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Diao, R.

Publications and source records attributed to Diao, R..

8 recordsLinked to original sources

Mu-seq reveals previously undetected long-range contact patterns in the Escherichia coli nucleoid

The spatial organization of the Escherichia coli chromosome is a key determinant of its functional and regulatory landscape, but widely used chromatin conformation capture methods, such as Hi-C, provide an incomplete view of the underlying three-dimensional genome connectivity. Here, we introduce Mu-seq, a massively multiplexed Mu transposition-based approach that maps genome-wide DNA-DNA proximity in live cells without crosslinking. By distributing engineered, barcoded Mu prophages throughout the chromosome and tracking their replicative transposition events, Mu-seq generates dense, uniform sampling of physical contacts across the genome, enabling a direct, in vivo snapshot of chromosome architecture. Applying this method in E. coli, we constructed a contact dataset spanning over 200,000 unique loci, revealing interaction patterns not accessible to current crosslinking-based techniques. Mu-seq findings can be broken down into three layers. First, it recapitulates polymer-like enrichment of short-range contacts. Second, contacts identified by Mu-seq are strongly shaped by chromatin state: transcriptionally active and accessible regions disproportionately contribute to long-range connectivity, whereas protein-occupied domains, including H-NS-enriched regions, are depleted of insertions and contacts. Third, beyond distance-dependent polymer behavior and chromatin accessibility, Mu-seq reveals specific long-range associations among functionally related loci, such as ribosomal operons. These findings suggest that bacterial chromosome organization can bring functionally related regions into shared three-dimensional neighborhoods even when they are widely separated along the linear genome. Together, these findings support a layered model of nucleoid organization, in which transcriptionally active loops extend outward from a dense, protein-rich axial core. Mu-seq thus provides a powerful, complementary in vivo framework for probing bacterial chromosome architecture, uncovering regulatory and structural features of genome organization that are largely invisible to crosslinking-based approaches.

microbiology↗

On the state of protein function prediction: a report on the fourth CAFA challenge

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). ResultsCAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced "partial knowledge" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.

bioinformatics↗

The Small GTPase Rab7 Regulates Release of Mitochondria in Extracellular Vesicles in Response to Lysosomal Dysfunction

Mitochondrial quality control is critical for cardiac homeostasis as these organelles are responsible for generating most of the energy needed to sustain contraction. Dysfunctional mitochondria are normally degraded via intracellular degradation pathways that converge on the lysosome. Here, we identified an alternative mechanism to eliminate mitochondria when lysosomal function is compromised. We show that lysosomal inhibition leads to increased secretion of mitochondria in large extracellular vesicles (EVs). The EVs are produced in multivesicular bodies, and their release is independent of autophagy. Deletion of the small GTPase Rab7 in cells or adult mouse heart leads to increased secretion of EVs containing ubiquitinated cargos, including intact mitochondria. The secreted EVs are captured by macrophages without activating inflammation. Hearts from aged mice or Danon disease patients have increased levels of secreted EVs containing mitochondria indicating activation of vesicular release during cardiac pathophysiology. Overall, these findings establish that mitochondria are eliminated in large EVs through the endosomal pathway when lysosomal degradation is inhibited.

physiology↗

Escherichia coli YigI is a conserved γ-proteobacterial acyl-CoA thioesterase permitting metabolism of unusual fatty acid substrates

Thioesterases play a critical role in metabolism, membrane biosynthesis, and overall homeostasis for all domains of life. In this present study, we characterize a putative thioesterase from Escherichia coli MG1655 and define its role as a cytosolic enzyme. Building on structure-guided functional predictions, we show that YigI is a medium- to -long chain acyl-CoA thioesterase that is involved in the degradation of conjugated linoleic acid (CLA) in vivo, showing overlapping specificity with two previously defined E. coli thioesterases TesB and FadM. We then bioinformatically identify the regulatory relationships that induce YigI expression, which include: an acidic environment, high oxygen availability, and exposure to aminoglycosides. Our findings define a role for YigI and shed light on why the E. coli genome harbors numerous thioesterases with closely related functions. ImportancePrevious research has shown that long chain acyl-CoA thioesterases are needed for E. coli to grow in the presence of carbon sources such as conjugated linoleic acid, but that E. coli must possess at least one such enzyme that had not previously been characterized. Building off of structure-guided function predictions, we showed that the poorly annotated protein YigI is indeed the previously unidentified third acyl CoA thioesterase. We found that the three potentially overlapping acyl-CoA thioesterases appear to be induced by non-overlapping conditions, and use that information as a starting point for identifying the precise functions catalyzed by each such thioesterase, which is an important prerequisite for their industrial application and for more accurate metabolic modeling of E. coli.

microbiology↗

Teaching Python for Data Science: Collaborative development of a modular & interactive curriculum

We are bioinformatics trainees at the University of Michigan who started a local chapter of Girls Who Code to provide a fun and supportive environment for high school women to learn the power of coding. Our goal was to cover basic coding topics and data science concepts through live coding and hands-on practice. However, we could not find a resource that exactly met our needs. Therefore, over the past three years, we have developed a curriculum and instructional format using Jupyter notebooks to effectively teach introductory Python for data science. This method, inspired by The Carpentries organization, uses bite-sized lessons followed by independent practice time to reinforce coding concepts, and culminates in a data science capstone project using real-world data. We believe our open curriculum is a valuable resource to the wider education community and hope that educators will use and improve our lessons, practice problems, and teaching best practices. Anyone can contribute to our educational materials on GitHub.

scientific communication and education↗

Developing and deploying an integrated workshop curriculum teaching computational skills for reproducible research

Inspired by well-established material and pedagogy provided by The Carpentries (Wilson 2016), we developed a two-day workshop curriculum that teaches introductory R programming for managing, analyzing, plotting and reporting data using packages from the tidyverse (Wickham et al. 2019), the Unix shell, version control with git, and GitHub. While the official Software Carpentry curriculum is comprehensive, we found that it contains too much content for a two-day workshop. We also felt that the independent nature of the lessons left learners confused about how to integrate the newly acquired programming skills in their own work. Thus, we developed a new curriculum that aims to teach novices how to implement reproducible research principles in their own data analysis. The curriculum integrates live coding lessons with individual-level and group-based practice exercises, and also serves as a succinct resource that learners can reference both during and after the workshop. Moreover, it lowers the entry barrier for new instructors as they do not have to develop their own teaching materials or sift through extensive content. We developed this curriculum during a two-day sprint, successfully used it to host a two-day virtual workshop with almost 40 participants, and updated the material based on instructor and learner feedback. We hope that our new curriculum will prove useful to future instructors interested in teaching workshops with similar learning objectives.

scientific communication and education↗

Differential processing and localization of human Nocturnin controls metabolism of mRNA and nicotinamide dinucleotide metabolites

Nocturnin (NOCT) is a eukaryotic enzyme that belongs to a superfamily of exoribonucleases, endonucleases, and phosphatases. In this study, we analyze the expression, processing, localization, and cellular functions of human NOCT. We demonstrate that the NOCT protein is differentially expressed and processed in a cell and tissue type specific manner as a means to control its localization to the cytoplasm or mitochondria. Our studies also show that the N-terminus of NOCT is necessary and sufficient to confer mitochondrial localization. We then measured the impact of cytoplasmic NOCT on the transcriptome and report that it regulates the levels of hundreds of mRNAs that are enriched for components of signaling pathways, neurological functions, and regulators of osteoblast differentiation. Recent biochemical data indicate that NOCT dephosphorylates nicotinamide adenine dinucleotide (NAD) metabolites, and thus we measured the effect of NOCT on these cofactors in cells. We find that NOCT increases NAD(H) and decreases NADP(H) levels in a manner dependent on its intracellular localization. Collectively, our data indicate that NOCT can regulate levels of both mRNAs and NADP(H) cofactors in manner specified by its intracellular localization.

molecular biology↗

IDEA: a web server for Interactive Differential Expression Analysis with R Packages

Differential expression (DE) analysis is a fundamental task in the downstream analysis of the next-generation sequencing (NGS) data. Up to now, a number of R packages have been developed for detecting differentially expressed genes. Although R language has an interaction-oriented programming design, for many biology researchers, a lack of basic programming skills has greatly hindered the application of these R packages. To address this issue, we developed the Interactive Differential Expression Analyzer (IDEA), a Shiny-based web application integrating the differential expression analysis related R packages into a graphical user interface (GUI), allowing users to run the analysis without writing any new code. A wide variety of charts and tables are generated to facilitate the interpretation of the results. In addition, IDEA also provides a combined analysis framework which helps to reconcile any discrepancy from different computational methods. As a public data analysis server, IDAE is implemented in HTML, CSS and JavaScript, and is freely available at http://idea.renlab.org.

bioinformatics↗