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

ABSTRACT: 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.

SUBMITTER: Hadwen J

PROVIDER: S-EPMC8231191 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThrough next-generation sequencing, the amount of sequence data potentially available for phylogenetic analyses has increased exponentially in recent years. Simultaneously, the risk of incorporating 'noisy' data with misleading phylogenetic signal has also increased, and may disproportionately influence the topology of weakly supported nodes and lineages featuring rapid radiations and/or elevated rates of evolution.ResultsWe investigated the influence of phylogenetic noise in large data sets by applying two fundamental strategies, variable site removal and long-branch exclusion, to the phylogenetic analysis of a full plastome alignment of 107 species of Pinus and six Pinaceae outgroups. While high overall phylogenetic resolution resulted from inclusion of all data, three historically recalcitrant nodes remained conflicted with previous analyses. Close investigation of these nodes revealed dramatically different responses to data removal. Whereas topological resolution and bootstrap support for two clades peaked with removal of highly variable sites, the third clade resolved most strongly when all sites were included. Similar trends were observed using long-branch exclusion, but patterns were neither as strong nor as clear. When compared to previous phylogenetic analyses of nuclear loci and morphological data, the most highly supported topologies seen in Pinus plastome analysis are congruent for the two clades gaining support from variable site removal and long-branch exclusion, but in conflict for the clade with highest support from the full data set.ConclusionsThese results suggest that removal of misleading signal in phylogenomic datasets can result not only in increased resolution for poorly supported nodes, but may serve as a tool for identifying erroneous yet highly supported topologies. For Pinus chloroplast genomes, removal of variable sites appears to be more effective than long-branch exclusion for clarifying phylogenetic hypotheses.

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

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