Methodological advances over the past two decades have propelled plant microbiome research, allowing the field to comprehensively test ideas proposed over a century ago and generate many new hypotheses. Studying the distribution of microbial taxa and genes across plant habitats has revealed the importance of various ecological and evolutionary forces shaping plant microbiota. In particular, selection imposed by plant habitats strongly shapes the diversity and composition of microbiota and leads to microbial adaptation associated with navigating the plant immune system and utilizing plant-derived resources. Reductionist approaches have demonstrated that the interaction between plant immunity and the plant microbiome is, in fact, bidirectional and that plants, microbiota, and the environment shape a complex chemical dialogue that collectively orchestrates the plant microbiome. The next stage in plant microbiome research will require the integration of ecological and reductionist approaches to establish a general understanding of the assembly and function in both natural and managed environments. Expected final online publication date for the Annual Review of Microbiology, Volume 74 is September 8, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Processing of external information by mammalian cells often involves seemingly redundant isoforms of signaling molecules and transcription factors. Understanding the functional relevance of coexpressed isoforms that respond to the same signal and control a shared set of genes is still limited. Here we show, using imaging of individual living mammalian cells, that the closely related transcription factors NFAT1 and NFAT4 possess distinct nuclear localization dynamics in response to cell stimulation. NFAT4 shows a fast response, with rapid stochastic bursts of nuclear localization. Burst frequency grows with signal level, while response amplitude is fixed. In contrast, NFAT1 has a slow, continuous response, and its amplitude increases with signal level. These diverse dynamical features observed for single cells are translated into different impulse response strategies at the cell population level. We suggest that dynamic response diversity of seemingly redundant genes can provide cells with enhanced capabilities of temporal information processing.

Elaboration of a compound leaf shape depends on extended morphogenetic activity in developing leaves. In tomato (Solanum lycopersicum), the CIN-TCP transcription factor LANCEOLATE (LA) promotes leaf differentiation. LA is negatively regulated by miR319 during the early stages of leaf development, and decreased sensitivity of LA mRNA to miR319 recognition in the semi-dominant mutant La leads to prematurely increased LA expression, precocious leaf differentiation and a simpler and smaller leaf. Increased levels or responses of the plant hormone gibberellin (GA) in tomato leaves also led to a simplified leaf form. Here, we show that LA activity is mediated in part by GA. Expression of the SlGA20 oxidase1 (SlGA20ox1) gene, which encodes an enzyme in the GA biosynthesis pathway, is increased in gain-of-function La mutants and reduced in plants that over-express miR319. Conversely, the transcript levels of the GA deactivation gene SlGA2 oxidase4 (SlGA2ox4) are increased in plants over-expressing miR319. The miR319 over-expression phenotype is suppressed by exogenous GA application and by a mutation in the PROCERA (PRO) gene, which encodes an inhibitor of the GA response. SlGA2ox4 is expressed in initiating leaflets during early leaf development. Its expression expands as a result of miR319 over-expression, and its over-expression leads to increased leaf complexity. These results suggest that LA activity is partly mediated by positive regulation of the GA response, probably by regulation of GA levels.

From natural ecology1–4 to clinical therapy5–8, cells are often exposed to mixtures of multiple drugs. Two competing null models are used to predict the combined effect of drugs: response additivity (Bliss) and dosage additivity (Loewe)9–11. Here, noting that these models diverge with increased number of drugs, we contrast their predictions with growth measurements of four phylogenetically distant microorganisms including Escherichia coli, Staphylococcus aureus, Enterococcus faecalis and Saccharomyces cerevisiae, under combinations of up to ten different drugs. In all species, as the number of drugs increases, Bliss maintains accuracy while Loewe systematically loses its predictive power. The total dosage required for growth inhibition, which Loewe predicts should be fixed, steadily increases with the number of drugs, following a square-root scaling. This scaling is explained by an approximation to Bliss where, inspired by R. A. Fisher’s classical geometric model12, dosages of independent drugs add up as orthogonal vectors rather than linearly. This dose-orthogonality approximation provides results similar to Bliss, yet uses the dosage language as in Loewe and is hence easier to implement and intuit. The rejection of dosage additivity in favour of effect additivity and dosage orthogonality provides a framework for understanding how multiple drugs and stressors add up in nature and the clinic.

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