Biology subjects

Prajapati, M.

Publications and source records attributed to Prajapati, M..

4 recordsLinked to original sources

bioRxiv2025

Mitochondrially Transcribed dsRNA Mediates Manganese-induced Neuroinflammation

Manganese is an essential trace element required for various biological functions, but in excess is neurotoxic and leads to significant health concerns. The mechanisms underlying manganese neurotoxicity remain poorly understood. Neuropathological studies of affected brain regions reveal astrogliosis, neuronal loss, and neuroinflammation. Here, we present a novel manganese-dependent mechanism linking mitochondrial dysfunction to neuroinflammation. We found that manganese disruption of the mitochondrial transcriptome processing results in the accumulation of double stranded RNA (dsRNA). This dsRNA is released into the cytoplasm, where it activates the cytosolic sensor MDA5, triggering type I interferon responses and inflammatory cytokine production. This mechanism is evident in 100 day human cerebral organoids, where manganese-increased mitochondrial dsRNA and induced inflammatory responses in mature astrocytes. Similarly, we observed an increase in mitochondrial dsRNA content, the activation of an inflammatory transcriptome and the production of cytokines in female and male mouse brains carrying mutations in the Slc30a10 gene, a model for human hypermanganesemia with dystonia 1 disorder. These findings highlight a previously unrecognized role for mitochondrial dsRNA in manganese-induced neuroinflammation and provide insights into the molecular pathogenesis of manganism. We propose that this mitochondrial dsRNA-induced inflammatory pathway could be active in other neurological diseases caused by environmental or genetic factors. Significance StatementEnvironmental exposures and genetic defects that perturb manganese homeostasis are an underappreciated cause of neurodegeneration and neuroinflammation. We describe a new paradigm for inducible neuroinflammation, where manganese disruption of mitochondrial transcriptome processing leads to the accumulation of mitochondrial double-stranded RNA (dsRNA), which activate antiviral responses in the cytoplasm driving type I interferon dependent inflammation. This manganese-dsRNA axis is induced in cell lines in vitro and a subpopulation of mature astrocytes in exposed human cerebral organoids. Brain cortex of mice deficient in the manganese efflux transporter Slc30a10, a genetic model of chronic manganese accumulation, show dsRNA accumulation, and up-regulation of type I interferon response and astrogliosis markers, supporting a role for this pathway in neurotoxicity and parkinsonism.

neuroscience↗

bioRxiv2024

scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery

PurposeSingle-cell RNA sequencing (scRNA-seq) is producing vast amounts of individual cell profiling data. Analysis of such datasets presents a significant challenge in accurately annotating cell types and their associated biomarkers. scRNA-seq datasets analysis will help us understand diseases such as Alzheimers, Cancer, Diabetes, Coronavirus disease 2019 (COVID-19), Systemic Lupus Ery-thematosus (SLE), etc. Recently different pipelines based on machine learning (ML) and Deep Neural Network (DNN) methods have been employed to tackle these issues utilizing scRNA-seq datasets. These pipelines have arisen as a promising resource and are capable of extracting meaningful and concise features from noisy, diverse, and high-dimensional data to enhance annotations and subsequent analysis. Existing tools require high computational resources to execute large sample datasets. MethodsWe have developed a cutting-edge platform known as scaLR (Single Cell Analysis using Low Resource) that efficiently processes data in batches, and reduces the required resources for processing large datasets and running NN models. scaLR is equipped with data processing, feature extraction, training, evaluation, and downstream analysis. The data processing module consists of sample-wise & standard scaler normalization and splitting of data. Its novel feature extraction algorithm, first trains the model on a feature subset and stores feature importance for all the features in that subset. At the end of this process, top K features are selected based on their importance. The model is trained on top K features, its performance evaluation and associated downstream analysis provide significant biomarkers for different cell types and diseases/traits. ResultsTo showcase the capabilities of scaLR, we utilized several scRNA-seq datasets of Peripheral Blood Mononuclear Cells (PBMCs), Alzheimers patients, and large datasets from human and mouse embryonic development. Our findings indicate that scaLR offers comparable prediction accuracy and requires less model training time and compute resources than existing Python-based pipelines and frameworks. Moreover, scaLR efficiently handles large sample datasets (>11.4 million cells) with minimal resource usage (29GB RAM, 12GB GPU, and 8 CPUs) while maintaining high prediction accuracy and being capable of ranking the biomarker association with specific cell types and diseases. ConclusionWe present scaLR a Python-based platform, engineered to utilize minimal computational resources while maintaining comparable execution times to existing frameworks. It is highly scalable and capable of efficiently handling datasets containing millions of cell samples and providing their classification and important biomarkers.

bioinformatics↗

bioRxiv2024

Manganese transporter SLC30A10 and iron transporters SLC40A1 and SLC11A2 impact dietary manganese absorption

SLC30A10 deficiency is a disease of severe manganese excess attributed to loss of SLC30A10-dependent manganese excretion via the gastrointestinal tract. Patients develop dystonia, cirrhosis, and polycythemia. They are treated with chelators but also respond to oral iron, suggesting that iron can outcompete manganese for absorption in this disease. Here we explore the latter observation. Intriguingly, manganese absorption is increased in Slc30a10-deficient mice despite manganese excess. Studies of multiple mouse models indicate that increased dietary manganese absorption reflects two processes: loss of manganese export from enterocytes into the gastrointestinal tract lumen by SLC30A10, and increased absorption of dietary manganese by iron transporters SLC11A2 (DMT1) and SLC40A1 (ferroportin). Our work demonstrates that aberrant absorption contributes prominently to SLC30A10 deficiency and expands our understanding of biological interactions between iron and manganese. Based on these results, we propose a reconsideration of the role of iron transporters in manganese homeostasis is warranted.

genetics↗

bioRxiv2023

Hypoxia-inducible factor 2 is a key determinant of manganese excess and polycythemia in SLC30A10 deficiency

Manganese is an essential yet potentially toxic metal. Initially reported in 2012, mutations in SLC30A10 are the first known inherited cause of manganese excess. SLC30A10 is an apical membrane transport protein that exports manganese from hepatocytes into bile and from enterocytes into the lumen of the gastrointestinal tract. SLC30A10 deficiency results in impaired gastrointestinal manganese excretion, leading to severe manganese excess, neurologic deficits, liver cirrhosis, polycythemia, and erythropoietin excess. Neurologic and liver disease are attributed to manganese toxicity. Polycythemia is attributed to erythropoietin excess, but the basis of erythropoietin excess in SLC30A10 deficiency has yet to be established. Here we demonstrate that erythropoietin expression is increased in liver but decreased in kidneys in Slc30a10-deficient mice. Using pharmacologic and genetic approaches, we show that liver expression of hypoxia-inducible factor 2 (Hif2), a transcription factor that mediates the cellular response to hypoxia, is essential for erythropoietin excess and polycythemia in Slc30a10-deficient mice, while hypoxia-inducible factor 1 (HIF1) plays no discernible role. RNA-seq analysis determined that Slc30a10-deficient livers exhibit aberrant expression of a large number of genes, most of which align with cell cycle and metabolic processes, while hepatic Hif2 deficiency attenuates differential expression of half of these genes in mutant mice. One such gene downregulated in Slc30a10-deficient mice in a Hif2-dependent manner is hepcidin, a hormonal inhibitor of dietary iron absorption. Our analyses indicate that hepcidin downregulation serves to increase iron absorption to meet the demands of erythropoiesis driven by erythropoietin excess. Finally, we also observed that hepatic Hif2 deficiency attenuates tissue manganese excess, although the underlying cause of this observation is not clear at this time. Overall, our results indicate that HIF2 is a key determinant of pathophysiology in SLC30A10 deficiency. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=148 HEIGHT=200 SRC="FIGDIR/small/529270v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@c25fc8org.highwire.dtl.DTLVardef@11e70feorg.highwire.dtl.DTLVardef@18c5518org.highwire.dtl.DTLVardef@26b61f_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗