Integrated Epigenome Profiling of Repressive Histone Modifications, DNA Methylation and Gene Expression in Normal and Malignant Urothelial Cells

Epigenetic regulation of gene expression is commonly altered in human cancer. We have observed alterations of DNA methylation and microRNA expression that reflect the biology of bladder cancer. This common disease arises by distinct pathways with low and high-grade differentiation. We hypothesized that epigenetic gene regulation reflects an interaction between histone and DNA modifications, and differences between normal and malignant urothelial cells represent carcinogenic events within bladder cancer. To test this we profiled two repressive histone modifications (H3K9m3 and H3K27m3) using ChIP-Seq, cytosine methylation using MeDIP and mRNA expression in normal and malignant urothelial cell lines. In genes with low expression we identified H3K27m3 and DNA methylation each in 20–30% of genes and both marks in 5% of genes. H3K9m3 was detected in 5–10% of genes but was not associated with overall expression. DNA methylation was more closely related to gene expression in malignant than normal cells. H3K27m3 was the epigenetic mark most specifically correlated to gene silencing. Our data suggest that urothelial carcinogenesis is accompanied by a loss of control of both DNA methylation and H3k27 methylation. From our observations we identified a panel of genes with cancer specific-epigenetic mediated aberrant expression including those with reported carcinogenic functions and members potentially mediating a positive epigenetic feedback loop. Pathway enrichment analysis revealed genes marked by H3K9m3 were involved with cell homeostasis, those marked by H3K27m3 mediated pro-carcinogenic processes and those marked with cytosine methylation were mixed in function. In 150 normal and malignant urothelial samples, our gene panel correctly estimated expression in 65% of its members. Hierarchical clustering revealed that this gene panel stratified samples according to the presence and phenotype of bladder cancer

Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies

[Image: see text] Proteogenomics has the potential to advance genome annotation through high quality peptide identifications derived from mass spectrometry experiments, which demonstrate a given gene or isoform is expressed and translated at the protein level. This can advance our understanding of genome function, discovering novel genes and gene structure that have not yet been identified or validated. Because of the high-throughput shotgun nature of most proteomics experiments, it is essential to carefully control for false positives and prevent any potential misannotation. A number of statistical procedures to deal with this are in wide use in proteomics, calculating false discovery rate (FDR) and posterior error probability (PEP) values for groups and individual peptide spectrum matches (PSMs). These methods control for multiple testing and exploit decoy databases to estimate statistical significance. Here, we show that database choice has a major effect on these confidence estimates leading to significant differences in the number of PSMs reported. We note that standard target:decoy approaches using six-frame translations of nucleotide sequences, such as assembled transcriptome data, apparently underestimate the confidence assigned to the PSMs. The source of this error stems from the inflated and unusual nature of the six-frame database, where for every target sequence there exists five “incorrect” targets that are unlikely to code for protein. The attendant FDR and PEP estimates lead to fewer accepted PSMs at fixed thresholds, and we show that this effect is a product of the database and statistical modeling and not the search engine. A variety of approaches to limit database size and remove noncoding target sequences are examined and discussed in terms of the altered statistical estimates generated and PSMs reported. These results are of importance to groups carrying out proteogenomics, aiming to maximize the validation and discovery of gene structure in sequenced genomes, while still controlling for false positives

A transcriptomic analysis of Echinococcus granulosus larval stages:implications for parasite biology and host adaptation

The cestode Echinococcus granulosus--the agent of cystic echinococcosis, a zoonosis affecting humans and domestic animals worldwide--is an excellent model for the study of host-parasite cross-talk that interfaces with two mammalian hosts. To develop the molecular analysis of these interactions, we carried out an EST survey of E. granulosus larval stages. We report the salient features of this study with a focus on genes reflecting physiological adaptations of different parasite stages.We generated ~10,000 ESTs from two sets of full-length enriched libraries (derived from oligo-capped and trans-spliced cDNAs) prepared with three parasite materials: hydatid cyst wall, larval worms (protoscoleces), and pepsin/H(+)-activated protoscoleces. The ESTs were clustered into 2700 distinct gene products. In the context of the biology of E. granulosus, our analyses reveal: (i) a diverse group of abundant long non-protein coding transcripts showing homology to a middle repetitive element (EgBRep) that could either be active molecular species or represent precursors of small RNAs (like piRNAs); (ii) an up-regulation of fermentative pathways in the tissue of the cyst wall; (iii) highly expressed thiol- and selenol-dependent antioxidant enzyme targets of thioredoxin glutathione reductase, the functional hub of redox metabolism in parasitic flatworms; (iv) candidate apomucins for the external layer of the tissue-dwelling hydatid cyst, a mucin-rich structure that is critical for survival in the intermediate host; (v) a set of tetraspanins, a protein family that appears to have expanded in the cestode lineage; and (vi) a set of platyhelminth-specific gene products that may offer targets for novel pan-platyhelminth drug development.This survey has greatly increased the quality and the quantity of the molecular information on E. granulosus and constitutes a valuable resource for gene prediction on the parasite genome and for further genomic and proteomic analyses focused on cestodes and platyhelminths

Open and Sustainable AI:challenges, opportunities and the road ahead in the life sciences

Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an unprecedented capacity, with novel applications and advances being made almost daily. In order to maximise return on the growing investments in AI-based life science research and accelerate this progress, it has become urgent to address the exacerbation of long-standing research challenges arising from the rapid adoption of AI methods. We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability and reproducibility, and highlight their consequent impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI (OSAI) model development. In response, this perspective introduces a practical set of OSAI recommendations directly mapped to over 300 components of the AI ecosystem. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI. Built upon life science community consensus and aligned to existing efforts, the outputs of this perspective are designed to aid the future development of policy and structured pathways for guiding AI implementation

Transcriptome Analysis Reveals Strain-Specific and Conserved Stemness Genes in Schmidtea mediterranea

The planarian Schmidtea mediterranea is a powerful model organism for studying stem cell biology due to its extraordinary regenerative ability mediated by neoblasts, a population of adult somatic stem cells. Elucidation of the S. mediterranea transcriptome and the dynamics of transcript expression will increase our understanding of the gene regulatory programs that regulate stem cell function and differentiation. Here, we have used RNA-Seq to characterize the S. mediterranea transcriptome in sexual and asexual animals and in purified neoblast and differentiated cell populations. Our analysis identified many uncharacterized genes, transcripts, and alternatively spliced isoforms that are differentially expressed in a strain or cell type-specific manner. Transcriptome profiling of purified neoblasts and differentiated cells identified neoblast-enriched transcripts, many of which likely play important roles in regeneration and stem cell function. Strikingly, many of the neoblast-enriched genes are orthologs of genes whose expression is enriched in human embryonic stem cells, suggesting that a core set of genes that regulate stem cell function are conserved across metazoan species

Next-generation transcriptome assembly

Transcriptomics studies often rely on partial reference transcriptomes that fail to capture the full catalog of transcripts and their variations. Recent advances in sequencing technologies and assembly algorithms have facilitated the reconstruction of the entire transcriptome by deep RNA sequencing (RNA-seq), even without a reference genome. However, transcriptome assembly from billions of RNA-seq reads, which are often very short, poses a significant informatics challenge. This Review summarizes the recent developments in transcriptome assembly approaches - reference-based, de novo and combined strategies-along with some perspectives on transcriptome assembly in the near future

Increased frequency of peripheral B and T cells expressing granulocyte monocyte colony-stimulating factor in rheumatoid arthritis patients

Objectives: Granulocyte monocyte colony-stimulating factor (GM-CSF) is currently considered a crucial inflammatory mediator and a novel therapeutic target in rheumatoid arthritis (RA), despite the fact that its precise cellular sources remain uncertain. We studied the expression of GM-CSF in peripheral lymphocytes from RA patients and its change with antirheumatic therapies. Methods: Intracellular GM-CSF expression was assessed by flow cytometry in stimulated peripheral B (CD19+) and T (CD3+) cells from RA patients (n = 40), disease (n = 31 including osteoarthritis n = 15, psoriatic arthritis n = 10, and systemic rheumatic diseases n = 6) and healthy (n = 16) controls. The phenotype of GM-CSF+ B cells was assessed as well as longitudinal changes in GM-CSF+ lymphocytes during methotrexate (MTX, n = 10) or anti-tumor necrosis factor (anti-TNF, n = 10) therapy. Results: Among untreated RA patients with active disease (Disease Activity Score 28-C-reactive protein = 5.6 ± 0.89) an expanded population of peripheral GM-CSF+ B (4.1 ± 2.2%) and T (3.4 ± 1.6%) cells was detected compared with both disease (1.7 ± 0.9%, p < 0.0001 and 1.7 ± 1.3%, p < 0.0001, respectively) and healthy (0.3 ± 0.2%, p < 0.0001 and 0.6 ± 0.6%, p < 0.0001) controls. RA GM-CSF+ B cells displayed more commonly a plasmablast or transitional phenotype (37.12 ± 18.34% vs. 14.26 ± 9.46%, p = 0.001 and 30.49 ± 15.04% vs. 2.45 ± 1.84%, p < 0.0001, respectively) and less a memory phenotype (21.46 ± 20.71% vs. 66.99 ± 16.63%, p < 0.0001) compared to GM-CSF- cells. GM-CSF expression in RA patients did not correlate to disease duration, activity or serological status. Anti-TNF treatment led to a statistically significant decrease in GM-CSF+ B and T cells while MTX had no significant effect. Discussion: This is the first study showing an expanded population of GM-CSF+ B and T lymphocytes in patients with active RA which declined after anti-TNF therapy. © 2018 Makris, Adamidi, Koutsianas, Tsalapaki, Hadziyannis and Vassilopoulos