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Background The application of RNA-seq technology has become more extensive and the number of analysis procedures available has increased over the past years. Selecting an appropriate workflow has become an important issue for researchers in the field. Methods In our study, six popular analytical procedures/pipeline were compared using four RNA-seq datasets from mouse, human, rat, and macaque, respectively. The gene expression value, fold change of gene expression, and statistical significance were evaluated to compare the similarities and differences among the six procedures. qRT-PCR was performed to validate the differentially expressed genes (DEGs) from all six procedures. Results Cufflinks - Cuffdiff demands the highest computing resources and Kallisto - Sleuth demands the least. Gene expression values, fold change, p and q values of differential expression (DE) analysis are highly correlated among procedures using HTseq for quantification. For genes with medium expression abundance, the expression values determined using the different procedures were similar. Major differences in expression values come from genes with particularly high or low expression levels. HISAT2 - StringTie - Ballgown is more sensitive to genes with low expression levels, while Kallisto - Sleuth may only be useful to evaluate genes with medium to high abundance. When the same thresholds for fold change and p value are chosen in DE analysis, StringTie - Ballgown produce the least number of DEGs, while HTseq - DESeq2 , - edgeR or - limma generally produces more DEGs. The performance of Cufflinks - Cuffdiff and Kallisto - Sleuth varies in different datasets. For DEGs with medium expression levels, the biological verification rates were similar among all procedures. Conclusion Results are highly correlated among RNA-seq analysis procedures using HTseq for quantification. Difference in gene expression values mainly come from genes with particularly high or low expression levels. Moreover, biological validation rates of DEGs from all six procedures were similar for genes with medium expression levels. Investigators can choose analytical procedures according to their available computer resources, or whether genes of high or low expression levels are of interest. If computer resources are abundant, one can utilize multiple procedures to obtain the intersection of results to get the most reliable DEGs, or to obtain a combination of results to get a more comprehensive DE profile for transcriptomes.

Cumulus oophorus complexes (COCs) are the first extracellular barriers that sperm must pass through to fuse with oocytes, which have an important role in oocyte maturation and fertilization. However, little is known about the molecular mechanisms of COCs involved in fertilization. In this study, COCs were collected and then randomly divided into a test group that interacted with sperm and a control group that did not interact with sperm. Then, the total RNA was extracted; RNA transcriptome and small RNA libraries were prepared, sequenced, and analyzed. The results showed that 1283 differentially expressed genes (DEGs), including 560 upregulated and 723 downregulated genes. In addition, 57 differentially expressed miRNAs (DEMIs) with 35 upregulated and 22 downregulated were also detected. After the RNA-seq results were verified by RT-qPCR, 86 effective DEGs and 40 DEMIs were finally screened and a DEMI-DEG regulatory network was constructed. From this, the top ten hub target genes were HNF4A, SPN, WSCD1, TMEM239, SLC2A4, E2F2, SIAH3, ADORA3, PIK3R2, and GDNF, and they were all downregulated. The top ten hub DEMIs were miR-6876-5p, miR-877-3p, miR-6818-5p, miR-4690-3p, miR-6789-3p, miR-6837-5p, miR-6861-5p, miR-4421, miR-6501-5p, and miR-6875-3p, all of which were upregulated. The KEGG signaling pathway enrichment analysis showed that the effective DEGs were significantly enriched in the calcium, AMPK, and phospholipase D signaling pathways. Our study identified several DEGs and DEMIs and potential miRNA-mRNA regulatory pathways in COCs and these may contribute to fertilization. This study may provide novel insights into potential biomarkers for fertilization failure.

Background Follicle selection in chickens refers to the process of selecting one follicle from a group of small yellow follicles (SY, 6–8 mm in diameter) for development into 12–15 mm hierarchical follicles (usually F6 follicles), which is an important process affecting laying performance in the poultry industry. Although transcriptomic analysis of chicken ovarian follicles has been reported, integrated analysis of chicken follicles for selection by using both transcriptomic and proteomic approaches is still rarely performed. In this study, we compared the proteomes and transcriptomes of SY and F6 follicles in laying hens and identified several genes involved in chicken follicle selection. Results Transcriptomic analysis revealed 855 differentially expressed genes (DEGs) between SY follicles and F6 follicles in laying hens, among which 202 were upregulated and 653 were downregulated. Proteomic analysis revealed 259 differentially expressed proteins (DEPs), including 175 upregulated and 84 downregulated proteins. Among the identified DEGs and DEPs, changes in the expression of seven genes, including VLDLR1, WIF1, NGFR, AMH, BMP15, GDF6 and MMP13 , and nine proteins, including VLDLR, VTG1, VTG3, PSCA, APOB, APOV1, F10, ZP2 and ZP3L2, were validated. Further analysis indicated that the mRNA level of chicken VLDLR was higher in F6 follicles than in SY follicles and was also higher in granulosa cells (GCs) than in thecal cells (TCs), and it was stimulated by FSH in GCs. Conclusions By comparing the proteomes and transcriptomes of SY and F6 follicles in laying hens, we identified several differentially expressed proteins/genes that might play certain roles in chicken follicle selection. These data may contribute to the identification of functional genes and proteins involved in chicken follicle selection.

The objective of this study was to identify potentially significant genes and long non-coding RNAs (lncRNAs) in colon cancer for a panel of lncRNA signatures that could be used as prognostic markers for colon adenocarcinoma (COAD) based on the data from The Cancer Genome Atlas (TCGA). RNA-seq V2 exon data of COAD were downloaded from the TCGA data portal for 285 tumor samples and 41 normal tissue samples adjacent to tumors. Differentially expressed mRNAs and lncRNAs were identified. A functional enrichment analysis of differentially expressed mRNAs was performed, followed by protein-protein interaction (PPI) network construction and significant module selection. Additionally, the regulatory relationships in differentially expressed mRNAs and lncRNAs were assessed, and an lncRNA-lncRNA co-regulation and functional synergistic analysis were performed. Furthermore, the risk score model and Cox regression analysis based on the expression levels of lncRNAs were used to develop a prognostic lncRNA signature. A total of 976 differentially expressed mRNAs and 169 differentially expressed lncRNAs were identified. MDFI and MEOX2 were the PPI network hubs. We found these lncRNAs to be mainly involved in vascular smooth muscle contraction and the cGMP-PKG signaling pathway. Several lncRNA-lncRNA pairs had co-regulatory relationships or functional synergistic effects, including BVES-AS1/MYLK-AS1, ADAMTS9-AS1/MYLK-AS1 and FENDRR/MYLK-AS1. The differential expression profile analysis of four candidate lncRNAs (MYLK-AS1, BVES-AS1, ADAMTS9-AS1, and FENDRR) in COAD tumors were confirmed by reverse transcription-quantitative PCR. Moreover, this study identified a 14-lncRNA signature that could predict the survival for COAD patients.

Colorectal cancer (CRC) is the fourth most common cause of cancer-related mortality worldwide. The tumor, node, metastasis (TNM) stage remains the standard for CRC prognostication. Identification of meaningful microRNA (miRNA) and gene modules or representative biomarkers related to the pathological stage of colon cancer helps to predict prognosis and reveal the mechanisms behind cancer progression. We applied a systems biology approach by combining differential expression analysis and weighted gene co-expression network analysis (WGCNA) to detect the pathological stage-related miRNA and gene modules and construct a miRNA-gene network. The Cancer Genome Atlas (TCGA) colon adenocarcinoma (CAC) RNA-sequencing data and miRNA-sequencing data were subjected to WGCNA analysis, and the GSE29623, GSE35602 and GSE39396 were utilized to validate and characterize the results of WGCNA. Two gene modules (Gmagenta and Ggreen) and one miRNA module were associated with the pathological stage. Six hub genes (COL1A2, THBS2, BGN, COL1A1, TAGLN and DACT3) were related to prognosis and validated to be associated with the pathological stage. Five hub miRNAs were identified to be related to prognosis (hsa-miR-125b-5p, hsa-miR-145-5p, hsa-let-7c-5p, hsa-miR-218-5p and hsa-miR-125b-2-3p). A total of 18 hub genes and seven hub miRNAs were predominantly expressed in tumor stroma. Proteoglycans in cancer, focal adhesion, extracellular matrix (ECM)-receptor interaction and so on were common pathways of the three modules. Hsa-let-7c-5p was located at the core of miRNA-gene network. These findings help to advance the understanding of tumor stroma in the progression of CAC and provide prognostic biomarkers as well as therapeutic targets.

Lipid-rich Pinus koraiensis seed kernels are highly regarded for their nutritional and health benefits. To ascertain the molecular mechanism of lipid synthesis, we conducted widely targeted metabolomic profiling together with a transcriptome analysis of the kernels in P. koraiensis cones at various developmental stages. The findings reveal that 148 different types of lipid metabolites, or 29.6% of total metabolites, are present in kernels. Among those metabolites, the concentrations of linoleic acid, palmitic acid, and α-linolenic acid were higher, and they steadily rose as the kernels developed. An additional 10 hub genes implicated in kernel lipid synthesis were discovered using weighted gene co-expression network analysis (WGCNA), gene interaction network analysis, oil body biosynthesis, and transcriptome analysis. This study used lipid metabolome and transcriptome analyses to investigate the mechanisms of key regulatory genes and lipid synthesis molecules during kernel development, which served as a solid foundation for future research on lipid metabolism and the creation of P. koraiensis kernel food.

Noncoding RNAs play diverse and crucial roles across various cell types, with many long noncoding RNAs (lncRNAs) implicated in germ cell development. Although lncRNAs remain largely uncharacterized, they play essential roles in key biological processes, including X-inactivation, pluripotency, genomic imprinting, and cell differentiation. In this study, we conducted a comprehensive bioinformatics analysis using publicly accessible single-cell RNA sequencing data (scRNA-seq) from Gene Expression Omnibus repository. The dataset includes four distinct cell types from different stages of porcine embryonic development: E11 derived epiblast cells, E14 derived somatic and primordial germ cells, E31 derived primordial germ cells. Our analysis identified a large number of lncRNAs and assessed their expression patterns, highlighting their critical roles in embryonic development. We also explored the relationship between lncRNAs and protein-coding genes, particularly focusing on the ten eleven translocation (TET) family genes, which are known for their role in DNA demethylation during early embryogenesis. We identified approximately 0.15 million lncRNA transcripts in porcine early embryos. Additionally, we investigated the differential expression profiles of both lncRNAs and protein-coding genes across different cell types, observing both similarities and differences in gene expression as the embryo differentiates. Finally, we used LncTar to predict potential interactions between co-expressed TET family genes and differentially expressed lncRNAs, providing further insight into their functional relationships in early embryonic development.

Background C 4 photosynthesis is a mechanism that plants have evolved to reduce the rate of photorespiration during the carbon fixation process. The C 4 pathway allows plants to adapt to high temperatures and light while more efficiently using resources, such as water and nitrogen. Despite decades of studies, the evolution of the C 4 pathway from a C 3 ancestor remains a biological enigma. Interestingly, species with C 3 -C 4 intermediates photosynthesis are usually found closely related to the C 4 lineages. Indeed, current models indicate that the assembly of C 4 photosynthesis was a gradual process that included the relocalization of photorespiratory enzymes, and the establishment of intermediate photosynthesis subtypes. More than a third of the C 4 origins occurred within the grass family (Poaceae). In particular, the Otachyriinae subtribe (Paspaleae tribe) includes 35 American species from C 3 , C 4 , and intermediates taxa making it an interesting lineage to answer questions about the evolution of photosynthesis. Results To explore the molecular mechanisms that underpin the evolution of C 4 photosynthesis, the transcriptomic dynamics along four different leaf segments, that capture different stages of development, were compared among Otachyriinae non-model species. For this, leaf transcriptomes were sequenced, de novo assembled, and annotated. Gene expression patterns of key pathways along the leaf segments showed distinct differences between photosynthetic subtypes. In addition, genes associated with photorespiration and the C 4 cycle were differentially expressed between C 4 and C 3 species, but their expression patterns were well preserved throughout leaf development. Conclusions New, high-confidence, protein-coding leaf transcriptomes were generated using high-throughput short-read sequencing. These transcriptomes expand what is currently known about gene expression in leaves of non-model grass species. We found conserved expression patterns of C 4 cycle and photorespiratory genes among C 3 , intermediate, and C 4 species, suggesting a prerequisite for the evolution of C 4 photosynthesis. This dataset represents a valuable contribution to the existing genomic resources and provides new tools for future investigation of photosynthesis evolution.

Colorectal cancer (CRC) is one of the principal causes of cancer-associated mortality worldwide. The high incidence of liver metastasis is the leading risk factor of mortality in patients with CRC, and the mechanisms of CRC liver metastasis are poorly understood. In the present study, 7 datasets, including 3 gene expression profile datasets and 4 microRNA (miRNA) expression profile datasets were downloaded from the NCBI Gene Expression Omnibus (GEO) database to identify potential key genes and miRNAs, which may be candidate biomarkers for CRC liver metastasis. Differentially expressed (DE) genes (DEGs) and DE miRNAs of primary CRC tumor tissues and liver metastatic CRC tumor tissues were selected using the GEO2R tool. Gene Ontology and Kyoto Encyclopedia of Gene and Genome pathway enrichment analyses were conducted using the Database for Annotation, Visualization and Integrated Discovery online database. Furthermore, Cytoscape with cytoHubba and the Molecular Complex Detection (MCODE) plug-in were used to visualize a protein-protein interaction (PPI) network for these DEGs, and to screen hub genes and gene modules in the PPI network. In addition, the online databases, TargetScan, miRanda, PITA, miRWalk and miRDB, were used to identify the target genes of the DE miRNAs. In the present study, 141 DEGs (97 upregulated and 44 downregulated) and 3 DE miRNAs (2 upregulated and 1 downregulated) were screened from the 3 gene expression microarray datasets and 4 miRNA expression microarray datasets, respectively. In total, 10 hub genes with a high degree of connectivity were selected from the PPI network, including albumin (ALB), coagulation factor II (F2), thrombin, apolipoprotein H (APOH), serpin family C member 1 (SERPINC1), apolipoprotein A1 (APOA1), α-1-microglobulin/bikunin precursor (AMBP), apolipoprotein C3 (APOC3), plasminogen (PLG), α-2 HS glycoprotein (AHSG) and apolipoprotein B (APOB). The most important module was detected in the PPI network using the MCODE plug-in. A total of 20 DEGs were identified to be potential target genes of these DE miRNAs, and novel miRNA-DEGs regulatory axes were constructed. In vitro experiments were performed to demonstrate that miR-885 promoted CRC cell migration by, at least partially, decreasing the expression of von Willebrand factor (vWF) and insulin-like growth factor binding protein 5 (IGFBP5). In conclusion, by using integrated bioinformatics analysis and in vitro experiments, key candidate genes were identified and novel miRNA-mRNA regulatory axes in CRC liver metastasis were constructed, which may improve understanding of the molecular mechanisms underlying CRC liver metastasis.

Background The epididymis is a highly regionalized tubular organ possesses vectorial functions of sperm concentration, maturation, transport, and storage. The epididymis-expressed genes and proteins are characterized by regional and developmental dependent pattern. However, a systematic and comprehensive insight into the postnatal development dependent changes in gene and protein expressions of porcine epididymis is still lacking. Here, the RNA and protein of epididymis of Duroc pigs at different postnatal development stages were extracted by using commercial RNeasy Midi kit and extraction buffer (7 M Urea, 2 M thiourea, 3% CHAPS, and 1 mM PMSF) combined with sonication, respectively, which were further subjected to transcriptomic and proteomic profiling. Results Transcriptome analysis indicated that 198 and 163 differentially expressed genes (DEGs) were continuously up-regulated and down-regulated along with postnatal development stage changes, respectively. Most of the up-regulated DEGs linked to functions of endoplasmic reticulum and lysosome, while the down-regulated DEGs mainly related to molecular process of extracellular matrix. Moreover, the following key genes INSIG1 , PGRMC1 , NPC2 , GBA , MMP2 , MMP14 , SFRP1 , ELN , WNT-2 , COL3A1 , and SPARC were highlighted. A total of 49 differentially expressed proteins (DEPs) corresponding to postnatal development stages changes were uncovered by the proteome analysis. Several key proteins ACSL3 and ACADM, VDAC1 and VDAC2, and KNG1, SERPINB1, C3, and TF implicated in fatty acid metabolism, voltage-gated ion channel assembly, and apoptotic and immune processes were emphasized. In the integrative network, the key genes and proteins formed different clusters and showed strong interactions. Additionally, NPC2 , COL3A1 , C3, and VDAC1 are located at the hub position in each cluster. Conclusions The identified postnatal development dependent genes and proteins in the present study will pave the way for shedding light on the molecular basis of porcine epididymis functions and are useful for further studies on the specific regulation mechanisms responsible for epididymal sperm maturation.