Biology subjects

Gravina, A.

Publications and source records attributed to Gravina, A..

3 recordsLinked to original sources

bioRxiv2021

Controlling astrocyte-mediated synaptic pruning signals for schizophrenia drug repurposing with Deep Graph Networks

Schizophrenia is a debilitating psychiatric disorder, leading to both physical and social morbidity. Worldwide 1% of the population is struggling with the disease, with 100,000 new cases annually only in the United States. Despite its importance, the goal of finding effective treatments for schizophrenia remains a challenging task, and previous work conducted expensive large-scale phenotypic screens. This work investigates the benefits of Machine Learning for graphs to optimize drug phenotypic screens and predict compounds that mitigate abnormal brain reduction induced by excessive glial phagocytic activity in schizophrenia subjects. Given a compound and its concentration as input, we propose a method that predicts a score associated with three possible compound effects, i.e., reduce, increase, or not influence phagocytosis. We leverage a high-throughput screening to prove experimentally that our method achieves good generalization capabilities. The screening involves 2218 compounds at five different concentrations. Then, we analyze the usability of our approach in a practical setting, i.e., prioritizing the selection of compounds in the SWEETLEAD library. We provide a list of 64 compounds from the library that have the most potential clinical utility for glial phagocytosis mitigation. Lastly, we propose a novel approach to computationally validate their utility as possible therapies for schizophrenia. Author summaryPhagocytosis is a fundamental biological process to protect biological organisms from exogenous infectious particles as well as to preserve equilibrium and efficiency of the host by removing its unwanted cells. A dysregulation of the phagocytic activity can lead to severe consequences for the host. In this study, we focus on a recent theory that relates an excessive phagocytic activity in brain cells, and a consequent abnormal reduction in brain volume, to the development of schizophrenia. Our working hypothesis is that pharmaceutical compounds that can reduce excessive of phagocytic activity might prove effective as a schizophrenia treatment. Rather than attempting to develop ex-novo such a chemical compound, we rely on a more cost-effective and efficient approach that seeks candidate therapies in a set of approved chemical compounds. To achieve this, we train a machine learning model capable of predicting, with good accuracy, the ability of a molecular compound to increase or decrease phagocytosis in the target brain cells. Our approach leverages learning models capable of directly processing the molecular graph of the compound, leading to the identification of 64 candidate drugs of potential clinical utility.

bioinformatics↗

bioRxiv2020

From random to predictive: a context-specific interaction framework improves selection of drug protein-protein interactions for unknown drug pathways

With high drug attrition, interaction network methods are increasingly attractive as quick and inexpensive methods for prediction of drug safety and efficacy effects when a drug pathway is unknown. However, these methods suffer from high false positive rates for selecting drug phenotypic effects, their performance is often no better than random (AUROC ~0.5), and this limits the use of network methods in regulatory and industrial decision making. In contrast to many network engineering approaches that apply mathematical thresholds to discover phenotype associations, we hypothesized that interaction networks associated with true positive drug phenotypes are context specific. We tested this hypothesis on 16 designated medical event (DMEs) phenotypes which are a subset of adverse events that are of upmost concern to FDA review using a novel data set extracted from drug labels. We demonstrated that context-specific interactions (CSIs) distinguished true from false positive DMEs with an 50% improvement over non-context-specific approaches (AUROC 0.77 compared to 0.51). By reducing false positives, CSI analysis has the potential to advance network techniques to influence decision making in regulatory and industry settings. Author summaryDrugs bind proteins that interact with multiple downstream proteins and these protein networks are responsible for drug efficacy and safety. Protein interaction network methods predict drug effects aggregating information about proteins around drug-binding protein targets. However, many frameworks exist for identifying proteins relevant to a drugs effect. We consider three frameworks for selecting these proteins and show increased performance from a context-specific approach on selecting proteins relevant to severe drug side effects. The context-specific approach leverages the idea that the proteins responsible for a drug side effect are specific to each side-effect. By discovering the relevant proteins, we can better understand downstream effects of drugs and better anticipate drug side effects for new drugs in development. Further, we focus on designated medical events, a subset of the most severe drug side-effects that are high priority for regulatory review.

systems biology↗

bioRxiv2019

Maleness-on-the-Y (MoY) orchestrates male sex determination in major agricultural fruit fly pests

In insects, rapidly evolving primary sex-determining signals are transduced by a conserved regulatory module producing sex-specific proteins that direct sex determination and sexual differentiation1-4. In the agricultural pest Ceratitis capitata (medfly), a Y-linked maleness factor (M) is thought to repress the autoregulatory splicing of transformer (Cctra), which is required in XX individuals to establish and maintain female sex determination5,6. Despite previous attempts of isolating Y-linked genes using the medfly whole genome, the M factor has remained elusive7. Here, we report the identification of a Y-linked gene, Maleness-on the-Y (MoY), and show that it encodes a small novel protein which is both necessary and sufficient for medfly male sex determination. Transient silencing of MoY in XY individuals leads to the development of fertile females while transient expression of MoY in XX individuals results in fertile males. Notably, a cross between these sex reverted individuals gives rise to both fertile males and females indicating that a functional MoY can be maternally transmitted. In contrast to the diversity of M factors found in dipteran species8-11, we discovered MoY orthologues in seven other Tephritid species spanning [~]111 millions of years of evolution (Mya). We confirmed their male determining function in the olive fly (Bactrocera oleae) and the oriental fruit fly (Bactrocera dorsalis). This unexpected conservation of the primary MoY signal in a large number of important agricultural pests12 will facilitate the development of transferable genetic control strategies in these species, for example sterile male releases or sex-ratio-distorting gene drives.

genetics↗