Westhues, M. | bioRxiv Science

bioRxiv2023

Vascular and neural transcriptomics reveal stage-dependent pathways to inflammation and cognitive dysfunction in a rat model of hypertension

Chronic arterial hypertension causes cerebral microvascular dysfunction and doubles dementia risk in aging. However, cognitive health preservation by therapeutic blood pressure lowering alone is limited and depends on disease duration, the degree of irreversible tissue damage and whether microvascular function can be restored. This study aimed to understand molecular and cellular temporo-spatial pathomechanisms in the course of hypertension. We investigated the effects of initial, early chronic and late chronic hypertension in the frontal brain of rats by applying behavioral tests, histopathology, immunofluorescence, FACS, microvascular/neural tissue RNA sequencing as well as 18F-FDG PET imaging. Chronic hypertension caused frontal brain-specific behavioral deficits. Our results highlight stage-dependent responses to continuous microvascular stress and wounding by hypertension. Early responses included a fast recruitment of activated microglia to the blood vessels, immigration of peripheral immune cells, blood-brain-barrier leakage and an energy-demanding hypermetabolic state. Vascular adaptation mechanisms were observed in later stages and included angiogenesis and vessel wall strengthening by upregulation of cellular adhesion molecules and extracellular matrix. Additionally, we identified late chronic accumulation of Igfbp-5 in the brains of hypertensive rats, which is also a signature of Alzheimers dementia and attenuates protective Igf-1 signaling. Our study advances the knowledge of involved pathomechanisms and highlights the stage-dependent nature of hypertensive pathobiology. This groundwork might be helpful for basic and clinical research to identify stage-dependent markers in the human disease course, investigate stage-dependent interventions besides blood pressure lowering and better understand the relationship between poor vascular health and neurodegenerative diseases.

neuroscience↗

bioRxiv2017

Omics-Based Hybrid Prediction In Maize

Accurate prediction of traits with complex genetic architecture is crucial for selecting superior candidates in animal and plant breeding and for guiding decisions in personalized medicine. Whole-genome prediction (WGP) has revolutionized these areas but has inherent limitations in incorporating intricate epistatic interactions. Downstream \"omics\" data are expected to integrate interactions within and between different biological strata and provide the opportunity to improve trait prediction. Yet, predicting traits from parents to progeny has not been addressed by a combination of \"omics\" data. Here, we evaluate several \"omics\" predictors -- genomic, transcriptomic and metabolic data -- measured on parent lines at early developmental stages, and demonstrate that the integration of transcriptomic with genomic data leads to higher success rates in the correct prediction of untested hybrid combinations in maize. Despite the high predictive ability of genomic data, transcriptomic data alone outperformed them and other predictors for the most complex heterotic trait, dry matter yield. An eQTL analysis revealed that transcriptomic data integrate genomic information from both, adjacent and distant sites relative to the expressed genes. Together, these findings suggest that downstream predictors capture physiological epistasis that is transmitted from parents to their hybrid offspring. We conclude that the use of downstream \"omics\" data in prediction can exploit important information beyond structural genomics for leveraging the efficiency of hybrid breeding.\n\nKey messageComplementing genomic data with other \"omics\" predictors can increase the probability of success for predicting the best hybrid combinations using complex agronomic traits.\n\nConflict of InterestThe authors declare that they have no conflict of interest.

genetics↗