Showing papers by "Jon Clardy published in 2020"

Enteroendocrine cells sense bacterial tryptophan catabolites to activate enteric and vagal neuronal pathways

Abstract: SUMMARY The intestinal epithelium senses nutritional and microbial stimuli using epithelial sensory enteroendocrine cells (EECs). EECs can communicate nutritional information to the nervous system, but similar mechanisms for microbial information are unknown. Using in vivo real-time measurements of EEC and nervous system activity in zebrafish, we discovered that the bacteria Edwardsiella tarda specifically activates EECs through the receptor transient receptor potential ankyrin A1 (Trpa1) and increases intestinal motility in an EEC-dependent manner. Microbial, pharmacological, or optogenetic activation of Trpa1+EECs directly stimulates vagal sensory ganglia and activates cholinergic enteric neurons through 5-HT. We identified a distinct subset of indole derivatives of tryptophan catabolites produced by E. tarda that potently activate zebrafish EEC Trpa1 signaling and also directly activate human and mouse Trpa1. These results establish a molecular pathway by which EECs regulate enteric and vagal neuronal pathways in response to specific microbial signals.

Total synthesis reveals atypical atropisomerism in a small-molecule natural product, tryptorubin A

Abstract: Molecular shape defines function in both biological and material settings, and chemists have developed an ever-increasing vernacular to describe these shapes. Noncanonical atropisomers-shape-defined molecules that are formally topologically trivial but are interconvertible only by complex, nonphysical multibond torsions-form a unique subset of atropisomers that differ from both canonical atropisomers (e.g., binaphthyls) and topoisomers (i.e., molecules that have identical connectivity but nonidentical molecular graphs). Small molecules, in contrast to biomacromolecules, are not expected to exhibit such ambiguous shapes. Using total synthesis, we found that the peptidic alkaloid tryptorubin A can be one of two noncanonical atropisomers. We then devised a synthetic strategy that drives the atropospecific synthesis of a noncanonical atrop-defined small molecule.

A symbiotic bacterium of shipworms produces a compound with broad spectrum anti-apicomplexan activity.

Abstract: Apicomplexan parasites cause severe disease in both humans and their domesticated animals. Since these parasites readily develop drug resistance, development of new, effective drugs to treat infection caused by these parasites is an ongoing challenge for the medical and veterinary communities. We hypothesized that invertebrate-bacterial symbioses might be a rich source of anti-apicomplexan compounds because invertebrates are susceptible to infections with gregarines, parasites that are ancestral to all apicomplexans. We chose to explore the therapeutic potential of shipworm symbiotic bacteria as they are bona fide symbionts, are easily grown in axenic culture and have genomes rich in secondary metabolite loci [1,2]. Two strains of the shipworm symbiotic bacterium, Teredinibacter turnerae, were screened for activity against Toxoplasma gondii and one strain, T7901, exhibited activity against intracellular stages of the parasite. Bioassay-guided fractionation identified tartrolon E (trtE) as the source of the activity. TrtE has an EC50 of 3 nM against T. gondii, acts directly on the parasite itself and kills the parasites after two hours of treatment. TrtE exhibits nanomolar to picomolar level activity against Cryptosporidium, Plasmodium, Babesia, Theileria, and Sarcocystis; parasites representing all branches of the apicomplexan phylogenetic tree. The compound also proved effective against Cryptosporidium parvum infection in neonatal mice, indicating that trtE may be a potential lead compound for preclinical development. Identification of a promising new compound after such limited screening strongly encourages further mining of invertebrate symbionts for new anti-parasitic therapeutics.

Cycloheximide-Producing Streptomyces Associated With Xyleborinus saxesenii and Xyleborus affinis Fungus-Farming Ambrosia Beetles.

Abstract: Symbiotic microbes help a myriad of insects acquire nutrients. Recent work suggests that insects also frequently associate with actinobacterial symbionts that produce molecules to help defend against parasites and predators. Here we explore a potential association between Actinobacteria and two species of fungus-farming ambrosia beetles, Xyleborinus saxesenii and Xyleborus affinis. We isolated and identified actinobacterial and fungal symbionts from laboratory reared nests, and characterized small molecules produced by the putative actinobacterial symbionts. One 16S rRNA phylotype of Streptomyces (XylebKG-1) was abundantly and consistently isolated from the galleries and adults of X. saxesenii and X. affinis nests. In addition to Raffaelea sulphurea, the symbiont that X. saxesenii cultivates, we also repeatedly isolated a strain of Nectria sp. that is an antagonist of this mutualism. Inhibition bioassays between Streptomyces griseus XylebKG-1 and the fungal symbionts from X. saxesenii revealed strong inhibitory activity of the actinobacterium toward the fungal antagonist Nectria sp. but not the fungal mutualist R. sulphurea. Bioassay guided HPLC fractionation of S. griseus XylebKG-1 culture extracts, followed by NMR and mass spectrometry, identified cycloheximide as the compound responsible for the observed growth inhibition. A biosynthetic gene cluster putatively encoding cycloheximide was also identified in S. griseus XylebKG-1. The consistent isolation of a single 16S phylotype of Streptomyces from two species of ambrosia beetles, and our finding that a representative isolate of this phylotype produces cycloheximide, which inhibits a parasite of the system but not the cultivated fungus, suggests that these actinobacteria may play defensive roles within these systems.

A conserved coccidian gene is involved in Toxoplasma sensitivity to the anti-apicomplexan compound, tartrolon E.

Abstract: New treatments for the diseases caused by apicomplexans are needed. Recently, we determined that tartrolon E (trtE), a secondary metabolite derived from a shipworm symbiotic bacterium, has broad-spectrum anti-apicomplexan parasite activity. TrtE inhibits apicomplexans at nM concentrations in vitro, including Cryptosporidium parvum, Toxoplasma gondii, Sarcocystis neurona, Plasmodium falciparum, Babesia spp. and Theileria equi. To investigate the mechanism of action of trtE against apicomplexan parasites, we examined changes in the transcriptome of trtE-treated T. gondii parasites. RNA-Seq data revealed that the gene, TGGT1_272370, which is broadly conserved in the coccidia, is significantly upregulated within 4 h of treatment. Using bioinformatics and proteome data available on ToxoDB, we determined that the protein product of this tartrolon E responsive gene (trg) has multiple transmembrane domains, a phosphorylation site, and localizes to the plasma membrane. Deletion of trg in a luciferase-expressing T. gondii strain by CRISPR/Cas9 resulted in a 68% increase in parasite resistance to trtE treatment, supporting a role for the trg protein product in the response of T. gondii to trtE treatment. Trg is conserved in the coccidia, but not in more distantly related apicomplexans, indicating that this response to trtE may be unique to the coccidians, and other mechanisms may be operating in other trtE-sensitive apicomplexans. Uncovering the mechanisms by which trtE inhibits apicomplexans may identify shared pathways critical to apicomplexan parasite survival and advance the search for new treatments.

Parsing Molecules for Drug Discovery.

Abstract: C events are painfully reminding us of our vulnerability to infectious disease. Most readers will not need to be reminded that the effectiveness of our currently used antibacterial agents is diminishing due to growing bacterial resistance, and developing new agents by modifying currently used agents has failed to fully address our needs. As a result, the search for new agents, especially ones with new chemotypes and targets, has become increasingly urgent. Recently, Stokes et al. reported a deep learning approach to discover antibacterials that would meet these requirements. Their approach, which is conceptually simple and technically challenging, required three things: (1) a training set, molecules with known structures and known ability to either stop or not stop bacterial growth, (2) a program to “learn” from this data set [in this case, the program was Chemprop, a directed message passing neural network (more on this below)], and (3) a screening set, a large numberof structures fromwhichChemprop could identify likely antibacterials. The authors began by constructing the training set. Rather than relyingonpreviously constructed sets or trying to assemble a data set from the literature, the authors created their data set by experimentally screening 2335 compounds with known structures for their ability to inhibit the growth of Escherichia coli. Themolecules in this initial training library consistedmostly of Food and Drug Administration-approved drugs but also included natural products isolated from plant, animal, and microbial sources. Because the screen is blind to the mechanism of action and there are many ways to inhibit bacterial growth, positive screening hits will work through different mechanisms. In principle, amodel trained on this data set could predict activity for molecules that act through different and possibly new mechanisms of action. The authors then used the training data set from their phenotypic screen to train the directed message passing neural network (DMPNN), Chemprop, to predict which compounds are likely to inhibit the growth of E. coli. The term parsing may remind readers of long ago grammar classes in which sentences were parsed (verb, noun, adjectives, etc.). Chemprop parsed molecules into smaller fragments [amide bond, aromatic ring, methyl group, etc. (Figure 1)]. A directedmessage passing neural network can learn which substructures of a molecule are associated with a given property, in this case the ability to inhibit the growth of E. coli. DMPNNs can learn substructures of molecules by passing “messages”, which contain information about the bonds and atoms, around local regions of themolecule. Through message passing, the neural network could identify functional groups and other local features. Because DMPNNs distinguish only local features of the molecule, Chemprop also uses features of the entire molecule computed using RDkit, an existing toolkit for computing molecular properties, such as the number of hydrogen bond donors and acceptors the and number of rotatable bonds, to make its predictions. Previously, Chemprop had outperformed many other machine learning methods for predicting chemical properties. Deep learning approaches have the potential to increase the efficiency of high-throughput screening. While high-throughput functional screening can screen hundreds of thousands to a million compounds for a given activity, those numbers comprise only a tiny fraction of small molecule space. A deep learning computational screen can make predictions for at least 10 compounds, many multiples of what could be experimentally screened, and a computational screen is not limited to available molecules. However, models based on deep learning are not sufficiently accurate to pick out the most promising compound from 10 compounds, but they can prioritize compounds for functional screening with much higher hit rates. This ability to focus screens on the most likely molecules has obvious advantages for drug discovery. Stokes et al. demonstrated Chemprop’s ability to increase the efficiency of screens for antibiotics by applying it to three different compound libraries, the Drug Repurposing Hub, the WuXi antituberculosis (https://www.broadinstitute.org/ therapeutics-platform/compound-libraries), and the ZINC15 libraries (http://zinc15.docking.org/). The model was exceptionally accurate for the Drug Repurposing Hub, with a true positive rate of 51.5% for the top 99 ranked molecules and a false negative rate of only 3.2% for the 63 lowest ranked molecules. Themuch higher hit rate for the top-rankedmolecules illustrates deep learning’s ability to prioritizemolecules for screening. From the set of the top-ranked molecules, the authors zeroed in on a synthetic small molecule that was previously identified as a selective c-Jun N-terminal kinase inhibitor. The small molecule (SU3327, renamed halicin) showed a micromolar minimal inhibitory concentration (MIC) against a range of bacteria, including high-priority pathogens such as Acinetobacter baumannii, Staphylococcus aureus, Clostridium dif f icile, and carbapenem-

Bacterially produced small molecules stimulate diatom growth

Abstract: Diatoms are photosynthetic microalgae that fix a significant fraction of the worlds carbon. Because of their photosynthetic efficiency and high-lipid content, diatoms are priority candidates for biofuel production. Here, we report that sporulating Bacillus thuringiensis when in co-culture with a marine diatom Phaeodactylum tricornutum significantly increases the diatom cell count. Bioassay-guided purification led to the identification of two diketopiperazines (DKPs) that both stimulate P. tricornutum growth and increase its lipid content. RNA-seq analysis revealed upregulation of a small set of P. tricornutum genes involved in iron starvation response and nutrient recycling when DKP was added to the diatom culture. This work demonstrates that two DKPs produced by a bacterium could positively impact P. tricornutum growth and lipid content, offering new approaches to enhance P. tricornutum-based biofuel production. As increasing numbers of DKPs are isolated from marine microbes, the work gives potential clues to bacterially produced growth factors for marine microalgae. One sentence summaryTwo diketopiperazines (DKPs) produced by sporulating bacterium Bacillus thuringiensis stimulate diatom P. tricornutum growth and increase diatom lipid content.

Compositions and methods for modulating protease activity

Abstract: Disclosed herein, inter alia, are compositions for modulating protease activity and treating cancer.