Sequence-based Identification Research Articles

Mitochondrial DNA sequence-based identification of two subterranean termite species, from Riyadh Province, Kingdom of Saudi Arabia.

Termites are economically important wood-destroying and agricultural pests. The termite fauna almost consists of 2900 described species in 286 genera worldwide. In the present study, hundreds of termite samples from 42 different locations in the Riyadh province were collected. These samples were previously used for morphometric identification and reported two subterranean termite species, Coptotermes heimi and Psammotermes hypostoma, in the family Rhinotermitidae. In the present study, these samples were analysed using DNA barcoding with the mitochondrial cytochrome c oxidase subunit 1 gene to confirm the conventional taxonomical identification on a molecular basis. The obtained COI gene sequences of all 42 termite specimens were submitted to GenBank (accession numbers: ON529959-ON529969, OP825131-OP825132, and OP890882-OP890910). Eleven of the 42 samples were thus identified as C. heimi and the remaining 31 samples as P. hypostoma, which were phylogenetically analysed. All the 11 C. heimi sequences were grouped in a single clade, indicating close relatedness. While 31 sequences of P. hypostoma constituted two clades in the phylogenetic tree. Pairwise nucleotide sequence identity and divergence analysis showed that C. heimi sequences showed high nucleotide identities of 87.6-99.5% and less divergence ranging from 0.5% to 13.6%. Similarly, sequences of P. hypostoma also showed high nucleotide identity of 78.6-100% and low divergence among them ranging from 0-10.7%. A further application, significance, and shortcomings of COI-based DNA barcoding have been discussed. DNA barcoding using the COI gene is a reliable tool to distinguish C. heimi and P. hypostoma genotypes.

Biofilm marker discovery with cloud-based dockerized metagenomics analysis of microbial communities.

In an environment, microbes often work in communities to achieve most of their essential functions, including the production of essential nutrients. Microbial biofilms are communities of microbes that attach to a nonliving or living surface by embedding themselves into a self-secreted matrix of extracellular polymeric substances. These communities work together to enhance their colonization of surfaces, produce essential nutrients, and achieve their essential functions for growth and survival. They often consist of diverse microbes including bacteria, viruses, and fungi. Biofilms play a critical role in influencing plant phenotypes and human microbial infections. Understanding how these biofilms impact plant health, human health, and the environment is important for analyzing genotype-phenotype-driven rule-of-life functions. Such fundamental knowledge can be used to precisely control the growth of biofilms on a given surface. Metagenomics is a powerful tool for analyzing biofilm genomes through function-based gene and protein sequence identification (functional metagenomics) and sequence-based function identification (sequence metagenomics). Metagenomic sequencing enables a comprehensive sampling of all genes in all organisms present within a biofilm sample. However, the complexity of biofilm metagenomic study warrants the increasing need to follow the Findability, Accessibility, Interoperability, and Reusable (FAIR) Guiding Principles for scientific data management. This will ensure that scientific findings can be more easily validated by the research community. This study proposes a dockerized, self-learning bioinformatics workflow to increase the community adoption of metagenomics toolkits in a metagenomics and meta-transcriptomics investigation. Our biofilm metagenomics workflow self-learning module includes integrated learning resources with an interactive dockerized workflow. This module will allow learners to analyze resources that are beneficial for aggregating knowledge about biofilm marker genes, proteins, and metabolic pathways as they define the composition of specific microbial communities. Cloud and dockerized technology can allow novice learners-even those with minimal knowledge in computer science-to use complicated bioinformatics tools. Our cloud-based, dockerized workflow splits biofilm microbiome metagenomics analyses into four easy-to-follow submodules. A variety of tools are built into each submodule. As students navigate these submodules, they learn about each tool used to accomplish the task. The downstream analysis is conducted using processed data obtained from online resources or raw data processed via Nextflow pipelines. This analysis takes place within Vertex AI's Jupyter notebook instance with R and Python kernels. Subsequently, results are stored and visualized in Google Cloud storage buckets, alleviating the computational burden on local resources. The result is a comprehensive tutorial that guides bioinformaticians of any skill level through the entire workflow. It enables them to comprehend and implement the necessary processes involved in this integrated workflow from start to finish. This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Comparative Bioremediation of Tetradecane, Cyclohexanone and Cyclohexane by Filamentous Fungi from Polluted Habitats in Kazakhstan.

Studying the fates of oil components and their interactions with ecological systems is essential for developing comprehensive management strategies and enhancing restoration following oil spill incidents. The potential expansion of Kazakhstan's role in the global oil market necessitates the existence of land-specific studies that contribute to the field of bioremediation. In this study, a set of experiments was designed to assess the growth and biodegradation capacities of eight fungal strains sourced from Kazakhstan soil when exposed to the hydrocarbon substrates from which they were initially isolated. The strains were identified as Aspergillus sp. SBUG-M1743, Penicillium javanicum SBUG-M1744, SBUG-M1770, Trichoderma harzianum SBUG-M1750 and Fusarium oxysporum SBUG-1746, SBUG-M1748, SBUG-M1768 and SBUG-M1769 using the internal transcribed spacer (ITS) region. Furthermore, microscopic and macroscopic evaluations agreed with the sequence-based identification. Aspergillus sp. SBUG-M1743 and P. javanicum SBUG-M1744 displayed remarkable biodegradation capabilities in the presence of tetradecane with up to a 9-fold biomass increase in the static cultures. T. harzianum SBUG-M1750 exhibited poor growth, which was a consequence of its low efficiency of tetradecane degradation. Monocarboxylic acids were the main degradation products by SBUG-M1743, SBUG-M1744, SBUG-M1750, and SBUG-M1770 indicating the monoterminal degradation pathway through β-oxidation, while the additional detection of dicarboxylic acid in SBUG-M1768 and SBUG-M1769 cultures was indicative of the fungus' ability to undertake both monoterminal and diterminal degradation pathways. F. oxysporum SBUG-M1746 and SBUG-M1748 in the presence of cyclohexanone showed a doubling of the biomass with the ability to degrade the substrate almost completely in shake cultures. F. oxysporum SBUG-M1746 was also able to degrade cyclohexane completely and excreted all possible metabolites of the degradation pathway. Understanding the degradation potential of these fungal isolates to different hydrocarbon substrates will help in developing effective bioremediation strategies tailored to local conditions.

AlPaCas: allele-specific CRISPR gene editing through a protospacer-adjacent-motif (PAM) approach.

Gene therapy of dominantly inherited genetic diseases requires either the selective disruption of the mutant allele or the editing of the specific mutation. The CRISPR-Cas system holds great potential for the genetic correction of single nucleotide variants (SNVs), including dominant mutations. However, distinguishing between single-nucleotide variations in a pathogenic genomic context remains challenging. The presence of a PAM in the disease-causing allele can guide its precise targeting, preserving the functionality of the wild-type allele. The AlPaCas (Aligning Patients to Cas) webserver is an automated pipeline for sequence-based identification and structural analysis of SNV-derived PAMs that satisfy this demand. When provided with a gene/SNV input, AlPaCas can: (i) identify SNV-derived PAMs; (ii) provide a list of available Cas enzymes recognizing the SNV (s); (iii) propose mutational Cas-engineering to enhance the selectivity towards the SNV-derived PAM. With its ability to identify allele-specific genetic variants that can be targeted using already available or engineered Cas enzymes, AlPaCas is at the forefront of advancements in genome editing. AlPaCas is open to all users without a login requirement and is freely available at https://schubert.bio.uniroma1.it/alpacas.

Comparison of the performance of multiple whole-genome sequence-based tools for the identification of Bacillus cereus sensu stricto biovar Thuringiensis.

The Bacillus cereus sensu stricto (s.s.) species comprises strains of biovar Thuringiensis (Bt) known for their bioinsecticidal activity, as well as strains with foodborne pathogenic potential. Bt strains are identified (i) based on the production of insecticidal crystal proteins, also known as Bt toxins, or (ii) based on the presence of cry, cyt, and vip genes, which encode Bt toxins. Multiple bioinformatics tools have been developed for the detection of crystal protein-encoding genes based on whole-genome sequencing (WGS) data. However, the performance of these tools is yet to be evaluated using phenotypic data. Thus, the goal of this study was to assess the performance of four bioinformatics tools for the detection of crystal protein-encoding genes. The accuracy of sequence-based identification of Bt was determined in reference to phenotypic microscope-based screening for the production of crystal proteins. A total of 58 diverse B. cereus sensu lato strains isolated from clinical, food, environmental, and commercial biopesticide products underwent WGS. Isolates were examined for crystal protein production using phase contrast microscopy. Crystal protein-encoding genes were detected using BtToxin_Digger, BTyper3, IDOPS (identification of pesticidal sequences), and Cry_processor. Out of 58 isolates, the phenotypic production of crystal proteins was confirmed for 18 isolates. Specificity and sensitivity of Bt identification based on sequences were 0.85 and 0.94 for BtToxin_Digger, 0.97 and 0.89 for BTyper3, 0.95 and 0.94 for IDOPS, and 0.88 and 1.00 for Cry_processor, respectively. Cry_processor predicted crystal protein production with the highest specificity, and BtToxin_Digger and IDOPS predicted crystal protein production with the highest sensitivity. Three out of four tested bioinformatics tools performed well overall, with IDOPS achieving high sensitivity and specificity (>0.90).IMPORTANCEStrains of Bacillus cereus sensu stricto (s.s.) biovar Thuringiensis (Bt) are used as organic biopesticides. Bt is differentiated from the foodborne pathogen Bacillus cereus s.s. by the production of insecticidal crystal proteins. Thus, reliable genomic identification of biovar Thuringiensis is necessary to ensure food safety and facilitate risk assessment. This study assessed the accuracy of whole-genome sequencing (WGS)-based identification of Bt compared to phenotypic microscopy-based screening for crystal protein production. Multiple bioinformatics tools were compared to assess their performance in predicting crystal protein production. Among them, identification of pesticidal sequences performed best overall at WGS-based Bt identification.

Sequence-based identification of amyloidogenic β-hairpins reveals a prostatic acid phosphatase fragment promoting semen amyloid formation

β-Structure-rich amyloid fibrils are hallmarks of several diseases, including Alzheimer’s (AD), Parkinson’s (PD), and type 2 diabetes (T2D). While amyloid fibrils typically consist of parallel β-sheets, the anti-parallel β-hairpin is a structural motif accessible to amyloidogenic proteins in their monomeric and oligomeric states. Here, to investigate implications of β-hairpins in amyloid formation, potential β-hairpin-forming amyloidogenic segments in the human proteome were predicted based on sequence similarity with β-hairpins previously observed in Aβ, α-synuclein, and islet amyloid polypeptide, amyloidogenic proteins associated with AD, PD, and T2D, respectively. These three β-hairpins, established upon binding to the engineered binding protein β-wrapin AS10, are characterized by proximity of two sequence segments rich in hydrophobic and aromatic amino acids, with high β-aggregation scores according to the TANGO algorithm. Using these criteria, 2505 potential β-hairpin-forming amyloidogenic segments in 2098 human proteins were identified. Characterization of a test set of eight protein segments showed that seven assembled into Thioflavin T-positive aggregates and four formed β-hairpins in complex with AS10 according to NMR. One of those is a segment of prostatic acid phosphatase (PAP) comprising amino acids 185–208. PAP is naturally cleaved into fragments, including PAP(248−286) which forms functional amyloid in semen. We find that PAP(185−208) strongly decreases the protein concentrations required for fibril formation of PAP(248−286) and of another semen amyloid peptide, SEM1(86−107), indicating that it promotes nucleation of semen amyloids. In conclusion, β-hairpin-forming amyloidogenic protein segments could be identified in the human proteome with potential roles in functional or disease-related amyloid formation.

Genome sequence-based identification of Enterobacter strains and description of Enterobacter pasteurii sp. nov.

Accurate taxonomy is essential for microbial biological resource centers, since the microbial resources are often used to support new discoveries and subsequent research. Here, we used genome sequence data, alongside matrix-assisted laser desorption/ionization time-of-flight mass spectrometer biotyper-based protein profiling, to accurately identify six Enterobacter cloacae complex strains. This approach effectively identified distinct species within the E. cloacae complex, including Enterobacter asburiae, "Enterobacter xiangfangensis," and Enterobacter quasihormaechei. Moreover, the study revealed the existence of a novel species within the Enterobacter genus, for which we proposed the name Enterobacter pasteurii sp. nov. In summary, this study demonstrates the significance of adopting a genome sequence-driven taxonomy approach for the precise identification of bacterial strains in a biological resource center and expands our understanding of the E. cloacae complex.

Technique for the identification of insect species in processed foods based on three short DNA sequences

For pest control, it is necessary to identify insect species in food. However, it is difficult to identify the insect species in processed food based on their morphological characteristics owing to deformation or fragmentation. DNA sequence-based identification is useful not only to corroborate morphological identification, but also as an alternative identification approach. Therefore, markers are required to obtain the target specific amplicons from insect DNA sequences fragmented by food processing. We developed a DNA sequence-based insect species identification technique that combined two markers in the cytochrome oxidase subunit I (COI) region and one marker in the 16S ribosomal RNA (rRNA) region, with amplicons up to 200 bp in size. The identification results from this technique were consistent with those from the morphological identification of 89% of the 38 insect samples selected from seven orders. This technique identified the insect species in insect samples recovered from model food that were heat-treated at 118 °C for 18 min.

Concatenated 16S rRNA sequence analysis improves bacterial taxonomy.

Background: Microscopic, biochemical, molecular, and computer-based approaches are extensively used to identify and classify bacterial populations. Advances in DNA sequencing and bioinformatics workflows have facilitated sophisticated genome-based methods for microbial taxonomy although sequencing of the 16S rRNA gene is widely employed to identify and classify bacterial communities as a cost-effective and single-gene approach. However, the 16S rRNA sequence-based species identification accuracy is limited because of the occurrence of multiple copies of the 16S rRNA gene and higher sequence identity between closely related species. The availability of the genomes of several bacterial species provided an opportunity to develop comprehensive species-specific 16S rRNA reference libraries. Methods: Sequences of the 16S rRNA genes were retrieved from the whole genomes available in the Genome databases. With defined criteria, four 16S rRNA gene copy variants were concatenated to develop a species-specific reference library. The sequence similarity search was performed with a web-based BLAST program, and MEGA software was used to construct the phylogenetic tree. Results: Using this approach, species-specific 16S rRNA gene libraries were developed for four closely related Streptococcusspecies ( S. gordonii, S. mitis, S. oralis, and S. pneumoniae). Sequence similarity and phylogenetic analysis using concatenated 16S rRNA copies yielded better resolution than single gene copy approaches. Conclusions: The approach is very effective in classifying genetically closely related bacterial species and may reduce misclassification of bacterial species and genome assemblies.

First Report of Brown Leaf Spot Caused by Epicoccum tobaicum on Oat (Avena sativa) in Korea.

Oat (Avena sativa) is one of the Korean winter crops, and oat consumption has been increasing because it is widely perceived as a superfood. Recently, various fungal diseases have been reported likely due to climate changes during the winter season in Korea (Choi et al., 2018; Kim, 2020). During the 2020-2021 winter to spring, we surveyed new fungal diseases among cereals, including oats, in the southern region of Korea. In April 2021, brown leaf spots on oat leaves were observed in Gangjin, Jeollanam-do, Korea. These brown spots were irregular circles, ranging from 2-7 mm in diameter. Samples from three infected leaves were surface sterilized by treating them with 70% ethanol for 1 min and 1% NaOCl for 1 min. The samples were subsequently rinsed at least twice with distilled water and dried with a sterile paper towel before being placed on 1.5% water agar supplemented with 100 ppm streptomycin. Hyphal tips derived from infected tissues after incubation at 25C for 7 days were transferred to a fresh potato dextrose agar (PDA). Three isolates, labeled as KJO-AN2-S1, KJO-AN2-S2 and KJO-AN2-S3, were obtained via single hyphal tip purification. Colonies on PDA were pigmented vermilion and subsequently turned to saffron color with irregular margins after 7 days. Conidia produced on PDA were golden to dark brown, globose to subglobose, solitary, and measured 15.5-21.5 μm in diameter (n=50). Cultural and morphological characteristics suggested that these isolates belong to Epicoccum species (Chen et al. 2017). For identification by sequencing, the ITS (MW691866-68), tub2 (MW691872-74), and rpb2 (MW691869-71) regions of three isolates were amplified using the primer pairs ITS5/ITS4 (White et al., 1990), Btub2Fd/ Btub4Rd (Woudengerg et al., 2009), and RPB2-5F2 (Sung et al., 2007)/fRPB2-7cR (Liu et al., 1999), respectively. A maximum likelihood phylogenetic analysis based on the concatenated ITS, tub2, and rpb2 sequences placed the three isolates within a clade comprising E. tobaicum CBS 384.36. A mycelial plug (5 mm diameter) was inoculated onto wounded and unwounded leaves of healthy 12-day-old oat (cv. Choyang) seedlings. The control leaves were inoculated with a sterile PDA plug. All inoculated and control plants were placed in a plastic box and incubated at 20℃ in darkness with 100% humidity. After 1 day, the inoculated mycelial plug or sterile PDA plug from plants was removed; the plants in plastic boxes were then transferred to a growth chamber set at 20℃ with 12 h light and 60-70% humidity. While brown spot lesions were observed in both unwounded and wounded leaves 7 days post-inoculation, both wounded and unwounded control leaves remained asymptomatic. The pathogen was recovered from all symptomatic leaf tissues but could not be isolated from control leaves. The re-isolated pathogen was identified as E. tobaicum through morphological characterization and sequence-based identification, fulfilling Koch's postulates. This study is the first to report a causal relationship between E. tobaicum and brown leaf spot disease of oat in Korea. Identification of this newly emerging fungal disease on oats will help prepare for effectively managing this disease.

Peptide Mapping with LC-HRMS and In Silico Tools for Evaluation of Potential Allergenicity of Commercially Available Whey Protein Hydrolysates

Hypoallergenic formulas containing hydrolyzed protein are intended for use by infants to prevent cow’s milk protein allergy. The degree of hydrolysis of epitopic areas determines the residual allergenicity of whey protein hydrolysates (WPHs). However, only amino-acid-based infant formulas (IFs) are considered entirely nonallergenic. The aim of this study was to investigate four commercially available WPHs with different degree of hydrolysis (H1–H4) for potential allergenicity, by applying LC-HRMS analysis of peptides, and using in silico tools to search for the immunoglobulin (IgE)-binding allergenic epitopes from the Immune Epitope Database. Additionally, the molecular weight distribution of proteins and peptides in the WPHs was measured by SE-UPLC. Based on the peptide coverage and peptide-length distribution profiles, the WPHs showed different extents of hydrolysis: extensively (H1 and H2), partially (H3), and slightly hydrolyzed (H4). Altogether, numerous peptides related to 46 specific IgE-binding epitopes from β-lactoglobulin and α-lactalbumin were found in all the WPHs, regardless of their extent of hydrolysis. Sequence-based identification of the specific peptide composition, with an application of in silico tools, is a reliable approach for discovering the potential allergenicity of protein hydrolysates for IFs.

Intergenic spacer (IGS-1) region sequence-based identification, genotypic analysis, and antifungal susceptibility of clinical Trichosporon species

ObjectivesMolecular genotyping of Trichosporon species using intergenic spacer region (IGS-1) sequencing and antifungal drug susceptibility testing of T. asahii clinical isolates from Indian patients. Materials and methodsFifty-five Trichosporon strains were characterized using IGS-1 sequencing from 2006 to 2018 and tested against 5 antifungals using CLSI M27-A3 guidelines. ResultsIn this study, broad-spectrum antibiotics with steroids, catheters, and ICU stays were major underlying risk factors. These cases were most commonly associated with diabetes (type-2), chronic obstructive pulmonary disease, and hypertension. Out of fifty-five isolates, 47 (85%) were identified as T. asahii, and the remaining 6 were T. inkin (11%) and 2 were Cutaneotrichosporon dermatis (3.6%). The most common genotype of T. asahii was G3 (22; 49%) subsequently G4 (12; 23%), G1 (8; 17%), and G7 (2; 4%). One new genotype of T asahii was found in addition to the fifteen already known genotypes. Indian T. asahii isolates showed a low level of amphotericin B (range 0.06–4 ​mg/l) resistance but relatively higher in fluconazole (range 0.25–64 ​mg/l). Although, comparatively low MIC ranges were found in the case of voriconazole (0.03–1 ​mg/l), posaconazole (0.06–1 ​mg/l) and itraconazole (0.06–1 ​mg/l). Voriconazole appeared to be the most active drug in T. asahii isolates. The MICs for all the drugs were comparatively lower in the case of non-Trichosporon asahii strains. ConclusionT. asahii was the most common Trichosporon isolate. Speciation is necessary for optimal antifungal therapy. Voriconazole-based treatment, Steroids, removal of catheters and control of underlying conditions results in positive outcomes.

Knowledge Gaps, Research Needs, and Opportunities in Plant Disease Diagnostic Assay Development and Validation

Sequence-based identification and detection technologies have revolutionized plant disease diagnostics in the last few decades, opening the door to highly selective and specific diagnostic assays. However, diagnostic assay development and validation remain hindered by many factors that would be ameliorated by research prioritization and coordination. Massive amounts of sequence data, in the forms of evolutionarily informative markers and now whole genomes, are available yet not easily accessible or properly organized to guide efficient assay development and deployment. For many key pathogen groups, particularly those that are not easily culturable, data are scant. Identification of pathogen-specific sequences in microbial genomes, the ultimate diagnostic markers, requires massive data processing and is slowed by a lack of quality data curation pipelines that efficiently translate data into problem solving. New technologies have the potential to enable detection at extremely low titers, but many of these have not yet found their way into diagnostic labs, much less the field. Here, we review the current status of diagnostics readiness in select groups of pathogens and pathosystems and define research priorities to fill knowledge and resource gaps to support scientists engaged in diagnostics. These priorities include improved coordination of data generation and curation and validation of new technologies, such as LAMP, MiFi, RPA, and CRISPR-Cas, to accelerate the implementation of more specific and sensitive assays. Finally, novel approaches, such as detection of volatile profiles and microbiome analyses, have the potential to complement pathogen diagnostics focusing on specific pathogens. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 “No Rights Reserved” license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2023.

DrugFinder: Druggable Protein Identification Model Based on Pre-Trained Models and Evolutionary Information

The identification of druggable proteins has always been the core of drug development. Traditional structure-based identification methods are time-consuming and costly. As a result, more and more researchers have shifted their attention to sequence-based methods for identifying druggable proteins. We propose a sequence-based druggable protein identification model called DrugFinder. The model extracts the features from the embedding output of the pre-trained protein model Prot_T5_Xl_Uniref50 (T5) and the evolutionary information of the position-specific scoring matrix (PSSM). Afterwards, to remove redundant features and improve model performance, we used the random forest (RF) method to select features, and the selected features were trained and tested on multiple different machine learning classifiers, including support vector machines (SVM), RF, naive Bayes (NB), extreme gradient boosting (XGB), and k-nearest neighbors (KNN). Among these classifiers, the XGB model achieved the best results. DrugFinder reached an accuracy of 94.98%, sensitivity of 96.33% and specificity of 96.83% on the independent test set, which is much better than the results from existing identification methods. Our model also performed well on another additional test set related to tumors, achieving an accuracy of 88.71% and precision of 93.72%. This further demonstrates the strong generalization capability of the model.

Identification and virulence of five isolates of root-knot nematode Meloidogyne floridensis on vegetables.

Meloidogyne floridensis is of particular concern because it reproduces on tomato, pepper, corn, and tobacco cultivars that have resistance to the common tropical root-knot nematode (RKN) species (i.e., M. incognita, M. arenaria, and M. javanica). During a survey of 436 randomly selected vegetable fields in Georgia in 2018, six M. floridensis infested fields were found and cultured from single egg-mass isolates on a susceptible tomato (cv. Rutgers), and speciated using molecular analyses. Five isolates of M. floridensis were identified from collard, cowpea, cucumber, watermelon, and tomato fields by DNA sequence-based identification targeting mitochondrial genes [cytochrome c oxidase subunit II, tRNAHis, large subunit rRNA, and NADH dehydrogenase subunit 5 gene]. Two greenhouse trials determined the host preference and reproduction level for each M. floridensis isolate. Evaluations were conducted on Rutgers tomato, a resistant tomato (cv. Skyway carrying the Mi-1.2 gene), and vegetable crops associated with the origin of M. floridensis populations. This study confirmed that most associated vegetables, except collards, were good hosts to M. floridensis, having a reproduction factor > 1. All isolates were able to reproduce aggressively on the resistant tomato. We found variations among M. floridensis isolates in pathogenicity and reproduction levels on the vegetable crops tested which should be considered when using or developing host resistance.

Diagnostic Utility of a Mycobacterium Multiplex PCR Detection Panel for Tuberculosis and Nontuberculous Mycobacterial Infections.

The prevalence of nontuberculous mycobacterial diseases is increasing worldwide, and tuberculosis remains highly prevalent. Rapid and accurate microbial diagnoses of both tuberculosis and nontuberculous mycobacterial infections are required. A novel PCR-reverse sequence-specific oligonucleotide probe (PCR-rSSO) method-based mycobacterial detection panel (Myco-Panel) test was developed for the rapid identification of 30 mycobacterial species and subspecies. Clinical respiratory samples were collected from patients with suspected or confirmed tuberculosis and nontuberculous mycobacterial lung disease and those with other respiratory diseases. Myco-Panel tests were performed on the samples, and liquid mycobacterial culture and identification were performed for reference according to housekeeping gene sequences of mycobacteria in positive culture tubes. Furthermore, to assess the detection performance for several mycobacterial species rarely recovered in Japan, the accuracy of the Myco-Panel test was investigated using stock mycobacterial type strains and clinical isolates. A total of 178 clinical respiratory samples were analyzed. The Myco-Panel and sequence-based identification results for mycobacterial cultures were 83.1% concordant (kappa coefficient, 0.785 [95% confidence interval, 0.716 to 0.854]). The Myco-Panel correctly identified 281 of the 283 type strains and clinical isolates tested. The Myco-Panel test could accurately detect several mycobacterial species from clinical respiratory samples and mycobacterial suspensions. Rapid and accurate identification of pathogens using respiratory samples is possible using the Myco-Panel. IMPORTANCE Species identification is important for the diagnosis of mycobacterial infections and decisions on treatment regimens. The Myco-Panel test accurately detects clinically common mycobacterial species that cause respiratory infections from clinical respiratory samples and mycobacterial suspensions. The rapid identification of multiple mycobacterial species will provide clinically useful information for the management of patients. Although we understand that the current diagnostic criteria require mycobacterial culture results in general for the diagnosis of nontuberculous mycobacterial infection, mycobacterial culture examination is a time-consuming process. The detection of potentially causative agents directly from clinical samples will aid in practical diagnosis and decision-making for rapid treatment initiation. This is a new laboratory method for species identification, and evaluating its performance is important.

Concatenated 16S rRNA sequence analysis improves bacterial taxonomy

Background: Microscopic, biochemical, molecular, and computer-based approaches are extensively used to identify and classify bacterial populations. Advances in DNA sequencing and bioinformatics workflows have facilitated sophisticated genome-based methods for microbial taxonomy although sequencing of the 16S rRNA gene is widely employed to identify and classify bacterial communities as a cost-effective and single-gene approach. However, the 16S rRNA sequence-based species identification accuracy is limited because of the occurrence of multiple copies of the 16S rRNA gene and higher sequence identity between closely related species. The availability of the genomes of several bacterial species provided an opportunity to develop comprehensive species-specific 16S rRNA reference libraries. Methods: Sequences of the 16S rRNA genes were retrieved from the whole genomes available in the Genome databases. With defined criteria, four 16S rRNA gene copy variants were concatenated to develop a species-specific reference library. The sequence similarity search was performed with a web-based BLAST program, and MEGA software was used to construct the phylogenetic tree. Results: Using this approach, species-specific 16S rRNA gene libraries were developed for four closely related Streptococcus species (S. gordonii, S. mitis, S. oralis, and S. pneumoniae). Sequence similarity and phylogenetic analysis using concatenated 16S rRNA copies yielded better resolution than single gene copy approaches. Conclusions: The approach is very effective in classifying genetically closely related bacterial species and may reduce misclassification of bacterial species and genome assemblies.

Genome-wide comparisons reveal broad variations in intraspecific SNP frequencies among species in Agaricomycetes, Basidiomycota

Background:Genome sequence analyses can provide crucial information for understanding population history, speciation, and taxonomy. In Class Agaricomycetes where most mushroom-forming fungi belong, most species so far have been defined based on morphological, ecological, and/or molecular features. At present, there is little information on how species defined based on such features reflect their genome sequence diversity. In this study, we investigated genome-wide single nucleotide polymorphism (SNP) frequencies between strains within species to understand the patterns of variation.Methods:A total of 112 species in 72 genera of Agaricomycetes contained the nuclear and/or mitochondrial genome sequences from at least two strains each in public databases. Together, we obtained 398 and 106 available nuclear and mitochondrial genomes respectively from these taxa. Pairwise strain comparisons of the nuclear and mitochondrial genomes within individual species were conducted to obtain their SNP frequencies.Results:The SNP frequencies between nuclear genomes within individual species ranged 0–7.69% while for the mitochondrial genome, the pairwise strain SNP frequencies ranged 0–4.41%. The Spearman’s non-parametric rank correlation test showed a weak but statistically significant positive correlation between the paired nuclear and mitochondrial genome SNP frequencies. Overall, we observed a significantly higher SNP frequency in the nuclear genome than in the mitochondrial genomes between strains within most species. Interestingly, across the broad Basidiomycetes, the ratios of mitochondrial genome SNPs and nuclear genome SNPs between pairs of strains within each species were almost all lower than 1, with a mean of 0.24.Conclusions:Our analyses revealed broad variations among species in their intraspecific SNP frequencies in both the nuclear and mitochondrial genomes. However, there was broad consensus among the examined species in their mitochondrial to nuclear genome SNP ratios, suggesting that such a ratio could potentially serve as an indicator for genome sequence-based species identification.

Bulinus snails in the Lake Victoria Basin in Kenya: Systematics and their role as hosts for schistosomes.

The planorbid gastropod genus Bulinus consists of 38 species that vary in their ability to vector Schistosoma haematobium (the causative agent of human urogenital schistosomiasis), other Schistosoma species, and non-schistosome trematodes. Relying on sequence-based identifications of bulinids (partial cox1 and 16S) and Schistosoma (cox1 and ITS), we examined Bulinus species in the Lake Victoria Basin in Kenya for naturally acquired infections with Schistosoma species. We collected 6,133 bulinids from 11 sites between 2014-2021, 226 (3.7%) of which harbored Schistosoma infections. We found 4 Bulinus taxa from Lake Victoria (B. truncatus, B. tropicus, B. ugandae, and B. cf. transversalis), and an additional 4 from other habitats (B. globosus, B. productus, B. forskalii, and B. scalaris). S. haematobium infections were found in B. globosus and B. productus (with infections in the former predominating) whereas S. bovis infections were identified in B. globosus, B. productus, B. forskalii, and B. ugandae. No nuclear/mitochondrial discordance potentially indicative of S. haematobium/S. bovis hybridization was detected. We highlight the presence of Bulinus ugandae as a distinct lake-dwelling taxon closely related to B. globosus yet, unlike all other members of the B. africanus species group, is likely not a vector for S. haematobium, though it does exhibit susceptibility to S. bovis. Other lake-dwelling bulinids also lacked S. haematobium infections, supporting the possibility that they all lack compatibility with local S. haematobium, thereby preventing widespread transmission of urogenital schistosomiasis in the lake's waters. We support B. productus as a distinct species from B. nasutus, B. scalaris as distinct from B. forskalii, and add further evidence for a B. globosus species complex with three lineages represented in Kenya alone. This study serves as an essential prelude for investigating why these patterns in compatibility exist and whether the underlying biological mechanisms may be exploited for the purpose of limiting schistosome transmission.

QuoteTarget: A sequence-based transformer protein language model to identify potentially druggable protein targets.

The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure-based drug target protein-identification algorithms are limited by the insufficient number of proteins with experimentally resolved structures. Moreover, sequence-based algorithms cannot effectively extract information from protein sequences and thus display insufficient accuracy. Here, we combined the sequence-based self-supervised pretraining protein language model ESM1b with a graph convolutional neural network classifier to develop an improved, sequence-based drug target protein identification method. This complete model, named QuoteTarget, efficiently encodes proteins based on sequence information alone and achieves an accuracy of 95% with the nonredundant drug target and nondrug target datasets constructed for this study. When applied to all proteins from Homo sapiens, QuoteTarget identified 1213 potential undeveloped drug target proteins. We further inferred residue-binding weights from the well-trained network using the gradient-weighted class activation mapping (Grad-Cam) algorithm. Notably, we found that without any binding site information input, significant residues inferred by the model closely match the experimentally confirmed drug molecule-binding sites. Thus, our work provides a highly effective sequence-based identifier for drug target proteins, as well to yield new insights into recognizing drug molecule-binding sites. The entire model is available at https://github.com/Chenjxjx/drug-target-prediction.