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A novel Actinidia cytorhabdovirus characterized using genomic and viral protein interaction features.

ABSTRACT: A novel cytorhabdovirus, tentatively named Actinidia virus D (AcVD), was identified from kiwifruit (Actinidia chinensis) in China using high-throughput sequencing technology. The genome of AcVD consists of 13,589 nucleotides and is organized into seven open reading frames (ORFs) in its antisense strand, coding for proteins in the order N-P-P3-M-G-P6-L. The ORFs were flanked by a 3' leader sequence and a 5' trailer sequence and are separated by conserved intergenic junctions. The genome sequence of AcVD was 44.6%-51.5% identical to those of reported cytorhabdoviruses. The proteins encoded by AcVD shared the highest sequence identities, ranging from 27.3% (P6) to 44.5% (L), with the respective proteins encoded by reported cytorhabdoviruses. Phylogenetic analysis revealed that AcVD clustered together with the cytorhabdovirus Wuhan insect virus 4. The subcellular locations of the viral proteins N, P, P3, M, G, and P6 in epidermal cells of Nicotiana benthamiana leaves were determined. The M protein of AcVD uniquely formed filament structures and was associated with microtubules. Bimolecular fluorescence complementation assays showed that three proteins, N, P, and M, self-interact, protein N plays a role in the formation of cytoplasm viroplasm, and protein M recruits N, P, P3, and G to microtubules. In addition, numerous paired proteins interact in the nucleus. This study presents the first evidence of a cytorhabdovirus infecting kiwifruit plants and full location and interaction maps to gain insight into viral protein functions.

SUBMITTER: Wang Y

PROVIDER: S-EPMC8435229 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Proteins are considered indispensable for facilitating an organism's viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins. Despite being a popular choice in machine learning (ML) applications, the deep learning (DL) method is not suggested for this specific research work based on sequence features due to the restricted availability of high-quality training sets of positive and negative samples. However, some DL works on limited availability of data are also executed at recent times which will be our future scope of work. Conventional ML techniques are thus utilized in this work due to their superior performance compared to DL methodologies. In consideration of the aforementioned, a technique called EPI-SF is proposed here, which employs ML to identify essential proteins within the protein-protein interaction network (PPIN). The protein sequence is the primary determinant of protein structure and function. So, initially, relevant protein sequence features are extracted from the proteins within the PPIN. These features are subsequently utilized as input for various machine learning models, including XGB Boost Classifier, AdaBoost Classifier, logistic regression (LR), support vector classification (SVM), Decision Tree model (DT), Random Forest model (RF), and Naïve Bayes model (NB). The objective is to detect the essential proteins within the PPIN. The primary investigation conducted on yeast examined the performance of various ML models for yeast PPIN. Among these models, the RF model technique had the highest level of effectiveness, as indicated by its precision, recall, F1-score, and AUC values of 0.703, 0.720, 0.711, and 0.745, respectively. It is also found to be better in performance when compared to the other state-of-arts based on traditional centrality like betweenness centrality (BC), closeness centrality (CC), etc. and deep learning methods as well like DeepEP, as emphasized in the result section. As a result of its favorable performance, EPI-SF is later employed for the prediction of novel essential proteins inside the human PPIN. Due to the tendency of viruses to selectively target essential proteins involved in the transmission of diseases within human PPIN, investigations are conducted to assess the probable involvement of these proteins in COVID-19 and other related severe diseases.

Project description:The Southwest Indian Ridge (SWIR) is one of the typical representatives of deep-sea ultraslow-spreading ridges, and has increasingly become a hot spot of studying subsurface geological activities and deep-sea mining management. However, the understanding of microbial activities is still limited on active hydrothermal vent chimneys in SWIR. In this study, samples from an active black smoker and a diffuse vent located in the Longqi hydrothermal region were collected for deep metagenomic sequencing, which yielded approximately 290 GB clean data and 295 mid-to-high-quality metagenome-assembled genomes (MAGs). Sulfur oxidation conducted by a variety of Gammaproteobacteria, Alphaproteobacteria, and Campylobacterota was presumed to be the major energy source for chemosynthesis in Longqi hydrothermal vents. Diverse iron-related microorganisms were recovered, including iron-oxidizing Zetaproteobacteria, iron-reducing Deferrisoma, and magnetotactic bacterium. Twenty-two bacterial MAGs from 12 uncultured phyla harbored iron oxidase Cyc2 homologs and enzymes for organic carbon degradation, indicated novel chemolithoheterotrophic iron-oxidizing bacteria that affected iron biogeochemistry in hydrothermal vents. Meanwhile, potential interactions between microbial communities and chimney minerals were emphasized as enriched metabolic potential of siderophore transportation, and extracellular electron transfer functioned by multi-heme proteins was discovered. Composition of chimney minerals probably affected microbial iron metabolic potential, as pyrrhotite might provide more available iron for microbial communities. Collectively, this study provides novel insights into microbial activities and potential mineral-microorganism interactions in hydrothermal vents. IMPORTANCE Microbial activities and interactions with minerals and venting fluid in active hydrothermal vents remain unclear in the ultraslow-spreading SWIR (Southwest Indian Ridge). Understanding about how minerals influence microbial metabolism is currently limited given the obstacles in cultivating microorganisms with sulfur or iron oxidoreduction functions. Here, comprehensive descriptions on microbial composition and metabolic profile on 2 hydrothermal vents in SWIR were obtained based on cultivation-free metagenome sequencing. In particular, autotrophic sulfur oxidation supported by minerals was presumed, emphasizing the role of chimney minerals in supporting chemosynthesis. Presence of novel heterotrophic iron-oxidizing bacteria was also indicated, suggesting overlooked biogeochemical pathways directed by microorganisms that connected sulfide mineral dissolution and organic carbon degradation in hydrothermal vents. Our findings offer novel insights into microbial function and biotic interactions on minerals in ultraslow-spreading ridges.

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A novel Actinidia cytorhabdovirus characterized using genomic and viral protein interaction features.

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