Biology subjects

Charland, K.

Publications and source records attributed to Charland, K..

2 recordsLinked to original sources

bioRxiv2025

A genetically-diverse mouse model reveals a complex gene-environment regulation of cognitive resilience and susceptibility to Alzheimer disease

Alzheimers disease (AD) has a complex etiology arising from largely unknown interactions between genetic and environmental (GxE) factors. Even in populations with causal familial Alzheimers disease mutations, there is variation in disease onset and progression, suggesting that clinical symptoms are modified by genetics and environment. Identification of such modifiers is critical, as mechanisms that promote resilience to high-risk AD mutations, unhealthy diet, or aging represent promising therapeutic targets for AD; global resilience factors that protect against multiple "hits" are among the highest priority for discovery. Both genetic and environmental protective factors in AD have been identified; however, GxE factors are incredibly difficult to study in human populations given complex genomes, poor self-reporting, limited data from underrepresented groups, and incompletely documented exposomes. Here, we (1) validate novel GxE tools using population of mouse strains that model the polygenic nature of human AD, (2) characterize individuals with cognitive resilience to high-risk genetic and dietary perturbations, (3) define and quantitate roles for genetics, sex, age, and diet, and (4) present data for the discovery of complex interactions that are nearly impossible to elucidate from humans or inbred mice. We found that a high-fat high-sugar (HFHS) diet is not universally damaging, as some strains showed an improved AD-related cognitive outcome when fed a HFHS diet, suggesting the need for personalized recommendations for dietary interventions in AD. Cognitive resilience to AD is polygenic; however, we found a locus on Chr 10 that was modestly associated with cognitive resilience to AD in females, and this association was strengthened by HFHS diet, pointing to an unexpected interaction between specific genetic loci and unhealthy diet in AD risk and resilience. This study is the first of its kind to explore characteristics of AD resilience and GxE interactions in a genetically diverse mouse model. We present a subset of strains that exemplify global cognitive resilience to be leveraged for deep mechanistic studies aimed toward development of resilience-based, personalized therapeutic interventions. HighlightsO_LIAD-BXD mouse models and data repository provide unprecedented tools for understanding individual differences arising from Gene x Environment factors in aging and AD. C_LIO_LIEvidence of global cognitive resilience to AD-relevant outcomes that is partially mediated by a resilience to aging and dietary risk factors. C_LIO_LICognitive resilience in AD-BXDs its polygenic nature, with a modest locus on Chr 10 whose association with resilience was amplified by a high-fat/high-sugar diet. C_LIO_LIThe genetic factors mediating resilience to AD in humans also predict cognitive resilience in AD-BXDs, suggesting robust, translatable global resilience factors. C_LI

neuroscience↗

bioRxiv2020

Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burdens at the national sub-national scale in Colombia

The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed for the national and departmental levels in Colombia to predict weekly dengue cases at 12-weeks ahead. A national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, population counts, income inequality, and education. Our RF model trained on the national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the departments data. Additionally, the forecast errors of the national RF model were smaller to those of the national ANN model and were increased with the forecast horizon increasing from one-week ahead (mean absolute error, MAE: 5.80; root mean squared error, RMSE: 11.10) to 12-weeks ahead (MAE: 13.38; RMSE: 26.82). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study showed the potential of RF in dengue forecasting with also demonstrating the feasibility of using a national model to forecast at finer spatial scales. Furthermore, sociodemographic predictors are important to include to capture longer-term trends in dengue. Author summaryDengue virus has the highest disease burden of all mosquito-borne viral diseases, infecting 390 million people annually in 128 countries. Forecasting is an important warning mechanism that can help with proactive planning and response for clinical and public health services. In this study, we compare two different machine learning approaches to dengue forecasting: random forest (RF) and neural networks (NN). National and local (departmental-level) models were compared and used to predict dengue cases in the future. The results showed that the counts of future dengue cases were more accurately estimated by RF than by NN. It was also shown that environmental and meteorological predictors were more important for forecast accuracy for shorter-term forecasts while socio-demographic predictors were more important for longer-term forecasts. Finally, the national model applied to local data was more accurate in dengue forecasting compared to the local model. This research contributes to the field of disease forecasting and highlights different considerations for future forecasting studies.

immunology↗