We profiled three novel T. gondii inhibitors identified from an antimalarial phenotypic high throughput screen (HTS) campaign: styryl 4-oxo-1,3-benzoxazin-4-one KG3, tetrahydrobenzo[b]pyran KG7, and benzoquinone hydrazone KG8. These compounds inhibit T. gondii in vitro with IC values ranging from 0.3 to 2μM, comparable to that of 0.25 to 1.5μM for the control drug pyrimethamine. KG3 had no measurable cytotoxicity against five mammalian cell lines, whereas KG7 and KG8 inhibited the growth of 2 of 5 cell lines with KG8 being the least selective for T. gondii. None of the compounds were mutagenic in an Ames assay. Experimental gLogD and calculated PSA values for the three compounds were well within the ranges predicted to be favorable for good ADME, even though each compound had relatively low aqueous solubility. All three compounds were metabolically unstable, especially KG3 and KG7. Multiple IP doses of 5mg/kg KG7 and KG8 increased survival in a T. gondii mouse model. Despite their liabilities, we suggest that these compounds are useful starting points for chemical prospecting, scaffold-hopping, and optimization.
Burying beetles (Nicrophorus spp.) are among the relatively few insects that provide parental care while not belonging to the eusocial insects such as ants or bees. This behavior incurs energy costs as evidenced by immune deficits and shorter life-spans in reproducing beetles. In the absence of an assembled transcriptome, relatively little is known concerning the molecular biology of these beetles. This work details the assembly and analysis of the Nicrophorus orbicollis transcriptome at multiple developmental stages. RNA-Seq reads were obtained by next-generation sequencing and the transcriptome was assembled using the Trinity assembler. Validation of the assembly was performed by functional characterization using Gene Ontology (GO), Eukaryotic Orthologous Groups (KOG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Differential expression analysis highlights developmental stage-specific expression patterns, and immunity-related transcripts are discussed. The data presented provides a valuable molecular resource to aid further investigation into immunocompetence throughout this organism's sexual development.
Increased technological methods have enabled the investigation of biology at nanoscale levels. Such systems require the use of computational methods to comprehend the complex interactions that occur. The dynamics of metabolic systems have been traditionally described utilizing differential equations without fully capturing the heterogeneity of biological systems. Stochastic modeling approaches have recently emerged with the capacity to incorporate the statistical properties of such systems. However, the processing of stochastic algorithms is a computationally intensive task with intrinsic limitations. Alternatively, the queueing theory approach, historically used in the evaluation of telecommunication networks, can significantly reduce the computational power required to generate simulated results while simultaneously reducing the expansion of errors. We present here the application of queueing theory to simulate stochastic metabolic networks with high efficiency. With the use of glycolysis as a well understood biological model, we demonstrate the power of the proposed modeling methods discussed herein. Furthermore, we describe the simulation and pharmacological inhibition of glycolysis to provide an example of modeling capabilities.
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