Biology subjects

Rajagopal, V.

Publications and source records attributed to Rajagopal, V..

18 recordsLinked to original sources

bioRxiv2025

Cardiac myofibril networks induce shear stress

Myofibril arrangement is critical to cardiac muscle function in health and disease. Historically, analysis of the impact of myofibril organisation on force and cell contraction has relied on the assumption of uniaxial arrays. However, improvements in imaging indicate that myofibrils form complex networks, though how these networks modulate force has yet to be explored. Here, morphological analysis of sheep left-ventricular cardiomyocytes is utilised to inform a non-linear finite element model of cell contraction. Analysis of deep learning segmentations of Z-Discs demonstrate that myofibrils are oriented about the major axis (mean = 0.03{degrees}) but deviate locally by up to 30{degrees} (standard deviation = 6.56{degrees}). Simulations produce unique deformations for geometries informed by myofibril orientations, displaying internal rotation and off-axis deformations. Moreover, anisotropy generates shear stresses distinct from the uniaxial case, demonstrating spatial relationships that balance shear across the cell. These findings highlight the impact of myofibril networks on forces during cell contraction.

biophysics↗

bioRxiv2024

An atlas of RNA-dependent proteins in cell division reveals the riboregulation of mitotic protein-protein interactions

Ribonucleoprotein complexes are dynamic assemblies of RNA with RNA-binding proteins (RBPs), which can modulate the fate of the RNA molecules from transcription to degradation. Vice versa, RNA can regulate the interactions and functions of the associated proteins. Dysregulation of RBPs is linked to diseases such as cancer and neurological disorders. RNA and RBPs are present in mitotic structures like the centrosomes and spindle microtubules, but their influence on mitotic spindle integrity remains unknown. Thus, we applied the R-DeeP strategy for the proteome-wide identification of RNA-dependent proteins and complexes to cells synchronized in mitosis versus interphase. The resulting atlas of RNA-dependent proteins in cell division can be accessed through the R-DeeP 3.0 database (R-DeeP3.dkfz.de). It revealed key mitotic factors as RNA-dependent such as AURKA, KIFC1 and TPX2 that were linked to RNA despite their lack of canonical RNA-binding domains. KIFC1 was identified as a new interaction partner and phosphorylation substrate of AURKA at S349 and T359. In addition, KIFC1 interacted with both, AURKA and TPX2, in an RNA-dependent manner. Our data suggest a riboregulation of mitotic protein-protein interactions during spindle assembly, offering new perspectives on the control of cell division processes by RNA-protein complexes. HighlightsO_LIDifferential R-DeeP screens in mitosis and interphase are provided as a resource in a user-friendly database at R-DeeP3.dkfz.de C_LIO_LIAn atlas of RNA-dependent proteins in cell division identifies a substantial number of unconventional RNA-binding proteins among mitotic factors C_LIO_LIInvestigation of protein-protein interactions reveals KIFC1 as a new AURKA and TPX2 interaction partner during spindle assembly C_LIO_LIKIFC1, AURKA and TPX2 interact with each other in an RNA-dependent manner and directly bind to RNA C_LIO_LIAURKA phosphorylates KIFC1 at residues S349 and T359 C_LI

cell biology↗

bioRxiv2024

Energy-based Modelling of Single Actin Filament Polymerisation Using Bond Graphs

Bond graphs provide an energy-based methodology for modelling complex systems in a hierarchical fashion; at the moment, the method allows biological system with both chemical and electrical subsystems to be modelled. Herein, the bond graph approach is extended to include chemomechanical transduction thus extending the range of biological systems to be modelled. Actin filament polymerisation and force generation is used as an example of chemo-mechanical transduction and it is shown that the TF (transformer) bond graph component provides a practical, and conceptually simple, alternative to the Brownian ratchet approach of Peskin, Odell, Oster & Mogilner. Furthermore it is shown that the bond graph approach leads to the same equation as the Brownian ratchet approach in the simplest case. The approach is illustrated by showing that flexibility and non-normal incidence can be modelled by simply adding additional bond graph components and that compliance leads to non-convexity of the force-velocity curve. Energy flows are fundamental to life; for this reason, the energy based approach is utilised to investigate the power transmission by the actin filament and its corresponding efficiency. The bond graph model is fitted to experimental data by adjusting model physical parameters.

systems biology↗

bioRxiv2023

A deep catalog of protein-coding variation in 985,830 individuals

Coding variants that have significant impact on function can provide insights into the biology of a gene but are typically rare in the population. Identifying and ascertaining the frequency of such rare variants requires very large sample sizes. Here, we present the largest catalog of human protein-coding variation to date, derived from exome sequencing of 985,830 individuals of diverse ancestry to serve as a rich resource for studying rare coding variants. Individuals of African, Admixed American, East Asian, Middle Eastern, and South Asian ancestry account for 20% of this Exome dataset. Our catalog of variants includes approximately 10.5 million missense (54% novel) and 1.1 million predicted loss-of-function (pLOF) variants (65% novel, 53% observed only once). We identified individuals with rare homozygous pLOF variants in 4,874 genes, and for 1,838 of these this work is the first to document at least one pLOF homozygote. Additional insights from the RGC-ME dataset include 1) improved estimates of selection against heterozygous loss-of-function and identification of 3,459 genes intolerant to loss-of-function, 83 of which were previously assessed as tolerant to loss-of-function and 1,241 that lack disease annotations; 2) identification of regions depleted of missense variation in 457 genes that are tolerant to loss-of-function; 3) functional interpretation for 10,708 variants of unknown or conflicting significance reported in ClinVar as cryptic splice sites using splicing score thresholds based on empirical variant deleteriousness scores derived from RGC-ME; and 4) an observation that approximately 3% of sequenced individuals carry a clinically actionable genetic variant in the ACMG SF 3.1 list of genes. We make this important resource of coding variation available to the public through a variant allele frequency browser. We anticipate that this report and the RGC-ME dataset will serve as a valuable reference for understanding rare coding variation and help advance precision medicine efforts.

genetics↗

bioRxiv2023

BondGraphs.jl: Composable energy-based modelling in systems biology

SummaryBondGraphs.jl is a Julia implementation of bond graphs. Bond graphs provide a modelling framework that describes energy flow through a physical system and by construction enforce thermodynamic constraints. The framework is widely used in engineering and has recently been shown to be a powerful approach for modelling biology. Models are mutable, hierarchical, multi-scale, multi-physics, and BondGraphs.jl is compatible with the Julia modelling ecosystem. Availability and ImplementationBondGraphs.jl is freely available under the MIT license. Source code and documentation can be found at https://github.com/jedforrest/BondGraphs.jl. Contactpan.m@unimelb.edu.au, mstumpf@unimelb.edu.au Supplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2023

Energy dependence of signalling dynamics and robustness in bacterial two component systems

One of the best known ways bacteria cells understand and respond to the environment are through Two-Component Systems (TCS). These signalling systems are highly diverse in function and can detect a range of physical stimuli including molecular concentrations and temperature, with a range of responses including chemotaxis and anaerobic energy production. TCS exhibit a range of different molecular structures and energy costs, and multiple types co-exist in the same cell. TCSs that incur relatively high energy cost are abundant in biology, despite strong evolutionary pressure to efficiently spend energy. We are motivated to discern what benefits, if any, the more energetically expensive variants had for a cell. We seek to answer this question by modelling energy flow through two variants of TCS. This was accomplished using bond graphs, a physics-based modelling framework that accurately models energy transfer through different physical domains. Our analysis demonstrates that energy availability can affect a cells signal sensitivity, noise filtering effectiveness, and the stimulus level where cell response is maximal. We also found that these properties are determined not by the molecular parameters themselves, but the reaction rate parameters that govern the reaction systems as a whole. This suggests possible connections between the molecular structure and evolutionary purpose of any two-component system. This opens the door to new synthetic circuit design in systems biology, and we propose new hypotheses about this link between structure and purpose that could be experimentally verified. Author summaryTwo-component systems are the main way many bacteria sense and respond to their environment. They exist in such well-studied bacteria as E. coli where they have been shown to detect a range of stimuli including nutrients, temperature, acidity, and pressure. Two-component systems are ubiquitous in bacteria yet have a deceptively simple structure. Knowing how they operate and the purpose of variations in signalling structure is helpful to our understanding of cellular biology and the design of synthetic biological circuits. Critical unanswered questions remain about the energy usage and functional benefits of these systems. We sought to improve our understanding of two-component systems by applying a physics-based modelling framework. We found that tracking energy flow through the cell reveals new energy-dependent behaviour in signalling sensitivity, noise filtering, and maximal cell response. We also found that these properties are not strictly dependent on the molecular properties themselves, but from the configuration of the reaction system as a whole.

biophysics↗

bioRxiv2022

Effects of altered cellular ultrastructure on energy metabolism in diabetic cardiomyopathy - an in-silico study

Diabetic cardiomyopathy is a leading cause of heart failure in diabetes. At the cellular level, diabetic cardiomyopathy leads to altered mitochondrial energy metabolism and cardiomyocyte ultrastructure. We combined electron microscopy and computational modelling to understand the impact of diabetes induced ultrastructural changes on cardiac bioenergetics. We collected transverse micrographs of multiple control and type I diabetic rat cardiomyocytes using electron microscopy. Micrographs were converted to finite element meshes, and bioenergetics was simulated over them using a biophysical model. The simulations also incorporated depressed mitochondrial capacity for oxidative phosphorylation and creatine kinase reactions to simulate diabetes induced mitochondrial dysfunction. Analysis of micrographs revealed a 14% decline in mitochondrial area fraction in diabetic cardiomyocytes, and an irregular arrangement of mitochondria and myofibrils. Simulations predicted that this irregular arrangement, coupled with depressed activity of mitochondrial creatine kinase enzymes, leads to large spatial variation in ADP/ATP profile of diabetic cardiomyocytes. However, when spatially averaged, myofibrillar ADP/ATP ratios of a cardiomyocyte do not change with diabetes. Instead, average concentration of inorganic phosphate rises by 40% due to lower mitochondrial area fraction and dysfunction in oxidative phosphorylation. These simulations indicate that a disorganized cellular ultrastructure negatively impacts metabolite transport in diabetic cardiomyopathy.

cell biology↗

bioRxiv2021

CardioVinci: building blocks for virtual cardiac cells using deep learning

Recent advances in high-throughput microscopy imaging have made it easier to acquire large volumes of cell images. Thanks to electron microscopy (EM) imaging, they provide a high-resolution and sufficient field of view that suits imaging large cell types, including cardiomyocytes. A significant bottleneck with these large datasets is the time taken to collect, extract and statistically analyse 3D changes in cardiac ultrastructures. We address this bottleneck with CardioVinci.

bioinformatics↗

bioRxiv2021

Cortical Tension Initiates the Positive Feedback Loop Between E-cadherin and F-actin

Adherens junctions (AJs) physically link two cells at their contact interface via extracellular homophilic interactions between cadherin molecules and intracellular connections between cadherins and the actomyosin cortex. Both cadherin and actomyosin cytoskeletal dynamics are reciprocally regulated by mechanical and chemical signals, which subsequently determine the strength of cell-cell adhesions and the emergent organization and stiffness of the tissues they form. However, an understanding of the integrated system is lacking. We present a new mechanistic computational model of intercellular junction maturation in a cell doublet to investigate the mechano-chemical crosstalk that regulates AJ formation and homeostasis. The model couples a 2D lattice-based model of cadherin dynamics with a continuum, reaction-diffusion model of the reorganizing actomyosin network through its regulation by Rho signaling at the intercellular junction. We demonstrate that local immobilization of cadherin induces cluster formation in a cis less dependent manner. We further investigate how cadherin and actin regulate and cooperate. By considering the force balance during AJ maturation and the force-sensitive property of the cadherin/F-actin linking molecules, we show that cortical tension applied on the contact rim can explain the ring distribution of cadherin and F-actin on the cell-cell contact of the cell-doublet. Meanwhile, the positive feedback loop between cadherin and F-actin is necessary for maintenance of the ring. Different patterns of cadherin distribution can be observed as an emergent property of disturbances of this feedback loop. We discuss these findings in light of available experimental observations on underlying mechanisms related to cadherin/F-actin binding and the mechanical environment. Significance StatementThe formation, maintenance and disassembly of adherens junctions (AJs) is fundamental to organ development, tissue integrity as well as tissue function. E-cadherins and F-actin are two major players of the adherens junctions (AJs). Although it is well known that cadherins and F-actin affect each other, how these two players work together to maintain the intercellular contact is unclear. Using a novel mechano-chemical model of E-cadherin and F-actin remodeling, we demonstrate that a positive feedback loop between cadherins and F-actin allows them to stabilize each other locally. Mechanical and chemical stimuli applied to the cell adhesion change E-cadherin and F-actin distribution by consolidating or interrupting the feedback loop locally. Our study mechanistically links mechanical force to E-cadherin patterning at cell-cell junctions.

biophysics↗

bioRxiv2020

An in-silico study to determine whether changes to mitochondria organization through engineered mitochondrial dynamics can enhance bioenergetics in cardiomyocytes

Mitochondria are the powerhouse of the cell and owing to their unique energetic demands, heart muscles contain a high density of mitochondria. In conditions such as heart failure and diabetes-induced heart disease, changes in the organization of cardiac mitochondria are common. While recent studies have also shown that cardiac mitochondria split and fuse throughout the cell, a mechanistic understanding of how mitochondrial dynamics may affect energy output is lacking. Using a mathematical model that has been fitted to experimental data, we test if briefly altering fission or fusion rates improves ATP production and supply in cardiomyocytes. Unexpectedly, we found that cardiac bioenergetics, e.g., the ADP/ATP ratio, were robust to changes in fusion and fission rates and consequently mitochondria organization. Our study highlights complex nonlinear feedback loops that are at play in the cross-talk between mitochondrial dynamics and bioenergetics. The study motivate further in-silico and experimental investigations to determine the mechanistic basis for new therapies that target mitochondrial dynamics.

systems biology↗

bioRxiv2020

Ca2+ release via IP3Rs increases RyR mediated Ca2+ spark frequency in ventricular cardiomyocytes without altering spark amplitude and duration

Calcium (Ca2+) plays a critical role in the excitation contraction coupling (ECC) process that governs the contraction of cardiomyocytes during each heartbeat. While ryanodine receptors (RyRs) are the primary Ca2+ channels responsible for mediating cell-wide Ca2+ transients during ECC, Ca2+ release via inositol 1,4,5-trisphosphate (IP3) receptors (IP3Rs) have been reported to elicit ECC-modulating effects. Recent studies suggest that the proximal localization of IP3Rs at dyads grants their ability to modify the occurrence of Ca2+ sparks (elementary Ca2+ release events that constitute ECC-associated Ca2+ transients) which may underlie the modulatory effects on ECC. Here, we aim to uncover the mechanism by which IP3Rs affect Ca2+ spark dynamics. To this end, we developed a mathematical model of the dyad that incorporates IP3Rs to reveal their impact on local Ca2+ handling and corresponding Ca2+ spark formation. Consistent with published experimental data, our model predicts that the propensity for Ca2+ spark formation increases with IP3R activity. Our simulations support the hypothesis that IP3R activity elevates Ca2+ within the dyad, sensitizing proximal RyRs for future release. However, this lowers Ca2+ in the JSR available for release and thus results in Ca2+ sparks with the same duration but lower amplitudes.

biophysics↗

bioRxiv2020

EM-stellar: benchmarking deep learning for electron microscopy image segmentation

The inherent low contrast of electron microscopy (EM) datasets presents a significant challenge for rapid segmentation of cellular ultrastructures from EM data. This challenge is particularly prominent when working with high resolution big-datasets that are now acquired using electron tomography and serial block-face imaging techniques. Deep learning (DL) methods offer an exciting opportunity to automate the segmentation process by learning from manual annotations of a small sample of EM data. While many DL methods are being rapidly adopted to segment EM data no benchmark analysis has been conducted on these methods to date. We present EM-stellar, a Jupyter Notebook platform that is hosted on google Colab that can be used to benchmark the performance of a range of state-of-the-art DL methods on user-provided datasets. Using EM-Stellar we show that the performance of any DL method is dependent on the properties of the images being segmented. It also follows that no single DL method performs consistently across all performance evaluation metrics.

bioinformatics↗

bioRxiv2020

Membrane tension can enhance adaptation to maintain polarity of migrating cells

Migratory cells are known to adapt to environments that contain wide-ranging levels of chemoattractant. While biochemical models of adaptation have been previously proposed, here we discuss a different mechanism based on mechanosensing, where the interaction between biochemical signaling and cell tension facilitates adaptation. We describe and analyze a model of mechanochemical-based adaptation coupling a mechanics-based physical model of cell tension coupled with the wave-pinning reaction-diffusion model for Rac activity. Mathematical analysis of this model, simulations of a simplified 1D cell geometry, and 2D finite element simulations of deforming cells reveal that as a cell protrudes under the influence of high stimulation levels, tension mediated inhibition of GTPase signaling causes the cell to polarize even when initially over-stimulated. Specifically, tension mediated inhibition of GTPase activation, which has been experimentally observed in recent years, facilitates this adaptation by countering the high levels of environmental stimulation. These results demonstrate how tension related mechanosensing may provide an alternative (and potentially complementary) mechanism for cell adaptation. Statement of SignificanceMigratory cells such as human neutrophils encounter environments that contain wide-ranging levels of chemoattractant. In order to move, these cells must maintain an organized front-rear signaling polarity despite this wide variation in environmental stimuli. Past research has demonstrated a number of biochemical based mechanisms by which cells adapt to variable signal levels. Here we demonstrate that the interplay between Rho GTPase signaling and tension mediated feedbacks may provide an alternative mechanochemical mechanism for adaptation to high levels of signaling.

systems biology↗

bioRxiv2020

EM-net: Deep learning for electron microscopy image segmentation

Recent high-throughput electron microscopy techniques such as focused ion-beam scanning electron microscopy (FIB-SEM) provide thousands of serial sections which assist the biologists in studying sub-cellular structures at high resolution and large volume. Low contrast of such images hinder image segmentation and 3D visualisation of these datasets. With recent advances in computer vision and deep learning, such datasets can be segmented and reconstructed in 3D with greater ease and speed than with previous approaches. However, these methods still rely on thousands of ground-truth samples for training and electron microscopy datasets require significant amounts of time for carefully curated manual annotations. We address these bottlenecks with EM-net, a scalable deep convolutional neural network for EM image segmentation. We have evaluated EM-net using two datasets, one of which belongs to an ongoing competition on EM stack segmentation since 2012. We show that EM-net variants achieve better performances than current deep learning methods using small- and medium-sized ground-truth datasets. We also show that the ensemble of top EM-net base classifiers outperforms other methods across a wide variety of evaluation metrics.

bioinformatics↗

bioRxiv2019

Detecting RyR clusters with CaCLEAN: influence of spatial distribution and structural heterogeneity

In cardiomyocytes, the coordinated release of calcium ions from intracellular stores through ryanodine receptor (RyR) clusters is key to the generation of a calcium transient and induction of contraction. Recently, a deconvolution algorithm from radio astronomy was adapted and applied to analysis of calcium fluorescence imaging of cardiomyocytes. The algorithm, CaCLEAN, showed potential in revealing both the spatial locations of RyR clusters and their functional response in living cells. However, whether the RyR clusters identified by CaCLEAN analysis of the imaging data were true or false positives remained unvalidated in the absence of ground truth values. In this work, a structurally realistic finite element model was developed to simulate reaction-diffusion of calcium emanating from RyR clusters during the rising phase (first 30 ms) of the calcium transient. The effect of two sets of factors were examined with the model: (1) the number and spacing of simulated RyR clusters and (2) the effect of mitochondria acting as barriers to diffusion. Confocal fluorescence microscopy images were simulated from the model results and analysed using CaCLEAN. The performance of CaCLEAN was found to be sensitive to cluster spacing and distance from the imaging plane. In a case with sparsely-packed clusters, detection recall and precision were 0.82 for clusters up to 610 nm from the imaging plane; in a densely-packed cluster case, recall and precision were 0.69 for clusters up to 280 nm from the imaging plane. Users interested in applying CaCLEAN to their data should therefore consider the likely density of cluster distributions and the trade-off between precision and recall when determining the maximum relevant depth of RyR clusters in their application.

bioengineering↗

bioRxiv2018

Insights on the impact of mitochondrial organisation on bioenergetics in high-resolution computational models of cardiac cell architecture

Recent electron microscopy data have revealed that cardiac mitochondria are not arranged in crystalline columns, but are organised with several mitochondria aggregated into columns of varying sizes often spanning the cell cross-section. This raises the question - how does the mitochondrial arrangement affect the metabolite distributions within cardiomyocytes and their impact on force dynamics? Here we employed finite element modelling of cardiac bioenergetics, using computational meshes derived from electron microscope images, to address this question. Our results indicate that heterogeneous mitochondrial distributions can lead to significant spatial variation across the cell in concentrations of inorganic phosphate, creatine (Cr) and creatine phosphate (PCr). However, our model predicts that sufficient activity of the creatine kinase (CK) system, coupled with rapid diffusion of Cr and PCr, maintains near uniform ATP and ADP ratios across the cell cross sections. This homogenous distribution of ATP and ADP should also evenly distribute force production and twitch duration with contraction. These results suggest that the PCr shuttle, and associated enzymatic reactions, act to maintain uniform force dynamics in the cell despite the heterogeneous mitochondrial organization. However, our model also predicts that under hypoxia - activity of mitochondrial CK enzyme and diffusion of high-energy phosphate compounds may be insufficient to sustain uniform ATP/ADP distribution and hence force generation.

systems biology↗

bioRxiv2018

An Automated Workflow for Segmenting Single Adult Cardiac Cells from Large-Volume Serial Block-Face Scanning Electron Microscopy Data

This paper presents a new algorithm to automatically segment the myofibrils, mitochondria and nuclei within single adult cardiac cells that are part of a large serial-block-face scanning electron microscopy (SBF-SEM) dataset. The algorithm only requires a set of manually drawn contours that roughly demarcate the cell boundary at routine slice intervals (every 50th, for example). The algorithm correctly classified pixels within the single cell with 97% accuracy when compared to manual segmentations. One entire cell and the partial volumes of two cells were segmented. Analysis of segmentations within these cells showed that myofibrils and mitochondria occupied 47.5% and 51.6% on average respectively, while the nuclei occupy 0.7% of the cell for which the entire volume was captured in the SBF-SEM dataset. Mitochondria clustering increased at the periphery of the nucleus region and branching points of the cardiac cell. The segmentations also showed high area fraction of mitochondria (up to 70% of the 2D image slice) in the sub-sarcolemmal region, whilst it was closer to 50% in the intermyofibrillar space. We finally demonstrate that our segmentations can be turned into 3D finite element meshes for cardiac cell computational physiology studies. We offer our large dataset and MATLAB implementation of the algorithm for research use at www.github.com/CellSMB/sbfsem-cardiac-cell-segmenter/. We anticipate that this timely tool will be of use to cardiac computational and experimental physiologists alike who study cardiac ultrastructure and its role in heart function.

bioengineering↗

bioRxiv2017

Transcriptomic Imputation of Bipolar Disorder and Bipolar subtypes reveals 29 novel associated genes

Bipolar disorder is a complex neuropsychiatric disorder presenting with episodic mood disturbances. In this study we use a transcriptomic imputation approach to identify novel genes and pathways associated with bipolar disorder, as well as three diagnostically and genetically distinct subtypes. Transcriptomic imputation approaches leverage well-curated and publicly available eQTL reference panels to create gene-expression prediction models, which may then be applied to \"impute\" genetically regulated gene expression (GREX) in large GWAS datasets. By testing for association between phenotype and GREX, rather than genotype, we hope to identify more biologically interpretable associations, and thus elucidate more of the genetic architecture of bipolar disorder.\n\nWe applied GREX prediction models for 13 brain regions (derived from CommonMind Consortium and GTEx eQTL reference panels) to 21,488 bipolar cases and 54,303 matched controls, constituting the largest transcriptomic imputation study of bipolar disorder (BPD) to date. Additionally, we analyzed three specific BPD subtypes, including 14,938 individuals with subtype 1 (BD-I), 3,543 individuals with subtype 2 (BD-II), and 1,500 individuals with schizoaffective subtype (SAB).\n\nWe identified 125 gene-tissue associations with BPD, of which 53 represent independent associations after FINEMAP analysis. 29/53 associations were novel; i.e., did not lie within 1Mb of a locus identified in the recent PGC-BD GWAS. We identified 37 independent BD-I gene-tissue associations (10 novel), 2 BD-II associations, and 2 SAB associations. Our BPD, BD-I and BD-II associations were significantly more likely to be differentially expressed in post-mortem brain tissue of BPD, BD-I and BD-II cases than we might expect by chance. Together with our pathway analysis, our results support long-standing hypotheses about bipolar disorder risk, including a role for oxidative stress and mitochondrial dysfunction, the post-synaptic density, and an enrichment of circadian rhythm and clock genes within our results.

genetics↗