Deciphering the Monilinia fructicola Genome to Discover Effector Genes Possibly Involved in Virulence

Brown rot is the most economically important fungal disease of stone fruits and is primarily caused by Monilinia laxa and Monlinia fructicola. Both species co-occur in European orchards although M. fructicola is considered to cause the most severe yield losses in stone fruit. This study aimed to generate a high-quality genome of M. fructicola and to exploit it to identify genes that may contribute to pathogen virulence. PacBio sequencing technology was used to assemble the genome of M. fructicola. Manual structural curation of gene models, supported by RNA-Seq, and functional annotation of the proteome yielded 10,086 trustworthy gene models. The genome was examined for the presence of genes that encode secreted proteins and more specifically effector proteins. A set of 134 putative effectors was defined. Several effector genes were cloned into Agrobacterium tumefaciens for transient expression in Nicotiana benthamiana plants, and some of them triggered necrotic lesions. Studying effectors and their biological properties will help to better understand the interaction between M. fructicola and its stone fruit host plants.

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SAUTE: sequence assembly using target enrichment

BMC Bioinformatics ◽

10.1186/s12859-021-04174-9 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Alexandre Souvorov ◽

Richa Agarwala

Keyword(s):

De Bruijn Graph ◽

Challenging Problem ◽

Target Enrichment ◽

Rna Seq ◽

Sequencing Technology ◽

Insert Size ◽

Systematic Biases ◽

Target Sequences ◽

Genomic Regions ◽

Higher Sensitivity

Abstract Background Illumina is the dominant sequencing technology at this time. Short length, short insert size, some systematic biases, and low-level carryover contamination in Illumina reads continue to make assembly of repeated regions a challenging problem. Some applications also require finding multiple well supported variants for assembled regions. Results To facilitate assembly of repeat regions and to report multiple well supported variants when a user can provide target sequences to assist the assembly, we propose SAUTE and SAUTE_PROT assemblers. Both assemblers use de Bruijn graph on reads. Targets can be transcripts or proteins for RNA-seq reads and transcripts, proteins, or genomic regions for genomic reads. Target sequences are nucleotide and protein sequences for SAUTE and SAUTE_PROT, respectively. Conclusions For RNA-seq, comparisons with Trinity, rnaSPAdes, SPAligner, and SPAdes assembly of reads aligned to target proteins by DIAMOND show that SAUTE_PROT finds more coding sequences that translate to benchmark proteins. Using AMRFinderPlus calls, we find SAUTE has higher sensitivity and precision than SPAdes, plasmidSPAdes, SPAligner, and SPAdes assembly of reads aligned to target regions by HISAT2. It also has better sensitivity than SKESA but worse precision.

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Pseudomonas syringae Strains Naturally Lacking the Classical P. syringae hrp/hrc Locus Are Common Leaf Colonizers Equipped with an Atypical Type III Secretion System

Molecular Plant-Microbe Interactions ◽

10.1094/mpmi-23-2-0198 ◽

2010 ◽

Vol 23 (2) ◽

pp. 198-210 ◽

Cited By ~ 69

Author(s):

Christopher R. Clarke ◽

Rongman Cai ◽

David J. Studholme ◽

David S. Guttman ◽

Boris A. Vinatzer

Keyword(s):

Pseudomonas Syringae ◽

Type Iii Secretion ◽

Plant Cells ◽

Type Iii Secretion System ◽

Ice Nucleation ◽

Secretion System ◽

Effector Proteins ◽

Population Densities ◽

Type Iii ◽

Effector Genes

Pseudomonas syringae is best known as a plant pathogen that causes disease by translocating immune-suppressing effector proteins into plant cells through a type III secretion system (T3SS). However, P. syringae strains belonging to a newly described phylogenetic subgroup (group 2c) are missing the canonical P. syringae hrp/hrc cluster coding for a T3SS, flanking effector loci, and any close orthologue of known P. syringae effectors. Nonetheless, P. syringae group 2c strains are common leaf colonizers and grow on some tested plant species to population densities higher than those obtained by other P. syringae strains on nonhost species. Moreover, group 2c strains have genes necessary for the production of phytotoxins, have an ice nucleation gene, and, most interestingly, contain a novel hrp/hrc cluster, which is only distantly related to the canonical P. syringae hrp/hrc cluster. This hrp/hrc cluster appears to encode a functional T3SS although the genes hrpK and hrpS, present in the classical P. syringae hrp/hrc cluster, are missing. The genome sequence of a representative group 2c strain also revealed distant orthologues of the P. syringae effector genes avrE1 and hopM1 and the P. aeruginosa effector genes exoU and exoY. A putative life cycle for group 2c P. syringae is discussed.

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Batch Effect Removal via Batch-Free Encoding

10.1101/380816 ◽

2018 ◽

Cited By ~ 1

Author(s):

Uri Shaham

Keyword(s):

Deep Learning ◽

Biological Properties ◽

Batch Effect ◽

Training Data ◽

Rna Seq ◽

Batch Effects ◽

Training Time ◽

Learning Techniques ◽

Downstream Analysis ◽

Biological Patterns

AbstractBiological measurements often contain systematic errors, also known as “batch effects”, which may invalidate downstream analysis when not handled correctly. The problem of removing batch effects is of major importance in the biological community. Despite recent advances in this direction via deep learning techniques, most current methods may not fully preserve the true biological patterns the data contains. In this work we propose a deep learning approach for batch effect removal. The crux of our approach is learning a batch-free encoding of the data, representing its intrinsic biological properties, but not batch effects. In addition, we also encode the systematic factors through a decoding mechanism and require accurate reconstruction of the data. Altogether, this allows us to fully preserve the true biological patterns represented in the data. Experimental results are reported on data obtained from two high throughput technologies, mass cytometry and single-cell RNA-seq. Beyond good performance on training data, we also observe that our system performs well on test data obtained from new patients, which was not available at training time. Our method is easy to handle, a publicly available code can be found at https://github.com/ushaham/BatchEffectRemoval2018.

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Underwater CAM photosynthesis elucidated by Isoetes genome

Nature Communications ◽

10.1038/s41467-021-26644-7 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

David Wickell ◽

Li-Yaung Kuo ◽

Hsiao-Pei Yang ◽

Amra Dhabalia Ashok ◽

Iker Irisarri ◽

...

Keyword(s):

Time Course ◽

Acid Metabolism ◽

Arid Environments ◽

Rna Seq ◽

Plant Type ◽

Circadian Control ◽

Evolutionary Paths ◽

Cam Photosynthesis ◽

High Quality Genome ◽

Bacterial Type

AbstractTo conserve water in arid environments, numerous plant lineages have independently evolved Crassulacean Acid Metabolism (CAM). Interestingly, Isoetes, an aquatic lycophyte, can also perform CAM as an adaptation to low CO2 availability underwater. However, little is known about the evolution of CAM in aquatic plants and the lack of genomic data has hindered comparison between aquatic and terrestrial CAM. Here, we investigate underwater CAM in Isoetes taiwanensis by generating a high-quality genome assembly and RNA-seq time course. Despite broad similarities between CAM in Isoetes and terrestrial angiosperms, we identify several key differences. Notably, Isoetes may have recruited the lesser-known ‘bacterial-type’ PEPC, along with the ‘plant-type’ exclusively used in other CAM and C4 plants for carboxylation of PEP. Furthermore, we find that circadian control of key CAM pathway genes has diverged considerably in Isoetes relative to flowering plants. This suggests the existence of more evolutionary paths to CAM than previously recognized.

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High quality genome resource of mango bacterial black spot pathogen Xanthomonas citri pv. mangiferaeindicae GXG07 isolated from Guangxi, China

Plant Disease ◽

10.1094/pdis-08-21-1714-a ◽

2021 ◽

Author(s):

Fengzhi Bie ◽

Yiming Li ◽

Zhibin Liu ◽

Meijing Qin ◽

Shuping Li ◽

...

Keyword(s):

Black Spot ◽

Economic Losses ◽

High Fidelity ◽

Whole Genome ◽

Sequencing Technology ◽

Xanthomonas Citri ◽

High Quality ◽

Genomic Resources ◽

High Quality Genome ◽

Growing Region

Xanthomonas citri pv. mangiferaeindicae (Xcm) is the causal agent of mango bacterial black spot which is present in many mango growing regions and leads to great economic losses to mango industry. Due to the limitation of high-quality genomic resources, little is known about the molecular pathogenesis of Xcm. Here, we used PacBio High Fidelity reads (HiFi) sequencing technology to sequence and analyze the whole genome of an Xcm strain GXG07 isolated from Guangxi, the largest mango growing region in China. PacBio HiFi reads with a mean coverage of 450× had been assembled into three contigs of 5,166,537, 79,634 and 30,169 bp, revealing that the genome of Xcm GXG07 contains one chromosome and two plasmids. This genome provides a resource to better understand the biology and pathogenicity of mango bacterial black spot.

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Identification and Characterization of Benzimidazole Resistance in Monilinia fructicola from Stone Fruit Orchards in California

Applied and Environmental Microbiology ◽

10.1128/aem.69.12.7145-7152.2003 ◽

2003 ◽

Vol 69 (12) ◽

pp. 7145-7152 ◽

Cited By ~ 127

Author(s):

Zhonghua Ma ◽

Michael A. Yoshimura ◽

Themis J. Michailides

Keyword(s):

Dna Sequences ◽

Point Mutations ◽

Brown Rot ◽

Stone Fruit ◽

Monilinia Fructicola ◽

Tubulin Gene ◽

Benzimidazole Fungicides ◽

Specific Pcr ◽

Allele Specific ◽

Fruit Orchards

ABSTRACT Low and high levels of resistance to the benzimidazole fungicides benomyl and thiophanate-methyl were observed in field isolates of Monilinia fructicola, which is the causative agent of brown rot of stone fruit. Isolates that had low levels of resistance (hereafter referred to as LR isolates) and high levels of resistance (hereafter referred to as HR isolates) were also cold and heat sensitive, respectively. Results from microsatellite DNA fingerprints showed that genetic identities among the populations of sensitive (S), LR, and HR isolates were very high (>0.96). Analysis of DNA sequences of theβ -tubulin gene showed that the LR isolates had a point mutation at codon 6, causing a replacement of the amino acid histidine by tyrosine. Codon 198, which encodes a glutamic acid in S and LR isolates, was converted to a codon for alanine in HR isolates. Based on these point mutations in the β-tubulin gene, allele-specific PCR assays were developed for rapid detection of benzimidazole-resistant isolates of M. fructicola from stone fruit.

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Radio Frequency Treatment to Control Postharvest Brown Rot in Stone Fruit

Post-harvest Pathology ◽

10.1007/978-3-319-07701-7_8 ◽

2014 ◽

pp. 89-99

Author(s):

M. Sisquella ◽

C. Casals ◽

P. Picouet ◽

I. Viñas ◽

R. Torres ◽

...

Keyword(s):

Radio Frequency ◽

Brown Rot ◽

Stone Fruit

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Spatial and temporal characteristics of dicarboximide‐resistant strains ofMonilinia fructicolaand brown rot incidence in stone fruit

Plant Pathology ◽

10.1046/j.1365-3059.1998.00266.x ◽

1998 ◽

Vol 47 (4) ◽

pp. 530-536 ◽

Cited By ~ 15

Author(s):

P. A. G. Elmer ◽

R. E. Gaunt ◽

C. M. Frampton

Keyword(s):

Brown Rot ◽

Stone Fruit ◽

Temporal Characteristics ◽

Resistant Strains ◽

Spatial And Temporal Characteristics

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A Two-Stage Poisson Model for Testing RNA-Seq Data

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1627 ◽

2011 ◽

Vol 10 (1) ◽

Cited By ~ 39

Author(s):

Paul L. Auer ◽

Rebecca W Doerge

Keyword(s):

Rna Sequencing ◽

Statistical Approach ◽

Poisson Model ◽

Real Data ◽

Rna Seq ◽

Sequencing Data ◽

Sequencing Technology ◽

Two Stage ◽

Individual Gene ◽

Unique Nature

RNA sequencing technology is providing data of unprecedented throughput, resolution, and accuracy. Although there are many different computational tools for processing these data, there are a limited number of statistical methods for analyzing them, and even fewer that acknowledge the unique nature of individual gene transcription. We introduce a simple and powerful statistical approach, based on a two-stage Poisson model, for modeling RNA sequencing data and testing for biologically important changes in gene expression. The advantages of this approach are demonstrated through simulations and real data applications.

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