Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data

Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different populations and tissue samples have greatly limited their use in clinical and research settings. We overcame these issues by performing hierarchical clustering on the 5000 most variably expressed autosomal genes from motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N = 112). Three molecular phenotypes linked to ALS pathogenesis were identified: synaptic and neuropeptide signalling, oxidative stress and apoptosis, and neuroinflammation. Cluster validation was achieved by applying linear discriminant analysis models to cases from TargetALS US motor cortex (N = 93), as well as Italian (N = 15) and Dutch (N = 397) blood expression datasets, for which there was a high assignment probability (80–90%) for each molecular subtype. The ALS and motor cortex specificity of the expression signatures were tested by mapping KCL BrainBank controls (N = 59), and occipital cortex (N = 45) and cerebellum (N = 123) samples from TargetALS to each cluster, before constructing case-control and motor cortex-region logistic regression classifiers. We found that the signatures were not only able to distinguish people with ALS from controls (AUC 0.88 ± 0.10), but also reflect the motor cortex-based disease process, as there was perfect discrimination between motor cortex and the other brain regions. Cell types known to be involved in the biological processes of each molecular phenotype were found in higher proportions, reinforcing their biological interpretation. Phenotype analysis revealed distinct cluster-related outcomes in both motor cortex datasets, relating to disease onset and progression-related measures. Our results support the hypothesis that different mechanisms underpin ALS pathogenesis in subgroups of patients and demonstrate potential for the development of personalised treatment approaches. Our method is available for the scientific and clinical community at https://alsgeclustering.er.kcl.ac.uk

The Deep Dementia Phenotyping (DEMON) Network: A global platform for innovation using data science and artificial intelligence.

This is the final version. Available from Wiley via the DOI in this record. BACKGROUND: The increasing availability of large high-dimensional data from experimental medicine, population-based and clinical cohorts, clinical trials, and electronic health records has the potential to transform dementia research. Our ability to make best use of this rich data will depend on utilisation of advanced machine learning and artificial intelligence (AI) techniques and collaboration across disciplinary and geographic boundaries. METHOD: The Deep Dementia Phenotyping (DEMON) Network launched in 20191 to support the growing interest in machine learning and AI. Led by Director Prof David Llewellyn and Deputy Director Dr Janice Ranson, the leadership team additionally includes 5 Theme Leads and 14 Working Group Leads, supported by an international Steering Committee of world-leading academics. Core funding is provided by Alzheimer's Research UK, the Alan Turing Institute and the University of Exeter, with additional support from strategic partners including the UK Dementia Research Institute and the Alzheimer's Society. Grand Challenges were established at a National Strategy Workshop in June 2020. Multidisciplinary Working Groups were formed to coordinate practical activities in seven key areas: Genetics and omics, experimental medicine, drug discovery and trials optimisation, biomarkers, imaging, dementia prevention, and applied models and digital health. Additional Special Interest Groups coordinate topic specific collaborations. RESULT: Membership on 4th February 2022 comprised 1,321 individuals from 61 countries across 6 continents (see Figure). Areas of expertise include dementia research (904; 68%), data science (692; 52%), clinical practice (244; 18%), industry (162; 12%), and regulation (26; 2%). Individual membership is free, and regular knowledge transfer events are provided including a monthly seminar series, talks and workshops, training, networking, and early career development. Each Working Group meets monthly, with multiple grants, reviews, and original research articles in progress. Eight state of the science position papers are in preparation, resulting from a Symposium held in April 2021. In January 2022, 110 early career researchers participated in the Network's flagship event 'NEUROHACK', a 4-day competitive global hackathon, with pilot grants awarded to those generating the most innovative solutions. CONCLUSION: The DEMON Network is a rapidly growing global platform for innovation that is supporting the global dementia research community to collaborate. Find out more at demondementia.com

Characterization of SDS-degrading Delftia acidovorans and in situ monitoring of its temporal succession in SDS-contaminated surface waters

Incomplete removal of sodium dodecyl sulfate (SDS) in wastewater treatment plants may result in SDS residues escaping and finding their way into receiving water bodies like rivers, lakes, and sea. Introduction of effective microorganisms into the aerobic treatment facilities can reduce unpleasant by-products and SDS residues. Selecting effective microorganisms for SDS treatment is a big challenge. Current study reports the isolation, identification, and in situ monitoring of an effective SDS-degrading isolate from detergent-polluted river waters. Screening was carried out by the conventional enrichment culture technique and the isolate was tentatively identified by using fatty acid methyl ester and 16S ribosomal RNA (rRNA) sequence analyses. Fatty acids produced by the isolate investigated were assumed as typical for the genus Comamonas. 16S rRNA sequence analysis also confirmed that the isolate had 95 % homology with Delftia acidovorans known as Comamonas or Pseudomonas acidovorans previously. D. acidovorans exhibited optimum growth at SDS concentration of 1 g l(-1) but tolerated up to 10 g l(-1) SDS. 87 % of 1.0 g l(-1) pure SDS was degraded after 11 days of incubation. The temporal succession of D. acidovorans in detergent-polluted river water was also monitored in situ by using Comamonas-specific fluorescein-labeled Cte probe. Being able to degrade SDS and populate in SDS-polluted surface waters, D. acidovorans isolates seem to be very helpful in elimination of SDS

Structural variation analysis of 6,500 whole genome sequences in amyotrophic lateral sclerosis

AbstractThere is a strong genetic contribution to Amyotrophic lateral sclerosis (ALS) risk, with heritability estimates of up to 60%. Both Mendelian and small effect variants have been identified, but in common with other conditions, such variants only explain a little of the heritability. Genomic structural variation might account for some of this otherwise unexplained heritability. We therefore investigated association between structural variation in a set of 25 ALS genes, and ALS risk and phenotype. As expected, the repeat expansion in the C9orf72 gene was identified as associated with ALS. Two other ALS-associated structural variants were identified: inversion in the VCP gene and insertion in the ERBB4 gene. All three variants were associated both with increased risk of ALS and specific phenotypic patterns of disease expression. More than 70% of people with respiratory onset ALS harboured ERBB4 insertion compared with 25% of the general population, suggesting respiratory onset ALS may be a distinct genetic subtype.</jats:p

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons