Network Integrative Genomic and Transcriptomic Analysis of Carbapenem-Resistant Klebsiella pneumoniae Strains Identifies Genes for Antibiotic Resistance and Virulence

Relevance of the Adjuvant Effect between Cellular Homeostasis and Resistance to Antibiotics in Gram-Negative Bacteria with Pathogenic Capacity: A Study of Klebsiella pneumoniae

Antibiotic resistance has become a global issue. The most significant risk is the acquisition of these mechanisms by pathogenic bacteria, which can have a severe clinical impact and pose a public health risk. This problem assumes that bacterial fitness is a constant phenomenon and should be approached from an evolutionary perspective to develop the most appropriate and effective strategies to contain the emergence of strains with pathogenic potential. Resistance mechanisms can be understood as adaptive processes to stressful conditions. This review examines the relevance of homeostatic regulatory mechanisms in antimicrobial resistance mechanisms. We focus on the interactions in the cellular physiology of pathogenic bacteria, particularly Gram-negative bacteria, and specifically Klebsiella pneumoniae. From a clinical research perspective, understanding these interactions is crucial for comprehensively understanding the phenomenon of resistance and developing more effective drugs and treatments to limit or attenuate bacterial sepsis, since the most conserved adjuvant phenomena in bacterial physiology has turned out to be more optimized and, therefore, more susceptible to alterations due to pharmacological action.

Use of a combined antibacterial synergy approach and the ANNOgesic tool to identify novel targets within the gene networks of multidrug-resistant Klebsiella pneumoniae

Since the 1980s, the development of new drug classes for the treatment of multidrug-resistant Klebsiella pneumoniae has become limited, highlighting the urgent need for novel antibiotics. To address this challenge, this study aimed to explore the synergistic interactions between chemical compounds and representative antibiotics, such as carbapenem and colistin. The primary objective of this study was not only to mitigate the adverse impact of multidrug-resistant K. pneumoniae on public health but also to establish a sustainable balance among humans, animals, and the environment. Phenotypical measurements were conducted using the broth microdilution technique to determine the drug sensitivity of bacterial strains. Additionally, a genotypical approach was employed, involving traditional RNA sequencing analysis to identify differentially expressed genes and the computational ANNOgesic tool to detect noncoding RNAs. This study revealed the existence of various pathways and regulatory RNA elements that form a functional network. These pathways, characterized by the expression of specific genes, contribute to the combined treatment effect and bacterial survival strategies. The connections between pathways are facilitated by regulatory RNA elements that respond to environmental changes. These findings suggest an adaptive response of bacteria to harsh environmental conditions.IMPORTANCENoncoding RNAs were identified as key players in post-transcriptional regulation. Moreover, this study predicted the presence of novel small regulatory RNAs that interact with target genes, as well as the involvement of riboswitches and RNA thermometers in conjunction with associated genes. These findings will contribute to the discovery of potential antimicrobial therapeutic candidates. Overall, this study offers valuable insights into the synergistic effects of chemical compounds and antibiotics, highlighting the role of regulatory RNA elements in bacterial response, and survival strategies. The identification of novel noncoding RNAs and their interactions with target genes, riboswitches, and RNA thermometers holds promise for the development of antimicrobial therapies.

Uncovering the secrets of resistance: An introduction to computational methods in infectious disease research

Antimicrobial resistance (AMR) is a growing global concern with significant implications for infectious disease control and therapeutics development. This chapter presents a comprehensive overview of computational methods in the study of AMR. We explore the prevalence and statistics of AMR, underscoring its alarming impact on public health. The role of AMR in infectious disease outbreaks and its impact on therapeutics development are discussed, emphasizing the need for novel strategies. Resistance mutations are pivotal in AMR, enabling pathogens to evade antimicrobial treatments. We delve into their importance and contribution to the spread of AMR. Experimental methods for quantitatively evaluating resistance mutations are described, along with their limitations. To address these challenges, computational methods provide promising solutions. We highlight the advantages of computational approaches, including rapid analysis of large datasets and prediction of resistance profiles. A comprehensive overview of computational methods for studying AMR is presented, encompassing genomics, proteomics, structural bioinformatics, network analysis, and machine learning algorithms. The strengths and limitations of each method are briefly outlined. Additionally, we introduce ResScan-design, our own computational method, which employs a protein (re)design protocol to identify potential resistance mutations and adaptation signatures in pathogens. Case studies are discussed to showcase the application of ResScan in elucidating hotspot residues, understanding underlying mechanisms, and guiding the design of effective therapies. In conclusion, we emphasize the value of computational methods in understanding and combating AMR. Integration of experimental and computational approaches can expedite the discovery of innovative antimicrobial treatments and mitigate the threat posed by AMR.

Outbreak report of polymyxin-carbapenem-resistant Klebsiella pneumoniae causing untreatable infections evidenced by synergy tests and bacterial genomes

Polymyxin-carbapenem-resistant Klebsiella pneumoniae (PCR-Kp) with pan (PDR)- or extensively drug-resistant phenotypes has been increasingly described worldwide. Here, we report a PCR-Kp outbreak causing untreatable infections descriptively correlated with bacterial genomes. Hospital-wide surveillance of PCR-Kp was initiated in December-2014, after the first detection of a K. pneumoniae phenotype initially classified as PDR, recovered from close spatiotemporal cases of a sentinel hospital in Rio de Janeiro. Whole-genome sequencing of clinical PCR-Kp was performed to investigate similarities and dissimilarities in phylogeny, resistance and virulence genes, plasmid structures and genetic polymorphisms. A target phenotypic profile was detected in 10% (12/117) of the tested K. pneumoniae complex bacteria recovered from patients (8.5%, 8/94) who had epidemiological links and were involved in intractable infections and death, with combined therapeutic drugs failing to meet synergy. Two resistant bacterial clades belong to the same transmission cluster (ST437) or might have different sources (ST11). The severity of infection was likely related to patients' comorbidities, lack of antimicrobial therapy and predicted bacterial genes related to high resistance, survival, and proliferation. This report contributes to the actual knowledge about the natural history of PCR-Kp infection, while reporting from a time when there were no licensed drugs in the world to treat some of these infections. More studies comparing clinical findings with bacterial genetic markers during clonal spread are needed.

Genome-scale metabolic modeling reveals increased reliance on valine catabolism in clinical isolates of Klebsiella pneumoniae

Infections due to carbapenem-resistant Enterobacteriaceae have recently emerged as one of the most urgent threats to hospitalized patients within the United States and Europe. By far the most common etiological agent of these infections is Klebsiella pneumoniae, frequently manifesting in hospital-acquired pneumonia with a mortality rate of ~50% even with antimicrobial intervention. We performed transcriptomic analysis of data collected previously from in vitro characterization of both laboratory and clinical isolates which revealed shifts in expression of multiple master metabolic regulators across isolate types. Metabolism has been previously shown to be an effective target for antibacterial therapy, and genome-scale metabolic network reconstructions (GENREs) have provided a powerful means to accelerate identification of potential targets in silico. Combining these techniques with the transcriptome meta-analysis, we generated context-specific models of metabolism utilizing a well-curated GENRE of K. pneumoniae (iYL1228) to identify novel therapeutic targets. Functional metabolic analyses revealed that both composition and metabolic activity of clinical isolate-associated context-specific models significantly differs from laboratory isolate-associated models of the bacterium. Additionally, we identified increased catabolism of L-valine in clinical isolate-specific growth simulations. These findings warrant future studies for potential efficacy of valine transaminase inhibition as a target against K. pneumoniae infection.

Porphyromonas gingivalis resistance and virulence: An integrated functional network analysis

BACKGROUND

The gram-negative bacteria Porphyromonas gingivalis (PG) is the most prevalent cause of periodontal diseases and multidrug-resistant (MDR) infections. Periodontitis and MDR infections are severe due to PG's ability to efflux antimicrobial and virulence factors. This gives rise to colonisation, biofilm development, evasion, and modulation of the host defence system. Despite extensive studies on the MDR efflux pump in other pathogens, little is known about the efflux pump and its association with the virulence factor in PG. Prolonged infection of PG leads to complete loss of teeth and other systemic diseases. This necessitates the development of new therapeutic interventions to prevent and control MDR.

OBJECTIVE

The study aims to identify the most indispensable proteins that regulate both resistance and virulence in PG, which could therefore be used as a target to fight against the MDR threat to antibiotics.

METHODS

We have adopted a hierarchical network-based approach to construct a protein interaction network. Firstly, individual networks of four major efflux pump proteins and two virulence regulatory proteins were constructed, followed by integrating them into one. The relationship between proteins was investigated using a combination of centrality scores, k-core network decomposition, and functional annotation, to computationally identify the indispensable proteins.

RESULTS

Our study identified four topologically significant genes, PG_0538, PG_0539, PG_0285, and PG_1797, as potential pharmacological targets. PG_0539 and PG_1797 were identified to have significant associations between the efflux pump and virulence genes. This type of underpinning research may help in narrowing the drug spectrum used for treating periodontal diseases, and may also be exploited to look into antibiotic resistance and pathogenicity in bacteria other than PG.

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

PangenomeNet: a pan-genome-based network reveals functional modules on antimicrobial resistome for Escherichia coli strains

BACKGROUND

Discerning genes crucial to antimicrobial resistance (AMR) mechanisms is becoming more and more important to accurately and swiftly identify AMR pathogenic strains. Pangenome-wide association studies (e.g. Scoary) identified numerous putative AMR genes. However, only a tiny proportion of the putative resistance genes are annotated by AMR databases or Gene Ontology. In addition, many putative resistance genes are of unknown function (termed hypothetical proteins). An annotation tool is crucially needed in order to reveal the functional organization of the resistome and expand our knowledge of the AMR gene repertoire.

RESULTS

We developed an approach (PangenomeNet) for building co-functional networks from pan-genomes to infer functions for hypothetical genes. Using Escherichia coli as an example, we demonstrated that it is possible to build co-functional network from its pan-genome using co-inheritance, domain-sharing, and protein-protein-interaction information. The investigation of the network revealed that it fits the characteristics of biological networks and can be used for functional inferences. The subgraph consisting of putative meropenem resistance genes consists of clusters of stress response genes and resistance gene acquisition pathways. Resistome subgraphs also demonstrate drug-specific AMR genes such as beta-lactamase, as well as functional roles shared among multiple classes of drugs, mostly in the stress-related pathways.

CONCLUSIONS

By demonstrating the idea of pan-genome-based co-functional network on the E. coli species, we showed that the network can infer functional roles of the genes, including those without functional annotations, and provides holistic views on the putative antimicrobial resistomes. We hope that the pan-genome network idea can help formulate hypothesis for targeted experimental works.

Phenotypic and Genotypic Features of Klebsiella pneumoniae Harboring Carbapenemases in Egypt: OXA-48-Like Carbapenemases as an Investigated Model

This study aimed at the characterization of carbapenem-resistant Klebsiella pneumoniae isolates focusing on typing of the bla_OXA-48-like genes. Additionally, the correlation between the resistance pattern and biofilm formation capacity of the carbapenem-resistant K. pneumoniae isolates was studied. The collected isolates were assessed for their antimicrobial resistance and carbapenemases production by a modified Hodge test and inhibitor-based tests. The carbapenemases encoding genes (bla_KPC, bla_NDM, bla_VIM, bla_IMP, and bla_OXA-48-like) were detected by PCR. Isolates harboring bla_OXA-48-like genes were genotyped by Enterobacterial Repetitive Intergenic Consensus-Polymerase Chain Reaction (ERIC-PCR) and plasmid profile analysis. The discriminatory power of the three typing methods (antibiogram, ERIC-PCR, and plasmid profile analysis) was compared by calculation of Simpson's Diversity Index (SDI). The transferability of bla_OXA-48 gene was tested by chemical transformation. The biofilm formation capacity and the prevalence of the genes encoding the fimbrial adhesins (fimH-1 and mrkD) were investigated. The isolates showed remarkable resistance to β-lactams and non-β-lactams antimicrobials. The coexistence of the investigated carbapenemases encoding genes was prevalent except for only 15 isolates. The plasmid profile analysis had the highest discriminatory power (SDI = 0.98) in comparison with ERIC-PCR (SDI = 0.89) and antibiogram (SDI = 0.78). The transferability of bla_OXA-48 gene was unsuccessful. All isolates were biofilm formers with the absence of a significant correlation between the biofilm formation capacity and resistance profile. The genes fimH-1 and mrkD were prevalent among the isolates. The prevalence of carbapenemases encoding genes, especially bla_OXA-48-like genes in Egyptian healthcare settings, is worrisome and necessitates further strict dissemination control measures.

Integrated Co-functional Network Analysis on the Resistance and Virulence Features in Acinetobacter baumannii

Acinetobacter baumannii is one of the most troublesome bacterial pathogens that pose major public health threats due to its rapidly increasing drug resistance property. It is not only derived from clinic setting but also emerges from aquaculture as a fish pathogen, which could pass the resistant genes in the food chain. Understanding the mechanism of antibiotic resistance development and pathogenesis will aid our battle with the infections caused by A. baumannii. In this study, we constructed a co-functional network by integrating multiple sources of data from A. baumannii and then used the k-shell decomposition to analyze the co-functional network. We found that genes involving in basic cellular physiological function, including genes for antibiotic resistance, tended to have high k-shell values and locate in the internal layer of our network. In contrast, the non-essential genes, such as genes associated with virulence, tended to have lower k-shell values and locate in the external layer. This finding allows us to fish out the potential antibiotic resistance factors and virulence factors. In addition, we constructed an online platform ABviresDB (https://acba.shinyapps.io/ABviresDB/) for visualization of the network and features of each gene in A. baumannii. The network analysis in this study will not only aid the study on A. baumannii but also could be referenced for the research of antibiotic resistance and pathogenesis in other bacteria.

Development, spread and persistence of antibiotic resistance genes (ARGs) in the soil microbiomes through co-selection

Bacterial pathogens resistant to multiple antibiotics are emergent threat to the public health which may evolve in the environment due to the co-selection of antibiotic resistance, driven by poly aromatic hydrocarbons (PAHs) and/or heavy metal contaminations. The co-selection of antibiotic resistance (AMR) evolves through the co-resistance or cross-resistance, or co-regulatory mechanisms, present in bacteria. The persistent toxic contaminants impose widespread pressure in both clinical and environmental setting, and may potentially cause the maintenance and spread of antibiotic resistance genes (ARGs). In the past few years, due to exponential increase of AMR, numerous drugs are now no longer effective to treat infectious diseases, especially in cases of bacterial infections. In this mini-review, we have described the role of co-resistance and cross-resistance as main sources for co-selection of ARGs; while other co-regulatory mechanisms are also involved with cross-resistance that regulates multiple ARGs. However, co-factors also support selections, which results in development and evolution of ARGs in absence of antibiotic pressure. Efflux pumps present on the same mobile genetic elements, possibly due to the function of Class 1 integrons (Int1), may increase the presence of ARGs into the environment, which further is promptly changed as per environmental conditions. This review also signifies that mutation plays important role in the expansion of ARGs due to presence of diverse types of anthropogenic pollutants, which results in overexpression of efflux pump with higher bacterial fitness cost; and these situations result in acquisition of resistant genes. The future aspects of co-selection with involvement of systems biology, synthetic biology and gene network approaches have also been discussed.

Transcriptome-based design of antisense inhibitors potentiates carbapenem efficacy in CRE Escherichia coli

In recent years, the prevalence of carbapenem-resistant Enterobacteriaceae (CRE) has risen substantially, and the study of CRE resistance mechanisms has become increasingly important for antibiotic development. Although much research has focused on genomic resistance factors, relatively few studies have examined CRE pathogens through changes in gene expression. In this study, we examined the gene expression profile of a CRE Escherichia coli clinical isolate that is sensitive to meropenem but resistant to ertapenem to explore transcriptomic contributions to resistance and to identify gene knockdown targets for carbapenem potentiation. We sequenced total and short RNA to analyze the gene expression response to ertapenem or meropenem treatment and found significant expression changes in genes related to motility, maltodextrin metabolism, the formate hydrogenlyase complex, and the general stress response. To validate these findings, we used our laboratory's Facile Accelerated Specific Therapeutic (FAST) platform to create antisense peptide nucleic acids (PNAs), gene-specific molecules designed to inhibit protein translation. PNAs were designed to inhibit the pathways identified in our transcriptomic analysis, and each PNA was then tested in combination with each carbapenem to assess its effect on the antibiotics' minimum inhibitory concentrations. We observed significant PNA-antibiotic interaction with five different PNAs across six combinations. Inhibition of the genes hycA, dsrB, and bolA potentiated carbapenem efficacy in CRE E. coli, whereas inhibition of the genes flhC and ygaC conferred added resistance. Our results identify resistance factors and demonstrate that transcriptomic analysis is a potent tool for designing antibiotic PNA.

BiomeNet: a database for construction and analysis of functional interaction networks for any species with a sequenced genome

Motivation

Owing to advanced DNA sequencing and genome assembly technology, the number of species with sequenced genomes is rapidly increasing. The aim of the recently launched Earth BioGenome Project is to sequence genomes of all eukaryotic species on Earth over the next 10 years, making it feasible to obtain genomic blueprints of the majority of animal and plant species by this time. Genetic models of the sequenced species will later be subject to functional annotation, and a comprehensive molecular network should facilitate functional analysis of individual genes and pathways. However, network databases are lagging behind genome sequencing projects as even the largest network database provides gene networks for less than 10% of sequenced eukaryotic genomes, and the knowledge gap between genomes and interactomes continues to widen.

Results

We present BiomeNet, a database of 95 scored networks comprising over 8 million co-functional links, which can build and analyze gene networks for any species with the sequenced genome. BiomeNet transfers functional interactions between orthologous proteins from source networks to the target species within minutes and automatically constructs gene networks with the quality comparable to that of existing networks. BiomeNet enables assembly of the first-in-species gene networks not available through other databases, which are highly predictive of diverse biological processes and can also provide network analysis by extracting subnetworks for individual biological processes and network-based gene prioritizations. These data indicate that BiomeNet could enhance the benefits of decoding the genomes of various species, thus improving our understanding of the Earth’ biodiversity.

Availability and implementation

The BiomeNet is freely available at http://kobic.re.kr/biomenet/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Antibody-recruiting protein-catalyzed capture agents to combat antibiotic-resistant bacteria

Antibiotic resistant infections are projected to cause over 10 million deaths by 2050, yet the development of new antibiotics has slowed. This points to an urgent need for methodologies for the rapid development of antibiotics against emerging drug resistant pathogens. We report on a generalizable combined computational and synthetic approach, called antibody-recruiting protein-catalyzed capture agents (AR-PCCs), to address this challenge. We applied the combinatorial protein catalyzed capture agent (PCC) technology to identify macrocyclic peptide ligands against highly conserved surface protein epitopes of carbapenem-resistant Klebsiella pneumoniae, an opportunistic Gram-negative pathogen with drug resistant strains. Multi-omic data combined with bioinformatic analyses identified epitopes of the highly expressed MrkA surface protein of K. pneumoniae for targeting in PCC screens. The top-performing ligand exhibited high-affinity (EC₅₀ ∼50 nM) to full-length MrkA, and selectively bound to MrkA-expressing K. pneumoniae, but not to other pathogenic bacterial species. AR-PCCs that bear a hapten moiety promoted antibody recruitment to K. pneumoniae, leading to enhanced phagocytosis and phagocytic killing by macrophages. The rapid development of this highly targeted antibiotic implies that the integrated computational and synthetic toolkit described here can be used for the accelerated production of antibiotics against drug resistant bacteria.