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Short Abstract: Pseudomonas aeruginosa is both an antibiotic-resistant opportunistic pathogen and an important model of type I clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-associated protein (CRISPR-Cas) systems. Comparative genomics has identified several CRISPR-Cas subtypes, and it was previously unclear how these immune modules might influence the genome content of P. aeruginosa. To better understand the distribution of CRISPR-Cas subtypes and their impact on genome composition, we annotated 672 P. aeruginosa clinical isolates. We found that CRISPR-Cas systems modulate genome size and accessory elements. In addition, we identified a novel, putatively mobile type I-C CRISPR-Cas system. In the process, we also created a global spacer library that provides a new means of identifying accessory fragments, and facilitates CRISPR typing of many P. aeruginosa strains. Finally, we have made the assemblies of 282 newly-sequenced P. aeruginosa isolates public as an NCBI BioProject (ID: PRJNA297679).
Short Abstract: We propose a knowledge-based approach to refining geospatial metadata for a GenBank record by deriving it from contextual information. We have explored different potential sources from which this knowledge can be derived, and incorporate these sources into our existing rule-based extraction system for linking geospatial locations to GenBank records. For example, contextual information can come from similar GenBank records. Even when no journal article is linked to a record, it is possible to infer some geospatial metadata from similar records For instance, record AB847567 is not linked to any PubMed article and only has the location ‘Viet Nam’ in the record. However, our system can use the geospatial metadata (Quang Ninh, Vietnam) of the similar record AB824281, which was collected in the same year, from the same type of host, and by the same set of authors, to infer that the location of collection of AB847567 could also be Quang Ninh, Vietnam. Our system will utilize the record with the most specific geospatial metadata, in a given collection, that is determined to be similar to the record of interest. To identify similar records, similarity scores between records will be computed, based on their metadata features such as PubMed IDs, author names, author affiliations, hosts, or date of collection, empirically applying different weights to each feature.
Other contextual info could come From GenBank records referring to the same article, or from the work environment of the author of the GenBank record and/or of the referenced paper.
Short Abstract: Molecular techniques for the detection of foodborne bacterial pathogens depend on the specificity of DNA sequence targets for individual species. Targeted sequencing of metagenomic DNA extracted from food sources and environmental samples offers advantages for improving sensitivity and reducing costs by multiplexing samples on high-throughput sequencing platforms. To identify conserved genes from within each species of interest, genes from established MLST schemes were initially selected and expanded as needed to ten genes for each species. The design includes 100 different species chosen from normal gut flora and known enteric pathogens. Alignment of multiple sequences for each species allowed identification of variable and conserved regions for the purpose of target selection and primer design. Previously published MLST primers have degenerate bases or are inconsistent with representatives of the species. The design additionally includes primers for 8-12 virulence genes each from Escherichia coli, Salmonella, and Listeria monocytogenes. Large scale multiplex PCR was used to amplify all target regions from mixed genomic DNA samples followed by sequencing of all amplicons. Subtyping of bacterial species present in the sequences was performed by mapping the reads to MLST databases for determination of MLST types. For proof-of-principle, E. coli C ATCC 8739 in a mixture of 6 species was able to be identified correctly from the sequencing reads as E. coli MLST sequence type 7. This approach may prove powerful for regulatory analyses where next generation sequencing can be employed as a highly sensitive analytical tool for culture independent detection of foodborne pathogens.
Short Abstract: The interaction of barley with the obligate biotrophic powdery mildew fungus, Blumeria graminis f. sp. hordei (Bgh), is an ideal model to address fundamental questions in host resistance and susceptibility. Effector proteins secreted by Bgh regulate host defenses to promote nutrient acquisition and colonization. The Blumeria genome harbors 527 predicted secreted effectors, designated BECs (Blumeria Effector Candidates) or CSEPs (Candidate Secreted Effector Proteins).
Fast-neutron mutagenesis of barley lines containing the Mla6 resistance gene led to the isolation of several loss-of-function deletions (mla6, bln1, rar3 and mla6 + bln1) in immune signaling. Transcriptome sequencing of Bgh-infected wild-type barley and derived mutants was performed at 6 developmental time points [0, 16, 20, 24, 32 and 48 hours after inoculation (HAI)].
Previously, eight BECs were shown to contribute to infection by host-induced gene silencing. Of these eight, BEC1011 and BEC1040 are down regulated during penetration in all susceptible mutants (mla6, rar3, mla6 + bln1) as compared to wild type. BEC1011, a bonafide effector that interferes with pathogen-induced host cell death, alternates to up regulation during haustorial development.
Distinct groups of effectors were differentially expressed at 16 HAI, and again at 32 HAI in Blumeria. This was followed by a similar spike of differential expression at 20 HAI, and again at 48 HAI in barley, suggesting a time delay effect between Blumeria effector and barley gene expression. This evidence suggests that Bgh is able detect the type of loss-of-function in barley and modify expression of its effectors which affects the accumulation of host transcripts.
Short Abstract: Human immunodeficiency virus type 1 (HIV-1) continuously evolves in the infected host due to random mutations. The viral envelope glycoproteins (Envs), which are a primary target for vaccine design, show particularly high sequence diversity. We compared the phenotypic diversity in Env that exists in contemporaneous viruses (i.e., circulating in the individual at a given time point), its divergence in longitudinally-monitored individuals and its diversity in geographically-defined populations. Envs cloned from 120 HIV-infected individuals were tested for different structural features, including integrity of epitopes and glycosylation patterns of Env segments. We found that each feature is maintained in the infected host at a defined degree of variance, which is highly conserved among different hosts. Based on this propensity for ‘dispersion’, which we designate ‘Volatility’, we modeled evolution of Env structural features as a linear diffusion process that progresses with increasing genetic distance. In addition to the stochastic (Volatility-controlled) component that controls progression of variance, preference of Env to occupy specific structural states defines predictable drift patterns from less preferred states. By accounting for the contribution of stochastic and deterministic components, we were able to accurately predict the longitudinal divergence of Env features that developed in 18 patients monitored for up to 11 years. Furthermore, the historical changes that occurred in Env structural features over the past three decades and their current population-level distribution are explained by the Volatility of each feature. The ability to predict changes in HIV-1 Env will likely aid the design of a protective AIDS vaccine.
Short Abstract: The World Health Organization has estimated that in 2015 there were 214 million cases of malaria that led to approximately 438,000 deaths. Effective drug treatments for malaria have been available, but resistance to these treatments has emerged and there is growing evidence that even the most recently developed antimalarials are losing their effectiveness. The development of novel therapeutics largely relies on a better understanding of parasite biology and pathogenesis. In order to unveil temporal-specific patterns of parasite gene expression, we conducted a transcriptomic analysis of the RNA-Seq data collected at seven time points during the red blood cell cycle (Otto et al. Mol. Micro 2010). All sequence reads were mapped to the latest reference genome sequence of the malaria parasite Plasmodium falciparum, PF3D7v3.0. Differential expression analysis was conducted for pair-wise comparisons across time points using the Exact Test as implemented in the EdgeR Bioconductor package. The Bonferroni method and the Benjamini and Hochberg's algorithm were used for multiple testing correction. Gene Ontology enrichment analysis was conducted to identify over-represented functional categories and STRING analysis was conducted to reveal important protein-protein associations. Transcriptomic and network analyses of the intraerythrocytic developmental cycle revealed an orchestrated transcriptional machinery and a just-in-time mechanism for transcriptional regulation in the malaria parasite.
Short Abstract: In rapidly evolving organisms such as RNA viruses, evolutionary and
epidemiological dynamics occur on the same time scale. Phylodynamics attempt to
detect and quantify the signatures of epidemiological processes in viral
phylogenies. Due to the rapid advancement of nucleotide sequencing technology,
viral sequence data data have become increasingly feasible to collect on a
population level. Through phylodynamic methods, these data offer a window into
epidemiological processes which would otherwise be virtually impossible to
study on a realistic scale. However, the vast majority of phylodynamic methods
assume a homogeneously mixed host population, where every pair of individuals
is equally likely to come into contact. This assumption is clearly unrealistic
for most human communities.
We developed a method based on kernel approximate Bayesian computation to fit
contact network models to viral phylogenies without calculating intractible
likelihoods. Our method can be used to fit any network model from which
simulated networks can be generated. We applied our method to investigate a
preferential attachment model using simulated and real-world HIV sequence
datasets. On simulated data, we found that the preferential attachment power
and the number of infected nodes could often be accurately estimated, while the
edge density and total number of nodes were more challenging to determine. We
found significant heterogeneity in the networks underlying several real-world
datasets, with estimated preferential attachment power ranging from 0.35 to
1.16. These results underscore the importance of taking contact structure into
account when investigating viral epidemics, and suggest kernel-ABC as an
effective tool for such investigations.
Short Abstract: Pseudomonas aeruginosa is an opportunistic pathogen implicated in myriad of infections, and a leading pathogen responsible for mortality in patients with cystic fibrosis (CF). Horizontal transfers of genes among the microorganisms living within CF patients have led to a more morbid and multi-drug resistant strains such as P. aeruginosa LESB58 that has the propensity to acquire virulence and antibiotic resistance genes. Often these genes are acquired in large clusters, referred to as “genomic islands”. To decipher genomic islands and understand their contributions to the evolution of virulence and antibiotic resistance in P. aeruginosa LESB58, we utilized a recursive segmentation and clustering procedure, presented here as a genome-mining tool, “GEMINI”. GEMINI was validated on experimentally verified islands in LESB58 strain before examining its potential to decipher novel islands. Of the 6062 genes in LESB58, 596 genes were identified to be resident on 20 genomic islands of which 12 have not been previously reported. Comparative genomics provided evidence in support of our novel predictions. Furthermore, GEMINI unravelled the mosaic structure of islands that are composed of segments of likely different evolutionary origins, and demonstrated its ability to identify potential strain biomarkers. These newly found islands likely have contributed to the hyper-virulence and multidrug resistance of LESB58.
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