# Patent

## Title
Biomarkers for detecting microbial infection

## Abstract

The present invention provides specific peptide biomarkers and sets of peptide biomarkers for use in methods of detecting or identifying bacterial biomarkers in a sample, wherein said bacterial biomarkers can be used to detect Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Escherichia coli, and/or Moraxella catarrhalis in a sample. Kits and diagnostic methods are also provided.

## Background
STATEMENT OF PRIORITY
This application is a 35 U.S.C. § 371 national phase application of International Application No. PCT/EP2019/061462, filed May 3, 2019, which claims priority to Great Britain Patent Application No. 1807380.9, filed May 4, 2018, the entire contents of each of which is incorporated by reference herein in its entirety.
STATEMENT REGARDING ELECTRONIC FILING OF A SEQUENCE LISTING
A Sequence Listing in ASCII text format, submitted under 37 C.F.R. § 1.821, entitled 9737-83seqlist_ST25.txt, 62,194 bytes in size, generated on Nov. 19, 2021 and filed via EFS-Web, is provided in lieu of a paper copy. This Sequence Listing is incorporated by reference into the specification for its disclosures.
FIELD OF THE INVENTION
This invention relates to the identification of specific biomarkers for microbial infection, e.g., bacterial infection, e.g. respiratory tract infections, and their use in detecting microbes/bacteria in a sample, such as detecting respiratory tract infections directly in clinical samples.
BACKGROUND OF THE INVENTION
Correct and effective diagnosis and treatment of patients suffering from infections is an ever-present goal in healthcare, impacting both socio-economic factors but also mortality rates in cases of severe infections. With the increased attention to microbes/bacteria displaying multi-resistance and virulence traits, tools and methods responding to meet the need of better diagnostics are continuously being developed and tested. The more established and commonly used methods involve traditional cultivation-based methods and profiling based on phenotypes, but also polymerase chain reaction (PCR) assays and DNA sequencing of biomarker genes for genotype classification. Motivations to remove some of the inherent drawbacks of these methods includes automation (less manual labor) and increased speed, which has resulted in development of so-called next-generation sequencing (NGS) tools. This has also significantly impacted on the analysis cost very positively.
Still, both traditional and newer technologies often lack information about gene expression levels, and therefore current diagnostics and assays are trending towards looking also at the expressed genome, i.e. the proteome, for more in-depth analysis and characterization and typing of microbes/bacteria. During the last decade, mass spectrometry has emerged as an important tool in the clinic for characterization and typing of microbes/bacteria with the implementation of MALDI-TOF instruments. This technique is very rapid and accurate down to a certain level of information, including what species is present in the sample. Still, in general both MALDI-TOF and other currently implemented technologies based on sequencing approaches benefit significantly from isolating and culturing the microbes/bacteria prior to analysis and characterization from the patient samples, and in many cases the isolation and culturing step is a pre-requisite in order to perform the analysis. In order to further increase the speed of analysis and diagnosis, and also provide a more in-depth level of information about the infecting microbes/bacteria, new methods are still needed.
One of the most common causes of hospital visits are respiratory tract infections. Difficulties to accurately judge whether the infection is bacterial or non-bacterial (i.e. viral or fungal infections) based on fever and other symptoms may lead to over-prescription of antibiotics and the use of broad spectrum antibiotics as common practice, especially in cases of milder respiratory tract infections. Additional tests are usually needed to confirm the infectious agent, often including a culturing step, and this step may further force doctors to prescribe antibiotics “just-in-case”. This may lead to contributing to the spread of antibiotic resistance, and also increased risk for patients for other adverse events with the treatment.
Knowledge of the identity of a pathogen causing disease facilitates adequate medical treatment, and, e.g., in clinical settings, information about the identity of a microbe/bacterium can be particularly useful. For example, knowledge regarding the identity of microbe/bacterium causing an infection can help to guide treatment decisions. A quick decision on the most suitable antimicrobial treatment can significantly shorten the duration of the infection and in some cases, such as meningitides or sepsis, potentially be life-saving.
Respiratory tract infections are one of the leading causes of death, causing several million deaths worldwide. Viral infection cause the majority of acute respiratory tract infections, but bacterial infections are also a significant cause, including infections by Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Moraxella catarrhalis, Chlamydia pneumoniae, Mycoplasma pneumoniae, Klebsiella pneumoniae, Escherichia coli, and/or Pseudomonas aeruginosa. Streptococcus pneumoniae is a human pathogen and one of the most common causes of bacteremia, pneumoniae, meningitis in young children, leading to hundreds of deaths in USA and Europe, and pneumococcal pneumoniae is the most common community-acquired bacterial pneumonia (approx. 100 per 100000 adults each year).
A method of typing and characterization of microbes such as bacteria using bottom-up tandem mass-spectrometry proteomics is disclosed by Boulund et al. in Molecular & Cellular Proteomics 16.6, pages 1052-1063 (2017). The method, called “TCUP” compares the generated peptide sequence data to reference databases.
The listing or discussion of an apparently prior-published document in this specification should not necessarily be taken as an acknowledgement that the document is part of the state of the art or is common general knowledge.
DISCLOSURE OF THE INVENTION
The inventors have developed methods for detecting and/or identifying microbes and/or microbial biomarkers, e.g. bacteria and/or bacterial biomarkers. In particular, they have developed a method to detect specific biomarker sequences originating from microbes, such as bacteria in samples, e.g. clinical samples, using, e.g., mass spectrometry. Preferably, such biomarker sequences are peptides.
Thus, there is provided a method or diagnostic assay to detect specific biomarker peptides from microbes/bacteria in clinical samples using mass spectrometry. The specific biomarker peptides may be generated by proteolytic digestion of microbial/bacterial proteins. They may be identified by any suitable means, e.g. mass spectrometry analysis. The methods provided herein do not require a step of culturing microbes/bacteria prior to the biomarker detection step. Thus, they may advantageously be carried out on a sample that has not been cultured.
Embodiments and preferred embodiments of the invention are defined in the claims.
The invention provides a method of detecting or identifying a microbial/bacterial biomarker in a sample, wherein said microbial/bacterial biomarker is a peptide selected from the list of peptides 1-50 of Table 5, peptides 1-48 of Table 6, peptides 1-50 of Table 7, peptides 1-50 of Table 8, and/or peptides 1-50 of Table 9. The invention also provides a method of detecting or identifying a microbial/bacterial biomarker in a sample, wherein said microbial/bacterial biomarker is a peptide selected from those disclosed in Table 2 and/or 3. More preferably, said microbial/bacterial biomarker is a peptide selected from the list of peptides of table 10, peptides of table 11, peptides of table 12, and/or peptides of table 13. More preferably, said microbial/bacterial biomarker is a peptide selected from the list of peptides of table 14, 15, 16, and/or 17.
Any reference herein to a microbial biomarker should be understood to encompass, and preferably be, a bacterial biomarker, particularly a bacterial biomarker selected from the list of peptides 1-50 of Table 5, peptides 1-48 of Table 6, peptides 1-50 of Table 7, peptides 1-50 of Table 8, and/or peptides 1-50 of Table 9. Alternatively or in addition, the microbial biomarker may be a peptide selected from those disclosed in Table 2 and/or 3. More preferably, said microbial/bacterial biomarker is a peptide selected from the list of peptides of table 10, peptides of table 11, peptides of table 12, and/or peptides of table 13. More preferably, said microbial/bacterial biomarker is a peptide selected from the list of peptides of table 14, 15, 16, and/or 17.
The shorthand “microbes/bacteria” and derivatives thereof, such as “microbial/bacterial” should be understood to mean a microbe, which may be a bacterium.
The method may be used to detect and/or confirm the presence of microbes/bacteria in a sample.
The method may be used to identify a microbe/bacterium in a sample. Thus, it may be used to determine and/or confirm whether a particular type of microbe/bacterium is present in the sample. More particularly, it may be used to determine and/or confirm that a particular type of microbe/bacterium is present or absent in the sample.
The method may involve detecting a microbial/bacterial biomarker using mass spectrometry and/or an affinity reagent specific for the biomarker.
The microbial/bacterial biomarker may be indicative of the presence of a microbe/bacterium in the sample. Preferably, the biomarker may be indicative of the presence of a specific type of microbe/bacterium. Preferably, the biomarker may be discriminatory for a specific type of microbe/bacterium. Thus, the method may, e.g., be used to detect and/or identify a microbe/bacterium in the sample. The identification may, e.g., be at the family, genus, species, sub-species, or strain level. Preferably, it is at the family, genus or species level, e.g. the genus level or the species level.
It should be understood that any of the methods provided herein for detecting a microbial/bacterial biomarker in a sample are also suitable for determining that the sample contains microbes/bacteria (or contained microbes/bacteria prior to being processed for analysis via the method provided herein) and/or for detecting or diagnosing a microbial/bacterial infection of the sample/the subject from whom the sample is derived. Thus, any of the methods of detecting a microbial/bacterial biomarker are alternatively viewed as methods of detecting microbes/bacteria, methods of detecting a microbial/bacterial infection, and/or methods of diagnosing a microbial/bacterial infection. As mentioned elsewhere herein, preferably the identity of a specific type of microbe/bacterium is detected/determined/diagnosed.
Similarly, any of the methods provided herein for detecting a microbe/bacterium in a sample are also suitable for determining that the sample contains microbes/bacteria and/or for detecting or diagnosing a microbial/bacterial infection of the sample. Thus, any of the methods of detecting microbes/bacteria are alternatively viewed as methods of detecting a microbial/bacterial infection, and/or methods of diagnosing a microbial/bacterial infection, and/or methods of measuring, determining or detecting the presence or level of one or more biomarker peptides as described herein.
The biomarker peptide may be generated by proteolytic digestion of microbial/bacterial proteins. Thus, the method may include a step of proteolysis prior to the detection step. A proteolysis step may break one or more proteins down into one or more peptides, thereby generating peptides that may be biomarker peptides. However, certain biomarker peptides may be naturally present within the microbe/bacterium, displayed on its surface, and/or secreted by the microbe/bacterium. For example, a biomarker peptide may be part of a larger polypeptide or protein and be an epitope for an affinity reagent. The epitope is preferably accessible to the affinity agent when the protein is intact in native and/or denatured form. Therefore, a step of proteolysis may not necessarily be required and in some embodiments, the method does not include a step of proteolysis.
The microbial/bacterial biomarkers mentioned above were identified through a novel process devised by the present inventors, which involves the identification and selection of specific biomarker peptides through bioinformatics processing of mass spectrometry data. More particularly, the method of biomarker identification may include several approaches including bioinformatics (i.e. comparing the genomes of microbial/bacterial species and identifying specific proteins and protein fragments, such as peptides, that are unique to the microbial/bacterial species); and experimentally verified identification of such proteins, protein fragments and/or peptides using cultures of microbes/bacteria and/or spiked clinical samples in order to optimize the sample processing and mass spectrometry analysis; and/or detection of such biomarker peptides directly in processed clinical samples.
Certain methods of identifying potential peptide biomarkers of microbial taxonomy are known in the art. However, the prior art approaches sometimes have limited sensitivity, so there is a risk that they may not be sufficiently sensitive accurately to detect/identify microbes in a complex sample, such as a clinical specimen.
For example, a process involving bottom-up tandem mass spectrometry proteomics and bioinformatics is known in the art as “TCUP” and details thereof are provided in Boulund et al. supra. TCUP relies on curated genomic databases and when a tandem mass spectra is matched to a peptide sequence it uses the algorithm Lowest Common Ancestor (LCA) to find unique peptides for different levels of taxonomy (Family, Genus, Species). In essence, a pan-genomic approach, finding nucleotide sequences that differ between different species, thus identifying gene-markers, enables the discovery of possible genetic markers. The same can be seen in translating these nucleotide sequences into peptide sequences.
The present invention provides an advantageous method of identifying peptide biomarkers of microbial/bacterial taxonomy, e.g. peptide biomarkers specific for a first microbial/bacterial species. The method involves the experimental verification of putative biomarkers.
Thus, in a further related aspect of the invention, there is provided a method of identifying a set of peptide biomarkers specific for a first microbial/bacterial species, said method comprising experimental verification of a first set of biomarkers. The first set of biomarkers may be considered to be a set of putative or preliminary biomarkers. The experimental verification of a biomarker may include mass spectrometric analysis of a clinical sample spiked with the first microbial/bacterial species. Alternatively or in addition, it may include mass spectrometric analysis of a (non-spiked) clinical sample previously determined to be positive for the first microbial/bacterial species.
Exemplary flowcharts of suitable methods are provided in FIGS. 1, 2, 3 and 6, but these are for illustrative purposes only and are not limiting.
The experimental verification of the biomarkers may be used to revise the first set of (putative) peptide biomarkers, thereby generating a second set of peptide biomarkers.
The experimental verification may typically involve determining whether a putative peptide biomarker for a first microbial/bacterial species can reliably be detected in a clinical sample comprising the first microbial/bacterial species. The set of biomarkers may be revised accordingly.
The revision may involve the removal of a putative peptide biomarker from the set of biomarkers, e.g. if the putative peptide biomarker cannot reliably be detected in the clinical sample. Alternatively or in addition, it may involve the addition of a putative peptide biomarker to the set of biomarkers, e.g. if a putative peptide biomarker can reliably be detected in the clinical sample. Alternatively or in addition, it may involve the retention of a putative peptide biomarker in the set of biomarkers, e.g. if the putative peptide biomarker can reliably be detected in the clinical sample it may be retained in the set of biomarkers and may optionally be given a higher ranking than prior to the experimental verification step.
Preferably, there is provided a method of identifying a peptide biomarker, or set of peptide biomarkers, specific for a first microbial/bacterial species, said method comprising

    
    
        (a) mass spectrometric analysis of a plurality of peptides from a first microbial/bacterial species to generate mass spectral data;
        (b) analyzing the mass spectral data to match it to peptide sequence identities, thereby generating peptide sequence data;
        (c) comparing the generated peptide sequence data to a reference database to map the peptides to reference genome sequences;
        (d) identifying peptides that are putative biomarkers for the first microbial species;
        (e) ranking the peptides of step (d) and selecting the top ranked peptides to generate a first set of peptide biomarkers suitable for detecting/identifying the first microbial/bacterial species; and
        (f) experimentally verifying one or more of the biomarkers included in the set of peptide biomarkers generated in step (e) to identify a peptide biomarker, or generate a revised set of peptide biomarkers, suitable for detecting/identifying the first microbial/bacterial species.
    
    


Steps (a) and (b) may be carried out using simulated data generated by in silico peptide digestion and/or using experimental proteomics data generated by tandem mass spectrometry as explained in Boulund et al. supra.
Step (c) may comprise aligning the peptide sequence data to the complete genomes of one or more microbial/bacterial species translated into all six reading frames and applying filtering steps as explained in Boulund et al. supra.
Step (e) may involve carrying out steps (a) to (d) two or more times, e.g. 2-20 or 8-15 times, e.g. about 10 times and ranking the peptides according to how reliably they were identified in step (d).
Step (f) may, for example, comprise

    
    
        (i) mass spectrometric analysis of a plurality of peptides in a clinical sample spiked with the first microbial/bacterial species; and revising the set of peptide biomarkers to generate a revised set of peptide biomarkers.

Alternatively or in addition, step (f) may, for example, comprise

        (ii) mass spectrometric analysis of a plurality of peptides in a clinical sample known to be infected with the first microbial/bacterial species; and revising the set of peptide biomarkers to generate a revised set of peptide biomarkers.

If the method comprises both steps (i) and (ii), these steps may be in any suitable order. Thus, step (i) may precede step (ii) and/or it may be subsequent to step (ii).

It must be appreciated that the set of biomarkers which is revised will depend on the steps that the method comprises. Thus, the set of biomarkers which is revised may be the first set of peptide biomarkers generated in step (e), or it may be the revised set of biomarkers generated in step (i), or the revised set of biomarkers generated in step (ii).

    
    


The set of biomarkers identified via any of the methods described above (or indeed any other biomarker or set of biomarkers as described herein) may be considered to be an “inclusion list”.
Inclusion lists may be used during mass spectrometric analysis. A peptide inclusion list may be considered to be instructions for the mass spectrometer to look for the selected set of masses corresponding to the peptides in the inclusion list. An inclusion lists can be used in various different modes of action. For example, an “inclusion list” mode instructs the mass spectrometer only to look for the selected set of masses corresponding to the peptides in the inclusion list. An “inclusion list plus pick others” mode instructs the mass spectrometer to first look for the masses in the inclusion list and pick them for fragmentation; however, if the mass spectrometer does not find the masses in the inclusion list, it will “pick other” ions for fragmentation, usually the top ten most intense ions. The use of an inclusion list may be beneficial if the sample is complex, such as a clinical sample containing both human and microbial/bacterial sources of proteins from which peptides may originate.
Thus, inclusion lists may be used in targeted MS analysis in order to enhance the detection probability of the peptides in the inclusion list, thus lowering the detection limit of the peptides and thereby increasing the sensitivity of the analysis of the sample.
There is provided a peptide biomarker inclusion list prepared according to the method of the invention. There is provided a peptide biomarker inclusion list suitable for the targeted mass spectrometric identification of Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Escherichia coli, or Moraxella catarrhalis. 
Provided is a peptide biomarker inclusion list suitable for the targeted mass spectrometric identification of Moraxella catarrhalis which comprises or consists of exactly, about, or at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45 or 50, or all of the peptides of Table 5, 11, or 15, and optionally variants thereof, preferably as defined below. Alternatively or in addition, said biomarker inclusion list may comprise or consist of one or more of the Moraxella catarrhalis biomarkers disclosed in Table 2.
Provided is a peptide biomarker inclusion list suitable for the targeted mass spectrometric identification of Haemophilus influenza which comprises or consists of exactly, about, or at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45 or 50, or all of the peptides of Table 6, 12 or 16, and optionally variants thereof, preferably as defined below. Alternatively or in addition, said biomarker inclusion list may comprise or consist of one or more of the Haemophilus influenza biomarkers disclosed in Table 2.
Provided is a peptide biomarker inclusion list suitable for the targeted mass spectrometric identification of Staphylococcus aureus which comprises or consists of exactly, about, or at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45 or 50, or all of the peptides of Table 7, 10 or 14, and optionally variants thereof, preferably as defined below. Alternatively or in addition, said biomarker inclusion list may comprise or consist of one or more of the Staphylococcus aureus biomarkers disclosed in Table 2.
Provided is a peptide biomarker inclusion list suitable for the targeted mass spectrometric identification of Streptococcus pneumonia which comprises or consists of exactly, about, or at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45 or 50, or all of the peptides of Table 8, 13, or 17, and optionally variants thereof, preferably as defined below. Alternatively or in addition, said biomarker inclusion list may comprise or consist of one or more of the Streptococcus pneumonia biomarkers disclosed in Table 2 and/or 3.
Provided is a peptide biomarker inclusion list suitable for the targeted mass spectrometric identification of Escherichia coli which comprises or consists of exactly, about, or at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45 or 50, or all of the peptides of Table 9 and optionally variants thereof, preferably as defined below.
Any reference herein to a “biomarker inclusion list” should be understood to encompass and preferably be a biomarker inclusion list prepared according to the method provided herein, more preferably a biomarker inclusion list as defined above by reference to Table 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, and/or 17, respectively, and/or by reference to Table 2 or 3 respectively.
Also provided is a peptide biomarker inclusion list suitable for the targeted mass spectrometric identification of two or more different bacteria selected from Moraxella catarrhalis, Haemophilus influenza, Staphylococcus aureus, Streptococcus pneumonia, and Escherichia coli, which comprises or consists of exactly, about, or at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45 or 50, or all of the peptides of Table 5, 11, or 15, and optionally variants thereof, preferably as defined below; exactly, about, or at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45 or 50, or all of the peptides of Table 6, 12, or 16, and optionally variants thereof, preferably as defined below; exactly, about, or at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45 or 50, or all of the peptides of Table 7, 10 or 14, and optionally variants thereof, preferably as defined below; exactly, about, or at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45 or 50, or all of the peptides of Table 8, 13, or 17, and optionally variants thereof, preferably as defined below; exactly, about, or at least, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45 or 50, or all of the peptides of Table 9 and optionally variants thereof, preferably as defined below; and/or one or more or all of the peptides of Table 2 and/or 3.
Such combinations of inclusion list may be useful for detecting co-infection of two or more (e.g. three, four or five) different bacteria selected from Moraxella catarrhalis, Haemophilus influenza, Staphylococcus aureus, Streptococcus pneumonia, and Escherichia coli. Indeed, preferred embodiments of the methods of the invention can be used to detect or identify two or more (e.g. three, four or five) different bacteria selected from Moraxella catarrhalis, Haemophilus influenza, Staphylococcus aureus, Streptococcus pneumonia, and Escherichia coli in the same sample. Again, such methods can thereby be used to detect co-infections. In such methods and inclusion lists, appropriate peptides (or proteins) or inclusion lists to use (or combine) can be selected depending on the different types of bacteria to be detected or identified, e.g. in a potential coinfection, e.g. can be selected based on the Tables and inclusion lists provided herein. Detection of two or more (e.g. 3 or 4) different bacteria selected from Moraxella catarrhalis, Haemophilus influenza, Staphylococcus aureus and Streptococcus pneumonia, is also provided.
Also provided is a method to detect a microbial/bacterial biomarker, microbe/bacterium or microbial/bacterial infection using a biomarker inclusion list, wherein said method is characterised by using a biomarker inclusion list as provided herein to perform targeted mass spectrometric analysis of a sample.
Also provided is to perform a parallel reaction monitoring (PRM) method with increased sensitivity and high selectivity by targeting the most appropriate species-unique peptides identified in the verification phase (Tables 10-13 and Tables 14-17). The peptide identities were verified by matching their fragment patterns to a spectral library containing annotated reference fragment spectra from the qualification and verification phases. Matching against spectra libraries, combined with correction for retention time and mass accuracy, provided high selectivity and sensitivity (FIG. 7). An advantage of the targeted PRM approach is that no advanced database matching is required to confirm the presence of already known peptides in clinical samples.
Also provided is software comprising instructions to cause a computer to carry out this method.
Also provided is a system for use in detecting a microbial/bacterial biomarker, microbe/bacterium or microbial/bacterial infection, said system comprising a computer carrying the software provided herein, or otherwise programmed to carry out the method to detect a microbial/bacterial biomarker, microbe/bacterium or microbial/bacterial infection, e.g. using a biomarker inclusion list as provided herein.
Also provided is a computer readable medium comprising the software provided herein to carry out the method to detect a microbial/bacterial biomarker, microbe/bacterium or microbial/bacterial infection using a biomarker inclusion list as provided herein and/or which comprises a digitally encoded biomarker inclusion list.
Also provided is use of the software, system, or computer readable medium as provided herein to detect a microbial/bacterial biomarker, microbe/bacterium or microbial/bacterial infection.
There is also provided a set of peptides comprising or consisting of 2-50, 3-50, 5-50, 10-50 or 20-50 (e.g. any integer between 2 and 50, or all peptides of a particular list, more preferably 2, 3, 4, 5, 6, 7, 8, 9, or 10, or up to 15 or 20, more preferably 1, 2, 3, 4 or 5) different peptides (a) to (e), (j) or (k), or different peptides (f-i), selected from

    
    
        (a) the peptides listed in Table 5 and optionally variants thereof, preferably as defined below;
        (b) the peptides listed in Table 6 and optionally variants thereof, preferably as defined below;
        (c) the peptides listed in Table 7 and optionally variants thereof, preferably as defined below;
        (d) the peptides listed in Table 8 and optionally variants thereof, preferably as defined below;
        (e) the peptides listed in Table 9 and optionally variants thereof, preferably as defined below;
        (f) the peptides listed in Table 10 and optionally variants thereof, preferably as defined below;
        (g) the peptides listed in Table 11 and optionally variants thereof, preferably as defined below;
        (h) the peptides listed in Table 12 and optionally variants thereof, preferably as defined below;
        (i) the peptides listed in Table 13 and optionally variants thereof, preferably as defined below;
        (j) the peptides listed in Table 2 and optionally variants thereof, preferably as defined below; or
        (k) the peptides listed in Table 3 and optionally variants thereof, preferably as defined below.
        
            Other preferred sets of peptides are provided in Tables 14 to 17. Combinations of these sets are also provided, e.g. combinations of the peptides listed in Tables 10 to 13 or Tables 14 to 17, with one or more of the lists of peptides in Tables 5, 6, 7, 8 or 9.
        
        
    
    


The set is particularly suitable for detecting/identifying a single type of microbe/bacterium in a sample, preferably a clinical sample. The type of microbe/bacterium is preferably selected from Moraxella catarrhalis, Haemophilus influenza, Staphylococcus aureus, Streptococcus pneumonia, and Escherichia coli. 
Preferably the set comprises or consists of at or at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 45, 48 or all different peptides as defined above, e.g. about 5, 10, 20, 30, 40, 48 or 50. Preferably the set comprises or consists of 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, or up to 15 or 20, more preferably 1, 2, 3, 4 or 5 different peptides, e.g. selected from the above lists (a) to (k) or selected from the Tables 5-9, preferably Tables 10-13, more preferably Tables 14-17. Preferably, the set comprises no more than 20, 10, 5, 4, 3, 2 or 1 peptides that are not listed in the Table 5, 6, 7, 8, 9, respectively that the peptides are selected from. Preferably, it does not comprise any peptides that are not listed in Table 5, 6, 7, 8, or 9, respectively that the peptides are selected from. Thus, if the set comprises peptides selected from Table 5, it preferably does not comprise any peptides selected from Table 6, 7, 8, or 9.
There is also provided a set of peptides comprising or consisting of 2-50, 3-50, 5-50, 10-50 or 20-50 (e.g. any integer between 2 and 50, or all peptides of a particular list, more preferably 2, 3, 4, 5, 6, 7, 8, 9, or 10, or up to 15 or 20, more preferably 1, 2, 3, 4 or 5) different peptides (a) to (e), (j) or (k), or different peptides (f-i), selected from

    
    
        (a) the peptides listed in Table 5 and optionally variants thereof, preferably as defined below;
        (b) the peptides listed in Table 6 and optionally variants thereof, preferably as
        (c) the peptides listed in Table 7 and optionally variants thereof, preferably as defined below;
        (d) the peptides listed in Table 8 and optionally variants thereof, preferably as defined below;
        (e) the peptides listed in Table 9 and optionally variants thereof, preferably as defined below;
        (f) the peptides listed in Table 10 and optionally variants thereof, preferably as defined below;
        (g) the peptides listed in Table 11 and optionally variants thereof, preferably as defined below;
        (h) the peptides listed in Table 12 and optionally variants thereof, preferably as defined below;
        (i) the peptides listed in Table 13 and optionally variants thereof, preferably as defined below;
        (j) the peptides listed in Table 2 and optionally variants thereof, preferably as defined below; and
        (k) the peptides listed in Table 3 and optionally variants thereof, preferably as defined below.
        
            Other preferred sets of peptides are provided in Tables 14 to 17. Combinations of these sets are also provided, e.g. combinations of the peptides listed in Tables 10 to 13 or Tables 14 to 17, with one or more of the lists of peptides in Tables 5, 6, 7, 8 or 9. Appropriate and preferred numbers of peptides to be included in the set are described elsewhere herein.
        
        
    
    


The set is particularly suitable for detecting/identifying two or more different types of microbe/bacterium in a sample, preferably a clinical sample. The types of microbe/bacterium are preferably selected from Moraxella catarrhalis, Haemophilus influenza, Staphylococcus aureus, Streptococcus pneumonia, and Escherichia coli. 
Preferably, the set comprises peptides from 2 or more of (a) to (k), more preferably (a) to (i) e.g. peptides from 2-5, 2-3, 2-4, 3-5, or 3-4 of (a) to (k) or (a) to (i), e.g. at least one peptide selected from (b) and at least one peptide selected from (c); or at least one peptide selected from each of (a) to (k) or (a) to (i).
The above sets of peptides, or other individual peptides or sets of peptides as described elsewhere herein, can also be used for detection or identification of the presence of said peptides or the proteins from which said peptides derive in a sample, for example a clinical sample or any other sample as described elsewhere herein. Thus, such peptides or sets of peptides as described herein can be used in detection or diagnostic kits of the invention. In such detection or identification methods, conveniently the peptides or sets of peptides can be attached to a solid support or immobilized as described elsewhere herein, for example presented in an array (peptide array), e.g. printed array (see for example https://www.pepperprint.com/products/pepperchipr-custom-microarrays/), for example on a chip or biochip.
These peptides or sets of peptides can then be used to detect the presence of peptides or proteins in a clinical sample by for example detecting an elicited immune response towards one or more of the peptide biomarkers which is prompted by a bacterial infection. In this regard, during an infection the immune system produces antibodies against the pathogens of the infection. Some of the antibodies will thus likely be directed towards proteins from which the peptide biomarkers of the present invention originate, or even directly towards the specific peptide sequences themselves, for example within the proteins. Thus, a sample, for example a clinical sample such as a serum or plasma sample, which will potentially contain such antibodies, could be applied to the peptides or sets of peptides of the invention, for example when presented on a solid support or array, and the binding of antibodies towards the biomarker peptides could be measured or detected. If there is binding of antibodies to one or more of the biomarker peptides, this would suggest the presence of an infection of the pathogen from which the biomarker peptides originate. Thus, this can be used for detecting, identifying or diagnosing infection in accordance with the invention, advantageously with potential for use at the point of care. Such point-of-care assays, which are enabled by the use of arrays or affinity reagents as described elsewhere herein, are preferred in some embodiments.
There is also provided a peptide comprising or consisting of an amino acid sequence selected from the amino acid sequences of

    
    
        (a) the peptides listed in Table 5 and optionally variants thereof, preferably as defined below;
        (b) the peptides listed in Table 6 and optionally variants thereof, preferably as defined below;
        (c) the peptides listed in Table 7 and optionally variants thereof, preferably as defined below;
        (d) the peptides listed in Table 8 and optionally variants thereof, preferably as defined below;
        (e) the peptides listed in Table 9 and optionally variants thereof, preferably as defined below;
        (f) the peptides listed in Table 10 and optionally variants thereof, preferably as defined below;
        (g) the peptides listed in Table 11 and optionally variants thereof, preferably as defined below;
        (h) the peptides listed in Table 12 and optionally variants thereof, preferably as defined below;
        (i) the peptides listed in Table 13 and optionally variants thereof, preferably as
        (j) the peptides listed in Table 2 and optionally variants thereof, preferably as defined below; and/or
        (k) the peptides listed in Table 3 and optionally variants thereof, preferably as defined below.
        
            Other preferred peptides are provided in Tables 14 to 17.
        
        
    
    


The variant may be a peptide comprising or consisting of a fragment of one of the peptides disclosed in any one of Tables 5-13, 14-17, 2 and/or 3, preferably Tables 5-13 or 14-17. Preferably, such a variant comprises a fragment of at least 4, 5, 6, 7, 8, 9 or 10 continuous amino acids of one of the peptide sequences disclosed in any one of Tables 5-9, 14-17, 2 and/or 3, preferably Tables 5-13 or 14-17. Preferably, the variant amino acid sequence consists of exactly, about or no more than 50, 40, 30, 25, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6 or 5 amino acids.
Alternatively or in addition, the variant may comprise or consist of an amino acid sequence having at least 75, 80, 85, 90, 95, 98 or 99% sequence identity with the amino acid sequence of any one of the peptides disclosed in any one of Tables 5-13, 14-17, 2 or 3, preferably Tables 5-13 or 14-17, e.g. with SEQ ID NO: 52 or SEQ ID NO: 171.
Alternatively or in addition, the variant comprise or consist of an amino acid sequence having 1-5 amino acid substitutions, additions and/or deletions, e.g. 1-4, 1-3 or 1-2, e.g. 1 or less than 2, compared to the amino acid sequence of any one of the peptides disclosed in any one of Tables 5-13, 14-17, 2 or 3, preferably Tables 5-13, or 14-17 e.g. SEQ ID NO: 52 or SEQ ID NO: 171. The substitutions may be conservative amino acid substitutions, non-conservative amino acid substitutions, or a mix thereof. Preferably, only conservative amino acid substitutions are present.
The variant preferably consists of no more than 50, 40, 30, 25, 24, 23, 22, 21, 20, 19 or 18 amino acids. If the peptide comprises SEQ ID NO: 171 it may, e.g., consist of 16 or 17 amino acids.
The peptide or variant thereof is preferably isolated, synthetic or recombinant.
Also provided is a peptidomimetic which is a mimetic of any of the peptides provided herein, such as any one of the peptides disclosed in any one of Tables 5-13, 14-17, 2 or 3, preferably Tables 5-13 or 14-17.
Also provided is a kit comprising one or more of the peptide biomarker sets provided herein and/or one or more of the peptides provided herein and/or one or more of the peptidomimetics provided herein, e.g. a kit comprising or consisting of at or at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 45, 48 or all different peptides of a particular set as defined above, e.g. about 5, 10, 20, 30, 40, 48 or 50. Preferably the set comprises or consists of 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, or up to 15 or 20, more preferably 1, 2, 3, 4 or 5 different peptides, e.g selected from the above lists (a) to (k), or selected from the Tables 5-9, preferably Tables 10-13, more preferably Tables 14-17.
Also provided is a nucleic acid molecule encoding one or more of the biomarker peptides provided herein, e.g. one or more of the peptides provided herein.
Also provided is the complement of such a nucleic acid molecule.
Also provided is a vector comprising a nucleic acid molecule as provided herein. Preferably, the vector comprises a promoter sequence operably linked to the sequence encoding a peptide provided herein.
Also provided is a host cell comprising such a peptide, nucleic acid molecule, and/or vector. Also provided is a kit comprising a nucleic acid molecule, vector, host, peptide and/or peptide set as provided herein.
Also provided is an affinity reagent that can specifically bind to one of the biomarker peptides provided herein, preferably to one of the peptides provided herein, most preferably to one of the peptides listed in Tables 5 to 9, preferably Tables 10 to 13, more preferably Tables 14 to 17, or one of the proteins listed in Tables 10 to 17. Sets of such affinity reagents are also provided, e.g. sets comprising or consisting of 2-50, 3-50, 5-50, 10-50 or 20-50 (e.g. any integer between 2 and 50, or all peptides of a particular list), more preferably 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, or up to 15 or 20, more preferably 1, 2, 3, 4 or 5 different peptides. Sets of affinity reagents that can specifically bind to a set of peptides or set of proteins as described herein (e.g. sets based on the peptides of Tables 5 to 9, preferably Tables 10 to 13, more preferably Tables 14 to 17, or sets based on the proteins described in Tables 10 to 13, preferably Tables 14 to 17) are also provided. A
The nucleic acid molecule, vector, host cell or affinity reagent is preferably isolated, synthetic or recombinant.
The biomarker may preferably be present in, or derived from, a protein selected from (a) general stress protein 24 (Gls24); and (b) methionine adenosyltransferase (MetK-2), or any of the other proteins listed in Tables 10 to 13, preferably Tables 14 to 17. Thus, also provided is a method of detecting microbes/bacteria in a sample, preferably in a clinical specimen, comprising detecting a protein selected from (a) general stress protein 24 (Gls24); and (b) methionine adenosyltransferase (MetK-2) or any of the other proteins listed in Tables 10 to 13, preferably Tables 14 to 17.
Also provided is an affinity reagent that can specifically bind to a protein selected from (a) general stress protein 24 (Gls24); and (b) methionine adenosyltransferase (MetK-2) or any of the other proteins listed in Tables 10 to 13, preferably Tables 14 to 17.
The biomarker may be present in a secreted protein and/or a surface-exposed protein, thereby allowing the detection of microbes/bacteria without requiring a microbial/bacterial lysis and/or proteolysis step. Thus, also provided is a method of detecting microbes/bacteria in a sample, preferably in a clinical specimen, comprising detecting a secreted protein or a surface-exposed protein. The method may, e.g., be used to detect whole microbes/bacteria. The method may involve detecting the protein using an affinity reagent specific for the protein.
Affinity reagents (or sets of affinity reagents) can conveniently be provided as an array, e.g. in a printed array format.
Also provided is a diagnostic kit for detecting specific biomarker peptides, wherein the kit comprises an affinity reagent (or a set of affinity reagents) as provided herein. The kit may further comprise one or more reagents suitable for proteolysis of microbial/bacterial proteins; one or more cell lysis agents; and/or one or more additional components, such as a buffer.
For example, it may comprise a suitable protease, details of which are provided elsewhere herein; and/or a cell lysis agent such as lysis beads and/or a detergent.
Also provided is a diagnostic kit for detecting microbes/bacteria, which may be whole microbes/bacteria, wherein the kit comprises an affinity reagent specific for a protein selected from (a) general stress protein 24 (Gls24); and (b) methionine adenosyltransferase (MetK-2) or any of the other proteins listed in Tables 10 to 13, preferably Tables 14 to 17.
In the cases of S. pneumoniae and H. influenzae, the most prominent peptides originate from only a few proteins, whereas, for M. catarrhalis and S. aureus, the peptides derive from a variety of different proteins. For M. catarrhalis and S. aureus, many of the species-unique peptides originated from highly abundant cytosolic proteins, including ribosomal proteins. Since cytosolic house-keeping proteins, in general, are relatively highly expressed, regardless of growth conditions (e.g., ex situ cultivation or in vivo within a host), the most prominent species-unique peptides would most likely originate from the house-keeping proteins when analyzing clinical samples. These proteins are also used often as targets for identification, using traditional gene-based approaches, as well as MALDI-TOF MS.
In contrast, many of the proteins identified from the species-unique peptides for S. pneumoniae and H. influenzae include those associated with the surface of the cells. This might be due to the differences in taxonomic structure of the different species. M. catarrhalis and S. aureus are phylogenetically more distant from their closest related species; hence, their house-keeping proteins, including ribosomal proteins, do not display substantial overlap in amino acid sequences of the species closest to them. However, for S. pneumoniae and H. influenzae, the taxonomic structures around these species are more complex and the house-keeping proteins, including ribosomal proteins, display larger amounts of overlapping amino acid sequences, thus making it more difficult to find species-unique peptides from these proteins. Surface-associated proteins have different functions, helping the bacteria survive in diverse and dynamic ecological niches and, particularly, these proteins are often involved in host-pathogen interactions, effectively functioning, for example, as virulence factors. Many of the proteins identified from S. pneumoniae and H. influenzae by their respective species-unique peptides belong to the group of surface-associated virulence factors, which could be explained by the fact that these proteins are the ones differentiating them from their closest relatives, as well as being expressed significantly in clinical samples, making detection of these species-unique peptides possible.
Also provided is a pharmaceutical composition comprising a peptide, peptidomimetic, nucleic acid molecule, vector, host cell, or affinity reagent as provided herein. The pharmaceutical composition may be a vaccine.
Any of the detection methods provided herein may form part of a diagnostic method. Thus, provided is a method of diagnosis comprising detecting a microbial/bacterial biomarker in a sample, wherein said microbial/bacterial biomarker is a peptide selected from any one of the peptides disclosed in Tables 5-13, 14-17, 2 or 3, preferably Tables 5-9, preferably Tables 10-13, more preferably Tables 14-17, or a variant thereof as defined herein. As discussed elsewhere herein, said detection step may involve mass spectrometry and/or an affinity agent. Said method may comprise a step of making a determination whether microbes/bacteria, preferably a specific type of microbes/bacteria, are present in the sample and preferably on the basis of that determination making a diagnosis. Thus, a determination may be made that microbes/bacteria are present and on that basis a diagnosis of a microbial/bacterial infection may be made; or a determination may be made that microbes/bacteria are not present and on that basis a diagnosis of that no microbial/bacterial infection is present may be made.
Also provided is a method of diagnosis comprising detecting microbes/bacteria, preferably S. pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Escherichia coli, and/or Moraxella catarrhalis in a sample, comprising detecting (a) general stress protein 24 (Gls24); and/or (b) methionine adenosyltransferase (MetK-2) or any of the other proteins listed in Tables 10 to 13 (as appropriate for the bacteria concerned), preferably Tables 14 to 17 (as appropriate for the bacteria concerned). As discussed elsewhere herein, said detection step may involve an affinity agent (or a set of affinity reagents). Said method may comprise a step of making a determination that S. pneumonia, Staphylococcus aureus, Haemophilus influenzae, Escherichia coli, and/or Moraxella catarrhalis, is present in the sample and preferably on the basis of that determination making a diagnosis.
It can be noted that for all embodiments of the invention described herein, alternative bacterial biomarkers for various bacteria are listed in Table 18.
In addition, other preferred peptide biomarkers for various bacteria are shown in FIG. 7. Other preferred peptide biomarkers for various bacteria are shown in Tables 10-13, more preferably Tables 14-17.
Also provided is a peptide, peptidomimetic, nucleic acid molecule, vector, host cell, affinity reagent or pharmaceutical composition as provided herein for use in diagnosis or therapy.
Thus, there is provided a method of diagnosis, comprising contacting a subject or a clinical specimen with an agent selected from a peptide, peptidomimetic, nucleic acid molecule, vector, host cell, affinity reagent, kit, or pharmaceutical composition as provided herein. The diagnostic method may be performed in vivo, ex vivo, or in vitro.
Also provided is a method of treatment, comprising administering to a subject in need thereof a therapeutically effective amount of an agent selected from a peptide, peptidomimetic, nucleic acid molecule, vector, host cell, affinity reagent, kit, or pharmaceutical composition as provided herein. Also provided is the use of an agent selected from a peptide, peptidomimetic, nucleic acid molecule, vector, host cell, affinity reagent, kit, or pharmaceutical composition as provided herein in the manufacture of a composition for use in diagnosis or therapy.
The treatment may be vaccination.
As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Also, as used herein, “and/or” refers to and encompasses any and all possible combinations of one or more of the associated listed items.
When a range is employed (e.g., a range from x to y) it is it meant that the measurable value is a range from about x to about y, or any range therein, such as about x1 to about y1, etc.
It will be further understood that the terms “comprises” and/or “comprising” when used herein specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Unless otherwise defined, all terms, including technical and scientific terms used herein, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
A microbe, also known as a micro-organism, is an organism which is too small to be visible to the naked eye, i.e. is microscopic. A microbe may be selected from bacteria, fungi, archaea, algae, protozoa, and viruses. Although the terms bacteria, fungi, archaea, algae, protozoa and viruses technically denote the plural form, it is common practice to use them also to denote the singular form. Consequently, the terms “microbes/bacteria” and “microbe/bacterium” are used interchangeably herein.
The microbe may be selected from bacteria, fungi, archaea, algae, virus and protozoa, preferably bacteria.
The microbe/bacterium may be a pathogen, e.g. a pathogen of the respiratory tract. For example, the microbe/bacterium may be selected from Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Moraxella catarrhalis, Chlamydia pneumoniae, Mycoplasma pneumoniae, Klebsiella pneumoniae, Escherichia coli, and Pseudomonas aeruginosa. Preferably, it is selected from Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Escherichia coli, and Moraxella catarrhalis. 
Any reference herein to a “type of microbe” or “type of bacterium” means a microbe/bacterium of the desired type, which may be at the genus, species or strain level as desired. Thus, for example, if it is desired to determine whether any members of the genus Staphylococcus are present in a sample, the type of bacterium is the genus Staphylococcus; whereas if it is desired to determine whether any members of the species Staphylococcus aureus are present in a sample, the type of bacterium is the species Staphylococcus aureus. 
Mass spectrometry is a sensitive technique used to detect, identify and quantitate molecules based on their mass-to-charge (m/z) ratio. Suitable mass spectrometry for identifying peptides includes tandem mass spectrometry (MS/MS). This may be combined with other techniques such as liquid chromatography (LC) or gas chromatography (GC), e.g. LC-MS/MS.
Any of the peptides, nucleic acid molecules, vectors and/or affinity reagents provided herein may be isolated, recombinant, synthetic and/or mutant. They may incorporate, or be linked to, a moiety, which may be a detectable moiety.
The peptide may incorporate one or more, e.g. at least 1, 2, 3, 4 or 5 amino acids which possess a side chain that is not coded for by the standard genetic code, termed herein “non-coded amino acids”. These may be selected from amino acids which are formed through metabolic processes such as ornithine or taurine, and/or artificially modified amino acids such as 9H-fluoren-9-ylmethoxycarbonyl (Fmoc), (tert)-(B)utyl (o)xy (c)arbonyl (Boc), 2,2,5,7,8-pentamethylchroman-6-sulphonyl (Pmc) protected amino acids, or amino acids having the benzyloxy-carbonyl (Z) group.
The peptide may comprise or consist of only amino acids having the L-configuration. Alternatively, it may comprise one or more amino acids having the D configuration, e.g. it may contain at least 1, 2, 3, 4 or 5 D-amino acids.
By “peptidomimetic” is meant a compound which is functionally equivalent or similar to a peptide and which can adopt a three-dimensional structure similar to its peptide counterparts, but which is not solely composed of amino acids linked by peptide bonds.
For example, the backbone of the peptidomimetic may incorporate one or more nitrogen atoms instead of one or more carbon atoms. A preferred class of peptidomimetics are peptoids, i.e. N-substituted glycines. Peptoids are closely related to their natural peptide counterparts, but they differ chemically in that their side chains are appended to nitrogen atoms along the molecule's backbone, rather than to the α-carbons as they are in amino acids.
The peptidomimetic may incorporate one or more di-amino acids and/or β-amino acids.
By “affinity reagent” is meant an agent that can specifically bind to a target. In the present context, the target is a peptide or protein, more particularly a specific peptide, preferably selected from the peptides or proteins disclosed in any one of Tables 5-13, 14-17, 2 or 3, preferably Tables 5-9, more preferably Tables 10-13 or 14-17, or variants thereof as defined herein. Thus, the affinity reagent is an agent that can specifically bind to a specific target peptide (or protein). By that is meant that the affinity reagent will be specific for a target peptide (or protein) having a particular amino acid sequence, so the affinity reagent will bind to this peptide (or protein), but will not bind to, or not significantly bind to, a non-target peptide (or protein) having a different amino acid sequence. For example, an affinity reagent specific for the peptide (or protein) having the sequence APLLFGG will not bind to, or not significantly bind to, a peptide (or protein) having the sequence YFPAA.
The term “does not significantly bind to” a non-target peptide (or protein) should be understood such that any binding of the affinity reagent to a non-target peptide (or protein) does not prohibit the use of said affinity reagent for the purpose of detecting a target peptide (or protein) in a method provided herein. Thus, by “insignificant” binding to a non-target peptide (or protein) is meant that the binding of the affinity reagent to non-target peptides (or proteins) is weaker than its binding to the target peptide (or protein). Some minor cross-reaction with non-target peptides (or proteins) may thus occur, but this level of binding can be considered to be “background” binding.
The affinity reagent may, for example, be selected from an antibody, a nucleic acid aptamer, and an affinity protein scaffold. In some embodiments, peptides, e.g. a set of peptides, as described herein can be used as affinity reagents, e.g. to detect antibodies in appropriate samples.
The term “antibody” extends to all antibodies and antigen binding fragments thereof, including whole antibodies, dimeric, trimeric and multimeric antibodies; bispecific antibodies; chimeric antibodies; recombinant and engineered antibodies, and fragments thereof.
The term “antibody” is thus used to refer to any antibody-like molecule that has an antigen binding region, and this term includes antibody fragments that comprise an antigen binding domain such as Fab′, Fab, F(ab′)2, single domain antibodies (DABs), Fv, scFv (single chain Fv), dsFv, ds-scFv, Fd, minibodies, diabodies, triabodys (scFv-Fab fusions); sc-diabodys; Bispecific T-cell Engager (BITE) (scFv-scFv tandems to attract T cells); dual variable domain (DVD)-Ig; small immunoprotein (SIP); DART (ds-stabilized diabody “Dual Affinity ReTargeting”).
The techniques for preparing and using various antibody-based constructs and fragments are well known in the art (see Kabat et al., 1991, specifically incorporated herein by reference).
The term “aptamer” extends to any single-stranded oligonucleotides that are capable of binding a target molecule with specificity. The aptamer may comprise or consist of DNA and/or RNA. It may comprise or consist of 10-100 nucleotides, preferably 20 to 60 nucleotides, more preferably about 40 nucleotides. Thus, it may preferably comprise or consist of at least 10, 20, 30, or 40 nucleotides and no more than 100, 90, 80, 70, 60, or 50 nucleotides.
The techniques for preparing and using various aptamers are well known in the art. For example, a method for aptamer engineering known as SELEX (systematic evolution of ligands by exponential enrichment) is widely used (see Tuerk and Gold, Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase Science 1990; 249(4968):505-10 specifically incorporated herein by reference).
The term “affinity protein scaffold” extends to any affinity reagents based on an affinity moiety linked to a non-antibody protein scaffold. The “affinity moiety” is a moiety that is capable of binding a target molecule with specificity. The affinity moiety may, for example, comprise or consist of an oligopeptide or an oligonucleotide. For example, the affinity moiety may comprise or consist of an aptamer. The affinity moiety may comprise or consist of 3-100 amino acids, preferably 5 to 40 amino acids, more preferably about 10-20 amino acids. Thus, it may preferably comprise or consist of at least 5, 6, 7, 8, 9 or 10 and no more than 40, 30, 20, 18, 15 or 12 amino acids.
The affinity reagent is preferably an antibody or an aptamer. The affinity reagent may incorporate, or be linked to, a detectable moiety.
A “detectable moiety” is a label such as, e.g., an enzymatic or radioactive label. Suitable examples include biotin; fluorescein isothiocyanate, rhodamine or luciferin; or an enzyme such as alkaline phosphatase, beta-galactosidase or horseradish peroxidase.
The “nucleic acid molecule” may be a DNA or an RNA molecule. It may have any length, e.g. comprising or consisting of at least 9 nucleotides and preferably no more than 1000 nucleotides. The nucleic acid molecule may be a probe, in which case it preferably comprises no more than 100, 90, 80, 70, 60, 50, 40 or 30 nucleotides. Preferably, it comprises or consists of about 9-50, 9-40, 9-35, 9-30 or 9-21 nucleotides, e.g. about 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or 30 nucleotides. The nucleic acid molecule may incorporate, or be linked to, a detectable moiety.
The nucleic acid molecule may be immobilized on a solid support. In the case of a set of nucleic acid molecules, the nucleic acid molecules may be immobilized on one or more solid supports. The solid support may e.g. be a sheet, filter, membrane, plate, chip or biochip. Indeed, any of the affinity reagents as described herein (or the peptides or sets of peptides as described herein), may similarly be immobilized on one or more solid supports.
The “sample” may be any sample that may contain microbes/bacteria and/or microbial/bacterial biomarkers. It may contain live microbes/bacteria and/or dead microbes/bacteria, e.g. microbes/bacteria that have been lysed, and/or microbial/bacterial biomarkers that have been released by microbes/bacteria into their environment.
The sample may thus contain microbes/bacteria and/or microbial/bacterial biomarkers, or be suspected of containing microbes/bacteria and/or microbial/bacterial biomarkers.
The sample may be, or comprise, a microbial/bacterial culture, which may, e.g., be a colony or a liquid culture. However, in some embodiments the sample is not a microbial/bacterial culture. Advantageously, the methods provided herein do not require a step of culturing microbes/bacteria prior to the detection step. Therefore, the method does preferably not include a microbial/bacterial culture step. Preferably, the method is carried out on a sample that has not been cultured. By “cultured” or “culture step” is meant incubating the sample under conditions to encourage the growth of microbes/bacteria, e.g. incubating in the presence of suitable nutrients at a suitable temperature, pH and the like for a suitable period of time, e.g. at least a few hours or a few days.
The sample may e.g. be an environmental specimen, a food, or a beverage.
It may be an ex vivo or in vitro sample, e.g., a specimen, which may be a clinical specimen. The specimen may optionally be a provided specimen, i.e. a specimen that was previously obtained or removed from a subject. Optionally, the method may include a step of obtaining a specimen from a subject.
A (clinical) sample that has been spiked with microbes/bacteria, i.e. to which microbes/bacteria have been deliberately added, is referred to herein as a “spiked (clinical) sample”. Typically, a spiked sample will have been spiked with a single known type of microbe/bacterium (“single spiked sample”), although spiked samples spiked with two or more types of known microbes/bacteria (“multiple spiked sample”) may alternatively be used. In some embodiments, a spiked sample may additionally have been spiked with one or more unidentified types of microbes/bacteria.
Any reference to a (clinical) sample that does not include the term “spiked” should be understood to refer to a non-spiked sample, i.e. a sample which has not deliberately been altered to contain added microbes/bacteria.
The terms “subject” and “patient” are used interchangeably herein unless specified otherwise. The “subject” may be a human or a non-human animal, preferably a mammal, for example any livestock, domestic or laboratory animal. Specific examples include mice, rats, pigs, cats, dogs, sheep, rabbits, cows and monkey. Preferably, however, the subject is a human.
The subject may, e.g., be a subject having a microbial/bacterial infection, a subject suspected of having a microbial/bacterial infection, a subject diagnosed as having an infection of unknown cause, or a subject suspected of having an infection of unknown cause. By “unknown cause” is meant in this context that it is unknown whether the infection is caused by bacteria, viruses, a combination of bacteria and viruses, or caused by a different causal agent. Samples obtained from such subjects, or any other subjects as described herein are preferred samples for analysis in accordance with the present invention.
The specimen may optionally be selected, for example, from a tissue specimen, and/or body fluid specimen. For example, it may be a surgical resection specimen, a biopsy specimen, a swab, a lavage, and/or a smear.
A body fluid may, for example, be selected from blood, plasma, serum, sputum, lavage fluid, pus, urine, saliva, phlegm, vomit, faeces, amniotic fluid, cerebrospinal fluid, pleural fluid, semen, vaginal secretion, interstitial fluid, and/or lymph. The blood may be a peripheral blood sample.
The specimen is preferably derived from the respiratory tract. It may be derived from the upper respiratory tract, e.g. the nose, mouth, sinuses, and/or throat; and/or from the lower respiratory tract, e.g. the trachea, bronchial tubes, and/or the lungs. It may, for example, be a nasopharyngeal swab, a nasopharyngeal wash, a bronchoalveolar lavage or an endotracheal aspirate.
The sample may be processed prior to the biomarker detection step. Thus, the method may include a sample processing step, and/or it may be carried out on a previously processed sample. The sample processing may include one or more wash steps, microbial/bacterial enrichment steps, lysis steps, proteolysis steps, and/or purification steps.
By “microbial/bacterial enrichment” is meant a step to enrich the proportion of microbes/bacteria in the sample. This may involve the removal of some or all non-microbial biomass, such as animal biomass. This may involve the selective lysis of animal cells and optionally the degradation of DNA. For example, the MolYsisTM kit may be used.
By “lysis” is meant that the microbes/bacteria are lysed, i.e. their cell membranes are disrupted such that the cellular content, including proteins and peptides, is released from the microbial/bacterial cell. The lysis step may be physical, e.g., be carried out using bead beating; and/or it may be reagent based, e.g. using a denaturing or non-denaturing detergent such as Sodium deoxycholate, SDS, Triton X-100 or CHAPS.
By “proteolysis” is meant the (hydrolytic) breakdown of proteins/polypeptides into polypeptides, oligopeptides and/or free amino acids. A proteolysis step may include contacting a sample with a suitable protease. By “protease” is meant an enzyme which catalyses hydrolytic proteolysis. The protease may be selected from endoproteases and exoproteases, endoproteases being preferred. Suitable endoproteases include Serine proteases, Threonine proteases, Cysteine proteases, Aspartate proteases, Metalloproteases and Glutamic acid proteases. Examples of suitable serine endoproteases include pepsin, trypsin, chymotrypsin and elastase. Thus, it can be seen that peptides or sets of peptides of or for use in the present invention may not be or may not correspond to naturally occurring peptides, for example are not or do not correspond to peptides which are found in the form of that peptide in an appropriate subject, even though the peptides may be present in larger native proteins found in the subject (e.g. the peptides are fragments of such proteins).
A purification step is any step that removes unwanted material, which may, e.g. be cellular debris such as membrane fractions, nucleic acid and the like; chemical agents; salts and the like. Purification steps may involve a centrifugation step to generate a pellet and a supernatant. The unwanted material, which may be in the pellet or in the supernatant depending on the nature of the purification step, may easily be discarded. A purification step may conveniently be carried out using appropriate commercially available columns. For example, peptides may be desalted using Pep Clean C18 spin columns.
Digestion of microbial/bacterial lysates and generation of peptides may, e.g., be performed using the Lipid-based Protein Immobilization (LPI) methodology, as discussed in the Examples.
The diagnosis may be the diagnosis of a microbial/bacterial infection of any part of the respiratory tract. The diagnosis may be carried out on a subject who has not previously been diagnosed as having an infection; on a subject who has previously been diagnosed as having an infection, wherein a diagnosis as to whether the infection is microbial/bacterial has not yet been made; or on a subject who has previously been diagnosed as having a microbial/bacterial infection, wherein the identity of the microbial/bacterial species causing the infection has not yet been determined.
The therapy is preferably the treatment a microbial/bacterial infection, which may be the vaccination against a current and/or future microbial/bacterial infection.
The infection may be selected from otitis (ear infection), meningitis (infection of the lining of the brain and/or spinal cord), bacteremia (blood stream infection), an infection of any part of the respiratory tract, Epiglotittis (swelling of the windpipe that can cause breathing trouble), Cellulitis (skin infection) and/or Infectious arthritis (inflammation of the joint). If it is otitis, it may be otitis externa, otitis media or otitis interna (labyrinthitis).
The infection may preferably be an infection of any part of the respiratory tract, e.g. tonsillitis, sinusitis, laryngitis, bronchitis, and/or pneumonia. It may be an acute respiratory infection.
As used herein, the term “acute respiratory infection” or “ARI” refers to an infection, or an illness showing symptoms and/or physical findings consistent with an infection (e.g., symptoms such as coughing, wheezing, fever, sore throat, and/or congestion; physical findings such as elevated heart rate, elevated breath rate, abnormal white blood cell count, and/or low arterial carbon dioxide tension (PaCO2), etc.), of the upper or lower respiratory tract, often due to a microbial/bacterial or viral pathogen, and/or characterized by rapid progression of symptoms over hours to days. ARIs may primarily be of the upper respiratory tract (URIs), the lower respiratory tract (LRIs), or a combination of the two. ARIs may have systemic effects due to spread of the infection beyond the respiratory tract or due to collateral damage induced by the immune response. An example of the former includes Staphylococcus aureus pneumonia that has spread to the blood stream and can result in secondary sites of infection, including endocarditis (infection of the heart valves), septic arthritis (joint infection), or osteomyelitis (bone infection). An example of the latter includes influenza pneumonia leading to acute respiratory distress syndrome and respiratory failure.
The microbial/bacterial infection is preferably caused by a microbe/bacterium selected from Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Escherichia coli, and/or Moraxella catarrhalis. 
As used herein, the terms “treatment,” “treat,” and “treating” refer to reversing, alleviating, inhibiting the progress of a disease or disorder as described herein; or delaying, eliminating or reducing the incidence or onset of a disorder or disease as described herein, as compared to that which would occur in the absence of the measure taken. Thus, the treatment may be prophylactic.
The terms “prophylaxis” or “prophylactic use” and “prophylactic treatment” as used herein, refer to any medical or public health procedure whose purpose is to prevent the disease herein disclosed. As used herein, the terms “prevent”, “prevention” and “preventing” refer to the reduction in the risk of acquiring or developing a given condition, or the reduction or inhibition of the recurrence or said condition in a subject who is not ill. An example of a prophylactic treatment is vaccination against a disease.
Any of the methods of detecting or identifying or diagnosing as described herein may thus optionally further comprise a therapeutic step, for example if a positive detection, identification, or diagnosis is reached. Appropriate therapies for the various bacterial infections described herein would be well known to a person skilled in the art, and any of these could be used. Preferably however a therapeutic step using an antibiotic or other antibacterial or antimicrobial treatment for the particular bacteria concerned could be carried out. Alternatively a treatment involving vaccination against the bacteria which has been detected and/or a future microbial/bacterial infection can be carried out. Thus, methods of treatment of microbial/bacterial infection in a subject, comprising the methods of detecting or identifying or diagnosing as described herein, are also provided. Such methods of treatment may comprise obtaining or receiving results of the level or presence of one or more of the bacterial biomarkers, or bacteria, in a subject (e.g. a sample from a subject) as described herein, and administering a treatment for example as described above, for example when the level or presence of said biomarkers is indicative of disease or infection, e.g. is above an appropriate control or reference level.
A “subject in need” of the methods of the invention can be a subject known to have, or suspected of having, a microbial/bacterial infection, such as a respiratory tract infection. The subject may be a subject who has an increased susceptibility to microbial/bacterial infections, but who does not yet have symptoms of microbial/bacterial infection. The term “increased susceptibility” in this context refers to a subject that has a likelihood of suffering from said disease that is at least 10% higher than that for the average population.
By “biomarker” is meant an objective, quantifiable characteristic of a specific type of microbe/bacterium. The biomarker may be a single peptide, or a plurality of peptides.
A pharmaceutical composition as provided herein may further comprise a pharmaceutically acceptable excipient. The excipient may include any excipients known in the art, for example any carrier or diluent or any other ingredient or agent such as buffer, antioxidant, chelator, binder, coating, disintegrant, filler, flavour, colour, glidant, lubricant, preservative, sorbent and/or sweetener.
All patents, patent applications and publications referred to herein are incorporated by reference in their entirety. In the event of conflicting terminology, the present specification is controlling. Further, the embodiments described in one aspect of the present invention are not limited to the aspect described. The embodiments may also be applied to a different aspect of the invention as long as the embodiments do not prevent these aspects of the invention from operating for their intended purpose.
It should be appreciated that the invention may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.



FIGURES
The following drawings are provided to illustrate various aspects of the present inventive concept and are not intended to limit the scope of the present invention unless specified herein.
FIG. 1. Exemplary workflow for generating peptide biomarker database identified by MS, followed by ranking by number of hits to generate a preliminary inclusion list, i.e. a first set of peptide biomarkers.
FIG. 2. Exemplary workflow for experimentally verifying and revising an inclusion list (set of peptide biomarkers) involving spiking of negative samples and ranking of peptides according to sensitivity of detection.
FIG. 3. Exemplary workflow for experimentally verifying and revising an inclusion list. The workflow includes analysing clinical samples, exemplified by nasal swabs, followed by bioinformatics processing using a targeted approach (“inclusion list only” mode or “inclusion list plus pick others” mode) and optionally also an open (TCUP) approach, followed by and revision of the inclusion list. The revision of the inclusion list may include verifying any peptide biomarkers of the inclusion list on the basis of their detection in the clinical sample; removing any peptide biomarkers on the basis of their non-detection in the clinical sample; and/or the addition of new biomarkers on the basis of their detection in the clinical sample.
FIG. 4. Comparison of dilution series for detecting certain peptide species using targeted (here: “inclusion list plus pick others” mode) and normal MS analysis approach.
FIG. 5. Identification of species unique peptides in clinical samples using parallel reaction monitoring (PRM) methods and comparison to results from cultures.
FIG. 6. Exemplary workflow of generating an inclusion list (set of peptide biomarkers) using spiked negative samples for a first revision of the preliminary inclusion list and clinical samples to for a further revision of the (revised) inclusion list. In the first step thousands of peptides (+15000 peptides) in cultures from the different microbes/bacteria (Sp, Hi, Mc, Sa) are identified. In the second step negative clinical samples are spiked with the respective microbes/bacteria in order to mimic clinical samples, thus reducing the list of peptides to hundreds instead of thousands of candidate peptide biomarkers. Finally validation of the peptides is performed by analysing real positive clinical samples thus resulting in a final inclusion list of peptide biomarkers.
FIG. 7. Direct analyses of clinical respiratory tract samples using PRM, targeting the most promising peptides. The peptide peaks are labeled with numbers corresponding their sequences from Table 10 (Staphylococcus aureus), Table 11 (Moraxella catarrhalis), Table 12 (Haemophilus influenzae) and Table 13 (Streptococcus pneumonia).



TABLES







TABLE 1







Bacterial strains selected from the Culture Collection University


of Gothenburg (CCUG). The strains were selected by to represent


the genetic diversity within each species.










Haemophilus
Moraxella
Streptococcus
Staphylococcus


influenzae
catarrhalis
pneumoniae
aureus





CCUG 23945T
CCUG 353T
CCUG 28588T
CCUG 41582T


CCUG 35273
CCUG 34455
CCUG 35272
CCUG 62707


CCUG 4559
CCUG 63408
CCUG 1350
CCUG 68900


CCUG 60440
CCUG 18284
CCUG 11780
CCUG 62271


CCUG 26214
CCUG 56314
CCUG 33774
CCUG 1964


CCUG 23969
CCUG 36757
CCUG 7206
CCUG 39740


CCUG 33775
CCUG 41836
CCUG 35180
CCUG 49245


CCUG 32226
CCUG 18283

CCUG 64138


CCUG 9188
CCUG 27321

CCUG 1979



CCUG 33013

CCUG 69160



CCUG 63533

CCUG 62274





CCUG 1988





CCUG 1914
















TABLE 2





Species discriminatory peptide biomarkers found


in dilution series (10{circumflex over ( )}4).
















H. influenzae
M. catarrhalis





GVAADAISATGYGK
VGDEIEIIGIKPTAK


SEQ ID NO: 36
SEQ ID NO: 251


 


AVVYNNEGTNVELGGR
TDEQLQAELDNK


SEQ ID NO: 41
SEQ ID NO: 252


 


FGQGEAPVVAAPEVVSK
GLITNSIENTNNITK


SEQ ID NO: 249
SEQ ID NO: 253


 


DGQVTGALATLGEPYK
QIVSNAGDEASVIVNEVK


SEQ ID NO: 250
SEQ ID NO: 19


 


LSVIAEQSNSTR
NTIEGENSVAIGSNNTVK


SEQ ID NO: 47
SEQ ID NO: 254


 


YDANNIIAGIAYGR
QQTEAIDALNK


SEQ ID NO: 42
SEQ ID NO: 255





S. pneumoniae
S. aureus (10{circumflex over ( )}3)





VQYEGGTEDELIR
ITYTMIGDPSQTITR


SEQ ID NO: 256
SEQ ID NO: 262


 


VSDVAESTGEFTSEQFEK
AILNNENNVLNVSIQLDGQYGGHK


SEQ ID NO: 52
SEQ ID NO: 263


 


GLDVTDEEGDDVTNGIFVGAK
GLEVGQIVESGAEADIK


SEQ ID NO: 257
SEQ ID NO: 264


 


SQTEQGEINIER
TVEVDGYNAIQVGFEDK


SEQ ID NO: 258
SEQ ID NO: 265


 


PAPAPQPAPAPKPEK
SINPADTSQVIANASK


SEQ ID NO: 259
SEQ ID NO: 266


 


AEADKPETEAGKER
GGLTDTFTNAFSSGNNVTQGVSVEVGEK


SEQ ID NO: 260
SEQ ID NO: 267


 


GAANGVVSHENTR
NFDVLDEATGLAQR


SEQ ID NO: 53
SEQ ID NO: 268


 


AEGVATASETAEAASAAKPEEK



SEQ ID NO: 261
















TABLE 3







Results of Example 2











Putative protein from which peptide


Peptide
Present
is potentially derived





AEKPADQQAEEDYAR
YES
hypothetical protein


SEQ ID NO: 181




 


AIEAGQTVDFSDLAIK
YES
glycerol facilitator-aquaporin


SEQ ID NO: 185




 


AIENPFAVEVADVETEK
YES
phosphoglucomutase


SEQ ID NO: 269




 


AVVVNPESTGVAIEEK
YES
alcohol dehydrogenase


SEQ ID NO: 270




 


IDTFGTGTVAESQLEK
YES
IS1380-Spn1 transposase


SEQ ID NO: 171




 


ILDLNEEEGRVSLSIK
YES
30S ribosomal protein S1


SEQ ID NO: 271




 


LLAGADPDDGTEVIEAK
YES
pneumococcal surface protein A


SEQ ID NO: 174




 


LYQNAEEVINK
YES
NADH oxidase


SEQ ID NO: 272




 


LYQNAEEVINKLSDK
YES
NADH oxidase


SEQ ID NO: 273




 


NLPVGSDGTFTPEDYVGR
YES
methionyl-tRNA synthetase


SEQ ID NO: 61




 


NVEIIEDDKQGVIR
YES
30S ribosomal protein S8


SEQ ID NO: 60




 


VSDVAESTGEFTSEQFEK
YES
general stress protein


SEQ ID NO: 52




 


VSDVTTLEEARPATTPSSPNVR
YES
capsular polysaccharide biosynthesis


SEQ ID NO: 274

protein Wzd
















TABLE 4





E. coli strains selected from the Culture Collection University


of Gothenburg (CCUG). The strains were selected by to represent


the genetic diversity within each species.




















CCUG
24T


Human urine, cystitis
Sweden


CCUG
65156
A
ECOR4
Human faeces, healthy
USA (Iowa)






woman


CCUG
65157
B1
ECOR29
Kangaroo faeces, healthy
USA (Nevada)


CCUG
65158
C
ECOR70
Gorilla faeces, healthy
USA







(Washington)


CCUG
65159
D
ECOR37
Marmoset faeces, healthy
USA







(Washington)


CCUG
65160
E
ECOR47
Sheep faeces, healthy
New guinea


CCUG
65161
F
ECOR39
Human faeces, healthy
Sweden






woman


CCUG
65162
B2
ECOR53
Human faeces, healthy
USA (Iowa)






woman


CCUG
65163
B2
ECOR56
Human urine, woman
Sweden


ATCC
35320
A
ECOR1
Human, woman
USA (Iowa)


ATCC
35345
B1
ECOR26
Human, infantis
USA







(Massachusetts)


ATCC
35359
D
ECOR40
Human, woman
Sweden


ATCC
35379
B2
ECOR60
Human, woman
Sweden
















TABLE 5





Moraxella catarrhalis biomarker peptides


SEQ ID NO

















SEQ ID NO: 27
 1
SQIYQTTASVSGAR


 


SEQ ID NO: 22
 2
VDATVDAQNPTK


 


SEQ ID NO: 28
 3
LLNETTGQVVPK


 


SEQ ID NO: 20
 4
AIAQVGSISANSDATIGELISK


 


SEQ ID NO: 72
 5
AQYDITQNAGTER


 


SEQ ID NO: 21
 6
ELSNTAAETQPK


 


SEQ ID NO: 19
 7
QIVSNAGDEASVIVNEVK


 


SEQ ID NO: 18
 8
VVLAGDTVVSDR


 


SEQ ID NO: 26
 9
YVVEGANMPLDAQAIDIVR


 


SEQ ID NO: 31
10
GLPVSNSGAPISVPVGQATLGR


 


SEQ ID NO: 29
11
SSENVVVVSVR


 


SEQ ID NO: 25
12
THTSALAEENQQASIPR


 


SEQ ID NO: 24
13
FNATAALGGYGSK


 


SEQ ID NO: 33
14
AVATQQATVSAEYLQK


 


SEQ ID NO: 73
15
TFHVGGAASAASVDNSVSVGNAGSVR


 


SEQ ID NO: 35
16
LGAQEAELVSNSK


 


SEQ ID NO: 23
17
QSDVGQLTGK


 


SEQ ID NO: 32
18
VNYNGDTDTVTLSGVAK


 


SEQ ID NO: 34
19
ADSGLSESEIEEMIR


 


SEQ ID NO: 30
20
AISYGNSADAQPYVGAK


 


SEQ ID NO: 74
21
ANLDTSTEEAR


 


SEQ ID NO: 75
22
ASSENTQNIAK


 


SEQ ID NO: 76
23
DADAVEAGQVIAK


 


SEQ ID NO: 77
24
FAATADAITK


 


SEQ ID NO: 78
25
LNTQGASFDYPVASNATEQGR


 


SEQ ID NO: 79
26
NKADADASFETLTK


 


SEQ ID NO: 80
27
AKLESLTEDMVAR


 


SEQ ID NO: 81
28
IITNNHAIALNLAAEGYGIAK


 


SEQ ID NO: 82
29
ILADIAMHDAAAFTAITEK


 


SEQ ID NO: 83
30
IYRPEIYNANSVAGQIYK


 


SEQ ID NO: 84
31
LDITETTDDSR


 


SEQ ID NO: 85
32
ALESNVEEGLLDLSGR


 


SEQ ID NO: 86
33
EFYAAETLPAESR


 


SEQ ID NO: 87
34
MNIEQTLQSAEDTAR


 


SEQ ID NO: 88
35
QDPANQEVYTK


 


SEQ ID NO: 89
36
SVTATDNTTQATVIK


 


SEQ ID NO: 90
37
VGVMAGPEQAVAEVAGQVAK


 


SEQ ID NO: 91
38
AHIGLAQAQFPEGLASSQVDALAR


 


SEQ ID NO: 92
39
ALATDYSHVVAPATTTGK


 


SEQ ID NO: 93
40
ENTVIVDGAGDKASIEAR


 


SEQ ID NO: 94
41
LPVDKETAPSDDATATTQFSR


 


SEQ ID NO: 95
42
LTYTDGSDPGSYYR


 


SEQ ID NO: 96
43
NLGAAVNEVTANEQSAEAKAPEDQQY


 


SEQ ID NO: 97
44
SDALYVVEDSVK


 


SEQ ID NO: 98
45
LADEGDIDVR


 


SEQ ID NO: 99
46
LTQATAQASAPQGR


 


SEQ ID NO: 100
47
LYPNDPTYQAASEK


 


SEQ ID NO: 101
48
NQADIANNINNIYELAQQQDQHSSDIK


 


SEQ ID NO: 102
49
SEVLDGMNSAYNPVVEDK


 


SEQ ID NO: 103
50
SLENDLGVSLLHR
















TABLE 6







Haemophilus influenza biomarker peptides









SEQ ID NO







SEQ ID NO: 36
 1
GVAADAISATGYGK


 


SEQ ID NO: 41
 2
AVVYNNEGTNVELGGR


 


SEQ ID NO: 37
 5
ANLKPQAQATLDSIYGEMSQVK


 


SEQ ID NO: 42
 6
YDANNIIAGIAYGR


 


SEQ ID NO: 38
 7
ADSVANYFVAK


 


SEQ ID NO: 48
 8
SADLTNEVAVGDVVEAK


 


SEQ ID NO: 104
 9
FGGNAQQTAQLPR


 


SEQ ID NO: 39
10
GSYEVLDGLDVYGK


 


SEQ ID NO: 40
11
LSQERADSVANYFVAK


 


SEQ ID NO: 51
12
AQYIVEQVIGQAR


 


SEQ ID NO: 43
13
ATHNFGDGFYAQGYLETR


 


SEQ ID NO: 105
14
GLSVGDQIQAGINSPIK


 


SEQ ID NO: 45
15
QQVNGALSTLGYR


 


SEQ ID NO: 50
16
TSPTQNLSLDAFVAR


 


SEQ ID NO: 44
17
AVVYNNEGTKVELGGR


 


SEQ ID NO: 47
18
LSVIAEQSNSTR


 


SEQ ID NO: 49
19
SADLTSEVAVGDVVEAK


 


SEQ ID NO: 46
20
YVPTNGNTVGYTFK


 


SEQ ID NO: 106
21
ATGEINLDGENLLTTK


 


SEQ ID NO: 107
22
ATHNLGDGFYAQGYLETR


 


SEQ ID NO: 108
23
GIASGTEVSFGTYGLK


 


SEQ ID NO: 109
24
GVAAIVTLSSTGR


 


SEQ ID NO: 110
25
NNEGTNVELGGR


 


SEQ ID NO: 111
26
TISDGITSAEDKEYGVLK


 


SEQ ID NO: 112
27
AILPPQEIEQGTVK


 


SEQ ID NO: 113
28
ATNLSAEQLNVTDASEK


 


SEQ ID NO: 114
29
FKQTAPSNNEVENELTNEQLTK


 


SEQ ID NO: 115
30
GIDGLVLGANYLLAQER


 


SEQ ID NO: 116
31
IAEQSNSTIKDQK


 


SEQ ID NO: 117
32
TAQFSTGGVYIDSR


 


SEQ ID NO: 118
33
YAYVTLGNNTFGEVK


 


SEQ ID NO: 119
34
ANLKPQAQATLDSVYGEISQVK


 


SEQ ID NO: 120
35
AQQLSTDVKNK


 


SEQ ID NO: 121
36
EITEDPAIYPSADILK


 


SEQ ID NO: 122
37
GLKVENTNNPIQVPVGTK


 


SEQ ID NO: 123
38
GVITVSAVGDQINPTLAR


 


SEQ ID NO: 124
39
INATEGAATLTAESGK


 


SEQ ID NO: 125
40
LSVIAEQSNSTADDQK


 


SEQ ID NO: 126
41
LSVIAEQSNTTVDDQK


 


SEQ ID NO: 127
42
LVSAQSGTESDNFGHIITK


 


SEQ ID NO: 128
43
NEGTNVELGGR


 


SEQ ID NO: 129
44
RAELEATAAANLAAAQAR


 


SEQ ID NO: 130
45
SADLTSEVAVGDVVDAK


 


SEQ ID NO: 131
46
SIIAEQSNSTIKDQK


 


SEQ ID NO: 132
47
SVDLTSEVAVGDVVEAK


 


SEQ ID NO: 133
48
TIADGITSAEDKEYGVLNNSK


 


SEQ ID NO: 134
49
TIIGANLSQLTQNELSAGK


 


SEQ ID NO: 135
50
TQTSTSIGFNAK
















TABLE 7







Staphylococcus aureus biomarker peptides









SEQ ID NO







SEQ ID NO: 2
 1
QAGVGAAVVAELSER


 


SEQ ID NO: 3
 2
ELINNIQSGQR


 


SEQ ID NO: 1
 3
TVQPIDVDTIVASVEK


 


SEQ ID NO: 4
 4
LGISDGDVEETEDAPK


 


SEQ ID NO: 5
 5
ALLNNMVQGVSQGYVK


 


SEQ ID NO: 8
 6
ILAESPNLAISSSSR


 


SEQ ID NO: 136
 7
NALIIEDTGDNNVVK


 


SEQ ID NO: 6
 8
SNVNDATDYSSETPEGK


 


SEQ ID NO: 10
 9
ATEATNATNNQSTQVSQATSQPINFQVQK


 


SEQ ID NO: 15
10
AEENGLTVVDAFNFEAPK


 


SEQ ID NO: 16
11
EKANELLKDNAELIASFSR


 


SEQ ID NO: 11
12
IHLVGDEIANGQGIGR


 


SEQ ID NO: 7
13
ANNVATDANHSYTSR


 


SEQ ID NO: 248
14
AQENGLTVVDAFNFEAPK


 


SEQ ID NO: 137
15
DLSFGENYGVVMEELR


 


SEQ ID NO: 17
16
LLGINATIVMPETAPQAK


 


SEQ ID NO: 12
17
NISNNVLVTIDAAQGK


 


SEQ ID NO: 9
18
NVVEIPLNDEEQSK


 


SEQ ID NO: 14
19
SQGVSEEELNESIDR


 


SEQ ID NO: 13
20
TAKPVAEVESQTEVTE


 


SEQ ID NO: 138
21
TPTEQTKPVQPK


 


SEQ ID NO: 139
22
VMGVDYVSNITEAR


 


SEQ ID NO: 140
23
YLGDEEISVSELK


 


SEQ ID NO: 141
24
AEAQANQMVGDAVEK


 


SEQ ID NO: 142
25
ANELLKDNAELIASFSR


 


SEQ ID NO: 143
26
ATDAENVEKEEAITK


 


SEQ ID NO: 144
27
AVAGAAGGADAAAEK


 


SEQ ID NO: 145
28
ELINGVFTDINPYIK


 


SEQ ID NO: 146
29
HIGTPGEVLEPGQQVNVK


 


SEQ ID NO: 147
30
KAQSEQDQAFLSK


 


SEQ ID NO: 148
31
MIAVLIPDDGSGK


 


SEQ ID NO: 149
32
NAGIGSGFSNDMYEKEGAK


 


SEQ ID NO: 150
33
QNLPVLDVPEDVVEEGVR


 


SEQ ID NO: 151
34
VVITAQTINEETEPELYDAEGNLINNSK


 


SEQ ID NO: 152
35
ADSGTVIQAISK


 


SEQ ID NO: 153
36
ATIDGLQNLKNAEDVAK


 


SEQ ID NO: 154
37
DSDIATTATKVELATK


 


SEQ ID NO: 155
38
FIAETYLDDVEQFNTVR


 


SEQ ID NO: 156
39
FIEETPELFDIQPSLDR


 


SEQ ID NO: 157
40
GLWNENKENEVIER


 


SEQ ID NO: 158
41
IFSEVEPNPSTNTVYK


 


SEQ ID NO: 159
42
LAEQKATDAENVEKEEA


 


SEQ ID NO: 160
43
LAVNEMLNAIQNK


 


SEQ ID NO: 161
44
LNDVEQTNTPGSLNPK


 


SEQ ID NO: 162
45
MQEVGVTAISGETIIK


 


SEQ ID NO: 163
46
NLSEQGINEATR


 


SEQ ID NO: 164
47
NMLPEVKPSSEVYGK


 


SEQ ID NO: 165
48
NVKDNAIVLEAISGADVNDSTSAPVDDVDFTSDIGKDIK


 


SEQ ID NO: 166
49
QNLPVLDVPEDVVEEGVRK


 


SEQ ID NO: 167
50
SGADVNDSTSAPVDDVDFTSDIGKDIK
















TABLE 8







Streptococcus pneumonia biomarker peptides









SEQ ID NO







SEQ ID NO: 52
 1
VSDVAESTGEFTSEQFEK


 


SEQ ID NO: 53
 2
GAANGVVSHENTR


 


SEQ ID NO: 54
 3
EEAPVASQSK


 


SEQ ID NO: 60
 4
NVEIIEDDKQGVIR


 


SEQ ID NO: 55
 5
SADQQAEEDYAR


 


SEQ ID NO: 61
 6
NLPVGSDGTFTPEDYVGR


 


SEQ ID NO: 65
 7
AVAAADAADAGAAK


 


SEQ ID NO: 63
 8
DIGLANDGSIVGINYAK


 


SEQ ID NO: 67
 9
TLSPEEYAVTQENQTER


 


SEQ ID NO: 56
10
APLQSELDTK


 


SEQ ID NO: 66
11
GQDWVIAAEVVTKPEVK


 


SEQ ID NO: 62
12
TLELEIAESDVK


 


SEQ ID NO: 64
13
IAELEYEVQR


 


SEQ ID NO: 70
14
IGVISVVEDGDEALAK


 


SEQ ID NO: 68
15
KDEAEAAFATIR


 


SEQ ID NO: 57
16
LKEIDESDSEDYVK


 


SEQ ID NO: 69
17
SQPSSETELSGNKQEQER


 


SEQ ID NO: 71
18
VAYFNEIDTYSEVK


 


SEQ ID NO: 58
19
AKLEEAEKKATEAK


 


SEQ ID NO: 59
20
AVNEPEKPAEESENPAPAPK


 


SEQ ID NO: 168
21
DVPENLITAVVQSNK


 


SEQ ID NO: 169
22
EAEANFNTEQAK


 


SEQ ID NO: 170
23
EIDESDSEDYLKEGLR


 


SEQ ID NO: 171
24
IDTFGTGTVAESQLEK


 


SEQ ID NO: 172
25
LKEIDESDSEDYVKEGFR


 


SEQ ID NO: 173
26
LKEIDESDSEDYVKEGLR


 


SEQ ID NO: 174
27
LLAGADPDDGTEVIEAK


 


SEQ ID NO: 175
28
NGNYETAEGSEETSSEVK


 


SEQ ID NO: 176
29
NTLLELGLDESQIK


 


SEQ ID NO: 177
30
VAAGDLLVTADLNAIR


 


SEQ ID NO: 178
31
VIPKETELATTK


 


SEQ ID NO: 179
32
VVPEAEQLAETK


 


SEQ ID NO: 180
33
AEKDYDAAMKNAEDAK


 


SEQ ID NO: 181
34
AEKPADQQAEEDYAR


 


SEQ ID NO: 182
35
AESTGEFTSEQFEK


 


SEQ ID NO: 183
36
AGITYSEGLVFESK


 


SEQ ID NO: 184
37
AGVVVVDNTSYFR


 


SEQ ID NO: 185
38
AIEAGQTVDFSDLAIK


 


SEQ ID NO: 186
39
ALTPEEVQKR


 


SEQ ID NO: 187
40
AQNTESTVVQLNNGDVK


 


SEQ ID NO: 188
41
DAEHAEEVAPQVK


 


SEQ ID NO: 189
42
DIILAQTEENLTR


 


SEQ ID NO: 190
43
DLENVETVIEKEDVETNASNGQR


 


SEQ ID NO: 191
44
EAGDQATYFDEIR


 


SEQ ID NO: 192
45
EGFVKNVEIIEDDKQGVIR


 


SEQ ID NO: 193
46
ELATQIYQVAR


 


SEQ ID NO: 194
47
GSDGKQFYNNYNDAPLK


 


SEQ ID NO: 195
48
GSIESMHNLPVNLAGAR


 


SEQ ID NO: 196
49
IAEATKEVQQAYLAYQQASNESQR


 


SEQ ID NO: 197
50
IGGGYAGQSGAIR
















TABLE 9







Escherichia coli biomarker peptides









SEQ ID NO







SEQ ID NO: 198
 1
AIDDLVKGFEELDTSK


 


SEQ ID NO: 199
 2
ANSSTTTAAEPLK


 


SEQ ID NO: 200
 3
QVPILQKDDSR


 


SEQ ID NO: 201
 4
VFDVNEPLSQINQAK


 


SEQ ID NO: 202
 5
VPVFAGDTEDDITAR


 


SEQ ID NO: 203
 6
SVQTVTGQPDVDQVVLDEAIKNR


 


SEQ ID NO: 204
 7
LIAAAPTAVAPEESGFYAR


 


SEQ ID NO: 205
 8
NAEFLQAYGVAIADGPLK


 


SEQ ID NO: 206
 9
EIAFEELGSQAR


 


SEQ ID NO: 207
10
AEVPSGTVLAEKQELVR


 


SEQ ID NO: 208
11
APRPAPAPQAPAQNTTPVTK


 


SEQ ID NO: 209
12
RTEPAAPVASTK


 


SEQ ID NO: 210
13
SDTYGWQEDSTYIR


 


SEQ ID NO: 211
14
SYEEELAKDPR


 


SEQ ID NO: 212
15
RTEPAAPVASTKAPAATSTPAPK


 


SEQ ID NO: 213
16
ADGINPEELLGNSSAAAPR


 


SEQ ID NO: 214
17
IVQSPDVIPADSEAGR


 


SEQ ID NO: 215
18
MAERPEVQDALSAEGLK


 


SEQ ID NO: 216
19
NAEFLQAYGVAIADGPLKGLAAR


 


SEQ ID NO: 217
20
QQAEVTEKAR


 


SEQ ID NO: 218
21
APAATSTPAPK


 


SEQ ID NO: 219
22
AFDSQTEDSSPAIGR


 


SEQ ID NO: 220
23
PNELLNSLAAVK


 


SEQ ID NO: 221
24
APAKESAPAAAAPAAQPALAAR


 


SEQ ID NO: 222
25
MNAFDSQTEDSSPAIGR


 


SEQ ID NO: 223
26
SGDLTAFEPELLKEHNAR


 


SEQ ID NO: 224
27
SLSDTLEEVLSSSGEK


 


SEQ ID NO: 225
28
NIPVELHVLLNDDAETPTR


 


SEQ ID NO: 226
29
QAQINGLEMAFLSAEEKR


 


SEQ ID NO: 227
30
QEAAPAAAPAPAAGVK


 


SEQ ID NO: 228
31
SRLPQNITLTEV


 


SEQ ID NO: 229
32
HLAKAPAKESAPAAAAPAAQPALAA




R


 


SEQ ID NO: 230
33
LTSSTATAATSKPVTSVASGPR


 


SEQ ID NO: 231
34
NVEYLVVEAAGATR


 


SEQ ID NO: 232
35
SDDMSMGLPSSAGEHGVLR


 


SEQ ID NO: 233
36
VRYEQSVAEEAVVAPVVEETVAAE




PIVQEAPAPR


 


SEQ ID NO: 234
37
AVTNSPVVVALDYHNR


 


SEQ ID NO: 235
38
EAPLAVELDHDKVMNMQVK


 


SEQ ID NO: 236
39
IMSGNSETETQEVGFKER


 


SEQ ID NO: 237
40
KRPEQPALATFAMPDVPPAPTPAE




PAAPVVAPAPK


 


SEQ ID NO: 238
41
SQPIFNDKQFQEALSR


 


SEQ ID NO: 239
42
ALDLSAEEKAAVR


 


SEQ ID NO: 240
43
ALEKVVGLQTEAPLKR


 


SEQ ID NO: 241
44
EAAIQVSNVAIFNATTGK


 


SEQ ID NO: 242
45
ETATTAPVQTASPAQTTATPAAGGK


 


SEQ ID NO: 243
46
FSAVLEQGAIAAGSDNK


 


SEQ ID NO: 244
47
LHHANDTDSFSATNVH


 


SEQ ID NO: 245
48
NVEYLVVEAAGTTR


 


SEQ ID NO: 246
49
SLEHEVTLVDDTLVR


 


SEQ ID NO: 247
50
TNGSLNAAEATETLR
















TABLE 10







The most prominent species-unique peptides of S. aureus. The corresponding


Gen Bank accession numbers and descriptions of the proteins are shown.










SEQ ID NO
Peptide sequence
Protein






SEQ ID NO: 1
TVQPIDVDTIVASVEK
AKJ16950.1
2-oxoisovalerate


SEQ ID NO: 2
QAGVGAAVVAELSER

dehydrogenase


 


SEQ ID NO: 3
ELINNIQSGQR
AKJ17520.1
preprotein translocase





subunit YajC


 


SEQ ID NO: 4
LGISDGDVEETEDAPK
AKJ17148.1
recombinase RecA


 


SEQ ID NO: 5
ALLNNMVQGVSQGYVK
AKJ18065.1
50S ribosomal protein





L6


 


SEQ ID NO: 6
SNVNDATDYSSETPEGK
AKJ17216.1
transketolase


 


SEQ ID NO: 7
ANNVATDANHSYTSR
AKJ17623.1
hypothetical protein


 


SEQ ID NO: 8
ILAESPNLAISSSSR
AKJ16422.1
HAD family hydrolase


 


SEQ ID NO: 9
NVVEIPLNDEEQSK
AKJ16109.1
lactate dehydrogenase


 


SEQ ID NO: 10
ATEATNATNNQSTQVSQATS
AKJ16987.1
heme transporter IsdA



QPINFQVQK




 


SEQ ID NO: 11
IHLVGDEIANGQGIGR
AKJ17576.1
pyruvate kinase


SEQ ID NO: 12
NISNNVLVTIDAAQGK




 


SEQ ID NO: 13
TAKPVAEVESQTEVTE
AKJ16406.1
DNA-directed RNA





polymerase subunit





beta′


 


SEQ ID NO: 14
SQGVSEEELNESIDR
AKJ16022.1
acetaldehyde





dehydrogenase


 


SEQ ID NO: 15
AEENGLTVVDAFNFEAPK
AKJ18079.1
50S ribosomal protein





L4


 


SEQ ID NO: 16
EKANELLKDNAELIASFSR
AKJ18460.1
fructose-16-





bisphosphate aldolase


 


SEQ ID NO: 17
LLGINATIVMPETAPQAK
AKJ17317.1
threonine dehydratase
















TABLE 11







The most prominent species-unique peptides of M. catarrhalis. The


corresponding GenBank accession numbers and descriptions of the


proteins are shown.










SEQ ID NO
Peptide sequence
Protein






SEQ ID NO: 18
VVLAGDTVVSDR
WP_003666427.1
TonB-dependent





receptor


 


SEQ ID NO: 19
QIVSNAGDEASVIVNEVK
WP_063454121.1
chaperonin GroEL


SEQ ID NO: 20
AIAQVGSISANSDATIGELISK




 


SEQ ID NO: 21
ELSNTAAETQPK
WP_003659702.1
30S ribosomal protein





S1


 


SEQ ID NO: 22
VDATVDAQNPTK
WP_003660336.1
hypothetical protein


SEQ ID NO: 23
QSDVGQLTGK




 


SEQ ID NO: 24
FNATAALGGYGSK
WP_063454085.1
cell surface protein


 


SEQ ID NO: 25
THTSALAEENQQASIPR
WP_063454087.1
cell division protein





FtsZ


 


SEQ ID NO: 26
YVVEGANMPLDAQAIDIVR
WP_049156084.1
NADP-specific





glutamate





dehydrogenase


 


SEQ ID NO: 27
SQIYQTTASVSGAR
WP_003657351.1
Ohr family





peroxiredoxin


 


SEQ ID NO: 28
LLNETTGQVVPK
WP_003657987.1
DUF4377 domain-





containing protein


 


SEQ ID NO: 29
SSENVVVVSVR
WP_063454071.1
electron transfer





flavoprotein subunit





beta


 


SEQ ID NO: 30
AISYGNSADAQPYVGAK
WP_003658939.1
porin family protein


 


SEQ ID NO: 31
GLPVSNSGAPISVPVGQATL
WP_003658974.1
F0F1 ATP synthase



GR

subunit beta


 


SEQ ID NO: 32
VNYNGDTDTVTLSGVAK
WP_003656943.1
peptidoglycan-binding





protein LysM


 


SEQ ID NO: 33
AVATQQATVSAEYLQK
WP_003657125.1
ABC transporter





substrate-binding





protein


 


SEQ ID NO: 34
ADSGLSESEIEEMIR
WP_003669031.1
molecular chaperone





DnaK


 


SEQ ID NO: 35
LGAQEAELVSNSK
WP_003660298.1
CTP synthase
















TABLE 12







The most prominent species-unique peptides of H. influenzae. The


corresponding GenBank accession numbers and descriptions of the


proteins are shown.









SEQ ID NO
Peptide sequence
Protein













SEQ ID NO: 36
GVAADAISATGYGK
WP_038441355.1
porin OmpA


SEQ ID NO: 37
ANLKPQAQATLDSIYGEMSQ





VK




SEQ ID NO: 38
ADSVANYFVAK




SEQ ID NO: 39
GSYEVLDGLDVYGK




SEQ ID NO: 40
LSQERADSVANYFVAK




 


SEQ ID NO: 41
AVVYNNEGTNVELGGR
WP_058222193.1
porin


SEQ ID NO: 42
YDANNIIAGIAYGR




SEQ ID NO: 43
ATHNFGDGFYAQGYLETR




SEQ ID NO: 44
AVVYNNEGTKVELGGR




SEQ ID NO: 45
QQVNGALSTLGYR




SEQ ID NO: 46
YVPTNGNTVGYTFK




SEQ ID NO: 47
LSVIAEQSNSTR




 


SEQ ID NO: 48
SADLTNEVAVGDVVEAK
WP_011272719.1
30S ribosomal protein


SEQ ID NO: 49
SADLTSEVAVGDVVEAK

S1


 


SEQ ID NO: 50
TSPTQNLSLDAFVAR
WP_058222202.1
ShlB/FhaC/HecB




WP_050846043.1
family hemolysin





secretion/activation





protein


 


SEQ ID NO: 51
AQYIVEQVIGQAR
WP_011272712.1
pyruvate





dehydrogenase





(acetyl-transferring),





homodimeric type
















TABLE 13







The most prominent species-unique peptides of S. pneumoniae. The


corresponding GenBank accession numbers and descriptions of the


proteins are shown.









SEQ ID NO
Peptide sequence
Protein













SEQ ID NO: 52
VSDVAESTGEFTSEQFEK
WP_000064115.1
Asp23/Gls24 family


SEQ ID NO: 53
GAANGVVSHENTR

envelope stress





response protein


 


SEQ ID NO: 54
EEAPVASQSK
WP_001035310.1
hypothetical protein


SEQ ID NO: 55
SADQQAEEDYAR




SEQ ID NO: 56
APLQSELDTK




SEQ ID NO: 57
LKEIDESDSEDYVK




SEQ ID NO: 58
AKLEEAEKKATEAK




SEQ ID NO: 59
AVNEPEKPAEESENPAPAPK




 


SEQ ID NO: 60
NVEIIEDDKQGVIR
WP_000245505.1
30S ribosomal protein





S8


 


SEQ ID NO: 61
NLPVGSDGTFTPEDYVGR
WP_001291372.1
methionine--tRNA





ligase


 


SEQ ID NO: 62
TLELEIAESDVK
WP_000458177.1
hypothetical protein


 


SEQ ID NO: 63
DIGLANDGSIVGINYAK
WP_000927809.1
sugar ABC transporter





substrate-binding





protein


 


SEQ ID NO: 64
IAELEYEVQR
WP_001008677.1
Asp-tRNA(Asn)/Glu-





tRNA(Gln)





amidotransferase





subunit GatB


 


SEQ ID NO: 65
AVAAADAADAGAAK
WP_001196960.1
50S ribosomal protein





L7/L12


 


SEQ ID NO: 66
GQDWVIAAEVVTKPEVK
WP_000116461.1
trigger factor


 


SEQ ID NO: 67
TLSPEEYAVTQENQTER
WP_000998307.1
peptide-methionine





(R)-S-oxide reductase


 


SEQ ID NO: 68
KDEAEAAFATIR
WP_001284361.1
thiol-activated toxin





pneumolysin


 


SEQ ID NO: 69
SQPSSETELSGNKQEQER
WP_078148305.1
sialidase


 


SEQ ID NO: 70
IGVISVVEDGDEALAK
WP_000808063.1
elongation factor Ts


 


SEQ ID NO: 71
VAYFNEIDTYSEVK
WP_000685088.1
nucleotide sugar





dehydrogenase
















TABLE 14





The five most prominent species-unique peptides of S. aureus. The


corresponding GenBank accession numbers and descriptions of the


proteins are shown.


















TVQPIDVDTIVASVEK
SEQ ID NO: 1
AKJ16950.1
2-oxoisovalerate dehydrogenase


QAGVGAAVVAELSER
SEQ ID NO: 2




 


ELINNIQSGQR
SEQ ID NO: 3
AKJ17520.1
preprotein translocase subunit YajC


 


LGISDGDVEETEDAPK
SEQ ID NO: 4
AKJ17148.1
recombinase RecA


 


ALLNNMVQGVSQGYVK
SEQ ID NO: 5
AKJ18065.1 
50S ribosomal protein L6
















TABLE 15





The five most prominent species-unique peptides of M. catarrhalis. The


corresponding GenBank accession numbers and descriptions of the


proteins are shown.


















VVLAGDTVVSDR
SEQ ID NO: 18
WP_003666427.1
TonB-dependent receptor


 


QIVSNAGDEASVIVNEVK
SEQ ID NO: 19
WP_063454121.1
chaperonin GroEL


AIAQVGSISANSDATIGELISK
SEQ ID NO: 20




 


ELSNTAAETQPK
SEQ ID NO: 21
WP_003659702.1
30S ribosomal protein S1


 


VDATVDAQNPTK
SEQ ID NO: 22
WP_003660336.1
hypothetdol protein
















TABLE 16





The five most prominent species-unique peptides of H. influenzae. The


corresponding GenBank accession numbers and descriptions of the


proteins are shown.


















GVAADAISATGYGK
SEQ ID NO: 36
WP_038441355.1
porin OmpA


ANLKPQAQATLDSIYGEMSQVK
SEQ ID NO: 37




 


AWYNNEGTNVELGGR
SEQ ID NO: 41
WP_058222193.1
porin


YDANNIIAGIAYGR
SEQ ID NO: 42




 


SADLTNEVAVGDVVEAK
SEQ ID NO: 48
WP_011272719.1
30S ribosomal protein S1
















TABLE 17





The five most prominent species-unique peptides of S. pneumoniae. The


corresponding GenBank accession numbers and descriptions of the


proteins are shown.


















VSDVAESTGEFTSEQFEK
SEQ ID NO: 52
WP_000064115.1
Asp23/G1s24 family envelope stress


GAANGVVSHENTR
SEQ ID NO: 53

response protein


 


EEAPVASQSK
SEQ ID NO: 54
WP_001035310.1
hypothetical protein


SADQQAEEDYAR
SEQ ID NO: 55




 


NVEIIEDDKQGVIR
SEQ ID NO: 60
WP_000245505.1
30S ribosomal protein S8









EXAMPLES
Examples 1—Biomarker Identification
In order to identify candidate peptide biomarkers, several strains from each of the four target species, H. influenzae, M. catarrhalis, S. pneumonia, and S. aureus, including the Type strain of each species, were selected to represent the genetic variability within the species (Table 1). Bacterial cells were grown, washed and prepared by bead beating as described in the examples and methods (FIG. 1).
Digestion of bacterial lysates and generation of peptides was performed using the Lipid-based Protein Immobilization (LPI) methodology. Peptides were analyzed by LC-MS/MS and subsequently, the tandem mass spectra were processed by a bioinformatics pipeline, TCUP, to discover species unique peptides, also described in the examples and methods. For S. pneumoniae 7 strains were analyzed in triplicate (21 MS runs), resulting in 782 species unique peptide candidates found in at least one of the 21 MS runs. For H. influenzae 9 strains were analyzed in triplicate (26 MS runs; 1 run failed), resulting in 2978 species unique peptide candidates found in at least one of the 26 MS runs. For M. catarrhalis 11 strains were analyzed in triplicate (33 MS runs), resulting in 5810 species unique peptide candidates found in at least one of the 33 MS runs. For S. aureus 13 strains were analyzed in triplicate (36 MS runs; 3 runs failed), resulting in 5847 species unique peptide candidates found in at least one of the 36 MS runs. From the sum of these species unique peptides, a targeted database containing 15417 peptides were created.
The peptides within this database were ranked according to frequency of detection to generate a preliminary inclusion list (see FIG. 1).
The preliminary inclusion list was experimentally verified as set out below.
In order to find biomarkers which were being expressed in sufficient amounts as well as being detected most easily by the mass spectrometer, different ranges of numbers of cells per ml were spiked to negative clinical samples. Thus, spiked negative samples were used to evaluate which peptides should be included in an inclusion list (50 to 100 peptides per species). Negative samples were spiked with a range of cells, ranging from 1 million cells per ml down to 100 cells per ml.
Removal of human biomass was performed by use of the MolYsis kit (Molzym, Germany) and in-solution digestion was performed using sodium deoxycholate (SDC), also described in the enclosed examples and methods. The samples were analysed via tandem MS and the tandem mass spectra were processed by a bioinformatics pipeline, TCUP, using the targeted database of mentioned above (15417 peptides) or via an open, non-targeted approach.
The peptides found in the most diluted spiked samples were considered to be promising as peptide biomarker candidates, due to a sufficient expression level and suitable properties for ionization, fragmentation and detection in the mass spectrometer (FIG. 2 and Table 2). On this basis, 100 peptides were selected as good candidate peptides per species, creating a revised inclusion list per species. Each inclusion list contained about 100 peptides and was divided into two lists of 50 for ease of handling.
In the next step of the process, true positive clinical samples were analysed to verify/revise the inclusion lists (FIG. 3). As before, removal of human biomass was performed by use of the MolYsis kit (Molzym, Germany), bacteria were lyzed using bead beating and in-solution digestion was performed using sodium deoxycholate (SDC), again described in the enclosed examples and methods. The clinical samples were analyzed using both an open approach (running all the raw files through TCUP) and a targeted approach (matching the raw data against the inclusion lists and/or targeted database mentioned above, 15417 peptides). The benefit of the open approach is that it is not targeted and thus peptides not present in the targeted database (15417 peptides) can be detected, whereas the drawback is lesser sensitivity. The benefit of the targeted approach is a higher sensitivity, but the drawback lies in the greater risk of false positives.
Approximately 1600 MS analyses were carried out, including on approximately 500 clinical samples containing S. pneumoniae, H. influenzae, M. catarrhalis and S. aureus (as determined by traditional culture-dependent methods, including MALDI-TOF MS-based identification). This analysis was used to validate (or invalidate, as the case may be) the candidate peptide biomarkers in the inclusion lists mentioned above.
The inclusion lists were revised as follows (see FIG. 3): If any peptide biomarkers identified in the true positive clinical samples were already present in the inclusion lists, this validated their relevance. If any peptide biomarkers present in the inclusion lists were not detected in any of the true positive clinical samples, they were removed from the inclusion lists, or were given a lower ranking.
If any peptide biomarkers that were not present in the inclusion list, but were present in the targeted database (15417 peptides), were identified in the true positive clinical samples, their ranking was noted and they were then included in an updated inclusion list.
Some peptides were ranked low in the first run based on bacterial cultures, e.g. found only in 1 out of 21 MS runs (in case of S. pneumoniae), but were nevertheless found in all clinical samples. This was likely due to different expression levels in the blood agar cultures as compared to actual clinical samples. For example, a virulence factor might be highly expressed in a clinical sample, whereas it is only moderately expressed in a blood agar culture.
The same strategy also is also being used to generate a peptide biomarker inclusion list for E. coli. 
Some peptides were also selected for PRM studies (FIGS. 5 and 7). Parallel reaction monitoring (PRM) is an ion monitoring technique based on high-resolution and high-precision mass spectrometry. The principle of this technique is comparable to SRM/MRM, but it is more convenient in assay development for absolute quantification of proteins and peptides. It is most suitable for quantification of multiple proteins in complex sample with an attomole-level detection. PRM is based on Q-Orbitrap as the representative quadrupole-high resolution mass spectrum platform. Unlike the SRM, which performs one transition at a time, the PRM performs a full scan of each transition by a precursor ion, that is, parallel monitoring of all fragments from the precursor ion. First, the PRM uses the quadrupole (Q1) to select the precursor ion, and the selection window is usually m/z≤2; then, the precursor ion is fragmented in the collision cell (Q2); finally, Orbitrap replaces Q3, scans all product ions with high resolution and high accuracy. Therefore, PRM technology not only has the SRM/MRM target quantitative analysis capabilities, but also have the qualitative ability. (1) The mass accuracy can reach to ppm level, which can eliminate the background interference and false positive better than SRM/MRM, and improve the detection limit and sensitivity in complex background effectively; (2) Full scan of product ions, without the need to select the ion pair and optimize the fragmentation energy, easier to establish the assay; (3) a wider linear range: increased to 5-6 orders of magnitude. Using this approach, it was possible to verify the presence of a biomarker peptide in a clinical sample by observing the same retention time and the same set of fragment ions when comparing to an analysis of a bacterial culture containing the same peptide.
Methods:
Cultivation of Bacteria and Preparation of Samples
In order to generate lists of candidate peptide biomarkers, approximately ten strains from each of the four target species, H. influenzae, M. catarrhalis, S. pneumoniae and S. aureus, including the Type strain of each species, were selected to represent the genetic variability within the species (Table 1).
Bacterial strains were grown on Blood Agar medium. S. pneumoniae and M. catarrhalis were grown at 36° C. with 5% CO2 overnight, S. aureus at 37° C. overnight, and H. influenzae was grown on chocolate agar medium at the same conditions as S. pneumoniae and M. catarrhalis. Bacterial biomass was collected and resuspended in phosphate-buffered saline (PBS). Bacterial densities were measured at A600 (A600=1 corresponding to 1*10{circumflex over ( )}9 bacteria). For each experiment, the same amounts of bacterial biomass were established, by adjusting the A to 1.0 in 1.0 ml of PBS. The bacterial biomass was washed with PBS three times by centrifuging the sample for 5 min at 12,000 g, discarding the supernatant, and resuspending the pellet in 1.0 ml of PBS. The bacteria were finally resuspended in 150 μl of PBS. The bacterial cell suspensions were transferred to 200-μl vials containing glass beads (Sigma-Aldrich, G1145). The bacterial cells were lysed by bead-beating, using a TissueLyser (Qiagen, 85220), with the following settings: frequency 1/25 s and 5 min. The bacterial lysates were frozen at −20° C. until analysis.
Spiking of Negative Samples for Discovery of Candidate Peptide Biomarkers
Clinical samples (respiratory tract nasopharyngeal and nasal swabs) deemed negative by culture and MALDI-TOF-MS were collected and spiked with cells of the Type strains of the four species H. influenzae, M. catarrhalis, S. pneumoniae and S. aureus. The spikes of added cells ranged from 1 million cells/ml down to 100 cells/ml.
Clinical Sample Clean-Up Using MolYsis Kit
The clinical samples (respiratory tract nasopharyngeal and nasal swabs), collected in Amies media, were supplemented with STGG (Skim milk, tryptone, glucose, glycerol) and frozen until processing.
For removal of human biomass (mucus, cells and proteins), the MolYsis kit (MolYsis Basic5 kit, Molzym, Germany) was used according to protocol provided by the supplier. The biomass was collected by centrifuging the samples 15000 g (5 min) in 1.5 ml Eppendorf tubes. Supernatant was discarded. The pellet was resuspended in 1 ml (500 μl SU buffer+500 μl PBS). CM buffer (250 μl) was added and the sample was vortexed for 15 s and then allowed to stand at room temp for 10 min. The samples were transferred to 2.0 ml tubes. If visible clusters of bacteria/mucus were present the sample was pipetted up and down until they were dissolved. DB1 buffer (250 μl) was added and the sample was vortexed before allowing standing at room temp for 15 min. If visible clusters of bacteria/mucus were present the sample was pipetted up and down until dissolving. The sample was centrifuged—15000 g for 10 min to collect bacteria. Supernatant discarded and pellet saved. Pellet was resuspended in 1 ml RS buffer.
The sample was centrifuged—15000 g for 5 min to collect bacteria. Supernatant discarded and pellet saved. Pellet was resuspended in 1 ml PBS buffer. The sample was centrifuged—15000 g for 5 min to collect bacteria. Supernatant discarded and pellet saved. Supernatant was discarded and the bacteria were resuspended in 120 μl ammonium bicarbonate (20 mM pH 8). The sample was subjected to bead beating in order to break the cells and release as many proteins as possible, making them accessible for digestion. Glass beads (Sigma-Aldrich G1145) had already been placed in the vials. The bead beater used was a TissueLyser from Qiagen. Settings: Frequency 1/25 s and continuous shaking for a total time of 5 min. The bead beaten samples were frozen until analysis.
Digestion of Clinical Samples Using In-Solution Digestion with Sodium Deoxycholate (SDC)
Frozen samples where thawed. SDC 1% was added from a 5% stock and bead beating was repeated. Samples were removed from the glass beads and transferred to new tubes (1.5 ml). The remaining glass beads were rinsed by adding 100 μl 1% SDC in ammonium bicarbonate (20 mM) and transferred to the samples. Trypsin (2 μg/ml, 100 μl ammonium bicarbonate, 20 mM pH 8) was added and samples were allowed to be digested for 8 h at 37 degrees Celsius. Formic acid (3 μl, neat) was subsequently added to remove SDC. Samples were centrifuged at 15000 g (10 min) to pellet biomass/debris. Pellet was discarded and supernatant (peptides) was transferred to a new tube (1.5 ml). Samples were kept frozen at −20 degrees Celsius until analysis.
Peptide Analysis Using Tandem Mass Spectrometry
The tryptic peptides were desalted on Pep Clean C18 spin columns (Thermo Fisher Scientific, Inc., Waltham, Mass.), according to the manufacturer's guidelines, dried, and reconstituted with 15 μl of 0.1% formic acid (Sigma-Aldrich) in 3% gradient-grade acetonitrile (Merck KGaA, Darmstadt, Germany). A 2.0 μl sample was injected, with an Easy-nLC autosampler (Thermo Fisher Scientific), and analyzed, using an interfaced Q Exactive hybrid mass spectrometer (Thermo Fisher Scientific). The peptides were trapped on a pre-column (45μ 0.075-mm inner diameter) and separated on a reversed-phase column, 200 0.075 mm, packed in-house with 3-m Reprosil-Pur C18-AQ particles (Dr. Maisch, Ammerbuch, Germany). The nanoLC (liquid chromatography) gradient was running at 200 nl/min, starting at 7% acetonitrile (ACN) in 0.2% formic acid, increased to 27% CAN for 25 min, then increased to 40% ACN for 5 min, and finally to 80% ACN for 5 min and held at 80% ACN for 10 min. Electrospray ionization was applied under a voltage of 1.8 kV and a capillary temperature of 320° C. in data-dependent positive ion mode. Full scan (MS1) spectra were acquired in the Orbitrap over the m/z range 400-1600, with a charge range of 2-6, at a resolution of 70,000, until reaching an AGC target value of 1e6 at a maximum of 250 ms. MS/MS spectra were acquired, using higher energy collision dissociation, at 30% from m/z 110 for the 10 most abundant parent ions, at a resolution of 35,000, using a precursor isolation window of 2 Da until reaching an AGC target value of 1e5 during an injection time of 110 ms. Dynamic exclusion for 30 s after selection for MS/MS was enabled to allow for detection of as many precursors as possible.
TCUP—Typing and Characterization Using Proteomics
The input to TCUP is a set of peptides identified from spectra generated by bottom-up tandem MS specified as a file in FASTA format. TCUP is general and can be used with peptide data from any spectral matching software, including de novo methods (e.g. SEQUEST (18), X!Tandem (19, 20), TIDE (21), Mascot (22), PEAKS (23), PepNovo (24), and Lutefisk (25)). The output from TCUP is in Excel format and includes the following: 1) the relative abundances of all organisms identified in a sample at and below a user-specified taxonomic level; 2) specific genes in the reference genomes that are matched by peptides in the analysis; and 3) the relative abundances of identified antimicrobial resistance genes. TCUP is implemented in Python 3.5, and the code and usage documentation are freely available under the ISC license from the project's repository. After alignment to the translated reference genome sequences, each peptide is matched to zero, one, or multiple reference genomes. To remove matches that are too dissimilar and unlikely to contain any relevant information about the taxonomic affiliation, two filtering steps were applied. The first step requires matches to have an identity of at least 90% and a coverage of 100% (only complete peptide matches are considered). Also, peptides shorter than six amino acids are removed. In the second filtering step, all matches with sequence identity of at least 5% below the best match for that peptide are discarded.
After filtering, the remaining peptides are assigned to nodes in a taxonomic tree, using the lowest common ancestor algorithm (30). The taxonomic affiliation of a sample is then assigned based on the set of discriminative peptides, i.e. the peptides with a lowest common ancestor at a node that is at or below the user-specified taxonomic level. The taxonomic tree used in TCUP is based on the full NCBI Taxonomy (31) (taxdump downloaded Nov. 17, 2015), in which each reference genome is associated with a unique node. Our implementation extends the SQLite3 database used in the ETE3 package (32) with a table of taxonomic affiliations for all reference genome sequences included in the reference database.
Example 2—Detection of S. Pneumoniae Through Detection of Peptide Biomarkers in a Clinical Sample
Step 1. Ten respiratory tract samples (nasopharyngeal swabs) deemed positive for S. Pneumoniae by traditional methods, including culturing and isolation of bacterial isolates, followed by MALDI-TOF-MS identification, were selected. The samples were in the form of swabs in commercial Amies media (Copan Diagnostics Inc).
Step 2. 50% of the liquid Amies media (0.5 ml) was transferred to a cryotube and supplemented with STGG buffer (Skim milk, tryptone, glucose, glycerol) for storage at −20 degrees Celsius until analysis.
Step 3. Human biomass was removed from the sample using the MolYsis kit (Molzym Gbh, Germany), according to manufacturer's protocol.
Step 4. Sample was homogenized using bead beating and subsequently, the bacterial proteins were digested into peptides using trypsin in a buffer supplemented with sodium deoxycholate (SDC).
Step 5. The peptides were desalted and purified using C18 spin column clean-up. After drying in speedvac, the peptides were resuspended in dilute formic acid.
Step 6. The peptides were analyzed using LC-MS/MS, using the inclusion lists in a mode called inclusion list plus pick others. In this fashion were the MS instrument first looks only at the masses of the selected peptides in the inclusion list. If there are no masses matching to the 50 peptides in the inclusion list during the MS instrument cycle time (milliseconds), the instrument looks for everything else and picks the top ten most intense ion peaks (pick others).
Step 7. The raw files were run through TCUP to match, identify and report the peptides identified in the samples. The results from this analysis are shown in Table 3. Thirteen peptides were identified belonging to the inclusion list among the ten clinical samples, thus resulting in a positive match for S. Pneumoniae. 
Example 3—Example Showing a Panel of the Peptide Biomarkers for Performing Clinical Diagnostics
Table 18 below shows an exemplary panel of the peptide biomarkers proposed for use in performing clinical diagnostics. Five different samples containing one or more of the four pathogens are analyzed. In each sample, a particular combination of the peptide biomarkers (shown by SEQ ID NOs) is shown. The detection of the peptide biomarkers are detected by any of the suggested methodologies, i.e. targeted MS approaches, or based on antibody detection or other suitable methodologies.
















S. aureus
M. catarrhalis
H. influenzae
S. pneumoniae




















Sample 1
SEQ ID NO: 1





S. aureus
SEQ ID NO: 2


Sample 2

SEQ ID NO: 20
SEQ ID NO: 36


M. catarrhalis

SEQ ID NO: 21
SEQ ID NO: 37


H. influence

SEQ ID NO: 22
SEQ ID NO: 41





SEQ ID NO: 42


Sample 3

SEQ ID NO: 18

SEQ ID NO: 52


M catarrhalis

SEQ ID NO: 19

SEQ ID NO: 53


S. pneumoniae

SEQ ID NO: 20

SEQ ID NO: 54






SEQ ID NO: 55


Sample 4


SEQ ID NO: 41
SEQ ID NO: 54


H. influence


SEQ ID NO: 42
SEQ ID NO: 55


S. pneumoniae


SEQ ID NO: 48
SEQ ID NO: 60


Sample 5



SEQ ID NO: 52


S. pneumoniae



SEQ ID NO: 55

## Claims

1. A non-naturally occurring set of peptides comprising 2-50 different peptides,
wherein at least two peptides of the 2-50 different peptides are desalted and wherein the at least two peptides of the 2-50 different peptides consist of an amino acid sequence selected from one of the following groups:
(i) SEQ ID NOs:52-71 and 168-197;
(ii) SEQ ID NOs:36-51 and 104-135;
(iii) SEQ ID NOs:1-17, 136-167, and 248;
(iv) SEQ ID NOs:18-35 and 72-103;
(v) SEQ ID NOs:198-247;
(vi) SEQ ID NOs:52-71;
(vii) SEQ ID NOs:36-51;
(viii) SEQ ID NOs:1-17;
(ix) SEQ ID NOs:18-35
(x) SEQ ID NOs:19, 36, 41, 42, 47, 52, 53, 249-268; or
(xi) SEQ ID NOs:52, 60, 61, 171, 174, 181, 185, 269-274.

2. The non-naturally occurring set of peptides according to claim 1, wherein the at least two peptides of the 2-50 different peptides consist of an amino acid sequence selected from one of the following groups:
(i) SEQ ID NOs:52-55 and 60;
(ii) SEQ ID NOs:36, 37, 41, 42, and 48;
(iii) SEQ ID NOs:1-5; or
(iv) SEQ ID NOs:18-22.

3. A method of detecting or identifying a bacterial biomarker in a sample, the method comprising: detecting in the sample at least one desalted peptide of the non-naturally occurring set of peptides of claim 1.

4. The method according to claim 3, wherein the method is
used to detect or identify one of Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Escherichia coli, and Moraxella catarrhalis.

5. The method of claim 3, wherein said method is used to detect or identify two or more bacteria selected from Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Escherichia coli, and Moraxella catarrhalis in the sample.

6. The method of claim 3, wherein the detecting comprises detecting at least two desalted peptides in the non-naturally occurring set of peptides.

7. The method of claim 3, wherein the detecting is carried out using mass spectrometry and/or an affinity reagent specific for the biomarker.

8. The method of claim 3, further comprising, prior to the detecting step, a cell lysis step and/or a proteolysis step.

9. The method of claim 3, further comprising, responsive to detecting in the sample the at least one desalted peptide, diagnosing bacterial infection.

10. A diagnostic kit for detecting specific biomarker peptides in a sample, wherein the kit comprises the non-naturally occurring set of peptides according to claim 1, wherein the kit further comprises a protease.

11. A non-naturally occurring peptide having an amino acid sequence consisting of one of SEQ ID NOs:1-71, wherein the non-naturally occurring peptide is desalted.

12. The non-naturally occurring peptide of claim 11, wherein the non-naturally occurring peptide has an amino acid sequence consisting of one of SEQ ID NOs:1-5, 18-22, 36, 37, 41, 42, 48, 52-55 and 60.

13. A pharmaceutical composition comprising the non-naturally occurring peptide according to claim 11.