Biology subjects

Castro, C. M.

Publications and source records attributed to Castro, C. M..

5 recordsLinked to original sources

bioRxiv2026

Reticulate History of Southern South American Wild Potatoes: Recurrent Hybridization, Cytonuclear Discordance, and Phylogenetic Placement of Solanum malmeanum

Hybridization is a powerful process in plant evolution generating reticulate patterns of speciation, introgression, and chloroplast capture. In the Petota clade of Solanum, which spans cultivated and wild potatoes, these processes cause much of the taxonomic complexity of the group. We examined the evolutionary history of the Commersoniana group (Solanum commersonii and S. malmeanum) relative to S. chacoense (Tuberosa Group) using an integrative framework of phylogenomics and morphology. With target-capture sequencing (Angiosperms353) of nuclear and plastid loci recovered from herbarium specimens, together with data mining we built nuclear and plastome phylogenies and ran symmetry tests, gene concordance analyses, and coalescent simulations to weigh incomplete lineage sorting against chloroplast capture. The data reveal cytonuclear discordance and events consistent with chloroplast capture from S. chacoense into Commersoniana lineages supported by both empirical and simulated results. Phylogenetic networks confirm repeated historical and recent reticulation, especially involving S. malmeanum. Non-metric Multidimensional Scaling (NMDS) of morphological traits did not recover clear species boundaries but was consistent with a hybrid origin for the group. These results indicate that recurrent hybridization and event consistent with chloroplast capture are strongly influencing evolution in the examined wild potatoes, complicating classical taxonomy and informing conservation and breeding. They also underscore the value of combining genomic and phenotypic data to resolve species boundaries in plant groups with tangled hybrid histories.

evolutionary biology↗

bioRxiv2026

Extracellular vesicles facilitate the horizontal transfer of drug resistance and stem-like properties between ovarian tumor cells

Ovarian cancer stem cells (CSCs) can seed recurrent drug-resistant disease. Likewise, non-CSCs can acquire CSC phenotypic properties. How this process is orchestrated is of interest to inform how it might be prevented. We tested the hypothesis that ovarian CSC and/or drug-resistant tumor cells confer stem-like properties via extracellular vesicles (EVs). We focused our investigation on how EVs might mediate EZH2 signaling to promote a phenotypic change in drug-sensitive, non-CSCs. To accomplish this, we utilized paired PARP inhibitor-sensitive and - resistant ovarian cancer (OvCa) cell lines, EZH2 knockdown lines, and patient-derived organoids (PDOs) originating from recurrent high-grade serous OvCa. Small EVs isolated from drug-sensitive, CSC and/or drug-resistant enriched cultures, PARP inhibitor (olaparib) resistant lines, or drug-treated (olaparib or carboplatin) lines were cultured with treatment naive or sensitive lines for defined time points. The impact of small EV exposure was determined by assessing cell number, metabolic activity, viability, sphere and colony-forming capacity, ALDH activity, DNA damage, and changes in associated signaling pathways. We found that EVs from CSC or drug-resistant enriched cell fractions communicate CSC-like phenotypes to the more sensitive tumor cells via EZH2 canonical and non-canonical signaling pathways, promoting stemness. We conclude that EV-mediated activation of EZH2 signaling represents a targetable mechanism contributing to stemness-associated drug resistance in OvCa. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=160 SRC="FIGDIR/small/699925v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@6b4ffborg.highwire.dtl.DTLVardef@1501931org.highwire.dtl.DTLVardef@1a5f31aorg.highwire.dtl.DTLVardef@1fb475f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Oligodendroglia vulnerability in the human dorsal striatum in Parkinson's disease

Oligodendroglia are the responsible cells for myelination in the central nervous system and their involvement in Parkinsons disease (PD) is poorly understood. We performed snRNA-seq and image-based spatial transcriptomics of human caudate nucleus and putamen (dorsal striatum) from PD and Control brain donors to elucidate the diversity of oligodendroglia and how they are affected by the disease. We have defined fifteen subclasses, from precursor to mature cells, four of which are disease-associated. These PD-specific populations are characterized by the overexpression of heat shock proteins and distinct expression signatures, including immune responses and myelination alterations. We have also identified disruptions in cell communication and oligodendrocyte development, evidenced by changes in neurotransmitter receptors expression and cell adhesion molecules. These transcriptomic changes correlated with impaired myelin integrity and altered oligodendrocyte distribution in the striatum. Thus, we uncover oligodendroglia as a critical cell type in PD and a potential new therapeutic target.

neuroscience↗

bioRxiv2023

Profiling extracellular vesicles in circulation enables the early detection of ovarian cancer

Ovarian cancer is a heterogeneous group of tumors in both cell type and natural history. While outcomes are generally favorable when detected early, the most common subtype, high-grade serous carcinoma (HGSOC), typically presents at an advanced stage and portends less favorable prognoses. Its aggressive nature has thwarted early detection efforts through conventional detection methods such as serum CA125 and ultrasound screening and thus inspired the investigation of novel biomarkers. Here, we report the systematic development of an extracellular-vesicle (EV)-based test to detect early-stage HGSOC. Our study is based on emerging insights into HGSOC biology, notably that it arises from precursor lesions within the fallopian tube before traveling to ovarian and/or peritoneal surfaces. To identify HGSOC marker candidates, we established murine fallopian tube (mFT) cells with oncogenic mutations in Brca1/2, Tp53, and Pten genes, and performed proteomic analyses on mFT EVs. The identified markers were then evaluated with an orthotopic HGSOC animal model. In serially-drawn blood samples of tumor-bearing mice, mFT-EV markers increased with tumor initiation, supporting their potential use in early cancer detection. A pilot human clinical study (n = 51) further narrowed EV markers to five candidates, EpCAM, CD24, VCAN, HE4, and TNC. Combined expression of these markers achieved high OvCa diagnostic accuracy (cancer vs. non-cancer) with a sensitivity of 0.89 and specificity of 0.93. The same five markers were also effective in a three-group classification: non-cancer, early-stage (I & II) HGSOC, and late-stage (III & IV) HGSOC. In particular, they differentiated early-stage HGSOC from the rest with a specificity of 0.91. Minimally invasive and repeatable, this EV-based testing could be a versatile and serial tool for informing patient care and monitoring women at high risk for ovarian cancer.

cancer biology↗

bioRxiv2017

Deep-learning based hologram classification for molecular diagnostics

Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditional lens-based microcopy. However, extensive computation is required to reconstruct object images from the complex diffraction patterns produced by LDIH, which limits LDIH utility for point-of-care applications, particularly in resource limited settings. Here, we describe a deep transfer learning (DTL) based approach to process LDIH images in the context of cellular analyses. Specifically, we captured holograms of cells labeled with molecular-specific microbeads and trained neural networks to classify these holograms without reconstruction. Using raw holograms as input, the trained networks were able to classify individual cells according to the number of cell-bound microbeads. The DTL-based approach including a VGG19 pretrained network showed robust performance even with noisy experimental data. Combined with the developed DTL approach, LDIH could be realized as a low-cost, portable tool for point-of-care diagnostics.

bioengineering↗