Phenotypic landscape of a bacterial cell

The explosion of sequence information in bacteria makes developing high-throughput, cost-effective approaches to matching genes with phenotypes imperative. Using E. coli as proof of principle, we show that combining large-scale chemical genomics with quantitative fitness measurements provides a high-quality data set rich in discovery. Probing growth profiles of a mutant library in hundreds of conditions in parallel yielded > 10,000 phenotypes that allowed us to study gene essentiality, discover leads for gene function and drug action, and understand higher-order organization of the bacterial chromosome. We highlight new information derived from the study, including insights into a gene involved in multiple antibiotic resistance and the synergy between a broadly used combinatory antibiotic therapy, trimethoprim and sulfonamides. This data set, publicly available at http://ecoliwiki.net/tools/chemgen/, is a valuable resource for both the microbiological and bioinformatic communities, as it provides high-confidence associations between hundreds of annotated and uncharacterized genes as well as inferences about the mode of action of several poorly understood drugs.     

Genetic Screens Identify Host Factors for SARS-CoV-2 and Common Cold Coronaviruses

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不受欢迎的生物多样性：香港的外来植物物种

香港早在19世纪中叶开始就有外来植物入侵的记录,迄今为止,已发现多达238种已归化的外来或怀疑为外来的植物,其中又以薇甘菊（Mikania micrantha)、五爪金龙（Ipomoea cairica)、假臭草（Eupatorium catarium)、大黍（Panicum maximum)等最常见,外来植物最常见于受人为干扰的生境,例如荒废农田及路旁等,而较少在天然林地生境及贫瘠的灌草丛中发现,外来植物的对本地生态系统的影响主要局限于低地生境,它们常形成单优种群,减少了生境及贫瘠的灌草丛中发现,外来植物对本地生态系统的影响主要局限于低地生境,它们常形成单优种群,减少少了生境及动植物的多样性,外来动物对香港原生植物影响最大的是于20世纪70年代入侵的松树线虫（Bur-saphelenchus xylophilus)。外来的脊椎动物也有可能对香港的植物被演替产生影响,目前香港的外来植物当中,有些在大陆较少分布或没有记录,作为华南最大的港口,香港对外来物种的引入扮演着重要的角色,因此制定控制外来种在香港及华南地区的引入及传播的政策及措施非常重要。     

The next frontier of systems biology: higher-order and interspecies interactions

Systems approaches are not so different in essence from classical genetic and biochemical approaches, and in the future may become adopted so widely that the term 'systems biology' itself will become obsolete.     

Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae

Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two studies, we marked 7504 as being of substantially lower confidence. Additionally, applying our metric and a stringent cutoff we identified a set of 9074 interactions (including 4456 that were not among the 12,122 interactions) with accuracy comparable to that of conventional small scale methodologies. Finally we organized proteins into coherent multisubunit complexes using hierarchical clustering. This work thus provides a highly accurate physical interaction map of yeast in a format that is readily accessible to the biological community.     

Replication Fork Collapse and Genome Instability in a Deoxycytidylate Deaminase Mutant

Ribonucleotide reductase (RNR) and deoxycytidylate deaminase (dCMP deaminase) are pivotal allosteric enzymes required to maintain adequate pools of deoxyribonucleoside triphosphates (dNTPs) for DNA synthesis and repair. Whereas RNR inhibition slows DNA replication and activates checkpoint responses, the effect of dCMP deaminase deficiency is largely unknown. Here, we report that deleting the Schizosaccharomyces pombe dcd1⁺ dCMP deaminase gene (SPBC2G2.13c) increases dCTP ∼30-fold and decreases dTTP ∼4-fold. In contrast to the robust growth of a Saccharomyces cerevisiae dcd1Δ mutant, fission yeast dcd1Δ cells delay cell cycle progression in early S phase and are sensitive to multiple DNA-damaging agents, indicating impaired DNA replication and repair. DNA content profiling of dcd1Δ cells differs from an RNR-deficient mutant. Dcd1 deficiency activates genome integrity checkpoints enforced by Rad3 (ATR), Cds1 (Chk2), and Chk1 and creates critical requirements for proteins involved in recovery from replication fork collapse, including the γH2AX-binding protein Brc1 and Mus81 Holliday junction resolvase. These effects correlate with increased nuclear foci of the single-stranded DNA binding protein RPA and the homologous recombination repair protein Rad52. Moreover, Brc1 suppresses spontaneous mutagenesis in dcd1Δ cells. We propose that replication forks stall and collapse in dcd1Δ cells, burdening DNA damage and checkpoint responses to maintain genome integrity.     

Learning a Prior on Regulatory Potential from eQTL Data

Genome-wide RNA expression data provide a detailed view of an organism's biological state; hence, a dataset measuring expression variation between genetically diverse individuals (eQTL data) may provide important insights into the genetics of complex traits. However, with data from a relatively small number of individuals, it is difficult to distinguish true causal polymorphisms from the large number of possibilities. The problem is particularly challenging in populations with significant linkage disequilibrium, where traits are often linked to large chromosomal regions containing many genes. Here, we present a novel method, Lirnet, that automatically learns a regulatory potential for each sequence polymorphism, estimating how likely it is to have a significant effect on gene expression. This regulatory potential is defined in terms of “regulatory features”—including the function of the gene and the conservation, type, and position of genetic polymorphisms—that are available for any organism. The extent to which the different features influence the regulatory potential is learned automatically, making Lirnet readily applicable to different datasets, organisms, and feature sets. We apply Lirnet both to the human HapMap eQTL dataset and to a yeast eQTL dataset and provide statistical and biological results demonstrating that Lirnet produces significantly better regulatory programs than other recent approaches. We demonstrate in the yeast data that Lirnet can correctly suggest a specific causal sequence variation within a large, linked chromosomal region. In one example, Lirnet uncovered a novel, experimentally validated connection between Puf3—a sequence-specific RNA binding protein—and P-bodies—cytoplasmic structures that regulate translation and RNA stability—as well as the particular causative polymorphism, a SNP in Mkt1, that induces the variation in the pathway.