Biology subjects

Delbare, S. Y. N.

Publications and source records attributed to Delbare, S. Y. N..

6 recordsLinked to original sources

bioRxiv2026

No detectable effect of P2 neurons on female post-mating changes in sleep and feeding in Drosophila melanogaster

In many insects, females exhibit behavioral changes after mating. This plasticity can be mediated by interactions between male-derived seminal fluid proteins (SFPs) and the female nervous system. The Drosophila melanogaster seminal fluid protein Sex Peptide (SP) causes mated females to sleep less during the day and consume more food than unmated females, among other behavioral changes. Although the SP-sensing neural circuits that convey information to the females brain are known, the downstream neurons that affect post-mating behaviors remain unknown. P2 neurons in the flys central brain are good candidates for regulating post-mating changes in sleep and food consumption, as they express feeding- and sleep-responsive neuropeptide F (NPF), directly innervate the flys sleep center (the dorsal fan-shaped body, dFB), and are responsible for social-isolation-induced changes in sleep and food consumption. Using the GAL4/UAS system, we activated or silenced P2 neurons in mated and unmated females. We measured sleep duration using Drosophila Activity Monitors (DAM) and food consumption using Capillary Feeder (CAFE) and blue-dye assays. We found that P2-neurons are not responsible for post-mating changes in sleep and food consumption: mated females slept less during the day and ate more compared to unmated females irrespective of P2 neuron perturbation. Together, these results indicate that P2 neurons are unlikely to be primary downstream neurons through which male-transferred SP regulates post-mating sleep and food consumption. Future studies could determine whether P2 neurons regulate other post-mating behaviors or modulate sleep and feeding behaviors in a context-dependent manner.

genetics↗

bioRxiv2022

Time series transcriptome analysis uncovers regulatory networks and a role for the circadian clock in the Drosophila melanogaster female's response to Sex Peptide

Sex Peptide, a seminal fluid protein of D. melanogaster males, has been described as driving a virgin-to-mated switch in females, through eliciting an array of responses, including increased egg laying, activity and food intake and a decreased re-mating rate. While it is known that Sex Peptide achieves this, at least in part, by altering neuronal signaling in females, the identity of key molecular regulators that act downstream of Sex Peptide is not known. Here, we used a high-resolution time series RNA-sequencing dataset of female heads at 10 time points within the first 24 hours after mating to investigate the genetic architecture, at the gene- and exon-level, of the females response to Sex Peptide. We find that Sex Peptide is not essential to trigger a virgin-to-mated transcriptional switch, which involves changes in a metabolic gene regulatory network. However, Sex Peptide is needed to maintain and diversify metabolic changes and to trigger changes in a neuronal gene regulatory network. We further find that Sex Peptide might interact with the females circadian clock to orchestrate transcriptional changes across different regulatory networks. That a male seminal fluid protein can alter a females rhythmic gene expression has implications for our understanding of both reproductive and circadian behaviors.

genomics↗

bioRxiv2021

An Empirical Bayes Approach to Estimating Dynamic Models of Co-regulated Gene Expression

Time-course gene expression datasets provide insight into the dynamics of complex biological processes, such as immune response and organ development. It is of interest to identify genes with similar temporal expression patterns because such genes are often biologically related. However, this task is challenging due to the high dimensionality of these datasets and the nonlinearity of gene expression time dynamics. We propose an empirical Bayes approach to estimating ordinary differential equation (ODE) models of gene expression, from which we derive a similarity metric between genes called the Bayesian lead-lag R2 (LLR2). Importantly, the calculation of the LLR2 leverages biological databases that document known interactions amongst genes; this information is automatically used to define informative prior distributions on the ODE models parameters. As a result, the LLR2 is a biologically-informed metric that can be used to identify clusters or networks of functionally-related genes with co-moving or time-delayed expression patterns. We then derive data-driven shrinkage parameters from Steins unbiased risk estimate that optimally balance the ODE models fit to both data and external biological information. Using real gene expression data, we demonstrate that our methodology allows us to recover interpretable gene clusters and sparse networks. These results reveal new insights about the dynamics of biological systems.

genomics↗

bioRxiv2020

Dense time-course gene expression profiling of the Drosophila melanogaster innate immune response

BackgroundImmune responses need to be initiated rapidly, and maintained as needed, to prevent establishment and growth of infections. At the same time, resources need to be balanced with other physiological processes. On the level of transcription, studies have shown that this balancing act is reflected in tight control of the initiation kinetics and shutdown dynamics of specific immune genes. ResultsTo investigate genome-wide expression dynamics and trade-offs after infection at a high temporal resolution, we performed an RNA-seq time course on D. melanogaster with 20 time points post Imd stimulation. A combination of methods, including spline fitting, cluster analysis, and Granger causality inference, allowed detailed dissection of expression profiles, lead-lag interactions, and functional annotation of genes through guilt-by-association. We identified Imd-responsive genes and co-expressed, less well characterized genes, with an immediate-early response and sustained up-regulation up to five days after stimulation. In contrast, stress response and Toll-responsive genes, among which were Bomanins, demonstrated early and transient responses. We further observed a strong trade-off with metabolic genes, which strikingly recovered to pre-infection levels before the immune response was fully resolved. ConclusionsThis high-dimensional dataset enabled the comprehensive study of immune response dynamics through the parallel application of multiple temporal data analysis methods. The well annotated data set should also serve as a useful resource for further investigation of the D. melanogaster innate immune response, and for the development of methods for analysis of a post-stress transcriptional response time-series at whole-genome scale.

systems biology↗

bioRxiv2020

Interactions between the microbiome and mating influence the female's transcriptional profile in Drosophila melanogaster

Drosophila melanogaster females undergo a variety of post-mating changes that influence their activity, feeding behavior, metabolism, egg production and gene expression. These changes are induced either by mating itself or by sperm or seminal fluid proteins. In addition, studies have shown that axenic females--those lacking a microbiome--have altered fecundity compared to females with a microbiome, and that the microbiome of the females mate can influence reproductive success. However, the extent to which post-mating changes in transcript abundance are affected by microbiome state is not well-characterized. Here we investigated fecundity and the post-mating transcript abundance profile of axenic or control females after mating with either axenic or control males. We observed interactions between the females microbiome and her mating status: transcripts of genes involved in reproduction and genes with neuronal functions were differentially abundant depending on the females microbiome status, but only in mated females. In addition, immunity genes showed varied responses to either the microbiome, mating, or a combination of those two factors. We further observed that the males microbiome status influences the fecundity of both control and axenic females, while only influencing the transcriptional profile of axenic females. Our results indicate that the microbiome plays a vital role in the post-mating switch of the females transcriptome.

genomics↗

bioRxiv2018

Female genetic contributions to sperm competition in Drosophila melanogaster

In many species, sperm can remain viable in the reproductive tract of a female well beyond the typical interval to remating. This creates an opportunity for sperm from different males to compete for oocyte fertilization inside the females reproductive tract. In Drosophila melanogaster, sperm morphology and seminal fluid content affect male success in sperm competition. On the other hand, although genome-wide association studies (GWAS) have demonstrated that female genotype plays a role in sperm competition outcome as well, the biochemical, sensory and physiological processes by which females detect and selectively use sperm from different males remain elusive.\n\nHere, we functionally tested 27 candidate genes implicated via a GWAS for their contribution to the females role in sperm competition, measured as changes in the relative success of the first male to mate (P1). Of these 27 candidates, we identified eight genes that affect P1 when knocked down in females, and also showed that six of them do so when knocked down in the female nervous system. Two genes in particular, Rim and caup, lowered P1 when knocked down in sensory pickpocket (ppk)+ neurons and octopaminergic Tdc2+ neurons, respectively. These results establish a functional role for the females nervous system in the process of sperm competition and expand our understanding of the genetic, neuronal and mechanistic basis of female responses to multiple matings. We propose that through their nervous system, females actively assess male compatibility based on courtship or ejaculates and modulate sperm competition outcome accordingly.

genetics↗