Coding-Sequence Determinants of Gene Expression in Escherichia coli

Escherichia coli is a commensal of the vertebrate gut that is increasingly involved in various intestinal and extra-intestinal infections as an opportunistic pathogen. Numerous pathotypes that represent groups of strains with specific pathogenic characteristics have been described based on heterogeneous and complex criteria. The democratization of whole-genome sequencing has led to an accumulation of genomic data that render possible a population phylogenomic approach to the emergence of virulence. Few lineages are responsible for the pathologies compared with the diversity of commensal strains. These lineages emerged multiple times during E. coli evolution, mainly by acquiring virulence genes located on mobile elements, but in a specific chromosomal phylogenetic background. This repeated emergence of stable and cosmopolitan lineages argues for an optimization of strain fitness through epistatic interactions between the virulence determinants and the remaining genome.

The population genetics of pathogenic Escherichia coli.

Evolvability is the ability of a biological system to produce phenotypic variation that is both heritable and adaptive. It has long been the subject of anecdotal observations and theoretical work. In recent years, however, the molecular causes of evolvability have been an increasing focus of experimental work. Here, we review recent experimental progress in areas as different as the evolution of drug resistance in cancer cells and the rewiring of transcriptional regulation circuits in vertebrates. This research reveals the importance of three major themes: multiple genetic and non-genetic mechanisms to generate phenotypic diversity, robustness in genetic systems, and adaptive landscape topography. We also discuss the mounting evidence that evolvability can evolve and the question of whether it evolves adaptively.

/pdf/the-causes-of-evolvability-and-their-evolution-2ce7trp6x2.pdf

The causes of evolvability and their evolution.

In this issue, as in previous issues, there are a number of papers that present the results of essentially descriptive surveys. Other papers deal with surveillance which is not quite the same as one or more surveys. In both cases wemay ask: What do these contribute to injury prevention? A general answer is far more than most may realize. Readers need to be reminded that few surveys are exclusively descriptive. Most include an analytic component, by which I mean they provide breakdowns or analyses of the injury under scrutiny by other featuresage and sex most commonly, but often by many others as well. Occasionally, these analyses are interpreted as identifying 'risk factors', and to some extent this may be correct. It is, however, this 'second level' of analysis -one that goes beyond simply estimating magnitude to identifying groups at greatest risk, for example, that makes many otherwise apparently simple descriptive studies so interesting and potentially valuable. Depending on what is actually surveyed, or how it is analyzed, most such studies provide both an estimate of the size of a problem and its severity. For example, a survey of playground injuries that includes all medically attended visits as well as hospital admissions, gives a rough approximation of this problem from both perspectives. Although some surveys focus only on a specific problem, such as head injuries, most are inclusive that is, they record all injuries regardless of origin, seen in a particular setting over a specified period. If the results are to be used to influence program priorities, it is important to know not only how many of each type of injury are seen, but also the proportions in each category that are serious whether measured by deaths, disability, hospital or emergency room admissions, or simply as doctor visits. When both numbers and severity are taken into consideration, alongside their preventability, a program priority or target should readily emerge. For example, the paper by Walsh et al (p 16), pointing so clearly to the dominance of head injuries, cannot but help not only those in the Newcastle community focus still more attention on this problem, but should also reinforce the work of others already concentrating on it. Learning further that injury admissions of all kinds, regardless of severity, are relatively little affected by age, is also a sobering and perhaps unexpected discovery. Equally unexpected is the finding reported by Sacks et al (p 52) that nearly three persons per 1000 are bitten by dogs and that children have three times the rate of medical visits than adults. Is this a problem we have tended to overlook? In light of these considerations the journal will continue to welcome descriptive reports. They remain the foundation on which much subsequent research and program decisions are built. In stating our willingness to publish yet another survey, however, my personal view is that survey researchers are obliged to go further. First, they must try to do more than simply send reprints to groups they believe can or should use their results. I am convinced the best way to foster implementation is through face-to-face meetings. An added reason for doing so is that such meetings help both parties better understand the perspectives and problems of the other. A second obligation is to move forward towards increasingly more illuminating types of studies. Descriptions should be followed by analyses of the kind referred to above, in a search for possible risk factors. This step, in turn, leads to still more sophisticated designs, such as case-control studies, that shed more light on possible risk factors. Eventually descriptive research should lead to randomised trials or other methods for evaluating existing or planned interventions. Taken together, these steps are certain to increase our rapidly growing knowledge of 'what works'. The only problem that then remains is how to put this into action. That is where advocacy and political action comes in.

Surveillance 전략 및 구현방안

Understanding the trends in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution is paramount to control the COVID-19 pandemic. We analyzed more than 300,000 high-quality genome sequences of SARS-CoV-2 variants available as of January 2021. The results show that the ongoing evolution of SARS-CoV-2 during the pandemic is characterized primarily by purifying selection, but a small set of sites appear to evolve under positive selection. The receptor-binding domain of the spike protein and the region of the nucleocapsid protein associated with nuclear localization signals (NLS) are enriched with positively selected amino acid replacements. These replacements form a strongly connected network of apparent epistatic interactions and are signatures of major partitions in the SARS-CoV-2 phylogeny. Virus diversity within each geographic region has been steadily growing for the entirety of the pandemic, but analysis of the phylogenetic distances between pairs of regions reveals four distinct periods based on global partitioning of the tree and the emergence of key mutations. The initial period of rapid diversification into region-specific phylogenies that ended in February 2020 was followed by a major extinction event and global homogenization concomitant with the spread of D614G in the spike protein, ending in March 2020. The NLS-associated variants across multiple partitions rose to global prominence in March to July, during a period of stasis in terms of interregional diversity. Finally, beginning in July 2020, multiple mutations, some of which have since been demonstrated to enable antibody evasion, began to emerge associated with ongoing regional diversification, which might be indicative of speciation.

https://www.pnas.org/content/pnas/118/29/e2104241118.full.pdf

Ongoing global and regional adaptive evolution of SARS-CoV-2.

The same mutation can have different effects in different individuals. One important reason for this is that the outcome of a mutation can depend on the genetic context in which it occurs. This dep...

https://www.annualreviews.org/doi/pdf/10.1146/annurev-genom-083118-014857

The Causes and Consequences of Genetic Interactions (Epistasis)

A central question in genetics and evolution is the extent to which the outcomes of mutations change depending on the genetic context in which they occur1–3. Pairwise interactions between mutations have been systematically mapped within4–18 and between
 19
 genes, and have been shown to contribute substantially to phenotypic variation among individuals
 20
 . However, the extent to which genetic interactions themselves are stable or dynamic across genotypes is unclear21, 22. Here we quantify more than 45,000 genetic interactions between the same 87 pairs of mutations across more than 500 closely related genotypes of a yeast tRNA. Notably, all pairs of mutations interacted in at least 9% of genetic backgrounds and all pairs switched from interacting positively to interacting negatively in different genotypes (false discovery rate < 0.1). Higher-order interactions are also abundant and dynamic across genotypes. The epistasis in this tRNA means that all individual mutations switch from detrimental to beneficial, even in closely related genotypes. As a consequence, accurate genetic prediction requires mutation effects to be measured across different genetic backgrounds and the use of  higher-order epistatic terms. Mutagenesis of a yeast tRNA shows that the effects of mutations and how they interact varies both in magnitude and sign between genotypes.

Pairwise and higher-order genetic interactions during the evolution of a tRNA

Allosteric communication between distant sites in proteins is central to biological regulation but still poorly characterized, limiting understanding, engineering and drug development1-6. An important reason for this is the lack of methods to comprehensively quantify allostery in diverse proteins. Here we address this shortcoming and present a method that uses deep mutational scanning to globally map allostery. The approach uses an efficient experimental design to infer en masse the causal biophysical effects of mutations by quantifying multiple molecular phenotypes-here we examine binding and protein abundance-in multiple genetic backgrounds and fitting thermodynamic models using neural networks. We apply the approach to two of the most common protein interaction domains found in humans, an SH3 domain and a PDZ domain, to produce comprehensive atlases of allosteric communication. Allosteric mutations are abundant, with a large mutational target space of network-altering 'edgetic' variants. Mutations are more likely to be allosteric closer to binding interfaces, at glycine residues and at specific residues connecting to an opposite surface within the PDZ domain. This general approach of quantifying mutational effects for multiple molecular phenotypes and in multiple genetic backgrounds should enable the energetic and allosteric landscapes of many proteins to be rapidly and comprehensively mapped. 

/pdf/mapping-the-energetic-and-allosteric-landscapes-of-protein-1nmuf1vh.pdf

Mapping the energetic and allosteric landscapes of protein binding domains

http://repositori.upf.edu/bitstream/10230/53077/1/Faure_2022.pdf

Information that regulates gene expression is encoded throughout each gene but if different regulatory regions can be understood in isolation, or if they interact, is unknown. Here we measure mRNA levels for 10,000 open reading frames (ORFs) transcribed from either an inducible or constitutive promoter. We find that the strength of cotranslational regulation on mRNA levels is determined by promoter architecture. By using a novel computational genetic screen of 6402 RNA-seq experiments, we identify the RNA helicase Dbp2 as the mechanism by which cotranslational regulation is reduced specifically for inducible promoters. Finally, we find that for constitutive genes, but not inducible genes, most of the information encoding regulation of mRNA levels in response to changes in growth rate is encoded in the ORF and not in the promoter. Thus, the ORF sequence is a major regulator of gene expression, and a nonlinear interaction between promoters and ORFs determines mRNA levels.

/pdf/promoter-architecture-determines-cotranslational-regulation-1s0ca472qy.pdf

Júlia Domingo

Papers

The Causes and Consequences of Genetic Interactions (Epistasis)

Pairwise and higher-order genetic interactions during the evolution of a tRNA

Mapping the energetic and allosteric landscapes of protein binding domains

Mapping the energetic and allosteric landscapes of protein binding domains

Promoter architecture determines cotranslational regulation of mRNA.