Biology subjects

Bajic, V. B.

Publications and source records attributed to Bajic, V. B..

6 recordsLinked to original sources

bioRxiv2018

A multidrug resistant clinical P. aeruginosa isolate in the MLST550 clonal complex: uncoupled quorum sensing modulates the interplay of virulence and resistance

Pseudomonas aeruginosa is a prevalent and pernicious pathogen equipped with both extraordinary capabilities to infect the host and to develop antimicrobials resistance (AMR). Monitoring the emergence of AMR high risk clones and understanding the interplay of their pathogenicity and antibiotic resistance is of paramount importance to avoid resistance dissemination and to control P. aeruginosa infections. In this study, we report the identification of a multidrug resistant (MDR) P. aeruginosa strain PA154197 isolated from a blood stream infection in Hong Kong. PA154197 belongs to a distinctive MLST550 clonal complex shared by two international P. aeruginosa isolates VW0289 and AUS544. Comparative genome and transcriptome analysis with the reference strain PAO1 led to the identification of a variety of genetic variations in antibiotic resistance genes and the hyper-expression of three multidrug efflux pumps MexAB-OprM, MexEF-OprN, and MexGHI-OpmD in PA154197. Unlike many resistant isolates displaying an attenuated virulence, PA154197 produces a significantly high level of the P. aeruginosa major virulence factor pyocyanin (PYO) and displays an uncompromised virulence compared to PAO1. Further analysis revealed that the secondary quorum sensing system Pqs which primarily controls the PYO production is hyper-active in PA154197 independent of the master QS systems Las and Rhl. Together, these investigations disclose a unique, uncoupled QS mediated pathoadaptation mechanism in clinical P. aeruginosa which may account for the high pathogenic potentials and antibiotics resistance in the MDR isolate PA154197.

microbiology↗

bioRxiv2018

Proteome-level assessment of origin, prevalence and function of Leucine-Aspartic Acid (LD) motifs

Short Linear Motifs (SLiMs) contribute to almost every cellular function by connecting appropriate protein partners. Accurate prediction of SLiMs is difficult due to their shortness and sequence degeneracy. Leucine-aspartic acid (LD) motifs are SLiMs that link paxillin family proteins to factors controlling (cancer) cell adhesion, motility and survival. The existence and importance of LD motifs beyond the paxillin family is poorly understood. To enable a proteome-wide assessment of these motifs, we developed an active-learning based framework that iteratively integrates computational predictions with experimental validation. Our analysis of the human proteome identified a dozen proteins that contain LD motifs, all being involved in cell adhesion and migration, and revealed a new type of inverse LD motif consensus. Our evolutionary analysis suggested that LD motif signalling originated in the common unicellular ancestor of opisthokonts and amoebozoa by co-opting nuclear export sequences. Inter-species comparison revealed a conserved LD signalling core, and reveals the emergence of species-specific adaptive connections, while maintaining a strong functional focus of the LD motif interactome. Collectively, our data elucidate the mechanisms underlying the origin and adaptation of an ancestral SLiM.

bioinformatics↗

bioRxiv2017

The genome sequence of the wild tomato Solanum pimpinellifolium provides insights into salinity tolerance

Solanum pimpinellifolium, a wild relative of cultivated tomato, offers a wealth of breeding potential for several desirable traits such as tolerance to abiotic and biotic stresses. Here, we report the genome and annotation of S. pimpinellifolium LA0480. The LA0480 genome size (811 Mb) and the number of annotated genes (25,970) are within the range observed for other sequenced tomato species. We developed and utilized the Dragon Eukaryotic Analyses Platform (DEAP) to functionally annotate the LA0480 protein-coding genes. Additionally, we used DEAP to compare protein function between S. pimpinellifolium and cultivated tomato. Our data suggest enrichment in genes involved in biotic and abiotic stress responses. Moreover, we present phenotypic data from one field experiment that demonstrate a greater salinity tolerance for fruit-and yield-related traits in S. pimpinellifolium compared with cultivated tomato. To understand the genomic basis for these differences in S. pimpinellifolium and S. lycopersicum, we analyzed 15 genes that have previously been shown to mediate salinity tolerance in plants. We show that S. pimpinellifolium has a higher copy number of the inositol-3-phosphate synthase and phosphatase genes, which are both key enzymes in the production of inositol and its derivatives. Moreover, our analysis indicates that changes occurring in the inositol phosphate pathway may contribute to the observed higher salinity tolerance in LA0480. Altogether, our work provides essential resources to understand and unlock the genetic and breeding potential of S. pimpinellifolium, and to discover the genomic basis underlying its environmental robustness.

plant biology↗

bioRxiv2017

Motif signatures of transcribed enhancers

In mammalian cells, transcribed enhancers (TrEn) play important roles in the initiation of gene expression and maintenance of gene expression levels in spatiotemporal manner. One of the most challenging questions in biology today is how the genomic characteristics of enhancers relate to enhancer activities. This is particularly critical, as several recent studies have linked enhancer sequence motifs to specific functional roles. To date, only a limited number of enhancer sequence characteristics have been investigated, leaving space for exploring the enhancers genomic code in a more systematic way. To address this problem, we developed a novel computational method, TELS, aimed at identifying predictive cell type/tissue specific motif signatures. We used TELS to compile a comprehensive catalog of motif signatures for all known TrEn identified by the FANTOM5 consortium across 112 human primary cells and tissues. Our results confirm that distinct cell type/tissue specific motif signatures characterize TrEn. These signatures allow discriminating successfully a) TrEn from random controls, proxy of non-enhancer activity, and b) cell type/tissue specific TrEn from enhancers expressed and transcribed in different cell types/tissues. TELS codes and datasets are publicly available at http://www.cbrc.kaust.edu.sa/TELS.

bioinformatics↗

bioRxiv2016

CpG traffic lights are markers of regulatory regions in humans

DNA methylation is involved in regulation of gene expression. Although modern methods profile DNA methylation at single CpG sites, methylation levels are usually averaged over genomic regions in the downstream analyses. In this study we demonstrate that single CpG methylation can serve as a more accurate predictor of gene expression compared to average promoter / gene body methylation. CpG positions with significant correlation between methylation and expression of a gene nearby (named CpG traffic lights) are evolutionary conserved and enriched for exact TSS positions and active enhancers. Among all promoter types, CpG traffic lights are especially enriched in poised promoters. Genes that harbor CpG traffic lights are associated with development and signal transduction. Methylation levels of individual CpG traffic lights vary between cell types dramatically with the increased frequency of intermediate methylation levels, indicating cell population heterogeneity in CpG methylation levels. Being in line with the concept of the inherited stochastic epigenetic variation, methylation of such CpG positions might contribute to transcriptional regulation. Alternatively, one can hypothesize that traffic lights are markers of absent gene expression resulting from inactivation of their regulatory elements. The CpG traffic lights provide a promising insight into mechanisms of enhancer activity and gene regulation linking methylation of single CpG to expression.

bioinformatics↗

bioRxiv2016

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease.

Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share patterns of transcriptional regulation. Accordingly, shared transcriptional regulation (coexpression) may help prioritise loci associated with a given trait, and help to identify the biological processes underlying it. Using cap analysis of gene expression (CAGE) profiles of promoter and enhancer-derived RNAs across 1824 human samples, we have quantified coexpression of RNAs originating from trait-associated regulatory regions using a novel analytical method (network density analysis; NDA). For most traits studied, sequence variants in regulatory regions were linked to tightly coexpressed networks that are likely to share important functional characteristics. These networks implicate particular cell types and tissues in disease pathogenesis; for example, variants associated with ulcerative colitis are linked to expression in gut tissue, whereas Crohns disease variants are restricted to immune cells. We show that this coexpression signal provides additional independent information for fine mapping likely causative variants. This approach identifies additional genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. This approach enables a deeper biological understanding of the causal basis of complex traits.\n\nONE SENTENCE SUMMARYWe discover that variants associated with a specific disease share expression profiles across tissues and cell types, enabling fine mapping and identification of new disease-associated variants, illuminating key cell types involved in disease pathogenesis.

genomics↗