Biology subjects

Peixoto, M.

Publications and source records attributed to Peixoto, M..

4 recordsLinked to original sources

bioRxiv2025

Multi-omics analysis of TNBC organoids identifies phosphorylation of the membrane trafficking machinery as a key event associated with FER-mediated invasion

Triple Negative Breast Cancer (TNBC) is characterised by unfavourable outcome due to the combination of its metastatic propensity, chemo-refractory behaviour and the lack of effective targeted interventions. Expression of the feline sarcoma-related (FER) kinase constitutes an independent prognostic factor that correlates with poor patient survival. FER promotes invasive behaviour in TNBC cells by regulating endosomal sorting and recycling (ESR) of adhesion proteins. Yet, the ESR machinery supporting invasion in TNBC, particularly within 3D environments, remains poorly understood. Here, we used FER-expressing TNBC patient-derived xenograft organoids (PDXOs) and MDA-MB-231 cells to identify the membrane trafficking machinery promoting invasion. Using a combination of proteomics, phospho-proteomics, and single cell RNA-sequencing, we show that the invasion of FER-expressing PDXO cells in collagen-I is mainly associated with the differential phosphorylation of membrane trafficking regulators, including SEC16A and a marked increase in Rab4-positive tubules. SEC16A depletion impairs cell invasion and reduces the number of focal adhesions and Rab4-positive tubules. Importantly, FER regulates SEC16A levels and localization, specifically in TNBC. Altogether, we identified SEC16A as a key player in FER-driven TNBC invasion, highlighting the membrane trafficking machinery as a promising target for the development of new therapeutic strategies.

cancer biology↗

bioRxiv2024

SimpleMating: R-package for prediction and optimization of breeding crosses using genomic selection

Selecting parents and crosses is a critical step for a successful breeding program. The ability to design crosses with high means that will maintain genetic variation in the population is the goal for long-term applications. Herein, we describe a new computational package for mate allocation in a breeding program. SimpleMating is a flexible and open-source R package originally designed to predict and optimize breeding crosses in crops with different reproductive systems and breeding designs. Divided into modules, SimpleMating first estimates the cross performance (criterion), such as mean parental average, cross total genetic value, and/or usefulness of a set of crosses. The second module implements an optimization algorithm to maximize a target criterion while minimizing next-generation inbreeding. The software is flexible, allowing users to specify the desired number of crosses, maximum/minimum number of crosses per parent, and the maximum value of the parent relationship for creating crosses. As an outcome, SimpleMating generates a mating plan from the target parental population using single or multi-trait criteria. As example, we implemented and tested SimpleMating in a simulated maize breeding program obtained through stochastic simulations. The crosses designed via SimpleMating showed large genetic mean over time (up to 22% more genetic gain than conventional genomic selection programs, with lower genetic diversity decrease over time), supporting the use of this tool, as well as the use of data-driven decisions in applied breeding programs.

genetics↗

bioRxiv2022

An innovative 2D-mesh model to improve deltoid moment arms, muscle forces and glenohumeral joint reaction force estimations

We proposed a 2D-mesh representation of the deltoid muscle to improve a realism of forces and moments predictions in a multibody shoulder model. Our 2D-mesh model was qualitatively compared to a commonly used via-point model during an arm elevation in the scapular plane and to previous in-vitro studies in terms of deltoid moment arms. The most notable improvement was observed for the moment arms of the anterior deltoid, which shows better agreement with in-vitro studies. Consequently, joint reaction force above 90{degrees} of arm elevation was more consistent with in-vivo data, validating the 2D-mesh representation of the deltoid muscle.

bioinformatics↗

bioRxiv2021

Identification of fetal liver stromal subsets in spectral cytometry using the parameter autofluorescence

The fetal liver is the main hematopoietic organ during embryonic development. The fetal liver is also the unique anatomical site where hematopoietic stem cells expand before colonizing the bone marrow, where they ensure life-long blood cell production and become mostly resting. The identification of the different cell types that comprise the hematopoietic stroma in the fetal liver is essential to understand the signals required for the expansion and differentiation of the hematopoietic stem cells. We used a panel of monoclonal antibodies to identify fetal liver stromal cells in a 5-laser equipped spectral flow cytometry analyzer. The "Autofluorescence Finder" of SONY ID7000 software identified two distinct autofluorescence emission spectra. Using autofluorescence as a fluorescence parameter we could assign the two autofluorescent signals to three distinct cell types and identified surface markers that characterize these populations. We found that one autofluorescent population corresponds to hepatoblasts and cholangiocytes whereas the other expresses mesenchymal transcripts and was identified as stellate cells. Importantly, after birth, autofluorescence becomes the unique identifying property of hepatoblasts because mature cholangiocytes are no longer autofluorescent. These results show that autofluorescence used as a parameter in spectral flow cytometry is a useful tool to identify new cell subsets that are difficult to analyze in conventional flow cytometry.

developmental biology↗