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Meade RK, Smith CM. Immunological roads diverged: mapping tuberculosis outcomes in mice. Trends Microbiol 2024:S0966-842X(24)00170-7. [PMID: 39034171 DOI: 10.1016/j.tim.2024.06.007] [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: 05/06/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/23/2024]
Abstract
The journey from phenotypic observation to causal genetic mechanism is a long and challenging road. For pathogens like Mycobacterium tuberculosis (Mtb), which causes tuberculosis (TB), host-pathogen coevolution has spanned millennia, costing millions of human lives. Mammalian models can systematically recapitulate host genetic variation, producing a spectrum of disease outcomes. Leveraging genome sequences and deep phenotyping data from infected mouse genetic reference populations (GRPs), quantitative trait locus (QTL) mapping approaches have successfully identified host genomic regions associated with TB phenotypes. Here, we review the ongoing optimization of QTL mapping study design alongside advances in mouse GRPs. These next-generation resources and approaches have enabled identification of novel host-pathogen interactions governing one of the most prevalent infectious diseases in the world today.
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Affiliation(s)
- Rachel K Meade
- Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, USA; University Program in Genetics and Genomics, Duke University, Durham, NC, USA
| | - Clare M Smith
- Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, USA; University Program in Genetics and Genomics, Duke University, Durham, NC, USA.
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2
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Koyuncu D, Tavolara T, Gatti DM, Gower AC, Ginese ML, Kramnik I, Yener B, Sajjad U, Niazi MKK, Gurcan M, Alsharaydeh A, Beamer G. B cells in perivascular and peribronchiolar granuloma-associated lymphoid tissue and B-cell signatures identify asymptomatic Mycobacterium tuberculosis lung infection in Diversity Outbred mice. Infect Immun 2024; 92:e0026323. [PMID: 38899881 PMCID: PMC11238564 DOI: 10.1128/iai.00263-23] [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] [Received: 07/25/2023] [Accepted: 04/09/2024] [Indexed: 06/21/2024] Open
Abstract
Because most humans resist Mycobacterium tuberculosis infection, there is a paucity of lung samples to study. To address this gap, we infected Diversity Outbred mice with M. tuberculosis and studied the lungs of mice in different disease states. After a low-dose aerosol infection, progressors succumbed to acute, inflammatory lung disease within 60 days, while controllers maintained asymptomatic infection for at least 60 days, and then developed chronic pulmonary tuberculosis (TB) lasting months to more than 1 year. Here, we identified features of asymptomatic M. tuberculosis infection by applying computational and statistical approaches to multimodal data sets. Cytokines and anti-M. tuberculosis cell wall antibodies discriminated progressors vs controllers with chronic pulmonary TB but could not classify mice with asymptomatic infection. However, a novel deep-learning neural network trained on lung granuloma images was able to accurately classify asymptomatically infected lungs vs acute pulmonary TB in progressors vs chronic pulmonary TB in controllers, and discrimination was based on perivascular and peribronchiolar lymphocytes. Because the discriminatory lesion was rich in lymphocytes and CD4 T cell-mediated immunity is required for resistance, we expected CD4 T-cell genes would be elevated in asymptomatic infection. However, the significantly different, highly expressed genes were from B-cell pathways (e.g., Bank1, Cd19, Cd79, Fcmr, Ms4a1, Pax5, and H2-Ob), and CD20+ B cells were enriched in the perivascular and peribronchiolar regions of mice with asymptomatic M. tuberculosis infection. Together, these results indicate that genetically controlled B-cell responses are important for establishing asymptomatic M. tuberculosis lung infection.
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Affiliation(s)
- Deniz Koyuncu
- Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Thomas Tavolara
- Wake Forest University, School of Medicine, Winston Salem, North Carolina, USA
| | | | - Adam C Gower
- Boston University Clinical and Translational Science Institute, Boston, Massachusetts, USA
| | - Melanie L Ginese
- Tufts University Cummings School of Veterinary Medicine, North Grafton, Massachusetts, USA
| | - Igor Kramnik
- NIEDL, Boston University, Boston, Massachusetts, USA
| | - Bülent Yener
- Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Usama Sajjad
- Wake Forest University, School of Medicine, Winston Salem, North Carolina, USA
| | | | - Metin Gurcan
- Wake Forest University, School of Medicine, Winston Salem, North Carolina, USA
| | | | - Gillian Beamer
- Aiforia Inc., Cambridge, Massachusetts, USA
- Texas Biomedical Research Institute, San Antonio, Texas, USA
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3
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Gatti DM, Tyler AL, Mahoney JM, Churchill GA, Yener B, Koyuncu D, Gurcan MN, Niazi MKK, Tavolara T, Gower A, Dayao D, McGlone E, Ginese ML, Specht A, Alsharaydeh A, Tessier PA, Kurtz SL, Elkins KL, Kramnik I, Beamer G. Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice. PLoS Pathog 2024; 20:e1011915. [PMID: 38861581 PMCID: PMC11195971 DOI: 10.1371/journal.ppat.1011915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 06/24/2024] [Accepted: 04/17/2024] [Indexed: 06/13/2024] Open
Abstract
Mycobacterium tuberculosis infects two billion people across the globe, and results in 8-9 million new tuberculosis (TB) cases and 1-1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. Here, we investigate the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using immune and inflammatory mediators; and clinical, microbiological, and granuloma correlates of disease identified five new loci on mouse chromosomes 1, 2, 4, 16; and three known loci on chromosomes 3 and 17. Further, multiple positively correlated traits shared loci on chromosomes 1, 16, and 17 and had similar patterns of allele effects, suggesting these loci contain critical genetic regulators of inflammatory responses to M. tuberculosis. To narrow the list of candidate genes, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks to generate scores representing functional relationships. The scores were used to rank candidates for each mapped trait, resulting in 11 candidate genes: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Although all candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling, and all contain single nucleotide polymorphisms (SNPs), SNPs in only four genes (S100a8, Itgb5, Fstl1, Zfp318) are predicted to have deleterious effects on protein functions. We performed methodological and candidate validations to (i) assess biological relevance of predicted allele effects by showing that Diversity Outbred mice carrying PWK/PhJ alleles at the H-2 locus on chromosome 17 QTL have shorter survival; (ii) confirm accuracy of predicted allele effects by quantifying S100A8 protein in inbred founder strains; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this body of work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and functionally relevant gene candidates that may be major regulators of complex host-pathogens interactions contributing to granuloma necrosis and acute inflammation in pulmonary TB.
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Affiliation(s)
- Daniel M. Gatti
- The Jackson Laboratory, Bar Harbor, Maine, United States of America
| | - Anna L. Tyler
- The Jackson Laboratory, Bar Harbor, Maine, United States of America
| | | | | | - Bulent Yener
- Rensselaer Polytechnic Institute, Troy, New York, United States of America
| | - Deniz Koyuncu
- Rensselaer Polytechnic Institute, Troy, New York, United States of America
| | - Metin N. Gurcan
- Wake Forest University School of Medicine, Winston Salem, North Carolina, United States of America
| | - MK Khalid Niazi
- Wake Forest University School of Medicine, Winston Salem, North Carolina, United States of America
| | - Thomas Tavolara
- Wake Forest University School of Medicine, Winston Salem, North Carolina, United States of America
| | - Adam Gower
- Clinical and Translational Science Institute, Boston University, Boston, Massachusetts, United States of America
| | - Denise Dayao
- Tufts University Cummings School of Veterinary Medicine, North Grafton, Massachusetts, United States of America
| | - Emily McGlone
- Tufts University Cummings School of Veterinary Medicine, North Grafton, Massachusetts, United States of America
| | - Melanie L. Ginese
- Tufts University Cummings School of Veterinary Medicine, North Grafton, Massachusetts, United States of America
| | - Aubrey Specht
- Tufts University Cummings School of Veterinary Medicine, North Grafton, Massachusetts, United States of America
| | - Anas Alsharaydeh
- Texas Biomedical Research Institute, San Antonio, Texas, United States of America
| | - Philipe A. Tessier
- Department of Microbiology and Immunology, Laval University School of Medicine, Quebec, Canada
| | - Sherry L. Kurtz
- Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Karen L. Elkins
- Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Igor Kramnik
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, Massachusetts, United States of America
| | - Gillian Beamer
- Texas Biomedical Research Institute, San Antonio, Texas, United States of America
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Acharya V, Choi D, Yener B, Beamer G. Prediction of Tuberculosis From Lung Tissue Images of Diversity Outbred Mice Using Jump Knowledge Based Cell Graph Neural Network. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2024; 12:17164-17194. [PMID: 38515959 PMCID: PMC10956573 DOI: 10.1109/access.2024.3359989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
Tuberculosis (TB), primarily affecting the lungs, is caused by the bacterium Mycobacterium tuberculosis and poses a significant health risk. Detecting acid-fast bacilli (AFB) in stained samples is critical for TB diagnosis. Whole Slide (WS) Imaging allows for digitally examining these stained samples. However, current deep-learning approaches to analyzing large-sized whole slide images (WSIs) often employ patch-wise analysis, potentially missing the complex spatial patterns observed in the granuloma essential for accurate TB classification. To address this limitation, we propose an approach that models cell characteristics and interactions as a graph, capturing both cell-level information and the overall tissue micro-architecture. This method differs from the strategies in related cell graph-based works that rely on edge thresholds based on sparsity/density in cell graph construction, emphasizing a biologically informed threshold determination instead. We introduce a cell graph-based jumping knowledge neural network (CG-JKNN) that operates on the cell graphs where the edge thresholds are selected based on the length of the mycobacteria's cords and the activated macrophage nucleus's size to reflect the actual biological interactions observed in the tissue. The primary process involves training a Convolutional Neural Network (CNN) to segment AFBs and macrophage nuclei, followed by converting large (42831*41159 pixels) lung histology images into cell graphs where an activated macrophage nucleus/AFB represents each node within the graph and their interactions are denoted as edges. To enhance the interpretability of our model, we employ Integrated Gradients and Shapely Additive Explanations (SHAP). Our analysis incorporated a combination of 33 graph metrics and 20 cell morphology features. In terms of traditional machine learning models, Extreme Gradient Boosting (XGBoost) was the best performer, achieving an F1 score of 0.9813 and an Area under the Precision-Recall Curve (AUPRC) of 0.9848 on the test set. Among graph-based models, our CG-JKNN was the top performer, attaining an F1 score of 0.9549 and an AUPRC of 0.9846 on the held-out test set. The integration of graph-based and morphological features proved highly effective, with CG-JKNN and XGBoost showing promising results in classifying instances into AFB and activated macrophage nucleus. The features identified as significant by our models closely align with the criteria used by pathologists in practice, highlighting the clinical applicability of our approach. Future work will explore knowledge distillation techniques and graph-level classification into distinct TB progression categories.
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Affiliation(s)
| | - Diana Choi
- Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA 02155, USA
| | - BüLENT Yener
- Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Gillian Beamer
- Research Pathology, Aiforia Technologies, Cambridge, MA 02142, USA
- Texas Biomedical Research Institute, San Antonio, TX 78227, USA
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Gatti DM, Tyler AL, Mahoney JM, Churchill GA, Yener B, Koyuncu D, Gurcan MN, Niazi M, Tavolara T, Gower AC, Dayao D, McGlone E, Ginese ML, Specht A, Alsharaydeh A, Tessier PA, Kurtz SL, Elkins K, Kramnik I, Beamer G. Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.21.572738. [PMID: 38187647 PMCID: PMC10769337 DOI: 10.1101/2023.12.21.572738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Mycobacterium tuberculosis, the bacillus that causes tuberculosis (TB), infects 2 billion people across the globe, and results in 8-9 million new TB cases and 1-1.5 million deaths each year. Most patients have no known genetic basis that predisposes them to disease. We investigated the complex genetic basis of pulmonary TB by modelling human genetic diversity with the Diversity Outbred mouse population. When infected with M. tuberculosis, one-third develop early onset, rapidly progressive, necrotizing granulomas and succumb within 60 days. The remaining develop non-necrotizing granulomas and survive longer than 60 days. Genetic mapping using clinical indicators of disease, granuloma histopathological features, and immune response traits identified five new loci on mouse chromosomes 1, 2, 4, 16 and three previously identified loci on chromosomes 3 and 17. Quantitative trait loci (QTLs) on chromosomes 1, 16, and 17, associated with multiple correlated traits and had similar patterns of allele effects, suggesting these QTLs contain important genetic regulators of responses to M. tuberculosis. To narrow the list of candidate genes in QTLs, we used a machine learning strategy that integrated gene expression signatures from lungs of M. tuberculosis-infected Diversity Outbred mice with gene interaction networks, generating functional scores. The scores were then used to rank candidates for each mapped trait in each locus, resulting in 11 candidates: Ncf2, Fam20b, S100a8, S100a9, Itgb5, Fstl1, Zbtb20, Ddr1, Ier3, Vegfa, and Zfp318. Importantly, all 11 candidates have roles in infection, inflammation, cell migration, extracellular matrix remodeling, or intracellular signaling. Further, all candidates contain single nucleotide polymorphisms (SNPs), and some but not all SNPs were predicted to have deleterious consequences on protein functions. Multiple methods were used for validation including (i) a statistical method that showed Diversity Outbred mice carrying PWH/PhJ alleles on chromosome 17 QTL have shorter survival; (ii) quantification of S100A8 protein levels, confirming predicted allele effects; and (iii) infection of C57BL/6 mice deficient for the S100a8 gene. Overall, this work demonstrates that systems genetics using Diversity Outbred mice can identify new (and known) QTLs and new functionally relevant gene candidates that may be major regulators of granuloma necrosis and acute inflammation in pulmonary TB.
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Affiliation(s)
- D M Gatti
- The Jackson Laboratory, Bar Harbor, ME
| | - A L Tyler
- The Jackson Laboratory, Bar Harbor, ME
| | | | | | - B Yener
- Rensselaer Polytechnic Institute, Troy, NY
| | - D Koyuncu
- Rensselaer Polytechnic Institute, Troy, NY
| | - M N Gurcan
- Wake Forest University School of Medicine, Winston Salem, NC
| | - Mkk Niazi
- Wake Forest University School of Medicine, Winston Salem, NC
| | - T Tavolara
- Wake Forest University School of Medicine, Winston Salem, NC
| | - A C Gower
- Clinical and Translational Science Institute, Boston University, Boston, MA
| | - D Dayao
- Tufts University Cummings School of Veterinary Medicine, North Grafton, MA
| | - E McGlone
- Tufts University Cummings School of Veterinary Medicine, North Grafton, MA
| | - M L Ginese
- Tufts University Cummings School of Veterinary Medicine, North Grafton, MA
| | - A Specht
- Tufts University Cummings School of Veterinary Medicine, North Grafton, MA
| | - A Alsharaydeh
- Texas Biomedical Research Institute, San Antonio, TX
| | - P A Tessier
- Department of Microbiology and Immunology, Laval University School of Medicine, Quebec, Canada
| | - S L Kurtz
- Center for Biologics, Food and Drug Administration, Bethesda, MD
| | - K Elkins
- Center for Biologics, Food and Drug Administration, Bethesda, MD
| | - I Kramnik
- NIEDL, Boston University, Boston, MA
| | - G Beamer
- Texas Biomedical Research Institute, San Antonio, TX
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6
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Chen L, Yuan L, Sun T, Liu R, Huang Q, Deng S. The performance of VCS(volume, conductivity, light scatter) parameters in distinguishing latent tuberculosis and active tuberculosis by using machine learning algorithm. BMC Infect Dis 2023; 23:881. [PMID: 38104064 PMCID: PMC10725592 DOI: 10.1186/s12879-023-08531-2] [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] [Received: 02/24/2023] [Accepted: 08/11/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND Tuberculosis is a chronic infectious disease caused by mycobacterium tuberculosis (MTB) and is the ninth leading cause of death worldwide. It is still difficult to distinguish active TB from latent TB,but it is very important for individualized management and treatment to distinguish whether patients are active or latent tuberculosis infection. METHODS A total of 220 subjects, including active TB patients (ATB, n = 97) and latent TB patients (LTB, n = 113), were recruited in this study .46 features about blood routine indicators and the VCS parameters (volume, conductivity, light scatter) of neutrophils(NE), monocytes(MO), and lymphocytes(LY) were collected and was constructed classification model by four machine learning algorithms(logistic regression(LR), random forest(RF), support vector machine(SVM) and k-nearest neighbor(KNN)). And the area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) to estimate of the model's predictive performance for dentifying active and latent tuberculosis infection. RESULTS After verification,among the four classifications, LR and RF had the best performance (AUROC = 1, AUPRC = 1), followed by SVM (AUROC = 0.967, AUPRC = 0.971), KNN (AUROC = 0.943, AUPRC = 0.959) in the training set. And LR had the best performance (AUROC = 0.977, AUPRC = 0.957), followed by SVM (AUROC = 0.962, AUPRC = 0.949), RF (AUROC = 0.903, AUPRC = 0.922),KNN(AUROC = 0.883, AUPRC = 0.901) in the testing set. CONCLUSIONS The machine learning algorithm classifier based on leukocyte VCS parameters is of great value in identifying active and latent tuberculosis infection.
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Affiliation(s)
- Lijiao Chen
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, 400042, P.R. China
| | - Lingke Yuan
- Science in Computational Finance, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Tingting Sun
- College of Medical Technology, Chongqing Medical and Pharmaceutical College, Chongqing, China
| | - Ruiqing Liu
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, 400042, P.R. China
| | - Qing Huang
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, 400042, P.R. China.
| | - Shaoli Deng
- Department of Laboratory Medicine, Daping Hospital, Army Medical University, Chongqing, 400042, P.R. China.
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7
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Mu Y, Tizhoosh HR, Dehkharghanian T, Campbell CJV. Whole slide image representation in bone marrow cytology. Comput Biol Med 2023; 166:107530. [PMID: 37837726 DOI: 10.1016/j.compbiomed.2023.107530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/17/2023] [Accepted: 09/27/2023] [Indexed: 10/16/2023]
Abstract
One of the goals of AI-based computational pathology is to generate compact representations of whole slide images (WSIs) that capture the essential information needed for diagnosis. While such approaches have been applied to histopathology, few applications have been reported in cytology. Bone marrow aspirate cytology is the basis for key clinical decisions in hematology. However, visual inspection of aspirate specimens is a tedious and complex process subject to variation in interpretation, and hematopathology expertise is scarce. The ability to generate a compact representation of an aspirate specimen may form the basis for clinical decision-support tools in hematology. In this study, we leverage our previously published end-to-end AI-based system for counting and classifying cells from bone marrow aspirate WSIs, which enables the direct use of individual cells as inputs rather than WSI patches. We then construct bags of individual cell features from each WSI, and apply multiple instance learning to extract their vector representations. To evaluate the quality of our representations, we conducted WSI retrieval and classification tasks. Our results show that we achieved a mAP@10 of 0.58 ±0.02 in WSI-level image retrieval, surpassing the random-retrieval baseline of 0.39 ±0.1. Furthermore, we predicted five diagnostic labels for individual aspirate WSIs with a weighted-average F1 score of 0.57 ±0.03 using a k-nearest-neighbors (k-NN) model, outperforming guessing using empirical class prior probabilities (0.26 ±0.02). We present the first example of exploring trainable mechanisms to generate compact, slide-level representations in bone marrow cytology with deep learning. This method has the potential to summarize complex semantic information in WSIs toward improved diagnostics in hematology, and may eventually support AI-assisted computational pathology approaches.
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Affiliation(s)
- Youqing Mu
- University of Toronto, Toronto, Canada; McMaster University, Hamilton, Canada
| | - H R Tizhoosh
- Rhazes Lab, Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
| | - Taher Dehkharghanian
- McMaster University, Hamilton, Canada; University Health Network, Toronto, Canada
| | - Clinton J V Campbell
- McMaster University, Hamilton, Canada; William Osler Health System, Brampton, Canada.
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8
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Klever AM, Alexander KA, Almeida D, Anderson MZ, Ball RL, Beamer G, Boggiatto P, Buikstra JE, Chandler B, Claeys TA, Concha AE, Converse PJ, Derbyshire KM, Dobos KM, Dupnik KM, Endsley JJ, Endsley MA, Fennelly K, Franco-Paredes C, Hagge DA, Hall-Stoodley L, Hayes D, Hirschfeld K, Hofman CA, Honda JR, Hull NM, Kramnik I, Lacourciere K, Lahiri R, Lamont EA, Larsen MH, Lemaire T, Lesellier S, Lee NR, Lowry CA, Mahfooz NS, McMichael TM, Merling MR, Miller MA, Nagajyothi JF, Nelson E, Nuermberger EL, Pena MT, Perea C, Podell BK, Pyle CJ, Quinn FD, Rajaram MVS, Mejia OR, Rothoff M, Sago SA, Salvador LCM, Simonson AW, Spencer JS, Sreevatsan S, Subbian S, Sunstrum J, Tobin DM, Vijayan KKV, Wright CTO, Robinson RT. The Many Hosts of Mycobacteria 9 (MHM9): A conference report. Tuberculosis (Edinb) 2023; 142:102377. [PMID: 37531864 PMCID: PMC10529179 DOI: 10.1016/j.tube.2023.102377] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/10/2023] [Accepted: 07/17/2023] [Indexed: 08/04/2023]
Abstract
The Many Hosts of Mycobacteria (MHM) meeting series brings together basic scientists, clinicians and veterinarians to promote robust discussion and dissemination of recent advances in our knowledge of numerous mycobacterial diseases, including human and bovine tuberculosis (TB), nontuberculous mycobacteria (NTM) infection, Hansen's disease (leprosy), Buruli ulcer and Johne's disease. The 9th MHM conference (MHM9) was held in July 2022 at The Ohio State University (OSU) and centered around the theme of "Confounders of Mycobacterial Disease." Confounders can and often do drive the transmission of mycobacterial diseases, as well as impact surveillance and treatment outcomes. Various confounders were presented and discussed at MHM9 including those that originate from the host (comorbidities and coinfections) as well as those arising from the environment (e.g., zoonotic exposures), economic inequality (e.g. healthcare disparities), stigma (a confounder of leprosy and TB for millennia), and historical neglect (a confounder in Native American Nations). This conference report summarizes select talks given at MHM9 highlighting recent research advances, as well as talks regarding the historic and ongoing impact of TB and other infectious diseases on Native American Nations, including those in Southwestern Alaska where the regional TB incidence rate is among the highest in the Western hemisphere.
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Affiliation(s)
- Abigail Marie Klever
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, USA; Infectious Diseases Institute, The Ohio State University, OH, USA
| | - Kathleen A Alexander
- Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, USA; CARACAL/Chobe Research Institute Kasane, Botswana
| | - Deepak Almeida
- Center for Tuberculosis Research, Johns Hopkins University, Baltimore, MD, USA
| | - Matthew Z Anderson
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, USA; Infectious Diseases Institute, The Ohio State University, OH, USA; Department of Microbiology, The Ohio State University, Columbus, OH, USA
| | | | - Gillian Beamer
- Host Pathogen Interactions and Population Health Program, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Paola Boggiatto
- Agricultural Research Service, United States Department of Agriculture, Ames, IA, USA
| | - Jane E Buikstra
- Center for Bioarchaeological Research, Arizona State University, Tempe, AZ, USA
| | - Bruce Chandler
- Division of Public Health, Alaska Department of Health, AK, USA
| | - Tiffany A Claeys
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, USA; Infectious Diseases Institute, The Ohio State University, OH, USA
| | - Aislinn E Concha
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
| | - Paul J Converse
- Center for Tuberculosis Research, Johns Hopkins University, Baltimore, MD, USA
| | - Keith M Derbyshire
- Division of Genetics, The Wadsworth Center, New York State Department of Health, Albany, NY, USA; Department of Biomedical Sciences, University at Albany, Albany, NY, USA
| | - Karen M Dobos
- Department of Microbiology, Immunology, and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, CO, USA
| | - Kathryn M Dupnik
- Center for Global Health, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Janice J Endsley
- Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX, USA
| | - Mark A Endsley
- Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX, USA
| | - Kevin Fennelly
- Pulmonary Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Rockville, MD, USA
| | - Carlos Franco-Paredes
- Department of Microbiology, Immunology, and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, CO, USA; Hospital Infantil de México Federico Gómez, México, USA
| | | | - Luanne Hall-Stoodley
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, USA; Infectious Diseases Institute, The Ohio State University, OH, USA
| | - Don Hayes
- Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | | | - Courtney A Hofman
- Department of Anthropology, University of Oklahoma, Norman, OK, USA; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, Norman, OK, USA
| | - Jennifer R Honda
- Department of Cellular and Molecular Biology, University of Texas Health Science Center at Tyler, Tyler, TX, USA
| | - Natalie M Hull
- Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, OH, USA
| | - Igor Kramnik
- Pulmonary Center, The Department of Medicine, Boston University Chobanian & Aveedisian School of Medicine, National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | - Karen Lacourciere
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA
| | - Ramanuj Lahiri
- United States Department of Health and Human Services, Health Resources and Services Administration, Health Systems Bureau, National Hansen's Disease Program, Baton Rouge, LA, USA
| | - Elise A Lamont
- Department of Microbiology and Immunology, University of Minnesota, Minneapolis, MN, USA
| | - Michelle H Larsen
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, NY, USA
| | | | - Sandrine Lesellier
- French Agency for Food, Environmental & Occupational Health & Safety (ANSES), Laboratory for Rabies and Wildlife,Nancy, France
| | - Naomi R Lee
- Department of Chemistry and Biochemistry, Northern Arizona University, Flagstaff, AZ, USA
| | - Christopher A Lowry
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
| | - Najmus S Mahfooz
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, USA; Infectious Diseases Institute, The Ohio State University, OH, USA
| | - Temet M McMichael
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, USA; Infectious Diseases Institute, The Ohio State University, OH, USA
| | - Marlena R Merling
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, USA; Infectious Diseases Institute, The Ohio State University, OH, USA
| | - Michele A Miller
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Jyothi F Nagajyothi
- Center for Discovery and Innovation, Hackensack Meridian Health, Nutley, NJ, USA
| | - Elizabeth Nelson
- Microbial Paleogenomics Unit, Dept of Genomes and Genetics, Institut Pasteur, Paris, France
| | - Eric L Nuermberger
- Center for Tuberculosis Research, Johns Hopkins University, Baltimore, MD, USA
| | - Maria T Pena
- United States Department of Health and Human Services, Health Resources and Services Administration, Health Systems Bureau, National Hansen's Disease Program, Baton Rouge, LA, USA
| | - Claudia Perea
- Animal & Plant Health Inspection Service, United States Department of Agriculture, Ames, IA, USA
| | - Brendan K Podell
- Department of Microbiology, Immunology, and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, CO, USA
| | - Charlie J Pyle
- Department of Molecular Genetics & Microbiology, Duke University School of Medicine, Durham, NC, USA; Department of Immunology, Duke University School of Medicine, Durham, NC, USA
| | - Fred D Quinn
- Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
| | - Murugesan V S Rajaram
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, USA; Infectious Diseases Institute, The Ohio State University, OH, USA
| | - Oscar Rosas Mejia
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, USA; Infectious Diseases Institute, The Ohio State University, OH, USA
| | | | - Saydie A Sago
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, USA
| | - Liliana C M Salvador
- School of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson, AZ, USA
| | - Andrew W Simonson
- Department of Microbiology and Molecular Genetics and the Center for Vaccine Research, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - John S Spencer
- Department of Microbiology, Immunology, and Pathology, Mycobacteria Research Laboratories, Colorado State University, Fort Collins, CO, USA
| | - Srinand Sreevatsan
- Pathobiology & Diagnostic Investigation Department, College of Veterinary Medicine, Michigan State University, East Lansing, MI, USA
| | - Selvakumar Subbian
- Public Health Research Institute (PHRI), New Jersey Medical School, Rutgers University, Newark, NJ, USA
| | | | - David M Tobin
- Department of Molecular Genetics & Microbiology, Duke University School of Medicine, Durham, NC, USA; Department of Immunology, Duke University School of Medicine, Durham, NC, USA
| | - K K Vidya Vijayan
- Department of Microbiology and Immunology, Center for AIDS Research, and Children's Research Institute, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Caelan T O Wright
- Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Richard T Robinson
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, USA; Infectious Diseases Institute, The Ohio State University, OH, USA.
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9
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Tavolara TE, Gurcan MN, Niazi MKK. Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels. Cancers (Basel) 2022; 14:cancers14235778. [PMID: 36497258 PMCID: PMC9738801 DOI: 10.3390/cancers14235778] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/16/2022] [Accepted: 11/19/2022] [Indexed: 11/25/2022] Open
Abstract
Recent methods in computational pathology have trended towards semi- and weakly-supervised methods requiring only slide-level labels. Yet, even slide-level labels may be absent or irrelevant to the application of interest, such as in clinical trials. Hence, we present a fully unsupervised method to learn meaningful, compact representations of WSIs. Our method initially trains a tile-wise encoder using SimCLR, from which subsets of tile-wise embeddings are extracted and fused via an attention-based multiple-instance learning framework to yield slide-level representations. The resulting set of intra-slide-level and inter-slide-level embeddings are attracted and repelled via contrastive loss, respectively. This resulted in slide-level representations with self-supervision. We applied our method to two tasks- (1) non-small cell lung cancer subtyping (NSCLC) as a classification prototype and (2) breast cancer proliferation scoring (TUPAC16) as a regression prototype-and achieved an AUC of 0.8641 ± 0.0115 and correlation (R2) of 0.5740 ± 0.0970, respectively. Ablation experiments demonstrate that the resulting unsupervised slide-level feature space can be fine-tuned with small datasets for both tasks. Overall, our method approaches computational pathology in a novel manner, where meaningful features can be learned from whole-slide images without the need for annotations of slide-level labels. The proposed method stands to benefit computational pathology, as it theoretically enables researchers to benefit from completely unlabeled whole-slide images.
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10
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Andagalu B, Lu P, Onyango I, Bergmann-Leitner E, Wasuna R, Odhiambo G, Chebon-Bore LJ, Ingasia LA, Juma DW, Opot B, Cheruiyot A, Yeda R, Okudo C, Okoth R, Chemwor G, Campo J, Wallqvist A, Akala HM, Ochiel D, Ogutu B, Chaudhury S, Kamau E. Age-dependent antibody profiles to plasmodium antigens are differentially associated with two artemisinin combination therapy outcomes in high transmission setting. Front Med (Lausanne) 2022; 9:991807. [PMID: 36314027 PMCID: PMC9606348 DOI: 10.3389/fmed.2022.991807] [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: 07/12/2022] [Accepted: 09/27/2022] [Indexed: 11/28/2022] Open
Abstract
The impact of pre-existing immunity on the efficacy of artemisinin combination therapy is largely unknown. We performed in-depth profiling of serological responses in a therapeutic efficacy study [comparing artesunate-mefloquine (ASMQ) and artemether-lumefantrine (AL)] using a proteomic microarray. Responses to over 200 Plasmodium antigens were significantly associated with ASMQ treatment outcome but not AL. We used machine learning to develop predictive models of treatment outcome based on the immunoprofile data. The models predict treatment outcome for ASMQ with high (72–85%) accuracy, but could not predict treatment outcome for AL. This divergent treatment outcome suggests that humoral immunity may synergize with the longer mefloquine half-life to provide a prophylactic effect at 28–42 days post-treatment, which was further supported by simulated pharmacokinetic profiling. Our computational approach and modeling revealed the synergistic effect of pre-existing immunity in patients with drug combination that has an extended efficacy on providing long term treatment efficacy of ASMQ.
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Affiliation(s)
- Ben Andagalu
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Pinyi Lu
- Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States,Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD, United States
| | - Irene Onyango
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Elke Bergmann-Leitner
- Biologics Research and Development, Walter Reed Army Institute of Research, Silver Spring, MD, United States
| | - Ruth Wasuna
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Geoffrey Odhiambo
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Lorna J. Chebon-Bore
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Luicer A. Ingasia
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Dennis W. Juma
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Benjamin Opot
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Agnes Cheruiyot
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Redemptah Yeda
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Charles Okudo
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Raphael Okoth
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Gladys Chemwor
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Joseph Campo
- Antigen Discovery Inc., Irvine, CA, United States
| | - Anders Wallqvist
- Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States
| | - Hoseah M. Akala
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | - Daniel Ochiel
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya
| | | | - Sidhartha Chaudhury
- Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD, United States,Center for Enabling Capabilities, Walter Reed Army Institute of Research, Silver Spring, MD, United States
| | - Edwin Kamau
- Department of Emerging and Infectious Diseases (DEID), United States Army Medical Research Directorate-Africa (USAMRD-A), Kenya Medical Research Institute (KEMRI)/Walter Reed Project, Kisumu, Kenya,U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, United States,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States,*Correspondence: Edwin Kamau, ,
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11
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Su Z, Tavolara TE, Carreno-Galeano G, Lee SJ, Gurcan MN, Niazi M. Attention2majority: Weak multiple instance learning for regenerative kidney grading on whole slide images. Med Image Anal 2022; 79:102462. [PMID: 35512532 PMCID: PMC10382794 DOI: 10.1016/j.media.2022.102462] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 04/11/2022] [Accepted: 04/13/2022] [Indexed: 10/18/2022]
Abstract
Deep learning consistently demonstrates high performance in classifying and segmenting medical images like CT, PET, and MRI. However, compared to these kinds of images, whole slide images (WSIs) of stained tissue sections are huge and thus much less efficient to process, especially for deep learning algorithms. To overcome these challenges, we present attention2majority, a weak multiple instance learning model to automatically and efficiently process WSIs for classification. Our method initially assigns exhaustively sampled label-free patches with the label of the respective WSIs and trains a convolutional neural network to perform patch-wise classification. Then, an intelligent sampling method is performed in which patches with high confidence are collected to form weak representations of WSIs. Lastly, we apply a multi-head attention-based multiple instance learning model to do slide-level classification based on high-confidence patches (intelligently sampled patches). Attention2majority was trained and tested on classifying the quality of 127 WSIs (of regenerated kidney sections) into three categories. On average, attention2majority resulted in 97.4%±2.4 AUC for the four-fold cross-validation. We demonstrate that the intelligent sampling module within attention2majority is superior to the current state-of-the-art random sampling method. Furthermore, we show that the replacement of random sampling with intelligent sampling in attention2majority results in its performance boost (from 94.9%±3.1 to 97.4%±2.4 average AUC for the four-fold cross-validation). We also tested a variation of attention2majority on the famous Camelyon16 dataset, which resulted in 89.1%±0.8 AUC1. When compared to random sampling, the attention2majority demonstrated excellent slide-level interpretability. It also provided an efficient framework to arrive at a multi-class slide-level prediction.
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12
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Pan T, Pedrycz W, Cui J, Yang J, Wu W. Interpretability of Neural Networks with Probability Density Functions. ADVANCED THEORY AND SIMULATIONS 2022. [DOI: 10.1002/adts.202100459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Tingting Pan
- School of Mathematical Sciences Dalian University of Technology Dalian 116024 China
- Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province Dalian 116024 China
- Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta T6G 2G7 Canada
| | - Witold Pedrycz
- Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta T6G 2G7 Canada
| | - Jiahui Cui
- School of Mathematical Sciences Dalian University of Technology Dalian 116024 China
- Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province Dalian 116024 China
| | - Jie Yang
- School of Mathematical Sciences Dalian University of Technology Dalian 116024 China
- Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province Dalian 116024 China
| | - Wei Wu
- School of Mathematical Sciences Dalian University of Technology Dalian 116024 China
- Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province Dalian 116024 China
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13
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Koyuncu D, Niazi MKK, Tavolara T, Abeijon C, Ginese ML, Liao Y, Mark C, Specht A, Gower AC, Restrepo BI, Gatti DM, Kramnik I, Gurcan M, Yener B, Beamer G. CXCL1: A new diagnostic biomarker for human tuberculosis discovered using Diversity Outbred mice. PLoS Pathog 2021; 17:e1009773. [PMID: 34403447 PMCID: PMC8423361 DOI: 10.1371/journal.ppat.1009773] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 09/07/2021] [Accepted: 06/30/2021] [Indexed: 12/12/2022] Open
Abstract
More humans have died of tuberculosis (TB) than any other infectious disease and millions still die each year. Experts advocate for blood-based, serum protein biomarkers to help diagnose TB, which afflicts millions of people in high-burden countries. However, the protein biomarker pipeline is small. Here, we used the Diversity Outbred (DO) mouse population to address this gap, identifying five protein biomarker candidates. One protein biomarker, serum CXCL1, met the World Health Organization’s Targeted Product Profile for a triage test to diagnose active TB from latent M.tb infection (LTBI), non-TB lung disease, and normal sera in HIV-negative, adults from South Africa and Vietnam. To find the biomarker candidates, we quantified seven immune cytokines and four inflammatory proteins corresponding to highly expressed genes unique to progressor DO mice. Next, we applied statistical and machine learning methods to the data, i.e., 11 proteins in lungs from 453 infected and 29 non-infected mice. After searching all combinations of five algorithms and 239 protein subsets, validating, and testing the findings on independent data, two combinations accurately diagnosed progressor DO mice: Logistic Regression using MMP8; and Gradient Tree Boosting using a panel of 4: CXCL1, CXCL2, TNF, IL-10. Of those five protein biomarker candidates, two (MMP8 and CXCL1) were crucial for classifying DO mice; were above the limit of detection in most human serum samples; and had not been widely assessed for diagnostic performance in humans before. In patient sera, CXCL1 exceeded the triage diagnostic test criteria (>90% sensitivity; >70% specificity), while MMP8 did not. Using Area Under the Curve analyses, CXCL1 averaged 94.5% sensitivity and 88.8% specificity for active pulmonary TB (ATB) vs LTBI; 90.9% sensitivity and 71.4% specificity for ATB vs non-TB; and 100.0% sensitivity and 98.4% specificity for ATB vs normal sera. Our findings overall show that the DO mouse population can discover diagnostic-quality, serum protein biomarkers of human TB. More humans die of tuberculosis (TB) than any other infectious disease, yet diagnostic tools remain limited. Here, we used the Diversity Outbred mouse population to discover candidate protein biomarkers of human TB. By applying statistical methods and machine learning to multidimensional data, we identified CXCL1 and MMP8 as the two most promising protein biomarker candidates. When evaluated in samples from human patients, CXCL1 achieved the World Health Organization’s targeted profile for a triage diagnostic test, discriminating active TB from important clinical differential diagnoses: latent Mtb infection and non-TB lung disease in HIV-negative adults. Overall, our studies show how a translationally relevant animal population model can accelerate TB biomarker discovery, validation, and testing for humans.
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Affiliation(s)
- Deniz Koyuncu
- Rensselaer Polytechnic Institute, Department of Electrical, Computer, and Systems Engineering, Troy, New York, United States of America
| | - Muhammad Khalid Khan Niazi
- Wake Forest School of Medicine, Bowman Gray Center for Medical Education, Winston-Salem, North Carolina, United States of America
| | - Thomas Tavolara
- Wake Forest School of Medicine, Bowman Gray Center for Medical Education, Winston-Salem, North Carolina, United States of America
| | - Claudia Abeijon
- Tufts University, Cummings School of Veterinary Medicine, North Grafton, Massachusetts, United States of America
| | - Melanie L. Ginese
- Tufts University, Cummings School of Veterinary Medicine, North Grafton, Massachusetts, United States of America
| | | | - Carolyn Mark
- Kansas State University, College of Veterinary Medicine, Manhattan, Kansas, United States of America
| | - Aubrey Specht
- Tufts University, Cummings School of Veterinary Medicine, North Grafton, Massachusetts, United States of America
| | - Adam C. Gower
- Boston University Clinical and Translational Science Institute, Boston, Massachusetts, United States of America
| | - Blanca I. Restrepo
- The University of Texas Health Science Center at Houston School of Public Health in Brownsville, Texas, United States of America
| | - Daniel M. Gatti
- The Jackson Laboratory, Bar Harbor, Maine, United States of America
| | - Igor Kramnik
- Boston University, National Emerging Infectious Diseases Laboratories, Boston, Massachusetts, United States of America
| | - Metin Gurcan
- Wake Forest School of Medicine, Bowman Gray Center for Medical Education, Winston-Salem, North Carolina, United States of America
| | - Bülent Yener
- Rensselaer Polytechnic Institute, Department of Computer Science, Troy, New York, United States of America
| | - Gillian Beamer
- Tufts University, Cummings School of Veterinary Medicine, North Grafton, Massachusetts, United States of America
- * E-mail:
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