Biology subjects

Ghedin, E.

Publications and source records attributed to Ghedin, E..

24 records · Page 2Linked to original sources

bioRxiv2019

Prediction pipeline for discovery of regulatory motifs associated with Brugia malayi molting

Filarial nematodes can cause debilitating diseases in humans. They have complicated life cycles involving an insect vector and mammalian hosts, and they go through a number of developmental molts. While whole genome sequences of parasitic worms are now available, very little is known about transcription factor (TF) binding sites and their cognate transcription factors that play a role in regulating development. To address this gap, we developed a novel motif prediction pipeline, Emotif Alpha, that integrates ten different motif discovery algorithms, multiple statistical tests, and a comparative analysis of conserved elements between the filarial worms Brugia malayi and Onchocerca volvulus, and the free-living nematode Caenorhabditis elegans. We identified stage-specific TF binding motifs in B. malayi, with a particular focus on those potentially involved in the L3-L4 molt, a stage important for the establishment of infection in the mammalian host. Using an in vitro molting system, we tested and validated three of these motifs demonstrating the accuracy of the motif prediction pipeline.

bioinformatics↗

bioRxiv2019

High-resolution proteomics identifies potential new markers of Zika and dengue infections

Distinguishing between Zika and dengue virus infections is critical for treatment and anticipation of complications. However, existing biomarkers have high error rates. To identify new potential diagnostic signatures, we used next-generation proteomics to profile 122 serum samples from 62 Zika or dengue patients. We quantified >500 proteins and identified 26 proteins that were significantly differentially expressed. These proteins typically function in infection and wound healing, with several also linked to pregnancy and brain. Integrating machine learning approaches, we used 7 proteins to predict ZIKV infection correctly in 72% of the cases, outperforming other tools. The three most predictive proteins were Platelet Factor 4 Variant 1, Fibrinogen Alpha, and Gelsolin. Finally, we showed that temporal changes in protein signatures from the same patient can disambiguate some diagnoses and serve as indicators for past infections. Taken together, we demonstrate that serum proteomics can be highly valuable to diagnose even challenging samples.

systems biology↗

bioRxiv2019

Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets

The filarial nematode Brugia malayi represents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteria Wolbachia--present in many filariae--which is vital to the worm.\n\nGenome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but only recently have been applied to eukaryotic organisms. Here, we present iDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 99 reactions essential to the survival of B. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.

systems biology↗

bioRxiv2019

Microbial composition of the human nasopharynx varies according to influenza virus type and vaccination status.

Factors that contribute to enhanced susceptibility to severe bacterial disease after influenza infection are not well defined, but likely include the microbiome of the respiratory tract. Vaccination against influenza, while having variable effectiveness, could also play a role in microbial community stability. We collected nasopharyngeal samples from 215 individuals infected with influenza A/H3N2 or influenza B and profiled the microbiota by target sequencing of the 16S rRNA gene. We identified signature taxonomic groups by performing linear discriminant analysis and effective size comparisons (LEfSe) and defined bacterial community types using Dirichlet Multinomial Mixture (DMM) models. Influenza infection was shown to be significantly associated with microbial composition of the nasopharynx according to the virus type and the vaccination status of the patient. We identified 4 microbial community types across the combined cohort of influenza patients and healthy individuals with one community type most representative of the influenza-infected group. We also identified microbial taxa for which relative abundance was significantly higher in the unvaccinated elderly group; these taxa include species known to be associated with pneumonia.\n\nImportanceOur results suggest that there is a significant association between the composition of the microbiota in the nasopharynx and the influenza virus type causing the infection. We observe that vaccination status, especially in more senior individuals, also has an association with the microbial community profile. This indicates that vaccination against influenza, even when ineffective to prevent disease, could play a role in controlling secondary bacterial complications.

microbiology↗

bioRxiv2018

Cell-to-cell variation in defective virus expression and effects on host responses during influenza virus infection

Virus and host factors contribute to cell-to-cell variation in viral infections and determine the outcome of the overall infection. However, the extent of the variability at the single cell level and how it impacts virus-host interactions at a systems level are not well understood. To characterize the dynamics of viral transcription and host responses, we used single-cell RNA sequencing to quantify at multiple time points the host and viral transcriptomes of human A549 cells and primary bronchial epithelial cells infected with influenza A virus. We observed substantial variability of viral transcription between cells, including the accumulation of defective viral genomes (DVGs) that impact viral replication. We show a correlation between DVGs and viral-induced variation of the host transcriptional program and an association between differential induction of innate immune response genes and attenuated viral transcription in subpopulations of cells. These observations at the single cell level improve our understanding of the complex virus-host interplay during influenza infection.\n\nIMPORTANCEDefective influenza virus particles generated during viral replication carry incomplete viral genomes and can interfere with the replication of competent viruses. These defective genomes are thought to modulate disease severity and pathogenicity of the influenza infection. Different defective viral genomes also introduce another source of variation across a heterogeneous cell population. Evaluating the impact of defective virus genomes on host cell responses cannot be fully resolved at the population level, requiring single cell transcriptional profiling. Here we characterized virus and host transcriptomes in individual influenza-infected cells, including that of defective viruses that arise during influenza A virus infection. We established an association between defective virus transcription and host responses and validated interfering and immunostimulatory functions of identified dominant defective virus genome species in vitro. This study demonstrates the intricate effects of defective viral genomes on host transcriptional responses and highlights the importance of capturing host-virus interactions at the single-cell level.

microbiology↗

bioRxiv2017

Transmission Bottleneck Size Estimation from Pathogen Deep-Sequencing Data, with an Application to Human Influenza A Virus

The bottleneck governing infectious disease transmission describes the size of the pathogen population transferred from a donor to a recipient host. Accurate quantification of the bottleneck size is of particular importance for rapidly evolving pathogens such as influenza virus, as narrow bottlenecks would limit the extent of transferred viral genetic diversity and, thus, have the potential to slow the rate of viral adaptation. Previous studies have estimated the transmission bottleneck size governing viral transmission through statistical analyses of variants identified in pathogen sequencing data. The methods used by these studies, however, did not account for variant calling thresholds and stochastic dynamics of the viral population within recipient hosts. Because these factors can skew bottleneck size estimates, we here introduce a new method for inferring transmission bottleneck sizes that explicitly takes these factors into account. We compare our method, based on beta-binomial sampling, with existing methods in the literature for their ability to recover the transmission bottleneck size of a simulated dataset. This comparison demonstrates that the beta-binomial sampling method is best able to accurately infer the simulated bottleneck size. We then apply our method to a recently published dataset of influenza A H1N1p and H3N2 infections, for which viral deep sequencing data from inferred donor-recipient transmission pairs are available. Our results indicate that transmission bottleneck sizes across transmission pairs are variable, yet that there is no significant difference in the overall bottleneck sizes inferred for H1N1p and H3N2. The mean bottleneck size for influenza virus in this study, considering all transmission pairs, was Nb = 196 (95% confidence interval 66-392) virions. While this estimate is consistent with previous bottleneck size estimates for this dataset, it is considerably higher than the bottleneck sizes estimated for influenza from other datasets.\n\nAuthor SummaryThe transmission bottleneck size describes the size of the pathogen population transferred from the donor to recipient host at the onset of infection and is a key factor in determining the rate at which a pathogen can adapt within a host population. Recent advances in sequencing technology have enabled the bottleneck size to be estimated from pathogen sequence data, though there is not yet a consensus on the statistical method to use. In this study, we introduce a new approach for inferring the transmission bottleneck size from sequencing data that accounts for the criteria used to identify sequence variants and stochasticity in pathogen replication dynamics. We show that the failure to account for these factors may lead to underestimation of the transmission bottleneck size. We apply this method to a previous dataset of human influenza A infections, showing that transmission is governed by a loose transmission bottleneck and that the bottleneck size is highly variable across transmission events. This work advances our understanding of the bottleneck size governing influenza infection and introduces a method for estimating the bottleneck size that can be applied to other rapidly evolving RNA viruses, such as norovirus and RSV.

microbiology↗