Biology subjects

Price, M. N.

Publications and source records attributed to Price, M. N..

5 recordsLinked to original sources

bioRxiv2018

Defining the toxicity limits on microbial range in a metal-contaminated aquifer

In extreme environments, toxic compounds restrict which microorganisms persist. However, in complex mixtures of inhibitory compounds, it is challenging to determine which specific compounds cause changes in abundance and prevent some microorganisms from growing. We focused on a contaminated aquifer in Oak Ridge, Tennessee, U.S.A. that has low pH and high concentrations of uranium, nitrate and many other inorganic ions. In the most contaminated wells, the microbial community is enriched in the Rhodanobacter genus. Rhodanobacter relative abundance is positively correlated with low pH and high concentrations of U, Mn, Al, Cd, Zn, Ni, Co, Ca, NO3-, Mg, Cl, SO42-, Sr, K and Ba and we sought to determine which of these correlated parameters are selective pressures that favor the growth of Rhodanobacter over other taxa. Using high-throughput cultivation, we determined that of the ions correlated high Rhodanobacter abundance, only low pH and high U, Mn, Al, Cd, Zn, Co and Ni (a) are selectively inhibitory of a sensitive Pseudomonas isolate from a background well versus a representative resistant Rhodanobacter isolate from a contaminated well, and (b) reach toxic concentrations in the most contaminated wells that can inhibit the sensitive Pseudomonas isolate. We prepared mixtures of inorganic ions representative of the most contaminated wells and verified that few other isolates aside from Rhodanobacter can tolerate these 8 parameters. These results clarify which toxic inorganic ions are causal factors that impact the microbial community at this field site and are not merely correlated with taxonomic shifts.

microbiology↗

bioRxiv2018

Dub-seq: dual-barcoded shotgun expression library sequencing for high-throughput characterization of functional traits

A major challenge in genomics is the knowledge gap between sequence and its encoded function. Gain-of-function methods based on gene overexpression are attractive avenues for phenotype-based functional screens, but are not easily applied in high-throughput across many experimental conditions. Here, we present Dual Barcoded Shotgun Expression Library Sequencing (Dub-seq), a method that greatly increases the throughput of genome-wide overexpression assays. In Dub-seq, a shotgun expression library is cloned between dual random DNA barcodes and the precise breakpoints of DNA fragments are associated to the barcode sequences prior to performing assays. To assess the fitness of individual strains carrying these plasmids, we use DNA barcode sequencing (BarSeq), which is amenable to large-scale sample multiplexing. As a demonstration of this approach, we constructed a Dub-seq library with total Escherichia coli genomic DNA, performed 155 genome-wide fitness assays in 52 experimental conditions, and identified 813 genes with high-confidence overexpression phenotypes across 4,151 genes assayed. We show that Dub-seq data is reproducible, accurately recapitulates known biology, and identifies hundreds of novel gain-of-function phenotypes for E. coli genes, a subset of which we verified with assays of individual strains. Dub-seq provides complementary information to loss-of-function approaches such as transposon site sequencing or CRISPRi and will facilitate rapid and systematic functional characterization of microbial genomes.\n\nImportanceMeasuring the phenotypic consequences of overexpressing genes is a classic genetic approach for understanding protein function; for identifying drug targets, antibiotic and metal resistance mechanisms; and for optimizing strains for metabolic engineering. In microorganisms, these gain-of-function assays are typically done using laborious protocols with individually archived strains or in low-throughput following qualitative selection for a phenotype of interest, such as antibiotic resistance. However, many microbial genes are poorly characterized and the importance of a given gene may only be apparent under certain conditions. Therefore, more scalable approaches for gain-of-function assays are needed. Here, we present Dual Barcoded Shotgun Expression Library Sequencing (Dub-seq), a strategy that couples systematic gene overexpression with DNA barcode sequencing for large-scale interrogation of gene fitness under many experimental conditions at low cost. Dub-seq can be applied to many microorganisms and is a valuable new tool for large-scale gene function characterization.

microbiology↗

bioRxiv2017

An Oxidative Pathway of Deoxyribose Catabolism

Using genome-wide mutant fitness assays in diverse bacteria, we identified novel oxidative pathways for the catabolism of 2-deoxy-D-ribose and 2-deoxy-D-ribonate. We propose that deoxyribose is oxidized to deoxyribonate, oxidized to ketodeoxyribonate, and cleaved to acetyl-CoA and glyceryl-CoA. We have genetic evidence for this pathway in three genera of bacteria, and we confirmed the oxidation of deoxyribose to ketodeoxyribonate in vitro. In Pseudomonas simiae, the expression of enzymes in the pathway is induced by deoxyribose or deoxyribonate, while in Paraburkholderia bryophila and in Burkholderia phytofirmans, the pathway proceeds in parallel with the known deoxyribose 5-phosphate aldolase pathway. We identified another oxidative pathway for the catabolism of deoxyribonate, with acyl-CoA intermediates, in Klebsiella michiganensis. Of these four bacteria, only P. simiae relies entirely on an oxidative pathway to consume deoxyribose. The deoxyribose dehydrogenase of P. simiae is either non-specific or evolved recently, as this enzyme is very similar to a novel vanillin dehydrogenase from Pseudomonas putida that we identified. So, we propose that these oxidative pathways evolved primarily to consume deoxyribonate, which is a waste product of metabolism.\n\nImportanceDeoxyribose is one of the building blocks of DNA and is released when cells die and their DNA degrades. We identified a bacterium that can grow with deoxyribose as its sole source of carbon even though its genome does not encode any of the known genes for breaking down deoxyribose. By growing many mutants of this bacterium together on deoxyribose and using DNA sequencing to measure the change in the mutants abundance, we identified multiple protein-coding genes that are required for growth on deoxyribose. Based on the similarity of these proteins to enzymes of known function, we propose a 6-step pathway in which deoxyribose is oxidized and then cleaved. Diverse bacteria use a portion of this pathway to break down a related compound, deoxyribonate, which is a waste product of human metabolism and is present in urine. Our study illustrates the utility of large-scale bacterial genetics to identify previously unknown metabolic pathways.

microbiology↗

bioRxiv2017

PaperBLAST: Text-mining papers for information about homologs

Large-scale genome sequencing has identified millions of protein-coding genes whose function is unknown. Many of these proteins are similar to characterized proteins from other organisms, but much of this information is missing from annotation databases and is hidden in the scientific literature. To make this information accessible, PaperBLAST uses EuropePMC to search the full text of scientific articles for references to genes. PaperBLAST also takes advantage of curated resources that link protein sequences to scientific articles (Swiss-Prot, GeneRIF, and EcoCyc). PaperBLASTs database includes over 700,000 scientific articles that mention over 400,000 different proteins. Given a protein of interest, PaperBLAST quickly finds similar proteins that are discussed in the literature and presents snippets of text from relevant articles or from the curators. PaperBLAST is available at http://papers.genomics.lbl.gov/.

bioinformatics↗

bioRxiv2016

Validating Regulatory Predictions from Diverse Bacteria with Mutant Fitness Data

Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacteriums growth across many conditions, to validate regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomics predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.

microbiology↗