Biology subjects

Ranu, N.

Publications and source records attributed to Ranu, N..

6 recordsLinked to original sources

bioRxiv2024

Myosin ATPase inhibition fails to rescue the metabolically dysregulated proteome of nebulin-deficient muscle

Nemaline myopathy (NM) is a genetic muscle disease, primarily caused by mutations in the NEB gene (NEB-NM) and with muscle myosin dysfunction as a major molecular pathogenic mechanism. Recently, we have observed that the myosin biochemical super-relaxed state was significantly impaired in NEB-NM, inducing an aberrant increase in ATP consumption and remodelling of the energy proteome in diseased muscle fibres. As the small-molecule Mavacamten is known to promote the myosin super-relaxed state and reduce the ATP demand, here, we tested its potency in the context of NEB-NM. We first conducted in vitro experiments in isolated single myofibres from patients and found that Mavacamten successfully reversed the myosin ATP over-consumption. Following this, we assessed its short-term in vivo effects by using the conditional nebulin knock-out (cNeb KO) mouse model and by subsequently performing global proteomics profiling in dissected soleus myofibres. After a four-week treatment period, we observed a remodelling of a large number of proteins in both cNeb KO mice and their wild-type siblings. Nevertheless, these changes were not related to the energy proteome, indicating that short-term Mavacamten treatment is not sufficient to properly counterbalance the metabolically dysregulated proteome of cNeb KO mice. Taken together, our findings emphasize Mavacamten potency in vitro but challenge its short-term efficacy in vivo. Key points summaryO_LINo cure exists for nemaline myopathy, a type of genetic skeletal muscle disease mainly derived from mutations in genes encoding myofilament proteins. C_LIO_LIApplying Mavacamten, a small molecule directly targeting the myofilament, to isolated membrane-permeabilized muscle fibres from human patients restored myosin energetic disturbances. C_LIO_LITreating a mouse model of nemaline myopathy in vivo with Mavacamten for four weeks, remodeled the skeletal muscle fibre proteome without any noticeable effects on energetic proteins. C_LIO_LIShort-term Mavacamten treatment may not be sufficient to reverse the muscle phenotype in nemaline myopathy. C_LI

physiology↗

bioRxiv2023

KatG catalase deficiency confers bedaquiline hyper-susceptibility to isoniazid resistant Mycobacterium tuberculosis

Multidrug-resistant tuberculosis (MDR-TB) is a growing source of global mortality and threatens global control of tuberculosis (TB) disease. The diarylquinoline bedaquiline (BDQ) recently emerged as a highly efficacious drug against MDR-TB, defined as resistance to the first-line drugs isoniazid (INH) and rifampin. INH resistance is primarily caused by loss-of-function mutations in the catalase KatG, but mechanisms underlying BDQs efficacy against MDR-TB remain unknown. Here we employ a systems biology approach to investigate BDQ hyper-susceptibility in INH-resistant Mycobacterium tuberculosis. We found hyper-susceptibility to BDQ in INH-resistant cells is due to several physiological changes induced by KatG deficiency, including increased susceptibility to reactive oxygen species and DNA damage, remodeling of transcriptional programs, and metabolic repression of folate biosynthesis. We demonstrate BDQ hyper-susceptibility is common in INH-resistant clinical isolates. Collectively, these results highlight how altered bacterial physiology can impact drug efficacy in drug-resistant bacteria.

microbiology↗

bioRxiv2023

A Pooled Cell Painting CRISPR Screening Platform Enables de novo Inference of Gene Function by Self-supervised Deep Learning

Pooled CRISPR screening has emerged as a powerful method of mapping gene functions thanks to its scalability, affordability, and robustness against well or plate-specific confounders present in array-based screening 1-6. Most pooled CRISPR screens assay for low dimensional phenotypes (e.g. fitness, fluorescent markers). Higher-dimensional assays such as perturb-seq are available but costly and only applicable to transcriptomics readouts 7-11. Recently, pooled optical screening, which combines pooled CRISPR screening and microscopy-based assays, has been demonstrated in the studies of the NFkB pathway, essential human genes, cytoskeletal organization and antiviral response 12-15. While the pooled optical screening methodology is scalable and information-rich, the applications thus far employ hypothesis-specific assays. Here, we enable hypothesis-free reverse genetic screening for generic morphological phenotypes by re-engineering the Cell Painting 16 technique to provide compatibility with pooled optical screening. We validated this technique using well-defined morphological genesets (124 genes), compared classical image analysis and self-supervised learning methods using a mechanism-of-action (MoA) library (300 genes), and performed discovery screening with a druggable genome library (1640 genes) 17. Across these three experiments we show that the combination of rich morphological data and deep learning allows gene networks to emerge without the need for target-specific biomarkers, leading to better discovery of gene functions.

genomics↗

bioRxiv2019

NickSeq for genome-wide strand-specific identification of DNA single-strand break sites with single nucleotide resolution

DNA single-strand breaks (SSBs), or nicks, are the most common form of DNA damage. Nicks occur at rates of tens of thousands per cell per day, and result from many sources including oxidative stress and endogenous enzyme activities. Accumulation of nicks, due to high rates of occurrence or defects in repair enzymes, has been implicated in multiple diseases. However, improved methods for nick analysis are needed to learn how their locations and number affect cells, disease progression, and health outcomes. In addition to natural processes including DNA repair, leading genome-editing technologies rely on nuclease activity, including nick generation, at target sites. There is currently a pressing need for methods to study unintended nicking activity genome-wide to evaluate the impact of emerging genome editing tools on cells and organisms. Here we developed a new method, NickSeq, for efficient strand-specific profiling of nicks in complex DNA samples with single nucleotide resolution and low false-positive rates. NickSeq produces deep sequence datasets enriched for reads near nick sites and establishes a readily detectable mutational signal that allows for determination of the nick site and strand. In this work, we apply NickSeq to profile off-target activity of the Nb.BsmI nicking endonuclease and an engineered spCas9 nickase. NickSeq will be useful in exploring the relevance of spontaneously occurring or repair-induced DNA breaks in human disease, DNA breaks caused by DNA damaging agents including therapeutics, and the activity of engineered nucleases in genome editing and other biotechnological applications.

genomics↗

bioRxiv2017

Targeting individual cells by barcode in pooled sequence libraries

There is rising interest in applying single-cell transcriptome analysis and other single-cell sequencing methods to resolve differences between cells. Pooled processing of thousands of single cells is now routinely practiced by introducing cell-specific DNA barcodes early in cell processing protocols1-5. However, researchers must sequence a large number of cells to sample rare subpopulations6-8, even when fluorescence-activated cell sorting (FACS) is used to pre-enrich rare cell populations. Here, a new molecular enrichment method is used in conjunction with FACS enrichment to enable efficient sampling of rare dendritic cell (DC) populations, including the recently identified AXL+SIGLEC6+ (AS DCs) subset7, within a 10X Genomics single-cell RNA-Seq library. DC populations collectively represent 1-2% of total peripheral blood mononuclear cells (PBMC), with AS DC representing only 1-3% of human blood DCs and 0.01-0.06% of total PBMCs.

bioengineering↗

bioRxiv2017

Estimation of immune cell content in tumour tissue using single-cell RNA-seq data

As interactions between the immune system and tumour cells are governed by a complex network of cell-cell interactions, knowing the specific immune cell composition of a solid tumour may be essential to predict a patients response to immunotherapy. Here, we analyse in depth how to derive the cellular composition of a solid tumour from bulk gene expression data by mathematical deconvolution, using indication- and cell type-specific reference gene expression profiles (RGEPs) from tumour-derived single-cell RNA sequencing data. We demonstrate that tumour-derived RGEPs are essential for the successful deconvolution and that RGEPs from peripheral blood are insufficient. We distinguish nine major cell types as well as three T cell subtypes. As the ratios of CD4+, CD8+ and regulatory T cells have been shown to predict overall survival, we extended our analysis to include the estimation of prognostic ratios that may enable the application in a clinical setting. Using the tumour derived RGEPs, we can estimate, for the first time, the content of cancer associated fibroblasts, endothelial cells and the malignant cells in a patient sample by a deconvolution approach. In addition, improved tumour cell gene expression profiles can be obtained by this method by computationally removing contamination from non-malignant cells. Given the difficulty around sample preparation and storage to obtain high quality single-cell RNA-seq data in the clinical context, the presented method represents a computational solution to derive the cellular composition of a tissue sample.

bioinformatics↗