RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease

The roles of RNA processing in translating genotype to phenotype

A goal of human genetics studies is to determine the mechanisms by which genetic variation produces phenotypic differences that affect human health. Efforts in this respect have previously focused on genetic variants that affect mRNA levels by altering epigenetic and transcriptional regulation. Recent studies show that genetic variants that affect RNA processing are at least equally as common as, and are largely independent from, those variants that affect transcription. We highlight the impact of genetic variation on pre-mRNA splicing and polyadenylation, and on the stability, translation and structure of mRNAs as mechanisms that produce phenotypic traits. These results emphasize the importance of including RNA processing signals in analyses to identify functional variants.

The roles of RNA processing in translating genotype to phenotype

A goal of human genetics studies is to determine the mechanisms by which genetic variation produces phenotypic differences that affect human health. Efforts in this respect have previously focused on genetic variants that affect mRNA levels by altering epigenetic and transcriptional regulation. Recent studies show that genetic variants that affect RNA processing are at least equally as common as, and are largely independent from, those variants that affect transcription. We highlight the impact of genetic variation on pre-mRNA splicing and polyadenylation, and on the stability, translation and structure of mRNAs as mechanisms that produce phenotypic traits. These results emphasize the importance of including RNA processing signals in analyses to identify functional variants.

Neurogenesis: Regulation by Alternative Splicing and Related Posttranscriptional Processes

The complexity of the mammalian brain requires highly specialized protein function and diversity. As neurons differentiate and the neuronal circuitry is established, several mRNAs undergo alternative splicing and other posttranscriptional changes that expand the variety of protein isoforms produced. Recent advances are beginning to shed light on the molecular mechanisms that regulate isoform switching during neurogenesis and the role played by specific RNA binding proteins in this process. Neurogenesis and neuronal wiring were recently shown to also be regulated by RNA degradation through nonsense-mediated decay. An additional layer of regulatory complexity in these biological processes is the interplay between alternative splicing and long noncoding RNAs. Dysregulation of posttranscriptional regulation results in defective neuronal differentiation and/or synaptic connections that lead to neurodevelopmental and psychiatric disorders.

Rational Design of Cyclic Peptide Inhibitors of U2AF Homology Motif (UHM) Domains To Modulate Pre-mRNA Splicing

U2AF homology motifs (UHMs) are atypical RNA recognition motif domains that mediate critical protein-protein interactions during the regulation of alternative pre-mRNA splicing and other processes. The recognition of UHM domains by UHM ligand motif (ULM) peptide sequences plays important roles during early steps of spliceosome assembly. Splicing factor 45 kDa (SPF45) is an alternative splicing factor implicated in breast and lung cancers, and splicing regulation of apoptosis-linked pre-mRNAs by SPF45 was shown to depend on interactions between its UHM domain and ULM motifs in constitutive splicing factors. We have developed cyclic peptide inhibitors that target UHM domains. By screening a focused library of linear and cyclic peptides and performing structure-activity relationship analysis, we designed cyclic peptides with 4-fold improved binding affinity for the SPF45 UHM domain compared to native ULM ligands and 270-fold selectivity to discriminate UHM domains from alternative and constitutive splicing factors. These inhibitors are useful tools to modulate and dissect mechanisms of alternative splicing regulation.

GSDMB induces an asthma phenotype characterized by increased airway responsiveness and remodeling without lung inflammation

Gasdermin B (GSDMB) on chromosome 17q21 demonstrates a strong genetic linkage to asthma, but its function in asthma is unknown. Here we identified that GSDMB is highly expressed in lung bronchial epithelium in human asthma. Overexpression of GSDMB in primary human bronchial epithelium increased expression of genes important to both airway remodeling [TGF-β1, 5-lipoxygenase (5-LO)] and airway-hyperresponsiveness (AHR) (5-LO). Interestingly, hGSDMB^Zp3-Cre mice expressing increased levels of the human GSDMB transgene showed a significant spontaneous increase in AHR and a significant spontaneous increase in airway remodeling, with increased smooth muscle mass and increased fibrosis in the absence of airway inflammation. In addition, hGSDMB^Zp3-Cre mice showed increases in the same remodeling and AHR mediators (TGF-β1, 5-LO) observed in vitro in GSDMB-overexpressing epithelial cells. GSDMB induces TGF-β1 expression via induction of 5-LO, because knockdown of 5-LO in epithelial cells overexpressing GSDMB inhibited TGF-β1 expression. These studies demonstrate that GSDMB, a gene highly linked to asthma but whose function in asthma is previously unknown, regulates AHR and airway remodeling without airway inflammation through a previously unrecognized pathway in which GSDMB induces 5-LO to induce TGF-β1 in bronchial epithelium.

Double-target Antisense U1snRNAs Correct Mis-splicing Due to c.639+861C>T and c.639+919G>A GLA Deep Intronic Mutations

Fabry disease is a rare X-linked lysosomal storage disorder caused by deficiency of the α-galactosidase A (α-Gal A) enzyme, which is encoded by the GLA gene. GLA transcription in humans produces a major mRNA encoding α-Gal A and a minor mRNA of unknown function, which retains a 57-nucleotide-long cryptic exon between exons 4 and 5, bearing a premature termination codon. NM_000169.2:c.639+861C>T and NM_000169.2:c.639+919G>A GLA deep intronic mutations have been described to cause Fabry disease by inducing overexpression of the alternatively spliced mRNA, along with a dramatic decrease in the major one. Here, we built a wild-type GLA minigene and two minigenes that carry mutations c.639+861C>T and c.639+919G>A. Once transfected into cells, the minigenes recapitulate the molecular patterns observed in patients, at the mRNA, protein, and enzymatic level. We constructed a set of specific double-target U1asRNAs to correct c.639+861C>T and c.639+919G>A GLA mutations. Efficacy of U1asRNAs in inducing the skipping of the cryptic exon was evaluated upon their transient co-transfection with the minigenes in COS-1 cells, by real-time polymerase chain reaction (PCR), western blot analysis, and α-Gal A enzyme assay. We identified a set of U1asRNAs that efficiently restored α-Gal A enzyme activity and the correct splicing pathways in reporter minigenes. We also identified a unique U1asRNA correcting both mutations as efficently as the mutation-specific U1asRNAs. Our study proves that an exon skipping-based approach recovering α-Gal A activity in the c.639+861C>T and c.639+919G>A GLA mutations is active.

Developmental regulation of RNA processing by Rbfox proteins

The Rbfox genes encode an ancient family of sequence-specific RNA binding proteins (RBPs) that are critical developmental regulators in multiple tissues including skeletal muscle, cardiac muscle, and brain. The hallmark of Rbfox proteins is a single high-affinity RRM domain, highly conserved from insects to humans, that binds preferentially to UGCAUG motifs at diverse regulatory sites in pre-mRNA introns, mRNA 3'UTRs, and pre-miRNAs hairpin structures. Versatile regulatory circuits operate on Rbfox pre-mRNA and mRNA to ensure proper expression of Rbfox1 protein isoforms, which then act on the broader transcriptome to regulate alternative splicing networks, mRNA stability and translation, and microRNA processing. Complex Rbfox expression is encoded in large genes encompassing multiple promoters and alternative splicing options that govern spatiotemporal expression of structurally distinct and tissue-specific protein isoforms with different classes of RNA targets. Nuclear Rbfox1 is a candidate master regulator that binds intronic UGCAUG elements to impact splicing efficiency of target alternative exons, many in transcripts for other splicing regulators. Tissue-specificity of Rbfox-mediated alternative splicing is executed by combinatorial regulation through the integrated activity of Rbfox proteins and synergistic or antagonistic splicing factors. Studies in animal models show that Rbfox1-related genes are critical for diverse developmental processes including germ cell differentiation and memory in Drosophila, neuronal migration and function in mouse brain, myoblast fusion and skeletal muscle function, and normal heart function. Finally, genetic and biochemical evidence suggest that aberrations in Rbfox-regulated circuitry are risk factors for multiple human disorders, especially neurodevelopmental disorders including epilepsy and autism, and cardiac hypertrophy. WIREs RNA 2017, 8:e1398. doi: 10.1002/wrna.1398 For further resources related to this article, please visit the WIREs website.

Does conservation account for splicing patterns?

BACKGROUND

Alternative mRNA splicing is critical to proteomic diversity and tissue and species differentiation. Exclusion of cassette exons, also called exon skipping, is the most common type of alternative splicing in mammals.

RESULTS

We present a computational model that predicts absolute (though not tissue-differential) percent-spliced-in of cassette exons more accurately than previous models, despite not using any 'hand-crafted' biological features such as motif counts. We achieve nearly identical performance using only the conservation score (mammalian phastCons) of each splice junction normalized by average conservation over 100 bp of the corresponding flanking intron, demonstrating that conservation is an unexpectedly powerful indicator of alternative splicing patterns. Using this method, we provide evidence that intronic splicing regulation occurs predominantly within 100 bp of the alternative splice sites and that conserved elements in this region are, as expected, functioning as splicing regulators. We show that among conserved cassette exons, increased conservation of flanking introns is associated with reduced inclusion. We also propose a new definition of intronic splicing regulatory elements (ISREs) that is independent of conservation, and show that most ISREs do not match known binding sites or splicing factors despite being predictive of percent-spliced-in.

CONCLUSIONS

These findings suggest that one mechanism for the evolutionary transition from constitutive to alternative splicing is the emergence of cis-acting splicing inhibitors. The association of our ISREs with differences in splicing suggests the existence of novel RNA-binding proteins and/or novel splicing roles for known RNA-binding proteins.

RNA Sequence Context Effects Measured In Vitro Predict In Vivo Protein Binding and Regulation

Many RNA binding proteins (RBPs) bind specific RNA sequence motifs, but only a small fraction (∼15%-40%) of RBP motif occurrences are occupied in vivo. To determine which contextual features discriminate between bound and unbound motifs, we performed an in vitro binding assay using 12,000 mouse RNA sequences with the RBPs MBNL1 and RBFOX2. Surprisingly, the strength of binding to motif occurrences in vitro was significantly correlated with in vivo binding, developmental regulation, and evolutionary age of alternative splicing. Multiple lines of evidence indicate that the primary context effect that affects binding in vitro and in vivo is RNA secondary structure. Large-scale combinatorial mutagenesis of unfavorable sequence contexts revealed a consistent pattern whereby mutations that increased motif accessibility improved protein binding and regulatory activity. Our results indicate widespread inhibition of motif binding by local RNA secondary structure and suggest that mutations that alter sequence context commonly affect RBP binding and regulation.

ExonImpact: Prioritizing Pathogenic Alternative Splicing Events

Alternative splicing (AS) is a closely regulated process that allows a single gene to encode multiple protein isoforms, thereby contributing to the diversity of the proteome. Dysregulation of the splicing process has been found to be associated with many inherited diseases. However, among the pathogenic AS events, there are numerous "passenger" events whose inclusion or exclusion does not lead to significant changes with respect to protein function. In this study, we evaluate the secondary and tertiary structural features of proteins associated with disease-causing and neutral AS events, and show that several structural features are strongly associated with the pathological impact of exon inclusion. We further develop a machine-learning-based computational model, ExonImpact, for prioritizing and evaluating the functional consequences of hitherto uncharacterized AS events. We evaluated our model using several strategies including cross-validation, and data from the Gene-Tissue Expression (GTEx) and ClinVar databases. ExonImpact is freely available at http://watson.compbio.iupui.edu/ExonImpact.

Defining the genetic basis of early onset hereditary spastic paraplegia using whole genome sequencing

We performed whole genome sequencing (WGS) in nine families from India with early-onset hereditary spastic paraplegia (HSP). We obtained a genetic diagnosis in 4/9 (44 %) families within known HSP genes (DDHD2 and CYP2U1), as well as perixosomal biogenesis disorders (PEX16) and GM1 gangliosidosis (GLB1). In the remaining patients, no candidate structural variants, copy number variants or predicted splice variants affecting an extended candidate gene list were identified. Our findings demonstrate the efficacy of using WGS for diagnosing early-onset HSP, particularly in consanguineous families (4/6 diagnosed), highlighting that two of the diagnoses would not have been made using a targeted approach.

Identification of Claudin 1 Transcript Variants in Human Invasive Breast Cancer

BACKGROUND

The claudin 1 tight junction protein, solely responsible for the barrier function of epithelial cells, is frequently down regulated in invasive human breast cancer. The underlying mechanism is largely unknown, and no obvious mutations in the claudin 1 gene (CLDN1) have been identified to date in breast cancer. Since many genes have been shown to undergo deregulation through splicing and mis-splicing events in cancer, the current study was undertaken to investigate the occurrence of transcript variants for CLDN1 in human invasive breast cancer.

METHODS

RT-PCR analysis of CLDN1 transcripts was conducted on RNA isolated from 12 human invasive breast tumors. The PCR products from each tumor were resolved by agarose gel electrophoresis, cloned and sequenced. Genomic DNA was also isolated from each of the 12 tumors and amplified using PCR CLDN1 specific primers. Sanger sequencing and single nucleotide polymorphism (SNP) analyses were conducted.

RESULTS

A number of CLDN1 transcript variants were identified in these breast tumors. All variants were shorter than the classical CLDN1 transcript. Sequence analysis of the PCR products revealed several splice variants, primarily in exon 1 of CLDN1; resulting in truncated proteins. One variant, V1, resulted in a premature stop codon and thus likely led to nonsense mediated decay. Interestingly, another transcript variant, V2, was not detected in normal breast tissue samples. Further, sequence analysis of the tumor genomic DNA revealed SNPs in 3 of the 4 coding exons, including a rare missense SNP (rs140846629) in exon 2 which represents an Ala124Thr substitution. To our knowledge this is the first report of CLDN1 transcript variants in human invasive breast cancer. These studies suggest that alternate splicing may also be a mechanism by which claudin 1 is down regulated at both the mRNA and protein levels in invasive breast cancer and may provide novel insights into how CLDN1 is reduced or silenced in human breast cancer.

Sequence variation between 462 human individuals fine-tunes functional sites of RNA processing

Recent advances in the cost-efficiency of sequencing technologies enabled the combined DNA- and RNA-sequencing of human individuals at the population-scale, making genome-wide investigations of the inter-individual genetic impact on gene expression viable. Employing mRNA-sequencing data from the Geuvadis Project and genome sequencing data from the 1000 Genomes Project we show that the computational analysis of DNA sequences around splice sites and poly-A signals is able to explain several observations in the phenotype data. In contrast to widespread assessments of statistically significant associations between DNA polymorphisms and quantitative traits, we developed a computational tool to pinpoint the molecular mechanisms by which genetic markers drive variation in RNA-processing, cataloguing and classifying alleles that change the affinity of core RNA elements to their recognizing factors. The in silico models we employ further suggest RNA editing can moonlight as a splicing-modulator, albeit less frequently than genomic sequence diversity. Beyond existing annotations, we demonstrate that the ultra-high resolution of RNA-Seq combined from 462 individuals also provides evidence for thousands of bona fide novel elements of RNA processing-alternative splice sites, introns, and cleavage sites-which are often rare and lowly expressed but in other characteristics similar to their annotated counterparts.

The power of multiplexed functional analysis of genetic variants

New technologies have recently enabled saturation mutagenesis and functional analysis of nearly all possible variants of regulatory elements or proteins of interest in single experiments. Here we discuss the past, present, and future of such multiplexed (functional) assays for variant effects (MAVEs). MAVEs provide detailed insight into sequence-function relationships, and they may prove critical for the prospective clinical interpretation of genetic variants.

Gene Panel Testing in Epileptic Encephalopathies and Familial Epilepsies

In recent years, several genes have been causally associated with epilepsy. However, making a genetic diagnosis in a patient can still be difficult, since extensive phenotypic and genetic heterogeneity has been observed in many monogenic epilepsies. This study aimed to analyze the genetic basis of a wide spectrum of epilepsies with age of onset spanning from the neonatal period to adulthood. A gene panel targeting 46 epilepsy genes was used on a cohort of 216 patients consecutively referred for panel testing. The patients had a range of different epilepsies from benign neonatal seizures to epileptic encephalopathies (EEs). Potentially causative variants were evaluated by literature and database searches, submitted to bioinformatic prediction algorithms, and validated by Sanger sequencing. If possible, parents were included for segregation analysis. We identified a presumed disease-causing variant in 49 (23%) of the 216 patients. The variants were found in 19 different genes including SCN1A, STXBP1, CDKL5, SCN2A, SCN8A, GABRA1, KCNA2, and STX1B. Patients with neonatal-onset epilepsies had the highest rate of positive findings (57%). The overall yield for patients with EEs was 32%, compared to 17% among patients with generalized epilepsies and 16% in patients with focal or multifocal epilepsies. By the use of a gene panel consisting of 46 epilepsy genes, we were able to find a disease-causing genetic variation in 23% of the analyzed patients. The highest yield was found among patients with neonatal-onset epilepsies and EEs.

Fine-mapping analysis revealed complex pleiotropic effect and tissue-specific regulatory mechanism of TNFSF15 in primary biliary cholangitis, Crohn's disease and leprosy

Genetic polymorphism within the 9q32 locus is linked with increased risk of several diseases, including Crohn’s disease (CD), primary biliary cholangitis (PBC) and leprosy. The most likely disease-causing gene within 9q32 is TNFSF15, which encodes the pro-inflammatory cytokine TNF super-family member 15, but it was unknown whether these disparate diseases were associated with the same genetic variance in 9q32, and how variance within this locus might contribute to pathology. Using genetic data from published studies on CD, PBC and leprosy we revealed that bearing a T allele at rs6478108/rs6478109 (r² = 1) or rs4979462 was significantly associated with increased risk of CD and decreased risk of leprosy, while the T allele at rs4979462 was associated with significantly increased risk of PBC. In vitro analyses showed that the rs6478109 genotype significantly affected TNFSF15 expression in cells from whole blood of controls, while functional annotation using publicly-available data revealed the broad cell type/tissue-specific regulatory potential of variance at rs6478109 or rs4979462. In summary, we provide evidence that variance within TNFSF15 has the potential to affect cytokine expression across a range of tissues and thereby contribute to protection from infectious diseases such as leprosy, while increasing the risk of immune-mediated diseases including CD and PBC.

The functional relevance of somatic synonymous mutations in melanoma and other cancers

Recent technological advances in sequencing have flooded the field of cancer research with knowledge about somatic mutations for many different cancer types. Most cancer genomics studies focus on mutations that alter the amino acid sequence, ignoring the potential impact of synonymous mutations. However, accumulating experimental evidence has demonstrated clear consequences for gene function, leading to a widespread recognition of the functional role of synonymous mutations and their causal connection to various diseases. Here, we review the evidence supporting the direct impact of synonymous mutations on gene function via gene splicing; mRNA stability, folding, and translation; protein folding; and miRNA-based regulation of expression. These results highlight the functional contribution of synonymous mutations to oncogenesis and the need to further investigate their detection and prioritization for experimental assessment.

Whole-Exome Sequencing Identifies Loci Associated with Blood Cell Traits and Reveals a Role for Alternative GFI1B Splice Variants in Human Hematopoiesis

Circulating blood cell counts and indices are important indicators of hematopoietic function and a number of clinical parameters, such as blood oxygen-carrying capacity, inflammation, and hemostasis. By performing whole-exome sequence association analyses of hematologic quantitative traits in 15,459 community-dwelling individuals, followed by in silico replication in up to 52,024 independent samples, we identified two previously undescribed coding variants associated with lower platelet count: a common missense variant in CPS1 (rs1047891, MAF = 0.33, discovery + replication p = 6.38 × 10(-10)) and a rare synonymous variant in GFI1B (rs150813342, MAF = 0.009, discovery + replication p = 1.79 × 10(-27)). By performing CRISPR/Cas9 genome editing in hematopoietic cell lines and follow-up targeted knockdown experiments in primary human hematopoietic stem and progenitor cells, we demonstrate an alternative splicing mechanism by which the GFI1B rs150813342 variant suppresses formation of a GFI1B isoform that preferentially promotes megakaryocyte differentiation and platelet production. These results demonstrate how unbiased studies of natural variation in blood cell traits can provide insight into the regulation of human hematopoiesis.

Leveraging gene-environment interactions and endotypes for asthma gene discovery

Asthma is a heterogeneous clinical syndrome that includes subtypes of disease with different underlying causes and disease mechanisms. Asthma is caused by a complex interaction between genes and environmental exposures; early-life exposures in particular play an important role. Asthma is also heritable, and a number of susceptibility variants have been discovered in genome-wide association studies, although the known risk alleles explain only a small proportion of the heritability. In this review, we present evidence supporting the hypothesis that focusing on more specific asthma phenotypes, such as childhood asthma with severe exacerbations, and on relevant exposures that are involved in gene-environment interactions (GEIs), such as rhinovirus infections, will improve detection of asthma genes and our understanding of the underlying mechanisms. We will discuss the challenges of considering GEIs and the advantages of studying responses to asthma-associated exposures in clinical birth cohorts, as well as in cell models of GEIs, to dissect the context-specific nature of genotypic risks, to prioritize variants in genome-wide association studies, and to identify pathways involved in pathogenesis in subgroups of patients. We propose that such approaches, in spite of their many challenges, present great opportunities for better understanding of asthma pathogenesis and heterogeneity and, ultimately, for improving prevention and treatment of disease.

Genome-wide characteristics of de novo mutations in autism

De novo mutations (DNMs) are important in Autism Spectrum Disorder (ASD), but so far analyses have mainly been on the ~1.5% of the genome encoding genes. Here, we performed whole genome sequencing (WGS) of 200 ASD parent-child trios and characterized germline and somatic DNMs. We confirmed that the majority of germline DNMs (75.6%) originated from the father, and these increased significantly with paternal age only (p=4.2×10^-10). However, when clustered DNMs (those within 20kb) were found in ASD, not only did they mostly originate from the mother (p=7.7×10^-13), but they could also be found adjacent to de novo copy number variations (CNVs) where the mutation rate was significantly elevated (p=2.4×10^-24). By comparing DNMs detected in controls, we found a significant enrichment of predicted damaging DNMs in ASD cases (p=8.0×10^-9; OR=1.84), of which 15.6% (p=4.3×10^-3) and 22.5% (p=7.0×10^-5) were in the non-coding or genic non-coding, respectively. The non-coding elements most enriched for DNM were untranslated regions of genes, boundaries involved in exon-skipping and DNase I hypersensitive regions. Using microarrays and a novel outlier detection test, we also found aberrant methylation profiles in 2/185 (1.1%) of ASD cases. These same individuals carried independently identified DNMs in the ASD risk- and epigenetic- genes DNMT3A and ADNP. Our data begins to characterize different genome-wide DNMs, and highlight the contribution of non-coding variants, to the etiology of ASD.

Deep learning for computational biology

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.

Transcriptome-Wide Analysis Reveals Modulation of Human Macrophage Inflammatory Phenotype Through Alternative Splicing

OBJECTIVE

Human macrophages can shift phenotype across the inflammatory M1 and reparative M2 spectrum in response to environmental challenges, but the mechanisms promoting inflammatory and cardiometabolic disease-associated M1 phenotypes remain incompletely understood. Alternative splicing (AS) is emerging as an important regulator of cellular function, yet its role in macrophage activation is largely unknown. We investigated the extent to which AS occurs in M1 activation within the cardiometabolic disease context and validated a functional genomic cell model for studying human macrophage-related AS events.

APPROACH AND RESULTS

From deep RNA-sequencing of resting, M1, and M2 primary human monocyte-derived macrophages, we found 3860 differentially expressed genes in M1 activation and detected 233 M1-induced AS events; the majority of AS events were cell- and M1-specific with enrichment for pathways relevant to macrophage inflammation. Using genetic variant data for 10 cardiometabolic traits, we identified 28 trait-associated variants within the genomic loci of 21 alternatively spliced genes and 15 variants within 7 differentially expressed regulatory splicing factors in M1 activation. Knockdown of 1 such splicing factor, CELF1, in primary human macrophages led to increased inflammatory response to M1 stimulation, demonstrating CELF1's potential modulation of the M1 phenotype. Finally, we demonstrated that an induced pluripotent stem cell-derived macrophage system recapitulates M1-associated AS events and provides a high-fidelity macrophage AS model.

CONCLUSIONS

AS plays a role in defining macrophage phenotype in a cell- and stimulus-specific fashion. Alternatively spliced genes and splicing factors with trait-associated variants may reveal novel pathways and targets in cardiometabolic diseases.

Splice-switching antisense oligonucleotides as therapeutic drugs

Splice-switching oligonucleotides (SSOs) are short, synthetic, antisense, modified nucleic acids that base-pair with a pre-mRNA and disrupt the normal splicing repertoire of the transcript by blocking the RNA–RNA base-pairing or protein–RNA binding interactions that occur between components of the splicing machinery and the pre-mRNA. Splicing of pre-mRNA is required for the proper expression of the vast majority of protein-coding genes, and thus, targeting the process offers a means to manipulate protein production from a gene. Splicing modulation is particularly valuable in cases of disease caused by mutations that lead to disruption of normal splicing or when interfering with the normal splicing process of a gene transcript may be therapeutic. SSOs offer an effective and specific way to target and alter splicing in a therapeutic manner. Here, we discuss the different approaches used to target and alter pre-mRNA splicing with SSOs. We detail the modifications to the nucleic acids that make them promising therapeutics and discuss the challenges to creating effective SSO drugs. We highlight the development of SSOs designed to treat Duchenne muscular dystrophy and spinal muscular atrophy, which are currently being tested in clinical trials.

Different RNA splicing mechanisms contribute to diverse infective outcome of classical swine fever viruses of differing virulence: insights from the deep sequencing data in swine umbilical vein endothelial cells

Molecular mechanisms underlying RNA splicing regulation in response to viral infection are poorly understood. Classical swine fever (CSF), one of the most economically important and highly contagious swine diseases worldwide, is caused by classical swine fever virus (CSFV). Here, we used high-throughput sequencing to obtain the digital gene expression (DGE) profile in swine umbilical vein endothelial cells (SUVEC) to identify different response genes for CSFV by using both Shimen and C strains. The numbers of clean tags obtained from the libraries of the control and both CSFV-infected libraries were 3,473,370, 3,498,355, and 3,327,493 respectively. In the comparison among the control, CSFV-C, and CSFV-Shimen groups, 644, 158, and 677 differentially expressed genes (DEGs) were confirmed in the three groups. Pathway enrichment analysis showed that many of these DEGs were enriched in spliceosome, ribosome, proteasome, ubiquitin-mediated proteolysis, cell cycle, focal adhesion, Wnt signalling pathway, etc., where the processes differ between CSFV strains of differing virulence. To further elucidate important mechanisms related to the differential infection by the CSFV Shimen and C strains, we identified four possible profiles to assess the significantly expressed genes only by CSFV Shimen or CSFV C strain. GO analysis showed that infection with CSFV Shimen and C strains disturbed ‘RNA splicing’ of SUVEC, resulting in differential ‘gene expression’ in SUVEC. Mammalian target of rapamycin (mTOR) was identified as a significant response regulator contributed to impact on SUVEC function for CSFV Shimen. This computational study suggests that CSFV of differing virulence could induce alterations in RNA splicing regulation in the host cell to change cell metabolism, resulting in acute haemorrhage and pathological damage or infectious tolerance.

ELAVL2-regulated transcriptional and splicing networks in human neurons link neurodevelopment and autism

The role of post-transcriptional gene regulation in human brain development and neurodevelopmental disorders remains mostly uncharacterized. ELAV-like RNA-binding proteins (RNAbps) are a family of proteins that regulate several aspects of neuronal function including neuronal excitability and synaptic transmission, both critical to the normal function of the brain in cognition and behavior. Here, we identify the downstream neuronal transcriptional and splicing networks of ELAVL2, an RNAbp with previously unknown function in the brain. Expression of ELAVL2 was reduced in human neurons and RNA-sequencing was utilized to identify networks of differentially expressed and alternatively spliced genes resulting from haploinsufficient levels of ELAVL2. These networks contain a number of autism-relevant genes as well as previously identified targets of other important RNAbps implicated in autism spectrum disorder (ASD) including RBFOX1 and FMRP. ELAVL2-regulated co-expression networks are also enriched for neurodevelopmental and synaptic genes, and include genes with human-specific patterns of expression in the frontal pole. Together, these data suggest that ELAVL2 regulation of transcript expression is critical for neuronal function and clinically relevant to ASD.

Novel bioinformatic developments for exome sequencing

With the widespread adoption of next generation sequencing technologies by the genetics community and the rapid decrease in costs per base, exome sequencing has become a standard within the repertoire of genetic experiments for both research and diagnostics. Although bioinformatics now offers standard solutions for the analysis of exome sequencing data, many challenges still remain; especially the increasing scale at which exome data are now being generated has given rise to novel challenges in how to efficiently store, analyze and interpret exome data of this magnitude. In this review we discuss some of the recent developments in bioinformatics for exome sequencing and the directions that this is taking us to. With these developments, exome sequencing is paving the way for the next big challenge, the application of whole genome sequencing.

Lessons from non-canonical splicing

Recent improvements in experimental and computational techniques that are used to study the transcriptome have enabled an unprecedented view of RNA processing, revealing many previously unknown non-canonical splicing events. This includes cryptic events located far from the currently annotated exons and unconventional splicing mechanisms that have important roles in regulating gene expression. These non-canonical splicing events are a major source of newly emerging transcripts during evolution, especially when they involve sequences derived from transposable elements. They are therefore under precise regulation and quality control, which minimizes their potential to disrupt gene expression. We explain how non-canonical splicing can lead to aberrant transcripts that cause many diseases, and also how it can be exploited for new therapeutic strategies.

Defective control of pre-messenger RNA splicing in human disease

Examples of associations between human disease and defects in pre-messenger RNA splicing/alternative splicing are accumulating. Although many alterations are caused by mutations in splicing signals or regulatory sequence elements, recent studies have noted the disruptive impact of mutated generic spliceosome components and splicing regulatory proteins. This review highlights recent progress in our understanding of how the altered splicing function of RNA-binding proteins contributes to myelodysplastic syndromes, cancer, and neuropathologies.

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

Liver-Specific Deletion of SRSF2 Caused Acute Liver Failure and Early Death in Mice

The liver performs a variety of unique functions critical for metabolic homeostasis. Here, we show that mice lacking the splicing factor SRSF2 but not SRSF1 in hepatocytes have severe liver pathology and biochemical abnormalities. Histological analyses revealed generalized hepatitis with the presence of ballooned hepatocytes and evidence of fibrosis. Molecular analysis demonstrated that SRSF2 governs splicing of multiple genes involved in the stress-induced cell death pathway in the liver. More importantly, SRSF2 also functions as a potent transcription activator, required for efficient expression of transcription factors mainly responsible for energy homeostasis and bile acid metabolism in the liver. Consistent with the effects of SRSF2 in gene regulation, accumulation of total cholesterol and bile acids was prominently observed in the mutant liver, followed by enhanced generation of reactive oxygen species and increased endoplasmic reticulum stress, as revealed by biochemical and ultrastructural analyses. Taking these observations together, inactivation of SRSF2 in liver caused dysregulated splicing events and hepatic metabolic disorders, which trigger endoplasmic reticulum stress, oxidative stress, and finally liver failure.

MiasDB: A Database of Molecular Interactions Associated with Alternative Splicing of Human Pre-mRNAs

Alternative splicing (AS) is pervasive in human multi-exon genes and is a major contributor to expansion of the transcriptome and proteome diversity. The accurate recognition of alternative splice sites is regulated by information contained in networks of protein-protein and protein-RNA interactions. However, the mechanisms leading to splice site selection are not fully understood. Although numerous databases have been built to describe AS, molecular interaction databases associated with AS have only recently emerged. In this study, we present a new database, MiasDB, that provides a description of molecular interactions associated with human AS events. This database covers 938 interactions between human splicing factors, RNA elements, transcription factors, kinases and modified histones for 173 human AS events. Every entry includes the interaction partners, interaction type, experimental methods, AS type, tissue specificity or disease-relevant information, a simple description of the functionally tested interaction in the AS event and references. The database can be queried easily using a web server (http://47.88.84.236/Miasdb). We display some interaction figures for several genes. With this database, users can view the regulation network describing AS events for 12 given genes.

Genome-Wide Functional Annotation of Human Protein-Coding Splice Variants Using Multiple Instance Learning

The vast majority of human multiexon genes undergo alternative splicing and produce a variety of splice variant transcripts and proteins, which can perform different functions. These protein-coding splice variants (PCSVs) greatly increase the functional diversity of proteins. Most functional annotation algorithms have been developed at the gene level; the lack of isoform-level gold standards is an important intellectual limitation for currently available machine learning algorithms. The accumulation of a large amount of RNA-seq data in the public domain greatly increases our ability to examine the functional annotation of genes at isoform level. In the present study, we used a multiple instance learning (MIL)-based approach for predicting the function of PCSVs. We used transcript-level expression values and gene-level functional associations from the Gene Ontology database. A support vector machine (SVM)-based 5-fold cross-validation technique was applied. Comparatively, genes with multiple PCSVs performed better than single PCSV genes, and performance also improved when more examples were available to train the models. We demonstrated our predictions using literature evidence of ADAM15, LMNA/C, and DMXL2 genes. All predictions have been implemented in a web resource called "IsoFunc", which is freely available for the global scientific community through http://guanlab.ccmb.med.umich.edu/isofunc .

Parallel computation of genome-scale RNA secondary structure to detect structural constraints on human genome

Background

RNA secondary structure around splice sites is known to assist normal splicing by promoting spliceosome recognition. However, analyzing the structural properties of entire intronic regions or pre-mRNA sequences has been difficult hitherto, owing to serious experimental and computational limitations, such as low read coverage and numerical problems.

Results

Our novel software, “ParasoR”, is designed to run on a computer cluster and enables the exact computation of various structural features of long RNA sequences under the constraint of maximal base-pairing distance. ParasoR divides dynamic programming (DP) matrices into smaller pieces, such that each piece can be computed by a separate computer node without losing the connectivity information between the pieces. ParasoR directly computes the ratios of DP variables to avoid the reduction of numerical precision caused by the cancellation of a large number of Boltzmann factors. The structural preferences of mRNAs computed by ParasoR shows a high concordance with those determined by high-throughput sequencing analyses.

Using ParasoR, we investigated the global structural preferences of transcribed regions in the human genome. A genome-wide folding simulation indicated that transcribed regions are significantly more structural than intergenic regions after removing repeat sequences and k-mer frequency bias. In particular, we observed a highly significant preference for base pairing over entire intronic regions as compared to their antisense sequences, as well as to intergenic regions. A comparison between pre-mRNAs and mRNAs showed that coding regions become more accessible after splicing, indicating constraints for translational efficiency. Such changes are correlated with gene expression levels, as well as GC content, and are enriched among genes associated with cytoskeleton and kinase functions.

Conclusions

We have shown that ParasoR is very useful for analyzing the structural properties of long RNA sequences such as mRNAs, pre-mRNAs, and long non-coding RNAs whose lengths can be more than a million bases in the human genome. In our analyses, transcribed regions including introns are indicated to be subject to various types of structural constraints that cannot be explained from simple sequence composition biases. ParasoR is freely available at https://github.com/carushi/ParasoR.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1067-9) contains supplementary material, which is available to authorized users.

The dark matter of the cancer genome: aberrations in regulatory elements, untranslated regions, splice sites, non-coding RNA and synonymous mutations

Cancer is a disease of the genome caused by oncogene activation and tumor suppressor gene inhibition. Deep sequencing studies including large consortia such as TCGA and ICGC identified numerous tumor‐specific mutations not only in protein‐coding sequences but also in non‐coding sequences. Although 98% of the genome is not translated into proteins, most studies have neglected the information hidden in this “dark matter” of the genome. Malignancy‐driving mutations can occur in all genetic elements outside the coding region, namely in enhancer, silencer, insulator, and promoter as well as in 5′‐UTR and 3′‐UTR. Intron or splice site mutations can alter the splicing pattern. Moreover, cancer genomes contain mutations within non‐coding RNA, such as microRNA, lncRNA, and lincRNA. A synonymous mutation changes the coding region in the DNA and RNA but not the protein sequence. Importantly, oncogenes such as TERT or miR‐21 as well as tumor suppressor genes such as TP53/p53,APC,BRCA1, or RB1 can be affected by these alterations. In summary, coding‐independent mutations can affect gene regulation from transcription, splicing, mRNA stability to translation, and hence, this largely neglected area needs functional studies to elucidate the mechanisms underlying tumorigenesis. This review will focus on the important role and novel mechanisms of these non‐coding or allegedly silent mutations in tumorigenesis.

RNA splicing is a primary link between genetic variation and disease

Noncoding variants play a central role in the genetics of complex traits, but we still lack a full understanding of the molecular pathways through which they act. We quantified the contribution of cis-acting genetic effects at all major stages of gene regulation from chromatin to proteins, in Yoruba lymphoblastoid cell lines (LCLs). About ~65% of expression quantitative trait loci (eQTLs) have primary effects on chromatin, whereas the remaining eQTLs are enriched in transcribed regions. Using a novel method, we also detected 2893 splicing QTLs, most of which have little or no effect on gene-level expression. These splicing QTLs are major contributors to complex traits, roughly on a par with variants that affect gene expression levels. Our study provides a comprehensive view of the mechanisms linking genetic variation to variation in human gene regulation.

Oxidative Stress Triggers Body-Wide Skipping of Multiple Exons of the Spinal Muscular Atrophy Gene

Humans carry two nearly identical copies of Survival Motor Neuron gene: SMN1 and SMN2. Loss of SMN1 leads to spinal muscular atrophy (SMA), the most frequent genetic cause of infant mortality. While SMN2 cannot compensate for the loss of SMN1 due to predominant skipping of exon 7, correction of SMN2 exon 7 splicing holds the promise of a cure for SMA. Previously, we used cell-based models coupled with a multi-exon-skipping detection assay (MESDA) to demonstrate the vulnerability of SMN2 exons to aberrant splicing under the conditions of oxidative stress (OS). Here we employ a transgenic mouse model and MESDA to examine the OS-induced splicing regulation of SMN2 exons. We induced OS using paraquat that is known to trigger production of reactive oxygen species and cause mitochondrial dysfunction. We show an overwhelming co-skipping of SMN2 exon 5 and exon 7 under OS in all tissues except testis. We also show that OS increases skipping of SMN2 exon 3 in all tissues except testis. We uncover several new SMN2 splice isoforms expressed at elevated levels under the conditions of OS. We analyze cis-elements and transacting factors to demonstrate the diversity of mechanisms for splicing misregulation under OS. Our results of proteome analysis reveal downregulation of hnRNP H as one of the potential consequences of OS in brain. Our findings suggest SMN2 as a sensor of OS with implications to SMA and other diseases impacted by low levels of SMN protein.

Genome-Wide Association of the Laboratory-Based Nicotine Metabolite Ratio in Three Ancestries

Introduction:

Metabolic enzyme variation and other patient and environmental characteristics influence smoking behaviors, treatment success, and risk of related disease. Population-specific variation in metabolic genes contributes to challenges in developing and optimizing pharmacogenetic interventions. We applied a custom genome-wide genotyping array for addiction research (Smokescreen), to three laboratory-based studies of nicotine metabolism with oral or venous administration of labeled nicotine and cotinine, to model nicotine metabolism in multiple populations. The trans-3′-hydroxycotinine/cotinine ratio, the nicotine metabolite ratio (NMR), was the nicotine metabolism measure analyzed.

Methods:

Three hundred twelve individuals of self-identified European, African, and Asian American ancestry were genotyped and included in ancestry-specific genome-wide association scans (GWAS) and a meta-GWAS analysis of the NMR. We modeled natural-log transformed NMR with covariates: principal components of genetic ancestry, age, sex, body mass index, and smoking status.

Results:

African and Asian American NMRs were statistically significantly (P values ≤ 5E-5) lower than European American NMRs. Meta-GWAS analysis identified 36 genome-wide significant variants over a 43 kilobase pair region at CYP2A6 with minimum P = 2.46E-18 at rs12459249, proximal to CYP2A6. Additional minima were located in intron 4 (rs56113850, P = 6.61E-18) and in the CYP2A6-CYP2A7 intergenic region (rs34226463, P = 1.45E-12). Most (34/36) genome-wide significant variants suggested reduced CYP2A6 activity; functional mechanisms were identified and tested in knowledge-bases. Conditional analysis resulted in intergenic variants of possible interest (P values < 5E-5).

Conclusions:

This meta-GWAS of the NMR identifies CYP2A6 variants, replicates the top-ranked single nucleotide polymorphism from a recent Finnish meta-GWAS of the NMR, identifies functional mechanisms, and provides pan-continental population biomarkers for nicotine metabolism.

Implications:

This multiple ancestry meta-GWAS of the laboratory study-based NMR provides novel evidence and replication for genome-wide association of CYP2A6 single nucleotide and insertion–deletion polymorphisms. We identify three regions of genome-wide significance: proximal, intronic, and distal to CYP2A6. We replicate the top-ranking single nucleotide polymorphism from a recent GWAS of the NMR in Finnish smokers, identify a functional mechanism for this intronic variant from in silico analyses of RNA-seq data that is consistent with CYP2A6 expression measured in postmortem lung and liver, and provide additional support for the intergenic region between CYP2A6 and CYP2A7.

PHKA2 mutation spectrum in Korean patients with glycogen storage disease type IX: prevalence of deletion mutations

BACKGROUND

Molecular diagnosis of glycogen storage diseases (GSDs) is important to enable accurate diagnoses and make appropriate therapeutic plans. The aim of this study was to evaluate the PHKA2 mutation spectrum in Korean patients with GSD type IX.

METHODS

Thirteen Korean patients were tested for PHKA2 mutations using direct sequencing and a multiplex polymerase chain reaction method. A comprehensive review of the literature on previously reported PHKA2 mutations in other ethnic populations was conducted for comparison.

RESULTS

Among 13 patients tested, six unrelated male patients with GSD IX aged 2 to 6 years at the first diagnostic work-up for hepatomegaly with elevated aspartate transaminase (AST) and alanine transaminase (ALT) were found to have PHKA2 mutations. These patients had different PHKA2 mutations: five were known mutations (c.537 + 5G > A, c.884G > A [p.Arg295His], c.3210_3212delGAG [p.Arg1072del], exon 8 deletion, and exons 27-33 deletion) and one was a novel mutation (exons 18-33 deletion). Notably, the most common type of mutation was gross deletion, in contrast to other ethnic populations in which the most common mutation type was sequence variant.

CONCLUSIONS

This study expands our knowledge of the PHKA2 mutation spectrum of GSD IX. Considering the PHKA2 mutation spectrum in Korean patients with GSD IX, molecular diagnostic methods for deletions should be conducted in conjunction with direct sequence analysis to enable accurate molecular diagnosis of this disease in the Korean population.

Novel bioinformatic developments for exome sequencing

With the widespread adoption of next generation sequencing technologies by the genetics community and the rapid decrease in costs per base, exome sequencing has become a standard within the repertoire of genetic experiments for both research and diagnostics. Although bioinformatics now offers standard solutions for the analysis of exome sequencing data, many challenges still remain; especially the increasing scale at which exome data are now being generated has given rise to novel challenges in how to efficiently store, analyze and interpret exome data of this magnitude. In this review we discuss some of the recent developments in bioinformatics for exome sequencing and the directions that this is taking us to. With these developments, exome sequencing is paving the way for the next big challenge, the application of whole genome sequencing.

Systems Nutrigenomics Reveals Brain Gene Networks Linking Metabolic and Brain Disorders

Nutrition plays a significant role in the increasing prevalence of metabolic and brain disorders. Here we employ systems nutrigenomics to scrutinize the genomic bases of nutrient–host interaction underlying disease predisposition or therapeutic potential. We conducted transcriptome and epigenome sequencing of hypothalamus (metabolic control) and hippocampus (cognitive processing) from a rodent model of fructose consumption, and identified significant reprogramming of DNA methylation, transcript abundance, alternative splicing, and gene networks governing cell metabolism, cell communication, inflammation, and neuronal signaling. These signals converged with genetic causal risks of metabolic, neurological, and psychiatric disorders revealed in humans. Gene network modeling uncovered the extracellular matrix genes Bgn and Fmod as main orchestrators of the effects of fructose, as validated using two knockout mouse models. We further demonstrate that an omega-3 fatty acid, DHA, reverses the genomic and network perturbations elicited by fructose, providing molecular support for nutritional interventions to counteract diet-induced metabolic and brain disorders. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine.

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Fructose promotes transcriptomic and epigenomic reprogramming to perturb brain networks linking metabolism and brain function.

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The extracellular matrix genes Bgn and Fmod emerge as key regulators of gene networks responsive to fructose.

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The omega-3 fatty acid DHA reverses fructose-induced genomic and network reprogramming.

Meng et al. report fructose as a powerful inducer of genomic and epigenomic variability with the capacity to reorganize gene networks critical for central metabolic regulation and neuronal processes in the brain; conversely, an omega-3 fatty acid, DHA, has the potential to normalize the genomic impact of fructose. Our findings help explain the pathogenic actions of fructose on prevalent metabolic and brain disorders and provide proof-of-concept for nutritional remedies supported by nutrigenomics evidence. Our integrative approach complementing rodent and human studies supports the applicability of nutrigenomics principles to predict disease susceptibility and to guide personalized medicine.

Investigation of Rare Single-Nucleotide PCDH15 Variants in Schizophrenia and Autism Spectrum Disorders

Both schizophrenia (SCZ) and autism spectrum disorders (ASD) are neuropsychiatric disorders with overlapping genetic etiology. Protocadherin 15 (PCDH15), which encodes a member of the cadherin super family that contributes to neural development and function, has been cited as a risk gene for neuropsychiatric disorders. Recently, rare variants of large effect have been paid attention to understand the etiopathology of these complex disorders. Thus, we evaluated the impacts of rare, single-nucleotide variants (SNVs) in PCDH15 on SCZ or ASD. First, we conducted coding exon-targeted resequencing of PCDH15 with next-generation sequencing technology in 562 Japanese patients (370 SCZ and 192 ASD) and detected 16 heterozygous SNVs. We then performed association analyses on 2,096 cases (1,714 SCZ and 382 ASD) and 1,917 controls with six novel variants of these 16 SNVs. Of these six variants, four (p.R219K, p.T281A, p.D642N, c.3010-1G>C) were ultra-rare variants (minor allele frequency < 0.0005) that may increase disease susceptibility. Finally, no statistically significant association between any of these rare, heterozygous PCDH15 point variants and SCZ or ASD was found. Our results suggest that a larger sample size of resequencing subjects is necessary to detect associations between rare PCDH15 variants and neuropsychiatric disorders.

Dopamine pathway gene variants may modulate cognitive performance in the DHS - Mind Study

BACKGROUND

There is an established association between type 2 diabetes and accelerated cognitive decline. The exact mechanism linking type 2 diabetes and reduced cognitive function is less clear. The monoamine system, which is extensively involved in cognition, can be altered by type 2 diabetes status. Thus, this study hypothesized that sequence variants in genes linked to dopamine metabolism and associated pathways are associated with cognitive function as assessed by the Digit Symbol Substitution Task, the Modified Mini-Mental State Examination, the Stroop Task, the Rey Auditory-Verbal Learning Task, and the Controlled Oral Word Association Task for Phonemic and Semantic Fluency in the Diabetes Heart Study, a type 2 diabetes-enriched familial cohort (n = 893).

METHODS

To determine the effects of candidate variants on cognitive performance, genetic association analyses were performed on the well-documented variable number tandem repeat located in the 3' untranslated region of the dopamine transporter, as well as on single-nucleotide polymorphisms covering genes in the dopaminergic pathway, the insulin signaling pathway, and the convergence of both. Next, polymorphisms in loci of interest with strong evidence for involvement in dopamine processing were extracted from genetic datasets available in a subset of the cohort (n = 572) derived from Affymetrix(®) Genome-Wide Human SNP Array 5.0 and 1000 Genomes imputation from this array.

RESULTS

The candidate gene analysis revealed one variant from the DOPA decarboxylase gene, rs10499695, to be associated with poorer performance on a subset of Rey Auditory-Verbal Learning Task measuring retroactive interference (P = 0.001, β = -0.45). Secondary analysis of genome-wide and imputed data uncovered another DOPA decarboxylase variant, rs62445903, also associated with retroactive interference (P = 7.21 × 10(-7), β = 0.3). These data suggest a role for dopaminergic genes, specifically a gene involved in regulation of dopamine synthesis, in cognitive performance in type 2 diabetes.

Multivariate models from RNA-Seq SNVs yield candidate molecular targets for biomarker discovery: SNV-DA

BACKGROUND

It has recently been shown that significant and accurate single nucleotide variants (SNVs) can be reliably called from RNA-Seq data. These may provide another source of features for multivariate predictive modeling of disease phenotype for the prioritization of candidate biomarkers. The continuous nature of SNV allele fraction features allows the concurrent investigation of several genomic phenomena, including allele specific expression, clonal expansion and/or deletion, and copy number variation.

RESULTS

The proposed software pipeline and package, SNV Discriminant Analysis (SNV-DA), was applied on two RNA-Seq datasets with varying sample sizes sequenced at different depths: a dataset containing primary tumors from twenty patients with different disease outcomes in lung adenocarcinoma and a larger dataset of primary tumors representing two major breast cancer subtypes, estrogen receptor positive and triple negative. Predictive models were generated using the machine learning algorithm, sparse projections to latent structures discriminant analysis. Training sets composed of RNA-Seq SNV features limited to genomic regions of origin (e.g. exonic or intronic) and/or RNA-editing sites were shown to produce models with accurate predictive performances, were discriminant towards true label groupings, and were able to produce SNV rankings significantly different from than univariate tests. Furthermore, the utility of the proposed methodology is supported by its comparable performance to traditional models as well as the enrichment of selected SNVs located in genes previously associated with cancer and genes showing allele-specific expression. As proof of concept, we highlight the discovery of a previously unannotated intergenic locus that is associated with epigenetic regulatory marks in cancer and whose significant allele-specific expression is correlated with ER+ status; hereafter named ER+ associated hotspot (ERPAHS).

CONCLUSION

The use of models from RNA-Seq SNVs to identify and prioritize candidate molecular targets for biomarker discovery is supported by the ability of the proposed method to produce significantly accurate predictive models that are discriminant towards true label groupings. Importantly, the proposed methodology allows investigation of mutations outside of exonic regions and identification of interesting expressed loci not included in traditional gene annotations. An implementation of the proposed methodology is provided that allows the user to specify SNV filtering criteria and cross-validation design during model creation and evaluation.

Applications of Deep Learning in Biomedicine

Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

Coevolution Theory of the Genetic Code at Age Forty: Pathway to Translation and Synthetic Life

The origins of the components of genetic coding are examined in the present study. Genetic information arose from replicator induction by metabolite in accordance with the metabolic expansion law. Messenger RNA and transfer RNA stemmed from a template for binding the aminoacyl-RNA synthetase ribozymes employed to synthesize peptide prosthetic groups on RNAs in the Peptidated RNA World. Coevolution of the genetic code with amino acid biosynthesis generated tRNA paralogs that identify a last universal common ancestor (LUCA) of extant life close to Methanopyrus, which in turn points to archaeal tRNA introns as the most primitive introns and the anticodon usage of Methanopyrus as an ancient mode of wobble. The prediction of the coevolution theory of the genetic code that the code should be a mutable code has led to the isolation of optional and mandatory synthetic life forms with altered protein alphabets.

Identification of CYP4V2 mutation in 36 Chinese families with Bietti crystalline corneoretinal dystrophy

Bietti crystalline corneoretinal dystrophy (BCD) is an inherited eye disease that is most common in the Chinese. It is caused by a mutation in the CYP4V2 gene. In this study, 43 Chinese BCD families were recruited; most patients manifested the characteristic phenotype of BCD, with 2 families initially misdiagnosed with retinitis pigmentosa. Five patients in our cohort presented with BCD and choroidal neovascularization (CNV), and 1 patient presented with typical BCD and abnormality in the terminals of both fingers and toes. A total of 17 pathogenic mutations involving 68 alleles were identified from 36 families using targeted exon sequencing and Sanger sequencing; we achieved a diagnostic rate of approximately 84%. Fifteen families were found to carry homozygous mutations, 17 families carried compound heterozygous mutations, and 4 families carried a single heterozygous mutation. Of the mutations identified, four variants c.802-8_810del17bpinsGC, c.802-8_810del17bpinsGT, c.992A > C (p.H331P), and c.1091-2A > G accounted for 71% of the mutations identified in CYP4V2. These mutations were hotspots in Chinese populations for BCD. Five among them were novel and predicted to be disease-causing, including c.65T > A (p.L22H), c.681_4delTGAG (p.S227Rfs*1), c.802-8_810del17bpinsGT, c.965_7delAAG (p.321delE), and c.994G > A (p.D332N). No apparent correlation between genotype and phenotype was identified. Our findings broaden the spectrum of CYP4V2 mutations that cause BCD and the phenotypic spectrum of the disease in Chinese families. These results will be useful for the genetic diagnosis of BCD, genetic consultation, and gene therapy in the future.

Deep Learning for Population Genetic Inference

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.