Domain Annotation Research Articles

Mining for New Sources of Resistance to Powdery Mildew in Genetic Resources of Winter Wheat.

Genetic pathogen control is an economical and sustainable alternative to the use of chemicals. In order to breed resistant varieties, information about potentially unused genetic resistance mechanisms is of high value. We phenotyped 8,316 genotypes of the winter wheat collection of the German Federal ex situ gene bank for Agricultural and Horticultural Crops, Germany, for resistance to powdery mildew (PM), Blumeria graminis f. sp. tritici, one of the most important biotrophic pathogens in wheat. To achieve this, we used a semi-automatic phenotyping facility to perform high-throughput detached leaf assays. This data set, combined with genotyping-by-sequencing (GBS) marker data, was used to perform a genome-wide association study (GWAS). Alleles of significantly associated markers were compared with SNP profiles of 171 widely grown wheat varieties in Germany to identify currently unexploited resistance conferring genes. We also used the Chinese Spring reference genome annotation and various domain prediction algorithms to perform a domain enrichment analysis and produced a list of candidate genes for further investigation. We identified 51 significantly associated regions. In most of these, the susceptible allele was fixed in the tested commonly grown wheat varieties. Eleven of these were located on chromosomes for which no resistance conferring genes have been previously reported. In addition to enrichment of leucine-rich repeats (LRR), we saw enrichment of several domain types so far not reported as relevant to PM resistance, thus, indicating potentially novel candidate genes for the disease resistance research and prebreeding in wheat.

Dynamic Instance Domain Adaptation.

Most existing studies on unsupervised domain adaptation (UDA) assume that each domain's training samples come with domain labels (e.g., painting, photo). Samples from each domain are assumed to follow the same distribution and the domain labels are exploited to learn domain-invariant features via feature alignment. However, such an assumption often does not hold true-there often exist numerous finer-grained domains (e.g., dozens of modern painting styles have been developed, each differing dramatically from those of the classic styles). Therefore, forcing feature distribution alignment across each artificially-defined and coarse-grained domain can be ineffective. In this paper, we address both single-source and multi-source UDA from a completely different perspective, which is to view each instance as a fine domain. Feature alignment across domains is thus redundant. Instead, we propose to perform dynamic instance domain adaptation (DIDA). Concretely, a dynamic neural network with adaptive convolutional kernels is developed to generate instance-adaptive residuals to adapt domain-agnostic deep features to each individual instance. This enables a shared classifier to be applied to both source and target domain data without relying on any domain annotation. Further, instead of imposing intricate feature alignment losses, we adopt a simple semi-supervised learning paradigm using only a cross-entropy loss for both labeled source and pseudo labeled target data. Our model, dubbed DIDA-Net, achieves state-of-the-art performance on several commonly used single-source and multi-source UDA datasets including Digits, Office-Home, DomainNet, Digit-Five, and PACS.

Unsupervised SAR and Optical Image Matching Using Siamese Domain Adaptation

Due to its highly complementary information about remote sensing, synthetic aperture radar (SAR) and optical imagery matching have drawn much attention in recent years. Compared with traditional methods, deep learning-based SAR-optical image matching models largely rely on supervision with ground truths, where the matching accuracy suffers because of unseen image domains. To mitigate loads in burdensome labeling tasks, transferring deep learning models trained with annotated source domains to nonannotated target domains has attracted great concern. Due to the domain gap, the difference between the source and target domains is likely to deteriorate the matching accuracy on target data if the training process is directly conducted without proper domain adaptation (DA). In this research, a Siamese DA (SDA) approach with a combined loss function is developed in the context of multimodality image matching. Then, a novel rotation/scale-invariant transformation module with regression modules is designed to extract rotation/scale-equivariant features. Finally, the causal inference-based self-learning method and the multiresolution histogram matching approach are employed to enhance the unsupervised matching performance. Experimental results on the RadarSat/Planet dataset and the Sentinel-1/2 dataset demonstrate that the developed model can achieve competitive matching performance with a low overlap ratio between domains and little data labeling. By alleviating the domain discrepancy, the developed model drastically reduces the average L2 score of the unsupervised matching from 9.576 to 0.658, while the less-than-one-pixel matching error rate is enhanced from 66.3% to 90.6%.

Domain generalization on medical imaging classification using episodic training with task augmentation

Medical imaging datasets usually exhibit domain shift due to the variations of scanner vendors, imaging protocols, etc. This raises the concern about the generalization capacity of machine learning models. Domain generalization (DG), which aims to learn a model from multiple source domains such that it can be directly generalized to unseen test domains, seems particularly promising to medical imaging community. To address DG, recent model-agnostic meta-learning (MAML) has been introduced, which transfers the knowledge from previous training tasks to facilitate the learning of novel testing tasks. However, in clinical practice, there are usually only a few annotated source domains available, which decreases the capacity of training task generation and thus increases the risk of overfitting to training tasks in the paradigm. In this paper, we propose a novel DG scheme of episodic training with task augmentation on medical imaging classification. Based on meta-learning, we develop the paradigm of episodic training to construct the knowledge transfer from episodic training-task simulation to the real testing task of DG. Motivated by the limited number of source domains in real-world medical deployment, we consider the unique task-level overfitting and we propose task augmentation to enhance the variety during training task generation to alleviate it. With the established learning framework, we further exploit a novel meta-objective to regularize the deep embedding of training domains. To validate the effectiveness of the proposed method, we perform experiments on histopathological images and abdominal CT images.

Semi-Supervised Pipeline for Autonomous Annotation of SARS-CoV-2 Genomes.

SARS-CoV-2 genomic sequencing efforts have scaled dramatically to address the current global pandemic and aid public health. However, autonomous genome annotation of SARS-CoV-2 genes, proteins, and domains is not readily accomplished by existing methods and results in missing or incorrect sequences. To overcome this limitation, we developed a novel semi-supervised pipeline for automated gene, protein, and functional domain annotation of SARS-CoV-2 genomes that differentiates itself by not relying on the use of a single reference genome and by overcoming atypical genomic traits that challenge traditional bioinformatic methods. We analyzed an initial corpus of 66,000 SARS-CoV-2 genome sequences collected from labs across the world using our method and identified the comprehensive set of known proteins with 98.5% set membership accuracy and 99.1% accuracy in length prediction, compared to proteome references, including Replicase polyprotein 1ab (with its transcriptional slippage site). Compared to other published tools, such as Prokka (base) and VAPiD, we yielded a 6.4- and 1.8-fold increase in protein annotations. Our method generated 13,000,000 gene, protein, and domain sequences—some conserved across time and geography and others representing emerging variants. We observed 3362 non-redundant sequences per protein on average within this corpus and described key D614G and N501Y variants spatiotemporally in the initial genome corpus. For spike glycoprotein domains, we achieved greater than 97.9% sequence identity to references and characterized receptor binding domain variants. We further demonstrated the robustness and extensibility of our method on an additional 4000 variant diverse genomes containing all named variants of concern and interest as of August 2021. In this cohort, we successfully identified all keystone spike glycoprotein mutations in our predicted protein sequences with greater than 99% accuracy as well as demonstrating high accuracy of the protein and domain annotations. This work comprehensively presents the molecular targets to refine biomedical interventions for SARS-CoV-2 with a scalable, high-accuracy method to analyze newly sequenced infections as they arise.

Establishment of quantitative RNAi-based forward genetics in Entamoeba histolytica and identification of genes required for growth.

While Entamoeba histolytica remains a globally important pathogen, it is dramatically understudied. The tractability of E. histolytica has historically been limited, which is largely due to challenging features of its genome. To enable forward genetics, we constructed and validated the first genome-wide E. histolytica RNAi knockdown mutant library. This library allows for Illumina deep sequencing analysis for quantitative identification of mutants that are enriched or depleted after selection. We developed a novel analysis pipeline to precisely define and quantify gene fragments. We used the library to perform the first RNAi screen in E. histolytica and identified slow growth (SG) mutants. Among genes targeted in SG mutants, many had annotated functions consistent with roles in cellular growth or metabolic pathways. Some targeted genes were annotated as hypothetical or lacked annotated domains, supporting the power of forward genetics in uncovering functional information that cannot be gleaned from databases. While the localization of neither of the proteins targeted in SG1 nor SG2 mutants could be predicted by sequence analysis, we showed experimentally that SG1 localized to the cytoplasm and cell surface, while SG2 localized to the cytoplasm. Overexpression of SG1 led to increased growth, while expression of a truncation mutant did not lead to increased growth, and thus aided in defining functional domains in this protein. Finally, in addition to establishing forward genetics, we uncovered new details of the unusual E. histolytica RNAi pathway. These studies dramatically improve the tractability of E. histolytica and open up the possibility of applying genetics to improve understanding of this important pathogen.

Predicting S-nitrosylation proteins and sites by fusing multiple features.

Protein S-nitrosylation is one of the most important post-translational modifications, a well-grounded understanding of S-nitrosylation is very significant since it plays a key role in a variety of biological processes. For an uncharacterized protein sequence, it is a very meaningful problem for both basic research and drug development when we can firstly identify whether it is a S-nitrosylation protein or not, and then predict the specific S-nitrosylation site(s). This work has proposed two models for identifying S-nitrosylation protein and its PTM sites. Firstly, three kinds of features are extracted from protein sequence: KNN scoring of functional domain annotation, PseAAC and bag-of-words based on the physical and chemical properties of amino acids. Secondly, the synthetic minority oversampling technique is used to balance the data sets, and some state-of-the-art classifiers and feature fusion strategies are performed on the balanced data sets. In the five-fold cross-validation for predicting S-nitrosylation proteins, the results of Accuracy (ACC), Matthew's correlation coefficient (MCC) and area under ROC curve (AUC) are 81.84%, 0.5178, 0.8635, respectively. Finally, a model for predicting S-nitrosylation sites has been constructed on the basis of tripeptide composition (TPC) and the composition of k-spaced amino acid pairs (CKSAAP). To eliminate redundant information and improve work efficiency, elastic nets are employed for feature selection. The five-fold cross-validation tests have indicated the promising success rates of the proposed model. For the convenience of related researchers, the web-server named "RF-SNOPS" has been established at http://www.jci-bioinfo.cn/RF-SNOPS.

Development and application of the semantic annotation framework for digital images

Purpose The purpose of this paper is to semantically annotate the content of digital images with the use of Semantic Web technologies and thus facilitate retrieval, integration and knowledge discovery. Design/Methodology/Approach After a review and comparison of the existing semantic annotation models for images and a deep analysis of the characteristics of the content of images, a multi-dimensional and hierarchical general semantic annotation framework for digital images was proposed. On this basis, taking histories images, advertising images and biomedical images as examples, by integrating the characteristics of images in these specific domains with related domain knowledge, the general semantic annotation framework for digital images was customized to form a domain annotation ontology for the images in a specific domain. The application of semantic annotation of digital images, such as semantic retrieval, visual analysis and semantic reuse, were also explored. Findings The results showed that the semantic annotation framework for digital images constructed in this paper provided a solution for the semantic organization of the content of images. On this basis, deep knowledge services such as semantic retrieval, visual analysis can be provided. Originality/Value The semantic annotation framework for digital images can reveal the fine-grained semantics in a multi-dimensional and hierarchical way, which can thus meet the demand for enrichment and retrieval of digital images.

CPLM 4.0: an updated database with rich annotations for protein lysine modifications.

Here, we reported the compendium of protein lysine modifications (CPLM 4.0, http://cplm.biocuckoo.cn/), a data resource for various post-translational modifications (PTMs) specifically occurred at the side-chain amino group of lysine residues in proteins. From the literature and public databases, we collected 450 378 protein lysine modification (PLM) events, and combined them with the existing data of our previously developed protein lysine modification database (PLMD 3.0). In total, CPLM 4.0 contained 592 606 experimentally identified modification events on 463 156 unique lysine residues of 105 673 proteins for up to 29 types of PLMs across 219 species. Furthermore, we carefully annotated the data using the knowledge from 102 additional resources that covered 13 aspects, including variation and mutation, disease-associated information, protein-protein interaction, protein functional annotation, DNA & RNA element, protein structure, chemical-target relation, mRNA expression, protein expression/proteomics, subcellular localization, biological pathway annotation, functional domain annotation, and physicochemical property. Compared to PLMD 3.0 and other existing resources, CPLM 4.0 achieved a >2-fold increase in collection of PLM events, with a data volume of ∼45GB. We anticipate that CPLM 4.0 can serve as a more useful database for further study of PLMs.

TMT-Based proteomics analysis of LPS-induced acute lung injury

Purpose: The proteome during lipopolysaccharide (LPS)-induced acute lung injury (ALI) in mice is unclear. Materials and Methods: In this study, eight-week-old male C57BL/6 mice were intraperitoneally injected with LPS and sacrificed 18 hours after LPS administration to identify protein expression levels in lung tissue using tandem mass tag (TMT) analysis for relative quantification. Hematoxylin-eosin (HE) staining was used to evaluate lung injury in mice. Immunohistochemical staining was used to calculate the production of myeloperoxidase (MPO) and TUNEL staining was performed to detect apoptosis. GO functional clustering and KEGG pathway enrichment analyses were performed to determine functions of differentially expressed proteins (DEPs) and transduction pathways. Domain annotation and subcellular localization analysis of the DEPs were also performed. Furthermore, parallel reaction monitoring (PRM) analysis was used to verify the top 30 DEPs. Results: A total of 5188 proteins were found to be expressed in lung tissues from LPS- and saline-treated mice. Among these proteins, 293 were differentially expressed between the two groups; 255 proteins were upregulated in the LPS-treated ALI mice, while 38 were downregulated. GO analysis showed that the DEPs are mainly extracellular, and KEGG analysis suggested that the DEPs are mainly enriched in the NOD-like receptor signaling pathway, complement and coagulation cascades and natural killer cell-mediated cytotoxicity. Enrichment of the DEPs is mainly peptidase S1A, serine proteases, peptidase S1, and the serpin domain. 26.6% of the DEPs are in the nucleus, 24.6% are in the cytosol, 19.1% are in the extracellular space, and 18.8% are in the plasma membrane. PRM validation showed that the trend of 30 DEPs was same with TMT analysis. Among these, Cytochrome b-245 heavy chain (Cybb), Monocyte differentiation antigen CD14 (Cd14) and Neutrophil gelatinase-associated lipocalin (NGAL) were the most obvious change. Conclusions: Our results may help to identify markers and therapeutic targets for LPS-induced ALI.

Proteomic Analysis of Histone Crotonylation Suggests Diverse Functions in Myzus persicae.

Myzus persicae is one of the most important economic pests of cultivated crops. In the present study, we used an integrated approach involving high-performance liquid chromatography fractionation, affinity enrichment, and mass spectrometry-based proteomics to carry out a comprehensive proteomic analysis of lysine crotonylation in M. persicae. Altogether, 7530 lysine crotonylation sites were identified in 2452 protein groups. Intensive bioinformatic analyses were then carried out to annotate those lysine crotonylated targets identified in terms of Gene Ontology annotation, domain annotation, subcellular localization, Kyoto Encyclopedia of Genes and Genomes pathway annotation, functional cluster analysis, etc. Analysis results showed that lysine-crotonylated proteins were involved in many biological processes, such as the amino acid metabolism, aminoacyl-tRNA biosynthesis, spliceosomes, ribosomes, and so forth. Notably, the interaction network showed that there were 199 crotonylated proteins involved in the amino acid metabolism and numerous crotonylation targets associated with fatty acid biosynthesis and degradation. The results provide a system-wide view of the entire M. persicae crotonylome and a rich data set for functional analysis of crotonylated proteins in this economically important pest, which marks an important beginning for the further research.

Dependency Parsing-based Entity Relation Extraction over Chinese Complex Text

Open Relation Extraction (ORE) plays a significant role in the field of Information Extraction. It breaks the limitation that traditional relation extraction must pre-define relational types in the annotated corpus and specific domains restrictions, to realize the goal of extracting entities and the relation between entities in the open domain. However, with the increase of sentence complexity, the precision and recall of Entity Relation Extraction will be significantly reduced. To solve this problem, we present an unsupervised Clause_CORE method based on Chinese grammar and dependency parsing features. Clause_CORE is used for complex sentences processing, including decomposing complex sentence and dynamically complementing sentence components, which can reduce sentences complexity and maintain the integrity of sentences at the same time. Then, we perform dependency parsing for complete sentences and implement open entity relation extraction based on the model constructed by Chinese grammar rules. The experimental results show that the performance of Clause_CORE method is better than that of other advanced Chinese ORE systems on Wikipedia and Sina news datasets, which proves the correctness and effectiveness of the method. The results on mixed datasets of news data and encyclopedia data prove the generalization and portability of the method.

DomainViz: intuitive visualization of consensus domain distributions across groups of proteins.

The prediction of functional domains is typically among the first steps towards understanding the function of new proteins and protein families. There are numerous databases of annotated protein domains that permit researchers to identify domains on individual proteins of interest. However, it is necessary to perform high-throughput domain searches to gain evolutionary insight into the functions of proteins and protein families. Unfortunately, at present, it is difficult to search for, and visualize domain conservation across multiple proteins and/or multiple groups of proteins in an intuitive manner. Here we present DomainViz, a new web-server that streamlines the identification and visualization of domains across multiple protein sequences. Currently, DomainViz uses the well-established PFAM and Prosite databases for domain searching and assembles intuitive, publication-ready ‘monument valley’ plots (mv-plots) that display the extent of domain conservation along two dimensions: positionality and frequency of occurrence in the input protein sequences. In addition, DomainViz produces a conventional domain-ordering figure. DomainViz can be used to explore the conservation of domains within a single protein family, across multiple families, and across families from different species to support studies into protein function and evolution. The web-server is publicly available at: https://uhrigprotools.biology.ualberta.ca/domainviz.

MyCLADE: a multi-source domain annotation server for sequence functional exploration.

The ever-increasing number of genomic and metagenomic sequences accumulating in our databases requires accurate approaches to explore their content against specific domain targets. MyCLADE is a user-friendly webserver designed for targeted functional profiling of genomic and metagenomic sequences based on a database of a few million probabilistic models of Pfam domains. It uses the MetaCLADE multi-source domain annotation strategy, modelling domains based on multiple probabilistic profiles. MyCLADE takes a list of protein sequences and possibly a target set of domains/clans as input and, for each sequence, it provides a domain architecture built from the targeted domains or from all Pfam domains. It is linked to the Pfam and QuickGO databases in multiple ways for easy retrieval of domain and clan information. E-value, bit-score, domain-dependent probability scores and logos representing the match of the model with the sequence are provided to help the user to assess the quality of each annotation. Availability and implementation: MyCLADE is freely available at http://www.lcqb.upmc.fr/myclade.

Improving protein domain classification for third-generation sequencing reads using deep learning

BackgroundWith the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce important insights into the biological functions of the underlying data. However, the high error rate in TGS data raises a new challenge to established domain analysis pipelines. The state-of-the-art methods are not optimized for noisy reads and have shown unsatisfactory accuracy of domain classification in TGS data. New computational methods are still needed to improve the performance of domain prediction in long noisy reads.ResultsIn this work, we introduce ProDOMA, a deep learning model that conducts domain classification for TGS reads. It uses deep neural networks with 3-frame translation encoding to learn conserved features from partially correct translations. In addition, we formulate our problem as an open-set problem and thus our model can reject reads not containing the targeted domains. In the experiments on simulated long reads of protein coding sequences and real TGS reads from the human genome, our model outperforms HMMER and DeepFam on protein domain classification.ConclusionsIn summary, ProDOMA is a useful end-to-end protein domain analysis tool for long noisy reads without relying on error correction.

Building blocks and blueprints for bacterial autolysins.

Bacteria utilize a wide variety of endogenous cell wall hydrolases, or autolysins, to remodel their cell walls during processes including cell division, biofilm formation, and programmed death. We here systematically investigate the composition of these enzymes in order to gain insights into their associated biological processes, potential ways to disrupt them via chemotherapeutics, and strategies by which they might be leveraged as recombinant antibacterial biotherapies. To do so, we developed LEDGOs (lytic enzyme domains grouped by organism), a pipeline to create and analyze databases of autolytic enzyme sequences, constituent domain annotations, and architectural patterns of multi-domain enzymes that integrate peptidoglycan binding and degrading functions. We applied LEDGOs to eight pathogenic bacteria, gram negatives Acinetobacter baumannii, Klebsiella pneumoniae, Neisseria gonorrhoeae, and Pseudomonas aeruginosa; and gram positives Clostridioides difficile, Enterococcus faecium, Staphylococcus aureus, and Streptococcus pneumoniae. Our analysis of the autolytic enzyme repertoires of these pathogens reveals commonalities and differences in their key domain building blocks and architectures, including correlations and preferred orders among domains in multi-domain enzymes, repetitions of homologous binding domains with potentially complementarity recognition modalities, and sequence similarity patterns indicative of potential divergence of functional specificity among related domains. We have further identified a variety of unannotated sequence regions within the lytic enzymes that may themselves contain new domains with important functions.

Comparative phosphoproteome analysis to identify candidate phosphoproteins involved in blue light-induced brown film formation in Lentinula edodes.

Light plays an important role in the growth and differentiation of Lentinula edodes mycelia, and mycelial morphology is influenced by light wavelengths. The blue light-induced formation of brown film on the vegetative mycelial tissues of L. edodes is an important process. However, the mechanisms of L. edodes’ brown film formation, as induced by blue light, are still unclear. Using a high-resolution liquid chromatography-tandem mass spectrometry integrated with a highly sensitive immune-affinity antibody method, phosphoproteomes of L. edodes mycelia under red- and blue-light conditions were analyzed. A total of 11,224 phosphorylation sites were identified on 2,786 proteins, of which 9,243 sites on 2,579 proteins contained quantitative information. In total, 475 sites were up-regulated and 349 sites were down-regulated in the blue vs red group. To characterize the differentially phosphorylated proteins, systematic bioinformatics analyses, including gene ontology annotations, domain annotations, subcellular localizations, and Kyoto Encyclopedia of Genes and Genomes pathway annotations, were performed. These differentially phosphorylated proteins were correlated with light signal transduction, cell wall degradation, and melanogenesis, suggesting that these processes are involved in the formation of the brown film. Our study provides new insights into the molecular mechanisms of the blue light-induced brown film formation at the post-translational modification level.

Protein Domain Annotations of the SARS-CoV-2 Proteomics as a Blue-Print for Mapping the Features for Drug and Vaccine Designs

SARS-CoV-2 virus, as the causal agent for the COVID-19 pandemic, remains an enigma in the bioinformatics sense. Current efforts in drug and vaccine design in primarily targeting general devised protein domain while overlooking the specific features in the proteomics repertoire. However, the NCBI Conserved Domain Database (CDD) could annotate the specific features that are indispensable for a more advanced drug and vaccine design. In this regard, the annotation efforts were initiated with CDD database, and visualized with the 3D Protein Visualizer of Cn3D. The exsistence of the ATP and ADP binding protein with respected domains were found to be a very potential target for drug design. It is recommended that nucleoside inhibitor that could mimick the ATP molecule could serve as a potential drug lead agains SARS-CoV-2.

SEDA: A Desktop Tool Suite for FASTA Files Processing.

SEDA (SEquence DAtaset builder) is a multiplatform desktop application for the manipulation of FASTA files containing DNA or protein sequences. The convenient graphical user interface gives access to a collection of simple (filtering, sorting, or file reformatting, among others) and advanced (BLAST searching, protein domain annotation, gene annotation, and sequence alignment) utilities not present in similar applications, which eases the work of life science researchers working with DNA and/or protein sequences, especially those who have no programming skills. This paper presents general guidelines on how to build efficient data handling protocols using SEDA, as well as practical examples on how to prepare high-quality datasets for single gene phylogenetic studies, the characterization of protein families, or phylogenomic studies. The user-friendliness of SEDA also relies on two important features: (i) the availability of easy-to-install distributable versions and installers of SEDA, including a Docker image for Linux, and (ii) the facility with which users can manage large datasets. SEDA is open-source, with GNU General Public License v3.0 license, and publicly available at GitHub (https://github.com/sing-group/seda). SEDA installers and documentation are available at https://www.sing-group.org/seda/.

A genome-wide screening for RNAi pathway proteins in Acari

BackgroundRNA interference (RNAi) is a highly conserved, sequence-specific gene silencing mechanism present in Eukaryotes. Three RNAi pathways are known, namely micro-RNA (miRNA), piwi-interacting RNA (piRNA) and short interfering RNA (siRNA). However, little knowledge exists about the proteins involved in these pathways in Acari. Moreover, variable successes has been obtained in gene knockdown via siRNA pathway in their functional genomics and management. We hypothesized that the clue may be in the variability of the composition and the efficacy of siRNA machinery among Acari.ResultsBoth comparative genomic analyses and domain annotation suggest that all the analyzed species have homologs of putative core proteins that mediate cleaving of targeted genes via the three RNAi pathways. We identified putative homologs of Caenorhabditis elegans RNA-dependent RNA polymerase (RdRP) protein in all species though no secondary Argonaute homologs that operate with this protein in siRNA amplification mechanism were found, suggesting that the siRNA amplification mechanism present in Acari may be distinct from that described in C. elegans. Moreover, the genomes of these species do not encode homologs of C. elegans systemic RNAi defective-1 (Sid-1) protein that mediate silencing of the mRNA target throughout the treated organisms suggesting that the phenomena of systemic RNAi that has been reported in some Acari species probably occur through a different mechanism. However, homologs of putative RNAi spreading defective-3 (Rsd-3) protein and scavenger receptors namely Eater and SR-CI that mediate endocytosis cellular update of dsRNA in C. elegans and Drosophila melanogaster were found in Acari genomes. This result suggests that cellular dsRNA uptake in Acari is endocytosis-dependent. Detailed phylogenetic analyses of core RNAi pathway proteins in the studied species revealed that their evolution is compatible with the proposed monophyletic evolution of this group.ConclusionsOur analyses have revealed the potential activity of all three pathways in Acari. Still, much experimental work remains to be done to confirm the mechanisms behind these pathways in particular those that govern systemic/parental RNAi and siRNA amplification in Acari. Disclosure of these mechanisms will facilitate the development of new and specific management tools for the harmful species and enrichment of the beneficial species.