Root microbiota drive direct integration of phosphate stress and immunity

Plants live in biogeochemically diverse soils that harbor extraordinarily diverse microbiota. Plant organs associate intimately with a subset of these microbes; this community’s structure can be altered by soil nutrient content. Plant-associated microbes can compete with the plant and with each other for nutrients; they can also provide traits that increase plant productivity. It is unknown how the plant immune system coordinates microbial recognition with nutritional cues during microbiome assembly. We establish that a genetic network controlling phosphate stress response influences root microbiome community structure, even under non-stress phosphate conditions. We define a molecular mechanism regulating coordination between nutrition and defense in the presence of a synthetic bacterial community. We demonstrate that the master transcriptional regulators of phosphate stress response in Arabidopsis also directly repress defense, consistent with plant prioritization of nutritional stress over defense. Our work will impact efforts to define and deploy useful microbes to enhance plant performance

Design of synthetic bacterial communities for predictable plant phenotypes

Specific members of complex microbiota can influence host phenotypes, depending on both the abiotic environment and the presence of other microorganisms. Therefore, it is challenging to define bacterial combinations that have predictable host phenotypic outputs. We demonstrate that plant-bacterium binary-association assays inform the design of small synthetic communities with predictable phenotypes in the host. Specifically, we constructed synthetic communities that modified phosphate accumulation in the shoot and induced phosphate starvation-responsive genes in a predictable fashion. We found that bacterial colonization of the plant is not a predictor of the plant phenotypes we analyzed. Finally, we demonstrated that characterizing a subset of all possible bacterial synthetic communities is sufficient to predict the outcome of untested bacterial consortia. Our results demonstrate that it is possible to infer causal relationships between microbiota membership and host phenotypes and to use these inferences to rationally design novel communitie

An integrated workflow for phenazine-modifying enzyme characterization

Increasing availability of new genomes and putative biosynthetic gene clusters (BGCs) has extended the opportunity to access novel chemical diversity for agriculture, medicine, environmental and industrial purposes. However, functional characterization of BGCs through heterologous expression is limited because expression may require complex regulatory mechanisms, specific folding or activation. We developed an integrated workflow for BGC characterization that integrates pathway identification, modular design, DNA synthesis, assembly and characterization. This workflow was applied to characterize multiple phenazine-modifying enzymes. Phenazine pathways are useful for this workflow because all phenazines are derived from a core scaffold for modification by diverse modifying enzymes (PhzM, PhzS, PhzH, and PhzO) that produce characterized compounds. We expressed refactored synthetic modules of previously uncharacterized phenazine BGCs heterologously in Escherichia coli and were able to identify metabolic intermediates they produced, including a previously unidentified metabolite. These results demonstrate how this approach can accelerate functional characterization of BGCs

Biogenesis and functions of bacterial S-layers.

The outer surface of many archaea and bacteria is coated with a proteinaceous surface layer (known as an S-layer), which is formed by the self-assembly of monomeric proteins into a regularly spaced, two-dimensional array. Bacteria possess dedicated pathways for the secretion and anchoring of the S-layer to the cell wall, and some Gram-positive species have large S-layer-associated gene families. S-layers have important roles in growth and survival, and their many functions include the maintenance of cell integrity, enzyme display and, in pathogens and commensals, interaction with the host and its immune system. In this Review, we discuss our current knowledge of S-layer and related proteins, including their structures, mechanisms of secretion and anchoring and their diverse functions

Cutaneous recording of electroencephalograms in electrically stunned broiler chickens

Methodology was developed to record electroencephalograms (EEGs) from chickens using skin surface contact electrodes and telemetry transmitter and receiving units prior to and immediately after electrical stunning. Optimal location of the three electrodes was determined using scaleless ”featherless” chickens. Broilers required plucking of feathers on the neck caudal to the comb ( 2 x 3 cm) under mild anesthesia the day prior to recording EEGs. The telemetry transmitter was protected from the stunning voltage with a custom-built circuit designed to reduce high amplitude AC and DC voltages to less than 0.8 V. This configuration permitted recording of EEG signals prior to and within 3.5 s after termination of the applied stunning current. EEGs were recorded during two different electrical stunning protocols with the current applied to a standing chicken (wattle + and vent -). The first stun protocol was at 8 mA, 12 V (500 Hz) pulse DC for 11 s immediately followed by 12 V (60 Hz) AC for 4 s. The broilers were given several minutes to recover and then stunned again using the second stun protocol set at 103 mA (60 Hz AC) for 4 s, which was sufficient to induce cardiac arrest. The EEG recordings of the second stun protocol were evaluated to determine wave characteristics and the duration of poststun brain activity. The poststun EEG recordings depicted a brief period of high amplitude spikes, which progressively diminished in amplitude with time. This high amplitude polyspike wave form has been assumed to be analogous to the insensibility period that occurs during epileptic seizures in humans. This poststun data, in both wave form and duration of brain activity (39 s), appears similar to that described in the literature for chickens (32 s). Use of the cutaneous-telemetry system to record brain EEG activity in chickens following electrical stunning may provide the opportunity to quantitatively optimize stunning voltage, current, and frequency. Optimal stun parameters should minimize the time to death, and diminish skeletal muscle contraction and the carcass defects associated with electrical stunning

Cutaneous recording of electroencephalograms in electrically stunned broiler chickens

Methodology was developed to record electroencephalograms (EEGs) from chickens using skin surface contact electrodes and telemetry transmitter and receiving units prior to and immediately after electrical stunning. Optimal location of the three electrodes was determined using scaleless ”featherless” chickens. Broilers required plucking of feathers on the neck caudal to the comb ( 2 x 3 cm) under mild anesthesia the day prior to recording EEGs. The telemetry transmitter was protected from the stunning voltage with a custom-built circuit designed to reduce high amplitude AC and DC voltages to less than 0.8 V. This configuration permitted recording of EEG signals prior to and within 3.5 s after termination of the applied stunning current. EEGs were recorded during two different electrical stunning protocols with the current applied to a standing chicken (wattle + and vent -). The first stun protocol was at 8 mA, 12 V (500 Hz) pulse DC for 11 s immediately followed by 12 V (60 Hz) AC for 4 s. The broilers were given several minutes to recover and then stunned again using the second stun protocol set at 103 mA (60 Hz AC) for 4 s, which was sufficient to induce cardiac arrest. The EEG recordings of the second stun protocol were evaluated to determine wave characteristics and the duration of poststun brain activity. The poststun EEG recordings depicted a brief period of high amplitude spikes, which progressively diminished in amplitude with time. This high amplitude polyspike wave form has been assumed to be analogous to the insensibility period that occurs during epileptic seizures in humans. This poststun data, in both wave form and duration of brain activity (39 s), appears similar to that described in the literature for chickens (32 s). Use of the cutaneous-telemetry system to record brain EEG activity in chickens following electrical stunning may provide the opportunity to quantitatively optimize stunning voltage, current, and frequency. Optimal stun parameters should minimize the time to death, and diminish skeletal muscle contraction and the carcass defects associated with electrical stunning

Synthetic communities alter plant phenotypes according to the strain makeup of the blocks from which they were composed.

<p>(A) Heat map showing strains (<i>n</i> = 78) tested in binary association and that were selected because they cause positive (P), negative (N), or indifferent (I) effects on shoot Pi content in the growth conditions defined in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003962#pbio.2003962.g002" target="_blank">Fig 2A</a>. Strains are sorted within each group according to their mean effect on shoot Pi accumulation. Color scale shows log(fold-change) of shoot Pi content with respect to axenically grown plants. Bars and labels at the bottom show the 9 bacterial blocks used for the design of synthetic communities. Log(fold-change) is calculated from 6 pools of 10 plants in 2 independent experiments. See also <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003962#pbio.2003962.s015" target="_blank">S3</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003962#pbio.2003962.s016" target="_blank">S4</a> Tables. (B) Schematic representation of the synthetic communities designed using pairs of blocks. Sections in the circle are the 9 bacterial blocks from (A); black curved segments represent synthetic communities. Outer curved segments and curves inside the circle represent synthetic communities made of adjacent and nonadjacent bacterial blocks, respectively. (C) Heat map shows the scaled effect of each synthetic community on 4 plant phenotypes: Pi content (Pi), primary root elongation (Main), shoot area (Area) and total root network (Net) across the 4 growth conditions defined in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003962#pbio.2003962.g002" target="_blank">Fig 2A</a>. (D) Similar to (C) for individual bacterial functional blocks. In both (C) and (D), the values correspond to the scaled coefficients from a linear model. The values have been scaled through dividing by the standard deviation of all coefficients for the same phenotype and condition (each column in the plots). In all cases, 0 (white) represents no change with respect to axenically grown plants. The method to estimate the block and synthetic community effects are described in Materials and methods section 3j, and statistical significance (<i>p</i>-value < 0.05) is indicated with an “X” inside each tile, while the results of testing for significance for changes in Pi content are presented in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003962#pbio.2003962.s017" target="_blank">S5 Table</a>. Area, shoot area; Pi, phosphate; SynCom; synthetic community.</p

Synthetic communities modify plant transcriptional profiles.

<p>(A) Comparison of differentially expressed genes between all the positive (green) and all the negative blocks (magenta), in all conditions (top) or at 30 μM Pi (bottom). The first column shows all differentially expressed genes sorted by their log(fold-change), while the following columns indicate different functional annotations. Numbers at the top of each column show how many genes are marked and colored asterisks indicate a significant enrichment of the function among genes more expressed by positive (green asterisk) or negative (magenta asterisk) blocks. Panels (B) and (C) compare the expression of <i>IPS1</i>, a gene activated by low Pi, with (B) the JA response marker <i>VSP2</i> and (C) the glucosinolate biosynthesis marker <i>SUR1</i>. Expression values are RPKM on a log₁₀ scale. (D) Comparison of 103 differentially expressed genes between 2 negative bacterial blocks (N1, N3) under low Pi (30 μM) condition. Rows represent genes and columns specific synthetic communities under different conditions. Color in the heat map shows the average expression of the corresponding genes across 2 independent experiments (3 replicates per experiment). Hierarchical clustering dendrograms are shown for both genes and conditions. Color in the dendrogram indicates the block that is included in the corresponding condition (column) or that up-regulates the corresponding gene (columns). Darker magenta color corresponds to block N3, and lighter magenta color corresponds to block N1, as in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003962#pbio.2003962.g003" target="_blank">Fig 3B</a>. Genes involved in stress response (Stress) are indicated on the right, and the logFC in expression between blocks N1 and N3 is also indicated, with positive values indicating a higher expression in the presence of block N1. Panels (E) and (F) compare the expression of <i>IPS1</i> with (E), a phosphate starvation response–induced ubiquitin-conjugating E2 enzyme, <i>PHO2</i> (F), and an auxin-regulated gene, <i>ARGOS</i>. Expression values are RPKM in a log₁₀ scale. Ellipses highlight samples from plants inoculated with synthetic communities P3N3 (pink asterisk) and N2N3 (blue asterisk). For panels (B), (C), (E), and (F), points on the axes represent samples in which the expression of the corresponding gene was not detected. ABA, abscisic acid; <i>ARGOS</i>, AUXIN-REGULATED GENE INVOLVED IN ORGAN SIZE; <i>IPS1</i>, INDUCED BY PHOSPHATE STARVATION1; JA, jasmonic acid; logFC, log(fold-change); <i>PHO2</i>, PHOSPHATE2; Pi, phosphate; PSR, Pi starvation response; RPKM, reads per kilobase per million; SA, salicylic acid; Stress, stress response; <i>SUR1</i>, SUPERROOT1; <i>VSP2</i>, VEGETATIVE STORAGE PROTEIN2.</p