Biology subjects

Izquierdo, P.

Publications and source records attributed to Izquierdo, P..

8 recordsLinked to original sources

bioRxiv2026

Uncovering genetic mechanisms underlying trait variation in switchgrass using explainable artificial intelligence

Uncovering the genetic architecture of quantitative traits is challenging because polygenic control yields small individual gene effects and because gene-gene and genotype-by-environment interactions add further complexity. To understand the genetic basis of polygenic traits and their plasticity across environments, we integrated genome-wide SNPs and RNA-seq transcript data with interpretable statistical and machine learning models in a switchgrass (Panicum virgatum) diversity panel grown at contrasting field sites in Michigan and Texas. Notably, in addition to single environments, our trait prediction models were able to predict phenotypic differences, across environments i.e., plasticity. By interpreting trait prediction models with explainable machine learning methods, we identified genes that are the most predictive of flowering time and annual biomass production across environments, based on their associated gene expression levels and nearby SNPs. This approach recovered canonical flowering regulators and revealed novel, environment-specific candidate flowering genes. Further, transcriptome models consistently recovered more switchgrass genes homologous to experimentally validated genes in Arabidopsis and rice than SNP-based models. Feature interaction scores from the models also allow the identification of trait and environment-dependent gene-gene interactions, where flowering time showed stronger and more abundant interactions than biomass. While some of the interactions identified are consistent with the link between flowering time and yield, most are novel predictors that need to be further evaluated. These findings provide insight into the genetic basis of trait variation and plasticity across environments and help prioritize candidate genes for functional validation and cultivar improvement.

genetics↗

bioRxiv2025

Predictive models of the genetic bases underlying budding yeast fitness in multiple environments

The ability of organisms to adapt and survive depends on the effects of genes and the environment on fitness. However, the multigenic nature of fitness traits and genotype-by-environment interactions hinder our ability to understand the genetic basis of fitness. Here, we established fitness prediction models for 35 environments using machine learning and existing fitness data and different types of genetic variants for a population of Saccharomyces cerevisiae isolates. Models revealed that the predictive ability of genetic variants varied across environments, with copy number variants explaining the majority of fitness variation in most cases. Model interpretation further showed that different variant types identified distinct sets of genes associated with predictive variants. These gene sets were significantly enriched in experimentally validated genes affecting fitness in only a subset of environments, indicating that many genes influencing fitness remain unexplored. Notably, non-experimentally validated genes were more important than validated ones for fitness predictions. Gene contributions to fitness predictions were both isolate and environment dependent, pointing to gene-by-gene and gene-by-environment interactions. Further interpretation of models uncovered experimentally validated and novel candidate genetic interactions for a well characterized stress, the fungicide benomyl. These findings highlight the feasibility of identifying the genetic basis of fitness by using different types of genetic variants and offer novel targets for future functional analysis. Author SummaryOrganisms adapt to changing environments by acquiring beneficial traits, which are largely determined by genetic variation. However, predicting how genetic variation influences adaptation, and thus survival, remains a challenge. Here, we used machine learning to identify genes, gene-gene interactions, and gene-by-environment interactions underlying fitness. Specifically, we used machine learning to predict how different genetic variants--such as changes in single nucleotides, presence/absence of a sequence, and differences in copy number--affect fitness in yeast across 35 different environmental conditions. Our results show that prediction accuracy and our ability to interpret the underlying biology depend on the genetic variant type. For example, the best predictions were obtained using differences in copy number. We also found that the contributions of genetic variants to yeast fitness depend on the genetic background. Importantly, our models uncovered known and novel genes that were important across multiple and specific environments and revealed genetic interactions for a well characterized stress, offering insights into how organisms cope with environmental stress. These findings advance our understanding of the genetic basis of fitness and provide a framework for future functional studies and the design of stress-resilient yeast strains.

genomics↗

bioRxiv2025

Reducing the environmental footprint of laboratory research using visual communications: A pilot study in Spain

Addressing the environmental impact of laboratory research provides a meaningful opportunity for workplace-level mitigation. However, in Spain, the importance of sustainability in research is only starting to be recognised at a national level. We developed a scalable, visual-oriented communications strategy that was designed with input from scientific staff and included a live seminar and a coherent set of graphic materials. Five pillars were targeted: three key areas of consumption (energy, water, and plastic use), procurement, and colleague engagement. The scheme was piloted in two of the largest biomedical research centres in Spain: the Severo Ochoa Molecular Biology Centre (CBM) and the Spanish National Centre for Biotechnology (CNB). The pilot engaged 50 laboratories and services across both centres, of which 34 were followed up for 5 months. The results indicate engagement with sustainability measures and shifts toward best practice across metrics, including equipment switch-off, water use, plastic item reuse, and purchasing decisions. During the follow-up period, 23% of participants adopted new measures related to freezer maintenance, decluttering, or sample organization, 55% removed old data from storage to save energy, 100% reduced their use of lifts, and 94% discussed research sustainability with their laboratory/group. The findings also revealed key areas for improvement, including the management of shared resources and resistance to change protocols. These results offer a wide range of quantitative and qualitative insights to guide further efforts and scaling at regional and national levels. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/675379v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@b50baeorg.highwire.dtl.DTLVardef@11ccdeorg.highwire.dtl.DTLVardef@a13b46org.highwire.dtl.DTLVardef@13a9b0b_HPS_FORMAT_FIGEXP M_FIG C_FIG Sustainability spotlightThis paper describes a scalable, visual-oriented communications strategy that can be used to promote sustainable behaviours in research laboratories. We present pilot results indicating engagement in sustainability and shifts toward best practice across metrics. Our work contributes to UN Sustainable Development Goal (SDG) 9 (industry and infrastructure) and SDG 12 (responsible consumption and production).

scientific communication and education↗

bioRxiv2023

A data-driven evaluation of Arabidopsis-centric research and the model species concept

The selection of Arabidopsis as a model organism played a pivotal role in advancing genomic science, firmly establishing the cornerstone of today s plant molecular biology. Competing frameworks to select an agricultural- or ecological-based model species, or to decentralize plant science and study a multitude of diverse species, were selected against in favor of building core knowledge in a species that would facilitate genome-enabled research that could assumedly be transferred to other plants. Here, we examine the ability of models based on Arabidopsis gene expression data to predict tissue identity in other flowering plant species. Comparing different machine learning algorithms, models trained and tested on Arabidopsis data achieved near perfect precision and recall values using the K-Nearest Neighbor method, whereas when tissue identity is predicted across the flowering plants using models trained on Arabidopsis data, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64, depending on the algorithm used. Below-ground tissue is more predictable than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance from Arabidopsis. This suggests that gene expression signatures rather than marker genes are more valuable to create models for tissue and cell type prediction in plants. Our data-driven results highlight that, in hindsight, the assertion that knowledge from Arabidopsis is translatable to other plants is not always true. Considering the current landscape of abundant sequencing data and computational resources, it may be prudent to reevaluate the scientific emphasis on Arabidopsis and to prioritize the exploration of plant diversity.

plant biology↗

bioRxiv2023

Amyloidosis and ageing have differential effectson microglial Ca2+ activity in the mouse brain

In microglia, changes in intracellular calcium concentration ([Ca2+]i) may regulate process motility, inflammasome activation, and phagocytosis. However, while neurons and astrocytes exhibit frequent spontaneous Ca2+ activity, microglial Ca2+ signals are much rarer and poorly understood. Here, we studied [Ca2+]i changes of microglia in acute brain slices using Fluo-4-loaded cells and mice expressing GCaMP5g in microglia. Spontaneous Ca2+ transients occurred [~]5 times more frequently in individual microglial processes than in their somata. We assessed whether microglial Ca2+ responses change in Alzheimers disease (AD) using AppNL- G-F knock-in mice. Proximity to A{beta} plaques strongly affected microglial Ca2+ activity. Although spontaneous Ca2+ transients were unaffected in microglial processes, they were 5-fold more frequent in microglial somata near A{beta} plaques than in wild-type microglia. Microglia away from A{beta} plaques in AD mice showed intermediate properties for morphology and Ca2+ responses, partly resembling those of wild-type microglia. By contrast, somatic Ca2+ responses evoked by tissue damage were less intense in microglia near A{beta} plaques than in wild-type microglia, suggesting different mechanisms underlying spontaneous vs. damage-evoked Ca2+ signals. Finally, as similar processes occur in neurodegeneration and old age, we studied whether ageing affected microglial [Ca2+]i. Somatic damage-evoked Ca2+ responses were greatly reduced in microglia from old mice, as in the AD mice. In contrast to AD, however, old age did not alter the occurrence of spontaneous Ca2+ signals in microglial somata but reduced the rate of events in processes. Thus, we demonstrate distinct compartmentalised Ca2+ activity in microglia from healthy, aged and AD-like brains.

neuroscience↗

bioRxiv2021

Synapse development is regulated by microglial THIK-1 K+ channels

Microglia are the resident immune cells of the central nervous system. They constantly survey the brain parenchyma for redundant synapses, debris or dying cells, which they remove through phagocytosis. Microglial ramification, motility and cytokine release are regulated by tonically active THIK-1 K+ channels on the microglial plasma membrane. Here, we examined whether these channels play a role in phagocytosis. Using pharmacological blockers and THIK-1 knockout (KO) mice, we found that lack of THIK-1 activity reduced microglial phagocytosis, which may result in impaired pruning of synapses. In hippocampus, mice lacking THIK-1 expression had an increased number of glutamatergic synapses during development. This resulted from an increased number of presynaptic terminals, due to impaired removal by THIK-1 KO microglia. In microglia in brain slices from fresh human biopsies, modulating THIK-1 function had effects similar to those in rodents: blocking THIK-1 rapidly reduced microglial process ramification and increased synaptic density. The dependence of synapse number on THIK-1 K+ channels, which control microglial surveillance and phagocytic ability, implies that changes in THIK-1 expression level over the lifespan or in disease states may contribute to altering neural circuit function. SignificanceMicroglia are the brains resident immune cells, surveying it with motile processes, which can remove pathogens but also prune unnecessary junctions between the neurons (synapses). A potassium channel, THIK-1, in the microglial membrane allows efflux of potassium from these cells, and thereby regulates their membrane voltage as well as their process motility and release of inflammatory mediators. Here, using THIK-1-blocking drugs and THIK-1-deficient mice, we demonstrate that THIK-1 controls removal of synaptic material by microglia, which reduces the number of functional synapses. We also show that blocking THIK-1, as some anaesthetics do, affects microglial structure and increases the number of synapses in living brain slices from both rodents and humans, and could thus alter network function in the brain.

neuroscience↗

bioRxiv2020

Monitoring phagocytic uptake of amyloid β into glial cell lysosomes in real time

Phagocytosis by glial cells is essential to regulate brain function during development and disease. Given recent interest in using amyloid {beta} (A{beta})-targeted antibodies as a therapy for patients with Alzheimers disease, removal of A{beta} by phagocytosis is likely protective early in Alzheimers disease, but remains poorly understood. Impaired phagocytic function of glial cells surrounding A{beta} plaques during later stages in Alzheimers disease likely contributes to worsened disease outcomes, but the underlying mechanisms of how this occurs remain unknown. We have developed a human A{beta}1-42 analogue (A{beta}pH) that exhibits green fluorescence upon internalization into the acidic phagosomes of cells but is non-fluorescent at physiological pH. This allowed us to image, for the first time, glial uptake of A{beta}pH in real time in live animals. Microglia phagocytose more A{beta}pH than astrocytes in culture, in brain slices and in vivo. A{beta}pH can be used to investigate the phagocytic mechanisms removing A{beta} from the extracellular space, and thus could become a useful tool to study A{beta} clearance at different stages of Alzheimers disease.

neuroscience↗

bioRxiv2018

A TauP301L mouse model of dementia; development of pathology, synaptic transmission, microglial response and cognition throughout life

BackgroundLate stage Alzheimers disease and other dementias are associated with neurofibrillary tangles and neurodegeneration. Here we describe a mouse (TauD35) carrying human Tau with the P301L mutation that results in Tau hyperphosphorylation and tangles. Previously we have compared gene expression in TauD35 mice to mice which develop plaques but no tangles. A similar comparison of other pathological features throughout disease progression is made here between amyloid{beta} and Tau mice described in Parts I and II of this study.\n\nMethodsIn vitro CA1 patch clamp and field recordings were used to investigate synaptic transmission and plasticity. Plaque load and microglia were investigated with immunohistochemistry. Cognition, locomotor activity and anxiety-related behaviours were assessed with a forced-alternation T-maze, open field and light/dark box.\n\nResultsTransgene copy number in TauD35 mice fell into two groups (HighTAU and LowTAU), allowing assessment of dose-dependent effects of overexpression and resulting in tangle load increasing 100-fold for a 2-fold change in protein levels. Tangles were first detected at 8 (HighTAU) or 13 months (LowTAU) but the effects on synaptic transmission and plasticity and behaviour were subtle. However severe neurodegeneration occurred in HighTAU mice at around 17 months preceded by considerable proliferation but little additional activation of microglia. Proliferation only started as neurodegeneration began at 13 months. Similarly to HighTau mice at 13 months of age, LowTAU mice at 24 months of age showed a comparable tangle load and microglial proliferation. However, LowTAU mice showed no neurodegeneration at this stage and considerable microglial activation, stressing the dependence of these effects on overexpression and/or age.\n\nConclusionsComparison of the effects of amyloid{beta} and plaques without tangles in a model of preclinical Alzheimers disease to the effects of tangles without amyloid{beta} plaques in the late stage model described here may clarify the progressive stages of Alzheimers disease. While Tau hyperphosphorylation and neurofibrillary tangles are eventually sufficient to cause severe neurodegeneration, initial effects on synaptic transmission and the immune response are subtle. In contrast while even with a heavy plaque load little if any neurodegeneration occurs, considerable effects on synaptic transmission and the immune system result, even before plaques are detectable.

neuroscience↗