Biology subjects

Sinha, S.

Publications and source records attributed to Sinha, S..

At least 145 records · Page 8Linked to original sources

bioRxiv2022

Machine Learning Identifies Signatures of Macrophage Reactivity and Tolerance that Predict Disease Outcomes

Single-cell transcriptomic studies have greatly improved organ-specific insights into macrophage polarization states are essential for the initiation and resolution of inflammation in all tissues; however, such insights are yet to translate into therapies that can predictably alter macrophage fate. Using machine learning algorithms on human macrophages, here we reveal the continuum of polarization states that is shared across diverse contexts. A path, comprised of 338 genes accurately identified both physiologic and pathologic spectra of "reactivity" and "tolerance", and remained relevant across tissues, organs, species and immune cells (> 12,500 diverse datasets). This 338-gene signature identified macrophage polarization states at single-cell resolution, in physiology and across diverse human diseases, and in murine pre-clinical disease models. The signature consistently outperformed conventional signatures in the degree of transcriptome-proteome overlap, and in detecting disease states; it also prognosticated outcomes across diverse acute and chronic diseases, e.g., sepsis, liver fibrosis, aging and cancers. Crowd-sourced genetic and pharmacologic studies confirmed that model-rationalized interventions trigger predictable macrophage fates. These findings provide a formal and universally relevant definition of macrophage states and a predictive framework (http://hegemon.ucsd.edu/SMaRT) for the scientific community to develop macrophage-targeted precision diagnostics and therapeutics. One Sentence SummarySignatures of macrophage reactivity and tolerance (SMaRT) predict disease outcomes

systems biology↗

bioRxiv2022

Dose-dependent disruption of hepatic zonation by 2,3,7,8-tetrachlorodibenzo-p-dioxin in mice: integration of single-nuclei RNA sequencing and spatial transcriptomics.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) dose-dependently induces the development of hepatic fat accumulation and inflammation with fibrosis in mice initially in the portal region. Conversely, differential gene and protein expression is first detected in the central region. To further investigate cell-specific and spatially resolved dose-dependent changes in gene expression elicited by TCDD, single-nuclei RNA sequencing and spatial transcriptomics were used for livers of male mice gavaged with TCDD every 4 days for 28 days. The proportion of 11 cell (sub)types across 131,613 nuclei dose-dependently changed with 68% of all portal and central hepatocyte nuclei in control mice being overtaken by macrophages following TCDD treatment. We identified 368 (portal fibroblasts) to 1,339 (macrophages) differentially expressed genes. Spatial analyses revealed initial loss of portal identity that eventually spanned the entire liver lobule with increasing dose. Induction of R-spondin 3 (Rspo3) and pericentral Apc, suggested dysregulation of the Wnt/{beta}-catenin signaling cascade in zonally resolved steatosis. Collectively, the integrated results suggest disruption of zonation contributes to the pattern of TCDD-elicited NAFLD pathologies. SYNOPSIS O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY Single-nuclei RNA sequencing (snRNAseq) and spatial transcriptomics were integrated to investigate cell-specific and spatially resolved dose-dependent changes elicited by TCDD. We show that TCDD causes a loss of zonal characteristics that disrupts spatially defined metabolic functions. - Dose-dependent analyses show higher responsiveness of central hepatocytes despite hepatotoxicity occurring initially in the portal region. - Integration of snRNAseq and spatial transcriptomics demonstrates a loss of hepatocytes with portal characteristics. - TCDD disrupted spatially resolved expression of {beta}-catenin signaling members that are critical in maintaining liver zonation. - Spatial transcriptomics and snRNAseq shows induction of R-spondin3 from nonparenchymal cells which serve as cue for the {beta}-catenin pathway.

pharmacology and toxicology↗

bioRxiv2022

Synthetic lethality-based prediction of cancer treatment response from histopathology images

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Importantly, ENLIGHT-DeepPT successfully predicts true responders in five independent patients cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, trained and tested on the same cohort in cross validation. Its future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries. Statement of SignificanceENLIGHT-DeepPT is the first approach shown to successfully predict response to multiple targeted and immune cancer therapies from H&E slides. In distinction from all previous H&E slides prediction approaches, it does not require supervised training on a specific cohort for each drug/indication treatment but is trained to predict expression on the TCGA cohort and then can predict response to an array of treatments without any further training. ENLIGHT-DeepPT can provide rapid treatment recommendations to oncologists and help advance precision oncology in underserved regions and low-income countries.

cancer biology↗

bioRxiv2022

SARS-CoV-2 Omicron Variant Wave in India: Advent, Phylogeny and Evolution

SARS-CoV-2 evolution has continued to generate variants, responsible for new pandemic waves locally and globally. Varying disease presentation and severity has been ascribed to inherent variant characteristics and vaccine immunity. This study analyzed genomic data from 305 whole genome sequences from SARS-CoV-2 patients before and through the third wave in India. Delta variant was responsible for disease in patients without comorbidity(97%), while Omicron BA.2 caused disease primarily in those with comorbidity(77%). Tissue adaptation studies brought forth higher propensity of Omicron variants to bronchial tissue than lung, contrary to observation in Delta variants from Delhi. Study of codon usage pattern distinguished the prevalent variants, clustering them separately, Omicron BA.2 isolated in February grouped away from December strains, and all BA.2 after December acquired a new mutation S959P in ORF1b (44.3% of BA.2 in the study) indicating ongoing evolution. Loss of critical spike mutations in Omicron BA.2 and gain of immune evasion mutations including G142D, reported in Delta but absent in BA.1, and S371F instead of S371L in BA.1 could possibly be due to evolutionary trade-off and explain very brief period of BA.1 in December 2021, followed by complete replacement by BA.2.

genomics↗

bioRxiv2022

The proto-oncogene DEK regulates neuronal excitability and tau accumulation in Alzheimer's disease vulnerable neurons

Neurons from layer II of the entorhinal cortex (ECII) are the first to accumulate tau protein aggregates and degenerate during prodromal Alzheimers disease. Here, we use a data-driven functional genomics approach to model ECII neurons in silico and identify the proto-oncogene DEK as a potential driver of tau pathology. By modulating DEK levels in EC neurons in vitro and in vivo, we first validate the accuracy and cell-type specificity of our network predictions. We then show that Dek silencing changes the inducibility of immediate early genes and alters neuron excitability, leading to dysregulation of neuronal plasticity genes. We further find that loss of function of DEK leads to tau accumulation in the soma of ECII neurons, reactivity of surrounding microglia, and eventually microglia-mediated neuron loss. This study validates a pathological gene discovery tool that opens new therapeutic avenues and sheds light on a novel pathway driving tau pathology in vulnerable neurons.

neuroscience↗

bioRxiv2022

Hierarchical cue control of drug seeking in the face of cost

Addiction is characterized by intermittent drug seeking despite rising costs. This behavior is heavily influenced by environmental stimuli that signal drug availability and reinforce seeking. We aimed to establish the relationship between three key aspects of human drug use in rats: the intermittent, binge nature of drug intake, the motivational conflict of drug seeking in the face of escalating negative costs, and the ability of different drug cues to interact to modulate relapse. Rats were trained to self administer cocaine on an intermittent access schedule, where brief drug availability states were signaled by a shift in the ambient lighting of the environment and cocaine delivery was signaled by a separate proximal cue. Rats then went through a conflict procedure, where foot shock intensity associated with cocaine seeking was escalated until intake was suppressed. We completed relapse tests where the drug delivery cue was non contingently presented alone, or in the context of dynamic drug availability state transitions. Intermittent access spurred psychomotor sensitization and binge-like cocaine intake. The intensity of binge-like drug taking during training was predictive of later drug seeking despite escalating costs. In relapse tests, the ability of a proximal drug cue to trigger relapse was gated by the presence of a global cue signaling drug-availability state transitions. Our results suggest that the pattern of drug intake plays a role in many features of addiction, including modifying an individuals willingness to endure high costs associated with drug seeking. Further, our results indicate that drug-related sensory information can be hierarchically organized to exert a dynamic modulating influence on drug-seeking motivation.

neuroscience↗

bioRxiv2022

Coupling of NOD2 to GIV is Required for Bacterial Sensing

ABSTRACT/SUMMARYSensing of pathogens by Nucleotide oligomerization domain (NOD)-like 2 receptor (NOD2) induces a protective inflammatory response that coordinates bacterial clearance. Polymorphisms in NOD2 impair bacterial clearance, leading to chronic gut inflammation in Crohns disease (CD) via mechanisms that remain incompletely understood. We identify GIV/Girdin (CCDC88A) as a NOD2-interactor that shapes bacterial sensing-and-signaling in macrophages. Myeloid-specific GIV depletion exacerbated and protracted infectious colitis and abolished the protective effect of muramyl dipeptide (MDP) in both chemical colitis and severe sepsis. In the presence of GIV, macrophages enhance anti-bacterial pathways downstream of NOD2, clear microbes rapidly and concomitantly suppress inflammation. GIVs actions are mediated via its C-terminus, which directly binds the terminal leucine-rich repeat (LRR#10) of NOD2; binding is augmented by MDP and ATP, precedes receptor oligomerization, and is abolished by the 1007fs CD-risk variant which lacks LRR#10. Findings illuminate mechanisms that underlie protective NOD2 signaling and loss of function in the major 1007fs variant. In briefThis work reveals a mechanism by which macrophages use their innate immune sensor, NOD2, to protect the host against overzealous inflammation during bacterial infections, and the consequences of its loss, as occurs in the most important Crohns disease-risk variant. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/489574v2_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@139a1feorg.highwire.dtl.DTLVardef@ba5354org.highwire.dtl.DTLVardef@b4d933org.highwire.dtl.DTLVardef@11fe12e_HPS_FORMAT_FIGEXP M_FIG GRAPHIC ABSTRACT C_FIG HIGHLIGHTSO_LIGIV is a functional and direct interactor of the terminal LRR repeat of NOD2 C_LIO_LIMice lacking M[FE] GIV develop dysbiosis, protracted ileocolitis and sepsis C_LIO_LIMDP/NOD2-dependent protective host responses require GIV C_LIO_LICD-risk NOD2 1007fs variant lacking the terminal LRR#10 cannot bind GIV C_LI

immunology↗

bioRxiv2022

N-terminal helices and A domain of archaeal FtsY facilitate SRP54 binding and the association with cell membrane

The process of protein translocation is essential to the maintenance of cellular life and has been critically addressed in eukaryotes and bacteria. However, little information is available regarding protein translocation across archaeal membranes. The signal recognition particle (SRP) plays an important role in this process. It binds the signal peptide at the N-terminus of the polypeptide chain and interacts with the cognate SRP receptor (FtsY) located on the target membrane to form a targeting complex (TC). Concomitant GTP hydrolysis by SRP and FtsY delivers the polypeptide to the adjacent protein-conducting channel. The present study aims to characterize the structural domains of FtsY contributing to the targeting complex (TC) formation in Sulfolobus acidocaldarius, a thermo-acidophilic crenarchaeon. The contacting residues between SRP54 and FtsY were mapped along the N1-N3 helices. Interestingly, the previously reported crystal structure did not take the N-terminal A domain into account - a region rich in negatively charged residues. Such observation led us to investigate the contribution of each of the three participating helices (N1-3) in terms of membrane association and functional TC formation. Through biophysical analyses of SRP-FtsY and FtsY-membrane interaction, and biochemical characterization of the reciprocal GTPase activity, this work sought to elucidate the minimal structural motif controlling the archaeal TC assembly.

biochemistry↗

bioRxiv2022

A phenotypic screen of Marfan syndrome iPSC-derived vascular smooth muscle cells uncovers GSK3β as a new target

Marfan syndrome (MFS) is a rare connective tissue disorder caused by mutations in FBN1. Patients with MFS notably suffer from aortic aneurysm and dissection. Despite considerable effort, animal models have proven to be poorly predictive for therapeutic intervention in human aortic disease. Using a "humanised" model system may be more appropriate in identifying new therapeutic targets. Patient-derived induced pluripotent stem cells can be differentiated into vascular smooth muscle cells (VSMCs) and recapitulate major features of MFS. We have screened 1,022 small molecules in our in vitro model, exploiting the highly-proteolytic nature of MFS-VSMCs, and identified 36 effective compounds. Further analysis identified GSK3{beta} as a recurring target in the compound screen. GSK3{beta} inhibition/knockdown did not ameliorate the proliferation defect in MFS-VSMCs but improved MFS-VSMC apoptosis and proteolysis. To conclude, we have identified GSK3{beta} as a novel target for MFS, forming the foundation for future work in MFS and other aortic diseases.

cell biology↗

bioRxiv2022

Evolution of decreased aggression in honey bees targeted brain cell gene regulatory networks

Understanding how genotypic variation results in phenotypic variation, a major challenge in biology, is especially difficult for collective behaviour because collective group phenotypes arise from complex interactions between group members1. Honeybees aggressively defend their colony from attacks with highly integrated collective behaviour in which different groups of bees play specific roles, giving rise to distinct colony-level differences in aggression. A previous genome-wide association study of a population of Africanized honeybees (Apis mellifera scutellata) from Puerto Rico that recently evolved decreased aggression identified hundreds of genes with single nucleotide polymorphisms (SNPs) that associated with colony-level variation in aggression2. Many of these SNPs also showed strong signals of selection for decreased aggression2,3, but their influence on brain function was unknown. Using brain single-cell (sc) transcriptomics and sc gene regulatory network analysis, we show here that variants of these genes give rise to genetic differences in transcription factor-target gene relationships. These differences involved the activity of several TFs, some that have been previously associated with aggression, like single stranded-binding protein c31A, and some that have been associated with tissue morphogenesis but not behaviour, like apontic. The activity of these and other TFs was located in specific brain cell populations related to olfaction and vision, the two sensory modalities that bees use in colony defence. They also implicate metabolism of serotonin, a neurochemical already known to influence honeybee aggression, but not from a genetic perspective. Surprisingly, genetic differences were more pronounced in the brains of forager bees than in similarly aged but more aggressive soldier bees, pointing to an evolutionary change in division of labour for colony defence. Our results demonstrate how group genetics can shape a collective phenotype by modulating individual brain gene regulatory network architecture.

genomics↗

bioRxiv2022

Universality of distribution of tumor mutation burden - a biomarker for the tumor evolution and disease risk

Cancers, resulting in uncontrolled cell proliferation, are driven by accumulation of somatic mutations. Genome-wide sequencing has produced a catalogue of millions of somatic mutations, which contain the evolutionary history of the cancers. However, the connection between the mutation accumulation and disease development and risks is poorly understood. Here, we analyzed more than 1,200,000 mutations from 5,000 cancer patients with whole-exome sequencing, and discovered two novel signatures for 16 cancer types in The Cancer Genome Atlas (TCGA) database. A clock-like mutational process, a strong correlation between Tumor Mutation Burden (TMB) and the Patient Age at Diagnosis (PAD), is observed for cancers with low TMB (mean value less than 3 mutations per million base pairs) but is absent in cancers with high TMB. We also validate this finding using whole-genome sequencing data from more than 2,000 patients for 24 cancer types. Surprisingly, we discovered that the distribution of TMB are universal. At low TMB it exhibits a Gaussian distribution and transitions to a power law at hight TMB. The differences in cancer risk between the sexes are also mainly driven by the disparity in mutation burden. The TMB variations, imprinted at the chromosome level, also reflect accumulation of mutation clusters within small chromosome segments in high TMB cancers. By analyzing the characteristics of mutations based on multi-region sequencing, we found that a combination of TMB and intratumor heterogeneity could be a potential biomarker for predicting the patient survival and response to treatment.

cancer biology↗

bioRxiv2022

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

BackgroundPrecision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. MethodsWe present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patients response to a variety of therapies in multiple cancer types, without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: Personalized Oncology (PO), aimed at prioritizing treatments for a single patient, and Clinical Trial Design (CTD), selecting the most likely responders in a patient cohort. FindingsEvaluating ENLIGHTs performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patients treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable to that of supervised predictors developed for specific indications and drugs. In combination with the IFN-{gamma}signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders, while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. ConclusionENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. FundingThis research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

bioinformatics↗

bioRxiv2022

Statistical Mechanical theory for spatio-temporal evolution of Intra-tumor heterogeneity in cancers: Analysis of Multiregion sequencing data

Variations in characteristics from one region (sub-population) to another is commonly observed in complex systems, such as glasses and a collection of cells. Such variations are manifestations of heterogeneity, whose spatial and temporal behavior is hard to describe theoretically. In the context of cancer, intra-tumor heterogeneity (ITH), characterized by cells with genetic and phenotypic variability that co-exist within a single tumor, is often the cause of ineffective therapy and recurrence of cancer. Next-generation sequencing, obtained by sampling multiple regions of a single tumor (multi-region sequencing, M-Seq), has vividly demonstrated the pervasive nature of ITH, raising the need for a theory that accounts for evolution of tumor heterogeneity. Here, we develop a statistical mechanical theory to quantify ITH, using the Hamming distance, between genetic mutations in distinct regions within a single tumor. An analytic expression for ITH, expressed in terms of cell division probability () and mutation probability (p), is validated using cellular-automaton type simulations. Application of the theory successfully captures ITH extracted from M-seq data in patients with exogenous cancers (melanoma and lung). The theory, based on punctuated evolution at the early stages of the tumor followed by neutral evolution, is accurate provided the spatial variation in the tumor mutation burden is not large. We show that there are substantial variations in ITH in distinct regions of a single solid tumor, which supports the notion that distinct subclones could co-exist. The simulations show that there are substantial variations in the sub-populations, with the ITH increasing as the distance between the regions increases. The analytical and simulation framework developed here could be used in the quantitative analyses of the experimental (M-Seq) data. More broadly, our theory is likely to be useful in analyzing dynamic heterogeneity in complex systems such as super-cooled liquids.

biophysics↗

bioRxiv2022

Tracer particles sense local stresses in an evolving multicellular spheroid without affecting the anomalous dynamics of the cancer cells

Measurements of local stresses on the cancer cells (CCs), inferred by embedding inert compressible tracer particles (TPs) in a growing multicellular spheroid (MCS), show that pressure decreases monotonically as the distance from the core of the MCS increases. How faithfully do the TPs report the local stresses in the CCs is an important question because pressure buildup in the MCS is dynamically generated due to CC division, which implies that the CC dynamics should be minimally altered by the TPs. Here using theory and simulations, we show that although the TP dynamics is unusual, exhibiting sub-diffusive behavior on times less than the CC division times and hyper-diffusive dynamics on in the long-time limit, they do not affect the long-time CC dynamics or the local CC stress distributions. The CC pressure profile within the MCS, which decays from a high value at the core to the periphery, is almost identical with and without the TPs. That the TPs have insignificant effect on the local stresses in the MCS implies that they are reliable reporters of the CC microenvironment.

biophysics↗

bioRxiv2022

Barrier-free liquid condensates of nanocatalysts as effective concentrators of catalysis

Molecular confinement of catalysts enhances the catalytic activity significantly. However, physicochemical barriers in traditional confinements restrict the free-passage of substrates/products. To obtain a barrier-free confinement of catalysts, here we explored the liquid-liquid phase separation. Using favourable ionic strength and crowding agents, we recruit the protein-inorganic-composites in phase separated liquid condensates from a solution. The phase separation propensity of these nanocomposites was seen to be independent of the native conformation of the component protein. Using standard catalytic oxidation-reduction reactions, we show that the close-proximity yet diffusive nature of catalysts in solution amplifies the homogeneous catalytic-efficiency of metal particles significantly. Overall, our work demonstrates the roadmap of using inorganic catalysts in homogeneous homogenous solution phase with amplified efficiency and longevity.

biophysics↗

bioRxiv2022

Mechanical feedback controls the emergence of dynamical memory in growing tissue monolayers

The growth of a tissue, which depends on cell-cell interactions and biologically relevant process such as cell division and apoptosis, is regulated by a mechanical feedback mechanism. We account for these effects in a minimal two-dimensional model in order to investigate the consequences of mechanical feedback, which is controlled by a critical pressure, pc. A cell can only grow and divide if the pressure it experiences, due to interaction with its neighbors, is less than pc. Because temperature is an irrelevant variable in the model, the cell dynamics is driven by self-generated active forces (SGAFs) that are created by cell division. It is shown that even in the absence of intercellular interactions, cells undergo diffusive behavior. The SGAF-driven diffusion is indistinguishable from the well-known dynamics of a free Brownian particle at a fixed finite temperature. When intercellular interactions are taken into account, we find persistent temporal correlations in the force-force autocorrelation function (FAF) that extends over timescale of several cell division times. The time-dependence of the FAF reveals memory effects, which increases as pc increases. The observed non-Markovian effects emerge due to the interplay of cell division and mechanical feedback, and is inherently a non-equilibrium phenomenon.

biophysics↗

bioRxiv2022

Phase Separation of Shell Protein and Enzyme: An Insight into the Biogenesis of a Prokaryotic Metabolosome

Bacterial microcompartments are substrate specific metabolic modules that are conditionally expressed in certain bacterial species. These all protein structures have size in the range of 100-150 nm and are formed by the self-assembly of thousands of protein subunits, all encoded by genes belonging to a single operon. The operon contains genes that encode for both enzymes and shell proteins. The shell proteins self-assemble to form the outer coat of the compartment and enzymes are encapsulated within. A perplexing question in MCP biology is to understand the mechanism which governs the formation of these small yet complex assemblages of proteins. In this work we use 1,2-propanediol utilization microcompartments (PduMCP) as a paradigm to identify the factors that drive the self-assembly of MCP proteins. We find that a major shell protein PduBB tend to self-assemble under macromolecular crowded environment and suitable ionic strength. Microscopic visualization and biophysical studies reveal phase separation to be the principle mechanism behind the self-association of shell protein in the presence of salts and macromolecular crowding. The shell protein PduBB interacts with the enzyme diol-dehydratase PduCDE and co-assemble into phase separated liquid droplets. The co-assembly of PduCDE and PduBB results in the enhancement of catalytic activity of the enzyme. A combination of spectroscopic and biochemical techniques shows the relevance of divalent cation Mg2+ in providing stability to intact PduMCP in vivo. Together our results suggest a combination of protein-protein interactions and phase separation guiding the self-assembly of Pdu shell protein and enzyme in solution phase. Significance StatementPresent work shows how surrounding environment modulates the self-assembly behavior of a major shell protein of 1,2-propanediol utilization microcompartment (PduMCP). Appropriate ionic strength and macromolecular crowding bring about liquid-liquid phase separation of the shell protein. Under crowded environment Mg2+ displayed unique property to drive the formation of shell protein liquid condensates. The co-phase separation of a native enzyme along with the shell protein enables it to outperform the enzyme in isolation. This adds on to the existing concept of phase separation being the underlying principle behind the genesis of lipid free organelles in prokaryotes. Further, our results indicate that the divalent metal ion Mg2+ plays an intricate role in the outfitting of the structure-function integrity of PduMCPs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/476392v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1b95271org.highwire.dtl.DTLVardef@1d3a5b5org.highwire.dtl.DTLVardef@2432eaorg.highwire.dtl.DTLVardef@8aad07_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

Predicting patient treatment response and resistance via single-cell transcriptomics of their tumors

Tailoring the best treatments to cancer patients is an important open challenge. Here, we build a precision oncology data science and software framework for PERsonalized single-Cell Expression-based Planning for Treatments In Oncology (PERCEPTION). Our approach capitalizes on recently published matched bulk and single-cell transcriptome profiles of large-scale cell-line drug screens to build treatment response models from patients single-cell (SC) tumor transcriptomics. First, we show that PERCEPTION successfully predicts the response to monotherapy and combination treatments in screens performed in cancer and patient-tumor-derived primary cells based on SC-expression profiles. Second, it successfully stratifies responders to combination therapy based on the patients tumors SC-expression in two very recent multiple myeloma and breast cancer clinical trials. Thirdly, it captures the development of clinical resistance to five standard tyrosine kinase inhibitors using tumor SC-expression profiles obtained during treatment in a lung cancer patients cohort. Notably, PERCEPTION outperforms state-of-the-art bulk expression-based predictors in all three clinical cohorts. In sum, this study provides a first-of-its-kind conceptual and computational method that is predictive of response to therapy in patients, based on the clonal SC gene expression of their tumors.

bioinformatics↗