Fecal metabolite profiling identifies liver transplant recipients at risk for postoperative infection.

Metabolites produced by the intestinal microbiome modulate mucosal immune defenses and optimize epithelial barrier function. Intestinal dysbiosis, including loss of intestinal microbiome diversity and expansion of antibiotic-resistant pathobionts, is accompanied by changes in fecal metabolite concentrations and increased incidence of systemic infection. Laboratory tests that quantify intestinal dysbiosis, however, have yet to be incorporated into clinical practice. We quantified fecal metabolites in 107 patients undergoing liver transplantation (LT) and correlated these with fecal microbiome compositions, pathobiont expansion, and postoperative infections. Consistent with experimental studies implicating microbiome-derived metabolites with host-mediated antimicrobial defenses, reduced fecal concentrations of short- and branched-chain fatty acids, secondary bile acids, and tryptophan metabolites correlate with compositional microbiome dysbiosis in LT patients and the relative risk of postoperative infection. Our findings demonstrate that fecal metabolite profiling can identify LT patients at increased risk of postoperative infection and may provide guideposts for microbiome-targeted therapies.

Bifidobacteria metabolize lactulose to optimize gut metabolites and prevent systemic infection in patients with liver disease.

Progression of chronic liver disease is precipitated by hepatocyte loss, inflammation and fibrosis. This process results in the loss of critical hepatic functions, increasing morbidity and the risk of infection. Medical interventions that treat complications of hepatic failure, including antibiotic administration for systemic infections and lactulose treatment for hepatic encephalopathy, can impact gut microbiome composition and metabolite production. Here, using shotgun metagenomic sequencing and targeted metabolomic analyses on 847 faecal samples from 262 patients with acute or chronic liver disease, we demonstrate that patients hospitalized for liver disease have reduced microbiome diversity and a paucity of bioactive metabolites, including short-chain fatty acids and bile acid derivatives, that impact immune defences and epithelial barrier integrity. We find that patients treated with the orally administered but non-absorbable disaccharide lactulose have increased densities of intestinal bifidobacteria and reduced incidence of systemic infections and mortality. Bifidobacteria metabolize lactulose, produce high concentrations of acetate and acidify the gut lumen in humans and mice, which, in combination, can reduce the growth of antibiotic-resistant bacteria such as vancomycin-resistant Enterococcus faecium in vitro. Our studies suggest that lactulose and bifidobacteria serve as a synbiotic to reduce rates of infection in patients with severe liver disease.

Association between Chronic Liver Disease Caused by Viral Hepatitis, Hospitalisation for COVID-19 and Mortality.

OBJECTIVE:  To evaluate the association between chronic liver disease (CLD) caused by viral hepatitis and COVID-19 hospitalisation, as well as the risk of disease progression and mortality among COVID-19 hospitalised patients in relation to their prior CLD status. STUDY DESIGN: A cohort study. Place and Duration of the study: Bahawal Victoria Hospital and Sir Sadiq Abbasi hospital, affiliated with Qauid-e-Azam Medical College, Bahawalpur, Pakistan, from July to December 2021. METHODOLOGY: In the main group analysis, the risk of hospitalisation for COVID-19 among CLD patients was determined, with the presence of CLD due to chronic viral hepatitis B and C as the exposure variable and hospitalisation for COVID-19 as the outcome measure. Patients hospitalised for a medical condition other than COVID-19 (non-COVID medical admissions) served as an external control group. In the sub-group analysis, the risk of disease severity and mortality were determined among COVID-19 admitted patients having a prior status of CLD, with disease progression to death serving as the primary outcome measure while the exposure variable remained the same as in the main analysis. RESULTS: A total of 3,976 participants [mean age 51 ±14.8 years; 54.1% men; 1616 hospitalised with COVID-19, including 27 (1.7%) exposed to CLD; and 2,360 non-COVID medical admissions, including 208 (8.8%) exposed to CLD] were evaluated. There was less likelihood of hospitalisation for COVID-19 among patients with CLD (1.7% vs. 8.8%; RR=0.270; 95% CI=0.189, 0.386; p<0.001). There was less risk of death among CLD patients admitted for COVID-19 when compared with those admitted for non-COVID CLD-related complications (14.8% vs. 35.1%; RR= 0.422; 95% CI=0.168-1.06; p=0.035). Among COVID-19 admissions, CLD was associated with a decreased risk of death compared with other comorbid conditions (14.8% vs. 36.9%; RR=0.401; 95% CI=0.162-0.994; p=0.04). CONCLUSION: CLD caused by viral hepatitis was significantly less likely to be present among COVID-19 hospitalised patients. There was a lower risk of severe COVID-19 and mortality owing to it among CLD patients compared to those with other comorbid conditions. KEY WORDS: COVID-19, Hospitalisations, Chronic liver disease, Viral hepatitis, COVID-19 severity, Death outcome.

Exploiting social graph networks for emotion prediction.

Emotion prediction plays an essential role in mental healthcare and emotion-aware computing. The complex nature of emotion resulting from its dependency on a person's physiological health, mental state, and his surroundings makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict self-reported happiness and stress levels. In addition to a person's physiology, we also incorporate the environment's impact through weather and social network. To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users of the graph network and integrates it with the temporal dynamics of data to predict emotion for all users. The construction of social networks does not incur additional costs in terms of ecological momentary assessments or data collection from users and does not raise privacy concerns. We propose an architecture that automates the integration of the user's social network in affect prediction and is capable of dealing with the dynamic distribution of real-life social networks, making it scalable to large-scale networks. The extensive evaluation highlights the prediction performance improvement provided by the integration of social networks. We further investigate the impact of graph topology on the model's performance.

Reduced immunomodulatory metabolite concentrations in peri-transplant fecal samples from heart allograft recipients.

Background: Emerging evidence is revealing the impact of the gut microbiome on hematopoietic and solid organ transplantation. Prior studies postulate that this influence is mediated by bioactive metabolites produced by gut-dwelling commensal bacteria. However, gut microbial metabolite production has not previously been measured among heart transplant (HT) recipients. Methods: In order to investigate the potential influence of the gut microbiome and its metabolites on HT, we analyzed the composition and metabolite production of the fecal microbiome among 48 HT recipients at the time of HT. Results: Compared to 20 healthy donors, HT recipients have significantly reduced alpha, i.e. within-sample, microbiota diversity, with significantly lower abundances of key anaerobic commensal bacteria and higher abundances of potentially pathogenic taxa that have been correlated with adverse outcomes in other forms of transplantation. HT recipients have a wide range of microbiota-derived fecal metabolite concentrations, with significantly reduced levels of immune modulatory metabolites such as short chain fatty acids and secondary bile acids compared to healthy donors. These differences were likely due to disease severity and prior antibiotic exposures but were not explained by other demographic or clinical factors. Conclusions: Key potentially immune modulatory gut microbial metabolites are quantifiable and significantly reduced among HT recipients compared to healthy donors. Further study is needed to understand whether this wide range of gut microbial dysbiosis and metabolite alterations impact clinical outcomes and if they can be used as predictive biomarkers or manipulated to improve transplant outcomes.

Immunomodulatory fecal metabolites are associated with mortality in COVID-19 patients with respiratory failure.

Respiratory failure and mortality from COVID-19 result from virus- and inflammation-induced lung tissue damage. The intestinal microbiome and associated metabolites are implicated in immune responses to respiratory viral infections, however their impact on progression of severe COVID-19 remains unclear. We prospectively enrolled 71 patients with COVID-19 associated critical illness, collected fecal specimens within 3 days of medical intensive care unit admission, defined microbiome compositions by shotgun metagenomic sequencing, and quantified microbiota-derived metabolites (NCT #04552834). Of the 71 patients, 39 survived and 32 died. Mortality was associated with increased representation of Proteobacteria in the fecal microbiota and decreased concentrations of fecal secondary bile acids and desaminotyrosine (DAT). A microbiome metabolic profile (MMP) that accounts for fecal secondary bile acids and desaminotyrosine concentrations was independently associated with progression of respiratory failure leading to mechanical ventilation. Our findings demonstrate that fecal microbiota composition and microbiota-derived metabolite concentrations can predict the trajectory of respiratory function and death in patients with severe SARS-Cov-2 infection and suggest that the gut-lung axis plays an important role in the recovery from COVID-19.

Long non-coding RNAs and their targets as potential biomarkers in breast cancer.

Breast cancer is among the lethal types of cancer with a high mortality rate, globally. Its high prevalence can be controlled through improved analysis and identification of disease-specific biomarkers. Recently, long non-coding RNAs (lncRNAs) have been reported as key contributors of carcinogenesis and regulate various cellular pathways through post-transcriptional regulatory mechanisms. The specific aim of this study was to identify the novel interactions of aberrantly expressed genetic components in breast cancer by applying integrative analysis of publicly available expression profiles of both lncRNAs and mRNAs. Differential expression patterns were identified by comparing the breast cancer expression profiles of samples with controls. Significant co-expression networks were identified through WGCNA analysis. WGCNA is a systems biology approach used to elucidate the pattern of correlation between genes across microarray samples. It is also used to identify the highly correlated modules. The results obtained from this study revealed significantly differentially expressed and co-expressed lncRNAs and their cis- and trans-regulating mRNA targets which include RP11-108F13.2 targeting TAF5L, RPL23AP2 targeting CYP4F3, CYP4F8 and AL022324.2 targeting LRP5L, AL022324.3, and Z99916.3, respectively. Moreover, pathway analysis revealed the involvement of identified mRNAs and lncRNAs in major cell signalling pathways, and target mRNAs expression is also validated through cohort data. Thus, the identified lncRNAs and their target mRNAs represent novel biomarkers that could serve as potential therapeutics for breast cancer and their roles could also be further validated through wet labs to employ them as potential therapeutic targets in future.

In-silico pharmacophoric and molecular docking-based drug discovery against the Main Protease (Mpro) of SARS-CoV-2, a causative agent COVID-19.

COVID-19 (Coronavirus Disease 2019) caused by a novel 'SARS-CoV-2' virus resulted in public health emergencies across the world. An effective vaccine to cure this virus is not yet available, thus requires concerted efforts at various scales. In this study, we employed Computer-Aided Drug Design (CADD) based approach to identify the drug-like compounds - inhibiting the replication of the main protease (Mpro) of SARS-CoV-2. Our database search using an online tool "ZINC pharmer" retrieved ~1500 compounds based on pharmacophore features. Lipinski's rule was applied to further evaluate the drug-like compounds, followed by molecular docking-based screening, and the selection of screening ligand complex with Mpro based on S-score (higher than reference inhibitor) and root-mean-square deviation (RMSD) value (less than reference inhibitor) using AutoDock 4.2. Resultantly, ~200 compounds were identified having strong interaction with Mpro of SARS-CoV-2. After evaluating their binding energy using the AutoDock 4.2 software, three compounds (ZINC20291569, ZINC90403206, ZINC95480156) were identified that showed highest binding energy with Mpro of SARS-CoV-2 and strong inhibition effect than the N3 (reference inhibitor). A good binding energy, drug likeness and effective pharmacokinetic parameters suggest that these candidates have greater potential to stop the replication of SARS-CoV-2, hence might lead to the cure of COVID-19.

MicroRNAs and their target mRNAs as potential biomarkers among smokers and non-smokers with lung adenocarcinoma.

Lung adenocarcinoma is one of the major causes of mortality. Current methods of diagnosis can be improved through identification of disease specific biomarkers. MicroRNAs are small non-coding regulators of gene expression, which can be potential biomarkers in various diseases. Thus, the main objective of this study was to gain mechanistic insights into genetic abnormalities occurring in lung adenocarcinoma by implementing an integrative analysis of miRNAs and mRNAs expression profiles in the case of both smokers and non-smokers. Differential expression was analysed by comparing publicly available lung adenocarcinoma samples with controls. Furthermore, weighted gene co-expression network analysis is performed which revealed mRNAs and miRNAs significantly correlated with lung adenocarcinoma. Moreover, an integrative analysis resulted in identification of several miRNA-mRNA pairs which were significantly dysregulated in non-smokers with lung adenocarcinoma. Also two pairs (miR-133b/Protein Kinase C Zeta (PRKCZ) and miR-557/STEAP3) were found specifically dysregulated in smokers. Pathway analysis further revealed their role in important signalling pathways including cell cycle. This analysis has not only increased the authors' understanding about lung adenocarcinoma but also proposed potential biomarkers. However, further wet laboratory studies are required for the validation of these potential biomarkers which can be used to diagnose lung adenocarcinoma.