Functional Annotation Of Genes Research Articles

GWAS combined with QTL mapping reveals the genetic loci of leaf morphological characters in Nicotiana tabacum

BackgroundLeaf morphology plays a crucial role in photosynthetic efficiency and yield potential in crops. Cigar tobacco plants, which are derived from common tobacco (Nicotiana tabacum L.), possess special leaf characteristics including thin and delicate leaves with few visible veins, making it a good system for studying the genetic basis of leaf morphological characters.ResultsIn this study, GWAS and QTL mapping were simultaneously performed using a natural population containing 185 accessions collected worldwide and an F2 population consisting of 240 individuals, respectively. A total of 26 QTLs related to leaf morphological traits were mapped in the F2 population at three different developmental stages, and some QTL intervals were repeatedly detected for different traits and at different developmental stages. Among the 206 significant SNPs identified in the natural population using GWAS, several associated with the leaf thickness phenotype were co-mapped via QTL mapping. By analyzing linkage disequilibrium and transcriptome data from different tissues combined with gene functional annotations, 7 candidate genes from the co-mapped region were identified as the potential causative genes associated with leaf thickness.ConclusionsThese results presented a valuable cigar tobacco resource showing the genetic diversity regarding its leaf morphological traits at different developmental stages. It also provides valuable information for novel genes and molecular markers that will be useful for further functional verification and for molecular breeding of leaf morphological traits in crops in the future.

Identification of key genes for triacylglycerol biosynthesis and storage in herbaceous peony (Paeonia lactifolra Pall.) seeds based on full-length transcriptome

BackgroundThe herbaceous peony (Paeonia lactiflora Pall.) is extensively cultivated in China due to its root being used as a traditional Chinese medicine known as ‘Radix Paeoniae Alba’. In recent years, it has been discovered that its seeds incorporate abundant unsaturated fatty acids, thereby presenting a potential new oilseed plant. Surprisingly, little is known about the full-length transcriptome sequencing of Paeonia lactiflora, limiting research into its gene function and molecular mechanisms.ResultsA total of 484,931 Reads of Inserts (ROI) sequences and 1,455,771 full-Length non-chimeric reads (FLNC) sequences were obtained for CDS prediction, TF analysis, SSR analysis and lncRNA identification. In addition, gene function annotation and gene structure analysis were performed. A total of 4905 transcripts were related to lipid metabolism biosynthesis pathway, belonging to 28 enzymes. We use these data to identify 10 oleosin (OLE) and 5 diacylglycerol acyltransferase (DGAT) gene members after de-redundancy. The analysis of physicochemical properties and secondary structure showed them similarity in gene family respectively. The phylogenetic analysis showed that the distribution of OLE and DGAT family members was roughly the same as that of Arabidopsis. Quantitative real-time polymerase chain reaction (qRT–PCR) analyses revealed expression changes in different seed development stages, and showed a trend of increasing and then decreasing.ConclusionIn summary, these results provide new insights into the molecular mechanism of triacylglycerol (TAG) biosynthesis and storage during the seedling stage in Paeonia lactiflora. It provides theoretical references for selecting and breeding oil varieties and understanding the functions of oil storage as well as lipid synthesis related genes in Paeonia lactiflora.

Effects of concentrate feeding sequence on VFA production, and cecal microbiota of Dezhou donkeys by metagenomic technology.

Microorganisms residing in the cecum of donkeys are crucial for physiological processes, nutrient metabolism, and immune function. Feeding methods can affect the dynamic balance of animal gut microbiota, thereby affecting indicators such as volatile fatty acids. This study explores suitable feeding methods to promote actual production by changing the feeding order of concentrate. Fifteen Dezhou donkeys with similar age and weight profiles were randomly divided into three groups with the concentrate feeding sequence: fiber-to-concentrate (FC), concentrate-to-fiber (CF), and total mixed ration (TMR). The experiment spanned a duration of 82 days. The analyses conducted were primarily aimed at determining the effects of feeding on gut microbes, primarily using metagenomic sequencing techniques. The experimental findings revealed that the levels of valeric acid were notably higher in the CF and TMR groups compared to the FC group (p < 0.05). These results suggest that the feeding sequence exerts a certain impact on the microbial composition within the cecum of Dezhou donkeys. At the phylum level, the predominant microbiota consisted of Firmicutes and Bacteroidetes, with the CF group displaying a higher relative abundance of Firmicutes compared to both the FC and TMR groups. At the genus level, Prevotella, Bacteroides, and Fibrobacter were the dominant bacterial genera identified in cecum. The functional gene annotation analysis indicated a significantly lower abundance of lacZ (K01190), Por/nifJ (K03737), and ppdK (K01006) genes in CF group relative to the FC and TMR groups (p < 0.05), highlighting their roles in galactose metabolism and glycolysis, respectively. Moreover, the CF group exhibited a higher concentration of antibiotic resistance genes (tetO and tet44) in the gut microbiota compared to the TMR and FC groups (p < 0.05), underscoring the presence of numerous antibiotic resistance genes within the phyla Bacteroidetes, Firmicutes, and Proteobacteria. In conclusion, different precision feed sequences significantly impact the levels of volatile fatty acids in Dezhou fattening donkeys, modify the composition and functional genes of the cecal microbiota, and elucidate the microbial mechanisms influenced by the feeding sequence on the growth and metabolism. These insights are anticipated to provide a foundation for the rational design of precision feed sequences in practical agricultural settings.

Bioinformatics analysis of the potential biomarkers of Multiple Sclerosis and Guillain-Barré syndrome.

Recent research emphasizes the intricate interplay of genetics and epigenetics in neurological disorders, notably Multiple Sclerosis (MS) and Guillain-Barre Syndrome (GBS), both of which exhibit cardiovascular dysregulation, with GBS often featuring serious bradyarrhythmias requiring prompt recognition and treatment. While cardiovascular autonomic dysfunction in MS is typically less severe, orthostatic intolerance affects around half of MS patients. Their distinction lies in their autoimmune responses, MS is an autoimmune disease affecting the central nervous system, causes demyelination and axon damage, leading to cognitive, ocular, and musculoskeletal dysfunction. In contrast, GBS primarily affects the peripheral nervous system, resulting in paralysis and respiratory complications. Despite their differences, both diseases share environmental risk factors such as viral infections and Vitamin D deficiency. This study aims to explore shared gene expression pathways, functional annotations, and molecular pathways between MS and GBS to enhance diagnostics, pathogenesis understanding, and treatment strategies through molecular analysis techniques. Through the gene expression analysis, five significant genes were found UTS2, TNFSF10, GBP1, VCAN, FOS. Results shows that Common DEGs are linked to apoptosis, bacterial infections, and atherosclerosis. Molecular docking analysis suggests Aflatoxin B1 as a potential therapeutic compound due to its high binding affinity with common differentially expressed proteins.

Expression Levels and Regulation of Selenoprotein Genes in Patients With Coronavirus Disease 2019

Objective To investigate the expression levels of selenoprotein genes in the patients with coronavirus disease 2019 (COVID-19) and the possible regulatory mechanisms.Methods The dataset GSE177477 was obtained from the Gene Expression Omnibus,consisting of a symptomatic group (n=11),an asymptomatic group (n=18),and a healthy control group (n=18).The dataset was preprocessed to screen the differentially expressed genes (DEG) related to COVID-19,and gene ontology functional annotation and Kyoto encyclopedia of genes and genomes enrichment analysis were performed for the DEGs.The protein-protein interaction network of DEGs was established,and multivariate Logistic regression was employed to analyze the effects of selenoprotein genes on the presence/absence of symptoms in the patients with COVID-19.Results Compared with the healthy control,the symptomatic COVID-19 patients presented up-regulated expression of GPX1,GPX4,GPX6,DIO2,TXNRD1,SELENOF,SELENOK,SELENOS,SELENOT,and SELENOW and down-regulated expression of TXNRD2 and SELENON (all P<0.05).The asymptomatic patients showcased up-regulated expression of GPX2,SELENOI,SELENOO,SELENOS,SELENOT,and SELENOW and down-regulated expression of SELP (all P<0.05).The results of multivariate Logistic regression analysis showed that the abnormally high expression of GPX1 (OR=0.067,95%CI=0.005-0.904,P=0.042) and SELENON (OR=56.663,95%CI=3.114-856.999,P=0.006) was the risk factor for symptomatic COVID-19,and the abnormally high expression of SELP was a risk factor for asymptomatic COVID-19 (OR=15.000,95%CI=2.537-88.701,P=0.003).Conclusions Selenoprotein genes with differential expression are involved in the regulation of COVID-19 development.The findings provide a new reference for the prevention and treatment of COVID-19.

Functional annotation and meta-analysis of maize transcriptomes reveal genes involved in biotic and abiotic stress

BackgroundEnvironmental stress factors, such as biotic and abiotic stress, are becoming more common due to climate variability, significantly affecting global maize yield. Transcriptome profiling studies provide insights into the molecular mechanisms underlying stress response in maize, though the functions of many genes are still unknown. To enhance the functional annotation of maize-specific genes, MaizeGDB has outlined a data-driven approach with an emphasis on identifying genes and traits related to biotic and abiotic stress.ResultsWe mapped high-quality RNA-Seq expression reads from 24 different publicly available datasets (17 abiotic and seven biotic studies) generated from the B73 cultivar to the recent version of the reference genome B73 (B73v5) and deduced stress-related functional annotation of maize gene models. We conducted a robust meta-analysis of the transcriptome profiles from the datasets to identify maize loci responsive to stress, identifying 3,230 differentially expressed genes (DEGs): 2,555 DEGs regulated in response to abiotic stress, 408 DEGs regulated during biotic stress, and 267 common DEGs (co-DEGs) that overlap between abiotic and biotic stress. We discovered hub genes from network analyses, and among the hub genes of the co-DEGs we identified a putative NAC domain transcription factor superfamily protein (Zm00001eb369060) IDP275, which previously responded to herbivory and drought stress. IDP275 was up-regulated in our analysis in response to eight different abiotic and four different biotic stresses. A gene set enrichment and pathway analysis of hub genes of the co-DEGs revealed hormone-mediated signaling processes and phenylpropanoid biosynthesis pathways, respectively. Using phylostratigraphic analysis, we also demonstrated how abiotic and biotic stress genes differentially evolve to adapt to changing environments.ConclusionsThese results will help facilitate the functional annotation of multiple stress response gene models and annotation in maize. Data can be accessed and downloaded at the Maize Genetics and Genomics Database (MaizeGDB).

Genome-Wide Association Study and Candidate Gene Mining of Seed Size Traits in Soybean

Seed size traits, including seed length (SL), seed width (SW), and seed thickness (ST), are crucial appearance parameters that determine soybean seed weight, yield, and ultimate utilization. However, there is still a large gap in the understanding of the genetic mechanism of these traits. Here, 281 soybeans were utilized to analyze the genetic architecture of seed size traits in different years through multiple (single-locus and multi-locus) genome-wide association study (GWAS) models, and candidate genes were predicted by integrating information on gene function and transcriptome sequencing data. As a result, two, seven, and three stable quantitative trait nucleotides (QTNs) controlling SL, SW, and ST were detected in multiple environments using the single-locus GWAS model, and concurrently detected by the results of the multi-locus GWAS models. These stable QTNs are located on 10 linkage disequilibrium blocks, with single genome regions ranging in size from 20 to 440 kb, and can serve as the major loci controlling soybean seed size. Furthermore, by combining gene functional annotation and transcriptome sequencing data of seeds at different developmental stages from two extreme soybean accessions, nine candidate genes, including Glyma.05G038000, Glyma.05G244100, Glyma.05G246900, Glyma.07G070200, Glyma.11G010000, Glyma.11G012400, Glyma.17G165500, Glyma.17G166500, and Glyma.20G012600 within the major loci that may regulate soybean seed size, were mined. Overall, these findings offer valuable insights for molecular improvement breeding as well as gene functional studies to unravel the mechanism of soybean seed size.

GC–MS analysis of Lactobacillus plantarum YW11 metabolites and its computational analysis on familial pulmonary fibrosis hub genes

Abstract The purpose of this research was to examine the interaction between metabolites of Lactobacillus plantarum YW11, characterized through GC–mass spectra (MS) analysis, and the FN1 protein in cases of familial pulmonary fibrosis, found from hub genes analysis. GC–MS analysis was performed to identify metabolites in L. plantarum. Then, gene expression analysis and functional annotations were conducted to investigate the hub genes. A network of hub genes and transcription factors (TFs) was constructed, highlighting the significance of FN1 in the disease’s etiology. Molecular docking was employed to explore the interaction between the characterized metabolites and the FN1 protein. Toxicity analysis was also carried out. Thirty-two active compounds of L. plantarum YW11 were characterized by GC–MS. The gene expression analysis identified 295 differentially expressed genes, including 10 hub genes and 6 TFs, providing further support for the involvement of FN1 protein in the disease. The results of the molecular docking studies suggest the therapeutic potential of targeting FN1, with the best docking result observed for the interaction between FN1 and the 2,4-di-tert-butylphenol metabolite (energy of −6.9 kcal/mol). The toxicity analysis and molecular dynamic simulations support the suitability of 2,4-di-tert-butylphenol as a candidate for targeting FN1.

SapBase: A central portal for functional and comparative genomics of Sapindaceae species.

The Sapindaceae family, encompassing a wide range of plant forms such as herbs, vines, shrubs, and trees, is widely distributed across tropical and subtropical regions. This family includes economically important crops like litchi, longan, rambutan, and ackee. With the wide application of genomic technologies in recent years, several Sapindaceae plant genomes have been decoded, leading to an accumulation of substantial omics data in this field. This surge in data highlights the pressing need for a unified genomic data center capable of storing, sharing, and analyzing these data. Here, we introduced SapBase, that is, the Sapindaceae Genome Database. SapBase houses seven published plant genomes alongside their corresponding gene structure and functional annotations, small RNA annotations, gene expression profiles, gene pathways, and synteny block information. It offers user-friendly features for gene information mining, co-expression analysis, and inter-species comparative genomic analysis. Furthermore, we showcased SapBase's extensive capacities through a detailed bioinformatic analysis of a MYB gene in litchi. Thus, SapBase could serve as an integrative genomic resource and analysis platform for the scientific exploration of Sapinaceae species and their comparative studies with other plants.

New insights into the dominance of mixed fermentation of Staphylococcus cohnii and Staphylococcus saprophyticus in Chinese bacon: Complete genomic and comparative genomic perspectives

Previous studies have demonstrated that Staphylococcus cohnii WX_M8 and S. saprophyticus MY_A10 significantly enhanced the flavor of Chinese bacon in a mixed fermentation. However, due to the complexity of the processing, the contribution of the bacteria is deceptive when investigating only the phenotypic changes at the time of fermentation. In order to clarify the metabolic mechanisms of mixed fermentation, a technological characterization, whole genome and comparative genomics analysis, and metabolites were approached in this study. Results showed that differences in tolerance characteristics existed between WX_M8 and MY_A10. And the genomes of both the two strains consisted of one chromosome and four circular plasmids. Their genome sizes were 2.74 Mp and 2.62 Mp, the GC contents were 32.45% and 33.18%, and the predicted coding genes (CDS) were 2564 and 2541, respectively. Based on the annotation of gene functions and assessment of metabolic pathways in the KEGG database, WX_M8 and MY_A10 strains were found to harbor complete protein degradation and amino acid metabolic pathways, pyruvate and butanol metabolic pathways, and isoleucine metabolic pathways, and their diverse enzyme-encoding genes superimposed the metabolic functions, whereas the alcohol dehydrogenase genes, adh and frmA, achieved complementary functions in the production of esters. Comparative genomics analysis revealed a diversity of encoding genes of aminotransferases and a greater metabolism for sulfur-containing amino acids, aromatic amino acids, and branched-chain amino acids in the mixed fermentation of strains WX_M8 and MY_A10. Metabolites analysis showed that MY_A10 focused on the production of soluble peptides and free amino acids (FAAs), while WX_M8 focused on volatile organic compounds (VOCs), resulting in a significant enhancement of the flavor of Chinese bacon when the two were mixed fermented. This result may provide direction for strains WX_M8 and MY_A10 to be used as starter cultures and targeted to regulate flavor.

Enhancing Gene Co-Expression Network Inference for the Malaria Parasite Plasmodium falciparum.

Malaria results in more than 550,000 deaths each year due to drug resistance in the most lethal Plasmodium (P.) species P. falciparum. A full P. falciparum genome was published in 2002, yet 44.6% of its genes have unknown functions. Improving the functional annotation of genes is important for identifying drug targets and understanding the evolution of drug resistance. Genes function by interacting with one another. So, analyzing gene co-expression networks can enhance functional annotations and prioritize genes for wet lab validation. Earlier efforts to build gene co-expression networks in P. falciparum have been limited to a single network inference method or gaining biological understanding for only a single gene and its interacting partners. Here, we explore multiple inference methods and aim to systematically predict functional annotations for all P. falciparum genes. We evaluate each inferred network based on how well it predicts existing gene-Gene Ontology (GO) term annotations using network clustering and leave-one-out crossvalidation. We assess overlaps of the different networks' edges (gene co-expression relationships), as well as predicted functional knowledge. The networks' edges are overall complementary: 47-85% of all edges are unique to each network. In terms of the accuracy of predicting gene functional annotations, all networks yielded relatively high precision (as high as 87% for the network inferred using mutual information), but the highest recall reached was below 15%. All networks having low recall means that none of them capture a large amount of all existing gene-GO term annotations. In fact, their annotation predictions are highly complementary, with the largest pairwise overlap of only 27%. We provide ranked lists of inferred gene-gene interactions and predicted gene-GO term annotations for future use and wet lab validation by the malaria community. The different networks seem to capture different aspects of the P. falciparum biology in terms of both inferred interactions and predicted gene functional annotations. Thus, relying on a single network inference method should be avoided when possible. Attached.

MiRNA-seq provides novel insight into the response to hyper- and hypo- salinity acclimation in Crassostrea hongkongensis

The Hong Kong oyster, Crassostrea hongkongensis, is a significant bivalve species with economic importance. It primarily inhabits the estuarine intertidal zones in southern China, making it susceptible to salinity fluctuations. Consequently, investigating the molecular mechanisms governing salinity regulation in C. hongkongensis is essential. In this study, we conducted miRNA-seq on C. hongkongensis to compare miRNA expression differences under varying salinities (5‰, 25‰, and 35‰). The miRNA sequencing revealed 51 known miRNAs and 95 novel miRNAs across nine small RNA libraries (S5, S25, and S35). Among these miRNAs, we identified 6 down-regulated differentially expressed (DE) miRNAs in response to hypo-salinity stress (5‰), while 1 up-regulated DE miRNA and 5 down-regulated DE miRNAs were associated with hyper-salinity stress (35‰). Additionally, we predicted 931 and 768 potential target genes for hypo- and hyper-salinity stress, respectively. Functional gene annotation indicated that the target genes under hypo-salinity stress were linked to vesicle-mediated transport and metal ion binding. Conversely, those under hyper-salinity stress were primarily involved in signal transduction and metabolic processes. These findings have provided insights into the regulatory role of miRNAs, their potential target genes and associated pathways in oyster hypo- and hyper-salinity stress, which establish a foundation for future studies on the roles of miRNAs in salinity acclimation mechanisms in C. hongkongensis.

Differential Gene Analysis of Langerhans Cell Histiocytosis and the Significance of MMP1-Targeted Drug Repositioning.

Langerhans cell histiocytosis (LCH) is a rare condition predominantly affecting young children. Activation of the MAPK pathway has offered key new insights into the pathogenesis of LCH; however, the precise mechanisms underlying its occurrence and development are still far from being completely elucidated. There is still a relapse/reactivation rate in patients with multisystem LCH. Therefore, this study aimed to investigate other potential LCH pathophysiologies and prospective therapeutic targets. The gene expression omnibus (GEO) database was used to retrieve gene expression profiles of LCH (GSE16395). Three distinct types of analyses were performed after identifying the common differentially expressed genes (DEGs) in LCH: hub gene identification, functional annotation, module construction, drug repositioning, and expression analysis via immunohistochemistry (IHC). We identified 417 common DEGs and 50 central hub genes. This functional study highlighted the significance of keratinization, skin development, and inflammation. In addition, we predicted new drug candidates (RS2 drugs targeting matrix metalloprotease1, MMP1) that could be used for LCH treatment. Finally, gene-miRNA and gene-TF networks and immune cell infiltration were analyzed for MMP1-related genes. MMP1 expression levels in LCH tissues were validated by IHC. Our study identified the central communal genes and novel drug candidates. These shared pathways and hub genes offer new perspectives on future mechanisms of action and therapeutic targets.

Integrating transcriptomic and proteomic data for a comprehensive molecular perspective on the association between sarcopenia and osteoporosis

BackgroundOsteoporosis and sarcopenia are common age-related conditions characterized by the progressive loss of bone density and muscle mass, respectively. Their co-occurrence, often referred to as osteosarcopenia, presents significant challenges in elderly care due to increased fragility and functional impairment. Existing studies have identified shared pathological mechanisms between these conditions, including inflammation, hormonal imbalances, and metabolic dysregulation, but a comprehensive understanding of their molecular interplay remains incomplete. ObjectiveThis study aims to deepen our understanding of the molecular interactions between sarcopenia and osteoporosis through an integrated omics approach, revealing potential therapeutic targets and biomarkers. MethodsEmploying a combination of proteomics and transcriptomics analyses, this study analyzed bone and muscle tissue samples from patients diagnosed with osteoporosis and osteosarcopenia. Techniques included high-throughput sequencing and label-free proteomics, supported by advanced bioinformatics tools for data analysis and functional annotation of genes and proteins. ResultsThe study found marked differences in gene and protein expressions between osteoporosis and osteosarcopenia tissues. Specifically, genes like PDIA5, TUBB1, and CYFIP2 in bone, along with MYH7 and NCAM1 in muscle, exhibited differential expression at both mRNA and protein levels. Pathway analyses revealed the significance of oxidative-reduction balance, cellular metabolism, and immune response in the progression of these conditions. Importantly, the study pinpointed osteoclast differentiation and NF-kappa B signaling pathways as critical in the molecular dynamics of osteosarcopenia, suggesting potential targets for therapy. ConclusionsThis study utilized transcriptomics and proteomics to identify key genes and proteins impacting sarcopenia and osteoporosis, employing advanced network tools to delineate interaction networks and crucial signaling pathways. It highlighted genes like PDIA5 and TUBB1, consistently expressed in both analyses, involved in pathways such as osteoclast differentiation and cytokine interactions. These insights enhance understanding of the molecular interplay in bone and muscle degeneration with aging, suggesting directions for future research into therapeutic interventions and prevention strategies for age-related degenerative diseases.

Bulked Segregant RNA-Seq Reveals Different Gene Expression Patterns and Mutant Genes Associated with the Zigzag Pattern of Tea Plants (Camellia sinensis).

The unique zigzag-patterned tea plant is a rare germplasm resource. However, the molecular mechanism behind the formation of zigzag stems remains unclear. To address this, a BC1 genetic population of tea plants with zigzag stems was studied using histological observation and bulked segregant RNA-seq. The analysis revealed 1494 differentially expressed genes (DEGs) between the upright and zigzag stem groups. These DEGs may regulate the transduction and biosynthesis of plant hormones, and the effects on the phenylpropane biosynthesis pathways may cause the accumulation of lignin. Tissue sections further supported this finding, showing differences in cell wall thickness between upright and curved stems, potentially due to lignin accumulation. Additionally, 262 single-nucleotide polymorphisms (SNPs) across 38 genes were identified as key SNPs, and 5 genes related to zigzag stems were identified through homologous gene function annotation. Mutations in these genes may impact auxin distribution and content, resulting in the asymmetric development of vascular bundles in curved stems. In summary, we identified the key genes associated with the tortuous phenotype by using BSR-seq on a BC1 population to minimize genetic background noise.

Small molecule drug discovery for glioblastoma treatment based on bioinformatics and cheminformatics approaches.

Background: Glioblastoma (GBM) is a common and highly aggressive brain tumor with a poor prognosis for patients. It is urgently needed to identify potential small molecule drugs that specifically target key genes associated with GBM development and prognosis. Methods: Differentially expressed genes (DEGs) between GBM and normal tissues were obtained by data mining the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Gene function annotation was performed to investigate the potential functions of the DEGs. A protein-protein interaction (PPI) network was constructed to explore hub genes associated with GBM. Bioinformatics analysis was used to screen the potential therapeutic and prognostic genes. Finally, potential small molecule drugs were predicted using the DGIdb database and verified using chemical informatics methods including absorption, distribution, metabolism, excretion, toxicity (ADMET), and molecular docking studies. Results: A total of 429 DEGs were identified, of which 19 hub genes were obtained through PPI analysis. The hub genes were confirmed as potential therapeutic targets by functional enrichment and mRNA expression. Survival analysis and protein expression confirmed centromere protein A (CENPA) as a prognostic target in GBM. Four small molecule drugs were predicted for the treatment of GBM. Conclusion: Our study suggests some promising potential therapeutic targets and small molecule drugs for the treatment of GBM, providing new ideas for further research and targeted drug development.

Aerobic Anoxygenic Phototrophic Bacteria in the Marine Environments Revealed by Raman/Fluorescence-Guided Single-Cell Sorting and Targeted Metagenomics.

Aerobic anoxygenic phototrophic bacteria (AAPB) contribute profoundly to the global carbon cycle. However, most AAPB in marine environments are uncultured and at low abundance, hampering the recognition of their functions and molecular mechanisms. In this study, we developed a new culture-independent method to identify and sort AAPB using single-cell Raman/fluorescence spectroscopy. Characteristic Raman and fluorescent bands specific to bacteriochlorophyll a (Bchl a) in AAPB were determined by comparing multiple known AAPB with non-AAPB isolates. Using these spectroscopic biomarkers, AAPB in coastal seawater, pelagic seawater, and hydrothermal sediment samples were screened, sorted, and sequenced. 16S rRNA gene analysis and functional gene annotations of sorted cells revealed novel AAPB members and functional genes, including one species belonging to the genus Sphingomonas, two genera affiliated to classes Betaproteobacteria and Gammaproteobacteria, and function genes bchCDIX, pucC2, and pufL related to Bchl a biosynthesis and photosynthetic reaction center assembly. Metagenome-assembled genomes (MAGs) of sorted cells from pelagic seawater and deep-sea hydrothermal sediment belonged to Erythrobacter sanguineus that was considered as an AAPB and genus Sphingomonas, respectively. Moreover, multiple photosynthesis-related genes were annotated in both MAGs, and comparative genomic analysis revealed several exclusive genes involved in amino acid and inorganic ion metabolism and transport. This study employed a new single-cell spectroscopy method to detect AAPB, not only broadening the taxonomic and genetic contents of AAPB in marine environments but also revealing their genetic mechanisms at the single-genomic level.

Bayesian shrinkage models for integration and analysis of multiplatform high‐dimensional genomics data

AbstractWith the increasing availability of biomedical data from multiple platforms of the same patients in clinical research, such as epigenomics, gene expression, and clinical features, there is a growing need for statistical methods that can jointly analyze data from different platforms to provide complementary information for clinical studies. In this paper, we propose a two‐stage hierarchical Bayesian model that integrates high‐dimensional biomedical data from diverse platforms to select biomarkers associated with clinical outcomes of interest. In the first stage, we use Expectation Maximization‐based approach to learn the regulating mechanism between epigenomics (e.g., gene methylation) and gene expression while considering functional gene annotations. In the second stage, we group genes based on the regulating mechanism learned in the first stage. Then, we apply a group‐wise penalty to select genes significantly associated with clinical outcomes while incorporating clinical features. Simulation studies suggest that our model‐based data integration method shows lower false positives in selecting predictive variables compared with existing method. Moreover, real data analysis based on a glioblastoma (GBM) dataset reveals our method's potential to detect genes associated with GBM survival with higher accuracy than the existing method. Moreover, most of the selected biomarkers are crucial in GBM prognosis as confirmed by existing literature.

Mechanism of Shexiang Tongxin Dropping Pills in treating diabetic cardiomyopathy based on network pharmacology and animal experiments

This study aimed to explore the mechanism of Shexiang Tongxin Dropping Pills(STDP) in treating diabetic cardiomyopathy(DCM) based on network pharmacology, molecular docking, and animal experiments. BATMAN, TCMSP, and GeneCards were searched for the active ingredients and targets of STDP against DCM. STRING and Cytoscape were used to build the protein-protein interaction(PPI) network and &quot;drug-active ingredient-target&quot; network. Gene Ontology(GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis of the targets were carried out based on DAVID. The molecular docking of key receptor proteins with corresponding active ingredients was performed using AutoDock Vina. The rat model of DCM was established by a high-fat diet combined with intraperitoneal injection of streptozotocin. Rats were assigned into control, model, low-(20 mg·kg~(-1)) and high-dose(40 mg·kg~(-1)) STDP, and metformin(200 mg·kg~(-1)) groups. After 8 weeks of continuous administration, the cardiac function, myocardial pathological changes, and myocardial collagen fiber deposition of rats in each group were detected by echocardiography, hematoxylin-eosin(HE) staining, and Sirius red staining, respectively. The myocardial hypertrophy was detected by WGA staining. The expression levels of p38 mitogen-activated protein kinase(p38), phosphorylation-p38(p-p38), c-Jun N-terminal kinase(JNK), phosphorylation-JNK(p-JNK), caspase-3, and C-caspase-3 in the myocardial tissue of rats in each group were measured by Western blot. The network pharmacology predicted 199 active ingredients and 1 655 targets of STDP and 463 targets of DCM. One hundred and thirty-four potential targets of STDP for treating DCM were obtained, and the AGE-RAGE signaling pathway in diabetic complications was screened out. Molecular docking results showed that miltirone, dehydromiltirone, and tryptanthrin had strong binding affinity with RAGE. The results of animal experiments confirmed that STDP effectively protected the cardiac function of DCM rats. Compared with the DCM model group, the STDP groups showed significantly down-regulated protein levels of p-p38, p-JNK, and C-caspase-3. To sum up, STDP may protect the cardiac function of DCM rats by regulating the AGE-RAGE signaling pathway.

Comprehensive bioinformatics analysis of KIF20A as a prognosis biomarker for clear cell renal cell carcinoma.

It has been shown that kinesin family member 20A (KIF20A) is involved in the development of several cancers. However, research on clear cell renal cell carcinoma (ccRCC) and KIF20A is still exploratory. The current research was carried out to determine whether KIF20A expression has any prognosis value in ccRCC. Data were downloaded from The Cancer Genome Atlas (TCGA) database to validate the KIF20A mRNA expression and to perform clinicopathological analysis. Receiver operating characteristic (ROC) curves were used in evaluating KIF20A's diagnostic performance for ccRCC. The prognostic value of KIF20A in ccRCC was estimated by the Kaplan-Meier survival curve and Cox regression analysis. Gene set enrichment analysis (GSEA), functional annotations, and immune infiltration analysis were used to determine the potential mechanism of KIF20A's role in ccRCC. The increase in KIF20A mRNA expression was associated with sex, clinical stage, histologic grade, and TNM stage. ROC curve indicated that KIF20A could distinguish ccRCC from normal kidney samples. Survival study showed that high KIF20A expression predicted poor ccRCC prognosis. Thus, KIF20A expression could be used as an independent overall survival (OS) risk factor for ccRCC patients. Co-expression analysis identified TPX2 as a strong, positively correlated factor with KIF20A in ccRCC. Functional enrichment analyses and GSEA showed that KIF20A and TPX2 participated in various tumor-related pathways. Moreover, KIF20A and TPX2 expression were significantly associated with the level of immune infiltration into ccRCC. KIF20A may be a therapeutic target and a prognostic biomarker for ccRCC.