Biology subjects

Padilla, N.

Publications and source records attributed to Padilla, N..

7 recordsLinked to original sources

bioRxiv2025

Advancing the design of the kissing bug kill trap for surveillance of triatomines

Standardized surveillance and control of kissing bugs (Hemiptera: Reduviidae: Triatominae), the insect vectors of the Chagas disease parasite Trypanosoma cruzi, which causes Chagas disease, remains difficult. The Kissing Bug Kill Trap consists of solar powered LED lights mounted over a column of black funnels. It operates autonomously to capture, kill and preserve adult triatomines. We conducted experiments from 2022-2024 testing potential ways to improve trap performance, ease of deployment, and minimize cost. Thirteen prototypes evaluated in Texas, Guatemala, and Mexico captured 1,531 triatomines. In 2022-2023 we selected a six-funnel trap suspended from a single support pole with an angle bracket, and with four LED lights and a solar panel mounted above the rain-guard, as a reference trap. In 2023, traps with smaller funnels, blue funnels, and blue lights were inferior to the reference trap based on high by-catch of other arthropods and/or fewer triatomines caught per day. In 2024, traps with more or fewer than six funnels or with LED lights mounted on or below the rain guard did not outperform the reference trap. The experiments added five new triatomine species to the four already known to be caught by the Kissing Bug Kill Trap and revealed differences and similarities in phenology of dispersal flights of Triatoma gerstaeckeri over a three-year period in Texas. The reference trap was selected as the pre-commercial prototype, based on its suitability for triatomine surveillance and potential for reducing the risk of T. cruzi infection by intercepting dispersing adult triatomines before they reach human habitats.

ecology↗

bioRxiv2024

Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfa-tase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among sub-missions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.

genetics↗

bioRxiv2022

Choosing variant interpretation tools for clinical applications: context matters

Our inability to solve the Variant Interpretation Problem (VIP) has become a bottleneck in the biomedical/clinical application of Next-Generation Sequencing. This situation has favored the development and use of bioinformatics tools for the VIP. However, choosing the optimal tool for our purposes is difficult because of the high variability of clinical contexts across and within countries. Here, we introduce the use of cost models as a new approach to compare pathogenicity predictors that considers clinical context. An interesting feature of this approach, absent in standard performance measures, is that it treats pathogenicity predictors as rejection classifiers. These classifiers, commonly found in machine learning applications to healthcare, reject low-confidence predictions. Finally, to explore whether context has any impact on predictor selection, we have developed a computational procedure that solves the problem of comparing an arbitrary number of tools across all possible clinical scenarios. We illustrate our approach using a set of seventeen pathogenicity predictors for missense variants. Our results show that there is no optimal predictor for all possible clinical scenarios. We also find that considering rejection gives a view of classifiers contrasting with that of standard performance measures. The Python code for comparing pathogenicity predictors across the clinical space using cost models is available to any interested user at: https://github.com/ClinicalTranslationalBioinformatics/clinical_space_partition SummariesJosu Aguirre earned his doctorate at the Clinical and Translational Bioinformatics group, at the Vall dHebron Institute of Research (VHIR). Natalia Padilla earned is a post-doctoral researcher at the Clinical and Translational Bioinformatics group, at the Vall dHebron Institute of Research (VHIR). Selen Ozkan is a Ph.D. student at the Clinical and Translational Bioinformatics group, at the Vall dHebron Institute of Research (VHIR). Casandra Riera earned her doctorate at the Clinical and Translational Bioinformatics group, at the Vall dHebron Institute of Research (VHIR). Lidia Feliubadalo earned her doctorate at the Universitat de Barcelona, presently she is a high-level technician working at the Catalan Institute of Oncology (ICO) in the diagnosis of hereditary cancers. Xavier de la Cruz is ICREA Research Professor at the Vall dHebron Institute of Research (VHIR). His research interests revolve around the application of machine learning methods to healthcare problems.

bioinformatics↗

bioRxiv2020

New genes involved in Angelman syndrome-like: expanding the genetic spectrum

Angelman syndrome (AS) is a neurogenetic disorder characterized by severe developmental delay with absence of speech, happy disposition, frequent laughter, hyperactivity, stereotypies, ataxia and seizures with specific EEG abnormalities. There is a 10-15% of patients with an AS phenotype whose genetic cause remains unknown (Angelman-like syndrome, AS-like). Whole-exome sequencing (WES) was performed on a cohort of 14 patients with clinical features of AS and no molecular diagnosis. As a result, we identified 10 de novo and 1 X-linked pathogenic/likely pathogenic variants in 10 neurodevelopmental genes (SYNGAP1, VAMP2, TBL1XR1, ASXL3, SATB2, SMARCE1, SPTAN1, KCNQ3, SLC6A1 and LAS1L) and one deleterious de novo variant in a candidate gene (HSF2). Our results highlight the wide genetic heterogeneity in AS-like patients and expands the differential diagnosis. New AS-like genes do not interact directly with UBE3A gene product but are involved in synapsis and neuron system development.

genetics↗

bioRxiv2020

Geographic heterogeneity in Anopheles albimanus microbiota is lost within one generation of laboratory colonization

BackgroundResearch on mosquito-microbe interactions may lead to new tools for mosquito and mosquito-borne disease control. To date, such research has largely utilized laboratory-reared mosquitoes that typically lack the microbial diversity of wild populations. A logical progression in this area involves working under controlled settings using field-collected mosquitoes or, in most cases, their progeny. Thus, an understanding of how laboratory colonization affects the assemblage of mosquito microbiota would aid in advancing mosquito microbiome studies and their applications beyond laboratory settings. MethodsUsing high throughput 16S rRNA amplicon sequencing, we characterized the internal and cuticle surface microbiota of F1 progeny of wild-caught adult Anopheles albimanus from four locations in Guatemala. A total of 132 late instar larvae and 135 2-5day old, non-blood-fed virgin adult females that were reared under identical laboratory conditions, were pooled (3 individuals/pool) and analyzed. ResultsResults showed geographical heterogeneity in both F1 larval internal (p=0.001; pseudo-F = 9.53) and cuticle surface (p=0.001; pseudo-F = 8.51) microbiota, and only F1 adult cuticle surface (p=0.001; pseudo-F = 4.5) microbiota, with a more homogenous adult internal microbiota (p=0.12; pseudo-F = 1.6) across collection sites. Overall, ASVs assigned to Leucobacter, Thorsellia, Chryseobacterium and uncharacterized Enterobacteriaceae, dominated F1 larval internal microbiota, while Acidovorax, Paucibacter, and uncharacterized Comamonadaceae, dominated the larval cuticle surface. F1 adults comprised a less diverse microbiota compared to larvae, with ASVs assigned to the genus Asaia dominating both internal and cuticle surface microbiota, and constituting at least 70% of taxa in each microbial niche. ConclusionsThese results suggest that location-specific heterogeneity in filed mosquito microbiota can be transferred to F1 progeny under normal laboratory conditions, but this may not last beyond the F1 larval stage without adjustments to maintain field-derived microbiota. Our findings provide the first comprehensive characterization of laboratory-colonized F1 An. albimanus progeny from field-derived mothers. This provides a background for studying how parentage and environmental conditions differentially or concomitantly affect mosquito microbiome composition, and how this can be exploited in advancing mosquito microbiome studies and their applications beyond laboratory settings.

microbiology↗

bioRxiv2019

Co-occurrence of kdr mutations V1016I and F1534C in pyrethroid-resistant Aedes aegypti (Diptera: Culicidae) populations from Costa Rica

Aedes aegypti (Linnaeus, 1762) is considered the most important mosquito vector species for several arboviruses (e.g., dengue, chikungunya, Zika) in Costa Rica. The main strategy for the control and prevention of Aedes-borne diseases relies on insecticide-based vector control. However, the emergence of insecticide resistance in the mosquito populations present a big threat for the prevention actions. The characterization of the mechanisms driving the insecticide resistance in Ae. aegypti are vital for decision making in vector control programs. Therefore, we analyzed the voltage-gated sodium channel gene for the presence of the V1016I and F1534C kdr mutations in pyrethroid-resistant Ae. aegypti populations from Puntarenas and Limon provinces, Costa Rica. The CDC bottle bioassays showed that both Costa Rican Ae. aegypti populations were resistant to permethrin and deltamethrin. In the case of kdr genotyping, results revealed the co-occurrence of V1016I and F1534C mutations in permethrin and deltamethrin-resistant populations, as well as the fixation of the 1534C allele. Therefore, our findings make an urgent call to expand the knowledge about the insecticide resistance status and mechanisms in the Costa Rican populations of Ae. aegypti which must be a priority to develop an effective resistance management plan.

genetics↗

bioRxiv2019

Pyrethroid exposure alters Anopheles albimanus microbiota and resistant mosquitoes harbor more insecticide-metabolizing bacteria

A deeper understanding of the mechanisms underlying insecticide resistance is needed to mitigate its threat to malaria vector control. Building upon our earlier identified associations between mosquito microbiota and insecticide resistance, we demonstrate for the first time, type-specific effects of pyrethroid exposure on internal and cuticle surface bacteria in adult progeny of field-collected Anopheles albimanus. In contrast, larval cuticle surface--but not internal--bacteria were affected by pyrethroid exposure. Being over five-folds more abundant in pyrethroid resistant adults, as compared to susceptible or non-insecticide-exposed mosquitoes, Klebsiella (alphacypermethrin), Pantoea and Asaia (permethrin) were identified as potential markers of pyrethroid resistance in An. albimanus. We also show for the first time that An. albimanus larvae and adult cuticles harbor more diverse bacterial communities than their internal microbial niches. Our findings indicate insecticide selection pressures on mosquito microbiota, and support the hypothesis of an undescribed microbe-mediated mechanism of insecticide metabolism in mosquitoes.

microbiology↗