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Campbell, M. K.

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

3 recordsLinked to original sources

Rapid Mechanistic Bridging of an Alzheimer's Disease Plasma Protein Staging Panel Across Brain Proteomics Cohorts

Background: Blood-based biomarkers are transforming Alzheimer's Disease (AD) diagnosis and staging. Recent multi-protein plasma panels accurately identify individuals with advanced Braak pathology, but whether these circulating biomarkers truly reflect the molecular remodeling underlying AD neuropathology remains unclear. Pre-existing datasets could answer such questions, but their reuse requires harmonized protein expression data, standardized sample annotations, and consistent study metadata. Methods: A recently reported seven-protein plasma staging panel was evaluated across multiple post-mortem proteomics brain cohorts using pre-structured datasets on the Tesorai platform. Five datasets with Braak stage data were identified and re-analyzed. A linear model was used to distinguish late (V-VI) from early Braak stage (0-IV). Because phosphorylated tau 217 (p-tau217) and amyloid beta 1-40 (A{beta}40) were not reported for any studies, raw spectra were reprocessed to quantify these proteoforms. Results: Across the cohorts, the plasma-derived biomarker panel consistently discriminated early from late disease despite heterogeneous protein coverage. Reprocessing of raw spectra recovered tau phosphopeptides absent from the original protein summaries, enabling inclusion of p-tau217, while A{beta}40 remained undetected. Together, these findings show that proteins comprising a recently proposed blood-based staging panel are associated with proteomic remodeling in the AD brain across independent cohorts. Conclusions: These findings provide biological validation for a recently proposed blood-based seven-protein staging panel by demonstrating that its constituent biomarkers are associated with disease-stage proteomic changes in AD brain tissue. More broadly, re-mining legacy mass spectrometry data for disease-relevant proteoforms, combined with pre-structured datasets, can accelerate evaluation of emerging blood-based biomarkers against neuropathological and molecular features of disease.

neuroscience↗

CNS-penetrant NLRP3 inhibitor achieves durable weight loss and reverses hypothalamic inflammation in diet-induced obesity

The NLRP3 inflammasome is a key mediator of innate immunity that integrates inflammatory and metabolic stress signals. Increased and/or chronic activation of this critical pathway has been implicated in obesity, with hypothalamic neuroinflammation linked to dysregulation of energy balance. TN-783 is an investigational, CNS-penetrant, small-molecule NLRP3 inhibitor that potently suppressed inflammasome activation across multiple in vitro assays. In diet-induced obese (DIO) mice, only TN-783, and not the peripherally restricted NLRP3 inhibitor TN-101, produced progressive and sustained weight loss, underscoring the requirement for central target engagement. Weight loss was driven by a persistent reduction in food intake across both acute and chronic phases, without altering energy expenditure. This effect was further characterized by selective reduction of fat mass, with minimal impact on lean tissues. Mechanistically, NLRP3 inhibition attenuated DIO-induced hypothalamic neuroinflammation and partially reversed obesity-associated molecular changes based on transcriptomic and proteomic profiling of the hypothalamus. Beyond monotherapy, TN-783 enhanced the effects of the GLP-1 receptor agonist semaglutide by amplifying weight loss, reinitiating weight loss after semaglutide effect had plateaued, and maintaining the weight loss benefit after semaglutide withdrawal. Discontinuation of TN-783 resulted in reversal of both weight and feeding effects, indicating that its therapeutic activity requires ongoing target engagement rather than permanent remodeling of metabolic pathways. Collectively, these observations support central NLRP3 inhibition as a distinct and promising approach for obesity treatment, offering robust induction and sustained maintenance of weight loss while preserving reversibility.

neuroscience↗

Wild flies hedge their thermal preference bets in response to seasonal fluctuations

Fluctuating environmental pressures can challenge organisms by repeatedly shifting the optimum phenotype. Two contrasting evolutionary strategies to cope with these fluctuations are 1) evolution of the mean phenotype to follow the optimum (adaptive tracking) or 2) diversifying phenotypes so that at least some individuals have high fitness in the current fluctuation (bet-hedging). Bet-hedging could underlie stable differences in the behavior of individuals that are present even when genotype and environment are held constant. Instead of being simply noise, behavioral variation across individuals may reflect an evolutionary strategy of phenotype diversification. Using geographically diverse wild-derived fly strains and high-throughput assays of individual preference, we tested whether thermal preference variation in Drosophila melanogaster could reflect a bet-hedging strategy. We also looked for evidence that populations from different regions differentially adopt bet-hedging or adaptive-tracking strategies. Computational modeling predicted regional differences in the relative advantage of bet-hedging, and we found patterns consistent with that in regional variation in thermal preference heritability. In addition, we found that temporal patterns in mean preference support bet-hedging predictions and that there is a genetic basis for thermal preference variability. Our empirical results point to bet-hedging in thermal preference as a potentially important evolutionary strategy in wild populations.

evolutionary biology↗