Biology subjects

Xavier, A.

Publications and source records attributed to Xavier, A..

7 recordsLinked to original sources

bioRxiv2025

Evolutionary history, ecological divergence, and introgression in the Oncocyclus irises species complex in the Southern Levant

Speciation is a dynamic process shaped by the interaction of gene flow, geographic isolation, and ecological divergence. The Oncocyclus irises of the Southern Levant represent a young radiation of narrowly endemic species considered to be in the course of speciation. In this study, we used RAD-sequencing and single nucleotide polymorphism analysis across nine described species to investigate patterns of genomic divergence, gene flow, and local adaptation. Phylogenomic analyses revealed a mix of well-supported clades for some species, previously defined by their morphology and distribution, and non-monophyletic lineages, with several species exhibiting shallow divergence and shared genetic ancestry. We found evidence for gene flow and historical introgression between Iris petrana, I. atrofusca and I. mariae, while other cases of non-monophyly appear driven by incomplete lineage sorting. Both geographic distance (IBD) and environmental factors (IBE), mainly altitude, temperature, and aridity, were significantly associated with genetic structure, suggesting that local adaptation contributed to divergence following range expansion. Based on our findings we propose that the divergence of the Oncocyclus iris species in the Southern Levant supports a stepping-stone dispersion model, in which north-to-south dispersal was followed by local adaptation, and introgression in secondary contact zones. Overall, these findings highlight the complexity of speciation and the need for integrative approaches to study the interplay between historical divergence, contemporary gene flow, and environmental differentiation in shaping genomic patterns.

evolutionary biology↗

bioRxiv2024

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winners strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

genetics↗

bioRxiv2021

Identification and validation of putative target genes regulated by miR-34 in cervical cancer

PurposeWe aimed to identify the epithelial-mesenchymal transition and associated putative targets of the miR-34 family in transcriptomic data of cervical epithelial squamous cell carcinoma (CESC) to find new therapeutic targets for better disease management. MethodsA combined computational analysis of the miR-34 family; gene expression in heterogeneous primary CESCs derived from TCGA; and the integration of miR-34b and EMT-regulated genes was performed. Four EMT-associated miR-34b gene targets were analysed in primary human CESC and non-cancerous cervical tissues by qRT-PCR. Effects of endogenous miR-34b expression and its associated gene modulations in cervical cancer cells (C33A and HeLa) were analysed using qRT-PCR, western blotting and immunofluorescence, transwell migration and invasion assays. ResultsThe results showed that the miR-34 family might regulate the mTOR pathway, cell cycle (CCND2) and cell adhesion functions (FZD4). Further, we showed that a low negative correlation (r2= -0.07) between miR-34b/EM[I] score and four EMT signature genes (E>MP7, CAV1, ID2, FN1) were significantly regulated by miR-34b. Also, FN1 was indicated as its putative target with the highest negative EMT score and high binding energy between MRE/3UTR. Further, these transcriptomic signatures in CESC revealed a significant inverse correlation across the stages of primary human CESC. These genes were repressed at transcriptional and translational levels in miR-34b-3p expressing C33A and HeLa cells, contributing to their reduced cell migration and invasive properties. ConclusionsOur studies revealed the potential targets of the miR-34 family, especially miR-34b, that can emerge as potential biomarkers and promising therapeutic targets in CESC disease management.

cancer biology↗

bioRxiv2020

Reproducibility in systems biology modelling

The reproducibility crisis has emerged as an important concern across many fields of science including life science, since many published results failed to reproduce. Systems biology modelling, which involves mathematical representation of biological processes to study complex system behaviour, was expected to be least affected by this crisis. While lack of reproducibility of experimental results and computational analysis could be a repercussion of several compounded factors, it was not fully understood why systems biology models with well-defined mathematical expressions fail to reproduce and how prevalent it is. Hence, we systematically attempted to reproduce 455 kinetic models of biological processes published in peer-reviewed research articles from 152 journals; which is collectively a work of about 1400 scientists from 49 countries. Our investigation revealed that about half (49%) of the models could not be reproduced using the information provided in the published manuscripts. With further effort, an additional 12% of the models could be reproduced either by empirical correction or support from authors. The other 37% remained non-reproducible models due to missing parameter values, missing initial concentration, inconsistent model structure, or a combination of these factors. Among the corresponding authors of the non-reproducible model we contacted, less than 30% responded. Our analysis revealed that models published in journals across several fields of life science failed to reproduce, revealing a common problem in the peer-review process. Hence, we propose an 8-point reproducibility scorecard that can be used by authors, reviewers and journal editors to assess each model and address the reproducibility crisis.

systems biology↗

bioRxiv2019

Quantitative Genomic Dissection of Soybean Yield Components

Soybean is a crop of major economic importance with low rates of genetic gains for grain yield compared to other field crops. A deeper understanding of the genetic architecture of yield components may enable better ways to tackle the breeding challenges. Key yield components include the total number of pods, nodes and the ratio pods per node. We evaluated the SoyNAM population, containing approximately 5600 lines from 40 biparental families that share a common parent, in 6 environments distributed across 3 years. The study indicates that the yield components under evaluation have low heritability, a reasonable amount of epistatic control, and partially oligogenic architecture: 18 quantitative trait loci were identified across the three yield components using multi-approach signal detection. Genetic correlation between yield and yield components was highly variable from family-to-family, ranging from -0.2 to 0.5. The genotype-by-environment correlation of yield components ranged from -0.1 to 0.4 within families. The number of pods can be utilized for indirect selection of yield. The selection of soybean for enhanced yield components can be successfully performed via genomic prediction, but the challenging data collections necessary to recalibrate models over time makes the introgression of QTLs a potentially more feasible breeding strategy. The genomic prediction of yield components was relatively accurate across families, but less accurate predictions were obtained from within-family predictions and predicting families not observed included in the calibration set.

genetics↗

bioRxiv2018

Panoptic vDISCO imaging reveals neuronal connectivity, remote trauma effects and meningeal vessels in intact transparent mice

Analysis of entire transparent rodent bodies could provide holistic information on biological systems in health and disease. However, it has been challenging to reliably image and quantify signal from endogenously expressed fluorescent proteins in large cleared mouse bodies due to the low signal contrast. Here, we devised a pressure driven, nanobody based whole-body immunolabeling technology to enhance the signal of fluorescent proteins by up to two orders of magnitude. This allowed us to image subcellular details in transparent mouse bodies through bones and highly autofluorescent tissues, and perform quantifications. We visualized for the first-time whole-body neuronal connectivity of an entire adult mouse and discovered that brain trauma induces degeneration of peripheral axons. We also imaged meningeal lymphatic vessels and immune cells through the intact skull and vertebra in naive animals and trauma models. Thus, our new approach can provide an unbiased holistic view of biological events affecting the nervous system and the rest of the body.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC=\"FIGDIR/small/374785_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (62K):\norg.highwire.dtl.DTLVardef@8fad72org.highwire.dtl.DTLVardef@c41ed5org.highwire.dtl.DTLVardef@1c470eaorg.highwire.dtl.DTLVardef@1d144cb_HPS_FORMAT_FIGEXP M_FIG C_FIG Note: Manuscript videos are available at Supplementary material section of BioRxiv and at the following link http://vdisco.isd-muc.de/

neuroscience↗

bioRxiv2018

Non-canonical role of E3 ligase RIPLET in innate immunity as a co-receptor for RIG-I

Ubiquitin (Ub) and its E3 ligases play diverse biological roles, from proteasomal degradation to innate immune signaling. The conventional view posits that E3 ligases function primarily through conjugating Ub to their substrate molecules. We report here that RIPLET, an essential E3 ligase in antiviral immunity, promotes the antiviral signaling activity of the viral RNA receptor RIG-I through both Ub-dependent and -independent manners. RIPLET utilizes its dimeric structure and the bivalent binding mode to preferentially recognize RIG-I preoligomerized on dsRNA. RIPLET can also adopt an alternative binding mode for filamentous oligomers of RIG-I assembled on longer dsRNAs, cross-bridging RIG-I filaments in both ubiquitin-dependent and-independent manners. The resultant receptor clustering leads to the formation of aggregate-like cytosolic granules and the stimulation of RIG-I-mediated antiviral signaling in a RNA-length dependent manner. These observations show the unexpected role of an E3 ligase as a co-receptor that directly participates in receptor oligomerization and ligand discrimination. This study also highlights previously unrecognized mechanisms by which an E3 ligase induces receptor clustering and signal amplification, and offers insights into the unique cellular function of membrane-less granule assembly.\n\nOne-sentence summaryRIPLET functions as a molecular glue to assemble signaling scaffolds for the antiviral innate immune receptor RIG-I.

immunology↗