What is mutation? A chapter in the series: How microbes "jeopardize" the modern synthesis

Genome and transcriptomic analysis of the adaptation of Escherichia coli to environmental stresses

In natural niches, bacteria are forced to spend most of their lives under various environmental stresses, such as nutrient limitation, heavy metal pollution, heat and antibiotic stress. To cope with adverse environments, bacterial genome can during the life cycle, produce potential adaptive mutants. The genomic changes, especially mutations, in the genes that encode RNA polymerase and transcription factors, might lead to variations in the transcriptome. These variations enable bacteria to cope with environmental stresses through physiological adaptation in response to stress. This paper reviews the recent contributions of genomic and transcriptomic analyses in understanding the adaption mechanism of Escherichia coli to environmental stresses. Various genomic changes have been observed in E. coli strains in laboratory or under natural stresses, including starvation, heavy metals, acidic conditions, heat shock and antibiotics. The mutations include slight changes (one to several nucleotides), deletions, insertions, chromosomal rearrangements and variations in copy numbers. The transcriptome of E. coli largely changes due to genomic mutations. However, the transcriptional profiles vary due to variations in stress selections. Cellular adaptation to the selections is associated with transcriptional changes resulting from genomic mutations. Changes in genome and transcriptome are cooperative and jointly affect the adaptation of E. coli to different environments. This comprehensive review reveals that coordination of genome mutations and transcriptional variations needs to be explored further to provide a better understanding of the mechanisms of bacterial adaptation to stresses.

Targeted protein evolution in the gut microbiome by diversity-generating retroelements

Diversity-generating retroelements (DGRs) accelerate evolution by rapidly diversifying variable proteins. The human gastrointestinal microbiota harbors the greatest density of DGRs known in nature, suggesting they play adaptive roles in this environment. We identified >1,100 unique DGRs among human-associated Bacteroides species and discovered a subset that diversify adhesive components of Type V pili and related proteins. We show that Bacteroides DGRs are horizontally transferred across species, that some are highly active while others are tightly controlled, and that they preferentially alter the functional characteristics of ligand-binding residues on adhesive organelles. Specific variable protein sequences are enriched when Bacteroides strains compete with other commensal bacteria in gnotobiotic mice. Analysis of >2,700 DGRs from diverse phyla in mother-infant pairs shows that Bacteroides DGRs are preferentially transferred to vaginally delivered infants where they actively diversify. Our observations provide a foundation for understanding the roles of stochastic, targeted genome plasticity in shaping host-associated microbial communities.

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype-phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype-genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype-genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.

"Metabolic burden" explained: stress symptoms and its related responses induced by (over)expression of (heterologous) proteins in Escherichia coli

BACKGROUND

Engineering bacterial strains to redirect the metabolism towards the production of a specific product has enabled the development of industrial biotechnology. However, rewiring the metabolism can have severe implications for a microorganism, rendering cells with stress symptoms such as a decreased growth rate, impaired protein synthesis, genetic instability and an aberrant cell size. On an industrial scale, this is reflected in processes that are not economically viable.

MAIN TEXT

In literature, most stress symptoms are attributed to "metabolic burden", however the actual triggers and stress mechanisms involved are poorly understood. Therefore, in this literature review, we aimed to get a better insight in how metabolic engineering affects Escherichia coli and link the observed stress symptoms to its cause. Understanding the possible implications that chosen engineering strategies have, will help to guide the reader towards optimising the envisioned process more efficiently.

CONCLUSION

This review addresses the gap in literature and discusses the triggers and effects of stress mechanisms that can be activated when (over)expressing (heterologous) proteins in Escherichia coli. It uncovers that the activation of the different stress mechanisms is complex and that many are interconnected. The reader is shown that care has to be taken when (over)expressing (heterologous) proteins as the cell's metabolism is tightly regulated.

Mutation and evolution: Conceptual possibilities

Although random mutation is central to models of evolutionary change, a lack of clarity remains regarding the conceptual possibilities for thinking about the nature and role of mutation in evolution. We distinguish several claims at the intersection of mutation, evolution, and directionality and then characterize a previously unrecognized category: complex conditioned mutation. Empirical evidence in support of this category suggests that the historically famous fluctuation test should be revisited, and new experiments should be undertaken with emerging experimental techniques to facilitate detecting mutation rates within specific loci at an ultra-high, individual base pair resolution.

Antivirals for Broader Coverage against Human Coronaviruses

Coronaviruses (CoVs) are enveloped positive-sense single-stranded RNA viruses with a genome that is 27-31 kbases in length. Critical genes include the spike (S), envelope (E), membrane (M), nucleocapsid (N) and nine accessory open reading frames encoding for non-structural proteins (NSPs) that have multiple roles in the replication cycle and immune evasion (1). There are seven known human CoVs that most likely appeared after zoonotic transfer, the most recent being SARS-CoV-2, responsible for the COVID-19 pandemic. Antivirals that have been approved by the FDA for use against COVID-19 such as Paxlovid can target and successfully inhibit the main protease (MPro) activity of multiple human CoVs; however, alternative proteomes encoded by CoV genomes have a closer genetic similarity to each other, suggesting that antivirals could be developed now that target future CoVs. New zoonotic introductions of CoVs to humans are inevitable and unpredictable. Therefore, new antivirals are required to control not only the next human CoV outbreak but also the four common human CoVs (229E, OC43, NL63, HKU1) that circulate frequently and to contain sporadic outbreaks of the severe human CoVs (SARS-CoV, MERS and SARS-CoV-2). The current study found that emerging antiviral drugs, such as Paxlovid, could target other CoVs, but only SARS-CoV-2 is known to be targeted in vivo. Other drugs which have the potential to target other human CoVs are still within clinical trials and are not yet available for public use. Monoclonal antibody (mAb) treatment and vaccines for SARS-CoV-2 can reduce mortality and hospitalisation rates; however, they target the Spike protein whose sequence mutates frequently and drifts. Spike is also not applicable for targeting other HCoVs as these are not well-conserved sequences among human CoVs. Thus, there is a need for readily available treatments globally that target all seven human CoVs and improve the preparedness for inevitable future outbreaks. Here, we discuss antiviral research, contributing to the control of common and severe CoV replication and transmission, including the current SARS-CoV-2 outbreak. The aim was to identify common features of CoVs for antivirals, biologics and vaccines that could reduce the scientific, political, economic and public health strain caused by CoV outbreaks now and in the future.

Targeted Hypermutation as a Survival Strategy: A Theoretical Approach

Targeted hypermutation has proven to be a useful survival strategy for bacteria under severe stress and is also used by multicellular organisms in specific instances such as the mammalian immune system. This might appear surprising, given the generally observed deleterious effects of poor replication fidelity/high mutation rate. A previous theoretical model designed to explore the role of replication fidelity in the origin of life was applied to a simulated hypermutation scenario. The results confirmed that the same model is useful for analyzing hypermutation and can predict the effects of the same parameters (survival probability, replication fidelity, mutation effect, and others) on the survival of cellular populations undergoing hypermutation as a result of severe stress.

Hypermutator emergence in experimental Escherichia coli populations is stress-type dependent

Genotypes exhibiting an increased mutation rate, called hypermutators, can propagate in microbial populations because they can have an advantage due to the higher supply of beneficial mutations needed for adaptation. Although this is a frequently observed phenomenon in natural and laboratory populations, little is known about the influence of parameters such as the degree of maladaptation, stress intensity, and the genetic architecture for adaptation on the emergence of hypermutators. To address this knowledge gap, we measured the emergence of hypermutators over ~1,000 generations in experimental Escherichia coli populations exposed to different levels of osmotic or antibiotic stress. Our stress types were chosen based on the assumption that the genetic architecture for adaptation differs between them. Indeed, we show that the size of the genetic basis for adaptation is larger for osmotic stress compared to antibiotic stress. During our experiment, we observed an increased emergence of hypermutators in populations exposed to osmotic stress but not in those exposed to antibiotic stress, indicating that hypermutator emergence rates are stress type dependent. These results support our hypothesis that hypermutator emergence is linked to the size of the genetic basis for adaptation. In addition, we identified other parameters that covaried with stress type (stress level and IS transposition rates) that might have contributed to an increased hypermutator provision and selection. Our results provide a first comparison of hypermutator emergence rates under varying stress conditions and point towards complex interactions of multiple stress-related factors on the evolution of mutation rates.

Escherichia coli survival in response to ciprofloxacin antibiotic stress correlates with increased nucleoid length and effective misfolded protein management

The evolution of antibiotic resistance is a fundamental problem in disease management but is rarely quantified on a single-cell level owing to challenges associated with capturing the spatial and temporal variation across a population. To evaluate cell biological phenotypic responses, we tracked the single-cell dynamics of filamentous bacteria through time in response to ciprofloxacin antibiotic stress. We measured the degree of phenotypic variation in nucleoid length and the accumulation of protein damage under ciprofloxacin antibiotic and quantified the impact on bacterial survival. Increased survival was correlated with increased nucleoid length and the variation in this response was inversely correlated with antibiotic concentration. Survival time was also increased through clearance of misfolded proteins, an unexpected mechanism of stress relief deployed by the filamentous bacteria. Our results reveal a diverse range of survival tactics employed by bacteria in response to ciprofloxacin and suggest potential evolutionary routes to resistance.

The origin of genetic and metabolic systems: Evolutionary structuralinsights

DNA is derived from reverse transcription and its origin is related to reverse transcriptase, DNA polymerase and integrase. The gene structure originated from the evolution of the first RNA polymerase. Thus, an explanation of the origin of the genetic system must also explain the evolution of these enzymes. This paper proposes a polymer structure model, termed the stable complex evolution model, which explains the evolution of enzymes and functional molecules. Enzymes evolved their functions by forming locally tightly packed complexes with specific substrates. A metabolic reaction can therefore be considered to be the result of adaptive evolution in this way when a certain essential molecule is lacking in a cell. The evolution of the primitive genetic and metabolic systems was thus coordinated and synchronized. According to the stable complex model, almost all functional molecules establish binding affinity and specific recognition through complementary interactions, and functional molecules therefore have the nature of being auto-reactive. This is thermodynamically favorable and leads to functional duplication and self-organization. Therefore, it can be speculated that biological systems have a certain tendency to maintain functional stability or are influenced by an inherent selective power. The evolution of dormant bacteria may support this hypothesis, and inherent selectivity can be unified with natural selection at the molecular level.

Genomic Legacies of Ancient Adaptation Illuminate GC-Content Evolution in Bacteria

Bacterial evolution is characterized by strong purifying selection as well as rapid adaptive evolution in changing environments. In this context, the genomic GC content (genomic GC) varies greatly but presents some level of phylogenetic stability, making it challenging to explain based on current hypotheses. To illuminate the evolutionary mechanisms of the genomic GC, we analyzed the base composition and functional inventory of 11,083 representative genomes. A phylogenetically constrained bimodal distribution of the genomic GC, which mainly originated from parallel divergences in the early evolution, was demonstrated. Such variation of the genomic GC can be well explained by DNA replication and repair (DRR), in which multiple pathways correlate with the genomic GC. Furthermore, the biased conservation of various stress-related genes, especially the DRR-related ones, implies distinct adaptive processes in the ancestral lineages of high- or low-GC clades which are likely induced by major environmental changes. Our findings support that the mutational biases resulting from these legacies of ancient adaptation have changed the course of adaptive evolution and generated great variation in the genomic GC. This highlights the importance of indirect effects of natural selection, which indicates a new model for bacterial evolution. IMPORTANCE GC content has been shown to be an important factor in microbial ecology and evolution, and the genomic GC of bacteria can be characterized by great intergenomic heterogeneity, high intragenomic homogeneity, and strong phylogenetic inertia, as well as being associated with the environment. Current hypotheses concerning direct selection or mutational biases cannot well explain these features simultaneously. Our findings of the genomic GC showing that ancient adaptations have transformed the DRR system and that the resulting mutational biases further contributed to a bimodal distribution of it offer a more reasonable scenario for the mechanism. This would imply that, when thinking about the evolution of life, diverse processes of adaptation exist, and combined effects of natural selection should be considered.

Soil bacterial community changes along elevation gradients in karst graben basin of Yunnan-Kweichow Plateau

Elevation gradients could provide natural experiments to examine geomorphological influences on biota ecology and evolution, however little is known about microbial community structures with soil depths along altitudinal gradients in karst graben basin of Yunnan-Kweichow Plateau. Here, bulk soil in A layer (0 ~ 10 cm) and B layer (10 ~ 20 cm) from two transect Mounts were analyzed by using high-throughput sequencing coupled with physicochemical analysis. It was found that the top five phyla in A layer were Proteobacteria, Acidobacteria, Actinobacteria, Bacteroidetes, and Verrucomicrobia, and the top five phyla in B layer were Proteobacteria, Acidobacteria, Actinobacteria, Verrucomicrobia, and Chloroflexi in a near-neutral environment. Edaphic parameters were different in two layers along altitudinal gradients. Besides that, soil microbial community compositions varied along altitudinal gradient, and soil organic carbon (SOC) and total nitrogen (TN) increased monotonically with increasing elevation. It was further observed that Shannon indexes with increasing altitudes in two transect Mounts decreased monotonically with significant difference (p = 0.001), however beta diversity followed U-trend with significant difference (p = 0.001). The low proportions of unique operational taxonomic units (OTUs) appeared at high altitude areas which impact the widely accepted elevation Rapoport's rules. The dominant Bradyrhizobium (alphaproteobacterial OTU 1) identified at high altitudes in two layers constitutes the important group of free-living diazotrophs and could bring fixed N into soils, which simultaneously enhances SOC and TN accumulation at high altitudes (p < 0.01). Due to different responses of bacterial community to environmental changes varying with soil depths, altitudinal gradients exerted negative effects on soil bacterial communities via soil physical properties and positive effects on soil bacterial diversities via soil chemical properties in A layer, however the results in B layer were opposite. Overall, our study is the first attempt to bring a deeper understanding of soil microbial structure patterns along altitudinal gradients at karst graben basin areas.

Mutagenic Characteristics of Six Heavy Metals in Escherichia coli: The Commonality and Specificity

The history of long-term environmental exposure to heavy metals can be recorded in the genome as sporadic and specific mutations. Variable environments introduce diverse and adaptive mutations to organisms. To reveal the information hidden in genomes about environmental exposure to heavy metals, we performed long-term mutation accumulation (MA) experiments with Escherichia coli, analyzed genomes from 36 populations across 1650 generations with 6 heavy metal exposure regimes (arsenic, cadmium, chromium, copper, nickel, and lead), and inferred metal-specific evolution modes at the genomic level. All heavy metals induced genetic mutations with a mean rate of 3.459 × 10-9 per nucleotide per generation. The mutational spectrum exhibited distinct signatures; however, heavy metals also shared common mutation signatures prominently associated with all cancer types. The mutated genes showed an average similarity of 54.4% within the same exposure regime, whereas only 38.8% between exposure regimes. In terms of biological insights, mutated genes were enriched to fundamental cellular processes such as metabolism, motility, and transport. Our study elucidates the mutagenic commonality and specificity of environmental heavy metals, which are highly specific at mutational features and locus, but conserved at gene and functional levels, and may play crucial roles in the convergence of adaptation to heavy metals.

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations

Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer. Breast adenocarcinoma starts in secretary cells. Breast adenocarcinoma is the most common of all cancers that occur in women. According to a survey within the United States of America, there are more than 282,000 breast adenocarcinoma patients registered each 12 months, and most of them are women. Recognition of cancer in its early stages saves many lives. A proposed framework is developed for the early detection of breast adenocarcinoma using an ensemble learning technique with multiple deep learning algorithms, specifically: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bi-directional LSTM. There are 99 types of driver genes involved in breast adenocarcinoma. This study uses a dataset of 4127 samples including men and women taken from more than 12 cohorts of cancer detection institutes. The dataset encompasses a total of 6170 mutations that occur in 99 genes. On these gene sequences, different algorithms are applied for feature extraction. Three types of testing techniques including independent set testing, self-consistency testing, and a 10-fold cross-validation test is applied to validate and test the learning approaches. Subsequently, multiple deep learning approaches such as LSTM, GRU, and bi-directional LSTM algorithms are applied. Several evaluation metrics are enumerated for the validation of results including accuracy, sensitivity, specificity, Mathew’s correlation coefficient, area under the curve, training loss, precision, recall, F1 score, and Cohen’s kappa while the values obtained are 99.57, 99.50, 99.63, 0.99, 1.0, 0.2027, 99.57, 99.57, 99.57, and 99.14 respectively.

Machine learning techniques for identification of carcinogenic mutations, which cause breast adenocarcinoma

Breast adenocarcinoma is the most common of all cancers that occur in women. According to the United States of America survey, more than 282,000 breast cancer patients are registered each year; most of them are women. Detection of cancer at its early stage saves many lives. Each cell contains the genetic code in the form of gene sequences. Changes in the gene sequences may lead to cancer. Replication and/or recombination in the gene base sometimes lead to a permanent change in the nucleotide sequence of the genome, called a mutation. Cancer driver mutations can lead to cancer. The proposed study develops a framework for the early detection of breast adenocarcinoma using machine learning techniques. Every gene has a specific sequence of nucleotides. A total of 99 genes are identified in various studies whose mutations can lead to breast adenocarcinoma. This study uses the dataset taken from 4127 human samples, including men and women from more than 12 cohorts. A total of 6170 mutations in gene sequences are used in this study. Decision Tree, Random Forest, and Gaussian Naïve Bayes are applied to these gene sequences using three evaluation methods: independent set testing, self-consistency testing, and tenfold cross-validation testing. Evaluation metrics such as accuracy, specificity, sensitivity, and Mathew’s correlation coefficient are calculated. The decision tree algorithm obtains the best accuracy of 99% for each evaluation method.

Stress-Induced Mutagenesis, Gambler Cells, and Stealth Targeting Antibiotic-Induced Evolution

Mechanisms of evolution and evolution of antibiotic resistance are both fundamental and world health problems. Stress-induced mutagenesis defines mechanisms of mutagenesis upregulated by stress responses, which drive adaptation when cells are maladapted to their environments—when stressed. Work in mutagenesis induced by antibiotics had produced tantalizing clues but not coherent mechanisms. We review recent advances in antibiotic-induced mutagenesis that integrate how reactive oxygen species (ROS), the SOS and general stress responses, and multichromosome cells orchestrate a stress response-induced switch from high-fidelity to mutagenic repair of DNA breaks. Moreover, while sibling cells stay stable, a mutable “gambler” cell subpopulation is induced by differentially generated ROS, which signal the general stress response. We discuss other evolvable subpopulations and consider diverse evolution-promoting molecules as potential targets for drugs to slow evolution of antibiotic resistance, cross-resistance, and immune evasion. An FDA-approved drug exemplifies “stealth” evolution-slowing drugs that avoid selecting resistance to themselves or antibiotics.

Comparative evaluation of Nanopore polishing tools for microbial genome assembly and polishing strategies for downstream analysis

Assembling high-quality microbial genomes using only cost-effective Nanopore long-read systems such as Flongle is important to accelerate research on the microbial genome and the most critical point for this is the polishing process. In this study, we performed an evaluation based on BUSCO and Prokka gene prediction in terms of microbial genome assembly for eight state-of-the-art Nanopore polishing tools and combinations available. In the evaluation of individual tools, Homopolish, PEPPER, and Medaka demonstrated better results than others. In combination polishing, the second round Homopolish, and the PEPPER × medaka combination also showed better results than others. However, individual tools and combinations have specific limitations on usage and results. Depending on the target organism and the purpose of the downstream research, it is confirmed that there remain some difficulties in perfectly replacing the hybrid polishing carried out by the addition of a short-read. Nevertheless, through continuous improvement of the protein pores, related base-calling algorithms, and polishing tools based on improved error models, a high-quality microbial genome can be achieved using only Nanopore reads without the production of additional short-read data. The polishing strategy proposed in this study is expected to provide useful information for assembling the microbial genome using only Nanopore reads depending on the target microorganism and the purpose of the research.

Drug-induced resistance evolution necessitates less aggressive treatment

Increasing body of experimental evidence suggests that anticancer and antimicrobial therapies may themselves promote the acquisition of drug resistance by increasing mutability. The successful control of evolving populations requires that such biological costs of control are identified, quantified and included to the evolutionarily informed treatment protocol. Here we identify, characterise and exploit a trade-off between decreasing the target population size and generating a surplus of treatment-induced rescue mutations. We show that the probability of cure is maximized at an intermediate dosage, below the drug concentration yielding maximal population decay, suggesting that treatment outcomes may in some cases be substantially improved by less aggressive treatment strategies. We also provide a general analytical relationship that implicitly links growth rate, pharmacodynamics and dose-dependent mutation rate to an optimal control law. Our results highlight the important, but often neglected, role of fundamental eco-evolutionary costs of control. These costs can often lead to situations, where decreasing the cumulative drug dosage may be preferable even when the objective of the treatment is elimination, and not containment. Taken together, our results thus add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and motivate further experimental and clinical investigation of the mutagenicity and other hidden collateral costs of therapies.

The Adaptive Potential of the Middle Domain of Yeast Hsp90

The distribution of fitness effects (DFEs) of new mutations across different environments quantifies the potential for adaptation in a given environment and its cost in others. So far, results regarding the cost of adaptation across environments have been mixed, and most studies have sampled random mutations across different genes. Here, we quantify systematically how costs of adaptation vary along a large stretch of protein sequence by studying the distribution of fitness effects of the same ≈2,300 amino-acid changing mutations obtained from deep mutational scanning of 119 amino acids in the middle domain of the heat shock protein Hsp90 in five environments. This region is known to be important for client binding, stabilization of the Hsp90 dimer, stabilization of the N-terminal-Middle and Middle-C-terminal interdomains, and regulation of ATPase–chaperone activity. Interestingly, we find that fitness correlates well across diverse stressful environments, with the exception of one environment, diamide. Consistent with this result, we find little cost of adaptation; on average only one in seven beneficial mutations is deleterious in another environment. We identify a hotspot of beneficial mutations in a region of the protein that is located within an allosteric center. The identified protein regions that are enriched in beneficial, deleterious, and costly mutations coincide with residues that are involved in the stabilization of Hsp90 interdomains and stabilization of client-binding interfaces, or residues that are involved in ATPase–chaperone activity of Hsp90. Thus, our study yields information regarding the role and adaptive potential of a protein sequence that complements and extends known structural information.

Dynamics of Diversity and Abundance of Sulfonamide Resistant Bacteria in a Silt Loam Soil Fertilized by Compost

Although composting is effective in deactivating antibiotic substances in manure, the influence of compost fertilization on the occurrence and dissemination of antibiotic resistance in arable soils remains to be controversial. Herein, the abundance and diversity of two sulfonamide resistance genes (sul1 and sul2) in soil fertilized by compost spiked with two concentrations of sulfadiazine (1 and 10 mg kg⁻¹) were studied intensively by qPCR and high throughput sequencing based on a two-month microcosm experiment. The concentration of sulfadiazine decreased rapidly after spiking from 25% at Day 1 to less than 2.7% at Day 60. Relative abundance of both sul1 and sul2 were significantly higher in soil amended with compost than the non-amended control at Day 1 and slightly decreased with incubation time except for sul2 in the S10 treatment. Soil bacterial communities were transiently shifted by compost fertilization regardless of the presence of sulfadiazine. Relative abundance of genera in three hubs positively interlinked with sul1 and sul2 were significantly higher in compost treated soil than the control at Day 1, 7 and 21, but not at Day 60. High throughput sequencing analyses revealed that most detected (>67% in relative abundance) sul1 and sul2 genotypes sharing >99% similarity with those found in gammaproteobacterial pathogens frequently were commonly present in compost and soil. These results indicated that compost fertilization might increase the abundance rather than diversity of sulfadiazine-resistant populations in soil, which may be facilitated by the presence of sulfadiazine.

Roadmap on biology in time varying environments

Biological organisms experience constantly changing environments, from sudden changes in physiology brought about by feeding, to the regular rising and setting of the Sun, to ecological changes over evolutionary timescales. Living organisms have evolved to thrive in this changing world but the general principles by which organisms shape and are shaped by time varying environments remain elusive. Our understanding is particularly poor in the intermediate regime with no separation of timescales, where the environment changes on the same timescale as the physiological or evolutionary response. Experiments to systematically characterize the response to dynamic environments are challenging since such environments are inherently high dimensional. This roadmap deals with the unique role played by time varying environments in biological phenomena across scales, from physiology to evolution, seeking to emphasize the commonalities and the challenges faced in this emerging area of research.

Global climate change, diet, and the complex relationship between human host and microbiome: Towards an integrated picture

Dietary changes can alter the human microbiome with potential detrimental consequences for health. Given that environment, health, and evolution are interconnected, we ask: Could diet-driven microbiome perturbations have consequences that extend beyond their immediate impact on human health? We address this question in the context of the urgent health challenges posed by global climate change. Drawing on recent studies, we propose that not only can diet-driven microbiome changes lead to dysbiosis, they can also shape life-history traits and fuel human evolution. We posit that dietary shifts prompt mismatched microbiome-host genetics configurations that modulate human longevity and reproductive success. These mismatches can also induce a heritable intra-holobiont stress response, which encourages the holobiont to re-establish equilibrium within the changed nutritional environment. Thus, while mismatches between climate change-related genetic and epigenetic configurations within the holobiont increase the risk and severity of diseases, they may also affect life-history traits and facilitate adaptive responses. These propositions form a framework that can help systematize and address climate-related dietary challenges for policy and health interventions.

On the waiting time until coordinated mutations get fixed in regulatory sequences

In this paper we consider the time evolution of a population of size N with overlapping generations, in the vicinity of m genes. We assume that this population is subject to point mutations, genetic drift, and selection. More specifically, we analyze the statistical distribution of the waiting time T_m until the expression of these genes have changed for all individuals, when transcription factors recognize and attach to short DNA-sequences (binding sites) within regulatory sequences in the neighborhoods of the m genes. The evolutionary dynamics is described by a multitype Moran process, where each individual is assigned a m×L regulatory array that consists of regulatory sequences with L nucleotides for all m genes. We study how the waiting time distribution depends on the number of genes, the mutation rate, the length of the binding sites, the length of the regulatory sequences, and the way in which the targeted binding sites are coordinated for different genes in terms of selection coefficients. These selection coefficients depend on how many binding sites have appeared so far, and possibly on their order of appearance. We also allow for back mutations, whereby some acquired binding sites may be lost over time. It is further assumed that the mutation rate is small enough to warrant a fixed state population, so that all individuals have the same regulatory array, at any given time point, until the next successful mutation arrives in some individual and spreads to the rest of the population. We further incorporate stochastic tunneling, whereby successful mutations get mutated before their fixation. A crucial part of our approach is to divide the huge state space of regulatory arrays into a small number of components, assuming that the array component varies as a Markov process over time. This implies that T_m is the time until this Markov process hits an absorbing state, with a phase-type distribution. A number of interesting results can be derived from our general setup, for instance that the expected waiting time increases exponentially with m, for a selectively neutral model, when back-mutations are possible.

Competition for nutritional resources masks the true frequency of bacterial mutants

Background

It is widely assumed that all mutant microorganisms present in a culture are able to grow and form colonies, provided that they express the features required for selection. Unlike wild-type Escherichia coli, PHO-constitutive mutants overexpress alkaline phosphatase and hence can hydrolyze glycerol-2-phosphate (G2P) to glycerol and form colonies on plates having G2P as the sole carbon source. These mutations mostly occur in the pst operon. However, the frequency of PHO-constitutive colonies on the G2P selective plate is exceptionally low.

Results

We show that the rate in which spontaneous PHO-constitutive mutations emerge is about 8.0 × 10⁻⁶/generation, a relatively high rate, but the growth of most existing mutants is inhibited by their neighboring wild-type cells. This inhibition is elicited only by non-mutant viable bacteria that can take up and metabolize glycerol formed by the mutants. Evidence indicates that the few mutants that do form colonies derive from microclusters of mutants on the selective plate. A mathematical model that describes the fate of the wild-type and mutant populations under these circumstances supports these results.

Conclusion

This scenario in which neither the wild-type nor the majority of the mutants are able to grow resembles an unavoidable “tragedy of the commons” case which results in the collapse of the majority of the population. Cooperation between rare adjacent mutants enables them to overcome the competition and eventually form mutant colonies. The inhibition of PHO-constitutive mutants provides an example of mutant frequency masked by orders of magnitude due to a competition between mutants and their ancestral wild-type cells. Similar “tragedy of the commons-like” cases may occur in other settings and should be taken into consideration while estimating true mutant frequencies and mutation rates.

Supplementary Information

The online version contains supplementary material available at (doi:10.1186/s12915-020-00913-1).

High-throughput synthetic rescue for exhaustive characterization of suppressor mutations in human genes

Inherited or acquired mutations can lead to pathological outcomes. However, in a process defined as synthetic rescue, phenotypic outcome created by primary mutation is alleviated by suppressor mutations. An exhaustive characterization of these mutations in humans is extremely valuable to better comprehend why patients carrying the same detrimental mutation exhibit different pathological outcomes or different responses to treatment. Here, we first review all known suppressor mutations' mechanisms characterized by genetic screens on model species like yeast or flies. However, human suppressor mutations are scarce, despite some being discovered based on orthologue genes. Because of recent advances in high-throughput screening, developing an inventory of human suppressor mutations for pathological processes seems achievable. In addition, we review several screening methods for suppressor mutations in cultured human cells through knock-out, knock-down or random mutagenesis screens on large scale. We provide examples of studies published over the past years that opened new therapeutic avenues, particularly in oncology.

Interaction between the Oral Microbiome and Dental Composite Biomaterials: Where We Are and Where We Should Go

Dental composites are routinely placed as part of tooth restoration procedures. The integrity of the restoration is constantly challenged by the metabolic activities of the oral microbiome. This activity directly contributes to a less-than-desirable half-life for the dental composite formulations currently in use. Therefore, many new antimicrobial dental composites are being developed to counteract the microbial challenge. To ensure that these materials will resist microbiome-derived degradation, the model systems used for testing antimicrobial activities should be relevant to the in vivo environment. Here, we summarize the key steps in oral microbial colonization that should be considered in clinically relevant model systems. Oral microbial colonization is a clearly defined developmental process that starts with the formation of the acquired salivary pellicle on the tooth surface, a conditioned film that provides the critical attachment sites for the initial colonizers. Further development includes the integration of additional species and the formation of a diverse, polymicrobial mature biofilm. Biofilm development is discussed in the context of dental composites, and recent research is highlighted regarding the effect of antimicrobial composites on the composition of the oral microbiome. Future challenges are addressed, including the potential of antimicrobial resistance development and how this could be counteracted by detailed studies of microbiome composition and gene expression on dental composites. Ultimately, progress in this area will require interdisciplinary approaches to effectively mitigate the inevitable challenges that arise as new experimental bioactive composites are evaluated for potential clinical efficacy. Success in this area could have the added benefit of inspiring other fields in medically relevant materials research, since microbial colonization of medical implants and devices is a ubiquitous problem in the field.

Visualizing mutagenic repair: novel insights into bacterial translesion synthesis

DNA repair is essential for cell survival. In all domains of life, error-prone and error-free repair pathways ensure maintenance of genome integrity under stress. Mutagenic, low-fidelity repair mechanisms help avoid potential lethality associated with unrepaired damage, thus making them important for genome maintenance and, in some cases, the preferred mode of repair. However, cells carefully regulate pathway choice to restrict activity of these pathways to only certain conditions. One such repair mechanism is translesion synthesis (TLS), where a low-fidelity DNA polymerase is employed to synthesize across a lesion. In bacteria, TLS is a potent source of stress-induced mutagenesis, with potential implications in cellular adaptation as well as antibiotic resistance. Extensive genetic and biochemical studies, predominantly in Escherichia coli, have established a central role for TLS in bypassing bulky DNA lesions associated with ongoing replication, either at or behind the replication fork. More recently, imaging-based approaches have been applied to understand the molecular mechanisms of TLS and how its function is regulated. Together, these studies have highlighted replication-independent roles for TLS as well. In this review, we discuss the current status of research on bacterial TLS, with emphasis on recent insights gained mostly through microscopy at the single-cell and single-molecule level.

Genomic Stress Responses Drive Lymphocyte Evolvability: An Ancient and Ubiquitous Mechanism

Somatic diversification of antigen receptor genes depends on the activity of enzymes whose homologs participate in a mutagenic DNA repair in unicellular species. Indeed, by engaging error-prone polymerases, gap filling molecules and altered mismatch repair pathways, lymphocytes utilize conserved components of genomic stress response systems, which can already be found in bacteria and archaea. These ancient systems of mutagenesis and repair act to increase phenotypic diversity of microbial cell populations and operate to enhance their ability to produce fit variants during stress. Coopted by lymphocytes, the ancient mutagenic processing systems retained their diversification functions instilling the adaptive immune cells with enhanced evolvability and defensive capacity to resist infection and damage. As reviewed here, the ubiquity and conserved character of specialized variation-generating mechanisms from bacteria to lymphocytes highlight the importance of these mechanisms for evolution of life in general.

Convergent Evolution, Evolving Evolvability, and the Origins of Lethal Cancer

Advances in curative treatment to remove the primary tumor have increased survival of localized cancers for most solid tumor types, yet cancers that have spread are typically incurable and account for >90% of cancer-related deaths. Metastatic disease remains incurable because, somehow, tumors evolve resistance to all known compounds, including therapies. In all of these incurable patients, de novo lethal cancer evolves capacities for both metastasis and resistance. Therefore, cancers in different patients appear to follow the same eco-evolutionary path that independently manifests in affected patients. This convergent outcome, that always includes the ability to metastasize and exhibit resistance, demands an explanation beyond the slow and steady accrual of stochastic mutations. The common denominator may be that cancer starts as a speciation event when a unicellular protist breaks away from its multicellular host and initiates a cancer clade within the patient. As the cancer cells speciate and diversify further, some evolve the capacity to evolve: evolvability. Evolvability becomes a heritable trait that influences the available variation of other phenotypes that can then be acted upon by natural selection. Evolving evolvability may be an adaptation for cancer cells. By generating and maintaining considerable heritable variation, the cancer clade can, with high certainty, serendipitously produce cells resistant to therapy and cells capable of metastasizing. Understanding that cancer cells can swiftly evolve responses to novel and varied stressors create opportunities for adaptive therapy, double-bind therapies, and extinction therapies; all involving strategic decision making that steers and anticipates the convergent coevolutionary responses of the cancers.

Design, Synthesis, and Antimicrobial Evaluation of New Annelated Pyrimido[2,1-c][1,2,4]triazolo[3,4-f][1,2,4]triazines

A series of 34 new pyrimido[2,1-c][1,2,4]triazine-3,4-diones were synthesized and fully characterized using IR, NMR, MS, and microanalytical analysis. In vitro investigation of 12 compounds of this series revealed promising antimicrobial activity of the conjugates 15a and 15f-j that were tagged with electron-withdrawing groups, with sensitivities ranging from 77% to as high as 100% of the positive control. The investigation of antimicrobial activity included Bacillus subtilis ATCC 6633, Staphylococcus aureus ATCC 6535, Pseudomonas aeruginosa ATCC 27853, and Escherichia coli ATCC 8739 (EC), and fungal strains Candida albicans ATCC 10231 and Aspergillus brasiliensis ATCC 16404.

Co-evolution in the Jungle: From Leafcutter Ant Colonies to Chromosomal Ends

Biological entities are multicomponent systems where each part is directly or indirectly dependent on the others. In effect, a change in a single component might have a consequence on the functioning of its partners, thus affecting the fitness of the entire system. In this article, we provide a few examples of such complex biological systems, ranging from ant colonies to a population of amino acids within a single-polypeptide chain. Based on these examples, we discuss one of the central and still challenging questions in biology: how do such multicomponent consortia co-evolve? More specifically, we ask how telomeres, nucleo-protein complexes protecting the integrity of linear DNA chromosomes, originated from the ancestral organisms having circular genomes and thus not dealing with end-replication and end-protection problems. Using the examples of rapidly evolving topologies of mitochondrial genomes in eukaryotic microorganisms, we show what means of co-evolution were employed to accommodate various types of telomere-maintenance mechanisms in mitochondria. We also describe an unprecedented runaway evolution of telomeric repeats in nuclei of ascomycetous yeasts accompanied by co-evolution of telomere-associated proteins. We propose several scenarios derived from research on telomeres and supported by other studies from various fields of biology, while emphasizing that the relevant answers are still not in sight. It is this uncertainty and a lack of a detailed roadmap that makes the journey through the jungle of biological systems still exciting and worth undertaking.

Selective Inbreeding: Genetic Crosses Drive Apparent Adaptive Mutation in the Cairns-Foster System of Escherichia coli

The Escherichia coli system of Cairns and Foster employs a lac frameshift mutation that reverts rarely (10-9/cell/division) during unrestricted growth. However, when 108 cells are plated on lactose medium, the nongrowing lawn produces ∼50 Lac+ revertant colonies that accumulate linearly with time over 5 days. Revertants carry very few associated mutations. This behavior has been attributed to an evolved mechanism ("adaptive mutation" or "stress-induced mutagenesis") that responds to starvation by preferentially creating mutations that improve growth. We describe an alternative model, "selective inbreeding," in which natural selection acts during intercellular transfer of the plasmid that carries the mutant lac allele and the dinB gene for an error-prone polymerase. Revertant genome sequences show that the plasmid is more intensely mutagenized than the chromosome. Revertants vary widely in their number of plasmid and chromosomal mutations. Plasmid mutations are distributed evenly, but chromosomal mutations are focused near the replication origin. Rare, heavily mutagenized, revertants have acquired a plasmid tra mutation that eliminates conjugation ability. These findings support the new model, in which revertants are initiated by rare pre-existing cells (105) with many copies of the F'lac plasmid. These cells divide under selection, producing daughters that mate. Recombination between donor and recipient plasmids initiates rolling-circle plasmid over-replication, causing a mutagenic elevation of DinB level. A lac+ reversion event starts chromosome replication and mutagenesis by accumulated DinB. After reversion, plasmid transfer moves the revertant lac+ allele into an unmutagenized cell, and away from associated mutations. Thus, natural selection explains why mutagenesis appears stress-induced and directed.

Adaptive mutability of colorectal cancers in response to targeted therapies

The emergence of drug resistance limits the efficacy of targeted therapies in human tumors. The prevalent view is that resistance is a fait accompli: when treatment is initiated, cancers already contain drug-resistant mutant cells. Bacteria exposed to antibiotics transiently increase their mutation rates (adaptive mutability), thus improving the likelihood of survival. We investigated whether human colorectal cancer (CRC) cells likewise exploit adaptive mutability to evade therapeutic pressure. We found that epidermal growth factor receptor (EGFR)/BRAF inhibition down-regulates mismatch repair (MMR) and homologous recombination DNA-repair genes and concomitantly up-regulates error-prone polymerases in drug-tolerant (persister) cells. MMR proteins were also down-regulated in patient-derived xenografts and tumor specimens during therapy. EGFR/BRAF inhibition induced DNA damage, increased mutability, and triggered microsatellite instability. Thus, like unicellular organisms, tumor cells evade therapeutic pressures by enhancing mutability.

The road to resistance

The way that bacteria grow - either floating in liquid or attached to a surface - affects their ability to evolve antimicrobial resistance and our ability to treat infections.

Irregularities in genetic variation and mutation rates with environmental stresses

The appearance of new mutations is determined by the equilibrium between DNA error formation and repair. In bacteria like Escherichia coli, stresses are thought shift this balance towards increased mutagenesis. Recent findings, however, suggest a very uneven relationship between stress and mutations. Only a subset of stressful environments increase the net rate of mutation and different forms of nutritional stress (such as oxygen, carbon or phosphorus limitations) result in markedly different mutation rates after similar reductions in growth rate. Moreover, different stresses result in altered mutational spectra, with some increasing transposition and others increasing indel formation. Single-base substitution rates are lower with some stresses than in unstressed bacteria. Indeed, changes to the mix of mutations with stress are more widespread than a marked increase in net mutation rate. Much remains to be learned on how environments have unique mutational signatures and why some stresses are more mutagenic than others. Even beyond stress-induced genetic variation, the fundamental unresolved question in the stress-mutation relationship is the adaptive value of different types of mutations and mutation rates; is transposition, for example, more advantageous under anaerobic conditions? It remains to be investigated whether stress-specific genetic variation impacts on evolvability differentially in distinct environments.