Bioinformatics Analysis Methods Research Articles

Comprehensive Analysis of Expression and Prognostic Value of MS4As in Glioma.

Glioma is the most common malignancy of the nervous system with high mortality rates. The MS4A family members have been reported as potential prognostic biomarkers in several cancers; however, the relationship between the MS4A family and glioma has not been clearly confirmed. In our study, we explored the prognostic value of MS4As as well as their potential pro-cancer mechanisms of glioma. Using bioinformatics analysis methods based on the data from public databases, we found that the expression of MS4A4A, MS4A4E, MS4A6A, MS4A7, TMEM176A, and TMEM176B was significantly overexpressed in glioma tissues compared with that of normal tissues. The Kaplan–Meier method and Cox proportional hazards models revealed that high levels of MS4As can be associated with a poorer prognosis; TMEM176A, TMEM176B, age, WHO grade, and IDH status were identified as independent prognostic factors. Enrichment analysis predicted that MS4As were related to tumor-related pathways and immune response, which might regulate the process of MS4As promoting tumorigenesis. Additionally, we analyzed the correlations of MS4A expression with immune cells and immune inhibitory molecules. Finally, data from the cell culture suggested that knockdown of the TMEM176B gene contributes to the decreased proliferation and migration of glioma cells. In conclusion, MS4A4A, MS4A4E, MS4A6A, MS4A7, TMEM176A, and TMEM176B may act as potential diagnostic or prognostic biomarkers in glioma and play a role in forming the immune microenvironment in gliomas.

High-throughput sequencing reveals rhizosphere fungal community composition and diversity at different growth stages of Populus euphratica in the lower reaches of the Tarim River.

BackgroundPopulus euphratica is one of the most ancient and primitive tree species of Populus spp and plays an important role in maintaining the ecological balance in desert areas. To decipher the diversity, community structure, and relationship between rhizosphere fungi and environmental factors at different growth stages of P. euphratica demands an in-depth investigation.MethodsIn this study, P. euphratica at different growth stages (young, medium, overripe, and decline periods) was selected as the research object, based on the determination of the physicochemical properties of its rhizosphere soil, the fungal community structure and diversity of P. euphratica and their correlation with soil physicochemical properties were comprehensively analyzed through high-throughput sequencing technology (internal transcribed spacer (ITS)) and bioinformatics analysis methods.ResultsAccording to the analysis of OTU annotation results, the rhizosphere soil fungal communities identified in Populus euphratica were categorized into10 phyla, 36 classes, 77 orders, 165 families, 275 genera and 353 species. The alpha diversity analysis showed that there was no obvious change between the different growth stages, while beta diversity analysis showed that there were significantly differences in the composition of rhizosphere soil fungal communities between mature and overripe trees (R2 = 0.31, P = 0.001), mature and deadwood (R2 = 0.28, P = 0.001). Ascomycota and Basidiomycota were dominant phyla in the rhizosphere fungal community and the dominant genera were Geopora, Chondrostereum and unidentified_Sordariales_sp. The relative abundance of the top ten fungi at each classification level differed greatly in different stages. Canonical correspondence analysis (CCA) and Spearman’s correlation analysis showed that conductivity (EC) was the main soil factor affecting the composition of Populus euphratica rhizosphere soil fungal community (P < 0.01), followed by total dissolvable salts (TDS) and available potassium (AK) (P < 0.05).ConclusionsOur data revealed that the rhizosphere fungal communities at the different growth stages of P. euphratica have differences, conductivity (EC) was the key factor driving rhizosphere fungi diversity and community structure, followed by total dissolvable salts (TDS) and available potassium (AK).

Development and validation of novel prognostic models based on RNA-binding proteins in breast cancer.

ObjectivesWe aimed to construct novel prognostic models based on RNA-binding proteins (RBPs) in breast cancer (BRCA) and explore their roles in this disease and their effects on tumor-infiltrating immune cells (TIICs).MethodsDatasets were downloaded from the Gene Expression Omnibus (GEO) database. Functions and prognostic values of RBPs were systematically investigated using a series of bioinformatics analysis methods. TIICs were assessed using CIBERSORT.ResultsOverall, 138 differentially expressed RBPs were identified, of which 86 were upregulated and 52 were downregulated. Of these, 13 RBPs were identified as prognosis-related and adopted to construct an overall survival (OS) model, while 12 RBPs were used for the relapse-free survival (RFS) model. High-risk patients had poorer OS and RFS rates than low-risk patients. The results indicate that the OS and RFS models are good prognostic models with reliable predictive abilities. In addition, the proportions of CD8, CD4 naïve, and CD4 memory resting T cells, as well as resting dendritic cells, were significantly different between the low-risk and high-risk groups in the OS model.ConclusionsOS and RFS signatures can be used as reliable BRCA prognostic biomarkers. This work will help understand the prognostic roles and functions of RBPs in BRCA.

Establishment of prognostic risk model and drug sensitivity based on prognostic related genes of esophageal cancer

At present, the treatment of esophageal cancer (EC) is mainly surgical and drug treatment. However, due to drug resistance, these therapies can not effectively improve the prognosis of patients with the EC. Therefore, a multigene prognostic risk scoring system was constructed by bioinformatics analysis method to provide a theoretical basis for the prognosis and treatment decision of EC. The gene expression profiles and clinical data of esophageal cancer patients were gathered from the Cancer Genome Atlas TCGA database, and the differentially expressed genes (DEGs) were screened by R software. Genes with prognostic value were screened by Kaplan Meier analysis, followed by functional enrichment analysis. A cox regression model was used to construct the prognostic risk score model of DEGs. ROC curve and survival curve were utilized to evaluate the performance of the model. Univariate and multivariate Cox regression analysis was used to evaluate whether the model has an independent prognostic value. Network tool mirdip was used to find miRNAs that may regulate risk genes, and Cytoscape software was used to construct gene miRNA regulatory network. GSCA platform is used to analyze the relationship between gene expression and drug sensitivity. 41 DEGs related to prognosis were pre-liminarily screened by survival analysis. A prognostic risk scoring model composed of 8 DEGs (APOA2, COX6A2, CLCNKB, BHLHA15, HIST1H1E, FABP3, UBE2C and ERO1B) was built by Cox regression analysis. In this model, the prognosis of the high-risk score group was poor (P < 0.001). The ROC curve showed that (AUC = 0.862) the model had a good performance in predicting prognosis. In Cox regression analysis, the comprehensive risk score can be employed as an independent prognostic factor of the EC. HIST1H1E, UBE2C and ERO1B interacted with differentially expressed miRNAs. High expression of HIST1H1E was resistant to trametinib, selumetinib, RDEA119, docetaxel and 17-AAG, High expression of UBE2C was resistant to masitinib, and Low expression of ERO1B made the EC more sensitive to FK866. We constructed an EC risk score model composed of 8 DEGs and gene resistance analysis, which can provide reference for prognosis prediction, diagnosis and treatment of the EC patients.

Construction and Validation of Angiogenesis-Related Prognostic Risk Signature to Facilitate Survival Prediction and Biomarker Excavation of Breast Cancer Patients

This study is aimed at exploring the potential mechanism of angiogenesis, a biological process-related gene in breast cancer (BRCA), and constructing a risk model related to the prognosis of BRCA patients. We used multiple bioinformatics databases and multiple bioinformatics analysis methods to complete our exploration in this research. First, we use the RNA-seq transcriptome data in the TCGA database to conduct a preliminary screening of angiogenesis-related genes through univariate Cox curve analysis and then use LASSO regression curve analysis for secondary screening. We successfully established a risk model consisting of seven angiogenesis-related genes in BRCA. The results of ROC curve analysis show that the risk model has good prediction accuracy. We can successfully divide BRCA patients into the high-risk and low-risk groups with significant prognostic differences based on this risk model. In addition, we used angiogenesis-related genes to perform cluster analysis in BRCA patients and successfully divided BRCA patients into three clusters with significant prognostic differences, namely, cluster 1, cluster 2, and cluster 3. Subsequently, we combined the clinical-pathological data for correlation analysis, and there is a significant correlation between the risk model and the patient's T and stage. Multivariate Cox regression curve analysis showed that the age of BRCA patients and the risk score of the risk model could be used as independent risk factors in the progression of BRCA. In particular, based on this angiogenesis-related risk model, we have drawn a matching nomogram that can predict the 5-, 7-, and 10-year overall survival rates of BRCA patients. Subsequently, we performed a series of pan-cancer analyses of CNV, SNV, OS, methylation, and immune infiltration for this risk model gene and used GDSC data to explore drug sensitivity. Subsequently, to gain insight into the protein expression of these risk model genes in BRCA, we used the immunohistochemical data in the THPA database for verification. The results showed that the protein expressions of IL18, RUNX1, SCG2, and THY1 molecules in BRCA tissues were significantly higher than those in normal breast tissues, while the protein expressions of PF4 and TNFSF12 molecules in BRCA tissues were significantly lower than those in normal breast tissues. Finally, we conducted multiple GSEA analyses to explore the biological pathways these risk model genes can cross in cancer progression. In summary, we believe that this study can provide valuable data and clues for future studies on angiogenesis in BRCA.

Гибридная стратегия биоинформатического моделирования (in silico) для изучения биологически активных пептидов молочного белка

Bioinformatic analysis methods are an auxiliary tool in the preliminary stage of research into biocatalytic conversion of proteins with predicted release of biologically active peptides. However, there are a number of factors ignored in current strategies for designing biologically active peptides, which prevents the complete prediction of their biological properties. This determines the relevance of the research objective, i.e. developing a hybrid strategy for bioinformatic modeling to study biologically active peptides of milk protein. The new strategy ranks key criteria based on high-performance algorithms of proteomic database.&#x0D; The research featured the scientific publications on in silico methods applied to biologically active peptides. Modern taxonometric methods of information retrieval were applied using the RSCI, Scopus and Web of Science databases.&#x0D; The article introduces and describes step by step the optimal in silico hybrid strategy algorithm for studying biologically active milk protein peptides. The algorithm takes into account the safety assessment of all hydrolysis products, their physicochemical and technological properties. The strategy algorithm relies on analytical data on the protein profile, the amino acid sequence of proteins that make up the raw material, taking into account their polymorphism, and the subsequent identification of bioactive amino acid sites in the protein structure. The algorithm selects optimal enzyme preparations, as well as models the hydrolysis and assesses the peptide bioactivity using proteomic databases. &#x0D; At the preliminary stage of protein hydrolysis, the new in silico strategy scientifically predicts the targeted release of stable peptide complexes of biologically active peptides with proven bioactivity, safety and sensory characteristics. The hybrid algorithm contributes to accumulation of the necessary primary data so as to reduce the time and cost of laboratory experiments.

Microbial community spatial structures in Luzhou-flavored liquor pit muds with different brewing materials.

BackgroundAlthough studies have shown that Bacteroidetes, Clostridiales, and Lactobacillales are the main components of the microbial community in pit mud during the brewing of Luzhou-flavored liquor, little is known about the effect of brewing materials on spatial structures of this microbiome.MethodsHigh-throughput sequencing of the V4–V5 region of prokaryotic 16S rRNA gene was performed to analyze the microbial community diversity and spatial heterogeneity in Luzhou-flavored liquor pit muds with different brewing ingredients. The structural characteristics and heterogeneous spatial distribution of the pit mud microbial communities were examined using bioinformatics and multivariate statistical analysis methods.ResultsOur results showed that Euryarchaeota, Actinobacteria, Bacteroidetes, Chlorobi, Chloroflexi, Firmicutes, Proteobacteria, Synergistetes, Tenericutes, and WWE1 were the dominant phyla in the pit mud microbiome. The Shannon and Simpson indices of the pit mud microbiome with three grains (M3G) in the upper layer were significantly lower than those in middle layer and bottom, whereas those of the pit mud microbiome with five grains (M5G) in bottom were significantly lower than those in middle layer (p < 0.05). There were significant differences in the microbial community compositions between the pit muds with different brewing ingredients and locations in the same pit (p < 0.05). T78 of Anaerolinaceae, Butyrivibrio, Dehalobacter_Syntrophobotulus, Desulfosporosinus, Asteroleplasma, and vadinCA02 of Synergistaceae were significantly enriched in M3G, whereas Prevotella, Vagococcus, Caldicoprobacter, Butyrivibrio, Coprococcus, Dorea, Sporanaerobacter, Tepidimicrobium, TissierellaSoehngenia, RFN20 of Erysipelotrichaceae, Sutterella, 125ds10 of Alteromonadales, Vibrio, and Sphaerochaeta were significantly enriched in M5G. This study provides a theoretical basis for exploring the influence of brewing ingredients in pit muds on the production of Luzhou-flavored liquor and the specific influence of pit mud microorganisms in different locations on liquor production.

Identification of Key Genes Associated with Endothelial Cell Dysfunction in Atherosclerosis Using Multiple Bioinformatics Tools.

Atherosclerosis is the most notable cardiovascular disease, the latter being the main cause of death globally. Endothelial cell dysfunction plays a major role in the pathogenesis of atherosclerosis. However, it is currently unclear which genes are involved between endothelial cell dysfunction and atherosclerosis. This study was aimed at identifying these genes. Based on the GSE83500 dataset, the quantification of endothelial cell function was conducted using single-sample gene set enrichment analysis; the coexpression modules were conducted using weighted correlation network analysis. After building module-trait relationships, tan and yellow modules were regarded as hub modules. 10 hub genes from each hub module were identified by the protein-protein interaction network analysis. The key genes (RAB5A, CTTN, ITGB1, and MMP9) were obtained by comparing the expression differences of the hub gene between atherosclerotic and normal groups from the GSE28829 and GSE43292 datasets, respectively. ROC analysis showed the diagnostic value of key genes. Moreover, the differential expression of key genes in normal and atherosclerotic aortic walls was verified. In vitro, we establish a model of ox-LDL-injured endothelial cells and transfect RAB5A overexpression and shRNA plasmids. The results showed that overexpression of RAB5A ameliorates the proliferation and migration function of ox-LDL-injured endothelial cells, including the ability of tubule formation. It was speculated that the interferon response, Notch signaling pathways, etc. were involved in this function of RAB5A by using gene set variation analysis. With the multiple bioinformatics analysis methods, we detected that yellow and tan modules are related to the abnormal proliferation and migration of endothelial cells associated with atherosclerosis. RAB5A, CTTN, ITGB1, and MMP9 can be used as potential targets for therapy and diagnostic markers. In vitro, overexpression of RAB5A can ameliorate the proliferation and migration function of ox-LDL-injured endothelial cells, and the possible molecules involved in this process were speculated.

Bioinformation Analysis Reveals IFIT1 as Potential Biomarkers in Central Nervous System Tuberculosis.

ObjectiveCentral nervous system tuberculosis is the most serious form of extrapulmonary tuberculosis. We aim to discover potential biomarkers involved in the development of the disease.MethodsThrough gene difference analysis, construction of a protein interaction network and tissue specific analysis and other bioinformatics analysis methods, we found out the relatively high expression of important substances in the central nervous system, interferon induced protein with tetratricopeptide repeats 1. Subsequently, the lesion tissue and the resection margin tissue away from the lesion were collected from the 6 cases of central nervous system tuberculosis patients diagnosed from 2019 to 2020, and the pathological manifestations were observed by Hematoxylin and Eosin (H&E) staining, and the expression of IFIT1 was verified by immunohistochemistry.ResultsA total of 101 differential genes were analyzed between extrapulmonary tuberculosis patients and normal people, and they were mainly enriched in the interferon pathway. The protein interaction network unearthed 34 key genes. Through tissue specific analysis, it was found that IFIT1 is relatively high in the central nervous system. H&E staining showed the expression of multinucleated macrophages, and immunohistochemistry showed that IFIT1 was significantly positively expressed in the lesion tissue.ConclusionIFIT1 is an important substance involved in central nervous system tuberculosis.

APOA1 Level is Negatively Correlated with the Severity of COVID-19.

ObjectiveWe used public data to analyze the proteomics, metabolomics and transcriptomics characteristics of COVID-19 patients to identify potential therapeutic targets. More importantly, we also collected clinical data for verification to make the analysis results more reliable.MethodsDownload the serum proteomics and metabolomics data of COVID-19 patients and describe their changes in serum proteins and metabolites, and use bioinformatics analysis methods to identify potential biomarkers and therapeutic targets. Finally, clinical data and experimental data of cell infection were combined for verification.ResultsIt was found that the serum apolipoprotein A1 (APOA1) protein level in COVID-19 patients was down-regulated (log2FC = −0.39, false discovery rate (FDR) < 0.001), and the degree of reduction in the severe group was more significant (kruskal-test p = 2.5e-05). What is more, APOA1 was not only expressed lower in male patients (Wilcox-test p = 0.012), but also negatively correlated with C-reactive protein (CRP, r = −0.37, p = 0.019). The experiment data from SARS-CoV-2 infected cells further showed that the protein and transcript level of APOA1 gradually decreased as the infection time increased, and the transcription level (log2FC = −8.3, FDR = 0.0015) was more down-regulated than protein level (log2FC = −0.95, FDR = 0.0014). More importantly, the collected clinical data also confirmed that APOA1 was down-regulated in COVID-19 patients (kruskal-test p = 0.001), and APOA1 levels are negatively correlated with IL6 (r = −0.396, p = 2.22e-07), D-dimers (DD, r = −0.262, p = 8.19e-04), prothrombin time (PT, r = −0.464, p = 6.68e-10) and thrombin time (TT, r = −0.279, p = 3.46e-04).ConclusionThe degree of down-regulation of APOA1 is positively correlated with the severity of COVID-19, and the expression level of APOA1 is negatively correlated with CRP, IL6, DD, PT, TT, and positively correlated with HD and LDL. This indicates that APOA1 may be a key molecule in tandem acute inflammatory response, coagulation abnormalities and cholesterol metabolism disorder in COVID-19, and could be a potential therapeutic target.

GFPT2 pan-cancer analysis and its prognostic and tumor microenvironment associations*

Abstract Objective Glutamine fructose-6-phosphate transaminase 2 (GFPT2) is involved in a wide range of biological functions in human cancer. However, few studies have comprehensively analyzed the correlation between GFPT2 and different cancer prognoses and tumor microenvironments (TMEs). Methods We evaluated the expression level and prognostic value of GFPT2 using updated public databases and multiple comprehensive bioinformatics analysis methods and explored the relationship between GFPT2 expression and immune infiltration, immune neoantigens, tumor mutational burden (TMB), and microsatellite instability in pan-cancer. Results GFPT2 was highly expressed in five cancers. GFPT2 expression correlates with the prognosis of several cancers from The Cancer Genome Atlas (TCGA) and is significantly associated with stromal and immune scores in pan-cancer. High GFPT2 expression in BLCA, BRCA, and CHOL was positively correlated with the infiltration of immune cells, such as B-cells, CD4+ T, CD8+ T cells, dendritic cells, neutrophils, and macrophages. Conclusion High GFPT2 expression may modify the outcomes of patients with BLCA, BRCA, or CHOL cancers by increasing immune cell infiltration. These findings may provide insights for further investigation into GFPT2 as a potential target in pan-cancer.

Construction and validation of a metabolic gene-associated prognostic model for cervical carcinoma and the role on tumor microenvironment and immunity.

Metabolic reprogramming is a common feature of tumor cells and is associated with tumorigenesis and progression. In this study, a metabolic gene-associated prognostic model (MGPM) was constructed using multiple bioinformatics analysis methods in cervical carcinoma (CC) tissues from The Cancer Genome Atlas (TCGA) database, which comprised fifteen differentially expressed metabolic genes (DEMGs). Patients were divided into a high-risk group with shorter overall survival (OS) and a low-risk group with better survival. Receiver operating characteristic (ROC) curve analysis showed that the MGPM precisely predicted the 1-, 3- and 5-year survival of CC patients. As expected, MGPM exhibited a favorable prognostic significance in the training and testing datasets of TCGA. And the clinicopathological parameters including stage, tumor (T) and metastasis (M) classifications had significant differences in low- and high-risk groups, which further demonstrated the MGPM had a favorite prognostic prediction ability. Additionally, patients with low-ESTMATEScore had a shorter OS and when those combined with high-risk scores presented a worse prognosis. Through “CIBERSORT” package and Wilcoxon rank-sum test, patients in the high-risk group with a poor prognosis showed lower levels of infiltration of T cell CD8 (P < 0.001), T cells memory activated (P = 0.010) and mast cells resting (P < 0.001), and higher levels of mast cells activated (P < 0.001), and we also found these patients had a worse response for immunosuppressive therapy. These findings demonstrate that MGPM accurately predicts survival outcomes in CC patients, which will be helpful for further optimizing immunotherapies for cancer by reprogramming its cell metabolism.

RNA-seq of buffalo fibroblasts over-expressed pluripotent-related genes to investigate characteristics of its preliminarily reprogrammed stage

Induced pluripotent stem cells (iPSCs) can enhance the efficiency of buffalo genetic improvements because of their differentiation potential and proliferation ability, which are similar to those of embryonic stem cells. However, very few studies have focussed on buffalo iPSCs, and a stable induction system has not been established for buffalo somatic cell reprogramming. In this study, we constructed a PiggyBac transposon vector co-expressing buffalo OCT4, C-MYC, KLF4 and SOX2 genes (PB_OMKS) separated by the nucleotide sequence of three 2A peptides and established the buffalo foetal skin fibroblast (BFSF) cell line BFSF_OMKS. RNA-seq technology and bioinformatics analysis methods were mainly employed to perform a transcriptome analysis between BFSF and BFSF_OMKS. The results revealed that over-expression of OCT4, C-MYC, KLF4 and SOX2 in BFSFs led to the activation of reprogramming-related LIF, activin, BMP4, SMAD1/5/9 and Wnt signals. These results increased our understanding of buffalo somatic cell reprogramming mechanisms and could provide a possible theory for the selection of small-molecule cocktails to promote reprogramming.

KIF11, a plus end-directed kinesin, as a key gene in benzo(a)pyrene-induced non-small cell lung cancer

Evidence indicates that Benzo(a)pyrenediol-epoxide (BPDE) can damage lung cells, resulting in carcinogenesis with complex mechanisms. We aimed to explore the genes and pathway variations in this process. First, the key gene was screened out and identified through data mining, and then, it was in turn validated by bioinformatics analysis and experimental methods. Consequently, 106 up-regulated and 260 down-regulated differentially expressed genes were yielded, which were enriched in various pathways, such as Cell cycle, and p53 signaling pathway. Then, KIF11 was identified as the key gene. Overexpression of KIF11 in lung cancer had a correlation with advanced pathological grade, advanced T stage, and presence of lymph node metastasis, which predicted poor prognosis. In summary, the present study revealed that KIF11 might be a key gene in the tumorigenesis of BPDE-related lung cancer, raising the possibility of KIF11 as a target for BPDE-induced lung cancer prevention and therapy.

Lnc-CPLC promotes the progression of colorectal cancer via regulating ZBTB34 by competitively binding miR-4319.

Long noncoding RNAs (lncRNAs) have been considered as regulatory molecules that play crucial roles in diverse biological processes, including the regulation of tumor progression. However, in colorectal cancer (CRC), due to the complex regulatory relationships involved in lncRNAs, the details of the specific mechanism still need to be elucidated. To discover the key regulatory role of lncRNA in CRC, we used bioinformatics analysis methods for preliminary screening. Through the combination of in vitro and in vivo verification, we further comprehensively analyzed the specific regulation of the key gene and the related key lncRNA in CRC. We found that ZBTB34 and lnc-CPLC (CRC progression-associated lncRNA)had a strong correlation, which plays a key role in the regulation of CRC. Furthermore, by exerting the "sponge" function, lnc-CPLC could bind to miR4319 and release its binding to the 3'UTR of ZBTB34 mRNA. Our results reveal the mechanism of the lnc-CPLC/miR-4319/ZBTB34 signal axis in CRC and provide evidence for elucidating the complex molecular mechanisms in tumors.

Significantly Different Lipid Profile Analysis of Litopenaeus vannamei under Low-Temperature Storage by UPLC-Q-Exactive Orbitrap/MS.

Low-temperature storage is one of the most important preservation methods for aquatic product storage. However, the effects of low-temperature storage on the lipid profiles of shrimp are unclear. Herein, UPLC-Q-Exactive Orbitrap/MS combined with LipidSearch software was applied to analyze the effect of three low storage temperatures (4 °C, −2 °C, and −18 °C) on the lipidomics of Litopenaeus vannamei. A total of 15 lipid classes were analyzed, and PC, PE, DG, and TG accounted for vast majority of peak areas. Furthermore, 531 individual lipid variables enriched in 12 metabolic pathways were identified via bioinformatics analysis methods. A total of 56 significantly different lipid molecular species (55 belonging to PC, PE, DG, and TG) were selected as potential biomarkers of lipid oxidation via correlational analysis between physical properties (texture and color) and individual lipid variables. The results indicated that the three low storage temperatures caused different effects on the lipidomics profile of L. vannamei, and PC, PE, DG, and TG could become potential focuses in further studies of lipid oxidation in L. vannamei.

Identification, expression patterns and RNA interference of Capa peptide receptors in Dendroctonus armandi larvae under cold

AbstractCapa peptides are of great importance for both understanding the control of salt balance by neuropeptides and revealing the mechanisms of response to cold stress. Presently, research on the physiological function of neuropeptides typically includes identifying and analysing specific receptors and their interactions. Given their role in osmotic balance and the importance of cold tolerance, this study attempted to identify the sequence and function of Capa peptide receptors (CapaRs) in Dendroctonus armandi and to prove that CapaRs play an important role in the cold stress responses. In this study, two full‐length cDNAs encoding more than 400 amino acids of Dendroctonus armandi Capa peptide receptor genes (DaCapaRs) and their sequence features were identified using bioinformatic analysis methods. The alignment of DaCapaRs with homologous proteins from other species indicated that the most similar are Dendroctonus ponderosae Capa peptide receptors. The expression of DaCapaRs was high in December and in the larvae, adults and head and midgut of Dendroctonus armandi. In addition, with decreasing temperatures and long‐term exposure to cold conditions, the expression of DaCapaRs increased markedly when the temperature was 4 to –10°C. RNA interference analysis demonstrated that DaCapaRs play an important role in the cold tolerance of overwintering larvae and indicated that a reverse interaction occurs between DaCapaRs and aquaporins. The results showed that DaCapaRs play an essential role in understanding the neuropeptide regulation of cold tolerance in overwintering Dendroctonus armandi larvae and that it can assist in understanding adaptive evolution of Dendroctonus armandi and its relationship with the ecological environment of the Qinling Mountains.

Use of transcriptomic analysis to identify microRNAs related to the effect of stress on thymus immune function in a chicken stress model

In modern poultry production, stress-induced immunosuppression leads to serious economic losses and harm to animals, but the molecular mechanisms governing the effects of stress on the chicken thymus have not been elucidated. In this study, we successfully constructed a stress model of 7-day-old Gushi chickens by adding exogenous corticosterone (CORT) to their diet and determined the microRNA (miRNA) expression profile of thymus tissues using RNA-seq technology. The results identified 51 differentially expressed miRNAs (DEMs), including 30 upregulated miRNAs and 21 downregulated miRNAs. A total of 164 target genes of the DEMs were predicted based on bioinformatic analysis methods, and Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of these target genes were performed. The results from the GO enrichment analysis of the target genes identified 349 significantly enriched terms, including terms associated with the stress response and immune function that are primarily involved in the negative regulation of phagocytosis, the response to stress and the cellular response to stimulus. The KEGG pathway analysis indicated that the enriched pathways related to immunity or stress included the MAPK signaling pathway, lysosomes, endocytosis, and the RIG-I-like receptor signaling pathway. Among these pathways, DEMs (such as gga-miR-2954, gga-miR-106-5p, and gga-miR-16-5p) and corresponding target genes (such as IL11Ra, SIKE1, and CX3CL1) might be strongly correlated with thymic immunity in chickens. The results of this study provide a reference for further research on the molecular regulatory mechanisms governing the effect of stress on the immune function of the chicken thymus.

Identification of immune related cells and crucial genes in the peripheral blood of ankylosing spondylitis by integrated bioinformatics analysis.

BackgroundAnkylosing spondylitis (AS) is a progressive rheumatic disease and studies reveal that the immune system is critical for the pathogenesis of AS. In the present study, various bioinformatics analysis methods were comprehensively applied, designed to identify potential key genes and inflammation states of AS.MethodsThe transcriptome profiles of GSE25101 and GSE73754 obtained from the Gene Expression Omnibus (GEO) database were merged for subsequent analyses. The differentially expressed genes (DEGs) were identified using the Bioconductor package Limma and threshold values. Functional enrichment and pathway enrichment analyses were performed using the clusterProfiler package and Gene Set Enrichment Analysis (GSEA). Next, protein–protein interaction (PPI) network of the identified DEGs was constructed by the online database, the Search Tool for the Retrieval of Interacting Genes (STRING), visualization and analysis were performed through Cytoscape software. Subsequently, we applied CIBERSORT algorithm to identify subpopulation proportions of immune cells in peripheral blood samples. Finally, we validated the hub genes with the GSE18781 dataset. Samples were collected from patients to validate gene and protein expression using qRT-PCR and ELISA.ResultsA total of 334 DEGs were identified, including 182 upregulated and 152 downregulated DEGs, between AS patients and normal human controls, which were primarily involved in immune response, autophagy, and natural killer cell-mediated cytotoxicity. The most prominent module and candidate biomarkers were identified from the PPI network. Biomarkers were selected for validation and their expressions were significantly decreased in peripheral blood samples which was consistent with transcriptome sequencing results. Nine genes with AUC > 0.70 were considered to be AS hub genes for ROC curve analysis, including GZMA, GZMK, PRF1, GNLY, NKG7, KLRB1, KLRD1, IL2RB and CD247. Furthermore, CIBERSORT results suggest that AS contained a higher proportion of CD8+ T cells, naive CD4+ T cells, neutrophils, and lower levels of gamma delta T cells compared with the normal controls.ConclusionIn this study, we identified DEGs combined with their closely related biological functions and propose that granule-associated proteins and immune infiltration maybe involved in the progression of ankylosing spondylitis. These validated hub genes may provide new perspectives for understanding the molecular mechanisms of ankylosing spondylitis.

The Evolving Faces of the SARS-CoV-2 Genome.

Surveillance of the evolving SARS-CoV-2 genome combined with epidemiological monitoring and emerging vaccination became paramount tasks to control the pandemic which is rapidly changing in time and space. Genomic surveillance must combine generation and sharing sequence data with appropriate bioinformatics monitoring and analysis methods. We applied molecular portrayal using self-organizing maps machine learning (SOM portrayal) to characterize the diversity of the virus genomes, their mutual relatedness and development since the beginning of the pandemic. The genetic landscape obtained visualizes the relevant mutations in a lineage-specific fashion and provides developmental paths in genetic state space from early lineages towards the variants of concern alpha, beta, gamma and delta. The different genes of the virus have specific footprints in the landscape reflecting their biological impact. SOM portrayal provides a novel option for ‘bioinformatics surveillance’ of the pandemic, with strong odds regarding visualization, intuitive perception and ‘personalization’ of the mutational patterns of the virus genomes.