Biology subjects

Lanzas, C.

Publications and source records attributed to Lanzas, C..

7 recordsLinked to original sources

bioRxiv2026

Scalable phylodynamic inference of mutational and epistatic fitness effects with multi-feature birth-death models

Fitness is a complex phenotype determined by many different genomic features, their interactions with each other and the environment. For rapidly evolving microbes like viruses and bacteria, phylodynamic multi-feature birth-death (MFBD) models provide a way to infer the fitness of lineages with different genotypes from reconstructed phylogenies. However, existing phylodynamic methods assume very simple additive or multiplicative genotype-to-fitness (G-to-F) mappings that typically ignore epistatic interactions among features. We developed a gradient-based Bayesian MFBD framework for estimating the fitness effects of multiple features while allowing for both additive and epistatic G-to-F maps. The framework uses Hamiltonian Monte Carlo for posterior inference and incorporates shrinkage priors to regularize weakly supported fitness effects without cross-validation. In simulations, Bayesian inference performs well at recovering additive fitness effects, with accuracy and precision improving consistently with increasing tree size. While epistatic effects are more difficult to estimate because they depend on the co-occurrence of mutations, shrinkage improves estimation when interaction effects are weak or sparsely supported. Simulations with misspecified additive models show that ignoring moderate or strong epistasis can bias estimates of individual mutational effects. We applied the framework to a time-calibrated \textit{Escherichia coli} ST131 phylogeny with 1763 samples and estimated fitness effects for several mutations in the quinolone resistance-determining region (QRDR). These results show that Bayesian phylodynamic inference can be used to learn the genomic determinants of fitness while quantifying uncertainty in mutational and epistatic fitness effects.

evolutionary biology↗

bioRxiv2025

Risk factors for fluoroquinolone- and macrolide-resistance among swine Campylobacter coli using multi-layered chain graphs

Campylobacter spp. resistant to fluoroquinolones and macrolides are serious public health threats. Studies aiming to identify risk factors for drug-resistant Campylobacter have narrowly focused on antimicrobial use at the farm level. Using chain graphs, we quantified risk factors for fluoroquinolones- and macrolide-resistance in Campylobacter coli isolated from two distinctive swine production systems, conventional and antibiotic-free (ABF). The chain graphs were learned using genotypic and phenotypic resistance data from 1082 isolates and host exposures obtained through surveys for 18 cohorts of pigs. The gyrA T86I point mutation alone explained at least 58 % of the variance in ciprofloxacin minimum inhibitory concentration (MIC) for ABF and 79 % in conventional farms. For macrolides, genotype and host exposures explained similar variance in azithromycin and erythromycin MIC. Among host exposures, heavy metal exposures were identified as risk factors in both conventional and ABF. Chain graph models can generate insights into the complex epidemiology of antimicrobial resistance by characterizing context-specific risk factors and facilitating causal discovery. Author summaryAntimicrobial resistance is influenced by multiple factors, including exposures to selecting agents, such as antibiotics, antiseptics, or heavy metals, and factors affecting the transmission of resistant pathogens, such as biosecurity and hygiene measures. Understanding what specific factors are associated with resistance in a given context is challenging. We developed an approach based on probabilistic graphical models to investigate context-specific antimicrobial resistance risk factors. We applied the approach to Campylobacter coli isolated from pigs in antibiotic-free and conventional farms. We demonstrated how for fluoroquinolones, risk factors were similar across both types of farms, but risk factors for macrolides were different across settings.

microbiology↗

bioRxiv2025

Influence of Sequencing Technology on Pangenome-level Analysis and Detection of Antimicrobial Resistance Genes in ESKAPE Pathogens

As sequencing costs decrease, short-read and long-read technologies are indispensable tools for uncovering the genetic drivers behind bacterial pathogen resistance. This study explores the differences between the use of short-read (Illumina) and long-read (Oxford Nanopore Technologies, ONT) sequencing in detecting antimicrobial resistance (AMR) genes in ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter cloacae). Utilizing a dataset of 1,385 whole genome sequences and applying commonly used bioinformatic methods in bacterial genomics, we assessed the differences in genomic completeness, pangenome structure, and AMR gene and point mutation identification. Illumina presented higher genome completeness, while ONT identified a broader pangenome. Hybrid assembly outperformed both Illumina and ONT at identifying key AMR genetic determinants, presented results closer to Illuminas completeness, and revealed ONT-like pangenomic content. Notably, Illumina consistently detected more AMR-related point mutations than its counterparts. This highlights the importance of method selection based on research goals. Differences were also observed for specific gene classes and bacterial species, underscoring the need for a nuanced understanding of technology limitations. Overall, this study reveals the strengths and limitations of each approach, advocating for the use of Illumina for common AMR analysis; ONT for studying complex genomes and novel species, and hybrid assembly for a more comprehensive characterization, leveraging the benefits of both technologies. Impact StatementThis study provides a comprehensive comparison of short-read (Illumina) and long-read (Oxford Nanopore Technologies, ONT) sequencing technologies in the context of antimicrobial resistance (AMR) detection in ESKAPE pathogens. By analyzing a large dataset of 1,385 whole genome sequences, the research offers valuable insights into the strengths and limitations of each approach, as well as the benefits of the novel approach of hybrid assembly. These findings have broad utility across microbiology, genomics, and infectious disease research. In particular, they apply to the work of researchers and clinicians dealing with AMR surveillance, investigation into outbreaks, and bacterial genome analysis. Given the nuance with which technological differences in genomic completeness, pangenome structure, and AMR determinant detection have been explored in this study, it is a good basis for informed method selection for future research. While the output represents an incremental advance, its significance lies in its practical implications. It thus enables researchers to take more reasonable decisions in designing genomic studies of bacterial pathogens by showing the complementarity of various sequencing approaches and their specific strengths. This could lead to more accurate and comprehensive detection of AMR, which would contribute ultimately to improved antibiotic stewardship and public health strategies. Data SummaryThe authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. RepositoriesAll the sequences used for this study are publicly accessible from GenBank, and their individual IDs are disclosed in Supplementary Table 1.

genomics↗

bioRxiv2024

Quantifying the genomic determinants of fitness in E. coli ST131 using phylodynamics

Antimicrobial resistant pathogens such as Escherichia coli sequence type 131 (ST131) pose a serious threat to public health globally. In the United States, ST131 acquired multiple antimicrobial resistance (AMR) genes and rapidly grew to its current high prevalence in healthcare settings. Notably, this coincided with the introduction and widespread use of antibiotics such as fluoroquinolones, suggesting AMR as the major driver of ST131s expansion. Yet, within ST131, there remains considerable diversity between strains in resistance profiles and their repertoires of virulence factors, stress factors, plasmids, and other accessory elements. Understanding which genomic features contribute to ST131s competitive advantage and their relative effects on population-level fitness therefore poses a considerable challenge. Here we use phylodynamic birth-death models to estimate the relative fitness of different ST131 lineages from bacterial phylogenies. By extending these phylodynamic methods to allow multiple genomic features to shape bacterial fitness, we further quantify the relative contribution of individual AMR genes to ST131s fitness. Our analysis indicates that while many genomic elements, including various AMR genes, virulence factors, and plasmids, have all contributed substantially to ST131s rapid growth, major increases in ST131s fitness are largely attributable to mutations in gyrase A that confer resistance to fluoroquinolones. Author summaryST131 is a pandemic lineage of E. coli that has spread globally and is now responsible for a large percentage of blood and urinary tract infections that cannot be treated with many common antibiotics. While antibiotic resistance has undoubtedly given ST131 a competitive edge, the relative importance of resistance compared with other factors shaping a pathogens growth or transmission potential (i.e. fitness) is often difficult to measure in natural settings. Here, we present a method that allows us to look at the entire spectrum of factors determining a pathogens fitness and estimate the individual contribution of each component to pathogens overall fitness. Our results suggest that resistance to fluoroquinolones, a widely used class of antibiotics, provides ST131 with a disproportionately large fitness advantage relative to many other factors with more moderate fitness effects. Understanding what determines the fitness of ST131 therefore provides insights that can be used to curb the spread of resistance and monitor for emerging lineages with high pandemic potential due to shared fitness enhancing attributes.

evolutionary biology↗

bioRxiv2023

Distilling Mechanistic Models From Multi-Omics Data

High-dimensional multi-omics data sets are increasingly accessible and now routinely being generated as part of medical and biological experiments. However, the ability to infer mechanisms of these data remains low due to the abundance of confounding data. The gap between data generation and interpretation highlights the need for strategies to harmonize and distill complex multi-omics data sets into concise, mechanistic descriptions. To this end, a four-step analysis approach for multiomics data is herein demonstrated, comprising: filling missing data and harmonizing data sources, inducing sparsity, developing mechanistic models, and interpretation. This strategy is employed to generate a parsimonious mechanistic model from high-dimensional transcriptomics and metabolomics data collected from a murine model of Clostridioides difficile infection. This approach highlighted the role of the Stickland reactor in the production of toxins during infection, in agreement with recent literature. The methodology present here is demonstrated to be feasible for interpreting multi-omics data sets and it, to the authors knowledge, one of the first reports of a successful implementation of such a strategy.

systems biology↗

bioRxiv2022

Extensions of mean-field approximations for environmentally-transmitted pathogen networks

Many pathogens spread via environmental transmission, without requiring host- to-host direct contact. While models for environmental transmission exist, many are simply constructed intuitively with structures analogous to standard models for direct transmission. As model insights are generally sensitive to the underlying model assumptions, it is important that we are able understand the details and consequences of these assumptions. We construct a simple network model for an environmentally-transmitted pathogen and rigorously derive systems of ordinary differential equations (ODEs) based on different assumptions. We explore two key assumptions, namely homogeneity and independence, and demonstrate that relaxing these assumptions can lead to more accurate ODE approximations. We compare these ODE models to a stochastic implementation of the network model over a variety of parameters and network structures, demonstrating that with fewer restrictive assumptions we are able to achieve higher accuracy in our approximations and highlighting more precisely the errors produced by each assumption. We show that less restrictive assumptions lead to more complicated systems of ODEs and the potential for unstable solutions. Due to the rigour of our derivation, we are able to identify the reason behind these errors and propose potential resolutions.

microbiology↗

bioRxiv2021

Modeling the role of mortality-based response triggers on the effectiveness of African swine fever control strategies

African swine fever (ASF) is considered the most impactful transboundary swine disease. In the absence of effective vaccines, control strategies are heavily dependent on mass depopulation and movement restrictions. Here we developed a nested multiscale model for the transmission of ASF, combining spatially explicit network model of animal movements with a deterministic compartmental model for the dynamics of two ASF strains within-pixels of 3 km x 3 km, amongst the pig population in one Brazilian state. The model outcomes are epidemic duration, number of secondary infected farms and pigs, and distance of ASF spread. The model also shows the spatial distribution of ASF epidemics. We analyzed quarantine-based control interventions in the context of mortality trigger thresholds for the deployment of control strategies. The mean epidemic duration of a moderately virulent strain was 11.2 days assuming the first infection is detected (best-case scenario) and 15.9 days when detection is triggered at 10 % mortality. For a highly virulent strain, the epidemic duration was 6.5 days and 13.1 days, respectively. The distance from the source to infected locations and the spatial distribution was not dependent on strain virulence. Under the best-case scenario, we projected an average number of infected farms of 23.77 farms and 18.8 farms for the moderate and highly virulent strains, respectively. At 10% mortality-trigger, the predicted number of infected farms was on average 46.27 farms and 42.96 farms, respectively. We also demonstrated that the establishment of ring quarantine zones regardless of size (i.e., 5 km, 15 km) was outperformed by backward animal movement tracking. The proposed modeling framework provides an evaluation of ASF epidemic potential, providing a ranking of quarantine-based control strategies that could assist animal health authorities in planning the national preparedness and response plan.

ecology↗