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Sinha, N.

Publications and source records attributed to Sinha, N..

At least 37 records · Page 2Linked to original sources
bioRxiv2022

Deep characterization of cancer drugs mechanism of action by integrating large-scale genetic and drug screens

Knowing a drugs mechanism of action (MOA) is essential for its clinical success by selecting the best indications, likely responders, and combinations. Yet knowledge of many drugs MOA remains lacking. Here we present DeepTarget, a computational tool for deep characterization of cancer drugs MOA by integrating existing large-scale genetic and drug screens. Spanning [~]1500 drugs across [~]18K possible target genes, DeepTarget predicts: (1) a drugs primary target(s), (2) whether it specifically targets the wild-type or mutated target forms, and (3) the secondary target(s) that mediate its response when the primary target is not expressed. We first tested and successfully validated DeepTarget in a total of eleven unseen gold-standard datasets, with an average AUC of 0.82, 0.77, and 0.92 for the above three prediction abilities, respectively. We then proceed to use it in a wide range of applications: First, we find that DeepTargets predicted specificity of a drug to its target is strongly associated with its success in clinical trials and is higher in its FDA-approved indications. Second, DeepTarget predicts candidate drugs for targeting currently undruggable cancer oncogenes and their mutant forms. Finally, DeepTarget predicts new targets for drugs with unknown MOA and new secondary targets of approved drugs. Taken together, DeepTarget is a new computational framework for accelerating drug prioritization and its target discovery by leveraging large-scale genetic and drug screens.

cancer biology↗
bioRxiv2022

A suberized exodermis is required for tomato drought tolerance

Plant roots integrate environmental signals and developmental programs using exquisite spatiotemporal control. This is apparent in the deposition of suberin, an apoplastic diffusion barrier, which regulates the entry and exit of water, solutes and gases, and is environmentally plastic. Suberin is considered a hallmark of endodermal differentiation, but we find that it is absent in the tomato endodermis during normal development. Instead, suberin is present in the exodermis, a cell type that is absent in the model organism Arabidopsis thaliana. Here, we uncover genes driving exodermal suberization and describe its effects on drought responses in tomato, unravelling the similarities and differences with the paradigmatic Arabidopsis endodermis. Cellular resolution imaging, gene expression, and mutant analyses reveal loss of this program from the endodermis, and its co-option in the exodermis. Functional genetic analyses of the tomato MYB92 transcription factor and ASFT enzyme demonstrate the importance of exodermal suberin for a plant water-deficit response. Controlling the degree of exodermal suberization could be a new strategy for breeding climate-resilient plants.

plant biology↗
bioRxiv2022

A knowledge integration strategy for the selection of a robust multi-stress biomarkers panel for Bacillus subtilis

Recent advances in high-throughput omics technologies have enhanced the identification of molecular biomarkers specific to phenotypes or states in bacteria. Using these biomarkers to monitor the state of bacteria used in biotechnological processes promises to increase process efficiency. However, live-cell monitoring systems applied to recognise bacterial cellular states in real time can only accommodate a small number of gene expression biomarkers. Computational methods are required to identify and prioritise robust biomarkers for experimental characterisation and verification. This study focused on designing a knowledge integration strategy for the selection of an optimal minimised gene expression biomarker panel to sense various stress states in Bacillus subtilis. We developed a computational method that ranks the candidate biomarker panels based on complementary information from machine learning model, gene regulatory network and co-expression network. We identified a recommended biomarker panel showing high stress sensing power for a variety of conditions included in both the dataset used for biomarker identification (mean f1-score achieved at 0.99) and the independent datasets from different sources (mean f1-score achieved at 0.98). We discovered a significant correlation between stress sensing power and evaluation metrics such as the number of associated regulators in a B. subtilis gene regulatory network (GRN) and the number of associated modules in a B. subtilis co-expression network (CEN). GRNs and CENs provide information relevant to the diversity of biological processes encoded by biomarker genes. We demonstrate that quantitatively relating meaningful evaluation metrics with stress sensing power has potential for recognising biomarkers that show better sensitivity and robustness to an extended set of stress conditions. We conclude that this approach is readily applicable to biomarker discovery model selection.

systems biology↗
bioRxiv2022

Synthetic lethality-based prediction of cancer treatment response from histopathology images

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Importantly, ENLIGHT-DeepPT successfully predicts true responders in five independent patients cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, trained and tested on the same cohort in cross validation. Its future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries. Statement of SignificanceENLIGHT-DeepPT is the first approach shown to successfully predict response to multiple targeted and immune cancer therapies from H&E slides. In distinction from all previous H&E slides prediction approaches, it does not require supervised training on a specific cohort for each drug/indication treatment but is trained to predict expression on the TCGA cohort and then can predict response to an array of treatments without any further training. ENLIGHT-DeepPT can provide rapid treatment recommendations to oncologists and help advance precision oncology in underserved regions and low-income countries.

cancer biology↗
bioRxiv2022

The genomic basis of the plant island syndrome in Darwin's giant daisies

Oceanic archipelagos comprise multiple disparate environments over small geographic areas and are isolated from other biotas. These conditions have led to some of the most spectacular adaptive radiations, which have been key to our understanding of evolution, and offer a unique chance to characterise the genomic basis underlying rapid and pronounced phenotypic changes. Repeated patterns of evolutionary change in plants on oceanic archipelagos, i.e. the plant island syndrome, include changes in leaf morphology, acquisition of perennial life-style, and change of ploidy. Here, we describe the genome of the critically endangered and Galapagos endemic Scalesia atractyloides Arnot., obtaining a chromosome-resolved 3.2-Gbp assembly with 43,093 candidate gene models. Using a combination of fossil transposable elements, k-mer spectra analyses and orthologue assignment, we identify the two ancestral subgenomes and date their divergence and the polyploidization event, concluding that the ancestor of all Scalesia species on the Galapagos was an allotetraploid. There are a comparable number of genes and transposable elements across the two subgenomes, and while their synteny has been mostly conserved, we find multiple inversions that may have facilitated adaptation. We identify clear signatures of selection across genes associated with vascular development, life-growth, adaptation to salinity and changes in flowering time, thus finding compelling evidence for a genomic basis of island syndrome in Darwins giant daisy radiation. This work advances understanding of factors influencing subgenome divergence in polyploid genomes, and characterizes the quick and pronounced genomic changes in a specular and diverse radiation of an iconic island plant radiation.

evolutionary biology↗
bioRxiv2021

Algorithmic Parameter Estimation and Uncertainty Quantification for Hodgkin-Huxley Neuron Models

Experimental data-based parameter search for Hodgkin-Huxley-style (HH) neuron models is a major challenge for neuroscientists and neuroengineers. Current search strategies are often computationally expensive, are slow to converge, have difficulty handling nonlinearities or multimodalities in the objective function, or require good initial parameter guesses. Most important, many existing approaches lack quantification of uncertainties in parameter estimates even though such uncertainties are of immense biological significance. We propose a novel method for parameter inference and uncertainty quantification in a Bayesian framework using the Markov chain Monte Carlo (MCMC) approach. This approach incorporates prior knowledge about model parameters (as probability distributions) and aims to map the prior to a posterior distribution of parameters informed by both the model and the data. Furthermore, using the adaptive parallel tempering strategy for MCMC, we tackle the highly nonlinear, noisy, and multimodal loss function, which depends on the HH neuron model. We tested the robustness of our approach using the voltage trace data generated from a 9-parameter HH model using five levels of injected currents (0.0, 0.1, 0.2, 0.3, and 0.4 nA). Each test consisted of running the ground truth with its respective currents to estimate the model parameters. To simulate the condition for fitting a frequency-current (F-I) curve, we also introduced an aggregate objective that runs MCMC against all five levels simultaneously. We found that MCMC was able to produce many solutions with acceptable loss values (e.g., for 0.0 nA, 889 solutions were within 0.5% of the best solution and 1,595 solutions within 1% of the best solution). Thus, an adaptive parallel tempering MCMC search provides a "landscape" of the possible parameter sets with acceptable loss values in a tractable manner. Our approach is able to obtain an intelligently sampled global view of the solution distributions within a search range in a single computation. Additionally, the advantage of uncertainty quantification allows for exploration of further solution spaces, which can serve to better inform future experiments.

neuroscience↗
bioRxiv2021

Protein reservoirs of seeds are composites of amyloid and amyloid-like structures

The seed protein functions and their localization in seed storage protein bodies (SSPB) are known for several decades. However, the structural and functional complexity of these SSPB is not known. Interestingly, the plant SSPB is morphologically similar to the amyloid-containing protein bodies found in other organisms and individual SSPB proteins were previously shown to form fibrillar structures under non-native conditions in-vitro. Therefore, we hypothesized that the seed storage protein bodies (SSPB) may have similar structures in-vivo for controlling seed functions. Since comprehensive in-vivo characterization of the SSPB and the structure-function relationship remains unexplored, we show firstly that wheat, barley, chickpea, and mungbean SSPB exhibit a speckled-pattern of amyloids interspersed in an amyloid-like matrix in-situ, suggesting their composite nature. This is confirmed by multiple amyloid-specific probes, biophysical characterization, electron-microscopy, peptide-fingerprinting, and differential degradation during germination. Moreover, the role of amyloid composites in seed germination is proved by the effect of signalling molecules and their correlation to germination parameters, using in-situ seed sections, ex-vivo protoplasts and in-vitro SSPB. These results would lay down foundation for understanding the amyloid composite structure during SSPB biogenesis and their structure-function evolution. It would further facilitate the exploration of molecular and atomic-level structural details of SSPB amyloids. SummaryO_ST_ABSRationaleC_ST_ABSThe function of plant seed storage protein bodies (SSPB) in germination is known for decades. SSPB have aggregated and electron-rich morphology. However their structural complexity remains elusive. Based on their morphological similarity to amyloid-containing protein-bodies of other organisms, and amyloid formation by some plant proteins under non-native conditions, we hypothesized that SSPB might contain in-vivo amyloid structures for modulating seed functions. MethodsTo unambiguously identify seed amyloids in the presence of complex carbohydrate-structures of plant tissues, multi-spectral methods were used including amyloid-staining probes, high-resolution-transmission-electron-microscopy, x-ray diffraction and infra-red-spectroscopy. SSPB amyloids role in germination was shown using amyloid probes, MS/MS analysis, and plant hormones/proteases in-situ seed-sections and ex-vivo protoplasts. Key resultsThe SSPB exhibit a composite structure of amyloid, amyloid-like aggregates and soluble proteins. During germination phases, the amyloids degrade slowly compared to the amyloid-like structures. Inhibition of amyloid degradation results in lower germination-index, confirming amyloids role in germination and seedling-growth. ConclusionThe study for the first time illustrates the presence of composite amyloid structures in-vivo in plant seeds and determines their function in seed germination and seedling-growth. It would open original research questions for decrypting composite amyloid structure formation during SSPB biogenesis and their evolutionary advancement across plant species.

plant biology↗
bioRxiv2021

Using a recently approved tumor mutational burden biomarker to stratify patients for immunotherapy may introduce a sex bias

The U.S. Food and Drug Administration (FDA) recently approved the treatment with pembrolizumab, an immune checkpoint inhibitor (ICI) targeting PD1 (anti-PD1), for patients with advanced solid tumors with a high tumor mutational burden (TMB) (defined as TMB [≥]10 mutations/Mb). However, following recent studies suggest that TMB levels and response to ICI treatment may differ between male and female melanoma patients, we investigated whether using this high-TMB threshold for selecting patients for anti-PD1 treatment may induce a sex-dependent bias. We analyzed a large ICI cohort of 1,286 patients across nine cancer types treated with anti-PD1/PDL1. We find that using this threshold would indeed result in an unwarranted sex bias in melanoma, successfully stratifying female but not male patients. While this threshold is currently not a regulatory prerequisite for ICI treatment in melanoma, it is important to raise awareness to this bias. Notably, no sex-dependent significant differences were observed in the response of melanoma patients to anti-CTLA4 therapies, different chemotherapies or combination therapies. Beyond melanoma, the high-TMB threshold additionally introduces a sex bias of considerable magnitude in glioblastoma and in patients with cancers of unknown origin, however, these results are not statistically significant. A power analysis shows that these biases may become significant with larger sample size, warranting further careful testing in larger cohorts.

cancer biology↗
bioRxiv2021

Immune determinants of the association between tumor mutational burden and immunotherapy response across cancer types

The FDA has recently approved high tumor mutational burden (TMB), defined by [&ge;]10 mutations/Mb, as a biomarker for the treatment of solid tumors with pembrolizumab, an immune checkpoint inhibitor (ICI) that targets PD1. However, recent studies testify that high TMB levels are only able to stratify ICI responders in a subset of cancer types, where the mechanisms underlying this observation have remained unknown. We hypothesized that the tumor immune microenvironment (TME) may modulate the stratification power of TMB (termed TMB power) in a cancer type, leading to this observation. To systematically study this hypothesis, we analyzed TCGA expression data to infer the levels of 31 immune-related factors characteristic of the TME of different cancer types. We integrated this information with TMB and response data of 2,277 patients treated with anti-PD1 or anti-PD-L1 ICI to identify the key immune factors that can determine TMB power across 14 different cancer types. We find that high levels of M1 macrophages and low resting dendritic cells in the TME characterize cancer types with high TMB power. A model based on these two immune factors is strongly predictive of the TMB power in a given cancer type (Spearman Rho=0.76, P<3.6x10-04). Using this model, we provide predictions of the TMB power in nine additional cancer types, including rare cancers, for which TMB and ICI response data are not yet publicly available on a large scale. Our analysis indicates that TMB-High may be highly predictive of ICI response in cervical squamous cell carcinoma, suggesting that such a study should be prioritized.

cancer biology↗
bioRxiv2021

In-vivo protein nitration and de-nitration facilitate Vibrio cholerae cell survival under anaerobic condition: Consequences of Nitrite induced protein nitration

Protein tyrosine nitration (PTN), a highly selective post translational modification, occurs in both prokaryotic and eukaryotic cells under nitrosative stress1. It is reported that the activities of many proteins are altered due to PTN2. PTN is found to be associated with many pathophysiological conditions like neurodegenerative and cardiac diseases etc.3. However, its physiological function is not yet clear. Like all other gut pathogens Vibrio cholerae also faces nitrosative stress in the gut environment which makes its proteome more vulnerable to PTN. Here, we report for the first time in-vivo PTN in V. cholerae. We show that in-vivo protein nitration is nitrite dependent and nitration-denitration phenomenon actually facilitates V. cholerae cell survival in anaerobic or hypoxic condition. In our study, we found that the extent of in-vivo nitration is negatively correlated with the intracellular nitrite content and maximum nitration occurs during log phase of V. cholerae. Most interestingly, a significant denitration was associated with increase in intracellular nitrate content during anaerobic incubation of aerobically grown late log phase cultures. In-vivo nitration could provide an avenue for toxic nitrite storage and nitrosative stress tolerance mechanism in many gut pathogens, whereas denitration could supply nitrate for cell survival in anaerobic nitrate deficient environment.

microbiology↗
bioRxiv2021

Discerning amyloid network in plants

Amyloids are proteinaceous fibrillar structures and are known for their pathogenic and functional roles across the kingdoms. Besides proteinaceous deposits, amyloid-like structures are present in small metabolite assemblies and fibrillar hydrogels. Recent cryoelectron microscopy studies have shed light on the heterogeneous nature of the amyloid structures and their association with carbohydrate or lipid molecules, suggesting that amyloids are not exclusively proteinaceous. The association of amyloids with carbohydrates is further supported because the gold-standard dye of amyloid detection, Congo red, also binds to carbohydrates, probably due to similar stacking interactions. We name the association between amyloids, carbohydrates and other biomolecules as amyloid-network and propose that plants might contain such structures. Specifically, we hypothesize that cereal seeds containing glutamine-repeat-rich granules of storage proteins may have amyloid-like structures. This is because, polyQ repeats are associated with protein aggregation and amyloid formation in humans and are linked to multiple neurodegenerative conditions. Also seed storage proteins and seed cell wall proteins possess carbohydrate affinity. Thus, plant seeds might contain an intercalated network of proteins and carbohydrates, lending strength, stability and dynamics to these structures. In this paper, we show that, plant seeds have a mesh-like network that shows apple-green birefringence on staining with Congo red, a characteristic of amyloids. This congophilic network is more prominent in protein-rich seed sections of wheat and lentils, as compared to starch-rich compartments of potato. The findings suggest an amyloid network in the seeds and might be extended to other plant tissues. Further investigation with mass spectrometry and other techniques would detail the exact compositional analysis of these networks.

plant biology↗
bioRxiv2020

Regeneration of lung epithelial cells by Fullerene C60 nanoformulation: A possible treatment strategy for acute respiratory distress syndrome (ARDS)

Acute respiratory distress syndrome (ARDS) involves death of lung epithelial cells. ARDS is a leading reason behind mortality in respiratory infections. Here we show a proof-of-concept that a Fullerene nanoformulation can be used for the regeneration of cells treated with apoptosis-inducing molecules, suggeting its potential for ARDS therapy.

pharmacology and toxicology↗
bioRxiv2020

Leaf form diversification in an heirloom tomato results from alterations in two different HOMEOBOX genes

Domesticated plants and animals display tremendous diversity in various phenotypic traits and often this diversity is seen within the same species. Tomato (Solanum lycopersicum; Solanaceae) cultivars show wide variation in leaf morphology, but the influence of breeding efforts in sculpting this diversity is not known. Here, we demonstrate that a single nucleotide deletion in the homeobox motif of BIPINNATA, which is a BEL-LIKE HOMEODOMAIN gene, led to a highly complex leaf phenotype in an heirloom tomato, Silvery Fir Tree (SiFT). Additionally, a comparative gene network analysis revealed that reduced expression of the ortholog of WUSCHEL RELATED HOMEOBOX 1 is also important for the narrow leaflet phenotype seen in SiFT. Phylogenetic and comparative genome analysis using whole-genome sequencing data suggests that the bip mutation in SiFT is likely a de novo mutation, instead of standing genetic variation. These results provide new insights into natural variation in phenotypic traits introduced into crops during improvement processes after domestication.

plant biology↗
bioRxiv2019

Node abnormality predicts seizure outcome and relates to long-term relapse after epilepsy surgery

ObjectiveWe assessed pre-operative structural brain networks and clinical characteristics of patients with drug resistant temporal lobe epilepsy (TLE) to identify correlates of post-surgical seizure outcome at 1 year and seizure relapses up to 5 years.\n\nMethodsWe retrospectively examined data from 51 TLE patients who underwent anterior temporal lobe resection (ATLR) and 29 healthy controls. For each patient, using the pre-operative structural, diffusion, and post-operative structural MRI, we generated two networks: pre-surgery network and surgically-spared network. The pre-surgery network is the whole-brain network before surgery and the surgically-spared network is a subnetwork of the pre-surgery network which is expected to remain unaffected by surgery and hence present post-operatively. Standardising these networks with respect to controls, we determined the number of abnormal nodes before surgery and expected to remain after surgery. We incorporated these 2 abnormality measures and 13 commonly acquired clinical data from each patient in a robust machine learning framework to estimate patient-specific chances of seizures persisting after surgery.\n\nResultsPatients with more abnormal nodes had lower chance of seizure freedom at 1 year and even if seizure free at 1 year, were more likely to relapse within five years. In the surgically-spared networks of poor outcome patients, the number of abnormal nodes was greater and their locations more widespread than in good outcome patients. We achieved 0.84 {+/-} 0.06 AUC and 0.89 {+/-} 0.09 specificity in detecting unsuccessful seizure outcomes at 1-year. Moreover, the model-predicted likelihood of seizure relapse was significantly correlated with the grade of surgical outcome at year-one and associated with relapses up-to five years post-surgery.\n\nConclusionNode abnormality offers a personalised non-invasive marker, that can be combined with clinical data, to better estimate the chances of seizure freedom at 1 year, and subsequent relapse up to 5 years after ATLR.

neuroscience↗
bioRxiv2019

The endonuclease Cue2 cleaves mRNAs at stalled ribosomes during No Go Decay.

Translation of problematic sequences in mRNAs leads to ribosome collisions that trigger a sequence of quality control events including ribosome rescue, degradation of the stalled nascent polypeptide via the Ribosome-mediated Quality control Complex (RQC), and targeting of the mRNA for decay (No Go Decay or NGD). Previous studies provide strong evidence for the existence of an endonuclease involved in the process of NGD though the identity of the endonuclease and the extent to which it contributes to mRNA decay remain unknown. Using a reverse genetic screen in yeast, we identify Cue2 as the conserved endonuclease that is recruited to stalled ribosomes to promote NGD. Ribosome profiling and biochemistry provide strong evidence that Cue2 cleaves mRNA within the A site of the colliding ribosome. Finally, we show that NGD primarily proceeds via Xrn1-mediated exonucleolytic decay. Cue2-mediated endonucleolytic decay normally constitutes a secondary decay pathway, but becomes a major contributor in cells depleted of factors required for the resolution of stalled ribosome complexes (the RQT factors including Slh1). Together these results provide insights into how multiple decay processes converge to process problematic mRNAs in eukaryotic cells.\n\nOne Sentence SummaryCue2 is the endonuclease that cleaves mRNA at ribosome stall sites.

molecular biology↗
bioRxiv2019

Higher prevalence of homologous recombination-deficiency in lung squamous carcinoma from African Americans

To improve our understanding of the longstanding disparities in incidence and mortality across multiple cancer types among minority populations, we performed a systematic comparative analysis of molecular features in tumors from African American (AA) and European American (EA) ancestry. Our pan-cancer analysis on the cancer genome atlas (TCGA) and a more focused analysis of genome-wide somatic copy number profiles integrated with tumor-normal RNA sequencing in a racially balanced cohort of 222 non-small cell lung cancers (NSCLC) reveals more aggressive genomic characteristics of AA tumors. In general, we find AA tumors exhibit higher genomic instability (GI), homologous recombination-deficiency (HRD) levels, and more aggressive molecular features such as chromothripsis across many cancer types, including lung squamous carcinoma (LUSC). GI and HRD levels are strongly correlated across AA tumors, indicating that HRD plays an important role in GI in these patients. The prevalence of germline HRD is higher in AA tumors, suggesting that the somatic differences observed have genetic ancestry origins. Finally, we identify AA-specific copy number-based arm, focal and gene level recurrent features in lung cancer, including a higher frequency of PTEN deletion and KRAS amplification and a lower frequency of CDKN2A deletion. These results highlight the importance of including minority and under-represented populations in genomics research and may have therapeutic implications.

cancer biology↗
bioRxiv2019

Allele-specific silencing ameliorates restrictive cardiomyopathy due to a human myosin regulatory light chain mutation

BackgroundRestrictive cardiomyopathy (RCM) is a rare heart disease associated with mutations in sarcomeric genes and with phenotypic overlap with hypertrophic cardiomyopathy. There is no approved therapy. Here, we explore the potential of an interfering RNA (RNAi) therapeutic for a human sarcomeric mutation in MYL2 causative of restrictive cardiomyopathy in a mouse model. MethodsAAV9-M7.8L shRNA was selected from a pool of RNAi oligonucleotides containing the SNV in different positions to specifically target the mutated allele causative of RCM by FACS screening. Two groups of RLC-N47K transgenic mice were injected with a single dose of AAV9-M7.8L shRNA at 3 days of age and at 60 days of age. Mice were subjected to treadmill exercise and echocardiography after treatment to determine VO2max and left ventricular mass. At the end of treatment, heart, lung, liver and kidney tissue was harvested to determine viral tropism and for transcriptome and proteomic analysis. Cardiomyocytes were isolated for single cell studies. ResultsOne time injection of AAV9-M7.8L RNAi in 3-day-old humanized RLC mutant transgenic mice silenced the mutated allele (RLC-47K) with minimal effects on the normal allele (RLC-47N) assayed 16 weeks post-injection. AAV9-M7.8L RNAi suppressed the expression of hypertrophic biomarkers, reduced heart weight and attenuated a pathological increase in left ventricular mass (LVM). Single adult cardiac myocytes from mice treated with AAV9-M7.8L showed partial restoration of the maximal contraction velocity with marked reduction in hypercontractility as well as relaxation kinetics and improved time to maximal calcium reuptake velocity. In addition, cardiac stress protein biomarkers, such as calmodulin-dependent protein kinase II (CAMKII) and the transcription activator Brg1 were reduced suggesting recovery towards a healthy myocardium. Transcriptome analyses further revealed no significant changes of argonaute (AGO1, AGO2) and endoribonuclease dicer (DICER1) transcripts while endogenous microRNAs were preserved suggesting the RNAi pathway was not saturated. ConclusionsOur results show the feasibility, efficacy, and safety of RNAi therapeutics directed at human restrictive cardiomyopathy. This is a promising step towards targeted therapy for a prevalent human disease. Clinical PerspectiveWhat is new? O_LIRestrictive cardiomyopathy due to a mutation of human MYL2 modeled in cells and mice can be treated with RNA interference C_LIO_LIReduction of disease-causing allele improves function at the cellular and organ level C_LIO_LIOff target effects evaluated and not found to be significant C_LI What are the clinical implications? O_LIAllele-specific RNA silencing of human alleles may be effective in treating inherited restrictive cardiomyopathy C_LIO_LIRNA therapies targeting individual mutations may need to be developed prior to consideration of clinical translation C_LI

molecular biology↗