Identification of a 5-Protein Biomarker Molecular Signature for Predicting Alzheimer's Disease

Background: Alzheimer’s disease (AD) is a progressive brain disease with a huge cost to human lives. The impact of the disease is also a growing concern for the governments of developing countries, in particular due to the increasingly high number of elderly citizens at risk. Alzheimer’s is the most common form of dementia, a common term for memory loss and other cognitive impairments. There is no current cure for AD, but there are drug and non-drug based approaches for its treatment. In general the drug-treatments are directed at slowing the progression of symptoms. They have proved to be effective in a large group of patients but success is directly correlated with identifying the disease carriers at its early stages. This justifies the need for timely and accurate forms of diagnosis via molecular means. We report here a 5-protein biomarker molecular signature that achieves, on average, a 96% total accuracy in predicting clinical AD. The signature is composed of the abundances of IL-1α, IL-3, EGF, TNF-α and G-CSF. Methodology/Principal Findings: Our results are based on a recent molecular dataset that has attracted worldwide attention. Our paper illustrates that improved results can be obtained with the abundance of only five proteins. Our methodology consisted of the application of an integrative data analysis method. This four step process included: a) abundance quantization, b) feature selection, c) literature analysis, d) selection of a classifier algorithm which is independent of the feature selection process. These steps were performed without using any sample of the test datasets. For the first two steps, we used the application of Fayyad and Irani’s discretization algorithm for selection and quantization, which in turn creates an instance of the (alpha-beta)-k-Feature Set problem; a numerical solution of this problem led to the selection of only 10 proteins. Conclusions/Significance: the previous study has provided an extremely useful dataset for the identification of A biomarkers. However, our subsequent analysis also revealed several important facts worth reporting: 1. A 5-protein signature (which is a subset of the 18-protein signature of Ray et al.) has the same overall performance (when using the same classifier). 2. Using more than 20 different classifiers available in the widely-used Weka software package, our 5- protein signature has, on average, a smaller prediction error indicating the independence of the classifier and the robustness of this set of biomarkers (i.e. 96% accuracy when predicting AD against non-demented control). 3. Using very simple classifiers, like Simple Logistic or Logistic Model Trees, we have achieved the following results on 92 samples: 100 percent success to predict Alzheimer’s Disease and 92 percent to predict Non Demented Control on the AD dataset

Landscape Features Impact on Soil Available Water, Corn Biomass, and Gene Expression during the Late Vegetative Stage

Crop yields at summit positions of rolling landscapes often are lower than backslope yields. The differences in plant response may be the result of many different factors. We examined corn (Zea mays L.) plant productivity, gene expression, soil water, and nutrient availability in two landscape positions located in historically high (backslope) and moderate (summit and shoulder) yielding zones to gain insight into plant response differences. Growth characteristics, gene expression, and soil parameters (water and N and P content) were determined at the V12 growth stage of corn. At tassel, plant biomass, N content, 13C isotope discrimination (Δ), and soil water was measured. Soil water was 35% lower in the summit and shoulder compared with the lower backslope plots. Plants at the summit had 16% less leaf area, biomass, and N and P uptake at V12 and 30% less biomass at tassel compared with plants from the lower backslope. Transcriptome analysis at V12 indicated that summit and shoulder-grown plants had 496 downregulated and 341 upregulated genes compared with backslope-grown plants. Gene set and subnetwork enrichment analyses indicated alterations in growth and circadian response and lowered nutrient uptake, wound recovery, pest resistance, and photosynthetic capacity in summit and shoulder-grown plants. Reducing plant populations, to lessen demands on available soil water, and applying pesticides, to limit biotic stress, may ameliorate negative water stress responses

Novel pathogenic variants and quantitative phenotypic analyses of Robinow syndrome:WNT signaling perturbation and phenotypic variability

Robinow syndrome (RS) is a genetically heterogeneous disorder with six genes that converge on the WNT/planar cell polarity (PCP) signaling pathway implicated (DVL1, DVL3, FZD2, NXN, ROR2, and WNT5A). RS is characterized by skeletal dysplasia and distinctive facial and physical characteristics. To further explore the genetic heterogeneity, paralog contribution, and phenotypic variability of RS, we investigated a cohort of 22 individuals clinically diagnosed with RS from 18 unrelated families. Pathogenic or likely pathogenic variants in genes associated with RS or RS phenocopies were identified in all 22 individuals, including the first variant to be reported in DVL2. We retrospectively collected medical records of 16 individuals from this cohort and extracted clinical descriptions from 52 previously published cases. We performed Human Phenotype Ontology (HPO) based quantitative phenotypic analyses to dissect allele-specific phenotypic differences. Individuals with FZD2 variants clustered into two groups with demonstrable phenotypic differences between those with missense and truncating alleles. Probands with biallelic NXN variants clustered together with the majority of probands carrying DVL1, DVL2, and DVL3 variants, demonstrating no phenotypic distinction between the NXN-autosomal recessive and dominant forms of RS. While phenotypically similar diseases on the RS differential matched through HPO analysis, clustering using phenotype similarity score placed RS-associated phenotypes in a unique cluster containing WNT5A, FZD2, and ROR2 apart from non-RS-associated paralogs. Through human phenotype analyses of this RS cohort and OMIM clinical synopses of Mendelian disease, this study begins to tease apart specific biologic roles for non-canonical WNT-pathway proteins