Biology subjects

Khare, S.

Publications and source records attributed to Khare, S..

At least 19 recordsLinked to original sources

bioRxiv2026

HaloMPNN: retraining ProteinMPNN on halophilic proteomes for salt-tolerant enzyme design

Machine learning-guided protein sequence redesign is now routinely used to optimize multiple properties relevant for protein engineering, most prominently thermostability and recombinant expression levels. Salt tolerance is a valuable property for "blue-biotechnology"- enabled biomanufacturing, yet no generative computational method exists to redesign proteins for increased salt tolerance. We hypothesized that a training dataset heavily biased toward salt-adapted proteomes would yield a model capable of designing proteins with halophilic properties. To test this, we retrained the sequence redesign model ProteinMPNN on proteins from "salt-in" extreme halophiles such as Haloarcula marismortui, a Dead Sea archaeon that grows optimally near 3-4 M NaCl, roughly six times the salinity of seawater, and accumulates molar concentrations of salts in its cytoplasm. Our model, HaloMPNN, redesigns non-halophilic proteins so that their properties shift towards those of natural halophilic proteins: lower predicted isoelectric point, greater surface acidity, and reduced surface and core hydrophobicity. Redesigning a broad range of non-halophilic proteins with SolubleMPNN, ProteinMPNN, and HyperMPNN shows that this shift is specific to HaloMPNN rather than a generic consequence of sequence redesign. HaloMPNN therefore offers both a route to designing candidate salt-tolerant enzymes and a means of identifying the characteristics that underlie halophilic adaptation.

bioengineering↗

bioRxiv2026

HLiCA: An integrated cell atlas of the healthy human liver

The human liver is composed of a heterogeneous mix of cell types. How these distinct populations contribute individually and collectively to liver function remains poorly understood. Although single-cell technologies have advanced our understanding of liver biology, individual studies have often been limited by small donor cohorts and inconsistent cell type annotations. Integrating multiple datasets can overcome these challenges and better capture biological variability. We present the Human Liver Cell Atlas (HLiCA), an integrated reference of non-disease liver cells assembled from eight datasets across six research centers, encompassing more than 525,000 cells from 110 donors. Developed in collaboration with the Human Cell Atlas Liver Bionetwork, the HLiCA incorporates expert-curated cell annotations refined through community feedback and dedicated cell type annotation meetings. The HLiCA classifies cells into six lineages and expands the cell type resolution to include 47 distinct cell types. Starting from raw sequencing reads, we realigned all data and performed rigorous benchmarking to ensure robust integration across technical and biological variables. Genetic ancestry was inferred for all samples to evaluate the range of ancestral backgrounds represented in the atlas. The expanded cell type annotation enabled identification of previously unrecognized liver cell types, including NRXN1+ stromal cells. Their presence was validated using spatial transcriptomics, which localized NRXN1+ stromal cells to periportal regions. With the number of donors included in the HLiCA we were able to examine cell type specific associations with demographic covariates. In hepatocytes, drug metabolism genes showed differential expression between sexes, and in cholangiocytes, mucus-production genes varied with age. As the largest and most genetically diverse human liver cell atlas to date, the HLiCA provides a comprehensive, well-annotated reference for the field, annotated by expert consensus. This resource will enable deeper interrogation of liver cellular diversity, architecture, and function in the healthy human liver and serve as a reference to understand changes that occur with disease.

genomics↗

bioRxiv2026

Supercharged binding modules can modulate engineered poly(ethylene terephthalate) hydrolase thermostability and functional persistence

Poly(ethylene terephthalate) (PET) is a highly recalcitrant polyester plastic whose resistance to degradation has contributed to widespread environmental accumulation. Enzymatic PET depolymerization has emerged as a promising bioremediation strategy, but PET hydrolysis remains challenging due to the insoluble and semi-crystalline nature of PET and the poor thermostability of many PET hydrolases at elevated temperatures. Here, several electrostatically supercharged PET binding modules (PBM) were fused to a PET-hydrolyzing Cutinase Catalytic Domain (CD) from the thermophilic microbe Thermobifida fusca to investigate how engineered PBM surface charge influences PET hydrolysis behavior. All PBM designs were derived from a native T. fusca family-2a carbohydrate binding module (CBM) as starting template. Since PET exhibited a substantially negative zeta potential, and accordingly, all positively supercharged PBMs displayed the strongest PET binding interactions in pull-down binding assays. However, stronger PET binding did not translate to improved hydrolysis activity for the fusion constructs. Instead, a slightly negatively charged PBM-CD fusion (D2 construct) exhibited activity comparable to the Cutinase CD on finely milled PET powder while showing substantially improved activity on intact PET discs, suggesting potential advantages for depolymerization of minimally processed PET feedstocks. Thermostability analysis identified an approximately 10 {degrees}C increase in melting temperature for the D2 fusion construct, corresponding to enhanced catalytic persistence and a shifted optimal hydrolysis temperature. Consequently, this construct exhibited an approximately 2-fold increase in long-term hydrolysis activity on milled PET and up to a 10-fold increase on intact PET discs, even at high solids loadings, compared to the native Cutinase CD. Collectively, these findings demonstrate that thermostability, rather than adsorption to PET alone, is a dominant factor governing functional persistence of PET hydrolases.

bioengineering↗

bioRxiv2025

Combinatorial Protein Language Model-Guided Engineering of TEV Protease for Enhanced Stability and Production

Industrial enzyme engineering focuses on improvement of enzyme production yield, stability, catalytic activity, and substrate specificity, but often suffers from low efficiency with time-consuming and labor-intensive design and screening processes of massive libraries. Recent advances in AI and machine learning created protein language models trained by numerous datasets and shed new lights to speed up the enzyme engineering processes with high accuracy structural prediction. Here, we developed a highly efficient enzyme engineering strategy combining three protein language models (xTrimoMPNN-Thermo, ESM-IF, and MPNNsol) and use it to generate TEV protease variants with improved expression, stability, and function. The results indicated that a small number of TEV protease designs (<50 designs) were sufficient to develop variants with desired properties, demonstrating its high efficiency. Our strategy could be broadly applied to accelerate designing and engineering various industrial enzymes.

bioengineering↗

bioRxiv2025

Constrained Diffusion for Protein Design with Hard Structural Constraints

AO_SCPLOWBSTRACTC_SCPLOWDiffusion models offer a powerful means of capturing the manifold of realistic protein structures, enabling rapid design for protein engineering tasks. However, existing approaches observe critical failure modes when precise constraints are necessary for functional design. To this end, we present a constrained diffusion framework for structure-guided protein design, ensuring strict adherence to functional requirements while maintaining precise stereochemical and geometric feasibility. The approach integrates proximal feasibility updates with ADMM decomposition into the generative process, scaling effectively to the complex constraint sets of this domain. We evaluate on challenging protein design tasks, including motif scaffolding and vacancy-constrained pocket design, while introducing a novel curated benchmark dataset for motif scaffolding in the PDZ domain. Our approach achieves state-of-the-art, providing perfect satisfaction of bonding and geometric constraints with no degradation in structural diversity.

molecular biology↗

bioRxiv2025

Single-Cell Integration of Chromatin Accessibility and Transcriptomics Reveals Regulatory Networks in Ovarian Tumor-Infiltrating Adaptive NK Cells

Natural killer (NK) cells are traditionally recognized for their rapid, non-specific responses against virus-infected or malignantly transformed cells, functioning as key effectors of innate immunity. However, a distinct subset known as adaptive NK (aNK) cells has been shown to acquire memory-like properties following viral infections, indicating their capacity for antigen-specific immune recall. Intriguingly, aNK cells have also been identified within the tumor microenvironment, where they can mediate tumor-specific recall responses. Yet, the regulatory mechanisms governing their function in tumor-infiltrating aNK cells remain largely undefined. In this study, we integrated publicly available multiomics datasets from ovarian cancer, including single-cell chromatin accessibility (scATAC-seq) and single-cell RNA sequencing (scRNA-seq), to identify chromatin-accessible regions and construct transcription factors (TF)-gene regulatory networks. To validate and extend these findings, we performed Smart-seq3 on NK cells isolated from ovarian tumors and applied SCENIC analysis to identify TF-driven gene regulation. By integrating results from both analyses, we identified PRDM1 and STAT2 as key TFs, along with their downstream targets CRCP and MTFP1, specifically enriched in tumor-infiltrating aNK cells. The expression levels of CRCP and MTFP1 positively correlated with NK cell infiltration in ovarian cancer tissues, suggesting their potential functions in supporting tumor-specific NK cell memory responses. In addition, external validation using data from the PROMIX clinical trial demonstrated that PRDM1 and STAT2 expression levels are positively associated with both overall survival and aNK cell-associated transcriptional features. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/676040v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@6df026org.highwire.dtl.DTLVardef@1b16dc8org.highwire.dtl.DTLVardef@19e916borg.highwire.dtl.DTLVardef@15d431a_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

In silico discovery of pathogenic PD-L1 nsSNVs with altered glycosylation and immunotherapy binding

Immune checkpoint inhibitors (ICIs), particularly anti-PD-L1 monoclonal antibodies, block extracellular interactions between programmed death ligand-1 (PD-L1) and programmed cell death protein-1 (PD-1) to enhance antitumour immunity. Here, we present a streamlined workflow integrating public databases, bioinformatics tools, and in silico molecular dynamics (MD) simulations and in vitro experimental assays to identify consequential pathogenic nonsynonymous PD-L1 variants (PD-L1SNVs). Four variants predicted as pathogenic across six bioinformatic tools (SIFT, PolyPhen-2, PANTHER, SNP-PhD, SNP&GO, and Pmut) destabilized the anti-PD-L1 ICI atezolizumab-drug binding epitope in MD studies, correlating with reduced drug affinity. Live-cell assays directly linked PD-L1SNVs to intracellular trafficking defects and aberrant glycosylation, even distal from N-glycosylation sites. Allostery modeling (AlloSigMA) linked these disruptions to long-range structural perturbations. Our work establishes bioinformatics-driven predictions of functionally impactful pathogenic PD-L1SNVs and highlight how glycosylation/trafficking defects may serve as key mechanisms compromising therapeutic efficacy. These insights in predicting and validating pathogenic PD-L1SNVs has significant implications for guiding the personalized selection of effective PD-L1-targeted therapies in clinical settings. One Sentence Summary: Uncovering the implication of non-synonymous single nucleotide variants (nsSNVs) on the structure and function of Programmed Death Ligand-1.

bioinformatics↗

bioRxiv2025

VDR-calcium axis regulates the diet-driven metabolic shift during weaning

Weaning in mammals is associated with a shift in the metabolism, driven by the differences in the macronutrient composition of milk and post-weaning diet. Milk has a higher fat content compared with the carbohydrate-enriched solid food. Malnutrition during this stage could affect this transition with long-term adverse effects. The role of micronutrients during this transition is not well understood. Mice lacking a functional vitamin D receptor (VDR) progressively develop severe skeletal muscle and adipose atrophy after weaning, suggesting a role for vitamin D signaling in the metabolic transition during weaning. Here, we demonstrate that after weaning, VDR knock-out mice exhibit systemic energy deprivation and higher lipolysis in inguinal white adipose tissue, probably due to increased norepinephrine signaling via protein kinase A (PKA) and extracellular signalling-regulated kinase (ERK) pathways. Energy deprivation in vdr-/- mice is associated with defective liver glycogenolysis, characterized by increased expression of protein phosphatase-1 and decreased phosphorylation of glycogen phosphorylase. However, restoration of serum calcium levels by a rescue diet is sufficient to restore energy metabolism in vdr-/- mice. Interestingly, maintaining a high-fat-containing milk-based diet post-weaning could prevent the onset of energy deprivation, liver glycogen storage defect, and adipose atrophy in these mice without restoring serum calcium levels. Our data show that the vitamin D-calcium axis is essential for the adaptation of mice to the dietary shift from high-fat-containing milk to post-weaning carbohydrate-enriched diets. It also reveals a novel macronutrient-micronutrient interaction that shapes the metabolic flexibility of the individual based on the dietary composition of nutrients.

physiology↗

bioRxiv2024

A Novel Early Onset Spinocerebellar Ataxia 13 BAC Mouse Model with Cerebellar Hypoplasia, Tremor, and Ataxic Gait

Spinocerebellar ataxia 13 (SCA13) is an autosomal dominant neurological disorder caused by mutations in KCNC3. Our previous studies revealed that KCNC3 mutation R423H results in an early-onset form of SCA13. Previous biological models of SCA13 include zebrafish and Drosophila but no mammalian systems. More recently, mouse models with KCNC3 mutations presented behavioral abnormalities but without obvious pathological changes in the cerebellum, a hallmark of patients with SCA13. Here, we present a novel transgenic mouse model by bacterial artificial chromosome (BAC) recombineering to express the full-length mouse Kcnc3 expressing the R424H mutation. This BAC-R424H mice exhibited behavioral and pathological changes mimicking the clinical phenotype of the disease. The BAC-R424H mice (homologous to R423H in human) developed early onset clinical symptoms with aberrant gait, tremor, and cerebellar hypoplasia/atrophy. Histopathological analysis of the cerebellum in BAC-R424H mice showed progressive Purkinje cell loss and thinning of the molecular cell layer. Additionally, Purkinje cells of BAC-R424H mice showed significantly lower spontaneous firing frequency with a corresponding increase in inter-spike interval compared to that of wild-type mice. Our SCA13 transgenic mice recapitulate both neuropathological and behavioral changes manifested in human SCA13 R423H patients and provide an advantageous approach to understanding the role of voltage-gated potassium channel in cerebellar morphogenesis and function. This mammalian in vivo model will lead to further understanding of the R423H allelic form of SCA13 from the molecular to the behavioral level and serve as a platform for testing potential therapeutic compounds.

neuroscience↗

bioRxiv2024

Association of cerebellar inflammation and neurodegeneration in a novel spinocerebellar ataxia type 13 mouse model

BackgroundNeuroinflammation is a recognized pathological characteristic of neurodegenerative diseases. Spinocerebellar ataxia 13 (SCA13) is a progressive neurodegenerative disease with no effective treatments. Our previous studies reported human mutations in KCNC3 gene are causative for SCA13. Human R423H allelic mutation induces early-onset neurodegeneration and aberrant intracellular retention of Epidermal Growth Factor Receptor (EGFR) in drosophila. However, the neurodegeneration and inflammatory response induced by the R424H allele are unknown in a mammalian model of disease. MethodIn this study, a single Kcnc3 R424H mutation (Analogous to the human SCA13 R423H isoform) transgenic mice were created using CRISPR/Cas 9 technique. Motor function (gait, tremor, coordination and balance) and cerebellar volume (scanned and imaged with 7T MRI) of the R424H transgenic mice were evaluated at multiple timepoints. Neurodegeneration (Purkinje cells loss) as well as cerebellar (astroglia, microglia and macrophage activation) and peripheral (plasma cytokines levels) inflammatory responses were examined and analyzed. ResultThe R424H transgenic mice showed marked neurological motor dysfunction with high-frequency tremor, aberrant gait, and short latency to fall in Rotarod testing at 3 and 6 months of age. Abnormal spontaneous firing was recorded in electrophysiology of Purkinje cells. Pathological changes in our R424H transgenic mice included progressive Purkinje cell degeneration and cerebellar atrophy. Over-active microglia, astrocytes, and macrophages were observed in the cerebella of transgenic mice. Pearson correlation analyses indicated that the number of Calbindin positive cells, a Purkinje cell marker, showed a strong inverse correlation with the positive cell number of EGFR, phosphorylated EGFR (pEGFR), and CD68. The expression of EGFR/pEGFR was positively correlated with CD68 and Glial Fibrillary Acidic Protein. ConclusionTransgenic R424H mice provide a novel SCA13 model showing significant motor deficits, Purkinje cells loss, cerebellar inflammation, and atrophy. Our study suggests that the activation of inflammatory immune cells (astroglia, microglia and macrophages) and strong expression of EGFR/ pEGFR signal in these immune cells are associated with Purkinje cell loss in the cerebellum. This abnormal neuroinflammation may play a significant role in the aggressive procession of neurodegeneration.

neuroscience↗

bioRxiv2024

Framework for Martini-based Coarse-grained Model of Enzymes: Model Development and Experimental Validation

Recent experiments have shown that enzyme activity can preserved in harsh environments by complexing enzyme with polymer into a Protein-Polymer Hybrid (PPH). In a successful PPH, heteropolymer strands bind to the enzyme surface and restrain the folded protein without adversely affecting the binding and active sites. It is believed that hybridization is driven by non-covalent interactions at the enzyme surface including hydrophobicity and electrostatics. Molecular modeling of these interactions is not practical at the all-atom scale due to the long timescales and large particle counts needed to characterize binding. Protein structure at the scale of amino acid residues is parsimoniously represented by a coarse-grained model in which one particle represents several atoms, significantly reducing the cost of simulation. In this study we present two coarse-grained enzyme models - lipase and dehalogenase - prepared using a top-down modeling strategy. We simulate each enzyme in aqueous solution and calculate statistics of protein surface features and shape descriptors. The values from the coarse-grained data are compared with the same calculations performed on all-atom reference systems, revealing key similarities of surface chemistry at the two scales. Structural measures are calculated from the all-atom reference systems and compared with estimates from small-angle X-ray scattering (SAXS) experiments, with good agreement between the two. The described procedures of modeling and analysis comprise a framework for the development of coarse-grained models of protein surfaces with validation to experiment.

biophysics↗

bioRxiv2024

Statins attenuate Wnt/β-catenin signaling by targeting SATB family proteins in colorectal cancer

Colorectal cancer is the second leading cause of cancer-related deaths worldwide, highlighting the need for improved treatments and advanced molecular research. A recent therapeutic approach focuses on repurposing drugs to target dysregulated pathways involved in tumorigenesis. Among these, statins, commonly known for lowering cholesterol, have attracted attention for their potential anti-cancer properties. Here, we provide direct evidence for the same by assessing the impact of statin treatment on lipid, transcript, and protein levels. Our findings reveal that statins specifically target key components of the Wnt/{beta}-catenin pathway, a major factor in adenoma formation, including the SATB (Special AT-rich Binding protein) family proteins. While SATB1 is recognized as a regulator of tumorigenesis, particularly under Wnt signaling, SATB2 appears to exert an opposing role. We demonstrate that statin treatment reciprocally alters the expression pattern of these proteins. Furthermore, a human clinical trial evaluating statins as an anti-cancer therapy supports the hypothesis that differential expression of SATB proteins is crucial in tumorigenic outcomes. In conclusion, this modulation by statin treatment suggests promising new therapeutic avenues through drug repurposing.

cancer biology↗

bioRxiv2024

Enhanced NK cell proficiency in solid tumors through antigen-specific memory via NKG2C/A-HLA-E/ABC recruited to tumors by CXCR2

Natural killer (NK) cells have emerged as promising effectors in cancer immunotherapy due to their ability to recognize and eliminate tumor cells. To investigate the immunological memory and tumor reactivity of adaptive (a)NK cells in the context of desmoplastic tumors, we used human ovarian cancer as a model. Through in vitro culture systems resembling dendritic cell (DC)-mediated T cell activation, we demonstrated that aNK cells exhibit antigen-specific cytotoxic responses and memory generation towards ovarian tumor antigens. Furthermore, mature DCs presenting tumor-associated antigens induced the expansion of aNK cells, suggesting antigen-specific proliferation. Single-cell transcriptomics revealed a distinct genetic signature of aNK cells in tumor samples, characterized by a cytotoxic phenotype and interactions with myeloid cells, particularly DCs. The spatial analysis confirmed the intratumoral presence of aNK cells, with higher abundance in the tumor nest compared to conventional (c)NK cells. Functional assays demonstrated the cytotoxicity of expanded aNK cells against autologous ovarian tumors, accompanied by an activated receptor profile. Importantly, aNK cells displayed antigen-specific memory responses towards primary tumors, maintaining specificity over time. Blockade of NKG2C and HLA-E influenced aNK cell recall responses, indicating their roles in the adaptive NK cell immune memory. Additionally, CXCR2 was essential for efficient aNK cell migration toward tumors. These findings shed light on the therapeutic potential of aNK cells in ovarian cancer immunotherapy, highlighting their ability to develop immunological memory and effectively eradicate tumor cells.

immunology↗

bioRxiv2023

Global Transcriptomics of Congenital Hepatic Fibrosis in Autosomal Recessive Polycystic Kidney Disease using PCK rats

Congenital hepatic fibrosis / Autosomal recessive polycystic kidney disease (CHF/ARPKD) is an inherited neonatal disease induced by mutations in the PKHD1 gene and characterized by cysts, and robust pericystic fibrosis in liver and kidney. The PCK rat is an excellent animal model which carries a Pkhd1 mutation and exhibits similar pathophysiology. We performed RNA-Seq analysis on liver samples from PCK rats over a time course of postnatal day (PND) 15, 20, 30, and 90 using age-matched Sprague-Dawley (SD) rats as controls to characterize molecular mechanisms of CHF/ARPKD pathogenesis. A comprehensive differential gene expression (DEG) analysis identified 1298 DEGs between PCK and SD rats. The genes overexpressed in the PCK rats at PND 30 and 90 were involved cell migration (e.g. Lamc2, Tgfb2, and Plet1), cell adhesion (e.g. Spp1, Adgrg1, and Cd44), and wound healing (e.g. Plat, Celsr1, Tpm1). Connective tissue growth factor (Ctgf) and platelet-derived growth factor (Pdgfb), two genes associated with fibrosis, were upregulated in PCK rats at all time-points. Genes associated with MHC class I molecules (e.g. RT1-A2) or involved in ribosome assembly (e.g. Pes1) were significantly downregulated in PCK rats. Upstream regulator analysis showed activation of proteins involved tissue growth (MTPN) and inflammation (STAT family members) and chromatin remodeling (BRG1), and inhibition of proteins involved in hepatic differentiation (HNF4) and reduction of fibrosis (SMAD7). The increase in mRNAs of four top upregulated genes including Reg3b, Aoc1, Tm4sf20, and Cdx2 was confirmed at the protein level using immunohistochemistry. In conclusion, these studies indicate that a combination of increased inflammation, cell migration and wound healing, and inhibition of hepatic function, decreased antifibrotic gene expression are the major underlying pathogenic mechanisms in CHF/ARPKD.

pathology↗

bioRxiv2022

Outcome of Crash Course Training on Protein Structure Prediction with Artificial Intelligence

Protein structure predictions have broad impact on several science disciplines such as biology, bioengineering, and medical science. AlphaFold2[1] and RoseTTAFold[2] are the current state-of-the-art AI methods to predict the structures of proteins with an accuracy comparable to lower-resolution experimental methods. In its 2021 year review, both these methods were recognized as "breakthrough of the year" by Science magazine[3] and "method of the year" by Nature magazine [4]. It is timely and important to provide training and support on these emerging methods. Our crash course "Enabling Protein Structure Prediction with Artificial Intelligence "was conducted in collaboration with domain experts and research computing professionals. The crash course was well received by the community as there were 750 registrants from all over the world. Here we provide the summary of the crash course, describe our findings in organizing the crash course, and explain what preparation steps helped us with the hands-on training. CCS CONCEPTSComputing methodologies a Machine learning a Machine learning approaches a Bio-inspired approaches

scientific communication and education↗

bioRxiv2022

Codon optimality is the primary contributor to the exceptional mutational sensitivity of CcdA antitoxin in its operonic context

Deep mutational scanning studies suggest that synonymous mutations are typically silent and that most exposed, non active-site residues are tolerant to mutations. Here we show that the ccdA antitoxin component of the E.coli ccdAB toxin-antitoxin system is unusually sensitive to mutations when studied in the operonic context. A large fraction ([~]80%) of single-codon mutations, including many synonymous mutations in the ccdA gene shows inactive phenotype, but they retain native-like binding affinity towards cognate toxin, CcdB. Therefore, the observed phenotypic effects are largely not due to alterations in protein structure/stability, consistent with a large region of CcdA being intrinsically disordered. E. coli codon preference and strength of ribosome-binding associated with translation of downstream ccdB gene are found to be major contributors of the observed mutant phenotypes. In select cases, proteomics studies reveal altered ratios of CcdA:CcdB protein levels in vivo, suggesting that the ccdA mutations likely alter relative translation efficiencies of the two genes in the operon. We extend these results by studying single-site synonymous mutations that lead to loss of function phenotypes in the relBE operon upon introduction of rarer codons. Thus, in their operonic context, genes are likely to be more sensitive to both synonymous and non-synonymous point mutations than inferred previously.

molecular biology↗

bioRxiv2021

Redefining tumor classification and clinical stratification through a colorectal cancer single-cell atlas

Colorectal cancer (CRC), a disease of high incidence and mortality, has had few treatment advances owing to a large degree of inter- and intratumoral heterogeneity. Attempts to classify subtypes of colorectal cancer to develop treatment strategies has been attempted by Consensus Molecular Subtypes (CMS) classification. However, the cellular etiology of CMS classification is incompletely understood and controversial. Here, we generated and analyzed a single-cell transcriptome atlas of 49,859 CRC cells from 16 patients, validated with an additional 31,383 cells from an independent CRC patient cohort. We describe subclonal transcriptomic heterogeneity of CRC tumor epithelial cells, as well as discrete stromal populations of cancer-associated fibroblasts (CAFs). Within CRC CAFs, we identify the transcriptional signature of specific subtypes (CAF-S1 and CAF-S4) in more than 1,500 CRC patients using bulk transcriptomic data that significantly stratifies overall survival in multiple independent cohorts. We also uncovered two CAF-S1 subpopulations, ecm-myCAF and TGF{beta}-myCAF, known to be associated with primary resistance to immunotherapies. We demonstrate that scRNA analysis of malignant, stromal, and immune cells exhibit a more complex picture than portrayed by bulk transcriptomic-based Consensus Molecular Subtypes (CMS) classification. By demonstrating an abundant degree of heterogeneity amongst these cell types, our work shows that CRC is best represented in a transcriptomic continuum crossing traditional classification systems boundaries. Overall, this CRC cell map provides a framework to re-evaluate CRC tumor biology with implications for clinical trial design and therapeutic development.

cancer biology↗

bioRxiv2020

Environmental and physiological factors affecting high-throughput measurements of bacterial growth

Bacterial growth in nutrient-rich and starvation conditions is intrinsically tied to the environmental history and physiological state of the population. While high-throughput technologies have enabled rapid analyses of mutant libraries, technical and biological challenges complicate data collection and interpretation. Here, we present a framework for the execution and analysis of growth measurements with improved accuracy over standard approaches. Using this framework, we demonstrate key biological insights that emerge from consideration of culturing conditions and history. We determined that quantification of the background absorbance in each well of a multi-well plate is critical for accurate measurements of maximal growth rate. Using mathematical modeling, we demonstrated that maximal growth rate is dependent on initial cell density, which distorts comparisons across strains with variable lag properties. We established a multiple-passage protocol that alleviates the substantial effects of glycerol on growth in carbon-poor media, and we tracked growth rate-mediated fitness increases observed during a long-term evolution of Escherichia coli in low glucose concentrations. Finally, we showed that growth of Bacillus subtilis in the presence of glycerol induces a long lag in the next passage due to inhibition of a large fraction of the population. Transposon mutagenesis linked this phenotype to the incorporation of glycerol into lipoteichoic acids, revealing a new role for these envelope components in resuming growth after starvation. Together, our investigations underscore the complex physiology of bacteria during bulk passaging and the importance of robust strategies to understand and quantify growth. Abstract ImportanceHow starved bacteria adapt to and multiply in replete nutrient conditions is intimately linked to their history of previous growth, their physiological state, and the surrounding environment. While automated equipment has enabled high-throughput growth measurements, data interpretation and knowledge gaps regarding the determinants of growth kinetics complicate comparisons between strains. Here, we present a framework for growth measurements that improves accuracy and attenuates the effects of growth history. We determined that background absorbance quantification and multiple passaging cycles allows for accurate growth-rate measurements even in carbon-poor media, which we used to reveal growth-rate increases during long-term laboratory evolution of Escherichia coli. Using mathematical modeling, we showed that maximum growth rate depends on initial cell density. Finally, we demonstrated that growth of Bacillus subtilis with glycerol inhibits the future growth of most of the population, due to lipoteichoic-acid synthesis. These studies highlight the challenges of accurate quantification of bacterial growth behaviors.

microbiology↗