Biology subjects

Maharjan, S.

Publications and source records attributed to Maharjan, S..

3 recordsLinked to original sources

bioRxiv2021

Loss of N1-methylation of G37 in tRNA induces ribosome stalling and reprograms gene expression

N1-methylation of G37 is required for a subset of tRNAs to maintain the translational reading-frame. While loss of m1G37 increases ribosomal +1 frameshifting, whether it incurs additional translational defects is unknown. Here we address this question by applying ribosome profiling to gain a genome-wide view of the effects of m1G37 deficiency on protein synthesis. Using E. coli as a model, we show that m1G37 deficiency induces ribosome stalling at codons that are normally translated by m1G37-containing tRNAs. Stalling occurs during decoding of affected codons at the ribosomal A site, indicating a distinct mechanism than that of +1 frameshifting, which occurs after the A site. Enzyme and cell-based assays show that m1G37 deficiency reduces tRNA aminoacylation and in some cases peptide-bond formation. We observe changes of gene expression in m1G37 deficiency similar to those in the starvation-induced stringent response, consistent with the notion that loss of tRNA aminoacylation activates the response. This work demonstrates a previously unrecognized function of m1G37 that emphasizes its role throughout the entire elongation cycle of protein synthesis, providing new insight into its essentiality for bacterial growth and survival.

microbiology↗

bioRxiv2021

A Statistical Model for Describing and Simulating Microbial Community Profiles

Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, nonindependence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework. To address this challenge, we developed SparseDOSSA (Sparse Data Observations for the Simulation of Synthetic Abundances): a statistical model of microbial ecological population structure, which can be used to parameterize real-world microbial community profiles and to simulate new, realistic profiles of known structure for methods evaluation. Specifically, SparseDOSSAs model captures marginal microbial feature abundances as a zero-inflated log-normal distribution, with additional model components for absolute cell counts and the sequence read generation process, microbemicrobe, and microbe-environment interactions. Together, these allow fully known covariance structure between synthetic features (i.e. "taxa") or between features and "phenotypes" to be simulated for method benchmarking. Here, we demonstrate SparseDOSSAs performance for 1) accurately modeling human-associated microbial population profiles; 2) generating synthetic communities with controlled population and ecological structures; 3) spiking-in true positive synthetic associations to benchmark analysis methods; and 4) recapitulating an end-to-end mouse microbiome feeding experiment. Together, these represent the most common analysis types in assessment of real microbial community environmental and epidemiological statistics, thus demonstrating SparseDOSSAs utility as a general-purpose aid for modeling communities and evaluating quantitative methods. An open-source implementation is available at http://huttenhower.sph.harvard.edu/sparsedossa2.

bioinformatics↗

bioRxiv2020

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3

Culture-independent analyses of microbial communities have advanced dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1,262 metagenomes) and IBD (1,635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4,077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.

microbiology↗