Isolation of Lupeol from the Stem Bark of Leptadenia hastata (Pers.) Decne

Dried stem bark powder of Leptadania hastata was subjected to maceration with methanol to afford crude methanol extract, which was partitioned with n-hexane, ethylacetate, chloroform and n- butanol to afford different their respective fractions. Extensive phytochemical screening of the n-hexane fraction using column chromatography resulted to the isolation of a white solid substance. The substance was identified as of lupeol using IR, 1D \u2013 NMR, 2D \u2013 NMR data and by comparison with reference spectral data

Statistical analysis of co-occurrence patterns in microbial presence-absence datasets.

Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson's correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard's index of similarity (J) can yield improvements over Pearson's correlation coefficient. However, the standard null model for Jaccard's index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard's index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa

Identification of genetic risk loci and causal insights associated with Parkinson\u27s disease in African and African admixed populations: a genome-wide association study

\ua9 2023 Elsevier LtdBackground: An understanding of the genetic mechanisms underlying diseases in ancestrally diverse populations is an important step towards development of targeted treatments. Research in African and African admixed populations can enable mapping of complex traits, because of their genetic diversity, extensive population substructure, and distinct linkage disequilibrium patterns. We aimed to do a comprehensive genome-wide assessment in African and African admixed individuals to better understand the genetic architecture of Parkinson\u27s disease in these underserved populations. Methods: We performed a genome-wide association study (GWAS) in people of African and African admixed ancestry with and without Parkinson\u27s disease. Individuals were included from several cohorts that were available as a part of the Global Parkinson\u27s Genetics Program, the International Parkinson\u27s Disease Genomics Consortium Africa, and 23andMe. A diagnosis of Parkinson\u27s disease was confirmed clinically by a movement disorder specialist for every individual in each cohort, except for 23andMe, in which it was self-reported based on clinical diagnosis. We characterised ancestry-specific risk, differential haplotype structure and admixture, coding and structural genetic variation, and enzymatic activity. Findings: We included 197 918 individuals (1488 cases and 196 430 controls) in our genome-wide analysis. We identified a novel common risk factor for Parkinson\u27s disease (overall meta-analysis odds ratio for risk of Parkinson\u27s disease 1\ub758 [95% CI 1\ub737–1\ub780], p=2\ub7397 7 10−14) and age at onset at the GBA1 locus, rs3115534-G (age at onset β=–2\ub700 [SE=0\ub757], p=0\ub70005, for African ancestry; and β=–4\ub715 [0\ub758], p=0\ub7015, for African admixed ancestry), which was rare in non-African or non-African admixed populations. Downstream short-read and long-read whole-genome sequencing analyses did not reveal any coding or structural variant underlying the GWAS signal. The identified signal seems to be associated with decreased glucocerebrosidase activity. Interpretation: Our study identified a novel genetic risk factor in GBA1 in people of African ancestry, which has not been seen in European populations, and it could be a major mechanistic basis of Parkinson\u27s disease in African populations. This population-specific variant exerts substantial risk on Parkinson\u27s disease as compared with common variation identified through GWAS and it was found to be present in 39% of the cases assessed in this study. This finding highlights the importance of understanding ancestry-specific genetic risk in complex diseases, a particularly crucial point as the Parkinson\u27s disease field moves towards targeted treatments in clinical trials. The distinctive genetics of African populations highlights the need for equitable inclusion of ancestrally diverse groups in future trials, which will be a valuable step towards gaining insights into novel genetic determinants underlying the causes of Parkinson\u27s disease. This finding opens new avenues towards RNA-based and other therapeutic strategies aimed at reducing lifetime risk of Parkinson\u27s disease. Funding: The Global Parkinson\u27s Genetics Program, which is funded by the Aligning Science Across Parkinson\u27s initiative, and The Michael J Fox Foundation for Parkinson\u27s Research