Keller, An hypothesis for approaching swarms of myxobacteria,

Genome-wide surveys have suggested that genetic variation affecting the regulation of mRNA expression, processing, and translation predominates over those that directly alter the amino acid sequence of encoded proteins. While the latter are easy to spot with use of extensive sequencing, regulatory variants often remain hidden. We have developed a comprehensive approach to detecting such regulatory variants, unexpectedly finding that many key genes involved in disease and drug therapy carry frequent regulatory variants. In parallel, others have pursued genome-wide association studies (GWAS), finding indications of numerous disease risk genes, but the overwhelming majority of the genetic risk remains unknown. Our research program therefore is beginning to address the question as to why the underlying genetic factors remain uncertain. One hypothesis is that regulatory variants could play a key role, but to account for disease risk we must search for frequent alleles that can fill the gap. For such variants to reach high frequency, positive selection during evolution is likely to play a role. In this seminar I will discuss why GWAS may have missed such genes/alleles, and what our approach should be to discover the main disease risk alleles, with an eye on the nexus between evolution, wellness, fitness, and disease.

A Model for Gliding and Aggregation of Myxobacteria
Photo provided by Flickr

In summary, here we show that a mechanism that had not hitherto been seen in animal cells, coattraction, may be at the core of collective migration where its role is to maintain the cohesion of cell clusters. This cohesion allows CIL to operate and to generate coherent polarity, imparting directionality to the cell group (). We have shown that local or social interactions between cells are key to achieve collective migration. We predict that collective migration in many cell types is achieved by a balance between a dispersive force (such as CIL) and an attraction, like coattraction, as we have shown to naturally exist in NC cells and to be sufficient to induce collective migration in hematocytes that otherwise move individually (F and 7G). Interestingly, similar balances are widely accepted to explain the swarming behavior in collective animal movements (), suggesting that similar strategies for producing collective movement have emerged at different magnitudes and levels of complexity. The coattraction between NC cells is reminiscent of the behavior of Dictyostelium, where individual cells release and respond to a chemoattractant to produce a multicellular aggregate. However, coattraction between single NC cells seems to be weak and, therefore, unlikely to lead to aggregation. Slime bacteria, or myxobacteria, also swarm under adverse environmental conditions such as starvation. proposed a mechanism for this swarming that is remarkably similar to our own propositions. Based on these examples, it is possible to speculate that there is a limited number of strategies that lead to effective collective migration and that these strategies are repeated over the course of evolution.


the myxobacteria serve as a model problem for ..

Simulations of the Gliding Behavior and Aggregation of ..
Photo provided by Flickr

Licensed vaccines or new vaccines are tested in clinical trials in humans. Subjects are categorized as good and poor responders to a vaccine on the basis of biological markers. Samples collected are analyzed by one or more ‘-omics’ approaches, and results are integrated by computational methods to generate meaningful data sets. Two outcomes can emerge from these bioinformatic analyses: the generation of new hypotheses that can be tested by mechanistic studies or in animal models and result in enhanced vaccination strategies, or the determination of new biomarkers that can be validated in other clinical trials and result in enhancement of the vaccination outcome measurement. Both of these arms will ‘feed’ new clinical trials that will follow the same path and, after many iterations of the cycle, lead to the development of better vaccination strategies and define better predictors of immunogenicity. This process might allow the generation of personalized vaccination.