Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences.

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.

A liver immune rheostat regulates CD8 T cell immunity in chronic HBV infection.

Chronic hepatitis B virus (HBV) infection affects 300 million patients worldwide1,2, in whom virus-specific CD8 T cells by still ill-defined mechanisms lose their function and cannot eliminate HBV-infected hepatocytes3-7. Here we demonstrate that a liver immune rheostat renders virus-specific CD8 T cells refractory to activation and leads to their loss of effector functions. In preclinical models of persistent infection with hepatotropic viruses such as HBV, dysfunctional virus-specific CXCR6+ CD8 T cells accumulated in the liver and, as a characteristic hallmark, showed enhanced transcriptional activity of cAMP-responsive element modulator (CREM) distinct from T cell exhaustion. In patients with chronic hepatitis B, circulating and intrahepatic HBV-specific CXCR6+ CD8 T cells with enhanced CREM expression and transcriptional activity were detected at a frequency of 12-22% of HBV-specific CD8 T cells. Knocking out the inhibitory CREM/ICER isoform in T cells, however, failed to rescue T cell immunity. This indicates that CREM activity was a consequence, rather than the cause, of loss in T cell function, further supported by the observation of enhanced phosphorylation of protein kinase A (PKA) which is upstream of CREM. Indeed, we found that enhanced cAMP-PKA-signalling from increased T cell adenylyl cyclase activity augmented CREM activity and curbed T cell activation and effector function in persistent hepatic infection. Mechanistically, CD8 T cells recognizing their antigen on hepatocytes established close and extensive contact with liver sinusoidal endothelial cells, thereby enhancing adenylyl cyclase-cAMP-PKA signalling in T cells. In these hepatic CD8 T cells, which recognize their antigen on hepatocytes, phosphorylation of key signalling kinases of the T cell receptor signalling pathway was impaired, which rendered them refractory to activation. Thus, close contact with liver sinusoidal endothelial cells curbs the activation and effector function of HBV-specific CD8 T cells that target hepatocytes expressing viral antigens by means of the adenylyl cyclase-cAMP-PKA axis in an immune rheostat-like fashion.

Genome-wide DNA-methylation landscape defines specialization of regulatory T cells in tissues.

Regulatory T cells (Treg cells) perform two distinct functions: they maintain self-tolerance, and they support organ homeostasis by differentiating into specialized tissue Treg cells. We found that epigenetic modifications defined the molecular characteristics of tissue Treg cells. Tagmentation-based whole-genome bisulfite sequencing revealed more than 11,000 regions that were methylated differentially in pairwise comparisons of tissue Treg cell populations and lymphoid T cells. Similarities in the epigenetic landscape led to the identification of a common tissue Treg cell population that was present in many organs and was characterized by gain and loss of DNA methylation that included many gene sites associated with the TH2 subset of helper T cells, such as the gene encoding cytokine IL-33 receptor ST2, as well as the production of tissue-regenerative factors. Furthermore, the ST2-expressing population was dependent on the transcriptional regulator BATF and could be expanded by IL-33. Thus, tissue Treg cells integrate multiple waves of epigenetic reprogramming that define their tissue-restricted specialization.

Corrigendum: Genome-wide DNA-methylation landscape defines specialization of regulatory T cells in tissues.

This corrects the article DOI: 10.1038/ni.3799.

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.

Biologically informed variational autoencoders allow predictive modeling of genetic and drug-induced perturbations.

MOTIVATION: Variational autoencoders (VAEs) have rapidly increased in popularity in biological applications and have already successfully been used on many omic datasets. Their latent space provides a low-dimensional representation of input data, and VAEs have been applied, e.g. for clustering of single-cell transcriptomic data. However, due to their non-linear nature, the patterns that VAEs learn in the latent space remain obscure. Hence, the lower-dimensional data embedding cannot directly be related to input features. RESULTS: To shed light on the inner workings of VAE and enable direct interpretability of the model through its structure, we designed a novel VAE, OntoVAE (Ontology guided VAE) that can incorporate any ontology in its latent space and decoder part and, thus, provide pathway or phenotype activities for the ontology terms. In this work, we demonstrate that OntoVAE can be applied in the context of predictive modeling and show its ability to predict the effects of genetic or drug-induced perturbations using different ontologies and both, bulk and single-cell transcriptomic datasets. Finally, we provide a flexible framework, which can be easily adapted to any ontology and dataset. AVAILABILITY AND IMPLEMENTATION: OntoVAE is available as a python package under https://github.com/hdsu-bioquant/onto-vae.

Author Correction: BCAT1 restricts αKG levels in AML stem cells leading to IDHmut-like DNA hypermethylation.

In Extended Data Fig. 1a of this Letter, the flow cytometry plot depicting the surface phenotype of AML sample DD08 was a duplicate of the plot for AML sample DD06. Supplementary Data 4 has been added to the Supplementary Information of the original Letter to clarify the proteome data acquisition and presentation. The original Letter has been corrected online.

dsMTL: a computational framework for privacy-preserving, distributed multi-task machine learning.

MOTIVATION: In multi-cohort machine learning studies, it is critical to differentiate between effects that are reproducible across cohorts and those that are cohort-specific. Multi-task learning (MTL) is a machine learning approach that facilitates this differentiation through the simultaneous learning of prediction tasks across cohorts. Since multi-cohort data can often not be combined into a single storage solution, there would be the substantial utility of an MTL application for geographically distributed data sources. RESULTS: Here, we describe the development of 'dsMTL', a computational framework for privacy-preserving, distributed multi-task machine learning that includes three supervised and one unsupervised algorithms. First, we derive the theoretical properties of these methods and the relevant machine learning workflows to ensure the validity of the software implementation. Second, we implement dsMTL as a library for the R programming language, building on the DataSHIELD platform that supports the federated analysis of sensitive individual-level data. Third, we demonstrate the applicability of dsMTL for comorbidity modeling in distributed data. We show that comorbidity modeling using dsMTL outperformed conventional, federated machine learning, as well as the aggregation of multiple models built on the distributed datasets individually. The application of dsMTL was computationally efficient and highly scalable when applied to moderate-size (n < 500), real expression data given the actual network latency. AVAILABILITY AND IMPLEMENTATION: dsMTL is freely available at https://github.com/transbioZI/dsMTLBase (server-side package) and https://github.com/transbioZI/dsMTLClient (client-side package). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The endogenous cellular protease inhibitor SPINT2 controls SARS-CoV-2 viral infection and is associated to disease severity.

COVID-19 outbreak is the biggest threat to human health in recent history. Currently, there are over 1.5 million related deaths and 75 million people infected around the world (as of 22/12/2020). The identification of virulence factors which determine disease susceptibility and severity in different cell types remains an essential challenge. The serine protease TMPRSS2 has been shown to be important for S protein priming and viral entry, however, little is known about its regulation. SPINT2 is a member of the family of Kunitz type serine protease inhibitors and has been shown to inhibit TMPRSS2. Here, we explored the existence of a co-regulation between SPINT2/TMPRSS2 and found a tightly regulated protease/inhibitor expression balance across tissues. We found that SPINT2 negatively correlates with SARS-CoV-2 expression in Calu-3 and Caco-2 cell lines and was down-regulated in secretory cells from COVID-19 patients. We validated our findings using Calu-3 cell lines and observed a strong increase in viral load after SPINT2 knockdown, while overexpression lead to a drastic reduction of the viral load. Additionally, we evaluated the expression of SPINT2 in datasets from comorbid diseases using bulk and scRNA-seq data. We observed its down-regulation in colon, kidney and liver tumors as well as in alpha pancreatic islets cells from diabetes Type 2 patients, which could have implications for the observed comorbidities in COVID-19 patients suffering from chronic diseases.

Global hypomethylation in childhood asthma identified by genome-wide DNA-methylation sequencing preferentially affects enhancer regions.

BACKGROUND: Childhood asthma is a result of a complex interaction of genetic and environmental components causing epigenetic and immune dysregulation, airway inflammation and impaired lung function. Although different microarray based EWAS studies have been conducted, the impact of epigenetic regulation in asthma development is still widely unknown. We have therefore applied unbiased whole genome bisulfite sequencing (WGBS) to characterize global DNA-methylation profiles of asthmatic children compared to healthy controls. METHODS: Peripheral blood samples of 40 asthmatic and 42 control children aged 5-15 years from three birth cohorts were sequenced together with paired cord blood samples. Identified differentially methylated regions (DMRs) were categorized in genotype-associated, cell-type-dependent, or prenatally primed. Network analysis and subsequent natural language processing of DMR-associated genes was complemented by targeted analysis of functional translation of epigenetic regulation on the transcriptional and protein level. RESULTS: In total, 158 DMRs were identified in asthmatic children compared to controls of which 37% were related to the eosinophil content. A global hypomethylation was identified affecting predominantly enhancer regions and regulating key immune genes such as IL4, IL5RA, and EPX. These DMRs were confirmed in n = 267 samples and could be linked to aberrant gene expression. Out of the 158 DMRs identified in the established phenotype, 56 were perturbed already at birth and linked, at least in part, to prenatal influences such as tobacco smoke exposure or phthalate exposure. CONCLUSION: This is the first epigenetic study based on whole genome sequencing to identify marked dysregulation of enhancer regions as a hallmark of childhood asthma.

BCAT1 restricts αKG levels in AML stem cells leading to IDHmut-like DNA hypermethylation.

The branched-chain amino acid (BCAA) pathway and high levels of BCAA transaminase 1 (BCAT1) have recently been associated with aggressiveness in several cancer entities. However, the mechanistic role of BCAT1 in this process remains largely uncertain. Here, by performing high-resolution proteomic analysis of human acute myeloid leukaemia (AML) stem-cell and non-stem-cell populations, we find the BCAA pathway enriched and BCAT1 protein and transcripts overexpressed in leukaemia stem cells. We show that BCAT1, which transfers α-amino groups from BCAAs to α-ketoglutarate (αKG), is a critical regulator of intracellular αKG homeostasis. Further to its role in the tricarboxylic acid cycle, αKG is an essential cofactor for αKG-dependent dioxygenases such as Egl-9 family hypoxia inducible factor 1 (EGLN1) and the ten-eleven translocation (TET) family of DNA demethylases. Knockdown of BCAT1 in leukaemia cells caused accumulation of αKG, leading to EGLN1-mediated HIF1α protein degradation. This resulted in a growth and survival defect and abrogated leukaemia-initiating potential. By contrast, overexpression of BCAT1 in leukaemia cells decreased intracellular αKG levels and caused DNA hypermethylation through altered TET activity. AML with high levels of BCAT1 (BCAT1high) displayed a DNA hypermethylation phenotype similar to cases carrying a mutant isocitrate dehydrogenase (IDHmut), in which TET2 is inhibited by the oncometabolite 2-hydroxyglutarate. High levels of BCAT1 strongly correlate with shorter overall survival in IDHWTTET2WT, but not IDHmut or TET2mut AML. Gene sets characteristic for IDHmut AML were enriched in samples from patients with an IDHWTTET2WTBCAT1high status. BCAT1high AML showed robust enrichment for leukaemia stem-cell signatures, and paired sample analysis showed a significant increase in BCAT1 levels upon disease relapse. In summary, by limiting intracellular αKG, BCAT1 links BCAA catabolism to HIF1α stability and regulation of the epigenomic landscape, mimicking the effects of IDH mutations. Our results suggest the BCAA-BCAT1-αKG pathway as a therapeutic target to compromise leukaemia stem-cell function in patients with IDHWTTET2WT AML.

High-Fidelity 3D Stray Magnetic Field Mapping of Smartphones to Address Safety Considerations with Active Implantable Electronic Medical Devices.

Case reports indicate that magnets in smartphones could be a source of electromagnetic interference (EMI) for active implantable medical devices (AIMD), which could lead to device malfunction, compromising patient safety. Recognizing this challenge, we implemented a high-fidelity 3D magnetic field mapping (spatial resolution 1 mm) setup using a three-axis Hall probe and teslameter, controlled by a robot (COSI Measure). With this setup, we examined the stray magnetic field of an iPhone 13 Pro, iPhone 12, and MagSafe charger to identify sources of magnetic fields for the accurate risk assessment of potential interferences with AIMDs. Our measurements revealed that the stray fields of the annular array of magnets, the wide-angle camera, and the speaker of the smartphones exceeded the 1 mT limit defined by ISO 14117:2019. Our data-driven safety recommendation is that an iPhone 13 Pro should be kept at least 25 mm away from an AIMD to protect it from unwanted EMI interactions. Our study addresses safety concerns due to potential device-device interactions between smartphones and AIMDs and will help to define data-driven safety guidelines. We encourage vendors of electronic consumer products (ECP) to provide information on the magnetic fields of their products and advocate for the inclusion of smartphones in the risk assessment of EMI with AIMDs.

Automated 3D light-sheet screening with high spatiotemporal resolution reveals mitotic phenotypes.

3D cell cultures enable the in vitro study of dynamic biological processes such as the cell cycle, but their use in high-throughput screens remains impractical with conventional fluorescent microscopy. Here, we present a screening workflow for the automated evaluation of mitotic phenotypes in 3D cell cultures by light-sheet microscopy. After sample preparation by a liquid handling robot, cell spheroids are imaged for 24 h in toto with a dual-view inverted selective plane illumination microscope (diSPIM) with a much improved signal-to-noise ratio, higher imaging speed, isotropic resolution and reduced light exposure compared to a spinning disc confocal microscope. A dedicated high-content image processing pipeline implements convolutional neural network-based phenotype classification. We illustrate the potential of our approach using siRNA knockdown and epigenetic modification of 28 mitotic target genes for assessing their phenotypic role in mitosis. By rendering light-sheet microscopy operational for high-throughput screening applications, this workflow enables target gene characterization or drug candidate evaluation in tissue-like 3D cell culture models.

Single-cell analyses reveal SARS-CoV-2 interference with intrinsic immune response in the human gut.

Exacerbated pro-inflammatory immune response contributes to COVID-19 pathology. However, despite the mounting evidence about SARS-CoV-2 infecting the human gut, little is known about the antiviral programs triggered in this organ. To address this gap, we performed single-cell transcriptomics of SARS-CoV-2-infected intestinal organoids. We identified a subpopulation of enterocytes as the prime target of SARS-CoV-2 and, interestingly, found the lack of positive correlation between susceptibility to infection and the expression of ACE2. Infected cells activated strong pro-inflammatory programs and produced interferon, while expression of interferon-stimulated genes was limited to bystander cells due to SARS-CoV-2 suppressing the autocrine action of interferon. These findings reveal that SARS-CoV-2 curtails the immune response and highlights the gut as a pro-inflammatory reservoir that should be considered to fully understand SARS-CoV-2 pathogenesis.

Quantitative proteomics reveals specific metabolic features of acute myeloid leukemia stem cells.

Acute myeloid leukemia is characterized by the accumulation of clonal myeloid blast cells unable to differentiate into mature leukocytes. Chemotherapy induces remission in the majority of patients, but relapse rates are high and lead to poor clinical outcomes. Because this is primarily caused by chemotherapy-resistant leukemic stem cells (LSCs), it is essential to eradicate LSCs to improve patient survival. LSCs have predominantly been studied at the transcript level, thus information about posttranscriptionally regulated genes and associated networks is lacking. Here, we extend our previous report on LSC proteomes to healthy age-matched hematopoietic stem and progenitor cells (HSPCs) and correlate the proteomes to the corresponding transcriptomes. By comparing LSCs to leukemic blasts and healthy HSPCs, we validate candidate LSC markers and highlight novel and potentially targetable proteins that are absent or only lowly expressed in HSPCs. In addition, our data provide strong evidence that LSCs harbor a characteristic energy metabolism, adhesion molecule composition, as well as RNA-processing properties. Furthermore, correlating proteome and transcript data of the same individual samples highlights the strength of proteome analyses, which are particularly potent in detecting alterations in metabolic pathways. In summary, our study provides a comprehensive proteomic and transcriptomic characterization of functionally validated LSCs, blasts, and healthy HSPCs, representing a valuable resource helping to design LSC-directed therapies.

Simultaneous T2 and T2∗ mapping of multiple sclerosis lesions with radial RARE-EPI.

PURPOSE: The characteristic MRI features of multiple sclerosis (MS) lesions make it conceptually appealing to pursue parametric mapping techniques that support simultaneous generation of quantitative maps of 2 or more MR contrast mechanisms. We present a modular rapid acquisition with relaxation enhancement (RARE)-EPI hybrid that facilitates simultaneous T2 and T2∗ mapping (2in1-RARE-EPI). METHODS: In 2in1-RARE-EPI the first echoes in the echo train are acquired with a RARE module, later echoes are acquired with an EPI module. To define the fraction of echoes covered by the RARE and EPI module, an error analysis of T2 and T2∗ was conducted with Monte Carlo simulations. Radial k-space (under)sampling was implemented for acceleration (R = 2). The feasibility of 2in1-RARE-EPI for simultaneous T2 and T2∗ mapping was examined in a phantom study mimicking T2 and T2∗ relaxation times of the brain. For validation, 2in1-RARE-EPI was benchmarked versus multi spin-echo (MSE) and multi gradient-echo (MGRE) techniques. The clinical applicability of 2in1-RARE-EPI was demonstrated in healthy subjects and MS patients. RESULTS: There was a good agreement between T2 / T2∗ values derived from 2in1-RARE-EPI and T2 / T2∗ reference values obtained from MSE and MGRE in both phantoms and healthy subjects. In patients, MS lesions in T2 and T2∗ maps deduced from 2in1-RARE-EPI could be just as clearly delineated as in reference maps calculated from MSE/MGRE. CONCLUSION: This work demonstrates the feasibility of radially (under)sampled 2in1-RARE-EPI for simultaneous T2 and T2∗ mapping in MS patients.

A comprehensive comparison of tools for differential ChIP-seq analysis.

ChIP-seq has become a widely adopted genomic assay in recent years to determine binding sites for transcription factors or enrichments for specific histone modifications. Beside detection of enriched or bound regions, an important question is to determine differences between conditions. While this is a common analysis for gene expression, for which a large number of computational approaches have been validated, the same question for ChIP-seq is particularly challenging owing to the complexity of ChIP-seq data in terms of noisiness and variability. Many different tools have been developed and published in recent years. However, a comprehensive comparison and review of these tools is still missing. Here, we have reviewed 14 tools, which have been developed to determine differential enrichment between two conditions. They differ in their algorithmic setups, and also in the range of applicability. Hence, we have benchmarked these tools on real data sets for transcription factors and histone modifications, as well as on simulated data sets to quantitatively evaluate their performance. Overall, there is a great variety in the type of signal detected by these tools with a surprisingly low level of agreement. Depending on the type of analysis performed, the choice of method will crucially impact the outcome.

LedPred: an R/bioconductor package to predict regulatory sequences using support vector machines.

UNLABELLED: Supervised classification based on support vector machines (SVMs) has successfully been used for the prediction of cis-regulatory modules (CRMs). However, no integrated tool using such heterogeneous data as position-specific scoring matrices, ChIP-seq data or conservation scores is currently available. Here, we present LedPred, a flexible SVM workflow that predicts new regulatory sequences based on the annotation of known CRMs, which are associated to a large variety of feature types. LedPred is provided as an R/Bioconductor package connected to an online server to avoid installation of non-R software. Due to the heterogeneous CRM feature integration, LedPred excels at the prediction of regulatory sequences in Drosophila and mouse datasets compared with similar SVM-based software. AVAILABILITY AND IMPLEMENTATION: LedPred is available on GitHub: https://github.com/aitgon/LedPred and Bioconductor: http://bioconductor.org/packages/release/bioc/html/LedPred.html under the MIT license. CONTACT: aitor.gonzalez@univ-amu.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Environment-induced epigenetic reprogramming in genomic regulatory elements in smoking mothers and their children.

Epigenetic mechanisms have emerged as links between prenatal environmental exposure and disease risk later in life. Here, we studied epigenetic changes associated with maternal smoking at base pair resolution by mapping DNA methylation, histone modifications, and transcription in expectant mothers and their newborn children. We found extensive global differential methylation and carefully evaluated these changes to separate environment associated from genotype-related DNA methylation changes. Differential methylation is enriched in enhancer elements and targets in particular "commuting" enhancers having multiple, regulatory interactions with distal genes. Longitudinal whole-genome bisulfite sequencing revealed that DNA methylation changes associated with maternal smoking persist over years of life. Particularly in children prenatal environmental exposure leads to chromatin transitions into a hyperactive state. Combined DNA methylation, histone modification, and gene expression analyses indicate that differential methylation in enhancer regions is more often functionally translated than methylation changes in promoters or non-regulatory elements. Finally, we show that epigenetic deregulation of a commuting enhancer targeting c-Jun N-terminal kinase 2 (JNK2) is linked to impaired lung function in early childhood.