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Dengue virus (DENV) infections have unpredictable clinical outcomes, ranging from asymptomatic or minor febrile illness to severe and fatal disease. The severity of dengue infection is at least partly related to the replacement of circulating DENV serotypes and/or genotypes. To describe clinical profiles of patients and the viral sequence diversity corresponding to non-severe and severe cases, we collected patient samples from 2018 to 2022 at Evercare Hospital Dhaka, Bangladesh. Serotyping of 495 cases and sequencing of 179 cases showed that the dominant serotype of DENV shifted from DENV2 in 2017 and 2018 to DENV3 in 2019. DENV3 persisted as the only representative serotype until 2022. Co-circulation of clades B and C of the DENV2 cosmopolitan genotype in 2017 was replaced by circulation of clade C alone in 2018 with all clones disappearing thereafter. DENV3 genotype I was first detected in 2017 and was the only genotype in circulation until 2022. We observed a high incidence of severe cases in 2019 when the DENV3 genotype I became the only virus in circulation. Phylogenetic analysis revealed clusters of severe cases in several different subclades of DENV3 genotype I. Thus, these serotype and genotype changes in DENV may explain the large dengue outbreaks and increased severity of the disease in 2019.

Background Large sequence datasets are difficult to visualize and handle. Additionally, they often do not represent a random subset of the natural diversity, but the result of uncoordinated and convenience sampling. Consequently, they can suffer from redundancy and sampling biases. Results Here we present Treemmer, a simple tool to evaluate the redundancy of phylogenetic trees and reduce their complexity by eliminating leaves that contribute the least to the tree diversity. Conclusions Treemmer can reduce the size of datasets with different phylogenetic structures and levels of redundancy while maintaining a sub-sample that is representative of the original diversity. Additionally, it is possible to fine-tune the behavior of Treemmer including any kind of meta-information, making Treemmer particularly useful for empirical studies.

Background Phylogenetic trees are essential diagrams used in different sciences, such as evolutionary biology or taxonomy, and they depict the relationships between a given set of taxa sharing a common ancestor. So far, a multitude of tools have already been developed to infer phylogeny, and even more to visualize the resulting trees. However, editing generated graphical plots to obtain ready-to-publish figures is still a major issue. Most available tools do not take into consideration important aspects in nomenclature, such as the use of italics for taxon names or the superscript T that must be displayed after the strain/specimen designation to denote the type strain/specimen, at least not automatically. A gap also exists to easily highlight tree branches conserved across different phylogenies containing the same taxa. The lack of available tools to achieve these tasks is challenging for scientists, since manual formatting of phylogenetic trees is very time-consuming. Results Here, we present a tool named ‘gitana’, running in Linux/Windows/Mac operating systems with R software installed. It creates ready-to-publish trees with formatting taxon nomenclature and editing options such as rerooting, clade highlighting or collapsing, among other features. Moreover, ‘gitana’ performs node comparisons among phylogenies comprising the same taxa to identify conserved branches. Conclusions ‘gitana’ is a user-friendly tool to output high-quality and ready-to-publish phylogenetic trees for users without R-coding skills. It combines dedicated functions of popular R packages for phylogeny and graphical visualization into an easy one-line-command. The users’ manual and source code are freely available at https://github.com/cristinagalisteo/gitana .

A phylogenetic tree can reflect the evolutionary relationships between species or gene families, and they play a critical role in modern biological research. In this review, we summarize common methods for constructing phylogenetic trees, including distance methods, maximum parsimony, maximum likelihood, Bayesian inference, and tree-integration methods (supermatrix and supertree). Here we discuss the advantages, shortcomings, and applications of each method and offer relevant codes to construct phylogenetic trees from molecular data using packages and algorithms in R. This review aims to provide comprehensive guidance and reference for researchers seeking to construct phylogenetic trees while also promoting further development and innovation in this field. By offering a clear and concise overview of the different methods available, we hope to enable researchers to select the most appropriate approach for their specific research questions and datasets.

DNA-barcoding is a species identification tool that uses a short section of the genome that provides a genetic signature of the species. The main advantage of this novel technique is that it requires a small sample of tissue from the tested organism. In most animal groups, this technique is very effective. However, in plants, the recommended standard markers, such as rbcL a, may not always work, and their efficacy remains to be tested in many plant groups, particularly from the Neotropical region. We examined the discriminating power of rbcL a in 55 tropical cloud forest vascular plant species from 38 families (Oaxaca, Mexico). We followed the CBOL criteria using BLASTn, genetic distance, and monophyly tree-based analyses (neighbor-joining, NJ, maximum likelihood, ML, and Bayesian inference, BI). rbcL a universal primers amplified 69.0% of the samples and yielded 91.3% bi-directional sequences. Sixty-three new rbcL a sequences were established. BLAST discriminates 80.8% of the genus but only 15.4% of the species. There was nil minimum interspecific genetic distances in Quercus, Oreopanax , and Daphnopsis . Contrastingly, Ericaceae (5.6%), Euphorbiaceae (4.6%), and Asteraceae (3.3%) species displayed the highest within-family genetic distances. According to the most recent angiosperm classification, NJ and ML trees successfully resolved (100%) monophyletic species. ML trees showed the highest mean branch support value (87.3%). Only NJ and ML trees could successfully discriminate Quercus species belonging to different subsections: Quercus martinezii (white oaks) from Q. callophylla and Q. laurina (red oaks). The ML topology could distinguish species in the Solanaceae clade with similar BLAST matches. Also, the BI topology showed a polytomy in this clade, and the NJ tree displayed low-support values. We do not recommend genetic-distance approaches for species discrimination. Severe shortages of rbcL a sequences in public databases of neotropical species hindered effective BLAST comparisons. Instead, ML tree-based analysis displays the highest species discrimination among the tree-based analyses. With the ML topology in selected genera, rbcL a helped distinguish infra-generic taxonomic categories, such as subsections, grouping affine species within the same genus, and discriminating species. Since the ML phylogenetic tree could discriminate 48 species out of our 55 studied species, we recommend this approach to resolve tropical montane cloud forest species using rbcL a, as an initial step and improve DNA amplification methods.

Background With the accumulation of phylogenomic data and the growing demand for bioinformatics analyses, it has become increasingly important and complex to construct evolutionary relationships for different research purposes. Therefore, the ability to support multiple scenarios has become an essential need for phylogenetic visualization. Results In this study, we present PhyloScape, a web-based application for interactive visualization of phylogenetic trees that can be used stand-alone or as a toolkit deployed on the users’ website. The platform supports customizable multiple visualization features and is equipped with a flexible metadata annotation system, providing researchers with publishable, interactive views of trees. PhyloScape extensions include views of amino acid identity, geometry, and protein structure, which are applicable to various areas such as microbial taxonomy, pathogen phylogeny, and plant conservation. Trees published on the website can be efficiently shared and integrated into the users’ own system via a unique address. Conclusions As a scalable platform, PhyloScape provides a variety of online plug-ins that users can easily combine for specific scenarios. PhyloScape is freely available at http://darwintree.cn/PhyloScape .

The properties of the variable domain of heavy-chain (VHH) antibodies are particularly relevant in cancer therapy. To isolate tumor cell-specific VHH antibodies, VHH phage libraries were constructed from multiple tumor cells. After enriching the libraries against particular tumor cell lines, a next-generation sequencer was used to screen the pooled phages of each library for potential antibody candidates. Based on high amplification folds, 50 sequences from each library were used to construct phylogenetic trees. Several clusters with identical CDR3 were observed. Groups X, Y, and Z were assigned as common sequences among the different trees. These identical groups over the trees were considered to be cross-reactive antibodies. To obtain monoclonal antibodies, we assembled 200 sequences (top 50 sequences from each library) and rebuilt a combined molecular phylogenetic tree. Groups were categorized as A–G. For each group, we constructed a phagemid and determined its binding specificity with tumor cells. The phage-binding results were consistent with the phylogenetic tree-generated groups, which indicated particular tumor-specific clusters; identical groups showed cross-reactivity. The strategy used in the current study is effective for screening and isolating monoclonal antibodies. Specific antibodies can be identified, even when the target markers of cancer cells are unknown.

The aim of this study was to compare between morphological and molecular identification methods. Two strains (A1, A2) of Aspergillus sp. isolated from El-Baida marsh, were subjected to be identified using morphological characterization, and molecular analysis performed by amplification of the ITS1 and ITS4 regions of rDNA. Morphological analysis indicated that both strains were identified as Aspergillus oryzae. However, by molecular methods A1 strain was identified as Aspergillus alliaceus and A2 strain as Aspergillus oryzae. Our results showed that phenotypic methods were insufficient for correct identification, and the use of genotypic methods is the most reliable. Enzymatic activity of the A2 isolate was evaluated using a plate assay for Lipase, Amylase and Protease production; the results illustrated the capacity of the fungus of the production of the three enzymes, which are mostly used in the production of pharmaceuticals, foods, beverages, and textile. They are found in a vast variety of sources such as animals, plants and microorganisms but microbial enzymes are more stable than the others. In addition, the reality that this fungus is known as a safe microorganism in several domains (food, beverage, cosmetic, and pharmaceutical industries), this opens the research in the future.

Background In the pursuit of a better understanding of biodiversity, evolutionary biologists rely on the study of phylogenetic relationships to illustrate the course of evolution. The relationships among natural organisms, depicted in the shape of phylogenetic trees, not only help to understand evolutionary history but also have a wide range of additional applications in science. One of the most challenging problems that arise when building phylogenetic trees is the presence of missing biological data. More specifically, the possibility of inferring wrong phylogenetic trees increases proportionally to the amount of missing values in the input data. Although there are methods proposed to deal with this issue, their applicability and accuracy is often restricted by different constraints. Results We propose a framework, called PhyloMissForest, to impute missing entries in phylogenetic distance matrices and infer accurate evolutionary relationships. PhyloMissForest is built upon a random forest structure that infers the missing entries of the input data, based on the known parts of it. PhyloMissForest contributes with a robust and configurable framework that incorporates multiple search strategies and machine learning, complemented by phylogenetic techniques, to provide a more accurate inference of lost phylogenetic distances. We evaluate our framework by examining three real-world datasets, two DNA-based sequence alignments and one containing amino acid data, and two additional instances with simulated DNA data. Moreover, we follow a design of experiments methodology to define the hyperparameter values of our algorithm, which is a concise method, preferable in comparison to the well-known exhaustive parameters search. By varying the percentages of missing data from 5% to 60%, we generally outperform the state-of-the-art alternative imputation techniques in the tests conducted on real DNA data. In addition, significant improvements in execution time are observed for the amino acid instance. The results observed on simulated data also denote the attainment of improved imputations when dealing with large percentages of missing data. Conclusions By merging multiple search strategies, machine learning, and phylogenetic techniques, PhyloMissForest provides a highly customizable and robust framework for phylogenetic missing data imputation, with significant topological accuracy and effective speedups over the state of the art.