VanderGiessen, M. | Search bioRxiv

bioRxiv2025

Hemoglobin in the diet modulates post-blood feeding behavioral rhythms and gene expression in Aedes aegypti

Female Aedes aegypti mosquitoes rely on blood meals to acquire nutrients essential for egg development. However, blood ingestion also introduces physiological stressors--including thermal, osmotic, and oxidative stress--particularly during the digestion of heme-containing proteins. In Drosophila melanogaster, oxidative stress response genes are regulated by the circadian clock, and core circadian transcription factors are redox-sensitive. Additionally, sufficient iron ingestion is necessary to maintain normal circadian behaviors, as iron metabolism genes influence circadian behaviors, and flies lacking iron storage and transport are arrhythmic. However, whether similar interactions between iron metabolism and circadian rhythms exist in mosquitoes remains unclear. Here, we leverage the known alteration of mosquitoes activity rhythms following a bloodmeal to investigate whether dietary hemoglobin contributes to post-feeding activity suppression and gene regulation in the female Ae. aegypti. Using vertebrate blood and artificial blood mimic diets of equal nutritional values but with and without hemoglobin, we compared mosquito egg production, locomotor activity, sleep profiles, and transcript abundance of genes involved in circadian regulation and host-seeking behavior. Hemoglobin intake significantly reduced post-feeding activity, increased sleep amounts, and suppressed transcription of the core circadian gene period in mosquito heads. Short periods of sleep deprivation during the post-blood feeding period of inactivity did not alter egg production, timing of deposition, or viability. Our findings reveal that hemoglobin-derived heme influences behavioral and molecular responses in Ae. aegypti after a blood meal, pointing to a complex regulatory network linking heme acquisition, oxidative stress, daily rhythms, and behavior.

animal behavior and cognition↗

bioRxiv2024

Machine Learning Identifies Genes Linked to Neurological Disorders Induced by Equine Encephalitis viruses (EEV), Traumatic Brain Injuries (TBI), and Organophosphorus nerve agents (OPNA)

Venezuelan, eastern, and western equine encephalitis viruses (collectively referred to as equine encephalitis viruses---EEV) cause serious neurological diseases and are a significant threat to the civilian population and the warfighter. Likewise, organophosphorus nerve agents (OPNA) are highly toxic chemicals that pose serious health threats of neurological deficits to both military and civilian personnel around the world. Consequently, only a select few approved research groups are permitted to study these dangerous chemical and biological warfare agents. This has created a significant gap in our scientific understanding of the mechanisms underlying neurological diseases. Valuable insights may be gleaned by drawing parallels to other extensively researched neuropathologies, such as traumatic brain injuries (TBI). By examining combined gene expression profiles, common and unique molecular characteristics may be discovered, providing new insights into medical countermeasures (MCMs) for TBI, EEV infection and OPNA neuropathologies and sequelae. In this study, we collected transcriptomic datasets for neurological disorders caused by TBI, EEV, and OPNA injury, and implemented a framework to normalize and integrate gene expression datasets derived from various platforms. Effective machine learning approaches were developed to identify critical genes that are associated, either shared among the three neuropathologies or to either TBI, EEV, and OPNA. With the aid of deep neural networks, we were able to extract important association signals for accurate prediction of different neurological disorders by using integrated gene expression datasets of VEEV, OPNA, and TBI samples. Gene ontology and pathway analyses further identified neuropathologic features with specific gene product attributes and functions, shedding light on the fundamental biology of these neurological disorders. Collectively, we highlight a workflow to analyze published transcriptomic data using machine learning, which can be used for both identification of gene biomarkers that are unique to specific neurological conditions, as well as genes shared across multiple neuropathologies. These shared genes could serve as potential neuroprotective drug targets for conditions like EEV, TBI, and OPNA.

neuroscience↗