Phylogenetic predictions of carbapenemase activity from the Guiana extended-spectrum (GES) family of ß-lactamases.

Objectives: We investigated the amino acid substitutions in the GES family of ESBLs that were most likely to be involved in the evolution of carbapenemase activity. Methods: To identify the substitutions that are functionally important, we analysed the evolutionary history of the GES ß-lactamases using an alignment and phylogeny to identify sites in GES that show evidence of positive selection and the selected phenotypes. Results and Conclusions: Data indicate that the substitutions G170S and G243A are associated with carbapenemase activity. The substitutions Q43E, E104K and T237A are most likely associated with ESBL activity.

The Klebsiella pneumoniae carbapenemase (KPC) ß-Lactamase Has Evolved in Response to Ceftazidime Avibactam.

Klebsiella pneumoniae carbapenemase KPC is an important resistance gene that has disseminated globally in response to carbapenem use. It is now being implicated as a resistance determinant in Ceftazidime Avibactam (CAZ-AVI) resistance. Given that CAZ-AVI is a last-resort antibiotic, it is critical to understand how resistance to this drug is evolving. In particular, we were interested in determining the evolutionary response of KPC to CAZ-AVI consumption. Through phylogenetic reconstruction, we identified the variable sites under positive selection in the KPC gene that are correlated with Ceftazidime Avibactam (CAZ-AVI) resistance. Our approach was to use a phylogeny to identify multiple independent occurrences of mutations at variable sites and a literature review to correlate CAZ-AVI resistance with the mutations we identified. We found the following sites that are under positive selection: P104, W105, A120, R164, L169, A172, D179, V240, Y241, T243, Y264, and H274. The sites that correlate with CAZ-AVI resistance are R164, L169, A172, D179, V240, Y241, T243, and H274. Overall, we found that there is evidence of positive selection in KPC and that CAZ-AVI is the major selective pressure.

Distribution of ß-Lactamase Genes in Clinical Isolates from California Central Valley Hospital Deviates from the United States Nationwide Trends.

The evolution and dissemination of antibiotic resistance genes throughout the world are clearly affected by the selection and migration of resistant bacteria. However, the relative contributions of selection and migration at a local scale have not been fully explored. We sought to identify which of these factors has the strongest effect through comparisons of antibiotic resistance gene abundance between a distinct location and its surroundings over an extended period of six years. In this work, we used two repositories of extended spectrum ß-lactamase (ESBL)-producing isolates collected since 2013 from patients at Dignity Health Mercy Medical Center (DHMMC) in Merced, California, USA, and a nationwide database compiled from clinical isolate genomes reported by the National Center for Biotechnology Information (NCBI) since 2013. We analyzed the stability of average resistance gene frequencies over the years since collection of these clinical isolates began for each repository. We then compared the frequencies of resistance genes in the DHMMC collection with the averages of the nationwide frequencies. We found DHMMC gene frequencies are stable over time and differ significantly from nationwide frequencies throughout the period of time we examined. Our results suggest that local selective pressures are a more important influence on the population structure of resistance genes in bacterial populations than migration. This, in turn, indicates the potential for antibiotic resistance to be controlled at a regional level, making it easier to limit the spread through local stewardship.

Adaptive Processes Change as Multiple Functions Evolve.

Epistasis influences the gene-environment interactions that shape bacterial fitness through antibiotic exposure, which can ultimately affect the availability of certain resistance phenotypes to bacteria. The substitutions present within blaTEM-50 confer both cephalosporin and ß-lactamase inhibitor resistance. We wanted to compare the evolution of blaTEM-50 with that of another variant, blaTEM-85, which differs in that blaTEM-85 contains only substitutions that contribute to cephalosporin resistance. Differences between the landscapes and epistatic interactions of these TEM variants are important for understanding their separate evolutionary responses to antibiotics. We hypothesized the substitutions within blaTEM-50 would result in more epistatic interactions than for blaTEM-85 As expected, we found more epistatic interactions between the substitutions present in blaTEM-50 than in blaTEM-85 Our results suggest that selection from many cephalosporins is required to achieve the full potential resistance to cephalosporins but that a single ß-lactam and inhibitor combination will drive evolution of the full potential resistance phenotype. Surprisingly, we also found significantly positive increases in growth rates as antibiotic concentration increased for some of the strains expressing blaTEM-85 precursor genotypes but not the blaTEM-50 variants. This result further suggests that additive interactions more effectively optimize phenotypes than epistatic interactions, which means that exposure to numerous cephalosporins actually increases the ability of a TEM enzyme to confer resistance to any single cephalosporin.

Growth rate assays reveal fitness consequences of ß-lactamases.

NON-TECHNICAL SUMMARY: Antibiotic resistance is a global human health problem. We partnered with Dignity Health Mercy Medical Center to study antibiotic resistance in clinical isolates. We tested whether growth rates, a sensitive assay used to measure the fitness of bacterial samples, correlate with a clinical test to measure antibiotic resistance. We found a strong correlation between these two methods suggesting that growth rates could be reliably applied to evolutionary studies of clinically relevant problems. Moreover, the sensitivity of the growth rates assay enabled us to identify fitness effects of specific antibiotic resistance genes.

Statistical Package for Growth Rates Made Easy.

Growth rates are an important tool in microbiology because they provide high throughput fitness measurements. The release of GrowthRates, a program that uses the output of plate reader files to automatically calculate growth rates, has facilitated experimental procedures in many areas. However, many sources of variation within replicate growth rate data exist and can decrease data reliability. We have developed a new statistical package, CompareGrowthRates (CGR), to enhance the program GrowthRates and accurately measure variation in growth rate data sets. We define a metric, Variability-score (V-score), that can help determine if variation within a data set might result in false interpretations. CGR also uses the bootstrap method to determine the fraction of bootstrap replicates in which a strain will grow the fastest. We illustrate the usage of CGR with growth rate data sets similar to those in Mira, Meza, et al. (Adaptive landscapes of resistance genes change as antibiotic concentrations change. Mol Biol Evol. 32(10): 2707-2715). These statistical methods are compatible with the analytic methods described in Growth Rates Made Easy and can be used with any set of growth rate output from GrowthRates.

Does Antibiotic Resistance Evolve in Hospitals?

Nosocomial outbreaks of bacteria are well documented. Based on these incidents, and the heavy usage of antibiotics in hospitals, it has been assumed that antibiotic resistance evolves in hospital environments. To test this assumption, we studied resistance phenotypes of bacteria collected from patient isolates at a community hospital over a 2.5-year period. A graphical model analysis shows no association between resistance and patient information other than time of arrival. This allows us to focus on time-course data. We introduce a hospital transmission model, based on negative binomial delay. Our main contribution is a statistical hypothesis test called the Nosocomial Evolution of Resistance Detector (NERD). It calculates the significance of resistance trends occurring in a hospital. It can inform hospital staff about the effects of various practices and interventions, can help detect clonal outbreaks, and is available as an R package. We applied the NERD method to each of the 16 antibiotics in the study via 16 hypothesis tests. For 13 of the antibiotics, we found that the hospital environment had no significant effect on the evolution of resistance; the hospital is merely a piece of the larger picture. The p-values obtained for the other three antibiotics (cefepime, ceftazidime, and gentamicin) indicate that particular care should be taken in hospital practices with these antibiotics. One of the three, ceftazidime, was significant after accounting for multiple hypotheses, indicating a trend of decreased resistance for this drug.

Adaptive Landscapes of Resistance Genes Change as Antibiotic Concentrations Change.

Most studies on the evolution of antibiotic resistance are focused on selection for resistance at lethal antibiotic concentrations, which has allowed the detection of mutant strains that show strong phenotypic traits. However, solely focusing on lethal concentrations of antibiotics narrowly limits our perspective of antibiotic resistance evolution. New high-resolution competition assays have shown that resistant bacteria are selected at relatively low concentrations of antibiotics. This finding is important because sublethal concentrations of antibiotics are found widely in patients undergoing antibiotic therapies, and in nonmedical conditions such as wastewater treatment plants, and food and water used in agriculture and farming. To understand the impacts of sublethal concentrations on selection, we measured 30 adaptive landscapes for a set of TEM ß-lactamases containing all combinations of the four amino acid substitutions that exist in TEM-50 for 15 ß-lactam antibiotics at multiple concentrations. We found that there are many evolutionary pathways within this collection of landscapes that lead to nearly every TEM-genotype that we studied. While it is known that the pathways change depending on the type of ß-lactam, this study demonstrates that the landscapes including fitness optima also change dramatically as the concentrations of antibiotics change. Based on these results we conclude that the presence of multiple concentrations of ß-lactams in an environment result in many different adaptive landscapes through which pathways to nearly every genotype are available. Ultimately this may increase the diversity of genotypes in microbial populations.

Rational design of antibiotic treatment plans: a treatment strategy for managing evolution and reversing resistance.

The development of reliable methods for restoring susceptibility after antibiotic resistance arises has proven elusive. A greater understanding of the relationship between antibiotic administration and the evolution of resistance is key to overcoming this challenge. Here we present a data-driven mathematical approach for developing antibiotic treatment plans that can reverse the evolution of antibiotic resistance determinants. We have generated adaptive landscapes for 16 genotypes of the TEM ß-lactamase that vary from the wild type genotype "TEM-1" through all combinations of four amino acid substitutions. We determined the growth rate of each genotype when treated with each of 15 ß-lactam antibiotics. By using growth rates as a measure of fitness, we computed the probability of each amino acid substitution in each ß-lactam treatment using two different models named the Correlated Probability Model (CPM) and the Equal Probability Model (EPM). We then performed an exhaustive search through the 15 treatments for substitution paths leading from each of the 16 genotypes back to the wild type TEM-1. We identified optimized treatment paths that returned the highest probabilities of selecting for reversions of amino acid substitutions and returning TEM to the wild type state. For the CPM model, the optimized probabilities ranged between 0.6 and 1.0. For the EPM model, the optimized probabilities ranged between 0.38 and 1.0. For cyclical CPM treatment plans in which the starting and ending genotype was the wild type, the probabilities were between 0.62 and 0.7. Overall this study shows that there is promise for reversing the evolution of resistance through antibiotic treatment plans.

Growth rates made easy.

In the 1960s-1980s, determination of bacterial growth rates was an important tool in microbial genetics, biochemistry, molecular biology, and microbial physiology. The exciting technical developments of the 1990s and the 2000s eclipsed that tool; as a result, many investigators today lack experience with growth rate measurements. Recently, investigators in a number of areas have started to use measurements of bacterial growth rates for a variety of purposes. Those measurements have been greatly facilitated by the availability of microwell plate readers that permit the simultaneous measurements on up to 384 different cultures. Only the exponential (logarithmic) portions of the resulting growth curves are useful for determining growth rates, and manual determination of that portion and calculation of growth rates can be tedious for high-throughput purposes. Here, we introduce the program GrowthRates that uses plate reader output files to automatically determine the exponential portion of the curve and to automatically calculate the growth rate, the maximum culture density, and the duration of the growth lag phase. GrowthRates is freely available for Macintosh, Windows, and Linux. We discuss the effects of culture volume, the classical bacterial growth curve, and the differences between determinations in rich media and minimal (mineral salts) media. This protocol covers calibration of the plate reader, growth of culture inocula for both rich and minimal media, and experimental setup. As a guide to reliability, we report typical day-to-day variation in growth rates and variation within experiments with respect to position of wells within the plates.

Using complete genome comparisons to identify sequences whose presence accurately predicts clinically important phenotypes.

In clinical settings it is often important to know not just the identity of a microorganism, but also the danger posed by that particular strain. For instance, Escherichia coli can range from being a harmless commensal to being a very dangerous enterohemorrhagic (EHEC) strain. Determining pathogenic phenotypes can be both time consuming and expensive. Here we propose a simple, rapid, and inexpensive method of predicting pathogenic phenotypes on the basis of the presence or absence of short homologous DNA segments in an isolate. Our method compares completely sequenced genomes without the necessity of genome alignments in order to identify the presence or absence of the segments to produce an automatic alignment of the binary string that describes each genome. Analysis of the segment alignment allows identification of those segments whose presence strongly predicts a phenotype. Clinical application of the method requires nothing more that PCR amplification of each of the set of predictive segments. Here we apply the method to identifying EHEC strains of E. coli and to distinguishing E. coli from Shigella. We show in silico that with as few as 8 predictive sequences, if even three of those predictive sequences are amplified the probability of being EHEC or Shigella is >0.99. The method is thus very robust to the occasional amplification failure for spurious reasons. Experimentally, we apply the method to screening a set of 98 isolates to distinguishing E. coli from Shigella, and EHEC from non-EHEC E. coli strains and show that all isolates are correctly identified.

Clustering acinetobacter strains by optical mapping.

Optical mapping is a technique that produces an ordered restriction map of a bacterial or eukaryotic chromosome. We have developed a new method, the BOP method, to compare experimental optical maps with in silico optical maps of complete genomes to infer the presence/absence of short DNA sequences (bops) in each genome. The BOP method, as implemented by the Optical Mapping suite of four programs, circumvents the necessity of whole-genome multiple alignments and permits reliable strain typing and clustering on the basis of optical maps. We have applied the Optical Mapping Suite to 125 strains of Acinetobacter sp., including 11 completely sequenced genomes and 114 Acinetobacter complex from three US military hospitals. We found that optical mapping completely resolves all 125 strains. Signal to noise analysis showed that when the 125 strains were considered together almost 1/3 of the experimental fragments were misidentified. We found that the set of 125 genomes could be divided into three clusters, two of which included sequenced genomes. Signal to noise analysis after clustering showed that only 3.5% of the experimental restriction fragments were misidentified. Minimum spanning trees of the two clusters that included sequenced genomes are presented. The programs we have developed provide a more rigorous approach for analyzing optical map data than previously existed.

Designing antibiotic cycling strategies by determining and understanding local adaptive landscapes.

The evolution of antibiotic resistance among bacteria threatens our continued ability to treat infectious diseases. The need for sustainable strategies to cure bacterial infections has never been greater. So far, all attempts to restore susceptibility after resistance has arisen have been unsuccessful, including restrictions on prescribing [1] and antibiotic cycling [2], [3]. Part of the problem may be that those efforts have implemented different classes of unrelated antibiotics, and relied on removal of resistance by random loss of resistance genes from bacterial populations (drift). Here, we show that alternating structurally similar antibiotics can restore susceptibility to antibiotics after resistance has evolved. We found that the resistance phenotypes conferred by variant alleles of the resistance gene encoding the TEM ß-lactamase (bla(TEM)) varied greatly among 15 different ß-lactam antibiotics. We captured those differences by characterizing complete adaptive landscapes for the resistance alleles bla(TEM-50) and bla(TEM-85), each of which differs from its ancestor bla(TEM-1) by four mutations. We identified pathways through those landscapes where selection for increased resistance moved in a repeating cycle among a limited set of alleles as antibiotics were alternated. Our results showed that susceptibility to antibiotics can be sustainably renewed by cycling structurally similar antibiotics. We anticipate that these results may provide a conceptual framework for managing antibiotic resistance. This approach may also guide sustainable cycling of the drugs used to treat malaria and HIV.

The peaks and geometry of fitness landscapes.

Fitness landscapes are central in the theory of adaptation. Recent work compares global and local properties of fitness landscapes. It has been shown that multi-peaked fitness landscapes have a local property called reciprocal sign epistasis interactions. The converse is not true. We show that no condition phrased in terms of reciprocal sign epistasis interactions only, implies multiple peaks. We give a sufficient condition for multiple peaks phrased in terms of two-way interactions. This result is surprising since it has been claimed that no sufficient local condition for multiple peaks exist. We show that our result cannot be generalized to sufficient conditions for three or more peaks. Our proof depends on fitness graphs, where nodes represent genotypes and where arrows point toward more fit genotypes. We also use fitness graphs in order to give a new brief proof of the equivalent characterizations of fitness landscapes lacking genetic constraints on accessible mutational trajectories. We compare a recent geometric classification of fitness landscape based on triangulations of polytopes with qualitative aspects of gene interactions. One observation is that fitness graphs provide information that are not contained in the geometric classification. We argue that a qualitative perspective may help relating theory of fitness landscapes and empirical observations.

Extracellular stress and lipopolysaccharide modulate Acinetobacter baumannii surface-associated motility.

Acinetobacter baumannii is a nosocomial bacterial pathogen, and infections attributed to this species are further complicated by a remarkable ability to acquire antimicrobial resistance genes and to survive in a desiccated state. While the antibiotic resistance and biofilm formation of A. baumannii is well-documented, less is known about the virulence attributes of this organism. Recent studies reported A. baumannii strains display a motility phenotype, which appears to be partially dependent upon Type IV pili, autoinducer molecules, and the response to blue light. In this study, we wanted to determine the prevalence of this trait in genetically diverse clinical isolates, and any additional required factors, and environmental cues that regulate motility. When strains are subjected to a wide array of stress conditions, A. baumannii motility is significantly reduced. In contrast, when extracellular iron is provided or salinity is reduced, motility is significantly enhanced. We further investigated whether the genes required for the production of lipopolysaccharide (lpsB) and K1 capsule (epsA/ptk) are required for motility as demonstrated in other Gram-negative bacteria. Transposon mutagenesis resulted in reduced motility by the insertion derivatives of each of these genes. The presence of the parental allele provided in trans, in the insertion mutant background, could only restore motility in the lpsB mutant. The production of core LPS directly contributes to the motility phenotype, while capsular polysaccharide may have an indirect effect. Further, the data suggest motility is regulated by extracellular conditions, indicating that A. baumannii is actively sensing the environment and responding accordingly.

Ethanol and reactive species increase basal sequence heterogeneity of hepatitis C virus and produce variants with reduced susceptibility to antivirals.

Hepatitis C virus (HCV) exhibits a high level of genetic variability, and variants with reduced susceptibility to antivirals can occur even before treatment begins. In addition, alcohol decreases efficacy of antiviral therapy and increases sequence heterogeneity of HCV RNA but how ethanol affects HCV sequence is unknown. Ethanol metabolism and HCV infection increase the level of reactive species that can alter cell metabolism, modify signaling, and potentially act as mutagen to the viral RNA. Therefore, we investigated whether ethanol and reactive species affected the basal sequence variability of HCV RNA in hepatocytes. Human hepatoma cells supporting a continuous replication of genotype 1b HCV RNA (Con1, AJ242652) were exposed to ethanol, acetaldehyde, hydrogen peroxide, or L-buthionine-S,R-sulfoximine (BSO) that decreases intracellular glutathione as seen in patients. Then, NS5A region was sequenced and compared with genotype 1b HCV sequences in the database. Ethanol and BSO elevated nucleotide and amino acid substitution rates of HCV RNA by 4-18 folds within 48 hrs which were accompanied by oxidative RNA damage. Iron chelator and glutathione ester decreased both RNA damage and mutation rates. Furthermore, infectious HCV and HCV core gene were sufficient to induce oxidative RNA damage even in the absence of ethanol or BSO. Interestingly, the dn/ds ratio and percentage of sites undergoing positive selection increased with ethanol and BSO, resulting in an increased detection of NS5A variants with reduced susceptibility to interferon alpha, cyclosporine, and ribavirin and others implicated in immune tolerance and modulation of viral replication. Therefore, alcohol is likely to synergize with virus-induced oxidative/nitrosative stress to modulate the basal mutation rate of HCV. Positive selection induced by alcohol and reactive species may contribute to antiviral resistance.

The gut as reservoir of antibiotic resistance: microbial diversity of tetracycline resistance in mother and infant.

The microbiota in the human gastrointestinal tract (GIT) is highly exposed to antibiotics, and may be an important reservoir of resistant strains and transferable resistance genes. Maternal GIT strains can be transmitted to the offspring, and resistances could be acquired from birth. This is a case study using a metagenomic approach to determine the diversity of microorganisms conferring tetracycline resistance (Tc(r)) in the guts of a healthy mother-infant pair one month after childbirth, and to investigate the potential for horizontal transfer and maternal transmission of Tc(r) genes. Fecal fosmid libraries were functionally screened for Tc(r), and further PCR-screened for specific Tc(r) genes. Tc(r) fosmid inserts were sequenced at both ends to establish bacterial diversity. Mother and infant libraries contained Tc(r), although encoded by different genes and organisms. Tc(r) organisms in the mother consisted mainly of Firmicutes and Bacteroidetes, and the main gene detected was tet(O), although tet(W) and tet(X) were also found. Identical Tc(r) gene sequences were present in different bacterial families and even phyla, which may indicate horizontal transfer within the maternal GIT. In the infant library, Tc(r) was present exclusively in streptococci carrying tet(M), tet(L) and erm(T) within a novel composite transposon, Tn6079. This transposon belongs to a family of broad host range conjugative elements, implying a potential for the joint spread of tetracycline and erythromycin resistance within the infant's gut. In addition, although not found in the infant metagenomic library, tet(O) and tet(W) could be detected in the uncloned DNA purified from the infant fecal sample. This is the first study to reveal the diversity of Tc(r) bacteria in the human gut, to detect a likely transmission of antibiotic resistance from mother to infant GITs and to indicate the possible occurrence of gene transfers among distantly related bacteria coinhabiting the GIT of the same individual.

Natural evolution of TEM-1 ß-lactamase: experimental reconstruction and clinical relevance.

TEM-1 ß-lactamase is one of the most well-known antibiotic resistance determinants around. It confers resistance to penicillins and early cephalosporins and has shown an astonishing functional plasticity in response to the introduction of novel drugs derived from these antibiotics. Since its discovery in the 1960s, over 170 variants of TEM-1 - with different amino acid sequences and often resistance phenotypes - have been isolated in hospitals and clinics worldwide. Next to this well-documented 'natural' evolution, the in vitro evolution of TEM-1 has been the focus of attention of many experimental studies. In this review, we compare the natural and laboratory evolution of TEM-1 in order to address the question to what extent the evolution of antibiotic resistance can be repeated, and hence might have been predicted, under laboratory conditions. We also use the comparison to gain an insight into the adaptive relevance of hitherto uncharacterized substitutions present in clinical isolates and to predict substitutions not yet observed in nature. Based on new structural insights, we review what is known about substitutions in TEM-1 that contribute to the extension of its resistance phenotype. Finally, we address the clinical relevance of TEM alleles during the past decade, which has been dominated by the emergence of another ß-lactamase, CTX-M.

What antimicrobial resistance has taught us about horizontal gene transfer.

Horizontal gene transfer (HGT) has been responsible for the dissemination of numerous antimicrobial-resistance determinants throughout diverse bacterial species. The rapid and broad dissemination of resistance determinants by HGT, and subsequent selection for resistance imposed by the use of antimicrobials, threatens to undermine the usefulness of antimicrobials. However, vigilant surveillance of the emerging antimicrobial resistance in clinical settings and subsequent studies of resistant isolates create a powerful system for studying HGT and detecting rare events. Two of the most closely monitored phenotypes are resistance to beta-lactams and resistance to fluoroquinolones. Studies of resistance to these antimicrobials have revealed that (1) transformation occurs between different species of bacteria including some recipient species that were not previously known to be competent for natural transformation; (2) transduction may be playing an important role in generating novel methicillin-resistant Staphylococcus aureus (MRSA) strains, although the details of transferring the SCCmec element are not yet fully understood; (3) Resistance genes are probably moving to plasmids from chromosomes more rapidly than in the past; and (4) Resistance genes are aggregating upon plasmids. The linkage of numerous resistance genes on individual plasmids may underlie the persistence of resistance to specific antimicrobials even when use of those antimicrobials is discontinued. Further studies of HGT and methods for controlling HGT may be necessary to maintain the usefulness of antimicrobials.