An official website of the United States government
Comprising 501 genera and around 14,000 species, Papilionoideae is not only the largest subfamily of Fabaceae (Leguminosae; legumes), but also one of the most extraordinarily diverse clades among angiosperms. Papilionoids are a major source of food and forage, are ecologically successful in all major biomes, and display dramatic variation in both floral architecture and plastid genome (plastome) structure. Plastid DNA-based phylogenetic analyses have greatly improved our understanding of relationships among the major groups of Papilionoideae, yet the backbone of the subfamily phylogeny remains unresolved. In this study, we sequenced and assembled 39 new plastomes that are covering key genera representing the morphological diversity in the subfamily. From 244 total taxa, we produced eight datasets for maximum likelihood (ML) analyses based on entire plastomes and/or concatenated sequences of 77 protein-coding sequences (CDS) and two datasets for multispecies coalescent (MSC) analyses based on individual gene trees. We additionally produced a combined nucleotide dataset comprising CDS plus matK gene sequences only, in which most papilionoid genera were sampled. A ML tree based on the entire plastome maximally supported all of the deep and most recent divergences of papilionoids (223 out of 236 nodes). The Swartzieae, ADA (Angylocalyceae, Dipterygeae, and Amburaneae), Cladrastis, Andira, and Exostyleae clades formed a grade to the remainder of the Papilionoideae, concordant with nine ML and two MSC trees. Phylogenetic relationships among the remaining five papilionoid lineages (Vataireoid, Dermatophyllum , Genistoid s.l., Dalbergioid s.l., and Baphieae + Non-Protein Amino Acid Accumulating or NPAAA clade) remained uncertain, because of insufficient support and/or conflicting relationships among trees. Our study fully resolved most of the deep nodes of Papilionoideae, however, some relationships require further exploration. More genome-scale data and rigorous analyses are needed to disentangle phylogenetic relationships among the five remaining lineages.
more »
« less
Applications of Machine Learning for the Classification of Porcine Reproductive and Respiratory Syndrome Virus Sublineages Using Amino Acid Scores of ORF5 Gene
Porcine reproductive and respiratory syndrome is an infectious disease of pigs caused by PRRS virus (PRRSV). A modified live-attenuated vaccine has been widely used to control the spread of PRRSV and the classification of field strains is a key for a successful control and prevention. Restriction fragment length polymorphism targeting the Open reading frame 5 (ORF5) genes is widely used to classify PRRSV strains but showed unstable accuracy. Phylogenetic analysis is a powerful tool for PRRSV classification with consistent accuracy but it demands large computational power as the number of sequences gets increased. Our study aimed to apply four machine learning (ML) algorithms, random forest, k-nearest neighbor, support vector machine and multilayer perceptron, to classify field PRRSV strains into four clades using amino acid scores based on ORF5 gene sequence. Our study used amino acid sequences of ORF5 gene in 1931 field PRRSV strains collected in the US from 2012 to 2020. Phylogenetic analysis was used to labels field PRRSV strains into one of four clades: Lineage 5 or three clades in Linage 1. We measured accuracy and time consumption of classification using four ML approaches by different size of gene sequences. We found that all four ML algorithms classify a large number of field strains in a very short time (<2.5 s) with very high accuracy (>0.99 Area under curve of the Receiver of operating characteristics curve). Furthermore, the random forest approach detects a total of 4 key amino acid positions for the classification of field PRRSV strains into four clades. Our finding will provide an insightful idea to develop a rapid and accurate classification model using genetic information, which also enables us to handle large genome datasets in real time or semi-real time for data-driven decision-making and more timely surveillance.
more »
« less
- PAR ID:
- 10313522
- Date Published:
- Journal Name:
- Frontiers in Veterinary Science
- Volume:
- 8
- ISSN:
- 2297-1769
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The spatial distribution of forest stands is one of the fundamental properties of forests. Timely and accurately obtained stand distribution can help people better understand, manage, and utilize forests. The development of remote sensing technology has made it possible to map the distribution of tree species in a timely and accurate manner. At present, a large amount of remote sensing data have been accumulated, including high-spatial-resolution images, time-series images, light detection and ranging (LiDAR) data, etc. However, these data have not been fully utilized. To accurately identify the tree species of forest stands, various and complementary data need to be synthesized for classification. A curve matching based method called the fusion of spectral image and point data (FSP) algorithm was developed to fuse high-spatial-resolution images, time-series images, and LiDAR data for forest stand classification. In this method, the multispectral Sentinel-2 image and high-spatial-resolution aerial images were first fused. Then, the fused images were segmented to derive forest stands, which are the basic unit for classification. To extract features from forest stands, the gray histogram of each band was extracted from the aerial images. The average reflectance in each stand was calculated and stacked for the time-series images. The profile curve of forest structure was generated from the LiDAR data. Finally, the features of forest stands were compared with training samples using curve matching methods to derive the tree species. The developed method was tested in a forest farm to classify 11 tree species. The average accuracy of the FSP method for ten performances was between 0.900 and 0.913, and the maximum accuracy was 0.945. The experiments demonstrate that the FSP method is more accurate and stable than traditional machine learning classification methods.more » « less
-
Abstract Profile mixture models capture distinct biochemical constraints on the amino acid substitution process at different sites in proteins. These models feature a mixture of time-reversible models with a common matrix of exchangeabilities and distinct sets of equilibrium amino acid frequencies known as profiles. Combining the exchangeability matrix with each profile generates the matrix of instantaneous rates of amino acid exchange for that profile. Currently, empirically estimated exchangeability matrices (e.g. the LG matrix) are widely used for phylogenetic inference under profile mixture models. However, these were estimated using a single profile and are unlikely optimal for profile mixture models. Here, we describe the GTRpmix model that allows maximum likelihood estimation of a common exchangeability matrix under any profile mixture model. We show that exchangeability matrices estimated under profile mixture models differ from the LG matrix, dramatically improving model fit and topological estimation accuracy for empirical test cases. Because the GTRpmix model is computationally expensive, we provide two exchangeability matrices estimated from large concatenated phylogenomic-supermatrices to be used for phylogenetic analyses. One, called Eukaryotic Linked Mixture (ELM), is designed for phylogenetic analysis of proteins encoded by nuclear genomes of eukaryotes, and the other, Eukaryotic and Archaeal Linked mixture (EAL), for reconstructing relationships between eukaryotes and Archaea. These matrices, combined with profile mixture models, fit data better and have improved topology estimation relative to the LG matrix combined with the same mixture models. Starting with version 2.3.1, IQ-TREE2 allows users to estimate linked exchangeabilities (i.e. amino acid exchange rates) under profile mixture models.more » « less
-
ABSTRACT In recent years, considerable progress has been made in topologically and functionally characterizing integral outer membrane proteins (OMPs) of Treponema pallidum subspecies pallidum , the syphilis spirochete, and identifying its surface-exposed β-barrel domains. Extracellular loops in OMPs of Gram-negative bacteria are known to be highly variable. We examined the sequence diversity of β-barrel-encoding regions of tprC , tprD , and bamA in 31 specimens from Cali, Colombia; San Francisco, California; and the Czech Republic and compared them to allelic variants in the 41 reference genomes in the NCBI database. To establish a phylogenetic framework, we used T. pallidum 0548 ( tp0548 ) genotyping and tp0558 sequences to assign strains to the Nichols or SS14 clades. We found that (i) β-barrels in clinical strains could be grouped according to allelic variants in T. pallidum subsp. pallidum reference genomes; (ii) for all three OMP loci, clinical strains within the Nichols or SS14 clades often harbored β-barrel variants that differed from the Nichols and SS14 reference strains; and (iii) OMP variable regions often reside in predicted extracellular loops containing B-cell epitopes. On the basis of structural models, nonconservative amino acid substitutions in predicted transmembrane β-strands of T. pallidum repeat C (TprC) and TprD2 could give rise to functional differences in their porin channels. OMP profiles of some clinical strains were mosaics of different reference strains and did not correlate with results from enhanced molecular typing. Our observations suggest that human host selection pressures drive T. pallidum subsp. pallidum OMP diversity and that genetic exchange contributes to the evolutionary biology of T. pallidum subsp. pallidum . They also set the stage for topology-based analysis of antibody responses to OMPs and help frame strategies for syphilis vaccine development. IMPORTANCE Despite recent progress characterizing outer membrane proteins (OMPs) of Treponema pallidum , little is known about how their surface-exposed, β-barrel-forming domains vary among strains circulating within high-risk populations. In this study, sequences for the β-barrel-encoding regions of three OMP loci, tprC , tprD , and bamA , in T. pallidum subsp. pallidum isolates from a large number of patient specimens from geographically disparate sites were examined. Structural models predict that sequence variation within β-barrel domains occurs predominantly within predicted extracellular loops. Amino acid substitutions in predicted transmembrane strands that could potentially affect porin channel function were also noted. Our findings suggest that selection pressures exerted within human populations drive T. pallidum subsp. pallidum OMP diversity and that recombination at OMP loci contributes to the evolutionary biology of syphilis spirochetes. These results also set the stage for topology-based analysis of antibody responses that promote clearance of T. pallidum subsp. pallidum and frame strategies for vaccine development based upon conserved OMP extracellular loops.more » « less
-
The anaerobic gut fungi (AGF,Neocallimastigomycota) represent a basal zoosporic phylum within the kingdomFungi. Twenty genera are currently described, all of which were isolated from the digestive tracts of mammalian herbivores. Here, we report on the isolation and characterization of novel AGF taxa from faecal samples of tortoises. Twenty-nine fungal isolates were obtained from seven different tortoise species. Phylogenetic analysis using the D1/D2 region of the LSU rRNA gene, ribosomal internal transcribed spacer 1, and RNA polymerase II large subunit grouped all isolates into two distinct, deep-branching clades (clades T and B), with a high level of sequence divergence to their closest cultured relative (Khoyollomyces ramosus). Average amino acid identity values calculated using predicted peptides from the isolates’ transcriptomes ranged between 60.80–66.21 % (clade T), and 61.24–64.83 % (clade B) when compared to all other AGF taxa; values that are significantly below recently recommended thresholds for genus (85%) and family (75%) delineation in theNeocallimastigomycota. Both clades displayed a broader temperature growth range (20–45 °C, optimal 30 °C for clade T, and 30–42 °C, optimal 39 °C for clade B) compared to all other AGF taxa. Microscopic analysis demonstrated that strains from both clades produced filamentous hyphae, polycentric rhizoidal growth patterns, and monoflagellated zoospores. Isolates in clade T were characterized by the production of unbranched, predominantly narrow hyphae, and small zoospores, while isolates in clade B were characterized by the production of multiple sporangiophores and sporangia originating from a single central swelling resulting in large multi-sporangiated structures. Based on the unique phylogenetic positions, AAI values, and phenotypic characteristics, we propose to accommodate these isolates into two novel genera (TestudinimycesandAstrotestudinimyces), and species (T. gracilisandA. divisus) within the orderNeocallimastigales. The type species are strains T130AT(T. gracilis) and B1.1T(A. divisus).more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fvets.2021.683134
