In birds, the sperm storage tubules ( SST ) are dispersed in uterovaginal junction ( UVJ ) and highly correlated with differential capacity of sperm storage ( SS ) in and among species with unspecified mechanisms. Here, the SS duration of 252 egg layer breeders was evaluated in 5 rounds with 3 phenotypic traits to screen high- and low-SS individuals, respectively, followed with transcriptome of UVJ tissues and metabolome of serum (high-SS vs. low-SS) to decipher the candidate genes and biochemical markers correlated with differential SS capacity. Histological characterization suggested slightly higher density of SST in UVJ (high-SS vs. low-SS). Transcriptome analyses identified 596 differentially expressed genes (336 upregulated vs. 260 downregulated), which were mainly enriched in gene ontology terms of homeostasis, steroid and lipid metabolism and hormone activity, and 12 significant pathways ( P < 0.05) represented by calcium, steroid, and lipid metabolism. Immunohistochemical staining of GNAQ, ST6GAL1, ADFP, and PCNA showed similar distribution in UVJ tissues between 2 groups. Several candidates ( HSD11B2 , DIO2 , AQP3 , GNAQ, NANS, ST6GAL1 ) combined with 4 (11β-prostaglandin F2α, prostaglandin B1, 7α-hydroxytestosterone, and N-acetylneuraminic acid) of 40 differential metabolites enriched in serum metabolome were considered as regulators and biomarkers of SS duration in egg layer breeders. The integrated transcriptome and metabolome analyses of chicken breeder hens will provide novel insights for exploration and improvement of differential SS capacity in birds.

with AD/MCI pathology. The results were integrated into a database named SCAD-Brain (scRNA-seq analysis for AD Brain, https://www.bioinform.cn/SCAD/). The users can access, reuse, and analyze the data by visiting the website, to perform cell marker analysis, gene expression analysis, pathway enrichment analysis, cell communication analysis, and cell trajectory analysis for multiple brain regions from patients with AD/MCI and AD mouse models. SCAD-Brain provides a user-friendly platform to explore and visualize scRNA-seq data of AD brain, assist in experiments design, verify hypotheses, and especially investigate the cellular heterogeneity of AD brain.. Methods . . Data collection and quality controlRaw data of AD brain scRNA-seq from the GEO database and Synapse database were collected in October 2021. For data collection through the GEO database, medical subject headings of AD and scRNA-seq were collected from the MeSH database to build

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Background The early diagnosis of Alzheimer's disease (AD) in large-scale high-risk population is a major challenge. Blood-based biomarkers could enable widespread testing for AD. RNA-seq technology is becoming an effective method in investigating diagnostic biomarkers for diseases, but platforms exploring RNA-seq data in AD blood are lacking. Methods We collected the raw RNA-seq data in the blood of AD patients or AD mouse models, mild cognitive impairment (MCI) patients, and normal people or wild type mouse from the Gene Expression Omnibus (GEO) and Synapse databases. And the RNA-seq data was analyzed by the standard pipeline. We applied R-Shiny to develop the website of RAD-Blood (RNA-seq analysis of AD blood, http://www.bioinform.cn/RAD-Blood/) to present the plentiful analysis results. Results RAD-Blood was specifically designed to analyze existing blood RNA-seq data sets (mRNA-seq, miRNA-seq, and scRNA-seq) from patients and mouse models with AD pathology. The RAD-Blood provides differential expression, gene set enrichment, immune abundance and its correlation with gene expression, cell type annotation, T cell receptor, and cell communication analyses for RNA-seq data in AD/MCI/normal blood, with rich results forms and colorful figures. We used a case study to show the capacity of RAD-Blood in finding blood biomarkers in AD/MCI blood. By using RAD-Blood, we found 274 protein-coding genes whose mRNA expression was consistently up-regulated or down-regulated from normal to MCI to AD. Among the consistently down-regulated genes, four are the markers of the blood erythroid cell. Compared with normal people, the population of erythroid cells in AD patients decreased. Despite the reduction in cell count, interactions between blood erythroid cells with other cells increased dramatically, which is mainly mediated through the major histocompatibility complex I (MHC-I) signaling pathway. These findings have not been reported by existing studies, which suggests that RAD-Blood is a solution for finding potential novel signatures in the blood of AD and MCI. Conclusions RAD-Blood is a user-friendly web server for multi-level analysis and visualization of gene/miRNA expression and immune profile in AD blood, and allows broad utility in exploring potential AD blood biomarkers, testing hypotheses, guiding experiment design, and investigating the peripheral pathogenic mechanisms and proposing potential early diagnosis standard of AD.