Random Mutations Research Articles

Directed evolution improves the catalytic activity of laccase in papermaking

As a biocatalyst, laccase has been widely studied and applied in the papermaking industry. However, the low catalytic efficiency and poor stability of natural laccase limit its application in the pulping process. To develop the laccase with high activity and strong tolerance, we carried out directed evolution for modification of the laccase derived from Bacillus pumilus and screened out the mutants F282L/F306L and Q275P from the random mutant library by high-throughput screening. The specific activities of F282L/F306L and Q275P were 280.87 U/mg and 453.94 U/mg, respectively, which were 1.42 times and 2.30 times that of the wild-type laccase. Q275P demonstrated significantly improved thermal stability, with the relative activity 20% higher than that of the wild-type laccase after incubation at 40 ℃, 50 ℃, and 70 ℃ for 4 h. F282L/F306L and Q275P showed greater tolerance to metal ions and organic solvents than the wild-type laccase. The Km value of the wild-type laccase was 374.97 μmo/L, and those of F282L/F306L and Q275P were reduced to 318.96 μmo/L and 360.71 μmo/L, respectively, which suggested that the substrate affinity of laccase was improved after mutation. The kcat values of F282L/F306L and Q275P for the substrate ABTS were 574.00 s-1 and 898.03 s-1, respectively, which were 1.1 times and 1.7 times that of the wild-type laccase, indicating the improved catalytic efficiency. Q275P demonstrated better performance than the wild-type laccase in pulping, as manifested by the reduction of 0.82 in the Kappa number and the increases of 2.00% ISO, 7.8%, and 7.2% in whiteness, tensile index, and breaking length, respectively. This work lays a foundation for improving the adaptation of laccase to the environment of the papermaking industry.

Engineering a membrane protein chaperone to ameliorate the proteotoxicity of mutant huntingtin

Toxic protein aggregates are associated with various neurodegenerative diseases, including Huntington’s disease (HD). Since no current treatment delays the progression of HD, we develop a mechanistic approach to prevent mutant huntingtin (mHttex1) aggregation. Here, we engineer the ATP-independent cytosolic chaperone PEX19, which targets peroxisomal membrane proteins to peroxisomes, to remove mHttex1 aggregates. Using yeast toxicity-based screening with a random mutant library, we identify two yeast PEX19 variants and engineer equivalent mutations into human PEX19 (hsPEX19). These variants effectively delay mHttex1 aggregation in vitro and in cellular HD models. The mutated hydrophobic residue in the α4 helix of hsPEX19 variants binds to the N17 domain of mHttex1, thereby inhibiting the initial aggregation process. Overexpression of the hsPEX19-FV variant rescues HD-associated phenotypes in primary striatal neurons and in Drosophila. Overall, our data reveal that engineering ATP-independent membrane protein chaperones is a promising therapeutic approach for rational targeting of mHttex1 aggregation in HD.

Genome-Wide A → G and C → T Mutations Induced by Functional TadA Variants in Escherichia coli.

The fusion expression of deoxyribonucleic acid (DNA) replication-related proteins with nucleotide deaminase enzymes promotes random mutations in bacterial genomes, thereby increasing genetic diversity among the population. Most previous studies have focused on cytosine deaminase, which produces only C → T mutations, significantly limiting the variety of mutation types. In this study, we developed a fusion expression system by combining DnaG (RNA primase) with adenine deaminase TadA-8e (DnaG-TadA) in Escherichia coli, which is capable of rapidly introducing A → G mutations into the E. coli genome, resulting in a 664-fold increase in terms of mutation rate. Additionally, we tested a dual-functional TadA variant, TadAD, and then fused it with DnaG. This construct introduced both C → T and A → G mutations into the E. coli genome, with the mutation rate increased by 370-fold upon coexpression with a uracil glycosylase inhibitor (DnaG-TadAD-UGI). We applied DnaG-TadA and DnaG-TadAD-UGI systems to the adaptive laboratory evolution for Cd2+ and kanamycin resistance, achieving an 8.0 mM Cd2+ and 200 μg/mL kanamycin tolerance within just 17 days and 132 h, respectively. Compared to conventional evolution methods, the final tolerance levels were increased by 320 and 266%, respectively. Our work offers a novel strategy for random mutagenesis in E. coli and potentially other prokaryotic species.

TRIM25: A Global Player of Cell Death Pathways and Promising Target of Tumor-Sensitizing Therapies.

Therapy resistance still constitutes a common hurdle in the treatment of many human cancers and is a major reason for treatment failure and patient relapse, concomitantly with a dismal prognosis. In addition to "intrinsic resistance", e.g., acquired by random mutations, cancer cells typically escape from certain treatments ("acquired resistance") by a large variety of means, including suppression of apoptosis and other cell death pathways via upregulation of anti-apoptotic factors or through inhibition of tumor-suppressive proteins. Therefore, ideally, the tumor-cell-restricted induction of apoptosis is still considered a promising avenue for the development of novel, tumor (re)sensitizing therapies. A growing body of evidence has highlighted the multifaceted role of tripartite motif 25 (TRIM25) in controlling different aspects of tumorigenesis, including chemotherapeutic drug resistance. Accordingly, overexpression of TRIM25 is observed in many tumors and frequently correlates with a poor patient survival. In addition to its originally described function in antiviral innate immune response, TRIM25 can play critical yet context-dependent roles in apoptotic- and non-apoptotic-regulated cell death pathways, including pyroposis, necroptosis, ferroptosis, and autophagy. The review summarizes current knowledge of molecular mechanisms by which TRIM25 can interfere with different cell death modalities and thereby affect the success of currently used chemotherapeutics. A better understanding of the complex repertoire of cell death modulatory effects by TRIM25 is an essential prerequisite for validating TRIM25 as a potential target for future anticancer therapy to surmount the high failure rate of currently used chemotherapies.

Understanding the Scope of Cytochrome P450-Catalyzed Radical Dimerization of Diketopiperazines.

DtpC was isolated from the ditryptophenaline biosynthetic pathway found in filamentous fungi as a cytochrome P450 (P450) that catalyzes the dimerization of diketopiperazines. More recently, several similar P450s were discovered. While a vast majority of such P450s generate asymmetric diketopiperazine dimers, DtpC and other fungal P450s predominantly catalyze the formation of symmetric dimer products. Dimeric compounds can have interesting biological activities, and the mode of dimerization can substantially affect their bioactivities substantially. Here, we set out to examine the mechanism and scope of diketopiperazine dimerization catalyzed by DtpC using both chemically modified substrate molecules and DtpC mutants that were selected by the screening of randomly mutated recombinant variants. Use of N1- and N10-methylated diketopiperazine substrates supports the proposal that the initial radical formation occurs by extraction of the N1 indole nitrogen for this fungal P450 dimerase. Further in vitro studies revealed that DtpC was capable of accepting a range of structurally variable substrates, including N-demethylated diketopiperazines, and forming symmetric homo- and heterodimeric products. Moreover, the introduction of single mutations identified through the screening of random mutants at and around the substrate-binding pocket led to the conversion of DtpC into a catalyst that predominantly generated asymmetric dimers of various diketopiperazines. The versatility of DtpC can serve as a good starting point for directed evolution of P450s that can serve as versatile catalysts for generation of various dimers of not only diketopiperazines derived from standard and nonstandard amino acids but also possibly structurally more divergent analogs of diketopiperazines.

Navigating Host Immunity and Concurrent Ozone Stress: Strain-Resolved Metagenomics Reveals Maintenance of Intraspecific Diversity and Genetic Variation in Xanthomonas on Pepper.

The evolving threat of new pathogen variants in the face of global environmental changes poses a risk to a sustainable crop production. Predicting and responding to how climate change affects plant-pathosystems is challenging, as environment affects host-pathogen interactions from molecular to the community level, and with eco-evolutionary feedbacks at play. To address this knowledge gap, we studied short-term within-host eco-evolutionary changes in the pathogen, Xanthomonas perforans, on resistant and susceptible pepper in the open-top chambers (OTCs) under elevated Ozone (O3) conditions in a single growing season. We observed increased disease severity with greater variance on the resistant cultivar under elevated O3, yet no apparent change on the susceptible cultivar. Despite the dominance of a single pathogen genotype on the susceptible cultivar, the resistant cultivar supported a heterogeneous pathogen population. Altered O3 levels led to a strain turnover, with a relatively greater gene flux on the resistant cultivar. Both standing genetic variation and de novo parallel mutations contributed toward evolutionary modifications during adaptation onto the resistant cultivar. The presence of elevated O3, however, led to a relatively higher genetic polymorphism, with random and transient mutations. Population heterogeneity along with genetic variation, and the promotion of interdependency are mechanisms by which pathogen responds to stressors. While parallel mutations may provide clues to predicting long-term pathogen evolution and adaptive potential. And,a high proportion of transient mutations suggest less predictable pathogen evolution under climatic alterations. This knowledge is relevant as we study the risk of pathogen emergence and the mechanisms and constraints underlying long-term pathogen adaptation under climatic shifts.

On-chip non-contact mechanical cell stimulation - quantification of SKOV-3 alignment to suspended microstructures.

Although the accumulation of random genetic mutations has been traditionally viewed as the main cause of cancer progression, altered mechanobiological profiles of the cells and microenvironment also play a major role as a mutation-independent element. To probe the latter, we have previously reported a microfluidic cell-culture platform with an integrated flexible actuator and its application for sequential cyclic compression of cancer cells. The platform is composed of a control microchannel in a top layer for introducing external pressure, and a polydimethylsiloxane (PDMS) membrane from which a monolithically-integrated actuator protrudes downwards into a cell-culture microchannel. When actuated, the integrated actuator, referred to as micro-piston, transfers the pressure from the control channel as a mechanical force to the cells underneath. When not actuated, the micro-piston remains suspended above cells, separated from the latter via a liquid-filled gap of ∼108 μm. Despite the lack of direct physical contact between the micro-piston and cells in the latter arrangement, we observed distinct alignment of SKOV-3 ovarian cancer cells to the piston shape. To characterize this observation, micro-piston localization, shape, and size were adjusted and the directionality of a mono-layer of SKOV-3 cells relative to the suspended structure was probed. Cell alignment analysis was performed in a novel, label-free approach by measuring elongation angles of whole cell bodies with respect to micro-piston peripheries. Alignment of SKOV-3 cells to the structure outline was significant for circular, triangular and square micro-piston when compared to control areas without micro-piston on the same chip. The effect was present irrespective of whether cells were loaded with micro-pistons in static position (∼108 μm gap) or actively retracted using vacuum (>108 μm gap). Similar alignment was not observed for MCF7 cancer cells and MCF10A non-cancerous epithelial cells. The reported observation of directional movement and growth of SKOV-3 cells towards the region under micro-pistons point towards a to-date unexplored mechanotactic behavior of these cells, warranting future investigations regarding the mechanisms involved and the role these may play in cancer.

Granulomatous inflammatory responses are elicited in the liver of PD-1 knockout mice by de novo genome mutagenesis.

Programmed death-1 (PD-1) is a negative regulator of immune responses. Upon deletion of PD-1 in mice, symptoms of autoimmunity developed only after they got old. In a model experiment in cancer immunotherapy, PD-1 was shown to prevent cytotoxic T lymphocytes from attacking cancer cells that expressed neoantigens derived from genome mutations. Furthermore, the larger number of genome mutations in cancer cells led to more robust anti-tumor immune responses after the PD-1 blockade. To understand the common molecular mechanisms underlying these findings, we hypothesize that we might have acquired PD-1 during evolution to avoid/suppress autoimmune reactions against neoantigens derived from mutations in the genome of aged individuals. To test the hypothesis, we introduced random mutations into the genome of young PD-1-/- and PD-1+/+ mice. We employed two different procedures of random mutagenesis: administration of a potent chemical mutagen N-ethyl-N-nitrosourea (ENU) into the peritoneal cavity of mice and deletion of MSH2, which is essential for the mismatch-repair activity in the nucleus and therefore for the suppression of accumulation of random mutations in the genome. We observed granulomatous inflammatory changes in the liver of the ENU-treated PD-1 knockout (KO) mice but not in the wild-type (WT) counterparts. Such lesions also developed in the PD-1/MSH2 double KO mice but not in the MSH2 single KO mice. These results support our hypothesis about the physiological function of PD-1 and address the mechanistic reasons for immune-related adverse events observed in cancer patients having PD-1-blockade immunotherapies.

Identification and characterization of PpUGT91BP1 as a trillin synthase from Paris polyphylla.

Polyphyllins are the active ingredients of the medicinal plant Paris polyphylla. The biosynthesis of different types of polyphyllins all require the catalysis of glycosyltransferases. Even though significant efforts have been made to identify PpUGTs capable of catalyzing the initial glycosylation reaction, the specific glycosyltransferases responsible for the synthesis of trillin have not been reported in P. polyphylla. Here, we identified a new trillin synthase, named PpUGT91BP1, which was highly expressed in the rhizome. Importantly, PpUGT91BP1 could specifically glycosylate diosgenin but not pennogenin. To improve its catalytic efficiency, we introduced random mutations through error-prone PCR and conducted an activity-based screening. Three mutants with significantly enhanced trillin synthase activity were identified. Finally, we successfully reconstituted trillin biosynthesis in Nicotiana benthamiana, achieving a yield of 3.69 mg per gram of plant dry weight using the mutant PpUGT91BP1. Taken together, our results deepen the understanding of PpUGT91 family's role in polyphyllin biosynthesis in P. polyphylla, facilitating rational selection of better P. polyphylla cultivars and guiding future studies in metabolic engineering of polyphllins.

From Micro to Macro: Avian Chromosome Evolution is Dominated by Natural Selection.

Birds display striking variation in chromosome number, defying the traditional view of highly conserved avian karyotypes. However, the evolutionary drivers of this variability remain unclear. To address this, we fit probabilistic models of chromosome number evolution across birds, enabling us to estimate rates of evolution for total chromosome number and the number of microchromosomes and macrochromosomes while simultaneously accounting for the impact of other evolving traits. Our analyses revealed higher rates of chromosome fusion than fission across all bird lineages. Notably, much of this signal was driven by Passeriformes, where migratory species showed a particularly strong bias towards fusions compared to sedentary counterparts. Furthermore, a robust correlation between the rearrangement rates of microchromosomes and macrochromosomes suggests that genome-wide processes drive rates of structural evolution. Additionally, we found that lineages with larger population sizes exhibited higher rates of both fusion and fission, indicating that positive selection plays a dominant role in driving divergence in chromosome number. Our findings illuminate the evolutionary dynamics of avian karyotypes and highlight that, while the fitness effects of random structural mutations are often deleterious, beneficial mutations may dominate karyotype divergence in some clades.

SARM: A network State-Aware Adaptive Routing Mutation method for power IoT

With the rapid development of Power Internet of Things (PIoT), the application of Internet of Things technology in smart grids is becoming increasingly widespread, but it also enlarges the attack surface of the system. Against this backdrop, traditional defense methods are limited by the asymmetry of network attack and defense, which makes it difficult to effectively resist the ievolving sniffing attacks and link flooding attacks. In order to improve the security of PIoT, this paper proposes an Adaptive Route Mutation based on Network State Awareness (SARM). It generates a route mutation space using a path state matrix and optimizes the time complexity of mutation space generation with a backtracking method. Furthermore, the SARM can dynamically adjust the route mutation strategy according to the real-time network state to realize self-adaptive defense. In conclusion, SARM is evaluated through simulations conducted with Mininet. Compared to Random Routing Mutation (RRM), it enhances defense against Sniffing and Distributed Denial of Service attacks by approximately 30% and 35% respectively. Additionally, in various example topologies, SARM consistently outperforms RRM.

Engineering thermostability of industrial enzymes for enhanced application performance.

Thermostability is a key factor for the industrial application of enzymes. This review categorizes enzymes by their applications and discusses the importance of engineering thermostability for practical use. It summarizes fundamental theories and recent advancements in enzyme thermostability modification, including directed evolution, semi-rational design, and rational design. Directed evolution uses high-throughput screening to generate random mutations, while semi-rational design combines hotspot identification with screening. Rational design focuses on key residues to enhance stability by improving rigidity, foldability, and reducing aggregation. The review also covers rational strategies like engineering folding energy, surface charge, machine learning methods, and consensus design, along with tools that support these approaches. Practical examples are critically assessed to highlight the benefits and limitations of these strategies. Finally, the challenges and potential contributions of artificial intelligence in enzyme thermostability engineering are discussed.

Machine learning-guided engineering of phosphocholine cytidylyltransferase for improved activity and halotolerance in citicoline production

Phosphocholine cytidylyltransferase (CCT) is an important biocatalyst for citicoline (CDP-choline) production. However, it suffers from relatively low catalytic activity and halotolerance when it was applied in the one-pot catalytic system coupling with the acetylphosphate based ATP regeneration system. A machine learning guided directed evolution approach was applied to simultaneously improve activity and halotolerance of Saccharomyces cerevisiae CCT (ScCCT), through random mutation on “hot-spot” regions predicted by selected models trained on different combinatory datasets generated by site-directed mutagenesis and random mutagenesis. The most desirable variant M3 (P347S/P365L/K340T) exhibited an approximately 1.4 folds improvement in final titer of CDP-choline (182 mM) comparing with WT. This research proves that machine learning can improve effectiveness of random mutagenesis, and provides a possible solution to engineering membrane protein with complicated evolutionary fitness landscapes, which can be difficult for classic enzyme engineering approaches.

Combining evolution and protein language models for an interpretable cancer driver mutation prediction with D2Deep.

The mutations driving cancer are being increasingly exposed through tumor-specific genomic data. However, differentiating between cancer-causing driver mutations and random passenger mutations remains challenging. State-of-the-art homology-based predictors contain built-in biases and are often ill-suited to the intricacies of cancer biology. Protein language models have successfully addressed various biological problems but have not yet been tested on the challenging task of cancer driver mutation prediction at a large scale. Additionally, they often fail to offer result interpretation, hindering their effective use in clinical settings. The AI-based D2Deep method we introduce here addresses these challenges by combining two powerful elements: (i) a nonspecialized protein language model that captures the makeup of all protein sequences and (ii) protein-specific evolutionary information that encompasses functional requirements for a particular protein. D2Deep relies exclusively on sequence information, outperforms state-of-the-art predictors, and captures intricate epistatic changes throughout the protein caused by mutations. These epistatic changes correlate with known mutations in the clinical setting and can be used for the interpretation of results. The model is trained on a balanced, somatic training set and so effectively mitigates biases related to hotspot mutations compared to state-of-the-art techniques. The versatility of D2Deep is illustrated by its performance on non-cancer mutation prediction, where most variants still lack known consequences. D2Deep predictions and confidence scores are available via https://tumorscope.be/d2deep to help with clinical interpretation and mutation prioritization.

High fitness paths can connect proteins with low sequence overlap.

The structure and function of a protein are determined by its amino acid sequence. While random mutations change a protein's sequence, evolutionary forces shape its structural fold and biological activity. Studies have shown that neutral networks can connect a local region of sequence space by single residue mutations that preserve viability. However, the larger-scale connectedness of protein morphospace remains poorly understood. Recent advances in artificial intelligence have enabled us to computationally predict a protein's structure and quantify its functional plausibility. Here we build on these tools to develop an algorithm that generates viable paths between distantly related extant protein pairs. The intermediate sequences in these paths differ by single residue changes over subsequent steps - substitutions, insertions and deletions are admissible moves. Their fitness is evaluated using the protein language model ESM2, and maintained as high as possible subject to the constraints of the traversal. We document the qualitative variation across paths generated between progressively divergent protein pairs, some of which do not even acquire the same structural fold. The ease of interpolating between two sequences could be used as a proxy for the likelihood of homology between them.

New genetic gray wolf optimizer with a random selective mutation for wind farm layout optimization

The wake effect is a relevant factor in determining the optimal distribution of wind turbines within the boundaries of a wind farm. This reduces the incident wind speed on downstream wind turbines, which results in a decrease in energy production for the wind farm. This paper proposes a novel approach for optimizing the distribution of wind turbines using a new Genetic Gray Wolf Optimizer (GGWO). The GGWO employs a teamwork model inspired by wolf prey hunting, guided by four leaders: Alpha, Beta, Delta, and Omicron wolves, each with different hierarchical weights. To improve the competitiveness of the wolves, GGWO utilizes genetic algorithm operators such as crossover blending, normal mutation, and a new genetic operator called Random Selective Mutation (RSM), which improves solution search efficiency. The proposed GGWO is compared to other algorithms such as Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO). The studies examine varying wind speeds in magnitude and direction throughout the year, as well as different wind farm boundaries. The outcomes show that GGWO successfully identifies the ideal locations for wind turbines, scoring better scores in terms of total simulation duration and annual energy generation for the wind farm. It surpasses the performance of GWO, ABC, and PSO algorithms and exhibits comparable competitiveness with more intricate algorithms like ACO.

High-Affinity Peptides for Target Protein Screened in Ultralarge Virtual Libraries.

High-throughput virtual screening (HTVS) has emerged as a pivotal strategy for identifying high-affinity peptides targeting functional proteins, which are crucial for diagnostic and therapeutic applications. In the HTVS of peptides, expanding the library capacity to enhance peptide sequence diversity, thereby screening out excellent affinity peptide candidates, remains a significant challenge. This study presents a de novo design strategy that leverages directed mutation driven HTVS to evolve vast virtual libraries and screen peptides with ultrahigh affinities for various target proteins. Utilizing a computer-generated library of 104 random 15-mer peptide scaffolds, we employed a self-developed algorithm for parallelized HTVS with Autodock Vina. The top 1% of designs underwent random mutations at a rate of 20% for six generations, theoretically expanding the library to 1014 members. This approach was applied to various protein targets, including a tumor marker (alpha fetoprotein, AFP) and virus surface proteins (SARS-CoV-2 RBD and norovirus P-domain). Starting from the same 104 random 15-mer peptide library, peptides with high affinities in the nanomolar range for three protein targets were successfully identified. The energy-saving and high-efficient design strategy presents new opportunities for the cost-effective development of more effective high-affinity peptides for various environmental and health applications.

Cas9-PE: a robust multiplex gene editing tool for simultaneous precise editing and site-specific random mutation in rice

In molecular design breeding, the simultaneous introduction of desired functional genes through specific nucleotide modifications and the elimination of genes regulating undesired phenotypic traits or agronomic components require advanced gene editing tools. Due to limited editing efficiency, even with the use of highly precise editing tools, such as prime editing (PE), simultaneous editing of multiple mutation types poses a challenge. Here, we replaced Cas9 nickase (nCas9) with Cas9 to construct a Cas9-mediated PE (Cas9-PE) system in rice. This system not only enables precise editing, but also allows for site-specific random mutation. Moreover, leveraging the precision of Cas9-PE, we established a transgene-free multiplex gene editing system using a co-editing strategy. This strategy involved the Agrobacterium-mediated transient expression of the precise editing rice endogenous acetolactate synthase gene ALSS627I to confer herbicide bispyribac-sodium (BS) resistance as a selection marker. This study provides a versatile and efficient multiplex gene editing tool for molecular design breeding.

Extended Spectrum β-Lactamase: Tackling Antibiotic Resistance and Overcoming Treatment Challenges

Antibiotics, also known as antibacterials, kill or inhibit bacterial growth but are ineffective against viruses, fungi, or parasites, often leading to misuse. They are categorized by molecular structure, mode of action, and spectrum of activity. Antimicrobial Resistance (AMR) occurs when pathogens no longer respond to antimicrobial drugs, arising naturally or through acquisition. Resistance mechanisms include enzymatic (most common), genetic and physical. Bacteria produce various β-lactamases, such as Extended Spectrum β-lactamases (ESBLs), AmpC enzymes, and carbapenemase to exert resistance to Beta-Lactam (βL) class of antibiotics. ESBL families include TEM, SHV, and CTX-M, with E. coli being the most prevalent host. Any Gram-Negative Bacteria (GNB) can be an ESBL producer, but most common ones are the Enterobacteriaceae including Escherichia coli, Klebsiella pneumoniae, Klebsiella oxytoca and Proteus mirabilis. ESBL-producing Enterobacteriaceae (ESBL-E) resist penicillin, aztreonam, and cephalosporins except cephamycins and carbapenems, posing a significant public health risk. Genetic resistance mechanisms involve random mutations and horizontal gene transfer through either of the following processes namely conjugation, transformation, transduction. Physical mechanisms include efflux pump production and decreased porin channels. In some microbiological laboratories, ESBL production are often not determined, rather resistance based on MIC values to third generation Cephalosporins are considered as resistance due to ESBL production. Antibiotic use in agriculture and medicine has increased Multi-drug resistant (MDR) ESBL-producing E. coli and evidenced in retail meat and among meat shop employees. Community-acquired ESBL-E infections are a growing concern, with hospital transmission primarily occurring among patients sharing rooms with ESBL carriers. Empirical and definitive therapies for ESBL-E infections must be adjusted based on Antibiotic Susceptibility Testing (AST). The MERINO trial identified urinary tract infections as the most common source of ESBL-E bacteremia, with E. coli being predominant. For critically ill patients with non-urinary tract infections, Meropenem or Imipenem-cilastatin are recommended. For uncomplicated UTIs, Nitrofurantoin, Cotrimoxazole, and Piperacillin-Tazobactam (Pip-Taz) are effective, while Cotrimoxazole, Fluoroquinolones, and Ceftolozane-tazobactam are suitable for complicated UTIs. New β-lactamase inhibitors like avibactam, vaborbactam, and relebactam are promising for treatment. Misuse of antibiotics, such as inappropriate dosing and duration, contributes to AMR, a growing global challenge. Deaths from AMR, estimated at 1.27 million in 2019, could reach 10 million by 2050. ESBLs drive the use of broad-spectrum antibiotics, accelerating resistance development. Inadequate therapy exacerbates infections, leading to prolonged hospital stays, complications, and increased mortality. Balancing new drug development with resistance emergence is crucial to combat AMR.

Accumulating waves of random mutations before fixation.

Mutations provide variation for evolution to emerge. A quantitative analysis of how mutations arising in single individuals expand and possibly fixate in a population is essential for studying evolutionary processes. While it is intuitive to expect that a continuous influx of mutations will lead to a continuous flow of mutations fixating in a stable constant population, joint fixation of multiple mutations occur frequently in stochastic simulations even under neutral selection. We quantitatively measure and analyze the distribution of joint fixation events of neutral mutations in constant populations and discussed the connection with previous results. We propose a new concept, the mutation "waves," where multiple mutations reach given frequencies simultaneously. We show that all but the lowest frequencies of the variant allele frequency distribution are dominated by single mutation "waves," which approximately follow an exponential distribution in terms of size. Consequently, large swaths of empty frequencies are observed in the variant allele frequency distributions, with a few frequencies having numbers of mutations far in excess of the expected average values over multiple realizations. We quantify the amount of time each frequency is empty of mutations and further show that the discrete mutation waves average out to a continuous distribution named as the wave frequency distribution, the shape of which is predictable based on few model parameters.