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Mukherjee, S.

Publications and source records attributed to Mukherjee, S..

At least 199 records · Page 11Linked to original sources

Plant-associated microbiomes promote nutrient turnover in impoverished substrates of a biodiversity hotspot

The substrates of the Brazilian campos rupestres have extremely low concentrations of key nutrients, mainly phosphorus, imposing severe restrictions to plant growth. Regardless, this ecosystem harbors enormous biodiversity which raises the question of how nutrients are cycled and acquired by the biosphere. To uncover the nutrient turnover potential of plant-associated microorganisms in the campos rupestres, we investigated the compositions and functions of microbiomes associated with two species of the Velloziaceae family that grow over distinct substrates (soil and rock). Amplicon, metagenomic, and metagenome-assembled genome sequence data showed that the campos rupestres harbor a novel assemblage of plant-associated prokaryotes and fungi. Compositional analysis revealed that the plant-associated soil and rock communities differed in taxonomic structure but shared a core of highly efficient colonizers that were strongly coupled with nutrient mobilization. Investigation of functional and abundance data revealed that the plant hosts actively recruit communities by exuding organic compounds and that the root-associated microbiomes possess a diverse repertoire of phosphorus turnover mechanisms. We also showed that the microbiomes of both plant species encompass novel populations capable of mobilizing nitrogen and that the substrate strongly influences the dynamics of this cycle. Our results show that the interplay between plants and their microbiomes shapes nutrient turnover in the campos rupestres. We highlight that investigation of microbial diversity is fundamental to understand plant fitness in stressful environments.

genomics↗

A Topological Data Analytic Approach for Discovering Biophysical Signatures in Protein Dynamics

Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution. Author SummaryStructural features of proteins often serve as signatures of their biological function and molecular binding activity. Elucidating these structural features is essential for a full understanding of underlying biophysical mechanisms. While there are existing methods aimed at identifying structural differences between protein variants, such methods do not have the capability to jointly infer both geometric and dynamic changes, simultaneously. In this paper, we propose SINATRA Pro, a computational framework for extracting key structural features between two sets of proteins. SINATRA Pro robustly outperforms standard techniques in pinpointing the physical locations of both static and dynamic signatures across various types of protein ensembles, and it does so with improved resolution.

biophysics↗

Inflammatory blockade prevents injury to the developing pulmonary gas exchange surface in preterm primates

Malformations of or injuries to the developing lung are associated with perinatal morbidity and mortality with lifelong consequences for subsequent pulmonary health. One fetal exposure linked with poor health outcomes is chorioamnionitis, which impacts up to 25-40% of preterm births. Severe chorioamnionitis with prematurity is associated with significantly increased risk of pulmonary disease and secondary infections in childhood, suggesting that fetal inflammation may significantly alter developmental ontogeny of the lung. To test this hypothesis, we used intra-amniotic lipopolysaccharide (LPS, endotoxin) to generate experimental chorioamnionitis in prenatal Rhesus macaque (Macaca mulatta), a model which shares critical structural and temporal aspects of human lung development. Inflammatory injury directly disrupts the developing gas exchange surface of the primate lung, with extensive damage to alveolar structure, particularly the close association and coordinated differentiation of alveolar type 1 pneumocytes and specialized alveolar capillary endothelium. Single cell RNA sequencing analysis defined a multicellular alveolar signaling niche driving alveologenesis which was extensively disrupted by perinatal inflammation, leading to loss of gas exchange surface and alveolar simplification similar to that found in chronic lung disease of newborns. Blockade of IL1{beta} and TNF ameliorated endotoxin-induced inflammatory lung injury by blunting stromal response to inflammation and modulating innate immune activation in myeloid cells, restoring structural integrity and key signaling networks in the developing alveolus. These data provide new insight into the pathophysiology of developmental lung injury and suggest that modulating inflammation is a promising therapeutic approach to prevent fetal consequences of chorioamnionitis.

developmental biology↗

In silico study for decoding the correlated role of MCM7 gene in progression of breast cancer and Alzheimers disorder

Breast cancer and Alzheimers disease (AD) are two of the progressive and detrimental disorders affecting large population around the globe. While the chemotherapy of breast cancer is well established and enriched, the AD still lacks it due to unrecognized peripheral biomarkers for detection and targeted therapy. This study aimed to identify common molecular signature markers in breast cancer (grade 1, 2, and 3) and AD for the diagnosis and prognosis. We used two microarray datasets (GSE42568, GSE33000) respectively for both disorders that led to identification of two common differentially expressed genes (DEGs), namely MCM7 and CD209, as common players in both these two conditions. While the pattern of expression of CD209 gene running upregulated in both disorders, the MCM7 showed unusual contrary in its pattern of expression. The expression of MCM7 is downregulated in breast cancer but upregulated in AD. Gene set and protein overrepresentation analysis, protein-protein interaction (PPI), and protein subcellular localizations analyses of this underrated MCM7 gene was performed with further prediction and validation of its structure. The findings may pave the way in designing therapeutic approaches to ameliorate AD.

bioinformatics↗

A potent myeloid response is rapidly activated in the lungs of premature Rhesus macaques exposed to intra-uterine inflammation

Intrauterine inflammation/infection (IUI), which is present in up to 40% of premature births, leads to elevated levels of pro-inflammatory mediators and microbial products within the amniotic fluid, which come in close contact to fetal mucosae. Yet, knowledge on the fetal mucosal responses to IUI exposure remains limited. To address these questions, we used a non-human primate model of IUI, in which pregnant Rhesus macaques received intra-amniotic (IA) LPS, compared with IA saline. We found that IA LPS exposure induced a robust and rapid inflammation of the fetal lung, but not the intestine. This inflammatory response was characterized by high levels of pro-inflammatory cytokines in the lung and the alveolar wash, and a potent myeloid cell response, dominated by neutrophils and monocytes/macrophages. scRNAseq analyses of fetal lungs showed that the infiltrating (neutrophils and inflammatory monocytes) and the resident (alveolar and interstitial macrophages) myeloid cells exhibited transcriptional profiles consistent with exposure to TLR ligands, as well as to cytokines, notably IL-1 and TNF. However, blocking IL-1 signaling or TNF, alone or simultaneously by administering inhibitors intra-amniotically and subcutaneously to the dam only partially blunted fetal lung inflammation. Together, our novel data indicate that the fetal innate immune system can mount a rapid multi-factorial mucosal innate response to IUI, responding both to direct signaling by bacterial products and to indirect cytokine-mediated pathways of activation. These data thus provide more mechanistic insights into the association between IUI exposure and the post-natal lung morbidities of the premature infant.

immunology↗

Dynamic FRET-FLIM based screens of signal transduction pathways: a feasibility study

Fluorescence Lifetime Imaging (FLIM) is an intrinsically quantitative method to screen for protein-protein interactions and frequently used to record the outcome of signal transduction events. With new highly sensitive and photon efficient FLIM instrumentation, the technique also becomes attractive to screen, with high temporal resolution, for fast changes in Forster Resonance Energy Transfer (FRET), such as those occurring upon activation of cell signaling. We studied the effects of siRNA-mediated individual knockdown of an extensive set of 22 different phosphodiesterases (PDEs) on baseline levels and agonist-induced changes of the second messenger cAMP. Using HeLa cells stably expressing our FRET-FLIM sensor we imaged many hundreds of cells at 5 second intervals for each condition. Following segmentation of cells by the deep-learning implementation Cellpose, FLIM time traces were calculated and fitted for dynamic analysis with custom-made Python scripts. Taking advantage of the quantitative FLIM data, we found very limited effects of PDE knockdown on baseline and agonist-induced peak levels of cAMP. However, cAMP breakdown in the decay phase was significantly slower when PDE3A and, to a lesser amount, PDE10A were knocked down, identifying these isoforms as dominant in HeLa cells. In conclusion, we present a robust platform that combines photon-efficient FLIM instrumentation with systematic gene knockdown and an automated open-source analysis pipeline. Our quantitative platform provides detailed kinetic analysis of cellular signals in individual cells with unprecedented throughput.

cell biology↗

Determining clinically relevant features in cytometry data using persistent homology

Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. We present that persistent homology, a mathematical structure that summarizes the topological features, can distinguish different sources of data, such as from groups of healthy donors or patients, effectively. Analysis of publicly available cytometry data describing non-naive CD8+ T cells in COVID-19 patients and healthy controls shows that systematic structural differences exist between single cell protein expressions in COVID-19 patients and healthy controls. Our method identifies proteins of interest by a decision-tree based classifier and passes them to a kernel-density estimator (KDE) for sampling points from the density distribution. We then compute persistence diagrams from these sampled points. The resulting persistence diagrams identify regions in cytometry datasets of varying density and identify protruded structures such as elbows. We compute Wasserstein distances between these persistence diagrams for random pairs of healthy controls and COVID-19 patients and find that systematic structural differences exist between COVID-19 patients and healthy controls in the expression data for T-bet, Eomes, and Ki-67. Further analysis shows that expression of T-bet and Eomes are significantly downregulated in COVID-19 patient non-naive CD8+ T cells compared to healthy controls. This counter-intuitive finding may indicate that canonical effector CD8+ T cells are less prevalent in COVID-19 patients than healthy controls. This method is applicable to any cytometry dataset for discovering novel insights through topological data analysis which may be difficult to ascertain otherwise with a standard gating strategy or in the presence of large batch effects. Author summaryIdentifying differences between cytometry data seen as a point cloud can be complicated by random variations in data collection and data sources. We apply persistent homology used in topological data analysis to describe the shape and structure of the data representing immune cells in healthy donors and COVID-19 patients. By looking at how the shape and structure differ between healthy donors and COVID-19 patients, we are able to definitively conclude how these groups differ despite random variations in the data. Furthermore, these results are novel in their ability to capture shape and structure of cytometry data, something not described by other analyses.

systems biology↗

NCodR: A multi-class SVM classification to distinguish between non-coding RNAs in Viridiplantae

Non-coding RNAs (ncRNAs) are major players in the regulation of gene expression. This study analyses seven classes of ncRNAs in plants using sequence and secondary structure-based RNA folding measures. We observe distinct regions in the distribution of AU content along with overlapping regions for different ncRNA classes. Additionally, we find similar averages for minimum folding energy index across various ncRNAs classes except for pre-miRNAs and lncRNAs. Various RNA folding measures show similar trends among the different ncRNA classes except for pre-miRNAs and lncRNAs. We observe different k-mer repeat signatures of length three among various ncRNA classes. However, in pre-miRs and lncRNAs, a diffuse pattern of k-mers is observed. Using these attributes, we train eight different classifiers to discriminate various ncRNA classes in plants. Support-vector machines employing radial basis function show the highest accuracy (average F1 of ~91%) in discriminating ncRNAs, and the classifier is implemented as a web server, NCodR.

plant biology↗

In silico analyses on the comparative sensing of SARS-CoV-2 mRNA by intracellular TLRs of human

The worldwide outbreak of COVID-19 pandemic caused by SARS-CoV-2 leads to loss of mankind and global economic stability. The continuous spreading of the disease and its pathogenesis takes millions of lives of peoples and the unavailability of appropriate therapeutic strategy makes it much more severe. Toll-like receptors (TLRs) are the crucial mediators and regulators of host immunity. The role of several TLRs in immunomodulation of host by SARS-CoV-2 is recently demonstrated. However, the functionality of human intracellular TLRs including TLR3,7,8 and 9 is still being untested for sensing of viral RNA. This study is hoped to rationalize the comparative binding and sensing of SARS-CoV-2 mRNA towards the intracellular TLRs, considering the solvent-based force-fields operational in the cytosolic aqueous microenvironment that predominantly drive these reactions. Our in-silico study on the binding of all mRNAs with the intracellular TLRs shown that the mRNA of NSP10, S2, and E proteins of SARS-CoV-2 are potent enough to bind with TLR3, TLR9, and TLR7 and trigger downstream cascade reactions, and may be used as an option for validation of therapeutic option and immunomodulation against COVID-19.

bioinformatics↗

TET2 drives 5hmc marking of GATA6 and epigenetically defines pancreatic ductal adenocarcinoma transcriptional subtypes

Background and AimsPancreatic ductal adenocarcinoma (PDAC) is characterised by advanced disease stage at presentation, aggressive disease biology and resistance to therapy resulting in extremely poor five-year survival <10%. PDAC is classified into transcriptional subtypes with distinct survival characteristics, although how these arise is not known. Epigenetic deregulation, rather than genetics, has been proposed to underpin progression but exactly why is unclear and hindered by analysis of clinical samples. MethodsGenome-wide epigenetic mapping of DNA modifications 5-hydroxymethylcytosine (5mc) and 5-hydroxymethylcytosine (5hmc) using oxidative bisulphite sequencing (oxBS). Bioinformatics using iCluster and mutational profiling to identify overlap with transcriptional signatures in FFPE from resected patients and confirmation in vivo. ResultsWe find that more aggressive squamous-like PDAC subtypes result from epigenetic inactivation of loci including GATA6 that promote differentiated classical-pancreatic subtypes. We show that squamous-like PDAC transcriptional subtypes are associated with greater loss of 5hmc due to reduced expression of the 5mc-hydroxylase TET2. Furthermore, we find that SMAD4 directly supports TET2 levels in the pancreas and classical-pancreatic tumors and loss of SMAD4 expression is associated reduced 5hmc, GATA6 and squamous-like tumors. Importantly, enhancing TET2 stability using Metformin and VitaminC/ascorbic acid (AA) restores 5hmc and GATA6 levels, reverting squamous-like tumor phenotypes and WNT-dependence in vitro and in vivo. ConclusionsWe identify epigenetic deregulation of pancreatic differentiation as an underpinning event behind the emergence of transcriptomic subtypes in PDAC. Our data shows that restoring epigenetic control increases biomarkers of classical-pancreatic tumors and raises the possibility that combination of Vitamin C and Metformin may prolong survival in patients with squamous-like pancreatic cancer.

cancer biology↗

The DOE JGI Metagenome Workflow

The DOE JGI Metagenome Workflow performs metagenome data processing, including assembly, structural, functional, and taxonomic annotation, and binning of metagenomic datasets that are subsequently included into the Integrated Microbial Genomes and Microbiomes (IMG/M) comparative analysis system (I. Chen, K. Chu, K. Palaniappan, M. Pillay, A. Ratner, J. Huang, M. Huntemann, N. Varghese, J. White, R. Seshadri, et al, Nucleic Acids Rsearch, 2019) and provided for download via the Joint Genome Institute (JGI) Data Portal (https://genome.jgi.doe.gov/portal/). This workflow scales to run on thousands of metagenome samples per year, which can vary by the complexity of microbial communities and sequencing depth. Here we describe the different tools, databases, and parameters used at different steps of the workflow, to help with interpretation of metagenome data available in IMG and to enable researchers to apply this workflow to their own data. We use 20 publicly available sediment metagenomes to illustrate the computing requirements for the different steps and highlight the typical results of data processing. The workflow modules for read filtering and metagenome assembly are available as a Workflow Description Language (WDL) file (https://code.jgi.doe.gov/BFoster/jgi_meta_wdl.git). The workflow modules for annotation and binning are provided as a service to the user community at https://img.jgi.doe.gov/submit and require filling out the project and associated metadata descriptions in Genomes OnLine Database (GOLD) (S. Mukherjee, D. Stamatis, J. Bertsch, G. Ovchinnikova, H. Katta, A. Mojica, I Chen, and N. Kyrpides, and T. Reddy, Nucleic Acids Research, 2018). IMPORTANCEThe DOE JGI Metagenome Workflow is designed for processing metagenomic datasets starting from Illumina fastq files. It performs data pre-processing, error correction, assembly, structural and functional annotation, and binning. The results of processing are provided in several standard formats, such as fasta and gff and can be used for subsequent integration into the Integrated Microbial Genome (IMG) system where they can be compared to a comprehensive set of publicly available metagenomes. As of 7/30/2020 7,155 JGI metagenomes have been processed by the JGI Metagenome Workflow.

bioinformatics↗

Regulation of gene transcription by thyroid hormone receptor β agonists in clinical development for the treatment of non-alcoholic steatohepatitis (NASH)

Thyroid hormones are important modulators of metabolic activity in mammals and alter cholesterol and fatty acid levels through activation of the nuclear thyroid hormone receptor (THR). Currently, there are several THR{beta} agonists in clinical trials for the treatment of non-alcoholic steatohepatitis (NASH) that have demonstrated the potential to reduce liver fat and restore liver function. In this study, we tested three THR{beta}-agonism-based NASH treatment candidates, GC-1 (sobetirome), MGL-3196 (resmetirom), and VK2809, and compared their selectivity for THR{beta} and their ability to modulate the expression of genes specific to cholesterol and fatty acid biosynthesis and metabolism in vitro using human hepatic cells and in vivo using a rat model. Treatment with GC-1 upregulated the transcription of CPT1A in the human hepatocyte-derived Huh-7 cell line with a dose-response comparable to that of the native THR ligand, triiodothyronine (T3). VK2809A (active parent of VK2809), MGL-3196, and VK2809 were approximately 30-fold, 1,000-fold, and 2,000-fold less potent than T3, respectively. Additionally, these relative potencies were confirmed by quantification of other direct gene targets of THR, namely, ANGPTL4 and DIO1. In primary human hepatocytes, potencies were conserved for every compound except for VK2809, which showed significantly increased potency that was comparable to that of its active counterpart, VK2809A. In high-fat diet fed rats, a single dose of T3 significantly reduced total cholesterol levels and concurrently increased liver Dio1 and Me1 RNA expression. MGL-3196 treatment resulted in concentration-dependent decreases in total and low-density lipoprotein cholesterol with corresponding increases in liver gene expression, but the compound was significantly less potent than T3. In conclusion, we have implemented a strategy to rank the efficacy of THR{beta} agonists by quantifying changes in the transcription of genes that lead to metabolic alterations, an effect that is directly downstream of THR binding and activation.

pharmacology and toxicology↗

Insulin inhibits protein phosphatase 2A to impair β-adrenergic receptor function

Insulin impairs {beta}2-adrenergic receptor ({beta}2AR) function through G protein-coupled receptor kinase 2 (GRK2) by phosphorylation but less is known about dephosphorylation mechanisms mediated by protein phosphatase 2A (PP2A). Pharmacologic or genetic inhibition of phosphoinositide 3-kinase {gamma} (PI3K{gamma}) unexpectedly resulted in significant reduction of insulin-mediated {beta}2AR phosphorylation. Interestingly, {beta}2AR-associated phosphatase activity was inhibited by insulin but was reversed by knock-down of PI3K{gamma} showing negative regulation of PP2A by PI3K{gamma}. Co-immunoprecipitation and surface plasmon resonance studies using purified proteins showed that GRK2 and PI3K{gamma} form a complex and could be recruited to {beta}2ARs as GRK2 interacts with insulin receptor substrate following insulin treatment. Consistently, {beta}-blocker pretreatment did not reduce insulin-mediated {beta}2AR phosphorylation indicating agonist- and G{beta}{gamma}-independent non-canonical regulation of receptor function. Mechanistically, PI3K{gamma} inhibits PP2A activity at the {beta}AR complex by phosphorylating an intracellular inhibitor of PP2A (I2PP2A). Knock-down or CRISPR ablation of endogenous I2PP2A unlocked PP2A inhibition mediating {beta}2AR dephosphorylation showing an unappreciated acute regulation of PP2A in mediating insulin-{beta}2AR cross-talk. SummaryInsulin impairs {beta}2-adrenergic receptor ({beta}2AR) function through G protein-coupled receptor kinase 2 (GRK2). We show that insulin simultaneously inhibits protein phosphatase 2A (PP2A) sustaining {beta}2AR functional impairment. Unexpectedly, releasing PP2A inhibition by PI3K{gamma} preserves {beta}2AR function despite intact insulin-driven GRK2-mechanisms.

biochemistry↗

The IgG3 Subclass of β1-adrenergic receptor autoantibody is an endogenous biaser of β1AR signaling

Autoantibodies recognizing human {beta}1ARs generated due to dysregulation in autoimmune response are generally associated with deleterious cardiac outcomes. However, cellular studies show that isolates of {beta}1AR autoantibody from patients differentially modulate {beta}1AR function. {beta}1AR autoantibodies belong to the IgG class of immunoglobulins, however it is not known whether the IgG sub-classes mediate variability in {beta}1AR responses. To determine whether the IgG3 subclass of {beta}1AR autoantibodies uniquely modulate {beta}1AR function, HEK293 cells stably expressing human {beta}1ARs were utilized. Treatment of cells with IgG3(-) serum resulted in significant increase of cAMP compared to IgG3(+) serum. Pre-treatment of cells with IgG3(+) serum impaired dobutamine-mediated Adenylate Cyclase (AC) activity and cAMP generation whereas, it surprisingly increased AC activity and cAMP generation with {beta}-blocker metoprolol. Consistently, purified IgG3(+) {beta}1AR autoantibodies impaired dobutamine-mediated cAMP while elevating metoprolol-mediated AC activity and cAMP. Despite IgG3(+) autoantibodies reducing cAMP response to dobutamine, they mediate significant ERK activation upon dobutamine. IgG3(+) {beta}1AR autoantibodies did not alter {beta}2AR function, reflecting their specificity. The study shows that IgG3(+) {beta}1AR autoantibody impairs agonist-mediated G-protein coupling while preferentially mediating G-protein-independent ERK activation. Furthermore, it uniquely biases {beta}-blocker towards G-protein coupling. This unique biasing capabilities of IgG3(+) {beta}1AR autoantibodies may underlie the beneficial outcomes in patients.

pharmacology and toxicology↗

Isolation of potential plant growth-promoting bacteria from nodules of legumes grown in arid Botswana soil

As the world population increases, improvements in crop growth and yield will be needed to meet rising food demands, especially in countries that have not developed agricultural practices optimized for their own soils and crops. In many African countries, farmers improve agricultural productivity by applying synthetic fertilizers and pesticides to crops, but their continued use over the years has had serious environmental consequences including air and water pollution as well as loss of soil fertility. To reduce the overuse of synthetic amendments, we are developing inocula for crops that are based on indigenous soil microbes, especially those that enhance plant growth and improve agricultural productivity in a sustainable manner. We first isolated environmental DNA from soil samples collected from an agricultural region to study the composition of the soil microbiomes and then used Vigna unguiculata (cowpea), an important legume crop in Botswana and other legumes as "trap" plants using the collected soil to induce nitrogen-fixing nodule formation. We have identified drought-tolerant bacteria from Botswana soils that stimulate plant growth; many are species of Bacillus and Paenibacillus. In contrast, the cowpea nodule microbiomes from plants grown in these soils house mainly rhizobia particularly Bradyrhizobium, but also Methylobacterium spp. Hence, the nodule microbiome is much more limited in non-rhizobial diversity compared to the soil microbiome, but also contains a number of potential pathogenic bacteria.

microbiology↗

Differential brain atrophy and neurogenetic profiles in cognitively-defined Alzheimer's disease subgroups

Elucidating mechanisms underlying the clinical heterogeneity observed among individuals with Alzheimers disease (AD) is key to facilitate personalized treatments. We categorized 679 individuals with AD into subgroups based on a relative impairment in one cognitive domain (i.e. AD-Memory, AD-Executive-Functioning, AD-Language and AD-Visuospatial-Functioning). We compared atrophy patterns derived from MRI and identified patterns that closely matched the respective cognitive profiles, i.e. medial temporal lobe atrophy in AD-Memory, fronto-parietal in AD-Executive-Functioning, asymmetric left-temporal in AD-Language, and posterior in AD-Visuospatial-Functioning. We then determined spatial correlations between subgroup-specific atrophy and a transcriptomic atlas of gene expression, which revealed both shared (e.g. mitochondrial respiration and synaptic function/plasticity) and subgroup-specific (e.g. cell-cycle for AD-Memory, protein metabolism in AD-Language, and modification of gene expression in AD-Visuospatial-Functioning) biological pathways associated with each subgroups atrophy patterns. We conclude that cognitive heterogeneity in AD is related to neuroanatomical differences, and specific biological pathways may be involved in their emergence.

neuroscience↗

Morphological and genomic shifts in mole-rat 'queens' increase fecundity but reduce skeletal integrity

In some mammals and many social insects, highly cooperative societies are characterized by reproductive division of labor, in which breeders and nonbreeders become behaviorally and morphologically distinct. While differences in behavior and growth between breeders and nonbreeders have been extensively described, little is known of their molecular underpinnings. Here, we investigate the consequences of breeding for skeletal morphology and gene regulation in highly cooperative Damaraland mole-rats. By experimentally assigning breeding queen status versus nonbreeder status to age-matched littermates, we confirm that queens experience vertebral growth that likely confers advantages to fecundity. However, they also up-regulate bone resorption pathways and show reductions in femoral mass, which predicts increased vulnerability to fracture. Together, our results show that, as in eusocial insects, reproductive division of labor in mole-rats leads to gene regulatory rewiring and extensive morphological plasticity. However, in mole-rats, concentrated reproduction is also accompanied by costs to bone strength.

evolutionary biology↗

NBS1-CtIP-Mediated DNA End Resection Regulates cGAS Binding to Micronuclei

Cyclic GMP-AMP synthase (cGAS), an important component of immune signaling, is hyperactivated in cells defective for DNA damage response (DDR) signaling. However, a direct role for DDR factors in the regulation of cGAS functions is mostly unknown. Here, we provide novel evidence that Nijmegen breakage syndrome 1 (NBS1) protein, a well-studied DNA double-strand break (DSB) sensor, in coordination with ATM, a protein kinase, and CtBP-interacting protein (CtIP), a DNA end resection factor, functions as an upstream regulator of cGAS binding to micronuclei. Upon NBS1 binding to micronuclei via its fork-head-associated domain, it recruits ATM and CtIP via its N- and C-terminal domains, respectively. Subsequently, ATM stabilizes NBS1s interaction with micronuclei, and CtIP converts DSB ends into single-strand DNA ends, and these two key events preclude cGAS from binding to micronuclei. Notably, we show that purified cGAS cannot form a complex with DNA substrates that mimic resected DNA ends in vitro. Thus, NBS1 together with its binding partners modify the chromatin architecture of the micronuclei and that plays a critical role in cGASs binding to micronuclei.

immunology↗