Clinically relevant aberrant Filip1l DNA methylation detected in a murine model of cutaneous squamous cell carcinoma

Background: Cutaneous squamous cell carcinomas (cSCC) are among the most common and highly mutated human malignancies. Understanding the impact of DNA methylation in cSCC may provide avenues for new therapeutic strategies. Methods: We used reduced-representation bisulfite sequencing for DNA methylation analysis of murine cSCC. Differential methylation was assessed at the CpG level using limma. Next, we compared with human cSCC Infinium HumanMethylation BeadArray data. Genes were considered to be of major relevance when they featured at least one significantly differentially methylated CpGs (RRBS) / probes (Infinium) with at least a 30% difference between tumour vs. control in both a murine gene and its human orthologue. The human EPIC Infinium data were used to distinguish two cSCC subtypes, stem-cell-like and keratinocyte-like tumours. Findings: We found increased average methylation in mouse cSCC (by 12.8%, p = 0.0011) as well as in stem-cell like (by 3.1%, p=0.002), but not keratinocyte-like (0.2%, p = 0.98), human cSCC. Comparison of differentially methylated genes revealed striking similarities between human and mouse cSCC. Locus specific methylation changes in mouse cSCC often occurred in regions of potential regulatory function, including enhancers and promoters. A key differentially methylated region was located in a potential enhancer of the tumour suppressor gene Filip1l and its expression was reduced in mouse tumours. Moreover, the FILIP1L, locus showed hypermethylation in human cSCC and lower expression in human cSCC cell lines. Interpretation: Deregulation of DNA methylation is an important feature of murine and human cSCC that likely contributes to silencing of tumour suppressor genes, as shown for Filip1l. 2021 The Author(s). Published by Elsevier B.V

Exploratory analysis of the human breast DNA methylation profile upon soymilk exposure

Upon soy consumption, isoflavone metabolites attain bioactive concentrations in breast tissue possibly affecting health. Though in vitro epigenetic activity of soy metabolites has been described, the in vivo impact on the epigenome is largely unknown. Therefore, in this case-control study, the breast glandular tissue DNA methylome was explored in women undergoing an aesthetic breast reduction. After a run-in phase, 10 generally healthy Belgian or Dutch women received soymilk for 5 days. MethylCap-seq methylation profiles were compared with those of 10 matched controls. Isoflavones and their microbial metabolites were quantified in urine, serum, and glandular breast tissue (liquid chromatography-mass spectrometry) and 17 beta-estradiol in glandular breast tissue (immunoassay). Global DNA methylation levels were obtained for 6 cases and 5 controls using liquid chromatography-mass spectrometry. Although lower MethylCap-seq coverages were observed, mass spectrometry results and computational LINE-1 methylation analysis did not provide evidence supporting global methylation alterations upon treatment. At a false discovery rate of 0.05, no differentially methylated loci were identified. Moreover, a set of previously identified loci was specifically tested, but earlier reported results could not be validated. In conclusion, after a 5-day soymilk treatment, no major general epigenetic reprogramming in breast tissue could be found in this exploratory study

The genome of the extremophile Artemia provides insight into strategies to cope with extreme environments

BACKGROUND : Brine shrimp Artemia have an unequalled ability to endure extreme salinity and complete anoxia. This study aims to elucidate its strategies to cope with these stressors. RESULTS AND DISCUSSION : Here, we present the genome of an inbred A. franciscana Kellogg, 1906. We identified 21,828 genes of which, under high salinity, 674 genes and under anoxia, 900 genes were differentially expressed (42%, respectively 30% were annotated). Under high salinity, relevant stress genes and pathways included several Heat Shock Protein and Leaf Embryogenesis Abundant genes, as well as the trehalose metabolism. In addition, based on differential gene expression analysis, it can be hypothesized that a high oxidative stress response and endocytosis/exocytosis are potential salt management strategies, in addition to the expression of major facilitator superfamily genes responsible for transmembrane ion transport. Under anoxia, genes involved in mitochondrial function, mTOR signalling and autophagy were differentially expressed. Both high salt and anoxia enhanced degradation of erroneous proteins and protein chaperoning. Compared with other branchiopod genomes, Artemia had 0.03% contracted and 6% expanded orthogroups, in which 14% of the genes were differentially expressed under high salinity or anoxia. One phospholipase D gene family, shown to be important in plant stress response, was uniquely present in both extremophiles Artemia and the tardigrade Hypsibius dujardini, yet not differentially expressed under the described experimental conditions. CONCLUSIONS : A relatively complete genome of Artemia was assembled, annotated and analysed, facilitating research on its extremophile features, and providing a reference sequence for crustacean research.Additional file 1. Assembly characteristics of all assembled crustacean genomes. Characteristics listed are: species, whether the species genome is annotated yes or no, N50 of the fragments with the highest assembly hierarchy, number of fragments with the highest assembly hierarchy in the assembly, haploid genome size, assembly size, completeness of the assembly (=haploid GS/assembly size), taxonomic lineage (NCBI taxonomy), reference for the genome paper.Additional file 2. Evolution of Artemia assembly quality metrics throughout the assembly steps. Evolution of the scaffold N50, the number of fragments and the genome completeness (assembly size/ genome size) in the subsequent Artemia assembly stagesAdditional file 3 BUSCO analysis results for the A. franciscana genome assembly and annotation.Additional file 4. BLAST results for mitochondrial genes in the Artemia genome. Listed: Query accession and gene name, presence of a (significant) BLAST hit in the Artemia proteome with the highest bit score, E-value and bit score of the hit, scaffold length of the scaffold on which the hit lies, percentage of mitochondrial genes on this scaffold.Additional file 5. Taxonomic groups of alien genomes identified in the Artemia genome.Additional file 6 Expanded or contracted Artemia orthogroups compared to other Branchiopoda. Listed: Orthogroup ID, number of genes in this orthogroup in A. franciscana, D. pulex, L. arcticus, and E. texana, expanded or contracted status of the orthogroup in Artemia compared to D. pulex, L. arcticus and E. texana, conservation in Branchiopoda (whether this orthogroup contains genes for each branchiopod), comma-separated IPR description of Artemia genes in this orthogroup, Artemia genes in this orthogroup.Additional file 7 GO enrichment of Artemia compared to other Branchiopoda. Listed: GO ID, name and category, false discovery rate (FDR) and P value of the Fisher’s exact test enrichment analysis in Blast2GO, number of Artemia genes from expanded/contracted orthogroups in this GO ID, number of whole Artemia genome genes in this GO category, number of Artemia genes from expanded/contracted orthogroups in this GO ID without GO annotation. The Fisher’s Exact Test is sensitive in the direction of the test: the genes that are present in the test-set and also in the reference genome set will be deleted from the reference, but not from the test set, resulting in zero sequences in the reference set and values above zero in the test set. Significantly enriched GOs (FDR ≤ 0.05, biological process) of Artemia genes in expanded or contracted orthogroups compared to Branchiopoda are given.Additional file 8 Expanded or contracted Artemia and H. dujardini orthogroups compared to other Arthropoda. Listed: Orthogroup ID, number of genes in this orthogroup in A. franciscana and in the other arthropod species, expanded or contracted status of the orthogroup in Artemia compared to the other arthropod species, comma-separated IPR description of Artemia genes in this orthogroup, H. dujardini genes in this orthogroup, Artemia genes in this orthogroup.Additional file 9. STAR mapping statistics for differential expression analysis in Artemia. Listed: sample name, total number of reads for this sample, percentage of uniquely mapped reads, absolute number of uniquely mapped reads, percentage of multi mapped reads, absolute number of multi mapped reads.Additional file 10. Summarization statistics for differential expression analysis in Artemia. Listed: sample name, total counts, percentage of counts assigned to a gene annotation, absolute counts assigned to a gene annotation. * notice that this amount can be more than the sum of uniquely mapped + multi-mapped in the mapping statistics since multimapped reads are considered.Additional file 11 Differentially expressed genes under high salinity (p < 0.05). Listed: functional annotation of the differentially expressed gene, gene ID in the genome annotation and on the ORCAE platform, p value, average log fold change of gene expression under high salinity, gene regulation of the differentially expressed gene (up or down), InterPro description of the gene family to which the gene belongs.Additional file 12 Differentially expressed genes under anoxia (p < 0.05). Listed: functional annotation of the differentially expressed gene, gene ID in the genome annotation and on the ORCAE platform, p value, log fold change of gene expression under anoxia, gene regulation of the differentially expressed gene (up or down), InterPro description of the gene family to which the gene belongs.Additional file 13 GO enrichment in Artemia under high salinity. Significantly Enriched GOs (FDR ≤ 0.05) of Artemia genes differentially expressed under high salinity. Listed: GO ID, name and category, false discovery rate (FDR) and P value of the Fisher’s exact test enrichment analysis in Blast2GO, number of DEG under high salinity in this GO category, number of whole Artemia genome genes in this GO category, number of DEG under high salinity without GO annotation. The Fisher’s Exact Test is sensitive in the direction of the test: the genes that are present in the test-set and also in the reference genome set will be deleted from the reference, but not from the test set, resulting in zero sequences in the reference set and values above zero in the test set.Additional file 14 Pathway enrichment in Artemia under high salinity. Significantly enriched (Fisher’s exact test corrected for multiple testing, FDR ≤ 0.05) pathways of Artemia genes differentially expressed under high salinity. Listed in first worksheet (STRING annotation): gene number, ORCAE gene ID, STRING Daphnia pulex gene ID, BLAST identity and bit score, gene name and gene annotation. Listed in second worksheet (STRING pathway enrichment): KEGG Daphnia pulex pathway name, pathway description, number of DEG under high salinity in this pathway, number of genes in the D. pulex genome that belong to this pathway, enrichment FDR, matching D. pulex gene IDs, matching gene names in pathways shown in figures and additional files, matching D. pulex gene ID labels.Additional file 15. Consolidation of DEG analysis, GO enrichment and pathway enrichment in Artemia under high salinity.Additional file 16. The enriched Carbon metabolism pathway in Artemia under high salinity. Up- and downregulated genes are indicated on the KEGG map dpx01200.Additional file 17. GO enrichment in Artemia under anoxia. Significantly enriched GOs (FDR ≤ 0.05) of Artemia genes differentially expressed under anoxia. Listed: GO ID, name and category, false discovery rate (FDR) and P value of the Fisher’s exact test enrichment analysis in Blast2GO, number of DEG under anoxia in this GO ID, number of whole Artemia genome genes in this GO ID, number of DEG under anoxia without GO annotation. The Fisher’s Exact Test is sensitive in the direction of the test: the genes that are present in the test set and also in the reference genome set will be deleted from the reference, but not from the test set, resulting in zero sequences in the reference set and values above zero in the test set.Additional file 18 Pathway enrichment in Artemia under anoxia. Significantly enriched (Fisher’s exact test corrected for multiple testing, FDR ≤ 0.05) pathways of Artemia genes differentially expressed under anoxia. Listed in first worksheet (STRING annotation): gene number, ORCAE gene ID, STRING Daphnia pulex gene ID, BLAST identity and bit score, gene name and gene annotation. Listed in second worksheet (STRING pathway enrichment): KEGG Daphnia pulex pathway name, pathway description, number of DEG under anoxia in this pathway, number of genes in the D. pulex genome that belong to this pathway, enrichment FDR, matching D. pulex gene IDs, matching gene names in pathways shown in figures and additional files, matching D. pulex gene ID labels.Additional file 19. Consolidation of DEG analysis, GO enrichment and pathway enrichment in Artemia under anoxia.Additional file 20. The enriched N-glycan biosynthesis pathway in Artemia under anoxia. Up- and downregulated genes are indicated on the KEGG map dpx00510.Additional file 21. The enriched Basal transcription factors pathway in Artemia under anoxia. Up- and downregulated genes are indicated on the KEGG map dpx03022.Additional file 22. Augustus custom training files for Artemia. Includes probabilities, parameters and weights used for Augustus training for annotation of the Artemia genome.Additional file 23. EuGene custom parameter file for Artemia. Includes parameters used for EuGene training for annotation of the Artemia genome.Additional file 24. Sequence GC-content profiles for all samples used for differential expression analysis.The Flemish Government Special Research Fund and the Laboratory of Aquaculture & Artemia Reference Center.http://www.biomedcentral.com/bmcgenomicsam2022BiochemistryGeneticsMicrobiology and Plant Patholog

Quantitative transcriptomic and epigenomic data analysis : a primer

The advent of microarray and second generation sequencing technology has revolutionized the field of molecular biology, allowing researchers to quantitatively assess transcriptomic and epigenomic features in a comprehensive and cost-efficient manner. Moreover, technical advancements have pushed the resolution of these sequencing techniques to the single cell level. As a result, the bottleneck of molecular biology research has shifted from the bench to the subsequent omics data analysis. Even though most methodologies share the same general strategy, state-of-the-art literature typically focuses on data type specific approaches and already assumes expert knowledge. Here, however, we aim at providing conceptual insight in the principles of genome-wide quantitative transcriptomic and epigenomic (including open chromatin assay) data analysis by describing a generic workflow. By starting from a general framework and its assumptions, the need for alternative or additional data-analytical solutions when working with specific data types becomes clear, and are hence introduced. Thus, we aim to enable readers with basic omics expertise to deepen their conceptual and statistical understanding of general strategies and pitfalls in omics data analysis and to facilitate subsequent progression to more specialized literature

DNA methylation regulates transcription factor specific neurodevelopmental but not sexually dimorphic gene expression dynamics in zebra finch telencephalon

Supplementary Information for the manuscript: "DNA methylation regulates transcription factor specific neurodevelopmental but not sexually dimorphic gene expression dynamics in zebra finch telencephalon

Promoter hypermethylation of neural-related genes is compatible with stemness in solid cancers

Abstract Background DNA hypermethylation is an epigenetic feature that modulates gene expression, and its deregulation is observed in cancer. Previously, we identified a neural-related DNA hypermethylation fingerprint in colon cancer, where most of the top hypermethylated and downregulated genes have known functions in the nervous system. To evaluate the presence of this signature and its relevance to carcinogenesis in general, we considered 16 solid cancer types available in The Cancer Genome Atlas (TCGA). Results All tested cancers showed significant enrichment for neural-related genes amongst hypermethylated genes. This signature was already present in two premalignant tissue types and could not be explained by potential confounders such as bivalency status or tumor purity. Further characterization of the neural-related DNA hypermethylation signature in colon cancer showed particular enrichment for genes that are overexpressed during neural differentiation. Lastly, an analysis of upstream regulators identified RE1-Silencing Transcription factor (REST) as a potential mediator of this DNA methylation signature. Conclusion Our study confirms the presence of a neural-related DNA hypermethylation fingerprint in various cancers, of genes linked to neural differentiation, and points to REST as a possible regulator of this mechanism. We propose that this fingerprint indicates an involvement of DNA hypermethylation in the preservation of neural stemness in cancer cells

Role of coelomocytes and immunity in axenic dietary restriction

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