Biology subjects

Biase, F.

Publications and source records attributed to Biase, F..

6 recordsLinked to original sources

bioRxiv2024

Genome-wide association analyses in dairy heifers highlight genes overlapping with mouse and human fertility and human health traits

Heifer Infertility and disease are important challenges in dairy cattle production. We investigated genetic differences between Holstein heifers with varying fertility potential and health. We carried out a genome-wide association analysis comparing heifers that conceived at first insemination against those requiring multiple attempts or failing to become pregnant, as well as heifers culled due to health issues. There were 12 significant SNPs (P<5x10-5) associated with fertility and 35 SNPs associated with health traits. There were 166 significant SNPs when infertile, sub-fertile and animals culled due to health issues were grouped. Two SNPs identified in the analysis of infertility were found near NUFIP1 and within TENM4 genes, both genes are linked to embryonic lethality in mouse knockouts. Follow-up CRISPR-Cas9 mediated disruption of NUFIP1 significantly (P<0.05) reduced in vitro blastocyst development in cattle embryos, while TENM4 editing did not alter in vitro blastocyst development. Additionally, SNPs overlapped with previously identified reproduction-related QTL (CNTN4, DLG2, PARP10, PRICKLE, TMEM150B) or health-related QTL (FAM162A, PARP10). We also identified genes within or near genes previously associated with age at menarche (CADM2, DLG2, FHIT, LSAMP and TENM4) or lung function or pulmonary diseases (ASCC2, BCAS3, BTBD9, CADM2, CNTN4, CPEB4, CTNNA2, DEUP1, DGKH, DLG2, ENOX1, EPHB1, ERC2, ERGIC1, EYA2, FAM162A, FGF18, FHIT, GRID1, KCNIP4, LINGO2, LRMDA, MALRD1, NEBL, PLA2G6, PLXDC2, PRPF18, SLC8A1, TEAD4, TSPAN9) in humans. These results further support genetic components of fertility and health in cattle. The findings also show overlapping genetic architecture between fertility and health traits, with a degree of conservation across mammals. Summary sentenceSeveral genetic variants that influence female fertility and health in cattle were identified, and many genes harboring or near significant polymorphisms are common to equivalent phenotypes in mice and humans.

genomics↗

bioRxiv2022

Robust identification of regulatory variants (eQTLs) using a differential expression framework developed for RNA-sequencing

BackgroundA gap currently exists between genetic variants and the underlying cell and tissue biology of a trait, and expression quantitative trait loci (eQTL) studies provide important information to help close that gap. However, two concerns that arise with eQTL analyses using RNA-sequencing data are normalization of data across samples and the data not following a normal distribution. Multiple pipelines have been suggested to address this. For instance, the most recent analysis of the human and farm Genotype-Tissue Expression (GTEx) project proposes using trimmed means of M-values (TMM) to normalize the data followed by an inverse normal transformation. ResultsIn this study, we reasoned that eQTL analysis could be carried out using the same framework used for differential gene expression (DGE), which uses a negative binomial model, a statistical test feasible for count data. Using the GTEx framework, we identified 38 significant eQTLs (P<5x10-8) following the ANOVA model and 15 significant eQTLs (P<5x10-8) following the additive model. Using a differential gene expression framework, we identified 2,471 and nine significant eQTLs (P<5x10-8) following an analytical framework equivalent to the ANOVA and additive model, respectively. When we compared the two approaches, there was no overlap of significant eQTLs between the two frameworks. Because we defined specific contrasts, we identified trans eQTLs that more closely resembled what we expect from genetic variants showing complete dominance between alleles. Yet, these were not identified by the GTEx framework. ConclusionsOur results show that transforming RNA-sequencing data to fit a normal distribution prior to eQTL analysis is not required when the DGE framework is employed, thus this may be more suitable for finding genes whose expression are impacted by genetic variants. Our approach detected biologically relevant variants that otherwise would not have been identified due to data transformation to fit a normal distribution.

genomics↗

bioRxiv2022

Delayed processing of blood samples impairs the accuracy of mRNA-based biomarkers

BackgroundThe transcriptome of peripheral white blood cells (PWBCs) contains valuable physiological information, thus making them a prime biological sample for investigating mRNA-based biomarkers. However, prolonged storage of whole blood samples can alter gene transcript abundance in PWBCs, compromising the results of biomarker discovery. Here, we designed an experiment to interrogate the impacts of delayed processing of whole blood samples on gene transcript abundance in PWBCs. We hypothesized that storing blood samples for 24 hours at 4{degrees}C would cause RNA degradation resulting in altered transcriptome profiles. ResultsWe produced RNA-sequencing data for 30 samples collected from five estrus synchronized heifers (Bos taurus). We quantified transcript abundance for 12,414 protein-coding genes in PWBCs. Analysis of parameters of RNA quality revealed no statistically significant differences (P>0.05) between samples collected from the jugular vein and coccygeal vein, as well as among samples processed after one, three, six, or eight hours. However, samples processed after 24 hours of storage had a lower RNA integrity number value (P=0.03) in comparison to those processed after one hour of storage. Next, we analyzed RNA-sequencing data between samples using those processed after one hour of storage as the baseline for comparison. Interestingly, evaluation of 3/5 bias revealed no differences between genes with lower transcript abundance in samples stored for 24 hours relative to one hour. In addition, sequencing coverage of transcripts was similar between samples from the 24-hour and one-hour groups. We identified four and 515 genes with differential transcript abundance in samples processed after storage for eight and 24 hours, respectively, relative to samples processed after one hour. ConclusionsThe PWBCs respond to prolonged cold storage by increasing genes related to active chromatin compaction which in turn reduces gene transcription. This alteration in transcriptome profiles can impair the accuracy of mRNA-based biomarkers. Therefore, blood samples collected for mRNA-based biomarker discovery should be refrigerated immediately and processed within six hours post sampling.

genomics↗

bioRxiv2020

Rewiring of gene expression in PWBC at artificial insemination is predictive of pregnancy outcome in cattle (Bos taurus)

Infertility is a challenging phenomenon in cattle that reduces the sustainability of beef production worldwide. Here, we tested the hypothesis that gene expression profiles of protein-coding genes expressed in peripheral white blood cells (PWBCs), and circulating micro RNAs in plasma, are associated with female fertility, measured by pregnancy outcome. We drew blood samples from 17 heifers on the day of artificial insemination and analyzed transcript abundance for 10496 genes in PWBCs and 290 circulating micro RNAs. The females were later classified as pregnant to artificial insemination, pregnant to natural breeding or not pregnant. We identified 1860 genes producing significant differential coexpression (eFDR<0.002) based on pregnancy outcome. Additionally, 237 micro RNAs and 2274 genes in PWBCs presented differential coexpression based on pregnancy outcome. Furthermore, using a machine learning prediction algorithm we detected a subset of genes whose abundance could be used for blind categorization of pregnancy outcome. Our results provide strong evidence that transcript abundance in circulating white blood cells is associated with fertility in heifers.

genomics↗

bioRxiv2018

Fine-tuned adaptation of embryo-endometrium pairs at implantation revealed by gene regulatory networks

Interactions between embryo and endometrium at implantation are critical for the progression and the issue of pregnancy. These reciprocal actions involve exchange of paracrine signals that govern implantation and placentation. However, it remains unknown how these interactions between the conceptus and the endometrium are coordinated at the level of an individual pregnancy. Under the hypothesis that gene expression of endometrium is dependent on gene expression of extraembryonic tissues, we performed an integrative analysis of transcriptome profiles of paired conceptuses and endometria obtained from pregnancies initiated by artificial insemination. We quantified strong dependence (|r|>0.95, eFDR<0.01) in transcript abundance of genes expressed in the extraembryonic tissues and genes expressed in the endometrium. The profiles of connectivity revealed distinct co-expression patterns of extraembryonic tissues with caruncular and intercaruncular areas of the endometrium. Notably, a subset of highly co-expressed genes between conceptus (n=229) and caruncular areas of the endometrium (n=218, r>0.9999, eFDR<0.001) revealed a blueprint of gene expression specific to each pregnancy. Functional analyses of genes co-expressed between conceptus and endometrium revealed significantly enriched functional modules with critical contribution for implantation and placentation, including \"in utero embryonic development\", \"placenta development\" and \"regulation of transcription\". Functional modules were remarkably specific to caruncular or intercaruncular areas of the endometrium. The quantitative and functional association between genes expressed in conceptus and endometrium emphasize a coordinated communication between these two entities in mammals. To our knowledge, we provide first evidence that implantation in mammalian pregnancy relies on the ability of the conceptus and the endometrium to develop a fine-tuned adaptive response characteristic of each pregnancy.

developmental biology↗

bioRxiv2018

Rainbow-seq: combining cell lineage tracking with single-cell RNA sequencing in preimplantation embryos

Single-cell RNA-seq experiments cannot record cell division history and therefore cannot directly connect intercellular differences at a later developmental stage to their progenitor cells. We developed Rainbow-seq to combine cell division lineage tracing with single-cell RNA-seq. With distinct fluorescent protein genes as lineage markers, Rainbow-seq enables each single-cell RNA-seq experiment to simultaneously read single-cell transcriptomes and decode the lineage marker genes. We traced the lineages deriving from each blastomere in two-cell mouse embryos and observed inequivalent contributions to the embryonic and abembryonic poles in 72% of the blastocysts evaluated. Rainbow-seq on four- and eight-cell embryos with lineage tracing triggered at two-cell stage exhibited remarkable transcriptome-wide differences between the two cell lineages at both stages, including genes involved in negative regulation of transcription and signaling. These data provide critical insights on cell fate choices in cleavage embryos. Rainbow-seq bridged a critical gap between cellular division history and single-cell RNA-seq assays.

developmental biology↗