Reactome: a database of reactions, pathways and biological processes

Integrated Transcriptome and Metabolomic Analysis Reveal Anti-Angiogenic Properties of Disarib, a Novel Bcl2-Specific Inhibitor

Transcriptomic profiling of several drugs in cancer cell lines has been utilised to obtain drug-specific signatures and guided combination therapy to combat drug resistance and toxicity. Global metabolomics reflects changes due to altered activity of enzymes, environmental factors, etc. Integrating transcriptomics and metabolomics can provide genotype-phenotype correlation, providing meaningful insights into alterations in gene expression and its outcome to understand differential metabolism and guide therapy. This study uses a multi-omics approach to understand the global gene expression and metabolite changes induced by Disarib, a novel Bcl2-specific inhibitor in the Ehrlich adenocarcinoma (EAC) breast cancer mouse model. RNAseq analysis was performed on EAC mouse tumours treated with Disarib and compared to the controls. The expression of 6 oncogenes and 101 tumour suppressor genes interacting with Bcl2 and Bak were modulated upon Disarib treatment. Cancer hallmark pathways like DNA repair, Cell cycle, angiogenesis, and mitochondrial metabolism were downregulated, and programmed cell death platelet-related pathways were upregulated. Global metabolomic profiling using LC-MS revealed that Oncometabolites like carnitine, oleic acid, glycine, and arginine were elevated in tumour mice compared to normal and were downregulated upon Disarib treatment. Integrated transcriptomic and metabolomic profiles identified arginine metabolism, histidine, and purine metabolism to be altered upon Disarib treatment. Pro-angiogenic metabolites, arginine, palmitic acid, oleic acid, and myristoleic acid were downregulated in Disarib-treated mice. We further validated the effect of Disarib on angiogenesis by qRT-PCR analysis of genes in the VEGF pathway. Disarib treatment led to the downregulation of pro-angiogenic markers. Furthermore, the chorioallantoic membrane assay displayed a reduction in the formation of the number of secondary blood vessels upon Disarib treatment. Disarib reduces tumours by reducing oncometabolite and activating apoptosis and downregulating angiogenesis.

Drug repurposing: An emerging strategy in alleviating skin cancer

Skin cancer is one of the most common forms of cancer. Several million people are estimated to have affected with this condition worldwide. Skin cancer generally includes melanoma and non-melanoma with the former being the most dangerous. Chemotherapy has been one of the key therapeutic strategies employed in the treatment of skin cancer, especially in advanced stages of the disease. It could be also used as an adjuvant with other treatment modalities depending on the type of skin cancer. However, there are several shortfalls associated with the use of chemotherapy such as non-selectivity, tumour resistance, life-threatening toxicities, and the exorbitant cost of medicines. Furthermore, new drug discovery is a lengthy and costly process with minimal likelihood of success. Thus, drug repurposing (DR) has emerged as a new avenue where the drug approved formerly for the treatment of one disease can be used for the treatment of another disease like cancer. This approach is greatly beneficial over the de novo approach in terms of time and cost. Moreover, there is minimal risk of failure of repurposed therapeutics in clinical trials. There are a considerable number of studies that have reported on drugs repurposed for the treatment of skin cancer. Thus, the present manuscript offers a comprehensive overview of drugs that have been investigated as repurposing candidates for the efficient treatment of skin cancers mainly melanoma and its oncogenic subtypes, and non-melanoma. The prospects of repurposing phytochemicals against skin cancer are also discussed. Furthermore, repurposed drug delivery via topical route and repurposed drugs in clinical trials are briefed. Based on the findings from the reported studies discussed in this manuscript, drug repurposing emerges to be a promising approach and thus is expected to offer efficient treatment at a reasonable cost in devitalizing skin cancer.

CAR-T cell therapy targeting B cell maturation antigen is effective for relapsed/refractory multiple myeloma, including cases with poor performance status

In this open-label, single-arm, phase I/II clinical trial, we evaluated the efficacy of anti-B cell maturation antigen (BCMA) chimeric antigen receptor (CAR)-T cell (HDS269B) therapy in 49 relapsed/refractory multiple myeloma (RRMM) patients, including 20 with Eastern Cooperative Oncology Group (ECOG) grade 3-4. After HDS269B infusion (9 × 10⁶ CAR⁺ cells/kg), 17 patients (34.69%, 11 ECOG 0-2, 6 ECOG 3-4) developed cytokine release syndrome [grade 1-2: 14 patients (28.57%); grade 3: 3 patients (6.12%)]. The objective response rate (ORR) was 77%, with a complete response (CR) achieved in 47%. Ongoing response >12 months occurred in 15 patients, and was extended beyond 38 months in one patient. The median progression-free survival (PFS) and overall survival (OS) were 10 months (95% CI 5.3-14.7) and 29 months (95% CI 10.0-48.0), respectively. The PFS (12 months) and OS (18 months) rates were 41.64% and 62.76%, respectively. In patients with ECOG 0-2 and 3-4, ORR was 79.31% (23/29) and 75.0% (15/20) and PFS were 15 months (95% CI 5.4-24.6) and 4 months (95% CI 0-11.7), respectively. OS was not reached in ECOG 0-2 patients, but was 10.5 months (95% CI 0-22) in ECOG 3-4 patients. Single-cell sequencing indicated that treatment efficacy might be related to mTORC1 signaling. Thus, HDS269B therapy is safe and effective for RRMM patients, even those with ECOG 3-4.

Discovery of novel PRMT5 inhibitors bearing a methylpiperazinyl moiety

Background: PRMT5 is an epigenetics-related enzyme, which plays a critical role in cancer development. Hence PRMT5 inhibition has been validated as a promising therapeutic strategy. Methods & Results: We synthesized a series of methylpiperazinyl derivatives as novel PRMT5 inhibitors that were achieved by scaffold-hopping from EPZ015666 by virtual screening followed by rational drug design. Among all compounds 43g, bearing a thiourea linker, showed antitumor activity across multiple cancer cell lines and reduced the level of symmetric arginine dimethylation of SmD3 dose-dependently. Moreover, 43g selectively inhibited PRMT5 among protein arginine methyltransferase isoforms. Further proteomics analysis revealed that 43g remarkably reduced the global arginine dimethylation level in a cellular context. Conclusion: This work provides new chemical templates for future structural optimization of PRMT5-related cancer treatments.

N(6)-methyladenosine methylation-regulated polo-like kinase 1 cell cycle homeostasis as a potential target of radiotherapy in pancreatic adenocarcinoma

In pancreatic cancer, methyltransferase-like 3 (METTL3), a N(6)-methyladenosine (m6A) methyltransferase, has a favorable effect on tumors and is a risk factor for patients' prognosis. However, the details of what genes are regulated by METTL3 remain unknown. Several RNAs are methylated, and what genes are favored in pancreatic cancer remains unclear. By epitranscriptomic analysis, we report that polo-like kinase 1 (PLK1) is an important hub gene defining patient prognosis in pancreatic cancer and that RNA methylation is involved in regulating its cell cycle-specific expression. We found that insulin like growth factor 2 mRNA binding protein 2 (IGF2BP2) binds to m6A of PLK1 3' untranslated region and is involved in upregulating PLK1 expression and that demethylation of this site activates the ataxia telangiectasia and Rad3-related protein pathway by replicating stress and increasing mitotic catastrophe, resulting in increased radiosensitivity. This suggests that PLK1 methylation is essential for cell cycle maintenance in pancreatic cancer and is a new therapeutic target.

Analyzing the Systems Biology Effects of COVID-19 mRNA Vaccines to Assess Their Safety and Putative Side Effects

COVID-19 vaccines have been instrumental tools in reducing the impact of SARS-CoV-2 infections around the world by preventing 80% to 90% of hospitalizations and deaths from reinfection, in addition to preventing 40% to 65% of symptomatic illnesses. However, the simultaneous large-scale vaccination of the global population will indubitably unveil heterogeneity in immune responses as well as in the propensity to developing post-vaccine adverse events, especially in vulnerable individuals. Herein, we applied a systems biology workflow, integrating vaccine transcriptional signatures with chemogenomics, to study the pharmacological effects of mRNA vaccines. First, we derived transcriptional signatures and predicted their biological effects using pathway enrichment and network approaches. Second, we queried the Connectivity Map (CMap) to prioritize adverse events hypotheses. Finally, we accepted higher-confidence hypotheses that have been predicted by independent approaches. Our results reveal that the mRNA-based BNT162b2 vaccine affects immune response pathways related to interferon and cytokine signaling, which should lead to vaccine success, but may also result in some adverse events. Our results emphasize the effects of BNT162b2 on calcium homeostasis, which could be contributing to some frequently encountered adverse events related to mRNA vaccines. Notably, cardiac side effects were signaled in the CMap query results. In summary, our approach has identified mechanisms underlying both the expected protective effects of vaccination as well as possible post-vaccine adverse effects. Our study illustrates the power of systems biology approaches in improving our understanding of the comprehensive biological response to vaccination against COVID-19.

Metabolomic and Transcriptomic Profiling Identified Significant Genes in Thymic Epithelial Tumor

Thymomas and thymic carcinomas are malignant thymic epithelial tumors (TETs) with poor outcomes if non-resectable. However, the tumorigenesis, especially the metabolic mechanisms involved, is poorly studied. Untargeted metabolomics analysis was utilized to screen for differential metabolic profiles between thymic cancerous tissues and adjunct noncancerous tissues. Combined with transcriptomic data, we comprehensively evaluated the metabolic patterns of TETs. Metabolic scores were constructed to quantify the metabolic patterns of individual tumors. Subsequent investigation of distinct clinical outcomes and the immune landscape associated with the metabolic scores was conducted. Two distinct metabolic patterns and differential metabolic scores were identified between TETs, which were enriched in a variety of biological pathways and correlated with clinical outcomes. In particular, a high metabolic score was highly associated with poorer survival outcomes and immunosuppressive status. More importantly, the expression of two prognostic genes (ASNS and BLVRA) identified from differential metabolism-related genes was significantly associated with patient survival and may play a key role in the tumorigenesis of TETs. Our findings suggest that differential metabolic patterns in TETs are relevant to tumorigenesis and clinical outcome. Specific transcriptomic alterations in differential metabolism-related genes may serve as predictive biomarkers of survival outcomes and potential targets for the treatment of patients with TETs.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

Cooperation between KDM6B overexpression and TET2 deficiency in the pathogenesis of chronic myelomonocytic leukemia

Loss-of-function TET2 mutations are recurrent somatic lesions in chronic myelomonocytic leukemia (CMML). KDM6B encodes a histone demethylase involved in innate immune regulation that is overexpressed in CMML. We conducted genomic and transcriptomic analyses in treatment naïve CMML patients and observed that the patients carrying both TET2 mutations and KDM6B overexpression constituted 18% of the cohort and 42% of patients with TET2 mutations. We therefore hypothesized that KDM6B overexpression cooperated with TET2 deficiency in CMML pathogenesis. We developed a double-lesion mouse model with both aberrations, and discovered that the mice exhibited a more prominent CMML-like phenotype than mice with either Tet2 deficiency or KDM6B overexpression alone. The phenotype includes monocytosis, anemia, splenomegaly, and increased frequencies and repopulating activity of bone marrow (BM) hematopoietic stem and progenitor cells (HSPCs). Significant transcriptional alterations were identified in double-lesion mice, which were associated with activation of proinflammatory signals and repression of signals maintaining genome stability. Finally, KDM6B inhibitor reduced BM repopulating activity of double-lesion mice and tumor burden in mice transplanted with BM-HSPCs from CMML patients with TET2 mutations. These data indicate that TET2 deficiency and KDM6B overexpression cooperate in CMML pathogenesis of and that KDM6B could serve as a potential therapeutic target in this disease.

ASpediaFI: Functional Interaction Analysis of Alternative Splicing Events

Alternative splicing (AS) regulates biological processes governing phenotypes and diseases. Differential AS (DAS) gene test methods have been developed to investigate important exonic expression from high-throughput datasets. However, the DAS events extracted using statistical tests are insufficient to delineate relevant biological processes. In this study, we developed a novel application, Alternative Splicing Encyclopedia: Functional Interaction (ASpediaFI), to systemically identify DAS events and co-regulated genes and pathways. ASpediaFI establishes a heterogeneous interaction network of genes and their feature nodes (i.e., AS events and pathways) connected by co-expression or pathway gene set knowledge. Next, ASpediaFI explores the interaction network using the random walk with restart algorithm and interrogates the proximity from a query gene set. Finally, ASpediaFI extracts significant AS events, genes, and pathways. To evaluate the performance of our method, we simulated RNA sequencing (RNA-seq) datasets to consider various conditions of sequencing depth and sample size. The performance was compared with that of other methods. Additionally, we analyzed three public datasets of cancer patients or cell lines to evaluate how well ASpediaFI detects biologically relevant candidates. ASpediaFI exhibits strong performance in both simulated and public datasets. Our integrative approach reveals that DAS events that recognize a global co-expression network and relevant pathways determine the functional importance of spliced genes in the subnetwork. ASpediaFI is publicly available at https://bioconductor.org/packages/ASpediaFI.

An Interaction-Based Method for Refining Results From Gene Set Enrichment Analysis

Purpose: To demonstrate an interaction-based method for the refinement of Gene Set Enrichment Analysis (GSEA) results.

Method: Intravitreal injection of miR-124-3p antagomir was used to knockdown the expression of miR-124-3p in mouse retina at postnatal day 3 (P3). Whole retinal RNA was extracted for mRNA transcriptome sequencing at P9. After preprocessing the dataset, GSEA was performed, and the leading-edge subsets were obtained. The Apriori algorithm was used to identify the frequent genes or gene sets from the union of the leading-edge subsets. A new statistic d was introduced to evaluate the frequent genes or gene sets. Reverse transcription quantitative PCR (RT-qPCR) was performed to validate the expression trend of candidate genes after the knockdown of miR-124-3p.

Results: A total of 115,140 assembled transcript sequences were obtained from the clean data. With GSEA, the NOD-like receptor signaling pathway, C-type-like lectin receptor signaling pathway, phagosome, necroptosis, JAK-STAT signaling pathway, Toll-like receptor signaling pathway, leukocyte transendothelial migration, chemokine signaling pathway, NF-kappa B signaling pathway and RIG-I-like signaling pathway were identified as the top 10 enriched pathways, and their leading-edge subsets were obtained. After being refined by the Apriori algorithm and sorted by the value of the modulus of d, Prkcd, Irf9, Stat3, Cxcl12, Stat1, Stat2, Isg15, Eif2ak2, Il6st, Pdgfra, Socs4 and Csf2ra had the significant number of interactions and the greatest value of d to downstream genes among all frequent transactions. Results of RT-qPCR validation for the expression of candidate genes after the knockdown of miR-124-3p showed a similar trend to the RNA-Seq results.

Conclusion: This study indicated that using the Apriori algorithm and defining the statistic d was a novel way to refine the GSEA results. We hope to convey the intricacies from the computational results to the low-throughput experiments, and to plan experimental investigations specifically.

ProMetheusDB: An In-Depth Analysis of the High-Quality Human Methyl-proteome

Protein arginine (R) methylation is a post-translational modification involved in various biological processes, such as RNA splicing, DNA repair, immune response, signal transduction, and tumor development. Although several advancements were made in the study of this modification by mass spectrometry, researchers still face the problem of a high false discovery rate. We present a dataset of high-quality methylations obtained from several different heavy methyl stable isotope labeling with amino acids in cell culture experiments analyzed with a machine learning–based tool and show that this model allows for improved high-confidence identification of real methyl-peptides. Overall, our results are consistent with the notion that protein R methylation modulates protein–RNA interactions and suggest a role in rewiring protein–protein interactions, for which we provide experimental evidence for a representative case (i.e., NONO [non-POU domain–containing octamer-binding protein]–paraspeckle component 1 [PSPC1]). Upon intersecting our R-methyl-sites dataset with the PhosphoSitePlus phosphorylation dataset, we observed that R methylation correlates differently with S/T-Y phosphorylation in response to various stimuli. Finally, we explored the application of heavy methyl stable isotope labeling with amino acids in cell culture to identify unconventional methylated residues and successfully identified novel histone methylation marks on serine 28 and threonine 32 of H3. The database generated, named ProMetheusDB, is freely accessible at https://bioserver.ieo.it/shiny/app/prometheusdb.

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hmSEEKER 2.0 identifies methyl-peptides from hmSILAC data through machine learning.

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Arginine methylation plays a role in modulating protein–protein interactions.

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Arginine methylations occur more frequently in proximity of phosphorylation sites.

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hmSEEKER 2.0 was used to identify methylations occurring on nonstandard amino acids.

Driver gene detection through Bayesian network integration of mutation and expression profiles

MOTIVATION

The identification of mutated driver genes and the corresponding pathways is one of the primary goals in understanding tumorigenesis at the patient level. Integration of multi-dimensional genomic data from existing repositories, e.g., The Cancer Genome Atlas (TCGA), offers an effective way to tackle this issue. In this study, we aimed to leverage the complementary genomic information of individuals and create an integrative framework to identify cancer-related driver genes. Specifically, based on pinpointed differentially expressed genes, variants in somatic mutations and a gene interaction network, we proposed an unsupervised Bayesian network integration (BNI) method to detect driver genes and estimate the disease propagation at the patient and/or cohort levels. This new method first captures inherent structural information to construct a functional gene mutation network and then extracts the driver genes and their controlled downstream modules using the minimum cover subset method.

RESULTS

Using other credible sources (e.g. Cancer Gene Census and Network of Cancer Genes), we validated the driver genes predicted by the BNI method in three TCGA pan-cancer cohorts. The proposed method provides an effective approach to address tumor heterogeneity faced by personalized medicine. The pinpointed drivers warrant further wet laboratory validation.

AVAILABILITY AND IMPLEMENTATION

The supplementary tables and source code can be obtained from https://xavieruniversityoflouisiana.sharefile.com/d-se6df2c8d0ebe4800a3030311efddafe5.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Rapid Molecular Diagnosis of Genetically Inherited Neuromuscular Disorders Using Next-Generation Sequencing Technologies

Neuromuscular diseases are genetically highly heterogeneous, and differential diagnosis can be challenging. Over a 3-year period, we prospectively analyzed 268 pediatric and adult patients with a suspected diagnosis of inherited neuromuscular disorder (INMD) using comprehensive gene-panel analysis and next-generation sequencing. The rate of diagnosis increased exponentially with the addition of genes to successive versions of the INMD panel, from 31% for the first iteration (278 genes) to 40% for the last (324 genes). The global mean diagnostic rate was 36% (97/268 patients), with a diagnostic turnaround time of 4–6 weeks. Most diagnoses corresponded to muscular dystrophies/myopathies (68.37%) and peripheral nerve diseases (22.45%). The most common causative genes, TTN, RYR1, and ANO5, accounted for almost 30% of the diagnosed cases. Finally, we evaluated the utility of the differential diagnosis tool Phenomizer, which established a correlation between the phenotype and molecular findings in 21% of the diagnosed patients. In summary, comprehensive gene-panel analysis of all genes implicated in neuromuscular diseases facilitates a rapid diagnosis and provides a high diagnostic yield.

Whole-Genome Sequencing Identifies PPARGC1A as a Putative Modifier of Cancer Risk in BRCA1/2 Mutation Carriers

While BRCA1 and BRCA2 mutations are known to confer the largest risk of breast cancer and ovarian cancer, the incomplete penetrance of the mutations and the substantial variability in age at cancer onset among carriers suggest additional factors modifying the risk of cancer in BRCA1/2 mutation carriers. To identify genetic modifiers of BRCA1/2, we carried out a whole-genome sequencing study of 66 ovarian cancer patients that were enriched with BRCA carriers, followed by validation using data from the Pan-Cancer Analysis of Whole Genomes Consortium. We found PPARGC1A, a master regulator of mitochondrial biogenesis and function, to be highly mutated in BRCA carriers, and patients with both PPARGC1A and BRCA1/2 mutations were diagnosed with breast or ovarian cancer at significantly younger ages, while the mutation status of each gene alone did not significantly associate with age of onset. Our study suggests PPARGC1A as a possible BRCA modifier gene. Upon further validation, this finding can help improve cancer risk prediction and provide personalized preventive care for BRCA carriers.

Network Pharmacology Approach for Medicinal Plants: Review and Assessment

Natural products have played a critical role in medicine due to their ability to bind and modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive scaffolds for the treatment of multiple disorders. The less adverse effects, affordability, and easy accessibility highlight their potential in traditional remedies. Identifying pharmacological targets from active ingredients of medicinal plants has become a hot topic for biomedical research to generate innovative therapies. By developing an unprecedented opportunity for the systematic investigation of traditional medicines, network pharmacology is evolving as a systematic paradigm and becoming a frontier research field of drug discovery and development. The advancement of network pharmacology has opened up new avenues for understanding the complex bioactive components found in various medicinal plants. This study is attributed to a comprehensive summary of network pharmacology based on current research, highlighting various active ingredients, related techniques/tools/databases, and drug discovery and development applications. Moreover, this study would serve as a protocol for discovering novel compounds to explore the full range of biological potential of traditionally used plants. We have attempted to cover this vast topic in the review form. We hope it will serve as a significant pioneer for researchers working with medicinal plants by employing network pharmacology approaches.

BioTAGME: A Comprehensive Platform for Biological Knowledge Network Analysis

The inference of novel knowledge and new hypotheses from the current literature analysis is crucial in making new scientific discoveries. In bio-medicine, given the enormous amount of literature and knowledge bases available, the automatic gain of knowledge concerning relationships among biological elements, in the form of semantically related terms (or entities), is rising novel research challenges and corresponding applications. In this regard, we propose BioTAGME, a system that combines an entity-annotation framework based on Wikipedia corpus (i.e., TAGME tool) with a network-based inference methodology (i.e., DT-Hybrid). This integration aims to create an extensive Knowledge Graph modeling relations among biological terms and phrases extracted from titles and abstracts of papers available in PubMed. The framework consists of a back-end and a front-end. The back-end is entirely implemented in Scala and runs on top of a Spark cluster that distributes the computing effort among several machines. The front-end is released through the Laravel framework, connected with the Neo4j graph database to store the knowledge graph.

Gene Expression Profiling of Glioblastoma to Recognize Potential Biomarker Candidates

Glioblastoma is an aggressive malignant tumor of the brain and spinal cord. Due to the blood-brain barrier, the accessibility of its treatments still remains significantly challenging. Unfortunately, the recurrence rates of glioblastoma upon surgery are very high too. Hence, understanding the molecular drivers of disease progression is valuable. In this study, we aimed to investigate the molecular drivers responsible for glioblastoma progression and identify valid biomarkers. Three microarray expression profiles GSE90604, GSE50601, and GSE134470 containing healthy and glioblastoma-affected samples revealed overlapping differentially expressed genes (DEGs). The interrelational pathway enrichment analysis elucidated the halt of cell cycle checkpoints and activation of signaling pathways and led to the identification of 6 predominant hub genes. Validation of hub genes in comparison with The Cancer Genome Atlas datasets identified the potential biomarkers of glioblastoma. The study evaluated two significantly upregulated genes, SPARC (secreted protein acidic and rich in cysteine) and VIM (vimentin) for glioblastoma. The genes CACNA1E (calcium voltage-gated channel subunit alpha1 e), SH3GL2 (SH3 domain-containing GRB2-like 2, endophilin A1), and DDN (dendrin) were identified as under-expressed genes as compared to the normal and pan-cancer tissues along with prominent putative prognostic biomarker potentials. The genes DDN and SH3GL2 were found to be upregulated in the proneural subtype, while CACNA1E in the mesenchymal subtype of glioblastoma exhibits good prognostic potential. The mutational analysis also revealed the benign, possibly, and probably damaging substitution mutations. The correlation between the DEG and survival in glioblastoma was evaluated using the Kaplan-Meier plots, and VIM had a greater life expectancy of 60.25 months. Overall, this study identified key candidate genes that might serve as predictive biomarkers for glioblastoma.

Bone Progenitors Pull the Strings on the Early Metabolic Rewiring Occurring in Prostate Cancer Cells

Metastatic prostate cancer (PCa) cells soiling in the bone require a metabolic adaptation. Here, we identified the metabolic genes fueling the seeding of PCa in the bone niche. Using a transwell co-culture system of PCa (PC3) and bone progenitor cells (MC3T3 or Raw264.7), we assessed the transcriptome of PC3 cells modulated by soluble factors released from bone precursors. In a Principal Component Analysis using transcriptomic data from human PCa samples (GSE74685), the altered metabolic genes found in vitro were able to stratify PCa patients in two defined groups: primary PCa and bone metastasis, confirmed by an unsupervised clustering analysis. Thus, the early transcriptional metabolic profile triggered in the in vitro model has a clinical correlate in human bone metastatic samples. Further, the expression levels of five metabolic genes (VDR, PPARA, SLC16A1, GPX1 and PAPSS2) were independent risk-predictors of death in the SU2C-PCF dataset and a risk score model built using this lipid-associated signature was able to discriminate a subgroup of bone metastatic PCa patients with a 23-fold higher risk of death. This signature was validated in a PDX pre-clinical model when comparing MDA-PCa-183 growing intrafemorally vs. subcutaneously, and appears to be under the regulatory control of the Protein Kinase A (PKA) signaling pathway. Secretome analyses of conditioned media showcased fibronectin and type-1 collagen as critical bone-secreted factors that could regulate tumoral PKA. Overall, we identified a novel lipid gene signature, driving PCa aggressive metastatic disease pointing to PKA as a potential hub to halt progression.

Genome-wide analysis of somatic noncoding mutation patterns in cancer

We established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types. Protein-coding events captured well-established drivers. Noncoding events near tissue-specific genes, such as ALB in the liver or KLK3 in the prostate, characterized localized passenger mutation patterns and may reflect tumor-cell-of-origin imprinting. Noncoding events in regulatory promoter and enhancer regions frequently involved cancer-relevant genes such as BCL6, FGFR2, RAD51B, SMC6, TERT, and XBP1 and represent possible drivers. Unlike most noncoding regulatory events, XBP1 mutations primarily accumulated outside the gene's promoter, and we validated their effect on gene expression using CRISPR-interference screening and luciferase reporter assays. Broadly, our study provides a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics.

Transcriptional Profiling and Deriving a Seven-Gene Signature That Discriminates Active and Latent Tuberculosis: An Integrative Bioinformatics Approach

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (M.tb.). Our integrative analysis aims to identify the transcriptional profiling and gene expression signature that distinguish individuals with active TB (ATB) disease, and those with latent tuberculosis infection (LTBI). In the present study, we reanalyzed a microarray dataset (GSE37250) from GEO database and explored the data for differential gene expression analysis between those with ATB and LTBI derived from Malawi and South African cohorts. We used BRB array tool to distinguish DEGs (differentially expressed genes) between ATB and LTBI. Pathway enrichment analysis of DEGs was performed using DAVID bioinformatics tool. The protein–protein interaction (PPI) network of most upregulated genes was constructed using STRING analysis. We have identified 375 upregulated genes and 152 downregulated genes differentially expressed between ATB and LTBI samples commonly shared among Malawi and South African cohorts. The constructed PPI network was significantly enriched with 76 nodes connected to 151 edges. The enriched GO term/pathways were mainly related to expression of IFN stimulated genes, interleukin-1 production, and NOD-like receptor signaling pathway. Downregulated genes were significantly enriched in the Wnt signaling, B cell development, and B cell receptor signaling pathways. The short-listed DEGs were validated in a microarray data from an independent cohort (GSE19491). ROC curve analysis was done to assess the diagnostic accuracy of the gene signature in discrimination of active and latent tuberculosis. Thus, we have derived a seven-gene signature, which included five upregulated genes FCGR1B, ANKRD22, CARD17, IFITM3, TNFAIP6 and two downregulated genes FCGBP and KLF12, as a biomarker for discrimination of active and latent tuberculosis. The identified genes have a sensitivity of 80–100% and specificity of 80–95%. Area under the curve (AUC) value of the genes ranged from 0.84 to 1. This seven-gene signature has a high diagnostic accuracy in discrimination of active and latent tuberculosis.

Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx

Metabolic “dark matter” describes currently unknown metabolic processes, which form a blind spot in our general understanding of metabolism and slow down the development of biosynthetic cell factories and naturally derived pharmaceuticals. Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. In this work, we use 489 generalized enzymatic reaction rules to map both known and unknown metabolic processes around a biochemical database of 1.5 million biological compounds. We predict over 5 million reactions and integrate nearly 2 million naturally and synthetically-derived compounds into the global network of biochemical knowledge, named ATLASx. ATLASx is available to researchers as a powerful online platform that supports the prediction and analysis of biochemical pathways and evaluates the biochemical vicinity of molecule classes (https://lcsb-databases.epfl.ch/Atlas2).

“Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. Here the authors present ATLASx, a repository of known and predicted enzymatic reaction, connecting millions of compounds to help synthetic biologists and metabolic engineers to design and explore metabolic pathways.”

Transcriptome-wide association study of coronary artery disease identifies novel susceptibility genes

The majority of risk loci identified by genome-wide association studies (GWAS) are in non-coding regions, hampering their functional interpretation. Instead, transcriptome-wide association studies (TWAS) identify gene-trait associations, which can be used to prioritize candidate genes in disease-relevant tissue(s). Here, we aimed to systematically identify susceptibility genes for coronary artery disease (CAD) by TWAS. We trained prediction models of nine CAD-relevant tissues using EpiXcan based on two genetics-of-gene-expression panels, the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) and the Genotype-Tissue Expression (GTEx). Based on these prediction models, we imputed gene expression of respective tissues from individual-level genotype data on 37,997 CAD cases and 42,854 controls for the subsequent gene-trait association analysis. Transcriptome-wide significant association (i.e. P < 3.85e-6) was observed for 114 genes. Of these, 96 resided within previously identified GWAS risk loci and 18 were novel. Stepwise analyses were performed to study their plausibility, biological function, and pathogenicity in CAD, including analyses for colocalization, damaging mutations, pathway enrichment, phenome-wide associations with human data and expression-traits correlations using mouse data. Finally, CRISPR/Cas9-based gene knockdown of two newly identified TWAS genes, RGS19 and KPTN, in a human hepatocyte cell line resulted in reduced secretion of APOB100 and lipids in the cell culture medium. Our CAD TWAS work (i) prioritized candidate causal genes at known GWAS loci, (ii) identified 18 novel genes to be associated with CAD, and iii) suggested potential tissues and pathways of action for these TWAS CAD genes.

An Integrative Network Approach to Identify Common Genes for the Therapeutics in Tuberculosis and Its Overlapping Non-Communicable Diseases

Tuberculosis (TB) is the leading cause of death from a single infectious agent. The estimated total global TB deaths in 2019 were 1.4 million. The decline in TB incidence rate is very slow, while the burden of noncommunicable diseases (NCDs) is exponentially increasing in low- and middle-income countries, where the prevention and treatment of TB disease remains a great burden, and there is enough empirical evidence (scientific evidence) to justify a greater research emphasis on the syndemic interaction between TB and NCDs. The current study was proposed to build a disease-gene network based on overlapping TB with NCDs (overlapping means genes involved in TB and other/s NCDs), such as Parkinson’s disease, cardiovascular disease, diabetes mellitus, rheumatoid arthritis, and lung cancer. We compared the TB-associated genes with genes of its overlapping NCDs to determine the gene-disease relationship. Next, we constructed the gene interaction network of disease-genes by integrating curated and experimentally validated interactions in humans and find the 13 highly clustered modules in the network, which contains a total of 86 hub genes that are commonly associated with TB and its overlapping NCDs, which are largely involved in the Inflammatory response, cellular response to cytokine stimulus, response to cytokine, cytokine-mediated signaling pathway, defense response, response to stress and immune system process. Moreover, the identified hub genes and their respective drugs were exploited to build a bipartite network that assists in deciphering the drug-target interaction, highlighting the influential roles of these drugs on apparently unrelated targets and pathways. Targeting these hub proteins by using drugs combination or drug repurposing approaches will improve the clinical conditions in comorbidity, enhance the potency of a few drugs, and give a synergistic effect with better outcomes. Thus, understanding the Mycobacterium tuberculosis (Mtb) infection and associated NCDs is a high priority to contain its short and long-term effects on human health. Our network-based analysis opens a new horizon for more personalized treatment, drug-repurposing opportunities, investigates new targets, multidrug treatment, and can uncover several side effects of unrelated drugs for TB and its overlapping NCDs.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen, and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semiautomated integration of rapidly emerging data sets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 63 278 host-host protein, and 1221 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is made publicly accessible via a web interface and via API calls based on the Bolt protocol. Details for accessing the database are provided on a landing page (https://neo4covid19.ncats.io/). We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.

Methods to Improve Molecular Diagnosis in Genomic Cold Cases in Pediatric Neurology

During the last decade, genetic testing has emerged as an important etiological diagnostic tool for Mendelian diseases, including pediatric neurological conditions. A genetic diagnosis has a considerable impact on disease management and treatment; however, many cases remain undiagnosed after applying standard diagnostic sequencing techniques. This review discusses various methods to improve the molecular diagnostic rates in these genomic cold cases. We discuss extended analysis methods to consider, non-Mendelian inheritance models, mosaicism, dual/multiple diagnoses, periodic re-analysis, artificial intelligence tools, and deep phenotyping, in addition to integrating various omics methods to improve variant prioritization. Last, novel genomic technologies, including long-read sequencing, artificial long-read sequencing, and optical genome mapping are discussed. In conclusion, a more comprehensive molecular analysis and a timely re-analysis of unsolved cases are imperative to improve diagnostic rates. In addition, our current understanding of the human genome is still limited due to restrictions in technologies. Novel technologies are now available that improve upon some of these limitations and can capture all human genomic variation more accurately. Last, we recommend a more routine implementation of high molecular weight DNA extraction methods that is coherent with the ability to use and/or optimally benefit from these novel genomic methods.

MHC-II Signature Correlates With Anti-Tumor Immunity and Predicts anti-PD-L1 Response of Bladder Cancer

A large proportion of anti-tumor immunity research is focused on major histocompatibility complex class I (MHC-I) molecules and CD8+ T cells. Despite mounting evidence has shown that CD4+ T cells play a major role in anti-tumor immunity, the role of the MHC-II molecules in tumor immunotherapy has not been thoroughly researched and reported. In this study, we defined a MHC-II signature for the first time by calculating the enrichment score of MHC-II protein binding pathway with a single sample gene set enrichment analysis (ssGSEA) algorithm. To evaluate and validate the predictive value of the MHC class II (MHC-II) signature, we collected the transcriptome, mutation data and matched clinical data of bladder cancer patients from IMvigor210, The Cancer Genome Atlas (TCGA) databases and Gene Expression Omnibus (GEO) databases. Comprehensive analyses of immunome, transcriptome, metabolome, genome and drugome were performed in order to determine the association of MHC-II signature and tumor immunotherapy. We identified that MHC-II signature is an independent and favorable predictor of immune response and the prognosis of bladder cancer treated with immune checkpoint inhibitors (ICIs), one that may be superior to tumor mutation burden. MHC-II signature was significantly associated with increased immune cell infiltration and levels of immune-related gene expression signatures. Additionally, transcriptomic analysis showed immune activation in the high-MHC-II signature subgroup, whereas it showed fatty acid metabolism and glucuronidation in the low-MHC-II signature subgroup. Moreover, exploration of corresponding genomic profiles highlighted the significance of tumor protein p53 (TP53) and fibroblast growth factor receptor 3 (FGFR3) alterations. Our results also allowed for the identification of candidate compounds for combined immunotherapy treatment that may be beneficial for patients with bladder cancer and a high MHC-II signature. In conclusion, this study provides a new perspective on MHC-II signature, as an independent and favorable predictor of immune response and prognosis of bladder cancer treated with ICIs.

A Novel Homozygous TTC7A Missense Mutation Results in Familial Multiple Intestinal Atresia and Combined Immunodeficiency

Rare autosomal-recessive variants in tetratricopeptide repeat domain 7A (TTC7A) gene have been shown to cause intestinal and immune disorders of variable severity. Missense mutations in TTC7A gene, usually retaining most of the functional motifs, is associated with relative milder clinical presentations. In this study, we reported a patient who was suffering from severe multiple intestinal atresia (MIA) with combined immunodeficiency (CID) that led to the pyloric diaphragm, ileum atresia, colon stenosis, and multiple episodes of sepsis. In spite of several surgeries and supportive treatment, the patient died of severe sepsis and multiple organ failure at age of 3 months. The whole exome sequencing (WES) of peripheral blood samples identified a novel homozygous TTC7A missense mutation (c. 206T>C, p. L69P), inherited from his parents with consanguineous marriage. In silico analysis revealed that a hydrogen bond present between Gly65 and Leu69 in the wild-type TTC7A was disrupted by the Leu69Pro mutation. Moreover, this homozygous missense mutation led to a reduced TTC7A expression in lymphocytes and intestinal tissues, accompanied by impeded lymphocyte development. Further studies demonstrated that the PI4K-FAM126A-EFR3A pathway was impaired in colon tissues. Our data strongly support the linkage of severe MIA-CID with the missense mutation in TTC7A gene. More knowledge of the TTC7A protein functions will have important therapeutic implications for patients with MIA-CID.

Pharmacological inhibition of LSD1 triggers myeloid differentiation by targeting GSE1 oncogenic functions in AML

The histone demethylase LSD1 is over-expressed in hematological tumors and has emerged as a promising target for anticancer treatment, so that several LSD1 inhibitors are under development and testing, in preclinical and clinical settings. However, the complete understanding of their complex mechanism of action is still unreached. Here, we unraveled a novel mode of action of the LSD1 inhibitors MC2580 and DDP-38003, showing that they can induce differentiation of AML cells through the downregulation of the chromatin protein GSE1. Analysis of the phenotypic effects of GSE1 depletion in NB4 cells showed a strong decrease of cell viability in vitro and of tumor growth in vivo. Mechanistically, we found that a set of genes associated with immune response and cytokine-signaling pathways are upregulated by LSD1 inhibitors through GSE1-protein reduction and that LSD1 and GSE1 colocalize at promoters of a subset of these genes at the basal state, enforcing their transcriptional silencing. Moreover, we show that LSD1 inhibitors lead to the reduced binding of GSE1 to these promoters, activating transcriptional programs that trigger myeloid differentiation. Our study offers new insights into GSE1 as a novel therapeutic target for AML.

Early-life viral infections are associated with disadvantageous immune and microbiota profiles and recurrent respiratory infections

The respiratory tract is populated by a specialized microbial ecosystem, which is seeded during and directly following birth. Perturbed development of the respiratory microbial community in early-life has been associated with higher susceptibility to respiratory tract infections (RTIs). Given a consistent gap in time between first signs of aberrant microbial maturation and the observation of the first RTIs, we hypothesized that early-life host-microbe cross-talk plays a role in this process. We therefore investigated viral presence, gene expression profiles and nasopharyngeal microbiota from birth until 12 months of age in 114 healthy infants. We show that the strongest dynamics in gene expression profiles occurred within the first days of life, mostly involving Toll-like receptor (TLR) and inflammasome signalling. These gene expression dynamics coincided with rapid microbial niche differentiation. Early asymptomatic viral infection co-occurred with stronger interferon activity, which was related to specific microbiota dynamics following, including early enrichment of Moraxella and Haemophilus spp. These microbial trajectories were in turn related to a higher number of subsequent (viral) RTIs over the first year of life. Using a multi-omic approach, we found evidence for species-specific host-microbe interactions related to consecutive susceptibility to RTIs. Although further work will be needed to confirm causality of our findings, together these data indicate that early-life viral encounters could impact subsequent host-microbe cross-talk, which is linked to later-life infections.

Multimodal analysis suggests differential immuno-metabolic crosstalk in lung squamous cell carcinoma and adenocarcinoma

Immunometabolism within the tumor microenvironment is an appealing target for precision therapy approaches in lung cancer. Interestingly, obesity confers an improved response to immune checkpoint inhibition in non-small cell lung cancer (NSCLC), suggesting intriguing relationships between systemic metabolism and the immunometabolic environment in lung tumors. We hypothesized that visceral fat and ¹⁸F-Fluorodeoxyglucose uptake influenced the tumor immunometabolic environment and that these bidirectional relationships differ in NSCLC subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). By integrating ¹⁸F-FDG PET/CT imaging, bulk and single-cell RNA-sequencing, and histology, we observed that LUSC had a greater dependence on glucose than LUAD. In LUAD tumors with high glucose uptake, glutaminase was downregulated, suggesting a tradeoff between glucose and glutamine metabolism, while in LUSC tumors with high glucose uptake, genes related to fatty acid and amino acid metabolism were also increased. We found that tumor-infiltrating T cells had the highest expression of glutaminase, ribosomal protein 37, and cystathionine gamma-lyase in NSCLC, highlighting the metabolic flexibility of this cell type. Further, we demonstrate that visceral adiposity, but not body mass index (BMI), was positively associated with tumor glucose uptake in LUAD and that patients with high BMI had favorable prognostic transcriptional profiles, while tumors of patients with high visceral fat had poor prognostic gene expression. We posit that metabolic adjunct therapy may be more successful in LUSC rather than LUAD due to LUAD's metabolic flexibility and that visceral adiposity, not BMI alone, should be considered when developing precision medicine approaches for the treatment of NSCLC.

A Systematic Analysis on the Genes and Their Interaction Underlying the Comorbidity of Alzheimer's Disease and Major Depressive Disorder

Background

During the past years, clinical and epidemiological studies have indicated a close relationship between Alzheimer's disease (AD) and other mental disorders like major depressive disorder (MDD). At the same time, a number of genes genetically associated with AD or MDD have been detected. However, our knowledge on the mechanisms that link the two disorders is still incomplete, and controversies exist. In such a situation, a systematic analysis on these genes could provide clues to understand the molecular features of two disorders and their comorbidity.

Methods

In this study, we compiled the genes reported to be associated with AD or MDD by a comprehensive search of human genetic studies and genes curated in disease-related database. Then, we investigated the features of the shared genes between AD and MDD using the functional enrichment analysis. Furthermore, the major biochemical pathways enriched in the AD- or MDD-associated genes were identified, and the cross talks between the pathways were analyzed. In addition, novel candidate genes related to AD and MDD were predicted in the context of human protein-protein interactome.

Results

We obtained 650 AD-associated genes, 447 MDD-associated genes, and 77 shared genes between AD and MDD. The functional analysis revealed that biological processes involved in cognition, neural development, synaptic transmission, and immune-related processes were enriched in the common genes, indicating a complex mechanism underlying the comorbidity of the two diseases. In addition, we conducted the pathway enrichment analysis and found 102 shared pathways between AD and MDD, which involved in neuronal development, endocrine, cell growth, and immune response. By using the pathway cross-talk analysis, we found that these pathways could be roughly clustered into four modules, i.e., the immune response-related module, the neurodevelopmental module, the cancer or cell growth module, and the endocrine module. Furthermore, we obtained 37 novel candidate genes potentially related to AD and MDD with node degrees > 5.0 by mapping the shared genes to human protein-protein interaction network (PPIN). Finally, we found that 37 novel candidate genes are significantly expressed in the brain.

Conclusion

These results indicated shared biological processes and pathways between AD and MDD and provided hints for the comorbidity of AD and MDD.

RNA-RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection

Some studies reported that genomic RNA of SARS-CoV-2 can absorb a few host miRNAs that regulate immune-related genes and then deprive their function. In this perspective, we conjecture that the absorption of the SARS-CoV-2 genome to host miRNAs is not a coincidence, which may be an indispensable approach leading to viral survival and development in host. In our study, we collected five datasets of miRNAs that were predicted to interact with the genome of SARS-CoV-2. The targets of these miRNAs in the five groups were consistently enriched immune-related pathways and virus-infectious diseases. Interestingly, the five datasets shared no one miRNA but their targets shared 168 genes. The signaling pathway enrichment of 168 shared targets implied an unbalanced immune response that the most of interleukin signaling pathways and none of the interferon signaling pathways were significantly different. Protein-protein interaction (PPI) network using the shared targets showed that PPI pairs, including IL6-IL6R, were related to the process of SARS-CoV-2 infection and pathogenesis. In addition, we found that SARS-CoV-2 absorption to host miRNA could benefit two popular mutant strains for more infectivity and pathogenicity. Conclusively, our results suggest that genomic RNA absorption to host miRNAs may be a vital approach by which SARS-CoV-2 disturbs the host immune system and infects host cells.

Identification of Molecular Biomarkers and Key Pathways for Esophageal Carcinoma (EsC): A Bioinformatics Approach

Esophageal carcinoma (EsC) is a member of the cancer group that occurs in the esophagus; globally, it is known as one of the fatal malignancies. In this study, we used gene expression analysis to identify molecular biomarkers to propose therapeutic targets for the development of novel drugs. We consider EsC associated four different microarray datasets from the gene expression omnibus database. Statistical analysis is performed using R language and identified a total of 1083 differentially expressed genes (DEGs) in which 380 are overexpressed and 703 are underexpressed. The functional study is performed with the identified DEGs to screen significant Gene Ontology (GO) terms and associated pathways using the Database for Annotation, Visualization, and Integrated Discovery repository (DAVID). The analysis revealed that the overexpressed DEGs are principally connected with the protein export, axon guidance pathway, and the downexpressed DEGs are principally connected with the L13a-mediated translational silencing of ceruloplasmin expression, formation of a pool of free 40S subunits pathway. The STRING database used to collect protein-protein interaction (PPI) network information and visualize it with the Cytoscape software. We found 10 hub genes from the PPI network considering three methods in which the interleukin 6 (IL6) gene is the top in all methods. From the PPI, we found that identified clusters are associated with the complex I biogenesis, ubiquitination and proteasome degradation, signaling by interleukins, and Notch-HLH transcription pathway. The identified biomarkers and pathways may play an important role in the future for developing drugs for the EsC.

NETME: on-the-fly knowledge network construction from biomedical literature

BACKGROUND

The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling.

RESULTS

We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41109-021-00435-x.

IL-17 Pathway Members as Potential Biomarkers of Effective Systemic Treatment and Cardiovascular Disease in Patients with Moderate-to-Severe Psoriasis

Psoriasis is a chronic inflammatory condition associated with atherosclerotic cardiovascular disease (CVD). Systemic anti-psoriatic treatments mainly include methotrexate and biological therapies targeting TNF, IL-12/23 and IL-17A. We profiled plasma proteins from patients with moderate-to-severe psoriasis to explore potential biomarkers of effective systemic treatment and their relationship to CVD. We found that systemically well-treated patients (PASI < 3.0, n = 36) had lower circulating levels of IL-17 pathway proteins compared to untreated patients (PASI > 10, n = 23). Notably, IL-17C and PI3 were decreased with all four examined systemic treatment types. Furthermore, in patients without CVD, we observed strong correlations among IL-17C/PI3/PASI (r ≥ 0.82, p ≤ 1.5 × 10⁻¹²) pairs or between IL-17A/PASI (r = 0.72, p = 9.3 × 10⁻⁸). In patients with CVD, the IL-17A/PASI correlation was abolished (r = 0.2, p = 0.24) and the other correlations were decreased, e.g., IL-17C/PI3 (r = 0.61, p = 4.5 × 10⁻⁵). Patients with moderate-to-severe psoriasis and CVD had lower levels of IL-17A compared to those without CVD (normalized protein expression [NPX] 2.02 vs. 2.55, p = 0.013), and lower IL-17A levels (NPX < 2.3) were associated with higher incidence of CVD (OR = 24.5, p = 0.0028, 95% CI 2.1–1425.1). As a result, in patients with moderate-to-severe psoriasis, we propose circulating IL-17C and PI3 as potential biomarkers of effective systemic anti-psoriatic treatment, and IL-17A as potential marker of CVD.

Identification of Prognostic Biomarkers in Papillary Thyroid Cancer and Developing Non-Invasive Diagnostic Models Through Integrated Bioinformatics Analysis

BACKGROUND

Papillary thyroid cancer (PTC) is the most frequent subtype of thyroid carcinoma, mainly detected in patients with benign thyroid nodules (BTN). Due to the invasiveness of accurate diagnostic tests, there is a need to discover applicable biomarkers for PTC. So, in this study, we aimed to identify the genes associated with prognosis in PTC. Besides, we performed a machine learning tool to develop a non-invasive diagnostic approach for PTC.

METHODS

For the study purposes, the miRNA dataset GSE130512 was downloaded from the GEO database and then analyzed to identify the common differentially expressed miRNAs in patients with non-metastatic PTC (nm-PTC)/metastatic PTC (m-PTC) compared with BTNs. The SVM was also applied to differentiate patients with PTC from those patients with BTN using the common DEMs. A protein-protein interaction network was also constructed based on the targets of the common DEMs. Next, functional analysis was performed, the hub genes were determined, and survival analysis was then executed.

RESULTS

A total of three common miRNAs were found to be differentially expressed among patients with nm-PTC/m-PTC compared with BTNs. In addition, it was established that the autophagosome maturation, ciliary basal body-plasma membrane docking, antigen processing as ubiquitination & proteasome degradation, and class I MHC mediated antigen processing & presentation are associated with the pathogenesis of PTC. Furthermore, it was illustrated that RPS6KB1, CCNT1, SP1, and CHD4 might serve as new potential biomarkers for PTC prognosis.

CONCLUSION

RPS6KB1, CCNT1, SP1, and CHD4 may be considered new potential biomarkers used for prognostic aims in PTC. However, performing validation tests is inevitable in the future.

Establishment and characterization of prostate organoids from treatment-naïve patients with prostate cancer

Three-dimensional (3D) organoid culture systems are emerging as potential reliable tools to investigate basic developmental processes of human disease, especially cancer. The present study used established and modified culture conditions to report successful generation and characterization of patient-derived organoids from fresh primary tissue specimens of patients with treatment-naïve prostate cancer (PCa). Fresh tissue specimens were collected, digested enzymatically and the resulting cell suspensions were plated in a 3D environment using Matrigel as an extracellular matrix. Previously established 12-factor medium for organoid culturing was modified to create a minimal 5-factor medium. Organoids and corresponding tissue specimens were characterized using transcriptomic analysis, immunofluorescent analysis, and immunohistochemistry. Furthermore, patient-derived organoids were used to assess the drug response. Treatment-naïve patient-derived PCa organoids were obtained from fresh radical prostatectomy specimens. These PCa organoids mimicked the heterogeneity of corresponding parental tumor tissue. Histopathological analysis demonstrated similar tissue architecture and cellular morphology, as well as consistent immunohistochemical marker expression. Also, the results confirmed the potential of organoids as an in vitro model to assess potential personalized treatment responses as there was a differential drug response between different patient samples. In conclusion, the present study investigated patient-derived organoids from a cohort of treatment-naïve patients. Derived organoids mimicked the histological features and prostate lineage profiles of their corresponding parental tissue and may present a potential model to predict patient-specific treatment response in a pre-clinical setting.

Existing Drug Repurposing for Glioblastoma to Discover Candidate Drugs as a New a Approach

Aims:

Repurposing of drugs has been hypothesized as a means of identifying novel treatment methods for certain diseases.

Background:

Glioblastoma (GB) is an aggressive type of human cancer; the most effective treatment for glioblastoma is chemotherapy, whereas, when repurposing drugs, a lot of time and money can be saved.

Objective:

Repurposing of the existing drug may be used to discover candidate drugs for individualized treatments of GB.

Method:

We used the bioinformatics method to obtain the candidate drugs. In addition, the drugs were verified by MTT assay, Transwell® assays, TUNEL staining, and in vivo tumor formation experiments, as well as statistical analysis.

Result:

We obtained 4 candidate drugs suitable for the treatment of glioma, camptothecin, doxorubicin, daunorubicin and mitoxantrone, by the expression spectrum data IPAS algorithm analysis and drug-pathway connectivity analysis. These validation experiments showed that camptothecin was more effective in treating the GB, such as MTT assay, Transwell® assays, TUNEL staining, and in vivo tumor formation.

Conclusion:

With regard to personalized treatment, this present study may be used to guide the research of new drugs via verification experiments and tumor formation. The present study also provides a guide to systematic, individualized drug discovery for complex diseases and may contribute to the future application of individualized treatments.

Surgical resection of glioblastomas induces pleiotrophin-mediated self-renewal of glioblastoma stem cells in recurrent tumors

Background

Glioblastomas are highly resistant to therapy, and virtually all patients experience tumor recurrence after standard-of-care treatment. Surgical tumor resection is a cornerstone in glioblastoma therapy, but its impact on cellular phenotypes in the local postsurgical microenvironment has yet to be fully elucidated.

Methods

We developed a preclinical orthotopic xenograft tumor resection model in rats with integrated ¹⁸F-FET PET/CT imaging. Primary and recurrent tumors were subject to bulk and single-cell RNA sequencing. Differentially expressed genes and pathways were investigated and validated using tissue specimens from the xenograft model, 23 patients with matched primary/recurrent tumors, and a cohort including 190 glioblastoma patients. Functional investigations were performed in vitro with multiple patient-derived cell cultures.

Results

Tumor resection induced microglia/macrophage infiltration, angiogenesis as well as proliferation and upregulation of several stem cell-related genes in recurrent tumor cells. Expression changes of selected genes SOX2, POU3F2, OLIG2, and NOTCH1 were validated at the protein level in xenografts and early recurrent patient tumors. Single-cell transcriptomics revealed the presence of distinct phenotypic cell clusters in recurrent tumors which deviated from clusters found in primary tumors. Recurrent tumors expressed elevated levels of pleiotrophin (PTN), secreted by both tumor cells and tumor-associated microglia/macrophages. Mechanistically, PTN could induce tumor cell proliferation, self-renewal, and the stem cell program. In glioblastoma patients, high PTN expression was associated with poor overall survival and identified as an independent prognostic factor.

Conclusion

Surgical tumor resection is an iatrogenic driver of PTN-mediated self-renewal in glioblastoma tumor cells that promotes therapeutic resistance and tumor recurrence.

Meta-Analysis and Bioinformatics Detection of Susceptibility Genes in Diabetic Nephropathy

The latest meta-analysis of genome-wide linkage studies (GWLS) identified nine cytogenetic locations suggestive of a linkage with diabetic nephropathy (DN) due to type 1 diabetes mellitus (T1DM) and seven locations due to type 2 diabetes mellitus (T2DM). In order to gain biological insight about the functional role of the genes located in these regions and to prioritize the most significant genetic loci for further research, we conducted a gene ontology analysis with an over representation test for the functional annotation of the protein coding genes. Protein analysis through evolutionary relationships (PANTHER) version 16.0 software and Cytoscape with the relevant plugins were used for the gene ontology analysis, and the overrepresentation test and STRING database were used for the construction of the protein network. The findings of the over-representation test highlight the contribution of immune related molecules like immunoglobulins, cytokines, and chemokines with regard to the most overrepresented protein classes, whereas the most enriched signaling pathways include the VEGF signaling pathway, the Cadherin pathway, the Wnt pathway, the angiogenesis pathway, the p38 MAPK pathway, and the EGF receptor signaling pathway. The common section of T1DM and T2DM results include the significant over representation of immune related molecules, and the Cadherin and Wnt signaling pathways that could constitute potential therapeutic targets for the treatment of DN, irrespective of the type of diabetes.

Comprehensive Mapping of the Cell Response to Borrelia bavariensis in the Brain Microvascular Endothelial Cells in vitro Using RNA-Seq

Borrelia bavariensis can invade the central nervous system (CNS) by crossing the blood-brain barrier (BBB). It is predicted that B. bavariensis evokes numerous signaling cascades in the human brain microvascular endothelial cells (hBMECs) and exploits them to traverse across the BBB. The complete picture of signaling events in hBMECs induced by B. bavariensis remains uncovered. Using RNA sequencing, we mapped 11,398 genes and identified 295 differentially expressed genes (DEGs, 251 upregulated genes and 44 downregulated genes) in B. bavariensis challenged hBMECs. The results obtained from RNA-seq were validated with qPCR. Gene ontology analysis revealed the participation of DEGs in a number of biological processes like cell communication, organization of the extracellular matrix, vesicle-mediated transport, cell response triggered by pattern recognition receptors, antigen processing via MHC class I, cellular stress, metabolism, signal transduction, etc. The expression of several non-protein coding genes was also evoked. In this manuscript, we discuss in detail the correlation between several signaling cascades elicited and the translocation of BBB by B. bavariensis. The data revealed here may contribute to a better understanding of the mechanisms employed by B. bavariensis to cross the BBB.

Network Medicine-Based Analysis of Association Between Gynecological Cancers and Metabolic and Hormonal Disorders

Different metabolic and hormonal disorders like type 2 diabetes mellitus (T2DM), obesity, and polycystic ovary syndrome (PCOS) have tangible socio-economic impact. Prevalence of these metabolic and hormonal disorders is steadily increasing among women. There are clinical evidences that these physiological conditions are related to the manifestation of different gynecological cancers and their poor prognosis. The relationship between metabolic and hormonal disorders with gynecological cancers is quite complex. The need for gene level association study is extremely important to find markers and predicting risk factors. In the current work, we have selected metabolic disorders like T2DM and obesity, hormonal disorder PCOS, and 4 different gynecological cancers like endometrial, uterine, cervical, and triple negative breast cancer (TNBC). The gene list was downloaded from DisGeNET database (v 6.0). The protein interaction network was constructed using HIPPIE (v 2.2) and shared proteins were identified. Molecular comorbidity index and Jaccard coefficient (degree of similarity) between the diseases were determined. Pathway enrichment analysis was done using ReactomePA and significant modules (clusters in a network) of the constructed network was analyzed by MCODE plugin of Cytoscape. The comorbid conditions like PCOS-obesity found to increase the risk factor of ovarian and triple negative breast cancers whereas PCOS alone has highest contribution to the endometrial cancer. Different gynecological cancers were found to be differentially related to the metabolic/hormonal disorders and comorbid condition.

m6A-express: uncovering complex and condition-specific m6A regulation of gene expression

N⁶-methyladenosine (m⁶A) is the most abundant form of mRNA modification and controls many aspects of RNA metabolism including gene expression. However, the mechanisms by which m⁶A regulates cell- and condition-specific gene expression are still poorly understood, partly due to a lack of tools capable of identifying m⁶A sites that regulate gene expression under different conditions. Here we develop m⁶A-express, the first algorithm for predicting condition-specific m⁶A regulation of gene expression (m⁶A-reg-exp) from limited methylated RNA immunoprecipitation sequencing (MeRIP-seq) data. Comprehensive evaluations of m⁶A-express using simulated and real data demonstrated its high prediction specificity and sensitivity. When only a few MeRIP-seq samples may be available for the cellular or treatment conditions, m⁶A-express is particularly more robust than the log-linear model. Using m⁶A-express, we reported that m⁶A writers, METTL3 and METTL14, competitively regulate the transcriptional processes by mediating m⁶A-reg-exp of different genes in Hela cells. In contrast, METTL3 induces different m⁶A-reg-exp of a distinct group of genes in HepG2 cells to regulate protein functions and stress-related processes. We further uncovered unique m⁶A-reg-exp patterns in human brain and intestine tissues, which are enriched in organ-specific processes. This study demonstrates the effectiveness of m⁶A-express in predicting condition-specific m⁶A-reg-exp and highlights the complex, condition-specific nature of m⁶A-regulation of gene expression.

High-Quality Genome-Scale Reconstruction of Corynebacterium glutamicum ATCC 13032

Corynebacterium glutamicum belongs to the microbes of enormous biotechnological relevance. In particular, its strain ATCC 13032 is a widely used producer of L-amino acids at an industrial scale. Its apparent robustness also turns it into a favorable platform host for a wide range of further compounds, mainly because of emerging bio-based economies. A deep understanding of the biochemical processes in C. glutamicum is essential for a sustainable enhancement of the microbe's productivity. Computational systems biology has the potential to provide a valuable basis for driving metabolic engineering and biotechnological advances, such as increased yields of healthy producer strains based on genome-scale metabolic models (GEMs). Advanced reconstruction pipelines are now available that facilitate the reconstruction of GEMs and support their manual curation. This article presents iCGB21FR, an updated and unified GEM of C. glutamicum ATCC 13032 with high quality regarding comprehensiveness and data standards, built with the latest modeling techniques and advanced reconstruction pipelines. It comprises 1042 metabolites, 1539 reactions, and 805 genes with detailed annotations and database cross-references. The model validation took place using different media and resulted in realistic growth rate predictions under aerobic and anaerobic conditions. The new GEM produces all canonical amino acids, and its phenotypic predictions are consistent with laboratory data. The in silico model proved fruitful in adding knowledge to the metabolism of C. glutamicum: iCGB21FR still produces L-glutamate with the knock-out of the enzyme pyruvate carboxylase, despite the common belief to be relevant for the amino acid's production. We conclude that integrating high standards into the reconstruction of GEMs facilitates replicating validated knowledge, closing knowledge gaps, and making it a useful basis for metabolic engineering. The model is freely available from BioModels Database under identifier MODEL2102050001.

Development of a bioinformatics platform for analysis of quantitative transcriptomics and proteomics data: the OMnalysis

BACKGROUND

In the past decade, RNA sequencing and mass spectrometry based quantitative approaches are being used commonly to identify the differentially expressed biomarkers in different biological conditions. Data generated from these approaches come in different sizes (e.g., count matrix, normalized list of differentially expressed biomarkers, etc.) and shapes (e.g., sequences, spectral data, etc.). The list of differentially expressed biomarkers is used for functional interpretation and retrieve biological meaning, however, it requires moderate computational skills. Thus, researchers with no programming expertise find difficulty in data interpretation. Several bioinformatics tools are available to analyze such data; however, they are less flexible for performing the multiple steps of visualization and functional interpretation.

IMPLEMENTATION

We developed an easy-to-use Shiny based web application (named as OMnalysis) that provides users with a single platform to analyze and visualize the differentially expressed data. The OMnalysis accepts the data in tabular form from edgeR, DESeq2, MaxQuant Perseus, R packages, and other similar software, which typically contains the list of differentially expressed genes or proteins, log of the fold change, log of the count per million, the P value, q-value, etc. The key features of the OMnalysis are multiple image type visualization and their dimension customization options, seven multiple hypothesis testing correction methods to get more significant gene ontology, network topology-based pathway analysis, and multiple databases support (KEGG, Reactome, PANTHER, biocarta, NCI-Nature Pathway Interaction Database PharmGKB and STRINGdb) for extensive pathway enrichment analysis. OMnalysis also fetches the literature information from PubMed to provide supportive evidence to the biomarkers identified in the analysis. In a nutshell, we present the OMnalysis as a well-organized user interface, supported by peer-reviewed R packages with updated databases for quick interpretation of the differential transcriptomics and proteomics data to biological meaning.

AVAILABILITY

The OMnalysis codes are entirely written in R language and freely available at https://github.com/Punit201016/OMnalysis. OMnalysis can also be accessed from - http://lbmi.uvlf.sk/omnalysis.html. OMnalysis is hosted on a Shiny server at https://omnalysis.shinyapps.io/OMnalysis/. The minimum system requirements are: 4 gigabytes of RAM, i3 processor (or equivalent). It is compatible with any operating system (windows, Linux or Mac). The OMnalysis is heavily tested on Chrome web browsers; thus, Chrome is the preferred browser. OMnalysis works on Firefox and Safari.