Gathering insights on disease etiology from gene expression profiles of healthy tissues

Epigenetic clocks and programmatic aging

The last decade has seen remarkable progress in the characterization of methylation clocks that can serve as indicators of biological age in humans and many other mammalian species. While the biological processes of aging that underlie these clocks have remained unclear, several clues have pointed to a link to developmental mechanisms. These include the presence in the vicinity of clock CpG sites of genes that specify development, including those of the Hox (homeobox) and polycomb classes. Here we discuss how recent advances in programmatic theories of aging provide a framework within which methylation clocks can be understood as part of a developmental process of aging. This includes how such clocks evolve, how developmental mechanisms cause aging, and how they give rise to late-life disease. The combination of ideas from evolutionary biology, biogerontology and developmental biology open a path to a new discipline, that of developmental gerontology (devo-gero). Drawing on the properties of methylation clocks, we offer several new hypotheses that exemplify devo-gero thinking. We suggest that polycomb controls a trade-off between earlier developmental fidelity and later developmental plasticity. We also propose the existence of an evolutionarily-conserved developmental sequence spanning ontogenesis, adult development and aging, that both constrains and determines the evolution of aging.

Transcriptomic analysis reveals a tissue-specific loss of identity during ageing and cancer

INTRODUCTION

Understanding changes in cell identity in cancer and ageing is of great importance. In this work, we analyzed how gene expression changes in human tissues are associated with tissue specificity during cancer and ageing using transcriptome data from TCGA and GTEx.

RESULTS

We found significant downregulation of tissue-specific genes during ageing in 40% of the tissues analyzed, which suggests loss of tissue identity with age. For most cancer types, we have noted a consistent pattern of downregulation in genes that are specific to the tissue from which the tumor originated. Moreover, we observed in cancer an activation of genes not usually expressed in the tissue of origin as well as an upregulation of genes specific to other tissues. These patterns in cancer were associated with patient survival. The age of the patient, however, did not influence these patterns.

CONCLUSION

We identified loss of cellular identity in 40% of the tissues analysed during human ageing, and a clear pattern in cancer, where during tumorigenesis cells express genes specific to other organs while suppressing the expression of genes from their original tissue. The loss of cellular identity observed in cancer is associated with prognosis and is not influenced by age, suggesting that it is a crucial stage in carcinogenesis.

m6A-TSHub: Unveiling the Context-specific m6A Methylation and m6A-affecting Mutations in 23 Human Tissues

As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs (lncRNAs), N6-methyladenosine (m6A) RNA methylation has been shown to participate in essential biological processes. Recent studies have revealed the distinct patterns of m6A methylome across human tissues, and a major challenge remains in elucidating the tissue-specific presence and circuitry of m6A methylation. We present here a comprehensive online platform, m6A-TSHub, for unveiling the context-specific m6A methylation and genetic mutations that potentially regulate m6A epigenetic mark. m6A-TSHub consists of four core components, including (1) m6A-TSDB, a comprehensive database of 184,554 functionally annotated m6A sites derived from 23 human tissues and 499,369 m6A sites from 25 tumor conditions, respectively; (2) m6A-TSFinder, a web server for high-accuracy prediction of m6A methylation sites within a specific tissue from RNA sequences, which was constructed using multi-instance deep neural networks with gated attention; (3) m6A-TSVar, a web server for assessing the impact of genetic variants on tissue-specific m6A RNA modifications; and (4) m6A-CAVar, a database of 587,983 The Cancer Genome Atlas (TCGA) cancer mutations (derived from 27 cancer types) that were predicted to affect m6A modifications in the primary tissue of cancers. The database should make a useful resource for studying the m6A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue (or cancer type). m6A-TSHub is accessible at www.xjtlu.edu.cn/biologicalsciences/m6ats.

WSB1 Involvement in Prostate Cancer Progression

Prostate cancer (PC) is polygenic disease involving many genes, and more importantly a host of gene-gene interactions, including transcriptional factors. The WSB1 gene is a transcriptional target of numerous oncoproteins, and its dysregulation can contribute to tumor progression by abnormal activation of targeted oncogenes. Using data from the Cancer Genome Atlas, we tested the possible involvement of WSB1 in PC progression. A multi-dimensional scaling (MDS) model was applied to clarify the association of WSB1 expression with other key genes, such as c-myc, ERG, Enhancer of Zeste 1 and 2 (EHZ1 and EZH2), WNT10a, and WNT 10b. An increased WSB1 expression was associated with higher PC grades and with a worse prognosis. It was also positively related to EZH1, EZH2, WNT10a, and WNT10b. Moreover, MDS showed the central role of WSB1 in influencing the other target genes by its central location on the map. Our study is the first to show a link between WSB1 expression and other genes involved in PC progression, suggesting a novel role for WSB1 in PC progression. This network between WSB1 and EZH2 through WNT/β-catenin may have an important role in PC progression, as suggested by the association between high WSB1 expression and unfavorable prognosis in our analysis.

Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation

As the most pervasive epigenetic mark present on mRNA and lncRNA, N6-methyladenosine (m6A) RNA methylation regulates all stages of RNA life in various biological processes and disease mechanisms. Computational methods for deciphering RNA modification have achieved great success in recent years; nevertheless, their potential remains underexploited. One reason for this is that existing models usually consider only the sequence of transcripts, ignoring the various regions (or geography) of transcripts such as 3'UTR and intron, where the epigenetic mark forms and functions. Here, we developed three simple yet powerful encoding schemes for transcripts to capture the submolecular geographic information of RNA, which is largely independent from sequences. We show that m6A prediction models based on geographic information alone can achieve comparable performances to classic sequence-based methods. Importantly, geographic information substantially enhances the accuracy of sequence-based models, enables isoform- and tissue-specific prediction of m6A sites, and improves m6A signal detection from direct RNA sequencing data. The geographic encoding schemes we developed have exhibited strong interpretability, and are applicable to not only m6A but also N1-methyladenosine (m1A), and can serve as a general and effective complement to the widely used sequence encoding schemes in deep learning applications concerning RNA transcripts.

Genes and Diseases: Insights from Transcriptomics Studies

Results of expression studies can be useful to clarify the genotype-phenotype relationship. However, according to data from recent literature, there is a large group of genes that are revealed as differentially expressed (DE) in many studies, regardless of the biological context. Additional analyses could shed more light on the relationships between genes, their differential expression, and diseases. We generated a set of 9972 disease genes from five gene-phenotype databases (OMIM, ORPHANET, DDG2P, DisGeNet and MalaCards) and a report of the International Union of Immunological Societies. To study transcriptomics of disease and non-disease genes in healthy tissues, we obtained data from the Human Protein Atlas (HPA) website. We analyzed the dependency between expression in healthy tissues and gene occurrence in Gene Expression Omnibus series using tools within the Enrichr libraries. The results of expression studies were annotated with Gene Ontology (GO) and Human Phenotype Ontology (HPO) terms. Using transcriptomics analysis of healthy tissues, we validated the previous findings of higher expression levels of disease genes in pathologically linked tissues compared to other tissues. Preferentially DE genes were generally highly expressed in one or multiple tissues and were enriched for disease genes. According to the results of GO enrichment analyses, both down- and up-regulated DE genes most often took part in immune response, translation and tissue-specific processes. A connection between DE-related pathology and the diversity of HPO terms was found. Investigating a link between expression and phenotype contributes to understanding the mode of development and progression of human diseases.

An integrative analysis of the age-associated multi-omic landscape across cancers

Age is the most important risk factor for cancer, as cancer incidence and mortality increase with age. However, how molecular alterations in tumours differ among patients of different age remains largely unexplored. Here, using data from The Cancer Genome Atlas, we comprehensively characterise genomic, transcriptomic and epigenetic alterations in relation to patients' age across cancer types. We show that tumours from older patients present an overall increase in genomic instability, somatic copy-number alterations (SCNAs) and somatic mutations. Age-associated SCNAs and mutations are identified in several cancer-driver genes across different cancer types. The largest age-related genomic differences are found in gliomas and endometrial cancer. We identify age-related global transcriptomic changes and demonstrate that these genes are in part regulated by age-associated DNA methylation changes. This study provides a comprehensive, multi-omics view of age-associated alterations in cancer and underscores age as an important factor to consider in cancer research and clinical practice.

Size matters: height, cell number and a person's risk of cancer

The multistage model of carcinogenesis predicts cancer risk will increase with tissue size, since more cells provide more targets for oncogenic somatic mutation. However, this increase is not seen among mammal species of different sizes (Peto's paradox), a paradox argued to be due to larger species evolving added cancer suppression. If this explanation is correct, the cell number effect is still expected within species. Consistent with this, the hazard ratio for overall cancer risk per 10 cm increase in human height (HR₁₀) is about 1.1, indicating a 10% increase in cancer risk per 10 cm; however, an alternative explanation invokes an indirect effect of height, with factors that increase cancer risk independently increasing adult height. The data from four large-scale surveillance projects on 23 cancer categories were tested against quantitative predictions of the cell-number hypothesis, predictions that were accurately supported. For overall cancer risk the HR₁₀ predicted versus observed was 1.13 versus 1.12 for women and 1.11 versus 1.09 for men, suggesting that cell number variation provides a null hypothesis for assessing height effects. Melanoma showed an unexpectedly strong relationship to height, indicating an additional effect, perhaps due to an increasing cell division rate mediated through increasing IGF-I with height. Similarly, only about one-third of the higher incidence of non-reproductive cancers in men versus women can be explained by cell number. The cancer risks of obesity are not correlated with effects of height, consistent with different primary causation. The direct effect of height on cancer risk suggests caution in identifying height-related SNPs as cancer causing.

Postmitotic cell longevity-associated genes: a transcriptional signature of postmitotic maintenance in neural tissues

Different cell types have different postmitotic maintenance requirements. Nerve cells, however, are unique in this respect as they need to survive and preserve their functional complexity for the entire lifetime of the organism, and failure at any level of their supporting mechanisms leads to a wide range of neurodegenerative conditions. Whether these differences across tissues arise from the activation of distinct cell type-specific maintenance mechanisms or the differential activation of a common molecular repertoire is not known. To identify the transcriptional signature of postmitotic cellular longevity (PMCL), we compared whole-genome transcriptome data from human tissues ranging in longevity from 120 days to over 70 years and found a set of 81 genes whose expression levels are closely associated with increased cell longevity. Using expression data from 10 independent sources, we found that these genes are more highly coexpressed in longer-living tissues and are enriched in specific biological processes and transcription factor targets compared with randomly selected gene samples. Crucially, we found that PMCL-associated genes are downregulated in the cerebral cortex and substantia nigra of patients with Alzheimer's and Parkinson's disease, respectively, as well as Hutchinson-Gilford progeria-derived fibroblasts, and that this downregulation is specifically linked to their underlying association with cellular longevity. Moreover, we found that sexually dimorphic brain expression of PMCL-associated genes reflects sexual differences in lifespan in humans and macaques. Taken together, our results suggest that PMCL-associated genes are part of a generalized machinery of postmitotic maintenance and functional stability in both neural and non-neural cells and support the notion of a common molecular repertoire differentially engaged in different cell types with different survival requirements.

From humans to hydra: patterns of cancer across the tree of life

Cancer is a disease of multicellularity; it originates when cells become dysregulated due to mutations and grow out of control, invading other tissues and provoking discomfort, disability, and eventually death. Human life expectancy has greatly increased in the last two centuries, and consequently so has the incidence of cancer. However, how cancer patterns in humans compare to those of other species remains largely unknown. In this review, we search for clues about cancer and its evolutionary underpinnings across the tree of life. We discuss data from a wide range of species, drawing comparisons with humans when adequate, and interpret our findings from an evolutionary perspective. We conclude that certain cancers are uniquely common in humans, such as lung, prostate, and testicular cancer; while others are common across many species. Lymphomas appear in almost every animal analysed, including in young animals, which may be related to pathogens imposing selection on the immune system. Cancers unique to humans may be due to our modern environment or may be evolutionary accidents: random events in the evolution of our species. Finally, we find that cancer‐resistant animals such as whales and mole‐rats have evolved cellular mechanisms that help them avoid neoplasia, and we argue that there are multiple natural routes to cancer resistance.

WSB1 overcomes oncogene-induced senescence by targeting ATM for degradation

Oncogene-induced senescence (OIS) or apoptosis through the DNA-damage response is an important barrier of tumorigenesis. Overcoming this barrier leads to abnormal cell proliferation, genomic instability, and cellular transformation, and finally allows cancers to develop. However, it remains unclear how the OIS barrier is overcome. Here, we show that the E3 ubiquitin ligase WD repeat and SOCS box-containing protein 1 (WSB1) plays a role in overcoming OIS. WSB1 expression in primary cells helps the bypass of OIS, leading to abnormal proliferation and cellular transformation. Mechanistically, WSB1 promotes ATM ubiquitination, resulting in ATM degradation and the escape from OIS. Furthermore, we identify CDKs as the upstream kinase of WSB1. CDK-mediated phosphorylation activates WSB1 by promoting its monomerization. In human cancer tissue and in vitro models, WSB1-induced ATM degradation is an early event during tumorigenic progression. We suggest that WSB1 is one of the key players of early oncogenic events through ATM degradation and destruction of the tumorigenesis barrier. Our work establishes an important mechanism of cancer development and progression in premalignant lesions.

WSB1 promotes tumor metastasis by inducing pVHL degradation

In this study, Kim et al. identified WD repeat and SOCS box-containing protein 1 (WSB1) as a novel regulator of pVHL through WSB1's E3 ligase activity. These findings provide important new insights into the understanding of misregulation of the pVHL–HIF pathway in cancer cell invasion and metastasis.

The von Hippel-Lindau tumor suppressor pVHL is an E3 ligase that targets hypoxia-inducible factors (HIFs). Mutation of VHL results in HIF up-regulation and contributes to processes related to tumor progression such as invasion, metastasis, and angiogenesis. However, very little is known with regard to post-transcriptional regulation of pVHL. Here we show that WD repeat and SOCS box-containing protein 1 (WSB1) is a negative regulator of pVHL through WSB1's E3 ligase activity. Mechanistically, WSB1 promotes pVHL ubiquitination and proteasomal degradation, thereby stabilizing HIF under both normoxic and hypoxic conditions. As a consequence, WSB1 up-regulates the expression of HIF-1α’s target genes and promotes cancer invasion and metastasis through its effect on pVHL. Consistent with this, WSB1 protein level negatively correlates with pVHL level and metastasis-free survival in clinical samples. This work reveals a new mechanism of pVHL's regulation by which cancer acquires invasiveness and metastatic tendency.

Concordance of gene expression in human protein complexes reveals tissue specificity and pathology

Disease-causing variants in human genes usually lead to phenotypes specific to only a few tissues. Here, we present a method for predicting tissue specificity based on quantitative deregulation of protein complexes. The underlying assumption is that the degree of coordinated expression among proteins in a complex within a given tissue may pinpoint tissues that will be affected by a mutation in the complex and coordinated expression may reveal the complex to be active in the tissue. We identified known disease genes and their protein complex partners in a high-quality human interactome. Each susceptibility gene's tissue involvement was ranked based on coordinated expression with its interaction partners in a non-disease global map of human tissue-specific expression. The approach demonstrated high overall area under the curve (0.78) and was very successfully benchmarked against a random model and an approach not using protein complexes. This was illustrated by correct tissue predictions for three case studies on leptin, insulin-like-growth-factor 2 and the inhibitor of NF-κB kinase subunit gamma that show high concordant expression in biologically relevant tissues. Our method identifies novel gene-phenotype associations in human diseases and predicts the tissues where associated phenotypic effects may arise.

Systems biology and bioinformatics in aging research: a workshop report

In an "aging society," health span extension is most important. As in 2010, talks in this series of meetings in Rostock-Warnemünde demonstrated that aging is an apparently very complex process, where computational work is most useful for gaining insights and to find interventions that counter aging and prevent or counteract aging-related diseases. The specific topics of this year's meeting entitled, "RoSyBA: Rostock Symposium on Systems Biology and Bioinformatics in Ageing Research," were primarily related to "Cancer and Aging" and also had a focus on work funded by the German Federal Ministry of Education and Research (BMBF). The next meeting in the series, scheduled for September 20-21, 2013, will focus on the use of ontologies for computational research into aging, stem cells, and cancer. Promoting knowledge formalization is also at the core of the set of proposed action items concluding this report.