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This study was conducted to examine the activity of antioxidants against xanthine oxidase in bioactive extracts from Jatropha podagrica stem bark. Among the extracts, the EtOAc extract showed the strongest antioxidant activity and xanthine oxidase inhibition. Column chromatography isolation using different ratios of ‘ethyl acetate-hexane’ (2:8, 3:7, and 4:6) yielded five fractions (M1-M5), which were identified and confirmed in the previous study. Specifically, the fractions included methyl gallate and gallic acid (M1); methyl gallate, gallic acid, and fraxetin (M2); methyl gallate, fraxetin and tomentin (M3); methyl gallate and fraxetin (M4); and fraxetin (M5). Employing allopurinol as the positive control (IC50= 20.2 μg/mL), M3, was found to have the highest level of xanthine oxidase inhibitors (IC50 = 69.7 μg/mL), followed by M5 (fraxetin, IC50 = 106.9 μg/mL). The results of this study proposed Jatropha podagrica as an alternative and potential source for the treatment of gout symptoms. Keywords: Xanthine Oxidase, Jatropha podagrica, Inhibitor, Stem, Bark.

Amino acid substitution models are a key component in phylogenetic analyses of protein sequences. All commonly used amino acid models available to date are time-reversible, an assumption designed for computational convenience but not for biological reality. Another significant downside to time-reversible models is that they do not allow inference of rooted trees without outgroups. In this article, we introduce a maximum likelihood approach nQMaker, an extension of the recently published QMaker method, that allows the estimation of time nonreversible amino acid substitution models and rooted phylogenetic trees from a set of protein sequence alignments. We show that the nonreversible models estimated with nQMaker are a much better fit to empirical alignments than pre-existing reversible models, across a wide range of data sets including mammals, birds, plants, fungi, and other taxa, and that the improvements in model fit scale with the size of the data set. Notably, for the recently published plant and bird trees, these nonreversible models correctly recovered the commonly estimated root placements with very high-statistical support without the need to use an outgroup. We provide nQMaker as an easy-to-use feature in the IQ-TREE software (http://www.iqtree.org), allowing users to estimate nonreversible models and rooted phylogenies from their own protein data sets. The data sets and scripts used in this article are available at https://doi.org/10.5061/dryad.3tx95x6hx. [amino acid sequence analyses; amino acid substitution models; maximum likelihood model estimation; nonreversible models; phylogenetic inference; reversible models.]

MotivationSequence simulation plays a vital role in phylogenetics with many applications, such as evaluating phylogenetic methods, testing hypotheses, and generating training data for machine-learning applications. We recently introduced a new simulator for multiple sequence alignments called AliSim, which outperformed existing tools. However, with the increasing demands of simulating large data sets, AliSim is still slow due to its sequential implementation; for example, to simulate millions of sequence alignments, AliSim took several days or weeks. Parallelization has been used for many phylogenetic inference methods but not yet for sequence simulation.ResultsThis paper introduces AliSim-HPC, which, for the first time, employs high-performance computing for phylogenetic simulations. AliSim-HPC parallelizes the simulation process at both multi-core and multi-CPU levels using the OpenMP and MPI libraries, respectively. AliSim-HPC is highly efficient and scalable, which reduces the runtime to simulate 100 large alignments from one day to 9 minutes using 256 CPU cores from a cluster with 6 computing nodes, a 162-fold speedup.Availability and implementationAliSim-HPC is open source and available as part of the new IQ-TREE version v2.2.2.2 athttps://github.com/iqtree/iqtree2/releaseswith a user manual athttp://www.iqtree.org/doc/AliSim.Contactm.bui@anu.edu.au

Hundreds or thousands of loci are now routinely used in modern phylogenomic studies. Concatenation approaches to tree inference assume that there is a single topology for the entire dataset, but different loci may have different evolutionary histories due to incomplete lineage sorting, introgression, and/or horizontal gene transfer; even single loci may not be treelike due to recombination. To overcome this shortcoming, we introduce the mixture across sites and trees (MAST) model, which uses a mixture of bifurcating trees to represent multiple histories in a single concatenated alignment. The MAST model allows each tree to have its own topology, branch lengths, substitution model, nucleotide or amino acid frequencies, and model of rate heterogeneity across sites. We implemented the MAST model in a maximum-likelihood framework in the popular phylogenetic software, IQ-TREE. Simulations show that we can accurately recover the true model parameters, including branch lengths and tree weights (i.e. frequencies) for a given set of tree topologies. We also show that we can use standard statistical inference approaches to reject a single-tree model when data are simulated under multiple trees (and vice versa). We applied the MAST model to multiple primate datasets and found that it can recover the signal of incomplete lineage sorting in the Great Apes, as well as the asymmetry in minor trees caused by introgression among several macaque species. When applied to a dataset of four Platyrrhine species for which standard concatenated maximum likelihood and gene tree approaches disagree, we find that MAST gives the highest weight to the tree favored by gene tree approaches. These results suggest that the MAST model is able to analyse a concatenated alignment using maximum likelihood, while avoiding some of the biases that come with assuming there is only a single tree. The MAST model can therefore offer unique biological insights when applied to datasets with multiple evolutionary histories. We discuss how it can be extended in the future.

Motivation Site concordance factors (sCFs) have become a widely used way to summarize discordance in phylogenomic datasets. However, the original version of sCFs was calculated by sampling a quartet of tip taxa and then applying parsimony-based criteria for discordance. This approach has the potential to be strongly affected by multiple hits at a site (homoplasy), especially when substitution rates are high or taxa are not closely related. Results Here, we introduce a new method for calculating sCFs. The updated version uses likelihood to generate probability distributions of ancestral states at internal nodes of the phylogeny. By sampling from the states at internal nodes adjacent to a given branch, this approach substantially reduces—but does not abolish—the effects of homoplasy and taxon sampling. Availability and implementation Updated sCFs are implemented in IQ-TREE 2.2.2. The software is freely available at https://github.com/iqtree/iqtree2/releases. Supplementary information Supplementary information is available at Bioinformatics online.

Using time-reversible Markov models is a very common practice in phylogenetic analysis, because although we expect many of their assumptions to be violated by empirical data, they provide high computational efficiency. However, these models lack the ability to infer the root placement of the estimated phylogeny. In order to compensate for the inability of these models to root the tree, many researchers use external information such as using outgroup taxa or additional assumptions such as molecular-clocks. In this study, we investigate the utility of non-reversible models to root empirical phylogenies and introduce a new bootstrap measure, the rootstrap, which provides information on the statistical support for any given root position.Availability and implementationrootstrap support is implemented in IQ-TREE 2 and a tutorial is available at the iqtree webpage http://www.iqtree.org/doc/Rootstrap. In addition, a python script is available at https://github.com/suhanaser/Rootstrap. [phylogenetic inference, root estimation, bootstrap, non-reversible models]

SummaryPhylogenetics plays a crucial role in the interpretation of genomic data1. Phylogenetic analyses of SARS-CoV-2 genomes have allowed the detailed study of the virus’s origins2, of its international3,4 and local4–9 spread, and of the emergence10 and reproductive success11 of new variants, among many applications. These analyses have been enabled by the unparalleled volumes of genome sequence data generated and employed to study and help contain the pandemic12. However, preferred model-based phylogenetic approaches including maximum likelihood and Bayesian methods, mostly based on Felsenstein’s ‘pruning’ algorithm13,14, cannot scale to the size of the datasets from the current pandemic4,15, hampering our understanding of the virus’s evolution and transmission16. We present new approaches, based on reworking Felsenstein’s algorithm, for likelihood-based phylogenetic analysis of epidemiological genomic datasets at unprecedented scales. We exploit near-certainty regarding ancestral genomes, and the similarities between closely related and densely sampled genomes, to greatly reduce computational demands for memory and time. Combined with new methods for searching amongst candidate evolutionary trees, this results in our MAPLE (‘MAximum Parsimonious Likelihood Estimation’) software giving better results than popular approaches such as FastTree 217, IQ-TREE 218, RAxML-NG19 and UShER15. Our approach therefore allows complex and accurate proba-bilistic phylogenetic analyses of millions of microbial genomes, extending the reach of genomic epidemiology. Future epidemiological datasets are likely to be even larger than those currently associated with COVID-19, and other disciplines such as metagenomics and biodiversity science are also generating huge numbers of genome sequences20–22. Our methods will permit continued use of preferred likelihood-based phylogenetic analyses.

Moringa oleifera (MO) grows throughout most of the tropics and has several industrial and medicinal uses. Besides the various uses of the plant parts such as its leaves, seed kernels, roots, or stem barks, the twigs (MT) of this plant are usually regarded as excessive parts. Although there have been few studies conducted to determine the value of this plant part, in fact, its potential uses—especially the pharmaceutical effects—of this biomaterial remains an up-to-date topic for scientists to discover due to the lack of interest so far. This study aims to identify the optimized fractions of different solvents for the extraction of antioxidants, for xanthine oxidase inhibition agents, and for anti-microbial activities. The two most active fractions obtained by column chromatography were the Hexane-Ethyl Acetate elution at a 9:1 (E1) and 8:2 (E2) ratio, respectively. With regard to antioxidant activity, E1 and E2 displayed relatively high DPPH radical scavenging capacity (IC50 = 87.7 and 99.0 µg/mL), which was only four times weaker than the control BHT (IC50 = 21.4 µg/mL). The highest inhibition activity against xanthine oxidase was also observed clearly in E1 and E2, which showed relatively low IC50 (54.7 and 42.0 µg/mL, respectively). These levels were inconsiderably higher than that of the positive control (IC50 = 20.8 µg/mL), proving that E1 and E2 exerted relatively strong antioxidant activity in terms of XOD inhibition. Regarding the antimicrobial test, E2 showed the highest inhibition activities against E. coli, K. pneumoiae, L. monocytogenes, B. subtilis, and P. mirabilis. The result indicates that (1) E1 and E2 were the strongest fractions for constraining free radical agents and several bacteria, and thus, (2) Moringa oleifera twigs are also a potential source for the prevention of gout-related symptoms.

We implement two measures for quantifying genealogical concordance in phylogenomic datasets: the gene concordance factor (gCF) and the novel site concordance factor (sCF). For every branch of a reference tree, gCF is defined as the percentage of “decisive” gene trees containing that branch. This measure is already in wide usage, but here we introduce a package that calculates it while accounting for variable taxon coverage among gene trees. sCF is a new measure defined as the percentage of decisive sites supporting a branch in the reference tree. gCF and sCF complement classical measures of branch support in phylogenetics by providing a full description of underlying disagreement among loci and sites. An easy to use implementation and tutorial is freely available in the IQ-TREE software package (http://www.iqtree.org).