Cyriac Abby Philips1,2, Philip Augustine3, Karthik Ganesan4, Shatakshi Ranade5, Varun Chopra5, Kunal Patil5, Sonie Shende5, Rizwan Ahamed3, Sandeep Kumbar3, Sasidharan Rajesh6, Tom George6, Meera Mohanan7, Narain Mohan8, Nikhil Phadke5, Mridula Rani5, Arjun Narayanan9, Suchetha M Jagan9. 1. The Liver Unit and Monarch Liver Lab, Cochin Gastroenterology Group, Ernakulam Medical Center, Kochi, 682 028, India. abbyphilips@gmail.com. 2. Philip Augustine Associates (P) Ltd, Ernakulam Medical Center, Room no: 3. Ground Floor, Kochi, 682 028, India. abbyphilips@gmail.com. 3. Gastroenterology and Advanced G.I. Endoscopy, Cochin Gastroenterology Group, Ernakulam Medical Center, Kochi, 682 028, India. 4. Biomedical Software and Instrumentation, Department of Bioinformatics, Helicalbio, Ann Arbor, MI, USA. 5. Molecular, Cellular and Developmental Biology, Genepath-Dx, Pune, 411 004, India. 6. Interventional Radiology, The Liver Unit and Gastroenterology, Cochin Gastroenterology Group, Ernakulam Medical Center, Kochi, 682 028, India. 7. Anaesthesia and Critical Care, Cochin Gastroenterology Group, Ernakulam Medical Center, Kochi, 682 028, India. 8. The Liver Unit and Monarch Liver Lab, Cochin Gastroenterology Group, Ernakulam Medical Center, Kochi, 682 028, India. 9. National Urban Health Mission, Ernakulam District Hospital, Kochi, 682 011, India.
Abstract
BACKGROUND: Dysbiotic gut bacteria engage in the development and progression of severe alcoholic hepatitis (SAH). We aimed to characterize bacterial communities associated with clinical events (CE), identify significant bacteria linked to CE, and define bacterial relationships associated with specific CE and outcomes at baseline and after treatment in SAH. METHODS: We performed 16-s rRNA sequencing on stool samples (n=38) collected at admission and the last follow-up within 90 days in SAH patients (n=26; 12 corticosteroids; 14 granulocyte colony-stimulating factor, [G-CSF]). Validated pipelines were used to plot bacterial communities, profile functional metabolism, and identify significant taxa and functional metabolites. Conet/NetworkX® was utilized to identify significant non-random patterns of bacterial co-presence and mutual exclusion for clinical events. RESULTS: All the patients were males with median discriminant function (DF) 64, Child-Turcotte-Pugh (CTP) 12, and model for end-stage liver disease (MELD) score 25.5. At admission, 27%, 42%, and 58% had acute kidney injury (AKI), hepatic encephalopathy (HE), and infections respectively; 38.5% died at end of follow-up. Specific bacterial families were associated with HE, sepsis, disease severity, and death. Lachnobacterium and Catenibacterium were associated with HE, and Pediococcus with death after steroid treatment. Change from Enterococcus (promotes AH) to Barnesiella (inhibits E. faecium) was significant after G-CSF. Phenylpropanoid-biosynthesis (innate-immunity) and glycerophospholipid-metabolism (cellular-integrity) pathways in those without infections and the death, respectively, were upregulated. Mutual interactions between Enterococcus cecorum, Acinetobacter schindleri, and Mitsuokella correlated with admission AKI. CONCLUSIONS: Specific gut microbiota, their interactions, and metabolites are associated with complications of SAH and treatment outcomes. Microbiota-based precision medicine as adjuvant treatment may be a new therapeutic area.
BACKGROUND: Dysbiotic gut bacteria engage in the development and progression of severe alcoholic hepatitis (SAH). We aimed to characterize bacterial communities associated with clinical events (CE), identify significant bacteria linked to CE, and define bacterial relationships associated with specific CE and outcomes at baseline and after treatment in SAH. METHODS: We performed 16-s rRNA sequencing on stool samples (n=38) collected at admission and the last follow-up within 90 days in SAH patients (n=26; 12 corticosteroids; 14 granulocyte colony-stimulating factor, [G-CSF]). Validated pipelines were used to plot bacterial communities, profile functional metabolism, and identify significant taxa and functional metabolites. Conet/NetworkX® was utilized to identify significant non-random patterns of bacterial co-presence and mutual exclusion for clinical events. RESULTS: All the patients were males with median discriminant function (DF) 64, Child-Turcotte-Pugh (CTP) 12, and model for end-stage liver disease (MELD) score 25.5. At admission, 27%, 42%, and 58% had acute kidney injury (AKI), hepatic encephalopathy (HE), and infections respectively; 38.5% died at end of follow-up. Specific bacterial families were associated with HE, sepsis, disease severity, and death. Lachnobacterium and Catenibacterium were associated with HE, and Pediococcus with death after steroid treatment. Change from Enterococcus (promotes AH) to Barnesiella (inhibits E. faecium) was significant after G-CSF. Phenylpropanoid-biosynthesis (innate-immunity) and glycerophospholipid-metabolism (cellular-integrity) pathways in those without infections and the death, respectively, were upregulated. Mutual interactions between Enterococcus cecorum, Acinetobacter schindleri, and Mitsuokella correlated with admission AKI. CONCLUSIONS: Specific gut microbiota, their interactions, and metabolites are associated with complications of SAH and treatment outcomes. Microbiota-based precision medicine as adjuvant treatment may be a new therapeutic area.
Authors: Muhammad Afzaal; Farhan Saeed; Yasir Abbas Shah; Muzzamal Hussain; Roshina Rabail; Claudia Terezia Socol; Abdo Hassoun; Mirian Pateiro; José M Lorenzo; Alexandru Vasile Rusu; Rana Muhammad Aadil Journal: Front Microbiol Date: 2022-09-26 Impact factor: 6.064