The Splicing Code Goes Deep.

The importance of genomic sequence context in generating transcriptome diversity through RNA splicing is independently unmasked by two studies in this issue (Jaganathan et al., 2019; Baeza-Centurion et al., 2019).

A deep catalogue of protein-coding variation in 983,578 individuals.

Rare coding variants that substantially affect function provide insights into the biology of a gene1-3. However, ascertaining the frequency of such variants requires large sample sizes4-8. Here we present a catalogue of human protein-coding variation, derived from exome sequencing of 983,578 individuals across diverse populations. In total, 23% of the Regeneron Genetics Center Million Exome (RGC-ME) data come from individuals of African, East Asian, Indigenous American, Middle Eastern and South Asian ancestry. The catalogue includes more than 10.4 million missense and 1.1 million predicted loss-of-function (pLOF) variants. We identify individuals with rare biallelic pLOF variants in 4,848 genes, 1,751 of which have not been previously reported. From precise quantitative estimates of selection against heterozygous loss of function (LOF), we identify 3,988 LOF-intolerant genes, including 86 that were previously assessed as tolerant and 1,153 that lack established disease annotation. We also define regions of missense depletion at high resolution. Notably, 1,482 genes have regions that are depleted of missense variants despite being tolerant of pLOF variants. Finally, we estimate that 3% of individuals have a clinically actionable genetic variant, and that 11,773 variants reported in ClinVar with unknown significance are likely to be deleterious cryptic splice sites. To facilitate variant interpretation and genetics-informed precision medicine, we make this resource of coding variation from the RGC-ME dataset publicly accessible through a variant allele frequency browser.

Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences.

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes, and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods on various prediction tasks, including evolutionary sequence divergence on lineage-specific splicing and splicing-altering mutations in human populations and neurodevelopmental disorders, and demonstrated that DeltaSplice outperformed consistently. DeltaSplice predicted ∼15% of splicing quantitative trait loci (sQTLs) in the human brain as causal splicing-altering variants. It also predicted splicing-altering de novo mutations outside the splice sites in a subset of patients affected by autism and other neurodevelopmental disorders (NDDs), including 19 genes with recurrent splicing-altering mutations. Integration of splicing-altering mutations with other types of de novo mutation burdens allowed the prediction of eight novel NDD-risk genes. Our work expanded the capacity of in silico splicing models with potential applications in genetic diagnosis and the development of splicing-based precision medicine.

Modeling RNA-Binding Protein Specificity In Vivo by Precisely Registering Protein-RNA Crosslink Sites.

RNA-binding proteins (RBPs) regulate post-transcriptional gene expression by recognizing short and degenerate sequence motifs in their target transcripts, but precisely defining their binding specificity remains challenging. Crosslinking and immunoprecipitation (CLIP) allows for mapping of the exact protein-RNA crosslink sites, which frequently reside at specific positions in RBP motifs at single-nucleotide resolution. Here, we have developed a computational method, named mCross, to jointly model RBP binding specificity while precisely registering the crosslinking position in motif sites. We applied mCross to 112 RBPs using ENCODE eCLIP data and validated the reliability of the discovered motifs by genome-wide analysis of allelic binding sites. Our analyses revealed that the prototypical SR protein SRSF1 recognizes clusters of GGA half-sites in addition to its canonical GGAGGA motif. Therefore, SRSF1 regulates splicing of a much larger repertoire of transcripts than previously appreciated, including HNRNPD and HNRNPDL, which are involved in multivalent protein assemblies and phase separation.

Integrated analysis of mRNA-seq and miRNA-seq for host susceptibilities to influenza A (H7N9) infection in inbred mouse lines.

Host genetic factors play an important role in diverse host outcomes after influenza A (H7N9) infection. Studying differential responses of inbred mouse lines with distinct genetic backgrounds to influenza virus infection could substantially increase our understanding of the contributory roles of host genetic factors to disease severity. Here, we utilized an integrated approach of mRNA-seq and miRNA-seq to investigate the transcriptome expression and regulation of host genes in C57BL/6J and DBA/2J mouse strains during influenza virus infection. The differential pathogenicity of influenza virus in C57BL/6J and DBA/2J has been fully demonstrated through immunohistochemical staining, histopathological analyses, and viral replication assessment. A transcriptional molecular signature correlates to differential host response to infection has been uncovered. With the introduction of temporal expression pattern analysis, we demonstrated that host factors responsible for influenza virus replication and host-virus interaction were significantly enriched in genes exhibiting distinct temporal dynamics between different inbred mouse lines. A combination of time-series expression analysis and temporal expression pattern analysis has provided a list of promising candidate genes for future studies. An integrated miRNA regulatory network from both mRNA-seq and miRNA-seq revealed several regulatory modules responsible for regulating host susceptibilities and disease severity. Overall, a comprehensive framework for analyzing host susceptibilities to influenza infection was established by integrating mRNA-seq and miRNA-seq data of inbred mouse lines. This work suggests novel putative molecular targets for therapeutic interventions in seasonal and pandemic influenza.

Predicting mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies.

Exome sequencing is becoming a standard tool for mapping Mendelian disease-causing (or pathogenic) non-synonymous single nucleotide variants (nsSNVs). Minor allele frequency (MAF) filtering approach and functional prediction methods are commonly used to identify candidate pathogenic mutations in these studies. Combining multiple functional prediction methods may increase accuracy in prediction. Here, we propose to use a logit model to combine multiple prediction methods and compute an unbiased probability of a rare variant being pathogenic. Also, for the first time we assess the predictive power of seven prediction methods (including SIFT, PolyPhen2, CONDEL, and logit) in predicting pathogenic nsSNVs from other rare variants, which reflects the situation after MAF filtering is done in exome-sequencing studies. We found that a logit model combining all or some original prediction methods outperforms other methods examined, but is unable to discriminate between autosomal dominant and autosomal recessive disease mutations. Finally, based on the predictions of the logit model, we estimate that an individual has around 5% of rare nsSNVs that are pathogenic and carries ~22 pathogenic derived alleles at least, which if made homozygous by consanguineous marriages may lead to recessive diseases.

A comprehensive framework for prioritizing variants in exome sequencing studies of Mendelian diseases.

Exome sequencing strategy is promising for finding novel mutations of human monogenic disorders. However, pinpointing the casual mutation in a small number of samples is still a big challenge. Here, we propose a three-level filtration and prioritization framework to identify the casual mutation(s) in exome sequencing studies. This efficient and comprehensive framework successfully narrowed down whole exome variants to very small numbers of candidate variants in the proof-of-concept examples. The proposed framework, implemented in a user-friendly software package, named KGGSeq (http://statgenpro.psychiatry.hku.hk/kggseq), will play a very useful role in exome sequencing-based discovery of human Mendelian disease genes.

Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences.

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods on various prediction tasks, including evolutionary sequence divergence on lineage-specific splicing and splicing-altering mutations in human populations and neurodevelopmental disorders, and demonstrated that DeltaSplice outperformed consistently. DeltaSplice predicted ~15% of splicing quantitative trait loci (sQTLs) in the human brain as causal splicing-altering variants. It also predicted splicing-altering de novo mutations outside the splice sites in a subset of patients affected by autism and other neurodevelopmental disorders, including 19 genes with recurrent splicing-altering mutations. Among the new candidate disease risk genes, MFN1 is involved in mitochondria fusion, which is frequently disrupted in autism patients. Our work expanded the capacity of in silico splicing models with potential applications in genetic diagnosis and the development of splicing-based precision medicine.

Lineage-specific splicing regulation of MAPT gene in the primate brain.

Divergence of precursor messenger RNA (pre-mRNA) alternative splicing (AS) is widespread in mammals, including primates, but the underlying mechanisms and functional impact are poorly understood. Here, we modeled cassette exon inclusion in primate brains as a quantitative trait and identified 1,170 (∼3%) exons with lineage-specific splicing shifts under stabilizing selection. Among them, microtubule-associated protein tau (MAPT) exons 2 and 10 underwent anticorrelated, two-step evolutionary shifts in the catarrhine and hominoid lineages, leading to their present inclusion levels in humans. The developmental-stage-specific divergence of exon 10 splicing, whose dysregulation can cause frontotemporal lobar degeneration (FTLD), is mediated by divergent distal intronic MBNL-binding sites. Competitive binding of these sites by CRISPR-dCas13d/gRNAs effectively reduces exon 10 inclusion, potentially providing a therapeutically compatible approach to modulate tau isoform expression. Our data suggest adaptation of MAPT function and, more generally, a role for AS in the evolutionary expansion of the primate brain.

Prioritizing genes responsible for host resistance to influenza using network approaches.

BACKGROUND: The genetic make-up of humans and other mammals (such as mice) affects their resistance to influenza virus infection. Considering the complexity and moral issues associated with experiments on human subjects, we have only acquired partial knowledge regarding the underlying molecular mechanisms. Although influenza resistance in inbred mice has been mapped to several quantitative trait loci (QTLs), which have greatly narrowed down the search for host resistance genes, only few underlying genes have been identified. RESULTS: To prioritize a list of promising candidates for future functional investigation, we applied network-based approaches to leverage the information of known resistance genes and the expression profiles contrasting susceptible and resistant mouse strains. The significance of top-ranked genes was supported by different lines of evidence from independent genetic associations, QTL studies, RNA interference (RNAi) screenings, and gene expression analysis. Further data mining on the prioritized genes revealed the functions of two pathways mediated by tumor necrosis factor (TNF): apoptosis and TNF receptor-2 signaling pathways. We suggested that the delicate balance between TNF's pro-survival and apoptotic effects may affect hosts' conditions after influenza virus infection. CONCLUSIONS: This study considerably cuts down the list of candidate genes responsible for host resistance to influenza and proposed novel pathways and mechanisms. Our study also demonstrated the efficacy of network-based methods in prioritizing genes for complex traits.

Systematic dissection of coordinated stromal remodeling identifies Sox10+ glial cells as a therapeutic target in myelofibrosis.

Remodeling of the tissue niche is often evident in diseases, yet, the stromal alterations and their contribution to pathogenesis are poorly characterized. Bone marrow fibrosis is a maladaptive feature of primary myelofibrosis (PMF). We performed lineage tracing and found that most collagen-expressing myofibroblasts were derived from leptin-receptor-positive (LepR+) mesenchymal cells, whereas a minority were from Gli1-lineage cells. Deletion of Gli1 did not impact PMF. Unbiased single-cell RNA sequencing (scRNA-seq) confirmed that virtually all myofibroblasts originated from LepR-lineage cells, with reduced expression of hematopoietic niche factors and increased expression of fibrogenic factors. Concurrently, endothelial cells upregulated arteriolar-signature genes. Pericytes and Sox10+ glial cells expanded drastically with heightened cell-cell signaling, suggesting important functional roles in PMF. Chemical or genetic ablation of bone marrow glial cells ameliorated fibrosis and improved other pathology in PMF. Thus, PMF involves complex remodeling of the bone marrow microenvironment, and glial cells represent a promising therapeutic target.

Rare coding variants in CHRNB2 reduce the likelihood of smoking.

Human genetic studies of smoking behavior have been thus far largely limited to common variants. Studying rare coding variants has the potential to identify drug targets. We performed an exome-wide association study of smoking phenotypes in up to 749,459 individuals and discovered a protective association in CHRNB2, encoding the ß2 subunit of the α4ß2 nicotine acetylcholine receptor. Rare predicted loss-of-function and likely deleterious missense variants in CHRNB2 in aggregate were associated with a 35% decreased odds for smoking heavily (odds ratio (OR) = 0.65, confidence interval (CI) = 0.56-0.76, P = 1.9 × 10-8). An independent common variant association in the protective direction ( rs2072659 ; OR = 0.96; CI = 0.94-0.98; P = 5.3 × 10-6) was also evident, suggesting an allelic series. Our findings in humans align with decades-old experimental observations in mice that ß2 loss abolishes nicotine-mediated neuronal responses and attenuates nicotine self-administration. Our genetic discovery will inspire future drug designs targeting CHRNB2 in the brain for the treatment of nicotine addiction.

A deep catalog of protein-coding variation in 985,830 individuals.

Coding variants that have significant impact on function can provide insights into the biology of a gene but are typically rare in the population. Identifying and ascertaining the frequency of such rare variants requires very large sample sizes. Here, we present the largest catalog of human protein-coding variation to date, derived from exome sequencing of 985,830 individuals of diverse ancestry to serve as a rich resource for studying rare coding variants. Individuals of African, Admixed American, East Asian, Middle Eastern, and South Asian ancestry account for 20% of this Exome dataset. Our catalog of variants includes approximately 10.5 million missense (54% novel) and 1.1 million predicted loss-of-function (pLOF) variants (65% novel, 53% observed only once). We identified individuals with rare homozygous pLOF variants in 4,874 genes, and for 1,838 of these this work is the first to document at least one pLOF homozygote. Additional insights from the RGC-ME dataset include 1) improved estimates of selection against heterozygous loss-of-function and identification of 3,459 genes intolerant to loss-of-function, 83 of which were previously assessed as tolerant to loss-of-function and 1,241 that lack disease annotations; 2) identification of regions depleted of missense variation in 457 genes that are tolerant to loss-of-function; 3) functional interpretation for 10,708 variants of unknown or conflicting significance reported in ClinVar as cryptic splice sites using splicing score thresholds based on empirical variant deleteriousness scores derived from RGC-ME; and 4) an observation that approximately 3% of sequenced individuals carry a clinically actionable genetic variant in the ACMG SF 3.1 list of genes. We make this important resource of coding variation available to the public through a variant allele frequency browser. We anticipate that this report and the RGC-ME dataset will serve as a valuable reference for understanding rare coding variation and help advance precision medicine efforts.

Evaluation of next-generation sequencing software in mapping and assembly.

Next-generation high-throughput DNA sequencing technologies have advanced progressively in sequence-based genomic research and novel biological applications with the promise of sequencing DNA at unprecedented speed. These new non-Sanger-based technologies feature several advantages when compared with traditional sequencing methods in terms of higher sequencing speed, lower per run cost and higher accuracy. However, reads from next-generation sequencing (NGS) platforms, such as 454/Roche, ABI/SOLiD and Illumina/Solexa, are usually short, thereby restricting the applications of NGS platforms in genome assembly and annotation. We presented an overview of the challenges that these novel technologies meet and particularly illustrated various bioinformatics attempts on mapping and assembly for problem solving. We then compared the performance of several programs in these two fields, and further provided advices on selecting suitable tools for specific biological applications.

WITHDRAWN: Evaluation of next-generation sequencing software in mapping and assembly.

Next-generation high-throughput DNA sequencing technologies have advanced progressively in sequence-based genomic research and novel biological applications with the promise of sequencing DNA at unprecedented speed. These new non-Sanger-based technologies feature several advantages, when compared with traditional sequencing methods in terms of higher sequencing speed, lower per run cost and higher accuracy. However, reads from next-generation sequencing (NGS) platforms, such as 454/Roche, ABI/SOLiD and Illumina/Solexa, are usually short, thereby restricting the applications of NGS platforms in genome assembly and annotation. We presented an overview of the challenges that these novel technologies meet and particularly illustrated various bioinformatics attempts on mapping and assembly for problem solving. We then compared the performance of several programs in these two fields and further provided advices on selecting suitable tools for specific biological applications.Journal of Human Genetics advance online publication, 16 June 2011; doi:10.1038/jhg.2011.62.

A graph model based study on regulatory impacts of transcription factors of Drosophila melanogaster and comparison across species.

Transcription factor binding sites and the cis-regulatory modules they compose are central determinants of gene regulation. The gene regulations in some model species have been well addressed. However, not as much is known about the fly due to the lack of experimental data. To study the transcription regulation of Drosophila melanogaster genes, we analyzed the regulation data from ChIP chip experiments as well as the regulatory database. A graph-based approach is applied to study the impacts of each transcription factor to the regulatory network. The model is also applied to Saccharomyces cerevisiae and Homo sapiens to study the behaviors of transcription factors in different species. Gene ontology annotations were used for further studies of the biological significance of studied transcription factors.

Stage-specific requirement for Mettl3-dependent m6A mRNA methylation during haematopoietic stem cell differentiation.

Haematopoietic stem cells (HSCs) maintain balanced self-renewal and differentiation, but how these functions are precisely regulated is not fully understood. N6-methyladenosine (m6A) messenger RNA methylation has emerged as an important mode of epitranscriptional gene expression regulation affecting many biological processes. We show that deletion of the m6A methyltransferase Mettl3 from the adult haematopoietic system led to an accumulation of HSCs in the bone marrow and a marked reduction of reconstitution potential due to a blockage of HSC differentiation. Interestingly, deleting Mettl3 from myeloid cells using Lysm-cre did not impact myeloid cell number or function. RNA sequencing revealed 2,073 genes with significant m6A modifications in HSCs. Myc was identified as a direct target of m6A in HSCs. Mettl3-deficient HSCs failed to upregulate MYC expression following stimulation to differentiate and enforced expression of Myc rescued differentiation defects of Mettl3-deficient HSCs. Our results reveal a key role of m6A in governing HSC differentiation.

A rare variant in MLKL confers susceptibility to ApoE ɛ4-negative Alzheimer's disease in Hong Kong Chinese population.

Alzheimer's disease (AD) is the most common neurodegenerative disorders in the elderly. To identify rare genetic factors other than apolipoprotein E ɛ4 allele (ApoE ɛ4) contributing to the pathogenesis of late-onset AD (LOAD), we conducted a whole-exome analysis of 246 ApoE ɛ4-negative LOAD cases and 172 matched controls in Hong Kong Chinese population. LOAD patients showed a significantly higher burden of rare loss-of-function variants in genes related to immune function than healthy controls. Among the genes involved in immune function, we identified a rare stop-gain variant (p.Q48X) in mixed lineage kinase domain like pseudokinase (MLKL) gene present exclusively in 6 LOAD cases. MLKL is expressed in neurons, and the its expression levels in the p.Q48X carriers were significantly lower than that in age-matched wild-type controls. The ratio of Aß42 to Aß40 significantly increased in MLKL knockdown cells compared to scramble controls. MLKL loss-of-function mutation might contribute to late-onset ApoE ɛ4-negative AD in the Hong Kong Chinese population.

Mutations in Hnrnpa1 cause congenital heart defects.

Incomplete penetrance of congenital heart defects (CHDs) was observed in a mouse model. We hypothesized that the contribution of a major genetic locus modulates the manifestation of the CHDs. After genome-wide linkage mapping, fine mapping, and high-throughput targeted sequencing, a recessive frameshift mutation of the heterogeneous nuclear ribonucleoprotein A1 (Hnrnpa1) gene was confirmed (Hnrnpa1ct). Hnrnpa1 was expressed in both the first heart field (FHF) and second heart field (SHF) at the cardiac crescent stage but was only maintained in SHF progenitors after heart tube formation. Hnrnpa1ct/ct homozygous mutants displayed complete CHD penetrance, including truncated and incomplete looped heart tube at E9.5, ventricular septal defect (VSD) and persistent truncus arteriosus (PTA) at E13.5, and VSD and double outlet right ventricle at P0. Impaired development of the dorsal mesocardium and sinoatrial node progenitors was also observed. Loss of Hnrnpa1 expression leads to dysregulation of cardiac transcription networks and multiple signaling pathways, including BMP, FGF, and Notch in the SHF. Finally, two rare heterozygous mutations of HNRNPA1 were detected in human CHDs. These findings suggest a role of Hnrnpa1 in embryonic heart development in mice and humans.

PTPN21 exerts pro-neuronal survival and neuritic elongation via ErbB4/NRG3 signaling.

Although expression quantitative trait locus, eQTL, serves as an explicit indicator of gene-gene associations, challenges remain to disentangle the mechanisms by which genetic variations alter gene expression. Here we combined eQTL and molecular analyses to identify an association between two seemingly non-associated genes in brain expression data from BXD inbred mice, namely Ptpn21 and Nrg3. Using biotinylated receptor tracking and immunoprecipitation analyses, we determined that PTPN21 de-phosphorylates the upstream receptor tyrosine kinase ErbB4 leading to the up-regulation of its downstream signaling. Conversely, kinase-dead ErbB4 (K751R) or phosphatase-dead PTPN21 (C1108S) mutants impede PTPN21-dependent signaling. Furthermore, PTPN21 also induced Elk-1 activation in embryonic cortical neurons and a novel Elk-1 binding motif was identified in a region located 1919bp upstream of the NRG3 initiation codon. This enables PTPN21 to promote NRG3 expression through Elk-1, which provides a biochemical mechanism for the PTPN21-NRG3 association identified by eQTL. Biologically, PTPN21 positively influences cortical neuronal survival and, similar to Elk-1, it also enhances neuritic length. Our combined approaches show for the first time, a link between NRG3 and PTPN21 within a signaling cascade. This may explain why these two seemingly unrelated genes have previously been identified as risk genes for schizophrenia.