Connecting omics signatures and revealing biological mechanisms with iLINCS

Developmental toxicity: artificial intelligence-powered assessments

Regulatory agencies require comprehensive toxicity testing for prenatal drug exposure, including new drugs in development, to reduce concerns about developmental toxicity, that is, drug-induced toxicity and adverse effects in pregnant women and fetuses. However, defining developmental toxicity endpoints and optimal analysis of associated public big data remain challenging. Recently, artificial intelligence (AI) approaches have had a critical role in analyzing complex, high-dimensional data, uncovering subtle relationships between chemical exposures and associated developmental risks. Here, we present an overview of major big data resources and data-driven models that focus on predicting various toxicity endpoints. We also highlight emerging, interpretable AI models that integrate multimodal data and domain knowledge to reveal toxic mechanisms underlying complex endpoints, and outline a potential framework that leverages multiple interpretable models to comprehensively evaluate chemical-induced developmental toxicity.

Exploring the genomic and transcriptomic profiles of glycemic traits and drug repurposing

BACKGROUND

Type 2 diabetes is an increasingly prevalent metabolic disorder with moderate to high heritability. Glycemic indices are crucial for diagnosing and monitoring the disease. Previous genome-wide association study (GWAS) have identified several risk loci associated with type 2 diabetes, but data from the Taiwanese population remain relatively sparse and primarily focus on type 2 diabetes status rather than glycemic trait levels.

METHODS

We conducted a comprehensive genome-wide meta-analysis to explore the genetics of glycemic traits. The study incorporated a community-based cohort of 145,468 individuals and a hospital-based cohort of 35,395 individuals. The study integrated genetics, transcriptomics, biological pathway analyses, polygenic risk score calculation, and drug repurposing for type 2 diabetes.

RESULTS

This study assessed hemoglobin A1c and fasting glucose levels, validating known loci (FN3K, SPC25, MTNR1B, and FOXA2) and discovering new genes, including MAEA and PRC1. Additionally, we found that diabetes, blood lipids, and liver- and kidney-related traits share genetic foundations with glycemic traits. A higher PRS was associated with an increased risk of type 2 diabetes. Finally, eight repurposed drugs were identified with evidence to regulate blood glucose levels, offering new avenues for the management and treatment of type 2 diabetes.

CONCLUSIONS

This research illuminates the unique genetic landscape of glucose regulation in Taiwanese Han population, providing valuable insights to guide future treatment strategies for type 2 diabetes.

Bioinformatics approach reveals the modulatory role of JUN in atorvastatin-mediated anti-breast cancer effects

Atorvastatin, a widely prescribed cholesterol-lowering drug, has recently shown potential anticancer effects. However, its influence on gene expression and its biological functions in cancer, in particular breast cancer, still unclear. We aim to identify the dysregulated genes associated with atorvastatin treatment and the main players in their biological network. A total of 103 differentially expressed genes (DEGs) in the unified signature were identified, and the functional enrichment analysis suggested their relation to multiple cancer-related pathways. JUN was identified as the hub gene in the protein-protein interaction (PPI) network and was shown to be responsive to atorvastatin in breast cancer. Atorvastatin exhibited notable predicted cytotoxicity against breast cancer lines, with the activity positively correlated with JUN expression. JUN was significantly downregulated in breast cancer expression inversely correlated with cancer progression, whereas higher JUN expression was linked with better survival outcomes. Atorvastatin may directly interact with JUN protein forming a more compact and stable conformation. These findings demystify the potential therapeutic mechanism of atorvastatin in breast cancer, possibly by fine tuning of JUN expression. As such, JUN might serve as a valuable prognostic biomarker in breast cancer.

L2S2: chemical perturbation and CRISPR KO LINCS L1000 signature search engine

As part of the Library of Integrated Network-Based Cellular Signatures (LINCS) NIH initiative, 248 human cell lines were profiled with the L1000 assay to measure the effect of 33 621 small molecules and 7508 single-gene CRISPR knockouts. From this massive dataset, we computed 1.678 million sets of up- and down-regulated genes. These gene sets are served for search by the LINCS L1000 Signature Search (L2S2) web server application. With L2S2, users can identify small molecules and single gene CRISPR KOs that produce gene expression profiles similar or opposite to their submitted single or up/down gene sets. L2S2 also includes a consensus search feature that ranks perturbations across all cellular contexts, time points, and concentrations. To demonstrate the utility of L2S2, we crossed the L2S2 gene sets with gene sets collected for the RummaGEO resource. The analysis identified clusters of differentially expressed genes that match drug classes, tissues, and diseases, pointing to many opportunities for drug repurposing and drug discovery. Overall, the L2S2 web server application can be used to further the development of personalized therapeutics while expanding our understanding of complex human diseases. The L2S2 web server application is available at https://l2s2.maayanlab.cloud.

Multi-omics analysis reveals aspirin is associated with reduced risk of Alzheimer's disease

The urgent need for safe and effective therapies for Alzheimer's disease (AD) has spurred a growing interest in repurposing existing drugs to treat or prevent AD. In this study, we combined multi-omics and clinical data to investigate possible repurposing opportunities for AD. We performed transcriptome-wide association studies (TWAS) to construct gene expression signatures of AD from publicly available GWAS summary statistics, using both transcriptome prediction models for 49 tissues from the Genotype-Tissue Expression (GTEx) project and microglia-specific models trained on eQTL data from the Microglia Genomic Atlas (MiGA). We then identified compounds capable of reversing the AD-associated changes in gene expression observed in these signatures by querying the Connectivity Map (CMap) drug perturbation database. Out of >2,000 small-molecule compounds in CMap, aspirin emerged as the most promising AD repurposing candidate. To investigate the longitudinal effects of aspirin use on AD, we collected drug exposure and AD coded diagnoses from three independent sources of real-world data: electronic health records (EHRs) from Vanderbilt University Medical Center (VUMC) and the National Institutes of Health All of Us Research Program, along with national healthcare claims from the MarketScan Research Databases. In meta-analysis of EHR data from VUMC and All of Us , we found that aspirin use before age 65 was associated with decreased risk of incident AD (hazard ratio=0.76, 95% confidence interval [CI]: 0.64-0.89, P =0.001). Consistent with the findings utilizing EHR data, analysis of claims data from MarketScan revealed significantly lower odds of aspirin exposure among AD cases compared to matched controls (odds ratio=0.32, 95% CI: 0.28-0.38, P <0.001). Our results demonstrate the value of integrating genetic and clinical data for drug repurposing studies and highlight aspirin as a promising repurposing candidate for AD, warranting further investigation in clinical trials.

Playbook workflow builder: Interactive construction of bioinformatics workflows

The Playbook Workflow Builder (PWB) is a web-based platform to dynamically construct and execute bioinformatics workflows by utilizing a growing network of input datasets, semantically annotated API endpoints, and data visualization tools contributed by an ecosystem of collaborators. Via a user-friendly user interface, workflows can be constructed from contributed building-blocks without technical expertise. The output of each step of the workflow is added into reports containing textual descriptions, figures, tables, and references. To construct workflows, users can click on cards that represent each step in a workflow, or construct workflows via a chat interface that is assisted by a large language model (LLM). Completed workflows are compatible with Common Workflow Language (CWL) and can be published as research publications, slideshows, and posters. To demonstrate how the PWB generates meaningful hypotheses that draw knowledge from across multiple resources, we present several use cases. For example, one of these use cases prioritizes drug targets for individual cancer patients using data from the NIH Common Fund programs GTEx, LINCS, Metabolomics, GlyGen, and ExRNA. The workflows created with PWB can be repurposed to tackle similar use cases using different inputs. The PWB platform is available from: https://playbook-workflow-builder.cloud/.

Comprehensive proteomics metadata and integrative web portals facilitate sharing and integration of LINCS multiomics data

The Library of Integrated Network-based Cellular Signatures (LINCS), an NIH Common Fund program, has cataloged and analyzed cellular function and molecular activity profiles in response to >80,000 perturbing agents that are potentially disruptive to cells. Because of the importance of proteins and their modifications to the response of specific cellular perturbations, four of the six LINCS centers have included significant proteomics efforts in the characterization of the resulting phenotype. This manuscript aims to describe this effort and the data harmonization and integration of the LINCS proteomics data discussed in recent LINCS papers.

Identification and multi-omics analysis of essential coding and long non-coding genes in colorectal cancer

Essential genes are indispensable for the survival of cancer cell. CRISPR/Cas9-based pooled genetic screens have distinguished the essential genes and their functions in distinct cellular processes. Nevertheless, the landscape of essential genes at the single cell levels and the effect on the tumor microenvironment (TME) remains limited. Here, we identified 396 essential protein-coding genes (ESPs) by integration of 8 genome-wide CRISPR loss-of-function screen datasets of colorectal cancer (CRC) cell lines and single-cell RNA sequencing (scRNA-seq) data of CRC tissues. Then, 29 essential long non-coding genes (ESLs) were predicted using Hypergeometric Test (HT) and Personalized PageRank (PPR) algorithms based on ESPs and co-expressed network constructed from scRNA-seq. CRISPR/Cas9 knockout experiment verified the effect of several ESPs and ESLs on the survival of CRC cell line. Furthermore, multi-omics features of ESPs and ESLs were illustrated by examining their expression patterns and transcription factor (TF) regulatory network at the single cell level, as well as DNA mutation and DNA methylation events at bulk level. Finally, through integrating multiple intracellular regulatory networks with cell-cell communication network (CCN), we elucidated that CD47 and MIF are regulated by multiple CRC essential genes, and the anti-cancer drugs sunitinib can interfere the expression of them potentially. Our findings provide a comprehensive asset of CRC ESPs and ESLs, sheding light on the mining of potential therapy targets for CRC.

Targeting Trefoil Factor Family 3 in Obstructive Airway Diseases: A Computational Approach to Novel Therapeutics

Background

Airway remodeling, a hallmark of chronic obstructive pulmonary disease (COPD) and mustard lung disease, is influenced by the Trefoil Factor 3 (TFF3). This study sought to pinpoint a compound with minimal toxicity that can effectively suppress TFF3 expression and activity.

Methods

We employed an integrative approach, combining gene expression analysis, molecular docking, and molecular dynamics simulations to identify potential TFF3 inhibitors. Gene expression analysis utilized Z-scores from the Library of Integrated Network-Based Cellular Signatures (LINCS) database to identify compounds altering TFF3 expression. Drug-like properties were assessed through Lipinski's "Rule of Five." Molecular docking was conducted with AutoDock Vina (version 1.1.2), and molecular dynamics simulations were performed using Groningen Machine for Chemical Simulations (GROMACS) version 5.1. Toxicity evaluation leveraged a Graph Convolutional Network (GCN). Statistical significance was set at P<0.05.

Results

Eight of the compounds assessed significantly reduced TFF3 expression, with binding affinities (ΔG) ranging from -7 to -9.4 kcal/mol. Notably, genistein emerged as the frontrunner, showcasing potent TFF3 downregulation, minimal toxicity, and a robust inhibitory profile, as evidenced by molecular dynamics simulations. The significance of gene expression changes was indicated by Z-scores provided by the LINCS database rather than exact P values.

Conclusion

Genistein holds promise as a therapeutic agent for TFF3-mediated conditions, including mustard lung disease. Its potential to address the current therapeutic gaps is evident, but its clinical utility necessitates further in vitro and in vivo validation. A preprint of this article has already been published (https://assets.researchsquare.com/files/rs-3907985/v1/41b7e6e6-4d70-4573-81e6-4d5a913950bd.pdf?c=1707752778).

Identification of targetable epigenetic vulnerabilities for uveal melanoma

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults, with a strong predilection for hepatic metastasis, occurring in approximately 50% of cases. Metastatic UM remains highly resistant to therapy and is almost invariably fatal. The strongest genetic drivers of UM metastasis are loss-of-function mutations in tumor suppressor BAP1, an epigenetic regulator that serves as the ubiquitin hydrolase subunit of the polycomb repressive deubiquitinase (PR-DUB) complex, and a key player in global epigenetic regulation. Inactivation of BRCA Associated Protein 1 (BAP1) has been shown to induce widespread epigenetic alterations across multiple model systems. To identify novel therapeutic strategies, we investigated whether targeting the epigenome could reveal new vulnerabilities in UM. We performed high-throughput compound screening using a curated epigenetic inhibitor library and identified BET (bromodomain and extra-terminal domain) inhibition as a particularly promising approach. Interestingly, we observed significant heterogeneity in the efficacy of different BET inhibitors in UM. While previous clinical trials with two BET inhibitors have failed to show efficacy in UM, our findings highlight substantial differences in the potency of specific BET inhibitors for this malignancy. Notably, the BET inhibitor mivebresib (ABBV-075) significantly improved survival rates by 50% in a metastatic UM xenograft mouse model and completely prevented detectable metastases in the bones, spinal cord, and brain. Unexpectedly, RNA sequencing revealed a strong transcriptional overlap between BET inhibition and histone deacetylase (HDAC) inhibition-- an approach currently under clinical evaluation for UM treatment. Both BET and HDAC inhibitors reversed gene expression signatures associated with high metastatic risk and induced a neuronal differentiation-like phenotype in UM cells. Together, our findings demonstrate that UM cells exhibit a distinct vulnerability to BET inhibition and establish BET inhibitors as promising candidates for further clinical evaluation for metastatic UM.

Adaptive Proteomic Changes in Protein Metabolism and Mitochondrial Alterations Associated with Resistance to Trastuzumab and Pertuzumab Therapy in HER2-Positive Breast Cancer

HER2 (human epidermal growth factor receptor 2) is overexpressed in approximately 15-20% of breast cancers, leading to aggressive tumour growth and poor prognosis. Anti-HER2 therapies, such as trastuzumab and pertuzumab, have significantly improved the outcomes for patients with HER2-positive breast cancer by blocking HER2 signalling. However, intrinsic and acquired resistance remains a major clinical challenge, limiting the long-term effectiveness of these therapies. Understanding the mechanisms of resistance is essential for developing strategies to overcome it and improve the therapeutic outcomes. We generated multiple HER2-positive breast cancer cell line models resistant to trastuzumab and pertuzumab combination therapy. Using mass spectrometry-based proteomics, we conducted a comprehensive analysis to identify the mechanisms underlying resistance. Proteomic analysis identified 618 differentially expressed proteins, with a core of 83 overexpressed and 118 downregulated proteins. Through a series of advanced bioinformatics analyses, we identified significant protein alterations and signalling pathways potentially responsible for the development of resistance, revealing key alterations in the protein metabolism, mitochondrial function, and signalling pathways, such as MAPK, TNF, and TGFβ. These findings identify mitochondrial activity and detoxification processes as pivotal mechanisms underlying the resistance to anti-HER2 therapy. Additionally, we identified key proteins, including ANXA1, SLC2A1, and PPIG, which contribute to the tumour progression and resistance phenotype. Our study suggests that targeting these pathways and proteins could form the basis of novel therapeutic strategies to overcome resistance in HER2-positive breast cancer.

Copper Drives Remodeling of Metabolic State and Progression of Clear Cell Renal Cell Carcinoma

SIGNIFICANCE

The work establishes a requirement for glucose-dependent coordination between energy production and redox homeostasis, which is fundamental for the survival of cancer cells that accumulate Cu and contributes to tumor growth.

Progress in toxicogenomics to protect human health

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

Single-Cell RNA Profiling of Ocular Adnexal Sebaceous Carcinoma Reveals a Complex Tumor Microenvironment and Identifies New Biomarkers

PURPOSE

Ocular adnexal sebaceous carcinoma (OaSC) is an aggressive malignancy that often necessitates orbital exenteration. Its tumor composition and transcriptional profile remain largely unknown, which poses a significant barrier to medical advances. Here, we report the first in-depth transcriptomic analysis of OaSC at the single-cell resolution and discern mechanisms underlying cancer progression for the discovery of potential globe-sparing immunotherapies, targeted therapies, and biomarkers to guide clinical management.

DESIGN

Laboratory investigation with a retrospective observational case series.

METHODS

Single-cell RNA sequencing was performed on six patient specimens: three primary tumors, two tumors with pagetoid spread, and a normal tarsus sample. Cellular components were identified via gene signatures. Molecular pathways underlying tumorigenesis and pagetoid spread were discerned via gene ontology analysis of the differentially expressed genes between specimens. CALML5 immunohistochemistry was performed on an archival cohort of OaSC, squamous cell carcinoma, ocular surface squamous neoplasia (OSSN), and basal cell carcinoma cases.

RESULTS

Analysis of 29,219 cells from OaSC specimens revealed tumor, immune, and stromal cells. Tumor-infiltrating immune cells include a diversity of cell types, including exhausted T-cell populations. In primary OaSC tumors, mitotic nuclear division and oxidative phosphorylation pathways are upregulated, while lipid biosynthesis and metabolism pathways are downregulated. Epithelial tissue migration pathways are upregulated in tumor cells undergoing pagetoid spread. Single-cell RNA sequencing analyses also revealed that CALML5 is upregulated in OaSC tumor cells. Diffuse nuclear and cytoplasmic CALML5 staining was present in 28 of 28 (100%) OaSC cases. Diffuse nuclear and membranous CALML5 staining was present in 5 of 25 (20%) squamous cell carcinoma and OSSN cases, while diffuse nuclear staining was present in 1 of 12 (8%) basal cell carcinoma cases.

CONCLUSIONS

This study reveals a complex OaSC tumor microenvironment and confirms that the CALML5 immunohistochemical stain is a sensitive diagnostic marker.

Reversal gene expression assessment for drug repurposing, a case study of glioblastoma

BACKGROUND

Glioblastoma (GBM) is a rare brain cancer with an exceptionally high mortality rate, which illustrates the pressing demand for more effective therapeutic options. Despite considerable research efforts on GBM, its underlying biological mechanisms remain unclear. Furthermore, none of the United States Food and Drug Administration (FDA) approved drugs used for GBM deliver satisfactory survival improvement.

METHODS

This study presents a novel computational pipeline by utilizing gene expression data analysis for GBM for drug repurposing to address the challenges in rare disease drug development, particularly focusing on GBM. The GBM Gene Expression Profile (GGEP) was constructed with multi-omics data to identify drugs with reversal gene expression to GGEP from the Integrated Network-Based Cellular Signatures (iLINCS) database.

RESULTS

We prioritized the candidates via hierarchical clustering of their expression signatures and quantification of their reversal strength by calculating two self-defined indices based on the GGEP genes' log2 foldchange (LFC) that the drug candidates could induce. Among five prioritized candidates, in-vitro experiments validated Clofarabine and Ciclopirox as highly efficacious in selectively targeting GBM cancer cells.

CONCLUSIONS

The success of this study illustrated a promising avenue for accelerating drug development by uncovering underlying gene expression effect between drugs and diseases, which can be extended to other rare diseases and non-rare diseases.

Drug response-based precision therapeutic selection for tamoxifen-resistant triple-positive breast cancer

Breast cancer adaptability to the drug environment reduces the chemotherapeutic response and facilitates acquired drug resistance. Cancer-specific therapeutics can be more effective against advanced-stage cancer than standard chemotherapeutics. To extend the paradigm of cancer-specific therapeutics, clinically relevant acquired tamoxifen-resistant MCF-7 proteome was deconstructed to identify possible druggable targets (N = 150). Twenty-eight drug inhibitors were used against identified druggable targets to suppress non-resistant (NC) and resistant cells (RC). First, selected drugs were screened using growth-inhibitory response against NC and RC. Seven drugs were shortlisted for their time-dependent (10-12 days) cytotoxic effect and further narrowed to three effective drugs (e.g., cisplatin, doxorubicin, and hydroxychloroquine). The growth-suppressive effectiveness of selected drugs was validated in the complex spheroid model (progressive and regressive). In the progressive model, doxorubicin (RC: 83.64 %, NC: 54.81 %), followed by cisplatin (RC: 76.66 %, NC: 68.94 %) and hydroxychloroquine (RC: 68.70 %, NC: 61.78 %) showed a significant growth-suppressive effect. However, in fully grown regressive spheroid, after 4th drug treatment, cisplatin significantly suppressed RC (84.79 %) and NC (40.21 %), while doxorubicin and hydroxychloroquine significantly suppressed only RC (76.09 and 76.34 %). Our in-depth investigation effectively integrated the expression data with the cancer-specific therapeutic investigation. Furthermore, our three-step sequential drug-screening approach unbiasedly identified cisplatin, doxorubicin, and hydroxychloroquine as an efficacious drug to target heterogeneous cancer cell populations. SIGNIFICANCE STATEMENT: Hormonal-positive BC grows slowly, and hormonal-inhibitors effectively suppress the oncogenesis. However, development of drug-resistance not only reduces the drug-response but also increases the chance of BC aggressiveness. Further, alternative chemotherapeutics are widely used to control advanced-stage BC. In contrast, we hypothesized that, compared to standard chemotherapeutics, cancer-specific drugs can be more effective against resistant-cancer. Although cancer-specific treatment identification is an uphill battle, our work shows proteome data can be used for drug selection. We identified multiple druggable targets and, using ex-vivo methods narrowed multiple drugs to disease-condition-specific therapeutics. We consider that our investigation successfully interconnected the expression data with the functional disease-specific therapeutic investigation and selected drugs can be used for effective resistant treatment with higher therapeutic response.

OSI-027 as a Potential Drug Candidate Targeting Upregulated Hub Protein TAF1 in Potential Mechanism of Sinonasal Squamous Cell Carcinoma: Insights from Proteomics and Molecular Docking

Sinonasal squamous cell carcinoma (SNSCC) is a rare tumor with high mortality and recurrence rates. However, SNSCC carcinogenesis mechanisms and potential therapeutic drugs have not been fully elucidated. This study investigated the key molecular mechanisms and hub proteins involved in SNSCC carcinogenesis using proteomics and bioinformatic analysis. Dysregulated proteins were validated by RT-qPCR in SNSCC and nasal polyp (NP) tissues. Proteomic analysis revealed that differentially expressed proteins were clustered using MCODE scores ≥ 4 into three modules. The specific hub proteins in each module were analyzed in carcinogenesis pathways using STRING, highlighting potential mechanisms of histone modification and spliceosome dysregulation. Spliceosome components SNRNP200 and SF3A3 were significantly downregulated in SNSCC by RT-qPCR. Web-based applications L1000CDS2 and iLINCS were applied to identify 10 potential repurposable drugs that could reverse the gene expression pattern associated with SNSCC. Docking studies of TAF1, a protein in histone modification, with these 10 small molecule inhibitors indicated OSI-027 to be the most promising due to its strong binding interactions with key residues. These findings suggest that hub proteins involved in the underlying mechanism of SNSCC carcinogenesis may serve as valuable targets for drug development, with OSI-027 emerging as a novel candidate against TAF1 in SNSCC.

Identification of Transcriptional Regulators of Immune Evasion Across Cancers: An Alternative Immunotherapeutic Strategy for Cholangiocarcinoma

BACKGROUND

Cancer immune evasion is a multifaceted process that synchronizes pro-tumoral immune infiltration, immunosuppressive inflammation, and inhibitory immune checkpoint expression (IC). Current immunotherapies combat this issue by reinstating immunosurveillance of tumors; however, it benefits a limited patient population. Thus, a more effective immunotherapeutic strategy is warranted to cater to specific patient populations. This investigation introduces a novel immunotherapeutic strategy via inhibition of master regulators of immune evasion (MR-IE).

METHODS

Samples of the TCGA Pan-Cancer Atlas transcriptomic data were subset and stratified based on IC and estimated immune cell infiltration. Transcriptomic analysis was conducted to unravel pathways associated with the immune evasion process. Transcription factor enrichment and survival analyses were conducted to identify and rank candidate MR-IEs per cancer type.

RESULTS

Inhibition of the top-ranking MR-IE candidate of cholangiocarcinoma (CCA), MYC, modulated the gene and protein expression of PD-L1. Moreover, pro-tumoral inflammatory markers, IFNA21 and CX3CL1, were downregulated, and anti-tumoral cytokines, IL-18 and IL-16, were upregulated. Lastly, MYC inhibition potentiated fourth-generation anti-folate receptor alpha (FRα) CAR-T cell therapy against CCA cells.

CONCLUSIONS

Cumulatively, this study highlights the promise of MR-IE inhibition as a novel potent immunotherapeutic strategy for the treatment of CCA and offers a candidate list of MR-IEs per cancer type for further validation.

Transcriptomic Analysis of the Amygdala in Subjects with Schizophrenia, Bipolar Disorder and Major Depressive Disorder Reveals Differentially Altered Metabolic Pathways

BACKGROUND AND HYPOTHESIS

The amygdala, crucial for mood, anxiety, fear, and reward regulation, shows neuroanatomical and molecular divergence in psychiatric disorders like schizophrenia, bipolar disorder and major depression. This region is also emerging as an important regulator of metabolic and immune pathways. The goal of this study is to address the paucity of molecular studies in the human amygdala. We hypothesize that diagnosis-specific gene expression alterations contribute to the unique pathophysiological profiles of these disorders.

STUDY DESIGN

We used a cohort of subjects diagnosed with SCZ, BPD or MDD, and nonpsychiatrically ill control subjects (n = 15/group), together with our bioinformatic 3-pod analysis consisting of full transcriptome pathway analysis, targeted pathway analysis, leading-edge gene analysis and iLINCS perturbagen analysis.

STUDY RESULTS

We identified altered expression of metabolic pathways in each disorder. Subjects with SCZ displayed downregulation of mitochondrial respiration and nucleotide metabolism pathways. In comparison, we observed upregulation of mitochondrial respiration pathways in subjects with MDD, while subjects with BPD displayed enrichment of pathways involved in carbohydrate metabolism. Several pathways associated with brain metabolism including immune system processes and calcium ion transport were also differentially altered between diagnosis groups.

CONCLUSION

Our findings suggest metabolic pathways, including downregulation of energy metabolism pathways in SCZ and upregulation of energy metabolism pathways in MDD, are uniquely altered in the amygdala in these disorders, which may impact approaches for therapeutic strategies.

A Multistep In Silico Approach Identifies Potential Glioblastoma Drug Candidates via Inclusive Molecular Targeting of Glioblastoma Stem Cells

Glioblastoma (GBM) is the highest grade of glioma for which no effective therapy is currently available. Despite extensive research in diagnosis and therapy, there has been no significant improvement in GBM outcomes, with a median overall survival continuing at a dismal 15-18 months. In recent times, glioblastoma stem cells (GSCs) have been identified as crucial drivers of treatment resistance and tumor recurrence, and GBM therapies targeting GSCs are expected to improve patient outcomes. We used a multistep in silico screening strategy to identify repurposed candidate drugs against selected therapeutic molecular targets in GBM with potential to concomitantly target GSCs. Common differentially expressed genes (DEGs) were identified through analysis of multiple GBM and GSC datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). For identification of target genes, we selected the genes with most significant effect on overall patient survival. The relative mRNA and protein expression of the selected genes in TCGA control versus GBM samples was also validated and their cancer dependency scores were assessed. Drugs targeting these genes and their corresponding proteins were identified from LINCS database using Connectivity Map (CMap) portal and by in silico molecular docking against each individual target using FDA-approved drug library from the DrugBank database, respectively. The molecules thus obtained were further evaluated for their ability to cross blood brain barrier (BBB) and their likelihood of resulting in drug resistance by acting as p-glycoprotein (p-Gp) substrates. The growth inhibitory effect of these final shortlisted compounds was examined on a panel of GBM cell lines and compared with temozolomide through the drug sensitivity EC50 values and AUC from the PRISM Repurposing Secondary Screen, and the IC50 values were obtained from GDSC portal. We identified RPA3, PSMA2, PSMC2, BLVRA, and HUS1 as molecular targets in GBM including GSCs with significant impact on patient survival. Our results show GSK-2126458/omipalisib, linifanib, drospirenone, eltrombopag, nilotinib, and PD198306 as candidate drugs which can be further evaluated for their anti-tumor potential against GBM. Through this work, we identified repurposed candidate therapeutics against GBM utilizing a GSC inclusive targeting approach, which demonstrated high in vitro efficacy and can prospectively evade drug resistance. These drugs have the potential to be developed as individual or combination therapy to improve GBM outcomes.

RummaGEO: Automatic mining of human and mouse gene sets from GEO

The Gene Expression Omnibus (GEO) has millions of samples from thousands of studies. While users of GEO can search the metadata describing studies, there is a need for methods to search GEO at the data level. RummaGEO is a gene expression signature search engine for human and mouse RNA sequencing perturbation studies extracted from GEO. To develop RummaGEO, we automatically identified groups of samples and computed differential expressions to extract gene sets from each study. The contents of RummaGEO are served for gene set, PubMed, and metadata search. Next, we analyzed the contents of RummaGEO to identify patterns and perform global analyses. Overall, RummaGEO provides a resource that is enabling users to identify relevant GEO studies based on their own gene expression results. Users of RummaGEO can incorporate RummaGEO into their analysis workflows for integrative analyses and hypothesis generation.

DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer

The field of computational drug repurposing aims to uncover novel therapeutic applications for existing drugs through high-throughput data analysis. However, there is a scarcity of drug repurposing methods leveraging the cellular-level information provided by single-cell RNA sequencing data. To address this need, we propose DrugReSC, an innovative approach to drug repurposing utilizing single-cell RNA sequencing data, intending to target specific cell subpopulations critical to disease pathology. DrugReSC constructs a drug-by-cell matrix representing the transcriptional relationships between individual cells and drugs and utilizes permutation-based methods to assess drug contributions to cellular phenotypic changes. We demonstrate DrugReSC's superior performance compared to existing drug repurposing methods based on bulk or single-cell RNA sequencing data across multiple cancer case studies. In summary, DrugReSC offers a novel perspective on the utilization of single-cell sequencing data in drug repurposing methods, contributing to the advancement of precision medicine for cancer.

GABA(A) Receptor Activation Drives GABARAP-Nix Mediated Autophagy to Radiation-Sensitize Primary and Brain-Metastatic Lung Adenocarcinoma Tumors

In non-small cell lung cancer (NSCLC) treatment, radiotherapy responses are not durable and toxicity limits therapy. We find that AM-101, a synthetic benzodiazepine activator of GABA(A) receptor, impairs the viability and clonogenicity of both primary and brain-metastatic NSCLC cells. Employing a human-relevant ex vivo 'chip', AM-101 is as efficacious as docetaxel, a chemotherapeutic used with radiotherapy for advanced-stage NSCLC. In vivo, AM-101 potentiates radiation, including conferring a significant survival benefit to mice bearing NSCLC intracranial tumors generated using a patient-derived metastatic line. GABA(A) receptor activation stimulates a selective-autophagic response via the multimerization of GABA(A) receptor-associated protein, GABARAP, the stabilization of mitochondrial receptor Nix, and the utilization of ubiquitin-binding protein p62. A high-affinity peptide disrupting Nix binding to GABARAP inhibits AM-101 cytotoxicity. This supports a model of GABA(A) receptor activation driving a GABARAP-Nix multimerization axis that triggers autophagy. In patients receiving radiotherapy, GABA(A) receptor activation may improve tumor control while allowing radiation dose de-intensification to reduce toxicity.

Reversal Gene Expression Assessment for Drug Repurposing, a Case Study of Glioblastoma

Glioblastoma (GBM) is a rare brain cancer with an exceptionally high mortality rate, which illustrates the pressing demand for more effective therapeutic options. Despite considerable research efforts on GBM, its underlying biological mechanisms remain unclear. Furthermore, none of the United States Food and Drug Administration (FDA) approved drugs used for GBM deliver satisfactory survival improvement. This study presents a novel computational pipeline by utilizing gene expression data analysis for GBM for drug repurposing to address the challenges in rare disease drug development, particularly focusing on GBM. The GBM Gene Expression Profile (GGEP) was constructed with multi-omics data to identify drugs with reversal gene expression to GGEP from the Integrated Network-Based Cellular Signatures (iLINCS) database. We prioritized the candidates via hierarchical clustering of their expression signatures and quantification of their reversal strength by calculating two self-defined indices based on the GGEP genes' log2 foldchange (LFCs) that the drug candidates could induce. Among eight prioritized candidates, in-vitro experiments validated Clofarabine and Ciclopirox as highly efficacious in selectively targeting GBM cancer cells. The success of this study illustrated a promising avenue for accelerating drug development by uncovering underlying gene expression effect between drugs and diseases, which can be extended to other rare diseases and non-rare diseases.

Accelerating drug discovery and repurposing by combining transcriptional signature connectivity with docking

We present an in silico approach for drug discovery, dubbed connectivity enhanced structure activity relationship (ceSAR). Building on the landmark LINCS library of transcriptional signatures of drug-like molecules and gene knockdowns, ceSAR combines cheminformatic techniques with signature concordance analysis to connect small molecules and their targets and further assess their biophysical compatibility using molecular docking. Candidate compounds are first ranked in a target structure-independent manner, using chemical similarity to LINCS analogs that exhibit transcriptomic concordance with a target gene knockdown. Top candidates are subsequently rescored using docking simulations and machine learning-based consensus of the two approaches. Using extensive benchmarking, we show that ceSAR greatly reduces false-positive rates, while cutting run times by multiple orders of magnitude and further democratizing drug discovery pipelines. We further demonstrate the utility of ceSAR by identifying and experimentally validating inhibitors of BCL2A1, an important antiapoptotic target in melanoma and preterm birth-associated inflammation.

Regulation of MYC by CARD14 in human epithelium is a determinant of epidermal homeostasis and disease

Caspase recruitment domain family member 14 (CARD14) and its variants are associated with both atopic dermatitis (AD) and psoriasis, but their mechanistic impact on skin barrier homeostasis is largely unknown. CARD14 is known to signal via NF-κB; however, CARD14-NF-κB signaling does not fully explain the heterogeneity of CARD14-driven disease. Here, we describe a direct interaction between CARD14 and MYC and show that CARD14 signals through MYC in keratinocytes to coordinate skin barrier homeostasis. CARD14 directly binds MYC and influences barrier formation in an MYC-dependent fashion, and this mechanism is undermined by disease-associated CARD14 variants. These studies establish a paradigm that CARD14 activation regulates skin barrier function by two distinct mechanisms, including activating NF-κB to bolster the antimicrobial (chemical) barrier and stimulating MYC to bolster the physical barrier. Finally, we show that CARD14-dependent MYC signaling occurs in other epithelia, expanding the impact of our findings beyond the skin.

RNA Sequencing in Disease Diagnosis

RNA sequencing (RNA-seq) enables the accurate measurement of multiple transcriptomic phenotypes for modeling the impacts of disease variants. Advances in technologies, experimental protocols, and analysis strategies are rapidly expanding the application of RNA-seq to identify disease biomarkers, tissue- and cell-type-specific impacts, and the spatial localization of disease-associated mechanisms. Ongoing international efforts to construct biobank-scale transcriptomic repositories with matched genomic data across diverse population groups are further increasing the utility of RNA-seq approaches by providing large-scale normative reference resources. The availability of these resources, combined with improved computational analysis pipelines, has enabled the detection of aberrant transcriptomic phenotypes underlying rare diseases. Further expansion of these resources, across both somatic and developmental tissues, is expected to soon provide unprecedented insights to resolve disease origin, mechanism of action, and causal gene contributions, suggesting the continued high utility of RNA-seq in disease diagnosis.

Transcriptomic Analysis of Arachidonic Acid Pathway Genes Provides Mechanistic Insight into Multi-Organ Inflammatory and Vascular Diseases

Arachidonic acid (AA) metabolites have been associated with several diseases across various organ systems, including the cardiovascular, pulmonary, and renal systems. Lipid mediators generated from AA oxidation have been studied to control macrophages, T-cells, cytokines, and fibroblasts, and regulate inflammatory mediators that induce vascular remodeling and dysfunction. AA is metabolized by cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 (CYP) to generate anti-inflammatory, pro-inflammatory, and pro-resolutory oxidized lipids. As comorbid states such as diabetes, hypertension, and obesity become more prevalent in cardiovascular disease, studying the expression of AA pathway genes and their association with these diseases can provide unique pathophysiological insights. In addition, the AA pathway of oxidized lipids exhibits diverse functions across different organ systems, where a lipid can be both anti-inflammatory and pro-inflammatory depending on the location of metabolic activity. Therefore, we aimed to characterize the gene expression of these lipid enzymes and receptors throughout multi-organ diseases via a transcriptomic meta-analysis using the Gene Expression Omnibus (GEO) Database. In our study, we found that distinct AA pathways were expressed in various comorbid conditions, especially those with prominent inflammatory risk factors. Comorbidities, such as hypertension, diabetes, and obesity appeared to contribute to elevated expression of pro-inflammatory lipid mediator genes. Our results demonstrate that expression of inflammatory AA pathway genes may potentiate and attenuate disease; therefore, we suggest further exploration of these pathways as therapeutic targets to improve outcomes.

Developmental pyrethroid exposure in mouse leads to disrupted brain metabolism in adulthood

Environmental and genetic risk factors, and their interactions, contribute significantly to the etiology of neurodevelopmental disorders (NDDs). Recent epidemiology studies have implicated pyrethroid pesticides as an environmental risk factor for autism and developmental delay. Our previous research showed that low-dose developmental exposure to the pyrethroid pesticide deltamethrin in mice caused male-biased changes in the brain and in NDD-relevant behaviors in adulthood. Here, we used a metabolomics approach to determine the broadest possible set of metabolic changes in the adult male mouse brain caused by low-dose pyrethroid exposure during development. Using a litter-based design, we exposed mouse dams during pregnancy and lactation to deltamethrin (3 mg/kg or vehicle every 3 days) at a concentration well below the EPA-determined benchmark dose used for regulatory guidance. We raised male offspring to adulthood and collected whole brain samples for untargeted high-resolution metabolomics analysis. Developmentally exposed mice had disruptions in 116 metabolites which clustered into pathways for folate biosynthesis, retinol metabolism, and tryptophan metabolism. As a cross-validation, we integrated metabolomics and transcriptomics data from the same samples, which confirmed previous findings of altered dopamine signaling. These results suggest that pyrethroid exposure during development leads to disruptions in metabolism in the adult brain, which may inform both prevention and therapeutic strategies.

Transcriptomic Analysis of Metastatic Uveal Melanoma and Differences in Male and Female Patients

BACKGROUND/AIM

Uveal melanoma is an ocular malignancy whose prognosis severely worsens following metastasis. In order to improve the understanding of molecular physiology of metastatic uveal melanoma, we identified genes and pathways implicated in metastatic vs non-metastatic uveal melanoma.

PATIENTS AND METHODS

A previously published dataset from Gene Expression Omnibus (GEO) was used to identify differentially expressed genes between metastatic and non-metastatic samples as well as to conduct pathway and perturbagen analyses using Gene Set Enrichment Analysis (GSEA), EnrichR, and iLINCS.

RESULTS

In male metastatic uveal melanoma samples, the gene LOC401052 is significantly down-regulated and FHDC1 is significantly up-regulated compared to non-metastatic male samples. In female samples, no significant differently expressed genes were found. Additionally, we identified many significant up-regulated immune response pathways in male metastatic uveal melanoma, including "T cell activation in immune response". In contrast, many top up-regulated female pathways involve iron metabolism, including "heme biosynthetic process". iLINCS perturbagen analysis identified that both male and female samples have similar discordant activity with growth factor receptors, but only female samples have discordant activity with progesterone receptor agonists.

CONCLUSION

Our results from analyzing genes, pathways, and perturbagens demonstrate differences in metastatic processes between sexes.

Orthogonal proteogenomic analysis identifies the druggable PA2G4-MYC axis in 3q26 AML

The overexpression of the ecotropic viral integration site-1 gene (EVI1/MECOM) marks the most lethal acute myeloid leukemia (AML) subgroup carrying chromosome 3q26 abnormalities. By taking advantage of the intersectionality of high-throughput cell-based and gene expression screens selective and pan-histone deacetylase inhibitors (HDACis) emerge as potent repressors of EVI1. To understand the mechanism driving on-target anti-leukemia activity of this compound class, here we dissect the expression dynamics of the bone marrow leukemia cells of patients treated with HDACi and reconstitute the EVI1 chromatin-associated co-transcriptional complex merging on the role of proliferation-associated 2G4 (PA2G4) protein. PA2G4 overexpression rescues AML cells from the inhibitory effects of HDACis, while genetic and small molecule inhibition of PA2G4 abrogates EVI1 in 3q26 AML cells, including in patient-derived leukemia xenografts. This study positions PA2G4 at the crosstalk of the EVI1 leukemogenic signal for developing new therapeutics and urges the use of HDACis-based combination therapies in patients with 3q26 AML.

Diffuse intrinsic pontine glioma (DIPG): A review of current and emerging treatment strategies

Diffuse intrinsic pontine glioma (DIPG) is a childhood malignancy of the brainstem with a dismal prognosis. Despite recent advances in its understanding at the molecular level, the prognosis of DIPG has remained unchanged. This article aims to review the current understanding of the genetic pathophysiology of DIPG and to highlight promising therapeutic targets. Various DIPG treatment strategies have been investigated in pre-clinical studies, several of which have shown promise and have been subsequently translated into ongoing clinical trials. Ultimately, a multifaceted therapeutic approach that targets cell-intrinsic alterations, the micro-environment, and augments the immune system will likely be necessary to eradicate DIPG.

Interactions between integrin α9β1 and VCAM-1 promote neutrophil hyperactivation and mediate poststroke DVT

ABSTRACT

Venous thromboembolic events are significant contributors to morbidity and mortality in patients with stroke. Neutrophils are among the first cells in the blood to respond to stroke and are known to promote deep vein thrombosis (DVT). Integrin α9 is a transmembrane glycoprotein highly expressed on neutrophils and stabilizes neutrophil adhesion to activated endothelium via vascular cell adhesion molecule 1 (VCAM-1). Nevertheless, the causative role of neutrophil integrin α9 in poststroke DVT remains unknown. Here, we found higher neutrophil integrin α9 and plasma VCAM-1 levels in humans and mice with stroke. Using mice with embolic stroke, we observed enhanced DVT severity in a novel model of poststroke DVT. Neutrophil-specific integrin α9-deficient mice (α9fl/flMrp8Cre+/-) exhibited a significant reduction in poststroke DVT severity along with decreased neutrophils and citrullinated histone H3 in thrombi. Unbiased transcriptomics indicated that α9/VCAM-1 interactions induced pathways related to neutrophil inflammation, exocytosis, NF-κB signaling, and chemotaxis. Mechanistic studies revealed that integrin α9/VCAM-1 interactions mediate neutrophil adhesion at the venous shear rate, promote neutrophil hyperactivation, increase phosphorylation of extracellular signal-regulated kinase, and induce endothelial cell apoptosis. Using pharmacogenomic profiling, virtual screening, and in vitro assays, we identified macitentan as a potent inhibitor of integrin α9/VCAM-1 interactions and neutrophil adhesion to activated endothelial cells. Macitentan reduced DVT severity in control mice with and without stroke, but not in α9fl/flMrp8Cre+/- mice, suggesting that macitentan improves DVT outcomes by inhibiting neutrophil integrin α9. Collectively, we uncovered a previously unrecognized and critical pathway involving the α9/VCAM-1 axis in neutrophil hyperactivation and DVT.

Differentially Altered Metabolic Pathways in the Amygdala of Subjects with Schizophrenia, Bipolar Disorder and Major Depressive Disorder

Background and hypothesis

A growing number of studies implicate a key role for metabolic processes in psychiatric disorders. Recent studies suggest that ketogenic diet may be therapeutically effective for subgroups of people with schizophrenia (SCZ), bipolar disorder (BPD) and possibly major depressive disorder (MDD). Despite this promise, there is currently limited information regarding brain energy metabolism pathways across these disorders, limiting our understanding of how brain metabolic pathways are altered and who may benefit from ketogenic diets. We conducted gene expression profiling on the amygdala, a key region involved in in the regulation of mood and appetitive behaviors, to test the hypothesis that amygdala metabolic pathways are differentially altered between these disorders.

Study Design

We used a cohort of subjects diagnosed with SCZ, BPD or MDD, and non-psychiatrically ill control subjects (n=15/group), together with our bioinformatic 3-pod analysis consisting of full transcriptome pathway analysis, targeted pathway analysis, leading-edge gene analysis and iLINCS perturbagen analysis.

Study Results

We identified differential expression of metabolic pathways in each disorder. Subjects with SCZ displayed downregulation of mitochondrial respiration and nucleotide metabolism pathways. In comparison, we observed upregulation of mitochondrial respiration pathways in subjects with MDD, while subjects with BPD displayed enrichment of pathways involved in carbohydrate metabolism. Several pathways associated with brain metabolism including immune system processes and calcium ion transport were also differentially altered between diagnosis groups.

Conclusion

Our findings suggest metabolic pathways are differentially altered in the amygdala in these disorders, which may impact approaches for therapeutic strategies.

RummaGEO: Automatic Mining of Human and Mouse Gene Sets from GEO

The Gene Expression Omnibus (GEO) is a major open biomedical research repository for transcriptomics and other omics datasets. It currently contains millions of gene expression samples from tens of thousands of studies collected by many biomedical research laboratories from around the world. While users of the GEO repository can search the metadata describing studies for locating relevant datasets, there are currently no methods or resources that facilitate global search of GEO at the data level. To address this shortcoming, we developed RummaGEO, a webserver application that enables gene expression signature search of a large collection of human and mouse RNA-seq studies deposited into GEO. To develop the search engine, we performed offline automatic identification of sample conditions from the uniformly aligned GEO studies available from ARCHS4. We then computed differential expression signatures to extract gene sets from these studies. In total, RummaGEO currently contains 135,264 human and 158,062 mouse gene sets extracted from 23,395 GEO studies. Next, we analyzed the contents of the RummaGEO database to identify statistical patterns and perform various global analyses. The contents of the RummaGEO database are provided as a web-server search engine with signature search, PubMed search, and metadata search functionalities. Overall, RummaGEO provides an unprecedented resource for the biomedical research community enabling hypothesis generation for many future studies. The RummaGEO search engine is available from: https://rummageo.com/.

Single-cell transcriptional signature-based drug repurposing and in vitro evaluation in colorectal cancer

BACKGROUND

The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health.

METHODS

In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116).

RESULTS

According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29).

CONCLUSIONS

Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

ViCEKb: Vitiligo-linked Chemical Exposome Knowledgebase

Vitiligo is a complex disease wherein the environmental factors, in conjunction with the underlying genetic predispositions, trigger the autoimmune destruction of melanocytes, ultimately leading to depigmented patches on the skin. While genetic factors have been extensively studied, the knowledge on environmental triggers remains sparse and less understood. To address this knowledge gap, we present the first comprehensive knowledgebase of vitiligo-triggering chemicals namely, Vitiligo-linked Chemical Exposome Knowledgebase (ViCEKb). ViCEKb involves an extensive and systematic manual effort in curation of published literature and subsequent compilation of 113 unique chemical triggers of vitiligo. ViCEKb standardizes various chemical information, and categorizes the chemicals based on their evidences and sources of exposure. Importantly, ViCEKb contains a wide range of metrics necessary for different toxicological evaluations. Notably, we observed that ViCEKb chemicals are present in a variety of consumer products. For instance, Propyl gallate is present as a fragrance substance in various household products, and Flutamide is used in medication to treat prostate cancer. These two chemicals have the highest level of evidence in ViCEKb, but are not regulated for their skin sensitizing effects. Furthermore, an extensive cheminformatics-based investigation revealed that ViCEKb chemical space is structurally diverse and comprises unique chemical scaffolds in comparison with skin specific regulatory lists. For example, Neomycin and 2,3,5-Triglycidyl-4-aminophenol have unique chemical scaffolds and the highest level of evidence in ViCEKb, but are not regulated for their skin sensitizing effects. Finally, a transcriptomics-based analysis of ViCEKb chemical perturbations in skin cell samples highlighted the commonality in their linked biological processes. Overall, we present the first comprehensive effort in compilation and exploration of various chemical triggers of vitiligo. We believe such a resource will enable in deciphering the complex etiology of vitiligo and aid in the characterization of human chemical exposome. ViCEKb is freely available for academic research at: https://cb.imsc.res.in/vicekb.

Targeting MYC at the intersection between cancer metabolism and oncoimmunology

MYC activation is a known hallmark of cancer as it governs the gene targets involved in various facets of cancer progression. Of interest, MYC governs oncometabolism through the interactions with its partners and cofactors, as well as cancer immunity via its gene targets. Recent investigations have taken interest in characterizing these interactions through multi-Omic approaches, to better understand the vastness of the MYC network. Of the several gene targets of MYC involved in either oncometabolism or oncoimmunology, few of them overlap in function. Prominent interactions have been observed with MYC and HIF-1α, in promoting glucose and glutamine metabolism and activation of antigen presentation on regulatory T cells, and its subsequent metabolic reprogramming. This review explores existing knowledge of the role of MYC in oncometabolism and oncoimmunology. It also unravels how MYC governs transcription and influences cellular metabolism to facilitate the induction of pro- or anti-tumoral immunity. Moreover, considering the significant roles MYC holds in cancer development, the present study discusses effective direct or indirect therapeutic strategies to combat MYC-driven cancer progression.

Central insulin dysregulation in antipsychotic-naïve first-episode psychosis: In silico exploration of gene expression signatures

Antipsychotic drug (AP)-naïve first-episode psychosis (FEP) patients display premorbid cognitive dysfunctions and dysglycemia. Brain insulin resistance may link metabolic and cognitive disorders in humans. This suggests that central insulin dysregulation represents a component of the pathophysiology of psychosis spectrum disorders (PSDs). Nonetheless, the links between central insulin dysregulation, dysglycemia, and cognitive deficits in PSDs are poorly understood. We investigated whether AP-naïve FEP patients share overlapping brain gene expression signatures with central insulin perturbation (CIP) in rodent models. We systematically compiled and meta-analyzed peripheral transcriptomic datasets of AP-naïve FEP patients along with hypothalamic and hippocampal datasets of CIP rodent models to identify common transcriptomic signatures. The common signatures were used for pathway analysis and to identify potential drug treatments with discordant (reverse) signatures. AP-naïve FEP and CIP (hypothalamus and hippocampus) shared 111 and 346 common signatures respectively, which were associated with pathways related to inflammation, endoplasmic reticulum stress, and neuroplasticity. Twenty-two potential drug treatments were identified, including antidiabetic agents. The pathobiology of PSDs may include central insulin dysregulation, which contribute to dysglycemia and cognitive dysfunction independently of AP treatment. The identified treatments may be tested in early psychosis patients to determine if dysglycemia and cognitive deficits can be mitigated.

Current advances in comprehensive omics data mining for oncology and cancer research

The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.

Investigation of normalization procedures for transcriptome profiles of compounds oriented toward practical study design

The transcriptome profile is a representative phenotype-based descriptor of compounds, widely acknowledged for its ability to effectively capture compound effects. However, the presence of batch differences is inevitable. Despite the existence of sophisticated statistical methods, many of them presume a substantial sample size. How should we design a transcriptome analysis to obtain robust compound profiles, particularly in the context of small datasets frequently encountered in practical scenarios? This study addresses this question by investigating the normalization procedures for transcriptome profiles, focusing on the baseline distribution employed in deriving biological responses as profiles. Firstly, we investigated two large GeneChip datasets, comparing the impact of different normalization procedures. Through an evaluation of the similarity between response profiles of biological replicates within each dataset and the similarity between response profiles of the same compound across datasets, we revealed that the baseline distribution defined by all samples within each batch under batch-corrected condition is a good choice for large datasets. Subsequently, we conducted a simulation to explore the influence of the number of control samples on the robustness of response profiles across datasets. The results offer insights into determining the suitable quantity of control samples for diminutive datasets. It is crucial to acknowledge that these conclusions stem from constrained datasets. Nevertheless, we believe that this study enhances our understanding of how to effectively leverage transcriptome profiles of compounds and promotes the accumulation of essential knowledge for the practical application of such profiles.

Drug repurposing based on differentially expressed genes suggests drug combinations with possible synergistic effects in treatment of lung adenocarcinoma

Lung adenocarcinoma is one of the leading causes of cancer-related mortality globally. Various treatment approaches and drugs had little influence on overall survival; thus, new drugs and treatment strategies are needed. Drug repositioning (repurposing) seems a favorable approach considering that developing new drugs needs much more time and costs. We performed a meta-analysis on 6 microarray datasets to obtain the main genes with significantly altered expression in lung adenocarcinoma. Following that, we found major gene clusters and hub genes. We assessed their enrichment in biological pathways to get insight into the underlying biological process involved in lung adenocarcinoma pathogenesis. The L1000 database was explored for drug perturbations that might reverse the expression of differentially expressed genes in lung adenocarcinoma. We evaluated the potential drug combinations that interact the most with hub genes and hence have the most potential to reverse the disease process. A total of 2148 differentially expressed genes were identified. Six main gene clusters and 27 significant hub genes mainly involved in cell cycle regulation have been identified. By assessing the interaction between 3 drugs and hub genes and information gained from previous clinical investigations, we suggested the three possible repurposed drug combinations, Vorinostat - Dorsomorphin, PP-110 - Dorsomorphin, and Puromycin - Vorinostat with a high chance of success in clinical trials.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Identification of drug candidates targeting monocyte reprogramming in people living with HIV

Introduction

People living with HIV (PLHIV) are characterized by functional reprogramming of innate immune cells even after long-term antiretroviral therapy (ART). In order to assess technical feasibility of omics technologies for application to larger cohorts, we compared multiple omics data layers.

Methods

Bulk and single-cell transcriptomics, flow cytometry, proteomics, chromatin landscape analysis by ATAC-seq as well as ex vivo drug stimulation were performed in a small number of blood samples derived from PLHIV and healthy controls from the 200-HIV cohort study.

Results

Single-cell RNA-seq analysis revealed that most immune cells in peripheral blood of PLHIV are altered in their transcriptomes and that a specific functional monocyte state previously described in acute HIV infection is still existing in PLHIV while other monocyte cell states are only occurring acute infection. Further, a reverse transcriptome approach on a rather small number of PLHIV was sufficient to identify drug candidates for reversing the transcriptional phenotype of monocytes in PLHIV.

Discussion

These scientific findings and technological advancements for clinical application of single-cell transcriptomics form the basis for the larger 2000-HIV multicenter cohort study on PLHIV, for which a combination of bulk and single-cell transcriptomics will be included as the leading technology to determine disease endotypes in PLHIV and to predict disease trajectories and outcomes.

Elucidating disease-associated mechanisms triggered by pollutants via the epigenetic landscape using large-scale ChIP-Seq data

BACKGROUND

Despite well-documented effects on human health, the action modes of environmental pollutants are incompletely understood. Although transcriptome-based approaches are widely used to predict associations between chemicals and disorders, the molecular cues regulating pollutant-derived gene expression changes remain unclear. Therefore, we developed a data-mining approach, termed "DAR-ChIPEA," to identify transcription factors (TFs) playing pivotal roles in the action modes of pollutants.

METHODS

Large-scale public ChIP-Seq data (human, n = 15,155; mouse, n = 13,156) were used to predict TFs that are enriched in the pollutant-induced differentially accessible genomic regions (DARs) obtained from epigenome analyses (ATAC-Seq). The resultant pollutant-TF matrices were then cross-referenced to a repository of TF-disorder associations to account for pollutant modes of action. We subsequently evaluated the performance of the proposed method using a chemical perturbation data set to compare the outputs of the DAR-ChIPEA and our previously developed differentially expressed gene (DEG)-ChIPEA methods using pollutant-induced DEGs as input. We then adopted the proposed method to predict disease-associated mechanisms triggered by pollutants.

RESULTS

The proposed approach outperformed other methods using the area under the receiver operating characteristic curve score. The mean score of the proposed DAR-ChIPEA was significantly higher than that of our previously described DEG-ChIPEA (0.7287 vs. 0.7060; Q = 5.278 × 10-42; two-tailed Wilcoxon rank-sum test). The proposed approach further predicted TF-driven modes of action upon pollutant exposure, indicating that (1) TFs regulating Th1/2 cell homeostasis are integral in the pathophysiology of tributyltin-induced allergic disorders; (2) fine particulates (PM2.5) inhibit the binding of C/EBPs, Rela, and Spi1 to the genome, thereby perturbing normal blood cell differentiation and leading to immune dysfunction; and (3) lead induces fatty liver by disrupting the normal regulation of lipid metabolism by altering hepatic circadian rhythms.

CONCLUSIONS

Highlighting genome-wide chromatin change upon pollutant exposure to elucidate the epigenetic landscape of pollutant responses outperformed our previously described method that focuses on gene-adjacent domains only. Our approach has the potential to reveal pivotal TFs that mediate deleterious effects of pollutants, thereby facilitating the development of strategies to mitigate damage from environmental pollution.

Treatment of Status Epilepticus after Traumatic Brain Injury Using an Antiseizure Drug Combined with a Tissue Recovery Enhancer Revealed by Systems Biology

We tested a hypothesis that in silico-discovered compounds targeting traumatic brain injury (TBI)-induced transcriptomics dysregulations will mitigate TBI-induced molecular pathology and augment the effect of co-administered antiseizure treatment, thereby alleviating functional impairment. In silico bioinformatic analysis revealed five compounds substantially affecting TBI-induced transcriptomics regulation, including calpain inhibitor, chlorpromazine, geldanamycin, tranylcypromine, and trichostatin A (TSA). In vitro exposure of neuronal-BV2-microglial co-cultures to compounds revealed that TSA had the best overall neuroprotective, antioxidative, and anti-inflammatory effects. In vivo assessment in a rat TBI model revealed that TSA as a monotherapy (1 mg/kg/d) or in combination with the antiseizure drug levetiracetam (LEV 150 mg/kg/d) mildly mitigated the increase in plasma levels of the neurofilament subunit pNF-H and cortical lesion area. The percentage of rats with seizures during 0-72 h post-injury was reduced in the following order: TBI-vehicle 80%, TBI-TSA (1 mg/kg) 86%, TBI-LEV (54 mg/kg) 50%, TBI-LEV (150 mg/kg) 40% (p < 0.05 vs. TBI-vehicle), and TBI-LEV (150 mg/kg) combined with TSA (1 mg/kg) 30% (p < 0.05). Cumulative seizure duration was reduced in the following order: TBI-vehicle 727 ± 688 s, TBI-TSA 898 ± 937 s, TBI-LEV (54 mg/kg) 358 ± 715 s, TBI-LEV (150 mg/kg) 42 ± 64 (p < 0.05 vs. TBI-vehicle), and TBI-LEV (150 mg/kg) combined with TSA (1 mg/kg) 109 ± 282 s (p < 0.05). This first preclinical intervention study on post-TBI acute seizures shows that a combination therapy with the tissue recovery enhancer TSA and LEV was safe but exhibited no clear benefit over LEV monotherapy on antiseizure efficacy. A longer follow-up is needed to confirm the possible beneficial effects of LEV monotherapy and combination therapy with TSA on chronic post-TBI structural and functional outcomes, including epileptogenesis.

Mutational Landscape and Precision Medicine in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the fourth most common malignancy worldwide and exhibits a universal burden as the incidence of the disease continues to rise. In addition to curative-intent therapies such as liver resection and transplantation, locoregional and systemic therapy options also exist. However, existing treatments carry a dismal prognosis, often plagued with high recurrence and mortality. For this reason, understanding the tumor microenvironment and mutational pathophysiology has become the center of investigation for disease control. The use of precision medicine and genetic analysis can supplement current treatment modalities to promote individualized management of HCC. In the search for personalized medicine, tools such as next-generation sequencing have been used to identify unique tumor mutations and improve targeted therapies. Furthermore, investigations are underway for specific HCC biomarkers to augment the diagnosis of malignancy, the prediction of whether the tumor environment is amenable to available therapies, the surveillance of treatment response, the monitoring for disease recurrence, and even the identification of novel therapeutic opportunities. Understanding the mutational landscape and biomarkers of the disease is imperative for tailored management of the malignancy. In this review, we summarize the molecular targets of HCC and discuss the current role of precision medicine in the treatment of HCC.

A Functional Pipeline of Genome-Wide Association Data Leads to Midostaurin as a Repurposed Drug for Alzheimer's Disease

Genome-wide association studies (GWAS) constitute a powerful tool to identify the different biochemical pathways associated with disease. This knowledge can be used to prioritize drugs targeting these routes, paving the road to clinical application. Here, we describe DAGGER (Drug Repositioning by Analysis of GWAS and Gene Expression in R), a straightforward pipeline to find currently approved drugs with repurposing potential. As a proof of concept, we analyzed a meta-GWAS of 1.6 × 107 single-nucleotide polymorphisms performed on Alzheimer's disease (AD). Our pipeline uses the Genotype-Tissue Expression (GTEx) and Drug Gene Interaction (DGI) databases for a rational prioritization of 22 druggable targets. Next, we performed a two-stage in vivo functional assay. We used a C. elegans humanized model over-expressing the Aβ1-42 peptide. We assayed the five top-scoring candidate drugs, finding midostaurin, a multitarget protein kinase inhibitor, to be a protective drug. Next, 3xTg AD transgenic mice were used for a final evaluation of midostaurin's effect. Behavioral testing after three weeks of 20 mg/kg intraperitoneal treatment revealed a significant improvement in behavior, including locomotion, anxiety-like behavior, and new-place recognition. Altogether, we consider that our pipeline might be a useful tool for drug repurposing in complex diseases.

A polycistronic transgene design for combinatorial genetic perturbations from a single transcript in Drosophila

Experimental models that capture the genetic complexity of human disease and allow mechanistic explorations of the underlying cell, tissue, and organ interactions are crucial to furthering our understanding of disease biology. Such models require combinatorial manipulations of multiple genes, often in more than one tissue at once. The ability to perform complex genetic manipulations in vivo is a key strength of Drosophila, where many tools for sophisticated and orthogonal genetic perturbations exist. However, combining the large number of transgenes required to establish more representative disease models and conducting mechanistic studies in these already complex genetic backgrounds is challenging. Here we present a design that pushes the limits of Drosophila genetics by allowing targeted combinatorial ectopic expression and knockdown of multiple genes from a single inducible transgene. The polycistronic transcript encoded by this transgene includes a synthetic short hairpin cluster cloned within an intron placed at the 5' end of the transcript, followed by two protein-coding sequences separated by the T2A sequence that mediates ribosome skipping. This technology is particularly useful for modeling genetically complex diseases like cancer, which typically involve concurrent activation of multiple oncogenes and loss of multiple tumor suppressors. Furthermore, consolidating multiple genetic perturbations into a single transgene further streamlines the ability to perform combinatorial genetic manipulations and makes it readily adaptable to a broad palette of transgenic systems. This flexible design for combinatorial genetic perturbations will also be a valuable tool for functionally exploring multigenic gene signatures identified from omics studies of human disease and creating humanized Drosophila models to characterize disease-associated variants in human genes. It can also be adapted for studying biological processes underlying normal tissue homeostasis and development that require simultaneous manipulation of many genes.