A Novel Mathematical Approach to Define the Genes/SNPs Conferring Risk or Protection in Sporadic Amyotrophic Lateral Sclerosis Based on Auto Contractive Map Neural Networks and Graph Theory

Serum Proteome in a Sporadic Amyotrophic Lateral Sclerosis Geographical Cluster

This study is meant to characterize the serum proteome in a small geographical cluster of sporadic ALS subjects originating from a restricted geographical area and sharing the same environmental exposure, in a broader context of evaluating the relevance of environmental factors to disease onset, status, and progression. An Artificial Neural Network based software is used to compare the relative abundance of proteins identified as different (by means of bi-dimensional electrophoresis and mass spectrometry) in the serum proteome of patients and age-matched healthy controls. The patient's group is characterized by altered levels of acute phase reactants and of proteins involved in lipid homeostasis, along with over-representation of the APOE*4 allele. Characterization of the serum proteome in a small cluster of sporadic ALS patients, originating from a geographically restricted area with a high prevalence of the disease and evaluation of the results with software based on artificial neural networks, highlights the association of the relative abundance of some proteins (most notably, acute phase reactants and lipid homeostasis proteins) with the disease presence and status.

Blood trace metals in a sporadic amyotrophic lateral sclerosis geographical cluster

Amyotrophic lateral sclerosis (ALS) is a fatal disorder with unknown etiology, in which genetic and environmental factors interplay to determine the onset and the course of the disease. Exposure to toxic metals has been proposed to be involved in the etiology of the disease either through a direct damage or by promoting oxidative stress. In this study we evaluated the concentration of a panel of metals in serum and whole blood of a small group of sporadic patients, all living in a defined geographical area, for which acid mine drainage has been reported. ALS prevalence in this area is higher than in the rest of Italy. Results were analyzed with software based on artificial neural networks. High concentrations of metals (in particular Se, Mn and Al) were associated with the disease group. Arsenic serum concentration resulted lower in ALS patients, but it positively correlated with disease duration. Comet assay was performed to evaluate endogenous DNA damage that resulted not different between patients and controls. Up to now only few studies considered geographically well-defined clusters of ALS patients. Common geographical origin among patients and controls gave us the chance to perform metallomic investigations under comparable conditions of environmental exposure. Elaboration of these data with software based on machine learning processes has the potential to be extremely useful to gain a comprehensive view of the complex interactions eventually leading to disease, even in a small number of subjects.

Predictors of Response to Cholinesterase Inhibitors Treatment of Alzheimer's Disease: Date Mining from the TREDEM Registry

BACKGROUND

The pharmacological treatment of Alzheimer's disease (AD) is based largely on cholinesterase inhibitors (ChEI).

OBJECTIVE

To investigate whether or not some non-pharmacological and contextual factors measured prior to starting treatment such as past occupation, lifestyles, marital status, degree of autonomy and cognitive impairment, living alone or with others, and the degree of brain atrophy are associated with a better response to ChEI treatment.

METHODS

Eighty-four AD and six AD with cerebrovascular disease (AD + CVD) outpatients of Treviso Dementia (TREDEM) Registry, with an average cholinesterase inhibitors treatment length of four years, were considered. The outpatients had undergone a complete evaluation and some non-pharmacological and contextual factors were collected. We defined responder a patient with a delta score T0 - T1 equal or inferior to 2.0 points per year of MMSE and a non-responder a patient with a delta score T0 - T1 superior to 2.0 points per year. In order to identify hidden relationships between variables related to response and non-response, we use a special kind of artificial neural network called Auto-CM, able to create a semantic connectivity map of the variables considered in the study.

RESULTS

A higher cognitive profile, a previous intellectual occupation, healthier lifestyles, being married and not living alone, a higher degree of autonomy, and lower degree of brain atrophy at baseline resulted in affecting the response to long-term ChEI therapy.

CONCLUSION

Non-pharmacological and contextual factors appear to influence the effectiveness of treatment with ChEI in the long term.

Networks in Coronary Heart Disease Genetics As a Step towards Systems Epidemiology

We present the use of innovative machine learning techniques in the understanding of Coronary Heart Disease (CHD) through intermediate traits, as an example of the use of this class of methods as a first step towards a systems epidemiology approach of complex diseases genetics. Using a sample of 252 middle-aged men, of which 102 had a CHD event in 10 years follow-up, we applied machine learning algorithms for the selection of CHD intermediate phenotypes, established markers, risk factors, and their previously associated genetic polymorphisms, and constructed a map of relationships between the selected variables. Of the 52 variables considered, 42 were retained after selection of the most informative variables for CHD. The constructed map suggests that most selected variables were related to CHD in a context dependent manner while only a small number of variables were related to a specific outcome. We also observed that loss of complexity in the network was linked to a future CHD event. We propose that novel, non-linear, and integrative epidemiological approaches are required to combine all available information, in order to truly translate the new advances in medical sciences to gains in preventive measures and patients care.

Application of artificial neural networks to link genetic and environmental factors to DNA methylation in colorectal cancer

AIMS

We applied artificial neural networks (ANNs) to understand the connections among polymorphisms of genes involved in folate metabolism, clinico-pathological features and promoter methylation levels of MLH1, APC, CDKN2A(INK4A), MGMT and RASSF1A in 83 sporadic colorectal cancer (CRC) tissues, and to link dietary and lifestyle factors with gene promoter methylation.

MATERIALS & METHODS

Promoter methylation was assessed by means of methylation-sensitive high-resolution melting and genotyping by PCR-RFLP technique. Data were analyzed with the Auto Contractive Map, a special kind of ANN able to define the strength of the association of each variable with all the others and to visually show the map of the main connections.

RESULTS

We observed a strong connection between the low methylation levels of the five CRC genes and the MTR 2756AA genotype. Several other connections were revealed, including those between dietary and lifestyle factors and the methylation levels of CRC genes.

CONCLUSION

ANNs revealed the complexity of the interconnections among factors linked to DNA methylation in CRC.

Biomarkers in autism spectrum disorder: the old and the new

RATIONALE

Autism spectrum disorder (ASD) is a complex heterogeneous neurodevelopmental disorder with onset during early childhood and typically a life-long course. The majority of ASD cases stems from complex, 'multiple-hit', oligogenic/polygenic underpinnings involving several loci and possibly gene-environment interactions. These multiple layers of complexity spur interest into the identification of biomarkers able to define biologically homogeneous subgroups, predict autism risk prior to the onset of behavioural abnormalities, aid early diagnoses, predict the developmental trajectory of ASD children, predict response to treatment and identify children at risk for severe adverse reactions to psychoactive drugs.

OBJECTIVES

The present paper reviews (a) similarities and differences between the concepts of 'biomarker' and 'endophenotype', (b) established biomarkers and endophenotypes in autism research (biochemical, morphological, hormonal, immunological, neurophysiological and neuroanatomical, neuropsychological, behavioural), (c) -omics approaches towards the discovery of novel biomarker panels for ASD, (d) bioresource infrastructures and (e) data management for biomarker research in autism.

RESULTS

Known biomarkers, such as abnormal blood levels of serotonin, oxytocin, melatonin, immune cytokines and lymphocyte subtypes, multiple neuropsychological, electrophysiological and brain imaging parameters, will eventually merge with novel biomarkers identified using unbiased genomic, epigenomic, transcriptomic, proteomic and metabolomic methods, to generate multimarker panels. Bioresource infrastructures, data management and data analysis using artificial intelligence networks will be instrumental in supporting efforts to identify these biomarker panels.

CONCLUSIONS

Biomarker research has great heuristic potential in targeting autism diagnosis and treatment.

Application of artificial neural networks to investigate one-carbon metabolism in Alzheimer's disease and healthy matched individuals

Folate metabolism, also known as one-carbon metabolism, is required for several cellular processes including DNA synthesis, repair and methylation. Impairments of this pathway have been often linked to Alzheimer's disease (AD). In addition, increasing evidence from large scale case-control studies, genome-wide association studies, and meta-analyses of the literature suggest that polymorphisms of genes involved in one-carbon metabolism influence the levels of folate, homocysteine and vitamin B12, and might be among AD risk factors. We analyzed a dataset of 30 genetic and biochemical variables (folate, homocysteine, vitamin B12, and 27 genotypes generated by nine common biallelic polymorphisms of genes involved in folate metabolism) obtained from 40 late-onset AD patients and 40 matched controls to assess the predictive capacity of Artificial Neural Networks (ANNs) in distinguish consistently these two different conditions and to identify the variables expressing the maximal amount of relevant information to the condition of being affected by dementia of Alzheimer's type. Moreover, we constructed a semantic connectivity map to offer some insight regarding the complex biological connections among the studied variables and the two conditions (being AD or control). TWIST system, an evolutionary algorithm able to remove redundant and noisy information from complex data sets, selected 16 variables that allowed specialized ANNs to discriminate between AD and control subjects with over 90% accuracy. The semantic connectivity map provided important information on the complex biological connections among one-carbon metabolic variables highlighting those most closely linked to the AD condition.