NetPath: a public resource of curated signal transduction pathways

Comprehensive physiological and genomic characterization of a potential probiotic strain, Lactiplantibacillus plantarum ILSF15, isolated from the gut of tribes of Odisha, India

Characterizing probiotic features of organisms isolated from diverse environments can lead to the discovery of novel strains with promising functional features and health attributes. The present study attempts to characterize a novel probiotic strain isolated from the gut of the tribal population of Odisha, India. Based on 16S rRNA-based phylogeny, the strain was identified as a species of the Lactiplantibacillus genus and was named Lactiplantibacillus plantarum strain ILSF15. The current investigation focuses on elucidating this strain's genetic and physiological properties associated with probiotic attributes such as biosafety risk, host adaptation/survival traits, and beneficial functional features. The novel strain was observed, in vitro, exhibiting features such as acid/bile tolerance, adhesion to the host enteric epithelial cells, cholesterol assimilation, and pathogen exclusion, indicating its ability to survive the harsh environment of the human GIT and resist the growth of harmful microorganisms. Additionally, the L. plantarum ILSF15 strain was found to harbor genes associated with the metabolism and synthesis of various bioactive molecules, including amino acids, carbohydrates, lipids, and vitamins, highlighting the organism's ability to efficiently utilize diverse resources and contribute to the host's nutrition and health. Several genes involved in host adaptation/survival strategies and host-microbe interactions were also identified from the ILSF15 genome. Moreover, L. plantarum strains, in general, were found to have an open pangenome characterized by high genetic diversity and the absence of specific lineages associated with particular habitats, signifying its versatile nature and potential applications in probiotic and functional food industries.

HNCDrugResDb: a platform for deciphering drug resistance in head and neck cancers

Drug resistance poses a significant obstacle to the success of anti-cancer therapy in head and neck cancers (HNCs). We aim to develop a platform for visualizing and analyzing molecular expression alterations associated with HNC drug resistance. Through data mining, we convened differentially expressed molecules and context-specific signaling events involved in drug resistance. The driver genes, interaction networks and transcriptional regulations were explored using bioinformatics approaches. A total of 2364 differentially expressed molecules were identified in 78 distinct drug-resistant cells against 14 anti-cancer drugs, comprising 1131 mRNAs, 746 proteins, 62 lncRNAs, 257 miRNAs, 1 circRNA, and 166 post-translational modifications. Among these, 255 molecules were considerably, the signature driver genes of HNC drug resistance. Further, we also developed a landscape of signaling pathways and their cross-talk with diverse signaling modules involved in drug resistance. Additionally, a publicly-accessible database named "HNCDrugResDb" was designed with browse, query, and pathway explorer options to fetch and enrich molecular alterations and signaling pathways altered in drug resistance. HNCDrugResDb is also enabled with a Drug Resistance Analysis tool as an initial platform to infer the likelihood of resistance based on the expression pattern of driver genes. HNCDrugResDb is anticipated to have substantial implications for future advancements in drug discovery and optimization of personalized medicine approaches.

Polyhexamethylene guanidine aerosol causes irreversible changes in blood proteins that associated with the severity of lung injury

Polyhexamethylene guanidine (PHMG) is a positively charged polymer used as a disinfectant that kills microbes but can cause pulmonary fibrosis if inhaled. After the long-term risks were confirmed in South Korea, it became crucial to measure toxicity through diverse surrogate biomarkers, not only proteins, especially after these hazardous chemicals had cleared from the body. These biomarkers, identified by their biological functions rather than simple numerical calculations, effectively explained the imbalance of pulmonary surfactant caused by fibrosis from PHMG exposure. These long-term studies on children exposed to PHMG has shown that blood protein indicators, primarily related to apolipoproteins and extracellular matrix, can distinguish the degree of exposure to humidifier disinfectants (HDs). We defined the extreme gradient boosting models and computed reflection scores based on just ten selected proteins, which were also verified in adult women exposed to HD. The reflection scores successfully discriminated between the HD-exposed and unexposed groups in both children and adult females (AUROC: 0.957 and 0.974, respectively) and had a strong negative correlation with lung function indicators. Even after an average of more than 10 years, blood is still considered a meaningful specimen for assessing the impact of environmental exposure to toxic substances, with proteins providing in identifying the pathological severity of such conditions.

A Comprehensive Network Map of Interleukin-26 Signaling Pathway

Interleukin-26 (IL-26) is a cytokine that belongs to the IL-20 subfamily and is primarily expressed in T helper 1 cells and Th17 memory CD4+ cells. Its receptor complex, consisting of IL-20R1 and IL-10R2, activates a signaling pathway involving several proteins such as Janus kinase 1 and tyrosine-protein kinase, signal transducer and activator of transcription (STAT) 1, and STAT3. This leads to the initiation of downstream signaling cascades that play a crucial role in various biological processes, including inflammation, immune response regulation, atopic dermatitis, macrophage differentiation, osteoclastogenesis, antibacterial host defense, anti-apoptosis, and tumor growth. In this study, we curated literature data pertaining to IL-26 signaling. The curated map includes a total of seven activation/inhibition events, 16 catalysis events, 33 gene regulation events, 25 protein expression types, two transport events, and three molecular associations.

Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction

Network inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/netlab-ku/pyPARAGON.

DrugRepoBank: a comprehensive database and discovery platform for accelerating drug repositioning

In recent years, drug repositioning has emerged as a promising alternative to the time-consuming, expensive and risky process of developing new drugs for diseases. However, the current database for drug repositioning faces several issues, including insufficient data volume, restricted data types, algorithm inaccuracies resulting from the neglect of multidimensional or heterogeneous data, a lack of systematic organization of literature data associated with drug repositioning, limited analytical capabilities and user-unfriendly webpage interfaces. Hence, we have established the first all-encompassing database called DrugRepoBank, consisting of two main modules: the 'Literature' module and the 'Prediction' module. The 'Literature' module serves as the largest repository of literature-supported drug repositioning data with experimental evidence, encompassing 169 repositioned drugs from 134 articles from 1 January 2000 to 1 July 2023. The 'Prediction' module employs 18 efficient algorithms, including similarity-based, artificial-intelligence-based, signature-based and network-based methods to predict repositioned drug candidates. The DrugRepoBank features an interactive and user-friendly web interface and offers comprehensive functionalities such as bioinformatics analysis of disease signatures. When users provide information about a drug, target or disease of interest, DrugRepoBank offers new indications and targets for the drug, proposes new drugs that bind to the target or suggests potential drugs for the queried disease. Additionally, it provides basic information about drugs, targets or diseases, along with supporting literature. We utilize three case studies to demonstrate the feasibility and effectiveness of predictively repositioned drugs within DrugRepoBank. The establishment of the DrugRepoBank database will significantly accelerate the pace of drug repositioning. Database URL:  https://awi.cuhk.edu.cn/DrugRepoBank.

Integrative and comparative genomic analyses of mammalian macrophage responses to intracellular mycobacterial pathogens

Mycobacterium tuberculosis, the causative agent of human tuberculosis (hTB), is a close evolutionary relative of Mycobacterium bovis, which causes bovine tuberculosis (bTB), one of the most damaging infectious diseases to livestock agriculture. Previous studies have shown that the pathogenesis of bTB disease is comparable to hTB disease, and that the bovine and human alveolar macrophage (bAM and hAM, respectively) transcriptomes are extensively reprogrammed in response to infection with these intracellular mycobacterial pathogens. In this study, a multi-omics integrative approach was applied with functional genomics and GWAS data sets across the two primary hosts (Bos taurus and Homo sapiens) and both pathogens (M. bovis and M. tuberculosis). Four different experimental infection groups were used: 1) bAM infected with M. bovis, 2) bAM infected with M. tuberculosis, 3) hAM infected with M. tuberculosis, and 4) human monocyte-derived macrophages (hMDM) infected with M. tuberculosis. RNA-seq data from these experiments 24 h post-infection (24 hpi) was analysed using three computational pipelines: 1) differentially expressed genes, 2) differential gene expression interaction networks, and 3) combined pathway analysis. The results were integrated with high-resolution bovine and human GWAS data sets to detect novel quantitative trait loci (QTLs) for resistance to mycobacterial infection and resilience to disease. This revealed common and unique response macrophage pathways for both pathogens and identified 32 genes (12 bovine and 20 human) significantly enriched for SNPs associated with disease resistance, the majority of which encode key components of the NF-κB signalling pathway and that also drive formation of the granuloma.

A signaling network map of Lipoxin (LXA4): an anti-inflammatory molecule

Lipoxins (LXs) are a class of endogenous bioactive lipid mediators that are involved in the regulation of inflammation. They exert immunomodulatory effects by regulating the behaviour of various immune cells, including neutrophils, macrophages, and T and B cells, by promoting the clearance of apoptotic neutrophils. This helps to dampen inflammation and promote tissue repair. LXs regulate the expression of many inflammatory genes by modulating the levels of transcription factors, such as nuclear factor κB (NF-κB), activator protein-1 (AP-1), nerve growth factor-regulated factor 1A binding protein 1 (NGF), and peroxisome proliferator activated receptor γ (PPAR-γ), which are elevated in various diseases, such as respiratory tract diseases, renal diseases, cancer, neurodegenerative diseases, and viral infections. Lipoxin-mediated signaling is involved in chronic inflammation, cancer, diabetes-associated kidney disease, lung injury, liver injury, endometriosis, respiratory tract diseases, neurodegenerative diseases, chronic cerebral hypoperfusion, and retinal degeneration. In this study, we systematically investigated the intricate network of lipoxin signaling by analyzing the relevant literature. The resulting map comprised 467 molecules categorized as activation/inhibition, enzyme catalysis, gene and protein expression, molecular associations, and translocation events. This map serves as a valuable resource for understanding the complexity of lipoxin signaling and its impact on various cellular functions.

An integrated analysis of the structural changes and gene expression of spleen in human visceral leishmaniasis with and without HIV coinfection

The spleen plays a pivotal role in the pathogenesis of visceral leishmaniasis. In severe forms of the disease, the spleen undergoes changes that can compromise its function in surveilling blood-circulating pathogens. In this study, we present an integrated analysis of the structural and gene expression alterations in the spleens of three patients with relapsing visceral leishmaniasis, two of whom were coinfected with HIV. Our findings reveal that the IL6 signaling pathway plays a significant role in the disorganization of the white pulp, while BCL10 and ICOSLG are associated with spleen organization. Patients coinfected with HIV and visceral leishmaniasis exhibited lower splenic CD4+ cell density and reduced expression of genes such as IL15. These effects may contribute to a compromised immune response against L. infantum in coinfected individuals, further impacting the structural organization of the spleen.

Puzzle of Proteoform Variety-Where Is a Key?

One of the human proteome puzzles is an imbalance between the theoretically calculated and experimentally measured amounts of proteoforms. Considering the possibility of combinations of different post-translational modifications (PTMs), the quantity of possible proteoforms is huge. An estimation gives more than a million different proteoforms in each cell type. But, it seems that there is strict control over the production and maintenance of PTMs. Although the potential complexity of proteoforms due to PTMs is tremendous, available information indicates that only a small part of it is being implemented. As a result, a protein could have many proteoforms according to the number of modification sites, but because of different systems of personal regulation, the profile of PTMs for a given protein in each organism is slightly different.

Proteomics and Its Current Application in Biomedical Area: Concise Review

Biomedical researchers tirelessly seek cutting-edge technologies to advance disease diagnosis, drug discovery, and therapeutic interventions, all aimed at enhancing human and animal well-being. Within this realm, proteomics stands out as a pivotal technology, focusing on extensive studies of protein composition, structure, function, and interactions. Proteomics, with its subdivisions of expression, structural, and functional proteomics, plays a crucial role in unraveling the complexities of biological systems. Various sophisticated techniques are employed in proteomics, including polyacrylamide gel electrophoresis, mass spectrometry analysis, NMR spectroscopy, protein microarray, X-ray crystallography, and Edman sequencing. These methods collectively contribute to the comprehensive understanding of proteins and their roles in health and disease. In the biomedical field, proteomics finds widespread application in cancer research and diagnosis, stem cell studies, and the diagnosis and research of both infectious and noninfectious diseases. In addition, it plays a pivotal role in drug discovery and the emerging frontier of personalized medicine. The versatility of proteomics allows researchers to delve into the intricacies of molecular mechanisms, paving the way for innovative therapeutic approaches. As infectious and noninfectious diseases continue to emerge and the field of biomedical research expands, the significance of proteomics becomes increasingly evident. Keeping abreast of the latest developments in proteomics applications becomes paramount for the development of therapeutics, translational research, and study of diverse diseases. This review aims to provide a comprehensive overview of proteomics, offering a concise outline of its current applications in the biomedical domain. By doing so, it seeks to contribute to the understanding and advancement of proteomics, emphasizing its pivotal role in shaping the future of biomedical research and therapeutic interventions.

GINv2.0: a comprehensive topological network integrating molecular interactions from multiple knowledge bases

Knowledge bases have been instrumental in advancing biological research, facilitating pathway analysis and data visualization, which are now widely employed in the scientific community. Despite the establishment of several prominent knowledge bases focusing on signaling, metabolic networks, or both, integrating these networks into a unified topological network has proven to be challenging. The intricacy of molecular interactions and the diverse formats employed to store and display them contribute to the complexity of this task. In a prior study, we addressed this challenge by introducing a "meta-pathway" structure that integrated the advantages of the Simple Interaction Format (SIF) while accommodating reaction information. Nevertheless, the earlier Global Integrative Network (GIN) was limited to reliance on KEGG alone. Here, we present GIN version 2.0, which incorporates human molecular interaction data from ten distinct knowledge bases, including KEGG, Reactome, and HumanCyc, among others. We standardized the data structure, gene IDs, and chemical IDs, and conducted a comprehensive analysis of the consistency among the ten knowledge bases before combining all unified interactions into GINv2.0. Utilizing GINv2.0, we investigated the glycolysis process and its regulatory proteins, revealing coordinated regulations on glycolysis and autophagy, particularly under glucose starvation. The expanded scope and enhanced capabilities of GINv2.0 provide a valuable resource for comprehensive systems-level analyses in the field of biological research. GINv2.0 can be accessed at: https://github.com/BIGchix/GINv2.0 .

DUX4 is a common driver of immune evasion and immunotherapy failure in metastatic cancers

Cancer immune evasion contributes to checkpoint immunotherapy failure in many patients with metastatic cancers. The embryonic transcription factor DUX4 was recently characterized as a suppressor of interferon-γ signaling and antigen presentation that is aberrantly expressed in a small subset of primary tumors. Here, we report that DUX4 expression is a common feature of metastatic tumors, with ~10-50% of advanced bladder, breast, kidney, prostate, and skin cancers expressing DUX4. DUX4 expression is significantly associated with immune cell exclusion and decreased objective response to PD-L1 blockade in a large cohort of urothelial carcinoma patients. DUX4 expression is a significant predictor of survival even after accounting for tumor mutational burden and other molecular and clinical features in this cohort, with DUX4 expression associated with a median reduction in survival of over one year. Our data motivate future attempts to develop DUX4 as a biomarker and therapeutic target for checkpoint immunotherapy resistance.

GPR56 signaling pathway network and its dynamics in the mesenchymal transition of glioblastoma

G protein-coupled receptor 56 (GPR56/ADGRG1) is a multifunctional adhesion GPCR involved in diverse biological processes ranging from development to cancer. In our earlier study, we reported that GPR56 is expressed heterogeneously in glioblastoma (GBM) and is involved in the mesenchymal transition, making it a promising therapeutic target (Ganesh et al., 2022). Despite its important role in cancer, its mechanism of action or signaling is not completely understood. Thus, based on transcriptomic, proteomic, and phosphoproteomic differential expression data of GPR56 knockdown U373-GBM cells included in our above study along with detailed literature mining of the molecular events plausibly associated with GPR56 activity, we have constructed a signaling pathway map of GPR56 as may be applicable in mesenchymal transition in GBM. The map incorporates more than 100 molecular entities including kinases, receptors, ion channels, and others associated with Wnt, integrin, calcium signaling, growth factors, and inflammation signaling pathways. We also considered intracellular and extracellular factors that may influence the activity of the pathway entities. Here we present a curated signaling map of GPR56 in the context of GBM and discuss the relevance and plausible cross-connectivity across different axes attributable to GPR56 function. GPR56 signaling and mesenchymal transition.

Unraveling Connective Tissue Growth Factor as a Therapeutic Target and Assessing Kahweol as a Potential Drug Candidate in Triple-Negative Breast Cancer Treatment

Triple-negative breast cancer (TNBC) is characterized by aggressive behavior and limited treatment options, necessitating the identification of novel therapeutic targets. In this study, we investigated the clinical significance of connective tissue growth factor (CTGF) as a prognostic marker and explored the potential therapeutic effects of kahweol, a coffee diterpene molecule, in TNBC treatment. Initially, through a survival analysis on breast cancer patients from The Cancer Genome Atlas (TCGA) database, we found that CTGF exhibited significant prognostic effects exclusively in TNBC patients. To gain mechanistic insights, we performed the functional annotation and gene set enrichment analyses, revealing the involvement of CTGF in migratory pathways relevant to TNBC treatment. Subsequently, in vitro experiments using MDA-MB 231 cells, a representative TNBC cell line, demonstrated that recombinant CTGF (rCTGF) administration enhanced cell motility, whereas CTGF knockdown using CTGF siRNA resulted in reduced motility. Notably, rCTGF restored kahweol-reduced cell motility, providing compelling evidence for the role of CTGF in mediating kahweol's effects. At the molecular level, kahweol downregulated the protein expression of CTGF as well as critical signaling molecules, such as p-ERK, p-P38, p-PI3K/AKT, and p-FAK, associated with cell motility. In summary, our findings propose CTGF as a potential prognostic marker for guiding TNBC treatment and suggest kahweol as a promising antitumor compound capable of regulating CTGF expression to suppress cell motility in TNBC. These insights hold promise for the development of targeted therapies and improved clinical outcomes for TNBC patients.

AgingReG: a curated database of aging regulatory relationships in humans

Aging and cellular senescence are characterized by a progressive loss of physiological integrity, which could be triggered by aging factors such as physiological, pathological and external factors. Numerous studies have shown that gene regulatory events play crucial roles in aging, increasing the need for a comprehensive repository of regulatory relationships during aging. Here, we established a manually curated database of aging factors (AgingReG, https://bio.liclab.net/Aging-ReG/), focusing on the regulatory relationships during aging with experimental evidence in humans. By curating thousands of published literature, 2157 aging factor entries (1345 aging gene entries, 804 external factor entries and eight aging-related pathway entries) and related regulatory information were manually curated. The regulatory relationships were classified into four types according to their functions: (i) upregulation, which indicates that aging factors upregulate the expression of target genes during aging; (ii) downregulation, which indicates that aging factors downregulate the expression of target genes during aging; (iii) activation, which indicates that aging factors influence the activity of target genes during aging and (iv) inhibition, which indicates that aging factors inhibit the activation of target molecule activity, leading to declined or lost target activity. AgingReG involves 651 upregulating pairs, 632 downregulating pairs, 330 activation-regulating pairs and 34 inhibition-regulating pairs, covering 195 disease types and more than 800 kinds of cells and tissues from 1784 published literature studies. AgingReG provides a user-friendly interface to query, browse and visualize detailed information about the regulatory relationships during aging. We believe that AgingReG will serve as a valuable resource database in the field of aging research. Database URL: https://bio.liclab.net/Aging-ReG/.

Differential gene expression analysis based on linear mixed model corrects false positive inflation for studying quantitative traits

Differential gene expression (DGE) analysis has been widely employed to identify genes expressed differentially with respect to a trait of interest using RNA sequencing (RNA-Seq) data. Recent RNA-Seq data with large samples pose challenges to existing DGE methods, which were mainly developed for dichotomous traits and small sample sizes. Especially, existing DGE methods are likely to result in inflated false positive rates. To address this gap, we employed a linear mixed model (LMM) that has been widely used in genetic association studies for DGE analysis of quantitative traits. We first applied the LMM method to the discovery RNA-Seq data of dorsolateral prefrontal cortex (DLPFC) tissue (n = 632) with four continuous measures of Alzheimer's Disease (AD) cognitive and neuropathologic traits. The quantile-quantile plots of p-values showed that false positive rates were well calibrated by LMM, whereas other methods not accounting for sample-specific mixed effects led to serious inflation. LMM identified 37 potentially significant genes with differential expression in DLPFC for at least one of the AD traits, 17 of which were replicated in the additional RNA-Seq data of DLPFC, supplemental motor area, spinal cord, and muscle tissues. This application study showed not only well calibrated DGE results by LMM, but also possibly shared gene regulatory mechanisms of AD traits across different relevant tissues.

A network map of cytoskeleton-associated protein 4 (CKAP4) mediated signaling pathway in cancer

Cytoskeleton-associated protein 4 (CKAP4) is a non-glycosylated type II transmembrane protein that serves as a cell surface-activated receptor. It is expressed primarily in the plasma membranes of bladder epithelial cells, type II alveolar pneumocytes, and vascular smooth muscle cells. CKAP4 is involved in various biological activities including cell proliferation, cell migration, keratinocyte differentiation, glycogenesis, fibrosis, thymic development, cardiogenesis, neuronal apoptosis, and cancer. CKAP4 has been described as a pro-tumor molecule that regulates the progression of various cancers, including lung cancer, breast cancer, esophageal squamous cell carcinoma, hepatocellular carcinoma, cervical cancer, oral cancer, bladder cancer, cholangiocarcinoma, pancreatic cancer, myeloma, renal cell carcinoma, melanoma, squamous cell carcinoma, colorectal cancer, and osteosarcoma. CKAP4 and its isoform bind to DKK1 or DKK3 (Dickkopf proteins) or antiproliferative factor (APF) and regulates several downstream signaling cascades. The CKAP4 complex plays a crucial role in regulating the signaling pathways including PI3K/AKT and MAPK1/3. Recently, CKAP4 has been recognized as a potential target for cancer therapy. Due to its biomedical importance, we integrated a network map of CKAP4. The available literature on CKAP4 signaling was manually curated according to the NetPath annotation criteria. The consolidated pathway map comprises 41 activation/inhibition events, 21 catalysis events, 35 molecular associations, 134 gene regulation events, 83 types of protein expression, and six protein translocation events. CKAP4 signaling pathway map data is freely accessible through the WikiPathways Database ( https://www.wikipathways.org/index.php/Pathway:WP5322 ). Generation of CKAP4 signaling pathway map.

Early induction of Hes1 by bone morphogenetic protein 9 plays a regulatory role in osteoblastic differentiation of a mesenchymal stem cell line

Bone morphogenic protein 9 (BMP9) is one of the most potent inducers of osteogenic differentiation among the 14 BMP members, but its mechanism of action has not been fully demonstrated. Hes1 is a transcriptional regulator with basic helix-loop-helix (bHLH) domain and is a well-known Notch effector. In this study, we investigated the functional roles of early induction of Hes1 by BMP9 in a mouse mesenchymal stem cell line, ST2. Hes1 mRNA was transiently and periodically induced by BMP9 in ST2, which was inhibited by BMP signal inhibitors but not by Notch inhibitor. Interestingly, Hes1 knockdown in ST2 by siRNA increased the expression of osteogenic differentiation markers such as Sp7 and Ibsp and matrix mineralization in comparison with control siRNA transfected ST2. In contrast, forced expression of Hes1 by using the Tet-On system suppressed the expression of osteogenic markers and matrix mineralization by BMP9. We also found that the early induction of Hes1 by BMP9 suppressed the expression of Alk1, an essential receptor for BMP9. In conclusion, BMP9 rapidly induces the expression of Hes1 via the SMAD pathway in ST2 cells, which plays a negative regulatory role in osteogenic differentiation of mesenchymal stem cells induced by BMP9.

A network map of GDNF/RET signaling pathway in physiological and pathological conditions

Glial cell line-derived neurotrophic factor (GDNF) signals through a multi-component receptor system predominantly consisting of glycosyl-phosphatidylinositol-anchored GDNF family receptor alpha-1 (GFRα1) and the Rearranged during transfection (RET) receptor tyrosine kinase. GDNF/RET signaling is vital to the central and peripheral nervous system, kidney morphogenesis, and spermatogenesis. In addition, the dysregulation of the GDNF/RET signaling has been implicated in the pathogenesis of cancers. Despite the extensive research on GDNF/RET signaling, a molecular network of reactions induced by GDNF reported across the published literature. However, a comprehensive GDNF/RET pathway resource is currently unavailable. We describe an integrated signaling pathway reaction map of GDNF/RET consisting of 1151 molecular reactions. These include information pertaining to 52 molecular association events, 70 enzyme catalysis events, 36 activation/inhibition events, 22 translocation events, 856 gene regulation events, and 115 protein-level expression events induced by GDNF in diverse cell types. We developed a comprehensive GDNF/RET signaling network map based on these molecular reactions. The pathway map was made accessible through WikiPathways database ( https://www.wikipathways.org/index.php/Pathway:WP5143 ). Biocuration and development of gene regulatory network map of GDNF/RET signaling pathway.

Single-cell multiomic analysis of thymocyte development reveals drivers of CD4+ T cell and CD8+ T cell lineage commitment

The development of CD4+ T cells and CD8+ T cells in the thymus is critical to adaptive immunity and is widely studied as a model of lineage commitment. Recognition of self-peptide major histocompatibility complex (MHC) class I or II by the T cell antigen receptor (TCR) determines the CD8+ or CD4+ T cell lineage choice, respectively, but how distinct TCR signals drive transcriptional programs of lineage commitment remains largely unknown. Here we applied CITE-seq to measure RNA and surface proteins in thymocytes from wild-type and T cell lineage-restricted mice to generate a comprehensive timeline of cell states for each T cell lineage. These analyses identified a sequential process whereby all thymocytes initiate CD4+ T cell lineage differentiation during a first wave of TCR signaling, followed by a second TCR signaling wave that coincides with CD8+ T cell lineage specification. CITE-seq and pharmaceutical inhibition experiments implicated a TCR-calcineurin-NFAT-GATA3 axis in driving the CD4+ T cell fate. Our data provide a resource for understanding cell fate decisions and implicate a sequential selection process in guiding lineage choice.

A network map of macrophage-stimulating protein (MSP) signaling

Macrophage-stimulating protein (MSP), a serum-derived growth factor belonging to the plasminogen-related kringle domain family, is mainly produced by the liver and released into the blood. MSP is the only known ligand for RON ("Recepteur d'Origine Nantais", also known as MST1R), which is a member of the receptor tyrosine kinase (RTK) family. MSP is associated with many pathological conditions, including cancer, inflammation, and fibrosis. Activation of the MSP/RON system regulates main downstream signaling pathways, including phosphatidylinositol 3-kinase/ AKT serine/threonine kinase/ (PI3-K/AKT), mitogen-activated protein kinases (MAPK), c-Jun N-terminal kinase (JNK) & Focal adhesion kinase (FAK). These pathways are mainly involved in cell proliferation, survival, migration, invasion, angiogenesis & chemoresistance. In this work, we created a pathway resource of signaling events mediated by MSP/RON considering its contribution to diseases. We provide an integrated pathway reaction map of MSP/RON that is composed of 113 proteins and 26 reactions based on the curation of data from the published literature. The consolidated pathway map of MSP/RON mediated signaling events contains seven molecular associations, 44 enzyme catalysis, 24 activation/inhibition, six translocation events, 38 gene regulation events, and forty-two protein expression events. The MSP/RON signaling pathway map can be freely accessible through the WikiPathways Database URL: https://classic.wikipathways.org/index.php/Pathway:WP5353 .

A network map of discoidin domain receptor 1(DDR1)-mediated signaling in pathological conditions

Discoidin domain receptor 1 (DDR1) is one of the receptors that belong to a family of non-integrin collagen receptors. In common, DDR1 is predominantly found in epithelial and smooth muscle cells and its mainly involved in organogenesis during embryonic development. However, it's also overexpressed in several pathological conditions, including cancer and inflammation. The DDR1 is reported in numerous cancers, including breast, prostate, pancreatic, bladder, lung, liver, pituitary, colorectal, skin, gastric, glioblastoma, and inflammation. DDR1 activates through the collagen I, IV, IGF-1/IGF1R, and IGF2/IR, regulating downstream signaling molecules such as MAPKs, PI3K/Akt, and NF-kB in diseases. Despite its biomedical importance, there is a lack of consolidated network map of the DDR1 signaling pathway, which prompted us for curation of literature data pertaining to the DDR1 system following the NetPath criteria. We present here the compiled pathway map comprises 39 activation/inhibition events, 17 catalysis events, 22 molecular associations, 65 gene regulation events, 35 types of protein expression, and two protein translocation events. The detailed DDR1 signaling pathway map is made freely accessible through the WikiPathways Database ( https://www.wikipathways.org/index.php/ Pathway: https://www.wikipathways.org/index.php/Pathway:WP5288 ).

An assembly of TROP2-mediated signaling events

Trophoblast cell surface antigen 2 (TROP2) is a calcium-transducing transmembrane protein mainly involved in embryo development. The aberrant expression of TROP2 is observed in numerous cancers, including triple-negative breast cancer, gastric, colorectal, pancreatic, squamous cell carcinoma of the oral cavity, and prostate cancers. The main signaling pathways mediated by TROP2 are calcium signaling, PI3K/AKT, JAK/STAT, MAPKs, and β-catenin signaling. However, collective information about the TROP2-mediated signaling pathway is not available for visualization or analysis. In this study, we constructed a TROP2 signaling map with respect to its role in different cancers. The data curation was done manually by following the NetPath annotation criteria. The described map consists of different molecular events, including 8 activation/inhibition, 16 enzyme catalysis, 19 gene regulations, 12 molecular associations, 39 induced-protein expressions, and 2 protein translocation. The data of the TROP2 pathway map is made freely accessible through the WikiPathways Database ( https://www.wikipathways.org/index.php/Pathway:WP5300 ). Development of TROP2 signaling pathway map.

Computational Construction of Toxicant Signaling Networks

Humans and animals are regularly exposed to compounds that may have adverse effects on health. The Toxicity Forecaster (ToxCast) program was developed to use high throughput screening assays to quickly screen chemicals by measuring their effects on many biological end points. Many of these assays test for effects on cellular receptors and transcription factors (TFs), under the assumption that a toxicant may perturb normal signaling pathways in the cell. We hypothesized that we could reconstruct the intermediate proteins in these pathways that may be directly or indirectly affected by the toxicant, potentially revealing important physiological processes not yet tested for many chemicals. We integrate data from ToxCast with a human protein interactome to build toxicant signaling networks that contain physical and signaling protein interactions that may be affected as a result of toxicant exposure. To build these networks, we developed the EdgeLinker algorithm, which efficiently finds short paths in the interactome that connect the receptors to TFs for each toxicant. We performed multiple evaluations and found evidence suggesting that these signaling networks capture biologically relevant effects of toxicants. To aid in dissemination and interpretation, interactive visualizations of these networks are available at http://graphspace.org.

Evaluating the Pharmacokinetics and Pharmacodynamics of Chemotherapeutics within a Spatial SILAC-Labeled Spheroid Model System

Tumors have considerable cellular heterogeneity that is impossible to explore with simple cell cultures. Spheroid cultures contain pathophysiological and chemical gradients similar to in vivo tumors and show complex responses to therapeutics, similar to a tumor. Using pulsed isotopic labels, we demonstrate the pronounced differential response of the proteome to the drug Regorafenib, a multikinase inhibitor, in HCT 116 spheroids. Regorafenib treatment of outer spheroids inhibits proteins involved in critical pathways such as mTOR signaling, extracellular signal-regulated kinase (ERK)/mitogen-activated protein kinase (MAPK) signaling, and colorectal cancer metastasis signaling, resulting in decreased proliferation and cellular apoptosis. By contrast, analysis of the treated core cells shows upregulation of MAPK1 and KRAS, possibly implicating drug resistance within these late apoptotic cells. Thus, pulsed isotopic labeling enables evaluation of the distinct proteomic responses for cells residing in the different chemical microenvironments of the spheroid. This platform promises great utility in assisting researchers' predictions of pharmacodynamic therapeutic responses within complex tumors.

Multi-scale systems genomics analysis predicts pathways, cell types, and drug targets involved in normative variation in peri-adolescent human cognition

An open challenge in human genetics is to better understand the systems-level impact of genotype variation on developmental cognition. To characterize the genetic underpinnings of peri-adolescent cognition, we performed genotype-phenotype and systems analysis for binarized accuracy in nine cognitive tasks from the Philadelphia Neurodevelopmental Cohort (~2,200 individuals of European continental ancestry aged 8-21 years). We report a region of genome-wide significance within the 3' end of the Fibulin-1 gene (P = 4.6 × 10-8), associated with accuracy in nonverbal reasoning, a heritable form of complex reasoning ability. Diffusion tensor imaging data from a subset of these participants identified a significant association of white matter fractional anisotropy with FBLN1 genotypes (P < 0.025); poor performers show an increase in the C and A allele for rs77601382 and rs5765534, respectively, which is associated with increased fractional anisotropy. Integration of published human brain-specific 'omic maps, including single-cell transcriptomes of the developing human brain, shows that FBLN1 demonstrates greatest expression in the fetal brain, as a marker of intermediate progenitor cells, demonstrates negligible expression in the adolescent and adult human brain, and demonstrates increased expression in the brain in schizophrenia. Collectively these findings warrant further study of this gene and genetic locus in cognition, neurodevelopment, and disease. Separately, genotype-pathway analysis identified an enrichment of variants associated with working memory accuracy in pathways related to development and to autonomic nervous system dysfunction. Top-ranking pathway genes include those genetically associated with diseases with working memory deficits, such as schizophrenia and Parkinson's disease. This work advances the "molecules-to-behavior" view of cognition and provides a framework for using systems-level organization of data for other biomedical domains.

Atypical teratoid/rhabdoid tumoroids reveal subgroup-specific drug vulnerabilities

Atypical teratoid/rhabdoid tumors (ATRTs) represent a rare, but aggressive pediatric brain tumor entity. They are genetically defined by alterations in the SWI/SNF chromatin remodeling complex members SMARCB1 or SMARCA4. ATRTs can be further classified in different molecular subgroups based on their epigenetic profiles. Although recent studies suggest that the different subgroups have distinct clinical features, subgroup-specific treatment regimens have not been developed thus far. This is hampered by the lack of pre-clinical in vitro models representative of the different molecular subgroups. Here, we describe the establishment of ATRT tumoroid models from the ATRT-MYC and ATRT-SHH subgroups. We demonstrate that ATRT tumoroids retain subgroup-specific epigenetic and gene expression profiles. High throughput drug screens on our ATRT tumoroids revealed distinct drug sensitivities between and within ATRT-MYC and ATRT-SHH subgroups. Whereas ATRT-MYC universally displayed high sensitivity to multi-targeted tyrosine kinase inhibitors, ATRT-SHH showed a more heterogeneous response with a subset showing high sensitivity to NOTCH inhibitors, which corresponded to high expression of NOTCH receptors. Our ATRT tumoroids represent the first pediatric brain tumor organoid model, providing a representative pre-clinical model which enables the development of subgroup-specific therapies.

RNA2Immune: A Database of Experimentally Supported Data Linking Non-coding RNA Regulation to The Immune System

Non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as important regulators of the immune system and are involved in the control of immune cell biology, disease pathogenesis, as well as vaccine responses. A repository of ncRNA-immune associations will facilitate our understanding of ncRNA-dependent mechanisms in the immune system and advance the development of therapeutics and prevention for immune disorders. Here, we describe a comprehensive database, RNA2Immune, which aims to provide a high-quality resource of experimentally supported database linking ncRNA regulatory mechanisms to immune cell function, immune disease, cancer immunology, and vaccines. The current version of RNA2Immune documents 50,433 immune-ncRNA associations in 42 host species, including (1) 6690 ncRNA associations with immune functions involving 31 immune cell types; (2) 38,672 ncRNA associations with 348 immune diseases; (3) 4833 ncRNA associations with cancer immunology; and (4) 238 ncRNA associations with vaccine responses involving 26 vaccine types targeting 22 diseases. RNA2Immune provides a user-friendly interface for browsing, searching, and downloading ncRNA-immune system associations. Collectively, RNA2Immune provides important information about how ncRNAs influence immune cell function, how dysregulation of these ncRNAs leads to pathological consequences (immune diseases and cancers), and how ncRNAs affect immune responses to vaccines. RNA2Immune is available at http://bio-bigdata.hrbmu.edu.cn/rna2immune/home.jsp.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

A comprehensive network map of IL-17A signaling pathway

Interleukin-17A (IL-17A) is one of the member of IL-17 family consisting of other five members (IL-17B to IL-17F). The Gamma delta (γδ) T cells and T helper 17 (Th17) cells are the major producers of IL-17A. Aberrant signaling by IL-17A has been implicated in the pathogenesis of several autoimmune diseases including idiopathic pulmonary fibrosis, acute lung injury, chronic airway diseases, and cancer. Activation of the IL-17A/IL-17 receptor A (IL-17RA) system regulates phosphoinositide 3-kinase/AKT serine/threonine kinase/mammalian target of rapamycin (PI3K/AKT/mTOR), mitogen-activated protein kinases (MAPKs) and activation of nuclear factor-κB (NF-κB) mediated signaling pathways. The IL-17RA activation orchestrates multiple downstream signaling cascades resulting in the release of pro-inflammatory cytokines such as interleukins (IL)-1β, IL-6, and IL-8, chemokines (C-X-C motif) and promotes neutrophil-mediated immune response. Considering the biomedical importance of IL-17A, we developed a pathway resource of signaling events mediated by IL-17A/IL-17RA in this study. The curation of literature data pertaining to the IL-17A system was performed manually by the NetPath criteria. Using data mined from the published literature, we describe an integrated pathway reaction map of IL-17A/IL-17RA consisting of 114 proteins and 68 reactions. That includes detailed information on IL-17A/IL-17RA mediated signaling events of 9 activation/inhibition events, 17 catalysis events, 3 molecular association events, 68 gene regulation events, 109 protein expression events, and 6 protein translocation events. The IL-17A signaling pathway map data is made freely accessible through the WikiPathways Database ( https://www.wikipathways.org/index.php/Pathway : WP5242).

Growing Directed Acyclic Graphs: Optimization Functions for Pathway Reconstruction Algorithms

A major challenge in molecular systems biology is to understand how proteins work to transmit external signals to changes in gene expression. Computationally reconstructing these signaling pathways from protein interaction networks can help understand what is missing from existing pathway databases. We formulate a new pathway reconstruction problem, one that iteratively grows directed acyclic graphs (DAGs) from a set of starting proteins in a protein interaction network. We present an algorithm that provably returns the optimal DAGs for two different cost functions and evaluate the pathway reconstructions when applied to six diverse signaling pathways from the NetPath database. The optimal DAGs outperform an existing k-shortest paths method for pathway reconstruction, and the new reconstructions are enriched for different biological processes. Growing DAGs is a promising step toward reconstructing pathways that provably optimize a specific cost function.

A molecular network map of orexin-orexin receptor signaling system

Orexins are excitatory neuropeptides, which are predominantly associated with feeding behavior, sleep-wake cycle and energy homeostasis. The orexinergic system comprises of HCRTR1 and HCRTR2, G-protein-coupled receptors of rhodopsin family and the endogenous ligands processed from HCRT pro-hormone, Orexin A and Orexin B. These neuropeptides are biosynthesized by the orexin neurons present in the lateral hypothalamus area, with dense projections to other brain regions. The orexin-receptor signaling is implicated in various metabolic as well as neurological disorders, making it a promising target for pharmacological interventions. However, there is limited information available on the collective representation of the signal transduction pathways pertaining to the orexin-orexin receptor signaling system. Here, we depict a compendium of the Orexin A/B stimulated reactions in the form of a basic signaling pathway map. This map catalogs the reactions into five categories: molecular association, activation/inhibition, catalysis, transport, and gene regulation. A total of 318 downstream molecules were annotated adhering to the guidelines of NetPath curation. This pathway map can be utilized for further assessment of signaling events associated with orexin-mediated physiological functions and is freely available on WikiPathways, an open-source pathway database ( https://www.wikipathways.org/index.php/Pathway:WP5094 ).

CRdb: a comprehensive resource for deciphering chromatin regulators in human

Chromatin regulators (CRs) regulate epigenetic patterns on a partial or global scale, playing a critical role in affecting multi-target gene expression. As chromatin immunoprecipitation sequencing (ChIP-seq) data associated with CRs are rapidly accumulating, a comprehensive resource of CRs needs to be built urgently for collecting, integrating, and processing these data, which can provide abundant annotated information on CR upstream and downstream regulatory analyses as well as CR-related analysis functions. This study established an integrative CR resource, named CRdb (http://cr.liclab.net/crdb/), with the aim of curating a large number of available resources for CRs and providing extensive annotations and analyses of CRs to help biological researchers clarify the regulation mechanism and function of CRs. The CRdb database comprised a total of 647 CRs and 2,591 ChIP-seq samples from more than 300 human tissues and cell types. These samples have been manually curated from NCBI GEO/SRA and ENCODE. Importantly, CRdb provided the abundant and detailed genetic annotations in CR-binding regions based on ChIP-seq. Furthermore, CRdb supported various functional annotations and upstream regulatory information on CRs. In particular, it embedded four types of CR regulatory analyses: CR gene set enrichment, CR-binding genomic region annotation, CR-TF co-occupancy analysis, and CR regulatory axis analysis. CRdb is a useful and powerful resource that can help in exploring the potential functions of CRs and their regulatory mechanism in diseases and biological processes.

Integrating Multi-Omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulatory, and Metabolic Pathways

Alteration of the status of the metabolic enzymes could be a probable way to regulate metabolic reprogramming, which is a critical cellular adaptation mechanism especially for cancer cells. Coordination among biological pathways, such as gene-regulatory, signaling, and metabolic pathways is crucial for regulating metabolic adaptation. Also, incorporation of resident microbial metabolic potential in human body can influence the interplay between the microbiome and the systemic or tissue metabolic environments. Systemic framework for model-based integration of multi-omics data can ultimately improve our understanding of metabolic reprogramming at holistic level. However, the interconnectivity and novel meta-pathway regulatory mechanisms are relatively lesser explored and understood. Hence, we propose a computational protocol that utilizes multi-omics data to identify probable cross-pathway regulatory and protein-protein interaction (PPI) links connecting signaling proteins or transcription factors or miRNAs to metabolic enzymes and their metabolites using network analysis and mathematical modeling. These cross-pathway links were shown to play important roles in metabolic reprogramming in cancer scenarios.

The network map of urotensin-II mediated signaling pathway in physiological and pathological conditions

Urotensin-II is a polypeptide ligand with neurohormone-like activity. It mediates downstream signaling pathways through G-protein-coupled receptor 14 (GPR14) also known as urotensin receptor (UTR). Urotensin-II is the most potent endogenous vasoconstrictor in mammals, promoting cardiovascular remodelling, cardiac fibrosis, and cardiomyocyte hypertrophy. It is also involved in other physiological and pathological activities, including neurosecretory effects, insulin resistance, atherosclerosis, kidney disease, and carcinogenic effects. Moreover, it is a notable player in the process of inflammatory injury, which leads to the development of inflammatory diseases. Urotensin-II/UTR expression stimulates the accumulation of monocytes and macrophages, which promote the adhesion molecules expression, chemokines activation and release of inflammatory cytokines at inflammatory injury sites. Therefore, urotensin-II turns out to be an important therapeutic target for the treatment options and management of associated diseases. The main downstream signaling pathways mediated through this urotensin-II /UTR system are RhoA/ROCK, MAPKs and PI3K/AKT. Due to the importance of urotensin-II systems in biomedicine, we consolidated a network map of urotensin-II /UTR signaling. The described signaling map comprises 33 activation/inhibition events, 31 catalysis events, 15 molecular associations, 40 gene regulation events, 60 types of protein expression, and 11 protein translocation events. The urotensin-II signaling pathway map is made freely accessible through the WikiPathways Database ( https://www.wikipathways.org/index.php/Pathway:WP5158 ). The availability of comprehensive urotensin-II signaling in the public resource will help understand the regulation and function of this pathway in normal and pathological conditions. We believe this resource will provide a platform to the scientific community in facilitating the identification of novel therapeutic drug targets for diseases associated with urotensin-II signaling.

Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning

Cancer is one of the leading causes of death worldwide and can be caused by environmental aspects (for example, exposure to asbestos), by human behavior (such as smoking), or by genetic factors. To understand which genes might be involved in patients’ survival, researchers have invented prognostic genetic signatures: lists of genes that can be used in scientific analyses to predict if a patient will survive or not. In this study, we joined together five different prognostic signatures, each of them related to a specific cancer type, to generate a unique pan-cancer prognostic signature, that contains 207 unique probesets related to 187 unique gene symbols, with one particular probeset present in two cancer type-specific signatures (203072_at related to the MYO1E gene). We applied our proposed pan-cancer signature with the Random Forests machine learning method to 57 microarray gene expression datasets of 12 different cancer types, and analyzed the results. We also compared the performance of our pan-cancer signature with the performances of two alternative prognostic signatures, and with the performances of each cancer type-specific signature on their corresponding cancer type-specific datasets. Our results confirmed the effectiveness of our prognostic pan-cancer signature. Moreover, we performed a pathway enrichment analysis, which indicated an association between the signature genes and a protein-protein interaction analysis, that highlighted PIK3R2 and FN1 as key genes having a fundamental relevance in our signature, suggesting an important role in pan-cancer prognosis for both of them.

A comprehensive survey of the approaches for pathway analysis using multi-omics data integration

Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method's practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.

CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research

BACKGROUND

Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated.

METHODS

To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with >440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes.

RESULTS

Analysis of >8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy.

CONCLUSIONS

Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/ . The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway .

Investigation of cell signalings and therapeutic targets in PTPRK-RSPO3 fusion-positive colorectal cancer

Introduction

Colorectal cancer (CRC) is one of the most deadly and common diseases in the world, accounting for over 881,000 casualties in 2018. The PTPRK-RSPO3 (P:R) fusion is a structural variation in CRC and well known for its ability to activate WNT signaling and tumorigenesis. However, till now, therapeutic targets and actionable drugs are limited in this subtype of cancer.

Materials and method

The purpose of this study is to identify key genes and cancer-related pathways specific for P:R fusion-positive CRC. In addition, we also inferred the actionable drugs in bioinformatics analysis using the Cancer Genome Atlas (TCGA) data.

Results

2,505 genes were altered in RNA expression specific for P:R fusion-positive CRC. By pathway analysis based on the altered genes, ten major cancer-related signaling pathways (Apoptosis, Direct p53, EGFR, ErbB, JAK-STAT, tyrosine kinases, Pathways in Cancer, SCF-KIT, VEGFR, and WNT-related Pathway) were significantly altered in P:R fusion-positive CRC. Among these pathways, the most altered cancer genes (ALK, ACSL3, AXIN, MYC, TP53, GNAQ, ACVR2A, and FAS) specific for P:R fusion and involved in multiple cancer pathways were considered to have a key role in P:R fusion-positive CRC. Based on the drug-target network analysis, crizotinib, alectinib, lorlatinib, brigatinib, ceritinib, erdafitinib, infigratinib and pemigatinib were selected as putative therapeutic candidates, since they were already used in routine clinical practice in other cancer types and target genes of the drugs were involved in multiple cancer-pathways.

CAF08 adjuvant enables single dose protection against respiratory syncytial virus infection in murine newborns

Respiratory syncytial virus is a leading cause of morbidity and mortality in children, due in part to their distinct immune system, characterized by impaired induction of Th 1 immunity. Here we show application of cationic adjuvant formulation CAF08, a liposomal vaccine formulation tailored to induce Th 1 immunity in early life via synergistic engagement of Toll-like Receptor 7/8 and the C-type lectin receptor Mincle. We apply quantitative phosphoproteomics to human dendritic cells and reveal a role for Protein Kinase C-δ for enhanced Th1 cytokine production in neonatal dendritic cells and identify signaling events resulting in antigen cross-presentation. In a murine in vivo model a single immunization at birth with CAF08-adjuvanted RSV pre-fusion antigen protects newborn mice from RSV infection by induction of antigen-specific CD8+ T-cells and Th1 cells. Overall, we describe a pediatric adjuvant formulation and characterize its mechanism of action providing a promising avenue for development of early life vaccines against RSV and other respiratory viral pathogens.

Dynamic changes in O-GlcNAcylation regulate osteoclast differentiation and bone loss via nucleoporin 153

Bone mass is maintained by the balance between osteoclast-induced bone resorption and osteoblast-triggered bone formation. In inflammatory arthritis such as rheumatoid arthritis (RA), however, increased osteoclast differentiation and activity skew this balance resulting in progressive bone loss. O-GlcNAcylation is a posttranslational modification with attachment of a single O-linked β-D-N-acetylglucosamine (O-GlcNAc) residue to serine or threonine residues of target proteins. Although O-GlcNAcylation is one of the most common protein modifications, its role in bone homeostasis has not been systematically investigated. We demonstrate that dynamic changes in O-GlcNAcylation are required for osteoclastogenesis. Increased O-GlcNAcylation promotes osteoclast differentiation during the early stages, whereas its downregulation is required for osteoclast maturation. At the molecular level, O-GlcNAcylation affects several pathways including oxidative phosphorylation and cell-cell fusion. TNFα fosters the dynamic regulation of O-GlcNAcylation to promote osteoclastogenesis in inflammatory arthritis. Targeted pharmaceutical or genetic inhibition of O-GlcNAc transferase (OGT) or O-GlcNAcase (OGA) arrests osteoclast differentiation during early stages of differentiation and during later maturation, respectively, and ameliorates bone loss in experimental arthritis. Knockdown of NUP153, an O-GlcNAcylation target, has similar effects as OGT inhibition and inhibits osteoclastogenesis. These findings highlight an important role of O-GlcNAcylation in osteoclastogenesis and may offer the potential to therapeutically interfere with pathologic bone resorption.

Challenges and Limitations of Biological Network Analysis

High-Throughput technologies are producing an increasing volume of data that needs large amounts of data storage, effective data models and efficient, possibly parallel analysis algorithms. Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other features, graph-based comparison of organisms' properties. For instance, in biological pathway representation, the nodes can represent proteins, RNA and fat molecules, while the edges represent the interaction between molecules. Otherwise, biological networks such as Protein-Protein Interaction (PPI) Networks, represent the biochemical interactions among proteins by using nodes that model the proteins from a given organism, and edges that model the protein-protein interactions, whereas pathway networks enable the representation of biochemical-reaction cascades that happen within the cells or tissues. In this paper, we discuss the main models for standard representation of pathways and PPI networks, the data models for the representation and exchange of pathway and protein interaction data, the main databases in which they are stored and the alignment algorithms for the comparison of pathways and PPI networks of different organisms. Finally, we discuss the challenges and the limitations of pathways and PPI network representation and analysis. We have identified that network alignment presents a lot of open problems worthy of further investigation, especially concerning pathway alignment.

Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we systematically compare 16 cell-cell communication inference resources and 7 methods, plus the consensus between the methods' predictions. Among the resources, we find few unique interactions, a varying degree of overlap, and an uneven coverage of specific pathways and tissue-enriched proteins. We then examine all possible combinations of methods and resources and show that both strongly influence the predicted intercellular interactions. Finally, we assess the agreement of cell-cell communication methods with spatial colocalisation, cytokine activities, and receptor protein abundance and find that predictions are generally coherent with those data modalities. To facilitate the use of the methods and resources described in this work, we provide LIANA, a LIgand-receptor ANalysis frAmework as an open-source interface to all the resources and methods.

High-throughput functional evaluation of human cancer-associated mutations using base editors

Comprehensive phenotypic characterization of the many mutations found in cancer tissues is one of the biggest challenges in cancer genomics. In this study, we evaluated the functional effects of 29,060 cancer-related transition mutations that result in protein variants on the survival and proliferation of non-tumorigenic lung cells using cytosine and adenine base editors and single guide RNA (sgRNA) libraries. By monitoring base editing efficiencies and outcomes using surrogate target sequences paired with sgRNA-encoding sequences on the lentiviral delivery construct, we identified sgRNAs that induced a single primary protein variant per sgRNA, enabling linking those mutations to the cellular phenotypes caused by base editing. The functions of the vast majority of the protein variants (28,458 variants, 98%) were classified as neutral or likely neutral; only 18 (0.06%) and 157 (0.5%) variants caused outgrowing and likely outgrowing phenotypes, respectively. We expect that our approach can be extended to more variants of unknown significance and other tumor types.

Construction of a prognostic immune-related lncRNA model and identification of the immune microenvironment in middle- or advanced-stage lung squamous carcinoma patients

Background

Methods

Results

Conclusion

Upregulation of cytokine signalling in platelets increases risk of thrombophilia in severe COVID-19 patients

Abnormal coagulation dynamics, including disseminated intravascular coagulopathy, pulmonary embolism, venous thromboembolism and risk of thrombosis are often associated with the severity of COVID-19. However, very little is known about the contribution of platelets in above pathogenesis. In order to decipher the pathophysiology of thrombophilia in COVID-19, we recruited severely ill patients from ICU, based on the above symptoms and higher D-dimer levels, and compared these parameters with their asymptomatic counterparts. Elevated levels of platelet-derived microparticles and platelet-leukocyte aggregates suggested the hyperactivation of platelets in ICU patients. Strikingly, platelet transcriptome analysis showed a greater association of IL-6 and TNF signalling pathways in ICU patients along with higher plasma levels of IL-6 and TNFα. In addition, upregulation of pathways like blood coagulation and hemostasis, as well as inflammation coexisted in platelets of these patients. Further, the increment of necrotic pathway and ROS-metabolic processes in platelets was suggestive of its procoagulant phenotype in ICU patients. This study suggests that higher plasma IL-6 and TNFα may trigger platelet activation and coagulation, and in turn aggravate thrombosis and hypercoagulation in severe COVID-19 patients. Therefore, the elevated IL-6 and TNFα, may serve as potential risk factors for platelet activation and thrombophilia in these patients.

Identification of significant genes with a poor prognosis in skin cutaneous malignant melanoma based on a bioinformatics analysis

Background

Skin cutaneous malignant melanoma (SKCM) is a deadly mutated malignancy that arises from melanocytes in the basal layer of the skin. This study sought to identify effective treatment targets that could serve as prospective therapeutic targets to improve patient outcomes.

Methods

The GSE83583, GSE111766, and GSE104849 data sets from the GPL10558 platform in the Gene Expression Omnibus (GEO) were used in this study. The candidate genes were identified using the GEO2R tool and a Venn diagram. The Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Gene and Genome (KEGG) preliminary analyses of the differentially expressed genes (DEGs) were conducted using the Database for Annotation, Visualization and Integrated Discovery, and R software. The protein-protein interaction (PPI) network was examined using Cytoscape software. The survminer package was used to examine the overall survival of patients with the identified genes. The Human Protein Atlas (HPA) was used to verify the protein levels of significant genes with poor prognosis. The highly expressed genes in the melanoma tissues were visualized using the ggplot2 package.

Results

In total, 160 DEGs from 124 melanoma tissues and 9 normal melanocyte tissues were examined in this study. Cytoscape displayed 19 central nodes from the 160 DEGs. The re-analysis showed that the cytochrome P450 family 1 subfamily B member 1 (CYP1B1) and protein kinase C beta (PRKCB) were significantly enriched in the micro ribonucleic acids (RNAs) in cancer.

Conclusions

CYP1B1 and PRKCB were overexpressed in and correlated with the poor prognosis of SKCM. Our findings might help explore the prognosis and diagnostic markers of SKCM.