Additional file 9. (AdditionalFile9.pdf) - Enriched regulatory features in liver-specific peaks a... more Additional file 9. (AdditionalFile9.pdf) - Enriched regulatory features in liver-specific peaks according to i-cisTarget online tool [68].
Additional file 2. Identified peaks and annotation for hypothalamus. The first 10 columns corresp... more Additional file 2. Identified peaks and annotation for hypothalamus. The first 10 columns correspond to ENCODE narrowPeak format, the following columns are the annotation output of ChIPseeker [62], and the last column indicates which of the peaks are tissue-specific.
Additional file 12. Enriched regulatory features in muscle-specific peaks according to i-cisTarge... more Additional file 12. Enriched regulatory features in muscle-specific peaks according to i-cisTarget online tool [68].
Additional file 3. Identified peaks and annotation for liver. The first 10 columns correspond to ... more Additional file 3. Identified peaks and annotation for liver. The first 10 columns correspond to ENCODE narrowPeak format, the following columns are the annotation output of ChIPseeker [62], and the last column indicates which of the peaks are tissue-specific.
Additional file 1: Table S1. Statistical power to detect the genetic variance of bull fertility p... more Additional file 1: Table S1. Statistical power to detect the genetic variance of bull fertility phenotypes. Statistical power analyses for detecting the genetic variance with the genomic relationship matrices models used are provided for each measured phenotype, within each breed.
Additional file 5. Distribution of peaks by chromosome for muscle (A), liver (B) and hypothalamus... more Additional file 5. Distribution of peaks by chromosome for muscle (A), liver (B) and hypothalamus (C).
We describe an analytical pipeline to exploit the results from RNA sequencing (RNA-Seq) experimen... more We describe an analytical pipeline to exploit the results from RNA sequencing (RNA-Seq) experiments combining a series of processes from data normalization to network inference. The pipeline makes use of numerical approaches aimed at identifying key regulators via the regulatory impact factor (Reverter et al. 2010) metrics. It also employs the partial correlation and an information theory (Reverter and Chan 2008) for the identification of significant edges in the construction of gene co-expression networks. Key nodes in the network include differentially expressed genes, transcription factors, tissue specific genes as well as genes harboring SNPs found to be associated with the phenotype(s) of interest. The pipeline has already been successfully employed in two beef cattle studies, dealing with the onset of puberty and feed efficiency. In the present paper, we describe a pipeline to analyze RNA-Seq data, focus on relevant genes, generate gene co-expression networks and identify emer...
The objectives were identify the best model to describe the growth curve in Brahman cattle and ch... more The objectives were identify the best model to describe the growth curve in Brahman cattle and characterize single nucleotide polymorphism (SNP) associated with growth curve parameters estimates. Body weights were measured at birth, sixth, twelfth, fifteenth, eighteenth and twenty-fourth months of age. Four nonlinear models were tested and evaluated for goodness of fit. Brody’s model was the most appropriate to describe the growth of Brahman cattle. The genome wide association studies were performed for three growth curve parameters (A, b and K). There were 136 SNPs significantly associated with A spread over all chromosomes. There were found significant SNPs in chromosomes thirteen and twenty five, which has no reports in QTLDatabase. These SNPs can be useful in posterior marker assisted selection and pathway analysis to describe the complexity of growth process in beef cattle.
Abstract Puberty is a whole‐body event, driven by the hypothalamic integration of peripheral sign... more Abstract Puberty is a whole‐body event, driven by the hypothalamic integration of peripheral signals such as leptin or IGF‐1. In the process of puberty, reproductive development is simultaneous to growth, including muscle growth. To enhance our understanding of muscle function related to puberty, we performed transcriptome analyses of muscle samples from six pre‐ and six post‐pubertal Brahman heifers (Bos indicus). Our aims were to perform differential expression analyses and co‐expression analyses to derive a regulatory gene network associate with puberty. As a result, we identified 431 differentially expressed (DEx) transcripts (genes and non‐coding RNAs) when comparing pre‐ to post‐pubertal average gene expression. The DEx transcripts were compared with all expressed transcripts in our samples (over 14,000 transcripts) for functional enrichment analyses. The DEx transcripts were associated with “extracellular region,” “inflammatory response” and “hormone activity” (adjusted p < .05). Inflammatory response for muscle regeneration is a necessary aspect of muscle growth, which is accelerated during puberty. The term “hormone activity” may signal genes that respond to progesterone signalling in the muscle, as the presence of this hormone is an important difference between pre‐ and post‐pubertal heifers in our experimental design. The DEx transcript with the highest average expression difference was a mitochondrial gene, ENSBTAG00000043574 that might be another important link between energy metabolism and puberty. In the derived co‐expression gene network, we identified six hub genes: CDC5L, MYC, TCF3, RUNX2, ATF2 and CREB1. In the same network, 48 key regulators of DEx transcripts were identified, using a regulatory impact factor metric. The hub gene TCF3 was also a key regulator. The majority of the key regulators (22 genes) are members of the zinc finger family, which has been implicated in bovine puberty in other tissues. In conclusion, we described how puberty may affect muscle gene expression in cattle.
Co-expression networks tightly coordinate the spatiotemporal patterns of gene expression unfoldin... more Co-expression networks tightly coordinate the spatiotemporal patterns of gene expression unfolding during development. Due to the dynamic nature of developmental processes simply overlaying gene expression patterns onto static representations of co-expression networks may be misleading. Here, we aim to formally quantitate topological changes of co-expression networks during embryonic development using a publicly available Drosophila melanogaster transcriptome data set comprising 14 time points. We deployed a network approach which inferred 10 discrete co-expression networks by smoothly sliding along from early to late development using 5 consecutive time points per window. Such an approach allows changing network structure, including the presence of hubs, modules and other topological parameters to be quantitated. To explore the dynamic aspects of gene expression captured by our approach, we focused on regulator genes with apparent influence over particular aspects of development. Those key regulators were selected using a differential network algorithm to contrast the first 7 (early) with the last 7 (late) developmental time points. This assigns high scores to genes whose connectivity to abundant differentially expressed target genes has changed dramatically between states. We have produced a list of key regulators – some increasing (e.g., Tusp, slbo, Sidpn, DCAF12, and chinmo) and some decreasing (Rfx, bap, Hmx, Awh, and mld) connectivity during development – which reflects their role in different stages of embryogenesis. The networks we have constructed can be explored and interpreted within Cytoscape software and provide a new systems biology approach for the Drosophila research community to better visualize and interpret developmental regulation of gene expression.
Animal breeding programs have used molecular genetic tools as an auxiliary method to identify and... more Animal breeding programs have used molecular genetic tools as an auxiliary method to identify and select animals with superior genetic merit for milk production and milk quality traits as well as disease resistance. Genes of the major histocompatibility complex (MHC) are important molecular markers for disease resistance that could be applied for genetic selection. The aim of this study was to identify single nucleotide polymorphisms (SNPs) and haplotypes in DRB2 , DRB3 , DMA , and DMB genes in Murrah breed and to analyze the association between molecular markers and milk, fat, protein and mozzarella production, fat and protein percentage, and somatic cell count. Two hundred DNA samples from Murrah buffaloes were used. The target regions of candidate genes were amplified by polymerase chain reaction (PCR) followed by sequencing and identification of polymorphisms. Allele and genotype frequencies, as well as linkage disequilibrium between SNPs, were calculated. Genotypes were used in association analyses with milk production and quality traits. Except for the DMA gene, identified as monomorphic, the other genes presented several polymorphisms. The DMB , DRB2 , and DRB3 genes presented two, six, and seven SNPs, respectively. Fifty-seven haplotype blocks were constructed from 15 SNPs identified, which was used in association analyses. All the studied traits had at least one associated haplotype. In conclusion, it is suggested that the haplotypes found herein can be associated with important traits related to milk production and quality.
Puberty in cattle is regulated by an endocrine axis, which includes a complex milieu of neuropept... more Puberty in cattle is regulated by an endocrine axis, which includes a complex milieu of neuropeptides in the hypothalamus and pituitary gland. The neuropeptidome of hypothalamic-pituitary gland tissue of pre- (PRE) and postpubertal (POST) Bos indicus-influenced heifers was characterized, followed by quantitative analysis of 51 fertility-related neuropeptides in these tissues. Comparison of peptide abundances with gene expression levels allowed assessment of post-transcriptional peptide processing. On the basis of classical cleavage, 124 mature neuropeptides from 35 precursor proteins were detected in hypothalamus and pituitary gland tissues of three PRE and three POST Brangus heifers. An additional 19 peptides (cerebellins, PEN peptides) previously reported as neuropeptides that did not follow classical cleavage were also identified. In the pre-pubertal hypothalamus, a greater diversity of neuropeptides (25.8%) was identified relative to post-pubertal heifers, while in the pituitary...
Numerical approaches to high-density single nucleotide polymorphism (SNP) data are often employed... more Numerical approaches to high-density single nucleotide polymorphism (SNP) data are often employed independently to address individual questions. We linked independent approaches in a bioinformatics pipeline for further insight. The pipeline driven by heterozygosity and Hardy-Weinberg equilibrium (HWE) analyses was applied to characterize Bos taurus and Bos indicus ancestry. We infer a gene co-heterozygosity network that regulates bovine fertility, from data on 18,363 cattle with genotypes for 729,068 SNP. Hierarchical clustering separated populations according to Bos taurus and Bos indicus ancestry. The weights of the first principal component were subjected to Normal mixture modelling allowing the estimation of a gene's contribution to the Bos taurus-Bos indicus axis. We used deviation from HWE, contribution to Bos indicus content and association to fertility traits to select 1,284 genes. With this set, we developed a co-heterozygosity network where the group of genes annotated...
Understanding the genetic architecture of beef cattle growth cannot be limited simply to the geno... more Understanding the genetic architecture of beef cattle growth cannot be limited simply to the genome-wide association study (GWAS) for body weight at any specific ages, but should be extended to a more general purpose by considering the whole growth trajectory over time using a growth curve approach. For such an approach, the parameters that are used to describe growth curves were treated as phenotypes under a GWAS model. Data from 1,255 Brahman cattle that were weighed at birth, 6, 12, 15, 18, and 24 months of age were analyzed. Parameter estimates, such as mature weight (A) and maturity rate (K) from nonlinear models are utilized as substitutes for the original body weights for the GWAS analysis. We chose the best nonlinear model to describe the weight-age data, and the estimated parameters were used as phenotypes in a multi-trait GWAS. Our aims were to identify and characterize associated SNP markers to indicate SNP-derived candidate genes and annotate their function as related to...
Additional file 9. (AdditionalFile9.pdf) - Enriched regulatory features in liver-specific peaks a... more Additional file 9. (AdditionalFile9.pdf) - Enriched regulatory features in liver-specific peaks according to i-cisTarget online tool [68].
Additional file 2. Identified peaks and annotation for hypothalamus. The first 10 columns corresp... more Additional file 2. Identified peaks and annotation for hypothalamus. The first 10 columns correspond to ENCODE narrowPeak format, the following columns are the annotation output of ChIPseeker [62], and the last column indicates which of the peaks are tissue-specific.
Additional file 12. Enriched regulatory features in muscle-specific peaks according to i-cisTarge... more Additional file 12. Enriched regulatory features in muscle-specific peaks according to i-cisTarget online tool [68].
Additional file 3. Identified peaks and annotation for liver. The first 10 columns correspond to ... more Additional file 3. Identified peaks and annotation for liver. The first 10 columns correspond to ENCODE narrowPeak format, the following columns are the annotation output of ChIPseeker [62], and the last column indicates which of the peaks are tissue-specific.
Additional file 1: Table S1. Statistical power to detect the genetic variance of bull fertility p... more Additional file 1: Table S1. Statistical power to detect the genetic variance of bull fertility phenotypes. Statistical power analyses for detecting the genetic variance with the genomic relationship matrices models used are provided for each measured phenotype, within each breed.
Additional file 5. Distribution of peaks by chromosome for muscle (A), liver (B) and hypothalamus... more Additional file 5. Distribution of peaks by chromosome for muscle (A), liver (B) and hypothalamus (C).
We describe an analytical pipeline to exploit the results from RNA sequencing (RNA-Seq) experimen... more We describe an analytical pipeline to exploit the results from RNA sequencing (RNA-Seq) experiments combining a series of processes from data normalization to network inference. The pipeline makes use of numerical approaches aimed at identifying key regulators via the regulatory impact factor (Reverter et al. 2010) metrics. It also employs the partial correlation and an information theory (Reverter and Chan 2008) for the identification of significant edges in the construction of gene co-expression networks. Key nodes in the network include differentially expressed genes, transcription factors, tissue specific genes as well as genes harboring SNPs found to be associated with the phenotype(s) of interest. The pipeline has already been successfully employed in two beef cattle studies, dealing with the onset of puberty and feed efficiency. In the present paper, we describe a pipeline to analyze RNA-Seq data, focus on relevant genes, generate gene co-expression networks and identify emer...
The objectives were identify the best model to describe the growth curve in Brahman cattle and ch... more The objectives were identify the best model to describe the growth curve in Brahman cattle and characterize single nucleotide polymorphism (SNP) associated with growth curve parameters estimates. Body weights were measured at birth, sixth, twelfth, fifteenth, eighteenth and twenty-fourth months of age. Four nonlinear models were tested and evaluated for goodness of fit. Brody’s model was the most appropriate to describe the growth of Brahman cattle. The genome wide association studies were performed for three growth curve parameters (A, b and K). There were 136 SNPs significantly associated with A spread over all chromosomes. There were found significant SNPs in chromosomes thirteen and twenty five, which has no reports in QTLDatabase. These SNPs can be useful in posterior marker assisted selection and pathway analysis to describe the complexity of growth process in beef cattle.
Abstract Puberty is a whole‐body event, driven by the hypothalamic integration of peripheral sign... more Abstract Puberty is a whole‐body event, driven by the hypothalamic integration of peripheral signals such as leptin or IGF‐1. In the process of puberty, reproductive development is simultaneous to growth, including muscle growth. To enhance our understanding of muscle function related to puberty, we performed transcriptome analyses of muscle samples from six pre‐ and six post‐pubertal Brahman heifers (Bos indicus). Our aims were to perform differential expression analyses and co‐expression analyses to derive a regulatory gene network associate with puberty. As a result, we identified 431 differentially expressed (DEx) transcripts (genes and non‐coding RNAs) when comparing pre‐ to post‐pubertal average gene expression. The DEx transcripts were compared with all expressed transcripts in our samples (over 14,000 transcripts) for functional enrichment analyses. The DEx transcripts were associated with “extracellular region,” “inflammatory response” and “hormone activity” (adjusted p < .05). Inflammatory response for muscle regeneration is a necessary aspect of muscle growth, which is accelerated during puberty. The term “hormone activity” may signal genes that respond to progesterone signalling in the muscle, as the presence of this hormone is an important difference between pre‐ and post‐pubertal heifers in our experimental design. The DEx transcript with the highest average expression difference was a mitochondrial gene, ENSBTAG00000043574 that might be another important link between energy metabolism and puberty. In the derived co‐expression gene network, we identified six hub genes: CDC5L, MYC, TCF3, RUNX2, ATF2 and CREB1. In the same network, 48 key regulators of DEx transcripts were identified, using a regulatory impact factor metric. The hub gene TCF3 was also a key regulator. The majority of the key regulators (22 genes) are members of the zinc finger family, which has been implicated in bovine puberty in other tissues. In conclusion, we described how puberty may affect muscle gene expression in cattle.
Co-expression networks tightly coordinate the spatiotemporal patterns of gene expression unfoldin... more Co-expression networks tightly coordinate the spatiotemporal patterns of gene expression unfolding during development. Due to the dynamic nature of developmental processes simply overlaying gene expression patterns onto static representations of co-expression networks may be misleading. Here, we aim to formally quantitate topological changes of co-expression networks during embryonic development using a publicly available Drosophila melanogaster transcriptome data set comprising 14 time points. We deployed a network approach which inferred 10 discrete co-expression networks by smoothly sliding along from early to late development using 5 consecutive time points per window. Such an approach allows changing network structure, including the presence of hubs, modules and other topological parameters to be quantitated. To explore the dynamic aspects of gene expression captured by our approach, we focused on regulator genes with apparent influence over particular aspects of development. Those key regulators were selected using a differential network algorithm to contrast the first 7 (early) with the last 7 (late) developmental time points. This assigns high scores to genes whose connectivity to abundant differentially expressed target genes has changed dramatically between states. We have produced a list of key regulators – some increasing (e.g., Tusp, slbo, Sidpn, DCAF12, and chinmo) and some decreasing (Rfx, bap, Hmx, Awh, and mld) connectivity during development – which reflects their role in different stages of embryogenesis. The networks we have constructed can be explored and interpreted within Cytoscape software and provide a new systems biology approach for the Drosophila research community to better visualize and interpret developmental regulation of gene expression.
Animal breeding programs have used molecular genetic tools as an auxiliary method to identify and... more Animal breeding programs have used molecular genetic tools as an auxiliary method to identify and select animals with superior genetic merit for milk production and milk quality traits as well as disease resistance. Genes of the major histocompatibility complex (MHC) are important molecular markers for disease resistance that could be applied for genetic selection. The aim of this study was to identify single nucleotide polymorphisms (SNPs) and haplotypes in DRB2 , DRB3 , DMA , and DMB genes in Murrah breed and to analyze the association between molecular markers and milk, fat, protein and mozzarella production, fat and protein percentage, and somatic cell count. Two hundred DNA samples from Murrah buffaloes were used. The target regions of candidate genes were amplified by polymerase chain reaction (PCR) followed by sequencing and identification of polymorphisms. Allele and genotype frequencies, as well as linkage disequilibrium between SNPs, were calculated. Genotypes were used in association analyses with milk production and quality traits. Except for the DMA gene, identified as monomorphic, the other genes presented several polymorphisms. The DMB , DRB2 , and DRB3 genes presented two, six, and seven SNPs, respectively. Fifty-seven haplotype blocks were constructed from 15 SNPs identified, which was used in association analyses. All the studied traits had at least one associated haplotype. In conclusion, it is suggested that the haplotypes found herein can be associated with important traits related to milk production and quality.
Puberty in cattle is regulated by an endocrine axis, which includes a complex milieu of neuropept... more Puberty in cattle is regulated by an endocrine axis, which includes a complex milieu of neuropeptides in the hypothalamus and pituitary gland. The neuropeptidome of hypothalamic-pituitary gland tissue of pre- (PRE) and postpubertal (POST) Bos indicus-influenced heifers was characterized, followed by quantitative analysis of 51 fertility-related neuropeptides in these tissues. Comparison of peptide abundances with gene expression levels allowed assessment of post-transcriptional peptide processing. On the basis of classical cleavage, 124 mature neuropeptides from 35 precursor proteins were detected in hypothalamus and pituitary gland tissues of three PRE and three POST Brangus heifers. An additional 19 peptides (cerebellins, PEN peptides) previously reported as neuropeptides that did not follow classical cleavage were also identified. In the pre-pubertal hypothalamus, a greater diversity of neuropeptides (25.8%) was identified relative to post-pubertal heifers, while in the pituitary...
Numerical approaches to high-density single nucleotide polymorphism (SNP) data are often employed... more Numerical approaches to high-density single nucleotide polymorphism (SNP) data are often employed independently to address individual questions. We linked independent approaches in a bioinformatics pipeline for further insight. The pipeline driven by heterozygosity and Hardy-Weinberg equilibrium (HWE) analyses was applied to characterize Bos taurus and Bos indicus ancestry. We infer a gene co-heterozygosity network that regulates bovine fertility, from data on 18,363 cattle with genotypes for 729,068 SNP. Hierarchical clustering separated populations according to Bos taurus and Bos indicus ancestry. The weights of the first principal component were subjected to Normal mixture modelling allowing the estimation of a gene's contribution to the Bos taurus-Bos indicus axis. We used deviation from HWE, contribution to Bos indicus content and association to fertility traits to select 1,284 genes. With this set, we developed a co-heterozygosity network where the group of genes annotated...
Understanding the genetic architecture of beef cattle growth cannot be limited simply to the geno... more Understanding the genetic architecture of beef cattle growth cannot be limited simply to the genome-wide association study (GWAS) for body weight at any specific ages, but should be extended to a more general purpose by considering the whole growth trajectory over time using a growth curve approach. For such an approach, the parameters that are used to describe growth curves were treated as phenotypes under a GWAS model. Data from 1,255 Brahman cattle that were weighed at birth, 6, 12, 15, 18, and 24 months of age were analyzed. Parameter estimates, such as mature weight (A) and maturity rate (K) from nonlinear models are utilized as substitutes for the original body weights for the GWAS analysis. We chose the best nonlinear model to describe the weight-age data, and the estimated parameters were used as phenotypes in a multi-trait GWAS. Our aims were to identify and characterize associated SNP markers to indicate SNP-derived candidate genes and annotate their function as related to...
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Papers by Marina R. S. Fortes