The distinct cell physiology of Bradyrhizobium at the population and cellular level.

The α-Proteobacteria belonging to Bradyrhizobium genus are microorganisms of extreme slow growth. Despite their extended use as inoculants in soybean production, their physiology remains poorly characterized. In this work, we produced quantitative data on four different isolates: B. diazoefficens USDA110, B. diazoefficiens USDA122, B. japonicum E109 and B. japonicum USDA6 which are representative of specific genomic profiles. Notably, we found conserved physiological traits conserved in all the studied isolates: (i) the lag and initial exponential growth phases display cell aggregation; (ii) the increase in specific nutrient concentration such as yeast extract and gluconate hinders growth; (iii) cell size does not correlate with culture age; and (iv) cell cycle presents polar growth. Meanwhile, fitness, cell size and in vitro growth widely vary across isolates correlating to ribosomal RNA operon number. In summary, this study provides novel empirical data that enriches the comprehension of the Bradyrhizobium (slow) growth dynamics and cell cycle.

Optimal threshold estimation for binary classifiers using game theory.

Many bioinformatics algorithms can be understood as binary classifiers. They are usually compared using the area under the receiver operating characteristic ( ROC) curve. On the other hand, choosing the best threshold for practical use is a complex task, due to uncertain and context-dependent skews in the abundance of positives in nature and in the yields/costs for correct/incorrect classification. We argue that considering a classifier as a player in a zero-sum game allows us to use the minimax principle from game theory to determine the optimal operating point. The proposed classifier threshold corresponds to the intersection between the ROC curve and the descending diagonal in ROC space and yields a minimax accuracy of 1-FPR. Our proposal can be readily implemented in practice, and reveals that the empirical condition for threshold estimation of "specificity equals sensitivity" maximizes robustness against uncertainties in the abundance of positives in nature and classification costs.

Convergent evolution and mimicry of protein linear motifs in host-pathogen interactions.

Pathogen linear motif mimics are highly evolvable elements that facilitate rewiring of host protein interaction networks. Host linear motifs and pathogen mimics differ in sequence, leading to thermodynamic and structural differences in the resulting protein-protein interactions. Moreover, the functional output of a mimic depends on the motif and domain repertoire of the pathogen protein. Regulatory evolution mediated by linear motifs can be understood by measuring evolutionary rates, quantifying positive and negative selection and performing phylogenetic reconstructions of linear motif natural history. Convergent evolution of linear motif mimics is widespread among unrelated proteins from viral, prokaryotic and eukaryotic pathogens and can also take place within individual protein phylogenies. Statistics, biochemistry and laboratory models of infection link pathogen linear motifs to phenotypic traits such as tropism, virulence and oncogenicity. In vitro evolution experiments and analysis of natural sequences suggest that changes in linear motif composition underlie pathogen adaptation to a changing environment.

Protein folding transition states probed by loop extension.

We propose a new way to characterize protein folding transition states by (1) insertion of one or more residues into an unstructured protein loop, (2) measurement of the effect on protein folding kinetics and thermodynamics, and (3) analysis of the results in terms of a rate-equilibrium free energy relationship, alpha(Loop). alpha(Loop) reports on the fraction of molecules that form the perturbed loop in the transition state. Interpretation of the changes in equilibrium free energy using standard polymer theory can help detect residual structure in the unfolded state. We illustrate our approach with data for the model proteins CI2 and the alpha spectrin SH3 domain.