Integrating CRISPR Technologies and Artificial Intelligence to Predict and Modulate Host-Microbe Interactions

Integrating CRISPR Technologies and Artificial Intelligence to Predict and Modulate Host-Microbe Interactions | 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 Research Article Integrating CRISPR Technologies and Artificial Intelligence to Predict and Modulate Host-Microbe Interactions Mustafa Hasan Zainel Talha, Saba Saadoon Khazaal, Heba Ameen Kadhom, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7699724/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 Understanding the intricate dynamics between host immunity and gut microbiota is fundamental for developing precision immunotherapies. However, existing tools lack the capacity to manipulate microbial genomes in a targeted, adaptive, and interpretable way while capturing downstream systemic effects. This gap limits the clinical translation of host–microbiome research, particularly in inflammatory and autoimmune diseases. The study aims to design and validate a CRISPR–AI integrated framework for real-time modulation of host immune responses through microbial gene editing. By dynamically targeting microbial determinants of host cytokine networks, the study seeks to optimize immunomodulatory outcomes in a programmable and biologically coherent manner. A hybrid methodological pipeline was implemented, combining CRISPR-Cas-based genome editing across 87 microbial strains with AI-driven modeling of host immune responses in 240 gastrointestinal tissue samples. Techniques included PLS-DA classification, Bayesian DAG-based causal inference, multivariate ANOVA, and dynamic feedback from cytokine expression. Gene loci MCR-21 and GNT-4B were targeted for immune optimization. A novel metric, the Biological Signal Integrity Score (BSIS), was introduced to quantify post-editing immunological coherence. IL-6 concentrations decreased by 52.4%, TNF-αby 46.1%, and IL-1β by 41.3% (all p < 0.001) following CRISPR editing of key microbial genes. Beneficial taxa such as Faecalibacterium prausnitzii increased by 2.7-fold, while harmful species like Enterococcus faecalis declined 3.1-fold. The model achieved 94.3% classification accuracy (PLS-DA) and 0.962 ROC-AUC for phenotype prediction. Causal inference identified 11 high-confidence edges (score > 0.85) linking microbial edits to cytokine cascades. BSIS reached 0.783, indicating high signal integrity post-editing. The article establishes a powerful cyber-biological framework to engineer host immune modulation by editing microbial genomes in response to real-time physiological feedback. The integration of CRISPR targeting, immune profiling, and AI-based optimization paves the way for next-generation precision therapeutics that are both adaptive and biologically grounded. The system enables recursive refinement, making it applicable to complex inflammatory conditions and personalized microbiome-based interventions. CRISPR-Cas systems host-microbiome interaction cytokine modulation precision immunotherapy deep learning synthetic biology AI in biotechnology Full Text Additional Declarations No competing interests reported. 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-7699724","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535433828,"identity":"f8123217-ee06-440e-b76c-44fe8be69df0","order_by":0,"name":"Mustafa Hasan Zainel Talha","email":"","orcid":"","institution":"Al-Mustansiriya University","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"Hasan Zainel","lastName":"Talha","suffix":""},{"id":535433829,"identity":"2bc59bd3-a301-4f76-a311-8f9e9d213941","order_by":1,"name":"Saba Saadoon Khazaal","email":"","orcid":"","institution":"Al-Mustansiriya 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However, existing tools lack the capacity to manipulate microbial genomes in a targeted, adaptive, and interpretable way while capturing downstream systemic effects. This gap limits the clinical translation of host–microbiome research, particularly in inflammatory and autoimmune diseases. The study aims to design and validate a CRISPR–AI integrated framework for real-time modulation of host immune responses through microbial gene editing. By dynamically targeting microbial determinants of host cytokine networks, the study seeks to optimize immunomodulatory outcomes in a programmable and biologically coherent manner. A hybrid methodological pipeline was implemented, combining CRISPR-Cas-based genome editing across 87 microbial strains with AI-driven modeling of host immune responses in 240 gastrointestinal tissue samples. Techniques included PLS-DA classification, Bayesian DAG-based causal inference, multivariate ANOVA, and dynamic feedback from cytokine expression. 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