MultiFacTV: module detection from higher-order time series biological data.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Identification of ST3GAL6-AS1 Interactive Protein in Multiple Myeloma

The WRKY transcription factors are pivotal in regulating several imperious plant growth transcriptional, biological, and metabolic processes. They are integrally involved in various stress responses in plants, significantly biotic and abiotic stresses. WRKY transcription family has been investigated in a large number of plant species, but their characterization and identification are not carried in date palm (Phoenix dactylifera L.) so far. The present study identified 66 non-redundant WRKY-encoding genes that were dispersed unevenly on all 16 chromosomes of the date palm. Based on phylogenetic analysis, PdWRKY genes were classified into three Groups (I, II, and III) while Group II was further divided into five sub-groups i.e., IIa, IIb, IIc, IId, and IIe. Intron-exon analysis exhibited the presence of 2-5 introns within all PdWRKY genes and a variable number of conserved motifs were observed in PdWRKY proteins. Moreover, the signatory WRKY domain was completely conserved, and many other domains were found to be conserved irregularly in PdWRKY proteins. More than fifty cis-acting regulatory elements were identified in upstream promoter regions. The transcriptomic data exhibited the expression patterns and variations of PdWRKY genes in shoot and root tissues stressed under drought and salt conditions. Interestingly, PdWRKY49 and PdWRKY58 showed similar responses in both shoot and root tissues under salt stress while in drought stress almost all PdWRKY genes upregulated in root tissues and showed downregulation in shoot tissues except PdWRKY59 in both tissues respectively. Our findings demonstrated that WRKY proteins frequently act as repressors, along with as activators. Furthermore, the present study indicates that WRKY transcription factors could potentially orchestrate a diverse array of seemingly unrelated functions.

PurposeConsidering the significance of retinal disorders and the growing need to employ tissue engineering in this field, in-silico studies can be used to establish a cost-effective method. This in-silico study was performed to find the most effective growth factors contributing to retinal tissue engineering.Methods In this study, a regeneration gene database was used. All 21 protein-coding genes participating in retinal regeneration were considered as a protein–protein interaction (PPI) network via the “STRING App” in “Cytoscape 3.7.2” software. The resultant graph possessed 21 nodes as well as 37 edges. Gene ontology (GO) analysis, as well as the centrality analysis, revealed the most effective proteins in retinal regeneration.ResultsAccording to the biological processes and the role of each protein in different pathways, selecting the correct one is possible through the information that the network provides. Eye development, detection of the visible light, visual perception, photoreceptor cell differentiation, camera-type eye development, eye morphogenesis, and angiogenesis are the major biological processes in retinal regeneration. Based on the GO analysis, SHH, STAT3, FGFR1, OPN4, ITGAV, RAX, and RPE65 are effective in retinal regeneration via the biological processes. In addition, based on the centrality analysis, four proteins have the greatest influence on retinal regeneration: SHH, IGF1, STAT3, and ASCL1.ConclusionWith the intention of applying the most impressive growth factors in retinal engineering, it seems logical to pay attention to SHH, STAT3, and RPE65. Utilizing these proteins can lead to fabricate high efficiency engineered retina via all aforementioned biological processes.

Drill-hole spacing analysis (DHSA) and optimization are becoming commonplace for uncertainty assessment and management in the mining industry. However, there is no standardized DHSA workflow, and the outputs of certain methodologies are not interchangeable. We group available simulation-based DHSA methods according to their accounted uncertainty into (i) raw uncertainty, assessed by drawing realizations from synthetic data-sets with the drilling spacings to be tested; and (ii) model uncertainty, in which these synthetic data-sets are used to assess the variation between the estimated model and the (unknown) actual value. DHSA workflows available in the literature ignore the differences between both types of uncertainty. Commonly, the DHSA algorithm is chosen without a detailed analysis of its uncertainty output, which may lead to misleading results and suboptimal decisions. While available solutions are based only on assessing raw or model uncertainty, the proposed approach simultaneously analyses both and their relationship for models at different stages of the mine. The integrated analysis results deliver more information to support decision-making than available methods. Principles, practical considerations, and discussions of the advantages of the proposed integrated analysis are presented. The approach is applied to a real gold deposit to illustrate its use.

Blood biomarkers hold potential for the early diagnosis of ischaemic stroke (IS). We aimed to evaluate the current weight of evidence and identify potential biomarkers and biological pathways for further investigation. We searched PubMed, EMBASE, the Cochrane Library and Web of Science, used R package meta4diag for diagnostic meta-analysis and applied Gene Ontology (GO) analysis to identify vital biological processes (BPs). Among 8544 studies, we included 182 articles with a total of 30,446 participants: 15675 IS, 2317 haemorrhagic stroke (HS), 1798 stroke mimics, 846 transient ischaemic attack and 9810 control subjects. There were 518 pooled biomarkers including 203 proteins, 114 genes, 108 metabolites and 88 transcripts. Our study generated two shortlists of biomarkers for future research: one with optimal diagnostic performance and another with low selection bias. Glial fibrillary acidic protein was eligible for diagnostic meta-analysis, with summary sensitivities and specificities for differentiating HS from IS between 3 h and 24 h after stroke onset ranging from 73% to 80% and 77% to 97%, respectively. GO analysis revealed the top five BPs associated with IS. This study provides a holistic view of early diagnostic biomarkers in IS. Two shortlists of biomarkers and five BPs warrant future investigation.

Soybean shoot metabolism of isopropyl-3-chlorocarbanilate: Ortho and para aryl hydroxylation

Barley plants (Hordeum vutgare L.) grown for 10 d in nitrogen-free hydroponic culture, after a rapid initial phase absorbed supplied NH4 (+) at a constant rate of 15.1 ±1.2 μ mol h(-1) g(-1) f. wt in the light, arid at a rate of 13.81 ± 1.6 μ mol h(-1) g(-1) f. wt in darkness. Ammonium-grown plants assimilated NH4 (+) at a rate of 7.5 ± 0.33 μmol h-1 g(-1) f. wt and at a 50% lower rate in darkness. Nitrogen-free grown plants showed low concentrations of free amino acids in both root and shoot tissues. Supplying NH4 (+) caused an immediate increase in the concentration of free amino in the root tissues of both illuminated and darkened plants over a 120 mm period. The increase in concentration of glutamine then exhibited a lag period of 120 min, after which it resumed, but to a very small extent. Glutamine also accumulated in shoot tissue of illuminated plants at increasing rates, attaining a concentration which, 8 h after NH4 (+) supply, was 1.61-fold greater than that attained in the roots. In shoots of darkened plants, by contrast, the concentration of glutamine increased slowly and was always smaller than that in the root tissue. Overall formation of glutamine (in shoots and roots) occurred at decreasing rates during the first 4 h, and then at increasing rates. The increase was more pronounced in illuminated plants than in darkened plants, liven 24 h after NH4 (+) was supplied, glutamine content in root tissue was lower than that in shoot tissue. However, 48 h later, the concentrations of glutamine in root and shoot were similar, attaining values that were almost 47-fold (in root) and 134-fold (in shoot) greater than initial values. Significant levels of asparagine were detected in the root and in the shoot 24 h after adding NH4 (+) . These increased further during the succeeding period. Ammonium supply caused a transitory drop in the concentration of ATP in root tissue, along with noticeable transitory variations in glucose-6-P concentration. A permanent decrease in free glucose concentration was also detected. Addition of NH4 (+) caused 2- and 1.43-fold increases in respiratory oxygen consumption by roots of illuminated and darkened plants, respectively. Both in the light and in the dark, the root tissue accumulated methylammonium up to a concentration of 55-67 μmol h(-1) g(-1) f. wt. Methylammonium was never found in shoot tissue of either illuminated or darkened plants. Methylammonium stimulated respiration of root barley plants by a factor of 1.2. Regulatory aspects of NH4 (+) metabolism are discussed.

The COVID-19 disease, caused by Severe Acute Respiratory Syndrome Coronavirus 2, proved to be a global pandemic with effect on mitochondrial functions. Mitochondrial genetics plays an essential role in disease severity. We identified differential methylation patterns in mitochondrial DNA and nuclear encoded mitochondrial genes in Indian COVID-19 patients through bisulphite sequencing. The study groups included severe deceased, recovered and healthy individuals. Differentially methylated regions (DMR) were obtained. Based on DMRs, differentially methylated genes (DMGs) having methylation in promoter regions were identified. A significantly higher amount of differential methylation is observed in the promoter regions of the genes indicating alterations in gene regulation. Gene ontological and pathway analysis was conducted. In gene ontology analysis, biological processes, cellular components, and molecular functions repeatedly revealed terms associated with the mitochondrial complex assembly (I and IV) and oxidative phosphorylation. KEGG pathway analysis showed that the highest number of DMGs were associated with metabolic pathways, thermogenesis, diabetic cardiomyopathy and oxidative phosphorylation. Significantly elevated levels of mitochondrial dynamics proteins DNM1L, TOMM20 and TOMM22 were found in COVID-19 patients. In conclusion, the mitochondria related DNA methylation could affect the expression of genes involved in the progression of COVID-19 contributing to mitochondrial dysfunction in the disease.

High-grade serous ovarian cancer (HGSOC) is a preferential omental metastasis malignancy. Since omental adipose tissue is an endocrine organ, we used liquid chromatography tandem mass spectrometry (LC-MS/MS) to compare the peptides secreted from omental adipose tissues of HGSOC and benign serous ovarian cysts (BSOC). Among the differentially secreted peptides, we detected 58 upregulated peptides, 197 downregulated peptides, 24 peptides that were only in the HGSOC group and 20 peptides that were only in the BSOC group (absolute fold change ≥ 2 and P < 0.05). Then, the basic characteristics of the differential peptides were analyzed, such as lengths, molecular weights, isoelectric points, and cleavage sites. Furthermore, we summarized the possible functions according to the precursor protein functions of the differentially expressed peptides by Gene Ontology (GO) analysis with the Annotation, Visualization, and Integrated Discovery (DAVID) database and canonical pathway analysis with IPA. For the GO analysis, the differentially secreted peptides were mainly associated with binding in molecular function and cellular processes in biology process. For the canonical pathways, the differentially secreted peptides were related to calcium signaling, protein kinase A signaling, and integrin-linked kinase (ILK) signaling. We also identified 67 differentially secreted peptides that located in the functional domains of the precursor proteins. These functional domains were mainly related to energy metabolism and immunoregulation. Our study might provide drugs that could potentially treat HGSOC or omental metastases of HGSOC cells.

BackgroundSepsis is a highly heterogeneous syndrome with diverse immune status and varied bioprocesses among individuals. The heterogeneity of sepsis could be associated with N6-methyladenosine (m6A) RNA methylation, due to m6A as a common and reversible posttranscriptional RNA modification involved in the regulation of whole bioprocesses. Therefore, we aim to identify m6A induced molecular subtypes of sepsis and furthermore explore the probable mechanism.MethodsGene expression datasets with 479 consecutive patients admitted for sepsis to the intensive care unit (ICU) in the Amsterdam Academic Medical Center were included in present study at first. Secondly, twelve m6A methylation regulatory genes were determined via systematic review in published researches. Furthermore, we utilized unsupervised clustering (consensus k means clustering) to identify m6A induced molecular subtypes in sepsis based on m6A prognostic molecular, and assess the association of these subtypes with clinical traits and survival outcomes. Moreover, the probable mechanism and regulatory relationship of m6A in sepsis was also explored through Gene Set Enrichment Analysis (GSEA), Weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) analysis and Co-expression analysis.ResultsThree m6A subtypes with different outcome were identified in sepsis cohort through unsupervised clustering on m6A prognostic molecular, designated Cluster 1/2/3 (log-rank P=0.004). The best outcome was found for patients classified as having cluster 3, and at 28 days, 21 of 144 people with cluster 3 had died [hazard ratio (HR) vs. all other clusters 5.42 (95% CI: 0.359–0.819); P=0.011], compared with 57 of 224 people with cluster 1 (HR 0.579, 95% CI: 0.364–0.920; P=0.037), and 36 of 112 people with cluster 2 (HR 0.477, 95% CI: 0.272–0.833; P=0.003). For exploration of the relationship between m6A subtypes and immunity, the GSEA found that patients in cluster 1 suffered from hyper-activated immunocompetent status; patients in cluster 2 indicated immunosuppressive status; and patients in cluster 3 showed the moderate immune activity (P<0.05). Co-expression analysis furthermore identified 82 immune molecules and 40 autophagy-related molecules could be regulated by prognostic m6A RNA methylation regulators (P<0.05) and correlation coefficient >0.6. In addition, WGCNA and GO analysis indicated that autophagy was significantly and widely activated in patients with cluster 3 (P<0.05).ConclusionsAccording to the heterogeneity in m6A methylation regulatory genes, three distinct subtypes in sepsis were identified with different RNA epigenetics, immune status, biological processes and outcomes, which initially uncovered that heterogeneity of sepsis may be largely caused by m6A RNA methylation.

Renal cell carcinoma (RCC) is a common form of cancer of the urinary tract. The present study aimed to identify driver genes in RCC using a bioinformatics approach. GSE53757 and GSE40435 microarray data were analyzed, and differentially expressed genes were filtered prior to gene ontology (GO) and pathway analysis. A protein-protein interaction (PPI) network was established. Overall survival and recurrence were investigated and based on data presented in cBioPortal. The COPS7B gene within the PPI network was selected for further study in vitro. The present study identified 174 and 149 genes possessing a significant signal to noise ratio in GSE53757 and GSE40435, respectively. In total, 53 of these genes were selected based upon inclusion in both datasets. GO analysis indicated that PRKCDBP, EHD2, KCNJ10, ATP1A1, KCNJ1 and EHD2 may be involved in various biological processes. Furthermore, ALDH6A1, LDHA, SUCLG1 and ABAT may be involved in the propanoate metabolism pathway. A network consisting of 106 genes, and one typical cluster were constructed. In addition, COPS7B was selected, as it was associated with decreased overall survival and increased recurrence rates, in order to elucidate its function in RCC. Furthermore, upregulation of COPS7B was demonstrated to be predictive of advanced stage disease and metastasis of RCC. Finally, COPS7B-knockdown inhibited RCC cell proliferation and invasion ability. Collectively, these results provided novel insights into COPS7B function, indicating that COPS7B may serve as a prognostic marker and therapeutic target in RCC.

BackgroundTorcetrapib, a cholesteryl ester transfer protein (CETP) inhibitor which raises high-density lipoprotein (HDL) cholesterol and reduces low-density lipoprotein (LDL) cholesterol level, has been documented to increase mortality and cardiac events associated with adverse effects. However, it is still unclear the underlying mechanisms of the off-target effects of torcetrapib.ResultsIn the present study, we developed a systems biology approach by combining a human reassembled signaling network with the publicly available microarray gene expression data to provide unique insights into the off-target adverse effects for torcetrapib. Cytoscape with three plugins including BisoGenet, NetworkAnalyzer and ClusterONE was utilized to establish a context-specific drug-gene interaction network. The DAVID functional annotation tool was applied for gene ontology (GO) analysis, while pathway enrichment analysis was clustered by ToppFun. Furthermore, potential off-targets of torcetrapib were predicted by a reverse docking approach. In general, 10503 nodes were retrieved from the integrative signaling network and 47660 inter-connected relations were obtained from the BisoGenet plugin. In addition, 388 significantly up-regulated genes were detected by Significance Analysis of Microarray (SAM) in adrenal carcinoma cells treated with torcetrapib. After constructing the human signaling network, the over-expressed microarray genes were mapped to illustrate the context-specific network. Subsequently, three conspicuous gene regulatory networks (GRNs) modules were unearthed, which contributed to the off-target effects of torcetrapib. GO analysis reflected dramatically over-represented biological processes associated with torcetrapib including activation of cell death, apoptosis and regulation of RNA metabolic process. Enriched signaling pathways uncovered that IL-2 Receptor Beta Chain in T cell Activation, Platelet-Derived Growth Factor Receptor (PDGFR) beta signaling pathway, IL2-mediated signaling events, ErbB signaling pathway and signaling events mediated by Hepatocyte Growth Factor Receptor (HGFR, c-Met) might play decisive characters in the adverse cardiovascular effects associated with torcetrapib. Finally, a reverse docking algorithm in silico between torcetrapib and transmembrane receptors was conducted to identify the potential off-targets. This screening was carried out based on the enriched signaling network analysis.ConclusionsOur study provided unique insights into the biological processes of torcetrapib-associated off-target adverse effects in a systems biology visual angle. In particular, we highlighted the importance of PDGFR, HGFR, IL-2 Receptor and ErbB1tyrosine kinase might be direct off-targets, which were highly related to the unfavorable adverse effects of torcetrapib and worthy of further experimental validation.

Purpose: To evaluate the molecular mechanism of kojic acid by network pharmacology.Methods: This study was conducted by designing a protein-protein interaction network through the STITCH database and analyzing biological processes via Cytoscape plugin ClueGO.Results: A total of 19 protein targets of kojic acid including TYR, NOS3, NOS2, and NOS1 were found. The PPI network helped to understand the mode of action of kojic acid at a molecular level. Gene Ontology (GO) analysis resulted in the retrieval of 104 GO terms which were related to variousphysiological processes. GO analysis revealed that kojic acid might be involved in the regulation of several biological processes such as circadian gene expression and transcription initiation of RNA polymerase 2.Conclusion: The findings from this study reveal that the retrieved GO pathways are known to be involved in several diseases such as inflammation, cancer, aging, pigmentation, and melisma. Furthermore, these pathways are directly or indirectly related to kojic acid. Thus, this study has contributed to a better understanding of the mode of action of kojic acid.

Objective To investigate the mechanism of development and rupture of intracranial aneurysm (IA) on the protein level and to search for target protein. Methods Specimens were collected from 3 cases of IA undergoing microsurgical clipping and STA (superficial temporal artery) in 3 cases as controls undergoing craniotomy through pterional approach from October 2015 to May 2016 at Department of Neurosurgery, Huashan Hospital Affiliated to Fudan University. All those samples were analyzed by bioinformatics and proteomics. The GO (gene ontology) analysis was performed for study of biological process, cell component and molecular function. PPI (protein-protein interaction) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used to analyze pathway and differentially expressed proteins. Results Compared between the IA and STA groups, there were 106 differentially expressed proteins identified. Among them, 20 were significantly dysregulated including 6 down-regulated proteins and 14 up-regulated ones. The GO analysis showed that differentially expressed proteins in IA group were mainly related to cellular biosynthetic process, and membrane-bounded organelles represented the most significant cellular structural difference between the IA and STA groups. Further analysis through PPI and KEGG pathway showed that the most significantly up-regulated protein was THBS4, which played a key role in extracellular matrix interaction; and the most significantly down-regulated protein was PTGS1, which was important for platelet activation. Conclusions Label-free quantitative mass spectrometry and bioinformatics could be effective ways to analyze the proteins in IA tissues. THBS could be a key protein in IA progression and a potential target for intervention. Key words: Intracranial aneurysm; Label-free; Proteomics; Computational biology