bioRxiv Science⌕ Search

Biology subjects

Monteiro, C.

Publications and source records attributed to Monteiro, C..

10 recordsLinked to original sources

Mitigation of intertidal temperature extremes by algal canopies

In a rapidly warming world, species must either adapt to changing environmental conditions, shift their distributions through the movement and survival of individuals, or face local or global extinction. Within ecosystems, structural complexity of either environmental or biogenic origin may provide microclimate buffering, promoting persistence. In coastal environments, it has long been stated that macroalgae act as important ecosystem engineers that can moderate thermal extremes, however the magnitude of this buffering has rarely been quantified. Moreover, the extent to which thermal buffering varies among macroalgal species, and how it relates to thermal heterogeneity across scales remains poorly understood. Here, we measured thermal buffering provided by intertidal macroalgae using high-frequency, high-spatial-resolution in situ temperature logging. We compared microclimates beneath algal cover with those on experimentally cleared bare rock across two tidal levels, two solar exposures and two seasons along six shores within a biogeographical transition zone. Thermal buffering was highly heterogeneous across the study area, varying with species identity, algal cover, tidal level, and solar exposure. Together, these factors generated a complex thermal mosaic, with even small algal specimens providing measurable buffering. We found that latitude had little influence on mean daytime low-tide temperatures, whereas local-scale factors had pronounced effects. Lower tidal position, reduced solar exposure, and algal cover each moderated thermal conditions and, in combination, provided an average cooling of approximately 4 {degrees}C. These findings highlight the importance of fine-scale habitat heterogeneity in shaping thermal exposure and identify features that could be incorporated into local climate-adaptation strategies, supporting coastal conservation and restoration efforts.

ecology↗

Tfh dysfunction underlies poor responses to HBV vaccination

The primary goal of vaccination is to induce durable protection through generation of antigen-specific humoral immunity and cellular immune memory. Yet the same vaccine may elicit durable protection in some and less durable responses in others. Antigen-specific antibody responses arise from germinal centers (GCs), where T follicular helper (Tfh) cells help cognate B cells undergo somatic hypermutation (SHM), affinity maturation to generate humoral protection and cellular immune memory. In humans, however, the mechanisms linking GC dynamics to downstream antibody output remain incompletely characterized. To identify the molecular and cellular events that underlie differential vaccine responsiveness, we leveraged the known, pronounced variability in vaccine antigen-specific antibody concentration and durability observed after Hepatitis B virus (HBV) immunization. We longitudinally profiled 101 healthy adults undergoing either de novo (n=59) or booster (n=42) immunization. We then stratified participants into High (>100 mIU/mL) and Low (<100 mIU/mL) responder groups based on vaccine antigen-specific antibody responses. From this cohort, we obtained core needle biopsies (CNBx) of vaccine-draining lymph nodes from 10 participants (High Responder n=6, Low Responder n=4), 10-21 days post-final vaccination and performed single-cell RNA sequencing with paired antigen receptor sequencing. Lymph node BCR repertoire analysis revealed convergent, semi-public clonotypes across unrelated participants with elevated SHM. Transcriptomic profiling of GC-B cells uncovered stark functional divergence between response groups, with High Responders showing robust activation across both dark zone (DZ) and light zone (LZ) compartments and enrichment of MYC and mTORC1 signaling. In contrast, Low Responders exhibited a contracted LZ compartment and a stalled GC B cell response. This divergence in B cell responses corresponded to changes in the T cell compartment. High Responders harbored an expanded Tfh subset marked by increased expression of CXCL13, ICOS, and GNG4, indicative of their GC localization and capacity to provide B cell help. Moreover, receptor-ligand analysis indicated that these GNG4+Tfh formed coordinated costimulatory networks capable of driving LZ GC-B cell licensing. Low Responders demonstrated a divergent program in which their GNG4+Tfh displayed aberrant inflammatory transcriptional activity. This inflammatory signaling was associated with reduced specificity of BCL6 regulon activity despite maintained expression of canonical Tfh genes. Together, these findings reveal that variable HBV vaccine responsiveness was associated with transcriptional network alterations in the GNG4+Tfh subset, offering a molecular framework for understanding poor vaccine responses. One Sentence SummaryLymph node Tfh dysfunction is associated with poor vaccine responses to Hepatitis B virus vaccination

immunology↗

Co-encapsulated liposomal formulations of tubacin and erlotinib.HCl: a comprehensive stability strategy

The co-encapsulation of two anticancer drugs within liposomes is a promising strategy for delivering a synergistic drug ratio to tumors. In this preliminary study, we systematically investigated the formulation stability of liposomes co-encapsulating tubacin and erlotinib.HCl, as well as the development of a freeze-drying (FD) method as a stabilization strategy. Notably, the effect of cryoprotectants on size and drug encapsulation conservation was studied both before and after freezing and thawing (FD). Tubacin was passively loaded into the lipid bilayer, while erlotinib.HCl was remotely loaded via an ammonium sulfate gradient. Lipid composition studies revealed that erlotinib.HCl encapsulation was the highest at pH 3. Additionally, DSPC:Chol ratios significantly impacted drug loading: high cholesterol content improved erlotinib.HCl retention but reduced tubacin encapsulation. Despite optimization, substantial drug losses occurred during storage at 4{degrees}C, leading to the development of freeze-drying as a stabilization strategy. Sucrose and trehalose were evaluated as cryoprotectants at different concentrations. Sucrose (10% w/v) demonstrated higher cryoprotection, maintaining liposome sizes within a range optimal for the enhanced permeability and retention (EPR) effect. However, the inclusion of internal sucrose did not further improve stability. Process optimization identified 150 mM ammonium sulfate as the optimal concentration for remote loading while preserving formulation stability. Comparative evaluation of two freeze-drying protocols revealed that the controlled stepwise system (Telstar(R)) outperformed the alternative approach, demonstrating superior drug retention and particle size preservation. Asymmetric flow field-flow fractionation coupled with dynamic light scattering and multi-angle light scattering (AF4-DLS-MALS) analysis confirmed minimal particle aggregation post-lyophilization, with preserved monodispersity and spherical morphology. These findings highlight the critical importance of cryoprotectant selection, buffer optimization, and freeze-drying protocol design for developing stable, co-encapsulated liposomal formulations.

cancer biology↗

Heat Stress on the brown Seaweed Ascophyllum nodosum: differential population sensitivity to future climate.

Accurate forecasts of the biological impacts of climate change require a better understanding of how small-scale temperature variability affects individual physiology and population dynamics. But doing so for intertidal species with large distribution ranges, while also accounting for the effect of local adaptation, presents numerous technical challenges. Here, we assessed the macroecological consequences of thermal stress on the cold-adapted brown seaweed Ascophyllum nodosum across its European distribution. We collected specimens from ten populations spanning latitudes 41{degrees}N to 60{degrees}N and subjected them to simulated intertidal heat stress using a novel, custom-built experimental setup that replicated realistic conditions, including tidal cycles, light conditions, and temperature trajectories based on in situ data. Our factorial design comprised eight experimental treatments, combining two high-tide water temperatures (15 {degrees}C and 20.5 {degrees}C) with four low-tide peak temperatures (28.5 {degrees}C to 40.5 {degrees}C). Physiological performance was evaluated through measurements of growth, mortality, and oxygen production. Results indicate that thermal stress is more closely associated with the magnitude of temperature change between high and low tides rather than the absolute maximum temperatures reached. Algae exposed to warmer water temperatures (20.5 {degrees}C) consistently outperformed those in colder water (15 {degrees}C), suggesting that cold upwelled waters at the species southern limit may not be essential for survival. Southern populations demonstrated higher resilience to thermal stress than central and northern counterparts. Integrating these physiological responses with climate projections, we employed demographic models to forecast long-term population dynamics. The models predict that future climatic conditions could exceed the thermal resilience of specific populations, leading to uneven impacts across the European distribution of the species. Notwithstanding, range contractions may occur at the warm edge of the distribution, where populations, though more resilient to thermal stress, could still be overwhelmed by the pace of warming. This study underscores the importance of realistic experimental simulations in evaluating species thermal tolerance and highlights the potential for climate change to differentially impact populations along large latitudinal gradients.

ecology↗

Leveraging AI for improved reproducibility of mathematical disease models: insights from a retinitis pigmentosa case study

Mathematical modeling of disease and drug action is becoming an indispensable component of drug development, underscored by recent examples of models predicting trial results. To be able to rely on such approaches, decision-makers need to be able to verify those results independently with in silico confirmatory studies. Artificial Intelligence (AI) offers a valuable avenue for improving the reproducibility of complex models, enabling their swift and software-agnostic deployment. This paper highlights AIs impact through a case study on an Ordinary Differential Equation (ODE) model of Retinitis Pigmentosa (RP). We use a version of Chat GPT 4, a sophisticated large language model (LLM) developed by OpenAI, as customized by Mathpix company with additional capabilities. This setup facilitated the extraction of equations from the PDF and converted into a human-readable, text-based definition language called Antimony, which is part of the Python package tellurium. Subsequently, the model was converted into Systems Biology Markup Language (SBML) using tellurium and uploaded onto the jink[o] platform for simulation. The RP model was efficiently and accurately implemented using AI techniques. Furthermore, we were able to reproduce the model behavior presented in the literature. Our findings advocate for the broader application of AI in mathematical model re-implementations to ensure reliability and reproducibility of the results.

systems biology↗

Molecularly distinct memory CD4+ T cells are induced by SARS-CoV-2 infection and mRNA vaccination

Adaptive immune responses are induced by vaccination and infection, yet little is known about how CD4+ T cell memory differs when primed in these two contexts. Notably, viral infection is generally associated with higher levels of systemic inflammation than is vaccination. To assess whether the inflammatory milieu at the time of CD4+ T cell priming has long-term effects on memory, we compared Spike-specific memory CD4+ T cells in 22 individuals around the time of the participants third SARS-CoV-2 mRNA vaccination, with stratification by whether the participants first exposure to Spike was via virus or mRNA vaccine. Multimodal single-cell profiling of Spike-specific CD4+ T cells revealed 755 differentially expressed genes that distinguished infection- and vaccine-primed memory CD4+ T cells. Spike-specific CD4+ T cells from infection-primed individuals had strong enrichment for cytotoxicity and interferon signaling genes, whereas Spike-specific CD4+ T cells from vaccine-primed individuals were enriched for proliferative pathways by gene set enrichment analysis. Moreover, Spike-specific memory CD4+ T cells established by infection had distinct epigenetic landscapes driven by enrichment of IRF-family transcription factors, relative to T cells established by mRNA vaccination. This transcriptional imprint was minimally altered following subsequent mRNA vaccination or breakthrough infection, reflecting the strong bias induced by the inflammatory environment during initial memory differentiation. Together, these data suggest that the inflammatory context during CD4+ T cell priming is durably imprinted in the memory state at transcriptional and epigenetic levels, which has implications for personalization of vaccination based on prior infection history. One Sentence SummarySARS-CoV-2 infection versus SARS-CoV-2 mRNA vaccination prime durable transcriptionally and epigenetically distinct Spike-specific CD4+ T cell memory landscapes.

immunology↗

Empirical methods for the validation of Time-To-Event mathematical models taking into account uncertainty and variability: Application to EGFR+ Lung Adenocarcinoma.

BackgroundOver the past several decades, metrics have been defined to assess the quality of various types of models and to compare their performance depending on their capacity to explain the variance found in real-life data. However, available validation methods are mostly designed for statistical regressions rather than for mechanistic models. To our knowledge, in the latter case, there are no consensus standards, for instance for the validation of predictions against real-world data given the variability and uncertainty of the data. In this work, we focus on the prediction of time-to-event curves using as an application example a mechanistic model of non-small cell lung cancer. We designed four empirical methods to assess both model performance and reliability of predictions: two methods based on bootstrapped versions of parametric statistical tests: log-rank and combined weighted log-ranks (MaxCombo); and two methods based on bootstrapped prediction intervals, referred to here as raw coverage and the juncture metric. We also introduced the notion of observation time uncertainty to take into consideration the real life delay between the moment when an event happens, and the moment when it is observed and reported. ResultsWe highlight the advantages and disadvantages of these methods according to their application context. We have shown that the context of use of the model has an impact on the model validation process. Thanks to the use of several validation metrics we have highlighted the limit of the model to predict the evolution of the disease in the whole population of mutations at the same time, and that it was more efficient with specific predictions in the target mutation populations. The choice and use of a single metric could have led to an erroneous validation of the model and its context of use. ConclusionsWith this work, we stress the importance of making judicious choices for a metric, and how using a combination of metrics could be more relevant, with the objective of validating a given model and its predictions within a specific context of use. We also show how the reliability of the results depends both on the metric and on the statistical comparisons, and that the conditions of application and the type of available information need to be taken into account to choose the best validation strategy.

systems biology↗

Fine-scale survey of intertidal macroalgae reveals recent changes in a cold-water biogeographic stronghold

Global warming has been causing severe impacts on marine ecosystems, a notorious one being shifts in the geographical ranges of species. The north-western coast of the Iberian Peninsula is an especially interesting zone to study distributional shifts as it has a strong latitudinal thermal gradient, is influenced by the Canary upwelling system (which partially cancels coastal warming) and holds some of the most diverse macroalgae communities in Europe. Notably, it is within this region that many cold-water species, common in northern Europe, have their southernmost distribution refuge. Recent studies hypothesize that the environmental conditions may be nonetheless changing and already threatening this biodiversity hotspot. The main goal of this study was to carry out a fine-scale assessment of the distributional limits of several macroalgae in North-western Iberia, as well as identify possible population and range shifts using historical data (2001-2005) as reference. In addition, invasive species were also surveyed. We also assessed if the regions of (i) Galicia, (ii) Northern Portugal, and (iii) Central Portugal displayed distinctive characters regarding macroalgae composition and abundance. We identified an increase in abundance of some invasive macroalgae as well as a decrease in the abundance of some cold-water species. In the most severe cases, cold-water species were extirpated along hundreds of km. The compounded effect of the decrease in the abundance of cold-water species and the increase in the abundance of invasive species is leading to the homogenization of macroalgae communities in north-western Iberia.

ecology↗

Integration of heterogeneous biological data in multiscale mechanistic model calibration: application to lung adenocarcinoma

Mechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data. Determining parameters and their values is particularly challenging in the case of models of pathophysiology, for which data for calibration is sparse. Multiple data sources might be required, and data may not be in a uniform or desirable format. We describe a calibration strategy to address the challenges of scarcity and heterogeneity of calibration data. Our strategy focuses on parameters whose initial values cannot be easily derived from the literature, and our goal is to determine the values of these parameters via calibration with constraints set by relevant data. When combined with a covariance matrix adaptation evolution strategy (CMA-ES), this step-by-step approach can be applied to a wide range of biological models. We describe a stepwise, integrative and iterative approach to multiscale mechanistic model calibration, and provide an example of calibrating a pathophysiological lung adenocarcinoma model. Using the approach described here we illustrate the successful calibration of a complex knowledge-based mechanistic model using only the limited heterogeneous datasets publicly available in the literature.

systems biology↗

Temperature dependent sex-biased gene expression in the gametophytes of the kelp Saccharina latissima

Saccharina latissima is an economically and ecologically relevant kelp species in Europe and North America. In kelps, the sexuality is expressed during the haploid life stage and the microscopic gametophytes exhibit significant sexual dimorphism. To understand the sex-dependent impact of temperature on the gametophyte stage, we analyzed for the first time, gene expression profiles of male and female gametophytes at three different temperatures (4{degrees}C, 12{degrees}C and 20{degrees}C) characteristic for the species distribution range by using RNA-sequencing. We identified several differentially expressed genes between sexes; while female biased genes were enriched in general metabolism and energy production, male biased genes function within cell cycle and signaling. In our study, temperature modulated sex-biased gene expression, with only a small percentage of differentially expressed genes consistently male (7%) or female-biased (12%) at the three temperatures. Female gametophytes responded stronger to higher temperatures than males, suggesting that males are more heat tolerant. Differences between S. latissima and other brown algal gender-dependent gene expression might mirror the different evolutionary and ecological contexts. Genomic information on kelp gametophyte is still scarce and thus this study adds to our knowledge on sex differences in abiotic stress responses in macroalgae at the transcriptomic level.\n\nHighlightThe transcriptomic basis for sexual dimorphism and associated metabolic needs are described for the kelp Saccharina latissima. Temperature modulates sex-biased gene expression resulting in a stronger stress response in females.

plant biology↗