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Lam, D.

Publications and source records attributed to Lam, D..

3 recordsLinked to original sources

Population dynamics of Escherichia coli in the gastrointestinal tracts of Tanzanian children

The stability of the Escherichia coli populations in the human gastrointestinal tract are not fully appreciated, and represent a significant knowledge gap regarding gastrointestinal community structure, as well as resistance to incoming pathogenic bacterial species and antibiotic treatment. The current study examines the genomic content of 240 Escherichia coli isolates from children 2 to 35 months old in Tanzania. The E. coli strains were isolated from three time points spanning a six month time period, with or without antibiotic treatment. The resulting isolates were sequenced, and the genomes compared. The findings in this study highlight the transient nature of E. coli strains in the gastrointestinal tract of children, as during a six-month interval, no one individual contained phylogenomically related isolates at all three time points. While the majority of the isolates at any one time point were phylogenomically similar, most individuals did not contain phylogenomically similar isolates at more than two time points. Examination of global genome content, canonical E. coli virulence factors, multilocus sequence type, serotype, and antimicrobial resistance genes identified diversity even among phylogenomically similar strains. There was no apparent increase in the antimicrobial resistance gene content after antibiotic treatment. The examination of the E. coli from longitudinal samples from multiple children in Tanzania provides insight into the genomic diversity and population variability of resident E. coli within the rapidly changing environment of the gastrointestinal tract.\n\nImportanceThis study increases the number of resident Escherichia coli genome sequences, and explores E. coli diversity through longitudinal sampling. We investigate the genomes of E. coli isolated from human gastrointestinal tracts as part of an antibiotic treatment program among rural Tanzanian children. Phylogenomics demonstrates that resident E. coli are diverse, even within a single host. Though the E. coli isolates of the gastrointestinal community tend to be phylogenomically similar at a given time, they differed across the interrogated time points, demonstrating the variability of the members of the E. coli community. Exposure to antibiotic treatment did not have an apparent impact on the E. coli community or the presence of resistance and virulence genes within E. coli genomes. The findings of this study highlight the variable nature of bacterial members of the human gastrointestinal tract.

microbiology

A Library of Phosphoproteomic and Chromatin Signatures for Characterizing Cellular Responses to Drug Perturbations

Though the added value of proteomic measurements to gene expression profiling has been demonstrated, profiling of gene expression on its own remains the dominant means of understanding cellular responses to perturbation. Direct protein measurements are typically limited due to issues of cost and scale; however, the recent development of high-throughput, targeted sentinel mass spectrometry assays provides an opportunity for proteomics to contribute at a meaningful scale in high-value areas for drug development. To demonstrate the feasibility of a systematic and comprehensive library of perturbational proteomic signatures, we profiled 90 drugs (in triplicate) in six cell lines using two different proteomic assays -- one measuring global changes of epigenetic marks on histone proteins and another measuring a set of peptides reporting on the phosphoproteome -- for a total of more than 3,400 samples. This effort represents a first-of-its-kind resource for proteomics. The majority of tested drugs generated reproducible responses in both phosphosignaling and chromatin states, but we observed differences in the responses that were cell line-and assay-specific. We formalized the process of comparing response signatures within the data using a concept called connectivity, which enabled us to integrate data across cell types and assays. Furthermore, it facilitated incorporation of transcriptional signatures. Consistent connectivity among cell types revealed cellular responses that transcended cell-specific effects, while consistent connectivity among assays revealed unexpected associations between drugs that were confirmed by experimental follow-up. We further demonstrated how the resource could be leveraged against public domain external datasets to recognize therapeutic hypotheses that are consistent with ongoing clinical trials for the treatment of multiple myeloma and acute lymphocytic leukemia (ALL). These data are available for download via the Gene Expression Omnibus (accession GSE101406), and web apps for interacting with this resource are available at https://clue.io/proteomics.\n\nHighlightsO_LIFirst-of-its-kind public resource of proteomic responses to systematically administered perturbagens\nC_LIO_LIDirect proteomic profiling of phosphosignaling and chromatin states in cells for 90 drugs in six different cell lines\nC_LIO_LIExtends Connectivity Map concept to proteomic data for integration with transcriptional data\nC_LIO_LIEnables recognition of unexpected, cell type-specific activities and potential translational therapeutic opportunities\nC_LI

systems biology

A Next Generation Connectivity Map: L1000 Platform And The First 1,000,000 Profiles

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.\n\nHIGHLIGHTSO_LIA new gene expression profiling method, L1000, dramatically lowers cost\nC_LIO_LIThe Connectivity Map database now includes 1.3 million publicly accessible L1000 perturbational profiles\nC_LIO_LIThis expanded Connectivity Map facilitates discovery of small molecule mechanism of action and functional annotation of genetic variants\nC_LIO_LIThe work establishes feasibility and utility of a truly comprehensive Connectivity Map\nC_LI

genomics