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Agarwal, D.

Publications and source records attributed to Agarwal, D..

At least 19 recordsLinked to original sources

Oxidative Stress and Interferon Signaling Drive Differential Pathogenesis of Ancestral and Contemporary Zika Viruses in Human Cerebral Organoids

Neurotropic Zika viruses (ZIKV) cause serious human disease with pandemic potential. Pathogenesis severities resulting from Asian/American versus African ZIKV lineage infections range from mild to severe, respectively; however, mechanisms underlying differential ZIKV pathogenesis remain unclear, as do effective therapeutic strategies. The limitations of mechanistic understanding are due in part to the challenges of comparing data generated in disparate experimental models, as well as approaches that did not test both ancestral and contemporary ZIKV infections. The goal of this work was to define differential pathogenesis mechanisms among ancestral and contemporary ZIKVs by direct infection comparisons using a relevant human stem cell-derived cerebral organoid experimental model. While Asian/American ZIKV lineage infections enhanced antiviral and interferon gene expression responses that correlated with viral RNA clearance from organoid ventricles, ancestral African lineage ZIKV infections enhanced apoptotic and stress response signaling that correlated with diminished STAT2 signaling protein levels, ongoing ZIKV replication, and production of damaging reactive oxygen species (ROS). We discovered that, surprisingly, severe ancestral Zika virus pathogenesis was dramatically reduced by Trolox, a hydroxyl radical scavenger antioxidant, thereby confirming ROS imbalance as a major pathogenesis driver. These results demonstrate that ZIKV lineage infections and pathogenesis are differentiated by their signaling responses and suggest that preventing or controlling hydroxyl radical imbalance may offer therapeutic benefits to address microcephaly and Congenital Zika Virus Syndrome. One Sentence SummaryDifferential signal transduction responses to lineage-specific Zika virus infections cause reduction-oxidation imbalance-mediated pathogenesis that is blocked by Trolox, an antioxidant.

microbiology↗

A High-Resolution Atlas of the Brain Predicts Lineage and Birth Order Underly Neuronal Identity.

Gene expression shapes the nervous system at every biological level, from molecular and cellular processes defining neuronal identity and function to systems-level wiring and circuit dynamics underlying behaviour. Here, we generate the first high-resolution, single-cell transcriptomic atlas of the adult Drosophila melanogaster central brain by integrating multiple datasets, achieving an unprecedented tenfold coverage of every neuron in this complex tissue. We show that a neurons genetic identity overwhelmingly reflects its developmental origin, preserving a genetic address based on both lineage and birth order. We reveal foundational rules linking neurogenesis to transcriptional identity and provide a framework for systematically defining neuronal types. This atlas provides a powerful resource for mapping the cellular substrates of behaviour by integrating annotations of hemilineage, cell types/subtypes and molecular signatures of underlying physiological properties. It lays the groundwork for a long-sought bridge between developmental processes and the functional circuits that give rise to behaviour.

neuroscience↗

Assessment of dispersion metrics for estimating single-cell transcriptional variability

Single-cell RNA sequencing data enables analysis of transcript levels of single cells across different cell types and conditions. Recent work has highlighted the value of measuring gene-specific transcriptional variability, or noise, within a genetically identical population of cells in addition to mean expression given that these differences contribute to biological processes including development and disease. However, measuring transcriptional noise remains a challenge. Here, we systematically compared statistical methods by simulating single-cell data by varying both dispersion and count size to assess the relative responsiveness to noise of several commonly used statistical metrics: the Gini index, variance-to-mean ratio, variance, and Shannon entropy. We found that the variance-to-mean ratio scales approximately linearly with increasing dispersion and is scale-invariant. In contrast, the Gini index displayed paradoxical behavior, and Shannon entropy was not scale-invariant. Thus, we next applied the variance-to-mean ratio to measure transcriptional variability in a publicly available single-cell dataset of embryonic hearts from a mouse model of maternal hyperglycemia. Our data show that many genes display transcriptional variability within the same cell type, and that this variation does not correlate with gene characteristics such as transcript level, promoter GC content, or evolutionary gene age. Notably, many of the genes and pathways with highest transcriptional variability were not identified as differentially expressed, and have in fact been implicated in maternal hyperglycemia in other studies, suggesting that transcriptional variability can provide additional biologically relevant information beyond what is observed from studying mean expression alone.

genomics↗

13C tracing in synaptosomes reveals that SGLT2 inhibition with dapagliflozin prevents metabolic deficits in the 5X-FAD model of Alzheimer's Disease

Metabolic dysfunction is linked to several forms of age-related neurodegeneration including Alzheimers Disease (AD), and targeting brain energy metabolism is an increasingly attractive mode of therapeutic intervention. However, commonly used in vitro methods to identify specific metabolic pathways of interest in preclinical models of neurodegenerative disease have considerable limitations. They are prone to subselection of sample material, unable to identify cell type-specific effects, or cannot identify metabolic defects upstream of mitochondria. Here we address these challenges by validating a method for stable isotope tracing with isolated synaptic nerve terminals, or synaptosomes. We further applied this approach to study glucose metabolism in synaptosomes isolated from the 5X-FAD mouse model of AD treated with the antidiabetic sodium-glucose linked transporter-2 (SGLT-2) inhibitor Dapagliflozin. Treatment with Dapagliflozin preserved steady-state levels of synaptosomal metabolites and enrichment from labeled glucose into citrate that was reduced in the 5X-FAD model. These changes correlated with trends towards improved spatial working memory but not amyloid burden. The results highlight the utility of stable isotope tracing in synaptosomes to identify precise sites of metabolic dysfunction and mechanisms of action for metabolic drug candidates in preclinical models of neurodegeneration.

biochemistry↗

4D mitochondrial network assumes distinct and predictive phenotypes through human lung and intestinal epithelial development

Mitochondria form a dynamic three-dimensional network within the cell that exhibits a wide range of morphologies and behaviors. Depending on cell state, cell type, and cell fate, a cells mitochondrial phenotype might range from relatively isolated mitochondrial segments to complex branching networks, and from stationary mitochondria to highly motile structures. While isolated mitochondrial phenotypes have been described for a subset of cell states, types, and fates, an integrated map of how mitochondrial phenotypes change over the full course of tissue development has so far been lacking. Here, we identify the mitochondrial phenotypes that appear throughout the course of lung and intestinal epithelial development from stem cells to differentiated tissue. Using human stem cell-derived intestinal and branching lung organoids that mimic developing human organs as model systems, we extract and analyze key mitochondrial biophysical phenotypes in human development. To achieve this, we employ lattice light-sheet microscopy (LLSM), which enables high-resolution, 4D (x, y, z, time) imaging of mitochondria in organoid tissues with minimal damage to the sample. We image at key developmental time points from stem cell differentiation into mature organoid tissue. For data processing, we utilize the MitoGraph and MitoTNT software packages along with our developed custom computational tools. These tools allow for automated 4D organoid to single cell image processing and quantitative 4D single cell mitochondrial temporal network tracking. This work represents the first 4D high spatiotemporal-resolution quantification of live human organoid tissues at the single-cell level through development. We identified distinct mitochondrial phenotypes unique to each organoid type and found correlations between mitochondrial phenotypes, cellular age, and cell type. Furthermore, we demonstrate that mitochondrial network characteristics can predict both organoid type and cell age. Our findings reveal fundamental aspects of mitochondrial biology that were previously unobservable, offering new insights into cell-type-specific mitochondrial dynamics and enabling new findings in relevant human model systems. We believe that our findings and methods will be essential for advancing 4D cell biology, providing a powerful framework for characterizing organelles in organoid tissues.

cell biology↗

Physicochemical and functional comparison of food-grade and precision-fermented bovine lactoferrin

This study provided a comprehensive physiochemical and functional comparison between food-grade, commercially available bovine lactoferrin (cLF) and two precision-fermented variants (pfLF_highFe and pfLF_lowFe) at varying iron saturation levels. LC-MS and FTIR/CD spectroscopy confirmed high purity and structural fidelity at primary and secondary levels, while UV-vis analysis revealed comparable molar absorptivity across all preparations. Fluorescence spectroscopy demonstrated tertiary structural variability with redshifts observed as a result of iron binding. Molecular weight (Mw) comparisons made using SDS-PAGE showed pfLF_highFe and pfLF_lowFe closely resembled cLF, while glycosylation analysis demonstrated very similar N-glycan compositions and site occupancy across low iron samples. The isoelectric points (pI) of pfLF_highFe and pfLF_lowFe were similar to cLF; however, varied slightly as a function of iron saturation, as did surface hydrophobicity (S0) measurements. Thermal denaturation analyses revealed characteristic behaviour of bovine lactoferrin preparations, with differences again observed according to the preparations iron saturation. Functional studies highlighted the iron chelating ability of native-LF preparations, cLF and pfLF_lowFe, and accompanying antimicrobial activity, but comparable interaction capacity with Lipid A (LPS) demonstrates preservation of structural and certain functional capabilities in precision-fermented bovine lactoferrin irrespective of iron saturation levels.

synthetic biology↗

CD4+ tissue resident memory Th17 cells drive IL-17A-mediated joint pathology in Spondyloarthritis

ObjectivesInterleukin (IL)-17A is a key driver of Spondyloarthritis (SpA) joint pathology. We aimed to identify its cellular source in synovial tissue from patients with two forms of SpA namely Axial SpA (AxSpA) and Psoriatic arthritis (PsA). MethodsSynovial tissue from patients with SpA was profiled using single-cell RNA sequencing (scRNA-seq: AxSpA, n=5 and PsA, n=6) or spatial RNA profiling (PsA, n=4). CellPhoneDB was used to infer cell-cell communication. Tissue resident memory Th17 (TRM17)-like cells were generated in vitro using blood memory CD4+ T cells from SpA patients. An epigenetic inhibitor library, siRNA and clustered regularly interspaced short palindromic repeats (CRISPR) were used to identify epigenetic regulator(s) for TRM17. ResultsscRNA-seq showed that CD4+CXCR6+ TRM17 cells are the predominant spontaneous IL17A producers in SpA synovium. Cell-cell communication and single-cell spatial analysis support the interaction between TRM17 and CLEC10A+ dendritic cells, which were activated in SpA. Both sublining and lining fibroblasts in SpA synovium showed evidence of IL-17A activation. In vitro-generated CD4+ TRM17-like cells phenocopied joint tissue TRM17, producing IL-17A/F upon T cell receptor (TCR) stimulation, which was further enhanced by cytokines. Perturbation of BRD1 inhibited the generation of TRM17-like cells. ConclusionsCD4+ TRM17 cells are the predominant source of IL-17A in SpA synovial tissue. TCR stimulation is essential for the secretion of IL-17A by CD4+TRM17-like cells. The epigenetic regulator BRD1 contributes to the generation of CD4+TRM17. Depleting CD4+TRM17 cells in SpA is thus a therapeutic strategy with potential to induce long-term remission. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSInterleukin (IL)-17A plays a key role in the immunopathogenesis of Spondyloarthritis (SpA), but its cellular source in joint tissue has not been determined previously. The induction and accumulation of CD4+ tissue resident memory Th17 (TRM17) cells following the clearance of pathogens has been described in skin, lung and kidney. Whether CD4+ TRM17 cells also accumulate in the joint and contribute to the pathology of SpA is not clear. What this study addsO_LICD4+ TRM17 cells are present in SpA synovial tissue and are the predominant source of IL17A C_LIO_LICD4+ TRM17 cells in SpA joints express IL17A without any in vitro exogenous stimulation C_LIO_LIT cell receptor (TCR) rather than cytokine stimulation is essential for IL-17A production by CD4+ TRM17-like cells C_LIO_LIThe epigenetic regulator BRD1 contributes to the generation of CD4+ TRM17-like cells. C_LI How this study might affect research, practice or policyOur findings identify CD4+ TRM17 cells as the primary source of IL-17A in SpA synovium, a previously unrecognized role for these cells. Key questions remain: How do CD4+ TRM17 cells relate to IL-17A producers in synovial fluid? What mechanisms induce and maintain them in the joint? How do they interact with other cells to promote arthritis? These questions warrant further investigation. In addition, our data suggest that targeting CD4+ TRM17 cells, the "factory" of IL-17A in SpA synovial tissue, has the potential to induce long-term remission, encouraging future efforts to develop new therapies to deplete CD4+ TRM17 cells in SpA.

immunology↗

Fine needle aspiration of human lymph nodes reveals cell populations and soluble interactors pivotal to immunological priming

Lymph node (LN) fine needle aspiration (LN FNA) represents a powerful technique for minimally invasive sampling of human lymph nodes in vivo and has been used to good effect to directly study aspects of the human germinal center response. However, systematic deep phenotyping of the cellular populations and cell-free proteins recovered by LN FNA has not been performed. Thus, we studied human cervical LN FNAs as a proof-of-concept and used single-cell RNA-sequencing and proteomic analysis to benchmark this compartment, define the purity of LN FNA material, and facilitate future studies into this immunologically pivotal environment. Our data provide evidence that LN FNAs contain bone fide LN-resident innate immune populations, with minimal contamination of cells or proteins from blood. Examination of these populations reveals unique biology not predictable from equivalent blood-derived populations. LN FNA supernatants represent a specific source of lymph- and lymph node-derived proteins, and can, in combination with transcriptomic approaches, identify likely receptor-ligand interactions. This study provides the first description of the types and abundance of immune cell populations and cell-free proteins that can be efficiently studied by LN FNA. These findings are of broad utility for understanding LN physiology both in health and disease, including infectious or autoimmune perturbations, and in the case of cervical nodes, neuroscience.

immunology↗

Gene Expression Profiling and Physiological Adaptations of Pearl Spot (Etroplus suratensis) under Varying Salinity Conditions

Eutroplus suratensis (Pearl spot) is naturally found in estuarine environments and has been noted to have a high salinity tolerance, with the ability to thrive in freshwater as well as seawater. By examining the impact of various salinity levels on the growth and survival of Pearl spot, the present study aims to enhance aquaculture profitability by assessing their adaptability and physiological adjustments to changes in salinity, as well as determining their potential to acclimate to a broad range of salinity regimes. Pearl spot fingerlings were placed in tanks with varying salinities (15, 25, 35, 45, 60, and 75ppt) and monitored for mortality at 24-hour intervals up to 120 hours. Results revealed no mortality in the control group (0ppt), as well as in the 15, 25, and 35ppt treatment groups. However, the remaining groups (45, 60, and 75ppt) showed differing levels of mortality, with 44% mortality observed in the 45ppt group and 100% mortality in both the 60 and 75ppt groups. The impact of different salinity levels on the expression of pearl spot genes such as IGF-1, SOD, CAT, NaKATPase, OSTF-1, and HSP70 was investigated, along with a histological examination of the gills. The results showed significant physiological and cellular damage caused by the salinity levels. The expression analysis showed that liver IGF-1 mRNA expression increased by 2.6-fold at 15ppt, and HSP70 mRNA expression in the liver also showed a significant increase with rising salinity levels. In addition, OSTF1 expression exhibited an increase at 15ppt, whereas SOD and CAT expression reached their highest levels at 25ppt. At 15ppt, the expression of NKA mRNA increased significantly by 2.8-fold. The studys overall findings suggested that the fish demonstrated strong molecular-level performance between 15 to 25ppt salinity levels, with the best results observed at 15ppt. These findings suggest that utilizing a salinity level of 15ppt for Pearl spot production could be viable for profitable aquaculture.

animal behavior and cognition↗

Panpipes: a pipeline for multiomic single-cell data analysis.

Single-cell multiomic analysis of the epigenome, transcriptome and proteome allows for comprehensive characterisation of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customisable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.

bioinformatics↗

Gliotransmission of D-serine promotes thirst-directed behaviors in Drosophila

Thirst emerges from a range of cellular changes that ultimately motivate an animal to consume water. Although thirst-responsive neuronal signals have been reported, the full complement of brain responses is unclear. Here we identify molecular and cellular adaptations in the brain using single-cell sequencing of water deprived Drosophila. Water deficiency primarily altered the glial transcriptome. Screening the regulated genes revealed astrocytic expression of the astray-encoded phosphoserine phosphatase to bi-directionally regulate water consumption. Astray synthesizes the gliotransmitter D-serine and vesicular release from astrocytes is required for drinking. Moreover, dietary D-serine rescues aay-dependent drinking deficits while facilitating water consumption and expression of water-seeking memory. D-serine action requires binding to neuronal NMDA-type glutamate receptors. Fly astrocytes contribute processes to tripartite synapses and the proportion of astrocytes that are themselves activated by glutamate increases with water deprivation. We propose that thirst elevates astrocytic D-serine release, which awakens quiescent glutamatergic circuits to enhance water procurement.

neuroscience↗

Systematic Single Cell Pathway Analysis (SCPA) reveals novel pathways engaged during early T cell activation

Next generation sequencing technologies have revolutionized the study of T cell biology, capturing previously unrecognized diversity in cellular states and functions. Pathway analysis is a key analytical stage in the interpretation of such transcriptomic data, providing a powerful method for detecting alterations in important biological processes. Current pathway analysis tools are built on models developed for bulk-RNA sequencing, limiting their effectiveness when applied to more complex single cell RNA-sequencing (scRNA-seq) datasets. We recently developed a sensitive and distribution-free statistical framework for multisample distribution testing, which we implement here in the open-source R package Single Cell Pathway Analysis (SCPA). After demonstrating the effectiveness of SCPA over commonly used methods, we generate a scRNA-seq T cell dataset and characterize pathway activity over early cellular activation and between T cell populations. This revealed unexpected regulatory pathways in T cells, such as an intrinsic type I interferon system regulating T cell survival and a reliance on arachidonic acid metabolism throughout T cell activation. A systems level characterization of pathway activity in T cells across multiple human tissues also revealed alpha defensin expression as a hallmark of bone marrow derived T cells. Overall, our work here provides a widely applicable tool for single cell pathway analysis, and highlights unexpected regulatory mechanisms of T cells using a novel T cell dataset.

immunology↗

Computationally validated SARS-CoV-2 CTL and HTL Multi-Patch Vaccines designed by reverse epitomics approach, shows potential to cover large ethnically distributed human population worldwide

BackgroundThe SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) is a positive-sense single-stranded RNA coronavirus responsible for the ongoing 2019-2020 COVID-19 outbreak. The highly contagious COVID-19 disease has spread to 216 countries in less than six months. Though several vaccine candidates are being claimed, an effective vaccine is yet to come. In present study we have designed and theoretically validated novel Multi-Patch Vaccines against SARS-CoV-2. MethodologyA novel reverse epitomics approach, "overlapping-epitope-clusters-to-patches" method is utilized to identify multiple antigenic regions from the SARS-CoV-2 proteome. These antigenic regions are here termed as "Ag-Patch or Ag-Patches", for Antigenic Patch or Patches. The identification of Ag-Patches is based on clusters of overlapping epitopes rising from a particular region of SARS-CoV-2 protein. Further, we have utilized the identified Ag-Patches to design Multi-Patch Vaccines (MPVs), proposing a novel methodology for vaccine design and development. The designed MPVs were analyzed for immunologically crucial parameters, physiochemical properties and cDNA constructs. ResultsWe identified 73 CTL (Cytotoxic T-Lymphocyte), 49 HTL (Helper T-Lymphocyte) novel Ag-Patches from the proteome of SARS-CoV-2. The identified Ag-Patches utilized to design MPVs cover 768 (518 CTL and 250 HTL) overlapping epitopes targeting different HLA alleles. Such large number of epitope coverage is not possible for multi-epitope vaccines. The large number of epitopes covered implies large number of HLA alleles targeted, and hence large ethnically distributed human population coverage. The MPVs:Toll-Like Receptor ectodomain complex shows stable nature with numerous hydrogen bond formation and acceptable root mean square deviation and fluctuation. Further, the cDNA analysis favors high expression of the MPVs constructs in human cell line. ConclusionHighly immunogenic novel Ag-Patches are identified from the entire proteome of SARS CoV-2 by a novel reverse epitomics approach. We conclude that the novel Multi-Patch Vaccines could be a highly potential novel approach to combat SARS-CoV-2, with greater effectiveness, high specificity and large human population coverage worldwide. O_FIG O_LINKSMALLFIG WIDTH=187 HEIGHT=200 SRC="FIGDIR/small/284992v1_ufig1.gif" ALT="Figure 1"> View larger version (84K): org.highwire.dtl.DTLVardef@176f27org.highwire.dtl.DTLVardef@82a4fcorg.highwire.dtl.DTLVardef@11db43forg.highwire.dtl.DTLVardef@12495b2_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOABSTRACT FIGURE:C_FLOATNO A Multi-Patch Vaccine design to combat SARS-CoV-2 and a method to prepare thereof. Multi-Patch Vaccine designing to combat SARS-CoV-2 infection by reverse epitomics approach, "Overlapping-epitope-clusters-to-patches" method. C_FIG

immunology↗

Single-cell sequencing of human iPSC-derived cerebellar organoids shows recapitulation of cerebellar development

Current protocols for producing cerebellar neurons from human pluripotent stem cells (hPSCs) are reliant on animal co-culture and mostly exist as monolayers, which have limited capability to recapitulate the complex arrangement of the brain. We developed a method to differentiate hPSCs into cerebellar organoids that display hallmarks of in vivo cerebellar development. Single-cell profiling followed by comparison to an atlas of the developing murine cerebellum revealed transcriptionally-discrete populations encompassing all major cerebellar cell types. Matrigel encapsulation altered organoid growth dynamics, resulting in differential regulation of cell cycle, migration and cell-death pathways. However, this was at the expense of reproducibility. Furthermore, we showed the contribution of basement membrane signalling to both cellular composition of the organoids and developmentally-relevant gene expression programmes. This model system has exciting implications for studying cerebellar development and disease most notably by providing xeno-free conditions, representing a more biologically relevant and therapeutically tractable culture setting.

neuroscience↗

A human single-cell atlas of the Substantia nigra reveals novel cell-specific pathways associated with the genetic risk of Parkinson's disease and neuropsychiatric disorders.

We describe a human single-nuclei transcriptomic atlas for the Substantia nigra (SN), generated by sequencing ~ 17,000 nuclei from matched cortical and SN samples. We show that the common genetic risk for Parkinsons disease (PD) is associated with dopaminergic neuron (DaN)-specific gene expression, including mitochondrial functioning, protein folding and ubiquitination pathways. We identify a distinct cell type association between PD risk and oligodendrocyte-specific gene expression. Unlike Alzheimers disease (AD), we find no association between PD risk and microglia or astrocytes, suggesting that neuroinflammation plays a less causal role in PD than AD. Beyond PD, we find associations between SN DaNs and GABAergic neuron gene expression patterns with multiple neuropsychiatric disorders. Nevertheless, we find that each neuropsychiatric disorder is associated with a distinct set of genes within that neuron type. This atlas guides our aetiological understanding by associating SN cell type expression profiles with specific disease risk.

neuroscience↗

Hispanic/Latino gastric adenocarcinoma patients have distinct molecular profiles including a high rate of germline CDH1 mutations

Hispanic/Latino patients have a higher incidence of gastric cancer and worse cancer-related outcomes as compared to patients of other backgrounds. Whether there is a molecular basis for these disparities is unknown, as very few Hispanic/Latino patients were included in previous studies. We performed a large, integrated genomic analysis of gastric cancer samples from Hispanic/Latino patients. Whole-exome sequencing (WES) and RNA sequencing were performed on 57 Hispanic/Latino gastric cancer patient samples. Germline analysis was conducted on 83 patients. Functional testing of CDH1 germline mutations was performed in Chinese hamster ovary cells. Tumors from Hispanic/Latino patients were significantly enriched for the genomically-stable subtype (as defined by The Cancer Genome Atlas), compared to Asians and Whites (65% vs 21% vs 20%, P < 0.001). Transcriptomic analysis identified molecular signatures that were prognostic. Of the 43 Hispanic/Latino patients with diffuse-type gastric cancer, 7 (16%) had germline mutations in CDH1. Mutation carriers were significantly younger than non-carriers (41 vs 50 years, P < 0.05). E-cadherin expression was reduced in 5 of 6 mutation carrier tumor samples available for analysis. In silico algorithms predicted 5 variants were deleterious. For the two variants that were predicted to be benign, we demonstrated that the mutations conferred increased migratory capability, suggesting pathogenicity. Hispanic/Latino gastric cancer patients possess unique genomic landscapes. This includes a high rate of CDH1 germline mutations that may partially explain their aggressive clinical phenotypes. Individualized screening, genetic counseling, and treatment protocols based on patient ethnicity and race may be necessary.

cancer biology↗

Transfer learning in single-cell transcriptomics improves data denoising and pattern discovery

Single-cell RNA sequencing (scRNA-seq) data is noisy and sparse. Here, we show that transfer learning across datasets remarkably improves data quality. By coupling a deep autoencoder with a Bayesian model, SAVER-X extracts transferable gene-gene relationships across data from different labs, varying conditions, and divergent species to denoise target new datasets.

bioinformatics↗

Analysing protein post-translational modform regions by linear programming

Post-translational modifications (PTMs) at multiple sites can collectively influence protein function but the scope of such PTM coding has been challenging to determine. The number of potential combinatorial patterns of PTMs on a single molecule increases exponentially with the number of modification sites and a population of molecules exhibits a distribution of such \"modforms\". Estimating these \"modform distributions\" is central to understanding how PTMs influence protein function. Although mass-spectrometry (MS) has made modforms more accessible, we have previously shown that current MS technology cannot recover the modform distribution of heavily modified proteins. However, MS data yield linear equations for modform amounts, which constrain the distribution within a high-dimensional, polyhedral \"modform region\". Here, we show that linear programming (LP) can efficiently determine a range within which each modform value must lie, thereby approximating the modform region. We use this method on simulated data for mitogen-activated protein kinase 1 with the 7 phosphorylations reported on UniProt, giving a modform region in a 128 dimensional space. The exact dimension of the region is determined by the number of linearly independent equations but its size and shape depend on the data. The average modform range, which is a measure of size, reduces when data from bottom-up (BU) MS, in which proteins are first digested into peptides, is combined with data from top-down (TD) MS, in which whole proteins are analysed. Furthermore, when the modform distribution is structured, as might be expected of real distributions, the modform region for BU and TD combined has a more intricate polyhedral shape and is substantially more constrained than that of a random distribution. These results give the first insights into high-dimensional modform regions and confirm that fast LP methods can be used to analyse them. We discuss the problems of using modform regions with real data, when the actual modform distribution will not be known.

systems biology↗