Biology subjects

Herrera, D.

Publications and source records attributed to Herrera, D..

8 recordsLinked to original sources

bioRxiv2026

Zinc excess promotes lysosome remodeling by activating HLH-30/TFEB through the action of the high zinc sensor HIZR-1.

Zinc is an essential element that plays many roles in animals. Since excess zinc is toxic, animals have evolved sophisticated mechanisms to achieve homeostasis. A conserved mechanism is to store excess zinc in specialized lysosomes, which contributes to zinc detoxification and provides a supply when zinc becomes limiting. In C. elegans excess zinc conditions promote an increase in lysosome-related organelles called bilobed granules. Defining how zinc-regulated transcription drives the observed lysosome remodeling is key to understanding zinc homeostasis processes. Here we describe a positively regulated zinc cascade controlled by hizr-1 and hlh-30/TFEB, which encode the C. elegans high zinc sensor and the master regulator of the autophagy-lysosomal pathway, respectively; essential for connecting excess zinc homeostasis to lysosome biogenesis and remodeling. Our transcriptomic, genetic, and bioinformatic studies indicate that hizr-1 and hlh-30 are necessary and sufficient to activate transcription of lysosome genes under excess zinc conditions. In our regulatory model, zinc binding activates HIZR-1 protein, which accumulates in the nucleus and activates genes containing the HZA enhancer, including hlh-30. HLH-30 protein accumulates in the nucleus and activates genes containing the E-box enhancer. Genetic analysis of loss-of-function mutants demonstrated that both hizr-1 and hlh-30 are necessary for animals to tolerate excess zinc. Furthermore, HLH-30 promotes the increase in the number of acidified compartments, whereas HIZR-1 promotes the increase in the volume of the expansion compartment. These results define a genetic pathway that responds to excess zinc by increasing the number of lysosome-related organelles and their capacity to store and detoxify cytosolic zinc.

genetics↗

bioRxiv2025

Endophytes induce systemic spatial reprogramming of metabolism in poplar roots under drought

Beneficial endophytes help plants thrive in challenging environments by altering their hosts metabolism, but how these cellular scale metabolic changes propagate to the systems biology scale is unknown. In this work, we employed a high-resolution chemical imaging approach to map metabolic changes at the root zone and cell type levels and found that a 9-strain consortium of beneficial endophytes differentially altered the metabolome of droughted root tissues according to cell types and locations along the root system architecture. Using machine learning (ML) models, we identified root metabolites and exudates that have predictive power over treatment class and could therefore be used as systems biology indicators of drought and endophyte inoculation status. We calculated the correlation between each endophyte and metabolite and found that this relationship shifts under drought conditions, indicating the dynamic role endophytes play in a plants microbiome and metabolism in response to environmental changes.

plant biology↗

bioRxiv2024

Moving towards more holistic validation of machine learning-based approaches in ecology and evolution

Machine-learning (ML) is revolutionizing field and laboratory studies of animals. However, a challenge when deploying ML for classification tasks is ensuring the models are reliable. Currently, we evaluate models using performance metrics (e.g., precision, recall, F1), but these can overlook the ultimate aim, which is not the outputs themselves (e.g. detected species or individual identities, or behaviour) but their incorporation for hypothesis testing. As improving performance metrics has diminishing returns, particularly when data are inherently noisy (as human-labelled, animal-based data often are), researchers are faced with the conundrum of investing more time in maximising metrics versus doing the actual research. This raises the question: how much noise can we accept in ML models? Here, we start by describing an under-reported factor that can cause metrics to underestimate model performance. Specifically, ambiguity between categories or mistakes in labelling validation data produces hard ceilings that limit performance metrics. This likely widespread issue means that many models could be performing better than their metrics suggest. Next, we argue and show that imperfect models (e.g. low F1 scores) can still be useable. Using a case study on ML-identified behaviour from vulturine guineafowl accelerometer data, we first propose a simulation framework to evaluate robustness of hypothesis testing using models that make classification errors. Second, we show how to determine the utility of a model by supplementing existing performance metrics with biological validations. This involves applying ML models to unlabelled data and using the models outputs to test hypotheses for which we can anticipate the outcome. Together, we show that effects sizes and expected biological patterns can be detected even when performance metrics are relatively low (e.g., F1: 60-70%). In doing so, we provide a roadmap for validation approaches of ML classification models tailored to research in animal behaviour, and other fields with noisy, biological data. HighlightsEvaluating machine learning (ML) models must go beyond performance metrics Mislabels in validation data leads to underestimation of models performance Underestimated metrics can cause research delays despite models being useful We propose simulations and biological validations to evaluate model performance Models with low standard metrics can still be powerful for hypothesis testing

ecology↗

bioRxiv2024

Modular Fluorescent Cholesterol Naphthalimide Probes And Their Application For Cholesterol Trafficking Studies In Cells

Development of fluorescent cholesterol analogs to better understand subcellular cholesterol trafficking is of great interest for cell biology and medicine. Our approach utilizes a bifunctional 1,8-naphthalimide scaffold with a push-pull character, modified on one side with a head group and a linker on the other side connecting it to cholesterol via an ester bond. Through structure-function studies, weve explored how different substituents--linkers and head groups--affect the ability of these fluorescent cholesterol naphthalimide analogs (CNDs) to mimic natural cholesterol behavior at both molecular and cellular levels. We categorized the resulting analogs into three groups: neutral, charged, and those featuring a hydroxyl group. Each compound was assessed for its solvatochromic behavior in organic solvents and model membranes. Extensive all-atom molecular dynamics simulations helped us examine how these analogs perform in model membranes compared to cholesterol. Additionally, we investigated the partitioning of these fluorescent probes in phase-separated giant unilamellar vesicles. We evaluated the uptake and distribution of these probes within mouse fibroblast cells and astrocytes, for their subcellular distributions in lysosomes and compared that to BODIPY-cholesterol, a well-regarded fluorescent cholesterol analog. The internalization efficiency of the fluorescent probes varies in different cell types and is affected mainly by the head groups. Our results demonstrate that the modular design significantly simplifies the creation of fluorescent cholesterol probes bearing distinct spectral, biophysical, and cellular targeting features, which makes it a valuable toolkit for the investigation of subcellular distribution and trafficking of cholesterol and its derivatives.

biochemistry↗

bioRxiv2024

Tumor-derived hypoxic small extracellular vesicles promote endothelial cell migration and tube formation via ALS2/Rab5/β-catenin signaling

Tumor hypoxia has been associated with cancer progression, angiogenesis, and metastasis via modifications in the release and cargo composition of extracellular vesicles secreted by tumor cells. Indeed, hypoxic extracellular vesicles are known to trigger a variety of angiogenic responses via different mechanisms. We recently showed that hypoxia promotes endosomal signaling in tumor cells via HIF-1-dependent induction of the guanine exchange factor ALS2, which activates Rab5, leading to downstream events involved in cell migration and invasion. Since Rab5-dependent signaling is required for endothelial cell migration and angiogenesis, we explored the possibility that hypoxia promotes the release of small extracellular vesicles containing ALS2, which in turn activate Rab5 in recipient endothelial cells leading to pro-angiogenic properties. In doing so, we found that hypoxia promoted ALS2 expression and incorporation as cargo within small extracellular vesicles, leading to subsequent transfer to recipient endothelial cells, promoting cell migration, tube formation and downstream Rab5 activation. Consequently, ALS2-containing small extracellular vesicles increased early endosome size and number in recipient endothelial cells, which was followed by subsequent sequestration of components of the {beta}-catenin destruction complex within endosomal compartments, leading to stabilization and nuclear localization of {beta}-catenin. These events converged in the expression of {beta}-catenin target genes involved in angiogenesis. Knockdown of ALS2 in donor-tumor cells, which precluded its incorporation into small extracellular vesicles, prevented Rab5-downstream events and endothelial cell responses, which depended on Rab5 activity and guanine exchange factor activity of ALS2. These findings indicate that vesicular ALS2, secreted in hypoxia, promotes endothelial cell events leading to angiogenesis. Finally, these events might explain how tumor angiogenesis proceeds in hypoxic conditions.

cell biology↗

bioRxiv2023

Profiling sorghum-microbe interactions with a specialized photoaffinity probe identifies key sorgoleone binders in Acinetobacter pittii

Sorghum (Sorghum bicolor) is a major food and bioenergy grass species cultivated worldwide. To promote more robust and sustainable growth of this important crop, we need a deeper understanding of the plant-microbe interactions between sorghum and soil microbial communities that benefit plant host resiliency and enhance nutrient acquisition. The release of specific metabolites from plant roots, or root exudation, drives these plant-microbe interactions, but the molecular pathways by which root exudates shape the sorghum rhizosphere microbiome require further elucidation. To investigate these complex interkingdom interactions in the sorghum rhizosphere, we developed a photoaffinity probe based on sorgoleone, a hydrophobic secondary metabolite and allelochemical produced in sorghum seedling root exudates. Here, we apply a new synthetic sorgoleone diazirine alkyne photoaffinity probe (SoDA-PAL) to the identification of sorgoleone-binding proteins in Acinetobacter pittii SO1, a potential plant growth promoting microbe derived from Sorghum bicolor rhizosphere soil. Competitive photoaffinity labeling of A. pittii whole cell lysates with SoDA-PAL identified 137 statistically enriched proteins that were complementary to a previously identified gene cluster involved in sorgoleone catabolism. Proteins identified by SoDA-PAL included a select set of putative transporters, transcription regulators, and a subset of proteins with lipid and secondary metabolic activities. We confirm binding of SoDA-PAL to a putative hydrolase in the /{beta} fold family (OH685_09420) through structural bioinformatics and in-vitro recombinant protein analysis. This photoaffinity labeling approach using metabolite-based probes can be extended in the future to proteomic profiling of complex rhizosphere microbiomes to discover genes that can be leveraged to promote beneficial plant-microbe interactions. ImportanceHere we demonstrate a photoaffinity-based chemical probe modeled after sorgoleone, a known secondary metabolite released from the roots of sorghum, can be used to dissect complicated plant-microbe interactions. Applying this probe to the sorghum-associated bacterium Acinetobacter pittii identified diverse proteins that directly interact with sorgoleone. We show that probe labeling is dose-dependent and is sensitive to competition with purified sorgoleone, demonstrating the probe is selective for protein targets that directly interact with sorgoleone. By using the probe to broadly profile proteins that interact with sorgoleone, we identified bacterial catabolic pathways, unintuitive transcriptional regulation pathways, and vital exchange mechanisms involving transporters that may be involved in sorgoleone utilization and cellular response toward this plant metabolite. We envision that this workflow will expand our understanding of the sorghum root exudate interactome and elucidate the molecular mechanisms by which specific metabolites shape the sorghum rhizosphere microbiome.

biochemistry↗

bioRxiv2020

Flexible contextual modulation of naturalistic texture perception in peripheral vision

Peripheral vision comprises most of our visual field, and is essential in guiding visual behavior. Its characteristic capabilities and limitations, which distinguish it from foveal vision, have been explained by the most influential theory of peripheral vision as the product of representing the visual input using summary-statistics. Despite its success, this account may provide a limited understanding of peripheral vision, because it neglects processes of perceptual grouping and segmentation. To test this hypothesis, we studied how contextual modulation, namely the modulation of the perception of a stimulus by its surrounds, interacts with segmentation in human peripheral vision. We used naturalistic textures, which are directly related to summary-statistics representations. We show that segmentation cues affect contextual modulation, and that this is not captured by our implementation of the summary-statistics model. We then characterize the effects of different texture statistics on contextual modulation, providing guidance for extending the model, as well as for probing neural mechanisms of peripheral vision.

neuroscience↗

bioRxiv2018

Seasonal patterns of dengue fever in rural Ecuador: 2009-2016

Season is a major determinant of infectious disease rates, including arboviruses spread by mosquitoes, such as dengue, chikungunya, and Zika. Seasonal patterns of disease are driven by a combination of climatic or environmental factors, such as temperature or rainfall, and human behavioral time trends, such as school year schedules, holidays, and weekday-weekend patterns. These factors affect both disease rates and healthcare-seeking behavior. Seasonality of dengue fever has been studied in the context of climatic factors, but short-and long-term time trends are less well-understood. With 2009-- 2016 medical record data from patients diagnosed with dengue fever at two hospitals in rural Ecuador, we used Poisson generalized linear modeling to determine short-and long-term seasonal patterns of dengue fever, as well as the effect of day of the week and public holidays. In a subset analysis, we determined the impact of school schedules on school-aged children. With a separate model, we examined the effect of climate on diagnosis patterns. In the first model, the most important predictors of dengue fever were annual sinusoidal fluctuations in disease, long-term trends, day of the week, and hospital. Seasonal trends showed single peaks in case diagnoses, during April. Compared to an average day, cases were more likely to be diagnosed on Tuesdays (risk ratio (RR): 1.26, 95% confidence interval (CI) 1.05--1.51) and Thursdays (RR: 1.25, 95% CI 1.02--1.53), and less likely to be diagnosed on Saturdays (RR: 0.81, 95% CI 0.65--1.01) and Sundays (RR: 0.74, 95% CI 0.58--0.95). Public holidays were not significant predictors of dengue fever diagnoses, except for an increase in diagnoses on the day after Christmas (RR: 2.77, 95% CI 1.46--5.24). School schedules did not impact dengue diagnoses in school-aged children. In the climate model, important climate variables included the monthly total precipitation (RR: 2.14, 95% CI 1.26--3.64), an interaction between total precipitation and monthly absolute minimum temperature (RR: 0.93, 95% CI 0.88--0.98), an interaction between total precipitation and monthly precipitation days (RR: 0.90, 95% CI 0.82--0.99), and a three-way interaction between minimum temperature, total precipitation, and precipitation days (RR: 1.01, 95% CI 1.00--1.02). This is the first report of long-term dengue fever seasonality in Ecuador, one of few reports from rural patients, and one of very few studies utilizing daily disease reports. These results can inform local disease prevention efforts, public health planning, as well as global and regional models of dengue fever trends.\n\nAuthor summaryDengue fever exhibits a seasonal pattern in many parts of the world, much of which has been attributed to climate and weather. However, additional factors may contribute to dengue seasonality. With 2009-- 2016 medical record data from rural Ecuador, we studied the short-and long-term seasonal patterns of dengue fever, as well as the effect of school schedules and public holidays. We also examined the effect of climate on dengue. We found that dengue diagnoses peak once per year during April, but that diagnoses are also affected by day of the week. Dengue was also impacted by regional climate and complex interactions between local weather variables. This is the first report of long-term dengue fever seasonality in Ecuador, one of few reports from rural patients, and one of very few studies utilizing daily disease reports. This is the first report on the impacts of school schedules, holidays, and weekday-weekend patterns on dengue diagnoses. These results suggest a potential impact of human behaviors on dengue exposure risk. More broadly, these results can inform local disease prevention efforts and public health planning, as well as global and regional models of dengue fever trends.

epidemiology↗