Immune recognition of human Hsp60 by Lyme disease patient sera

The 37-kDa protein (P37) of Borrelia burgdorferi is an antigen that elicits an early immunoglobulin M (IgM) antibody response in Lyme disease patients. The P37 gene was cloned from aB. burgdorferi genomic library by screening with antibody from a Lyme disease patient who had developed a prominent humoral response to the P37 antigen. DNA sequence analysis of this clone revealed the identity of P37 to be FlaA, an outer sheath protein of the periplasmic flagella. Recombinant P37 expression was accomplished inEscherichia coli by using a gene construct with the leader peptide deleted and fused to a 38-kDa E. coli protein. The recombinant antigen was reactive in IgM immunoblots using serum samples from patients clinically diagnosed with early Lyme disease that had been scored positive for B. burgdorferi anti-P37 reactivity. Lyme disease patient samples serologically negative for theB. burgdorferi P37 protein did not react with the recombinant. Recombinant P37 may be a useful component of a set of defined antigens for the serodiagnosis of early Lyme disease. This protein can be utilized as a marker in diagnostic immunoblots, aiding in the standardization of the present generation of IgM serologic tests.

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Evidence That the Variable Regions of the Central Domain of VlsE Are Antigenic during Infection with Lyme Disease Spirochetes

Infection and Immunity ◽

10.1128/iai.70.8.4196-4203.2002 ◽

2002 ◽

Vol 70 (8) ◽

pp. 4196-4203 ◽

Cited By ~ 76

Author(s):

John V. McDowell ◽

Shian-Ying Sung ◽

Linden T. Hu ◽

Richard T. Marconi

Keyword(s):

Lyme Disease ◽

Immune Evasion ◽

Antigenic Variation ◽

Protein A ◽

Disease Patient ◽

Serum Samples ◽

Variable Regions ◽

Central Domain ◽

Humoral Immune ◽

Antigenic Properties

ABSTRACT It has been postulated that the vls system of the Lyme disease spirochetes contributes to immune evasion through antigenic variation. While it is clear that vlsE undergoes sequence change within its variable regions at a high frequency during the early stages of infection, a definitive role in immune evasion has not been demonstrated. In this report we assessed the murine and human humoral immune response to recombinant (r)-VlsE variants that originally arose during infection in mice. Immunoblot analyses of r-VlsE variants were conducted by using serum samples collected from mice infected with Borrelia burgdorferi clones that carried different vlsE variants. All of the r-VlsE variants were recognized by infection sera regardless of the identity of the infecting clone or isolate. In addition, all variants were immunoreactive with a panel of human Lyme disease patient serum samples. It is evident from these analyses that the infection-induced VlsE variants share common epitopes that reside within conserved segments of these proteins. However, preabsorption experiments revealed that the variable regions of the central domain of VlsE, which undergo rapid mutation during infection, also influence the antigenic properties of the protein. A subset of the antibodies elicited against vlsE variants that differ in the sequences of their variable regions were found to be variant specific. Hence, in spite of a robust antibody response to conserved segments of VlsE, infection-induced sequence changes within the variable regions alter the antigenicity of VlsE. These results provide the first direct evidence of antigenic variation in the VlsE protein.

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Identification of OppA2 Linear Epitopes as Serodiagnostic Markers for Lyme Disease

Clinical and Vaccine Immunology ◽

10.1128/cvi.00792-13 ◽

2014 ◽

Vol 21 (5) ◽

pp. 704-711 ◽

Cited By ~ 15

Author(s):

Giacomo Signorino ◽

Paul M. Arnaboldi ◽

Mary M. Petzke ◽

Raymond J. Dattwyler

Keyword(s):

Lyme Disease ◽

Disease Patient ◽

Laboratory Diagnosis ◽

Etiologic Agent ◽

Early Infection ◽

Protein Antigens ◽

Content Type ◽

Linear Epitopes ◽

Bacterial Lysates ◽

Oligopeptide Permease

ABSTRACTLaboratory diagnosis of Lyme disease is based on the serological detection of antibodies against the etiologic agentBorrelia burgdorferi. Current diagnostics are insensitive at detecting early infection, when treatment is most effective. This deficiency results from the limited number ofB. burgdorferiantigens expressed in early infection and the use of an insensitive two-tier paradigm, put in place to deal with insufficient specificity associated with the use of whole-protein antigens and/or bacterial lysates as serodiagnostic targets. Whole-protein antigens contain epitopes that are unique toB. burgdorferias well as cross-reactive epitopes found in other bacteria. One method for overcoming the limitations imposed by cross-reactive epitopes is the use of short peptides containing epitopes unique toB. burgdorferias antigen targets. This eliminates nonspecific epitopes. Using overlapping peptide libraries, we performed epitope mapping of linear epitopes in oligopeptide permease A2 (OppA2), a member of the oligopeptide permease (Opp) family of peptide transporters, expressed during earlyB. burgdorferiinfection. We identified 9 epitopes, synthesized peptides containing these epitopes, and screened those using panels of blood from patients with early Lyme disease, rheumatoid arthritis (RA), or syphilis or from healthy individuals. Two of the peptides, OppA2 (191-225) (amino acids comprising the peptide are shown in parentheses) and OppA2 (381-400), are highly conserved among the three major pathogenicBorreliaspecies responsible for most Lyme disease cases in North America and Europe. They detected antibodies in Lyme disease patient sera with sufficient sensitivity and specificity to indicate that they could have value in a serological assay for Lyme disease.

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Feature Selection from Lyme Disease Patient Survey Using Machine Learning

Algorithms ◽

10.3390/a13120334 ◽

2020 ◽

Vol 13 (12) ◽

pp. 334

Author(s):

Joshua Vendrow ◽

Jamie Haddock ◽

Deanna Needell ◽

Lorraine Johnson

Keyword(s):

Machine Learning ◽

Lyme Disease ◽

Large Scale ◽

Disease Patient ◽

Patient Survey ◽

Machine Learning Techniques ◽

Medical Community ◽

Support Vector ◽

Global Rating ◽

K Nearest Neighbors

Lyme disease is a rapidly growing illness that remains poorly understood within the medical community. Critical questions about when and why patients respond to treatment or stay ill, what kinds of treatments are effective, and even how to properly diagnose the disease remain largely unanswered. We investigate these questions by applying machine learning techniques to a large scale Lyme disease patient registry, MyLymeData, developed by the nonprofit LymeDisease.org. We apply various machine learning methods in order to measure the effect of individual features in predicting participants’ answers to the Global Rating of Change (GROC) survey questions that assess the self-reported degree to which their condition improved, worsened, or remained unchanged following antibiotic treatment. We use basic linear regression, support vector machines, neural networks, entropy-based decision tree models, and k-nearest neighbors approaches. We first analyze the general performance of the model and then identify the most important features for predicting participant answers to GROC. After we identify the “key” features, we separate them from the dataset and demonstrate the effectiveness of these features at identifying GROC. In doing so, we highlight possible directions for future study both mathematically and clinically.

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Phe2vec: Automated Disease Phenotyping based on Unsupervised Embeddings from Electronic Health Records

10.1101/2020.11.14.20231894 ◽

2020 ◽

Author(s):

Jessica K. De Freitas ◽

Kipp W. Johnson ◽

Eddye Golden ◽

Girish N. Nadkarni ◽

Joel T. Dudley ◽

...

Keyword(s):

Lyme Disease ◽

Electronic Health Records ◽

Disease Patient ◽

Clinical History ◽

Health Records ◽

Patient Cohort ◽

Phenotype Definition ◽

Disease Definition ◽

Electronic Health ◽

Medical Concepts

AbstractObjectiveWe introduce Phe2vec, an automated framework for disease phenotyping from electronic health records (EHRs) based on unsupervised learning. We assess its effectiveness against standard rule-based algorithms from the Phenotype KnowledgeBase (PheKB).Materials and MethodsPhe2vec is based on pre-computing embeddings of medical concepts and patients’ longitudinal clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are similarly linked to a disease if their embedded representation is close to the phenotype. We evaluated Phe2vec using 49,234 medical concepts from structured EHRs and clinical notes from 1,908,741 patients in the Mount Sinai Health System. We assessed performance on ten diverse diseases having a PheKB algorithm, and one disease without, namely Lyme disease.ResultsPhe2vec phenotypes derived using Word2vec, GloVe, and Fasttext embeddings led to promising performance in disease definition and patient cohort identification as compared with standard PheKB definitions. When comparing head-to-head Phe2vec and PheKB disease patient cohorts using chart review, Phe2vec performed on par or better in nine out of ten diseases in terms of predictive positive values. Additionally, Phe2vec effectively identified phenotype definition and patient cohort for Lyme disease, a condition not covered in PheKB.DiscussionPhe2vec offers a solution to improve time-consuming phenotyping pipelines. Differently from other automated approaches in the literature, it is fully unsupervised, can easily scale to any disease and was validated against widely adopted expert-based standards.ConclusionPhe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts.

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Learning and Mapping Lyme Disease Patient Trajectories from Electronic Medical Data for Stratification of Disease Risk and Therapeutic Response

10.1101/239020 ◽

2017 ◽

Cited By ~ 1

Author(s):

Osamu Ichikawa ◽

Benjamin S. Glicksberg ◽

Brian Kidd ◽

Li Li ◽

Joel T. Dudley

Keyword(s):

Lyme Disease ◽

Precision Medicine ◽

Disease Risk ◽

Disease Patient ◽

Tear Film ◽

Drug Repurposing ◽

Not Otherwise Specified ◽

Precise Understanding ◽

New Findings ◽

Machine Learning Approach

ABSTRACTBackgroundLyme disease (LD) is an epidemic, tick-borne illness with approximately 329,000 incidences diagnosed each year in United States. Long-term use of antibiotics is associated with serious complications, including post-treatment Lyme disease syndrome (PTLDS). The landscape of comorbidities and health trajectories associated with LD and associated treatments is not fully understood. Consequently, there is an urgent need to improve clinical management of LD based on a more precise understanding of disease and patient stratification.MethodsWe used a precision medicine machine-learning approach based on high-dimensional electronic medical records (EMRs) to characterize the heterogeneous comorbidities in a LD population and develop systematic predictive models for identifying medications that influence the risk of subsequent comorbidities.FindingsWe identified 3, 16, and 17 comorbidities at broad disease categories associated with LD within 2, 5, and 10 years of diagnosis, respectively. At higher resolution of ICD-9 levels, we pinpointed specific co-morbid diseases on a timescale that matched the symptoms associated with PTLDS. We identified 7, 30, and 35 medications that influenced the risks of the reported comorbidities within 2, 5, and 10 years, respectively. These medications included six previously associated with the identified comorbidities and 29 new findings. For instance, the first-line antibiotic doxycycline exhibited a consistently protective effect for typical symptoms of LD, including ‘backache Not Otherwise Specified (NOS)’ and ‘chronic rhinitis’, but consistently increased the risk of ‘cataract NOS’, ‘tear film insufficiency NOS’, and ‘nocturia’.InterpretationOur approach and findings suggest new hypotheses for precision medicine treatments regimens and drug repurposing opportunities tailored to the phenotypic profiles of LD patients.FundingThe Steven & Alexandra Cohen Foundation

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Immunoreactivity of Polish Lyme Disease Patient Sera to Specific Borrelia Antigens—Part 1

Diagnostics ◽

10.3390/diagnostics11112157 ◽

2021 ◽

Vol 11 (11) ◽

pp. 2157

Author(s):

Iwona Wojciechowska-Koszko ◽

Magdalena Mnichowska-Polanowska ◽

Paweł Kwiatkowski ◽

Paulina Roszkowska ◽

Monika Sienkiewicz ◽

...

Keyword(s):

Lyme Disease ◽

Clinical Picture ◽

Screening Test ◽

Disease Patient ◽

Healthy People ◽

Serological Diagnosis ◽

Elisa Assay ◽

Igg Antibodies ◽

Igm Antibodies ◽

Positive Results

The diverse clinical picture and the non-specificity of symptoms in Lyme disease (LD) require the implementation of effective diagnostics, which should take into account the heterogeneity of Borrelia antigens. According to available guidelines, laboratories should use a two-tier serological diagnosis based on the enzyme-linked immunosorbent (ELISA) screening test and confirmation of the immunoblot (IB). The aim of the study was to investigate the immunoreactivity of LD patient sera to Borrelia antigens and to attempt to identify the genospecies responsible for LD using an ELISA–IB assay combination. Eighty patients with suspected LD and 22 healthy people participated in the study. All samples were tested with ELISA and IB assays in both IgM and IgG antibodies. In the case of the ELISA assay, more positive results were obtained in the IgM class than in the IgG class. In the case of the IB assay, positive results dominated in the IgG class. Positive results obtained in the IB assay most often showed IgM antibodies against the OspC and flagellin antigens, whereas the IgG antibodies were against VlsE, BmpA, OspC, p41, and p83 antigens. The IB assay is an important part of LD serodiagnosis and should be mandatory in diagnostic laboratories.

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The Long-Term Persistence of Borrelia burgdorferi Antigens and DNA in the Tissues of a Patient with Lyme Disease

Antibiotics ◽

10.3390/antibiotics8040183 ◽

2019 ◽

Vol 8 (4) ◽

pp. 183 ◽

Cited By ~ 11

Author(s):

Eva Sapi ◽

Rumanah S. Kasliwala ◽

Hebo Ismail ◽

Jason P. Torres ◽

Michael Oldakowski ◽

...

Keyword(s):

Polymerase Chain Reaction ◽

T Lymphocytes ◽

Lyme Disease ◽

Confocal Microscopy ◽

Human Body ◽

Disease Patient ◽

Chain Reaction ◽

Antibiotic Resistant ◽

Polymerase Chain

Whether Borrelia burgdorferi, the causative agent of Lyme disease, can persist for long periods in the human body has been a controversial question. The objective of this study was to see if we could find B. burgdorferi in a Lyme disease patient after a long clinical course and after long-term antibiotic treatment. Therefore, we investigated the potential presence of B. burgdorferi antigens and DNA in human autopsy tissues from a well-documented serum-, PCR-, and culture-positive Lyme disease patient, a 53-year-old female from northern Westchester County in the lower Hudson Valley Region of New York State, who had received extensive antibiotic treatments during extensive antibiotic treatments over the course of her 16-year-long illness. We also asked what form the organism might take, with special interest in the recently found antibiotic-resistant aggregate form, biofilm. We also examined the host tissues for the presence of inflammatory markers such as CD3+ T lymphocytes. Autopsy tissue sections of the brain, heart, kidney, and liver were analyzed by histological and immunohistochemical methods (IHC), confocal microscopy, fluorescent in situ hybridization (FISH), polymerase chain reaction (PCR), and whole-genome sequencing (WGS)/metagenomics. We found significant pathological changes, including borrelial spirochetal clusters, in all of the organs using IHC combined with confocal microscopy. The aggregates contained a well-established biofilm marker, alginate, on their surfaces, suggesting they are true biofilm. We found B. burgdorferi DNA by FISH, polymerase chain reaction (PCR), and an independent verification by WGS/metagenomics, which resulted in the detection of B. burgdorferi sensu stricto specific DNA sequences. IHC analyses showed significant numbers of infiltrating CD3+ T lymphocytes present next to B. burgdorferi biofilms. In summary, we provide several lines of evidence that suggest that B. burgdorferi can persist in the human body, not only in the spirochetal but also in the antibiotic-resistant biofilm form, even after long-term antibiotic treatment. The presence of infiltrating lymphocytes in the vicinity of B. burgdorferi biofilms suggests that the organism in biofilm form might trigger chronic inflammation.

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Immunoproteomic analysis of Borrelia miyamotoi for the identification of serodiagnostic antigens

Scientific Reports ◽

10.1038/s41598-019-53248-5 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Emma K. Harris ◽

Marisa R. Harton ◽

Maria Angela de Mello Marques ◽

John T. Belisle ◽

Claudia R. Molins ◽

...

Keyword(s):

Lyme Disease ◽

Specific Antigen ◽

Disease Patient ◽

Borrelia Miyamotoi ◽

Serum Samples ◽

Human Patient ◽

Highly Sensitive ◽

Associated Proteins ◽

Multiple Variable ◽

Health Significance

AbstractThe tick-borne spirochete, Borrelia miyamotoi, is an emerging pathogen of public health significance. Current B. miyamotoi serodiagnostic testing depends on reactivity against GlpQ which is not highly sensitive on acute phase serum samples. Additionally, anti-B. miyamotoi antibodies can cross-react with C6 antigen testing for B. burgdorferi, the causative agent of Lyme disease, underscoring the need for improved serological assays that produce accurate diagnostic results. We performed an immunoproteomics analysis of B. miyamotoi proteins to identify novel serodiagnostic antigens. Sera from mice infected with B. miyamotoi by subcutaneous inoculation or tick bite were collected for immunoblotting against B. miyamotoi membrane-associated proteins separated by 2-dimensional electrophoresis (2DE). In total, 88 proteins in 40 2DE immunoreactive spots were identified via mass spectrometry. Multiple variable large proteins (Vlps) and a putative lipoprotein were among those identified and analyzed. Reactivity of anti-B. miyamotoi sera against recombinant Vlps and the putative lipoprotein confirmed their immunogenicity. Mouse anti-B. burgdorferi serum was cross-reactive to all recombinant Vlps, but not against the putative lipoprotein by IgG. Furthermore, antibodies against the recombinant putative lipoprotein were present in serum from a B. miyamotoi-infected human patient, but not a Lyme disease patient. Results presented here provide a comprehensive profile of B. miyamotoi antigens that induce the host immune response and identify a putative lipoprotein as a potentially specific antigen for B. miyamotoi serodetection.

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Evaluation of Selected Borrelia burgdorferi lp54 Plasmid-Encoded Gene Products Expressed during Mammalian Infection as Antigens To Improve Serodiagnostic Testing for Early Lyme Disease

Clinical and Vaccine Immunology ◽

10.1128/cvi.00399-15 ◽

2015 ◽

Vol 22 (11) ◽

pp. 1176-1186 ◽

Cited By ~ 6

Author(s):

Zachary P. Weiner ◽

Rebecca M. Crew ◽

Kevin S. Brandt ◽

Amy J. Ullmann ◽

Martin E. Schriefer ◽

...

Keyword(s):

Lyme Disease ◽

Borrelia Burgdorferi ◽

Recombinant Proteins ◽

Erythema Migrans ◽

Disease Patient ◽

Enzyme Linked Immunosorbent Assay ◽

Serum Samples ◽

Early Stages ◽

Lyme Carditis ◽

Mammalian Infection

ABSTRACTLaboratory testing for the diagnosis of Lyme disease is performed primarily by serologic assays and is accurate for detection beyond the acute stage of the infection. Serodiagnostic assays to detect the early stages of infection, however, are limited in their sensitivity, and improvement is warranted. We analyzed a series ofBorrelia burgdorferiproteins known to be induced within feeding ticks and/or during mammalian infection for their utility as serodiagnostic markers against a comprehensive panel of Lyme disease patient serum samples. The antigens were assayed for IgM and IgG reactivity in line immunoblots and separately by enzyme-linked immunosorbent assay (ELISA), with a focus on reactivity against early Lyme disease with erythema migrans (EM), early disseminated Lyme neuroborreliosis, and early Lyme carditis patient serum samples. By IgM immunoblotting, we found that recombinant proteins BBA65, BBA70, and BBA73 reacted with early Lyme EM samples at levels comparable to those of the OspC antigen used in the current IgM blotting criteria. Additionally, these proteins reacted with serum samples from patients with early neuroborreliosis and early carditis, suggesting value in detecting early stages of this disease progression. We also found serological reactivity against recombinant proteins BBA69 and BBA73 with early-Lyme-disease samples using IgG immunoblotting and ELISA. Significantly, some samples that had been scored negative by the Centers for Disease Control and Prevention-recommended 2-tiered testing algorithm demonstrated positive reactivity to one or more of the antigens by IgM/IgG immunoblot and ELISA. These results suggest that incorporating additionalin vivo-expressed antigens into the current IgM/IgG immunoblotting tier in a recombinant protein platform assay may improve the performance of early-Lyme-disease serologic testing.

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