Biology subjects

Roy, A.

Publications and source records attributed to Roy, A..

At least 145 records · Page 8Linked to original sources

bioRxiv2021

Loss of Protein Kinase D2 Activity Protects Against Bleomycin-induced Dermal Fibrosis in Mice

BackgroundDermal fibrosis occurs in many human diseases, particularly systemic sclerosis (SSc) where persistent inflammation leads to collagen deposition and fiber formation in skin and multiple organs. The family of protein kinase D (PKD) has been linked to inflammatory responses in various pathological conditions, however, its role in inflammation-induced dermal fibrosis has not been well defined. Here, using a murine fibrosis model that gives rise to dermal lesions similar to those in SSc, we investigated the role of PKD in dermal fibrosis in mice lacking PKD2 activity. MethodsHomozygous kinase-dead PKD2 knock-in mice (PKD2SSAA/SSAA-KI) were obtained through intercrossing mice heterozygous for PKD2S707A/S711A (PKD2SSAA). The wild-type and KI mice were subjected to repeated subcutaneous injection of bleomycin (BLM) to induce dermal inflammation and fibrosis. As controls, mice were injected with PBS. At the end of the experiment, mouse skin at the injection site was dissected, stained, and analyzed for morphological changes and expression of inflammatory and fibrotic markers. PKD-regulated signaling pathways were examined by real-time RT-qPCR and Western blotting. In a separate experiment, BLM-treated mice were administered with or without a PKD inhibitor, CRT0066101 (CRT). The effects of CRT on dermal fibrosis were analyzed similarly. The identity of the PKD expressing cells were probed using myeloid lineage markers CD45, CD68 in BLM-treated mouse tissues. ResultsDermal thickness and collagen fibers of kinase-dead PKD2-KI mice were significantly reduced in response to BLM treatment as compared to the wild-type mice. These mice also exhibited reduced -smooth muscle actin (-SMA) and collagen expression. At molecular levels, both transforming growth factor {beta}1 (TGF-{beta}1) and interleukin-6 (IL-6) mRNAs were decreased in the KI mice treated with BLM as compared to those in the wild-type mice. Similarly, CRT significantly blocked BLM-induced dermal fibrosis and inhibited the expression of -SMA, collagen, and IL-6 expression. Further analysis indicated that PKD2 was mainly expressed in CD45+/CD68+ myeloid cells that could be recruited to the lesional sites to promote the fibrotic process of the skin in response to BLM. ConclusionsKnock-in of the kinase-dead PKD2 or inhibition of PKD activity in mice protected against BLM-induced dermal fibrosis by reducing dermis thickness and expression of fibrotic biomarkers including -SMA, collagen, and inflammatory/fibrotic mediators including TGF-{beta}1 and IL-6. PKD2 does this potentially through modulating the recruitment and function of myeloid cells in skin of BLM-treated mice. Overall, our study demonstrated a potential critical role of PKD catalytic activity in inflammation-induced dermal fibrosis.

genetics↗

bioRxiv2021

Generative Adversarial Networks (GAN) for the simulation of central-place foraging trajectories

AO_SCPLOWBSTRACTC_SCPLOWO_LIMiniature electronic device such as GPS have enabled ecologists to document relatively large amount of animal trajectories. Modeling such trajectories may attempt (1) to explain mechanisms underlying observed behaviors and (2) to elucidate ecological processes at the population scale by simulating multiple trajectories. Existing approaches to animal movement modeling mainly addressed the first objective and they are yet soon limited when used for simulation. Individual-based models based on ad-hoc formulation and empirical parametrization lack of generability, while state-space models and stochastic differential equations models, based on rigorous statistical inference, consist in 1st order Markovian models calibrated at the local scale which can lead to overly simplistic description of trajectories. C_LIO_LIWe introduce a state-of-the-art tool from artificial intelligence - Generative Adversarial Networks (GAN) - for the simulation of animal trajectories. GAN consist in a pair of deep neural networks that aim at capturing the data distribution of some experimental dataset, and that enable the generation of new instances of data that share statistical similarity. In this study, we aim on one hand to identify relevant deep networks architecture for simulating central-place foraging trajectories and on the second hand to evaluate GAN benefits over classical methods such as state-switching Hidden Markov Models (HMM). C_LIO_LIWe demonstrate the outstanding ability of GAN to simulate realistic seabirds foraging trajectories. In particular, we show that deep convolutional networks are more efficient than LSTM networks and that GAN-derived synthetic trajectories reproduce better the Fourier spectral density of observed trajectories than those simulated using HMM. Therefore, unlike HMM, GAN capture the variability of large-scale descriptive statistics such as foraging trips distance, duration and tortuosity. C_LIO_LIGAN offer a relevant alternative to existing approaches to modeling animal movement since it is calibrated to reproduce multiple scales at the same time, thus freeing ecologists from the assumption of first-order markovianity. GAN also provide an ultra-flexible and robust framework that could further take environmental conditions, social interactions or even bio-energetics model into account and tackle a wide range of key challenges in movement ecology. C_LI

ecology↗

bioRxiv2021

Mice With Monoallelic GNAO1 Loss Exhibit Reduced Inhibitory Synaptic Input To Cerebellar Purkinje Cells

GNAO1 encodes Go, a heterotrimeric G protein alpha subunit in the Gi/o family. In this report, we used a Gnao1 mouse model "G203R" previously described as a "gain-of-function" Gnao1 mutant with movement abnormalities and enhanced seizure susceptibility. Here, we report an unexpected second mutation resulting in a loss-of-function Go protein and describe alterations in central synaptic transmission. Whole cell patch clamp recordings from Purkinje cells (PCs) in acute cerebellar slices from Gnao1 mutant mice showed significantly lower frequencies of spontaneous and miniature inhibitory postsynaptic currents (sIPSCs and mIPSCs) compared to WT mice. There was no significant change in sEPSCs or mEPSCs. Whereas mIPSC frequency was reduced, mIPSC amplitudes were not affected, suggesting a presynaptic mechanism of action. A modest decrease in the number of molecular layer interneurons was insufficient to explain the magnitude of IPSC suppression. Paradoxically, Gi/o inhibitors (pertussis toxin), enhanced the mutant-suppressed mIPSC frequency and eliminated the difference between WT and Gnao1 mice. While GABAB receptor regulates mIPSCs, neither agonists nor antagonists of this receptor altered function in the mutant mouse PCs. This study is the first electrophysiological investigation of the role of Gi/o protein in cerebellar synaptic transmission using an animal model with a loss-of-function Gi/o protein. Significance StatementThis is the first report on the electrophysiological mechanisms of a movement disorder animal model with monoallelic Gnao1 loss. This study illustrates the role of Go protein in regulating GABA release in mouse cerebellum. This study could also facilitate the discovery of new drugs or drug repurposing for GNAO1-associated disorders. Moreover, since GNAO1 shares pathways with other genes related to movement disorders, developing drugs for the treatment of GNAO1-associated movement disorders could further the pharmacological intervention for other monogenic movement disorders.

neuroscience↗

bioRxiv2021

Kinase activity profiling identifies putative downstream targets of cGMP/PKG signaling in inherited retinal neurodegeneration

Inherited retinal diseases (IRDs) are a group of neurodegenerative disorders that lead to photoreceptor cell death and eventually blindness. IRDs are characterised by a high genetic heterogeneity, making it imperative to design mutation-independent therapies. Mutations in a number of IRD disease genes have been associated with a rise of cyclic 3,5-guanosine monophosphate (cGMP) levels in photoreceptors. Accordingly, the cGMP-dependent protein kinase (PKG) has emerged as a new potential target for the mutation-independent treatment of IRDs. However, the substrates of PKG and the downstream degenerative pathways triggered by its activity have yet to be determined. Here, we performed kinome activity profiling of different murine organotypic retinal explant cultures (diseased rd1 and wild-type controls) using multiplex peptide microarrays to identify proteins whose phosphorylation was significantly altered by PKG activity. In addition, we tested the downstream effect of a known PKG inhibitor CN03 in these organotypic retina cultures. Among the PKG substrates were potassium channels belonging to the Kv1 family (KCNA3, KCNA6), Cyclic AMP-responsive element-binding protein 1 (CREB1), DNA topoisomerase 2- (TOP2A), 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (F263), and the glutamate ionotropic receptor kainate 2 (GRIK2). The retinal expression of these PKG targets was further confirmed by immunofluorescence and could be assigned to various neuronal cell types, including photoreceptors, horizontal cells, and ganglion cells. Taken together, this study confirmed the key role of PKG in photoreceptor cell death and identified new downstream targets of cGMP/PKG signalling that will improve the understanding of the degenerative mechanisms underlying IRDs.

neuroscience↗

bioRxiv2021

High-grade Ovarian Cancer Associated H/ACA snoRNAs Promote Cancer Cell Proliferation and Survival

Small nucleolar RNAs (snoRNAs) are an omnipresent class of non-coding RNAs involved in the modification and processing of ribosomal RNA (rRNA). As snoRNAs are required for ribosome production, the increase of which is a hallmark of cancer development, their expression would be expected to increase in proliferating cancer cells. However, the nature and extent of snoRNAs contribution to the biology of cancer cells remain largely unexplored. In this study, we examined the abundance patterns of snoRNA in high-grade serous ovarian carcinomas (HGSC) and serous borderline tumours (SBT) and identified a subset of snoRNA associated with increased invasiveness. This subgroup of snoRNA accurately discriminates between SBT and HGSC underlining their potential as biomarkers of tumour aggressiveness. Remarkably, knockdown of HGSC-associated H/ACA snoRNAs, but not their host genes, inhibits cell proliferation and induces apoptosis of model ovarian cancer cell lines. Wound healing and cell migration assays confirmed the requirement of these HGSC-associated snoRNA for cell invasion and increased tumour aggressiveness. Together our data indicate that H/ACA snoRNAs promote tumour aggressiveness through the induction of cell proliferation and resistance to apoptosis.

cancer biology↗

bioRxiv2021

IRE1/XBP1 signaling promotes skeletal muscle regeneration through a cell non-autonomous mechanism

Skeletal muscle regeneration is regulated by coordinated activation of multiple signaling pathways activated in both injured myofibers and satellite cells. The unfolded protein response (UPR) is a major mechanism that detects and alleviates protein-folding stresses in ER. However, the role of individual arms of the UPR in skeletal muscle regeneration remains less understood. In the present study, we demonstrate that IRE1 (also known as ERN1) and its downstream target, XBP1, are activated in skeletal muscle of mice upon injury. Myofiber-specific ablation of IRE1 or XBP1 in mice diminishes skeletal muscle regeneration that is accompanied with reduced number of satellite cells and their fusion to injured myofibers. Ex vivo cultures of myofiber explants demonstrate that ablation of IRE1 reduces the proliferative capacity of myofiber- associated satellite cells. Myofiber-specific deletion of IRE1 dampens Notch signaling and canonical NF-{kappa}B pathway in skeletal muscle of mice. Our results also demonstrate that targeted ablation of IRE1 reduces skeletal muscle regeneration in the mdx mice, a model of Duchenne muscular dystrophy. Collectively, our results reveal that the IRE1-mediated signaling promotes muscle regeneration through augmenting the proliferation of satellite cells in a cell non- autonomous manner.

cell biology↗

bioRxiv2021

HepT1-derived murine models of high-risk hepatoblastoma display vascular invasion, metastasis, and circulating tumor cells

Hepatoblastoma (HB) is the most common pediatric primary liver malignancy, and survival for high-risk disease approaches 50%. Mouse models of HB fail to recapitulate hallmarks of high-risk disease. The aim of this work was to generate murine models that show high-risk features including multifocal tumors, vascular invasion, metastasis, and circulating tumor cells (CTCs). HepT1 cells were injected into the livers or tail veins of mice, and tumor growth was monitored with magnetic resonance and bioluminescent imaging. Blood was analyzed with fluorescence activated cell sorting to identify CTCs. Intra- and extra-hepatic tumor samples were harvested for immunohistochemistry and RNA and DNA sequencing. Cell lines were grown from tumor samples and profiled with RNA sequencing. With intrahepatic injection of HepT1 cells, 100% of animals grew liver tumors and showed vascular invasion, metastasis, and CTCs. Mutation profiling revealed genetic alterations in seven cancer-related genes, while transcriptomic analyses showed changes in gene expression with cells that invade vessels. Tail vein injection of HepT1 cells resulted in multifocal, metastatic disease. These unique models will facilitate further meaningful studies of high-risk HB. Summary StatementIn this work, we developed and thoroughly characterized several unique models of hepatoblastoma derived from the HepT1 cell line that show high-risk features.

cancer biology↗

bioRxiv2021

Using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design

We are interested in generating new small molecules which could act as inhibitors of a biological target, when there is limited prior information on target-specific inhibitors. This form of drug-design is assuming increasing importance with the advent of new disease threats for which known chemicals only provide limited information about target inhibition. In this paper, we propose the combined use of deep neural networks and Inductive Logic Programming (ILP) that allows the use of symbolic domain-knowledge (B) to explore the large space of possible molecules. Assuming molecules and their activities to be instances of random variables X and Y, the problem is to draw instances from the conditional distribution of X, given Y, B (DX|Y,B). We decompose this into the constituent parts of obtaining the distributions DX|B and DY|X,B, and describe the design and implementation of models to approximate the distributions. The design consists of generators (to approximate DX|B and DX|Y,B) and a discriminator (to approximate DY|X,B). We investigate our approach using the well-studied problem of inhibitors for the Janus kinase (JAK) class of proteins. We assume first that if no data on inhibitors are available for a target protein (JAK2), but a small numbers of inhibitors are known for homologous proteins (JAK1, JAK3 and TYK2). We show that the inclusion of relational domain-knowledge results in a potentially more effective generator of inhibitors than simple random sampling from the space of molecules or a generator without access to symbolic relations. The results suggest a way of combining symbolic domain-knowledge and deep generative models to constrain the exploration of the chemical space of molecules, when there is limited information on target-inhibitors. We also show how samples from the conditional generator can be used to identify potentially novel target inhibitors.

molecular biology↗

bioRxiv2021

Early life trauma leads to violent behavior and its inheritance by impairing local thyroid hormone availability in brain

Escalated and inappropriate levels of aggressive behavior referred to as pathological in psychiatry can lead to violent outcomes with detrimental impact on health and society. Early life trauma triggers adulthood violence and criminality, though molecular mechanisms remain elusive. Here, we provide prefrontal cortex and hypothalamus specific transcriptome profiles of peripubertal stress (PPS) exposed Balb/c adult male mice exhibiting escalated aggression and adult female mice resilient to such aberrant behavioral responses. We identify transthyretin (TTR) as a key regulator of PPS induced escalated aggression and its intergenerational inheritance. TTR mediated long-term perturbation in hypothalamic thyroid hormone (TH) availability contributed to male aggressive behavior without affecting circulating hormone. Ttr gene ablation in hypothalamus impaired local TH signaling including levels of TH transporters (Mct8, Oatp1c1), deiodinase 2(DIO2) and TH responsive genes (Nrgn, Trh and Hr). Escalated aggressive behavior and impaired TTR-TH signaling was also inherited in F1 male progenies. Further, we deciphered Ttr promoter hyper methylation in hypothalamus of such abnormally aggressive males across generations. Interestingly prefrontal cortex showed opposite pattern of Ttr expression as well as long term epigenetic changes. Also, T4 increase by levothyroxine in PFC did not produce any behavioral changes. Our findings reveal that trauma during puberty trigger lasting escalated aggression by epigenetic programming of TTR and consequent impaired thyroid availability in brain. TTR-TH signaling in brain can serve as potential target in reversal of escalated aggression and related psychopathologies.

neuroscience↗

bioRxiv2021

An insight into the evolutionary history of Indoplanorbis exustus (Bulinidae: Gastropoda)at the scale of population and species

The history of a lineage is intertwined with the history of the landscape it resides in. Here we showcase how the geo-tectonic and climatic evolution in South Asia and surrounding landmasses have shaped the biogeographic history of Indoplanorbis exustus, a tropical Asian, freshwater, pulmonated snail. We amplified partial COI gene fragment from all over India and combined this with a larger dataset from South and Southeast Asia to carry out phylogenetic reconstruction, species delimitation analysis, and population genetic analyses. Two nuclear genes were also amplified from one individual per putative species to carry out divergence dating and ancestral area reconstruction analyses. The results suggest that Indoplanorbis dispersed out of Africa into India during Eocene. Furthermore, molecular data suggests Indoplanorbis is a species complex consisting of multiple putative species. The primary diversification took place in Northern Indian plains or the Northeast India. The speciation events appear to be primarily allopatric caused by a series of aridification events starting from late Miocene to early Pleistocene. None of the species seemed to have any underlying genetic structure suggestive of high vagility. All the species underwent population fluctuations during the Pleistocene likely driven by the Quaternary climatic fluctuations.

evolutionary biology↗

bioRxiv2021

Cryptic speciation in freshwaters: are speciation in lentic species shaped by paleohydrological events?

The Indian subcontinent is extremely diverse in terms of its flora and fauna. However, there is a severe underestimation of the biotic diversity in invertebrate groups as well as a lack of understanding of the processes generating diversity in these groups. Here we aimed to jointly estimate the cryptic diversity of two freshwater snail species complexes - Pila globosa and Pila virens, and uncover the processes behind the speciation in these groups. We employed phylogenetic, morphometric, population genetic and niche modelling technique to address these questions. We found out that both species complexes consist of several genetically and ecologically distinct putative species. The speciation was primarily driven by allopatric isolation into different river basins. A combination of paleoclimatic and paleohydrological changes during the Miocene have shaped the speciation events. Local climatic adaptation also could have contributed towards some of the speciation events. The study sheds light on the complex interaction between the biology of the species and the environment that shape the diversification patterns in a group.

evolutionary biology↗

bioRxiv2021

Fast and Accurate Estimation of Gas-Phase Entropy from the Molecular Surface Curvature

Estimating entropy is crucial for understanding and modifying biological systems, such as protein-ligand binding. Current computational methods to estimate entropy require extensive, or at times prohibitively extensive, computational resources. This article presents SHAPE (SHape-based Accurate Predictor of Entropy), a new method that estimates the gas-phase entropy of small molecules purely from their surface geometry. The gas-phase entropy of small molecules can be computed in {approx}0.01 CPU hours with run time complexity of [Formula], where Na is the number of atoms. The accuracy of SHAPE is within 1 - 2% of computationally expensive quantum mechanical or molecular mechanical calculations. We further show that the inclusion of gas-phase entropy, estimated using SHAPE, improves the rank-order correlation between binding affinity and binding score from 0.18 to 0.40. The speed and accuracy of SHAPE make it well-suited for inclusion in molecular docking or QSAR (quantitative structure-activity relationships) methods. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/445640v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1cc10d1org.highwire.dtl.DTLVardef@6448d6org.highwire.dtl.DTLVardef@1e9e308org.highwire.dtl.DTLVardef@385eb2_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

Developmental stage- and site-specific transitions in lineage specification and gene regulatory networks in human hematopoietic stem and progenitor cells

Human hematopoiesis is a dynamic process that starts in utero 4 weeks post-conception. Understanding the site- and stage-specific variation in hematopoiesis is important if we are to understand the origin of hematological disorders, many of which occur at specific points in the human lifespan. To unravel how the hematopoietic stem/progenitor cell (HSPC) compartments change during human ontogeny and the underlying gene regulatory mechanisms, we compared 57,489 HSPCs from 5 different tissues spanning 4 developmental stages through the human lifetime. Single-cell transcriptomic analysis identified significant site- and developmental stage-specific transitions in cellular architecture and gene regulatory networks. Uncommitted stem cells showed progression from cycling to quiescence and increased inflammatory signalling during ontogeny. We demonstrate the utility of this dataset for understanding aberrant hematopoiesis through comparison to two cancers that present at distinct timepoints in postnatal life - juvenile myelomonocytic leukemia, a childhood cancer, and myelofibrosis, which classically presents in older adults.

developmental biology↗

bioRxiv2021

Deep Learning and trajectory representation for the prediction of seabird diving behaviour

AO_SCPLOWBSTRACTC_SCPLOWAt-sea behaviour of seabirds have received significant attention in ecology over the last decades as it is a key process in the ecology and fate of these populations. It is also, through the position of top predator that these species often occupy, a relevant and integrative indicator of the dynamics of the marine ecosystems they rely on. Seabird trajectories are recorded through the deployment of GPS, and a variety of statistical approaches have been tested to infer probable behaviours from these location data. Recently, deep learning tools have shown promising results for the segmentation and classification of animal behaviour from trajectory data. Yet, these approaches have not been widely used and investigation is still needed to identify optimal network architecture and to demonstrate their generalization properties. From a database of about 300 foraging trajectories derived from GPS data deployed simultaneously with pressure sensors for the identification of dives, this work has benchmarked deep neural network architectures trained in a supervised manner for the prediction of dives from trajectory data. It first confirms that deep learning allows better dive prediction than usual methods such as Hidden Markov Models. It also demonstrates the generalization properties of the trained networks for inferring dives distribution for seabirds from other colonies and ecosystems. In particular, convolutional networks trained on Peruvian boobies from a specific colony show great ability to predict dives of boobies from other colonies and from distinct ecosystems. We further investigate accross-species generalization using a transfer learning strategy known as fine-tuning. Starting from a convolutional network pre-trained on Guanay cormorant data reduced by two the size of the dataset needed to accurately predict dives in a tropical booby from Brazil. We believe that the networks trained in this study will provide relevant starting point for future fine-tuning works for seabird trajectory segmentation. To make easier such applications, we share online the different models we trained on the considered datasets (https://github.com/AmedeeRoy/BirdDL/models). Author SummaryOver the last decades, the use of miniaturized electronic devices enabled the tracking of many wide-ranging animal species. The deployment of GPS has notably informed on migratory, habitat and foraging strategies of numerous seabird species. A key challenge in movement ecology is to identify specific behavioural patterns (e.g. travelling, resting, foraging) through the observed movement data. In this work, we address the inference of seabird diving behaviour from GPS data using deep learning methods. We demonstrate the performance of deep networks to accurately identify movement patterns from GPS data over state-of-the-art tools, and we illustrate their great accross-species generalization properties (i.e. the ability to generalize prediction from one seabird species to aother). Our results further supports the relevance of deep learning schemes as ready-to-use tools which could be used by ecologists to segmentate animal trajectories on new (small) datasets, including when these datasets do not include groundtruthed labelled data for a supervised training.

ecology↗

bioRxiv2021

Genomic surveillance and phylodynamic analyses reveal emergence of novel mutation and co-mutation patterns within SARS-CoV2 variants prevalent in India

Emergence of distinct viral clades has been observed in SARS-CoV2 variants across the world and India. Identification of the genomic diversity and the phylodynamic profiles of the prevalent strains of the country are critical to understand the evolution and spread of the variants. We performed whole-genome sequencing of 54 SARS-CoV2 strains collected from COVID-19 patients in Kolkata, West Bengal during August to October 2020. Phylogeographic and phylodynamic analyses were performed using these 54 and other sequences from India and abroad available in GISAID database. Spatio-temporal evolutionary dynamics of the pathogen across various regions and states of India over three different time periods in the year 2020 were analyzed. We estimated the clade dynamics of the Indian strains and compared the clade specific mutations and the co-mutation patterns across states and union territories of India over the time course. We observed that GR, GH and G (GISAID) or 20B and 20A (Nextstrain) clades were the prevalent clades in India during middle and later half of the year 2020. However, frequent mutations and co-mutations observed within the major clades across time periods do not show much overlap, indicating emergence of newer mutations in the viral population prevailing in the country. Further, we explored the possible association of specific mutations and co-mutations with the infection outcomes manifested within the Indian patients.

genomics↗

bioRxiv2021

Large-scale design and refinement of stable proteins using sequence-only models

Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be tested computationally and experimentally in order to find stable ones, which is expensive in terms of time and resources. Here we report a neural network model that predicts protein stability based only on sequences of amino acids, and demonstrate its performance by evaluating the stability of almost 200,000 novel proteins. These include a wide range of sequence perturbations, providing a baseline for future work in the field. We also report a second neural network model that is able to generate novel stable proteins. Finally, we show that the predictive model can be used to substantially increase the stability of both expert-designed and model-generated proteins.

biophysics↗

bioRxiv2021

Suppression of PIK3CA-driven epileptiform activity by acute pathway control

Patients harboring mutations in the PI3K-AKT-MTOR signaling pathway often develop a spectrum of neurodevelopmental disorders including epilepsy. A significant proportion of them remain unresponsive to conventional anti-seizure medications. Understanding mutation-specific pathophysiology is thus critical for molecularly targeted therapies. We previously determined that mouse models expressing patient-related activating mutation in PIK3CA are epileptic and acutely treatable with PI3K inhibition, irrespective of dysmorphology (Roy et al. 2015). Using the same mutant model, we have now identified physiological mechanisms underlying the dysregulated neuronal excitability and its acute attenuation. We show that Pik3ca-driven hyperexcitability in hippocampal pyramidal neurons is mediated by changes in multiple non-synaptic, cell-intrinsic properties. These are distinct from mechanisms driving epilepsy in TSC/RHEB models. Further, we report that acute inhibition of PI3K or AKT, but not MTOR, suppresses the intrinsic epileptiform nature of the mutant neurons. These data represent an important step towards precision therapeutics against intractable epilepsy, using pathway drugs originally developed as anti-cancer agents.

neuroscience↗

bioRxiv2021

Targeting an anchored phosphatase-deacetylase unit restores renal ciliary homeostasis.

Pathophysiological defects in water homeostasis can lead to renal failure. Autosomal dominant polycystic kidney disease (ADPKD) is a common genetic disorder associated with abnormal cytoskeletal dynamics in the kidney collecting ducts and perturbed calcium and cAMP signaling in the ciliary compartment. We show that collecting ducts in mice lacking the A-Kinase anchoring protein AKAP220 exhibit enhanced development of primary cilia. Mechanistic studies reveal that AKAP220-associated protein phosphatase 1 (PP1) mediates this phenotype by promoting changes in the stability of histone deacetylase 6 (HDAC6) with concomitant defects in actin dynamics. This proceeds through a previously unrecognized adaptor function for PP1 as all ciliogenesis and cytoskeletal phenotypes are recapitulated in mIMCD3 knock-in cells expressing a phosphatase-targeting defective AKAP220-{Delta}PP1 mutant. Pharmacological blocking of local HDAC6 activity alters cilia development and reduces cystogenesis in kidney-on-chip and organoid models of ADPKD. These findings identify the AKAP220-PPI-HDAC6 pathway as a key effector in primary cilia development.

cell biology↗