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Sarkar, S.

Publications and source records attributed to Sarkar, S..

191 records · Page 11Linked to original sources

Enhancer Reprogramming Confers Dependence on Glycolysis and IGF signaling in KMT2D Mutant Melanoma

Epigenetic modifiers have emerged as important regulators of tumor progression. We identified histone methyltransferase KMT2D as a potent tumor-suppressor through an in vivo epigenome-focused pooled RNAi screen in melanoma. KMT2D harbors frequent somatic point mutations in multiple tumor types. How these events contribute to tumorigenesis and whether they impart therapeutic vulnerability are poorly understood. To address these questions, we generated a genetically engineered mouse model of melanoma based on conditional and melanocyte-specific deletion of KMT2D. We demonstrate KMT2D as a bona fide tumor suppressor which cooperates with activated BRAF. KMT2D-deficient tumors showed substantial reprogramming of key metabolic pathways including glycolysis. Glycolysis enzymes, intermediate metabolites and glucose consumption rate were aberrantly upregulated in KMT2D mutant cells. The pharmacological inhibition of glycolysis reduced proliferation and tumorigenesis preferentially in KMT2D mutant cells. Mechanistically, KMT2D loss caused drastic reduction of H3K4me1-marked active enhancer states. Loss of distal enhancer and subsequent reduction in expression of IGFBP5 activated IGF1R-AKT to increase glycolysis in KMT2D-deficient cells. We conclude that KMT2D loss promotes tumorigenesis by facilitating increased usage of glycolysis pathway for enhanced biomass needs via enhancer reprogramming. Our data imply that inhibition of glycolysis or IGFR pathway could be a potential therapeutic strategy in KMT2D mutant tumors.

cancer biology↗

Encoding of wind direction by central neurons in Drosophila

Wind is a major navigational cue for insects, but how wind direction is decoded by central neurons in the insect brain is unknown. Here, we find that walking flies combine signals from both antennae to orient to wind during olfactory search behavior. Movements of single antennae are ambiguous with respect to wind direction, but the difference between left and right antennal displacements yields a linear code for wind direction in azimuth. Second-order mechanosensory neurons share the ambiguous responses of single antenna and receive input primarily from the ipsilateral antenna. Finally, we identify a novel set of neurons, which we call wedge projection neurons, that integrate signals across the two antennae and receive input from at least three classes of second-order neurons to produce a more linear representation of wind direction. This study establishes how a feature of the sensory environment - the wind direction - is decoded by single neurons that compare information across two sensors.

neuroscience↗

Engineering a decoy substrate in soybean to enable recognition of the Soybean Mosaic Virus NIa protease

In Arabidopsis, recognition of the AvrPphB effector protease from Pseudomonas syringae is mediated by the disease resistance (R) protein RPS5, which is activated by AvrPphB-induced cleavage of the Arabidopsis protein kinase PBS1. The recognition specificity of RPS5 can be altered by substituting the AvrPphB cleavage site within PBS1 with cleavage sequences for other proteases, including proteases from viruses. AvrPphB also activates defense responses in soybean (Glycine max), suggesting that soybean may contain an R protein analogous to RPS5. It was unknown, however, whether this response is mediated by cleavage of a soybean PBS1-like protein. Here we show that soybean contains three PBS1 orthologs and that their products are cleaved by AvrPphB. Further, transient expression of soybean PBS1 derivatives containing a five-alanine insertion at their AvrPphB cleavage sites activated cell death in soybean protoplasts, demonstrating that soybean likely contains an AvrPphB-specific resistance protein that is activated by a conformational change in soybean PBS1 proteins. Significantly, we show that a soybean PBS1 decoy protein modified to contain a cleavage site for the Soybean mosaic virus (SMV) NIa protease triggers cell death in soybean protoplasts when cleaved by this protease, indicating that the PBS1 decoy approach will work in soybean using endogenous PBS1 genes. Lastly, we show that activation of the AvrPphB-dependent cell death response effectively inhibits systemic spread of SMV in soybean. These data also indicate that decoy engineering may be feasible in other crop plant species that recognize AvrPphB protease activity.

plant biology↗

Myocardin ablation in a cardiac-renal rat model

Cardiorenal syndrome is defined by primary heart failure conditions influencing or leading to renal injury or dysfunction. Dilated cardiomyopathy (DCM) is a major co-existing form of heart failure (HF) with renal diseases. Myocardin (MYOCD), a cardiac-specific co-activator of serum response factor (SRF), is increased in DCM mice and patient cardiac tissues and plays a crucial role in the pathophysiology of DCM. Inhibiting the increased MYOCD has shown to be partially rescuing the DCM mice phenotype. However, expression levels of MYOCD in the cardiac tissues of the cardiorenal syndromic patients and the beneficial effect of inhibiting MYOCD in a cardiorenal syndrome model remains to be explored. Here, we analyzed the expression levels of MYOCD in the DCM patients with and without renal diseases. We also explored, whether cardiac specific silencing of MYOCD expression could ameliorate the cardiac remodeling and improve cardiac function in a renal artery ligated rat model (RAL). We observed an increase in MYOCD levels in the endomyocardial biopsies of DCM patients associated with renal failure compared to DCM alone. Silencing of MYOCD in RAL rats by a cardiac homing peptide conjugated MYOCD siRNA resulted in attenuation of cardiac hypertrophy, fibrosis and restoration of the left ventricular functions. Our data suggest hyper-activation of MYOCD in the pathogenesis of the cardiorenal failure cases. Also, MYOCD silencing showed beneficial effects by rescuing cardiac hypertrophy, fibrosis, size and function in a cardiorenal rat model.

molecular biology↗

Modeling the relative role of human mobility, land-use and climate factors on dengue outbreak emergence in Sri Lanka

BackgroundMore than 80,000 dengue cases including 215 deaths were reported nationally in less than seven months between 2016-2017, a fourfold increase in the number of reported cases compared to the average number over 2010-2016. The region of Negombo, located in the Western province, experienced the greatest number of dengue cases in the country and is the focus area of our study, where we aim to capture the spatial-temporal dynamics of dengue transmission.\n\nMethodsWe present a statistical modeling framework to evaluate the spatial-temporal dynamics of the 2016-2017 dengue outbreak in the Negombo region of Sri Lanka as a function of human mobility, land-use, and climate patterns. The analysis was conducted at a 1 km x 1 km spatial resolution and a weekly temporal resolution.\n\nResultsOur results indicate human mobility to be a stronger indicator for local outbreak clusters than land-use or climate variables. The minimum daily temperature was identified as the most influential climate variable on dengue cases in the region; while among the set of land-use patterns considered, urban areas were found to be most prone to dengue outbreak, followed by areas with stagnant water and then coastal areas. The results are shown to be robust across spatial resolutions.\n\nConclusionsOur study highlights the potential value of using travel data to target vector control within a region. In addition to illustrating the relative relationship between various potential risk factors for dengue outbreaks, the results of our study can be used to inform where and when new cases of dengue are likely to occur within a region, and thus help more effectively and innovatively, plan for disease surveillance and vector control.

epidemiology↗

Structural and Practical Identifiability of Dual-input Kinetic Modeling in Dynamic PET of Liver Inflammation

Dynamic 18F-FDG PET with tracer kinetic modeling has the potential to noninvasively evaluate human liver inflammation using the FDG blood-to-tissue transport rate K1. Accurate kinetic modeling of dynamic liver PET data and K1 quantification requires the knowledge of dual-blood input function from the hepatic artery and portal vein. While the arterial input function can be derived from the aortic region on dynamic PET images, it is difficult to extract the portal vein input function accurately from PET. The optimization-derived dual-input kinetic modeling approach has been proposed to overcome this problem by jointly estimating the portal vein input function and FDG tracer kinetics from time activity curve fitting. In this paper, we further characterize the model properties by analyzing the structural identifiability of the model parameters using the Laplace transform and practical identifiability using Monte Carlo simulation based on fourteen patient datasets. The theoretical analysis has indicated that all the kinetic parameters of the dual-input kinetic model are structurally identifiable, though subject to local solutions. The Monte Carlo simulation results have shown that FDG K1 can be estimated reliably in the whole-liver region of interest with reasonable bias, standard deviation, and high correlation between estimated and original values, indicating of practical identifiability of K1. The result has also demonstrated the correlation between K1 and histological liver inflammation scores is reliable. FDG K1 quantification by the optimization-derived dual-input kinetic model is promising for assessing liver inflammation.

bioengineering↗

Transgenerational effect of adult density through paternal effect in Drosophila melanogaster

O_LINotwithstanding recent evidences, paternal environment is thought to be a potential but unlikely source of fitness variation that can affect trait evolution. Here we studied intergenerational effects of males exposure to varying adult density in Drosophila melanogaster laboratory populations.\nC_LIO_LIWe held sires at normal (N), medium (M) and high (H) adult densities for two days before allowing them to mate with virgin females. This treatment did not introduce selection through differential mortality. Further, we randomly paired males and females and allowed a single round of mating between the sires and the dams. We then collected eggs from the dams and measured the egg size. Finally, we investigated the effect of the paternal treatment on juvenile and adult (male) fitness components.\nC_LIO_LIWe found a significant treatment effect on juvenile competitive ability where the progeny sired by the H-males had higher competitive ability. Since we did not find the treatment to affect egg size, this effect is unlikely to be mediated through variation in female provisioning.\nC_LIO_LIMale fitness components were also found to have a significant treatment effect: M-sons had lower dry weight at eclosion, higher mating latency and lower competitive mating success.\nC_LIO_LIWhile being the first study to show both adaptive and non-adaptive effect of the paternal density in Drosophila, our results highlight the importance of considering paternal environment as important source of fitness variation.\nC_LI

evolutionary biology↗

Integrating genotype and weather variables for soybean yield prediction using deep learning

Realized performance of complex traits is dependent on both genetic and environmental factors, which can be difficult to dissect due to the requirement for multiple replications of many genotypes in diverse environmental conditions. To mediate these problems, we present a machine learning framework in soybean (Glycine max (L.) Merr.) to analyze historical performance records from Uniform Soybean Tests (UST) in North America, with an aim to dissect and predict genotype response in multiple envrionments leveraging pedigree and genomic relatedness measures along with weekly weather parameters. The ML framework of Long Short Term Memory - Recurrent Neural Networks works by isolating key weather events and genetic interactions which affect yield, seed oil, seed protein and maturity enabling prediction of genotypic responses in unseen environments. This approach presents an exciting avenue for genotype x environment studies and enables prediction based systems. Our approaches can be applied in plant breeding programs with multi-environment and multi-genotype data, to identify superior genotypes through selection for commercial release as well as for determining ideal locations for efficient performance testing.

bioinformatics↗

Dynamic PET of Human Liver Inflammation: Impact of Kinetic Modeling with Optimization-Derived Dual-Blood Input Function

The hallmark of nonalcoholic steatohepatitis is hepatocellular inflammation and injury in the setting of hepatic steatosis. Recent work has indicated that dynamic 18F-FDG PET with kinetic modeling has the potential to assess hepatic inflammation noninvasively, while static FDG-PET did not show a promise. Because the liver has dual blood supplies, kinetic modeling of dynamic liver PET data is challenging in human studies. This paper aims to identify the optimal dual-input kinetic modeling approach for dynamic FDG-PET of human liver inflammation. Fourteen patients with nonalcoholic fatty liver disease were included. Each patient underwent 1-hour dynamic FDG-PET/CT scan and had liver biopsy within six weeks. Three models were tested for kinetic analysis: traditional two-tissue compartmental model with an image-derived single-blood input function (SBIF), model with population-based dual-blood input function (DBIF), and new model with optimization-derived DBIF through a joint estimation framework. The three models were compared using Akaike information criterion (AIC), F test and histopathologic inflammation score. Results showed that the optimization-derived DBIF model improved liver time activity curve fitting and achieved lower AIC values and higher F values than the SBIF and population-based DBIF models in all patients. The optimization-derived model significantly increased FDG K1 estimates by 101% and 27% as compared with traditional SBIF and population-based DBIF. K1 by the optimization-derived model was significantly associated with histopathologic grades of liver inflammation while the other two models did not provide a statistical significance. In conclusion, modeling of DBIF is critical for dynamic liver FDG-PET kinetic analysis in human studies. The optimization-derived DBIF model is more appropriate than SBIF and population-based DBIF for dynamic FDG-PET of liver inflammation.

physiology↗

Induction of autophagy by trehalose limits opportunistic mycobacterial infections in HIV-infected macrophages

Opportunistic bacterial infections amongst HIV-infected individuals pose serious health challenge. While immediate control of bacterial pathogens is typically attributed to innate defense mechanisms, whether HIV-mediated modulation of innate mechanisms like autophagy promote opportunistic infections, remains obscure. Using U1.1 and U937 macrophages, we show, HIV activation or infection inhibits autophagy and helps survival of pathogenic Mycobacterium tuberculosis and non-pathogenic non-tuberculous mycobacterial strains (NTMs) like Mycobacterium avium complex and Mycobacterium fortuitum. HIV achieves this by blocking xenophagy flux, which could be reversed by the autophagy inducer trehalose that kills intracellular Mtb and NTMs. We found trehalose acts as a PI (3,5) P2 agonist and activates TRPML1 to induce autophagy. Remarkably, trehalose treatment significantly reduced p24 levels in PBMCs infected with clinical HIV strains and in PBMCs derived from treatment-naive HIV patients. Taken together, our study highlights the immense potential of autophagy modulators in the therapeutic intervention of HIV and associated opportunistic infection.

immunology↗

Development of a novel Francisella tularensis Live Vaccine Strain expressing ovalbumin provides insight into Francisella tularensis-specific CD8+ T cell responses.

Progress towards a safe and effective vaccine for the prevention of tularemia has been hindered by a lack of knowledge regarding the correlates of protective adaptive immunity and a lack of tools to generate this knowledge. CD8+ T cells are essential for protective immunity against virulent strains of Francisella tularensis, but to-date, it has not been possible to study these cells in a pathogen-specific manner. Here, we report the development of a tool for expression of the model antigen ovalbumin (OVA) in F. tularensis, which allows for the study of CD8+ T cell responses to the bacterium. We demonstrate that in response to intranasal infection with the F. tularensis Live Vaccine Strain, pathogen-specific CD8+ T cells expand after the first week and produce IFN-{gamma} but not IL-17. Effector and central memory subsets develop with disparate kinetics in the lungs, draining lymph node and spleen. Notably, F. tularensis-specific cells are poorly retained in the lungs after clearance of infection. We also show that intranasal vaccination leads to more pathogen-specific CD8+ T cells in the lung-draining lymph node compared to scarification vaccination, but that an intranasal booster overcomes this difference. Together, our data show that this novel tool can be used to study multiple aspects of the CD8+ T cell response to F. tularensis. Use of this tool will enhance our understanding of immunity to this deadly pathogen.

immunology↗