Biology subjects

Rodrigues, L.

Publications and source records attributed to Rodrigues, L..

13 recordsLinked to original sources

bioRxiv2026

NeoDBS: Open-Source Platform for Visualization and Analysis of Electrophysiological Recordings from Deep Brain Stimulation Systems

Optimization of deep brain stimulation (DBS) therapy for neurological and neuropsychiatric disorders depends on objective quantitative biomarkers that can guide stimulation parameter adjustments. With the recent introduction of new-generation DBS systems capable of simultaneously stimulating brain activity and recording local field potentials (LFP), there is increasing demand for platforms that enable efficient visualization and analysis of these signals for electrophysiological biomarkers identification. To address the limitations of currently available toolboxes that require advanced signal processing skills and rely on proprietary software, we present NeoDBS, an open-source Python platform designed for ingestion and advance signal visualization and processing of LFP signals from DBS systems through an easy-to-use graphical interface. NeoDBS is a user-centered platform that offers predefined analysis pipelines with the aim of facilitating electrophysiological biomarker investigation for DBS across different brain disorders. Custom analysis pipelines are also available for users to leverage the signal analysis tools to their research needs. Critical functionalities for longitudinal biomarker research are featured in NeoDBS, such as batch file processing and event-locked analysis for in-clinic and at-home recordings. This combination of accessibility, user-experience and advanced signal processing tools makes NeoDBS an environment that propels easy and fast electrophysiological biomarker research for DBS, across patients, sessions, and stimulation parameters.

bioengineering↗

bioRxiv2025

Highly prevalent co-infections and genetic diversity of Anaplasma species in asymptomatic small ruminants in a Mediterranean context revealed by a gltA-based nested multiplex PCR assay

Anaplasmosis is a widespread tick-borne disease affecting various host species. Small ruminants (sheep and goats) can be infected by multiple Anaplasma species:. To evaluate the circulation and co-infection rates of five Anaplasma species (A. ovis, A. capra, A. bovis, A. marginale and A. phagocytophilum) in asymptomatic small ruminants in Corsica--a Mediterranean island of France--we designed a singleplex, duplex, and multiplex nested PCR (nPCR) scheme based on the gltA gene. Anaplasmosis was found to be highly endemic on the island, with 85.2% of the tested small ruminants infected by one or more Anaplasma species (82.8% of sheep and 89.5% of goats). We confirmed the presence of A. ovis and A. capra, and, for the first time in Corsica, detected A. bovis and A. phagocytophilum in both sheep (respective infection rates: 64.3%, 34.3%, 4.3%, and 7.1%) and goats (respective rates: 84.2%, 13.2%, 18.4%, and 5.3%). A. marginale was not detected. Approximately one-third of the infected animals were co-infected (35.1% of infected sheep and 32.4% of infected goats). A. ovis was always involved in co-infections, most often associated with A. capra (54.8% of co-infections). Importantly, A. bovis was never detected as a mono-infection in asymptomatic animals, but was always found associated with A. ovis (32.3% of co-infections). Sequence analysis of the gltA and 16S rRNA genes confirmed the species identification of all detected Anaplasma species. A probably new Anaplasma species was detected for the first time in Corsica using the 16S rRNA nPCR. Furthermore, we found several circulating A. phagocytophilum genetic variants. Sequence analysis of A. ovis 16S rRNA revealed a single SNP that delineates two sequence groups (Ao1goat and Ao2sheep), which were significantly linked to the host of origin. Additionally, two groups of A. bovis gltA sequences were identified (Ab1 and Ab2, with 87.4% sequence homology), forming two novel clades. While the Ab1 clade is closely related to the six previously described clades, the Ab2 clade formed a separate phylogenetic group that also contained isolates from Corsican symptomatic goats. In conclusion, Anaplasma species are highly prevalent in the small ruminant population of Corsica, confronted with a great diversity of co-infecting species and genetic variants.

microbiology↗

bioRxiv2025

Molecular Basis of EWS Interdomain Self-Association and Its Role in Condensate Formation

Ewing sarcoma, the second most common pediatric bone and soft tissue cancer, is caused by aberrant fusion of the EWS low-complexity domain (EWSLCD) to the DNA-binding domain of the transcription factor FLI1. The resulting fusion, EWS::FLI1, directly interacts with and engages in a dynamic interplay with EWS that drives tumorigenesis and regulates the function of both proteins. While EWSLCD is known to promote self-association, the role of the RNA-binding domains (RBDs) of EWS, which include RGG repeat regions and a structured RNA-recognition motif (RRM), remains less well understood. Here, we investigate the interplay between EWSLCD and RBDs using biomolecular condensation assays, microscopy, NMR spectroscopy, and molecular simulations. Our studies reveal that RBDs differentially influence EWSLCD condensate formation and suggest that electrostatics and polypeptide-chain length likely contribute to this interaction. NMR spectroscopy and molecular dynamics simulations further demonstrate that EWSLCD and the central RNA-binding region, comprising the RRM and RGG2 domains, engage in transient, non-specific interactions that are broadly distributed across both regions and involve diverse residue types. Specifically, tyrosine, polar residues, and proline within EWSLCD preferentially interact with arginine, glycine, and proline residues in the RBD. Atomistic simulations of EWS confirm that the full-length protein exhibits a similar interaction profile with conserved chemical specificity, supporting a model in which a network of weak, distributed interdomain contacts underlies EWS self-association. Together, these findings provide molecular insight into the mechanisms of EWS condensate formation and lay the groundwork for understanding how interdomain interactions regulate EWS and EWS::FLI1 function. SIGNIFICANCEThis study shows that the RNA-binding regions of the RNA-binding protein EWS subtly tune biomolecular condensation through numerous transient contacts with the low complexity domain that is common to both EWS and the oncogenic fusion EWS::FLI1. These results provide insight into condensate formation of EWS, which may be useful for understanding the oncogenic mechanisms of EWS::FLI1 and broadly, into pathogenic biomolecular condensate formation.

biophysics↗

bioRxiv2024

Resilience in a time of stress: revealing the molecular underpinnings of coral survival following thermal bleaching events

Coral bleaching events from thermal stress are increasing globally in duration, frequency, and intensity. Bleaching occurs when a corals algal symbionts are expelled, resulting in a loss of color. Some coral colonies survive bleaching, reacquire their symbionts and recover. In this study, we experimentally bleached Montipora capitata colonies to examine molecular and physiological signatures of intrinsic differences between corals that recover (resilient) compared to those that die (susceptible). All colonies were collected from the same bay and monitored for eight months post-bleaching to identify specific colonies exhibiting long-term resilience and survival. Using an integrated systems-biology approach that included quantitative mass spectrometry-based proteomics, 16S rRNA of the microbiome, total lipids, symbiont density and diversity, we explored molecular-level mechanisms of tolerance in pre- and post-bleached colonies and found biomarkers of resilience that can confidently identify resilient and susceptible corals before thermal-induced bleaching events. Prior to thermal stress, resilient corals were characterized by a more diverse microbiome and increased abundances of proteins involved in multiple carbon and nitrogen acquisition strategies, symbiont retention and acquisition, and pathogen resistance. Susceptible corals had early signs of symbiont rejection and had resorted to utilizing urea uptake pathways for carbon and nitrogen. Further, molecular signatures identified prior to bleaching were amplified after bleaching, suggesting these pathways may be deterministic in a colonys fate. Our results have important implications for the future of reefs, revealing molecular factors necessary for survival through thermally-induced bleaching events and providing diagnostic biomarkers for coral reef management. Significance statementCorals are being negatively impacted by the increase in the number and duration of thermal-induced bleaching events. There are, however, some individuals within a single species that will bleach and, after time, reacquire symbionts and physiologically recover while neighboring colonies will die. Here, we used a multidisciplinary approach to understand the biochemical details of the physiological changes of resilient and susceptible Montipora capitata to thermal-induced bleaching. Resilient corals were characterized by their use of multiple carbon and nitrogen acquisition strategies, metabolically active symbiont relationships, abundant antiviral proteins, and a diverse microbiome. We reveal a multi-factor molecular-level approach for confidently identifying resilient and susceptible coral colonies so that environmental managers can rapidly select quality candidates for propagation while in the field.

ecology↗

bioRxiv2023

Optimization of chondroitin production in E. coli using genome scale models

Chondroitin is a natural occurring glycosaminoglycan with applications as a nutraceutical and pharmaceutical ingredient and can be extracted from animal tissues. Microbial chondroitin-like polysaccharides emerged as a safer and more sustainable alternative source. However, chondroitin titers using either natural or recombinant microorganisms are still far from meeting the increasing demand. The use of genome-scale models and computational predictions can assist the design of microbial cell factories with possible improved titers of these value-added compounds. Genome-scale models have been used to predict genetic modifications in Escherichia coli engineered strains that would potentially lead to improved chondroitin production. Additionally, using synthetic biology approaches, a pathway for producing chondroitin has been designed and engineered in E. coli. Afterwards, the most promising mutants identified based on bioinformatics predictions were constructed and evaluated for chondroitin production in flask fermentation. This resulted in the production of 118 mg/L of extracellular chondroitin by overexpressing both superoxide dismutase (sodA) and a lytic murein transglycosylase (mltB). Then, batch and fed-batch fermentations at bioreactor scale were also evaluated, in which the mutant overexpressing mltB led to an extracellular chondroitin production of 427 mg/L and 535 mg/L, respectively. The computational approach herein described identified several potential novel targets for improved chondroitin biosynthesis, which may ultimately lead to a more efficient production of this glycosaminoglycan.

bioengineering↗

bioRxiv2023

Intraspecific density changes the impact of interspecific competitors on parasite infection traits in multiple infections

Co-infections are frequent, with consequences for parasite life-history traits expressed at within- and between-host levels. However, little is known about whether the effect of interspecific competitors on traits are correlated or independent or if they change with intraspecific competition. To address this, we investigated the occurrence of genetic correlations among within- and betweenhost traits at different intra-specific densities of inbred lines of the spider mite, Tetranychus urticae with its competitor T. evansi. First, we found T. evansi presence on a shared host leaf produced a negative (non-genetic) correlation between virulence (leaf damage) and number of daughters (transmitting stages) at intermediate intraspecific densities; this same relationship was not significant without competitors. Second, we show interspecific competitors increases transmission to adjacent leaf discs, measured as movement of adult T. urticae females, but only at low and intermediate intraspecific densities. Finally we tested whether within-host traits (virulence and transmitting stages) were correlated with between-host traits (movement to adjacent patches), with or without competitors, at different conspecific densities. We found traits were mostly independent; interspecific competitors may increase transmission across hosts without affecting virulence (or vice versa). These independent effects on within- and between-host traits indicate competition may impact epidemiology and parasite trait evolution.

evolutionary biology↗

bioRxiv2023

Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine

BackgroundReference histomorphometric data of healthy human kidneys are largely lacking due to laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning, computational image analysis, and feature analysis to investigate the relationship of histomorphometry with patient age, sex, and serum creatinine (SCr) in a multinational set of reference kidney tissue sections. MethodsA panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in the digitized images of 79 periodic acid-Schiff-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were quantified from the segmented classes. Regression analysis aided in determining the relationship of histomorphometric parameters with age, sex, and SCr. ResultsOur deep-learning model achieved high segmentation performance for all test compartments. The size and density of nephrons and arteries/arterioles varied significantly among healthy humans, with potentially large differences between geographically diverse patients. Nephron size was significantly dependent on SCr. Slight, albeit significant, differences in renal vasculature were observed between sexes. Glomerulosclerosis percentage increased, and cortical density of arteries/arterioles decreased, as a function of age. ConclusionsUsing deep learning, we automated precise measurements of kidney histomorphometric features. In the reference kidney tissue, several histomorphometric features demonstrated significant correlation to patient demographics and SCr. Deep learning tools can increase the efficiency and rigor of histomorphometric analysis. Significance statementAlthough the importance of kidney morphometry is well explored in disease contexts, the definition of variance in reference tissue is not. Advancements in digital and computational pathology have rendered quantitative analysis of unprecedented tissue volumes via the single press of a button. The authors leverage the unique benefits of panoptic segmentation to perform the largest ever quantitation of reference kidney morphometry. Regression analysis identified several kidney morphometric features that varied significantly with patient age and sex, and the results suggested that the set size of nephrons might depend more intricately on creatinine than previously thought.

cell biology↗

bioRxiv2022

Hormone-induced enhancer assembly requires an optimal level of hormone receptor multivalent interactions

Transcription factors (TFs) activate enhancers to drive cell-specific gene programs in response to signals, but our understanding of enhancer assembly during signaling events is incomplete. Here, we show that Androgen Receptor (AR), a steroid hormone-regulated transcription factor, forms condensates through multivalent interactions in response to androgen signaling to orchestrate enhancer assembly. We demonstrate that the intrinsically disordered N-terminal domain (NTD) of AR drives 1,6-Hexanediol-sensitive condensate formation and that NTD deletion or aromatic residue mutation reduces AR self-association and abolishes AR transcriptional activity. AR NTD can be substituted by intrinsically disordered regions (IDRs) from selective proteins for AR condensation capacity and transactivation function. Surprisingly, strengthened AR condensation capacity caused by extending the polyQ tract within AR NTD also leads to impaired transcriptional activity without affecting AR binding on enhancers. Furthermore, either NTD deletion or polyQ extension reduces heterotypic multivalent interactions between AR and other enhancer components. These results suggest the importance of an optimal level of AR condensation in mediating AR-AR homotypic and AR-cofactor heterotypic interactions to regulate enhancer assembly in response to signals. Our study supports the notion that alteration of the fine-tuned multivalent IDR-IDR interactions might underlie AR-related human pathologies, thereby providing novel molecular insights for potential therapeutic strategies to treat prostate cancer and other AR-involved diseases by targeting AR multivalent interactions.

molecular biology↗

bioRxiv2021

A tool for federated training of segmentation models on whole slide images.

The largest bottleneck to the development of convolutional neural network (CNN) models in the computational pathology domain is the collection and curation of diverse training datasets. Training CNNs requires large cohorts of image data, and model generalizability is dependent on training data heterogeneity. Including data from multiple centers enhances the generalizability of CNN based models, but this is hindered by the logistical challenges of sharing medical data. In this paper we explore the feasibility of training our recently developed cloud-based segmentation tool (Histo-Cloud) using federated learning. We show that a federated trained model to segment interstitial fibrosis and tubular atrophy (IFTA) using datasets from three institutions is comparable to a model trained by pooling the data on one server when tested on a fourth (holdout) institutions data. Further, training a model to segment glomeruli for a federated dataset (split by staining) demonstrates similar performance.

pathology↗

bioRxiv2021

A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology

BackgroundImage-based machine learning tools hold great promise for clinical applications in nephropathology and kidney research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often face prohibitive challenges in using these tools to their full potential, including the lack of technical expertise, suboptimal user interface, and limited computation power. MethodsWe have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. ResultsBy segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in murine models of aging, diabetic nephropathy, and HIV associated nephropathy. ConclusionThe ability to access this tool over the internet will facilitate widespread use by computational non-experts. Histo-Cloud is open source and adaptable for segmentation of any histological structure regardless of stain. Histo-Cloud will greatly accelerate and facilitate the generation of datasets for machine learning in the analysis of kidney histology, empowering computationally novice end-users to conduct deep feature analysis of tissue slides.

pathology↗

bioRxiv2020

A kinetic model of the central carbon metabolism for acrylic acid production in Escherichia coil

Acrylic acid is a value-added chemical used in industry to produce diapers, coatings, paints, and adhesives, among many others. Due to its economic importance, there is currently a need for new and sustainable ways to synthesise it. Recently, the focus has been laid in the use of Escherichia coli to express the full bio-based pathway using 3-hydroxypropionate as an intermediary through three distinct pathways (glycerol, malonyl-CoA, and {beta}-alanine). Hence, the goals of this work were to use COPASI software to assess which of the three pathways has a higher potential for industrial-scale production, from either glucose or glycerol, and identify potential targets to improve the biosynthetic pathways yields. When compared to the available literature, the models developed during this work successfully predict the production of 3-hydroxypropionate, using glycerol as carbon source in the glycerol pathway, and using glucose as a carbon source in the malonyl-CoA and {beta}-alanine pathways. Finally, this work allowed to identify four potential over-expression targets (glycerol-3-phosphate dehydrogenase (G3pD), acetyl-CoA carboxylase (AccC), aspartate aminotransferase (AspAT), and aspartate carboxylase (AspC)) that should, theoretically, result in higher AA yields. Author summaryAcrylic acid is an economically important chemical compound due to its high market value. Nevertheless, the majority of acrylic acid consumed worldwide its produced from petroleum derivatives by a purely chemical process, which is not only expensive, but it also contributes towards environment deterioration. Hence, justifying the current need for sustainable novel production methods that allow higher profit margins. Ideally, to minimise production cust, the pathway should consist in the direct bio-based production from microbial feedstocks, such as Escherichia coli, but the current yields achieved are still to low to compete with conventional method. In this work, even though the glycerol pathway presented higher yields, we identified the malonyl-CoA route, when using glucose as carbon source, as having the most potential for industrial-scale production, since it is cheaper to implement. Furthermore, we also identified potential optimisation targets for all the tested pathways, that can help the bio-based method to compete with the conventional process.

bioengineering↗

bioRxiv2020

Consequences of population structure for sex allocation and sexual conflict

Both sex allocation and sexual conflict can be modulated by spatial structure. However, how the interplay between the type of dispersal and the scale of competition simultaneously affects these traits in sub-divided populations is rarely considered. We investigated sex allocation and sexual conflict evolution in meta-populations of the spider mite Tetranychus urticae evolving under budding (pairing females from the same patch) or random (pairing females from different patches) dispersal and either local (fixed sampling from each subpopulation) versus global (sampling as a function of subpopulation productivity) competition. Females evolving under budding dispersal produced less female-biased offspring sex ratios than those from the random dispersal selection regimes, contradicting theoretical predictions. In turn, the scale of competition did not have a strong effect on sex allocation. Males evolved under budding dispersal induced less female harm than those exposed to random dispersal, but there was no reduction in female fitness following exposure to multiple mates from either selection regime. This work highlights that population structure can impact the evolution of sex allocation and sexual conflict. We also discuss how selection on either trait may reciprocally affect the evolution of the other, for example via effects on fecundity.

evolutionary biology↗

bioRxiv2018

Projecting the end of the Zika virus epidemic in Latin America: a modelling analysis

Background Zika virus (ZIKV) emerged in Latin America & the Caribbean (LAC) region in 2013, and has had serious implications for population health in the region. In 2016, the World Health Organization declared the ZIKV outbreak a Public Health Emergency of International Concern following a cluster of associated neurological disorders and neonatal malformations. In 2017, Zika cases declined, but future incidence in LAC remains uncertain due to gaps in our understanding, considerable variation in surveillance and a lack of a comprehensive collation of data from affected countries.\n\nMethods Our analysis combines information on confirmed and suspected Zika cases across LAC countries and a spatio-temporal dynamic transmission model for ZIKV infection to determine key transmission parameters and projected incidence in 91 major cities within 35 countries. Seasonality was determined by spatio-temporal estimates of Aedes aegypti vector capacity. We used country and state-level data from 2015 to mid-2017 to infer key model parameters, country-specific disease reporting rates, and the 2018 projected incidence. A 10-fold cross-validation approach was used to validate parameter estimates to out-of-sample epidemic trajectories.\n\nResults There was limited transmission in 2015, but in 2016 and 2017 there was sufficient opportunity for wide-spread ZIKV transmission in most cities, resulting in the depletion of susceptible individuals. We predict that the highest number of cases in 2018 within some Brazilian States (Sao Paulo and Rio de Janeiro), Colombia and French Guiana, but the estimated number of cases were no more than a few hundred. Model estimates of the timing of the peak in incidence were correlated (p<0.05) with the reported peak in incidence. The reporting rate varied across countries, with lower reporting rates for those with only confirmed cases compared to those who reported both confirmed and suspected cases.\n\nConclusions The findings suggest that the ZIKV epidemic is by and large over, with incidence projected to be low in most cities in LAC in 2018. Local low levels of transmission are probable but the estimated rate of infection suggests that most cities have a population with high levels of herd immunity.

epidemiology↗