Expression of the translation termination factor eRF1 is autoregulated by translational readthrough and 3'UTR intron-mediated NMD in Neurospora crassa

Eukaryotic release factor 1 (eRF1) is a translation termination factor that binds to the ribosome at stop codons. The expression of eRF1 is strictly controlled, since its concentration defines termination efficiency and frequency of translational readthrough. Here, we show that eRF1 expression in Neurospora crassa is controlled by an autoregulatory circuit that depends on the specific 3'UTR structure of erf1 mRNA. The stop codon context of erf1 promotes readthrough that protects the mRNA from its 3'UTR-induced nonsense-mediated mRNA decay (NMD). High eRF1 concentration leads to inefficient readthrough, thereby allowing NMD-mediated erf1 degradation. We propose that eRF1 expression is controlled by similar autoregulatory circuits in many fungi and seed plants and discuss the evolution of autoregulatory systems of different translation termination factors

Strategies, processes, outcomes, and costs of implementing experience sampling-based monitoring in routine mental health care in four European countries:study protocol for the IMMERSE effectiveness-implementation study

BACKGROUND: Recent years have seen a growing interest in the use of digital tools for delivering person-centred mental health care. Experience Sampling Methodology (ESM), a structured diary technique for capturing moment-to-moment variation in experience and behaviour in service users' daily life, reflects a particularly promising avenue for implementing a person-centred approach. While there is evidence on the effectiveness of ESM-based monitoring, uptake in routine mental health care remains limited. The overarching aim of this hybrid effectiveness-implementation study is to investigate, in detail, reach, effectiveness, adoption, implementation, and maintenance as well as contextual factors, processes, and costs of implementing ESM-based monitoring, reporting, and feedback into routine mental health care in four European countries (i.e., Belgium, Germany, Scotland, Slovakia).METHODS: In this hybrid effectiveness-implementation study, a parallel-group, assessor-blind, multi-centre cluster randomized controlled trial (cRCT) will be conducted, combined with a process and economic evaluation. In the cRCT, 24 clinical units (as the cluster and unit of randomization) at eight sites in four European countries will be randomly allocated using an unbalanced 2:1 ratio to one of two conditions: (a) the experimental condition, in which participants receive a Digital Mobile Mental Health intervention (DMMH) and other implementation strategies in addition to treatment as usual (TAU) or (b) the control condition, in which service users are provided with TAU. Outcome data in service users and clinicians will be collected at four time points: at baseline (t0), 2-month post-baseline (t1), 6-month post-baseline (t2), and 12-month post-baseline (t3). The primary outcome will be patient-reported service engagement assessed with the service attachment questionnaire at 2-month post-baseline. The process and economic evaluation will provide in-depth insights into in-vivo context-mechanism-outcome configurations and economic costs of the DMMH and other implementation strategies in routine care, respectively.DISCUSSION: If this trial provides evidence on reach, effectiveness, adoption, implementation and maintenance of implementing ESM-based monitoring, reporting, and feedback, it will form the basis for establishing its public health impact and has significant potential to bridge the research-to-practice gap and contribute to swifter ecological translation of digital innovations to real-world delivery in routine mental health care.TRIAL REGISTRATION: ISRCTN15109760 (ISRCTN registry, date: 03/08/2022).</p

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19

Recent Updates on the Significance of KRAS Mutations in Colorectal Cancer Biology

The most commonly mutated isoform of RAS among all cancer subtypes is KRAS. In this review, we focus on the special role of KRAS mutations in colorectal cancer (CRC), aiming to collect recent data on KRAS-driven enhanced cell signalling, in vitro and in vivo research models, and CRC development-related processes such as metastasis and cancer stem cell formation. We attempt to cover the diverse nature of the effects of KRAS mutations on age-related CRC development. As the incidence of CRC is rising in young adults, we have reviewed the driving forces of ageing-dependent CRC

A Novel Cell-Based Model for a Rare Disease: The Tks4-KO Human Embryonic Stem Cell Line as a Frank-Ter Haar Syndrome Model System

Tyrosine kinase substrate with four SH3 domains (Tks4) scaffold protein plays roles in cell migration and podosome formation and regulates systemic mechanisms such as adult bone homeostasis and adipogenesis. Mutations in the Tks4 gene (SH3PXD2b) cause a rare developmental disorder called Frank-Ter Haar syndrome (FTHS), which leads to heart abnormalities, bone tissue defects, and reduced adiposity. We aimed to produce a human stem cell-based in vitro FTHS model system to study the effects of the loss of the Tks4 protein in different cell lineages and the accompanying effects on the cell signalome. To this end, we used CRISPR/Cas9 (clustered, regularly interspaced, short palindromic repeats (CRISPR)/CRISPR associated (Cas9)) to knock out the SH3PXD2b gene in the HUES9 human embryonic stem cell line (hESC), and we obtained stable homo- and heterozygous knock out clones for use in studying the potential regulatory roles of Tks4 protein in embryonic stem cell biology. Based on pluripotency marker measurements and spontaneous differentiation capacity assays, we concluded that the newly generated Tks4-KO HUES9 cells retained their embryonic stem cell characteristics. We propose that the Tks4-KO HUES9 cells could serve as a tool for further cell differentiation studies to investigate the involvement of Tks4 in the complex disorder FTHS. Moreover, we successfully differentiated all of the clones into mesenchymal stem cells (MSCs). The derived MSC cultures showed mesenchymal morphology and expressed MSC markers, although the expression levels of mesodermal and osteogenic marker genes were reduced, and several EMT (epithelial mesenchymal transition)-related features were altered in the Tks4-KO MSCs. Our results suggest that the loss of Tks4 leads to FTHS by altering cell lineage differentiation and cell maturation processes, rather than by regulating embryonic stem cell potential

The National COVID Cohort Collaborative (N3C): Rationale, Design, Infrastructure, and Deployment

ObjectiveCoronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.Materials and methodsThe Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.ResultsOrganized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.ConclusionsThe N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19