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Abstract DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual’s age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and preliminarily explore the biological significance of methylation-associated genes using machine learning. A biological age prediction model was constructed using human methylation data through data preprocessing, feature selection procedures, statistical analysis, and machine learning techniques. Subsequently, 15 methylation data sets were subjected to in-depth analysis using SHAP, GO enrichment, and KEGG analysis. XGBoost, LightGBM, and CatBoost identified 15 groups of methylation sites associated with biological age. The cg23995914 locus was identified as the most significant contributor to predicting biological age by calculating SHAP values. Furthermore, GO enrichment and KEGG analyses were employed to initially explore the methylated loci’s biological significance.

【Objective】The transparent traits in Pterophyllum scalare has high economic and scientific value. This article aims to explore the differential expression genes (DEG) between transparent gill cover (TGC) and opaque gill cover (OGC) tissues of P. scalare, and explore the relevant signaling pathways affecting the transparent gill cover traits of P. scalare, so as to provide a theoretical basis for the subsequent screening of key genes regulating the transparent gill cover trait of P. scalare and related mechanism studies.【Method】The gill cover tissue of P. scalare (strain: red-topped tri-color) was selected as the research material, and 6 samples were selected from each group of OGC and TGC. Transcriptomic sequencing was carried out based on the Illumina Novaseq 6000 sequencing platform with using Fastp to perform quality control on raw sequencing data. The transcript and the corresponding single gene (Unigene) were obtained by de novo assembling of quality control data by Trinity software, and the initial assembly sequence was deredundancy analysis and result evaluation by CD-HIT software and BUSCO software, respectively. After redundancy, unigene were functionally annotated based on sequence homology. RSEM software was used to calculate the expression of unigene in each sample. DEGs between OGC and TGC were calculated using DESeq2 software. GO and KEGG pathway enrichment analysis was performed on DEGs using Goatools software and KOBAS software, respectively.【Result】(1) After quality control of sequencing data, the average error rate of each sample sequencing base was less than 0.1%, Q20 was higher than 97.78%, and Q30 was higher than 93.58%, and the sequencing quality was reliable. After de novo assembling, 200 303 transcripts were obtained, corresponding to 123 178 unigenes. After deredundancy analysis, a total of 147 932 transcripts are obtained, corresponding to 108 070 unigenes, with an average length of 991.85 bp and N50 is 2 430 bp. (2) A total of 40 180 unigenes were annotated in the NR, KEGG, eggNOG, GO, Pfam, and Swiss-Prot databases, accounting for 37.98% of the total, of which 10 747 genes were annotated simultaneously in 6 databases, accounting for 26.75% of the total. (3) TGC relative to OGC has a total of 432 DEGs, of which TGC significantly up-regulated 267 genes and down-regulated 165 genes relative to OGC. (4) GO enrichment results showed that 432 DEGs were mainly enriched in 5 biological pathways: IMP biosynthesis, IMP metabolism, "de novo" IMP biosynthesis, amino acid binding, and modified amino acid binding. (5) KEGG enrichment results showed that 432 DEGs were mainly enriched in 7 biological pathways: neuroactive ligand-receptor interaction, tyrosine metabolism, purine metabolism, a carbon pool of folate, phenylalanine metabolism, nucleotide metabolism, ubiquinone and other terpene-quinone biosynthesis.【Conclusion】432 genes related to the transparent gill cover trait of P. scalare were obtained by transcriptomic sequencing, which were mainly involved in 12 signaling pathways. The results will provide reference for the mining and functional study of genes related to transparent gill cover trait in P. scalare.

The objective of this study is to analyze environmental genetic selection signals in large-scale sheep populations with conflicting environmental adaptations, aiming to identify and isolate genes associated with environmental adaptations in sheep populations. Kirghiz sheep, which inhabit high-altitude environments year-round, demonstrate the ability to adapt to extreme conditions. In this study, 42 Kirghiz sheep, 24 Tien-Shan in Kyrgyzstan sheep, 189 Qira black sheep, and 160 Chinese Merino sheep were genotyped using Illumina Ovine SNP50K chip. Regions exhibiting a selection signal threshold of 5%, as well as PI analysis and haplotype statistical scanning gene data were annotated, and intersecting genes were identified as candidate genes. Through Fst and haplotype statistical analysis revealed the key gene PDGFD and its vicinity’s impact on fat deposition in sheep tails. Additionally, Fst and PI analysis uncovered genes related to high-altitude adaptation as well as those linked to animal growth and reproduction.Further GO and KEGG enrichment pathway analyses unveiled pathways associated with high-altitude adaptation such as negative regulation of peptidyl-tyrosine phosphorylation and xenobiotic metabolism processes.This investigation into the adaptability of Kirghiz sheep provides theoretical support and practical guidance for the conservation and genetic enhancement of Kirghiz sheep germplasm resources.

The network pharmacology (NP) approach is a valuable novel methodology for understanding the complex pharmacological mechanisms of medicinal herbs. In addition, various in silico analysis techniques combined with the NP can improve the understanding of various issues used in natural product research. This study assessed the therapeutic effects of Arum ternata (AT), Poria cocos (PC), and Zingiber officinale (ZO) on hyperlipidemia after network pharmacologic analysis. A protein–protein interaction (PPI) network of forty-one key targets was analyzed to discover core functional clusters of the herbal compounds. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) term enrichment analysis identified significant categories of hypolipidemic mechanisms. The STITCH database indicated a high connection with several statin drugs, deduced by the similarity in targets. AT, PC, and ZO regulated the genes related to the energy metabolism and lipogenesis in HepG2 cells loaded with free fatty acids (FFAs). Furthermore, the mixture of three herbs had a combinational effect. The herbal combination exerted superior efficacy compared to a single herb, particularly in regulating acetyl-CoA carboxylase (ACC) and carnitine palmitoyltransferase 1 (CPT-1). In conclusion, the network pharmacologic approach was used to assess potential targets of the herbal combination for treatment. Experimental data from FFA-induced HepG2 cells suggested that the combination of AT, PC, and ZO might attenuate hyperlipidemia and its associated hepatic steatosis.

The Wenshenyang recipe (WSYR) has the effect of treating infertility, but the mechanisms underlying this activity have not been fully elucidated. In this study, network pharmacology and RNA sequencing were combined, with database-based “dry” experiments and transcriptome analysis-based “wet” experiments used conjointly to analyse the mechanism of WSYR in the treatment of infertility. In the dry analysis, 43 active compounds in WSYR and 44 therapeutic targets were obtained through a database search, 15 infertility pathways were significantly enriched, and key targets, such as ESR1, TP53, AKT1, IL-6, and IL-10 were identified. Then the wet experiments were performed to detect the expression changes of the 412 genes from 15 infertility pathways identified by dry analysis. HK-2 cells were treated with the three herbs of WSYR and subjected to targeted RNA sequencing. Based on the results, 92 of the 412 genes in 15 infertility pathways were identified as DEGs. Additionally, key targets, such as ESR2, STAT1, STAT3, and IL6, were also identified in the wet experiments. RT-qPCR experiments further verified that WSYR played an anti-inflammatory role by upregulating IL-4 and IL-10 and Epimedium brevicornu Maxim (Yinyanghuo) showed broader effect than Drynaria fortunei (Kunze) J. Sm (Gusuibu) and Cistanche deserticola Y.C.Ma (Roucongrong). By screening compounds of WSYR using molecular docking models of ESR1 and ESR2, it was further found that xanthogalenol in Gusuibu, arachidonate in Roucongrong, and anhydroicaritin in Yinyanghuo had good affinity for estrogen receptors. These findings provide evidence for an estrogen-regulating role of the three herbs in WSYR.

Critical in revealing cell heterogeneity and identifying new cell subtypes, cell clustering based on single-cell RNA sequencing (scRNA-seq) is challenging. Due to the high noise, sparsity, and poor annotation of scRNA-seq data, existing state-of-the-art cell clustering methods usually ignore gene functions and gene interactions. In this study, we propose a feature extraction method, named FEGFS, to analyze scRNA-seq data, taking advantage of known gene functions. Specifically, we first derive the functional gene sets based on Gene Ontology (GO) terms and reduce their redundancy by semantic similarity analysis and gene repetitive rate reduction. Then, we apply the kernel principal component analysis to select features on each non-redundant functional gene set, and we combine the selected features (for each functional gene set) together for subsequent clustering analysis. To test the performance of FEGFS, we apply agglomerative hierarchical clustering based on FEGFS and compared it with seven state-of-the-art clustering methods on six real scRNA-seq datasets. For small datasets like Pollen and Goolam, FEGFS outperforms all methods on all four evaluation metrics including adjusted Rand index (ARI), normalized mutual information (NMI), homogeneity score (HOM), and completeness score (COM). For example, the ARIs of FEGFS are 0.955 and 0.910, respectively, on Pollen and Goolam; and those of the second-best method are only 0.938 and 0.910, respectively. For large datasets, FEGFS also outperforms most methods. For example, the ARIs of FEGFS are 0.781 on both Klein and Zeisel, which are higher than those of all other methods but slight lower than those of SC3 (0.798 and 0.807, respectively). Moreover, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation and in inferring cell lineage trajectories. As for application, take glioma as an example; we demonstrated that our clustering methods could identify important cell clusters related to glioma and also inferred key marker genes related to these cell clusters.

Abstract Background Tail-anchored membrane proteins (TAMPs) differ from other integral membrane proteins, because they contain a single transmembrane domain at the extreme carboxyl-terminus and are therefore obliged to target to membranes post-translationally. Although 3–5% of all transmembrane proteins are predicted to be TAMPs only a small number are well characterized. Results To identify novel putative TAMPs across different species, we used TAMPfinder software to identify 859, 657 and 119 putative TAMPs in human (Homo sapiens), plant (Arabidopsis thaliana), and yeast (Saccharomyces cerevisiae), respectively. Bioinformatics analyses of these putative TAMP sequences suggest that the list is highly enriched for authentic TAMPs. To experimentally validate the software predictions several human and plant proteins identified by TAMPfinder that were previously uncharacterized were expressed in cells and visualized at subcellular membranes by fluorescence microscopy and further analyzed by carbonate extraction or by bimolecular fluorescence complementation. With the exception of the pro-apoptotic protein harakiri, which is, peripherally bound to the membrane this subset of novel proteins behave like genuine TAMPs. Comprehensive bioinformatics analysis of the generated TAMP datasets revealed previously unappreciated common and species-specific features such as the unusual size distribution of and the propensity of TAMP proteins to be part of larger complexes. Additionally, novel features of the amino acid sequences that anchor TAMPs to membranes were also revealed. Conclusions The findings in this study more than double the number of predicted annotated TAMPs and provide new insights into the common and species-specific features of TAMPs. Furthermore, the list of TAMPs and annotations provide a resource for further investigation.

Abstract Background MiRNAs can regulate gene expression directly or indirectly, and long noncoding RNAs as competing endogenous RNA (ceRNAs) can bind to miRNAs competitively and affect mRNA expression. The ceRNA network is still unclear in breast cancer. In this study, a ceRNA network was constructed, and new treatment and prognosis targets and biomarkers for breast cancer were explored. Methods A total of 1 096 cancer tissues and 112 adjacent normal tissues to cancer from the TCGA database were used to screen out significant differentially expressed mRNAs (DEMs), lncRNAs (DELs), and miRNAs (DEMis) to construct a ceRNA network. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to predict potential functions. Survival analysis was performed to predict which functions were significant for prognosis. Results From the analysis, 2 139 DEMs, 1 059 DELs, and 84 DEMis were obtained. Targeting predictions for DEMis‐DELs and DEMis‐DEMs can yield 26 DEMs, 90 DELs, and 18 DEMis. We performed GO enrichment analysis, and the results showed that the upregulated DEMs were involved in nucleosomes, extracellular regions, and nucleosome assembly, while the downregulated DEMs were mainly involved in Z disk, muscle contraction, and structural constituents of muscle. KEGG pathway analysis was performed on all DEMs, and the pathways were enriched in retinol metabolism, steroid hormone biosynthesis, and tyrosine metabolism. Through survival analysis of the ceRNA network, we identified four DEMs, two DELs, and two DEMis that were significant for poor prognosis. Conclusions This study suggested that constructing a ceRNA network and performing survival analysis on the network could screen out new significant treatment and prognosis targets and biomarkers.

The purpose of the study is to identify and analyze patterns of the diurnal dynamics of the expression of genes that differ in the shape of the curve. It can be expected that the similarity of the patterns of daily expression (shape of the curve) of genes is a reflection of the synchronization of gene expression by common external and internal signals or participation in similar biological processes. Different signals that have daily dynamics (light, activity, nutrition, stress, temperature, etc.) can affect different levels of expression regulation, which can be manifested in various forms of patterns of daily gene expression. In our research, we used experimental data on gene expression at the level of translation (ribosome profling) in the liver and kidney of a mouse (GSE67305 and GSE81283). To identify genes with a daily rhythm of expression, we used a oneway analysis of variance. To identify similar­in­shape curves of the daily dynamics of gene expression, we propose an approach based on cluster analysis. The distance between the genes was calculated by aligning the phases and fnding the maximum cross­correlation between the patterns of the daily expression of these genes by the cyclic shift. This approach allowed us to identify genes that have not only expression patterns with a single maximum (sinusoidal, asymmetrical, shifted to the left or right, pulsed), but also complex composite signals with several extremes. As a result, the groups of genes united by the similarity of the shape of the daily expression curve without regard to their phase characteristics were identifed. GO enrichment analysis of groups of genes with sharply different patterns of daily expression (sinusoidal and pulsed) in the mouse kidneys and liver showed that the group of genes with a sinusoidal pattern was more associated with regulation of circadian rhythm and metabolism. The group of genes with a pulsed pattern is largely associated with the protective functions of the organism, which require the quick response. Thus, our studies have confrmed the effectiveness of the proposed approach to the analysis of the diurnal dynamics of gene expression. The identifed dynamic patterns of diurnal expression are important for the further study of complex circadian regulation, synchronization and interaction of biological processes with diurnal dynamics in mammals.

Purpose: To develop a comprehensive differential expression gene profile as well as a prediction model based on the expression analysis of pediatric sepsis specimens.Methods: In this study, compared with control specimens, a total of 708 differentially expressed genes in pediatric sepsis (case–control at a ratio of 1:3) were identified, including 507 up-regulated and 201 down-regulated ones. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed genes indicated the close interaction between neutrophil activation, neutrophil degranulation, hematopoietic cell lineage, Staphylococcus aureus infection, and periodontitis. Meanwhile, the results also suggested a significant difference for 16 kinds of immune cell compositions between two sample sets. The two potential selected biomarkers (MMP and MPO) had been validated in septic children patients by the ELISA method.Conclusion: This study identified two potential hub gene biomarkers and established a differentially expressed genes-based prediction model for pediatric sepsis, which provided a valuable reference for future clinical research.

A number of methods are being developed and used for analysis of gene expression data such as RNA-Seq data. Most of these tools focus on finding genes that are responsible for the disease conditions. Methods such as co-expression network generation, module detection and differential co-expression analysis are used to look into specific changes in the gene expression data among different conditions. In this paper, a comparative study of four differential co-expression analysis tools are presented, namely, WGCNA, DiffCorr, MODA and CEMiTool, for RNA-Seq data. The different methods used by these tools are studied and tested on schizophrenia and bipolar disorder datasets and their effectiveness in finding the related differentially co-expressed genes and pathways are being discussed. The relevancy of the resultant genes and pathways are decided on the basis of whether the genes and pathways are associated with the given disease conditions.

Abstract Background The aim of this research was to investigate the retinal transcriptome changes in long‐term streptozotocin (STZ)‐induced rats' retinas using RNA sequencing (RNA‐seq), to explore the molecular mechanisms of diabetic retinopathy (DR), and to identify novel targets for the treatment of DR by comparing the gene expression profile we obtained. Methods In this study, 6 healthy male SD rats were randomly divided into wild‐type (WT) group and streptozotocin (STZ)‐induced group, 3 rats each group. After 6 months, 3 normal retina samples and 3 DM retina samples (2 retinas from the same rat were considered as 1 sample) were tested and differentially expressed genes (DEGs) were measured by RNA‐seq technology. Then, we did Gene Ontology (GO) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis and validated the results of RNA‐seq through qRT–PCR. Results A total of 118 DEGs were identified, of which 72 were up‐regulated and 46 were down‐regulated. The enriched GO terms showed that 3 most significant enrichment terms were binding (molecular function), cell part (cellular component), and biological regulation (biological process). The results of the KEGG pathway analysis revealed a significant enrichment in cell adhesion molecules, PI3K‐Akt signaling pathway, and allograft rejection, etc. Conclusion Our research has identified specific DEGs and also speculated their potential functions, which will provide novel targets to explore the molecular mechanisms of DR.

The paper presents the results of a study of the tissue­specificity of the circadian phase characteristics of biological processes in the mouse liver and kidneys. We performed a comparative analysis of the translatomes in these two organs based on experimental data on the daily dynamics of the level of translation of mouse genes from the GEO database (GSE67305 and GSE81283) obtained by ribosome profiling. Genes with a pronounced daily dynamics of translation were revealed (3 358 genes in the liver and 2 938 in the kidneys). Further, for each of the 12 time points (ZT0–ZT22), for each tissue (liver, kidneys), groups of genes that were in a phase with an increased level of translation were identified. It was assumed that the gene is in a phase with an increased level of translation if at a given time point its ribosome profiling rate for both replicas exceeded the daily average value for this gene. The greatest number of rhythmic genes in the liver has an increased level of translation at the beginning of the dark phase of the day corresponding to increased animal activity. In the kidneys, the differences in the distribution of the number of genes in the phase of an elevated translation level by the time of day were less pronounced, and the maximum number of such genes was observed from the middle of the light phase of the day to the middle of the dark one. A statistical analysis of enrichment of Gene Ontology terms in these twelve gene groups in the liver and kidneys was perform ed. Analyzing the processes, the rhythmicity of which is typical of both liver and kidneys, we have identified the processes, the circadian phase characteristics of which in these tissues coincide and the processes having essentially different temporal phase patterns for these tissues. Processes with strict tissue­specific rhythmic translation have also been identified. The approach used in our work allows us to analyze the organo/tissue­specificity of the phase characteristics of biological processes, and the results emphasize the need to take into account the phase circadian characteristics when comparing the features of the course of biological processes in various organs.