Temporal changes in fecal microbiota of patients infected with COVID-19: a longitudinal cohort

MD3F: Multivariate Distance Drift Diffusion Framework for High-Dimensional Datasets

High-dimensional biomedical datasets have become easier to collect in the last two decades with the advent of multi-omic and single-cell experiments. These can generate over 1000 measurements per sample or per cell. More recently, focus has been drawn toward the need for longitudinal datasets, with the appreciation that important dynamic changes occur along transitions between health and disease. Analysis of longitudinal omics data comes with many challenges, including type I error inflation and corresponding loss in power when thousands of hypothesis tests are needed. Multivariate analysis can yield approaches with higher statistical power; however, multivariate methods for longitudinal data are currently limited. We propose a multivariate distance-based drift-diffusion framework (MD3F) to tackle the need for a multivariate approach to longitudinal, high-throughput datasets. We show that MD3F can result in surprisingly simple yet valid and powerful hypothesis testing and estimation approaches using generalized linear models. Through simulation and application studies, we show that MD3F is robust and can offer a broadly applicable method for assessing multivariate dynamics in omics data.

Metagenomic next-generation sequencing of nasopharyngeal microbiota in COVID-19 patients with different disease severities

Throughout the COVID-19 pandemic, extensive research has been conducted on SARS-COV-2 to elucidate its genome, prognosis, and possible treatments. However, few looked at the microbial markers that could be explored in infected patients and that could predict possible disease severity. The aim of this study is to compare the nasopharyngeal microbiota of healthy subjects, moderate, under medication, and recovered SARS-COV-2 patients. In 2020, 38 nasopharyngeal swabs were collected from 6 healthy subjects, 14 moderates, 10 under medication and 8 recovered SARS-COV-2 patients at the Prince Mohammed Bin Abdulaziz Hospital Riyadh. Metatranscriptomic sequencing was performed using Minion Oxford nanopore sequencing. No significant difference in alpha as well as beta diversity was observed among all four categories. Nevertheless, we have found that Streptococcus spp including Streptococcus pneumoniae and Streptococcus thermophilus were among the top 15 most abundant species detected in COVID-19 patients but not in healthy subjects. The genus Staphylococcus was found to be associated with COVID-19 patients compared to healthy subjects. Furthermore, the abundance of Leptotrichia was significantly higher in healthy subjects compared to recovered patients. Corynebacterium on the other hand, was associated with under-medication patients. Taken together, our study revealed no differences in the overall microbial composition between healthy subjects and COVID-19 patients. Significant differences were seen only at specific taxonomic level. Future studies should explore the nasopharyngeal microbiota between controls and COVID-19 patients while controlling for confounders including age, gender, and comorbidities; since these latter could affect the results and accordingly the interpretation.IMPORTANCEIn this work, no significant difference in the microbial diversity was seen between healthy subjects and COVID-19 patients. Changes in specific taxa including Leptotrichia, Staphylococcus, and Corynebacterium were only observed. Leptotrichia was significantly higher in healthy subjects, whereas Staphylococcus and Corynebacterium were mostly associated with COVID-19, and specifically with under-medication SARS-COV-2 patients, respectively. Although the COVID-19 pandemic has ended, the SARS-COV-2 virus is continuously evolving and the emergence of new variants causing more severe disease should be always kept in mind. Microbial markers in SARS-COV-2 infected patients can be useful in the early suspicion of the disease, predicting clinical outcomes, framing hospital and intensive care unit admission as well as, risk stratification. Data on which microbial marker to tackle is still controversial and more work is needed, hence the importance of this study.

Harnessing and delivering microbial metabolites as therapeutics via advanced pharmaceutical approaches

Microbial metabolites have emerged as key players in the interplay between diet, the gut microbiome, and host health. Two major classes, short-chain fatty acids (SCFAs) and tryptophan (Trp) metabolites, are recognized to regulate inflammatory, immune, and metabolic responses within the host. Given that many human diseases are associated with dysbiosis of the gut microbiome and consequent reductions in microbial metabolite production, the administration of these metabolites represents a direct, multi-targeted treatment. While a multitude of preclinical studies showcase the therapeutic potential of both SCFAs and Trp metabolites, they often rely on high doses and frequent dosing regimens to achieve systemic effects, thereby constraining their clinical applicability. To address these limitations, a variety of pharmaceutical formulations approaches that enable targeted, delayed, and/or sustained microbial metabolite delivery have been developed. These approaches, including enteric encapsulations, esterification to dietary fiber, prodrugs, and nanoformulations, pave the way for the next generation of microbial metabolite-based therapeutics. In this review, we first provide an overview of the roles of microbial metabolites in maintaining host homeostasis and outline how compromised metabolite production contributes to the pathogenesis of inflammatory, metabolic, autoimmune, allergic, infectious, and cancerous diseases. Additionally, we explore the therapeutic potential of metabolites in these disease contexts. Then, we provide a comprehensive and up-to-date review of the pharmaceutical strategies that have been employed to enhance the therapeutic efficacy of microbial metabolites, with a focus on SCFAs and Trp metabolites.

Explore the changes of intestinal flora in patients with coronavirus disease 2019 based on bioinformatics

Background

Studies have revealed that there were significant changes in intestinal flora composition in patients with coronavirus disease 2019 (COVID-19) compared to non-COVID-19 patients, regardless of whether they were treated with medication. Therefore, a comprehensive study of the intestinal flora of COVID-19 patients is needed to further understand the mechanisms of COVID-19 development.

Methods

In total, 20 healthy samples and 20 COVID-19 samples were collected in this study. Firstly, alpha diversity and beta diversity were analyzed to assess whether there were difference in species richness and diversity as well as species composition between COVID-19 and control groups. The observed features index, Evenness index, PD index, and Shannon index were utilized to measure alpha diversity. The principal coordinates analysis (PCoA) and non-metric multidimensional scaling (NMDS) were performed to analyzed beta diversity. Linear discriminant analysis Effect Size (LEfSe) was utilized to analyze the variability in the abundance of bacterial taxa from different classification levels. The random forest (RF), Least absolute shrinkage and selection operator (LASSO), and univariate logistic regression were utilized to identify key Amplicon Sequence Variant (ASVs). Finally, the relevant networks of bacterial taxa were created in COVID-19 and control groups, separately.

Results

There were more species in the control group than in COVID-19 group. The observed features index, Shannon index, and Evenness index in the control groups were markedly higher than in the COVID-19 group. Therefore, there were marked variations in bacterial taxa composition between the COVID-19 and control groups. The nine bacterial taxa were significantly more abundant in the COVID-19 group, such as g-Streptococcus, f-Streptococcaceae, o-Lactobacillales, c-Bacilli and so on. In the control group, 26 bacterial taxa were significantly more abundant, such as c-Clostrjdia, o-Oscillospirales, f-Ruminococcaceae, etc. The 5 key ASVs were obtained through taking the intersection of the characteristic ASVs obtained by the three algorithms, namely ASV6, ASV53, ASV92, ASV96, and ASV105, which had diagnostic value for COVID-19. The relevance network in the control group was more complex compared to the COVID-19 group.

Conclusion

Our findings provide five key ASVs for diagnosis of COVID-19, providing a scientific reference for further studies of COVID-19.