Patent Application
20220170066
The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Apr. 27, 2021, is named “CSURF_065620-642266_ST25.txt”, and is 2.58 KB in size.
The present disclosure relates to human disease detection tools and methods, and in particular pertains to tools and methods for detecting Lyme disease and southern tick-associated rash illness (STARI), and for distinguishing Lyme disease from STARI.
Lyme disease is a multisystem bacterial infection that in the United States is primarily caused by infection with Borrelia burgdorferi sensu stricto. Over 300,000 cases of Lyme disease are estimated to occur annually in the United States, with over 3.4 million laboratory diagnostic tests performed each year. Symptoms associated with this infection include fever, chills, headache, fatigue, muscle and joint aches, and swollen lymph nodes; however, the most prominent clinical manifestation in the early stage is the presence of one or more erythema migrans (EM) skin lesions. This annular, expanding erythematous skin lesion occurs at the site of the tick bite in 70 to 80% of infected individuals and is typically 5 cm or more in diameter. Although an EM lesion is a hallmark for Lyme disease, other types of skin lesions can be confused with EM, including the rash of southern tick-associated rash illness (STARI).
A strict geographic segregation of Lyme disease and STARI does not exist, as there are regions where STARI and Lyme disease are co-prevalent. Clinically, the skin lesions of STARI and early Lyme disease are indistinguishable, and no laboratory tool or method exists for the diagnosis of STARI or differentiation of STARI from Lyme disease. The only biomarkers evaluated for differential diagnosis of early Lyme disease and STARI have been serum antibodies to B. burgdorferi. However, these tests have poor sensitivity for early stages of Lyme disease, and thus a lack of B. burgdorferi antibodies cannot be used as a reliable differential marker for STARI. Thus, there is a need for diagnostic methods to differentiate between Lyme disease and STARI, and that facilitate proper treatment, patient management and disease surveillance.
In one aspect, the present disclosure encompasses a method for analyzing a blood sample from a subject, the method comprising: (a) deproteinizing the blood sample to produce a metabolite extract; (b) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; and (c) providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D.
In another aspect, the present disclosure encompasses a method for classifying a subject as having Lyme disease or STARI, the method comprising: (a) deproteinizing a blood sample from a subject to produce a metabolite extract, wherein the subject has at least one symptom that is associated with Lyme disease or STARI; (b) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (c) providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D; and (d) inputting the abundance values from step (c) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease or STARI.
In another aspect, the present disclosure encompasses a method for treating a subject with Lyme disease, the method comprising: (a) obtaining a disease score from a test; (b) diagnosing the subject with Lyme disease based on the disease score; and (c) administering a treatment to the subject with Lyme disease, wherein the test comprises measuring the amount of each molecular feature in Table A, Table B, Table C, or Table D; providing abundance values for each molecular feature measured; and inputting the abundance values into a classification model trained with samples derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease from subjects with STARI, and optionally from healthy subjects. In certain examples, the test comprises (i) deproteinizing a blood sample from a subject to produce a metabolite extract; (ii) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (iii) providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D; and (iv) inputting the abundance values from step (iii) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease. In further examples, the subject has at least one symptom of Lyme disease. In still further examples, the Lyme disease is early Lyme disease and optionally the symptom is an EM rash.
In another aspect, the present disclosure encompasses a method for treating a subject with STARI, the method comprising: (a) obtaining a disease score from a test; (b) diagnosing the subject with STARI based on the disease score; and (c) administering a treatment to the subject with STARI, wherein the test comprises measuring the amount of each molecular feature in Table A, Table B, Table C, or Table D; providing abundance values for each molecular feature measured; and inputting the abundance values into a classification model trained with samples derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with STARI. In certain examples, the test comprises (i) deproteinizing a blood sample from a subject to produce a metabolite extract; (ii) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (iii) providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D; and (iv) inputting the abundance values from step (iii) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with STARI from subjects with Lyme disease, including early Lyme disease, and optionally from healthy subjects. In further examples, the subject has at least one symptom of STARI. In still further examples, the symptom is an EM or an EM-like rash.
Other aspects and iterations of the invention are described below.
The disclosure contains at least one photograph executed in color. Copies of this patent application publication with color photographs will be provided by the Office upon request and payment of the necessary fee.
Lyme disease is an illness caused by a Borrelia species (e.g., Borrelia burgdorferi, Borrelia garinii, Borellia afzelii, etc.) and is transmitted to humans through the bite of infected blacklegged ticks (Ixodes species). Lyme disease can go through several stages and may cause different symptoms, depending on how long a subject has been infected and where in the body the infection has spread. The stages of Lyme disease include Stage 1, Stage 2, and Stage 3. Stage 1 Lyme disease may also be referred to as “early localized Lyme disease” or “early Lyme disease” and usually develops about 1 day to about 4 weeks after infection. Non-limiting examples of symptoms of Stage 1 Lyme disease include erythema migrans and flu-like symptoms, such as lack of energy, headache, stiff neck, fever, chills, muscle pain, joint pain, and swollen lymph nodes. Stage 1 Lyme disease may result in one or more than one symptom. In some cases, Stage 1 Lyme disease does not result in any symptoms. Stage 2 Lyme disease may also be referred to as “early disseminated infection” and usually develops about 1 month to about 4 months after infection. Non-limiting examples of symptoms of Stage 2 Lyme disease include an erythema migrans (or additional erythema migrans rash sites), pain, weakness, numbness in the arms and/or legs, Bell's palsy (facial drooping), headaches, fainting, poor memory, reduced ability to concentrate, conjunctivitis, episodes of pain, redness and swelling in one or more large joints, rapid heartbeats (palpitations), and serious heart problems. Stage 3 Lyme disease may also be referred to as “late persistent Lyme disease” and usually develops months to years after infection. Non-limiting examples of symptoms of Stage 3 Lyme disease include arthritis, numbness and tingling in the hands, numbness and tingling in the feet, numbness and tingling in the back, tiredness, Bell's palsy (facial drooping), problems with memory, mood, sleep speaking, and heart problems (pericarditis). A subject diagnosed with Lyme disease, or suspected of having Lyme disease, may be identified on the basis of one or more symptoms, geographic location, and possibility of tick bite. Currently, several routine diagnostic tests are known for diagnosing Lyme disease. Typically these tests detect and/or quantify antibodies to one or more Borellia antigens, and are performed using common immunoassay methods such as enzyme-linked immunoassays (EIA or ELISA), immunofluorescence assays, or Western immunoblots. Generally, these tests are most reliable only a few weeks after an infection. Positive PCR and/or positive culture may also be used. (See, e.g., Moore et al., “Current Guidelines, Common Clinical Pitfalls, and Future Directions for Laboratory Diagnosis of Lyme Disease, United States,” Emerg Infect Dis. 2016, Vol. 22, No. 7). In one example, diagnostic testing may comprise a commercially-available C6 EIA. The C6 Lyme EIA measures antibody reactivity to a synthetic peptide corresponding to the sixth invariable region of VlsE, a highly conserved surface protein of the causative Borrelia burgdorferi bacterium. Alternatively, or in addition, diagnostic testing may comprise using IgM and/or IgG immunoblots following a positive or equivocal first-tier assay. As used herein, a subject that is negative for antibodies to Lyme disease causing Borrelia species need only be negative by one method of testing.
Southern tick-associated rash illness (STARI) is an illness associated with a bite from the lone star tick, Amblyomma americanum. The causative agent of STARI is unknown. The rash of STARI is a red, expanding “bull's-eye” lesion that develops around the site of a lone star tick bite. The rash of STARI may be referred to as an EM rash or an EM-like rash. The rash usually appears within 7 days of tick bite and expands to a diameter of 8 centimeters (3 inches) or more. Non-limiting examples of additional symptoms associated with STARI include discomfort and/or itching at the bite site, muscle pain, joint pain, fatigue, fever, chills, and headache. A subject diagnosed with STARI, or suspected of having STARI, may be identified on the basis of one or more symptom, geographic location, and possibility of tick bite.
Complicating the clinical differentiation between Lyme disease, and in particular early Lyme disease, and STARI are shared symptoms (for example, an EM or EM-like rash), co-prevalence of STARI and Lyme disease in certain geographic regions, and poor sensitivity of common diagnostic methods for early stages Lyme disease. The present disclosure provides a biosignature that identifies Lyme disease and southern tick-associated rash illness (STARI), and distinguishes one from the other. Various aspects of the biosignature and its use are described in detail below.
So that the present disclosure may be more readily understood, certain terms are first defined. 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 examples of the disclosure pertain. Many methods and materials similar, modified, or equivalent to those described herein can be used in the practice of the examples of the present disclosure without undue experimentation, the preferred materials and methods are described herein. In describing and claiming the examples of the present disclosure, the following terminology will be used in accordance with the definitions set out below.
The term “about,” as used herein, refers to variation of in the numerical quantity that can occur, for example, through typical measuring techniques and equipment, with respect to any quantifiable variable, including, but not limited to, mass, volume, time, distance, wave length, frequency, voltage, current, and electromagnetic field. Further, given solid and liquid handling procedures used in the real world, there is certain inadvertent error and variation that is likely through differences in the manufacture, source, or purity of the ingredients used to make the compositions or carry out the methods and the like. The term “about” also encompasses these variations, which can be up to ±5-10%, but can also be ±9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, etc. Whether or not modified by the term “about,” the claims include equivalents to the quantities.
As used herein, the term “accuracy” refers to the ability of a test (e.g., a diagnostic test, a classification model, etc.) to correctly differentiate one type of subject (e.g., a subject with Lyme disease) from one or more different types of subjects (e.g., a subject with STARI, a healthy subject, etc.). Accuracy is equal to (true positive result)+(true negative result)/(true positive result)+(true negative result)+(false positive result)+(false negative result).
The term “biosignature” refers to a plurality of molecular features forming a distinctive pattern which is indicative of a disease or condition of an animal, preferably a human.
The term “molecular feature” refers to a small molecule metabolite in a blood sample that has a mass less than 3000 Da. The term “abundance value” refers to an amount of a molecular feature in a blood sample. The abundance value for a molecular feature may be identified via any suitable method known in the art. Molecular features are defined herein by a positive ion m/z charge ratio ±a suitable tolerance to account for instrument variability (e.g., ±15 ppm) and optionally one or more additional characteristic such as retention time or a chemical structure based on accurate mass; and abundance values for each molecular feature are obtained from a measurement of the area under the peak for the monoisotopic mass of each molecular feature determined by mass spectrometry. Given that the present disclosure identifies the molecular features (i.e., small molecule metabolites) forming each biosignature, alternative methods for measuring the amount of the metabolites may be used without departing from the scope of the invention.
As used herein, the term “ROC” means “receiver operating characteristic”. A ROC analysis may be used to evaluate the diagnostic performance, or predictive ability, of a test or a method of analysis. A ROC graph is a plot of sensitivity and specificity of a test at various thresholds or cut-off values. Each point on a ROC curve represents the sensitivity and its respective specificity. A threshold value can be selected based on an ROC curve, wherein the threshold value is a point where sensitivity and specificity both have acceptable values. The threshold value can be used in applying the test for diagnostic purposes. It will be understood that if only specificity is optimized, then the test will be less likely to generate a false positive (diagnosis of the disease in more subjects who do not have the disease) at the cost of an increased likelihood that some cases of disease will not be identified (e.g., false negatives). If only sensitivity is optimized, the test will be more likely to identify most or all of the subjects with the disease, but will also diagnose the disease in more subjects who do not have the disease (e.g., false positives). A user is able to modify the parameters, and therefore select an ROC threshold value suitable for a given clinical situation, in ways that will be readily understood by those skilled in the art.
Another useful feature of the ROC curve is an area under the curve (AUC) value, which quantifies the overall ability of the test to discriminate between different sample properties, for example, to discriminate between subjects with Lyme disease and those STARI; to discriminate between subjects with STARI and healthy subjects; subjects or to discriminate between subjects with Lyme disease, STARI, and healthy subjects. A test that is no better at identifying true positives than random chance will generate a ROC curve with an AUC of 0.5. A test having perfect specificity and sensitivity (i.e., generating no false positives and no false negatives) will have an AUC of 1.00. In reality, most tests will have an AUC somewhere between these two values.
As used herein, the term “sensitivity” refers to the percentage of truly positive observations which is classified as such by a test, and indicates the proportion of subjects correctly identified as having a given condition. In other words, sensitivity is equal to (true positive result)/[(true positive result)+(false negative result)].
As used herein, the term “specificity” refers to the percentage of truly negative observations which is classified as such by a test, and indicates the proportion of subjects correctly identified as not having a given condition. Specificity is equal to (true negative result)/[(true negative result)+(false positive result).
As used herein, the term “subject” refers to a mammal, preferably a human. The mammals include, but are not limited to, humans, primates, livestock, rodents, and pets. A subject may be waiting for medical care or treatment, may be under medical care or treatment, or may have received medical care or treatment.
As used interchangeably herein, the terms “control group,” “normal group,” “control subject,” or “healthy subject” refer to a subject, or a group of subjects, not previously diagnosed with the disease in question and/or treated for the disease in question for atherapeutically effective amount of time (e.g., 12 months or more).
As used herein, the term “blood sample” refers to a biological sample derived from blood, preferably peripheral (or circulating) blood. The blood sample can be whole blood, plasma or serum.
The terms “treat,” “treating,” or “treatment” as used herein, refer to the provision of medical care by a trained and licensed health professional to a subject in need thereof. The medical care may be a diagnostic test, a therapeutic treatment, and/or a prophylactic or preventative measure. The object of therapeutic and prophylactic treatment is to prevent or slow down (lessen) an undesired physiological change or disease/disorder. Beneficial or desired clinical results of therapeutic or prophylactic treatments include, but are not limited to, alleviation of symptoms, diminishment of extent of disease, stabilized (i.e., not worsening) state of disease, a delay or slowing of disease progression, amelioration or palliation of the disease state, and remission (whether partial or total), whether detectable or undetectable. “Treatment” can also mean prolonging survival as compared to expected survival if not receiving treatment. Alternatively, the medical care may be a recommendation for no intervention. For example, no medical intervention may be needed for diseases that are self-limiting. Those in need of treatment include those already with the disease, condition, or disorder as well as those prone to have the disease, condition or disorder or those in which the disease, condition or disorder is to be prevented.
In an aspect, the present disclosure provides a biosignature that provides an accuracy of detecting Lyme disease equal to or greater than about 80%. In another aspect, the present disclosure provides a biosignature that provides an accuracy of detecting STARI disease equal to or greater than about 80%.
A method for identifying a Lyme disease biosignature and/or a STARI biosignature is detailed in the examples. Generally speaking, the method comprises: a) obtaining test blood samples and control blood samples; b) analyzing the test blood samples and control blood samples by mass spectrometry to obtain abundance values for a plurality of molecular features in the test blood samples and the control blood samples; and c) applying a statistical modeling technique to select for a plurality of molecular features that distinguish test blood samples from control blood samples with an accuracy equal to or greater than about 80%. Test blood samples are from subjects with Lyme disease and/or STARI, either of which is confirmed using known diagnostic methods as described above; and control bloods samples are from subjects confirmed to be free of Lyme, STARI, or both using known diagnostic methods for each. The molecular features that distinguish test blood samples from control blood samples comprise the biosignature for that disease.
A blood sample may be a whole blood sample, a plasma sample, or a serum sample. Any of a variety of methods generally known in the art for collecting a blood sample may be utilized. Generally speaking, the sample collection method preferably maintains the integrity of the sample such that abundance values for each molecular feature can be accurately measured. A blood sample may be used “as is”, or a blood sample may be processed to remove undesirable constituents. In preferred examples, a blood sample is processed using standard techniques to remove high-molecular weight species, and thereby obtain an extract comprising small molecule metabolites. This is referred to herein as “deproteinization” or a “deproteinization step.” For example, a solvent or solvent mixture (e.g., methanol or the like) may be added to a blood sample to precipitate these high-molecular weight species followed by a centrifugation step to separate the precipitate and the metabolite-containing supernatant. In another example, proteases may be the added to a blood sample. In another example, size exclusion chromatography may be used.
Analysis using mass spectrometry, preferably high resolution mass spectrometry, yields abundance measures for a plurality of molecular features. The abundance value for each molecular feature may be obtained from a measurement of the area under the peak for the monoisotopic mass of each molecular feature. Identification and extraction of molecular features involves finding and quantifying all the known and unknown compounds/metabolites down to the lowest abundance, and extracting all relevant spectral and chromatographic information. Algorithms are available to identify and extract molecular features. Such algorithms include for example the Molecular Feature Extractor (MFE) by Agilent. MFE locates ions that are covariant (rise and fall together in abundance) but the analysis is not exclusively based on chromatographic peak information. The algorithm uses the accuracy of the mass measurements to group related ions-related by charge-state envelope, isotopic distribution, and/or the presence of adducts and dimers. It assigns multiple species (ions) that are related to the same neutral molecule (for example, ions representing multiple charge states or adducts of the same neutral molecule) to a single compound that is referred to as a feature. Using this approach, the MFE algorithm can locate multiple compounds within a single chromatographic peak. Specific parameters for MFE may include a minimum ion count of 600, an absolute height of 2,000 ion counts, ion species H+ and Na+, charge state maximum 1, and compound ion count threshold of 2 or more ions. Once the molecular feature has been identified and extracted, the area under the peak for the monoisotopic mass is used to determine the abundance value for the molecular feature. The monoisotopic mass is the sum of the masses of the atoms in a molecule using the unbound, ground-state, rest mass of the principal (most abundant) isotope for each element instead of the isotopic average mass. Monoisotopic mass is typically expressed in unified atomic mass units (u), also called daltons (Da).
A molecular feature is identified as a potential molecular feature for utilization in a biosignature of the present disclosure if it is present in at least 50% of either the test blood samples or the control blood samples. For example, the molecular feature may be present in at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or 100% of either the test blood samples or the control blood samples. Additionally, a molecular feature is identified as a potential molecular feature for utilization in a biosignature of the present disclosure if it is significantly different in abundance between the test blood samples and the control blood samples. Specifically, a molecular feature is identified as being significantly different if the difference in abundance value of the molecular feature in the test blood samples versus the abundance value of the molecular feature in the blood biological samples has a p-value is less than 0.1, preferably less than 0.05, less than 0.01, less than 0.005, or less than 0.001.
To increase the stringency of the biosignature, replicates of the test blood samples and control blood samples may be analyzed. For example, the test blood samples and control blood samples may be analyzed in duplicate. Alternatively, the test biological samples and control biological samples may be analyzed in triplicate. Additionally, the test blood samples and control blood samples may be analyzed four, five or six times. The replicate analysis is used to down-select the plurality of molecular features. The down-selection results in a biosignature with increased stringency.
Once a plurality of potential molecular features has been generated, a statistical modeling technique may be applied to select for the molecular features that provide an accuracy of disease detection that is clinically meaningful. Several statistical models are available to select the molecular features that comprise a biosignature of the present disclosure. Non-limiting examples of statistical modeling techniques include LDA, classification tree (CT) analysis, random forests, and LASSO (least absolute shrinkage and selection operator) logistic regression analysis. Various methods are known in the art for determining an optimal cut-off that maximizes sensitivity and/or specificity to serve as a threshold for discriminating samples obtained from subjects with Lyme disease or STARI. For the biosignatures in Table A, Table C, and Table D, the cut-off is determined by a data point of the highest specificity at the highest sensitivity on the ROC curve. However, the cut-off can be set as required by situational circumstances. For example, in certain clinical situations it may be desirable to minimize false-positive rates. These clinical situations may include, but are not limited to, the use of an experimental treatment (e.g., in a clinical trial) or the use of a treatment associated with serious adverse events and/or a higher than average number of side effects. Alternatively, it may be desirable to minimize false-negative rates in other clinical situations. Non-limiting examples may include treatment with a non-pharmacological intervention, the use of a treatment with a good risk-benefit profile, or treatment with an additional diagnostic agent. For the biosignatures in Table A, molecular feature stability across many samples and different LC-MS analyses was used as the cut-off.
In one example, the present disclosure provides a biosignature comprising each molecular feature in Table A, wherein the molecular features in the table are defined at least by their m/z ratio ±15 ppm, in some examples ±10 ppm, in some examples ±5 ppm (depending upon instrument variability). A biosignature comprising each molecular feature in Table A provides a 9800 probability of accurately detecting a sample from a subject with Lyme disease, including early Lyme disease, and an 89% probability of accurately detecting a sample from a subject with STARI, when discriminating between a classification of Lyme disease and STARI. A skilled artisan will appreciate that in certain examples one or more molecular feature may be eliminated from the model without a clinically meaningful, negative impact on the model.
In one example, the present disclosure provides a biosignature comprising each molecular feature in Table B, wherein the molecular features in the table are defined at least by their m/z ratio ±15 ppm, in some examples ±10 ppm, in some examples ±5 ppm (depending upon instrument variability). The biosignature comprises each molecular feature in Table B that maintains an absolute abundance fold change of 2 or greater between Lyme disease and STARI, and maintains an abundance coefficient of variation of 0.2 or less between STARI blood samples, and maintains an abundance coefficient of variation of 0.2 or less between Lyme disease blood samples. A skilled artisan will appreciate that in certain examples one or more molecular features may be eliminated from the model without a clinically meaningful, negative impact on the model.
In one example, the present disclosure provides a biosignature comprising each molecular feature in Table C, wherein the molecular features in the table are defined at least by their m/z ratio ±15 ppm, in some examples ±10 ppm, in some examples ±5 ppm (depending upon instrument variability). The biosignature comprising each molecular feature in Table C provides an 85% probability of accurately detecting a sample from a subject with Lyme disease, including early Lyme disease, an 92% probability of accurately detecting a sample from a subject with STARI, and a 93% probability of accurately detecting a sample from a healthy subject, when discriminating between a status (classification) of Lyme disease, STARI, and healthy. A skilled artisan will appreciate that in certain examples one or more molecular features may be eliminated from the model without a clinically meaningful, negative impact on the model.
In one example, the present disclosure provides a biosignature comprising each molecular feature in Table D, wherein the molecular features in the table are defined at least by their m/z ratio ±15 ppm, in some examples ±10 ppm, in some examples ±5 ppm (depending upon instrument variability). The biosignature comprising each molecular feature in Table D provides an 97% probability of accurately detecting a sample from a subject with Lyme disease, including early Lyme disease, and an 89% probability of accurately detecting a sample from a subject with STARI, when discriminating between a classification of Lyme disease and STARI. The biosignature comprising each molecular feature in Table D provides an 85% probability of accurately detecting a sample from a subject with Lyme disease, including early Lyme disease, a 92% probability of accurately detecting a sample from a subject with STARI, and a 93% probability of accurately a sample from a healthy subject, when discriminating between a classification of Lyme disease, STARI, and healthy. A skilled artisan will appreciate that one or more molecular features may be eliminated from the model without a clinically meaningful, negative impact on the model.
In another aspect, the present disclosure provides a method for analyzing a blood sample from a subject. The method comprises performing liquid chromatography coupled to mass spectrometry on a blood sample, and providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D. Preferably, the method further comprises deproteinizing a blood sample from a subject to produce a metabolite extract and then performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract. The method may comprise providing abundance values for each molecular feature in Table A or Table C. The method may comprise providing abundance values for each molecular feature in Table B or Table D. The method may comprise providing abundance values for each molecular feature in Table A, Table B, or Table D. The method may comprise providing abundance values for each molecular feature in Table C or Table D.
A subject may be a human or a non-human mammal including, but not limited to, a livestock animal, a companion animal, a lab animal, or a zoological animal. A subject may be a rodent, e.g., a mouse, a rat, a guinea pig, etc. A subject may also be a livestock animal. Non-limiting examples of suitable livestock animals may include pigs, cows, horses, goats, sheep, llamas and alpacas. Alternatively, a subject may be a companion animal. Non-limiting examples of companion animals may include pets such as dogs, cats, rabbits, and birds. A subject may be a zoological animal. As used herein, a “zoological animal” refers to an animal that may be found in a zoo. Such animals may include non-human primates, large cats, wolves, and bears. In preferred examples, a subject is human.
Methods of the present disclosure for analyzing a blood sample may be used to monitor the progression or resolution of Lyme disease or STARI. A skilled artisan will also appreciate that infection with Borrelia species that cause Lyme disease, or with the causative agent(s) of STARI, likely commences prior to diagnosis or the onset of symptoms associated with the disease. For at least these reasons, a suitable blood sample may be from a subject that may or may not have a symptom associated with Lyme disease or STARI. Non-limiting examples of symptoms associated with Lyme disease and STARI are described above. A subject may have at least one symptom associated with Lyme disease, at least one symptom associated with STARI, or at least one symptom associated with Lyme disease and STARI. As a non-limiting example, a subject can have an erythema migrans (EM) rash or an EM-like rash. Alternatively, a subject may not have a symptom of Lyme disease or STARI but may be at risk of having Lyme disease or STARI. Non-limiting examples of risk factors for Lyme disease or STARI include living in or visiting a region endemic for Lyme disease or STARI, spending time in wooded or grassy areas, camping, fishing, gardening, hiking, hunting and/or picnicking in a region endemic for Lyme disease or STARI, and not removing tick(s) promptly or properly. In each of the above examples, suitable subjects, whether or not they have a symptom associated with Lyme disease or STARI at the time a blood sample is obtained, may or may not have received (or be receiving) treatment for Lyme disease, STARI, or another disease with symptoms similar to Lyme disease or STARI.
A blood sample may be a whole blood sample, a plasma sample, or a serum sample. Any of a variety of methods generally known in the art for collecting a blood sample may be utilized. Generally speaking, the sample collection method preferably maintains the integrity of the sample such that abundance values for each molecular feature in Table A, Table B, Table C, or Table D can be accurately measured according to the disclosure. A blood sample may be used “as is”, or a blood sample may be processed to remove undesirable constituents. In preferred examples, a blood sample is processed using standard techniques to remove high-molecular weight species, and thereby obtain an extract comprising small molecule metabolites. This is referred to herein as “deproteinization” or a “deproteinization step.” For example, a solvent or solvent mixture (e.g., methanol or the like) may be added to a blood sample to precipitate these high-molecular weight species followed by a centrifugation step to separate the precipitate and the metabolite-containing supernatant. In another example, proteases may be the added to a blood sample. In another example, size exclusion chromatography may be used.
A single blood sample may be obtained from a subject. Alternatively, the molecular features may be detected in blood samples obtained over time from a subject. As such, more than one blood sample may be collected from a subject over time. For instance, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or more blood samples may be collected from a subject over time. For example, 2, 3, 4, 5, or 6 blood samples are collected from a subject over time. Alternatively, 6, 7, 8, 9, or 10 blood samples are collected from a subject over time. Further, 10, 11, 12, 13, or 14 blood samples are collected from a subject over time. Still further, 14, 15, 16 or more blood samples are collected from a subject over time. The blood samples collected from the subject over time may be used to monitor Lyme disease or STARI in a subject. Alternatively, the blood samples collected from the subject over time may be used to monitor response to treatment in a subject.
When more than one sample is collected from a subject over time, blood samples may be collected 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more days apart. For example, blood samples may be collected 0.5, 1, 2, 3, or 4 days apart. Alternatively, blood samples may be collected 4, 5, 6, or 7 days apart. Further, blood samples may be collected 7, 8, 9, or 10 days apart. Still further, blood samples may be collected 10, 11, 12 or more days apart.
Once a sample is obtained, it is processed in vitro to measure abundance values for each molecular feature in Table A, Table B, Table C, or Table D. All suitable methods for measuring the abundance value for each of the molecular features known to one of skill in the art are contemplated within the scope of the invention. For example, mass spectrometry may be used to measure abundance values for each molecular feature in Table A, Table B, Table C, or Table D. The abundance values may be determined through direct infusion into the mass spectrometer. Alternatively, techniques coupling a chromatographic step with a mass spectrometry step may be used. The chromatographic step may be liquid chromatography. In certain examples, the abundance value for each of the molecular features may be determined utilizing liquid chromatography followed by mass spectrometry (LC-MS). In some examples, the liquid chromatography is high performance liquid chromatography (HPLC). Non-limiting examples of HPLC include partition chromatography, normal phase chromatography, displacement chromatography, reversed phase chromatography, size exclusion chromatography, ion exchange chromatography, bioaffinity chromatography, aqueous normal phase chromatography or ultrafast liquid chromatography. As used herein “mass spectrometry” describes methods of ionization coupled with mass selectors. Non-limiting examples of methods of ionization include matrix-assisted laser desorption/ionization (MALDI), electrospray ionization (ESI), and atmospheric pressure chemical ionization (ACPI). Non-limiting examples of mass selectors include quadropole, time of flight (TOF), and ion trap. Further, the mass selectors may be used in combination such as quadropole-TOF or triple quadropole.
In one example, an aliquot of a serum metabolite extract may be applied to a Poroshell 120, EC-C8, 2.1×100 mm, 2.7 μm LC Column (Agilent Technologies, Palo Alto, Calif.), and metabolites may be eluted with a nonlinear gradient of acetonitrile in formic acid (e.g., 0.1%) at a flow rate of 250 μl/min with an Agilent 1200 series LC system. The eluent may be introduced directly into an Agilent 6520 quadrapole time of flight mass (Q-TOF) spectrometer and MS may be performed as previously described (27, 50). LC-MS and LC-MS/MS data may be collected under the following parameters: gas temperature, 310° C.; drying gas at 10 liters per min; nebulizer at 45 lb per in2; capillary voltage, 4,000 V; fragmentation energy, 120 V; skimmer, 65 V; and octapole RF setting, 750 V. The positive-ion MS data for the mass range of 75 to 1,700 Da may be acquired at a rate of 2 scans per sec in both centroid and profile modes in 4-GHz high-resolution mode. Positive-ion reference masses may be used to ensure mass accuracy. To monitor instrument performance, quality control samples having a metabolite extract of healthy control serum may be analyzed in duplicate at the beginning of each analysis day and every 20 samples during the analysis day. In view of the specifics disclosed in this example, a skilled artisan will be able to optimize conditions as needed when using alternative equipment or approaches.
In another aspect, the present disclosure provides a method for classifying a subject as having Lyme disease or STARI. The method comprises analyzing a blood sample from a subject as described in Section III to provide abundance values for each molecular feature in Table A, Table B, Table C, or Table D; and comparing the abundance values to a reference set of abundance values. The statistical significance of any difference between the abundance values measured in the subject's blood sample as compared to the abundance values from the reference set is then determined. If the difference is statistically significant then a subject may be classified as having Lyme disease or STARI; if the difference is not statistically significant then a subject may be classified as not having Lyme disease or STARI. For instance, when using p-values, the abundance value of a molecular feature in a test blood sample is identified as being significantly different from the abundance value of the molecular feature in the reference set when the p-value is less than 0.1, preferably less than 0.05, less than 0.01, less than 0.005, or less than 0.001. Abundance values for the molecular features from the reference set may be determined before, after, or at the same time, as the abundance values for the molecular features from the subject's blood sample. Alternatively, abundance values for the molecular features from a reference set stored in a database may be used.
Any suitable reference set known in the art may be used; alternatively a new reference set may be generated. A suitable reference set comprises the abundance values for each of the molecular feature in Table A, Table B, Table C, or Table D in blood sample(s) obtained from control subjects known to be positive for Lyme disease, known to be positive for STARI, known to be negative for Lyme disease, known to be negative for STARI, known to be negative for Lyme disease and STARI, healthy subjects, or any combination thereof. Further, control subjects known to be negative for Lyme disease and/or STARI may also be known to be suffering from a disease with overlapping symptoms, may exhibit serologic cross-reactivity with Lyme disease, and/or may be suffering for another spirochetal infection. A subject suffering from a disease with overlapping symptoms may have one or more of the symptoms of Lyme disease described above. Non-limiting examples of diseases with overlapping symptoms include tick-bite hypersensitivity reactions, certain cutaneous fungal infections and bacterial cellulitis with non-Lyme EM-like lesions, syphilis, fibromyalgia, lupus, mixed connective tissue disorders (MCTD), chronic fatigue syndrome (CFS), rheumatoid arthritis, depression, mononucleosis, multiple sclerosis, sarcoidosis, endocarditis, colitis, Crohn's disease, early ALS, early Alzheimers disease, encephalitis, Fifth's disease, gastroesophageal reflux disease, infectious arthritis, interstitial cystis, irritable bowel syndrome, juvenile arthritis, Menieres syndrome, osteoarthritis, prostatitis, psoriatic arthritis, psychiatric disorders (bipolar, depression, etc.), Raynaud's syndrome, reactive arthritis, scleroderma, Sjogren's syndrome, sleep disorders, and thyroid disease. Specifically, a disease with overlapping symptoms is selected from the group consisting of syphilis and fibromyalgia. Further, the disclosure provides a method of correctly distinguishing a subject with early Lyme disease from a subject exhibiting serologic cross-reactivity with Lyme disease. A 2-tier serology-based assay is frequently used to diagnose Lyme disease. However, such an assay suffers from poor sensitivity in subjects with early Lyme disease. Non-limiting examples of diseases that exhibit serologic cross-reactivity with Lyme disease include infectious mononucleosis, syphilis, periodontal disease caused by Treponema denticola, granulocytic anaplasmosis, Epstein-Barr virus infection, malaria, Helicobacter pylori infections, bacterial endocarditis, rheumatoid arthritis, multiple sclerosis, infections caused by other spirochetes, and lupus. Specifically, a disease with serologic cross-reactivity is selected from the group consisting of infectious mononucleosis and syphilis. Non-limiting examples of other spirochetal infections include syphilis, severe periodontitis, leptospirosis, relapsing fever, rate-bite fever, bejel, yaws, pinta, and intestinal spirochaetosis. Specifically, another spirochetal infection is selected from the group consisting of syphilis and severe periodontitis.
In one example, a method for classifying a subject as having Lyme disease comprises: (a) deproteinizing a blood sample from a subject to produce a metabolite extract; (b) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (c) providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D; and (d) inputting the abundance values from step (c) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease. In one example, the subject has at least one symptom associated with Lyme disease. In a specific example, the subject has an erythema migrans rash or an EM-like rash. In another example, the subject does not have a symptom of Lyme disease but is at risk of having Lyme disease. In each of the above examples, the subject may or may not have received (or be receiving) treatment for Lyme disease, STARI, or another disease with symptoms similar to Lyme disease or STARI.
In another example, a method for classifying a subject as having STARI comprises: (a) deproteinizing a blood sample from a subject to produce a metabolite extract; (b) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (c) providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D; and (d) inputting the abundance values from step (c) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with STARI. In one example, the subject has at least one symptom associated with STARI. In a specific example, the subject has an erythema migrans rash or an EM-like rash. In another example, the subject does not have a symptom of STARI but is at risk of having STARI. In each of the above examples, the subject may or may not have received (or be receiving) treatment for Lyme disease, STARI, or another disease with symptoms similar to Lyme disease or STARI.
In another example, a method for classifying a subject as having Lyme disease or STARI comprises: (a) deproteinizing a blood sample from a subject to produce a metabolite extract; (b) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (c) providing abundance values for each molecular feature in Table A, Table B, Table C, or Table D; and (d) inputting the abundance values from step (c) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease from subjects STARI, and optionally further distinguishes healthy subjects. In one example, the subject has at least one symptom associated with Lyme disease and/or at least one symptom associated with STARI. In a specific example, the subject has an erythema migrans rash or an EM-like rash. In another example, the subject does not have a symptom of Lyme disease or STARI but is at risk of having Lyme disease or STARI. In each of the above examples, the subject may or may not have received (or be receiving) treatment for Lyme disease, STARI, or another disease with symptoms similar to Lyme disease or STARI.
In each of the above examples, the classification model has been trained with samples derived from suitable controls. Any suitable classification system known in the art may be used, provided the model produced therefrom has an accuracy of at least 80% for detecting a sample from a subject with Lyme disease, including early Lyme disease, and/or an accuracy of at least 80% for detecting a sample from a subject with STARI. For example, a classification model may have an accuracy of about 80%, about 85%, about 90%, about 95%, or greater for detecting a sample from a subject with Lyme disease, including early Lyme disease, and/or an accuracy of about 80%, about 85%, about 90%, about 95%, or greater for detecting a sample from a subject with STARI. Non-limiting examples of suitable classification models include LASSO, RF, ridge regression, elastic net, linear discriminant analysis, logistic regression, support vector machines, CT, and kernel estimation. In various examples, the model has a sensitivity from about 0.8 to about 1, and/or a specificity from about 0.8 to about 1. In certain examples, area under the ROC curve may be used to evaluate the suitability of a model, and an AUC ROC value of about 0.8 or greater indicates the model has a suitable accuracy.
The classification model produces a disease score and the disease score distinguishes: (i) samples from subjects with Lyme disease from samples from subjects with STARI, or (ii) distinguishes samples from subjects with Lyme disease from samples from control subjects, or (iii) distinguishes samples from subjects with STARI from samples from control subjects, or (iv) distinguishes samples from subjects with Lyme disease, samples from subjects with STARI and samples from control subjects from one another. As a non-limiting example, LASSO scores for a subject's sample may be calculated by multiplying the respective regression coefficients resulting from LASSO analysis by the transformed abundance of each MF in the biosignature and summing for each sample. In a further example, the sample score may be transformed into probabilities for each sample being classified to each sample group. As another non-limiting example, the transformed abundances of all MFs are used to classify the sample into one of the sample groups in each classification tree developed in an RF model, where the levels of chosen MFs are used sequentially to classify the samples, and the final classification is determined by majority votes among all such classification trees in the RF model. Scores from alternative classification models may be calculated as is known in the art.
In one example, abundance values are provided for each molecular feature in Table A, Table B, or Table D; the suitable controls comprise a blood sample known to be positive for Lyme disease and a blood sample known to be positive for STARI; and the classification model has an accuracy of at least 80%, at least 85%, at least 90%, or at least 95% for detecting a sample from a subject with Lyme disease and an accuracy of at least 80% or at least 85% for detecting a sample from a subject with STARI. Alternatively, abundance values are provided for each molecular feature in Table A, Table B, or Table D; the suitable controls include a blood sample known to be positive for Lyme disease, a blood sample known to be positive for STARI, and a blood sample known to be negative for both Lyme disease and STARI; and the classification model has an accuracy of at least 80%, at least 85%, or at least 90%, even more preferably at least 95% for detecting a sample from a subject with Lyme disease and an accuracy of at least 80% or at least 85% for detecting a sample from a subject with STARI. In still another alternative, abundance values are provided for each molecular feature in Table C or Table D; the suitable controls include a blood sample known to be positive for Lyme disease, a blood sample known to be positive for STARI, and a blood sample known to be negative for both Lyme disease and STARI; and the classification model has an accuracy of at least 80%, preferably at least 85% for detecting a sample from a subject with Lyme disease; an accuracy of at least 80%, at least 85%, or at least 90% for detecting a sample from a subject with STARI; and an accuracy of at least 80%, at least 85%, at least 90%, or at least 95% for detecting a sample from a healthy subject.
Another aspect of the disclosure is a method for treating a subject based on the subject's classification as having Lyme disease or STARI as described in Section IV. Treatment may be with a non-pharmacological treatment, a pharmacological treatment, or an additional diagnostic test.
In one example, the method comprises (a) obtaining a disease score from a test; (b) diagnosing the subject with Lyme disease based on the disease score; and (c) administering a treatment to the subject with Lyme disease, wherein the test comprises measuring the amount of each molecular feature in Table A, Table B, Table C, or Table D; providing abundance values for each molecular feature measured; and inputting the abundance values into a classification model trained with samples derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease from subjects with STARI, and optionally from healthy subjects. In some examples, the test is a method of Section IV. In further examples, the test comprises (i) deproteinizing a blood sample from a subject to produce a metabolite extract; (ii) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (iii) providing abundance values for each molecular feature in Table A, Table B, Table C or Table D; and (iv) inputting the abundance values from step (iii) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with Lyme disease. Suitable controls are described above. In one example, abundance values are provided for each molecular feature in Table A, Table B, or Table D; the suitable controls comprise a blood sample known to be positive for Lyme disease and a blood sample known to be positive for STARI; and the classification model has an accuracy of at least 80%, at least 85%, at least 90%, or at least 95% for detecting a sample from a subject with Lyme disease and an accuracy of at least 80% or at least 85% for detecting a sample from a subject with STARI. Alternatively, abundance values are provided for each molecular feature in Table A, Table B, or Table D; the suitable controls include a blood sample known to be positive for Lyme disease, a blood sample known to be positive for STARI, and a blood sample known to be negative for both Lyme disease and STARI; and the classification model has an accuracy of at least 80%, at least 85%, or at least 90%, even more preferably at least 95% for detecting a sample from a subject with Lyme disease and an accuracy of at least 80% or at least 85% for detecting a sample from a subject with STARI. In still another alternative, abundance values are provided for each molecular feature in Table C or Table D; the suitable controls include a blood sample known to be positive for Lyme disease, a blood sample known to be positive for STARI, and a blood sample known to be negative for both Lyme disease and STARI; and the classification model has an accuracy of at least 80%, preferably at least 85% for detecting a sample from a subject with Lyme disease; an accuracy of at least 80%, at least 85%, or at least 90% for detecting a sample from a subject with STARI; and an accuracy of at least 80%, at least 85%, at least 90%, or at least 95% for detecting a sample from a healthy subject.
Treatment may comprise one or more standard treatments for Lyme disease. Non-limiting examples of standard pharmacological treatments for Lyme disease include an antibiotic, an antibacterial agent, a vaccine, an immune modulator, an anti-inflammatory agent, or a combination thereof. Suitable antibiotics include, but are not limited to, amoxicillin, doxycycline, cefuroxime axetil, amoxicillin-clavulanic acid, macrolides, ceftriaxone, cefotaxmine, and penicillin G. Antibiotics may be administered orally or parenterally. Alternatively, treatment may comprise one or more experimental pharmacological treatment (e.g., treatment in a clinical trial). In each of the above examples, treatment may be for the acute or disseminated stage of the disease, or may be a prophylactic treatment. For example, following successful resolution of a primary Borrrelia infection, the subject may be treated with a vaccine to prevent future infections. In still other examples, treatment may comprise further diagnostic testing. For example, if a subject has early Lyme disease but was negative for Lyme disease by current diagnostic testing (e.g., first-tier testing performed using the C6 EIA and second tier testing using IgM and/or IgG immunoblots following a positive or equivocal first-tier assay), additional testing may be ordered after an amount of time has elapsed (e.g., 3, 5, 7, 10, 14 days or more) to confirm the initial diagnosis.
In another example, the method comprises (a) obtaining a disease score from a test; (b) diagnosing the subject with STARI based on the disease score; and (c) administering a treatment to the subject with STARI, wherein the test comprises measuring the amount of each molecular feature in Table A, Table B, Table C, or Table D; providing abundance values for each molecular feature measured; and inputting the abundance values into a classification model trained with samples derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with STARI from subjects with Lyme disease, including early Lyme disease, and optionally from healthy subjects. In some examples, the test is a method of Section IV. In further examples, the test comprises (i) deproteinizing a blood sample from a subject to produce a metabolite extract; (ii) performing liquid chromatography coupled to mass spectrometry on a sample of the metabolite extract; (iii) providing abundance values for each molecular feature in Table A, Table B, Table C or Table D; and (iv) inputting the abundance values from step (iii) into a classification model trained with samples of metabolite extracts derived from suitable controls, wherein the classification model produces a disease score and the disease score distinguishes subjects with STARI. Suitable controls are described above. In one example, abundance values are provided for each molecular feature in Table A, Table B, or Table D; the suitable controls comprise a blood sample known to be positive for Lyme disease and a blood sample known to be positive for STARI; and the classification model has an accuracy of at least 80%, at least 85%, at least 90%, or at least 95% for detecting a sample from a subject with Lyme disease and an accuracy of at least 80% or at least 85% for detecting a sample from a subject with STARI. Alternatively, abundance values are provided for each molecular feature in Table A, Table B, or Table D; the suitable controls include a blood sample known to be positive for Lyme disease, a blood sample known to be positive for STARI, and a blood sample known to be negative for both Lyme disease and STARI; and the classification model has an accuracy of at least 80%, at least 85%, or at least 90%, even more preferably at least 95% for detecting a sample from a subject with Lyme disease and an accuracy of at least 80% or at least 85% for detecting a sample from a subject with STARI. In still another alternative, abundance values are provided for each molecular feature in Table C or Table D; the suitable controls include a blood sample known to be positive for Lyme disease, a blood sample known to be positive for STARI, and a blood sample known to be negative for both Lyme disease and STARI; and the classification model has an accuracy of at least 80%, preferably at least 85% for detecting a sample from a subject with Lyme disease; an accuracy of at least 80%, at least 85%, or at least 90% for detecting a sample from a subject with STARI; and an accuracy of at least 80%, at least 85%, at least 90%, or at least 95% for detecting a sample from a healthy subject.
Treatment may comprise one or more standard treatments for STARI. There are no therapeutic agents specifically approved for STARI, in part because the causative agent is not known. Nonetheless, non-limiting examples of standard pharmacological treatments for STARI include an antibiotic, an antibacterial agent, a vaccine, an immune modulator, an anti-inflammatory agent, or a combination thereof. Suitable antibiotics include, but are not limited to, amoxicillin, doxycycline, cefuroxime axetil, amoxicillin-clavulanic acid, macrolides, ceftriaxone, cefotaxmine, and penicillin G. Antibiotics may be administered orally or parenterally. Alternatively, treatment may comprise one or more experimental pharmacological treatment (e.g., treatment in a clinical trial). In each of the above examples, treatment may be for acute disease, or may be a prophylactic treatment. For example, following successful treatment of STARI (as defined the by the current clinical standard of the time), the subject may be treated with a vaccine to prevent future infections. In still other another example, treatment may comprise further diagnostic testing. For example, if a subject is diagnosed with STARI, additional testing may be ordered after an amount of time has elapsed (e.g., 3, 5, 7, 10, 14 days or more) to confirm the initial diagnosis. In yet another example, treatment may consist of supportive care only, e.g., non-pharmacological treatments or over-the-counter pharmaceutical agents to alleviate symptoms, such as fever, aches, etc.
In certain examples, obtaining a result from a test of Section IV comprises analyzing a blood sample obtained from the subject as described in Section III and/or classifying the subject as described in Section IV. In certain examples, obtaining a result from a test of Section IV comprises requesting (e.g., placing a medical order or prescription) from a third party a test that analyzes a blood sample obtained from the subject as described in Section III and classifies the subject as described in Section IV, or requesting from a third party a test that analyzes a blood sample obtained from the subject as described in Section III, and then performing the classification as described in Section IV.
In each of the above examples, the method may further comprise obtaining a second result (for a sample obtained from the subject after treatment has begun) from the same test of Section IV as before treatment and adjusting treatment based on the test result.
Accordingly, yet another aspect of the disclosure is a method for monitoring the effectiveness of a therapeutic agent intended to treat a subject with Lyme disease or STARI. The method comprises obtaining a result from a test of Section IV, administering a therapeutic agent to the subject, obtaining a result from the same test of Section IV as before treatment, wherein the treatment is effective if the disease score classifies the subject as more healthy than before. A first sample obtained before treatment began may be used as a baseline. Alternatively, the first sample may be obtained after treatment has begun. Samples may be collected from a subject over time, including 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more days apart. For example, blood samples may be collected 0.5, 1, 2, 3, or 4 days apart. Alternatively, blood samples may be collected 4, 5, 6, or 7 days apart. Further, blood samples may be collected 7, 8, 9, or 10 days apart. Still further, blood samples may be collected 10, 11, 12 or more days apart.
The following examples are included to demonstrate preferred examples of the invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples that follow represent techniques discovered by the inventors to function well in the practice of the invention. Those of skill in the art should, however, in light of the present disclosure, appreciate that changes may be made in the specific examples that are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention. Therefore, all matter set forth or shown in the examples and accompanying drawings is to be interpreted as illustrative and not in a limiting sense.
The following examples illustrate various iterations of the invention.
Lyme disease is a multisystem bacterial infection that in the United States is primarily caused by infection with Borrelia burgdorferi sensu stricto. Over 300,000 cases of Lyme disease are estimated to occur annually in the United States, with over 3.4 million laboratory diagnostic tests performed each year (1, 2). Symptoms associated with this infection include fever, chills, headache, fatigue, muscle and joint aches, and swollen lymph nodes; however, the most prominent clinical manifestation in the early stage is the presence of one or more erythema migrans (EM) skin lesions (3). This annular, expanding erythematous skin lesion occurs at the site of the tick bite in 70 to 80% of infected individuals and is typically 5 cm or more in diameter (4, 5). Although an EM lesion is a hallmark for Lyme disease, other types of skin lesions can be confused with EM (3, 5, 6). These include rashes caused by tick-bite hypersensitivity reactions, certain cutaneous fungal infections, bacterial cellulitis and the rash of southern tick-associated rash illness (STARI) (7, 8).
STARI is associated with a bite from the lone star tick (Amblyomma americanum) and, in addition to the development of an EM-like skin lesion, individuals with STARI can present with mild systemic symptoms (including muscle and joint pains, fatigue, fever, chills, and headache) that are similar to those occurring in patients with Lyme disease (7, 9, 10). These characteristics of STARI have led some to postulate that the etiology of this illness is a Borrelia species, including B. burgdorferi (10, 11) or B. lonestari (12-15); however, multiple studies have refuted that STARI is caused by B. burgdorferi (7, 16-19) and additional cases associating B. lonestari with STARI have not emerged (20, 21). Additionally, STARI patients have been screened serologically for reactivity to rickettsial agents, but no evidence was obtained to demonstrate that rickettsia causes this illness (10, 22). Thus, at present no infectious etiology is known for STARI.
STARI cases occur over the geographic region where the lone star tick is present. This includes a region that currently expands from central Texas and Oklahoma upward into the Midwestern states and eastward, including the southern states and along the Atlantic coast into Maine (23). Unlike STARI, Lyme disease is transmitted to humans through the bite of the blacklegged tick (Ixodes scapularis) that is present in the northeastern, mid-Atlantic, and north-central United States, and the western blacklegged tick (I. pacificus), which is present on the Pacific Coast (24). The geographic distribution of human Lyme disease and the vectors for this disease is expanding (24-26), and there is a similar expansion of areas inhabited by the lone star tick (23). Importantly, a strict geographic segregation of Lyme disease and STARI does not exist, as there are regions where STARI and Lyme disease are co-prevalent (25). Thus, there is a growing need for diagnostic methods to differentiate between Lyme disease and STARI, and that facilitate proper treatment, patient management and disease surveillance.
Clinically, the skin lesions of STARI and early Lyme disease are indistinguishable, and no laboratory tool or method exists for the diagnosis of STARI or differentiation of STARI from Lyme disease. The only biomarkers evaluated for differential diagnosis of early Lyme disease and STARI have been serum antibodies to B. burgdorferi (10, 16). However, these tests have poor sensitivity for early stages of Lyme disease, and thus a lack of B. burgdorferi antibodies cannot be used as a reliable differential marker for STARI.
The experiments herein describe the development of a metabolomics-driven approach to identify biomarkers that discriminate early Lyme disease from STARI, and provide evidence that these two diseases are biochemically distinct. A retrospective cohort of well-characterized sera from patients with early Lyme disease and STARI was evaluated to identify a differentiating metabolic biosignature. Using statistical modeling, this metabolic biosignature accurately classified test samples that included healthy controls. Additionally, the metabolic biosignature revealed that N-acyl ethanolamine (NAE) and primary fatty acid amide (PFAM) metabolism differed significantly between these two diseases.
Clinical samples: A total of 220 well-characterized retrospective serum samples from three different repositories were used to develop and test a metabolic biosignature that accurately classifies early Lyme disease and STARI (Table 2). All samples from Lyme disease patients were culture confirmed and/or PCR positive for B. burgdorferi. The median age for early Lyme disease patients was 45 years and 74% were males. STARI patients had an overall median age of 45 years and 55% were males.
To establish a Lyme disease diagnostic baseline, the recommended two-tiered serology testing for Lyme disease was performed on all samples. First-tier testing was performed using the C6 EIA and was positive for 66% of Lyme disease samples. When STARI and healthy controls were tested by the C6 EIA, two STARI samples (2%) and five healthy controls (9%) tested positive or equivocal. Two-tiered testing using IgM and IgG immunoblots as the second-tier test following a positive or equivocal first-tier assay resulted in a sensitivity of 44% for early Lyme disease samples (duration of illness was not considered for IgM immunoblot testing). The sensitivity of two-tiered testing for early Lyme disease samples included in the Discovery/Training-Sets and the Test-Sets was 38% and 53%, respectively. All STARI and healthy control samples were negative by two-tiered testing (Table 2).
Development of a metabolic biosignature for early Lyme disease and STARI differentiation: Metabolic profiling by liquid chromatography-mass spectrometry (LC-MS) of a retrospective cohort of well-characterized sera from patients with early Lyme disease (n=40) and STARI (n=36) (Table 2 and
In silico analysis of metabolic pathways: Presumptive chemical identification was applied to the 261 MFs. This yielded predicted chemical formulae for 149 MFs, and 122 MFs were assigned a putative chemical structure based on interrogation of each MF's monoisotopic mass (+ or −15 ppm) against the Metlin database and the Human Metabolome Database (HMDB) (Table 3). An in silico interrogation of potentially altered metabolic pathways was performed using the presumptive identifications for the 122 MFs and MetaboAnalyst (28). Four differentiating pathways were predicted to have the greatest impact, with the most significant being glycerophospholipid metabolism and sphingolipid metabolism (
Elucidation of altered NAE metabolism: The prediction of altered metabolic pathways was based on the presumptive structural identification of the early Lyme disease versus STARI differentiating MFs. Thus, to further define the metabolic differences between these two patient groups, structural confirmation of selected MFs was undertaken. Two MFs that displayed relatively large abundance differences (m/z 300.2892, RT 19.66; and m/z 328.3204, RT 20.72) were putatively identified as sphingosine-C18 or 3-ketosphinganine, and sphingosine-C20 or N,N-dimethyl sphingosine, respectively. However, both of these MFs had alternative predicted structures of palmitoyl ethanolamide and stearoyl ethanolamide, respectively. The interrogation of authentic standards against these two serum MFs revealed RTs and MS/MS spectra that identified the m/z 300.2892 and m/z 328.3204 products as palmitoyl ethanolamide (
The large number of differentiating MFs associated with NAE metabolism suggested that this is a major biological difference between STARI and early Lyme disease (
The 261 MF biosignature list revealed metabolic dissimilarity between Lyme disease and STARI: To test whether early Lyme disease and STARI represent distinct metabolic states that would be reflected in the comparison of MF abundances in these two disease states to those of healthy controls, the abundance fold-change for each structurally confirmed MF in early Lyme disease and STARI sera as compared to healthy controls was determined. This revealed that the majority of these MFs maintained fold change differences with respect to healthy controls that allowed for segregation of early Lyme disease and STARI patient samples (
Diagnostic classification of early Lyme disease vs STARI: Classification models were used to determine whether the 261 MF biosignature could be applied to discriminate early Lyme disease from STARI (Table 1 and
Diagnostic classification of early Lyme disease vs STARI vs healthy controls: Separate three-way classification models using LASSO and RF were developed by including LC-MS data collected for healthy controls in the Training-Set samples (
Of the 38 MFs selected by LASSO for the two-way classification model, 33 were included in the 82 MFs of the LASSO three-way classification model (Table 3). The 82 MFs of the LASSO three-way classification included seven of the 14 structurally confirmed metabolites: 3-ketosphingosine, glycocholic acid and pentadecanoyl ethanolamide, as well as the four included in the LASSO two-way classification model (Table 3).
Biosignature was not influenced by geographic variability: Since retrospective samples collected by multiple laboratories were used in these studies, analyses were performed to assess whether a geographic bias was introduced. To address this, three healthy control groups and three STARI groups (all early Lyme disease samples came from one geographic region) were evaluated by linear discriminant analysis using the 82 MFs of the LASSO three-way classification model (
Discussion: The inability to detect B. burgdorferi by PCR or culture and the lack of a serological response to B. burgdorferi antigens in STARI patients is widely accepted as evidence that the etiologies of STARI and Lyme disease differ (7, 16). This is further supported by the different tick species associated with these two diseases (8, 25). Nevertheless, the strong overlap in clinical symptoms, including the development of an EM-like skin lesion, creates confusion and controversy for the clinical differentiation of STARI and Lyme disease (30). The data reported here demonstrated marked differences between the metabolic profiles of early Lyme disease and STARI patients, and thus provide compelling positive data to support the concept that these two illnesses are distinct entities. Interestingly, metabolic pathway analyses and the structural identification of several MFs with significant abundance differences between early Lyme disease and STARI identified multiple NAEs. These endogenous lipid mediators are derived from phosphatidylcholine and phospahtidylethanolamine via the endocannabinoid system (
This current work expands demonstrates the ability to distinguish early Lyme disease from an illness with nearly identical symptoms or what would be considered a Lyme disease-like illness (37). The existing diagnostic algorithm for Lyme disease is a two-tiered serologic approach that utilizes an EIA or IFA as a first-tier test followed by IgM and IgG immunoblotting as the second-tier test (38). For early Lyme disease, the sensitivity of this diagnostic is 29-40% and the specificity is 95-100% (39). The current antibody-based approaches do not distinguish between active and previous infections, an important limitation. In the current study all of the STARI samples were negative by two-tiered testing, and only 2% were positive by the first-tier EIA. Early Lyme disease samples were 44% positive (38% positivity for the early Lyme disease samples used in the Discovery and Training Sets and 53% positivity for early Lyme disease samples used in the Test Sets) by two-tiered testing. In contrast, when classification modeling was applied to the 261 MFs of the early Lyme disease-STARI biosignature, diagnostic accuracy for early Lyme disease was dramatically increased (85 to 98% accuracy depending on the model) as compared to serology. Classification by RF or LASSO was overall highly accurate for early Lyme disease and STARI, in particular when using the two-way classification models. Interestingly, when healthy controls were introduced and used to develop a three-way classification model there was a slight increase in the accuracy for STARI and decrease in the accuracy for early Lyme disease, but healthy controls were classified with a 93-95% accuracy. This was surprising as healthy controls were not used to create the initial 261 MF biosignature, and furthers supported that STARI and early Lyme disease are metabolically distinct from healthy controls, but in different ways.
To date the development of a diagnostic tool for STARI or for differentiation of early Lyme disease and STARI has received little attention. As the geographic distribution of Lyme disease continues to expand (25, 26), so will the geographic range where there is overlap of Lyme disease and STARI. Thus, a diagnostic tool that accurately differentiates these two diseases could have a major impact on patient management. Lyme disease is treated with antibiotics, and although there is no defined infectious etiology for STARI, this illness is also commonly treated in a similar manner (7, 20, 40). Establishment of a robust diagnostic tool would not only facilitate antibiotic stewardship, it would also allow for proper studies to assess the true impact of therapies for STARI. Lyme disease is also a reportable disease and in order to maintain accurate disease surveillance in low incidence areas, it is essential that diseases such as STARI be excluded (30). Additionally, vaccines are currently being developed for Lyme disease (41-44) and as these are tested, it will be important to identify STARI patients in order to properly assess vaccine efficacy.
To apply the discoveries of this work towards the development of an assay that can be used for the clinical differentiation of early Lyme disease and STARI, it should first be determined whether an emphasis should be placed on the diagnosis of Lyme disease or STARI. As there is no defined etiology of STARI, and Lyme disease is not necessarily self-limiting without antibiotics and can have subsequent complications if untreated, we envision that the final assay would focus on being highly sensitive for early Lyme disease and be primarily applied in regions where Lyme disease and STARI overlap. Although existing laboratory tests for Lyme disease emphasize specificity, this strategy needs to be reconsidered for a differential diagnostic test of STARI and early Lyme disease, since any illness presenting with an EM in a region with a known incidence of Lyme disease would likely be treated with antibiotics (7, 20, 40). As with all diagnostic tests, use of a metabolic biosignature for differentiation of early Lyme disease and STARI would need to be performed in conjunction with clinical evaluation of the patient, and consideration of their medical history and epidemiologic risk for these two diseases.
The approach outlined in this study applies semi-quantitative mass spectrometry and the use of biochemical signatures for the classification of patients. Clinical application of such an approach would likely occur in a specialized clinical diagnostic laboratory. However, it should be noted that the second-tier immunoblot assays for the serological diagnosis of Lyme disease are already performed in specialized laboratories (1, 45, 46). Mass spectrometry assays are currently used in clinical laboratories for the analyses of small molecule metabolites. The majority of these tests are under Clinical Laboratory Improvement Amendments (CLIA) guidelines, but an FDA cleared mass spectrometry-based test for inborn metabolic errors is in use (47). The most accurate quantification of metabolites by mass spectrometry is achieved by Multiple Reaction Monitoring (MRM) assays (48). Such assays are developed with the knowledge of a MF's chemical structure. To this end, the chemical structure of 14 MFs have been identified. The chemical structure of the remainder of the MFs can be identified by the methods described herein. It should be noted that the NAEs and PFAMs that were revealed via our pathway analyses are amenable to MRM assays (49). These metabolites are now being investigated for their ability to accurately classify STARI and early Lyme disease.
The data reported here were generated from the analysis of retrospectively collected serum samples from various repositories that have been archived for different lengths of time. To reduce the impact of the potential variability associated with these samples, stringent criteria were applied to the data analysis. In addition to the requirement of a significant fold change, those MFs selected for the final early Lyme disease-STARI biosignature were required to be present in at least 80% of samples within a sample group and maintain the median fold-change difference in at least 50% of samples within a group. While the STARI and healthy control sera were collected by multiple laboratories and from multiple geographic locations, the early Lyme disease sera were obtained from a single laboratory. This is a potential limitation of the study. However, linear discriminate analysis was applied to assess the variability within the healthy control and STARI samples collected by different laboratories. This analysis demonstrated little to no variability among the STARI or healthy control samples indicating that the criteria used for MF selection effectively reduced non-biological variability. As noted, data were collected by non-absolute semi-quantitative mass spectrometry. Nevertheless, this is a common practice applied in the development of differentiating biosignatures for infectious diseases (27, 50-53), and the workflow ensured that the most robust MFs were selected and used for classification modeling.
Without knowledge of a known etiologic agent, it is recognized that STARI simply encompasses a clinical syndrome. The STARI samples used in this current work included those collected in studies used to define this illness (9), as well as samples collected outside those original studies. Additional samples collected prospectively will be useful to assess the applicability of our current metabolic biosignature in a real world scenario. Future sample collection will also target patient populations with non-Lyme EM-like lesions, including tick-bite hypersensitivity reactions, certain cutaneous fungal infections and bacterial cellulitis. Additionally, other factors such as confections with other vector-borne pathogens will need to be addressed with prospective studies. In the Southeastern United States, there is evidence for enzootic transmission of B. burgdorferi; however, it is debatable whether Lyme disease occurs in this region (11, 30, 54, 55). The current study was not designed to provide evidence for or against the presence of Lyme disease in the southern United States. Nevertheless, metabolic profiling offers a novel approach that is orthogonal to the methods currently employed to address this issue.
STARI is an illness that has received little attention over the years, but is a confounding factor in diagnosing early Lyme disease in areas where both illnesses overlap and contributes to the debate surrounding the presence of Lyme disease in the southern United States. No diagnostic tool exists for STARI or for differentiating early Lyme disease from STARI. Based on documented differences between early Lyme disease and STARI (9, 16, 56), we metabolically profiled serum to develop a biochemical biosignature that when applied could accurately classify early Lyme disease and STARI patients (See Example 1). This example describes the design of the study described in Example 1.
An unbiased-metabolomics study was designed to directly compare the metabolic host responses between these two illnesses, and subsequently evaluate how this metabolic biosignature distinguishes these two illnesses. The use of unbiased metabolomics for biosignature discovery does not lend itself to power calculations to determine sample size. Thus, sample sizes were selected based on our previous studies (27, 50, 51). To obtain a sufficient number of well-characterized STARI sera, retrospectively collected samples from two separate studies were used. Specifically, the first set of STARI serum samples (n=33) was obtained from the CDC repository. These samples were collected through a prospective study performed between 2007 and 2009 (57). Patients were enrolled through CDC outreach efforts (n=17) or by contract with the University of North Carolina at Chapel Hill (n=16). The states where patients were recruited included NC, 18; VA, 4; TN, 3; KY, 2; GA, 2; IA, 2; AL, 1; and NE, 1. All samples were collected pre-treatment with the exception of one patient who was treated with doxycycline 1-2 days before the serum sample was obtained. The second set of STARI samples (n=22) was obtained from the New York Medical College serum repository (20). These samples were collected between 2001 and 2004 from patients living in Missouri.
Sufficient numbers of well-characterized early Lyme disease serum samples were acquired from New York, an area of high incidence for Lyme disease and low incidence of STARI (9). Specifically, all early Lyme disease samples (n=70) were culture and/or PCR positive for B. burgdorferi and were collected pre-treatment. To ensure appropriate representation of both non-disseminated and disseminated forms of early EM Lyme disease, samples from patients with a single EM that were skin culture and/or PCR positive for B. burgdorferi and blood culture negative (n=35), and patients with multiple EMs or a single EM that were blood culture positive (n=35) were used. Early Lyme disease samples were collected between 1992 and 2007, and 1 to 33 days post-onset of symptoms. To understand the relationship of our findings to a healthy control population serum samples from healthy donors were also included in the study. These were procured from repositories at New York Medical College, the CDC and the University of Central Florida. A detailed description of inclusion and exclusion criteria for each patient and donor population is provided in Table 2. All participating institutions obtained institutional review board (IRB) approval for this study. IRB review and approval for this study ensured that the retrospective samples used had been collected under informed consent.
All samples were analyzed in duplicate and were randomized prior to processing for LC-MS analyses. Healthy control sera were used as quality control samples for each LC-MS experiment. The serum samples and respective LC-MS data files of each patient group and healthy controls were randomly separated into a Discovery-Set/Training-Sets 1 and 2, and Test-Sets 1 and 2. Specifically, 40 of the 70 early Lyme disease and 36 of the 55 STARI samples were randomly selected as the Discovery-Set samples. This sample set was used for molecular feature selection. To train the classification models, two training-sets were used. The first, Training-Set 1, was identical to the Discovery-Set (i.e. contained the same early Lyme disease and STARI samples) and the second, Training-Set 2, had the same samples as Training-Set 1 with the addition of 38 of the 58 healthy control samples. Lastly, Test-Sets 1 and 2 were created. Test-Set 1 was comprised of 30 early Lyme disease and 19 STARI samples that were not included in the Discovery/Training samples sets. Test-Set 2 had the same samples as those used in Test-Set 1 with the addition of 20 healthy control samples that were not included in the Training-Set 2 samples. Test-Sets 1 and 2 were exclusively used for blinded testing of the classification models.
Randomization into Discovery/Training-Sets or Test-Sets was done in a manner that ensured bias was not introduced based on the repository from which STARI samples were obtained or on whether the early Lyme disease samples were from a non-disseminated or disseminated case. Biosignature development was performed by screening MFs based on stringent criteria outlined in
This example describes methods used for Lyme disease serologic testing of all serum samples used in the examples above. Standard two-tiered testing was performed on all samples (38). The C6 B. burgdorferi (Lyme) ELISA (Immunetics, Boston, Mass.) was used as a first-tier test, and any positive or equivocal samples were reflexed to Marblot IgM and IgG immunoblots (MarDx Diagnostics, Inc., Carlsbad, Calif.) as the second-tier test. Serologic assays were performed according to the manufacturer's instructions, and the data were interpreted according to established CDC guidelines (38). Duration of illness, however, was not considered for test interpretation.
This example describes liquid chromatography-mass spectrometry (LC-MS) methods used in the examples above. Serum samples were randomized prior to extraction of small molecule metabolites and LC-MS analyses. Small molecule metabolites were extracted from sera as previously reported (27). An aliquot (10 μl) of the serum metabolite extract was applied to a Poroshell 120, EC-C8, 2.1×100 mm, 2.7 μm LC Column (Agilent Technologies, Palo Alto, Calif.). The metabolites were eluted with a 2-98% nonlinear gradient of acetonitrile in 0.1% formic acid at a flow rate of 250 μl/min with an Agilent 1200 series LC system. The eluent was introduced directly into an Agilent 6520 quadrapole time of flight mass (Q-TOF) spectrometer and MS was performed as previously described (27, 50). LC-MS and LC-MS/MS data were collected under the following parameters: gas temperature, 310° C.; drying gas at 10 liters per min; nebulizer at 45 lb per in2; capillary voltage, 4,000 V; fragmentation energy, 120 V; skimmer, 65 V; and octapole RF setting, 750 V. The positive-ion MS data for the mass range of 75 to 1,700 Da were acquired at a rate of 2 scans per sec. Data were collected in both centroid and profile modes in 4-GHz high-resolution mode. Positive-ion reference masses of 121.050873 m/z and 922.009798 m/z were introduced to ensure mass accuracy. To monitor instrument performance, quality control samples having a metabolite extract of healthy control serum (BioreclamationIVT, Westbury, N.Y.) was analyzed in duplicate at the beginning of each analysis day and every 20 samples during the analysis day.
This example describes the methods used for biosignature development as described in the examples above. LC-MS data from an initial Discovery-Set of samples comprised of randomly selected early Lyme disease (n=40) and randomly selected STARI patients (n=36) that were exclusively used for molecular feature selection and classification model training were processed with the Molecular Feature Extractor algorithm tool of the Agilent MassHunter Qualitative Analysis software version B.05.00 (Agilent Technologies, Santa Clara, Calif.). The MFs were aligned between data files with a 0.25 min retention time window and 15 ppm mass tolerance. Comparative analyses of differentiating MFs between patient groups were performed using the workflow presented in
Abundance data from the Targeted-Discovery-Set data files were normalized using a two-step method. First, abundances (area under the peak for the monoisotopic mass) of each Discovery MF were normalized by the median intensity of the stable MFs detected in each individual sample (58). Stable MFs were those identified in the original extraction of LC-MS data files with the Agilent MassHunter Qualitative Analysis software and present in at least 50% of all sample data files. Secondly, median fold changes of stable MFs between the initial quality control sample (applied at the beginning of the LC-MS analysis) and each of the subsequent quality control samples (applied every 20 clinical samples throughout the LC-MS analysis) were calculated. The median fold change calculated for the quality control sample that directly followed each series of 20 clinical samples was multiplied against the normalized Discovery-MF abundances in the clinical samples of that series. This second normalization step was performed to correct for instrument variability. To apply stringency to the development of a final early Lyme disease-STARI biosignature, MFs were filtered based on consistency in the duplicate LC-MS data sets by requiring the same directional abundance change between the patient groups. Specifically, MFs with at least a ≥2-fold abundance difference and a 1.5-fold abundance difference between the medians of the two groups (early Lyme disease and STARI) for LC-MS analysis-1 and LC-MS analysis-2, respectively, were selected. Further criteria applied to ensure that the most robust MFs were being selected included: removing MFs with >20% missing values in both groups, and selecting only MFs where at least 50% of the samples within a patient group produced a fold change of ≥2 in comparison to the mean of the other patient group. This selection process resulted in the MFs included in the early Lyme disease-STARI biosignature.
This example describes the methods used for prediction and verification of MF chemical structure. Confirmation of the chemical structures of selected MFs was performed by LC-MS-MS to provide level-1 or level-2 identifications (59). Commercial standards palmitoyl ethanolamide, stearoyl ethanolamide, eicosanoyl ethanolamide, glycerophospho-N-palmitoyl ethanolamine, pentadecanoyl ethanolamide, and erucamide were obtained from Cayman Chemical (Ann Arbor, Mich., USA). Commercial standards piperine and nonanedioic acid were obtained from Sigma Aldrich (Saint Louis, Mo., USA). Commercial standards methyl oleate, stearamide, palmitamide, CMPF, and glycocholic acid were obtained from Santa Cruz Biotechnology, Inc. (Santa Cruz, Calif., USA). The LC conditions used were the same as those used for the LC-MS analyses of serum metabolites. MS/MS spectra of the targeted MFs and commercial standards were obtained with an Agilent 6520 Q-TOF mass spectrometer. Electrospray ionization was performed in the positive ion mode as described for MS analyses, except the mass spectrometer was operated in the 2 GHz extended dynamic range mode. The positive ion MS/MS data (50 to 1,700 Da) were acquired at a rate of 1 scan per sec. Precursor ions were selected by the quadrupole and fragmented via collision-induced dissociation (CID) with nitrogen at collision energies of 10, 20, or 40 eV. To provide a level-1 identification, the MS/MS spectra of the targeted metabolites were compared to spectra of commercial standards. Additionally, LC retention time comparisons between the targeted MF and the respective standard were made. A retention time window of ±5 sec was applied as a cutoff for identification. The MS/MS spectra of selected serum metabolites were compared to spectra in the Metlin database for a level-2 identification.
Metabolic pathway analysis in the examples above was performed by MetaboAnalyst. The experimentally obtained monoisotopic masses corresponding to the MFs of the 261 biosignature list were searched against HMDB using a 15 ppm window. The resulting list of potential metabolite structures were applied to the MetaboAnalyst pathway analysis tool (28) Settings for pathway analysis included applying Homo sapiens pathway library; the Hypergeometric Test for the over-representation analysis and Relative-betweenness centrality to estimate node importance in the pathway topology.
Methods for statistical analyses and classification modeling are described in this example. Methods to filter the list of MFs and to normalize abundances are described in the section on biosignature development. Prior to analysis, the normalized abundances were log 2 transformed and each MF was scaled to have a mean of zero and standard deviation of 1. Statistical analyses were performed using R software (60).
For classification modeling, Training- and Test-Set samples were used as previously described (27, 50) and as shown in
A linear discriminant analysis was performed with the 82 MFs selected by the three-way LASSO model using linear discriminant analysis function in R. MF abundance data included in the linear discriminant analysis were from healthy controls from Colorado, Florida, and New York, and from STARI patients from North Carolina, Missouri, and other states. Before linear discriminant analysis data were transformed by taking the log 2 value and standardizing to the mean 0 and variance 1 within each MF. Samples were differentiated by healthy and STARI.
±A total of 38 MFs were selected by the LASSO model for two-way modeling and 82 MFs were selected by the LASSO for three-way modeling.
B. burgdorferi. Patients
americanum (lone star)
±Two patients were from southwest Iowa and one was from southeast Virginia; both areas are considered to have low risk for Lyme disease and a higher prevalence of A. americanum as compared to I. scapularis.
†The gender of these donors was approximately 50% females and? 50% males.
#The samples were obtained from the same geographic location as the early Lyme disease samples.
§Age ranged from 18-74 for all donors (n = 100). Only a subset of 30 donors were used for this study.
¥Healthy controls from Florida were used to verify that the dysregulation of MFs between EL and STARI were not due to regional differences.
4†
3±
†The 4 hits in the glycerophospholipid metabolism pathway were phosphatidic acid, phosphatidylethanolamine, phosphatidylcholine and lysophosphotidylcholine.
±The 3 hits in the sphingolipid metabolism pathway were in sphingosine, dehydrosphinganine and sulfatide.
This application is a continuation application of U.S. application Ser. No. 16/703,401, filed Dec. 4, 2019, which application is a continuation of International Application No. PCT/US2018/036688, with an international filing date of Jun. 8, 2018, which PCT application claims the benefit of U.S. provisional application No. 62/516,824, filed Jun. 8, 2017. The contents of the above-mentioned applications are hereby incorporated by reference in their entirety.
This invention was made with government support under AI100228 and AI099094, each awarded by National Institutes of Health. The government has certain rights in the invention.
| Number | Date | Country | |
|---|---|---|---|
| 62516824 | Jun 2017 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16703401 | Dec 2019 | US |
| Child | 17534170 | US | |
| Parent | PCT/US2018/036688 | Jun 2018 | US |
| Child | 16703401 | US |
