ChemGAPP: a tool for chemical genomics analysis and phenotypic profiling.

MOTIVATION: High-throughput chemical genomic screens produce informative datasets, providing valuable insights into unknown gene function on a genome-wide level. However, there is currently no comprehensive analytic package publicly available. We developed ChemGAPP to bridge this gap. ChemGAPP integrates various steps in a streamlined and user-friendly format, including rigorous quality control measures to curate screening data. RESULTS: ChemGAPP provides three sub-packages for different chemical-genomic screens: ChemGAPP Big for large-scale screens; ChemGAPP Small for small-scale screens; and ChemGAPP GI for genetic interaction screens. ChemGAPP Big, tested against the Escherichiacoli KEIO collection, revealed reliable fitness scores which displayed biologically relevant phenotypes. ChemGAPP Small demonstrated significant changes in phenotype in a small-scale screen. ChemGAPP GI was benchmarked against three sets of genes with known epistasis types and successfully reproduced each interaction type. AVAILABILITY AND IMPLEMENTATION: ChemGAPP is available at https://github.com/HannahMDoherty/ChemGAPP, as a standalone Python package as well as Streamlit applications.

Plasmid permissiveness of wastewater microbiomes can be predicted from 16S rRNA sequences by machine learning.

MOTIVATION: Wastewater treatment plants (WWTPs) harbor a dense and diverse microbial community. They constantly receive antimicrobial residues and resistant strains, and therefore provide conditions for horizontal gene transfer (HGT) of antimicrobial resistance (AMR) determinants. This facilitates the transmission of clinically important genes between, e.g. enteric and environmental bacteria, and vice versa. Despite the clinical importance, tools for predicting HGT remain underdeveloped. RESULTS: In this study, we examined to which extent water cycle microbial community composition, as inferred by partial 16S rRNA gene sequences, can predict plasmid permissiveness, i.e. the ability of cells to receive a plasmid through conjugation, based on data from standardized filter mating assays using fluorescent bio-reporter plasmids. We leveraged a range of machine learning models for predicting the permissiveness for each taxon in the community, representing the range of hosts a plasmid is able to transfer to, for three broad host-range resistance IncP plasmids (pKJK5, pB10, and RP4). Our results indicate that the predicted permissiveness from the best performing model (random forest) showed a moderate-to-strong average correlation of 0.49 for pB10 [95% confidence interval (CI): 0.44-0.55], 0.43 for pKJK5 (0.95% CI: 0.41-0.49), and 0.53 for RP4 (0.95% CI: 0.48-0.57) with the experimental permissiveness in the unseen test dataset. Predictive phylogenetic signals occurred despite the broad host-range nature of these plasmids. Our results provide a framework that contributes to the assessment of the risk of AMR pollution in wastewater systems. AVAILABILITY AND IMPLEMENTATION: The predictive tool is available as an application at https://github.com/DaneshMoradigaravand/PlasmidPerm.

The lipoprotein DolP affects cell separation in Escherichia coli, but not as an upstream regulator of NlpD.

Bacterial amidases are essential to split the shared envelope of adjunct daughter cells to allow cell separation. Their activity needs to be precisely controlled to prevent cell lysis. In Escherichia coli, amidase activity is controlled by three regulatory proteins NlpD, EnvC and ActS. However, recent studies linked the outer membrane lipoprotein DolP (formerly YraP) as a potential upstream regulator of NlpD. In this study we explored this link in further detail. To our surprise DolP did not modulate amidase activity in vitro and was unable to interact with NlpD in pull-down and MST (MicroScale Thermophoresis) assays. Next, we excluded the hypothesis that ΔdolP phenocopied ΔnlpD in a range of envelope stresses. However, morphological analysis of double deletion mutants of amidases (AmiA, AmiB AmiC) and amidase regulators with dolP revealed that ΔamiAΔdolP and ΔenvCΔdolP mutants display longer chain length compared to their parental strains indicating a role for DolP in cell division. Overall, we present evidence that DolP does not affect NlpD function in vitro, implying that DolP is not an upstream regulator of NlpD. However, DolP may impact daughter cell separation by interacting directly with AmiA or AmiC, or by a yet undiscovered mechanism.

Detection of vancomycin-resistant Enterococcus faecium hospital-adapted lineages in municipal wastewater treatment plants indicates widespread distribution and release into the environment.

Vancomycin-resistant Enterococcus faecium (VREfm) is a leading cause of healthcare-associated infection. Reservoirs of VREfm are largely assumed to be nosocomial although there is a paucity of data on alternative sources. Here, we describe an integrated epidemiological and genomic analysis of E. faecium associated with bloodstream infection and isolated from wastewater. Treated and untreated wastewater from 20 municipal treatment plants in the East of England, United Kingdom was obtained and cultured to isolate E. faecium, ampicillin-resistant E. faecium (AREfm), and VREfm. VREfm was isolated from all 20 treatment plants and was released into the environment by 17/20 plants, the exceptions using terminal ultraviolet light disinfection. Median log10 counts of AREfm and VREfm in untreated wastewater from 10 plants in direct receipt of hospital sewage were significantly higher than 10 plants that were not. We sequenced and compared the genomes of 423 isolates from wastewater with 187 isolates associated with bloodstream infection at five hospitals in the East of England. Among 481 E. faecium isolates belonging to the hospital-adapted clade, we observed genetic intermixing between wastewater and bloodstream infection, with highly related isolates shared between a major teaching hospital in the East of England and 9/20 plants. We detected 28 antibiotic resistance genes in the hospital-adapted clade, of which 23 were represented in bloodstream, hospital sewage, and municipal wastewater isolates. We conclude that our findings are consistent with widespread distribution of hospital-adapted VREfm beyond acute healthcare settings with extensive release of VREfm into the environment in the East of England.

A One Health Study of the Genetic Relatedness of Klebsiella pneumoniae and Their Mobile Elements in the East of England.

BACKGROUND: Klebsiella pneumoniae is a human, animal, and environmental commensal and a leading cause of nosocomial infections, which are often caused by multiresistant strains. We evaluate putative sources of K. pneumoniae that are carried by and infect hospital patients. METHODS: We conducted a 6-month survey on 2 hematology wards at Addenbrooke's Hospital, Cambridge, United Kingdom, in 2015 to isolate K. pneumoniae from stool, blood, and the environment. We conducted cross-sectional surveys of K. pneumoniae from 29 livestock farms, 97 meat products, the hospital sewer, and 20 municipal wastewater treatment plants in the East of England between 2014 and 2015. Isolates were sequenced and their genomes compared. RESULTS: Klebsiella pneumoniae was isolated from stool of 17/149 (11%) patients and 18/922 swabs of their environment, together with 1 bloodstream infection during the study and 4 others over a 24-month period. Each patient carried 1 or more lineages that was unique to them, but 2 broad environmental contamination events and patient-environment transmission were identified. Klebsiella pneumoniae was isolated from cattle, poultry, hospital sewage, and 12/20 wastewater treatment plants. There was low genetic relatedness between isolates from patients/their hospital environment vs isolates from elsewhere. Identical genes encoding cephalosporin resistance were carried by isolates from humans/environment and elsewhere but were carried on different plasmids. CONCLUSION: We identified no patient-to-patient transmission and no evidence for livestock as a source of K. pneumoniae infecting humans. However, our findings reaffirm the importance of the hospital environment as a source of K. pneumoniae associated with serious human infection.

Recent independent emergence of multiple multidrug-resistant Serratia marcescens clones within the United Kingdom and Ireland.

Serratia marcescens, a member of the Enterobacteriaceae family, is a Gram-negative bacterium responsible for a wide range of nosocomial infections. The emergence of multidrug-resistant strains is an increasing danger to public health. To design effective means to control the dissemination of S. marcescens, an in-depth analysis of the population structure and variation is required. Utilizing whole-genome sequencing, we characterized the population structure and variation, as well as the antimicrobial resistance determinants, of a systematic collection of antimicrobial-resistant S. marcescens associated with bloodstream infections in hospitals across the United Kingdom and Ireland between 2001 and 2011. Our results show that S. marcescens is a diverse species with a high level of genomic variation. However, the collection was largely composed of a limited number of clones that emerged from this diverse background within the past few decades. We identified potential recent transmissions of these clones, within and between hospitals, and showed that they have acquired antimicrobial resistance determinants for different beta-lactams, ciprofloxacin, and tetracyclines on multiple occasions. The expansion of these multidrug-resistant clones suggests that the treatment of S. marcescens infections will become increasingly difficult in the future.

Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data.

The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The detection of antibiotic resistance is typically made by measuring a few known determinants previously identified from genome sequencing, and thus requires the prior knowledge of its biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average accuracy of 0.91 on held-out data (range 0.81-0.97). While the best models most frequently employed gene content, an average accuracy score of 0.79 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and significantly improved prediction only for two antibiotics, including ciprofloxacin. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data can be informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic.

What Is Resistance? Impact of Phenotypic versus Molecular Drug Resistance Testing on Therapy for Multi- and Extensively Drug-Resistant Tuberculosis.

Rapid and accurate drug susceptibility testing (DST) is essential for the treatment of multi- and extensively drug-resistant tuberculosis (M/XDR-TB). We compared the utility of genotypic DST assays with phenotypic DST (pDST) using Bactec 960 MGIT or Löwenstein-Jensen to construct M/XDR-TB treatment regimens for a cohort of 25 consecutive M/XDR-TB patients and 15 possible anti-TB drugs. Genotypic DST results from Cepheid GeneXpert MTB/RIF (Xpert) and line probe assays (LPAs; Hain GenoType MTBDRplus 2.0 and MTBDRsl 2.0) and whole-genome sequencing (WGS) were translated into individual algorithm-derived treatment regimens for each patient. We further analyzed if discrepancies between the various methods were due to flaws in the genotypic or phenotypic test using MIC results. Compared with pDST, the average agreement in the number of drugs prescribed in genotypic regimens ranged from just 49% (95% confidence interval [CI], 39 to 59%) for Xpert and 63% (95% CI, 56 to 70%) for LPAs to 93% (95% CI, 88 to 98%) for WGS. Only the WGS regimens did not contain any drugs to which pDST showed resistance. Importantly, MIC testing revealed that pDST likely underestimated the true rate of resistance for key drugs (rifampin, levofloxacin, moxifloxacin, and kanamycin) because critical concentrations (CCs) were too high. WGS can be used to rule in resistance even in M/XDR strains with complex resistance patterns, but pDST for some drugs is still needed to confirm susceptibility and construct the final regimens. Some CCs for pDST need to be reexamined to avoid systematic false-susceptible results in low-level resistant isolates.

Some Synonymous and Nonsynonymous gyrA Mutations in Mycobacterium tuberculosis Lead to Systematic False-Positive Fluoroquinolone Resistance Results with the Hain GenoType MTBDRsl Assays.

In this study, using the Hain GenoType MTBDRsl assays (versions 1 and 2), we found that some nonsynonymous and synonymous mutations in gyrA in Mycobacterium tuberculosis result in systematic false-resistance results to fluoroquinolones by preventing the binding of wild-type probes. Moreover, such mutations can prevent the binding of mutant probes designed for the identification of specific resistance mutations. Although these mutations are likely rare globally, they occur in approximately 7% of multidrug-resistant tuberculosis strains in some settings.

Role of Alanine Racemase Mutations in Mycobacterium tuberculosis d-Cycloserine Resistance.

A screening of more than 1,500 drug-resistant strains of Mycobacterium tuberculosis revealed evolutionary patterns characteristic of positive selection for three alanine racemase (Alr) mutations. We investigated these mutations using molecular modeling, in vitro MIC testing, as well as direct measurements of enzymatic activity, which demonstrated that these mutations likely confer resistance to d-cycloserine.

Recombination accelerates adaptation on a large-scale empirical fitness landscape in HIV-1.

Recombination has the potential to facilitate adaptation. In spite of the substantial body of theory on the impact of recombination on the evolutionary dynamics of adapting populations, empirical evidence to test these theories is still scarce. We examined the effect of recombination on adaptation on a large-scale empirical fitness landscape in HIV-1 based on in vitro fitness measurements. Our results indicate that recombination substantially increases the rate of adaptation under a wide range of parameter values for population size, mutation rate and recombination rate. The accelerating effect of recombination is stronger for intermediate mutation rates but increases in a monotonic way with the recombination rates and population sizes that we examined. We also found that both fitness effects of individual mutations and epistatic fitness interactions cause recombination to accelerate adaptation. The estimated epistasis in the adapting populations is significantly negative. Our results highlight the importance of recombination in the evolution of HIV-I.

Wild-Type and Non-Wild-Type Mycobacterium tuberculosis MIC Distributions for the Novel Fluoroquinolone Antofloxacin Compared with Those for Ofloxacin, Levofloxacin, and Moxifloxacin.

Antofloxacin (AFX) is a novel fluoroquinolone that has been approved in China for the treatment of infections caused by a variety of bacterial species. We investigated whether it could be repurposed for the treatment of tuberculosis by studying its in vitro activity. We determined the wild-type and non-wild-type MIC ranges for AFX as well as ofloxacin (OFX), levofloxacin (LFX), and moxifloxacin (MFX), using the microplate alamarBlue assay, of 126 clinical Mycobacterium tuberculosis strains from Beijing, China, of which 48 were OFX resistant on the basis of drug susceptibility testing on Löwenstein-Jensen medium. The MIC distributions were correlated with mutations in the quinolone resistance-determining regions of gyrA (Rv0006) and gyrB (Rv0005). Pharmacokinetic/pharmacodynamic (PK/PD) data for AFX were retrieved from the literature. AFX showed lower MIC levels than OFX but higher MIC levels than LFX and MFX on the basis of the tentative epidemiological cutoff values (ECOFFs) determined in this study. All strains with non-wild-type MICs for AFX harbored known resistance mutations that also resulted in non-wild-type MICs for LFX and MFX. Moreover, our data suggested that the current critical concentration of OFX for Löwenstein-Jensen medium that was recently revised by the World Health Organization might be too high, resulting in the misclassification of phenotypically non-wild-type strains with known resistance mutations as wild type. On the basis of our exploratory PK/PD calculations, the current dose of AFX is unlikely to be optimal for the treatment of tuberculosis, but higher doses could be effective.

The impact of natural transformation on adaptation in spatially structured bacterial populations.

BACKGROUND: Recent studies have demonstrated that natural transformation and the formation of highly structured populations in bacteria are interconnected. In spite of growing evidence about this connection, little is known about the dynamics of natural transformation in spatially structured bacterial populations. RESULTS: In this work, we model the interdependency between the dynamics of the bacterial gene pool and those of environmental DNA in space to dissect the effect of transformation on adaptation. Our model reveals that even with only a single locus under consideration, transformation with a free DNA fragment pool results in complex adaptation dynamics that do not emerge in previous models focusing only on the gene shuffling effect of transformation at multiple loci. We demonstrate how spatial restriction on population growth and DNA diffusion in the environment affect the impact of transformation on adaptation. We found that in structured bacterial populations intermediate DNA diffusion rates predominantly cause transformation to impede adaptation by spreading deleterious alleles in the population. CONCLUSION: Overall, our model highlights distinctive evolutionary consequences of bacterial transformation in spatially restricted compared to planktonic bacterial populations.

Adaptation through genetic time travel? Fluctuating selection can drive the evolution of bacterial transformation.

Natural transformation is a process whereby bacteria actively take up DNA from the surrounding environment and incorporate it into their genome. Natural transformation is widespread in bacteria, but its evolutionary significance is still debated. Here, we hypothesize that transformation may confer a fitness advantage in changing environments through a process we term 'genetic time travel': by taking up old genes that were retained in the environment, the bacteria may revert to a past genotypic state that proves advantageous in the present or a future environment. We scrutinize our hypothesis by means of a mathematical model involving two bacterial types (transforming and non-transforming), a single locus under natural selection and a free DNA pool. The two bacterial types were competed in environments with changing selection regimes. We demonstrate that for a wide range of parameter values for the DNA turnover rate, the transformation rate and the frequency of environmental change, the transforming type outcompetes the non-transforming type. We discuss the empirical plausibility of our hypothesis, as well as its relationship to other hypotheses for the evolution of transformation in bacteria and sex more generally, speculating that 'genetic time travel' may also be relevant in eukaryotes that undergo horizontal gene transfer.

In-silico identification of deleterious non-synonymous SNPs of TBX1 gene: Functional and structural impact towards 22q11.2DS.

The TBX1 gene plays a critical role in the development of 22q11.2 deletion syndrome (22q11.2DS), a complex genetic disorder associated with various phenotypic manifestations. In this study, we performed in-silico analysis to identify potentially deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) within the TBX1 gene and evaluate their functional and structural impact on 22q11.2DS. A comprehensive analysis pipeline involving multiple computational tools was employed to predict the pathogenicity of nsSNPs. This study assessed protein stability and explored potential alterations in protein-protein interactions. The results revealed the rs751339103(C>A), rs780800634(G>A), rs1936727304(T>C), rs1223320618(G>A), rs1248532217(T>C), rs1294927055 (C>T), rs1331240435 (A>G, rs1601289406 (A>C), rs1936726164 (G>A), and rs911796187(G>A) with a high-risk potential for affecting protein function and stability. These nsSNPs were further analyzed for their impact on post-translational modifications and structural characteristics, indicating their potential disruption of molecular pathways associated with TBX1 and its interacting partners. These findings provide a foundation for further experimental studies and elucidation of potential therapeutic targets and personalized treatment approaches for individuals affected by 22q11.2DS.

Reduced peptidoglycan synthesis capacity impairs growth of E. coli at high salt concentration.

Gram-negative bacteria have a thin peptidoglycan layer between the cytoplasmic and outer membranes protecting the cell from osmotic challenges. Hydrolases of this structure are needed to cleave bonds to allow the newly synthesized peptidoglycan strands to be inserted by synthases. These enzymes need to be tightly regulated and their activities coordinated to prevent cell lysis. To better understand this process in Escherichia coli, we probed the genetic interactions of mrcA (encodes PBP1A) and mrcB (encodes PBP1B) with genes encoding peptidoglycan amidases and endopeptidases in envelope stress conditions. Our extensive genetic interaction network analysis revealed relatively few combinations of hydrolase gene deletions with reduced fitness in the absence of PBP1A or PBP1B, showing that none of the amidases or endopeptidases is strictly required for the functioning of one of the class A PBPs. This illustrates the robustness of the peptidoglycan growth mechanism. However, we discovered that the fitness of ∆mrcB cells is significantly reduced under high salt stress and in vitro activity assays suggest that this phenotype is caused by a reduced peptidoglycan synthesis activity of PBP1A at high salt concentration.IMPORTANCEEscherichia coli and many other bacteria have a surprisingly high number of peptidoglycan hydrolases. These enzymes function in concert with synthases to facilitate the expansion of the peptidoglycan sacculus under a range of growth and stress conditions. The synthases PBP1A and PBP1B both contribute to peptidoglycan expansion during cell division and growth. Our genetic interaction analysis revealed that these two penicillin-binding proteins (PBPs) do not need specific amidases, endopeptidases, or lytic transglycosylases for function. We show that PBP1A and PBP1B do not work equally well when cells encounter high salt stress and demonstrate that PBP1A alone cannot provide sufficient PG synthesis activity under this condition. These results show how the two class A PBPs and peptidoglycan hydrolases govern cell envelope integrity in E. coli in response to environmental challenges and particularly highlight the importance of PBP1B in maintaining cell fitness under high salt conditions.

The emergence of highly resistant and hypervirulent Klebsiella pneumoniae CC14 clone in a tertiary hospital over 8 years.

BACKGROUND: Klebsiella pneumoniae is a major bacterial and opportunistic human pathogen, increasingly recognized as a healthcare burden globally. The convergence of resistance and virulence in K. pneumoniae strains has led to the formation of hypervirulent and multidrug-resistant strains with dual risk, limiting treatment options. K. pneumoniae clones are known to emerge locally and spread globally. Therefore, an understanding of the dynamics and evolution of the emerging strains in hospitals is warranted to prevent future outbreaks. METHODS: In this study, we conducted an in-depth genomic analysis on a large-scale collection of 328 multidrug-resistant (MDR) K. pneumoniae strains recovered from 239 patients from a single major hospital in the western coastal city of Jeddah in Saudi Arabia from 2014 through 2022. We employed a broad range of phylogenetic and phylodynamic methods to understand the evolution of the predominant clones on epidemiological time scales, virulence and resistance determinants, and their dynamics. We also integrated the genomic data with detailed electronic health record (EHR) data for the patients to understand the clinical implications of the resistance and virulence of different strains. RESULTS: We discovered a diverse population underlying the infections, with most strains belonging to Clonal Complex 14 (CC14) exhibiting dominance. Specifically, we observed the emergence and continuous expansion of strains belonging to the dominant ST2096 in the CC14 clade across hospital wards in recent years. These strains acquired resistance mutations against colistin and extended spectrum ß-lactamase (ESBL) and carbapenemase genes, namely blaOXA-48 and blaOXA-232, located on three distinct plasmids, on epidemiological time scales. Strains of ST2096 exhibited a high virulence level with the presence of the siderophore aerobactin (iuc) locus situated on the same mosaic plasmid as the ESBL gene. Integration of ST2096 with EHR data confirmed the significant link between colonization by ST2096 and the diagnosis of sepsis and elevated in-hospital mortality (p-value < 0.05). CONCLUSIONS: Overall, these results demonstrate the clinical significance of ST2096 clones and illustrate the rapid evolution of an emerging hypervirulent and MDR K. pneumoniae in a clinical setting.

The evolution of natural competence: disentangling costs and benefits of sex in bacteria.

One of the most challenging questions in evolutionary biology is how sex has evolved in the face of substantial fitness costs. In this study, we focus on the evolution of bacterial sex in the form of natural transformation, where cells take up exogenous DNA and integrate it into the genome. Besides the physiological cost of producing a DNA uptake system, transformation can potentially impose a genetic cost as a result of an overrepresentation of deleterious mutations in the extracellular DNA pool. On the other hand, the uptake of DNA can be beneficial not only because of genetic effects but also because of the immediate nutritional value of the DNA. To disentangle these fitness costs and benefits, we developed a mathematical model and competed three bacterial types during adaptation to a new environment: competent cells capable of DNA import and digestion; competent cells capable of DNA import, digestion, and recombination; and noncompetent cells. Our results indicate a complex interplay between several physiological and ecological factors, including the rate at which DNA is taken up, the rate of DNA decay in the medium, and the nutritional value of DNA. In finite populations, the recombining type is often favored through the Fisher-Muller effect.

The effect of bacterial recombination on adaptation on fitness landscapes with limited peak accessibility.

There is ample empirical evidence revealing that fitness landscapes are often complex: the fitness effect of a newly arisen mutation can depend strongly on the allelic state at other loci. However, little is known about the effects of recombination on adaptation on such fitness landscapes. Here, we investigate how recombination influences the rate of adaptation on a special type of complex fitness landscapes. On these landscapes, the mutational trajectories from the least to the most fit genotype are interrupted by genotypes with low relative fitness. We study the dynamics of adapting populations on landscapes with different compositions and numbers of low fitness genotypes, with and without recombination. Our results of the deterministic model (assuming an infinite population size) show that recombination generally decelerates adaptation on these landscapes. However, in finite populations, this deceleration is outweighed by the accelerating Fisher-Muller effect under certain conditions. We conclude that recombination has complex effects on adaptation that are highly dependent on the particular fitness landscape, population size and recombination rate.