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Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients' lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71-0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-β signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis., (© 2024. The Author(s).)

Background: Glioblastoma (GBM) is a highly aggressive and invasive brain tumor associated with high patient mortality. A large fraction of GBM tumors have been identified as epidermal growth factor receptor ( EGFR ) amplified and ~50% also are EGFRvIII mutant positive. In a previously reported multicenter phase II study, we have described the response of recurrent GBM (rGBM) patients to dacomitinib, an EGFR tyrosine kinase inhibitor (TKI). As a continuation of that report, we leverage the tumor cargo-encapsulating extracellular vesicles (EVs) and explore their genetic composition as carriers of tumor biomarker., Methods: Serum samples were longitudinally collected from EGFR-amplified rGBM patients who clinically benefitted from dacomitinib therapy (responders) and those who did not (nonresponders), as well as from a healthy cohort of individuals. The serum EV transcriptome was evaluated to map the RNA biotype distribution and distinguish GBM disease., Results: Using long RNA sequencing, we show enriched detection of over 10 000 coding RNAs from serum EVs. The EV transcriptome yielded a unique signature that facilitates differentiation of GBM patients from healthy donors. Further analysis revealed genetic enrichment that enables stratification of responders from nonresponders prior to dacomitinib treatment as well as following administration., Conclusion: This study demonstrates that genetic composition analysis of serum EVs may aid in therapeutic stratification to identify patients with dacomitinib-responsive GBM., Competing Interests: J.S., R.K., S.C., and W.Y. are employees of Exosome Diagnostics, a Bio-Techne Brand. A.S.C. is an employee of Bright Peak Therapeutics. None of the authors declare any conflicts of interests. This paper follows a previous clinical publication [DOI: 10.1200/PO.19.00295] and provides detailed analysis on serum EV transcriptomic biomarkers to predict response to dacomitinib in EGFR-amplified recurrent glioblastoma., (© The Author(s) 2023. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) has a prevalence of ∼25% worldwide, with significant public health consequences yet few effective treatments. Human genetics can help elucidate novel biology and identify targets for new therapeutics. Genetic variants in mitochondrial amidoxime-reducing component 1 ( MTARC1 ) have been associated with NAFLD and liver-related mortality; however, its pathophysiological role and the cell type(s) mediating these effects remain unclear. We aimed to investigate how MTARC1 exerts its effects on NAFLD by integrating human genetics with in vitro and in vivo studies of mARC1 knockdown., Methods: Analyses including multi-trait colocalisation and Mendelian randomisation were used to assess the genetic associations of MTARC1 . In addition, we established an in vitro long-term primary human hepatocyte model with metabolic readouts and used the Gubra Amylin NASH (GAN)-diet non-alcoholic steatohepatitis mouse model treated with hepatocyte-specific N -acetylgalactosamine (GalNAc)-siRNA to understand the in vivo impacts of MTARC1 ., Results: We showed that genetic variants within the MTARC1 locus are associated with liver enzymes, liver fat, plasma lipids, and body composition, and these associations are attributable to the same causal variant ( p.A165T, rs2642438 G>A ), suggesting a shared mechanism. We demonstrated that increased MTARC1 mRNA had an adverse effect on these traits using Mendelian randomisation, implying therapeutic inhibition of mARC1 could be beneficial. In vitro mARC1 knockdown decreased lipid accumulation and increased triglyceride secretion, and in vivo GalNAc-siRNA-mediated knockdown of mARC1 lowered hepatic but increased plasma triglycerides. We found alterations in pathways regulating lipid metabolism and decreased secretion of 3-hydroxybutyrate upon mARC1 knockdown in vitro and in vivo ., Conclusions: Collectively, our findings from human genetics, and in vitro and in vivo hepatocyte-specific mARC1 knockdown support the potential efficacy of hepatocyte-specific targeting of mARC1 for treatment of NAFLD., Impact and Implications: We report that genetically predicted increases in MTARC1 mRNA associate with poor liver health. Furthermore, knockdown of mARC1 reduces hepatic steatosis in primary human hepatocytes and a murine NASH model. Together, these findings further underscore the therapeutic potential of targeting hepatocyte MTARC1 for NAFLD., Competing Interests: A provisional patent application directed to the subject matter disclosed in this manuscript has been filed by Novo Nordisk A/S. LCL, LC, LSH, RRK, CM, JN, CED, STH, RP, IS, EJL, TND, AC, SH, BG, MEG, ETM, AHAE, WGH, KC, JF, JMMH, BA, and MAR are Novo Nordisk A/S or Novo Nordisk Ltd employees. EW and NWP are former Dicerna employees. SBR is a former employee of Dicerna Pharmaceuticals Inc. and presently employed by the Novartis Institutes for BioMedical Research (NIBR), Cambridge, MA, USA. WL is a former employee of Dicerna Pharmaceuticals and presently employed with Biogen Inc, Cambridge, MA, USA. LH is a scientific consultant for Novo Nordisk A/S. The remaining authors declare no competing interests. Please refer to the accompanying ICMJE disclosure forms for further details., (© 2023 The Author(s).)

Molecular profiling to characterize the effects of environmental exposures is important from the human health and performance as well as the occupational medicine perspective in space exploration. We have developed a novel exosome-based platform that allows profiling of biological processes in the body from a variety of body fluids. The technology is suitable for diagnostic applications as well as studying the pathophysiology of the Space Associated Neuro-Ocular Syndrome in astronauts and monitoring patients with chronically impaired cerebrospinal fluid drainage or elevated intracranial pressure. In this proof-of-concept, we demonstrate that: (a) exosomes from different biofluids contain a specific population of RNA transcripts; (b) urine collection hardware aboard the ISS is compatible with exosome gene expression technology; (c) cDNA libraries from exosomal RNA can be stored in dry form and at room temperature, representing an interesting option for the creation of longitudinal molecular catalogs that can be stored as a repository for retrospective analysis., (© 2022. The Author(s).)

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., Competing Interests: Crislyn D’Souza-Schorey, Satyajyoti Senapati and Hsueh-Chia Chang hold equity in AgenDx Biosciences. Hsueh-Chia Chang is a cofounder of Aopia Biosciences and holds equity. Daniel T. Chiu has financial interest and is a scientific founder and/or board member of the following companies and their respective affiliates: Micareo, Inc, Lamprogen, Inc, Cellectricon AB, and Fluicell AB. Saumya Das is a founding member of Dyrnamix Inc. that did not play any role in this study. Kendal Van Keuren-Jensen is a board member of Dyrnamix Inc. and HTG that did not play any role in this study. Aleksandar Milosavljevic is a founder of IP Genesis, Inc. that did not play any role in this study. Louise C. Laurent has stock in Illumina, Inc. Anil K. Sood has the following disclosures: Consultant (Merck, Kiyatec), shareholder (BioPath), research funding (MTrap). John Nolan holds equity in Cellarcus Biosciences Inc. L. James Lee holds equity at Spot Biosystems. David A. Routenberg, Sigal Shachar and Alexander K. Tucker-Schwartz are employees of Meso Scale Diagnostics, LLC. Andrey Turchinovich is founder of SciBerg e.Kfm, Mannheim, Germany. Angela M. Zivkovic is cofounder and board member at Innate Biology, Inc., (© 2022 The Author(s).)

The core Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers; amyloid-β (Aß), total tau (t-tau), and phosphorylated tau (p-tau181), are strong indicators of the presence of AD pathology, but do not correlate well with disease progression, and can be difficult to implement in longitudinal studies where repeat biofluid sampling is required. As a result, blood-based biomarkers are increasingly being sought as alternatives. In this study, we aimed to evaluate a promising blood biomarker discovery technology, Olink Proximity Extension Assays for technical reproducibility characteristics in order to highlight the advantages and disadvantages of using this technology in biomarker discovery in AD. We evaluated the performance of five Olink Proteomic multiplex proximity extension assays (PEA) in plasma samples. Three technical control samples included on each plate allowed calculation of technical variability. Biotemporal stability was measured in three sequential annual samples from 54 individuals with and without AD. Coefficients of variation (CVs), analysis of variance (ANOVA), and variance component analyses were used to quantify technical and individual variation over time. We show that overall, Olink assays are technically robust, with the largest experimental variation stemming from biological differences between individuals for most analytes. As a powerful illustration of one of the potential pitfalls of using a multi-plexed technology for discovery, we performed power calculations using the baseline samples to demonstrate the size of study required to overcome the need for multiple test correction with this technology. We show that the power of moderate effect size proteins was strongly reduced, and as a result investigators should strongly consider pooling resources to perform larger studies using this multiplexed technique where possible., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Carlyle, Kitchen, Mattingly, Celia, Trombetta, Das, Hyman, Kivisäkk and Arnold.)

To gain insights into the mechanisms driving cardiovascular complications in COVID-19, we performed a case-control plasma proteomics study in COVID-19 patients. Our results identify the senescence-associated secretory phenotype, a marker of biological aging, as the dominant process associated with disease severity and cardiac involvement. FSTL3, an indicator of senescence-promoting Activin/TGFβ signaling, and ADAMTS13, the von Willebrand Factor-cleaving protease whose loss-of-function causes microvascular thrombosis, were among the proteins most strongly associated with myocardial stress and injury. Findings were validated in a larger COVID-19 patient cohort and the hamster COVID-19 model, providing new insights into the pathophysiology of COVID-19 cardiovascular complications with therapeutic implications., Competing Interests: This work was supported by the National Institutes of Health (R01AG061034 and R35HL15531 [to Dr Rosenzweig]; R01HL092577, R01HL128914, and K24HL105780 [to Dr Ellinor]; R01HL134893, R01HL140224, K24HL153669 [to Dr Ho]; T32HL094301 [to Dr Weber]; K08HL140200 [to Dr Rhee]; and K76AG064328 [to Dr Roh]), the Fondation Leducq (14CVD01 [to Dr Ellinor]), the American Heart Association (18SFRN34110082 [to Dr Ellinor]), a Sarnoff Cardiovascular Research Foundation Fellowship award (to Dr Trager), the Fred and Ines Yeatts Fund for Innovative Research (to Dr Roh), the Hassenfeld Scholars Award (to Dr Roh), Fast Grants, Emergent Ventures, Mercatus Center at George Mason University (to Dr Martinot), and a research grant from Bayer AG to the Broad Institute (to Drs Ellinor and Ho). Dr Ellinor is supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of cardiovascular diseases; and has served on advisory boards or consulted for Bayer AG, Quest Diagnostics, MyoKardia and Novartis. Dr Ho has received research grants from Bayer AG and Gilead Sciences; and has received research supplies from EcoNugenics. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2022 The Authors.)

Proteomic characterization of human brain tissue is increasingly utilized to identify potential novel biomarkers and drug targets for a variety of neurological diseases. In whole-tissue studies, results may be driven by changes in the proportion of the largest and most abundant organelles or tissue cell-type composition. Spatial proteomics approaches enhance our knowledge of disease mechanisms and changing signalling pathways at the subcellular level by taking into account the importance of cellular localization, which critically influences protein function. Density gradient-based ultracentrifugation methods allow for subcellular fractionation and have been utilized in cell lines, mouse and human brain tissue to quantify thousands of proteins in specific enriched organelles such as the pre- and post-synapse. Serial ultracentrifugation methods allow for the analysis of multiple cellular organelles from the same biological sample, and to our knowledge have not been previously applied to frozen post-mortem human brain tissue. The use of frozen human tissue for tissue fractionation faces two major challenges, the post-mortem interval, during which proteins may leach from their usual location into the cytosol, and freezing, which results in membrane breakdown. Despite these challenges, in this proof-of-concept study, we show that the majority of proteins segregate reproducibly into crude density-based centrifugation fractions, that the fractions are enriched for the appropriate organellar markers and that significant differences in protein localization can be observed between tissue from individuals with Alzheimer's disease and control individuals., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.)

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., Competing Interests: EV is co-founder, member of the Board of Directors, on the SAB, equity holder, and paid consultant for Eclipse BioInnovations. EV’s interests have been reviewed and approved by the Baylor College of Medicine in accordance with its conflict of interest policies. RS is a paid consultant for Lacerta Therapeutics. Author RY was employed by the company Aginome Scientific, Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Alexander, Kitchen, Tosar, Roth, Mestdagh, Max, Rozowsky, Kaczor-Urbanowicz, Chang, Balaj, Losic, Van Nostrand, LaPlante, Mateescu, White, Yu, Milosavljevic, Stolovitzky and Spengler.)

Background: COVID-19 has resulted in significant morbidity and mortality worldwide. Lateral flow assays can detect anti-Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) antibodies to monitor transmission. However, standardized evaluation of their accuracy and tools to aid in interpreting results are needed., Methods: We evaluated 20 IgG and IgM assays selected from available tests in April 2020. We evaluated the assays' performance using 56 pre-pandemic negative and 56 SARS-CoV-2-positive plasma samples, collected 10-40 days after symptom onset, confirmed by a molecular test and analyzed by an ultra-sensitive immunoassay. Finally, we developed a user-friendly web app to extrapolate the positive predictive values based on their accuracy and local prevalence., Results: Combined IgG + IgM sensitivities ranged from 33.9 to 94.6%, while combined specificities ranged from 92.6 to 100%. The highest sensitivities were detected in Lumiquick for IgG (98.2%), BioHit for both IgM (96.4%), and combined IgG + IgM sensitivity (94.6%). Furthermore, 11 LFAs and 8 LFAs showed perfect specificity for IgG and IgM, respectively, with 15 LFAs showing perfect combined IgG + IgM specificity. Lumiquick had the lowest estimated limit-of-detection (LOD) (0.1 μg/mL), followed by a similar LOD of 1.5 μg/mL for CareHealth, Cellex, KHB, and Vivachek., Conclusion: We provide a public resource of the accuracy of select lateral flow assays with potential for home testing. The cost-effectiveness, scalable manufacturing process, and suitability for self-testing makes LFAs an attractive option for monitoring disease prevalence and assessing vaccine responsiveness. Our web tool provides an easy-to-use interface to demonstrate the impact of prevalence and test accuracy on the positive predictive values.

Background: Inflammatory breast cancer (IBC) has a highly invasive and metastatic phenotype. However, little is known about its genetic drivers. To address this, we report the largest cohort of whole-genome sequencing (WGS) of IBC cases., Methods: We performed WGS of 20 IBC samples and paired normal blood DNA to identify genomic alterations. For comparison, we used 23 matched non-IBC samples from the Cancer Genome Atlas Program (TCGA). We also validated our findings using WGS data from the International Cancer Genome Consortium (ICGC) and the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We examined a wide selection of genomic features to search for differences between IBC and conventional breast cancer. These include (i) somatic and germline single-nucleotide variants (SNVs), in both coding and non-coding regions; (ii) the mutational signature and the clonal architecture derived from these SNVs; (iii) copy number and structural variants (CNVs and SVs); and (iv) non-human sequence in the tumors (i.e., exogenous sequences of bacterial origin)., Results: Overall, IBC has similar genomic characteristics to non-IBC, including specific alterations, overall mutational load and signature, and tumor heterogeneity. In particular, we observed similar mutation frequencies between IBC and non-IBC, for each gene and most cancer-related pathways. Moreover, we found no exogenous sequences of infectious agents specific to IBC samples. Even though we could not find any strongly statistically distinguishing genomic features between the two groups, we did find some suggestive differences in IBC: (i) The MAST2 gene was more frequently mutated (20% IBC vs. 0% non-IBC). (ii) The TGF β pathway was more frequently disrupted by germline SNVs (50% vs. 13%). (iii) Different copy number profiles were observed in several genomic regions harboring cancer genes. (iv) Complex SVs were more frequent. (v) The clonal architecture was simpler, suggesting more homogenous tumor-evolutionary lineages., Conclusions: Whole-genome sequencing of IBC manifests a similar genomic architecture to non-IBC. We found no unique genomic alterations shared in just IBCs; however, subtle genomic differences were observed including germline alterations in TGFβ pathway genes and somatic mutations in the MAST2 kinase that could represent potential therapeutic targets.

FtsJ RNA 2'-O-methyltransferase 1 ( FTSJ1 ) gene has been implicated in X-linked intellectual disability (XLID), but the molecular pathogenesis is unknown. We show that Ftsj1 is responsible for 2'- O -methylation of 11 species of cytosolic transfer RNAs (tRNAs) at the anticodon region, and these modifications are abolished in Ftsj1 knockout (KO) mice and XLID patient-derived cells. Loss of 2'- O -methylation in Ftsj1 KO mouse selectively reduced the steady-state level of tRNA Phe in the brain, resulting in a slow decoding at Phe codons. Ribosome profiling showed that translation efficiency is significantly reduced in a subset of genes that need to be efficiently translated to support synaptic organization and functions. Ftsj1 KO mice display immature synaptic morphology and aberrant synaptic plasticity, which are associated with anxiety-like and memory deficits. The data illuminate a fundamental role of tRNA modification in the brain through regulation of translation efficiency and provide mechanistic insights into FTSJ1-related XLID., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Objective: Adiposity is associated with oxidative stress, inflammation, and glucose intolerance. Previous data suggest that platelet gene expression is associated with key cardiometabolic phenotypes, including body mass index but stable in healthy individuals over time. However, modulation of gene expression in platelets in response to metabolic shifts (eg, weight reduction) is unknown and may be important to defining mechanism. Approach and Results: Platelet RNA sequencing and aggregation were performed from 21 individuals with massive weight loss (>45 kg) following bariatric surgery. Based on RNA sequencing data, we measured the expression of 67 genes from isolated platelet RNA using high-throughput quantitative reverse transcription quantitative PCR in 1864 FHS (Framingham Heart Study) participants. Many transcripts not previously studied in platelets were differentially expressed with bariatric surgical weight loss, appeared specific to platelets (eg, not differentially expressed in leukocytes), and were enriched for a nonalcoholic fatty liver disease pathway. Platelet aggregation studies did not detect alteration in platelet function after significant weight loss. Linear regression models demonstrated several platelet genes modestly associated with cross-sectional cardiometabolic phenotypes, including body mass index. There were no associations between studied transcripts and incident diabetes or cardiovascular end points., Conclusions: In summary, while there is no change in platelet aggregation function after significant weight loss, the human platelet experiences a dramatic transcriptional shift that implicates pathways potentially relevant to improved cardiometabolic risk postweight loss (eg, nonalcoholic fatty liver disease). Further studies are needed to determine the mechanistic importance of these observations.

Small RNA sequencing has been widely adopted to study the diversity of extracellular RNAs (exRNAs) in biofluids; however, the analysis of exRNA samples can be challenging: they are vulnerable to contamination and artifacts from different isolation techniques, present in lower concentrations than cellular RNA, and occasionally of exogenous origin. To address these challenges, we present exceRpt, the exRNA-processing toolkit of the NIH Extracellular RNA Communication Consortium (ERCC). exceRpt is structured as a cascade of filters and quantifications prioritized based on one's confidence in a given set of annotated RNAs. It generates quality control reports and abundance estimates for RNA biotypes. It is also capable of characterizing mappings to exogenous genomes, which, in turn, can be used to generate phylogenetic trees. exceRpt has been used to uniformly process all ∼3,500 exRNA-seq datasets in the public exRNA Atlas and is available from genboree.org and github.gersteinlab.org/exceRpt., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Motivation: Analysis of RNA sequencing (RNA-Seq) data in human saliva is challenging. Lack of standardization and unification of the bioinformatic procedures undermines saliva's diagnostic potential. Thus, it motivated us to perform this study., Results: We applied principal pipelines for bioinformatic analysis of small RNA-Seq data of saliva of 98 healthy Korean volunteers including either direct or indirect mapping of the reads to the human genome using Bowtie1. Analysis of alignments to exogenous genomes by another pipeline revealed that almost all of the reads map to bacterial genomes. Thus, salivary exRNA has fundamental properties that warrant the design of unique additional steps while performing the bioinformatic analysis. Our pipelines can serve as potential guidelines for processing of RNA-Seq data of human saliva., Availability and Implementation: Processing and analysis results of the experimental data generated by the exceRpt (v4.6.3) small RNA-seq pipeline (github.gersteinlab.org/exceRpt) are available from exRNA atlas (exrna-atlas.org). Alignment to exogenous genomes and their quantification results were used in this paper for the analyses of small RNAs of exogenous origin., Contact: dtww@ucla.edu., (© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com)

Background: To address the lack of standard terminology to describe extracellular RNA (exRNA) data/metadata, we have launched an inter-community effort to extend the Gene Ontology (GO) with subcellular structure concepts relevant to the exRNA domain. By extending GO in this manner, the exRNA data/metadata will be more easily annotated and queried because it will be based on a shared set of terms and relationships relevant to extracellular research., Methods: By following a consensus-building process, we have worked with several academic societies/consortia, including ERCC, ISEV, and ASEMV, to identify and approve a set of exRNA and extracellular vesicle-related terms and relationships that have been incorporated into GO. In addition, we have initiated an ongoing process of extractions of gene product annotations associated with these terms from Vesiclepedia and ExoCarta, conversion of the extracted annotations to Gene Association File (GAF) format for batch submission to GO, and curation of the submitted annotations by the GO Consortium. As a use case, we have incorporated some of the GO terms into annotations of samples from the exRNA Atlas and implemented a faceted search interface based on such annotations., Results: We have added 7 new terms and modified 9 existing terms (along with their synonyms and relationships) to GO. Additionally, 18,695 unique coding gene products (mRNAs and proteins) and 963 unique non-coding gene products (ncRNAs) which are associated with the terms: "extracellular vesicle", "extracellular exosome", "apoptotic body", and "microvesicle" were extracted from ExoCarta and Vesiclepedia. These annotations are currently being processed for submission to GO., Conclusions: As an inter-community effort, we have made a substantial update to GO in the exRNA context. We have also demonstrated the utility of some of the new GO terms for sample annotation and metadata search.

We describe a chemical method to label and purify 4-thiouridine (s(4)U)-containing RNA. We demonstrate that methanethiosulfonate (MTS) reagents form disulfide bonds with s(4)U more efficiently than the commonly used HPDP-biotin, leading to higher yields and less biased enrichment. This increase in efficiency allowed us to use s(4)U labeling to study global microRNA (miRNA) turnover in proliferating cultured human cells without perturbing global miRNA levels or the miRNA processing machinery. This improved chemistry will enhance methods that depend on tracking different populations of RNA, such as 4-thiouridine tagging to study tissue-specific transcription and dynamic transcriptome analysis (DTA) to study RNA turnover., (Copyright © 2015 Elsevier Inc. All rights reserved.)

The large diversity and volume of extracellular RNA (exRNA) data that will form the basis of the exRNA Atlas generated by the Extracellular RNA Communication Consortium pose a substantial data integration challenge. We here present the strategy that is being implemented by the exRNA Data Management and Resource Repository, which employs metadata, biomedical ontologies and Linked Data technologies, such as Resource Description Framework to integrate a diverse set of exRNA profiles into an exRNA Atlas and enable integrative exRNA analysis. We focus on the following three specific data integration tasks: (a) selection of samples from a virtual biorepository for exRNA profiling and for inclusion in the exRNA Atlas; (b) retrieval of a data slice from the exRNA Atlas for integrative analysis and (c) interpretation of exRNA analysis results in the context of pathways and networks. As exRNA profiling gains wide adoption in the research community, we anticipate that the strategies discussed here will increasingly be required to enable data reuse and to facilitate integrative analysis of exRNA data.

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.

Background: Aberrant CpG island promoter DNA hypermethylation is frequently observed in cancer and is believed to contribute to tumor progression by silencing the expression of tumor suppressor genes. Previously, we observed that promoter hypermethylation in breast cancer reflects cell lineage rather than tumor progression and occurs at genes that are already repressed in a lineage-specific manner. To investigate the generality of our observation we analyzed the methylation profiles of 1,154 cancers from 7 different tissue types., Results: We find that 1,009 genes are prone to hypermethylation in these 7 types of cancer. Nearly half of these genes varied in their susceptibility to hypermethylation between different cancer types. We show that the expression status of hypermethylation prone genes in the originator tissue determines their propensity to become hypermethylated in cancer; specifically, genes that are normally repressed in a tissue are prone to hypermethylation in cancers derived from that tissue. We also show that the promoter regions of hypermethylation-prone genes are depleted of repetitive elements and that DNA sequence around the same promoters is evolutionarily conserved. We propose that these two characteristics reflect tissue-specific gene promoter architecture regulating the expression of these hypermethylation prone genes in normal tissues., Conclusions: As aberrantly hypermethylated genes are already repressed in pre-cancerous tissue, we suggest that their hypermethylation does not directly contribute to cancer development via silencing. Instead aberrant hypermethylation reflects developmental history and the perturbation of epigenetic mechanisms maintaining these repressed promoters in a hypomethylated state in normal cells.

Background: Affymetrix GeneChips and Illumina BeadArrays are the most widely used commercial single channel gene expression microarrays. Public data repositories are an extremely valuable resource, providing array-derived gene expression measurements from many thousands of experiments. Unfortunately many of these studies are underpowered and it is desirable to improve power by combining data from more than one study; we sought to determine whether platform-specific bias precludes direct integration of probe intensity signals for combined reanalysis., Results: Using Affymetrix and Illumina data from the microarray quality control project, from our own clinical samples, and from additional publicly available datasets we evaluated several approaches to directly integrate intensity level expression data from the two platforms. After mapping probe sequences to Ensembl genes we demonstrate that, ComBat and cross platform normalisation (XPN), significantly outperform mean-centering and distance-weighted discrimination (DWD) in terms of minimising inter-platform variance. In particular we observed that DWD, a popular method used in a number of previous studies, removed systematic bias at the expense of genuine biological variability, potentially reducing legitimate biological differences from integrated datasets., Conclusion: Normalised and batch-corrected intensity-level data from Affymetrix and Illumina microarrays can be directly combined to generate biologically meaningful results with improved statistical power for robust, integrated reanalysis.

Background: Systematic processing noise, which includes batch effects, is very common in microarray experiments but is often ignored despite its potential to confound or compromise experimental results. Compromised results are most likely when re-analysing or integrating datasets from public repositories due to the different conditions under which each dataset is generated. To better understand the relative noise-contributions of various factors in experimental-design, we assessed several Illumina and Affymetrix datasets for technical variation between replicate hybridisations of Universal Human Reference (UHRR) and individual or pooled breast-tumour RNA., Results: A varying degree of systematic noise was observed in each of the datasets, however in all cases the relative amount of variation between standard control RNA replicates was found to be greatest at earlier points in the sample-preparation workflow. For example, 40.6% of the total variation in reported expressions were attributed to replicate extractions, compared to 13.9% due to amplification/labelling and 10.8% between replicate hybridisations. Deliberate probe-wise batch-correction methods were effective in reducing the magnitude of this variation, although the level of improvement was dependent on the sources of noise included in the model. Systematic noise introduced at the chip, run, and experiment levels of a combined Illumina dataset were found to be highly dependent upon the experimental design. Both UHRR and pools of RNA, which were derived from the samples of interest, modelled technical variation well although the pools were significantly better correlated (4% average improvement) and better emulated the effects of systematic noise, over all probes, than the UHRRs. The effect of this noise was not uniform over all probes, with low GC-content probes found to be more vulnerable to batch variation than probes with a higher GC-content., Conclusions: The magnitude of systematic processing noise in a microarray experiment is variable across probes and experiments, however it is generally the case that procedures earlier in the sample-preparation workflow are liable to introduce the most noise. Careful experimental design is important to protect against noise, detailed meta-data should always be provided, and diagnostic procedures should be routinely performed prior to downstream analyses for the detection of bias in microarray studies.

Respiratory epithelium is the target of therapies, such as gene therapy, for cystic fibrosis (CF) lung disease. To determine the usefulness of the nasal epithelium as a pre-screen for lung-directed therapies, we profiled gene expression in CF and non-CF nasal and bronchial epithelium samples using Illumina HumanRef-8 Expression BeadChips. 863 genes were differentially expressed between CF and non-CF bronchial epithelium but only 15 were differentially expressed between CF and non-CF nasal epithelium (≥1.5-fold, P≤0.05). The most enriched pathway in CF bronchial epithelium was inflammatory response, whereas in CF nasal epithelium it was amino acid metabolism. We also compared nasal and bronchial epithelium in each group and identified differential expression of cellular movement genes in CF patients and cellular growth genes in non-CF subjects. We conclude that CF and non-CF nasal and bronchial epithelium are transcriptionally distinct and CF nasal epithelium is not a good surrogate for the lung respiratory epithelium., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Early life neglect is an important public health problem which can lead to lasting psychological dysfunction. Good animal models are necessary to understand the mechanisms responsible for the behavioral and anatomical pathology that results. We recently described a novel model of early life neglect, maternal separation with early weaning (MSEW), that produces behavioral changes in the mouse that persist into adulthood. To begin to understand the mechanism by which MSEW leads to these changes we applied cDNA microarray, next-generation RNA-sequencing (RNA-seq), label-free proteomics, multiple reaction monitoring (MRM) proteomics, and methylation analysis to tissue samples obtained from medial prefrontal cortex to determine the molecular changes induced by MSEW that persist into adulthood. The results show that MSEW leads to dysregulation of markers of mature oligodendrocytes and genes involved in protein translation and other categories, an apparent downward biasing of translation, and methylation changes in the promoter regions of selected dysregulated genes. These findings are likely to prove useful in understanding the mechanism by which early life neglect affects brain structure, cognition, and behavior.

Background: Microarray technology is a popular means of producing whole genome transcriptional profiles, however high cost and scarcity of mRNA has led many studies to be conducted based on the analysis of single samples. We exploit the design of the Illumina platform, specifically multiple arrays on each chip, to evaluate intra-experiment technical variation using repeated hybridisations of universal human reference RNA (UHRR) and duplicate hybridisations of primary breast tumour samples from a clinical study., Results: A clear batch-specific bias was detected in the measured expressions of both the UHRR and clinical samples. This bias was found to persist following standard microarray normalisation techniques. However, when mean-centering or empirical Bayes batch-correction methods (ComBat) were applied to the data, inter-batch variation in the UHRR and clinical samples were greatly reduced. Correlation between replicate UHRR samples improved by two orders of magnitude following batch-correction using ComBat (ranging from 0.9833-0.9991 to 0.9997-0.9999) and increased the consistency of the gene-lists from the duplicate clinical samples, from 11.6% in quantile normalised data to 66.4% in batch-corrected data. The use of UHRR as an inter-batch calibrator provided a small additional benefit when used in conjunction with ComBat, further increasing the agreement between the two gene-lists, up to 74.1%., Conclusion: In the interests of practicalities and cost, these results suggest that single samples can generate reliable data, but only after careful compensation for technical bias in the experiment. We recommend that investigators appreciate the propensity for such variation in the design stages of a microarray experiment and that the use of suitable correction methods become routine during the statistical analysis of the data.