In Silico Prediction and Prioritisation of Novel Selective Antimicrobial Drug Targets in Escherichia Coli

doi:10.21203/rs.3.rs-131560/v1

Download PDF

Research Article

In Silico Prediction and Prioritisation of Novel Selective Antimicrobial Drug Targets in Escherichia Coli

https://doi.org/10.21203/rs.3.rs-131560/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Treatment of infections caused by Escherichia coli and other Enterobacteriaceae often requires broad-spectrum antimicrobials, which cause perturbations of the gut microbiota (dysbiosis). Novel antimicrobial drugs interfering with pathogen-specific targets would minimize the risk of such dysbiosis. Here, we employed an in silico approach to identify essential proteins in E. coli, including pathogenic ST131, that are either absent or have low homology to humans and beneficial taxa of the gut microbiota. We identified 37 potential new targets with little or no homology to the proteomes seven taxa representative of the healthy gut microbiota. The suitability of these proteins as drug targets was further analysed through essentiality and conservation in the closely related pathogen Klebsiella pneumoniae. None of them are targets of commercially used antibiotics. Eighteen proteins are involved in four functionally connected essential biological processes (replication, chromosome segregation, cell division, and outer membrane biogenesis). Our results indicate that it may be possible to selectively interfere with essential biological processes in Enterobacteriaceae that are absent or mediated by unrelated proteins in beneficial bacterial taxa residing in the gut. The identified targets can be used to discover antimicrobial drugs that are effective against these opportunistic pathogens with a decreased potential of causing dysbiosis.

Drug Discovery, Design, & Development

Bioinformatics

General Microbiology

Escherichia coli

Treatment of infections

silico

Klebsiella

Due to the worldwide increase in resistance observed among certain bacterial pathogens, there is a pressing need for novel antimicrobials. Most of the antimicrobial drugs approved for human use since the end of the antibiotic golden age in the 1960s belong to known antimicrobial classes and are primarily active against Gram-positive bacteria¹. Thus, there is an urgent need for truly new antimicrobial compounds targeting Gram-negative bacteria. In 2017 the World Health Organization released a list of global priority antimicrobial-resistant (AMR) pathogens in order to guide research, discovery and development of new antibiotics². The highest category (‘Priority 1: Critical’) comprises Acinetobacter baumannii, Pseudomonas aeruginosa and several Enterobacteriaceae, including Escherichia coli. The latter enteric species is one of the most frequent causes of bacterial infections. Specific hyper virulent clones such as E. coli O25b sequence type (ST) 131 are responsible for a wide range of extra-intestinal diseases globally, ranging from urinary tract infection to septicaemia and meningitis³. Treatment of E. coli infections is often complicated by the high frequency of multidrug resistance. In particular, ST131 is a major contributor to the global spread of fluoroquinolone resistance and extended-spectrum b-lactamase-mediated resistance to β-lactams, and is responsible for millions of multidrug-resistant infections each year⁴.

Oral antimicrobial therapy impacts the healthy gut microbiota by inducing a loss of beneficial microbes followed by expansion of opportunistic pathogenic bacteria, such as Enterobacteriaceae⁵. This phenomenon, generally referred to as dysbiosis, can in extreme cases lead to life-threatening secondary infections caused by Clostridioides difficile⁶, which are becoming increasingly common and difficult to treat. Certain antimicrobial-sensitive taxa residing in the healthy gut microbiota, e.g. Bacteroidetes and Lachnospiraceae have been shown to provide protection from C. difficile infections through colonisation resistance⁵Moreover, the gut-associated taxa Lactobacillus and Bifidobacterium both contain strains that are associated with beneficial effects on health and are used as probiotics in various food supplements, including Lactobacillus planetarum, L. paracasei, L. acidophilus, Bifidobacterium infantis, B. longum, and B. breve⁷.

Pathogen-targeted antimicrobial drugs with limited effect on beneficial organisms could potentially decrease the risk of dysbiosis and antibiotic-induced secondary infections. One approach to discover such drugs is the employment of target-based assays to identify compounds that selectively interfere with the viability of E. coli and other Enterobacteriaceae without affecting the healthy gut microbiota. This requires identification of targets that are specific for this bacterial family. Here, we performed an in silico study to identify protein drug targets in E. coli that: i) are present in pathogenic E. coli ST131; ii) do not display significant homology to proteins in the host; iii) are absent or have low homology in selected members of the healthy gut microbiota. Furthermore, the selected targets were analysed for their conservation and essentiality in the closely related pathogen K. pneumoniae. Finally, the "druggability" of the selected targets was assessed based on subcellular localisation (SCL), availability of three-dimensional structures and presence of known inhibitors.

Homology searches

The GenBank record for E. coli BW25113 (GenBank: CP009273.1) was downloaded, and the protein sequences from the 358 genes found to be essential by Goodall et al.⁸ were extracted. Five genes were removed due to being labelled as pseudogenes (ttcC, yedN, ygeF, ygeN) or putative protein (yddL).

To establish presence of proteins in E. coli O25b:H4-ST131 (NCBI:txid941322), NCBI+ BLASTp was used to BLAST the 353 protein sequences against this organism using the non-redundant BLAST database. Percent alignment was calculated by dividing the length of the hit by the length of the query protein, extracted from the NCBI record. A dual cut-off was used, where hits E-value ≤ 10E^-10, or percent ID ≥ 70% and percent alignment ≥ 75% were excluded. These cut-offs were selected to be equal to, or more stringent than those used in previous in silico studies^52,53,56,57. Any hits below the first or above the second cut-off were removed, and the remaining were taken on to the next step.

To find human analogues, the NCBI+ command line remote BLAST tool was used to BLAST the remaining protein sequences using first the Entrez queries ‘Homo sapiens [Organism]’ against the non-redundant database, and the output was sorted using the same cut-offs as described above.

Presence in beneficial gut taxa

To establish presence in the simplified gut microbiota, remote BLASTp command line applications were used to search in specific gut taxa using the Entrez queries ‘Faecalibacterium [Organism]’, ‘Bacteroides [Organism], ‘Ruminococcus [Organism]’, ‘Prevotella [Organism]’, ‘Lactobacillus [Organism]’, ‘Lachnospiraceae [Organism]' and ‘Bifidobacterium [Organism]' using the non-redundant database. These filters provided 274,718 different entries as listed in Supplementary Materials 3-9. The results were downloaded, and analysed using the same methodology and cut-offs as described above.

All hits sorted as above cut-off in the homology search against the simplified microbiota were collected and the number of hits for each protein manually evaluated. A table with the number of hits, together with the information for the highest scoring hit for each protein was generated. The proteins with less than one hits were manually inspected, and proteins with high scoring similarity were removed.

Presence and essentiality in K. pneumoniae

To find conservation in K. pneumoniae command line applications for BLASTp was used to BLAST remotely against ‘Klebsiella pneumoniae subsp. pneumoniae KPNIH1 [Organism]’ using the non-redundant database. The supplementary dataset generated by Ramage et al.¹⁷ was downloaded and used to search for the potential target genes using gene names.

Subcellular localisation and structural information

The SCL for each protein was manually checked by querying the UniProt/SwissProt database, and retrieving the information found under ‘Subcellular Localisation’.

Information about 3D structure for proteins in E. coli K-12 was manually retrieved through the UniProt/SwissProt entries for each protein individually. The PDB accession number, molecules in complex and resolution were recorded.

Dataavailability

The raw data generated during the current study are available from the corresponding author on reasonable request.

Essential genes in the target pathogen

Due to the lack of understanding of the essential genome in pathogenic strains such as ST131, the model strain BW25113 was used as a basis for our study. The predicted amino acid sequences of 353 out of the 358 essential genes identified by Goodall et al.⁸ were retrieved (Figure 1). The remaining five genes (ttcC, yddL, yedN, ygeF, ygeN), all labelled ‘pseudogenes’ or ‘putative protein’ and were not found in the Keio collection⁹, were excluded from further analysis. Their assignment as essential by Goodall et al.⁸ may be thus an artefact of the methodology employed in the original study.

The sequences of the 353 proteins were compared to those found in E. coli O25b:H4-ST131 using a pre-defined cut-off (E-value ≤ 10E^-10 or ≥ 70% sequence identity, and ≥ 75% alignment length, see Materials and Methods). All of the 353 essential BW25113 proteins were associated with at least one hit in ST131, apart from YqeL (Supplementary Materials 1). However, 15 proteins were excluded as they scored below the cut-off threshold. Inspection of these sequences revealed that many of them were prophage-related or uncharacterized proteins. The presence of phages in a bacterial genome is expected to vary with the specific strain history, and this may explain the observed difference between the laboratory strain BW25113 and the epidemic clonal lineage ST131. This left 337 essential and conserved E. coli proteins in the pipeline for further analyses.

Homology to proteins in mammalian hosts

The second step of the analysis aimed at removing E. coli targets homologous to the human proteome. A high degree of similarity between the pathogen’s target and one or more proteins in the host proteome may result in off-target binding of a drug, leading to toxicity and unwanted side effects. The 337 selected essential proteins were therefore compared to the human proteome, leading to 186 proteins fulfilling the same stringent cut-offs as above (Figure 1, Supplementary Materials 2).

Homology to proteins in beneficial taxa of the gut microbiota

The next step in the selection pipeline aimed to exclude proteins with high similarity to those found in representatives of the beneficial gut microbiota. Given the complexity and variability of the gut microbiome, we decided to focus on seven taxa containing species have previously been shown to have beneficial and protective effects on the host^5,6,10–16: Faecalibacterium, Prevotella, Ruminococcus,Bacteroides, Lactobacillus, Lachnospiraceae and Bifidobacterium (Supplementary Materials 3-9). The 186 proteins were blasted against the abovementioned taxa using the same cut-off values as before (Figure 1). As expected, this step was the most selective, leaving just 31 proteins to further analysis (Table 1) and removed all targets of commercially available antibiotics, including ParC (target of fluoroquinolones), FtsI, MrdA (targets of b-lactams), parts of the 30S and 50S ribosome (targets of macrolides, aminoglycosides tetracyclines) and RNA polymerase (target of rifamycins).

Among the identified 31 proteins, only PheM and TrpL, each encoding a leader peptide in the Phe tRNA synthetase and Trp biosynthetic operon, were found to be missing completely in all taxa. No hits for YobI (a protein of unknown function) were found in any of the taxa apart from Faecalibacterium, where one single hit was found (E-value 5.1, 61.9 % alignment and 69.2% id). SafA (part of the low pH stress response) was found to be missing in Lachnospiraceae, Bifidobacterium and Faecalibacterium. Furthermore, WzyE (probable ECA polymerase), MreD (rod shape determining protein), LolA and LolB (both part of the lipoprotein transport pathway) lacked hits in Bifidobacterium, FtsL (a cell division protein) and MukF (involved in chromosome partition) in Bacteroides and YciS (lipopolysaccharide assembly protein A) in Faecalibacterium. All other proteins were associated with hits below the cut-off in all taxa.

Due to the high stringency applied in the above step, potentially valuable targets may have been missed in the selection process. Thus, a second analysis of the microbiota BLAST results was undertaken to find proteins associated with only a few hits over cut-off. Six proteins (BamD, YfgZ, HolA, YrfF, LptD and ZipA) were each found to be associated with one hit only and had all been excluded based on E-value cut-off rather than sequence identity. Thus, these six proteins were included in further analyses, leading to 37 proteins as potential E. coli-selective targets (Table 1).

Target conservation in K. pneumoniae

We evaluated presence and essentiality of the selected targets in K. pneumoniae KPNIH1, another global priority pathogen closely related to E. coli. The essentiality of the 37 proteins was checked against the library generated by Ramage et al.¹⁷, together with conservation of the amino acid sequence as above (Figure 1, Supplementary Materials 10). Eighteen were found to be essential in both organisms, all displaying high sequence conservation, apart from ZipA and HipB. However, the majority of the targets not reported to be essential in K. pneumoniae fulfilled the selection criteria, apart from HigA, IraM, SafA, YobI and TrpL (Table 1).

Biological function of selected targets

Of the 37 identified targets (Table 1), several were found to share or have similar biological functions (Figure 2). One of the largest groups comprise of the proteins involved in outer membrane (OM) biogenesis and maintenance (Figure 2). Here, BamD is directly associated with the OM, and is part of the b-barrel assembly machinery (BAM). LptA, LptD, LptE and LptF are all part of the lipopolysaccharide (LPS) transport (Lpt) machinery. LolA and LolB are found in the periplasmic space and the periplasmic side of the OM respectively, and belong to the lipoprotein transport machinery responsible for delivering OM lipoproteins to all three of the OM assembly machineries (LOL, BAM and LPT)¹⁸. SecE is part of the SecYEG protein translocation machinery responsible for transporting proteins into the periplasm¹⁹, and PssA is involved in phospholipid biosynthesis²⁰. Furthermore, the inner membrane-protein YciS (also known as LapA, lipopolysaccharide assembly protein A) is part of a machinery responsible for envelope stress-response and regulation of LPS production²¹. Finally, although not associated with OM maintenance, TonB is part of the machinery responsible for actively importing iron across the OM in the cell²² (Figure 2).

Another two functional groups comprise of the proteins responsible for DNA replication (HolA, HolD, PriB, DnaT and YgfZ) and cell division (MukB, MukE, MukF, FtsB, FtsL, FtsQ and ZipA) (Figure 2). DNA replication is a tightly controlled mechanism and DNA Polymerase III holoenzyme is the major replication complex in E. coli, where both HolA (d subunit) and HolD (y subunit) make up parts of the clamp loading complex²³. DNA damage can cause this machinery to be stalled and disassemble on the chromosome, leading to replication failure. To re-start replication the cell must make use of the replication restart primosome, where both the PriB helicase and DnaT primase are found²⁴. YgfZ has been shown to be part of the system regulating chromosomal replication²⁵. MukBEF are unique to the g-proteobacteria and are involved in cell division, making up the only E. coli condensin for chromosome replication, segregation and organisation²⁶. Further downstream in this process the transmembrane complex FtsBL is found²⁷, together with FtsQ²⁸ and ZipA. In a related process, MreD is involved in determining cell shape²⁹

Among the proteins in the stress response category, CydX (Figure 2) is part of the CydAB cytochrome bd oxidase complex involved in aerobic respiration and maintaining the charge across the membrane used for synthesizing ATP³⁰. IraM is a regulator of s^S, the stationary phase sigma factor responsible for controlling expression of a plethora of genes involved in stress response³¹. Although the exact function of YobI has not yet been established, it has been shown to accumulate upon heat shock³².

Among the biosynthetic genes, WzyE has been implicated to be involved in assembly of the enterobacterial common antigen³³, TrpL is involved in controlling tryptophan biosynthesis³⁴ and HemD is a uroporphyrinogen III synthase³⁵ (Figure 2).

HipB and HigA together make up the category of anti-toxins of the Type II Toxin-Antitoxin system, and work to counteract the effect of their cognate toxins³⁶. As the sole members of their functional groups PheM is a target of transcriptional regulation (Figure 2) and is responsible for attenuation of the phenylalanyl-tRNA synthetase³⁷, while SafA is a two-component system connector³⁸.

Finally, no information regarding biological function could be found for the three proteins YcaR, YrfF and YdhL (Figure 2).

Target localisation

An essential requirement for to develop an efficient antimicrobial drug is target access. This is especially important in Gram-negative bacteria, where the double membrane structure acts as a permeability barrier, efficiently blocking many compounds from accessing intracellular targets. Subcellular localisation (SCL) was therefore considered to evaluate protein’s druggability. Swiss-Prot, the manually annotated section of UniProtKB, was used to find information on SCL for each of the 37 selected proteins (Figure 2, Table 1). The target proteins were found to be located in either the Inner Membrane (IM), Outer Membrane, Cytoplasm, Nucleoid or Periplasm (Figure 2). Notably PssA was annotated as located in both the IM and the cytoplasm. For eleven proteins (PriB, HemD, DnaT, HolA, HolD, YdhL, HigA, HipB, PheM, TrpL and YobI), no SCL had been experimentally determined. Here, the four OM associated proteins (LptD, LptE, LolB and BamD) are promising potential targets, especially LptD, which contains extracellular domains.

Existence of known inhibitors

Next, the literature was searched for previously reported inhibitors of the selected targets. As expected, none of the targets presented in Table 1 are inhibited by commercially available antibiotics. Through analysis of scientific literature we were able to identify inhibitors targeting a few of the listed targets but, to our knowledge, none has gone beyond laboratory studies: the ZipA/FtsZ interaction has been reported to be inhibited by certain antimicrobial compounds^39,40; the insect peptide Thanatin blocks LptA⁴¹; compound IMB-881 blocks the interaction between LptA and LptC⁴²; JB-95 inhibits b-barrel proteins including LptD⁴³; MAC13243 inhibits LolA⁴⁴; BamD is inhibited by an inhibitory peptide⁴⁵ while the compound IMB-H4 has been shown to block BamA-BamD interaction⁴⁶, and MukB is inhibited by the small molecules Michellamine B and NSC260594⁴⁷. Finally, multiple inhibitory compounds targeting TonB have been identified^48–50. Thanatin has been shown to possess antimicrobial activity against several Gram-negative bacteria beyond E. coli, including K. pneumoniae, Salmonella typhimurium and Enterobacter cloacae⁴¹. IMB-H4 was also able to inhibit growth in K. pneumoniae, P. aeruginosa and A. baumannii⁴⁶. NSC176319 was found to be active against S. aureus and permeabilised P. aeruginosa and A. baumannii⁴⁷. JB-95 was reported to have antimicrobial activity against A. baumannii, P. aeruginosa and Staphylococcus aureus⁴³, MAC13243 has been shown to also be active against P. aeruginosa⁴⁴ and TonB inhibition has been shown to affect A. baumanii⁴⁹. With the information provided in this study, some of these inhibitors may represent starting scaffolds for development into pathogen-specific antibacterials. In addition, they might represent useful tools in validating future target-based assays.

Target structure

Structure-guided drug design is a powerful in silico approach that can rapidly screen millions of compounds for their ability to dock into a desired target, and identified hits can subsequently be tested in vitro. Thus, 3D structures at a high enough resolution represent an advantage for the targets identified in this study.

Information retrieved from the Protein Data Bank (PDB)⁵¹ showed that 3D structures at a resolution of <3 Å existed for 18 proteins, >3 Å for 6 proteins and no structure could be found for 3 proteins, while YrfF was associated with a structure but no resolution information was reported in the database, and no structure had been reported for the remaining nine protein targets (Table 1).

The originality of the present study lies in the identification of cellular targets that may lead to the discovery of innovative pathogen-selective antimicrobial drugs with limited effect on the healthy gut microbiota. Similar in silico studies have previously been conducted for verotoxigenic E. coli O157:H7, K. pneumoniae, Yersinia pseudotuberculosis and Enterobacteriaceae^52–56. However, these studies were not designed to identify targets with low homology to the corresponding proteins in beneficial taxa residing in the intestinal tract or suffered from limitations related to the lack of a well-established essential genome for the target pathogen, or the comparisons to the human proteome.

We identified 37 potential drug targets selective for E. coli based on protein sequence homology. A large proportion of the identified proteins are functionally related between themselves. For example, MukBEF and FtsBL together with FtsQ and ZipA are all involved in regulation of the cell cycle; HolA, HolD and DnaT and PriB are part of two different complexes that are both part of the replication system in E. coli; PheM belongs to the separate, but related category of transcription regulation. OM maintenance was another large category, which is unsurprising as this structure is unique to Gram-negative bacteria while the majority of the bacteria used in this study as representatives of the healthy gut microbiota are Gram-positive. Targets belonging to this category include proteins directly associated with the OM (LptD, LptE, BamD, LolB) as well as proteins found in various IM and periplasm related processes (LptA, LptF, LolA, SecE, YciS, TonB, MreD). The fact that multiple proteins from a single pathway were identified indicates that this is potentially a good cellular function to target. A number of proteins were found to be uniquely involved in various biosynthetic processes (HemD, PssA, TrpL and WzyE), stress response (CydX, IraM and YobI), or toxin-antitoxin systems (HigA and HipB). Proteins involved in two-competent system (SafA) or tRNA processing (YgfZ) were also found. Finally, four proteins without clear functions were identified (YcaR, YdhL, YgfZ and YrfF), indicating that there is more to discover regarding E. coli biology.

When searching for homologues in seven representative taxa of the healthy gut microbiota, only TrpL and PheM lacked hits in all these groups, indicating that these structures are unique to E. coli and possibly other opportunistic pathogenic bacteria residing in the intestinal tract. However, no info on SCL and 3D structure is available for either of these two proteins and only PheM is present and highly conserved in K. pneumoniae, although it is not proven to be essential in this species. Several proteins lacked hits in one or multiple taxa: YobI was only found in Faecalibacterium; SafA was missing Lachnospiraceae, Bifidobacterium and Faecalibacterium; no hits for WzyE, MreD, LolA, LolB and FtsL were found in Bifidobacterium; FtsL and MukF were missing in Bacteroides, and finally Faecalibacterium was lacking hits for YciS. These proteins may also be appropriate targets for developing pathogen-targeted antibiotics due to their absence in some gut beneficial bacteria. The remaining proteins were associated with hits below the cut-off, indicating that a similar protein may exist but that may be divergent enough that off-target activity can be avoided.

The potential to target an infecting pathogen without affecting the beneficial microbiota makes E. coli -selective antibiotics especially attractive. The clinical value of such antibiotics would be even higher if their spectrum covered other pathogenic bacterial species such. K. pneumoniae is a close relative to E. coli, and a major source of MDR infections in health care settings. The potential target proteins found in E. coli were therefore checked for sequence similarity and essentiality in K. pneumoniae. While the majority of the proteins were highly conserved in both species, only 18 out of the 37 targets were found to be essential in this pathogen. This could be due to disparity of information since the E. coli essential genome is well characterized, whereas the one in K. pneumoniae has been less studied. However, these 18 proteins are good candidates for developing pathogen-selective antibiotics that target both species.

An issue regarding essentiality in K. pneumoniae is its reliance on a relatively uncharacterised genome. The essentiality status is defined by a single study, which may be affected by different parameters than the one used to identify the E. coli ‘essentialome’. The lack of a well-characterised essential genome in K. pneumoniae leads to limitations in this selection step, as misclassification in terms of essentiality can occur and due to the scarcity of information and verification through consensus from multiple studies was not possible here. However, sequence conservation indicates that majority of proteins are highly conserved across the two species. Due to the close relativeness between the two species, investigations into the essentiality of the highly conserved but reported non-essential proteins may be worthwhile to further characterise their suitability as drug targets.

Inhibitors interfering with the proteins identified here were only found for seven of the selected targets: ZipA (Compound 10b, Compound 4)^39,40, LptA (Thanatin, IMB-881)^41,42, LptD (JB-95)⁴³, LolA (MAC13243)⁴⁴, BamD (peptide and IMB-H4)^45,46, MukB (Michellamine B and NSC176319)⁴⁷, and TonB (multiple small bacteriostatic molecules and inhibitory compounds)^48–50. None of these are currently developed for clinical use, however the presence of inhibitors indicates that the targets are druggable. Furthermore, many of the identified inhibitors target functions related to OM related processes, further strengthening these functions as viable for development of antimicrobials.

Due to the double-membrane structure found in Gram-negative bacteria, the accessibility of a potential drug target is essential. Proteins found in the OM are therefore considered to be targets with extra potential. Here, we identified LptD, LptE, LolB and BamD as OM associated proteins. Out of these proteins, LptD also has extracellular domains, indicating that it may be possible to find an inhibitor that interferes with these portions and which would therefore not have to cross the OM. Interestingly, multiple of the OM proteins have already been targeted by various inhibitors, indicating that this is a viable strategy when developing novel antimicrobials. Notably, the four proteins listed above have an excellent ‘druggability’ potential since in addition to their optimal SCL, they are also essential in K. pneumoniae and have known 3D structures. Furthermore, all proteins were shown to be essential in K. pneumoniae and share a high degree of sequence similarity with this species, while they were found to only share low levels of sequence identity with the selected gut taxa used here. Here, LolB and LptE fell below the cut-offs used with alignment similarity ≤ 16%, and percent shared identity < 49%. Both LptD and BamD were recovered in the manual inspection of the proteins that were excluded due to falling above one of the cut-offs. However, both proteins were found to be excluded due to their E-value, and only be associated with one hit each: LptD in Bacteroides (12.5% alignment, 100% id and E-value 6,03E-62) and BamD in Lactobacillus (55.9% alignment, 24.8% id and E-value 6.06E-10). The selectivity of BamD and LptD can be further evaluated by future in vitro studies using known inhibitors that specifically interfere with these proteins.⁴³^,^45,46

All in silico studies suffer from the drawback of cut-off criteria that may have little biological relevance. Stringent cut-off values potentially exclude valuable drug targets while loose criteria may result in an unmanageable list of targets. In the present study, while keeping stringent cut-offs throughout, we were able to identify 31 proteins present only in a selected number of beneficial gut microbes and worthy of further investigations. However, a manual analysis of the rejected sequences showed that six proteins could be recovered and that may be important targets for future drug development efforts.

Another issue related to in silico studies is that essentiality may differ between in vitro and in vivo conditions. The essential genome established by Goodall et al.⁸ was characterized in rich media conditions, and therefore may miss genes essential for metabolism inside the host^8,57. Certain biosynthetic pathways may be downregulated as the pathogen instead relies on the host to supply nutrients such as amino acids, vitamins and nucleobases⁵⁷. However, targeting biosynthetic pathways involved in maintenance of the cell is likely to represent a target relevant in vitro as well as in vivo⁵⁸.

In silico studies like this are a first but essential step towards the discovery of novel pathogen-targeted antimicrobials. The results of our study provide a starting point towards the identification and development of novel specific antimicrobials targeting E. coli. Future wet-lab studies are required to validate the presumptive selective targets identified by the study. High-throughput screens can be applied to find inhibitors interfering with the specific protein targets e.g. through knock-down strains with reduced expression of the target protein. The antimicrobial activity of the identified inhibitors could subsequently be evaluated on a comprehensive strain collection representative of the healthy gut microbiota or directly on faecal samples using a metagenomics approach in order to assess their selective toxicity towards E. coli and other pathogenic Enterobacteriaceae.

Acknowledgements

We would like to thank Bastian V.H. Hornung and Defne Surjoun for their kind help with development of the Python scripts and pipelines. FSF is a recipient of a PhD fellowship from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant agreement no. 765147.

Author contributions

FSF, LG and SD designed the study. FSF conducted the experiments. FSF, LG, SD and BJ analysed the results. All authors reviewed the manuscript.

Additional information

Competing interests
The author(s) declare no competing interests.

Lewis, K. Platforms for antibiotic discovery. Nat. Rev. Drug Discov. 12, 371–387 (2013).
World health organization releases global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics. 32, 76 (2018).
Köhler, C. D. & Dobrindt, U. What defines extraintestinal pathogenic Escherichia coli? Int. J. Med. Microbiol. 301, 642–647 (2011).
Pitout, J. D. D. & DeVinney, R. Escherichia coli ST131: A multidrug-resistant clone primed for global domination. F1000Research 6, 1–7 (2017).
Buffie, C. G. & Pamer, E. G. Microbiota-mediated colonization resistance against intestinal pathogens. Nat. Rev. Immunol. 13, 790–801 (2013).
Rodriguez, C., Taminiau, B., Van Broeck, J., Delmée, M. & Daube, G. Clostridium difficile infection and intestinal microbiota interactions. Microb. Pathog. 89, 201–209 (2015).
Hills, R. D. et al. Gut microbiome: Profound implications for diet and disease. Nutrients 11, 1–40 (2019).
Goodall, E. C. A. et al. The Essential Genome of Escherichia coli K-12. MBio 9, 1–18 (2018).
Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: The Keio collection. Mol. Syst. Biol. 2, (2006).
Tamanai-Shacoori, Z. et al. Roseburia spp.: a marker of health? Future Microbiol. 12, 157–170 (2017).
Naaber, P. et al. Inhibition of Clostridium difficile strains by intestinal Lactobacillus species. J. Med. Microbiol. 53, 551–554 (2004).
Ferreira-Halder, C. V., Faria, A. V. de S. & Andrade, S. S. Action and function of Faecalibacterium prausnitzii in health and disease. Best Pract. Res. Clin. Gastroenterol. 31, 643–648 (2017).
Precup, G. & Vodnar, D.-C. Gut Prevotella as a possible biomarker of diet and its eubiotic versus dysbiotic roles: a comprehensive literature review. Br. J. Nutr. 122, 131–140 (2019).
Hsiao, A. et al. Members of the human gut microbiota involved in recovery from Vibrio cholerae infection. Nature 515, 423–426 (2014).
Vacca, M. et al. The controversial role of human gut lachnospiraceae. Microorganisms 8, 1–25 (2020).
Grimm, V., Westermann, C. & Riedel, C. U. Bifidobacteria-Host Interactions - An Update on Colonisation Factors. Biomed Res. Int. 2014, (2014).
Ramage, B. et al. Comprehensive Arrayed Transposon Mutant Library of Klebsiella pneumoniae Outbreak Strain KPNIH1. J. Bacteriol. 199, 1–9 (2017).
Grabowicz, M. Lipoprotein Transport: Greasing the Machines of Outer Membrane Biogenesis. BioEssays 40, 1700187 (2018).
Lycklama A Nijeholt, J. A., De Keyzer, J., Prabudiansyah, I. & Driessen, A. J. M. Characterization of the supporting role of SecE in protein translocation. FEBS Lett. 587, 3083–3088 (2013).
Matsumoto, K. Phosphatidylserine synthase from bacteria. Biochim. Biophys. Acta - Lipids Lipid Metab. 1348, 214–227 (1997).
Klein, G., Kobylak, N., Lindner, B., Stupak, A. & Raina, S. Assembly of Lipopolysaccharide in Escherichia coli Requires the Essential LapB Heat Shock Protein. J. Biol. Chem. 289, 14829–14853 (2014).
Postle, K. & Larsen, R. A. TonB-dependent energy transduction between outer and cytoplasmic membranes. BioMetals 20, 453–465 (2007).
Xu, Z. Q. & Dixon, N. E. Bacterial replisomes. Curr. Opin. Struct. Biol. 53, 159–168 (2018).
Huang, Y. H. & Huang, C. Y. Structural insight into the DNA-binding mode of the primosomal proteins PriA, PriB, and AnaT. Biomed Res. Int. 2014, (2014).
Ote, T. et al. Involvement of the Escherichia coli folate-binding protein YgfZ in RNA modification and regulation of chromosomal replication initiation. Mol. Microbiol. 59, 265–275 (2006).
Rybenkov, V. V., Herrera, V., Petrushenko, Z. M. & Zhao, H. MukBEF, a Chromosomal Organizer. J. Mol. Microbiol. Biotechnol. 24, 371–383 (2014).
Condon, S. G. F. et al. The FtsLB subcomplex of the bacterial divisome is a tetramer with an uninterrupted FtsL helix linking the transmembrane and periplasmic regions. J. Biol. Chem. 293, 1623–1641 (2018).
Buddelmeijer, N. & Beckwith, J. A complex of the Escherichia coli cell division proteins FtsL, FtsB and FtsQ forms independently of its localization to the septal region. Mol. Microbiol. 52, 1315–1327 (2004).
Kruse, T., Bork-Jensen, J. & Gerdes, K. The morphogenetic MreBCD proteins of Escherichia coli form an essential membrane-bound complex. Mol. Microbiol. 55, 78–89 (2005).
Vanorsdel, C. E. et al. The escherichia coli CydX protein is a member of the CydAB cytochrome bd oxidase complex and is required for cytochrome bd oxidase activity. J. Bacteriol. 195, 3640–3650 (2013).
Bougdour, A., Cunning, C., Baptiste, P. J., Elliott, T. & Gottesman, S. Multiple pathways for regulation of σS (RpoS) stability in Escherichia coli via the action of multiple anti-adaptors. Mol. Microbiol. 68, 298–313 (2008).
Hemm, M. R. et al. Small stress response proteins in Escherichia coli: Proteins missed by classical proteomic studies. J. Bacteriol. 192, 46–58 (2010).
Kajimura, J., Rahman, A. & Rick, P. D. Assembly of cyclic enterobacterial common antigen in Escherichia coli K-12. J. Bacteriol. 187, 6917–6927 (2005).
Zurawski, G., Elseviers, D., Stauffer, G. V. & Yanofsky, C. Translational control of transcription termination at the attenuator of the Escherichia coli tryptophan operon. Proc. Natl. Acad. Sci. U. S. A. 75, 5988–5992 (1978).
Jordan, P. M., Mgbeje, B. I., Thomas, S. D. & Alwan, A. F. Nucleotide sequence for the hemD gene of Escherichia coli encoding uroporphyrinogen III synthase and initial evidence for a hem operon. Biochem. J. 249, 613–616 (1988).
Fraikin, N., Goormaghtigh, F. & Van Melderen, L. Type II Toxin-Antitoxin Systems: Evolution and Revolutions. J. Bacteriol. 202, 1–14 (2020).
Springer, M. et al. Attenuation control of the Escherichia coli phenylalanyl-tRNA synthetase operon. J. Mol. Biol. 181, 467–478 (1985).
Ishii, E., Eguchi, Y. & Utsumi, R. Mechanism of activation of PhoQ/PhoP two-component signal transduction by SafA, an auxiliary protein of PhoQ histidine kinase in escherichia coli. Biosci. Biotechnol. Biochem. 77, 814–819 (2013).
Tsao, D. H. H. et al. Discovery of novel inhibitors of the ZipA/FtsZ complex by NMR fragment screening coupled with structure-based design. Bioorganic Med. Chem. 14, 7953–7961 (2006).
Jennings, L. D. et al. Design and synthesis of indolo[2,3-a]quinolizin-7-one inhibitors of the ZipA-FtsZ interaction. Bioorganic Med. Chem. Lett. 14, 1427–1431 (2004).
Vetterli, S. U. et al. Thanatin targets the intermembrane protein complex required for lipopolysaccharide transport in Escherichia coli. Sci. Adv. 4, 1–9 (2018).
Zhang, X. et al. Identification of an anti-Gram-negative bacteria agent disrupting the interaction between lipopolysaccharide transporters LptA and LptC. Int. J. Antimicrob. Agents 53, 442–448 (2019).
Urfer, M. et al. A peptidomimetic antibiotic targets outer membrane proteins and disrupts selectively the outer membrane in Escherichia coli. J. Biol. Chem. 291, 1921–1932 (2016).
Pathania, R. et al. Chemical genomics in Escherichia coli identifies an inhibitor of bacterial lipoprotein targeting. Nat. Chem. Biol. 5, 849–856 (2009).
Hagan, C. L., Wzorek, J. S. & Kahne, D. Inhibition of the β-barrel assembly machine by a peptide that binds BamD. Proc. Natl. Acad. Sci. 112, 2011–2016 (2015).
Li, Y. et al. Identification of a Compound That Inhibits the Growth of Gram-Negative Bacteria by Blocking BamA–BamD Interaction. Front. Microbiol. 11, 1–13 (2020).
Zhao, H. et al. Small Molecule Condensin Inhibitors. ACS Infect. Dis. 4, 1737–1745 (2018).
Yep, A., McQuade, T., Kirchhoff, P., Larsen, M. & Mobley, H. L. T. Inhibitors of TonB Function Identified by a High-Throughput Screen for Inhibitors of Iron Acquisition in Uropathogenic Escherichia coli CFT073. MBio 5, 1–10 (2014).
Nairn, B. L. et al. Fluorescence High-Throughput Screening for Inhibitors of TonB Action. J. Bacteriol. 199, 1–20 (2017).
Hanson, M., Jordan, L. D., Shipelskiy, Y., Newton, S. M. & Klebba, P. E. High-Throughput Screening Assay for Inhibitors of TonB-Dependent Iron Transport. J. Biomol. Screen. 21, 316–322 (2016).
Berman, H. M. et al. The protein data bank. Acta Crystallogr. Sect. D Biol. Crystallogr. 58, 899–907 (2002).
Ramos, P. I. P. et al. An integrative, multi-omics approach towards the prioritization of Klebsiella pneumoniae drug targets. Sci. Rep. 8, 10755 (2018).
Hadizadeh, M. et al. Genome-Wide Identification of Potential Drug Target in Enterobacteriaceae Family: A Homology-Based Method. Microb. Drug Resist. 24, 8–17 (2018).
Georrge, J. J. & Umrania, V. In silico identification of putative drug targets in Klebsiella pneumonia MGH78578. Indian J. Biotechnol. 10, 432–439 (2011).
Duffield, M. et al. Predicting conserved essential genes in bacteria: in silico identification of putative drug targets. Mol. Biosyst. 6, 2482 (2010).
Mondal, S. I. et al. Identification of potential drug targets by subtractive genome analysis of Escherichia coli O157:H7: an in silico approach. Adv. Appl. Bioinforma. Chem. 8, 49 (2015).
Mobegi, F. M. et al. From microbial gene essentiality to novel antimicrobial drug targets. BMC Genomics 15, 958 (2014).
Bakheet, T. M. & Doig, A. J. Properties and identification of antibiotic drug targets. BMC Bioinformatics 11, (2010).

Table 1 The identified potential protein targets with gene name, PDB accession number, RCSB PDB structural information, Swissprot SCL, available inhibitors, sequence conservation and essential status in K. pneumoniae. N/A = Not available, N/D = Not determined.

Download PDF

Version 1

posted

You are reading this latest preprint version

In Silico Prediction and Prioritisation of Novel Selective Antimicrobial Drug Targets in Escherichia Coli

Status:

Version 1

Abstract

Figures

Introduction

Methods

Results

Discussion

Declarations

References

Tables

Supplementary Files

Status:

Version 1