The ribosome's comeback: new role in body weight regulation.

The recent discovery of an association between ribosomal DNA (rDNA) copy number and body mass index (BMI) by Law et al. sheds light on a possible role of 45S rDNA in body-weight regulation. This finding opens new avenues for further investigations into the effect of rDNA on various human phenotypes.

Deletion in RMST lncRNA impairs hypothalamic neuronal development in a human stem cell-based model of Kallmann Syndrome.

Rhabdomyosarcoma 2-associated transcript (RMST) long non-coding RNA has previously been shown to cause Kallmann syndrome (KS), a rare genetic disorder characterized by congenital hypogonadotropic hypogonadism (CHH) and olfactory dysfunction. In the present study, we generated large deletions of approximately 41.55 kb in the RMST gene in human pluripotent stem cells using CRISPR/Cas9 gene editing. To evaluate the impact of RMST deletion, these cells were differentiated into hypothalamic neurons that include 10-15% neurons that express gonadotrophin-releasing hormone (GnRH). We found that deletion in RMST did not impair the neurogenesis of GnRH neurons, however, the hypothalamic neurons were electro-physiologically hyperactive and had increased calcium influx activity compared to control. Transcriptomic and epigenetic analyses showed that RMST deletion caused altered expression of key genes involved in neuronal development, ion channels, synaptic signaling and cell adhesion. The in vitro generation of these RMST-deleted GnRH neurons provides an excellent cell-based model to dissect the molecular mechanism of RMST function in Kallmann syndrome and its role in hypothalamic neuronal development.

The impact of COVID-19 on "biological aging".

The global impact of the SARS-CoV-2 pandemic has been unprecedented, posing a significant public health challenge. Chronological age has been identified as a key determinant for severe outcomes associated with SARS-CoV-2 infection. Epigenetic age acceleration has previously been observed in various diseases including human immunodeficiency virus (HIV), Cytomegalovirus (CMV), cardiovascular diseases, and cancer. However, a comprehensive review of this topic is still missing in the field. In this review, we explore and summarize the research work focusing on biological aging markers, i.e., epigenetic age and telomere attrition in COVID-19 patients. From the reviewed articles, we identified a consistent pattern of epigenetic age dysregulation and shortened telomere length, revealing the impact of COVID-19 on epigenetic aging and telomere attrition.

An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health.

BACKGROUND: Diabetes is a chronic condition that can result in many long-term physiological, metabolic, and neurological complications. Therefore, early detection of diabetes would help to determine a proper diagnosis and treatment plan. METHODS: In this study, we employed machine learning (ML) based case-control study on a diabetic cohort size of 1000 participants form Qatar Biobank to predict diabetes using clinical and bone health indicators from Dual Energy X-ray Absorptiometry (DXA) machines. ML models were utilized to distinguish diabetes groups from non-diabetes controls. Recursive feature elimination (RFE) was leveraged to identify a subset of features to improve the performance of model. SHAP based analysis was used for the importance of features and support the explainability of the proposed model. RESULTS: Ensemble based models XGboost and RF achieved over 84% accuracy for detecting diabetes. After applying RFE, we selected only 20 features which improved the model accuracy to 87.2%. From a clinical standpoint, higher HDL-Cholesterol and Neutrophil levels were observed in the diabetic group, along with lower vitamin B12 and testosterone levels. Lower sodium levels were found in diabetics, potentially stemming from clinical factors including specific medications, hormonal imbalances, unmanaged diabetes. We believe Dapagliflozin prescriptions in Qatar were associated with decreased Gamma Glutamyltransferase and Aspartate Aminotransferase enzyme levels, confirming prior research. We observed that bone area, bone mineral content, and bone mineral density were slightly lower in the Diabetes group across almost all body parts, but the difference against the control group was not statistically significant except in T12, troch and trunk area. No significant negative impact of diabetes progression on bone health was observed over a period of 5-15 yrs in the cohort. CONCLUSION: This study recommends the inclusion of ML model which combines both DXA and clinical data for the early diagnosis of diabetes.

DIAPH3 predicts survival of patients with MGMT-methylated glioblastoma.

Background: Glioblastoma is one of the most aggressive primary brain tumors, with a poor outcome despite multimodal treatment. Methylation of the MGMT promoter, which predicts the response to temozolomide, is a well-established prognostic marker for glioblastoma. However, a difference in survival can still be detected within the MGMT methylated group, with some patients exhibiting a shorter survival than others, emphasizing the need for additional predictive factors. Methods: We analyzed DIAPH3 expression in glioblastoma samples from the cancer genome atlas (TCGA). We also retrospectively analyzed one hundred seventeen histological glioblastomas from patients operated on at Saint-Luc University Hospital between May 2013 and August 2019. We analyzed the DIAPH3 expression, explored the relationship between mRNA levels and Patient's survival after the surgical resection. Finally, we assessed the methylation pattern of the DIAPH3 promoter using a targeted deep bisulfite sequencing approach. Results: We found that 36% and 1% of the TCGA glioblastoma samples exhibit copy number alterations and mutations in DIAPH3, respectively. We scrutinized the expression of DIAPH3 at single cell level and detected an overlap with MKI67 expression in glioblastoma proliferating cells, including neural progenitor-like, oligodendrocyte progenitor-like and astrocyte-like states. We quantitatively analyzed DIAPH3 expression in our cohort and uncovered a positive correlation between DIAPH3 mRNA level and patient's survival. The effect of DIAPH3 was prominent in MGMT-methylated glioblastoma. Finally, we report that the expression of DIAPH3 is at least partially regulated by the methylation of three CpG sites in the promoter region. Conclusion: We propose that combining the DIAPH3 expression with MGMT methylation could offer a better prediction of survival and more adapted postsurgical treatment for patients with MGMT-methylated glioblastoma.

DiaNet v2 deep learning based method for diabetes diagnosis using retinal images.

Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and accessible diagnostic methods are essential. Current diagnostic tests like fasting plasma glucose (FPG), oral glucose tolerance tests (OGTT), random plasma glucose (RPG), and hemoglobin A1c (HbA1c) have limitations, leading to misclassifications and discomfort for patients. The aim of this study is to enhance diabetes diagnosis accuracy by developing an improved predictive model using retinal images from the Qatari population, addressing the limitations of current diagnostic methods. This study explores an alternative approach involving retinal images, building upon the DiaNet model, the first deep learning model for diabetes detection based solely on retinal images. The newly proposed DiaNet v2 model is developed using a large dataset from Qatar Biobank (QBB) and Hamad Medical Corporation (HMC) covering wide range of pathologies in the the retinal images. Utilizing the most extensive collection of retinal images from the 5545 participants (2540 diabetic patients and 3005 control), DiaNet v2 is developed for diabetes diagnosis. DiaNet v2 achieves an impressive accuracy of over 92%, 93% sensitivity, and 91% specificity in distinguishing diabetic patients from the control group. Given the high prevalence of diabetes and the limitations of existing diagnostic methods in clinical setup, this study proposes an innovative solution. By leveraging a comprehensive retinal image dataset and applying advanced deep learning techniques, DiaNet v2 demonstrates a remarkable accuracy in diabetes diagnosis. This approach has the potential to revolutionize diabetes detection, providing a more accessible, non-invasive and accurate method for early intervention and treatment planning, particularly in regions with high diabetes rates like MENA.

Effects of paternal and chronological age on BEGAIN methylation and its possible role in autism.

Children from old fathers carry an increased risk for autism spectrum (ASD) and other neurodevelopmental disorders, which may at least partially be mediated by paternal age effects on the sperm epigenome. The brain enriched guanylate kinase associated (BEGAIN) protein is involved in protein-protein interactions at and transmission across synapses. Since several epigenome-wide methylation screens reported a paternal age effect on sperm BEGAIN methylation, here we confirmed a significant negative correlation between BEGAIN promoter methylation and paternal age, using more sensitive bisulfite pyrosequencing and a larger number of sperm samples. Paternal age-associated BEGAIN hypomethylation was also observed in fetal cord blood (FCB) of male but not of female offspring. There was no comparable maternal age effect on FCB methylation. In addition, we found a significant negative correlation between BEGAIN methylation and chronological age (ranging from 1 to 70 years) in peripheral blood samples of male but not of female donors. BEGAIN hypomethylation was more pronounced in male children, adolescents and adults suffering from ASD compared to controls. Both genetic variation (CC genotype of SNP rs7141087) and epigenetic factors may contribute to BEGAIN promoter hypomethylation. The age- and sex-specific BEGAIN methylation trajectories in the male germ line and somatic tissues, in particular the brain, support a role of this gene in ASD development.

Epigenetic age acceleration in surviving versus deceased COVID-19 patients with acute respiratory distress syndrome following hospitalization.

BACKGROUND: Aging has been reported as a major risk factor for severe symptoms and higher mortality rates in COVID-19 patients. Molecular hallmarks such as epigenetic alterations and telomere attenuation reflect the biological process of aging. Epigenetic clocks have been shown to be valuable tools for measuring biological age in various tissues and samples. As such, these epigenetic clocks can determine accelerated biological aging and time-to-mortality across various tissues. Previous reports have shown accelerated biological aging and telomere attrition acceleration following SARS-CoV-2 infection. However, the effect of accelerated epigenetic aging on outcome (death/recovery) in COVID-19 patients with acute respiratory distress syndrome (ARDS) has not been well investigated. RESULTS: In this study, we measured DNA methylation age and telomere attrition in 87 severe COVID-19 cases with ARDS under mechanical ventilation. Furthermore, we compared dynamic changes in epigenetic aging across multiple time points until recovery or death. Epigenetic age was measured using the Horvath, Hannum, DNAm skin and blood, GrimAge, and PhenoAge clocks, whereas telomere length was calculated using the surrogate marker DNAmTL. Our analysis revealed significant accelerated epigenetic aging but no telomere attrition acceleration in severe COVID-19 cases. In addition, we observed epigenetic age deceleration at inclusion versus end of follow-up in recovered but not in deceased COVID-19 cases using certain clocks. When comparing dynamic changes in epigenetic age acceleration (EAA), we detected higher EAA using both the Horvath and PhenoAge clocks in deceased versus recovered patients. The DNAmTL measurements revealed telomere attrition acceleration in deceased COVID-19 patients between inclusion and end of follow-up and a significant change in dynamic telomere attrition acceleration when comparing patients who recovered versus those who died. CONCLUSIONS: EAA and telomere attrition acceleration were associated with treatment outcomes in hospitalized COVID-19 patients with ARDS. A better understanding of the long-term effects of EAA in COVID-19 patients and how they might contribute to long COVID symptoms in recovered individuals is urgently needed.

A scoping review of artificial intelligence-based methods for diabetes risk prediction.

The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.

Ribosomal DNA Copy Number Variation is Coupled with DNA Methylation Changes at the 45S rDNA Locus.

The human ribosomal DNA (rDNA) copy number (CN) has been challenging to analyse, and its sequence has been excluded from reference genomes due to its highly repetitive nature. The 45S rDNA locus encodes essential components of the cell, nevertheless rDNA displays high inter-individual CN variation that could influence human health and disease. CN alterations in rDNA have been hypothesized as a possible factor in autism spectrum disorders (ASD) and were shown to be altered in Schizophrenia patients. We tested whether whole-genome bisulphite sequencing can be used to simultaneously quantify rDNA CN and measure DNA methylation at the 45S rDNA locus. Using this approach, we observed high inter-individual variation in rDNA CN, and limited intra-individual copy differences in several post-mortem tissues. Furthermore, we did not observe any significant alterations in rDNA CN or DNA methylation in Autism Spectrum Disorder (ASD) brains in 16 ASD vs 11 control samples. Similarly, no difference was detected when comparing neurons form 28 Schizophrenia (Scz) patients vs 25 controls or oligodendrocytes from 22 Scz samples vs 20 controls. However, our analysis revealed a strong positive correlation between CN and DNA methylation at the 45S rDNA locus in multiple tissues. This was observed in brain and confirmed in small intestine, adipose tissue, and gastric tissue. This should shed light on a possible dosage compensation mechanism that silences additional rDNA copies to ensure homoeostatic regulation of ribosome biogenesis.