Biology subjects

Pinheiro, F.

Publications and source records attributed to Pinheiro, F..

8 recordsLinked to original sources

bioRxiv2026

No-code microbial growth phenotyping with GUIbiont

Microbial growth screens generate thousands of curves, but cross-experiment comparison and mapping growth phenotypes to genotypes or environments routinely require custom code. GUIbiont is a no-code browser application for quality control, curve fitting, clustering and metadata-linked analysis with machine learning techniques. Interactive sessions export as Julia scripts, allowing users to reproduce or extend browser analyses. Validated across 3,885 E. coli deletion strains and 13,608 defined-media curves, GUIbiont recovered known auxotrophic and nutrient-dependent phenotypes.

microbiology↗

bioRxiv2026

Global analysis of thermal and chemical denaturation using CheMelt: Thermodynamic dissection of highly thermostable de novo designed proteins

De novo protein design often produces thermostable proteins that denature above 100 {degrees}C, which complicates the analysis of their stability. Thermostable proteins can be unfolded by combined chemical and thermal denaturation followed by global analysis of multiple melting curves. Here, we have developed CheMelt, a new online tool for global analysis of unfolding data via an intuitive graphical user interface. We use nanoscale differential scanning fluorimetry followed by CheMelt data analysis to dissect the combined thermal and chemical denaturation of thirty-five de novo designed protein binders. Thirteen present sufficient fluorescence changes to extract thermodynamic parameters of unfolding. These de novo designed proteins have systematically lower {Delta}Cp and m-values than comparable natural proteins. We show that a high thermostability of a designed protein does not necessarily imply a high equilibrium stability, and demonstrate the potential of CheMelt in dissecting thermodynamic properties for protein design and engineering. CheMelt can be accessed at https://spc.embl-hamburg.de/app/chemelt

biophysics↗

bioRxiv2025

Screening de novo designed protein binders in unpurified lysate using flow induced dispersion analysis

Computational protein design can create binders against targets of interest, but identifying binders with sufficient affinity still requires biochemical screening of many designs. In this work, we test flow-induced dispersion analysis (FIDA) as a method for screening binders in a time and cost-effective manner. FIDA uses Taylor dispersion analysis to determine the hydrodynamic radius of fluorescently labelled biomolecules and their complexes. Here, we use FIDA to assess the binding of RFdiffusion-designed protein binders against the small helical peptide ALFA-tag and the GK domain of PSD-95. Successful binders can be identified in a single measurement using heat-treated bacterial lysates allowing rapid identification of binders with high affinity, and thermostability. Subsequent titration experiments show micromolar affinities for ALFA-tag binders and nanomolar affinities for GK domain binders. The lack of immobilization, the minimal sample volume, and the compatibility with complex biological samples, positions FIDA as a valuable tool for the screening and characterization of computationally generated protein binders.

biophysics↗

bioRxiv2024

Kinbiont: From time series to ecological and evolutionary responses in microbial systems

Microbial behavior is quantitatively characterized by observables inferred from kinetics experiments. Growth rate and biomass yield, for example, are used to map response patterns across different conditions including antibiotic growth inhibition and yield dependence on substrate. As microbial kinetics datasets grow, there is immense potential to advance our understanding of ecological and evolutionary processes. But how can we turn these data into actionable insights about microbial responses? Here we introduce Kinbiont - an ecosystem of numerical methods integrating advanced ordinary differential equation solvers, non-linear optimization, signal processing, and interpretable machine learning algorithms. Kinbiont offers a model-based data analysis pipeline covering all aspects of microbial kinetics, from pre-processing to result interpretation. We demonstrate Kinbionts performance using synthetic and real datasets, including bacterial growth, diauxic curves, phage-bacteria co-cultures, and ecotoxicological responses. Kinbiont can aid biological discovery through data-driven generation of hypotheses that can be tested in targeted experiments.

microbiology↗

bioRxiv2024

VPS34-IN1 inhibits cap-mediated translation and synergizes with STING to drive type-I IFN expression in human plasmacytoid DCs

Inhibition of the phosphatidylinositol kinase vacuolar protein sorting 34 (VPS34) with the pharmacological compound VPS34-IN1 has a range of effects on the dynamics of endosomes. While VPS34 inhibition has been suggested as a potential therapeutic approach for treating certain cancers, our findings indicate that it has minimal cytotoxic effects on leukemic blastic plasmacytoid dendritic cell neoplasms (BPDCN). VPS34-IN1, however, interferes with plasmacytoid dendritic cells (pDCs) function by blocking the recruitment of serum and glucocorticoid-regulated kinase 3 (SGK3) to endosomes, which is shown to be necessary for Toll-like receptor 7 (TLR7) signaling. In a contrasting parallel, VPS34-IN1 triggers the activation of the stimulator of interferon genes (STING) and significantly enhances pDCs response to the STING agonist 23-cyclic guanosine monophosphate-adenosine monophosphate (23-cGAMP). This cooperative action with VPS34-IN1 leads to strongly increased expression of type-I interferons (IFNs), associated with an alteration of STING degradation and importantly, inhibition of cap-mediated mRNA translation. Inhibition of protein synthesis by VPS34-IN1 appears to be central to this synergy with STING activation, notably by compromising the expression of IFIT1/ISG56, a negative regulator of innate signaling. Thus, despite their limited toxicity towards different cancer lines, inhibitors targeting VPS34 and SGK3 may present promising compounds for controlling the expression of type-I IFNs in response to various microbial stimuli and pathological contexts. One-sentence summaryPharmacological inhibition of VPS34 affects multiple signaling pathways downstream of innate immunity receptors and consequently can inhibit or potentiate type-I Interferon induction according to the danger or microbial stimuli received by plasmacytoid DCs.

immunology↗

bioRxiv2024

Resource allocation in biochemically structured metabolic networks

Microbes tune their metabolism to environmental challenges by changing protein expression levels, metabolite concentrations, and reaction rates simultaneously. Here, we establish an analytical model for microbial resource allocation that integrates enzyme biochemistry and the global architecture of metabolic networks. We describe the production of protein biomass from external nutrients in pathways of Michaelis-Menten enzymes and compute the resource allocation that maximizes growth under constraints of mass conservation and metabolite dilution by cell growth. This model predicts generic patterns of growth-dependent microbial resource allocation to proteome and metabolome. In a nutrient-rich medium, optimal protein expression depends primarily on the biochemistry of individual synthesis steps, while metabolite concentrations and fluxes decrease along successive reactions in a metabolic pathway. Under nutrient limitation, individual protein expression levels change linearly with growth rate, the direction of change depending again on the enzymes biochemistry. Metabolite levels and fluxes show a stronger, nonlinear decline with growth rate. We identify a simple, metabolite-based regulatory logic by which cells can be tuned to near-optimal growth. Finally, our model predicts evolutionary stable states of metabolic networks, including local biochemical parameters and the global metabolite mass fraction, in tune with empirical data.

systems biology↗

bioRxiv2020

Predicting trajectories and mechanisms of antibiotic resistance evolution

Bacteria evolve resistance to antibiotics by a multitude of mechanisms. A central, yet unsolved question is how resistance evolution affects cell growth at different drug levels. Here we develop a fitness model that predicts growth rates of common resistance mutants from their effects on cell metabolism. We map metabolic effects of resistance mutations in drug-free environments and under drug challenge; the resulting fitness trade-off defines a Pareto surface of resistance evolution. We predict evolutionary trajectories of dosage-dependent growth rates and resistance levels, as well as the prevalent resistance mechanism depending on drug and nutrient levels. These predictions are confirmed by empirical growth curves and genomic data of E. coli populations. Our results show that resistance evolution, by coupling major metabolic pathways, is strongly intertwined with systems biology and ecology of microbial populations.

evolutionary biology↗

bioRxiv2020

Adaptive evolution of hybrid bacteria by horizontal gene transfer

Horizontal gene transfer is an important factor in bacterial evolution that can act across species boundaries. Yet, we know little about rate and genomic targets of cross-lineage gene transfer, and about its effects on the recipient organisms physiology and fitness. Here, we address these questions in a parallel evolution experiment with two Bacillus subtilis lineages of 7% sequence divergence. We observe rapid evolution of hybrid organisms: gene transfer swaps ~12% of the core genome in just 200 generations, and 60% of core genes are replaced in at least one population. By genomics, transcriptomics, fitness assays, and statistical modeling, we show that transfer generates adaptive evolution and functional alterations in hybrids. Specifically, our experiments reveal a strong, repeatable fitness increase of evolved populations in the stationary growth phase. By genomic analysis of the transfer statistics across replicate populations, we infer that selection on HGT has a broad genetic basis: 40% of the observed transfers are adaptive. At the level of functional gene networks, we find signatures of negative and positive selection, consistent with hybrid incompatibilities and adaptive evolution of network functions. Our results suggest that gene transfer navigates a complex cross-lineage fitness landscape, bridging epistatic barriers along multiple high-fitness paths. Significance statementIn a parallel evolution experiment, we probe lateral gene transfer between two Bacillus subtilis lineages close to the species boundary. We show that laboratory evolution by horizontal gene transfer can rapidly generate hybrid organisms with broad genomic and functional alterations. By combining genomics, transcriptomics, fitness assays and statistical modeling, we map the selective effects underlying gene transfer. We show that transfer takes place under genome-wide positive and negative selection, generating a net fitness increase in hybrids. The evolutionary dynamics efficiently navigates this fitness landscape, finding viable paths with increasing fraction of transferred genes.

evolutionary biology↗