Biology subjects

Stadler, T.

Publications and source records attributed to Stadler, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Bayesian phylogenetics with incomplete lineage tracing data

Single-cell lineage tracing is rapidly emerging as a powerful method to track the dynamics of cell populations through time. Specifically, CRISPR-Cas9-based dynamic lineage tracing methods generate high-resolution data with the potential to investigate increasingly complex biological processes. However, single-cell readouts are inherently lossy, resulting in significant sparsity in lineage barcode alignments. Here, we present a mechanistic model of barcode loss implemented in the Bayesian framework BEAST 2, enabling phylogenetic inference from sparse datasets without the need for data filtering. Through simulations, we find that incorporating a mechanistic model of loss improves accuracy across all estimates in phylogenetic analyses by enabling previously unusable data to contribute to the inference. We further demonstrate the model's utility across two datasets: one from a monoclonal cell culture and another from murine gastruloids. Consistent with our simulation findings, applying our model to these datasets recovers higher growth rates than workflows relying on standard data filtering, confirming that such filtering can lead to an underestimation of population expansion. Crucially, we demonstrate that our framework enables inference even from datasets whose sparsity previously prevented analysis, providing a robust foundation for extracting biological insights from lineage tracing experiments.

bioinformatics↗

bioRxiv2026

Empirical evidence and robustness of clock models with spikes

1Time-calibrated phylogenies provide information on past macroevolutionary history. Time calibration can be obtained from fossil ages or node calibrations in combination with a clock model describing evolutionary rates. Relaxed clocks, which allow rates of evolution to vary across lineages, are widely used in phylogenetic research but lack a mechanistic link between rate variation and the evolutionary process. A recently developed class of clock models attempts to introduce biological mechanisms by coupling speciation events with spikes of evolutionary change. However, their empirical support and overall impact on phylogenetic inference remain underexplored. Here, we evaluate the support for spike clock models across a range of empirical datasets and use simulations to quantify the effects of model misspecification and missing data across clock models. We find that spike clock models are supported as the best-fitting model in six of the seven datasets analyzed, suggesting widespread evidence of a punctuated mode of evolution. Although the choice of clock models does not strongly affect the resulting divergence time estimates, spike clock models tend to give more constrained uncertainty intervals of speciation and extinction rate estimates in some empirical analyses. We interpret this as the consequence of information transfer from sequence evolution into the inferred branching process. Our simulations show that a general clock model that incorporates both branch-specific clock rates and spikes is the most robust across simulated datasets. In particular, models with spikes are robust to missing data, capable of accurately estimating speciation and extinction rates even when fossil data is completely absent. In summary, we highlight here that evolutionary spikes leave a detectable signal in the alignment data, and correctly accounting for them leads to improved estimates of the branching parameters and tree topologies.

evolutionary biology↗

bioRxiv2026

Bayesian neural networks enable inference of complex phylodynamic processes

Phylogenetic branching patterns carry essential information about population and diversification dynamic processes, including speciation, extinction, and epidemiological transmission. Phylodynamic models offer a rigorous mathematical framework for quantifying these dynamics from phylogenetic trees. Extensions of these models enable the incorporation of external covariates as predictors of phylodynamic parameters, for instance allowing us to link traits or environmental variables with changes in speciation, extinction, or transmission rates. However, the dependencies between predictors and phylodynamic parameters are typically restricted to linear, additive effects and may thus fail to capture complex, potentially non-linear relationships underlying evolutionary dynamics. To address this limitation, we propose a new framework, BELLA, which leverages unsupervised Bayesian neural networks (BNNs) to flexibly model functional relationships between key phylodynamic parameters and a broad set of predictors, including categorical traits, quantitative variables, and time series data. Based on these covariates, the BNN weights are estimated through Markov chain Monte Carlo and can be inferred jointly with the phylogenetic tree topology and branching times, obtained directly from sequence alignment data. Using extensive simulations, we demonstrate that this approach accurately recovers predictor-parameter relationships, mitigates overfitting, and remains robust across both macroevolutionary and epidemiological contexts. By incorporating tools from explainable artificial intelligence, we further show that our framework reliably identifies the most influential predictors and yields interpretable descriptions of their impact on phylodynamic rates. Finally, we apply our method to two empirical analyses: linking SARS-CoV-2 migration dynamics with travel data during its early spread in Europe, and inferring trait and time-dependent speciation and extinction rates in the Cenozoic diversification of platyrrhines. Our unsupervised BNN framework substantially expands the capabilities of phylodynamic inference providing a powerful and flexible approach to model complex macroevolutionary and epidemiological processes. Significance statementUnderstanding how species diversify or pathogens spread requires linking phylogenetic trees to biological traits and environmental characteristics. We present BELLA, an unsupervised Bayesian neural network that learns complex, nonlinear links between predictors--such as traits or environmental variables--and key rates such as speciation, extinction, transmission, and migration directly from sequence data--which in turn inform on the phylogenetic relationships. BELLA does not require any training data, enabling inferences that were previously not tractable. Across simulated epidemics and macroevolutionary histories, BELLA is more accurate than state-of-the-art linear models relating predictors and rates, while remaining interpretable through explainable artificial intelligence. Empirical applications in epidemiology reveal nonlinear effects of travel among countries on the early spread of SARS-CoV-2 in Europe, while a macroevolutionary application shows selective extinction events in the diversifcation history of New World monkeys. The BELLA framework expands the reach of phylodynamics in biology and public health.

evolutionary biology↗

bioRxiv2025

Single-cell phylodynamics reveal rapid late-stage colorectal cancer expansions

Single-cell whole-genome sequencing of 335 cells from seven colorectal cancers, coupled with Bayesian phylodynamic modeling, revealed tumors often originate decades before diagnosis, remain indolent, and expand rapidly within two years. Strong spatial structuring with minimal interregional mixing was observed, while substitution rates varied widely and were decoupled from growth. Our findings highlight extended evolutionary stasis and sudden expansion, informing strategies for early detection and intervention.

evolutionary biology↗

bioRxiv2025

Bayesian phylodynamics for developmental biology: incorporating age-dependence

As novel technologies for single-cell lineage tracing emerge, phylogenetic and phylodynamic tools are increasingly being used to study developmental processes. However, traditional phylodynamic methods, which were originally developed to study viral evolution, rely on assumptions that are difficult to justify in developmental contexts. Notably, due to cells dividing after characteristic generation times rather than after exponential waiting times--as assumed by the traditionally used birth-death model--empirical cell lineage trees deviate from birth-death phylogenies. Here, we present a non-trivial extension of the birth-death phylodynamic model that captures this characteristic feature of development. By applying our method to a public dataset of stem cell colonies, we show how previous estimates of the underlying population-dynamic parameters were biased by the choice of a birth-death tree prior. Beyond developmental biology, our framework provides an approach for analyzing systems where classical birth-death assumptions may be violated or where empirical tree shapes are poorly captured by those expected under standard phylodynamic models. Our method is available as a BEAST2 package. SignificanceApplying phylodynamic inference methods to data from developmental biology requires reassessment of the foundational assumptions underlying these tools. We show that cell population dynamics can be captured by an age-dependent branching process, as opposed to the widely used birth-death process. We develop computational methodology for efficient phylodynamic inference under this age-dependent model, thus providing a tool for connecting cell population dynamics to lineage trees. Our method is furthermore, to our knowledge, the first performant implementation of an age-dependent phylodynamic likelihood, and may be more generally applicable to systems which are ill-characterized by traditional birth-death models.

developmental biology↗

bioRxiv2025

Characterizing Co-Circulating Respiratory Virus Genomic Diversity in Switzerland with Hybrid-Capture Sequencing and Phylogenetic Reconstructions: Insights into the 2023/24 Season

Respiratory viruses circulate yearly with strain-specific patterns. Although SARS-CoV-2 and Influenza A/B genomic surveillance is well-developed, most respiratory viruses are unevenly monitored, lacking geographical diversity to capture wider population dynamics. Consequently, insights into respiratory virus evolution are limited. We investigated the genetic diversity of these viruses within one country. During the 2023/24 season, we conducted whole-genome sequencing of 1129 clinical samples using a hybrid-capture protocol. These samples were pre-tested by real-time PCR panels throughout Switzerland. Leveraging publicly-available full-length genomes, we constructed background datasets representative of geographical diversity and built phylogenies for all viruses with more than 40 new high-quality genomes from this study. We detected 981 viruses and recovered 461 high-quality genomes, including 18 co-infections, from 437 PCR-positive and 6 PCR-negative samples. All viruses detected by PCR were also detected by sequencing in 56% of samples. The four most prevalent viruses were Influenza A/H1N1, SARS-CoV-2, RSV-A, and HPIV-3, and their seasonal spread was consistent with wastewater monitoring and influenza-like illness reports. Swiss viral genomes were representative of the global genomic diversity, with evidence for multiple introductions into Switzerland, and we identified putative Swiss clusters. In this proof-of-concept study, we focus on 3 viruses (Influenza A/H1N1, RSV-A/B, and HPIV-3), and we demonstrate the streamlined implementation of a broad respiratory virus genomic surveillance workflow with an off-the-shelf protocol and publicly-available software. In addition, we highlight additional evolutionary insights that can only be derived from genomic surveillance. Going forward, this dataset will be a useful resource for future investigations into respiratory viral genomic diversity.

genetics↗

bioRxiv2025

Response to "No evidence that transmissible cancer has shifted from emergence to endemism in Tasmanian devils"

Herein, we rebut the critique of Patton et al. (2020), entitled, "No evidence that a transmissible cancer has shifted from emergence to endemism", by Stammnitz et al. (2024). First and foremost, the authors do not conduct any phylogenetic or epidemiological analyses to rebut the inferences from the main results of the Patton et al. (2020) article, rendering the title of their rebuttal without evidence or merit. Additionally, Stammnitz et al. (2024) present a phylogenetic tree based on only 32 copy number variants (not typically used in phylogenetic analyses and evolve in a completely different way than DNA sequences) to "rebut" our tree that was inferred from 436.1 kb of sequence data and nearly two orders of magnitude more parsimony-informative sites (2520 SNPs). As such it is not surprising that their phylogeny did not have a similar branching pattern to ours, given that support for each branch of their tree was weak and the essentially formed a polytomy. That is, one could rotate their resulting tree in any direction and by nature, it would not match ours. While the authors are correct that we used suboptimal filtering of our raw whole genome sequencing data, re-analyses of the data with 30X coverage, as suggested, resulted in a mutation rate similar to that reported in Stammnitz et al. (2024). Most importantly, when we re-analyzed our data, as well as Stammnitz et al.s own data, the results of the Patton et al. (2020) article are supported with both datasets. That is, the effective transmission rate of DFTD has transitioned over time to approach one, suggesting endemism; and, the spread of DFTD is rapid and omnidirectional despite the observed east-to-west wave of spread. Overall, Stammnitz et al. (2024) not only fail to provide evidence to contradict the findings of Patton et al. (2020), but rather help support the results with their own data.

evolutionary biology↗

bioRxiv2025

Post-sampling degradation of viral RNA in wastewater impacts the quality of PCR-based concentration estimates

Successful wastewater-based infectious disease surveillance programs depend on regular, reliable molecular detection of nucleic acids in municipal wastewater systems. This process is challenged by the gradual degradation of the viral content of the wastewater over time. Testing protocols are complex and often cannot be performed on site, resulting in delays between collection and testing. The evidence of continued degradation of viral RNA when stored at low temperatures is currently limited to a small number of studies with mixed results. Taking advantage of variable delays between sample collection and processing, we used a Bayesian temporal model and data from two winter periods of a national surveillance program in Switzerland to determine the rate at which the measured viral concentrations of four respiratory viruses declined as a result of RNA degradation between sample collection and processing. We found evidence of substantial degradation between the collection and processing of samples with daily rates of up to -0.28 (-0.38 - -0.19 95% CrI). We established that reduction in viral concentrations resulting from post-sampling degradation was responsible for a number of measurements falling below quantifiable limits. For one treatment plant, we estimate that 39 measurements fell below the limit of detection due to RNA degradation over the course of a single season. Measurements are more likely to be lost early in the seasonal epidemic when concentrations are still low. This delays consistent reliable measurement and sets back epidemiological assessments relevant to public health management strategies.

microbiology↗

bioRxiv2025

Enhancing Evolutionary Timelines: The Impact of Stratigraphic Range Information on Phylogenetic Inference

Coherent phylogenetic analyses of molecular and fossil datasets have deepened our understanding of evolutionary biology. A core model facilitating such coherent analyses is the fossilised birth-death (FBD) model which directly incorporates fossils within phylogenetic trees. However, a limitation of the FBD model is that it cannot assign multiple fossil occurrences of the same species, limiting our ability to accurately represent age information for species which have been sampled repeatedly. To address this gap, the Stratigraphic Ranges Fossilized Birth-Death (SRFBD) model has recently been introduced. This model can account for sampled strati-graphic ranges and integrate over occurrences within the range, enabling us to include more complete fossil age information within the inference. Here, building upon this mathematical work, we develop a computational method making the model accessible to the community, for more accurate total-evidence inference of dated phylogenetic trees and evolutionary parameters. In particular, we integrate the SRFBD model into BEAST2, facilitating the use of a diverse array of genomic substitution models and the inclusion of both morphological and molecular data when inferring phylogenies. We present a thorough validation of the SRFBD implementation against simulated data. We then demonstrate the differences in posterior parameter estimates and phylogenies when applying FBD and SRFBD models to two example datasets, the Spheniscidae (penguins) and the Canidae (dogs). In both examples, the SRFBD model produces older divergence times, lower diversification and turnover rates, and considerably higher sampling proportions compared to FBD model.

evolutionary biology↗

bioRxiv2025

Highly pathogenic avian influenza management in high-density poultry farming areas

The continuous spread of highly pathogenic avian influenza H5 viruses poses significant challenges, particularly in regions with high poultry farm densities where conventional control measures are less effective. Using phylogeographic and phylodynamic tools, we analysed virus spread in Southwestern France in 2020-21, a region with recurrent outbreaks. Following a single introduction, the virus spread regionally, mostly affecting duck farms, peaking in mid-December with a velocity of 27.8 km/week and an effective reproduction number between farms (Re) of 3.8, suggesting the virus can spread beyond current control radii. Transmission declined after late December following preventive culling. Farm infectiousness was estimated around 9 days. Duck farm density was the main driver of virus spread and we identified farm density and proximity thresholds required to maintain effective control (Re < 1). These findings offer actionable guidance to support regional biosecurity and to improve the robustness of the poultry sector to mitigate future outbreaks.

evolutionary biology↗

bioRxiv2025

Phylodynamic estimation of the within-host mutation rate of extended-spectrum beta-lactamase-producing Enterobacterales

Despite their clinical relevance, the within-host evolution of extended-spectrum beta-lactamase-producing Enterobacterales (ESBL-PE) is still poorly understood. To estimate the within-host mutation rates of ESBL-producing Escherichia coli and Klebsiella pneumoniae species complex, we fitted phylodynamic models to genomic sequence data of longitudinally collected rectal swabs from 64 colonized hospital patients. We estimated an average within-host mutation rate of 7.71e-07 [4.60e-07,1.10e-06] mutations/site/year for E. coli and 4.20e-07 [1.57e-07,7.14e-07] mutations/site/year for K. pneumoniae species complex, with limited variation observed across patients and sequence types. These estimates are presumably the most accurate reported so far and are useful for future epidemiological and evolutionary studies. Impact statementUnderstanding the within-patient mutation rates of extended-spectrum beta-lactamase (ESBL)-producing E. coli and K. pneumoniae species complex is critical for elucidating their evolutionary dynamics and transmission potential. In this study, we employed Bayesian phylodynamic models to analyze longitudinal isolates collected over a 10-year period from hospitalized patients. Our findings reveal limited variability in mutation rates across patients and sequence types, suggesting evolutionary stability within these pathogens in hospital settings. This work provides new insights into the persistence and evolutionary dynamics of ESBL-PE, offering valuable guidance for antimicrobial resistance surveillance and infection prevention strategies. Data summaryThe data used in this study are publicly available in the NCBI database under the BioProject number PRJNA910977. Supporting metadata are provided in Appendix 1. The code for the phylodynamic analyses, including the BEAST2 XML files, is available at https://github.com/EtthelWindels/esbl-pe_mutation. All supporting data, code and protocols have been provided within the article, supplementary data files or public repositories.

genomics↗

bioRxiv2025

Strategies for resolving cellular phylogenies from sequential lineage tracing data

A combination of recent advancements in molecular recording devices and sequencing technologies has made it possible to generate lineage tracing data on the order of thousands of cells. Dynamic lineage recorders are able to generate random, heritable mutations which accumulate continuously on the timescale of developmental processes; this genetic information is then recovered using single-cell RNA sequencing. These data have the potential to hold rich phylogenetic information due to the irreversible nature of the editing process, a key feature of the employed CRISPR-based systems that deviates from traditional assumptions about molecular mutation processes. Recent technologies have furthermore made it possible for mutations to be acquired sequentially. Understanding the information content of these recorders remains an open area of investigation. Here, we model a sequentially-edited recording system and analyse the experimental conditions over which exact phylogenetic reconstruction occurs with high probability. We find, using simulation and theory, explicit parameter regimes over which simple and efficient distance-based reconstruction methods can accurately resolve the cellular phylogeny. We furthermore illustrate how our theoretical results could be used to help inform experimental design.

developmental biology↗

bioRxiv2025

Assessing the inference of single-cell phylogenies and population dynamics from genetic lineage tracing data

Multicellular organisms develop from a single cell by repeated rounds of cell division, differentiation, and death, which can be represented as a single-cell phylogenetic tree. Genetic lineage tracing allows us to investigate this development by tracking the ancestry of individual cells as populations grow and change over time. However, accurate reconstruction of the cell phylogeny and quantification of the corresponding phylodynamic parameters - cell division, differentiation and death rates - from this tracking data remains challenging and needs to be systematically evaluated. We perform simulations and assess, using the Bayesian framework, the joint inference of time-scaled cell phylogenies and phylodynamic parameters from CRISPR lineage recordings with random or sequential edits. Principally, we characterize the inference improvements as the recorder capacity increases. We observe more accurate phylogenetic reconstruction from sequential compared to random recordings, but no substantial improvement in phylodynamic inference when using the additional information contained in the order of edits. Overall, we find that CRISPR lineage recordings carry a strong signal on the rates of cell division when appropriate models are used. However, we detect biases in the inferred rates of cell division and death under phylodynamic model misspecification, i.e. when fitting classic memoryless birth-death processes to synchronous cell divisions. Moreover, for scenarios when cells differentiate into distinct types, we demonstrate that Bayesian phylodynamic analysis of sparse end-point measurements can resolve these cell differentiation trajectories by lineage and time. Under prototypical dynamics, we recover cell type-specific division and death rates, and cell type transition rates in over 80% of simulations. Overall, this simulation study explores how much information on cellular development can be extracted from state-of-the-art genetic lineage tracing data using phylogenetic and phylodynamic methodology. Author summaryNovel technologies provide means to trace the development of cell populations over time by introducing heritable and editable genetic sequences that record lineage information in the cells genome. Reconstructing a populations history from such sequences sparsely sampled at a single time point is, however, computationally challenging. In this work, we use simulations and statistical inference to evaluate how accurately we can recover the relationships among cells and estimate the temporal dynamics of cell populations from genetic lineage tracing data generated from distinct recording systems, and compare their information content. Our results show that it is possible to quantify how cells divide, differentiate, and die based on such data, though certain statistical limitations remain. Addressing these limitations in future research will be essential for deepening our understanding of cell development in complex tissues and organisms, in both health and disease.

cell biology↗

bioRxiv2025

Viral Simulation Reveals Overestimation Bias in Within-Host Phylodynamic Migration Rate Estimates Under Selection

Phylodynamic methods are widely used to infer the population dynamics of viruses between and within hosts. For HIV-1, these methods have been used to estimate migration rates between different anatomical compartments within a host. These methods typically assume that the genomic regions used for reconstruction are evolving without selective pressure, even though other parts of the viral genome are known to experience strong selection. In this study, we investigate how selection affects phylodynamic migration rate estimates. To this end, we developed a novel agent-based simulation tool, virolution, to simulate the evolution of virus within two anatomical compartments of a host. Using this tool, we generated viral sequences and genealogies assuming both, neutral evolution and purifying selection that is concordant in both compartments. We found that, under the selection regime, migration rates are significantly over-estimated with a stochastic mixture model and a structured coalescent model in the Bayesian inference framework BEAST2. Our results reveal that commonly used phylogeographic methods, which assume neutral evolution, can significantly bias migration rate estimates in selective regimes. This study underscores the need for assessing the robustness of phylodynamic analysis with respect to more realistic selection regimes.

evolutionary biology↗

bioRxiv2024

Bayesian phylodynamic inference of multi-type population trajectories using genomic dat

Phylodynamic methods provide a coherent framework for the inference of population parameters directly from genetic data. They are an important tool for understanding both the spread of epidemics as well as long-term macroevolutionary trends in speciation and extinction. In particular, phylodynamic methods based on multi-type birth-death models have been used to infer the evolution of discrete traits, the movement of individuals or pathogens between geographic locations or host types, and the transition of infected individuals between disease stages. In these models, population heterogeneity is treated by assigning individuals to different discrete types. Typically, methods which allow inference of parameters under multi-type birth-death models integrate over the possible birth-death trajectories (i.e. the type-specific population size functions) to reduce the computational demands of the inference. As a result, it has not been possible to use these methods to directly infer the dynamics of trait-specific population sizes, infected host counts or other such demographic quantities. In this paper we present a method which infers these multi-type trajectories with minimal additional computational cost beyond that of existing methods. We demonstrate the practicality of our approach by applying it to a previously-published set of MERS-CoV genomes, inferring the numbers of human and camel cases through time, together with the number and timing of spillovers from the camel reservoir. This application highlights the multi-type population trajectorys ability to elucidate properties of the population which are not directly ancestral to its sampled members.

bioinformatics↗

bioRxiv2024

Improving inference in wastewater-based epidemiology by modelling the statistical features of digital PCR

Digital polymerase chain reaction (dPCR) is a powerful technique for quantifying gene targets in environmental samples, with various applications such as biodiversity monitoring and wastewater-based epidemiology. However, statistical analyses of environmental dPCR data often assume, explicitly or implicitly, that concentration measurements have a normal or log-normal error structure, which does not reflect the underlying partitioning statistics of dPCR. Using simulations and real-world environmental data, we show that (log-)normality assumptions are violated for dPCR measurements, leading to inaccurate estimates of gene concentrations and underlying biological processes. To enable reliable analyses of environmental dPCR data, we present a dPCR-specific likelihood model that accounts for concentration-dependent measurement noise and non-detects as characteristic of dPCR assays. We demonstrate that this approach overcomes biases in inference from environmental data, such as estimating free-eDNA decay in seawater or pathogen transmission from wastewater monitoring. Our method is implemented in the R packages "dPCRfit" for regression analyses and "EpiSewer" for wastewater surveillance.

bioinformatics↗

bioRxiv2024

Sciphy: A Bayesian phylogenetic framework using sequential genetic lineage tracing data.

CRISPR-based lineage tracing offers a promising avenue to decipher single cell lineage trees, especially in organisms that are challenging for microscopy. A recent advancement in this domain is lineage tracing based on sequential genome editing, which not only records genetic edits but also the order in which they occur. To capitalize on this enriched data, we introduce SciPhy, a simulation and inference tool integrated within the BEAST 2 framework. SciPhy utilizes a Bayesian phylogenetic approach to estimate time-scaled phylogenies and cell population parameters. After validating SciPhy using simulations, we apply it to two lineage tracing datasets for which we estimate time-scaled trees together with cell proliferation rates. Using simulated and real lineage tracing data obtained from a monoclonal culture of HEK293T cells, we compare SciPhy to other lineage reconstruction methods, and find that SciPhy consistently constructs distinct, and more accurate lineage trees. In particular, for HEK293T cells, SciPhy trees stand out for their later estimated cell division times. In addition, SciPhy reports uncertainty as well as proliferation rates, neither of which are available within a UPGMA analysis. Our second example applies SciPhy to the study of murine gastruloids, and showcases the use of complex models of time-varying population growth to capture realistic aspects of this in-vitro model of early mammalian development. Together, these examples showcase the application of advanced phylogenetic and phylodynamic tools to explore and quantify cell lineage trees, laying the groundwork for enhanced and confident analyses to decode the complexities of biological development in multicellular organisms. SciPhys codebase is publicly available at https://github.com/azwaans/SciPhy.

developmental biology↗

bioRxiv2024

Onset of infectiousness explains differences in transmissibility across Mycobacterium tuberculosis lineages

Mycobacterium tuberculosis complex (MTBC) lineages show substantial variability in virulence, but the epidemiological consequences of this variability have not been studied in detail. Here, we aimed for a lineage-specific epidemiological characterization by applying phylodynamic models to genomic data from different countries, representing the most abundant MTBC lineages. Our results show that all lineages are associated with similar durations and levels of infectiousness, resulting in similar reproductive numbers. However, L1 and L6 are associated with a delayed onset of infectiousness, leading to longer periods between subsequent transmission events. Together, our findings highlight the role of MTBC phylogenetic diversity in tuberculosis disease progression and transmission.

genomics↗