Heavy Metals and Essential Metals Are Associated with Cerebrospinal Fluid Biomarkers of Alzheimer's Disease.

Various metals have been associated with the pathogenesis of Alzheimer's disease (AD), principally heavy metals that are environmental pollutants (such as As, Cd, Hg, and Pb) and essential metals whose homeostasis is disturbed in AD (such as Cu, Fe, and Zn). Although there is evidence of the involvement of these metals in AD, further research is needed on their mechanisms of toxicity. To further assess the involvement of heavy and essential metals in AD pathogenesis, we compared cerebrospinal fluid (CSF) AD biomarkers to macro- and microelements measured in CSF and plasma. We tested if macro- and microelements' concentrations (heavy metals (As, Cd, Hg, Ni, Pb, and Tl), essential metals (Na, Mg, K, Ca, Fe, Co, Mn, Cu, Zn, and Mo), essential non-metals (B, P, S, and Se), and other non-essential metals (Al, Ba, Li, and Sr)) are associated with CSF AD biomarkers that reflect pathological changes in the AD brain (amyloid ß1-42, total tau, phosphorylated tau isoforms, NFL, S100B, VILIP-1, YKL-40, PAPP-A, and albumin). We used inductively coupled plasma mass spectroscopy (ICP-MS) to determine macro- and microelements in CSF and plasma, and enzyme-linked immunosorbent assays (ELISA) to determine protein biomarkers of AD in CSF. This study included 193 participants (124 with AD, 50 with mild cognitive impairment, and 19 healthy controls). Simple correlation, as well as machine learning algorithms (redescription mining and principal component analysis (PCA)), demonstrated that levels of heavy metals (As, Cd, Hg, Ni, Pb, and Tl), essential metals (Ca, Co, Cu, Fe, Mg, Mn, Mo, Na, K, and Zn), and essential non-metals (P, S, and Se) are positively associated with CSF phosphorylated tau isoforms, VILIP-1, S100B, NFL, and YKL-40 in AD.

Patterns of diverse gene functions in genomic neighborhoods predict gene function and phenotype.

Genes with similar roles in the cell cluster on chromosomes, thus benefiting from coordinated regulation. This allows gene function to be inferred by transferring annotations from genomic neighbors, following the guilt-by-association principle. We performed a systematic search for co-occurrence of >1000 gene functions in genomic neighborhoods across 1669 prokaryotic, 49 fungal and 80 metazoan genomes, revealing prevalent patterns that cannot be explained by clustering of functionally similar genes. It is a very common occurrence that pairs of dissimilar gene functions - corresponding to semantically distant Gene Ontology terms - are significantly co-located on chromosomes. These neighborhood associations are often as conserved across genomes as the known associations between similar functions, suggesting selective benefits from clustering of certain diverse functions, which may conceivably play complementary roles in the cell. We propose a simple encoding of chromosomal gene order, the neighborhood function profiles (NFP), which draws on diverse gene clustering patterns to predict gene function and phenotype. NFPs yield a 26-46% increase in predictive power over state-of-the-art approaches that propagate function across neighborhoods, thus providing hundreds of novel, high-confidence gene function inferences per genome. Furthermore, we demonstrate that copy number-neutral structural variation that shapes gene function distribution across chromosomes can predict phenotype of individuals from their genome sequence.

Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer's disease patients.

Based on a set of subjects and a collection of attributes obtained from the Alzheimer's Disease Neuroimaging Initiative database, we used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer's disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, ciliary neurotrophic factor, brain natriuretic peptide, Fas ligand, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Moreover, applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p ≤ 0.01) were found between PAPP-A and clinical tests: Alzheimer's Disease Assessment Scale, Clinical Dementia Rating Sum of Boxes, Mini Mental State Examination, etc. The high importance of this finding lies in the fact that PAPP-A is a metalloproteinase, known to cleave insulin-like growth factor binding proteins. Since it also shares similar substrates with A Disintegrin and the Metalloproteinase family of enzymes that act as α-secretase to physiologically cleave amyloid precursor protein (APP) in the non-amyloidogenic pathway, it could be directly involved in the metabolism of APP very early during the disease course. Therefore, further studies should investigate the role of PAPP-A in the development of AD more thoroughly.