Biology subjects

Andreani, V.

Publications and source records attributed to Andreani, V..

7 recordsLinked to original sources

bioRxiv2026

The microbiome determines the phenotype in CTLA-4 insufficient mice and men

CTLA-4 (haplo)insufficiency displays incomplete penetrance and phenotypic heterogeneity, indicating the involvement of additional disease modifiers beyond the genetic defect. Microbiome analyses reveal a positive association between disease severity and intestinal dysbiosis, highlighting the microbiome as a critical contributor. To investigate this relationship mechanistically, we generated Ctla4/- wildlings harboring a natural microbiota. Unlike specific pathogen free (SPF) counterparts, which remain healthy, Ctla4/- wildlings spontaneously develop disease phenotypes resembling human CTLA-4 haploinsufficiency. Disease onset is followed by reduced microbial diversity and expansion of pathobionts. Integrative immunophenotyping shows that the natural microbiota synergizes with Ctla4 haploinsufficiency to reshape innate and adaptive immune compartments, generating a sustained pro-inflammatory milieu and reduced CTLA-4 expression in the cecum. Furthermore, microbiota-derived metabolites promote inflammatory cytokine production in both murine and human primary T cells via NF-{kappa}B activation. Collectively, Ctla4/- wildlings constitute an effective model for dissecting microbiome-immune crosstalk in CTLA-4 (haplo)insufficiency and for exploring therapeutic strategies.

immunology↗

bioRxiv2024

Evaluating the predictive power of combined gene expression dynamics from single cells on antibiotic survival

Heteroresistance can allow otherwise drug-susceptible bacteria to survive and resume growth after antibiotic exposure. This temporary form of antibiotic tolerance can be caused by the upregulation of stress response genes or a decrease in cell growth rate. However, it is not clear how expression of multiple genes contributes to the tolerance phenotype. By using fluorescent reporters for stress related genes, we conducted real time measurements of expression prior to, during, and after antibiotic exposure. We first identified relationships between growth rate and reporter levels based on auto and cross correlation analysis, revealing consistent patterns where changes in growth rate were anticorrelated with fluorescence following a delay. We then used pairs of stress gene reporters and time lapse fluorescence microcopy to measure the growth rate and reporter levels in cells that survived or died following antibiotic exposure. Using these data, we asked whether combined information about reporter expression and growth rate could improve our ability to predict whether a cell would survive or die following antibiotic exposure. We developed a Bayesian inference model to predict how the combination of dual reporter expression levels and growth rate impact ciprofloxacin survival in Escherichia coli. We found clear evidence of the impact of growth rate and the gadX promoter activity on survival. Unexpectedly, our results also revealed examples where additional information from multiple genes decreased prediction accuracy, highlighting an important and underappreciated effect that can occur when integrating data from multiple simultaneous measurements.

systems biology↗

bioRxiv2023

Generating information-dense nucleotide sequences with optimal string packing

Dense arrangements of binding sites within nucleotide sequences can collectively influence downstream transcription rates or initiate biomolecular interactions. For example, natural promoter regions can harbor many overlapping transcription factor binding sites that influence the rate of transcription initiation. Despite the prevalence of overlapping binding sites in nature, rapid design of nucleotide sequences with many overlapping sites remains a challenge. Here, we show that this is an NP-hard problem, coined here as the nucleotide String Packing Problem (SPP). We then introduce a computational technique that efficiently assembles sets of DNA-protein binding sites into dense, contiguous stretches of double-stranded DNA. For the efficient design of nucleotide sequences spanning hundreds of base pairs, we reduce the SPP to an Orienteering Problem with integer distances, and then leverage modern integer linear programming solvers. Our method optimally packs libraries of 20-100 binding sites into dense nucleotide arrays of 50-300 base pairs in 0.05-10 seconds. Unlike approximation algorithms or meta-heuristics, our approach finds provably optimal solutions. We demonstrate how our method can generate large sets of diverse sequences suitable for library generation, where the frequency of binding site usage across the returned sequences can be controlled by modulating the objective function. As an example, we then show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The nucleotide string packing approach we present can accelerate the design of sequences with complex DNA-protein interactions. When used in combination with synthesis and high-throughput screening, this design strategy could help interrogate how complex binding site arrangements impact either gene expression or biomolecular mechanisms in varied cellular contexts. Author SummaryThe way protein binding sites are arranged on DNA can control the regulation and transcription of downstream genes. Areas with a high concentration of binding sites can enable complex interplay between transcription factors, a feature that is exploited by natural promoters. However, designing synthetic promoters that contain dense arrangements of binding sites is a challenge. The task involves overlapping many binding sites, each typically about 10 nucleotides long, within a constrained sequence area, which becomes increasingly difficult as sequence length decreases, and binding site variety increases. We introduce an approach to design nucleotide sequences with optimally packed protein binding sites, which we call the nucleotide String Packing Problem (SPP). We show that the SPP can be solved efficiently using integer linear programming to identify the densest arrangements of binding sites for a specified sequence length. We show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The presented approach enables the rapid design and study of nucleotide sequences with complex, dense binding site architectures.

systems biology↗

bioRxiv2023

Comprehensive screening of a light-inducible split Cre recombinase with domain insertion profiling

Splitting proteins with light- or chemically-inducible dimers provides a mechanism for post-translational control of protein function. However, current methods for engineering stimulus-responsive split proteins often require significant protein engineering expertise and laborious screening of individual constructs. To address this challenge, we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out using sequencing. We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein. To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures. Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.

synthetic biology↗

bioRxiv2022

Mapping single-cell responses to population-level dynamics during antibiotic treatment

Treatment of sensitive bacteria with beta-lactam antibiotics often leads to two salient population-level features: a transient increase in total population biomass before a subsequent decline, and a linear correlation between growth and killing rates. However, it remains unclear how these population-level responses emerge from collective single-cell responses. During beta-lactam treatment, it is well recognized that individual cells often exhibit varying degrees of filamentation before lysis. We show that the probability of cell lysis increases with the extent of filamentation and that this dependence is characterized by unique parameters that are specific to bacterial strain, antibiotic dose, and growth condition. Modeling demonstrates how the single-cell lysis probabilities can give rise to population-level biomass dynamics, which were experimentally validated. This mapping provides insights into how the population biomass time-kill curve emerges from single cells and allows the representation of both single-and population-level responses with universal parameters.

microbiology↗

bioRxiv2021

A model-based approach to characterize enzyme-mediated response to antibiotic treatments: going beyond the SIR classification

Commensal and pathogenic E. coli strains are increasingly found to be resistant to {beta}-lactams, one of the most widely prescribed classes of antibiotics. Understanding escape to such treatments is complex since {beta}-lactams have several cellular targets and since several mechanisms might be involved in treatment escape in a combined manner. Surprisingly, the accumulated knowledge has not yet proven effective enough to predict the bacterial response to antibiotic treatments at both cellular and population levels with quantitative accuracy for -producing bacteria. Here, we propose a mathematical model that captures in a comprehensive way key phenomena happening at the molecular, cellular, and population levels, as well as their interactions. Our growth-fragmentation model gives a central role to cellular heterogeneity and filamentation as a way for cells to gain time until the degradation of the antibiotic by the {beta}-lactamases released by the dead cell population. Importantly, the model can account for the observed temporal evolution of the total (live and dead) biomass and of the live cell numbers for various antibiotic concentrations. To our knowledge, this is the first model able to quantitatively reconciliate these two classical views on cell death (OD and CFUs) for clinical isolates expressing extended-spectrum beta-lactamases (ESBL). Moreover, our model has a strong predictive power. When calibrated using a slight extension of OD-based data that we propose here, it can predict the CFU profiles in initial and delayed treatments despite inoculum effects, and suggest non-trivial optimal treatments. Generating quality data in quantity has been essential for model development and validation on non-model E. coli strains. We developed protocols to increase the reproducibility of growth kinetics assays and to increase the throughput of CFU assays.

biophysics↗

bioRxiv2018

Dissecting bacterial resistance and resilience in antibiotic responses

An essential property of microbial communities is the ability to survive a disturbance. Survival can be achieved through resistance, the ability to absorb effects of a disturbance without a significant change, or resilience, the ability to recover after being perturbed by a disturbance. These concepts have long been applied to the analysis of ecological systems, though their interpretations are often subject to debate. Here we show that this framework readily lends itself to the dissection of the bacterial response to antibiotic treatment, where both terms can be unambiguously defined. The ability to tolerate the antibiotic treatment in the short term corresponds to resistance, which primarily depends on traits associated with individual cells. In contrast, the ability to recover after being perturbed by an antibiotic corresponds to resilience, which primarily depends on traits associated with the population. This framework effectively reveals the phenotypic signatures of bacterial pathogens expressing extended spectrum {beta}-lactamases (ESBLs), when treated by a {beta}-lactam antibiotic. Our analysis has implications for optimizing treatment of these pathogens using a combination of a {beta}-lactam and a {beta}-lactamase (Bla) inhibitor. In particular, our results underscore the need to dynamically optimize combination treatments based on the quantitative features of the bacterial response to the antibiotic or the Bla inhibitor.

cell biology↗