Biology subjects

Simmons, A.

Publications and source records attributed to Simmons, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Multi-omic analysis reveals maturation programs in human pluripotent stem cell-derived cardiomyocytes during long-term culture

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) hold tremendous promise for disease modeling, drug discovery, and cardiac regenerative therapies. However, the immature phenotype of hPSC-CMs remains a major barrier limiting their translational utility. Here, we performed integrated multi-omic profiling to identify molecular pathways and regulatory programs associated with hPSC-CM maturation during long-term culture. hPSC-CMs were cultured for 113 days and analyzed using metabolomics, proteomics, and transcriptomics across progressive stages of maturation. Long-term culture induced widespread multi-omic remodeling, including significant changes in 142/934 metabolites, 550/3,556 proteins, and 2,892/23,309 transcripts from Day 30 to Day 113. Metabolomic analyses revealed early increases in phospholipid biosynthesis and mitochondrial beta oxidation of fatty acids from Day 30 to Day 60, suggesting metabolic priming precedes later maturation events. In contrast, proteomic remodeling was more prominent during later stages of maturation and was characterized by enhanced calcium handling and cell cycle exit. Transcriptomic analyses demonstrated progressive increases in ion channel expression, t-tubule organization, fatty acid metabolism, creatine shuttle pathways, and cell cycle arrest programs. Transcriptomic and integrative multi-omic pathway analyses identified coordinated suppression of TGF{beta}, MAPK, Wnt, and Hedgehog signaling together with activation of integrin-related, respiratory electron transport, muscle contraction, and Slit-Robo signaling pathways during maturation. Moreover, multi-omic transcription factor activity analysis prioritized a GATA4-centered network of putative cardiomyocyte maturation regulators including SOX7, SOX18, TBX2, and ZFPM2 (FOG2). Together, these findings elucidate the degree and pace of hPSC-CM maturation during long-term culture and establish an integrated multi-omic framework for identifying strategies to accelerate hPSC-CM maturation.

Cell Biology↗

bioRxiv2026

Spatial atlas highlights contribution of C. difficile in early-stage colorectal cancer

Background and AimsIntratumoral heterogeneity with respect to both host and microbial components has emerged as an important contributor to colorectal cancer (CRC) biology. Although the overall taxonomy and abundance of the CRC tumor microbiome have become well characterized, much less is known about bacterial niches within the tumor microenvironment (TME). Escherichia coli and Fusobacterium nucleatum are highly prevalent and abundant species linked to CRC. Less abundant organisms, like Clostridioides difficile and Enterocloster aldenensis, are emerging as potentially important CRC-associated bacteria. Additionally, many studies are confounded by preoperative oral antibiotics or the inclusion of late-stage cancers, both of which may alter the gut microbiome. To better understand the spatial relationships between bacteria and CRC, we generated a molecular atlas based on surgically resected tissue specimens collected from a unique cohort of early-stage CRC patients in whom oral antibiotics were not administered preoperatively. MethodsFrom 20 CRC specimens, we performed matched histopathological analysis, fluorescence in situ hybridization (FISH), whole exome sequencing (WES), 16S rRNA amplicon bacterial DNA sequencing, codetection by indexing (CODEX) multiplex immunofluorescence, and spatial transcriptomics of 756 regions among the specimens. We used 16S rRNA amplicon sequencing data to design and experimentally validate custom bacterial probes that were applied to the spatial transcriptomics. Human colonic organoids were used to validate the relationship between Clostridioides difficile toxin B (TcdB) and an E-twenty-six family transcription factor, ELF3. ResultsWe identified eight intratumoral bacterial niches consisting of a variety of bacterial species, and each niche was associated with unique tumor features. We further defined tumor gene expression patterns correlating with individual bacterial species and show that C. difficile has a disproportionate impact on the tumor transcriptome given its relatively low abundance. Specifically, TcdB induces nuclear localization of ELF3, increased cytosolic {beta}-catenin protein, and upregulated WNT signaling. ConclusionOur study integrates multi-omics to identify bacterial species with biological and spatial relevance in CRC regardless of abundance. These findings will enable further studies to define diagnostic and therapeutic targets for bacteria-associated CRC.

Cancer Biology↗

bioRxiv2026

Higher Lipid Saturation in Well-Irrigated Georgia Cotton Plants: A Field-Based NMR Metabolomics Study

Cotton (Gossypium hirsutum) is an economically important crop, but it faces increasing pest outbreaks, especially in non-irrigated areas. In this study, 20 cotton farms using center-pivot irrigation were sampled in southern Georgia to assess chemical differences between non-irrigated and irrigated areas. Proton (1H) nuclear magnetic resonance (NMR) data were obtained from cotton leaves, and Principal Component Analysis (PCA) was performed to assess differences in chemical composition. Across all samples, farm site accounted for most of the variability, but within each farm site, the PCA scores plots showed clear separation between non-irrigated and irrigated conditions in 10 sites. Inspecting the PCA loadings revealed significant resonances resembling a lipid-like signal. After reverse-phase fractionation, we observed that many of these resonances appeared together in later fractions, suggesting a lipid, specifically a fatty acid such as linoleic acid. We hypothesized that differences in net lipid saturation level may drive separation between non-irrigated and irrigated samples. Six farm sites had a significantly or marginally significantly higher degree of unsaturation in irrigated samples, while one farm site had significantly higher unsaturation in non-irrigated samples. Our results indicate that drought stress likely affects lipid profile composition, which could be driving higher herbivorous pest densities in drought-stressed crops.

plant biology↗

bioRxiv2026

Postnatal Reprogramming Shapes Human Intestinal Epithelial Immune Competency

At birth, the intestine must rapidly adapt to enable nutritional function and immune microbial tolerance. Here, integrating single-cell multi-omics and spatial transcriptomics we define the circuits underpinning this process. We identify asynchronous developmental trajectories with postnatal epithelial reprogramming characterised by coordinated changes in metabolism, junctional structure and innate defence. At birth epithelial stem cells demonstrate dynamic enhancer remodelling, with accessibility often preceding transcription. Fetal stemness elements remain accessible despite reduced transcription across epithelial lineages, retaining plasticity potential. Post-natal epithelia experience sequential homing of myeloid cells followed by innate T cells with peri-epithelial B cells localising later in infancy. Using developmentally staged organoids, we show that epithelial responses to inflammatory stimuli are age-dependent and constrained in early life. We identify BHLHE40 as an early-life regulator that attenuates the impact of interferon- and NF-{kappa}B-driven signalling. Altogether we define the events driving epithelial licensing and barrier adaptation at birth and through infancy.

cell biology↗

bioRxiv2026

Spatially-resolved single cell atlas of liposarcoma reveals lineage hierarchies, immune niches, and regulatory circuits

Well-differentiated and dedifferentiated liposarcoma (WDLPS and DDLPS) exhibit markedly different clinical behaviors, with DDLPS showing greater aggressiveness, higher recurrence and metastasis rates, and worse outcomes. Using single-nucleus multiome sequencing, epigenomic profiling, and spatial transcriptomics, we characterized cellular and epigenetic heterogeneity between these subtypes at single-cell and spatial resolution. We found distinct phenotypic states reflecting altered lineage differentiation and plasticity: DDLPS is dominated by early-differentiated progenitor-like cells, sclerotic WDLPS displays broader mesenchymal lineage plasticity, and adipocytic WDLPS contains abundant committed adipocytes. The DDLPS immune microenvironment was dominated by immunosuppressive macrophages, whereas WDLPS harbored more T cells and inflammatory macrophages. Notably, sclerotic WDLPS displayed intermediate cellular and molecular features, suggesting it may represent a distinct WDLPS subtype. Importantly, we identified novel gene regulatory circuits underlying each state, including FABP4/PPARG programs in adipocytic WDLPS, GLI2/TCF7L2/RBPJ/KLF7 programs in sclerotic WDLPS, and KLF7/FOSL2/SP3/GLI2/RBPJ programs in DDLPS. H3K27ac-marked enhancers were enriched near adipocytic marker genes in WDLPS and mesenchymal markers in DDLPS. Together, these findings reveal the cellular heterogeneity of tumor and immune compartments across liposarcoma subtypes and identify regulatory programs driving their differentiation states. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/713651v1_ufig1.gif" ALT="Figure 1"> View larger version (73K): org.highwire.dtl.DTLVardef@1c84ee1org.highwire.dtl.DTLVardef@1b2ad42org.highwire.dtl.DTLVardef@18ce5a6org.highwire.dtl.DTLVardef@138f615_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Deregulated Myt3 translation predisposes islet β-cells to dysfunction under obesity-induced metabolic stress

In response to obesity-related metabolic stress, islet {beta}-cells adapt (or compensate) by increasing their secretory function and mass. Yet, for unknown reasons, this compensation is reversed in some individuals at some point to induce {beta}-cell failure and overt type 2 diabetes (T2D). We have previously shown that transcription factor Myt3 (St18) and its paralogs, Myt1 and Myt2, prevent {beta}-cell failure. Myt3 was induced at post-transcriptional levels by obesity-related stress in both mouse and human {beta} cells and its downregulation accompanied {beta}-cell dysfunction during T2D development. Single-nucleotide polymorphisms in MYT3 were associated with an increased risk of developing human diabetes. We now demonstrate that Myt3 translation is regulated by an upstream open-reading frame that overlaps with the main Myt3 open-reading frame in mice. Disrupting this overlap enhances Myt3 translation in mouse {beta} cells without metabolic stress but decreases it under high-fat-diet challenges. Consequently, this deregulation results in {beta}-cell dysfunction and glucose intolerance in mice, accompanied by compromised expression of several {beta}-cell function genes. These findings suggest that stress-induced Myt3 translation is part of the compensation mechanism that prevents {beta}-cell failure.

genetics↗

bioRxiv2025

Myelin transcription factors 1 and 3 have overlapping but distinct roles in insulin secretion and survival of human β cells

Aims/hypothesisGenetic and environmental factors work together to cause islet beta-cell failure, leading to type 2 diabetes (T2D). How these factors are integrated to regulate beta cells remains largely unclear. Based on our previous findings that the family of Myelin transcription factors (MYT1, MYT1L, and ST18) prevents mouse beta-cell failure by repressing the overactivation of stress response, their regulation by obesity-related nutrition signals in human beta cells, and their association with T2D, we postulate that these factors prevent human beta-cell failure under normal physiology and obesity-related stress. MethodsMYT1 or ST18 were knocked down in primary human beta cells using shRNA. Beta-cell survival, secretory function, and gene expression were examined after islet cells were cultured in vitro or xenotransplanted into mice under normal or obesity-related stress. ResultsIn culture, MYT1-knockdown (KD) caused beta-cell death, while ST18-KD compromised glucose-stimulated insulin secretion. Under obesity-induced stress as xenotransplants, ST18-KD also caused beta-cell death. Accordingly, MYT1-KD deregulated several genes and genesets in cell death and cellular stress response, while ST18-KD deregulated those regulating stress response, mitochondria, and ion channels. Corresponding to these gene expression changes, ST18-KD reduces glucose-stimulated Ca2+ influx in beta cells. In addition, the MYT1- and ST18-regulated genes are enriched for T2D-associated loci, with an enrichment of 2.05-fold relative to random distribution. Conclusions/interpretationThe MYT TFs complement each other to integrate genetic and environmental factors to prevent beta-cell failure and T2D, with their major effects exerted on beta-cell viability and/or Ca2+ influx. Graphical AbstractNutrient-responsive transcription factors MYT1 and ST18 regulate human beta-cell survival and secretory functions. Under low metabolic stress, MYT1 regulates cell survival while ST18 regulates insulin secretion. Under high metabolic stress, ST18 also regulates beta-cell survival. MYT1, at least partly, regulates cell survival through stress-related apoptotic processes, while ST18 regulates insulin secretion via Ca2+ influx. Research in context summaryO_ST_ABSBackgroundC_ST_ABSO_LIThe myelin transcription factors (MYT TFs, including MYT1, MYT1l, and ST18) prevent mouse beta-cell failure by depressing the overactivation of stress-response genes. C_LIO_LISNPs in all three MYT loci are associated with human type 2 diabetes. C_LIO_LIThe expression and nuclear localization of MYT1 and ST18 were increased in primary human beta cells under acute metabolic stress but downregulated in type 2 diabetes. C_LIO_LIThe co-knockdown of MYT1, MYT1L, and ST18 in a human beta-cell line resulted in apoptosis. C_LI Key questionO_LIHow does MYT1 or ST18 regulate human beta-cell function and survival? C_LI New findingsO_LIMYT1 prevents human beta-cell death under normal and metabolic stress conditions, corresponding to deregulation of a few genes involved in cell death under cellular stress. C_LIO_LIST18 promotes insulin secretion under normal physiological conditions by regulating Ca2+ influx and prevents beta-cell death under metabolic stress. C_LIO_LIThe MYT1- and ST18-regulated genes are enriched for type 2 diabetes-risk loci. C_LI Clinical impactO_LIRegulators of the MYT-TF activities could be explored to delay/prevent beta-cell failure and the development of type 2 diabetes. C_LI

cell biology↗

bioRxiv2024

Embryological insights into the evolution of genome regulation using haploid and diploid whiteflies, Bemisia tabaci

The whitefly, Bemisia tabaci, is a hemipteran with a haplodiploid sex determination system, which provides a natural experiment for examining genome function in haploid and diploid embryos. Yet the embryogenesis of B. tabaci remains understudied. Our previous work has established a possible role of DNA methyltransferase 1 (Dnmt1) in genome stability. In this study we used maternal RNA interference (RNAi) and immunohistochemistry to study the complex dynamics between DNMT1 and ploidy during embryogenesis. We found that both haploid and diploid B. tabaci have a similar developmental timeline to other holometabolous insects. We also found that, like other obligatory haploid insects, ploidy does not affect developmental timing, suggesting that maternal factors play a greater role than ploidy in developmental timing. For embryos with reduced expression of Dnmt1, we found that the loss of DNMT1 disrupts blastoderm development and affects nuclei morphology in both haploid and diploid embryos. Our results suggest that DNMT1 is required for blastoderm development.

developmental biology↗

bioRxiv2023

Human enteric glia diversity in health and disease: new avenues for the treatment of Hirschsprung disease

Hirschsprung disease (HSCR) is caused by an absence of the enteric nervous system (ENS), which is crucial for intestinal function. The ENS is composed of enteric neurons and glia, and is mostly derived from migrating vagal neural crest cells. Trunk-derived Schwann cells also play a significant role in postnatal maintenance of the ENS. However, the diversity of the ENS in health and disease remains largely unknown. Here, we performed single cell RNA sequencing on pediatric controls and HSCR individuals, and identified two major classes of enteric glia, being canonical and Schwann-like enteric glia. We show that the latter are the main contributors of enteric glia heterogeneity after birth and importantly, that they are preserved in aganglionic segments of HSCR individuals. In a zebrafish model of HSCR, which also shows preservation of Schwann-like enteric glia, enteric neurogenesis could be stimulated, demonstrating a potential novel therapy for HSCR.

pathology↗

bioRxiv2023

Distinct roles for sequential NREM and REM acetylcholine-regulated states during sleep-dependent memory consolidation

Across vertebrate species, sleep consists of repeating cycles of NREM followed by REM. However, the respective functions of NREM, REM, and their stereotypic cycling pattern are not well understood. Using a simplified biophysical network model, we show that NREM and REM sleep can play differential and critical roles in memory consolidation primarily regulated, based on state-specific changes in cholinergic signaling. Within this network, decreasing and increasing muscarinic acetylcholine (ACh) signaling during bouts of NREM and REM, respectively, differentially alters neuronal excitability and excitatory/inhibitory balance. During NREM, deactivation of inhibitory neurons leads to network-wide disinhibition and bursts of synchronized activity led by firing in engram neurons. These features strengthen connections from the original engram neurons to less-active network neurons. In contrast, during REM, an increase in network inhibition suppresses firing in all but the most-active excitatory neurons, leading to competitive strengthening/pruning of the memory trace. We tested the predictions of the model against in vivo recordings from mouse hippocampus during active sleep-dependent memory storage. Consistent with modeling results, we find that functional connectivity between CA1 neurons changes differentially at transition from NREM to REM sleep during learning. Returning to the model, we find that an iterative sequence of state-specific activations during NREM/REM cycling is essential for memory storage in the network, serving a critical role during simultaneous consolidation of multiple memories. Together these results provide a testable mechanistic hypothesis for the respective roles of NREM and REM sleep, and their universal relative timing, in memory consolidation. Significance statementUsing a simplified computational model and in vivo recordings from mouse hippocampus, we show that NREM and REM sleep can play differential roles in memory consolidation. The specific neurophysiological features of the two sleep states allow for expansion of memory traces (during NREM) and prevention of overlap between different memory traces (during REM). These features are likely essential in the context of storing more than one new memory simultaneously within a brain network.

neuroscience↗

bioRxiv2023

Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data

The use of biomedical knowledge graphs (BMKG) for knowledge representation and data integration has increased drastically in the past several years due to the size, diversity, and complexity of biomedical datasets and databases. Data extraction from a single dataset or database is usually not particularly challenging. However, if a scientific question must rely on integrative analysis across multiple databases or datasets, it can often take many hours to correctly and reproducibly extract and integrate data towards effective analysis. To overcome this issue, we created Petagraph, a large-scale BMKG that integrates biomolecular data into a schema incorporating the Unified Medical Language System (UMLS). Petagraph is instantiated on the Neo4j graph platform, and to date, has fifteen integrated biomolecular datasets. The majority of the data consists of entities or relationships related to genes, animal models, human phenotypes, drugs, and chemicals. Quantitative data sets containing values from gene expression analyses, chromatin organization, and genetic analyses have also been included. By incorporating models of biomolecular data types, the datasets can be traversed with hundreds of ontologies and controlled vocabularies native to the UMLS, effectively bringing the data to the ontologies. Petagraph allows users to analyze relationships between complex multi-omics data quickly and efficiently.

bioinformatics↗

bioRxiv2022

A robust deep learning platform to predict CD8+ T-cell epitopes

T-cells play a crucial role in the adaptive immune system by inducing an anti-tumour response, defending against pathogens, and maintaining tolerance against self-antigens, which has sparked interest in the development of T-cell-based vaccines and immunotherapies. Because screening antigens driving the T-cell response is currently low-throughput and laborious, computational methods for predicting CD8+ T-cell epitopes have emerged. However, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8+ T-cell epitopes. Therefore, we developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning platform for predicting CD8+ T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8+ T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to dissimilarity to self from cancer studies. We used TRAP to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. Thus, this study presents a novel computational platform for accurately predicting CD8+ T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics. HighlightsO_LIHLA bias and out-of-distribution problem are causes of poor performance of current state-of-the-art algorithms C_LIO_LITransfer learning, deep learning architecture, context-specific and HLA-generalised approaches improve CD8+ T-cell epitope prediction C_LIO_LITRAP reports degree of correctness to improve reliability of the prediction C_LIO_LIA novel metric termed RSAT estimates immunogenicity of pathogenic peptides, as a complementary to dissimilarity to self from cancer studies C_LI

bioinformatics↗

bioRxiv2022

A contamination focused approach for optimizing thesingle-cell RNA-seq experiment

Achieving high data quality in single-cell RNA-seq (scRNA-seq) experiments has always been a significant challenge stemming from minute signal that can be detected in individual cells. Droplet-based scRNA-seq additionally suffers from ambient contamination, comprising nucleic acid materials released by dead cells into the loading buffer and co-encapsulated with real cells, which further washes out real biological signals. Here, we developed quantitative, ambient contamination-based metrics and an associated software package that can both evaluate current datasets and guide new experimental optimizations. We performed a series of experimental optimizations using the inDrops platform to address the mechanical and microfluidic cell encapsulation aspect of an scRNA-seq experiment, with a focus on minimizing ambient contamination. We report improvements that can be achieved via cell fixation, microfluidic loading, microfluidic dilution, and nuclei versus cell preparation; many of these parameters are inaccessible on commercial platforms. We provide insights into previously obscured factors that can affect scRNA-seq data quality and suggest mitigation strategies that can guide future experiments.

genomics↗

bioRxiv2022

A systems approach evaluating the impact of SARS-CoV-2 variant of concern mutations on CD8+ T cell responses

T cell recognition of SARS-CoV-2 antigens after vaccination and/or natural infection has played a central role in resolving SARS-CoV-2 infections and generating adaptive immune memory. However, the clinical impact of SARS-CoV-2-specific T cell responses is variable and the mechanisms underlying T cell interaction with target antigens are not fully understood. This is especially true given the virus rapid evolution, which leads to new variants with immune escape capacity. In this study, we used the Omicron variant as a model organism and took a systems approach to evaluate the impact of mutations on CD8+ T cell immunogenicity. We computed an immunogenicity potential score for each SARS-CoV-2 peptide antigen from the ancestral strain and Omicron, capturing both antigen presentation and T cell recognition probabilities. By comparing ancestral vs. Omicron immunogenicity scores, we reveal a divergent and heterogeneous landscape of impact for CD8+ T cell recognition of mutated targets in Omicron variants. While T cell recognition of Omicron peptides is broadly preserved, we observed mutated peptides with deteriorated immunogenicity that may assist breakthrough infection in some individuals. We then combined our scoring scheme with an in-silico mutagenesis, to characterise the position- and residue-specific theoretical mutational impact on immunogenicity. While we predict many escape trajectories from the theoretical landscape of substitutions, our study suggests that Omicron mutations in T cell epitopes did not develop under cell-mediated pressure. Our study provides a generalisable platform for fostering a deeper understanding of existing and novel variant impact on antigen-specific vaccine- and/or infection-induced T cell immunity.

immunology↗

bioRxiv2021

HLA-dependent variation in SARS-CoV-2 CD8+ T cell cross-reactivity with human coronaviruses

Pre-existing T cell immunity to SARS-CoV-2 in individuals without prior exposure to SARS-CoV-2 has been reported in several studies. While emerging evidence hints toward prior exposure to common-cold human coronaviruses (HCoV), the extent of- and conditions for-cross-protective immunity between SARS-CoV-2 and HCoVs remain open. Here, by leveraging a comprehensive pool of publicly available functionally evaluated SARS-CoV-2 peptides, we report 126 immunogenic SARS-CoV-2 peptides with high sequence similarity to 285 MHC-presented target peptides from at least one of four HCoV, thus providing a map describing the landscape of SARS-CoV-2 shared and private immunogenic peptides with functionally validated T cell responses. Using this map, we show that while SARS-CoV-2 immunogenic peptides in general exhibit higher level of dissimilarity to both self-proteome and -microbiomes, there exist several SARS-CoV-2 immunogenic peptides with high similarity to various human protein coding genes, some of which have been reported to have elevated expression in severe COVID-19 patients. We then combine our map with a SARS-CoV-2-specific TCR repertoire data from COVID-19 patients and healthy controls and show that whereas the public repertoire for the majority of convalescent patients are dominated by TCRs cognate to private SARS-CoV-2 peptides, for a subset of patients, more than 50% of their public repertoires that show reactivity to SARS-CoV-2, consist of TCRs cognate to shared SARS-CoV-2-HCoV peptides. Further analyses suggest that the skewed distribution of TCRs cognate to shared and private peptides in COVID-19 patients is likely to be HLA-dependent. Finally, by utilising the global prevalence of HLA alleles, we provide 10 peptides with known cognate TCRs that are conserved across SARS-CoV-2 and multiple human coronaviruses and are predicted to be recognised by a high proportion of the global population. Overall, our work indicates the potential for HCoV-SARS-CoV-2 reactive CD8+ T cells, which is likely dependent on differences in HLA-coding genes among individuals. These findings may have important implications for COVID-19 heterogeneity and vaccine-induced immune responses as well as robustness of immunity to SARS-CoV-2 and its variants.

immunology↗

bioRxiv2021

BMP pathway antagonism by Grem1 regulates epithelial cell fate in intestinal regeneration

In the intestine, the homeostatic effect of Bone Morphogenetic Protein (BMP) on cell fate has predominantly been inferred through pathway inactivation. Here, we assessed the impact of autocrine Bmp4 expression on secretory cell fate. Ligand exposure reduced proliferation, expedited terminal differentiation, abrogated long-term secretory cell survival and prevented dedifferentiation. As stem cell plasticity is required for regenerative adaptive reprogramming, we spatiotemporally mapped and functionally explored BMPs role in epithelial restitution. Following ulceration, physiological attenuation of BMP signalling arose through upregulation of the secreted antagonist, Grem1, from topographically distinct populations of stromal cells. Concomitant expression supported functional compensation, following Grem1 deletion from tissue-resident fibroblasts. BMP pathway manipulation showed that antagonist-mediated BMP attenuation was obligatory, but functionally sub-maximal, as regeneration was impaired or enhanced by epithelial overexpression of Bmp4 or Grem1 respectively. Mechanistically, Bmp4 abrogated regenerative stem cell reprogramming, despite a convergent impact of YAP/TAZ on cell fate in remodelled wounds.

cell biology↗

bioRxiv2020

Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens

T cell recognition of a cognate peptide-MHC complex (pMHC) presented on the surface of infected or malignant cells, is of utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T Cell Receptors (TCR) would greatly facilitate identification of vaccine targets for both pathogenic diseases as well as personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the centre of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology, has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as SARS-CoV-2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in training data of the models seem to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen vs. cancer peptides. Overall, we demonstrate that accurate and reliable prediction of immunogenic CD8+ T cell targets remains unsolved, thus we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors.

immunology↗

bioRxiv2020

CD8+ T cell cross-reactivity against SARS-CoV-2 conferred by other coronavirus strains and influenza virus

While individuals infected with coronavirus disease 2019 (COVID-19) manifested a broad range in susceptibility and severity to the disease, the pre-existing immune memory of related pathogens can influence the disease outcome. Here, we investigated the potential extent of T cell cross-reactivity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that can be conferred by other coronaviruses and influenza virus, and generated a map of public and private predicted CD8+ T cell epitopes between coronaviruses. Moreover, to assess the potential risk of self-reactivity and/or diminished T cell response for peptides identical or highly similar to the host, we identified predicted epitopes with high sequence similarity with human proteome. Lastly, we compared predicted epitopes from coronaviruses with epitopes from influenza virus deposited in IEDB to support vaccine development against different virus strains. We believe the comprehensive in silico profile of private and public predicted epitopes across coronaviruses and influenza viruses will facilitate design of vaccines capable of protecting against various viral infections.

immunology↗