Less wiring, more firing: low-performing older adults compensate for impaired white matter with greater neural activity.

The reliable neuroimaging finding that older adults often show greater activity (over-recruitment) than younger adults is typically attributed to compensation. Yet, the neural mechanisms of over-recruitment in older adults (OAs) are largely unknown. Rodent electrophysiology studies have shown that as number of afferent fibers within a circuit decreases with age, the fibers that remain show higher synaptic field potentials (less wiring, more firing). Extrapolating to system-level measures in humans, we proposed and tested the hypothesis that greater activity in OAs compensates for impaired white-matter connectivity. Using a neuropsychological test battery, we measured individual differences in executive functions associated with the prefrontal cortex (PFC) and memory functions associated with the medial temporal lobes (MTLs). Using event-related functional magnetic resonance imaging, we compared activity for successful versus unsuccessful trials during a source memory task. Finally, we measured white-matter integrity using diffusion tensor imaging. The study yielded 3 main findings. First, low-executive OAs showed greater success-related activity in the PFC, whereas low-memory OAs showed greater success-related activity in the MTLs. Second, low-executive OAs displayed white-matter deficits in the PFC, whereas low-memory OAs displayed white-matter deficits in the MTLs. Finally, in both prefrontal and MTL regions, white-matter decline and success-related activations occurred in close proximity and were negatively correlated. This finding supports the less-wiring-more-firing hypothesis, which provides a testable account of compensatory over-recruitment in OAs.

Variation in DNA-Damage Responses to an Inhalational Carcinogen (1,3-Butadiene) in Relation to Strain-Specific Differences in Chromatin Accessibility and Gene Transcription Profiles in C57BL/6J and CAST/EiJ Mice.

BACKGROUND: The damaging effects of exposure to environmental toxicants differentially affect genetically distinct individuals, but the mechanisms contributing to these differences are poorly understood. Genetic variation affects the establishment of the gene regulatory landscape and thus gene expression, and we hypothesized that this contributes to the observed heterogeneity in individual responses to exogenous cellular insults. OBJECTIVES: We performed an in vivo study of how genetic variation and chromatin organization may dictate susceptibility to DNA damage, and influence the cellular response to such damage, caused by an environmental toxicant. MATERIALS AND METHODS: We measured DNA damage, messenger RNA (mRNA) and microRNA (miRNA) expression, and genome-wide chromatin accessibility in lung tissue from two genetically divergent inbred mouse strains, C57BL/6J and CAST/EiJ, both in unexposed mice and in mice exposed to a model DNA-damaging chemical, 1,3-butadiene. RESULTS: Our results showed that unexposed CAST/EiJ and C57BL/6J mice have very different chromatin organization and transcription profiles in the lung. Importantly, in unexposed CAST/EiJ mice, which acquired relatively less 1,3-butadiene-induced DNA damage, we observed increased transcription and a more accessible chromatin landscape around genes involved in detoxification pathways. Upon chemical exposure, chromatin was significantly remodeled in the lung of C57BL/6J mice, a strain that acquired higher levels of 1,3-butadiene-induced DNA damage, around the same genes, ultimately resembling the molecular profile of CAST/EiJ. CONCLUSIONS: These results suggest that strain-specific changes in chromatin and transcription in response to chemical exposure lead to a "compensation" for underlying genetic-driven interindividual differences in the baseline chromatin and transcriptional state. This work represents an example of how chemical and environmental exposures can be evaluated to better understand gene-by-environment interactions, and it demonstrates the important role of chromatin response in transcriptomic changes and, potentially, in deleterious effects of exposure. https://doi.org/10.1289/EHP1937.

Removing reference mapping biases using limited or no genotype data identifies allelic differences in protein binding at disease-associated loci.

BACKGROUND: Genetic variation can alter transcriptional regulatory activity contributing to variation in complex traits and risk of disease, but identifying individual variants that affect regulatory activity has been challenging. Quantitative sequence-based experiments such as ChIP-seq and DNase-seq can detect sites of allelic imbalance where alleles contribute disproportionately to the overall signal suggesting allelic differences in regulatory activity. METHODS: We created an allelic imbalance detection pipeline, AA-ALIGNER, to remove reference mapping biases influencing allelic imbalance detection and evaluate accuracy of allelic imbalance predictions in the absence of complete genotype data. Using the sequence aligner, GSNAP, and varying amounts of genotype information to remove mapping biases we investigated the accuracy of allelic imbalance detection (binomial test) in CREB1 ChIP-seq reads from the GM12878 cell line. Additionally we thoroughly evaluated the influence of experimental and analytical parameters on imbalance detection. RESULTS: Compared to imbalances identified using complete genotypes, using imputed partial sample genotypes, AA-ALIGNER detected >95 % of imbalances with >90 % accuracy. AA-ALIGNER performed nearly as well using common variants when genotypes were unknown. In contrast, predicting additional heterozygous sites and imbalances using the sequence data led to >50 % false positive rates. We evaluated effects of experimental data characteristics and key analytical parameter settings on imbalance detection. Overall, total base coverage and signal dispersion across the genome most affected our ability to detect imbalances, while parameters such as imbalance significance, imputation quality thresholds, and alignment mismatches had little effect. To assess the biological relevance of imbalance predictions, we used electrophoretic mobility shift assays to functionally test for predicted allelic differences in CREB1 binding in the GM12878 lymphoblast cell line. Six of nine tested variants exhibited allelic differences in binding. Two of these variants, rs2382818 and rs713875, are located within inflammatory bowel disease-associated loci. CONCLUSIONS: AA-ALIGNER accurately detects allelic imbalance in quantitative sequence data using partial genotypes or common variants filling a critical methodological gap in these analyses, as full genotypes are rarely available. Importantly, we demonstrate how experimental and analytical features impact imbalance detection providing guidance for similar future studies.