New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background

<p>Abstract</p> <p>Background</p> <p>Few genetic factors predisposing to the sporadic form of amyotrophic lateral sclerosis (ALS) have been identified, but the pathology itself seems to be a true multifactorial disease in which complex interactions between environmental and genetic susceptibility factors take place. The purpose of this study was to approach genetic data with an innovative statistical method such as artificial neural networks to identify a possible genetic background predisposing to the disease. A DNA multiarray panel was applied to genotype more than 60 polymorphisms within 35 genes selected from pathways of lipid and homocysteine metabolism, regulation of blood pressure, coagulation, inflammation, cellular adhesion and matrix integrity, in 54 sporadic ALS patients and 208 controls. Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis</p> <p>Results</p> <p>Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis. An unexpected discovery of a strong genetic background in sporadic ALS using a DNA multiarray panel and analytical processing of the data with advanced artificial neural networks was found. The predictive accuracy obtained with Linear Discriminant Analysis and Standard Artificial Neural Networks ranged from 70% to 79% (average 75.31%) and from 69.1 to 86.2% (average 76.6%) respectively. The corresponding value obtained with Advanced Intelligent Systems reached an average of 96.0% (range 94.4 to 97.6%). This latter approach allowed the identification of seven genetic variants essential to differentiate cases from controls: apolipoprotein E arg158cys; hepatic lipase -480 C/T; endothelial nitric oxide synthase 690 C/T and glu298asp; vitamin K-dependent coagulation factor seven arg353glu, glycoprotein Ia/IIa 873 G/A and E-selectin ser128arg.</p> <p>Conclusion</p> <p>This study provides an alternative and reliable method to approach complex diseases. Indeed, the application of a novel artificial intelligence-based method offers a new insight into genetic markers of sporadic ALS pointing out the existence of a strong genetic background.</p

Lipid Biomarkers for Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal degenerative disease primarily characterized by the selective loss of upper and lower motor neurons. To date, there is still an unmet need for robust and practical biomarkers that could estimate the risk of the disease and its progression. Based on metabolic modifications observed at the level of the whole body, different classes of lipids have been proposed as potential biomarkers. This review summarizes investigations carried out over the last decade that focused on changes in three major lipid species, namely cholesterol, triglycerides and fatty acids. Despite some contradictory findings, it is becoming increasingly accepted that dyslipidemia, and related aberrant energy homeostasis, must be considered as essential components of the pathological process. Therefore, it is tempting to envisage dietary interventions as a means to counterbalance the metabolic disturbances and ameliorate the patient's quality of life

Artificial Adaptive Systems and predictive medicine: a revolutionary paradigm shift

An individual patient is not the average representative of the population. Rather he or she is a person with unique characteristics. An intervention may be effective for a population but not necessarily for the individual patient. The recommendation of a guideline may not be right for a particular patient because it is not what he or she wants, and implementing the recommendation will not necessarily mean a favourable outcome

Polymorphisms in folate-metabolizing genes, chromosome damage, and risk of Down syndrome in Italian women: identification of key factors using artificial neural networks

<p>Abstract</p> <p>Background</p> <p>Studies in mothers of Down syndrome individuals (MDS) point to a role for polymorphisms in folate metabolic genes in increasing chromosome damage and maternal risk for a Down syndrome (DS) pregnancy, suggesting complex gene-gene interactions. This study aimed to analyze a dataset of genetic and cytogenetic data in an Italian group of MDS and mothers of healthy children (control mothers) to assess the predictive capacity of artificial neural networks assembled in TWIST system in distinguish consistently these two different conditions and to identify the variables expressing the maximal amount of relevant information to the condition of being mother of a DS child.</p> <p>The dataset consisted of the following variables: the frequency of chromosome damage in peripheral lymphocytes (BNMN frequency) and the genotype for 7 common polymorphisms in folate metabolic genes (<it>MTHFR </it>677C>T and 1298A>C, <it>MTRR </it>66A>G, <it>MTR </it>2756A>G, <it>RFC1 </it>80G>A and <it>TYMS </it>28bp repeats and 1494 6bp deletion). Data were analysed using TWIST system in combination with supervised artificial neural networks, and a semantic connectivity map.</p> <p>Results</p> <p>TWIST system selected 6 variables (BNMN frequency, <it>MTHFR </it>677TT, <it>RFC1 </it>80AA, <it>TYMS </it>1494 6bp +/+, <it>TYMS </it>28bp 3R/3R and <it>MTR </it>2756AA genotypes) that were subsequently used to discriminate between MDS and control mothers with 90% accuracy. The semantic connectivity map provided important information on the complex biological connections between the studied variables and the two conditions (being MDS or control mother).</p> <p>Conclusions</p> <p>Overall, the study suggests a link between polymorphisms in folate metabolic genes and DS risk in Italian women.</p

HnRNP K mislocalisation and dysfunction in neurodegenerative disease and ageing

Heterogeneous nuclear ribonucleoproteins (hnRNPs) are a diverse, multi-functional family of RNA-binding proteins. Many such proteins, including TDP-43 and FUS, have been strongly implicated in the pathogenesis of frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS). By contrast hnRNP K, the focus of this thesis, has been underexplored in the context of neurodegenerative disease. The first work to be described here involves a comprehensive pathological assessment of hnRNP K protein’s neuronal localisation profile in FTLD, ALS and control brain tissue. Following pathological examination, hnRNP K mislocalisation from the nucleus to the cytoplasm within pyramidal neurons of the cortex was identified as a novel neuropathological feature that is associated with both neurodegenerative disease and ageing. Double immunofluorescence was used to confirm these neurons were anatomically distinct from those harbouring the classical TDP-43 or Tau proteinaceous inclusions used in the pathological diagnosis of FTLD. Nuclear loss and mislocalisation of hnRNP K to the cytoplasm was then identified to also occur in two further neuronal cell types within the dentate nucleus of the cerebellum and the CA4 region of the hippocampus. As with pyramidal neurons, similar associations were identified between disease, age and hnRNP K mislocalisation in neurons of the dentate nucleus. Hence, neuronal mislocalisation of hnRNP K across the brain has potentially broad relevance to dementia and the ageing process. Almost all hnRNPs have been found to perform essential homeostatic functions in regulating appropriate target gene splicing activity. Recently, several hnRNPs have been found to have important roles in repressing the inclusion of non-conserved, so-called ‘cryptic exons’ within mature mRNA transcripts. Inclusion of cryptic exons following TDP-43 nuclear depletion and subsequent reductions in the functional levels of target transcripts and proteins is an emerging pathogenic theme of several neurodegenerative diseases including FTLD and ALS. To recapitulate the functional implications of the hnRNP K nuclear depletion that is observed in brain tissue, a hnRNP K knockdown neuronal model was developed utilising an iPSC-derived CRISPR-interference based platform. RNA-seq analysis revealed that nuclear hnRNP K protein depletion within cortical neurons is associated with the robust activation of several cryptic exon events in mRNA targets of hnRNP K as well as the upregulation of other abnormal splicing events termed ‘skiptic exons’. Several of these novel splicing events were validated molecularly using three-primer PCRs. Finally, an in situ hybridisation (ISH) based technology (BaseScope™) platform was optimised to visualise novel cryptic events in post-mortem brain tissue. The platform was used to detect a recently discovered cryptic exon within synaptic gene UNC13A and another in the insulin receptor (INSR) gene, two newly described targets of TDP-43. These events were found specifically in FTLD-TDP or ALS brains, validating it as a specific marker of TDP-43-proteinopathy. A methodological pipeline was also developed to delineate the spatial relationship between cryptic exons and associated TDP-43 pathology. Hence, providing a platform for the future detection, validation and analyses of novel cryptic exons associated with hnRNP K protein depletion in pyramidal neurons

Role of excitotoxicity in the degeneration of motor neurones in ALS

Amyotrophic lateral sclerosis (ALS) is a fatal neurological disorder involving selective degeneration of motor neurones. In a proportion of patients, ALS is caused by mutations in copper/zinc superoxide dismutase (SOD1), and mice expressing these mutants develop a syndrome that resembles human ALS. Using SOD1G93A transgenic mice, the role of glutamate-mediated toxicity and related intracellular pathways in motor neurone death has been investigated. GluR2 AMPA receptor subunits were decreased in spinal motor neurones of SOD1G93A mice before symptom onset. Since GluR2-lacking receptors are highly permeable to Ca2+, a known trigger of excitotoxic neuronal death, this phenomenon may have a pathogenic role. In support of this, treatment with an AMPA antagonist, ZK-187638, ameliorated motor symptoms and prolonged the survival of SOD1G93A mice. GluR2-lacking receptors are also permeable to zinc, which can be highly toxic. In situ hybridisation showed low expression of the zinc extruder ZnT-1 on motor neurones, which may contribute to their vulnerability to excitotoxicity. Expression of glutamate transporters on astrocytes may also contribute to excitotoxic motor neurone death. In transgenic mice and in astrocytes transfected with SOD1G93A, immunocytochemistry. Western blotting, in situ hybridisation and RT-PCR revealed down-regulation of GLT-1 protein, but not mRNA in astrocytes. However, the time-course in mice suggests that GLT-1 downregulation is not a primary pathological event. Activation of p38MAPK, a kinase putatively involved in glutamate mediated neurotoxicity, was observed in motor neurones at the onset of the disease, but SB 239063, a p38MAPK inhibitor, did not ameliorate motor dysfunction or survival of SOD1G93A mice. Altogether the results suggest that AMPA receptor mediated excitotoxicity is a primary cause of motor neurone degeneration in ALS. These observations may contribute to the development of new therapeutic strategies for this devastating disease

Prediction of Resting Energy Expenditure in Children: May Artificial Neural Networks Improve Our Accuracy?

The inaccuracy of resting energy expenditure (REE) prediction formulae to calculate energy metabolism in children may lead to either under- or overestimated real caloric needs with clinical consequences. The aim of this paper was to apply artificial neural networks algorithms (ANNs) to REE prediction. We enrolled 561 healthy children (2-17 years). Nutritional status was classified according to World Health Organization (WHO) criteria, and 113 were obese. REE was measured using indirect calorimetry and estimated with WHO, Harris-Benedict, Schofield, and Oxford formulae. The ANNs considered specific anthropometric data to model REE. The mean absolute error (mean \ub1 SD) of the prediction was 95.8 \ub1 80.8 and was strongly correlated with REE values (R2 = 0.88). The performance of ANNs was higher in the subgroup of obese children (101 \ub1 91.8) with a lower grade of imprecision (5.4%). ANNs as a novel approach may give valuable information regarding energy requirements and weight management in children

Positron emission tomography imaging biomarkers of frontotemporal dementia

There are currently no disease modifying treatments available for frontotemporal dementia (FTD). Pathological heterogeneity within and between FTD phenotypes and genotypes makes accurate diagnosis challenging. Biomarkers that can aid diagnosis and monitor disease progression will be critical for clinical trials of potential treatments. Positron emission tomography (PET) imaging provides insights into molecular changes in the brain during life that are otherwise only directly quantifiable at postmortem. In this thesis I aimed to identify potential biomarkers of FTD using PET imaging. In Chapter 3 I use PET imaging of glucose metabolism to identify early neuronal dysfunction in presymptomatic genetic FTD, revealing specific involvement of the anterior cingulate in a subgroup of mutation carriers. In Chapter 4 I evaluate the utility of a PET tracer of tau protein deposition in genetic FTD against volumetric imaging, which appears to provide a more sensitive biomarker of disease than this tau PET tracer in FTD. In Chapter 5 I investigate neuroinflammation via PET imaging and identify different areas of neuroinflammation in different FTD genotypes, suggesting an association between neuroinflammation and protein deposition and that PET imaging of neuroinflammation might provide a sensitive biomarker in MAPT-related FTD. In Chapter 6 I investigate synaptic and mitochondrial dysfunction via PET imaging in FTD, the latter of which has been previously unexplored. I reveal marked differences in both markers in FTD versus controls which suggests both might provide sensitive biomarkers of disease. Furthermore, in Chapter 7 I evaluate the same biomarkers at longitudinal follow up where I find continued reductions in mitochondrial function over time suggesting mitochondrial PET imaging may provide a biomarker of disease progression in FTD. Future replication of the findings in this thesis in larger cohorts might facilitate the advancement of clinical trials in FTD