Transientgrb10aKnockdown Permanently Alters Growth, Cardiometabolic Phenotype and the Transcriptome inDanio rerio

Abstract

Embryonic growth trajectory is a risk factor for chronic metabolic and cardiovascular disorders and influences birth weight along with early post-natal weight gain in humans. Grb10 is a negative regulator of the main pathways driving embryonic growth and knock-out in mammals increases insulin sensitivity and growth trajectory. This study investigates in Danio rerio the long-term cardiometabolic consequences and associated transcriptomic profiles of morpholino induced early life disruption of grb10a expression. The associated transient knockdown of grb10a increased embryonic growth (+7 %) and metabolic rate (+25 %), while decreasing heart rate (−50 %) in early life. Juvenile growth and respiratory rate were also elevated (+30 % and 7-fold increase respectively). This was associated with permanent remodelling of the transcriptional landscape and the dysregulation of multiple growth-related pathways. This study indicates that zebrafish are a suitable model for life-long investigation of the link between early growth and later life disease risk.

Monocarboxylate Transporters Are Involved in Extracellular Matrix Remodelling in Pancreatic Ductal Adenocarcinoma

“…The causal relationship between MCTs and ECMs was evaluated using a hypernetwork modelling approach as previously described [ 29 , 31 ]. Briefly, hypernetworks represent network structures where edges define a relationship between nodes (e.g., transcripts) and can be shared by many nodes ( Appendix A , Figure A1 ); this is the definition of “higher order interactions” [ 28 ].…”

“…Hierarchical clustering was used to identify the clusters formed within the hypernetwork. The correlations between the highly connected cluster of transcripts from the hypernetwork and the rest of the transcriptome were determined by interrogating the incidence matrix, M [ 31 ]. This produced a subset of the whole transcriptome that correlated with 90% of the transcripts from the hypernetwork cluster.…”

“…Hypernetworks model higher-order interactions or relationships between omic elements (represented by nodes) based on large numbers of shared correlations (represented by edges) [ 29 ]. Such interactions that are normally not captured by traditional pairwise transcriptomic approaches provide a model of functional relationships between these omic elements [ 30 , 31 ].…”

Monocarboxylate Transporters are Involved in Extracellular Matrix Remodelling in Pancreatic Ductal Adenocarcinoma

“…The causal relationship between MCTs and ECMs was evaluated using a hypernetwork modelling approach as previously described [24,26]. Briefly, hypernetworks represent network structures where edges define a relationship between nodes (e.g., transcripts) and can be shared by many nodes (Figure A1); this is the definition of "higher order interactions" [23] .…”

“…Hypernetworks model higher-order interactions or relationships between 'omic elements (represented by nodes) based on large numbers of shared correlations (represented by edges) [24]. Such interactions which are normally not captured by traditional pairwise transcriptomic approaches provide a model of functional relationships between these 'omic elements [25,26].…”