Search

Pictures from Figures 1A and 1B;2A,2B and 2C;6A and 6D;7B Dataset from Figures2-7with data analysis(including Wound Healing assay, Transwell migration and invasion assay) on MCF7, MDA and H2122 AS cell lines

In order to effectively manage and cure cancer we should move beyond the general view of cancer as a random process of genetic alterations leading to uncontrolled cell proliferation or simply a predictable evolutionary process involving selection for traits that increase cell fitness. In our view, cancer is a systemic disease that involves multiple interactions not only among cells within tumors or between tumors and surrounding tissues but also with the entire organism and its internal “milieu”. We define the internal body climate as an emergent property resulting from spatial and temporal interactions among internal components themselves and with the external environment. The body climate itself can either prevent, promote or support cancer initiation and progression (top-down effect; i.e., body climate-induced effects on cancer), as well as be perturbed by cancer (bottom-up effect; i.e., cancer-induced body climate changes) to further favor cancer progression and spread. This positive feedback loop can move the system towards a “cancerized” organism and ultimately results in its demise. In our view, cancer not only affects the entire system; it is a reflection of an imbalance of the entire system. This model provides an integrated framework to study all aspects of cancer as a systemic disease, and also highlights unexplored links that can be altered to both prevent body climate changes that favor cancer initiation, progression and dissemination as well as manipulate or restore the body internal climate to hinder the success of cancer inception, progression and metastasis or improve therapy outcomes. To do so, we need to (i) identify cancer-relevant factors that affect specific climate components, (ii) develop ‘body climate biomarkers’, (iii) define ‘body climate scores’, and (iv) develop strategies to prevent climate changes, stop or slow the changes, or even revert the changes (climate restoration).

Additional images, tables and information regarding the mutants and their sensitivity to stress

Although life-history trade-offs are central to life-history evolution, their mechanistic basis is often unclear. Traditionally, trade-offs are understood in terms of competition for limited resources among traits within an organism, which could be mediated by signal transduction pathways at the level of cellular metabolism. Nevertheless, trade-offs are also thought to be produced as a consequence of the performance of one activity generating negative consequences for other traits, or be the result of genes or pathways that simultaneously regulate two life-history traits in opposite directions (antagonistic pleiotropy), independent of resource allocation. Yet, examples of genes with antagonistic effects on life-history traits are limited. This study provides direct evidence for a gene���RLS1, that is involved in increasing survival in nutrient-limiting environments at a cost to immediate reproduction in the single-celled photosynthetic alga, Chlamydomonas reinhardtii. Specifically, we show that RLS1 mutants are unable to properly suppress their reproduction in phosphate-deprived conditions. Although these mutants have an immediate reproductive advantage relative to the parental strain, their long-term survival is negatively affected. Our data argue that RLS1 is a bona fide life-history trade-off gene that suppresses immediate reproduction and ensures survival by downregulating photosynthesis in limiting environments, as part of the general acclimation response to nutrient deprivation in photosynthetic organisms.

Detailed experimental methodology, table of primers, and additional data/figures

Ruiz-Trillo, Iñaki; Nedelcu, Aurora M. (eds.)., Sharpe, Susan et al., The book integrates our understanding of the factors and processes underlying the evolution of multicellularity by providing several complementary perspectives (both theoretical and experimental) and using examples from various lineages in which multicellularity evolved. Recent years marked an increased interest in understanding how and why these transitions occurred, and data from various fields are providing new insights into the forces driving the several independent transitions to multicellular life as well as into the genetic and molecular basis for the evolution of this phenotype. The ultimate goal of this book is to facilitate the identification of general and unifying principles and mechanisms.

Microbial ecology is plagued by problems of an abstract nature. Cell sizes are so small and population sizes so large that both are virtually incomprehensible. Niches are so far from our everyday experience as to make their very definition elusive. Organisms that may be abundant and critical to our survival are little understood, seldom described and/or cultured, and sometimes yet to be even seen. One way to confront these problems is to use data of an even more abstract nature: molecular sequence data. Massive environmental nucleic acid sequencing, such as metagenomics or metatranscriptomics, promises functional analysis of microbial communities as a whole, without prior knowledge of which organisms are in the environment or exactly how they are interacting. But sequence-based ecological studies nearly always use a comparative approach, and that requires relevant reference sequences, which are an extremely limited resource when it comes to microbial eukaryotes [1]. In practice, this means sequence databases need to be populated with enormous quantities of data for which we have some certainties about the source. Most important is the taxonomic identity of the organism from which a sequence is derived and as much functional identification of the encoded proteins as possible. In an ideal world, such information would be available as a large set of complete, well-curated, and annotated genomes for all the major organisms from the environment in question. Reality substantially diverges from this ideal, but at least for bacterial molecular ecology, there is a database consisting of thousands of complete genomes from a wide range of taxa, supplemented by a phylogeny-driven approach to diversifying genomics [2]. For eukaryotes, the number of available genomes is far, far fewer, and we have relied much more heavily on random growth of sequence databases [3],[4], raising the question as to whether this is fit for purpose.