Computational approaches for detecting disease-associated alternative splicing events.

Alternative splicing (AS) is a key transcriptional regulation pathway. Recent studies have shown that AS events are associated with the occurrence of complex diseases. Various computational approaches have been developed for the detection of disease-associated AS events. In this review, we first describe the metrics used for quantitative characterization of AS events. Second, we review and discuss the three types of methods for detecting disease-associated splicing events, which are differential splicing analysis, aberrant splicing detection and splicing-related network analysis. Third, to further exploit the genetic mechanism of disease-associated AS events, we describe the methods for detecting genetic variants that potentially regulate splicing. For each type of methods, we conducted experimental comparison to illustrate their performance. Finally, we discuss the limitations of these methods and point out potential ways to address them. We anticipate that this review provides a systematic understanding of computational approaches for the analysis of disease-associated splicing.

ASOs are an effective treatment for disease-associated oligodendrocyte signatures in premanifest and symptomatic SCA3 mice.

Spinocerebellar ataxia type 3 (SCA3) is the most common dominantly inherited ataxia. Currently, no preventive or disease-modifying treatments exist for this progressive neurodegenerative disorder, although efforts using gene silencing approaches are under clinical trial investigation. The disease is caused by a CAG repeat expansion in the mutant gene, ATXN3, producing an enlarged polyglutamine tract in the mutant protein. Similar to other paradigmatic neurodegenerative diseases, studies evaluating the pathogenic mechanism focus primarily on neuronal implications. Consequently, therapeutic interventions often overlook non-neuronal contributions to disease. Our lab recently reported that oligodendrocytes display some of the earliest and most progressive dysfunction in SCA3 mice. Evidence of disease-associated oligodendrocyte signatures has also been reported in other neurodegenerative diseases, including Alzheimer's disease, amyotrophic lateral sclerosis, Parkinson's disease, and Huntington's disease. Here, we assess the effects of anti-ATXN3 antisense oligonucleotide (ASO) treatment on oligodendrocyte dysfunction in premanifest and symptomatic SCA3 mice. We report a severe, but modifiable, deficit in oligodendrocyte maturation caused by the toxic gain-of-function of mutant ATXN3 early in SCA3 disease that is transcriptionally, biochemically, and functionally rescued with anti-ATXN3 ASO. Our results highlight the promising use of an ASO therapy across neurodegenerative diseases that requires glial targeting in addition to affected neuronal populations.

Fast decoding cell type-specific transcription factor binding landscape at single-nucleotide resolution.

Decoding the cell type-specific transcription factor (TF) binding landscape at single-nucleotide resolution is crucial for understanding the regulatory mechanisms underlying many fundamental biological processes and human diseases. However, limits on time and resources restrict the high-resolution experimental measurements of TF binding profiles of all possible TF-cell type combinations. Previous computational approaches either cannot distinguish the cell context-dependent TF binding profiles across diverse cell types or can only provide a relatively low-resolution prediction. Here we present a novel deep learning approach, Leopard, for predicting TF binding sites at single-nucleotide resolution, achieving the average area under receiver operating characteristic curve (AUROC) of 0.982 and the average area under precision recall curve (AUPRC) of 0.208. Our method substantially outperformed the state-of-the-art methods Anchor and FactorNet, improving the predictive AUPRC by 19% and 27%, respectively, when evaluated at 200-bp resolution. Meanwhile, by leveraging a many-to-many neural network architecture, Leopard features a hundredfold to thousandfold speedup compared with current many-to-one machine learning methods.

Evidence of widespread, independent sequence signature for transcription factor cobinding.

Transcription factors (TFs) are the vocabulary that genomes use to regulate gene expression and phenotypes. The interactions among TFs enrich this vocabulary and orchestrate diverse biological processes. Although simple models identify open chromatin and the presence of TF motifs as the two major contributors to TF binding patterns, it remains elusive what contributes to the in vivo TF cobinding landscape. In this study, we developed a machine learning algorithm to explore the contributors of the cobinding patterns. The algorithm substantially outperforms the state-of-the-field models for TF cobinding prediction. Game theory-based feature importance analysis reveals that, for most of the TF pairs we studied, independent motif sequences contribute one or more of the two TFs under investigation to their cobinding patterns. Such independent motif sequences include, but are not limited to, transcription initiation-related proteins and known TF complexes. We found the motif sequence signatures and the TFs are rarely mutual, corroborating a hierarchical and directional organization of the regulatory network and refuting the possibility of artifacts caused by shared sequence similarity with the TFs under investigation. We modeled such regulatory language with directed graphs, which reveal shared, global factors that are related to many binding and cobinding patterns.

Representation of molecules for drug response prediction.

The rapid development of machine learning and deep learning algorithms in the recent decade has spurred an outburst of their applications in many research fields. In the chemistry domain, machine learning has been widely used to aid in drug screening, drug toxicity prediction, quantitative structure-activity relationship prediction, anti-cancer synergy score prediction, etc. This review is dedicated to the application of machine learning in drug response prediction. Specifically, we focus on molecular representations, which is a crucial element to the success of drug response prediction and other chemistry-related prediction tasks. We introduce three types of commonly used molecular representation methods, together with their implementation and application examples. This review will serve as a brief introduction of the broad field of molecular representations.

An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease.

Alzheimer's disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer's brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer's Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer's brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.

TissueNexus: a database of human tissue functional gene networks built with a large compendium of curated RNA-seq data.

Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and disease. Tissue functional gene networks (FGNs) are essential models for mapping complex gene interactions. We present TissueNexus, a database of 49 human tissue/cell line FGNs constructed by integrating heterogeneous genomic data. We adopted an advanced machine learning approach for data integration because Bayesian classifiers, which is the main approach used for constructing existing tissue gene networks, cannot capture the interaction and nonlinearity of genomic features well. A total of 1,341 RNA-seq datasets containing 52,087 samples were integrated for all of these networks. Because the tissue label for RNA-seq data may be annotated with different names or be missing, we performed intensive hand-curation to improve quality. We further developed a user-friendly database for network search, visualization, and functional analysis. We illustrate the application of TissueNexus in prioritizing disease genes. The database is publicly available at https://www.diseaselinks.com/TissueNexus/.

Impaired Oligodendrocyte Maturation Is an Early Feature in SCA3 Disease Pathogenesis.

Spinocerebellar ataxia Type 3 (SCA3), the most common dominantly inherited ataxia, is a polyglutamine neurodegenerative disease for which there is no disease-modifying therapy. The polyglutamine-encoding CAG repeat expansion in the ATXN3 gene results in expression of a mutant form of the ATXN3 protein, a deubiquitinase that causes selective neurodegeneration despite being widely expressed. The mechanisms driving neurodegeneration in SCA3 are unclear. Research to date, however, has focused almost exclusively on neurons. Here, using equal male and female age-matched transgenic mice expressing full-length human mutant ATXN3, we identified early and robust transcriptional changes in selectively vulnerable brain regions that implicate oligodendrocytes in disease pathogenesis. We mapped transcriptional changes across early, mid, and late stages of disease in two selectively vulnerable brain regions: the cerebellum and brainstem. The most significant disease-associated module through weighted gene coexpression network analysis revealed dysfunction in SCA3 oligodendrocyte maturation. These results reflect a toxic gain-of-function mechanism, as ATXN3 KO mice do not exhibit any impairments in oligodendrocyte maturation. Genetic crosses to reporter mice revealed a marked reduction in mature oligodendrocytes in SCA3-disease vulnerable brain regions, and ultrastructural microscopy confirmed abnormalities in axonal myelination. Further study of isolated oligodendrocyte precursor cells from SCA3 mice established that this impairment in oligodendrocyte maturation is a cell-autonomous process. We conclude that SCA3 is not simply a disease of neurons, and the search for therapeutic strategies and disease biomarkers will need to account for non-neuronal involvement in SCA3 pathogenesis.SIGNIFICANCE STATEMENT Despite advances in spinocerebellar ataxia Type 3 (SCA3) disease understanding, much remains unknown about how the disease gene causes brain dysfunction ultimately leading to cell death. We completed a longitudinal transcriptomic analysis of vulnerable brain regions in SCA3 mice to define the earliest and most robust changes across disease progression. Through gene network analyses followed up with biochemical and histologic studies in SCA3 mice, we provide evidence for severe dysfunction in oligodendrocyte maturation early in SCA3 pathogenesis. Our results advance understanding of SCA3 disease mechanisms, identify additional routes for therapeutic intervention, and may provide broader insight into polyglutamine diseases beyond SCA3.

Systematic dissection of genomic features determining the vast diversity of conotoxins.

BACKGROUND: Conus, a highly diverse species of venomous predators, has attracted significant attention in neuroscience and new drug development due to their rich collection of neuroactive peptides called conotoxins. Recent advancements in transcriptome, proteome, and genome analyses have facilitated the identification of conotoxins within Conus' venom glands, providing insights into the genetic features and evolutionary patterns of conotoxin genes. However, the underlying mechanism behind the extraordinary hypervariability of conotoxins remains largely unknown. RESULTS: We analyzed the transcriptomes of 34 Conus species, examining various tissues such as the venom duct, venom bulb, and salivary gland, leading to the identification of conotoxin genes. Genetic variation analysis revealed that a subset of these genes (15.78% of the total) in Conus species underwent positive selection (Ka/Ks > 1, p < 0.01). Additionally, we reassembled and annotated the genome of C. betulinus, uncovering 221 conotoxin-encoding genes. These genes primarily consisted of three exons, with a significant portion showing high transcriptional activity in the venom ducts. Importantly, the flanking regions and adjacent introns of conotoxin genes exhibited a higher prevalence of transposon elements, suggesting their potential contribution to the extensive variability observed in conotoxins. Furthermore, we detected genome duplication in C. betulinus, which likely contributed to the expansion of conotoxin gene numbers. Interestingly, our study also provided evidence of introgression among Conus species, indicating that interspecies hybridization may have played a role in shaping the evolution of diverse conotoxin genes. CONCLUSIONS: This study highlights the impact of adaptive evolution and introgressive hybridization on the genetic diversity of conotoxin genes and the evolution of Conus. We also propose a hypothesis suggesting that transposable elements might significantly contribute to the remarkable diversity observed in conotoxins. These findings not only enhance our understanding of peptide genetic diversity but also present a novel approach for peptide bioengineering.

Micro-dissection and integration of long and short reads to create a robust catalog of kidney compartment-specific isoforms.

Studying isoform expression at the microscopic level has always been a challenging task. A classical example is kidney, where glomerular and tubulo-interstitial compartments carry out drastically different physiological functions and thus presumably their isoform expression also differs. We aim at developing an experimental and computational pipeline for identifying isoforms at microscopic structure-level. We microdissected glomerular and tubulo-interstitial compartments from healthy human kidney tissues from two cohorts. The two compartments were separately sequenced with the PacBio RS II platform. These transcripts were then validated using transcripts of the same samples by the traditional Illumina RNA-Seq protocol, distinct Illumina RNA-Seq short reads from European Renal cDNA Bank (ERCB) samples, and annotated GENCODE transcript list, thus identifying novel transcripts. We identified 14,739 and 14,259 annotated transcripts, and 17,268 and 13,118 potentially novel transcripts in the glomerular and tubulo-interstitial compartments, respectively. Of note, relying solely on either short or long reads would have resulted in many erroneous identifications. We identified distinct pathways involved in glomerular and tubulo-interstitial compartments at the isoform level, creating an important experimental and computational resource for the kidney research community.

Anchor: trans-cell type prediction of transcription factor binding sites.

The ENCyclopedia of DNA Elements (ENCODE) consortium has generated transcription factor (TF) binding ChIP-seq data covering hundreds of TF proteins and cell types; however, due to limits on time and resources, only a small fraction of all possible TF-cell type pairs have been profiled. One solution is to build machine learning models trained on currently available epigenomic data sets that can be applied to the remaining missing pairs. A major challenge is that TF binding sites are cell-type-specific, which can be attributed to cellular contexts such as chromatin accessibility. Meanwhile, indirect TF-DNA binding and interactions between TFs complicate this regulatory process. Technical issues such as sequencing biases and batch effects render the prediction task even more challenging. Many pioneering efforts have been made to predict TF binding profiles based on DNA sequence and DNase-seq footprints, but to what extent a model can be generalized to completely untested cell conditions remains unknown. In this study, we describe our first place solution to the 2017 ENCODE-DREAM in vivo TF binding site prediction challenge. By carefully addressing multisource biases and information imbalance across cell types, we created a pipeline that significantly outperforms the current state-of-the-art methods. The proposed method is sufficiently complex enough to model nonlinear interactions between TF binding motifs and chromatin accessibility information up to 1500 bp from the genomic region of interest.

Gene expression profiles of diabetic kidney disease and neuropathy in eNOS knockout mice: Predictors of pathology and RAS blockade effects.

Diabetic kidney disease (DKD) and diabetic peripheral neuropathy (DPN) are two common diabetic complications. However, their pathogenesis remains elusive and current therapies are only modestly effective. We evaluated genome-wide expression to identify pathways involved in DKD and DPN progression in db/db eNOS-/- mice receiving renin-angiotensin-aldosterone system (RAS)-blocking drugs to mimic the current standard of care for DKD patients. Diabetes and eNOS deletion worsened DKD, which improved with RAS treatment. Diabetes also induced DPN, which was not affected by eNOS deletion or RAS blockade. Given the multiple factors affecting DKD and the graded differences in disease severity across mouse groups, an automatic data analysis method, SOM, or self-organizing map was used to elucidate glomerular transcriptional changes associated with DKD, whereas pairwise bioinformatic analysis was used for DPN. These analyses revealed that enhanced gene expression in several pro-inflammatory networks and reduced expression of development genes correlated with worsening DKD. Although RAS treatment ameliorated the nephropathy phenotype, it did not alter the more abnormal gene expression changes in kidney. Moreover, RAS exacerbated expression of genes related to inflammation and oxidant generation in peripheral nerves. The graded increase in inflammatory gene expression and decrease in development gene expression with DKD progression underline the potentially important role of these pathways in DKD pathogenesis. Since RAS blockers worsened this gene expression pattern in both DKD and DPN, it may partly explain the inadequate therapeutic efficacy of such blockers.

Enabling Eating Detection in a Free-living Environment: Integrative Engineering and Machine Learning Study.

BACKGROUND: Monitoring eating is central to the care of many conditions such as diabetes, eating disorders, heart diseases, and dementia. However, automatic tracking of eating in a free-living environment remains a challenge because of the lack of a mature system and large-scale, reliable training set. OBJECTIVE: This study aims to fill in this gap by an integrative engineering and machine learning effort and conducting a large-scale study in terms of monitoring hours on wearable-based eating detection. METHODS: This prospective, longitudinal, passively collected study, covering 3828 hours of records, was made possible by programming a digital system that streams diary, accelerometer, and gyroscope data from Apple Watches to iPhones and then transfers the data to the cloud. RESULTS: On the basis of this data collection, we developed deep learning models leveraging spatial and time augmentation and inferring eating at an area under the curve (AUC) of 0.825 within 5 minutes in the general population. In addition, the longitudinal follow-up of the study design encouraged us to develop personalized models that detect eating behavior at an AUC of 0.872. When aggregated to individual meals, the AUC is 0.951. We then prospectively collected an independent validation cohort in a different season of the year and validated the robustness of the models (0.941 for meal-level aggregation). CONCLUSIONS: The accuracy of this model and the data streaming platform promises immediate deployment for monitoring eating in applications such as diabetic integrative care.

COVID-19 drug repurposing: A review of computational screening methods, clinical trials, and protein interaction assays.

The situation of coronavirus disease 2019 (COVID-19) pandemic is rapidly evolving, and medical researchers around the globe are dedicated to finding cures for the disease. Drug repurposing, as an efficient way for drug development, has received a lot of attention. However, the huge amount of studies makes it challenging to keep up to date with the literature on COVID-19 therapeutic development. This review addresses this challenge by grouping the COVID-19 drug repurposing research into three large groups, including clinical trials, computational research, and in vitro protein-binding experiments. Particularly, to facilitate future drug discovery and the creation of effective drug combinations, drugs are organized by their mechanisms of action and reviewed by their efficacy measured by clinical trials. Providing this subtyping information, we hope this review would serve the scientists, clinicians, and the pharmaceutical industry who are looking at the new therapeutics for COVID-19 treatment.

Deep Residual Neural Networks Resolve Quartet Molecular Phylogenies.

Phylogenetic inference is of fundamental importance to evolutionary as well as other fields of biology, and molecular sequences have emerged as the primary data for this task. Although many phylogenetic methods have been developed to explicitly take into account substitution models of sequence evolution, such methods could fail due to model misspecification or insufficiency, especially in the face of heterogeneities in substitution processes across sites and among lineages. In this study, we propose to infer topologies of four-taxon trees using deep residual neural networks, a machine learning approach needing no explicit modeling of the subject system and having a record of success in solving complex nonlinear inference problems. We train residual networks on simulated protein sequence data with extensive amino acid substitution heterogeneities. We show that the well-trained residual network predictors can outperform existing state-of-the-art inference methods such as the maximum likelihood method on diverse simulated test data, especially under extensive substitution heterogeneities. Reassuringly, residual network predictors generally agree with existing methods in the trees inferred from real phylogenetic data with known or widely believed topologies. Furthermore, when combined with the quartet puzzling algorithm, residual network predictors can be used to reconstruct trees with more than four taxa. We conclude that deep learning represents a powerful new approach to phylogenetic reconstruction, especially when sequences evolve via heterogeneous substitution processes. We present our best trained predictor in a freely available program named Phylogenetics by Deep Learning (PhyDL, https://gitlab.com/ztzou/phydl; last accessed January 3, 2020).

Machine learning empowers phosphoproteome prediction in cancers.

MOTIVATION: Reversible protein phosphorylation is an essential post-translational modification regulating protein functions and signaling pathways in many cellular processes. Aberrant activation of signaling pathways often contributes to cancer development and progression. The mass spectrometry-based phosphoproteomics technique is a powerful tool to investigate the site-level phosphorylation of the proteome in a global fashion, paving the way for understanding the regulatory mechanisms underlying cancers. However, this approach is time-consuming and requires expensive instruments, specialized expertise and a large amount of starting material. An alternative in silico approach is predicting the phosphoproteomic profiles of cancer patients from the available proteomic, transcriptomic and genomic data. RESULTS: Here, we present a winning algorithm in the 2017 NCI-CPTAC DREAM Proteogenomics Challenge for predicting phosphorylation levels of the proteome across cancer patients. We integrate four components into our algorithm, including (i) baseline correlations between protein and phosphoprotein abundances, (ii) universal protein-protein interactions, (iii) shareable regulatory information across cancer tissues and (iv) associations among multi-phosphorylation sites of the same protein. When tested on a large held-out testing dataset of 108 breast and 62 ovarian cancer samples, our method ranked first in both cancer tissues, demonstrating its robustness and generalization ability. AVAILABILITY AND IMPLEMENTATION: Our code and reproducible results are freely available on GitHub: https://github.com/GuanLab/phosphoproteome_prediction. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Antisense Oligonucleotide Therapy Targeted Against ATXN3 Improves Potassium Channel-Mediated Purkinje Neuron Dysfunction in Spinocerebellar Ataxia Type 3.

Spinocerebellar ataxia type 3 (SCA3) is the second-most common CAG repeat disease, caused by a glutamine-encoding expansion in the ATXN3 protein. SCA3 is characterized by spinocerebellar degeneration leading to progressive motor incoordination and early death. Previous studies suggest that potassium channel dysfunction underlies early abnormalities in cerebellar cortical Purkinje neuron firing in SCA3. However, cerebellar cortical degeneration is often modest both in the human disease and mouse models of SCA3, raising uncertainty about the role of cerebellar dysfunction in SCA3. Here, we address this question by investigating Purkinje neuron excitability in SCA3. In early-stage SCA3 mice, we confirm a previously identified increase in excitability of cerebellar Purkinje neurons and associate this excitability with reduced transcripts of two voltage-gated potassium (KV) channels, Kcna6 and Kcnc3, as well as motor impairment. Intracerebroventricular delivery of antisense oligonucleotides (ASO) to reduce mutant ATXN3 restores normal excitability to SCA3 Purkinje neurons and rescues transcript levels of Kcna6 and Kcnc3. Interestingly, while an even broader range of KV channel transcripts shows reduced levels in late-stage SCA3 mice, cerebellar Purkinje neuron physiology was not further altered despite continued worsening of motor impairment. These results suggest the progressive motor phenotype observed in SCA3 may not reflect ongoing changes in the cerebellar cortex but instead dysfunction of other neuronal structures within and beyond the cerebellum. Nevertheless, the early rescue of both KV channel expression and neuronal excitability by ASO treatment suggests that cerebellar cortical dysfunction contributes meaningfully to motor dysfunction in SCA3.

TAIJI: approaching experimental replicates-level accuracy for drug synergy prediction.

MOTIVATION: Combination therapy is widely used in cancer treatment to overcome drug resistance. High-throughput drug screening is the standard approach to study the drug combination effects, yet it becomes impractical when the number of drugs under consideration is large. Therefore, accurate and fast computational tools for predicting drug synergistic effects are needed to guide experimental design for developing candidate drug pairs. RESULTS: Here, we present TAIJI, a high-performance software for fast and accurate prediction of drug synergism. It is based on the winning algorithm in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, which is a unique platform to unbiasedly evaluate the performance of current state-of-the-art methods, and includes 160 team-based submission methods. When tested across a broad spectrum of 85 different cancer cell lines and 1089 drug combinations, TAIJI achieved a high prediction correlation (0.53), approaching the accuracy level of experimental replicates (0.56). The runtime is at the scale of minutes to achieve this state-of-the-field performance. AVAILABILITY AND IMPLEMENTATION: TAIJI is freely available on GitHub (https://github.com/GuanLab/TAIJI). It is functional with built-in Perl and Python. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BaiHui: cross-species brain-specific network built with hundreds of hand-curated datasets.

MOTIVATION: Functional gene networks, representing how likely two genes work in the same biological process, are important models for studying gene interactions in complex tissues. However, a limitation of the current network-building scheme is the lack of leveraging evidence from multiple model organisms as well as the lack of expert curation and quality control of the input genomic data. RESULTS: Here, we present BaiHui, a brain-specific functional gene network built by probabilistically integrating expertly-hand-curated (by reading original publications) heterogeneous and multi-species genomic data in human, mouse and rat brains. To facilitate the use of this network, we deployed a web server through which users can query their genes of interest, visualize the network, gain functional insight from enrichment analysis and download network data. We also illustrated how this network could be used to generate testable hypotheses on disease gene prioritization of brain disorders. AVAILABILITY AND IMPLEMENTATION: BaiHui is freely available at: http://guanlab.ccmb.med.umich.edu/BaiHui/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High-Throughput Single-Cell Sequencing of both TCR-ß Alleles.

Allelic exclusion is a vital mechanism for the generation of monospecificity to foreign Ags in B and T lymphocytes. In this study, we developed a high-throughput barcoded method to simultaneously analyze the VDJ recombination status of both mouse TCR-ß alleles in hundreds of single cells using next-generation sequencing.