Frugal innovation: doing more with less for more

The global economy will face significant challenges over the next few decades. On the one hand, it must meet the needs of 7 billion consumers (growing to 9 billion by 2050), including the currently unmet basic needs of large numbers in developing countries in areas such as food, energy, housing and health. On the other hand, it must achieve this growth without exceeding the resources available on the planet or causing environmental devastation. This paper argues that such change is possible through a systemic shift to a frugal economy that involves radical, frugal innovation across sectors. Such a transformation will involve the participation of large and small firms, consumers and governments alike. The paper introduces the notion of frugal innovation—the creation of faster, better and cheaper solutions for more people that employ minimal resources—and discusses strategies and examples of such change already taking place in core sectors like manufacturing, food, automotive and energy in developing and developed economies. It also outlines the role of the interaction between large and small firms as well as between firms and consumers in making change possible, as well as the role of governments in driving change where market mechanisms alone will not suffice. This article is part of the themed issue ‘Material demand reduction’

The Influence of cis-Regulatory Elements on DNA Methylation Fidelity

It is now established that, as compared to normal cells, the cancer cell genome has an overall inverse distribution of DNA methylation (“methylome”), i.e., predominant hypomethylation and localized hypermethylation, within “CpG islands” (CGIs). Moreover, although cancer cells have reduced methylation “fidelity” and genomic instability, accurate maintenance of aberrant methylomes that underlie malignant phenotypes remains necessary. However, the mechanism(s) of cancer methylome maintenance remains largely unknown. Here, we assessed CGI methylation patterns propagated over 1, 3, and 5 divisions of A2780 ovarian cancer cells, concurrent with exposure to the DNA cross-linking chemotherapeutic cisplatin, and observed cell generation-successive increases in total hyper- and hypo-methylated CGIs. Empirical Bayesian modeling revealed five distinct modes of methylation propagation: (1) heritable (i.e., unchanged) high- methylation (1186 probe loci in CGI microarray); (2) heritable (i.e., unchanged) low-methylation (286 loci); (3) stochastic hypermethylation (i.e., progressively increased, 243 loci); (4) stochastic hypomethylation (i.e., progressively decreased, 247 loci); and (5) considerable “random” methylation (582 loci). These results support a “stochastic model” of DNA methylation equilibrium deriving from the efficiency of two distinct processes, methylation maintenance and de novo methylation. A role for cis-regulatory elements in methylation fidelity was also demonstrated by highly significant (p<2.2×10−5) enrichment of transcription factor binding sites in CGI probe loci showing heritably high (118 elements) and low (47 elements) methylation, and also in loci demonstrating stochastic hyper-(30 elements) and hypo-(31 elements) methylation. Notably, loci having “random” methylation heritability displayed nearly no enrichment. These results demonstrate an influence of cis-regulatory elements on the nonrandom propagation of both strictly heritable and stochastically heritable CGIs

Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements

Background: Recent assays for individual-specific genome-wide DNA methylation profiles have enabled epigenome-wide association studies to identify specific CpG sites associated with a phenotype. Computational prediction of CpG site-specific methylation levels is important, but current approaches tackle average methylation within a genomic locus and are often limited to specific genomic regions. Results: We characterize genome-wide DNA methylation patterns, and show that correlation among CpG sites decays rapidly, making predictions solely based on neighboring sites challenging. We built a random forest classifier to predict CpG site methylation levels using as features neighboring CpG site methylation levels and genomic distance, and co-localization with coding regions, CGIs, and regulatory elements from the ENCODE project, among others. Our approach achieves 91% -- 94% prediction accuracy of genome-wide methylation levels at single CpG site precision. The accuracy increases to 98% when restricted to CpG sites within CGIs. Our classifier outperforms state-of-the-art methylation classifiers and identifies features that contribute to prediction accuracy: neighboring CpG site methylation status, CpG island status, co-localized DNase I hypersensitive sites, and specific transcription factor binding sites were found to be most predictive of methylation levels. Conclusions: Our observations of DNA methylation patterns led us to develop a classifier to predict site-specific methylation levels that achieves the best DNA methylation predictive accuracy to date. Furthermore, our method identified genomic features that interact with DNA methylation, elucidating mechanisms involved in DNA methylation modification and regulation, and linking different epigenetic processes

Mechanisms of human telomerase reverse transcriptase (hTERT) regulation: clinical impacts in cancer

Background Limitless self-renewal is one of the hallmarks of cancer and is attained by telomere maintenance, essentially through telomerase (hTERT) activation. Transcriptional regulation of hTERT is believed to play a major role in telomerase activation in human cancers. Main body The dominant interest in telomerase results from its role in cancer. The role of telomeres and telomere maintenance mechanisms is well established as a major driving force in generating chromosomal and genomic instability. Cancer cells have acquired the ability to overcome their fate of senescence via telomere length maintenance mechanisms, mainly by telomerase activation. hTERT expression is up-regulated in tumors via multiple genetic and epigenetic mechanisms including hTERT amplifications, hTERT structural variants, hTERT promoter mutations and epigenetic modifications through hTERT promoter methylation. Genetic (hTERT promoter mutations) and epigenetic (hTERT promoter methylation and miRNAs) events were shown to have clinical implications in cancers that depend on hTERT activation. Knowing that telomeres are crucial for cellular self-renewal, the mechanisms responsible for telomere maintenance have a crucial role in cancer diseases and might be important oncological biomarkers. Thus, rather than quantifying TERT expression and its correlation with telomerase activation, the discovery and the assessment of the mechanisms responsible for TERT upregulation offers important information that may be used for diagnosis, prognosis, and treatment monitoring in oncology. Furthermore, a better understanding of these mechanisms may promote their translation into effective targeted cancer therapies. Conclusion Herein, we reviewed the underlying mechanisms of hTERT regulation, their role in oncogenesis, and the potential clinical applications in telomerase-dependent cancers.info:eu-repo/semantics/publishedVersio

The epigenetic landscape of renal cancer

This is an accepted manuscript of an article published by Nature in Nature Reviews: Nephrology on 28/11/2016, available online: https://doi.org/10.1038/nrneph.2016.168 The accepted version of the publication may differ from the final published version.The majority of kidney cancers are associated with mutations in the von Hippel-Lindau gene and a small proportion are associated with infrequent mutations in other well characterized tumour-suppressor genes. In the past 15 years, efforts to uncover other key genes involved in renal cancer have identified many genes that are dysregulated or silenced via epigenetic mechanisms, mainly through methylation of promoter CpG islands or dysregulation of specific microRNAs. In addition, the advent of next-generation sequencing has led to the identification of several novel genes that are mutated in renal cancer, such as PBRM1, BAP1 and SETD2, which are all involved in histone modification and nucleosome and chromatin remodelling. In this Review, we discuss how altered DNA methylation, microRNA dysregulation and mutations in histone-modifying enzymes disrupt cellular pathways in renal cancers