Protective Effects of Systiva® Seed Treatment Fungicide for the Control of Winter Wheat Foliar Diseases Caused at Early Stages Due to Climate Change

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Abstract

Foliar fungal diseases are a serious threat to winter wheat production and climate change appears to favor pathogens associated with leaf blotch and tan spot symptoms in the Mediterranean area. The present work aimed to highlight these risks and propose appropriate disease management strategies by evaluating the seed treatment with the Systiva® (BASF) fungicide as a means to protect the crop against foliar fungal infections during the early growing stages. Towards that aim, plant tissue symptoms affected by the pathogens Pyrenophora tritici-repentis and Septoria spp. were systematically recorded in a study field in the region of Larissa, central Greece for three years (2016–2018), and the findings were associated with the monthly weather anomalies. Consequently, for the growing period of 2021–2022, a field experiment was established in the same disease prone field, comparing different doses of the seed treatment with Systiva® fungicide against leaf blotch and tan spot diseases. The evaluation was made by visual disease assessments, remote sensing with an unmanned aerial vehicle (UAV) and metagenomics analysis. Parallel measurements on straw residues were also made to characterize the plant residues perithecia (pseudothecia). Visual leaf disease assessments and UAV remote sensing data showed that Systiva® treatments at doses of 125 cc and 150 cc per 100 kg of wheat seed can reduce the percentage of infected wheat plants caused by foliar fungal pathogens at wheat growth stages GS23-25 and GS30-31. Moreover, the metagenomics analyses performed on the microbial communities revealed that Systiva® can decrease the degree of infection by P. tritici-repentis and Z. tritici but do not provide sufficient protection against P. nodorum. Foliar diseases were influenced by the soil surface area covered with straw residue with a high proportion of natural inoculum (pseudothecia/ascospores).

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DADA2: High resolution sample inference from Illumina amplicon data

Benjamin J. Callahan, Paul McMurdie, Michael Rosen … (2016)

We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.

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Qiong Wang, George Garrity, James Tiedje … (2007)

The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.

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James J Kozich, Sarah Westcott, Nielson T Baxter … (2013)

Rapid advances in sequencing technology have changed the experimental landscape of microbial ecology. In the last 10 years, the field has moved from sequencing hundreds of 16S rRNA gene fragments per study using clone libraries to the sequencing of millions of fragments per study using next-generation sequencing technologies from 454 and Illumina. As these technologies advance, it is critical to assess the strengths, weaknesses, and overall suitability of these platforms for the interrogation of microbial communities. Here, we present an improved method for sequencing variable regions within the 16S rRNA gene using Illumina's MiSeq platform, which is currently capable of producing paired 250-nucleotide reads. We evaluated three overlapping regions of the 16S rRNA gene that vary in length (i.e., V34, V4, and V45) by resequencing a mock community and natural samples from human feces, mouse feces, and soil. By titrating the concentration of 16S rRNA gene amplicons applied to the flow cell and using a quality score-based approach to correct discrepancies between reads used to construct contigs, we were able to reduce error rates by as much as two orders of magnitude. Finally, we reprocessed samples from a previous study to demonstrate that large numbers of samples could be multiplexed and sequenced in parallel with shotgun metagenomes. These analyses demonstrate that our approach can provide data that are at least as good as that generated by the 454 platform while providing considerably higher sequencing coverage for a fraction of the cost.