Biology subjects

Hu, J. C.

Publications and source records attributed to Hu, J. C..

5 recordsLinked to original sources

bioRxiv2020

Insights from the reanalysis of high-throughput chemical genomics data for Escherichia coli K-12

Despite the demonstrated success of genome-wide genetic screens and chemical genomics studies at predicting functions for genes of unknown function or predicting new functions for well-characterized genes, their potential to provide insights into gene function hasnt been fully explored. We systematically reanalyzed a published high-throughput phenotypic dataset for the model Gram-negative bacterium Escherichia coli K-12. The availability of high-quality annotation sets allowed us to compare the power of different metrics for measuring phenotypic profile similarity to correctly infer gene function. We conclude that there is no single best method; the three metrics tested gave comparable results for most gene pairs. We also assessed how converting qualitative phenotypes to discrete, qualitative phenotypes affected the association between phenotype and function. Our results indicate that this approach may allow phenotypic data from different studies to be combined to produce a larger dataset that may reveal functional connections between genes not detected in individual studies.

genomics↗

bioRxiv2020

microbialPhenotypes: An R package that analyzes high-throughput microbial phenotype data

Various microbial high-throughput phenotyping techniques have been vastly conducted to infer functions of genes, generating large numbers of valuable datasets whose potential in providing insights to characterize genes hasnt been fully exploited. Therefore, computational tools that allow unbiased, systematic analysis of these data also have become vital. Here we describe a package that evaluates high-throughput microbial phenotype data by one or several sets of associated functional annotations are provided. In addition, some helper functions are provided to help clean high-throughput microbial phenotype data.

bioinformatics↗

bioRxiv2020

Chemical-genetic interrogation of RNA polymerase mutants reveals structure-function relationships and physiological tradeoffs

The multi-subunit bacterial RNA polymerase (RNAP) and its associated regulators carry out transcription and integrate myriad regulatory signals. Numerous studies have interrogated the inner workings of RNAP, and mutations in genes encoding RNAP drive adaptation of Escherichia coli to many health- and industry-relevant environments, yet a paucity of systematic analyses has hampered our understanding of the fitness benefits and trade-offs from altering RNAP function. Here, we conduct a chemical-genetic analysis of a library of RNAP mutants. We discover phenotypes for non-essential insertions, show that clustering mutant phenotypes increases their predictive power for drawing functional inferences, and illuminate a connection between transcription and cell division. Our findings demonstrate that RNAP chemical-genetic interactions provide a general platform for interrogating structure-function relationships in vivo and for identifying physiological trade-offs of mutations, including those relevant for disease and biotechnology. This strategy should have broad utility for illuminating the role of other important protein complexes.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2020

Phenotypic associations among cell cycle genes in Saccharomyces cerevisiae

A long-standing effort in biology is to precisely define and group phenotypes that characterize a biological process, and the genes that underpin them. In Saccharomyces cerevisiae and other organisms, functional screens have generated rich lists of phenotypes associated with individual genes. However, it is often challenging to identify sets of phenotypes and genes that are most closely associated with a given biological process. Here, we focused on the 166 phenotypes arising from loss-of-function and the 86 phenotypes from gain-of-function mutations in 571 genes currently assigned to cell cycle-related ontologies in S. cerevisiae. To reduce this complexity, we applied unbiased, computational approaches of correspondence analysis to identify a minimum set of phenotypic variables that accounts for as much of the variability in the data as possible. Loss-of-function phenotypes can be reduced to 20 dimensions, while gain-of-function ones to 14 dimensions. We also pinpoint the contributions of phenotypes and genes in each set. The approach we describe not only simplifies the categorization of phenotypes associated with cell cycle progression but can also serve as a discovery tool for gene function.

genetics↗

bioRxiv2018

Bacteria-to-human protein networks reveal origins of endogenous DNA damage

DNA damage provokes mutations and cancer, and results from external carcinogens or endogenous cellular processes. Yet, the intrinsic instigators of DNA damage are poorly understood. Here we identify proteins that promote endogenous DNA damage when overproduced: the DNA-damaging proteins (DDPs). We discover a large network of DDPs in Escherichia coli and deconvolute them into six DNA-damage-causing function clusters, demonstrating DDP mechanisms in three: reactive-oxygen increase by transmembrane transporters, chromosome loss by replisome binding, and replication stalling by transcription factors. Their 284 human homologs are over-represented among known cancer drivers, and their expression in tumors predicts heavy mutagenesis and poor prognosis. Half of tested human homologs, when overproduced in human cells, promote DNA damage and mutation, with DNA-damaging mechanisms like those in E. coli. Together, our work reveals DDP networks that provoke endogenous DNA damage and may indicate functions of many human known and newly implicated cancer-promoting proteins.

cancer biology↗