The Extracellular RNA Communication Consortium: Establishing Foundational Knowledge and Technologies for Extracellular RNA Research.

The Extracellular RNA Communication Consortium (ERCC) was launched to accelerate progress in the new field of extracellular RNA (exRNA) biology and to establish whether exRNAs and their carriers, including extracellular vesicles (EVs), can mediate intercellular communication and be utilized for clinical applications. Phase 1 of the ERCC focused on exRNA/EV biogenesis and function, discovery of exRNA biomarkers, development of exRNA/EV-based therapeutics, and construction of a robust set of reference exRNA profiles for a variety of biofluids. Here, we present progress by ERCC investigators in these areas, and we discuss collaborative projects directed at development of robust methods for EV/exRNA isolation and analysis and tools for sharing and computational analysis of exRNA profiling data.

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.

Phospho-RNA-seq: a modified small RNA-seq method that reveals circulating mRNA and lncRNA fragments as potential biomarkers in human plasma.

Extracellular RNAs (exRNAs) in biofluids have attracted great interest as potential biomarkers. Although extracellular microRNAs in blood plasma are extensively characterized, extracellular messenger RNA (mRNA) and long non-coding RNA (lncRNA) studies are limited. We report that plasma contains fragmented mRNAs and lncRNAs that are missed by standard small RNA-seq protocols due to lack of 5' phosphate or presence of 3' phosphate. These fragments were revealed using a modified protocol ("phospho-RNA-seq") incorporating RNA treatment with T4-polynucleotide kinase, which we compared with standard small RNA-seq for sequencing synthetic RNAs with varied 5' and 3' ends, as well as human plasma exRNA Analyzing phospho-RNA-seq data using a custom, high-stringency bioinformatic pipeline, we identified mRNA/lncRNA transcriptome fingerprints in plasma, including tissue-specific gene sets. In a longitudinal study of hematopoietic stem cell transplant patients, bone marrow- and liver-enriched exRNA genes were tracked with bone marrow recovery and liver injury, respectively, providing proof-of-concept validation as a biomarker approach. By enabling access to an unexplored realm of mRNA and lncRNA fragments, phospho-RNA-seq opens up new possibilities for plasma transcriptomic biomarker development.

Optimized permutation testing for information theoretic measures of multi-gene interactions.

BACKGROUND: Permutation testing is often considered the "gold standard" for multi-test significance analysis, as it is an exact test requiring few assumptions about the distribution being computed. However, it can be computationally very expensive, particularly in its naive form in which the full analysis pipeline is re-run after permuting the phenotype labels. This can become intractable in multi-locus genome-wide association studies (GWAS), in which the number of potential interactions to be tested is combinatorially large. RESULTS: In this paper, we develop an approach for permutation testing in multi-locus GWAS, specifically focusing on SNP-SNP-phenotype interactions using multivariable measures that can be computed from frequency count tables, such as those based in Information Theory. We find that the computational bottleneck in this process is the construction of the count tables themselves, and that this step can be eliminated at each iteration of the permutation testing by transforming the count tables directly. This leads to a speed-up by a factor of over 103 for a typical permutation test compared to the naive approach. Additionally, this approach is insensitive to the number of samples making it suitable for datasets with large number of samples. CONCLUSIONS: The proliferation of large-scale datasets with genotype data for hundreds of thousands of individuals enables new and more powerful approaches for the detection of multi-locus genotype-phenotype interactions. Our approach significantly improves the computational tractability of permutation testing for these studies. Moreover, our approach is insensitive to the large number of samples in these modern datasets. The code for performing these computations and replicating the figures in this paper is freely available at https://github.com/kunert/permute-counts .

Partial Information Decomposition and the Information Delta: A Geometric Unification Disentangling Non-Pairwise Information.

Information theory provides robust measures of multivariable interdependence, but classically does little to characterize the multivariable relationships it detects. The Partial Information Decomposition (PID) characterizes the mutual information between variables by decomposing it into unique, redundant, and synergistic components. This has been usefully applied, particularly in neuroscience, but there is currently no generally accepted method for its computation. Independently, the Information Delta framework characterizes non-pairwise dependencies in genetic datasets. This framework has developed an intuitive geometric interpretation for how discrete functions encode information, but lacks some important generalizations. This paper shows that the PID and Delta frameworks are largely equivalent. We equate their key expressions, allowing for results in one framework to apply towards open questions in the other. For example, we find that the approach of Bertschinger et al. is useful for the open Information Delta question of how to deal with linkage disequilibrium. We also show how PID solutions can be mapped onto the space of delta measures. Using Bertschinger et al. as an example solution, we identify a specific plane in delta-space on which this approach's optimization is constrained, and compute it for all possible three-variable discrete functions of a three-letter alphabet. This yields a clear geometric picture of how a given solution decomposes information.

Modeling bias and variation in the stochastic processes of small RNA sequencing.

The use of RNA-seq as the preferred method for the discovery and validation of small RNA biomarkers has been hindered by high quantitative variability and biased sequence counts. In this paper we develop a statistical model for sequence counts that accounts for ligase bias and stochastic variation in sequence counts. This model implies a linear quadratic relation between the mean and variance of sequence counts. Using a large number of sequencing datasets, we demonstrate how one can use the generalized additive models for location, scale and shape (GAMLSS) distributional regression framework to calculate and apply empirical correction factors for ligase bias. Bias correction could remove more than 40% of the bias for miRNAs. Empirical bias correction factors appear to be nearly constant over at least one and up to four orders of magnitude of total RNA input and independent of sample composition. Using synthetic mixes of known composition, we show that the GAMLSS approach can analyze differential expression with greater accuracy, higher sensitivity and specificity than six existing algorithms (DESeq2, edgeR, EBSeq, limma, DSS, voom) for the analysis of small RNA-seq data.

sRNAnalyzer-a flexible and customizable small RNA sequencing data analysis pipeline.

Although many tools have been developed to analyze small RNA sequencing (sRNA-Seq) data, it remains challenging to accurately analyze the small RNA population, mainly due to multiple sequence ID assignment caused by short read length. Additional issues in small RNA analysis include low consistency of microRNA (miRNA) measurement results across different platforms, miRNA mapping associated with miRNA sequence variation (isomiR) and RNA editing, and the origin of those unmapped reads after screening against all endogenous reference sequence databases. To address these issues, we built a comprehensive and customizable sRNA-Seq data analysis pipeline-sRNAnalyzer, which enables: (i) comprehensive miRNA profiling strategies to better handle isomiRs and summarization based on each nucleotide position to detect potential SNPs in miRNAs, (ii) different sequence mapping result assignment approaches to simulate results from microarray/qRT-PCR platforms and a local probabilistic model to assign mapping results to the most-likely IDs, (iii) comprehensive ribosomal RNA filtering for accurate mapping of exogenous RNAs and summarization based on taxonomy annotation. We evaluated our pipeline on both artificial samples (including synthetic miRNA and Escherichia coli cultures) and biological samples (human tissue and plasma). sRNAnalyzer is implemented in Perl and available at: http://srnanalyzer.systemsbiology.net/.

A genome wide dosage suppressor network reveals genomic robustness.

Genomic robustness is the extent to which an organism has evolved to withstand the effects of deleterious mutations. We explored the extent of genomic robustness in budding yeast by genome wide dosage suppressor analysis of 53 conditional lethal mutations in cell division cycle and RNA synthesis related genes, revealing 660 suppressor interactions of which 642 are novel. This collection has several distinctive features, including high co-occurrence of mutant-suppressor pairs within protein modules, highly correlated functions between the pairs and higher diversity of functions among the co-suppressors than previously observed. Dosage suppression of essential genes encoding RNA polymerase subunits and chromosome cohesion complex suggests a surprising degree of functional plasticity of macromolecular complexes, and the existence of numerous degenerate pathways for circumventing the effects of potentially lethal mutations. These results imply that organisms and cancer are likely able to exploit the genomic robustness properties, due the persistence of cryptic gene and pathway functions, to generate variation and adapt to selective pressures.

Small RNA profiling of low biomass samples: identification and removal of contaminants.

BACKGROUND: Sequencing-based analyses of low-biomass samples are known to be prone to misinterpretation due to the potential presence of contaminating molecules derived from laboratory reagents and environments. DNA contamination has been previously reported, yet contamination with RNA is usually considered to be very unlikely due to its inherent instability. Small RNAs (sRNAs) identified in tissues and bodily fluids, such as blood plasma, have implications for physiology and pathology, and therefore the potential to act as disease biomarkers. Thus, the possibility for RNA contaminants demands careful evaluation. RESULTS: Herein, we report on the presence of small RNA (sRNA) contaminants in widely used microRNA extraction kits and propose an approach for their depletion. We sequenced sRNAs extracted from human plasma samples and detected important levels of non-human (exogenous) sequences whose source could be traced to the microRNA extraction columns through a careful qPCR-based analysis of several laboratory reagents. Furthermore, we also detected the presence of artefactual sequences related to these contaminants in a range of published datasets, thereby arguing in particular for a re-evaluation of reports suggesting the presence of exogenous RNAs of microbial and dietary origin in blood plasma. To avoid artefacts in future experiments, we also devise several protocols for the removal of contaminant RNAs, define minimal amounts of starting material for artefact-free analyses, and confirm the reduction of contaminant levels for identification of bona fide sequences using 'ultra-clean' extraction kits. CONCLUSION: This is the first report on the presence of RNA molecules as contaminants in RNA extraction kits. The described protocols should be applied in the future to avoid confounding sRNA studies.

Symmetries among Multivariate Information Measures Explored Using Möbius Operators.

Relations between common information measures include the duality relations based on Möbius inversion on lattices, which are the direct consequence of the symmetries of the lattices of the sets of variables (subsets ordered by inclusion). In this paper we use the lattice and functional symmetries to provide a unifying formalism that reveals some new relations and systematizes the symmetries of the information functions. To our knowledge, this is the first systematic examination of the full range of relationships of this class of functions. We define operators on functions on these lattices based on the Möbius inversions that map functions into one another, which we call Möbius operators, and show that they form a simple group isomorphic to the symmetric group S3. Relations among the set of functions on the lattice are transparently expressed in terms of the operator algebra, and, when applied to the information measures, can be used to derive a wide range of relationships among diverse information measures. The Möbius operator algebra is then naturally generalized which yields an even wider range of new relationships.

Role of MicroRNAs in Renal Parenchymal Diseases-A New Dimension.

Since their discovery in 1993, numerous microRNAs (miRNAs) have been identified in humans and other eukaryotic organisms, and their role as key regulators of gene expression is still being elucidated. It is now known that miRNAs not only play a central role in the processes that ensure normal development and physiology, but they are often dysregulated in various diseases. In this review, we present an overview of the role of miRNAs in normal renal development and physiology, in maladaptive renal repair after injury, and in the pathogenesis of renal parenchymal diseases. In addition, we describe methods used for their detection and their potential as therapeutic targets. Continued research on renal miRNAs will undoubtedly improve our understanding of diseases affecting the kidneys and may also lead to new therapeutic agents.

Sources and Functions of Extracellular Small RNAs in Human Circulation.

Various biotypes of endogenous small RNAs (sRNAs) have been detected in human circulation, including microRNAs, transfer RNAs, ribosomal RNA, and yRNA fragments. These extracellular sRNAs (ex-sRNAs) are packaged and secreted by many different cell types. Ex-sRNAs exhibit differences in abundance in several disease states and have, therefore, been proposed for use as effective biomarkers. Furthermore, exosome-borne ex-sRNAs have been reported to elicit physiological responses in acceptor cells. Exogenous ex-sRNAs derived from diet (most prominently from plants) and microorganisms have also been reported in human blood. Essential issues that remain to be conclusively addressed concern the (a) presence and sources of exogenous ex-sRNAs in human bodily fluids, (b) detection and measurement of ex-sRNAs in human circulation, (c) selectivity of ex-sRNA export and import, (d) sensitivity and specificity of ex-sRNA delivery to cellular targets, and (e) cell-, tissue-, organ-, and organism-wide impacts of ex-sRNA-mediated cell-to-cell communication. We survey the present state of knowledge of most of these issues in this review.

Molecular evidence of stress-induced acute heart injury in a mouse model simulating posttraumatic stress disorder.

Posttraumatic stress disorder (PTSD) is a common condition induced by life-threatening stress, such as that experienced by soldiers under battlefield conditions. Other than the commonly recognized behavioral and psychological dysfunction, epidemiological studies have also revealed that PTSD patients have a higher risk of other diseases, such as cardiovascular disorders. Using a PTSD mouse model, we investigated the longitudinal transcriptomic changes in heart tissues after the exposure to stress through intimidation. Our results revealed acute heart injury associated with the traumatic experience, reflecting the underlying biological injury processes of the immune response, extracellular matrix remodeling, epithelial-to-mesenchymal cell transitions, and cell proliferation. Whether this type of injury has any long-term effects on heart function is yet to be determined. The differing responses to stress leading to acute heart injury in different inbred strains of mice also suggest that this response has a genetic as well as an environmental component. Accordingly, the results from this study suggest a molecular basis for the observed higher risk of cardiovascular disorders in PTSD patients, which raises the likelihood of cardiac dysfunction induced by long-term stress exposures.

Systems genomics evaluation of the SH-SY5Y neuroblastoma cell line as a model for Parkinson's disease.

BACKGROUND: The human neuroblastoma cell line, SH-SY5Y, is a commonly used cell line in studies related to neurotoxicity, oxidative stress, and neurodegenerative diseases. Although this cell line is often used as a cellular model for Parkinson's disease, the relevance of this cellular model in the context of Parkinson's disease (PD) and other neurodegenerative diseases has not yet been systematically evaluated. RESULTS: We have used a systems genomics approach to characterize the SH-SY5Y cell line using whole-genome sequencing to determine the genetic content of the cell line and used transcriptomics and proteomics data to determine molecular correlations. Further, we integrated genomic variants using a network analysis approach to evaluate the suitability of the SH-SY5Y cell line for perturbation experiments in the context of neurodegenerative diseases, including PD. CONCLUSIONS: The systems genomics approach showed consistency across different biological levels (DNA, RNA and protein concentrations). Most of the genes belonging to the major Parkinson's disease pathways and modules were intact in the SH-SY5Y genome. Specifically, each analysed gene related to PD has at least one intact copy in SH-SY5Y. The disease-specific network analysis approach ranked the genetic integrity of SH-SY5Y as higher for PD than for Alzheimer's disease but lower than for Huntington's disease and Amyotrophic Lateral Sclerosis for loss of function perturbation experiments.

Chromosomal haplotypes by genetic phasing of human families.

Assignment of alleles to haplotypes for nearly all the variants on all chromosomes can be performed by genetic analysis of a nuclear family with three or more children. Whole-genome sequence data enable deterministic phasing of nearly all sequenced alleles by permitting assignment of recombinations to precise chromosomal positions and specific meioses. We demonstrate this process of genetic phasing on two families each with four children. We generate haplotypes for all of the children and their parents; these haplotypes span all genotyped positions, including rare variants. Misassignments of phase between variants (switch errors) are nearly absent. Our algorithm can also produce multimegabase haplotypes for nuclear families with just two children and can handle families with missing individuals. We implement our algorithm in a suite of software scripts (Haploscribe). Haplotypes and family genome sequences will become increasingly important for personalized medicine and for fundamental biology.

RNASEQR--a streamlined and accurate RNA-seq sequence analysis program.

Next-generation sequencing (NGS) technologies-based transcriptomic profiling method often called RNA-seq has been widely used to study global gene expression, alternative exon usage, new exon discovery, novel transcriptional isoforms and genomic sequence variations. However, this technique also poses many biological and informatics challenges to extracting meaningful biological information. The RNA-seq data analysis is built on the foundation of high quality initial genome localization and alignment information for RNA-seq sequences. Toward this goal, we have developed RNASEQR to accurately and effectively map millions of RNA-seq sequences. We have systematically compared RNASEQR with four of the most widely used tools using a simulated data set created from the Consensus CDS project and two experimental RNA-seq data sets generated from a human glioblastoma patient. Our results showed that RNASEQR yields more accurate estimates for gene expression, complete gene structures and new transcript isoforms, as well as more accurate detection of single nucleotide variants (SNVs). RNASEQR analyzes raw data from RNA-seq experiments effectively and outputs results in a manner that is compatible with a wide variety of specialized downstream analyses on desktop computers.

MicroRNA as biomarkers and regulators in B-cell chronic lymphocytic leukemia.

Early cancer detection and disease stratification or classification are critical to successful treatment. Accessible, reliable, and informative cancer biomarkers can be medically valuable and can provide some relevant insights into cancer biology. Recent studies have suggested improvements in detecting malignancies by the use of specific extracellular microRNAs (miRNAs) in plasma. In chronic lymphocytic leukemia (CLL), an incurable hematologic disorder, sensitive, early, and noninvasive diagnosis and better disease classification would be very useful for more effective therapies. We show here that circulating miRNAs can be sensitive biomarkers for CLL, because certain extracellular miRNAs are present in CLL patient plasma at levels significantly different from healthy controls and from patients affected by other hematologic malignancies. The levels of several of these circulating miRNAs also displayed significant differences between zeta-associated protein 70 (ZAP-70)(+) and ZAP-70(-) CLL. We also determined that the level of circulating miR-20a correlates reliably with diagnosis-to-treatment time. Network analysis of our data, suggests a regulatory network associated with BCL2 and ZAP-70 expression in CLL. This hypothesis suggests the possibility of using the levels of specific miRNAs in plasma to detect CLL and to determine the ZAP-70 status.

RCytoscape: tools for exploratory network analysis.

BACKGROUND: Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail, and ill-equipped to explore the variety and quantities of data available today, and the complex causes we seek to understand. RESULTS: RCytoscape integrates R (an open-ended programming environment rich in statistical power and data-handling facilities) and Cytoscape (powerful network visualization and analysis software). RCytoscape extends Cytoscape's functionality beyond what is possible with the Cytoscape graphical user interface. To illustrate the power of RCytoscape, a portion of the Glioblastoma multiforme (GBM) data set from the Cancer Genome Atlas (TCGA) is examined. Network visualization reveals previously unreported patterns in the data suggesting heterogeneous signaling mechanisms active in GBM Proneural tumors, with possible clinical relevance. CONCLUSIONS: Progress in bioinformatics and computational biology depends upon exploratory and confirmatory data analysis, upon inference, and upon modeling. These activities will eventually permit the prediction and control of complex biological systems. Network visualizations--molecular maps--created from an open-ended programming environment rich in statistical power and data-handling facilities, such as RCytoscape, will play an essential role in this progression.

ApoE Modifier Alleles for Alzheimer's Disease Discovered by Information Theory Dependency Measures: MIST Software Package.

Information theory-based measures of variable dependency (previously published) have been implemented into a software package, MIST. The design of the software and its potential uses are described, and a demonstration is presented in the discovery of modifier alleles of the ApoE gene in affecting Alzheimer's disease (AD) by analyzing the UK Biobank dataset. The modifier genes uncovered overlap strongly with genes found to be associated with AD. Others include many known to influence AD. We discuss a range of uses of the dependency calculations using MIST that can uncover additional genetic effects in similar complex datasets, like higher degrees of interaction and phenotypic pleiotropy.

SRM targeted proteomics in search for biomarkers of HCV-induced progression of fibrosis to cirrhosis in HALT-C patients.

The current gold standard for diagnosis of hepatic fibrosis and cirrhosis is the traditional invasive liver biopsy. It is desirable to assess hepatic fibrosis with noninvasive means. Targeted proteomic techniques allow an unbiased assessment of proteins and might be useful to identify proteins related to hepatic fibrosis. We utilized selected reaction monitoring (SRM) targeted proteomics combined with an organ-specific blood protein strategy to identify and quantify 38 liver-specific proteins. A combination of protein C and retinol-binding protein 4 in serum gave promising preliminary results as candidate biomarkers to distinguish patients at different stages of hepatic fibrosis due to chronic infection with hepatitis C virus (HCV). Also, alpha-1-B glycoprotein, complement factor H and insulin-like growth factor binding protein acid labile subunit performed well in distinguishing patients from healthy controls.