{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T01:49:10Z","timestamp":1790646550011,"version":"4.1.0"},"reference-count":85,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biology"],"abstract":"<jats:p>The gut microbiota is recognized as a programmable metabolic organ that governs systemic homeostasis. Recent advances (2023\u20132025) have pivoted Type 2 Diabetes Mellitus (T2DM) research from a host-centric perspective toward a failure of bidirectional host\u2013microbe metabolic flux. This review evaluates the molecular mechanisms underpinning this shift, focusing on microbial metabolite signaling, virome-mediated modulation, and the emergence of drug\u2013microbiome interactions as critical therapeutic variables. We highlight the transformative role of AI-guided mapping and digital twin simulations in modeling high-resolution metabolic flux and predicting the stability of engineered microbial consortia. By integrating meta-transcriptomics and epigenomics, we characterize the functional plasticity of the microbiome under therapeutic stress. We argue that framing the microbiota as a programmable infrastructure\u2014integrated with AI analytics and metabolic engineering\u2014enables adaptive, real-time interventions. This synthesis offers a blueprint for transitioning from correlative observations toward precision microbiome engineering to achieve sustained metabolic resilience.<\/jats:p>","DOI":"10.3390\/biology15120974","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T03:06:34Z","timestamp":1782875194000},"page":"974","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["The Programmable Microbiome: Integrative AI and Multi-Omics Frameworks for Precision T2DM Management"],"prefix":"10.3390","volume":"15","author":[{"given":"Barlina","family":"Konwar","sequence":"first","affiliation":[{"name":"Department of Chemistry and Chemistry Institute of Functional Materials, Pusan National University, Busan 46241, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3703-5461","authenticated-orcid":false,"given":"Kwang-sun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Chemistry Institute of Functional Materials, Pusan National University, Busan 46241, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chong, S., Lin, M., Chong, D., Jensen, S., and Lau, N.S. 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