Exploration of the Key Genes Involved in Non-alcoholic Fatty Liver Disease and Possible MicroRNA Therapeutic Targets

Background

MicroRNAs (miRNAs) are promising therapeutic agents for non-alcoholic fatty liver disease (NAFLD). This study aimed to identify key genes/proteins involved in NAFLD pathogenesis and progression and to evaluate miRNAs influencing their expression.

Methods

Gene expression profiles from datasets GSE151158, GSE163211, GSE135251, GSE167523, GSE46300, and online databases were analyzed to identify significant NAFLD-related genes. Then, protein-protein interaction networks and module analysis identified hub genes/proteins, which were validated using real-time PCR in oleic acid-treated HepG2 cells. Functional enrichment analysis evaluated signaling pathways and biological processes. Gene-miRNA interaction networks identified miRNAs targeting critical NAFLD genes.

Results

The most critical overexpressed hub genes/proteins included: TNF, VEGFA, TLR4, CYP2E1, ACE, SCD, FASN, SREBF2, and TGFB1 based on PPI network analysis, of which TNF, TLR4, SCD, FASN, SREBF2, and TGFB1 were up-regulated in oleic acid-treated HepG2 cells. Functional enrichment analysis for biological processes highlighted programmed necrotic cell death, lipid metabolic process response to reactive oxygen species, and inflammation. In the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, the highest adjusted P-value signaling pathways encompassed AGE-RAGE in diabetic complications, TNF, and HIF-1 signaling pathways. In gene-miRNA network analysis, miR-16 and miR-124 were highlighted as the miRNAs exerting the most influence on important NAFLD-related genes.

Conclusion

In silico analyses identified NAFLD therapeutic targets and miRNA candidates to guide further experimental investigation.

Prediction of protein N-terminal acetylation modification sites based on CNN-BiLSTM-attention model

N-terminal acetylation is one of the most common and important post-translational modifications (PTM) of eukaryotic proteins. PTM plays a crucial role in various cellular processes and disease pathogenesis. Thus, the accurate identification of N-terminal acetylation modifications is important to gain insight into cellular processes and other possible functional mechanisms. Although some algorithmic models have been proposed, most have been developed based on traditional machine learning algorithms and small training datasets. Their practical applications are limited. Nevertheless, deep learning algorithmic models are better at handling high-throughput and complex data. In this study, DeepCBA, a model based on the hybrid framework of convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism deep learning, was constructed to detect the N-terminal acetylation sites. The DeepCBA was built as follows: First, a benchmark dataset was generated by selecting low-redundant protein sequences from the Uniport database and further reducing the redundancy of the protein sequences using the CD-HIT tool. Subsequently, based on the skip-gram model in the word2vec algorithm, tripeptide word vector features were generated on the benchmark dataset. Finally, the CNN, BiLSTM, and attention mechanism were combined, and the tripeptide word vector features were fed into the stacked model for multiple rounds of training. The model performed excellently on independent dataset test, with accuracy and area under the curve of 80.51% and 87.36%, respectively. Altogether, DeepCBA achieved superior performance compared with the baseline model, and significantly outperformed most existing predictors. Additionally, our model can be used to identify disease loci and drug targets.

Effects of polystyrene microplastics on the metabolic level of Pseudomonas aeruginosa

Given the widespread presence of Pseudomonas aeruginosa in water and its threat to human health, the metabolic changes in Pseudomonas aeruginosa when exposed to polystyrene microplastics (PS-MPs) exposure were studied, focusing on molecular level. Through non-targeted metabolomics, a total of 64 differential metabolites were screened out under positive ion mode and 44 under negative ion mode. The content of bacterial metabolites changed significantly, primarily involving lipids, nucleotides, amino acids, and organic acids. Heightened intracellular oxidative damage led to a decrease in lipid molecules and nucleotide-related metabolites. The down-regulation of amino acid metabolites, such as L-Glutamic and L-Proline, highlighted disruptions in cellular energy metabolism and the impaired ability to synthesize proteins as a defense against oxidation. The impact of PS-MPs on organic acid metabolism was evident in the inhibition of pyruvate and citrate, thereby disrupting the cells' normal participation in energy cycles. The integration of Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that PS-MPs mainly caused changes in metabolic pathways, including ABC transporters, Aminoacyl-tRNA biosynthesis, Purine metabolism, Glycerophospholipid metabolism and TCA cycle in Pseudomonas aeruginosa. Most of the differential metabolites enriched in these pathways were down-regulated, demonstrating that PS-MPs hindered the expression of metabolic pathways, ultimately impairing the ability of cells to synthesize proteins, DNA, and RNA. This disruption affected cell proliferation and information transduction, thus hampering energy circulation and inhibiting cell growth. Findings of this study supplemented the toxic effects of microplastics and the defense mechanisms of microorganisms, in turn safeguarding drinking water safety and human health.

Relationships between high-concentration toxic metals in sediment and evolution of microbial community structure and carbon-nitrogen metabolism functions under long-term stress perspective

Microorganisms are highly sensitive to toxic metal pollution and play an important role in the material cycling and energy flow of the water ecosystem. Herein, 13 sediment samples from Junchong Reservoir (Guangxi Province, China) were collected in December 2021. The spatial distribution of pollution levels for toxic metals and the effects of toxic metals on the composition, functional characteristics, and metabolism of microorganisms were investigated. The results demonstrated that the area is a proximate area to industrial zones with severity of toxic metal pollution. Their mean concentrations of As, Cu, Zn, and Pb were up to 128.79 mg/kg, 57.62 mg/kg, 594.77 mg/kg, and 97.12 mg/kg respectively. There was a strong correlation between As, Cu, Zn, and Pb, with the highest correlation coefficient reaching 0.94. As the level of toxic metal pollution increases, the diversity and abundance of microorganisms gradually decrease. Compared to those with lower pollution levels, the Shannon index in regions with higher pollution levels decreases by up to 0.373, and the Chao index decreases by up to 143.507. However, the relative abundance of Bacteroidota, Patescibacteria, and Chloroflexi increased by 23%, 20%, and 5%, respectively, indicating their higher adaptability to toxic metals. Furthermore, microbial carbon and nitrogen metabolism were also affected by the presence of toxic metals. FAPROTAX analysis demonstrated an abundant reduction of ecologically functional groups associated with carbon and nitrogen transformations under high toxic metal pollution levels. KEGG pathway analysis indicated that carbon fixation and nitrogen metabolism pathways were inhibited with increasing toxic metal concentrations. These findings would contribute to a better understanding of the effects of toxic metal pollution on sediment microbial communities and function, shedding light on the ecological consequences of toxic metal contamination.

Identification and analysis of the molecular targets of statins in colorectal cancer

Colorectal cancer (CRC) is the third most common cancer in the world. According to several types of research, statins may impact the development and treatment of CRC. This work aimed to use bioinformatics to discover the relationship between statin targets and differentially expressed genes (DEGs) in CRC patients and determine the possible molecular effect of statins on CRC suppression. We used CRC datasets from the GEO database to select CRC-related DEGs. DGIdb and STITCH databases were used to identify gene targets of subtypes of statin. Further, we identified the statin target of CRC DEGs hub genes by using a Venn diagram of CRC DEGs and statin targets. Funrich and enrichr databases were carried out for the KEGG pathway and gene ontology (GO) enrichment analysis, respectively. GSE74604 and GSE10950 were used to identify CRC DEGs. After analyzing datasets,1370 genes were identified as CRC DEGs, and 345 targets were found for statins. We found that 35 genes are CRC DEGs statin targets. We found that statin targets in CRC were enriched in the receptor and metallopeptidase activity for molecular function, cytoplasm and plasma membrane for cellular component, signal transduction, and cell communication for biological process genes were substantially enriched based on FunRich enrichment. Analysis of the KEGG pathways revealed that the overexpressed DEGs were enriched in the IL-17, PPAR, and Toll-like receptor signaling pathways. Finally, CCNB1, DNMT1, AURKB, RAC1, PPARGC1A, CDKN1A, CAV1, IL1B, and HSPD1 were identified as hub CRC DEGs statin targets. The genetic and molecular aspects of our findings reveal that statins might have a therapeutic effect on CRC.

Startup of a large height-diameter ratio bioreactor by alternate feeding: performance of partial nitrification and enrichment of ammonia-oxidizing bacteria (AOB)

In order to solve the complicated control of dissolved oxygen (DO) for partial nitrification in bioreactors treating high NH 4 + - N wastewater, a large height-diameter ratio anammox pre-reactor system was developed. And in this reactor, NO 2 - - N accumulation rate can reach 85.76% by alternate feeding with high NH 4 + - N wastewater (150 mg NH 4 + - N / L ) and low NH 4 + - N wastewater (50 mg c) with low DO (0.19 mg/L-0.62 mg/L). Based on 16S rRNA identification technology, it was found that Nitrosomonas had a significant effect on NH 4 + - N oxidization in this study. And when the reactor treated higher concentration wastewater (250 mg NH 4 + - N / L ), the growth rate of Nitrosomonas was higher than that of Nitrospira (nitrite-oxidizing bacteria, NOB), which was conducive to improving the NO 2 - - N accumulation rate and realizing partial nitrification stably. It was also found that the material exchange frequency of the microbial flora during alternate feeding with different NH 4 + - N concentration wastewaters was higher than that during feeding with higher NH 4 + - N concentration wastewater (250 mg/L) by Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolism pathways analysis. This study can provide valuable insights and lay the foundation for building anammox pre-reactors.

Using liquid chromatography and mass spectrometry to predict potential biomarkers for missed miscarriage and its metabolic pathways in a tertiary center: A cross-sectional analytic study

OBJECTIVE

To determine and compare the serum metabolites in missed abortion versus normal early pregnancy using ultra-high-performance liquid chromatography and tandem time-of-flight mass spectrometry, and to determine how these metabolites can be used to predict the potential biomarkers and possible metabolic pathways of a missed abortion.

METHODS

The serum of patients with a missed abortion was used as the experimental group and the serum of patients with an induced abortion during normal early pregnancy was used as the control group. Principal component analysis and orthogonal partial least square discriminant analysis were additionally used to observe the difference in metabolite distribution between the two groups. A variable weight value (variable importance in the projection; VIP) obtained from the orthogonal partial least squares discriminant analysis model more than 1 and P less than 0.05 were taken to indicate significant differences in metabolite screening. After this, enrichment analysis of the metabolic pathways of these metabolites was conducted using Fisher precise test in order to find the metabolic pathway with the highest correlation with the differential metabolites.

RESULTS

In total, 30 patients were included in the experimental group, with 30 patients in the control group. Fifty-five metabolites (VIP > 1, P < 0.05) with significant differences related to missed abortion were selected, among which 35 metabolites increased and 20 decreased in patients with a missed abortion. KEGG pathway enrichment analysis showed that the four metabolic pathways with the highest correlation were cholesterol metabolism, arginine biosynthesis, cell apoptosis, and the FoxO signaling pathway.

CONCLUSION

The missed abortion serum metabolites and changes in related metabolic pathways reported in this study provide a basis for the early prediction and diagnosis of a missed abortion.

Transcriptomic Analysis of the Rat Dorsal Root Ganglion After Fracture

In fractures, pain signals are transmitted from the dorsal root ganglion (DRG) to the brain, and the DRG generates efferent signals to the injured bone to participate in the injury response. However, little is known about how this process occurs. We analyzed DRG transcriptome at 3, 7, 14, and 28 days after fracture. We identified the key pathways through KEGG and GO enrichment analysis. We then used IPA analysis to obtain upstream regulators and disease pathways. Finally, we compared the sequencing results with those of nerve injury to identify the unique transcriptome changes in DRG after fracture. We found that the first 14 days after fracture were the main repair response period, the 3rd day was the peak of repair activity, the 14th day was dominated by the stimulus response, and on the 28th day, the repair response had reached a plateau. ECM-receptor interaction, protein digestion and absorption, and the PI3K-Akt signaling pathway were most significantly enriched, which may be involved in repair regeneration, injury response, and pain transmission. Compared with the nerve injury model, DRG after fracture produced specific alterations related to bone repair, and the bone density function was the most widely activated bone-related function. Our results obtained some important genes and pathways in DRG after fracture, and we also summarized the main features of transcriptome function at each time point through functional annotation clustering of GO pathway, which gave us a deeper understanding of the role played by DRG in fracture.

The Relevance of Reperfusion Stroke Therapy for miR-9-3p and miR-9-5p Expression in Acute Stroke-A Preliminary Study

Reperfusion stroke therapy is a modern treatment that involves thrombolysis and the mechanical removal of thrombus from the extracranial and/or cerebral arteries, thereby increasing penumbra reperfusion. After reperfusion therapy, 46% of patients are able to live independently 3 months after stroke onset. MicroRNAs (miRNAs) are essential regulators in the development of cerebral ischemia/reperfusion injury and the efficacy of the applied treatment. The first aim of this study was to examine the change in serum miRNA levels via next-generation sequencing (NGS) 10 days after the onset of acute stroke and reperfusion treatment. Next, the predictive values of the bioinformatics analysis of miRNA gene targets for the assessment of brain ischemic response to reperfusion treatment were explored. Human serum samples were collected from patients on days 1 and 10 after stroke onset and reperfusion treatment. The samples were subjected to NGS and then validated using qRT-PCR. Differentially expressed miRNAs (DEmiRNAs) were used for enrichment analysis. Hsa-miR-9-3p and hsa-miR-9-5p expression were downregulated on day 10 compared to reperfusion treatment on day 1 after stroke. The functional analysis of miRNA target genes revealed a strong association between the identified miRNA and stroke-related biological processes related to neuroregeneration signaling pathways. Hsa-miR-9-3p and hsa-miR-9-5p are potential candidates for the further exploration of reperfusion treatment efficacy in stroke patients.

The synthesis of ectoine enhance the assimilation of ammonia nitrogen in hypersaline wastewater by the salt-tolerant assimilation bacteria sludge

In contrast to nitrification-denitrification microorganisms that convert ammonia nitrogen in hypersaline wastewater into nitrogen for discharge, this research utilizes sludge enriched with salt-tolerant assimilation bacteria (STAB) to assimilate organic matter and ammonia nitrogen in hypersaline wastewater into ectoine - a biomass with high economic value and resistance to external osmotic pressure. The study investigates the relationship between the synthesis of ectoine and nitrogen removal efficiency of STAB sludge in three sequencing batch reactors (SBR) operated at different salinities (50, 75, and 100 g/L) and organic matter concentrations. The research reveals that, under low concentration carbon sources (TOC/N = 4, NH4+-N = 60 mg/L), the ammonia nitrogen removal efficiency of SBR reactors increased by 14.51 % and 17.25 % within 5 d and 2 d, respectively, when salinity increased from 50 g/L to 75 g/L and 100 g/L. Under high concentration carbon sources (TOC/N = 8, NH4+-N = 60 mg/L), the ammonia nitrogen removal efficiency of STAB sludge in the three reactors stabilized at 80.20 %, 76.71 %, and 72.87 %, and the total nitrogen removal efficiency was finally stabilized at 80.47 %, 73.15 %, and 65.53 %, respectively. The nitrogen removal performance by ammonium-assimilating of STAB sludge is more sustainable under low salinity, while it is more short-term explosive under high salinity. Moreover, the intracellular ectoine concentration of STAB sludge was found to be related to this behavior. Empirical formulas confirm that STAB sludge synthesizes ectoine from nutrients in wastewater through assimilation, and intracellular ectoine has a threshold defect (150 mg/gVss). The ectoine metabolism pathways of STAB sludge was constructed using the Kyoto Encyclopedia of Genes and Genomes (KEGG). The ammonia nitrogen in sewage is converted into glutamic acid under the action of assimilation genes. It then undergoes a tricarboxylic acid cycle to synthesize the crucial precursor of ectoine - aspartic acid. Subsequently, ectoine is produced through ectoine synthase. The findings suggest that when the synthesis of intracellular ectoine reaches saturation, it inhibits the continuous nitrogen removal performance of STAB sludge under high salinity. STAB sludge does not actively release ectoine through channels under stable external osmotic pressure.

Short-chain fatty acids in breast milk and their relationship with the infant gut microbiota

Introduction

The short-chain fatty acids (SCFAs) contained in breast milk play a key role in infant growth, affecting metabolism and enhancing intestinal immunity by regulating inflammation.

Methods

In order to examine the associations between the microbiota and SCFA levels in breast milk, and explore the roles of SCFAs in regulating the infant gut microbiota, we enrolled 50 paired mothers and infants and collected both breast milk and infant fecal samples. Breast milk SCFA contents were determined by UPLC-MS, and whole genome shotgun sequencing was applied to determine the microbial composition of breast milk and infant feces. The SCFA levels in breast milk were grouped into tertiles as high, medium, or low, and the differences of intestinal microbiota and KEGG pathways were compared among groups.

Results

The results demonstrated that breast milk butyric acid (C4) is significantly associated with Clostridium leptum richness in breastmilk. Additionally, the specific Bifidobacterium may have an interactive symbiosis with the main species of C4-producing bacteria in human milk. Women with a low breast milk C4 tertile are associated with a high abundance of Salmonella and Salmonella enterica in their infants' feces. KEGG pathway analysis further showed that the content of C4 in breast milk is significantly correlated with the infants' metabolic pathways of lysine and arginine biosynthesis.

Discussion

This study suggests that interactive symbiosis of the microbiota exists in breast milk. Certain breast milk microbes could be beneficial by producing C4 and further influence the abundance of certain gut microbes in infants, playing an important role in early immune and metabolic development.

KNeXT: a NetworkX-based topologically relevant KEGG parser

Automating the recreation of gene and mixed gene-compound networks from Kyoto Encyclopedia of Genes and Genomes (KEGG) Markup Language (KGML) files is challenging because the data structure does not preserve the independent or loosely connected neighborhoods in which they were originally derived, referred to here as its topological environment. Identical accession numbers may overlap, causing neighborhoods to artificially collapse based on duplicated identifiers. This causes current parsers to create misleading or erroneous graphical representations when mixed gene networks are converted to gene-only networks. To overcome these challenges we created a python-based KEGG NetworkX Topological (KNeXT) parser that allows users to accurately recapitulate genetic networks and mixed networks from KGML map data. The software, archived as a python package index (PyPI) file to ensure broad application, is designed to ingest KGML files through built-in APIs and dynamically create high-fidelity topological representations. The utilization of NetworkX's framework to generate tab-separated files additionally ensures that KNeXT results may be imported into other graph frameworks and maintain programmatic access to the original x-y axis positions to each node in the KEGG pathway. KNeXT is a well-described Python 3 package that allows users to rapidly download and aggregate specific KGML files and recreate KEGG pathways based on a range of user-defined settings. KNeXT is platform-independent, distinctive, and it is not written on top of other Python parsers. Furthermore, KNeXT enables users to parse entire local folders or single files through command line scripts and convert the output into NCBI or UniProt IDs. KNeXT provides an ability for researchers to generate pathway visualizations while persevering the original context of a KEGG pathway. Source code is freely available at https://github.com/everest-castaneda/knext.

Comprehensive transcriptomic analysis unveils the interplay of mRNA and LncRNA expression in shaping collagen organization and skin development in Dezhou donkeys

The primary focus of donkey hide gelatin processing lies in the dermal layer of donkey hide due to its abundant collagen content. However, the molecular mechanism involved in collagen organization and skin development in donkey skin tissue across various developmental stages remains incomplete. The current study aims to investigate the transcriptomic screening of lncRNAs and mRNA associated with skin development and collagen organization across different ages in Dezhou donkeys' skin. In the pursuit of this objective, we used nine skin tissue samples obtained from Dezhou donkeys at various ages including 8-month fetal stage, followed by 2 and 8 years. RNA-seq analysis was performed for the transcriptomic profiling of differentially expressed genes (DEGs) and lncRNAs associated with skin development in different age groups. Our investigation revealed the presence of 6,582, 6,455, and 405 differentially expressed genes and 654, 789, and 29 differentially expressed LncRNAs within the skin tissues of Dezhou donkeys when comparing young donkeys (YD) vs. middle-aged donkeys (MD), YD vs. old donkeys (OD), and MD vs. OD, respectively. Furthermore, we identified Collagen Type I Alpha 1 Chain (COL1A1), Collagen Type III Alpha 1 Chain (COL3A1), and Collagen Type VI Alpha 5 Chain (COL6A5) as key genes involved in collagen synthesis, with COL1A1 being subject to cis-regulation by several differentially expressed LncRNAs, including ENSEAST00005041187, ENSEAST00005038497, and MSTRG.17248.1, among others. Interestingly, collagen organizational and skin development linked pathways including Protein digestion and absorption, metabolic pathways, Phosphatidylinositol 3-Kinase-Protein Kinase B signaling pathway (PI3K-Akt signaling pathway), Extracellular Matrix-Receptor Interaction (ECM-receptor interaction), and Relaxin signaling were also reported across different age groups in Dezhou donkey skin. These findings enhance our comprehension of the molecular mechanisms underlying Dezhou donkey skin development and collagen biosynthesis and organization, thus furnishing a solid theoretical foundation for future research endeavors in this domain.

Transcriptome Analysis of White- and Red-Fleshed Apple Fruits Uncovered Novel Genes Related to the Regulation of Anthocyanin Biosynthesis

The red flesh coloration of apples is a result of a biochemical pathway involved in the biosynthesis of anthocyanins and anthocyanidins. Based on apple genome analysis, a high number of regulatory genes, mainly transcription factors such as MYB, which are components of regulatory complex MYB-bHLH-WD40, and several structural genes (PAL, 4CL, CHS, CHI, F3H, DFR, ANS, UFGT) involved in anthocyanin biosynthesis, have been identified. In this study, we investigated novel genes related to the red-flesh apple phenotype. These genes could be deemed molecular markers for the early selection of new apple cultivars. Based on a comparative transcriptome analysis of apples with different fruit-flesh coloration, we successfully identified and characterized ten potential genes from the plant hormone transduction pathway of auxin (GH3); cytokinins (B-ARR); gibberellins (DELLA); abscisic acid (SnRK2 and ABF); brassinosteroids (BRI1, BZR1 and TCH4); jasmonic acid (MYC2); and salicylic acid (NPR1). An analysis of expression profiles was performed in immature and ripe fruits of red-fleshed cultivars. We have uncovered genes mediating the regulation of abscisic acid, salicylic acid, cytokinin, and jasmonic acid signaling and described their role in anthocyanin biosynthesis, accumulation, and degradation. The presented results underline the relationship between genes from the hormone signal transduction pathway and UFGT genes, which are directly responsible for anthocyanin color transformation as well as anthocyanin accumulation during apple-fruit ripening.

Network pharmacology study to explore the multiple molecular mechanism of SH003 in the treatment of non-small cell lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) is one of the leading causes of human death worldwide. Herbal prescription SH003 has been developed to treat several cancers including NSCLC. Due to the multi-component nature of SH003 with multiple targets and pathways, a network pharmacology study was conducted to analyze its active compounds, potential targets, and pathways for the treatment of NSCLC.

METHODS

We systematically identified oral active compounds within SH003, employing ADME criteria-based screening from TM-MC, OASIS, and TCMSP databases. Concurrently, SH003-related and NSCLC-associated targets were amalgamated from various databases. Overlapping targets were deemed anti-NSCLC entities of SH003. Protein-protein interaction networks were constructed using the STRING database, allowing the identification of pivotal proteins through node centrality measures. Empirical validation was pursued through LC-MS analysis of active compounds. Additionally, in vitro experiments, such as MTT cell viability assays and western blot analyses, were conducted to corroborate network pharmacology findings.

RESULTS

We discerned 20 oral active compounds within SH003 and identified 239 core targets shared between SH003 and NSCLC-related genes. Network analyses spotlighted 79 hub genes, including TP53, JUN, AKT1, STAT3, and MAPK3, crucial in NSCLC treatment. GO and KEGG analyses underscored SH003's multifaceted anti-NSCLC effects from a genetic perspective. Experimental validations verified SH003's impact on NSCLC cell viability and the downregulation of hub genes. LC-MS analysis confirmed the presence of four active compounds, namely hispidulin, luteolin, baicalein, and chrysoeriol, among the eight compounds with a median of > 10 degrees in the herb-compounds-targets network in SH003. Previously unidentified targets like CASP9, MAPK9, and MCL1 were unveiled, supported by existing NSCLC literature, enhancing the pivotal role of empirical validation in network pharmacology.

CONCLUSION

Our study pioneers the harmonization of theoretical predictions with practical validations. Empirical validation illuminates specific SH003 compounds within NSCLC, simultaneously uncovering novel targets for NSCLC treatment. This integrated strategy, accentuating empirical validation, establishes a paradigm for in-depth herbal medicine exploration. Furthermore, our network pharmacology study unveils fresh insights into SH003's multifaceted molecular mechanisms combating NSCLC. Through this approach, we delineate active compounds of SH003 and target pathways, reshaping our understanding of its therapeutic mechanisms in NSCLC treatment.

Identifying Key Genes and Functionally Enriched Pathways in Osteoporotic Patients by Weighted Gene Co-Expression Network Analysis

Osteoporosis is a systemic bone disease characterized by low bone mineral density and bone microstructure damage, resulting in increased bone fragility and fracture risk. The present study aimed to identify key genes and functionally enriched pathways in osteoporotic patients. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to microarray datasets of blood samples of osteoporotic patients from the Sao Paulo Ageing & Health [SPAH] study (26 osteoporotic samples and 31 normal samples) to construct co-expression networks and identify hub gene. The results showed that HDGF, AP2M1, DNAJC6, TMEM183B, MFSD2B, IGKV1-5, IGKV1-8, IGKV3-7, IGKV3D-11, and IGKV1D-42 are genes which were associated with the disease status of osteoporosis. Differentially expressed genes are enriched in proteasomal protein catabolic process, ubiquitin ligase complex, and ubiquitin-like protein transferase activity. Functional enrichment analysis demonstrated that genes in the tan module were enriched in immune-related functions, indicating that the immune system plays a critical role in osteoporosis. Validation assay demonstrated that the HDGF, AP2M1, TMEM183B, and MFSD2B levels were decreased in osteoporosis samples compared with healthy controls, while the levels of IGKV1-5, IGKV1-8, and IGKV1D-42 were increased in osteoporosis samples compared with healthy controls. In conclusion, our data identified and validated the association of HDGF, AP2M1, TMEM183B, MFSD2B, IGKV1-5, IGKV1-8, and IGKV1D-42 with osteoporosis in elderly women. These results suggest that these transcripts have potential clinical significance and may help to explain the molecular mechanisms and biological functions of osteoporosis.

Network pharmacology and molecular dynamic simulation integrated strategy for the screening of active components and mechanisms of phytochemicals from Datura innoxia on Alzheimer and cognitive decline

Alzheimer's disease (AD) ranks as the most prevalent neurodegenerative disorder with dementia and it accounts for more than 70% of all cases. Despite extensive reporting on the experimental investigation of Datura innoxia (DI) and its phytochemical components in the treatment of AD, the urgent need for elucidation of the principle of multi-mechanism and multi-level treatment of AD remains. In this research, molecular docking and network pharmacology were used to evaluate active compounds and molecular targets of DI for the treatment of AD. The phytochemical compounds of DI were obtained from the Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) as well as the Traditional Chinese Medicine System Pharmacology (TCMSP) databases. The screening includes the 28 most abundant components of DI and the Swiss Target Prediction database was used to predict targets of these compounds. The GeneCards database was used to collect AD-related genes. Both DI and AD targets were imported into a Venn diagram, and the 28 overlapped genes were identified as potential DI anti-AD targets. The results showed that Dinoxin B, Meteloidine, Scopoline, and Tropic acid had no effect on AD-related genes. Furthermore, the GO enrichment analysis indicates that DI influences molecular functions and biological processes such as learning or memory and modulation of chemical synaptic transmission as well as the membrane raft and membrane microdomain. The KEGG pathway analysis revealed that the key pathways implicated in DI's anti-AD actions include serotonergic synapse, IL-17 signaling pathway, and AGE-RAGE signaling pathway in diabetic complications. Based on the STRING and Cytoscape network-analysis platforms, the top ten anti-AD core targets include APP, CASP3, IL6, BACE1, IL1B, ACE, PSEN1, GAPDH, GSK3B and ACHE. The molecular docking and molecular dynamic simulation of the top two molecules against the top three target proteins confirmed the strong binding affinity and stability at the docked site. Overall, our findings pave the path for further research into the development and optimization of potential anti-AD agents from DI.Communicated by Ramaswamy H. Sarma.

Fecal Metagenomics to Identify Biomarkers of Food Intake in Healthy Adults: Findings from Randomized, Controlled, Nutrition Trials

BACKGROUND

Undigested components of the human diet affect the composition and function of the microorganisms present in the gastrointestinal tract. Techniques like metagenomic analyses allow researchers to study functional capacity, thus revealing the potential of using metagenomic data for developing objective biomarkers of food intake.

OBJECTIVES

As a continuation of our previous work using 16S and metabolomic datasets, we aimed to utilize a computationally intensive, multivariate, machine-learning approach to identify fecal KEGG (Kyoto encyclopedia of genes and genomes) Orthology (KO) categories as biomarkers that accurately classify food intake.

METHODS

Data were aggregated from 5 controlled feeding studies that studied the individual impact of almonds, avocados, broccoli, walnuts, barley, and oats on the adult gastrointestinal microbiota. Deoxyribonucleic acid from preintervention and postintervention fecal samples underwent shotgun genomic sequencing. After preprocessing, sequences were aligned and functionally annotated with Double Index AlignMent Of Next-generation sequencing Data v2.0.11.149 and MEtaGenome ANalyzer v6.12.2, respectively. After the count normalization, the log of the fold change ratio for resulting KOs between pre- and postintervention of the treatment group against its corresponding control was utilized to conduct differential abundance analysis. Differentially abundant KOs were used to train machine-learning models examining potential biomarkers in both single-food and multi-food models.

RESULTS

We identified differentially abundant KOs in the almond (n = 54), broccoli (n = 2474), and walnut (n = 732) groups (q < 0.20), which demonstrated classification accuracies of 80%, 87%, and 86% for the almond, broccoli, and walnut groups using a random forest model to classify food intake into each food group's respective treatment and control arms, respectively. The mixed-food random forest achieved 81% accuracy.

CONCLUSIONS

Our findings reveal promise in utilizing fecal metagenomics to objectively complement self-reported measures of food intake. Future research on various foods and dietary patterns will expand these exploratory analyses for eventual use in feeding study compliance and clinical settings.

Comparison of different drying technologies for walnut (Juglans regia L.) pellicles: Changes from phenolic composition, antioxidant activity to potential application

The analysis of the phenolic profile in the walnut pellicle (WP) and its exploitability can help to promote the valorization of the industrial waste from walnut production. Three forms of 33 monomeric phenols in WPs were quantified based on our previously established LC-MS/MS method. The levels of protocatechuic acid and 4-hydroxybenzoic acid in the WPs were the highest, exceeding 400 μg/g. Antioxidant tests revealed that all three phenolic forms of WPs were effective antioxidants (IC50: 2.12-35.05 µg/mL). The findings also revealed that drying temperature had a substantial type-dependent effect on phenolics and their antioxidant ability in WPs. KEGG enrichment analysis found that drying method has the greatest impact on WPs phenols in six metabolic pathways. Besides, 11 active substances in WPs were identified by a compound-targeted activity screening approach, indicating that WPs could be used as a natural antioxidant source in the development of medical and nutraceutical products.

Metabolite Profiling and Comparative Metabolomics Analysis of Jiaozhou Chinese Cabbage (Brassica rapa L. ssp. pekinensis) Planted in Different Areas

BACKGROUND

Chinese cabbage (Brassica rapa L. ssp. pekinensis) is one of the most popular vegetables in China because of its taste and health benefits. The area of production has obvious effects on the quality of Chinese cabbage. However, metabolite profiling and variations in different production areas are still unclear.

METHODS

Here, widely targeted metabolite analyses based on the ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) approach were performed to study the metabolite profiling of Chinese cabbage planted in the Jiaozhou and Jinan areas.

RESULTS

A total of 531 metabolites were detected, of which 529 were present in the Chinese cabbage from both areas, 108 were found to be chemicals related to Chinese traditional medicine, and 79 were found to correspond to at least one disease. Chinese cabbage is rich in nutritious substances such as lipids, phenolic acids, amino acids and derivatives, nucleotides and derivatives, organic acids, flavonoids, glucosinolates, saccharides, alcohols, and vitamins. Comparative analysis showed that the metabolic profiles differed between areas, and 89 differentially altered metabolites (DAMs) were characterized. Of these, 78 DAMs showed higher levels in Jinan Chinese cabbage, whereas 11 had higher levels in Jiaozhou Chinese cabbage. Two metabolites, S-(Methyl)glutathione and nicotinic acid adenine dinucleotide, were unique in Jiaozhou Chinese cabbage. Based on Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the DAMs were enriched into 23 pathways, of which tryptophan metabolism and thiamine metabolism were the significant enrichment pathways.

CONCLUSIONS

This study provides new insights into the metabolite profiles and production areas affecting the metabolite variations of Chinese cabbage, which will be useful for functional Chinese cabbage cultivation.

OMICs approaches and technologies for understanding low-high feed efficiency traits in chicken: implication to breeding

In poultry production, there has been a trend of continuous increase in cost of feed ingredients which represents the major proportion of the production costs. Feed costs can be reduced by improving feed efficiency traits which increase the possibility of using various indigestible feed sources and decrease the environmental impact of the enhanced poultry production. Therefore, feed efficiency has been used as one of the most important economic traits of selection in the breeding program of chickens. Recently, many OMICs experimental studies have been designed to characterize biological differences between the high and low feed efficiency chicken phenotypes. Biological complexity cannot be fully captured by main individual OMICs such as genomics, transcriptomics, proteomics and metabolomics. Therefore, researchers have combined multiple assays from the same set of samples to create multi-OMICs datasets. OMICs findings are crucial in improving existing approaches to poultry breeding. The current review aimed to highlight the components of feed efficiency and general OMICs approaches and technologies. Besides, individual and multi-OMICs based understanding of chicken feed efficiency traits and the application of the acquired knowledge in the chicken breeding program were addressed.

Enzyme Databases in the Era of Omics and Artificial Intelligence

Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.

Global transcriptomic analysis of breast cancer and normal mammary epithelial cells infected with Borrelia burgdorferi

The bacterial spirochete Borrelia burgdorferi, the causative agent of Lyme Disease, can disseminate and colonize various tissues and organs, orchestrating severe clinical symptoms including arthritis, carditis, and neuroborreliosis. Previous research has demonstrated that breast cancer tissues could provide an ideal habitat for diverse populations of bacteria, including B. burgdorferi, which is associated with a poor prognosis. Recently, we demonstrated that infection with B. burgdorferi enhances the invasion and migration of triple-negative MDA-MB-231 cells which represent a type of breast tumor with more aggressive cancer traits. In this study, we hypothesized that infection by B. burgdorferi affects the expression of cancer-associated genes to effectuate breast cancer phenotypes. We applied the high-throughput technique of RNA-sequencing on B. burgdorferi-infected MDA-MB-231 breast cancer and normal-like MCF10A cells to determine the most differentially expressed genes (DEG) upon infection. Overall, 142 DEGs were identified between uninfected and infected samples in MDA-MB-231 while 95 DEGs were found in MCF10A cells. A major trend of the upregulation of C-X-C and C-C motif chemokine family members as well as genes and pathways was associated with infection, inflammation, and cancer. These genes could serve as potential biomarkers for pathogen-related tumorigenesis and cancer progression which could lead to new therapeutic opportunities.

Benchmark Dataset for Training Machine Learning Models to Predict the Pathway Involvement of Metabolites

Metabolic pathways are a human-defined grouping of life sustaining biochemical reactions, metabolites being both the reactants and products of these reactions. But many public datasets include identified metabolites whose pathway involvement is unknown, hindering metabolic interpretation. To address these shortcomings, various machine learning models, including those trained on data from the Kyoto Encyclopedia of Genes and Genomes (KEGG), have been developed to predict the pathway involvement of metabolites based on their chemical descriptions; however, these prior models are based on old metabolite KEGG-based datasets, including one benchmark dataset that is invalid due to the presence of over 1500 duplicate entries. Therefore, we have developed a new benchmark dataset derived from the KEGG following optimal standards of scientific computational reproducibility and including all source code needed to update the benchmark dataset as KEGG changes. We have used this new benchmark dataset with our atom coloring methodology to develop and compare the performance of Random Forest, XGBoost, and multilayer perceptron with autoencoder models generated from our new benchmark dataset. Best overall weighted average performance across 1000 unique folds was an F1 score of 0.8180 and a Matthews correlation coefficient of 0.7933, which was provided by XGBoost binary classification models for 11 KEGG-defined pathway categories.

Network pharmacology based virtual screening of Flavonoids from Dodonea angustifolia and the molecular mechanism against inflammation

Inflammation is a nonspecific immune response against injury caused by a harmful agent that strives to restore tissue function and homeostasis. Dodonaea angustifolia L.f. (Sapindaceae) is a medium-sized shrub used to treat a variety of diseases in traditional medicine. In the current study, integrated network-pharmacology and molecular docking approaches were used to identify the active constituents, their possible targets, signaling pathways, and anti-inflammatory effects of flavonoids from D.angustifolia. D. angustifolia active ingredients were acquired from the Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT), and Traditional Chinese Medicine System Pharmacology (TCMSP) databases. The screening included the ten most prevalent D. angustifolia components, and the SwissTargetPrediction database was utilized to anticipate the targets of these compounds. Anti-inflammatory genes were found using the GeneCards database. The 175 overlapping genes were discovered as prospective D. angustifolia anti-inflammatory targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that the overlapped targets were closely related to the major pathogenic processes linked to inflammation, such as response to organonitrogen compound, protein kinase activity, phosphotransferase activity, pI3k-Akt signaling pathway, metabolic pathways, and chemical carcinogenesis. Compound-target-pathway, and protein-protein interaction networks revealed 6-Methoxykaempferol and 5-Hydroxy-7,8 dimethoxyflavone as key compounds, and AKT1, VEGFA, and EGFR as key targets. Furthermore, molecular docking followed by molecular dynamic (MD) simulation of D. angustifolia active ingredients with core proteins fully complemented the binding affinity of these compounds and indicated stable complexes at the docked site. These findings reveal D. angustifolia 's multi-target, multi-compound, and multi-pathway strategies against inflammation. Our study paved the way for further research into the mechanism for developing D. angustifolia -based natural products as alternative therapies for inflammation.

Cellular signaling modulated by miRNA-3652 in ovarian cancer: unveiling mechanistic pathways for future therapeutic strategies

MicroRNAs (miRNAs) are small non-coding RNA molecules that play pivotal roles in regulating gene expression and have been implicated in the pathogenesis of numerous cancers. miRNA-3652, though relatively less explored, has recently emerged as a potential key player in ovarian cancer's molecular landscape. This review aims to delineate the functional significance and tumor progression role of miRNA-3652 in ovarian cancer, shedding light on its potential as both a diagnostic biomarker and therapeutic target. A comprehensive literature search was carried out using established databases, the focus was on articles that reported the role of miRNA-3652 in ovarian cancer, encompassing mechanistic insights, functional studies, and its association with clinical outcomes. This updated review highlighted that miRNA-3652 is intricately involved in ovarian cancer cell proliferation, migration, and invasion, its dysregulation was linked to altered expression of critical genes involved in tumor growth and metastasis; furthermore, miRNA-3652 expression levels were found to correlate with clinical stages, prognosis, and response to therapy in ovarian cancer patients. miRNA-3652 holds significant promise as a vital molecular player in ovarian cancer's pathophysiology. Its functional role and impact on tumor progression make it a potential candidate for diagnostic and therapeutic applications in ovarian cancer. Given the pivotal role of miRNA-3652 in ovarian cancer, future studies should emphasize in-depth mechanistic explorations, utilizing advanced genomic and proteomic tools. Collaboration between basic scientists and clinicians will be vital to translating these findings into innovative diagnostic and therapeutic strategies, ultimately benefiting ovarian cancer patients. Video Abstract.

Bioinformatic Analysis to Identify and Cellular Experiments to Validate Autophagy-related Genes in Psoriasis

PURPOSE

To explore differentially expressed genes (DEGs) associated with autophagy in psoriasis using bioinformatics analysis and verify them in an M5-induced psoriatic cell model.

METHODS

We obtained gene expression microarray data from patients with psoriasis and normal skin tissues from the dataset GSE78097 of the NCBI Gene Expression Omnibus (GEO) database. R software was used to identify DEGs associated with autophagy in psoriasis. Proteinprotein interaction (PPI) and correlation analyses were used to show interactions between certain genes. Their potential biological roles were determined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, all the DEGs associated with autophagy in psoriasis were validated in a psoriatic cell model by RT-qPCR.

RESULTS

28 DEGs associated with autophagy were identified. These genes were linked to one another, and the most connected hub gene was VEGFA, according to PPI analysis. GO and KEGG enrichment analyses revealed various biological pathways associated with autophagy. The RT-qPCR findings of the expression of 18 genes in the psoriatic cell model confirmed the bioinformatics analysis results. The five genes with the most significant differences were IL24, CCL2, NAMPT, PPP1R15A, and SPHK1.

CONCLUSION

We identified DEGs associated with autophagy in patients with psoriasis. IL24, CCL2, NAMPT, PPP1R15A, and SPHK1 were identified as important genes that may influence psoriasis development through the regulation of autophagy.

Effect of different size microplastic particles on the construction of algal-bacterial biofilms and microbial communities

Algal-bacterial symbiotic system is a biological purification system that combines sewage treatment with resource utilization and has the dual effects of carbon sequestration and pollution reduction. In this study, an immobilized algal-bacterial biofilm system was constructed for the treatment of natural sewage. Effects of exposure to microplastics (MPs) with different particle diameters (0.065 μm, 0.5 μm and 5 μm) were determined in terms of algal biomass recovery efficiency, the composition of extracellular polymeric substances (EPS) and morphologic characteristics. The impacts of MPs on the bacterial diversity and community structure of biofilms were also examined. The metagenomic analysis of key microorganisms and related metabolism pathways involved in system was further investigated. Results showed that following exposure to 5 μm MP, a maximum algal recovery efficiency of 80% was achieved, with a minimum PSII primary light energy conversion efficiency (Fv/Fm ratio) of 0.513. Furthermore, 5 μm MP caused the highest level of damage to the algal-bacterial biofilm, enhancing the secretion of protein-rich EPS. The biofilm morphology became rough and loose following exposure to 0.5 μm and 5 μm MP. Community diversity and richness were significantly high in biofilms exposed to 5 μm MP. Proteobacteria (15.3-24.1%), Firmicutes (5.0-7.8%) and Actinobacteria (4.2-4.9%) were dominant in all groups, with exposure to 5 μm MP resulting in the highest relative abundance for these species. The addition of MPs promoted the related metabolic functions while inhibited the degradation of harmful substances by algal-bacterial biofilms. The findings have environmental significance for the practical application of algal-bacterial biofilms for sewage treatment, providing novel insights into the potential effects of MPs on immobilized algal-bacterial biofilm systems.

Gibbs Energy and Gene Expression Combined as a New Technique for Selecting Drug Targets for Inhibiting Specific Protein-Protein Interactions

One of the most important aspects of successful cancer therapy is the identification of a target protein for inhibition interaction. Conventionally, this consists of screening a panel of genes to assess which is mutated and then developing a small molecule to inhibit the interaction of two proteins or to simply inhibit a specific protein from all interactions. In previous work, we have proposed computational methods that analyze protein-protein networks using both topological approaches and thermodynamic quantification provided by Gibbs free energy. In order to make these approaches both easier to implement and free of arbitrary topological filtration criteria, in the present paper, we propose a modification of the topological-thermodynamic analysis, which focuses on the selection of the most thermodynamically stable proteins and their subnetwork interaction partners with the highest expression levels. We illustrate the implementation of the new approach with two specific cases, glioblastoma (glioma brain tumors) and chronic lymphatic leukoma (CLL), based on the publicly available patient-derived datasets. We also discuss how this can be used in clinical practice in connection with the availability of approved and investigational drugs.

Underlying Mechanisms of Bergenia spp. to Treat Hepatocellular Carcinoma Using an Integrated Network Pharmacology and Molecular Docking Approach

Hepatocellular carcinoma (HCC) is the fifth most common and fatal cancer reported, representing 72.5% of malignancies around the world. The majority of HCC incidents have been associated with infections caused by hepatitis B and C viruses. Many first- and second-line conventional drugs, e.g., sorafenib, cabozantinib, or ramucirumab, have been used for the management of HCC. Despite different combinational therapies, there are still no defined biomarkers for an early stage diagnosis of HCC. The current study evaluated the potential of Bergenia stracheyi, Bergenia ciliata, Bergenia pacumbis, and Bergenia purpurascens, which belong to the family Saxifragaceae, to treat HCC using an integrated network pharmacology and molecular docking approach. Four active phytochemicals were selected based on oral bioavailability (OB) and drug likeness (DL) parameters. The criteria of phytochemical selection were set to OB > 30% and DL > 0.18. Similarly, the gene targets related to Bergenia spp. and the genes related to HCC were retrieved from different databases. The integration of these genes revealed 98 most common overlapping genes, which were mainly interrelated with HCC pathogenesis. Ultimately, the 98 Bergenia-HCC associated genes were used for protein-protein interaction (PPI), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Ontology (GO) enrichment analyses. Finally, the topological analysis revealed the top ten hub genes with maximum degree rank. From the top ten genes, STAT3, MAPK3, and SRC were selected due to their involvement in GO annotation and KEGG pathway. To confirm the network pharmacology results, molecular docking analysis was performed to target STAT3, MAPK3, and SRC receptor proteins. The phytochemical (+)-catechin 3-gallate exhibited a maximum binding score and strong residue interactions with the active amino acids of MAPK3-binding pockets (S-score: -10.2 kcal/mol), SRC (S-score: -8.9 kcal/mol), and STAT3 (S-score: -8.9 kcal/mol) as receptor proteins. (+)-Catechin 3-gallate and β-sitosterol induced a significant reduction in cell viability in HepG2 after 24 h of treatment in a dose-dependent manner. The results of this study explore the potential of (+)-catechin 3-gallate and β-sitosterol, which can be used in the future as potential drug candidates to suppress HCC.

Statins as a Potential Treatment for Non-Alcoholic Fatty Liver Disease: Target Deconvolution using Protein-protein Interaction Network Analysis

BACKGROUND

The hallmark of non-alcoholic fatty liver disease (NAFLD) is aberrant buildup of triglycerides (TGs) in hepatocytes. Many genes promote NAFLD development. Using bioinformatics tools, we investigated the possible effect of statins on genes involved in NAFLD progression.

METHODS

Protein interactions of statins and NAFLD were searched in gene-drug and gene-disease databases. A Protein-Protein interaction (PPI) network was constructed to find hub genes and Molecular Complex Detection (MCODE) of NAFLD-related genes. Shared protein targets between protein targets of statins and NAFLD-associated genes were identified. Next, targets of each statin were assayed with all modular clusters in the MCODEs related to NAFLD. Biological process and pathway enrichment analysis for shared proteins was performed.

RESULTS

Screening protein targets for conventional statins and curated NAFLD-related genes identified 343 protein targets and 70 genes, respectively. A Venn diagram of NAFLD-related genes and protein targets of statins showed 24 shared proteins. The biological pathways on KEGG enrichment associated with the 24 shared protein sets were evaluated and included cytokine-cytokine receptor interaction, adipocytokine, PPAR, TNF and AMPK signaling pathways. Gene Ontology analysis showed major involvement in lipid metabolic process regulation and inflammatory response. PPI network analysis of 70 protein targets indicated 13 hub genes (PPARA, IL4, CAT, LEP, SREBF1, PRKCA, CYP2E1, NFE2L2, PTEN, NR1H4, ADIPOQ, GSTP1 and TGFB1). Comparing all seven statins with the three MCODE clusterings and 13 hub genes revealed that simvastatin as the most associated statin with NAFLD.

CONCLUSIONS

Simvastatin has the most impact on NAFLD-related genes versus other statins.

Analysis of metabolites of bactrain camel milk in Alxa of China before and after fermentation with fermenting agent TR1 based on untargeted LC-MS/MS based metabolomics

Camel milk produces many beneficial functional compounds and affects body health through metabolism. The differential metabolites of bactrain camel milk in Alxa before and after fermentation were identified by liquid chromatography-tandem mass spectrometry based metabolomics (LC-MS/MS). The differential metabolite pathway types were also identified in this paper. We obtained the following results that 148 and 82 differential metabolites were detected in positive and negative ion mode respectively, 85 differential metabolites were shown a significant upward trend and 63 with downward trend after fermentation in positive ion mode. Meanwhile, 32 differential metabolites characterized upward trend and 50 characterized downward trend in negative ion mode. The differential metabolites were mainly organic acids, amino acids, esters, vitamins and other substances contained in camel milk. Among them, most up-regulated substances had the functions of lowering blood pressure, lowering blood sugar, treatment of inflammation, antibiosis and other effects. Many harmful substances were significantly down-regulated after camel milk fermentation. However, there were also some metabolites whose prebiotic functions have been weakened by camel milk fermentation, which may provide reference values for healthcare function, exploitation and application of camel milk.

High-throughput transcriptome sequencing reveals the protective role of adenosine receptor-related genes in paraquat-exposed Caenorhabditis elegans

This study sought to identify the genes associated with adenosine's protective action against paraquat (PQ)-induced oxidative stress via the adenosine receptor (ADOR-1) in Caenorhabditis elegans (C. elegans). The C. elegans was divided into 3 groups-2 groups exposed to PQ, one in presence, and one in absence of adenosine-and a control group that was not treated. Each group's total RNA was extracted and sequenced. When the transcriptomes of these groups were analyzed, several genes were found to be differently expressed. These differentially expressed genes were significantly enriched in adenosine-response biological processes and pathways, including gene ontology terms related to neuropeptide and kyoto encyclopedia of genes and genomes pathways associated to cAMP pathway regulator activity. Quantitative reverse-transcription PCR confirmed that G-protein-coupled receptors signaling pathway involving dop-1, egl-30, unc-13, kin-1, and goa-1 genes may play crucial roles in modulating adenosine's protective action. Interestingly, there are no significant variations in the expression of the ador-1 gene across the 3 treatments, thereby indicating that adenosine receptor exerts a consistent and stable influence on its related pathways irrespective of the presence or absence of PQ. Furthermore, the wild-type group with ador-1 gene has higher survival rate than that of the ador-1-/RNA interference group while treated with PQ in the presence of adenosine. Conclusively, our study uncovered a number of novel PQ-response genes and adenosine receptor-related genes in C. elegans, which may function as major regulators of PQ-induced oxidative stress and indicate the possible protective effects of adenosine.

Analysis of ceRNA Regulatory Mechanism of Rape Pollen Allergy Based on Whole-Transcriptome Sequencing of Peripheral Blood Mononuclear Cells

Background

Rape pollen allergy is a common allergic reaction disorder that affects the health and life of patients seriously. The research on ceRNA regulatory network in rape pollen allergy is poor.

Methods

High throughput whole-transcriptome sequencing was conducted on rape pollen allergic samples and non-allergic samples. Differentially expressed microRNAs (DEmiRNAs), circRNAs (DEcircRNAs), long non-coding RNA (DElncRNAs), mRNA (DEmRNAs) were identified and a ceRNA regulatory network was constructed by Cytoscape. Functional enrichment analyses were performed on DEmRNAs in the ceRNA network. Then, the least absolute shrinkage and selection operator (LASSO) regression model was used to identify characteristic genes for rape pollen allergy. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic ability of characteristic genes.

Results

A total of 25 DEmiRNAs, 258 DEcircRNAs, 304 DElncRNAs, and 383 DEmRNAs in the allergic group compared with the non-allergic group were uncovered, respectively. A ceRNA network containing 21 miRNAs, 57 circRNAs, 28 lncRNAs, and 33 mRNAs was generated with 139 nodes and 160 edges. The signal transduction-related processes, immune-related processes, the ion, inorganic substance, and hormone regulation processes were associated with mRNAs in the ceRNA network. The results of pathway enrichment illustrated that mRNAs in the ceRNA were significantly linked to IL-17 signaling pathway, inflammatory mediator regulation of trp channels, GMP-PKG signaling pathway, signaling by GPCR, and GPCR downstream signaling pathway. Then, five characteristic genes (KCNQ3, CCR5, FOSB, CFAP43, and PRKG1) were defined by the LASSO algorithm. The AUC values of these genes indicated that these genes had a powerful discrimination ability in discriminating allergic samples from non-allergic samples.

Conclusion

Taken together, we revealed the ceRNA regulatory network in rape pollen allergy and excavated five characteristic genes (KCNQ3, CCR5, FOSB, CFAP43, and PRKG1) with the diagnostic value that may be a potential target in diagnosis and treatment.

The Extracellular Vesicle Citrullinome and Signature in a Piglet Model of Neonatal Seizures

Neonatal seizures are commonly associated with acute perinatal brain injury, while understanding regarding the downstream molecular pathways related to seizures remains unclear. Furthermore, effective treatment and reliable biomarkers are still lacking. Post-translational modifications can contribute to changes in protein function, and post-translational citrullination, which is caused by modification of arginine to citrulline via the calcium-mediated activation of the peptidylarginine deiminase (PAD) enzyme family, is being increasingly linked to neurological injury. Extracellular vesicles (EVs) are lipid-bilayer structures released from cells; they can be isolated from most body fluids and act as potential liquid biomarkers for disease conditions and response to treatment. As EVs carry a range of genetic and protein cargo that can be characteristic of pathological processes, the current study assessed modified citrullinated protein cargo in EVs isolated from plasma and CSF in a piglet neonatal seizure model, also following phenobarbitone treatment. Our findings provide novel insights into roles for PAD-mediated changes on EV signatures in neonatal seizures and highlight the potential of plasma- and CSF-EVs to monitor responses to treatment.

Investigating possible dilated cardiomyopathy targets via bioinformatic analysis

Dilated cardiomyopathy (DCM) is the most common cardiomyopathy associated with heart failure; however, the underlying mechanism remains unclear. Initially, gene expression data of patients with DCM from the GSE4172 and GSE21610 datasets were obtained from the Gene Expression Omnibus website. Differentially expressed genes (DEGs) were analyzed with a false discovery rate < 0.05 and log2 fold change > 1.2. Furthermore, both the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA) were used to investigate the functional annotations. STRING and Cytoscape tools were used to form the protein-protein interaction (PPI) network and authenticate hub genes. Thereafter, the signature of immune-related genes (IRGs) was selected from the DEGs and data via the IMMPORT website. Hub genes were selected from the differentially expressed IRGs that formed the PPI network. Finally, the receiver-operating characteristic curves of the key genes were measured as biomarkers of DCM. A total of 173 independent DEGs (103 upregulated and 70 downregulated genes) were found in the microarray datasets GSE4172 and GSE21610. KEGG analysis and GSEA indicated that the BMP signaling pathway and apoptosis-related signals have a key effect on DCM development. The 10 hub genes also indicated the key effect of the BMP signaling pathway on DCM. A total of 224 differentially expressed IRGs and 20 featured IRGs were identified. Finally, BMP6, CD69, RUNX2, and SPP1 were identified as possible targets for DCM. Our data suggest a possible molecular regulatory mechanism for DCM therapy. Moreover, BMP6, CD69, RUNX2, and SPP1 may have key effects on the development of DCM.

Effects of upward flow rate and modified biochar location on the performance and microecology of an anaerobic reactor treating kitchen waste

Increasing the value of food waste through anaerobic digestion is an attractive strategy. Meanwhile, the anaerobic digestion of kitchen waste also faces some technical challenges. In this study, four EGSB reactors were equipped with Fe-Mg-chitosan bagasse biochar at different locations, and the reflux pump flow rate was increased to change the upward flow rate of the reactor. The effects of adding modified biochar at different locations under different upward flow rate on the efficacy and microecology of anaerobic reactors treating kitchen waste were investigated. Results showed that Chloroflexi was the dominant microorganism when the modified biochar was added to the lower, middle, and upper parts of the reactor and mixed in the reactor, accounting for 54%, 56%, 58%, and 47%, respectively, on day 45. With the increased upward flow rate, the abundance of Bacteroidetes and Chloroflexi increased, while Proteobacteria and Firmicutes decreased. It was worth noting that the best COD removal effect was achieved when the anaerobic reactor upward flow rate was v2 = 0.6 m/h and the modified biochar was added in the upper part of the reactor, during which the average COD removal rate reached 96%. In addition, mixing modified biochar throughout the reactor while increasing the upward flow rate provided the greatest stimulus for the secretion of tryptophan and aromatic proteins in the sludge extracellular polymeric substances. The results provided a certain technical reference for improving the efficiency of anaerobic digestion of kitchen waste and scientific support for the application of modified biochar to the anaerobic digestion process.

Dynamic changes of serum metabolite profiling in septic mice based on high performance liquid chromatography of quadrupole time of flight mass spectrometry analysis

The objective of this study is to gain insights into the underlying metabolic transformations that occurred during the whole progression of cecal ligation and puncture (CLP)-induced sepsis, thus providing new targets for its treatment. High-performance liquid chromatography of quadrupole time of flight mass spectrometry (HPLC-Q-TOF-MS/MS) combined with multivariate statistical techniques was used to detect the s in serum from septic mice. Fifty male mice were divided into two groups, including the sham group (n = 7) and the CLP-induced sepsis group (n = 43). Animals were sacrificed at 1, 3, 5, and 7 days post-CLP and then serum were collected for metabolomic analysis. Multivariate regression analysis was carried out through MetaboAnalyst 5.0, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), to identify the s and screen out the related differential metabolites. Besides, the KEGG pathway analysis was used to analyze the related metabolic pathways in which the identified metabolites were involved. Based on the fold change (FC > 2.0 or <0.5), variable important in projection (VIP > 1.2), and P value (P < 0.05), we found 26, 17, 21, and 17 metabolites in septic mice at 1, 3, 5, and 7 days post-CLP, respectively, compared with that of the sham group. The PCA and PLS-DA pattern recognition showed a cluster-type distribution between the sham group and the CLP group. Dysregulated amino acid metabolism, as well as disturbed nucleotide metabolism, is observed. Several important metabolic pathways were identified between the sham group and the CLP group. Among them, phenylalanine metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis showed striking at day 1 post-CLP. At day 3, phenylalanine, tyrosine, and tryptophan biosynthesis changed significantly. However, as the disease process, only pyrimidine metabolism showed the most significant alternation, compared to the sham group. Several differential metabolites were identified in the CLP group compared with that of the sham group and they were presented with dynamic alternation at different time points post-CLP, indicating metabolic disturbance occurred throughout the whole sepsis progression.

Assessment of lipid metabolism-disrupting effects of non-phthalate plasticizer diisobutyl adipate through in silico and in vitro approaches

Diisobutyl adipate (DIBA), as a novel non-phthalate plasticizer, is widely used in various products. However, little effort has been made to investigate whether DIBA might have adverse effects on human health. In this study, we integrated an in silico and in vitro strategy to assess the impact of DIBA on cellular homeostasis. Since numerous plasticizers could activate peroxisome proliferator-activated receptor γ (PPARγ) pathway to interrupt metabolism systems, we first utilized molecular docking to analyze interaction between DIBA and PPARγ. Results indicated that DIBA had strong affinity with the ligand-binding domain of PPARγ (PPARγ-LBD) at Histidine 499. Afterwards, we used cellular models to investigate in vitro effects of DIBA. Results demonstrated that DIBA exposure increased intracellular lipid content in murine and human hepatocytes, and altered transcriptional expression of genes related to PPARγ signaling and lipid metabolism pathways. At last, target genes regulated by DIBA were predicted and enriched for Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Protein-protein interaction (PPI) network and transcriptional factors (TFs)-genes network were established accordingly. Target genes were enriched in Phospholipase D signaling pathway, phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) and Epidermal growth factor receptor (EGFR) signaling pathway which were related to lipid metabolism. These findings suggested that DIBA exposure might disturb intracellular lipid metabolism homeostasis via targeting PPARγ. This study also demonstrated that this integrated in silico and in vitro methodology could be utilized as a high throughput, cost-saving and effective tool to assess the potential risk of various environmental chemicals on human health.

Insights into autotrophic carbon fixation strategies through metagonomics in the sediments of seagrass beds

Seagrass beds contributes up to 10% ocean carbon storage. Carbon fixation in seagrass bed greatly affect global carbon cycle. Currently, six carbon fixation pathways are widely studied: Calvin, reductive tricarboxylic acid (rTCA), Wood-Ljungdahl (WL), 3-hydroxypropionate (3HP), 3-hydroxypropionate/4-hydroxybutyrate (3HP/4HB) and dicarboxylate/4-hydroxybutyrate (DC/4-HB). Despite the knowledges about carbon fixation increase, the carbon fixation strategies in seagrass bed sediment remain unexplored. We collected seagrass bed sediment samples from three sites with different characteristics in Weihai, a city in Shandong, China. The carbon fixation strategies were investigated through metagenomics. The results exhibited that five pathways were present, of which Calvin and WL were the most dominant. The community structure of microorganisms containing the key genes of these pathways were further analyzed, and those dominant microorganisms with carbon fixing potential were revealed. Phosphorus significantly negatively corelated with those microorganisms. This study provides an insight into the strategies of carbon fixation in seagrass bed sediments.

Integrated bioinformatics analysis for exploring hub genes and related mechanisms affecting the progression of gastric cancer

Objective Gastric cancer (GC) is a high-risk tumor disease worldwide. The goal of the current study was to explore new diagnostic and prognostic indicators for gastric cancer. Methods Database GSE19826 and GSE103236 were gained from the Gene Expression Omnibus (GEO) to screen for differentially expressed genes (DEGs), which were then grouped together as co-DEGs. GO and KEGG pathway analysis were used to investigate the function of these genes. The protein-protein interaction (PPI) network of DEGs was constructed by STRING. Results GSE19826 selected 493 DEGs in GC and gastric normal tissues, including 139 up-regulated genes and 354 down-regulated genes. A total of 478 DEGs were selected by GSE103236, including 276 up-regulated genes and 202 downregulated genes. 32 co-DEGs were overlapped from two databasesand involved in digestion, regulation of response to wounding, wound healing, potassium ion imports across plasma membrane, regulation of wound healing, anatomical structure homeostasis, and tissue homeostasis. KEGG analysis revealed that co-DEGs were mainly involved in ECM-receptor interaction, tight junction, protein digestion and absorption, gastric acid secretion and cell adhesion molecules. Twelve hub genes were screened by Cytoscape, including cholecystokinin B receptor (CCKBR), Collagen type I alpha 1 (COL1A1), COL1A2, COL2A1, COL6A3, COL11A1, matrix metallopeptidase 1 (MMP1), MMP3, MMP7, MMP10, tissue inhibitor of matrix metalloprotease 1 (TIMP1) and secreted phosphoprotein 1 (SPP1). Conclusions Twelve key genes affecting the progression of gastric cancer were obtained by bioinformatics, which may be potential biomarkers for the diagnosis and prognosis of GC.

Genome-Wide Tissue-Specific Genes Identification for Novel Tissue-Specific Promoters Discovery in Soybean

Promoters play a crucial role in controlling the spatial and temporal expression of genes at transcriptional levels in the process of higher plant growth and development. The spatial, efficient, and correct regulation of exogenous genes expression, as desired, is the key point in plant genetic engineering research. Constitutive promoters widely used in plant genetic transformation are limited because, sometimes, they may cause potential negative effects. This issue can be solved, to a certain extent, by using tissue-specific promoters. Compared with constitutive promoters, a few tissue-specific promoters have been isolated and applied. In this study, based on the transcriptome data, a total of 288 tissue-specific genes were collected, expressed in seven tissues, including the leaves, stems, flowers, pods, seeds, roots, and nodules of soybean (Glycine max). KEGG pathway enrichment analysis was carried out, and 52 metabolites were annotated. A total of 12 tissue-specific genes were selected via the transcription expression level and validated through real-time quantitative PCR, of which 10 genes showed tissue-specific expression. The 3-kb 5' upstream regions of ten genes were obtained as putative promoters. Further analysis showed that all the 10 promoters contained many tissue-specific cis-elements. These results demonstrate that high-throughput transcriptional data can be used as effective tools, providing a guide for high-throughput novel tissue-specific promoter discovery.

Functional annotation of rhizospheric phageome of the wild plant species Moringa oleifera

Introduction

The study aims to describe phageome of soil rhizosphere of M.oleifera in terms of the genes encoding CAZymes and other KEGG enzymes.

Methods

Genes of the rhizospheric virome of the wild plant species Moringa oleifera were investigated for their ability to encode useful CAZymes and other KEGG (Kyoto Encyclopedia of Genes and Genomes) enzymes and to resist antibiotic resistance genes (ARGs) in the soil.

Results

Abundance of these genes was higher in the rhizospheric microbiome than in the bulk soil. Detected viral families include the plant viral family Potyviridae as well as the tailed bacteriophages of class Caudoviricetes that are mainly associated with bacterial genera Pseudomonas, Streptomyces and Mycobacterium. Viral CAZymes in this soil mainly belong to glycoside hydrolase (GH) families GH43 and GH23. Some of these CAZymes participate in a KEGG pathway with actions included debranching and degradation of hemicellulose. Other actions include biosynthesizing biopolymer of the bacterial cell wall and the layered cell wall structure of peptidoglycan. Other CAZymes promote plant physiological activities such as cell-cell recognition, embryogenesis and programmed cell death (PCD). Enzymes of other pathways help reduce the level of soil H₂O₂ and participate in the biosynthesis of glycine, malate, isoprenoids, as well as isoprene that protects plant from heat stress. Other enzymes act in promoting both the permeability of bacterial peroxisome membrane and carbon fixation in plants. Some enzymes participate in a balanced supply of dNTPs, successful DNA replication and mismatch repair during bacterial cell division. They also catalyze the release of signal peptides from bacterial membrane prolipoproteins. Phages with the most highly abundant antibiotic resistance genes (ARGs) transduce species of bacterial genera Pseudomonas, Streptomyces, and Mycobacterium. Abundant mechanisms of antibiotic resistance in the rhizosphere include "antibiotic efflux pump" for ARGs soxR, OleC, and MuxB, "antibiotic target alteration" for parY mutant, and "antibiotic inactivation" for arr-1.

Discussion

These ARGs can act synergistically to inhibit several antibiotics including tetracycline, penam, cephalosporin, rifamycins, aminocoumarin, and oleandomycin. The study highlighted the issue of horizontal transfer of ARGs to clinical isolates and human gut microbiome.

Integrated analysis of the lncRNA-associated ceRNA network in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease that worsens with age. Long non-coding RNAs (lncRNAs) dysregulation and its associated competing endogenous RNA (ceRNA) network have a potential connection with the occurrence and development of AD. A total of 358 differentially expressed genes (DEGs) were screened via RNA sequencing, including 302 differentially expressed mRNAs (DEmRNAs) and 56 differential expressed lncRNAs (DElncRNAs). Anti-sense lncRNA is the main type of DElncRNA, which plays a major role in the cis and trans regulation. The constructed ceRNA network consisted of 4 lncRNAs (NEAT1, LINC00365, FBXL19-AS1, RAI1-AS1719) and 4 microRNAs (miRNAs) (HSA-Mir-27a-3p, HSA-Mir-20b-5p, HSA-Mir-17-5p, HSA-Mir-125b-5p), and 2 mRNAs (MKNK2, F3). Functional enrichment analysis revealed that DEmRNAs are involved in related biological functions of AD. The co-expressed DEmRNAs (DNAH11, HGFAC, TJP3, TAC1, SPTSSB, SOWAHB, RGS4, ADCYAP1) of humans and mice were screened and verified by real-time quantitative polymerase chain reaction (qRT-PCR). In this study, we analyzed the expression profile of human AD-related lncRNA genes, constructed a ceRNA network, and performed functional enrichment analysis of DEmRNAs between human and mice. The obtained gene regulatory networks and target genes can be used to further analyze AD-related pathological mechanisms to optimize AD diagnosis and treatment.

De novo transcriptome analysis and identification of defensive genes in garlic (Allium sativum L.) using high-throughput sequencing

BACKGROUND

Garlic (Allium sativum L.) is the second most widely cultivated Allium which is mainly grown in temperate regions and used as a flavoring agent in a wide variety of foods. Garlic contains various bioactive compounds whose metabolic pathways, plant-pathogen interactions, defensive genes, identify interaction networks, and functional genomics were not previously predicted in the garlic at the genomic level. To address this issue, we constructed two garlic Illumina 2000 libraries from tissues of garlic clove and leaf.

RESULTS

Approximately 43 million 125 bp paired-end reads were obtained in the two libraries. A total of 239,973 contigs were generated by de novo assembly of both samples and were compared with the sequences in the NCBI non-redundant protein database (Nr). In total, 42% of contigs were matched to known proteins in public databases including Nr, Gene Ontology (GO), and Cluster Orthologous Gene Database (COG), and then, contigs were mapped to 138 via functional annotation against the Kyoto Encyclopedia of Genes and Genomes pathway database (KEGG). In addition, a number of regulatory genes including the CCHC (Zn) family, followed by WD40, bromodomain, bZIP, AP2-EREBP, BED-type (Zn) proteins, and defense response proteins related to different conserved domains, such as RGA3, NBS-LRR, TIR-NBS-LRR, LRR, NBS-ARC, and CC-NBS-LRR were discovered based on the transcriptome dataset. We compared the ortholog gene family of the A. sativum transcriptome to A. thaliana, O. sativa, and Z. mays and found that 12,077 orthologous gene families are specific to A. sativum L. Furthermore, we identified genes involved in plant defense mechanisms, their protein-protein interaction network, and plant-pathogen interaction pathways.

CONCLUSIONS

Our study contains an extensive sequencing and functional gene-annotation analysis of A. sativum L. The findings provide insights into the molecular basis of TFs, defensive genes, and a reference for future studies on the genetics and breeding of A. sativum L.

Combining in silico and in vitro approaches to understand the involvement of MEP and shikimate pathway in Agrobacterium tumefaciens for enhanced CoQ10 production

AIMS

To carry out integrated comparative metabolic pathway analysis to understand Coenzyme Q10 (CoQ10) production in Agrobacterium tumefaciens.

METHODS AND RESULTS

Comparative analysis of the CoQ10 metabolic pathways in 10 organisms using a G2KO program and KEGG database shed light on the completeness of the production pathway in A. tumefaciens. The specific role of the key precursors and the enzymes in the metabolic network was further validated using pathway inhibitors and enhancers. While the use of fosmidomycin and glyphosate displayed 54.54%-99% suppression in CoQ10 production, supplementation of polyprenyl pyrophosphate from the MEP (methylerythritol 4-phosphate) pathway and 4-hydroxybenzoate precursor from the shikimate pathway, enhanced CoQ10 production by 2.3-fold.

CONCLUSIONS

The present study provides a comprehensive understanding of the CoQ10 biosynthetic pathway in A. tumefaciens, which would assist rational metabolic engineering strategies for boosting CoQ10 biosynthesis.

Configuration of gut bacterial community profile and their potential functionality in the digestive tract of the wild and cultivated Indonesian shortfin elver-phase eels (Anguilla bicolor bicolor McClelland, 1844)

This study aimed to explore the bacteria present in the digestive tracts of wild and cultivated Indonesian shortfin eel during the elver phase. The eel has high export potential due to its vitamin and micronutrient content, but slow growth and vulnerability to collapse in farm conditions hinder its cultivation. The microbiota in the eel's digestive tract is crucial for its health, particularly during the elver phase. This study used Next Generation Sequencing to analyze the community structure and diversity of bacteria in the eels' digestive tracts, focusing on the V3-V4 regions of the 16S rRNA gene. Mothur software was used for data analysis and PAST v.3.26 was used to calculate alpha diversity. The results showed that Proteobacteria (64.18%) and Firmicutes (33.55%) were the predominant phyla in the digestive tract of cultivated eels, while Bacteroidetes (54.16%), Firmicutes (14.71%), and Fusobacteria (10.56%) were predominant in wild eels. The most prevalent genera in cultivated and wild elver were Plesiomonas and Cetobacterium, respectively. The microbiota in the digestive tract of cultivated eels was diverse despite uneven distribution. The KEGG database analysis revealed that the primary function of the microbiome was to facilitate the eel's absorption of nutrients by contributing significantly to the metabolism of carbohydrates and amino acids. This study's findings can aid in assessing eel health and improving eel farming conditions.

Identification of key upregulated genes involved in foam cell formation and the modulatory role of statin therapy

BACKGROUND

We aimed to investigate the possible effect of statins on important genes/proteins involved in foam cell formation.

METHODS

The gene expression profile of the GSE9874, GSE54666, and GSE7138from the Omnibus database were usedto identify genes involved in foam cell formation. The protein-protein interaction (PPI) network and MCODE analysis of the intersection of three databases were analyzed. We used molecular docking analysis to investigate the possible interaction of different statins with the overexpressed hub genes obtained from PPI analysis.

RESULTS

The intersection among the three datasets showed 54 upregulated and 26 down-regulated genes. The most critical overexpressed genes/proteins obtained as hub genes included: G6PD, NPC1, ABCA1, ABCG1, PGD, PLIN2, PPAP2B, and TXNRD1 based on PPI analysis. Functional enrichment analysis of 81 intersection DEGs at the biological process level focusing on the cholesterol metabolic process, secondary alcohol biosynthetic process and the cholesterol biosynthetic process. Under cellular components, the analysis confirmed that these 81 intersection DEGs were mainly applied in endoplasmic reticulum membrane, lysosome and lytic vacuole. The molecular functions were identified as sterol binding, oxidoreductase activity and NADP binding. The molecular docking showed that all statins appear to affect important protein targets overexpressed in foam cell formation. However, lipophilic statins, especially pitavastatin and lovastatin, had a greater effect than hydrophilic statins. The most significant protein target of all the overexpressed genes interacting with all statin types was ABCA1.

CONCLUSION

The effect of lipophilic statins was shown for several critical proteins in foam cell formation.

Effect of Amino Acids on Fusarium oxysporum Growth and Pathogenicity Regulated by TORC1-Tap42 Gene and Related Interaction Protein Analysis

Free amino acids (AAs) formed in fermented meat products are important nitrogen sources for the survival and metabolism of contaminating fungi. These AAs are mainly regulated by the TORC1-Tap42 signaling pathway. Fusarium spp., a common contaminant of fermented products, is a potential threat to food safety. Therefore, there is an urgent need to clarify the effect of different AAs on Fusarium spp. growth and metabolism. This study investigated the effect of 18 AAs on Fusarium oxysporum (Fo17) growth, sporulation, T-2 toxin (T-2) synthesis and Tri5 expression through Tap42 gene regulation. Co-immunoprecipitation and Q Exactive LC-MS/MS methods were used to detect the interacting protein of Tap42 during specific AA treatment. Tap42 positively regulated L-His, L-Ile and L-Tyr absorption for Fo17 colony growth. Acidic (L-Asp, L-Glu) and sulfur-containing (L-Cys, L-Met) AAs significantly inhibited the Fo17 growth which was not regulated by Tap42. The L-Ile and L-Pro addition significantly activated the sporulation of ΔFoTap42. L-His and L-Ser inhibited the sporulation of ΔFoTap42. In T-2 synthesis, ΔFoTap42 was increased in GYM medium, but was markedly inhibited in L-Asp and L-Glu addition groups. Dose-response experiments showed that 10-70 mg/mL of neutral AA (L-Thr) and alkaline AA (L-His) significantly increased the T-2 production and Tri5 expression of Fo17, but Tri5 expression was not activated in ΔFoTap42. Inhibition of T-2 synthesis and Tri5 expression were observed in Fo17 following the addition of 30-70 mg/mL L-Asp. KEGG enrichment pathway analysis demonstrated that interacting proteins of Tap42 were from glycerophospholipid metabolism, pentose phosphate pathway, glyoxylate and dicarboxylate metabolism, glycolysis and gluconeogenesis, and were related to the MAPK and Hippo signaling pathways. This study enhanced our understanding of AA regulation in fermented foods and its effect on Fusarium growth and metabolism, and provided insight into potential ways to control fungal contamination in high-protein fermented foods.

Proteomics profiling for the global and acetylated proteins of papillary thyroid cancers

BACKGROUND

Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy cancer among the malignancies of thyroid. Despite of wide usages of proteomics in PTC, the profile of acetylated proteins in PTC remains unsettled, which is helpful for understanding the carcinogenesis mechanism and identifying useful biomarkers for PTC.

METHODS

The surgically removed specimens of cancer tissues (Ca-T) and adjacent normal tissues (Ca-N) from 10 female patients pathological diagnosed as PTC (TNM stage III) were enrolled in the study. After preparing the pooled extracts of the whole proteins and the acetylated proteins from 10 cases, TMT labeling and LC/MS/MS methods were applied to the assays of global proteomics and acetylated proteomics separately. Bioinformatics analysis, including KEGG, gene ontology (GO) and hierarchical clustering were performed. Some differentially expressed proteins (DEPs) and differentially expressed acetylated proteins (DEAPs) were validated by individual Western blots.

RESULTS

Controlled with the normal tissues adjacent to the lesions, 147 out of 1923 identified proteins in tumor tissues were considered as DEPs in global proteomics, including 78 up-regulated and 69 down-regulated ones, while 57 out of 311 identified acetylated proteins in tumor tissues were DEAPs in acetylated proteomics, including 32 up-regulated and 25 down-regulated, respectively. The top 3 up- and down-regulated DEPs were fibronectin 1, KRT1B protein and chitinase-3-like protein 1, as well as keratin, type I cytoskeletal 16, A-gamma globin Osilo variant and Huntingtin interacting protein-1. The top 3 up- and down-regulated DEAPs were ribosomal protein L18a-like protein, alpha-1-acid glycoprotein 2 and eukaryotic peptide chain release factor GTP-binding subunit ERF3A, as well as trefoil factor 3, thyroglobulin and histone H2B. Functional GO annotation and KEGG pathway analysis based on the DEPs and DEAPs showed completely different changing pictures. Contrary to the top 10 up- and -down regulated DEPs, most of which were addressed in PTC and other types of carcinomas, changes of the majority DEAPs were not mentioned in the literatures.

CONCLUSIONS

Taken the profiling of the global and acetylated proteomics together will provide more broad view of protein alterations on the carcinogenesis and new direction for selecting biomarker for diagnosis of PTC.