ASU Scholarship Showcase

Background: The discovery of genetic associations is an important factor in the understanding of human illness to derive disease pathways. Identifying multiple interacting genetic mutations associated with disease remains challenging in studying the etiology of complex diseases. And although recently new single nucleotide polymorphisms (SNPs) at genes implicated in immune response, cholesterol/lipid metabolism, and cell membrane processes have been confirmed by genome-wide association studies (GWAS) to be associated with late-onset Alzheimer's disease (LOAD), a percentage of AD heritability continues to be unexplained. We try to find other genetic variants that may influence LOAD risk utilizing data mining methods.

Methods: Two different approaches were devised to select SNPs associated with LOAD in a publicly available GWAS data set consisting of three cohorts. In both approaches, single-locus analysis (logistic regression) was conducted to filter the data with a less conservative p-value than the Bonferroni threshold; this resulted in a subset of SNPs used next in multi-locus analysis (random forest (RF)). In the second approach, we took into account prior biological knowledge, and performed sample stratification and linkage disequilibrium (LD) in addition to logistic regression analysis to preselect loci to input into the RF classifier construction step.

Results: The first approach gave 199 SNPs mostly associated with genes in calcium signaling, cell adhesion, endocytosis, immune response, and synaptic function. These SNPs together with APOE and GAB2 SNPs formed a predictive subset for LOAD status with an average error of 9.8% using 10-fold cross validation (CV) in RF modeling. Nineteen variants in LD with ST5, TRPC1, ATG10, ANO3, NDUFA12, and NISCH respectively, genes linked directly or indirectly with neurobiology, were identified with the second approach. These variants were part of a model that included APOE and GAB2 SNPs to predict LOAD risk which produced a 10-fold CV average error of 17.5% in the classification modeling.

Conclusions: With the two proposed approaches, we identified a large subset of SNPs in genes mostly clustered around specific pathways/functions and a smaller set of SNPs, within or in proximity to five genes not previously reported, that may be relevant for the prediction/understanding of AD.

Background: Glioblastoma is the most aggressive primary central nervous tumor and carries a very poor prognosis. Invasion precludes effective treatment and virtually assures tumor recurrence. In the current study, we applied analytical and bioinformatics approaches to identify a set of microRNAs (miRs) from several different human glioblastoma cell lines that exhibit significant differential expression between migratory (edge) and migration-restricted (core) cell populations. The hypothesis of the study is that differential expression of miRs provides an epigenetic mechanism to drive cell migration and invasion.

Results: Our research data comprise gene expression values for a set of 805 human miRs collected from matched pairs of migratory and migration-restricted cell populations from seven different glioblastoma cell lines. We identified 62 down-regulated and 2 up-regulated miRs that exhibit significant differential expression in the migratory (edge) cell population compared to matched migration-restricted (core) cells. We then conducted target prediction and pathway enrichment analysis with these miRs to investigate potential associated gene and pathway targets. Several miRs in the list appear to directly target apoptosis related genes. The analysis identifies a set of genes that are predicted by 3 different algorithms, further emphasizing the potential validity of these miRs to promote glioblastoma.

Conclusions: The results of this study identify a set of miRs with potential for decreased expression in invasive glioblastoma cells. The verification of these miRs and their associated targeted proteins provides new insights for further investigation into therapeutic interventions. The methodological approaches employed here could be applied to the study of other diseases to provide biomedical researchers and clinicians with increased opportunities for therapeutic interventions.

Background: Peanut consumption favorably influences satiety. This study examined the acute effect of peanut versus grain bar preloads on postmeal satiety and glycemia in healthy adults and the long-term effect of these meal preloads on body mass in healthy overweight adults.

Methods: In the acute crossover trial (n = 15; 28.4 ± 2.9 y; 23.1 ± 0.9 kg/m²), the preload (isoenergetic peanut or grain bar with water, or water alone) was followed after 60 min with ingestion of a standardized glycemic test meal. Satiety and blood glucose were assessed immediately prior to the preload and to the test meal, and for two hours postmeal at 30-min intervals. In the parallel-arm, randomized trial (n = 44; 40.5 ± 1.6 y, 31.8 ± 0.9 kg/m²), the peanut or grain bar preload was consumed one hour prior to the evening meal for eight weeks. Body mass was measured at 2-week intervals, and secondary endpoints included blood hemoglobin A1c and energy intake as assessed by 3-d diet records collected at pre-trial and trial weeks 1 and 8.

Results: Satiety was elevated in the postprandial period following grain bar ingestion in comparison to peanut or water ingestion (p = 0.001, repeated-measures ANOVA). Blood glucose was elevated one hour after ingestion of the grain bar as compared to the peanut or water treatments; yet, total glycemia did not vary between treatments in the two hour postprandial period. In the 8-week trial, body mass was reduced for the grain bar versus peanut groups after eight weeks (−1.3 ± 0.4 kg versus −0.2 ± 0.3 kg, p = 0.033, analysis of covariance). Energy intake was reduced by 458 kcal/d in the first week of the trial for the grain bar group as compared to the peanut group (p = 0.118). Hemoglobin A1c changed significantly between groups during the trial (−0.25 ± 0.07% and −0.18 ± 0.12% for the grain bar and peanut groups respectively, p = 0.001).

Conclusions: Compared to an isoenergetic peanut preload, consumption of a grain bar preload one hour prior to a standardized meal significantly raised postmeal satiety. Moreover, consumption of the grain bar prior to the evening meal was associated with significant weight loss over time suggesting that glycemic carbohydrate ingestion prior to meals may be a weight management strategy.

Background: Height is an important health assessment measure with many applications. In the medical practice and in research settings, height is typically measured with a stadiometer. Although lasers are commonly used by health professionals for measurement including facial imaging, corneal thickness, and limb length, it has not been utilized for measuring height. The purpose of this feasibility study was to examine the ease and accuracy of a laser device for measuring height in children and adults.

Findings: In immediate succession, participant height was measured in triplicate using a stadiometer followed by the laser device. Measurement error for the laser device was significantly higher than that for the stadiometer (0.35 and 0.20 cm respectively). However, the measurement techniques were highly correlated (r² = 0.998 and 0.990 for the younger [<12 y, n = 25] and older [≥12 y, n = 100] participants respectively), and the estimated reliability between measurement techniques was 0.999 (ICC; 95 % CI: 0.998,1.000) and 0.995 (ICC; 95 % CI: 0.993,0.997) for the younger and older groups respectively. The average differences between the two styles of measurement (e.g., stadiometer minus laser) were significantly different from zero: +0.93 and +0.45 cm for the younger and older groups respectively.

Conclusions: These data demonstrate that laser technology can be adapted to measure height in children and adults. Although refinement is needed, the laser device for measuring height merits further development.