Protein-Protein interaction network analyses of human WNT proteins involved in neural development

Identification of prognostic coagulation-related signatures in clear cell renal cell carcinoma through integrated multi-omics analysis and machine learning

Clear cell renal cell carcinoma is a threat to public health with high morbidity and mortality. Clinical evidence has shown that cancer-associated thrombosis poses significant challenges to treatments, including drug resistance and difficulties in surgical decision-making in ccRCC. However, the coagulation pathway, one of the core mechanisms of cancer-associated thrombosis, recently found closely related to the tumor microenvironment and immune-related pathway, is rarely researched in ccRCC. Therefore, we integrated bulk RNA-seq data, DNA mutation and methylation data, single-cell data, and proteomic data to perform a comprehensive analysis of coagulation-related genes in ccRCC. First, we demonstrated the importance of the coagulation-related gene set by consensus clustering. Based on machine learning, we identified 5 coagulation signature genes and verified their clinical value in TCGA, ICGC, and E-MTAB-1980 databases. It's also demonstrated that the specific expression patterns of coagulation signature genes driven by CNV and methylation were closely correlated with pathways including apoptosis, immune infiltration, angiogenesis, and the construction of extracellular matrix. Moreover, we identified two types of tumor cells in single-cell data by machine learning, and the coagulation signature genes were differentially expressed in two types of tumor cells. Besides, the signature genes were proven to influence immune cells especially the differentiation of T cells. And their protein level was also validated.

Circadian clock crosstalks with autism

BACKGROUND

The mechanism underlying autism spectrum disorder (ASD) remains incompletely understood, but researchers have identified over a thousand genes involved in complex interactions within the brain, nervous, and immune systems, particularly during the mechanism of brain development. Various contributory environmental effects including circadian rhythm have also been studied in ASD. Thus, capturing the global picture of the ASD-clock network in combined form is critical.

METHODS

We reconstructed the protein-protein interaction network of ASD and circadian rhythm to understand the connection between autism and the circadian clock. A graph theoretical study is undertaken to evaluate whether the network attributes are biologically realistic. The gene ontology enrichment analyses provide information about the most important biological processes.

RESULTS

This study takes a fresh look at metabolic mechanisms and the identification of potential key proteins/pathways (ribosome biogenesis, oxidative stress, insulin/IGF pathway, Wnt pathway, and mTOR pathway), as well as the effects of specific conditions (such as maternal stress or disruption of circadian rhythm) on the development of ASD due to environmental factors.

CONCLUSION

Understanding the relationship between circadian rhythm and ASD provides insight into the involvement of these essential pathways in the pathogenesis/etiology of ASD, as well as potential early intervention options and chronotherapeutic strategies for treating or preventing the neurodevelopmental disorder.

Machine Learning Advances in Predicting Peptide/Protein-Protein Interactions Based on Sequence Information for Lead Peptides Discovery

Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high-throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead peptides have been expanded and incorporated into rational drug design. Predictions of peptide-protein interactions (PepPIs) and protein-protein interactions (PPIs) are both opportunities and challenges in computational biology, which will help to better understand the mechanisms of disease and provide the impetus for the discovery of lead peptides. This paper comprehensively reviews computational models for PepPI and PPI predictions. It begins with an introduction of various databases of peptide ligands and target proteins. Then it discusses data formats and feature representations for proteins and peptides. Furthermore, classical ML methods and emerging deep learning (DL) methods that can be used to train prediction models of PepPI and PPI are classified into four categories, and their advantages and disadvantages are analyzed. To assess the relative performance of different models, different validation protocols and evaluation indexes are discussed. The goal of this review is to help researchers quickly get started to develop computational frameworks using these integrated resources and eventually promote the discovery of lead peptides.

Molecular mechanisms of atrazine toxicity on H19-7 hippocampal neurons revealed by integrated miRNA and mRNA "omics"

Atrazine (ATR) is a widely applied herbicide in Asia and South America with slow natural degradation and documented deleterious effects on human and animal health, including hippocampal toxicity. However, relatively little is known about the molecular mechanisms responsible for ATR-induced hippocampal damage. Screening for differentially expressed mRNAs and microRNAs (miRNAs), and construction of potential miRNA-mRNA regulatory networks can reveal such mechanisms, so we analyzed the mRNA and miRNA expression profiles of rat hippocampus-derived H19-7 cells in response to ATR (500 μM) and conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes enrichment (KEGG) analyses. Integration of miRNA sequencing (miRNA-seq) and mRNA sequencing (mRNA-seq) results identified 114 differentially expressed miRNAs (DEMIs, 40 upregulated and 74 downregulated), and 510 differentially expressed mRNAs (DEMs, 177 upregulated and 333 downregulated) targeted by these DEMIs. The top 10 hub mRNAs (Fos, Prkcb, Ncf1, Vcam1, Atf3, Pak3, Pak1, Cacna1s, Junb, and Ccl2) and 19 related miRNAs (rno-miR-194-5p, rno-miR-24-3p, rno-miR-3074, rno-miR-1949, rno-miR-218a-1-3p, rno-miR-1843a-5p, rno-miR-1843b-5p, rno-miR-296-3p, rno-miR-320-3p, rno-miR-219a-1-3p, rno-miR-122-5p, rno-miR-1839-5p, rno-miR-1843a-3p, rno-miR-215, rno-miR-3583-3p, rno-miR-194-3p, rno-miR-128-1-5p, rno-miR-1956-5p, and rno-miR-466b-2-3p) were validated by quantitative real-time PCR. GO analysis indicated that these DEMs were enriched in genes associated with synaptic plasticity and antioxidant capacity, while KEGG analysis suggested that enriched DEMs were involved in calcium signaling, axon guidance, MAPK signaling, and glial carcinogenesis. The miRNA-mRNA regulatory network identified here may provide potential biomarkers and novel strategies for the treatment of hippocampal neurotoxicity induced by ATR.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Combining Network Pharmacology with Experimental Validation to Elucidate the Mechanism of Salvianolic Acid B in Treating Diabetic Peripheral Neuropathy

Background. Salvianolic acid B (Sal B) is a bioactive component of Radix Salviae, which has antiinflammation and antiapoptotic activity in diabetic complications. However, the molecular mechanism of action of Sal B on diabetic peripheral neuropathy (DPN) is unknown. This study was designed to identify a mechanism for Sal B in the treatment of DPN by using a pharmacology network, molecular docking, and in vitro experiments. Methods. Sal B and DPN-related targets from Gene Cards and OMIM platforms were retrieved and screened. Then, an analysis of possible targets with STRING and Cytoscape software was conducted. KEGG signaling pathways were determined using the R software. Subsequently, the binding capacity of Sal B to target proteins was analyzed by molecular docking and in vitro experiments. Results. A total of 501 targets related to Sal B and 4662 targets related to DPN were identified. Among these targets, 108 intersection targets were shared by Sal B and DPN. After topological and cluster analysis, 11 critical targets were identified, including p38MAPK. KEGG analysis revealed that the AGE-RAGE signaling pathway likely plays an important role in Sal B action on DPN. The p38MAPK protein is a key target in the AGE-RAGE signaling pathway. Molecular docking results suggested that Sal B and p38MAPK have excellent binding affinity (<−5 kcal/mol). The in vitro experiments revealed that Sal B downregulates the expressions of p-P38MAPK, inflammatory cytokines, and apoptosis targets, which are upregulated by hyperglycemia. Conclusion. Sal B may alter DPN by inhibiting inflammation and apoptosis activated by p38MAPK.

CRHR1 mediates the transcriptional expression of pituitary hormones and their receptors under hypoxia

Hypothalamus-pituitary-adrenal (HPA) axis plays critical roles in stress responses under challenging conditions such as hypoxia, via regulating gene expression and integrating activities of hypothalamus-pituitary-targets cells. However, the transcriptional regulatory mechanisms and signaling pathways of hypoxic stress in the pituitary remain to be defined. Here, we report that hypoxia induced dynamic changes in the transcription factors, hormones, and their receptors in the adult rat pituitary. Hypoxia-inducible factors (HIFs), oxidative phosphorylation, and cAMP signaling pathways were all differentially enriched in genes induced by hypoxic stress. In the pituitary gene network, hypoxia activated c-Fos and HIFs with specific pituitary transcription factors (Prop1), targeting the promoters of hormones and their receptors. HIF and its related signaling pathways can be a promising biomarker during acute or constant hypoxia. Hypoxia stimulated the transcription of marker genes for microglia, chemokines, and cytokine receptors of the inflammatory response. Corticotropin-releasing hormone receptor 1 (CRHR1) mediated the transcription of Pomc, Sstr2, and Hif2a, and regulated the function of HPA axis. Together with HIF, c-Fos initiated and modulated dynamic changes in the transcription of hormones and their receptors. The receptors were also implicated in the regulation of functions of target cells in the pituitary network under hypoxic stress. CRHR1 played an integrative role in the hypothalamus-pituitary-target axes. This study provides new evidence for CRHR1 involved changes of hormones, receptors, signaling molecules and pathways in the pituitary induced by hypoxia.

Systematic analysis of the lysine malonylome in Sanghuangporus sanghuang

Background

Sanghuangporus sanghuang is a well-known traditional medicinal mushroom associated with mulberry. Despite the properties of this mushroom being known for many years, the regulatory mechanisms of bioactive compound biosynthesis in this medicinal mushroom are still unclear. Lysine malonylation is a posttranslational modification that has many critical functions in various aspects of cell metabolism. However, at present we do not know its role in S. sanghuang. In this study, a global investigation of the lysine malonylome in S. sanghuang was therefore carried out.

Results

In total, 714 malonyl modification sites were matched to 255 different proteins. The analysis indicated that malonyl modifications were involved in a wide range of cellular functions and displayed a distinct subcellular localization. Bioinformatics analysis indicated that malonylated proteins were engaged in different metabolic pathways, including glyoxylate and dicarboxylate metabolism, glycolysis/gluconeogenesis, and the tricarboxylic acid (TCA) cycle. Notably, a total of 26 enzymes related to triterpene and polysaccharide biosynthesis were found to be malonylated, indicating an indispensable role of lysine malonylation in bioactive compound biosynthesis in S. sanghuang.

Conclusions

These findings suggest that malonylation is associated with many metabolic pathways, particularly the metabolism of the bioactive compounds triterpene and polysaccharide. This paper represents the first comprehensive survey of malonylation in S. sanghuang and provides important data for further study on the physiological function of lysine malonylation in S. sanghuang and other medicinal mushrooms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08120-0.

Predictions for high COL1A1 and COL10A1 expression resulting in a poor prognosis in esophageal squamous cell carcinoma by bioinformatics analyses

Background

Esophageal squamous cell carcinoma (ESCC) is one of the most common malignant neoplasms of the digestive tract worldwide. The lack of key molecular biomarkers is associated with the poor prognosis in ESCC patients. The present study was aimed to identify candidate genes for diagnostic, prognostic, and therapeutic applications in ESCC by bioinformatics.

Methods

Two datasets of ESCC (GSE20347 and GSE70409) from gene expression omnibus (GEO) were analyzed using GEO2R online tool to identify the differentially expressed genes (DEGs). Subsequently, functions and pathways enrichment analyses of DEGs and their protein-protein interaction (PPI) network analyses were performed. When key DEGs were identified, their relationship with ESCC prognosis was further validated.

Results

There were 134 commonly changed DEGs (33 up-regulated and 101 down-regulated) from GSE20347 and GSE70409 datasets were identified using integrated bioinformatical analysis. Gene ontology (GO) and pathway enrichment analysis was performed to annotate genes and gene products, highlight biological processes (BPs) and systemic functional information. Through the PPI network and cluster analysis, two clusters containing 21 key DEGs were detected and 14 of them were validated based on TCGA and GTEx data. Among these key DEGs, COL1A1 and COL10A1 were significantly associated with the prognosis in ESCC cases.

Conclusions

In conclusion, a total of 14 key DEGs and outcome in ESCC were identified by integrated bioinformatics analyses. COL1A1 and COL10A1 might be novel potential diagnostic and prognostic biomarkers in ESCC.