Although selection of the early age at puberty in gilts will make for a favorable effect on the reproductivity of sow, a large proportion of phenotypic variation in age at puberty of gilts cannot be explained by genetics. Previous studies have implicated hypothalamic DNA methylation in the onset of puberty in mammals. However, the underlying molecular mechanism regarding the regulation of the onset of puberty has remained largely unexplored in gilts. Herein, the genome-scale DNA methylation of hypothalamus was acquired, using the reduced representation bisulfite sequencing, to compare and describe the changes of DNA methylation across Pre-, In- and Post-pubertal gilts. In this study, the average methylation levels of CpGs and CpHs (where H = C, T, or A) in CpG islands- and gene-related regions were gradually decreased in hypothalamic methylomes during the pubertal transition. Comparisons of Pre- vs. In-, In- vs. Post-, and Pre- vs. Post-pubertal stage revealed that there were 85726, 92914, and 100421 differentially methylated CpGs and 5940, 14804, and 16893 differentially methylated CpHs (where H = C, T, or A) in the hypothalamic methylomes. The methylation changes of CpHs were more dynamic than that of CpGs, and methylation changes of CpGs and CpHs were likely to be, respectively, involved in the developmental processes of reproduction and the molecular processes of cellular communications in the hypothalamus. Moreover, methylation changes of CpHs were observed to overrepresent in the quantitative trait loci of age at puberty, and the biological function of these CpH methylation changes was enriched in the pancreas development in gilts. Furthermore, the mRNA levels of several differentially CpG or CpH methylated genes related to the transcription of RNA II polymerase, GnRH signaling pathway, Estrogen signaling pathway, PI3K-AKt signaling pathway, and Insulin signaling pathway, including MAX, MMP2, FGF11, IGF1R, FGF21, and GSK3B, were significantly changed across these pubertal stages in the hypothalamus. These results will help our understanding of how DNA methylation contributes to phenotypic variation of age at puberty.

Periodontitis is the most prevalent inflammatory disease of the periodontium, and is related to oral and systemic health. Exosomes are emerging as non-invasive biomarker for liquid biopsy. We here evaluated the levels of programmed death-ligand 1 (PD-L1) mRNA in salivary exosomes from patients with periodontitis and non-periodontitis controls. The purposes of this study were to establish a procedure for isolation and detection of mRNA in exosomes from saliva of periodontitis patients, to characterize the level of salivary exosomal PD-L1, and to illustrate its clinical relevance. Bioinformatics analysis suggested that periodontitis was associated with an inflammation gene expression signature, that PD-L1 expression positively correlated with inflammation in periodontitis based on gene set enrichment analysis (GSEA) and that PD-L1 expression was remarkably elevated in periodontitis patients versus control subjects. Exosomal RNAs were successfully isolated from saliva of 61 patients and 30 controls and were subjected to qRT-PCR. Levels of PD-L1 mRNA in salivary exosomes were higher in periodontitis patients than controls (P < 0.01). Salivary exosomal PD-L1 mRNA showed significant difference between the stages of periodontitis. In summary, the protocols for isolating and detecting exosomal RNA from saliva of periodontitis patients were, for the first time, characterized. The current study suggests that assay of exosomes-based PD-L1 mRNA in saliva has potential to distinguish periodontitis from the healthy, and the levels correlate with the severity/stage of periodontitis.

In the last years, a series of methods for genomic prediction (GP) have been established, and the advantages of GP over pedigree best linear unbiased prediction (BLUP) have been reported. However, the majority of previously proposed GP models are purely based on mathematical considerations while seldom take the abundant biological knowledge into account. Prediction ability of those models largely depends on the consistency between the statistical assumptions and the underlying genetic architectures of traits of interest. In this study, gene annotation information was incorporated into GP models by constructing haplotypes with SNPs mapped to genic regions. Haplotype allele similarity between pairs of individuals was measured through different approaches at single gene level and then converted into whole genome level, which was then treated as a special kernel and used in kernel based GP models. Results shown that the gene annotation guided methods gave higher or at least comparable predictive ability in some traits, especially in the Arabidopsis dataset and the rice breeding population. Compared to SNP models and haplotype models without gene annotation, the gene annotation based models improved the predictive ability by 0.56~26.67% in the Arabidopsis and 1.62~16.53% in the rice breeding population, respectively. However, incorporating gene annotation slightly improved the predictive ability for several traits but did not show any extra gain for the rest traits in a chicken population. In conclusion, integrating gene annotation into GP models could be beneficial for some traits, species, and populations compared to SNP models and haplotype models without gene annotation. However, more studies are yet to be conducted to implicitly investigate the characteristics of these gene annotation guided models.

Background: In western swine breeds, up to 30% of embryonic losses occur during early pregnancy, and the majority of embryonic losses happens during implantation. In this period, maternal recognition of pregnancy begins to occur and blastocysts undergo dramatic morphologic changes. As with other species, changes in the uterine environment plays an important role in the process of embryo implantation in pigs. Erhualian (ER) pigs, one of the Chinese Taihu swine breeds, are known to have the highest litter size in the world. Experiments demonstrated that the greater embryonic survival on gestation day (GD) 12 in Chinese Taihu pigs is one important factor that contributes to enhanced litter size. This is largely controlled by maternal genes. In this study, endometrial samples were collected from pregnant Landrace×Large Yorkshire (LL) sows (parity 3) and ER sows (parity 3) on GD12 and the expression profiles of microRNAs (miRNAs) in the endometrium were compared between ER and LL using miRNA-seq technology. Results: A total of 288 miRNAs were identified in the pig endometrium, including 202 previously known and 86 novel miRNAs. The Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that highly abundant miRNAs might affect endometrial remodeling. Comparison between LL and ER sows revealed that 96 known miRNAs were differentially expressed between the two groups (including 78 up-regulated and 18 down-regulated miRNAs in ER compared to LL). Bioinformatics analysis showed that the target genes of some differentially expressed miRNAs were involved in pathways related to angiogenesis, proliferation, apoptosis, and tissue remodeling, which play critical roles in implantation by regulating endometrial structural changes and secretions of hormones, growth factors, and nutrients. Furthermore, the results demonstrated that insulin-like growth factor-1 protein expression was directly inhibited by miR-206. The lower expression of miR-206 in ER compared to LL might facilitate the angiogenesis of the endometrium during embryo implantation. Conclusions: The identified miRNAs that are differentially expressed in the endometrium of ER and LL pigs will contribute to the understanding of the role of miRNAs in embryonic implantation and the molecular mechanisms of the highest embryonic survival in Chinese ER pigs.

Background: Soft tissue sarcoma (STS) is a group of tumors with a low incidence and a complex type. Therefore, it is an arduous task to accurately diagnose and treat them. Glycolysis-related genes are closely related to tumor progression and metastasis. Hence, our study is dedicated to the development of risk characteristics and nomograms based on glycolysis-related genes to assess the survival possibility of patients with STS. Methods: All data sets used in our research include gene expression data and clinical medical characteristics in the Genomic Data Commons Data Portal (National Cancer Institute) Soft Tissue Sarcoma (TCGA SARC) and GEO database, gene sequence data of corresponding non-diseased human tissues in the Genotype Tissue Expression (GTEx).Next, transcriptome data in TCGA SARC was analyzed as the training set to construct a glycolysis-related gene risk signature and nomogram, which were confirmed in external test set. Results: We identified and verified the 7 glycolysis-related gene signature that is highly correlated with the overall survival (OS) of STS patients, which performed excellently in the evaluation of the size of AUC, and calibration curve. As well as, the results of the analysis of univariate and multivariate Cox regression demonstrated that this 7 glycolysis-related gene characteristic acts independently as an influence predictor for STS patients. Therefore, a prognostic-related nomogram combing 7 gene signature with clinical influencing features was constructed to predict OS of patients with STS in the training set that demonstrated strong predictive values for survival. Conclusion: These results demonstrate that both glycolysis-related gene risk signature and nomogram were efficient prognostic indicators for patients with STS. These findings may contribute to make individualize clinical decisions on prognosis and treatment.

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

Background: Necroptosis, a type of programmed cell death, has been implicated in a variety of cancer-related biological processes. However, the roles of necroptosis-related genes in thyroid cancer yet remain unknown. Methods: A necroptosis-related gene signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis and Cox regression analysis. The predictive value of the prognostic signature was validated in an internal cohort. Additionally, the single-sample gene set enrichment analysis (ssGSEA) was used to examine the relationships between necroptosis and immune cells, immunological functions, and immune checkpoints. Next, the modeled genes expressions were validated in 96 pairs of clinical tumor and normal tissue samples. Finally, the effects of modeled genes on PTC cells were studied by RNA interference approaches in vitro. Results: In this study, the risk signature of seven necroptosis-related genes was created to predict the prognosis of papillary thyroid cancer (PTC) patients, and all patients were divided into high- and low-risk groups. Patients in the high-risk group fared worse in terms of overall survival than those in the low-risk group. The area under the curve (AUC) of the receiving operating characteristic (ROC) curves proved the predictive capability of created signature. The risk score was found to be an independent risk factor for prognosis in multivariate Cox analysis. The low-risk group showed increased immune cell infiltration and immunological activity, implying that they might respond better to immune checkpoint inhibitor medication. Next, GEO database and qRT-PCR in 96 pairs of matched tumorous and non-tumorous tissues were used to validate the expression of the seven modeled genes in PTCs, and the results were compatible with TCGA database. Finally, overexpression of IPMK, KLF9, SPATA2 could significantly inhibit the proliferation, invasion and migration of PTC cells. Conclusion: The created necroptosis associated risk signature has the potential to have prognostic capability in PTC for patient outcome. The findings of this study could pave the way for further research into the link between necroptosis and tumor immunotherapy.

Introduction: Abdominal aortic aneurysms (AAA) are among the most lethal non-cancerous diseases. A comprehensive analysis of the AAA-related disease model has yet to be conducted. Methods: Weighted correlation network analysis (WGCNA) was performed for the AAA-related genes. Machine learning random forest and LASSO regression analysis were performed to develop the AAA-related score. Immune characteristics and epigenetic characteristics of the AAA-related score were explored. Results: Our study developed a reliable AAA-related disease model for predicting immunity and m1A/m5C/m6A/m7G epigenetic regulation. Discussion: The pathogenic roles of four model genes, UBE2K, TMEM230, VAMP7, and PUM2, in AAA, need further validation by in vitro and in vivo experiments.

Bacteroides ovatus ELH-B2 is considered as a potential next-generation probiotic due to its preventive effects on lipopolysaccharides-associated inflammation and intestinal microbiota disorders in mice. To study safety issues associated with B. ovatus ELH-B2, we conducted comprehensive and systematic experiments, including in vitro genetic assessments of potential virulence and antimicrobial resistance genes, and an in vivo acute toxicity study of both immunocompetent and immunosuppressed mice via cyclophosphamide treatment. The results indicated that this novel strain is non-toxigenic, fragilysin is not expressed, and most of potential virulence genes are correlated with cellular structures such as capsular polysaccharide and polysaccharide utilizations. The antibiotic resistance features are unlikely be transferred to other intestinal microorganisms as no plasmids nor related genomic islands were identified. Side effects were not observed in mice. B. ovatus ELH-B2 also alleviated the damages caused by cyclophosphamide injection.

Genomic prediction with imputed whole-genome sequencing (WGS) data is an attractive approach to improve predictive ability with low cost. However, high accuracy has not been realized using this method in livestock. In this study, we imputed 435 individuals from 600K single nucleotide polymorphism (SNP) chip data to WGS data using different reference panels. We also investigated the prediction accuracy of genomic best linear unbiased prediction (GBLUP) using imputed WGS data from different reference panels, linkage disequilibrium (LD)-based marker pruning, and pre-selected variants based on Genome-wide association society (GWAS) results. Results showed that the imputation accuracies from 600K to WGS data were 0.873 ± 0.038, 0.906 ± 0.036, and 0.979 ± 0.010 for the internal, external, and combined reference panels, respectively. In most traits of chickens, the prediction accuracy of imputed WGS data obtained from the internal reference panel was greater than or equal to that of the combined reference panel; the external reference panel had the lowest prediction accuracy. Compared with 600K chip data, GBLUP with imputed WGS data had only a small increase (1-3%) in prediction accuracy. Using only variants selected from imputed WGS data based on GWAS results resulted in almost no increase for most traits and even increased the bias of the regression coefficient. The impact of the degree of LD of selected and remaining variants on prediction accuracy was different. For average daily gain (ADG), residual feed intake (RFI), intestine length (IL), and body weight in 91 days (BW91), the accuracy of GBLUP increased as the degree of LD of selected variants decreased, but the opposite relationship occurred for the remaining variants. But for breast muscle weight (BMW) and average daily feed intake (ADFI), the accuracy of GBLUP increased as the degree of LD of selected variants increased, and the degree of LD of remaining variants had a small effect on prediction accuracy. Overall, the optimal imputation strategy to obtain WGS data for genomic prediction should consider the relationship between selected individuals and target population individuals to avoid heterogeneity of imputation. LD-based marker pruning can be used to improve the accuracy of genomic prediction using imputed WGS data.

In recent years, studies have shown that phytopathogenic fungi possess the ability of cross-kingdom regulation of host plants through small RNAs (sRNAs). Magnaporthe oryzae, a causative agent of rice blast, introduces disease by penetrating the rice tissues through appressoria. However, little is known about the transboundary regulation of M. oryzae sRNAs during the interaction of the pathogen with its host rice. Therefore, investigation of the regulation of M. oryzae through sRNAs in the infected rice plants has important theoretical and practical significance for disease control and production improvement. Based on the high-throughput data of M. oryzae sRNAs and the mixed sRNAs during infection, the differential expressions of sRNAs in M. oryzae before and during infection were compared, it was found that expression levels of 366 M. oryzae sRNAs were upregulated significantly during infection. We trained a SVM model which can be used to predict differentially expressed sRNAs, which has reference significance for the prediction of differentially expressed sRNAs of M. oryzae homologous species, and can facilitate the research of M. oryzae in the future. Furthermore, fifty core targets were selected from the predicted target genes on rice for functional enrichment analysis, the analysis reveals that there are nine biological processes and one KEGG pathway associated with rice growth and disease defense. These functions correspond to thirteen rice genes. A total of fourteen M. oryzae sRNAs targeting the rice genes were identified by data analysis, and their authenticity was verified in the database of M. oryzae sRNAs. The 14 M. oryzae sRNAs may participate in the transboundary regulation process and act as sRNA effectors to manipulate the rice blast process.

Poultry feed constitutes the largest cost in poultry production, estimated to be up to 70% of the total cost. Moreover, there is pressure on the poultry industry to increase production to meet the protein demand of humans and simultaneously reduce emissions to protect the environment. Therefore, improving feed efficiency plays an important role to improve profits and the environmental footprint in broiler production. In this study, using imputed whole-genome sequencing data, genome-wide association analysis (GWAS) was performed to identify single-nucleotide polymorphisms (SNPs) and genes associated with residual feed intake (RFI) and its component traits. Furthermore, a transcriptomic analysis between the high-RFI and the low-RFI groups was performed to validate the candidate genes from GWAS. The results showed that the heritability estimates of average daily gain (ADG), average daily feed intake (ADFI), and RFI were 0.29 (0.004), 0.37 (0.005), and 0.38 (0.004), respectively. Using imputed sequence-based GWAS, we identified seven significant SNPs and five candidate genes [MTSS I-BAR domain containing 1, folliculin, COP9 signalosome subunit 3, 5',3'-nucleotidase (mitochondrial), and gametocyte-specific factor 1] associated with RFI, 20 significant SNPs and one candidate gene (inositol polyphosphate multikinase) associated with ADG, and one significant SNP and one candidate gene (coatomer protein complex subunit alpha) associated with ADFI. After performing a transcriptomic analysis between the high-RFI and the low-RFI groups, both 38 up-regulated and 26 down-regulated genes were identified in the high-RFI group. Furthermore, integrating regional conditional GWAS and transcriptome analysis, ras-related dexamethasone induced 1 was the only overlapped gene associated with RFI, which also suggested that the region (GGA14: 4767015-4882318) is a new quantitative trait locus associated with RFI. In conclusion, using imputed sequence-based GWAS is an efficient method to identify significant SNPs and candidate genes in chicken. Our results provide valuable insights into the genetic mechanisms of RFI and its component traits, which would further improve the genetic gain of feed efficiency rapidly and cost-effectively in the context of marker-assisted breeding selection.

The timing of puberty in mammals marks the point at which reproduction becomes possible. Abnormalities in the timing of puberty may exert a series of negative effects on subsequent health outcomes. Alternative splicing (AS) has not only emerged as a significant factor in the transcription of genes but it is also reported to play a role in the timing of puberty. However, to date, the changes and dynamics of AS during the onset of puberty is extremely seldom explored. In the present study, we used gilts as a research model to investigated the dynamics of AS and differentially expressed AS (DEAS) events within the hypothalamus-pituitary-ovary (HPO) axis across pre-, in-, and post-puberty. We detected 3,390, 6,098, and 9,085 DEAS events in the hypothalamus, pituitary, and ovary when compared across pre-, in-, and post-pubertal stages, respectively. Within the entire HPO axis, we also identified 22,889, 22,857, and 21,055 DEAS events in the pre-, in-, and post-pubertal stages, respectively. Further analysis revealed that the differentially spliced genes (DSGs) associated with staged DEAS events were likely to be enriched in the oxytocin signaling pathway, thyroid hormone signaling pathway, GnRH signaling pathway, and oocyte meiosis signaling pathway. The DSGs associated with DEAS events across the entire HPO axis were enriched in endocytosis signaling pathway, the MAPK signaling pathway, and the Rap1 signaling pathway. Moreover. the ASs of TAC1, TACR3, CYP19A1, ESR1, ESRRA, and FSHR were likely to regulate the functions of the certain HPO tissues during the onset of puberty. Collectively, the AS dynamics and DEAS events were comprehensively profiled in hypothalamus, pituitary, and ovary across the pre-, in-, and post-pubertal stages in pigs. These findings may enhance our knowledge of how puberty is regulated by AS and shed new light on the molecular mechanisms underlying the timing of puberty in mammals.

Neuronal Ceroid Lipofuscinoses (NCLs) are progressive degenerative diseases mainly affect brain and retina. They are characterized by accumulation of autofluorescent storage material, mitochondrial ATPase subunit C, or sphingolipid activator proteins A and D in lysosomes of most cells. Heterogenous storage material in NCLs is not completely disease-specific. Most of CLN proteins and their natural substrates are not well-characterized. Studies have suggested variants of Late-Infantile NCLs (LINCLs) include the major type CLN2 and minor types CLN5, CLN6, CLN7, and CLN8. Therefore, combination of clinical and molecular analysis has become a more effective diagnosis method. We studied 4 late-infantile NCL siblings characterized by seizures, ataxia as early symptoms, followed by progressive regression in intelligence and behavior, but mutations are located in different genes. Symptoms and progression of 4 types of LINCLs are compared. Pathology of LINCLs is also discussed. We performed Nest-Generation Sequencing on these phenotypically similar families. Three novel variants c.1551+1insTGAT in TPP1, c.244G>T in CLN6, c.554-5A>G in MFSD8 were identified. Potential outcome of the mutations in structure and function of proteins are studied. In addition, we observed some common and unique clinical features of Chinese LINCL patient as compared with those of Western patients, which greatly improved our understanding of the LINCLs.

Background: The aim of this study was to explore the Human Papillomavirus (HPV) genotype composition and intra-genotype variants within individual samples of low- and high-grade cervical cytology by deep sequencing. Clinical, cytological, sequencing, and functional/structural data were forged into an integrated variant profiling pipeline for the detection of potentially vaccine-resistant genotypes or variants. Methods: Low- and high-grade intraepithelial lesion (LSIL and HSIL) cytology samples with +HPV were subjected to amplicon (L1 gene fragment) sequencing by dideoxy (Sanger) and deep methods. Taxonomic, abundance, diversity, and phylogenetic analyses were conducted to determine HPV genotypes/sub-lineages, relative abundance, species diversity and phylogenetic distances within and between samples. Variant detection and functional analysis of translated L1 amino acid sequences determined structural variations of interest. Results: Pure and mixed HPV infections were common among LSIL (n = 6) and HSIL (n = 6) samples. Taxonomic profiling revealed loss of species richness and gain of dominance by carcinogenic genotypes in HSIL samples. Phylogenetic analysis showed excellent correlation between HPV-type specific genetic distances and carcinogenic potential. For combined LSIL/HSIL samples (n = 12), 11 HPV genotypes and 417 mutations were detected: 375 single-nucleotide variants (SNV), 29 insertion/deletion (indel), 12 multi-nucleotide variants (MNV), and 1 replacement variant. The proportion of nonsynonymous mutations was lower for HSIL (0.38) than for LSIL samples (0.51) (p < 0.05). HPV variant analysis pinpointed nucleotide-level mutations and amino acid-level structural modifications. Conclusion: HPV L1 intra-host and intra-genotype variants are abundant in LSIL and HSIL samples with potential functional/structural consequences. An integrated multi-omics approach to variant analysis may provide a sensitive and practical means of detecting changes in HPV evolution and dynamics within individuals or populations.

Colon cancer has a huge incidence and mortality worldwide every year. Immunotherapy could be a new therapeutic option for patients with advanced colon cancer. Tumor mutation burden (TMB) and immune infiltration are considered critical in immunotherapy but their characteristics in colon cancer are still controversial.

Background and Objective: Neuroblastoma (NB), the most common pediatric solid tumor apart from brain tumor, is associated with dismal long-term survival. The aim of this study was to identify a gene signature to predict the prognosis of NB patients. Materials and Methods: GSE49710 dataset from the Gene Expression Omnibus (GEO) database was downloaded and differentially expressed genes (DEGs) were analyzed using R package "limma" and SPSS software. The gene ontology (GO) and pathway enrichment analysis were established via DAVID database. Random forest (RF) and risk score model were used to pick out the gene signature in predicting the prognosis of NB patients. Simultaneously, the receiving operating characteristic (ROC) and Kaplan-Meier curve were plotted. GSE45480 and GSE16476 datasets were employed to validate the robustness of the gene signature. Results: A total of 131 DEGs were identified, which were mainly enriched in cancer-related pathways. Four genes (ERCC6L, AHCY, STK33, and NCAN) were selected as a gene signature, which was included in the top six important features in RF model, to predict the prognosis in NB patients, its area under the curve (AUC) could reach 0.86, and Cox regression analysis revealed that the 4-gene signature was an independent prognostic factor of overall survival and event-free survival. As well as in GSE16476. Additionally, the robustness of discriminating different groups of the 4-gene signature was verified to have a commendable performance in GSE45480 and GSE49710. Conclusion: The present study identified a gene-signature in predicting the prognosis in NB, which may provide novel prognostic markers, and some of the genes may be as treatment targets according to biological experiments in the future.

Since the emergence of the Phytophthora sojae infection, economic losses of 10-20 billion U.S. dollars have been annually reported. Studies have revealed that P. sojae works by releasing effect factors such as small RNA in the process of infecting soybeans, but research on the interaction mechanism between plants and fungi at the small RNA level remains vague and unclear. For this reason, studying the resistance mechanism of the hosts after P. sojae invades soybeans has critical theoretical and practical significance for increasing soybean yield. The present article is premised on the high-throughput data published by the National Center of Biotechnology Information (NCBI). We selected 732 sRNA sequences through big data analysis whose expression level increased sharply after soybean was infected by P. sojae and 36 sRNA sequences with massive expression levels newly generated after infection. This article analyzes the resistance mechanism of soybean to P. sojae from two aspects of plant's own passive stress and active resistance. These 768 sRNA sequences are targeted to soybean mRNA and P. sojae mRNA, and 2,979 and 1,683 targets are obtained, respectively. The PageRank algorithm was used to screen the core functional clusters, and 50 core nodes targeted to soybeans were obtained, which were analyzed for functional enrichment, and 12 KEGG_Pathway and 18 Go(BP) were obtained. The node targeted to P. sojae was subjected to functional enrichment analysis to obtain 11 KEGG_Pathway. The results show that there are multiple Go(BP) and KEGG_Pathway related to soybean growth and defense and reverse resistance of P. sojae. In addition, by comparing the small RNA prediction model of soybean resistance with Phytophthora pathogenicity constructed by the three machine learning methods of random forest, support vector machine, and XGBoost, about the accuracy, precision, recall rate, and F-measure, the results show that the three models have satisfied classification effect. Among the three models, XGBoost had an accuracy rate of 86.98% in the verification set.

Plasmodium falciparum surface-related antigen (SRA) is located on the surfaces of gametocyte and merozoite and has the structural and functional characteristics of potential targets for multistage vaccine development. However, little information is available regarding the genetic polymorphism of pfsra. To determine the extent of genetic variation about P. falciparum by characterizing the sra sequence, 74 P. falciparum samples were collected from migrant workers who returned to China from 12 countries of Africa between 2015 and 2019. The full length of the sra gene was amplified and sequenced. The average pairwise nucleotide diversities (π) of P. falciparum sra gene was 0.00132, and the haplotype diversity (Hd) was 0.770. The average number of nucleotide differences (k) for pfsra was 3.049. The ratio of non-synonymous (dN) to synonymous (dS) substitutions across sites (dN/dS) was 1.365. Amino acid substitutions of P. falciparum SRA could be categorized into 35 unique amino acid variants. Neutrality tests showed that the polymorphism of PfSRA was maintained by positive diversifying selection, which indicated its role as a potential target of protective immune responses and a vaccine candidate. Overall, the ability of the N-terminal of PfSRA antibodies to evoke inhibition of merozoite invasion of erythrocytes and conserved amino acid at low genetic diversity suggest that the N-terminal of PfSRA could be evaluated as a vaccine candidate against P. falciparum infection.

Background: Oxidative stress is related to oncogenic transformation in kidney renal clear cell carcinoma (KIRC). We intended to identify a prognostic antioxidant gene signature and investigate its relationship with immune infiltration in KIRC. Methods: With the support of The Cancer Genome Atlas (TCGA) database, we researched the gene expression and clinical data of KIRC patients. Antioxidant related genes with significant differences in expression between KIRC and normal samples were then identified. Through univariate and multivariate Cox analysis, a prognostic gene model was established and all patients were divided into high- and low-risk subgroups. Single sample gene set enrichment analysis was adopted to analyze the immune infiltration, HLA expression, and immune checkpoint genes in different risk groups. Finally, the prognostic nomogram model was established and evaluated. Results: We identified six antioxidant genes significantly correlated with the outcome of KIRC patients as independent predictors, namely DPEP1 (HR = 0.97, P < 0.05), GSTM3 (HR = 0.97, P < 0.05), IYD (HR = 0.33, P < 0.05), KDM3B (HR = 0.96, P < 0.05), PRDX2 (HR = 0.99, P < 0.05), and PRXL2A (HR = 0.96, P < 0.05). The high- and low-risk subgroups of KIRC patients were grouped according to the six-gene signature. Patients with higher risk scores had poorer prognosis, more advanced grade and stage, and more abundance of M0 macrophages, regulatory T cells, and follicular helper T cells. There were statistically significant differences in HLA and checkpoint gene expression between the two risk subgroups. The performance of the nomogram was favorable (concordance index = 0.766) and reliably predicted the 3-year (AUC = 0.792) and 5-year (AUC = 0.766) survival of patients with KIRC. Conclusion: The novel six antioxidant related gene signature could effectively forecast the prognosis of patients with KIRC, supply insights into the interaction between cellular antioxidant mechanisms and cancer, and is an innovative tool for selecting potential patients and targets for immunotherapy.