Effects of mutations in the eEF1A2 gene in mouse gene expression profiles and identification of potential markers for motor neuron degeneration

The elongation factor 1 alpha (eEF1A) exists in mammals as two highly conserved isoforms: eEF1A1 and eEF1A2 which share 98% amino acid sequence similarity. When bound with GTP, both forms recruit aminoacylated-tRNA for delivery to the ribosome during translation elongation. eEF1A1 is expressed ubiquitously during development and is downregulated in mature neurones, cardiomyocytes and myocytes. Downregulation is observed concurrently with eEF1A2 expression increasing in the terminally differentiated cells. This shift in expression may be resultant of non-canonical roles that can differ between isoforms, and although eEF1A1 is well characterised, less is known about eEF1A2. Given the tissue-specific nature of this shift, it suggests that eEF1A2 may be involved in the development of neurodegeneration. eEF1A2 in humans has been implicated in severe neurodevelopmental disorders, in which sufferers can display symptoms of repeated seizures, intellectual disability and autism. However, patients carry differing mutations in eEF1A2 and each case can present varied severity of symptoms. To explore the effects that mutations in eEF1A2 have, two mouse lines were generated using CRISPR/Cas9; a mutation that was found in humans, D252H and a deletion that arose in the founders, Del.22.ex3. Homozygous (-/-) mice displayed a severe neurodegenerative phenotype. In Del.22.ex3, eEF1A2 is absent in homozygotes, whereas in D252H, mice express eEF1A2 but the protein is impaired or non-functional. An analysis of the founder mice identified mosaic alleles, some had incorporated the target mutation but a range of insertions and deletions were also present. The expression of eEF1A2 was observed to be reduced across the mosaic mice. The extent of neuronal damage that loss of functioning eEF1A2 may cause was investigated by immunohistochemistry. Identification of biomarkers for prognostic purposes for potential therapies of motor neuron degeneration was conducted by a bottom up proteomic approach. Label-free quantitative mass spectrometry was used to define the proteome of spinal cords from homozygotes and wild types for comparative study and identified potential biomarkers. In complement, an analysis on microarray data from wasted mice spinal cords identified differentially expressed genes. Some of these supported proteins of interest as being significantly differentially regulated, whilst not being confounded by varying protein turnover rates or stability. Proteins and genes that were significantly differentially expressed underwent gene ontology enrichment analysis exploring which pathways and functions were overrepresented to better understand pathogenesis, some of which demonstrated affiliation with neuronal disorders and cell metabolism. Understanding the loss of eEF1A2 and its neuronal degeneration phenotype, the affected protein and genetic expression patterns across the spinal cord has elucidated proteins enriched for particular pathways, and provided possible prognostic benchmarks for future therapeutic development. However these finding are only preliminary and more penetrating study is required into the differences of expression profiles between healthy and diseased mice with more replicates, as well as establishing whether the changes observed are within the translationally impaired motor neurons or glial cells

A Digital One Health framework to integrate data for public health decision-making

The current implementation of One Health (OH) primarily focuses on multi-sectoral collaboration but often overlooks opportunities to integrate contextual and pathogen-related data into a unified data resource. This lack of integration hampers effective, data-driven decision-making in OH activities. In this perspective, we examine the existing strategies for data sharing and identify gaps and barriers to integration. To overcome these challenges, we propose the Digital OH (DOH) framework for data integration, which consolidates data-sharing principles within five pillars for the OH community of practice: (a) Harmonization of standards to establish trust, (b) Automation of data capture to enhance quality and efficiency, (c) Integration of data at point of capture to limit bureaucracy, (d) Onboard data analysis to articulate utility, and (e) Archiving and governance to safeguard the OH data resource. We discuss an upcoming pilot program as a use case focusing on antimicrobial resistance surveillance to illustrate the application of this framework. Our ambition is to leverage technology to create data as a shared resource using DOH not only to overcome current structural barriers but also to address prevailing ethical and legal concerns. By doing so, we can enhance the efficiency and effectiveness of decision-making processes in the OH community of practice, at a national, regional, and international level