Coessentiality networks derived from CRISPR screens in cell lines provide a powerful framework for identifying functional modules in the cell and for inferring the role of uncharacterized genes. However, these networks integrate signal across all underlying data, and can mask strong interactions that occur in only a subset of the cell lines analyzed. Here we decipher dynamic functional interactions by identifying significant cellular contexts, primarily by oncogenic mutation, lineage, and tumor type, and discovering coessentiality relationships that depend on these contexts. We recapitulate well-known gene-context interactions such as oncogene-mutation, paralog buffering, and tissue-specific essential genes, show how mutation rewires known signal transduction pathways, including RAS/RAF and IGF1R-PIK3CA, and illustrate the implications for drug targeting. We further demonstrate how context-dependent functional interactions can elucidate lineage-specific gene function, as illustrated by the maturation of proreceptors IGF1R and MET by proteases FURIN and CPD. This approach advances our understanding of context-dependent interactions and how they can be gleaned from these data.

The commensal bacterial strains used in this study induced the expression of a large number of genes in colonocyte-like cultured cells and changed the expression of several genes involved in important cellular processes such as regulation of transcription, protein biosynthesis, metabolism, cell adhesion, ubiquitination, and apoptosis. Such changes induced by the presence of probiotic bacteria may shape the physiologic and pathologic responses they trigger in the host.

The environment in areas where geological and mining activities, such as the extraction of ores containing heavy metals, take place, is heavily polluted with dusts resulting from these activities, as well as with residual waters from the mines. Depending on the meteorological conditions, as well as on the conditions under which sedimentable dusts or dusts in suspension are emitted into the air, the distance from the main pollution source varies considerably. In order to estimate the pollution level and the danger presented by this phenomenon, some analyses are required with regard to determining the concentration of heavy pollutant metals in air, soil and plants samples, as well as in dusts from the air. For the precise determination of the major components, as well as the minor ones, and also the ones in traces, the analytical techniques used must have low detection limits and the lowest matrix effects possible [1,2]. The methods that respond to these requirements are from the category of inductively coupled plasma atomic emission spectrometry (ICP-AES). The research was done in an area where there are industrial units whose main field of activity is extraction of certain ores which contain Pb, Cu and Zn as major components, as well as Cr, Mn, Ni, Co, Ag, Au, Al, and Fe as minor components or in traces. It is obvious that the presence of these metals in the air, water and soil has a negative impact on human health, plants and animals. This paper is a study of these aspects in an area where pollution with heavy metals reaches alarming quotas