Visualizing metagenomic and metatranscriptomic data: A comprehensive review

Multiomics-Based Profiling of the Fecal Microbiome Reveals Potential Disease-Specific Signatures in Pediatric IBD (PIBD)

Inflammatory bowel disease (IBD), which includes Crohn's Disease (CD) and Ulcerative Colitis (UC), is a chronic gastrointestinal (GI) disorder affecting 1 in 100 people in the United States. Pediatric IBD (PIBD) is estimated to impact 15 per 100,000 children in North America. Factors such as the gut microbiome (GM), genetic predisposition to the disease, and certain environmental factors are thought to be involved in pathogenesis. However, the pathophysiology of IBD is incompletely understood, and diagnostic biomarkers and effective treatments, particularly for PIBD, are limited. Recent work suggests that these factors may interact to influence disease development, and multiomic approaches have emerged as promising tools to elucidate the pathophysiology. We employed metagenomics, metabolomics- and metatranscriptomics-based approaches to examine the microbiome, its genetic potential, and its activity to identify factors associated with PIBD. Metagenomics-based analyses revealed pathways such as octane oxidation and glycolysis that were differentially expressed in UC patients. Additionally, metatranscriptomics-based analyses suggested enrichment of glycan degradation and two component systems in UC samples as well as protein processing in the endoplasmic reticulum, ribosome, and protein export in CD and UC samples. In addition, metabolomics-based approaches revealed patterns of differentially abundant metabolites between healthy and PIBD individuals. Interestingly, overall microbiome community composition (as measured by alpha and beta diversity indices) did not appear to be associated with PIBD. However, we observed a small number of differentially abundant taxa in UC versus healthy controls, including members of the Classes Gammaproteobacteria and Clostridia as well as members of the Family Rikenellaceae. Accordingly, when identifying potential biomarkers for PIBD, our results suggest that multiomics-based approaches afford enhanced potential to detect putative biomarkers for PIBD compared to microbiome community composition sequence data alone.

Harnessing plant-microbe interactions: strategies for enhancing resilience and nutrient acquisition for sustainable agriculture

The rhizosphere, a biologically active zone where plant roots interface with soil, plays a crucial role in enhancing plant health, resilience, and stress tolerance. As a key component in achieving Sustainable Development Goal 2, the rhizosphere is increasingly recognized for its potential to promote sustainable agricultural productivity. Engineering the rhizosphere microbiome is emerging as an innovative strategy to foster plant growth, improve stress adaptation, and restore soil health while mitigating the detrimental effects of conventional farming practices. This review synthesizes recent advancements in omics technologies, sequencing tools, and synthetic microbial communities (SynComs), which have provided insights into the complex interactions between plants and microbes. We examine the role of root exudates, composed of organic acids, amino acids, sugars, and secondary metabolites, as biochemical cues that shape beneficial microbial communities in the rhizosphere. The review further explores how advanced omics techniques like metagenomics and metabolomics are employed to elucidate the mechanisms by which root exudates influence microbial communities and plant health. Tailored SynComs have shown promising potential in enhancing plant resilience against both abiotic stresses (e.g., drought and salinity) and biotic challenges (e.g., pathogens and pests). Integration of these microbiomes with optimized root exudate profiles has been shown to improve nutrient cycling, suppress diseases, and alleviate environmental stresses, thus contributing to more sustainable agricultural practices. By leveraging multi-disciplinary approaches and optimizing root exudate profiles, ecological engineering of plant-microbiome interactions presents a sustainable pathway for boosting crop productivity. This approach also aids in managing soil-borne diseases, reducing chemical input dependency, and aligning with Sustainable Development Goals aimed at global food security and ecological sustainability. The ongoing research into rhizosphere microbiome engineering offers significant promise for ensuring long-term agricultural productivity while preserving soil and plant health for future generations.

Advancing Gut Microbiome Research: The Shift from Metagenomics to Multi-Omics and Future Perspectives

The gut microbiome, a dynamic and integral component of human health, has co-evolved with its host, playing essential roles in metabolism, immunity, and disease prevention. Traditional microbiome studies, primarily focused on microbial composition, have provided limited insights into the functional and mechanistic interactions between microbiota and their host. The advent of multi-omics technologies has transformed microbiome research by integrating genomics, transcriptomics, proteomics, and metabolomics, offering a comprehensive, systems-level understanding of microbial ecology and host-microbiome interactions. These advances have propelled innovations in personalized medicine, enabling more precise diagnostics and targeted therapeutic strategies. This review highlights recent breakthroughs in microbiome research, demonstrating how these approaches have elucidated microbial functions and their implications for health and disease. Additionally, it underscores the necessity of standardizing multi-omics methodologies, conducting large-scale cohort studies, and developing novel platforms for mechanistic studies, which are critical steps toward translating microbiome research into clinical applications and advancing precision medicine.

FGeneBERT: function-driven pre-trained gene language model for metagenomics

Metagenomic data, comprising mixed multi-species genomes, are prevalent in diverse environments like oceans and soils, significantly impacting human health and ecological functions. However, current research relies on K-mer, which limits the capture of structurally and functionally relevant gene contexts. Moreover, these approaches struggle with encoding biologically meaningful genes and fail to address the one-to-many and many-to-one relationships inherent in metagenomic data. To overcome these challenges, we introduce FGeneBERT, a novel metagenomic pre-trained model that employs a protein-based gene representation as a context-aware and structure-relevant tokenizer. FGeneBERT incorporates masked gene modeling to enhance the understanding of inter-gene contextual relationships and triplet enhanced metagenomic contrastive learning to elucidate gene sequence-function relationships. Pre-trained on over 100 million metagenomic sequences, FGeneBERT demonstrates superior performance on metagenomic datasets at four levels, spanning gene, functional, bacterial, and environmental levels and ranging from 1 to 213 k input sequences. Case studies of ATP synthase and gene operons highlight FGeneBERT's capability for functional recognition and its biological relevance in metagenomic research.

Unveiling the potential of bioslurry and biogenic ZnO nanoparticles formulation as significant bionanofertilizer by ameliorating rhizospheric microbiome of Vigna radiata

Advancements in nanotechnology, particularly the use of bionanofertilizers, show promise for sustainable agriculture by enhancing soil health and reducing reliance on conventional fertilizers. This study explored the impact of a bioslurry and biogenic zinc oxide (ZnO) nanoparticle formulation on microbial diversity in the rhizosphere of Vigna radiata (mung bean) using 16S rRNA sequencing. High-quality reads from both untreated and treated soil samples revealed a dominance of Archaea, though its proportion was reduced in the treated sample (66% in untreated, 58% in treated). The treated soil showed an increased abundance of beneficial bacterial phyla, including Acidobacteria (+ 6%), Actinobacteria (+ 2%), and Firmicutes (+ 2%). Notably, Acidobacteria-6 and Chloroacidobacteria, essential for nutrient cycling, were enriched in treated soil. Alpha diversity (Chao1 and Shannon indices) was lower in treated samples, indicating selective enhancement of beneficial microbes. Functional analyses like Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) and Statistical Analysis of Taxonomic and Functional Profiles (STAMP) analysis highlighted increased pathways related to motility, chemotaxis, and metabolic processes in the treated soil. These findings suggest that ZnO NPs and bioslurry treatment at 250 ppm improves soil microbial composition and functional attributes, supporting its potential as a bionanofertilizer for soil health restoration and enhanced plant growth.