Clinically significant changes in genes and variants associated with epilepsy over time: implications for re-analysis.

Despite the significant advances in understanding the genetic architecture of epilepsy, many patients do not receive a molecular diagnosis after genomic testing. Re-analysing existing genomic data has emerged as a potent method to increase diagnostic yields-providing the benefits of genomic-enabled medicine to more individuals afflicted with a range of different conditions. The primary drivers for these new diagnoses are the discovery of novel gene-disease and variants-disease relationships; however, most decisions to trigger re-analysis are based on the passage of time rather than the accumulation of new knowledge. To explore how our understanding of a specific condition changes and how this impacts re-analysis of genomic data from epilepsy patients, we developed Vigelint. This approach combines the information from PanelApp and ClinVar to characterise how the clinically relevant genes and causative variants available to laboratories change over time, and this approach to five clinical-grade epilepsy panels. Applying the Vigelint pipeline to these panels revealed highly variable patterns in new, clinically relevant knowledge becoming publicly available. This variability indicates that a more dynamic approach to re-analysis may benefit the diagnosis and treatment of epilepsy patients. Moreover, this work suggests that Vigelint can provide empirical data to guide more nuanced, condition-specific approaches to re-analysis.

Molecular Basis for RNA Cytidine Acetylation by NAT10.

Human NAT10 acetylates the N4 position of cytidine in RNA, predominantly on rRNA and tRNA, to facilitate ribosome biogenesis and protein translation. NAT10 has been proposed as a therapeutic target in cancers as well as aging-associated pathologies such as Hutchinson-Gilford Progeria Syndrome (HGPS). The ∼120 kDa NAT10 protein uses its acetyl-CoA-dependent acetyltransferase, ATP-dependent helicase, and RNA binding domains in concert to mediate RNA-specific N4-cytidine acetylation. While the biochemical activity of NAT10 is well known, the molecular basis for catalysis of eukaryotic RNA acetylation remains relatively undefined. To provide molecular insights into the RNA-specific acetylation by NAT10, we determined the single particle cryo-EM structures of Chaetomium thermophilum NAT10 ( Ct NAT10) bound to a bisubstrate cytidine-CoA probe with and without ADP. The structures reveal that NAT10 forms a symmetrical heart-shaped dimer with conserved functional domains surrounding the acetyltransferase active sites harboring the cytidine-CoA probe. Structure-based mutagenesis with analysis of mutants in vitro supports the catalytic role of two conserved active site residues (His548 and Tyr549 in Ct NAT10), and two basic patches, both proximal and distal to the active site for RNA-specific acetylation. Yeast complementation analyses and senescence assays in human cells also implicates NAT10 catalytic activity in yeast thermoadaptation and cellular senescence. Comparison of the NAT10 structure to protein lysine and N-terminal acetyltransferase enzymes reveals an unusually open active site suggesting that these enzymes have been evolutionarily tailored for RNA recognition and cytidine-specific acetylation.

Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures.

Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME.

Understanding trends in loneliness during the COVID-19 pandemic in The Netherlands: the moderating role of gender, age, and living arrangement.

Objectives: Evidence suggests that the COVID-19 pandemic and the preventive lockdown measures increased loneliness levels. However, most studies are cross-sectional or rely on a pre-post (pandemic) design. This study relies on multiple observations to analyze the impact of the lockdown on loneliness levels in the Netherlands, and test whether it differed by gender, age, and living arrangement.Methods: Longitudinal data from the Covid-Questionnaire within the Lifelines Cohort Study from the northern Netherlands was used. Data was gathered between March 2020 and July 2021 with a total of 21 waves and 769,526 observations nested in 74,844 individuals. The outcome was a multi-dimensional Loneliness Index. The association between the lockdown period and loneliness levels was estimated using fixed-effects linear regression. Moderation effects were tested by means of two-way interactions.Results: Loneliness levels increased during stricter lockdown periods, and decreased when preventive measures were relaxed. Women and young adults experienced stronger fluctuations in their loneliness levels, whereas living arrangement did not play a notable moderating role.Conclusion: This study calls for special attention to be paid to the public issue of loneliness during periods of lockdown. Women and young adults appear as particularly vulnerable groups during the Covid-19 pandemic.

The impact of the COVID lockdown on alcohol consumption in the Netherlands. The role of living arrangements and social isolation.

BACKGROUND: The COVID-19 pandemic and the subsequent lockdown have a strong impact on health and health behaviours, such as alcohol consumption. Although there is some evidence of an overall decline in alcohol consumption during the lockdown, studies also show an increase in risky drinking patterns, e.g. solitary drinking, and differences between subgroups of individuals, e.g. depending on their living arrangement. Yet most studies rely on cross-sectional designs with retrospective questions, and small samples. METHODS: A longitudinal study was conducted using 13 waves of the COVID-Questionnaire within the Lifelines cohort from the northern Netherlands (n = 63,194). The outcome was alcohol consumption (glasses per week) between April 2020 and July 2021. Linear fixed-effects models were fitted to analyse trends in alcohol consumption, and these were compared with pre-COVID drinking levels. Moreover, the role of living arrangement and feelings of social isolation as potential moderators was tested. RESULTS: Alcohol consumption during the pandemic was lower than in previous years, and the seasonal pattern differed from the pre-COVID one, with levels being lower when lockdown measures were stricter. Moreover, the seasonal pattern differed by living arrangement: those living alone saw a relative increase in drinking throughout tight lockdown periods, whereas those living with children showed the strongest increase during the summer. Social isolation showed a weaker moderation effect. CONCLUSIONS: Overall alcohol levels were down in the pandemic, and in particular during strict lockdowns. Those living on their own and those who felt more isolated reacted more strongly to the lockdown, the longer it lasted.

Deep learning in cancer diagnosis, prognosis and treatment selection.

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning. In this review, we provide an overview of emerging deep learning techniques and how they are being applied to oncology. We focus on the deep learning applications for omics data types, including genomic, methylation and transcriptomic data, as well as histopathology-based genomic inference, and provide perspectives on how the different data types can be integrated to develop decision support tools. We provide specific examples of how deep learning may be applied in cancer diagnosis, prognosis and treatment management. We also assess the current limitations and challenges for the application of deep learning in precision oncology, including the lack of phenotypically rich data and the need for more explainable deep learning models. Finally, we conclude with a discussion of how current obstacles can be overcome to enable future clinical utilisation of deep learning.

Verifying explainability of a deep learning tissue classifier trained on RNA-seq data.

For complex machine learning (ML) algorithms to gain widespread acceptance in decision making, we must be able to identify the features driving the predictions. Explainability models allow transparency of ML algorithms, however their reliability within high-dimensional data is unclear. To test the reliability of the explainability model SHapley Additive exPlanations (SHAP), we developed a convolutional neural network to predict tissue classification from Genotype-Tissue Expression (GTEx) RNA-seq data representing 16,651 samples from 47 tissues. Our classifier achieved an average F1 score of 96.1% on held-out GTEx samples. Using SHAP values, we identified the 2423 most discriminatory genes, of which 98.6% were also identified by differential expression analysis across all tissues. The SHAP genes reflected expected biological processes involved in tissue differentiation and function. Moreover, SHAP genes clustered tissue types with superior performance when compared to all genes, genes detected by differential expression analysis, or random genes. We demonstrate the utility and reliability of SHAP to explain a deep learning model and highlight the strengths of applying ML to transcriptome data.

Coordinated removal of repressive epigenetic modifications during induced reversal of cell identity.

Epigenetic modifications operate in concert to maintain cell identity, yet how these interconnected networks suppress alternative cell fates remains unknown. Here, we uncover a link between the removal of repressive histone H3K9 methylation and DNA methylation during the reprogramming of somatic cells to pluripotency. The H3K9me2 demethylase, Kdm3b, transcriptionally controls DNA hydroxymethylase Tet1 expression. Unexpectedly, in the absence of Kdm3b, loci that must be DNA demethylated are trapped in an intermediate hydroxymethylated (5hmC) state and do not resolve to unmethylated cytosine. Ectopic 5hmC trapping precludes the chromatin association of master pluripotency factor, POU5F1, and pluripotent gene activation. Increased Tet1 expression is important for the later intermediates of the reprogramming process. Taken together, coordinated removal of distinct chromatin modifications appears to be an important mechanism for altering cell identity.

Defining Reprogramming Checkpoints from Single-Cell Analyses of Induced Pluripotency.

Elucidating the mechanism of reprogramming is confounded by heterogeneity due to the low efficiency and differential kinetics of obtaining induced pluripotent stem cells (iPSCs) from somatic cells. Therefore, we increased the efficiency with a combination of epigenomic modifiers and signaling molecules and profiled the transcriptomes of individual reprogramming cells. Contrary to the established temporal order, somatic gene inactivation and upregulation of cell cycle, epithelial, and early pluripotency genes can be triggered independently such that any combination of these events can occur in single cells. Sustained co-expression of Epcam, Nanog, and Sox2 with other genes is required to progress toward iPSCs. Ehf, Phlda2, and translation initiation factor Eif4a1 play functional roles in robust iPSC generation. Using regulatory network analysis, we identify a critical role for signaling inhibition by 2i in repressing somatic expression and synergy between the epigenomic modifiers ascorbic acid and a Dot1L inhibitor for pluripotency gene activation.

The role of α-ketoglutarate-dependent proteins in pluripotency acquisition and maintenance.

α-Ketoglutarate is an important metabolic intermediate that acts as a cofactor for several chromatin-modifying enzymes, including histone demethylases and the Tet family of enzymes that are involved in DNA demethylation. In this review, we focus on the function and genomic localization of these α-ketoglutarate-dependent enzymes in the maintenance of pluripotency during cellular reprogramming to induced pluripotent stem cells and in disruption of pluripotency during in vitro differentiation. The enzymatic function of many of these α-ketoglutarate-dependent proteins is required for pluripotency acquisition and maintenance. A better understanding of their specific function will be essential in furthering our knowledge of pluripotency.

Alternative Routes to Induced Pluripotent Stem Cells Revealed by Reprogramming of the Neural Lineage.

During the reprogramming of mouse embryonic fibroblasts (MEFs) to induced pluripotent stem cells, the activation of pluripotency genes such as NANOG occurs after the mesenchymal to epithelial transition. Here we report that both adult stem cells (neural stem cells) and differentiated cells (astrocytes) of the neural lineage can activate NANOG in the absence of cadherin expression during reprogramming. Gene expression analysis revealed that only the NANOG+E-cadherin+ populations expressed stabilization markers, had upregulated several cell cycle genes; and were transgene independent. Inhibition of DOT1L activity enhanced both the numbers of NANOG+ and NANOG+E-cadherin+ colonies in neural stem cells. Expressing SOX2 in MEFs prior to reprogramming did not alter the ratio of NANOG colonies that express E-cadherin. Taken together these results provide a unique pathway for reprogramming taken by cells of the neural lineage.

Collaborative rewiring of the pluripotency network by chromatin and signalling modulating pathways.

Reprogramming of somatic cells to induced pluripotent stem cells (iPSCs) represents a profound change in cell fate. Here, we show that combining ascorbic acid (AA) and 2i (MAP kinase and GSK inhibitors) increases the efficiency of reprogramming from fibroblasts and synergistically enhances conversion of partially reprogrammed intermediates to the iPSC state. AA and 2i induce differential transcriptional responses, each leading to the activation of specific pluripotency loci. A unique cohort of pluripotency genes including Esrrb require both stimuli for activation. Temporally, AA-dependent histone demethylase effects are important early, whereas Tet enzyme effects are required throughout the conversion. 2i function could partially be replaced by depletion of components of the epidermal growth factor (EGF) and insulin growth factor pathways, indicating that they act as barriers to reprogramming. Accordingly, reduction in the levels of the EGF receptor gene contributes to the activation of Esrrb. These results provide insight into the rewiring of the pluripotency network at the late stage of reprogramming.

Autosis is a Na+,K+-ATPase-regulated form of cell death triggered by autophagy-inducing peptides, starvation, and hypoxia-ischemia.

A long-standing controversy is whether autophagy is a bona fide cause of mammalian cell death. We used a cell-penetrating autophagy-inducing peptide, Tat-Beclin 1, derived from the autophagy protein Beclin 1, to investigate whether high levels of autophagy result in cell death by autophagy. Here we show that Tat-Beclin 1 induces dose-dependent death that is blocked by pharmacological or genetic inhibition of autophagy, but not of apoptosis or necroptosis. This death, termed "autosis," has unique morphological features, including increased autophagosomes/autolysosomes and nuclear convolution at early stages, and focal swelling of the perinuclear space at late stages. We also observed autotic death in cells during stress conditions, including in a subpopulation of nutrient-starved cells in vitro and in hippocampal neurons of neonatal rats subjected to cerebral hypoxia-ischemia in vivo. A chemical screen of ~5,000 known bioactive compounds revealed that cardiac glycosides, antagonists of Na(+),K(+)-ATPase, inhibit autotic cell death in vitro and in vivo. Furthermore, genetic knockdown of the Na(+),K(+)-ATPase α1 subunit blocks peptide and starvation-induced autosis in vitro. Thus, we have identified a unique form of autophagy-dependent cell death, a Food and Drug Administration-approved class of compounds that inhibit such death, and a crucial role for Na(+),K(+)-ATPase in its regulation. These findings have implications for understanding how cells die during certain stress conditions and how such cell death might be prevented.