Genome-wide Analyses Identify KIF5A as a Novel ALS Gene

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.Peer reviewe

Altered brain-wide auditory networks in a zebrafish model of fragile X syndrome

Loss or disrupted expression of the FMR1 gene causes fragile X syndrome (FXS), the most common monogenetic form of autism in humans. Although disruptions in sensory processing are core traits of FXS and autism, the neural underpinnings of these phenotypes are poorly understood. Using calcium imaging to record from the entire brain at cellular resolution, we investigated neuronal responses to visual and auditory stimuli in larval zebrafish, using fmr1 mutants to model FXS. The purpose of this study was to model the alterations of sensory networks, brain-wide and at cellular resolution, that underlie the sensory aspects of FXS and autism.Combining functional analyses with the neurons' anatomical positions, we found that fmr1 animals have normal responses to visual motion. However, there were several alterations in the auditory processing of fmr1 animals. Auditory responses were more plentiful in hindbrain structures and in the thalamus. The thalamus, torus semicircularis, and tegmentum had clusters of neurons that responded more strongly to auditory stimuli in fmr1 animals. Functional connectivity networks showed more inter-regional connectivity at lower sound intensities (a - 3 to - 6 dB shift) in fmr1 larvae compared to wild type. Finally, the decoding capacities of specific components of the ascending auditory pathway were altered: the octavolateralis nucleus within the hindbrain had significantly stronger decoding of auditory amplitude while the telencephalon had weaker decoding in fmr1 mutants.We demonstrated that fmr1 larvae are hypersensitive to sound, with a 3-6 dB shift in sensitivity, and identified four sub-cortical brain regions with more plentiful responses and/or greater response strengths to auditory stimuli. We also constructed an experimentally supported model of how auditory information may be processed brain-wide in fmr1 larvae. Our model suggests that the early ascending auditory pathway transmits more auditory information, with less filtering and modulation, in this model of FXS

BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows

Peer reviewe

EDAM-bioimaging: the ontology of bioimage informatics operations, topics, data, and formats (update 2020)

EDAM is a well-established ontology of operations, topics, types of data, and data formats that are used in bioinformatics and its neighbouring fields [1,2] . EDAM-bioimaging is an extension of EDAM dedicated to bioimage analysis, bioimage informatics, and bioimaging. It is being developed in collaboration between the ELIXIR research infrastructure and the NEUBIAS and COMULIS COST Actions, in close contact with the Euro-BioImaging research infrastructure and the Global BioImaging network. EDAM-bioimaging contains an inter-related hierarchy of concepts including bioimage analysis and related operations, bioimaging topics and technologies, and bioimage data and their formats. The modelled concepts enable interoperable descriptions of software, publications, data, workflows, and training materials, fostering open science and "reproducible" bioimage analysis. New developments in EDAM-bioimaging at the time of publication [3] include among others: A concise but relatively comprehensive ontology of Machine learning, Artificial intelligence, and Clustering (to the level relevant in particular in bioimaging, biosciences, and also scientific data analysis in general) Added and refined topics and synonyms within Sample preparation and Tomography, and finalised coverage of imaging techniques (all of these to the high-level extent that influences choices of downstream analysis, i.e. the scope of EDAM) EDAM-bioimaging continues being under active development, with a growing and diversifying community of contributors. It is used in BIII.eu, the registry of bioimage analysis tools, workflows, and training materials, and emerging also in descriptions of Debian Med packages available in Debian and Bio-Linux, and tools in bio.tools. Development of EDAM-bioimaging has been carried out in a successful open community manner, in a fruitful collaboration between numerous bioimaging experts and ontology developers. The last stable release at the time of poster publication is version alpha06 [3], and the live development version can be viewed and commented on WebProtégé (free registration required). New contributors are warmly welcome! [1] Ison, J., Kalaš, M., Jonassen, I., Bolser, D., Uludag, M., McWilliam, H., Malone, J., Lopez, R., Pettifer, S. and Rice, P. (2013). EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats. Bioinformatics, 29(10): 1325-1332. DOI: 10.1093/bioinformatics/btt113 Open Access [2] Kalaš, M., Ménager, H., Schwämmle, V., Ison, J. and EDAM Contributors (2017). EDAM – the ontology of bioinformatics operations, types of data, topics, and data formats (2017 update) [version 1; not peer reviewed]. F1000Research, 6(ISCB Comm J):1181 (Poster) DOI: 10.7490/f1000research.1114459.1 Open Access [3] Matúš Kalaš, Laure Plantard, Martin Jones, Nataša Sladoje, Marie-Charlotte Domart, Matthia Karreman, Arrate Muñoz-Barrutia, Raf Van de Plas, Ivana Vrhovac Madunić, Dean Karaica, Laura Nicolás Sáenz, Estibaliz Gómez de Marisca, Daniel Sage, Robert Haase Joakim Lindblad, and all contributors to previous versions (2020). edamontology/edam-bioimaging: alpha06 (Version alpha06). Zenodo. DOI: 10.5281/zenodo.3695725 Open Acces