KEGG for representation and analysis of molecular networks involving diseases and drugs

Identification of Novel Gene Signatures using Next-Generation Sequencing Data from COVID-19 Infection Models: Focus on Neuro-COVID and Potential Therapeutics

SARS-CoV-2 is the causative agent for coronavirus disease-19 (COVID-19) and belongs to the family Coronaviridae that causes sickness varying from the common cold to more severe illnesses such as severe acute respiratory syndrome, sudden stroke, neurological complications (Neuro-COVID), multiple organ failure, and mortality in some patients. The gene expression profiles of COVID-19 infection models can be used to decipher potential therapeutics for COVID-19 and related pathologies, such as Neuro-COVID. Here, we used the raw RNA-seq reads (Single-End) in quadruplicates derived using Illumina Next Seq 500 from SARS-CoV-infected primary human bronchial epithelium (NHBE) and mock-treated NHBE cells obtained from the Gene Expression Omnibus (GEO) (GSE147507), and the quality control (QC) was evaluated using the CLC Genomics Workbench 20.0 (Qiagen, United States) before the RNA-seq analysis using BioJupies web tool and iPathwayGuide for gene ontologies (GO), pathways, upstream regulator genes, small molecules, and natural products. Additionally, single-cell transcriptomics data (GSE163005) of meta clusters of immune cells from the cerebrospinal fluid (CSF), such as T-cells/natural killer cells (NK) (TcMeta), dendritic cells (DCMeta), and monocytes/granulocyte (monoMeta) cell types for comparison, namely, Neuro-COVID versus idiopathic intracranial hypertension (IIH), were analyzed using iPathwayGuide. L1000 fireworks display (L1000FWD) and L1000 characteristic direction signature search engine (L1000 CDS2) web tools were used to uncover the small molecules that could potentially reverse the COVID-19 and Neuro-COVID-associated gene signatures. We uncovered small molecules such as camptothecin, importazole, and withaferin A, which can potentially reverse COVID-19 associated gene signatures. In addition, withaferin A, trichostatin A, narciclasine, camptothecin, and JQ1 have the potential to reverse Neuro-COVID gene signatures. Furthermore, the gene set enrichment analysis (GSEA) preranked method and Metascape web tool were used to decipher and annotate the gene signatures that were potentially reversed by these small molecules. In conclusion, our study unravels a rapid approach for applying next-generation knowledge discovery (NGKD) platforms to discover small molecules with therapeutic potential against COVID-19 and its related disease pathologies.

TPPU treatment of burned mice dampens inflammation and generation of bioactive DHET which impairs neutrophil function

Oxylipins modulate the behavior of immune cells in inflammation. Soluble epoxide hydrolase (sEH) converts anti-inflammatory epoxyeicosatrienoic acid (EET) to dihydroxyeicosatrienoic acid (DHET). An sEH-inhibitor, TPPU, has been demonstrated to ameliorate lipopolysaccharide (LPS)- and sepsis-induced inflammation via EETs. The immunomodulatory role of DHET is not well characterized. We hypothesized that TPPU dampens inflammation and that sEH-derived DHET alters neutrophil functionality in burn induced inflammation. Outbred mice were treated with vehicle, TPPU or 14,15-DHET and immediately subjected to either sham or dorsal scald 28% total body surface area burn injury. After 6 and 24 h, interleukin 6 (IL-6) serum levels and neutrophil activation were analyzed. For in vitro analyses, bone marrow derived neutrophil functionality and mRNA expression were examined. In vivo, 14,15-DHET and IL-6 serum concentrations were decreased after burn injury with TPPU administration. In vitro, 14,15-DHET impaired neutrophil chemotaxis, acidification, CXCR1/CXCR2 expression and reactive oxygen species (ROS) production, the latter independent from p38MAPK and PI3K signaling. We conclude that TPPU administration decreases DHET post-burn. Furthermore, DHET downregulates key neutrophil immune functions and mRNA expression. Altogether, these data reveal that TPPU not only increases anti-inflammatory and inflammation resolving EET levels, but also prevents potential impairment of neutrophils by DHET in trauma.

DNA methylome and transcriptome landscapes of cancer-associated fibroblasts reveal a smoking-associated malignancy index

Unlike the better-studied aberrant epigenome in the tumor, the clinicopathologic impact of DNA methylation in the tumor microenvironment (TME), especially the contribution from cancer-associated fibroblasts (CAFs), remains elusive. CAFs exhibit profound patient-to-patient tumorigenic heterogeneity. We asked whether such heterogeneity may be exploited to quantify the level of TME malignancy. We developed a robust and efficient methylome/transcriptome co-analytical system for CAFs and paired normal fibroblasts (NFs) from non-small-cell lung cancer patients. We found 14,781 CpG sites of CAF/NF differential methylation, of which 3,707 sites showed higher methylation changes in ever-smokers than in nonsmokers. Concomitant CAF/NF differential gene expression analysis pointed to a subset of 54 smoking-associated CpG sites with strong methylation-regulated gene expression. A methylation index that summarizes the β values of these CpGs was built for NF/CAF discrimination (MIND) with high sensitivity and specificity. The potential of MIND in detecting premalignancy across individual patients was shown. MIND succeeded in predicting tumor recurrence in multiple lung cancer cohorts without reliance on patient survival data, suggesting that the malignancy level of TME may be effectively graded by this index. Precision TME grading may provide additional pathological information to guide cancer prognosis and open up more options in personalized medicine.

Resurgence of phosphotyrosine binding domains: Structural and functional properties essential for understanding disease pathogenesis

BACKGROUND

Phosphotyrosine Binding (PTB) Domains, usually found on scaffold proteins, are pervasive in many cellular signaling pathways. These domains are the second-largest family of phosphotyrosine recognition domains and since their initial discovery, dozens of PTB domains have been structurally determined.

SCOPE OF REVIEW

Due to its signature sequence flexibility, PTB domains can bind to a large variety of ligands including phospholipids. PTB peptide binding is divided into classical binding (canonical NPXY motifs) and non-classical binding (all other motifs). The first atypical PTB domain was discovered in cerebral cavernous malformation 2 (CCM2) protein, while only one third in size of the typical PTB domain, it remains functionally equivalent.

MAJOR CONCLUSIONS

PTB domains are involved in numerous signaling processes including embryogenesis, neurogenesis, and angiogenesis, while dysfunction is linked to major disorders including diabetes, hypercholesterolemia, Alzheimer's disease, and strokes. PTB domains may also be essential in infectious processes, currently responsible for the global pandemic in which viral cellular entry is suspected to be mediated through PTB and NPXY interactions.

GENERAL SIGNIFICANCE

We summarize the structural and functional updates in the PTB domain over the last 20 years in hopes of resurging interest and further analyzing the importance of this versatile domain.

Channeling of Carbon Flux Towards Carotenogenesis in Botryococcus braunii: A Media Engineering Perspective

Microalgae, due to their unique properties, gained attention for producing promising feedstocks having high contents of proteins, antioxidants, carotenoids, and terpenoids for applications in nutraceutical and pharmaceutical industries. Optimizing production of the high-value renewables (HVRs) in microalgae requires an in-depth understanding of their functional relationship of the genes involved in these metabolic pathways. In the present study, bioinformatic tools were employed for characterization of the protein-encoding genes of methyl erythritol phosphate (MEP) pathway involved in carotenoid and squalene biosynthesis based upon their conserved motif/domain organization. Our analysis demonstrates nearly 200 putative genes showing a conservation pattern within divergent microalgal lineages. Furthermore, phylogenomic studies confirm the close evolutionary proximity among these microalgal strains in the carotenoid and squalene biosynthetic pathways. Further analysis employing STRING predicts interactions among two rate-limiting genes, i.e., phytoene synthase (PSY) and farnesyl diphosphate farnesyl synthase (FPPS), which are specifically involved in the synthesis of carotenoids and squalene. Experimentally, to understand the carbon flux of these rate-limiting genes involved in carotenogenesis, an industrial potential strain, namely, Botryococcus braunii, was selected in this study for improved biomass productivity (i.e., 100 mg L^-1 D^-1) along with enhanced carotenoid content [0.18% dry cell weight (DCW)] when subjected to carbon supplementation. In conclusion, our approach of media engineering demonstrates that the channeling of carbon flux favors carotenogenesis rather than squalene synthesis. Henceforth, employing omics perspectives will further provide us with new insights for engineering regulatory networks for enhanced production of high-value carbon biorenewables without compromising growth.

Evaluating Targeted Therapies in Ovarian Cancer Metabolism: Novel Role for PCSK9 and Second Generation mTOR Inhibitors

BACKGROUND

Dysregulated lipid metabolism is emerging as a hallmark in several malignancies, including ovarian cancer (OC). Specifically, metastatic OC is highly dependent on lipid-rich omentum. We aimed to investigate the therapeutic value of targeting lipid metabolism in OC. For this purpose, we studied the role of PCSK9, a cholesterol-regulating enzyme, in OC cell survival and its downstream signaling. We also investigated the cytotoxic efficacy of a small library of metabolic (n = 11) and mTOR (n = 10) inhibitors using OC cell lines (n = 8) and ex vivo patient-derived cell cultures (PDCs, n = 5) to identify clinically suitable drug vulnerabilities. Targeting PCSK9 expression with siRNA or PCSK9 specific inhibitor (PF-06446846) impaired OC cell survival. In addition, overexpression of PCSK9 induced robust AKT phosphorylation along with increased expression of ERK1/2 and MEK1/2, suggesting a pro-survival role of PCSK9 in OC cells. Moreover, our drug testing revealed marked differences in cytotoxic responses to drugs targeting metabolic pathways of high-grade serous ovarian cancer (HGSOC) and low-grade serous ovarian cancer (LGSOC) PDCs. Our results show that targeting PCSK9 expression could impair OC cell survival, which warrants further investigation to address the dependency of this cancer on lipogenesis and omental metastasis. Moreover, the differences in metabolic gene expression and drug responses of OC PDCs indicate the existence of a metabolic heterogeneity within OC subtypes, which should be further explored for therapeutic improvements.

Metabolomics Reveals the Allelopathic Potential of the Invasive Plant Eupatorium adenophorum

Phytotoxic chemicals produced by alien invasive plants exert inhibitory effects on native species to facilitate their invasiveness. The allelopathic process of invaders has been hypothesized as the “Novel Weapon Hypothesis”. However, this hypothesis has been controversial for decades due to lack of molecular evidence, and the underlying mechanism of allelopathy still remains ambiguous. Herein, we explore the allelopathic mechanisms of Eupatorium adenophorum, a world-widely spread noxious weed, by the methods of laboratory bioassay and metabolomics analyses in the recipient plant, Arabidopsis thaliana. The bioassay revealed significant phytotoxicity of E. adenophorum extracts. A total of 234 metabolites in A. thaliana were detected by Gas Chromatographic−Mass Spectrometric analysis. There were 48, 99 and 94 impacted metabolites in A. thaliana treated by 50, 25 and 12.5% aqueous extracts compared to control. When mapping all the impacted metabolites to the biological pathways in the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, we found mineral absorption, ABC transporters, amino acid biosynthesis, metabolic pathways and biosynthesis of plant secondary metabolites were mainly impacted. Synthesized with partial least-squares discriminate analysis (PLS-DA) results of metabolic profiles in A. thaliana, we found that citrate cycle was suppressed, metabolism of amino acids was disordered and phosphate absorption was inhibited. Subsequent investigation demonstrated that the phosphorus content in A. thaliana tissues exposed in allelopathic extracts was much lower, indicating inhibition of phosphate uptake. Our study revealed by metabolomics approaches that E. adenophorum is an allelopathic species.

Mechanistic targets for BPH and prostate cancer-a review

All men, almost, suffer from prostatic disorders in average life expectancy. In the year of 1950s, the first autopsy of prostate gland discovered the link between Benign prostatic hyperplasia (BPH) and Prostate Cancer (PCa). After that, many histology, biochemistry, epidemiology studies explained the association and associated risk factor for the same. From the various scientific evidence, it is proved that both diseases share some common transcription factors and signalling pathways. Still, BPH cannot be considered as the first step of PCa progression. To define, the relationship between both of the diseases, a well-defined large epidemiological study is needed. Along with androgen signalling, imbalanced apoptosis, oxidative stress, and microbial infection also crucial factors that significantly affect the pathogenesis of BPH. Various signalling pathways are involved in the progression of BPH. Androgen signalling is the driving force for the progress of PCa. In PCa androgen signalling is upregulated as compared to a healthy prostate. Some dominant Androgen-regulated genes and their functions have been discussed in this work.

High expression of LAMA3/AC245041.2 gene pair associated with KRAS mutation and poor survival in pancreatic adenocarcinoma: a comprehensive TCGA analysis

Background

Pancreatic adenocarcinoma (PAAD) is one of the most challenging cancers with high morbidity and mortality. KRAS mutations could occur as an early event in PAAD. The present study aimed to identify the differentially expressed lncRNAs (DE-lncRNAs) and differentially expressed mRNAs (DE-mRNAs) in KRAS-mutant PAAD to explore the pathogenesis and the underlying molecular mechanism of PAAD development.

Methods

Clinical data of TCGA–PAAD patients were downloaded from the TCGA database and subjected to survival analysis along with the KRAS mutation information data. Weighted gene correlation network analysis (WGCNA) and univariate Cox regression analysis were conducted to construct prognostic risk models to identify the hub DE-mRNAs and DE-lncRNAs associated with PAAD prognosis. GO and KEGG enrichment analyses of the identified hub DE-mRNAs were performed. Multivariate cox regression analysis was performed to analyze the overall prognosis of age, gender, pathologic_T, and KRAS mutations, following which the differences in the clinical characteristics of risk score1 and risk score2 were analyzed. Finally, the mRNAs–lncRNA–TFs regulatory network was constructed.

Results

Functional enrichment analysis was performed after screening 1671 DE-mRNAs and 324 DE-lncRNAs. It was observed that the associated pathways were enriched mainly in the modulation of chemical synaptic transmission, synaptic membrane, ion-gated channel activity, ligand−receptor interactions that stimulate neural tissue, among others. The univariate Cox regression analysis screened 117 mRNAs and 36 lncRNAs, and the risk ratio models of the mRNAs and lncRNAs were constructed. LAMA3 (mRNA) and AC245041.2 (lncRNA) exhibited a strong expression correlation in the respective two risk models. The genes in the samples with a high expression of these two genes were enriched in several pathways associated with transcription factors (TFs), among which the TFs ATF5, CSHL1, NR1I2, SIPA1, HOXC13, HSF2, and HOXA10 were shared by the two groups. The core enrichment genes in the common TF pathways were collated, and the mRNAs–lncRNAs–TFs regulatory network was constructed.

Conclusion

In the present study, novel prognostic mRNAs and lncRNAs were identified, and their respective prognostic models and nomograms were constructed to guide clinical practice. An mRNAs–lncRNAs–TFs regulatory network was also constructed, which could assist further research in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-021-00322-2.

In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis

We have performed theoretical calculations with 70 drugs that have been considered in 231 clinical trials as possible candidates to repurpose drugs for schizophrenia based on their interactions with the dopaminergic system. A hypothesis of shared pharmacophore features was formulated to support our calculations. To do so, we have used the crystal structure of the D2-like dopamine receptor in complex with risperidone, eticlopride, and nemonapride. Linagliptin, citalopram, flunarizine, sildenafil, minocycline, and duloxetine were the drugs that best fit with our model. Molecular docking calculations, molecular dynamics outcomes, blood-brain barrier penetration, and human intestinal absorption were studied and compared with the results. From the six drugs selected in the shared pharmacophore features input, flunarizine showed the best docking score with D2, D3, and D4 dopamine receptors and had high stability during molecular dynamics simulations. Flunarizine is a frequently used medication to treat migraines and vertigo. However, its antipsychotic properties have been previously hypothesized, particularly because of its possible ability to block the D2 dopamine receptors.

Primary Chicken and Duck Endothelial Cells Display a Differential Response to Infection with Highly Pathogenic Avian Influenza Virus

Highly pathogenic avian influenza viruses (HPAIVs) in gallinaceous poultry are associated with viral infection of the endothelium, the induction of a ‘cytokine storm, and severe disease. In contrast, in Pekin ducks, HPAIVs are rarely endothelial tropic, and a cytokine storm is not observed. To date, understanding these species-dependent differences in pathogenesis has been hampered by the absence of a pure culture of duck and chicken endothelial cells. Here, we use our recently established in vitro cultures of duck and chicken aortic endothelial cells to investigate species-dependent differences in the response of endothelial cells to HPAIV H5N1 infection. We demonstrate that chicken and duck endothelial cells display a different transcriptional response to HPAI H5N1 infection in vitro—with chickens displaying a more pro-inflammatory response to infection. As similar observations were recorded following in vitro stimulation with the viral mimetic polyI:C, these findings were not specific to an HPAIV H5N1 infection. However, similar species-dependent differences in the transcriptional response to polyI:C were not observed in avian fibroblasts. Taken together, these data demonstrate that chicken and duck endothelial cells display a different response to HPAIV H5N1 infection, and this may help account for the species-dependent differences observed in inflammation in vivo.

A comparison expression analysis of CXCR4, CXCL9 and Caspase-9 in dermal vascular endothelial cells between keloids and normal skin on chemotaxis and apoptosis

This present study was designed to explore key biological characteristics and biomarkers associated with dermal vascular endothelial cells of keloids. GSE121618 dataset was downloaded in the Gene Expression Omnibus (GEO) Database, including the KECs group and NVECs group. Through GEO2R, we have screened the differentially expressed genes (DEGs) and performed gene ontology (GO), Gene Set Enrichment Analysis (GSEA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Then, we constructed a protein-protein interaction (PPI) network and analyzed hub genes via the Search Tool for the Retrieval of Interacting Genes (STRING) Online Database and Cytoscape software. Furthermore, experiments were performed to validate the expression of selected genes, including H&E staining, immunohistochemical staining, Western blot, and RT-qPCR. A total of 1040 DEGs were selected with GEO2R online tools. Most of the enriched pathways and processes focus on cell migration, tube development, chemotaxis, cell motility, and regulation of apoptosis. With the assistance of STRING and Cytoscape, hub genes were selected. In our validation experiments of RT-qPCR, the mRNA expression of selected genes has significant differences between different groups in tissue and cell experiments. As was shown in immunohistochemical staining, the proteins of CXCR4, CXCL9, and Caspase-9 had higher expression levels in tissue samples of the Keloid group than the Normal skin group. Western blot and RT-qPCR in dermal vascular endothelial cell experiments were consistent with the aforementioned results. This study has provided a deeper analysis of the pathogenesis of dermal vascular endothelial cells in keloids. Genes of CXCR4, CXCL9, and Caspase-9 may influence the processes of inflammatory responses and vascular endothelial cell apoptosis to exert crucial effects in the development of keloids. Abbreviations: GEO: gene expression omnibus; DEGs: differentially expressed genes; KVECs: keloid vascular endothelial cells; NVECs: normal skin vascular endothelial cells; GO: gene ontology; KEGG: Kyoto encyclopedia of genes and genomes; PPI: protein protein interaction; BP: biological process; CC: cellular component; MF: molecular function; GSEA: gene set enrichment analysis; STRING: search tool for the retrieval of interacting genes; MCODE: molecular complex detection.

Biological Mechanisms Induced by Soybean Agglutinin Using an Intestinal Cell Model of Monogastric Animals

Soybean agglutinin (SBA) has a toxic effect on most animals. The anti-nutritional mechanisms of SBA are not fully understood, in terms of cell survival activity and metabolism of intestinal cells. This study aims to investigate the effects of SBA on the cell cycle, apoptosis, and to verify the mechanism of SBA anti-nutritional characters based on proteomic-based analysis. The IPEC-J2 cell line was cultured with medium containing 0.0, 0.5, or 2.0 mg/mL SBA. With increasing SBA levels, the percentage of the cells at G0/G1 phase, cell apoptosis rates, expressions of Bax and p21, and the activities of Casp-3 and Casp-9 were increased, while cyclin D1 and Bcl-2 expressions were declined (p < 0.05). The proteomic analysis showed that the numbers of differentially expressed proteins, induced by SBA, were mainly enriched in different pathways including DNA replication, base excision repair, nucleus excision repair, mismatch repair, amide and peptide biosynthesis, ubiquitin-mediated proteolysis, as well as structures and functions of mitochondria and ribosome. In conclusion, the anti-nutritional mechanism of SBA is a complex cellular process. Such process including DNA related activities; protein synthesis and metabolism; signal-conducting relation; as well as subcellular structure and function. This study provides comprehensive information to understand the toxic mechanism of SBA in monogastrics.

A multi-objective hybrid machine learning approach-based optimization for enhanced biomass and bioactive phycobiliproteins production in Nostoc sp. CCC-403

The cyanobacterial phycobiliproteins (PBPs) are an important natural colorant for nutraceutical industries. Here, a multi-objective hybrid machine learning-based optimization approach was used for enhanced cell biomass and PBPs production simultaneously in Nostoc sp. CCC-403. A central composite design (CCD) was employed to design an experimental setup for four input parameters, including three BG-11 medium components and pH. We achieved a 61.76% increase in total PBPs production and an almost 90% increase in cell biomass by our prediction model. We also established a test genome-scale metabolic network (GSMN) for Nostoc sp. and identified potential metabolic fluxes contributing to PBPs enhanced production. This study highlights the advantage of the hybrid machine learning approach and GSMN to achieve optimization for more than one objective and serves as the foundation for future efforts to convert cyanobacteria as an economically viable source for biofuels and natural products.

Identification and validation of a new gene signature predicting prognosis of hepatocellular carcinoma patients by network analysis of stemness indices

Background: Stem cells play an important role in hepatocellular carcinoma (HCC). However, their precise effect on HCC tumorigenesis and progression remains unclear. The present study aimed to characterize stem cell-related gene expression in HCC.Methods: The mRNA expression-based stemness index (mRNAsi) was used to analyze the clinical characteristics and prognosis of HCC patients. The weighted gene co-expression network analysis (WGCNA) was used to construct a gene co-expression network of 374 HCC patients. Finally, six genes were used to construct the prognosis signature.Results: HCC patients had a higher mRNAsi score than healthy people, suggesting poor prognosis. Two gene modules highly related to mRNAsi were identified. Multivariate Cox analysis was carried out to establish a Cox proportional risk regression model. The risk score for each patient was the sum of the product of each gene expression and its coefficient. Survival analysis suggested that the low-risk group had a significantly better prognosis.Conclusions: The established six-gene signature was able to predict patient prognosis accurately. This new signature should be verified in prospective studies in order to determine patient prognosis in clinical decision-making.

In silico drug repositioning using deep learning and comprehensive similarity measures

BACKGROUND

Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug-disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized.

METHODS

In this work, we develop a deep gated recurrent units model to predict potential drug-disease interactions using comprehensive similarity measures and Gaussian interaction profile kernel. More specifically, the similarity measure is used to exploit discriminative feature for drugs based on their chemical fingerprints. Meanwhile, the Gaussian interactions profile kernel is employed to obtain efficient feature of diseases based on known disease-disease associations. Then, a deep gated recurrent units model is developed to predict potential drug-disease interactions.

RESULTS

The performance of the proposed model is evaluated on two benchmark datasets under tenfold cross-validation. And to further verify the predictive ability, case studies for predicting new potential indications of drugs were carried out.

CONCLUSION

The experimental results proved the proposed model is a useful tool for predicting new indications for drugs or new treatments for diseases, and can accelerate drug repositioning and related drug research and discovery.

Advances in the Study of Metabolomics and Metabolites in Some Species Interactions

In the natural environment, interactions between species are a common natural phenomena. The mechanisms of interaction between different species are mainly studied using genomic, transcriptomic, proteomic, and metabolomic techniques. Metabolomics is a crucial part of system biology and is based on precision instrument analysis. In the last decade, the emerging field of metabolomics has received extensive attention. Metabolomics not only provides a qualitative and quantitative method for studying the mechanisms of interactions between different species, but also helps clarify the mechanisms of defense between the host and pathogen, and to explore new metabolites with various biological activities. This review focuses on the methods and progress of interspecies metabolomics. Additionally, the prospects and challenges of interspecies metabolomics are discussed.

Characterization of the small RNA transcriptomes of cell protrusions and cell bodies of highly metastatic hepatocellular carcinoma cells via RNA sequencing

Increasing evidence suggest that hepatocellular carcinoma (HCC) HCCLM3 cells initially develop pseudopodia when they metastasize, and microRNAs (miRNAs/miRs) and circular RNAs (circRNAs) have been demonstrated to serve important roles in the development, progression and metastasis of cancer. The present study aimed to isolate the cell bodies (CBs) and cell protrusions (CPs) from HCCLM3 cells, and screen the miRNAs and circRNAs associated with HCC infiltration and metastasis in CBs and CPs. The Boyden chamber assay has been confirmed to effectively isolate the CBs and CPs from HCCLM3 cells via observation of microtubule immunofluorescence, DAPI staining and nuclear protein H3 western blotting. Following high-throughput sequencing of the successfully isolated CBs and CPs, 64 pairs of miRNAs, including 23 pairs of upregulated genes and 41 pairs of downregulated genes, and 260 sets of circRNAs, including 127 upregulated genes and 133 downregulated genes, were significantly differentially expressed, using the following criteria: HP/HB ratio, fold change ≥|1.5|, P<0.05). PCR analysis verified that changes in the expression levels of hsa-let-7a-5p, hsa-let-7c-3p, hsa-miR-30c-5p, hsa_circ_0059580, hsa_circ_0067475, hsa_circ_0002100 and hsa_circ_00072309 were consistent with the sequencing results. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to analyze the functions and roles of the differentially expressed miRNAs and circRNAs. The interaction maps between miRNAs and circRNAs were constructed, and signaling pathway maps were analyzed to determine the molecular mechanism and regulation of the differentially expressed miRNAs and circRNAs. Taken together, the results of the present study suggest that the Boyden chamber assay can be used to effectively isolate the somatic CBs and CPs of HCC, which can be used to screen the miRNAs and circRNAs associated with invasion and metastasis of HCC.

Bioinformatics-based Identification of Key Pathways and Hub Genes of Traumatic Brain Injury in a Rat Model

Traumatic brain injury (TBI) is a common injury caused by external forces that lead to damaged brain function or pathological changes in the brain tissue. To explore the molecular mechanism and the hub genes of TBI, we downloaded gene expression profiles of the TBI model of rat and the sham control for the subsequent gene set enrichment analysis, pathway analysis and protein-protein interactions analysis. The results of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that multiple biological pathways, including immune response, inflammatory response and cellular response to interleukin-1, as well as signaling pathways, such as tumor necrosis factor signaling pathway, chemokine signaling pathway, cytokine-cytokine receptor interaction, Toll-like receptor signaling pathway and nuclear factor kappa B signaling pathway were implicated in the TBI. In conclusion, this study provides insights into the molecular mechanism of TBI by screening the differentially expressed genes and hub genes that can be used as biomarkers and therapeutic targets.

Peel Learning for Pathway-Related Outcome Prediction

MOTIVATION

Traditional regression models are limited in outcome prediction due to their parametric nature. Current deep learning methods allow for various effects and interactions and have shown improved performance, but they typically need to be trained on a large amount of data to obtain reliable results. Gene expression studies often have small sample sizes but high dimensional correlated predictors so that traditional deep learning methods are not readily applicable.

RESULTS

In this paper, we proposed peel learning, a novel neural network that incorporates the prior relationship among genes. In each layer of learning, overall structure is peeled into multiple local substructures. Within the substructure, dependency among variables is reduced through linear projections. The overall structure is gradually simplified over layers and weight parameters are optimized through a revised backpropagation. We applied PL to a small lung transplantation study to predict recipients' post-surgery primary graft dysfunction using donors' gene expressions within several immunology pathways, where PL showed improved prediction accuracy compared to conventional penalized regression, classification trees, feed-forward neural network, and a neural network assuming prior network structure. Through simulation studies, we also demonstrated the advantage of adding specific structure among predictor variables in neural network, over no or uniform group structure, which is more favorable in smaller studies. The empirical evidence is consistent with our theoretical proof of improved upper bound of PL's complexity over ordinary neural networks.

AVAILABILITY AND IMPLEMENTATION

PL algorithm was implemented in Python and the open-source code and instruction will be available at https://github.com/Likelyt/Peel-Learning.

Identification and characterization of long non-coding RNA and their response against citrus bark cracking viroid infection in Humulus lupulus

Long non-coding RNAs (lncRNAs) are a highly heterogeneous class of non-protein-encoding transcripts that play an essential regulatory role in diverse biological processes, including stress responses. The severe stunting disease caused by Citrus bark cracking viroid (CBCVd) poses a major threat to the production of Humulus lupulus (hop) plants. In this study, we systematically investigate the characteristics of the lncRNAs in hop and their role in CBCVd-infection using RNA-sequencing data. Following a stringent filtration criterion, a total of 3598 putative lncRNAs were identified with a high degree of certainty, of which 19% (684) of the lncRNAs were significantly differentially expressed (DE) in CBCVd-infected hop, which were predicted to be mainly involved in plant-pathogen interactions, kinase cascades, secondary metabolism and phytohormone signal transduction. Besides, several lncRNAs and CBCVd-responsive lncRNAs were identified as the precursor of microRNAs and predicted as endogenous target mimics (eTMs) for hop microRNAs involved in CBCVd-infection.

Scalp and serum profiling of frontal fibrosing alopecia reveals scalp immune and fibrosis dysregulation with no systemic involvement

BACKGROUND

Frontal fibrosing alopecia (FFA) is a progressive, scarring alopecia of the frontotemporal scalp that poses a substantial burden on quality of life. Large-scale global profiling of FFA is lacking, preventing the development of effective therapeutics.

OBJECTIVE

To characterize FFA compared to normal and alopecia areata using broad molecular profiling and to identify biomarkers linked to disease severity.

METHODS

This cross-sectional study assessed 33,118 genes in scalp using RNA sequencing and 350 proteins in serum using OLINK high-throughput proteomics. Disease biomarkers were also correlated with clinical severity and a fibrosis gene set.

RESULTS

Genes differentially expressed in lesional FFA included markers related to Th1 (IFNγ/CXCL9/CXCL10), T-cell activation (CD2/CD3/CCL19/ICOS), fibrosis (CXCR3/FGF14/FGF22/VIM/FN1), T-regulatory (FOXP3/TGFB1/TGFB3), and Janus kinase/JAK (JAK3/STAT1/STAT4) (Fold changes [FCH]>1.5, FDR<.05 for all). Only one protein, ADM, was differentially expressed in FFA serum compared to normal (FCH>1.3, FDR<.05). Significant correlations were found between scalp biomarkers (IL-36RN/IL-25) and FFA severity, as well as between JAK/STAT and fibrosis gene-sets (r>.6; P <.05).

LIMITATIONS

This study was limited by a small sample size and predominantly female FFA patients.

CONCLUSION

Our data characterize FFA as an inflammatory condition limited to scalp, involving Th1/JAK skewing, with associated fibrosis and elevated T-regulatory markers, suggesting the potential for disease reversibility with JAK/STAT inhibition.

Enhanced Thermogenesis in Triple-Negative Breast Cancer Is Associated with Pro-Tumor Immune Microenvironment

Mild cold stress induced by housing mice with a 4T1 triple-negative breast cancer (TNBC) cell implantation model at 22 °C increases tumor growth rate with a pro-tumorigenic immune microenvironment (lower CD8 +T cells, higher myeloid-derived suppressor cells (MDSCs) and regulatory T-cells (Tregs)). Since cold stress also activates thermogenesis, we hypothesized that enhanced thermogenesis is associated with more aggressive cancer biology and unfavorable tumor microenvironment (TME) in TNBC patients. A total of 6479 breast cancer patients from METABRIC, TCGA, GSE96058, GSE20194, and GSE25066 cohorts were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) thermogenesis score. High-thermogenesis TNBC was associated with a trend towards worse survival and with angiogenesis, adipogenesis, and fatty acid metabolism pathways. On the other hand, low-thermogenesis TNBC enriched most of the hallmark cell-proliferation-related gene sets (i.e., mitotic spindle, E2F targets, G2M checkpoint, MYC targets), as well as immune-related gene sets (i.e., IFN-α and IFN-γ response). Favorable cytotoxic T-cell-attracting chemokines CCL5, CXCL9, CXCL10, and CXCL11 were lower; while the MDSC- and Treg-attracting chemokine CXCL12 was higher. There were higher M2 but lower M1 macrophages and Tregs. In conclusion, high-thermogenesis TNBC is associated with pro-tumor immune microenvironment and may serve as biomarker for testing strategies to overcome this immunosuppression.

IBDDB: a manually curated and text-mining-enhanced database of genes involved in inflammatory bowel disease

To date, research on inflammatory bowel disease (IBD, encompassing Crohn's disease and ulcerative colitis), a chronic complex disorder, has generated a large amount of data scattered across published literature (1 06 333) listed in PubMed on 14 October 2020, and no dedicated database currently exists that catalogues information on genes associated with IBD. We aimed to manually curate 289 genes that are experimentally validated to be linked with IBD and its known phenotypes. Furthermore, we have developed an integrated platform providing information about different aspects of these genes by incorporating several resources and an extensive text-mined knowledgebase. The curated IBD database (IBDDB) allows the selective display of collated 34 subject-specific concepts (listed as columns) exportable through a user-friendly IBDDB portal. The information embedded in concepts was acquired via text-mining of PubMed (manually cleaned and curated), accompanied by data-mining from varied resources. The user can also explore different biomedical entities and their co-occurrence with other entities (about one million) from 11 curated dictionaries in the indexed PubMed records. This functionality permits the user to generate and cross-examine a new hypothesis that is otherwise not easy to comprehend by just reading the published abstracts and papers. Users can download required information using various file formats and can display information in the form of networks. To our knowledge, no curated database of IBD-related genes is available so far. IBDDB is free for academic users and can be accessed at https://www.cbrc.kaust.edu.sa/ibd/.

Comparison of neutral lipid fatty acid composition in organisms from different trophic levels

The profiles of total fatty acids (TFAs) and the neutral lipid fatty acids (NLFAs) were compared for the bacterium Rhodopirellula rubra and the alga Raphidocelis subcapitata (conventional food source for Daphnia magna). D. magna NLFAs were assessed when this crustacean was fed with bacterium and alga, individually or in combination. After NLFA extraction, the profiles of the various organisms were characterized by gas chromatography. Results evidenced the relevance of the different composition of the fatty acid (FAs) fractions in the different organisms, R. rubra and R. subcapitata. In these species, the NFLA analyses revealed high amounts of long chain FAs (C₁₉). The FA profile of D. magna was influenced by the different diets provided although the preferred diet was the alga. D. magna showed the capacity to adapt to the available food resources as it defines its FA profile according to its needs, namely for the long chain FAs (C₁₉).

Transcriptomic Analysis of Pseudomonas aeruginosa Response to Pine Honey via RNA Sequencing Indicates Multiple Mechanisms of Antibacterial Activity

Pine honey is a unique type of honeydew honey produced exclusively in Eastern Mediterranean countries like Greece and Turkey. Although the antioxidant and anti-inflammatory properties of pine honey are well documented, few studies have investigated so far its antibacterial activity. This study investigates the antibacterial effects of pine honey against P. aeruginosa PA14 at the molecular level using a global transcriptome approach via RNA-sequencing. Pine honey treatment was applied at sub-inhibitory concentration and short exposure time (0.5× of minimum inhibitory concentration –MIC- for 45 min). Pine honey induced the differential expression (>two-fold change and p ≤ 0.05) of 463 genes, with 274 of them being down-regulated and 189 being up-regulated. Gene ontology (GO) analysis revealed that pine honey affected a wide range of biological processes (BP). The most affected down-regulated BP GO terms were oxidation-reduction process, transmembrane transport, proteolysis, signal transduction, biosynthetic process, phenazine biosynthetic process, bacterial chemotaxis, and antibiotic biosynthetic process. The up-regulated BP terms, affected by pine honey treatment, were those related to the regulation of DNA-templated transcription, siderophore transport, and phosphorylation. Pathway analysis revealed that pine honey treatment significantly affected two-component regulatory systems, ABC transporter systems, quorum sensing, bacterial chemotaxis, biofilm formation and SOS response. These data collectively indicate that multiple mechanisms of action are implicated in antibacterial activity exerted by pine honey against P. aeruginosa.

Use of Network Pharmacology to Explore the Mechanism of Gegen (Puerariae lobatae Radix) in the Treatment of Type 2 Diabetes Mellitus Associated with Hyperlipidemia

Rapid increases in metabolic disorders, such as type 2 diabetes mellitus (T2DM) and hyperlipidemia, are becoming a substantial challenge to worldwide public health. Traditional Chinese medicine has a long history and abundant experience in the treatment of diabetes and hyperlipidemia, and Puerariae lobatae Radix (known as Gegen in Chinese) is one of the most prevalent Chinese herbs applied to treat these diseases. The underlying mechanism by which Gegen simultaneously treats diabetes and hyperlipidemia, however, has not been clearly elucidated to date. Therefore, we systematically explored the potential mechanism of Gegen in the treatment of T2DM complicated with hyperlipidemia based on network pharmacology. We screened the potential targets of Gegen, T2DM, and hyperlipidemia in several online databases. Then, the hub targets were analyzed by performing protein-protein interaction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment assays, and finally, the complicated connections among compounds, targets, and pathways were visualized in Cytoscape. We found that isoflavones, including daidzein, genistein, and puerarin, as well as β-sitosterol, are the key active ingredients of Gegen responsible for its antidiabetic and antihyperlipidemia effects, which mainly target AKR1B1, EGFR, ESR, TNF, NOS3, MAPK3, PPAR, CYP19A1, INS, IL6, and SORD and multiple pathways, such as the PI3K-Akt signaling pathway; the AGE-RAGE signaling pathway in diabetic complications, fluid shear stress, and atherosclerosis; the PPAR signaling pathway; insulin resistance; the HIF-1 signaling pathway; the TNF signaling pathway; and others. These active ingredients also target multiple biological processes, including the regulation of glucose and lipid metabolism, the maintenance of metabolic homeostasis, and anti-inflammatory and antioxidant pathways. In conclusion, Gegen is a promising therapeutic phytomedicine for T2DM with hyperlipidemia that targets multiple proteins, biological processes, and pathways.

New Genotype of Yersinia pestis Found in Live Rodents in Yunnan Province, China

Yunnan Province, China is thought to be the original source of biovar Orientalis of Yersinia pestis, the causative agent of the third plague pandemic that has spread globally since the end of the 19th century. Although encompassing a large area of natural plague foci, Y. pestis strains have rarely been found in live rodents during surveillance in Yunnan, and most isolates are from rodent corpses and their fleas. In 2017, 10 Y. pestis strains were isolated from seven live rodents and three fleas in Heqing County of Yunnan. These strains were supposed to have low virulence to local rodents Eothenomys miletus and Apodemus chevrieri because the rodents were healthy and no dead animals were found in surrounding areas, as had occurred in previous epizootic disease. We performed microscopic and biochemical examinations of the isolates, and compared their whole-genome sequences and transcriptome with those of 10 high virulence Y. pestis strains that were isolated from nine rodents and one parasitic flea in adjacent city (Lijiang). We analyzed the phenotypic, genomic, and transcriptomic characteristics of live rodent isolates. The isolates formed a previously undefined monophyletic branch of Y. pestis that was named 1.IN5. Six SNPs, two indels, and one copy number variation were detected between live rodent isolates and the high virulence neighbors. No obvious functional consequence of these variations was found according to the known annotation information. Among genes which expression differential in the live rodent isolates compared to their high virulent neighbors, we found five iron transfer related ones that were significant up-regulated (| log₂ (FC) | > 1, p.adjust < 0.05), indicating these genes may be related to the low-virulence phenotype. The novel genotype of Y. pestis reported here provides further insights into the evolution and spread of plague as well as clues that may help to decipher the virulence mechanism of this notorious pathogen.

Analysis of key genes and related transcription factors in liver fibrosis based on bioinformatic technology

OBJECTIVE

To analyze differentially expressed genes (DEGs) related to liver fibrosis, and clarify the key genes and the possible targets in the progression of liver fibrosis.

METHODS

Using microarray datasets, GSE38199 was extracted from Gene Expression Omnibus (GEO), and a bioinformatics method was performed to find DEGs and transcription factors related to liver fibrosis.

RESULTS

A total of 58 DEGs were screened out according to GEO2R online analysis tool, which included 49 up-regulated and 9 down-regulated genes. These DEGs were mainly involved in formation with the extracellular region and extracellular exosome through gene ontology (GO) enrichment analysis. Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analysis showed that DEGs mainly participated in the PI3K-Akt signaling pathway, focal adhesion, ECM-receptor interaction, and metabolic pathways. Based on the results of the Protein-Protein Interaction (PPI) network and Molecular Complex Detection (MCODE) analysis, 9 key genes (COL1A1, FBN1, BGN, COL6A3, MMP2, FBLN5, LUM, PDGFRB, LOXL1) were screened out. A total of 30 transcription factors were found according to these 9 key genes, of which 4 transcription factors (Stat3, Trp53, NF-κB1, Sp1) were enriched.

CONCLUSION

Stat3, Trp53, NF-κB1, and Sp1 were all related to the development of liver fibrosis, and FBLN5 might be a target for liver fibrosis.

Muscle Glycogen Phosphorylase and Its Functional Partners in Health and Disease

Glycogen phosphorylase (PG) is a key enzyme taking part in the first step of glycogenolysis. Muscle glycogen phosphorylase (PYGM) differs from other PG isoforms in expression pattern and biochemical properties. The main role of PYGM is providing sufficient energy for muscle contraction. However, it is expressed in tissues other than muscle, such as the brain, lymphoid tissues, and blood. PYGM is important not only in glycogen metabolism, but also in such diverse processes as the insulin and glucagon signaling pathway, insulin resistance, necroptosis, immune response, and phototransduction. PYGM is implicated in several pathological states, such as muscle glycogen phosphorylase deficiency (McArdle disease), schizophrenia, and cancer. Here we attempt to analyze the available data regarding the protein partners of PYGM to shed light on its possible interactions and functions. We also underline the potential for zebrafish to become a convenient and applicable model to study PYGM functions, especially because of its unique features that can complement data obtained from other approaches.

MedFused: A framework to discover the relationships between drug chemical functional group impacts and side effects

It is a well-known fact that there are often side effects to the long-term use of certain medications. These side effects can vary from mild dizziness to, at its most serious, death. The main factors that cause these side effects are the chemical composition, the mode of treatment, and the dose. The dynamics that govern the reaction of a drug heavily depend on its structural composition. The structural composition of a drug is defined by the structural arrangement of the corresponding basic chemical functional groups. Hence, it is essential to investigate the effect of chemical functional groups on the side effects to synthesize drugs with minimal side effects. To support this process, we developed a framework named MedFused (Medical Functional Group Side Effects Database), which is composed of drugs (International Union of Pure and Applied Chemistry: IUPAC nomenclature), functional groups, and the side effects along with other valuable information such as STITCH (search tool for interactions of chemicals) compound ID, and the Unified Medical Language System (UMLS) concept ID. We develop a web framework that functions on the MedFused system database on top of the Django web framework. Our web server supports functionalities such as exploring the database and descriptive graph tools, which provide additional exploration capabilities to the framework. These descriptive tools include histograms, pie charts, and association charts, which further explore the system. Above these basic tools, MedFused includes functionality to discover the drug's "chemical functional group" impact on "side effects". The method conducts an association rule analysis on the relationships by considering the MedFused database as a collection of transactions. A specific transaction has a list of the functional groups of a drug and one side effect. Hence, a drug that has more than one side effect forms multiple transactions. Next, we generate a binary feature matrix based on the transactions and introduce a pruning mechanism to consider only the potential functional groups and side effects based on their support (frequencies), subjected to a predefined threshold (which can be changed accordingly). As the current version of the MedFused database has a limited number of side effects (hence low support), we restricted the analysis to identify the functional groups which have the most potential of causing a particular side effect, based on a confidence value of 1. Our framework can be further extended with more functions and tools as it supports the model view controller (MVC) architecture, which is inherited from the Django Python web framework.

Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways

Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.

Key aroma compounds and metabolic profiling of Debaryomyces hansenii L1-1-fermented Flos Sophorae

The extract from Debaryomyces hansenii L1-1-fermented Flos Sophorae has a unique aroma and could be used as a natural spice. The influence of yeast growth and culture medium pH on organoleptic properties of fermented substrates, as well as on the content of volatile aromatic compounds, total sugars, polysaccharides, reducing sugars, total proteins, and amino acids, were analyzed. Metabolic pathways were annotated to compare and contrast key aromatic compounds and metabolic profiles of water and ethanol extracts of D. hansenii L1-1-fermented Flos Sophorae. We found that cells grew most rapidly, pH values changed significantly, and the largest consumption of sugars and amino acids occurred within 48 hr, producing bouquet-like, fruity, and sweet odors, as well as the highest content of volatile aromatic compounds in the extracts. The main aroma metabolites were 2-phenylethanol, linalool, and α-terpineol. The sensory quality of the ethanol extracts was superior to that of the water extracts. Five aromatic compounds, isoamyl alcohol, 2-methylbutan-1-ol, isobutyric acid, 2,3-hexanedione, and 1-hexanol, were positively correlated with the water extract group and negatively correlated with the ethanol extract group, whereas 13 aromatic compounds, styrene, acetophenone, 2-octen-1-ol, linalool, naphthalene, α-terpineol, dihydrocarveol, (-)-myrtenol, methyl anthranilate, eugenol, γ-nonanolactone, jasmone, and β-ionone, showed the converse trend. Although 2-phenylethanol displayed the highest concentration in the extracts, it did not significantly contribute to the separation of ethanol and water extracts. In Flos Sophorae medium, D. hansenii mainly produces 2-phenylethanol from phenylalanine by the Ehrlich reaction, whereas it produces linalool and α-terpineol by the terpenoid backbone and monoterpenoid biosynthetic pathways; the variable contents of proline, arginine, and glutamate could respond to the arginine and proline metabolic pathways. PRACTICAL APPLICATIONS: Flos Sophorae, a collection of buds of Sophora japonica L., is a traditional Chinese medicinal and edible plant for its good aroma, taste, and nutritional value. Debaryomyces hansenii is a common, aroma-producing yeast. D. hansenii L1-1-fermented Flos Sophorae had a unique, bouquet-like aroma, slightly softer than the typical Flos Sophorae-like aroma. This study enriches our understanding of predominant aroma components and determines their contribution to the profiles of Flos Sophorae ferments obtained using D. hansenii L1-1. Researchers and manufacturers specializing in spices making can use these data to improve the aromatic profiles of natural spices produced by microorganisms, thereby obtaining unique aromas.

In-Depth Analysis of the Hidradenitis Suppurativa Serum Proteome Identifies Distinct Inflammatory Subtypes

Hidradenitis suppurativa is a chronic inflammatory dermatosis with presentations ranging from painful nodules and abscesses to draining tunnels. Using an unbiased proteomics approach, we assessed cardiovascular-, cardiometabolic-, and inflammation-related biomarkers in the serum of patients with moderate-to-severe hidradenitis suppurativa. The serum of patients with hidradenitis suppurativa clustered separately from that of healthy controls and had an upregulation of neutrophil-related markers (Cathepsin D, IL-17A, CXCL1). Patients with histologically diagnosed dermal tunnels had higher serum lipocalin-2 levels compared with those without tunnels. Consistent with this, patients with tunnels had a more neutrophilic-rich serum signature, marked by Cathepsin D, IL-17A, and IL-17D alterations. There was a significant serum‒skin correlation between proteins in the serum and the corresponding mRNA expression in skin biopsies, with healthy-appearing perilesional skin demonstrating a significant correlation with neutrophil-related proteins in the serum. CSF3 mRNA levels in lesional skin significantly correlated with neutrophil-related proteins in the serum, suggesting that CFS3 in the skin may be a driver of neutrophilic inflammation. Clinical significantly correlated with the levels of lipocalin-2 and IL-17A in the serum. Using an unbiased, large-scale proteomic approach, we demonstrate that hidradenitis suppurativa is a systemic neutrophilic dermatosis, with a specific molecular signature associated with the presence of dermal tunnels.

Two novel qualitative transcriptional signatures robustly applicable to non-research-oriented colorectal cancer samples with low-quality RNA

Currently, due to the low quality of RNA caused by degradation or low abundance, the accuracy of gene expression measurements by transcriptome sequencing (RNA‐seq) is very challenging for non‐research‐oriented clinical samples, majority of which are preserved in hospitals or tissue banks worldwide with complete pathological information and follow‐up data. Molecular signatures consisting of several genes are rarely applied to such samples. To utilize these resources effectively, 45 stage II non‐research‐oriented samples which were formalin‐fixed paraffin‐embedded (FFPE) colorectal carcinoma samples (CRC) using RNA‐seq have been analysed. Our results showed that although gene expression measurements were significantly affected, most cancer features, based on the relative expression orderings (REOs) of gene pairs, were well preserved. We then developed two REO‐based signatures, which consisted of 136 gene pairs for early diagnosis of CRC, and 4500 gene pairs for predicting post‐surgery relapse risk of stage II and III CRC. The performance of our signatures, which included hundreds or thousands of gene pairs, was more robust for non‐research‐oriented clinical samples, compared to that of two published concise REO‐based signatures. In conclusion, REO‐based signatures with relatively more gene pairs could be robustly applied to non‐research‐oriented CRC samples.

An Open Access Database of Licensed Cancer Drugs

A global, comprehensive and open access listing of approved anticancer drugs does not currently exist. Partial information is available from multiple sources, including regulatory authorities, national formularies and scientific agencies. Many such data sources include drugs used in oncology for supportive care, diagnostic or other non-antineoplastic uses. We describe a methodology to combine and cleanse relevant data from multiple sources to produce an open access database of drugs licensed specifically for therapeutic antineoplastic purposes. The resulting list is provided as an open access database, (http://www.redo-project.org/cancer-drugs-db/), so that it may be used by researchers as input for further research projects, for example literature-based text mining for drug repurposing.

Aripiprazole as a Candidate Treatment of COVID-19 Identified Through Genomic Analysis

Background: Antipsychotics modulate expression of inflammatory cytokines and inducible inflammatory enzymes. Elopiprazole (a phenylpiperazine antipsychotic drug in phase 1) has been characterized as a therapeutic drug to treat SARS-CoV-2 infection in a repurposing study. We aim to investigate the potential effects of aripiprazole (an FDA approved phenylpiperazine) on COVID-19-related immunological parameters. Methods: Differential gene expression profiles of non-COVID-19 vs. COVID-19 RNA-Seq samples (CRA002390 project in GSA database) and drug-naïve patients with non-affective psychosis at baseline and after three months of aripiprazole treatment were identified. An integrative transcriptomic analyses of aripiprazole effects on differentially expressed genes in COVID-19 patients was performed. Findings: 82 out the 377 genes (21.7%) with expression significantly altered by aripiprazole have also their expression altered in COVID-19 patients and in 93.9% of these genes their expression is reverted by aripiprazole. The number of common genes with expression altered in both analyses is significantly higher than expected (Fisher's Exact Test, two tail; p value = 3.2e-11). 11 KEGG pathways were significantly enriched with genes with altered expression both in COVID-19 patients and aripiprazole medicated non-affective psychosis patients (p adj<0.05). The most significant pathways were associated to immune responses and mechanisms of hyperinflammation-driven pathology (i.e.,"inflammatory bowel disease (IBD)" (the most significant pathway with a p adj of 0.00021), "Th1 and Th2 cell differentiation" and "B cell receptor signaling pathway") that have been also associated with COVID19 clinical outcome. Interpretation: This exploratory investigation may provide further support to the notion that a protective effect is exerted by aripiprazole (phenylpiperazine) by modulating the expression of genes that have shown to be altered in COVID-19 patients. Along with many ongoing studies and clinical trials, repurposing available medications could be of use in countering SARS-CoV-2 infection, but require further studies and trials.

Identification of a ceRNA Network in Lung Adenocarcinoma Based on Integration Analysis of Tumor-Associated Macrophage Signature Genes

As research into tumor-immune interactions progresses, immunotherapy is becoming the most promising treatment against cancers. The tumor microenvironment (TME) plays the key role influencing the efficacy of anti-tumor immunotherapy, in which tumor-associated macrophages (TAMs) are the most important component. Although evidences have emerged revealing that competing endogenous RNAs (ceRNAs) were involved in infiltration, differentiation and function of immune cells by regulating interactions among different varieties of RNAs, limited comprehensive investigation focused on the regulatory mechanism between ceRNA networks and TAMs. In this study, we aimed to utilize bioinformatic approaches to explore how TAMs potentially influence the prognosis and immunotherapy of lung adenocarcinoma (LUAD) patients. Firstly, according to TAM signature genes, we constructed a TAM prognostic risk model by the least absolute shrinkage and selection operator (LASSO) cox regression in LUAD patients. Then, differential gene expression was analyzed between high- and low-risk patients. Weighted gene correlation network analysis (WGCNA) was utilized to identify relevant gene modules correlated with clinical characteristics and prognostic risk score. Moreover, ceRNA networks were built up based on predicting regulatory pairs in differentially expressed genes. Ultimately, by synthesizing information of protein-protein interactions (PPI) analysis and survival analysis, we have successfully identified a core regulatory axis: LINC00324/miR-9-5p (miR-33b-5p)/GAB3 (IKZF1) which may play a pivotal role in regulating TAM risk and prognosis in LUAD patients. The present study contributes to a better understanding of TAMs associated immunosuppression in the TME and provides novel targets and regulatory pathway for anti-tumor immunotherapy.

The Metabolic Landscape of RAS-Driven Cancers from biology to therapy

Our understanding of how the RAS protein family, and in particular mutant KRAS promote metabolic dysregulation in cancer cells has advanced significantly over the last decade. In this Review, we discuss the metabolic reprogramming mediated by oncogenic RAS in cancer, and elucidating the underlying mechanisms could translate to novel therapeutic opportunities to target metabolic vulnerabilities in RAS-driven cancers.

In vivo therapeutic success of MicroRNA-155 antagomir in a mouse model of pulmonary fibrosis induced by bleomycin

BACKGROUND/AIMS

MicroRNAs (miRNAs) play critical regulatory roles in the pathogenesis of pulmonary fibrosis. The aim of this study was to explore whether miRNA antagomirs could serve as potential therapeutic agents in interstitial lung diseases.

METHODS

A mouse model of pulmonary fibrosis was established by intratracheal injection of bleomycin (BLM). Using microarray analysis, up-regulated miRNAs were identified during the development of pulmonary fibrosis. miR-155 was chosen as the candidate miRNA. Fifteen mice were then randomized into the following three groups: BLM + antagomiR-155 group, treated with BLM plus intravenously injected with antagomiR-155; BLM group, treated with intratracheal BLM plus phosphate-buffered saline (PBS); and a control group, treated with PBS only. Lung tissues were collected for histopathological analysis, hydroxyproline measurement, and Western blotting. Enzyme-linked immunosorbent assays were used for the measurement of cytokines associated with pulmonary fibrosis.

RESULTS

Histological changes and hydroxyproline levels induced by BLM were significantly inhibited by antagomiR-155. The levels of interleukin 4 (IL-4) and transforming growth factor-β (TGF-β) expression were increased after BLM treatment. However, miR-155 silencing decreased the expression of IL-4, TGF-β, and interferon-γ. TGF-β-activated kinase 1/mitogen-activated protein kinase kinase kinase 7 (MAP3K7)-binding protein 2 (TAB2) of the mitogen-activated protein kinase (MAPK) signaling pathway, was activated by BLM and inhibited by in vivo silencing of miR-155 via antagomiR-155.

CONCLUSION

In vivo treatment with antagomiR-155 alleviated the pathological changes induced by BLM and may be a promising therapeutic strategy for pulmonary fibrosis.

Enhanced Understanding of Molecular Interactions and Function Underlying Pain Processes Through Networks of Transcript Isoforms, Genes, and Gene Families

Introduction

Molecular networks based on the abundance of mRNA at the gene level and pathway networks that relate families or groups of paralog genes have supported the understanding of interactions between molecules. However, multiple molecular mechanisms underlying health and behavior, such as pain signal processing, are modulated by the abundances of the transcript isoforms that originate from alternative splicing, in addition to gene abundances. Alternative splice variants of growth factors, ion channels, and G-protein-coupled receptors can code for proteoforms that can have different effects on pain and nociception. Therefore, networks inferred using abundance from more agglomerative molecular units (eg, gene family, or gene) have limitations in capturing interactions at a more granular level (eg, gene, or transcript isoform, respectively) do not account for changes in the abundance at the transcript isoform level.

Objective

The objective of this study was to evaluate the relative benefits of network inference using abundance patterns at various aggregate levels.

Methods

Sparse networks were inferred using Gaussian Markov random fields and a novel aggregation criterion was used to aggregate network edges. The relative advantages of network aggregation were evaluated on two molecular systems that have different dimensions and connectivity, circadian rhythm and Toll-like receptor pathways, using RNA-sequencing data from mice representing two pain level groups, opioid-induced hyperalgesia and control, and two central nervous system regions, the nucleus accumbens and the trigeminal ganglia.

Results

The inferred networks were benchmarked against the Kyoto Encyclopedia of Genes and Genomes reference pathways using multiple criteria. Networks inferred using more granular information performed better than networks inferred using more aggregate information. The advantage of granular inference varied with the pathway and data set used.

Discussion

The differences in inferred network structure between data sets highlight the differences in OIH effect between central nervous system regions. Our findings suggest that inference of networks using alternative splicing variants can offer complementary insights into the relationship between genes and gene paralog groups.

Metformin Reduces Histone H3K4me3 at the Promoter Regions of Positive Cell Cycle Regulatory Genes in Lung Cancer Cells

Simple Summary

To understand the effect of metformin on epigenetic regulation, we analyzed histone H3 methylation, DNA methylation, and chromatin accessibility in lung cancer cells. Metformin showed little effect on DNA methylation or chromatin accessibility but significantly reduced H3K4me3 levels at the promoters of positive cell cycle regulatory genes. Metformin downregulated H3K4 methyltransferase MLL2 expression and knockdown of MLL2 resulted in suppression of H3K4me3 expression and lung cancer cell proliferation. We further evaluated the clinicopathological significance of MLL2 in tumor and matched normal tissues from 42 non-small cell lung cancer patients. MLL2 overexpression was significantly associated with poor recurrence-free survival in lung adenocarcinoma. Our study facilitates the understanding of the effect of metformin on the regulation of histone H3K4me3 at promoter regions of cell cycle regulatory genes in lung cancer cells, and MLL2 may be a potential therapeutic target for lung cancer therapy.

Abstract

This study aimed at understanding the effect of metformin on histone H3 methylation, DNA methylation, and chromatin accessibility in lung cancer cells. Metformin significantly reduced H3K4me3 level at the promoters of positive cell cycle regulatory genes such as CCNB2, CDK1, CDK6, and E2F8. Eighty-eight genes involved in cell cycle showed reduced H3K4me3 levels in response to metformin, and 27% of them showed mRNA downregulation. Metformin suppressed the expression of H3K4 methyltransferases MLL1, MLL2, and WDR82. The siRNA-mediated knockdown of MLL2 significantly downregulated global H3K4me3 level and inhibited lung cancer cell proliferation. MLL2 overexpression was found in 14 (33%) of 42 NSCLC patients, and a Cox proportional hazards analysis showed that recurrence-free survival of lung adenocarcinoma patients with MLL2 overexpression was approximately 1.32 (95% CI = 1.08–4.72; p = 0.02) times poorer than in those without it. Metformin showed little effect on DNA methylation and chromatin accessibility at the promoter regions of cell cycle regulatory genes. The present study suggests that metformin reduces H3K4me3 levels at the promoters of positive cell cycle regulatory genes through MLL2 downregulation in lung cancer cells. Additionally, MLL2 may be a potential therapeutic target for reducing the recurrence of lung adenocarcinoma.

Algorithmic Annotation of Functional Roles for Components of 3,044 Human Molecular Pathways

Current methods of high-throughput molecular and genomic analyses enabled to reconstruct thousands of human molecular pathways. Knowledge of molecular pathways structure and architecture taken along with the gene expression data can help interrogating the pathway activation levels (PALs) using different bioinformatic algorithms. In turn, the pathway activation profiles can characterize molecular processes, which are differentially regulated and give numeric characteristics of the extent of their activation or inhibition. However, different pathway nodes may have different functions toward overall pathway regulation, and calculation of PAL requires knowledge of molecular function of every node in the pathway in terms of its activator or inhibitory role. Thus, high-throughput annotation of functional roles of pathway nodes is required for the comprehensive analysis of the pathway activation profiles. We proposed an algorithm that identifies functional roles of the pathway components and applied it to annotate 3,044 human molecular pathways extracted from the Biocarta, Reactome, KEGG, Qiagen Pathway Central, NCI, and HumanCYC databases and including 9,022 gene products. The resulting knowledgebase can be applied for the direct calculation of the PALs and establishing large scale profiles of the signaling, metabolic, and DNA repair pathway regulation using high throughput gene expression data. We also provide a bioinformatic tool for PAL data calculations using the current pathway knowledgebase.

SWATH-MS Based Proteomic Profiling of Prostate Cancer Cells Reveals Adaptive Molecular Mechanisms in Response to Anti-Androgen Therapy

Prostate cancer (PCa) is the second most common cancer affecting men worldwide. PCa shows a broad-spectrum heterogeneity in its biological and clinical behavior. Although androgen targeted therapy (ATT) has been the mainstay therapy for advanced PCa, it inevitably leads to treatment resistance and progression to castration resistant PCa (CRPC). Thus, greater understanding of the molecular basis of treatment resistance and CRPC progression is needed to improve treatments for this lethal phenotype. The current study interrogated both proteomics and transcriptomic alterations stimulated in AR antagonist/anti-androgen (Bicalutamide and Enzalutamide) treated androgen-dependent cell model (LNCaP) in comparison with androgen-independent/castration-resistant cell model (C4-2B). The analysis highlighted the activation of MYC and PSF/SFPQ oncogenic upstream regulators in response to the anti-androgen treatment. Moreover, the study revealed anti-androgen induced genes/proteins related to transcription/translation regulation, energy metabolism, cell communication and signaling cascades promoting tumor growth and proliferation. In addition, these molecules were found dysregulated in PCa clinical proteomic and transcriptomic datasets, suggesting their potential involvement in PCa progression. In conclusion, our study provides key molecular signatures and associated pathways that might contribute to CRPC progression despite treatment with anti-androgens. Such molecular signatures could be potential therapeutic targets to improve the efficacy of existing therapies and/or predictive/prognostic value in CRPC for treatment response.

Screening Balanites aegyptiaca for inhibitors against putative drug targets in Microsporum gypseum - Subtractive proteome, docking and simulation approach

Microsporum gypseum is a keratinophilic fungi grouped under dermatophytes infecting skin, hair and nail portions in human and animals causing tinea corporis, tinea facei and tinea capitis. As both human and fungi are eukaryotes, the available drugs for treating dermatophytes produce some side effects due to drug interaction with human also. Apart from this, the gut microbiota has a very big role in the health of human which should not be affected by the drugs. Hence this study focused on finding a target which is unique and essential to M. gypseum and non-homologous to human and gut microbiota, non-homologous to human domain architecture, highly interacting with other proteins, sub-cellular localization of proteins and non-druggability analysis of the targets using subtractive proteomics approach which resulted with 3 novel drug targets from M. gypseum which were modeled using I-TASSER, refined by ModRefiner and validated by PROCHECK. Further these targets were docked with compounds identified through LC-MS of fractioned methanol extract of B. aegyptiaca fruit pulp using Glide module and the stability of the docked complex was analyzed by molecular dynamics simulation using Desmond module of Schrodinger. Cyanidin-3-O-rhamnoside had better interaction with all the targets and Taurocholic acid had better result with ECCP which suggests the multi-targeting potency of these two compounds against M. gypseum which has to be confirmed by in vitro and in vivo studies.

Restoration of type I interferon signaling in intrahepatically primed CD8+ T cells promotes functional differentiation

Hepatitis B virus–specific (HBV-specific) CD8⁺ T cells fail to acquire effector functions after priming in the liver, but the molecular basis for the dysfunction is poorly understood. By comparing the gene expression profile of intrahepatically primed, dysfunctional HBV-specific CD8⁺ T cells with that of systemically primed, functional effector counterparts, we found that the expression of interferon-stimulated genes (ISGs) is selectively suppressed in the dysfunctional CD8⁺ T cells. The ISG suppression was associated with impaired phosphorylation of STAT1 in response to IFN-α treatment. Importantly, a strong induction of type I interferons (IFN-Is) in the liver facilitated the functional differentiation of intrahepatically primed HBV-specific CD8⁺ T cells in association with the restoration of ISGs’ expression in the T cells. These results suggest that intrahepatic priming suppresses IFN-I signaling in CD8⁺ T cells, which may contribute to the dysfunction. The data also suggest a therapeutic value of the robust induction of intrahepatic IFN-Is for the treatment of chronic HBV infection.

Functional defects of hepatitis B virus–specific CD8⁺ T cells correlate with impairment of type I interferon signaling after intrahepatic priming.

ASYMPTOTICALLY INDEPENDENT U-STATISTICS IN HIGH-DIMENSIONAL TESTING

Many high-dimensional hypothesis tests aim to globally examine marginal or low-dimensional features of a high-dimensional joint distribution, such as testing of mean vectors, covariance matrices and regression coefficients. This paper constructs a family of U-statistics as unbiased estimators of the ℓ p -norms of those features. We show that under the null hypothesis, the U-statistics of different finite orders are asymptotically independent and normally distributed. Moreover, they are also asymptotically independent with the maximum-type test statistic, whose limiting distribution is an extreme value distribution. Based on the asymptotic independence property, we propose an adaptive testing procedure which combines p-values computed from the U-statistics of different orders. We further establish power analysis results and show that the proposed adaptive procedure maintains high power against various alternatives.

Predicting therapeutic drugs for hepatocellular carcinoma based on tissue-specific pathways

Hepatocellular carcinoma (HCC) is a significant health problem worldwide with poor prognosis. Drug repositioning represents a profitable strategy to accelerate drug discovery in the treatment of HCC. In this study, we developed a new approach for predicting therapeutic drugs for HCC based on tissue-specific pathways and identified three newly predicted drugs that are likely to be therapeutic drugs for the treatment of HCC. We validated these predicted drugs by analyzing their overlapping drug indications reported in PubMed literature. By using the cancer cell line data in the database, we constructed a Connectivity Map (CMap) profile similarity analysis and KEGG enrichment analysis on their related genes. By experimental validation, we found securinine and ajmaline significantly inhibited cell viability of HCC cells and induced apoptosis. Among them, securinine has lower toxicity to normal liver cell line, which is worthy of further research. Our results suggested that the proposed approach was effective and accurate for discovering novel therapeutic options for HCC. This method also could be used to indicate unmarked drug-disease associations in the Comparative Toxicogenomics Database. Meanwhile, our method could also be applied to predict the potential drugs for other types of tumors by changing the database.