ntubst2019EN

Precision medicine recently is recognized to be the better strategies to design personalized medicine that achieve the better cure rate and minimize the drug side effect. Creating or designing personalized medicine is heavily relied on identifying the precise biomarkers or targets. We aim to collect multi-omics big data from mouse models resembling human diseases and use artificial intelligence to predict microbial or immune targets. Then functional validate predicted targets in vivo that manage the immune systems and disease progression. The detail goals are as following:

(1) Introduce and establish cutting-edge technologies to profile Immune cell and gut microbiota in atherosclerotic mice (Big data collections) – High-parameter and high-throughput analysis are needed to collect the big data and the most recent techniques for flow cytometry analysis and next-generation sequencing, Spectral Flow Cytometry Analysis (30+ fluorochromes), Single-cell RNA-seq, CITE-seq, 16S rRNA-seq, and shotgun-seq, have been developed to profile the molecular and protein features of cell populations and the community of gut microbiota. We aim to use CITE-seq to profile immune cells from the gut, aortic arches, and peripheral blood and to determine microbiota community by 16S rRNA-seq and shotgun-seq in atherosclerotic mice. We will also use high-parameter and high-throughput spectral FACS analysis to validate the transcriptomic and proteomic data from CITE-seq by immune cell markers. Based on these big data collections, we can form the basis of integrative multi-omics data and use machine learning models to predict the targets.

(2) Use of artificial intelligence to predict target genes and gut microbes that manage immune responses and atherosclerosis progression (Machine learning model prediction) – From the big data collections on the immune cell profiles in different organs and communities of gut microbiota, the integrative models for multi-omics data are needed to build and use of the machine learning approaches will identify and quantify discriminatory features between health and disease progression. We aim to predict target genes and gut microbial species that manage immune responses and atherosclerosis progression.

(3) Establish the anaerobic chamber system to isolate and in vitro culture, the predicted gut microbes, then functionally validate gut microbial mixes in murine animal model (Functional validations) – Employing artificial intelligence and machine learning models allow us to predict the top gut microbial species that manage immune responses and atherosclerosis progression. Functional validations of these predicted gut microbial species are needed to precisely identify the targets. We aim to isolate and culture the predicted gut microbes in the anaerobic chamber system and inoculate gut microbial mixes into the germ-free mice. By further examination of the inflammatory responses and atherosclerosis induction in these mice, we will be able to identify which members of gut microbiota mediate the inflammation and induce atherosclerosis progression.

1. P Loke*, JD Lin*. Redefining inflammatory macrophage phenotypes across stages and tissues by single-cell transcriptomics. Science Immunology, 7(70), April 2022. (Rank: Q1, IF=30.663), *Corresponding Author.
2. JD Lin*, P Loke*. Helminth infections and cardiovascular diseases: A role for the microbiota and Møs? Journal of Leukocyte Biology, 110(6), September 2021. (Rank: Q2, IF=6.011, Citations≥2), *Corresponding Author.
3. C McElrath, V Espinosa, JD Lin#, J Peng, R Sridhar, O Dutta, HC Tseng, S V Smirnov, H Risman, M J Sandoval, V Davra, YJ Chang, B P Pollack, R B Birge, M Galan, A Rivera, J E Durbin, S V Kotenko. Critical role of interferons in gastrointestinal injury repair. Nature Communication, 12(1), May 2021. (Rank: Q1, IF=17.694, Citations≥25), #3rd Author.
4. J C Devlin*, J Axelrad*, A M Hine*, S Chang, S Sarkar, JD Lin#, K V Ruggles, D Hudesman, K Cadwell, P Loke. Single-cell transcriptional survey of ileal-anal pouch immune cells from ulcerative colitis patients. Gastroenterology, 160(5), April 2021. (Rank: Q1, IF=33.883, Citations≥7), *Co-first Author. #4th Author.
5. JD Lin, J C Devlin, F Yeung, C McCauley, J M. Leung, YH Chen, A Cronkite, C Hansen, C Drake-Dunn, K V. Ruggles, K Cadwell, A L Graham, P Loke. Rewilding Nod2 and Atg16l1 mutant mice uncover genetic and environmental contributions towards variation in microbial responses and immune cell composition. Cell Host & Microbe, 27(5), May 2020. (Rank: Q1, IF=31.316, Citations≥43)
6. F Yeung*, YH Chen*, JD Lin*, J M Leung, C McCauley, J C. Devlin, C Hansen, A Cronkite, Z Stephens, C Drake-Dunn, Y Fulmer, B Shopsin, K V. Ruggles, J L. Round, P Loke, A L Graham, K Cadwell. Altered immunity of laboratory mice in the natural environment is associated with fungal colonization. Cell Host & Microbe, 27(5), May, 2020. (Rank: Q1, IF=31.316, Citations≥74); *Co-first Author.
7. JD Lin, H Nishi, J Poles, X Niu, C Mccauley, K Rahman, E J. Brown, S T Yeung, N Vozhilla, A Weinstock, S Ramsey, E A Fisher and P Loke. Single-cell analysis of fate-mapped macrophages reveals heterogeneity, including stem-like properties, during atherosclerosis progression and regression. JCI insight, 4 (4), February, 2019. (Rank: Q1, IF=9.533; Citations≥171) (Top 1% cited in "Clinical Medicine" field)
8. U M Gundra, N M Girgis, M A Gonzalez, MS Tang, H J P Van Der Zande, JD Lin, M Ouimet, L J Ma, J Poles, N Vozhilla, E A Fisher, K J Moore, P Loke. Vitamin A mediates conversion of monocyte-derived macrophages into tissue-resident macrophages during alternative activation. Nature Immunology, 18 (6), June 2017. (Rank: Q1, IF=31.250; Citations≥112)
9. JD Lin, N Feng, A Sen, M Balan, HC Tseng, C McElrath, S V Smirnov, J Peng, L L Yasukawa, R K. Durbin, J E Durbin, H B Greenberg and S V. Kotenko. Distinct roles of type I and type III interferons in intestinal immunity to homologous and heterologous rotavirus infection. PLoS Pathog., 12 (4), April, 2016. (Rank: Q1, IF=7.464; Citations≥145)

• Applied Microbiology and Biotechnology
• Applied Microbiology Lab
• Seminar
• Research Training
• Basic Single-cell RNA-seq Data Analysis