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Bockenstedt LK, Belperron AA. Insights From Omics in Lyme Disease. J Infect Dis 2024; 230:S18-S26. [PMID: 39140719 DOI: 10.1093/infdis/jiae250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2024] Open
Abstract
Lyme disease is a zoonotic infection due to Ixodes tick-transmitted Borrelia burgdorferi sensu lato spirochetes and the most common vector-borne disease in the Northern Hemisphere. Despite nearly 50 years of investigation, the pathogenesis of this infection and its 2 main adverse outcomes-postinfectious Lyme arthritis and posttreatment Lyme disease syndrome-are incompletely understood. Advancement in sequencing and mass spectrometry have led to the rapid expansion of high-throughput omics technologies, including transcriptomics, metabolomics, and proteomics, which are now being applied to human diseases. This review summarizes findings of omics studies conducted on blood and tissue samples of people with acute Lyme disease and its postinfectious outcomes.
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Affiliation(s)
- Linda K Bockenstedt
- Section of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Alexia A Belperron
- Section of Rheumatology, Allergy and Immunology, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
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2
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Năstase AM, Barrett MP, Cárdenas WB, Cordeiro FB, Zambrano M, Andrade J, Chang J, Regato M, Carrillo E, Botana L, Moreno J, Regnault C, Milne K, Spence PJ, Rowe JA, Rogers S. Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites. PLoS Negl Trop Dis 2023; 17:e0011133. [PMID: 37486920 PMCID: PMC10399774 DOI: 10.1371/journal.pntd.0011133] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 02/01/2023] [Indexed: 07/26/2023] Open
Abstract
Acute febrile illnesses are still a major cause of mortality and morbidity globally, particularly in low to middle income countries. The aim of this study was to determine any possible metabolic commonalities of patients infected with disparate pathogens that cause fever. Three liquid chromatography-mass spectrometry (LC-MS) datasets investigating the metabolic effects of malaria, leishmaniasis and Zika virus infection were used. The retention time (RT) drift between the datasets was determined using landmarks obtained from the internal standards generally used in the quality control of the LC-MS experiments. Fitted Gaussian Process models (GPs) were used to perform a high level correction of the RT drift between the experiments, which was followed by standard peakset alignment between the samples with corrected RTs of the three LC-MS datasets. Statistical analysis, annotation and pathway analysis of the integrated peaksets were subsequently performed. Metabolic dysregulation patterns common across the datasets were identified, with kynurenine pathway being the most affected pathway between all three fever-associated datasets.
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Affiliation(s)
- Ana-Maria Năstase
- School of Infection & Immunity, University of Glasgow, Glasgow, Scotland, United Kingdom
| | - Michael P Barrett
- School of Infection & Immunity, University of Glasgow, Glasgow, Scotland, United Kingdom
| | - Washington B Cárdenas
- Laboratorio para Investigaciones Biomedicas, Facultad de Ciencias de la Vida, Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil, Ecuador
| | - Fernanda Bertuccez Cordeiro
- Laboratorio para Investigaciones Biomedicas, Facultad de Ciencias de la Vida, Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil, Ecuador
| | - Mildred Zambrano
- Servicio de Infectología e Epidemiología, Hospital de Niños Dr. Roberto Gilbert, Guayaquil, Ecuador
| | - Joyce Andrade
- Servicio de Infectología e Epidemiología, Hospital de Niños Dr. Roberto Gilbert, Guayaquil, Ecuador
| | - Juan Chang
- Servicio de Infectología e Epidemiología, Hospital de Niños Dr. Roberto Gilbert, Guayaquil, Ecuador
| | - Mary Regato
- Instituto Nacional de Investigación en Salud Pública (INSPI), Guayaquil, Ecuador
| | - Eugenia Carrillo
- WHO Collaborating Centre for Leishmaniasis, National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain
| | - Laura Botana
- WHO Collaborating Centre for Leishmaniasis, National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain
| | - Javier Moreno
- WHO Collaborating Centre for Leishmaniasis, National Centre for Microbiology, Instituto de Salud Carlos III, Madrid, Spain
| | - Clément Regnault
- School of Infection & Immunity, University of Glasgow, Glasgow, Scotland, United Kingdom
| | - Kathryn Milne
- Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Philip J Spence
- Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom
| | - J Alexandra Rowe
- Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Simon Rogers
- School of Computing Science, University of Glasgow, Glasgow, Scotland, United Kingdom
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3
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Lyme Borreliosis in Dogs: Background, Epidemiology, Diagnostics, Treatment and Prevention. FOLIA VETERINARIA 2023. [DOI: 10.2478/fv-2023-0009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2023] Open
Abstract
Abstract
Lyme borreliosis (LB) is a multisystemic tick-borne disease that can affect many organs and have various clinical manifestations in dogs. We attempted to summarise various aspects of Lyme disease: i. e., pathogenesis, epidemiology, benefits and risks of diagnostic approaches, treatment options, and prevention in dogs. Several diagnostic bottlenecks for LB in dogs and humans are compared. Because the occurrence of LB in both humans and dogs is closely related, monitoring its prevalence in dogs as sentinel animals is an excellent aid in assessing the risk of Lyme disease in a given geographic area. Although clinical symptoms in humans help clinicians diagnose LB, they are ineffective in dogs because canines rarely exhibit LB symptoms. Despite significant differences in sensitivity and specificity, sero-logical two-step detection of antibodies against Borrelia spp. (ELISA and Western blot) is the most commonly used method in humans and dogs. The limitations of the assay highlight the need for further research to develop new clinical markers and more accurate diagnostic tests. Due to the lack of a specific all-encompassing LB test, a definitive diagnosis of LB remains a difficult and time-consuming process in human and veterinary medicine. Understanding the disease prevalence and diagnostics, as well as preventing its spread with effective and timely treatment, are fundamental principles of good disease management.
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A systems biology approach to better understand human tick-borne diseases. Trends Parasitol 2023; 39:53-69. [PMID: 36400674 DOI: 10.1016/j.pt.2022.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/28/2022] [Accepted: 10/28/2022] [Indexed: 11/17/2022]
Abstract
Tick-borne diseases (TBDs) are a growing global health concern. Despite extensive studies, ill-defined tick-associated pathologies remain with unknown aetiologies. Human immunological responses after tick bite, and inter-individual variations of immune-response phenotypes, are not well characterised. Current reductive experimental methodologies limit our understanding of more complex tick-associated illness, which results from the interactions between the host, tick, and microbes. An unbiased, systems-level integration of clinical metadata and biological host data - obtained via transcriptomics, proteomics, and metabolomics - offers to drive the data-informed generation of testable hypotheses in TBDs. Advanced computational tools have rendered meaningful analysis of such large data sets feasible. This review highlights the advantages of integrative system biology approaches as essential for understanding the complex pathobiology of TBDs.
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Zhou CX, Li LY, Huang CQ, Guo XD, An XD, Luo FF, Cong W. Investigation of urine metabolome of BALB/c mouse infected with an avirulent strain of Toxoplasma gondii. Parasit Vectors 2022; 15:271. [PMID: 35906695 PMCID: PMC9338554 DOI: 10.1186/s13071-022-05408-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/13/2022] [Indexed: 11/21/2022] Open
Abstract
Background The protozoan parasite Toxoplasma gondii is a major concern for human and animal health. Although the metabolic understanding of toxoplasmosis has increased in recent years, the analysis of metabolic alterations through noninvasive methodologies in biofluids remains limited. Methods Here, we applied liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics and multivariate statistical analysis to analyze BALB/c mouse urine collected from acutely infected, chronically infected and control subjects. Results In total, we identified 2065 and 1409 metabolites in the positive electrospray ionization (ESI +) mode and ESI − mode, respectively. Metabolomic patterns generated from principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) score plots clearly separated T. gondii-infected from uninfected urine samples. Metabolites with altered levels in urine from T. gondii-infected mice revealed changes in pathways related to amino acid metabolism, fatty acid metabolism, and nicotinate and nicotinamide metabolism. Conclusions This is the first study to our knowledge on urine metabolic profiling of BALB/c mouse with T. gondii infection. The urine metabolome of infected mouse is distinctive and has value in the understanding of Toxoplasmosis pathogenesis and improvement of treatment. Graphical Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s13071-022-05408-2.
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Affiliation(s)
- Chun-Xue Zhou
- Department of Pathogen Biology, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, People's Republic of China.
| | - Ling-Yu Li
- Department of Pathogen Biology, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, People's Republic of China
| | - Cui-Qin Huang
- Engineering Research Center for the Prevention and Control of Animal Original Zoonosis, Fujian Province University & College of Life Science, Longyan University, Longyan, 364012, Fujian, People's Republic of China
| | - Xu-Dong Guo
- Department of Pathogen Biology, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, People's Republic of China
| | - Xu-Dian An
- Department of Pathogen Biology, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, People's Republic of China
| | - Fang-Fang Luo
- Department of Pathogen Biology, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, People's Republic of China
| | - Wei Cong
- Marine College, Shandong University, Weihai, 264209, Shandong, People's Republic of China.
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Senger RS, Sayed Issa A, Agnor B, Talty J, Hollis A, Robertson JL. Disease-Associated Multimolecular Signature in the Urine of Patients with Lyme Disease Detected Using Raman Spectroscopy and Chemometrics. APPLIED SPECTROSCOPY 2022; 76:284-299. [PMID: 35102746 DOI: 10.1177/00037028211061769] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A urine-based screening technique for Lyme disease (LD) was developed in this research. The screen is based on Raman spectroscopy, iterative smoothing-splines with root error adjustment (ISREA) spectral baselining, and chemometric analysis using Rametrix software. Raman spectra of urine from 30 patients with positive serologic tests (including the US Centers for Disease Control [CDC] two-tier standard) for LD were compared against subsets of our database of urine spectra from 235 healthy human volunteers, 362 end-stage kidney disease (ESKD) patients, and 17 patients with active or remissive bladder cancer (BCA). We found statistical differences (p < 0.001) between urine scans of healthy volunteers and LD-positive patients. We also found a unique LD molecular signature in urine involving 112 Raman shifts (31 major Raman shifts) with significant differences from urine of healthy individuals. We were able to distinguish the LD molecular signature as statistically different (p < 0.001) from the molecular signatures of ESKD and BCA. When comparing LD-positive patients against healthy volunteers, the Rametrix-based urine screen performed with 86.7% for overall accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), respectively. When considering patients with ESKD and BCA in the LD-negative group, these values were 88.7% (accuracy), 83.3% (sensitivity), 91.0% (specificity), 80.7% (PPV), and 92.4% (NPV). Additional advantages to the Raman-based urine screen include that it is rapid (minutes per analysis), is minimally invasive, requires no chemical labeling, uses a low-profile, off-the-shelf spectrometer, and is inexpensive relative to other available LD tests.
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Affiliation(s)
- Ryan S Senger
- Department of Biological Systems Engineering, 1757Virginia Tech, Blacksburg, Virginia, USA
- DialySensors Inc., Blacksburg, Virginia, USA
| | | | - Ben Agnor
- Department of Biological Systems Engineering, 1757Virginia Tech, Blacksburg, Virginia, USA
| | - Janine Talty
- Neuromusculoskeletal Medicine & OMM, Roanoke, Virginia, USA
| | | | - John L Robertson
- DialySensors Inc., Blacksburg, Virginia, USA
- Department of Biomedical Engineering and Mechanics, 1757Virginia Tech, Blacksburg, Virginia, USA
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Kehoe ER, Fitzgerald BL, Graham B, Islam MN, Sharma K, Wormser GP, Belisle JT, Kirby MJ. Biomarker selection and a prospective metabolite-based machine learning diagnostic for lyme disease. Sci Rep 2022; 12:1478. [PMID: 35087163 PMCID: PMC8795431 DOI: 10.1038/s41598-022-05451-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 01/06/2022] [Indexed: 12/14/2022] Open
Abstract
We provide a pipeline for data preprocessing, biomarker selection, and classification of liquid chromatography–mass spectrometry (LCMS) serum samples to generate a prospective diagnostic test for Lyme disease. We utilize tools of machine learning (ML), e.g., sparse support vector machines (SSVM), iterative feature removal (IFR), and k-fold feature ranking to select several biomarkers and build a discriminant model for Lyme disease. We report a 98.13% test balanced success rate (BSR) of our model based on a sequestered test set of LCMS serum samples. The methodology employed is general and can be readily adapted to other LCMS, or metabolomics, data sets.
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Affiliation(s)
- Eric R Kehoe
- Department of Mathematics, Colorado State University, Fort Collins, CO, 80523, USA.
| | - Bryna L Fitzgerald
- Department of Microbiology, Immunology & Pathology, Colorado State University, Fort Collins, CO, 80523, USA
| | - Barbara Graham
- Department of Microbiology, Immunology & Pathology, Colorado State University, Fort Collins, CO, 80523, USA
| | - M Nurul Islam
- Department of Microbiology, Immunology & Pathology, Colorado State University, Fort Collins, CO, 80523, USA
| | - Kartikay Sharma
- Department of Computer Science, Colorado State University, Fort Collins, CO, 80523, USA
| | - Gary P Wormser
- Department of Medicine, New York Medical College, Valhalla, NY, 10595, USA
| | - John T Belisle
- Department of Microbiology, Immunology & Pathology, Colorado State University, Fort Collins, CO, 80523, USA
| | - Michael J Kirby
- Department of Computer Science, Colorado State University, Fort Collins, CO, 80523, USA.,Department of Mathematics, Colorado State University, Fort Collins, CO, 80523, USA
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8
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Fitzgerald BL, Graham B, Delorey MJ, Pegalajar-Jurado A, Islam MN, Wormser GP, Aucott JN, Rebman AW, Soloski MJ, Belisle JT, Molins CR. Metabolic Response in Patients With Post-treatment Lyme Disease Symptoms/Syndrome. Clin Infect Dis 2021; 73:e2342-e2349. [PMID: 32975577 PMCID: PMC8492154 DOI: 10.1093/cid/ciaa1455] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Post-treatment Lyme disease symptoms/syndrome (PTLDS) occurs in approximately 10% of patients with Lyme disease following antibiotic treatment. Biomarkers or specific clinical symptoms to identify patients with PTLDS do not currently exist and the PTLDS classification is based on the report of persistent, subjective symptoms for ≥6 months following antibiotic treatment for Lyme disease. METHODS Untargeted liquid chromatography-mass spectrometry metabolomics was used to determine longitudinal metabolic responses and biosignatures in PTLDS and clinically cured non-PTLDS Lyme patients. Evaluation of biosignatures included (1) defining altered classes of metabolites, (2) elastic net regularization to define metabolites that most strongly defined PTLDS and non-PTLDS patients at different time points, (3) changes in the longitudinal abundance of metabolites, and (4) linear discriminant analysis to evaluate robustness in a second patient cohort. RESULTS This study determined that observable metabolic differences exist between PTLDS and non-PTLDS patients at multiple time points. The metabolites with differential abundance included those from glycerophospholipid, bile acid, and acylcarnitine metabolism. Distinct longitudinal patterns of metabolite abundance indicated a greater metabolic variability in PTLDS versus non-PTLDS patients. Small numbers of metabolites (6 to 40) could be used to define PTLDS versus non-PTLDS patients at defined time points, and the findings were validated in a second cohort of PTLDS and non-PTLDS patients. CONCLUSIONS These data provide evidence that an objective metabolite-based measurement can distinguish patients with PTLDS and help understand the underlying biochemistry of PTLDS.
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Affiliation(s)
| | - Barbara Graham
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, USA
| | - Mark J Delorey
- Centers for Disease Control and Prevention, Fort Collins, Colorado, USA
| | | | - M Nurul Islam
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, USA
| | - Gary P Wormser
- Division of Infectious Diseases, New York Medical College, Valhalla, New York, USA
| | - John N Aucott
- The Lyme Disease Research Center, Division of Rheumatology, Department of Medicine, Johns Hopkins University School of Medicine, Lutherville, Maryland, USA
| | - Alison W Rebman
- The Lyme Disease Research Center, Division of Rheumatology, Department of Medicine, Johns Hopkins University School of Medicine, Lutherville, Maryland, USA
| | - Mark J Soloski
- The Lyme Disease Research Center, Division of Rheumatology, Department of Medicine, Johns Hopkins University School of Medicine, Lutherville, Maryland, USA
| | - John T Belisle
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, Colorado, USA
| | - Claudia R Molins
- Centers for Disease Control and Prevention, Fort Collins, Colorado, USA
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Bobe JR, Jutras BL, Horn EJ, Embers ME, Bailey A, Moritz RL, Zhang Y, Soloski MJ, Ostfeld RS, Marconi RT, Aucott J, Ma'ayan A, Keesing F, Lewis K, Ben Mamoun C, Rebman AW, McClune ME, Breitschwerdt EB, Reddy PJ, Maggi R, Yang F, Nemser B, Ozcan A, Garner O, Di Carlo D, Ballard Z, Joung HA, Garcia-Romeu A, Griffiths RR, Baumgarth N, Fallon BA. Recent Progress in Lyme Disease and Remaining Challenges. Front Med (Lausanne) 2021; 8:666554. [PMID: 34485323 PMCID: PMC8416313 DOI: 10.3389/fmed.2021.666554] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 07/12/2021] [Indexed: 12/14/2022] Open
Abstract
Lyme disease (also known as Lyme borreliosis) is the most common vector-borne disease in the United States with an estimated 476,000 cases per year. While historically, the long-term impact of Lyme disease on patients has been controversial, mounting evidence supports the idea that a substantial number of patients experience persistent symptoms following treatment. The research community has largely lacked the necessary funding to properly advance the scientific and clinical understanding of the disease, or to develop and evaluate innovative approaches for prevention, diagnosis, and treatment. Given the many outstanding questions raised into the diagnosis, clinical presentation and treatment of Lyme disease, and the underlying molecular mechanisms that trigger persistent disease, there is an urgent need for more support. This review article summarizes progress over the past 5 years in our understanding of Lyme and tick-borne diseases in the United States and highlights remaining challenges.
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Affiliation(s)
- Jason R. Bobe
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Brandon L. Jutras
- Department of Biochemistry, Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, United States
| | | | - Monica E. Embers
- Tulane University Health Sciences, New Orleans, LA, United States
| | - Allison Bailey
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | | | - Ying Zhang
- State Key Laboratory for the Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Mark J. Soloski
- Division of Rheumatology, Department of Medicine, Lyme Disease Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | | | - Richard T. Marconi
- Department of Microbiology and Immunology, Virginia Commonwealth University Medical Center, Richmond, VA, United States
| | - John Aucott
- Division of Rheumatology, Department of Medicine, Lyme Disease Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Avi Ma'ayan
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | | | - Kim Lewis
- Department of Biology, Northeastern University, Boston, MA, United States
| | | | - Alison W. Rebman
- Division of Rheumatology, Department of Medicine, Lyme Disease Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Mecaila E. McClune
- Department of Biochemistry, Fralin Life Sciences Institute, Virginia Tech, Blacksburg, VA, United States
| | - Edward B. Breitschwerdt
- Department of Clinical Sciences, Comparative Medicine Institute, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, United States
| | | | - Ricardo Maggi
- Department of Clinical Sciences, Comparative Medicine Institute, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, United States
| | - Frank Yang
- Department of Microbiology and Immunology, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Bennett Nemser
- Steven & Alexandra Cohen Foundation, Stamford, CT, United States
| | - Aydogan Ozcan
- University of California, Los Angeles, Los Angeles, CA, United States
| | - Omai Garner
- University of California, Los Angeles, Los Angeles, CA, United States
| | - Dino Di Carlo
- University of California, Los Angeles, Los Angeles, CA, United States
| | - Zachary Ballard
- University of California, Los Angeles, Los Angeles, CA, United States
| | - Hyou-Arm Joung
- University of California, Los Angeles, Los Angeles, CA, United States
| | - Albert Garcia-Romeu
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Roland R. Griffiths
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Nicole Baumgarth
- Center for Immunology and Infectious Diseases and the Department of Pathology, Microbiology & Immunology, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
| | - Brian A. Fallon
- Columbia University Irving Medical Center, New York, NY, United States
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Aggarwal S, Acharjee A, Mukherjee A, Baker MS, Srivastava S. Role of Multiomics Data to Understand Host-Pathogen Interactions in COVID-19 Pathogenesis. J Proteome Res 2021; 20:1107-1132. [PMID: 33426872 PMCID: PMC7805606 DOI: 10.1021/acs.jproteome.0c00771] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Indexed: 12/15/2022]
Abstract
Human infectious diseases are contributed equally by the host immune system's efficiency and any pathogens' infectivity. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the coronavirus strain causing the respiratory pandemic coronavirus disease 2019 (COVID-19). To understand the pathobiology of SARS-CoV-2, one needs to unravel the intricacies of host immune response to the virus, the viral pathogen's mode of transmission, and alterations in specific biological pathways in the host allowing viral survival. This review critically analyzes recent research using high-throughput "omics" technologies (including proteomics and metabolomics) on various biospecimens that allow an increased understanding of the pathobiology of SARS-CoV-2 in humans. The altered biomolecule profile facilitates an understanding of altered biological pathways. Further, we have performed a meta-analysis of significantly altered biomolecular profiles in COVID-19 patients using bioinformatics tools. Our analysis deciphered alterations in the immune response, fatty acid, and amino acid metabolism and other pathways that cumulatively result in COVID-19 disease, including symptoms such as hyperglycemic and hypoxic sequelae.
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Affiliation(s)
- Shalini Aggarwal
- Department of Biosciences and
Bioengineering, Indian Institute of Technology
Bombay, Mumbai 400076,
India
| | - Arup Acharjee
- Department of Biosciences and
Bioengineering, Indian Institute of Technology
Bombay, Mumbai 400076,
India
| | - Amrita Mukherjee
- Department of Biosciences and
Bioengineering, Indian Institute of Technology
Bombay, Mumbai 400076,
India
| | - Mark S. Baker
- Department of Biomedical Science,
Faculty of Medicine, Health and Human Sciences, Macquarie
University, Sydney 2109,
Australia
| | - Sanjeeva Srivastava
- Department of Biosciences and
Bioengineering, Indian Institute of Technology
Bombay, Mumbai 400076,
India
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Abstract
Lyme borreliosis is caused by a growing list of related, yet distinct, spirochetes with complex biology and sophisticated immune evasion mechanisms. It may result in a range of clinical manifestations involving different organ systems, and can lead to persistent sequelae in a subset of cases. The pathogenesis of Lyme borreliosis is incompletely understood, and laboratory diagnosis, the focus of this review, requires considerable understanding to interpret the results correctly. Direct detection of the infectious agent is usually not possible or practical, necessitating a continued reliance on serologic testing. Still, some important advances have been made in the area of diagnostics, and there are many promising ideas for future assay development. This review summarizes the state of the art in laboratory diagnostics for Lyme borreliosis, provides guidance in test selection and interpretation, and highlights future directions.
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12
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Zheng J, Zhang L, Johnson M, Mandal R, Wishart DS. Comprehensive Targeted Metabolomic Assay for Urine Analysis. Anal Chem 2020; 92:10627-10634. [PMID: 32634308 DOI: 10.1021/acs.analchem.0c01682] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Among all the human biological fluids used for disease biomarker discovery or clinical chemistry, urine stands out. It can be collected easily and noninvasively, it is readily available in large volumes, it is typically free from protein contamination, and it is chemically complex-reflecting a wide range of physiological states and functions. However, the comprehensive metabolomic analysis of urine has been somewhat less studied compared to blood. Indeed, most published metabolomic assays are specifically optimized for serum or plasma. In an effort to improve this situation, we have developed a comprehensive, quantitative MS-based assay for urine analysis. The assay robustly detects and quantifies 142 urinary metabolites including 28 amino acids and derivatives, 17 organic acids, 22 biogenic amines and derivatives, 40 acylcarnitines, 34 lipids, and glucose/hexose, among which 67 metabolites are absolutely quantified and 75 metabolites are semiquantified. All the analysis methods in this assay are based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) using both positive and negative-mode multiple reaction monitoring (MRM). The recovery rates of spiked urine samples at three different concentration levels, that is, low, medium and high, are in the range of 80% to 120% with satisfactory precision values of less than 20%. This targeted metabolomic assay has been successfully applied to the analysis of large numbers of human urine samples, with results closely matching those reported in the literature as well as those obtained from orthogonal analysis via NMR spectroscopy. Moreover, the assay was specifically developed in a 96-well plate format, which enables automated, high-throughput sample analysis. The assay has already been used to analyze more than 1800 urine samples in our laboratory since early 2019.
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13
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Fitzgerald BL, Molins CR, Islam MN, Graham B, Hove PR, Wormser GP, Hu L, Ashton LV, Belisle JT. Host Metabolic Response in Early Lyme Disease. J Proteome Res 2020; 19:610-623. [PMID: 31821002 PMCID: PMC7262776 DOI: 10.1021/acs.jproteome.9b00470] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Lyme disease is a tick-borne bacterial illness that occurs in areas of North America, Europe, and Asia. Early infection typically presents as generalized symptoms with an erythema migrans (EM) skin lesion. Dissemination of the pathogen Borrelia burgdorferi can result in multiple EM skin lesions or in extracutaneous manifestations such as Lyme neuroborreliosis. Metabolic biosignatures of patients with early Lyme disease can potentially provide diagnostic targets as well as highlight metabolic pathways that contribute to pathogenesis. Sera from well-characterized patients diagnosed with either early localized Lyme disease (ELL) or early disseminated Lyme disease (EDL), plus healthy controls (HC), from the United States were analyzed by liquid chromatography-mass spectrometry (LC-MS). Comparative analyses were performed between ELL, or EDL, or ELL combined with EDL, and the HC to develop biosignatures present in early Lyme disease. A direct comparison between ELL and EDL was also performed to develop a biosignature for stages of early Lyme disease. Metabolic pathway analysis and chemical identification of metabolites with LC-tandem mass spectrometry (LC-MS/MS) demonstrated alterations of eicosanoid, bile acid, sphingolipid, glycerophospholipid, and acylcarnitine metabolic pathways during early Lyme disease. These metabolic alterations were confirmed using a separate set of serum samples for validation. The findings demonstrated that infection of humans with B. burgdorferi alters defined metabolic pathways that are associated with inflammatory responses, liver function, lipid metabolism, and mitochondrial function. Additionally, the data provide evidence that metabolic pathways can be used to mark the progression of early Lyme disease.
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Affiliation(s)
| | - Claudia R. Molins
- Centers for Disease Control and Prevention, Fort Collins, CO 80521, USA
| | - M. Nurul Islam
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO 80521, USA
| | - Barbara Graham
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO 80521, USA
| | - Petronella R. Hove
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO 80521, USA
| | - Gary P. Wormser
- Division of Infectious Diseases, Department of Medicine, New York Medical College, Valhalla, NY 10595, USA
| | - Linden Hu
- Sackler School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Laura V. Ashton
- Centers for Disease Control and Prevention, Fort Collins, CO 80521, USA
| | - John T. Belisle
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO 80521, USA
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NMR metabolome of Borrelia burgdorferi in vitro and in vivo in mice. Sci Rep 2019; 9:8049. [PMID: 31142787 PMCID: PMC6541645 DOI: 10.1038/s41598-019-44540-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 05/20/2019] [Indexed: 12/25/2022] Open
Abstract
Lyme borreliosis (LB), caused by bacteria of the Borrelia burgdorferi sensu lato (Borrelia) species, is the most common tick-borne infection in the northern hemisphere. LB diagnostics is based on clinical evaluation of the patient and on laboratory testing, where the main method is the detection of Borrelia specific antibodies in patient samples. There are, however, shortcomings in the current serology based LB diagnostics, especially its inability to differentiate ongoing infection from a previously treated one. Identification of specific biomarkers of diseases is a growing application of metabolomics. One of the main methods of metabolomics is nuclear magnetic resonance (NMR) spectroscopy. In the present study, our aim was to analyze whether Borrelia growth in vitro and infection in vivo in mice causes specific metabolite differences, and whether NMR can be used to detect them. For this purpose, we performed NMR analyses of in vitro culture medium samples, and of serum and urine samples of Borrelia infected and control mice. The results show, that there were significant differences in the concentrations of several amino acids, energy metabolites and aromatic compounds between Borrelia culture and control media, and between infected and control mouse serum and urine samples. For example, the concentration of L-phenylalanine increases in the Borrelia growth medium and in serum of infected mice, whereas the concentrations of allantoin and trigonelline decrease in the urine of infected mice. Therefore, we conclude that Borrelia infection causes measurable metabolome differences in vitro and in Borrelia infected mouse serum and urine samples, and that these can be detected with NMR.
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