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Pestalotiopsis guepinii is a pathogenic fungus that causes grey blight on Camellia pitardii. In this study, we investigated the enzyme activity and kinetics of these cell‐wall‐degrading enzymes (CWDEs) produced by Pestalotiopsis guepinii in both C. pitardii leaves and culture medium. Our enzyme activity experiments revealed that the activities of xylanase, pectin methyl‐galacturonase (PMG), β‐1,4‐endoglucanase (Cx), and β‐glucosidase were high in both C. pitardii leaves and culture medium. These enzymes played a significant role in the pathogenic process. However, the activity of laccase was found to be very low and had a minor impact on the pathogenic process. Furthermore, our enzyme dynamics experiments demonstrated that the optimal reaction temperature for PMG and Cx was 50°C, while for β‐glucosidase and xylanase, it was 60°C. The optimal reaction pH for Cx, β‐glucosidase, and xylanase was 5.0, whereas for PMG, it ranged from 5.0 to 6.0. This indicates that these four enzymes prefer acidic conditions. Moreover, we observed that the activities of Cx, PMG, and xylanase decreased with increasing reaction time. On the other hand, the activity of β‐glucosidase initially increased sharply and then decreased slowly. The maximum reaction rates of the four cell‐wall‐degrading enzymes were ranked as follows: xylanase > PMG > β‐glucosidase > Cx. Additionally, the affinities of these enzymes with substrates were ranked as follows: PMG < Cx < xylanase < β‐glucosidase. [ABSTRACT FROM AUTHOR]

Many fungi in the Diaporthe genus across the world are pathogenic. Diaporthe capsici. is a pathogenic fungus that can infect peppers and walnuts, causing their death. The aim of this study was to develop a genomic resource to provide substantial data and a theoretical basis for research on molecular pathogenesis, transcriptome, proteome, and metabonome of D. capsici. The whole genome of D. capsici was sequenced using the PacBio RSII sequencing platform, and functional annotation was performed using different public databases. The genome was found to be 57.56 Mb in size, with an N50 contig size of 5,171,887 bp, and encodes 14,425 putative genes. This is the first genome-scale assembly and annotation for D. capsici , which is the eighth species in Diaporthe to be sequenced. • The genome of Diaporthe capsici was sequenced, assembled and annotated. • The size of the genome is 57.56 Mb, ecoding 14,425 putative predicted genes. • The genes that may be involved in pathogenicity were searched throughout the genome. [ABSTRACT FROM AUTHOR]

Arthrinium phaeospermum (Corda) M.B. Ellis is a globally distributed pathogenic fungus with a wide host range; its hosts include not only plants, but also humans and animals. This study aimed to develop genomic resources for A. phaeospermum to provide solid data and a theoretical basis for further studies of its pathogenesis, transcriptomics, proteomics, metabolomics and RNA genomics. The genome was obtained from the mycelia of the strain AP-Z13 using a combination of analyses with the high-throughput Illumina HiSeq 4000 system and PacBio RSII LongRead sequencing platform. Functional annotation was performed by BLASTing protein sequences against those in different publicly available databases to obtain their corresponding annotations. The genome is 48.45 Mb in size, with an N90 scaffold size of 1,931,147 bp, and encodes 19,836 putative predicted genes. This is the first report of the genome-scale assembly and annotation for A. phaeospermum , the first species in the genus Arthrinium to be subjected to whole genome sequencing. • The genome of Arthrinium phaeospermum was sequenced, assembled and annotated. • The size of the genome is 48.45 Mb, ecoding 19,836 putative predicted genes. • The genome contains a large number of genes involved in vital movement. [ABSTRACT FROM AUTHOR]

Bambusa pervariabilis × Dendrocalamopsis grandis blight, caused by Arthrinium phaeospermum , is one of the most common and serious diseases in bamboo and occurs in the newly born twigs. Bamboo has suffered large dead areas, including more than 3000 hm2, which greatly threatens the process of returning farmlands to forests and the construction of ecological barriers. To identify differential metabolites and metabolic pathways associated with B. pervariabilis × D. grandis to A. phaeospermum , ultra-performance liquid chromatography (UPLC) and quadrupole-time of flight (Q-TOF) Mass Spectrometry (MS) combined with a data-dependent acquisition method was used to analyse the entire sample spectrum. In total, 13223 positive ion peaks and 10616 negative ion peaks were extracted. OPLS-DA and several other analyses were performed using the original data. The OPLS-DA models showed good quality and had strong predictive power, indicating clear trends in the analyses of the treatment and control groups. Clustering and KEGG pathway analyses were used to screen the differential metabolites in the treatment and control groups from the three B. pervariabilis × D. grandis varieties and reflected their metabolic responses induced by A. phaeospermum infection. The results showed that the three B. pervariabilis × D. grandis varieties mode showed significant changes in the following six resistance-related metabolites after A. phaeospermum invasion in positive and negative ion modes: proline, glutamine, dictamnine, apigenin 7-O-neohesperidoside, glutamate, and cis-Aconitate. The following four main metabolic pathways are involved: Arginine and proline metabolism, Glyoxylate and dicarboxylate metabolism, Biosynthesis of alkaloids derived from shikimate pathway, and Flavone and flavonol biosynthesis. This study lays a foundation for the later detection of differential metabolites and metabolic pathways for targeting, and provides a theoretical basis for disease-resistant breeding and the control of B. pervariabilis × D. grandis blight. Image 1 • The application methods of UPLC-Q-TOF MS in plant metabolomics are reviewed. • Non-targeted metabolomics can characterize all metabolites as much as possible. • Differential metabolomics are associated with the fungal stress in bamboo varieties. • Six metabolites and four metabolic pathways in resistance have significant changes. [ABSTRACT FROM AUTHOR]