Biology subjects

Tang, M.

Publications and source records attributed to Tang, M..

At least 73 records · Page 4Linked to original sources

bioRxiv2023

Comparative analysis of methodologies for detecting extrachromosomal circular DNA

Extrachromosomal circular DNA (eccDNA) is crucial in oncogene amplification, gene transcription regulation, and intratumor heterogeneity. While various analysis pipelines and experimental methods have been developed for eccDNA identification, their detection efficiencies have not been systematically assessed. To address this, we evaluated the performance of 7 analysis pipelines using three simulated datasets, in terms of accuracy, similarity, duplication rate, and computational resource consumption. We also compared the eccDNA detection efficiency of 7 experimental methods through twenty-one real sequencing datasets. Our results identified Circle-Map and CReSIL as the most effective pipelines for eccDNA detection through short-read and long-read sequencing data, respectively. Moreover, third-generation sequencing-based Circle-Seq showed superior efficiency in detecting copy number-amplified eccDNA over 10 kb in length. These results offer valuable insights for researchers in choosing the most suitable methods for eccDNA research.

cancer biology↗

bioRxiv2023

A Fuzzy sequencer for rapid DNA fragment counting and genotyping

High-throughput sequencing technologies generate a vast number of DNA sequence reads simultaneously, which are subsequently analyzed using the information contained within these fragmented reads. The assessment of sequencing technology relies on information efficiency, which measures the amount of information entropy produced per sequencing reaction cycle. In this study, we propose a fuzzy sequencing strategy that exhibits information efficiency more than twice of currently prevailing cyclic reversible terminator sequencing methods. To validate our approach, we developed a fully functional and high-throughput fuzzy sequencer. This sequencer implements a highly efficient fluorogenic sequencing-by-synthesis chemistry and underwent testing across various application scenarios, including copy-number variation detection, noninvasive prenatal testing, transcriptome profiling, mutation genotyping, and metagenomic profling. Our findings unequivocally demonstrate that the fuzzy sequencing strategy outperforms existing methods in terms of information efficiency and delivers accurate resequencing results with faster turnaround times. One Sentence SummaryA fuzzy sequencer can exceed current limit of information efficiency of DNA sequencers for resequencing applications.

genomics↗

bioRxiv2023

Monkeybread: A Python toolkit for the analysis of cellular niches in single-cell resolution spatial transcriptomics data

Spatial transcriptomics technologies enable the spatially resolved measurement of gene expression within a tissue specimen. With these technologies, researchers can investigate how cells organize into cellular niches which are defined as distinct regions in the tissue comprising a specific composition of cell types or phenotypes. While general-purpose software tools for the exploratory analysis of spatial transcriptomics data exist, there is a need for tools that specialize in the analysis of cellular organization into niches. This can further enhance the downstream application of these data towards drug target discovery, target validation, and biomarker development. We present Monkeybread: A Python toolkit for analyzing cellular organization and intercellular communication in single-cell resolution spatial transcriptomics data. We applied Monkeybread to a human melanoma sample to demonstrate its utility in identifying cellular niches with diverse immunogenic compositions in the tumor microenvironment. We found that these niches were differentially enriched for immunogenic and tolerogenic macrophage populations that could be correlated to T cell abundance. These findings highlight how Monkeybread can be used for revealing underlying biology of the tumor microenvironment, and in the future, for understanding the influence of these niches on response to available treatments and discovery of novel drug targets.

bioinformatics↗

bioRxiv2023

The tidyomics ecosystem: Enhancing omic data analyses

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

bioinformatics↗

bioRxiv2023

Task success in trained spiking neuronal network models coincides with emergence of cross-stimulus-modulated inhibition

The neocortex is composed of spiking neuronal units interconnected in a sparse, recurrent network. Neuronal networks exhibit spiking activity that transforms sensory inputs into appropriate behavioral outputs. In this study, we train biologically realistic spiking neural network (SNN) models to identify the architectural changes which enable task-appropriate computations. Specifically, we employ a binary state change detection task, where each state is defined by motion entropy. This task mirrors behavioral paradigms that mice perform in the lab. SNNs are composed of excitatory and inhibitory units randomly interconnected with connection likelihoods and strengths matched to observations from mouse neocortex. Following training, we discover that SNNs selectively adjust firing rates depending on state, and that excitatory and inhibitory connectivity between input and recurrent layers change in accordance with this rate modulation. Input channels that exhibit bias to one specific motion entropy input develop stronger connections to recurrent excitatory units during training, while channels that exhibit bias to the other input develop stronger connections to inhibitory units. Furthermore, recurrent inhibitory units which positively modulated firing rates to one input strengthened their connections to recurrent units of the opposite modulation. This specific pattern of cross-modulation inhibition emerged as the optimal solution when imposing Dales law throughout training of the SNNs. Removing this constraint led to the absence of the emergence of this architectural solution. This work highlights the critical role of interneurons and the specific architectural patterns of inhibition in shaping dynamics and information processing within neocortical circuits.

neuroscience↗

bioRxiv2023

Rationally designed multimeric nanovaccines using icosahedral DNA origami for molecularly controlled display of SARS-CoV-2 receptor binding domain

Multivalent antigen display on nanoparticles can enhance the immunogenicity of nanovaccines targeting viral moieties, such as the receptor binding domain (RBD) of SARS-CoV-2. However, particle morphology and size of current nanovaccines are significantly different from those of SARS-CoV-2. Additionally, surface antigen patterns are not controllable to enable the optimization of B cell activation. Herein, we employed an icosahedral DNA origami (ICO) as a display particle for SARS-CoV-2 RBD nanovaccines. The morphology and diameter of the particles were close to those of the virus (91 {+/-} 11 nm). The surface addressability of the DNA origami permitted facile modification of the ICO surface with numerous RBD antigen clusters (ICO-RBD) to form various antigen patterns. Using an in vitro screening system, we demonstrate that the antigen spacing, antigen copies within clusters and cluster number parameters of the surface antigen pattern all impact the ability of the nanovaccines to activate B cells. Importantly, the optimized ICO-RBD nanovaccines evoked stronger and more enduring humoral and T cell immune responses in mouse models compared to soluble RBD antigens. Our vaccines activated similar humoral immunity and slightly stronger cellular immunity compared to mRNA vaccines. These results provide reference principles for the rational design of nanovaccines and exemplify the utility of DNA origami as a display platform for vaccines against infectious disease.

bioengineering↗

bioRxiv2023

Tailored Photoacoustic Apertures with Superimposed Optical Holograms

A new method of generating potentially arbitrary photoacoustic wavefronts with optical holograms is presented. This method uses nanosecond laser pulses at 1064 nm that are split into four time-delayed components by means of a configurable multipass optical delay apparatus which serves to map the pulses onto phase-delayed regions of a given acoustic wavefront. A single spatial light modulator generates separate holograms for each component which are imaged onto a photoacoustic transducer comprised of a thermoelastic polymer. The spatially- and temporally-modulated holograms facilitate a phased array effect which enables beam steering of the resulting acoustic pulse. For a first experimental demonstration of the method, as verified by simulation, an acoustic beam is steered in four directions by around 5 degrees.

bioengineering↗

bioRxiv2023

DePARylation is critical for S phase progression and cell survival

Poly(ADP-ribose)ylation or PARylation by PAR polymerase 1 (PARP1) and dePARylation by poly(ADP-ribose) glycohydrolase (PARG) are equally important for the dynamic regulation of DNA damage response. PARG, the most active dePARylation enzyme, is recruited to sites of DNA damage via pADPr-dependent and PCNA-dependent mechanisms. Targeting dePARylation is considered an alternative strategy to overcome PARP inhibitor resistance. However, precisely how dePARylation functions in normal unperturbed cells remains elusive. To address this challenge, we conducted multiple CRISPR screens and revealed that dePARylation of S phase pADPr by PARG is essential for cell viability. Loss of dePARylation activity initially induced S phase-specific pADPr signaling, which resulted from unligated Okazaki fragments and eventually led to uncontrolled pADPr accumulation and PARP1/2-dependent cytotoxicity. Moreover, we demonstrated that proteins involved in Okazaki fragment ligation and/or base excision repair regulate pADPr signaling and cell death induced by PARG inhibition. In addition, we determined that PARG expression is critical for cellular sensitivity to PARG inhibition. Additionally, we revealed that PARG is essential for cell survival by suppressing pADPr. Collectively, our data not only identify an essential role for PARG in normal proliferating cells but also provide a potential biomarker for the further development of PARG inhibitors in cancer therapy.

cancer biology↗

bioRxiv2023

Contrasting carbon cycle along tropical forest aridity gradients in W Africa and Amazonia

2Tropical forests cover large areas of equatorial Africa and play a significant role in the global carbon cycle. However, there has been a lack of in-situ measurements to understand the forests gross and net primary productivity (GPP and NPP) and their allocation. Here we present the first detailed field assessment of the carbon budget of multiple forest sites in Africa, by monitoring 14 one-hectare plots along an aridity gradient in Ghana. When compared with an equivalent aridity gradient in Amazonia using the same measurement protocol, the studied West African forests generally had higher GPP and NPP and lower carbon use efficiency (CUE). The West African aridity gradient consistently shows the highest NPP, CUE, GPP, and autotrophic respiration at a medium-aridity site, Bobiri. Notably, NPP and GPP of the site are the highest yet reported anywhere in the tropics using similar methods. Widely used data products (MODIS and FLUXCOM) substantially underestimate productivity when compared to in situ measurements, in Amazonia and especially in Africa. Our analysis suggests that the high productivity of the African forests is linked to their large GPP allocation to canopy and semi-deciduous characteristics, which may be a result of a seasonal climate coupled with high soil fertility.

ecology↗

bioRxiv2023

Insights from the Early Cretaceous: The promise of Lycoptera aDNA sequencing

Obtaining high-quality ancient DNA (aDNA) is a crucial process for advancements in molecular paleontology. However, current research is restricted to a limited range of fossil types due to DNA degradation and contamination from environmental DNA (eDNA). Utilizing the technology of nanoparticle affinity beads, we successfully extracted DNA from Lycoptera davidi fossils and obtained 1,258,901 DNA sequences. To ascertain their origins, we developed and implemented a novel protocol, the "mega screen method", designed to categorize aDNA fragments. This approach successfully identified 243 Actinopterygii species-specific fragments (SSFs), which subsequent analysis confirmed as original in situ DNA (oriDNA) sequences. Lycoptera davidi, a ray-finned fish from the Early Cretaceous Jehol Biota, dates back approximately 120 million years (Ma). Notably, the absence of deamination in certain oriDNA and paleoenvironmental DNA (paeDNA) fragments calls into question its reliability as a definitive marker for aDNA identification, a phenomenon potentially attributable to the protective effects of fossil diagenesis. Additionally, we identified a novel mechanism, "coding region sliding replication and recombination", responsible for generating new transposase. This mechanism, distinct from conventional processes such as horizontal gene transfer or vertical inheritance from parents, can facilitate a rapid genomic diversification. The transposase CDS insertion into the GAS2 gene of Anabarilius grahami offers molecular insights into the genetic interactions between ancestral carp and Lycoptera and the swift emergence of the expansion of early fish species starting from the early Cretaceous. These findings enhance our understanding of fossil DNA preservation, lay the groundwork for an ancient species DNA database, and introduce innovative tools for exploring evolutionary relationships across geological timescales.

paleontology↗

bioRxiv2023

Mono-phosphorylation at Ser4 of Barrier-to-autointegration Factor (Banf1) significantly reduces its DNA binding capability by inducing critical changes in its local conformation and DNA binding surface

Barrier-to-Autointegration Factor (Banf1) is a small DNA-bridging protein. The binding status of Banf1 to DNA is regulated by its N-terminal phosphorylation and dephosphorylation, which plays a critical role in cell proliferation. Banf1 can be phosphorylated at Ser4 into mono-phosphorylated Banf1, which is further phosphorylated at Thr3 to form di-phosphorylated Banf1. It was observed decades ago that mono-phosphorylated Banf1 cannot bind to DNA. However, the underlying molecular- and atomic-level mechanisms remain unclear. A clear understanding of these mechanisms will aid in interfering with the cell proliferation process for better global health. Herein, we explored the detailed atomic bases of unphosphorylated Banf1-DNA binding and how mono- and di-phosphorylation of Banf1 impair these atomic bases to eliminate its DNA-binding capability, followed by exploring the DNA-binding capability of mono- and di-phosphorylation Banf1, using comprehensive and systematic molecular modelling and molecular dynamics simulations. This work presented in detail the residue-level binding energies, hydrogen bonds and water bridges between Banf1 and DNA, some of which have not been reported. Moreover, we revealed that mono-phosphorylation of Banf1 causes its N-terminal secondary structure changes, which in turn induce significant changes in Banf1s DNA binding surface, thus eliminating its DNA-binding capability. At the atomic level, we also uncovered the alterations in interactions due to the induction of mono-phosphorylation that result in the N-terminal secondary structure changes of Banf1. Additionally, our modelling showed that phosphorylated Banf1 with their dominant N-terminal secondary structures bind to DNA with a significantly lower affinity and the docked binding pose are not stable in MD simulations. These findings help future studies in predicting effect of mutations in Banf1 on its DNA-binding capability and open a novel avenue for the development of therapeutics such as cancer drugs, targeting cell proliferation by inducing conformational changes in Banf1s N-terminal domain.

biochemistry↗

bioRxiv2023

Covalent Targeting of Glutamate Cysteine Ligase to Inhibit Glutathione Synthesis

Dysregulated oxidative stress plays a major role in cancer pathogenesis and some types of cancer cells are particularly vulnerable to inhibiting cellular antioxidant capacity. Glutamate-cysteine ligase (GCL) is the first and rate-limiting step in the synthesis of the major cellular antioxidant glutathione (GSH). Developing a GCL inhibitor may be an attractive therapeutic strategy for certain cancer types that are particularly sensitive to oxidative stress. In this study, we reveal a cysteine-reactive covalent ligand EN25 that covalently targets an allosteric cysteine C114 on GCLM, the modifier subunit of GCL, leading to inhibition of GCL activity, lowering of cellular GSH levels, and impaired cell viability in ARID1A-deficient cancer cells that are particularly vulnerable to glutathione depletion, but not in ARID1A-positive cancer cells. Our studies uncover a novel potential ligandable site within GCLM that can be targeted to inhibit the GSH synthesis in cancer cells to target vulnerable cancer cell types.

biochemistry↗

bioRxiv2023

Predictive Coding Networks for Temporal Prediction

One of the key problems the brain faces is inferring the state of the world from a sequence of dynamically changing stimuli, and it is not yet clear how the sensory system achieves this task. A well-established computational framework for describing perceptual processes in the brain is provided by the theory of predictive coding. Although the original proposals of predictive coding have discussed temporal prediction, later work developing this theory mostly focused on static stimuli, and key questions on neural implementation and computational properties of temporal predictive coding networks remain open. Here, we address these questions and present a formulation of the temporal predictive coding model that can be naturally implemented in recurrent networks, in which activity dynamics rely only on local inputs to the neurons, and learning only utilises local Hebbian plasticity. Additionally, we show that temporal predictive coding networks can approximate the performance of the Kalman filter in predicting behaviour of linear systems, and behave as a variant of a Kalman filter which does not track its own subjective posterior variance. Importantly, temporal predictive coding networks can achieve similar accuracy as the Kalman filter without performing complex mathematical operations, but just employing simple computations that can be implemented by biological networks. Moreover, when trained with natural dynamic inputs, we found that temporal predictive coding can produce Gabor-like, motion-sensitive receptive fields resembling those observed in real neurons in visual areas. In addition, we demonstrate how the model can be effectively generalized to nonlinear systems. Overall, models presented in this paper show how biologically plausible circuits can predict future stimuli and may guide research on understanding specific neural circuits in brain areas involved in temporal prediction. Author summaryWhile significant advances have been made in the neuroscience of how the brain processes static stimuli, the time dimension has often been relatively neglected. However, time is crucial since the stimuli perceived by our senses typically dynamically vary in time, and the cortex needs to make sense of these changing inputs. This paper describes a computational model of cortical networks processing temporal stimuli. This model is able to infer and track the state of the environment based on noisy inputs, and predict future sensory stimuli. By ensuring that these predictions match the incoming stimuli, the model is able to learn the structure and statistics of its temporal inputs and produces responses of neurons resembling those in the brain. The model may help in further understanding neural circuits in sensory cortical areas.

neuroscience↗

bioRxiv2023

A Series of Composited Tumor DNA Reference Materials Containing Three Genes and Ten Mutation Positions for CNV and SNV Detection

Processes in clinic for tumors diagnosis and treatment need reference materials (RMs) to evaluate and calibrate. However, no RMs can provides properties of copy number variation (CNV) and single nucleotide variants (SNV) of genes EGFR, HER2, MET, PIK3CA, KRAS, BRAF, NRAS simultaneously. In this study, we used commercial cell lines to construct a series of tumor RMs containing property mentioned above. Furthermore, we evaluated their stability, homogeneity, and commutability by droplet digital PCR and next generation sequencing technology. The results showed that, for tumor CNV gDNA RM, the copy number is 7.3 copies/L (EGFR), 5.3 copies/L (HER2) and 8.2 copies/L (MET). For tumor 5% SNV gDNA RM, the mutation frequency of each mutation position showed as follow: EGFR-E746A750 (24.6%), EGFR-L858R (5.8%), EGFR-T790M (5.5%), EGFR-G719S (6.6%), PIK3CA-E545K (4.7%), PIK3CA-H1047R (5.8%), KRAS-G13D (8.2%), KRAS-G12D (6.5%), BRAF-V600E (4.6%), NRAS-Q61K (8.5%). All variable coefficient (CV) of tumor gDNA RM for homogeneity were less than 7%, that of CNV+SNV ctDNA RM were less than 17%. Besides, the CV for commutability of the all types of RMs were less than 17%. These RMs can be applied into a wide range type of sequencing panels and provides a closer simple background.

molecular biology↗

bioRxiv2023

VirtualFlow 2.0 - The Next Generation Drug Discovery Platform Enabling Adaptive Screens of 69 Billion Molecules

Identifying potent lead molecules for specific targets remains a major bottleneck in drug discovery. As structural information about proteins becomes increasingly available, ultra-large virtual screenings (ULVSs) which computationally evaluate billions of molecules offer a powerful way to accelerate early-stage drug discovery. Here, we introduce AdaptiveFlow, an open-source platform designed to make ULVSs more accessible, scalable, and efficient. AdaptiveFlow provides free access to a screening-ready version of the Enamine REAL Space (1), the largest library of ready-to-dock, drug-like molecules, containing 69 billion compounds that we prepared using the ligand preparation module of the platform. A key innovation of the platform is its use of a multi-dimensional grid of molecular properties, which helps researchers explore and prioritize chemical space more effectively and reduce the computational costs by a factor of approximately 1000. This grid forms the basis of a new method for identifying promising regions of chemical space, enabling systematic exploration and prioritization of compound libraries. An optional active learning component can further accelerate this process by adaptively steering the search toward molecules most likely to bind a given target. To support a broad range of applications, AdaptiveFlow is compatible with over 1,500 docking protocols. The platform achieves near-linear scaling on up to 5.6 million CPUs in the AWS Cloud, setting a new benchmark for large-scale cloud computing in drug discovery. Using this approach, we identified nanomolar inhibitors of two disease-relevant targets: ferroptosis suppressor protein 1 (FSP1) and poly(ADP-ribose) polymerase 1 (PARP-1) (2, 3). By leveraging newly solved crystal structures of FSP1 in complex with NAD+, FAD, and coenzyme Q1, we validated these hits experimentally and determined the co-crystal structures of FSP1 bound to small-molecule inhibitors, enabling insights into inhibitor binding mechanisms previously unknown. With its high scalability, flexibility, and open accessibility, AdaptiveFlow offers a powerful new resource for discovering and optimizing drug candidates at an unprecedented scale and speed.

bioinformatics↗

bioRxiv2023

USP39 promotes hepatocellular carcinogenesis through regulating alternative splicing in cooperation with SRSF6/HNRNPC

Abnormal alternative splicing (AS) caused by alterations in spliceosomal factors is implicated in cancers. Standard models posit that splice site selection is mainly determined by early spliceosomal U1, U2 snRNPs. Whether and how other mid/late-acting spliceosome components such as USP39 modulate tumorigenic splice site choice remain largely elusive. We observed that hepatocyte-specific knock-in of USP39 promoted hepatocarcinogenesis and potently regulated splice site selection. In human liver cancer cells, USP39 promoted tumor proliferation in a spliceosome-dependent manner. USP39 depletion deregulated hundreds of AS events, including the oncogenic splice-switching of KANK2. Mechanistically, we developed a novel RBP-motif enrichment analysis and found that USP39 modulated exon inclusion/exclusion by interacting with SRSF6/HNRNPC in both humans and mice. Our data represent a paradigm for the control of splice site selection by mid/late-acting spliceosome proteins and their interacting RBPs. USP39 and possibly other mid/late-acting spliceosome proteins may represent potential prognostic biomarkers and targets for cancer therapy. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/525324v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@34cbc1org.highwire.dtl.DTLVardef@2d0ee2org.highwire.dtl.DTLVardef@2c4bd6org.highwire.dtl.DTLVardef@26db00_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2023

Predicting the functional effect of compound heterozygous genotypes from large scale variant effect maps

BackgroundPathogenic variants in PHGDH, PSAT1, and PSPH cause a set of rare, autosomal recessive diseases known as serine biosynthesis defects. Serine biosynthesis defects present in a broad phenotypic spectrum that includes, at the severe end, Neu-Laxova syndrome, a lethal multiple congenital anomaly disease, intermediately in the form of infantile serine biosynthesis defects with severe neurological manifestations and growth deficiency, and at the mild end, as childhood disease with intellectual disability. However, because L-serine supplementation, especially if started early, can ameliorate and in some cases even prevent symptoms, knowledge of pathogenic variants is highly actionable. MethodsRecently, our laboratory established a yeast-based assay for human PSAT1 function. We have now applied it at scale to assay the functional impact of 1,914 SNV-accessible amino acid substitutions. In addition to assaying the functional impact of individual variants in yeast haploid cells, we can assay pairwise combinations of PSAT1 alleles that recapitulate human genotypes, including compound heterozygotes, in yeast diploids. ResultsResults of our assays of individual variants (in haploid yeast cells) agree well with clinical interpretations and protein structure-function relationships, supporting the use of our data as functional evidence under the ACMG interpretation guidelines. Results from our diploid assay successfully distinguish patient genotypes from those of healthy carriers and agree well with disease severity. Finally, we present a linear model that uses individual allele measurements (in haploid yeast cells) to accurately predict the biallelic function (in diploid yeast cells) of ~1.8 million allele combinations corresponding to potential human genotypes. ConclusionsTaken together, our work provides an example of how large-scale functional assays in model systems can be powerfully applied to the study of a rare disease.

genetics↗

bioRxiv2022

The deacetylase dependent and independent role of HDAC3 in cardiomyopathy

BackgroundCardiomyopathy is a common disease of cardiac muscle that negatively affects cardiac function. HDAC3 commonly functions as co-repressor by removing acetyl moieties from histone tails. However, a deacetylase-independent role of HDAC3 has also been described. Cardiac deletion of HDAC3 causes reduced cardiac contractility accompanied by lipid accumulation. The molecular function of HDAC3 in cardiomyopathy remains unknown. We have used the powerful genetic tools in Drosophila to investigate the enzymatic and non-enzymatic roles of HDAC3 in cardiomyopathy. Methods and ResultsUsing the Drosophila heart model, we showed that cardiac-specific HDAC3 knockdown leads to prolonged systoles and reduced cardiac contractility. Immunohistochemistry revealed structural abnormalities characterized by myofiber disruption in HDAC3 knock down hearts. Cardiac-specific HDAC3 knockdown showed increased levels of whole body triglycerides and increased fibrosis. The introduction of deacetylase-dead HDAC3 mutant in HDAC3 KD background showed comparable results with wild type HDAC3 in aspects of contractility and Pericardin deposition. However, deacetylase-dead HDAC3 mutants failed to improve triglyceride accumulation. ConclusionsOur data indicate that HDAC3 plays a deacetylase-independent role in maintaining cardiac contractility and preventing Pericardin deposition as well as a deacetylase-dependent role to maintain triglyceride homestasis.

physiology↗