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AIMS Microbiology

2017, Volume 3, Issue 3: 502-524. doi: 10.3934/microbiol.2017.3.502
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Review Special Issues

Plant probiotic bacteria: solutions to feed the world

  • 1.
    Instituto de Ciências Agrárias e Ambientais Mediterrânicas (ICAAM), Universidade de Évora, Évora, Portugal
  • 2.
    Laboratory of Fungal Genetics and Metabolism, Institute of Microbiology, Academy of Sciences of the Czech Republic, Prague, Czech Republic
  • Received: 27 April 2017 Accepted: 12 June 2017 Published: 21 June 2017

  • The increasing human population expected in the next decades, the growing demand of livestock products—which production requires higher amounts of feed products fabrication, the collective concern about food quality in industrialized countries together with the need to protect the fertility of soils, in particular, and the environment, in general, constitute as a whole big challenge that worldwide agriculture has to face nowadays. Some soil bacteria harbor mechanisms to promote plant growth, which include phytostimulation, nutrient mobilization, biocontrol of plant pathogens and abiotic stresses protection. These bacteria have also been proved as promoters of vegetable food quality. Therefore, these microbes, also so-called Plant Probiotic Bacteria, applied as biofertilizers in crop production, constitute an environmental friendly manner to contribute to produce the food and feed needed to sustain world population. In this review, we summarize some of the best-known mechanisms of plant probiotic bacteria to improve plant growth and develop a more sustainable agriculture.

    Citation: Esther Menendez, Paula Garcia-Fraile. Plant probiotic bacteria: solutions to feed the world[J]. AIMS Microbiology, 2017, 3(3): 502-524. doi: 10.3934/microbiol.2017.3.502

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    1.   Introduction

    The tumor microenvironment (TME) is a complex ecosystem comprising numerous cells, including immune cells, stromal cells and immunosuppressive cells [1]. Tumour endothelial cells (TECs) are a prominent component of TME and TECs, which have critical functions on tumor growth, progression and metastasis [2]. As tumor angiogenesis is essential for tumor growth and metastasis, inducing tumor-associated angiogenesis is a promising tactic in cancer progression [3]. Angiogenesis is now accepted as an important hallmark of cancer [4] and endothelial cells (which form tight adhesions to ensure vessel integrity) are essentials in inducing angiogenesis in TME [3]. In TME, TECs interact with tumor cells via juxtacrine and paracrine signaling during tumor intravasation and metastasis [5].

    Colorectal cancer (CRC) is the fourth most common cancer and a leading cause of cancer mortality worldwide [6]. Recently stated that distinct stromal transcriptional and interactions are altered in colon cancer development [7] and stromal cells contribute to the CRC transcriptome [8]. It was also stated that stromal gene expression defines poor prognosis in colorectal cancer [9] and induces poor-prognosis subtypes in colorectal cancer [10]. Altogether, stromal cells have substantial influence and regulatory roles in colon cancer and endothelial cells (ECs) may cause colon carcinogenesis as a part of the stromal component.

    So, analyzing the transcriptomes of colon TECs (CTECs) in TME reveals new targets and avenues and explores more uncontrolled factors in colon cancer research. We present a comprehensive analysis through bioinformatic tools and identified molecular and genetic alterations in TECs. We identified DEGs from microarray datasets of gene expression omnibus (GEO), hub genes from the interactions of deregulated genes and find out significant KEGG pathways. We also identified significant hub genes that are linked with poor survival of patients. Moreover, we found that hub genes are associated with immune cells infiltration and positively correlated with immune suppressive markers. This integrated analysis provides vital molecular insights into CTECs characterization, which may directly affect treatment recommendations for colon cancer patients. It provides opportunities for genome-guided clinical trials as well as drug development for the treating of colon cancer patients.

    2.   Materials and methods

    2.1.   Datasets

    We searched the NCBI gene expression omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) using the keywords "colon cancer endothelial cells, " "endothelial cells, " and "normal endothelial cells, " and identified one CTECs gene expression dataset GSE89287 (n = 24) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89287) [11]. This dataset included different cell types (endothelial cells, macrophages and epithelial cells) which were isolated from non-paired primary normal colon tissues and colorectal carcinomas and subsequently, RNA was isolated. In this study, we included only endothelial cells and excluded the other cell types from our study. Endothelial cells were isolated using the facs-sorting method. The dataset included 24 samples of endothelial cells, including 10 normal colon samples and 14 colon tumor samples. The platform of this data is GPL4133 (Agilent-014850 whole human genome microarray 4x44K G4112F) with a feature number version. Gene expression profiling interactive analysis (GEPIA) (http://gepia2.cancer-pku.cn/#index) dataset (n = 316) was used for the study of prognostic and expression levels of hub genes [12]. In addition, the correlation between the expression of hub genes and tumor-infiltrating immune cells was explored via gene modules in the TIMER dataset (n = 457) (https://cistrome.shinyapps.io/timer/) [13]. For identifying the gene-gene correlation, we used the TCGA-COAD dataset (n = 287) (https://gdc-portal.nci.nih.gov/) which was normalized into base log2.

    2.2.   Identification of differentially expressed genes (DEGs) between CTECs and CNECs

    We used the web tool Network Analyst [14]to identify the significant DEGs between CTECs and CNECs. Dataset was normalized by quantile normalization and the R package "limma" was utilized to identify the DEGs between CTECs and CNECs. We utilized the Network Analyst tool to identify the average expression level of single gene having multiple probes in this gene expression study. We identified the DEGs with a threshold of |LogFC| > 0.585 and adjusted P value < 0.05.

    2.3.   Gene-set enrichment analysis

    We performed gene-set enrichment analysis of the DEGs by GSEA [15]. We identified the KEGG [16] pathways that were significantly associated with the up-regulated and the down-regulated DEGs, respectively. We identified the significant pathways with a threshold of false discovery rate (FDR) < 0.05.

    2.4.   Construction of protein-protein interaction (PPI) network and modular analysis of PPI

    To better know the relationship among these screened DEGs, the PPI network was established using the STRING database [17]. The hub genes in the PPI network were identified according to a degree using the node explorer module of Network Analyst software [14]. A hub gene was defined as a gene that was connected to no less than 10 other DEGs. A cytoscape plug-in molecular complex detection (MCODE) was employed to detect the modules from the PPI network [18]. We identified the significant modules based on the MCODE score and node number. The threshold of the MCODE was Node Score Cut-off: 0.2, Haircut: true, K-Core: 2 and maximum depth from Seed: 100.

    2.5.   Survival analysis of hub genes

    We compared the overall survival (OS) and the disease-free survival (DFS) of colon cancer patients classified based on gene expression levels (expression levels > median versus expression levels < median). Kaplan-Meier survival curves were used to show the survival differences and the log-rank test was utilized to evaluate the significance of survival differences. The prognostic roles of screened hub genes in COAD were analyzed using expression profiling interactive analysis (GEPIA) databases [12]. Cox P < 0.05 was considered as significant between two groups of patients.

    2.6.   Validation of prognosis-associated hub gene expression levels in TCGA COAD dataset

    The expression levels of top hub genes were further validated using the gene expression profiling interactive analysis (GEPIA), a newly developed interactive web server for analyzing the RNA sequencing data [12]. We used TCGA COAD tumor data with matched normal. Hub genes with |log2FC| > 0.585 and P < 0.05 were considered statistically significant between the two groups.

    2.7.   Evaluation of immune scores and stromal scores in colon cancer

    We utilized the "ESTIMATE" R package to quantify the immune scores (predict the immune cell quantity) and stromal scores (predict the stromal cells quantity) for each of the tumor samples [19]. Then we calculated Spearman's correlations between the expression levels of prognostic hub genes and immune and stromal scores. The threshold value of correlation is R > 0.30 and P-value is less than 0.001 (Spearman's correlation test).

    2.8.   Analysis of immune cell infiltration and correlation of top hub genes with immune-inhibitory markers

    We analyzed the significant correlations of hub genes with the abundance of immune infiltrates, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages and dendritic cells, via gene modules in TIMER [13]. In addition, Spearman's correlations between the expression level of hub genes and the immune inhibitory marker genes of tumor-infiltrating immune cells were explored via correlation modules in TIMER [13]. Moreover, we identified pearson's correlations between the expression level of hub genes and the marker genes of inhibitory immune cells in TCGA-COAD datasets using R software. The R package "ggplot2" was employed for drawing a correlation graph [20]. The gene markers of tumor-infiltrating immune cells included markers of monocytes, TAMs, M2 macrophages, T-helper 1 (Th1) cells, Tregs and exhausted T cells. These gene markers are illustrated in prior studies [12,21,22,23,24].

    2.9.   Statistical analysis

    We used R programming software for the statistical analysis of this study. For the differential expression analysis of gene expression data, only genes with |logFC| > 0.585 and adjusted P-value < 0.05 were considered statistically significant between the two groups. In the survival analysis, Cox P < 0.05 was considered as statistically significant. Spearman's correlation test between the ssGSEA scores and the expression level of specific prognostic genes was performed because these data were not normally distributed (P < 0.05) [25]. For analyzing the correlations between the expression levels of hub genes with the expression levels of other marker genes, we employed Pearson's correlation test because these data were normally distributed [25].

    3.   Results

    3.1.   Identification of Differentially Expressed Genes

    Based on the log2FC and adjusted P-value, we identified 362 differentially expressed genes (DEGs) in CTECs relative to the CNECs, which included 117 up-regulated genes (Supplementary Table S1) and 245 down-regulated (Supplementary Table S2) in the CTECs samples. Table 1 demonstrates the top ten up-regulated genes (SPARC, IGFBP7, COL4A2, SELE, CXCL1, VWF, CCDC3, FSTL1, VWA1 and IFITM3) with significant statistical description. The most up-regulated gene SPARC is up-expressed in the stromal portion of CRC tissues [26]. The extracellular matrix-associated gene, COL4A2, is linked with cancer stemness [27]. Another up-regulated gene, CXCL1, is associated with increasing the metastatic ability of colon cancer by enhancing cell migration, matrix metalloproteinase-7 expression and EMT [28]. VWF, another up-regulated gene, is a plasma marker for the early detection of adenoma and colon cancer [29]. Table 2 illustrated the top ten down-regulated genes (IGKV1-5, IGLV1-44, IGKC, IGHV3-30, KRT81, IGHG1, IGHM, POU2AF1, IGKV2-24 and IGLJ3) with all significant statistical descriptions. Interestingly, most of the top down-regulated genes are associated with immunological activities. Some other down-regulated genes of CTECs have been demonstrated to be under-expressed in CRC. For example, the expression of POU2AF1 is down-regulated in the rat colon and in turn, reveals proximal-distal differences in the process of histone modifications and proto-oncogene expression [30]. Altogether, abnormally expressed numerous genes in CTECs compared to NTECs identified by the bioinformatic analysis have been associated with CRC pathology and carcinogenesis.

    Table 1.  List of top ten up-regulated genes.
    Entrez ID Symbols logFC P-Value Adjusted P-Value Name of gene
    6678 SPARC 2.21 0.0009 0.029 Secreted protein, acidic, cysteine-rich (osteonectin)
    3490 IGFBP7 2.19 0.0008 0.026 Insulin-like growth factor-binding protein 7
    1284 COL4A2 2.19 0.0002 0.016 Collagen, type IV, alpha 2
    6401 SELE 1.97 0.0017 0.038 Selectin E
    2919 CXCL1 1.85 0.0003 0.018 Chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating activity, alpha)
    7450 VWF 1.85 0.0023 0.044 Von Willebrand factor
    83643 CCDC3 1.82 0.0004 0.019 Coiled-coil domain containing 3
    11167 FSTL1 1.68 0.0005 0.022 Follistatin-like 1
    64856 VWA1 1.62 0.0002 0.016 Von Willebrand factor A domain containing 1
    10410 IFITM3 1.59 0.0003 0.018 Interferon-induced transmembrane protein 3
     | Show Table
    DownLoad: CSV
    Table 2.  List of top ten down-regulated genes.
    Entrez ID Symbols logFC P-Value Adjusted P-Value Name of gene
    28299 IGKV1-5 −3.30 0.00058 0.024 Immunoglobulin kappa variable 1−5
    28823 IGLV1-44 −3.28 0.00004 0.009 Immunoglobulin lambda variable 1−44
    3514 IGKC −3.23 0.00020 0.016 Immunoglobulin kappa constant
    28439 IGHV3-30 −3.19 0.00002 0.008 Immunoglobulin heavy variable 330
    3887 KRT81 −3.19 0.00006 0.009 Keratin 81
    3500 IGHG1 −3.07 0.00004 0.009 Immunoglobulin heavy constant gamma 1 (G1m marker)
    3507 IGHM −2.98 0.00017 0.016 Immunoglobulin heavy constant mu
    5450 POU2AF1 −2.91 0.00007 0.010 POU class 2 associating factor 1
    28923 IGKV2-24 −2.75 0.00094 0.029 Immunoglobulin kappa variable 2−24
    28831 IGLJ3 −2.74 0.00032 0.018 Immunoglobulin lambda joining 3
     | Show Table
    DownLoad: CSV

    3.2.   KEGG pathway enrichment analysis of significantly identified DEGs

    The functional enrichment analysis identifying the enriched up-regulated and down-regulated biological pathways that are associated with significant DEGs (Figure 1). GSEA pathway analysis revealed nine up-regulated pathways (focal adhesion, ECM-receptor interaction, pathways in cancer, small cell lung cancer, adherens junction, arrhythmogenic right ventricular cardiomyopathy (ARVC), pathogenic escherichia coli infection, cytokine-cytokine receptor interaction and proximal tubule bicarbonate reclamation) (Figure 1A) in CTECs. In addition, we also identified ten down-regulated pathways (protein export, cell adhesion molecules (CAMs), arrhythmogenic right ventricular cardiomyopathy (ARVC), N-Glycan biosynthesis, the intestinal immune network for IgA production, primary immunodeficiency, hypertrophic cardiomyopathy (HCM), ECM-receptor interaction, dilated cardiomyopathy and vasopressin-regulated water reabsorption) in CTECs (Figure 1B). Recently, it was stated that focal adhesion, ECM-receptor interaction, pathways in cancer, small cell lung cancer, adherens junction and cytokine-cytokine receptor interaction pathways are dysregulated in colon tumors stroma [7].

    Figure 1.  KEGG pathway enrichment analysis of significant DEGs. A. Significantly up-regulated pathways in colon tumor endothelial cells. B. Significantly down-regulated pathways in colon tumor endothelial cells. FDR: false discovery rate.
    DownLoad: Full-Size Img PowerPoint

    3.3.   Construction of PPI by using significant DEGs and identification of hub genes and functional clusters from the PPI

    Using all 362 DEGs, PPI networks were constructed using STRING software and hub genes were identified using the node explorer of network analyst software. Many significant DEGs are involved in the networks (Supplementary Table S3). The identification of hub genes explored that FN1, GAPDH, COL1A1, COL1A2, CTNNB1, LAMB1, LAMC1, SPARC, IGFBP3 and COL4A1 are top up-regulated hub genes and HSP90B1, PDIA6, ITGA4, DDOST, SDC1, CKAP4, CD38, MANF, ITGB7 and CCR2 are top down-regulated hub genes in CTECs respectively (Figure 2 and Table 3). Interestingly, we found that top-up-regulated (Table 1) SPARC also acts as a hub gene in the PPI network. FN1 suppressed apoptosis and promoted viability, invasion and migration of tumor cells in CRC [31]. It was showed that COL1A1 and COL1A2 are associated with colon cancer [32,33]. The high expression levels of CTNNB1 positively correlated with metastasis of colon cancer [34]. Deregulation of SDC1 contributes to cancer progression by promoting cell proliferation, metastasis, invasion and angiogenesis [35]. Altogether, it may be stated that CTECs-derived these hub genes may contribute to colon cancer pathogenesis.

    Figure 2.  The functional enrichment analysis of MCODE derived clusters. A. PPI interaction of Cluster 1. B. Enrichment of the KEGG pathways in Cluster 1. C. PPI interaction of Cluster 3. D. Enrichment of the 11 KEGG pathways in Cluster 3. E. Interaction of 23 nodes in Cluster 4. F. The enrichment of 11 KEGG pathways in Cluster 4.
    DownLoad: Full-Size Img PowerPoint
    Table 3.  The top ten up-regulated and top ten down-regulated hub genes are associated with PPI.
    Regulatory status Symbols Degree logFC Adjusted P-Value Name of gene
    Up-regulated FN1 65 1.14 0.020 Fibronectin 1
    GAPDH 58 0.73 0.010 Glyceraldehyde-3-phosphate dehydrogenase
    COL1A1 39 1.06 0.041 Collagen, type I, alpha 1
    COL1A2 32 1.01 0.032 Collagen, type I, alpha 2
    CTNNB1 32 0.79 0.007 Catenin (cadherin-associated protein), beta 1, 88kda
    LAMB1 31 1.10 0.021 Laminin, beta 1
    LAMC1 29 0.76 0.031 Laminin, gamma 1 (formerly LAMB2)
    SPARC 27 2.20 0.028 Secreted protein, acidic, cysteine-rich (osteonectin)
    IGFBP3 24 1.47 0.008 Insulin-like growth factor-binding protein 3
    COL4A1 24 1.10 0.017 Collagen, type IV, alpha 1
    Down-regulated HSP90B1 42 −0.83 0.008 Heat shock protein 90kda beta (Grp94), member 1
    PDIA6 32 −0.730 0.041 Protein disulfide isomerase family A, member 6
    ITGA4 25 −0.72 0.020 Integrin, alpha 4 (antigen CD49D, alpha 4 subunit of VLA-4 receptor)
    DDOST 22 −0.78 0.015 Dolichyl-diphosphooligosaccharide-protein glycosyltransferase
    SDC1 21 −1.09 0.009 Syndecan 1
    CKAP4 20 −0.74 0.020 Cytoskeleton-associated protein 4
    CD38 20 −1.48 0.012 CD38 molecule
    MANF 18 −0.67 0.018 Mesencephalic astrocyte-derived neurotrophic factor
    ITGB7 18 −1.89 0.017 Integrin, beta 7
    CCR2 17 −0.73112 0.022 Chemokine (C-C motif) receptor 2
     | Show Table
    DownLoad: CSV

    Moreover, MCODE identified 15 clusters from the original PPI networks. The description of MCODE derived clusters is illustrated in Table 4. The top significant cluster 1 contained 15 nodes and 105 edges (Figure 2 and Table 4). We identified the functional enrichment of KEGG pathways for all clusters by using the GSEA. Interestingly, we found that eight of the clusters out of 15 are associated with the enrichment of KEGG pathways. Gene set of Cluster 1 is related to the enrichment of 4 pathways: ECM-receptor interaction, small cell lung cancer, pathways in cancer and focal adhesion (Figure 2A, B). Cluster 3 (Figure 2C, D), Cluster 4 (Figure 2E, F) and Cluster 9 (Cell adhesion molecules and natural killer cell-mediated cytotoxicity) are associated with the enrichment of immune, stromal and cancer-associated pathways. Some of the enriched pathways in Cluster 3 included ECM-receptor interaction, focal adhesion, small cell lung cancer, cell adhesion molecules, the intestinal immune network for IgA production, pathways in cancer and hematopoietic cell lineage. In addition, some of the pathways enriched in Cluster 4 are the chemokine signaling pathway, adherens junction, cytokine-cytokine receptor interaction, focal adhesion, NOD-like receptor signaling pathway, pathways in cancer, leukocyte trans-endothelial migration and tight junction. Protein export is enriched in Cluster 2 and the B cell receptor signaling pathway is enriched in Cluster 5. Cluster 13 (Fatty acid metabolism and PPAR signaling pathway) and cluster 15 (Glycosphingolipid biosynthesis-Lacto and neolacto series and N-Glycan biosynthesis) are mainly associated with the enrichment of metabolic pathways. Uddin et al. also identified some of these pathways, including pathways in cancer, small cell lung cancer, ECM-receptor interaction, focal adhesion, natural killer cell-mediated cytotoxicity and cell adhesion molecules, are enriched in colon tumor stroma [7,9]. Altogether, it can be stated that this hub PPI from CTECs may contribute to colon carcinogenesis.

    Table 4.  MCODE identified 15 clusters from the PPI networks.
    Cluster Score Nodes Edges Node Ids
    1 15 15 105 FSTL1, IGFBP5, IGFBP3, APLP2, HSP90B1, PDIA6, FN1, PRSS23, TMEM132A, VWA1, CKAP4, LAMB1, IGFBP7, LAMC1, CYR61
    2 9.556 10 43 DDOST, SSR3, SEC61B, SSR4, SPCS2, SPCS1, SPCS3, TRAM1, OSTC, SEC11C
    3 9.2 11 46 ITGA8, ITGA5, COL4A2, LAMA4, COL12A1, COL4A1, SPARC, ITGB7, COL1A1, ITGA4, COL1A2
    4 5.364 23 59 CCR10, SERP1, OS9, DNAJB11, ABCG2, SDF2L1, HERPUD1, SEL1L, ACTG1, MET, GNG7, CCR2, CTNNB1, SDC1, XBP1, MMP1, DNAJB9, APLN, PNOC, CXCL1, TIMP2, CXCL2, SNAI1
    5 4.857 8 17 CD27, CD79A, UCHL1, IGLL5, SLAMF1, SLAMF7, CD81, CD38
    6 4.5 5 9 TUBA1B, CCT2, CCT3, DNAJA4, CCT4
    7 4.5 5 9 CLCA4, ZG16, SLC26A3, GUCA2A, GUCA2B
    8 4 5 8 DERL2, DERL3, ERLEC1, CRELD2, UBE2J1
    9 4 4 6 SPN, CD48, GZMB, CD4
    10 3 3 3 MZB1, TNFRSF17, POU2AF1
    11 3 3 3 FCGBP, CLCA1, RETNLB
    12 3 3 3 PLAC8, RNASET2, CECR1
    13 3 3 3 HMGCS2, ACSL1, ACADVL
    14 3 3 3 IFITM3, IFITM2, ISG20
    15 2.667 4 4 B4GALT3, ST3GAL6, MGAT1, MANEA
     | Show Table
    DownLoad: CSV

    3.4.   Hub genes are negatively associated with prognosis

    We considered the top ten up-regulated and top ten down-regulated genes from the original PPI network for survival analysis. We selected the TCGA-COAD database for screening OS and DFS. We got COL1A1, COL1A2, IGFBP3, SPARC and DDOST hub genes are significantly involved with patient survival time (Figure 3). We found that the higher expression level of up-regulated COL1A1, COL1A2, IGFBP3 and SPARC are worse for patient's DFS and COL1A2 is also associated with poor OS. In addition, the low expression level of down-regulated DDOST is more inferior for patient's OS and DFS. It showed that higher expressions of COL1A1 were related to poor survival in colon cancer patients [33]. COL1A2 gene is also associated with the prognosis of IIA stage colon cancer [32]. Stromal SPARC had a pro-metastatic impact in vitro and was a characteristic of aggressive tumors with a poor prognosis in CRC patients [26]. Collectively, hub genes of CTECs are prognostic markers in COAD and may be contributed to colon carcinogenesis.

    Figure 3.  Survival analysis of individual hub genes. Up-regulated COL1A1, COL1A2, IGFBP3 and SPARC and down-regulated DDOST have been associated with the poor prognosis in COAD.
    DownLoad: Full-Size Img PowerPoint

    3.5.   Comparisons of the expression levels of prognostic hub genes between colon cancer and normal tissue

    Interestingly, we found that all four up-regulated hub genes (COL1A1, COL1A2, IGFBP3 and SPARC) were consistently up-regulated in TCGA COAD samples versus normal samples (P < 0.05) (Figure 4). It indicates that CTECs may have a contribution to the COAD-derived transcriptomes in colon cancer. However, one of the down-regulated hub genes, DDOST, showed no significant expression differences between TCGA-COAD samples versus normal samples (P < 0.05), suggesting that this hub gene is expressed explicitly in CTECs.

    Figure 4.  Validation of the expression levels of prognostic hub genes between colon cancer and normal tissue (P < 0.05, |Log FC| > 0.585).
    DownLoad: Full-Size Img PowerPoint

    3.6.   Prognostic hub genes are correlated with immune infiltrations and associated with immune-inhibitory markers in colon TME

    Tumor-infiltrating lymphocytes are independent prognostic factors for better survival and sentinel lymph node status in cancers [36]. Genes highly expressed in the microenvironment are expected to negatively associate with tumor purity, while the opposite is expected for genes highly expressed in the tumor cells [13]. So, we assessed whether the expression of hub genes was correlated with immune infiltration levels in COAD. We investigated the correlations of prognostic hub genes (COL1A1, COL1A2, IGFBP3, SPARC and DDOST) with immune scores and stromal scores in COAD. Interestingly, we found that the expression levels of COL1A1, COL1A2, IGFBP3 and SPARC positively correlated with the immune scores (Figure 5A) and stromal scores (Figure 5B) (Spearman's correlation test, R > 0.30, P < 2.2e−16). This result indicated that the prognostic hub genes are associated with the regulation of the tumor microenvironment.

    Figure 5.  Up-regulated prognostic hub genes are associated with the regulation of the tumor microenvironment in COAD. The expression level of COL1A1, COL1A2, IGFBP3 and SPARC positively correlated with the immune scores (A) and stromal scores (B).
    DownLoad: Full-Size Img PowerPoint

    In addition, we identified the correlations between the expression of hub genes and the immune infiltration levels in COAD by using the TIMER tool. The results discovered that up-regulated COL1A1, COL1A2, IGFBP3 and SPARC expression have significant (P ≤ 0.05) correlations with tumor purity in COAD but down-regulated DDOST not correlated with tumor purity (Figure 6). In addition, COL1A1 expression has significant correlations with infiltrating levels of CD4+ T cells, macrophage, neutrophil and dendritic cells. We also found that a significant positive correlation between COL1A2, IGFBP3 and SPARC expression and infiltration of all five immune cells (CD4+ T cells, CD8+ T cells, Neutrophils, Macrophages and Dendritic cells) in COAD. Besides, we found significant negative correlations between DDOST expression and infiltration of all five immune cells (CD4+ T cells, CD8+ T cells, Neutrophils, Macrophages and Dendritic cells) (Figure 6). Recently it was stated that macrophages and neutrophils are associated with immunosuppressive inflammation to modulate anti-tumor immunity [37]. DCs can promote tumor metastasis by increasing Treg cells and reducing CD8+ T cell cytotoxicity [38]. These results suggest that the expression of COL1A1, COL1A2, IGFBP3, SPARC and DDOST has potential roles in tumor purity and immune cell infiltrations in colon cancer.

    Figure 6.  Hub genes are correlated with immune infiltration level and tumor purity. Expression of COL1A1, COL1A2, SPARC, IGFBP3 and DDOST are significantly negatively correlated to tumor purity and have significant positive correlations with infiltrating levels of CD4+ T cells, macrophages, neutrophils and dendritic cells in COAD. The correlation analyses were performed using TIMER [13]. RSEM: RNA-Seq by Expectation Maximization [39].
    DownLoad: Full-Size Img PowerPoint

    To elucidate the relationship between up-regulated hub genes COL1A1, COL1A2, IGFBP3 and SPARC and the diverse immune infiltrating cells, we find out the correlations between these genes and immune marker sets of various immune cells including monocytes, TAMs, M2 macrophages, Th1, Tregs and T cell exhaustion (Table 5). Surprisingly, we got that the hub genes COL1A1, COL1A2, IGFBP3 and SPARC are positively correlated (Pearson correlation test, P ≤ 0.001) with the immune markers of monocytes, TAMs, M2 macrophages, Th1, Tregs and T cell exhaustion. These findings suggest that the high expression level of hub genes plays an essential role in the infiltration of monocytes, TAMs, M2 macrophages, Th1, Tregs and T cell exhaustion in COAD. It was recently reported that LAYN acts as a prognostic biomarker for determining prognosis and immune infiltration in gastric and colon cancers [21]. Potent immunosuppressive T-regulatory cells (Tregs) are found in a vast array of tumor types and tumor-infiltrating Tregs are often associated with poor clinical outcomes. Tregs also promote cancer progression through their ability to limit anti-tumor immunity and promote angiogenesis [40]. Another marker, FOXP3, plays an important role in Treg cells which leads to the suppression of cytotoxic T cells attacking tumor cells [40]. TIM-3 is a crucial surface protein on exhausted T cells, a critical gene that regulating T cell exhaustion [41].

    Table 5.  Correlation analysis between survival-associated hub genes and markers of immune-inhibitory cells in TCGA-COAD data.
    Immune cell Gene markers SPARC COL1A1 COL1A2 IGFBP3
    R P R P R P R P
    Monocyte CD86 0.69 1.49E−42 0.65 2.71E−35 0.67 3.02E−39 0.55 2.39E−24
    CSF1R 0.71 1.22E−44 0.68 5.80E−41 0.72 1.60E−46 0.59 9.29E−28
    TAM CCL2 0.76 3.55E−56 0.69 1.00E−41 0.72 2.51E−47 0.58 2.49E−27
    CD68 0.49 8.62E−19 0.48 5.34E−18 0.49 1.16E−18 0.46 1.25E−16
    IL10 0.57 1.86E−26 0.52 4.70E−21 0.54 2.65E−23 0.42 1.87E−13
    M2 macrophage CD163 0.71 4.72E−46 0.71 4.11E−46 0.73 7.07E−49 0.52 1.88E−21
    VSIG4 0.73 8.85E−50 0.70 2.16E−43 0.71 6.58E−46 0.56 7.05E−25
    MS4A4A 0.70 2.77E−44 0.65 1.77E−35 0.67 7.62E−39 0.54 6.22E−23
    Th1 T-bet (TBX21) 0.36 3.16E−10 0.38 1.66E−11 0.38 3.46E−11 0.37 8.76E−11
    IFN-γ (IFNG) 0.18 0.001 0.21 0.00040 0.20 0.000834 0.16 0.00589
    TNF-α (TNF) 0.40 3.37E−12 0.38 1.65E−11 0.40 3.16E−12 0.21 0.00030
    Treg FOXP3 0.56 6.11E−25 0.55 3.52E−24 0.58 1.49E−27 0.50 1.01E−19
    CCR8 0.56 1.13E−24 0.54 4.14E−23 0.58 2.02E−27 0.48 1.23E−17
    TGFβ (TGFB1) 0.74 1.08E−50 0.73 1.99E−49 0.75 1.61E−52 0.62 6.64E−32
    T cell exhaustion PD-1 (PDCD1) 0.32 3.27E−08 0.32 2.35E−08 0.33 9.61E−09 0.35 8.10E−10
    CTLA4 0.42 1.40E−13 0.42 1.10E−13 0.44 7.38E−15 0.40 3.58E−12
    LAG3 0.25 1.56E−05 0.29 6.10E−07 0.28 1.94E−06 0.27 3.07E−06
    TIM-3(HAVCR2) 0.70 1.48E−43 0.67 2.34E−38 0.69 3.05E−41 0.54 1.58E−23
    TIGIT 0.39 4.84E−12 0.41 7.99E−13 0.42 5.46E−14 0.40 2.49E−12
    CXCL13 0.35 1.82E−09 0.36 5.46E−10 0.35 8.72E−10 0.31 1.34E−07
    LAYN 0.89 2.42E−97 0.79 1.39E−63 0.83 4.05E−75 0.67 2.35E−38
    *Note: R is Pearson correlation and P is p-value in Pearson's correlation test.
     | Show Table
    DownLoad: CSV

    Since the elevated expression of prognostic hub genes were positively associated with the immune inhibitory markers PD-1 and TGFB1 (Table 5), we expect the expression levels of PD-L1 (CD274), PD-L2 (PDCD1LG2) and TGFBR1 could be positively associated with the prognosis-associated hub genes. Interestingly, we found that PDL1 (CD274), PDL2 (PDCD1LG2) and TGFBR1 are moderate to highly correlated with all four-prognosis related highly expressed hub genes: COL1A1, COL1A2, SPARC and IGFBP3 (Figure 7). T cell immunity recovered by PD-1/PD-L1 immune checkpoint blockade has been demonstrated to be a promising cancer therapeutic strategy [42]. The expression of PD-L2 was observed in tumors, stroma and endothelial cells and PD-L2 expression is relevant to anti-PD-1 therapy in cancer [43]. TGFBR1 is one of the receptors for TGF-β ligands. The TGF-β signaling pathway is crucially associated with stimulation, induction and maintenance of EMT in cancers [44]. TGFBR1 is up-regulated in multiple malignancies and dysregulation of TGFBR1 leads to tumorigenesis by controlling cellular signaling, which in turn is associated with colorectal cancer risk [45]. Altogether, it suggests that the prognostic associated hub genes may regulate the immune-suppressive activities of TME through the interactions with the immune marker in colon cancer.

    Figure 7.  Prognostic hub genes are significantly positively correlated with immune suppressive PD-L1, PD-L2 and TGFBR1. Up-regulated four hub genes (SPARC, COL1A2, COL1A2 and IGFBP3) are significantly correlated with three prominent immune-inhibitory markers (PD-L1, PD-L2 and TGFBR1). The correlation analyses were performed using R software. The Pearson's correlation test p-values (P) and correlation coefficients (R) are shown in the figure.
    DownLoad: Full-Size Img PowerPoint

    4.   Discussion

    TECs are the substantial component of tumor stroma in TME and these cells are associated with tumor malignancies, progression and metastasis [5]. This present study conducted a differential expression analysis using gene expression profiling data from the GEO database. We identified 362 DEGs in CTECs, including 117 up-regulated genes (SPARC, COL4A2, CXCL1 and VWF) and 245 down-regulated genes. Interestingly, we found that many immunological genes are down-regulated in CTECs (Table 2). Subsequent pathway enrichment analysis revealed that up-regulated DEGs were significantly enriched with cancer, cellular development and immune regulation. Down-regulated DEGs were enriched considerably with mostly immunodeficiency and metabolism-related pathways (Figure 1). These results revealed the abnormal cellular growth, immune regulatory, cancerous and metabolism-associated pathways in CTECs.

    Next, we employed DEGs to construct a PPI network and extracted significant clusters from the original PPI network (Figure 2). Finally, we identified hub genes (degree > 10) that are dysregulated in CTECs. We identified CTECs-derived five hub genes (COL1A1, COL1A2, SPARC, IGFBP3 and DDOST) whose expression was significantly associated with the poor prognosis in TCGA-COAD data (Figure 3). These hub genes are mainly involved in protein digestion and absorption (COL1A1 and COL1A2), cellular signaling (SPARC and IGFBP3) and enzymatic action of metabolism (DDOST), suggesting that the deregulation of these cellular functions in CTECs may contribute to the altered prognosis in colon cancer. In addition, the expression analysis of these prognostic hub genes in colon cancer was further evaluated using the GEPIA database [12]. We found that four hub genes (COL1A1, COL1A2, SPARC and IGFBP3) are also significantly up-regulated in TCGA-COAD samples. Still, another down-regulated hub gene (DDOST) is not considerably down-regulated in TCGA-COAD samples (Figure 4). It indicates that CTECs-associated gene signature substantially contributed to colon carcinogenesis.

    Furthermore, our study showed that the immune infiltration levels and diverse immune marker sets are correlated with the expression levels of COL1A1, COL1A2, SPARC and IGFBP3 hub genes. Our analysis demonstrated that a significant positive correlation between the COL1A1, COL1A2, SPARC and IGFBP3 expression levels and infiltration level of CD4+ T cells, macrophages, neutrophils and DCs in COAD (Figure 6). These results are suggesting that the potential immunological roles of hub genes in colon TME. In addition, our study indicated that COL1A1, COL1A2, SPARC and IGFBP3 could activate Tregs and induce T cell exhaustion. The increase in up-regulated four hub genes expression positively correlates with Treg and T cell exhaustion markers (Table 5). Other markers of monocytes, TAM, M2 macrophage, Th1, are also positively correlated with the expression of these hub genes (Table 5). Some other crucial immune inhibitory markers, PD-L1, PD-L2 and TGFBR1, are significantly correlated with the expression level of COL1A1, COL1A2, SPARC and IGFBP3 (Figure 7). Altogether, the prognostic hub genes are associated with immunosuppressive colon TME.

    Since our analysis identified numerous CTECs-derived transcriptomes that are critically associated with colon cancer, we speculated that the endothelial cells might contribute to the other vascular pathologies, including the central nervous system (CNS) and other diseases. It was stated that cerebral endothelial cells play an active role in the pathogenesis of CNS inflammatory diseases [46]. Endothelial cells are associated with the leukocyte migration in CNS and regulating the leukocyte-endothelial cell interactions and the crosstalk between endothelial cells and glial cells or platelets in CNS [46]. The major vascular anomalies of the nervous system included cerebral cavernous malformations (CCMs) and arteriovenous malformations (AVMs) [47,48]. These CNS-associated vascular diseases are characterized by genetic alterations, such as gene polymorphisms and mutations [47,48,49,50,51]. For example, germline mutation enrichment in pathways controlling the endothelial cell homeostasis in patients with brain arteriovenous malformation [48]. Altogether, our findings may provide insights to find the roles of endothelial cells in diseases, including cancer, CNS-associated disease and other vascular pathologies.

    This study identified numerous deregulated transcriptomes in the CTECs that could be used as biomarkers for the diagnosis and prognosis of colon cancer and may provide therapeutic targets for colon cancer. However, to translate these findings into clinical application, further experimental and clinical validation would be necessary.

    5.   Conclusions

    In summary, we identified potential CTECs-derived significantly deregulated transcriptomes that are involving with the pathogenesis, prognosis and immune inhibition within the tumor microenvironment of colon cancer. This study reveals a potential regulatory mechanism of CTECs in the tumor microenvironment of colon cancer and may contribute to revealing CTECs-colon cancer cellular cross-talk.

    Conflicts of interest

    The authors declare no conflict of interest.

    Acknowledgments

    We thank all laboratory staff and technicians for their participation in this study. The present study was supported by the project from Xinjiang Health Poverty Alleviation Work Appropriate Technology Promotion (Grant no. SYTG-Y202035).

    Author contributions

    Jie Wang and Md. Nazim Uddin contributed equally to this work. Jie Wang and Md. Nazim Uddin conceived the research, designed analysis strategies. Rehana Akter wrote the manuscript and Yun Wu conceived the study and wrote the manuscript. All the authors read and approved the final manuscript.

    [1] Pimentel D (2012) World overpopulation. Environ Dev Sustain 14: 151. doi: 10.1007/s10668-011-9336-2
    [2] Tilman D, Balzer C, Hill J, et al. (2011) Global food demand and the sustainable intensification of agriculture. Proc Natl Acad Sci USA 108: 20260–20264. doi: 10.1073/pnas.1116437108
    [3] Béné C, Barange M, Subasinghe R, et al. (2015) Feeding 9 billion by 2050-Putting fish back on the menu. Food Sec 7: 261. doi: 10.1007/s12571-015-0427-z
    [4] Khush G (2001) Green revolution: the way forward. Nat Rev Genet 2: 815–822.
    [5] Araus J, Li J, Parry M, et al. (2014) Phenotyping and other breeding approaches for a New Green Revolution. J Integr Plant Biol 56: 422–424. doi: 10.1111/jipb.12202
    [6] Garcia-Fraile P, Carro L, Robledo M, et al. (2012) Rhizobium promotes non-legumes growth and quality in several production steps: towards a biofertilization of edible raw vegetables healthy for humans. PLoS One 7: e38122. doi: 10.1371/journal.pone.0038122
    [7] Flores-Felix JD, Silva LR, Rivera LP, et al. (2015) Plants probiotics as a tool to produce highly functional fruits: the case of Phyllobacterium and vitamin C in strawberries. PLoS One 10: e0122281. doi: 10.1371/journal.pone.0122281
    [8] Haas D, Keel C (2003) Regulation of antibiotic production in root-colonizing Pseudomonas spp. and relevance for biological control of plant disease. Ann Rev Phytopathol 41: 117–153.
    [9] Kloepper J, Schrot M (1978) Plant growth-promoting rhizobacteria on radishes. Proceedings of the 4th International Conference on Plant Pathogenic Bacteria 2: 879–882.
    [10] Gray EJ, Smith DL (2005) Intracellular and extracellular PGPR: commonalities and distinctions in the plant-bacterium signaling processes. Soil Biol Biochem 37: 395–412. doi: 10.1016/j.soilbio.2004.08.030
    [11] Hardoim PR, van Overbeek LS, Berg G, et al. (2015) The hidden world within plants: ecological and evolucionary considerations for defining functioning of microbial endophytes. Microbiol Mol Biol Rev 79: 293–320. doi: 10.1128/MMBR.00050-14
    [12] Brewin NJ (1991) Development of the legume root nodule. Ann Rev Cell Biol 7: 191–226. doi: 10.1146/annurev.cb.07.110191.001203
    [13] Suzaki T, Kawaguchi M (2014) Root nodulation: a developmental program involving cell fate conversion triggered by symbiotic bacterial infection. Curr Opin Plant Biol 21: 16–22. doi: 10.1016/j.pbi.2014.06.002
    [14] Pawlowski K, Demchenko KN (2012) The diversity of actinorhizal symbiosis. Protoplasma 249: 967–979. doi: 10.1007/s00709-012-0388-4
    [15] Vessey JK, Pawlowski K, Bergman B (2005) Root-based N2-fixing symbioses: Legumes, actinorhizal plants, Parasponia sp. and cycads. Plant Soil 274: 51–78. doi: 10.1007/s11104-005-5881-5
    [16] Khalid A, Arshad M, Shaharoona B, et al. (2009) Plant Growth Promoting Rhizobacteria and Sustainable Agriculture, In: Microbial Strategies for Crop Improvement, Berlin: Springer, 133–160.
    [17] Bhattacharyya PN, Jha DK (2012) Plant growth-promoting rhizobacteria (PGPR): emergence in agriculture. World J Microbiol Biotechnol 28: 1327–1350. doi: 10.1007/s11274-011-0979-9
    [18] García-Fraile P, Menéndez E, Rivas R (2015) Role of bacterial biofertilizers in agriculture and forestry. AIMS Bioeng 2: 183–205. doi: 10.3934/bioeng.2015.3.183
    [19] Vejan P, Abdullah R, Khadiran T, et al. (2016) Role of plant growth promoting rhizobacteria in agricultural sustainability-a review. Molecules 21: 573. doi: 10.3390/molecules21050573
    [20] Malusá E, Vassilev N (2014) A contribution to set a legal framework for biofertilisers. Appl Microbiol Biotechnol 98: 6599–6607. doi: 10.1007/s00253-014-5828-y
    [21] Reinhold-Hurek B, Hurek T (1998) Interactions of gramineous plants with Azoarcus spp. and other Diazotrophs: identification, localization, and perspectives to study their function. Crit Rev Plant Sci 17: 29–54.
    [22] Sabry SRS, Saleh SA, Batchelor CA, et al. (1997) Endophytic establishment of Azorhizobium caulinodans in wheat. Proc Biol Sci 264: 341–346. doi: 10.1098/rspb.1997.0049
    [23] Tejera N, Lluch C, Martínez-Toledo MV, et al. (2005) Isolation and characterization of Azotobacter and Azospirillum strains from the sugarcane rhizosphere. Plant Soil 270: 223–232. doi: 10.1007/s11104-004-1522-7
    [24] Yadegari M, Rahmani HA, Noormohammadi G, et al. (2010) Plant growth promoting rhizobacteria increase growth, yield and nitrogen fixation in Phaseolus vulgaris. J Plant Nutr 33: 1733–1743. doi: 10.1080/01904167.2010.503776
    [25] Isawa T, Yasuda M, Awazaki H, et al. (2010) Azospirillum sp. strain B510 enhances rice growth and yield. Microbes Environ 25: 58–61.
    [26] Hungria M, Nogueira MA, Araujo RS (2013) Co-inoculation of soybeans and common beans with rhizobia and azospirilla: strategies to improve sustainability. Biol Fert Soils 49: 791–801. doi: 10.1007/s00374-012-0771-5
    [27] Sahoo RK, Ansari MW, Pradhan M, et al. (2014) Phenotypic and molecular characterization of native Azospirillum strains from rice fields to improve crop productivity. Protoplasma 251: 943–953. doi: 10.1007/s00709-013-0607-7
    [28] Ramakrishnan K, Selvakumar G (2012) Effect of biofertilizers on enhancement of growth and yield on Tomato (Lycopersicum esculentum Mill.) Int. J Res Bot 2: 20–23.
    [29] Wani SA, Chand S, Ali T (2013) Potential use of Azotobacter chroococcum in crop production: an overview. Curr Agri Res J 1: 35–38. doi: 10.12944/CARJ.1.1.04
    [30] Sahoo RK, Ansari MW, Dangar TK, et al. (2014) Phenotypic and molecular characterisation of efficient nitrogen-fixing Azotobacter strains from rice fields for crop improvement. Protoplasma 251: 511–523. doi: 10.1007/s00709-013-0547-2
    [31] Beneduzi A, Peres D, Vargas LK, et al. (2008) Evaluation of genetic diversity and plant growth promoting activities of nitrogen-fixing bacilli isolated from rice fields in South Brazil. App Soil Ecol 39: 311–320. doi: 10.1016/j.apsoil.2008.01.006
    [32] Habibi S, Djedidi S, Prongjunthuek K, et al. (2014) Physiological and genetic characterization of rice nitrogen fixer PGPR isolated from rhizosphere soils of different crops. Plant Soil 379: 51–66. doi: 10.1007/s11104-014-2035-7
    [33] Rana A, Saharan B, Joshi M, et al. (2011) Identification of multi-trait PGPR isolates and evaluating their potential as inoculants for wheat. Ann Microbiol 61: 893–900. doi: 10.1007/s13213-011-0211-z
    [34] Kao CM, Chen SC, Chen YS, et al. (2003) Detection of Burkholderia pseudomallei in rice fields with PCR-based technique. Folia Microbiol (Praha) 48: 521–552. doi: 10.1007/BF02931334
    [35] Govindarajan M, Balandreau J, Kwon SW, et al. (2007) Effects of the inoculation of Burkholderia vietnamensis and related endophytic diazotrophic bacteria on grain yield of rice. Microb Ecol 55: 21–37.
    [36] Berge O, Heulin T, Achouak W, et al. (1991) Rahnella aquatilis, a nitrogen-fixing enteric bacterium associated with the rhizosphere of wheat and maize. Can J Microbiol 37: 195–203. doi: 10.1139/m91-030
    [37] Taulé C, Mareque C, Barlocco C, et al. (2012) The contribution of nitrogen fixation to sugarcane (Saccharum officinarum L.), and the identification and characterization of part of the associated diazotrophic bacterial community. Plant Soil 356: 35–49.
    [38] Simonet P, Normand P, Moiroud A, et al. (1990) Identification of Frankia strains in nodules by hybridization of polymerase chain reaction products with strain-specific oligonucleotide probes. Arch Microb 153: 235–240. doi: 10.1007/BF00249074
    [39] Muñoz-Rojas J, Caballero-Mellado J (2003) Population dynamics of Gluconacetobacter diazotrophicus in sugarcane cultivars and its effect on plant growth. Microb Ecol 46: 454–464. doi: 10.1007/s00248-003-0110-3
    [40] Elbeltagy A, Nishioka K, Sato T, et al. (2001) Endophytic colonization and in planta nitrogen fixation by a Herbaspirillum sp. isolated from wild rice species. Appl Environ Microbiol 67: 5285–5293.
    [41] Valverde A, Velazquez E, Gutierrez C, et al. (2003) Herbaspirillum lusitanum sp. nov., a novel nitrogen-fixing bacterium associated with root nodules of Phaseolus vulgaris. Int J Syst Evol Microbiol 53: 1979–1983.
    [42] Alves GC, Videira SS, Urquiaga S, et al. (2015) Differential plant growth promotion and nitrogen fixation in two genotypes of maize by several Herbaspirillum inoculants. Plant Soil 387: 307–321. doi: 10.1007/s11104-014-2295-2
    [43] Puri A, Padda KP, Chanway CP (2016) Evidence of nitrogen fixation and growth promotion in canola (Brassica napus L.) by an endophytic diazotroph Paenibacillus polymyxa P2b-2R. Biol Fert Soils 52: 119–125.
    [44] Peix A, Ramírez-Bahena MH, Velázquez E, et al. (2015) Bacterial associations with legumes. Crit Rev Plant Sci 34: 17–42. doi: 10.1080/07352689.2014.897899
    [45] Aloni R, Aloni E, Langhans M, et al. (2006) Role of cytokinin and auxin in shaping root architecture: regulating vascular differentiation, lateral root initiation, root apical dominance and root gravitropism. Ann Bot 97: 883–893. doi: 10.1093/aob/mcl027
    [46] Ahmed A, Hasnain S (2010) Auxin producing Bacillus sp.: Auxin quantification and effect on the growth Solanum tuberosum. Pure Appl Chem 82: 313–319.
    [47] Sokolova MG, Akimova GP, Vaishlya OB (2011) Effect of phytohormones synthesized by rhizosphere bacteria on plants. App Biochem Microbiol 47: 274–278. doi: 10.1134/S0003683811030148
    [48] Liu F, Xing S, Ma H, et al. (2013) Cytokinin-producing, plant growth-promoting rhizobacteria that confer resistance to drought stress in Platycladus orientalis container seedlings. Appl Microbiol Biotechnol 97: 9155–9164. doi: 10.1007/s00253-013-5193-2
    [49] Ortiz-Castro R, Valencia-Cantero E, López-Bucio J (2008) Plant growth promotion by Bacillus megaterium involves cytokinin signaling. Plant Signal Behav 3: 263–265. doi: 10.4161/psb.3.4.5204
    [50] Kang SM, Khan AL, Waqas M, et al. (2015) Gibberellin-producing Serratia nematodiphila PEJ1011 ameliorates low temperature stress in Capsicum annuum L. Eur J Soil Biol 68: 85–93. doi: 10.1016/j.ejsobi.2015.02.005
    [51] Asaf S, Khan MA, Khan AL, et al. (2017) Bacterial endophytes from arid land plants regulate endogenous hormone content and promote growth in crop plants: an example of Sphingomonas sp. and Serratia marcescens. J Plant Interact 12: 31–38. doi: 10.1080/17429145.2016.1274060
    [52] Suarez C, Cardinale M, Ratering S, et al. (2015) Plant growth-promoting effects of Hartmannibacter diazotrophicus on summer barley (Hordeum vulgare L.) under salt stress. Appl Soil Ecol 95: 23–30.
    [53] Bent E, Tuzun S, Chanway CP, et al. (2001) Alterations in plant growth and in root hormone levels of lodgepole pines inoculated with rhizobacteria. Can J Microbiol 47: 793–800. doi: 10.1139/w01-080
    [54] Bakaeva MD, Chetverikov SP, Korshunova TY, et al. (2017) The new bacterial strain Paenibacillus sp. IB-1: A producer of exopolysaccharide and biologically active substances with phytohormonal and antifungal activities. App Biochem Microbiol 53: 201–208.
    [55] Galland M, Gamet L, Varoquaux F, et al. (2012) The ethylene pathway contributes to root hair elongation induced by the beneficial bacteria Phyllobacterium brassicacearum STM196. Plant Sci 190: 74–81. doi: 10.1016/j.plantsci.2012.03.008
    [56] Shaharoona B, Naveed M, Arshad M, et al. (2008) Fertilizer-dependent efficiency of Pseudomonas for improving growth, yield, and nutrient use efficiency of wheat (Triticum aestivum L.). Appl Microbiol Biotechnol 79: 147–155. doi: 10.1007/s00253-008-1419-0
    [57] Ahmad M, Zahir ZA, Khalid M, et al. (2013) Efficacy of Rhizobium and Pseudomonas strains to improve physiology, ionic balance and quality of mung bean under salt-affected conditions on farmer's fields. Plant Physiol Biochem 63: 170–176. doi: 10.1016/j.plaphy.2012.11.024
    [58] Flores-Felix JD, Menendez E, Rivera LP, et al. (2013) Use of Rhizobium leguminosarum as a potential biofertilizer for Lactuca sativa and Daucus carota crops. J Plant Nutr Soil Sci 176: 876–882. doi: 10.1002/jpln.201300116
    [59] Brígido C, Nascimento FX, Duan J, et al. (2013) Expression of an exogenous 1-aminocyclopropane-1-carboxylate deaminase gene in Mesorhizobium spp. reduces the negative effects of salt stress in chickpea. FEMS Microbiol Lett 349: 46–53.
    [60] Flores-Félix JD, Marcos-García M, Silva LR, et al. (2015) Rhizobium as plant probiotic for strawberry production under microcosm conditions. Symbiosis 67: 25–32. doi: 10.1007/s13199-015-0373-8
    [61] Menéndez E, Escribano-Viana R, Flores-Félix JD, et al. (2016) Rhizobial biofertilizers for ornamental plants, In: Biological Nitrogen Fixation and Beneficial Plant-Microbe Interaction, Springer International Publishing, 13–21.
    [62] Brígido C, Glick BR, Oliveira S (2016) Survey of plant growth-promoting mechanisms in native Portuguese Chickpea Mesorhizobium isolates. Microb Ecol 73: 900–915.
    [63] Kong Z, Glick BR, Duan J, et al. (2015) Effects of 1-aminocyclopropane-1-carboxylate (ACC) deaminase-overproducing Sinorhizobium meliloti on plant growth and copper tolerance of Medicago lupulina. Plant Soil 391: 383–398. doi: 10.1007/s11104-015-2434-4
    [64] Khan AL, Waqas M, Kang SM, et al. (2014) Bacterial endophyte Sphingomonas sp. LK11 produces gibberellins and IAA and promotes tomato plant growth. J Microbiol 52: 689–695.
    [65] Verma VC, Singh SK, Prakash S (2011) Bio-control and plant growth promotion potential of siderophore producing endophytic Streptomyces from Azadirachta indica A. Juss. J Basic Microb 51: 550–556. doi: 10.1002/jobm.201000155
    [66] Boudjeko T, Tchinda RAM, Zitouni M, et al. (2017) Streptomyces cameroonensis sp. nov., a Geldanamycin producer that promotes Theobroma cacao growth. Microbes Environ 32: 24–31.
    [67] Estrada GA, Baldani VLD, de Oliveira DM, et al. (2013) Selection of phosphate-solubilizing diazotrophic Herbaspirillum and Burkholderia strains and their effect on rice crop yield and nutrient uptake. Plant Soil 369: 115–129. doi: 10.1007/s11104-012-1550-7
    [68] Jog R, Pandya M, Nareshkumar G, et al. (2014) Mechanism of phosphate solubilization and antifungal activity of Streptomyces spp. isolated from wheat roots and rhizosphere and their application in improving plant growth. Microbiology 160: 778–788.
    [69] Sheng XF (2005) Growth promotion and increased potassium uptake of cotton and rape by a potassium releasing strain of Bacillus edaphicus. Soil Biol Biochem 37: 1918–1922. doi: 10.1016/j.soilbio.2005.02.026
    [70] Han HS, Lee KD (2005) Phosphate and potassium solubilizing bacteria effect on mineral uptake, soil availability and growth of eggplant. Res J Agric Biol Sci 1: 176–180.
    [71] Han HS, Supanjani S, Lee KD (2006) Effect of co-inoculation with phosphate and potassium solubilizing bacteria on mineral uptake and growth of pepper and cucumber. Plant Soil Environ 52: 130–136.
    [72] Sugumaran P, Janarthanam B (2007) Solubilization of potassium containing minerals by bacteria and their effect on plant growth. World J Agric Sci 3: 350–355.
    [73] Singh G, Biswas DR, Marwaha TS (2010) Mobilization of potassium from waste mica by plant growth promoting rhizobacteria and its assimilation by maize (Zea mays) and wheat (Triticum aestivum L.): a hydroponics study under phytotron growth chamber. J Plant Nutr 33: 1236–1251.
    [74] Velázquez E, Silva LR, Ramírez-Bahena MH, et al. (2016) Diversity of potassium-solubilizing microorganisms and their interactions with plants, In: Potassium Solubilizing Microorganisms for Sustainable Agriculture, Springer India, 99–110.
    [75] Zhang C, Kong F (2014) Isolation and identification of potassium-solubilizing bacteria from tobacco rhizospheric soil and their effect on tobacco plants. Appl Soil Ecol 82: 18–25. doi: 10.1016/j.apsoil.2014.05.002
    [76] Subhashini DV (2015) Growth promotion and increased potassium uptake of tobacco by potassium-mobilizing bacterium Frateuria aurantia grown at different potassium levels in vertisols. Commun Soil Sci Plant Anal 46: 210–220. doi: 10.1080/00103624.2014.967860
    [77] Bagyalakshmi B, Ponmurugan P, Marimuthu S (2012) Influence of potassium solubilizing bacteria on crop productivity and quality of tea (Camellia sinensis). Afr J Agric Res 7: 4250–4259.
    [78] Beneduzi A, Ambrosini A, Passaglia LM (2012) Plant growth-promoting rhizobacteria (PGPR): Their potential as antagonists and biocontrol agents. Genet Mol Biol 35: 1044–1051. doi: 10.1590/S1415-47572012000600020
    [79] Radzki W, Gutierrez Manero FJ, Algar E, et al. (2013) Bacterial siderophores efficiently provide iron to iron-starved tomato plants in hydroponics culture. Anton Van Leeuw 104: 321–330. doi: 10.1007/s10482-013-9954-9
    [80] Ghavami N, Alikhani HA, Pourbabaei AA, et al. (2016) Effects of two new siderophore producing rhizobacteria on growth and iron content of maize and canola plants. J Plant Nutr 40: 736–746.
    [81] Egamberdiyeva D (2007) The effect of plant growth promoting bacteria on growth and nutrient uptake of maize in two different soils. Appl Soil Ecol 36: 184–189. doi: 10.1016/j.apsoil.2007.02.005
    [82] El-Akhal MR, Rincon A, Coba de la Pena T, et al. (2013) Effects of salt stress and rhizobial inoculation on growth and nitrogen fixation of three peanut cultivars. Plant Biol (Stuttg) 15: 415–421. doi: 10.1111/j.1438-8677.2012.00634.x
    [83] Lee SW, Lee SH, Balaraju K, et al. (2014) Growth promotion and induced disease suppression of four vegetable crops by a selected plant growth-promoting rhizobacteria (PGPR) strain Bacillus subtilis 21-1 under two different soil conditions. Acta Physiol Plant 36: 1353–1362. doi: 10.1007/s11738-014-1514-z
    [84] Sivasakthi S, Usharani G, Saranraj P (2014) Biocontrol potentiality of plant growth promoting bacteria (PGPR)-Pseudomonas fluorescens and Bacillus subtilis: A review. Afr J Agric Res 9: 1265–1277.
    [85] Li H, Ding X, Wang C, et al. (2016) Control of Tomato yellow leaf curl virus disease by Enterobacter asburiae BQ9 as a result of priming plant resistance in tomatoes. Turk J Biol 40: 150–159. doi: 10.3906/biy-1502-12
    [86] Singh RP, Jha PN (2016) The multifarious PGPR Serratia marcescens CDP-13 augments induced systemic resistance and enhanced salinity tolerance of wheat (Triticum aestivum L.). PloS One 11: e0155026. doi: 10.1371/journal.pone.0155026
    [87] Calvo J, Calvente V, de Orellano ME, et al. (2007) Biological control of postharvest spoilage caused by Penicillium expansum and Botrytis cinerea in apple by using the bacterium Rahnella aquatilis. Int J Food Microbiol 113: 251–257. doi: 10.1016/j.ijfoodmicro.2006.07.003
    [88] Allard S, Enurah A, Strain E, et al. (2014) In situ evaluation of Paenibacillus alvei in reducing carriage of Salmonella enterica serovar Newport on whole tomato plants. App Environ Microbiol 80: 3842–3849. doi: 10.1128/AEM.00835-14
    [89] Xu S, Kim BS (2016) Evaluation of Paenibacillus polymyxa strain SC09-21 for biocontrol of Phytophthora blight and growth stimulation in pepper plants. Trop Plant Pathol 41: 162–168. doi: 10.1007/s40858-016-0077-5
    [90] Yao L, Wu Z, Zheng Y, et al. (2010) Growth promotion and protection against salt stress by Pseudomonas putida Rs-198 on cotton. Eur J Soil Biol 46: 49–54. doi: 10.1016/j.ejsobi.2009.11.002
    [91] Kumar H, Bajpai VK, Dubey RC, et al. (2010) Wilt disease management and enhancement of growth and yield of Cajanus cajan (L) var. Manak by bacterial combinations amended with chemical fertilizer. Crop Protect 29: 591–598.
    [92] Pastor N, Masciarelli O, Fischer S, et al. (2016) Potential of Pseudomonas putida PCI2 for the protection of tomato plants against fungal pathogens. Curr Microbiol 73: 346–353. doi: 10.1007/s00284-016-1068-y
    [93] Raymond J, Siefert JL, Staples CR, et al. (2004) The natural history of nitrogen fixation. Mol Biol Evol 21: 541–554. doi: 10.1093/molbev/msh047
    [94] Grady EN, MacDonald J, Liu L, et al. (2016) Current knowledge and perspectives of Paenibacillus: a review. Microb Cell Fact 15: 203. doi: 10.1186/s12934-016-0603-7
    [95] Borriss R (2015) Bacillus, a plant-beneficial bacterium, In: Principles of Plant-Microbe Interactions, Springer International Publishing, 379–391.
    [96] Hurek T, Reinhold-Hurek B (2003) Azoarcus sp. strain BH72 as a model for nitrogen-fixing grass endophytes. J Biotechnol 106: 169–178.
    [97] Kao CM, Chen SC, Chen YS, et al. (2003) Detection of Burkholderia pseudomallei in rice fields with PCR-based technique. Folia Microbiol (Praha) 48: 521–552. doi: 10.1007/BF02931334
    [98] Tan Z, Hurek T, Vinuesa P, et al. (2001) Specific detection of Bradyrhizobium and Rhizobium strains colonizing rice (Oryza sativa) roots by 16S-23S ribosomal DNA intergenic spacer-targeted PCR. Appl Environ Microbiol 67: 3655–3664. doi: 10.1128/AEM.67.8.3655-3664.2001
    [99] Yanni YG, Rizk RY, El-Fattah FKA, et al. (2001) The beneficial plant growth-promoting association of Rhizobium leguminosarum bv. trifolii with rice roots. Aust J Plant Physiol 28: 845–870.
    [100] Yanni YG, Dazzo FB, Squartini A, et al. (2016) Assessment of the natural endophytic association between Rhizobium and wheat and its ability to increase wheat production in the Nile delta. Plant Soil 407: 367–383. doi: 10.1007/s11104-016-2895-0
    [101] Moulin L, Munive A, Dreyfus B, et al. (2001) Nodulation of legumes by members of the beta-subclass of Proteobacteria. Nature 411: 948–950. doi: 10.1038/35082070
    [102] Oldroyd GE, Downie JA (2008) Coordinating nodule morphogenesis with rhizobial infection in legumes. Annu Rev Plant Biol 59: 519–546. doi: 10.1146/annurev.arplant.59.032607.092839
    [103] Santi C, Bogusz D, Franche C (2013) Biological nitrogen fixation in non-legume plants. Ann Bot 111: 743–767. doi: 10.1093/aob/mct048
    [104] Spaepen S (2015) Plant Hormones Produced by Microbes, In: Lugtenberg B, Editor, Principles of Plant-Microbe Interactions, Switzerland: Springer International Publishing, 247–256.
    [105] Costacurta A, Vanderleyden J (1995) Synthesis of phytohormones by plant-associated bacteria. Crit Rev Microbiol 21: 1–18. doi: 10.3109/10408419509113531
    [106] Trewavas A (1981) How do plant growth substances work? Plant Cell Environ 4: 203–228. doi: 10.1111/j.1365-3040.1981.tb01048.x
    [107] Spaepen S, Vanderleyden J, Remans R (2007) Indole-3-acetic acid in microbial and microorganism-plant signaling. FEMS Microbiol Rev 31: 425–448. doi: 10.1111/j.1574-6976.2007.00072.x
    [108] Hayat R, Ali S, Amara U, et al. (2010) Soil beneficial bacteria and their role in plant growth promotion: a review. Ann Microbiol 60: 579–598. doi: 10.1007/s13213-010-0117-1
    [109] Arkhipova TN, Prinsen E, Veselov SU, et al. (2007) Cytokinin producing bacteria enhance plant growth in drying soil. Plant Soil 292: 305–315. doi: 10.1007/s11104-007-9233-5
    [110] Bottini R, Cassán F, Piccoli P (2004) Gibberellin production by bacteria and its involvement in plant growth promotion and yield increase. App Microbiol Biotechnol 65: 497–503.
    [111] Nagahama K, Ogawa T, Fujii T, et al. (1992) Classification of ethylene-producing bacteria in terms of biosynthetic pathways to ethylene. J Ferment Bioeng 73: 1–5. doi: 10.1016/0922-338X(92)90221-F
    [112] Glick BR, Penrose DM, Li J (1998) A model for the lowering of plant ethylene concentrations by plant growth-promoting bacteria. J Theor Biol 190: 63–68. doi: 10.1006/jtbi.1997.0532
    [113] Joo GJ, Kim YM, Kim JT, et al. (2005) Gibberellins-producing rhizobacteria increase endogenous gibberellins content and promote growth of red peppers. J Microbiol 43: 510–515.
    [114] Ghosh PK, Sen SK, Maiti TK (2015) Production and metabolism of IAA by Enterobacter spp. (Gammaproteobacteria) isolated from root nodules of a legume Abrus precatorius L. Biocatal Agric Biotechnol 4: 296–303.
    [115] Ma W, Penrose DM, Glick BR (2002) Strategies used by rhizobia to lower plant ethylene levels and increase nodulation. Can J Microbiol 48: 947–954. doi: 10.1139/w02-100
    [116] Saleem M, Arshad M, Hussain S, et al. (2007) Perspective of plant growth promoting rhizobacteria (PGPR) containing ACC deaminase in stress agriculture. J Ind Microbiol Biotechnol 34: 635–648. doi: 10.1007/s10295-007-0240-6
    [117] Glick BR, Cheng Z, Czarny J, et al. (2007) Promotion of plant growth by ACC deaminase-producing soil bacteria. Eur J Plant Pathol 119: 329–339. doi: 10.1007/s10658-007-9162-4
    [118] Glick BR (2014) Bacteria with ACC deaminase can promote plant growth and help to feed the world. Microbiol Res 169: 30–39. doi: 10.1016/j.micres.2013.09.009
    [119] Gamalero E, Glick BR (2015) Bacterial modulation of plant ethylene levels. Plant Physiol 169: 13–22. doi: 10.1104/pp.15.00284
    [120] Nascimento FX, Brígido C, Glick BR, et al. (2016) The role of rhizobial ACC deaminase in the nodulation process of leguminous plants. Int J Agron 2016.
    [121] Honma M, Shimomura T (1978) Metabolism of 1-aminocyclopropane-1-carboxylic acid. Agric Biol Chem 42: 1825–1831.
    [122] Magnucka EG, Pietr SJ (2015) Various effects of fluorescent bacteria of the genus Pseudomonas containing ACC deaminase on wheat seedling growth. Microbiol Res 181: 112–119. doi: 10.1016/j.micres.2015.04.005
    [123] Zerrouk IZ, Benchabane M, Khelifi L, et al. (2016) A Pseudomonas strain isolated from date-palm rhizospheres improves root growth and promotes root formation in maize exposed to salt and aluminum stress. J Plant Physiol 191: 111–119. doi: 10.1016/j.jplph.2015.12.009
    [124] Zahir ZA, Ghani U, Naveed M, et al. (2009) Comparative effectiveness of Pseudomonas and Serratia sp. containing ACC-deaminase for improving growth and yield of wheat (Triticum aestivum L.) under salt-stressed conditions. Arch Microbiol 191: 415–424.
    [125] Schachtman DP, Reid RJ, Ayling SM (1998) Phosphorus uptake by plants: from soil to cell. Plant Physiol 116: 447–453. doi: 10.1104/pp.116.2.447
    [126] Sharma SB, Sayyed RZ, Trivedi MH, et al. (2013) Phosphate solubilizing microbes: sustainable approach for managing phosphorus deficiency in agricultural soils. Springerplus 2: 587. doi: 10.1186/2193-1801-2-587
    [127] Zaidi A, Khan M, Ahemad M, et al. (2009) Plant growth promotion by phosphate solubilizing bacteria. Acta Microbiol Immunol Hungarica 56: 263–284. doi: 10.1556/AMicr.56.2009.3.6
    [128] Dastager SG, Deepa CK, Pandey A (2010) Isolation and characterization of novel plant growth promoting Micrococcus sp NII-0909 and its interaction with cowpea. Plant Physiol Biochem 48: 987–992. doi: 10.1016/j.plaphy.2010.09.006
    [129] Pindi PK, Satyanarayana SDV (2012) Liquid microbial consortium-a potential tool for sustainable soil health. J Biofertil Biopest 3: 1–9.
    [130] Peix A, Rivas-Boyero AA, Mateos PF, et al. (2001) Growth promotion of chickpea and barley by a phosphate solubilizing strain of Mesorhizobium mediterraneum under growth chamber conditions. Soil Biol Biochem 33: 103–110. doi: 10.1016/S0038-0717(00)00120-6
    [131] Liu FP, Liu HQ, Zhou HL, et al. (2014) Isolation and characterization of phosphate-solubilizing bacteria from betel nut (Areca catechu) and their effects on plant growth and phosphorus mobilization in tropical soils. Biol Fert Soils 50: 927–937. doi: 10.1007/s00374-014-0913-z
    [132] Panda P, Chakraborty S, Ray DP, et al. (2016) Screening of phosphorus solubilizing bacteria from tea rhizosphere soil based on growth performances under different stress conditions. Int J Biores Sci 3: 39–56. doi: 10.5958/2454-9541.2016.00005.0
    [133] Jaiswal DK, Verma JP, Prakash S, et al. (2016) Potassium as an important plant nutrient in sustainable agriculture: a state of the art, In: Potassium Solubilizing Microorganisms for Sustainable Agriculture, Springer India, 21–29.
    [134] Sheng XF, He LY (2006) Solubilization of potassium-bearing minerals by a wild-type strain of Bacillus edaphicus and its mutants and increased potassium uptake by wheat. Can J Microbiol 52: 66–72. doi: 10.1139/w05-117
    [135] Sangeeth KP, Bhai RS, Srinivasan V (2012). Paenibacillus glucanolyticus, a promising potassium solubilizing bacterium isolated from black pepper (Piper nigrum L.) rhizosphere. J Spices Arom Crops 21.
    [136] Basak BB, Biswas DR (2009) Influence of potassium solubilizing microorganism (Bacillus mucilaginosus) and waste mica on potassium uptake dynamics by sudan grass (Sorghum vulgare Pers.) grown under two Alfisols. Plant Soil 317: 235–255.
    [137] Neilands JB (1995) Siderophores: structure and function of microbial iron transport compounds. J Biol Chem 270: 26723–26726. doi: 10.1074/jbc.270.45.26723
    [138] Ahmed E, Holmstrom SJ (2014) Siderophores in environmental research: roles and applications. Microb Biotechnol 7: 196–208. doi: 10.1111/1751-7915.12117
    [139] Saha M, Sarkar S, Sarkar B, et al. (2016) Microbial siderophores and their potential applications: a review. Environ Sci Poll Res 23: 3984–3999. doi: 10.1007/s11356-015-4294-0
    [140] Wang W, Vinocur B, Altman A (2003) Plant responses to drought, salinity and extreme temperatures: towards genetic engineering for stress tolerance. Planta 218: 1–14. doi: 10.1007/s00425-003-1105-5
    [141] Zubair M, Shakir M, Ali Q, et al. (2016) Rhizobacteria and phytoremediation of heavy metals. Environ Technol Rev 5: 112–119. doi: 10.1080/21622515.2016.1259358
    [142] Liddycoat SM, Greenberg BM, Wolyn DJ (2009) The effect of plant growth-promoting rhizobacteria on asparagus seedlings and germinating seeds subjected to water stress under greenhouse conditions. Can J Microbiol 55: 388–394. doi: 10.1139/W08-144
    [143] Paul D, Nair S (2008) Stress adaptations in a Plant Growth Promoting Rhizobacterium (PGPR) with increasing salinity in the coastal agricultural soils. J Basic Microbiol 48: 378–384. doi: 10.1002/jobm.200700365
    [144] Yaish MW, Antony I, Glick BR (2015) Isolation and characterization of endophytic plant growth-promoting bacteria from date palm tree (Phoenix dactylifera L.) and their potential role in salinity tolerance. Anton Van Leeuw 107: 1519–1532.
    [145] Burd GI, Dixon DG, Glick BR (2000) Plant growth-promoting bacteria that decrease heavy metal toxicity in plants. Can J Microbiol 46: 237–245. doi: 10.1139/w99-143
    [146] Pérez-Montaño F, Alías-Villegas C, Bellogín RA, et al. (2014) Plant growth promotion in cereal and leguminous agricultural important plants: from microorganism capacities to crop production. Microbiol Res 169: 325–336. doi: 10.1016/j.micres.2013.09.011
    [147] Abou-Shanab RA, Angle JS, Delorme TA, et al. (2003) Rhizobacterial effects on nickel extraction from soil and uptake by Alyssum murale. New Phytol 158: 219–224. doi: 10.1046/j.1469-8137.2003.00721.x
    [148] Ma Y, Rajkumar M, Freitas H (2009) Isolation and characterization of Ni mobilizing PGPB from serpentine soils and their potential in promoting plant growth and Ni accumulation by Brassica spp. Chemosphere 75: 719–725. doi: 10.1016/j.chemosphere.2009.01.056
    [149] Dimkpa C, Svatoš A, Merten D, et al. (2008) Hydroxamate siderophores produced by Streptomyces acidiscabies E13 bind nickel and promote growth in cowpea (Vignaunguiculata L.) under nickel stress. Can J Microbiol 54: 163–172.
    [150] Carrillo-Castaneda G, Juarez MJ, Peralta-Videa J, et al. (2002) Plant growth-promoting bacteria promote copper and iron translocation from root to shoot in alfalfa seedlings. J Plant Nutr 26: 1801–1814. doi: 10.1081/PLN-120023284
    [151] Thomashow LS (1996) Biological control of plant root pathogens. Curr Opin Biotechnol 7: 343–347. doi: 10.1016/S0958-1669(96)80042-5
    [152] Ulloa-Ogaz, AL, Muñoz-Castellanos LN, Nevárez-Moorillón GV (2015) Biocontrol of phytopathogens: Antibiotic production as mechanism of control, In: Méndez-Vilas A, The Battle Against Microbial Pathogens: Basic Science, Technological Advances and Educational Programs, 305–309.
    [153] Fernando WD, Nakkeeran S, Zhang Y (2005) Biosynthesis of antibiotics by PGPR and its relation in biocontrol of plant diseases, In: PGPR: Biocontrol and Biofertilization, Springer Netherlands, 67–109.
    [154] Mazzola M, Fujimoto DK, Thomashow LS, et al. (1995) Variation in sensitivity of Gaeumannomyces graminis to antibiotics produced by fluorescent Pseudomonas spp. and effect on biological control of take-all of wheat. Appl Environ Microbiol 61: 2554–2559.
    [155] Durán P, Acuña JJ, Jorquera MA, et al. (2014) Endophytic bacteria from selenium-supplemented wheat plants could be useful for plant-growth promotion, biofortification and Gaeumannomyces graminis biocontrol in wheat production. Biol Fert Soils 50: 983–990. doi: 10.1007/s00374-014-0920-0
    [156] Maksimov IV, Abizgil'dina RR, Pusenkova LI (2011) Plant growth promoting rhizobacteria as alternative to chemical crop protectors from pathogens (review). Appl Biochem Microbiol 47: 333–345. doi: 10.1134/S0003683811040090
    [157] Silo-Suh LA, Lethbridge BJ, Raffel SJ, et al. (1994) Biological activities of two fungistatic antibiotics produced by Bacillus cereus UW85. Appl Environ Microbiol 60: 2023–2030.
    [158] Araújo FF, Henning AA, Hungria M (2005) Phytohormones and antibiotics produced by Bacillus subtilis and their effects on seed pathogenic fungi and on soybean root development. World J Microbiol Biotechnol 21: 1639–1645. doi: 10.1007/s11274-005-3621-x
    [159] Arora NK, Khare E, Oh JH, et al. (2008) Diverse mechanisms adopted by Pseudomonas fluorescens PGC2 during the inhibition of Rhizoctonia solani and Phytophthora capsici. World J Microbiol Biotechnol 24: 581–585. doi: 10.1007/s11274-007-9505-5
    [160] El-Tarabily KA, Sykes ML, Kurtböke ID, et al. (1996) Synergistic effects of a cellulase-producing Micromonospora carbonacea and an antibiotic-producing Streptomyces violascens on the suppression of Phytophthora cinnamomi root rot of Banksia grandis. Can J Bot 74: 618–624. doi: 10.1139/b96-078
    [161] El-Tarabily KA (2006) Rhizosphere-competent isolates of streptomycete and non-streptomycete actinomycetes capable of producing cell-wall-degrading enzymes to control Pythium aphanidermatum damping-off disease of cucumber. Can J Bot 84: 211–222. doi: 10.1139/b05-153
    [162] Martínez-Hidalgo P, Galindo-Villardón P, Trujillo ME, et al. (2014) Micromonospora from nitrogen fixing nodules of alfalfa (Medicago sativa L.). A new promising Plant Probiotic Bacteria. Sci Rep 4: 6389.
    [163] Martínez-Hidalgo P, García JM, Pozo MJ (2015) Induced systemic resistance against Botrytis cinerea by Micromonospora strains isolated from root nodules. Front Microbiol 6.
    [164] Hirsch AM, Valdés M (2010) Micromonospora: An important microbe for biomedicine and potentially for biocontrol and biofuels. Soil Biol Biochem 42: 536–542. doi: 10.1016/j.soilbio.2009.11.023
    [165] Schnepf E, Crickmore N, Van RJ, et al. (1998) Bacillus thuringiensis and its pesticidal crystal proteins. Microbiol Mol Biol Rev 62: 775–806.
    [166] Chattopadhyay A, Bhatnagar NB, Bhatnagar R (2004) Bacterial insecticidal toxins. Crit Rev Microbiol 30: 33–54. doi: 10.1080/10408410490270712
    [167] Muqarab R, Bano A (2016) Plant defence induced by PGPR against Spodoptera litura in tomato. Plant Biol 19: 406–412.
    [168] Sharma IP, Sharma AK (2017) Effective control of root-knot nematode disease with Pseudomonad rhizobacteria filtrate. Rhizosphere 3: 123–125. doi: 10.1016/j.rhisph.2017.02.001
    [169] Schippers B, Bakker AW, Bakker PAHM (1987) Interactions of deleterious and beneficial rhizosphere microorganisms and the effect of cropping practices. Ann Rev Phytopathol 25: 339–358. doi: 10.1146/annurev.py.25.090187.002011
    [170] Pal KK, Tilak KVBR, Saxcna AK, et al. (2001) Suppression of maize root diseases caused by Macrophomina phaseolina, Fusarium moniliforme and Fusarium graminearum by plant growth promoting rhizobacteria. Microbiol Res 156: 209–223. doi: 10.1078/0944-5013-00103
    [171] Yu X, Ai C, Xin L, et al. (2011) The siderophore-producing bacterium, Bacillus subtilis CAS15, has a biocontrol effect on Fusarium wilt and promotes the growth of pepper. Eur J Soil Biol 47: 138–145. doi: 10.1016/j.ejsobi.2010.11.001
    [172] Gamalero E, Marzachì C, Galetto L, et al. (2016) An 1-Aminocyclopropane-1-carboxylate (ACC) deaminase-expressing endophyte increases plant resistance to flavescence dorée phytoplasma infection. Plant Biosyst 151: 331–340.
    [173] Borriss R (2011) Use of plant-associated Bacillus strains as biofertilizers and biocontrol agents in agricultura, In: Bacteria in agrobiology: Plant growth responses, Springer Berlin Heidelberg, 41–76.
    [174] Senthilkumar M, Swarnalakshmi K, Govindasamy V, et al. (2009) Biocontrol potential of soybean bacterial endophytes against charcoal rot fungus, Rhizoctonia bataticola. Curr Microbiol 58: 288. doi: 10.1007/s00284-008-9329-z
    [175] Herrera SD, Grossi C, Zawoznik M, et al. (2016) Wheat seeds harbour bacterial endophytes with potential as plant growth promoters and biocontrol agents of Fusarium graminearum. Microbiol Res 186: 37–43.
    [176] Andreolli M, Lampis S, Zapparoli G, et al. (2016) Diversity of bacterial endophytes in 3 and 15 year-old grapevines of Vitis vinifera cv. Corvina and their potential for plant growth promotion and phytopathogen control. Microbiol Res 183: 42–52.
    [177] Pretty J, Sutherland WJ, Ashby J, et al. (2010). The top 100 questions of importance to the future of global agriculture. Int J Agric Sust 8: 219–236. doi: 10.3763/ijas.2010.0534

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