- by Nazem-Bokaee, H.Human genetic variation is a major determinant of organ metabolism, yet how naturally occurring variants shape quantitative metabolic phenotypes remains unclear. We present VariantFlux, a workflow that integrates ancestry-aware variant interpretation into genome-scale metabolic modelling to generate personalised, variant-constrained kidney reconstructions. Using the Human1 v1.19 model, we built a kidney-specific baseline model constrained by 482 metabolites and analysed 2,547 individuals from the 1000 Genomes Project, in whom ~50% of metabolic genes were predicted damaging by at least three computational tools. […]
- by McGill, C. J., Christensen, A., Namvari, S., Thorwald, M. A., Anson, H., Vermulst, M., Finch, C. E., Benayoun, B. A., Pike, C. J.Longevity-promoting interventions represent a promising strategy to mitigate brain aging and reduce Alzheimer disease (AD) risk. The NIA Interventions Testing Program identified the weak estrogen 17-alpha-estradiol (17aE2) as a compound that extends healthspan and lifespan in mice, with effects observed primarily in males. Our recent work demonstrated that 17aE2 healthspan benefits were modulated by human apolipoprotein E (APOE) genotype such that aging phenotypes were improved more strongly in middle-aged male mice with targeted-replacement of the AD-associated APOE4 allele compared to […]
- by Ptak, C. C., Eng, J., Radoshevich, L., Wright, M. E.Androgen receptor-interacting proteins (AR-IPs) comprise nearly 1,000 cataloged partners, yet how AR engages this interactome inside the nucleus, in what temporal order, and through what molecular handoffs, remains uncharted. Here, we construct a minute-scale temporal atlas of the nuclear AR proximal interactome by proximity-labeling quantitative mass spectrometry (PL-qMS) in androgen-treated LNCaP prostate cancer cells, capturing 84.2% of the known AR-interactome and resolving 3,378 nuclear AR-proximal interacting proteins (AR-PIPs) across six time points. The atlas recapitulates the cyclic sequential recruitment model […]
- by Ptak, C. C., O'Rourke, C., Eng, J., Radoshevich, L., Wright, M. E.Androgen receptor-interacting proteins (AR-IPs) comprise nearly 1,000 proteins, yet their organization across subcellular space and time remains uncharted. Proximity labeling captures direct binding partners along with neighboring proteins that populate a receptors local environment, thereby broadening AR-IPs into a broader population of AR-proximal interacting proteins (AR-PIPs). Here, we apply proximity labeling quantitative mass spectrometry (PL-qMS) to construct a spatiotemporal atlas of the extranuclear AR-proximal interactome in LNCaP prostate tumor cells. PL-qMS recovered 82.2% of the known AR-IPs and identified 3,947 […]
- by Im, H., Liu, Y., Thompson, J., Miranda, G. I., Loke, K., Bacon, R., Lucas, L., Amador-Noguez, D., Gray, S. M., Venturelli, O. S.Identifying design principles for robust inhibition of human pathogens is a major goal of microbiome engineering. By building synthetic microbial communities from the bottom-up guided by Bayesian active learning, we investigate the Clostridioides difficile growth landscape across thousands of species-metabolite conditions. Mechanistic consumer resource and machine learning models uncover significant interactions linking metabolites and species, and exhibit concordance with microbial interactions identified in human microbiome datasets. Guided by machine learning and mechanistic models, we elucidate microbial communities capable of robustly […]
- by Ozen, M., Agrahar, C., Zappa, F., Bianco, S., Acosta-Alvear, D., Lopez, C. F.Sparse experimental data often yields vast mechanistic hypothesis spaces with numerous equally probable models. Traditional model selection metrics like the Akaike Information Criterion fall short because they reduce models nonlinear dynamics to a scalar score that masks crucial mechanistic details. Here, we introduce an AI-driven framework treating model dynamics as learnable signatures. Using deep learning autoencoders, we embed the dynamic signatures of thousands of competing models into a low-dimensional latent space. Iterative clustering and physiological constraints systematically refine this space […]
- by Jaiswal, A. K., Singh, E., Patel, A., Sahoo, S. R.The reliable operation of biomolecular circuits depends on the availability of shared cellular resources such as ribosomes, whose levels can vary substantially across growth conditions and cellular contexts. Although resource competition among co-expressed genes is well recognized, the relationship between resource variation and the robustness of circuit dynamics has not been characterized quantitatively. This paper integrates a resource-aware gene expression model, contraction theory-based analytical bounds, and experimental validation to study the effect of translational resource variation on constitutive gene expression […]
- by Yang, J., Deng, Y., Luo, J., Wang, Y., Li, F., Chen, Y.Single-cell genome-scale metabolic models (scGEMs) enable characterization of metabolic heterogeneity underlying cellular states and phenotypes. However, methodological choices during scGEM construction can substantially alter model structure and predictions, challenging the reliability and comparability of resulting analyses. Here, we established a systematic benchmark to assess three key construction factors: data preprocessing method, model extraction method (MEM) and gene expression threshold. We evaluated 26 strategies representing different combinations of these factors across nine scRNA-seq datasets in three dimensions: accuracy, sensitivity to expression […]
- by Liu, N., Halbauer, J., Albadry, M., Dahmen, U., Gassler, N., Scicluna, B. P., Bauer, M., Press, A. T.Sepsis is classified into distinct transcriptomic endotypes. Yet specific cellular drivers for those subtypes remain ambiguous. Although dysregulated CXCL8-CXCR1/2 signaling and neutrophil hyperactivation are implicated in severe endotypes, the therapeutic window and organ-specific consequences of CXCR2 antagonism remain unclear. To determine whether murine transcriptomic subtypes (MTSs) recapitulate human consensus transcriptomic subtypes (CTSs) and evaluate how subtype state dictates the efficacy and trade-offs of Danirixin in polymicrobial sepsis. We reanalyzed septic patients whole-blood CITE-seq data to characterize CXCL8- CXCR2 signaling. Using […]
- by Manes, N. P., Zhang, F., Lin, B., Sun, J., Hassan, S. A., Armstrong, A. A., Shao, Y., Calzola, J. M., Kaplan-Stafford, P. R., Gottschalk, R. A., Marino, M. J., Kim, D., Germain, R. N., Fraser, I. D. C., Meier-Schellersheim, M., Nita-Lazar, A.Toll-like receptor (TLR) signaling must be activated rapidly and then terminated to support host defense without sustained inflammation. We developed a rule-based model of mouse macrophage TLR4 signaling at the molecular-interaction level using measured protein copy numbers, RNA-seq-based abundance estimates, literature- and structure-informed reaction rates, and 979 dynamic experimental constraints. The trained model reproduced much of the TLR4-induced NF-{kappa}B and MAP kinase response but consistently failed to capture deactivation of MyD88, TRAF6-associated species, and IKK/{beta}. The recurrent model failure conveyed […]
- by Solanki, U. S., Patel, A., Singh, A.Understanding noise propagation in gene regulatory circuits requires accounting for both model and resource constraints. In this work, we investigated the role of model order in influencing stochastic behaviour by deriving and analytically comparing reduced protein-only models with higher-order models that include mRNA and molecular complexes, and found that protein-based models can exhibit higher noise levels in the gene expression. Through frequency-response analysis, we explained that the higher-order models provide additional noise-filtering effects. We also analyzed a one-dimensional constrained model […]
- by Retkute, R., Gilligan, C.Disease surveillance data are often sparse, irregularly timed and heterogeneous across observational units, creating challenges for inference in mechanistic epidemiological models. We present observation-masked neural posterior estimation (OM-NPE), a simulation-based Bayesian inference framework for such settings. The approach represents observations on a common temporal grid and records observation availability through a binary mask, enabling heterogeneous surveillance records to be analysed using a single amortised neural posterior estimator. We demonstrate OM-NPE using two contrasting epidemiological systems. First, we fit an effective […]
- by Liu, Y., Thomas, J. P., Bohar, B., Modos, D., Powell, N., Paun, A., Korcsmaros, T.Ulcerative colitis (UC) is a genetically heterogeneous disease causing chronic intestinal inflammation. Genome-wide association studies have linked numerous non-coding single nucleotide polymorphisms (SNPs) to UC, yet how these variants relate to intestinal epithelial function and clinical outcomes remains largely unknown. To address this, we aimed to reconstruct patient-specific epithelial signalling and regulatory networks perturbed by non-coding SNPs and to dissect patient heterogeneity in a large UC phase III trial. We analysed genotype data from 452 participants in the etrolizumab phase […]
- by Baskar, P., Parnika, S., Lakhdive, A., Bej, S., Shameer, S., Vijayan, K.Identifying synthetic lethal (SL) interactions offers a principled framework for discovering disease-specific therapeutic targets. However, current machine learning approaches heavily rely on curated protein-protein interaction networks. Because these networks cover only [~]7,500 proteins, they severely restrict the search space of human gene pairs and introduce systematic biases toward well-characterized genes. To circumvent these limitations, we developed SLxGO, a network-independent machine learning framework that predicts SL interactions directly from semantic representations of Gene Ontology annotations encoded via BioBERT-derived embeddings. Benchmarked across […]
- by Humphries, E. M., Schliemann, M., O'Sullivan, N., Hains, P., Robinson, P. J., Küster, B.Formalin-fixed paraffin-embedded (FFPE) tissue is the dominant clinical pathology resource yet whether it faithfully preserves organ signalling biology and supports directional regulatory analysis remains unquantified. We generated a phosphoproteome map from eight healthy rat organs, separating preservation effects from biological variation. Using mass spectrometry, we quantified 54,710 phosphosites on 5,994 proteins across receptors, kinase cascades and nuclear regulators. Organ-specific phosphosite signatures matched known physiological and proliferative states. Paired antagonistic phosphosites converted into "activating-minus-inhibitory" indices that quantified net tissue-specific pathway activity, […]
- by Fabrini, G., Froehlich, F.Cells sense and respond to their environment through signalling pathways, and the dynamics of these pathways shape cell fate even within genetically identical populations. Two largely separate computational traditions describe this behaviour: mechanistic differential-equation models and representation-learning methods. Mechanistic models encode pathway topology and kinetics but cannot easily represent variation arising outside the modelled pathway. Representation learning, instead, maps genome-wide measurements onto low-dimensional manifolds but offers no mechanistic account of how the resulting cell states execute their functions. Reconciling these […]
- by Kebede, A. M., David, C. T., Rawlinson, S. M., Deffrasnes, C., Gooley, P. R., Forster, S. C., Moseley, G. W.Type-I IFNs mediate the principle antiviral response of cells by controlling the expression of hundreds of IFN-regulated genes (IRGs), many of which have antiviral functions. The best understood mediators of IFN signalling are STAT1 and STAT2, and STAT1/2-dependent gene induction is conventionally viewed as the primary outcome of type-I IFN signalling. To overcome the IFN response, viruses express proteins called IFN-antagonists, which target IFN signalling pathways (e.g. rabies virus P-protein (RABV-P) binds and inhibits IFN-activated STAT1/2) and so are typically […]
- by Raval, M., Zhou, Y., Wichman, M., Lynch, M., Krizanc, D., Thayer, K. M., Weir, M. P.Nucleotide modifications of the tRNA anticodon can affect protein translation fidelity and speed. Chemical modifications of the anticodon nucleotide 34 are regulated under cellular stress and associated with several translational defects and pathologies. Here, we investigate how these modifications influence A-site codon recognition interactions and their coupling to the CAR site that lies adjacent to nucleotide 34 in the ribosome. The conserved three-residue CAR interface hydrogen bonds in a sequence-dependent manner to the mRNA +1 codon 3-adjacent to the A-site […]
- by Biswas, A., Bokes, P., Singh, A.Sequestration of gene products through diverse mechanisms forms a fundamental layer of regulation in intracellular biochemical processes, including post-translational modification, promiscuous binding to genomic decoy sites, and partitioning into membraneless compartments formed through phase separation. Here, we develop a unified stochastic framework to quantify how such sequestration-type processes, when coupled to noisy gene expression, modulate cell-to-cell variation in protein levels. In this model, protein molecules reversibly switch between active (free) and inactive (sequestered) states, whose switching rates are arbitrary functions […]
- by Khetan, N., Zheng, J., Deng, C., Wu, E., Li, H.Aging is often regarded as the last chapter of development. There is growing evidence that aging and development are mechanistically linked. Here we describe a genetic switch involving EZH2 and EGR1 as opposing regulatory nodes that appears to connect aging and development across human tissues. We show that this switch defines two distinct states in fibroblasts: a proliferative (EZH2-high/EGR1-low) state and a non-proliferative and extracellular matrix-expressing (EGR1-high/EZH2-low) state. During replicative aging, cells shift from the proliferative state to the non-proliferative […]
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