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Background: In breast cancer, as in all cancers, genetic and epigenetic deregulations can result in out-of-context expressions of a set of normally silent tissue-specific genes. The activation of some of these genes in various cancers empowers tumours cells with new properties and drives enhanced proliferation and metastatic activity, leading to a poor survival prognosis. Results: In this work, we undertook an unprecedented systematic and unbiased analysis of out-of-context activations of a specific set of tissue-specific genes from testis, placenta and embryonic stem cells, not expressed in normal breast tissue as a source of novel prognostic biomarkers. To this end, we combined a strict machine learning framework of transcriptomic data analysis, and successfully created a new robust tool, validated in several independent datasets, which is able to identify patients with a high risk of relapse. This unbiased approach allowed us to identify a panel of five biomarkers, DNMT3B, EXO1, MCM10, CENPF and CENPE, that are robustly and significantly associated with disease-free survival prognosis in breast cancer. Based on these findings, we created a new Gene Expression Classifier (GEC) that stratifies patients. Additionally, thanks to the identified GEC, we were able to paint the specific molecular portraits of the particularly aggressive tumours, which show characteristics of male germ cells, with a particular metabolic gene signature, associated with an enrichment in pro-metastatic and pro-proliferation gene expression. Conclusions: The GEC classifier is able to reliably identify patients with a high risk of relapse at early stages of the disease. We especially recommend to use the GEC tool for patients with the luminal-A molecular subtype of breast cancer, generally considered of a favourable disease-free survival prognosis, to detect the fraction of patients undergoing a high risk of relapse. [ABSTRACT FROM AUTHOR]

Abstract: More and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be “personalized” according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non‐synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants. [ABSTRACT FROM AUTHOR]

Cancer cells have recently been shown to activate hundreds of normally silent tissuerestricted genes, including a specific subset associated with cancer progression and poor prognosis. Within these genes, a class of testis-specific genes designed as cancer/testis, attracted special attention because of their oncogenic roles as well as their potential use in immunotherapy. Here we focus on one of these genes encoding the testis-specific member of the bromodomain and extra-terminal (BET) family, known as BRDT. Aberrant activation of BRDT was first detected in lung cancers. In this study, we report that the frequency of BRDT's aberrant activation in lung cancer varies according to the histological subtypes and in contrast with other cancer/testis genes, it is rarely expressed in other solid tumours. The functional characterization of BRDT in its physiological setting in male germ cells is now painting a clear portrait of its normal activity and also suggests possible underlying oncogenic activities, when the gene is ectopically activated in cancers. Also, these functional studies of BRDT point to specific anti-cancer therapeutic strategies that could be used to “highjack” BRDT's action and turn it against cancer cells, which express this gene. Finally, BRDT's expression could be used as a biomarker for cell sensitivity to BET bromodomain inhibitors, which have become newly available as anti-cancer drugs. [ABSTRACT FROM AUTHOR]

Despite the recent progress in sequencing technologies, genome-wide association studies (GWAS) remain limited by a statistical-power issue: many polymorphisms contribute little to common trait variation and therefore escape detection. The small contribution sometimes corresponds to incomplete penetrance, which may result from probabilistic effects on molecular regulations. In such cases, genetic mapping may benefit from the wealth of data produced by single-cell technologies. We present here the development of a novel genetic mapping method that allows to scan genomes for single-cell Probabilistic Trait Loci that modify the statistical properties of cellular-level quantitative traits. Phenotypic values are acquired on thousands of individual cells, and genetic association is obtained from a multivariate analysis of a matrix of Kantorovich distances. No prior assumption is required on the mode of action of the genetic loci involved and, by exploiting all single-cell values, the method can reveal non-deterministic effects. Using both simulations and yeast experimental datasets, we show that it can detect linkages that are missed by classical genetic mapping. A probabilistic effect of a single SNP on cell shape was detected and validated. The method also detected a novel locus associated with elevated gene expression noise of the yeast galactose regulon. Our results illustrate how single-cell technologies can be exploited to improve the genetic dissection of certain common traits. The method is available as an open source R package called ptlmapper. [ABSTRACT FROM AUTHOR]

Background: Epigenomic studies on humans and model species have revealed substantial inter-individual variation in histone modification profiles. However, the pattern of this variation has not been precisely characterized, particularly regarding which genomic features are enriched for variability and whether distinct histone marks co-vary synergistically. Yeast allows us to investigate intra-species variation at high resolution while avoiding other sources of variation, such as cell type or subtype. Results: We profiled histone marks H3K4me3, H3K9ac, H3K14ac, H4K12ac and H3K4me1 in three unrelated wild strains of Saccharomyces cerevisiae at single-nucleosome resolution and analyzed inter-strain differences statistically. All five marks varied significantly at specific loci, but to different extents. The number of nucleosomes varying for a given mark between two strains ranged from 20 to several thousands; +1 nucleosomes were significantly less subject to variation. Genes with highly evolvable or responsive expression showed higher variability; however, the variation pattern could not be explained by known transcriptional differences between the strains. Synergistic variation of distinct marks was not systematic, with surprising differences between functionally related H3K9ac and H3K14ac. Interestingly, H3K14ac differences that persisted through transient hyperacetylation were supported by H3K4me3 differences, suggesting stabilization via cross talk. Conclusions: Quantitative variation of histone marks among S. cerevisiae strains is abundant and complex. Its relation to functional characteristics is modular and seems modest, with partial association with gene expression divergences, differences between functionally related marks and partial co-variation between marks that may confer stability. Thus, the specific context of studies, such as which precise marks, individuals and genomic loci are investigated, is primordial in population epigenomics studies. The complexity found in this pilot survey in yeast suggests that high complexity can be anticipated among higher eukaryotes, including humans. [ABSTRACT FROM AUTHOR]

The hopes of precision medicine rely on our capacity to measure various high-throughput genomic information of a patient and to integrate them for personalized diagnosis and adapted treatment. Reaching these ambitious objectives will require the development of efficient tools for the detection of molecular defects at the individual level. Here, we propose a novel method, PenDA, to perform Personalized Differential Analysis at the scale of a single sample. PenDA is based on the local ordering of gene expressions within individual cases and infers the deregulation status of genes in a sample of interest compared to a reference dataset. Based on realistic simulations of RNA-seq data of tumors, we showed that PenDA outcompetes existing approaches with very high specificity and sensitivity and is robust to normalization effects. Applying the method to lung cancer cohorts, we observed that deregulated genes in tumors exhibit a cancer-type-specific commitment towards up- or down-regulation. Based on the individual information of deregulation given by PenDA, we were able to define two new molecular histologies for lung adenocarcinoma cancers strongly correlated to survival. In particular, we identified 37 biomarkers whose up-regulation lead to bad prognosis and that we validated on two independent cohorts. PenDA provides a robust, generic tool to extract personalized deregulation patterns that can then be used for the discovery of therapeutic targets and for personalized diagnosis. An open-access, user-friendly R package is available at https://github.com/bcm-uga/penda. Author summary: The hopes of precision medicine rely on our capacity to measure individual molecular information for personalized diagnosis and treatment. These challenging perspectives will be only possible with the development of efficient methodological tools to identify patient-specific molecular defects from the many precise molecular information that one can access at the single-individual, single tissue or even single-cell levels. Such methods will provide a better understanding of disease-specific biological mechanisms and will promote the development of personalized therapeutic strategies. Here we describe a novel method, named PenDA, to perform differential analysis of gene expression at the individual level. Based on a realistic benchmark of simulated tumors, we demonstrated that PenDA reaches very high efficiency in detecting sample-specific deregulated genes. We then applied the method to two large cohorts associated with lung cancer. A detailed statistical analysis of the results allowed to isolate genes with specific deregulation patterns, like genes that are up-regulated in all tumors or genes that are expressed but never deregulated in any tumors. Given their specificities, these genes are likely to be of interest in therapeutic research. In particular, we were able to identified 37 new biomarkers associated to bad prognosis that we validated on two independent cohorts. [ABSTRACT FROM AUTHOR]

The article focuses on the role of nucleoside diphosphate kinases 1 and 2 (NME1/2) in regulating fatty acid accumulation in response to a high-fat diet. Topics discussed include the unique binding mode of NME1 with acetyl–coenzyme A (AcCoA), the impact of Nme2 knockout on triglyceride synthesis and liver steatosis in high-fat diet (HFD)-fed mice, and the epigenetic regulation of a protective liver response through targeted histone acetylation by NME2.

In low-risk myelodysplastic syndrome (LR-MDS), erythropoietin (EPO) is widely used for the treatment of chronic anemia. However, initial response to EPO has time-limited effects. Luspatercept reduces red blood cell transfusion dependence in LR-MDS patients. Here, we investigated the molecular action of luspatercept (RAP-536) in an in vitro model of erythroid differentiation of MDS, and also in a in vivo PDX murine model with primary samples of MDS patients carrying or not SF3B1 mutation. In our in vitro model, RAP-536 promotes erythroid proliferation by increasing the number of cycling cells without any impact on apoptosis rates. RAP-536 promoted late erythroid precursor maturation while decreasing intracellular reactive oxygen species level. RNA sequencing of erythroid progenitors obtained under RAP-536 treatment showed an enrichment of genes implicated in positive regulation of response to oxidative stress and erythroid differentiation. In our PDX model, RAP-536 induces a higher hemoglobin level. RAP-536 did not modify variant allele frequencies in vitro and did not have any effect against leukemic burden in our PDX model. These results suggest that RAP-536 promotes in vivo and in vitro erythroid cell differentiation by decreasing ROS level without any remarkable impact on iron homeostasis and on mutated allele burden. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) syndrome is characterized by chronic intermittent hypoxia and is associated with an increased risk of all-cause mortality, including cancer mortality. Hepatocellular carcinoma (HCC) is the most common type of liver cancer, characterized by increasing incidence and high mortality. However, the link between HCC and OSA-related chronic intermittent hypoxia remains unclear. Herein, we used a diethylnitrosamine (DEN)-induced HCC model to investigate whether OSA-related chronic intermittent hypoxia has an impact on HCC progression. To elucidate the associated mechanisms, we first evaluated the hypoxia status in the DEN-induced HCC model. Next, to simulate OSA-related intermittent hypoxia, we exposed cirrhotic rats with HCC to intermittent hypoxia during six weeks. We performed histopathological, immunohistochemical, RT-qPCR, and RNA-seq analysis. Chronic DEN injections strongly promoted cell proliferation, fibrosis, disorganized vasculature, and hypoxia in liver tissue, which mimics the usual events observed during human HCC development. Intermittent hypoxia further increased cell proliferation in DEN-induced HCC, which may contribute to an increased risk of HCC progression. In conclusion, our observations suggest that chronic intermittent hypoxia may be a factor worsening the prognosis of HCC. [ABSTRACT FROM AUTHOR]

Background: T cell acute lymphoblastic leukemia (T-ALL) defines a group of hematological malignancies with heterogeneous aggressiveness and highly variable outcome, making therapeutic decisions a challenging task. We tried to discover new predictive model for T-ALL before treatment by using a specific pipeline designed to discover aberrantly active gene. Results: The expression of 18 genes was significantly associated with shorter survival, including ACTRT2, GOT1L1, SPATA45, TOPAZ1 and ZPBP (5-GEC), which were used as a basis to design a prognostic classifier for T-ALL patients. The molecular characterization of the 5-GEC positive T-ALL unveiled specific characteristics inherent to the most aggressive T leukemic cells, including a drastic shut-down of genes located on the mitochondrial genome and an upregulation of histone genes, the latter characterizing high risk forms in adult patients. These cases fail to respond to the induction treatment, since 5-GEC either predicted positive minimal residual disease (MRD) or a short-term relapse in MRD negative patients. Conclusion: Overall, our investigations led to the discovery of a homogenous group of leukemic cells with profound alterations of their biology. It also resulted in an accurate predictive tool that could significantly improve the management of T-ALL patients. [ABSTRACT FROM AUTHOR]

Simple Summary: Hepatocellular carcinoma is the most frequent form of primary liver cancer, characterized by increasing incidence and high mortality. Animal models of hepatocellular carcinoma are widely used to study the biology of cancer and to test potential therapies. Herein, we describe how the rat model of DEN-induced hepatocellular carcinoma mimics the pathogenesis of hepatocellular carcinoma seen in humans, including liver damage, chronic inflammation, hepatocytes proliferation, liver fibrosis and cirrhosis, disorganized vasculature, and modulations of the liver's immune microenvironment. Our results should help the hepatocellular carcinoma field to better tailor the use of the DEN-induced rat liver cancer model for testing specific experimental hypotheses or to perform preclinical testing. Hepatocellular carcinoma (HCC) is the most common type of liver cancer. The majority of HCC cases are associated with liver fibrosis or cirrhosis developing from chronic liver injuries. The immune system of the liver contributes to the severity of tissue damage, the establishment of fibrosis and the disease's progression towards HCC. Herein, we provide a detailed characterization of the DEN-induced HCC rat model during fibrosis progression and HCC development with a special focus on the liver's inflammatory microenvironment. Fischer 344 male rats were treated weekly for 14 weeks with intra-peritoneal injections of 50 mg/kg DEN. The rats were sacrificed before starting DEN-injections at 0 weeks, after 8 weeks, 14 weeks and 20 weeks after the start of DEN-injections. We performed histopathological, immunohistochemical, RT-qPCR, RNA-seq and flow cytometry analysis. Data were compared between tumor and non-tumor samples from the DEN-treated versus untreated rats, as well as versus human HCCs. Chronic DEN injections lead to liver damage, hepatocytes proliferation, liver fibrosis and cirrhosis, disorganized vasculature, and a modulated immune microenvironment that mimics the usual events observed during human HCC development. The RNA-seq results showed that DEN-induced liver tumors in the rat model shared remarkable molecular characteristics with human HCC, especially with HCC associated with high proliferation. In conclusion, our study provides detailed insight into hepatocarcinogenesis in a commonly used model of HCC, facilitating the future use of this model for preclinical testing. [ABSTRACT FROM AUTHOR]

Background: Large-scale genetic and epigenetic deregulations enable cancer cells to ectopically activate tissue-specific expression programmes. A specifically designed strategy was applied to oral squamous cell carcinomas (OSCC) in order to detect ectopic gene activations and develop a prognostic stratification test.Methods: A dedicated original prognosis biomarker discovery approach was implemented using genome-wide transcriptomic data of OSCC, including training and validation cohorts. Abnormal expressions of silent genes were systematically detected, correlated with survival probabilities and evaluated as predictive biomarkers. The resulting stratification test was confirmed in an independent cohort using immunohistochemistry.Results: A specific gene expression signature, including a combination of three genes, AREG, CCNA1 and DDX20, was found associated with high-risk OSCC in univariate and multivariate analyses. It was translated into an immunohistochemistry-based test, which successfully stratified patients of our own independent cohort.Discussion: The exploration of the whole gene expression profile characterising aggressive OSCC tumours highlights their enhanced proliferative and poorly differentiated intrinsic nature. Experimental targeting of CCNA1 in OSCC cells is associated with a shift of transcriptomic signature towards the less aggressive form of OSCC, suggesting that CCNA1 could be a good target for therapeutic approaches. [ABSTRACT FROM AUTHOR]

Background: Although exposure to cigarette smoking during pregnancy has been associated with alterations of DNA methylation in the cord blood or placental cells, whether such exposure before pregnancy could induce epigenetic alterations in the placenta of former smokers has never been investigated.Methods: Our approach combined the analysis of placenta epigenomic (ENCODE) data with newly generated DNA methylation data obtained from 568 pregnant women, the largest cohort to date, either actively smoking during their pregnancy or formerly exposed to tobacco smoking.Results: This strategy resulted in several major findings. First, among the 203 differentially methylated regions (DMRs) identified by the epigenome-wide association study, 152 showed "reversible" alterations of DNA methylation, only present in the placenta of current smokers, whereas 26 were also found altered in former smokers, whose placenta had not been exposed directly to cigarette smoking. Although the absolute methylation changes were smaller than those observed in other contexts, such as in some congenital diseases, the observed alterations were consistent within each DMR. This observation was further supported by a demethylation of LINE-1 sequences in the placentas of both current (beta-coefficient (β) (95% confidence interval (CI)), - 0.004 (- 0.008; 0.001)) and former smokers (β (95% CI), - 0.006 (- 0.011; - 0.001)) compared to nonsmokers. Second, the 203 DMRs were enriched in epigenetic marks corresponding to enhancer regions, including monomethylation of lysine 4 and acetylation of lysine 27 of histone H3 (respectively H3K4me1 and H3K27ac). Third, smoking-associated DMRs were also found near and/or overlapping 10 imprinted genes containing regions (corresponding to 16 genes), notably including the NNAT, SGCE/PEG10, and H19/MIR675 loci.Conclusions: Our results pointing towards genomic regions containing the imprinted genes as well as enhancers as preferential targets suggest mechanisms by which tobacco could directly impact the fetus and future child. The persistence of significant DNA methylation changes in the placenta of former smokers supports the hypothesis of an "epigenetic memory" of exposure to cigarette smoking before pregnancy. This observation not only is conceptually revolutionary, but these results also bring crucial information in terms of public health concerning potential long-term detrimental effects of smoking in women. [ABSTRACT FROM AUTHOR]

Background: Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking. Results: Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30–35%, and that selection of cell-type informative probes has similar effect. We show that Cattell's rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms' performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. Conclusion: Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir. [ABSTRACT FROM AUTHOR]

Background Air pollution exposure represents a major health threat to the developing foetus. DNA methylation is one of the most well-known molecular determinants of the epigenetic status of cells. Blood DNA methylation has been proven sensitive to air pollutants, but the molecular impact of air pollution on new-borns has so far received little attention. Objectives We investigated whether nitrogen dioxide (NO 2 ), particulate matter (PM 10 ), temperature and humidity during pregnancy are associated with differences in placental DNA methylation levels. Methods Whole-genome DNA-methylation was measured using the Illumina's Infinium HumanMethylation450 BeadChip in the placenta of 668 newborns from the EDEN cohort. We designed an original strategy using a priori biological information to focus on candidate genes with a specific expression pattern in placenta (active or silent) combined with an agnostic epigenome-wide association study (EWAS). We used robust linear regression to identify CpGs and differentially methylated regions (DMR) associated with each exposure during short- and long-term time-windows. Results The candidate genes approach identified nine CpGs mapping to 9 genes associated with prenatal NO 2 and PM 10 exposure [false discovery rate (FDR) p  < 0.05]. Among these, the methylation level of 2 CpGs located in ADORA2B remained significantly associated with NO 2 exposure during the 2nd trimester and whole pregnancy in the EWAS (FDR p  < 0.05). EWAS further revealed associations between the environmental exposures under study and variations of DNA methylation of 4 other CpGs. We further identified 27 DMRs significantly (FDR p  < 0.05) associated with air pollutants exposure and 13 DMRs with meteorological conditions. Conclusions The methylation of ADORA2B , a gene whose expression was previously associated with hypoxia and pre-eclampsia, was consistently found here sensitive to atmospheric pollutants. In addition, air pollutants were associated to DMRs pointing towards genes previously implicated in preeclampsia, hypertensive and metabolic disorders. These findings demonstrate that air pollutants exposure at levels commonly experienced in the European population are associated with placental gene methylation and provide some mechanistic insight into some of the reported effects of air pollutants on preeclampsia. [ABSTRACT FROM AUTHOR]

Preventing the cytokine storm observed in COVID-19 is a crucial goal for reducing the occurrence of severe acute respiratory failure and improving outcomes. Here, we identify Aldo-Keto Reductase 1B10 (AKR1B10) as a key enzyme involved in the expression of pro-inflammatory cytokines. The analysis of transcriptomic data from lung samples of patients who died from COVID-19 demonstrates an increased expression of the gene encoding AKR1B10. Measurements of the AKR1B10 protein in sera from hospitalised COVID-19 patients suggests a significant link between AKR1B10 levels and the severity of the disease. In macrophages and lung cells, the over-expression of AKR1B10 induces the expression of the pro-inflammatory cytokines Interleukin-6 (IL-6), Interleukin-1β (IL-1β) and Tumor Necrosis Factor a (TNFα), supporting the biological plausibility of an AKR1B10 involvement in the COVID-19-related cytokine storm. When macrophages were stressed by lipopolysaccharides (LPS) exposure and treated by Zopolrestat, an AKR1B10 inhibitor, the LPS-induced production of IL-6, IL-1β, and TNFα is significantly reduced, reinforcing the hypothesis that the pro-inflammatory expression of cytokines is AKR1B10-dependant. Finally, we also show that AKR1B10 can be secreted and transferred via extracellular vesicles between different cell types, suggesting that this protein may also contribute to the multi-organ systemic impact of COVID-19. These experiments highlight a relationship between AKR1B10 production and severe forms of COVID-19. Our data indicate that AKR1B10 participates in the activation of cytokines production and suggest that modulation of AKR1B10 activity might be an actionable pharmacological target in COVID-19 management. [ABSTRACT FROM AUTHOR]

The molecular basis of residual histone retention after the nearly genome-wide histone-to-protamine replacement during late spermatogenesis is a critical and open question. Our previous investigations showed that in postmeiotic male germ cells, the genome-scale incorporation of histone variants TH2B-H2A.L.2 allows a controlled replacement of histones by protamines to occur. Here, we highlight the intrinsic ability of H2A.L.2 to specifically target the pericentric regions of the genome and discuss why pericentric heterochromatin is a privileged site of histone retention in mature spermatozoa. We observed that the intranuclear localization of H2A.L.2 is controlled by its ability to bind RNA, as well as by an interplay between its RNA-binding activity and its tropism for pericentric heterochromatin. We identify the H2A.L.2 RNA-binding domain and demonstrate that in somatic cells, the replacement of H2A.L.2 RNA-binding motif enhances and stabilizes its pericentric localization, while the forced expression of RNA increases its homogenous nuclear distribution. Based on these data, we propose that the specific accumulation of RNA on pericentric regions combined with H2A.L.2 tropism for these regions are responsible for stabilizing H2A.L.2 on these regions in mature spermatozoa. This situation would favor histone retention on pericentric heterochromatin. [ABSTRACT FROM AUTHOR]