Biology subjects

Ferreira, C.

Publications and source records attributed to Ferreira, C..

10 recordsLinked to original sources

bioRxiv2026

Nanoscale identification of organic pigments in biogenic calcite by atom probe tomography

Spatially-resolved mass spectrometry could contribute to the understanding of biological processes. Atom probe tomography (APT) is a technique which could, in principle, fill an analytical gap for nanoscale mass spectrometry. Yet, because of a limited understanding of the process of fragmentation of biomolecules, biological or biochemical interpretation of the obtained data has so far not been successful. Here, we report on analyses of synthetic and biogenic pigmented calcite minerals. The purple coloration of Sphaerechinus granularis sea urchin spines is due to the occlusion of polyhydroxylated naphthoquinone (PHNQ) molecules, mainly spinochrome A, within the calcite phase. Peaks relating to these pigments as well as to other intra-crystalline macromolecules were identified by APT analysis of the purple spine region with comparison to measurements of synthetic calcite single crystals, calcite crystals doped with PHNQ, and the uncoloured (white) region of sea urchin spines. To facilitate the molecular identification of peaks appearing solely in purple-coloured spines, which are possible fragments from PHNQ, we introduce novel APT data filtering techniques based on spatial and temporal correlations in the detection of ions of targeted mass-to-charge state ratios. This allows us to discuss the fragmentation of organic molecules under field conditions and improve the identification of peaks in the mass spectrum, while also determining nanoscale spatial distribution of fragments related to specifically targeted biomolecules in 3D and elucidate the relationship these organic pigment molecules have to the calcite lattice and inorganic elements such as magnesium. The study aims to improve identification of complex molecular structures by APT, including organic molecules, with the potential ability to provide structural information at a near-atomic scale for a range of materials.

biophysics↗

bioRxiv2026

Transplantation reveals birthdate-dependent post-mitotic competence in cortical neurons

Cortical glutamatergic neurons are generated through temporally ordered developmental programs that link neuronal birthdate to laminar position and identity. Yet, because differentiation continues after cell-cycle exit, it remains unclear how far post-mitotic identity can be reshaped by the environment and whether this residual competence varies across neuronal types. Here, we used transplantation to uncouple birthdate from final laminar position by placing newborn neurons in ectopic laminae in the postnatal mouse cortex. E13-born (i.e. deep layer-destined) and E15-born (i.e. superficial layer-destined) donor neurons displayed distinct birthdate-associated molecular programs before transplantation. After grafting into P0 hosts, both populations migrated and settled across cortical layers, enabling comparison across laminar environments. We simultaneously profiled transcriptomic, morphological and electrophysiological neuronal identities using Patch-seq. Transplanted neurons developed pyramidal morphologies and active membrane properties, but their final identity was not imposed by their laminar position. Thus, the postnatal cortex supports integration and maturation but does not impose canonical laminar identity. Critically, neuronal competence was asymmetric across birthdates. E15-born neurons remained committed toward a robust superficial layer neuron-like profile independently of cortical position, whereas E13-born neurons retained a broad, non-canonical differentiation rather than either converging on a deep- or superficial-layer reference state. This is consistent with the wider intrinsic repertoire of early-born neurons, which the postnatal environment does not resolve. Thus, post-mitotic identity is constrained by developmental history in a birthdate-dependent manner, with early- and late-born neurons retaining fundamentally different differentiation potentials after cell-cycle exit.

neuroscience↗

bioRxiv2026

Hybrid modelling and transfer learning for Bayesian optimisation of yeast protein production from food waste substrates

Food production is a significant contributor to global greenhouse gas emissions and deforestation, exacerbated by substantial food waste. Converting food waste into yeast protein offers a sustainable solution to enhance food security and contribute to a circular economy. However, due to the diverse and variable nature of food waste substrates, numerous experimental trials are required to optimise the preprocessing steps, yeast strain selection, nutrient addition, and fermentation conditions. This study presents a hybrid modelling approach where data-driven machine learning is used to predict microbial growth kinetics from process parameters. The hybrid model was trained on a comprehensive dataset consisting of 963 fermentation experiments from 55 publications, enabling transfer learning across 46 yeast strains and 79 food waste substrates. The hybrid modelling method was integrated with Bayesian optimisation, a sequential strategy to optimise expensive-to-evaluate functions, to efficiently maximise yeast biomass growth from different food waste substrates. The utility of the hybrid model was evaluated using five test datasets selected from previous literature and was shown to facilitate an average reduction of 66% in the number of experimental trials required to identify optimal fermentation conditions compared to without using the hybrid model. This proved that the transfer of knowledge between yeast strains and food wastes improved the optimisation efficiency of real, previously published datasets compared to traditional optimisation methods. The novelty and contributions of this study include the collation of the extensive dataset, provided as supplementary material; and the demonstration that transfer learning by training the hybrid model on this heterogeneous dataset can improve the optimisation efficiency for yeast biomass growth on new strains and substrates.

microbiology↗

bioRxiv2026

Host cell traversal by Plasmodium parasites is essential for sterilizing hepatic cellular immunity.

An effective vaccine capable of inducing sterile protection against Plasmodium, the causative agent of malaria, is critical to aid global eradication. Whole-organism vaccines using liver-infective sporozoites provide high levels of sterile protection against pre-erythrocytic infection. Yet, determinants of sporozoite immunogenicity remain poorly characterized. Using rodent models of vaccination, we demonstrate that the ability of Plasmodium sporozoites to actively migrate through multiple host cells prior to infecting hepatocytes is required for sterilizing immunity, regardless of the time of intrahepatic development of immunizing parasites. We further establish that host cell traversal is sufficient to trigger robust protection against Plasmodium hepatic infection. Impaired cell traversal precludes protective liver-resident memory CD8 T cell responses following vaccination, but not the production of anti-plasmodial antibodies. Our findings challenge the prevailing notion that intrahepatic parasite development is the sole determinant of whole-sporozoite vaccination-induced protection, and highlight parasite behavior traits as critical immunogenic events shaping sterilizing cellular immunity against Plasmodium liver stages.

immunology↗

bioRxiv2026

Progressive bilateral recruitment and resilient network reorganization during temporal lobe epilepsy

BackgroundTemporal lobe epilepsy (TLE) often originates from focal hippocampal injury but progressively evolves into a bilateral epileptic network engaging both hippocampi and distributed cortical regions. A mechanistic understanding of how this network emerges, and whether early perturbation of specific nodes can alter its trajectory, is essential for developing network-level therapeutic strategies. ObjectiveWe used a kainate-induced rodent model of TLE to (1) characterize the spatiotemporal emergence of epileptic discharges during the latent phase, (2) determine how bilaterally synchronized events develop, and (3) test whether transient chemogenetic silencing of either the ipsilateral epileptogenic focus (EF) or the contralateral hippocampus (CH) modifies large-scale epileptogenesis. MethodsFreely moving mice were implanted with multi-site electrodes spanning bilateral hippocampal subfields (dentate gyrus, CA1, subiculum) and cortical regions (M2, Cg1, PrL, V1, entorhinal cortex). Longitudinal LFP recordings were performed every other day during the latent and early chronic phases following KA or saline injection. DREADD-based chemogenetic inhibition of glutamatergic neurons was applied between days 2-7 post-KA. Epileptiform events were quantified via spike rates, waveform metrics, high-frequency oscillations (HFOs), and short-latency interregional co-spiking ResultsEarly after KA, epileptic spiking emerged locally in the ipsilateral dentate gyrus and progressively organized into HFO-coupled discharges. Contralateral hippocampal recruitment followed a distinctive biphasic time course, characterized by transient early activation, subsequent suppression, and later re-emergence with increasing bilateral coactivation. Cortical regions gradually developed higher spike rates and enhanced DG-related co-spiking, indicating large-scale network integration. Ipsilateral silencing modified local spike composition but did not prevent global network progression, whereas contralateral silencing accelerated ipsilateral epileptogenesis and strengthened pathological HFO expression. ConclusionEpileptogenesis in the KA model reflects a transition from a focal hippocampal insult to a resilient, bilateral cortico-hippocampal network. Targeting a single hippocampal node--even at early latent stages--is insufficient to halt this progression, highlighting the need for network-level therapeutic strategies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/701979v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@171797dorg.highwire.dtl.DTLVardef@df13d3org.highwire.dtl.DTLVardef@18e9594org.highwire.dtl.DTLVardef@1fe68f8_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

Genetically diverse Mycobacterium tuberculosis isolates manipulate inflammasome activation and IL-1β secretion independently of macrophage metabolic rewiring

The natural diversity of Mycobacterium tuberculosis is gaining relevance in dictating the outcome of tuberculosis (TB). We previously revealed a link between TB severity and M. tuberculosis-driven evasion of the macrophage cytosolic surveillance systems, with isolates from severe TB cases reducing inflammasome activation and interleukin (IL)-1{beta} production by infected cells. IL-1{beta} production and inflammasome activation are commonly associated with the metabolic reprogramming of stimulated macrophages. Thus, we questioned whether the differential modulation of the inflammasome and IL-1{beta} by M. tuberculosis isolates depended on distinct macrophage metabolic reprogramming. Using metabolic inhibitors, mice deficient for key metabolic regulators, and a metabolomics approach, we found that the macrophage metabolic landscape was similar regardless of the infecting M. tuberculosis isolate. Paralleling single-TLR activated macrophages, inhibition of glycolysis during infection impaired IL-1{beta} secretion. However, departing from TLR based models, in M. tuberculosis-infected macrophages IL-1{beta} secretion was independent of macrophage mitochondrial metabolic changes and the transcription factor hypoxia-inducible factor (HIF)-1. Additionally, we found a previously unappreciated impact of host metabolic inhibitors on the pathogen, and show that inhibition of the mycobacteria metabolism dampened both inflammasome activation and IL-1{beta} production. Collectively, our study raises awareness of the potential confounding effect of host metabolic inhibitors acting on the pathogen itself and demonstrates that the modulation of the inflammasome by M. tuberculosis may be uncoupled from the host metabolic reprogramming. Author SummaryMycobacterium tuberculosis is the causative agent of tuberculosis and one of the top infectious killers in the world, with around 1.3 million deaths reported annually. The genetic variability of this pathogen can shape its interaction with the host and modulate disease outcomes. We previously found that M. tuberculosis clinical isolates from patients with severe forms of tuberculosis evade cytosolic surveillance systems in macrophages. Here, we explored whether this evasion tactic was linked to metabolic alterations in the infected macrophages. We found that different M. tuberculosis isolates induced similar metabolic changes in infected macrophages. Additionally, we demonstrate that both host glycolysis and pathogens metabolism were pivotal for maximum IL-1{beta} production. These findings highlight the complexity of macrophage-pathogen interactions and emphasize that bacterial metabolism should be considered in metabolic studies and may be amenable to therapeutic intervention against tuberculosis.

immunology↗

bioRxiv2024

Radiation dermatitis in the hairless mouse model mimics human radiation dermatitis

Over half of all people diagnosed with cancer receive radiation therapy. Moderate to severe radiation dermatitis occurs in most human radiation patients, causing pain, aesthetic distress, and a negative impact on tumor control. No effective prevention or treatment for radiation dermatitis exists. The lack of well-characterized, clinically relevant animal models of human radiation dermatitis contributes to the absence of strategies to mitigate radiation dermatitis. Here, we establish and characterize a hairless SKH-1 mouse model of human radiation dermatitis by correlating temporal stages of clinical and pathological skin injury. We demonstrate that a single ionizing radiation treatment of 30 Gy using 6 MeV electrons induces severe clinical grade 3 peak toxicity at 12 days, defined by marked erythema, desquamation and partial ulceration, with resolution occurring by 25 days. Histopathology reveals that radiation-induced skin injury features temporally unique inflammatory changes. Upregulation of epidermal and dermal TGF-{beta}1 and COX-2 protein expression occurs at peak dermatitis, with sustained epidermal TGF-{beta}1 expression beyond resolution. Specific histopathological variables that remain substantially high at peak toxicity and early clinical resolution, including epidermal thickening, hyperkeratosis and dermal fibroplasia/fibrosis, serve as specific measurable parameters for in vivo interventional preclinical studies that seek to mitigate radiation-induced skin injury.

pathology↗

bioRxiv2023

bettercallsal: better calling of Salmonella serotypes from enrichment cultures using shotgun metagenomic profiling and its application in an outbreak setting.

Precise and rapid identification of Salmonella serotypes from suspect food matrices is critical for successful source attribution of illness outbreaks (Scallan et al., 2011). Currently, close to 3% of U.S. foodborne Salmonella outbreaks have been attributed to multiple Salmonella serotypes (2.85%, 2000 - 2020) (CDC, 2022). Recent foodborne outbreaks that have been attributed to multiple Salmonella serotypes force us to question whether these are rare events or if previous methods simply did not have the throughput to provide an accurate picture of the complex ecology that is connected to outbreak etiologies. (Hassan et al., 2019; FDA, 2021; Whitney et al., 2021). An in-silico benchmark dataset, comprising 29 unique Salmonella, 46 non-Salmonella bacterial and 10 viral genomes, was generated with varying read depths. For outbreak samples, analysis was performed on previously sequenced pre-enrichments and selective enrichments of papayas and peaches (fruits and leaves) that led to the identification of multiple serovars. Data analyses was performed using a custom-built k-mer tool, SeqSero2, Kallisto and bettercallsal. The in-silico dataset analyzed with bettercallsal had accuracy, recall and specificity of 95%, 96% and 98 % respectively. In the papaya outbreak samples, bettercallsal identified multiple serovar presence in concordance with Bioplex assay results and the genome hits assigned to the samples are Salmonella isolates from the papaya outbreak as evident by NCBI SNP cluster information. In peach outbreak samples, bettercallsal identified both the serovars (Alachua and Gaminara) in concordance with k-mer analysis and the Luminex xMap assay. bettercallsal outperformed k-mer, Kallisto and Seqsero2 in identifying multiple serovars from enrichment cultures using shotgun metagenomics sequencing. Most Salmonella subtyping work has relied upon WGS methods which focuses on the high-resolution analysis of single genomes, or multiple single genomes picked from colonies on agar. Here we introduce laboratory and bioinformatics innovations for a metagenomic outbreak response workflow that accurately identifies multiple Salmonella serovars at the same time in a much higher throughput approach. bettercallsal is one of the first analysis tools that can potentially identify multiple Salmonella spp. serotypes from a metagenomic or quasi-metagenomic datasets with accuracy and can provide early insights into the etiology of the sample.

genomics↗

bioRxiv2021

Straintables: An application that extracts sequences fromgenome assemblies and generates dissimilarity matrices

Background and ObjectivesThe dissimilarity matrix (DM) is an important component of phylogenetic analysis, and many software packages exist to build and show DMs. However, as the common input for this type of software are sequences in FASTA file format, the process of extracting and aligning each set of sequences to produce a big number of matrices can be laborious. Additionally, existing software do not facilitate the comparison of clusters of similarity across several DMs built for the same group of individuals, using different genomic regions. To address our requirements of such a tool, we designed Straintables to extract specific genomic region sequences from a group of intraspecies genomic assemblies, using extracted sequences to build dissimilarity matrices. MethodsA Python module with executable scripts was developed for a study on genetic diversity across strains of Toxoplasma gondii, being a general purpose system for DM calculation and visualization for preliminary phylogenetic studies. For automatic region sequence extraction from genomic assemblies we assembled a system that designs virtual primers using reference sequences located at genomic annotations, then matches those primers on genome files by using regex patterns. Extracted sequences are then aligned using Clustal Omega and compared to generate matrices. ResultsUsing this software saves the user from manual preparation and alignment of the sequences, a process that can be laborious when a large number of assemblies or regions are involved. The automatic sequence extraction process can be checked against BLAST results using extracted sequence as queries, where correct results were observed for same-species pools for various organisms. The package also contains a matrix visualization tool focused on cluster visualization, capable of drawing matrices into image files with custom settings, and features methods of reordering matrices to facilitate the comparison of clustering patterns across two or more matrices. ConclusionStraintables may replace and extend the functionality of existing matrix-oriented phylogenetic software, featuring automatic region extraction from genomic assemblies and enhanced matrix visualization capabilities emphasizing cluster identification. This module is open source, available at GitHub (https://github.com/Gab0/straintables) under a MIT license and also as a PIPY package. HighlightsSimple in-silico protocol for generation, visualization and comparison of dissimilarity matrices. Accurate automatic sequence extraction from multiple genomic assemblies by using virtual primers built from reference sequences in an annotation file. Draws matrices as images, with enhanced cluster visualization and customized options. Supports reordering of matrix indices to better visualize clustering pattern conservation across multiple regions.

bioinformatics↗

bioRxiv2017

De novo transcriptome sequencing and comparative analysis of midgut tissues of four non-model insects pertaining to Hemiptera, Coleoptera, Diptera and Lepidoptera

Despite the great morphological diversity of insects, there is a regularity in their digestive functions, which is apparently related to their physiology. In the present work we report the de novo midgut transcriptomes of four non-model insects from four distinct orders: Spodoptera frugiperda (Lepidoptera), Musca domestica (Diptera), Tenebrio molitor (Coleoptera) and Dysdercus peruvianus (Hemiptera). We employed a computational strategy to merge assemblies obtained with two different algorithms, which substantially increased the quality of the final transcriptomes. Unigenes were annotated and analyzed using the eggNOG database, which allowed us to assign some level of functional and evolutionary information to 79.7% to 93.1% of the transcriptomes. We found interesting transcriptional patterns, such as: i) the intense use of lysozymes in digestive functions of M. domestica larvae, which are streamlined and adapted to feed on bacteria; ii) the up-regulation of orthologous UDP-glycosyl transferase and cytochrome P450 genes in the whole midguts different species, supporting the presence of an ancient defense frontline to counter xenobiotics; iii) evidence supporting roles for juvenile hormone binding proteins in the midgut physiology, probably as a way to activate genes that help fight anti-nutritional substances (e.g. protease inhibitors). The results presented here shed light on the digestive and structural properties of the digestive systems of these distantly related species. Furthermore, the produced datasets will also be useful for scientists studying these insects.

genomics↗