Fractional killing is the main cause of tumour resistance to chemotherapy. This phenomenon is observed even in genetically identical cancer cells in homogeneous microenvironments. To understand this variable resistance, here we investigate the individual responses to TRAIL in a clonal population of HeLa cells using live-cell microscopy and computational modelling. We show that the cellular mitochondrial content determines the apoptotic fate and modulates the time to death, cells with higher mitochondrial content are more prone to die. We find that all apoptotic protein levels are modulated by the mitochondrial content. Modelling the apoptotic network, we demonstrate that these correlations, and especially the differential control of anti- and pro-apoptotic protein pairs, confer mitochondria a powerful discriminatory capacity of apoptotic fate. We find a similar correlation between the mitochondria and apoptotic proteins in colon cancer biopsies. Our results reveal a different role of mitochondria in apoptosis as the global regulator of apoptotic protein expression.

Directed evolution has been used for decades to engineer biological systems from the top-down. Generally, it has been applied at or below the organismal level, by iteratively sampling the mutational landscape in a guided search for genetic variants of higher function. Above the organismal level, a small number of studies have attempted to artificially select microbial communities and ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is still limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. To address this issue, we have developed a flexible modeling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions, in a wide range of ecological conditions. By artificially selecting hundreds of in-silico microbial metacommunities under identical conditions, we examine the fundamental limits of the two main breeding methods used so far, and prescribe modifications that significantly increase their power. We identify a range of directed evolution strategies that, particularly when applied in combination, are better suited for the top-down engineering of large, diverse, and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search for dynamically stable and ecologically and functionally resilient high-functioning communities.1 .

Directed evolution is a form of artificial selection that has been used for decades to find biomolecules and organisms with new or enhanced functional traits. Directed evolution can be conceptualized as a guided exploration of the genotype–phenotype map, where genetic variants with desirable phenotypes are first selected and then mutagenized to search the genotype space for an even better mutant. In recent years, the idea of applying artificial selection to microbial communities has gained momentum. In this article, we review the main limitations of artificial selection when applied to large and diverse collectives of asexually dividing microbes and discuss how the tools of directed evolution may be deployed to engineer communities from the top down. We conceptualize directed evolution of microbial communities as a guided exploration of an ecological structure–function landscape and propose practical guidelines for navigating these ecological landscapes. Expected final online publication date for the Annual Review of Biophysics, Volume 50 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Influenza virus stablishes a network of virus-host functional interactions, which depends on chromatin dynamic and therefore on epigenetic modifications. Using an unbiased search, we analyzed the epigenetic changes at DNA methylation and post-translational histone modification levels induced by the infection. DNA methylation was unaltered, while we found a general decrease on histone acetylation, which correlates with transcriptional inactivation and may cooperate with the impairment of cellular transcription that causes influenza virus infection. A particular increase in H3K79 methylation was observed and the use of an inhibitor of the specific H3K79 methylase, Dot1L enzyme, or its silencing, increased influenza virus replication. The antiviral response was reduced in conditions of Dot1L downregulation, since decreased nuclear translocation of NF-kB complex, and IFN-β, Mx1 and ISG56 expression was detected. The data suggested a control of antiviral signaling by methylation of H3K79 and consequently, influenza virus replication was unaffected in IFN pathway-compromised, Dot1L-inhibited cells. H3K79 methylation also controlled replication of another potent interferon-inducing virus such as vesicular stomatitis virus, but did not modify amplification of respiratory syncytial virus that poorly induces interferon signaling. Epigenetic methylation of H3K79 might have an important role in controlling interferon-induced signaling against viral pathogens.

The functions and services provided by ecosystems emerge from myriad interactions between organisms and their environment. The difficulty of incorporating this complexity into quantitative models has hindered our ability to predictively link species-level composition with ecosystem function. This represents a major obstacle towards engineering ecological systems for environmental and biotechnological purposes. Inspired by similar findings in evolutionary genetics, here we show that the function of ecological communities often follows simple equations that allow us to accurately predict and optimize ecological function. This predictability is facilitated by emergent "species-by-ecosystem" interactions that mirror the patterns of global epistasis observed in many genetic systems. Our results illuminate an unexplored path to quantitatively linking the composition and function of ecological communities, bringing the tasks of predicting biological function at the genetic, organismal, and ecological scales under the same quantitative formalism.