BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a prevalent cause of chronic liver disease and encompasses a broad spectrum of disorders, including simple steatosis, steatohepatitis, fibrosis, cirrhosis, and liver cancer. However, due to the global epidemic of NAFLD, where invasive liver biopsy is the gold standard for diagnosis, it is necessary to identify a more practical method for early NAFLD diagnosis with useful therapeutic targets; as such, molecular biomarkers could most readily serve these aims. To this end, we explored the hub genes and biological pathways in fibrosis progression in NAFLD patients.
METHODS: Raw data from microarray chips with GEO accession GSE49541 were downloaded from the Gene Expression Omnibus database, and the R package (Affy and Limma) was applied to investigate differentially expressed genes (DEGs) involved in the progress of low- (mild 0-1 fibrosis score) to high- (severe 3-4 fibrosis score) fibrosis stage NAFLD patients. Subsequently, significant DEGs with pathway enrichment were analyzed, including gene ontology (GO), KEGG and Wikipathway. In order to then explore critical genes, the protein-protein interaction network (PPI) was established and visualized using the STRING database, with further analysis undertaken using Cytoscape and Gephi software. Survival analysis was undertaken to determine the overall survival of the hub genes in the progression of NAFLD to hepatocellular carcinoma.
RESULTS: A total of 311 significant genes were identified, with an expression of 278 being upregulated and 33 downregulated in the high vs. low group. Gene functional enrichment analysis of these significant genes demonstrated major involvement in extracellular matrix (ECM)-receptor interaction, protein digestion and absorption, and the AGE-RAGE signaling pathway. The PPI network was constructed with 196 nodes and 572 edges with PPI enrichment using a p-value < 1.0 e-16. Based on this cut-off, we identified 12 genes with the highest score in four centralities: Degree, Betweenness, Closeness, and Eigenvector. Those twelve hub genes were CD34, THY1, CFTR, COL3A1, COL1A1, COL1A2, SPP1, THBS1, THBS2, LUM, VCAN, and VWF. Four of these hub genes, namely CD34, VWF, SPP1, and VCAN, showed significant association with the development of hepatocellular carcinoma.
CONCLUSIONS: This PPI network analysis of DEGs identified critical hub genes involved in the progression of fibrosis and the biological pathways through which they exert their effects in NAFLD patients. Those 12 genes offer an excellent opportunity for further focused research to determine potential targets for therapeutic applications.

Microsatellites are a ubiquitous occurrence in prokaryotic and eukaryotic genomes. Microsatellites have become one of the most popular classes of genetic markers due to their high reproducibility, multi-allelic nature, co-dominant mode of inheritance, abundance and wide genome coverage. We characterised microsatellites in the genomes and genes of two bat species, Pteropus vampyrus and Miniopterus natalensis. This characterisation was used for gene ontology analysis and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment of coding sequences (CDS).
Compared to M. natalensis, the genome size of P. vampyrus is larger and contains more microsatellites, but the total diversity of both species is similar. Mononucleotide and dinucleotide repeats were the most diverse in the genome of the two species. In each bat species, the microsatellite bias was obvious. The microsatellites with the largest number of repeat motifs in P. vampyrus from mononucleotide to hexanucleotide were (A)n, (AC)n, (CAA)n, (AAAC)n, (AACAA)n and (AAACAA)n, with frequencies of 97.94%, 58.75%, 30.53%, 22.82%, 54.68% and 22.87%, respectively, while in M. natalensis were (A)n, (AC)n, (TAT)n, (TTTA)n, (AACAA)n and (GAGAGG)n, with of 92.00%, 34.08%, 40.36%, 21.83%, 25.42% and 12.79%, respectively. In both species, the diversity of microsatellites was highest in intergenic regions, followed by intronic, untranslated and exonic regions and lowest in coding regions. Location analysis indicated that microsatellites were mainly concentrated at both ends of the genes. Microsatellites in the CDS are thus subject to higher selective pressure. In the GO analysis, two unique GO terms were found only in P. vampyrus and M. natalensis, respectively. In KEGG enriched pathway, the biosynthesis of other secondary metabolites and metabolism of other amino acids in metabolism pathways were present only in M. natalensis. The combined biological process, cellular components and molecular function ontology are reflected in the GO analysis and six functional enrichments in KEGG annotation, suggesting advantageous mutations during species evolution.
Our study gives a comparative characterization of the genomes of microsatellites composition in the two bat species. And also allow further study on the effect of microsatellites on gene function as well as provide an insight into the molecular basis for species adaptation to new and changing environments.

The role of noncoding RNAs (ncRNAs) in plant resistance to abiotic stresses is increasingly being discovered. Drought stress is one of the most common stresses that affecting plant growth, and high intensity drought has a significant impact on the normal growth of plants. In this study, a high-throughput sequencing was performed on plant tissue samples of Phyllostachys aureosulcata f. spectabilis C. D. Chu et C. S. Chao by drought treatment for 0, 2, 4 and 6 days. The sequencing results were analysed bioinformatically. We detected 336,946 RNAs among all 12 samples, including 192,098 message RNAs (mRNAs), 142,761 long noncoding RNAs (lncRNAs), 1,670 circular RNAs (circRNAs), and 417 microRNAs (miRNAs). We detected 2,419 differentially expressed (DE) ncRNAs, including 213 DE circRNAs, 2,088 DE lncRNAs and 118 DE miRNAs. Then, we used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to functionally predict DE ncRNAs. The results showed that most DE ncRNAs are involved in the response to drought stress, mainly in biochemical reactions involved in some metabolites, as well as in organelle activities. In addition, we validated two random circRNAs and demonstrated their circularity. We also found a stable internal reference gene available for Phyllostachys aureosulcata f. spectabilis and validated the accuracy of this experiment by quantitative real-time polymerase chain reaction (qRT-PCR).

Reperfusion Injury Acute ischemic stroke is increasing in people recently and Musk, as a commonly used Traditional Chinese Medicine (TCM), has been suggested as a potential agent against acute ischemic stroke, but the efficacies and underlying mechanisms of it remain unknown.
This study was aimed to test the hypotheses that volatile compounds of musk could attenuate nerve injury and identify the bioactive compounds and potential mechanisms of Musk.
Transient middle cerebral artery occlusion (MCAO) model in vivo in Sprague-Dawley rats (SD rats) was used to test this hypothesis. Collecting ingredients of Musk and their related targets were discerned from the Gas chromatography-olfactory mass spectrometry (GC-O-MS) experiment. Then the potential mechanisms and targets of the compounds were searched by network pharmacology techniques. Finally, the pathway was verified by Western Bolt (WB).
First, Musk treatment significantly up-regulated the relative levels of AKT1, PI3KA, and VEGFA in the hippocampus, and improved the sport functions in the post-MCAO ischemic rats in vivo. Next, twenty potential flavor active compounds were recognized by GC-O-MS. A total of 89 key targets including HIF-1, PIK3CA, TNF signaling pathway, and VEGF were identified. AKT1, HIF1A, PIK3CA, and VEGFA were viewed as the most important genes, which were validated by molecular docking simulation.
The Volatile compounds of musk can attenuate nerve injury and improving post-cerebral ischemic exercise functions by HIF1A pathways, and the combined data provide novel insight for Musk volatile compounds developed as new drug for improving reperfusion injury in acute ischemic stroke.

Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein-protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein-protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.

Trichoderma spp. are widely used biocontrol agents which are antagonistic to a variety of plant pathogens. Chlamydospores are a type of propagules produced by many fungi that have thick walls and are highly resistant to adverse environmental conditions. Chlamydospore preparations of Trichoderma spp. can withstand various storage conditions, have a longer shelf life than conidial preparations and have better application potential. However, large-scale production of chlamydospores has proven difficult. To understand the molecular mechanisms governing chlamydospore formation (CF) in Trichoderma fungi, we performed a comprehensive analysis of transcriptome dynamics during CF across 8 different developmental time points, which were divided into 4 stages according to PCA analysis: the mycelium growth stage (S1), early and middle stage of CF (S2), flourishing stage of CF (S3), and late stage of CF and mycelia initial autolysis (S4). 2864, 3206, and 3630 DEGs were screened from S2 vs S1, S3 vs S2, and S4 vs S3, respectively. We then identified the pathways and genes that play important roles in each stage of CF by GO, KEGG, STC and WGCNA analysis. The results showed that DEGs in the S2 vs S1 were mainly enriched in organonitrogen compound metabolism, those in S3 vs S2 were mainly involved in secondary metabolite, cell cycle, and N-glycan biosynthesis, and DEGs in S4 vs S3 were mainly involved in lipid, glycogen, and chitin metabolic processes. We speculated that mycelial assimilation and absorption of exogenous nitrogen in the early growth stage (S1), resulted in subsequent nitrogen deficiency (S2). At the same time, secondary metabolites and active oxygen free radicals released during mycelial growth produced an adverse growth environment. The resulting nitrogen-deficient and toxin enriched medium may stimulate cell differentiation by initiating cell cycle regulation to induce morphological transformation of mycelia into chlamydospores. High expression of genes relating to glycogen, lipid, mannan, and chitin synthetic metabolic pathways during the flourishing (S3) and late stages (S4) of CF may be conducive to energy storage and cell wall construction in chlamydospores. For further verifying the functions of the amino sugar and nucleotide sugar metabolism (tre00520) pathway in the CF of T. virens GV29-8 strain, the chitin synthase gene (TRIVIDRAFT_90152), one key gene of the pathway, was deleted and resulted in the dysplasia of mycelia and an incapability to form normal chlamydospores, which illustrated the pathway affecting the CF of T. virens GV29-8 strain. Our results provide a new perspective for understanding the genetics of biochemical pathways involved in CF of Trichoderma spp.

OBJECTIVE: Colon cancer is occurring at an increasing rate and ginger (Zingiber officinale), as a commonly used herbal medicine, has been suggested as a potential agent for colon cancer. This study was aimed to identify the bioactive components and potential mechanisms of ginger for colon cancer prevention by an integrated network pharmacology approach.
METHODS: The putative ingredients of ginger and its related targets were discerned from the TCMSP  and Swiss target prediction database. After that, the targets interacting with colon cancer were collected using Genecards, OMIM, and Drugbank databases. KEGG pathway and GO enrichment analysis were performed to explore the signaling pathways related to ginger for colon cancer treatments. The PPI and compound-target-disease networks were constructed using Cytoscape 3.8.1. Finally, Discovery studio software was employed to confirm the key genes and active components from ginger.
RESULTS: Six potential active compounds, 285 interacting targets in addition to 1356 disease-related targets were collected, of which 118 intersection targets were obtained. A total of 34 key targets including PIK3CA, SRC, and TP53 were identified through PPI network analysis. These targets were mainly focused on the biological processes of phosphatidylinositol 3-kinase signaling, cellular response to oxidative stress, and cellular response to peptide hormone stimulus. The KEGG enrichment manifested that three signaling pathways were closely related to colon cancer prevention of ginger, cancer, endocrine resistance, and hepatitis B pathways. TP53, HSP90AA1, and JAK2 were viewed as the most important genes, which were validated by molecular docking simulation.
CONCLUSIONS: This study demonstrated that ginger produced preventive effects against colon cancer by regulating multi-targets and multi-pathways with multi-components. And, the combined data provide novel insight for ginger compounds developed as new drug for anti-colon cancer.

The viability and competitiveness of Staphylococcus xylosus in meat mostly depend on the ability to adapt itself to rapid oxygen and nutrients depletion during meat fermentation. The utilization of nitrite instead of oxygen becomes a successful strategy for this strain to improve its performance in anaerobiosis; however, metabolic pathways of this strain underlying this adaptation, are partially known. The aim of this study was to provide an overview on proteomic changes of S. xylosus DSM 20266T cultured under anaerobiosis and nitrite exposure. Thus, two different cultures of this strain, supplemented or not with nitrite, were in vitro incubated in aerobiosis and anaerobiosis monitoring cell viability, pH, oxidation reduction potential and nitrite content. Protein extracts, obtained from cells, collected as nitrite content was depleted, were analyzed by 2DE/MALDI-TOF/TOF-MS. Results showed that DSM 20266T growth was significantly sustained by nitrite in anaerobiosis, whereas no differences were found in aerobiosis. Accordingly, nitrite content was depleted after 13 h only in anaerobiosis. At this time of sampling, a comparative proteomic analysis showed 45 differentially expressed proteins. Most differences were found between aerobic and anaerobic cultures without nitrite; the induction of glycolytic enzymes and glyoxylate cycle, the reduction of TCA enzymes, and acetate fermentation were found in anaerobiosis to produce ATP and maintain the cell redox balance. In anaerobic cultures the nitrite supplementation partially restored TCA cycle, and reduced the amount of glycolytic enzymes. These results were confirmed by phenotypic microarray that, for the first time, was carried out on cell previously adapted at the different growth conditions. Overall, metabolic changes were similar between aerobiosis and anaerobiosis NO2-adapted cells, whilst cells grown under anaerobiosis showed different assimilation profiles by confirming proteomic data; indeed, these latter extensively assimilated substrates addressed at both supplying glucose for glycolysis or fueling alternative pathways to TCA cycle. In conclusion, metabolic pathways underlying the ability of S. xylosus to adapt itself to oxygen starvation were revealed; the addition of nitrite allowed S. xylosus to take advantage of nitrite to this condition, restoring some metabolic pathway underlying aerobic behavior of the strain.

Compound-protein interactions play important roles in every cell via the recognition and regulation of specific functional proteins. The correct identification of compound-protein interactions can lead to a good comprehension of this complicated system and provide useful input for the investigation of various attributes of compounds and proteins. In this study, we attempted to understand this system by extracting properties from both proteins and compounds, in which proteins were represented by gene ontology and KEGG pathway enrichment scores and compounds were represented by molecular fragments. Advanced feature selection methods, including minimum redundancy maximum relevance, incremental feature selection, and the basic machine learning algorithm random forest, were used to analyze these properties and extract core factors for the determination of actual compound-protein interactions. Compound-protein interactions reported in The Binding Databases were used as positive samples. To improve the reliability of the results, the analytic procedure was executed five times using different negative samples. Simultaneously, five optimal prediction methods based on a random forest and yielding maximum MCCs of approximately 77.55 % were constructed and may be useful tools for the prediction of compound-protein interactions. This work provides new clues to understanding the system of compound-protein interactions by analyzing extracted core features. Our results indicate that compound-protein interactions are related to biological processes involving immune, developmental and hormone-associated pathways.

Granulosa cells (GCs) are those somatic cells closest to the female germ cell. GCs play a vital role in oocyte growth and development, and the oocyte is necessary for multiplication of a species. Zinc oxide (ZnO) nanoparticles (NPs) readily cross biologic barriers to be absorbed into biologic systems that make them promising candidates as food additives. The objective of the present investigation was to explore the impact of intact NPs on gene expression and the functional classification of altered genes in hen GCs in vivo, to compare the data from in vivo and in vitro studies, and finally to point out the adverse effects of ZnO NPs on the reproductive system. After a 24-week treatment, hen GCs were isolated and gene expression was quantified. Intact NPs were found in the ovary and other organs. Zn levels were similar in ZnO-NP-100 mg/kg- and ZnSO4-100 mg/kg-treated hen ovaries. ZnO-NP-100 mg/kg and ZnSO4-100 mg/kg regulated the expression of the same sets of genes, and they also altered the expression of different sets of genes individually. The number of genes altered by the ZnO-NP-100 mg/kg and ZnSO4-100 mg/kg treatments was different. Gene Ontology (GO) functional analysis reported that different results for the two treatments and, in Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, 12 pathways (out of the top 20 pathways) in each treatment were different. These results suggested that intact NPs and Zn(2+) had different effects on gene expression in GCs in vivo. In our recent publication, we noted that intact NPs and Zn(2+) differentially altered gene expression in GCs in vitro. However, GO functional classification and KEGG pathway enrichment analyses revealed close similarities for the changed genes in vivo and in vitro after ZnO NP treatment. Furthermore, close similarities were observed for the changed genes after ZnSO4 treatments in vivo and in vitro by GO functional classification and KEGG pathway enrichment analyses. Therefore, the effects of ZnO NPs on gene expression in vitro might represent their effects on gene expression in vivo. The results from this study and our earlier studies support previous findings indicating ZnO NPs promote adverse effects on organisms. Therefore, precautions should be taken when ZnO NPs are used as diet additives for hens because they might cause reproductive issues.