Biology subjects

Freitas, V.

Publications and source records attributed to Freitas, V..

4 recordsLinked to original sources

bioRxiv2026

Fluid shear stress modulates endocytic pathways and junctional targeting of tumor-derived extracellular vesicles in endothelial cells

Breast cancer is the most common malignancy in women, with triple-negative breast cancer (TNBC) representing the most aggressive subtype and carrying a poor metastatic prognosis. Metastasis requires tumor cells to cross the endothelial barrier, a process facilitated by tumor-derived extracellular vesicles (EVs), which can disrupt vascular integrity. Fluid shear stress (FSS), generated by blood flow, shapes endothelial physiology and may influence EV uptake, yet the mechanisms underlying TNBC-derived small EV (sEV) internalization remain unclear. Here, we investigated TNBC sEV-endothelial interactions using combined in silico and in vitro approaches. Human umbilical vein endothelial cells (HUVECs) were cultured under static or FSS conditions (20 dyn/cm{superscript 2}), followed by proteomic profiling and protein-protein interaction analyses with sEV proteomes. Uptake assays employed pharmacological inhibition (Dynasore, M{beta}CD, Pitstop2), Caveolin-1 (CAV-1) and Clathrin Heavy Chain (CLHC), siRNA-mediated knockdown, and junctional interaction analyses via confocal microscopy and co-immunoprecipitation. FSS downregulated proliferation- and angiogenesis-associated proteins while upregulating adhesion and cytoskeletal regulators assessed by proteomics. Network analysis identified clathrin- and caveolin-mediated endocytosis (CME and CavME), integrins, and early endosomes as central mediators of sEV uptake. Functionally, uptake was reduced by Pitstop2, M{beta}CD, and CAV-1/CLHC knockdown under static conditions, but silencing paradoxically enhanced uptake under FSS, suggesting compensatory flow-dependent pathways. Notably, under FSS, sEVs accumulated at endothelial junctions, colocalizing with VE-CAD and associating with CLDN5, indicating a potential disruption mechanism of adherens and tight junctions and consequent endothelial permeability. These findings identify CME and CavME as key uptake routes while underscoring FSS as a critical determinant of endothelial-tumor EV interactions. By revealing junctional targeting of sEVs, this work provides new mechanistic insight into vascular remodeling during metastasis and highlights EV pathways as potential therapeutic targets in TNBC. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/721946v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@f91c5org.highwire.dtl.DTLVardef@2b4dc8org.highwire.dtl.DTLVardef@ff94f1org.highwire.dtl.DTLVardef@18b714b_HPS_FORMAT_FIGEXP M_FIG C_FIG Uptake and localization of sEVs on HUVEC under (a) static and (b) fluid shear-stress conditions. sEVs: Small Extracellular Vesicles. CME: Clathrin-mediated Endocytosis. CavME: Caveolin-mediated Endocytosis. CLDN5: Claudin-5. VE-CAD: Vascular Endothelial Cadherin. FSS: Fluid shear-stress.

cancer biology↗

bioRxiv2025

Combining phenomic and genomic selection for pea breeding improvement

Pea (Pisum sativum L.) is a strategic crop in the development of sustainable agriculture. However, the genetic gain remains limited despite advances in breeding. Genomic selection holds promise to accelerate varietal improvement, but its high implementation cost restricts its use in crops. Phenomic selection, based on near-infrared spectroscopy data, is a cost-effective alternative demonstrated in various crops, but not yet undertaken in pea. This study aims to assess the predictive ability of phenomic selection, alone and combined with genomic selection, for yield-related traits in a panel of elite spring pea lines evaluated across twelve environments. Three cross-validation scenarios were implemented to simulate predictions across different years and locations. Our results show that phenomic prediction is as effective as genomic selection at predicting yield, and is more accurate for seed protein content. The integrative model, combining spectral and molecular data, consistently achieved the highest accuracy for most traits, particularly for complex traits such as grain yield and seed protein. In temporal prediction scenarios, the most accurate predictions were obtained using the spectra data from the same year as phenotyping. In spatial prediction scenarios, predictive accuracy varied by site and year, nevertheless, integrative phenomic-genomic models consistently outperformed univariate approaches. These findings confirm the potential of phenomic selection in pea and underscore the added value of combining near-infrared spectroscopy and genotyping data to improve the prediction of complex traits in breeding programs. In the face of increasing environmental variability, the integrative approach offers a valuable tool for accelerating genetic gain. Key messageThe integration of spectral data into prediction models enhances the predictive ability for complex traits in pea.

genetics↗

bioRxiv2025

Robustness of high-throughput prediction of leaf ecophysiological traits using near infra-red spectroscopy and poro-fluorometry

Water scarcity is a major threat to crop production and quality. Improving drought tolerance through variety selection requires a deeper understanding of plant ecophysiological responses, but large-scale phenotyping remains a bottleneck. This study assessed the potential of high-throughput tools (spectroscopy and poro-fluorometry) to predict leaf morphological and ecophysiological traits in a grapevine diversity panel grown in pots under well-watered outdoor conditions and under three contrasting soil water treatments in a greenhouse. We found a certain complementarity between measuring devices. Spectrometers could accurately predict leaf mass per area, water content, and water quantity (R{superscript 2} > 0.58), while the poro-fluorometer was efficient for predicting net CO2 assimilation (R{superscript 2} > 0.72), regardless of the water treatment. The prediction of leaf mass per area using spectrometers appeared to be quite robust across both outdoor and greenhouse experiments, while the prediction of water use efficiency was dependent on the water treatment, with much better predictions under moderate (R{superscript 2} > 0.73) than severe water deficit. Calibrated models were then applied to the full diversity panel using only high-throughput measurements to estimate trait values and their broad-sense heritability. Leaf mass per area, also measured directly, showed similar heritability whether based on observed or predicted data. Heritability estimates for predicted traits reached up to 0.5. Overall, our findings support the use of spectroscopy and poro-fluorometry as reliable, non-destructive tools for high-throughput phenotyping, enabling genetic studies on drought-related traits in grapevine.

plant biology↗

bioRxiv2022

Transcriptional drifts associated with environmental changes in endothelial cells

O_LIEnvironmental cues, such as physical forces and heterotypic cell interactions play a critical role in cell function, yet their collective contributions to transcriptional changes are unclear. Focusing on human endothelial cells, we performed broad individual sample analysis to identify transcriptional drifts associated with environmental changes that were independent of genetic background. Global gene expression profiling by RNA-seq and protein expression by LC-MS directed proteomics distinguished endothelial cells in vivo from genetically matched culture (in vitro) samples. Over 43% of the transcriptome was significantly changed by the in vitro environment. Subjecting cultured cells to long-term shear stress significantly rescued the expression of approximately 17% of genes. Inclusion of heterotypic interations by co-culture of endothelial cells with smooth muscle cells normalized approximately 9% of the original in vivo signature. We identify novel flow depedent genes, as well as, genes that necessitate heterotypic cell interactions to mimic the in vivo transcriptome. Our findings highlight specific genes and pathways that rely on contextual information and physical forces. C_LI

cell biology↗