Variable Region Sequences Influence 16S rRNA Performance

Molecular and computational insights into algD biofilm genes in multi drug resistant and extensively drug resistant Pseudomonasaeruginosa

Antibiotic-resistance and biofilm formation are the main virulence factors and present a serious treatment challenge in Pseudomonas aeruginosa. This study aimed to investigate antimicrobial resistance, genetic diversity, biofilm-specific algD gene, and computational analysis of clinical isolates. Forty two isolates of P. aeruginosa were examined by PCR, ELISA, sangers sequencing, phylogenetic analysis, MolProbity score, 3D structural modelling, Ramachandran plot, multiple sequence alignment, and protein domain analysis. According to the results, PCR analysis revealed algD gene presence in all isolates. ELISA showed 55 % (n = 23) of the samples produced strong biofilms, 38 % (n = 16) produced moderate biofilms, and 7 % (3) produced weak biofilms. The evolutionary relationships of 8 (S1-S8) P. aeruginosa strains with 81 reference strains were illustrated by the phylogenetic tree. Samples S1-S8 showed excellent MolProbity score (<1.00), low clashed scores (0.67-0.70), most residues in the favored regions (∼96.2-96.5 %), low Ramachandran outliers (0.53-0.56 %), low Rotamer outlier (0.62 %), low bad angles (<2), indicated high-quality models and values preferred percentages showed excellent models with structural refinement. Over all samples S5 and S6 stood out as the top choices for high-confidence modeling and applications. The essential catalytic domain UDP-glucose/GDP-mannose dehydrogenase was identified that could be used as important therapeutic targets. High quality models indicated suitability for downstream applications, such as studying protein-ligand interactions, understanding structural aspects of biofilm-resistant bacteria. This study improved our knowledge of the mechanisms underlying P. aeruginosa biofilm resistance and sets the stage for the development of novel therapeutic and diagnostic strategies to combat multidrug resistant strains.

Impact of Soil Microbiomes on Mung Bean Cultivation: Insights from 16S rRNA Metagenomics

Cyclic nutrient processes, soil health maintenance, and plant development are contingent upon soil microbiomes. The microbial makeup of the soil of Maruthupandiyar College, Thanjavur, is assessed using 16S rRNA gene sequencing. QIIME2, in conjunction with the SILVA database, analyzed the sequencing data to examine microbial diversity and composition. The experimental results revealed a diverse array of bacteria in soil physicochemical properties. The alpha and beta diversity assessment revealed significant microbial community complexity and distribution patterns disparities. The research revealed bacterial groups associated with biological nitrogen fixing, suggesting their potential to enhance mung bean growth. The current study illustrates the significance of microbial interactions in soil for sustaining soil fertility and enhancing crop output. Research findings provide essential insights into improving the sustainability of tropical agriculture through intentional microbial management to create sustainable soil health systems.

Changes in the Gastrointestinal Microbiota Induced by Proton Pump Inhibitors-A Review of Findings from Experimental Trials

The use of proton pump inhibitors (PPIs) has increased considerably in many Western countries, and there is concern that numerous conditions and diseases associated with PPI use may be adverse events. The main function of gastric acid is to defend the organism against orally ingested microorganisms, and there is also concern that alterations not only in the gastric microbiome but also the downstream intestinal microbiome may increase the risk of disease or alter the course of preexisting disease. The current study is a systematic review of the available evidence from experimental trials investigating the effects of PPIs on the gastrointestinal microbiota by next-generation sequencing. Thirteen studies were identified. The effects of PPIs were seen on alterations in diversity and richness in some of the studies, while a larger proportion of the studies detected alterations at various taxonomic levels. The general finding was that PPI use caused an increase in bacteria normally found in the oral microbiota in both the upper and lower GI tract. The most consistent taxonomic alterations seemed to be increases in oral flora along the axis Streptococcaceae and Streptococcus at genus level and various Streptococcus spp., as well as Veillonellaceae, Veillonella and Haemophilus.

Development of an automated amplicon-based next-generation sequencing pipeline for rapid detection of bacteria and fungi directly from clinical specimens

The timely identification of microbial pathogens is essential to guide targeted antimicrobial therapy and ultimately, successful treatment of an infection. However, the yield of standard microbiology testing (SMT) is directly related to the duration of antecedent antimicrobial therapy as SMT culture methods are dependent on the recovery of viable organisms, the fastidious nature of certain pathogens, and other pre-analytic factors. In the last decade, metagenomic next-generation sequencing (mNGS) has been successfully utilized as a diagnostic tool for various applications within the clinical laboratory. However, mNGS is resource, time, and labor-intensive-requiring extensive laborious preliminary benchwork, followed by complex bioinformatic analysis. We aimed to address these shortcomings by developing a largely Automated targeted Metagenomic next-generation sequencing (tmNGS) PipeLine for rapId inFectIous disEase Diagnosis (AMPLIFIED) to detect bacteria and fungi directly from clinical specimens. Therefore, AMPLIFIED may serve as an adjunctive approach to complement SMT. This tmNGS pipeline requires less than 1 hour of hands-on time before sequencing and less than 2 hours of total processing time, including bioinformatic analysis. We performed tmNGS on 50 clinical specimens with concomitant cultures to assess feasibility and performance in the hospital laboratory. Of the 50 specimens, 34 (68%) were from true clinical infections. Specimens from cases of true infection were more often tmNGS positive compared to those from the non-infected group (82.4% vs 43.8%, respectively, P = 0.0087). Overall, the clinical sensitivity of AMPLIFIED was 54.6% with 85.7% specificity, equating to 70.6% and 75% negative and positive predictive values, respectively. AMPLIFIED represents a rapid supplementary approach to SMT; the typical time from specimen receipt to identification of potential pathogens by AMPLIFIED is roughly 24 hours which is markedly faster than the days, weeks, and months required to recover bacterial, fungal, and mycobacterial pathogens by culture, respectively.

IMPORTANCE

To our knowledge, this represents the first application of an automated sequencing and bioinformatics pipeline in an exclusively pediatric population. Next-generation sequencing is time-consuming, labor-intensive, and requires experienced personnel; perhaps contributing to hesitancy among clinical laboratories to adopt such a test. Here, we report a strong case for use by removing these barriers through near-total automation of our sequencing pipeline.