exRNA-eCLIP intersection analysis reveals a map of extracellular RNA binding proteins and associated RNAs across major human biofluids and carriers

Although the role of RNA binding proteins (RBPs) in extracellular RNA (exRNA) biology is well established, their exRNA cargo and distribution across biofluids are largely unknown. To address this gap, we extend the exRNA Atlas resource by mapping exRNAs carried by extracellular RBPs (exRBPs). This map was developed through an integrative analysis of ENCODE enhanced crosslinking and immunoprecipitation (eCLIP) data (150 RBPs) and human exRNA profiles (6,930 samples). Computational analysis and experimental validation identified exRBPs in plasma, serum, saliva, urine, cerebrospinal fluid, and cell-culture-conditioned medium. exRBPs carry exRNA transcripts from small non-coding RNA biotypes, including microRNA (miRNA), piRNA, tRNA, small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), Y RNA, and lncRNA, as well as protein-coding mRNA fragments. Computational deconvolution of exRBP RNA cargo reveals associations of exRBPs with extracellular vesicles, lipoproteins, and ribonucleoproteins across human biofluids. Overall, we mapped the distribution of exRBPs across human biofluids, presenting a resource for the community.

Phase 2 of extracellular RNA communication consortium charts next-generation approaches for extracellular RNA research

The extracellular RNA communication consortium (ERCC) is an NIH-funded program aiming to promote the development of new technologies, resources, and knowledge about exRNAs and their carriers. After Phase 1 (2013-2018), Phase 2 of the program (ERCC2, 2019-2023) aims to fill critical gaps in knowledge and technology to enable rigorous and reproducible methods for separation and characterization of both bulk populations of exRNA carriers and single EVs. ERCC2 investigators are also developing new bioinformatic pipelines to promote data integration through the exRNA atlas database. ERCC2 has established several Working Groups (Resource Sharing, Reagent Development, Data Analysis and Coordination, Technology Development, nomenclature, and Scientific Outreach) to promote collaboration between ERCC2 members and the broader scientific community. We expect that ERCC2's current and future achievements will significantly improve our understanding of exRNA biology and the development of accurate and efficient exRNA-based diagnostic, prognostic, and theranostic biomarker assays.

Open Problems in Extracellular RNA Data Analysis: Insights From an ERCC Online Workshop

We now know RNA can survive the harsh environment of biofluids when encapsulated in vesicles or by associating with lipoproteins or RNA binding proteins. These extracellular RNA (exRNA) play a role in intercellular signaling, serve as biomarkers of disease, and form the basis of new strategies for disease treatment. The Extracellular RNA Communication Consortium (ERCC) hosted a two-day online workshop (April 19-20, 2021) on the unique challenges of exRNA data analysis. The goal was to foster an open dialog about best practices and discuss open problems in the field, focusing initially on small exRNA sequencing data. Video recordings of workshop presentations and discussions are available (https://exRNA.org/exRNAdata2021-videos/). There were three target audiences: experimentalists who generate exRNA sequencing data, computational and data scientists who work with those groups to analyze their data, and experimental and data scientists new to the field. Here we summarize issues explored during the workshop, including progress on an effort to develop an exRNA data analysis challenge to engage the community in solving some of these open problems.

exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids

To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies.

Comparative analysis of the transcriptome across distant species

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a 'universal model' based on a single set of organism-independent parameters.

Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project

We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor-binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor-binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.

CheV: CheW-like coupling proteins at the core of the chemotaxis signaling network

Microbes have chemotactic signaling systems that enable them to detect and follow chemical gradients in their environments. The core of these sensory systems consists of chemoreceptor proteins coupled to the CheA kinase via the scaffold or coupler protein CheW. Some bacterial chemotaxis systems replace or augment CheW with a related protein, CheV, which is less well understood. CheV consists of a CheW domain fused to a receiver domain that is capable of being phosphorylated. Our review of the literature, as well as comparisons of the CheV and CheW sequence and structure, suggest that CheV proteins conserve CheW residues that are crucial for coupling. Phosphorylation of the CheV receiver domain might adjust the efficiency of its coupling and thus allow the system to modulate the response to chemical stimuli in an adaptation process.

Annotating non-coding regions of the genome

Most of the human genome consists of non-protein-coding DNA. Recently, progress has been made in annotating these non-coding regions through the interpretation of functional genomics experiments and comparative sequence analysis. One can conceptualize functional genomics analysis as involving a sequence of steps: turning the output of an experiment into a 'signal' at each base pair of the genome; smoothing this signal and segmenting it into small blocks of initial annotation; and then clustering these small blocks into larger derived annotations and networks. Finally, one can relate functional genomics annotations to conserved units and measures of conservation derived from comparative sequence analysis.

Improved reconstruction of in silico gene regulatory networks by integrating knockout and perturbation data

We performed computational reconstruction of the in silico gene regulatory networks in the DREAM3 Challenges. Our task was to learn the networks from two types of data, namely gene expression profiles in deletion strains (the ‘deletion data’) and time series trajectories of gene expression after some initial perturbation (the ‘perturbation data’). In the course of developing the prediction method, we observed that the two types of data contained different and complementary information about the underlying network. In particular, deletion data allow for the detection of direct regulatory activities with strong responses upon the deletion of the regulator while perturbation data provide richer information for the identification of weaker and more complex types of regulation. We applied different techniques to learn the regulation from the two types of data. For deletion data, we learned a noise model to distinguish real signals from random fluctuations using an iterative method. For perturbation data, we used differential equations to model the change of expression levels of a gene along the trajectories due to the regulation of other genes. We tried different models, and combined their predictions. The final predictions were obtained by merging the results from the two types of data. A comparison with the actual regulatory networks suggests that our approach is effective for networks with a range of different sizes. The success of the approach demonstrates the importance of integrating heterogeneous data in network reconstruction.

Understanding modularity in molecular networks requires dynamics

The era of genome sequencing has produced long lists of the molecular parts from which cellular machines are constructed. A fundamental goal in systems biology is to understand how cellular behavior emerges from the interaction in time and space of genetically encoded molecular parts, as well as nongenetically encoded small molecules. Networks provide a natural framework for the organization and quantitative representation of all the available data about molecular interactions. The structural and dynamic properties of molecular networks have been the subject of intense research. Despite major advances, bridging network structure to dynamics-and therefore to behavior-remains challenging. A key concept of modern engineering that recurs in the functional analysis of biological networks is modularity. Most approaches to molecular network analysis rely to some extent on the assumption that molecular networks are modular-that is, they are separable and can be studied to some degree in isolation. We describe recent advances in the analysis of modularity in biological networks, focusing on the increasing realization that a dynamic perspective is essential to grouping molecules into modules and determining their collective function.

Evolutionary genomics reveals conserved structural determinants of signaling and adaptation in microbial chemoreceptors

As an important model for transmembrane signaling, methyl-accepting chemotaxis proteins (MCPs) have been extensively studied by using genetic, biochemical, and structural techniques. However, details of the molecular mechanism of signaling are still not well understood. The availability of genomic information for hundreds of species enables the identification of features in protein sequences that are conserved over long evolutionary distances and thus are critically important for function. We carried out a large-scale comparative genomic analysis of the MCP signaling and adaptation domain family and identified features that appear to be critical for receptor structure and function. Based on domain length and sequence conservation, we identified seven major MCP classes and three distinct structural regions within the cytoplasmic domain: signaling, methylation, and flexible bundle subdomains. The flexible bundle subdomain, not previously recognized in MCPs, is a conserved element that appears to be important for signal transduction. Remarkably, the N- and C-terminal helical arms of the cytoplasmic domain maintain symmetry in length and register despite dramatic variation, from 24 to 64 7-aa heptads in overall domain length. Loss of symmetry is observed in some MCPs, where it is concomitant with specific changes in the sensory module. Each major MCP class has a distinct pattern of predicted methylation sites that is well supported by experimental data. Our findings indicate that signaling and adaptation functions within the MCP cytoplasmic domain are tightly coupled, and that their coevolution has contributed to the significant diversity in chemotaxis mechanisms among different organisms.

Comparative genomic and protein sequence analyses of a complex system controlling bacterial chemotaxis

Molecular machinery governing bacterial chemotaxis consists of the CheA-CheY two-component system, an array of specialized chemoreceptors, and several auxiliary proteins. It has been studied extensively in Escherichia coli and, to a significantly lesser extent, in several other microbial species. Emerging evidence suggests that homologous signal transduction pathways regulate not only chemotaxis, but several other cellular functions in various bacterial species. The availability of genome sequence data for hundreds of organisms enables productive study of this system using comparative genomics and protein sequence analysis. This chapter describes advances in genomics of the chemotaxis signal transduction system, provides information on relevant bioinformatics tools and resources, and outlines approaches toward developing a computational framework for predicting important biological functions from raw genomic data based on available experimental evidence.

CDP840. A prototype of a novel class of orally active anti-inflammatory phosphodiesterase 4 inhibitors

The discovery, synthesis and biological activity of a series of triarylethane phosphodiesterase 4 inhibitors is described. Structure-activity relationship studies are presented for CDP840 (29), a potent, chiral, selective inhibitor of PDE 4 (IC(50) 4nM). CDP840 is non-emetic in the ferret at 30mgkg(-1) (po), active in models of inflammation and reverses ozone-induced bronchial hyperreactivity in the guinea pig.

Investigation of the in vitro metabolism profile of a phosphodiesterase-IV inhibitor, CDP-840: leading to structural optimization

CDP-840 is a selective and potent phosphodiesterase type IV inhibitor, whose in vitro metabolism profile was first investigated using liver microsomes from different species. At least 10 phase I oxidative metabolites (M1-M10) were detected in the microsomal incubations and characterized by capillary high-performance liquid chromatography continuous-flow liquid secondary ion mass spectrometry (CF-LSIMS). Significant differences in the microsomal metabolism of CDP-840 were found between rat and other species. The major route of metabolism in rat involved para-hydroxylation on the R4 phenyl. This pathway was not observed in human and several other species. The in vitro metabolism profile of CDP-840 was further examined using freshly isolated hepatocytes from rat, rabbit, and human. The hepatocyte incubations indicated more extensive metabolism relative to that in microsomes. In addition to the phase I oxidative metabolites observed in microsomal incubations, several phase II conjugates were identified and characterized by CF-LSIMS. Interspecies differences in phase II metabolism were also found in these hepatocyte incubations. The major metabolite in human hepatocytes was identified as the pyridinium glucuronide, which was not detected in rat hepatocytes. Simple structural modification on R4, such as p-Cl substitution, greatly reduced the species differences in microsomal metabolism. Furthermore, modifications on R3, such as the N-oxide, eliminated the N-glucuronide formation in human. These results not only helped in determining the suitability of animal species used in the preclinical safety studies but also provided valuable directions for the synthetic efforts in finding backup compounds that are more metabolically stable.