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Mukherjee, P.

Publications and source records attributed to Mukherjee, P..

At least 55 records · Page 3Linked to original sources

Activation of ERK by altered RNA splicing in cancer

Many cancers carry change-of-function mutations affecting RNA splicing factors, however, less is known about the functional consequences of upregulated RNA splicing factors in cancer. Here, we demonstrate that SMNDC1, a poorly studied splicing factor, which we found to be upregulated in multiple carcinomas and associated with poor patient prognosis, promotes cell proliferation, clonal expansion, and tumor growth by promoting the retention of G-rich exons, which otherwise would be excluded or retained at a lower rate after RNA splicing in normal cells. Inclusion of exon 4 (E4) of MAPK3 (ERK1), which encodes both kinase phosphorylation sites (Thr202/Tyr204), was among the promoted exons by SMNDC1. Forced exclusion of MAPK3-E4 using anti-sense oligos inhibited the ERK1 phosphorylation, expression of target genes and decreased tumor cell growth. These data support that cancer cells exploit a "splicing switch" to promote ERK kinase activity and offer a druggable alternative to block oncogenic signaling and altered RNA splicing in cancer cells SIGNIFICANCEERK signaling promotes tumor growth and survival. Exon 4 of MAPK3 (ERK1) encodes the activation phosphorylation sites of ERK1 kinase. Aberrant RNA splicing induced by SMNDC1 in cancer cells increases the retention of exon 4 during mRNA splicing, unleashes the kinase activity. SMNDC1 potentializes as a cancer therapeutic target.

cancer biology↗

A comparative meta-analysis of membraneless organelle associated proteins with age related proteome of C. elegans

Proteome imbalance can lead to protein misfolding and aggregation which is associated with pathologies. Protein aggregation can also be an active, organized process and can be exploited by cells as a survival strategy. In adverse conditions, it is beneficial to deposit the proteins in a condensate rather degrading and resynthesizing. Membraneless organelles (MLOs) are biological condensates formed through liquid-liquid phase separation (LLPS), involving cellular components such as nucleic acids and proteins. LLPS is a regulated process, which when perturbed, can undergo a transition from a physiological liquid condensate to pathological solid-like protein aggregates. To understand how the MLO-associated proteins (MLO-APs) behave during aging, we performed a comparative meta-analysis with age related proteome of C. elegans. We found that the MLO-APs are highly abundant throughout the lifespan. Interestingly, they are aggregating more in long-lived mutant worms compared to the age matched wildtype worms. GO term analysis revealed that the cell cycle and embryonic development are among the top enriched processes in addition to RNP components in insoluble proteome. Considering antagonistic pleotropic nature of these developmental genes and post mitotic status of C. elegans, we assume that these proteins phase transit during post development. As the organism ages, these MLO-APs either mature to become more insoluble or dissolve in uncontrolled manner. However, in the long-lived daf-2 mutant worms, the MLOs may attain protective states due to extended availability and association of molecular chaperones.

biochemistry↗

ImaGene: A web-based software platform for tumor radiogenomic evaluation and reporting

The field of radiomics has undergone several advancements in approaches to uncovering hidden quantitative features from tumor imaging data for use in guiding clinical decision-making for cancer patients. Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest (ROIs), while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. Potential associations between tumor genotype and phenotype can be identified from imaging and omics data via traditional correlation analysis, as well as through artificial intelligence (AI) models. However, at present the radiogenomics community lacks a unified software platform for which to conduct such analyses in a reproducible manner. To address this gap, we propose ImaGene, a web-based platform that takes tumor omics and imaging data sets as input, performs correlation analysis between them, and constructs AI models (optionally using only those features found to exhibit statistically significant correlation with some element of the opposing dataset). ImaGene has several modifiable configuration parameters, providing users complete control over their analysis. For each run, ImaGene produces a comprehensive report displaying a number of intuitive model diagnostics. To demonstrate the utility of ImaGene, exploratory studies surrounding Invasive Breast Carcinoma (IBC) and Head and Neck Squamous Cell Carcinoma (HNSCC) on datasets acquired from public databases are conducted. Potential associations are identified between several imaging features and 6 genes: CRABP1, SMTNL2, FABP1, HAS1, FAM163A and DSG1 for IBC, and 4 genes: CEACAM6, NANOG, ACSM2B, and UPK2 for HNSCC. In summary, the software provides researchers with a transparent tool for which to begin radiogenomic analysis and explore possible further directions in their research. We anticipate that ImaGene will become the standard platform for tumor analyses in the field of radiogenomics due to its ease of use, flexibility, and reproducibility, and that it can serve as an enabling centrepoint for an emerging radiogenomic knowledge base.

bioinformatics↗

NAD+ repletion by the PARP inhibitor PJ34 prevents Sarm1 activation and rotenone-induced cell death

Sarm1 is an evolutionary conserved innate immune adaptor protein that has emerged as a primary regulator of programmed axonal degeneration over the past decade. In vitro structural insights have revealed that although Sarm1 induces energy depletion by breaking down NAD+, it is also allosterically inhibited by NAD+. However, how NAD+ levels modulate the activation of intracellular Sarm1 has not been elucidated so far. This study focuses on understanding the events leading to Sarm1 activation in both neuronal and non-neuronal cells using the mitochondrial complex I inhibitor rotenone. Here we report the regulation of rotenone-induced cell death by loss of NAD+ that may act as a "biological trigger" of Sarm1 activation. Our study revealed that early loss of endogenous NAD+ levels arising due to PARP1 hyperactivation preceded Sarm1 induction following rotenone treatment. Interestingly, replenishing NAD+ levels by the PARP1 inhibitor, PJ34 restored mitochondrial homeostasis and prevented subsequent Sarm1 activation in rotenone treated cells. These cellular data were further validated in Drosophila melanogaster where a significant reduction in rotenone mediated loss of locomotor abilities and reduced dSarm expression was observed in the flies following PARP inhibition. Taken together, these observations not only uncovers a novel regulation of Sarm1 induction by endogenous NAD+ levels but also point towards an important understanding on how PARP inhibitors could be repurposed in the treatment of mitochondrial complex I deficiency disorders mediated by Sarm1.

cell biology↗

Elucidating the correlation between the number of TTTTGAT heptamer repeats and cholera toxin promoter activity in Vibrio cholerae O1 pandemic strains

A complex regulatory cascade controls expression of the cholera toxin genes (ctxAB) in Vibrio cholerae; which eventually leads to choleragen (CT) production and secretion, resulting in rice watery diarrhoea. The cholera toxin promoter (PctxAB) contains a series of heptad repeats (5-TTTTGAT-3); which have been previously shown to play crucial role in ctxAB transcriptional regulation by recruiting the transcriptional activators ToxT, ToxR, and the nucleoid-associated protein H-NS along the ctx promoter. The numbers of these repeats vary between the two biotypes of V. cholerae O1 strains, and even among strains of the same biotype. In this study, we examined PctxAB activation of V. cholerae O1 pandemic strains to understand the significance of the distal heptad repeats in regulating ctx expression. Interestingly, we found that ctx activation may depend on the number of TTTTGAT heptad repeats within PctxAB, and we posit that the occupation of the distal repeats by H-NS could further prevent transcriptional activation of ctx genes in V. cholerae. We hypothesize that ToxT-dependent transcriptional activation may not require entire displacement of H-NS and propose a revision in the currently accepted model of ToxT dependent PctxAB transcriptional activation. IMPORTANCECT production by pathogenic V. cholerae O1 strains is regulated through the transcriptional silencing of CTX promoter by H-NS and counter repression by ToxT. The highly AT rich PctxAB is composed of the tandem repeats 5{square} TTTTGAT 3{square}; the numbers of which differ among the classical and El Tor biotypes. However, it is still not very clear whether the numbers of these repeats could be correlated with promoter activation of the ctx operon. Here we report the role of the distal repeats in ctxAB expression levels. We demonstrate that PctxAB activation changes with the number of the heptad repats within the core promoter element, thereby suggesting a model for ctx regulation in the toxigenic strains of V. cholerae.

microbiology↗

Toward Optimized and Predictive Connectomics at Scale

Probabilistic MRI diffusion tractography is a sophisticated technique to investigate structural connectomes, but its steep computational cost prevents application to broader research and clinical settings. Major speedup can be achieved by reducing the number of tractography streamlines. To ensure this does not degrade connectome quality, we calculate the identifiability of connectomes between test and retest MRI as a proxy for information content. We find that reducing streamline count by up to two orders of magnitude from prevailing levels in literature has no significant impact on identifiability. Incidentally, we also observe that Jaccard similarity is more effective than Pearson correlation in achieving identifiability. This document was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their employees makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or Lawrence Livermore National Security, LLC. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or Lawrence Livermore National Security, LLC, and shall not be used for advertising or product endorsement purposes.

neuroscience↗

MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline

Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational cost. We developed MaPPeRTrac, a probabilistic tractography pipeline that simplifies and vastly accelerates this process on a wide range of high performance computing (HPC) environments. It fully automates the entire tractography pipeline, from management of raw MRI machine data to edge density imaging (EDI) of the structural connectome. Dependencies are containerized with Docker or Singularity and de-coupled from code to enable rapid proto-typing and modification. Data artifacts are strictly organized with the Brain Imaging Data Structure (BIDS) to ensure that they are findable, accessible, interoperable, and reusable following FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming frame-work, resulting in the creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so that it may accelerate brain connectome research for a broader user community.

neuroscience↗

Deep Linear Modeling of Hierarchical Functional Connectivity in the Human Brain

The human brain exhibits hierarchical modular organization, which is not depicted by conventional fMRI functional connectivity reconstruction methods such as independent component analysis (ICA). To map hierarchical brain connectivity networks (BCNs), we propose a novel class of deep (multilayer) linear models that are constructed such that each successive layer decomposes the features of the preceding layer. Three of these are multilayer variants of Sparse Dictionary Learning (SDL), Non-Negative Matrix Factorization (NMF) and Fast ICA (FICA). We present a fourth deep linear model, Deep Matrix Fitting (MF), which incorporates both rank reduction for data-driven hyperparameter determination as well as a distributed optimization function. We also introduce a novel framework for theoretical comparison of these deep linear models based on their combination of mathematical operators, the predictions of which are tested using simulated resting state fMRI data with known ground truth BCNs. Consistent with the theoretical predictions, Deep MF and Deep SDL performed best for connectivity estimation of 1st layer networks, whereas Deep FICA and Deep NMF were modestly better for spatial mapping. Deep MF provided the best overall performance, including computational speed. These deep linear models can efficiently map hierarchical BCNs without requiring the manual hyperparameter tuning, extensive fMRI training data or high-performance computing infrastructure needed by deep nonlinear models, such as convolutional neural networks (CNNs) or deep belief networks (DBNs), and their results are also more explainable from their mathematical structure. These benefits gain in importance as continual improvements in the spatial and temporal resolution of fMRI reveal more of the hierarchy of spatiotemporal brain architecture. These new models of hierarchical BCNs may also advance the development of fMRI diagnostic and prognostic biomarkers, given the recent recognition of disparities between low-level vs high-level network connectivity across a wide range of neurological and psychiatric disorders.

neuroscience↗

Repression of MUC1 promotes expansion and suppressive function of myeloid-derived suppressor cells in pancreatic ductal adenocarcinoma and breast cancer murine models.

Myeloid-derived suppressor cells (MDSCs) are immature myeloid cells that are responsible for immunosuppression in tumor microenvironment. Here we report the impact of mucin 1 (MUC1), a transmembrane glycoprotein, on proliferation and functional activity of MDSCs. To determine the role of MUC1 in MDSC phenotype, we analyzed MDSCs derived from wild type (WT) and MUC1-knockout (MUC1KO) mice bearing pancreatic ductal adenocarcinoma KCKO and breast cancer C57MG xenografts. We observed enhanced tumor growth in MUC1KO mice compared to WT mice in both pancreatic KCKO and breast C57MG cancer models due to increased MDSC population and enrichment of Tregs in tumor microenvironment. Our current study shows that knockdown of MUC1 in MDSCs promotes proliferation and immature suppressive phenotype indicated by increased level of iNOS, ARG1 activity and TGF-{beta} secretion under cancer conditions. Increased activity of MDSCs leads to repression of IL-2 and IFN-{gamma} production by T-cells. We were able to find that MDSCs from MUC1KO mice have higher levels of c-Myc and activated pSTAT3 as compared to MUC1 WT mice, that are signaling pathways leading to increased survival, proliferation and prevention of maturation. In summary, MUC1 regulates signaling pathways that maintain immunosuppressive properties of MDSCs. Thus, immunotherapy must target only tumor associated MUC1 on epithelial cells and not MUC1 on hematopoietic cells to avoid expansion and suppressive functions of MDSC.

cancer biology↗

Tractography-Pathology Correlations in Traumatic Brain Injury: A TRACK-TBI Study

Diffusion tractography MRI can infer changes in network connectivity in patients with traumatic brain injury (TBI), but pathological substrates of disconnected tracts have not been well-defined due to a lack of high-resolution imaging with histopathological validation. We developed an ex vivo MRI protocol to analyze tract terminations at 750 m resolution, followed by histopathologic evaluation of white matter pathology, and applied these methods to a 60-year-old man who died 26 days after TBI. Analysis of 74 cerebral hemispheric white matter regions revealed a heterogeneous distribution of tract disruptions. Associated histopathology identified variable white matter injury with patchy deposition of amyloid precursor protein and loss of neurofilament-positive axonal processes, myelin dissolution, astrogliosis, microgliosis, and perivascular hemosiderin-laden macrophages. Multiple linear regression revealed that tract disruption strongly correlated with neurofilament loss. Ex vivo diffusion MRI can detect tract disruptions in the human brain that reflect axonal injury.

neuroscience↗

Vascular Endothelial Growth Factor as an Immediate-Early Activator of UV-induced Skin Injury

The negative health consequences of acute ultraviolet (UV) exposure are evident, with reports of 30,000 emergency room visits annually to treat the effects of sunburn in the United States alone. Acute effects of sunburn include erythema, edema, and severe pain, and chronic overexposure to UV radiation can lead to skin cancer. While the pain associated with the acute effects of sunburn may be relieved by current interventions, existing post-sunburn treatments are not capable of reversing the cumulative and long-term pathological effects of UV exposure, an unmet clinical need. Here we show that activation of the vascular endothelial growth factor (VEGF) pathway is a direct and immediate consequence of acute UV exposure, and activation of VEGF signaling is necessary for the initiating the acute pathological effects of sunburn. In UV-exposed human subjects, VEGF signaling is activated within hours. Topical delivery of VEGF pathway inhibitors, targeted against the ligand VEGF-A (gold nanoparticles conjugated with anti-VEGF antibodies) and small molecule antagonists of VEGF receptor signaling, prevent the development of erythema and edema in UV-exposed mice. Collectively, these findings suggest targeting VEGF signaling may reduce the subsequent inflammation and pathology associated with UV-induced skin damage, which reveals a new post-exposure therapeutic window to potentially inhibit the known detrimental effects of UV on human skin.

molecular biology↗

A distinct phylogenetic cluster of Indian SARS-CoV-2 isolates

From an isolated epidemic, COVID-19 has now emerged as a global pandemic. The availability of genomes in the public domain following the epidemic provides a unique opportunity to understand the evolution and spread of the SARS-CoV-2 virus across the globe. The availability of whole genomes from multiple states in India prompted us to analyse the phylogenetic clusters of genomes in India. We performed whole-genome sequencing for 64 genomes making a total of 361 genomes from India, followed by phylogenetic clustering, substitution analysis, and dating of the different phylogenetic clusters of viral genomes. We describe a distinct phylogenetic cluster (Clade I / A3i) of SARS-CoV-2 genomes from India, which encompasses 41% of all genomes sequenced and deposited in the public domain from multiple states in India. Globally 3.5% of genomes, which till date could not be mapped to any distinct known cluster fall in this newly defined clade. The cluster is characterized by a core set of shared genetic variants - C6312A (T2016K), C13730T (A88V/A97V), C23929T, and C28311T (P13L). Further, the cluster is also characterized by a nucleotide substitution rate of 1.4 x 10-3 variants per site per year, lower than the prevalent A2a cluster, and predominantly driven by variants in the E and N genes and relative sparing of the S gene. Epidemiological assessments suggest that the common ancestor emerged in the month of February 2020 and possibly resulted in an outbreak followed by countrywide spread, as evidenced by the low divergence of the genomes from across the country. To the best of our knowledge, this is the first comprehensive study characterizing the distinct and predominant cluster of SARS-CoV-2 in India.

genomics↗

Tumor -Associated MUC1 Regulates TGF-β Signaling and Function in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDA) is one of the most lethal human cancers. Transforming Growth Factor Beta (TGF-{beta}) is a cytokine that switches from a tumor-suppressor to a tumor promoter throughout tumor development, by a yet unknown mechanism. Tumor associated MUC1 (tMUC1) is aberrantly glycosylated and overexpressed in >80% of PDAs and is associated with poor prognosis. The cytoplasmic tail of MUC1 (MUC1-CT) interacts with other oncogenic proteins promoting tumor progression and metastasis. We hypothesize that tMUC1 levels regulate TGF-{beta} functions in PDA in vitro and in vivo. We report that high-tMUC1 expression positively correlates to TGF-{beta}RII and negatively to TGF-{beta}RI receptors. In response to TGF-{beta}1, high tMUC1 expressing PDA cells undergo c-Src phosphorylation, and activation of the Erk/MAPK pathway; while low tMUC1 expressing cells activate the Smad2/3 pathway, enhancing cell death. Correspondingly, mice bearing tMUC1-high tumors responded to TGF-{beta}1 neutralizing antibody in vivo showing significantly retarded tumor growth. Analysis of clinical data from TCGA revealed significant alterations in gene-gene correlations in the TGF-{beta} pathway in tMUC1 high versus tMUC1 low samples. This study deepens our understanding of tMUC1-regulated TGF-{beta}s paradoxical function in PDA and establishes tMUC1 as a potential biomarker to predict response to TGF-{beta}-targeted therapies.

cancer biology↗

Overcoming Immunological Resistance Enhances the Efficacy of a Novel anti-tMUC1 CAR T Cell Treatment Against Pancreatic Ductal Adenocarcinoma

Chimeric antigen receptor engineered T cells (CAR T cells) have shown remarkable success in treating hematologic cancers. However, this efficacy has yet to translate to treatment in solid tumors. Pancreatic ductal adenocarcinoma (PDA) is a fatal malignancy with poor prognosis. Treatment options are limited and commonly associated with severe side effects. We have developed and characterized a second generation CAR engineered T cell using the light and heavy chain sequence derived from a novel monoclonal antibody, TAB004, that specifically binds the tumor associated antigen, tMUC1. tMUC1 is overexpressed in varying levels on ~85% of all human PDA. We present data showing that the TAB004 derived CAR T cells (tMUC1-CAR T cells) specifically bind to tMUC1 on PDA cells and is cytotoxic against the majority of the PDA cell lines. The tMUC1-CAR T cells do not bind or kill normal epithelial cells. We further demonstrate that the tMUC1-CAR T cells control the growth of orthotopic pancreatic tumors in vivo. PDAs are generally cold tumors with resistance to many standard treatment modalities, thus, it was not surprising that some of the PDA cell lines were refractory to CAR T cell treatment. qPCR analysis of several genes known to be associated with immune resistance revealed overexpression of indoleamine 2, 3-dioxygenases-1 (IDO1), Cyclooxygenase 1 and 2 (COX1 and COX2), Adenosine deaminases acting on RNA (ADAR1) and galectin-9 (Gal-9). We treated resistant PDA cells with a combination of CAR T cells and biological inhibitors of IDO1, COX1/2, ADAR1, and Gal-9. Results showed a significant enhancement of CAR T cell cytotoxicity against PDA cells when inhibiting IDO1, COX1/2, and Gal-9 but not ADAR1 or COX2. Overcoming CAR T cell resistance in PDA is a significant advancement in the field and may lead to future combination therapies that are less toxic but more efficient against this deadly disease.

cancer biology↗

All-Assay-Max2 pQSAR: Activity predictions as accurate as 4-concentration IC50s for 8,558 Novartis assays

Profile-QSAR (pQSAR) is a massively multi-task, 2-step machine learning method with unprecedented scope, accuracy and applicability domain. In step one, a \"profile\" of conventional single-assay random forest regression (RFR) models are trained on a very large number of biochemical and cellular pIC50 assays using Morgan 2 sub-structural fingerprints as compound descriptors. In step two, a panel of PLS models are built using the profile of pIC50 predictions from those RFR models as compound descriptors. Hence the name. Previously described for a panel of 728 biochemical and cellular kinase assays, we have now built an enormous pQSAR from 11,805 diverse Novartis IC50 and EC50 assays. This large number of assays, and hence of compound descriptors for PLS, dictated reducing the profile by only including RFR models whose predictions correlate with the assay being modeled. The RFR and pQSAR models were evaluated with our \"realistically novel\" held-out test set whose median average similarity to the nearest training set member across the 11,805 assays was only 0.34, thus testing a realistically large applicability domain. For the 11,805 single-assay RFR models, the median correlation of prediction with experiment was only R2 ext=0.05, virtually random, and only 8% of the models achieved our standard success threshold of R2 ext=0.30. For pQSAR, the median correlation was R2 ext=0.53, comparable to 4-concentration experimental IC50s, and 72% of the models met our R2 ext>0.30 standard, totaling 8558 successful models. The successful models included assays from all of the 51 annotated target sub-classes, as well as 4196 phenotypic assays, indicating that pQSAR can be applied to virtually any disease area. Every month, all models are updated to include new measurements, and predictions are made for 5.5 million Novartis compounds, totaling 50 billion predictions. Common uses have included virtual screening, selectivity design, toxicity and promiscuity prediction, mechanism-of-action prediction, and others.

bioinformatics↗

Spectral Graph Theory of Brain Oscillations

The relationship between the brains structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectomes Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled non-linear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30Hz) activity estimated from source localized scalp magneto-encephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. Significance StatementThe relationship between the brains structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectomes Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics.

neuroscience↗

Dual RNA-Seq meta-analysis in Plasmodium infection

Dual RNA-Seq is the simultaneous transcriptomic analysis of interacting symbionts, for example, in malaria. Potential cross-species interactions identified by correlated gene expression might highlight interlinked signaling, metabolic or gene regulatory pathways in addition to physically interacting proteins. Often, malaria studies address one of the interacting organisms - host or parasite - rendering the other "contamination". Here we perform a meta-analysis using such studies for cross-species expression analysis. We screened experiments for gene expression from host and Plasmodium. Out of 171 studies in Homo sapiens, Macaca mulatta and Mus musculus, we identified 63 potential studies containing host and parasite data. While 16 studies (1950 samples) explicitly performed dual RNA-Seq, 47 (1398 samples) originally focused on one organism. We found 915 experimental replicates from 20 blood studies to be suitable for co-expression analysis and used orthologs for meta-analysis across different host-parasite systems. Centrality metrics from the derived gene expression networks correlated with gene essentiality in the parasites. We found indications of host immune response to elements of the Plasmodium protein degradation system, an antimalarial drug target. We identified well-studied immune responses in the host with our co-expression networks as our approach recovers known broad processes interlinked between hosts and parasites in addition to individual host and parasite protein associations. The set of core interactions represents commonalities between human malaria and its model systems for prioritization in laboratory experiments. Our approach might also allow insights into the transferability of model systems for different pathways in malaria studies. ImportanceMalaria still causes about 400,000 deaths a year and is one the most studied infectious diseases. The disease is studied in mice and monkeys as lab models to derive potential therapeutic intervention in human malaria. Interactions between Plasmodium spp. and its hosts are either conserved across different host-parasite systems or idiosyncratic to those systems. Here we use correlation of gene expression from different RNA-Seq studies to infer common host-parasite interactions across human, mouse and monkey studies. We, firstly, find a set of very conserved interactors, worth further scrutiny in focussed laboratory experiments. Secondly, this work might help assess to which extent experiments and knowledge on different pathways can be transferred from models to humans for potential therapy.

bioinformatics↗

Co-translation drives the assembly of mammalian nuclear multisubunit complexes

AO_SCPLOWBSTRACTC_SCPLOWCells dedicate significant energy to build proteins often organized in multiprotein assemblies with tightly regulated stoichiometries. As genes encoding proteins assembling in the same multisubunit complexes are dispersed in the genome of eukaryotes, it is unclear how multisubunit complexes assemble. We show that mammalian nuclear transcription complexes (TFIID, TREX-2 and SAGA) composed of a large number of subunits but lacking precise architectural details are built co-translationally. We demonstrate that the dimerization domains and their positions in the interacting subunits determine the co-translational assembly pathway (simultaneous or sequential). Our results indicate that protein translation and complex assembly are linked in building mammalian multisubunit complexes and suggest that co-translational assembly is a general principle in mammalian cells to avoid non-specific interactions and protein aggregation. These findings will significantly advance structural biology by defining endogenous co-translational building blocks in the architecture of multisubunit complexes.

molecular biology↗