Biology subjects

Ray, A.

Publications and source records attributed to Ray, A..

At least 19 recordsLinked to original sources

bioRxiv2026

IFNγ-CXCL9-CXCR3 axis links T cell recruitment to skeletal muscle remodelling in human physical frailty

Introduction: Physical frailty is increasingly prevalent in our ageing society, and results in higher morbidity and healthcare costs. Yet, targeted therapeutics are lacking because underlying mechanisms are poorly defined. Human cohort studies have linked inflammation with frailty, but do not define the mechanisms by which the ageing immune system affects skeletal muscle and physical function. Defining this immune-muscle interaction is therefore crucial for identifying therapeutics for human physical frailty. Methods: We first studied two independent human ageing cohorts (discovery n=22; validation n=33). Physical frailty was quantified with age-adjusted timed-up-and-go (TUG) residuals. Peripheral blood was analysed for plasma OLINK proteomics, high-dimensional spectral immunophenotyping and in a subset, single-cell RNA-sequencing of PBMCs with paired TCR profiling. We then examined an independent human skeletal-muscle cohort (n=340) by bulk RNA-sequencing to derive our proposed immune-muscle axis. Finally, we interrogated individual steps of this axis in primary human in vitro models using IFN{gamma}-conditioned myotube-fibroblast co-cultures, PBMC transwell migration assays with CXCL9 neutralisation (anti-CXCL9) and receptor CXCR3 antagonism (AMG487). Macrophage co-cultured with CD8 were sequentially co-cultured with myotube/fibroblast co-culture, supernatant was then analysed for fibrosis mediators. We used fixed-effect meta-analysis, multivariable models and sequential mediation for analyses. Results: Physical frailty, by single-cell analyses, was associated with a compartmentalised immune-remodelling state involving inflammatory, tissue-remodelling and trafficking programmes in myeloid and T-cells. This finding was validated with plasma proteomics of the same human cohort, where physical frailty was significantly positively associated with an interferon-gamma (IFN{gamma})/CXCR3 (IL-18R1, IFN{gamma}, CXCL9) module, within which plasma CXCL9 was the strongest correlate. CD8-TEMRA and {gamma}{delta} T-cells were most responsive to CXCL9 in physical frailty. This expanded CD8-TEMRA compartment was polyclonal, consistent with bystander IFN{gamma} production rather than a shared antigen-specific response. In human muscle, CXCL9 was significantly positively associated with T-cell module, and through this, fibrosis. Conversely, fibrosis was associated with macrophages. Sequential mediation was consistent with a CXCL9-T-cell-macrophage-fibrosis pathway. Finally, in in-vitro human primary cell experiments, IFN{gamma}-conditioned myotube-fibroblast cultures produced CXCL9 and attracted CD8-TEMRA subsets. Anti-CXCL9 and CXCR3 antagonism significantly reduced this migration, indicating that recruitment of CD8-TEMRA cells was dependent on the CXCL9-CXCR3 axis. Incubating with activated CD8 cells shifted macrophages towards mixed CD163+CD86+ and pro-inflammatory CD163-CD86+ phenotypes. These CD8-co-cultured macrophages in myotube/fibroblast co-culture were associated with increased fibrosis markers transforming growth factor beta-1 and plasminogen activator inhibitor-1. Conclusion: We identified a CD8-TEMRA T-cell migration to human muscle dependent on IFN{gamma}-CXCL9-CXCR3 axis that is associated with human TUG-defined physical frailty which presents potential therapeutic avenues.

immunology↗

bioRxiv2026

Evolutionary Dynamics of AI, Politicization, Contestation, and Trust in Science Funding

Economic stability and progress in modern technological societies depend on vigorous and independent public funding of science and engineering research. When peer review or funding decisions are perceived as politically directed, scientists, funding agencies, and the public react in coupled and conflicting ways. We describe an evolutionary game-theoretic model to analyze how perceived political interference in science funding affects the interrelated behaviors of scientists, funding agencies, and the public. The model simulates scientists choosing to refuse peer reviews and retaliate, agencies responding by adopting AI-assisted review and altering reviewer pay, and the public accepting or rejecting these AI systems. Through numerical simulations, five principal findings are identified: (1) Operational capacity and institutional legitimacy are governed by separate conditions and can fail independently. (2) Legitimacy of the process is bistable, meaning final states are determined by the publics acceptance of AI. (3) Since the career cost for researchers refusing to review is generally low, resistance/retaliation cascades can readily ignite, leading identical institutions to entirely opposite fates. (4) Increasing reviewer pay only stabilizes participation within a strict budget-solvency frontier, and emergency pay can paradoxically erode the legitimacy it aims to protect. (5) Finally, finite-population simulations reveal that baseline scenarios partition into either legitimacy recovery without capacity or joint failure, confirming that the fundamental separation of capacity and legitimacy outcomes is a dominant structural feature driven primarily by initial scientific resistance and politicization levels. This theoretical work quantifies issues for future work in science policy. SignificanceModern industrial nations rely on public funding of science, certified through expert peer reviews whose authority rest as much on perceived institutional independence as on processing capacity. We model what happens when review or funding decisions are seen as politically directed, and artificial intelligence (AI) substitutes for resistant human reviewers. Treating scientists, the public, and an adaptive funding agency as interacting populations, the model shows that operational capacity and institutional legitimacy obey separate dynamics and can fail independently: automation can sustain a review pipeline while its authority collapses, or legitimacy can recover while backlogs persist. Under finite populations, identical institutions can reach markedly different fates by chance alone, and how often chance favors survival depends on the noise process assumed, not on a fixed institutional probability. This framework identifies which conditions govern whether resistance ignites or whether legitimacy, once threatened, is repaired, as explicit future priorities for empirical calibration and policy design. Lay AbstractModern societies depend on public funding for scientific research, a system that only functions if the public believes the process is fair. Using evolutionary game theory, this mathematical work explores what happens when scientific peer review is viewed as politically compromised, and funding agencies turn to artificial intelligence (AI) to replace human experts who protest the system. The mathematical model reveals several critical warnings for policymakers: O_LIAutomation Does Not Equal Trust: Keeping grant money flowing and maintaining public trust are two separate problems. An agency can successfully use AI to process grants while the public completely loses faith in its decisions, creating a "zombie" institution. C_LIO_LIThe Threat of "Proposal Flooding": When scientists protest, in principle they refuse to undertake peer review, and can overwhelm the funding agency by submitting many proposals. This retaliatory flooding and refusal to review, within a limited budget, can outpace any AI systems ability to keep up, causing the entire pipeline to collapse. C_LIO_LIEmergency Pay Can Backfire: Trying to secure scientists cooperation by raising reviewer pay during a highly politicized crisis can make things worse, as the public may view this emergency pay as a "bribe," which rapidly destroys whatever institutional trust remains. C_LIO_LIEarly Action is Critical: Human behavior is unpredictable, small early differences in how people react can push identical institutions toward completely different fates. This model tracks individual scientists and members of the public to capture their behavior, which appears to be model-dependent, thus producing sensitivity to early demographic fluctuations and chance factors. Under the standard assumption, every simulated agency ended up with an unmanageable backlog; the only thing chance decided was whether public trust recovered. Under a coarser and more commonly used shortcut, roughly half the agencies appeared to recover fully, and a "zombie" agency that kept processing grants while trust collapsed appeared in about one in six. Policymakers therefore must urgently focus on building trust through transparent, explainable AI rollouts before public rejection becomes permanent. C_LI

scientific communication and education↗

bioRxiv2026

Governance scale and network structure shape pollinator recovery under pesticide reduction

Reducing pesticide risks while maintaining food production remains a central challenge for sustainable agriculture. Although pesticide reduction is pursued through centralized regulation and farm-level Integrated Pest Management, how these governance pathways translate into pollinator recovery in agroecological systems remains poorly understood. Existing ecological network models often treat pesticide pressure asm external forcing and management actions as fixed parameters, limiting their ability to capture feedbacks among governance decisions, network structure, and population dynamics. Here, we develop a dynamical framework that embeds pesticide management within tripartite pollinator-plant-pest networks using a policy variable and a farm-level adoption variable. Across empirical and synthetic networks, we show that recovery is not determined by pesticide reduction alone, but by how management acts through ecological interaction structure. More modular networks require stronger intervention, and pollinators with similar degrees show different recovery outcomes, indicating that degree alone does not determine recovery potential. Further, increasing policy strength generally expands the persistence domain more than increasing farmer adoption alone. These results show that pesticide reduction does not automatically yield ecological recovery, and effective strategies must match governance scale to ecological condition and network structure.

ecology↗

bioRxiv2026

Plasmodium falciparum Niemann-Pick Type C1-Related protein relies on its physicochemical properties for membrane contact site localization required for cholesterol homeostasis

The Niemann-Pick Type C1-Related protein of the malaria parasite Plasmodium falciparum, PfNCR1, is a promising anti-malarial drug target facilitating cholesterol homeostasis at the interface of the malaria parasite with its host-red blood cell. PfNCR1 is localized to otherwise functionally uncharacterized regions covering [~]half of the host-parasite interface. These regions are defined by exceptionally narrow membrane contact sites, leaving only [~]3-4 nm vertical aqueous space in between the membranes. Determining the origin and functional consequence of localization to the closely apposed membrane is central for our understanding of PfNCR1 as drug target but also offers a window into the mechanism of the group of homologous proteins, associated with congenital conditions and cancer. Here we define the mechanism of PfNCR1s membrane contact site localization and its implication for cholesterol transport. We identified a 141 amino acid long (amphipathic) helix - linker - (amphipathic) helix domain ("HLH domain") unique to Plasmodium spp., that is necessary for efficient localization of PfNCR1 to the narrow membrane contact sites. Mechanistically, we show that this localization relies on the HLH domains physicochemical properties. GPI-anchoring the isolated HLH domain or a version of the HLH domain in which the helices are replaced by the amphipathic helix of human ATG3 are sufficient to target a fluorescent protein to regions of endogenous PfNCR1. Functionally, we demonstrate that the degree of PfNCR1 localization to narrow contact sites qualitatively correlates with its ability to maintain cholesterol homeostasis, linking PfNCR1s membrane contact to its recognized transport function. Collectively, the results establish the HLH domain as key element for PfNCR1s localization and effectiveness in cholesterol transport while also opening avenues to probe the narrow membrane contact site regions with engineered proteins.

microbiology↗

bioRxiv2026

SPECIES DISTRIBUTION PROJECTIONS UNDER INTERNAL CLIMATE VARIABILITY REVEAL MULTIPLE PLAUSIBLE FUTURES REQUIRING FLEXIBLE CLIMATE-READY DECISIONS

Traditional biodiversity projections using species distribution models (SDMs) assume that a given emissions pathway implies a largely predictable ecological response, yet how internal climate variability (ICV) translates into SDM outcomes remains largely uncharacterized. By projecting distributions for 34 marine and terrestrial species across 100 initial-condition members from CESM2-LENS2, we show that ICV alone produces qualitatively different outcomes, including reversals in projected range shifts. We classify these outcomes into four decision-relevant cases of increasing ICV susceptibility and find a pronounced marine-terrestrial divide that persists across time horizons. Responsiveness to ICV is not consistently explained by traits or local climate variability amplitude, implying it is atleast partly ecological in nature. Robust biodiversity planning therefore requires decisions stress-tested across distributions of plausible climate realizations rather than single best-estimate maps.

ecology↗

bioRxiv2026

miRNA-mRNA Interaction Network Analysis in Alzheimer's Disease for Biomarker Discovery

--Alzheimers disease (AD) is a complex neurodegenerative disorder characterized by widespread dysregulation of gene expression and regulatory pathways. MicroRNAs (miRNAs) act as key post-transcriptional regulators by modulating messenger RNAs (mRNAs), and their disruption can influence synaptic function, neuroinflammation, and neuronal survival. In this study, we present an integrative transcriptome-driven framework to identify AD-associated miRNA-mRNA regulatory signatures and potential biomarkers. Transcriptomic and clinical data were obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the GEO dataset GSE48552. Differential expression analysis (Welchs t-test with FDR correction) identified 148 significantly dysregulated genes (35 up-regulated, 113 down-regulated) between AD and cognitively normal controls. Experimentally validated and predicted miRNA-target interactions were integrated using miRTarBase and additional target resources, yielding 1,669,089 miRNA-gene interactions involving 3,055 unique miRNAs, with strong enrichment toward down-regulated gene targeting. Functional enrichment analysis revealed convergence of miRNA-regulated genes on synaptic signaling, neuronal communication, intracellular transport, apoptosis, oxidative stress, and PI3K-Akt/MAPK-related pathways. A bipartite miRNA-mRNA regulatory network (2,343 nodes; 14,603 edges) was constructed and analyzed using centrality metrics, highlighting key hub regulators including PBX1 and SLC7A5. Finally, supervised machine learning models trained on selected molecular features achieved strong performance, with ensemble approaches (XGBoost/LightGBM) demonstrating robust discrimination of AD from controls.

bioinformatics↗

bioRxiv2026

Microhomology-mediated end joining acts directly on replication forks to repair single-ended double strand breaks

Replication stress, intrinsic to oncogenesis, often leads to fork breakage and double-strand break (DSB) formation. Conventionally, break-induced replication (BIR) is considered the primary mechanism for repairing replication-associated single-ended DSBs (seDSBs). Here, we demonstrate that microhomology-mediated end joining (MMEJ) acts directly to repair seDSBs at broken replication forks (fork-MMEJ), preferentially on the leading strands, and functions cooperatively with BIR. We also showed that while fork-MMEJ is promoted by Pol{theta}, it operates independently of MRE11/CtIP-mediated end resection, relies on RPA, and produces asymmetric deletion patterns, which is distinct from canonical MMEJ (cMMEJ) defined at replication-independent double-ended DSBs (deDSBs). ATR, activated as end resection proceeds, serves as a pivotal switch to suppress fork-MMEJ while promoting BIR. Combined inactivation of ATR and Pol{theta} synergistically kills cancer cells under high replication stress with minimal toxicity to normal cells. Together, our study provides fundamental insights into the MMEJ mechanism and offers new strategies for cancer treatment.

molecular biology↗

bioRxiv2025

Fine-tuned Protein Language Model Identifies Antigen-specific B Cell Receptors from Immune Repertoires

Scalable identification of antigen-specific antibodies from whole immune repertoire V(D)J sequences is a central challenge in biomedical engineering. We show that protein language models (PLMs) fine-tuned on antibody heavy-chain sequences can directly predict antigen specificity from unselected immune repertoires. We assessed our model, Antigen Specificity Predictor (ASPred), against SARS-CoV-2, influenza, and HIV-AIDS antigens, observing comparable predictive performance. In the whole immune repertoire V(D)J sequences of mice immunized with the SARS-CoV-2 spike proteins receptor-binding domain (RBD), ASPred identified antibody sequences specific to RBD. Several candidate sequences were validated, including one as a heavy chain-only nanobody with 20.7 nM dissociation constant. Molecular dynamics simulations supported the predicted interactions at coarse-grained and atomic levels. Benchmarking against Barcode-Enabled Antigen Mapping (BEAM) of B cell receptor sequence data had highly significant overlaps with ASPred predictions, suggesting scalability. The predicted SARS-CoV-2 binders differed substantially from training sequences, demonstrating generalization beyond sequence memorization. Together, we establish that heavy chain antibody sequences encode sufficient information for PLMs to infer specificity, offering a scalable framework for antibody discovery with broad applications.

bioinformatics↗

bioRxiv2025

Insights into the recruitment of the H3K4me3 reader Spp1 by the meiotic double-strand break protein Mer2

The formation of DNA double-strand breaks (DSBs) by Spo11 is tied to the loop-axis organization of meiotic chromosomes. Prior to DSB formation, chromatin loops marked by histone H3K4 trimethylation become tethered to the chromosome axis through interactions between Spp1 and Mer2. Mer2 is an essential partner of Spo11 thought to assemble the DSB machinery via biomolecular condensation, but these molecular assemblies remain poorly characterized. Here, using AlphaFold modeling, biochemical reconstitution, and biophysical validation, we explored the relationship between Mer2, Spp1 and their DNA-bound complexes. The tetrameric coiled-coil domain of Mer2 has four rotationally symmetrical sites that can bind a C-terminal -helix of Spp1. However, binding of one Spp1 subunit appears to allosterically modulate the affinity for the adjacent sites, leading to the assembly of 4x2 Mer2-Spp1 complexes. Mer2 also accommodates multiple DNA duplexes, allowing the assembly of tripartite Mer2-Spp1-DNA complexes with branched DNA substrates and effective recruitment of Spp1 within nucleoprotein condensates. However, because the Spp1- and DNA-binding sites of Mer2 partially overlap, Spp1 recruitment reduces DNA binding by Mer2, which is compensated for by a patch of positively charged residues within Spp1. These findings provide insights into the structural organization of Mer2 and Spp1 and their role in the assembly of the meiotic DSB machinery.

molecular biology↗

bioRxiv2025

dCas9 targeted proteome profiling reveals p300-mediated reciprocal regulation of SMAD and SP1 as a driver of GM2 synthase transcription in renal cell carcinoma

Glycolipids constitute an important component of the plasma membrane based on both abundance as well as function. Gangliosides, being a class of structurally diverse and functionally varied glycolipids, can act both as a receptor as well as a ligand and therefore is established as a crucial player in several normal cellular processes. In certain diseases, and in particular cancer, select gangliosides are over-expressed often leading to disease manifestation. GM2-synthase, the enzyme responsible for the formation of a pro-tumorigenic ganglioside, GM2 is well reported to be over-expressed across various cancer tissues and cell lines. This over-expression of GM2-synthase has been linked with increased migration, invasion and epithelial to mesenchymal transition (1) as well as induction of a local and systemic host immune suppression in cancer. Despite only a handful of studies demonstrating an epigenetic regulation underlying the transcriptional regulation of GM2-synthase (B4GalNT1) gene, the detailed mechanism still remains unclear. Here we identified the total proteome associated with the GM2-synthase promoter through a two-step CRISPR-dCas9 based proteome profiling approach by categorizing all the identified proteins leading to a detailed elucidation of the molecular drivers behind GM2-synthase transcription. While the previous study identified an acetylation-dependent de-repression of the transcription factor SP1 causing GM2-synthase activation, the underlying molecular mechanism driving its activation wasnt clear. This study demonstrated that the histone acetyl transferase (2), p300 acts as a pivotal factor which on one hand cause acetylation-mediated degradation of SP1, and on the other hand activates SMAD2/4 to have a direct positive impact on GM2-synthase gene transcription. We identified p300 to have an activator role in GM2-synthase gene transcription through knock out, knock down and over-expression experiments. Furthermore, SP1 degradation, SMAD activation and their DNA binding patterns show the reciprocal role of p300 on SP1 and SMAD complexes. Altogether we have identified SMAD 2/4 as an activator complex, p300 as a positive regulator and uncovered a critical p300-SMAD-SP1 regulatory axis in GM2-synthase transcriptional regulation.

molecular biology↗

bioRxiv2025

QKI ensures splicing fidelity during cardiogenesis by engaging the U6 tri-snRNP to activate splicing at weak 5' splice sites

During organogenesis, precise pre-mRNA splicing is essential to assemble tissue architecture. Many developmentally essential exons bear weak 5' splice sites (5'SS) yet are spliced with high precision, implying unknown yet active splicing fidelity mechanisms. By combining transcriptome and alternative splicing profiling with temporal eCLIP mapping of RNA interactions across development, we identify the RNA-binding protein QKI as an essential direct regulator of splicing fidelity in key cardiac transcripts. Although QKI is dispensable for cardiac specification, its loss disrupts sarcomere assembly despite intact expression of sarcomere mRNAs through exon skipping and nuclear retention of mis-spliced RNAs. QKI-dependent exons in essential cardiac genes have weak 5'SS and frequently show poor complementarity with U6 snRNA. We show that QKI directly interacts with U6 snRNA using an overlapping interface to its traditional intronic binding activity, securing U4/U6{middle dot}U5 tri-snRNP to ensure splicing fidelity. Thus, QKI exemplifies how context-aware RBPs enforce splicing fidelity at structurally vulnerable splice sites during organogenesis.

cell biology↗

bioRxiv2025

Phospholipids that plug the pores of cholesteryl ester transfer protein control its lipid transfer dynamics

The mechanism by which cholesteryl ester transfer protein (CETP), a key drug target in cardiovascular disease, moves lipids between lipoproteins, has remained elusive. CETP is the primary protein that regulates plasma circulating levels of lipids (within high, low, and very low-density lipoproteins. The protein, in addition to its terminal openings, has two additional openings, plugged by two phospholipids (PLs). Small molecule inhibitors targeting CETP tunnel displace PL during CETP inhibition. Here, using steered molecular dynamics simulations followed by in-vitro mutagenesis we show that CETP-bound PLs are indispensable in establishing the optimal architecture of CETP tunnel. Our structural and functional analyses revealed that lipid traversal through CETPs central tunnel is facilitated through hydrophobic interaction-mediated diffusion. PLs are critical in synchronizing domain movements of CETP while accelerating triglyceride traversal through the tunnel and their activity regulated through salt-bridge interactions. Most notably, we show for the first time that phospholipids (PLs) bound within CETPs tunnel accelerate lipid movement through a novel "gliding" mechanism, representing the first known example of a lipid regulating the movement of another. We identified conserved phenylalanine (Phe) flaps that act as a regulatory valve, opening and closing concertedly to prevent lipid backflow in the absence of an active motor. This study provides in-depth understanding of the mechanism of lipid exchange by CETP guided, and accentuated by its interaction with PLs. The conservation of these critical structural elements across the BPI/LBP family suggests that this mechanism is broadly applicable, expanding our understanding of lipid transport in this clinically significant protein class. Significance StatementThe mechanism of lipid transport by the cholesteryl ester transfer protein (CETP), a major drug target, has long been a scientific mystery. Neutral lipid exchange through CETP is dependent on the hydrophobic characteristics of the tunnel that has been exploited during inhibitor design. We provide a detailed, molecular-level explanation, showing for the first time that phospholipids (PLs), previously thought to be static plugs, are indispensable for lipid transfer. They actively accelerate lipid movement through a novel gliding mechanism, the first documented instance of a lipid regulating another. This discovery identifies new therapeutic targets for CETP inhibition. Our findings also suggest that this fundamental mechanism is conserved across a large protein family, with broad implications for understanding lipid transport and function.

biophysics↗

bioRxiv2025

Differential Assembly of Mouse and Human Tumor Microenvironments

Mouse models are frequently used to develop treatments for human cancer. Yet, we lack a comprehensive understanding of the comparative organization of mouse and human tumor microenvironments (mu/huTMEs). Through immunoprofiling of commonly used mouse models, we found that the immune composition of most muTMEs resemble poorly infiltrated human tumors extensively biased toward high macrophages densities. Relatedly, we discover species-specific biases of chemokine expression networks, factors which drive TMEs assembly. Further, assessing coarse cellular networks, we find conserved correlations between some immune cell frequencies, while other relationships only appear conserved in the huTMEs matching muTME profiles. Despite this variable alignment, we define robust cell type-specific gene expression programs conserved in TMEs across species and cohorts and identify ones that are coordinated between cell populations in both species. Together, we isolate and offer methods to study the multiple areas of hazard and opportunities for using mice to model human cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=192 HEIGHT=200 SRC="FIGDIR/small/661217v1_ufig1.gif" ALT="Figure 1"> View larger version (71K): org.highwire.dtl.DTLVardef@22bcf7org.highwire.dtl.DTLVardef@a410b7org.highwire.dtl.DTLVardef@1455eb5org.highwire.dtl.DTLVardef@14cc6f5_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Nonlinear integration of sensory and motor inputs by a single neuron in C. elegans.

Context is important for sensory integration. Rather than simply considering sensory information independently, the brain integrates this information to inform behavior, however identifying this property at the single-neuron level is not trivial. In Caenorhabditis elegans, the paired interneurons AIBL and AIBR (AIB) have a compartmentalized organization of presynapses along its singular process. Sensory and sensory interneurons primarily synapse along the proximal process, while motor and motor interneurons synapse along the distal process. Since this neuron has graded potentials, the simplest model for AIB integration is simply a convolution of its presynaptic inputs. Through a series of experiments to manipulate sensory and motor input onto AIB, we find that while AIB activity is primarily a convolution of motor inputs, its sensory responses are not integrated independently. Instead, the gain in sensory input is a function of the temporal dynamics of motor input. Sensory information is reinforced when it matches the expected behavioral response. We find this property is also observed in other whole-brain datasets. Context-dependent behavioral responses to sensory input is well-documented. Here, we show this property can be localized to single neurons in the worm nervous system. This integration property likely plays an important role in context-dependent decision-making, as well as the highly variable dynamics of the worm nervous system.

neuroscience↗

bioRxiv2024

Global and genetic regulation of gene expression in human endothelial and vascular smooth muscle cells

BackgroundThe understanding of genetic and epigenetic regulation of gene expression in endothelial and vascular smooth muscle cells remains fragmented with limited experimental validation. MethodsChromatin conformation (Micro-C), DNA methylation (RRBS), chromatin accessibility (ATAC-seq), and transcriptome profiles (RNA-seq) were mapped in human induced pluripotent stem cell (hiPSC)-derived, isogenic endothelial and vascular smooth muscle cells (iECs and iVSMCs). CTCF and RAD21 were depleted to assess the functional relevance of chromatin architecture, and genome editing was used to evaluate the allelic effect of a blood pressure-associated single nucleotide polymorphism (SNP). ResultsSignificant correlations were identified between gene expression levels and chromatin interactions, chromatin accessibility, and DNA methylation in iECs and iVSMCs, with chromatin interactions showing the strongest association. Chromatin contact regions displayed distinct epigenetic landscapes depending on the types of regulatory element interactions involved. Perturbation of CTCF and RAD21 revealed their differential regulatory effects, particularly on the expression of genes overlapping chromatin contacts, with RAD21 exhibiting a broader regulatory impact. SNPs associated with several vascular traits were enriched in chromatin loops or accessible regions in iECs or iVSMCs. Precise genome editing demonstrated allele-dependent effects of SNP rs9833313 on the expression of SHOX2 located 247.4 kbp away but within the same chromatin loops as the SNP. ConclusionThis study provides an extensive epigenetic landscape of vascular cells that may drive novel research on the role of genetic and epigenetic mechanisms of vascular function and disease as demonstrated by our targeted experiments.

genomics↗

bioRxiv2024

A Large Language Model Guides the Affinity Maturation of Variant Antibodies Generated by Combinatorial Optimization

The ability of an antibody to bind an antigen with high specificity and strength (i.e., its binding affinity) are critical properties in the design of neutralizing antibodies. Recent technical advances in AI and a surge of experimental data on antigen-antibody interaction are driving innovations in the design and optimization of antibodies via affinity maturation. Here we introduce Ab-Affinity, a novel large language model which can accurately predict the binding affinity of specific antibodies against a target peptide within the SARS-CoV-2 spike protein. When used in conjunction with a genetic algorithm and simulated annealing, Ab-Affinity can generate novel antibodies with more than a 160-fold increase in predicted binding affinity compared to those obtained experimentally. Our experimental results show that the synthetic antibodies produced by Ab-Affinity have strong predicted biophysical properties. Molecular docking and molecular dynamics simulation of binding interactions of the best synthetic antibodies show enhanced interactions and stability on the target peptide epitope. In general, antibodies generated by Ab-Affinity are superior to those obtained with other existing computational methods.

molecular biology↗

bioRxiv2024

Convergent cellular adaptation to a quiescence-like state in response to freeze-thaw stress

Extreme stress, such as freeze-thaw, poses a severe challenge to many organisms, but the mechanisms underlying their adaptation to survive such stress remain elusive. Here, we show that Saccharomyces cerevisiae can rapidly evolve freeze-thaw tolerance through a physiological state transition, with survival increasing nearly two orders of magnitude from {approx}2% to {approx}70% in about 25 cycles of stress exposure. Evolved yeast cells exhibit a quiescence-like state, characterized by altered cellular physiology: increased intracellular trehalose accumulation, reduced membrane damage, cytoplasmic stiffening and an exit from a proliferative cycle. This mechano-chemically reinforced survival strategy emerges across independent evolutionary lines despite distinct genetic backgrounds, suggesting a convergent mechanism of adaptation. By integrating experimental evolution, biophysical measurements, genomic analysis, and a quantitative model that captures the adaptation dynamics, we reveal that stress tolerance can arise via a potentially generalizable, physiologically mediated adaptation strategy. These findings provide new insights into microbial survival under extreme conditions and suggest broader implications for cellular stress responses beyond yeast.

evolutionary biology↗

bioRxiv2024

Machine learning of honey bee olfactory behavior identifies repellent odorants in free flying bees in the field

Preventing beneficial insects like honey bees (Apis mellifera) from contacting pesticides on crops using odorants could counter current pollinator declines. However, the discovery of behaviorally aversive odorants is impeded by the complexity of the honey bee olfactory system where >180 olfactory receptors detect volatiles and generate valence. To solve this systems-level challenge we generated a machine-learning model to predict aversive valence from chemical structure using published olfactory behavior data in honey bees. We refine the predictive model by generating species level behavioral data for honey bees and Drosophila on an initial set of novel predicted repellents. The improved second computational model was then used to screen a chemical space of >50 million compounds and identify >130 repellent candidates. Behavioral validation using honey bees in the laboratory show a high predictive success. Additional testing of the top seven candidates using freely foraging honey bees in a field assay confirmed strong repellency, thus predicting a high probability to repel foraging bees from pesticide-treated crops. Machine learning, with iterative testing and modeling therefore provides a powerful approach for rational discovery of aversive volatiles for control of insects for which limited data is available. SIGNIFICANCE STATEMENTWith honey bee populations declining partly due to pesticide exposure, we aimed to find smells that could keep bees away from pesticide-treated crops. We overcome challenges studying the complex bee olfactory system by developing an AI model trained on existing bee behavior data to predict chemicals bees would find aversive. The predictive model screened millions of compounds, identifying more than 130 potential repellents. Behavior testing in the lab and in field tests confirmed the effectiveness of the bee repellents. This method could lead to bee-safe pesticide formulations, potentially protecting pollinator populations while maintaining crop protection.

animal behavior and cognition↗