Biology subjects

Pacheco, A. R.

Publications and source records attributed to Pacheco, A. R..

6 recordsLinked to original sources

bioRxiv2024

Dimalis: A complete standalone pipeline to analyse prokaryotic cell growth from time-lapse imaging

Real-time imaging of bacterial cell division, population growth and behaviour is essential for our understanding of microbial-catalyzed processes at the microscale. However, despite the relative ease by which high resolution imaging data can be acquired, the extraction of relevant cell features from images remains cumbersome. Here we present a versatile pipeline for automated extraction of bacterial cell features from standalone or time-resolved image series, with standardized data output for easy downstream processing. The input consist of phase-contrast images with or without additional fluorescence details, which are denoised to account for potential out-of-focus regions, and segmented to outline the morphologies of individual cells. Cells are then tracked over subsequent time frame images to provide genealogy or microcolony spatial information. We test the pipeline with eight different bacterial strains, cultured in microfluidics systems with or without nutrient flow, or on agarose miniature surfaces to follow microcolony growth. Examples of downstream processing in form of extraction of growth kinetic parameters or bistable cell differentiation are provided. The pipeline is wrapped in a Docker to facilitate installation, consistent processing and avoiding constant software updates.

microbiology↗

bioRxiv2022

First-in-human evaluation of cutaneous innate and adaptive immunomodulation by mosquito bites

Mosquito-borne viruses are a growing global threat. Initial viral inoculation occurs in the skin via the mosquito bite, eliciting immune responses that shape the establishment of infection and pathogenesis. We aimed to cutaneous innate and adaptive immune responses to mosquito bites in individuals from endemic areas. In this single-arm, cross-sectional interventional study, we enrolled 30 healthy adult participants aged 18 to 45 years of age from Cambodia between October 2020 and January 2021. We performed 3-mm skin biopsies at baseline as well as 30 minutes, 4 hours, and 48 hours after a controlled feeding by uninfected Aedes aegypti mosquitos. The primary endpoints were measurement of changes in early and late innate responses in bitten vs unbitten skin by gene expression profiling, immunophenotyping, and cytokine profiling. Results revealed induction of neutrophil degranulation and recruitment of skin-resident dendritic cells and M2-macrophages in bitten skin. As the immune reaction progressed over time, T cell priming and regulatory pathways were upregulated along with a shift to a Th2-driven response and CD8+ T cell activation. In accordance, participants bitten skin cells produced less pro-inflammatory cytokines when stimulated by Ae. aegypti salivary gland extract. No unexpected adverse events occurred, and one patient was lost to follow-up at Day 14. These results identify key immune genes, cell types, and pathways in the human response to mosquito bites that can be leveraged to develop novel therapeutics and vector-targeted vaccine candidates to arboviral diseases. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/489145v2_ufig1.gif" ALT="Figure 1"> View larger version (77K): org.highwire.dtl.DTLVardef@1ba2eaaorg.highwire.dtl.DTLVardef@4d9dd6org.highwire.dtl.DTLVardef@1b54dc8org.highwire.dtl.DTLVardef@5a104_HPS_FORMAT_FIGEXP M_FIG Panel shows evolution of the human skin response to Aedes aegypti bites over time. C_FIG

immunology↗

bioRxiv2020

An evolutionary algorithm for designing microbial communities via environmental modification

Despite a growing understanding of how environmental composition affects microbial community properties, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbor thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and dynamic flux balance analysis (dFBA) that selects optimal environmental compositions to produce target community phenotypes. In this framework, dFBA is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behavior of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to more closely approach this target. We apply this iterative process to in silico communities of varying sizes, showing how it can rapidly identify environments that yield desired phenotypes. Moreover, this novel combination of approaches produces testable predictions for the in vivo assembly of microbial communities with specific properties, and can facilitate rational environmental design processes for complex microbiomes.

bioinformatics↗

bioRxiv2020

Stoichiometric Modelling of String Chemistries

Uncovering the general principles that govern the architecture of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries, in silico representations of chemical reaction networks arising from a defined set of mathematical rules, can help address this challenge by enabling the exploration of alternative chemical universes and the possible metabolic networks that could emerge within them. Here we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We study string chemistries using tools borrowed from the field of stoichiometric constraint-based modeling of organismal metabolic networks, through a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET). In addition to exploring the complexity and connectivity properties of different string chemistries, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental molecules within the string chemistry framework. We found that the identities of the metabolites in the biomass reaction wield much more influence over the structure of the minimal metabolic networks than the identities of the nutrient metabolites -- a notion that could help us better understand the rise and evolution of biochemical organization. Our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.

bioinformatics↗

bioRxiv2018

CRISPR screen reveals that EHEC’s T3SS and Shiga toxin rely on shared host factors for infection

Enterohemorrhagic Escherichia coli (EHEC) has two critical virulence factors - a type III secretion system (T3SS) and Shiga toxins (Stx) - that are required for the pathogen to colonize the intestine and cause diarrheal disease. Here, we carried out a genome-wide CRISPR/Cas9 loss-of-function screen to identify host loci that facilitate EHEC infection of intestinal epithelial cells. Many of the guide RNAs identified targeted loci known to be associated with sphingolipid biosynthesis, particularly for production of globotriaosylceramide (Gb3), the Stx receptor. Two loci (TM9SF2 and LAPTM4A) with largely unknown functions were also targeted. Mutations in these loci not only rescued cells from Stx-mediated cell death, but also prevented cytotoxicity associated with the EHEC T3SS. These mutations interfered with early events associated with T3SS and Stx pathogenicity, markedly reducing entry of T3SS effectors into host cells and binding of Stx. The convergence of Stx and T3SS onto overlapping host targets provides guidance for design of new host-directed therapeutic agents to counter EHEC infection.\n\nImportanceEnterohemorrhagic Escherichia coli (EHEC) has two critical virulence factors - a type III secretion system (T3SS) and Shiga toxins (Stx) - that are required for colonizing the intestine and causing diarrheal disease. We screened a genome-wide collection of CRISPR mutants derived from intestinal epithelial cells and identified mutants with enhanced survival following EHEC infection. Many had mutations that disrupted synthesis of a subset of lipids (sphingolipids) that includes the Stx receptor globotriaosylceramide (Gb3), and hence protect against Stx intoxication. Unexpectedly, we found that sphingolipids also mediate early events associated with T3SS pathogenicity. Since antibiotics are contraindicated for the treatment of EHEC, therapeutics targeting sphingolipid biosynthesis are a promising alternative, as they could provide protection against both of the pathogens key virulence factors.

microbiology↗

bioRxiv2018

Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems

Metabolic exchange can mediate beneficial interactions among microbes, helping explain diversity in microbial communities. These interactions are often assumed to involve a fitness cost, prompting questions on how cooperative phenotypes can be stable and withstand the emergence of cheaters. Here we use genome-scale models of metabolism to investigate whether a radically different scenario, the pervasive release of \"costless\" metabolites (i.e. those that cause no fitness cost to the producing organism), can serve as a prominent mechanism for inter-microbial interactions. By carrying out over 1 million pairwise growth simulations for 14 microbial species in a combinatorial assortment of environmental conditions, we find that there is indeed a large space of metabolites that can be secreted at no cost, which can generate ample cross-feeding opportunities. In addition to providing an atlas of putative costless interdependencies, our modeling also demonstrates that oxygen availability significantly enhances mutualistic interactions by providing more opportunities for metabolic exchange through costless metabolites, resulting in an over-representation of specific ecological network motifs. In addition to helping explain natural diversity, we show how the exchange of costless metabolites can facilitate the engineering of stable synthetic microbial consortia.

systems biology↗