Human cytomegalovirus (HCMV) preferentially targets neural progenitor cells (NPCs) in congenitally infected fetal brains, inducing neurodevelopmental disorders. While HCMV expresses several microRNAs (miRNAs) during infection, their roles in NPC infection are unclear. Here, we characterized expression of cellular and viral miRNAs in HCMV‐infected NPCs during early infection by microarray and identified seven differentially expressed cellular miRNAs and six significantly upregulated HCMV miRNAs. Deep learning approaches were used to identify potential targets of significantly upregulated HCMV miRNAs against differentially expressed cellular messenger RNA (mRNAs), and the associations with miRNA‐mRNA expression changes were observed. Gene ontology enrichment analysis indicated cellular gene targets were significantly enriched in pathways involved in neurodevelopment and cell‐cycle processes. Viral modulation of selected miRNAs and cellular gene targets involved in neurodevelopmental processes were further validated by real‐time quantitative reverse transcription polymerase chain reaction. Finally, a predicted 3′ untranslated region target site of hcmv‐miR‐US25‐1 in Jag1, a factor important for neurogenesis, was confirmed by mutagenesis. Reduction of Jag1 RNA and protein levels in NPCs was observed in response to transient expression of hcmv‐miR‐US25‐1. A hcmv‐miR‐US25‐1 mutant virus (ΔmiR‐US25) displayed limited ability to downregulate Jag1 mRNA levels and protein levels during the early infection stage compared with the wild type virus. Our collective experimental and computational investigation of miRNAs and cellular mRNAs expression in HCMV‐infected NPCs yields new insights into the roles of viral miRNAs in regulating NPC fate and their contributions to HCMV neuropathogenesis.

Motivation Technical advances have revolutionized the life sciences and researchers commonly face challenges associated with handling large amounts of heterogeneous digital data. The Findable, Accessible, Interoperable and Reusable (FAIR) principles provide a framework to support effective data management. However, implementing this framework is beyond the means of most researchers in terms of resources and expertise, requiring awareness of metadata, policies, community agreements and other factors such as vocabularies and ontologies. Results We have developed the Globally Accessible Distributed Data Sharing (GADDS) platform to facilitate FAIR-like data-sharing in cross-disciplinary research collaborations. The platform consists of (i) a blockchain-based metadata quality control system, (ii) a private cloud-like storage system and (iii) a version control system. GADDS is built with containerized technologies, providing minimal hardware standards and easing scalability, and offers decentralized trust via transparency of metadata, facilitating data exchange and collaboration. As a use case, we provide an example implementation in engineered living material technology within the Hybrid Technology Hub at the University of Oslo. Availability and implementation Demo version available at https://github.com/pavelvazquez/GADDS. Supplementary information Supplementary data are available at Bioinformatics online.