Genome Biology

In the rhizobia, a group of symbiotic Gram-negative soil bacteria, RpoN (σ54, σN, NtrA) is best known as the sigma factor enabling transcription of the nitrogen fixation genes. Recent reports, however, demonstrate the involvement of RpoN in other symbiotic functions, although no large-scale effort has yet been undertaken to unravel the RpoN-regulon in rhizobia. We screened two complete rhizobial genomes (Mesorhizobium loti, Sinorhizobium meliloti) and four symbiotic regions (Rhizobium etli, Rhizobium sp. NGR234, Bradyrhizobium japonicum, M. loti) for the presence of the highly conserved RpoN-binding sites. A comparison was also made with two closely related non-symbiotic members of the Rhizobiales (Agrobacterium tumefaciens, Brucella melitensis). A highly specific weight-matrix-based screening method was applied to predict members of the RpoN-regulon, which were stored in a highly annotated and manually curated dataset. Possible enhancer-binding proteins (EBPs) controlling the expression of RpoN-dependent genes were predicted with a profile hidden Markov model. The methodology used to predict RpoN-binding sites proved highly effective as nearly all known RpoN-controlled genes were identified. In addition, many new RpoN-dependent functions were found. The dependency of several of these diverse functions on RpoN seems species-specific. Around 30% of the identified genes are hypothetical. Rhizobia appear to have recruited RpoN for symbiotic processes, whereas the role of RpoN in A. tumefaciens and B. melitensis remains largely to be elucidated. All species screened possess at least one uncharacterized EBP as well as the usual ones. Lastly, RpoN could significantly broaden its working range by direct interfering with the binding of regulatory proteins to the promoter DNA.

Differences in the transcription regulation network are at the root of much of the phenotypic variation observed among organisms. These differences may be achieved either by changing the repertoire of regulators and/or their targets, or by rewiring the network. Following these changes and studying their logic is crucial for understanding the evolution of regulatory networks. We use the well characterized transcription regulatory network of Escherichia coli K12 and follow the evolutionary changes in the repertoire of regulators and their targets across a large number of fully sequenced γ-proteobacteria. By focusing on close relatives of E. coli K12, we study the dynamics of the evolution of transcription regulation across a relatively short evolutionary timescale. We show significant differences in the evolution of repressors and activators. Repressors are only lost from a genome once their targets have themselves been lost, or once the network has significantly rewired. In contrast, activators are often lost even when their targets remain in the genome. As a result, E. coli K12 repressors that regulate many targets are rarely absent from organisms that are closely related to E. coli K12, while activators with a similar number of targets are often absent in these organisms. We demonstrate that the mode of regulation exerted by transcription factors has a strong effect on their evolution. Repressors co-evolve tightly with their target genes. In contrast, activators can be lost independently of their targets. In fact, loss of an activator can lead to efficient shutdown of an unnecessary pathway.

Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing. All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5–20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden. This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.

Hemiascomycetous yeasts have intron-poor genomes with very few cases of alternative splicing. Most of the reported examples result from intron retention in Saccharomyces cerevisiae and some have been shown to be functionally significant. Here we used transcriptome-wide approaches to evaluate the mechanisms underlying the generation of alternative transcripts in Yarrowia lipolytica, a yeast highly divergent from S. cerevisiae. Experimental investigation of Y. lipolytica gene models identified several cases of alternative splicing, mostly generated by intron retention, principally affecting the first intron of the gene. The retention of introns almost invariably creates a premature termination codon, as a direct consequence of the structure of intron boundaries. An analysis of Y. lipolytica introns revealed that introns of multiples of three nucleotides in length, particularly those without stop codons, were underrepresented. In other organisms, premature termination codon-containing transcripts are targeted for degradation by the nonsense-mediated mRNA decay (NMD) machinery. In Y. lipolytica, homologs of S. cerevisiae UPF1 and UPF2 genes were identified, but not UPF3. The inactivation of Y. lipolytica UPF1 and UPF2 resulted in the accumulation of unspliced transcripts of a test set of genes. Y. lipolytica is the hemiascomycete with the most intron-rich genome sequenced to date, and it has several unusual genes with large introns or alternative transcription start sites, or introns in the 5' UTR. Our results suggest Y. lipolytica intron structure is subject to significant constraints, leading to the under-representation of stop-free introns. Consequently, intron-containing transcripts are degraded by a functional NMD pathway.

Recently, it has become clear that some promoters function as long-range regulators of gene expression. However, direct and quantitative assessment of enhancer activity at long intergenic noncoding RNA (lincRNA) or mRNA gene bodies has not been performed. To unbiasedly assess the enhancer capacity across lincRNA and mRNA loci, we performed a massively parallel reporter assay (MPRA) on six lincRNA loci and their closest protein-coding neighbors. For both gene classes, we find significantly more MPRA activity in promoter regions than in gene bodies. However, three lincRNA loci, Lincp21, LincEnc1, and Peril, and one mRNA locus, Morc2a, display significant enhancer activity within their gene bodies. We hypothesize that such peaks may mark long-range enhancers, and test this in vivo using RNA sequencing from a knockout mouse model and high-throughput chromosome conformation capture (Hi-C). We find that ablation of a high-activity MPRA peak in the Peril gene body leads to consistent dysregulation of Mccc1 and Exosc9 in the neighboring topologically associated domain (TAD). This occurs irrespective of Peril lincRNA expression, demonstrating this regulation is DNA-dependent. Hi-C confirms long-range contacts with the neighboring TAD, and these interactions are altered upon Peril knockout. Surprisingly, we do not observe consistent regulation of genes within the local TAD. Together, these data suggest a long-range enhancer-like function for the Peril gene body. A multi-faceted approach combining high-throughput enhancer discovery with genetic models can connect enhancers to their gene targets and provides evidence of inter-TAD gene regulation.

Structural variations (SVs) or copy number variations (CNVs) greatly impact the functions of the genes encoded in the genome and are responsible for diverse human diseases. Although a number of existing SV detection algorithms can detect many types of SVs using whole genome sequencing (WGS) data, no single algorithm can call every type of SVs with high precision and high recall. We comprehensively evaluate the performance of 69 existing SV detection algorithms using multiple simulated and real WGS datasets. The results highlight a subset of algorithms that accurately call SVs depending on specific types and size ranges of the SVs and that accurately determine breakpoints, sizes, and genotypes of the SVs. We enumerate potential good algorithms for each SV category, among which GRIDSS, Lumpy, SVseq2, SoftSV, Manta, and Wham are better algorithms in deletion or duplication categories. To improve the accuracy of SV calling, we systematically evaluate the accuracy of overlapping calls between possible combinations of algorithms for every type and size range of SVs. The results demonstrate that both the precision and recall for overlapping calls vary depending on the combinations of specific algorithms rather than the combinations of methods used in the algorithms. These results suggest that careful selection of the algorithms for each type and size range of SVs is required for accurate calling of SVs. The selection of specific pairs of algorithms for overlapping calls promises to effectively improve the SV detection accuracy.

Systematic in vitro loss-of-function screens provide valuable resources that can facilitate the discovery of drugs targeting cancer vulnerabilities. We develop a deep learning-based method to predict tumor-specific vulnerabilities in patient samples by leveraging a wealth of in vitro screening data. Acquired dependencies of tumors are inferred in cases in which one allele is disrupted by inactivating mutations or in association with oncogenic mutations. Nucleocytoplasmic transport by Ran GTPase is identified as a common vulnerability in Her2-positive breast cancers. Vulnerability to loss of Ku70/80 is predicted for tumors that are defective in homologous recombination and rely on nonhomologous end joining for DNA repair. Our experimental validation for Ran, Ku70/80, and a proteasome subunit using patient-derived cells shows that they can be targeted specifically in particular tumors that are predicted to be dependent on them. This approach can be applied to facilitate the development of precision therapeutic targets for different tumors.

Polyadenylation plays a key role in producing mature mRNAs in eukaryotes. It is widely believed that the poly(A)-binding proteins (PABs) uniformly bind to poly(A)-tailed mRNAs, regulating their stability and translational efficiency. We observe that the homozygous triple mutant of broadly expressed Arabidopsis thaliana PABs, AtPAB2, AtPAB4, and AtPAB8, is embryonic lethal. To understand the molecular basis, we characterize the RNA-binding landscape of these PABs. The AtPAB-binding efficiency varies over one order of magnitude among genes. To identify the sequences accounting for the variation, we perform poly(A)-seq that directly sequences the full-length poly(A) tails. More than 10% of poly(A) tails contain at least one guanosine (G); among them, the G-content varies from 0.8 to 28%. These guanosines frequently divide poly(A) tails into interspersed A-tracts and therefore cause the variation in the AtPAB-binding efficiency among genes. Ribo-seq and genome-wide RNA stability assays show that AtPAB-binding efficiency of a gene is positively correlated with translational efficiency rather than mRNA stability. Consistently, genes with stronger AtPAB binding exhibit a greater reduction in translational efficiency when AtPAB is depleted. Our study provides a new mechanism that translational efficiency of a gene can be regulated through the G-content-dependent PAB binding, paving the way for a better understanding of poly(A) tail-associated regulation of gene expression.

Recovering metagenome-assembled genomes (MAGs) from shotgun sequencing data is an increasingly common task in microbiome studies, as MAGs provide deeper insight into the functional potential of both culturable and non-culturable microorganisms. However, metagenome-assembled genomes vary in quality and may contain omissions and contamination. These errors present challenges for detecting genes and comparing gene enrichment across sample types. To address this, we propose happi, an approach to testing hypotheses about gene enrichment that accounts for genome quality. We illustrate the advantages of happi over existing approaches using published Saccharibacteria MAGs, Streptococcus thermophilus MAGs, and via simulation.