Biology subjects

Cardoso, J.

Publications and source records attributed to Cardoso, J..

9 recordsLinked to original sources

bioRxiv2026

How a highly acidic SH3 domain binds to its intrinsically disordered partner through the formation of an encounter complex intermediate

Electrostatic interactions often play a role in determining the thermodynamic and kinetic properties of protein-protein interactions. However, the role of long-range electrostatic interactions in intrinsically disordered protein (IDP) binding is less clear, as they often bind in multiple steps including initial formation of a disordered encounter complex, followed by rearrangement into the bound state. We varied the salt concentration to probe the role of long-range electrostatic interactions in the binding of the highly charged AbpSH3 domain and the oppositely charged IDP ArkA. Using isothermal titration calorimetry, we observe that salt enthalpically destabilizes the bound complex. Molecular dynamics and NMR experiments reveal that salt has little effect on the bound state structure. However, simulations show that salt destabilizes the encounter complex intermediate, which primarily affects the association rate as measured by NMR. Consistent with these results, salt has the largest stabilizing effect on the apo SH3 domain, as cations substitute for the transient and long-range electrostatic interactions that can form with ArkA in the complex. We reveal a detailed picture of how a highly charged domain uses long-range, fuzzy, electrostatic interactions to help reach the bound state, a mechanism that is likely common among other highly charged domains that bind IDPs. TOC Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/741257v1_ufig1.gif" ALT="Figure 1000"> View larger version (21K): org.highwire.dtl.DTLVardef@dfc7acorg.highwire.dtl.DTLVardef@1ae0438org.highwire.dtl.DTLVardef@19704adorg.highwire.dtl.DTLVardef@1b40b15_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2026

Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistance

Precision oncology lacks scalable methods to identify the mechanisms that mediate therapeutic resistance for individual patients. Resistance often arises from focal cellular niches that are obscured by bulk profiling and costly to resolve with multi-omics. Here, we show that deep learning (DL), applied to routine histology, can localize focal tissue regions enriched for therapeutically relevant biology. Using 3111 breast cancer H&E slides with matched bulk transcriptomics, we trained weakly-supervised DL models to infer activities of immune, metabolic, and tumor-intrinsic phenotypes implicated in therapeutic resistance (AUROC>0.80; PCC>0.64). Accurate inference of these phenotypes should identify tissue regions enriched for the corresponding biological signal. Therefore, we validated phenotype inference and spatial localization with complementary analyses. Tissue-matched multiplexed immunofluorescence showed concordance between inferred immune states and corresponding cell fractions (p=0.006-0.106). Across multi-institutional cohorts, model-derived phenotypes recovered expected relationships with therapeutic outcomes (p<0.045). Finally, in a blinded evaluation, pathologists confirmed that model-derived high-attention regions were enriched for phenotype-specific morphology (p<2.408*10-5). Because evaluated phenotypes represent diverse mechanisms of resistance across therapeutic modalities, these findings provide a foundation for resistance-directed localization using therapeutic outcomes as supervision. By directing deep profiling toward model-prioritized regions, this framework could enable scalable nomination of candidate mediators of resistance for subsequent functional validation across real-world patient populations. One sentence summaryWeakly supervised deep learning localizes focal tissue regions enriched for therapeutically relevant biology in routine histology, thus offering a scalable strategy to study therapeutic resistance across large patient populations.

biophysics↗

bioRxiv2025

Auditory Gamma-Frequency Entrainment Abolishes Working Memory Deficits in a Rodent Model of Autism

Individuals on the autism spectrum often exhibit atypical responses to sensory stimuli and difficulties with behavioral regulation, reflecting altered functional patterns in core sensory-motor circuits. While impairments in higher cognitive functions are a hallmark of autism, they remain underexplored in rodent models compared to basic sensory-motor deficits. Furthermore, these sensory processing differences suggest the potential for using patterned auditory stimulation to modulate neural activity and mitigate symptoms. In this study, we investigated higher cognitive function in the valproic acid (VPA) rodent model of autism and evaluated whether auditory entrainment could ameliorate associated impairments. Pregnant dams received an intraperitoneal injection of VPA on embryonic day 12.5 (E12.5), and offspring were subsequently tested on a delayed non-match to place (DNMP) task to assess working memory. VPA-exposed animals showed significantly impaired performance and altered trial-by-trial learning dynamics, indicating deficits in working memory-based decision making. Notably, gamma-frequency auditory stimulation delivered during DNMP sessions led to increased gamma-frequency oscillations, and enhanced power in other bands (theta, beta) across brain regions, as assessed by LFP recordings in freely moving animals. More importantly, such stimulation eliminated the observed working memory impairments, enhancing performance both during and after entrainment. This provides strong evidence for the therapeutic potential of sensory entrainment in reversing autism-related cognitive deficits by modulating dysfunctional neural circuits.

neuroscience↗

bioRxiv2025

Differential patterns of cortical expansion in fetal and preterm brain development

The human cerebral cortex undergoes a complex developmental process during gestation, characterised by rapid cortical expansion and gyrification. This study investigates in vivo cortical growth trajectories using longitudinal MRI data from fetal and preterm cohorts. We employed anatomically constrained multimodal surface matching (aMSM) to quantify cortical surface area expansion and compare in-utero versus ex-utero cortical growth using cortical surface data from 22 to 44 weeks post-menstrual age (PMA), acquired as part of the Developing Human Connectome Project (dHCP). Our findings revealed distinct regional and temporal growth patterns during the 2nd and 3rdrd trimesters of healthy fetal cortical expansion. Ex utero brain development following preterm birth was shown to follow a modified trajectory compared to normal gestation, with potential implications for cortical organisation. Our methodology, combining biomechanically constrained surface registration with high quality fetal and neonatal imaging, provides a powerful framework for understanding early cortical development and deviations associated with preterm birth.

neuroscience↗

bioRxiv2024

Gut bacteria-derived succinate induces enteric nervous system regeneration

Enteric neurons control gut physiology by regulating peristalsis, nutrient absorption, and secretion1. Disruptions in microbial communities caused by antibiotics or enteric infections result in the loss of enteric neurons and long-term motility disorders2-5. However, the signals and underlying mechanisms of this microbiota-neuron communication are unknown. We studied the effects of microbiota on the recovery of the enteric nervous system after microbial dysbiosis caused by antibiotics. We found that both enteric neurons and glia are lost after antibiotic exposure, but recover when the pre-treatment microbiota is restored. Using murine gnotobiotic models and fecal metabolomics, we identified neurogenic bacterial species and their derived metabolite succinate as sufficient to rescue enteric neurons and glia. Unbiased single-nuclei RNA-seq analysis uncovered a novel neural precursor-like population marked by the expression of the neuronal gene Nav2. Genetic fate-mapping showed that Plp1+ enteric glia differentiate into neurons following antibiotic exposure. In contrast, Nav2+ neurons expand upon succinate treatment and indicate an alternative mode of neuronal regeneration under recovery conditions. Our findings highlight specific microbial species, metabolites, and the underlying cellular mechanisms involved in neuronal regeneration, with potential therapeutic implications for peripheral neuropathies.

neuroscience↗

bioRxiv2022

A new method and tool for volumetric estimation of brain structures after serial sectioning.

Volume estimations are crucial for many neuroscience studies, allowing the evaluation of changes in the size of brain areas that may have relevant functional consequences. Classical histological methods and modern human brain imaging techniques rely on obtaining physical or digital sections, with a known thickness, of the organ to be analyzed. This "slicing" strategy is associated with an ineludible loss of information about the three-dimensional organization of the analyzed structures, especially affecting the precision of volumetric measurements. To overcome this problem, several methods have been developed. One of the most commonly used approaches for volume estimation is the classical Cavalieris method. Within this book chapter, we provide first an overview of Cavalieris method and propose a new one, named the Truncated Cone Shape (TCS) method, for the estimation of volumes from tissue sections. Second, we compare the accuracy of both methods using computer-generated objects of different shapes and sizes. We conclude that, more frequently, the TCS method provides a better estimate of real volumes than Cavalieris method. And third, we describe a protocol to estimate volumes using a self-developed and freely available tool for ImageJ: VolumestJ (https://github.com/Jorvalgl/VolumestJ). This new tool helps to implement both Cavalieris and TCS methods using digital images of tissue sections. We consider that VolumestJ will facilitate the labor of researchers interested in volume estimations.

neuroscience↗

bioRxiv2020

Preliminary Minimum Reporting Requirements for Reporting In-Vivo Neural Interface Research: I. Implantable Neural Interfaces

The pace of research and development in neuroscience, neurotechnology, and neurorehabilitation is rapidly accelerating, with the number of publications doubling every 4.2 years. Maintaining this progress requires technological standards and scientific reporting guidelines to provide frameworks for communication and interoperability. The present lack of such standards for neurotechnologies limits the transparency, reproducibility, and meta-analysis of this growing body of research, posing an ongoing barrier to research, clinical, and commercial objectives. Continued neurotechnological innovation requires the development of some minimal standards to promote integration between this broad spectrum of technologies and therapies. To preserve design freedom and accelerate the translation of research into safe and effective technologies with maximal user benefit, such standards must be collaboratively co-developed by a full spectrum of neuroscience and neurotechnology stakeholders. This paper summarizes the preliminary recommendations of IEEE Working Group P2794, developing a Reporting Standard for in-vivo Neural Interface Research (RSNIR). Impact StatementThis work provides a preliminary set of reporting guidelines for implantable neural interface research, developed by IEEE WG P2794 in open collaboration between a range of stakeholders to accelerate the research, development, and integration of innovative neurotechnologies.

bioengineering↗

bioRxiv2017

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we present a new machine learning technique - Subtype and Stage Inference (SuStaIn) - able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available crosssectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal new subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes, and characterises within-group heterogeneity for the first time. In Alzheimers disease, SuStaIn uncovers three subtypes, uniquely revealing their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p=7.18x10--4) or temporal stage (p=3.96x10-5). SuStaIn thus offers new promise for enabling disease subtype discovery and precision medicine.

neuroscience↗

bioRxiv2017

Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories

Computational systems biology methods enable rational design of cell factories on a genomescale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, for the majority of these methods implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knock-out, knock-in, over-expression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated website including documentation, examples, and installation instructions can be found at http://cameo.bio. Users can also give cameo a try at http://try.cameo.bio.

bioengineering↗