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Review Special Issues

Plant probiotic bacteria: solutions to feed the world

  • 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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  • 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.


    Ovarian cancer is the most lethal gynecological malignancy worldwide [1]. The most common type of ovarian cancer is epithelial ovarian cancer and is the substantial cause of death in female cancer patients every year [2]. At the diagnosis stage, 58% of ovarian cancers are metastatic with a 5-year relative survival of only 30% of patients [2]. Ovarian surface epithelial carcinoma cells are capable of initiating ovarian cancer cells and are crucial for the malignant progression of cancer [3]. In the tumor microenvironment, epithelial component from high grade serous ovarian cancer is associated with cancer invasion, progression, and pathogenesis [4]. The differentially expressed genes of the epithelial component in ovarian cancer are associated with cell proliferation, invasion, motility, chromosomal instability, and gene silencing [5]. The epithelial component of ovarian cancer tissue also provided the prognostic factors, immunological factors, and molecular factors [6]. In ovarian cancer, key immune cells and soluble molecules in the TME regulated the prognosis [7]. These studies provide the clue that the human ovarian cancer epithelial-derived transcriptomes are associated with cancer initiation, invasion, migration, and metastasis.

    Previous studies revealed the association of coding and non-coding biomarkers including differentially expressed genes (DEGs), mRNA, and miRNA on ovarian cancer pathogenesis. Ting Gui et al identified biomarkers including key hub genes and signaling pathways that are involved with epithelial ovarian cancer progression [8]. Another study identified the biomarkers including DEGs, key hub genes, prognostic genes, significant clusters of genes, and functional enrichment analysis in epithelial ovarian cancer [9]. Daniela Matei et al identified mRNA levels that are significantly deregulated in primary cultures of ovarian epithelial cells derived from epithelial ovarian carcinoma [10]. Immunogenic mRNA and protein expression by cell cultures of epithelial ovarian cancer are also crucial factors to identify the disease progression [11]. MicroRNA (miRNA), the substantial onco-regulators in cancer, is associated with clinicopathological characteristics of epithelial ovarian cancer [12]. MiRNA regulating the initiation, proliferation, cell cycle, survivability, and resistance of chemosensitivity in ovarian cancer [13]. Some studies demonstrated the significance of bioinformatics analysis, data mining, gene regulatory networks, and prediction of biomarkers in human diseases [14,15,16,17]. Altogether, these studies provide the clue that the bioinformatics strategies could identify the epithelial-derived transcriptomes that are associated with the pathogenesis of ovarian cancer.

    Herein, we identified the deregulated transcriptional signatures in laser capture microdissected human ovarian cancer epithelia. Then we identified the TFs that are associated with regulating the deregulated transcriptional signatures in ovarian cancer epithelia. Moreover, we identified the key hub genes and hub genes associated clusters from the PPI network. Furthermore, we investigated the correlation of key genes with survival prognosis and immune infiltrations. Finally, we investigated the genetic alterations of key prognostic hub genes and their diagnostic efficacy in ovarian cancer. The study may provide novel insights to investigate the functional regulatory mechanisms of immune-related biomarkers and help to develop immune-related targeted therapy in the treatment of ovarian cancer. Also, this study will help the experimental biologists to further carry out their research to explore the clinical association of these identified biomarkers to treat ovarian cancer patients.

    We searched the NCBI gene expression omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) using the keywords "epithelial", "ovarian epithelial", "ovarian surface epithelium tumor", "cancer-associated epithelial", "ovarian cancer", and "tumor epithelial", and identified five ovarian epithelial gene expression datasets from the same platform. These included five datasets have the following criteria: the datasets extracted from the same platform (Affymetrix human genome U133 plus 2.0 array), the study organisms had to be Homo sapiens, the datasets had to contain ovarian epithelial cancer and normal ovarian epithelial tissue samples, and the sample size is greater than 20 with minimum 5 control samples. The five selected datasets are GSE14407 [3], GSE27651 [18], GSE38666 [19,20], GSE40595 [4], and GSE54388 [21]. The total samples included 120 ovarian epitheial tumors and 43 control epithelial. Moreover, we downloaded the TCGA ovarian cancer cohort (n = 307) (https://portal.gdc.cancer.gov/) and normalized it by base-2 log transformation. Finally, we downloaded the clinical data of the TCGA ovarian cancer cohort for analyzing the survival differences between the two groups (https://portal.gdc.cancer.gov/).

    The GEO datasets (GSE14407, GSE27651, GSE38666, GSE40595, and GSE54388) used in this study are available in The National Biotechnology Information Center Gene (NCBI-Gene) database (https://www.ncbi.nlm.nih.gov/gene). TCGA-OV cohort is downloaded from the Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). Genetical data of OV was utilized from the cBioPortal (http://www.cbioportal.org/).

    We used Network Analyst [22] to identify the DEGs between ovarian epithelial tumor and normal samples by a meta-analysis of five epithelial gene expression profiling datasets (GSE14407 [3], GSE27651 [18], GSE38666 [19,20], GSE40595 [4], and GSE54388 [21]). Datasets were normalized by quantile normalization or base-2 log transformation. We removed the batch effects of multiple datasets by using the ComBat method [23]. A total of 20, 184 common genes were found by integrating the datasets from five datasets using the Network Analyst [22] tool. We used the R package "limma" for identifying the DEGs between tumor and normal samples, and Cochran's combination test for performing the meta-analysis [24]. The false discovery rate (FDR) [25] was used to adjust for multiple tests. We identified the DEGs using a threshold of absolute combined effect size (ES) > 1.50 and FDR < 0.05.

    We utilized Cytoscape [26] plug-in iRegulon [27] to identify the MTRs for the upregulated and downregulated DEGs. In the Cytoscape plug-in iRegulon [27], a minimum normalized enrichment score (NES) > 3.0 was selected for each TFs. For this purpose, we used an extensive collection of TF motifs and a large collection of ChIP-seq tracks. The iRegulon method depends on a ranking-and-recovery system where all genes of the human genome are scored by a motif discovery step integrating the clustering of binding sites within cis-regulatory modules (CRMs) and the potential distal location of CRMs upstream or downstream of the transcription start site (TSS ± 10 kb). The recovery step calculates the normalized enrichment score (NES) of TFs for each set of genes, input for each of the individual analyses, leading to the prediction of the TFs based on NES and their putative direct target genes which exist in the input lists. This methodology optimizes the linking of TFs to motifs using both explicit annotations and predictions of TF orthologs and motif similarity. A transcription factor NES was computed for each group where an NES > 3.0 corresponds, and the maximum false discovery rate (FDR) on motif similarity was set as 0.001 [28].

    We performed gene-set enrichment analysis of the DEGs by using the GSEA [29]. We inputted all significant upregulated and downregulated DEGs into the GSEA tool [29] for identifying deregulated pathways. The KEGG [30] pathways significantly associated with the upregulated DEGs and the downregulated DEGs were identified, respectively. The P-value < 0.05 was considered significant when selecting the pathways.

    To better know the relationship among these identified DEGs, the PPI network was established using the STRING-based analysis [31]. To identify the rank of hub genes, we used Cytoscape plug-in tool cytoHubba [32]. Hub genes were identified based on the degree of interactions with neighbor genes. We selected the minimum required interaction score is 0.40 for identifying the PPI of DEGs. Hub genes were defined as a gene that was connected to a minimum of 10 other DEGs in the PPI. We visualize the PPI networks by utilizing the Cytoscape 3.6.1 software [26]. A Cytoscape plug-in molecular complex detection (MCODE) was employed to detect the modules from the PPI network [33]. 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.

    We used the R package "survival" to investigate the survival prognosis of ovarian cancer patients [34]. We used the clinical data of the TCGA OV cohort for analyzing the survival differences. We compared the overall survival (OS) of ovarian cancer patients that are 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 (P < 0.05) was utilized to evaluate the significance of survival differences between the groups.

    ESTIMATE is an algorithmic tool based on the R package for predicting tumor purity, immune score (predicting the infiltrations of immune cells), and stromal score (predicting the infiltrations of stromal cells) which uses the gene expression profiles of 141 immune genes and 141 stromal genes [35]. The presence of infiltrated immune cells and stromal cells in tumor tissues were calculated using related gene expression matrix data, represented by immune score and stromal score, respectively [35]. Then we calculated the correlations of key genes with immune scores and stromal scores. The threshold value of correlation is R > 0.20, and P-value is not less than 0.001 (Spearman's correlation test).

    One of the extension packages of GSEA, single-sample gene-set enrichment analysis (ssGSEA) was used to identify the enrichment scores of immune cells for each pairing of a sample and gene set in the tumor samples [36]. We collected the marker gene set for immune signatures and utilizing each gene set to quantify the ssGSEA scores of specific immune signatures [37,38,39,40]. We identified the enrichment levels (ssGSEA scores) of ten immune signatures included TILs, B cells, CD8+ T cells, CD4+ regulatory T cells, cytolytic activity, T cells activations, CAFs, pDC, macrophages, and neutrophils. All of the marker genes are displayed in Supplementary Table S1. Then we investigated Spearman's correlation between the ssGSEA scores and specific prognostic genes. The threshold of correlation of immune cells is the absolute value is not less than 0.20 with P-value < 0.05.

    We identified the genetic alterations associated with prognostic hub genes by using the cBioPortal (http://www.cbioportal.org/), an open-access tool for exploring and analyzing genetical alterations of multidimensional cancer studies [41]. In this study, we selected the TCGA OV epithelial data that contains 311 samples with mutation data and copy number alteration data (https://www.cbioportal.org/study/summary?id=ov_tcga).

    To assess diagnostic values of the prognostic genes, the receiver operating characteristic (ROC) curve was plotted and the area under the ROC curve (AUC) was calculated using the "pROC" R package [42] to evaluate the capability of distinguishing ovarian cancer epithelia and normal epithelia. The greater AUC value of individual genes indicated the differences between tumor and normal samples, and the key gene of AUC > 0.5 in the integrated five datasets was defined as a diagnostic efficiency of the gene [43]. If the P-value <  0.05, the selection of the prognostic genes are considered statistically significant.

    We evaluated Pearson's or Spearman's correlation test to verify the significant levels between the two variables. For analyzing the correlations between the expression levels of hub genes and the enrichment levels (ssGSEA scores) of immune signatures, we used Spearman's correlation test because these data were not normally distributed [44]. For analyzing the correlations between the expression levels of hub genes with the expression levels of other marker genes, we utilized Pearson's correlation test because these data were normally distributed [44]. We utilized the Network Analyst [22] tool for calculating the average expression of a gene having multiple probes in the same expression dataset. We used the R package "ggplot2" for plotting the graphs in this study [45].

    We identified 1339 differentially expressed genes (DEGs) between the ovarian epithelial tumor and normal epithelial, which included 541 upregulated (Tables 1 and S2) and 798 downregulated (Tables 2 and S3) genes in the tumor ovarian epithelial when compared with normal ovarian epithelial based on combined Effect size (ES). ES is the difference between two group means divided by standard deviation, which is considered combinable and comparable across different studies [22]. Table 1 describes the regulatory status of the top 25 (The highest combined effect size) upregulated genes including SOX17, CENPF, CD24, INAVA, MECOM, NEK2, DTL, WFDC2, PAX8, MUC1, and CXXC5. Lin Zhao et al reported that the expression of SOX17, INAVA, WFDC2, and CD24 are upregulated in ovarian cancer [46]. It was found that PAX8 and SOX17 regulate tumor angiogenesis in vitro and in vivo in ovarian cancer [47]. NEK2, another upregulated gene in ovarian tumor epithelia, is associated with drug resistance in ovarian cancer [48].

    Table 1.  Gene expression pattern of top 25 upregulated genes in ovarian tumor epithelium relative to ovarian normal epithelium.
    Entrez ID Gene symbol Combined ES Adjusted P-value
    64321 SOX17 3.6554 0
    100133941 CD24 3.6041 7.24E-07
    55765 INAVA 3.4924 0
    2122 MECOM 3.4549 4.69E-12
    4751 NEK2 3.404 2.02E-09
    51514 DTL 3.3758 5.58E-07
    10406 WFDC2 3.3672 0
    51523 CXXC5 3.2352 0
    1063 CENPF 3.1671 0.00011
    4582 MUC1 3.1454 1.28E-13
    701 BUB1B 3.1136 6.41E-10
    4072 EPCAM 3.0962 3.57E-14
    24137 KIF4A 3.0737 1.57E-13
    7849 PAX8 3.0656 0
    79581 SLC52A2 3.0212 0
    1164 CKS2 2.9878 0
    10112 KIF20A 2.9847 6.01E-07
    57565 KLHL14 2.9794 1.89E-08
    54845 ESRP1 2.9359 0
    11130 ZWINT 2.9358 5.54E-05
    81610 FAM83D 2.8954 8.96E-05
    332 BIRC5 2.8935 0.000116
    29968 PSAT1 2.8885 1.42E-13
    1356 CP 2.8825 0

     | Show Table
    DownLoad: CSV
    Table 2.  Gene expression pattern of top 25 downregulated genes in ovarian tumor epithelium relative to ovarian normal epithelium.
    Entrez ID Gene symbol Combined ES Adjusted P-value
    55600 ITLN1 -8.0963 1.96E-06
    150622 SILC1 -5.8072 2.46E-05
    10351 ABCA8 -5.5625 2.58E-08
    10216 PRG4 -5.3043 1.05E-06
    316 AOX1 -5.264 1.87E-14
    590 BCHE -5.199 1.66E-05
    8854 ALDH1A2 -5.039 1.76E-06
    4886 NPY1R -4.8974 8.46E-06
    125 ADH1B -4.872 3.77E-08
    65055 REEP1 -4.8554 0
    93663 ARHGAP18 -4.8506 0.00026109
    4753 NELL2 -4.8473 6.74E-05
    79804 HAND2-AS1 -4.7844 1.42E-06
    4969 OGN -4.7596 1.24E-07
    84709 MGARP -4.7055 2.86E-06
    51555 PEX5L -4.7042 0
    126 ADH1C -4.6513 1.57E-08
    9737 GPRASP1 -4.6473 2.23E-05
    8622 PDE8B -4.6407 1.49E-08
    56245 C21orf62 -4.6073 3.14E-06
    339896 GADL1 -4.5668 9.75E-09
    80310 PDGFD -4.5255 5.88E-13
    139221 PWWP3B -4.3353 1.34E-12
    3957 LGALS2 -4.2859 3.33E-05

     | Show Table
    DownLoad: CSV

    In addition, Table 2 describes the regulatory status of the top 25 (The lowest combined effect size) downregulated genes including ITLN1, SILC1, ABCA8, PRG4, AOX1, BCHE, ALDH1A2, NPY1R, ADH1B, and REEP1. It was found that the downregulation of mesothelial cell-derived ITLN1 in the omental tumor microenvironment facilitates ovarian cancer progression [49]. In the ovarian cancer cell line, ABCA8 was significantly downregulated and is associated with drug-resistant [50]. A tumor suppressor, ALDH1A2, was strongly downregulated 36-fold in 779 epithelial ovarian cancer cases compared with 18 normal controls [51]. Altogether, it indicates that the ovarian cancer epithelia-derived deregulated transcriptomes are associated with ovarian cancer pathogenesis.

    MTRs are crucial cancer-associated biomarkers and targets for metabolism-targeted cancer therapy [52]. To identify the significant MTRs that regulating the DEGs, we used the Cytoscape plug-in iRegulon tool. We identified 21 (E2F4, FOXM1, TFDP1, E2F1, SIN3A, PIR, SMAD1, E2F7, UBP1, NFYC, BDP1, E2F1, NFYA, MYBL2, TCF12, MTHFD1, TEAD4, MZF1, EP300, FHL2, and GRHL1) and 11 (JUN, DDX4, FOSL1, NOC2L, HMGA1, JUND, TCF12, EP300, NFIC, ZSCAN9, and FOS) MTRs for the upregulated and the downregulated genes in ovarian tumor epithelial, respectively (Table S4). We built the regulatory networks between the top 5 MTRs and upregulated DEGs (Figure 1A). In addition, we built the regulatory networks between the top 5 MTRs and downregulated DEGs (Figure 1B). In the networks, E2F4, top MTRs for upregulated genes, targeted 267 upregulated genes, and JUN, top-scored MTRs for downregulated genes, targeted 146 downregulated genes. Interestingly, three members of the E2 factor (E2F) family of transcription factors (E2F1, E2F7, and E2F1) were the MTRs that regulated the upregulated genes in ovarian tumors epithelium (Figure 1A). Deregulation of E2F transcription factors is associated with both proliferation-promoting and proliferation-inhibiting and their cross-talk is involved in the tumor biology of ovarian cancer and influences the clinical outcome of ovarian cancer [53,54]. Transcription factor JUN is associated with cellular proliferation, malignant transformation, and invasion in various tumors including ovarian cancer [55].

    Figure 1.  Regulatory networks of the master transcriptional regulators (MTRs) and their targeted differentially expressed genes (DEGs) between ovarian tumor epithelium and normal epithelium. A. Regulatory network of the top 5 MTRs and their targeted upregulated genes in ovarian tumor epithelium. B. Regulatory network of the top 5 MTRs and their targeted downregulated genes in ovarian tumor epithelium. The Green color octagon indicates MTRs, and the purple color oval indicates DEGs in ovarian tumor epithelial.

    The significantly enriched upregulated and downregulated biological pathways were identified by using the GSEA tool (Figure 2). GSEA identified 13 KEGG pathways that are significantly associated with upregulated DEGs (Figure 2A). The upregulated pathways included cell cycle, Oocyte meiosis, pathways in cancer, progesterone-mediated oocyte maturation, DNA replication, homologous recombination, and small cell lung cancer (Figure 2A). Besides, we identified the 42 KEGG pathways that are significantly linked with downregulated DEGs (Table S5). Some of these downregulated pathways are involved with immune regulation, including complement and coagulation cascades, cytokine-cytokine receptor interaction, Fc gamma R-mediated phagocytosis, apoptosis, and endocytosis (Table S5 and Figure 2B). Moreover, metabolic pathways including drug metabolism - cytochrome P450, metabolism of xenobiotics by cytochrome P450, tyrosine metabolism, histidine metabolism, arginine and proline metabolism, and steroid hormone biosynthesis are downregulated in the epithelium of ovarian cancer (Figure 2B).

    Figure 2.  Significantly enriched KEGG pathways that are associated epithelium-derived DEGs. A. Significantly enriched 13 KEGG pathways that are associated with upregulated DEGs. B. Significantly enriched top 20 KEGG pathways that are associated with downregulated DEGs. FDR is false discovery rate.

    We investigated the PPI of all significant epithelial-derived DEGs. The PPI information of STRING is inputted into the Cytoscape for identifying and visualizing the hub genes and significant clusters. We identified the 460 hub genes (minimum degree of interaction is 10 with other DEGs) (Table S6). The top 20 hub genes (with the maximum degree of interaction) including CDK1, CCNB1, AURKA, CDC20, CCNA2, BUB1, TOP2A, BUB1B, CCNB2, and CDC45 are shown in Figure 3. The overexpression of CDK1 is associated with cancer growth and survival rate in epithelial ovarian cancer [56]. CCNB1, another top hub gene, is abnormally expressed and significantly involved in carboplatin-resistant epithelial ovarian cancer [57]. The expression level of CCNB2 is distinguished between ovarian cancer and normal tissues [58].

    Figure 3.  The top 20 hub genes and their degree of protein-protein interaction. The PPI network was established using the STRING-based analysis. Cytoscape plug-in cytoHubba tool was used to identify their degree of interactions.

    We investigated the significant cluster-based analysis of 460 hub genes. The MCODE-based analysis identified 7 clusters (Score of MCODE > 5.0) from the original PPI networks. The description of MCODE derived clusters with their interacting gene lists is illustrated in Table 3. The top significant cluster 1 included 103 nodes and 4571 edges (Table 3). We identified the functional enrichment of KEGG pathways for all clusters by using the GSEA [29]. Interestingly, we found that all seven of the clusters are associated with the enrichment of KEGG pathways (FDR < 0.05). Gene set of cluster 1 is associated with the enrichment of the cell cycle and other cellular differentiation pathways (Table 3). Gene set of cluster 2 is mainly involved with immune regulation and cellular signaling (Table 3). Gene set of clusters 4 and 5 is mainly involved with cancer-associated pathways including pathways in cancer, Hedgehog signaling pathway, basal cell carcinoma, melanoma, Wnt signaling pathway, endocytosis, cytokine-cytokine receptor interaction, focal adhesion, regulation of actin cytoskeleton, MAPK signaling pathway, and TGF-beta signaling pathway (Table 3). Altogether, these results indicate that the epithelial tumor tissue-derived transcriptomes are contributed to ovarian cancer pathogenesis.

    Table 3.  MCODE identified significant 7 clusters from the PPI networks of DEGs and GSEA identified enrichment of KEGG pathways (FDR < 0.05) for a specific gene set of the individual cluster.
    Cluster Score of MCODE Nodes Edges Node symbol Enrichment of KEGG pathways (FDR < 0.05)
    1 89.627 103 4571 DEPDC1B, FOXM1, E2F8, KIF18B, AURKA, NUSAP1, PBK, TYMS, HJURP, DEPDC1, TOP2A, MCM2, RACGAP1, KIF20A, PRC1, CCNA2, CDC45, ECT2, MKI67, CDCA3, KIF4A, CDCA8, KIF2C, CEP55, TTK, TPX2, STIL, CENPF, CDCA5, CENPA, MELK, TACC3, KIF15, RAD51, SPAG5, FEN1, ESCO2, BIRC5, HMMR, CENPI, KIAA0101, MCM10, SPC25, RAD54L, CDC7, CASC5, CENPE, UHRF1, MND1, SGOL1, DTL, NEIL3, KIF11, TROAP, SKA3, NCAPG, ZWINT, DLGAP5, EXO1, TRIP13, FAM83D, FANCI, CENPU, NUF2, CDCA7, CDC25A, CCNE2, CDK1, PTTG1, DSCC1, CDC20, CKS2, E2F7, RRM2, SMC4, UBE2C, CKS1B, BUB1, PKMYT1, MAD2L1, MCM4, CCNB2, KIF23, BUB1B, KIF18A, GINS2, ESPL1, FAM64A, CCNB1, NCAPH, RAD51AP1, CDC6, OIP5, CENPM, ASF1B, CHEK1, CDKN3, KPNA2, CENPK, EZH2, NEK2, KIF14, TK1 Cell cycle, Oocyte meiosis, Progesterone-mediated oocyte maturation, p53 signaling pathway, DNA replication
    2 16 16 120 GNG12, GPSM2, NPY1R, CXCL6, GNG2, BDKRB1, HEBP1, CX3CL1, ADRA2C, APLNR, CXCR4, GNG11, LPAR3, ANXA1, PTGER3, GNAI1 Chemokine signaling pathway, Neuroactive ligand-receptor interaction, Cytokine-cytokine receptor interaction, Leukocyte transendothelial migration, Axon guidance
    3 7.6 26 95 SOX9, CP, EPCAM, TIMP1, SPARCL1, FOXO1, ALDH1A1, KAT2B, CALCRL, STC2, CD24, LAMB1, GPR39, PTGDR, SDC2, PTGER4, KLF4, PTH2R, VWA1, CHGB, GOLM1, MEF2C, RAMP3, SCTR, ADCYAP1, CHRDL1 Neuroactive ligand-receptor interaction
    4 7.6 31 114 LRP2, MMP7, PJA2, SYT1, TRIM4, WNT2B, NANOG, FBXL3, FBXL5, SPSB1, RCHY1, FGF13, GATA6, PACSIN3, STON2, MCAM, MUC1, PDGFRA, NOTCH1, BMP2, SH3GL2, CAV1, SFRP1, FBXL7, DAB2, FGF9, WNT7A, BMP4, SCARB2, MET, KITLG Pathways in cancer, Hedgehog signaling pathway, Basal cell carcinoma, Melanoma, Wnt signaling pathway, Endocytosis, Cytokine-cytokine receptor interaction, Melanogenesis, Focal adhesion, Regulation of actin cytoskeleton, MAPK signaling pathway, TGF-beta signaling pathway
    5 6 9 24 ANXA5, FGF2, VEGFA, KDR, SNAI2, ZEB2, VIM, FGF1, ZEB1 Pathways in cancer, Melanoma, VEGF signaling pathway, Focal adhesion, Regulation of actin cytoskeleton, Cytokine-cytokine receptor interaction, MAPK signaling pathway
    6 5.273 12 29 RAB33A, PTX3, RAB39B, IL18, LCN2, NFKB1, HPSE, RAB31, RAB27B, GSDMD, JUP, RAB8B Cytosolic DNA-sensing pathway, Acute myeloid leukemia, NOD-like receptor signaling pathway
    7 5 5 10 MAP1LC3B, NBR1, GABARAPL2, PIK3C3, ATG101 Regulation of autophagy

     | Show Table
    DownLoad: CSV

    We investigated the survival significance of the epithelium-derived all 460 significant hub genes in TCGA OV data. Our analysis revealed that the epithelium-derived upregulated genes included SCNN1A and CDCA3 (Figure 4A, B) and downregulated SOX6 gene (Figure 4C) is significantly correlated with shorter survival time of ovarian cancer patients (Figure 4). The overexpression of SCNN1A exerts substantial roles in cell growth, invasion, and migration in ovarian cancer through regulating the epithelial to mesenchymal transition, and is a potential indicator for a patient's prognosis [59]. Chongxiang Chen et al. reported that the expression of the CDCA3 gene is associated with the survival and tumorigenesis through the PLK1 pathway in ovarian cancer [60]. SOX6, a tumor suppressor, is downregulated in various cancers and associated with the inhibition of cellular proliferation, invasion, and tumor cell-induced angiogenesis of ovarian cancer cells [61].

    Figure 4.  Identification of prognostic hub genes in ovarian cancer. A-B. Upregulated genes that included SCNN1A and CDCA3 are significantly correlated with shorter survival time in ovarian cancer. C. Downregulated SOX6 gene is significantly correlated with shorter survival time in ovarian cancer. D. The GEO dataset GSE9891 was used for the analysis of survival differences in the SurvExpress tool. three prognostic gene signatures are significantly associated with shorter overall survival time in the high-risk groups.

    To verify the survival significance of the key three genes (SCNN1A, CDCA3, and SOX6), we inputted these prognostic three gene signatures into the SurvExpress tool (http://bioinformatica.mty.itesm.mx/SurvExpress) [62]. We used a GEO dataset GSE9891 (n = 285) [63] that is a built-in dataset in the SurvExpress tool (http://bioinformatica.mty.itesm.mx/SurvExpress). SurvExpress split the samples into two groups (high-risk group and low-risk group) based on the prognostic index and identify the significant survival differences between the two groups. Interestingly, we found that the three gene signatures are prognostic in overall survival (Figure 4D). The high-risk group patients had significantly lower survival times than low-risk group patients (Figure 4D).

    Since survival time of patients is correlated with immunological responses in human cancers [64], it is essential to identify the correlation of prognostic hub genes with the immune infiltrations. We investigated the correlations between the expression levels of three hub genes (SCNN1A, CDCA3, and SOX6) and the levels of immune signatures and tumor purity in the TME of OV. Interestingly, we found that the expression level of SOX6 is negatively correlated with immune scores (R = -0.34, P < 0.001) (Figure 5A), and positively correlated with tumor purity (R = 0.27, P < 0.001) (Figure 5B), but the expression level of SCNN1A and CDCA3 are not correlated with immune scores and tumor purity. Then, we investigated the correlation of three prognostic hub genes (SCNN1A, CDCA3, and SOX6) with the several immune stimulatory and inhibitory signatures including B cells, TILs, CD8+ T cells, CD4+ Regulatory T cells, cytolytic activity, T cell activation, pDC, neutrophils, CAFs, and macrophages. We found that the expression of SOX6 is negatively correlated with the infiltration of TILs, CD8+ T cells, CD4+ Regulatory T cells, cytolytic activity, T cell activation, pDC, neutrophils, and macrophages (Figure 5C). The expression level of SCNN1A and CDCA3 is not correlated with immune infiltrations. Therefore, we identified the correlations of SOX6 with the immune markers (all markers selected from the significant immune signatures). Interestingly, we found that the expression of SOX6 negatively correlated with 68 immune markers including CD8A, PRF1, GZMA, GZMB, NKG7, PRF1, CCL3, CCL4, CST7, CXCR3, and IL10RA in ovarian cancer (Table 5). Tumor immune infiltration is a key indicator in the progression of ovarian cancer [65]. In epithelial ovarian cancer, tumor-infiltrating T cells are predictors of prognosis and biological basis of treatment outcomes [66]. In intratumoral TME, the accumulation of CD8+ T cells is associated with the survival of high-grade serous ovarian carcinoma patients [67]. Our immunological analysis indicated that the epithelial-derived-transcriptomes are associated with the modulation of the immune microenvironment in ovarian cancer.

    Figure 5.  The expression level of SOX6 is associated with the tumor microenvironment and immune infiltration in ovarian cancer. A. The expression level of SOX6 is negatively correlated with immune scores (R = -0.34, P < 0.001). B. The expression level of SOX6 is positively correlated with tumor purity (R = 0.27, P < 0.001). C. The expression level of SOX6 is negatively correlated with ssGSEA scores of TILs, CD8+ T cells, CD4+ Regulatory T cells, cytolytic activity, T cell activation, pDC, neutrophils, and macrophages. (Spearman's correlation test, P < 0.001).

    We used the OV epithelial tumor data (n = 311) in cBioPortal (http://www.cbioportal.org/) to identify the genetic alterations of three prognostic hubs genes (SCNN1A, CDCA3, and SOX6). Queried SCNN1A, CDCA3, and SOX6 genes are altered in 36 (12%) of queried patients/samples.

    We found that the SCNN1A and CDCA3 are altered in 10 % of patients (Figure 6A). In addition, SOX6 is altered in 1.6 % of patients (Figure 6A). The genetic alterations of SCNN1A and CDCA3 included amplification and the genetic alterations of SOX6 included mutation, amplification, and multiple alterations (Figure 6B).

    Figure 6.  Genetical alterations of SCNN1A, CDCA3, and SOX6 in the ovarian epithelial tumor. A. SCNN1A (10%), CDCA3 (10%), and SOX6 (1.6 %) genes are mutated in the ovarian epithelial tumor. B. The genetic alterations of SCNN1A and CDCA3 are amplification and the genetic alterations of SOX6 are mutation, amplification, and multiple alterations in the ovarian epithelial tumor.

    We speculate that these six genes (SCNN1A, CDCA3, and SOX6) have diagnostic value in combined 5 datasets of ovarian epithelium. We used the combined 5 datasets (GSE14407 [3], GSE27651 [18], GSE38666 [19,20], GSE40595 [4], and GSE54388 [21]) of the ovarian epithelium to validate our hypothesis, and the results showed that the ROC curve of the expression levels of SCNN1A (AUC = 0.60) and SOX6 (AUC = 0.744) showed excellent diagnostic value for ovarian epithelial tumor and normal ovarian epithelial cells (Figure 7).

    Figure 7.  Evaluation of diagnostic efficacy of key prognostic genes in combined 5 datasets of ovarian epithelium.

    The receiver operating characteristic (ROC) curve of prognostic genes in combined 5 datasets (GSE14407 [3], GSE27651 [18], GSE38666 [19,20], GSE40595 [4], and GSE54388 [21]) of ovarian tumor epithelium and normal ovarian epithelium. The expression levels of SCNN1A (AUC = 0.60) and SOX6 (AUC = 0.744) showed diagnostic value in the ovarian epithelial tumor.

    Since bioinformatic studies are crucial for identifying the key signaling genes and pathways in human diseases [68,69,70,71], we aimed to explore the ovarian cancer epithelium-derived transcriptomes for identifying the substantial biomarkers. We identified DEGs in ovarian tumor epithelium by a meta-analysis of five gene expression datasets (Tables S2 and S3). Based on the significant DEGs, we identified pathways that are associated with the significant DEGs. The upregulated pathways were mainly involved in cellular development, cancer, and metabolism, and the downregulated pathways involved in immune-regulation and metabolism (Figure 2). The cell cycle, the top enriched upregulated pathway, is involved in ovarian cancer pathogenesis [68]. The downregulation of immune cells is directly associated with enhancing the pathogenesis of ovarian cancer through the release of various cytokines and chemokines [72]. Therefore, our investigated results are consistent with the role of DEGs in the enrichment of pathways that cause ovarian cancer pathogenesis. These results revealed the abnormal alterations of cellular development, immune regulation, and metabolism-related pathways in the compartment of the ovarian cancer epithelium. In addition, we identified hub genes that are interacted with other DEGs. We identified 21 and 11 transcription factors that are associated with regulating the upregulated and downregulated DEGs, respectively (Table S4). In ovarian epithelial cancer, TFs are substantial contributors to cancer risk and somatic development [73]. These TFs could be a unique target for the development of novel precision medicine strategies for ovarian cancer.

    Moreover, we found that several clusters are associated with the hub gene signatures. These clusters are associated with the enrichment level of KEGG pathways (Table 3). Pathway analysis revealed that the significant clusters are mainly involved with cancer, immune regulation, and cellular signaling (Table 3). Then, we found that the expression of three hub genes (SCNN1A, CDCA3, and SOX6) are significantly correlated with the shorter overall survival time of ovarian cancer patients (Figure 4). Since the level of immune infiltration is an independent predictor of a patient's survival in cancer [74], we analyzed the correlation of SCNN1A, CDCA3, and SOX6 with the immune infiltration levels in the ovarian tumor. First, we revealed the association of these three hub genes with the ovarian tumor microenvironment. The expression level of SOX6 is negatively correlated with immune scores, indicating that SOX6 is associated with lower the immunity (Figure 5A, B). Second, the expression level of SOX6 is negatively correlated with infiltrations of TILs, CD8+ T cells, CD4+ Regulatory T cells, cytolytic activity, T cell activation, pDC, neutrophils, and macrophages in epithelial ovarian cancer, suggesting that the SOX6 expression is linked with lowering the immune infiltrations in ovarian cancer (Figure 5C). Third, the 68 immune markers (we selected all markers of the significant immune signatures (Figure 4C)) including CD8A, GZMA, PRF1, GZMB, NKG7, CCL3, CCL4, and CCL5 are negatively correlated with the expression levels of SOX6, demonstrating that the SOX6 expression is related to the reduced expression levels of immune markers (Table 4). Altogether. It indicates that the expression of SOX6 is associated with the suppression of tumor immunity in ovarian epithelial cancer. The expression of SCNN1A and CDCA3 is not associated with the regulation of immune infiltrations in epithelial cancer. In addition, SCNN1A, CDCA3, and SOX6 are genetically altered in ovarian cancer. The genetic alteration of SCNN1A and CDCA3 is amplification and the genetic alterations of SOX6 are mutation, amplification, and multiple alterations in ovarian cancer (Figure 6).

    Table 4.  Correlated immune markers with SOX6 in ovarian cancer (TCGA OV cohort). R is Pearson's correlation; P is p-value; FDR is false discovery rate.
    Immune signatures ID Immune markers Name R P value FDR value
    CD8 T cell 925 CD8A CD8a molecule -0.26 5.87E-06 8.74E-05
    Cytolytic activity 3001 GZMA granzyme A -0.27 1.73E-06 3.45E-05
    5551 PRF1 perforin 1 -0.30 1.25E-07 4.38E-06
    T cell activation 3001 GZMA granzyme A -0.27 1.73E-06 3.45E-05
    3002 GZMB granzyme B -0.27 1.31E-06 2.74E-05
    4818 NKG7 natural killer cell granule protein 7 -0.26 3.72E-06 6.16E-05
    5551 PRF1 perforin 1 -0.30 1.25E-07 4.38E-06
    6348 CCL3 C-C motif chemokine ligand 3 -0.26 3.53E-06 5.90E-05
    6351 CCL4 C-C motif chemokine ligand 4 -0.31 3.79E-08 1.75E-06
    8530 CST7 cystatin F -0.23 3.78E-05 3.76E-04
    CD4 regulatory T cell 1493 CTLA4 cytotoxic T-lymphocyte associated protein 4 -0.26 4.69E-06 7.33E-05
    50943 FOXP3 forkhead box P3 -0.24 2.65E-05 2.80E-04
    Macrophages 968 CD68 CD68 molecule -0.29 3.66E-07 1.03E-05
    1536 CYBB cytochrome b-245 beta chain -0.24 2.61E-05 2.77E-04
    10457 GPNMB glycoprotein nmb -0.23 6.45E-05 5.71E-04
    23601 CLEC5A C-type lectin domain containing 5A -0.22 1.22E-04 9.47E-04
    Neutrophils 2124 EVI2B ecotropic viral integration site 2B -0.23 6.91E-05 6.03E-04
    4332 MNDA myeloid cell nuclear differentiation antigen -0.25 6.50E-06 9.44E-05
    55350 VNN3 vanin 3 -0.24 2.35E-05 2.55E-04
    pDC 2833 CXCR3 C-X-C motif chemokine receptor 3 -0.27 1.02E-06 2.24E-05
    3002 GZMB granzyme B -0.27 1.31E-06 2.74E-05
    122618 PLD4 phospholipase D family member 4 -0.20 3.03E-04 1.98E-03
    171558 PTCRA pre T cell antigen receptor alpha -0.25 1.14E-05 1.45E-04
    TILs 914 CD2 CD2 molecule -0.23 4.20E-05 4.09E-04
    915 CD3D CD3d molecule -0.25 1.41E-05 1.71E-04
    916 CD3E CD3e molecule -0.24 2.49E-05 2.68E-04
    919 CD247 CD247 molecule -0.25 1.11E-05 1.42E-04
    925 CD8A CD8a molecule -0.26 5.87E-06 8.74E-05
    CD86 CD86 molecule -0.29 1.73E-07 5.66E-06
    952 CD38 CD38 molecule -0.25 1.20E-05 1.50E-04
    962 CD48 CD48 molecule -0.31 2.05E-08 1.11E-06
    963 CD53 CD53 molecule -0.30 7.56E-08 2.97E-06
    1043 CD52 CD52 molecule -0.32 9.68E-09 6.45E-07
    1536 CYBB cytochrome b-245 beta chain -0.24 2.61E-05 2.77E-04
    1794 DOCK2 dedicator of cytokinesis 2 -0.21 2.88E-04 1.91E-03
    2124 EVI2B ecotropic viral integration site 2B -0.23 6.91E-05 6.03E-04
    2533 FYB1 FYN binding protein 1 -0.22 7.16E-05 6.19E-04
    2841 GPR18 G protein-coupled receptor 18 -0.25 8.27E-06 1.13E-04
    3071 NCKAP1L NCK associated protein 1 like -0.26 4.66E-06 7.29E-05
    3560 IL2RB interleukin 2 receptor subunit beta -0.27 1.99E-06 3.82E-05
    3561 IL2RG interleukin 2 receptor subunit gamma -0.27 2.48E-06 4.48E-05
    3587 IL10RA interleukin 10 receptor subunit alpha -0.24 1.75E-05 2.04E-04
    3676 ITGA4 integrin subunit alpha 4 -0.22 7.79E-05 6.61E-04
    3932 LCK LCK proto-oncogene, Src family tyrosine kinase -0.24 1.95E-05 2.21E-04
    3937 LCP2 lymphocyte cytosolic protein 2 -0.31 2.37E-08 1.21E-06
    4689 NCF4 neutrophil cytosolic factor 4 -0.27 2.38E-06 4.35E-05
    5341 PLEK pleckstrin -0.28 6.03E-07 1.50E-05
    5788 PTPRC protein tyrosine phosphatase receptor type C -0.24 2.37E-05 2.57E-04
    5790 PTPRCAP protein tyrosine phosphatase receptor type C associated protein -0.23 4.11E-05 4.02E-04
    6352 CCL5 C-C motif chemokine ligand 5 -0.29 2.63E-07 7.84E-06
    6793 STK10 serine/threonine kinase 10 -0.20 3.71E-04 2.33E-03
    6846 XCL2 X-C motif chemokine ligand 2 -0.32 1.36E-08 8.17E-07
    7293 TNFRSF4 TNF receptor superfamily member 4 -0.22 7.34E-05 6.31E-04
    8530 CST7 cystatin F -0.23 3.78E-05 3.76E-04
    9046 DOK2 docking protein 2 -0.28 3.81E-07 1.06E-05
    9404 LPXN leupaxin -0.21 2.27E-04 1.57E-03
    10791 VAMP5 vesicle associated membrane protein 5 -0.24 2.26E-05 2.48E-04
    10859 LILRB1 leukocyte immunoglobulin like receptor B1 -0.24 2.04E-05 2.29E-04
    10870 HCST hematopoietic cell signal transducer -0.25 7.56E-06 1.05E-04
    11151 CORO1A coronin 1A -0.39 2.12E-12 1.36E-09
    22914 KLRK1 killer cell lectin like receptor K1 -0.22 1.19E-04 9.31E-04
    23157 SEPTIN6 septin 6 -0.20 3.52E-04 2.24E-03
    29851 ICOS inducible T cell costimulator -0.23 3.46E-05 3.50E-04
    51316 PLAC8 placenta associated 8 -0.22 1.21E-04 9.45E-04
    54440 SASH3 SAM and SH3 domain containing 3 -0.30 8.70E-08 3.32E-06
    GIMAP4 GTPase, IMAP family member 4 -0.27 1.06E-06 2.31E-05
    55340 GIMAP5 GTPase, IMAP family member 5 -0.24 1.99E-05 2.24E-04
    55423 SIRPG signal regulatory protein gamma -0.27 2.30E-06 4.24E-05
    55843 ARHGAP15 Rho GTPase activating protein 15 -0.26 3.03E-06 5.27E-05
    63940 GPSM3 G protein signaling modulator 3 -0.24 3.09E-05 3.19E-04
    64098 PARVG parvin gamma -0.25 1.24E-05 1.53E-04
    64231 MS4A6A membrane spanning 4-domains A6A -0.25 6.90E-06 9.87E-05
    64333 ARHGAP9 Rho GTPase activating protein 9 -0.27 1.91E-06 3.72E-05
    80342 TRAF3IP3 TRAF3 interacting protein 3 -0.25 1.30E-05 1.60E-04
    139818 DOCK11 dedicator of cytokinesis 11 -0.28 5.53E-07 1.41E-05

     | Show Table
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    We downloaded and integrated publicly available five datasets for this study. Some of the previous studies scatteredly used these datasets to implement various purposes [75,76,77]. For example, Dan Yang et al identified hub genes and therapeutic drugs by using the four datasets including GSE54388 and GSE40595 [75]. However, none of the authors specifically using these five datasets together for identifying the key biomarkers in the epithelial compartment of ovarian cancer. As per our knowledge, this is the first time, we have integrated the five transcriptomic datasets from the same platform. One of the major advantages of this study is that we selected only laser capture microdissected human ovarian cancer epithelial samples with control. Another advantage of this study is that we integrated the microarray datasets from the same platform (Affymetrix Human Genome U133 Plus 2.0) to reduce the data platform heterogeneity. Moreover, we did a meta-analysis to identify the key genes in ovarian tumor epithelial samples. This meta-analysis method usually gives more conservative results (less DEGs but more confident) [22]. The major drawback of this study is that the ovarian epithelial-associated key genes and networks identified by bioinformatics analysis have not been validated by experimental analysis. Thus, although our findings could provide potential biomarkers for ovarian cancer diagnosis and prognosis, as well as therapeutic targets, further experimental and clinical validation is necessary to transform these results into practical application in ovarian cancer treatments.

    The identification of ovarian cancer epithelial-derived key biomarkers may provide insight into the association of these genes with survival prognosis and tumor immunity. Epithelial-derived key transcriptomes may be crucial indicators of the effectiveness of ovarian cancer diagnosis and treatment.

    All authors have contributed to the research concept, design, and interpretation of the data. All authors review and approved the final version of the manuscript.

    The external fund was not collected for this project.

    The authors declare that they have no conflict of interest.

    [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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