Footprint of Positive Selection in Treponema pallidum subsp. pallidum Genome Sequences Suggests Adaptive Microevolution of the Syphilis Pathogen

In the rabbit model of syphilis, infection phenotypes associated with the Nichols and Chicago strains of Treponema pallidum (T. pallidum), though similar, are not identical. Between these strains, significant differences are found in expression of, and antibody responses to some candidate virulence factors, suggesting the existence of functional genetic differences between isolates. The Chicago strain genome was therefore sequenced and compared to the Nichols genome, available since 1998. Initial comparative analysis suggested the presence of 44 single nucleotide polymorphisms (SNPs), 103 small (≤3 nucleotides) indels, and 1 large (1204 bp) insertion in the Chicago genome with respect to the Nichols genome. To confirm the above findings, Sanger sequencing was performed on most loci carrying differences using DNA from Chicago and the Nichols strain used in the original T. pallidum genome project. A majority of the previously identified differences were found to be due to errors in the published Nichols genome, while the accuracy of the Chicago genome was confirmed. However, 20 SNPs were confirmed between the two genomes, and 16 (80.0%) were found in coding regions, with all being of non-synonymous nature, strongly indicating action of positive selection. Sequencing of 16 genomic loci harboring SNPs in 12 additional T. pallidum strains, (SS14, Bal 3, Bal 7, Bal 9, Sea 81-3, Sea 81-8, Sea 86-1, Sea 87-1, Mexico A, UW231B, UW236B, and UW249C), was used to identify “Chicago-“ or “Nichols -specific” differences. All but one of the 16 SNPs were “Nichols-specific”, with Chicago having identical sequences at these positions to almost all of the additional strains examined. These mutations could reflect differential adaptation of the Nichols strain to the rabbit host or pathoadaptive mutations acquired during human infection. Our findings indicate that SNPs among T. pallidum strains emerge under positive selection and, therefore, are likely to be functional in nature

Supporting dynamic change detection: using the right tool for the task

Detecting task-relevant changes in a visual scene is necessary for successfully monitoring and managing dynamic command and control situations. Change blindness—the failure to notice visual changes—is an important source of human error. Change History EXplicit (CHEX) is a tool developed to aid change detection and maintain situation awareness; and in the current study we test the generality of its ability to facilitate the detection of changes when this subtask is embedded within a broader dynamic decision-making task. A multitasking air-warfare simulation required participants to perform radar-based subtasks, for which change detection was a necessary aspect of the higher-order goal of protecting one’s own ship. In this task, however, CHEX rendered the operator even more vulnerable to attentional failures in change detection and increased perceived workload. Such support was only effective when participants performed a change detection task without concurrent subtasks. Results are interpreted in terms of the NSEEV model of attention behavior (Steelman, McCarley, & Wickens, Hum. Factors 53:142–153, 2011; J. Exp. Psychol. Appl. 19:403–419, 2013), and suggest that decision aids for use in multitasking contexts must be designed to fit within the available workload capacity of the user so that they may truly augment cognition

A software tool ‘CroCo’ detects pervasive cross-species contamination in next generation sequencing data

International audienceBackground: Multiple RNA samples are frequently processed together and often mixed before multiplex sequencing in the same sequencing run. While different samples can be separated post sequencing using sample barcodes, the possibility of cross contamination between biological samples from different species that have been processed or sequenced in parallel has the potential to be extremely deleterious for downstream analyses