Histone lactylation in macrophages is predictive for gene expression changes during ischemia induced-muscle regeneration.

OBJECTIVES: We have previously shown that lactate is an essential metabolite for macrophage polarisation during ischemia-induced muscle regeneration. Recent in vitro work has implicated histone lactylation, a direct derivative of lactate, in macrophage polarisation. Here, we explore the in vivo relevance of histone lactylation for macrophage polarisation after muscle injury. METHODS: To evaluate macrophage dynamics during muscle regeneration, we subjected mice to ischemia-induced muscle damage by ligating the femoral artery. Muscle samples were harvested at 1, 2, 4, and 7 days post injury (dpi). CD45+CD11b+F4/80+CD64+ macrophages were isolated and processed for RNA sequencing, Western Blotting, and CUT&Tag-sequencing to investigate gene expression, histone lactylation levels, and histone lactylation genomic localisation and enrichment, respectively. RESULTS: We show that, over time, macrophages in the injured muscle undergo extensive gene expression changes, which are similar in nature and in timing to those seen after other types of muscle-injuries. We find that the macrophage histone lactylome is modified between 2 and 4 dpi, which is a crucial window for macrophage polarisation. Absolute histone lactylation levels increase, and, although subtly, the genomic enrichment of H3K18la changes. Overall, we find that histone lactylation is important at both promoter and enhancer elements. Lastly, H3K18la genomic profile changes from 2 to 4 dpi were predictive for gene expression changes later in time, rather than being a reflection of prior gene expression changes. CONCLUSIONS: Our results suggest that histone lactylation dynamics are functionally important for the function of macrophages during muscle regeneration.

GiRAFR improves gRNA detection and annotation in single-cell CRISPR screens.

Novel methods that combine single cell RNA-seq with CRISPR screens enable high-throughput characterization of transcriptional changes caused by genetic perturbations. Dedicated software is however lacking to annotate CRISPR guide RNA (gRNA) libraries and associate them with single cell transcriptomes. Here, we describe a CRISPR droplet sequencing (CROP-seq) dataset. During analysis, we observed that the most commonly used method fails to detect mutant gRNAs. We therefore developed a python tool to identify and characterize intact and mutant gRNAs, called GiRAFR. We show that mutant gRNAs are dysfunctional, and failure to detect and annotate them leads to an inflated estimate of the number of untransformed cells, attenuated downregulation of target genes, as well as an underestimated multiplet frequency. These findings are mirrored in publicly available datasets, where we find that up to 35% of cells are transduced with a mutant gRNA. Applying GiRAFR hence stands to improve the annotation and quality of single cell CRISPR screens.

Cell-free DNA methylome analysis for early preeclampsia prediction.

Preeclampsia (PE) is a leading cause for peripartal morbidity, especially if developing early in gestation. To enable prophylaxis in the prevention of PE, pregnancies at risk of PE must be identified early-in the first trimester. To identify at-risk pregnancies we profiled methylomes of plasma-derived, cell-free DNA from 498 pregnant women, of whom about one-third developed early-onset PE. We detected DNA methylation differences between control and PE pregnancies that enabled risk stratification at PE diagnosis but also presymptomatically, at around 12 weeks of gestation (range 9-14 weeks). The first-trimester risk prediction model was validated in an external cohort collected from two centers (area under the curve (AUC) = 0.75) and integrated with routinely available maternal risk factors (AUC = 0.85). The combined risk score correctly predicted 72% of patients with early-onset PE at 80% specificity. These preliminary results suggest that cell-free DNA methylation profiling is a promising tool for presymptomatic PE risk assessment, and has the potential to improve treatment and follow-up in the obstetric clinic.

H3K18 lactylation marks tissue-specific active enhancers.

BACKGROUND: Histone lactylation has been recently described as a novel histone post-translational modification linking cellular metabolism to epigenetic regulation. RESULTS: Given the expected relevance of this modification and current limited knowledge of its function, we generate genome-wide datasets of H3K18la distribution in various in vitro and in vivo samples, including mouse embryonic stem cells, macrophages, adipocytes, and mouse and human skeletal muscle. We compare them to profiles of well-established histone modifications and gene expression patterns. Supervised and unsupervised bioinformatics analysis shows that global H3K18la distribution resembles H3K27ac, although we also find notable differences. H3K18la marks active CpG island-containing promoters of highly expressed genes across most tissues assessed, including many housekeeping genes, and positively correlates with H3K27ac and H3K4me3 as well as with gene expression. In addition, H3K18la is enriched at active enhancers that lie in proximity to genes that are functionally important for the respective tissue. CONCLUSIONS: Overall, our data suggests that H3K18la is not only a marker for active promoters, but also a mark of tissue specific active enhancers.

DNA methylation-driven EMT is a common mechanism of resistance to various therapeutic agents in cancer.

BACKGROUND: Overcoming therapeutic resistance is one of the major hurdles in cancer care. One mechanism contributing to therapeutic resistance is a process in which epithelial cells switch to a mesenchymal state (epithelial-to-mesenchymal transition or EMT). The precise mechanisms driving EMT-mediated therapeutic resistance have, however, not been elucidated. RESULTS: Here, we study ten cell line pairs, for which parental cell lines were made resistant to either a targeted or chemotherapy-based treatment. First, we show by miRNA-200 overexpression that treatment resistance is driven by EMT. Next, we demonstrate that DNA methylation changes occur within each cell line pair and show that exposure to 5-azacytidine or knock down of DNA methyltransferases (DNMTs), both of which globally demethylate cells, result in EMT reversal and increased therapeutic sensitivity. This suggests DNA methylation to causally underlie EMT and treatment resistance. We also observe significant overlap in methylation profiles between resistant lines, suggesting a common epigenetic mechanism to cause resistance to therapy. In line with this hypothesis, cross-resistance to other targeted and chemotherapies is observed, while importantly, this is lost upon demethylation of the cells. Finally, we clinically validate that DNA methylation changes drive EMT-mediated resistance to sorafenib in patients with advanced hepatocellular carcinoma (HCC). Specifically, we develop a capture-based protocol to interrogate DNA methylation in low amounts of circulating tumor DNA (ctDNA). By interrogating the methylation status in liquid biopsies, longitudinally collected during sorafenib treatment, we assess whether DNA methylation changes also drive EMT and therapy resistance in a clinical setting. Particularly, by monitoring methylation changes in EMT genes, we are able to predict tumor response and acquired resistance to sorafenib. CONCLUSIONS: We propose methylation changes underlying EMT to constitute a common resistance mechanism to cancer therapies. This process can be reversed pharmacologically and monitored non-invasively in ctDNA to predict resistance to treatment.

Transition between fermentation and respiration determines history-dependent behavior in fluctuating carbon sources.

Cells constantly adapt to environmental fluctuations. These physiological changes require time and therefore cause a lag phase during which the cells do not function optimally. Interestingly, past exposure to an environmental condition can shorten the time needed to adapt when the condition re-occurs, even in daughter cells that never directly encountered the initial condition. Here, we use the molecular toolbox of Saccharomyces cerevisiae to systematically unravel the molecular mechanism underlying such history-dependent behavior in transitions between glucose and maltose. In contrast to previous hypotheses, the behavior does not depend on persistence of proteins involved in metabolism of a specific sugar. Instead, presence of glucose induces a gradual decline in the cells' ability to activate respiration, which is needed to metabolize alternative carbon sources. These results reveal how trans-generational transitions in central carbon metabolism generate history-dependent behavior in yeast, and provide a mechanistic framework for similar phenomena in other cell types.

Expression profiling of budding cells in colorectal cancer reveals an EMT-like phenotype and molecular subtype switching.

BACKGROUND: Tumour budding, described as the presence of single cells or small clusters of up to five tumour cells at the invasive margin, is established as a prognostic marker in colorectal carcinoma. In the present study, we aimed to investigate the molecular signature of tumour budding cells and the corresponding tumour bulk. METHODS: Tumour bulk and budding areas were microdissected and processed for RNA-sequencing. As little RNA was obtained from budding cells, a special low-input mRNA library preparation protocol was used. Gene expression profiles of budding as compared with tumour bulk were investigated for established EMT signatures, consensus molecular subtype (CMS), gene set enrichment and pathway analysis. RESULTS: A total of 296 genes were differentially expressed with an FDR <0.05 and a twofold change between tumour bulk and budding regions. Genes that were upregulated in the budding signature were mainly involved in cell migration and survival while downregulated genes were important for cell proliferation. Supervised clustering according to an established EMT gene signature categorised budding regions as EMT-positive, whereas tumour bulk was considered EMT-negative. Furthermore, a shift from CMS2 (epithelial) to CMS4 (mesenchymal) was observed as tumour cells transit from the tumour bulk to the budding regions. CONCLUSIONS: Tumour budding regions are characterised by a phenotype switch compared with the tumour bulk, involving the acquisition of migratory characteristics and a decrease in cell proliferation. In particular, most tumour budding signatures were EMT-positive and switched from an epithelial subtype (CMS2) in the tumour bulk to a mesenchymal subtype (CMS4) in budding cells.

Fine scale mapping of the 17q22 breast cancer locus using dense SNPs, genotyped within the Collaborative Oncological Gene-Environment Study (COGs).

Genome-wide association studies have found SNPs at 17q22 to be associated with breast cancer risk. To identify potential causal variants related to breast cancer risk, we performed a high resolution fine-mapping analysis that involved genotyping 517 SNPs using a custom Illumina iSelect array (iCOGS) followed by imputation of genotypes for 3,134 SNPs in more than 89,000 participants of European ancestry from the Breast Cancer Association Consortium (BCAC). We identified 28 highly correlated common variants, in a 53 Kb region spanning two introns of the STXBP4 gene, that are strong candidates for driving breast cancer risk (lead SNP rs2787486 (OR = 0.92; CI 0.90-0.94; P = 8.96 × 10(-15))) and are correlated with two previously reported risk-associated variants at this locus, SNPs rs6504950 (OR = 0.94, P = 2.04 × 10(-09), r(2) = 0.73 with lead SNP) and rs1156287 (OR = 0.93, P = 3.41 × 10(-11), r(2) = 0.83 with lead SNP). Analyses indicate only one causal SNP in the region and several enhancer elements targeting STXBP4 are located within the 53 kb association signal. Expression studies in breast tumor tissues found SNP rs2787486 to be associated with increased STXBP4 expression, suggesting this may be a target gene of this locus.

Age- and Tumor Subtype-Specific Breast Cancer Risk Estimates for CHEK2*1100delC Carriers.

PURPOSE: CHEK2*1100delC is a well-established breast cancer risk variant that is most prevalent in European populations; however, there are limited data on risk of breast cancer by age and tumor subtype, which limits its usefulness in breast cancer risk prediction. We aimed to generate tumor subtype- and age-specific risk estimates by using data from the Breast Cancer Association Consortium, including 44,777 patients with breast cancer and 42,997 controls from 33 studies genotyped for CHEK2*1100delC. PATIENTS AND METHODS: CHEK2*1100delC genotyping was mostly done by a custom Taqman assay. Breast cancer odds ratios (ORs) for CHEK2*1100delC carriers versus noncarriers were estimated by using logistic regression and adjusted for study (categorical) and age. Main analyses included patients with invasive breast cancer from population- and hospital-based studies. RESULTS: Proportions of heterozygous CHEK2*1100delC carriers in controls, in patients with breast cancer from population- and hospital-based studies, and in patients with breast cancer from familial- and clinical genetics center-based studies were 0.5%, 1.3%, and 3.0%, respectively. The estimated OR for invasive breast cancer was 2.26 (95%CI, 1.90 to 2.69; P = 2.3 × 10(-20)). The OR was higher for estrogen receptor (ER)-positive disease (2.55 [95%CI, 2.10 to 3.10; P = 4.9 × 10(-21)]) than it was for ER-negative disease (1.32 [95%CI, 0.93 to 1.88; P = .12]; P interaction = 9.9 × 10(-4)). The OR significantly declined with attained age for breast cancer overall (P = .001) and for ER-positive tumors (P = .001). Estimated cumulative risks for development of ER-positive and ER-negative tumors by age 80 in CHEK2*1100delC carriers were 20% and 3%, respectively, compared with 9% and 2%, respectively, in the general population of the United Kingdom. CONCLUSION: These CHEK2*1100delC breast cancer risk estimates provide a basis for incorporating CHEK2*1100delC into breast cancer risk prediction models and into guidelines for intensified screening and follow-up.