Co-Swarming and Local Collapse: Quorum Sensing Conveys Resilience to Bacterial Communities by Localizing Cheater Mutants in Pseudomonas aeruginosa

Background: Members of swarming bacterial consortia compete for nutrients but also use a co-operation mechanism called quorum sensing (QS) that relies on chemical signals as well as other secreted products (‘‘public goods’’) necessary for swarming. Deleting various genes of this machinery leads to cheater mutants impaired in various aspects of swarming cooperation. Methodology/Principal Findings: Pairwise consortia made of Pseudomonas aeruginosa, its QS mutants as well as B. cepacia cells show that a interspecies consortium can ‘‘combine the skills’ ’ of its participants so that the strains can cross together barriers that they could not cross alone. In contrast, deleterious mutants are excluded from consortia either by competition or by local population collapse. According to modeling, both scenarios are the consequence of the QS signalling mechanism itself. Conclusion/Significance: The results indirectly explain why it is an advantage for bacteria to maintain QS systems that can cross-talk among different species, and conversely, why certain QS mutants which can be abundant in isolated niches

A geographically matched control population efficiently limits the number of candidate disease-causing variants in an unbiased whole-genome analysis

Whole-genome sequencing is a promising approach for human autosomal dominant disease studies. However, the vast number of genetic variants observed by this method constitutes a challenge when trying to identify the causal variants. This is often handled by restricting disease studies to the most damaging variants, e.g. those found in coding regions, and overlooking the remaining genetic variation. Such a biased approach explains in part why the genetic causes of many families with dominantly inherited diseases, in spite of being included in whole-genome sequencing studies, are left unsolved today. Here we explore the use of a geographically matched control population to minimize the number of candidate disease-causing variants without excluding variants based on assumptions on genomic position or functional predictions. To exemplify the benefit of the geographically matched control population we apply a typical disease variant filtering strategy in a family with an autosomal dominant form of colorectal cancer. With the use of the geographically matched control population we end up with 26 candidate variants genome wide. This is in contrast to the tens of thousands of candidates left when only making use of available public variant datasets. The effect of the local control population is dual, it (1) reduces the total number of candidate variants shared between affected individuals, and more importantly (2) increases the rate by which the number of candidate variants are reduced as additional affected family members are included in the filtering strategy. We demonstrate that the application of a geographically matched control population effectively limits the number of candidate disease-causing variants and may provide the means by which variants suitable for functional studies are identified genome wide

Designing Usable Bioinformatics Tools for Specialized Users

Visualization - the process of interpreting data into visual forms - is increasingly important in science as data grows rapidly in volume and complexity. A common challenge faced by many biologists is how to benefit from this data deluge without being overwhelmed by it. Here, our main interest is in the visualization of genomes, sequence alignments, phylogenies and systems biology data. Bringing together new technologies, including design theory, and applying them into the above three areas in biology will improve the usability and user interaction.The main goal of this paper is to apply design principles to make bioinformatics resources, evaluate them using different usability methods, and provide recommended steps to design usable tools.</p

Number of candidate variants at successive steps of filtering and relaxation, with and without ACpop.

The numbers represent candidate variants in family CRC1 with and without access to the control population ACpop. The first step of the filtering strategy consisted of removing variants of poor quality. Thereafter, variants that were hypothesized to be non-disease-causing were removed. A minor allele frequency cut-off (MAF>1%) was used for the six public variant databases while all variants found in ACpop were removed. In the next step, variants were conditioned to exist in the 11 affected individuals. Two additional steps were applied where the conditions were relaxed. The first was to allow for two sporadic cases of colorectal cancer among the 11 affected individuals, and the second was to allow for reduced penetrance of the disease-causing variant in ACpop.</p

Summary of the 26 variants remaining after filtering analysis of family CRC1.

Summary of the 26 variants remaining after filtering analysis of family CRC1.</p

Visualization of the haplotype on chr20 and the segregation within family CRC1.

(A) Figure adapted from the UCSC Genome Browser illustrating the positions of the 23 candidate variants on chr20. The relevant section is enlarged below the whole chromosome. The three SNPs found to be associated with colorectal cancer in previous GWAS are depicted in the track below. An asterisk represents two variants in close proximity. (B) An abbreviated pedigree of family CRC1 showing the segregation of the haplotype within the family. Filled black diamonds represent individuals with colorectal cancer, half-filled diamonds represent individuals with stomach cancer, grey diamonds represent individuals who have had four or more adenomas removed, and empty diamonds represent individuals with undetermined phenotype. The haplotype on chr20 is represented by a rounded rectangle. Blue color indicates a carrier of the haplotype and an empty symbol denotes individuals found not to carry the haplotype.</p