Biology subjects

Zamora, P.

Publications and source records attributed to Zamora, P..

6 recordsLinked to original sources

bioRxiv2025

HERITABILITY AND QTL MAPPING OF AERIAL ROOTS AND OTHER YIELD COMPONENT TRAITS WITH IMPLICATIONS FOR N2 FIXATION IN ZEA MAYS

Zea mays L. spp. mays (hereafter maize) populations from an indigenous community of Mexico have been reported to perform well using traditional cultivation practices that exclude industrial fertilizers despite low soil nitrate levels. The local maize cultivar is characterized by extended maturity, tall stature, and presence of thick aerial roots that secrete an abundance of polysaccharide-rich root exudate, or mucilage, that is implicated in the recruitment of diazotrophic microbes to facilitate biological nitrogen fixation. Here we estimate the broad sense heritability of traits related to nitrogen fixation in a panel of Zea entries spanning pre-domestication, post-domestication and post-improvement, and identify QTL via F2:F3 families for traits including aerial root node counts and various other yield related traits (i.e. germination rate, time to reproductive maturity, total biomass and nitrogen content of shoot and grain). Across two separate field studies, aerial root node count (AR) demonstrates heritability of 64% and 73%, and genetic mapping reveals three distinct QTL distributed on chromosomes 1 and 9. Further, we report novel QTL for germination rate via stand counts after direct sowing (SC), Plant Total Nitrogen (PTN), Plant Dry Mass (PDM), and a joint QTL for Grain Total Nitrogen (GTN) and Grain Dry Mass (GDM). Finally, we identify overlap between QTL for multiple traits (including AR and SC) and regions of elevated introgression from wild Zea mays L. spp. mexicana (hereafter mexicana) into Totontepec maize, with a greater degree of overlap than expected under a uniform genomic distribution suggesting adaptive introgression from mexicana. Given its pronounced aerial root morphology, we propose that mexicana is the ancestral source for prominent aerial roots and corresponding abundant mucilage production in Totontepec maize and other maize traditional varieties. ARTICLE SUMMARYZea mays aerial roots (AR) and mucilage have been implicated in diazotrophic recruitment. This study estimates heritability and identifies QTL for AR and yield components via a diverse panel of Zea entries and F2:F3 mapping populations. Genetic mapping reveals three QTL for AR and several QTL for other yield traits, both novel and previously reported. This study also compares QTL and regions of elevated introgression from wild Zea mays L. spp. mexicana (hereafter mexicana) into a native cultivar. For AR QTL, enrichment for mexicana-derived haplotypes is greater than expected under a uniform genomic distribution, suggesting adaptive introgression from mexicana.

genetics↗

bioRxiv2025

Prioritizing Combinational Drug Screening: A Ranking System for In Vitro Drug Combinations in Neurofibromatosis Type 1

Neurofibromatosis type 1 (NF1) is a genetic disorder characterized by benign tumors, including plexiform neurofibromas, which can be difficult to treat. Currently, only two FDA-approved therapies exist: selumetinib, approved for pediatric patients with inoperable tumors, and mirdametinib, approved for patients aged two and older with symptomatic peripheral neuropathy where surgical resection is not possible. These limited options highlight the urgent need for novel therapeutic strategies, including combination therapies and therapies applicable to adult populations. In this study, we introduce the Composite Matrix Reduction Score (CMRS), a novel algorithm designed to evaluate the in vitro efficacy of drug combinations for NF1-related plexiform neurofibromas. Using a high-throughput 6x6 combinatorial matrix, we screened three cell lines: ipnNF95.11c (NF1+/-, non-tumor reference), and two NF1-/- tumor lines: ipNF05.5mc and ipNF95.6. Cell viability responses to drug combinations were normalized to vehicle controls, and combination effects were compared to single-agent responses. Tumor-to-non-tumor response ratios were aggregated to generate a composite ranking for each drug pair. Our results show that certain drug combinations outperformed single agents in reducing tumor cell viability, consistent with findings in other cancers. A focused analysis on selumetinib combinations supported the CMRS algorithm and identified potential synergistic partners that may surpass a single-agent therapy, highlighting candidates for continued investigation. CMRS provides a scalable, standardized framework for prioritizing drug combinations in NF1 and potentially other cancers. By integrating multi-cell line analysis, this approach enhances the identification of promising therapeutic candidates and mechanisms of action for further preclinical development.

cancer biology↗

bioRxiv2020

Molecular characterization of triple negative breast cancer formaldehyde-fixed paraffin-embedded samples by data independent acquisition proteomics

Triple negative breast cancer (TNBC) accounts for 15-20% of all breast carcinomas and it is clinically characterized by an aggressive phenotype and bad prognosis. TNBC does not benefit from any targeted therapy, so further characterization is needed to define subgroups with potential therapeutic value. In this work, the proteomes of one hundred twenty-five formalin-fixed paraffin-embedded samples from patients diagnosed with triple negative breast cancer were analyzed by mass spectrometry using data-independent acquisition. Hierarchical clustering, probabilistic graphical models and Significance Analysis of Microarrays were used to characterize molecular groups. Additionally, a predictive signature related with relapse was defined. Two molecular groups with differences in several biological processes as glycolysis, translation and immune response, were defined in this cohort, and a prognostic signature based on the abundance of proteins RBM3 and NIPSNAP1 was defined. This predictor split the population into low-risk and high-risk groups. The differential processes identified between the two molecular groups may serve to design new therapeutic strategies in the future and the prognostic signature could be useful to identify a population at high-risk of relapse that could be directed to clinical trials.

cancer biology↗

bioRxiv2018

Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients

AimsDifferences in metabolism among breast cancer subtypes suggest that metabolism plays an important role in this disease. Flux Balance Analysis is used to explore these differences as well as drug response.\n\nMaterials & MethodsProteomics data from breast tumors were obtained by mass-spectrometry. Flux Balance Analysis was performed to study metabolic networks. Flux activities from metabolic pathways were calculated and used to build prognostic models.\n\nResultsFlux activities of vitamin A, tetrahydrobiopterin and beta-alanine metabolism pathways split our population into low- and high-risk patients. Additionally, flux activities of glycolysis and glutamate metabolism split triple negative tumors into low- and high-risk groups.\n\nConclusionsFlux activities summarize Flux Balance Analysis data and can be associated with prognosis in cancer.

cancer biology↗

bioRxiv2018

Probabilistic graphical models and computational metabolic models applied to the analysis of metabolomics data in breast cancer

Metabolomics has a great potential in the development of new biomarkers in cancer. In this study, metabolomics and gene expression data from breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without other a priori information; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways. On the one hand, both analyses highlighted the importance of glutamine in breast cancer. Moreover, cell experiments showed that treating breast cancer cells with drugs targeting glutamine metabolism significantly affects cell viability. On the other hand, these computational methods suggested some hypotheses and have demonstrated their utility in the analysis of metabolomics data and in associating metabolomics with patients clinical outcome.

systems biology↗

bioRxiv2017

Probabilistic Graphical Models Relate Immune Status With Response To Neoadjuvant Chemo-Therapy In Breast Cancer

Breast cancer is the most frequent tumor in women and its incidence is increasing. Neoadjuvant chemotherapy has become standard of care as a complement to surgery in locally advanced or poor-prognosis early stage disease. The achievement of a complete response to neoadjuvant chemotherapy correlates with prognosis but it is not possible to predict who will obtain an excellent response. The molecular analysis of the tumor offers a unique opportunity to unveil predictive factors. In this work, gene expression profiling in 279 tumor samples from patients receiving neoadjuvant chemotherapy was performed and probabilistic graphical models were used. This approach enables addressing biological and clinical questions from a Systems Biology perspective, allowing to deal with large gene expression data and their interactions. Tumors presenting complete response to neoadjuvant chemotherapy had a higher activity of immune related functions compared to resistant tumors. Similarly, samples from complete responders presented higher expression of lymphocyte cell lineage markers, immune-activating and immune-suppressive markers, which may correlate with tumor infiltration by lymphocytes (TILs). These results suggest that the patients immune system plays a key role in tumor response to neoadjuvant treatment. However, future studies with larger cohorts are necessary to validate these hypotheses.

cancer biology↗