Patent Application
20110257888
This application is related to Japanese patent application No. 2010-93225 filed on Apr. 14, 2010 whose priority is claimed under 35 USC §119, the disclosure of which is incorporated herein by reference in its entirety.
1. Field of the Invention
The present invention relates to a method of determining whether or not a subject is affected with chronic fatigue syndrome (CFS). More specifically, the present invention relates to a method which can determine whether or not a subject is affected with CFS based on a measurement of an expression level of a given gene transcript in a biological sample from a subject.
2. Description of the Related Art
In modern societies, many people chronically feel fatigue. According to the surveillance in 1999 by Ministry of Health, Labour and Welfare of Japan, approximately one-third of Japanese feel chronic fatigue, and economical loss due to the fatigue is estimated about 1.2 trillion yen, suggesting that the fatigue may be a big social concern.
Chronic fatigue syndrome (CFS) is a disease characterized by irreversible intensive fatigue with unknown cause for more than 6 months. It is estimated that there are about 0.3 million and about 3 million patients of CFS in Japan and whole world, respectively, and that there are about 30 million patients-to-be.
At the moment, CFS can only be diagnosed by determination of disability in life based on reports from patients themselves together with by exclusion of possible other diseases accompanied by fatigue after detailed examinations; thus there is no objective determination method for this disease.
WO 98/15646 discloses a diagnosis method of CFS by detecting a blood protein RNase L.
Japanese Unexamined Patent Publication No. 2005-13147 discloses a method of determining a risk of developing CFS based on polymorphism of serotonin transporter gene in the genome of a subject.
Japanese Unexamined Patent Publication No. 2007-228878 discloses a method of diagnosing CFS based on expression levels of genes which have differential expressions in CFS patients.
US Patent Publication No. 2009/0010908 discloses numerous biomarkers (genes) which can be used in the diagnosis of CFS.
Gow et al. (John W Gow et al, “A gene signature for post-infectious chronic fatigue syndrome”, BMC Medical Genomics 2009, 2:38) also identified genes whose expression levels are specific in CFS patients.
However, it was difficult to precisely and stably diagnose CFS with prior techniques.
Thus, the object of the present invention is to provide a method which allows precise and stable determination as to whether a subject is affected with CFS.
The present inventors have carried out extensive studies in order to solve the above problem and found that the patients suffering from CFS can be clearly and stably distinguished from healthy subjects by measuring an expression level of a transcript of at least one gene belonging to certain categories (gene groups) in a biological sample from a tested subject, calculating a value representing a deviation of the measured expression level based on an expression level of a transcript of the corresponding gene in a biological sample from a healthy subject, averaging the calculated value in the category, and using the averaged values calculated from at least two categories to determine CFS.
Thus, the present invention provides a method of determining whether or not a subject is affected with chronic fatigue syndrome (CFS) comprising the steps of:
measuring, in a biological sample from the subject, an expression level of a transcript of at least one gene respectively from at least two gene groups selected from energy production-related gene group, virus infection-related gene group, cell death-related gene group, antioxidation-related gene group, immune function-related gene group and iron regulation-related gene group,
calculating a value representing a deviation of the measured expression level based on an expression level of a transcript of the corresponding gene in a population of healthy subjects,
obtaining an average by, (i) when one value representing a deviation is obtained for one gene from the gene group selected, taking the value representing a deviation for the gene as the average, or (ii) when two or more values representing a deviation are obtained for two or more genes from the gene group selected, calculating the average from the values representing a deviation for the two or more genes, and
determining whether or not the subject is affected with CFS by using the obtained average.
Further, the present invention provides a computer program product for enabling a computer to determine whether or not a subject is affected with chronic fatigue syndrome (CFS) comprising a computer readable medium, and software instructions, on the computer-readable medium, for enabling the computer to perform predetermined operations comprising:
receiving an expression level of a transcript of at least one gene respectively from at least two gene groups selected from energy production-related gene group, virus infection-related gene group, cell death-related gene group, antioxidation-related gene group, immune function-related gene group and iron regulation-related gene group measured in a biological sample from the subject,
calculating a value representing a deviation of the measured expression level based on an expression level of a transcript of the corresponding gene in a population of healthy subjects, and obtaining an average by, (i) when one value representing a deviation is obtained for one gene from the gene group selected, taking the value representing a deviation for the gene as the average, or (ii) when two or more values representing a deviation are obtained for two or more genes from the gene group selected, calculating the average from the values representing a deviation for the two or more genes,
determining whether or not the subject is affected with CFS by using the average, and
outputting the result obtained by the determining.
According to the present method, the determination of CFS can be easily carried out from biological samples of subjects, as well as the objective diagnosing tool can be provided. The present method can provide more precise indexes to support the determination of CFS compared to the previous methods.
1 Measuring apparatus of gene transcript expression level
2 Computer
3 Cable
According to the present method, an expression level of a transcript of at least one gene respectively from at least two gene groups is measured in a biological sample from a subject, at least two gene groups being selected from energy production-related gene group, virus infection-related gene group, cell death-related gene group, antioxidation-related gene group, immune function-related gene group and iron regulation-related gene group.
The biological sample is not specifically limited so long as it is obtained from living body and from which a transcript of a gene can be extracted. It may be blood (including whole blood, plasma and serum), saliva, urine, body hair and the like.
As used herein, “a transcript of a gene” and “a gene transcript” refer to a product obtained by transcription of the gene and includes ribonucleic acid (RNA), specifically messenger RNA (mRNA).
As used herein, “expression level of a transcript of a gene” refers to an existing amount of a transcript of a gene in the biological sample or an amount which reflects such existing amount. According to the present method, an amount of a transcript of a gene (mRNA) or an amount of complementary deoxyribonucleic acid (cDNA) or complementary RNA (cRNA) may be measured. Generally, the amount of mRNA in biological samples is minute, and therefore the amount of cDNA or cRNA which is obtained from mRNA by reverse transcription or in vitro transcription (IVT) is preferably measured.
Gene transcripts can be extracted from a biological sample using well-known RNA extraction methods. For example, RNA extracts may be obtained by centrifuging the biological sample to precipitate cells containing RNA, physically or enzymatically disrupting the cells and removing cell debris. The extraction of RNA may also be carried out using commercially available RNA extraction kits.
The thus obtained gene transcript extract may be subjected to a treatment for removing contaminants which are derived from the biological sample and are preferably to be excluded at the time of measurement of expression level of the transcript of the gene, such as globin mRNA if the biological sample is blood.
For the thus obtained gene transcript extract, an expression level of a transcript of at least one gene respectively from at least two gene groups selected from energy production-related gene group, virus infection-related gene group, cell death-related gene group, antioxidation-related gene group, immune function-related gene group and iron regulation-related gene group is measured.
The expression level of a gene transcript may be measured according to well-known methods. The measurement is preferably carried out by quantitative PCR or nucleic acid chip techniques because they allow expression analyses of numerous gene transcripts.
When the expression level of a gene transcript is measured with nucleic acid technology, the gene transcript extract or cDNA or cRNA generated from the gene transcript is brought into contact with nucleic probes of 20- to 25-mer long fixed on a substrate and changes in index of hybridization such as fluorescence, color, electric current and the like is determined to measure an expression level of the gene transcript of interest.
At least one nucleic probe may be used for one gene transcript, and more than one nucleic probe may be used according to the length of the gene transcript. The sequence of the probe may be appropriately selected by a person skilled in the art according to the sequence of the gene transcript to be measured.
The measurement of the expression level of a gene transcript using nucleic acid chip technology may be carried out on GeneChip® system provided by Affymetrix, Inc.
When nucleic acid chip technology is used, the gene transcript or its cDNA or cRNA is preferably fragmented in order to promote the hybridization with probes. The fragmentation may be carried out by well-known methods including heating in the presence of metal ions and fragmentation with nucleases such as ribonucleases or deoxyribonucleases.
The amount of the gene transcript or its cDNA or cRNA to be brought into contact with probes on nucleic acid chips may generally be 5 to 20 μg. The condition for the contact is generally at 45° C. for 16 hours or the like.
The transcript or its cDNA or cRNA which has hybridized with a probe can be detected for the formation of hybridization and for the amount of the hybridized transcript based on the changes in a fluorescent substance, dye or electric current passing through the nucleic acid chip.
When the hybridization is detected based on a fluorescent substance or dye, the gene transcript or its cDNA or cRNA is preferably labeled with a labeling substance in order to detect the fluorescent substance or dye. Such labeling substances may include those conventionally used in the art. Generally, in order to label cDNA or cRNA with biotin, biotinylated nucleotide or biotinylated ribonucleotide is mixed as a nucleotide or ribonucleotide substrate at the synthesis of cDNA or cRNA. When cDNA or cRNA is biotinylated, a binding partner for biotin, avidin or streptavidin, can bind to biotin on nucleic acid chips. If avidin or streptavidin is bound to an appropriate fluorescent substance, hybridization can be detected. The fluorescent substance may include fluorescein isothiocyanate (FITC), green fluorescence protein (GFP), luciferin, phycoerythrin and the like. It is usually convenient to use commercially available phycoerythrin-streptavidin conjugates.
Alternatively, a labeled anti-avidin or -streptavidin antibody may be brought into contact with avidin or streptavidin to detect a fluorescent substance or dye of the labeled antibody.
The above six gene groups comply with classifications of categories (GO Terms) defined by Gene Ontology (GO) project. GO Terms can be found in http://www.geneontology.org/index.shtml.
According to the present method, the expression level of a transcript of at least one gene respectively from at least two gene groups among the above six gene groups is measured.
The energy production-related gene group is a category of genes relating to adenosine triphosphate (ATP), which is an energy source in living body. The energy production-related gene group according to the present method preferably comprises (A-1) ATP synthase-related genes, (A-2) mitochondrial ribosomal protein-related genes, (A-3) NADH dehydrogenase-related genes and (A-4) mitochondrial DNA synthesis-related genes.
(A-1) ATP synthase-related genes are the genes which are classified into GO Term of “Mitochondrial proton-transporting ATP synthase complex” (GO: 0005753).
(A-2) Mitochondrial ribosomal protein-related genes are the genes encoding proteins which constitute mitochondrial ribosome or which are present in mitochondria.
(A-3) NADH dehydrogenase-related genes are the genes which are classified into GO Term of “NADH dehydrogenase (ubiquinone) activity” (GO Term: 0008753).
(A-4) Mitochondrial DNA synthesis-related genes are the genes which are classified into GO Term of “Mitochondrial DNA replication” (GO: 0006264).
The virus infection-related gene group preferably comprises genes encoding interferon-inducible proteins (i.e., interferon-related genes). These genes are classified into the virus infection-related gene group because interferon is produced upon virus infection.
The cell death-related gene group preferably comprises genes related to caspase and sphingomyelin which are known to be related to cell death. Specifically, the cell death-related gene group preferably comprises caspase-related genes and sphingomyelin synthase-related genes.
Caspase-related genes are the genes encoding caspase.
Sphingomyelin synthase-related genes are the genes of enzymes related to the synthesis of sphingomyelin.
The antioxidation-related gene group preferably comprises glutathione S-transferase related genes because glutathione is a known antioxidant.
Glutathione S-transferase related genes are the genes which are classified into GO Term of “Glutathione transferase activity” (GO: 0004364).
The immune function-related gene group is a group of genes related to immune system, and preferably comprises T-cell receptor-related genes and NK cell receptor-related genes.
T-cell receptor-related genes are the genes encoding T-cell receptors α, β, γ and the like.
NK cell receptor-related genes are the genes encoding NK cell receptors.
The iron regulation-related gene group preferably comprises iron-responsive element binding protein-related genes.
Iron-responsive element binding protein-related genes are the genes which are classified into GO Term of “Iron-responsive element binding” (GO: 0030350).
Examples of the genes belonging to each gene group are shown in Table 1.
Among the above genes listed in Table 1, according to the present method, it is preferable to measure an expression level of a transcript of at least one gene listed in Table 2 for at least two gene groups.
In Table 2, “Probe Set ID” is an ID number for identifying a probe set for gene recognition in GeneChip® from Affymetrix, Inc. The sequences of probes can be obtained from, for example, http://www.affymetrix.com/analysis/index.affx.
The sequences of these genes are already known and can be obtained from databases such as Entrez, Ensemble and Unigene, with their ID numbers shown in Table 2.
The gene transcript expression level obtained in this step is not specifically limited so long as it relatively represents an existing amount of the gene transcript in the biological sample. When nucleic acid chip technology is used for the measurement, the expression level may be signal obtained from nucleic acid chips based on fluorescence intensity, color intensity, amount of current and the like.
Such signal can be measured with a measuring apparatus for nucleic acid chips.
Next, a value representing a deviation of the measured expression level based on an expression level of a transcript of the corresponding gene in a population of healthy subjects is calculated.
As used herein, “a transcript of the corresponding gene” means a transcript of the same gene for which the expression level from the subject has been measured.
The expression level of a transcript of the corresponding gene in a population of healthy subjects can be obtained by measuring the expression level of the target gene transcript in biological samples obtained from healthy subjects according to the similar procedures used for a biological sample from the subject.
As used herein, “healthy subject” means a person who is confirmed as healthy by doctor's questions and general blood test. As used herein, “a patient of chronic fatigue syndrome” and “a CFS patient” mean a person who is diagnosed as CFS by a medical specialist.
“A population of healthy subjects” may be a population having statistically sufficient size such as a group comprising 30 or more, preferably 40 or more people.
The value representing a deviation can be calculated according to the following equation:
A value representing a deviation={(Expression level of a transcript of a gene in a subject)−(An average of expression levels of a transcript of the corresponding gene in a population of healthy subjects)}/(Standard deviation of expression levels of the transcript of the corresponding gene in the population of healthy subjects)
The above value representing a deviation is a value also known as Z score which represents the distance of the expression level of the gene transcript of the subject from the expression levels of the transcript in the healthy subject population.
Next, an average is obtained by, (i) when one value representing a deviation is obtained for one gene from the gene group selected, taking the value representing a deviation for the gene as the average, or (ii) when two or more values representing a deviation are obtained for two or more genes from the gene group selected, calculating the average from the values representing a deviation for the two or more genes.
Thus, as used herein, when a value representing a deviation for only one gene is obtained in the gene group for which the average is to be obtained, “an average” means the value representing the deviation for the one gene, and when values representing a deviation for two or more genes are obtained, it means a value obtained by averaging out these values representing a deviation.
The above average is obtained for at least two gene groups selected from energy production-related gene group, virus infection-related gene group, cell death-related gene group, antioxidation-related gene group, immune function-related gene group and iron regulation-related gene group. Preferably, the average is obtained for at least three gene groups, more preferably for at least four gene groups, still more preferably for at least five gene groups and most preferably for six gene groups.
The thus obtained averages are used to determine whether or not the subject is affected with CFS.
This determination can be carried out by feeding the above averages from the subject to a determination equation obtained from an average preliminary obtained by corresponding steps described above using biological samples from healthy subjects and an average preliminary obtained by corresponding steps described above using biological samples from CFS patients. The determination equation can be obtained by a known software Support Vector Machine (SVM).
The averages calculated from a biological sample from the subject may be fed to SVM to which the average from healthy subjects and the average from CFS patients have been fed to obtain the determination equation, thereby determining whether or not the subject is affected with CFS.
The present method preferably has the sensitivity, i.e., a probability of the method to determine a CFS patient as “positive”, of 80% or more, more preferably 85% or more and still more preferably 90% or more. The present method preferably has the specificity, i.e., a probability of the method to determine a healthy subject as “negative”, of 60% or more, more preferably 70% or more, still more preferably 80% or more and particularly preferably 90% or more.
Because the present method has such high sensitivity and specificity, it can provide precise and stable diagnoses.
The present invention also provides a computer program product for enabling a computer to carry out the present method. Thus, the computer program product of the present invention comprises a computer readable medium, and software instructions, on the computer-readable medium, for enabling the computer to perform predetermined operations comprising:
receiving an expression level of a transcript of at least one gene respectively from at least two gene groups selected from energy production-related gene group, virus infection-related gene group, cell death-related gene group, antioxidation-related gene group, immune function-related gene group and iron regulation-related gene group measured in a biological sample from the subject,
calculating a value representing a deviation of the measured expression level based on an expression level of a transcript of the corresponding gene in a population of healthy subjects, and obtaining an average by, (i) when one value representing a deviation is obtained for one gene from the gene group selected, taking the value representing a deviation for the gene as the average, or (ii) when two or more values representing a deviation are obtained for two or more genes from the gene group selected, calculating the average from the values representing a deviation for the two or more genes,
determining whether or not the subject is affected with CFS by using the average, and
outputting the result obtained by the determining.
In the computer 2, the obtained expression level is used to calculate the value representing a deviation, the value is converted to the average for each of at least two gene groups and the averages are used for the determination as to whether the subject is affected with CFS.
The present computer program product may be in cooperation with the computer 2 comprising a central processing unit, a memory part, a reader for compact disc, Floppy® disc etc., an input part such as a keyboard and an output part such as a display to carry out the present method.
First, the expression level of the gene transcript measured in the measuring apparatus of gene transcript expression level is fed to CPU of the computer 2 (step S11).
CPU then processes the fed expression level to obtain a value representing a deviation based on the expression level of a transcript of the corresponding gene in a population of healthy subjects and an average of the obtained value representing a deviation for each of at least two gene groups (step S12).
CPU further determines whether or not the subject is affected with CFS using the obtained average (step S13). This determination can be carried out by feeding the above averages to a determination equation obtained from an average preliminary obtained by using biological samples from healthy subjects and an average preliminary obtained by using biological samples from CFS patients.
Namely, it is preferable that the average preliminary obtained from healthy subjects and the average preliminary obtained from CFS patients have already been stored in the hard disk of the computer 2. More preferably, Support Vector Machine has already been installed in the hard disc of the computer 2 and the above averages have been stored in the SVM.
CPU feeds an average from the subject to the determination equation obtained from the preliminary stored averages, and displays on a displaying apparatus such as a display of a computer the determination results as to whether or not the subject is affected with CFS (step S14).
The present invention is further illustrated by means of the following Examples which do not limit the present invention.
Blood samples obtained from the following subjects were used as biological samples in the present Example.
The subjects were determined to be healthy or CFS by using SVM.
From 5 ml of blood taken with a syringe, total RNA was extracted with PAXgene Blood RNA system (PreanalytiX GmbH) according to the following procedures. All reagents and columns used are contained in PAXgene Blood RNA system.
Blood taken with a syringe (2.5 ml) was transferred to a blood collecting tube for RNA extraction, PAXgene Blood RNA Tube (PreanalytiX GmbH), mixed up and down for about 10 times and left to stand at room temperature for 2 hours. The blood was immediately used or stored at −80° C. The blood collecting tube for RNA extraction containing blood was centrifuged at 4000×g for 10 minutes and the supernatant was removed. The pellet was suspended in 4 ml of Ribonuclease free water and centrifuged at 4000×g for 10 minutes to remove the supernatant. The pellet was suspended in 350 μl of BRI buffer.
The content was transferred to a 1.5-mL tube and 300 μl of BR2 buffer and 40 μl of Protein Kinase solution were added. After voltexing for 5 seconds, the tube was incubated in a thermoshaker at 55° C. and 1000 rpm for 10 minutes. A PSC column was loaded with the content, centrifuged at 14000 rpm for 3 minutes and the obtained filtrate was transferred to a 1.5-mL tube. The tube was added with 350 μl of ethanol, voltexed and spun. A PRC column was loaded with 700 μl of the supernatant and centrifuged at 12000 rpm for 1 minute, and the filtrate was discarded. The remained supernatant was also passed through the PRC column in a similar manner. The PRC column was loaded with 350 μl of BR3 buffer and centrifuged at 12000 rpm for 1 minute, and the filtrate was discarded. The PRC column was loaded with 70 μl of RDD+10 μl of DNase and left to stand at room temperature for 15 minutes, and the filtrate was discarded. The PRC column was loaded with 350 μl of BR3 buffer and centrifuged at 12000 rpm for 1 minute, and the filtrate was discarded. The PRC column was then loaded with 500 μl of BR4 buffer and centrifuged at 12000 rpm for 1 minute, and the filtrate was discarded. The same procedure (centrifugation for 3 minutes) was repeated one more time. The empty PRC column was centrifuged at 12000 rpm for 1 minute. The column was placed with a new 1.5-mL tube, loaded with 4 μl of BR5 buffer and centrifuged at 12000 rpm for 1 minute. The same procedure was repeated one more time. The obtained filtrate was incubated at 65° C. for 5 minutes and placed on ice.
The total RNA obtained as the above procedures was subjected to the removal of globin RNA using GLOBINclear-Human kit (Ambion, Inc.) according to the following procedures.
To the solution of total RNA were added 0.1 volume of 5M NH4OAc, 5 μg of glycogen and 2.5 volumes of ethanol and the mixture was left to stand at −80° C. for 30 to 60 minutes. The mixture was centrifuged at 14000 rpm and 4° C. for 30 minutes and the supernatant was removed. The pellet was added with 1 mL of cold 80% ethanol, mixed, and centrifuged at 14000 rpm and 4° C. for 10 minutes to remove the supernatant. The same procedure was repeated one more time. The pellet was dried for 15 minutes and dissolved in 20 μl of nuclease-free water.
The thus concentrated RNA solution (1 to 10 μg, maximum 14 μl) was placed with a tube provided with GLOBINclear-Human kit, and 1 μl of Capture Oligo Mix provided with the kit and nuclease-free water up to 15 μl were added. The provided 2× Hybridization Buffer (15 μl) was added, voltexed, spun and incubated at 50° C. for 15 minutes.
Streptavidin Magnetic Beads (30 μl) were added which were prepared from Streptavidin Magnetic Beads, Streptavidin Bead Buffer and 2× Hybridization Buffer according to the instruction of the kit, all of which were provided with the kit, and the mixture was voltexed, spun, snapped to mix and incubated at 50° C. for 30 minutes. Thereafter, the mixture was voltexed, spun, and left to stand on a magnetic separation stand for 3 to 5 minutes. The supernatant was collected.
The supernatant was added with 100 μl of RNA Binding Buffer and 20 μl of voltexed Beads Suspension Mix and voltexed for 10 seconds. The mixture was spun and left to stand on a magnetic separation stand for 3 to 5 minutes. After the removal of the supernatant, 200 μl of RNA Wash Solution was added. The mixture was voltexed for 10 seconds, spun and left to stand on a magnetic separation stand for 3 to 5 minutes. After the removal of the supernatant, the pellet was dried, added with 20 μl of Elution Buffer heated to 58° C., voltexed for 10 seconds and incubated at 58° C. for 5 minutes. The mixture was further voltexed for 10 seconds, left to stand on a magnetic separation stand for 3 to 5 minutes and the supernatant was collected to recover RNA from which globin RNA was removed.
The thus obtained total RNA was used to prepare biotinylated target cRNA to be used for GeneChip® with GeneChip One-Cycle Target Labeling and Control Reagents (Affymetrix, Inc.) according to the following procedures, in order to measure expression levels of gene transcripts.
(4-1) Synthesis of 1st Strand of cDNA
The following reagents were incubated in a PCR tube at 70° C. for 10 minutes and then 4° C. for 2 minutes or more.
The following reagents were further added and the tube was tapped.
The tube was incubated at 42° C. for 2 minutes, added with 1 μl of Super Script II and incubated at 42° C. for 1 hour and then at 4° C. for 2 minutes or more to synthesize the 1st strand of cDNA.
(4-2) Synthesis of 2nd Strand of cDNA
The following reagents were added to the synthesized 1st strand of cDNA and the tube was tapped.
E. coli DNA ligase
E. coli DNA polymerase I
The mixture was incubated at 16° C. for 2 hours, added with 2 μl of T4 DNA polymerase, incubated at 16° C. for 5 minutes, added with 10 μl of 0.5M EDTA to synthesize the 2nd strand of cDNA.
(4-3) Washing of cDNA
The thus synthesized 2nd strand cDNA was transferred to a 1.5-mL tube, added with 600 μl of cDNA Binding Buffer and voltexed. The mixture (500 μl) was loaded to cDNA Cleanup Spin Column, which was then centrifuged at 10000 rpm for 1 minute, and the filtrate was discarded. The rest of cDNA was loaded to the column, which was then centrifuged in a similar manner. The column was placed with a new 2-mL tube, loaded with 750 μl of cDNA Wash Buffer, centrifuged and the filtrate was discarded. The column was centrifuged at 14000 rpm for 5 minutes. The column was placed with a new 1.5-mL tube, loaded with 14 μl of cDNA Elution Buffer, left to stand for 1 minute, and centrifuged at 14000 rpm for 1 minute to wash cDNA.
The obtained cDNA was transformed to biotinylated cRNA by in vitro transcription (IVT) according to the following procedures.
The following reagents were mixed in a PCR tube and incubated at 37° C. for 16 hours to obtain cRNA. The following reagents are attached to One-Cycle Target Labeling and Control Reagents kit.
(4-5) Washing of cRNA
The thus obtained cRNA was transferred to a 1.5-mL tube, added with 60 μl of RNase-free water and voltexed. To the tube was added 350 μl of IVT CRNA Binding Buffer, voltexed, added with 250 μl of 100% EtOH and mixed with pipetting. cRNA Cleanup Spin Column was loaded with the content, centrifuged at 1000 rpm for 15 seconds and placed with a new tube. The column was loaded with 500 μl of IVT cRNA Wash Buffer and centrifuged at 10000 rpm for 15 seconds, and the filtrate was discarded. The column was loaded with 500 μl of 80% EtOH and centrifuged at 10000 rpm for 15 seconds, and the filtrate was discarded. The column was centrifuged at 14000 rpm for 5 minutes to dry before the column was placed with a new tube. The column was loaded with 11 μl of RNase-free water, left to stand for 1 minute and centrifuged at 14000 rpm for 1 minute. Further, the column was loaded with 10 μl of RNase-free water, left to stand for 1 minute and centrifuged at 14000 rpm for 1 minute. The thus obtained filtrate was diluted at 200-fold and measured for absorbance to determine the amount of cRNA.
(4-6) Fragmentation of cRNA
The following reagents were mixed in a tube and incubated at 94° C. for 35 minutes to obtain fragmented cRNA before storage at 4° C.
The following reagents are attached to One-Cycle Target Labeling and Control Reagents kit.
Gene expression level was measured with fragmented and biotinylated cRNA obtained in step (4) by hybridization in GeneChip®. The nucleic acid chip used was Human Genome U133 Plus 2.0 Array. The hybridization conditions were as follows.
99° C. for 5 minutes→45° C. for 5 minutes→14000 rpm for 5 minutes
The chip was stained and washed on Fluidic Station 450 (Affymetrix, Inc.) apparatus using GeneChip Hybridization Wash and Stain kit (Affymetrix, Inc.) according to the supplier's instructions, which stains hybridized target cRNA with streptavidin-phycoerythrin conjugate.
The chip was scanned on GeneChip Scanner 3000 (Affymetrix, Inc.).
Scanned image data was transformed to CEL file using DNA microarray analysis software GeneChip Operating Software (GCOS; Affymetrix, Inc.), which was then normalized with ArrayAssist (Stratagene) software, and correlation coefficients between measurement results of samples from subjects were calculated. Normalized algorithm used was MAS5.0.
Among the genes corresponding to about 56,000 probe sets analyzed as above, only the maximum signal values were extracted for the genes for which two or more different probe sets were analyzed. Further, the genes having a signal value of 100 or less were excluded. As a result, the genes corresponding to about 17,000 probe sets were selected for the following analyses.
For the transcripts of genes corresponding to about 17,000 probe sets selected as above, all signal values obtained from healthy subjects 1 (63 samples) were used to calculate average and standard deviation. These values were entered to the following equation to obtain the values representing a deviation of each gene (Z scores) for the about 17,000 genes.
Z score={(a signal value of a transcript of a gene)−(an average of signal values of a transcript of the corresponding gene in healthy subjects (63 samples))}/(a standard deviation of signal values of the transcript of the corresponding gene in healthy subjects (63 samples))
The above about 17,000 genes were classified into GO Terms according to the classification in Gene Ontology (http://www.geneontology.org/index.shtml). Z scores obtained in (7-2) for the genes in each GO Term were averaged.
In a similar manner, averages in GO Terms were calculated for 100 samples from CFS patients.
The thus obtained averages in GO Terms from healthy subjects and CFS patients were subjected to T-test to obtain P values.
The GO Terms used were divided into several groups based on their functions or intracellular localizations and the groups which contain more GO Terms having P value<1.0E-05 were selected.
Hierarchical cluster analysis was carried out with Z scores of all genes contained in the selected groups, and clusters of genes which synchronously vary were selected.
Scores for clusters which correspond to the averages of Z scores of genes contained in each cluster were subjected to T-test for healthy subjects (63 samples) and CFS patients (100 samples). The clusters having P value<1.0E-05 were selected, which were energy production-related gene group, virus infection-related gene group, cell death-related gene group, antioxidation-related gene group, immune function-related gene group and iron regulation-related gene group. It is believed that these gene groups can be parameters for distinguishing healthy subjects and CFS patients. These gene groups and genes belonging thereto are shown in Table 2.
Among six gene groups identified in Example 1, the averages for healthy subjects 1 (63 samples) and CFS patients (100 samples) in each of the following groups (A) to (G) were fed to Support Vector Machine (SVM; contained in the analysis software ArrayAssist) to obtain determination equations:
(A) energy production-related gene group and virus infection-related gene group;
(B) energy production-related gene group and antioxidation-related gene group;
(C) virus infection-related gene group and immune function-related gene group;
(D) energy production-related gene group, antioxidation-related gene group and iron regulation-related gene group;
(E) energy production-related gene group, cell death-related gene group and immune function-related gene group;
(F) antioxidation-related gene group, iron regulation-related gene group and immune function-related gene group; and
(G) energy production-related gene group, virus infection-related gene group, cell death-related gene group, antioxidation-related gene group, immune function-related gene group and iron regulation-related gene group.
The SVM fed with these averages from 163 samples was used to assess the performance as to whether the samples were determined to be positive (CFS) or negative (healthy).
The results are shown in
In
These results show that the present method can identify CFS patients with sensitivity of 80% or more and specificity of 60% or more.
In addition, it is found that an increase in the number of gene groups to be measured improves accuracy of the determination.
The performance of the determination equation obtained in Example 2 was further assessed with 200 blood samples from healthy subjects 2 (average age: 20.4 years). The results are shown in
| Number | Date | Country | Kind |
|---|---|---|---|
| 2010-93225 | Apr 2010 | JP | national |
