Biology subjects

Chen, M.

Publications and source records attributed to Chen, M..

At least 73 records · Page 4Linked to original sources

bioRxiv2026

Systematic Profiling and Functional Characterization of Alternative Splicing in C. elegans Olfactory Learning

Alternative splicing (AS) is prevalent in neuronal gene expression. However, its function in learning has not been systematically characterized. Here, by profiling pan-neuronal translatome changes during a C. elegans learning paradigm, we show that AS remodels expression of neuronal genes with critical function in learning at the genome-wide scale. Intriguingly, AS operates on a functionally distinct gene set from those showing transcript abundance changes, serving as a separate regulatory layer in response to experience. Specifically, a neuronally enriched worm ortholog of a mitochondrial DNA helicase twnk-1 displays significant learning-associated AS changes, with both isoform types acting in a pair of sensory neurons to regulate learning with distinct functions. AS of twnk-1 modulates a cell-nonautonomous signal from neuronal mitochondria to peripheral tissues to regulate physiological states critical for learning. These results establish AS as a systematic regulator of learning and mechanistically reveal how AS shapes physiological states to facilitate learning.

neuroscience↗

bioRxiv2026

Revealing the spatiotemporal dynamics of methionine metabolism with a genetically encoded single-fluorophore biosensor

Methionine is an essential amino acid, used for protein synthesis, redox homeostasis, and methylation reactions throughout the cell. However, the compartmentalized dynamics of methionine have remained elusive, due to a lack of available tools to measure methionine with high spatial and temporal resolution. To address this limitation, we have developed a single fluorescent protein-based methionine optical reporter (Meteor) which reports subcellular changes in methionine with high dynamic range. Using Meteor, we demonstrate the subcellular uptake of methionine in multiple cell lines into several locations, including the mitochondrial matrix. Furthermore, we use Meteor to illuminate the dynamics of the methionine cycle in the cytoplasm and nucleus, finding cancer cells can rapidly increase methionine from metabolic precursors in both locations. Finally, demonstrated that Meteor can be used to visualize methionine dynamics in vivo using Caenorhabditis elegans. Thus, we have developed a new tool to measure methionine dynamics across scales with high dynamic range and spatiotemporal resolution.

biochemistry↗

bioRxiv2026

A new Persister Enrichment Model in Mycobacterium tuberculosis links multidrug persistence to drug sequencing and maltokinase-dependent carbon distribution.

Prolonged combination chemotherapy is required to cure Tuberculosis, in part because Mycobacterium tuberculosis (Mtb) forms antibiotic-tolerant persister cells that survive exposure to bactericidal drugs. Experimental systems to study multidrug persistence in Mtb remain limited. Here, we describe a rapid and tractable Persister Enrichment Model (PEM) in which removal of glycerol markedly increases the frequency of multidrug-tolerant Mtb cells that survive exposure to high concentrations of bactericidal antibiotics with distinct mechanisms of action. Persister enrichment in PEM depends on carbon source identity rather than carbon abundance and enables direct interrogation at the transition from active growth into multidrug tolerance. Using PEM, we show that the temporal order of isoniazid (INH) and rifampicin (RIF) exposure shapes the magnitude of multidrug persistence. A transposon sequencing screen under combination drug pressure identified maltokinase (mak) as a major determinant of multidrug persistence. Deletion of mak enhanced susceptibility to INH+RIF in PEM and impaired bacterial survival during the persistent phase of infection in mice. Metabolic analyses reveal that loss of mak destabilizes carbon distribution during co-catabolism of glycolytic carbon sources, leading to defective growth and antibiotic persistence. This defect is ameliorated by inactivating mutations in glucose-1-phosphate adenylyltransferase (glgC), which alleviate the buildup of ADP-glucose. Together, these findings establish PEM as a practical platform to study multidrug persistence, reveal that drug sequencing can influence entry into multidrug tolerance, and identify maltokinase-dependent carbon distribution as a metabolic vulnerability that constrains Mtb survival. ImportanceTuberculosis treatment requires prolonged multidrug chemotherapy in part because Mycobacterium tuberculosis forms antibiotic-tolerant persister cells that survive bactericidal drug exposure. However, experimental systems to study multidrug persistence remain limited, and the metabolic mechanisms underlying this phenotype are poorly understood. Here, we establish a rapid and experimentally tractable Persister Enrichment Model (PEM) that enriches multidrug-tolerant M. tuberculosis populations by removing glycerol from culture medium. Using this model, we demonstrate that the order of antibiotic exposure influences multidrug persistence both in vitro and in vivo and identify maltokinase-dependent carbon distribution as a critical determinant of persistence and long-term bacterial survival. These findings provide a new platform for studying multidrug tolerance and reveal carbon buffering pathways as potential therapeutic vulnerabilities that could help shorten tuberculosis treatment.

microbiology↗

bioRxiv2026

GAE-Δ: A Graph-Learning Framework for Gene Network Rewiring and Clinical Outcome Prediction from Multi-Omics Data

Cancer progression and outcomes are driven in part by changes to molecular networks that result from genetic and/or environmental perturbations. These network changes manifest across multiple interconnected network layers and include accumulation of somatic mutations, altered proteinprotein interactions and dysregulated gene-expression. Here we describe a graph autoencoder-based framework (Graph Autoencoder-Delta (GAE-{Delta})), for characterizing phenotype-specific gene role shifts across multi-omics data. Given samples stratified into two contrasting phenotypic groups and a prior gene interaction network, GAE-{Delta} constructs group-specific gene graphs for each omics modality and trains, for each modality, a single graph autoencoder jointly on both group graphs, so that the two group-conditional embeddings share a common latent space. Contrasting these embeddings defines a multi-omics embedding-shift representation for each gene that reflects how its network role reorganizes across phenotypic contexts. These gene-level shifts are subsequently used for unsupervised gene prioritization, multi-omics late fusion and sample-level classification. Applied to five TCGA cancer types with a survival endpoint, GAE-{Delta} achieves competitive or superior predictive performance compared with classical network-based methods and multi-omics matrix-factorisation methods (MOFA+, iNMF), with statistically significant AUC gains over MOFA+ in three of five cohorts and statistical ties on the remaining two. Beyond predictive performance, the consensus shift genes are significantly enriched for known cancer drivers in three of five cohorts (hypergeometric p < 0.01; 11-17x fold-enrichment), whereas matrix-factorisation baselines reach p < 0.05 in zero of five cohorts (best per-cancer p = 0.06), indicating that GAE-{Delta} captures biological signal that linear factor methods miss. In summary, the GAE-{Delta} approach provides for both improved outcome classification as well as for biological and mechanistic discovery through deep network-based integration of disease-associated multi-omics data.

bioinformatics↗

bioRxiv2026

Benchmarking full-length ITS metabarcoding across Illumina 2x500, PacBio, and Oxford Nanopore sequencing using mock and soil communities

Metabarcoding is a powerful tool for biodiversity comparisons, where standard-size DNA barcodes (>500 bases) offer better taxonomic resolution than shorter ones. Still, the choice of sequencing platforms and bioinformatics pipelines may strongly affect inferred diversity due to various technical biases. We assessed the relative performance of Illumina MiSeq i100 (2x500 paired-end), PacBio Revio and Oxford Nanopore MinION sequencing and bioinformatics pipelines, using full-length ITS amplicon sequencing datasets from a 103-species mock community and 45 composite soil samples. Despite numerous low-quality reads, PacBio yielded the lowest overall error rate and highest number of taxa. Illumina revealed the highest proportion of chimeric and index-switched reads, along with a strong bias towards shorter amplicons. MinION data analysed using PRONAME and Minovar - a bioinformatics pipeline presented here - had the largest proportion of low-quality data, and rare taxa were lost during data filtering and read polishing steps. Although Minovar enabled amplicon sequence variant (ASV) level precision for common taxa, we recommend clustering ASVs into OTUs. For PacBio, standard filtering approaches outperformed the ASV approach because they retained rare taxa. For Illumina, a stringent ASV approach or removal of rare OTUs would limit artefacts. Across all platforms, excess PCR cycles promoted chimeric and low-quality reads and lost quantitativity in biodiversity assessments. With moderate differences in effect sizes, all analytical approaches supported the conclusion that sampling design determines how we see soil biodiversity responses to land use. For biodiversity surveys based on the full-length ITS metabarcoding, we recommend using PacBio sequencing with standard, non-ASV pipelines.

bioinformatics↗

bioRxiv2026

Prediction of Transcription Factor DNA Binding Affinity with High-Throughput Kd Measurements and Deep Learning

Transcription factors (TFs) regulate gene expression through specific interactions with genomic DNA. While TF binding motifs from public databases describe sequence preferences, quantifying genome-wide affinity (Kd) is highly desirable for a more accurate thermodynamic description. Here, we report ivtFOODIE (in vitro FOOtprinting with DeamInasE), an assay that leverages deaminase-mediated cytosine-to-uracil conversion to measure Kd values for a given TF across accessible genomic regions from human cells. By pre-training on TF binding sites from JASPAR and fine-tuning with our ivtFOODIE data from 46 TFs representing 13 different DNA-binding domains (DBDs), we developed Seq2Kd, a deep learning model capable of predicting a TFs absolute binding affinity on DNA sequences. Seq2Kd enables de novo motif discovery of [~]500 previously uncharacterized human TFs and reveals the effects of genetic variation both in TF-coding regions and DNA-binding sites on gene expression and disease susceptibility. By correlating predicted affinity changes with the sign and magnitude of expression quantitative trait locus (eQTL) effects, we stratified TFs into activator-like and repressor-like groups. Compared to clinically benign variants, pathogenic single-nucleotide variants (SNVs) within regulatory and protein-coding regions show significantly larger predicted shifts in Kd. We provide an interactive web portal, the ENcyclopedia of Transcription-factor Interactions with Regulatory Elements (ENTIRE), which integrates the Seq2Kd model with the ivtFOODIE dataset. This resource offers thermodynamic prediction for TF-DNA interactions for functional genomics and human disease.

biophysics↗

bioRxiv2026

Gut Microbiota-derived Acetate Safeguards the Colonic Epithelial Acetyl-CoA Reserve to Avert Colonic Senescence

Aging is a major driver of tissue senescence, but little is known about the development of age-unrelated tissue senescence. Here we show that nucleocytosolic acetyl-CoA deficiency in colon epithelial cells, caused by Acly ablation and bacterial depletion, triggers age-independent, p53-dependent colonic senescence leading to severe systemic inflammation. Acetate supplementation, acetate-producing bacterial transplantation, targeted depletion of senescent cells or treatment with lysine deacetylase inhibitors blocks colonic senescence and inflammatory injury. Spontaneous colonic senescence develops following simultaneous deletion of epithelial Acly and Acss2, confirming that microbe-derived acetate maintains the epithelial acetyl-CoA pool via ACSS2 to avert colonic senescence. Mechanistically, acetyl-CoA deficiency deprives a cohort of mitochondrial and nuclear proteins of acetylation, leading to increased oxidative stress and DNA repair stress that trigger cellular senescence. Among these proteins, ATP5F1A-K161 acetylation and H4-K5/8 acetylation are required to protect colonic epithelial cells from developing senescence. These observations unveil a previously unknown mechanism that governs colonic senescence.

physiology↗

bioRxiv2026

A tunable aqueous architecture modulates functionaloutput in biomolecular condensates

Biomolecular condensates organize cellular biochemistry, yet the principles governing their internal solvent architectures remain poorly understood. Most current models focus on macromolecular scaffolds while treating the solvent as a passive, spatially uniform background. Here, we introduce Condensate Spatial Topography via Emission Lifetimes (ConSTEL) to map the continuous solvent polarity landscape inside biomolecular condensates. Using PopZ as a model system, we show that the condensate interior contains a persistent, tunable mosaic of aqueous environments whose apparent polarity, reported by Nile Red fluorescence lifetimes, is organized by thermodynamic state and chemical cues. This microphase-separated solvent architecture defines distinct mesoscale rheological regimes, with intermediate aqueous niches supporting fast, confined tracer motion and highly polar or non-polar extremes forming a slower, viscoelastic mesh. We further demonstrate that drug-like small molecules partition non-uniformly across this landscape according to their physicochemical properties, and that exceeding local solubility limits drives "reciprocal sculpting", in which mismatched guests remodel the host solvent architecture. Together, these results highlight internal solvent organization as an active, tunable determinant of condensate material properties, molecular transport, and partitioning, and suggest that predictive models of condensate function and pharmacology would benefit from incorporating the spatial arrangement of solvent environments alongside bulk composition.

biochemistry↗

bioRxiv2026

Coupled Binding and Folding of NS2B/NS3 Protease and Linker Effects Revealed by Topology-based Modeling

Orthoflavivirus, such as West Nile Virus (WNV), dengue virus (DENV) and ZIKA virus (ZIKV), are globally distributed pathogens that pose substantial threats to human health. Currently, there are still no effective antiviral drugs for WNV or ZIKV. Despite the availability of two licensed DENV vaccines, their use remains limited due to potential risks, highlighting an urgent need for antiviral drug development. The highly conserved orthoflavivirus protease NS2B/NS3 is required for viral replication, making it a promising anti-flavivirus target. A major challenge, however, is the highly charged active site of this enzyme, which requires charged chemical matters with low bioavailability. An alternative and more attractive strategy is to target potential allosteric sites or folding intermediate states of the protease. In this work, we employ the topology-based coarse-grained G[o] modeling to explore the coupled binding and folding pathways of WNV NS2B/NS3 protease and study the effects of the widely used experimental construct with a G4SG4 linker between NS2B and NS3 on stability and folding. Our results provide a holistic conformational landscape of the protease binding and folding, including several key intermediate states. We find that the presence of the G4SG4 linker alters the folding pathways and destabilizes the NS2B C-terminus. The latter is consistent with experimental observations that the G4SG4 linked protease has lower activity and adopts an open state without the substrate in crystal structures. Together, these findings provide for the first time a complete picture of the binding and folding of the NS2B/NS3 protease and identify important folding intermediate states that could be targeted for allosteric antiviral drug development. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=157 SRC="FIGDIR/small/722635v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@163c356org.highwire.dtl.DTLVardef@ad7b35org.highwire.dtl.DTLVardef@173ed8aorg.highwire.dtl.DTLVardef@1f026bf_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2026

Hippocampal Glucuronyl C5-epimerase promotes stress resilience by directly engaging PI3K through a non-enzymatic mechanism

Major depressive disorder (MDD) seriously affects human physical and mental health and causes global socioeconomic burdens. Stress-induced depressive etiology is linked to dendritic atrophy in the hippocampus, however, the underlying mechanism was poorly understood. Here, we identify that glucuronyl C5-epimerase (Glce), a heparan sulfae-modifying enzyme, as a critical regulator of hippocampal dendritic integrity and stress resilience. Genetic ablation of Glce in the hippocampal excitatory neurons is sufficient to induce dendritic atrophy and depressive-like behaviors, whereas its restoration rescues these deficits. Notably, Glce levels are reduced in the plasma of depressed individuals. We further find that Glce functions independently of its enzymatic activity, instead binding directly to and activating PI3K (p85/p110), thereby triggering an Akt/CREB/BDNF signaling cascade. Together, our findings uncover a non-canonical role for Glce and establish that Glce-PI3K-BDNF axis is essential for maintaining hippocampal structure and behavioral resilience, thereby highlighting a critical role of Glce in the pathophysiology of depression.

neuroscience↗

bioRxiv2026

Gut microbiota-derived indole-3-propionic acid preserves dorsal hippocampal catecholamines to prevent post-stroke cognitive impairment

BackgroundGut dysbiosis has been increasingly implicated in post-stroke cognitive impairment (PSCI), yet the causal contribution and therapeutic potential of gut microbiota-derived metabolites remain unclear. This study aimed to identify key microbiota-derived metabolites involved in PSCI and to elucidate their underlying mechanisms. ResultsWe found that both PSCI patients and middle cerebral artery occlusion (MCAO) mice exhibited distinct gut microbial alterations, characterized by a marked reduction in tryptophan-metabolizing bacteria and indole-3-propionic acid (IPA), a gut microbiota-derived tryptophan metabolite. Exogenous IPA administration alleviated PSCI-like phenotypes in MCAO mice. Mechanistically, IPA preserved tyrosine hydroxylase-positive (Th) fibers and catecholamine levels in the dorsal hippocampus. Further analyses showed that IPA binds to the adaptor protein Ywhab, promotes ERK activation, and enhances neuronal survival, thereby counteracting neuronal apoptosis-associated inflammation and subsequent Th fiber degeneration. ConclusionThese findings identify IPA as a gut microbiota-derived neuromodulator that mitigates PSCI by preserving dorsal hippocampal catecholaminergic transmission. IPA may therefore serve as a promising predictive biomarker and therapeutic candidate for PSCI.

neuroscience↗

bioRxiv2026

mGluR6 coordinates cone terminal targeting and synaptic layer assembly during human retinal development

The metabotropic glutamate receptor 6 (mGluR6), encoded by GRM6, is a core component of the ON-bipolar signaling cascade in the retina, but its role in human retinal development remains unclear. Here, we used temporally controlled CRISPR-based genetic ablation in human induced pluripotent stem cell-derived retinal organoids to define the developmental functions of mGluR6. Unexpectedly, we found that mGluR6 is expressed not only in depolarizing ON-bipolar cells but also transiently in cone photoreceptors during human retinal development, a pattern not observed in the mouse retina. Early loss of GRM6 prior to synaptogenesis disrupted cone pedicle architecture, leading to mislocalization of synaptic proteins including Bassoon, ELFN2, and TRPM1, and ultimately resulting in widening or duplication of the outer plexiform layer (OPL). In contrast, deletion after synapse formation did not alter OPL synapses or morphology, revealing a temporally restricted requirement for mGluR6 during circuit assembly. These findings uncover a previously unrecognized role for mGluR6 in coordinating cone terminal targeting and synaptic layer assembly during human retinal development and highlight the power of temporally controlled genetic manipulation in organoid systems to reveal species-specific mechanisms of neural circuit formation.

neuroscience↗

bioRxiv2026

GenNA: Conditional generation of nucleotide sequences guided by natural-language annotations

Deciphering the mapping between linear biomolecular sequences and complex biological functions remains a central challenge in genomics. Although existing generative nucleotide language models have made substantial progress in modeling sequence distributions, they generally lack explicit access to high-level biological semantics, limiting their ability to support semantics-guided conditional generation. To address this limitation, we present GenNA, a generative nucleotide foundation model guided by natural-language annotations. GenNA is pretrained on a multimodal nucleotide-text corpus spanning 2,221 eukaryotic species and comprising approximately 416 billion characters, and learns the relationships between sequence patterns and functional annotations within a unified autoregressive framework. Systematic evaluations show that, even without explicit supervision from biological rules, GenNA yields distinguishable perplexity scores in response to semantic mismatches between sequences and functional annotations, to different mutation types, and to perturbations of species labels. Moreover, across a range of natural-language-guided nucleotide generation tasks, the model produces sequences consistent with both target semantics and species context. Overall, GenNA provides a unified framework for natural-language-guided nucleotide modeling and conditional generation, and offers a feasible route toward integrating high-level functional descriptions with low-level sequence design.

bioinformatics↗

bioRxiv2026

Revisiting the genome assembly of Lupinus species reveals differential diploidization after a shared whole-genome duplication

Accurate genome assemblies are essential for comparative genomics, yet Hi-C-guided scaffolding can introduce structural errors that misrepresent chromosome architecture and bias evolutionary inferences. Here, we identified pervasive scaffolding errors - including artificial fusions, internal inversions, and incomplete contig mounting - in two previously published Lupinus genomes (L. cosentinii and L. digitatus) using a segmentation method based on LTR retrotransposon density. We re-assembled both genomes, producing chromosome-level references of 472.7 Mb (16 chromosomes) and 427.2 Mb (21 chromosomes), with BUSCO completeness >98.5%. Synteny validation and re-application of LTR profiling confirmed that all prior errors were resolved. Using these corrected genomes together with four additional Lupinus species and two outgroup legumes, we investigated post-polyploid evolution. Synonymous substitution rate (Ks) analysis revealed a genus-specific whole-genome duplication (WGD) event (Ks = 0.17) shared by all six Lupinus species. The proportion of WGD-derived genes varied markedly, from 60% in L. digitatus to only 36% in L. mutabilis, indicating differential diploidization. While all species retained a core set of WGD duplicates enriched in cytoskeleton organization, ion transport, and defense responses, each exhibited lineage-specific functional trajectories: cell wall modification in L. cosentinii and L. digitatus, nitrogen metabolism in L. albus and L. angustifolius, flower development in L. luteus, and stress/lipid metabolism in L. mutabilis. Our corrected assemblies provide optimal references for Lupinus comparative genomics, and our findings demonstrate that a shared WGD event can lead to both conserved and highly divergent post-polyploid fates, likely underpinning adaptive diversification within the genus.

genomics↗

bioRxiv2026

Dynamic dissociation of the IFT complex drives ciliary dysfunction during C. elegans ageing

Intraflagellar transport (IFT) is essential for cilia, and its dysfunction drives ciliopathies and systemic ageing. However, the in vivo dynamics of individual components in the IFT complexes remain obscured, leaving the mechanisms of age-dependent IFT failure largely unknown. Here, we report a dual-color super-resolution imaging strategy to dissect the structural integrity and kinetics of IFT trains in the sensory cilia of young and aged Caenorhabditis elegans. We show that IFT complexes are not static entities. Instead, distinct components undergo dynamic dissociation within IFT trains. This intra-complex dissociation causes a remarkable reduction in IFT velocity and is significantly exacerbated in the cilia of aged worms. Mechanistically, we identify the conserved TRiC/CCT chaperonin complex and daf-19/RFX, the master transcription factor driving IFT genes, as critical regulators of IFT stability. We demonstrate that their age-dependent downregulation drives the progressive IFT component dissociation. Our findings re-frame the IFT complex as a highly dynamic assembly, uncover a new dimension of IFT regulation, and identify the progressive uncoupling of IFT components as a key driver of ciliary dysfunction during ageing.

cell biology↗

bioRxiv2026

Correlation Between Information Entropy and Functions of Gene Sequences in the Evolutionary Context: A New Way to Construct Gene Regulatory Networks from Sequence

The information encoded in DNA sequences can be rigorously quantified using Shannon entropy and related measures. When placed in an evolutionary context, this quantification offers a principled yet underexplored route to constructing gene regulatory networks (GRNs) directly from sequence data. While most GRN inference methods rely exclusively on gene expression profiles, the regulatory code is ultimately written in the DNA sequence itself. Here we review the mathematical foundations of information theory as applied to gene sequences, survey existing computational methods for GRN inference--with emphasis on information-theoretic and sequence-based approaches--and examine how evolutionary conservation constrains sequence entropy to preserve biological function. We then propose a four-layer integrative framework that combines per-position Shannon entropy profiles, evolutionary conservation scoring via Jensen- Shannon divergence, expression-based mutual information and transfer entropy, and DNA foundation model embeddings to construct GRNs from sequence. Through worked examples on the Escherichia coli SOS regulatory sub-network, we demonstrate how conservation-weighted mutual information improves edge discrimination and how transfer entropy resolves regulatory directionality. The framework generates testable predictions: edges supported by low-entropy regulatory regions should show higher experimental validation rates, and cross-species entropy profile conservation should predict GRN topology conservation. This work bridges three scales of biological information--nucleotide-level entropy, evolutionary constraint patterns, and network-level regulatory logic--establishing information entropy as the natural mathematical language for sequence-to-network regulatory inference.

bioinformatics↗

bioRxiv2026

Redox imbalance dictates dependence on GOT1 versus GOT2 for rod photoreceptor health during aging and stress

Photoreceptor (PR) loss causes vision loss in many blinding diseases, and effective therapies to prevent this cell loss are lacking. Aspartate aminotransferases (GOTs), located in the cytosol (GOT1) and mitochondria (GOT2), are key components of the malate-aspartate shuttle, which transfers reducing equivalents from cytosol to mitochondria. Previous work has implicated the GOTs as potential modulators of blinding retinal disease. To determine the roles of GOT1 and GOT2 in rod PRs, we generated rod PR-specific Got1 or Got2 conditional knockout mice (Got1 or Got2 cKO). We previously showed that Got1 cKO causes PR degeneration and is accompanied by NADH accumulation and a decreased retinal NAD+/NADH ratio. Here, we show that NADH oxidation via metabolic or genetic means prolongs PR survival in Got1 cKO animals, implicating NADH accumulation, or reductive stress, as a key driver of PR degeneration. In contrast, Got2 cKO causes minimal PR degeneration and alterations in retinal NADH and the NAD+/NADH ratio that oppose reductive stress. Interestingly, GOT2, but not GOT1, is decreased in multiple models of PR degeneration, including retinal detachment (RD) where the NAD+/NADH ratio favors a reductive state. Notably, loss of Got2 in PRs demonstrates a neuroprotective effect after experimental RD suggesting decreased GOT2 expression may be part of a stress response to promote PR survival. Overall, this study illustrates the differential dependence on the GOTs for PR health, provides evidence that an overly reductive environment is detrimental to PR survival, and identifies GOT2 as a novel therapeutic target with potentially broad application in blinding diseases.

neuroscience↗

bioRxiv2026

A Systems Framework for Quantifying Programmability and Persistence Across Mammalian Cell Types

Cellular therapies, toxicity screening, and regenerative medicine depend on selecting mammalian cell types with optimal lifespan, persistence post-transplant, immunogenicity, and chemical resilience. This review synthesizes data from over 50 immune, parenchymal, stem, and emerging engineered cell populations--including gamma-delta T cells, iNKT cells, CAR-macrophages, and hypoimmune iPSC derivatives--drawing from in vivo lifespan studies (including 1{blacksquare}C birth-dating and deuterium labeling), engraftment dynamics, immune rejection risk, and stress sensitivity profiles. We introduce a Programmability & Persistence Score (PPS; 0-20) that integrates these features into a unified metric, complemented by Pareto frontier analysis to visualize multi-objective trade-offs. High-PPS cell types (e.g., HLA-matched HSCs, hypoimmune iPSCs, chondrocytes) are suited for long-term regenerative applications, while low-PPS sentinels (e.g., neutrophils, enterocytes) serve acute assays. We discuss mathematical extensions including multi-criteria decision analysis, fuzzy membership functions, and Bayesian frameworks that address limitations of linear additive scoring. Together, these integrated profiles support cell selection for gene editing, organ-on-chip systems, in vivo cell programming, and immunotherapy, bridging cell biology with translational engineering.

systems biology↗