Patent Application
20080172184
The human plasma proteome is a complex fluid that contains over 3000 individual proteins and peptides that are present in quantities that range from picograms to tens of milligrams per milliliter. The expression of specific proteins and specific changes in protein expression levels can be associated with specific conditions, e.g., disease, stage or progression of a condition, infection, etc. As such, analysis of protein levels and changes in protein levels can provide information useful for purposes such as condition diagnosis and therapeutic monitoring. In the clinical setting, certain diagnostic tests include obtaining proteomic profiles of human body fluid collected from a patient. Such diagnostic tests search for protein biomarkers or changes in expression of certain proteins found in body fluids, such as plasma or serum, which can be easily obtained from patients using minimally invasive, safe procedures.
A number of FDA-approved plasma and serum diagnostic assays currently exist; for example, serum and plasma electrophoresis, and a variety of immunochemical assays can be used to monitor the concentrations of specific proteins in plasma and serum. These existing low-to-moderate resolution assays have had a practical impact on medical diagnosis. Such assays can provide useful information at early stages of a disease, allowing for intervention and improved outcomes for patients, with lower associated monetary costs. However, specific protein levels or changes in protein levels associated with conditions of interest can be small, relative to the overall levels of proteins in a given fluid sample. As such, the sensitivity of a method for analyzing protein levels should be such that relatively low levels and minor fluctuations can be detected.
Recent developments in proteomics have brought increased interest in the human plasma and serum proteome as a source for biomarkers of human disease. Higher resolution methods like 2-D electrophoresis and mass spectrometry, coupled with often elaborate protocols for sample preparation and fractionation, have made it possible to identify apparent changes in the composition of the less abundant proteins and peptides in plasma that correlate with particular diseases. Typically no single protein emerges from such analyses as a wholly reliable biomarker, but instead changes in the patterns of panels of proteins often serve as the best diagnostic for a particular malady. These patterns often involve protein or peptide components of plasma that are present in low concentrations.
Interest in the array of existing proteins in a patient's serum has thus evolved to consider in more detail the low molecular weight peptides within serum, which represent a mixture of small intact proteins plus degradation fragments of larger proteins. This low molecular weight region of the serum proteome has been dubbed the “peptidome,” and has been touted as a “treasure trove of diagnostic information that has largely been ignored . . . .” See Liotta and Petricoin, J. Clin. Invest. (2006), and Liotta, et al., Nature (2003). Although some consider the peptidome “unidentified flying peptides” and have questioned the reliability of peptidome SELDI (surface-enhanced laser desorption ionization) patterns as a meaningful diagnostic until the functions of all of the peptide peaks in the peptidome have been properly identified, mass spectrometry, in particular SELDI methods, have made the peptidome accessible for analysis. See Anderson, Proteomics (2005). Many components of the “peptidome” have been found to be complexed with more abundant serum proteins, particular human serum albumin (HAS) and immunoglobulins. Such findings led to the concept of an “interactome,” which introduces the added complexity that serum and plasma can be “comprised of a ‘network’ of protein-protein and peptide-protein interactions,” in which potential biomarkers are bound to the more abundant proteins within the fluid. See Zhou, et al., Electrophoresis (2004). Interestingly, the paper that introduced the “interactome” concept concludes by saying that “the discovery of novel biomarkers in serum/plasma requires new biochemical and analytical approaches, and, most importantly, it is clear that no single sample preparation or detection method will suffice if biomarker investigations are to be broadly successful using current technologies.” See Zhou, et al., Electrophoresis, (2004).
Ten proteins make up 90% of the mass of plasma (by weight). These are, in order of abundance: albumin, IgG, Fibrinogen, Transferrin, IgA, α2-macroglobulin, α1-antitrypsin, complement C3, IgM and Haptoglobin. Another 12 proteins account for another 9% of the plasma mass, the 3 most abundant of which are the apolipoproteins A1 and B, and α1-acid glycoprotein. Twenty-two proteins thus comprise 99% of the mass of plasma, making it challenging to fractionate and quantify the remaining 1%.
The FDA-approved serum protein electrophoresis method monitors changes in the most abundant protein population. See O'Connell, et al., Am. Fam. Physician (2005). However, this method has sensitivity limitations and does not adequately detect changes in less-abundant proteins. Additionally, the equipment necessary for practicing this serum protein electrophoresis method is costly to obtain and maintain.
More recently, 2-D gel electrophoresis and mass spectrometry assays have been developed, which allow for detection of the least abundant components of plasma; however, samples must be prepared by following laborious prefractionation protocols to rid the plasma/serum of the proteins present in high concentrations. See Anderson, Proteomics (2005); Anderson and Anderson, Electrophoresis (1991); Gygi and Aebersold, Curr Opin Chem Biol (2000); Liotta, et al., JAMA (2001); Yates, Trends Genet (2000); and Adkin, et al., Mol Cell Proteomics (2002). Additionally, these assays are time consuming and the equipment necessary for practicing these methods can be costly to obtain and maintain.
Although, the human plasma proteome holds great promise as a convenient specimen for disease diagnosis and therapeutic monitoring, existing assays and technologies have various drawbacks, including sensitivity limitations, time and efficiency limitations, and associated costs that can be prohibitive. Additionally, existing assays and technologies do not fully exploit plasma as a source for biomarkers. For example, electrophoresis and mass spectrometry both separate plasma proteins based on protein size and charge, but assays and technologies based on other physical properties of protein are lacking.
Accordingly, there remains a need in the art for a method for obtaining proteomic profiles of samples, which will address the above-mentioned drawbacks of existing technologies. Additionally, a method with distinctive physical bases, relative to existing technologies, could also be used as an adjunct to existing technologies by identifying unique properties of the individual proteins within a sample.
The presently-disclosed subject matter meets some or all of the above-identified needs, as will become evident to those of ordinary skill in the art after a study of the information provided in this document.
This Summary describes several embodiments of the presently-disclosed subject matter, and in many cases lists variations and permutations of these embodiments. This Summary is merely exemplary of the numerous and varied embodiments. Mention of one or more representative features of a given embodiment is likewise exemplary. Such an embodiment can typically exist with or without the feature(s) mentioned; likewise, those features can be applied to other embodiments of the presently-disclosed subject matter, whether listed in this Summary or not. To avoid excessive repetition, this Summary does not list or suggest all possible combinations of such features.
The presently-disclosed subject matter includes a method of diagnosing or monitoring a condition of interest in a subject. In some embodiments, the method includes: generating a signature thermogram containing a protein composition pattern for a sample obtained from the subject; comparing the signature thermogram to a standard thermogram selected from a negative standard thermogram containing a protein composition pattern associated with an absence of the condition of interest, and a positive standard thermogram containing a protein composition pattern associated with a presence of the condition of interest; and identifying the subject as having the condition of interest or lacking the condition of interest.
In some embodiments, the method further includes identifying the subject as having the condition of interest when the signature thermogram is a good simulation of the positive standard thermogram. In some embodiments, the method further includes identifying the subject as having the condition of interest when the signature thermogram is a good simulation of the positive standard thermogram, and the signature thermogram is a poor simulation of the negative standard thermogram.
In some embodiments, the method further includes identifying the subject as lacking the condition of interest when the signature thermogram is a poor simulation of the positive standard thermogram. In some embodiments, the method further includes identifying the subject as lacking the condition of interest when the signature thermogram is a good simulation of the negative standard thermogram. In some embodiments, the method further includes identifying the subject as lacking the condition of interest when the signature thermogram is a poor simulation of the positive standard thermogram, and the signature thermogram is a good simulation of the negative standard thermogram.
In some embodiments of the method, each standard thermogram is a group-specific standard thermogram. In some embodiments, each group-specific standard thermogram is an ethnic group-specific standard thermogram. In some embodiments, each ethnic group-specific standard thermogram is: a Hispanic-specific standard thermogram if the subject is Hispanic; or a non-Hispanic-specific standard thermogram if the subject is non-Hispanic.
In some embodiments, the condition of interest is cancer. In some embodiments, the cancer is selected from: cervical cancer, endometrial cancer, lung cancer, melanoma, multiple myeloma, ovarian cancer, and vulvar cancer.
In some embodiments the condition of interest is a stage of cervical cancer selected from: moderate cervical dysplasia (CIN II), early stage cervical cancer, and stage IVB cervical cancer.
In some embodiments, the condition of interest is an autoimmune disease. In some embodiments, the autoimmune disease is selected from: rheumatoid arthritis, multiple sclerosis, and systemic lupus.
In some embodiments, the condition of interest is caused by a bacterial infection. In some embodiments, the condition is Lyme disease.
In some embodiments, the condition of interest is caused by a viral infection. In some embodiments, the condition is selected from: Dengue fever, and hepatitis.
In some embodiments, the condition of interest is selected from: amyotrophic lateral sclerosis (ALS), anemia, cardiac disease, diabetes, and renal disease.
In some embodiments, the method further includes comparing the signature thermogram to multiple positive standard thermograms, and identifying the subject as having the condition associated with the positive standard thermogram of which the signature thermogram is a good simulation. In some embodiments, the positive standard thermogram is associated with multiple sclerosis, and another of the positive standard thermograms is associated with amyotrophic lateral sclerosis (ALS). In some embodiments, the multiple positive standard thermograms include positive standard thermograms for different stages of a condition of interest.
In some embodiments, the method further includes providing a second sample obtained from the subject at a time point after the first sample is obtained; generating a second signature thermogram containing a protein composition pattern for the second sample; comparing the first signature thermogram to the second signature thermogram; and identifying the condition of interest as changed when the second signature thermogram is a poor simulation of the first signature thermogram, or identifying the condition of interest as being unchanged when the second signature thermogram is a good simulation of the first signature thermogram. In some embodiments the method further includes comparing the second signature thermogram to the negative standard thermogram, and identifying the subject as lacking the condition of interest if the second signature thermogram is a good simulation of the negative standard thermogram. In some embodiments, the method further includes comparing the second signature thermogram to positive standard thermograms for different stages of a condition of interest, and identifying the condition as progressing, unchanged, or regressing in the subject.
The presently-disclosed subject matter includes a method of assessing a treatment program for a subject. In some embodiments, the method includes providing a first sample obtained from the subject at a first time point of interest; generating a first signature thermogram containing a protein composition pattern for the first sample; providing a second sample obtained from the subject at a second time point of interest; generating a second signature thermogram containing a protein composition pattern for the second sample; comparing the first signature thermogram to the second signature thermogram; and identifying the presence or absence of a change in the condition of interest.
In some embodiments, the method further includes identifying the absence of a change in the condition of interest when the second signature thermogram is a good simulation of the first signature thermogram.
In some embodiments, the method further includes identifying the presence of a change in the condition of interest when the second signature thermogram is a poor simulation of the first signature thermogram.
In some embodiments, the first time point of interest occurs prior to the initiation of the treatment program, and the second time point of interest occurs following the initiation of the treatment program. In some embodiments, the method further includes comparing the second signature thermogram to a standard thermogram selected from: a negative standard thermogram containing a protein composition pattern associated with an absence of the condition of interest; and a positive standard thermogram containing a protein composition pattern associated with a presence of the condition of interest.
The presently-disclosed subject matter includes a method of screening for a composition useful for treating a condition of interest. In some embodiments, the method includes administering to a subject infected with the condition of interest a candidate treatment composition; providing a sample obtained from the subject; generating a signature thermogram containing a protein composition pattern for the sample; comparing the signature thermogram to a standard thermogram selected from: a negative standard thermogram containing a protein composition pattern associated with an absence of the condition of interest; and a positive standard thermogram containing a protein composition pattern associated with a presence of the condition of interest; and determining the utility of the candidate treatment composition.
The presently-disclosed subject matter includes a method of screening a composition, e.g. candidate drug or treatment, for plasma protein interactions. In some embodiments, the method includes interacting the composition with a first plasma sample; generating a first signature thermogram containing a protein composition pattern for the first plasma sample; comparing the first signature thermogram to a negative standard thermogram containing a protein composition pattern associated with an absence of plasma protein interactions; or a second signature thermogram generated using a second plasma sample not interacted with the composition; and identifying the composition as lacking substantial plasma protein interactions when the first signature thermogram is a good simulation of the negative standard thermogram, or the second signature thermogram.
The details of one or more embodiments of the presently-disclosed subject matter are set forth in this document. Modifications to embodiments described in this document, and other embodiments, will be evident to those of ordinary skill in the art after a study of the information provided in this document. The information provided in this document, and particularly the specific details of the described exemplary embodiments, is provided primarily for clearness of understanding and no unnecessary limitations are to be understood therefrom. In case of conflict, the specification of this document, including definitions, will control.
While the following terms are believed to be well understood by one of ordinary skill in the art, the following definitions are set forth to facilitate explanation of the presently-disclosed subject matter.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the presently-disclosed subject matter belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice or testing of the presently-disclosed subject matter, representative methods, devices, and materials are now described.
Following long-standing patent law convention, the terms “a”, “an”, and “the” refer to “one or more” when used in this application, including the claims. Thus, for example, reference to “a cell” includes a plurality of such cells, and so forth.
Unless otherwise indicated, all numbers expressing quantities of ingredients, properties such as reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term “about”. Accordingly, unless indicated to the contrary, the numerical parameters set forth in this specification and claims are approximations that can vary depending upon the desired properties sought to be obtained by the presently-disclosed subject matter.
As used herein, the term “about,” when referring to a value or to an amount of mass, weight, time, volume, concentration or percentage is meant to encompass variations of in some embodiments ±20%, in some embodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, in some embodiments ±0.5%, and in some embodiments ±0.1% from the specified amount, as such variations are appropriate to perform the disclosed method.
The presently-disclosed subject matter includes a method of diagnosing a condition of interest in a subject; a method of monitoring a condition of interest in a subject; a method for assessing the efficacy of a treatment program for a subject; a method of screening for compositions useful for treating a condition of interest; and a method of screening a composition for plasma protein interactions, including tendency of the composition to bind serum albumin.
As used herein, the term condition of interest refers to a variety of conditions. In some embodiments, the condition of interest can be cancer, including but not limited to cervical cancer, endometrial cancer, lung cancer, melanoma, multiple myeloma, ovarian cancer, and vulvar cancer. In some embodiments, the condition of interest can be an autoimmune disease, including but not limited to rheumatoid arthritis, multiple sclerosis, and systemic lupus. In some embodiments, the condition of interest can be caused by an infection, such as a bacterial or a viral infection; such conditions include but are not limited to Lyme disease, Dengue fever, and hepatitis. In some embodiments, the condition of interest can be another condition, including but not limited to amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig's disease, anemia, cardiac disease, diabetes, renal disease, or plasma cell dyscrasias and related disorders. In some embodiments, the condition of interest can be a particular stage of a condition, for example, a particular stage of cervical cancer, such as moderate cervical dysplasia (CIN II), early stage cervical cancer, or stage IVB cervical cancer.
As used herein, the term subject refers to both human and animal subjects. Thus, veterinary therapeutic uses are provided in accordance with the presently-disclosed subject matter. As such, the presently-disclosed subject matter provides for the treatment of mammals such as humans, as well as those mammals of importance due to being endangered, such as Siberian tigers; of economic importance, such as animals raised on farms for consumption by humans or animals used for scientific research, such as rabbits, rats, and mice; and/or animals of social importance to humans, such as animals kept as pets or in zoos. Examples of such animals include but are not limited to: carnivores such as cats and dogs; swine, including pigs, hogs, and wild boars; rodents such as guinea pigs and hamsters; primates such as monkeys; arthropods including insects, arachnids and crustaceans; fish; mollusks; ruminants and/or ungulates such as cattle, oxen, sheep, giraffes, deer, goats, bison, and camels; and horses. Also provided is the treatment of birds, including the treatment of those kinds of birds that are endangered and/or kept in zoos, as well as fowl, and more particularly domesticated fowl, i.e., poultry, such as turkeys, chickens, ducks, geese, guinea fowl, and the like, as they are also of economic importance to humans. Thus, also provided is the treatment of livestock, including, but not limited to, domesticated swine, ruminants, ungulates, horses (including race horses), poultry, and the like.
The methods of the presently-disclosed subject matter make use of a unique calorimetric process for obtaining proteomic profiles of samples. Calorimetry provides a direct means for detecting what is perhaps the most fundamental property of chemical and biochemical reactions—heat changes. Biological calorimetry dates from the time of Lavoisier (1743-1794), who invented a calorimetric method for measuring the heats of metabolism of living animals. The presently-disclosed subject matter can make use of the high sensitivity of modern microcalorimeters, which can reliably measure heat changes of about 0.1 microcalories.
With reference to
Differential scanning calorimetry (DSC) can be used for thermodynamic studies of protein denaturation. The thermodynamics of thermal-induced unfolding of proteins can be measured as directly as possible by DSC. With reference to
Every protein has, under a given set of buffer conditions, a characteristic denaturation thermogram that is unique, and which provides a fundamental thermodynamic signature for that protein. Thermograms can be more complex than the simple two-state melting shown in
A primary DSC thermogram is an extensive property of a protein solution, and as such it is directly proportional to the mass of the protein in solution. If the weight concentration of the protein is doubled, for example, the calorimetric heat response will double. Similarly, in a solution of mixtures of proteins, the heat response will be proportional to the mass of each protein component in the mixture. Mixtures of proteins can be resolved with respect to the fundamental characteristic melting curves of their component proteins. Each protein in a noninteracting mixture will denature at its characteristic melting temperature (Tm) and with its characteristic melting enthalpy. The observed overall thermogram will be the weighted sum of all of the individual protein thermograms, weighted according to the mass of each component. For example,
Samples obtained from subjects, e.g., human plasma/serum samples, include mixtures of proteins. The presence of and the expression level of specific proteins in a mixture of proteins found in a sample can be referred to as the proteomic profile of the sample. The proteomic profile of a sample obtained from a subject having a condition differs from the proteomic profile of a normal subject, i.e., condition-free subject. As such, information about a subject of unknown status (having condition vs. normal/lacking condition) can be obtained by comparing a thermogram generated from a sample obtained from the subject to a thermogram generated from a sample associated with a known status.
Such thermograms have many advantages, for example: they are easily obtained on unlabeled, underivitized, unfractionated plasma/serum samples; they consume only modest amounts of sample; they are obtained relatively quickly; they are based on rigorous, fundamental physical properties of proteins within the sample; they are quantitative, and reflect the exact protein composition of the sample; the procedures for obtaining thermograms are amenable to automated, high-throughput screening; and they provide a new window for viewing plasma/serum composition, based on thermal stability rather than on molecular weight and charge as is the case for electrophoresis and mass spectrometry.
The methods of the presently-disclosed subject matter make use of signature thermograms and standard thermograms. As used herein, the term signature thermogram refers to a thermogram generated using a particular sample of interest. The sample of interest is often a sample obtained from a particular subject. In some embodiments, a method is provided for diagnosing or monitoring a condition of interest in a subject. In such embodiments, the signature thermogram can be a thermogram generated using a sample obtained from the subject being diagnosed or monitored. In some embodiments, a method of screening a composition for use in treating a condition of interest in a subject is provided. In such embodiments, the signature thermogram can be a thermogram generated using a sample obtained from the subject receiving the composition. In some embodiments, it can be desirable to obtain multiple signature thermograms. In such embodiments, the multiple signature thermograms are generated using samples of interest that are related in a particular manner. In such embodiments, samples of interest can be collected from the same subject (i.e., samples related in that they are obtained from the same subject) at different time points during the course of the treatment program.
As used herein, the term standard thermogram refers to a thermogram that is used as a reference to which a signature thermogram can be compared. A standard thermogram can be generated using a standard sample. A standard thermogram can be an average of multiple thermograms generated using multiple standard samples. For example, twenty standard samples can be obtained and a thermogram can be generated from each sample. The twenty generated thermograms could then be averaged to generate a standard thermogram.
In some embodiments, it can be desirable to provide a negative standard thermogram and/or a positive standard thermogram to which a signature thermogram can be compared. A negative standard thermogram is generated using a negative standard sample. For example, a negative standard thermogram can be generated using a sample known to be associated with an absence of a condition of interest, e.g., a sample obtained from a subject known not to have a condition of interest. A positive standard thermogram is generated using a positive standard sample. For example, a positive standard thermogram can be generated using a sample known to be associated with a presence of a condition of interest, e.g., a sample obtained from a subject known to have a condition of interest.
The standard thermogram can be generated using a standard sample obtained from a subject that is selected based on certain common characteristics relative to the subject. For example, if the subject from which a sample is obtained to generate a signature thermogram is a mouse, then the standard sample can be obtained from a mouse. For another example, if the subject from which a sample is obtained to generate a signature thermogram is a human, then the standard sample can be obtained from a human.
In some embodiments, it can be desirable to provide a group-specific standard thermogram to which a signature thermogram can be compared. A group-specific standard thermogram is a standard thermogram generated using a standard sample obtained from a member of the same identified group as the subject.
In some embodiments, when the subject is a member of a particular ethnic group or race, it is desirable to provide a group-specific standard thermogram generated using a sample obtained from a subject of the same ethnic group or race. For example, in some embodiments, when the subject is of Hispanic origin, it is desirable to provide an ethnic group-specific standard thermogram generated using a sample obtained from a subject of Hispanic origin. Other identified groups can include, for example, groups including members of African origin, of native American origin, of Asian origin, or of another ethnic group. In some embodiments, a group is identified by virtue of having negative standard thermograms that are good simulations of one another, i.e., where the standard thermograms of subjects who are substantially free of disease, sickness, or infection are good simulations of one another, a group can be identified to include these subjects.
In some embodiments, when the subject is a member of a particular sex, it can be desirable to provide a group-specific standard thermogram generated using a standard sample obtained from a subject of the same sex as the subject. For example, in some embodiments, when the subject is a female, it is desirable to provide a group-specific standard thermogram generated using a sample obtained from a female. In some embodiments, when the subject is a male, it is desirable to provide a group-specific standard thermogram generated using a sample obtained from a male.
A standard thermogram can be generated at a time point before, at a time point concurrent with or close to, or at a time point after the generation of a signature thermogram to which it will be compared. In some embodiments, it can be desirable to have a standard thermogram prepared to compare with various future-generated signature thermograms. In some embodiments, it can be desirable to provide a kit including one or more standard thermograms and instructions for generating signature thermograms for comparing with the one or more standard thermograms.
When comparing thermograms in accordance with methods of the presently-disclosed subject matter, they can be good simulations of one another or poor simulations of one another. When comparing thermograms, when a first thermogram is not a good simulation of a second thermogram, then it is a poor simulation of the second thermogram. A first thermogram is a good simulation of a second thermogram when it has substantial similarity to the second thermogram. In some embodiments, it is evident whether a first thermogram has substantial similarity to the second thermogram by inspection of the thermograms superimposed on one another, e.g., a signature thermogram superimposed on graphs of the standard(s). For example,
One of ordinary skill in the art can use his or her knowledge to make appropriate determinations of whether a substantial similarity can be found in particular situations.
In some embodiments, substantial similarity can be found when each of the peaks of the first thermogram occur at about the same temperatures as each of the peaks of the second thermogram. In some embodiments, substantial similarity can be found when the peaks of the first thermogram occur at temperatures within one standard deviation of the peaks of the second thermogram. In some embodiments, substantial similarity can be found when the peaks of the first thermogram occur at temperatures within two standard deviations of the peaks of the second thermogram.
In some embodiments, substantial similarity can be found when each of the peaks of the signature thermogram yield about the same heat capacity as the peaks of the standard thermogram. In some embodiments, substantial similarity can be found when the heat capacity of the peaks of the signature thermogram is within one standard deviation of the heat capacity of the peaks of the standard thermogram. In some embodiments, substantial similarity can be found when the heat capacity of the peaks of the signature thermogram is within two standard deviation of the heat capacity of the peaks of the standard thermogram.
In some embodiments, substantial similarity can be determined by application of published statistical procedures, for example, quantile-quantile plots (Lodder and Hieftje (1988)) can be used and/or a two-way Kolmogorov-Smirnov test can be used (Young (1977)). Briefly, for these tests, the thermogram must be converted to a quantile distribution.
To construct a quantile-quantile plot, the quantile values derived from one thermogram is plotted against the quantile values derived from a second thermogram.
The same quantile values used to construct the quantile-quantile plot can be used to conduct a two-way Kolmogorov-Smirnov test, as implemented in standard statistical software packages and as is available online on service web sites (See, e.g., http://www.physics.csbsju.edu/stats/KS-test.html). The Kolmogorov-Smirnov test is designed to test the null hypothesis that two quantile distributions are not statistically different. The test returns a P-value for the confidence level with which the null hypothesis can be rejected. In this regard, in some embodiments, if the null hypothesis that the two quantile distributions are not statistically different (are good simulations) is rejected, it can be determined that the first thermogram is not substantially similar to the second thermogram. In some embodiments, the P-value is less than or equal to 0.5, 0.2, 0.1, 0.05, 0.02, 0.01, 0.005, 0.002, or 0.001.
By way of an example, when using the quantile values of
In some embodiments of the presently-disclosed subject matter, a method of diagnosing or monitoring a condition of interest in a subject is provided. With reference to
As will be understood by those skilled in the art, the sample obtained from the subject 102 can be any appropriate biological sample, such as a body fluid. Appropriate body fluids include, ascites fluid, blood, cerebral spinal fluid, serum, peritoneal fluid, plasma, saliva, senovial fluid, ocular fluid, urine, and the like. As will be understood by those skilled in the art, in some cases it can be desirable to select the type of sample being collected based on the selected condition of interest. For example, in some embodiments when the condition of interest is ALS, it can be desirable to obtain a cerebral spinal fluid sample.
In some embodiments, an obtained sample can be prepared in the following manner. A blood sample is drawn from the subject and plasma or serum is isolated from the blood using known methods. A small volume of about 100 μL of plasma or serum is dialyzed at about 4° C. against a standard buffer (e.g., 10 mM potassium phosphate, 150 mM NaCl, 0.38% (w/v) sodium citrate, pH 7.5 for plasma; 10 mM potassium phosphate, 150 mM NaCl, pH 7.5 for serum). Dialyzed plasma or serum is filtered to remove particulates and then diluted about 25-fold into the standard buffer.
With continued reference to
Using the MicroCal, LLC DSC as an example, a sample volume of approximately 0.4 mL is needed for liquid-handling for proper filling of the sample cell, although the effective cell volume is only approximately 0.133 mL. Each DSC run takes about 1-2 hours to complete. Total protein concentrations of the diluted sample can be determined by standard colorimetric, spectrophotometric, or refractometric methods. These concentrations can be used to normalize experimental thermograms to a g/L protein concentration scale. This normalized thermogram shows the “Excess Specific Heat Capacity” as a function of temperature for a plasma/serum sample (See, e.g., the dashed line thermogram of
With continued reference to
In some embodiments, the subject can be identified as having the condition of interest 112 when the signature thermogram is compared to a negative standard thermogram, and is found to be a poor simulation of the negative standard thermogram 114. In some embodiments, the subject can be identified as having the condition of interest 112 when the signature thermogram is compared to a positive standard thermogram, and is found to be a good simulation of the positive standard thermogram 116. In some embodiments, the subject can be identified as having the condition of interest 112 when the signature thermogram is compared to a positive standard thermogram and a negative standard thermogram, and is found to be a good simulation of the positive standard thermogram 116 and a poor simulation of the negative standard thermogram 114.
In some embodiments, the subject can be identified as lacking the condition of interest 118 when the signature thermogram is compared to a negative standard thermogram and is found to be a good simulation of the negative standard thermogram 120. In some embodiments, the subject can also be identified as lacking the condition of interest 118 when the signature thermogram is compared to a positive standard thermogram and is found to be a poor simulation of the positive standard thermogram 122. In some embodiments, the subject can also be identified as lacking the condition of interest 118 when the signature thermogram is compared to a negative standard thermogram and a positive standard thermogram, and is found to be a good simulation of the negative standard thermogram 120 and a poor simulation of the positive standard thermogram 122.
In some embodiments, the subject can be identified as having a condition, albeit unidentified for the time being, when the signature thermogram is found to be a poor simulation of the negative standard thermogram. Upon such a finding, the signature thermogram can then be compared to positive standard thermograms associated with conditions of interest in order to make a diagnosis.
In some embodiments, the signature thermogram can be compared to multiple positive standard thermograms, e.g., a database including multiple positive standard thermograms, each positive standard thermogram being associated with a particular condition of interest. The positive standard thermogram that most resembles the signature thermogram can be selected. The subject can be identified as having the condition associated with the positive standard thermogram that most resembles the signature thermogram. In some embodiments, the method can be useful to distinguish between two conditions having initial symptoms that are difficult to distinguish; for example, in some embodiments, the method can be used to distinguish multiple sclerosis and ALS in a subject.
As will be understood by those of ordinary skill in the art, it can sometimes be desirable to obtain multiple samples from the subject at various time points, in order to monitor the condition of interest. For example, in some embodiments, a second sample can be obtained from the subject at a time point after the first sample is obtained. A second signature thermogram containing a protein composition pattern for the second sample can be generated. The first signature thermogram can be compared to the second signature thermogram. The condition of interest can be identified as changed when the second signature thermogram is a poor simulation of the first signature thermogram. The condition of interest can be identified as unchanged when the second signature thermogram is a good simulation of the first signature thermogram.
In some embodiments, the second signature thermogram can also be compared to a negative standard thermogram. If the second signature thermogram is a good simulation of the negative standard thermogram, for a subject that had previously been identified as having a particular condition, the subject can be identified as having improved to the point of lacking the condition. In some embodiments, the second signature thermogram can also be compared to various positive standard thermograms associated with different stages of a particular condition. In this regard, it can be determined whether the condition is progressing, i.e., becoming more severe, or regressing, i.e., improving.
With reference now to
In some embodiments, a method of assessing efficacy of a treatment program for a subject 200 includes the following: obtaining a first sample from the subject prior to the initiation of the treatment program 202, obtaining a second sample from the subject following the initiation of the treatment program 204, generating a first signature thermogram using the first sample 206, generating a second signature thermogram using the second sample 208, comparing the first signature thermogram to the second signature thermogram 210, and identifying the presence or absence of a change in the condition of interest 214, 218.
The first sample is obtained from the subject before initiation of the treatment program 202 and is used to generate a first signature thermogram containing a protein composition pattern 206. In some embodiments, the subject has a condition of interest when the first sample is collected. In some embodiments, the subject does not have a condition of interest, but there is otherwise a reason for receiving a treatment program, as will be understood by those of ordinary skill in the art. For example, a subject lacking a condition of interest, but having a risk for obtaining the condition of interest could receive a treatment program, the efficacy of which can be assessed using the method of the presently-disclosed subject matter.
The second sample is obtained from the subject following the initiation of the treatment program 204 and is used to generate a second signature thermogram containing a protein composition pattern 208 associated with the treatment program of the subject. For example, the treatment program could include administration of a treatment composition and the second sample could be obtained after the subject has been receiving the treatment composition for a day, week, month, or other time period of interest. For another example, the treatment program could include providing radiation treatment and the second sample could be obtained after the subject has been receiving the radiation treatment for a specific period of time. In any event, the second sample is obtained at a time point of interest after the treatment program has been initiated. Additional samples can be obtained at different time points of interest to generate a time course describing the effect of the treatment program on the subject.
The signature thermograms are generated 206, 208 by running the samples on a differential scanning calorimeter (DSC) to obtain thermogram for the samples. Once the signature thermograms are generated, they are compared to one another 210. To minimize uncontrolled variables, the sample used to generate the first signature thermogram should be prepared in the same manner and be of the same type as the sample used to generate the second signature thermogram. Similarly, the calorimeter, software, and protocols used to generate the signature thermogram should be substantially the same as those used to generate the standard thermogram.
When the signature thermograms are compared and the second signature thermogram is found to be a good simulation of the first signature thermogram 216, then the treatment program can be identified as having not changed the condition of the subject 218, i.e., absence of a change.
When the signature thermograms are compared and the second signature thermogram is found to be a poor simulation of the first signature thermogram 212, then the treatment program can be identified as having changed the condition of the subject 214, i.e., presence of a change.
As will be understood by those of ordinary skill in the art, depending on the goal of the treatment program, an absence or a presence of a change can be indicative of an effective or an ineffective treatment program. As such, the determination of whether the presence or absence of a change is indicative of an effective treatment program will differ depending on the goal of the treatment program.
In some embodiments, when there is an absence of a change, the treatment program can be identified as an effective treatment program. In some embodiments, when there is an absence of a change, the treatment program can be identified as an ineffective treatment program. For example, if a prophylactic treatment program is administered to a subject lacking a condition of interest, with a goal of preventing an onset of the condition of interest, an absence of a change in the condition of the subject can be indicative of an effective (successful) treatment program. For another example, if a therapeutic treatment program is administered to a subject having a condition of interest, an absence of a change in the condition of the subject can be indicative of an effective treatment program if the goal is to prevent progression of the condition, or an ineffective treatment program if the goal is to cause a regression of the condition.
In some embodiments, when there is a presence of a change, the treatment program can be identified as an effective treatment program. In some embodiments, when there is a presence of a change, the treatment program can be identified as an ineffective treatment program. For example, in some embodiments, a prophylactic treatment program is administered to a subject who initially lacked a condition of interest; in such embodiments, a change in the condition can be indicative of an ineffective treatment program.
In some embodiments, it is apparent by inspecting the thermograms whether a change is indicative of an effective or an ineffective treatment program, e.g., change indicative of a regression of a condition, or a progression of a condition, as will be understood by those of ordinary skill in the art. In some embodiments, it can be desirable to additionally compare the signature thermogram to one or more standard thermograms. For example, in some embodiments a treatment program is administered to a subject who initially had a condition of interest; in such embodiments, a change in the condition can be indicative of either a regression or a progression of the condition. In such cases, as will be understood by those of ordinary skill in the art, it can be useful to additionally compare the second signature thermogram to one or more standard thermograms. For example, if the second signature thermogram is a good simulation of a negative standard thermogram, then the change can be indicative of a regression. In some embodiments, it can be useful to compare the second signature thermogram to a series of positive standard thermograms, each associated with a particular stage of the condition of interest. Such comparisons can also provide information about whether a change in the condition is indicative of a progression or a regression of the condition.
With reference now to
With regard to the step of interacting a sample associated with the condition of interest with a candidate treatment composition 302, in some embodiment, the candidate treatment composition can be administered to an infected subject 302. The subject can be any appropriate test subject, for example, a mouse, a rat, a rabbit, or another appropriate test subject. In some embodiments, the candidate treatment composition can be administered to a subject that is a model for a condition of interest, e.g., mouse model for a particular condition. The candidate composition can be administered by any appropriate method, depending on the characteristics of the composition being screened. A sample, e.g., body fluid sample, can then be obtained from the test subject for use in generating the signature thermogram. In some embodiments, the step of interacting a sample associated with the condition of interest with a candidate treatment composition includes administering the candidate treatment composition to cells in culture, which cells have been infected with or are otherwise associated with the condition of interest. A sample can then be extracted from the cells for use in generating the signature thermogram. The signature thermogram containing a protein composition pattern for the sample can be generated 304 using a differential scanning calorimeter (DSC).
With continued reference to
The standard thermogram can be a negative standard thermogram 308, in that it is associated with an absence of the condition of interest. The negative standard thermogram can be generated using a sample associated with an absence of the condition of interest, e.g., a sample obtained from a subject who is “normal,” or condition-free. In some embodiments, the negative standard sample can be obtained from a subject administered the candidate treatment composition, in which case it is obtained prior to the infection of the subject and prior to administration of the candidate treatment composition.
The standard thermogram can also be a positive standard thermogram 310, in that it is associated with a presence of the condition of interest. In some embodiments, the positive standard thermogram can be generated using a sample obtained from a subject who has the condition of interest. In some embodiments, the positive standard sample can be obtained from the subject administered the candidate treatment composition, in which case it is obtained after the subject is infected and prior to administration of the candidate treatment composition.
In some embodiments, the signature thermogram is a good simulation of the negative standard thermogram 312 associated with an absence of the condition of interest, and the candidate treatment composition can be identified as being useful 314.
In some embodiments, the signature thermogram is a good simulation of the positive standard thermogram 316 associated with a presence of the condition of interest. It can then be determined whether the candidate treatment composition is either useful for preventing a progression of the condition, or is ineffective if the goal is to cause a regression of the condition 318.
In some embodiments, the signature thermogram is a poor simulation of the negative standard thermogram 320 and/or a poor simulation of the positive standard thermogram 322. It can then be determined whether the candidate treatment composition is either useful for causing a regression of the condition, useful for preventing a progression of the condition, or is ineffective, i.e., not treatment affected, or causes a progression of the condition 324.
In order to make the determination of whether the candidate treatment composition is useful for causing a regression of the condition, useful for preventing a progression of the condition, or is ineffective, it can be desirable to obtain a series of samples collected over time, for use in generating a series of signature thermograms. The series of signature thermograms can be compared to identify any changes. In some embodiments, it is apparent by inspecting the series of signature thermograms whether a change is indicative of an effective or an ineffective treatment program. For example, if the series of signature thermograms display a trend towards a good simulation of the negative standard thermogram, then it can be determined that the candidate treatment composition causes a regression of the condition. For another example, if the series of signature thermograms display no change, then it can be determined that the candidate treatment composition prevents a progression of the condition. For another example, if the series of signature thermograms display a trend towards a good simulation of the positive standard thermogram, then it can be determined that the candidate treatment composition neither causes a regression of the condition nor prevents a progression of the condition, i.e., ineffective.
In some embodiments, it can be desirable to additionally compare the signature thermogram to one or more standard thermograms. In some embodiments, the series of signature thermograms can be compared to one or more positive standard thermograms associated with different stages of a condition of interest. For example, if the condition of interest is cervical cancer, standard thermograms associated with moderate cervical dysplasia (CIN II), early stage cervical cancer, and stage IVB cervical cancer could be provided. The series of signature thermograms could be used to determine whether the candidate treatment composition affects a regression of the cervical cancer from stage IVB cervical cancer, to early stage cervical cancer, to moderate cervical dysplasia; a progression from moderate cervical dysplasia, to early stage cervical cancer, to stage IVB cervical cancer; or no change.
In some embodiments, the candidate treatment composition can be administered to a test subject before the test subject has been infected with the condition of interest. The subject can then be infected, samples obtained, and thermograms generated. The thermograms can be compared to determine the ability of the candidate treatment composition to prevent or inhibit an onset or progression of a condition of interest.
The presently-disclosed subject matter further includes a method of screening a composition, e.g. candidate drug or treatment, for protein interactions, to identify and/or monitor the capacity of the composition to interact with protein. In some embodiments, the method includes: interacting the composition with a sample; generating a signature thermogram containing a protein composition pattern for the first sample; comparing the signature thermogram to a thermogram containing a protein composition pattern associated with an absence of protein interactions; identifying the candidate composition as lacking substantial plasma protein interactions when the first signature thermogram is a good simulation of the thermogram containing a protein composition pattern associated with an absence of protein interactions.
In some embodiments, the thermogram containing a protein composition pattern associated with an absence of protein interactions can be a negative standard thermogram. In some embodiments, the thermogram containing a protein composition pattern associated with an absence of protein interactions can be a second signature thermogram generated using a second sample not interacted with the composition.
In some embodiments, the sample is a plasma sample or a serum sample. In such embodiments, the method can be used to identify and/or monitor capacity of composition, e.g., candidate drug, to bind serum albumin and/or other serum or plasma protein interactions.
During drug development and efficacy studies, it can be desirable to identify and monitor interactions between a compound of interest (e.g., drug candidate) and components of plasma. For example, it will be appreciated by those of ordinary skill in the art that it can be desirable to identify and/or monitor a compound of interest for binding to serum albumin.
The presently-disclosed subject matter is further illustrated by the following specific but non-limiting examples. The following examples may include compilations of data that are representative of data gathered at various times during the course of development and experimentation related to the presently-disclosed subject matter.
Reproducible Thermogram for normal plasma.
The average normal thermogram in
In order to establish that frozen samples can be thawed and used in accordance with the methods of the presently-disclosed subject matter, thermograms were generated for freshly prepared samples, and compared to thermograms generated using samples that were thawed after being frozen. With reference to
Normal plasma thermogram is the weighted sum of the denaturation of individual plasma proteins. Applicants hypothesized that the thermogram seen in
This hypothesis was tested in two ways. With reference to
Referring now to
The data presented in
Thermograms of HSA-depleted serum.
Distinctive thermograms for samples associated with a condition of interest. Plasma samples for subjects suffering from various conditions were obtained from BBI Diagnostics (West Bridgewater, Mass.). For comparison, plasma samples from 15 normal subjects were studied. Thermograms were obtained and compared as described herein, and the results are shown in
The thermogram for Lyme disease (
Origin of the altered thermograms. What causes the dramatic alterations in thermograms seen in FIG. 13? One possibility is that the concentrations of the major proteins in plasma are changed. This possibility was tested by experiments, and it was found that such is not the case.
The most likely explanation for the shifts in the thermograms in
In order to test the hypothesis that shifted thermograms result from interactions, the following study was performed. Bromocresol green is a small organic molecule that binds to Site I of human serum albumin (HSA) with a binding constant of 7×105 M−1 (Peters (1996)). The consequences of such binding on plasma thermograms was studied by spiking a normal plasma sample with 30 micromolar bromocresol green. That concentration corresponds to roughly 1 equivalent of the compound per HSA protein molecule.
With reference to
The results of another study are shown in
The shifts in denaturation transition curves that accompany ligand binding to protein are well understood, and have been explained by a number of specific statistical mechanical and thermodynamic models (Brandts (1990) and Schellman (1958)). The effects of binding on the magnitude and exact shape of a melting transition curve depends precisely on the ligand binding affinity, enthalpy, and stoichiometry. Complex multiphasic transition curves can result from partial saturation. Peptide biomarkers in plasma could produce a myriad of thermogram shapes, depending on the exact proteins (and protein binding sites) that they occupy, and their affinity. The interactions of multiple unique biomarkers with different plasma proteins could produce unique, characteristic thermograms that reflect the underlying complexity of the interactions. While calorimetry may not sense signals arising from the denaturation of the biomarkers themselves, it is uniquely sensitive to interactions of these biomarkers with the more abundant plasma proteins.
Distinctive thermograms for samples associated with additional conditions of interest. Plasma samples were obtained from subjects diagnosed with cervical cancer (samples obtained from a gynecological cancer tissue bank maintained at the University of Louisville). Thermograms were generated using the cervical cancer samples. The samples were associated with either moderate cervical dysplasia (CIN II), early stage cervical cancer, or stage IVB cervical cancer. With reference to
Aliquots of these identical samples were also analyzed by the FDA approved serum protein electrophoresis assay. Densitometric scans of the stained gels are shown for comparison in
For the cervical cancers, thermograms were generated for several samples from the gynecological tissue bank. Samples from four normals, four CIN II cervical dysplasia, and four diagnosed cervical cancers were studied. These results are plotted in
Using methods described herein, thermograms are obtained using plasma samples from normal subjects and from subjects diagnosed with a variety of cancers in order to explore and discover the range of patterns resulting from these diseases. Deidentified plasma samples are obtained from a tissue bank maintained at the University of Louisville. This resource maintains “discard” pieces of benign, premalignant, and malignant gynecological tissues for each patient donor, along with pre- and post-operative blood and urine samples, and ascites fluid (when possible). Plasma is prepared from blood samples by standard methods and was stored at −80° C.
With reference to
With reference to
Using methods described herein, thermograms are obtained using plasma samples from normal subjects and from subjects diagnosed with a variety of conditions. With reference to
With reference to
With reference to
Gender-specific and ethnic group-specific thermograms were studied. With reference to
With reference to
Turning now to
The data set forth in
The results of the studies described herein indicate that the methods of the presently-disclosed subject matter are extremely sensitive to binding interactions between proteins. Changes in low-abundance “biomarkers” of conditions of interest that cannot be detected by known methods such as mass spectroscopy or 2-dimensional electrophoresis can be detected with sensitivity using the methods of the presently-disclosed subject matter.
The methods of the presently-disclosed subject are sensitive not only to changes in protein compositions in a noninteracting mixture, but also to interactions resulting from increased concentrations of smaller components (e.g., “biomarkers”) that would themselves not be directly observed. In either case, reproducible signature changes in thermograms relative to normal samples are seen.
Normal thermograms and thermograms for specific conditions of interest are reproducible and distinct. A thermogram for a specific condition of interest is different than a normal thermogram, and is also different than thermograms for other conditions of interest, i.e., they are poor simulations of one another. Each condition of interest has a distinctive and characteristic thermogram. Indeed, in some embodiments, different stages of a condition of interest have distinctive and characteristic thermograms. Therefore, the methods of the presently-disclosed subject matter have beneficial clinical utility and research utility. Benefits of the methods include, the sensitivity, simplicity, non-invasive sample collection, ability to work with low-volume samples, ease of sample preparation, and the capacity for high-throughput.
Materials and Methods
Pure protein samples. Human serum albumin (HSA) (lot # 113K7601), immunoglobulin G (IGG) (lot # 415781/1), immunoglobulin A (IGA) (lot # 105K3777), α1-acid glycoprotein (AAG) (lot # 073K7607), α1-antitrypsin (AAT) (lot # 033K7603), fibrinogen (FIB) (lot # 083K7604), transferrin (TRF) (lot # 123K14511), haptoglobin (HPT) (lot # 055K1664) and immunoglobulin M (IGM) (lot # 016K4876) were purchased from Sigma-Aldrich Chemical Co. (St. Louis, Mo.). α1-Antichymotrypsin (ACT) (lot # B58700), complement C3 (C3) (lot # D33204), complement C4 (C4) (lot # D34721), ceruloplasmin (CER) (lot # B70322), α2-macroglobulin (A2M) (lot # B73605) and prealbumin (PRE) (lot # B68296) were purchased from Calbiochem. C-reactive protein (CRP) (lot # 32F0305FP) was purchased from Life Diagnostics.
Manufactured mixtures. By using available purified plasma proteins, solution mixtures of any desired composition can be made and thermograms for these preparations can be obtained. Such is done, in order to match experimental thermograms of normal and diseased plasma/serum samples. This approach allows for an exploration of the effects of individual components on thermogram shape.
Standard reference serum. A serum reference material (sample # 16910) was purchased from Sigma-Aldrich Chemical Co. (St. Louis, Mo.). A standardized human serum sample can be provided with a certificate of analysis that includes certified values for the concentrations (g/L) of the 15 most abundant proteins, along with the uncertainty in the concentration determination. Concentrations of each sample are determined on the same sample independently by multiple different laboratories. Each sample is provided as a lyophilized portion under nitrogen, and a strict standardized protocol for reconstitution of the material is provided. Thermograms obtained for such materials are useful for multicomponent analysis, since the protein concentrations that are being sought by the numerical analyses procedure are precisely known for the experimental sample. The goodness of fits can thus be rigorously evaluated.
Plasma samples. Normal plasma samples (lot # JA053759, JA053761, JA053763, JA053764, JA053765, JA053766, JC014372, JM034968, JM034969, JM034970, JM034971) were purchased from Innovative Research (Southfield, Mich.) and were also obtained from the Gynecological Cancer Repository of the James Graham Brown Cancer Center. Plasma from subjects suffering from Lyme disease (lot # BM146897, BM140032, BM140031, BM140028), systemic lupus erythematosis (lot # BM142168, BM142160) and rheumatoid arthritis (lot # BM204810, BM205222, BM203373, BM202803, BM200182) were purchased from BBI Diagnostics (West Bridgewater, Mass.).
Sample preparation. IGM, C3, C4 and CRP were purchased as solutions in buffer, lyophilized to dryness and then re-constituted in a smaller volume of ultrapure water (18.2 MΩ-cm) to yield a concentration suitable for DSC. PRE, A2M, CER, ACT were purchased as a powder lyophilized from buffer and were reconstituted with ultrapure water. HSA, IGG, IGA, AAG, AAT, FIB, TRF and HPT were reconstituted with 10 mM potassium phosphate, 150 mM NaCl, pH 7.5. Reference serum was reconstituted according to the guidelines. Pure proteins and reference serum were dialyzed for 24 h at 4° C. against 10 mM potassium phosphate, 150 mM NaCl, pH 7.5 to ensure complete solvent exchange. Pure proteins were diluted with dialysate to a concentration suitable for DSC. Reference serum was diluted 25-fold with the dialysate. Plasma samples (100 μL) were dialyzed for 24 h at 4° C. against 10 mM potassium phosphate, 150 mM NaCl, 0.38% (w/v) sodium citrate, pH 7.5 to ensure complete solvent exchange then diluted 25-fold with the same buffer. All samples (0.45 micron, cellulose acetate or polyethersulfone) and buffers (0.22 micron, polyethersulfone) were filtered before use. Pure protein concentrations were quantitated spectrophotometrically using the following extinction coefficients (ε280; L−1·g−1·cm −1): HSA, 0.53; IGG, 1.38; IGA, 1.32; AAG, 0.89; AAT, 0.53; FIB, 1.55; TRF, 1.12; HPT, 1.2; IGM, 1.18; ACT, 0.62; C3, 0.97; C4, 0.92; CER, 1.49; A2M, 0.893; PRE, 1.41; CRP, 1.95.
DSC protocol. An automated capillary Differential Scanning Calorimeter (DSC) (MicroCal, LLC, Northampton, Mass.) was used for the studies described herein. Samples and dialysate were stored in 96-well plates at 5° C. until being loaded into the calorimeter using the robotic attachment. Scans were recorded from 20-110° C. at 1° C./min using the mid feedback mode, a filtering period of 2 s and with a pre-scan thermostat of 15 min. Data were analyzed using Origin 7.0. Sample scans were first corrected for the instrument baseline by subtracting an appropriate buffer scan. Nonzero baselines were then corrected by applying a linear baseline fit. Scans were finally normalized for the gram concentration of protein. For the pure protein samples, protein concentrations were determined spectrophotometrically as outlined herein. Total protein concentrations of the reference serum and plasma samples were measured by the bicinchoninic acid method (Pierce, Rockford, Ill.). Thermograms were plotted as Excess Specific Heat Capacity (cal/° C. g) versus temperature.
Clinical Laboratory Testing. Both total protein and the concentration of the individual major serum proteins are measured, for example, immunoglobulins G, A and M, transferrin, haptoglobin, prealbumin, complement factors C3 and C4, ceruloplasmin, apolipoproteins A1 and B, α1-antitrypsin, α1-acid glycoprotein, and C-reactive protein. In addition, serum (or plasma) protein electrophoresis is performed on each sample. All of these assays are performed by FDA approved, standard clinical laboratory procedures. The concentrations of the specific serum proteins and the SPE patterns are correlated with the thermograms determined by the methods described herein.
Lipoproteins (HDL, LDL, VLDL, and chylomicrons) are more complex than the other serum proteins. They contain not only the apolipoproteins, but also cholesterol and triglyceride, as well as other minor components. The lipoproteins are likely to cause a significant signal in the thermogram patterns. Therefore, cholesterol and triglyceride of the samples are also measured. Cholesterol and triglyceride is measured on the Vitros by enzymatic methods.
C-reactive protein (CRP) is normally present at a low concentration, which is unlikely to contribute to the thermogram pattern. However, during the acute phase reaction, which is common among sick patients, the concentration of CRP can be high enough to be detectable by the methods described herein.
Clinical assay methods. Protein electrophoresis was performed on agarose gels using the SPIFE 3000 and scanned with the QUICKSCAN 2000 (Helena Laboratories, Beaumont, Tex.). Total protein was measured by the biuret method on the Ortho Vitros 950 (Vitros) (Ortho-Clinical Diagnostic, Rochester, N.Y.) chemistry analyzer. Albumin was measured on the Vitros by the bromocresol green dye binding assay or by an immunoturbidometric assay on the Cobas Integra 800 (Integra) (Roche, Indianapolis, Ind.). Albumin concentrations were also determined from the fraction percent on the protein electrophoresis assay along with the total protein concentration. Specific serum proteins (IGG, IGA, TRF, HPT, IGM, C3, C4, PRE, CRP) were measured by immunoturbidimetry on the Integra.
Column depletion experiments. Reference serum was depleted of HSA using the SwellGel Blue™ albumin removal kit with some minor modifications to the manufacturer's protocol (Pierce, Rockford, Ill.). The serum sample was diluted 10-fold into 10 mM potassium phosphate, pH 7.5 in order to achieve salt conditions and albumin concentrations required for good column binding. Diluted serum (200 μL) was applied to a column containing 2 Swellgel™ discs. An HSA-depleted fraction was obtained following the standard protocol. A single 200 μL volume of the supplied binding/wash buffer was used to obtain a wash fraction. Finally, an eluted HSA fraction was obtained from a single 200 μL addition of the supplied elution buffer. In order to obtain a greater volume of each fraction for subsequent experiments, multiple columns were run using an identical protocol and each of the fractions pooled. Fractions for DSC analysis were dialyzed for 24 h at 4° C. against 10 mM potassium phosphate, 150 mM NaCl, pH 7.5 and diluted as necessary with dialysate. DSC scans were performed on an N-DSC II instrument (Calorimetry Sciences Corporation, Provo, Utah) from 20-110° C. at 1° C./min with a pre-scan equilibration time of 10 min. Data were analyzed using Origin 7.0.
Throughout this document, various references are mentioned. All such references are incorporated herein by reference, including the references set forth in the following list:
This application claims priority from U.S. Provisional Application Ser. Nos. 60/978,252 filed Oct. 8, 2007; and 60/884,730 filed Jan. 12, 2007, the entire disclosures of which are incorporated herein by this reference.
Subject matter described herein was made with government support under Grant Number R44 CA103437 awarded by the National Cancer Institute. The government has certain rights in the described subject matter.
| Number | Date | Country | |
|---|---|---|---|
| 60978252 | Oct 2007 | US | |
| 60884730 | Jan 2007 | US |
