Separating the Wheat from the Chaff: The Use of Upstream Regulator Analysis to Identify True Differential Expression of Single Genes within Transcriptomic Datasets

The development of DNA microarray and RNA-sequencing technology has led to an explosion in the generation of transcriptomic differential expression data under a wide range of biologic systems including those recapitulating the monogenic muscular dystrophies. Data generation has increased exponentially due in large part to new platforms, improved cost-effectiveness, and processing speed. However, reproducibility and thus reliability of data remain a central issue, particularly when resource constraints limit experiments to single replicates. This was observed firsthand in a recent rare disease drug repurposing project involving RNA-seq-based transcriptomic profiling of primary cerebrocortical cultures incubated with clinic-ready blood–brain penetrant drugs. Given the low validation rates obtained for single differential expression genes, alternative approaches to identify with greater confidence genes that were truly differentially expressed in our dataset were explored. Here we outline a method for differential expression data analysis in the context of drug repurposing for rare diseases that incorporates the statistical rigour of the multigene analysis to bring greater predictive power in assessing individual gene modulation. Ingenuity Pathway Analysis upstream regulator analysis was applied to the differentially expressed genes from the Care4Rare Neuron Drug Screen transcriptomic database to identify three distinct signaling networks each perturbed by a different drug and involving a central upstream modulating protein: levothyroxine (DIO3), hydroxyurea (FOXM1), dexamethasone (PPARD). Differential expression of upstream regulator network related genes was next assessed in in vitro and in vivo systems by qPCR, revealing 5× and 10× increases in validation rates, respectively, when compared with our previous experience with individual genes in the dataset not associated with a network. The Ingenuity Pathway Analysis based gene prioritization may increase the predictive value of drug–gene interactions, especially in the context of assessing single-gene modulation in single-replicate experiments.

Modulation of granulosa cell function via CRISPR-Cas fuelled editing of BMPR-IB gene in goats (Capra hircus)

BMPs are multifunctional growth factors implicated in regulating the ovarian function as key intra-ovarian factors. Biological effects of BMPs are mediated through binding with membrane bound receptors like BMPR-IB and initiating downstream Smad signaling pathway. FecB mutation, regarded as a loss of function mutation in the BMPR-IB gene was identified in certain sheep breeds having high fecundity. Similar type of fecundity genes in goats have not been discovered so far. Hence, the current study was designed to investigate the effects of BMPR-IB gene modulation on granulosa cell function in goats. The BMPR-IB gene was knocked out using CRISPR-Cas technology in granulosa cells and cultured in vitro with BMP-4 stimulation for three different durations In addition, the FecB mutation was introduced in the BMPR-IB gene applying Easi-CRISPR followed by BMP-4/7 stimulation for 72 h. Steroidogenesis and cell viability were studied to explore the granulosa cell function on BMPR-IB gene modulation. BMPRs were found to be expressed stage specifically in granulosa cells of goats. Higher transcriptional abundance of R-Smads, LHR and FSHR indicating sensitisation of Smad signaling and increased gonadotropin sensitivity along with a significant reduction in the cell proliferation and viability was observed in granulosa cells upon BMPR-IB modulation. The inhibitory action of BMP-4/7 on P4 secretion was abolished in both KO and KI cells. Altogether, the study has revealed an altered Smad signaling, steroidogenesis and cell viability upon modulation of BMPR-IB gene in granulosa cells similar to that are documented in sheep breeds carrying the FecB mutation.