Peach trees showing witches’‐broom disease symptoms in the northwest of Iran were sampled for phytoplasma detection. PCR assays and Sanger sequence analyses indicated that ʻCandidatus Phytoplasma ...phoeniciumʼ was associated with peach witchesʼ‐broom disease. Virtual RFLP analyses of the 16S rRNA gene indicated that ʻCa. Phytoplasma phoeniciumʼ strain, which was prevalent in the northwest of Iran belonged to 16SrIX‐C subgroup. For the genomic characterization of Iranian ʻCa. Phytoplasma phoeniciumʼ strain, total DNA extracted from the infected peach trees was subjected to Illumina next‐generation sequencing. The NGS sequencing resulted in 41157647 read pairs of raw data. The raw reads length was 150 bp and the insert size was 350 bp. Assembly and genes clustering finally resulted in 750 contigs with a total size of 228345 bp. The GC content of sequence was 33.2%, N50 value was 259 and L50 value was 256. According to NCBI‐ PGAP annotation the genome of Prunus persica phytoplasma PP2 contained 410 genes including 377 CDSs with protein, 10 rRNAs, nine tRNAs and 14 pseudogenes.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
In this study, Russian olive trees exhibiting witches’-broom symptoms were collected from urban green areas in Tabriz, in the northwest of Iran. PCR analysis confirmed that phytoplasma caused the ...disease and, according to the resulting Sanger sequencing electropherogram, a mixed infection with two or more phytoplasma species within the Russian olive trees was revealed. Next-generation sequencing analyses, using the Illumina method, were performed on total DNA from the infected Russian olive plants to recognize the microbial genomic content and assemble the whole genome of the causative pathogen(s). The use of MetaphlAn2 and Kraken2 to analyze species composition revealed the very diverse and unique compositions of different Prokaryotic and Eukaryotic species within the infected plants. Several bacteria and fungi were discovered inside the samples, among which Mycoplasmatota was significantly dominating. Interestingly, the results also revealed a high level of endosymbiotic bacteria and Archaea (Methanobacteria) genome contents within the samples. Bowtie2, metaSPAdes, and CD-HIT pipelines were used to perform the initial genome assembly, and the whole genome of the notable phytoplasma species was assembled and submitted to Genbank.
Colorectal cancer (CRC) is one of the most prevalent cancers in the world, especially in developed countries. In different studies, the association between CRC and dysbiosis of gut microbiome has ...been reported. However, most of these works focus on the taxonomic variation of the microbiome, which presents little, if any, functional insight about the reason behind and/or consequences of microbiome dysbiosis. In this study, we used a previously reported metagenome dataset which is obtained by sequencing 156 microbiome samples of healthy individuals as the control group (Co), as well as microbiome samples of patients with advanced colorectal adenoma (Ad) and colorectal carcinoma (Ca). Features of the microbiome samples have been analyzed at the level of species, as well as four functional levels, i.e., gene, KEGG orthology (KO) group, Enzyme Commission (EC) number, and reaction. It was shown that, at each of these levels, certain features exist which show significant changing trends during cancer progression. In the next step, a list of these features were extracted, which were shown to be able to predict the category of Co, Ad, and Ca samples with an accuracy of >85%. When only one group of features (species, gene, KO group, EC number, reaction) was used, KO-related features were found to be the most successful features for classifying the three categories of samples. Notably, species-related features showed the least success in sample classification. Furthermore, by applying an independent test set, we showed that these performance trends are not limited to our original dataset. We determined the most important classification features at each of the four functional levels. We propose that these features can be considered as biomarkers of CRC progression. Finally, we show that the intra-diversity of each sample at the levels of bacterial species and genes is much more than those of the KO groups, EC numbers, and reactions of that sample. Therefore, we conclude that the microbiome diversity at the species level, or gene level, is not necessarily associated with the diversity at the functional level, which again indicates the importance of KO-, EC-, and reaction-based features in metagenome analysis. The source code of proposed method is freely available from https://www.bioinformatics.org/mamed.
The “inversion recovery experiment” is used as a tool in nuclear magnetic resonance spectroscopy for characterization and identification of basic important amino acids. This methodology is based on ...determination of “longitudinal relaxation time (
T
1
)”of Carbon-13 of these molecular structures which relates to assignment of each carbon atom of amino acids. Not only the chemical shifts and frequencies of carbon atoms are different, but also the relaxation times of them in scale of seconds or less are different, without much overlapping. Due to larger shift effects in
13
C NMR spectra and larger paramagnetic origin for carbon-13 and other differences, it has made us to rely on
13
C nucleus as main clue in this work rather than
1
H nucleus. This procedure has helped us to identify the amino acids in terms of both “frequency” and “time” of relaxation for each carbon atom simultaneously. Applying “average linkage” as an agglomerative clustering method to the feature vectors extracted from NMR spectra of amino acids, a hierarchical clustering is provided. The obtained clusters reveal notable relationships between amino acids in a same cluster. After a time gap, the proposed clusters of amino acids which have similarities and differences with traditional grouping of amino acids provide a new perspective on amino acids characterization and related studies such as defining descriptors for proteins and peptides based on their sequence information.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Abstract
Peach trees showing witches’‐broom disease symptoms in the northwest of Iran were sampled for phytoplasma detection. PCR assays and Sanger sequence analyses indicated that
ʻCandidatus
...Phytoplasma phoeniciumʼ was associated with peach witchesʼ‐broom disease. Virtual RFLP analyses of the 16S rRNA gene indicated that
ʻCa
. Phytoplasma phoeniciumʼ strain, which was prevalent in the northwest of Iran belonged to 16SrIX‐C subgroup. For the genomic characterization of Iranian
ʻCa
. Phytoplasma phoeniciumʼ strain, total DNA extracted from the infected peach trees was subjected to Illumina next‐generation sequencing. The NGS sequencing resulted in 41157647 read pairs of raw data. The raw reads length was 150 bp and the insert size was 350 bp. Assembly and genes clustering finally resulted in 750 contigs with a total size of 228345 bp. The GC content of sequence was 33.2%, N50 value was 259 and L50 value was 256. According to NCBI‐ PGAP annotation the genome of
Prunus persica
phytoplasma PP2 contained 410 genes including 377 CDSs with protein, 10 rRNAs, nine tRNAs and 14 pseudogenes.
Full text
Available for:
BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
In this study, Russian olive trees exhibiting witches’-broom symptoms were collected from urban green areas in Tabriz, in the northwest of Iran. PCR analysis confirmed that phytoplasma caused the ...disease and, according to the resulting Sanger sequencing electropherogram, a mixed infection with two or more phytoplasma species within the Russian olive trees was revealed. Next-generation sequencing analyses, using the Illumina method, were performed on total DNA from the infected Russian olive plants to recognize the microbial genomic content and assemble the whole genome of the causative pathogen(s). The use of MetaphlAn2 and Kraken2 to analyze species composition revealed the very diverse and unique compositions of different Prokaryotic and Eukaryotic species within the infected plants. Several bacteria and fungi were discovered inside the samples, among which Mycoplasmatota was significantly dominating. Interestingly, the results also revealed a high level of endosymbiotic bacteria and Archaea (Methanobacteria) genome contents within the samples. Bowtie2, metaSPAdes, and CD-HIT pipelines were used to perform the initial genome assembly, and the whole genome of the notable phytoplasma species was assembled and submitted to Genbank.
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