Laura-Isobel McCall

Targeting Ergosterol Biosynthesis in Leishmania donovani: Essentiality of Sterol 14alpha-demethylase

Leishmania protozoan parasites (Trypanosomatidae family) are the causative agents of cutaneous, mucocutaneous and visceral leishmaniasis worldwide. While these diseases are associated with significant morbidity and mortality, there are few adequate treatments available. Sterol 14alpha-demethylase (CYP51) in the parasite sterol biosynthesis pathway has been the focus of considerable interest as a novel drug target in Leishmania. However, its essentiality in Leishmania donovani has yet to be determined. Here, we use a dual biological and pharmacological approach to demonstrate that CYP51 is indispensable in L. donovani. We show via a facilitated knockout approach that chromosomal CYP51 genes can only be knocked out in the presence of episomal complementation and that this episome cannot be lost from the parasite even under negative selection. In addition, we treated wild-type L. donovani and CYP51-deficient strains with 4-aminopyridyl-based inhibitors designed specifically for Trypanosoma cruzi CYP51. While potency was lower than in T. cruzi, these inhibitors had increased efficacy in parasites lacking a CYP51 allele compared to complemented parasites, indicating inhibition of parasite growth via a CYP51-specific mechanism and confirming essentiality of CYP51 in L. donovani. Overall, these results provide support for further development of CYP51 inhibitors for the treatment of visceral leishmaniasis.

Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery

Background Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity. Methodology/Principal Findings In the present study we developed a computational approach that utilized data from several public whole-cell, phenotypic high throughput screens that have been completed for T. cruzi by the Broad Institute, including a single screen of over 300,000 molecules in the search for chemical probes as part of the NIH Molecular Libraries program. We have also compiled and curated relevant biological and chemical compound screening data including (i) compounds and biological activity data from the literature, (ii) high throughput screening datasets, and (iii) predicted metabolites of T. cruzi metabolic pathways. This information was used to help us identify compounds and their potential targets. We have constructed a Pathway Genome Data Base for T. cruzi. In addition, we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds. Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC50 < 10μM. We progressed five compounds to an in vivo mouse efficacy model of Chagas disease and validated that the machine learning model could identify in vitro active compounds not in the training set, as well as known positive controls. The antimalarial pyronaridine possessed 85.2% efficacy in the acute Chagas mouse model. We have also proposed potential targets (for future verification) for this compound based on structural similarity to known compounds with targets in T. cruzi. Conclusions/ Significance We have demonstrated how combining chemoinformatics and bioinformatics for T. cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked. The approach we have taken is broadly applicable to other NTDs.

Best practices for analysing microbiomes

Complex microbial communities shape the dynamics of various environments, ranging from the mammalian gastrointestinal tract to the soil. Advances in DNA sequencing technologies and data analysis have provided drastic improvements in microbiome analyses, for example, in taxonomic resolution, false discovery rate control and other properties, over earlier methods. In this Review, we discuss the best practices for performing a microbiome study, including experimental design, choice of molecular analysis technology, methods for data analysis and the integration of multiple omics data sets. We focus on recent findings that suggest that operational taxonomic unit-based analyses should be replaced with new methods that are based on exact sequence variants, methods for integrating metagenomic and metabolomic data, and issues surrounding compositional data analysis, where advances have been particularly rapid. We note that although some of these approaches are new, it is important to keep sight of the classic issues that arise during experimental design and relate to research reproducibility. We describe how keeping these issues in mind allows researchers to obtain more insight from their microbiome data sets.Complex microbial communities shape the dynamics of various environments. In this Review, Knight and colleagues discuss the best practices for performing a microbiome study, including experimental design, choice of molecular analysis technology, methods for data analysis and the integration of multiple omics data sets.

Mass Spectrometry-Based Visualization of Molecules Associated with Human Habitats

The cars we drive, the homes we live in, the restaurants we visit, and the laboratories and offices we work in are all a part of the modern human habitat. Remarkably, little is known about the diversity of chemicals present in these environments and to what degree molecules from our bodies influence the built environment that surrounds us and vice versa. We therefore set out to visualize the chemical diversity of five built human habitats together with their occupants, to provide a snapshot of the various molecules to which humans are exposed on a daily basis. The molecular inventory was obtained through untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of samples from each human habitat and from the people that occupy those habitats. Mapping MS-derived data onto 3D models of the environments showed that frequently touched surfaces, such as handles (e.g., door, bicycle), resemble the molecular fingerprint of the human skin more closely than other surfaces that are less frequently in direct contact with humans (e.g., wall, bicycle frame). Approximately 50% of the MS/MS spectra detected were shared between people and the environment. Personal care products, plasticizers, cleaning supplies, food, food additives, and even medications that were found to be a part of the human habitat. The annotations indicate that significant transfer of chemicals takes place between us and our built environment. The workflows applied here will lay the foundation for future studies of molecular distributions in medical, forensic, architectural, space exploration, and environmental applications.

Mass Spectrometry-Based Chemical Cartography of a Cardiac Parasitic Infection.

Trypanosoma cruzi parasites are the causative agents of Chagas disease, a leading infectious form of heart failure whose pathogenesis is still not fully characterized. In this work, we applied untargeted liquid chromatography-tandem mass spectrometry to heart sections from T. cruzi-infected and uninfected mice. We combined molecular networking and three-dimensional modeling to generate chemical cartographical heart models. This approach revealed for the first time preferential parasite localization to the base of the heart and regiospecific distributions of nucleoside derivatives and eicosanoids, which we correlated to tissue-damaging immune responses. We further detected novel cardiac chemical signatures related to the severity and ultimate outcome of the infection. These signatures included differential representation of higher- vs lower-molecular-weight carnitine and phosphatidylcholine family members in specific cardiac regions of mice infected with lethal or nonlethal T. cruzi strains and doses. Overall, this work provides new insights into Chagas disease pathogenesis and presents an analytical chemistry approach that can be broadly applied to the study of host-microbe interactions.

Location, Location, Location: Five Facts about Tissue Tropism and Pathogenesis

Infectious disease burden remains high [1]. An improved knowledge of host–microorganism interactions is key to delineating disease progression and developing new control and treatment modalities. Microorganisms may be pathogenic (able to cause disease) or non-pathogenic (unable to cause disease). Disease severity is influenced by host and pathogen factors; in particular, pathogens may express different virulence factors that modulate disease severity and progression. Within this context, the interaction of the pathogen with organ and tissue niches is especially important. Tropism refers to the ability of a given pathogen to infect a specific location. Organ or tissue tropism reflects the ability of a given pathogen to infect a specific organ or sets of organs. Some pathogens are broadly tropic, infecting all or most organs, while others are restricted to a given tissue or even to certain tissue niches. From this point of view, the ability of a pathogen to infect specific organs may vary over the course of the disease and could be active, pathogen-mediated, or passive, requiring, for example, a prior skin break or vector bite. Within tissue niches, intracellular pathogens may also preferentially infect specific organelles or intracellular sites. This article will focus on tissue tropism and its relationship to pathogenesis with examples from Staphylococcus aureus bacteria, Trypanosoma brucei protozoan parasites, and the influenza virus.

Experimental Chagas disease-induced perturbations of the fecal microbiome and metabolome

Trypanosoma cruzi parasites are the causative agents of Chagas disease. These parasites infect cardiac and gastrointestinal tissues, leading to local inflammation and tissue damage. Digestive Chagas disease is associated with perturbations in food absorption, intestinal traffic and defecation. However, the impact of T. cruzi infection on the gut microbiota and metabolome have yet to be characterized. In this study, we applied mass spectrometry-based metabolomics and 16S rRNA sequencing to profile infection-associated alterations in fecal bacterial composition and fecal metabolome through the acute-stage and into the chronic stage of infection, in a murine model of Chagas disease. We observed joint microbial and chemical perturbations associated with T. cruzi infection. These included alterations in conjugated linoleic acid (CLA) derivatives and in specific members of families Ruminococcaceae and Lachnospiraceae, as well as alterations in secondary bile acids and members of order Clostridiales. These results highlight the importance of multi-‘omics’ and poly-microbial studies in understanding parasitic diseases in general, and Chagas disease in particular.

Metabolomics: Eavesdropping on silent conversations between hosts and their unwelcome guests

Interest in metabolomics has been rising over the past 15 years or more, driven by instrumental and computational advances, complementarity to other “omics” approaches, and usefulness for a variety of applications, including drug development, biomarker discovery, and basic research on pathogen tropism and metabolic potential. This growing interest has been paralleled by increasing applications of metabolomics studies to host–pathogen systems. Signals silently transmitted between host and pathogen via small molecules can be intercepted by researchers using metabolomic techniques for identification and quantification. In this Pearl, we will discuss basic metabolomics principles and examples of their application to the study of microbial pathogenesis. Metabolomics is the analysis of a complex biological sample to detect and quantify small (approximately 50–1,500 Da), chemically diverse molecular species known as metabolites, including biological molecules (output of core metabolism, secondary metabolites) and externally derived molecules (food additives, drugs, etc.) [1]. They are the outputs and intermediates of enzymatic reactions, as well as their regulators [2]. Metabolites can also regulate gene expression by, for example, direct binding of transcription factors or through upstream signaling pathways [3]. These multifactorial effects are why the metabolome is often considered closest to phenotype [4]. Common metabolomics methods include mass spectrometry (MS)–or nuclear magnetic resonance (NMR) spectroscopy–based approaches. NMR data acquisition is based on the resonance behavior of certain atoms (e.g., H) in a magnetic field, which is modulated by the surrounding chemical structure [5]. MS separates intact (MS1) or fragmented (MS2, MS/MS, tandem MS) charged particles based on their mass-over-charge ratio (m/z). The fragmentation pattern is characteristic of a molecule’s structure [6]. Studies can focus on a list of metabolites (targeted) or on all detectable metabolites under a given analysis setup (untargeted) [5]. Data processing and identification of NMR or MS signals are usually performed using a combination of computational techniques, manual curation, and comparison to authentic standards [1]. However, many of the detected metabolites will have no known matches, making metabolite identification a major challenge in metabolomics [6]. In addition, further comparison with authentic standards is necessary to confirm peak identifications. Metabolomics in the context of host–pathogen interactions seeks to determine how specific metabolic environments favor pathogen establishment and how metabolite composition varies under infection conditions. For example, metabolomics can be applied to identify biological processes taking place in the host in response to the pathogen or in the pathogen as it adapts and proliferates in host environments. These insights into the conversation between host and

Cysteine proteases in protozoan parasites

Cysteine proteases (CPs) play key roles in the pathogenesis of protozoan parasites, including cell/tissue penetration, hydrolysis of host or parasite proteins, autophagy, and evasion or modulation of the host immune response, making them attractive chemotherapeutic and vaccine targets. This review highlights current knowledge on clan CA cysteine proteases, the best-characterized group of cysteine proteases, from 7 protozoan organisms causing human diseases with significant impact: Entamoeba histolytica, Leishmania species (sp.), Trypanosoma brucei, T. cruzi, Cryptosporidium sp., Plasmodium sp., and Toxoplasma gondii. Clan CA proteases from three organisms (T. brucei, T. cruzi, and Plasmodium sp.) are well characterized as druggable targets based on in vitro and in vivo models. A number of candidate inhibitors are under development. CPs from these organisms and from other protozoan parasites should be further characterized to improve our understanding of their biological functions and identify novel targets for chemotherapy.