Biology subjects

Fu, J.

Publications and source records attributed to Fu, J..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

All-trans retinoic acid-mediated ADAR1 degradation synergizes with PD-1 blockade to suppress pancreatic cancer

As a double-stranded RNA-editing enzyme and an interferon-stimulated gene, double-stranded RNA-specific adenosine deaminase (ADAR1) suppresses interferon signaling and contributes to immunotherapy resistance. Suppression of ADAR1 overcomes immunotherapy resistance in preclinical models, but has not yet been translated to clinical settings. By conducting a screening of a subset of the FDA-approved drugs, we found that all-trans retinoic acid (ATRA, also known as tretinoin) caused ADAR1 protein degradation through ubiquitin-proteasome pathways and concomitantly increased PD-L1 expression in pancreatic and breast cancers. In addition, the combination of ATRA and PD-1 blockade reprogrammed the tumor microenvironment and unleashed antitumor immunity and thereby impeded tumor growth in pancreatic cancer mouse models. In a pilot clinical trial, a higher dose of ATRA plus the anti-PD-1 antibody nivolumab prolonged median overall survival in patients with chemotherapy-resistant pancreatic cancer compared to a lower dose of the same regimen. In this study, ATRA was the first drug to be found to cause ADAR1 degradation. We propose translation of a promising 2-pronged antitumor strategy using ATRA and nivolumab to convert immunologically "cold" into "hot" tumors susceptible to immune checkpoint blockade.

cancer biology↗

bioRxiv2024

A percolation-type criticality threshold controls immune protein coating of surfaces

When a material enters the body, it is immediately attacked by hundreds of proteins, organized into complex networks of binding interactions and reactions. How do such complex systems interact with a material, "deciding" whether to attack? We focus on the "complement" system of [~]40 blood proteins that bind microbes, nanoparticles, and medical devices, initiating inflammation. We show a sharp threshold for complement activation upon varying a fundamental material parameter, the surface density of potential complement attachment points. This sharp threshold manifests at scales spanning single nanoparticles to macroscale pathologies, shown here for diverse engineered and living materials. Computational models show these behaviors arise from a minimal subnetwork of complement, manifesting percolation-type critical transitions in the complement response. This criticality switch explains the "decision" of a complex signaling network to interact with a material, and elucidates the evolution and engineering of materials interacting with the body.

biophysics↗

bioRxiv2024

Expression of Capsanthin/Capsorubin Synthase in Tomato Alters Fruit Carotenoid Pools, and Flavor Volatiles Enhancing Provitamin A

We conceptualized a tomato biofortification strategy via engineering simultaneous accumulation of {beta}-carotene, a provitamin A and pepper-specialized ketocarotenoids, capsanthin, and capsorubin. Capsanthin/capsorubin synthase (CCS) in pepper, an enzyme phylogenetically related to lycopene {beta}-cyclases (LCYB) known for {beta}-carotene synthesis, was investigated for its in vivo role in ripening fruit. In pepper, silencing of CCS via virus-induced gene silencing reduced the flux from lycopene to {beta}-carotene (the {beta}-branch) with significant changes in carotenoid compositions. In a carotenogenic bacterial expression system, pepper CCS was more effective than tomato LCYB1 or LCYB2 in lycopene cyclization to {beta}-carotene and CCS synthesized capsanthin, but the LCYBs did not. Therefore, we overexpressed pepper CCS in different tomato varieties, including Micro-Tom WT, Micro-Tom mutant, pyp1-1(H7L) (defective in xanthophyll esterification), and five inbreds and characterized their carotenoid profiles. In CCS-transformed tomato fruit (WT and selected varieties), besides the biosynthesis of capsanthin and capsorubin, total carotenoids, {beta}-carotene, and xanthophyll esters remarkably increased compared to the controls, while such increments were weaker in the pyp1-1(H7L). CCS expression had a positive influence on the flux toward the {beta}-branch and the storage pool of xanthophyll esters consistent with its dual functions in lycopene cyclization and ketocarotenoid biosynthesis. The data further supported that xanthophyll esters facilitated carotenoid accumulation. While CCS-expression had no significant impact on growth or yield, fruit of CCS-transformed tomato had greater levels of {beta}-carotene-derived flavor volatiles than the controls. Consumption of 37-131-gram of CCS-derived hybrid fruit meets the provitamin A recommended dietary allowance, indicating greatly improved nutritional value.

plant biology↗

bioRxiv2024

Dynamics and determinants of the gut mobilome in early life

As mobile genetic elements (MGE) are critical yet understudied determinants of gut microbiome composition, we characterized the gut virome and plasmidome in 195 samples from 28 mother-infant dyads delivered by caesarean section. Infant mobilome increased in richness over the first 6 postnatal weeks, demonstrating high individual-specificity and temporal stability, establishing a personal persistent mobilome. Formula-fed infants exhibited greater mobilome richness than breastfed infants, with plasmid composition influenced by antibiotic exposure and birth weight. Plasmids constituted a significant reservoir of antibiotic resistance genes (ARG), with around 5% of infant gut plasmid taxonomic units carrying ARG. Notably, ARG profiles did not differ with antibiotic exposure at birth. We found that mother-infant sharing of viral and plasmid strains primarily occurred after 6 months of age. Overall, our integrative analysis offers novel insights into the dynamics, modulation, origin, and clinical implications of MGE in the developing gut microbiome.

genomics↗

bioRxiv2024

Mutant p53 Exploits Enhancers to Elevate Immunosuppressive Chemokine Expression and Impair Immune Checkpoint Inhibitors in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer without effective treatments. It is characterized by activating KRAS mutations and p53 alterations. However, how these mutations dysregulate cancer-cell-intrinsic gene programs to influence the immune landscape of the tumor microenvironment (TME) remains poorly understood. Here, we show that p53R172H establishes an immunosuppressive TME, diminishes the efficacy of immune checkpoint inhibitors (ICIs), and enhances tumor growth. Our findings reveal that the upregulation of the immunosuppressive chemokine Cxcl1 mediates these pro-tumorigenic functions of p53R172H. Mechanistically, we show that p53R172H associates with the distal enhancers of the Cxcl1 gene, increasing enhancer activity and Cxcl1 expression. p53R172H occupies these enhancers in an NF-{kappa}B-pathway-dependent manner, suggesting NF-{kappa}Bs role in recruiting p53R172H to the Cxcl1 enhancers. Our work uncovers how a common mutation in a tumor-suppressor transcription factor appropriates enhancers, stimulating chemokine expression and establishing an immunosuppressive TME that diminishes ICI efficacy in PDAC. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/609802v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@b2354dorg.highwire.dtl.DTLVardef@1c96300org.highwire.dtl.DTLVardef@829f26org.highwire.dtl.DTLVardef@19e7d2e_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

A transgene-free, human peri-gastrulation embryo model with trilaminar embryonic disc-, amnion- and yolk sac-like structures

The ultimate outcome of the gastrulation in mammalian development is a recognizable trilaminar disc structure containing organized cell lineages with spatially defined identities in an emerging coordinate system1-4. Despite its importance in human development, gastrulation remains difficult to study. Stem cell-based embryo models, including those that recapitulate different aspects of pre- and peri-gastrulation human development5-15, are emerging as promising tools for studying human embryogenesis16-18. However, it remains unclear whether existing human embryo models are capable of modeling the development of the trilaminar embryonic disc structure, a hallmark of human gastrulation. Here we report a transgene-free human embryo model derived solely from primed human pluripotent stem cells (hPSCs), which recapitulates various aspects of peri-gastrulation human development, including formation of trilaminar embryonic layers situated between dorsal amnion and ventral definitive yolk sac and primary hematopoiesis. We term this model the peri-gastrulation trilaminar embryonic disc (PTED) embryoid. The development of PTED embryoid does not follow natural developmental sequences of cell lineage diversification or spatial organization. Instead, it exploits both extrinsic control of tissue boundaries and intrinsic self-organizing properties and embryonic plasticity of the diverse peri-gastrulation-stage cell lineages, leading to the emergence of in vivo-like tissue organization and function at a global scale. Our lineage tracing study reveals that in PTED embryoids, embryonic and extraembryonic mesoderm cells, as well as embryonic and extraembryonic endoderm cells, share common progenitors emerging during peri-gastrulation development. Active hematopoiesis and blood cell generation are evident in the yolk sac-like structure of PTED embryoids. Together, PTED embryoids provide a promising and ethically less challenging model for studying self-organizing properties of peri-gastrulation human development.

developmental biology↗

bioRxiv2024

MultiOmicsAgent: Guided extreme gradient-boosted decision trees-based approaches for biomarker-candidate discovery in multi-omics data

MultiOmicsAgent (MOAgent) is an innovative, Python based open-source tool for biomarker discovery, utilizing machine learning techniques specifically extreme gradient-boosted decision trees to process multi-omics data. With its cross-platform compatibility, user-oriented graphical interface and a well-documented API, MOAgent not only meets the needs of both coding professionals and those new to machine learning but also addresses common data analysis challenges like data incompleteness, class imbalances and data leakage between disjoint data splits. MOAgents guided data analysis strategy opens up data-driven insights from digitized clinical biospecimen cohorts and makes advanced data analysis accessible and reliable for a wide audience. Biographical NoteJens Settelmeier, Julia Boshart, Martin Gesell are Ph.D. candidates, Jianbo Fu, Sebastian N. Steiner are Post Doc candidates and Sandra Goetze, Patrick Pedrioli senior scientists at the Institute of Translational Medicine at Health Sciences and Technology department at ETH Zurich, Switzerland, within Professor Bernd Wollscheids research group who has been working in the fields of bioinformatics, clinical multi-omics with a focus on spatial cell surface proteomics. Peter J. Schuffler is professor at the institute of Pathology at the TU Munich, Germany and has been working in the field of digital pathology and clinical multi-modal studies. Diyora Salimova is junior professor at the department of Applied Mathematics at the Albert-Ludwigs-University of Freibug, Germany and has been working in the field of stochastic processes, approximation theory and machine learning related topics. Key PointsO_LIMOAgent enables a guided biomarker-candidate discovery in multi-omics studies, providing a graphical interface and well-documented API. C_LIO_LIA user can run MOAgent on a personal computer without the requirement of coding a single line. C_LIO_LIMOAgent is a Python-based solution for biomarker-candidate discovery, using machine learning to analyze multi-omics data. C_LIO_LIMOAgent can address challenges like data incompleteness and class imbalances, ensuring reliable analysis. C_LIO_LIMOAgent makes advanced data analysis accessible, enhancing insights from clinical data. C_LI

bioinformatics↗

bioRxiv2024

Substrate Rigidity Modulates Segmentation Clock Dynamics in Isolated Presomitic Mesoderm Cells

The segmentation clock, a genetic oscillator in the presomitic mesoderm (PSM), is known to be influenced by biochemical signals, yet its potential regulation by mechanical cues remains unclear. The complex PSM microenvironment has made it challenging to isolate the effects of mechanical signals on clock behavior. Here we investigated how mechanical stimuli affect clock oscillations by culturing zebrafish PSM cells on bioengineered elastic substrates (PDMS micropost arrays) with tunable rigidities ranging from 0.6 to 1,200 kPa. We observed an inverse sigmoidal relationship between substrate rigidity and the percentage of oscillating PSM cells, with a switching rigidity threshold between 3-6 kPa. The oscillation periods of oscillating PSM cells showed a consistently broad distribution across the substrate rigidity conditions tested. Moreover, these oscillatory PSM cells exhibited distinct biophysical properties, including reduced motility, contractility, and sustained circularity, compared to non-oscillating ones. These findings highlight a role of cell-substrate interactions in regulating segmentation clock behavior, providing insights into the mechanobiology of somitogenesis. HighlightsO_LIOscillatory behaviors of single PSM cells respond to substrate rigidity in a switch-like manner, transitioning from an oscillatory state to a quiescent state at a critical rigidity threshold between 2.9 kPa and 6 kPa. C_LIO_LIIncreased substrate rigidity significantly suppresses the percentage of oscillating PSM cells, while oscillation period and cycle number show no consistent rigidity-dependent trend. C_LIO_LINon-oscillating PSM cells exhibit distinct biophysical properties compared to oscillating ones, including reduced circularity, a polarized and elongated morphology, greater motility, and increased contractility. C_LIO_LIAggregates of PSM cells exhibit similar trends in response to substrate rigidity changes, except for increased oscillation percentages across different rigidity conditions, suggesting a potential interplay between cell-cell and cell-matrix communications in influencing clock oscillation behavior. C_LI

developmental biology↗

bioRxiv2024

The landscape of gene loss and missense variation across the mammalian tree informs on gene essentiality

BackgroundThe degree of gene and sequence preservation across species provides valuable insights into the relative necessity of genes from the perspective of natural selection. Here, we developed novel interspecies metrics across 462 mammalian species, GISMO (Gene identity score of mammalian orthologs) and GISMO-mis (GISMO-missense), to quantify gene loss traversing millions of years of evolution. GISMO is a measure of gene loss across mammals weighed by evolutionary distance relative to humans, whereas GISMO-mis quantifies the ratio of missense to synonymous variants across mammalian species for a given gene. RationaleDespite large sample sizes, current human constraint metrics are still not well calibrated for short genes. Traversing over 100 million years of evolution across hundreds of mammals can identify the most essential genes and improve gene-disease association. Beyond human genetics, these metrics provide measures of gene constraint to further enable mammalian genetics research. ResultsOur analyses showed that both metrics are strongly correlated with measures of human gene constraint for loss-of-function, missense, and copy number dosage derived from upwards of a million human samples, which highlight the power of interspecies constraint. Importantly, neither GISMO nor GISMO-mis are strongly correlated with coding sequence length. Therefore both metrics can identify novel constrained genes that were too small for existing human constraint metrics to capture. We also found that GISMO scores capture rare variant association signals across a range of phenotypes associated with decreased fecundity, such as schizophrenia, autism, and neurodevelopmental disorders. Moreover, common variant heritability of disease traits are highly enriched in the most constrained deciles of both metrics, further underscoring the biological relevance of these metrics in identifying functionally important genes. We further showed that both scores have the lowest duplication and deletion rate in the most constrained deciles for copy number variants in the UK Biobank, suggesting that it may be an important metric for dosage sensitivity. We additionally demonstrate that GISMO can improve prioritization of recessive disorder genes and captures homozygous selection. ConclusionsOverall, we demonstrate that the most constrained genes for gene loss and missense variation capture the largest fraction of heritability, GISMO can help prioritize recessive disorder genes, and identify the most conserved genes across the mammalian tree. O_FIG O_LINKSMALLFIG WIDTH=168 HEIGHT=200 SRC="FIGDIR/small/594531v1_ufig1.gif" ALT="Figure 1"> View larger version (79K): org.highwire.dtl.DTLVardef@11c3e46org.highwire.dtl.DTLVardef@13c6e70org.highwire.dtl.DTLVardef@76a6c7org.highwire.dtl.DTLVardef@1bf8d86_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

bioRxiv2024

Towards anchoring evolutionary fitness for protein stability with virtual chemical environment recovery

Predicting the protein stability changes upon mutations is one of the effective ways to improve the efficiency of protein engineering. Here, we propose a dual-view ensemble learning-based framework, DVE-stability, for mutation-induced protein stability change prediction from single sequence. DVE-stability integrates the global and local dependencies of mutations to capture the intramolecular interactions from two views through ensemble learning, in which a structural microenvironment simulation module is designed to indirectly introduce the information of structural microenvironment at the sequence level. DVE-stability achieved state-of-the-art prediction performance on 7 single-point mutation benchmark datasets, and comprehensively surpassed other methods on 5 of them. Furthermore, DVE-stability outperformed other methods comprehensively through zero-shot inference on multiple-point mutation prediction task, demonstrating superior model generalizability to capture the epistasis of multiple-point mutations. More importantly, DVE-stability exhibited superior generalization performance in predicting rare beneficial mutations that are crucial for practical protein directed evolution scenarios. In addition, DVE-stability identified important intramolecular interactions via attention scores, demonstrating interpretable. Overall, DVE-stability provides a flexible and efficient tool for mutation-induced protein stability change prediction in an interpretable ensemble learning manner.

bioinformatics↗

bioRxiv2024

Dzip1 is dynamically expressed in the vertebrate germline and regulates the development of Xenopus primordial germ cells.

Primordial germ cells (PGCs) are the precursors of sperms and oocytes. Proper development of PGCs is crucial for the survival of the species. In many organisms, factors responsible for PGC development are synthesized during early oogenesis and assembled into the germ plasm. During early embryonic development, germ plasm is inherited by a few cells, leading to the formation of PGCs. While germline development has been extensively studied, how components of the germ plasm regulate PGC development is not fully understood. Here, we report that Dzip1 is dynamically expressed in vertebrate germline and is a novel component of the germ plasm in Xenopus and zebrafish. Knockdown of Dzip1 impairs PGC development in Xenopus embryos. At the molecular level, Dzip1 physically interacts with Dazl, an evolutionarily conserved RNA-binding protein that plays a multifaced role during germline development. We further showed that the sequence between amino acid residues 282 and 550 of Dzip1 is responsible for binding to Dazl. Disruption of the binding between Dzip1 and Dazl leads to defective PGC development. Taken together, our results presented here demonstrate that Dzip1 is dynamically expressed in the vertebrate germline and plays a novel function during Xenopus PGC development.

developmental biology↗

bioRxiv2024

RNA folding kinetics control riboswitch sensitivity in vivo

Riboswitches are ligand-responsive gene-regulatory RNA elements that perform key roles in maintaining cellular homeostasis. Understanding how riboswitch sensitivity is controlled is critical to understanding how highly conserved aptamer domains are deployed in a variety of contexts with different sensitivity demands. Here we uncover new roles by which RNA folding dynamics control riboswitch sensitivity in cells. By investigating the Clostridium beijerinckii pfl ZTP riboswitch, we identify multiple mechanistic routes of altering expression platform sequence and structure to slow RNA folding, all of which enhance riboswitch sensitivity. Applying these methods to riboswitches with diverse aptamer architectures that regulate transcription and translation with ON and OFF logic demonstrates the generality of our findings, indicating that any riboswitch that operates in a kinetic regime can be sensitized by slowing expression platform folding. Comparison of the most sensitized versions of these switches to equilibrium aptamer:ligand dissociation constants suggests a limit to the sensitivities achievable by kinetic RNA switches. Our results add to the growing suite of knowledge and approaches that can be used to rationally program cotranscriptional RNA folding for biotechnology applications, and suggest general RNA folding principles for understanding dynamic RNA systems in other areas of biology.

biochemistry↗

bioRxiv2024

Interpretable antibody-antigen interaction prediction by bridging structure to sequence

With the application of personalized and precision medicine, more precise and efficient antibody drug development technology is urgently needed. Identification of antibody-antigen interactions is crucial to antibody engineering. The time-consuming and expensive nature of wet-lab experiments calls for efficient computational methods. Taking into account the non-overlapping advantage of current structure-dependent and sequence-only computational methods, we propose an interpretable antibody-antigen interaction prediction method, S3AI. The introduction of structural knowledge, combined with explicit modeling of chemical rules, establishes a sequence-to-function route in S3AI, thereby facilitating its perception of intricate molecular interactions through providing route and priors guidance. S3AI significantly and comprehensively outperforms the state-of-the-art models and exhibits excellent generalization when predicting unknown antibody-antigen pairs, surpassing specialized prediction methods designed for out-of-distribution generalization in fair comparisons. More importantly, S3AI captures the universal pattern of antibody-antigen interactions, which not only identifies the CDRs responsible for specific binding to the antigen but also unearths the importance of CDR-H3 for the interaction. Structure-free design and superior performance make S3AI ideal for large-scale, parallelized antibody optimization and screening, enabling the rapid and precise identification of promising candidates within the extensive antibody space.

bioinformatics↗

bioRxiv2024

High-resolution analysis of the treated coeliac disease microbiome reveals increased inter-individual variability

BackgroundCoeliac disease (CeD) is an immune-mediated disorder primarily affecting the small intestine, characterised by an inflammatory immune reaction to dietary gluten. CeD onset results from a multifaceted interplay of genetic and environmental factors. While recent data show that alterations in gut microbiome composition could play an important role, many current studies are constrained by small sample sizes and limited resolution. MethodsTo address these limitations, we analysed faecal gut microbiota from two Dutch cohorts, CeDNN (128 treated CeD patients (tCeD), 106 controls) and the Lifelines Dutch Microbiome Project (24 self-reported tCeD, 654 controls), using shotgun metagenomic sequencing. Self-reported IBS (570 cases, 1710 controls) and IBD (93 cases, 465 controls) were used as comparative conditions of the gastrointestinal tract. Interindividual variation within the case and control groups was calculated at whole microbiome and strain level. Finally, species-specific gene repertoires were analysed in tCeD patients and controls. ResultsWithin-individual microbiome diversity was decreased in patients with self-reported IBS and IBD but not in tCeD patients. Each condition displayed a unique microbial pattern and, in addition to confirming previously reported microbiome associations, we identify an increase in the levels of Clostridium sp. CAG:253, Roseburia hominis, and Eggerthella lenta, amongst others. We further show that the observed changes can partially be explained by gluten-free diet adherence. We also observe increased interindividual variation of gut microbiome composition among tCeD patients and a higher bacterial mutation frequency in tCeD that contributes to higher interindividual variation at strain level. In addition, the immotile European subspecies of Eubacterium rectale, which has a distinct carbohydrate metabolism potential, was nearly absent in tCeD patients. ConclusionOur study sheds light on the complex interplay between the gut microbiome and CeD, revealing increased interindividual variation and strain-level variation in tCeD patients. These findings expand our understanding of the microbiomes role in intestinal health and disease. HighlightsO_LIThis is the largest microbiome collection of coeliac disease (CeD) patients assembled to date, providing insights into gut microbiome composition down to strain level. C_LIO_LICompared to controls, treated CeD (tCeD) patients adhering to a gluten-free diet show novel gut microbiome associations. C_LIO_LItCeD patients also have a less uniform gut microbiome than controls. C_LIO_LIBacteria display higher mutation frequency in tCeD compared to controls. C_LIO_LIHallmarks of the European subspecies of Eubacterium rectale are nearly absent in tCeD patients, implying selection at strain level. C_LI

microbiology↗

bioRxiv2024

Genetic polymorphisms of Leukocyte Immunoglobulin-Like Receptor B3 (LILRB3) gene in African American kidney transplant recipients are associated with post-transplant graft failure

BackgroundAfrican American (AA) kidney transplant recipients exhibit a higher rate of graft loss compared to other racial and ethnic populations, highlighting the need to identify causative factors underlying this disparity. MethodWe analyzed RNA sequences of pretransplant whole blood from subjects followed in three kidney transplant cohorts to identify single nucleotide polymorphisms (SNPs) associated with death censored graft loss (DCGL). We employed a meta-analysis to uncover key transcriptional signatures and pathways associated with the identified SNPs and used single cell RNA to define cellular specificity. We characterized SNP functions using in vitro immunological and survival assays and tested for associations between the identified SNPs and other immune-related diseases using a [~]30,100 subject, electronic health record (EHR)-linked database. ResultsWe uncovered a cluster of four consecutive missense SNPs in the Leukocyte Immunoglobulin-Like Receptor B3 (LILRB3, a negative immune response regulator) gene that strongly associated with DCGL. This LILRB3-4SNPs cluster encodes missense mutations at amino acids 617-618 proximal to a SHP-1/2 phosphatase-binding ITIM motif. LILRB3-4SNPs is specifically enriched within subjects of AA ancestry (8.6% prevalence vs 2.3% in Hispanic and 0.1% in European populations), is not linked to APOL1 G1/G2 alleles, and exhibited a strong association with DCGL. Analysis of PBMC and transplant biopsies from recipients with LILRB3-4SNPs showed evidence of enhanced adaptive immune responsiveness and ferroptosis-associated death in monocytes. Overexpression of the variant allele in THP-1 cells (macrophage line) induced augmented inflammation and ferroptosis, which were attenuated by a ferroptosis inhibitor, verifying a causal link. The LILRB3-4SNPs also associated with multiple systemic and organ-specific immune-related diseases in AAs, consistent with conferring a broadly relevant immune function. Conclusionthe LILRB3-4SNPs represent a functionally important, distinct genetic risk factor for kidney transplant outcome and development/severity of other immune-related diseases in patients of AA ancestry. Pharmacological targeting of ferroptosis should be tested to prevent or treat these disease processes in AA recipients carrying LILRB3-4SNPs.

genetics↗

bioRxiv2024

Neurotrophic factor Neuritin modulates T cell electrical and metabolic state for the balance of tolerance and immunity

The adaptive T cell response is accompanied by continuous rewiring of the T cells electric and metabolic state. Ion channels and nutrient transporters integrate bioelectric and biochemical signals from the environment, setting cellular electric and metabolic states. Divergent electric and metabolic states contribute to T cell immunity or tolerance. Here, we report that neuritin (Nrn1) contributes to tolerance development by modulating regulatory and effector T cell function. Nrn1 expression in regulatory T cells promotes its expansion and suppression function, while expression in the T effector cell dampens its inflammatory response. Nrn1 deficiency causes dysregulation of ion channel and nutrient transporter expression in Treg and effector T cells, resulting in divergent metabolic outcomes and impacting autoimmune disease progression and recovery. These findings identify a novel immune function of the neurotrophic factor Nrn1 in regulating the T cell metabolic state in a cell context-dependent manner and modulating the outcome of an immune response.

immunology↗

bioRxiv2024

A universal molecular mechanism driving aging

How cell replication ultimately results in aging and the Hayflick limit are not fully understood. Here we show that clock-like accumulation of DNA G-quadruplexes (G4s) throughout cell replication drives conserved aging mechanisms. G4 stimulates transcription-replication interactions to delay genome replication and impairs DNA re-methylation and histone modification recovery, leading to loss of heterochromatin. This creates a more permissive local environment for G4 formation in subsequent generations. As a result, G4s gradually accumulate on promoters throughout mitosis, driving clock-like DNA hypomethylation and chromatin opening. In patients and in vitro models, loss-of-function mutations in the G4-resolving enzymes WRN, BLM and ERCC8 accelerate the erosion of the epigenomic landscape around G4. G4-driven epigenomic aging is strongly correlated with biological age and is conserved in yeast, nematodes, insects, fish, rodents, and humans. Our results revealed a universal molecular mechanism of aging and provided mechanistic insight into how G-quadruplex processor mutations drive premature aging.

molecular biology↗

bioRxiv2024

Hunting for peptide binders of specific targets with data-centric generative language models

Bioactive peptides have become strong candidates for a variety of clinical therapies due to their diverse advantages, which promotes the development of deep generative models for peptide design. Considering that existing methods cannot effectively deal with the conformational flexibility of peptides and are difficult to capture accurate residue-to-residue interaction dependencies, we propose a unified weakly order-dependent autoregressive language modeling architecture (PepGenWOA) for bioactive peptide generative design with tolerating out-of-order input as inductive bias. The superiority of PepGenWOA is demonstrated by generating three classes of therapeutic peptides, including antimicrobial peptides, anticancer peptides, and peptide binders. For antimicrobial and anticancer peptide generation, PepGenWOA not only comprehensively outperforms state-of-the-art baseline models, but also exhibits a significant propensity to incorporate specific types of residues that are beneficial for antimicrobial or anticancer bioactivity. For characteristic-guided peptide binder generation, the pretrained PepGenWOA is fine-tuned with Mixture-of-Experts-style plugins with lifelong learning paradigm, thereby achieving the best trade-off between memory stability for world knowledge and learning plasticity for new downstream tasks. The subsequent structure-based virtual screening anchors the 7 most promising candidates from the mega-scale synthetic sequence space, reducing the number of candidates for in vitro experimental validation by 5 orders of magnitude. A target binding rate of 28.6% with binding specificity further confirms the effectiveness of the fine-tuning strategy. Overall, PepGenWOA is a unified architecture for peptide generative design that can be flexibly customized for different task requirements, thereby harnessing generative language models to reach the "dark space" of biological language.

bioinformatics↗