CRF-Like Diuretic Hormone Negatively Affects Both Feeding and Reproduction in the Desert Locust, Schistocerca gregaria

Diuretic hormones (DH) related to the vertebrate Corticotropin Releasing Factor (CRF) have been identified in diverse insect species. In the migratory locust, Locusta migratoria, the CRF-like DH (CRF/DH) is localized in the same neurosecretory cells as the Ovary Maturating Parsin (OMP), a neurohormone that stimulates oocyte growth, vitellogenesis and hemolymph ecdysteroid levels in adult female locusts. In this study, we investigated whether CRF-like DH can influence feeding and reproduction in the desert locust, Schistocerca gregaria. We identified two highly similar S. gregaria CRF-like DH precursor cDNAs, each of which also encodes an OMP isoform. Alignment with other insect CRF-like DH precursors shows relatively high conservation of the CRF/DH sequence while the precursor region corresponding to OMP is not well conserved. Quantitative real-time RT-PCR revealed that the precursor transcripts mainly occur in the central nervous system and their highest expression level was observed in the brain. Injection of locust CRF/DH caused a significantly reduced food intake, while RNAi knockdown stimulated food intake. Therefore, our data indicate that CRF-like DH induces satiety. Furthermore, injection of CRF/DH in adult females retarded oocyte growth and caused lower ecdysteroid titers in hemolymph and ovaries, while RNAi knockdown resulted in opposite effects. The observed effects of CRF/DH may be part of a wider repertoire of neurohormonal activities, constituting an integrating control system that affects food intake and excretion, as well as anabolic processes like oocyte growth and ecdysteroidogenesis, following a meal. Our discussion about the functional relationship between CRF/DH and OMP led to the hypothesis that OMP may possibly act as a monitoring peptide that can elicit negative feedback effects

An integrative approach for building personalized gene regulatory networks for precision medicine

Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare