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A molecular diagnostic system using single nucleotide polymorphisms (SNPs) was developed to identify four Sclerotinia species: S. sclerotiorum (Lib.) de Bary, S. minor Jagger, S. trifoliorum Erikss., and the undescribed species Sclerotinia species 1. DNAs of samples are hybridized with each of five 15-bp oligonucleotide probes containing an SNP site midsequence unique to each species. For additional verification, hybridizations were performed using diagnostic single nucleotide substitutions at a 17-bp sequence of the calmodulin locus. The accuracy of these procedures was compared to that of a restriction fragment length polymorphism (RFLP) method based on Southern hybridizations of EcoRI-digested genomic DNA probed with the ribosomal DNA-containing plasmid probe pMF2, previously shown to differentiate S. sclerotiorum, S. minor, and S. trifoliorum. The efficiency of the SNP-based assay as a diagnostic test was evaluated in a blind screening of 48 Sclerotinia isolates from agricultural and wild hosts. One isolate of Botrytis cinerea was used as a negative control. The SNP-based assay accurately identified 96% of Sclerotinia isolates and could be performed faster than RFLP profiling using pMF2. This method shows promise for accurate, high-throughput species identification.Sclerotinia is distinguished morphologically from other genera in the Sclerotiniaceae (Ascomycota, Pezizomycotina, Leotiomycetes) by the production of tuberoid sclerotia that do not incorporate host tissue, by the production of microconidia that function as spermatia but not as a disseminative asexual state, and by the development of a layer of textura globulosa composing the outer tissue of apothecia (8). Two hundred forty-six species of Sclerotinia have been reported, most distinguished morphotaxonomically (Index Fungorum [www.indexfungorum.org]). These include the four species of agricultural importance now recognized plus many that are imperfectly known, seldom collected, or apparently endemic to relatively small geographic areas (2, 5, 6, 7, 8, 9, 17).The main species of phythopathological interest in the genus Sclerotinia are S. sclerotiorum (Lib.) de Bary, S. minor Jagger, S. trifoliorum Erikss., and the undescribed species Sclerotinia species 1. Sclerotinia species 1 is an important cause of disease in vegetables in Alaska (16) and has been found in association with wild Taraxacum sp., Caltha palustris, and Aconitum septentrionalis in Norway (7). It is morphologically indistinguishable from S. sclerotiorum, but it was shown to be a distinct species based on distinctive polymorphisms in sequences from internal transcribed spacer 2 (ITS2) of the nuclear ribosomal repeat (7). The other three species have been delimited using morphological, cytological, biochemical, and molecular characters (3, 8, 9, 10, 12, 15). Interestingly, given that the ITS is sufficiently polymorphic in many fungal genera to resolve species, in Sclerotinia, only species 1 and S. trifoliorum are distinguished by characteristic ITS sequence polymorphisms; S. sclerotiorum and S. minor cannot be distinguished based on ITS sequence (2, 7).Sclerotinia sclerotiorum is a necrotrophic pathogen with a broad host range (1). S. minor has a more restricted host range but causes disease in a variety of important crops such as lettuce, peanut, and sunflower crops (11). S. trifoliorum has a much narrower host range, limited to the Fabaceae (3, 8, 9). Sclerotial and ascospore characteristics also serve to differentiate among the three species. Sclerotinia minor has small sclerotia that develop throughout the colony in vitro and aggregate to form crusts on the host, while the sclerotia of S. sclerotiorum and S. trifoliorum are large and form at the colony periphery in vitro, remaining separate on the host (8, 9). The failure of an isolate to produce sclerotia or apothecia in vitro is not unusual, especially after serial cultivation (8). The presence of dimorphic, tetranucleate ascospores characterizes S. trifoliorum, while S. sclerotiorum and S. minor both have uniformly sized ascospores that are binucleate and tetranucleate, respectively (9, 14).With the apparent exception of Sclerotinia species 1, morphological characteristics are sufficient to delimit Sclerotinia species given that workers have all manifestations of the life cycle in hand. In cultures freshly isolated from infected plants, investigators usually have mycelia and sclerotia but not apothecia. Restriction fragment length polymorphisms (RFLPs) in ribosomal DNA (rDNA) are diagnostic for Sclerotinia species (3, 10), but the assay requires cloned probes (usually accessed from other laboratories) hybridized to Southern blots from vertical gels, an impractical procedure for large samples. We have analyzed sequence data from previous phylogenetic studies (2) and have identified diagnostic variation for the rapid identification of the four Sclerotinia species. The single nucleotide polymorphism (SNP) assay that we report here is amenable to a high throughput of samples and requires only PCR amplification with a standard set of primers and oligonucleotide hybridizations to Southern blots in a dot format.The SNP assay was performed using two independent sets of species-specific oligonucleotide probes, all with SNP sites shown to differentiate the four Sclerotinia species (Fig. (Fig.1).1). A panel of 49 anonymously coded isolates (Table (Table1)1) was screened using these species-specific SNP probes, as outlined in Fig. Fig.1.1. The assay was validated by comparison to Southern hybridizations of EcoRI-digested genomic DNA hybridized with pMF2, a plasmid probe containing the portion of the rDNA repeat with the 18S, 5.8S, and 26S rRNA cistrons of Neurospora crassa (4, 10).Open in a separate windowFIG. 1.Protocol for the SNP-based identification of Sclerotinia species, with diagnostic SNP sites underlined and in boldface type for each hybridization probe.
Open in a separate windowaThe annotated genome for S. sclerotiorum strain 1980 (ATCC 18683) is publicly available through the Broad Institute, Cambridge, MA (http://www.broad.mit.edu/annotation/genome/sclerotinia_sclerotiorum/Home.html).bAll isolates from New York were provided by Gary C. Bergstrom, Cornell University, Ithaca, NY. Isolates Ss001 and Ssp005 were submitted as S. sclerotiorum, and Ssp001 through Ssp004 were submitted as S. trifoliorum.cAll isolates from Alaska, submitted as Sclerotinia species 1, were provided by Lori Winton, USDA-ARS Subarctic Agricultural Research Unit, University of Alaska, Fairbanks.dAll isolates from Finland, submitted as S. trifoliorum, were provided by Tapani Yli-Mattila, University of Turku, Turku, Finland.eAll isolates from Australia, presumed to be S. sclerotiorum but requiring species confirmation, were provided by Martin Barbetti, DAF Plant Protection Branch, South Perth, Australia.fThe probes that are diagnostic for S. minor, S. sclerotiorum, S. trifoliorum, and Sclerotinia species 1 are listed, with a “+” indicating a positive hybridization for the probe and a “−” indicating no hybridization of the probe. 相似文献
TABLE 1.
Isolates and hybridization results for all SNP-based oligonucleotide probesfCollector''s isolate | Anonymous code | Prescreened presumed species identity | Origin | Host | Species-specific SNP
| ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
IGS50 | CAL448 S.trifol | CAL124 | CAL448 S.minor | RAS148 | CAL446 S.sp1 | CAL19A | CAL19B | CAL448 S.sclero | |||||
LMK18 | 49 | Botrytis cinerea | Ontario, Canada | Allium cepa | − | − | − | − | − | − | − | − | − |
FA2-1 | 3 | Sclerotinia minor | North Carolina | Arachis hypogaea | − | − | + | + | − | − | − | − | − |
W1 | 5 | Sclerotinia minor | North Carolina | Cyperus esculentus | − | − | + | + | − | − | − | − | − |
W10 | 30 | Sclerotinia minor | North Carolina | Oenothra laciniata | − | − | + | + | − | − | − | − | − |
PF1-1 | 38 | Sclerotinia minor | North Carolina | Arachis hypogaea | − | − | + | + | − | − | − | − | − |
PF18-497 | 14 | Sclerotinia minor | Oklahoma | Arachis hypogaea | − | − | + | + | − | − | − | − | − |
PF17-482 | 46 | Sclerotinia minor | Oklahoma | Arachis hypogaea | − | − | + | + | − | − | − | − | − |
PF19-519 | 48 | Sclerotinia minor | Oklahoma | Arachis hypogaea | − | − | + | + | − | − | − | − | − |
LF-27 | 20 | Sclerotinia minor | United States | Lactuca sativa | − | − | + | + | − | − | − | − | − |
AR1281 | 1 | Sclerotinia sclerotiorum | Argentina | Arachis hypogaea | − | − | − | − | − | − | + | − | + |
AR1282 | 16 | Sclerotinia sclerotiorum | Argentina | Arachis hypogaea | − | − | − | − | − | − | + | − | + |
LMK211 | 6 | Sclerotinia sclerotiorum | Canada | Brassica napus | − | − | − | − | − | − | + | − | + |
LMK57 | 25 | Sclerotinia sclerotiorum | Norway | Ranunculus ficaria | − | − | − | − | − | − | + | − | + |
LMK754 | 15 | Sclerotinia sclerotiorum | Norway | Ranunculus ficaria | − | − | − | − | − | − | − | + | + |
UR19 | 39 | Sclerotinia sclerotiorum | Uruguay | Lactuca sativa | − | − | − | − | − | − | + | − | + |
UR478 | 9 | Sclerotinia sclerotiorum | Uruguay | Lactuca sativa | − | − | − | − | − | − | + | − | + |
CA901 | 32 | Sclerotinia sclerotiorum | California | Lactuca sativa | − | − | − | − | − | − | + | − | + |
CA995 | 40 | Sclerotinia sclerotiorum | California | Lactuca sativa | − | − | − | − | − | − | + | − | + |
CA1044 | 41 | Sclerotinia sclerotiorum | California | Lactuca sativa | − | − | − | − | − | − | + | − | + |
1980a | 34 | Sclerotinia sclerotiorum | Nebraska | Phaseolus vulgaris | − | − | − | − | − | − | + | − | + |
Ss001 | 13 | Sclerotinia sclerotiorum | New Yorkb | Glycine max | − | − | − | − | − | − | + | − | + |
Ssp005 | 31 | Sclerotinia sclerotiorum | New York | Glycine max | − | − | − | − | − | − | + | − | + |
H02-V28 | 33 | Sclerotinia species 1 | Alaskac | Unknown vegetable crop | − | − | − | − | + | + | − | − | − |
H01-V14 | 26 | Sclerotinia species 1 | Alaska | Unknown vegetable crop | − | − | − | − | + | + | − | − | − |
LMK745 | 21 | Sclerotinia species 1 | Norway | Taraxacum sp. | − | − | − | − | + | + | − | − | − |
02-26 | 11 | Sclerotinia trifoliorum | Finlandd | Trifolium pratense | + | − | − | − | − | − | − | − | − |
06-14 | 29 | Sclerotinia trifoliorum | Finland | Trifolium pratense | + | + | − | − | − | − | − | − | − |
202 | 2 | Sclerotinia trifoliorum | Finland | Trifolium pratense | + | + | − | − | − | − | − | − | − |
2-L9 | 45 | Sclerotinia trifoliorum | Finland | Trifolium pratense | + | + | − | − | − | − | − | − | − |
3-A5 | 24 | Sclerotinia trifoliorum | Finland | Trifolium pratense | − | − | − | − | − | − | − | − | − |
5-L9 | 12 | Sclerotinia trifoliorum | Finland | Trifolium pratense | + | + | − | − | − | − | − | − | − |
K1 | 4 | Sclerotinia trifoliorum | Finland | Trifolium pratense | + | + | − | − | − | − | − | − | − |
K2 | 37 | Sclerotinia trifoliorum | Finland | Trifolium pratense | + | + | − | − | − | − | − | − | − |
L-112 | 23 | Sclerotinia trifoliorum | Finland | Trifolium pratense | + | + | − | − | − | − | − | − | − |
L-119 | 44 | Sclerotinia trifoliorum | Finland | Trifolium pratense | + | + | − | − | − | − | − | − | − |
LMK36 | 19 | Sclerotinia trifoliorum | Tasmania | Trifolium repens | + | + | − | − | − | − | − | − | − |
Ssp001 | 18 | Sclerotinia trifoliorum | New York | Lotus corniculatus | + | + | − | − | − | − | − | − | − |
Ssp002 | 10 | Sclerotinia trifoliorum | New York | Lotus corniculatus | + | + | − | − | − | − | − | − | − |
Ssp003 | 28 | Sclerotinia trifoliorum | New York | Lotus corniculatus | + | + | − | − | − | − | − | − | − |
Ssp004 | 36 | Sclerotinia trifoliorum | New York | Lotus corniculatus | + | + | − | − | − | − | − | − | − |
LMK47 | 43 | Sclerotinia trifoliorum | Virginia | Medicago sativa | + | + | − | − | − | − | − | − | − |
MBRS-1 | 27 | Unknown | Australiae | Brassica spp. | − | − | − | − | − | − | + | − | + |
MBRS-2 | 7 | Unknown | Australia | Brassica spp. | − | − | − | − | − | − | + | − | + |
MBRS-3 | 42 | Unknown | Australia | Brassica spp. | − | − | − | − | − | − | + | − | + |
MBRS-5 | 22 | Unknown | Australia | Brassica spp. | − | − | − | − | − | − | + | − | + |
WW-1 | 35 | Unknown | Australia | Brassica spp. | − | − | − | − | − | − | + | − | + |
WW-2 | 8 | Unknown | Australia | Brassica spp. | − | − | − | − | − | − | + | − | + |
WW-3 | 17 | Unknown | Australia | Brassica spp. | − | − | − | − | − | − | + | − | + |
WW-4 | 47 | Unknown | Australia | Brassica spp. | − | − | − | − | − | − | + | − | + |
5.
Banumathi Sankaran Shilah A. Bonnett Kinjal Shah Scott Gabriel Robert Reddy Paul Schimmel Dmitry A. Rodionov Valérie de Crécy-Lagard John D. Helmann Dirk Iwata-Reuyl Manal A. Swairjo 《Journal of bacteriology》2009,191(22):6936-6949
GTP cyclohydrolase I (GCYH-I) is an essential Zn2+-dependent enzyme that catalyzes the first step of the de novo folate biosynthetic pathway in bacteria and plants, the 7-deazapurine biosynthetic pathway in Bacteria and Archaea, and the biopterin pathway in mammals. We recently reported the discovery of a new prokaryotic-specific GCYH-I (GCYH-IB) that displays no sequence identity to the canonical enzyme and is present in ∼25% of bacteria, the majority of which lack the canonical GCYH-I (renamed GCYH-IA). Genomic and genetic analyses indicate that in those organisms possessing both enzymes, e.g., Bacillus subtilis, GCYH-IA and -IB are functionally redundant, but differentially expressed. Whereas GCYH-IA is constitutively expressed, GCYH-IB is expressed only under Zn2+-limiting conditions. These observations are consistent with the hypothesis that GCYH-IB functions to allow folate biosynthesis during Zn2+ starvation. Here, we present biochemical and structural data showing that bacterial GCYH-IB, like GCYH-IA, belongs to the tunneling-fold (T-fold) superfamily. However, the GCYH-IA and -IB enzymes exhibit significant differences in global structure and active-site architecture. While GCYH-IA is a unimodular, homodecameric, Zn2+-dependent enzyme, GCYH-IB is a bimodular, homotetrameric enzyme activated by a variety of divalent cations. The structure of GCYH-IB and the broad metal dependence exhibited by this enzyme further underscore the mechanistic plasticity that is emerging for the T-fold superfamily. Notably, while humans possess the canonical GCYH-IA enzyme, many clinically important human pathogens possess only the GCYH-IB enzyme, suggesting that this enzyme is a potential new molecular target for antibacterial development.The Zn2+-dependent enzyme GTP cyclohydrolase I (GCYH-I; EC 3.5.4.16) is the first enzyme of the de novo tetrahydrofolate (THF) biosynthesis pathway (Fig. (Fig.1)1) (38). THF is an essential cofactor in one-carbon transfer reactions in the synthesis of purines, thymidylate, pantothenate, glycine, serine, and methionine in all kingdoms of life (38), and formylmethionyl-tRNA in bacteria (7). Recently, it has also been shown that GCYH-I is required for the biosynthesis of the 7-deazaguanosine-modified tRNA nucleosides queuosine and archaeosine produced in Bacteria and Archaea (44), respectively, as well as the 7-deazaadenosine metabolites produced in some Streptomyces species (33). GCYH-I is encoded in Escherichia coli by the folE gene (28) and catalyzes the conversion of GTP to 7,8-dihydroneopterin triphosphate (55), a complex reaction that begins with hydrolytic opening of the purine ring at C-8 of GTP to generate an N-formyl intermediate, followed by deformylation and subsequent rearrangement and cyclization of the ribosyl moiety to generate the pterin ring in THF (Fig. (Fig.1).1). Notably, the enzyme is dependent on an essential active-site Zn2+ that serves to activate a water molecule for nucleophilic attack at C-8 in the first step of the reaction (2).Open in a separate windowFIG. 1.Reaction catalyzed by GCYH-I, and metabolic fate of 7,8-dihydroneopterin triphosphate.A homologous GCYH-I is found in mammals and other higher eukaryotes, where it catalyzes the first step of the biopterin (BH4) pathway (Fig. (Fig.1),1), an essential cofactor in the biosynthesis of tyrosine and neurotransmitters, such as serotonin and l-3,4-dihydroxyphenylalanine (3, 52). Recently, a distinct class of GCYH-I enzymes, GCYH-IB (encoded by the folE2 gene), was discovered in microbes (26% of sequenced Bacteria and most Archaea) (12), including several clinically important human pathogens, e.g., Neisseria and Staphylococcus species. Notably, GCYH-IB is absent in eukaryotes.The distribution of folE (gene product renamed GCYH-IA) and folE2 (GCYH-IB) in bacteria is diverse (12). The majority of organisms possess either a folE (65%; e.g., Escherichia coli) or a folE2 (14%; e.g., Neisseria gonorrhoeae) gene. A significant number (12%; e.g., B. subtilis) possess both genes (a subset of 50 bacterial species is shown in Table Table1),1), and 9% lack both genes, although members of the latter group are mainly intracellular or symbiotic bacteria that rely on external sources of folate. The majority of Archaea possess only a folE2 gene, and the encoded GCYH-IB appears to be necessary only for the biosynthesis of the modified tRNA nucleoside archaeosine (44) except in the few halophilic Archaea that are known to synthesize folates, such as Haloferax volcanii, where GCYH-IB is involved in both archaeosine and folate formation (13, 44).
TABLE 1.
Distribution and candidate Zur-dependent regulation of alternative GCYH-I genes in bacteriaaOpen in a separate windowaGenes that are preceded by candidate Zur binding sites.bZur-regulated cluster is on the virulence plasmid pLVPK.cExamples of organisms with no folE genes are in boldface type.dZn-dependent regulation of B. subtilis folE2 by Zur was experimentally verified (17).Expression of the Bacillus subtilis folE2 gene, yciA, is controlled by the Zn2+-dependent Zur repressor and is upregulated under Zn2+-limiting conditions (17). This led us to propose that the GCYH-IB family utilizes a metal other than Zn2+ to allow growth in Zn2+-limiting environments, a hypothesis strengthened by the observation that an archaeal ortholog from Methanocaldococcus jannaschii has recently been shown to be Fe2+ dependent (22). To test this hypothesis, we investigated the physiological role of GCYH-IB in B. subtilis, an organism that contains both isozymes, as well as the metal dependence of B. subtilis GCYH-IB in vitro. To gain a structural understanding of the metal dependence of GCYH-IB, we determined high-resolution crystal structures of Zn2+- and Mn2+-bound forms of the N. gonorrhoeae ortholog. Notably, although the GCYH-IA and -IB enzymes belong to the tunneling-fold (T-fold) superfamily, there are significant differences in their global and active-site architecture. These studies shed light on the physiological significance of the alternative folate biosynthesis isozymes in bacteria exposed to various metal environments, and offer a structural understanding of the differential metal dependence of GCYH-IA and -IB. 相似文献6.
Cotyledons of tomato seedlings that germinated in a 20 µM AlK(SO4)2 solution remained chlorotic while those germinated in an aluminum free medium were normal (green) in color. Previously, we have reported the effect of aluminum toxicity on root proteome in tomato seedlings (Zhou et al.1). Two dimensional DIGE protein analysis demonstrated that Al stress affected three major processes in the chlorotic cotyledons: antioxidant and detoxification metabolism (induced), glyoxylate and glycolytic processes (enhanced), and the photosynthetic and carbon fixation machinery (suppressed).Key words: aluminum, cotyledons, proteome, tomatoDifferent biochemical processes occur depending on the developmental stages of cotyledons. During early seed germination, before the greening of the cotyledons, glyoxysomes enzymes are very active. Fatty acids are converted to glucose via the gluconeogenesis pathway.2,3 In greening cotyledons, chloroplast proteins for photosynthesis and leaf peroxisomal enzymes in the glycolate pathway for photorespiration are metabolized.2–4 Enzymes involved in regulatory mechanisms such as protein kinases, protein phosphatases, and mitochondrial enzymes are highly expressed.3,5,6The chlorotic cotyledons are similar to other chlorotic counterparts in that both contains lower levels of chlorophyll, thus the photosynthetic activities are not as active. In order to understand the impact of Al on tomato cotyledon development, a comparative proteome analysis was performed using 2D-DIGE following the as previously described procedure.1 Some proteins accumulated differentially in Al-treated (chlorotic) and untreated cotyledons (Fig. 1). Mass spectrometry of tryptic digestion fragments of the proteins followed by database search has identified some of the differentially expressed proteins (Open in a separate windowFigure 1Image of protein spots generated by Samspot analysis of Al treated and untreated tomato cotyledons proteomes separated on 2D-DIGE.
Open in a separate window 相似文献
Table 1
Proteins identified from tomato cotyledons of seeds germinating in Al-solutionSpot No. | Fold (treated/ctr) | ANOVA (p value) | Annotation | SGN accession |
1 | 2.34 | 0.001374 | 12S seed storages protein (CRA1) | SGN-U314355 |
2 | 2.13 | 0.003651 | unidentified | |
3 | 2.0 | 0.006353 | lipase class 3 family | SGN-U312972 |
4 | 1.96 | 0.002351 | large subunit of RUBISCO | SGN-U346314 |
5 | 1.95 | 2.66E-05 | arginine-tRNA ligase | SGN-U316216 |
6 | 1.95 | 0.003343 | unidentified | |
7 | 1.78 | 0.009219 | Monodehydroascorbate reductase (NADH) | SGN-U315877 |
8 | 1.78 | 0.000343 | unidentified | |
9 | 1.75 | 4.67E-05 | unidentified | |
12 | 1.70 | 0.002093 | unidentified | |
13 | 1.68 | 0.004522 | unidentified | |
15 | 1.66 | 0.019437 | Glutamate dehydrogenase 1 | SGN-U312368 |
16 | 1.66 | 0.027183 | unidentified | |
17 | 1.62 | 2.01E-08 | Major latex protein-related, pathogenesis-related | SGN-U312368 |
18 | −1.61 | 0.009019 | RUBisCo activase | SGN-U312543 |
19 | 1.61 | 0.003876 | Cupin family protein | SGN-U312537 |
20 | 1.60 | 0.000376 | unidentified | |
22 | 1.59 | 0.037216 | unidentified | |
0.003147 | unidentified | |||
29 | −1.56 | 0.001267 | RUBisCo activase | SGN-U312543 |
35 | 1.52 | 0.001955 | unidentified | |
40 | 1.47 | 0.007025 | unidentified | |
41 | 1.47 | 0.009446 | unidentified | |
45 | 1.45 | 0.001134 | unidentified | |
59 | −1.40 | 5.91E-05 | 12 S seed storage protein | SGN-U314355 |
61 | 1.39 | 1.96E-05 | MD-2-related lipid recognition domain containing protein | SGN-U312452 |
65 | 1.37 | 0.000608 | triosephosphate isomerase, cytosolic | SGN-U312988 |
68 | 1.36 | 0.004225 | unidentified | |
81 | 1.32 | 0.001128 | unidentified | |
82 | −1.31 | 0.001408 | 33 kDa precursor protein of oxygen-evolving complex | SGN-U312530 |
87 | 1.30 | 0.002306 | unidentified | |
89 | −1.3 | 0.000765 | unidentified | |
92 | 1.29 | 0.000125 | superoxide dismutase | SGN-U314405 |
98 | 1.28 | 0.000246 | triosephosphate isomerase, cytosolic | SGN-U312988 |
7.
Ari Jumpponen Karen Keating Gary Gadbury Kenneth L Jones J David Mattox 《Plant signaling & behavior》2010,5(9):1157-1161
Diverse fungal mutualists, pathogens and saprobes colonize plant leaves. These fungi face a complex environment, in which stochastic dispersal interplays with abiotic and biotic filters. However, identification of the specific factors that drive the community assembly seems unattainable. We mined two broad data sets and identified chemical elements, to which dominant molecular operational taxonomic units (OTUs) in the foliage of a native tree respond most extremely. While many associations could be identified, potential complicating issues emerged. Those were related to unevenly distributed OTU frequency data, a large number of potentially explanatory variables and the disproportionate effects of outlier observations.Key words: community assembly, environmental filter, fungi, heavy metal enrichment, nutrient enrichment, oak, Quercus macrocarpaHyperdiverse fungal communities inhabit the foliage of most plants1,2 and these fungal communities have been reported for virtually every plant that has been examined.3 Baas-Becking hypothesis states that environment selects microbial communities from the abundant and possibly globally distributed propagule pools.4 Although the foliage-associated communities—like other microbial communities—are suspected to be sensitive to environmental drivers, determination of the mechanisms that control the assembly of these foliar communities has remained difficult and elusive. Some of the proposed mechanisms include distance limitations to propagule dispersal,5–7 volume limitations to propagule loads,7 or limitations set by the environmental conditions either on the scale of the site of fungal colonization8 or more broadly on a landscape level.6,9 The forces that may control the fungal community assembly are overlaid by additional biotic controls that include compatibilities between the fungi and host species10,11 or genotypes6,12 and the competitive or facilitative interactions among the component fungal genotypes.6,10–13 Although a variety of potential controls for the foliage-associated fungal communities have been speculated, very little consensus exists on the relative importance of the different drivers. For example, while macronutrient and heavy metal enrichment may have an influence on the composition fungal communities14 and populations,15 relative importance of various chemical elements in the foliage remains yet to be investigated.To evaluate the use of multi-element fingerprinting data produced by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) in combination with high throughput 454-pyrosequencing for determining influential chemical elements in structuring of the leaf-associated fungal communities, we mined a recent dataset16 that explored the effects of urbanization on the diversity and composition of the fungal communities associated with a native tree Quercus macrocarpa. From a total list of more than 700 non-singleton fungal OTUs, we selected fifty with highest overall frequency to provide an observationrich dataset for elemental effect assessment; these OTUs accounted for 84.5% of all sequences. Even so, many of these OTUs had a number of zero frequencies (Fig. 1), highlighting one of the difficulties in the use of environmental sequencing data. We omitted one OTU (OTU630 with a likely affinity to Trimmatostroma cordae [Mycosphaerellaceae]) that was strongly affected by the original land use design (urbanization; Wilcoxon rank sum test with a Bonferroni adjustment) and therefore unlikely to be representative for the present analyses of elemental drivers. This OTU was replaced with one with the next highest frequency. The frequencies of these 50 OTUs were investigated in the context of concentrations for 29 elements after the omission of five (Ag, Au, C, δ13C, δ15N) in the final analyses because of their strong association with the land use or the difficulty of finding a biological relevance. Of the remaining elements three (Fe, Cr and Ni) had pairwise correlations exceeding 0.98 between the three pairings; others showed no similar high correlations. To allow comparable evaluation across the broad array of elements, all concentrations were standardized to have a mean equal to zero and a standard deviation equal to one.Open in a separate windowFigure 1Rank-ordered distribution of observed frequencies for those OTU s whose frequency had an extreme slope when associated with the concentrations of one or more chemical elements in the mixed effects model. The asterisk denotes one extreme frequency for OTU 313 with a value 0.8636. Numbers in parentheses indicate the number of observations with a frequency equal to zero. The OTU s were assigned to approximate taxa using BLAST:20 425: Alternaria alternata (Pleosporaceae); 46: Phoma glomerata (Pleosporaceae); 686: Aureobasidium pullulans (Dothioraceae); 520: Davidiella tassiana (Davidiellaceae); 567: Cladosporioum tenuissimum (Davidiellaceae); 313 Oidium heveae (mitosporic Erysiphaceae); 586: Erysiphe hypogena (Erysiphaceae); 671: Mycosphaerella microsora (Mycosphaerellaceae); 555: Pestalotiopsis sp. (Amphisphaeriaceae); 607: Pleiochaeta setosa (incertae sedis).To rank elements according to their magnitude of association with the abundance of each OTU, a total of 1,450 models (50 OTUs times 29 elements) relating element concentration to OTU abundance were fit to the data. For each model, OTU frequency was the dependent variable, element concentration and time (a factor with three levels) were fixed effects, and—to account for the spatial arrangement of the experimental units—random effects associated with tree nested within site were included in the error structure. Time by element interactions were also investigated and tested using a likelihood ratio test. These mixed effect models were fit using R and the package lme4 (www.rproject.org).Statistical “tests of significance” that produce p-values can be sensitive to assumptions or outliers. Because of this and the fact that our analyses evaluated a total of 1,450 models, p-values themselves were not considered a reliable measure of importance when associating elements with OTU frequency. Instead, we emphasized metrics that highlight extraordinary findings rather than rely on tests of statistical significance. This approach facilitates finding few elements that have the strongest effect on OTU frequency. Note that the use of standardized element concentrations (above) provided slope coefficients that are comparable across all models. “Extreme slopes”, i.e., models where the OTU response to element concentration was strongest, were identified as those with estimated slope coefficients in the lower or upper 2.5 percentile, i.e., those farther than 1.8 standard deviations from the mean across all estimated slopes (Fig. 2). Using this approach, we identified a total of 69 models with extreme slopes (Open in a separate windowFigure 2Distribution of estimated slopes (i.e., the slope for element concentration) for a model relating OTU frequency to element concentration, time and a concentration by time interaction, including a tree-nested-within-site random effect. The mean across all 1,450 OTU s is approximately zero; the two vertical lines identify upper and lower 2.5 percentiles, beyond which the slopes were considered extreme (large black symbols). The horizontal line identifies the cut off maximum leverage (0.24), above which the slopes were considered to have observations with high leverage. Models with observations with a high leverage were tested for extreme slopes by refitting without those observations. Models are ranked from bottom to top in order of increasing leverage and the element for which the high-leverage observations and extreme slopes were recorded are identified on the right y-axis.
Open in a separate windowPositive slopes are indicated by +, negative by −. Parentheses indicate where a statistically significant (α = 0.05) interaction was observed (likelihood ratio test). Extreme slopes with observations with high leverage are identified by an asterisk (*) and those where omission of high-leverage observations lead to a non-extreme slopes are identified by two asterisks (**). Note that eight of the ten OTU s in the table had an extreme slope with at least one element concentration after accounting for high leverage and interactions in the model.Unfortunately, the models with extreme slopes were often affected by high leverage observations (outliers in the explanatory variables) that may have exerted substantial influence on the magnitudes of the slopes. We accounted for this by computing leverage values based on the fixed effect model matrix (element concentration and time) for each model. High leverage was defined as those observations with leverage approximately twice the mean leverage over all samples for a particular model as is considered conventional by some authors.17 This value was approximately 0.24 for our models. The models with high leverage and extreme slopes were re-evaluated by refitting the model to the data after omission of the influential observations. Of the 69 models with extreme slopes only 22 were void of influential observations by our metric (Fig. 1). Our analyses included the possibility of identifying those models that were affected by numerous low frequencies and a few high frequency observations. We argue that the few higher frequencies are most likely indicative of those elements that also have extreme concentrations in the same samples; we did not want to miss such findings. Second, no one element controls the occurrence of all or even majority, of the OTUs, but the OTUs appear to respond positively or negatively to different drivers. This is strongly visible even among the eight that remained through our rigorous evaluation of a vast number of models. This can be interpreted in the context of a niche. Foliage represents a complex abiotic physicochemical habitat within which organisms are sorted based by stochastic arrival parameters, but also by either environmental tolerances or nutritional preferences. Those fungi best able to colonize and invade the available substrate under any given combination of the complex physical and chemical environmental matrix will persist and be detected most frequently. Thirdly, even for one OTU, many elements may have strong and occasionally opposing effects. For example, for OTU425, B, Cd, Ce, Cu, Na, had positive effects, whereas N, P, Sc had negative effects (18,19 it is tempting to speculate on species replacement or on tolerance to nutrient enrichment as a result of changes in the abiotic chemical environment. However, one must exercise caution: as we point out above, a number of other alternative factors come to play when a correlative relationship like this is considered across two discrete and complex datasets. Several heavy metal concentrations also showed either positive or negative associations with the fungal OTU frequencies. To exemplify, the frequencies of OTUs 313 and 425 were positively associated with the concentrations of Cd and OTU 46 was positively associated with Zn, whereas OTUs 313 and 586 were negatively associated Hg and Pb concentrations, respectively. Does this mean that these species differ in their sensitivities to these particular heavy metals? Not necessarily, but these observational data provide a starting point for more explicit hypothesis-driven experiments that allow for specific elucidation of the fungal responses to these elements and may guide future experimentation.We conducted a high-dimensional exploratory analysis to evaluate potential effects of element concentration on OTU frequencies. Using a repeated measures mixed effects model, we were able to compile a brief list of chemical elements with the most likely (based on these data) strongest effects on the abundances of the dominant components of the phyllosphere-associated fungal communities. Complicating the use of usual methods of statistical inference (i.e., use of p-values) was the sparseness in the occurrence of many OTUs across samples and outlying observations in the concentration of some elements. We chose the extreme slopes approach that allowed ranking associations between OTU frequency and element concentration with no assumptions regarding normality or equivariance that may be violated using traditional tools of inference (e.g., Analysis of Variance). Still, some of the observed associations may have been affected by extreme leverage points (outliers in the explanatory variables) and these were accounted for in the present analyses by model re-evaluation after omission of the high-leverage observations. While our analyses identified a number of biologically meaningful associations between chemical elements and molecular OTUs, rigorous experimentation is mandatory to establish causative relationships. 相似文献
Table 1
Slopes identified as extreme in our analysesElement | OTU 425 | OTU 46 | OTU 686 | OTU 520 | OTU 567 | OTU 313 | OTU 586 | OTU 671 | OTU 555 | OTU 607 |
B | +* | +* | +* | |||||||
Ba | − | − | ||||||||
Ca | −* | (−)* | −* | (+)* | +** | |||||
Cd | + | + | (+) | |||||||
Ce | + | (+) | − | |||||||
Co | +** | −* | ||||||||
Cr | −* | |||||||||
Cu | +* | −** | −* | |||||||
Fe | −* | |||||||||
Hg | +** | −* | ||||||||
K | (−) | + | − | + | (−) | (+) | ||||
Li | (+)* | (+)* | −* | |||||||
Mn | +* | |||||||||
Mo | −* | |||||||||
N | −* | +* | (+)* | |||||||
Na | + | |||||||||
Ni | −* | |||||||||
P | −* | (+)* | ||||||||
Pb | +** | −* | ||||||||
Rb | +** | +* | −* | −* | ||||||
S | (−)* | +* | +* | +* | ||||||
Sc | − | (−) | − | |||||||
Se | − | |||||||||
Sn | − | − | (−) | |||||||
Sr | +* | |||||||||
Y | +* | −* | +* | (+)* | ||||||
Zn | (−)* | +* | −** | (+)* |
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Holger Fahnenstich Ulf-Ingo Flügge Verónica G Maurino 《Plant signaling & behavior》2008,3(12):1122-1125
Reactive oxygen species (ROS) represent both toxic by-products of aerobic metabolism as well as signaling molecules in processes like growth regulation and defense pathways. The study of signaling and oxidative-damage effects can be separated in plants expressing glycolate oxidase in the plastids (GO plants), where the production of H2O2 in the chloroplasts is inducible and sustained perturbations can reproducibly be provoked by exposing the plants to different ambient conditions. Thus, GO plants represent an ideal non-invasive model to study events related to the perception and responses to H2O2 accumulation. Metabolic profiling of GO plants indicated that under high light a sustained production of H2O2 imposes coordinate changes on central metabolic pathways. The overall metabolic scenario is consistent with decreased carbon assimilation, which results in lower abundance of glycolytic and tricarboxylic acid cycle intermediates, while simultaneously amino acid metabolism routes are specifically modulated. The GO plants, although retarded in growth and flowering, can complete their life cycle indicating that the reconfiguration of the central metabolic pathways is part of a response to survive and thus, to adapt to stress conditions imposed by the accumulation of H2O2 during the light period.Key words: Arabidopsis thaliana, H2O2, oxidative stress, reactive oxygen species, signalingReactive oxygen species (ROS) are key molecules in the regulation of plant development, stress responses and programmed cell death. Depending on the identity of ROS species or its subcellular production site, different cellular responses are provoked.1 To assess the effects of metabolically generated H2O2 in chloroplasts, we have recently generated Arabidopsis plants in which the peroxisomal GO was targeted to chloroplasts.2 The GO overexpressing plants (GO plants) show retardation in growth and flowering time, features also observed in catalase, ascorbate peroxidase and MnSOD deficient mutants.3–5 The analysis of GO plants indicated that H2O2 is responsible for the observed phenotype. GO plants represent an ideal non-invasive model system to study the effects of H2O2 directly in the chloroplasts because H2O2 accumulation can be modulated by growing the plants under different ambient conditions. By this, growth under low light or high CO2 concentrations minimizes the oxygenase activity of RubisCO and thus the flux through GO whereas the exposition to high light intensities enhances photorespiration and thus the flux through GO.Here, we explored the impact of H2O2 production on the primary metabolism of GO plants by assessing the relative levels of various metabolites by gas chromatography coupled to mass spectrometry (GC-MS)6 in rosettes of plants grown at low light (30 µmol quanta m−2 s−1) and after exposing the plants for 7 h to high light (600 µmol quanta m−2 s−1). The results obtained for the GO5 line are shown in After 1 h at 30 µE After 7 h at 600 µE Alanine 0.88 ± 0.05 2.83 ± 0.68 Asparagine 1.39 ± 0.12 3.64 ± 0.21 Aspartate 0.88 ± 0.03 1.65 ± 0.10 GABA 1.14 ± 0.05 1.13 ± 0.05 Glutamate 0.97 ± 0.04 1.51 ± 0.07 Glutamine 1.06 ± 0.11 1.87 ± 0.06 Glycine 1.23 ± 0.07 0.30 ± 0.02 Isoleucine 3.52 ± 0.40 3.00 ± 0.15 Leucine 1.36 ± 0.22 0.57 ± 0.06 Lysine 1.49 ± 0.13 0.38 ± 0.02 Methionine 0.96 ± 0.05 4.54 ± 0.51 Phenylalanine 0.95 ± 0.03 0.94 ± 0.04 Proline 1.32 ± 0.22 1.60 ± 0.13 Serine 1.05 ± 0.04 1.49 ± 0.15 Threonine 4.74 ± 0.17 5.51 ± 0.34 Valine 0.91 ± 0.13 0.29 ± 0.02 Citrate/Isocitrate 0.65 ± 0.02 0.64 ± 0.02 2-oxoglutarate 0.95 ± 0.11 0.76 ± 0.05 Succinate 0.78 ± 0.04 0.72 ± 0.02 Fumarate 0.64 ± 0.03 0.31 ± 0.01 Malate 0.74 ± 0.03 0.60 ± 0.02 Pyruvate 1.19 ± 0.28 0.79 ± 0.04 Ascorbate 1.13 ± 0.14 2.44 ± 0.45 Galactonate-γ-lactone 1.81 ± 0.40 1.62 ± 0.28 Fructose 1.20 ± 0.13 0.37 ± 0.01 Glucose 1.38 ± 0.17 0.30 ± 0.01 Mannose 0.90 ± 0.27 1.34 ± 0.28 Sucrose 1.04 ± 0.07 0.49 ± 0.02 Fructose-6P 0.82 ± 0.15 1.20 ± 0.15 Glucose-6P 0.87 ± 0.06 1.25 ± 0.18 3-PGA 1.13 ± 0.11 0.35 ± 0.02 DHAP 1.38 ± 0.09 1.26 ± 0.08 Glycerate 0.99 ± 0.04 0.67 ± 0.01 Glycerol 1.07 ± 0.04 1.12 ± 0.05 Shikimate 1.18 ± 0.04 0.35 ± 0.01 Salicylic acid 1.04 ± 0.18 0.66 ± 0.18