Microbial Signatures in COVID-19: Distinguishing Mild and Severe Disease via Gut Microbiota

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has significantly impacted global healthcare, underscoring the importance of exploring the virus's effects on infected individuals beyond treatments and vaccines. Notably, recent findings suggest that SARS-CoV-2 can infect the gut, thereby altering the gut microbiota. This study aimed to analyze the gut microbiota composition differences between COVID-19 patients experiencing mild and severe symptoms. We conducted 16S rRNA metagenomic sequencing on fecal samples from 49 mild and 43 severe COVID-19 cases upon hospital admission. Our analysis identified a differential abundance of specific bacterial species associated with the severity of the disease. Severely affected patients showed an association with Enterococcus faecium, Akkermansia muciniphila, and others, while milder cases were linked to Faecalibacterium prausnitzii, Alistipes putredinis, Blautia faecis, and additional species. Furthermore, a network analysis using SPIEC-EASI indicated keystone taxa and highlighted structural differences in bacterial connectivity, with a notable disruption in the severe group. Our study highlights the diverse impacts of SARS-CoV-2 on the gut microbiome among both mild and severe COVID-19 patients, showcasing a spectrum of microbial responses to the virus. Importantly, these findings align, to some extent, with observations from other studies on COVID-19 gut microbiomes, despite variations in methodologies. The findings from this study, based on retrospective data, establish a foundation for future prospective research to confirm the role of the gut microbiome as a predictive biomarker for the severity of COVID-19.

An improved and extended dual-index multiplexed 16S rRNA sequencing for the Illumina HiSeq and MiSeq platform

BACKGROUND

Recent advancements in next-generation sequencing (NGS) technology have ushered in significant improvements in sequencing speed and data throughput, thereby enabling the simultaneous analysis of a greater number of samples within a single sequencing run. This technology has proven particularly valuable in the context of microbial community profiling, offering a powerful tool for characterizing the microbial composition at the species level within a given sample. This profiling process typically involves the sequencing of 16S ribosomal RNA (rRNA) gene fragments. By scaling up the analysis to accommodate a substantial number of samples, sometimes as many as 2,000, it becomes possible to achieve cost-efficiency and minimize the introduction of potential batch effects. Our study was designed with the primary objective of devising an approach capable of facilitating the comprehensive analysis of 1,711 samples sourced from diverse origins, including oropharyngeal swabs, mouth cavity swabs, dental swabs, and human fecal samples. This analysis was based on data obtained from 16S rRNA metagenomic sequencing conducted on the Illumina MiSeq and HiSeq sequencing platforms.

RESULTS

We have designed a custom set of 10-base pair indices specifically tailored for the preparation of libraries from amplicons derived from the V3-V4 region of the 16S rRNA gene. These indices are instrumental in the analysis of the microbial composition in clinical samples through sequencing on the Illumina MiSeq and HiSeq platforms. The utilization of our custom index set enables the consolidation of a significant number of libraries, enabling the efficient sequencing of these libraries in a single run.

CONCLUSIONS

The unique array of 10-base pair indices that we have developed, in conjunction with our sequencing methodology, will prove highly valuable to laboratories engaged in sequencing on Illumina platforms or utilizing Illumina-compatible kits.

[Study of the resistome of human microbial communities using a targeted panel of antibiotic resistance genes in COVID-19 patients]

AIM

To study overall drug resistance genes (resistome) in the human gut microbiome and the changes in these genes during COVID-19 in-hospital therapy.

MATERIALS AND METHODS

A single-center retrospective cohort study was conducted. Only cases with laboratory-confirmed SARS-CoV-2 RNA using polymerase chain reaction in oro-/nasopharyngeal swab samples were subject to analysis. The patients with a documented history of or current comorbidities of the hepatobiliary system, malignant neoplasms of any localization, systemic and autoimmune diseases, as well as pregnant women were excluded. Feces were collected from all study subjects for subsequent metagenomic sequencing. The final cohort was divided into two groups depending on the disease severity: mild (group 1) and severe (group 2). Within group 2, five subgroups were formed, depending on the use of antibacterial drugs (ABD): group 2A (receiving ABD), group 2AC (receiving ABD before hospitalization), group 2AD (receiving ABD during hospitalization), group 2AE (receiving ABD during and before hospitalization), group 2B (not receiving ABD).

RESULTS

The median number of antibiotic resistance (ABR) genes (cumulative at all time points) was significantly higher in the group of patients treated with ABD: 81.0 (95% CI 73.8-84.5) vs. 51.0 (95% CI 31.1-68.4). In the group of patients treated with ABD (2A), the average number of multidrug resistance genes (efflux systems) was significantly higher than in controls (group 2B): 47.0 (95% CI 46.0-51.2) vs. 21.5 (95% CI 7.0-43.9). Patients with severe coronavirus infection tended to have a higher median number of ABR genes but without statistical significance. Patients in the severe COVID-19 group who did not receive ABD before and during hospitalization also had more resistance genes than the patients in the comparison group.

CONCLUSION

This study demonstrated that fewer ABR genes were identified in the group with a milder disease than in the group with a more severe disease associated with more ABR genes, with the following five being the most common: SULI, MSRC, ACRE, EFMA, SAT.

[Composition of oropharyngeal microbiota in patients with COVID-19 of different pneumonia severity]

AIM

To identify features of the taxonomic composition of the oropharyngeal microbiota of COVID-19 patients with different disease severity.

MATERIALS AND METHODS

The study group included 156 patients hospitalized with confirmed diagnosis of COVID-19 in the clinical medical center of Yevdokimov Moscow State University of Medicine and Dentistry between April and June 2021. There were 77 patients with mild pneumonia according to CT (CT1) and 79 patients with moderate to severe pneumonia (CT2 and CT3). Oropharyngeal swabs were taken when the patient was admitted to the hospital. Total DNA was isolated from the samples, then V3V4 regions of the 16s rRNA gene were amplified, followed by sequencing using Illumina HiSeq 2500 platform. DADA2 algorithm was used to obtain amplicon sequence variants (ASV).

RESULTS

When comparing the microbial composition of the oropharynx of the patients with different forms of pneumonia, we have identified ASVs associated with the development of both mild and severe pneumonia outside hospital treatment. Based on the results obtained, ASVs associated with a lower degree of lung damage belong predominantly to the class of Gram-negative Firmicutes (Negativicutes), to various classes of Proteobacteria, as well as to the order Fusobacteria. In turn, ASVs associated with a greater degree of lung damage belong predominantly to Gram-positive classes of Firmicutes Bacilli and Clostridia. While being hospitalized, patients with severe pneumonia demonstrated negative disease dynamics during treatment significantly more often.

CONCLUSION

We have observed differences in the taxonomic composition of the oropharyngeal microbiota in patients with different forms of pneumonia developed outside hospital treatment against COVID-19. Such differences might be due to the presumed barrier function of the oropharyngeal microbiota, which reduces the risk of virus titer increase.

Phigaro: high-throughput prophage sequence annotation

SUMMARY

Phigaro is a standalone command-line application that is able to detect prophage regions taking raw genome and metagenome assemblies as an input. It also produces dynamic annotated 'prophage genome maps' and marks possible transposon insertion spots inside prophages. It is applicable for mining prophage regions from large metagenomic datasets.

AVAILABILITY AND IMPLEMENTATION

Source code for Phigaro is freely available for download at https://github.com/bobeobibo/phigaro along with test data. The code is written in Python.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

VERA: agent-based modeling transmission of antibiotic resistance between human pathogens and gut microbiota

MOTIVATION

The resistance of bacterial pathogens to antibiotics is one of the most important issues of modern health care. The human microbiota can accumulate resistance determinants and transfer them to pathogenic microbiota by means of horizontal gene transfer. Thus, it is important to develop methods of prediction and monitoring of antibiotics resistance in human populations.

RESULTS

We present the agent-based VERA model, which allows simulation of the spread of pathogens, including the possible horizontal transfer of resistance determinants from a commensal microbiota community. The model considers the opportunity of residents to stay in the town or in a medical institution, have incorrect self-treatment, treatment with several antibiotics types and transfer and accumulation of resistance determinants from commensal microorganism to a pathogen. In this model, we have also created an assessment of optimum observation frequency of infection spread among the population. Investigating model behavior, we show a number of non-linear dependencies, including the exponential nature of the dependence of the total number of those infected on the average resistance of a pathogen. As the model infection, we chose infection with Shigella spp., though it could be applied to a wide range of other pathogens.

AVAILABILITY AND IMPLEMENTATION

Source code and binaries VERA and VERA.viewer are freely available for download at github.com/lpenguin/microbiota-resistome. The code is written in Java, JavaScript and R for Linux platform.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Gut microbiota assessment in Moscow long-livers using next generation sequencing

Demographic aging poses a challenge to the medical community, pressing for research into the biological factors promoting longevity and its features. Below, we look at the gut microbiota as one of such factors. The aim of this non-longitudinal study was to profile the gut microbiota of centenarians and to compare it with that of relatively healthy, younger Moscow residents. The study recruited 20 people aged 97–100 years (mean age 98 ± 1 year); the control group consisted of 92 individuals aged 53 ± 13 years. For each stool sample, the variable V3–V4 regions of the microbial 16S rRNA gene were sequenced. Primary analysis, read filtering and taxonomic identification were conducted in the QIIME 1.9 environment; reconstruction of metabolic pathways was aided by PICRUSt. Statistical analysis was performed by means of Python v. 3.2. A few differences were detected between the gut microbiota of centenarians and younger individuals: Bifidobacterium (p = 0.026) and Coprococcus eutactus (р = 0.026) were more abundant in centenarians, whereas Bacteroides (p = 0.003) and Prevotella (р = 0.002) were better represented in younger participants. The potential for butyric acid synthesis was higher in the group of centenarians (p = 0.048). Surprisingly, the gut microbiota of centenarians was more diverse and surprisingly beneficial for advanced age. Besides, the gut microbiota of centenarians might have more pronounced anti-inflammatory potential due to its ability to better synthesize butyric acid.

Shifts in the Human Gut Microbiota Structure Caused by Quadruple Helicobacter pylori Eradication Therapy

The human gut microbiome plays an important role both in health and disease. Use of antibiotics can alter gut microbiota composition, which can lead to various deleterious events. Here we report a whole genome sequencing metagenomic/genomic study of the intestinal microbiota changes caused by Helicobacter pylori (HP) eradication therapy. Using approaches for metagenomic data analysis we revealed a statistically significant decrease in alpha-diversity and relative abundance of Bifidobacterium adolescentis due to HP eradication therapy, while the relative abundance of Enterococcus faecium increased. We have detected changes in general metagenome resistome profiles as well: after HP eradication therapy, the ermB, CFX group, and tetQ genes were overrepresented, while tetO and tetW genes were underrepresented. We have confirmed these results with genome-resolved metagenomic approaches. MAG (metagenome-assembled genomes) abundance profiles have changed dramatically after HP eradication therapy. Focusing on ermB gene conferring resistance to macrolides, which were included in the HP eradication therapy scheme, we have shown a connection between antibiotic resistance genes (ARGs) and some overrepresented MAGs. Moreover, some E. faecium strains isolated from stool samples obtained after HP eradication have manifested greater antibiotic resistance in vitro in comparison to other isolates, as well as the higher number of ARGs conferring resistance to macrolides and tetracyclines.

[Bioinformatics analysis of antimicrobial resistance genes and prophages colocalized in human gut metagenomes]

The constant increase of antibiotic-resistant strains of bacteria is caused by extensive uses of antibiotics in medicine and animal breeding. It was suggested that the gut microbiota serves as a reservoir for antibiotics resistance genes that can be carried from symbiotic bacteria to pathogenic ones, in particular, as a result of transduction. In the current study, we have searched for antibiotics resistance genes that are located inside prophages in human gut microbiota using PHASTER prophage predicting tool and CARD antibiotics resistance database. After analysing metagenomic assemblies of eight samples of antibiotic treated patients, lsaE, mdfA and cpxR/cpxA genes were identified inside prophages. The abovementioned genes confer resistance to antimicrobial peptides, pleuromutilin, lincomycins, streptogramins and multidrug resistance. Three (0.46%) of 659 putative prophages predicted in metagenomic assemblies contained antibiotics resistance genes in their sequences.