Biology subjects

Shelton, A. N.

Publications and source records attributed to Shelton, A. N..

4 recordsLinked to original sources

bioRxiv2024

Abundant and active community members respond to the diel cycle in hot spring phototrophic mats

Photosynthetic microbial mats in hot springs can provide insights into the diel behaviors of communities in extreme environments. In this habitat, photosynthesis dominates during the day, leading to super-oxic conditions, with a rapid transition to fermentation and anoxia at night. Multiple samples were collected from two springs over several years to generate metagenomic and metatranscriptomic datasets. Metagenome assembled genomes (MAGs) comprised 71 taxa (in 19 different phyla), of which twelve core taxa were present at high abundance in both springs. The eight most active taxa identified by metatranscriptomics were an oxygenic cyanobacterium (Synechococcus sp.), five anoxygenic phototrophs from three different phyla, and two understudied heterotrophs from phylum Armatimonadota. Surprisingly, in all eight taxa, a significant fraction of genes exhibited a diel expression pattern although peak timing varied considerably. The two abundant heterotrophs exhibit starkly different peak timing of expression, which we propose is shaped by their metabolic and genomic potential to use carbon sources that become differentially available during the diel cycle. Network analysis revealed pathway expression patterns that had not previously been linked to diel cycles, including ribosome biogenesis and chaperones. This provides a framework for analyzing metabolically coupled communities and the dominant role of the diel cycle.

microbiology↗

bioRxiv2022

On the synergies between ribosomal assembly and machine learning tools for microbial identification

Genome assembly tools are used to reconstruct genomic sequences from raw sequencing data, which are then used for identifying the organisms present in a metagenomic sample. More recently, machine learning approaches have been applied to a variety of bioinformatics problems, and in this paper, we explore their use for organism identification. We start out by evaluating several commonly used metagenomic assembly tools, including PhyloFlash, MEGAHIT, MetaSPAdes, Kraken2, Mothur, UniCycler, and PathRacer, and compare them against state-of-the art deep learning-based machine learning classification approaches represented by DNABERT and DeLUCS, in the context of two synthetic mock community datasets. Our analysis focuses on determining whether ensembling metagenome assembly tools with machine learning tools has the potential to improve identification performance relative to using the tools individually. We find that this is indeed the case, and analyze the level of effectiveness of potential tool ensembling for organisms with different characteristics (based on factors such as repetitiveness, genome size, and GC content). Author SummaryMetagenomic studies focus on the challenging problem of identifying the presence and abundance of different species in a sample. This process typically involves the creation of digital reads from the sample which correspond to small parts of the genome sequence, and then have to be assembled together by a genome assembly tool. More recently, machine learning approaches have been applied to a variety of bioinformatics problems, and in this paper, we explore their use for organism identification, and how they might complement traditional bioinformatics approaches. We conduct experiments with two representative state-of-the-art machine learning approaches and six metagenomic assembly tools in the context of two synthetic datasets. We find that for organisms with certain characteristics (levels of repetitiveness, GC content, and genome size), ensembling metagenome assembly tools with machine learning tools has the potential to improve species identification performance relative to using the tools individually.

bioinformatics↗

bioRxiv2019

Flexible cobamide metabolism in Clostridioides (Clostridium) difficile 630 Δerm

Clostridioides (Clostridium) difficile is an opportunistic pathogen known for its ability to colonize the human gut under conditions of dysbiosis. Several aspects of its carbon and amino acid metabolism have been investigated, but its cobamide (vitamin B12 and related cofactors) metabolism remains largely unexplored. C. difficile has seven predicted cobamide-dependent metabolisms encoded in its genome in addition to a nearly complete cobamide biosynthesis pathway and a cobamide uptake system. To address the importance of cobamides to C. difficile, we studied C. difficile 630 {Delta}erm and mutant derivatives under cobamide-dependent conditions in vitro. Our results show that C. difficile can use a surprisingly diverse array of cobamides for methionine and deoxyribonucleotide synthesis, and can use alternative metabolites or enzymes, respectively, to bypass these cobamide-dependent processes. C. difficile 630 {Delta}erm produces the cobamide pseudocobalamin when provided the early precursor 5-aminolevulinc acid or the late intermediate cobinamide, and produces other cobamides if provided an alternative lower ligand. The ability of C. difficile 630 {Delta}erm to take up cobamides and Cbi at micromolar or lower concentrations requires the transporter BtuFCD. Genomic analysis revealed genetic variations in in the btuFCD locus of different C. difficile strains, which may result in differences in the ability to take up cobamides and Cbi. These results together demonstrate that, like other aspects of its physiology, cobamide metabolism in C. difficile is versatile.\n\nImportanceThe ability of the opportunistic pathogen Clostridioides difficile to cause disease is closely linked to its propensity to adapt to conditions created by dysbiosis of the human gut microbiota. The cobamide (vitamin B12) metabolism of C. difficile has been underexplored, though it has seven metabolic pathways that are predicted to require cobamide-dependent enzymes. Here, we show that C. difficile cobamide metabolism is versatile, as it can use a surprisingly wide variety of cobamides and has alternative functions that can bypass some of its cobamide requirements. Furthermore, C. difficile does not synthesize cobamides de novo, but produces them when given cobamide precursors. Better understanding of C. difficile cobamide metabolism may lead to new strategies to treat and prevent C. difficile-associated disease.

microbiology↗

bioRxiv2018

Uneven distribution of cobamide biosynthesis and dependence in bacteria predicted by comparative genomics

The vitamin B12 family of cofactors known as cobamides are essential for a variety of microbial metabolisms. We used comparative genomics of 11,000 bacterial species to analyze the extent and distribution of cobamide production and use across bacteria. We find that 86% of bacteria in this data set have at least one of 15 cobamide-dependent enzyme families, yet only 37% are predicted to synthesize cobamides de novo. The distribution of cobamide biosynthesis varies at the phylum level, with 57% of Actinobacteria, 45% of Proteobacteria, and 30% of Firmicutes, and less than 1% of Bacteroidetes containing the complete biosynthetic pathway. Cobamide structure could be predicted for 58% of cobamide-producing species, based on the presence of signature lower ligand biosynthesis and attachment genes. Our predictions also revealed that 17% of bacteria that have partial biosynthetic pathways, yet have the potential to salvage cobamide precursors. These include a newly defined, experimentally verified category of bacteria lacking the first step in the biosynthesis pathway. These predictions highlight the importance of cobamide and cobamide precursor crossfeeding as examples of nutritional dependencies in bacteria.

microbiology↗