Background: Soil microbes exist throughout the soil profile and those inhabiting topsoil (0–20 cm) are believed to play a key role in nutrients cycling. However, the majority of the soil microbiology studies have exclusively focused on the distribution of soil microbial communities in the topsoil, and it remains poorly understood through the subsurface soil profile (i.e., 20–40 and 40–60 cm). Here, we examined how the bacterial community composition and functional diversity changes under intensive fertilization across vertical soil profiles [(0–20 cm (RS1), 20–40 cm (RS2), and 40–60 cm (RS3)] in the red soil of pomelo orchard, Pinghe County, Fujian, China. Results: Bacterial community composition was determined by 16S rRNA gene sequencing and interlinked with edaphic factors, including soil pH, available phosphorous (AP), available nitrogen (AN), and available potassium (AK) to investigate the key edaphic factors that shape the soil bacterial community along with different soil profiles. The most dominant bacterial taxa were Proteobacteria, Acidobacteria, Actinobacteria, Chloroflexi, Crenarchaeota, and Bacteriodetes. Bacterial richness and diversity was highest in RS1 and declined with increasing soil depth. The distinct distribution patterns of the bacterial community were found across the different soil profiles. Besides, soil pH exhibited a strong influence (pH ˃AP ˃AN) on the bacterial communities under all soil depths. The relative abundance of Proteobacteria, Actinobacteria, Crenarchaeota, and Firmicutes was negatively correlated with soil pH, while Acidobacteria, Chloroflexi, Bacteriodetes, Planctomycetes, and Gemmatimonadetes were positively correlated with soil pH. Co-occurrence network analysis revealed that network topological features were weakened with increasing soil depth, indicating a more stable bacterial community in the RS1. Bacterial functions were estimated using FAPROTAX and the relative abundance of functional bacterial community related to metabolic processes, including C-cycle, N-cycle, and energy production was significantly higher in RS1 compared to RS2 and RS3, and soil pH had a significant effect on these functional microbes. Conclusions: This study provided the valuable findings regarding the structure and functions of bacterial communities in red soil of pomelo orchards, and highlighted the importance of soil depth and pH in shaping the soil bacterial population, their spatial distribution and ecological functioning. These results suggest the alleviation of soil acidification by adopting integrated management practices to preserve the soil microbial communities for better ecological functioning.

Recent advances in high-throughput sequencing technologies and computational methods have added a new dimension to metagenomic data analysis i.e. genome-resolved metagenomics. In general terms, it refers to the recovery of draft or high-quality microbial genomes and their taxonomic classification and functional annotation. In recent years, several studies have utilized the genome-resolved metagenome analysis approach and identified previously unknown microbial species from human and environmental metagenomes. In this review, we describe genome-resolved metagenome analysis as a series of four necessary steps: (i) preprocessing of the sequencing reads, (ii) de novo metagenome assembly, (iii) genome binning and (iv) taxonomic and functional analysis of the recovered genomes. For each of these four steps, we discuss the most commonly used tools and the currently available pipelines to guide the scientific community in the recovery and subsequent analyses of genomes from any metagenome sample. Furthermore, we also discuss the tools required for validation of assembly quality as well as for improving quality of the recovered genomes. We also highlight the currently available pipelines that can be used to automate the whole analysis without having advanced bioinformatics knowledge. Finally, we will highlight the most widely adapted and actively maintained tools and pipelines that can be helpful to the scientific community in decision making before they commence the analysis.

The human microbiota is recognized as a vital “virtual” organ of the human body that influences human health, metabolism, and physiology. While the microbiomes of the gut, oral cavity, and skin have been extensively studied in the literature, relatively little work has been done on characterizing the microbiota of the human reproductive tract organs, and specifically on investigating its association to fertility. Here, we implemented a 16S ribosomal RNA (rRNA) amplicon sequencing approach to sequence and characterize the gut and genital tract microbiomes from several married Pakistani couples. The recruited individuals included 31 fertile and 35 infertile individuals, with ages ranging from 19–45 years. We identified several fluctuations in the diversity and composition of the gut and genital microbiota among fertile and infertile samples. For example, measures of α-diversity varied significantly between the genital samples donated by fertile and infertile men and there was overall greater between-sample variability in genital samples regardless of gender. In terms of taxonomic composition, Actinobacteria, Bacteroidetes, and Firmicutes fluctuated significantly between the gut microbiomes of fertile and infertile samples. Finally, biomarker analyses identified features (genera and molecular functions and pathways) that differed significantly between the fertile and infertile samples and in the past have been associated with bacterial vaginosis. However, we emphasize that 16S amplicon data alone has no bearing on individual health and is merely representative of microbial taxonomic differences that could also arise due to multiple other factors. Our findings, however, represent the first effort to characterize the microbiome associated with fertile and infertile couples in Pakistan and will hopefully pave the way for more comprehensive and broad-scale investigations in the future.

Antimicrobial resistance (AMR) is an escalating global health challenge, with the rapid proliferation of antibiotic resistance genes (ARGs) undermining the efficacy of existing treatments and threatening decades of medical progress. The advent of next-generation sequencing technologies, coupled with machine learning algorithms, has revolutionized ARG identification and prediction in high-throughput genomics and metagenomics. Despite these advancements, selecting the most appropriate ARG resources remains challenging owing to significant variability in database structures, data curation methodologies, annotation depth, and coverage of resistance determinants. This review comprehensively analyzes widely used ARG resources, focusing on databases and computational tools. We examine the structural and functional characteristics of leading ARG databases, their strengths and limitations, and the diversity of metadata they incorporate. Additionally, we explore cutting-edge computational tools, such as AMRFinderPlus, DeepARG, and HMD-ARG, evaluating their underlying algorithms, predictive capabilities, and suitability for different research contexts, including the detection of complex or low-abundance ARGs. This review bridges a critical gap in the literature, which often focuses on either databases or algorithms in isolation. Moreover, our findings are expected to support researchers in selecting appropriate resources for ARG detection and surveillance, enabling more accurate identification of resistance determinants and fostering the development of robust strategies to combat AMR.

Diabetes mellitus, a global health threat, is associated with living standards, age, genetics of the host, and other factors. Diabetes has three major types, Type 1 diabetes, Type 2 diabetes, and Gestational diabetes. Out of these, type 2 diabetes (T2D) is the most prevalent type. Understanding the pathophysiology of T2D is challenging since it requires a knowledge of the intricate relationships between glucose metabolism, insulin secretion, sensitivity, and glucose effectiveness. The human gut microbiome, a complex assembly of microorganisms including bacteria, viruses, and other microbes, has drawn much attention recently due to its association with T2D. Patients with T2D exhibit altered microbial composition and diversity, along with dysbiosis in the gut microbiome. Glucose-lowering drugs used for treatment purposes, like acarbose and metformin, are related to microbiome dysbiosis. These glucose-lowering drugs also affect the metabolic pathways and functions in T2D patients including short-chain acids (SCFAs) synthesis, an important metabolic player in diabetes linked to the gut microbiome. Therefore, to improve T2D treatment and management, the gut microbiome becomes an important target. However, this remains challenging and necessitates a comprehensive understanding of the mechanism of gut microbes and their role in disease onset. Clinical trials are being conducted to study the effects of gut microbiome on T2D progression and the effect of glucose-lowering drugs on gut microbiome composition and diversity. This review explores the relationships between gut microbiome and T2D, focusing on how glucose-lowering drugs impact the gut microbiome of T2D patients. It also highlights the possible treatment considerations along with the challenges.