Florian Gerber

Mistranslation drives the evolution of robustness in TEM-1 β-lactamase

Significance Translation is a fundamental biochemical process in which ribosomes use an mRNA’s nucleotide sequence as a template to synthesize a protein with a specific amino acid sequence. Errors in this process are deleterious because they can alter a protein’s structure. Yet such errors are surprisingly frequent. Here we ask whether and how evolution can affect the ability of proteins to cope with these errors. In principle, evolution could reduce the rate of such errors, or it could leave this rate unchanged but reduce the damaging effects of errors. We find that populations of proteins evolving in the laboratory pursue the second route, increasing their robustness to translation errors. Evolution may preferentially mitigate damage to a biological system than reduce the source of this damage. How biological systems such as proteins achieve robustness to ubiquitous perturbations is a fundamental biological question. Such perturbations include errors that introduce phenotypic mutations into nascent proteins during the translation of mRNA. These errors are remarkably frequent. They are also costly, because they reduce protein stability and help create toxic misfolded proteins. Adaptive evolution might reduce these costs of protein mistranslation by two principal mechanisms. The first increases the accuracy of translation via synonymous “high fidelity” codons at especially sensitive sites. The second increases the robustness of proteins to phenotypic errors via amino acids that increase protein stability. To study how these mechanisms are exploited by populations evolving in the laboratory, we evolved the antibiotic resistance gene TEM-1 in Escherichia coli hosts with either normal or high rates of mistranslation. We analyzed TEM-1 populations that evolved under relaxed and stringent selection for antibiotic resistance by single molecule real-time sequencing. Under relaxed selection, mistranslating populations reduce mistranslation costs by reducing TEM-1 expression. Under stringent selection, they efficiently purge destabilizing amino acid changes. More importantly, they accumulate stabilizing amino acid changes rather than synonymous changes that increase translational accuracy. In the large populations we study, and on short evolutionary timescales, the path of least resistance in TEM-1 evolution consists of reducing the consequences of translation errors rather than the errors themselves.

Hybridization alters spontaneous mutation rates in a parent-of-origin-dependent fashion in Arabidopsis.

Hybridization alters mutation rates in Arabidopsis. Over 70 years ago, increased spontaneous mutation rates were observed in Drosophila spp. hybrids, but the genetic basis of this phenomenon is not well understood. The model plant Arabidopsis (Arabidopsis thaliana) offers unique opportunities to study the types of mutations induced upon hybridization and the frequency of their occurrence. Understanding the mutational effects of hybridization is important, as many crop plants are grown as hybrids. Besides, hybridization is important for speciation and its effects on genome integrity could be critical, as chromosomal rearrangements can lead to reproductive isolation. We examined the rates of hybridization-induced point and frameshift mutations as well as homologous recombination events in intraspecific Arabidopsis hybrids using a set of transgenic mutation detector lines that carry mutated or truncated versions of a reporter gene. We found that hybridization alters the frequency of different kinds of mutations. In general, Columbia (Col) × Cape Verde Islands and Col × C24 hybrid progeny had decreased T→G and T→A transversion rates but an increased C→T transition rate. Significant changes in frameshift mutation rates were also observed in some hybrids. In Col × C24 hybrids, there is a trend for increased homologous recombination rates, except for the hybrids from one line, while in Col × Cape Verde Islands hybrids, this rate is decreased. The overall genetic distance of the parents had no influence on mutation rates in the progeny, as closely related accessions on occasion displayed higher mutation rates than accessions that are separated farther apart. However, reciprocal hybrids had significantly different mutation rates, suggesting parent-of-origin-dependent effects on the mutation frequency.

Extending R packages to support 64-bit compiled code

Software packages for spatial data often implement a hybrid approach of interpreted and compiled programming languages. The compiled parts are usually written in C, C++, or Fortran, and are efficient in terms of computational speed and memory usage. Conversely, the interpreted part serves as a convenient user-interface and calls the compiled code for computationally demanding operations. The price paid for the user friendliness of the interpreted component isbesides performancethe limited access to low level and optimized code. An example of such a restriction is the 64-bit vector support of the widely used statistical language R. On the R side, users do not need to change existing code and may not even notice the extension. On the other hand, interfacing 64-bit compiled code efficiently is challenging. Since many R packages for spatial data could benefit from 64-bit vectors, we investigate strategies to efficiently pass 64-bit vectors to compiled languages. More precisely, we show how to simply extend existing R packages using the foreign function interface to seamlessly support 64-bit vectors. This extension is shown with the sparse matrix algebra R package spam. The new capabilities are illustrated with an example of GIMMS NDVI3g data featuring a parametric modeling approach for a non-stationary covariance matrix. Graphical abstractDisplay Omitted HighlightsThe 64-bit capability of R in conjunction with compiled code is explored.A simple strategy to enhance entire R packages with long vectors is shown.The sparse matrix R package spam is extended to work with larger matrices.This concept enables spatial modeling with data structures featuring >231 elements.A non-stationary covariance model is fitted to a huge NDVI residual field.

Multiorder correction algorithms to remove image distortions from mass spectrometry imaging data sets.

Time-of-flight secondary ion mass spectrometry imaging is a rapidly evolving technology. Its main application is the study of the distribution of small molecules on biological tissues. The sequential image acquisition process remains susceptible to measurement distortions that can render imaging data less analytically useful. Most of these artifacts show a repetitive nature from tile to tile. Here we statistically describe these distortions and derive two different algorithms to correct them. Both a generalized linear model approach and the linear discriminant analysis approach are able to increase image quality for negative and positive ion mode data sets. Additionally, performing simulation studies with repetitive and nonrepetitive tiling error we show that both algorithms are only removing repetitive distortions. It is further shown that the spectral component of the data set is not altered by the use of these correction methods. Both algorithms presented in this work greatly increase the image quality and improve the analytical usefulness of distorted images dramatically.

Predicting missing values in spatio-temporal satellite data

Continuous, consistent, and long time-series from remote sensing are essential to monitoring changes on Earth’s surface. However, analyzing such data sets is often challenging due to missing values introduced by cloud cover, missing orbits, sensor geometry artifacts, and so on. We propose a new and accurate spatio-temporal prediction method to replace missing values in remote sensing data sets. The method exploits the spatial coherence and temporal seasonal regularity that are inherent in many data sets. The key parts of the method are: 1) the adaptively chosen spatio-temporal subsets around missing values; 2) the ranking of images within the subsets based on a scoring algorithm; 3) the estimation of empirical quantiles characterizing the missing values; and 4) the prediction of missing values through quantile regression. One advantage of quantile regression is the robustness to outliers, which enables more accurate parameter retrieval in the analysis of remote sensing data sets. In addition, we provide bootstrap-based quantification of prediction uncertainties. The proposed prediction method was applied to a Normalized Difference Vegetation Index data set from the Moderate Resolution Imaging Spectroradiometer and assessed with realistic test data sets featuring between 20% and 50% missing values. Validation against established methods showed that the proposed method has a good performance in terms of the root-mean-squared prediction error and significantly outperforms its competitors. This paper is accompanied by the open-source R package gapfill, which provides a flexible, fast, and ready-to-use implementation of the method.

Evaluating alternative explanations for an association of extinction risk and evolutionary uniqueness in multiple insular lineages: EXTINCTION AND EVOLUTIONARY UNIQUENESS

Studies in insular environments have often documented a positive association of extinction risk and evolutionary uniqueness (i.e., how distant a species is from its closest living relative). However, the cause of this association is unclear. One explanation is that species threatened with extinction are evolutionarily unique because they are old, implying that extinction risk increases with time since speciation (age‐dependent extinction). An alternative explanation is that such threatened species are last survivors of clades that have undergone an elevated extinction rate, and that their uniqueness results from the extinction of their close relatives. Distinguishing between these explanations is difficult but important, since they imply different biological processes determining extinction patterns. Here, we designed a simulation approach to distinguish between these alternatives using living species, and applied it to 12 insular radiations that show a positive association between extinction risk and evolutionary uniqueness. We also tested the sensitivity of results to underlying assumptions and variable extinction rates. Despite differences among the radiations considered, age‐dependent extinction was supported as best explaining the majority of the empirical cases. Biological processes driving characteristic changes in abundance with species duration (age‐dependency) may merit further investigation.