Curvularia brachyspora, Penicillium chrysogenum, Scopulariopsis brevicaulis, Cladosporium sphaerospermum, Alternaria alternata, and Stemphylium botryosum) and three fungal consortia (FC), FC1 (P. chrysogenum and C. brachyspora), FC2 (S. brevicaulis and S. botryosum), and FC3 , to remediate petroleum hydrocarbons (PHs). Qualitative and quantitative changes in polyaromatic hydrocarbons (PAHs) and saturated hydrocarbons (SH) mixtures and the patterns of PHs degradation have been examined using HPLC and GC. Studying the GC chromatogram of C. sphaerospermum revealed severe degradation of SHs exhibited by this species, and the normal-paraffin and isoparaffin degradation percentage have been valued 97.19% and 98.88%, respectively. A. alternata has shown the highest significant (at P ˂ 0.05) PAH degradation percent reaching 72.07%; followed by P. chrysogenum, 59.51%. HPLC data have revealed that high-molecular-weight PAH percent/total PAHs decreased significantly from 98.94% in control samples to 68.78% in samples treated with A. alternata. FC1 and FC2 consortia have exhibited the highest significant PH deterioration abilities than did the individual isolates, indicating that these fungal consortia exhibited positive synergistic effects. The study supports the critical idea of the potential PAH and SH biodegradation as a more ecologically acceptable alternative to their chemical degradation.Crude oil spills as a result of natural disasters or extraction and transportation operations are common nowadays. Oil spills have adverse effects on both aquatic and terrestrial ecosystems and pose a threat to human health. This study have been concerned with studying the capability of six fungal species ( Environment contamination with petroleum hydrocarbons (PHs) has occurred since ancient time naturally, but in the latest years, man-made oil spills have become common . PHs hav2  anthracene, benzo [a] pyrene, benzo [b] fluoranthene, benzo [k] fluoranthene, chrysene, dibenzo  anthracene, and indeno  pyrene using HPLC estimation. Benzo [a] pyrene has been classified as the first discovered carcinogenic compound [The important role of soil fungi in hydrocarbon oxidizing activities, consequently in the oil degradation activity, has been reported by many authors , 26. Howcompound .C. sphaerospermum followed by P. chrysogenum. For PAH deterioration, A. alternata and P. chrysogenum were the most potent strains. The higher hydrocarbon degradation rates presented by these fungal species could be due to their fast enzymatic production response and the massive growth exhibited by the four strains during their growth phases [According to the results obtained in the current study, the most potent strains in deterioration of SHs were the filamentous fungi h phases . Utilizah phases ; they foCladosporium and genus Penicillium are among the most potent filamentous fungi participating in aliphatic hydrocarbon biodegradation, whereas fungi belonging to Cunninghamella, Fusarium, Penicillinum, and Aspergillus can take part in aromatic hydrocarbon decomposition. Obire et al. [Cladosporium and Penicillium as significant soil fungi groups capable of utilizing PHs. Madani [Penicillium and Alternaria are rich in crude-oil-assimilating strains. The degradation of PHs has also been studied by Ameen et al. [C. sphaerospermum strongly degraded tritetracontane, 1,2-benzenedicarboxylicacid,mono(2-ethylhexyl) ester, and tetrapentacontane, 1,54-dibromo.Steliga  reportede et al.  reported. Madani  illustran et al. . The autP. chrysogenum has been previously recorded as a potent oil-utilizing species [P. chrysogenum, and the authors have reported complete n-alkane degradation. Isoprenoids such as pristine (C17) and pyrene (C18) have shown a considerable degradation rate. Verma et al. [A. alternata to degrade the PAHs compounds naphthalene, phenanthrene, and anthracene. The authors proposed that A. alternata could degrade the hydrocarbon by producing manganese-dependent peroxide and polyphenol oxidase enzyme. species , 35. Bal species  studied a et al.  reportedP. chrysogenum and C. brachyspora), FC2 (S. brevicaulis and S. botryosum), and FC3  consortia for PH degradation using GC and HPLC analysis. Fungal consortia FC1 and FC2 have exhibited more oil degradation ability than did the individual isolates. The results have shown that FC1 and FC2 consortia demonstrated positive synergistic effects. A wide range of metabolic and physiological factors are essential for degradation of different hydrocarbons. Usage of fungal consortia could result in metabolic integration as the metabolites created by a specific fungus through incomplete degradation may be used as a substrate by another fungus, increasing the possibility of hydrocarbons remediation effectively [This is the first study to describe the potentiality of FC1  exhibited reductions in PH biodegradation despite the high rate of hydrocarbon degradation by each single isolate separately.The lowest crude oil degradation exhibited by using FC3 could be due to the fungal growth reduction as a result of many factors such as the competition and antagonisms . These rC. sphaerospermum, P. chrysogenum, C. brachyspora, and A. alternata. The SH fraction consists of normal, branched, and cycloalkanes (naphthenes). The route of n-alkane biodegradation through a monoterminal attack with alcohol formation followed by the creation of aldehyde and a monocarboxylic acid has been demonstrated by Nkwelang et al. [The data in the present study have shown a significant SH degradation percent, exhibited by all isolates, particularly g et al. . Jurtehug et al.  reportedC. sphaerospermum, P. chrysogenum, and A. alternata in the present was study almost the same, indicating that both n-paraffin and isoparaffin have been attacked in the same rate by these strains. Pirnik and McKenna [N-paraffin and isoparaffin degradation percent exhibited by  McKenna  reportedCycloalkanes are more resilient to attack by microorganisms; however, there were several reports considering the fungal metabolism of cycloalkanes and correlated compounds . Cobet aSeveral authors reviewed the microbial degradation of aromatic hydrocarbons . AromatiC. sphaerospermum and P. chrysogenum presented the highest SH degradation percentage while A. alternata and P. chrysogenum exhibited the highest degradation efficiency of PAHs. It is recommended to use mixed cultures of P. chrysogenum and C. brachyspora for enhanced degradation of both SHs and PAHs. This study supports the critical idea of the potential PAH biodegradation as more a ecologically acceptable alternative to their chemical degradation. After large-scale production, the designated single organisms or the fungal consortium FC1 can be used, in field application, for PHs remediation.This study has succeeded in isolating a large number of petroleum-hydrocarbon-degrading taxa. Based on the results of the laboratory-scale studies,


Vibrio neptunius is a Gram-negative bacterium that has been shown to cause disease in marine bivalve mollusk larvae. Here, we report the draft genome sequences and annotations of five V. neptunius strains isolated from larvae of European oyster (Ostrea edulis) and Manila clam (Ruditapes philippinarum) at hatcheries in Galicia, northwest Spain. Vibrio neptunius is a Gram-negative bacterium that has been shown to cause disease in marine bivalve mollusk larvae. Here, we report the draft genome sequences and annotations of five V. neptunius strains isolated from larvae of European oyster (Ostrea edulis) and Manila clam (Ruditapes philippinarum) at hatcheries in Galicia, northwest Spain. Vibrio spp., is the main bottleneck of the production process in bivalve hatcheries, leading to high larval mortality rates and loss of production lots. V. neptunius was identified as a pathogen responsible for larval and spat mortality episodes in clam and oyster cultures, causing important economic losses for mollusk hatcheries , and 16S rRNA gene sequencing. All details on the isolation of the strains, phenotypic characterization process, and 16S rRNA gene sequencing were described by Prado et al. , and strains PP-307 and PP-313 were isolated from oyster larvae (Ostrea edulis).The 5 V. neptunius that had been grown overnight at 25°C, with shaking, in Trypticase soy broth supplemented with 1% NaCl, using the EasyDNA genomic DNA purification kit (Invitrogen). Sequencing was performed by the Fisabio Public Health Sequencing and Bioinformatics Service , using 300-bp paired-end libraries prepared with the Illumina Nextera XT kit, and ran on an Illumina MiSeq system. The reads were inspected for data quality using FastQC version 0.11.5 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc). Default parameters were used for all software unless otherwise noted. Reads were quality trimmed using PRINSEQ v. 0.20.4  was used to calculate the GC content of the genomes. Further information on the genome parameters is given in Genomic DNA was extracted from cultures of . 0.20.4 , clippin. 0.20.4 . Scaffol. 0.20.4 . An onliV. neptunius would allow the identification and characterization of sequences related to possible virulence factors.The sequencing of the genomes of pathogenic strains of PRJNA703775 under the accession numbers provided in These nucleotide sequences have been deposited in DDBJ/ENA/GenBank as BioProject


Of those, a few are prokaryotic species-specific. Despite their attractive advantages and performances, these methods have certain limitations. Therefore, this study proposes a novel predictor STALLION (STacking-based Predictor for ProkAryotic Lysine AcetyLatION), containing six prokaryotic species-specific models to identify Kace sites accurately. To extract crucial patterns around Kace sites, we employed 11 different encodings representing three different characteristics. Subsequently, a systematic and rigorous feature selection approach was employed to identify the optimal feature set independently for five tree-based ensemble algorithms and built their respective baseline model for each species. Finally, the predicted values from baseline models were utilized and trained with an appropriate classifier using the stacking strategy to develop STALLION. Comparative benchmarking experiments showed that STALLION significantly outperformed existing predictor on independent tests. To expedite direct accessibility to the STALLION models, a user-friendly online predictor was implemented, which is available at: http://thegleelab.org/STALLION.Protein post-translational modification (PTM) is an important regulatory mechanism that plays a key role in both normal and disease states. Acetylation on lysine residues is one of the most potent PTMs owing to its critical role in cellular metabolism and regulatory processes. Identifying protein lysine acetylation (Kace) sites is a challenging task in bioinformatics. To date, several machine learning-based methods for the  The final step of the ‘central dogma’ of molecular biology is the translation process, where RNA codes for specific proteins . Protein3CO) from acetyl coenzyme A is transferred to either the free α-amino group (NH3+) of the N-terminal residue (Nα-acetylation) or to the ε-amino group of internal lysine (Nε-acetylation) at specific sites . In total, we employed 11 encoding schemes that can be grouped into three major types: (i) sequence-based features include numerical representation of amino acid (NRA), binary encoding (BINA), amino acid composition (AAC), dipeptide composition (DPC) and conjoint triad (CTF); (ii) physicochemical properties based features include amino acid index (AAI), grouped dipeptide composition (GDPC), grouped tripeptide composition (GTPC)In NRF encoding, protein sequences are converted into numerical values  by mappiIn BINA encoding, each amino acid converts into a segment of a 21D orthogonal binary vector . For exaAmino acid composition (AAC): AAC computes the frequency of 21 amino acids from the given protein fragment sequence, which has been used as a descriptor for several peptide function predictions [dictions , 51. TheTwenty-one amino acids generate 441 (21 × 21) dipeptides. DPC computes the percentage of all possible dipeptide combinations from the given sequence, reflecting each protein sequence global information and local order amino acids information. Notably, DPC has been widely applied to capture segmental information around PTMs .In CTF, 20 amino acids were categorized into seven classes  according to their volumes of side chains and dipoles . Notablyhttps://www.genome.jp/aaindex/; [AAI is the publicly available database that represents the physicochemical properties of amino acids as the most intuitive features for describing biochemical reactions . We utiaindex/;  and encoEach amino acid has a specific physicochemical property. Accordingly, they have been categorized into five different groups: aliphatic (IMLVAG), aromatic (WYF), positively charged (HRK), negatively charged (ED) and uncharged (QNPCTSO). We assigned dummy residue (O) to the uncharged group. By utilizing these five groups, GDPC are classified into 25 classes that result in a 25D feature vector , 57.In GTPC , 59, thek residues . Unlike the composition of k-spaced amino acid pairs [The CKSAAGP also calculates the frequency of amino acid pairs separated by id pairs , it claset al. [In Zscale, each amino acid is characterized by five physicochemical descriptor variables, according to Sandberg et al. . A givenBLOSUM62 matrix is commonly applied in a BLAST sequence alignment program. Here, it was used to convert the protein sequence to describe the similarity of two sequence segments. Generally, this substitution matrix is applied to study sequence conservation of related proteins in large databases, which has been used as features in several predictors , 61. EacIn this study, we employed six different classifiers that included five decision-tree-based classifiers , AAC (21D), DPC (441D), CKSAAGP (150D), CTF (343D), Zscale (105D), BINA (441D), BLOS (420D), GTPC (125D), GDPC (25D) and NRF (20D) encoding schemes. We linearly integrated these 11 encodings for each sequence and obtained a 2616D feature vector. Thus, the training dataset of F-score, feature importance score (FIS) estimated by RF (RFIS) and FIS calculated by XGB (XFIS) according to their ability to distinguish Kace sites from non-Kace sites. Thereafter, we sorted the original feature dimension in descending order based on their scores. In total, we generated three feature lists , where F-score and RFIS contained the top 2000 features and XFIS included features that have only non-zero value (~500 features).Each sequence contains a high-dimensional feature vector (2616D) that may include irrelevant or redundant information. Consequently, the predictive performance decreased and required vast computational resources during model training , 78. We r and s denoted the ranked feature list and suboptimum subset, respectively. In SFS, k  moved most informative features from r to s, which was inputted into five different classifiers independently and the performance evaluated by employing a 5-fold cross-validation in s. This process was repeated until r became empty. Ultimately, the feature subset for each classifier that achieved superior performance in terms of Mathews correlation coefficient (MCC) was considered an optimal set for each species. Generally, one of the scoring functions and a classifier was to be used to determine the optimal feature set [Second, a sequential forward search (SFS) was applied independently on three feature lists to identify suboptimum feature subsets. Letter ture set , 76. HowFor each classifier, we selected the best model from three different suboptimum subset models. Consequently, we obtained five optimal baseline models for each species. Predicted probabilities and class labels received from baseline models were combined and considered as a new feature vector (10D). In general, the product of baseline models was trained with logistic regression while developing the final prediction model , 75. Howrfitting . Finallyn samples from the training dataset (KAce and non-KAce). In particular, for a given two sequences 1R and 2R with the fixed length, the similarity score F is computed as follows:jth position, and K is the sequence length. For two amino acids m and n, the similarity score is defined as follows:m, n) similarity score for two amino acids derived from the BLOSUM 62 substitution matrix [A is the substitution matrix and min(A)/max(A) represents the smallest/largest value in the matrix, respectively. In this study, we set X = 2, 4, 8, 16, 32, 64 and 128 to generate a 7D feature vector for a given sequence.We also tested K-nearest neighbor (KNN) encoding in this study, which is not part of STALLION. KNN encoding makes features for a given sequence based on the similarity of that sequence to the n matrix , A is thhttps://pypi.org/project/xgboost/) were built and optimized by Scikit-learn v0.18.1 package [F-score, RFIS and XFIS) scoring functions to rank the features using the same package. In addition, feature encodings employed in this study were computed using our in-house code. Notably, a few open-source packages such as iLearn [All cross-validations and independent evaluations were executed in a server with CentOS Linux 7.6 and Python 2.7.5. Notably, all ML classifiers . However, we noted that six encodings achieved similar performances and were significantly higher than the other five encodings  for S. typhimurium. Overall, four encodings  were found to be superior compared to their counterparts. Nevertheless, other encodings also possessed essential information to support Kace site prediction. To get an overview of the performance of each classifier on Kace prediction, we computed an average performance of 66 models (11 encodings × 6 species) for each classifier. The results showed that AB, XGB, RF, ERT and GB achieved average MCCs of 0.261, 0.255, 0.241, 0.232 and 0.230, respectively. Notably, all classifiers performed reasonably well in Kace site prediction; however, AB was found to be marginally superior. Rather than searching for the best model, integrating the above information and developing a robust model is admissible. In this study, we applied a stacking approach similar to recent studies [We systematically investigated the effect of various feature encodings and classifiers in prokaryotic Kace site predictions by employing five tree-based ensemble classifiers  and eleven feature encodings including sequence-based, physicochemical properties and position-specific scoring matrix. We performed a ten times randomized 5-fold cross-validation test for constructing each model for each species dataset and compared the performances among 55 models (11 encodings × 5 classifiers).  studies , 85, 86.F-score and RFIS assign a relative score for all given features. However, XFIS excludes ~70% of the features and designates a relative score for the remaining features. C. glutamicum species. Here, we observed that the performance increased steadily, achieved maximum accuracy and subsequently remained in an equilibrium state for most classifiers based on the F-score . For example, RF, ERT, GB, XGB and AB possessed 1000, 520, 790, 260 and 410 optimal feature sets, respectively, from F-score identified by SFS. The corresponding classifiers had 140, 1290, 211, 120 and 150D optimal feature sets from RFIS and 30, 38, 31, 52 and 44D optimal feature sets from XFIS. Similarly, the best model for each classifier from three different approaches showed different sizes of optimal feature sets. For example, RF possessed three models with 1000, 140 and 40D optimal feature sets. However, we selected the best model based on maximal accuracy. The same procedure was followed for the other species and the best three models were selected for each classifier, whose performances were compared with the control.The size of the optimal feature set varied among five classifiers for each one of the three different approaches , the optimal feature sets obtained from XFIS achieved superior performances for five classifiers compared to their counterparts (F-score and RFIS). In two species (S. typhimurium and B. subtilis), the optimal feature set extracted from F-score achieved excellent performances for five classifiers compared to their counterparts (XFIS and RFIS). However, for G. kaustophilus, the optimal feature sets derived from the F-score showed improved performance for RF and ERT classifiers. The remaining three classifiers showed improved performance upon the acquisition of optimal features from XFIS. Unexpectedly, the optimal feature set derived from RFIS did not show the best performance. Notably, the best models for five classifiers have been considered as baseline models in each species and utilized for subsequent analysis. Overall, our systematic feature selection analysis suggests that it is essential to apply different scoring functions to rank features and employ different classifiers individually for SFS to obtain their corresponding optimal feature set.B. subtilis, C. glutamicum and G. kaustophilus), the SVM classifier for two species (M. tuberculosis and S. typhimurium), and the XGB classifier for E. coli, whose performances are marginally superior to its counterparts. Six species models were commonly named as STALLION that achieved ACC, MCC and AUC of 0.403, 0.700 and 0.745, respectively for B. subtilis; 0.513, 0.756 and 0.809, respectively for C. glutamicum; 0.357, 0.678 and 0.733, respectively for E. coli; 0.603, 0.801 and 0.836, respectively for G. kaustophilus; 0.557, 0.779 and 0.782, respectively for M. tuberculosis; and 0.571, 0.785 and 0.770, respectively for S. typhimurium.Stacking is an ensemble technique that considers different predictive models to generate a stable stacked model. This approach employs an efficient scheme to decrease the generalization error rate of various predictive models . The preB. subtilis, 8.8–11.1% higher for C. glutamicum, 3.7–6.1% higher for E. coli, 24.9–28.2% higher for G. kaustophilus, 8.6–11.7% higher for M. tuberculosis and 26.2–29.3% higher for S. typhimurium. The superior performance of STALLION over the single feature-based models was primarily due to the novelty introduced in our approach, which included (i) feature fusion strategy, (ii) selecting the optimal feature set from hybrid features for each classifier independently and their respective baseline model construction and (iii) selecting an appropriate classifier for stacking model construction.To show the advantage of our proposed stacked approach, we compared STALLION with the single feature-based models. We selected the top 10 single feature-based models from B. subtilis, C. glutamicum, E. coli, M. tuberculosis and S. typhimurium). For example, in C. glutamicum, RF, ERT, GB, XGB and AB contained 30, 38, 31, 52 and 44D optimal feature subsets, respectively. Here, 52D had other feature subsets. However, in G. kaustophilus, different optimal subsets were combined to investigate their role.To understand the contribution of different features in the optimal feature set for each species, we analyzed their composition and distribution. It is worth mentioning that five classifier models have different optimal feature subsets for each species. Instead of focusing on each subset, we considered the maximum size of the optimal feature subset that potentially includes the other four subsets for five species  consistently contributed to the optimal feature set for all species. Still, the contribution level varied among them suggesting a supporting role played in Kace prediction. Furthermore, we observed that GTPC and GDPC, GTPC, NRF and GTPC and BINA did not contribute to the final prediction for C. glutamicum, E. coli, G. kaustophilus and M. tuberculosis, respectively. Overall, apart from AAI, the rest of the feature contribution varied considerably among species, thus suggesting that Kace sites in these species might have different characteristics.et al. [We further evaluated STALLION using independent datasets and compared their performances with the existing method. Since 2009, several computational tools have been reported for Kace site prediction. Notably, Chen et al.  recentlyet al. , 37 and http://computbiol.ncu.edu.cn/PAPred) and the predictions were computed based on the given default threshold. Notably, the ProAcePred 2.0 returns Kace site and its predicted probability value, but not non-Kace predicted probability values. Therefore, it might be unfeasible to compute the AUC value with partial probability information. However, we compared the performances between two methods in terms of MCC, which is an intuitive and straightforward metric while dealing with an imbalanced dataset, as mentioned in [B. subtilis, C. glutamicum, E. coli, G. kaustophilus, M. tuberculosis and S. typhimurium, respectively  and 9.1% in MCC value for S. typhimurium. STALLION provided better performance than ProAcePred2.0 because of the following: (i) Unlike ProAcePred 2.0, we excluded KNN feature encoding from the stacking framework based on our systematic analysis that identified the overfitting nature of KNN encoding during cross-validation (see section below); (ii) Unlike ProAcePred 2.0 simple feature selection approach, we employed a rigorous process by utilizing three different scoring functions and SFS to identify the optimal feature set independently for each classifier, which is time-consuming and (iii) Unlike a single model in ProAcePred 2.0, our stacking strategy integrates five tree-based ensemble baseline models leading to more accurate Kace site predictions.It is worth mentioning that the independent dataset for each species was submitted to the ProAcePred 2.0 web server  between the training and independent datasets in all six species significantly increased from 22.46 to 29.32%, clearly indicating the overestimation of KNN encoding during training regardless of the classifiers. Owing to the over-fitting nature of the KNN encoding scheme, we highly recommend testing KNN encoding transferability before incorporating it into any computational frameworks requiring huge computations.KNN feature encoding is widely applied for the identification of PTM sites, including previous Kace site prediction methods , 37. Simhttp://thegleelab.org/STALLION. The STALLION web server is maintained by an Apache HTTP server and configured in a 16-core CentOs Linux 7.6 server machine with 64GB RAM and a 2 TB hard disk. We have given the detailed instructions for using the STALLION in the following link: http://thegleelab.org/STALLION/Staltutorial.html. In addition, we provided the server running time of our independent datasets in the above link.Publicly accessible web servers can help experimental or biomedical researchers to identify the putative functional sites, which will aid further experimental characterization. To help the user identify high-throughput putative Kace sites from six prokaryotic species, we implemented the STALLION web server, which is freely accessible at: This study presented STALLION, a stacking framework for the accurate Kace site prediction from six different prokaryotic species. STALLION employed 11 distinct feature encoding schemes (categorized into three groups) to encode protein fragments. Subsequently, a rigorous feature selection approach was employed to carefully select the optimal feature set for each of the five different tree-based ensemble algorithms and constructed their respective baseline models for each species. Finally, the predicted output was derived from five baseline models which were trained with an appropriate classifier to build the stable, stacked STALLION models. Our proposed method STALLION outperformed the current state-of-the-art predictor for identifying Kace sites on the independent data sets across six different species. It is expected that STALLION methodology and a user-friendly web server based on the stacked model for six prokaryotic species will expedite the discovery of putative Kace sites and greatly assist the effort of a broader research community for functional characterization. Our study identified that heterogeneous and complementary features derived from different perspectives helped to improve predictor performance. We will continually attempt to investigate other informative features, examine their contribution and refine our prediction platform.Overall, the STALLION method has achieved a robust performance in Kace site prediction, whose prediction performance requires further improvement in several aspects. Novel computational frameworks have been reported recently, including a DL-based hybrid framework  and DL-bWe propose a stacking framework STALLION and implement it as a user-friendly webserver for accurate identification of prokaryotic Kace sites.STALLION utilized 11 different features encoding schemes and combined five tree-based ensemble algorithms to build stable stacked models.Extensive benchmarking experiments demonstrated that STALLION outperformed its constituent baseline models in both training and independent datasets, thus highlighting its excellent generalization capability.Supplementary_bbab376Click here for additional data file.


Botryosphaerella sudetica KNUA107, isolated from Ulleung Island, South Korea, as a bioresource and analyzed the effects of mixotrophic cultivation on its bioresource production efficiency. Internal transcribed spacer (ITS) , ribulose bisphosphate carboxylase large subunit (rbcL), and elongation factor Tu (tufa) regions were used for molecular identification and phylogenetic analysis. B. sudetica KNUA107 had a strong relationship with the green algae of Botryococcus and Botryosphaerella genera, which are colony-forming species, and was also associated with members of the Neochloris genus. To improve biomass productivity, we tested mixotrophic cultivation conditions using several organic carbon sources. Glucose supplementation stimulated B. sudetica KNUA107 growth and reduced the time needed to reach the stationary phase. In addition, the colony size was 1.5–2.0 times larger with glucose than in photoautotrophic cultures, and settleability improved in proportion to colony size. The total lipid content and biomass productivity were also higher in cultures supplemented with glucose. Among the lipid components, saturated fatty acids and monounsaturated fatty acids had the highest proportion. Our study suggests that B. sudetica KNUA107, which has enhanced efficiency in biomass production and lipid components under mixotrophic cultivation, has high potential as a bioresource.In this study, we characterized the potential of colony-forming green algae,  Green algae are photosynthetic organisms used as bioresources . They arPediastrum genus) and others form colonies that connect single cells   . Multice genera) . High se genera) , 2015. H genera) . Continu genera) .Although green algae are photosynthetic organisms, they can metabolize organic and inorganic carbons . SeveralBotryosphaerella sudetica KNUA107, isolated from Ulleung Island, as bioresources for bioenergy production. To enhance bioresource production, we used mixotrophic cultures supplemented with different organic carbon sources and screened their effect on algal growth. Settleability and lipid contents under various mixotrophic culture conditions were measured and analyzed. Based on these analyses, we identified an organic carbon source that enhanced bioresource productivity and confirmed the value of B. sudetica KNUA107 as a bioresource for bioenergy production.In this study, we evaluated the suitability of the colony-forming green alga, B. sudetica KNUA107 strain was assigned the GenBank accession number MW683220 in the NCBI database. B. sudetica KNUA107 grew the vegetative cell in the form of single cells and multicellular colonies in the liquid medium, and the colonies were irregular and appeared to be tightly bound between cells , followed by Botryococcus sp. (92%) and N. aquatica (91%). Previous data indicated that some members of these three green algae genera have colony-forming abilities and morphological characteristics similar to those of B. sudetica KNUA107  was further determined by phylogenetic analysis. ML trees were constructed based on ITS, rbcl, and tufa sequences of B. sudetica KNUA107 and several green algal species . Hence, B. sudetica KNUA107 had the closest phylogenetic relationship with B. sudetica. Additionally, B. sudetica KNUA107 has a colony-forming ability similar to previously studied B. sudetica algae  and confirmed that it is related to B. sudetica. We evaluated biomass and lipid productivity to demonstrate its potential as a bioresource for bioenergy production. Furthermore, we applied mixotrophic cultivation, which promotes bioenergy production in microalgae, to B. sudetica KNUA107. Several mixotrophic conditions were applied to the culture to investigate their effect on biomass productivity. Glucose was the only added carbon source that increased biomass in high-density cultures, increasing growth rate and settleability. Moreover, the glucose mixotrophic condition increased B. sudetica KNUA107 content in lipids and their saturated components. Therefore, B. sudetica KNUA107 is a suitable strain to produce bioresources under mixotrophic conditions. Our research elucidates the impact of organic carbon sources on biomass productivity and suggests the potential of colony-forming green algae as an industrially useful bioresource.In this study, we isolated a colony-forming green algae strain at Ulleung Island (–2 s–1), and with a 16:8 h light:dark cycle. The culture was rotated at 160 rpm in an orbital shaker . After a 2-week incubation, the algal sample was streaked onto a BG11 (pH 7.4) agar plate. Subsequently, single colonies grown were aseptically transferred to fresh BG11 agar plates. The transferring process was repeated until a colony consisting of one species was aseptically obtained  in April 2017. The sample was transferred to the laboratory and inoculated in 150 mL of BG11 medium (pH 7.4) in a 250 mL flask . The inoobtained .B. sudetica KNUA107 strains, cells that were incubated for 2 weeks in 150 mL of BG11 were collected by centrifugation at 4000 rpm for 10 min and washed twice with sterile distilled water. The collected algal cells were resuspended in 400 μL DNA extraction buffer  in 2 mL microfuge tubes. The suspended samples were vortexed for 5 min after adding 8 μL RNAse A  and sterile glass beads and heated at 65°C for 30 min. Cellular debris was removed by centrifugation at 13,000 rpm for 10 min, and 400 μL of the supernatant was transferred into a new tube. DNA was purified with a DNA purification kit  according to the manufacturer’s protocol. The internal transcribed spacer (ITS), ribulose bisphosphate carboxylase large subunit (rbcL), and elongation factor Tu (tufa) were used as marker genes   . DNA sequencing was conducted at the Genotech facility . The sequence information of previously reported related algal strains (with similar sequences) was retrieved from NCBI; B. sudetica KNUA107 collected from Ulleung islands received the GenBank accession number MW683220. The selected sequences were aligned using the MEGA (version 7.0) software package to analyze the phylogenetic relationship between algal strain  , and theWCGC-3′) . The ampl strain . Based ol strain .B. sudetica KNUA107 culture was prepared for photoautotrophic and mixotrophic cultivation. The algal cells were cultured with shaking at 160 rpm in an illuminated incubation room at 25°C. The prepared algal cells were diluted to approximately 156.67 ± 20.81 mg/L. BG11 medium was used for photoautotrophic cultivation  and sediment (1 mL). The measurements were performed for 20 min per set, with three sets per culture condition.Colony size, a phenotype associated with settleability, was measured , 2015 in530) was measured in mixtures with sufficient reaction, and the total lipid content was calculated based on the measured value  and at the end (30-day-old) of the cultivation period by centrifugation at 4,000 rpm for 30 min. The collected cells were then freeze-dried. The sulfo-phospho-vanillin colorimetric method was used to determine the total lipid content . Canola ed value .Lipids were extracted and analyzed by gas chromatography/mass spectrometry (GC/MS) to determine their composition and content . Freeze-The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/H-SYu: investigation and writing—original draft. Y-SK: investigation. H-SYu and Y-SK: conceptualization, data curation, formal analysis, and methodology. H-SYu: supervision. Y-SK and H-SYo: funding acquisition. H-SYu, Y-SK, and H-SYo: writing—review and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Prunus takesimensis Nakai has been presumed to be derived through anagenetic speciation on the island. Based on morphological similarities, P. sargentii Rehder distributed in adjacent continental areas and islands has been suggested as a purported continental progenitor. However, the overall genetic complexity and resultant non-monophyly of closely related flowering cherries have hindered the determination of their phylogenetic relationships as well as the establishment of concrete continental progenitors and insular derivative relationships. Based on extensive sampling of wild flowering cherries, including P. takesimensis and P. sargentii from Ulleung Island and its adjacent areas, the current study revealed the origin and evolution of P. takesimensis using multiple molecular markers. The results of phylogenetic reconstruction and population genetic structure analyses based on single nucleotide polymorphisms detected by multiplexed inter-simple sequence repeat genotyping by sequencing (MIG-seq) and complementary cpDNA haplotypes provided evidence for (1) the monophyly of P. takesimensis; (2) clear genetic differentiation between P. takesimensis (insular derivative) and P. sargentii ; (3) uncertain geographic origin of P. takesimensis, but highly likely via single colonization from the source population of P. sargentii in the Korean Peninsula; (4) no significant reduction in genetic diversity in anagenetically derived insular species, i.e., P. takesimensis, compared to its continental progenitor P. sargentii; (5) no strong population genetic structuring or geographical patterns in the insular derivative species; and (6) MIG-seq method as an effective tool to elucidate the complex evolutionary history of plant groups.Of the two major speciation modes of endemic plants on oceanic islands, cladogenesis and anagenesis, the latter has been recently emphasized as an effective mechanism for increasing plant diversity in isolated, ecologically homogeneous insular settings. As the only flowering cherry occurring on Ulleung Island in the East Sea (concurrently known as Sea of Japan),  Prunus comprises approximately 200 species of shrubs and trees , Ulleung Island is rich in flora with approximately 500 native vascular plant species, of which approximately 37 are endemic   in the Gálapagos Islands (Echium (Boraginaceae) (Aeonium (Crassulaceae)  (Dendroseris and Robinsonia (Asteraceae) in the Juan Fernández Islands (Nigella arvensis complex (Ranunculaceae) in the Aegean Archipelago (Helianthemum (Cistaceae) in the Canary Islands (The most commonly described evolutionary process in island biogeography is speciation associated with cladogenesis via adaptive radiation . Cladogeulaceae)  and the ulaceae)  in Hawai Islands ; Echium inaceae) , Aeoniumulaceae) , and theeraceae)  in the C Islands . In addihipelago  and Heli Islands . In non- Islands .A different mode of evolution, anagenetic speciation, which describes lineages changing over time without splitting events on islands, has been reported, explaining ca. 25% of insular endemic angiosperm species diversity . In anagRubus takesimensis Nakai (Campanula takesimana Nakai (Phedimus takesimensis (Nakai) ’t Hart (Dystaenia takesimana (Nakai) Kitag.  Kitag. , Acer okum Nakai , and Acese Nakai  have bee species , 2013. He island . Similaructuring . Phedimuiversity . To furtPrunus takesimensis Nakai is an exceptionally challenging taxon to elucidate its origin and evolution. It suffers from the difficulties in species delimitation and unresolved phylogenetic relationships of flowering cherries due to morphological continuity, lack of diagnostic morphological features, limited informative genetic polymorphisms from appropriate molecular markers, and frequent hybridization and introgression among congeneric species  and higher flower numbers per umbellate inflorescence  (P. takesimensis has entire and erect (or spreading) calyx lobes and lacks hair on the bud scale, inflorescence, leaf, petiole, and pedicel; however, rare individuals with hair on the pedicel can be found  . AdditioP. takesimensis has never been resolved as a monophyletic lineage in any prior molecular phylogenetic analyses. In addition, no comprehensive study has been conducted to determine its progenitor-derivative relationship or population genetic diversity and structure. For example, with the inclusion of limited samples of P. takesimensis, phylogenetic analyses of Prunus/Cerasus have been conducted to determine primary interspecific relationships  suggested genetic closeness between P. takesimensis and P. sargentii, although both species were not retrieved as monophyletic  as a maternally inherited marker. It has been hypothesized that P. takesimensis on Ulleung Island has been originated from its continental progenitor species, P. sargentii according to the common morphological characteristics  of P. takesimensis and 161 of other species in the subgenus Cerasus]. In particular, the purported continental progenitor species, P. sargentii, was sampled from likely source areas, such as the Korean Peninsula, including Jeju Island, Japan, and the Russian Far East, to determine the geographical source area. The primary objectives of this study were to (1) test the monophyly of P. takesimensis on Ulleung Island and resolve its sister group relationship for identifying its continental progenitor species and source populations in the phylogenetic framework employing genome-wide MIG-seq analysis as well as Sanger sequencing for nrDNA regions ITS and ETS, and seven concatenated cpDNA regions. Secondly, we performed population genetic analyses to (2) evaluate the patterns of genetic variation within the insular populations of P. takesimensis and compare them with the results in its continental progenitor species using MIG-seq and cpDNA data. These objectives allow us to better understand the origin of Ulleung Island endemic plants as well as the genetic consequences of anagenetic speciation in the East Sea.Given the abovementioned methodological challenges for the study of the evolutionary processes of flowering cherries in a phylogenetic framework, we performed a genome-wide single nucleotide polymorphism (SNP) analysis using multiplexed inter-SSR (ISSR) genotyping by sequencing (MIG-seq) in addition to Sanger-derived sequences for nrDNA (ITS and ETS) and seven concatenated cpDNA non-coding regions. MIG-seq is a polymerase chain reaction (PCR)-based next-generation sequencing (NGS) method, which has been recently developed as an effective method for the discovery of genome-wide SNPs from low-quantity or low-quality DNA . It has eristics . HoweverPrunus is broadly interpreted and divided into five subgenera. Leaves collected mostly from natural populations were dried with silica gel and used as DNA sources. DNA was extracted using the DNeasy Plant Mini Kit . We included extensive samples in analyses, a total of 350 accessions belonging to 15 Prunus species in the subgenus Cerasus , P. sargentii Rehder , P. sargentii var. verecunda (Koidz.) Chin S. Chang (four accessions from South Korea), P. serrulata var. spontanea (Max) Wilson (21 accessions: 16 from South Korea and five from Japan), P. serrulata var. quelpaertensis (Nakai) Uyeki , P. serrulata var. pubescens (Makino) Nakai (17 accessions: 16 from South Korea and one from Japan), P. yedoensis var. angustipetala Kim and Kim (one accession from Jeju Island), P. hallasanensis Kim & Kim (two accessions from Jeju Island), P. longistylus Kim and Kim (one accession from Jeju Island), P. speciosa (Koidz.) Ingram , P. incisa Thunb. (one accession from Japan), and P. apetala (Siebold and Zucc.) Franch and Sav. (three accessions from Japan). Two taxa belonging to the subgenus Cerasus, section Phyllomahaleb and section Eucerasus were also included: P. maximowiczii Ruprecht (three accessions from South Korea) and P. avium (L.) L. (one accession from Japan).This study followed the most widely accepted Rehder’s classification(1940), where the genus  Cerasus . First, Cerasus species were used for phylogenetic analyses based on nrDNA ITS and ETS as well as seven concatenated cpDNA non-coding regions. In total, 262 accessions from 13 species, including P. takesimensis (162 accessions), P. sargentii (46 accessions), and other species (54 accessions) were used to determine the phylogenetic position and genetic structure of P. takesimensis by MIG-seq SNP analysis. Ninety nine accessions of P. takesimensis and P. sargentii sampled at the population level were used for the analysis of the cpDNA haplotype network   for P. takesimensis and P. sargentii using five non-coding regions   were amp species . These d species , which wions see . The theions see  with defions see .Cerasus species, including P. takesimensis and P. sargentii. The first PCR was performed from genomic DNA using designated primers to target ISSR, followed by a second PCR to add the complementary sequences for the binding sites of the Illumina sequencing flow cell and indices (barcodes) as specified by 1 and TagDust 1.12  and the maximum distance allowed between stacks (-M 2). Deleveraging (d) and removal (r) algorithms were enabled. The program ‘cstacks’ was used to create a catalog, and ‘ustacks’ products were searched against the catalog in the program ‘sstacks.’ The SNP data matrix, including 262 samples, was generated by the program ‘populations’ using the optimized parameters based on the most robustness in phylogenetic analysis after testing variable parameters  as shown in The MIG-seq library was constructed by two-step amplification, as detailed by ust 1.12 . A totalust 1.12  to maintust 1.12 , generatP. takesimensis among other Cerasus species, we first conducted maximum likelihood (ML) analyses of the nrDNA ITS/ETS and the concatenated seven non-coding regions of the cpDNA datasets for 123 samples using W-IQ-TREE  was calculated from 1000 bootstrap replicates for the robustness of the clades (P. takesimensis and P. sargentii) from default setting of 100,000 random quartets with QFM quartet tree search algorithm and bootstrapping of 100 replicates in PAUP 4.0a169  and P. sargentii (5 populations), including 200 samples  (10.5061/dryad.x3ffbg7jn) , were poorly resolved with low nodal supports. However, MIG-seq phylogeny based on genome-wide SNPs  provided much greater resolution  of its purported continental progenitor species P. sargentii, mainly sampled from populations of the Korean Peninsula, Russia, and Miyagi, Japan. P. takesimensis was found to be monophyletic; a total of 162 accessions formed a highly supported monophyletic group  except for one accession (SIB969-005), which was also nested in the group of P. sargentii accessions collected from Mt. Odaesan in the central part of the Korean Peninsula. The clade comprising all but one accession of P. takesimensis shared the most recent common ancestor with P. sargentii JRS355-54 collected from Mt. Jirisan in the southern part of the Korean Peninsula  . In contrast, the accessions of P. sargentii collected from Okayama, Japan were distantly related to P. takesimensis and were embedded within the lineage of P. serrulata var. spontanea, comprising the accessions collected from the southern islands of South Korea . P. sargentii accessions collected from Jeju Island were nested in the group, which included other Cerasus species from Jeju Island and the Korean Peninsula and was in the sister relationship to CLADE A   and non-ffbg7jn) . It was solution . Specifieninsula . Within on (NRH) . Contrarargentii . Within  CLADE A .P. takesimensis (100% BS). However, the geographic origin of P. takesimensis could not be inferred from SVDQuartets analysis, as other clades resolved in ML tree were highly unresolved without providing any clue to the origin of P. takesimensis. Clade A in ML tree, including P. takesimensis and the populations of the Korean Peninsula, Russia, and Miyagi, Japan of P. sargentii, was not resolved in the SVDQuartets bootstrap consensus tree. Prunus takesimensis shared the most recent common ancestor with the lineage comprising P. sargentii and P. avium collected from Miyagi, Japan, but with very weak support value (<50% BS) (The SVDQuartets bootstrap consensus tree reconfirmed strongly the monophyly of <50% BS) .10.5061/dryad.x3ffbg7jn): petA-psbJ , petD-rpoA , rpl16 intron , trnL-rpl32 , and trnQ-rps16 . The TCS haplotype network contained 24 haplotypes in total: 11 haplotypes for P. takesimensis (69 accessions from 13 populations) and 14 haplotypes for P. sargentii (30 accessions from five populations). Of the 24 haplotypes, 10 were exclusive to P. takesimensis, 13 to P. sargentii, and only one (H7) was shared by both species in nine accessions of P. sargentii and one accession of P. takesimensis (SIB2115-6) as shown in P. sargentii than in P. takesimensis, despite the considerably broad sampling of P. takesimensis. The number of haplotypes within populations of P. sargentii ranged from two (ODS) to five (JJ and MYG) with higher gene diversity and nucleotide diversity  than in P. takesimensis . Most populations of P. takesimensis showed a simple combination of only two haplotypes, except NAR (three haplotypes), DDJ (four haplotypes), and SIB (five haplotypes).The aligned sequence length of five concatenated chloroplast non-coding regions used for the construction of the TCS haplotype network was 3,809 characters , with one accession from P. takesimensis forming a ring-like network structure with other closely related haplotypes H4, H8, and H9. Haplotype H11 was found only in a single accession of P. takesimensis (SIB2115-1) and was derived by one mutational step from a missing haplotype of another ring structure composed of P. sargentii haplotypes H13, H10, and H7. Three haplotypes of P. takesimensis, H22 (SIB population), H23 (JRG population), and H24 (DDJ population), were derived from H21 exclusively found in P. sargentii (ODS). The two dominant haplotypes found in the populations of P. takesimensis were H4 (shared by 32 accessions) and H2 (shared by 27 accessions), while the other haplotypes were represented by only a single accession  or two . In contrast, no dominant haplotypes were found in P. sargentii populations. With regard to the partitioning of genetic variation, including both species of P. sargentii and P. takesimensis, the majority of variation (59%) existed within populations, while approximately 30% and 10% of the variation existed between species and among populations within species, respectively  occurred within populations, while the remaining variation (13.6%) existed among populations. A similar level of genetic variation was found within populations (83.7%) for the insular derivative species P. takesimensis.The genealogical relationships among cpDNA haplotypes are shown in ectively . Within Cerasus species  using STRUCTURE 2.3.4   for both datasets based on the rate of change in the log probability of data between successive K values , while P. sargentii was represented by different genetic clusters  shared with other Cerasus species with admixed (or single) composition. One accession of P. takesimensis (SIB969-005) appeared as an outlier, showing genetic profiles similar to those of P. sargentii from ODS and RSS population. All accessions of P. speciosa were extremely uniform in its genetic composition comprising single cluster  from all K values. The populations of P. sargentii differed in genetic profiles; Jeju and Okayama populations showed admixed profiles similar to those of P. serrulata var. quelpaertensis and P. serrulata var. spontaneae, and were differentiated from three populations of Odaesan in the Korean Peninsula, Russia, and Miyagi, Japan. The results of comparison between both species, P. takesimensis and P. sargentii, also revealed clear differentiation in population genetic structures, as displayed in the bar plots of all K values  and northeastern populations  slightly differed in the proportions of the inferred clusters. One outlier accession (SIB969-005) was similar to P. sargentii from the ODS, RSS, and MYG populations.Based on SNP matrices generated by STACKS, the genetic structure was estimated first for 262 accessions of all RE 2.3.4 . Furtherffbg7jn) . The besK values  from STRK values . The sumclusters . SpecifiK values . There w10.5061/dryad.x3ffbg7jn) further confirmed the genetic relationship among accessions of Cerasus species. P. takesimensis and P. speciosa were distinct from other species in the PCA scatter plot constructed by PC1 and PC2  and P. sargentii  using STACKS. The expected and observed heterozygosity were higher in the populations of P. takesimensis  than in P. sargentii (mean 0.1241 and 0.1658) (PCA results based on 262 MIG-seq dataset in VCF format (see data matrices available in dryad under doi:  and PC2 . The oth 0.1658) .P. takesimensis as it diverged from its presumptive continental progenitor, P. sargentii, as well as other flowering cherry species. Morphologically, P. takesimensis has been recognized as a distinct species from other flowering cherries occurring in adjacent continents and other islands , but such morphological delineation has not been supported by genetic distinction in molecular phylogenetic analyses. For example, the P. serrulata/P. sargentii complex  was differentiated from P. sargentii , as the former possessed species diagnostic haplotypes exclusively  , formed a 100% BS-supported monophyletic group, suggesting its genetic cohesiveness on Ulleung Island  , 5. Howeg Island , 3. The  species .P. takesimensis, MIG-seq SNP phylogenetic reconstruction revealed the continental progenitor and insular derivative species relationship between P. sargentii and P. takesimensis, which provided clues to the origin of P. takesimensis. The MIG-seq method successfully uncovered the species delimitation and phylogenetic relationships of the genetic lineages confined to relatively narrow (or isolated) distribution areas, such as P. speciosa  and P. takesimensis on Ulleung Island. Furthermore, three geographic groups of P. sargentii are recognized in the MIG-seq ML phylogeny, that is, OKA , JJ (Jeju Island in South Korea), and the combined clade (Clade A) of MYG , ODS/SBK/HBK/JRS (the Korean Peninsula) and RSS (Russia), which facilitates tracing the geographic origin of the continental progenitor of P. takesimensis. The strongest phylogenetic evidence for detecting progenitor-derivative species pairs comes from insular endemic populations of one species (derivative) nested within source populations of another species (progenitor) (P. takesimensis were nested within the populations of P. sargentii (Clade A), and we could reconfirm the continental progenitor and insular derivative species relationship between P. sargentii and P. takesimensis, which was initially presumed based on morphological similarities. All but one accession of P. takesimensis (161 accessions) formed a monophyletic group (100% BS), sharing the most recent common ancestor with one accession (JRS35554) of P. sargentii collected from Mt. Jirisan in the southern part of the Korean Peninsula (86% BS)  . In the (86% BS) . This suP. takesimensis seems still uncertain, although a single origin of P. takesimensis from the P. sargentii population in Korean Peninsula is highly plausible   was nested within the ODS population of P. sargentii  instead of being nested within the conspecific monophyletic group on MIG-seq phylogeny. Another accession of P. takesimensis (SIB2115-6), which was not included in the MIG-seq analysis, shared the same haplotype (H7) as P. sargentii accessions. These two genetic outliers within the same population  potentially represent independent dispersal event and convergent or parallel evolution.However, the origin of lausible . SVDQuarlausible . Unlike <50% BS) . In addiP. takesimensis (100% BS) and identification of the clade containing P. sargentii populations from mainland South Korea, the Russian Far East, and Miyagi (northern Japan) being sister (97%) to P. takesimensis on Ulleung Island are reasonably well established  and P. sargentii var. verecunda , northern Japan, and the Russian Far East, may constitute the species P. sargentii as originally described  may represent P. serrulata or the results of introgression between the two closely related taxa. A future morphological and fine-scale molecular phylogenetic study is required to further unravel species delimitation as well as potential gene flow histories of flowering cherries in East Asia.While the evidence for monophyly of ablished , this sterecunda . In addierrulata , this stescribed . Prunus Dystaenia takesimana and D. ibukiensis) or slightly lower  genetic variations in island populations than continental progenitors without geographic partitioning  along with no geographical partitioning of the those variations over the Islands (Acer (nine loci), Myrceugenia (six loci), and Drimys (nine loci) species. Prunus species, specifically belonging to P. serrulata/P. sargentii complex, appeared more complicated genetically than those species, as they were not clearly distinguished based on 11 nuclear microsatellite loci genotyping . In this study, we assessed the genetic consequences of anagenetic speciation of P. takesimensis using different types of molecular data, that is, maternally inherited cpDNA haplotypes and genome-wide SNPs from MIG-seq analysis. No geographic partitioning of genetic variation was found within the populations of P. takesimensis in either analysis. The genetic diversity in P. takesimensis was lower than the one in P. sargentii in haplotype richness, polymorphic sites, gene diversity and nucleotide diversity from cpDNA haplotypes, but was slightly higher in heterozygosity (He) and nucleotide diversity (π) from much more extensive genome-wide SNP analysis  and HE (e): the insular derivative P. takesimensis (mean 0.1543 and 0.1905) versus the continental progenitor P. sargentii (mean 0.1241 and 0.1658)  . We, howP. takesimensis across Ulleung Island is clearly confirmed by phylogenetic and population genetic structure analyses based on genome-wide SNPs, which is consistent with the expected pattern for anagenetically derived endemic species  geographic structuring among populations of  species , 6. Lacksimensis , 51.90% kesimana , and 56.simensis . Gene flsimensis . In the cherries . MoreoveRubus takesimensis, and its continental progenitor, R. crataegifolius, showed significantly higher genetic diversity statistics in the continental progenitor R. crataegifolius in a few parameters  than the insular derivative R. takesimensis, even though R. takesimensis does not have low levels of genetic variation because it has experienced multiple introductions from geographically and genetically diverse source populations   , future 10.5061/dryad.x3ffbg7jn). Raw reads are available in the Short Read Archive (SRA) at NCBI under the bioProject number of PRJNA742508 titled as “262 Prunus MIG-Seq”.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Dryad (doi: M-SC and S-CK designed the experiment. M-SC and MM collected the samples. M-SC, KT, and JY performed the experiments and analyzed the data. M-SC drafted the manuscript. S-CK revised the manuscript. M-SC, KT, JY, MM, and S-CK approved the final manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Varroa destructor is the most important honey bee ectoparasite. Varroa mites are competent vectors of deformed wing virus (DWV), and the Varroa-virus complex is a major determinant of annual honey bee colony mortality and collapse. MicroRNAs (miRNAs) are 22-24 nucleotide non-coding RNAs produced by all plants and animals and some viruses that influence biological processes through post-transcriptional regulation of gene expression. Knowledge of miRNAs and their function in mite biology remains limited. Here we constructed small RNA libraries from male and female V. destructor using Illumina’s small RNA-Seq platform. A total of 101,913,208 and 91,904,732 small RNA reads (>18 nucleotides) from male and female mites were analyzed using the miRDeep2 algorithm. A conservative approach predicted 306 miRNAs, 18 of which were upregulated and 13 downregulated in female V. destructor compared with males. Quantitative real-time PCR validated the expression of selected differentially-expressed female Varroa miRNAs. This dataset provides a list of potential miRNA targets involved in regulating vital Varroa biological processes and paves the way for developing strategies to target Varroa and their viruses.The remarkably adaptive mite  Varroa destructor mites are considered the most damaging ectoparasite of the European honey bee (Apis mellifera)  , with collifera) . Varroa d adults , depletid adults . Varroa athogens , which tathogens  and requrvention . Indeed,rvention . Althougf losses .Varroa foundresses vector deformed wing virus (DWV), one of the major causes of honey bee colony collapse, to immature honey bees when they feed. There is now considerable evidence that Varroa mites are a vector of DWV  need further clarification to develop interventions to prevent Varroa infestation and Varroa-borne virus transmission to honey bees.r of DWV , furtherr of DWV . While Destation , inoculaal stage . Bees syal stage . By feedortality . These Virulence . AlthougVarroa contributes to driving changes in virus distribution, prevalence, and virulence  between August and October, 2019. The apiary comprises five healthy and well-established Italian Apis mellifera ligustica colonies that had successfully survived the mild winter in MS the previous year (2018) and that had not received Varroa treatment of any kind in 2019 prior to the sampling period. Capped brood and mites were collected from a single-well populated queen-right colony with two chambers hosted in a classic Langstroth hive. Adult female mites were collected from adult honey bee workers using the sugar shake method  for pooled Varroa males. Small RNA libraries were prepared using the Illumina TruSeq kit following the manufacturer’s instructions . Briefly, short adapter oligonucleotides were ligated to each end of the small RNAs in the samples. Individual cDNA copies were made with reverse transcriptase, and PCR was used to add sample-specific barcodes and Illumina sequencing adapters. The final concentration of all NGS libraries was determined using a Qubit fluorometric assay. The cDNA fragment size of each library was assessed using a DNA 1000 high-sensitivity chip on an Agilent 2100 Bioanalyzer . Before sequencing, samples passed the essential quality control (QC) test. After purification by polyacrylamide gel electrophoresis, the sample libraries were pooled and sequenced on an Illumina NextSeq 500  using the TruSeq SBS kit v3 (Illumina) and protocols defined by the manufacturer. Four small RNA libraries of pooled male and female Varroa mites were sequenced via Illumina small RNA high-throughput sequencing. RNA library preparation and indexing were performed by the University of Mississippi Medical Center genomics core facility.The RNA concentration was 286.5 ng/ul (260/280 = 1.8) for pooled V. destructor reference genome (GCF_002443255.1_Vdes_3.0_genomic.fna)  reads with the secondary structure of the miRNA precursor. BEDtools (The miRDeep2 version 2.0.0.8 software package  was usedmic.fna)  using thBEDtools  was usedVarroa were separately pooled to extract RNA for cDNA synthesis, and the miRNA-specific qRT-PCR reaction was performed for each sample. The Mir-X miRNA qRT-PCR TB Green kit from Takara Bio  was used for cDNA synthesis and miRNA expression analysis. Conditions for qRT-PCR were: initial denaturation 95°C for 10 mins and then 40 cycles of 95°C for 5 secs and 60°C for 20 secs on a C1000 Touch Thermal cycler . The primer sequences are shown in The miRprimer2 algorithm  was usedIn silico differential expression (DE) analysis of predicted miRNAs was performed using the DeApp interactive web interface (shiny package (web application framework for R). Low expression genetic features were removed after alignment if the counts per million (CPM) value was ≤1 in less than two samples. Sample normalization and a multidimensional scaling (MDS) plot are shown in p-value set to 0.05 and minimum fold-change of 1.5. The interface displays a dispersion plot showing the results of overall DE analysis along with statistical significance  and volcano plot corresponding to the specified parameters and cutoff values.nterface . DeApp iVarroa miRNAs , 101,914,732 small RNA reads were available from V. destructor, and several had homologs in D. melanogaster. The predicted miRNAs were categorized as high (n=50) and low confidence (n=80) miRNAs  detected 112 miRNA orthologs (https://www.ncbi.nlm.nih.gov/assembly/GCF_000856945.1/) as the DWV reference. In silico mapping of female Varroa small RNA sequences suggested that female samples were DWV-infected, since 0.18% of female Varroa mite sequences mapped to the DWV-B genome. The few viral miRNAs predicted in the analysis had very low mIRDeep2 scores so could not be regarded as predicted viral miRNAs.Varroa miRNAs were conserved in D. melanogaster, and previous studies revealed their conserved roles in development, replication, viral colonization, and viral inhibition in other arthropods . Such enrichment indicates that the proteins are at least partially biologically connected as a group. To minimize the number of false-positive targets, we opted only for targets predicted by all three miRNA target programs . The predicted miRNAs were therefore of high confidence, and the main KEGG pathways involved were oxidative phosphorylation (12 out of 80), endocytosis (4 out of 116), protein processing in the endoplasmic reticulum (2 out of 109), and other metabolic pathways (14 out of 810). Many target genes were predicted for the differentially-expressed miRNAs in the small RNA-seq data , thereby regulating reproductive events (Drosophila melanogaster embryos). Its temporal expression is less restricted in Drosophila and has been observed across all developmental stages, occasionally with significant variation in expression   . Interesnfection . miR-34-bopictus . Again, elopment  and antibopictus . Our qRTAe. aegypti, miR-375-3p is expressed in blood-fed mosquitoes, and miR-375 was found to regulate DENV replication, enhancing DENV-2 infection in an Ae. aegypti cell line  protein  and monocarboxylate transporter (MCT1) genes, as validated in Ae. aegypti, through which it affects Wolbachia density in host cells  was one of the first synthetic varroacides registered in the United States, and pyrethroid-resistant Varroa is a major challenge in controlling this mite. Pyrethroid-resistant Varroa are associated with point mutations in the voltage-gated sodium channel gene at position 925 , insecticide resistance, and also antiviral immune responses  can be found below: NCBI (accession: PRJNA794145).Conceptualization: DK and SK. Data curation: DK. Formal analysis: DK. Funding acquisition: SK and JA. Investigation: DK, MA, FT, MG, JA, and SK. Methodology: DK and SK. Project administration: SK. Resources: JA and SK. Supervision; SK. Validation: DK and SK. Visualization: DK. Writing, original draft: DK and SK. Writing, review & editing: DK, MA, MG, JA, and SK. All authors contributed to the article and approved the submitted version.This research was funded by a USDA ARS cooperative agreement and the Mississippi INBRE  from the National Institute of General Medical Sciences of the National Institutes of Health under award P20GM103476).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Elsinoë arachidis which is the cause of peanut scab. However, the biosynthesis pathway of ESCs in E. arachidis has not been elucidated and the potential pathogenic mechanism of E. arachidis is poorly understood. In this study, we report a high-quality genome sequence of E. arachidis. The size of the E. arachidis genome is 33.18Mb, which is comparable to the Ascomycota genome (average 36.91 Mb), encoding 9174 predicted genes. The self-detoxification family including transporters and cytochrome P450 enzymes were analysis, candidate effectors and cell wall degrading enzymes were investigated as the pathogenicity genes by using PHI and CAZy databases. Additionally, the E. arachidis genome contains 24 secondary metabolism gene clusters, in which ESCB1 was identified as the core gene of ESC biosynthesis. Taken together, the genome sequence of E. arachidis provides a new route to explore its potential pathogenic mechanism and the biosynthesis pathway of ESCs.Elsinochromes (ESCs) are virulence factors produced by  Elsinoë arachidis is a phytopathogenic fungus that causes peanut scab on Arachis hypogaea Linn., resulting in tremendous yield loss  in peanut planting regions in China [E. arachidis are urgently needed [in China , 2. Curry needed –6.Elsinoë produce elsinochromes (ESCs) [2–). Additionally, ESCs contribute to pathogenesis and are essential for full virulence which was validated by constructing mutants in E. fawcettii of a polyketide synthase-encoding gene which is the core gene of ESC biosynthesis [Cercospora spp.) is the most well-known member of the group of perylenequinone fungal toxins. The biological functions and biosynthetic pathway of cercosporin have been clarified. Like many toxins identified in ascomycete fungi, its metabolic pathway is dependent on polyketide synthase (PKS) [E. arachidis and their potential pathogenic mechanism remain to be explored. For instance, it is unclear whether, in addition to ESCs, there exist cell wall degrading enzymes or effectors that act as virulence factors in E. arachidis [Interestingly, several s (ESCs) , which aynthesis –10. Cercse (PKS) , and therachidis .Magnaporthe oryzae [Fusarium graminearum [Sclerotinia sclerotiorum and Botrytis cinerea [A growing number of studies have applied genome sequencing technology to the study of phytopathogenic fungi, such as e oryzae , Fusariuminearum , Sclerot cinerea , which hE. arachidis during host invasion. We report on the 33.18Mb genome sequence of E. arachidis, the secondary metabolism gene cluster, and the discovery of 6 PKS gene clusters in E. arachidis including the ESC biosynthetic gene cluster and the core gene ESCB1. Through our analysis of the whole genome, we show that E. arachidis has a complex pathogenesis, with, in addition to the toxin, several candidate virulence factors including effectors, enzymes, and transporters. Moreover, the putative pathogenicity genes provide new horizons to unravel the pathogenic mechanism of E. arachidis.The present study was aimed at exploring the possible virulence factors of E. arachidis strain LNFT-H01, which was purified by single spores and cultured on potato dextrose agar (PDA) under 5 microeinstein (μE) m-2s-1. The genome of LNFT-H01 was sequenced by PacBio RS II using a 20kb library of LNFT-H01 genomic DNA under 100 ×sequencing depth and assembled by Canu [In this paper, we used  by Canu –18. The E. arachidis within reference genomes by OrthoMCL, then constructed a phylogenetic tree by SMS implemented in the PhyML (http://www.atgc-montpellier.fr/ phyml-sms/) [E. arachidis and E. australis were analyzed using MCScanX, which can effectively determine the changes in chromosome structure and reveal the history of the gene family expansion [The evolutionary history can be deduced from conserved sequences and conserved biochemical functions. In addition, clustering the orthologous genes of different genomes can be helpful to integrate the information of conserved gene families and biological processes. We calculated the closest relatives to sequences from ml-sms/) , 20. Synxpansion .Due to the low conservation of repetitive sequence (RS) between species according to MITE Hunter, LTR FINDER, Repeat Scout, and PILER –25, we eE. arachidis genome. A combination of Augustus, Glimmer HMM, Genscan GeneID, and SNAP [The ab initio-based and homology-based methods were performed to predict gene numbers in the and SNAP –32 homoland SNAP  and the and SNAP . Non-codand SNAP  databaseand SNAP  was usedand SNAP  to identand SNAP  alignmenand SNAP  to searcand SNAP –43. The and SNAP –46.https://fungismash.Secondarymetabolites.org). In order to confirm the function of polyketide synthase (PKS), which is the core protein that responsible for the biosynthesis of mycotoxin in different organisms, PKS sequences were used to construct the phylogenetic tree by MEGA 10.0.5. The detailed information on PKS is reported in S9 Table. Domains of PKSs were identified via InterPro (https://www.ebi.ac.uk/interpro) and their location visualized by DOG 2.0.Secondary metabolite clusters were predicted by performing antiSMASH2 . RT-PCR was performed using TransScript® One-Step gDNA Removal and cDNA Synthesis . qPCR was done using SuperMix TransStart® Green qPCR SuperMix with primers ESCB1F (ATCCGAGGTCATTGGTGATG) and ESCB1R (GAGGTTGACATCTGGC ATTTG).Elsinochrome extraction and quantitation were performed as previously described . As for E. arachidis was performed using PacBio RS II (100×coverage). A total of 6.28 Gb high-quality sequencing raw data were assembled by CANU into 16 scaffolds  and the characteristics that are displayed in a circus-plot  was comparable in size to the Ascomycota genome (36.91 Mb) [M. oryzae, (38.10 Mb), Fusarium graminearum (35.45 Mb), and Sclerotinia sclerotiorum (38.68 Mb). However, phylogenetic analysis showed that the species used in this comparative study were distinct from one another. Notably, E. arachidis was only close to Sphaceloma murrayae and E. australis (E. arachidis was larger than S. murrayae (20.72 Mb) or E. australis (23.34 Mb). Additionally, synteny analysis indicated the highest synteny between E. arachidis and E. australis  repeat sequences were identified including LTR retrotransposons and DNA transposons (The assembled size of the 6.91 Mb) , as wellustralis , but in ustralis . Concernnsposons .E. arachidis  fungicides [The cytochrome P450 enzymes (CYPs) are multifunctional oxidoreductases that can aid in the detoxification of natural and environmental pollutants, involved in the primary and secondary metabolism [PHI data , mainly ngicides . CYP52X1E. arachidis , a biotrophic pathogen (B. graminis), and hemi-biotrophic pathogens (M. oryzae and F. graminearum)  was smaller than that of the necrotrophic plant pathogens S. sclerotiorum (53) and B. cinerea (62). However, it was significantly larger than that of B. graminis (2)  . The CAZinis (2) . In addiE. arachidis encodes 24 secondary metabolism clusters, including PKS (6), nonribosomal peptide synthetase (NRPS) (11), NRPS-PKS (1), terpene (6)  and non-reduced  due to the reducing activity of ER and KR.pene (6) . The numrachidis . We analDOG 2.0. . We founE. arachidis, EVM0003759 was in the same clade as EaPKS which is encoding for ESC biosynthesis in E. australis and therefore we named it ESCB1 (Elsinochrome Biosynthesis gene 1). Interestingly, EVM0004732 and EVM0005880 are related to the biosynthesis of melanin , E. arachidis has a larger genome size. This may be due to the lower proportion of repeat sequences in the E. fawcettii genome [i genome . The GC E. arachidis can maintain growth and development even in the presence of high toxin levels, which indicates an efficient self-detoxification mechanism. We identified ABC transporters and MFS transporters in E. arachidis indicating the complex transportation of substances in E. arachidis and that some of them may have an effect on the secretion of ESCs. Cytochrome P450 enzyme system, a multifunctional oxidoreductase, may involve in the self-detoxification of E. arachidis by providing redox conditions to maintain its own steady state for various physiological and biochemical reactions.Mycotoxins play an important part in the pathogenic mechanisms of pathogens. Mycotoxin ESCs, perylenequinones photosensitive toxins, can produce reactive oxygen species (ROS) and act on the cell membrane to destroy the cell structure. E. arachidis. However, compared with mycotoxins such as aflatoxins, fumonisin, and trichothecenes, and host-selective toxins such as T-toxin, still little is known about the biosynthetic pathways of perylenequinone mycotoxins. Cercosporin, the same group of perylenequinone toxins with ESC, has been proved that CTB1 (cercosporin synthase gene 1) which encoding polyketide synthase is the core gene of cercosporin biosynthesis pathway [Efpks1 has been shown to function the in ESC biosynthesis in E. fawcettii, but the specific biosynthesis pathway still needs to be further clarified [E. arachidis, 6 gene clusters related to polyketide synthase were obtained. The core genes were EVM0002563, EVM0003759, EVM0004732, EVM0005880, EVM0005988, and EVM0006869. Phylogenetic tree constructions showed that EVM0003759 is involved in ESCs synthesis, while EVM0004732 and EVM0005880 play a role in melanin synthesis. To our knowledge, this is the first time that melanin has been identified in E. arachidis. Interestingly, analysis of the position between the core genes of ESCs and melanin gene clusters, we found that the three genes are all located in Contig00003. This result also cast some doubt on whether PKS synthesis pathways from ESC and melanin are interrelated or competing.ESC is a crucial virulent factor in the pathogenic process of  pathway . Efpks1 larified , 9. WithAlternaria alternata and can limit the host-selective toxin AK-toxin production [Fusarium oxysporum [Pathogens employ complex mechanisms to break through the defenses of plants, including toxins, enzymes, and other pathogenic factors to help invasion and colonization. Analysis of the CAZy and PHI databases revealed that, in addition to ESCs, enzymes, effectors, and certain transcription factors may be involved in the pathogenic process. Increased virulence factors (3%) that cause increased pathogenicity include O-methylsterigmatocystin oxidoreductase, AK-toxin biosynthetic gene 7 (AKT7) and bZIP transcription factor MeaB. EVM0005728, EVM0001699 and EVM0004784 are related to AKT7, which encodes a cytochrome P450 monooxygenase in xysporum , which aE. arachidis. Analysis of its assembly and annotation allowed the identification of the presumptive PKS gene clusters. Based on our results, we hypothesize that ESCB1 maybe the core gene of the biosynthesis of ESC. Additionally, pathogenic factors including CAZymes and effectors may help E. arachidis to circumvent the defense mechanisms of peanuts. Our work lays the foundation of future research aimed at elucidating the detailed pathogenic mechanisms of E. arachidis.In conclusion, we reported the whole-genome sequence of E. arachidis by PacBio RS II. The basic information of the sequence, gene family and metabolic gene cluster of E. arachidis were clarified. Through further analysis of the key genes in different PKS gene clusters, the expression of ESCB1 (EVM0003759) under light and dark condition was initially determined to participate in the ESC biosynthetic pathway, and the flanking sequences of this gene cluster were annotation, including major facilitator superfamily transporter, cytochrome P450, monooxygenase and O-methyltransferase. In addition to ESC toxins, genes related to mycotoxin biosynthesis such as melanin are also noted. This information provides new ideas for further exploration of the pathogenic mechanism of E. arachidis.In conclusion, this is the first report of the high-quality genome of S1 Fig(TIF)Click here for additional data file.S2 FigE. arachidis and other fungi. (B) Collinear analysis.(A) A phylogenetic tree constructed the evolutionary relationships of (TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S1 Table(DOC)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOC)Click here for additional data file.


Synechococcus cultures in the laboratory are often associated with heterotrophic bacteria. Here, we report the genome sequence of the bacterium Microbacterium sp. strain R1, isolated from a culture of the estuarine Synechococcus strain CBW1107. Several secondary metabolites and transporter-related genes were identified in the genome of Microbacterium sp. strain R1. Synechococcus spp. are important primary producers in the marine environment, and heterotrophic bacteria can utilize dissolved organic matter released from Synechococcus spp. in the surrounding waters  by heterotrophic bacteria to provide essential nutrients ; then, bacterial genomic DNA was obtained from the cells following the phenol-chloroform method  for the contigs are 2,408,454 bp and 967.483×, respectively. Open reading frame (ORF) prediction and genome annotation were performed using the RAST server (http://rast.nmpdr.org)  have predicted functions. Default parameters were used for all software unless otherwise noted.m method . A librapdr.org) . A totalMicrobacterium sp. strain R1 were predicted using antiSMASH bacterial version. Four genes encoding nonribosomal peptide synthase, polyketide synthase type III, terpenes, and β-lactones were identified  annotation is also available. The raw sequence data are available under SRA accession no. SRR14683051. The BioProject accession no. is PRJNA667551, and the BioSample accession no. is SAMN16377710.The genome sequence of


A 59-year-old man with diabetes mellitus, hypertension, coronary artery disease, and gout presented to the dermatology clinic with an 18-month history of a pruritic widespread rash . Dull, eQuestion 1: What is the most likely diagnosis?A.Mycosis fungoidesB.Disseminated granuloma annulareC.Secondary syphilisD.Erythema gyratum repensE.SarcoidosisAnswers:A.Mycosis fungoides – Incorrect. Mycosis fungoides is the most common type of cutaneous T-cell lymphoma and classically presents with scaly erythematous patches and plaques with a predilection for the trunk, specifically the buttocks. Mycosis fungoides is also notorious for a variety of clinical presentations. This diagnosis was excluded by histopathology.B.Disseminated granuloma annulare – Incorrect. Disseminated granuloma annulare is a self-limiting benign skin condition presenting with symmetric annular or arciform plaques without surface change as well as discrete skin-colored papules. Disseminated granuloma annulare is not common and tends to be recalcitrant to therapy. Clinically, this diagnosis was favored until the histopathology was reviewed.C.arrow) solidified the diagnosis C.Angiotensin-converting enzyme levelsD.Treponema pallidum enzyme immunoassay (IgG)E.Chemiluminescence immunoassayAnswers:A.Rapid plasma regain – Correct. Nontreponemal tests such as rapid plasma regain, venereal disease research laboratory, and toluidine red unheated serum tests are traditionally used as initial screening tests but also as the main way of monitoring a patient's response to treatment due to their quantitative results.B.FTA-ABS – Incorrect. FTA-ABS is a qualitative treponemal test, thus providing no useful tracking of treatment response. The FTA-ABS assay is thought to be most sensitive in primary syphilis.C.Angiotensin-converting enzyme levels – Incorrect. Angiotensin-converting enzyme levels correlate with disease severity and activity in response to treatment in sarcoidosis, not syphilis.D.Treponema pallidum enzyme immunoassay (IgG) – Incorrect. The T. pallidum enzyme immunoassay is also a treponemal test that has become a favorite of large laboratories as a screening test due to its lower cost compared with the others.E.Chemiluminescence immunoassay – Incorrect. The chemiluminescence immunoassay is also a treponemal test. All treponemal tests are based upon the detection of antibodies directed against specific treponemal antigens and thus tend to be more specific than nontreponemal tests.None declared.