Justin M. Fear

Colony-Stimulating Factor 2 (CSF-2) Improves Development and Posttransfer Survival of Bovine Embryos Produced in Vitro

In this study, we tested the role of colony-stimulating factor 2 (CSF2) as one of the regulatory molecules that mediate maternal effects on embryonic development during the preimplantation period. Our objective was to verify effects of CSF2 on blastocyst yield, determine posttransfer survival, and evaluate properties of the blastocyst formed after CSF2 treatment. In vitro, CSF2 increased the percentage of oocytes that became morulae and blastocysts. Blastocysts that were treated with CSF2 tended to have a greater number of inner cell mass cells and had a higher ratio of inner cell mass to trophectoderm cells. There was no effect of CSF2 on the incidence of apoptosis. Treatment with CSF2 from d 5 to 7 after insemination increased embryonic survival as indicated by improved pregnancy rate at d 30-35 of gestation. Moreover, treatment with CSF2 from either d 1-7 or 5-7 after insemination reduced pregnancy loss after d 30-35. Results indicate that treatment with CSF2 can affect embryonic development and enhance embryo competence for posttransfer survival. The fact that treatment with CSF2 during such a narrow window of development altered embryonic function much later in pregnancy suggests that CSF2 may exert epigenetic effects on the developing embryo that result in persistent changes in function during the embryonic and fetal periods of development.

Allelic Imbalance in Drosophila Hybrid Heads: Exons, Isoforms, and Evolution

Unraveling how regulatory divergence contributes to species differences and adaptation requires identifying functional variants from among millions of genetic differences. Analysis of allelic imbalance (AI) reveals functional genetic differences in cis regulation and has demonstrated differences in cis regulation within and between species. Regulatory mechanisms are often highly conserved, yet differences between species in gene expression are extensive. What evolutionary forces explain widespread divergence in cis regulation? AI was assessed in Drosophila melanogaster-Drosophila simulans hybrid female heads using RNA-seq technology. Mapping bias was virtually eliminated by using genotype-specific references. Allele representation in DNA sequencing was used as a prior in a novel Bayesian model for the estimation of AI in RNA. Cis regulatory divergence was common in the organs and tissues of the head with 41% of genes analyzed showing significant AI. Using existing population genomic data, the relationship between AI and patterns of sequence evolution was examined. Evidence of positive selection was found in 30% of cis regulatory divergent genes. Genes involved in defense, RNAi/RISC complex genes, and those that are sex regulated are enriched among adaptively evolving cis regulatory divergent genes. For genes in these groups, adaptive evolution may play a role in regulatory divergence between species. However, there is no evidence that adaptive evolution drives most of the cis regulatory divergence that is observed. The majority of genes showed patterns consistent with stabilizing selection and neutral evolutionary processes.

Developmental Changes in Expression of Genes Involved in Regulation of Apoptosis in the Bovine Preimplantation Embryo

The early bovine preimplantation embryo is resistant to proapoptotic signals until around the 8- to 16-cell stage. We hypothesized that 2-cell embryos have higher amounts of antiapoptotic proteins and lower amounts of proapoptotic proteins when compared to embryos ≥16 cells. Steady-state concentrations of mRNA for the antiapoptotic genes BCL2 and HSPA1A were higher for MII oocytes, 2-cell embryos, and 2-cell embryos treated with alpha-amanitin as compared to ≥16-cell embryos. Steady-state concentrations of mRNA for the proapoptotic gene BAD increased in embryos ≥16 cells. There was no significant effect of stage of development on steady-state mRNA concentrations of BCL2L1, DFFA, or BAX. Using immunohistochemistry, it was found that BCL2 was present in greater relative concentrations for 2-cell embryos than for embryos ≥16 cells. These results were confirmed by Western blotting. Relative amounts of immunoreactive BAX detected by immunofluorescence were lower for 2-cell embryos than for embryos ≥16 cells. Using Western blotting, a high molecular weight (46 kDa) form of BAX was highest in ≥16-cell embryos, intermediate in 2-cell embryos, and lowest in MII oocytes. There were no effects of stage of development on relative amounts of immunoreactive BCL2L1, HSPA1A, or BAD, as determined by immunofluorescence. Treatment of embryos with alpha-amanitin from Day 0 to Day 5 or Day 4 to Day 5 after insemination reduced activation of group II caspases and terminal deoxynucleotidyl transferase dUTP nick end labeling after treatment with the proapoptotic signal C2 ceramide at Day 5 after fertilization. Thus, transcription of BAX or other proteins is required for acquisition of the capacity for apoptosis. Results support the idea that changes in amounts of BCL2 family members are important for the inhibition of apoptosis in the 2-cell embryo and in the establishment of the capacity for apoptosis later in development.

Male-specific Fruitless isoforms have different regulatory roles conferred by distinct zinc finger DNA binding domains.

BackgroundDrosophila melanogaster adult males perform an elaborate courtship ritual to entice females to mate. fruitless (fru), a gene that is one of the key regulators of male courtship behavior, encodes multiple male-specific isoforms (FruM). These isoforms vary in their carboxy-terminal zinc finger domains, which are predicted to facilitate DNA binding.ResultsBy over-expressing individual FruM isoforms in fru-expressing neurons in either males or females and assaying the global transcriptional response by RNA-sequencing, we show that three FruM isoforms have different regulatory activities that depend on the sex of the fly. We identified several sets of genes regulated downstream of FruM isoforms, including many annotated with neuronal functions. By determining the binding sites of individual FruM isoforms using SELEX we demonstrate that the distinct zinc finger domain of each FruM isoforms confers different DNA binding specificities. A genome-wide search for these binding site sequences finds that the gene sets identified as induced by over-expression of FruM isoforms in males are enriched for genes that contain the binding sites. An analysis of the chromosomal distribution of genes downstream of FruM shows that those that are induced and repressed in males are highly enriched and depleted on the X chromosome, respectively.ConclusionsThis study elucidates the different regulatory and DNA binding activities of three FruM isoforms on a genome-wide scale and identifies genes regulated by these isoforms. These results add to our understanding of sex chromosome biology and further support the hypothesis that in some cell-types genes with male-biased expression are enriched on the X chromosome.

A flexible Bayesian method for detecting allelic imbalance in RNA-seq data

BackgroundOne method of identifying cis regulatory differences is to analyze allele-specific expression (ASE) and identify cases of allelic imbalance (AI). RNA-seq is the most common way to measure ASE and a binomial test is often applied to determine statistical significance of AI. This implicitly assumes that there is no bias in estimation of AI. However, bias has been found to result from multiple factors including: genome ambiguity, reference quality, the mapping algorithm, and biases in the sequencing process. Two alternative approaches have been developed to handle bias: adjusting for bias using a statistical model and filtering regions of the genome suspected of harboring bias. Existing statistical models which account for bias rely on information from DNA controls, which can be cost prohibitive for large intraspecific studies. In contrast, data filtering is inexpensive and straightforward, but necessarily involves sacrificing a portion of the data.ResultsHere we propose a flexible Bayesian model for analysis of AI, which accounts for bias and can be implemented without DNA controls. In lieu of DNA controls, this Poisson-Gamma (PG) model uses an estimate of bias from simulations. The proposed model always has a lower type I error rate compared to the binomial test. Consistent with prior studies, bias dramatically affects the type I error rate. All of the tested models are sensitive to misspecification of bias. The closer the estimate of bias is to the true underlying bias, the lower the type I error rate. Correct estimates of bias result in a level alpha test.ConclusionsTo improve the assessment of AI, some forms of systematic error (e.g., map bias) can be identified using simulation. The resulting estimates of bias can be used to correct for bias in the PG model, without data filtering. Other sources of bias (e.g., unidentified variant calls) can be easily captured by DNA controls, but are missed by common filtering approaches. Consequently, as variant identification improves, the need for DNA controls will be reduced. Filtering does not significantly improve performance and is not recommended, as information is sacrificed without a measurable gain. The PG model developed here performs well when bias is known, or slightly misspecified. The model is flexible and can accommodate differences in experimental design and bias estimation.

Leveraging biological replicates to improve analysis in ChIP-seq experiments.

ChIP-seq experiments identify genome-wide profiles of DNA-binding molecules including transcription factors, enzymes and epigenetic marks. Biological replicates are critical for reliable site discovery and are required for the deposition of data in the ENCODE and modENCODE projects. While early reports suggested two replicates were sufficient, the widespread application of the technique has led to emerging consensus that the technique is noisy and that increasing replication may be worthwhile. Additional biological replicates also allow for quantitative assessment of differences between conditions. To date it has remained controversial about how to confirm peak identification and to determine signal strength across biological replicates, particularly when the number of replicates is greater than two. Using objective metrics, we evaluate the consistency of biological replicates in ChIP-seq experiments with more than two replicates. We compare several approaches for binding site determination, including two popular but disparate peak callers, CisGenome and MACS2. Here we propose read coverage as a quantitative measurement of signal strength for estimating sample concordance. Determining binding based on genomic features, such as promoters, is also examined. We find that increasing the number of biological replicates increases the reliability of peak identification. Critically, binding sites with strong biological evidence may be missed if researchers rely on only two biological replicates. When more than two replicates are performed, a simple majority rule (>50% of samples identify a peak) identifies peaks more reliably in all biological replicates than the absolute concordance of peak identification between any two replicates, further demonstrating the utility of increasing replicate numbers in ChIP-seq experiments.

Buffering of Genetic Regulatory Networks in Drosophila melanogaster.

Regulatory variation in gene expression can be described by cis- and trans-genetic components. Here we used RNA-seq data from a population panel of Drosophila melanogaster test crosses to compare allelic imbalance (AI) in female head tissue between mated and virgin flies, an environmental change known to affect transcription. Indeed, 3048 exons (1610 genes) are differentially expressed in this study. A Bayesian model for AI, with an intersection test, controls type I error. There are ∼200 genes with AI exclusively in mated or virgin flies, indicating an environmental component of expression regulation. On average 34% of genes within a cross and 54% of all genes show evidence for genetic regulation of transcription. Nearly all differentially regulated genes are affected in cis, with an average of 63% of expression variation explained by the cis-effects. Trans-effects explain 8% of the variance in AI on average and the interaction between cis and trans explains an average of 11% of the total variance in AI. In both environments cis- and trans-effects are compensatory in their overall effect, with a negative association between cis- and trans-effects in 85% of the exons examined. We hypothesize that the gene expression level perturbed by cis-regulatory mutations is compensated through trans-regulatory mechanisms, e.g., trans and cis by trans-factors buffering cis-mutations. In addition, when AI is detected in both environments, cis-mated, cis-virgin, and trans-mated–trans-virgin estimates are highly concordant with 99% of all exons positively correlated with a median correlation of 0.83 for cis and 0.95 for trans. We conclude that the gene regulatory networks (GRNs) are robust and that trans-buffering explains robustness.

Cheating death at the dawn of life: developmental control of apoptotic repression in the preimplantation embryo.

During early development, the mammalian embryo is resistant to pro-apoptotic signals because of biochemical properties of the mitochondrion and nucleus. Mitochondria of the bovine two-cell embryo are resistant to depolarization because of low amounts of the proapoptotic protein BAX and high concentrations of the anti-apoptotic protein BCL2. As development proceeds, BAX content increases, BCL2 content declines, and mitochondria becomes capable of pore formation and depolarization in response to pro-apoptotic signals. The nucleus of the two-cell embryo is resistant to degradation by the DNase DFFB because epigenetic modifications, including DNA methylation and histone deacetylation, mask internucleosomal sites for DNA cleavage. Blastomere DNA becomes progressively less methylated during development so that DNA becomes accessible to cleavage by DFFB.

Sex Differences in Drosophila Somatic Gene Expression: Variation and Regulation by doublesex

Sex differences in gene expression have been widely studied in Drosophila melanogaster. Sex differences vary across strains, but many molecular studies focus on only a single strain, or on genes that show sexually dimorphic expression in many strains. How extensive variability is and whether this variability occurs among genes regulated by sex determination hierarchy terminal transcription factors is unknown. To address these questions, we examine differences in sexually dimorphic gene expression between two strains in Drosophila adult head tissues. We also examine gene expression in doublesex (dsx) mutant strains to determine which sex-differentially expressed genes are regulated by DSX, and the mode by which DSX regulates expression. We find substantial variation in sex-differential expression. The sets of genes with sexually dimorphic expression in each strain show little overlap. The prevalence of different DSX regulatory modes also varies between the two strains. Neither the patterns of DSX DNA occupancy, nor mode of DSX regulation explain why some genes show consistent sex-differential expression across strains. We find that the genes identified as regulated by DSX in this study are enriched with known sites of DSX DNA occupancy. Finally, we find that sex-differentially expressed genes and genes regulated by DSX are highly enriched on the fourth chromosome. These results provide insights into a more complete pool of potential DSX targets, as well as revealing the molecular flexibility of DSX regulation.

SECIMTools: a suite of metabolomics data analysis tools

BackgroundMetabolomics has the promise to transform the area of personalized medicine with the rapid development of high throughput technology for untargeted analysis of metabolites. Open access, easy to use, analytic tools that are broadly accessible to the biological community need to be developed. While technology used in metabolomics varies, most metabolomics studies have a set of features identified. Galaxy is an open access platform that enables scientists at all levels to interact with big data. Galaxy promotes reproducibility by saving histories and enabling the sharing workflows among scientists.ResultsSECIMTools (SouthEast Center for Integrated Metabolomics) is a set of Python applications that are available both as standalone tools and wrapped for use in Galaxy. The suite includes a comprehensive set of quality control metrics (retention time window evaluation and various peak evaluation tools), visualization techniques (hierarchical cluster heatmap, principal component analysis, modular modularity clustering), basic statistical analysis methods (partial least squares - discriminant analysis, analysis of variance, t-test, Kruskal-Wallis non-parametric test), advanced classification methods (random forest, support vector machines), and advanced variable selection tools (least absolute shrinkage and selection operator LASSO and Elastic Net).ConclusionsSECIMTools leverages the Galaxy platform and enables integrated workflows for metabolomics data analysis made from building blocks designed for easy use and interpretability. Standard data formats and a set of utilities allow arbitrary linkages between tools to encourage novel workflow designs. The Galaxy framework enables future data integration for metabolomics studies with other omics data.