Microbial factors associated with the natural suppression of take-all wheat in New Zealand

Take-all, caused by the soilborne fungus, Gaeumannomyces graminis var. tritici (Ggt), is an important root disease of wheat that can be reduced by take-all decline (TAD) in successive wheat crops, due to general and/or specific suppression. A study of 112 New Zealand wheat soils in 2003 had shown that Ggt DNA concentrations (analysed using real-time PCR) increased with successive years of wheat crops (1-3 y) and generally reflected take-all severity in subsequent crops. However, some wheat soils with high Ggt DNA concentrations had low take-all, suggesting presence of TAD. This study investigated 26 such soils for presence of TAD and possible suppressive mechanisms, and characterised the microorganisms from wheat roots and rhizosphere using polymerase chain reaction (PCR) and denaturing gradient gel electrophoresis (DGGE). A preliminary pot trial of 29 soils (including three from ryegrass fields) amended with 12.5% w/w Ggt inoculum, screened their suppressiveness against take-all in a growth chamber. Results indicated that the inoculum level was too high to detect the differences between soils and that the environmental conditions used were unsuitable. Comparison between the Ggt DNA concentrations of the same soils collected in 2003 and in 2004 (collected for the pot trial), showed that most soils cropped with 2, 3 and 4 y of successive wheat had reduced Ggt DNA concentrations (by 195-2911 pg g-1 soil), and their disease incidences revealed 11 of the 29 test soils with potential take-all suppressiveness. Further pot trials improved the protocols, such that they were able to differentiate the magnitudes of suppressiveness among the soils. The first of the subsequent trials, using 4% w/w Ggt inoculum level, controlled conditions at 16°C, 80% RH with alternate 12 h light/dark conditions, and watering the plants twice weekly to field capacity (FC), screened 13 soils for their suppressiveness against take-all. The 13 soils consisted of 11 from the preliminary trial, one wheat soil that had been cropped with 9 y of wheat (considered likely to be suppressive), and a conducive ryegrass soil. The results revealed that 10 of these soils were suppressive to take-all. However, in only four of them were the effects related to high levels of microbial/biological involvement in the suppression, which were assessed in an experiment that first sterilised the soils. In a repeat trial using five of the soils H1, H3, M2, P7 (previously cropped with 3, 3, 4 and 9 y successive wheat, respectively) and H15 (previously cropped with 5 y of ryegrass), three of them (H1, H3 and M2) had reduced Ggt DNA concentrations (>1000 pg g-1 soil reductions), and were confirmed to be suppressive to take-all. A pot trial, in which 1% of each soil was transferred into a γ-irradiated base soil amended with 0.1% Ggt inoculum, indicated that soils H1 and H3 (3 y wheat) were specific in their suppressiveness, and M2 (4 y wheat) was general in its suppressiveness. The microbial communities within the rhizosphere and roots of plants grown in the soils, which demonstrated conduciveness, specific or general suppressiveness to take-all, were characterised using PCR-DGGE, and identities of the distinguishing microorganisms (which differentiated the soils) identified by sequence analysis. Results showed similar clusters of microorganisms associated with conducive and suppressive soils, both for specific and general suppression. Further excision, re-amplification, cloning and sequencing of the distinguishing bands showed that some actinomycetes (Streptomyces bingchengensis, Terrabacter sp. and Nocardioides sp.), ascomycetes (Fusarium lateritium and Microdochium bolleyi) and an unidentified fungus, were associated with the suppressive soils (specific and general). Others, such as the proteobacteria (Pseudomonas putida and P. fluorescens), an actinomycete (Nocardioides oleivorans), ascomycete (Gibberella zeae), and basidiomycete (Penicillium allii), were unique in the specific suppressiveness. This indicated commonality of some microorganisms in the take-all suppressive soils, with a selected distinguishing group responsible for specific suppressiveness. General suppressiveness was considered to be due to no specific microorganisms, as seen in soil M2. An attempt to induce TAD by growing successive wheat crops in pots of Ggt-infested soils was unsuccessful with no TAD effects shown, possibly due to variable Ggt DNA concentrations in the soils and addition of nutrients during the experiment. Increasing numbers of Pseudomonas fluorescens CFU in the rhizosphere of plants, during successive wheat crops was independent of the Ggt DNA concentrations and disease incidence, suggesting that increases in P. fluorescens numbers were associated with wheat monoculture. This study has demonstrated that TAD in New Zealand was due to both specific and general suppressiveness, and has identified the distinguishing microorganisms associated with the suppression. Since most of these distinguishing microorganisms are known to show antagonistic activities against Ggt or other soilborne pathogens, they are likely to act as antagonists of Ggt in the field. Future work should focus on validating their effects either individually, or interactively, on Ggt in plate and pot assays and under field conditions

MCE domain proteins::conserved inner membrane lipid-binding proteins required for outer membrane homeostasis

AbstractBacterial proteins with MCE domains were first described as being important for Mammalian Cell Entry. More recent evidence suggests they are components of lipid ABC transporters. In Escherichia coli, the single-domain protein MlaD is known to be part of an inner membrane transporter that is important for maintenance of outer membrane lipid asymmetry. Here we describe two multi MCE domain-containing proteins in Escherichia coli, PqiB and YebT, the latter of which is an orthologue of MAM-7 that was previously reported to be an outer membrane protein. We show that all three MCE domain-containing proteins localise to the inner membrane. Bioinformatic analyses revealed that MCE domains are widely distributed across bacterial phyla but multi MCE domain-containing proteins evolved in Proteobacteria from single-domain proteins. Mutants defective in mlaD, pqiAB and yebST were shown to have distinct but partially overlapping phenotypes, but the primary functions of PqiB and YebT differ from MlaD. Complementing our previous findings that all three proteins bind phospholipids, results presented here indicate that multi-domain proteins evolved in Proteobacteria for specific functions in maintaining cell envelope homeostasis.</jats:p

Pathogenicity of Gaeumannomyces graminis var. tritici increased by nitrogen applied to soil to enhance the decomposition rate of wheat residues

Soil cores removed after harvest of a wheat crop infected with the fungus, Gaeumannomyces graminis var. tritici (Ggt), were amended with nitrogen and fungal saprophytes to increase decay of crop residues and subsequently reduce soil inoculum. The cores were treated with one application of 50 kg nitrogen (N) per ha, Trichoderma strains, or both. Cores were assessed 0, 2, 4 and 7 months after harvest. At 7 months, the crop residues had decayed to a third of their original mass, with the decay not influenced by the treatments. DNA analysis confirmed Ggt DNA was present in the stubble stems, crowns and roots. The pathogenicity of Ggt was increased by N, as shown by a 5 to 8-fold increase in take-all severity in indicator wheat seedlings planted in the N-treated cores 2 to 4 months after harvest, compared with those without N. Ggt remained viable in all treatments to infect wheat seedlings 7 months after harvest

Structure of dual BON-domain protein DolP identifies phospholipid binding as a new mechanism for protein localisation

The Gram-negative outer-membrane envelops the bacterium and functions as a permeability barrier against antibiotics, detergents, and environmental stresses. Some virulence factors serve to maintain the integrity of the outer membrane, including DolP (formerly YraP) a protein of unresolved structure and function. Here, we reveal DolP is a lipoprotein functionally conserved amongst Gram-negative bacteria and that loss of DolP increases membrane fluidity. We present the NMR solution structure for Escherichia coli DolP, which is composed of two BON domains that form an interconnected opposing pair. The C-terminal BON domain binds anionic phospholipids through an extensive membrane:protein interface. This interaction is essential for DolP function and is required for sub-cellular localisation of the protein to the cell division site, providing evidence of subcellular localisation of these phospholipids within the outer membrane. The structure of DolP provides a new target for developing therapies that disrupt the integrity of the bacterial cell envelope

Patterns of Oncogene Coexpression at Single-Cell Resolution Influence Survival in Lymphoma.

UNLABELLED: Cancers often overexpress multiple clinically relevant oncogenes, but it is not known if combinations of oncogenes in cellular subpopulations within a cancer influence clinical outcomes. Using quantitative multispectral imaging of the prognostically relevant oncogenes MYC, BCL2, and BCL6 in diffuse large B-cell lymphoma (DLBCL), we show that the percentage of cells with a unique combination MYC+BCL2+BCL6- (M+2+6-) consistently predicts survival across four independent cohorts (n = 449), an effect not observed with other combinations including M+2+6+. We show that the M+2+6- percentage can be mathematically derived from quantitative measurements of the individual oncogenes and correlates with survival in IHC (n = 316) and gene expression (n = 2,521) datasets. Comparative bulk/single-cell transcriptomic analyses of DLBCL samples and MYC/BCL2/BCL6-transformed primary B cells identify molecular features, including cyclin D2 and PI3K/AKT as candidate regulators of M+2+6- unfavorable biology. Similar analyses evaluating oncogenic combinations at single-cell resolution in other cancers may facilitate an understanding of cancer evolution and therapy resistance. SIGNIFICANCE: Using single-cell-resolved multiplexed imaging, we show that selected subpopulations of cells expressing specific combinations of oncogenes influence clinical outcomes in lymphoma. We describe a probabilistic metric for the estimation of cellular oncogenic coexpression from IHC or bulk transcriptomes, with possible implications for prognostication and therapeutic target discovery in cancer. This article is highlighted in the In This Issue feature, p. 1027

Supplementary appendix from Patterns of Oncogene Coexpression at Single-Cell Resolution Influence Survival in Lymphoma

Supplementary methods. Supplementary Figure 1. Phenotyping of B-cells in non-malignant tissues. A, Quantitation of marker positivity across ten tonsil and two reactive lymph node samples (rLN). Analysis is spatially resolved between the GC and extra-GC zones. B, Spatial map of cellular coordinates based on cell segmentation of images in Figure 1B. Marker-positivity is indicated, and a total proportion of positive and negative cells is depicted as a pie chart. These maps were used to derive sub-population phenotypes depicted in Figure 1C. Scale bar is 100μm. C, Proliferation analysis (i.e., Ki67-positivity) among sub-populations in five tonsil samples. Median with interquartile range, whiskers denote 10th and 90th percentile. Supplementary figure 2. Example pseudo-colored mfIHC images for MYC, BCL2, BCL6 cases in DLBCL. Images of a range of mean fluorescent intensities are shown with equal scaling for reference. Supplementary figure 3. Global distribution of MYC, BCL2 and BCL6 sub-populations within DLBCL cohorts. Heat-maps displaying the percentage extent of individual markers and each sub-population within the DLBCL NUH, CMMC, SGH and MDA cohorts. Hierarchical k-means clustering of patients according to sub-population extent is applied. Positivity shading for single markers ranges between 0-100% positivity, whereas shading for sub-populations reflects 0-50% positivity and remains fully saturated until 100%. IPI Risk Group - International Prognostic Index Risk Group, FISH - fluorescence in situ hybridization. Supplementary figure 4. Intra-tumor heterogeneity of sub-populations. A, Correlation of sub-population extent quantification between two biopsies of the same patient for which at least two tissue microarray (TMA) biopsies are available. Correlation is shown separately for lymph node and extranodal biopsies. Spearman rho is indicated for each correlation. Axes are in exponential and equivalent in all panels. B, Sub-population percentage extent quantification across multiple TMA cores (columns) of the same patient (rows). Pie charts are ordered according to decreasing cell numbers evaluated per core. All patients from the NUH cohort with at least five cores are evaluated. A heterogenous cluster is highlighted by the red box. Supplementary figure 5. Spatial heterogeneity of sub-population interactions. A, Conceptual schematic of pair correlation function (PCF) plots depicting a clustered distribution (left, green) and a random distribution (right, grey). Representative counterpart spatial maps are above each plot. B, PCF analysis for sub-populations to investigate spatial clustering (top). Mean results for two independent cohorts (shading is cohort standard deviation). An example tissue microarray core is shown as physical distance reference for spatial analyses (bottom left). Absolute number of neighboring cells expected within a given radius (data from 3500 randomly selected cells across all images, mean with standard deviation) (bottom right). C, Actual spatial map of sub-populations of an example DLBCL case (top). Extent of all sub-populations within the sample is shown on the left. Simulated, hypothetical random distribution of cells for the same case (middle). PCF analysis for the shown sample and its matched simulated random distribution (bottom). Scale bars in B and C are 100µm. D, Mean deviations from expected neighbor abundance (Δ%) summarizing cell-cell interactions between sub-populations for the sample shown in (C). E, Sub-population interaction matrices from spatially distinct biopsies (cores in tissue microarray) for example DLBCL patients. Biopsies of stable, spatially homogenous, sub-population interaction profiles are grouped (top), whereas biopsies of a differing, heterogenous, interaction profile are grouped separately (bottom). Supplementary figure 6. Global deviations from expected spatial neighbor abundance (Δ%). Hierarchical clustering (minimum variance method) of measured Δ% for all cases in the SGH and MDA cohorts. Extents of sub-populations are indicated for reference (top). For the MDA cohort, multiple biopsies (n = 1-3) from the same patient were included in the analysis to determine spatial interaction similarity across spatially distinct regions (bottom). Supplementary figure 7. Correlation of predicted MYC, BCL2 and BCL6 sub-population percentage extent based on single oncogene positivity and observed percentage extent in DLBCL cohorts. Spearman rho, axes are equivalent in all panels. Supplementary figure 8. Variance of M+2+6- percentage extent in the context of positivity calling across a 15% cut-off. A, M+2+6- scoring variance across multiple pathological imaging fields. All whole-tissue DLBCL sections from University of Palermo (UP), and samples from the NUH TMA with at least four fields scored per patient and a mean M+2+6- score above 5% are shown. Mean with SD. Ordinates between 50-100% are compressed for clarity. Dashed line denotes M+2+6- 15% positivity. B, Stability of M+2+6- case positivity calling across scoring increasing number of imaging fields. All cases from panel A with at least five fields scored in this study are shown. Only one case is called M+2+6- Low (<15%) at the first image scored, and subsequently called M+2+6- High (≥15%) after two or more fields scored. Supplementary figure 9. Mapping of mRNA expression data into percentage extent data. A, Cumulative histogram of MYC, BCL2 and BCL6 protein percentage extent positivity in DLBCL cohorts (data transformed from Figure 4A) (top). B, Aggregated single oncogene cumulative distribution of MYC, BCL2 and BCL6 protein percentage extent positivity across all measured protein cohorts and its smoothed empirical cumulative distribution function (eCDF).C, Distribution of inferred single oncogene percentage extent in GEP cohorts. (see Supplementary table 6 for all values). Supplementary figure 10. Analysis of the GOYA clinical trial. A, Correlation of MYC mRNA with quantitative IHC score. Linear regression (left) and Wilcoxon rank sum test (right). B, Analysis as in (A) for BCL2. C, Kaplan-Meier curves for PFS and OS for patients stratified across the 15% M+2+6- metric (GEP-derived). Multivariate Cox proportional hazards model is available in Supplementary table 9. PFS - progression free survival, OS - overall survival. Supplementary figure 11. Proliferative advantage of cyclin D2 (CCND2) overexpressing B-cells. Representative FACS plots documenting to the expansion over time of the cyclin D2 positive GC B-cell population in cyclin D2 overexpressing GC B-cells (CCND2-Lyt2) and non-cyclin D2 overexpressing GC B-cells (Empty vector Lyt2). All GC B-cells co-overexpress BCL2, BCL6, MYC and GFP. Supplementary table 3. Non-parametric correlation of sub-population percentage extent with clinicopathological features. Supplementary table 4. Pooled univariate analysis for MYC, BCL2 and BCL6 single oncogene and sub-populations percentage extents as a continuous variable at 5% increments as predictors for overall survival (OS) in mfIHC cohorts of DLBCL (Cox proportional hazards model). Supplementary table 5. Univariate analysis of clinicopathological features as a predictor of overall survival (OS) after first-line R-CHOP treatment in the NUH, SGH and MDA cohorts of DLBCL (Cox proportional hazards model). Supplementary table 7. Pooled univariate analysis for sub-population metrics as a continuous variable at 5% increments as predictors for overall survival (OS) in GEP DLBCL cohorts (Cox proportional hazards model). Supplementary table 8. Multivariate analysis of continuous M+2+6- metric at 5% increments as a predictor of overall survival (OS) in cohorts with gene-expression data (Cox proportional hazards model). Supplementary table 9. Univariate and multivariate analysis of continuous M+2+6- metric as a continuous variable at 5% increments as predictor of progression-free survival (PFS) and overall survival (OS) in the GOYA trial cohort (Cox proportional hazards model). Supplementary table 10. Multivariate analysis of M+2+6- metric dichotomized at 15% as a predictor of overall survival (OS) in cohorts with gene-expression data (Cox proportional hazards model). Supplementary table 15. Clinicopathologic characteristics of DLBCL patients evaluated by multiplexed fluorescent immunohistochemistry (mfIHC) in this study. Supplementary table 16. Manual multiplexed fluorescent immunohistochemistry (mfIHC) staining protocol performed on the NUH and CMMC cohort TMA. Supplementary table 17. Automated multiplexed fluorescent immunohistochemistry (mfIHC) staining protocol performed on the SGH, MDA and BCA cohort TMA.</p

Supplementary tables from Patterns of Oncogene Coexpression at Single-Cell Resolution Influence Survival in Lymphoma

Supplementary table 1. Per-patient mfIHC MYC, BCL2 and BCL6 single oncogene and subpopulation scores for normal tonsil tissue and reactive lymph node tissue. Supplementary table 2. Per-patient mfIHC MYC, BCL2 and BCL6 single oncogene and subpopulation scores for DLBCL tissue (NUH, CMMC, SGH, MDA, BCA and UP). Supplementary table 6. Inferred percentage extents of MYC, BCL2, BCL6 and sub-population metrics in GEP cohorts. Supplementary table 11. Correlation of M+2+6- metric with gene expression in GEP cohorts. Supplementary table 12. Differential gene expression analysis of primary germinal center (GC) B-cells with M+2+ and M+2+6+ overexpression. Supplementary table 13. Differentially expressed genes between M+2+6- and all other malignant cells in scRNA-seq samples of DLBCL. Dichotomized non-parametric comparison, Wilcoxon rank sum test. Supplementary table 14. Analysis of positive enrichment of Wikipathways terms between M+2+6- and all other malignant cells in scRNA-seq samples of DLBCL by gprofiler2.</p