Niranjan Nagarajan

Surveillance of methicillin-resistant Staphylococcus aureus (MRSA) in the environment is crucial for reducing its transmission, particularly in hospitals, nursing homes, and high-contact public areas. Rapid Point-of-Need (PON) tools at healthcare facilities, without involving prolonged bacterial culture in sophisticated laboratories, are needed for environmental surveillance of MRSA. We have developed a gold nanoparticle (AuNP)-based “Swab-and-Stain” assay for rapid and on-site detection of PBP2a-expressing MRSA from environmental surfaces. The assay utilizes anti-PBP2a antibody immobilized on cotton swabs to specifically capture PBP2a-expressing MRSA and anti-MRSA antibody conjugated on AuNPs to detect the captured MRSA on the swabs via sandwiched complex formation. In the presence of PBP2a-expressing MRSA, the swabs were stained in red, as seen with the naked eye. The color intensity of the stain can be quantified by smartphone image analysis for MRSA quantification. Using PBP2a-expressing MRSA samples spiked in real hospital sink and drain matrices, this pilot study demonstrated a “Swab-and-Stain” assay as a proof-of-concept method for detecting MRSA at a concentration as low as 12 CFU/mL. The assay was validated to specifically detect MRSA spiked in real hospital sink and drain matrices. This “Swab-and-Stain” assay provides a rapid, portable, and easy-to-use platform with potential application for PON environmental surveillance of MRSA.

Background: The gut microbiome has been widely studied in the context of obesity, and yet the reported associations vary widely across populations and analytical approaches. In Asian populations where the prevalence of obesity is rapidly rising, the extent to which gut microbiome features could associate with adiposity in a robust and generalizable manner remains unclear. Methods: Population-scale shotgun metagenomic data was generated for adults (n=871) from the Health for Life in Singapore (HELIOS) cohort, comprising ethnic Chinese, Malay, and Indian participants. Integrated taxonomic, functional, and machine-learning–based analyses were used to assess associations between gut microbiome features and obesity, adjusting for demographic covariates and evaluating for robustness across multiple statistical frameworks. Results: Global microbiome structure exhibited weak separation by body mass index (BMI), with enterotype-like clustering providing limited discriminatory power for obesity status. Differential abundance analyses identified a small number of method-dependent taxa and pathways, with only limited recurrence across methods. Supervised machine learning models trained on taxonomic profiles achieved modest predictive performance, particularly for intermediate BMI classes, and did not reveal robust microbial signatures beyond those detected by univariate analyses. Conclusions: Our study highlights the importance of large-scale, multi-framework analyses for distinguishing robust microbiome–phenotype associations from weak, method-dependent signals. Together, our findings emphasize that obesity-associated microbiome signatures may be too weak, diffuse, and insufficient to explain adiposity in Asian populations.

The extent to which an RNA folds into structure ensembles and how different structures in the ensemble regulate eukaryotic gene expression is not fully understood. Here, we coupled chemical probing with direct RNA sequencing to identify structure modifications along a single RNA molecule (sm-PORE-cupine). We used direct signal alignment in addition to base mapping to increase the percentage of mappable sequences and showed that Bernoulli mixture model clustering can separate structure ensembles accurately. We applied sm-PORE-cupine to identify isoform-specific structure ensembles along the SARS-CoV-2 genome and structure ensembles in the Candida albicans transcriptome. We observed that RNAs are more structurally homogeneous in vitro, at higher temperatures and in the 3' untranslated regions of C. albicans. Structure ensembles are associated with changes in translation efficiency and decay in C. albicans, and we validated translation changes using reporter assays. sm-PORE-cupine expands the existing toolbox for studying RNA structure and function in diverse transcriptomes.

The human skin is repeatedly exposed to mechanical and environmental stress, particularly in common skin diseases such as eczema, and yet the determinants of recovery remain poorly understood. Using longitudinal, multimodal profiling of skin physiology, structure (Raman spectroscopy), and microbial communities (shotgun metagenomics), we investigated in a human cohort (n=36 subjects, ×2 sites, ×6 timepoints) how host-microbe interactions could jointly shape recovery. Despite baseline variability in physiological parameters, we established that our protocol enables a defined disruption of the stratum corneum. While recovery trajectories for host attributes were notably consistent across age groups and body sites, individual-specific differences in recovery timelines were observed. To assess the role of the skin microbiome, several key time-dependent changes in microbial species were identified including enrichment of select Cutibacterium and Staphylococcus species and depletion of Corynebacterium and Malassezia species. Clustering of microbiome stability profiles across subjects and sites identified 6 distinct groups which associate with varying host-recovery patterns and microbial functions. Finally, joint hazards modelling of recovery timing revealed significant contributions from microbial taxa, functions and stability groups, highlighting the under-appreciated role of host-microbial interactions in response to skin stress and in the recovery process.

The presence of microbiota in human tumors has been reported widely based on bioinformatic analyses of DNA sequencing datasets; however, the source of microbial sequences in atypical anatomical sites is challenging to validate, as these could derive from sampling, storage, handling and processing of samples, similar to what has been described in studies of ancient DNA. Contamination of microbial reference genomes can also be a source of microbial signals, causing misclassification of human reads. Here, we overview the required quality controls and validation approaches and summarize optimal practices to improve the rigor and standards of tumor microbiome studies.

Quality control is essential in numerous Bioinformatics pipelines, yet existing approaches often depend on inefficient mapping to reference genomes or potentially uncalibrated quality scores. We present skiver, a reference-free, alignment-free quality control tool that uses (k, v)-mer sketches. By deriving consensus from sketched (k, v)-mers, skiver estimates survival and hazard rates that capture the positional distribution of errors. Across simulated and real datasets from multiple sequencing platforms, skiver accurately estimates overall error rates, error-type proportions, and quality-score calibration. An iterative outlier-filtering strategy enables robust performance on complex datasets with multiple strains, alleles, and repetitive regions.

The majority of SARS-CoV-2 genomes obtained during the pandemic were derived by amplifying overlapping windows of the genome (‘tiled amplicons’), reconstructing their sequences and fitting them together. This leads to systematic errors in genomes unless the software is both aware of the amplicon scheme and of the error modes of amplicon sequencing. Additionally, over time, amplicon schemes need to be updated as new mutations in the virus interfere with the primer binding sites at the end of amplicons. Thus, waves of variants swept the world during the pandemic and were followed by waves of systematic errors in the genomes, which had significant impacts on the inferred phylogenetic tree. Here we reconstruct the genomes from all public data as of June 2024 using an assembly tool called Viridian ( https://github.com/iqbal-lab-org/viridian ), developed to rigorously process amplicon sequence data. With these high-quality consensus sequences we provide a global phylogenetic tree of 4,471,579 samples, viewable at https://viridian.taxonium.org . We provide simulation and empirical validation of the methodology, and quantify the improvement in the phylogeny.

Bifidobacterium represents a diverse genus of commensal gut bacteria with key roles in human health, from metabolizing indigestible fibers to protecting against pathogens. While metagenomic studies have highlighted significant strain diversity for Bifidobacterium species within individuals, their systematic isolation and phenotypic characterization can be hampered by the significant effort and biases inherent in traditional culturomics. Here, we explored the utility of a high-throughput single-cell dispensing system (B.SIGHT)-based workflow for accelerating the process of isolating diverse Bifidobacterium strains from fecal samples. Systematic assessment of this workflow revealed a high single-cell dispensing frequency (>88%) and the ability to preserve species diversity when a pool of Bifidobacterium strains was dispensed. Culturing-related factors including the use of an effective selection medium, such as the Bifidus Selective Medium supplemented with mupirocin, and the length of pre-dispensing incubation were found to be critical in determining isolation success. Leveraging this workflow, we obtained a total of 622 viable isolates from five Singaporean fecal samples, of which >98% were found to be from Bifidobacterium species. Whole-genome sequencing of 96 isolates identified six different Bifidobacterium species with both inter- and intra-subject strain and lineage diversity, and the majority (>66%) were novel relative to large public genomic databases. Our findings highlight the ability of this high-throughput culturomics workflow to accelerate the recovery of diverse and novel Bifidobacterium strains, enabling further interrogation of their functional characteristics and advancing our understanding of important bacterial species in the gut microbiome. IMPORTANCE: The field of high-throughput microbial culturomics is still in its early stages. Enhancing our ability to isolate and phenotypically test bacterial strains from complex communities is crucial for advancing microbiome research and healthcare development. Given the time and cost inefficiencies of traditional culturing methods, a more efficient, high-throughput approach to obtain isolates is needed. In the present study, we assessed a single-cell dispensing platform and developed a workflow to isolate diverse Bifidobacterium strains from fecal samples. We demonstrated here the capability of this novel technology to efficiently obtain hundreds of isolates of a targeted group, covering both species and strain diversities. This generalizable and scalable method can potentially allow for the high-throughput recovery of microbes from other taxonomic groups, providing a fundamental step in improving the culturomics framework to complement metagenomic approaches and enable isolate-level functional studies of important microbes.

Despite being important ecological modulators of the gut microbiome, bacteriophage diversity and function remain under-characterized. We show that short-read metagenomic surveys can miss even globally highly prevalent viral family-level clusters (VFCs), that can be readily assembled and characterized with long-read metagenomic data from a relatively small cohort (n=109). While gut Bacteroidota phages have been the prevailing focus in the literature, we show that highly prevalent gut phage families frequently have Firmicutes hosts (termed GuFi phages), with broad host ranges verified using proximity-ligation (Hi-C) sequencing data. High-throughput sequencing of virus-like particles from fecal samples detected frequent enrichment of GuFi phages across samples, revealing their under-appreciated impact on the gut microbiome. We report the first in vitro induction and imaging of members of prevalent GuFi clades including the candidate orders Heliusvirales , Astravirales (VFC 2) and Suryavirales (VFC 4). Our findings underscore the importance of GuFi phages with broad host ranges in the gut microbiome, and the utility of long-read sequencing for viral discovery, paving the way for deeper insights into the role of bacteriophages in human health and disease.