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Protein–protein interaction (PPI) prediction is vital for interpreting biological activities. Even though many diverse sorts of data and machine learning approaches have been employed in PPI prediction, performance still has to be enhanced. As a result, we adopted an Aquilla Influenced Shark Smell (AISSO)-based hybrid prediction technique to construct a sequence-dependent PPI prediction model. This model has two stages of operation: feature extraction and prediction. Along with sequence-based and Gene Ontology features, unique features were produced in the feature extraction stage utilizing the improved semantic similarity technique, which may deliver reliable findings. These collected characteristics were then sent to the prediction step, and hybrid neural networks, such as the Improved Recurrent Neural Network and Deep Belief Networks, were used to predict the PPI using modified score level fusion. These neural networks’ weight variables were adjusted utilizing a unique optimal methodology called Aquila Influenced Shark Smell (AISSO), and the outcomes showed that the developed model had attained an accuracy of around 88%, which is much better than the traditional methods; this model AISSO-based PPI prediction can provide precise and effective predictions. [ABSTRACT FROM AUTHOR]

Treating diabetic wounds effectively remains a significant clinical challenge. Emerging studies suggest that microRNAs (miRNAs) play crucial roles in various physiological and pathological processes and hold promise as therapeutic tools. This study investigates the miRNA expression profile in keratinocytes using a cell model of diabetic wounds. Microarray analysis revealed that 43 miRNAs from wounded keratinocytes incubated under diabetic conditions (high glucose/hypoxia) exhibited a two-fold change in expression compared to those incubated under normal conditions (low glucose/normoxia). Quantitative RT-PCR confirmed significant differences in the expression of eight miRNAs, with miR-3138 and miR-3679-5p being further analyzed for their roles in keratinocyte migration. Transfection with a miR-3138 mimic and a miR-3679-5p inhibitor indicated that upregulation of miR-3138 and downregulation of miR-3679-5p enhance keratinocyte migration in both normal and diabetic wounds. Pathway and gene ontology (GO) analyses identified potential pathways and functional annotations associated with miR-3138 and miR-3679-5p in diabetic wound healing. Potential human gene targets of miR-3138 and miR-3679-5p were predicted using a three-way comparison of the TargetScan, miRDB, and DIANA databases. This study elucidates the miRNA expression signature of human keratinocytes in a diabetes-like environment, providing deeper insights into the pathogenesis of diabetic wounds. [ABSTRACT FROM AUTHOR]

The present study examines the critical function of the ketoacyl-ACP synthase (KAS) genes in Chlamydomonas reinhardtii (C. reinhardtii)'s lipid production, especially in the presence of nitrogen stress. The growing need for sustainable energy sources has made biodiesel created from microalgae a viable option. Acetyl-CoA carboxylase (ACC) and KAS in C. reinhardtii are two important enzymes highlighted in this work, focusing on the molecular mechanisms of fatty acid and triacylglycerol production. The expression of the KAS genes (KASII and KASIII) at the mRNA and protein levels was examined by employing quantitative reverse transcription polymerase chain reaction (qRT-PCR) as well as western blot techniques. Moreover, protein–protein interaction (PPI) network and modular analysis, as well as gene ontology (GO) and pathway enrichment analysis (PEA), were performed. The findings highlighted the important role that KAS genes play in lipid synthesis, especially when nitrogen stress is present. Although the work offers insightful information on the intricate routes of lipid manufacturing in microalgae, it also highlights the need for more research using various methodologies. These results highlight the significance of KAS genes in boosting lipid yield under challenging circumstances and further our knowledge of microalgae-based biodiesel generation. [ABSTRACT FROM AUTHOR]

Abstract Protein–protein interaction (PPI) prediction is vital for interpreting biological activities. Even though many diverse sorts of data and machine learning approaches have been employed in PPI prediction, performance still has to be enhanced. As a result, we adopted an Aquilla Influenced Shark Smell (AISSO)-based hybrid prediction technique to construct a sequence-dependent PPI prediction model. This model has two stages of operation: feature extraction and prediction. Along with sequence-based and Gene Ontology features, unique features were produced in the feature extraction stage utilizing the improved semantic similarity technique, which may deliver reliable findings. These collected characteristics were then sent to the prediction step, and hybrid neural networks, such as the Improved Recurrent Neural Network and Deep Belief Networks, were used to predict the PPI using modified score level fusion. These neural networks’ weight variables were adjusted utilizing a unique optimal methodology called Aquila Influenced Shark Smell (AISSO), and the outcomes showed that the developed model had attained an accuracy of around 88%, which is much better than the traditional methods; this model AISSO-based PPI prediction can provide precise and effective predictions.

The PHLDA (pleckstrin homology-like domain family) gene family is popularly known as a potential biomarker for cancer identification, and members of the PHLDA family have become considered potentially viable targets for cancer treatments. The PHLDA gene family consists of PHLDA1, PHLDA2, and PHLDA3. The predictive significance of PHLDA genes in cancer remains unclear. To determine the role of pleckstrin as a prognostic biomarker in human cancers, we conducted a systematic multiomics investigation. Through various survival analyses, pleckstrin expression was evaluated, and their predictive significance in human tumors was discovered using a variety of online platforms. By analyzing the protein–protein interactions, we also chose a collection of well-known functional protein partners for pleckstrin. Investigations were also carried out on the relationship between pleckstrins and other cancers regarding mutations and copy number alterations. The cumulative impact of pleckstrin and their associated genes on various cancers, Gene Ontology (GO), and pathway analyses were used for their evaluation. Thus, the expression profiles of PHLDA family members and their prognosis in various cancers may be revealed by this study. During this multiomics analysis, we found that among the PHLDA family, PHLDA1 may be a therapeutic target for several cancers, including kidney, colon, and brain cancer, while PHLDA2 can be a therapeutic target for cancers of the colon, esophagus, and pancreas. Additionally, PHLDA3 may be a useful therapeutic target for ovarian, renal, and gastric cancer.

Jute (Corchorus sp.), a commercially important and eco-friendly crop, is widely cultivated in Bangladesh, India, and China. Some varieties of this tropical plant such as the Corchorus olitorius. Variety accession no. 2015 (acc. 2015) has been found to be low-temperature tolerant. The current study was designed to explore the genome-wide variations present in the tolerant plant acc. 2015 in comparison to the sensitive farmer popular variety Corchorus olitorius var. O9897 using the whole genome resequencing technique. Among different variations, intergenic Single Nucleotide Polymorphism (SNPs) and Insertion-Deletion (InDels) were found in the highest percentage whereas approximately 3% SNPs and 2% InDels were found in exonic regions in both plants. Gene enrichment analysis indicated the presence of acc. 2015 specific SNPs in the genes encoding peroxidase, ER lumen protein retaining receptor, and hexosyltransferase involved in stress response (GO:0006950) which were not present in sensitive variety O9897. Besides, distinctive copy number variation regions (CNVRs) comprising 120 gene loci were found in acc. 2015 with a gain of function from multiple copy numbers but absent in O9897. Gene ontology analysis revealed these gene loci to possess different receptors like kinases, helicases, phosphatases, transcription factors especially Myb transcription factors, regulatory proteins containing different binding domains, annexin, laccase, acyl carrier protein, potassium transporter, and vesicular transporter proteins that are responsible for low temperature induced adaptation pathways in plants. This work of identifying genomic variations linked to cold stress tolerance traits will help to develop successful markers that will pave the way to develop genetically modified cold-resistant jute lines for year-round cultivation to meet the demand for a sustainable fiber crop economy.

Cryopreservation of human testicular tissue, as a key element of anticancer therapy, includes the following stages: saturation with cryoprotectants, freezing, thawing, and removal of cryoprotectants. According to the point of view existing in "classical" cryobiology, the thawing mode is the most important consideration in the entire process of cryopreservation of any type of cells, including cells of testicular tissue. The existing postulate in cryobiology states that any frozen types of cells must be thawed as quickly as possible. The technologically maximum possible thawing temperature is 100 °C, which is used in our technology for the cryopreservation of testicular tissue. However, there are other points of view on the rate of cell thawing, according to how thawing should be carried out at physiological temperatures. In fact, there are morphological and functional differences between immature (from prepubertal patients) and mature testicular tissue. Accordingly, the question of the influence of thawing temperature on both types of tissues is relevant. The purpose of this study is to explore the transcriptomic differences of cryopreserved mature and immature testicular tissue subjected to different thawing methods by RNA sequencing. Collected and frozen testicular tissue samples were divided into four groups: quickly (in boiling water at 100 °C) thawed cryopreserved mature testicular tissue (group 1), slowly (by a physiological temperature of 37 °C) thawed mature testicular tissue (group 2), quickly thawed immature testicular tissue (group 3), and slowly thawed immature testicular tissue (group 4). Transcriptomic differences were assessed using differentially expressed genes (DEG), the Kyoto Encyclopedia of Genes and Genomes (KEGG), gene ontology (GO), and protein–protein interaction (PPI) analyses. No fundamental differences in the quality of cells of mature and immature testicular tissue after cryopreservation were found. Generally, thawing of mature and immature testicular tissue was more effective at 100 °C. The greatest difference in the intensity of gene expression was observed in ribosomes of cells thawed at 100 °C in comparison with cells thawed at 37 °C. In conclusion, an elevated speed of thawing is beneficial for frozen testicular tissue. [ABSTRACT FROM AUTHOR]

Objective To screen key autophagy-related genes in alcoholic hepatitis (AH) and investigate potential biomarkers and therapeutic targets for AH. Methods Two AH gene chips in Gene Expression Omnibus (GEO) and autophagy-related data sets obtained from MSigDB and GeneCards databases were used, and the key genes were verified and obtained by weighted gene co-expression network analysis (WGCNA). The screened key genes were subject to gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) and immune infiltration analyses. Messenger RNA (mRNA)- microRNA (miRNA) network was constructed to analyze the expression differences of key autophagy-related genes during different stages of AH, which were further validated by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) in the liver tissues of AH patients and mice. Results Eleven autophagy-related genes were screened in AH (EEF1A2, CFTR, SOX4, TREM2, CTHRC1, HSPB8, TUBB3, PRKAA2, RNASE1, MTCL1 and HGF), all of which were up-regulated. In the liver tissues of AH patients and mice, the relative expression levels of SOX4, TREM2, HSPB8 and PRKAA2 in the AH group were higher than those in the control group. Conclusions SOX4, TREM2, HSPB8 and PRKAA2 may be potential biomarkers and therapeutic targets for AH. [ABSTRACT FROM AUTHOR]

Introduction: Increasing global warming has made heat stress a serious threat to crop productivity and global food security in recent years. One of the most promising solutions to address this issue is developing heat-stress-tolerant plants. Hence, a thorough understanding of heat stress response mechanisms, particularly molecular ones, is crucial. Methods: Although numerous studies have used microarray expression profiling technology to explore this area, these experiments often face limitations, leading to inconsistent results. To overcome these limitations, a random effects meta-analysis was employed using advanced statisticalmethods. Ameta-analysis of 16 microarray datasets related to heat stress response in Arabidopsis thaliana was conducted. Results: The analysis revealed 1,972 significant differentially expressed genes between control and heat-stressed plants (826 over-expressed and 1,146 downexpressed), including 128 differentially expressed transcription factors from different families. The most significantly enriched biological processes, molecular functions, and KEGG pathways for over-expressed genes included heat response, mRNA splicing via spliceosome pathways, unfolded protein binding, and heat shock protein binding. Conversely, for down-expressed genes, the most significantly enriched categories included cell wall organization or biogenesis, protein phosphorylation, transmembrane transporter activity, ion transmembrane transporter, biosynthesis of secondary metabolites, and metabolic pathways. Discussion: Through our comprehensive meta-analysis of heat stress transcriptomics, we have identified pivotal genes integral to the heat stress response, offering profound insights into the molecular mechanisms by which plants counteract such stressors. Our findings elucidate that heat stress influences gene expression both at the transcriptional phase and posttranscriptionally, thereby substantially augmenting our comprehension of plant adaptive strategies to heat stress. [ABSTRACT FROM AUTHOR]

Background: Most patients who had coronavirus disease 2019 (COVID-19) fully recovered, but many others experienced acute sequelae or persistent symptoms. It is possible that acute COVID-19 recovery is just the beginning of a chronic condition. Even after COVID-19 recovery, it may lead to the exacerbation of hyperglycemia process or a new onset of diabetes mellitus (DM). In this study, we used a combination of bioinformatics and machine learning algorithms to investigate shared pathways and biomarkers in DM and COVID-19 convalescence. Methods: Gene transcriptome datasets of COVID-19 convalescence and diabetes mellitus from Gene Expression Omnibus (GEO) were integrated using bioinformatics methods and differentially expressed genes (DEGs) were found using the R programme. These genes were also subjected to Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to find potential pathways. The hub DEGs genes were then identified by combining protein-protein interaction (PPI) networks and machine learning algorithms. And transcription factors (TFs) and miRNAs were predicted for DM after COVID-19 convalescence. In addition, the inflammatory and immune status of diabetes after COVID-19 convalescence was assessed by singlesample gene set enrichment analysis (ssGSEA). Results: In this study, we developed genetic diagnostic models for 6 core DEGs beteen type 1 DM (T1DM) and COVID-19 convalescence and 2 core DEGs between type 2 DM (T2DM) and COVID-19 convalescence and demonstrated statistically significant differences (p<0.05) and diagnostic validity in the validation set. Analysis of immune cell infiltration suggests that a variety of immune cells may be involved in the development of DM after COVID-19 convalescence. Conclusion: We identified a genetic diagnostic model for COVID-19 convalescence and DM containing 8 core DEGs and constructed a nomogram for the diagnosis of COVID-19 convalescence DM. [ABSTRACT FROM AUTHOR]

BackgroundMost patients who had coronavirus disease 2019 (COVID-19) fully recovered, but many others experienced acute sequelae or persistent symptoms. It is possible that acute COVID-19 recovery is just the beginning of a chronic condition. Even after COVID-19 recovery, it may lead to the exacerbation of hyperglycemia process or a new onset of diabetes mellitus (DM). In this study, we used a combination of bioinformatics and machine learning algorithms to investigate shared pathways and biomarkers in DM and COVID-19 convalescence.MethodsGene transcriptome datasets of COVID-19 convalescence and diabetes mellitus from Gene Expression Omnibus (GEO) were integrated using bioinformatics methods and differentially expressed genes (DEGs) were found using the R programme. These genes were also subjected to Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to find potential pathways. The hub DEGs genes were then identified by combining protein-protein interaction (PPI) networks and machine learning algorithms. And transcription factors (TFs) and miRNAs were predicted for DM after COVID-19 convalescence. In addition, the inflammatory and immune status of diabetes after COVID-19 convalescence was assessed by single-sample gene set enrichment analysis (ssGSEA).ResultsIn this study, we developed genetic diagnostic models for 6 core DEGs beteen type 1 DM (T1DM) and COVID-19 convalescence and 2 core DEGs between type 2 DM (T2DM) and COVID-19 convalescence and demonstrated statistically significant differences (p

Adipose tissues are closely related to physiological functions and pathological conditions in most organs. Although differentiated 3T3-L1 preadipocytes have been used for in vitro adipose studies, the difference in cellular characteristics of adipogenic differentiation in two-dimensional (2D) culture and three-dimensional (3D) culture remain unclear. In this study, we evaluated gene expression patterns using RNA sequencing and metabolic functions using an extracellular flux analyzer in 3T3-L1 preadipocytes with and without adipogenic induction in 2D culture and 3D culture. In 2D culture, 565 up-regulated genes and 391 down-regulated genes were identified as differentially expressed genes (DEGs) by adipogenic induction of 3T3-L1 preadipocytes, whereas only 69 up-regulated genes and 59 down-regulated genes were identified as DEGs in 3D culture. Ingenuity Pathway Analysis (IPA) revealed that genes associated with lipid metabolism were identified as 2 out of the top 3 causal networks related to diseases and function in 3D spheroids, whereas only one network related to lipid metabolism was identified within the top 9 of these causal networks in the 2D planar cells, suggesting that adipogenic induction in the 3D culture condition exhibits a more adipocyte-specific gene expression pattern in 3T3-L1 preadipocytes. Real-time metabolic analysis revealed that the metabolic capacity shifted from glycolysis to mitochondrial respiration in differentiated 3T3-L1 cells in the 3D culture condition but not in those in the 2D cultured condition, suggesting that adipogenic differentiation in 3D culture induces a metabolic phenotype of well-differentiated adipocytes. Consistently, expression levels of mitochondria-encoded genes including mt-Nd6, mt-Cytb, and mt-Co1 were significantly increased by adipogenic induction of 3T3-L1 preadipocytes in 3D culture compared with those in 2D culture. Taken together, the findings suggest that induction of adipogenesis in 3D culture provides a more adipocyte-specific gene expression pattern and enhances mitochondrial respiration, resulting in more adipocyte-like cellular properties.

Non-alcoholic steatohepatitis (NASH) is a leading cause of chronic liver disease with limited treatment options. The widely distributed plant Aloe vera has shown protective effects against NASH in animals, yet the precise mechanism remains unknown. In this study, we investigated the potential mechanisms underlying the anti-NASH effects of Aloe vera using a network pharmacology and molecular docking approach. By searching online databases and analyzing the Gene Expression Omnibus dataset, we obtained 260 Aloe vera–NASH common targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses showed that the common targets were strongly associated with the key pathological processes implicated in NASH, including lipid and glucose metabolism, inflammation, apoptosis, oxidative stress, and liver fibrosis. Four core proteins, AKT serine/threonine kinase 1 (AKT1), tumor necrosis factor alpha (TNFα), transcription factor c-Jun, and tumor suppressor protein p53, were identified from compound–target–pathway and protein–protein interaction networks. Molecular docking analysis verified that the active ingredients of Aloe vera were able to interact with the core proteins, especially AKT1 and TNFα. The results demonstrate the multi-compound, multi-target, and multi-pathway mechanisms of Aloe vera against NASH. Our study has shown the scientific basis for further experiments in terms of the mechanism to develop Aloe vera-based natural products as complementary treatments for NASH. Furthermore, it identifies novel drug candidates based on the structures of Aloe vera's active compounds. [ABSTRACT FROM AUTHOR]

Background: Gallbladder Cancer (GBC) is one of the most common cancers of the biliary tract and the third commonest gastrointestinal (GI) malignancy worldwide. The disease is characterized by the late presentation and poor outcome despite treatment, and hence, newer therapies and targets need to be identified. Methods: The current study investigated various functionally enriched pathways in GBC pathogenesis involving the genes identified through Next Generation Sequencing (NGS) in a hospital-based cohort. The Pathway enrichment analysis and Gene Ontology (GO) were carried out after NGS, followed by the construction of the protein–protein interaction (PPI) network to discover associations among the genes. Results: Of the thirty-three patients with GBC who were screened through next-generation sequencing (NGS), 27somatic mutations were identified. These mutations involved a total of 14 genes. The p53 and KRAS were commonly found to be mutated, while mutations in other genes were seen in one case each, the mean number of mutations were 1.2, and maximum mutation in a single case (eight) was seen in one case. The bioinformatics analysis identified MAP kinase, PI3K-AKT, EGF/EGFR, and Focal Adhesion PI3K-AKT-mTOR signaling pathways and cross-talk between these. Conclusion: The results suggest that the complex crosstalk between the mTOR, MAPK, and multiple interacting cell signaling cascades can promote GBC progression, and hence, mTOR–MAPK targeted treatment will be an attractive option. [ABSTRACT FROM AUTHOR]

Postoperative cognitive dysfunction (POCD) is a cognitive deterioration and dementia that arise after a surgical procedure, affecting up to 40% of surgery patients over the age of 60. The precise etiology and molecular mechanisms underlying POCD remain uncovered. These reasons led us to employ integrative bioinformatics and machine learning methodologies to identify several biological signaling pathways involved and molecular signatures to better understand the pathophysiology of POCD. A total of 223 differentially expressed genes (DEGs) comprising 156 upregulated and 67 downregulated genes were identified from the circRNA microarray dataset by comparing POCD and non-POCD samples. Gene ontology (GO) analyses of DEGs were significantly involved in neurogenesis, autophagy regulation, translation in the postsynapse, modulating synaptic transmission, regulation of the cellular catabolic process, macromolecule modification, and chromatin remodeling. Pathway enrichment analysis indicated some key molecular pathways, including mTOR signaling pathway, AKT phosphorylation of cytosolic targets, MAPK and NF-κB signaling pathway, PI3K/AKT signaling pathway, nitric oxide signaling pathway, chaperones that modulate interferon signaling pathway, apoptosis signaling pathway, VEGF signaling pathway, cellular senescence, RANKL/RARK signaling pathway, and AGE/RAGE pathway. Furthermore, seven hub genes were identified from the PPI network and also determined transcription factors and protein kinases. Finally, we identified a new predictive drug for the treatment of SCZ using the LINCS L1000, GCP, and P100 databases. Together, our results bring a new era of the pathogenesis of a deeper understanding of POCD, identified novel therapeutic targets, and predicted drug inhibitors in POCD. [ABSTRACT FROM AUTHOR]

Aim: Porphyromonas gingivalis lipopolysaccharide (PgLPS) is a significant virulence factor and a driver of early innate immune responses in epithelial cells. The presence of PgLPS in immediate proximity to gingival epithelium induces significant inflammatory responses. In primary human gingival keratinocytes (HGK), we utilized transcriptome analysis to elucidate the change in early gene expression induced by PgLPS., Methods: HGK cell cultures were treated with PgLPS (4 h), and RNA was extracted and prepared for RNA sequence (RNAseq) analysis. Differentially expressed genes (DEGs) were identified, and potential interactions between these genes were subsequently examined using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analytic approaches to identify significantly enriched pathways. Expression of genes associated with relevant pathways was evaluated using real-time quantitative reverse-transcription polymerase chain reaction (RT-qPCR)., Results: RNAseq analysis identified 25 DEGs, and GO and KEGG analytic approaches showed related genes expressed in two general pathways. First, pathways broadly related to urokinase and coagulation included the genes PLAU, PLAUR, and SerpinB2. In RT-qPCR analysis, these genes were induced by PgLPS over time (4-24 h), and these data were consistent with PgLPS induction of cell migration. Second, interleukin-1 (IL-1) receptor binding and cytokine-activity pathways were also enriched. Genes associated with these pathways included IL36G, IL1B, IL1RN, and CXCL14. RT-qPCR analysis confirmed PgLPS induction of genes associated with the IL-1family. When expression of IL1B and IL36G genes was examined in relation to their respective antagonists, only IL36G gene expression was increased. CXCL14 gene expression was reduced over time, and this was consistent with RNAseq analysis., Conclusions: Genes associated with significantly enriched GO and KEGG pathways are relevant to aspects of periodontal disease (PDD) pathogenesis. First, PgLPS induced expression of PLAU, PLAUR, and SerpinB2, and these changes were consistent with an increase in cell migration that was found. Second, both IL36G and IL1B gene expression was significantly induced, but only IL36G in relation to its selective antagonist (IL36RN) was increased. These data support that early upregulation of IL36G may serve as an alarmin that can drive early innate immune inflammatory responses in HGK. Further in vivo testing of these findings is ongoing., (© 2024 The Author(s). Journal of Periodontal Research published by John Wiley & Sons Ltd.)

Background: Rhinitis is a disorder of the nasal mucosa with inflammatory responses, which negatively impairs work performance and life quality. Hedysarum Multijugum Maxim (HMM) has been widely used for preventing and treating rhinitis. However, the pharmacologic effects of HMM and its mechanisms against rhinitis have not been fully studied. Methods: The bioactive compounds of HMM were screened using the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). The targets genes of HMM for treating rhinitis were identified using PharmMapper and GeneCards database integration. Protein-protein interaction analysis (PPI) was carried out using the GeneMANIA database. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were carried out using clusterProfiler. The drug-target-disease-GO-KEGG networks were built via Cytoscape. Results: A total of 14 bioactive ingredients were screened and 25 target genes of HMM acting on rhinitis were discovered. PPI analysis demonstrated that these genes and their related genes had physical interactions or displayed co-expression characteristics. GO, KEGG and network analyses demonstrated that these targets were closely related to epithelial cell proliferation, regulation of inflammatory response, endocrine resistance, MAPK, VEGF, and TNF signaling pathways. Conclusion: In short, HMM displays multi-compounds, multi-targets to exert systematic pharmacologic actions on rhinitis.

Tinospora cordifolia, commonly known as "Giloe" in India, is a shrub belonging to the family Menispermaceae. It is an important medicinal plant known for its antipyretic, anti-inflammatory, antispasmodic, and antidiabetic properties and is used in the treatment of jaundice, gout, and rheumatism. Despite its economic importance, the limited information related to its genomic resources prohibits its judicious exploitation through molecular breeding or biotechnological approaches. In this study, we generated a meta-transcriptome assembly of 43,090 non-redundant transcripts by merging the RNASeq data obtained from Roche 454 GS-FLX, and Illumina platforms, and report the first transcriptome-based database for simple sequence repeats and transcription factors ("TinoTranscriptDB" (Tinospora cordifolia Transcriptome Database)). We annotated 26,716 (62%) of the total transcripts successfully from National Center for Biotechnology Information non-redundant protein (NCBI-NR), gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Swiss-Prot, and Pfam databases. This database contains information of 2620 perfect simple sequence repeats (P-SSRs) with a relative abundance of 340.12 (loci/Mb), and relative density of 6309.29 (bp/Mb). Excluding mono-nucleotides, the most abundant SSR motifs were tri-nucleotides (54.31%), followed by di-nucleotides (37.51%), tetra-nucleotides (4.54%), penta-nucleotides (3.16%) and hexa-nucleotides (0.45%). Additionally, we also identified 4,311 transcription factors (TFs) and categorized them into 55 sub-families. This database is expected to fill the gap in genomic resource availability in T. cordifolia and thus accelerate molecular breeding and related functional and other applied studies aimed towards genetic improvements of T. cordifolia and related species. [ABSTRACT FROM AUTHOR]

Results of expression studies can be useful to clarify the genotype-phenotype relationship. However, according to data from recent literature, there is a large group of genes that are revealed as differentially expressed (DE) in many studies, regardless of the biological context. Additional analyses could shed more light on the relationships between genes, their differential expression, and diseases. We generated a set of 9972 disease genes from five gene-phenotype databases (OMIM, ORPHANET, DDG2P, DisGeNet and MalaCards) and a report of the International Union of Immunological Societies. To study transcriptomics of disease and non-disease genes in healthy tissues, we obtained data from the Human Protein Atlas (HPA) website. We analyzed the dependency between expression in healthy tissues and gene occurrence in Gene Expression Omnibus series using tools within the Enrichr libraries. The results of expression studies were annotated with Gene Ontology (GO) and Human Phenotype Ontology (HPO) terms. Using transcriptomics analysis of healthy tissues, we validated the previous findings of higher expression levels of disease genes in pathologically linked tissues compared to other tissues. Preferentially DE genes were generally highly expressed in one or multiple tissues and were enriched for disease genes. According to the results of GO enrichment analyses, both down- and up-regulated DE genes most often took part in immune response, translation and tissue-specific processes. A connection between DE-related pathology and the diversity of HPO terms was found. Investigating a link between expression and phenotype contributes to understanding the mode of development and progression of human diseases. [ABSTRACT FROM AUTHOR]

Bovine leukemia virus (BLV) infection in cattle is omnipresent, which causes significantly economical losses worldwide. The objective of this study was to determine microRNA (miRNA) and transcript profiles and to establish their relationship in response to exposure to the virus. Small noncoding and messenger RNA were extracted and sequenced from serum and white blood cells (WBCs) derived from seven BLV seropositive and seven seronegative cows. Transcriptomic profiles were generated by sequencing RNA libraries from WBC. Bta-miR-206 and bta-miR-133a-3p were differentially expressed in serum (P < 0.05). In WBC, bta-miR-335-3p, bta-miR-375, and bta-novel-miR76-3p were differentially expressed (P < 0.03). There were 64 differentially expressed transcripts (DETs). Gene ontology (GO) analysis of the DETs overexpressed in the seropositive group with GOs of response to stimulus and immune system process predicted that the DETs could potentially negatively regulate viral life cycle and viral entry or release from host cells. In addition, the DETs depleted in the seropositive group could play a role in the downregulation of antigen processing and presentation of endogenous peptide antigen via MHC class I. The differentially expressed miRNAs targeted 17 DETs, among which the expressions of bta-miR-133a-3p and bta-miR-335-3p were significantly negatively correlated with the expressions of ENSBTAT00000079143 and ENSBTAT00000066733, respectively. Under high prediction criteria, 90 targets of the differentially expressed miRNAs were all non-DETs. The most enriched biological process GO term of the targets was the RNA-dependent DNA biosynthetic process, which could be associated with virus replication. These results suggested that the differentially expressed miRNAs fine-tune most of the target genes in responding to BLV exposure. In addition, Bta-miR-206 interacted with BLV regulatory genes rex and tax by targeting their coding regions. A further study of the miRNAs and the genes may reveal the molecular mechanisms of BLV infection and uncover possible ways to prevent the infection.

Abstract Background Osteoarthritis (OA) is a common skeletal system disease that has been partially attributed to genetic factors. The hand is frequently affected, which seriously affects the patient’s quality of life. However, the pathogenetic mechanism of hand osteoarthritis (hand OA) is still elusive. Methods A genome-wide association study (GWAS) summary of hand OA was obtained from the UK Biobank dataset, which contains data from a total of 452,264 White British individuals, including 37,782 OA patients. The transcriptome-wide association study (TWAS) of hand OA was performed using FUnctional Summary-based ImputatiON (FUSION) with the skeletal muscle and blood as gene expression references. The significant genes identified by TWAS were further subjected to gene set enrichment analysis (GSEA) with the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. Furthermore, we compared the genes and gene sets identified by our TWAS with that of a knee OA mRNA expression profile to detect the genes and gene sets shared by TWAS and mRNA expression profiles in OA. The mRNA expression profiles of 18 normal knee cartilages and 20 OA knee cartilages were acquired from the Gene Expression Omnibus database (accession number: GSE114007). Results TWAS identified 177 genes with P

• Unveiling low-temperature stress tolerance-related genomic variations in jute. • Sensitive variety O9897 specific SNPs were present in stress-related pathways. • Tolerant variety acc. 2015 possesses SNPs related to alternative cold tolerance functionalities. • InDels of acc. 2015 have tolerance utilities, but O9897 specific InDels cause ailments. • CNVs with gain of function were observed in cold stress-induced adaptation pathways. Jute (Corchorus sp.), a commercially important and eco-friendly crop, is widely cultivated in Bangladesh, India, and China. Some varieties of this tropical plant such as the Corchorus olitorius. Variety accession no. 2015 (acc. 2015) has been found to be low-temperature tolerant. The current study was designed to explore the genome-wide variations present in the tolerant plant acc. 2015 in comparison to the sensitive farmer popular variety Corchorus olitorius var. O9897 using the whole genome resequencing technique. Among different variations, intergenic Single Nucleotide Polymorphism (SNPs) and Insertion-Deletion (InDels) were found in the highest percentage whereas approximately 3% SNPs and 2% InDels were found in exonic regions in both plants. Gene enrichment analysis indicated the presence of acc. 2015 specific SNPs in the genes encoding peroxidase, ER lumen protein retaining receptor, and hexosyltransferase involved in stress response (GO:0006950) which were not present in sensitive variety O9897. Besides, distinctive copy number variation regions (CNVRs) comprising 120 gene loci were found in acc. 2015 with a gain of function from multiple copy numbers but absent in O9897. Gene ontology analysis revealed these gene loci to possess different receptors like kinases, helicases, phosphatases, transcription factors especially Myb transcription factors, regulatory proteins containing different binding domains, annexin, laccase, acyl carrier protein, potassium transporter, and vesicular transporter proteins that are responsible for low temperature induced adaptation pathways in plants. This work of identifying genomic variations linked to cold stress tolerance traits will help to develop successful markers that will pave the way to develop genetically modified cold-resistant jute lines for year-round cultivation to meet the demand for a sustainable fiber crop economy. [ABSTRACT FROM AUTHOR]

The cardiac conduction system (CCS) comprises critical components responsible for the initiation, propagation, and coordination of the action potential. Aberrant CCS development can cause conduction abnormalities, including sick sinus syndrome, accessory pathways, and atrioventricular and bundle branch blocks. Gene Ontology (GO; http://geneontology.org/) is an invaluable global bioinformatics resource which provides structured, computable knowledge describing the functions of gene products. Many gene products are known to be involved in CCS development; however, this information is not comprehensively captured by GO. To address the needs of the heart development research community, this study aimed to describe the specific roles of proteins reported in the literature to be involved with CCS development and/or function. 14 proteins were prioritized for GO annotation which led to the curation of 15 peer-reviewed primary experimental articles using carefully selected GO terms. 152 descriptive GO annotations, including those describing sinoatrial node and atrioventricular node development were created and submitted to the GO Consortium database. A functional enrichment analysis of 35 key CCS development proteins confirmed that this work has improved the in-silico interpretation of this CCS dataset. This work may improve future investigations of the CCS with application of high-throughput methods such as genome-wide association studies analysis, proteomics, and transcriptomics. [ABSTRACT FROM AUTHOR]

Abstract Background In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

The cardiac conduction system (CCS) comprises critical components responsible for the initiation, propagation, and coordination of the action potential. Aberrant CCS development can cause conduction abnormalities, including sick sinus syndrome, accessory pathways, and atrioventricular and bundle branch blocks. Gene Ontology (GO; http://geneontology.org/) is an invaluable global bioinformatics resource which provides structured, computable knowledge describing the functions of gene products. Many gene products are known to be involved in CCS development; however, this information is not comprehensively captured by GO. To address the needs of the heart development research community, this study aimed to describe the specific roles of proteins reported in the literature to be involved with CCS development and/or function. 14 proteins were prioritized for GO annotation which led to the curation of 15 peer-reviewed primary experimental articles using carefully selected GO terms. 152 descriptive GO annotations, including those describing sinoatrial node and atrioventricular node development were created and submitted to the GO Consortium database. A functional enrichment analysis of 35 key CCS development proteins confirmed that this work has improved the in-silico interpretation of this CCS dataset. This work may improve future investigations of the CCS with application of high-throughput methods such as genome-wide association studies analysis, proteomics, and transcriptomics.

Epithelial ovarian cancer (EOC) can be considered as a stressful and challenging disease among all women in the world, which has been associated with a poor prognosis and its molecular pathogenesis has remained unclear. In recent years, RNA Sequencing (RNA-seq) has become a functional and amazing technology for profiling gene expression. In the present study, RNA-seq raw data from Sequence Read Archive (SRA) of six tumor and normal ovarian sample was extracted, and then analysis and statistical interpretation was done with Linux and R Packages from the open-source Bioconductor. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of key genes and pathways involved in EOC. We identified 1091 Differential Expression Genes (DEGs) which have been reported in various studies of ovarian cancer as well as other types of cancer. Among them, 333 genes were up-regulated and 273 genes were down-regulated. In addition, Differentially Expressed Genes (DEGs) including RPL41, ALDH3A2, ERBB2, MIEN1, RBM25, ATF4, UPF2, DDIT3, HOXB8 and IL17D as well as Ribosome and Glycolysis/Gluconeogenesis pathway have had the potentiality to be used as targets for EOC diagnosis and treatment. In this study, unlike that of any other studies on various cancers, ALDH3A2 was most down-regulated gene in most KEGG pathways, and ATF4 was most up-regulated gene in leucine zipper domain binding term. In the other hand, RPL41 as a regulatory of cellular ATF4 level was up-regulated in many term and pathways and augmentation of ATF4 could justify the increase of RPL41 in the EOC. Pivotal pathways and significant genes, which were identified in the present study, can be used for adaptation of different EOC study. However, further molecular biological experiments and computational processes are required to confirm the function of the identified genes associated with EOC. • Ten Differential Expression Genes (DEGs) between six tumor and normal ovarian samples were identified. • Ribosome and Glycolysis/Gluconeogenesis pathways were most significant pathway in our study. • Unlike that of any other studies on various cancers, ALDH3A2 was most down-regulated gene in most KEGG pathways. • Differentially expressed ribosomal (Raps) genes (RPL41, RPL3, RPL32) were most up-regulated gene in the most of Go terms. • The most significantly up-regulated DEGs were related to the GO terms RNA binding (ontology: MF). [ABSTRACT FROM AUTHOR]

An exhaustive literature survey shows that finding protein/gene similarity is an important step towards solving widespread bioinformatics problems, such as predicting protein-protein interactions, analyzing Protein-Protein Interaction Networks (PPINs), gene prioritization, and disease gene/protein detection. In this article, we have proposed an improved 3-in-1 fused protein similarity measure called FuSim-II. It is built upon combining the weighted average of biological knowledge extracted from three potential genomic/ proteomic resources such as Gene Ontology (GO), PPIN, and protein sequence. Furthermore, we have shown the application of the proposed measure in detecting potential hub-proteins from a given PPIN. Aiming that, we have proposed a multi-objective clustering-based protein hub detection framework with FuSim-II working as the underlying proximity measure. The PPINs of H. Sapiens and M. Musculus organisms are chosen for experimental purposes. Unlike most of the existing hub-detection methods, the proposed technique does not require to follow any protein degree cut-off or threshold to define hubs. A thorough assessment of efficiency between proposed and existing eight protein similarity measures along with eight single/multi-objective clustering methods has been carried out. Internal cluster validity indices like Silhouette and Davies Bouldin (DB) are deployed to accomplish analytical study. Also, a comparative performance analysis between proposed and five existing hub-proteins detection algorithms is conducted through the enrichment of essentiality study. The reported results show the improved performance of FuSim-II over existing protein similarity measures in terms of identifying functionally related proteins as well as relevant hub-proteins. Supplementary material is available at http://csse.szu.edu.cn/staff/cuilz/eng/index.html. [ABSTRACT FROM AUTHOR]

Summary: Glutamine synthetase (GS; E.C.6.3.1.2) is a key enzyme in higher plants with two isozymes, cytosolic GS1 and plastidic GS2, and involves in the assimilation and recycling of NH4+ ions and maintenance of complex traits such as crop nitrogen‐use efficiency and yield. Our present understanding of crop nitrogen‐use efficiency and its correlation with the functional role of the GS family genes is inadequate, which delays harnessing the benefit of this key enzyme in crop improvement. In this report, we performed a comprehensive investigation on the phylogenetic relationship, structural properties, complex multilevel gene regulation, and expression patterns of the GS genes to enrich present understanding about the enzyme. Our Gene Ontology and protein–protein interactions analysis revealed the functional aspects of GS isozymes in stress mitigation, aging, nucleotide biosynthesis/transport, DNA repair and response to metals. The insight gained here contributes to the future research strategies in developing climate‐smart crops for global sustainability. [ABSTRACT FROM AUTHOR]

In order to explore the genomic basis for liver cancer metastasis, whole-exome sequencing (WES) was performed on patient-derived hepatocellular carcinoma (HCC) cell lines with differential metastatic potentials and analyzed their clonal evolution relationships. An evolutionary tree based on genomic single nucleotide polymorphism (SNP) was constructed in MegaX software. The WES data showed that the average percentage of heterogeneous mutations in each HCC cell lines was 16.55% (range, 15.38%–18.17%). C: G > T: A and T: A > C: G somatic transitions were the two most frequent substitutions. In these metastatic HCC cell lines, non-silent gene mutations were found in 21.88% of known driver genes and 10 classical signaling pathways. The protein interaction network was constructed by STRING, and hub genes were found in the shared trunk mutation genes and the heterogeneous branch mutations respectively. In cBioPortal database, some of the selected hub genes were found to be associated with poor overall survival (OS) of HCC patients. Among the mutated HCC driver genes, a novel KEAP1 mutation with a homozygous frameshift truncation at the c-terminal Nrf2 binding region was detected and verified in MHCC97-H and HCC97LM3 cells. In conclusion, WES data demonstrate that HCC cell lines from tumor biopsy specimens of the same patient have obtained different metastatic potentials through repeated selection in rodents in vivo, and they do indeed have a genetic relationship at the genomic level.

Abstract Background In the field of computational biology, analyzing complex data helps to extract relevant biological information. Sample classification of gene expression data is one such popular bio-data analysis technique. However, the presence of a large number of irrelevant/redundant genes in expression data makes a sample classification algorithm working inefficiently. Feature selection is one such high-dimensionality reduction technique that helps to maximize the effectiveness of any sample classification algorithm. Recent advances in biotechnology have improved the biological data to include multi-modal or multiple views. Different ‘omics’ resources capture various equally important biological properties of entities. However, most of the existing feature selection methodologies are biased towards considering only one out of multiple biological resources. Consequently, some crucial aspects of available biological knowledge may get ignored, which could further improve feature selection efficiency. Results In this present work, we have proposed a Consensus Multi-View Multi-objective Clustering-based feature selection algorithm called CMVMC. Three controlled genomic and proteomic resources like gene expression, Gene Ontology (GO), and protein-protein interaction network (PPIN) are utilized to build two independent views. The concept of multi-objective consensus clustering has been applied within our proposed gene selection method to satisfy both incorporated views. Gene expression data sets of Multiple tissues and Yeast from two different organisms (Homo Sapiens and Saccharomyces cerevisiae, respectively) are chosen for experimental purposes. As the end-product of CMVMC, a reduced set of relevant and non-redundant genes are found for each chosen data set. These genes finally participate in an effective sample classification. Conclusions The experimental study on chosen data sets shows that our proposed feature-selection method improves the sample classification accuracy and reduces the gene-space up to a significant level. In the case of Multiple Tissues data set, CMVMC reduces the number of genes (features) from 5565 to 41, with 92.73% of sample classification accuracy. For Yeast data set, the number of genes got reduced to 10 from 2884, with 95.84% sample classification accuracy. Two internal cluster validity indices - Silhouette and Davies-Bouldin (DB) and one external validity index Classification Accuracy (CA) are chosen for comparative study. Reported results are further validated through well-known biological significance test and visualization tool.

Abstract Background Nasopharyngeal carcinoma (NPC) is an epithelial malignancy with high morbidity rates in the east and southeast Asia. The molecular mechanisms of NPC remain largely unknown. We explored the pathogenesis, potential biomarkers, and prognostic indicators of NPC. Methods We analyzed mRNAs, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) in the whole transcriptome sequencing dataset of our hospital (five normal tissues vs. five NPC tissues) and six microarray datasets (62 normal tissues vs. 334 NPC tissues) downloaded from the Gene Expression Omnibus (GSE12452, GSE13597, GSE95166, GSE126683, and GSE70970, GSE43039). Differential expression analyses, gene ontology (GO) enrichment, kyoto encyclopedia of genes and genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were conducted. The lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks were constructed using the miRanda and TargetScan database, and a protein–protein interaction (PPI) network of differentially expressed genes (DEGs) was built using Search Tool for the Retrieval of Interacting Genes (STRING) software. Hub genes were identified using Molecular Complex Detection (MCODE), NetworkAnalyzer, and CytoHubba. Results We identified 61 mRNAs, 14miRNAs, and 10 lncRNAs as shared DEGs related to NPC in seven datasets. Changes in NPC were enriched in the chromosomal region, sister chromatid segregation, and nuclear chromosome segregation. GSEA indicated that the mitogen-activated protein kinase (MAPK) pathway, phosphatidylinositol-3 OH kinase/protein kinase B (PI3K-Akt) pathway, apoptotic pathway, and tumor necrosis factor (TNF) were involved in the initiation and development of NPC. Finally, 20 hub genes were screened out via the PPI network. Conclusions Several DEGs and their biological processes, pathways, and interrelations were found in our current study by bioinformatics analyses. Our findings may offer insights into the biological mechanisms underlying NPC and identify potential therapeutic targets for NPC.

Objective Drought stress is a major problem that plagues world's arable lands and poses major limitations to plant growth and productivity. Lavandula angustifolia L. is a medicinal and aromatic plant that has great value for its essential oil. Due to the relatively resistant nature of this plant to water deficit, it can be a good substitute for plants with high water demand. To identification of some of genes involved in response to drought stress, we carried out transcriptome analysis under normal and drought condition using RNA-Seq. Materials and methods Illumina HiSeq. 2000 was applied for sequencing of flower and leaf mRNAs under control and stress conditions. Also the activity of some antioxidant enzymes and level of Malondialdehyde and Dityrosine were measured. Results Among a total of 264126 transcripts, 1083 DEGs in flower and 150 DEGs in leaf were identified in response to the drought stress. Gene Ontology Enrichment of drought responsive DEGs including catalytic activity, response to stimulus and binding were identified. KEGG analysis showed different pathways associated with stress such as biosynthesis of secondary metabolites, glutathione and proline metabolism and plant hormone signal transduction. Also some unigenes related to antioxidant enzymes were highlighted in response to drought. Conclusion The transcriptome data generated here is the first report of RNA-Seq for Lavandula angustifolia L. under drought stress. Biochemical analysis results in this study were consistent with the RNA-seq results. Our findings offer insights into the molecular networks of Lavandula angustifolia L. in response to drought stress.

Abstract Objective Low birth weight (LBW) is a major public health issue as it increases the risk of noncommunicable diseases throughout life. However, the genome-wide DNA methylation patterns of full-term LBW infants (FT-LBWs) are still unclear. This exploratory study aimed to analyze the DNA methylation differences in FT-LBWs compared with those in full-term normal birth weight infants (FT-NBWs) whose mothers were nonsmokers and had no complications. Initially, 702 Japanese women with singleton pregnancies were recruited. Of these, four FT-LBWs and five FT-NBWs were selected as references for DNA methylation analysis, and 862,260 CpGs were assessed using Illumina Infinium MethylationEPIC BeadChip. Gene ontology enrichment analysis was performed using DAVID v6.8 software to identify the biological functions of hyper- and hypomethylated DNA in FT-LBWs. Results 483 hyper-differentially methylated genes (DMGs) and 35 hypo-DMGs were identified in FT-LBW promoter regions. Hyper-DMGs were annotated to 11 biological processes; “macrophage differentiation” (e.g., CASP8), “apoptotic mitochondrial changes” (e.g., BH3), “nucleotide-excision repair” (e.g., HUS1), and “negative regulation of inflammatory response” (e.g., NLRP12 and SHARPIN). EREG was classified into “ovarian cumulus expansion” within the “organism growth and organization” category. Our data imply that LBW might be associated with epigenetic modifications, which regulate the immune system and cell maturation.

Background: Neuroblastoma (NB) is a malignant tumor primarily found in children, presenting significant challenges in its development and prognosis. The role of necroptosis in the pathogenesis of NB has been acknowledged as crucial for treatment. This study aimed to investigate the key genes and functional pathways associated with necroptosis, as well as immune infiltration analysis, in NB. Furthermore, we aimed to evaluate the diagnostic significance of these genes for prognostic assessment and explore their potential immunological characteristics., Methods: The NB dataset (GSE19274, GSE73517, and GSE85047) was obtained from the Gene Expression Omnibus (GEO) database, and genes associated with necroptosis were collected from GeneCards and previous literature. First, we conducted differential expression analysis and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We employed gene set enrichment analysis (GSEA) to identify overlapping enriched functional pathways from the NB dataset. In addition, we constructed a protein-protein interaction (PPI) network, predicting relevant microRNAs (miRNAs) and transcription factors (TFs), as well as their corresponding drug predictions. Furthermore, the diagnostic value was assessed using receiver operating characteristic (ROC) curves. Finally, an immune infiltration analysis was performed., Results: We identified six necroptosis-related differentially expressed genes (NRDEGs) closely associated with necroptosis in NB. They were enriched in Tuberculosis, Apoptosis-multiple species, Salmonella infection, legionellosis, and platinum drug resistance. GSEA and PPI network analyses, along with mRNA-drug interaction network, revealed 38 potential drugs corresponding to BIRC2 , CAMK2G , CASP3 , and IL8 . ROC curve analysis showed that in GSE19274, FLOT2 with area under the ROC curve (AUC) of 0.850 and DAPK1 with AUC of 0.789., Conclusions: Our study elucidates the key genes and functional pathways associated with necroptosis in NB, offering valuable insights to enhance our comprehension of the pathogenesis of NB, and improve prognosis assessment., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-14/coif). The authors have no conflicts of interest to declare., (2024 Translational Cancer Research. All rights reserved.)

Non-alcoholic steatohepatitis (NASH) is a leading cause of chronic liver disease with limited treatment options. The widely distributed plant Aloe vera has shown protective effects against NASH in animals, yet the precise mechanism remains unknown. In this study, we investigated the potential mechanisms underlying the anti-NASH effects of Aloe vera using a network pharmacology and molecular docking approach. By searching online databases and analyzing the Gene Expression Omnibus dataset, we obtained 260 Aloe vera–NASH common targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses showed that the common targets were strongly associated with the key pathological processes implicated in NASH, including lipid and glucose metabolism, inflammation, apoptosis, oxidative stress, and liver fibrosis. Four core proteins, AKT serine/threonine kinase 1 (AKT1), tumor necrosis factor alpha (TNFα), transcription factor c-Jun, and tumor suppressor protein p53, were identified from compound–target–pathway and protein–protein interaction networks. Molecular docking analysis verified that the active ingredients of Aloe vera were able to interact with the core proteins, especially AKT1 and TNFα. The results demonstrate the multi-compound, multi-target, and multi-pathway mechanisms of Aloe vera against NASH. Our study has shown the scientific basis for further experiments in terms of the mechanism to develop Aloe vera-based natural products as complementary treatments for NASH. Furthermore, it identifies novel drug candidates based on the structures of Aloe vera’s active compounds.

Acetylation at lysine residue in a protein mediates multiple cellular biological processes, including tumorigenesis. This study aimed to investigate the acetylated protein profile alterations and acetylation-mediated molecular pathway changes in human nonfunctional pituitary neuroendocrine tumors (NF-PitNETs). The anti-acetyl antibody-based label-free quantitative proteomics was used to analyze the acetylomes between NF-PitNETs (n = 4) and control pituitaries (n = 4). A total of 296 acetylated proteins with 517 acetylation sites was identified, and the majority of which were significantly down-acetylated in NF-PitNETs (p<0.05 or only be quantified in NF-PitNETs/controls). These acetylated proteins widely functioned in cellular biological processes and signaling pathways, including metabolism, translation, cell adhesion, and oxidative stress. The randomly selected acetylated phosphoglycerate kinase 1 (PGK1), which is involved in glycolysis and amino acid biosynthesis, was further confirmed with immunoprecipitation and western blot in NF-PitNETs and control pituitaries. Among these acetylated proteins, 15 lysine residues within 14 proteins were down-acetylated and simultaneously up-ubiquitinated in NF-PitNETs to demonstrate a direct competition relationship between acetylation and ubiquitination. Moreover, the potential effect of protein acetylation alterations on NF-PitNETs invasiveness was investigated. Overlapping analysis between acetylomics data in NF-PitNETs and transcriptomics data in invasive NF-PitNETs identified 26 overlapped molecules. These overlapped molecules were mainly involved in metabolism-associated pathways, which means that acetylation-mediated metabolic reprogramming might be the molecular mechanism to affect NF-PitNET invasiveness. This study provided the first acetylomic profiling and acetylation-mediated molecular pathways in human NF-PitNETs, and offered new clues to elucidate the biological functions of protein acetylation in NF-PitNETs and discover novel biomarkers for early diagnosis and targeted therapy of NF-PitNETs. [ABSTRACT FROM AUTHOR]

The third leading cause of cancer-related deaths in the world, colorectal cancer (CRC) is a global health issue that should be addressed in both diagnostics and therapeutics to improve patient survival rate. Today, microarray data analysis is increasingly being used as a novel and effective method for classification of malignancies and making prognostic assessments. Built upon the concept of microarray data analysis and aimed at the identification of CRC-associated genes, our study has adopted an integrative analysis for the gene expression patterns of four microarray datasets in gene expression omnibus (GEO) and microRNAs (miRNAs) expression profiles. We downloaded four gene expression profiles, i.e., GSE37182, GSE25070, GSE10950, and GSE113513, miRNAs gene expression profiles and differentially expressed genes (DEGs). We used R software, the DAVID database, protein-protein interaction (PPI) networks, the Cytoscape program and receiver operating characteristic (ROC) curve for data analysis. Out of the four gene expression profiles, a total of 43 common DEGs were identified, including 10 hub genes, SLC26A3, CLCA1, GUCA2A, MS4A12, CLCA4, GUCA2B, KRT20, AQP8, MAOA, and ADH1A, and four differentially expressed miRNAs, miR-552, miR-423-5p, miR-502-3p, and miR-490-5p. The highly enriched modes of the signaling pathways among these DEGs were speculated to be involved in various processes including nitrogen metabolism, mineral absorption, pancreatic secretions, and tyrosine metabolism in Kyoto encyclopedia of genes and genomes (KEGG) database. According to our bioinformatics analysis, the DEGs identified in the present study could be considered as significant hallmarks in the molecular mechanisms of CRC development. Our findings may assist scientists with developing novel strategies not only for prediction of CRC, but also for screening and early diagnosis, and treatment of CRC patients. [ABSTRACT FROM AUTHOR]

Acetylation at lysine residue in a protein mediates multiple cellular biological processes, including tumorigenesis. This study aimed to investigate the acetylated protein profile alterations and acetylation-mediated molecular pathway changes in human nonfunctional pituitary neuroendocrine tumors (NF-PitNETs). The anti-acetyl antibody-based label-free quantitative proteomics was used to analyze the acetylomes between NF-PitNETs (n = 4) and control pituitaries (n = 4). A total of 296 acetylated proteins with 517 acetylation sites was identified, and the majority of which were significantly down-acetylated in NF-PitNETs (p

The proteomics of donkey milk from China’s major producing areas have not been well documented. To explore the protein profiles of donkey milk from China, this study preliminarily investigated caseins and whey proteins in products from two major producing areas using quantitative proteomics approach. Total 15 caseins genetic variants and 620 whey proteins were identified and relatively quantified. Functional categorization of whey proteins was clustered into 46 terms based on Gene ontology (GO) and 42 Brite B classes based on Kyoto Encyclopedia of Genes and Genomes (KEGG). Six of the seven identified isoforms of caseins were firstly observed in donkey milk. Whey proteins including α-lactalbumin, β-lactoglobulins and lysozyme were identified, but no immunoglobulins or lactoferrin was determined. An unprecedented low ratio of casein/whey indicated that the products were very good sources with high biological value. This study enhanced knowledge of protein composition and established proteins profiles of differentially expressed proteins in the selected products of donkey milk from China.

Background: Atherosclerotic cardiovascular diseases accounted for a quarter of global deaths. Most of these fatal diseases like coronary atherosclerotic disease (CAD) and stroke occur in the advanced stage of atherosclerosis, during which candidate therapeutic targets have not been fully established. This study aims to identify hub genes and possible regulatory targets involved in treatment of advanced atherosclerotic plaques.Material/Methods: Microarray dataset GSE43292 and GSE28829, both containing advanced atherosclerotic plaques group and early lesions group, were obtained from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was conducted to identify advanced plaque-related modules. Module conservation analysis was applied to assess the similarity of advanced plaque-related modules between GSE43292 and GSE28829. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of these modules were performed by Metascape. Differentially expressed genes (DEGs) were mapped into advanced plaque-related modules and module membership values of DEGs in each module were calculated to identify hub genes. Hub genes were further validated for expression in atherosclerotic samples, for distinguishing capacity of CAD and for potential functions in advanced atherosclerosis.Results: The lightgreen module (MElightgreen) in GSE43292 and the brown module (MEbrown) in GSE28829 were identified as advanced plaque-related modules. Conservation analysis of these two modules showed high similarity. GO and KEGG enrichment analysis revealed that genes in both MElightgreen and MEbrown were enriched in immune cell activation, secretory granules, cytokine activity, and immunoinflammatory signaling. RBM47, HCK, CD53, TYROBP, and HAVCR2 were identified as common hub genes, which were validated to be upregulated in advanced atherosclerotic plaques, to well distinguish CAD patients from non-CAD people and to regulate immune cell function-related mechanisms in advanced atherosclerosis.Conclusions: We have identified RBM47, HCK, CD53, TYROBP, and HAVCR2 as immune-responsive hub genes related to advanced plaques, which may provide potential intervention targets to treat advanced atherosclerotic plaques.

Background: Atherosclerotic cardiovascular diseases accounted for a quarter of global deaths. Most of these fatal diseases like coronary atherosclerotic disease (CAD) and stroke occur in the advanced stage of atherosclerosis, during which candidate therapeutic targets have not been fully established. This study aims to identify hub genes and possible regulatory targets involved in treatment of advanced atherosclerotic plaques. Material/Methods: Microarray dataset GSE43292 and GSE28829, both containing advanced atherosclerotic plaques group and early lesions group, were obtained from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was conducted to identify advanced plaque-related modules. Module conservation analysis was applied to assess the similarity of advanced plaque-related modules between GSE43292 and GSE28829. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of these modules were performed by Metascape. Differentially expressed genes (DEGs) were mapped into advanced plaque-related modules and module membership values of DEGs in each module were calculated to identify hub genes. Hub genes were further validated for expression in atherosclerotic samples, for distinguishing capacity of CAD and for potential functions in advanced atherosclerosis. Results: The lightgreen module (MElightgreen) in GSE43292 and the brown module (MEbrown) in GSE28829 were identified as advanced plaque-related modules. Conservation analysis of these two modules showed high similarity. GO and KEGG enrichment analysis revealed that genes in both MElightgreen and MEbrown were enriched in immune cell activation, secretory granules, cytokine activity, and immunoinflammatory signaling. RBM47, HCK, CD53, TYROBP, and HAVCR2 were identified as common hub genes, which were validated to be upregulated in advanced atherosclerotic plaques, to well distinguish CAD patients from non-CAD people and to regulate immune cell function-related mechanisms in advanced atherosclerosis. Conclusions: We have identified RBM47, HCK, CD53, TYROBP, and HAVCR2 as immune-responsive hub genes related to advanced plaques, which may provide potential intervention targets to treat advanced atherosclerotic plaques. [ABSTRACT FROM AUTHOR]

The PHLDA (pleckstrin homology-like domain family) gene family is popularly known as a potential biomarker for cancer identification, and members of the PHLDA family have become considered potentially viable targets for cancer treatments. The PHLDA gene family consists of PHLDA1, PHLDA2, and PHLDA3. The predictive significance of PHLDA genes in cancer remains unclear. To determine the role of pleckstrin as a prognostic biomarker in human cancers, we conducted a systematic multiomics investigation. Through various survival analyses, pleckstrin expression was evaluated, and their predictive significance in human tumors was discovered using a variety of online platforms. By analyzing the protein-protein interactions, we also chose a collection of well-known functional protein partners for pleckstrin. Investigations were also carried out on the relationship between pleckstrins and other cancers regarding mutations and copy number alterations. The cumulative impact of pleckstrin and their associated genes on various cancers, Gene Ontology (GO), and pathway analyses were used for their evaluation. Thus, the expression profiles of PHLDA family members and their prognosis in various cancers may be revealed by this study. During this multiomics analysis, we found that among the PHLDA family, PHLDA1 may be a therapeutic target for several cancers, including kidney, colon, and brain cancer, while PHLDA2 can be a therapeutic target for cancers of the colon, esophagus, and pancreas. Additionally, PHLDA3 may be a useful therapeutic target for ovarian, renal, and gastric cancer.

Background: In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results: In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion: A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting. [ABSTRACT FROM AUTHOR]