The Genomic Landscape of Compensatory Evolution Laboratory selection experiment explains how organisms compensate for the loss of genes during evolution, and reveals the deleterious side-effects of this process when adapting to novel environments.

We present a novel, international, multicenter, predictive tool to assess the risk of additional axillary metastases after tumor-positive sentinel node biopsy in breast cancer. The predictive model performed well in internal and external validation but needs to be further studied in each center before application to clinical use.

Antibiotic resistance typically induces a fitness cost that shapes the fate of antibiotic-resistant bacterial populations. However, the cost of resistance can be mitigated by compensatory mutations elsewhere in the genome, and therefore the loss of resistance may proceed too slowly to be of practical importance. We present our study on the efficacy and phenotypic impact of compensatory evolution in Escherichia coli strains carrying multiple resistance mutations. We have demonstrated that drug-resistance frequently declines within 480 generations during exposure to an antibiotic-free environment. The extent of resistance loss was found to be generally antibiotic-specific, driven by mutations that reduce both resistance level and fitness costs of antibiotic-resistance mutations. We conclude that phenotypic reversion to the antibiotic-sensitive state can be mediated by the acquisition of additional mutations, while maintaining the original resistance mutations. Our study indicates that restricting antimicrobial usage could be a useful policy, but for certain antibiotics only.

The optimal technique for sentinel lymph node biopsy (SLNB) is still debated. SLNB with peritumoral injection of Patent blue dye was performed in 129 clinically T1-T2 and N0 breast cancers in 127 patients (group A); it was later replaced by combined dye and radiocolloid-guided SLNB preceded by lymphoscintigraphy in 72 breast cancer patients (group B). This study compares these two methods. All patients underwent completion axillary dissection. Means of 1.4 and 1.3 SLNs were identified in groups A and B, respectively. The mean number of non-SLNs for the whole series was 14.9 (range 5-42). The first 53 cases of lymphatic mapping (dye only) comprised the institutional learning period during which the identification rate of at least 1 SLN in 30 consecutive attempts reached 90%. The identification rate for the subsequent 76 group A patients was 92%. The accuracy rate of SLNBs for overall axillary nodal status prediction and the false-negative rate for group A patients (after excluding the learning-phase cases) were 93% and 10%, respectively. All 72 group B cases had at least one SLN identified, and only one false-negative case occurred in this group (accuracy and false-negative rates of 99% and 3%, respectively). Both the dye-only and the combined SLNB methods are suitable for SLN identification, but the latter works better and results in higher accuracy, a higher negative predictive value, and a lower false-negative rate. It is therefore the method of choice.

Several models have previously been proposed to predict the probability of non-sentinel lymph node (NSLN) metastases after a positive sentinel lymph node (SLN) biopsy in breast cancer. The aim of this study was to assess the accuracy of two previously published nomograms (MSKCC, Stanford) and to develop an alternative model with the best predictive accuracy in a Czech population. In the basic population of 330 SLN-positive patients from the Czech Republic, the accuracy of the MSKCC and the Stanford nomograms was tested by the area under the receiver operating characteristics curve (AUC). A new model (MOU nomogram) was proposed according to the results of multivariate analysis of relevant clinicopathologic variables. The new model was validated in an independent test population from Hungary (383 patients). In the basic population, six of 27 patients with isolated tumor cells (ITC) in the SLN harbored additional NSLN metastases. The AUCs of the MSKCC and Stanford nomograms were 0.68 and 0.66, respectively; for the MOU nomogram it reached 0.76. In the test population, the AUC of the MOU nomogram was similar to that of the basic population (0.74). The presence of only ITC in SLN does not preclude further nodal involvement. Additional variables are beneficial when considering the probability of NSLN metastases. In the basic population, the previously published nomograms (MSKCC and Stanford) showed only limited accuracy. The developed MOU nomogram proved more suitable for the basic population, such as for another independent population from a mid-European country.

Central players of the adaptive immune system are the groups of proteins encoded in the major histocompatibility complex (MHC), which shape the immune response against pathogens and tolerance to self-peptides. The corresponding genomic region is of particular interest, as it harbors more disease associations than any other region in the human genome, including associations with infectious diseases, autoimmune disorders, cancers, and neuropsychiatric diseases. Certain MHC molecules can bind to a much wider range of epitopes than others, but the functional implication of such an elevated epitope-binding repertoire has remained largely unclear. It has been suggested that by recognizing more peptide segments, such promiscuous MHC molecules promote immune response against a broader range of pathogens. If so, the geographical distribution of MHC promiscuity level should be shaped by pathogen diversity. Three lines of evidence support the hypothesis. First, we found that in pathogen-rich geographical regions, humans are more likely to carry highly promiscuous MHC class II DRB1 alleles. Second, the switch between specialist and generalist antigen presentation has occurred repeatedly and in a rapid manner during human evolution. Third, molecular positions that define promiscuity level of MHC class II molecules are especially diverse and are under positive selection in human populations. Taken together, our work indicates that pathogen load maintains generalist adaptive immune recognition, with implications for medical genetics and epidemiology.

Proteins are necessary for cellular growth. Concurrently, however, protein production has high energetic demands associated with transcription and translation. Here, we propose that activity of molecular chaperones shape protein burden, that is the fitness costs associated with expression of unneeded proteins. To test this hypothesis, we performed a genome-wide genetic interaction screen in baker's yeast. Impairment of transcription, translation, and protein folding rendered cells hypersensitive to protein burden. Specifically, deletion of specific regulators of the Hsp70-associated chaperone network increased protein burden. In agreement with expectation, temperature stress, increased mistranslation and a chemical misfolding agent all substantially enhanced protein burden. Finally, unneeded protein perturbed interactions between key components of the Hsp70-Hsp90 network involved in folding of native proteins. We conclude that specific chaperones contribute to protein burden. Our work indicates that by minimizing the damaging impact of gratuitous protein overproduction, chaperones enable tolerance to massive changes in genomic expression.

On the system level, we quantitatively confirm previous findings about the specialized control provided by miRNAs. For knock-out tests we list the groups of our predicted most and least essential miRNAs. In addition, we provide possible explanations for (i) the low number of individually essential miRNAs in Caenorhabdtits elegans and (ii) the high number of ubiquitous miRNAs influencing cell and tissue-specific miRNA expression patterns in mouse and human.