Alignment-Free Identification of Microplastic Bioremediation Potential Using K-mer Frequency Patterns

misosoup: a metabolic modeling tool for identifying minimal microbial communities, facilitates the exploration of microbial ecology and biotechnological applications

ABSTRACT Microbial survival and function often depend on metabolic interactions within communities. Therefore, a central question in disentangling microbial organization is determining which minimal groups of strains are able to thrive in a given medium—referred to as “minimal communities.” Answering this question is essential for understanding microbial distribution, enhancing laboratory cultivation, and designing synthetic communities (SynComs). Here, we introduce misosoup, a Python package for identifying minimal communities (minimal supplying community search). Through genome-scale constraint-based metabolic modeling, misosoup enables the systematic identification of communities that support microbial growth in environments where individual strains fail to survive alone. We validate misosoup against experimentally verified minimal communities, demonstrating its ability to predict known cooperative interactions, cocultures, and consortia with biotechnological potential. We further illustrate the use of misosoup to investigate broad microbial ecology questions by applying it to a set of 60 marine microbes, finding pervasive cross-feeding-driven niche expansion, and showing how the detailed outputs provided by misosoup facilitate research on hot topics such as the identification of functional groups. In summary, misosoup provides a powerful tool for microbial ecology and community design, with potential applications in both research and biotechnological innovation. IMPORTANCE Microbes often rely on each other to survive, especially in environments where they cannot live alone. Understanding which small groups of microbes can thrive together—called minimal communities—is key to improving laboratory research, designing synthetic ecosystems, and exploring how microbes spread in nature. To support this, we developed misosoup, a Python tool that identifies these communities using advanced metabolic modeling. misosoup helps scientists discover how microbes cooperate by sharing nutrients, a process known as metabolic cross-feeding. When tested on sets of species from different origins, the tool showed that species could thrive in more environments when part of a group. This finding highlights the importance of cooperation in microbial life. misosoup not only predicts these interactions but also provides detailed insights that can guide ecological studies and biotechnological innovation. By revealing how microbes support each other, misosoup contributes to a deeper understanding of life’s interconnectedness and offers tools for solving real-world challenges. Microbes often rely on each other to survive, especially in environments where they cannot live alone. Understanding which small groups of microbes can thrive together—called minimal communities—is key to improving laboratory research, designing synthetic ecosystems, and exploring how microbes spread in nature. To support this, we developed misosoup, a Python tool that identifies these communities using advanced metabolic modeling. misosoup helps scientists discover how microbes cooperate by sharing nutrients, a process known as metabolic cross-feeding. When tested on sets of species from different origins, the tool showed that species could thrive in more environments when part of a group. This finding highlights the importance of cooperation in microbial life. misosoup not only predicts these interactions but also provides detailed insights that can guide ecological studies and biotechnological innovation. By revealing how microbes support each other, misosoup contributes to a deeper understanding of life’s interconnectedness and offers tools for solving real-world challenges.

Environmental filtering shapes biosynthetic potential and resistome of antarctic microbiomes

Environmental filtering is a major driver of microbial community assembly in Antarctic ecosystems, yet its influence on biosynthetic potential and antimicrobial resistance remains poorly understood. Here, we analyzed 319 medium- to high-quality metagenome-assembled genomes (MAGs) recovered from four Antarctic sites (Whalers Bay, Crater Lake, Fumarole Bay, and Hannah Point) to investigate the relationship between geochemical gradients, biosynthetic gene clusters (BGCs), and antimicrobial resistance genes (ARGs). Integrating genome-resolved metagenomics, biosynthetic mining, resistome profiling, and environmental analyses, we identified 1,197 BGCs, with terpene clusters representing more than 25% of the total. Several biosynthetic hotspots were detected, including an Acidobacteriota MAG harboring 62 BGCs. Resistome composition exhibited strong site-specific structuring and was significantly associated with geochemical variables, particularly cobalt, iron, organic carbon, and thermal variation. Network analyses revealed highly connected MAGs affiliated with Pseudomonadota and Actinomycetota, linking diverse BGC and ARG classes. At the same time, genomic co-localization of biosynthetic and resistance determinants suggests potential adaptive associations between secondary metabolism and self-resistance mechanisms. Together, these findings demonstrate that environmental filtering shapes both the taxonomic and functional organization of Antarctic microbiomes and highlight polar ecosystems as reservoirs of unexplored biosynthetic diversity with potential biotechnological relevance.

Metagenomic insights into antibiotic resistance genes and virulence factors in sediments of river Yamuna

Riverine sediments serve as critical reservoirs of microbial diversity and functional genes, reflecting both natural ecological processes and anthropogenic impacts. In the present study, we employed a shotgun metagenomic approach to investigate microbial community composition, antimicrobial resistance (AMR) genes, and virulence factors in sediments collected from three environmentally distinct locations of the Yamuna River near Agra, India, representing BSA, TGY, and YEA. The sediment DNA was subjected to high-throughput Illumina sequencing, followed by quality control, assembly, and open reading frame prediction. Taxonomic classification and diversity analyses were performed using MEGAN6 and R-based statistical tools, while AMR genes were identified from predicted metagenomic proteins using the Resistance Gene Identifier (RGI) against the CARD database, with high-confidence perfect and strict hits retained; ARGs were interpreted independently of species-level host assignment. Virulence factors were assessed through presence–absence profiling of functionally relevant gene categories. The results revealed pronounced spatial heterogeneity in microbial communities, with increasing taxonomic diversity, functional complexity, and evenness from BSA to TGY and YEA. TGY and YEA composite samples showed greater observed representation of high-confidence AMR gene predictions spanning multiple drug classes and resistance mechanisms, alongside a diverse repertoire of virulence-associated genes linked to motility, adhesion, and secretion systems. In contrast, the BSA site harbored a comparatively simpler resistome and virulome. Overall, this study highlights Yamuna River sediments as important reservoirs of resistance and virulence determinants and underscores the need for long-term genomic surveillance to inform risk assessment, pollution control, and sustainable river management strategies.

Defining the ESKAPE pathogen prophage repertoire with PHORAGER

PHORAGER (Prophage Hunting, vOtu Retrieval, Annotation and Genomic ExploRation), a scalable Nextflow pipeline for the standardised identification and quality assessment of prophages from bacterial genomes, validated using 30,824 publicly available ESKAPE pathogen genomes.

Metagenomics Approach for Identification of Arbuscular Mycorrhizal Fungal Sequences in Historical Herbarium Specimens.

Uneven global coverage of halophilic metagenomes limits comparative analyses of microbial adaptation to saline environments

Halophilic microorganisms are central to biotechnology, bioremediation, and astrobiology because they persist under extreme and polyextreme conditions analogous to extraterrestrial environments. Although metagenomics has transformed the study of halophilic biodiversity, available datasets remain fragmented and unevenly documented. To assess how halophilic metagenomic research reflects the global exploration of hypersaline environments, we analyzed PubMed-indexed studies and associated sequencing metadata deposited at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) using a curation workflow. Our quantitative analysis reveals a severe geographic bias linked to uneven global research investment (Gini coefficient = 0.736), with a small number of countries contributing to many publicly available datasets. In contrast, the environmental distribution of these samples showed moderate ecological uniformity (Pielou’s Evenness = 0.818), though we identified pervasive gaps in metadata completeness that hinder dataset interoperability. Our curated dataset highlights a strong research focus on polyextremophilic habitats, positioning these ecosystems as prime targets for biotechnological and astrobiological bioprospecting. Additionally, the geographical bias highlights the need for interoperable global data frameworks and more equitable investment in data generation and analysis, especially in underrepresented regions of the Global South.