Biology subjects

Lara, M. K.

Publications and source records attributed to Lara, M. K..

4 recordsLinked to original sources

bioRxiv2025

Interpretable EEG Biomarkers for Neurological Disease Models in Mice Using Bag-of-Waves Classifiers

Electroencephalograms (EEGs) are time-series records of the electrical potential from collective neural activity in the brain. EEG waveform patterns--rhythmic and irregular oscillations and transient patterns of sharp waves or spikes--are potential phenotypical biomarkers, reflecting genotype-specific neural activity. This is especially relevant to diagnosing epilepsy without direct seizure observations, which is common in clinical settings, as well as in animal models, which often have subtle neurological phenotypes without overt epilepsy. Herein, we investigate genotypic prediction from long-term EEG signals of freely behaving mice belonging to six groups defined by the presence or absence of a neurological disease-genotype (TSC1 gene knockout) in three different inbred strains with distinct genetic backgrounds. The potential complexity of genotype-related EEG patterns motivates a machine learning approach to automatically extract time-series descriptors, such as waveforms or spectral content, as biomarkers. We propose a machine learning approach to predict the genotypes of individual mice from the occurrence counts of waveforms that approximate short windows of the EEG. That is, a dictionary of waveforms is optimized to approximate windows from each genotype, and the vectors of waveform occurrence counts are the features for predicting genotypes via logistic regression models. Across two-fold cross-validation of the waveform dictionary learning, and leave-one-individual-out genotype prediction, we find that waveform counts pooled over multiple hour segments enable reliable prediction of mouse strain with an accuracy of 70% (chance rate of 38%), and for two of the three strains, DBA2 and C57B6, strain-specific classifiers reliably determined the epilepsy-genotype (TSC1 gene knockout) at a 67% sensitivity with a 100% specificity for DBA2 and 67% specificity for C57B6. None of the mice of these strains had evidence of overt seizures or EEG-based seizure detection. The methodologies and results show the potential of EEG waveforms as phenotypes and bag-of-waves as a feature representation for identifying epilepsy genotypes.

neuroscience↗

bioRxiv2025

Genome-wide association study of cocaine self-administration behavior in Heterogeneous Stock rats

BackgroundCocaine use disorder (CUD) is a major public health crisis with detrimental individual and societal effects. The specific genes mediating CUD remain largely unknown. MethodsWe conducted a genome-wide association study (GWAS) using outbred N/NIH Heterogeneous Stock (HS; n = 836, female = 415, male = 421) rats. We examined multiple CUD-related phenotypes that captured acquisition of self-administration, escalation of intake, and compulsive-like responding. ResultsConsistent with the existing literature, these traits were phenotypically and genetically correlated and exhibited modest heritability (h2 = 0.07 - 0.16). We identified six genome-wide significant associations. One locus on chromosome 19 was associated with the variable time between cocaine infusions (post infusion interval) and contains several carboxylesterase genes that are orthologous to the human CES1 gene; notably, carboxylesterases metabolize cocaine. Three non-synonymous coding variants in the genes Ces1c and Ces1d were in perfect linkage disequilibrium with this locus, suggesting that one or more of them might be the causal SNP. The other 5 loci also contained promising coding and expression variants, including Trak2, a gene previously associated with CUD in human GWAS and Slc10a7, Plcl1, and Satb2 which have been associated with alcohol and tobacco use disorder. ConclusionsThis is the largest genetic study of cocaine self-administration ever conducted in any species. Our results replicate previous loci associated with CUD in humans and provide several novel biological insights including the potential of pharmacological strategies targeting carboxylesterases for the treatment of CUD.

genetics↗

bioRxiv2023

Genome-wide association study of delay discounting in Heterogenous Stock rats

Delay discounting refers to the behavioral tendency to devalue rewards as a function of their delay in receipt. Heightened delay discounting has been associated with substance use disorders, as well as multiple co-occurring psychopathologies. Genetic studies in humans and animal models have established that delay discounting is a heritable trait, but only a few specific genes have been associated with delay discounting. Here, we aimed to identify novel genetic loci associated with delay discounting through a genome-wide association study (GWAS) using Heterogenous Stock rats, a genetically diverse outbred population derived from eight inbred founder strains. We assessed delay discounting in 650 male and female rats using an adjusting amount procedure in which rats chose between smaller immediate sucrose rewards or a larger reward at variable delays. Preference switch points were calculated for each rat and both exponential and hyperbolic functions were fitted to these indifference points. Area under the curve (AUC) and the discounting parameter k of both functions were used as delay discounting measures. GWAS for AUC, exponential k, and indifference points for a short delay identified significant loci on chromosomes 20 and 14. The gene Slc35f1, which encodes a member of the solute carrier family of nucleoside sugar transporters, was the only gene within the chromosome 20 locus. That locus also contained an eQTL for Slc35f1, suggesting that heritable differences in the expression of that gene might be responsible for the association with behavior. The gene Adgrl3, which encodes a member of the latrophilin family of G-protein coupled receptors, was the only gene within the chromosome 14 locus. These findings implicate novel genes in delay discounting and highlight the need for further exploration.

genomics↗

bioRxiv2023

A multi-network approach to Alzheimers Disease gene prioritization complements cis-regulatory prioritization with molecular quantitative trait loci

Gene prioritization within mapped disease-risk loci from genome-wide association studies (GWAS) remains one of the central bioinformatic challenges of human genetics. This problem is abundantly clear in Alzheimers Disease (AD) which has several dozen risk loci, but no therapeutically effective drug target. Dominant strategies emphasize alignment between molecular quantitative trait loci (mQTLs) and disease risk loci, under the assumption that cis-regulatory drivers of gene expression or protein abundance mediate disease risk. However, mQTL data do not capture clinically relevant time points or they derive from bulk tissue. These limitations are particularly significant in complex diseases like AD where access to diseased tissue occurs only in end-stage disease, while genetically encoded risk events accumulate over a lifetime. Network-based functional predictions, where bioinformatic databases of gene interaction networks are used to learn disease-associated gene networks to prioritize genes, complement mQTL-based prioritization. The choice of input network, however, can have a profound impact on the output gene rankings, and the optimal tissue network may not be known a priori. Here, we develop a natural extension of the popular NetWAS approach to gene prioritization that allows us to combine information from multiple networks at once. We applied our multi-network (MNFP) approach to AD GWAS data to prioritize candidate genes and compared the results to baseline, single-network models. Finally, we applied the models to prioritize genes in recently mapped AD risk loci and compared our prioritizations to the state-of-the-art mQTL approach used to functionally prioritize genes within those loci. We observed a significant concordance between the top candidates prioritized by our MNFP method and those prioritized by the mQTL approach. Our results show that network-based functional predictions are a strong complement to mQTL-based approaches and are significant to the AD genetics community as they provide a strong functional rationale to mechanistically follow-up novel AD-risk candidates. Author SummaryRisk genes give us insight into the failing molecular mechanisms that drive disease phenotypes. However, these risk genes are several layers of complexity removed from the emergent phenotypes they are influencing, the p-value that denotes their risk status gives little insight into the functional implications of that risk, and it is not clear when that risk gene may be having its effect. Methods like network-based functional prediction start to address several of these limitations by contextualizing risk genes in their broader genetic neighborhood within disease-relevant tissues. For complex diseases like Alzheimers, there are many possible relevant tissues incorporating everything from individual brain cell types to whole lobes of the brain. The work in this paper expands upon the traditional network-based functional prediction approach by considering a genes connections in multiple relevant tissue networks to prioritize candidate genes. Unlike traditional genetic risk studies, this kind prioritization benefits the Alzheimers genetics community as it provides a strong functional rationale to mechanistically follow-up on novel gene candidates.

genetics↗