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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 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. 

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 In the last decade, DNA-DNA proximity ligation assays opened powerful new ways to study the 3D organization of genomes and have become a mainstay experimental technology. Yet many aspects of these experiments remain poorly understood. We study the inner workings of DNA-DNA proximity ligation assays through numerical experiments and theoretical modeling. Chromosomes are modeled at nucleosome resolution and evolved in time via molecular dynamics. A virtual Hi-C experiment reproduces, in-silico, the different steps of the Hi-C protocol, including: crosslinking of chromatin to an underlying proteic matrix, enzymatic digestion of DNA, and subsequent proximity ligation of DNA open ends. The protocol is simulated on ensembles of different structures as well as individual structures, enabling the construction of ligation maps and the calculation of ligation probabilities as functions of genomic and Euclidean distance. The methods help to assess the effect of the many variables of the Hi-C experiment and of subsequent data processing methods on the quality of the final results. 

Abstract We introduce a general phenomenological framework for understanding how phenotypic plasticity gives rise to drug persisters. These persisters, often quiescent but sometimes which again return to cycling, survive in the presence of treatment and eventually can lead to mutants with true resistance. Our framework builds on recent experimental observations regarding variations between and among single-cell clones and the possible role of the drug itself in enhancing the survival strategy. Predictions of our approach include the existence of an optimum drug concentration as well as an optimum drug holiday schedule to minimize the persistence-based threat. 

ABSTRACT In starvingBacillus subtilisbacteria,the initiation of two survival programs—biofilm formation and sporulation—is controlled by the same phosphorylated master regulator, Spo0A~P. Its gene,spo0A,is transcribed from two promoters, Pvand Ps,that are, respectively, regulated by RNA polymerase (RNAP) holoenzymes bearing σ^Aand σ^H. Notably, transcription is directly autoregulated by Spo0A~P binding sites known as 0A1, 0A2, and 0A3 box, located in between the two promoters. It remains unclear whether, at the onset of starvation, these boxes activate or repressspo0Aexpression, and whether the Spo0A~P transcriptional feedback plays a role in the increase inspo0Aexpression. Based on the experimental data of the promoter activities under systematic perturbation of the promoter architecture, we developed a biophysical model of transcriptional regulation ofspo0Aby Spo0A~P binding to each of the 0A boxes. The model predicts that Spo0A~P binding to its boxes does not affect the RNAP recruitment to the promoters but instead affects the transcriptional initiation rate. Moreover, the effects of Spo0A~P binding to 0A boxes are mainly repressive and saturated early at the onset of starvation. Therefore, the increase inspo0Aexpression is mainly driven by the increase in RNAP holoenzyme levels. Additionally, we reveal that Spo0A~P affinity to 0A boxes is strongest at 0A3 and weakest at 0A2 and that there are attractive forces between the occupied 0A boxes. Our findings, in addition to clarifying how the sporulation master regulator is controlled, offer a framework to predict regulatory outcomes of complex gene-regulatory mechanisms. IMPORTANCECell differentiation is often critical for survival. In bacteria, differentiation decisions are controlled by transcriptional master regulators under transcriptional feedback control. Therefore, understanding how master regulators are transcriptionally regulated is required to understand differentiation. However, in many cases, the underlying regulation is complex, with multiple transcription factor binding sites and multiple promoters, making it challenging to dissect the exact mechanisms. Here, we address this problem for theBacillus subtilismaster regulator Spo0A. Using a biophysical model, we quantitatively characterize the effect of individual transcription factor binding sites on eachspo0Apromoter. Furthermore, the model allows us to identify the specific transcription step that is affected by transcription factor binding. Such a model is promising for the quantitative study of a wide range of master regulators involved in transcriptional feedback. 

ABSTRACT The assembly of the mitoribosomal small subunit involves folding and modification of rRNA, and its association with mitoribosomal proteins. This process is assisted by a dynamic network of assembly factors. Conserved methyltransferases Mettl15 and Mettl17 act on the solvent-exposed surface of rRNA. Binding of Mettl17 is associated with the early assembly stage, whereas Mettl15 is involved in the late stage, but the mechanism of transition between the two was unclear. Here, we integrate structural data fromTrypanosoma bruceiwith mammalian homologs and molecular dynamics simulations. We reveal how the interplay of Mettl15 and Mettl17 in intermediate steps links the distinct stages of small subunit assembly. The analysis suggests a model wherein Mettl17 acts as a platform for Mettl15 recruitment. Subsequent release of Mettl17 allows a conformational change of Mettl15 for substrate recognition. Upon methylation, Mettl15 adopts a loosely bound state which ultimately leads to its replacement by initiation factors, concluding the assembly. Together, our results indicate that assembly factors Mettl15 and Mettl17 cooperate to regulate the biogenesis process, and present a structural data resource for understanding molecular adaptations of assembly factors in mitoribosome. 

Abstract Squamate reptiles are a highly diverse and intriguing group of tetrapods, offering valuable insights into the evolution of amniotes. The Australian water dragon (Intellagama lesueurii) is a member of the Agamidae and sister to the core mesic Australian endemic radiation (Amphibolurinae). The species is renowned for its urban adaptability and complex social systems. We report a 1.8 Gb chromosome-length genome assembly together with the annotation of 23,675 protein-coding genes. Comparative analysis with other squamate genomes highlights gene family expansions associated with immune function, energetic homeostasis, and wound healing. This reference genome will serve as a valuable resource for studies of evolution and environmental resilience in lizards. 

Abstract While significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding protein function and facilitating drug discovery. Traditional docking methods, frequently used in studying protein-ligand interactions, typically treat proteins as rigid. While molecular dynamics simulations can propose appropriate protein conformations, they’re computationally demanding due to rare transitions between biologically relevant equilibrium states. In this study, we present DynamicBind, a deep learning method that employs equivariant geometric diffusion networks to construct a smooth energy landscape, promoting efficient transitions between different equilibrium states. DynamicBind accurately recovers ligand-specific conformations from unbound protein structures without the need for holo-structures or extensive sampling. Remarkably, it demonstrates state-of-the-art performance in docking and virtual screening benchmarks. Our experiments reveal that DynamicBind can accommodate a wide range of large protein conformational changes and identify cryptic pockets in unseen protein targets. As a result, DynamicBind shows potential in accelerating the development of small molecules for previously undruggable targets and expanding the horizons of computational drug discovery.