Biology subjects

Habib, M.

Publications and source records attributed to Habib, M..

4 recordsLinked to original sources

bioRxiv2024

Neuropathic pain and distinct CASPR2 autoantibody IgG subclasses drive neuronal hyperexcitability

Patients with autoantibodies (aAbs) against the contactin-associated protein-like 2 (CASPR2) suffer from a variety of clinical syndromes including neuropathic pain, in some patients even as the only symptom. CASPR2 is an adhesion protein of the neurexin IV family and part of the voltage-gated potassium channel complex (VGKC) in neurons of dorsal root ganglia (DRG). The subsequent pathological mechanisms following the binding of CASPR2 aAbs and their association with pain are only partially understood. CASPR2 aAbs are mainly of the IgG4 subclass. Previous studies have neglected subclass-dependent effects. Here we investigated 49 subclassified patient serum samples positive for CASPR2 aAbs. To unravel underlying molecular mechanisms, we used a combination of super-resolution lattice structural illumination microscopy (SIM2) and functional readouts by calcium imaging and electrophysiological recordings. CASPR2-positive patient sera subclassified in IgG4 together with at least one other IgG subclass (IgGX) and patients with only IgG4 were further subdivided into the pain and no pain group. Patient subclassification shed further light on the pathological mechanisms of CASPR2 aAbs. A decrease of CASPR2 expression after long-term exposure to CASPR2 aAbs was only observed for the patient group without pain. Upon withdrawal of the CASPR2 aAbs, CASPR2 expression returned to normal level. Structural alterations were obtained by increased distances between CASPR2 and associated potassium channels along DRG axons using high-resolution lattice SIM2 microscopy but only following binding of CASPR2 aAbs from patients with pain. Similarly, CASPR2 aAbs of patients with pain significantly increased overall neuronal excitability of cultured DRG neurons as measured by calcium imaging. Patch-clamp recordings revealed significantly decreased current amplitudes of voltage-gated potassium (Kv) channels after incubation with all four CASPR2 aAbs subclassifications with the most prominent effect of serum samples harboring IgG4 aAbs. Notably, a patient serum sample lacking IgG4 did not alter Kv channel function. Withdrawal of aAbs rescued Kv channel function to normal levels suggesting that the affected potassium channel function is rather due to a functional block of the VGKC rather than altered structural integrity of the VGKC. Taken together, we found IgG4 aAbs to be a major modifier of potassium channel function. The increase in DRG excitability is primarily due to impaired Kv channel conductance as a consequence of CASPR2 aAbs binding but additional and so far unidentified signal pathways contribute to this process in patients with neuropathic pain.

neuroscience↗

bioRxiv2023

Optimized design and in vivo application of optogenetically functionalized Drosophila dopamine receptors

Neuromodulatory signaling via G protein-coupled receptor (GPCRs) plays a pivotal role in regulating neural network function and animal behavior. Recent efforts have led to the development of optogenetic tools to induce G protein-mediated signaling, with the promise of acute and cell type-specific manipulation of neuromodulatory signals. However, designing and deploying optogenetically functionalized GPCRs (optoXRs) with accurate specificity and activity to mimic endogenous signaling in vivo remains challenging. Here we optimized the design of optoXRs by considering evolutionary conserved GPCR-G protein interactions and demonstrate the feasibility of this approach using two Drosophila Dopamine receptors (optoDopRs). We validated these optoDopRs showing that they exhibit high signaling specificity and light sensitivity in vitro. In vivo we detected receptor and cell type-specific effects of dopaminergic signaling in various behaviors including the ability of optoDopRs to rescue loss of the endogenous receptors. This work demonstrates that OptoXRs can enable optical control of neuromodulatory receptor specific signaling in functional and behavioral studies.

neuroscience↗

bioRxiv2023

Integrating disease genetics and drug bioassays to discover drug impacts on the human phenome

Unintended effects of medications on diverse diseases are widespread, resulting in not only harmful drug side effects, but also beneficial drug repurposing. This implies that drugs can unexpectedly influence disease networks. Then, discovering how biological effects of drugs relate to disease biology can both provide insight into the biological basis for latent drug effects, and can help predict new effects. Rich data now comprehensively profile both drug impacts on biological processes, and known drug associations with human phenotypes. At the same time, systematic phenome-wide genetic studies have linked each common phenotype with putative disease driver genes. Here, we develop Draphnet, a supervised linear model that integrates in vitro data on 429 drugs and gene associations of nearly 200 common phenotypes to learn a network connecting these molecular signals to explain drug effects on disease. The approach uses the -omics level similarity among drugs, and among phenotypes, to extrapolate impacts of drug on disease. Our predicted drug-phenotype relationships outperform a baseline predictive model. But more importantly, by projecting each drug to the space of its influence on disease driver genes, we propose the biological mechanism of unexpected effects of drugs on disease phenotypes. We show that drugs sharing downstream predicted biological effects share known biology (i.e., gene targets), supporting the potential of our method to provide insights into the biology of unexpected drug effects on disease. Using Draphnet to map a drugs known molecular effects to their downstream effect on the disease genome, we put forward disease genes impacted by drug targets, and we suggest new grouping of drugs based on shared effects on the disease genome. Our approach has multiple applications, including predicting drug uses and learning about drug biology, with potential implications for personalized medicine. Author summaryMedications can impact a number of cellular processes, resulting in both their intended treatment of a health condition, and also unintended harmful or beneficial effects on other diseases. We aim to understand and predict these drug effects by learning the network connecting the biological processes altered by drugs to the genes driving disease. Our model, called Draphnet, can predict drug side effects and indications, but beyond prediction we show that it is also able to learn a drugs expected effect on the disease genome. Using Draphnet to summarize the biological impact of each drug, we put forward the disease genes impacted by drugs or drug targets. For instance, both anti-inflammatories and some PPAR-agonists share downstream effect on the cholesterol ester transfer protein (CETP), a gene previously experimentally supported as an effector of fenofibrate. Our approach provides a biological basis for drug repurposing, potentially accelerating clinical advances.

bioinformatics↗

bioRxiv2022

Terraces in Species Tree Inference from Gene Trees

A terrace in a phylogenetic tree space is a region where all trees contain the same set of subtrees, due to certain patterns of missing data among the taxa sampled, resulting in an identical optimality score for a given data set. This was first investigated in the context of phylogenetic tree estimation from sequence alignments using maximum likelihood (ML) and maximum parsimony (MP). The concept of terraces was later extended to the species tree inference problem from a collection of gene trees, where a set of equally optimal species trees was referred to as a "pseudo" species tree terrace. Pseudo terraces do not consider the topological proximity of the trees in terms of the induced subtrees resulting from certain patterns of missing data. In this study, we mathematically characterize species tree terraces and investigate the mathematical properties and conditions that lead multiple species trees to induce/display an identical set of locus-specific subtrees owing to missing data. We report that species tree terraces are agnostic to gene tree topologies and the discordance therein. Therefore, we introduce and characterize a special type of gene tree topology-aware terrace which we call "peak terrace", and investigate conditions on the patterns of missing data that give rise to peak terraces. In addition to the theoretical and analytical results, we empirically investigated different challenges as well as various opportunities pertaining to the multiplicity of equally good species trees in terraced landscapes. Based on an extensive experimental study involving both simulated and real biological datasets, we present the prevalence of species tree terraces and the resulting ambiguity created for tree search algorithms. Remarkably, our findings indicate that the identification of terraces and the trees within them can substantially enhance the accuracy of summary methods. Furthermore, we demonstrate that reasonably accurate branch support can be computed by leveraging trees sourced from these terraces.

bioinformatics↗