Cattle infection response network and its functional modules

Network analysis reveals different hub genes and molecular pathways for pig in vitro fertilized early embryos and parthenogenotes

Maternal-to-zygotic transition (MZT) occurs when maternal transcripts decay and zygotic genome is activated gradually at early stage of embryo development. Previously, single cell RNA-seq (scRNA-seq) has helped us to uncover the MZT-associated mRNA dynamics of in vitro produced pig early embryos. Here, to further investigate functional modules and hub genes associated with MZT process, the weighted gene-coexpression network analysis (WGCNA) was performed on our previously generated 45 scRNA-seq datasets. For the in vitro fertilized embryo (IVF) group, 5 significant modules were identified (midnightblue/black/red and blue/brown modules, positively correlated with 1-cell (IVF1) and 8-cell (IVF8), respectively), containing genes mainly enriched in signaling pathways such as Wnt, regulation of RNA transcription, fatty acid metabolic process, poly(A) RNA binding and lysosome. For the parthenogenetically activated embryo (PA) group, 9 significant modules were identified (black/purple/red, brown/turquoise/yellow, and magenta/blue/green modules, positively correlated with MII oocytes, 1-cell (PA1), and 8-cell (PA8), respectively), mainly enriched in extracellular exosome, poly(A) RNA binding, mitochondrion, transcription factor activity. Moreover, some of identified hub genes within 3 IVF and 9 PA significant modules, including ADCY2, DHX34, KDM4A, GDF10, ABCC10, PAFAH2, HEXIM2, COQ9, DCAF11, SGK1, ESRRB etc., have been reported to play vital roles in different biological processes. Our findings provide information and resources for subsequent in-depth study on the regulation and function of MZT in pig embryos.

Longitudinal blood transcriptomic analysis to identify molecular regulatory patterns of bovine respiratory disease in beef cattle

Bovine respiratory disease (BRD) is the most common disease in beef cattle and leads to considerable economic losses in both beef and dairy cattle. It is important to uncover the molecular mechanisms underlying BRD and to identify biomarkers for early identification of BRD cattle in order to address its impact on production and welfare. In this study, a longitudinal transcriptomic analysis was conducted using blood samples collected from 24 beef cattle at three production stages in the feedlot: 1) arrival (Entry group); 2) when identified as sick (diagnosed as BRD) and separated for treatment (Pulled); 3) prior to marketing (Close-out, representing healthy animals). Expressed genes were significantly different in the same animal among Entry, Pulled and Close-out stages (false discovery rate (FDR) < 0.01 & |Fold Change| > 2). Beef steers at both Entry and Pulled stages presented obvious difference in GO terms (FDR < 0.05) and affected biological functions (FDR < 0.05 & |Z-score| > 2) when compared with animals at Close-out. However, no significant functional difference was observed between Entry and Pulled animals. The interferon signaling pathway showed the most significant difference between animals at Entry/Pulled and Close-out stages (P < .001 & |Z-score| > 2), suggesting the animals initiated antiviral responses at an early stage of infection. Six key genes including IFI6, IFIT3, ISG15, MX1, and OAS2 were identified as biomarkers to predict and recognize sick cattle at Entry. A gene module with 169 co-expressed genes obtained from WGCNA analysis was most positively correlated (R = 0.59, P = 6E-08) with sickness, which was regulated by 11 transcription factors. Our findings provide an initial understanding of the BRD infection process in the field and suggests a subset of novel marker genes for identifying BRD in cattle at an early stage of infection.

Common Gene Modules Identified for Chicken Adiposity by Network Construction and Comparison

Excessive fat deposition can cause chicken health problem, and affect production efficiency by causing great economic losses to the industry. However, the molecular underpinnings of the complex adiposity trait remain elusive. In the current study, we constructed and compared the gene co-expression networks on four transcriptome profiling datasets, from two chicken lines under divergent selection for abdominal fat contents, in an attempt to dissect network compositions underlying adipose tissue growth and development. After functional enrichment analysis, nine network modules important to adipogenesis were discovered to be involved in lipid metabolism, PPAR and insulin signaling pathways, and contained hub genes related to adipogenesis, cell cycle, inflammation, and protein synthesis. Moreover, after additional functional annotation and network module comparisons, common sub-modules of similar functionality for chicken fat deposition were identified for different chicken lines, apart from modules specific to each chicken line. We further validated the lysosome pathway, and found TFEB and its downstream target genes showed similar expression patterns along with chicken preadipocyte differentiation. Our findings could provide novel insights into the genetic basis of complex adiposity traits, as well as human obesity and related metabolic diseases.

Improved annotation of the domestic pig genome through integration of Iso-Seq and RNA-seq data

BACKGROUND

Our understanding of the pig transcriptome is limited. RNA transcript diversity among nine tissues was assessed using poly(A) selected single-molecule long-read isoform sequencing (Iso-seq) and Illumina RNA sequencing (RNA-seq) from a single White cross-bred pig.

RESULTS

Across tissues, a total of 67,746 unique transcripts were observed, including 60.5% predicted protein-coding, 36.2% long non-coding RNA and 3.3% nonsense-mediated decay transcripts. On average, 90% of the splice junctions were supported by RNA-seq within tissue. A large proportion (80%) represented novel transcripts, mostly produced by known protein-coding genes (70%), while 17% corresponded to novel genes. On average, four transcripts per known gene (tpg) were identified; an increase over current EBI (1.9 tpg) and NCBI (2.9 tpg) annotations and closer to the number reported in human genome (4.2 tpg). Our new pig genome annotation extended more than 6000 known gene borders (5' end extension, 3' end extension, or both) compared to EBI or NCBI annotations. We validated a large proportion of these extensions by independent pig poly(A) selected 3'-RNA-seq data, or human FANTOM5 Cap Analysis of Gene Expression data. Further, we detected 10,465 novel genes (81% non-coding) not reported in current pig genome annotations. More than 80% of these novel genes had transcripts detected in > 1 tissue. In addition, more than 80% of novel intergenic genes with at least one transcript detected in liver tissue had H3K4me3 or H3K36me3 peaks mapping to their promoter and gene body, respectively, in independent liver chromatin immunoprecipitation data.

CONCLUSIONS

These validated results show significant improvement over current pig genome annotations.