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Coronary artery disease (CAD) is a complex condition and remains the leading cause of mortality worldwide. Genome-wide association studies have identified a CAD risk locus on chromosome 10q23 that is independent of traditional risk factors, providing an opportunity to uncover novel molecular mechanisms contributing to CAD pathogenesis. METHODS: Improved fine-mapping approaches were used to prioritize noncoding variants at the 10q23 locus within the intronic region ofTSPAN14(tetraspanin 14). Regulatory elements harboring lead variants were functionally interrogated using chromatin accessibility, 3-dimensional chromatin organization, and clustered regularly interspaced short palindromic repeats–mediated deletion approaches in endothelial cells (ECs). Subsequently, TSPAN14 function in ECs was assessed using transcriptomic profiling, Notch signaling activation assays, and EC functional assay using gene knockout (KO) cell lines. RESULTS:Fine-mapping identified 2 lead variants, rs17680741 and rs12260962, located within regulatory elements predicted to affectTSPAN14expression in monocytes and ECs, respectively. Chromatin accessibility, organization, and enhancer deletion assays demonstrated EC-specific function for the rs12260962-harboring regulatory element inTSPAN14expression regulation. Loss ofTSPAN14resulted in significant transcriptomic changes related to Notch signaling, heart morphogenesis, cell adhesion, and wound healing. Functionally,TSPAN14 KOECs exhibited impaired cell-cell junction integrity, reduced repair capacity, and diminished mechanosensitive responses. CONCLUSIONS:Together, these data identify a regulatory element harboring CAD-associated variant at the 10q23 locus that modulatesTSPAN14expression and downstream Notch signaling in ECs, thereby linking genetic risk to endothelial dysfunction relevant to CAD pathogenesis. 

Abstract Markov chain Monte Carlo (MCMC) methods are a family of algorithms designed to approximate probability distributions that are otherwise intractable to sample from directly. However, as the dimensionality and complexity of the target distribution increase, the convergence of these methods can slow considerably, demanding substantial computational resources. In this context, quantum computing emerges as a promising framework to address this limitation, with different quantum circuit constructions serving as quantum MCMC methods. In this work, we present a quantum circuit based on the discrete quantum walk (DQW) algorithm that implements the algorithmic steps of the Metropolis–Hastings method, encoding the target distribution in a position quantum register. The circuit incorporates auxiliary registers to compute and discretize the acceptance probability, and uses controlled operations to modulate the walker’s evolution accordingly. Simulation results show that the marginal probability distribution encoded in the position register converges asymptotically toward the discretized target distribution for both single and multimodal distributions. We further analyze the effect of circuit parameters on convergence behavior and approximation quality, refining the construction by enabling the evaluation of multiple candidate moves per iteration, therefore reducing the number of iterations required for convergence within our implementation. Finally, we provide a qualitative comparison of our approach with related quantum MCMC methods, namely Szegedy’s quantum walk and QAOA, and discuss structural differences as well as the potential incorporation of adaptive proposal techniques within the proposed circuit framework. 

The Random Circuit Perturbation (RACIPE) algorithm enables the exploration of the dynamical behaviors of gene regulatory circuits (GRCs) by simulating an ensemble of differential equation models via randomization of kinetic parameters. Here, we release sRACIPE 2.0, a major update to the R/Bioconductor package, as a unified platform for modeling GRCs with diverse interaction types using both deterministic and stochastic simulations. The update also introduces new features for modeling perturbation, extrinsic signaling and time-corrected noise, and a new diagnostic tool to ensure proper simulations. We hope that this release will serve as a versatile modeling toolkit for the systems biology community. Availability and implementationThe package is available on GitHub at https://github.com/lusystemsbio/sRACIPE under the MIT license. It is also available on Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/sRACIPE.html. 

ABSTRACT Quinones play a central role in maintaining redox balance and conserving energy but can trigger oxidative stress at high levels. However, the mechanisms by which microbes regulate quinone levels remain poorly understood, hindering effective metabolic engineering to modulate microbes for quinone production. Here, we show that the biosynthesis of the menaquinone precursor 1,4-dihydroxy-2-naphthoic acid (DHNA) in the lactic acid bacteriumLactococcus lactisis regulated by a combined genetic, enzymatic, and metabolic mechanism. Using synthetic biology approaches, we found that enzymes MenF and MenD both contribute to DHNA regulation, with MenD playing a more prominent role in controlling DHNA concentrations. A mathematical model elucidates a two-phase regulatory pattern resulting from the interplay of reversible flux and allosteric feedback inhibition, where either MenF or MenD can serve as the regulatory enzyme, depending on their relative expression ratio. In addition, the overproduction of DHNA is constrained by substrate availability, ensuring a sufficient but not excessive DHNA level to benefit cell growth while mitigating cytotoxicity. Collectively, these mechanisms maintain a fine-tuned physiological quinone level and suggest that modulating substrate supplement and MenF-to-MenD ratio could be keys for engineering DHNA production. IMPORTANCEQuinones are crucial molecules in cellular respiration, helping cells produce energy and maintain balance in their redox state. However, excessive quinone levels can be toxic, making it vital for microbes to tightly regulate their production. Our study uncovers howLactococcus lactis, a key food fermenting bacterium, uses a multi-layer mechanism to maintain optimal levels of the menaquinone precursor 1,4-dihydroxy-2-naphthoic acid (DHNA). By combining biosensors, genetic perturbations, and modeling, we show how cells balance the benefits and toxicity of quinones. These findings not only reveal fundamental microbial physiology but also provide strategies to engineer microbes for improved quinone production. 

BackgroundCurative responses to immunotherapy require the generation of robust systemic immunity with limited toxicity. Recruitment of T cell populations such as precursor exhausted T cells (Tpex) from lymphoid tissues to tumors is a hallmark of effective treatment. However, the ability to efficiently induce this recruitment is lacking in current immunotherapy approaches. Furthermore, systemic administration of immunotherapies frequently results in dose-limiting toxicities, yielding an inadequate therapeutic window for eliciting durable responses. MethodsIn this investigation, we evaluated the safety and antitumor efficacy of locally administered interleukin 12 (IL-12) using a clinically translatable cytokine delivery platform (NCT05538624) to identify Tpex recruitment capabilities at tolerable cytokine doses. ResultsWe show IL-12 cytokine factories can effectively treat a broad spectrum of cancer types. Single-cell RNA sequencing data suggests that the antitumor efficacy seen in our studies was due to retinal pigmented epithelial cells-mIL12 treatment inducing differentiation of Tpex cells within the tumor microenvironment. When administered in combination with checkpoint therapy, IL-12 cytokine factory treatment generated systemic abscopal immunity, preventing subcutaneous tumor outgrowth in 8/9 mice with colorectal cancer and lung metastasis in mice with melanoma. Furthermore, this platform was well tolerated in a non-human primate without signs of toxicity. ConclusionsOur new immunotherapy approach provides a robust strategy for inducing Tpex recruitment and systemic immunity against a range of solid peritoneal malignancies, many incurable with current immunotherapy strategies. Notably, these features were achieved using IL-12, and by leveraging our technology, we avoided the toxicities that have prevented the translation of IL-12 to the clinic. Our findings provide a strong rationale for the clinical development of IL-12 cytokine factories. 

IntroductionT-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders. MethodsThis study presents a theoretical approach that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs. To evaluate our approach across different TCR systems, we utilized a dataset that includes peptide libraries tested against three distinct murine TCRs. A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key properties contributing to binding affinity. ResultsOur analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR peptide interactions. The results of our feature selection method are consistent with findings from hybrid approaches that utilize both sequence and structural data as input as well as experimental data. DiscussionOur theoretical approach highlights the role of feature selection in peptide-TCR interactions, providing a quantitative tool for uncovering the molecular mechanisms of the T-cell response and assisting in the design of more advanced targeted therapeutics. 

Abstract Under starvation conditions, a spot of a few millionMyxococcus xanthuscells on agar will migrate inward to form aggregates that mature into dome-shaped fruiting bodies. This migration is thought to occur within structures called ‘streams,’ which are considered crucial for initiating aggregation. The prevailing traffic jam model hypothesizes that intersections of streams cause cell crowding and ‘jamming,’ thereby initiating the process of aggregate formation. However, this hypothesis has not been rigorously tested, in part due to the lack of a standardized, quantifiable definition of streams. To address this gap, we captured time-lapse movies and conducted fluorescent cell tracking experiments using wild-type and two motility-deficient mutantM. xanthusstrains. By quantitatively defining streams and developing a novel stream detection mask, we show that streams are not essential for nascent aggregate formation, though they may accelerate the process. Moreover, our results indicate that streaming has a genetic component: disrupting only one of the twoM. xanthusmotility systems hinders stream formation. Together, these findings challenge the idea that stream intersections are required to drive aggregate formation and suggest thatM. xanthusaggregation may be driven by mechanisms independent of streaming, highlighting the need for alternative models to fully explain aggregation dynamics. 

Abstract The mitotic arrest deficiency 2 (MAD2) exists ininactiveandactiveforms under physiologic conditions. In itsactiveconformation, MAD2 binds to the cell division cycle protein 20 (Cdc20) and prevents the separation of duplicated chromosomes. In theinactiveconformation, the C‐terminal region of MAD2 covers the binding motif for the Cdc20 target. Here, we investigated the MAD2 activation mechanism using structure‐based models (SBMs) simulations, amino acid coevolution, and structural frustration analysis. MAD2 switches betweenactiveandinactiveconformations while maintaining core stability. Simulations reveal an intermediate state during the transition consistent with recent time‐resolved NMR experiments. Coevolution analysis captures native contacts for both states. These native contacts, present in both conformations, compete, driving transitions between different protein states. This competition leads to a frustrated energy landscape. Frustration analysis further shows that highly frustrated residues are present in both conformations, particularly in the fold‐switching segments. 

Abstract BackgroundB-type lamins are critical nuclear envelope proteins that interact with the three-dimensional genomic architecture. However, identifying the direct roles of B-lamins on dynamic genome organization has been challenging as their joint depletion severely impacts cell viability. To overcome this, we engineered mammalian cells to rapidly and completely degrade endogenous B-type lamins using Auxin-inducible degron technology. ResultsUsing live-cell Dual Partial Wave Spectroscopic (Dual-PWS) microscopy, Stochastic Optical Reconstruction Microscopy (STORM), in situ Hi-C, CRISPR-Sirius, and fluorescence in situ hybridization (FISH), we demonstrate that lamin B1 and lamin B2 are critical structural components of the nuclear periphery that create a repressive compartment for peripheral-associated genes. Lamin B1 and lamin B2 depletion minimally alters higher-order chromatin folding but disrupts cell morphology, significantly increases chromatin mobility, redistributes both constitutive and facultative heterochromatin, and induces differential gene expression both within and near lamin-associated domain (LAD) boundaries. Critically, we demonstrate that chromatin territories expand as upregulated genes within LADs radially shift inwards. Our results indicate that the mechanism of action of B-type lamins comes from their role in constraining chromatin motion and spatial positioning of gene-specific loci, heterochromatin, and chromatin domains. ConclusionsOur findings suggest that, while B-type lamin degradation does not significantly change genome topology, it has major implications for three-dimensional chromatin conformation at the single-cell level both at the lamina-associated periphery and the non-LAD-associated nuclear interior with concomitant genome-wide transcriptional changes. This raises intriguing questions about the individual and overlapping roles of lamin B1 and lamin B2 in cellular function and disease. 

Abstract Gene duplication is a fundamental part of evolutionary innovation. While single-gene duplications frequently exhibit asymmetric evolutionary rates between paralogs, the extent to which this applies to multi-gene duplications remains unclear. In this study, we investigate the role of genetic context in shaping evolutionary divergence within multi-gene duplications, leveraging microsynteny to differentiate source and target copies. Using a dataset of 193 mammalian genome assemblies and a bird outgroup, we systematically analyze patterns of sequence divergence between duplicated genes and reference orthologs. We find that target copies, those relocated to new genomic environments, exhibit elevated evolutionary rates compared to source copies in the ancestral location. This asymmetry is influenced by the distance between copies and the size of the target copy. We also demonstrate that the polarization of rate asymmetry in paralogs, the “choice” of the slowly evolving copy, is biased towards collective, block-wise polarization in multi-gene duplications. Our findings highlight the importance of genetic context in modulating post-duplication divergence, where differences in cis-regulatory elements and co-expressed gene clusters between source and target copies may be responsible. This study presents a large-scale test of asymmetric evolution in multi-gene duplications, offering new insight into how genome architecture shapes functional diversification of paralogs. Significance statementAfter a gene is duplicated, reduced selective constraints can lead the two copies to rapidly diverge, with one copy often evolving faster and occasionally gaining a new function. We quantify the influence of genetic context in choosing which copy of a duplicated gene has an elevated substitution rate. In a representative dataset of 193 mammalian genomes, we found strong evidence that gene copies pasted into new genomic locations tend to evolve faster than the corresponding copies in ancestral locations, suggesting an important role for the regulatory environment. The asymmetry in evolutionary rates of duplicated genes persists even for very large multigenic duplications, up to the scale of megabases, indicating that regulatory interactions frequently reach farther than previously thought.