Selection and Experimental Evaluation of Universal Primers to Study the Fungal Microbiome of Higher Plants

The impact of primer choice on results of metabarcoding studies was experimentally evaluated by analyzing fungal communities associated with leaves of four plant species. Significant differences in target specificity of primers were highlighted by a percentage of plant reads ranging from almost nothing to 30 to 35% of the total detected sequences. Overall, primer sets targeting the internal transcribed spacer 1 (ITS1) region proved to be more specific than those targeting the ITS2 region. A comparable taxa coverage was revealed for all investigated primer sets. However, each primer set detected only around 50% of the overall detected taxa highlighting that a consistent part of the actual fungal diversity remains undetected in studies conducted using a single couple of primers. The coverage was increased to 70 to 80% by combining results from two different primer sets. Some fungal taxa were preferentially or exclusively detected by certain primer sets and this association between primers and taxa was generally recurrent on several plant hosts. Data highlighted that a perfect set of primers to investigate the whole fungal diversity does not exist and that whatever the choice, only a fraction of the actual microbial diversity will be investigated. However, provided information may be valuable to select the best primers according to the objective of the analysis

Molecular analysis of Colletotrichum species in the carposphere and phyllosphere of olive.

A metagenomic approach based on the use of genus specific primers was developed and utilized to characterize Colletotrichum species associated with the olive phyllosphere and carposphere. Selected markers enabled the specific amplification of almost the entire ITS1-5.8S-ITS2 region of the rDNA and its use as barcode gene. The analysis of different olive samples (green and senescent leaves, floral residues, symptomatic and asymptomatic fruits, and litter leaves and mummies) in three different phenological phases (June, October and December) enabled the detection of 12 genotypes associated with 4 phylotypes identified as C. godetiae, C. acutatum s.s., C. gloeosporioides s.s. and C. kahawae. Another three genotypes were not identified at the level of species but were associated with the species complexes of C. acutatum, C. gloeosporioides and C. boninense sensu lato. Colletotrichum godetiae and C. acutatum s.s. were by far the most abundant while C. gloeosporioides s.s. was detected in a limited number of samples whereas ther phylotypes were rarely found. The high incidence of C. acutatum s.s. represents a novelty for Italy and more generally for the Mediterranean basin since it had been previously reported only in Portugal. As regards to the phenological phase, Colletotrichum species were found in a few samples in June and were diffused on all assessed samples in December. According to data new infections on olive tissues mainly occur in the late fall. Furthermore, Colletotrichum species seem to have a saprophytic behavior on floral olive residues. The method developed in the present study proved to be valuable and its future application may contribute to the study of cycle and aetiology of diseases caused by Colletotrichum species in many different pathosystems

Revealing Cues for Fungal Interplay in the Plant–Air Interface in Vineyards

Plant-associated microorganisms play a crucial role in plant health and productivity. Belowground microbial diversity is widely reported as a major factor in determining the composition of the plant microbiome. In contrast, much less is known about the role of the atmosphere in relation to the plant microbiome. The current study examined the hypothesis that the atmospheric microbiome influences the composition of fungal communities of the aboveground organs (flowers, fruit, and leaves) of table grape and vice versa. The atmosphere surrounding grape plantings exhibited a significantly higher level of fungal diversity relative to the nearby plant organs and shared a higher number of phylotypes (5,536 OTUs, 40.3%) with the plant than between organs of the same plant. Using a Bayesian source tracking approach, plant organs were determined to be the major source of the atmospheric fungal community (92%). In contrast, airborne microbiota had only a minor contribution to the grape microbiome, representing the source of 15, 4, and 35% of the fungal communities of leaves, flowers, and fruits, respectively. Moreover, data indicate that plant organs and the surrounding atmosphere shared a fraction of each other’s fungal communities, and this shared pool of fungal taxa serves as a two-way reservoir of microorganisms. Microbial association analysis highlighted more positive than negative interactions between fungal phylotypes. Positive interactions were more common within the same environment, while negative interactions appeared to occur more frequently between different environments, i.e., atmosphere, leaf, flower, and fruit. The current study revealed the interplay between the fungal communities of the grape phyllosphere with the surrounding air. Plants were identified as a major source of recruitment for the atmospheric microbiome, while the surrounding atmosphere contributed only a small fraction of the plant fungal community. The results of the study suggested that the plant–air interface modulates the plant recruitment of atmospheric fungi, taking a step forward in understanding the plant holobiont assembly and how the atmosphere surrounding plants plays a role in this process. The impact of plants on the atmospheric microbiota has several biological and epidemiological implications for plants and humans

Use of quantitative PCR detection methods to study biocontrol agents and phytopathogenic fungi and oomycetes in environmental samples

Quantitative polymerase chain reaction (qPCR) is a versatile technique for the accurate, sensitive, reliable and high-throughput detection and quantification of target DNA in various environmental samples, and in recent years, it has greatly contributed to the advancement of knowledge in the plant pathology field. Indeed, this technique is ideal to evaluate inoculum threshold levels and to study the epidemiology, biology and ecology of phytopathogenic fungi and oomycetes, thus opening up new research opportunities to investigate host-pathogen interactions and to address tasks related to quarantine, eradication and biosecurity. Moreover, it can be a useful tool in breeding programs. The present review analyses the most relevant applications of qPCR for the detection and quantification of filamentous fungi and oomycetes within host tissues and in soil, air and water, along with brief paragraphs focusing on new application fields such as the detection and quantification of mycotoxigenic fungi and biocontrol agents. The high potentiality of qPCR for present and future applications is highlighted together with a critical analysis of major drawbacks that need to be corrected to definitively confirm it as a preferential routine quantitative detection method. © 2013 Blackwell Verlag GmbH

Genotype networks based on ITS sequences of <i>Colletotrichum acutatum sensu lato</i> (A), <i>C. gloeosporioides s.l.</i> (B) and <i>C. boninense s.l.</i> (C), detected in different olive tissues in 3 different phenological phases (June, October and December).

<p>According to the caption (bottom right of the figure) different colors were used to connect detected genotypes and analyzed olive samples. Empty white boxes in the caption indicate analyzed samples that did not produce any positive amplification, while white boxes containing “na” indicate non-analyzed samples. The letters “T1”, “T2” and “A1” inside the circles were used to indicate sampling fields where genotypes were detected (Cfr. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114031#pone-0114031-t002" target="_blank">Table 2</a>). The size of each circle represents the relative frequency of genotypes in terms of number of samples in which they were detected. Genotypes were identified according to their phylogenetic collocation (Cfr. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114031#pone-0114031-g002" target="_blank">Fig. 1</a>) and named using the initials of the corresponding species as follows: <i>C. godetiae</i> (Glo), <i>C. acutatum s.s.</i> (Acu), <i>C. gloeosporioides s.s.</i> (Glo), <i>C. kahawae</i> (Kah), <i>C. acutatum s.l.</i> (Acusl), <i>C. gloesporioides s.l.</i> (Glosl) and <i>C. boninense s.l.</i> (Bonsl).</p

Summary of results of field surveys conducted with different olive tissues collected in 3 phenological phases from 8 different plants located in three fields (T1, T2, A1).

<p>*GPS coordinates: T1 (38°22'53.0"N, 15°56'27.5"E), T2 (38°22'15.1"N 15°55'38.3"E) and A1 (38°24'44.6"N, 15°56'23.1"E). (nd)  =  not analyzed samples; (-) analyzed samples that did not produce positive amplifications.</p><p>Detected phylotypes were associated with <i>Colletotrichum godetiae</i> (Cgo), <i>C. acutatum sensu stricto</i> (Ca), <i>C. gloeosporioides s.s.</i> (Cgl), <i>C. kahawae</i> (Ck), and non well-defined species of <i>C. acutatum</i> s.l (Casl), <i>C. gloeosporioides s.l.</i> (Cgsl) and <i>C. boninense s.l.</i> (Cbsl). Numbers in brackets represent the percentage of sequences associated with different phylotypes in each cloned PCR fragment.</p><p>Summary of results of field surveys conducted with different olive tissues collected in 3 phenological phases from 8 different plants located in three fields (T1, T2, A1).</p

List of <i>Colletotrichum</i> species and ITS genotypes identified in different olive tissues collected in three olive orchards on the Gioia Tauro plain (southern Italy).

<p>*Number of samples in which each genotype was detected</p><p>**Accession numbers</p><p>The number of samples and the orchards (Cfr. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114031#pone-0114031-t002" target="_blank">Table 2</a>) in which each genotype was detected is reported together with GenBank accession numbers for sequences. Genotypes were grouped according to their phylogenetic identification (Cfr. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114031#pone-0114031-g001" target="_blank">Fig. 1</a>).</p><p>List of <i>Colletotrichum</i> species and ITS genotypes identified in different olive tissues collected in three olive orchards on the Gioia Tauro plain (southern Italy).</p

List of species and isolates utilized to evaluate the specificity of <i>Colletotrichum</i>-genus-specific primers and corresponding positive (+) or negative (-) amplification results obtained in PCR reactions with pure culture DNA samples.

<p>List of species and isolates utilized to evaluate the specificity of <i>Colletotrichum</i>-genus-specific primers and corresponding positive (+) or negative (-) amplification results obtained in PCR reactions with pure culture DNA samples.</p

Phylogenetic trees built using unique sequences representative of all detected genotypes (♦) together with sequences of reference isolates of <i>Colletotrichum acutatum sensu lato</i>[1], <i>C. gloeosporioides s.l.</i>[2] and <i>C. boninense s.l.</i>[3].

<p>Genotypes were identified as <i>C. godetiae</i> (A), <i>C. acutatum s.s.</i> (C), <i>C. gloeosporioides s.s.</i> (D) and <i>C. kahawae</i> (F). Three additional genotypes were associated with 2 (B), 6 (D) and 3 (F) species within <i>C. acutatum s.l.</i>, <i>C. gloeosporioides s.l</i> and <i>C. boninense s.l.</i>, respectively. Separate analyses were conducted for each species complex. Numbers on nodes represent the posterior probabilities for the maximum likelihood method.</p