Mechanistic Deconvolution of Tuberculosis Treatment Failure: A Multi-Omic and Causal Network Approach

Mechanistic Deconvolution of Tuberculosis Treatment Failure: A Multi-Omic and Causal Network Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Mechanistic Deconvolution of Tuberculosis Treatment Failure: A Multi-Omic and Causal Network Approach Siddalingaiah H.S. This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8512316/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The global tuberculosis (TB) epidemic is increasingly characterized by 'recycled' cases—patients who fail treatment or relapse, fueling transmission and drug resistance. Current diagnostic tools are inadequate for predicting these unfavorable outcomes at the point of care. While blood transcriptomic signatures have been developed, they typically lack mechanistic resolution, serving as 'black box' indicators of generalized inflammation rather than revealing actionable pathology. Methods: We bridged this 'Resolution Gap' using a V2 Intelligence pipeline (combining Virtual Deconvolution and Causal Network Inference). We integrated public whole-blood transcriptomics (N=254) with Virtual Single-Cell Deconvolution and Physical Single-Cell Validation (PBMC3k Atlas). We further employed Causal Network Analysis to identify upstream regulatory hubs. Results: Our model predicted treatment failure with high accuracy (Mean ROC AUC=0.79 ± 0.04 SD; Range: 0.70-0.85). Validating across modalities, we confirmed that failure is strongly associated with a specific 'Neutrophil-High/T-cell-Low' immunophenotype, distinct from general inflammation. Conclusions: This study provides the first multi-omic, mechanistic map of TB treatment failure. We identify a specific neutrophil-associated pathology as the primary target for host-directed therapies, rigorously cross-validated across bulk and single-cell landscapes. Biological sciences/Molecular biology/Transcriptomics Biological sciences/Molecular biology/Proteomics/Protein–protein interaction networks Biological sciences/Immunology/Immunogenetics Biological sciences/Immunology/Gene regulation in immune cells/Immunogenetics Biological sciences/Immunology/Inflammation/Chronic inflammation Full Text Additional Declarations There is NO Competing Interest. Supplementary Files 03SUPPLEMENTARYMATERIALWITHFIGURES.docx Mechanistic Deconvolution of Tuberculosis Treatment Failure: A Multi-Omic and Causal Network Approach Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8512316","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":569820033,"identity":"b88801a6-0664-44d0-8232-c2bc9def74d5","order_by":0,"name":"Siddalingaiah H.S.","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACAwYGxgMJDAxyDAw8xGthAGkxJlELECc2EK3FnP/4gwMPd9Smbzh+9uCDDwx2croNBLRYzsgxOJB45njuhjN5yYYzGJKNzQ4QctgNHoYDiW3HcjccyDGTBrG3EdRyHugwoJZ0g/NviNVyIAHosLaaBIMbRNtyA+SXtgOGM2+8MTacYUCMX84ff/jwZ1udPN/5HMMHHyrs5AhqgYLDDApglQbEKQeBOgb5BuJVj4JRMApGwQgDAEutS5uyZWbkAAAAAElFTkSuQmCC","orcid":"","institution":"Shridevi Institute of Medical Sciences and Research Hospital","correspondingAuthor":true,"prefix":"","firstName":"Siddalingaiah","middleName":"","lastName":"H.S.","suffix":""}],"badges":[],"createdAt":"2026-01-04 10:25:07","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8512316/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8512316/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99797905,"identity":"52fc3842-bd91-49be-b5a8-41efc35f2ab5","added_by":"auto","created_at":"2026-01-08 13:46:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":618581,"visible":true,"origin":"","legend":"","description":"","filename":"TBTreatmentFailureNatureMedicinePublicationReadyv11REVISED.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8512316/v1_covered_13dbecd5-9dd8-4d95-be2b-e8bc9cb38355.pdf"},{"id":99715806,"identity":"59cd42c5-3f5a-456d-a38f-9e7fbf9b4082","added_by":"auto","created_at":"2026-01-07 14:31:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3316811,"visible":true,"origin":"","legend":"Mechanistic Deconvolution of Tuberculosis Treatment Failure: A Multi-Omic and Causal Network Approach","description":"","filename":"03SUPPLEMENTARYMATERIALWITHFIGURES.docx","url":"https://assets-eu.researchsquare.com/files/rs-8512316/v1/93fc069d7d002af64bdbfe94.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Mechanistic Deconvolution of Tuberculosis Treatment Failure: A Multi-Omic and Causal Network Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8512316/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8512316/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eThe global tuberculosis (TB) epidemic is increasingly characterized by 'recycled' cases—patients who fail treatment or relapse, fueling transmission and drug resistance. Current diagnostic tools are inadequate for predicting these unfavorable outcomes at the point of care. While blood transcriptomic signatures have been developed, they typically lack mechanistic resolution, serving as 'black box' indicators of generalized inflammation rather than revealing actionable pathology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eWe bridged this 'Resolution Gap' using a \u003cstrong\u003eV2 Intelligence\u003c/strong\u003e pipeline (combining Virtual Deconvolution and Causal Network Inference). We integrated public whole-blood transcriptomics (N=254) with \u003cstrong\u003eVirtual Single-Cell Deconvolution\u003c/strong\u003e and \u003cstrong\u003ePhysical Single-Cell Validation\u003c/strong\u003e (PBMC3k Atlas). We further employed Causal Network Analysis to identify upstream regulatory hubs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Our model predicted treatment failure with high accuracy (Mean ROC AUC=0.79 ± 0.04 SD; Range: 0.70-0.85). Validating across modalities, we confirmed that failure is strongly associated with a specific 'Neutrophil-High/T-cell-Low' immunophenotype, distinct from general inflammation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This study provides the first multi-omic, mechanistic map of TB treatment failure. 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