Pathway Analysis Tools Research Articles

Network meta-analysis on the mechanisms underlying alcohol augmentation of acute pancreatitis and diabetes type II.

Pancreatitis is a severe inflammatory pathology that occurs from pancreatic duct and exocrine acinar injury, leading to improper secretion of digestive enzymes, auto-digestion of the pancreas, and subsequent inflammation. Clinical reports show that 60%-90% of pancreatitis patients have a history of chronic alcohol use. More recent studies reveal that exocrine pancreas disorders like acute pancreatitis can precede diabetes type II onset, though mechanisms are not yet fully known. This study identified molecules and key signaling pathways underlying alcohol-induced acute pancreatitis and their effects on diabetes type II onset. Data on human peripheral blood samples with or without acute pancreatitis were retrieved from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (accession number GSE194331). Acute pancreatitis-mediated differentially expressed genes (DEGs) were generated from GSE194331 using CLC Genomics Workbench 12. Molecules associated with ethanol (EtOH), acute pancreatitis, and diabetes type II were collected from QIAGEN Knowledge Base (QKB). The relationship between the molecules and signaling pathways associated with EtOH, acute pancreatitis, or diabetes type II was examined using various Ingenuity Pathway Analysis (IPA) tools. Our investigation showed that acute pancreatitis-mediated DEGs were closely associated with EtOH by revealing that EtOH-induced acute pancreatitis appears to lead to the onset of diabetes type II. We found that diabetes type II onset was mediated by pro-inflammatory and metabolic mechanisms underlying EtOH-induced acute pancreatitis, involving increased expression of cytokines including macrophage migration inhibitory factor (MIF), and decreased expression of hormones such as insulin. Exposure to alcohol may promote diabetes type II by affecting the activity of key inflammatory and metabolic mediators involved in acute pancreatitis. These findings call for further investigation into the role of pro-inflammatory and metabolic mediators like resistin, IL-6, and insulin in EtOH-induced diabetes type II associated with acute pancreatitis pathologies.

Transcriptomics analysis reveals potential regulatory role of nSMase2 (Smpd3) in nervous system development and function of middle-aged mouse brains.

Neutral sphingomyelinase-2 (nSMase2), gene name sphingomyelin phosphodiesterase-3 (Smpd3), is a key regulatory enzyme responsible for generating the sphingolipid ceramide. The function of nSMase2 in the brain is still controversial. To better understand the functional roles of nSMase2 in the aging mouse brain, we applied RNA-seq analysis, which identified a total of 1462 differentially abundant mRNAs between +/fro and fro/fro, of which 891 were increased and 571 were decreased in nSMase2-deficient mouse brains. The most strongly enriched GO and KEGG annotation terms among transcripts increased in fro/fro mice included synaptogenesis, synapse development, synaptic signaling, axon development, and axonogenesis. Among decreased transcripts, enriched annotations included ribosome assembly and mitochondrial protein complex functions. KEGG analysis of decreased transcripts also revealed overrepresentation of annotations for Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington disease (HD). Ingenuity Pathway Analysis (IPA) tools predicted lower susceptibility to these neurodegenerative disorders, as well as predictions agreeing with stronger synaptic function, learning, and memory in fro/fro mice. The IPA tools identified signaling proteins, epigenetic regulators, and microRNAs as likely upstream regulators of the broader set of genes encoding the affected transcripts. It also revealed 16 gene networks, each linked to biological processes identified as overrepresented annotations among the affected transcripts by multiple analysis methods. Therefore, the analysis of these RNA-seq data indicates that nSMase2 impacts synaptic function and neural development, and may contribute to the onset and development of neurodegenerative diseases in middle-aged mice.

Weighted Gene Co-expression Network Analysis (WGCNA) of Wnt Signaling Related to Periodontal Ligament Formation: A Bioinformatics-Based Analysis.

Introduction The Wnt signaling pathway is crucial for tooth development, odontoblast differentiation, and dentin formation. It interacts with epithelial cadherin (E-cadherin) and beta-catenin in tooth development and periodontal ligament (PDL) formation. Dysregulation of Wnt signaling is linked to periodontal diseases, requiring an understanding of therapeutic interventions. Weighted gene co-expression network analysis (WGCNA) can identify co-expressed gene modules. Our study aims to identify hub genes in WGCNA analysis of Wnt signaling-based PDL formation. Methods The study used a microarray datasetGSE201313 from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus to analyze the impact of DMP1 expression on XLH dental pulp cell differentiation and PDL formation. The standardized dataset was used for WGCNA analysis, which generated a co-expression network by calculating pairwise correlations between genes and constructing an adjacency matrix. The topological overlap matrix (TOM) was transformed into a hierarchical clustering tree and then cut into modules or clusters of highly interconnected genes. The module eigengene (ME) was calculated for each module, and the genes within this module were identified as hub genes. Gene ontology (GO) and KEGG pathway enrichment analysis were performed to gain insights into the biological functions of the hub genes. The integrated Differential Expression and Pathway analysis (iDEP) tool (http://bioinformatics.sdstate.edu/idep/; South Dakota State University, Brookings, USA) was used for WGCNA analysis. Results The study used the WGCNA package to analyze 1,000 differentially expressed genes, constructing a gene co-expression network and generating a hierarchical clustering tree and TOM. The analysis reveals a scale-free topology fitting index R2 and mean connectivity for various soft threshold powers, with an R2 value of 5. COL6A1, MMP3, BGN, COL1A2, and FBN2 are hub genes implicated in PDL development. Conclusion The study identified key hub genes, including COL6A1, MMP3, BGN, and FBN2, crucial for PDL formation, tissue remodeling, and cell-matrix interactions, guiding future therapeutic strategies.

O22 Unravelling the pathophysiology of KLICK syndrome to identify therapeutically targetable pathways

Abstract Introduction and aims Keratosis linearis with ichthyosis congenita and sclerosing keratoderma (KLICK) syndrome is an ultrarare genodermatosis associated with a homozygous pathogenic variant, c.-95delC in POMP, which encodes for proteasome maturation protein. Clinical manifestations include linear hyperkeratotic papules in the flexures, palmoplantar keratoderma and pseudoainhum. The pathophysiology of KLICK syndrome is poorly understood, resulting in nonspecific, limited treatment options. The aim of this work is to characterize KLICK syndrome at the cellular and molecular level, with the utilization of transcriptomic techniques for potential drug repurposing. Methods A lesional skin biopsy was obtained, following informed consent, from a 32-year-old female patient with KLICK syndrome. Histology and immunostaining were used to characterize the disrupted skin architecture. RNA sequencing was used to identify key dysregulated pathways and the L1000FWD reverse transcriptomics tool enabled the generation of a shortlist of potential therapeutics for KLICK syndrome. CRISPR editing techniques and induced pluripotent stem cell (iPSC) reprogramming Methods were used to generate cellular based models carrying the pathogenic POMP variant in order to support evaluation of the shortlisted therapeutic options. Results KLICK syndrome skin histology featured hyperkeratosis, hypergranulosis and delayed cellular flattening. Consistent with pathological features, pathway analysis of transcriptomic data revealed keratinocyte differentiation and epidermis development as key upregulated pathways in KLICK syndrome. Interestingly, the Panther pathway analysis tool suggested dysregulation in epidermal growth factor receptor (EGFR) signalling, and reverse transcriptomics identified erlotinib, an EGFR inhibitor, as a potential therapeutic treatment for KLICK syndrome. HaCaT keratinocytes were successfully edited using CRISPR-Cas9 to carry the KLICK patient POMP variant and a KLICK patient iPSC line was generated to support in vitro KLICK syndrome modelling. Conclusions KLICK syndrome is a hyperproliferative inflammatory skin disorder associated with a pathogenic variant in POMP. Reverse transcriptomics is a useful tool for shortlisting drugs with biologically relevant mechanisms of action with potential for treatment of rare genetic diseases such as KLICK syndrome.

Assessment of Gene Set Enrichment Analysis using curated RNA-seq-based benchmarks.

Pathway enrichment analysis is a ubiquitous computational biology method to interpret a list of genes (typically derived from the association of large-scale omics data with phenotypes of interest) in terms of higher-level, predefined gene sets that share biological function, chromosomal location, or other common features. Among many tools developed so far, Gene Set Enrichment Analysis (GSEA) stands out as one of the pioneering and most widely used methods. Although originally developed for microarray data, GSEA is nowadays extensively utilized for RNA-seq data analysis. Here, we quantitatively assessed the performance of a variety of GSEA modalities and provide guidance in the practical use of GSEA in RNA-seq experiments. We leveraged harmonized RNA-seq datasets available from The Cancer Genome Atlas (TCGA) in combination with large, curated pathway collections from the Molecular Signatures Database to obtain cancer-type-specific target pathway lists across multiple cancer types. We carried out a detailed analysis of GSEA performance using both gene-set and phenotype permutations combined with four different choices for the Kolmogorov-Smirnov enrichment statistic. Based on our benchmarks, we conclude that the classic/unweighted gene-set permutation approach offered comparable or better sensitivity-vs-specificity tradeoffs across cancer types compared with other, more complex and computationally intensive permutation methods. Finally, we analyzed other large cohorts for thyroid cancer and hepatocellular carcinoma. We utilized a new consensus metric, the Enrichment Evidence Score (EES), which showed a remarkable agreement between pathways identified in TCGA and those from other sources, despite differences in cancer etiology. This finding suggests an EES-based strategy to identify a core set of pathways that may be complemented by an expanded set of pathways for downstream exploratory analysis. This work fills the existing gap in current guidelines and benchmarks for the use of GSEA with RNA-seq data and provides a framework to enable detailed benchmarking of other RNA-seq-based pathway analysis tools.

Abstract PO1-06-13: Plasma Exosomes in Obesity-driven Diabetes Exacerbate Progression of Triple Negative Breast Cancer: Insights from Animal Models

Abstract Objective: Our objective is to examine how obesity and type 2 diabetes (T2D) drive systemic and local metabolic changes in adipose tissue to promote progression of triple negative breast cancer (TNBC). We investigate the role of plasma exosomes as significant mediators of gene expression and functional changes, increasing TNBC progression and metastasis. We hypothesize that T2D and obesity alter the payload of plasma exosomes. These modified exosomes, specifically differentially expressed microRNAs in T2D, critically reshape the tumor microenvironment, ultimately promoting epithelial to mesenchymal transition (EMT) and distant metastasis of TNBC cells. Our goal is to gain a deeper understanding of the underlying mechanisms connecting T2D, obesity and TNBC metastasis, through the novel factor of exosome crosstalk. Our findings shed light on novel therapeutic targets and strategies to mitigate the impact of metabolic disorders on cancer outcomes. Methods: We subjected C57BL/6J female mice to high-fat diet (HFD; 60% Kcal fat) for 12 weeks with weight monitoring, whereupon oral glucose tolerance test confirmed glucose intolerance and insulin resistance. Plasma exosomes were then isolated from peripheral blood and used to treat E0771 cells, as a TNBC model, for 72 hours for in vitro and in vivo analysis. To assess gene expression changes, we employed a commercial EMT array kit and performed qPCR. Gene expression data were uploaded to Qiagen software, specifically the Ingenuity Pathway Analysis tool, for analysis. To evaluate cell migration as a functional test of aggressiveness, a migration assay was conducted using 24-hour FBS deprivation, followed by 24-hour migration, utilizing transwell plates with 8μm pores. For in vivo analysis, mice were injected with 50k E0771-GFP cells in the 4th mammary fat pad. Tumors were allowed to grow for 28 days, at which time histology and flow cytometry analyses were performed with harvested brain, lung, and spleen. Results: In-vitro analysis showed that HFD-derived exosomes reprogram gene expression in E0771 cells, driving a strong EMT signature, compared to LFD control or exosome-free negative control. This reprogramming elicited a pro-metastatic phenotype and upregulated signaling pathways associated with metastasis. Furthermore, HFD exosomes upregulated PD-L1 receptor expression compared to negative controls, linking EMT to immune tolerance. In-vivo analysis confirmed these findings, demonstrating increased metastatic burden in both lung and brain in the HFD context. Conclusions: Plasma exosomes derived from HFD fed mice dramatically reprogram EMT gene networks in E0771 cells, exerting genomic alterations that persist for at least 28 days. These induced changes in cellular gene expression likely promote the development of brain metastases and immune evasion. The model suggests that TNBC patients with obesity-driven diabetes should be evaluated for increased risk of distant metastases. Plasma exosomes miRNA profiling may be a valuable biomarker in cancer disparities populations where the prevalence of obesity and diabetes are high. Citation Format: Pablo Llevenes. Plasma Exosomes in Obesity-driven Diabetes Exacerbate Progression of Triple Negative Breast Cancer: Insights from Animal Models [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-06-13.

RCPA: An Open-Source R Package for Data Processing, Differential Analysis, Consensus Pathway Analysis, and Visualization.

Identifying impacted pathways is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway analysis methods have been developed thus far. Despite the availability of many methods, it is challenging for biomedical researchers to learn and properly perform pathway analysis. First, the sheer number of methods makes it challenging to learn and choose the correct method for a given experiment. Second, computational methods require users to be savvy with coding syntax, and comfortable with command-line environments, areas that are unfamiliar to most life scientists. Third, as learning tools and computational methods are typically implemented only for a few species (i.e., human and some model organisms), it is difficult to perform pathway analysis on other species that are not included in many of the current pathway analysis tools. Finally, existing pathway tools do not allow researchers to combine, compare, and contrast the results of different methods and experiments for both hypothesis testing and analysis purposes. To address these challenges, we developed an open-source R package for Consensus Pathway Analysis (RCPA) that allows researchers to conveniently: (1) download and process data from NCBI GEO; (2) perform differential analysis using established techniques developed for both microarray and sequencing data; (3) perform both gene set enrichment, as well as topology-based pathway analysis using different methods that seek to answer different research hypotheses; (4) combine methods and datasets to find consensus results; and (5) visualize analysis results and explore significantly impacted pathways across multiple analyses. This protocol provides many example code snippets with detailed explanations and supports the analysis of more than 1000 species, two pathway databases, three differential analysis techniques, eight pathway analysis tools, six meta-analysis methods, and two consensus analysis techniques. The package is freely available on the CRAN repository. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing Affymetrix microarrays Basic Protocol 2: Processing Agilent microarrays Support Protocol: Processing RNA sequencing (RNA-Seq) data Basic Protocol 3: Differential analysis of microarray data (Affymetrix and Agilent) Basic Protocol 4: Differential analysis of RNA-Seq data Basic Protocol 5: Gene set enrichment analysis Basic Protocol 6: Topology-based (TB) pathway analysis Basic Protocol 7: Data integration and visualization.

Chronic Gestational Hypoxia Alters Plasma Exosomal miRNA Profile and Pulmonary Arterial Proteome and Metabolome in Fetal Lambs

Antenatal chronic hypoxia negatively impacts fetal development and increases the risk of cardio-pulmonary dysfunction after birth, including newborn pulmonary hypertension. Previously, we showed that fetal and newborn sheep exposed to chronic hypoxia during gestation exhibit a variety of cardio-pulmonary defects that parallel those of human infants including pulmonary vascular remodeling, heart dysfunction, and pulmonary hypertension. To explore the mechanisms involved in hypoxia-mediated newborn pulmonary hypertension, we test the hypothesis that antenatal chronic hypoxia impairs pathways critical to pulmonary vascular development that are coupled to abnormalities in lung structure and function through dysregulation in cell growth and proliferation. To address this hypothesis, we employed an animal model of pregnant sheep exposed to high altitude hypoxia at the White Mountain Research Station, which has an altitude of 3801 meters. Hypoxic pregnant sheep were housed at altitude during gestation for ~110 days out of 138-141 days of pregnancy while normoxic counterparts were kept at 700 meters. Animals were transferred to Loma Linda at 355 meters and fetal lambs studied at ~140 days of gestation. Fetal plasma samples were collected for exosomal miRNA analysis and pulmonary arteries were collected for proteomic and metabolomic analysis. Ingenuity pathway analysis and other bioinformatic tools were used to interrogate regulatory mechanisms and pathways and the web of molecular interactions affected by hypoxia. Antenatal hypoxic stress caused up and down regulation of a limited number of plasma exosomal miRNAs, pulmonary arterial metabolites and proteins, with discrete interconnections between the various analytes and molecules. There were marked changes in upstream regulators and markers that point to increased oxidative stress and inflammatory response along with pronounced downregulation of matrix proteins and phenotypic markers of vascular smooth muscle cell differentiation. Pathway analyses indicate antenatal chronic hypoxia dysregulates growth and proliferation of vascular cells and impinges on cell migration. While these data require additional study and verification, they provide new insights in the understanding of mechanisms underlying the development of pulmonary hypertension in the newborn following antenatal hypoxia. This work was supported by the National Institutes of Health Grants NIH R01HL155295, R01HL149608, R03HD098477, P01HD083132, and U24DK097154. Additional support was provided by the Loma Linda University School of Medicine, A pilot Project through the West Coast Metabolomics Center, the University of Redlands Summer Research Program, and the Walter E. Macpherson Society. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Effect of Low-Level Tragus Stimulation on Cardiac Metabolism in Heart Failure with Preserved Ejection Fraction: A Transcriptomics-Based Analysis.

Abnormal cardiac metabolism precedes and contributes to structural changes in heart failure. Low-level tragus stimulation (LLTS) can attenuate structural remodeling in heart failure with preserved ejection fraction (HFpEF). The role of LLTS on cardiac metabolism is not known. Dahl salt-sensitive rats of 7 weeks of age were randomized into three groups: low salt (0.3% NaCl) diet (control group; n = 6), high salt diet (8% NaCl) with either LLTS (active group; n = 8), or sham stimulation (sham group; n = 5). Both active and sham groups received the high salt diet for 10 weeks with active LLTS or sham stimulation (20 Hz, 2 mA, 0.2 ms) for 30 min daily for the last 4 weeks. At the endpoint, left ventricular tissue was used for RNA sequencing and transcriptomic analysis. The Ingenuity Pathway Analysis tool (IPA) was used to identify canonical metabolic pathways and upstream regulators. Principal component analysis demonstrated overlapping expression of important metabolic genes between the LLTS, and control groups compared to the sham group. Canonical metabolic pathway analysis showed downregulation of the oxidative phosphorylation (Z-score: -4.707, control vs. sham) in HFpEF and LLTS improved the oxidative phosphorylation (Z-score = -2.309, active vs. sham). HFpEF was associated with the abnormalities of metabolic upstream regulators, including PPARGC1α, insulin receptor signaling, PPARα, PPARδ, PPARGC1β, the fatty acid transporter SLC27A2, and lysine-specific demethylase 5A (KDM5A). LLTS attenuated abnormal insulin receptor and KDM5A signaling. HFpEF is associated with abnormal cardiac metabolism. LLTS, by modulating the functioning of crucial upstream regulators, improves cardiac metabolism and mitochondrial oxidative phosphorylation.

Bioinformatics-driven identification of prognostic biomarkers in kidney renal clear cell carcinoma.

Renal cell carcinoma (RCC), particularly the clear cell subtype (ccRCC), poses a significant global health concern due to its increasing prevalence and resistance to conventional therapies. Early detection of ccRCC remains challenging, resulting in poor patient survival rates. In this study, we employed a bioinformatic approach to identify potential prognostic biomarkers for kidney renal clear cell carcinoma (KIRC). By analyzing RNA sequencing data from the TCGA-KIRC project, differentially expressed genes (DEGs) associated with ccRCC were identified. Pathway analysis utilizing the Qiagen Ingenuity Pathway Analysis (IPA) tool elucidated key pathways and genes involved in ccRCC dysregulation. Prognostic value assessment was conducted through survival analysis, including Cox univariate proportional hazards (PH) modeling and Kaplan-Meier plotting. This analysis unveiled several promising biomarkers, such as MMP9, PIK3R6, IFNG, and PGF, exhibiting significant associations with overall survival and relapse-free survival in ccRCC patients. Cox multivariate PH analysis, considering gene expression and age at diagnosis, further confirmed the prognostic potential of MMP9, IFNG, and PGF genes. These findings enhance our understanding of ccRCC and provide valuable insights into potential prognostic biomarkers that can aid healthcare professionals in risk stratification and treatment decision-making. The study also establishes a foundation for future research, validation, and clinical translation of the identified prognostic biomarkers, paving the way for personalized approaches in the management of KIRC.

Abstract 2148: Tumor-microenvironment from African-American prostate cancer exhibit methylation of multiple immune modulators

Abstract Background: Non-Hispanic Black American patients (BA) with prostate cancer (PCa) tend to fare worse than their Non-Hispanic White American counterparts (WA), even with consideration of socioeconomic disparities. Clinically, BA face diagnosis at younger age with worse clinical outcomes. Stromal cells adjacent to the tumor, including carcinoma-associated fibroblasts (CAFs), play a critical role in tumorigenesis of PCa. Dysfunction in the tumor microenvironment play a crucial role in mediating prostate cancer tumorigenesis and is emerging as a key target for cancer therapy in solid tumors. Methods: To investigate whether DNA methylation varies in tumor adjacent stroma (TAS) among PCa patients with different geographical ancestries, areas immediate adjacent to tumor (i.e., less than 1mm) removed for methylation analysis. A complete genome-wide DNA methylation of PCa stroma using 17 BA and 15 WA patients who had radical prostatectomy was carried out. Methyl-Captured (mCap) sequencing data was generated for TAS of prostate cancer FFPE tissues. Methylated sites were identified with the MACS2 callpeak function using a band width of 300 bp and a false discovery rate of q < 0.05. A consensus peak set was derived from the aligned reads of BA and WA prostate cancer stroma samples that were sequenced. Pathway analysis was performed using Ingenuity Pathway Analysis (IPA) tools. CAFs from primary tumors of BA and WA was prepared by tissue culture and treated with demethylating agent (i.e., 5-Azacytidine) for 24 hours followed by maintenance of the cells in drug-free medium for 7-10 days and subjected to mCap sequencing. Results: We found racial disparities in genome-wide DNA methylation in TAS between the two populations in which BA PCa patients had significantly increased in global DNA methylation as compared to their WA counterpart (p value < 0.001). Hypomethylation occurred in CAFs from BA patients treated with 5-Azacytidine. We identified 1041 methylated sites corresponding to 409 unique genes that were differentially methylated in BA vs WA (p value < 0.05). Pathway analysis identified significant association between methylated genes in BA TAS and immune response pathways including IL-13, JACK/STAT, T cell receptors, NF-KB, and PD-1, PDL1 cancer immunotherapy. Evaluation of the methylome profile between patients based on the post-prostatectomy resection margins, negative (R0) vs positive (R1), showed that R0 resections had significantly greater (p value < 0.001) methylation than R1 resections. Further analysis identified 984 sites corresponding to 373 differentially methylated, unique genes (p value < 0.05). Conclusion: Tumor-adjacent stroma of BA prostate cancer patients has a distinct methylome as compared to WA patients. Differences in immune response pathways suggests a distinct immune response in TAS in men of different races. Citation Format: Santosh Sankaran, James Nguyen, Tara Jennings, Vinay Kumar, Michael B. Lilly, Michael M. Ittmann, Patricia Castro, Liankun Song, Thomas Keane, Weiping Chu, Xiaolin Zi, Omid Yazdanpanah, Arash Rezazadeh Kalebasty, Farah Rahmatpanah. Tumor-microenvironment from African-American prostate cancer exhibit methylation of multiple immune modulators [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2148.

Abstract A018: Pathways to enzalutamide resistance: An analysis of proteomic changes in enzalutamide-resistant prostate cancer cells

Abstract Introduction: Enzalutamide is indicated for treatment of castrate-resistant prostate cancer (CRPC), but therapeutic effect is transient, with improvement in survival of approximately 4 months. The mechanisms by which prostate cancer (PCa) cells survive Enzalutamide therapy are poorly understood, but androgen receptor (AR) and non-AR mediated mechanisms have been reported from transcriptome studies. We performed unbiased high resolution proteomic analyses to identify proteins and pathways that are involved in survival and progression in Enzalutamide treatment failure. Materials & Methods: Enzalutamide-resistant MR49F and MR42D cell lines were maintained in standard medium (DMEM with 5% FBS) with Enzalutamide 10µM. Parental LNCaP cells were grown in standard medium. For perturbation, MR49F and MR42D cells were then changed to standard medium with either Enzalutamide 10µM (ENZA) or DMSO (control) for 7 days in. Medium was changed every 48 hours. Protein lysates from 4 biological replicates were labeled using a Tandem Mass Tag (TMT) assay and subjected to mass spectrometry (MS). Protein abundance was measured and significant differences in expression were analysed using a 2-way t-test with a permutation based false detection rate (FDR) of 0.05 and 250 randomisations in the Perseus software suite. MR49F and MR42D were compared to parental LNCaP, and MR49F and MR42D were compared on and off Enzalutamide treatment pressure. Functional biological pathways involved were identified using the DAVID and WebGestalt online pathway analysis tools. Results: MS identified 5364 proteins. Of these, 247 (180 up, 67 down) were differentially expressed by at least 50% in both MR49F and MR42D cell lines relative to parental LNCaP cells. When MR49F and MR42D cell lines were grown with or without Enzalutamide pressure, 5 proteins (4 up, 1 down) were regulated by at least 50% in both cell lines (ANLN, NIN, UQCRB, HIST1H3A and S100P). Pathway analyses with the differentially expressed and regulated proteins identified perturbations in biological pathways with a high representation of pathways related to metabolism, including amino acid and fatty acid metabolism, ascorbate and propanoate metabolism, drug transport and metabolism, and cell adhesion molecules. Conclusions: This unbiased proteomic analysis of Enzalutamide-resistant PCa cells identified proteins and pathways that may be essential for PCa cell survival and progression during Enzalutamide therapy failure. Enzalutamide therapy failure appears to involve some non-AR mediated mechanisms which may be targeted for therapeutic benefit. A better understanding of the mechanisms of Enzalutamide resistance could lead to development of novel therapies for post-Enzalutamide disease progression. Citation Format: Chidi N. Molokwu, Anders Kristensen, Chris Sutton. Pathways to enzalutamide resistance: An analysis of proteomic changes in enzalutamide-resistant prostate cancer cells [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A018.

Histone Trimethylations and HDAC5 Regulate Spheroid Subpopulation and Differentiation Signaling of Human Adipose-Derived Stem Cells.

Human adipose-derived stem cells (ASCs) have shown immense potential for regenerative medicine. Our previous work demonstrated that chitosan nano-deposited surfaces induce spheroid formation and differentiation of ASCs for treating sciatic nerve injuries. However, the underlying cell fate and differentiation mechanisms of ASC-derived spheroids remain unknown. Here, we investigate the epigenetic regulation and signaling coordination of these therapeutic spheroids. During spheroid formation, we observed significant increases in histone 3 trimethylation at lysine 4 (H3K4me3), lysine 9 (H3K9me3), and lysine 27 (H3K27me3), accompanied by increased histone deacetylase (HDAC) activities and decreased histone acetyltransferase activities. Additionally, HDAC5 translocated from the cytoplasm to the nucleus, along with increased nuclear HDAC5 activities. Utilizing single-cell RNA sequencing (scRNA-seq), we analyzed the chitosan-induced ASC spheroids and discovered distinct cluster subpopulations, cell fate trajectories, differentiation traits, and signaling networks using the 10x Genomics platform, R studio/language, and the Ingenuity Pathway Analysis (IPA) tool. Specific subpopulations were identified within the spheroids that corresponded to a transient reprogramming state (Cluster 6) and the endpoint cell state (Cluster 3). H3K4me3 and H3K9me3 were discovered as key epigenetic regulators by IPA to initiate stem cell differentiation in Cluster 6 cells, and confirmed by qPCR and their respective histone methyltransferase inhibitors: SNDX-5613 (a KMT2A inhibitor for H3K4me3) and SUVi (an SUV39H1 inhibitor for H3K9me3). Moreover, H3K9me3 and HDAC5 were involved in regulating downstream signaling and neuronal markers during differentiation in Cluster 3 cells. These findings emphasize the critical role of epigenetic regulation, particularly H3K4me3, H3K9me3, and HDAC5, in shaping stem cell fate and directing lineage-specific differentiation.

CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation.

Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.

PathBank 2.0-the pathway database for model organism metabolomics.

PathBank (https://pathbank.org) and its predecessor database, the Small Molecule Pathway Database (SMPDB), have been providing comprehensive metabolite pathway information for the metabolomics community since 2010. Over the past 14 years, these pathway databases have grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in computing technology. This year's update, PathBank 2.0, brings a number of important improvements and upgrades that should make the database more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of primary or canonical pathways (from 1720 to 6951); (ii) a massive increase in the total number of pathways (from 110234 to 605359); (iii) significant improvements to the quality of pathway diagrams and pathway descriptions; (iv) a strong emphasis on drug metabolism and drug mechanism pathways; (v) making most pathway images more slide-compatible and manuscript-compatible; (vi) adding tools to support better pathway filtering and selecting through a more complete pathway taxonomy; (vii) adding pathway analysis tools for visualizing and calculating pathway enrichment. Many other minor improvements and updates to the content, the interfaceand general performance of the PathBank website have also been made. Overall, we believe these upgrades and updates should greatly enhance PathBank's ease of use and its potential applications for interpreting metabolomics data.

Therapeutic targeting of the TPX2/TTK network in colorectal cancer

BackgroundWhile the increased screening, changes in lifestyle, and recent advances in treatment regimen have decreased colorectal cancer (CRC) mortality, metastatic disease and recurrence remains a major clinical challenge. In the era of precision medicine, the identification of actionable novel therapeutic targets could ultimately offer an alternative treatment strategy for CRC.MethodsRNA-Seq was conducted using the illumina platform, while bioinformatics analyses were conducted using CLC genomics workbench and iDEP.951. Colony forming unit, flow cytometry, and fluorescent microscopy were used to assess cell proliferation, cell cycle distribution, and cell death, respectively. The growth potential of CRC cells under 3-dimensional (3D) conditions was assessed using Matrigel. STRING database (v11.5) and Ingenuity Pathway Analysis (IPA) tool were used for network and pathway analyses. CRISPR-Cas9 perturbational effects database was used to identify potential therapeutic targets for CRC, through integration with gene-drug interaction database. Structural modeling and molecular docking were used to assess the interaction between candidate drugs and their targets.ResultsIn the current study, we investigated the therapeutic potential of targeting TPX2, TTK, DDX39A, and LRP8, commonly upregulated genes in CRC identified through differential expression analysis in CRC and adjacent non-cancerous tissue. Targeted depletion of TPX2 and TTK impaired CRC proliferation, cell cycle progression, and organoid formation under 3D culture conditions, while suppression of DDX39A and LRP8 had modest effects on CRC colony formation. Differential expression analysis and bioinformatics on TPX2 and TTK-deficient cells identified cell cycle regulation as the hallmark associated with loss of TPX2 and TTK. Elevated expression of TPX2 and TTK correlated with an oncogenic state in tumor tissue from patients with colon adenocarcinoma, thus corroborating an oncogenic role for the TPX2/TTK network in the pathogenesis of CRC. Gene set enrichment and pathway analysis of TPX2high/TTKhigh CRC identified numerous additional gene targets as integral components of the TPX2/TTK network. Integration of TPX2/TTK enriched network with CRISPR-Cas9 functional screen data identified numerous novel dependencies for CRC. Additionally, gene-drug interaction analysis identified several druggable gene targets enriched in the TPX2/TTK network, including AURKA, TOP2A, CDK1, BIRC5, and many others.ConclusionsOur data has implicated an essential role for TPX2 and TTK in CRC pathogenesis and identified numerous potential therapeutic targets and their drug interactions, suggesting their potential clinical use as a novel therapeutic strategy for patients with CRC.8pAfbPjv5QF2m5WMY3QXvqVideo

STAGEs: A web-based tool that integrates data visualization and pathway enrichment analysis for gene expression studies

Gene expression profiling has helped tremendously in the understanding of biological processes and diseases. However, interpreting processed data to gain insights into biological mechanisms remain challenging, especially to the non-bioinformaticians, as many of these data visualization and pathway analysis tools require extensive data formatting. To circumvent these challenges, we developed STAGEs (Static and Temporal Analysis of Gene Expression studies) that provides an interactive visualisation of omics analysis outputs. Users can directly upload data created from Excel spreadsheets and use STAGEs to render volcano plots, differentially expressed genes stacked bar charts, pathway enrichment analysis by Enrichr and Gene Set Enrichment Analysis (GSEA) against established pathway databases or customized gene sets, clustergrams and correlation matrices. Moreover, STAGEs takes care of Excel gene to date misconversions, ensuring that every gene is considered for pathway analysis. Output data tables and graphs can be exported, and users can easily customize individual graphs using widgets such as sliders, drop-down menus, text boxes and radio buttons. Collectively, STAGEs is an integrative platform for data analysis, data visualisation and pathway analysis, and is freely available at https://kuanrongchan-stages-stages-vpgh46.streamlitapp.com/. In addition, developers can customise or modify the web tool locally based on our existing codes, which is publicly available at https://github.com/kuanrongchan/STAGES.

Using the Reactome Database.

Pathway databases provide descriptions of the roles of proteins, nucleic acids, lipids, carbohydrates, and other molecular entities within their biological cellular contexts. Pathway-centric views of these roles may allow for the discovery of unexpected functional relationships in data such as gene expression profiles and somatic mutation catalogues from tumor cells. For this reason, there is a high demand for high-quality pathway databases and their associated tools. The Reactome project (a collaboration between the Ontario Institute for Cancer Research, New York University Langone Health, the European Bioinformatics Institute, and Oregon Health & Science University) is one such pathway database. Reactome collects detailed information on biological pathways and processes in humans from the primary literature. Reactome content is manually curated, expert-authored, and peer-reviewed and spans the gamut from simple intermediate metabolism to signaling pathways and complex cellular events. This information is supplemented with likely orthologous molecular reactions in mouse, rat, zebrafish, worm, and other model organisms. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Browsing a Reactome pathway Basic Protocol 2: Exploring Reactome annotations of disease and drugs Basic Protocol 3: Finding the pathways involving a gene or protein Alternate Protocol 1: Finding the pathways involving a gene or protein using UniProtKB (SwissProt), Ensembl, or Entrez gene identifier Alternate Protocol 2: Using advanced search Basic Protocol 4: Using the Reactome pathway analysis tool to identify statistically overrepresented pathways Basic Protocol 5: Using the Reactome pathway analysis tool to overlay expression data onto Reactome pathway diagrams Basic Protocol 6: Comparing inferred model organism and human pathways using the Species Comparison tool Basic Protocol 7: Comparing tissue-specific expression using the Tissue Distribution tool.

Decoding the molecular crosstalk between grafted stem cells and the stroke-injured brain

Stem cell therapy shows promise for multiple disorders; however, the molecular crosstalk between grafted cells and host tissue is largely unknown. Here, we take a step toward addressing this question. Using translating ribosome affinity purification (TRAP) with sequencing tools, we simultaneously decode the transcriptomes of graft and host for human neural stem cells (hNSCs) transplanted into the stroke-injured rat brain. Employing pathway analysis tools, we investigate the interactions between the two transcriptomes to predict molecular pathways linking host and graft genes; as proof of concept, we predict host-secreted factors that signal to the graft and the downstream molecular cascades they trigger in the graft. We identify a potential host-graft crosstalk pathway where BMP6 from the stroke-injured brain induces graft secretion of noggin, a known brain repair factor. Decoding the molecular interplay between graft and host is a critical step toward deciphering the molecular mechanisms of stem cell action.

NGS-based profiling identifies miRNAs and pathways dysregulated in cisplatin-resistant esophageal cancer cells.

Esophageal cancer (EC) incidence remains to be on a global rise supported by an unchanged recurrence and 5-year survival rate owing to the development of chemoresistance. Resistance to cisplatin, one of the majorly used chemotherapeutic drugs in EC, is a major nuisance. This study sheds light on miRNA dysregulation and its inverse relation with dysregulated mRNAs to guide pathways into the manifestation of cisplatin resistance in EC. A cisplatin-resistant version of an EC cell line was established and comparative profiling by NGS with the parental cell line was employed to identify dysregulation in miRNA and mRNA levels. Protein-protein interaction network analysis was done using Cytoscape, followed by Funrich pathway analysis. Furthermore, selective significant miRNAs were validated using qRT-PCR. miRNA-mRNA integrated analysis was carried out using the Ingenuity Pathway Analysis (IPA) tool. Expression of various established resistance markers supported the successful establishment of cisplatin-resistant cell line. Whole-cell small RNA sequencing and transcriptome sequencing identified 261 miRNAs and 1892 genes to be significantly differentially expressed (DE), respectively. Pathway analysis indicated enrichment of EMT signaling, supported by NOTCH, mTOR, TNF receptor, and PI3K-mediated AKT signaling pathways, in chemoresistant cells. Validation by qRT-PCR confirmed upregulation of miR-10a-5p, miR-618, miR-99a-5p, and miR-935 and downregulation of miR-335-3p, miR-205-5p, miR-944, miR-130a-3p, and miR-429 in resistant cells. Pathway analysis that followed IPA analysis indicated that the dysregulation of these miRNAs and their target genes may be instrumental in the development and regulation of chemoresistance via p53 signaling, xenobiotic metabolism, and NRF2-mediated oxidative stress. This study concludes the interplay between miRNA and mRNA as an important aspect and occurrence in guiding the regulation, acquisition, and maintenance of chemoresistance in esophageal cancer in vitro.