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Tillery, L.

Publications and source records attributed to Tillery, L..

2 recordsLinked to original sources

The transcriptome of Balamuthia mandrillaris trophozoites for structure-based drug design

Balamuthia mandrillaris, a pathogenic free-living amoeba (FLA), causes cutaneous skin lesions as well as the brain-eating disease: Balamuthia granulomatous amoebic encephalitis (GAE). These diseases, and diseases caused by other pathogenic FLA, Naegleria fowleri or Acanthamoeba species, are minimally studied from a drug discovery perspective; few targets have been validated or characterized at the molecular level, and little is known about the biochemical pathways necessary for parasite survival. Chemotherapies for CNS disease caused by B. mandrillaris require vast improvement. Current therapeutics are limited to a small number of drugs that were previously discovered in the last century through in vitro testing or identified after use in the small pool of surviving reports. Using our recently published methodology to identify potentially useful therapeutics, we screened a collection of 85 compounds that have previously been reported to have antiparasitic activity. We identified 59 compounds that impacted growth at concentrations below 220 M. Since there is no fully annotated genome or proteome, we used RNA-Seq to reconstruct the transcriptome of B. mandrillaris and locate the coding sequences of the specific genes potentially targeted by the compounds identified to inhibit trophozoite growth. We determined the sequence of 17 of these target genes and obtained expression clones for 15 that we validated by direct sequencing.

genomics

In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19

Rapid generation of diagnostics is paramount to understand epidemiology and to control the spread of emerging infectious diseases such as COVID-19. Computational methods to predict serodiagnostic epitopes that are specific for the pathogen could help accelerate the development of new diagnostics. A systematic survey of 27 SARS-CoV-2 proteins was conducted to assess whether existing B-cell epitope prediction methods, combined with comprehensive mining of sequence databases and structural data, could predict whether a particular protein would be suitable for serodiagnosis. Nine of the predictions were validated with recombinant SARS-CoV-2 proteins in the ELISA format using plasma and sera from patients with SARS-CoV-2 infection, and a further 11 predictions were compared to the recent literature. Results appeared to be in agreement with 12 of the predictions, in disagreement with 3, while a further 5 were deemed inconclusive. We showed that two of our top five candidates, the N-terminal fragment of the nucleoprotein and the receptor-binding domain of the spike protein, have the highest sensitivity and specificity and signal-to-noise ratio for detecting COVID-19 sera/plasma by ELISA. Mixing the two antigens together for coating ELISA plates led to a sensitivity of 94% (N=80 samples from persons with RT-PCR confirmed SARS-CoV2 infection), and a specificity of 97.2% (N=106 control samples).

bioinformatics