Biology subjects

Fisher, J.

Publications and source records attributed to Fisher, J..

At least 19 recordsLinked to original sources

bioRxiv2026

A data model for longitudinal multi-modal biomedical cohorts

Longitudinal multimodal cohort studies require data models that can integrate heterogeneous molecular, phenotypic, and contextual data while preserving relationships across participants, biospecimens, study visits, and computational transformations. Here, we present the data model developed for the Multi-Omics for Health and Disease (MOHD) Consortium, a modality-agnostic, provenance-driven framework designed to support an evolving longitudinal biomedical resource. The model uses a compact set of linked entities and a central dataset abstraction to represent experimental, phenotypic and contextual, and computational data within a common framework. Stable identifiers, explicit provenance relationships, and flexible grouping structures enable data to be tracked across biological sources, processing workflows, and releases without requiring modality-specific restructuring of the core model. To address the complexity of participant context, we developed a harmonization framework that transforms branched REDCap survey data into a longitudinal, machine-readable representation with normalized variables and explicit response status. Interoperability with external data models and ontologies is supported through reproducible translation workflows while maintaining a stable canonical representation. We demonstrate the model through its implementation in the first production-scale MOHD data release, encompassing 956 participants, nearly 7,000 datasets, and more than 40,000 files across multiple molecular modalities and study visits. This implementation demonstrates the ability of the model to accommodate heterogeneous and longitudinal data, evolving computational products, participant withdrawal, and distinct data-access environments. Together, the MOHD implementation provides a practical framework and generalizable design principles for building longitudinal multimodal biomedical resources that remain traceable, interoperable, and adaptable as data and analytical requirements evolve.

bioinformatics↗

bioRxiv2026

Spatiotemporal profiling of T cell repertoires in Lynch syndrome reveals convergent immune responses through neoplastic progression

Surveillance of individuals with Lynch syndrome (LS), who are carriers of heterozygous inactivating mismatch repair (MMR) gene mutations that predispose to colorectal cancer, enables the study of tumour-immune coevolution during tumour initiation. Somatic loss of MMR activity in the LS colon leads to the accrual of cancer driver mutations, and also of neoantigens likely to generate an adaptive immune response. However, the spatial and temporal immune dynamics in colon and peripheral blood are uncharacterised. Here we show that the neoplastic evolution of mismatch repair deficient (MMRd) clones leads to large-scale and sustained reorganisation of the T-cell repertoire in colon and peripheral blood. We performed dense spatiotemporal profiling of T cell receptor (TCR) repertoires from 109 colonic biopsies (11 tumour samples, 98 normal bowel) from seven individuals, with eight matched blood repertoires. We additionally analysed 13 colorectal polyp repertoires from eight participants, 286 cross sectional blood repertoires from 242 participants (198 LS and 44 non-LS) and 52 longitudinal peripheral blood samples from 26 individuals with LS. We detected tumour-associated T cell clones, some of which were widely present in normal bowel tissue and also detectable in peripheral blood. Sequence similarity analysis revealed conserved TCR architectures across tissue compartments and recurrent clustering patterns between patients, that were not present in non-Lynch controls, consistent with convergent MMRd-associated antigen-driven immune selection. We identified systemic signatures associated with local tumour immune activity and revealed evidence of structured repertoire remodelling before overt malignancy. Longitudinal profiling revealed frequent turnover of low-abundance clonotypes, whereas highly abundant clonotypes persisted for several years. Collectively, our data defines the local and circulating TCR landscape associated with colonic neoplasia in LS, and provides a framework for LS blood-based immune monitoring, early interception and biomarker development.

immunology↗

bioRxiv2026

Functional Characterization of Myeloid Neoplasm-associated DDX41 Variants Reveals Pathogenic Interaction with Acquired Hotspot Mutation

Germline variants in DDX41 are the most frequent genetic predisposition to adult hematologic malignancies. The most common variants are truncating, implicating loss of function in the pathogenesis. However, non-truncating variants account for 30-40% of cases, and their impact on essential DDX41 functions remains unknown. We utilized a genetic complementation assay to assess the functionality of 10 recurrent germline non-truncating variants of DDX41. All variants restored viability to Ddx41-deficient hematopoietic progenitor cells at exogenous expression levels. In contrast, the hotspot mutant p.R525H, which is somatically acquired at disease onset in >50% of patients, failed to restore viability. CRISPR-based modeling in cell lines and mice revealed heterogeneity: some variants were non-functional at endogenous expression levels whereas others maintained complete functionality, supporting normal cell proliferation and even lifelong hematopoiesis in a homozygous setting. Notably, co-expression of p.R525H with some variants caused impaired hematopoietic progenitor cell viability, indicating a dominant-negative effect of p.R525H. In contrast, other variants, all classified as variants of unknown significance, were unaffected by the presence of p.R525H. A screen of 100 disease-associated variants confirmed that many non-truncating germline variants are susceptible to p.R525H-mediated dominant-negative effects, whereas wild-type DDX41 is not. These findings indicate that DDX41 variant curation is complicated by variable effects on functionality and variant-specific interactions with somatically-acquired DDX41 mutations. The dominant-negative effect of p.R525H provides a mechanistic basis for the conclusion of recent patient cohort analyses that co-occurrence with a somatic hotspot mutation is a reliable indicator of DDX41-driven disease in carriers of non-truncating variants.

cancer biology↗

bioRxiv2026

Multiplexed measurements of protein-protein interactions and protein abundance across cellular conditions using Prod&PQ-seq

Methods to profile protein-protein interactions (PPIs) have limited scalability and can only study a handful of conditions and/or targets. Here, we introduce Prod&PQ-seq, a framework for multiplexed detection and quantification of PPIs and proteins. Our framework uses cross-linked cells, antibody-oligonucleotide conjugates (ab-oligos), and captures PPIs by the DNA-caliper, a specialized oligonucleotide for bidirectional priming of proximal ab-oligos. We benchmarked Prod&PQ-seq using recombinant complexes, titrations and cell mixture experiments and show that our framework is quantitative, reproducible, sensitive and specific. Applying Prod&PQ-seq to study Polycomb Repressive Complex 2 (PRC2) shows that EZH2 inhibition and expression of the oncohistone H3.3K27M weakens both PRC2-H3K27me3 interactions and PPIs within PRC2. Further, H3.1K27M and H3.3K27M variants lead to distinct PPI profiles such as the intensity of H3K27ac-K27M or H3K27ac-EED. Together, Prod&PQ-seq enables detection of changes in PPI composition and intensity and protein quantification across biological conditions, small molecule inhibition and genetic perturbations. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/697286v2_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@1ed0805org.highwire.dtl.DTLVardef@a9c0d9org.highwire.dtl.DTLVardef@b411fborg.highwire.dtl.DTLVardef@8a12e_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Exosome secretion is required for sonic hedgehog dispersal and signal gradient formation in the embryonic limb mesenchyme

Carrier-assisted diffusion and cytoneme transport have been postulated to disperse Hedgehog across diverse embryonic tissues, yet their relative contributions to patterning mesenchymal tissues remains poorly understood. Here, we combine novel signaling assays with quantitative microscopy to establish exosome secretion as a predominant and adaptable mechanism for Sonic Hedgehog (Shh) dispersal. Introducing a novel synchronous release system to visualize Shh trafficking in embryonic tissues, we demonstrate that Shh traffics through the exosome biogenesis pathway in the limb bud mesenchyme. Shh-bound exosomes diffuse through extracellular space, and can also bind and travel along cytonemes, providing a potential mechanism for directed and/or long-range transport. Using a synthetic patterning assay, we show that exosome secretion is essential to establish short-range Shh gradients in vitro. We propose that exosome-based Shh secretion, combined with different modes of extracellular transport, provides a tunable mechanism to sculpt Shh gradients on different length and time scales, across different embryonic tissues. HIGHLIGHTSO_LIA novel synchronous release system reveals trafficking dynamics in embryonic cells C_LIO_LISonic hedgehog is packaged and secreted on exosomes for extracellular dispersal C_LIO_LIExosome secretion is required to establish short-range Hedgehog gradients C_LIO_LIDiffusion and cytoneme transport provide tunable exosome dispersal strategies C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/692671v1_ufig1.gif" ALT="Figure 1"> View larger version (89K): org.highwire.dtl.DTLVardef@76b266org.highwire.dtl.DTLVardef@ba5b4borg.highwire.dtl.DTLVardef@bf9d67org.highwire.dtl.DTLVardef@1f6d699_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2025

Guidance receptor-mediated mechanocompliance of GBM cells facilitates immune-silent invasion

The lethality of glioblastoma (GBM) stems from diffuse infiltration and immune evasion, two hallmarks traditionally studied separately. Here, we identify as unifying mechanism how GBM cells utilize guidance receptors Plexin-D1 and Plexin-B2 to gain mechanocompliance, i.e., the ability to deform and remodel membrane/cytoskeleton during confined migration without triggering immune activation. We show that PLXND1 upregulation marks invasive fronts and predicts poor survival of glioma patients. Through live-cell imaging in microchannels, intracranial xenografts, single-nucleus transcriptomics, and lipidomics we demonstrate that Plexin-D1/B2 enable GBM cells to retract tumor microtubes (TMs), traverse constrictions, and escape microglial surveillance. Single and especially double deletion of PLXND1 and B2 resulted in TM overgrowth, membrane instability, and susceptibility to cell fragment shedding, leading to impaired migration and a shift to activation of tumor-associated myeloid cells. Our findings thus reveal a molecular strategy used by GBM cells to penetrate through interstitial space while escaping immune surveillance. SIGNIFICANCEPlexin-mediated mechanocompliance underlies the invasive yet immune-silent behavior of GBM cells, exposing a vulnerability that could be therapeutically exploited by forcing invading tumor cells into a mechanically fragile, immunogenic state.

cancer biology↗

bioRxiv2025

Agent-based simulations of lung tumour evolution suggest that ongoing cell competition drives realistic clonal expansions

Computational simulations of tumour evolution are increasingly used to infer the rules underlying cancer growth, with the goal of one day recommending tailored treatments. To make reliable inferences, such models must be able to reflect the properties of real tumours. Recent work has shown that lung tumours undergo frequent and late subclonal expansions, which are associated with poor prognosis. This paper tests three candidate simulations of three-dimensional tumour growth, which make different assumptions about the nature of competition between cells, for their ability to replicate these late expansions. Only a model which assumes stringent competition between tumour cells for existing tissue space, after the tumour has reached a fixed size, can produce multiregion sequencing data realistic to lung tumours. This work also assesses the influence of these assumptions on the inferred selection strength of individual tumours in a large cohort of lung cancers, and finds that inferences from a well-tailored model produce much higher estimates than poorly-tailored models of the effects of driver mutations on cell fitness. Table of Contents O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/686578v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@40fae9org.highwire.dtl.DTLVardef@18f2d2org.highwire.dtl.DTLVardef@1edec55org.highwire.dtl.DTLVardef@136d1da_HPS_FORMAT_FIGEXP M_FIG Computational simulations of tumour evolution are increasingly used to infer the rules underlying cancer growth, with the goal of one day recommending tailored treatments. Here we show that the properties of lung cancer sequencing data are best replicated by a model which assumes that cells compete both to proliferate and survive. C_FIG

cancer biology↗

bioRxiv2025

Perfusion Bioreactor Culture Incorporating Mechanical Confinement Enhances Mesenchymal Stem Cell Extracellular Vesicle Production and Wound Healing Potential

Mesenchymal stem cell extracellular vesicles (MSC EVs) have been widely studied for regenerative medicine and tissue repair applications. However, clinical translation of EV therapeutics has been hampered by low potency and lack of scalable production strategies. This work aims to develop a novel approach that exploits the mechanosensitivity of MSCs to enhance EV potency in the context of enhanced production via bioreactor culture. MSCs are well known to respond to mechanical stimuli such as substrate stiffness and shear stress, and here it is shown that exposing MSCs to another mechanoregulatory parameter, confinement, enhances the pro-angiogenic bioactivity of their EVs. This is consistent across both donor-derived primary MSCs and induced pluripotent stem cell-derived MSCs (iMSCs). The development of a 3D-printed perfusion bioreactor system that enables culture of confined MSCs under flow is also detailed here, resulting in a 67-fold increase in EV production compared to flask culture. iMSC EVs obtained downstream of this confinement-bioreactor culture induce greater vascularization and generally improve wound healing in a diabetic mouse model compared to iMSC EVs from conventional tissue culture. Overall, this work establishes the development of a scalable, bioreactor-based iMSC EV production platform that provides a solution to major translational bottlenecks of therapeutic EVs.

bioengineering↗

bioRxiv2025

Intra- and intercellular immune responses across diverse in vitro stimuli and inflammatory disease

In vitro stimulation of healthy human immune cells is commonly used to reproduce the immune states observed in disease, both to understand pathology and to test therapeutic approaches. However, experiments typically focus on individual cell types and stimuli and a comprehensive cellular comparison of common immunomodulators and their relevance to disease is lacking. To this end, we performed integrated single-cell transcriptomic profiling of human peripheral blood mononuclear cells treated with 11 different common in vitro stimuli, totalling over 150,000 cells from 21 immune cell subtypes. Comparative analysis of the immunomodulations revealed their shared and unique pathways, for instance stimulation via the T cell receptor (anti-CD3, CytoStimTM) and IFN- induced broad activation signatures including off-target effects across multiple cell types, whereas TNF- and LPS elicited more specific responses. Ligand-receptor interaction mapping also uncovered the common and distinct intercellular signalling pathways across stimuli. Comparing the stimuli to patient samples enabled identification of specific inflammatory disease features best replicated by each. For example, IFN- stimulation recapitulated signatures of SLE across cell types, whereas LPS induced SLE-like changes specifically within monocytes. Comparative cell-cell network analysis showed that in vitro stimuli were able to recreate some, but not all, aspects of intercellular interactions upregulated in SLE, highlighting the limitations of these model systems. This resource provides new insights into the similarities and differences of established immune stimuli at cellular resolution and facilitates appropriate use of in vitro systems to study pathways relevant to disease.

immunology↗

bioRxiv2025

MAGELLAN: Automated Generation of Interpretable Computational Models for Biological Reasoning

Computational models have become essential tools for understanding signalling networks and their non-linear dynamics. However, these models are typically constructed manually using prior knowledge and can be over-reliant on study bias. These limitations hinder their ability to make accurate predictions and incorporate new evidence. Scaling up the construction of models to take advantage of increasingly abundant omics data can bridge these gaps by providing a comprehensive view of signalling events and how they influence cellular phenotypes. In this study, we present MAGELLAN, a method leveraging message passing graph neural networks to build computational models directly from pathway data and discrete rules representing experimental results. We used this to construct a computational model of breast cancer signalling and re-parameterize a previously published non-small cell lung cancer (NSCLC) model, showing that MAGELLAN can predict genetic dependencies and achieve comparable model quality to expert-curated and manually trained models. Our approach enables the integration of prior knowledge networks and experimental data to build predictive models that are mechanistically interpretable. This approach simplifies model creation, making it more accessible and practical for experimentalists, and supports broader applications in drug discovery and biological research.

systems biology↗

bioRxiv2025

Cooperation Between Genotoxic Bacteria Accelerates Tumorigenesis in a Mouse Model of Colon Carcinogenesis

To identify causal links between gut microbes and tumorigenesis, we colonized groups of germ-free colon tumor-susceptible mice (ApcMin/+;Il10-/-) with 15 cultured human fecal microbiotas from healthy individuals, as well as patients with inflammatory bowel disease and colorectal cancer. The number of colonic tumors in ApcMin/+;Il10-/- mice varied by donor microbiota but not by the health status of the donor. In vitro screens of host cell proliferation, genotoxicity, and inflammation in bacteria-mammalian cell cocultures revealed that genotoxicity best predicted tumorigenic microbes in vivo with genotoxic microbes present in all tested individuals. The genotoxic subset of strains from each donor induced more tumors than the complete community - even when the complete community was not tumorigenic. Combining genotoxic microbes from multiple sources increased tumor number and decreased the time to tumor onset. Together these results suggest that most individuals harbor genotoxic bacterial strains and the balance of genotoxic to protective strains determines the timing and severity of tumorigenesis in vivo.

microbiology↗

bioRxiv2025

Predicting Personalised Therapeutic Combinations in Non-Small Cell Lung Cancer Using In Silico Modelling

The disease burden from non-small cell lung cancer (NSCLC) adenocarcinoma is substantial, with around a million new cases diagnosed globally each year, and a 5-year survival rate of less than 20%. A lack of therapeutic options personalized to individual patient genetics, and the targeted therapies that exist quickly succumbing to resistance, leads to high variation in survival. Patient stratification combined with greater personalisation of therapies have the potential to improve outcomes, however, the wide variation in mutations found in NSCLC adenocarcinoma patients mean that experimentally determining suitable treatment combinations is time-consuming and expensive. Here we present an in silico model encompassing tumour intrinsic key oncogenic signalling pathways, including EGFR, AKT, JAK/STAT and WNT for efficiently predicting rational drug-drug and drug-radiotherapy combination therapies in NSCLC. Using this model, we simulate diverse genetic profiles and test over 10,000 therapeutic combinations to identify optimal strategies to overcome resistance mechanisms specific to genetic profiles and p53 status. Our in silico model reproduces drug additivity experiments, predicts radio-sensitising genes validated in a CRISPR screen and identifies 53BP1 as a potential drug target that improves the therapeutic window during radiotherapy, as well as potential to use ATM inhibitors to overcome p53 loss-of-function driven radiotherapy resistance. We further use the in silico model to identify a 19-gene signature to stratify patients most likely to benefit from radiotherapy and validated this using TCGA data. These results further demonstrate the utility of in silico mechanistic modelling and present a bespoke computational resource for large-scale screening of personalised therapies applied to NSCLC.

cancer biology↗

bioRxiv2024

An Expanded Registry of Candidate cis-Regulatory Elements for Studying Transcriptional Regulation

Mammalian genomes contain millions of regulatory elements that control the complex patterns of gene expression. Previously, The ENCODE consortium mapped biochemical signals across many cell types and tissues and integrated these data to develop a Registry of 0.9 million human and 300 thousand mouse candidate cis-Regulatory Elements (cCREs) annotated with potential functions1. We have expanded the Registry to include 2.35 million human and 927 thousand mouse cCREs, leveraging new ENCODE datasets and enhanced computational methods. This expanded Registry covers hundreds of unique cell and tissue types, providing a comprehensive understanding of gene regulation. Functional characterization data from assays like STARR-seq, MPRA, CRISPR perturbation, and transgenic mouse assays now cover over 90% of human cCREs, revealing complex regulatory functions. We identified thousands of novel silencer cCREs and demonstrated their dual enhancer/silencer roles in different cellular contexts. Integrating the Registry with other ENCODE annotations facilitates genetic variation interpretation and trait-associated gene identification, exemplified by discovering KLF1 as a novel causal gene for red blood cell traits. This expanded Registry is a valuable resource for studying the regulatory genome and its impact on health and disease.

genomics↗

bioRxiv2024

Machine Learning Predictions Surpass Individual mRNAs as a Proxy of Single-cell Protein Expression

BackgroundExpansive repositories of single-cell RNA-seq data are now available. These data are often analysed assuming that mRNA abundance reflects the expression of their cognate proteins. However, post-transcriptional and translational regulation make mRNA an inadequate proxy for protein. High sparsity in low abundance mRNAs from single-cell transcriptomics data further complicates the extrapolation of protein expression levels. Although methods for single-cell surface protein quantification exist, they incur additional technical steps at greater expense and have yet to see wide-spread adoption. Computational approaches for protein imputation from scRNAseq data have been published, which learn transcriptome-wide patterns that predict protein expression. These models can then be applied to infer surface protein expression on RNA-seq only data, to increase the utility of existing data repositories. ResultsWe tested 8 such methods and compared the accuracy of predictions between approaches, and against cognate mRNAs as a direct proxy. Predictions from the trained models outperformed the use of mRNA expression as a proxy. We identify notable cases of cell surface proteins with very poor correlation with their mRNA that were predicted very successfully by imputation using the whole transcriptome. We find cell type signatures are a major determinant of predicted protein levels and, as such, prediction methods require representative training data. ConclusionsThese results reiterate that mRNA level is not a reliable predictor of cell surface protein expression, and that whole-transcriptome informed imputation can improve protein estimations given appropriately trained models.

bioinformatics↗

bioRxiv2024

InTraSeq: A Multimodal Assay that Uncovers New Single-Cell Biology and Regulatory Mechanisms

Single-cell RNA sequencing (scRNA-seq) has revolutionized cell biology by enabling the profiling of transcriptomes at a single-cell resolution, leading to important discoveries that have advanced our understanding of cellular and tissue heterogeneity, developmental trajectories, and disease progression. Despite these important advances, scRNA-seq is limited to measuring the transcriptome providing a partial view of cellular function. To address this limitation, multimodal scRNA-seq assays have emerged, allowing for the simultaneous measurement of RNA expression and protein. Intracellular Transcriptomic and Protein Sequencing (InTraSeq), a novel multimodal scRNA-seq technology described here, enables the concurrent measurement of mRNA, surface markers, cytoplasmic proteins, and nuclear proteins within individual cells through oligo-barcoded antibodies. This method offers a comprehensive approach to studying cellular function by combining RNA and protein profiling from the same sample and utilizing a relatively simple protocol. The InTraSeq method enables researchers to expand their view of critical intracellular protein expression including post-translational modifications (PTMs) and transcription factors, allowing for the identification of novel cellular subtypes and states that may be obscured by RNA-based analyses alone. This is particularly valuable in understanding the heterogeneity of cell populations and identifying distinct functional states. In this report, we used InTraSeq to characterize the complex cellular states and regulatory mechanisms during Th17 cell differentiation. We simultaneously profiled RNA and protein expression in over 85,000 cells, capturing transcriptional changes, changes in protein expression and the dynamics of signaling pathways at a high resolution. Our results revealed novel insights into Th17 cell differentiation, including the identification of key regulatory factors and their target genes. By simultaneously measuring mRNA, extra and intra-cellular proteins, signaling proteins, and PTMs, InTraSeq offers a comprehensive understanding of cellular processes and enables the identification of novel regulatory mechanisms.

cell biology↗

bioRxiv2024

Genetic architecture of the tomato fruit lipidome; new insights link lipid and volatile compounds

Tomato (Solanum lycopersicum L.) fruit flavor is determined by a combination of multiple volatile compounds, including several derived from lipids and fatty acids. Although fruit flavor has been intensively studied, the linkage between lipid metabolism and flavor remains largely undefined. Here, we performed a genome-wide association study (GWAS) and QTL mapping for the fruit lipid content from 550 tomato accessions and 107 backcross inbred lines (BILs) in two consecutive seasons. Over 130 lipid compounds were identified and mapped, allowing for the identification of over 600 metabolic QTL (mQTL). We further described and validated candidate genes associated with lipid content. Among them is a lipase-like protein (TomLLP) whose function was validated in vivo using overexpression lines in tomato and knockout mutants in Arabidopsis. We also identified functions for three enzymes: a class III lipase (Sl-LIP8), a cyclopropane-fatty-acyl-phospholipid synthase (CFAPS1), and Lipoxygenase C (TomLoxC). By utilizing knockout lines for CFAPS1 and CRISPR-Cas9 loss-of-function lines for Sl-LIP8 and TomLoxC, we demonstrated the functional importance of these enzymes in fruit lipid metabolism. Our study provides a comprehensive analysis of the tomato fruit lipidome and insights into key genes that shaped the natural variation in tomato lipid content and their links to flavor-associated volatile compounds.

molecular biology↗

bioRxiv2023

Characterization of tomato canal-1 mutant using a multi-omics approach

The recently described canal-1 tomato mutant, which has a variegated leaf phenotype, has been shown to affect canalization of yield. The corresponding protein is orthologous to AtSCO2 - SNOWY COTYLEDON2, which has suggested roles in thylakoid biogenesis. Here we characterize the canal-1 mutant through a multi-omics approach, by comparing mutant to wild-type tissues. While white canal-1 leaves are devoid of chlorophyll, green leaves of the mutant appear wild-type-like, despite an impaired protein function. Transcriptomic data suggest that green mutant leaves compensate for this impaired protein function by upregulation of transcription of photosystem assembly and photosystem component genes, thereby allowing adequate photosystem establishment, which is reflected in their wild-type-like proteome. White canal-1 leaves, however, likely fail to reach a certain threshold enabling this overcompensation, and plastids get trapped in an undeveloped state, while additionally suffering from high light stress, indicated by the overexpression of ELIP homolog genes. The metabolic profile of white and to a lesser degree also green tissues revealed upregulation of amino acid levels, that was at least partially mediated by transcriptional and proteomic upregulation. These combined changes are indicative of a stress response and suggest that white tissues behave as carbon sinks. In summary, our work demonstrates the relevance of the SCO2 protein in both photosystem assembly and as a consequence in the canalization of yield. Significance statementThe variegated canalized-1 tomato mutant was recently described and the underlying gene SCO2 suggested to be a yield canalization gene. Through a multi-omics approach we show that mutants require a transcriptional upregulation of photosystem components and assembly components, likely as overcompensation for partially impaired SCO2 function, to produce a wild type-like proteome and functional photosynthetic tissue Our data, furthermore, suggest that variation of green to white leaf area from plant to plant leads to the yield variation.

plant biology↗

bioRxiv2023

Neutralization Escape by SARS-CoV-2 Omicron Subvariant BA.2.86

The continued evolution of SARS-CoV-2 may lead to evasion of vaccine immunity and natural immunity. A highly mutated Omicron variant BA.2.86 has recently been identified with over 30 amino acid changes in Spike compared with BA.2 and XBB.1.5. As of September 4, 2023, BA.2.86 has been identified in 37 sequences from 10 countries, which is likely an underestimate due to limited surveillance. The ability of BA.2.86 to evade NAbs compared with other currently circulating Omicron variants remains unknown. Our data show that NAb responses to BA.2.86 were lower than to BA.2 but were comparable or slightly higher than to the current circulating recombinant variants XBB.1.5, XBB.1.16, EG.5, EG.5.1, and FL.1.5.1.

microbiology↗