Patent Application
20110183856
The present application includes a sequence listing on an accompanying compact disk containing a single file named “AFD 764 (GXP) Sequence Listing,” created on Nov. 7, 2005 and 2 KB in size.
The entire contents of that accompanying compact disk are incorporated by reference into this application.
The present application includes 18 tables on an accompanying compact disk containing the following files:
The entire contents of that accompanying compact disk are incorporated by reference into this application.
1. Field of the Invention
The present invention provides a specific set of gene expression markers from whole blood and/or peripheral blood leukocytes (PBL) that are indicative of a host response to exposure, response, and recovery from infectious pathogens. The present invention further provides methods for identifying the specific set of gene expression markers, methods of monitoring disease progression and treatment of infectious pathogen infections, methods of predicting the onset of the symptoms and/or manifestation of an infectious pathogen infection, and methods of diagnosing an infectious pathogen infection and classifying the pathogen involved.
The present invention also provides the following:
(1) methods for validating the differential gene expression markers in a cohort (such as a Basic Military Trainee (BMT) population). Such a method can be used to validate and/or expand upon a subset of biomarkers identified by alternative techniques for a specific disorder,
(2) methods for designing and implementing a process of determining pre-symptomatic gene expression changes in an exposed population,
(3) methods for statistical (e.g. Bayesian) inference to combine other (e.g. metadata) information into a overall diagnosis or assessment, and
(4) alternative measurement techniques other than Genechip microarrays, though not necessarily excluding Genechip microarray, that could be used to measure changes in a small, differentiating subset of genes (i.e., a subset of genes identified by the microarray-based method of the present invention) in a minimal volume of blood (lancet to produce drops of blood instead of intravenous blood draw to produce milliliters of blood) in a period of hours instead of days.
Moreover, the present invention relates to an overall business model, components of which include:
(1) assessment of the morbidity potential of individuals who were exposed to an infectious pathogen or agent of chembio-terrorism using pre-symptomatic gene expression markers,
(2) pre-assessment of the morbidity potential for select individuals (e.g. aircrews prior to the start of a 24 hour mission) or for general public use for pro-active intervention against infectious disease prior to the onset of major symptoms, and
(3) assessment of human behavioral activities (i.e. Exercising, eating, fasting, smoking, etc) that affect physiology and blood gene-expression, thus enabling discovery of biomarkers related to these behaviors that may be used to establish past activities of an individual at a certain probability of confidence.
The present invention further relates to:
(1) methods for extrapolating the methods developed herein (e.g., PAXgene processing and metadata) for use in other disease diagnostics (e.g., blood-related; autoimmune diseases, leukemia);
(2) methods for assembly of metadata in a format that allows it to be assimilated into inferential models of disease assessment; and
(3) methods for establishing a comprehensive human gene expression baseline database, against which perturbations, such a pathogen exposure, infection, and other disease states would be compared.
2. Discussion of the Background
Recent years have witnessed an explosive growth in the number of applications involving the use of DNA microarrays to monitor the expression of genes in various forms of tissues and cultured cells (1-5). Such “expression profiling” requires a measurable change in the relative abundance of transcribed messenger RNA (mRNA) in host cells in response to some type of perturbation. The measurement is usually performed indirectly by reverse transcription (RT) of the labile mRNA into more stable complementary DNA (cDNA) which is in turn labeled with a fluorophore (true for most work, but the Affymetrix process involves re-conversion of cDNA back to RNA, which is in turn labeled and hybridized) and allowed to hybridize with the microarrays containing a plurality of DNA “probe” molecules that bind the target cDNA of interest.
Typically, colored fluorophores are used to label the “control” and “experimental” pools of cDNA, allowing the relative transcript abundances to be deduced from the ratio of fluorescence intensities. Alternatively, a single color measurement can be enabled by scaling of the intensities between different microarrays, as in the case with Affymetrix high-density microarrays (vide infra) because the variation from among Affymetrix arrays are minimal compared to most spotted array platforms. Defining sets of genes that are modulated in response to the external perturbation is non-trivial and is complicated by “noise” due to biologic variability, microarray production batch, handling factors, and variability emerging during sample processing (6).
Significantly, the DNA probes themselves can be of highly variable lengths. Probes comprised of cDNA molecules (which are RT/PCR products of transcriptional isolates known as “Expressed Sequence Tags”; ESTs) can have varying lengths (usually hundreds of base pairs) and are often adsorbed (non-covalently) and then cross-linked (chemically or using ultraviolet radiation) to positively-charged poly-lysine or aminosilane-coated microscope slides. In contrast, probes comprised of defined “long” (70-mer) or “short” (25-mer) oligonucleotides are of fixed length and are almost invariably attached by a covalent bond via one terminus of the DNA molecule. Higher degrees of transcript detection sensitivity can usually be achieved with 70-mer probes compared to shorter ones (e.g. 20-25mers). However, specificity is reduced because 70-mer target/probe hybridizations are generally insensitive to small numbers (e.g., 2-3) of single base mismatches, whereas shorter probes are sensitive to single mismatches and thus provide greater specificity. In contrast, little can be said about transcript-specific cDNA binding to complementary cDNA probes prepared from EST libraries, because the length of the probes (hundreds of base pairs) can result in binding of multiple smaller transcription-specific cDNA molecules. The separation of these contributions would be impossible from a single fluorescent intensity signal as measured by a microarray scanner.
At least a few research groups have developed microarrays that are capable of distinguishing varying levels of “sequence resolution”. Within the human genome, only a small percentage of the total sequences called “exons” actually encode for functional polypeptides and these segments are interspersed with non-coding segments called “introns”. Shoemaker et al (7) developed “exon arrays” comprised of long (50-60 bases) targeting predicted exon regions, and “tiling arrays” which used sets of similar length overlapping oligonucleotides to completely blanket a genomic region of interest for human chromosome 22. This allows for determination of most RNA transcripts from this chromosome, including transcripts that are not traditionally considered as genes. Additionally, these microarrays should also be able to locate mutations in the chromosomal DNA itself. Further, this allows determination of which exons are represented in the formation of specific splice variants of transcripts coding for functional proteins.
For the present invention, the authors have used Affymetrix HG-U133A and HG-U133B Human Genome Expression Chips (Part No. 900444; for detailed information refer to the product literature available from the manufacturer, which is hereby incorporated by reference in its entirety) as well as the HG-U133 plus 2.0 chip (Part No. 900467) which contains probes from HG-U133A, HG-U133B, and an additional 10,000 probeset on one cartridge. A GeneChip® probe array contains “cells”, each having a large number of copies of a unique 25-mer probe and arranged in probe pairs consisting of a perfect match (PM) and a mis-match (MM) wherein the middle (number 13) position is varied. Normally, RNA is extracted from samples and reverse transcribed into cDNA then into double stranded cDNA with a T7 promoter region added. Then in vitro transcription is carried out to linearly amplify the RNA and incorporate biotinylated nucleotides to make biotin labeled cRNA. The labeled cRNA target is hybridized onto the microarray, usually over night, then follow by washing and detection via strepavidin conjugated fluorescent dyes the next day. Following hybridization of the labeled transcriptional targets to the microarray (for detailed information refer to the product literature available from Affymetrix entitled ‘Eukaryotic Sample and Array Processing,’ which is hereby incorporated by reference in its entirety), the Affymetrix GCOS software (manual available from Affymetrix) (8) is used to reduce the raw scanned image (.DAT) file to a simplified file format (.CEL file) with intensities assigned to each of the corresponding probe positions.
A graphical description of the probe pair layouts and the expression analysis algorithm is found in the Affymetrix GCOS manual on pages 505-523 (8). On the U133A and B GeneChips®, each (˜39,000) known and putative gene from the Unigene database U133 build of the human genome (for detailed information refer to the product literature available from the manufacturer, which is incorporated by reference in its entirety) are represented by 10 probe pairs spaced across some length of the gene, with some bias towards the 3′ end (maps and analysis available through the NetAffx website available through the Affymetrix website). The GCOS software executes algorithms to assign an overall intensity that is used to infer abundance of a transcript and calculate fold changes of expression between two or more experiments. It also provides a metric to indicate whether a gene is “present” (detectably expressed) or absent. Following these calculations, the individual probe intensities are not explicitly referenced but they remain part of the permanent data in the .CEL file for each experiment.
Thus, there are considerable differences in the interpretability of “gene expression” measurements, depending on the types and numbers of microarray probes used and the algorithms used to analyze the spatial patterns of intensity from the probes.
Of equal significance, relative to the “sequence resolution” of the measurement of transcript abundance in metazoan systems is the variation in the composition of “genes” and transcriptional gene products. Initial drafts of the human genome (9, 10) indicate that the human genome is comprised of approximately 30,000 genes, mostly identified by computational methods having significant limitations (11). Yet, orders of magnitude greater numbers of different proteins can be produced from these genes through the recombination of the internal coding sequences (exons) that are interspersed with non-coding sequences (introns). Hence, probes comprised of cDNA clones derived from a transcriptional library are biased towards detection of the complete gene product sequences that are obtained under a specific set of times and conditions, and cannot represent the multiform nature of mammalian gene expression in more general conditions where alternative splice variants will change the transcriptional sequence composition.
Several groups have also measured the gene expression profiles of individual immune cell types following exposure to microbes or microbial components in vitro. Groups at Whitehead Institute (12) and Stanford (13) have used Affymetrix and spotted cDNA microarray types, respectively, to observe relatively stereotyped responses of cultured human peripheral blood mononuclear cells (PBMCs; i.e. circulating macrophage precursor cells, T lymphocytes, B lymphocytes), eosinophils, and basophils when exposed to a variety of killed bacteria and bacterial cell wall components. The similarity of the responses is reflective of evolutionarily conserved pro-inflammatory responses within the innate immune system and do not suggest that pathogen-specific responses would be obviously detectable. Chaussable et al (14) describe a study with in vitro generated macrophages and dendritic cells, which provides insights into the innate immune response to diverse pathogens but is impractical for surveillance, as these cells types can only be isolated by laboratory procedures that will change their natural gene expression.
Peripheral Blood Leukocytes (PBLs) Drawn from the Infected Host
Craig Cummings, David Relman and Patrick Brown (Stanford University) hypothesized that the unique mixtures of virulence factors expressed by specific pathogens will give rise to a correspondingly unique transcriptional response in the host (15). They reasoned that an attractive host tissue source would be peripheral blood leukocytes (PBLs) because any pathogen gaining access to the body will elicit a multiplicity of immune response mechanisms, each characterized by combinations of specific gene modulations. They also pointed out that this technique might allow early diagnosis of even uncultivable or uncharacterized pathogens, that variations in host expression profiles could allow inference of time since exposure, and that a single technique could be used to diagnose a large number of different diseases.
Relman et al have used variations of the “Lymphochip” (16, 17) (which is comprised of probes for approximately 3,000-3,500 “lymphoid” genes comprised of cDNA clones prepared from transcriptional libraries of human lymphoid tissues) to analyze expression changes in cultured PBMCs (13), and in PBLs (PBL contributions-all white blood cells and the differential is typically 41-77% neutrophils, 20-51% lymphocytes, 1.7-9% monocytes and less than one percent of basophils and eosinophils), from RNA isolated from PAXgene Blood RNA tubes from 75 healthy human donors (18). The latter study (18) illustrated that relative gene expression levels in PBLs are related to variations in specific blood cell types, gender, age, and time of day. Relman et al have also observed changes in PBMC expression in non-human primates (NHPs) following experimental inoculation with Variola major, the virus responsible for human smallpox. In addition, Relman et al compared Ebola infection of NHP. However, the inventors herein are unaware of any disclosures that relate those changes to NHP inoculations using other pathogens or to baseline gene expression in humans. Because of the type of microarray (cDNA EST clones) it is not possible to ascribe particular transcriptional sequences that are responsible for assigning fold changes to particular genes. The present inventors are unaware of any written descriptions existing in the public domain that describe these data.
In short, all of Relman's papers use cDNA arrays and PBMCs (which require on site isolation centrifuge and technicians). If they used paxgene, they processed it within 24 hours. This is not practical for surveillance. Whereas in the present invention, the inventors demonstrate that the paxgene tubes can give decent gene expression profiles even when handled in conditions amendable to surveillance. Relman did not know and/or test this; hence they did everything within 24 hours to be safe in the notation that the RNA has not degraded. Also, for cDNA arrays, Relman required reference RNA with gene expression profiles similar to tissue of interest to compare 2 colors for all chips, which makes it impractical to study large population expressing different genes than what is contained within their reference RNA. Whereas the Affy chip is single color so no reference common RNA is needed allowing us to compare large numbers of chips overtime, especially when we spike in normalization control RNA.
At least one U.S. Pat. No. 6,316,197 B1 (19) makes claim to methods for determining characteristic gene expression changes from an infected host to diagnose exposure to biological warfare (or bioterrorism) agents. The inventors of that application described a series of steps that begin with the use of differential display PCR (DD-PCR) to discover genes that are expressed differently in cultured cells following incubation with biological toxins (e.g. Staphyloccocus enterotoxin B; SEB, and Botulinum toxin) or microbes (e.g. Bacillus anthracis). Briefly, DD-PCR involves the use of reverse transcriptase to convert host RNA transcripts to cDNAs, which are in turn amplified with PCR and separated by gel electrophoresis. Specific sequences are determined for each of the corresponding electrophoretic bands to identify the differentially expressed genes. The inventors of U.S. Pat. No. 6,316,197 described methods for measuring (including the use of reverse transcriptase PCR and DNA microarray hybridization) correlating the observed changes with methods for measurement in animals exposed to the same agents, and found gene expression changes that corresponded to those observed in culture. Overall, this work makes use of a commonly used method of discovering genes that are involved in differential biological responses and implicates several transcriptional markers that correlate with the exposure to several types of toxic insult. However, there is no ethical way to perform the same experiments using humans, and consequently, no manner of obtaining clinically relevant data for a human population. Nor is there an attempt in this work to compare the perturbations to a baseline human expression profile. Also, none of the methods disclosed by Relman et al are amendable to a surveillance setting
The concept of a microarray used for broad-spectrum pathogen identification has considerable and obvious appeal to both medical practice and national defense. This was best illustrated in the recommendations of the Defense Sciences Board (DSB) Summer 2000 Panel, which made recommendations to the DATSD (ATL) that the U.S. Defense Department develop a “Zebra Chip”; that is, a hypothetical microarray of unspecified technology that could include gene expression markers, that would be in widely distributed use (DoD TriCare System) as a routine clinical diagnostic for both common and uncommon (e.g. bioterrorism) infectious agents. In addition to having probes for common infectious agents, the Zebra Chip would also contain a large number of probes for unusual (“zebra”) pathogens. If such a device were in widespread use at the time of a biological terrorism event or a natural epidemic (e.g. SARS), the cost savings, both financial and in human suffering, could be enormous, due to the earliest possible detection of the agent when only minor (flu-like) symptoms were manifest.
Furthermore, there is a need to unambiguously define “baseline” expression profiles, against which the “perturbed” state profiles are compared, as they may be variable in time and between individuals.
Because it may not always be possible to identify the specific cause of an infection through pathogen genomic markers (e.g. using PCR or microarrays), there remains a critical need to determine alternative “biomarkers’ from the host that would elucidate the character of the disease etiology and guide the clinician in the proper management of the infection.
Heretofore, none of the published prior art methods are amendable to large long-term field studies/surveillance. All of the published methods are simply for a quick one-time gene expression study. Therefore, and in view of the foregoing, there remains a critical need of methods for determining characteristics gene expression changes that arise from an infected host to diagnose disease states, help guide treatment regimens, and assist in making treatment/operational decisions. Further, there exists a critical need for rapid, near real-time methods useful for field implementation that may be used individually or in combination with additional detection and diagnostic methods and apparatuses.
It is an object of the present invention to provide methods for determining the baseline gene expression in a healthy individual, as well as systematic changes in the gene, expression pattern characteristic to a pathogen or infection. More specifically, this object relates to methods for establishing a comprehensive human gene expression baseline database, against which perturbations, such a pathogen exposure, infection, and other disease states would be compared.
It is another object of the present invention to provide a method for validating the differential gene expression markers identified in a cohort.
It is yet another object of the present invention to design and implement a process to determine pre-symptomatic gene expression changes in an exposed population and from this to design/tailor therapeutic regimens.
Within the aforementioned objects, the present invention further provides methods for statistical (e.g. Bayesian) inference to combine other (e.g. metadata) information into an overall diagnosis or assessment.
The objects of the present invention may be extended to and the present invention embraces extrapolating the methods developed herein (e.g., PAXgene processing and metadata) for use in other disease diagnostics.
Further, it is an object of the present invention to provide a method for assembly of metadata in a format that allows it to be assimilated into inferential models of disease assessment.
It is an object of the present invention to further an overall business model, which includes:
(1) assessment of the morbidity potential of individuals who were exposed to an infectious pathogen or agent of chembio-terrorism using pre-symptomatic gene expression markers,
(2) pre-assessment of the morbidity potential for select individuals (e.g. aircrews prior to the start of a 24 hour mission) or for general public use for pro-active intervention against infectious disease prior to the onset of major symptoms, and
(3) assessment of human behavioral activities (i.e., Exercising, eating, fasting, smoking, etc.) that affect physiology and blood gene-expression, thus enabling discovery of biomarkers related to these behaviors that may be used to establish past activities of an individual at a certain probability of confidence.
(4) banking of samples (i.e. Paxgene) in conjunction with clinical information database for any phenotype of interest now or in the future.
In a certain object of the present invention is to provide a method for determining the gene expression profile for (i) a healthy person and/or (ii) a subject that has been exposed to one or more infectious pathogens by
a) collecting a biological sample (e.g., whole blood) from a subject;
b) isolating RNA from said sample;
c) removing DNA contaminants from said sample;
d) spiking into said sample a normalization control;
e) synthesizing cDNA from the RNA contained in said sample;
f) in vitro transcribing cRNA from said cDNA and labeling said cRNA;
g) hybridizing said cRNA to a gene chip followed by washing, staining, and scanning; and
h) acquiring a gene expression profile from said gene chip and analyzing the gene expression profile represented by the RNA in said sample on the basis of (i) the health of the subject or (ii) the disease(s) said subject has been exposed to while controlling for confounder variables.
Within this object, the following additional steps may also be performed to increase the overall sensitivity of the method and to enhance the reliability of the results obtained thereby:
In another object of the present invention, is a method for identifying gene expression markers for distinguishing between healthy, febrile, or convalescence in subjects that have been exposed to one or more infectious pathogens by:
a) acquiring a gene expression profile by the method according to the aforementioned object for a subject that has been exposed to one or more infectious pathogens;
b) acquiring a gene expression profile by the method according to the aforementioned object for a subject that has recovered from exposure to said one or more infectious pathogens;
c) acquiring a gene expression profile by the method according to the aforementioned object for a healthy subject that has not been exposes to those one or more infectious pathogens;
d) comparing the gene expression profiles for the subjects from (a), (b), and (c) by a pairwise comparison;
e) determining the identify of the minimal set of genes that classify the patient phenotype as healthy, febrile, or convalescent by class prediction algorithm based on said pairwise comparison; and
f) assigning the classification of healthy, febrile, or convalescent and/or classifying adenovirus febrile infection from background cases of other febrile illness in the cohort based on gene expression profile of the minimal set of genes determined in (e).
In yet another object of the present invention, is a method of classifying a subject in need thereof as healthy, febrile, or convalescence, by
a) collecting a biological sample (e.g., whole blood) from said subject;
b) isolating RNA from said sample;
c) removing DNA contaminants from said sample;
d) spiking into said sample a normalization control;
e) synthesizing cDNA from the RNA contained in said sample;
f) in vitro transcribing cRNA from said cDNA and labeling said cRNA;
g) hybridizing said cRNA to a gene chip followed by washing, staining, and scanning
h) acquiring a gene expression profile from said gene chip and analyzing the gene expression profile represented by the RNA in said sample; and
i) determining the gene expression profile in said subject of the minimal set of genes that classify the patient phenotype as healthy, febrile, or convalescent determined by the method described herein above;
j) classifying the subject in need thereof as being healthy, febrile, or convalescent by comparing the gene expression profile obtained in (i) to that of the classification assignment of healthy, febrile, or convalescent based on gene expression profile of the minimal set of genes as determined by the method described herein above.
The results procured by the present inventors provides a range of gene sets from a few genes to very large number of genes in various sets that could give the same percent correct classification results. The larger set size may provide a more robust prediction when the population involves more phenotypes. While the advantages and/or utility of the small set size may lie in the ability to make a quick independent diagnostic.
The above objects highlight certain aspects of the invention. Additional objects, aspects and embodiments of the invention are found in the following detailed description of the invention.
A more complete appreciation of the invention and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following figures in conjunction with the detailed description below.
Unless specifically defined, all technical and scientific terms used herein have the same meaning as commonly understood by a skilled artisan in enzymology, biochemistry, cellular biology, molecular biology, and the medical sciences.
All methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, with suitable methods and materials being described herein. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. Further, the materials, methods, and examples are illustrative only and are not intended to be limiting, unless otherwise specified.
The present invention provides a method for identifying human gene transcripts in blood, and their expression patterns, to identify a causative agent of respiratory infection, and provide a measure of recovery during the period of time following infection. The methods developed here can be extended to the discovery of gene expression profiles that will be indicative of exposure and predictive for the actual development of disease. These abilities have not previously been demonstrated in a human population. Gene expression: the following description details the importance of the present invention and its utility in gene expression analysis:
1. Identification of uncultivatable organisms: Mycoplasma pneumoniae, Bordetella pertussis and Chlamydia pneumoniae, which commonly cause respiratory disease in all age groups. These organisms require special transport media for sample collection of respiratory secretions. Even with optimal transport, it is tremendously difficult to cultivate these common organisms; therefore, healthcare workers are often unable to make a diagnosis and have little opportunity to direct antimicrobial therapy to potentially shorten the duration or to prevent transmission of disease with these organisms. Bordetella pertussis is the causative organism for whooping cough in children and carries a high morbidity. Adults infected with this organism often develop prolonged, dry cough and remain undiagnosed during the period of infectivity and possible transmission. It is likely that adults represent a typically undiagnosed reservoir of disease for this organism that can have significant impact on the health of children.
2. Analysis of organisms for which no sample can be taken, for example TB from children. Young children tend to have disseminated tuberculosis infection and will not tend to have a productive cough; this means that it is very difficult to collect sputum to look for the organism. Having an assay in blood that detects an immunologic signature for tuberculosis infection and disease in children would be a significant medical breakthrough. Worldwide, tuberculosis is a significant cause of morbidity and mortality in children, especially in impoverished regions of the world. Early detection of infection can significantly limit disease. Therefore, this area is of particular interest in the present invention.
3. Analysis of and identification of multiple organisms in a single blood sample.
4. Differentiation of a pathogen from colonization (discussed further below).
5. Determination of pre-symptomatic-exposed individuals.
6. Expansion to non-infectious/toxin exposure.
7. Identification of normal baseline for comparison for all studies.
Based on the foregoing and the embodiments specifically described herein, the present invention provides an opportunity to direct treatment options. In other words, by determining the gene expression patterns (both baseline healthy and ill) the artisan would be enabled to determine the diagnosis and the corresponding treatment, i.e. whether an individual has a bacterial infection-give antibiotics or viral infection-no antibiotics. In this manner the medical professional may reduce inappropriate antibiotic use and decrease resistance.
Further, the present invention may be employed to measure response to treatment—i.e., is there evidence that the host is resolving the infection? At times, individuals will be hospitalized and treated for respiratory infection, they appear to get better, but then develop fever again—the causes of fever can be: new infection-intravenous line is now infected or patient has developed urinary tract infection due to indwelling Foley catheter-typically multiple tests have to be sent-blood, urine, sputum to determine whether there is a new site of infection. Also, diseases like pancreatitis or cholecystitis that develops in very ill patients while hospitalized can be non-infectious causes of fever that develops after admission. Gene expression as described herein provides a means to take a single sample, blood, and differentiate infectious from non-infectious cause of fever and to identify whether a new pathogen at a new anatomic site is responsible for the new fever—e.g., if an individual was admitted with S. pneumoniae pneumonia and had gene expression pattern consistent with this, but then developed a new fever in the hospital and had a changing gene expression pattern consistent with a S. aureus (skin pathogen) infection, then the new gene expression pattern would direct the practitioner to look at IV sites and other skin sites, such as decubitus ulcers, for a new source of infection. If the gene expression pattern did not appear to be consistent with a response to an infectious agent, then the practitioner should consider diagnoses such as pancreatitis or cholecystitis. The development of fever during hospitalization is not uncommon and often is a vexing problem for the health care practitioner, especially in severely ill patients in the Intensive Care Unit. Therefore, techniques as described herein would be well received in the medical profession.
The present invention was accomplished following successful adaptation of a commercial technology (Affymetrix Human Genome U133 chip set) that has not been demonstrated prior to this to be effective for whole blood expression profiling due to interferences from high-abundance globin RNA (20). The demonstration of the enablement of the present invention has been assisted, in part, by the employment of enhanced sample preparation methods (e.g., PAXgene™). Further, by employing rigorous screening and control functions the present invention offers a significant advantage in that the data obtained thereby are free from the confounding environmental influences that pervade other gene monitoring studies. Moreover, the gene products used to distinguish between varying febrile respiratory disease states can be targeted for a variety of other assay types that do not require whole genome transcriptional monitoring or the attendant processing steps.
Herein, the present inventors demonstrate that high density DNA microarray technology can be adapted for insertion into an accelerated system for discovery of blood transcriptional markers of infectious disease and other factors important of health, occupational, and military significance.
When considering host gene expression profiling, the capacity to conduct thousands of assays simultaneously poses challenges regarding data analysis, storage, and management. While data storage and management issues are largely technical concerns for information technology specialists, no clear consensus on analysis techniques has emerged for making use of host gene expression profiles. The major role for bioinformatics is the identification of patterns associated responses to pathogens which may not only provide a means of detection, but also elucidation of genetic networks underlying initiation and progression of disease. The most commonly exploited tool for analysis of gene expression profiles is hierarchical clustering (21, 22) where the fundamental assumption is that similar trends, computed through a measure of distance, in the relative magnitudes of gene modulation imply similarity of function.
A critical need for the interpretation of large data files is the visualization of information, which can be readily accomplished by dendrograms that can be derived from cluster analysis. Interpretation of expression profiling data has been used to gain profound insights into gene function. Clustering of genes expressed in yeast coupled with statistical algorithms yielded a model of regulatory transcriptional sub-network (23). A significant demonstration of the utility of clustering has been offered by Hughes et al. (24), where a compendium of expression profiles of 300 diverse yeast mutations was used to identify novel open reading frames that encoded proteins of several cell functions. In regard to pathogen detection, different pathological conditions reflected by particular expression profiles could also be clustered (clustering by arrays rather than by genes), but variation among a broad set of genes or dimensions may reduce the ability to discern pathogen exposure states.
Efforts in functional genomics related to cancer research have yielded major successes in the pursuit of gene expression signatures. Expression-based criteria or class predictors have been defined based on neighborhood analysis (25), Bayesian regression models (26), and artificial neural networks (27-29). These predictors were successfully used to classify novel samples in a manner consistent with clinical assessments. In fact, classifications based on gene expression alone or class discovery has also been demonstrated, suggesting that gene expression profiling has the capacity to identify subtypes that have not been previously defined (25).
While promising, one should note that cancer line gene expression analyses are one-dimensional; in contrast, a host expression profile evoked by pathogen exposure would be expected to be temporal and “dose-dependent”. Comprehensive sets of gene expression profiles that explore temporal and dose ranges for pathogen exposure must be produced to map the continuum of gene expression changes.
The present invention has been developed, in part, based on the rigorous assessment of the RNA quality from PAX tubes from a relatively large sample of humans with various disease phenotypes, to determine the following: nested sets of genes that could optimally classify the four phenotypes of (a) healthy, (b) recovered, (c) febrile with adenovirus infection, and (d) febrile without adenovirus infection; lists of differential genes among the four phenotypes; and the pathways in blood cells involved in respiratory disease due to adenovirus infection versus non-adenovirus infection. These results demonstrate possibilities and issues involved in measurement of gene expression from whole blood at the population level; show the potential of using host gene-expression responses in blood cells to distinguish pathogen classes; elucidate functional pathways involved in adenoviral respiratory disease; and provide a data set to develop statistical models to answer other biological questions of interest.
The present invention was accomplished as a result of the availability of the BMT population of the U.S. Air Force to the present inventors. The BMT population offered advantages for surveillance studies. The major advantage is that the BMT population is racially and ethnically diverse and is representative of the racial/ethnic diversity observed in the United States. The BMT population undergoes environmental factors similar to those of other populations to include: smoking, exercise, stress, schooling (education), activities of daily living; while the activities of daily living may appear to be more regimented than their civilian counterparts, they largely reflect typical schedules (early breakfast, exercise, education for 6 hours, regular lunch and dinner, cleaning of dorms or TV in evening). These characteristics are advantageous for many research questions. One difference between the BMT and the civilian population is that there is a predominance of males in the BMT population (90% male, 10% female) and the age range is typically from 18-25 years. In order to address this, the present inventors are extending this study to a civilian population that includes individuals of all ages greater than 18, male and female, who present to medical clinics and hospital wards with symptoms of upper respiratory tract infection. The ability to ascribe differential gene expression profiles in a relatively homogeneous population is directly applicable to military applications and is enabling for the development of methods necessary for the discovery of a subset of markers that will be predictive for a larger population.
There has been considerable speculation within the research community that blood would provide the best range of gene expression biomarkers involved with the immune response to a broad range of viral and bacterial infections. A variety of blood cell isolation kits and reagents might be useful for collecting blood cells and isolating RNA for gene expression analysis, including CPT vacutainer tubes (Beckman Dickenson) which collect blood and after a spin can segregate the PBMCs; the Paxgene blood RNA system, which has an RNA stabilizer reagent inside the vacutainer tube for blood collection; and the Tempus blood collection tube from Applied Bioscience which also has a stabilizer, but is relatively new on the market.
Relman (18) has used PAXgene to successfully measure gene expression changes in blood using cDNA and long oligonucleotide (70-mer) microarrays. However, the stability of RNA in PAX tubes over handling conditions practical for multicenter surveillance was not assessed. Relman (18) processed all the PAX tubes within 24 hours of collection, which is not practical for large multicenter surveillance. Also, in principle, a higher degree of sequence resolution would be obtainable using shorter (25-mer) oligonucleotide arrays have high-density probe tiling (e.g. Affymetrix GeneChip) that blanket entire genomic regions of interest. However, prior observations have been that PAXgene produced an insufficient number of “percent present” calls (i.e. the percentage of total genes determined to be measurably expressed as determined by the Affymetrix GCOS gene expression software) on Affymetrix GeneChip expression microarrays. Presumably, the unsatisfactory level of “percent present” calls was caused by the interference of high abundance globin RNA on binding of lower abundance transcriptional markers. Thus, there have been no prior reports of the combined use of PAXgene blood RNA kits and the Affymetrix GeneChip® platform prior to that described herein.
From a logistical perspective, the use of PAXgene technology would be highly preferred for discovery of expression markers during opportunistic encounters of infectious agents with a mobile human population. This is because of the proposition of the unique abilities of the PAXgene reagents to rapidly terminate gene expression in cells and stabilize RNA at the time of blood draw, minimizing the confounding effects of variable RNA degradation and gene expression perturbations caused by varying storage and processing times and conditions in a military clinical setting, rather than controlled laboratory environment using controlled exposures and sampling times. Traditionally, studies of blood cells utilize gradient-density based methods to collect live mononuclear cells for analysis such as cell sorting, genotyping, and expression profiling. However, the RNA population may have changed or become degraded due to the processing of live cells, as transcript levels can fluctuate early after blood collection (30-32). Additionally, these methods do not isolate neutrophils, which typically pass through the gradient-density and are not collected for analysis. These methods are labor intensive and do not translate well to mobile populations. In contrast, the PAX tube contains a proprietary solution that reduces RNA degradation and gene induction as 2.5 ml of blood is flowed into the tube (30-32). However, the blood cells are killed and cannot be sorted, nor can DNA be isolated using procedures described in the PAX kit handbook (33).
Since the goal of the present inventors is to measure RNA transcript levels for diagnosis or epidemiologic surveillance, we decided that the RNA stabilization capability of the PAX tube complemented our interests, especially for situations where one cannot process the blood samples soon after collection. It is to be understood that alternative sample preparation methods may be used in the methods of the present application, so long as these alternative sample preparation methods do not compromise the integrity of the RNA material contained within the sample.
In view of the foregoing, the present inventors have developed a modified protocol for gene-expression analysis of RNA isolated from human blood collected and processed with the PAXgene Blood RNA System that works with the Affymetrix GeneChip® platform. The protocol was used to compare profiles of blood samples collected in PAX tubes that were handled in two ways that may provide practicality to surveillance and clinical studies (conditions E and O). These methods entailed collecting blood samples in a PAX tube and then either, (a) incubating the sample for a minimum of 2 hours at room temperature (condition E) and then isolating RNA from the PAX tube-collected blood samples, or (b) incubating the sample at room temperature for nine hours followed by storage at −20° C. for 6 days (condition O) and then isolating RNA from the PAX tube-collected blood samples.
The present inventors found differences between the two handling methods (although either of these conditions may be employed in the context of the present invention). Samples of condition E had higher DNA contamination, lower total RNA yield, and higher double-stranded cDNA yield than samples of condition O. ANOVA indicated that the two conditions contributed to differences in gene expression levels, but the magnitude was minimal, being 0.09% of the total variation. These results should facilitate incorporation of expression profiling protocols and handling methods into clinical and surveillance level procedures.
Genome-wide expression studies of human blood samples in the context of clinical diagnosis and epidemiologic surveillance face numerous challenges—one of the foremost being the capability to produce reliable detection of transcript levels. Many factors contribute to the variability of target detection, including: the method of blood collection, sample handling, RNA stabilization, RNA isolation, and other downstream processes.
The Affymetrix® GeneChip® platform can measure a significant subset of the transcriptome. In design, it incorporates a DNA oligonucleotide microarray, manufactured via photolithography to detect labeled cRNA targets amplified from RNA populations. However, some labs have observed a lower percentage of genes detected using RNA from whole blood compared to RNA from mononuclear cells regardless of the blood collection or processing method. This phenomenon may be due to the dilution of leukocyte RNA by RNA from reticulocytes, the activation of leukocytes during the isolation procedure, and/or the degradation of RNA isolated from the PAX tubes.
The RNA, isolated from blood in PAX tubes that is stored at room temperature, at −20° C., at −80° C., or after freeze-thaw cycles has been shown to be stable as determined by ribosomal RNA bands on agarose gel, fluorescence profiles on the bioanalyzer (Agilent Technologies), or RT-PCR for a few genes (31, 34-45). However, the integrity of the RNA at the transcriptome level as measured by Affymetrix microarrays has not been determined. In the context of multi-centered epidemiological studies, one needs to stabilize the transcriptome at the point of sample collection and during sample storage and transportation. Therefore, we compared the gene-expression profiles of parallel blood samples drawn into PAX tubes handled in two ways (Condition O and E described above) (
In the present specification, the present inventors relate a quality assured and controlled protocol that is capable of producing reliable gene-expression profiles, using the GeneChip® system and RNA isolated from whole blood using the PAXgene™ Blood RNA System. We used this protocol to compare quality control (QC) metrics and gene-expression profiles of PAX tube collected blood that was handled by the methods diagramed in
Our results implied several recommendations as to sample handling for multi-centered studies. Since there were differences between the conditions but they both showed good within-group reliability, one should preferably pick one method to reduce variability. In which case, condition O seemed advantageous over E, as it provided time before one had to process or freeze the samples and allowed for transportation while frozen. If one needed the flexibility of the range of handling methods between the conditions, then this would still be possible, as long as during subsequent analysis, one increased statistical stringency.
Therefore, in a preferred embodiment of the present invention blood samples are obtained and prepared for microarray analysis by the following general protocol:
(a) Blood Collection
(b) Target RNA Isolation
(c) Labeling and/or Amplification of Target RNA
(d) Hybridization onto Microarray
(e) Detection of Bound Target RNA
(f) Data Integration and Analysis.
Although the PaxGene-based methods worked well in the present invention, the present invention contemplates and includes additional optimized processes. One adjustment to the existing protocol is to omit the increase in proteinase K during RNA isolation. To this end, some reports have stated that sufficient pellet formation is possible by simply increasing centrifugation time. Therefore, it is also possible to increase the centrifugation time concomitant with the omission of the proteinase K increase. Alternatively, the protein K digestion step may be shortened by using a more concentrated proteinase K and a shorter incubation time. Also, the eluent volume during mRNA elution was 100 μl, but a 200 μl total eluent might give better yield. The in-solution DNase treatment was used to ascertain removal of DNA. However, the amount of DNA left after on-column DNase treatment might not interfere with subsequent steps.
Further, to improve preparation time on the PaxGene technology itself, vacuum-filtering methods may be employed to collect the cells rather than spinning the tubes to pellet the cells. Another permissible modification would be to use filtering methods to collect the supernatant after proteinase K digestion rather than spinning down the debris for a defined time (e.g., 30 min). Robotic systems could also be employed to considerably shorten liquid handling time.
For alternatives to existing protocols, other related sample collection methods and transcriptome measurement technologies may be used. These include:
Additional alternative and/or supplemental preparation methods are also contemplated, which may shorten duration time and reduce initial input RNA amount, for Example:
Other methods that are also contemplated to increase sensitivity of the sample preparation processes include:
As stated above, the present invention was accomplished following successful adaptation of a commercial technology (Affymetrix Human Genome U133 chip set) that has not been demonstrated prior to this to be effective for whole blood expression profiling due to interferences from high-abundance globin RNA (20). Therefore, globin reduction for whole blood RNA is an important step for improving gene expression profile from whole blood sample, since 70% total RNA in whole blood samples are globin mRNA, which would result in decreased percent present calls, decreased call concordance and increased signal variation.
In Example 4, the present inventors evaluated biotinylated globin oligos (Ambion) and PNA oligos (Affymetrix), which prove to be the two most effective methods to reduce globin mRNA from whole blood RNA. However, heretofore there was no systematic comparison on gene expression profiles derived from these two methods. The present inventors' studies using Jurkat RNA and globin spiked in Jurkat RNA (JG) in parallel with paxgene RNA provides a detailed insight of comparison between these two methods for cRNA profiles, present calls, call concordance, signal variation, multidimensional scaling and hierarchal cluster analysis in gene expression profiles.
Although neither of two globin reduction methods gave the same gene expression profile (gxp) as Jurkat RNA, the globinclear method using Biotinylated globin oligos gave closer gxp than PNA method. The data set forth in Example 4 indicate that the globinclear RNA resulted in significantly higher number of present calls (%), higher call concordance %, lower false negative discovery, and closer gene expression profile to no globin control relative to the single step PNA reduction method in Jurkat and JG RNA. However, it also resulted in higher signal variation, lower triplicate correlation coefficient and no difference in correlation to no globin control relative to the PNA method, possibly due to the multi-step procedure that involves a 2 hour processing time. It is notable that highly pure RNA free from RNase contamination is required for the globinclear method, necessitating in solution Dnase digested paxgene RNA to be subjected to cleaning and concentration using the Rneasy Minelute column (Qiagen). In contrast, the single step PNA process is easy to perform simply by adding the oligo mixture to the downstream application tube. But we noticed that higher ratios of 3′/5′ GAPDH and 3′/5′ Actin appeared in paxgene RNA samples and smaller cRNA size in PNA treated paxgene RNA. Reduction in cRNA size may lead to a higher ratio of the two control probe sets and likely is the cause of the higher CV seen with paxgene RNA.
PNA oligos specifically hybridized to the 3′ end of globin mRNA to prevent reverse transcription, while biotinylated capture globin specific oligos hybridized to globin mRNA followed by removal of globin mRNA via strepavidin magnetic beads. Thus, because the globin clear method physically separates globin mRNA from the sample, it allowed non 3′ bias techniques downstream, such as direct labeling of globinclear RNA for target preparation. Globinclear method produces a good quality RNA with the ratio of 260/280 beyond 2.0. However, from paxgene RNA not from J and JG RNA, the cRNA yield reduces to half of the amount of no treatment or PNA treated sample and at least 5 μg paxgene RNA is required to get enough cRNA for hybridization. Whereas, 1 μg paxgene RNA treated with PNA oligo is able to amplify enough cRNA (approximately 20 μg) for hybridization
In sum, the present inventors have compared pros and cons for the globinclear and PNA methods. Based on this comparison, the present inventors have found that the both of these methods may be used to reduce the amount of globin in whole blood RNA. Choice of methods depends on the individual project setup and goals. However, in either scenario by employing one of these methods a significantly higher number of present calls (%), higher call concordance %, lower false negative discovery, and closer gene expression profile to no globin control can be obtained.
Based on the foregoing, the present inventors have developed a method for identifying gene expression markers for distinguishing between healthy, febrile, or convalescence in subjects that have been exposed to one or more of various infectious pathogens.
In general, a preferred method of the present invention is as follows:
a) sample collection;
b) Isolation of RNA from said sample;
c) Removal of DNA contaminants from said sample;
d) Optional concentration and clean-up of RNA;
e) Spike-in controls for normalization;
f) Optional globin mRNA reduction/elimination;
g) Synthesis of cDNA;
h) IVT (in vitro transcription) labeling and cRNA synthesis;
i) cRNA quantification and quality control;
j) Gene chip hybridization, wash, stain, and scan;
k) Optional second gene chip hybridization, wash, stain, and scan;
l) Data acquisition and management; and
m) Statistical analysis.
Within the context of the present invention, including this preferred embodiment, the sample is preferably whole blood. However, within the context of the present invention, any RNA source may be utilized whether from whole blood or extracted from some other source. In a preferred embodiment, and as described above and in the Examples, when the sample is whole blood the collection device is a PAXgene blood RNA tube.
Within the context of the present invention, including this preferred embodiment, the RNA may be isolated by any known RNA isolation technique. As stated above, the RNA isolation technique may be facilitated by use of a commercially available kit, including the PAX kit system or Qiamp. Preferably, RNA isolation may be performed without on-comun Dnase treatment. In addition, in an embodiment of the present invention, RNA isolation may be performed with a Qiashredder column (Qiagen Corp.), which helps to increase the yield of RNA obtained from samples obtained from sick subjects.
Within the context of the present invention, including this preferred embodiment, the DNA may be removed by any known technique. In a preferred embodiment, the DNA is removed from the sample by in-solution Dnase treatment. The Dnase treatment may be performed with or without use of an inactivation reagent. In the case of use of an inactivation reagent, it is preferred that the inactivation reagent be added after a defined period after onset of Dnase treatment. In this case, the defined period is preferably set by the level of DNA remaining in the sample. In case where the DNase inactivation reagent is not used is because subsequent use of column to clean (hence DNase and metal ions are removed) and concentrate RNA for globinclear method.
Within the context of the present invention, including this preferred embodiment, the RNA may be concentrated and cleaned-up where necessary. For subsequent techniques in the preferred protocol of the present invention it is preferred that there be a total of at least 8 μg of RNA initially before going into column to clean and concentrate. As such, one or more of several techniques may be used to concentrate and clean-up the RNA. For example, a Minelute column may be used and the RNA eluted in BR5. Also it is possible to used ethanol precipitation techniques with resuspension in water although this is not compatible with globinclear downstream as this method does not clean the RNA enough (e.g., approximately 10 μl). Further, to determine whether additional concentration and/or clean-up is necessary the RNA and/or quality thereof may be assessed on a bioanalyzer or a nanodrop.
Within the context of the present invention, including this preferred embodiment, it is preferred for the subsequent steps (i.e., steps (e)-(m)) that the starting amount of total RNA be at least 5 μg, although 1 μg starting amount can work with PNA and no globin reduction methods.
Within the context of the present invention, including this preferred embodiment, it is important that prior to cDNA synthesis that a spike-in control be added to the reaction cocktail containing the subject RNA. This step is critical for normalization between diseases and patients and poses an improvement over existing techniques. The spike-in control for use in the present invention is preferably a polyA control or an ERCC universal control (http://www.cstl.nist.gov/biotech/workshops/ERCC2003/).
As stated above, 70% of mRNA in whole blood samples are globin mRNA, which would result in decreased percent present calls, decreased call concordance and increased signal variation. As such, in a particularly preferred embodiment, the globin RNA content is either reduced or eliminated. To this end, the term “reduced” is contemplated as meaning that there is a reduction in the total amount of globin RNA in the sample of at least 50%, preferably at least 60%, more preferably at least 70%, even more preferably at least 80%, still even more preferably at least 90%, and most preferably at least 95% as compared to the sample prior to the reduction treatment. Within the context of the present invention, the globin RNA reduction may be performed using biotinylated globin capture oligos (Ambion globinclear kit) or PNA (Affymetrix GeneChip globin reduction kit) according to modified manufacturers' procedures (see the Examples of the present invention).
When the globin RNA reduction method is that of using biotinylated globin capture oligos, it is preferred that biotinylated globin capture oligos are added to the total RNA and, subsequently, the globin mRNA were removed by contacting the RNA mixture with streptavidin beads (e.g., Strepavidin magnetic beads). Globinclear RNA was further purified using magnetic RNA bead. Alternatively, it is possible to replace the magnetic bead based total RNA isolation step with Qiagen column chromatography. In either event, the subject RNA is preferably eluted with water or BR5 (preferably diluted such that following speedvac concentration the total salt content is 1×BR5 or if water is used for elution, then speedvac to small volume and then increase to appropriate volume using BR5). Accordingly, when the globin RNA reduction method is that of using biotinylated globin capture oligos is employed it is a highly preferred embodiment that the RNA be concentrated and cleaned-up before and/or after said method. It is important to note that the Elution buffer that comes with the Globin clear kit does not work with downstream speed vac concentration and affymetrix target prep. Ambion test their Elution buffer with their Message Amp target prep method, whereas the present invention preferably uses Affymetrix target prep.
When the PNA method is used as the RNA reduction method, this step is performed simultaneously with cDNA synthesis. In this method, PNA is spiked in with the cDNA synthesis cocktail. Peptide nucleic acid (PNA) oligonucleotides specifically bind to the 3′ end of globin mRNA to inhibit reverse transcription during cDNA synthesis. However, when employing this method, care must be taken to preserve the stability of PNA and one has to take measures to prevent PNA aggregation and precipitation. It may also be advisable to run Jurkat globin as a control for efficient globin removal.
When the method above is practiced in the absence of a globin RNA reduction protocol low sensitivity and high variance are observed. When the PNA method is followed the sensitivity is boosted, low variance is observed, but this method only works for 3′biased reverse transcription assays. When the biotinylated globin capture oligo method is followed the best sensitivity is obtained, low variance is observed, and the RNA may be used for nay reverse transcription assay including non-3′ biased assays. With the biotinylated globin capture oligo method very high quality RNA is required, whereas the PNA method is useful even without high quality RNA. It is important to note that if ERCC controls are uses, then the data can be normalized across highly different gene expression profiles.
Within the context of the present invention, including this preferred embodiment, it is preferred that the purified target RNA be amplified via reverse transcription to cDNA utilizing a T7 polyT primer (or a random primer for non 3′-biased assay alternative for exon arrays) then to double stranded cDNA with a T7 promoter for subsequent in vitro transcription. Following production of double stranded cDNA, the double stranded cDNA should be cleaned-up and concentrated as appropriate.
Within the context of the present invention, including this preferred embodiment, commercially available in vitro transcription kits are preferably used to amplify and label the resulting cRNA. Examples of such kits are readily available through Enzo Biochem or Affymetrix. These methods may be performed as instructed by the manufacturer with a subsequent cRNA clean-up as appropriate.
Within the context of the present invention, including this preferred embodiment, the cRNA is quantiated and the quality of the sample assessed to determine the cRNA yield and purity of the sample, respectively. To determine whether additional concentration and/or whether further clean-up is necessary the RNA and/or quality thereof may be assessed on a bioanalyzer, nanodrop, and/or UV spectrophotometer (cuvette or plate reader). If necessary, if an increased cRNA yield is necessary, Ambions Message Amp kit may be used in accordance with the manufacturers' instructions. Among the quality controls within this embodiment are the ratio of 260/280, the yield of cRNA, etc.
Within the context of the present invention, including this preferred embodiment, gene chip (first, second, or subsequent chips) hybridization, washing, staining, and scanning may be conducted as directed by standard Affymetrix protocols. For example, hybridization may be conducted by contacting approximately 10 μg of biotin incorporated cRNA to the genechip in the Affymetrix hybridization oven for 15 to 17 hours at 45° C. of hybridization of labeled target onto the Genechip microarray. Conditions, including incubation time and temperature, may be further modified, so long as sensitivity and accuracy are maintained. In addition, the washing and staining conditions may also be modified so long as the sensitivity and accuracy of the technique are maintained. The nature, identity, and composition of the genechip for use in the present invention are not limited; however in a preferred embodiment the genechip is selected from Affymetrix U133A, U133B, and U133 plus 2.0. In a preferred embodiment, it is preferred that either U133 plus 2.0 or both U133A and U133B are used as the genechip.
As discussed below, data acquisition and handling may be performed by any means known by the skilled artisan. For example, data acquisition and handling may be performed by hand and passing through various programs, including the manufacturer developed software accompanying the genechip reader.
A more complete discussion of data management and statistical/functional analysis is provided in the description below and the Examples that follow.
However, briefly, data management is conducted by using Affymetrix GCOS gene expression software data are exported to Excel. MAS5.0 signal and present calls are exported and saved as tab-delimited text files, as are scaled and unsealed Signal values, to test normalization assumptions and strategies. The text files (and file names) are subsequently reformatted for import into Arraytools in house R-script. QC analysis software, datamatrix, and JMP IN (SAS Institute) programs are used for analysis of variance and further data exploitation. Where appropriate, the data for U133A and U133B are joined in Arraytools.
For analysis software the following can be mentioned:
To identify gene expression profiles resulting from pathogen exposure and to enable the general technology described herein, the following program was undertaken with an adenovirus model system.
Lackland Air Force Base (LAFB) in San Antonio, Tex. is the location of Basic Military Training for all recruits to the United States Air Force. Approximately 40,000 basic military trainees (BMTs) undergo a 6-week training course prior to assignment of duty. These BMTs are organized into flights of 50-60 individuals that eat, sleep, and train in close quarters. Each flight is paired with a brother or sister flight with which there is increased contact due to co-localization for scheduled activities, and multiple flights are grouped into squadrons which reside in the same dormitory building, subdivided into dorms for individual flights. Compared with their civilian peers, young healthy adults serving in the U.S. Military are at a significantly elevated risk of respiratory infections. Crowding and numerous stressors facilitate the transmission of respiratory pathogens. During the 6-week basic training course, approximately 20% of BMTs will develop fever and respiratory symptoms.
Adenoviruses are the most common respiratory pathogens seen in the BMT population today. Before an adenoviral vaccine was available, adenovirus was consistently isolated in 30-70% of BMTs with acute respiratory disease. The outbreaks often incapacitate commands, halting the flow of new trainees through basic training. In 1971, the adenoviral vaccine directed against serotypes 4 and 7 became routinely available to new military trainees. This vaccine had a dramatic impact on trainee illness, reducing total respiratory disease by 50-60%, and reducing adenovirus-specific disease rates by 95-99%. The use of the adenoviral vaccine continued uninterrupted for 25 years until the manufacturer of the vaccine halted production. After discontinuation of the vaccine, 1814 of the 3413 (53%) throat cultures from symptomatic military trainees yielded adenovirus during the period from October 1996 to June 1998. At that time, adenovirus types 4, 7, 3, and 21 accounted for 57%, 25%, 9%, and 7% of the isolates, respectively, and currently a predominance of adenovirus type 4 is recognized. Since the discontinuation of the adenoviral vaccine, approximately 20% of BMTs develop symptoms of fever and respiratory illness and 60% of these cases are due to adenovirus. Other pathogens such as influenza A, Mycoplasma pneumoniae, Chlamydia pneumoniae, Bordetella pertussis, and Streptococcus pyogenes continue to cause a significant minority of respiratory disease in this population. Mixed infections are known to occur but the frequency and types of pathogens involved in mixed infections are largely uncharacterized. Resolution of mixed pathogens is the topic of a related patent application by the present group of inventors (U.S. Provisional Patent Application No. 60/590,931, filed on Jul. 2, 2004). In the present invention, the present inventors do not attempt to characterize multiple pathogens but rely on the predominance of a single pathogen (human Adenovirus type 4; Ad4) to create a category of infection and compare cases of that to other categories comprised of non-Ad4 FRI and convalescent Ad4 FRI.
With the current state of the art, differentiating the serotypes and strains of adenovirus and influenza is a time-consuming and labor-intensive undertaking. Cultures of adenovirus may take a week to grow and subsequent typing of the adenovirus isolate must then be performed using hemagglutination-inhibition and neutralization assays which are cumbersome and subject to significant reciprocal cross-reactions, making serotype identification take as long as 2-3 weeks. By the time that the virus is identified, the BMT has often has already transmitted the infection to multiple others. There is great need for more rapid diagnostic assays and a need to detail the epidemiology of these respiratory outbreaks so that public health measures can be directed appropriately.
More importantly, especially with regard to the present invention, there are no known methods to determine reliable physiological markers that relate the exposure of an individual to an infectious pathogen to the actual infection. Thus, while a sample such as a throat swab or nasal wash might produce nucleic acid markers for the presence of a respiratory pathogen, there are no techniques available to determine whether the individual will become ill or has just recovered from infection caused by that pathogen(s). In addition, an organism may be recovered from a sampling of the respiratory tract. Generally, it may be unclear whether this organism is simply colonizing the respiratory tract or is the cause of disease; assaying for the presence of an immunologic signature to this organism is expected to assist in the differentiation of colonization from disease. Furthermore, within the group of individuals who present with febrile respiratory illness, there are no methods for determining the severity of infection, or the degree and type of interaction with the host immune system. The present invention describes methods for performing these latter assessments in a statistically valid manner.
In order to determine whether gene expression profiling could differentiate individuals infected and ill with adenovirus versus other infectious pathogens, the present inventors undertook an Institutional Review Board (IRB) approved study (vide infra). BMTs arriving at LAFB underwent informed consent to participate in this study. Approximately 15 ml of blood, filling 4 to 5 PAX tubes, were drawn from each volunteer. On day 1-3 of training, blood samples were drawn from healthy BMTs into PAX tubes by standard protocol (described herein elsewhere), but no nasal wash was collected for this group. A complete blood cell count (CBC) was also obtained. These individuals were determined to be healthy by screening with a standardized questionnaire, which eliminated any initial BMT with acute medical illness within 4 weeks of arriving at basic training.
In Phase II of the study, BMTs who presented at a later stage in training with a temperature greater than 100.4° F. and respiratory symptoms were consented for a nasal wash, throat swab and blood draw for PAX tubes and CBC. These individuals were categorized into either the febrile with- or without-adenovirus infection groups. At times, a rapid antigen capture assay for adenovirus was used to screen for individuals who were adenovirus negative; this was done to improve enrollment of individuals in this group. All results of rapid assay were confirmed with culture.
In Phase III of the study, approximately three weeks after sample collection from febrile volunteers with adenovirus, additional blood (PAX tube and CBC) and nasal wash were collected from these individuals when they recovered, forming the convalescent group.
All PAX tubes were maintained at room temperature for 2 hrs and then were frozen at −20° C. and shipped on dry-ice to the Navy Research Laboratory (NRL) in Washington, D.C. within 7 days for processing. Nasal washes were performed by standard protocol using 5 ml of normal saline to lavage the nasopharynx followed by collection of the eluent in a sterile container. Nasal wash eluent was stored at 4° C. for 1-24 hrs before being aliquoted and stored at −20° C. and shipped to NRL within 7 days for processing. The nasal wash and throat swab was sent for standard viral culture of adenovirus, influenza, parainfluenza 1, 2, and 3 and RSV. The nasal wash and throat swab were also tested by a multiplex PCR for adenovirus type 4 to further confirm culture results for this pathogen. Although the foregoing describes the protocol undertaken in the present study, it is understood that the present invention further contemplates alternative storage and shipment conditions so long as the integrity of the sample is not compromised.
All BMTs underwent a standardized questionnaire at initial presentation, during presentation with illness, and at follow-up. Questions posed to BMTs include: vaccination history, allergies, last meal, last exercise, last injury, medication taken, smoking history, observed subjective symptoms, and last menstruation (if appropriate). Among the observed subjective symptoms asked and monitored are: sore throat, sinus congestion, cough (productive or non-productive), fever, chills, nausea, vomiting, diarrhea, malaise, body aches, runny nose, headache, pain w/deep breath, and rash. All data was stored in electronic format using personal identification numbers and date of sample collection.
During the period of sample collection, two outbreaks of Streptococcus pyogenes occurred. Throat swab and blood samples were collected as above on acutely ill BMTs and on those who recovered from illness and were still in basic training. Diagnosis of Streptococcus pyogenes was confirmed by bacterial culture and subsequently by PCR.
For the experiment supporting the present invention all male BMTs who were determined to be healthy (no acute medical illness in 4 weeks prior to initiation of basic training) were eligible for study. In Phase II, any male BMT with T>100.4 and respiratory symptoms were eligible for consent. In the experiments described in the examples below, the patient population enrolled consisted of male BMTs between the ages of 17-25. Seventy percent were white, 12% Hispanic, 12% black and 6% Asian. Thirty BMTs who were determined to be healthy were enrolled, 30 who had fever and respiratory symptoms and determined to have adenovirus by rapid assay (confirmed by viral culture and PCR) were enrolled, 19 with fever, respiratory symptoms and non-adenoviral infection were enrolled. The 30 BMTs with fever, respiratory symptoms and adenovirus had another nasal wash and blood draw performed during convalescence from their illness.
Metadata for the experiments supporting the present invention were obtained by providing the healthy incoming BMTs with a standardized questionnaire. These individuals were excluded from inclusion if they had fever, sinus congestion, nausea/vomiting, burning with urination, cough, sore throat, diarrhea or chills in the 4 weeks prior to basic training. In order to determine conditions that might affect baseline gene expression, these individuals were screened for: race/ethnicity, vaccination status, time of most recent meal, time of last exercise, perceived stress level, allergies, recent injuries, current medications, and smoking history.
For Phase II, when BMTs were presenting with fever and respiratory symptoms, a standardized questionnaire was administered. In order to determine conditions that might affect baseline gene expression, these individuals were screened for: race/ethnicity, vaccination status, time of most recent meal, time of last exercise, perceived stress level, allergies, recent injuries, current medications, and smoking history. The duration and type of respiratory symptoms to include sore throat, sinus congestion, cough, fever, chills, nausea, vomiting, diarrhea, fatigue, body aches, runny nose, headache, chest pain and rash were recorded on standardized forms. A physical examination was recorded on standardized form to detail signs of illness in the BMT. Type and duration of medications taken were recorded.
For Phase III when the BMT with adenoviral illness had recovered (14-28 days after presenting ill) another standardized questionnaire was administered, including questions on time of most recent meal, time of last exercise, perceived stress level, allergies, recent injuries, current medications, and smoking history. The total duration of each symptom from the Phase II questionnaire was noted and the total period of recovery from each symptom was determined. A detailed history of medication use between the time of Phase II and Phase III was taken.
The ability to collect samples in a longitudinal study enables one to study gene expression throughout the course of an infectious illness. In a study as outlined hereinabove and further supported by the examples of the present application, the present inventors particularly followed BMTs who were ill with adenovirus through the time of their recovery from disease. The detailed database on type and duration of symptoms thus enabled the present inventors to determine whether these factors impact the gene expression signature for adenovirus and Streptococcus pyogenes. Further, the detailed database also enabled the present inventors to discriminate early versus late disease and the severity of disease (for example, expected duration of illness/symptoms).
The detailed and standardized collection of information such as recent meal, recent exercise, perceived stress level, recent injuries, current medications, and smoking history enable control of confounding variables, strengthening the conclusion that identified gene expression patterns are specific immunologic signatures of particular pathogens. This collected information also can be used to determine whether such conditions significantly impact gene expression patterns in a population. A statistical assessment of whether these factors are necessary or confounding for correct classification will determine whether it will be necessary to monitor for them in future experiments and applications.
In the future, gene expression patterns (immunologic signatures) for particular pathogens at different stages of disease may be used to predict morbidity and mortality. This may assist the healthcare professional in determining the appropriate level of care (type of medications to use, level of care required-admit to hospital or provide care in the outpatient setting). There currently are algorithms for determining whether individuals with respiratory infection (particularly pneumonia) should be admitted to the hospital (and to what level of care) and these algorithms rely on such factors as degree of fever, heart rate, respiratory rate, blood gases and blood chemistries (47, 48) (49). A detailed understanding of the state of immunologic activation of the ill individual through gene expression may further assist with determining severity of illness.
Moreover, understanding gene expression patterns, based on the inventive techniques herein, in individuals who are recovered from a particular infectious illness would enable forensic analysis of past outbreaks. Subsequently, this information may be used to determine whether certain pathogens are naturally endemic in specific geographic areas or whether new infections have been imported to regions (e.g., how many have been previously infected with West Nile Virus?).
Further, for an individual, the present invention enables determination of whether these individuals have been infected with a particular infectious pathogen in the past and from this information determines the likelihood of immunity/protection against future infection with the same or related organism. Such information would be valuable as it could guide whether vaccination or prophylaxis is necessary for particular deploying/deployed troops or hospital workers.
Having established a prospective, longitudinal study using PAX tubes, this gave the present inventors the opportunity to assess the quality of the modified protocol for gene-expression analysis of RNA using PAX tubes and the Affymetrix Genechip platform in a real world test bed of ongoing epidemics of upper respiratory disease.
Many factors contribute to the variability of target detection, with the quality of RNA being one of the most important. The quality of RNA from PAX tubes collected blood could be influenced by the disease status of the donors, sample handling, and other downstream processes. Previously, the present inventors showed that under two conditions representative of practical sample handling, the PAX system was capable of preserving blood RNA to produce good quality metrics and relatively stable transcriptome measurements (50). Recently, new RNA quality metrics have been proposed based on associations between experimental treatment of cells or purified RNA to induce RNA degradation and metrics derived from electropherograms of the RNA on the bioanalyzer (51). One new metric is the degradation factor (% Dgr/18S), which is the ratio of the average intensity of bands from degraded RNA, that is peaks of lesser molecular weight than the 18S ribosomal peak, to the 18S band intensity multiplied by 100. It is a continuous variable that is used to derive a categorical variable named ‘Alert’. Alert has five values:
Thus, for PAX system isolated RNA from the present inventors previous study (50) and current BMTs cohort, the distributions of RNA quality metrics were reported, which would be useful for comparisons and planning of protocols by other labs; determined the up-stream quality metrics that are most indicative of the quality of microarray target detection outcomes; and determined the effects of inter-individual hemoglobin variability on the sensitivity of target detection.
The present inventors demonstrate that the Alert metric was a robust indicator of microarray results and will be useful for high throughput RNA quality control, especially as one practically cannot look at all the electropherograms directly during an ongoing study and must be able to rely on an indicator to flag a sample for further evaluations.
The magnitude of the apoptosis factor suggested that a high percentage of blood cells underwent apoptotic cell death. This could be due to the PAX RNA stabilizing reagent inducing cell death via apoptosis upon contact with blood cells, or simply due to differences between whole blood and cultured cells from which the apoptosis factor was derived. If interested in studying apoptosis related pathways, one would have to investigate this property further with the PAX system technology. In this manner it may be possible to correlate the apoptosis factor with gene-expression profiles to implicate apoptotic pathways.
The stability of the RNA from PAX tube blood that was handled a variety of ways suggest that for future studies one can be more confident in the stability of RNA throughout the range of these handling conditions.
The present inventors were next able to explore appropriate methods of scaling of gene expression arrays when applied to detection of clinical phenotypes. While global scaling approaches have been advocated for other study designs and uses involving gene expression arrays, we concluded that the use of the 100 housekeeping genes provided the least biased approach, although 5 approaches were considered:
1) double scaled global normalization
2) no normalization at all
3) 100 hk gene scaling
4) 100 hk gene median normalization
5) empirical set of normalization gene
After QC/QA of the PAX tube RNA and the microarray scaling, we undertook class prediction and class comparison modeling (a summary appears in Tables 7, 10, and 11). The class prediction using gene-expression, suggestively, performed better than using CBC or electropherograms alone. This could be that gene-expression does in fact contain more information about the sample or that it simply has more variables thus providing more opportunities to find a good classifier by chance alone. More specifically, the p-value for the significance test of classification rate suggests that gene expression is better for classification than the CBC or electropherogram and that it is not likely a function of number of variables acquired because the CBC actually has 10 times as many as gene expression and performed poorly.
In order to study another patient population (broader age range, male and female, civilian) and to increase the number of pathogens recovered, another protocol was undertaken which focused on patients presenting to medical clinics and hospital wards at the Wilford Hall Medical Center at Lackland AFB (sometimes referred to herein as “the Hospital study”).
For the Hospital study, patient selection (Inclusion criteria) was conducted as follows. Adults (male and female) greater than the age of 18 were included. All were presenting to the hospital or hospital clinics with temperature >100.4° F. and respiratory symptoms. Nasal wash and throat swab were collected most commonly by a study nurse or by medical personnel who had been instructed by the study nurse. A portion of the nasal wash was used to screen for influenza A or B by rapid antigen capture assay (52) and this result was confirmed by culture and PCR. All nasal wash specimens were additionally cultured for Parainfluenza 1, 2, 3, RSV and adenovirus. Accordingly, in an embodiment of the present invention, the gene expression analysis may be combined with one or more pre-screening methods. For example, the pre-screening method may include abovementioned influenza A or B rapid antigen capture assay, a culture assay, a PCR-based assay, a method described in U.S. 60/590,931, filed on Jul. 2, 2004 (the entire contents of which are incorporated herein by reference).
A CBC will be obtained for all enrollees with differential. In addition, each enrollee will be given a standardized questionnaire including questions relating to race/ethnicity, vaccination status, time of most recent meal, time of last exercise, perceived stress level, allergies, recent injuries, current medications, and smoking history. The duration and type of respiratory symptoms to include sore throat, sinus congestion, cough, fever, chills, nausea, vomiting, diarrhea, fatigue, body aches, runny nose, headache, chest pain and rash are recorded on standardized forms. Physical examination findings are recorded on standardized forms.
This is a cross-sectional study that includes adults of all ages with differing severity of disease (some will be in the outpatient clinic setting and others admitted to the hospital). The ability to collect blood samples over more than one influenza season will enable the present inventors to determine the gene expression pattern to influenza A and B and may allow us to determine whether there is a specific gene expression pattern for different strains of influenza A (H1N1 vs. H3N2).
For this study, the present inventors will monitor whether individuals received the injectable form of the influenza vaccine and the timing of vaccine relative to illness. The present inventors will discern whether the gene expression pattern differs between individuals with “breakthrough” influenza-illness occurring greater than 2 weeks after time of influenza vaccine compared to the gene expression pattern seen in unvaccinated individuals with illness. The present inventors will perform the same comparison for those individuals who receive FluMist (MedImmune Vaccines) intranasal vaccination with a live, attenuated strain of influenza. Understanding gene expression patterns after vaccination may predict likelihood of protection from disease and likelihood of breakthrough illness; the efficacy of the influenza vaccine is considered to be 70-80%
Because the Lackland BMT population will be receiving FluMist as a strategy of prophylaxis during the 2004-2005 flu season, the present inventors will assess gene expression profiles in individuals who receive FluMist and develop flu-like symptoms and those without in the 7 days following vaccination; it is well know that individuals receiving FluMist may develop cough, sore throat and muscle aches in 2-7 days post-vaccination as they shed the attenuated virus (CID 2004:38 (1March), 760-762 full reference below), but the gene expression pattern post vaccination has not been determined. This study will allow us to determine whether there is a gene expression pattern that enables us to differentiate which individual is symptomatic after FluMist vaccination, but developing a protective immune response and which individual has actually developed cough, sore throat, muscle aches due to acquisition of circulating wild type influenza in the population. This is a critical distinction to make in a closed population, such as the BMTs or college students in dormitories, because it is this age group that is most appropriate to receive the FluMist vaccine and yet the most likely to have transmission of wild type influenza in closed quarters.
Individuals typically become infected with an infectious pathogen and remain asymptomatic during the incubation period prior to onset of disease. During this incubation period, the host begins to mount an immune response to the infecting pathogen. Typically the initial response is the innate immune response mounted by natural killer cells and neutrophils. Later in infection, the specific host immune response comprised of T lymphocyte, B lymphocyte and antibody responses becomes effective. In some infections, such as with the bioagent, Francisella tularensis, as few as 10 organisms can ultimately cause symptomatic disease; while this small number of organisms can be difficult to detect directly, the host immune response typically constitutes an amplified response of literally millions of immune cells and this immunologic signature can likely be detected prior to the onset of clinical symptoms.
There are clinical scenarios in which it would be advantageous to the health care provider, public health officers and commanders/public officials to determine not only who is infected with a particular pathogen, but who has also been exposed to this same pathogen either by direct exposure or through transmission from an infected index case. For example, if the infectious agent of smallpox was released and an index case was detected, it is anticipated that each index case would significantly expose close contacts (face-to-face contact within 3 feet) via respiratory droplets and nuclei. Typically, for each index case of smallpox as many as 10 other susceptible individuals may develop the disease. In view of the limited amount of smallpox vaccine and potential adverse reactions to the vaccine, predicting who amongst the exposed would develop disease could direct resources and limit adverse side effects of the vaccine. Gene expression studies can detect developing, specific immunologic signatures for pathogens and assist in determining who in a population has been significantly exposed and infected (carrying organism) and who amongst the exposed-infected will ultimately develop disease. Therefore, the methods of the present invention are particularly useful for the identification of gene expression signatures and the results obtained thereby may be used directly to guide and/or tailor therapeutic regimens.
To this end, the following study design permits the study of cues and expression profiles at various stages of pathogen exposure and onset. Since the majority of BMTs arriving to basic training from their respective home communities will be susceptible to infection with adenovirus, the present inventors are able to screen BMTs presenting with fever and respiratory symptoms to Lackland AFB clinics with a rapid assay for adenovirus. Once a BMT is identified as being infected with adenovirus, the BMTs with whom he/she has had face-to-face contact can be followed for infection and subsequent development of disease. Significantly exposed BMTs can have blood drawn for gene expression during the exposed/asymptomatic period and again after development of disease and during recovery. Gene expression patterns obtained from these time points are then analyzed to determine the gene expression pattern that best predicts development of disease.
In anticipation of the abovementioned study, BMTs who are ill with fever and respiratory symptoms during basic training are receiving a standardized questionnaire to determine other BMTs with whom they have had face-to-face contact within the last week; a database is being generated which labels the infected BMT as the current “index case” and all BMTs with who he/she has had recent contact as “exposed”. Data on the exposed and their relationship to the index case are maintained; for example, the exposed may have been the Training Instructor or Dorm Chief or Element Leader of the index case. If an exposed case next presents to a clinic with fever and respiratory illness, then that case is linked to the initial index case as well as to other BMTs to which he/she may now have exposed. The epidemiology is followed to determine whether there are situations in which the infectious respiratory disease is most likely transmitted; do Dorm Chief or Element Leaders most commonly transmit to individuals within their dorms or elements? This will direct the EOS clinical team on who constitutes the best case definition for “significant exposure” and, thus, which BMTs would be best to draw for gene expression studies in the “exposed” group. This group will be followed for subsequent development of disease and blood will be drawn if these individuals present with fever and respiratory symptoms.
Next the present inventors describe the present invention in terms of GXP Protocols and Data handling
There are several techniques to quantitatively measure mRNA at various level of throughput. Some of them are Northern blot, RT-PCR, Nuclease protection assay, Quantigene, SAGE, differential display, in situ hybridization, nanoarrays and microarrays. Some of these are not readily adapted for high throughput or can measure at the transcriptome level. For our purposes of surveillance and biomarker discovery, microarray based techniques are most amendable for these purposes. Once biomarkers are discovered, techniques that have short processing time, but less parallel processing capability may be more useful for diagnostic purposes, such as RT-PCR or Quantigene. Techniques to measure mRNA generally involves sample preparation, mRNA amplification and labeling if needed, followed by hybridization, then washing; staining, and/or detection of signals. There are variations to all these major steps. Sample preparation may be extensive such as for the Affymetrix genechip platform or minimal such as the Quantigene system from Genospectra. Ideally, for our purpose, we want to measure the most number of transcripts in the shortest time and the highest sensitivity and specificity. Although we have used the Genechip technology to discover biomarkers and pathways, there are many possible improvements on the current Affymetrix technology or other technologies that one can think of or already available to assess in the field (several of which are discussed herein and form a part of the present invention).
For the platform that the present inventors have tested, the Affymetrix genechip platforms, recent improvements include reducing the amount of initial RNA needed, shortened time of processing, or robotics to facilitate high throughput and reduce operator variability. Several options are available on the market to incorporate into the sample processing step of the Genechip platform. One is the new IVT kit from Affymetrix that can use 1 μg starting amount of total RNA versus 5 μg previously. Another is the double cycle IVT from Affymetrix that can start with 10 ng total RNA, however, the processing time and complexity of the assayed is increased. The Ovation kit can also amplify and label RNA starting with as low as 5 ng, and they claim the time is in 4 hours. However, it has not been extensively tested with the Genechip microarray. A recent publication also attempted to label the mRNA directly without amplification to shorten processing time, but the sensitivity was reduced.
There are many areas of improvements at various steps in the processing that the present inventors contemplate in the present invention. One is to combine and develop various steps in the surveillance process. For sample collection, instead of Paxgene, one could use microcapillary tubes to collect blood, then stabilize with RNAstat, then isolate RNA via several available kits for RNA isolation from small volumes of blood, such as the Dynabeads® mRNA DIRECT™ Kit that can isolate mRNA using only 1 tube in 15 min, then use the Ovation kit to amplify and label, followed by hybridization onto Genechip and wash and stain the next day. In addition, the hybridization time may be reduced from it current time of 16 hrs on the Genechip to a time ranging from 8-14 hours, preferably 10-12 hours, or even shorter times. To further reduce the hybridization time, the present invention contemplates applying a strong electric/magnetic field to the chip during hybridization. Also to reduce hybridization time, the hybridizing temperature may be increased and then ramp down to 45° C., the current temperature for hybridization.
To improve sensitivity, the skilled artisan may employ alternative signal emitters. Currently, the signal emitter is the strepavidin-phycoerythrin followed by further amplification with biotinylated anti-strepavidin. However, the present invention contemplates the use of the branch DNA from Genospectra to amplify signal, quantum dots followed by multiple scans as the quantum dots do not quench, alexi dyes, or biotin labeled viruses which greatly increase signals because of reduced quenching, higher quantum yields and up to 120 biotin molecule per virus, or RLS particles. Even further, the present invention contemplates the use of probes that are synthesized onto a conductive material, thereby it is possible to detect via electrical signals upon duplex formation, and then one can detect signals right away. In even a further embodiment, another mRNA measurement technology may be employed altogether, especially a nanoarray developed to measure mRNA from single cells.
In the present invention data acquisition is performed using scanner (genechip) and computer.
Data acquisition and handling may be performed by any means known by the skilled artisan. For example, data acquisition and handling may be performed by hand and passing through various programs. The present inventors are in the process of developing software to perform all necessary data analysis automatically and provide results.
The present invention also offers the practitioner and clinician an ability to monitor and/or validate expression profiles identified by other assays. For example, the Griffiths et al (71) report biomarkers for malaria determined by monitoring host gene expression in whole blood from patients suffering from acute malaria or other febrile illnesses. Cobb et al (72) report the effect of traumatic injury upon the gene expression profile of blood leukocytes. While Rubins et al (73) report the gene expression profile determined for primates suffering from smallpox. The methods of the present invention can be used to assess the accuracy and reliability of the biomarkers identified in these, and similar, and to determine whether these biomarkers can be utilized to trace disease progression.
In addition to determining the gene expression profiles in response to pathogen exposure, there are many more questions and hypotheses that could be explored with the database developed by the present inventors. Some of these questions are listed below:
The present invention will certainly find application in the measurement of “baseline” (i.e. normal) gene expression signature measurement. This would have great value in defining the establishment of baseline gene expression profiles across defined demographic populations. Such baseline measurements would have high value in discovery of fundamental differences between sexes, races, and the development and ageing processes. The value of such population gene expression profiling is illustrated in the phenomena such as Gulf War Illness following putative exposures to chemical weapons and environmental toxins wherein a variety of immune disorders were reported (53, 54) without the identification of a specific etiology. In response to Gulf War Illness, the Department of Defense initiated a broad baseline study known as the Millennium Cohort that has collected general health questionnaires from hundreds of thousands of active duty military personnel in hopes of establishing “baseline” indices of normal health. In contrast, baseline gene expression for 105 to 106 specific 25-mer transcriptional sequences would provide orders of magnitude greater information regarding the possible genomic and physiological etiologies of phenotypic or asymptomatic illnesses caused by external perturbations.
The present invention may also be used for diagnoses of: oncology diseases including: CML (bcr/abl0) (30), circulating tumor cell detection, colorectal cancer recurrence, neurology (MS), hemostatus and thrombosis, inflammatory disease (48 inflammatory genes for Rheumatoid Arthritis from Source Precision Medicine), diabetes, respiratory disease, and cytotoxicity and toxicology. (55). Generally, the present invention may find utility in any diseases or physiological states that have mRNA biomarkers from blood can use similar methods described herein.
Although it has been speculated that gene expression profiles could be diagnostic for asymptomatic disease diagnosis and prognosis, the practical reduction of that concept to practice has proven quite elusive. At least one prior study has shown that peripheral blood leukocytes obtained using PAXgene kits has yielded evidence of the utility of obtaining cDNA microarray baseline (i.e. healthy) expression signatures (Whitney et al 2003) (18). Other studies and prior art have shown time exposure of a known dosage of an infectious agent can lead to detectable signatures.
However, it has been exceptionally difficult, if not impossible to obtain experimental cohorts that allow simultaneous measurement of gene expression profiles in a homogeneous, isolated and experimentally accessible human population that contains statistically significant numbers of the following categories: (1) healthy baseline individuals in the identical physical environment as those who will be infected with a pathogen, (2) individuals who do not have an acquired immunity against a pathogen but encounter a low level of pathogen exposure to that pathogen, or have a high innate immunity, and exhibit distinguishable “successful” immune responses against the pathogen and do not become symptomatic for illness, (3) individuals who become ill following actual pathogen exposure and manifest symptoms without becoming febrile, (4) individuals who are exposed to the pathogen and develop illness with symptoms satisfying criteria for “febrile respiratory ill” (FRI) but who do not become so ill as to require hospitalization, (5) same as 4 except that severe illness develops and the individual meets medical criteria for hospitalization, and (6) individuals in various stages of recovery from categories 3-5.
While individuals are incubating an infectious agent and before the onset of symptoms, the innate immune system begins to mount a rudimentary response followed by a more effective specific immune response. During these phases, immune cells manufacture various cytokines and chemokines to mount an effective response. These biomarkers of the immune response provide an immunologic signature that may precede clinical symptoms.
Thus, there is a critical need to develop methods for discovery of unique gene expression patterns for various time points within the above mentioned classes, and the present invention successfully demonstrates those methods.
Assays for pre-symptomatic diagnosis and prognosis of infectious disease would find utility in a variety of applications where the information is of sufficient quality to provide decision-quality information. For example, individuals who are at risk to themselves, to others, or to the completion of an important task as a result of probable or imminent illness can be temporarily replaced until the impending illness is managed. Examples would include pilots (commercial or military) prior to long-range flights, surgeons, etc.
Another use would be in the mitigation of an act of bioterrorism or industrial accident where hundreds, thousands, or even millions of individuals would be exposed to varying degrees of a toxic or infectious agent. Data obtained following the 2001 anthrax attacks in Washington, D.C. and New York, N.Y. indicated that for every 1 person who obtained a sufficient exposure to anthrax cause illness and death, there were another 1,500 “worried well” persons who were candidates for prophylactic administration of antibiotics. This number could have been orders of magnitude higher if the agent had been infectious (e.g. smallpox virus) instead of anthrax. If the remedial action, such as the administration of a high dosage of vaccine, antibiotic, or drug carries an associated risk (e.g. highly adverse reaction in 1 out of every 250 persons) then the remedial action could be of greater threat to public health than the initial attack or accident without the appropriate assessment of risk within an exposed population. Alternatively, the vaccine, antibiotic, or drug may be in short supply and a triaging of exposed individuals would be highly desirable to make maximal use of available quantities. Thus, a set of pre-symptomatic indicators could be of critical importance in the appropriate application of countermeasures in the above-mentioned situations.
In the above-mentioned applications, it will be necessary to measure specific sets of transcriptional markers in a more rapid and cost-effective manner than that using a DNA microarray. Thus, the high density DNA microarray is a high-content discovery tool that teaches the distillation of the most meaningful transcriptional markers. Although, recent advances, such as shortening time of sample and target preparation with small initial amounts of RNA may allow the high density DNA microarray to be a direct diagnostic platform instead of simply being a biomarker discovery platform. Other platforms for highly parallel measurements of gene expression include SAGE and MPSS (56), but these methods are technically challenging. MPSS can provide the exact number of an RNA molecule per cell, even the ones at very low levels. Thus, MPSS might be used to confirm results from microarrays.
Definition of Subsequences within “Genes”
The first step in the reduction to an alternative platform involves a statistical reduction of the number of specific transcriptional markers that are required to still make a high percentage of classifications with an acceptable probability of error. Unlike discoveries of “gene expression” using microarrays prepared using cDNA molecules (several hundred base pairs of double stranded DNA) or even long oligonucleotides (e.g. single-stranded 70-mers), the Affymetrix gene expression microarrays probe all known genes with a combination of at least ten 25-mer probe pairs across the wherein one of the pair members is a perfect sequence match to the predicted gene sequence and the other is a mismatch, comprised of the same sequence as the its partner except for the middle (number 13 position) nucleotide. Complementary binding between a 25-mer probe and its target transcriptional marker is severely attenuated by even a single mismatch (unlike long oligonucleotide and cDNA probes). Hence, it is critical to recognize that only small oligonucleotide probes provide probe-wise interrogation of the highly heterogeneous transcriptome, the content of which varies with not only gene activation and deactivation but also with alternative exon splice variation, depending on exact physiological conditions.
Although the GCOS software makes “present” or “absent” calls for a known or predicted full length gene sequence based on an algorithm which considers the probe pair intensity profiles across the three prime end of the gene sequences, the result can be de-convoluted into individual probe pair intensities. The intensity values that are available for each probe set within each known gene sequence are relatively high confidence sequence identifications that are independent of whether that 25-mer transcriptional sequence has been spliced into different resultant mRNAs. A cDNA probe for a full length gene product would be entirely incapable of making such a discrimination, and the 70-mer probe array should show intermediate level of sequence determination, but would require higher hybridization stringency. Moreover, the error rate in a transcriptional sequence determined from the long oligonucleotide 70-mer would be intermediate to high inaccuracies.
In a manner similar to that described in the present invention for reducing the number of full sequence genes required to make classifications, the number of subsequences within the full length gene sequences may also be selected for use in classification, irrespective of whether the Affymetrix GCOS software identified the full length “gene” as being “present” or “absent”. In this manner, the classification problem will be reduced to a set of defined 25-mer subsequences having experimentally-verified abundance variations instead of full-length gene sequences which will be comprised of subsequences might or might not actually be present or change in abundance.
The Affymetrix GeneChip® platform provides an excellent format for the discovery genome-wide expression changes in research, and possibly for clinical diagnostics in situations that allows one or more days for a result (e.g. tumor prognosis). However, many applications, including infectious diagnostics, will be more critically time-dependent. Ideally, these assays will be performed in several hours.
In several very preferable embodiments, the information gleaned from whole genome GeneChip® experiments will be used produce a greatly reduced set of markers that can be measured rapidly in an alternative format that is optimized for both speed and simplicity. In one very preferable embodiment, a reduced set of gene expression markers is analyzed by reverse transcription PCR (RT/PCR) without requiring isolation of total RNA. An example of this can be found with the Ambion (Austin, Tex.) “Cells-to-Signal™” Kit, which allows RT/PCR amplification directly from cell lysates following a 5 minute incubation with the reagent, bypassing the need for mRNA isolation. Such a technique might be applied to whole blood lysates or to lysates of specific cell types that are separated from whole blood by any of a number of methods, including centrifugation, fluorescence-activated cell sorting (FACS), or by other flow cytometry techniques, such as with the use of the Agilent Bioanalyzer 2100 or the like.
The cDNA products from the preparations described above can be analyzed directly in small numbers using real-time PCR techniques (e.g. TaqMan, or Fluorescence Energy Transfer (FRET) techniques, molecular beacons, etc.) or in larger numbers using DNA microarrays having a much smaller probe content than the whole genome Affymetrix GeneChips in a system that is optimized for speed and simplicity (57). The microarrays used for this purpose could be selected from a large number of options described in a previous overview (58).
In a highly preferred embodiment, the volume of blood required to perform an assay of the type described above would be greatly reduced relative to that required for the experiments described in the present invention.
There are two small aliquot techniques available on the market currently. Both can amplify from nanograms amount of RNA to microgram amounts. One is from Affymetrix which supports its two-cycle amplification protocol. This protocol basically doubles the in vitro transcription step to obtain more cRNA products. Of course, this would also increase the workload and the time considerably. A new protocol for amplifying nanograms of RNA in a relative short time is available from Ovation™. Although this technique has not been extensively tested on the Affymetrix system, it holds much promise and is contemplated by the present invention. By these techniques only a few drops of blood is needed to isolate nanograms of RNA. Additional methods may be developed to collect drops of blood and RNA stabilization. One such possibility is to use RNAstat to stabilize the blood and for transportation and storage; followed by RNA isolation when needed.
Alternatively, the information obtained from whole genome GeneChip® experiments could be used produce assays that probe for the polypeptides that are coded for by the transcriptional markers detected by the GeneChip® whole genome assay. These polypeptides could be detected in blood or from cell lysates using microarrays comprised of antibodies (59) instead of DNA probes or by mass spectrometry methods that measure relative protein abundances.
However, it is a central hypothesis of the Epidemic Outbreak Surveillance (EOS) program and the present invention that the only economical method to realistically widely deploy a parallel pathogen surveillance assay in a clinical environment is to do so in parallel with assays that have validity in their own right for routine clinical diagnosis of common pathogens. That is, unlike a reimbursable diagnostic assay for a common pathogen, an un-reimbursable assay for bioweapons surveillance will only burden a clinical operation and will not be widely adopted. Because it may not always be possible to identify the specific cause of an infection through pathogen genomic markers (e.g. using PCR or microarrays), there remains a critical need to determine alternative “biomarkers’ from the host that would elucidate the character of the disease etiology and guide the clinician in the proper management of the infection. Gene expression monitoring is thought of as a potentially revolutionary technology that could provide hundreds if not thousands of such “biomarkers”. However, in order for gene expression-based bio-defense assays to move beyond scientific curiosity and into the realm of clinical diagnostics, a significant work must be carried out to demonstrate that the principle is applicable to routine clinical diagnostics. Hence, there is a critical need to develop databases of baseline (normal) human gene expression levels and to understand the nature of perturbations caused by various levels and stages of pathogen infection.
The above written description of the invention provides a manner and process of making and using it such that any person skilled in this art is enabled to make and use the same, this enablement being provided in particular for the subject matter of the appended claims.
As used above, the phrases “selected from the group consisting of,” “chosen from,” and the like include mixtures of the specified materials.
Where a numerical limit or range is stated herein, the endpoints are included. Also, all values and subranges within a numerical limit or range are specifically included as if explicitly written out.
The above description is presented to enable a person skilled in the art to make and use the invention, and is provided in the context of a particular application and its requirements. Various modifications to the preferred embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the invention. Thus, this invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
Having generally described this invention, a further understanding can be obtained by reference to certain specific examples, which are provided herein for purposes of illustration only, and are not intended to be limiting unless otherwise specified.
Informed consented Basic Military Trainees (BMTs) generously donated blood and/or nasal washes. Blood collection and RNA isolation was performed using the Paxgene Blood RNA System (PreAnalytiX), which consists of an evacuated tube (PAX tube) for blood collection and a processing kit (PAX kit) for isolation of total RNA from whole blood (35). The isolated RNA was amplified, labeled, and interrogated on HG-U133A (A) and HG-U133B (B) Genechips from Affymetrix. The Affymetrix GeneChip platform measures a significant subset of the transcriptome. In design, it incorporates a DNA oligonucleotide microarray, manufactured via photolithography to detect labeled cRNA targets amplified from RNA populations. Nasal washes were aliquot and sent for determination of adenovirus infection via culture and real-time PCR.
Lackland Air Force Base (LAFB) in San Antonio, Tex. is the location of Basic Military Training for all recruits to the United States Air Force. More than 50,000 Basic Military Trainees (BMTs) undergo a 6 week training course prior to assignment of duty. These BMTs are organized into flights of 50-60 individuals that eat, sleep and train in close quarters. Each flight is paired with a brother or sister flight with which there is increased contact due to co-localization for scheduled activities and multiple flights are grouped into squadrons which reside in the same dormitory building, subdivided into dorms for individual flights.
BMTs arriving to LAFB underwent informed consent to participate in this study. On day 1-3 of training, approximately 15 milliliters of blood were drawn from each BMT into a total of 5 Paxgene tubes, per standard protocol, to establish baseline gene expression profiles. BMTs who presented during training with a temperature of 100.5 or greater and respiratory symptoms were consented for a nasal wash and Paxgene blood draw. All Paxgene tubes were maintained at room temperature for 2 hours and then were frozen at −20 C and shipped on dry ice to the Naval Research Laboratory (NRL) within 7 days for processing. Nasal washes were performed by standard protocol using 5 cc of normal saline to lavage the nasopharynx with collection of the eluent in a sterile container. Nasal wash eluent was stored at 4° C. for 1-24 hours before being aliquoted and stored at −20° C. and shipped to NRL within 7 days for processing.
All BMTs underwent a standardized questionnaire at initial presentation, during presentation with illness, and at follow-up. Questions posed to BMTs include: vaccination history, allergies, last meal, last exercise, last injury, medication taken, smoking history, observed subjective symptoms, and last menstruation (if appropriate). Among the observed subjective symptoms asked and monitored are: sore throat, sinus congestion, cough (productive or non-productive), fever, chills, nausea, vomiting, diarrhea, malaise, body aches, runny nose, headache, pain w/deep breath, and rash. All data was stored in electronic format using personal identification numbers.
The present inventors sought to determine the gene expression patterns that developed in Basic Military Trainees (BMT) populations as they were naturally exposed to respiratory pathogens and subsequently developed disease during their 6 week training period. Up to 50% of BMTs experience upper respiratory tract infection (URI) during training and 40% of these will have fever and URI symptoms. Approximately 60-80% of febrile respiratory disease is due to adenovirus type 4. Other pathogens that cause a significant minority of disease include Streptococcus pyogenes, Chlamydia pneumoniae, Mycoplasma pneumoniae, and Bordetella pertussis.
BMTs maintain set schedules throughout the 6 week training program and are kept in close proximity; the BMT population offers a unique opportunity to evaluate gene expression profiles resulting from pathogen exposure and/or infection in the absence of confounding external/environmental factors.
In the first 18 months of the EOS program, a Lackland and Air Force Surgeon General Institutional Review Board (IRB)-approved protocol was implemented. This protocol continues to be supported by the Lackland 37th Training Wing Commander and the Base Commander. The present inventors implemented an experimental model for comparing whole blood expression profiles from four categories of BMTs:
1. Healthy (baseline),
2. Febrile Respiratory Illness (FRI) adenovirus 4 infected (Ad4+),
3. FRI without adenovirus (Ad4−), and
4. post-FRI Ad4+ (individuals recovered from adenoviral infection, i.e. #2 above).
Individuals were identified as healthy if they were in week 0 of basic training and had no respiratory symptoms in the prior 4 weeks. Individuals with FRI were identified by primary providers and study nurses as the BMTs presented to health clinics and dispensaries. All BMTs were consented and underwent blood draw to determine gene expression profiles. All ill BMTs were administered a standardized questionnaire to determine the type of presenting symptoms and the onset and duration of symptoms. Physical examination and complete blood counts were recorded. BMTs who were determined to have an adenoviral illness by rapid immunoassay/PCR/culture underwent a subsequent blood draw and nasal wash 14-21 days after their initial FRI presentation; the majority of these individuals had no further symptoms of infection at the time of the follow-up blood draw. PCR for adenovirus and culture for all respiratory viruses was performed on nasal washes. One hundred BMTs were entered on the study, including 30 healthy BMTs. Whole blood gene expression profiling for 33,000 known genes and open reading frames (ORFs) was performed on PAXgene blood RNA samples using Affymetrix U133A/B chip sets. Data from 76 BMTs is available with the following breakdown: healthy (n=38), febrile without adenovirus infection (n=14), febrile with adenovirus infection as determined by culture (n=24), and those who recovered from adenovirus associated febrile illness (n=26). Initial search for genes that show expression level differences of >=1.5 fold-change of the lower 90% confidence interval between groups showed that: 913 genes differ between healthy and febriles at 0.1% median false discovery rate (FDR); 203 genes differ between healthy and recovered at 2.0% FDR. Ongoing recruitment with the addition of a screening rapid assay for adenovirus has enabled increased enrollment of FRI Ad4− BMTs and will enable statistical analysis between the FRI Ad4+ and Ad4− groups.
PAX tube blood collection. Blood was collected into the PAX tubes from volunteers according to the manufacturer's directions (60). For the experiment described in
Total RNA isolation. After sample collection, the PAX tubes were incubated at room temperature for 2 or 9 hours, followed by immediate total RNA isolation or freezing at −20° C. for 6 days before further processing. For total RNA isolation, we followed the PAX kit handbook (33), but with modifications to aid tight pellet formation after proteinase K treatment. Loose pellets were problematic. To form tight pellets, we increased the proteinase K added from 40 μl to 80 μl (>600 mAU/ml) per sample and the 55° C. incubation time from 10 min to 30 min. After spinning the samples, if a tight pellet still did not form, then we remixed the samples, incubated at 55° C. for another 5 min, and followed by centrifugation. The optional on-column DNase digestion mentioned in the PAX kit handbook was not carried out. Thus, OD measurements at this point would not give accurate quantification due to DNA contamination; however, the 260/280 ratio may indicate other contaminants. Approximately 4 μl of the 80 μl eluted RNA was needed to obtain an absorbance greater than 0.1. All aliquots were diluted in 10 mM Tris-Cl pH 7.5 for OD readings.
In-solution DNase digestion. Subsequently, in-solution DNase treatment was carried out using the DNA-free™ kit (Ambion). Briefly, for each sample eluted in 80 μl BR5 buffer, we added 7 μl 10×DNase I buffer and 1 μl DNase, followed by mixing and incubation at 37° C. for 20 min. Afterwards, 7 μl of DNase inactivation reagent was added, incubated at room temperature for 2 min, and spun down to pellet the beads that were in the inactivation reagent. The treated RNA in the supernatant was pipetted off without disruption of the pellet. An aliquot of each RNA sample was run on the bioanalyzer for quantification and QC measurements.
Poly-A RNA isolation. After DNase treatment, duplicate samples were pooled, and mRNA was isolated using the Oligotex™ mRNA kit (Qiagen). The mRNA was eluted in 100 μl total of OEB buffer.
Sample concentration. Next, the samples were concentrated via ethanol precipitation. For each 100 μl sample, we added 1 μl glycogen (5 mg/ml) (Ambion), 15 μl 5M ammonium acetate, and 200 μl 100% ethanol chilled at −20° C. The reaction was incubated at −20° C. overnight. The next day, the samples were spun down at 13,791 g at 4° C. for 30 min. The pellet was washed twice with 80% ethanol chilled at −20° C.; air-dried; and resuspended in 12 μl of nuclease free water (Ambion).
Generation of cRNA. All subsequent steps were carried out as described in the GeneChip® expression analysis manual (6). Ten microliters of each sample were used in the first strand cDNA synthesis reaction. Ten microliters of purified double-stranded cDNA were used for synthesis of biotin-labeled cRNA. Fragmentation, hybridization, and detection were performed as described in the manual (6).
Measurements on the bioanalyzer. One microliter, from pre- and post-DNase total RNA, purified double stranded cDNA, purified cRNA diluted 1:10, and fragmented cRNA, was run on the bioanalyzer using the protocols described in the RNA 6000 Nano Assay (Agilent Technologies) (61). The usage of the bioanalyzer was analogous to gel electrophoresis, except that the gel matrix and samples were flowed through microfluidic channels of a cartridge, thus facilitating small sample usage and automated quantification.
Real-time PCR for gapdh gene. Each real-time PCR reaction for gapdh DNA included: 12.5 μl 2×SYBR green PCR master mix (Applied Biosystem), 0.5 μA 5′GTGAAGGTCGGAGTCAACGG forward primer (10 μM), 0.5 μl of 5′GCCAGTGGACTCCACGACGTA reverse primer (10 μM), 10.5 μl of water, and 1 μl of template from total RNA or cDNA samples. The reactions were carried out in the iCycler (Biorad) with cycling settings of 95° C. 3 min; 95° C. 30 s, 58° C. 30 s, and 72° C. 30 s for 40 cycles; followed by melting curve analysis and/or a 4° C. hold. The completed reactions were also analyzed by gel electrophoresis.
Reverse transcription. For RNA quality assessment during protocol development, synthesis of cDNA was carried out using the SuperScript™ First-Strand synthesis system for RT-PCR kit (Invitrogen Life Technologies).
Statistical analysis. Statview (SAS Institute) software was used to perform the nonparametric Mann-Whitney U test to determine statistically significant differences between 260/280 OD ratios, concentrations via 260 nm absorbance, concentrations via integration of fluorescence profiles, relative amounts of contaminating DNA via threshold cycle, RNA quality via ribosomal 28S/18S peak ratios, double stranded cDNA yields, purified cRNA yields, and 260/280 ratios of purified cRNA. A P-value of less than or equal to 0.05 was considered statistically significant.
Affymetrix Microarray Suite 5.0 (MAS 5.0) (62) was used for generation of QC metrics including: noise (RawQ), an indicator of variation in pixel intensities; average background; scale factor, an indicator of variation of intensities between chips; percent present calls, an indicator of the number of genes detected; and gapdh 3′/5′ signals and actin 3′/5′ signals, indicators of RNA degradation. Dataplot (63) was used to assess autocorrelations of QC metrics. Statview was used to make individual line charts and to set quality control limits at ±3 standard deviations from the mean.
MAS 5.0 CEL files, which contained intensity values of each probe, and gene expression present calls were imported into dChip (64, 65) for further analysis. In dChip, HG-U133A and HG-U133B chips were analyzed separately. dChip uses intensity values of probes on multiple arrays to calculate an expression index, which is a measure of transcript abundance. The expression index is analogous to the signal statistic output by MAS 5.0. dChip was used for hierarchical clustering and fold-change determinations, and the expression indices were exported to JMP IN (SAS Institute) for analysis of variance.
Adaptation of RNA from PAX tube for use with the GeneChip® system. RNA from a PAX tube was isolated using the protocol provided with the PAX kit. As determined by spectrometry, the yield was 4.8 μg; the 260/280 ratio was 2.01; and the concentration was 0.06 μg/μl. This was not sufficient for use with the GeneChip® protocol which prescribed an initial total RNA amount of 5 μg at 0.5 μg/μl (6). Thus, RNA isolated from two PAX tubes were pooled, followed by ethanol precipitation and resuspension in 15 μl of BR5 buffer. This resulted in a yield of 10.4 μg, a 260/280 ratio of 2.07, and a concentration of 0.7 μg/μl, which met the amounts recommended in the GeneChip® protocol.
The optional on-column DNase digestion step was performed as described in the PAX kit. However, for quality assurance, the presence of DNA in the purified RNA was assessed via real-time PCR for the gapdh gene. PCR could detect the presence of gapdh DNA (
The use of Oligotex purified mRNA was based on a preliminary experiment comparing the number of genes detected when using total RNA versus mRNA isolated from blood in PAX tubes. The resulting present calls, signifying the number of genes detected, were 33% for total RNA and 41% for mRNA on the HG-U133A chips. Comparisons were also made between mRNA isolated via Oligotex and mRNA isolated via ion-pair reversed-phase high performance liquid chromatography (IP RP HPLC) (66). The resulting present calls were 17% and 19% for IP RP HPLC and 35% and 40% for Oligotex mRNA. Since Oligotex isolated mRNA showed the highest percent present calls, the step was incorporated into the protocol.
The protocol used for gene-expression profiles of human blood samples using the PAXgene Blood RNA System and the GeneChip® platform includes at least 2 PAX tubes per donor, total RNA isolation without on-column DNase digestion but with in-solution DNase digestion, mRNA isolation, precipitation for concentration, followed by standard protocols from the GeneChip® manual.
Comparison of QC measures for conditions E and O. We compared the quality control measures of PAX tube-collected blood samples whose RNA were isolated after the minimum incubation time of 2 hours at room temperature (
To compare the purity and yield of total RNA from the two conditions, we performed spectrometric analysis on the RNA samples. There was no difference in the 260/280 ratio between the two treatments (Table 1, row 1), suggesting that RNA purity was equivalent for the samples. The yield before DNase treatment was 1.0 μg higher for condition E than O (Table 1, row 2). However, this measure may be confounded by differential DNA contamination in the samples. Thus, after in-solution DNase treatment, we quantitated the RNA using the bioanalyzer (
RNA from various samples produced different profiles on the bioanalyzer, and we would like to use such profiles for QC. Therefore, we overlaid RNA profiles from our samples to assess inter-sample variability and RNA quality (
Since the RNA were of similar quality for the two conditions, we continued through the procedures to make fragmented labeled cRNA. We used the bioanalyzer to monitor double stranded cDNA synthesis (
Since the QC metrics suggested that sample preparation was successful, we hybridized the samples to human HG-U133A chips followed by hybridization onto the HG-U133B chips using the same hybridization cocktails, which had been stored at −80° C. Hybridization, washing, detection, and scanning were done as described in the GeneChip® manual (6).
Afterwards, we assessed the QC metrics along with other samples processed in our facility (
Analysis of gene-expression profiles. To determine the contributions of handling conditions, microarray chips, and differing genes to the variation in measures of transcript abundance, we performed a three-way analysis of variance on dChip-derived gene expression indices from HG-U133A chips. Quantile-normal plot of expression indices from 6 chips indicated that the expression indices were not normally distributed. Thus, 100 genes were randomly sampled from the 22,577 genes, and their expression indices were transformed by adding ‘1’ to every value to remove zeros followed by a Box-Cox transformation to bring the distribution closer to normality. Subsequently, the transformed data was fitted into the following model:
Y
ijk
=M+C
i
+P
j
+G
k
+E
ijk
Where Y stands for the transformed expression indices, M for the grand mean, C for the two conditions (i=1, 2), G for the 100 sampled genes (k=1, 2, 3, . . . 100), and E for the residual error. P has three levels (j=1, 2, 3) and encompasses variations due to the order of the blood draw, order of processing, and/or between chips. For example, level j=1 of P contains expression indices from one chip of each condition, and these two chips detected targets from PAX tube samples that were drawn first (draw order numbered 1, 3 for condition E and 2, 4 for condition O,
The ‘Sum of Squares’ column indicates the magnitude of the variations explained by the factors listed under the ‘Source’ column, while the ‘% of total variation’ column converted the sum of squares into percentages. The F ratio (mean square of a factor/mean square of the residual) is used to test whether the variation explained by a factor is statistically greater than the variation of the residuals; a P-value of less than 0.05 indicated statistical significance. The results indicated that all three factors: C, P, and G, significantly explained portions of the total variation. However, the gene (G) factor explained most of the variation (99%), while the handling conditions contributed minimally (0.09%) to differences in gene expression levels. These results were generalizable to all genes on the chips since the 100 genes analyzed were randomly selected.
To determine the correlations of gene levels among the samples of the two conditions relative to other PAX-tube-derived samples processed in our lab, cluster analysis was performed. Samples were clustered via hierarchical clustering with average linkage, no gene filtering, and no standardization of genes or samples. The distances among samples were l-r, where r is Pearson's linear correlation coefficient. This distance measure quantified dissimilarities between entire expression profiles. The resulting dendrograms with descriptive ontologies of samples are shown in
To quantitate differences between the two conditions in terms of fold-changes, we compared fold changes of all genes between the conditions. From the set of non-filtered genes (˜22,600 genes for HG-U133 chips, with 7,600 genes for HG-U133A and 5600 genes for HG-U133B called present by MAS 5.0), we filtered for genes that showed greater than 1.3 fold changes between the conditions using the lower bound of the 90% confidence interval of fold-change estimates. This resulted in 5 genes for HG-U133A chips and 22 genes for HG-U133B chips (Table 4). When the lower bound was set to 1.5, only 1 gene remained for HG-U133A chips and none for HG-U133B chips. These results indicated that the differences between the two conditions were due to genes whose expression indices differ by no more than 1.5 fold of the 90% lower bound.
1The mean of expression indices of condition E (n = 3)
2The mean of expression indices of condition O (n = 3)
In comparing the two conditions, there were more genes that showed changes on the HG-U133B chips than on the HG-U133A chips, even though more genes were detected on the HG-U133A chips. Also, the genes that changed on the HG-U133B chips mostly went down in condition O.
Our results implied several recommendations as to sample handling for multi-centered studies. Since there were differences between the conditions but they both showed good within-group reliability, one should preferably pick one method to reduce variability. In which case, condition O seemed advantageous over E, as it provided time before one had to process or freeze the samples and allowed for transportation while frozen. If one needed the flexibility of the range of handling methods between the conditions, then this would still be possible, as long as during subsequent analysis, one increased statistical stringency, such as only passing genes greater than 1.5 fold change of the 90% lower bound.
Culture of adenovirus from nasal washes. All samples are cultured for Adenovirus, Parainfluenza 1,2, and 3, Influenza A and B and RSV. Standard cell types, including Rhesus Monkey Kidney-PMK or Cynomologous Monkey Kidney-CYN are most commonly used in addition to A549 cells. Standard culture and shell vial with direct fluorescent antibody are used. All respiratory cultures are held for 10-14 days until called negative.
Fluorogenic real-time PCR for adenovirus serotype 4 from nasal washes. DNA was extracted from 100 μl of nasal washes using the MasterPure™ DNA purification kit (Epicentre Technologies, Madison, Wis.) and resuspended in 10 μl nuclease free water (Ambion Inc., Austin, Tex.). Two different fluorogenic real-time PCR were used to detect adenovirus serotype 4 hexon and fiber genes. For hexon gene specific PCR, each reaction was 15 μl total volume containing 20 mM Tris-HCl (pH 8.4), 50 mM KCl, 4 mM MgCl2, 200 dNTPs (Invitrogen Life Technologies, Carlsbad, Calif.), 200 nM primers, 100 μM TaqMan probe (Integrated DNA technologies, Inc. Coralville, Iowa), 0.6 U of Platinum Taq DNA polymerase (Invitrogen Life Technologies, Carlsbad, Calif.), and 0.6 μl purified DNA from nasal washes. The sequences of adenovirus 4 specific hexon primers are: 5′-GTTGCTAACTACGATCCAGATATTG-3′ (forward; SEQ ID NO:1) and 5′-CCTGGTAAGTGTCTGTCAATCC-3′ (reverse; SEQ ID NO:2). The sequence of adenovirus 4 hexon specific probe is 5′-FAM-CAGTATGTGGAATCAGGCGGTGGACAGC-TAMRA-3′ (SEQ ID NO:3), where FAM is the fluorescent reporter, and TAMRA is the fluorescence quencher. The reaction conditions were: 94° C. 3 min denaturation, then 35 two-step cycles of ramping to 95° C. and 60° C. 20 s. For fiber gene specific PCR, each reaction was also 15 μl total volumes containing 1.5 μl FastStart DNA Master SYBR Green I (Roche Applied Science, Indianapolis, Ind.), 3 mM MgCl2, 200 nM primers, and 0.6 μl purified DNA from nasal washes. The sequences of adenovirus 4 specific fiber primers are: 5′-TCCCTACGATGCAGACAACG-3′ (forward; SEQ ID NO:4) and 5′-AGTGCCATCTATGCTATCTCC-3′ (reverse; SEQ ID NO:5). The reaction conditions were 94° C. 10 min denaturation, then 40 two-step cycles of ramping to 95° C. and 60° C. 20 s. Both reactions were carried out in the RAPID LightCycler™ (Idaho Technology Inc., Salt Lake City, Utah).
Total RNA isolation from blood. Frozen PAX tubes were thawed at room temperature for 2 hrs followed by total RNA isolation as described in the PAX kit handbook (60), but modified to aid in tight pellet formation by increasing proteinase K from 40 μl to 80 μl (>600 mAU/ml) per sample, extending the 55° C. incubation time from 10 min to 30 min, and the centrifugation time to 30 min or more. The optional on-column DNase digestion was not carried out. Purified total RNA was stored at −80° C.
Target preparation. For more complete removal of DNA from purified RNA samples, RNA isolated from multiple PAX tubes of blood from the same donor at a specific collection date were pulled, followed by in-solution DNase treatment using the DNA-free™ kit (Ambion). However, to facilitate removal of the DNase inactivating beads, the completed reaction was spun through a spin column (Qiagen, Cat#79523), rather than attempting to pipette off the supernatant without disturbing the bead pellet. Subsequently, one micro liter from each post-DNase total RNA sample was run on the bioanalyzer using the RNA 6000 Nano Assay (Agilent Technologies) for assessment of RNA quality and quantification of RNA amount. Next, for most samples, 5 μg of RNA were concentrated via ethanol precipitation. For each 100 μl of RNA sample, we added 1 μl glycogen (5 mg/ml) (Ambion), 15 μl 5M ammonium acetate, and 200 μl 100% ethanol chilled at −20° C. The reaction was incubated at −20° C. overnight. The next day, the samples were spun down at 13,791 g at 4° C. for 30 min. The pellet was washed twice with 80% ethanol chilled at −20° C.; air-dried; and resuspended in 10 or 12 μl of nuclease free water (Ambion). All subsequent steps were as described in the GeneChip® Expression Analysis Technical Manual (6).
Database integration. The database can be divided into two major categories: 1) metadata, all information relating to the sample processing that is not gene-expression measurements; and 2) gene-expression data. The metadata consists of several subcategories: clinical, laboratory handling, and quality metrics of microarray results.
Clinical data captures information about the patients as transcribed from the questionnaire, complete blood count (CBC), and about handling of the collected PAX tube blood samples.
Laboratory data contains information about the processing of blood samples. For steps from blood in PAX tubes to total RNA extraction, fields such as date of processing, reagent lots, and operator are captured. Subsequent bioanalyzer measurements of DNased treated RNA samples resulted in fluorescent intensities versus time data, which graphically, form the electropherograms and were treated as metadata as well. The electropherograms were analyzed by the Biosizing (Agilent Technologies) software to output 28S-to-18S intensity ratios and RNA yields, and by the Degradometer 1.1 (51) software to consolidate, scale, and calculate quality metrics such as degradation factors and apoptosis factors. For steps from after bioanalyzer analysis to hybridization, variables such as yields of cRNA and processing batches were recorded.
Quality metrics of microarray results data were information associated with the scanned chip. This included fields such as lot numbers of chips and date of scanned images stored in DAT files. Also included were fields from the Report fifes generated by the GeneChip Operating Software 1.1 (GCOS1.1) (Affymetrix), which summarized the quality of target detection for a chip.
Microsoft Access and Excel worksheets were used to enter manually clinical and laboratory handling data. Outputs from Degradometer 1.1 were in Excel worksheets. An in-house script called ReportToMatrix (script provided hereinbelow) was used to reformat and consolidate Report files into a data matrix in Excel. Metadata from GCOS1.1 were exported into Access.
Finally, the JMP IN (SAS Institute) software was used to join these various data tables together using identifiers, usually the volunteer's ID number and date of blood collection. The metadata table has more than a thousand columns.
In regard to the gene-expression data, the scanned images of chips were captured and stored in Microarray Suite 5.0 (MAS 5.0) (Affymetrix) and later transported to GCOS1.1. Signal values, which quantify the abundance of genes from intensities of probes, and detection calls, which qualify the detection of genes into present (P), marginal (M), or absent (A), were calculated in GCOS1.1 which uses the MAS5.0 algorithm. For both HG-U133A and B chips, the scaling factor and normalization value were set to 1, resulting in no scaling or normalization after generating Signal values. This allows for testing of various scaling and normalization procedures. Signals and detection calls were exported to Excel and saved as tab-delimited text files with A chips in one folder and B chips in another. Statistical analysis. Statistical quality control and relations among metadata variables were analyzed in JMP IN and StatView (SAS). ANOVA, t-tests, and class prediction of clinical phenotypes using CBC or electropherogram data were performed in BRB-Arraytools 3.2.0 Beta (Arraytools) developed by Dr. Richard Simon and Amy Peng Lam (available through the web-site for the Biometric Research Branch, Division of Cancer Research and Diagnosis, National Cancer Institute, U.S. National Institutes of Health). Arraytools is written for analysis of gene-expression data, but here we have imported certain quantitative metadata fields, such as CBC, to be treated as ‘genes’ by Arraytools to take advantage of its class prediction algorithm.
Relations between metadata variables and gene-expression profiles were analyzed in Arraytools. To facilitate import of text files with Signals and detection calls, in-house scripts were written in R to move files of interest into a different folder and renaming and reformatting the files to be compatible with ArrayTools: (Script provided herein below)
Script for Reformatting the Files to be Compatible with ArrayTools:
Selected metadata fields were imported into the Experiment descriptors worksheet of Arraytools. After data import, Arraytools were used to determine differential gene expression and ontology, class prediction, and quantitative trait correlations, with, between, and/or among clinical phenotypes.
CBC data were obtained from two machines. The first partitioned the white blood cells (WBC) into only three groups: lymphocytes, monocytes, and granulocytes, while the second partitioned the WBC into five groups: lymphocytes, monocytes, neutrophils, eosinophils, and basophils. Therefore, to make CBC comparable between the two machines, the following in-silico transformations were performed. Since granulcytes consist of neutrophils, eosinophils, and basophils, samples with five groups were converted to three by summing up the neutrophils, eosinophils, and basophils counts. Also, blood samples from 25 volunteers not in this study were run on both machines. Their CBC showed linear correlations between the two machines (data not shown). Therefore, linear regression equations were calculated for CBC variables between the two machines. These equations were used to normalize the CBC of the current BMT cohort.
The Degradometer 1.1 software scales the electropherograms using the spiked in marker peak (51).
Scaling was performed for gene-expression data. Since for each blood sample, the same hybridization cocktail went onto the A chip and then the B chip, concatenation of the data from the two chips together in-silico to form a virtual array would be logical and bypasses issues with analyzing the two chip types separately; also, the 100 control probe sets common between the A and B chips should detect genes to result in similar Signal distributions. Several methods were considered to concatenate the A and B chips profiles.
First, if each A and B chips were separately globally scaled to a target value of 500, then the resulting Scale Factors (SF) was significantly higher for the B chips than for A (data not shown) (t-test, p<0.0001), suggesting that generally, Signals from B chips were actually lower than from A. Confirmatory of this bias was that Signals of the 100 control genes were higher in B chips than in A after globally scaling each chip. The lower overall Signals in B are probably due to the B chip containing probesets that detect mostly low expressing genes (69). These observations suggested that the above step of globally scaling each chip was not appropriate to perform prior to concatenating data from the two array types.
Thus, another method was assessed, which was to scale all A and B chips using only the 100 control genes to a target value of 500. This resulted in stable SF over time (data not shown) and that there was no significant differences in SF among the four phenotypes of healthy, sick with adenovirus infection and convalescents, and sick without adenovirus infection (data not shown) (ANOVA, p=0.1047 A chips, p=0.1782 B chips). The 100 control genes were selected based on stability in expression from a large study of various tissue types (69); therefore, this scaling method would allow for the concatenation of corresponding A and B chips and also should remove assay variations independent of gene concentration. This scaling procedure was carried out using an in-house R script (Script provided herein below):
After scaling using the 100 control genes, the expression profiles from corresponding A and B chips were concatenated to form virtual arrays. Furthermore, the present inventors considered globally scaling these virtual arrays to further remove assay variations. However, the SF from this procedure showed differences among the four phenotypes: highest SF in the healthy group, then convalescents, followed by the febrile group (data not shown) (ANOVA, p<0.0001). Therefore, this step was not used for the whole data set, although it might still be useful in increasing the sensitivity of detection of genes with differential expression between groups with equivalent SF, such as between sick with- versus without-adenovirus infection. These results also suggested that relatively large subsets of transcripts differ among healthy, convalescents, and febrile, while relatively small subsets of transcripts differ between sick with- and without-adenovirus. These analysis steps were also carried out using an in-house R script (Script provided herein below):
Quality and variations of RNA derived from PAX system from the BMTs population. Many factors contribute to the variability of target detection, with the quality of RNA being one of the most important. The quality of RNA from PAX tubes collected blood could be influenced by the disease status of the donors, sample handling, and other downstream processes. Previously, we showed that under two conditions representative of practical sample handling, the PAX system was capable of preserving blood RNA to produce good quality metrics and relatively stable transcriptome measurements (50). Recently, new RNA quality metrics have been proposed based on associations between experimental treatment of cells or purified RNA to induce RNA degradation and metrics derived from electropherograms of the RNA on the bioanalyzer (51). One new metric is the degradation factor (% Dgr/18S), which is the ratio of the average intensity of bands from degraded RNA, that is peaks of lesser molecular weight than the 18S ribosomal peak, to the 18S band intensity multiplied by 100. It is a continuous variable that is used to derive a categorical variable named ‘Alert’. Alert has five values:
BLACK—indicating that the ribosomal peaks were not detected;
NULL—no RNA degradation and corresponds to degradation factor values ≦8;
YELLOW—for RNA degradation can be detected and values from >8 to 16;
ORANGE—for severe degradation and values from >16 to 24;
RED—for highest alert, strong degradation, for values from >24.
The degradation factor is a more sensitive indicator of RNA degradation than the traditional 28S to 18S band intensities ratio. Another new metric is the apoptosis factor (28S/18S), which is the ratio of the height of the 28S to 18S peak and is indicative of the percentage of cells undergoing apoptosis (51). Apoptosis factors from 1 to 3 inversely correlate with 80% to 0% of cultured cells positive for annexin V. Thus, for PAX system isolated RNA from our previous study (50) and current BMTs cohort, we report the distributions of RNA quality metrics, which would be useful for comparisons and planning of protocols by other labs; determined the up-stream quality metrics that are most indicative of the quality of microarray target detection outcomes; and determined the effects of inter-individual hemoglobin variability on the sensitivity of target detection.
Electropherograms from Thach et al (50) were reanalyzed for the two PAX tube handling conditions, wherein condition E as in fresh, the RNA was extracted after the minimum incubation time of 2 hours from phlebotomy, and condition O as in frozen, the blood sat for 9 hours at room temperature followed by storage at −20° C. for 6 days, followed by RNA extraction. The degradation factor was 5.34±0.53 (mean±SE, n=6) for E and 6.53±0.40 for O with no difference between the two handling methods (Wilcoxon, p=0.13); the magnitude indicated that no degradation was detected (data not shown). Linear correlation between the degradation factor and gapdh and actin 3′/5′ is tissue dependent (51), and was not detected here (data not shown). The apoptosis factor was 1.39±0.06 for E and 1.29±0.09 for O, also with no differences between conditions (Wilcoxon, p=0.38) (data not shown). These results confirmed the lack of major differences between the handling conditions.
The reanalysis above were from samples that only have technical variation, whereas the current BMTs cohort captures inter-individual and disease states variations and has more samples; therefore, electropherograms from the BMTs were assessed. The degradation factor for the BMTs cohort was 8.47±0.47 (meant SE, n=120) and the apoptosis factor was 1.17±0.02. The distribution of the Alerts was: 77 NULL, 36 YELLOW, 3 ORANGE, and 4 RED.
A closer look at the electropherograms of ORANGE and RED samples suggested that these samples, mostly from the same run, had high degradation factors due to increased noise in the bioanalyzer rather than true RNA degradation. In contrast to the reanalysis of Condition E and O samples above, linear correlations were detected between the degradation factor and gapdh and actin 3′/5′, probably because of greater variation and larger number of samples. However, the magnitudes of the correlations were modest (A chips gapdh r=0.526, actin r=0.303; B chips gapdh r=0.325, actin r=0.284). There was no significant correlation between 28S to 18S band intensity ratio versus degradation factor, gapdh 3′/5′, or actin 3′/5′. Also, only about 50% of the 28S to 18S band intensity ratio values derived from the bioanalyzer software fell between the 1.8 and 2.1 range, while the rest fell outside of this standard range.
Finally, the distribution of yields of total RNA as determined by the bioanalyzer ranges from 1 to 15 μg per PAX tube. These results suggest that of the metrics relating to RNA quality obtained at the bioanalyzer step: RNA yield, 28S to 18S band intensity ratio, degradation factor, and Alert, the variable Alert would be most useful in assessment of individual RNA samples for continuation of processing, as the other metrics had large variation outside of the traditional range, although microarrays with acceptable quality metrics were still obtained from those RNA samples.
In condition O, the frozen time was 6 day, whereas in the current BMT study, samples were frozen at −20° C. for up to 20 days, and a few samples had been frozen and thawed a couple of times. Therefore, to determine if frozen time and freeze-thaw affected RNA quality derived from PAX system, linear correlations were performed between the time the samples were frozen before RNA extraction and RNA quality metrics. There was no significant correlation detected between frozen time versus degradation factor, apoptosis factor, total RNA yield per PAX tube, 28S to 18S band intensity ratio, gapdh and actin 3′/5′. These results suggest that RNA derived from PAX system is stable over these conditions.
Many factors affect number of present calls, an indication of sensitivity of detection of targets. One obvious factor is average background. As average background increases, then number of present calls decrease. This was observed in the current data set, but the effect was minor (A chips, r=−0.397, p=0.00003; B chips, r=−0.211, p=0.032). A less obvious factor affecting sensitivity is the percent of globin transcripts of the mRNA population. When increasing amounts of globin mRNA transcripts were spiked into total RNA from cell line, the percent present calls decreases linearly (20). To determine if this effect is present and to quantitate its magnitude in the current data set, linear correlation was performed between Number Present and Mean Cell Hemoglobin (MCH), a measurement of picograms of hemoglobin per red blood cell that is likely to be directly related to globin mRNA amounts. A significant although minor effect was detected (r=0.229, p=0.020), but only for the B chips only. The equation of the regression line suggested that for every picogram increase in hemoglobin, there is a loss in present detection calls of 100 genes, or about 2% of the average number of present call genes detected on the B chips.
These results suggested that the quality of RNA from PAX tubes collected blood of the BMT population with various disease phenotypes and handling conditions are of good and reproducible quality for gene-expression analysis, although variation in hemoglobin amounts contributed a minor effect to the sensitivity of detection of target by the Genechip microarray. The Alert metric seemed to be a robust indicator for continuation to the target preparation steps, with values of NULL and YELLOW indicating acceptable microarray results.
Quality of microarray measurements of PAX system derived RNA from the BMTs population. The numbers of arrays processed and their allocations were determined. A total of 145 A and B chip sets were processed from hybridization cocktail samples from PAX system derived RNA. Of these, 128 were from the BMTs, and the remaining 17 were from civilians.
Of the 17, 6 were from the same donor and were samples used in the condition O versus E study (50); 6 were from another donor to compare using total versus poly A RNA; 2 were technical replicates from a third donor; and 3 were technical replicates from a female donor.
The 128 chips sets from the BMTs were run in 10 batches (variable name ‘RNA to hyb cocktail Batch #’). Batch 1 had 8 blood samples and polyA RNA was used as in Thach et al. (50). Batch 2 had 12 chip sets with 8 blood samples that were processed as in Batch 1, but the RNA was over fragmented; four of these samples had more than 5 μg of cRNA left over, so these were hybridized to the arrays resulting in the 12 chip sets for Batch 2. Batch 3 also had 12 chip sets with 8 blood samples that were processed using total RNA; 4 of the eight blood samples yielded enough total RNA to have duplicates using polyA RNA instead. The remaining batches totaling 96 chip sets were processed as the 8 total RNA blood samples from Batch 3. One of the 96 chip sets was from a convalescent BMT whose nasal wash still had positive adenoviral culture; therefore, this singular case was excluded from most analysis. The resulting 95 chip sets were used as the training set in class prediction analysis. The other 50 chip sets, regardless of processing differences were placed into the test set. The 95 chips sets and the 8 from Batch 3 summed to 103 chip sets that were processed similarly, and these 103 chip sets were used for most other analysis such as class comparisons. Each batch had about equal representation of the four phenotypes: healthy, febrile with adenovirus and convalescents, and febrile without adenovirus. Therefore, comparisons among these four groups should detect biological differences as these four groups have similar variations due to processing. These results above are summarized in Table 5 below:
The correlation of signals and concentrations and the sensitivity of the bioB, bioC, bioD, and cre cRNA spike-ins were evaluated. The spike-ins showed strong linear relationship with known concentration across all chips (data not shown) and that the percent present calls of bioB, whose concentration is at the level of assay sensitivity, was 100% of the time suggesting good sensitivity for all the chips. After scaling via 100 control genes, the spike-ins still showed strong linear relationship with known concentration, suggesting that the scaling procedure did not introduce significant artifacts (data not shown).
Individual control charts versus the date the microarray was scanned were plotted to look for stability of quality metrics, to determine outliers and excluded arrays when error in processing was known, and to compare our results with values from other labs and values proposed by Affymetrix. The in silico parameter settings were uniform throughout as expected. For the A chips, there was an upward drift in background and noise due to drifting in the scanner as these metrics returned to normal after recalibration of the scanner. Most of the B chips were processed before drifting and after recalibration so this factor did not affect them. The percent present was 32±10 (average ±3SD) for A chips and 21±6 for B chips. Batch 2 had been over fragmented resulting in high gapdh and actin 3′/5′ and was excluded from analysis where appropriate. All other chips showed gapdh and actin 3′/5′ values well less than three, the limit proposed by Affymetrix (68). All quality metrics, including background and noise were stable for the 103 chip sets from identical protocol.
These QC results suggested the reliability of our process and facilitated the inclusion and exclusion of microarrays to form subsets suitable for a particular statistical analysis to answer certain questions.
Class prediction of infection status. To determine if sets of genes could classify the four phenotypes, healthy, febrile with adenovirus and convalescents, and febrile without adenovirus, class prediction on the training set was performed. For supervised class prediction, the class labels were results from the gold standard assay of culture for adenovirus from samples of the febrile and convalescent groups. Unsupervised clustering of samples suggested that the predominant variation among gene expression profiles were febrile versus non-febrile patients (not shown).
Therefore, to determine sets of genes that could best classify febrile versus non-febrile patients, febrile with adenovirus versus without, and healthy versus convalescents, class prediction was performed and optimized for these three comparisons (
The optimized percent correctly classified and the optimal conditions for the three comparisons results are shown in Table 6 below:
Also shown in the table are optimized percent correct and conditions when using CBC or electropherograms data. The results showed that under optimal conditions for each data types, gene-expression data provided information that best classified the four groups, with 99% correct between febrile versus non-febrile, 87% between healthy and convalescents, and 91% between sick with adenovirus versus without. The optimal number of genes for equal optimal classifications among the four groups tended to be nested sets, with the smallest set that gave the same optimal class prediction accuracy containing genes with the most differential expression. This was likely so because some genes are correlated with each other and thus provided equivalent amounts of information for classification. Tables 7, 10, and 11 to provide the p-values as a measure of reliability of prediction and lists the minimal set of genes used to classify the following classes: febrile versus non-febrile patients—99% Feverstatus, p<5E-4, number of genes in classifier=47 (Table 7); healthy versus convalescents—87% accurate between healthy and convalescents, p=0.001, number of genes in classifier=8 (Table 10); and febrile with adenovirus versus without—91% Febriles with vs. without adenovirus infection, p<5E-4, number of genes in classifier=11 (Table 11).
From the genes listed above, a table of ‘Observed v. Expected’ table of GO classes and parent classes, in list of 47 genes shown above can be prepared to help elucidate the molecular function (Table 8) and/or biological processes (Table 9) in which the identified genes take part. Only GO classes and parent classes with at least 5 observations in the selected subset and with an ‘Observed vs. Expected’ ratio of at least 2 are shown.
Categorical and continuous metadata variables co-varying with the four phenotypes above were assessed. The only categorical variables that correlated with the four phenotypes involved the lots of the PAX system used. These covariates were unlikely to affect gene expression outcomes because the manufacturers have QC their products for consistency. ‘Perceived Stress’ showed increasing qualitative trend with sickness, but this was expected. This increase our confidence that our class prediction set of genes is due to infection health status rather than other confounding variables.
Tables 18, 22, and 26 provide a larger list of genes that still give high percent correct to classification, in order of: febrile versus non-febrile patients, febrile with adenovirus versus without adenovirus patients, and healthy versus convalescent patients, respectively. In Tables 18, 22, and 26, the composition of classifiers is listed for genes significant at the 0.001 level and is sorted by t-value.
Tables 16, 20, and 24 provide a detailed summary for the performance of classifiers during cross-validation used for Tables 18, 22, and 26.
Tables 17, 21, and 25 provide further details as to the performance of classifiers during cross-validation with respect to Performance of the Compound Covariate Predictor Classifier, Performance of the 1-Nearest Neighbor Classifier, Performance of the 3-Nearest Neighbors Classifier, Performance of the Nearest Centroid Classifier, Performance of the Support Vector Machine Classifier, and Performance of the Linear Diagonal Discriminant
Analysis Classifier. Specifically, Tables 17, 21, and 25 reports the parameters used for each classification method and each class.
For compilation of the data in Tables 17, 21, and 25, the following formulae were employed:
Let, for some class A,
Then the following parameters can characterize performance of classifiers:
Tables 19, 23, and 27 provides a table of ‘Observed v. Expected’ table of GO classes and parent classes, and lists the frequency of genes reported in Tables 18, 22, and 26 to help elucidate the cellular component, molecular function and/or biological processes in which the identified genes take part. Only GO classes and parent classes with at least 5 observations in the selected subset and with an ‘Observed vs. Expected’ ratio of at least 2 are shown.
Class comparisons. To determine lists of genes that are differentially expressed among the four phenotypes, class comparisons were performed. Tables 28, 30, and 32 show the list of genes found to be different between febrile versus non-febrile patients, febrile with adenovirus versus without, and healthy versus convalescents, respectively. Tables 29, 31, and 33 provide a table of ‘Observed v. Expected’ table of GO classes and parent classes, and lists the frequency of genes reported in Tables 28, 30, and 32 to help elucidate the cellular component, molecular function and/or biological processes in which the identified genes take part. Only GO classes and parent classes with at least 5 observations in the selected subset and with an ‘Observed vs. Expected’ ratio of at least 2 are shown.
Description of the Problem:
Summary of Results:
Genes which Discriminate Among Classes:
Description of the Problem:
Summary of Results:
Genes which Discriminate Among Classes:
Description of the Problem:
Summary of Results:
Genes which Discriminate Among Classes:
However, because of differences in CBC (Table 12 below), these differences in RNA could be due to cell type heterogeneity and/or differential expression at the per cell level. Although large expression differences are likely to be due to differential expression at the per cell level because the differences in CBC variables cannot likely to account for these large differences. Statistical models would have to be developed to sort out these two effects. Serendipitously, there were no differences in CBC for comparisons between febrile with adenovirus versus without (Table 12 below).
Differences in CBC between non-febriles versus febriles, healthy versus convalescents, but not between febriles with versus without adenovirus. P-value columns are from Wilcoxon testing for differences in CBC variables between the groups. Highlights indicate significant differences.
Therefore, one could surmise that the differentially expressed genes were at the per cell level, suggesting that the biomolecular pathways involving these genes are involved in differences between adenovirus infection and non-adenovirus infection. To determine these pathways, the gene list was integrated with the KEGG pathway and the Genetic Association databases using EASE (70) to elucidate the functions of these genes in known pathways.
The results for the KEGG pathway database search are as follows:
hsa00071 Fatty Acid Metabolism
hsa00190 Oxidative Phosphorylation
hsa00193 ATP Synthesis
hsa00230 Purine Metabolism
hsa00240 Pyrimidine Metabolism
hsa00252 Alanine and Aspartate Metabolism
hsa00361 Gamma-Hexachlorocyclohexane Degradation
hsa00510 N-Glycans Biosynthesis
hsa00532 Chondroitin/Heparan Sulfate Biosynthesis
hsa00561 Glycerolipid Metabolism
hsa00670 One Carbon Pool by Folate
hsa00740 Riboflavin metabolism
hsa00920 Sulfur metabolism
hsa00970 Aminoacyl-tRNA Biosynthesis
hsa03022 Basal Transcription Factors
hsa03050 Proteasome
hsa04010 MAPK Signaling Pathway
hsa04060 Cytokine-Cytokine Receptor Interaction
hsa04110 Cell Cycle
hsa04120 Ubiquitin Mediated Proteolysis
hsa04210 Apoptosis
hsa04310 Wnt Signaling Pathway
hsa04350 TGF-Beta Signaling Pathway
hsa04610 Complement and Coagulation Cascades
hsa04611
hsa04620 Toll-Like Receptor Signaling Pathway
hsa04630 Jak-STAT Signaling Pathway
hsa05110 Cholera—Infection
A batch search of the Genetic Association database was performed for the following genes: CX3CR1, TRIM14, ARF3, BRD7, PILRB, ENTPD1, CSF1R, RABGAP1, ICAM2, KLHL2, PUM1, MTHFS, LY6E, MRPL47, NPM1, C12orf8, TNFAIP3, CHES1, SIP1, MYOZ2, ATP5J, IF144, SEC14L1, G1P2, GTF2H1, FBXO2, USP18, ACPT, SP100, AIP, ABHD5, SCO2, PWWP1, RAN, GRN, MX1, SLC1A4, GZMB, SNRPA1, IMPDH1, TARDBP, ZCCHC2, IER5, CBLB, STAT1, WBSCR20A, MEA, TNRC6, MAK, TCF7L2, TINF2, HNRPH1, HNRPH2, GK, SART3, HIFX, PTP4A2, PSMD14, EIF3S4, BTN3A3, LETM1, TIMM23, HIVEP2, USP22, MTIL, C1QA, IL1RAP, MS4A7, NICAL, KBTBD7, C1orf29, PNUTL2, RPN2, ILF3, PCNA, HMGB1, BAG1, MCM2, TYMS, MTIX, CPD, COX15, MCM6, SN, C6orf133, BACE2, SYT6, OAS1, FACL2, OAS2, C6orf209, NUP98, PRKAR1A, OAS3, CHST12, FACL5, SLP1, CD59, IFIT1, IFI27, SORL1, RNPC4, IFIT4, HMGN4, CECR1, CDCA7, MTSS1, C6orf37, CDKN1C, RBPSUH, IL1R2, YWHAQ, RRM2, DARS, UBE2R2, SFRS7, FCGR2A, OASL, ID2, PLCL2, LGALS3BP, KPNA2, and MAP2K4.
Of these genes, the following hits were returned:
CX3CR1
SCO2
FCGR2A
Sample collection. With approval of the Lackland AFB IRB and after informed consent, approximately 25 ml of blood, filling 10 PAX tubes, were drawn from each healthy volunteer. Blood was drawn into PAX tubes by standard protocol {Preanalytix #23*}. All PAX tubes were maintained at room temperature for 2 hrs, then frozen at −20° C., stored at −80° C. for 5 days, and shipped on dry-ice to the Navy Research Laboratory in Washington, D.C. for processing.
Sample processing. Blood collection and RNA isolation was performed using the PAX System, which consists of an evacuated tube (PAX tube) for blood collection and a processing kit (PAX kit) for isolation of total RNA from whole blood {*Jurgensen #32; Jurgensen #33}. The isolated RNA underwent globin reduction procedures and was amplified, labeled, and interrogated on the HG-U133 plus 2.0 Genechip® microarrays (Affymetrix).
Total RNA isolation from blood. Frozen PAX tubes were thawed at room temperature for 2 hrs followed by total RNA isolation as described in the PAX kit handbook {*Preanalytix #24}, but modified to aid in tight pellet formation by increasing proteinase K from 40 μl to 80 μl (>600 mAU/ml) per sample, extending the 55° C. incubation time from 10 min to 30 min, and passing through a QIAshredder spin column (Qiagen). The optional on-column DNase digestion was not carried out. Purified total RNA was stored at −80° C.
Total RNA cleanup and concentration. For more complete removal of DNA from purified RNA, duplicate RNA samples were pooled, followed by in-solution DNase treatment using the DNA-free™ kit (Ambion), but without addition of DNase inactivation reagent. After DNase treatment, RNA were subjected to RNAeasy MinElute Cleanup (Qiagene cat#74204) and concentrated according to the manufacturer's procedure. Subsequently, one microliter from each sample was run on the bioanalyzer 2100 (Agilent) for assessment of RNA quality while the nanodrop (NanoDrop) was used for quantification. Usage of the bioanalyzer was analogous to capillary gel electrophoresis. This resulted in electropherograms displaying florescent intensity versus time, which correlates with the amount of RNA versus the size of RNA, respectively.
Globin reduction and target preparation. To remove globin mRNA, biotinylated globin capture oligos (Ambion Globinclear kit) and PNA (Affymetrix GeneChip Globin Reduction kit) were used according to modified manufacturers' procedures. In brief, for the Globinclear's procedure, biotinylated globin capture oligos were added to 5 μg total RNA and globin mRNA were removed by strepavidin magnetic beads. Then the remaining globin-reduced total RNA was purified using magnetic beads and eluted in 30 μl, of water. One microliter of RNA was used for bioanalyzer measurement and the remaining RNA was concentrated to 8 μL using Speed Vac concentration at room temperature. For the PNA globin reduction procedure, 5 μg of total RNA in 9 μl. BR5 from the RNAeasy MinElute Cleanup step was used for the downstream procedure. The column that came with the Globin Reduction kit was not used. All subsequent steps were as described in the GeneChip Expression Analysis Technical Manual version 701021 Rev. 3.
Database integration. Laboratory data contained information about the processing of samples from blood in PAX tubes to cRNA target preparation, as well as bioanalyzer and nanodrop measurements. Electropherograms were analyzed by the Biosizing software (Agilent) to output 28S/18S intensity ratios and RIN QC metrics while the nanodrop output RNA quantity and 260/280 ratios. Report files summarizing the quality of target detection for an array were generated by GeneChip® Operating Software 1.1 (Affymetrix). JMP (SAS) was used to join these various data tables together into a metadata table. For gene-expression data, Signal values were calculated using the Microarray Suite 5.0 algorithm with and without scaling to test the effects on various downstream analytical methods.
Statistical analysis. Statistical quality control and relations among metadata variables and gene expression profiles were analyzed in JMP. ANOVAs, multidimensional scaling, and functional analysis of gene-expression data were performed in Arraytools 3.2.0. Beta developed by Richard Simon and Amy Lam (http://linus.nci.nih.gov/BRB-ArrayTools.html). Heat-maps and dendrograms were graphed using dChip {Li, 2001 #41; Li, 2001 #42}. Scaled expression data showed no differences in Scale Factors among treatment groups.
Quality of RNA, globin reduction, and target preparation. The following RNA samples were used to study the effects of two globin reduction methods on gene expression profiles:
1) Jurkat RNA isolated from Jurkat cell line (J)
2) Jurkat RNA with globin mRNA spiked-in (JG)
3) Paxgene RNA from whole blood (B)
The globin reduction protocols tested were:
1) Ambion's Globinclear method using biotinylated globin capture oligos (A)
2) Affymetrix′ method using PNA oligos (P)
3) No globin reduction treatment as technical control (C).
The same lot of J and JG RNA were used throughout. RNA treated with Ambion globinclear had ˜90% recovery for J and JG RNA. The yields of cRNA for the Ambion group were the lowest among the three technical conditions for each RNA species; however, RNA purity judged by the ratio of 260/280 for Ambion globinclear group was the highest (Table 13).
Profiles of cRNA for J and JG RNA compared using the bioanalyzer (
There was no biological variation among paxgene RNA, since paxgene RNA used for each technical condition was derived from the pooled paxgene tubes collected from the same individual in one bleeding. Paxgene RNA with a ratio 260/280 between 1.9-2.1 was used as starting RNA and ˜75% recovery for paxgene RNA (Table 13)
Decreasing globin peaks and band were also seen in cRNA profiles derived from paxgene RNA samples treated with Ambion globinclear (BA) and PNA globin reduction (BP) compared to BC (no treatment) (arrow in
Quality of microarray measurements for each technical condition For microarray data quality assessment, poly A control graphs for each microarray were plotted using scaling signal intensity and non-scaling data. Linearity was achieved among the four control probe sets for all samples (data not shown). All of the constants and major variables, such as scale factors (SF), background, and noise (see Table 13) obtained from RPT report were assessed using the ANOVA and Wilcoxon tests. There was no statistically significant difference in SF and noise among JA, JC, JP, JGA, JGP and JGC, neither in BA, BP and BC. Thus, scaling signal intensities for all probe sets were used in the gene expression profile comparison. For Jurkat RNA, background was highest in JGC and was significantly different from the others, possibly due to the spiked globin mRNA. There was no difference in background among all paxgene RNA. Ratios of 3′/5′ GAPDH for all microarrays were all below 5 and indicated that there was no RNA degradation. A slightly higher ratio of 3′/5′ Actin and GAPDH was noted in paxgene RNA with PNA treatment, possibly due to the reduction of cRNA size (BP in
Globin removal increases number of present calls (%) and call concordance in gene expression Removal of globin by both methods significantly increased the number of present calls (%) in JGA, JGP, BA, BP compared to their corresponding controls, JGC and BC (ANOVA, Wilcoxon test); however, there was no difference among three technical conditions in Jurkat RNA using the ANOVA and Wilcoxon tests. Further analysis of these methods with the student t-test revealed statistically significant higher present calls in JGA than JGP (student t-test, p<0.05), but there was no significant difference in paxgene RNA between BA and BP (Table 13). The present Call concordance among Jurkat RNA for the three technical conditions was compared and a gene subset containing 19731 genes, called JCAP, which was not affected by technical conditions (JCAP in
In addition to assessing present call concordance, the overall call concordance excluding margin calls between Jurkat and JG RNA was tabulated and the percentages of false positive and negative among technical conditions were compared (Table 14). Our data demonstrated that JGA and JGP increased concordant present calls by 8% and 5%, respectively, relative to JGC had 7% and 4% increased false negative calls compared to JGA and JGP, respectively. False positive present calls occurred in 1% and 0.22% of JGA and JGP processed samples, respectively, compared to JGC. Calculated sensitivities for JGA, JGP and JGC compared to the “gold standard” of Jurkat RNA were 86%, 79.5% and 68.2%, respectively. Specificity was retained with all processing methods with specific values for JGA, JGP and JGC being 94.3%, 96.2% and 96.2%, respectively. The data suggests that the Ambion globinclear method had significantly higher sensitivity percent present calls without significant loss of specificity relative to JGC (Table 15).
Variance caused by two globin reduction methods Signal variation among triplicates was assessed by comparing the coefficient of variance (CV) (
In addition to CV (%) comparison, Pearson correlation coefficient (again-it was difficult for me to determine whether any of these observations was significant) was also calculated and compared in each triplicate between technical conditions within the same RNA species and between RNA species (Table 15). Higher signal correlation was seen within triplicates compared to that seen between technical conditions or between RNA species. In JG RNA, globin removal by biotinylated globin oligos (Ambion) had lower signal correlation with no treatment JGC (0.966), but JGP has higher correlation (0.983) with JGC. This indicated that globinclear JG RNA has more difference in gene expression profile relative to JGC than JGP. In paxgene RNA, PNA treatment has lower signal correlation (0.967) with no treatment (BC), but JGA higher correlation (0.978) with BC. This suggested that more difference in gene expression were seen in BP and BC than BA and BC. Removal of globin mRNA from paxgene RNA or JG RNA resulted in higher signal correlation in the same RNA species or between Jurkat and Jurkat+Globin RNA (between RNA species in Table 15).
Multidimensional scaling cluster analysis of gene expression profiles To further evaluate correlation between groups of samples for each technical condition, multidimensional scaling (MDS) cluster analysis was conducted. Since non-scaling data and scaling data exhibited similar clustering pattern, we only showed MDS plots using all probe sets with non-scaling signal intensities (
Hierarchal cluster analysis of gene expression profiles The overall expression profiles for Jurkat and JG RNA samples with different technical conditions were analyzed using center correlation and average linkage parameters (
We divided these differentially expressed genes into 4 groups as indicated on the right side of the dendrogram (
Using the same approach, gene expression profiles and differentially expressed gene profiles among BA, BP, and BC, with total of 9 paxgene blood RNA samples were analyzed and clustered using center correlation and average linkage. Our results revealed that removal of globin mRNA using biotinylated globin oligos and PNA oligos revealed more similar gene expression profile and were clustered within the same group possibly due to globin reduction
(
Entry criteria and sample collection. LAFB is the location of Basic Military Training for all recruits to the United States Air Force. The BMTs are organized into flights of 50-60 individuals that eat, sleep, and train in close quarters. As many as 40-50 BMTs/week present with FRI and 50-70% are due to adenovirus. With approval of LAFB IRB and after informed consent, approximately 15 ml of blood, filling 4 to 5 PAX tubes, were drawn from each volunteer. On day 1-3 of training, blood was drawn from healthy BMTs into PAX tubes by standard protocol {Preanalytix #23}, but no nasal wash was collected for this group. During training, BMTs who presented with a temperature of 38.1° C. or greater and FRI provided a nasal wash and blood draw. These individuals were categorized into either the FRI without adenovirus or with adenovirus group. Approximately three weeks after sample collection from the FRI volunteers with adenovirus, additional blood and nasal wash were collected to constitute samples for the convalescent group. All PAX tubes were maintained at room temperature for 2 hrs, then frozen at −20° C. and shipped on dry-ice to the Navy Research Laboratory in Washington, D.C. for processing. Nasal washes were performed using a standard protocol, with 5 ml of normal saline lavage of the nasopharynx, followed by collection of the eluent in a sterile container. Nasal wash eluent was stored at 4° C. for 1-24 hrs before being aliquotted and sent for adenoviral culture. All BMTs underwent standardized questionnaires before each sample collection. Healthy individuals were screened for acute medical illness within 4 weeks of arriving at basic training. BMTs were screened for race/ethnicity, allergies, recent injuries, and smoking history to assess confounding variables for gene expression. The duration and type of respiratory symptoms to include sore throat, sinus congestion, cough, fever, chills, nausea, vomiting, diarrhea, fatigue, body aches, runny nose, headache, chest pain and rash were recorded. A physical examination was recorded.
Sample processing. Blood collection and RNA isolation was performed using the PAX System, which consists of an evacuated tube (PAX tube) for blood collection and a processing kit (PAX kit) for isolation of total RNA from whole blood {Jurgensen #32; Jurgensen #33}. The isolated RNA was amplified, labeled, and interrogated on the HG-U133A and HG-U133B Genechip® microarrays (Affymetrix), noted here as A and B arrays, respectively.
Total RNA isolation from blood. Frozen PAX tubes were thawed at room temperature for 2 hrs followed by total RNA isolation as described in the PAX kit handbook {Preanalytix #24}, but modified to aid in tight pellet formation by increasing proteinase K from 40 μl to 80 μl (>600 mAU/ml) per sample, extending the 55° C. incubation time from 10 min to 30 min, and the centrifugation time to 30 min or more. The optional on-column DNase digestion was not carried out. Purified total RNA was stored at −80° C.
Target preparation. For more complete removal of DNA from purified RNA, duplicate RNA samples were pooled, followed by in-solution DNase treatment using the DNA-free™ kit (Ambion). However, to facilitate removal of the DNase inactivating beads, the completed reaction was spun through a spin column (Qiagen, Cat#79523), rather than attempting to pipette off the supernatant without disturbing the bead pellet. Subsequently, one microliter from each sample was run on the bioanalyzer (Agilent) for assessment of RNA quality and quantity. The usage of the bioanalyzer was analogous to capillary gel electrophoresis. This resulted in electropherograms displaying florescent intensity versus time (
Database integration. The database consisted of clinical data such as information transcribed from standardized questionnaires, the complete blood count (CBC), and the handling of blood samples. Laboratory data contained information about the processing of samples, from blood in PAX tubes to RNA extraction, as well as subsequent bioanalyzer measurements. Electropherograms were analyzed by the Biosizing (Agilent) software to output 28S/18S intensity ratios and RNA yields, and by the Degradometer 1.1 {Auer, 2003 #26} software to consolidate, scale, and calculate degradation and apoptosis factors. Report files summarizing the quality of target detection for an array were generated by GeneChip® Operating Software 1.1 (Affymetrix). JMP (SAS) was used to join these various data tables together into a metadata table with more than a thousand columns. For gene-expression data, Signal values were calculated using the Microarray Suite 5.0 algorithm with no scaling or normalization. This allows for subsequent testing of various scaling and normalization methods.
Statistical analysis. Statistical quality control and relations among metadata variables were analyzed in JMP. ANOVAs and class prediction of phenotypes using gene-expression data were performed in Arraytools 3.2.0 Beta developed by Richard Simon and Amy Lam (http://linus.nci.nih.gov/BRB-ArrayTools.html). Heat-maps and dendrograms were graphed using dChip {Li, 2001 #41; Li, 2001 #42}. Analysis of gene functions was aided by Arraytools and EASE {Hosack, 2003 #30}. Data analysis was performed primarily by D.T.
Scaling was carried out for gene-expression data. For each blood sample, the same hybridization cocktail went onto the A and then the B array, allowing concatenation of the data from the two arrays to form a virtual array. This bypassed issues with analyzing the two data sets separately. The 100 control probesets common between the A and B arrays were selected based on stability in expression from a large study of various tissue types {Affymetrix, 2002 #27}. Thus, all array data were scaled to a target value of 500 using the trimmed mean of the 100 control probesets. This resulted in stable Scale Factors (SF) over time and no differences in SF among the infection status phenotypes (ANOVA, P=0.1047 A arrays, P=0.1782 B arrays). This scaling method allowed for the concatenation of corresponding A and B arrays and should also remove variations that are not gene-specific.
Clinical Phenotypes. Thirty healthy, 19 with FRI and negative by culture for adenovirus, 30 with FRI and positive by culture for adenovirus, and 30 convalescing from adenovirus-positive FRI were enrolled in this study. Enrollees in these four infection status phenotypes were matched for age ±3 years and race/ethnicity. Only male BMTs were enrolled. After selection of samples meeting standards for gene expression analysis, 17 FRI without adenovirus had been ill for 5±3 days (median±SD), whereas 26 FRI with adenovirus had been ill for 8±4 days (P=0.006, Wilcoxon). The incidence of symptoms over all the groups was: sore throat (95.3%), cough (93%), sinus congestion (90.7%), headache (88%), chills (84%), rhinorrhea (81%), body aches (65%), malaise (63%), nausea (54%), diarrhea (14%), pleuritic chest pain (14%), vomiting (14%), and rash (0%), with no significant differences between the FRI groups. There was also no significant difference in allergies, recent injuries, and smoking history among the infection status phenotypes.
Quality and variations of RNA derived from PAX system from the BMT population. In order to identify clinically relevant gene expression profile differences for phenotypes in a population, it is essential that the RNA sample applied to the microarray is representative of the amount of transcripts in vivo. The PAX system was used to minimize handling of blood cells post collection and to immediately stabilize RNA and halt transcription. We previously have shown two methods using this PAX system that provide stable RNA for microarray analysis {Thach, 2003 #18}.
To assess RNA quality on each of the 95 microarrays analyzed in this study, recently published metrics derived from electropherograms of the RNA were used {Auer, 2003 #26}. Assessment of the degradation factor, which is the ratio of the average intensity of bands of lesser molecular weight than the 18S ribosomal peak to the 18S band intensity multiplied by 100, demonstrated minimal degradation of RNA (
Assessment of the apoptosis factor, which is the ratio of the height of the 28S to 18S peak {Auer, 2003 #26}, suggested that a high percentage of blood cells underwent apoptotic cell death. The distribution of the degradation factor, apoptosis factor, 28S/18S, and yields of total RNA are shown in
We determined if blood cell type heterogeneity affected the sensitivity of transcript detection. Assessment of complete blood count (CBC) variables that affect the number of present calls on the microarray demonstrated a linear correlation between number of probesets called Present and Mean Corpuscular Hemoglobin (MCH). A significant effect was detected (r=0.272; P=0.008, ANOVA) for the B arrays only (
Quality of microarray measurements of PAX system-derived RNA from the BMT population. Individual control charts versus the date of microarray scanning were plotted to look for stability of quality metrics over time, determine outliers, and compare with values proposed by the array manufacturer. The percent Present of transcripts was 32±10 (average±3SD) for A arrays and 21±6 for B arrays. The gapdh and actin 3′/5′ values were less than three, the upper-limit proposed by Affymetrix {Affymetrix, 2004 #29}. Noise was 3.6±1.3 for A arrays and 2.9±0.8 for B arrays. Average Background was 100±48 for A arrays and 78±33 for B arrays. After exclusions of array sets that were known to have been processed differently or erroneously, a total of 95 A and B array sets with stable quality metrics remained. These 95 sets were processed in batches with nearly equal representation of the four infection status phenotypes. Therefore, comparisons among these four groups should detect biological differences as these groups have similar variations due to processing.
Gene expression profiles. The gene expression profiles were displayed on a heat-map with hierarchical clustering of transcripts to characterize and visualize patterns in the profiles of our cohort (
Class prediction of infection status phenotype. The pattern recognition above suggested that there were transcripts with differences in expression levels among healthy, febrile, and recovered patients. Therefore, class prediction was performed, to find sets of transcripts that best classify the four infection status phenotypes. Probesets with >80% absent calls across samples were filtered resulting in 15,721 probesets for further analysis. For supervised class prediction, the class labels for the febrile group were determine from respiratory viral culture results identifying presence or absence of adenovirus.
Unlike data from cancer studies {Golub, 2004 #34; Valk, 2004 #9}, there are no reported transcript selection methods or class prediction algorithms that are optimal for classification of infectious diseases. Therefore, we determined the transcript selection method and classification algorithm that would result in the highest percent correct classification during leave-one-out cross-validation. To estimate the optimal transcript selection parameters for classification in each node, the cut-off level of the univariate P-value was varied, selecting for probesets that showed statistically significant differences between the two groups at a P-value that was equaled to or smaller than a set cut-off level. As the P-value cut-offs became more stringent, the number of probesets selected decreased. For each P-value cut-off level, the selected probesets were subsequently used to classify the samples using various algorithms along with cross-validation analysis. For classification of node 1, 2 and 3, an optimal P-value cut-off level of 10−2, 10−3, 10−5 (
Once an optimal P-value cut-off level was estimated and held constant, the additional criterion of fold-change cut-off threshold was varied (
The samples that were misclassified by various algorithms and the associated gene expression profiles for the selected transcript set are shown in
The estimated optimal percent-correct classification of non-febrile versus febrile, healthy versus convalescents, and febrile without versus with adenovirus infection patients were 99%, 87%, and 91%, respectively. To determine the reliability of these percentages, the permutation test was performed with 2000 permutations. This resulted in P-values of <0.0005, 0.001, and <0.0005, respectively.
Functions of genes in the classifier sets. The identifiers of the discovered transcript sets for the class prediction results are shown in
The 8 probeset classifier (Table 10) for distinguishing healthy versus convalescent, patients mapped to 7 transcripts, including RP127 and RPS7 associated with ribosomal structure; IGHM, the immunoglobulin heavy constant mu transcript; LAMA2, which is involved with cell adhesion, migration, and tissue remodeling; and transcripts related to other functions such as DAB2, KREMEN1, and EVA1.
The 10 transcript classifier (Table 11) for distinguishing febrile without adenovirus versus with adenovirus infection included the interleukin-1 receptor accessory protein, IL1 RAP; two interferon induced genes, IF127 and IF144, which were also in the classifier for fever status; and LGALS3BP, which is involved in cell-cell and cell-matrix interactions and has been found elevated in individuals infected with the human immunodeficiency virus. Other transcripts with known functions less clearly related to adenoviral FRI or with unknown functions included ZCCHC2, ZSIG11, NOP5/NOP58, MS4A7, LY6E, and BTN3A3.
After having rigorously assessed the RNA quality of samples processed with PAX tubes in a relatively large sample of humans with differing infection status phenotypes, we characterized and compared the transcriptomes from whole blood samples of healthy, FRI without and with adenovirus infection, and convalescent individuals, evaluated class prediction methodologies, discovered nested sets of transcripts that could optimally classify the infection status phenotypes and have begun to implicate pathways and gene functions involved in FRI.
We applied a previously reported quality control metric called the degradation factor {Auer, 2003 #26} to our RNA samples and determined that this factor correlates with quality control metrics (gapdh 3′/5′ and actin 3′/5′) present on the microarray. This degradation factor can easily be applied to microarray studies on large populations by assessing electropherogram data that is available from a bioanalyzer prior to processing microarrays and an indicator can be set to flag poor quality samples. We find that quality metrics typically used, such as the 28S/18S ratio have high variability outside the traditional standard range of 1.8 to 2.1 and poorly correlate with the quality control metrics present on the microarray.
When assessing signal to noise quality metrics, we discovered that MCH significantly affects number of present calls on the B array only, likely due to detection of low expression transcripts on the B array compared to the A array {Affymetrix, 2002 #27}. At the time of probe design, the probes on the A chip were associated with more annotation than those on the B chip. The MCH is a measure of picograms of hemoglobin per red blood cell and likely is directly related to amounts of globin mRNA in whole blood samples; prior studies have demonstrated that spiking of increasing amounts of globin mRNA transcripts into total RNA from a cell line decreases the percent present calls linearly {Affymetrix, 2003 #28}. This factor would need to be controlled in future microarray studies or globin mRNA would need to be reduced. In the present study, there was no difference of MCH among the infection status phenotypes.
During supervised analysis, we varied the fold-change cut-off threshold in addition to the P-value cut-off to optimize percent correct classification. These combined criteria select for transcripts that not only are statistically different between two groups, but also vary above a specific fold-change threshold, reducing transcripts that may represent noise. The accuracy of classification seemed to be resistant to transcript selection parameters and algorithms when the gene-expression profiles showed large consistent differences, such as between non-febrile versus febrile patients; stricter P-value and fold change cut-off levels were needed to select informative transcripts that classify the healthy and convalescent or the febrile patients to an accuracy of 87% and 91%, respectively.
Misclassified samples tended to belong to groups more likely to be heterogeneous, suggesting that the misclassification may be due to the lack of specificity of the class labels. In future studies of larger size, the convalescent group might be further sub-classified based on duration of recovery and the febrile without adenovirus group sub-classified based on specific pathogen identified. The majority of transcripts in the classifiers shown in
Our demonstration that one can predict the class of a patient with FRI due to adenovirus infection from background cases of FRI due to other etiologies support the possibility of using gene-expression in biosurveillance and pathogenesis. To our knowledge, this is the first in vivo demonstration of classification of infectious diseases via transcriptional signatures of the host. We intend to extend these findings to other respiratory pathogens, both viral and bacterial and to women, to further determine the capability of applying this technology to biodefense and infectious disease surveillance.
Numerous modifications and variations on the present invention are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the accompanying claims, the invention may be practiced otherwise than as specifically described herein.
Table 16—Performance of classifiers during cross-validation for Class Prediction for fever status (i.e., febrile versus non-febrile patients)
Table 17—Performance of classifiers during cross-validation, table of parameters for Table 16
Table 18—Composition of classifier, list of genes significant at the 0.01 level (sorted by t-value) for Class Prediction for fever status
Table 19—‘Observed v. Expected’ table of GO classes and parent classes, in list of significant genes shown in Table 18
Table 20—Performance of classifiers during cross-validation for Class Prediction for febrile with adenovirus versus without adenovirus patients
Table 21—Performance of classifiers during cross-validation, table of parameters for Table 20
Table 22—Composition of classifier, list of genes significant at the 0.01 level (sorted by t-value) for Class Prediction for rile with adenovirus versus without adenovirus patients
Table 23—‘Observed v. Expected’ table of GO classes and parent classes, in list of significant genes shown in Table 22
Table 24—Performance of classifiers during cross-validation for Class Prediction for healthy versus convalescent patients
Table 25—Performance of classifiers during cross-validation, table of parameters for Table 24
Table 26—Composition of classifier, list of genes significant at the 0.01 level (sorted by t-value) for Class Prediction for healthy versus convalescent patients
Table 27—‘Observed v. Expected’ table of GO classes and parent classes, in list of significant genes shown in Table 26
Table 28—List of genes that discriminate for fever status (i.e., febrile versus non-febrile patients)
Table 29—‘Observed v. Expected’ table of GO classes and parent classes, in list of significant genes shown in Table 28
Table 30—List of genes that discriminate for adenovirus versus without adenovirus patients
Table 31—‘Observed v. Expected’ table of GO classes and parent classes, in list of significant genes shown in Table 30
Table 32—List of genes that discriminate for healthy versus convalescent patients
Table 33—‘Observed v. Expected’ table of GO classes and parent classes, in list of significant genes shown in Table 32
The present application claims priority to U.S. 60/626,500, filed on Nov. 5, 2004, the entire contents of which are incorporated by reference.
The United States Government owns rights in the present invention pursuant to funding from the Defense Threat Reduction Agency (DTRA; Interagency Cost Reimbursement Order (IACRO #02-4118), MIPR numbers 01-2817, 02-2292, 02-2219, and 02-2887), the Office of the U.S. Air Force Surgeon General (HQ USAF SGR; MIPR Numbers NMIPRO35203650, NMIPRONMIPRO35203881, NMIPRONMIPRO35203881), the U.S. Army Medical Research Acquisition Activity (Contract # DAMD17-03-2-0089), the Defense Advance Research Projects Agency (DARPA; MIPR Number M189/02), and the Office of Naval Research (NRL Work Unit 6456).
| Number | Date | Country | |
|---|---|---|---|
| 60626500 | Nov 2004 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 11268373 | Nov 2005 | US |
| Child | 12713554 | US |
