Enricherator: A Bayesian Method for Inferring Regularized Genome-wide Enrichments from Sequencing Count Data.

A pervasive question in biological research studying gene regulation, chromatin structure, or genomics is where, and to what extent, does a signal of interest arise genome-wide? This question is addressed using a variety of methods relying on high-throughput sequencing data as their final output, including ChIP-seq for protein-DNA interactions,1 GapR-seq for measuring supercoiling,2 and HBD-seq or DRIP-seq for R-loop positioning.3,4 Current computational methods to calculate genome-wide enrichment of the signal of interest usually do not properly handle the count-based nature of sequencing data, they often do not make use of the local correlation structure of sequencing data, and they do not apply any regularization of enrichment estimates. This can result in unrealistic estimates of the true underlying biological enrichment of interest, unrealistically low estimates of confidence in point estimates of enrichment (or no estimates of confidence at all), unrealistic gyrations in enrichment estimates at very close (<10 bp) genomic loci due to noise inherent in sequencing data, and in a multiple-hypothesis testing problem during interpretation of genome-wide enrichment estimates. We developed a tool called Enricherator to infer genome-wide enrichments from sequencing count data. Enricherator uses the variational Bayes algorithm to fit a generalized linear model to sequencing count data and to sample from the approximate posterior distribution of enrichment estimates (https://github.com/jwschroeder3/enricherator). Enrichments inferred by Enricherator more precisely identify known binding sites in cases where low coverage between binding sites leads to false-positive peak calls in these noisy regions of the genome; these benefits extend to published datasets.

Regulation of the Drosophila transcriptome by Pumilio and the CCR4-NOT deadenylase complex.

The sequence-specific RNA-binding protein Pumilio controls Drosophila development; however, the network of mRNAs that it regulates remains incompletely characterized. In this study, we utilize knockdown and knockout approaches coupled with RNA-Seq to measure the impact of Pumilio on the transcriptome of Drosophila cells in culture. We also use an improved RNA co-immunoprecipitation method to identify Pumilio-bound mRNAs in Drosophila embryos. Integration of these datasets with the locations of Pumilio binding motifs across the transcriptome reveal novel direct Pumilio target genes involved in neural, muscle, wing, and germ cell development, and cellular proliferation. These genes include components of Wnt, TGF-beta, MAPK/ERK, and Notch signaling pathways, DNA replication, and lipid metabolism. We identify the mRNAs regulated by the CCR4-NOT deadenylase complex, a key factor in Pumilio-mediated repression, and observe concordant regulation of Pumilio:CCR4-NOT target mRNAs. Computational modeling reveals that Pumilio binding, binding site number, clustering, and sequence context are important determinants of regulation. In contrast, we show that the responses of direct mRNA targets to Pumilio-mediated repression are not influenced by their content of optimal synonymous codons. Moreover, contrary to a prevailing model, we do not detect a role for CCR4-NOT in the degradation of mRNAs with low codon optimality. Together, the results of this work provide new insights into the Pumilio regulatory network and mechanisms, and the parameters that influence the efficacy of Pumilio-mediated regulation.

Nucleoid-associated proteins shape the global protein occupancy and transcriptional landscape of a clinical isolate of Vibrio cholerae.

Vibrio cholerae, the causative agent of the diarrheal disease cholera, poses an ongoing health threat due to its wide repertoire of horizontally acquired elements (HAEs) and virulence factors. New clinical isolates of the bacterium with improved fitness abilities, often associated with HAEs, frequently emerge. The appropriate control and expression of such genetic elements is critical for the bacteria to thrive in the different environmental niches it occupies. H-NS, the histone-like nucleoid structuring protein, is the best studied xenogeneic silencer of HAEs in gamma-proteobacteria. Although H-NS and other highly abundant nucleoid-associated proteins (NAPs) have been shown to play important roles in regulating HAEs and virulence in model bacteria, we still lack a comprehensive understanding of how different NAPs modulate transcription in V. cholerae. By obtaining genome-wide measurements of protein occupancy and active transcription in a clinical isolate of V. cholerae, harboring recently discovered HAEs encoding for phage defense systems, we show that a lack of H-NS causes a robust increase in the expression of genes found in many HAEs. We further found that TsrA, a protein with partial homology to H-NS, regulates virulence genes primarily through modulation of H-NS activity. We also identified a few sites that are affected by TsrA independently of H-NS, suggesting TsrA may act with diverse regulatory mechanisms. Our results demonstrate how the combinatorial activity of NAPs is employed by a clinical isolate of an important pathogen to regulate recently discovered HAEs. Importance: New strains of the bacterial pathogen Vibrio cholerae, bearing novel horizontally acquired elements (HAEs), frequently emerge. HAEs provide beneficial traits to the bacterium, such as antibiotic resistance and defense against invading bacteriophages. Xenogeneic silencers are proteins that help bacteria harness new HAEs and silence those HAEs until they are needed. H-NS is the best-studied xenogeneic silencer; it is one of the nucleoid-associated proteins (NAPs) in gamma-proteobacteria and is responsible for the proper regulation of HAEs within the bacterial transcriptional network. We studied the effects of H-NS and other NAPs on the HAEs of a clinical isolate of V. cholerae. Importantly, we found that H-NS partners with a small and poorly characterized protein, TsrA, to help domesticate new HAEs involved in bacterial survival and in causing disease. Proper understanding of the regulatory state in emerging isolates of V. cholerae will provide improved therapies against new isolates of the pathogen.

Improving deep learning protein monomer and complex structure prediction using DeepMSA2 with huge metagenomics data.

Leveraging iterative alignment search through genomic and metagenome sequence databases, we report the DeepMSA2 pipeline for uniform protein single- and multichain multiple-sequence alignment (MSA) construction. Large-scale benchmarks show that DeepMSA2 MSAs can remarkably increase the accuracy of protein tertiary and quaternary structure predictions compared with current state-of-the-art methods. An integrated pipeline with DeepMSA2 participated in the most recent CASP15 experiment and created complex structural models with considerably higher quality than the AlphaFold2-Multimer server (v.2.2.0). Detailed data analyses show that the major advantage of DeepMSA2 lies in its balanced alignment search and effective model selection, and in the power of integrating huge metagenomics databases. These results demonstrate a new avenue to improve deep learning protein structure prediction through advanced MSA construction and provide additional evidence that optimization of input information to deep learning-based structure prediction methods must be considered with as much care as the design of the predictor itself.

Deep mutational scanning highlights a role for cytosolic regions in Hrd1 function.

Misfolded endoplasmic reticulum (ER) proteins are degraded through a process called ER-associated degradation (ERAD). Soluble, lumenal ERAD targets are recognized, retrotranslocated across the ER membrane, ubiquitinated, extracted from the membrane, and degraded by the proteasome using an ERAD pathway containing a ubiquitin ligase called Hrd1. To determine how Hrd1 mediates these processes, we developed a deep mutational scanning approach to identify residues involved in Hrd1 function, including those exclusively required for lumenal degradation. We identify several regions required for different Hrd1 functions. Most surprisingly, we find two cytosolic regions of Hrd1 required for lumenal ERAD substrate degradation. Using in vivo and in vitro approaches, we define roles for disordered regions between structural elements that are required for Hrd1 autoubiquitination and substrate interaction. Our results demonstrate that disordered cytosolic regions promote substrate retrotranslocation by controlling Hrd1 activation and establishing directionality of retrotranslocation for lumenal substrate across the ER membrane.

Tracking live-cell single-molecule dynamics enables measurements of heterochromatinassociated protein-protein interactions.

Visualizing and measuring molecular-scale interactions in living cells represents a major challenge, but recent advances in microscopy are bringing us closer to achieving this goal. Single-molecule super-resolution microscopy enables high-resolution and sensitive imaging of the positions and movement of molecules in living cells. HP1 proteins are important regulators of gene expression because they selectively bind and recognize H3K9 methylated (H3K9me) histones to form heterochromatin-associated protein complexes that silence gene expression. Here, we extended live-cell single-molecule tracking studies in fission yeast to determine how HP1 proteins interact with their binding partners in the nucleus. We measured how genetic perturbations that affect H3K9me alter the diffusive properties of HP1 proteins and each of their binding partners based on which we inferred their most likely interaction sites. Our results indicate that H3K9me promotes specific complex formation between HP1 proteins and their interactors in a spatially restricted manner, while attenuating their ability to form off-chromatin complexes. As opposed to being an inert platform or scaffold to direct HP1 binding, our studies propose a novel function for H3K9me as an active participant in enhancing HP1-associated complex formation in living cells.

FURNA: a database for function annotations of RNA structures.

Despite the increasing number of 3D RNA structures in the Protein Data Bank, the majority of experimental RNA structures lack thorough functional annotations. As the significance of the functional roles played by non-coding RNAs becomes increasingly apparent, comprehensive annotation of RNA function is becoming a pressing concern. In response to this need, we have developed FURNA (Functions of RNAs), the first database for experimental RNA structures that aims to provide a comprehensive repository of high-quality functional annotations. These include Gene Ontology terms, Enzyme Commission numbers, ligand binding sites, RNA families, protein binding motifs, and cross-references to related databases. FURNA is available at https://seq2fun.dcmb.med.umich.edu/furna/ to enable quick discovery of RNA functions from their structures and sequences.