Indigenous data governance approaches applied in research using routinely collected health data: a scoping review.

Globally, there is a growing acknowledgment of Indigenous Peoples' rights to control data related to their communities. This is seen in the development of Indigenous Data Governance standards. As health data collection increases, it's crucial to apply these standards in research involving Indigenous communities. Our study, therefore, aims to systematically review research using routinely collected health data of Indigenous Peoples, understanding the Indigenous Data Governance approaches and the associated advantages and challenges. We searched electronic databases for studies from 2013 to 2022, resulting in 85 selected articles. Of these, 65 (77%) involved Indigenous Peoples in the research, and 60 (71%) were authored by Indigenous individuals or organisations. While most studies (93%) provided ethical approval details, only 18 (21%) described Indigenous guiding principles, 35 (41%) reported on data sovereignty, and 28 (33%) addressed consent. This highlights the increasing focus on Indigenous Data Governance in utilising health data. Leveraging existing data sources in line with Indigenous data governance principles is vital for better understanding Indigenous health outcomes.

QSAR Classification Models for Prediction of Hydroxamate Histone Deacetylase Inhibitor Activity against Malaria Parasites.

Malaria, caused by Plasmodium parasites, results in >400,000 deaths annually. There is no effective vaccine, and new drugs with novel modes of action are needed because of increasing parasite resistance to current antimalarials. Histone deacetylases (HDACs) are epigenetic regulatory enzymes that catalyze post-translational protein deacetylation and are promising malaria drug targets. Here, we describe quantitative structure-activity relationship models to predict the antiplasmodial activity of hydroxamate-based HDAC inhibitors. The models incorporate P. falciparum in vitro activity data for 385 compounds containing a hydroxamic acid and were subject to internal and external validation. When used to screen 22 new hydroxamate-based HDAC inhibitors for antiplasmodial activity, model A7 (external accuracy 91%) identified three hits that were subsequently verified as having potent in vitro activity against P. falciparum parasites (IC50 = 6, 71, and 84 nM), with 8 to 51-fold selectivity for P. falciparum versus human cells.

Neisseria meningitidis Induces Pathology-Associated Cellular and Molecular Changes in Trigeminal Schwann Cells.

Neisseria meningitidis, a common cause of sepsis and bacterial meningitis, infects the meninges and central nervous system (CNS), primarily via paracellular traversal across the blood-brain barrier (BBB) or blood-cerebrospinal fluid barrier. N. meningitidis is often present asymptomatically in the nasopharynx, and the nerves extending between the nasal cavity and the brain constitute an alternative route by which the meningococci may reach the CNS. To date, the cellular mechanisms involved in nerve infection are not fully understood. Peripheral nerve glial cells are phagocytic and are capable of eliminating microorganisms, but some pathogens may be able to overcome this protection mechanism and instead infect the glia, causing cell death or pathology. Here, we show that N. meningitidis readily infects trigeminal Schwann cells (the glial cells of the trigeminal nerve) in vitro in both two-dimensional and three-dimensional cell cultures. Infection of trigeminal Schwann cells may be one mechanism by which N. meningitidis is able to invade the CNS. Infection of the cells led to multinucleation and the appearance of atypical nuclei, with the presence of horseshoe nuclei and the budding of nuclei increasing over time. Using sequential window acquisition of all theoretical mass spectra (SWATH-MS) proteomics followed by bioinformatics pathway analysis, we showed that N. meningitidis induced protein alterations in the glia that were associated with altered intercellular signaling, cell-cell interactions, and cellular movement. The analysis also suggested that the alterations in protein levels were consistent with changes occurring in cancer. Thus, infection of the trigeminal nerve by N. meningitidis may have ongoing adverse effects on the biology of Schwann cells, which may lead to pathology.

A Grand Challenge. 2. Phenotypic Profiling of a Natural Product Library on Parkinson's Patient-Derived Cells.

Harnessing the inherent biological relevance of natural products requires a method for the recognition of biological effects that may subsequently lead to the discovery of particular targets. An unbiased multidimensional profiling method was used to examine the activities of natural products on primary cells derived from a Parkinson's disease patient. The biological signature of 482 natural products was examined using multiparametric analysis to investigate known cellular pathways and organelles implicated in Parkinson's disease such as mitochondria, lysosomes, endosomes, apoptosis, and autophagy. By targeting several cell components simultaneously the chance of finding a phenotype was increased. The phenotypes were then clustered using an uncentered correlation. The multidimensional phenotypic screening showed that all natural products, in our screening set, were biologically relevant compounds as determined by an observed phenotypic effect. Multidimensional phenotypic screening can predict the cellular function and subcellular site of activity of new compounds, while the cluster analysis provides correlation with compounds with known mechanisms of action. This study reinforces the value of natural products as biologically relevant compounds.

A Linear Mixed Model Spline Framework for Analysing Time Course 'Omics' Data.

Time course 'omics' experiments are becoming increasingly important to study system-wide dynamic regulation. Despite their high information content, analysis remains challenging. 'Omics' technologies capture quantitative measurements on tens of thousands of molecules. Therefore, in a time course 'omics' experiment molecules are measured for multiple subjects over multiple time points. This results in a large, high-dimensional dataset, which requires computationally efficient approaches for statistical analysis. Moreover, methods need to be able to handle missing values and various levels of noise. We present a novel, robust and powerful framework to analyze time course 'omics' data that consists of three stages: quality assessment and filtering, profile modelling, and analysis. The first step consists of removing molecules for which expression or abundance is highly variable over time. The second step models each molecular expression profile in a linear mixed model framework which takes into account subject-specific variability. The best model is selected through a serial model selection approach and results in dimension reduction of the time course data. The final step includes two types of analysis of the modelled trajectories, namely, clustering analysis to identify groups of correlated profiles over time, and differential expression analysis to identify profiles which differ over time and/or between treatment groups. Through simulation studies we demonstrate the high sensitivity and specificity of our approach for differential expression analysis. We then illustrate how our framework can bring novel insights on two time course 'omics' studies in breast cancer and kidney rejection. The methods are publicly available, implemented in the R CRAN package lmms.

A practical Java tool for small-molecule compound appraisal.

BACKGROUND: The increased use of small-molecule compound screening by new users from a variety of different academic backgrounds calls for adequate software to administer, appraise, analyse and exchange information obtained from screening experiments. While software and spreadsheet solutions exist, there is a need for software that can be easily deployed and is convenient to use. RESULTS: The Java application cApp addresses this need and aids in the handling and storage of information on small-molecule compounds. The software is intended for the appraisal of compounds with respect to their physico-chemical properties, analysis in relation to adherence to likeness rules as well as recognition of pan-assay interference components and cross-linking with identical entries in the PubChem Compound Database. Results are displayed in a tabular form in a graphical interface, but can also be written in an HTML or PDF format. The output of data in ASCII format allows for further processing of data using other suitable programs. Other features include similarity searches against user-provided compound libraries and the PubChem Compound Database, as well as compound clustering based on a MaxMin algorithm. CONCLUSIONS: cApp is a personal database solution for small-molecule compounds which can handle all major chemical formats. Being a standalone software, it has no other dependency than the Java virtual machine and is thus conveniently deployed. It streamlines the analysis of molecules with respect to physico-chemical properties and drug discovery criteria; cApp is distributed under the GNU Affero General Public License version 3 and available from http://www.structuralchemistry.org/pcsb/. To download cApp, users will be asked for their name, institution and email address. A detailed manual can also be downloaded from this site, and online tutorials are available at http://www.structuralchemistry.org/pcsb/capp.php.

Panel docking of small-molecule libraries - Prospects to improve efficiency of lead compound discovery.

Computational docking as a means to prioritise small molecules in drug discovery projects remains a highly popular in silico screening approach. Contemporary docking approaches without experimental parametrisation can reliably differentiate active and inactive chemotypes in a protein binding site, but the absence of a correlation between the score of a predicted binding pose and the biological activity of the molecule presents a clear limitation. Several novel or improved computational approaches have been developed in the recent past to aid in screening and profiling of small-molecule ligands for drug discovery, but also more broadly in developing conceptual relationships between different protein targets by chemical probing. Among those new methodologies is a strategy known as inverse virtual screening, which involves the docking of a compound into different protein structures. In the present article, we review the different computational screening methodologies that employ docking of atomic models, and, by means of a case study, present an approach that expands the inverse virtual screening concept. By computationally screening a reasonably sized library of 1235 compounds against a panel of 48 mostly human kinases, we have been able to identify five groups of putative lead compounds with substantial diversity when compared to each other. One representative of each of the five groups was synthesised, and tested in kinase inhibition assays, yielding two compounds with micro-molar inhibition in five human kinases. This highly economic and cost-effective methodology holds great promise for drug discovery projects, especially in cases where a group of target proteins share high structural similarity in their binding sites.

Capturing nature's diversity.

Natural products are universally recognized to contribute valuable chemical diversity to the design of molecular screening libraries. The analysis undertaken in this work, provides a foundation for the generation of fragment screening libraries that capture the diverse range of molecular recognition building blocks embedded within natural products. Physicochemical properties were used to select fragment-sized natural products from a database of known natural products (Dictionary of Natural Products). PCA analysis was used to illustrate the positioning of the fragment subset within the property space of the non-fragment sized natural products in the dataset. Structural diversity was analysed by three distinct methods: atom function analysis, using pharmacophore fingerprints, atom type analysis, using radial fingerprints, and scaffold analysis. Small pharmacophore triplets, representing the range of chemical features present in natural products that are capable of engaging in molecular interactions with small, contiguous areas of protein binding surfaces, were analysed. We demonstrate that fragment-sized natural products capture more than half of the small pharmacophore triplet diversity observed in non fragment-sized natural product datasets. Atom type analysis using radial fingerprints was represented by a self-organizing map. We examined the structural diversity of non-flat fragment-sized natural product scaffolds, rich in sp3 configured centres. From these results we demonstrate that 2-ring fragment-sized natural products effectively balance the opposing characteristics of minimal complexity and broad structural diversity when compared to the larger, more complex fragment-like natural products. These naturally-derived fragments could be used as the starting point for the generation of a highly diverse library with the scope for further medicinal chemistry elaboration due to their minimal structural complexity. This study highlights the possibility to capture a high proportion of the individual molecular interaction motifs embedded within natural products using a fragment screening library spanning 422 structural clusters and comprised of approximately 2800 natural products.

Unlocking the puzzling biology of the black Périgord truffle Tuber melanosporum.

The black Périgord truffle (Tuber melanosporum Vittad.) is a highly prized food today, with its unique scent (i.e., perfume) and texture. Despite these attributes, it remains relatively poorly studied, lacking "omics" information to characterize its biology and biochemistry, especially changes associated with freshness and the proteins/metabolites responsible for its organoleptic properties. In this study, we have functionally annotated the truffle proteome from the 2010 T. melanosporum genome comprising 12,771 putative nonredundant proteins. Using sequential BLAST search strategies, we identified homologues for 2587 proteins with 2486 (96.0%) fungal homologues (available from http://biolinfo.org/protannotator/blacktruffle.php). A combined 1D PAGE and high-accuracy LC-MS/MS proteomic study was employed to validate the results of the functional annotation and identified 836 (6.5%) proteins, of which 47.5% (i.e., 397) were present in our bioinformatics studies. Our study, functionally annotating 6487 black Périgord truffle proteins and confirming 836 by proteomic experiments, is by far the most comprehensive study to date contributing significantly to the scientific community. This study has resulted in the functional characterization of novel proteins to increase our biological understanding of this organism and to uncover potential biomarkers of authenticity, freshness, and perfume maturation.

High-throughput optimization of surfaces for antibody immobilization using metal complexes.

Using a high-throughput surface discovery approach, we have generated a 1600-member library of metal-containing surfaces and screened them for antibody binding potential. The surface library assembly involved graft modification of argon plasma-treated polyvinylidenedifluoride (PVDF) membranes with alternating maleic anhydride-styrene copolymer followed by anhydride ring opening with a range of secondary amines and microarray contact printing of transition metal complexes. The microarrays of metal-containing surfaces were then tested for their antibody binding capacity by incubation with a biotinylated mouse antibody in a chemiluminescence assay. A total of 11 leads were identified from the first screen, constituting a "hit" rate of 0.7%. A smaller 135-member surface library was then synthesized and screened to optimize existing hits and generate additional leads. To demonstrate the applicability of these surfaces to other formats, high-binding surface leads were then transferred onto Luminex beads for use in a bead flow cytometric immunoassay. The novel one-step antibody coupling process increased assay sensitivity of a Luminex tumor necrosis factor immunoassay. These high-binding surfaces do not require prior incorporation of polyhistidine tags or posttreatments such as oxidation to achieve essentially irreversible binding of immunoglobulin G.

Diversity in medicinal chemistry space.

The chemical universe containing organic molecules within a reasonable molecular weight is vast and largely unexplored. Estimations of possible numbers of unique molecules range from 10(13) to 10(180). These numbers have to be compared with the few tens of millions of compounds currently known. Design of libraries that populate the medicinally relevant chemical subspace and tools that help to maximise the chance of identifying leads are necessary. This review describes various molecular representations that lead to the definition of chemical space, drug space or activity space. Strategies for compound selection in such spaces are discussed, as well as potential sources of diversity that could be used to explore the medicinal space in quest of new drugs.