Genetic variation at mouse and human ribosomal DNA influences associated epigenetic states

Background: Ribosomal DNA (rDNA) displays substantial inter-individual genetic variation in human and mouse. A systematic analysis of how this variation impacts epigenetic states and expression of the rDNA has thus far not been performed. Results: Using a combination of long- and short-read sequencing, we establish that 45S rDNA units in the C57BL/6J mouse strain exist as distinct genetic haplotypes that influence the epigenetic state and transcriptional output of any given unit. DNA methylation dynamics at these haplotypes are dichotomous and life-stage specific: at one haplotype, the DNA methylation state is sensitive to the in utero environment, but refractory to post-weaning influences, whereas other haplotypes entropically gain DNA methylation during aging only. On the other hand, individual rDNA units in human show limited evidence of genetic haplotypes, and hence little discernible correlation between genetic and epigenetic states. However, in both species, adjacent units show similar epigenetic profiles, and the overall epigenetic state at rDNA is strongly positively correlated with the total rDNA copy number. Analysis of different mouse inbred strains reveals that in some strains, such as 129S1/SvImJ, the rDNA copy number is only approximately 150 copies per diploid genome and DNA methylation levels are < 5%. Conclusions: Our work demonstrates that rDNA-associated genetic variation has a considerable influence on rDNA epigenetic state and consequently rRNA expression outcomes. In the future, it will be important to consider the impact of inter-individual rDNA (epi)genetic variation on mammalian phenotypes and diseases

Artificial intelligence for neurodegenerative experimental models

INTRODUCTION: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. METHODS: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. RESULTS: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. DISCUSSION: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. HIGHLIGHTS: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery

Significant Improvement in Diagnosis of Hepatitis C Virus Infection by a One-Step Strategy in a Central Laboratory : an Optimal Tool for Hepatitis C Elimination?

The remarkable effectivity of current antiviral therapies has led to consider the elimination of hepatitis C virus (HCV) infection. However, HCV infection is highly underdiagnosed; therefore, a global strategy for eliminating it requires improving the effectiveness of HCV diagnosis to identify hidden cases. The remarkable effectivity of current antiviral therapies has led to consider the elimination of hepatitis C virus (HCV) infection. However, HCV infection is highly underdiagnosed; therefore, a global strategy for eliminating it requires improving the effectiveness of HCV diagnosis to identify hidden cases. In this study, we assessed the effectiveness of a protocol for HCV diagnosis based on viral load reflex testing of anti-HCV antibody-positive patients (known as one-step diagnosis) by analyzing all diagnostic tests performed by a central laboratory covering an area of 1.5 million inhabitants in Barcelona, Spain, before (83,786 cases) and after (45,935 cases) the implementation of the reflex testing protocol. After its implementation, the percentage of anti-HCV-positive patients with omitted HCV RNA determination remarkably decreased in most settings, particularly in drug treatment centers and primary care settings, where omitted HCV RNA analyses had absolute reductions of 76.4 and 20.2%, respectively. In these two settings, the percentage of HCV RNA-positive patients identified as a result of reflex testing accounted for 55 and 61% of all anti-HCV-positive patients. HCV RNA results were provided in a mean of 2 days. The presence of HCV RNA and age of ≥65 years were significantly associated with advanced fibrosis, assessed using the serological FIB-4 index (odds ratio [OR], 5.92; 95% confidence interval [CI], 3.4 to 10.4). The implementation of viral load reflex testing in a central laboratory is feasible and significantly increases the diagnostic effectiveness of HCV infections, while allowing the identification of underdiagnosed cases

Artificial intelligence for neurodegenerative experimental models

Introductions: experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials.Methods: here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research.Results: considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability.Discussion: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data.Highlights: there are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.</p