Molecular Systems Biology

Dysregulated kinase activity drives oncogenic signaling, disrupts cellular homeostasis, and promotes tumour progression. The BRAFV600E mutation constitutively activates the MAPK pathway and is a key therapeutic target in melanoma and other cancers, but the functional relevance of most downstream phosphorylation events and mechanisms of drug resistance remain unclear. To address this, a global multi-omic model of BRAF inhibition response was established in BRAFV600E-mutant cells by integrating time-resolved and biophysical phosphoproteomics, transcriptomics, and thermal proteome profiling. Ultradeep phosphoproteomics revealed extensive phosphorylation changes upon BRAF inhibitor treatment, while biophysical phosphoproteomics identified phosphorylation events linked to altered protein solubility and subcellular localization, suggesting changes in nucleic acid interactions and nuclear reorganisation. Network-based integration of these datasets prioritized functionally relevant phosphorylation sites and kinases. Experimental validation identified CDK9, CLK3, and TNIK as critical regulators of BRAFV600E signaling and candidate targets for combinatorial inhibition capable of re-sensitising resistant cells. The transcription factor ETV3 emerged as a previously unrecognised effector of BRAF signaling. Biophysical proteomics data confirmed that ETV3 phosphorylation modulates DNA-binding, while functional assays combining knockdown, metabolomics, and drug screening demonstrated its role in coordinating transcriptional and metabolic adaptations to BRAF inhibition. This study provides a systems-level framework linking phosphorylation dynamics to protein function and phenotype, identifies ETV3 as a new node in oncogenic BRAF signaling, and illustrates how integrated, site-resolved models can reveal mechanisms of kinase-driven oncogenesis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=43 SRC="FIGDIR/small/704793v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@1df1401org.highwire.dtl.DTLVardef@9a77a5org.highwire.dtl.DTLVardef@124f819org.highwire.dtl.DTLVardef@1c6b57_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LITime- and cell type-resolved phosphoproteomics maps BRAF inhibition dynamics C_LIO_LIBiophysical phosphoproteomics, combining quantitative phosphoproteomics with solubility profiling or nuclear fractionation, reveals phosphorylation-driven changes of protein solubility and localization C_LIO_LIIntegration of abundance and biophysical phosphoproteomics data identifies functionally relevant phosphorylation events of BRAFV600E signaling C_LIO_LINetwork integration of multimodal phosphoproteomic, transcriptomic and thermal proteome profiling data links signaling to protein function and cellular phenotypes C_LIO_LIBiophysical evidence improves models and identifies non-canonical kinases driving BRAF signaling as well as novel downstream regulators such as ETV3 C_LIO_LIFollow-up experiments reveal a ETV3-GLUT3-mediated metabolic adaptation in BRAFV600E cells C_LI

Characterizing CRISPR interference (CRISPRi) phenotypes presents a fundamental temporal challenge: pre-existing overabundance of target proteins can mask early silencing, requiring extended growth for dilution, yet prolonged repression rapidly selects for escaper mutants. To resolve this, we integrated a tightly regulated CRISPRi system in Pseudomonas putida with an automated mini bioreactor platform operating in turbidostat mode. By maintaining continuous exponential growth, we mapped the exact temporal dynamics of essential gene silencing. We identified a critical observation window between 17 and 27 hours (7-9.5 cell doublings) where repression exerts its maximum physiological impact, directly preceding population takeover by target-site mutated escapers. Applying this workflow to the arginine biosynthesis pathway, multi-omics profiling disentangled transient physiological buffering from long-term mutational events, revealing that argH and argG knockdowns trigger highly diverse metabolomic perturbations. This scalable framework overcomes batch culture limitations, ensuring precise temporal control for accurate phenotypic characterization and reliable functional genomics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/709552v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@c3dd07org.highwire.dtl.DTLVardef@e42939org.highwire.dtl.DTLVardef@14e7228org.highwire.dtl.DTLVardef@128ae19_HPS_FORMAT_FIGEXP M_FIG C_FIG

An increased emphasis on disease prevention is essential to improve population health and reduce healthcare resource needs. Key requirements for effective population scale prevention programmes are an optimal balance between simplicity of screening and accuracy of risk prediction at individual level. We developed models of varying complexity to predict the incident risk of 24 diseases in the UK Biobank population, testing combinations of modalities including established clinical variable-based risk scoring, 1H-NMR metabolomics, polygenic risk scores (PRS), and self-answered questionnaires with individual electronic health record (EHR)-based past medical history (PMH). Our results show that prediction models that utilise just questionnaire and PMH data ("No Needle" model) or with added metabolomics and PRS ("Single Blood Draw" model) exhibit robust discriminative performance, at least as good or better than a comprehensive clinical variable model or established cardiovascular disease (CVD) risk scores. The "No Needle" model was also validated in an independent prospective cohort, the Estonian Biobank. We also tested scenarios for deployment of these models to improve the effectiveness of the NHS Health Check, a population-scale UK screening programme. Our results suggest high potential for less resource-intensive approaches than current clinically-based paradigms for prediction of incident disease.

The cell division cycle is characterised by oscillatory dynamics in regulatory mechanisms and biosynthesis, coordinated with genome replication and segregation. To understand these dynamics, quantitative cell cycle-dependent protein concentration data is essential. Unfortunately, accurate resolution of cell cycle-dependent protein dynamics is challenging because single-cell proteomics is currently infeasible and bulk proteomics requires inherently imperfect cell synchronisation. Here, we developed a computational method to deconvolve cell cycle-dependent protein concentration dynamics and applied it to new budding yeast bulk proteome data. Key to this method was a yeast population model, parameterised with experimental cell cycle progression and volume growth data, for quantifying the desynchronisation in sampled populations. We performed deconvolution on 3373 proteins, using cross-validation to determine regularisation parameters, and identified 563 proteins with cell cycle-dependent dynamics. Many of these dynamics were consistent with known yeast biology and dynamic proteins were enriched for several metabolic process, extending previous observations and supporting the emerging picture of metabolic activity as varying substantially over cell cycle phases. We consider the generated cell cycle-resolved budding yeast proteome data a key resource.

Codons function as translation units in open-reading-frames (ORF) of genes to encode for proteins. Transfer RNAs (tRNAs) mediate the connection of every codon to its cognate amino acid. Despite the cooperation between messenger and transfer RNA during translation, approaches to integrate codon usage and tRNA quantities remain to be established. Using matched mRNA- and tRNA-sequencing of peripheral blood cells, we apply a precision-biology approach quantitatively integrating transcriptomic codon- and corresponding tRNA-abundance. Thereby, we classify codons as highly or lowly supplied and compare optimality of synonymous codons. Additionally, we describe substantial differences regarding the conservation of a codons tRNA-supply among healthy donors. A meta-ORF-analysis demonstrates depletion of lowly supplied codons at translation start sites. Discrepancy between codon- and tRNA-abundance, and codon-preference depending on the distance to the translation start site, seem to be non-random and could affect translational speed and thus provide a novel level of regulation of protein abundance.

Quantitative systems pharmacology (QSP) models require calibration data from published literature, yet manual curation produces inconsistent documentation while large language model (LLM) extraction exhibits hallucination and fabrication errors unacceptable for quantitative modeling. We present MAPLE (Model-Aware Parameterization from Literature Evidence), a framework that uses structured validation schemas as a collaboration interface between LLMs and modelers. Two complementary schemas capture calibration data at different scales: one for isolated experiments that constrain individual parameters through simplified forward models, and one for clinical and in vivo endpoints that constrain the full model through species-level observables. Both schemas separate data extraction from modeling decisions, capturing literature values with full provenance in a machine-verifiable form. Targeted validators catch characteristic LLM errors: value-in-snippet matching detects hallucinated values, DOI resolution flags fabricated citations, and code execution catches malformed forward models. We evaluate MAPLE on 87 calibration targets for a pancreatic ductal adenocarcinoma (PDAC) QSP model, using two collaboration modes: batch LLM extraction followed by interactive curation, and interactive extraction where modeler and LLM collaborate in real time. Both modes required substantial modeler input: the modeler changed forward model types in 65% of SubmodelTargets, adjusted prior parameters in 46%, and revised source relevance assessments in all files. Interactively extracted targets embedded modeler effort in the extraction process, producing near-final output. The schemas ensure completeness and enable reproducible, provenance-rich calibration regardless of workflow.

Interpreting the functional consequences of genetic variation, especially rare missense variants, remains a significant challenge in human genetics. Computational variant effect predictors (VEPs) and multiplexed assays of variant effects (MAVEs) provide complementary approaches, with VEPs offering scalable predictions and MAVEs delivering detailed empirical measurements. However, MAVEs are resource intensive and cannot yet be applied broadly across the proteome, making it important to identify proteins where experimental mapping will be most informative. We hypothesised that MAVEs should be particularly valuable for proteins where computational predictors disagree, as such disagreement may highlight mechanistic blind spots. To test this, we analysed predictions from ten distinct VEPs across more than 13,000 human proteins and quantified inter-predictor concordance. We observed substantial variability across proteins in the degree of agreement across predictors and investigated structural, functional and gene-level features associated with this variation. We find that inter-VEP concordance showed no relationship with agreement to experimental MAVE data. If predictor agreement reflected how intrinsically predictable a protein is, these quantities would be expected to correlate. Their decoupling instead suggests that MAVEs may provide orthogonal information to VEPs, supporting the use of inter-VEP disagreement to prioritise proteins where experimental data will be most informative. We therefore propose using inter-VEP disagreement as a practical strategy to prioritise proteins for experimental characterization. Focusing on proteins with low predictor concordance should maximise the informational value of new MAVEs, and improve variant interpretation in both research and clinical contexts.

The ability to understand and affect the course of complex, multi-system diseases like diabetes has been limited by a lack of well-designed, high-quality and large multimodal datasets. The NIH Bridge2AI AI-READI project (aireadi.org) aims to address this shortfall by generating an AI-ready dataset to support AI discoveries in type 2 diabetes mellitus (T2DM). This manual of procedures provides a detailed description of the AI-READI protocol.

It is commonly assumed that lack of correlation is evidence for lack of causal relationship. Here, however we show that in transcriptional networks, causal linkages can exist in the absence of correlation. We find that a substantial proportion of transcribed genes in yeast and in mouse, show evidence of state-dependent (nonlinear) and temporally coordinated dynamics in their expression patterns (65-77%). Using a test that accommodates this fact, we uncover strong causal relationships that are invisible to correlation-based analyses for both yeast and mouse models. Specifically, for yeast we detect uncorrelated causal relationships for the transcriptional regulators WHI5 and YHP1, and can verify these relationships experimentally. These genes reside at important checkpoints in the cell cycle where multiple signals are integrated at single nodes, giving rise to causal relationships, that despite being uncorrelated, can be accurately detected (71-78%) using a nonlinear causality test.

Functional health is centered on five domains of Intrinsic Capacity (IC): locomotion, cognition, vitality, psychological and sensory capacity. Therefore, measuring IC at the domain-specific level is the cornerstone for developing preventive interventions to help individuals preserve their independence. In this study, we used 63 clinical features from the UK Biobank to develop IC age, an 18-year mortality risk estimator that approximates an individuals biological age associated with the decline of each IC domain. By establishing proteomic surrogates of IC age, we find immune system activation across domains and provide a proteomic framework that may facilitate scalable monitoring of functional health decline.

AbstractAcute kidney injury (AKI) and chronic kidney disease (CKD) are two interconnected clinical conditions, both defined by degree of functional impairment, but with heterogeneous clinical trajectories. Using new transcriptomic technologies, recent studies have described the cellular diversity in the healthy and injured kidney at the single cell level. Here, we used single nucleus transcriptomics to investigate the molecular diversity and commonalities in kidney biopsies from over 150 participants with AKI and CKD enrolled within the Kidney Precision Medicine Project (KPMP) and did so at the patient participant level. Using an unsupervised approach, we identified two multi-cellular programs associated with clinical and histopathological features of acute injury and chronic damage, respectively. We found that these programs are expressed across patients with AKI and CKD, supporting shared, rather than distinct, underlying molecular mechanisms. These programs capture tissue-level compositional changes towards adaptive and failed-repair states in tubular epithelial cells, as well as intra-cellular molecular changes characteristic of stress in all cell types. We identified subunits of the NFkB and AP-1 complexes, as well as members of the STAT family, as putative upstream regulators of the acute and chronic programs. We were able to map these continuous molecular measures of acute injury and chronic damage to urine and plasma protein profiles obtained at time of biopsy. These non-invasive protein signatures were predictive of renal outcomes in an independent cohort of 44 thousand participants from the UK biobank. In summary, unbiased identification of cellular programs in kidney disease biopsies defined molecular programs of injury cutting across conventional disease categorization and established a non-invasive molecular link to long term patient outcomes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/26347522v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@a2bf0forg.highwire.dtl.DTLVardef@ad93f6org.highwire.dtl.DTLVardef@1cd21c7org.highwire.dtl.DTLVardef@64b5ab_HPS_FORMAT_FIGEXP M_FIG C_FIG

PDAC exhibits rapid chemoresistance, yet how drug-tolerant states arise remains unclear. Existing approaches miss how network topology evolves across cell-state transitions under drug pressure. A 3D PANC-1 tissue model on decellularized intestinal matrix was used for scRNA-seq across four conditions (control, GEM, TGF-{beta}1, GEM+TGF-{beta}1). Pseudotime trajectory inference was combined with dynamic PPI network analysis. Findings were cross-examined in a PDAC atlas (726,107 cells, 231 patients; Loveless et al., 2025). GEM resistance involved E2F1, mTOR, CDK1, AURKA, TPX2, TOP2A, and BIRC5. TGF-{beta}1 drove EMT resistance via KRAS, glycolysis, and hypoxia, inducing SPOCK1, MBOAT2, COL5A1, ADAMTS6, THBS1, and FN1. Trajectory-coupled network analysis revealed an emergent bottleneck when G1[->]S and TGF-{beta}1-induced EMT co-occurred: CDK1 centrality spiked selectively, with CDKN1A as critical regulator. This CDK1-CDKN1A-WEE1 axis defines an "S-phase persistence" state enriched for GEM survivors. Atlas cross-examination confirmed 8.7-fold metastatic enrichment of triple-positive cells and EMT-cell-cycle coupling. Trajectory-coupled network topology analysis identifies CDK1-CDKN1A-WEE1 as a chemoresistance bottleneck corroborated in 726,107 patient cells. The framework generalizes to drug resistance across cancer types.

The daily cortisol cycle is a critical indicator of hypothalamic-pituitary-adrenal (HPA) axis function. The current analytical approaches produce several outputs difficult to integrate into simple statistical models, clinical workflows, and ML/AI pipelines requiring single-value inputs. We developed the Cortisol Sine Score (CSS), a model-free scalar metric that quantifies daily cortisol exposure by computing a weighted sum of cortisol measurements across the day, using sine-transformed time-of-day weights. The CSS produces positive values for morning-dominant patterns, negative values for evening-shifted profiles, and near-zero values for flattened rhythms characteristic of chronic stress and circadian disruption. We validated the CSS performance in 3,006 samples from 501 pregnant women enrolled in the March of Dimes program, with cortisol values measured at 6 time points per day collected during the second trimester of pregnancy. The CSS showed strong correlations with observed and model-estimated amplitude and acrophase from Cosinor regression and JTK_CYCLE approaches, with excellent classifying performance (AUC=0.89, high versus low). The CSS successfully captured established associations between social disadvantage and cortisol dysregulation, and demonstrated utility in predicting gut microbiome composition in metagenomic analyses. Importantly, the CSS maintains excellent fidelity to the full 6-sample protocol with as few as 3-4 daily measurements. The 4-sample protocol achieves great performance (r = 0.952, MAE = 0.087) while reducing participant burden. The 06:00 time point was identified as essential for accurate CSS quantification. The CSS bridges the gap between circadian analysis and practical implementation by providing a simple, interpretable, and robust assessment of cortisol daily cycle in large-scale epidemiological studies, clinical screening, and biomedical sensors. HighlightsO_LICurrent state-of-the-art approaches estimating the daily cortisol exposures produce multi-output information difficult to implement in simple statistical analyses or ML/AI multi-omics approaches C_LIO_LICortisol Sine Score is a novel model-free scalar metric expressing cortisol daily exposure and rhythmicity (morning vs evening exposure) C_LIO_LICortisol Sine Score was validated using 3006 salivary samples from clinical data and golden standards in circadian analyses such as Cosinor and JTK_CYCLE C_LIO_LICortisol Sine Score was the top performer in our benchmarking approach predicting association with social disadvantage and gut microbiome composition C_LIO_LIReliable with 3-4 daily samples, reducing participant burden C_LIO_LIOpen-source R package CortSineScore democratizes cortisol cycle analysis C_LI

Despite the fact that microbes in natural environments spend most of their time in growth arrest, we understand little about how this physiological state affects the performance of engineered genetic circuits. Here, we measure repression curves from a library of genetic NOT gates at single-cell resolution in Escherichia coli under both active growth and growth arrest to systematically investigate how growth arrest affects circuit behavior. We find that the impact of growth arrest on circuit performance is almost entirely dominated by a single effect: a >100-fold reduction in unrepressed expression levels. Growth arrest caused gene expression noise to increase moderately and had only minimal impacts on the sensitivity and sharpness of the repression curves. Our work shows both that conventional genetic circuit design paradigms are currently insufficient to develop circuits that can function properly under growth arrest, but also that addressing the reduction in just a single performance parameter would be sufficient to resolve this problem. This work expands our understanding of bacterial gene regulation under growth arrest and lays the groundwork for new design paradigms that will be essential in ensuring the safe and reliable performance of synthetic biology systems in real-world environments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/703179v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@3df103org.highwire.dtl.DTLVardef@9a2f5forg.highwire.dtl.DTLVardef@9c15aborg.highwire.dtl.DTLVardef@1529c39_HPS_FORMAT_FIGEXP M_FIG C_FIG

Extensive laboratory experimentation has revealed conserved molecular pathways controlling growth and stress responses in plants, yet how these programs operate in natural settings remains poorly understood. We investigated transcriptome organization in wild populations of Arabidopsis thaliana by sampling plants from 60 natural sites in Europe and North America across two seasons. Transcriptomes varied extensively among individuals and showed largely continuous rather than discrete structure across geography and season. Although disease and microbial colonization were common in the wild, wild transcriptomes did not simply recapitulate canonical laboratory stress signatures. Measured microbial infection, environmental, and phenotypic variables explained only a modest fraction of total expression variation, but infection-associated signals accounted for the largest share of the explainable component. Consistent with this, biotic-response networks defined in controlled laboratory experiments were well conserved in wild transcriptomes, whereas control and abiotic-response networks were substantially reorganized. Together, these results suggest that while core transcriptional modules remain recognizable across environments, regulatory relationships among modules differ markedly between laboratory and natural contexts. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/711176v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@2c5356org.highwire.dtl.DTLVardef@136b9corg.highwire.dtl.DTLVardef@fddf37org.highwire.dtl.DTLVardef@149c28c_HPS_FORMAT_FIGEXP M_FIG C_FIG

Antibiotic treatment failure, often driven by non-genetic mechanisms such as tolerance and persistence, remains a major global health challenge. {beta}-lactams, the most widely prescribed antibiotic class, are particularly compromised by tolerance in dormant, non-growing cells; yet, how these drugs act on cells resuscitating from dormancy remains poorly understood. Here, we investigate the resuscitation phase at an unprecedented scale using Hi-DFA (High-throughput Dynamic Fate Analyser), a single-cell microfluidic platform integrating time-lapse imaging with machine-learning-based image analysis for dynamic cell-fate tracking. We identify a distinct survival strategy: a significant fraction of resuscitating cells transiently slow their growth, facilitating survival upon {beta}-lactam exposure. This transiently tolerant phenotype is considerably less frequent in unstressed, exponentially growing cells, indicating that prior starvation history predisposes cells to this state. Using simulated in vitro pharmacokinetic treatment profiles, we show that suboptimal dosing selectively enriches for this transient tolerance state. A population dynamics model built from this single-cell antibiotic-response data suggests that these transient-tolerant cells, not typical starvation-triggered persisters, may be the primary drivers of rapid population regrowth post-treatment under clinically relevant conditions. Together, our findings define a distinct class of antibiotic survival shaped by stress history and treatment profile, offering a quantitative framework for optimising antibiotic dosing strategy.

The detection of monogenic diabetes illustrates the potential of precision medicine, with treatments tailored to specific genes and diagnosis involving targeted genetic testing. Current detection criteria are derived from White populations. We investigated detection of monogenic diabetes in an unselected multiethnic cohort comprising 1,706 participants diagnosed with diabetes before the age of 30-years. Using broad biomarker criteria (triple pancreatic antibody negative and detectable C-peptide) to select for next generation sequencing of monogenic diabetes genes, we found a non-significantly different minimum cohort prevalence of monogenic diabetes of 2.1% in White, 2.0% in South Asian, 2.5% in African-Caribbean, and 3.6% in Mixed participants. The detection rate, however, varied significantly (17.7% in White, 5.3%in South Asian, 8.0% in African-Caribbean, and 15.2% in Mixed participants, p<0.001). Those without monogenic diabetes showed significant variations in BMI. No difference in phenotype of monogenic diabetes across ancestry groups was observed. Non-white ethnicity participants were significantly more likely to have undiagnosed monogenic diabetes than White with on average a 10-year duration before receiving a correct diagnosis. By applying ancestry-specific BMI cut-offs (White <30, South Asian <27, African-Caribbean and Mixed <35 kg/m{superscript 2}), the overall detection rate increased from 8.8 to 16%, reducing the number needed to test to identify one case from 11 to 6 and boosting detection rates to 39, 11, 9 and 26% in White, South Asian, African-Caribbean and Mixed-ethnicity participants, respectively. These findings were validated in an external real-world dataset. Applying broad biomarker criteria for initial selection, mitigates clinical biases leading to misclassification of monogenic diabetes in non-White ethnicities. However, further tailoring criteria with ethnic-specific BMI cut-offs doubled detection rates, improving cost-effectiveness by minimising unnecessary testing. Our study highlights the need to develop precision medicine approaches accounting for phenotypic variation across diverse populations, to ensure accurate diagnoses and cost-efficient healthcare provision.

Many organs are organized into repeating anatomical units, yet how cellular heterogeneity is structured within and between these units remains poorly understood. Here we use spatial transcriptomics to dissect multiscale heterogeneity in the human thyroid gland, a tissue composed of hormone-producing follicles. Across human thyroid samples spanning non-inflamed to inflamed states, we develop a follicle-aware analytical framework that separates intra-follicular from inter-follicular variability. We find that heterogeneity among thyrocytes is not dominated by differences in hormone synthesis but instead by two opposing transcriptional programs: an active hormone-producing state and a damage-response thyrocyte (DRT) state enriched for stress, immune, and damage-response pathways. DRTs are spatially clustered, associated with DNA damage markers, and are enriched near immune niches. Notably, the balance between active and damage-response programs constitutes a major axis of variability across cells, follicles, and patients. Our findings highlight a damage-response epithelial thyrocyte state that may be fundamental to follicular function in the human thyroid and provide a general framework for studying heterogeneity in tissues composed of repeating anatomical units.

Linking the spatiotemporal dynamics of proteins in live cells to physiological functions is a fundamental challenge in biology and robust quantification of protein dynamics is a major step towards this endeavor. Single molecule tracking (SMT) has emerged as a powerful technique to investigate protein dynamics at the single molecule level in living cells. Most SMT analyses require familiarity with biophysical models and programming and the results from different analyses cannot be easily integrated. To mitigate these shortcomings, we developed QuantiTrack - a MATLAB-based SMT analysis software that can be operated from a simple graphical user interface. This provides a much-needed end-to-end solution where a user can load a movie, track single molecules, and perform a range of analyses. In addition to a detailed user guide with step-by-step instructions, QuantiTrack includes quality control metrics that can be used to systematically determine tracking parameters. As a practical example, we address by QuantiTrack a question relevant to hormonal therapy: How does the glucocorticoid receptor (GR), a hormone-regulated transcription factor (TF), respond to treatment and washout of its cognate hormone. Hormone washout results in rapid (in minutes) downregulation of GR target genes to basal levels. We observe dynamics of the Halo tagged GR (Halo-GR) and by integrating several analyses, show that hormone washout results in a substantially lower bound fraction of GR, reduced occupancy in the mobility state associated with GR activation, and shorter GR dwell times. These analyses showcase QuantiTrack as a convenient tool for comprehensive SMT analysis for a wide range of biologists.

Quantifying the selective effects of individual mutations is essential to understand how their population-wise frequencies evolve under natural selection and genetic drift. Large genomic datasets provide a real-life experiment that we exploit to characterize the efficiency of selection across different mutations types and populations. Using Direct Coupling Analysis, a model from statistical physics, we derive protein-informed scores for individual non-synonymous mutations identified in 81,440 Escherichia coli genomes. We show that these scores act as a latent variable capturing the probability that a mutation is beneficial, neutral, or mildly to highly deleterious. We contribute to the debate on the importance of synonymous mutations by demonstrating that their selection intensities span a single order of magnitude in the E. coli species, whereas non-synonymous mutations span six orders of magnitude. We further relate selection efficiency to genetic drift, defined as the inverse of population size, and to ecological lifestyle, and we identify a 10,000-fold reduction in selection efficiency between the entire E. coli species and its most pathogenic populations. Together, these results highlight how population genetics and protein variant fitness predictors inform one another: variation in selection efficiency is associated with shifts in the distribution of mutation scores, and population genetics data provide a benchmark to assess the accuracy of these scores. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/711857v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1df70corg.highwire.dtl.DTLVardef@1464860org.highwire.dtl.DTLVardef@139d4d3org.highwire.dtl.DTLVardef@1c3a4c5_HPS_FORMAT_FIGEXP M_FIG Schematic representation of the analysis of polymorphism in 81,440 Escherichia coli genomes. 458,443 polymorphic codon sites were identified and oriented using homologous sequences from closely related species. Mutations can be classified as synonymous or non-synonymous based on whether they alter the amino-acid sequence encoded, and real-valued scores predictive of fitness effects can be attributed to mutations within each of these classes. Codon scores reflect the global codon usage preference within the E. coli genome. DCA scores capture position- and amino-acid-specific preference as well as epistatic constraints and are obtained for each protein from a set of distantly related homologous sequences. Coupled with the abundance of polymorphic sites within different E. coli subpopulations, these different polymorphism classifications allow to precisely compare the intensity of selection between different types of mutations and across populations with distinct lifestyles, illustrated here by their pathogenic power. C_FIG