Literature-based predictions of Mendelian disease therapies.

In the effort to treat Mendelian disorders, correcting the underlying molecular imbalance may be more effective than symptomatic treatment. Identifying treatments that might accomplish this goal requires extensive and up-to-date knowledge of molecular pathways-including drug-gene and gene-gene relationships. To address this challenge, we present "parsing modifiers via article annotations" (PARMESAN), a computational tool that searches PubMed and PubMed Central for information to assemble these relationships into a central knowledge base. PARMESAN then predicts putatively novel drug-gene relationships, assigning an evidence-based score to each prediction. We compare PARMESAN's drug-gene predictions to all of the drug-gene relationships displayed by the Drug-Gene Interaction Database (DGIdb) and show that higher-scoring relationship predictions are more likely to match the directionality (up- versus down-regulation) indicated by this database. PARMESAN had more than 200,000 drug predictions scoring above 8 (as one example cutoff), for more than 3,700 genes. Among these predicted relationships, 210 were registered in DGIdb and 201 (96%) had matching directionality. This publicly available tool provides an automated way to prioritize drug screens to target the most-promising drugs to test, thereby saving time and resources in the development of therapeutics for genetic disorders.

Genetic inactivation of ß-catenin is salubrious, whereas its activation is deleterious in desmoplakin cardiomyopathy.

AIMS: Mutations in the DSP gene encoding desmoplakin, a constituent of the desmosomes at the intercalated discs (IDs), cause a phenotype that spans arrhythmogenic cardiomyopathy (ACM) and dilated cardiomyopathy. It is typically characterized by biventricular enlargement and dysfunction, myocardial fibrosis, cell death, and arrhythmias. The canonical wingless-related integration (cWNT)/ß-catenin pathway is implicated in the pathogenesis of ACM. The ß-catenin is an indispensable co-transcriptional regulator of the cWNT pathway and a member of the IDs. We genetically inactivated or activated ß-catenin to determine its role in the pathogenesis of desmoplakin cardiomyopathy. METHODS AND RESULTS: The Dsp gene was conditionally deleted in the 2-week-old post-natal cardiac myocytes using tamoxifen-inducible MerCreMer mice (Myh6-McmTam:DspF/F). The cWNT/ß-catenin pathway was markedly dysregulated in the Myh6-McmTam:DspF/F cardiac myocytes, as indicated by a concomitant increase in the expression of cWNT/ß-catenin target genes, isoforms of its key co-effectors, and the inhibitors of the pathway. The ß-catenin was inactivated or activated upon inducible deletion of its transcriptional or degron domain, respectively, in the Myh6-McmTam:DspF/F cardiac myocytes. Genetic inactivation of ß-catenin in the Myh6-McmTam:DspF/F mice prolonged survival, improved cardiac function, reduced cardiac arrhythmias, and attenuated myocardial fibrosis, and cell death caused by apoptosis, necroptosis, and pyroptosis, i.e. PANoptosis. In contrast, activation of ß-catenin had the opposite effects. The deleterious and the salubrious effects were independent of changes in the expression levels of the cWNT target genes and were associated with changes in several molecular and biological pathways, including cell death programmes. CONCLUSION: The cWNT/ß-catenin was markedly dysregulated in the cardiac myocytes in a mouse model of desmoplakin cardiomyopathy. Inactivation of ß-catenin attenuated, whereas its activation aggravated the phenotype, through multiple molecular pathways, independent of the cWNT transcriptional activity. Thus, suppression but not activation of ß-catenin might be beneficial in desmoplakin cardiomyopathy.

Deep learning for detecting and elucidating human T-cell leukemia virus type 1 integration in the human genome.

Human T-cell leukemia virus type 1 (HTLV-1), a retrovirus, is the causative agent for adult T cell leukemia/lymphoma and many other human diseases. Accurate and high throughput detection of HTLV-1 virus integration sites (VISs) across the host genomes plays a crucial role in the prevention and treatment of HTLV-1-associated diseases. Here, we developed DeepHTLV, the first deep learning framework for VIS prediction de novo from genome sequence, motif discovery, and cis-regulatory factor identification. We demonstrated the high accuracy of DeepHTLV with more efficient and interpretive feature representations. Decoding the informative features captured by DeepHTLV resulted in eight representative clusters with consensus motifs for potential HTLV-1 integration. Furthermore, DeepHTLV revealed interesting cis-regulatory elements in regulation of VISs that have significant association with the detected motifs. Literature evidence demonstrated nearly half (34) of the predicted transcription factors enriched with VISs were involved in HTLV-1-associated diseases. DeepHTLV is freely available at https://github.com/bsml320/DeepHTLV.

PANoptosis is a prominent feature of desmoplakin cardiomyopathy.

Introduction: Arrhythmogenic cardiomyopathy (ACM) is hereditary cardiomyopathy caused by pathogenic variants (mutations) in genes encoding the intercalated disc (ID), particularly desmosome proteins. ACM caused by mutations in the DSP gene encoding desmoplakin (DSP) is characterized by the prominence of cell death, myocardial fibrosis, and inflammation, and is referred to as desmoplakin cardiomyopathy. Aim: The aim of this article was to gain insight into the pathogenesis of DSP cardiomyopathy. Methods and Results: The Dsp gene was exclusively deleted in cardiac myocytes using tamoxifen-inducible MerCreMer (Myh6-Mcm Tam) and floxed Dsp (Dsp F/F) mice (Myh6-Mcm Tam:Dsp F/F). Recombination was induced upon subcutaneous injection of tamoxifen (30 mg/kg/d) for 5 days starting post-natal day 14. Survival was analyzed by Kaplan-Meier plots, cardiac function by echocardiography, arrhythmias by rhythm monitoring, and gene expression by RNA-Seq, immunoblotting, and immunofluorescence techniques. Cell death was analyzed by the TUNEL assay and the expression levels of specific markers were by RT-PCR and immunoblotting. Myocardial fibrosis was assessed by picrosirius red staining of the myocardial sections, RT-PCR, and immunoblotting. The Myh6-Mcm Tam: Dsp F/F mice showed extensive molecular remodeling of the IDs and the differential expression of ~10,000 genes, which predicted activation of KDM5A, IRFs, and NFκB and suppression of PPARGC1A and RB1, among others in the DSP-deficient myocytes. Gene set enrichment analysis predicted activation of the TNFα/NFκB pathway, inflammation, cell death programs, and fibrosis. Analysis of cell death markers indicated PANoptosis, comprised of apoptosis (increased CASP3, CASP8, BAD and reduced BCL2), necroptosis (increased RIPK1, RIPK3, and MLKL), and pyroptosis (increased GSDMD and ASC or PYCARD) in the DSP-deficient myocytes. Transcript levels of the pro-inflammatory and pro-fibrotic genes were increased and myocardial fibrosis comprised ~25% of the myocardium in the DSP-deficient hearts. The Myh6-Mcm Tam:Dsp F/F mice showed severe cardiac systolic dysfunction and ventricular arrhythmias, and died prematurely with a median survival rate of ~2 months. Conclusion: The findings identify PANoptosis as a prominent phenotypic feature of DSP cardiomyopathy and set the stage for delineating the specific molecular mechanisms involved in its pathogenesis. The model also provides the opportunity to test the effects of pharmacological and genetic interventions on myocardial fibrosis and cell death.

Evolutionarily conserved regulators of tau identify targets for new therapies.

Tauopathies are neurodegenerative diseases that involve the pathological accumulation of tau proteins; in this family are Alzheimer disease, corticobasal degeneration, and chronic traumatic encephalopathy, among others. Hypothesizing that reducing this accumulation could mitigate pathogenesis, we performed a cross-species genetic screen targeting 6,600 potentially druggable genes in human cells and Drosophila. We found and validated 83 hits in cells and further validated 11 hits in the mouse brain. Three of these hits (USP7, RNF130, and RNF149) converge on the C terminus of Hsc70-interacting protein (CHIP) to regulate tau levels, highlighting the role of CHIP in maintaining tau proteostasis in the brain. Knockdown of each of these three genes in adult tauopathy mice reduced tau levels and rescued the disease phenotypes. This study thus identifies several points of intervention to reduce tau levels and demonstrates that reduction of tau levels via regulation of this pathway is a viable therapeutic strategy for Alzheimer disease and other tauopathies.

Cross-species genetic screens identify transglutaminase 5 as a regulator of polyglutamine-expanded ataxin-1.

Many neurodegenerative disorders are caused by abnormal accumulation of misfolded proteins. In spinocerebellar ataxia type 1 (SCA1), accumulation of polyglutamine-expanded (polyQ-expanded) ataxin-1 (ATXN1) causes neuronal toxicity. Lowering total ATXN1, especially the polyQ-expanded form, alleviates disease phenotypes in mice, but the molecular mechanism by which the mutant ATXN1 is specifically modulated is not understood. Here, we identified 22 mutant ATXN1 regulators by performing a cross-species screen of 7787 and 2144 genes in human cells and Drosophila eyes, respectively. Among them, transglutaminase 5 (TG5) preferentially regulated mutant ATXN1 over the WT protein. TG enzymes catalyzed cross-linking of ATXN1 in a polyQ-length-dependent manner, thereby preferentially modulating mutant ATXN1 stability and oligomerization. Perturbing Tg in Drosophila SCA1 models modulated mutant ATXN1 toxicity. Moreover, TG5 was enriched in the nuclei of SCA1-affected neurons and colocalized with nuclear ATXN1 inclusions in brain tissue from patients with SCA1. Our work provides a molecular insight into SCA1 pathogenesis and an opportunity for allele-specific targeting for neurodegenerative disorders.

The WNT/ß-catenin pathway regulates expression of the genes involved in cell cycle progression and mitochondrial oxidative phosphorylation in the postmitotic cardiac myocytes.

INTRODUCTION: Aging is associated with cardiac myocyte loss, sarcopenia, and cardiac dysfunction. Adult cardiac myocytes are postmitotic cells with an insufficient proliferative capacity to compensate for myocyte loss. The canonical WNT (cWNT) pathway is involved in the regulation of cell cycle reentry in various cell types. The effects of the cWNT pathway on the expression of genes involved in cell cycle reentry in the postmitotic cardiac myocytes are unknown. AIM: The aim of the study was to identify genes whose expression is regulated by the ß-catenin, the indispensable component to the cWNT signaling, in the postmitotic myocytes. METHODS AND RESULTS: Cardiac myocyte-specific tamoxifen-inducible MerCreMer (Myh6-Mcm) mice were used to delete the floxed exon 3 or exons 8 to 13 of the Ctnnb1 gene to induce gain-of-function (GoF) or loss-of-function (LoF) the ß-catenin, respectively. Deletion of exon 3 leads to the expression of a stable ß-catenin. In contrast, deletion of exons 8-13 leads to the expression of transcriptionally inactive truncated ß-catenin, which is typically degraded. GoF or LoF of the ß-catenin was verified by reverse transcription-polymerase chain reaction (RT-PCR), immunoblotting, and immunofluorescence. Myocyte transcripts were analyzed by RNA-Sequencing (RNA-Seq) at 4 weeks of age. The GoF of ß-catenin was associated with differential expression of ~1700 genes, whereas its LoF altered expression of ~400 genes. The differentially expressed genes in the GoF myocytes were enriched in pathways regulating the cell cycle, including karyokinesis and cytokinesis, whereas the LoF was associated with increased expression of genes involved in mitochondrial oxidative phosphorylation. These findings were validated by RT-PCR in independent samples. Short-term GoF nor LoF of ß-catenin did not affect the number of cardiac myocytes, cardiac function, myocardial fibrosis, myocardial apoptosis, or adipogenesis at 4 weeks of age. CONCLUSION: Activation of the ß-catenin of the cWNT pathway in postmitotic myocytes leads to cell cycle reentry and expression of genes involved in cytokinesis without leading to an increase in the number of myocytes. In contrast, suppression of the ß-catenin modestly increases the expression of genes involved in oxidative phosphorylation. The findings provide insights into the role of ß-catenin of the cWNT pathway in the regulation of cell cycle reentry and oxidative phosphorylation in the postmitotic cardiac myocytes.

Effects of tamoxifen inducible MerCreMer on gene expression in cardiac myocytes in mice.

The Cre-LoxP technology, including the tamoxifen (TAM) inducible MerCreMer (MCM), is increasingly used to delineate gene function, understand the disease mechanisms, and test therapeutic interventions. We set to determine the effects of TAM-MCM on cardiac myocyte transcriptome. Expression of the MCM was induced specifically in cardiac myocytes upon injection of TAM to myosin heavy chain 6-MCM (Myh6-Mcm) mice for 5 consecutive days. Cardiac function, myocardial histology, and gene expression (RNA-sequencing) were analyzed 2 weeks after TAM injection. A total of 346 protein coding genes (168 up- and 178 down-regulated) were differentially expressed. Transcript levels of 85 genes, analyzed by a reverse transcription-polymerase chain reaction in independent samples, correlated with changes in the RNA-sequencing data. The differentially expressed genes were modestly enriched for genes involved in the interferon response and the tumor protein 53 (TP53) pathways. The changes in gene expression were relatively small and mostly transient and had no discernible effects on cardiac function, myocardial fibrosis, and apoptosis or induction of double-stranded DNA breaks. Thus, TAM-inducible activation of MCM alters cardiac myocytes gene expression, provoking modest and transient interferon and DNA damage responses without exerting other discernible phenotypic effects. Thus, the effects of TAM-MCM on gene expression should be considered in discerning the bona fide changes that result from the targeting of the gene of interest.

Association of CXCR6 with COVID-19 severity: delineating the host genetic factors in transcriptomic regulation.

The coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with ~ 80% asymptomatic or mild cases and ~ 5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms. We implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis, and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single-cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients. We discovered and replicated the genetically regulated expression of CXCR6 and CCR9 genes. These two genes have a protective effect on lung, and a risk effect on whole blood, respectively. The colocalization analysis of GWAS and cis-expression quantitative trait loci highlighted the regulatory effect on CXCR6 expression in lung and immune cells. In the lung-resident memory CD8+ T (TRM) cells, we found a 2.24-fold decrease of cell proportion among CD8+ T cells and lower expression of CXCR6 in the severe patients than moderate patients. Pro-inflammatory transcriptional programs were highlighted in the TRM cellular trajectory from moderate to severe patients. CXCR6 from the 3p21.31 locus is associated with severe COVID-19. CXCR6 tends to have a lower expression in lung TRM cells of severe patients, which aligns with the protective effect of CXCR6 from TWAS analysis.

Obstructive Sleep Apnea and Dementia-Common Gene Associations through Network-Based Identification of Common Driver Genes.

BACKGROUND: Obstructive Sleep Apnea (OSA) occurs in 7% of the adult population. The relationship between neurodegenerative diseases such as dementia and sleep disorders have long attracted clinical attention; however, no comprehensive data exists elucidating common gene expression between the two diseases. The objective of this study was to (1) demonstrate the practicability and feasibility of utilizing a systems biology approach called network-based identification of common driver genes (NICD) to identify common genomic features between two associated diseases and (2) utilize this approach to identify genes associated with both OSA and dementia. METHODS: This study utilized 2 public databases (PCNet, DisGeNET) and a permutation assay in order to identify common genes between two co-morbid but mutually exclusive diseases. These genes were then linked to their mechanistic pathways through Enrichr, producing a list of genes that were common between the two different diseases. RESULTS: 42 common genes were identified between OSA and dementia which were primarily linked to the G-coupled protein receptor (GPCR) and olfactory pathways. No single nucleotide polymorphisms (SNPs) were identified. CONCLUSIONS: This study demonstrates the viability of using publicly available databases and permutation assays along with canonical pathway linkage to identify common gene drivers as potential mechanistic targets for comorbid diseases.

Investigating Cellular Trajectories in the Severity of COVID-19 and Their Transcriptional Programs Using Machine Learning Approaches.

Single-cell RNA sequencing of the bronchoalveolar lavage fluid (BALF) samples from COVID-19 patients has enabled us to examine gene expression changes of human tissue in response to the SARS-CoV-2 virus infection. However, the underlying mechanisms of COVID-19 pathogenesis at single-cell resolution, its transcriptional drivers, and dynamics require further investigation. In this study, we applied machine learning algorithms to infer the trajectories of cellular changes and identify their transcriptional programs. Our study generated cellular trajectories that show the COVID-19 pathogenesis of healthy-to-moderate and healthy-to-severe on macrophages and T cells, and we observed more diverse trajectories in macrophages compared to T cells. Furthermore, our deep-learning algorithm DrivAER identified several pathways (e.g., xenobiotic pathway and complement pathway) and transcription factors (e.g., MITF and GATA3) that could be potential drivers of the transcriptomic changes for COVID-19 pathogenesis and the markers of the COVID-19 severity. Moreover, macrophages-related functions corresponded more to the disease severity compared to T cells-related functions. Our findings more proficiently dissected the transcriptomic changes leading to the severity of a COVID-19 infection.

The bone microenvironment invigorates metastatic seeds for further dissemination.

Metastasis has been considered as the terminal step of tumor progression. However, recent genomic studies suggest that many metastases are initiated by further spread of other metastases. Nevertheless, the corresponding pre-clinical models are lacking, and underlying mechanisms are elusive. Using several approaches, including parabiosis and an evolving barcode system, we demonstrated that the bone microenvironment facilitates breast and prostate cancer cells to further metastasize and establish multi-organ secondary metastases. We uncovered that this metastasis-promoting effect is driven by epigenetic reprogramming that confers stem cell-like properties on cancer cells disseminated from bone lesions. Furthermore, we discovered that enhanced EZH2 activity mediates the increased stemness and metastasis capacity. The same findings also apply to single cell-derived populations, indicating mechanisms distinct from clonal selection. Taken together, our work revealed an unappreciated role of the bone microenvironment in metastasis evolution and elucidated an epigenomic reprogramming process driving terminal-stage, multi-organ metastases.

The bone microenvironment increases phenotypic plasticity of ER+ breast cancer cells.

Estrogen receptor-positive (ER+) breast cancer exhibits a strong bone tropism in metastasis. How the bone microenvironment (BME) impacts ER signaling and endocrine therapy remains poorly understood. Here, we discover that the osteogenic niche transiently and reversibly reduces ER expression and activities specifically in bone micrometastases (BMMs), leading to endocrine resistance. As BMMs progress, the ER reduction and endocrine resistance may partially recover in cancer cells away from the osteogenic niche, creating phenotypic heterogeneity in macrometastases. Using multiple approaches, including an evolving barcoding strategy, we demonstrated that this process is independent of clonal selection, and represents an EZH2-mediated epigenomic reprogramming. EZH2 drives ER+ BMMs toward a basal and stem-like state. EZH2 inhibition reverses endocrine resistance. These data exemplify how epigenomic adaptation to BME promotes phenotypic plasticity of metastatic seeds, fosters intra-metastatic heterogeneity, and alters therapeutic responses. Our study provides insights into the clinical enigma of ER+ metastatic recurrences despite endocrine therapies.

Association of CXCR6 with COVID-19 severity: Delineating the host genetic factors in transcriptomic regulation.

BACKGROUND: The coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with ∼80% asymptomatic or mild cases and ∼5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms. METHODS: We implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients. RESULTS: We discovered and replicated the genetically regulated expression of CXCR6 and CCR9 genes. These two genes have a protective effect on the lung and a risk effect on whole blood, respectively. The colocalization analysis of GWAS and cis -expression quantitative trait loci highlighted the regulatory effect on CXCR6 expression in lung and immune cells. In the lung resident memory CD8 + T (T RM ) cells, we found a 3.32-fold decrease of cell proportion and lower expression of CXCR6 in the severe than moderate patients using the BALF transcriptomic dataset. Pro-inflammatory transcriptional programs were highlighted in T RM cells trajectory from moderate to severe patients. CONCLUSIONS: CXCR6 from the 3p21 . 31 locus is associated with severe COVID-19. CXCR6 tends to have a lower expression in lung T RM cells of severe patients, which aligns with the protective effect of CXCR6 from TWAS analysis. We illustrate one potential mechanism of host genetic factor impacting the severity of COVID-19 through regulating the expression of CXCR6 and T RM cell proportion and stability. Our results shed light on potential therapeutic targets for severe COVID-19.

ClearF: a supervised feature scoring method to find biomarkers using class-wise embedding and reconstruction.

BACKGROUND: Feature selection or scoring methods for the detection of biomarkers are essential in bioinformatics. Various feature selection methods have been developed for the detection of biomarkers, and several studies have employed information-theoretic approaches. However, most of these methods generally require a long processing time. In addition, information-theoretic methods discretize continuous features, which is a drawback that can lead to the loss of information. RESULTS: In this paper, a novel supervised feature scoring method named ClearF is proposed. The proposed method is suitable for continuous-valued data, which is similar to the principle of feature selection using mutual information, with the added advantage of a reduced computation time. The proposed score calculation is motivated by the association between the reconstruction error and the information-theoretic measurement. Our method is based on class-wise low-dimensional embedding and the resulting reconstruction error. Given multi-class datasets such as a case-control study dataset, low-dimensional embedding is first applied to each class to obtain a compressed representation of the class, and also for the entire dataset. Reconstruction is then performed to calculate the error of each feature and the final score for each feature is defined in terms of the reconstruction errors. The correlation between the information theoretic measurement and the proposed method is demonstrated using a simulation. For performance validation, we compared the classification performance of the proposed method with those of various algorithms on benchmark datasets. CONCLUSIONS: The proposed method showed higher accuracy and lower execution time than the other established methods. Moreover, an experiment was conducted on the TCGA breast cancer dataset, and it was confirmed that the genes with the highest scores were highly associated with subtypes of breast cancer.

Topological integration of RPPA proteomic data with multi-omics data for survival prediction in breast cancer via pathway activity inference.

BACKGROUND: The analysis of integrated multi-omics data enables the identification of disease-related biomarkers that cannot be identified from a single omics profile. Although protein-level data reflects the cellular status of cancer tissue more directly than gene-level data, past studies have mainly focused on multi-omics integration using gene-level data as opposed to protein-level data. However, the use of protein-level data (such as mass spectrometry) in multi-omics integration has some limitations. For example, the correlation between the characteristics of gene-level data (such as mRNA) and protein-level data is weak, and it is difficult to detect low-abundance signaling proteins that are used to target cancer. The reverse phase protein array (RPPA) is a highly sensitive antibody-based quantification method for signaling proteins. However, the number of protein features in RPPA data is extremely low compared to the number of gene features in gene-level data. In this study, we present a new method for integrating RPPA profiles with RNA-Seq and DNA methylation profiles for survival prediction based on the integrative directed random walk (iDRW) framework proposed in our previous study. In the iDRW framework, each omics profile is merged into a single pathway profile that reflects the topological information of the pathway. In order to address the sparsity of RPPA profiles, we employ the random walk with restart (RWR) approach on the pathway network. RESULTS: Our model was validated using survival prediction analysis for a breast cancer dataset from The Cancer Genome Atlas. Our proposed model exhibited improved performance compared with other methods that utilize pathway information and also out-performed models that did not include the RPPA data utilized in our study. The risk pathways identified for breast cancer in this study were closely related to well-known breast cancer risk pathways. CONCLUSIONS: Our results indicated that RPPA data is useful for survival prediction for breast cancer patients under our framework. We also observed that iDRW effectively integrates RNA-Seq, DNA methylation, and RPPA profiles, while variation in the composition of the omics data can affect both prediction performance and risk pathway identification. These results suggest that omics data composition is a critical parameter for iDRW.

An Inflammatory Landscape for Preoperative Neurologic Deficits in Glioblastoma.

Introduction: Patients with glioblastoma (GBM), one of the most aggressive forms of primary brain tumors, exhibit a wide range of neurologic signs, ranging from headaches to neurologic deficits and cognitive impairment, at first clinical presentation. While such variability is attributed to inter-individual differences in increased intracranial pressure, tumor infiltration, and vascular compromise, a direct association with disease stage, tumor size and location, edema, and necrotic cell death has yet to be established. The lack of specificity of neurologic symptoms often confounds the diagnosis of GBM. It also limits clinicians' ability to elect treatment regimens that not only prolong survival but also promote symptom management and high quality of life. Methods: To decipher the heterogeneous presentation of neurologic symptoms in GBM, we investigated differences in the molecular makeup of tumors from patients with and without preoperative neurologic deficits. We used the Ivy GAP (Ivy Glioblastoma Atlas Project) database to integrate RNA sequencing data from histologically defined GBM tumor compartments and neurologic examination records for 41 patients. We investigated the association of neurologic deficits with various tumor and patient attributes. We then performed differential gene expression and co-expression network analysis to identify a transcriptional signature specific to neurologic deficits in GBM. Using functional enrichment analysis, we finally provided a comprehensive and detailed characterization of involved pathways and gene interactions. Results: An exploratory investigation of the association of tumor and patient variables with the early development of neurologic deficits in GBM revealed a lack of robust and consistent clinicopathologic prognostic factors. We detected significant differences in the expression of 728 genes (FDR-adjusted p-value ≤ 0.05 and relative fold-change ≥ 1.5), unique to the cellular tumor (CT) anatomical compartment, between neurologic deficit groups. Upregulated differentially expressed genes in CT were enriched for mesenchymal subtype-predictive genes. Applying a systems approach, we then identified co-expressed gene sets that correlated with neurological deficit manifestation (FDR-adjusted p-value < 0.1). Collectively, these findings uncovered significantly enriched immune activation, oxidative stress response, and cytokine-mediated proinflammatory processes. Conclusion: Our study posits that inflammatory processes, as well as a mesenchymal tumor subtype, are implicated in the pathophysiology of preoperative neurologic deficits in GBM.

Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies.

BACKGROUND: Integrating the rich information from multi-omics data has been a popular approach to survival prediction and bio-marker identification for several cancer studies. To facilitate the integrative analysis of multiple genomic profiles, several studies have suggested utilizing pathway information rather than using individual genomic profiles. METHODS: We have recently proposed an integrative directed random walk-based method utilizing pathway information (iDRW) for more robust and effective genomic feature extraction. In this study, we applied iDRW to multiple genomic profiles for two different cancers, and designed a directed gene-gene graph which reflects the interaction between gene expression and copy number data. In the experiments, the performances of the iDRW method and four state-of-the-art pathway-based methods were compared using a survival prediction model which classifies samples into two survival groups. RESULTS: The results show that the integrative analysis guided by pathway information not only improves prediction performance, but also provides better biological insights into the top pathways and genes prioritized by the model in both the neuroblastoma and the breast cancer datasets. The pathways and genes selected by the iDRW method were shown to be related to the corresponding cancers. CONCLUSIONS: In this study, we demonstrated the effectiveness of a directed random walk-based multi-omics data integration method applied to gene expression and copy number data for both breast cancer and neuroblastoma datasets. We revamped a directed gene-gene graph considering the impact of copy number variation on gene expression and redefined the weight initialization and gene-scoring method. The benchmark result for iDRW with four pathway-based methods demonstrated that the iDRW method improved survival prediction performance and jointly identified cancer-related pathways and genes for two different cancer datasets. REVIEWERS: This article was reviewed by Helena Molina-Abril and Marta Hidalgo.

Beta-binomial modeling of CRISPR pooled screen data identifies target genes with greater sensitivity and fewer false negatives.

The simplicity and cost-effectiveness of CRISPR technology have made high-throughput pooled screening approaches accessible to virtually any laboratory. Analyzing the large sequencing data derived from these studies, however, still demands considerable bioinformatics expertise. Various methods have been developed to lessen this requirement, but there are still three tasks for accurate CRISPR screen analysis that involve bioinformatic know-how, if not prowess: designing a proper statistical hypothesis test for robust target identification, developing an accurate mapping algorithm to quantify sgRNA levels, and minimizing the parameters that need to be fine-tuned. To make CRISPR screen analysis more reliable as well as more readily accessible, we have developed a new algorithm, called CRISPRBetaBinomial or CB2 Based on the beta-binomial distribution, which is better suited to sgRNA data, CB2 outperforms the eight most commonly used methods (HiTSelect, MAGeCK, PBNPA, PinAPL-Py, RIGER, RSA, ScreenBEAM, and sgRSEA) in both accurately quantifying sgRNAs and identifying target genes, with greater sensitivity and a much lower false discovery rate. It also accommodates staggered sgRNA sequences. In conjunction with CRISPRcloud, CB2 brings CRISPR screen analysis within reach for a wider community of researchers.