Biology subjects

Wood, J. M.

Publications and source records attributed to Wood, J. M..

4 recordsLinked to original sources

bioRxiv2022

Deletion of proU suppresses proQ phenotypes in Escherichia coli

The ProQ protein interacts as an RNA chaperone with diverse RNA molecules in Escherichia coli. ProQ is implicated in the bacterial osmotic stress response. When the osmotic pressure is high, cells maintain their hydration by accumulating organic solutes denoted osmolytes. Transporters ProP and ProU (which is ProVWX) mediate osmolyte accumulation by Escherichia coli. Mutations at proQ impair ProP activity by reducing ProP levels (the ProQ transport phenotype) but do not impair ProU activity or reduce the level of ProX. The proQ- bacteria are longer than proQ+ bacteria during growth in either low or high salinity medium and they grow slowly at high salinity (the ProQ growth phenotype). In addition, spherical cells with crescent-shaped, nucleic acid-rich foci appear and cells lyse (the ProQ morphological phenotypes). In this work, the proQ transport phenotype was suppressed by deletions of proU, or by an insertion of IS5 in proU, when proP was expressed from the chromosome or from the heterologous, plasmid-based PBAD promoter. A point mutation disrupting the Walker B motif of ProV inactivated ProU but did not suppress the transport phenotype. ProP activities and ProP levels varied in parallel, so proQ and proU act at the same level to regulate ProP expression. Deletion of the proU operon also suppressed the growth and morphological phenotypes. The proU locus may overlap the gene encoding a regulatory sRNA that acts with ProQ, contributing to cellular morphogenesis and osmotic stress tolerance, or the relationship between ProQ and proU may be indirect.

microbiology↗

bioRxiv2022

Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome

BackgroundAntimicrobial Resistance (AMR) has a detrimental impact on human health on Earth and it is equally concerning in other environments such as space due to microgravity, radiation and confinement, especially for long-distance space travel. The International Space Station (ISS) is ideal for investigating microbial diversity and virulence. The shotgun metagenomics data of the ISS generated during the Microbial Tracking - 1 (MT-1) project and resulting metagenome-assembled genomes (MAGs) across three flights in eight different locations during 12 months were used in this study. The objective of this study was to identify the AMR genes associated with whole genomes of 227 cultivable strains, 21 shotgun metagenome sequences, and 24 MAGs retrieved from the ISS environmental samples that were treated with propidium monoazide (PMA; viable microbes). ResultsWe have analyzed the data using a deep learning model, allowing us to go beyond traditional cut-offs based only on high DNA sequence similarity and extending the catalog of AMR genes. Our results in PMA treated samples revealed AMR dominance in the last flight for Kalamiella piersonii, a bacteria related to urinary tract infection in humans. The analysis of 227 pure strains isolated from the MT-1 project revealed hundreds of antibiotic resistance genes from many isolates, including two top-ranking species that corresponded to strains of Enterobacter bugandensis and Bacillus cereus. Computational predictions were experimentally validated by antibiotic resistance profiles in these two species, showing a high degree of concordance. Specifically, disc assay data confirmed the high resistance of these two pathogens to various beta-lactam antibiotics. ConclusionOverall, our computational predictions and validation analyses demonstrate the advantages of machine learning to uncover concealed AMR determinants in metagenomics datasets, expanding the understanding of the ISS environmental microbiomes and their pathogenic potential in humans.

bioinformatics↗

bioRxiv2021

Chasing perfection: validation and polishing strategies for telomere-to-telomere genome assemblies

Advances in long-read sequencing technologies and genome assembly methods have enabled the recent completion of the first Telomere-to-Telomere (T2T) human genome assembly, which resolves complex segmental duplications and large tandem repeats, including centromeric satellite arrays in a complete hydatidiform mole (CHM13). Though derived from highly accurate sequencing, evaluation revealed that the initial T2T draft assembly had evidence of small errors and structural misassemblies. To correct these errors, we designed a novel repeat-aware polishing strategy that made accurate assembly corrections in large repeats without overcorrection, ultimately fixing 51% of the existing errors and improving the assembly QV to 73.9. By comparing our results to standard automated polishing tools, we outline common polishing errors and offer practical suggestions for genome projects with limited resources. We also show how sequencing biases in both PacBio HiFi and Oxford Nanopore Technologies reads cause signature assembly errors that can be corrected with a diverse panel of sequencing technologies

genomics↗

bioRxiv2020

Ecotype Simulation 2: An improved algorithm for efficiently demarcating microbial species from large sequence datasets

BackgroundMicrobial systematists have used molecular cutoffs to classify the vast diversity present within a natural microbial community without invoking ecological theory. The use of ecological theory is needed to identify whether or not demarcated groups are the ecologically distinct, fundamental units (ecotypes), necessary for understanding the system. Ecotype Simulation, a Monte-Carlo approach to modeling the evolutionary dynamics of a microbial population based on the Stable Ecotype Model of microbial speciation, has proven useful for finding these fundamental units. For instance, predicted ecotypes of Synechococcus forming microbial mats in Yellowstone National Park hot springs, which were previously considered to be a single species based on phenotype, have been shown to be ecologically distinct, with specialization to different temperature and light levels. Unfortunately, development of high-throughput DNA sequencing methods has outpaced the ability of the program to analyze all of the sequence data produced. ResultsWe developed an improved version of the program called Ecotype Simulation 2, which can rapidly analyze alignments of very large sequence datasets. For instance, while the older version takes days to analyze 200 sequences, the new version can analyze 1.92 x 105 sequences in about six hours. The faster simulation identified similar ecotypes as found with the slower version, but from larger amounts of sequence data. ConclusionsBased on ecological theory, Ecotype Simulation 2 provides a much-needed approach that will help guide microbial ecologists and systematists to the natural, fundamental units of bacterial diversity.

bioinformatics↗