Microbiome analysis

Abstract

A method for determining high-level taxonomic (preferably phylum-level) microbiome population information is described. The method uses asymmetric nucleic acid amplification of a portion of r16S DNA using primers which are conserved across the taxa of interest; and detects the amplified portion using a probe which has a predetermined number of mismatched bases across the taxa of interest. Melt curve analysis is used to distinguish numbers of mismatched bases and the relative abundance of each amplified target. From this, the relative abundance of each phylum of interest is determined.

Description

[0001]Microbiome Analysis Field of Invention: The present invention relates to a method and system for diagnosing disease by analysing the composition changes of the microbiome at a phylum level. Background: Disease diagnostics is limited to the analysis of a sample to identify the causative, e.g. is a specific bacteria responsible for a pathogenesis present in the sample. Current diagnostic methods typically focus on detecting the presence or absence of single or a few specific bacterial species, which can be time-consuming and costly to iterate through different potential causes. Further, traditional methods do not allow for monitoring of interactions between different microbial communities. Sequencing of microbiomes has yielded more detailed data analysis and complex data-rich information which has been useful in research that seeks to establish associations. Chakravorty et al. (J Clin Micro, vol 48, 2010, pp258-267) report an assay to identify bacteria at the species level across a number of clinically relevant genera using a highly multiplexed combination of mismatch-tolerant “sloppy” molecular beacon probes. These probes include probe sequences of up to 60 nt in length or more, and stem lengths of 5-7 nt. Melting temperatures of combinations of long probes are used to distinguish bacterial species. El-Hajj et al. (J Clin Micro, vol 47, 2009, pp1190-1198) uses a combination of four sloppy probes having relatively long lengths to produce a species specific Tm profile, thereby allowing 27 Mycobacteria to be differentiated. Luo Yongqiang et al. (Small Methods, vol 6, 2022, article number 2200185) describes a technique referred to as SAMBA, a further multiplexed analysis of melting temperature using sloppy molecular beacon probes to distinguish mixtures of bacteria. Summary of Invention: The present invention provides a method and system for analysing the composition of the microbiome and restricting the analysis to the phylum level to triage results against expected ratios for a specific sample type or patient demographic. By mapping the microbiome at a phylum level rather than looking individually at a species level it is possible to create a single test that may be used across many different disease states. This approach can help identify broad changes in the composition of the microbiome that are associated with the infection, rather than just detecting the presence or absence of specific species. It may be used both singularly, or longitudinally to provide different levels of information. Significantly, embodiments of the present invention can provide methods for distinguishing a number of bacterial and / or archeal phyla without the need for multiplexed probes; and can make use of probes which are typically shorter in length than sloppy molecular beacon probes described in the art for species-specific discrimination. Further, the probes need not be molecular beacon probes. This provides a simpler system than previously described. In a first aspect of the invention, there is provided a method for determining high-level taxonomic microbiome population information across a plurality of taxa of interest in a sample, the method comprising the steps of: a) subjecting the sample to asymmetric nucleic acid amplification of a portion of r16S DNA using primers which are conserved across the taxa of interest; b) detecting said amplified portion using a probe selected to provide varying numbers of mismatched bases across the taxa of interest; c) determining the relative abundance of probe:amplified portion detection events with each of said varying numbers of mismatched bases; and d) determining the relative abundance of each of said taxa of interest based on the determined relative abundance of step c). “r16S DNA” or “r16S gene” here refers to the genomic DNA sequences which encode the 16S ribosomal RNA component. It is possible that similar techniques may be applied to eukaryotic organisms, making use of the r18S DNA; this may be of benefit in particular when sampling environmental DNA. The microbiome may be understood as the population of microbes living in a particular habitat, typically including bacteria and archea, although some populations may also include microbial eukaryotes such as fungi or protists. Generally the habitat will be in association with a multicellular host organism; preferably a human. The habitat may be within a particular location associated with the organism, for example, gut, mouth, vagina etc. In some applications, the habitat may be associated with a particular condition; for example, sepsis has been linked with certain dysregulated host microbiomes, for example within the gut. In such cases, the microbiome may be that associated with sepsis or other conditions of the host. Typically, however, in the practice of the present invention the microbiome will be that in a particular location associated with the host organism, as is described herein. In some embodiments the habitat may not be in association with a multicellular host organism; for example, sampling of environmental DNA. By “high-level taxonomic microbiome population information” is meant population information which distinguishes between groups at a certain taxonomic level, but not between lower groups within that level. For example, typically the population information will distinguish between different microbial phyla, but no lower classifications (for example, class, order, family, genus). In some embodiments the same concept may be used to distinguish between lower ranked classifications (for example, class). It is intended that the method used is not so specific as to distinguish below the class level; and in particular, no species information is provided. In the preferred embodiments of the invention, the “microbiome” includes prokaryotic organisms, including bacteria and archea. While bacteria and archea are different domains, ranking higher than phyla, as we describe here they share sufficient similarity in the r16S genes to permit a phylum-level analysis. As there is no intention or requirement to identify organisms to the species level (or even to certain higher taxonomic levels), probe design is simplified and the use of multiplexed probes may not be necessary. Amplification may be by any convenient method; in embodiments a thermal cycling amplification is performed (for example, PCR). In other embodiments, isothermal amplification is performed (for example, LAMP, HAD, RCA, MCA, RPA). The use of asymmetric amplification is preferred, as this has been found in some embodiments to provide more uniform amplification across different numbers of starting target sequences, such as may be found in a variable population in which different phyla may be present at different population numbers. In embodiments, the V4 variable region of the r16S DNA is amplified. Suitable primers for relatively conserved regions flanking this region are known, and are described herein. Suitable probes for detecting the V4 region of r16S DNA are also disclosed herein. Probes will typically be labelled; for example, with a fluorescent label to allow detection. In preferred embodiments, each taxon of interest has a unique number and / or pattern of mismatched bases with respect to the probe. In some embodiments, the probe may include one or more universal bases (that is, a base which hybridises with reduced specificity compared to canonical Watson-Crick base pairing). For example, universal bases may include 2'-DeoxyInosine; 2'-DeoxyNebularine; 3-Nitropyrrole 2'- deoxynucleoside; 5-Nitroindole 2'-deoxynucleoside. Universal bases may be included in the probe where one potential mismatch is desired to be masked from detection, or where a potential mismatch is uninformative as to the taxa of interest. In embodiments, the probe may be less than 60 nt in length; or less than 55 nt, or less than 50, 48, 46, 44, 42, 40, 38, 36, 34, 32, or 30 nt in length. Preferred embodiments do not make use of molecular beacon probes; preferably the probe is a single nucleotide strand without any self-hybrisiding or self-complementary sequences. In embodiments, universal bases may also or instead be incorporated into one or more primers for allowing amplification of targets despite potential mismatches – for example, if, despite a conserved region being targeted with the primers, there may nonetheless be a mismatch against one or more taxa of interest. This will permit amplification of the target region despite such mismatches. The determination step of step c) may be performed using melt-curve analysis. That is, differing numbers of mismatches will alter the melt temperature of a target:probe duplex; detecting the number of melt events as temperature is raised will allow detection of peaks in a melt curve. These peaks may then be matched with expected melt temperatures of each of the varying numbers of mismatches. The determination step of step d) may be performed by calculating ratios of peak height, and mapping peak heights to predicted taxa of interest. The sample may be obtained from a human subject. The sample may be obtained from a region of the subject with a resident microbiome population. For example, the sample may be nasal, oral, buccal, gular, vaginal, urinary tract, or gut. In embodiments, the method may further comprise the step of e1) comparing the determined high-level taxonomic microbiome population information with predetermined high-level taxonomic microbiome population information indicative of a particular selected condition; the selected condition may include a disease state or physiological condition; a subject population characteristic (eg lifestyle, demographic, gender). In embodiments, the method may further comprise the step of e2) repeating steps a) to d) to obtain high-level taxonomic microbiome population information over time. The method may further comprise comparing said high-level taxonomic microbiome population information obtained over time, and determining changes and / or trends in population. The method may further comprise correlating said determined changes and / or trends with lifestyle information from the subject, and / or further medical tests. The method may yet further comprise providing the subject with lifestyle advice and / or a therapeutic regimen based on said determined changes and / or trends. For both of these embodiments (that is, those including steps e1) and / or e2)), determining the high-level taxonomic microbiome population can be particularly advantageous, as many conditions will be associated with significant imbalances of relative populations compared with a healthy microbiome. For example, over time one specific phylum may increase at the expense of other phyla; where the population is monitored over time the progress and development of the condition can be observed. Similarly, where the population is observed as a snapshot, but compared with predetermined population information indicative of a given condition, the imbalance in populations may have already developed and can potentially be readily observed. In an embodiment, the taxa of interest comprise or consist of the phyla Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria; and optionally also Spirochaetes. In another embodiment, the taxa of interest comprise or consist of the phyla Firmicutes, Chlamydiae, Proteobacteria, and Actinobacteria. In another embodiment, the taxa of interest comprise or consist of the phyla Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, and Fusobacteria. In a further aspect of the invention, there is provided a method for monitoring high-level taxonomic microbiome population information across a plurality of taxa of interest in samples over time, the method comprising the steps of: a) subjecting the sample to asymmetric nucleic acid amplification of a portion of r16S DNA using primers which are conserved across the taxa of interest; b) detecting said amplified portion using a probe selected to provide varying numbers of mismatched bases across the taxa of interest; c) determining the relative abundance of probe:amplified portion detection events with each of said varying numbers of mismatched bases; d) determining the relative abundance of each of said taxa of interest based on the determined relative abundance of step c); and e) repeating steps a) to d) over time for a plurality of samples. The plurality of samples will preferably be obtained from the same subject over time. BRIEF DESCRIPTION OF THE FIGURES Figure 1 shows a typical distribution of phyla in a saliva sample Figure 2 shows a 16S rRNA primer map Figure 3 shows a comparison of various V4 sequences Figure 4 shows an example of melt peaks obtained directly from saliva using probe design 1. Figure 5 shows melt peak data from two subjects Figure 6 shows melt peak data from several subjects Figure 7 shows BC data from Figure 6, with the peaks deconvoluted Figure 8 shows amplification plots and a melt curve analysis for negative control (in red), and a dilution series of 1,000, 10,000, and 100,000 copies Figure 9 shows in more detail the melt curve analysis of a 10,000 copy experiment The composition of the microbiome can be influenced by various internal and external factors. The results of the test will provide ratios of microbiome phyla distribution but will not provide any details of specific bacteria. In oral samples, some of the common causes of changes in phyla distribution include; Diet Changes in diet can alter the types of bacteria present in the oral cavity. For example, a diet high in sugar and refined carbohydrates can increase the abundance of bacteria associated with dental plaque and gingivitis. Oral Hygiene Poor oral hygiene can lead to an overgrowth of harmful bacteria and a decrease in the diversity of the oral microbiome. Regular brushing and flossing, along with regular dental cleanings, can help maintain a healthy oral microbiome. Antibiotic Use The use of antibiotics can alter the composition of the oral microbiome by killing off beneficial bacteria and allowing harmful bacteria to flourish. Early studies characterised the oral microbiome of a large cohort of healthy individuals using culture-independent methods. They found that the oral microbiome was composed of several major phyla, including Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria1. High-throughput sequencing to track the oral microbiome in cohorts of healthy individuals has more recently shown that although the relative abundance of different oral phyla varied between individuals, it remained relatively stable over time within individuals2. Current state of knowledge about the oral microbiome, including the composition of the oral microbiome and the role of different oral phyla in health and disease, concludes that changes in the relative abundance of different oral phyla may be used as a surrogate for oral health and as a predictor of potential health outcomes3. The test is a simple, non-invasive way to take the "temperature" of your microbiome and understand overall health.1Aas, J. A., Paster, B. J., Stokes, L. N., Olsen, I., Dewhirst, F. E. (2005). "Defining the normal bacterial flora of the oral cavity." Journal of Clinical Microbiology, 43(11), pp.5721-5732.2Ling, Z., et al. (2016). "Composition and temporal stability of the oral microbiota in healthy individuals." Microbiome, 4(1), pp.30.3Dongari-Bagtzoglou, A. (2017). "The oral microbiome and its role in health and disease." Periodontology 2000, 74(1), pp.14-25. Easy way to get insight into your microbiome health. It is important in maintaining a healthy microbiome for overall wellness, the "microbiome thermometer" can help track changes over time. The test is a proactive tool for monitoring health, just as you would check your temperature to assess for illness. Use the concept of a "microbiome thermometer" to explain the test results in a way that is easy for consumers to understand, by comparing changes in the microbiome to changes in temperature. Current diagnostic methods for bacterial infections have typically involved analysing the presence of specific bacterial species. However, for triage purposes, this approach may be unnecessary. In fact, looking at the overall distribution of the microbiome at a higher taxonomic level, such as the phylum level, can simplify the assay and provide more useful longitudinal information. Most infections are characterised by a significant increase in one bacterial species over others in the microbiome of the affected area. By identifying the sampling location, it is possible to map the microbiome at a phylum level instead of looking individually at a species level. This approach can help identify broad changes in the composition of the microbiome that are associated with the infection, rather than just detecting the presence or absence of specific species. It is not needed to know specifics for a triaging test, simply that something is imbalanced. For example, studies have shown that the abundance of certain bacterial phyla, such as Firmicutes and Bacteroidetes, can be associated with the development of bacterial throat infections. Similarly, changes in the abundance of phyla such as Actinobacteria and Proteobacteria have been linked to the development of dental caries. By focusing on phylum-level analysis, diagnostic tests can provide a more comprehensive picture of the changes that occur in the microbiome during an infection. This approach may also have the added benefit of simplifying the assay and reducing the cost of diagnostic testing. While current diagnostic methods for bacterial infections have focused on species-level analysis, there is growing evidence to suggest that phylum-level analysis may be a more useful approach for triage testing. By mapping the microbiome at a higher taxonomic level, diagnostic tests can provide more comprehensive information about changes in the composition of the microbiome during an infection, while also simplifying the assay and reducing costs. The microbiome has the potential to transform the way we measure physiology. This test has been designed to provide a quick and convenient way of identifying and reporting the distribution of an individual’s microbiome over time (longitudinal data) and register key imbalances. It has been developed for oral microbiome measurement using saliva but may also be used for vaginal samples to pro insight into women’s health. Significant shifts in the distribution of bacteria are important indicators of changes in underlying physiology. Snapshot would be used in conjunction with follow up NGS to identify the specific cause underlying an imbalance so that healthcare providers can recommend treatment where necessary. There are several advantages of this approach over current state of the art such as next generation sequencing (NGS); Cost This method is significantly less expensive than sequencing tests, making it more accessible and affordable and therefore being able to be used to collect longitudinal data on a regular basis to build up a picture over time Simplicity Generally easier to perform and interpret than sequencing tests, which makes it a good choice for non-expert users or for use in resource-limited settings Speed It can often be performed more quickly than sequencing tests, which can be important for users who need results quickly, such as those with acute symptoms or conditions Clinical utility Sufficient for some diagnostic or monitoring purposes, such as monitoring changes in the relative abundance of different phyla over time, and may not require the more detailed information provided by sequencing tests Privacy Typically provides less detailed information about an individual's specific microbiome than sequencing tests, which can be a privacy concern for some users in some use case This test may be used at different test intervals to provide either longitudinal use, and single use testing. The test may be carried out regularly to determine changes in distribution. Longitudinal data is ideal for individuals who want to monitor changes in their microbiome over time. This allows monitoring of shifts in the individual's microbiome over time alerting any imbalances. This may be useful for individuals who are undergoing treatment for a specific condition or for those who want to track the impact of lifestyle changes on their microbiome. For example, individuals who are trying to make dietary changes or taking probiotics may use the test to track the progress of their efforts. For oral samples collected over time, there are several changes in the microbiome distribution that might be seen; Changes in abundance of specific bacterial phyla Longitudinal data can provide insight into changes in the relative abundance of different bacterial phyla over time. For example, an increase in the abundance of the Firmicutes phylum might be associated with a healthy oral microbiome, while an increase in the abundance of the Proteobacteria phylum might indicate an imbalance in the microbiome Shifts in oral microbiome diversity Over time, the diversity of the oral microbiome may change. For example, a decrease in oral microbiome diversity may be associated with the development of oral diseases such as periodontitis or cavities Impacts of lifestyle changes Longitudinal data can provide information on how changes in diet, oral hygiene practices, or other lifestyle factors impact the oral microbiome. For example, switching to a sugar-free diet or increasing frequency of brushing and flossing might result in a change in the oral microbiome distribution Impacts of medical treatments The oral microbiome may be impacted by medical treatments such as antibiotics, antifungal medications, or other drugs. Longitudinal data can provide insight into how these treatments affect the oral microbiome and whether any changes persist over time Longitudinal use of the test may be particularly useful for women who want to monitor changes in their vaginal microbiome during different phases of their menstrual cycle, or during pregnancy. This can provide valuable insights into how the microbiome may be influenced by hormonal changes and may help identify any imbalances that may require intervention or require changes in nutrition. For vaginal samples collected over time there are several changes in the microbiome distribution that might be seen; Changes in abundance of specific bacterial phyla Longitudinal data can provide insight into changes in the relative abundance of different bacterial phyla over time. For example, an increase in the abundance of the Lactobacillus phylum might be associated with a healthy vaginal microbiome, while a decrease in the abundance of this phylum might indicate an imbalance in the microbiome Shifts in vaginal microbiome diversity Over time, the diversity of the vaginal microbiome may change. For example, a decrease in vaginal microbiome diversity may be associated with the development of vaginal infections such as bacterial vaginosis or yeast infections Impacts of hormonal changes Longitudinal data can provide information on how changes in hormonal levels, such as those that occur during the menstrual cycle or menopause, impact the vaginal microbiome Impacts of sexual activity Sexual activity can impact the vaginal microbiome, and longitudinal data can provide information on how changes in sexual activity or partners impact the vaginal microbiome distribution Impacts of medical treatments The vaginal microbiome may be impacted by medical treatments such as antibiotics, antifungal medications, or other drugs. Longitudinal data can provide insight into how these treatments affect the vaginal microbiome and whether any changes persist over time It's important to note that the changes in microbiome distribution seen in longitudinal data may not always be directly related to changes in oral health, and that additional tests and assessments may be necessary to fully understand the implications of any changes seen in the data. Alternatively, the test can be used as a one off (Single use mode) to determine if the microbiome is outside of normal distribution in terms of phyla. Single use of the test may be appropriate for individuals who want to get a snapshot of their current microbiome distribution and compare it to what is considered normal. This may be helpful for individuals who are experiencing symptoms such as persistent vaginal discharge or oral thrush and want to understand the possible cause. Detailed Description: Examples below show common human conditions and the top 5 bacteria populations in terms of phyla. Generally 4 phyla are common to each, underlined. Here we would limit our reporting to report these 4 with all others classified as a 5th‘others’ group Bacterial throat infection and phyla Bacterial throat infections are a common medical condition that can affect people of all ages, from infants to the elderly. These infections are caused by the invasion and multiplication of pathogenic bacteria in the throat, which can result in inflammation, soreness, and other symptoms. While bacterial throat infections can be caused by a variety of bacterial species, recent research has suggested that the composition of the saliva microbiome, particularly the abundance of certain bacterial phyla, may play a role in the development and progression of these infections. The saliva microbiome is a diverse community of microorganisms that inhabit the oral cavity and contribute to the maintenance of oral health. The composition of the saliva microbiome is influenced by a variety of factors, including diet, genetics, age, and environmental exposures. Recent studies have shown that alterations in the composition of the saliva microbiome can be associated with various health conditions, including bacterial throat infections. Studies investigating the relationship between the saliva microbiome and the presence of Streptococcus pyogenes, a common bacterial species that can cause bacterial throat infections have found that the saliva microbiome of patients with S. pyogenes infection was significantly different from that of healthy controls. Specifically, the abundance of Firmicutes, a phylum of Gram-positive bacteria that includes S. pyogenes, was significantly higher in the saliva of infected patients compared to healthy controls. This suggests that an increase in Firmicutes abundance in the saliva microbiome may be associated with the development of bacterial throat infections. Other bacterial species that can cause throat infections, including Haemophilus influenzae and Moraxella catarrhalis in patients with acute pharyngitis have significantly differing saliva microbiome from that of healthy controls. Specifically, the abundance of Bacteroidetes, a phylum of Gram-negative bacteria, was significantly higher in the saliva of infected patients compared to healthy controls. Additionally, the abundance of Actinobacteria, another phylum of Gram-positive bacteria, was significantly lower in infected patients compared to healthy controls. These findings suggest that alterations in the abundance of Bacteroidetes and Actinobacteria in the saliva microbiome may be associated with the development of bacterial throat infections. Examples of phyla associated with throat infection; Streptococcal pharyngitis (Strep throat) - Phylum Firmicutes, Genus Streptococcus Diphtheria - Phylum Actinobacteria, Genus Corynebacterium Chlamydial pharyngitis - Phylum Chlamydiae, Genus Chlamydia Gonococcal pharyngitis - Phylum Proteobacteria, Genus Neisseria Haemophilus influenzae pharyngitis - Phylum Proteobacteria, Genus Haemophilus Bacterial phyla that are commonly associated with vaginal infection Microbial communities are essential for human defence and coexist symbiotically with humans, contributing to metabolic functions and immune defence against pathogens. In the vaginal region, a stable microbial community primarily comprising Lactobacillus species plays a critical role in preventing genital infections by regulating pH levels, converting glycogen to lactic acid, and promoting bacteriocin and hydrogen peroxide production. Conversely, an abnormal vaginal microbial composition increases the risk of bacterial vaginosis, trichomoniasis, sexually transmitted diseases, preterm labour, and other birth defects. Factors such as race, ethnicity, pregnancy, hormonal changes, sexual activity, hygiene practices, and other conditions can affect microbial diversity. However, although vaginal flora undergoes substantial changes during different phases of the reproductive cycle, including menopause, the vaginal microbiota can be broadly classified into the five phyla Bacteroidetes, Firmicutes, Proteobacteria, Fusobacteria and Actinobacteria. Studies have characterised the vaginal microbiomes of large cohorts of reproductive- age women using 16S rRNA gene sequencing. This work has shown that the vaginal microbiome was composed of four main phyla: Bacteroidetes, Firmicutes, Actinobacteria, and Proteobacteria and that changes in the relative abundance of these phyla were associated with different health outcomes, such as bacterial vaginosis and yeast infections4,5,6. In vaginal samples, some of the common causes of changes in phyla distribution include; Hormonal Changes4Ravel, J., et al. (2011). "Vaginal microbiome of reproductive-age women." Microbiology and Molecular Biology Reviews, 75(3), pp.371-384.5Gajer, P., et al. (2012). "Temporal dynamics of the human vaginal microbiota." Science Translational Medicine, 4(132), pp.132ra52.6Ravel, J., et al. (2015). "Comparative genomics of Lactobacillus crispatus and L. jensenii, dominant lactobacilli in healthy women." Microbiome, 3(1), pp.41. Hormonal fluctuations can change the acidity of the vagina and affect the types of bacteria present. For example, hormonal changes during menstruation, pregnancy, or menopause can alter the vaginal microbiome Sexual Activity Sexual activity can introduce new bacteria into the vagina, potentially leading to changes in the microbiome Antibiotic Use Similar to oral samples, the use of antibiotics can disrupt the balance of bacteria in the vagina and lead to an overgrowth of harmful bacteria Examples of phyla associated with vaginal discord and imbalance; Bacterial vaginosis - Phylum Actinobacteria, Genus Gardnerella Trichomoniasis - Phylum Parabasalia, Genus Trichomonas Gonorrhea - Phylum Proteobacteria, Genus Neisseria Chlamydia - Phylum Chlamydiae, Genus Chlamydia Group B streptococcal infection - Phylum Firmicutes, Genus Streptococcus Bacterial phyla that are commonly associated with urinary tract health - This phylum includes several species that have been found to promote urinary tract health, such as Gardnerella vaginalis and Bifidobacterium. Firmicutes - Some species of Firmicutes, such as Lactobacillus, have been found to play a beneficial role in maintaining a healthy urinary tract microbiome and preventing urinary tract infections (UTIs). Proteobacteria - While some species of Proteobacteria can cause UTIs, others, such as Escherichia coli and Klebsiella, are important members of the gut microbiome and can play a role in preventing UTIs by outcompeting pathogenic bacteria. - This phylum includes several species that are found in the urinary tract, such as Prevotella and Bacteroides, and may play a role in maintaining a healthy urinary tract microbiome. - While Fusobacteria are most commonly associated with oral infections, some studies have found that they may also be present in the urinary tract and play a role in preventing UTIs. Bacterial phyla that are commonly associated with human health and wellbeing: Bacteroidetes - This phylum is associated with the breakdown and metabolism of dietary fibers, and helps to maintain a healthy gut microbiome. Bacteroides and Prevotella are common genera in this phylum. Firmicutes - While some Firmicutes bacteria can cause infections, others are important for digestion and maintaining a healthy gut microbiome. Examples include Lactobacillus, which helps to produce lactic acid and maintain vaginal health, and some species of Clostridium, which produce short-chain fatty acids that promote gut health. Actinobacteria - This phylum includes many beneficial bacteria, such as Bifidobacterium and Propionibacterium, which are important for gut health, and Streptomyces, which produces antibiotics and other compounds that have potential therapeutic uses. Proteobacteria - While many Proteobacteria can cause infections, others are important for human health, such as some species of Escherichia, which produce vitamin K2 and help to maintain a healthy gut microbiome. Verrucomicrobia - This phylum includes the mucin-degrading bacterium Akkermansia muciniphila, which has been associated with improved metabolic health and immune function. Bacterial phyla that are commonly associated with other oral infections: - This phylum includes several species that are commonly found in the oral cavity and can cause dental caries (tooth decay), such as Streptococcus mutans and Streptococcus sobrinus. Proteobacteria - This phylum includes several species that can cause periodontal disease, such as Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, and Tannerella forsythia. Bacteroidetes - This phylum includes several species that are commonly found in the oral cavity, such as Prevotella, Porphyromonas, and Bacteroides. Actinobacteria - This phylum includes several species that are commonly found in the oral cavity, such as Actinomyces, Corynebacterium, and Propionibacterium. Spirochaetes - This phylum includes several species that can cause oral infections, such as Treponema denticola, which is associated with periodontal disease. See Figure 1 for a typical distribution of phyla in a saliva sample. Examples of how different bacterial phyla may have varying antibiotic treatment considerations: 1. Firmicutes: This phylum includes various Gram-positive bacteria, such as Staphylococcus and Streptococcus species. Treatment options for Firmicutes may involve antibiotics like penicillins (e.g., penicillin G, amoxicillin) or cephalosporins (e.g., cephalexin) that are effective against Gram-positive bacteria. However, it's important to note that there can be variations in antibiotic susceptibility even within this phylum, and resistance mechanisms like methicillin resistance (MRSA) can limit treatment options. 2. Pseudomonadota (Proteobacteria): This phylum includes Gram-negative bacteria like Pseudomonas aeruginosa. Pseudomonas infections can be challenging to treat due to their intrinsic resistance and ability to develop resistance mechanisms. Treatment options for Pseudomonadota infections often involve antibiotics like beta-lactam / beta-lactamase inhibitor combinations (e.g., piperacillin / tazobactam) or carbapenems (e.g., meropenem). However, susceptibility testing is essential due to the potential for acquired resistance mechanisms, such as extended-spectrum beta-lactamases (ESBLs) or carbapenemases. 3. Actinomycetota: Actinomycetota, also known as Actinobacteria, includes Mycobacterium species, such as Mycobacterium tuberculosis. Mycobacteria, including M. tuberculosis, require specific treatment regimens due to their intrinsic resistance to many antibiotics. Standard treatment for tuberculosis typically involves a combination of antibiotics like isoniazid, rifampicin, pyrazinamide, and ethambutol. Drug susceptibility testing is crucial to guide appropriate treatment based on the specific strain's resistance profile. 4. Bacteroidota: Bacteroidota, also known as Bacteroidetes, includes various Gram-negative anaerobic bacteria found in the gut microbiota, such as Bacteroides species. Infections caused by Bacteroidota may require antibiotics effective against anaerobic bacteria, such as metronidazole or beta- lactam / beta-lactamase inhibitor combinations like amoxicillin / clavulanic acid. However, susceptibility testing is recommended due to potential resistance mechanisms. 5. Deinococcota: Deinococcota includes Deinococcus species, which are known for their remarkable resistance to radiation and desiccation. Antibiotic treatment for Deinococcota infections may follow standard guidelines based on susceptibility testing. Common antibiotics like penicillins, cephalosporins, or fluoroquinolones may be considered based on the specific pathogen causing the infection. 6. Campylobacterota: Campylobacterota, also known as Campylobacter, includes bacteria like Campylobacter jejuni, a common cause of bacterial gastroenteritis. In most cases, Campylobacter infections are self-limiting and do not require antibiotic treatment. However, severe or prolonged infections may require antibiotics like macrolides (e.g., azithromycin) or fluoroquinolones (e.g., ciprofloxacin) based on susceptibility testing. Detailed Description: Test design The test design utilises short regions of the 16s Ribosomal RNA (Figure 1). The 16S rRNA is a single-stranded RNA molecule that is approximately 1,500 nucleotides long, and is composed of several regions that are conserved across all bacteria and archaea, as well as variable regions that are unique to different bacterial and archaeal species. The overall structure of the 16S rRNA molecule is highly conserved, with several distinct domains that are involved in binding to other ribosomal components and interacting with the messenger RNA (mRNA) and transfer RNA (tRNA) during protein synthesis. The structure of the 16S rRNA molecule can be divided into three main regions: the 5' end, the central region, and the 3' end. The central region of the 16S rRNA molecule is the most conserved and contains several conserved motifs and secondary structures that are essential for ribosomal function. This region includes the 16S rRNA primary binding site for mRNA, which is located near the 3' end of the molecule, as well as several conserved stem-loop structures that are involved in tRNA binding. The variable regions of the 16S rRNA molecule are located primarily in the loops and bulges between the conserved stem-loop structures in the central region. These variable regions are highly divergent between different bacterial and archaeal species, and contain unique sequences that can be used to differentiate between different types of bacteria and archaea. The variable regions of the 16S rRNA molecule are commonly used in molecular biology and microbiology to identify and classify bacterial and archaeal species. This is done by amplifying and sequencing the variable regions of the 16S rRNA gene using PCR, and then comparing the resulting sequences to a reference database of known 16S rRNA sequences. By comparing the variable region sequences of an unknown bacterial or archaeal sample to known sequences in the database, researchers can identify the species or genus of the sample. The 16S ribosomal DNA (rDNA) is the gene that encodes for the 16S rRNA molecule. The 16S rDNA contains both conserved and variable regions, like the 16S rRNA. The conserved regions of the 16S rDNA are highly similar across all bacteria and archaea and are essential for the function of the ribosome. The variable regions of the 16S rDNA are located primarily in the loops and bulges between the conserved regions and contain unique sequences that can be used to differentiate between different bacterial and archaeal species. Like the 16S rRNA, the variable regions of the 16S rDNA can be amplified and sequenced using PCR and compared to a reference database of known sequences to identify and classify bacterial and archaeal species. This approach is known as 16S rDNA sequencing and is commonly used in microbiology and molecular biology to study microbial communities and identify bacterial and archaeal species. In summary, the 16S ribosomal DNA encodes for the 16S rRNA molecule and contains both conserved and variable regions that are essential for ribosomal function and can be used to differentiate between different bacterial and archaeal species. 16S rDNA sequencing is a widely used molecular biology technique that can identify and classify bacterial and archaeal species based on their 16S rDNA sequences. See Figure 2 for an illustration of different variable regions in the 16S rRNA region. Red regions (V2, V8) have a poor phylogenetic resolution at the phylum level. Green regions (V4, V5, V6) are associated with the shortest geodesic distance, which suggests that they may be the best choice for phylogeny-related analyses and the phylogenetic analysis of novel bacterial phyla. The figure refers to the primer map from Lutzonilab (http: / / lutzonilab.org / 16s-ribosomal-dna / ). Use of this information was approved by the original authors of the website. One of the main challenges in determining the ratio of bacteria in mixed populations is maintaining the relative proportions of each target. Generally, amplification methods such as PCR are not proportional. This is because they are exponential and exhibit other non-linear characteristics such as the plateau effect where the amount of amplification has a non-proportionate end point. Also, amplification of different regions using different oligonucleotides will have different amplification efficiencies due to sequence differences. This compounds discontinuities in proportionate amplification. Primer binding efficiency, the presence of secondary structures, and differences in amplicon length can also limit proportionality when using different target regions for each target. To limit these challenges the design in the current invention uses a combination of these features; 1. A single target is amplified across all phyla of bacteria represented in the sample using a single primer pair that is designed to conserved regions spanning a variable region 2. A single hybridisation probe is used to detect key sequence variations in the variable region amplified However, even if there is complete homology between the target sequences of closely related organisms (meaning the primers bind with equal efficiency to each target) and the amplification efficiency is identical or similar for each target, discrepancy in the relative abundance of the targets of a mixed population in the final PCR product can occur due to the plateau effect. The plateau effect occurs in the late stages of PCR, where the amplification rate slows down, and the reaction eventually reaches a plateau. This phenomenon is mainly caused by the depletion of reaction components (dNTPs, primers, or DNA polymerase), the accumulation of inhibitors (such as pyrophosphate), or the re- annealing of PCR products. The plateau effect can disproportionately impact low-abundance targets in the following ways; 1. High-abundance targets may reach the plateau phase earlier than low- abundance targets, leading to an over-representation of the high-abundance targets in the final PCR product. 2. Low-abundance targets may not reach the plateau phase within the same number of cycles as the high-abundance targets, resulting in an under- representation of the low-abundance targets in the final PCR product. Further ways of proportionality could be achieved by optionally including; 1. A linear amplification is used to amplify in as proportionate way possible, each target present in the sample. Each phylum would be amplified proportionately during the first cycles of amplification and then retained as the amplification became linear. For example asymmetric PCR, where the limiting of one primer in the primer pair over the other, creates a linear phase amplification with the excess primer after an initial exponential phase with both primers becomes limited. Methods of asymmetric amplification are not limited to PCR so the same process is also possible using non-PCR methods including isothermal amplification based on RPA or LAMP. There is limited literature on the use of asymmetric PCR for the amplification of mixed populations and the uniformity or proportionality of the amplification generated. Asymmetric PCR is primarily designed to generate single-stranded DNA products, rather than to maintain proportionality among mixed populations. Most studies utilizing asymmetric PCR focus on applications like generating single-stranded DNA for sequencing, hybridization assays, or molecular cloning, rather than analyzing mixed populations. When it comes to analyzing mixed populations of DNA targets, such as microbial communities, studies usually focus on optimizing standard PCR or employing alternative techniques, such as isothermal amplification methods (e.g., LAMP) or digital PCR, to improve proportionality and reduce biases. In a preferred embodiment, a variable region of V4 of 16S is amplified and interrogated in the analysis. The V4 region contains a relatively high degree of variation, and this variation can be used to differentiate between different bacterial species. However, despite this variability, there are still regions within the V4 region there are certain positions within the V4 region that are highly conserved across bacterial phyla. See Figure 3 for a comparison of various V4 sequences. Isothermal amplification methods, such as Loop-mediated isothermal amplification (LAMP), could also be used and offer several advantages over PCR that can contribute to even more proportional amplification of mixed populations of target sequences; 1. Constant temperature: Unlike PCR, which requires thermal cycling through various temperatures, isothermal methods like LAMP occur at a constant temperature (usually around 60-65°C). This eliminates the need to optimise annealing temperatures and reduces the risk of preferential amplification of certain sequences due to variations in annealing temperatures. 2. Strand displacement: LAMP uses a strand displacement DNA polymerase, which allows for continuous amplification without the need for denaturation and annealing steps. This reduces the influence of primer binding efficiency on amplification, potentially leading to more proportionate amplification. 3. Multiple primers: LAMP typically uses 4-6 primers that recognize 6-8 distinct regions within the target sequence. This increases the specificity of the reaction and can help ensure that all targets in a mixed population are amplified with similar efficiency. In contrast, traditional PCR typically uses only two primers that recognize two distinct regions, which can lead to preferential amplification if there are mismatches or variations in primer binding efficiency. 4. Reduced secondary structure effects: The constant temperature used in isothermal methods can help reduce the formation of secondary structures in the template DNA that can impede amplification. This can result in more uniform amplification across mixed populations of target sequences. 5. High tolerance to inhibitors: Isothermal methods like LAMP are often more resistant to inhibitors that may be present in biological samples. This can lead to more consistent amplification across different samples, reducing the likelihood of preferential amplification of certain sequences. Although LAMP is primarily known for producing double-stranded DNA products, during the amplification process, it also transiently produces single-stranded DNA (ssDNA) intermediates. These ssDNA intermediates are generated because of the strand displacement activity of the DNA polymerase used in the reaction. The LAMP reaction involves a set of four to six primers that target distinct regions of the DNA sequence, along with a DNA polymerase with strand displacement activity. The primers initiate the synthesis of new DNA strands, and the DNA polymerase displaces the original strands, generating ssDNA intermediates. As the LAMP reaction progresses, the newly synthesized DNA strands form stem-loop structures, and additional primers bind to the loop regions. This leads to the synthesis of even more DNA strands and the formation of complex, branched, double-stranded DNA products, referred to as "cauliflower-like" structures. While LAMP is not specifically designed to produce ssDNA as the primary product, it does generate ssDNA intermediates during the amplification process. These ssDNA intermediates of LAMP are typically not the main focus of the LAMP reaction, as the final products of interest have typically been the double-stranded DNA amplification products. However here there form target for hybridisation probes that may be useful for the analysis. Advantages: The present invention offers several advantages over traditional diagnostic methods for bacterial infections. By analysing the composition of the microbiome at a phylum level, the method and system can provide more comprehensive information about changes in the microbiome composition during an infection, while also simplifying the assay and reducing costs. This approach can also help identify broad changes in the composition of the microbiome that are associated with the infection, rather than just detecting the presence or absence of specific species. Conclusion: The present invention provides a method and system for diagnosing bacterial infections by analysing the composition of the microbiome at a phylum level. This approach can provide more comprehensive information about changes in the microbiome composition during an infection, while also simplifying the assay and reducing costs. The invention has the potential to improve the accuracy and efficiency of bacterial infection diagnosis, leading to better patient outcomes. Probe design The effect of nucleotides on melting can be ‘cancelled’ or ‘silenced’ using universal bases such as ideoxyl (for example to neutralise the effect of nucleotide shown as location I). Example probe 1 Primers V4fwd GTGICAGCIGCCGCGGTAA SEQ ID NO: 1 V4rev GACTACIIGGGTATCTAATCC SEQ ID NO: 2 Probe GCAAGCGTTATCCGGAATTATTGGGCGTAAAGIGCGCGTAGGCGGTT SEQ ID NO: 3 Variants Actinobacteria GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCTCGTAGGCGGTT 1 mismatches SEQ ID NO: 3 Bacteroidetes GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCT 4 mismatchesSEQ ID NO: 4Firmicutes GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGG-GCGTAGGCGGTT 2 mismatches SEQ ID NO: 5 Fusobacteria GCAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGCGGTT 2 mismatches SEQ ID NO: 6 Proteobacteria GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGCGGTT 3 mismatches SEQ ID NO: 7 Example probe 2 Primers V4fwd GTGICAGCIGCCGCGGTAASEQ ID NO: 1V4rev GACTACIIGGGTATCTAATCC SEQ ID NO: 2 Probe ACTGACGCTGAGGCACGAAAGCGTGGGGAGCGAACAG SEQ ID NO: 8 Variants Actinobacteria ACTGACGCTGAGGAGCGAAAGCGTGGGGAGCGAACAG 1 mismatches SEQ ID NO: 9 Bacteroidetes ACTGACGCTGAGGCACGAAAGCGTGGGGAGCGAACAG 0 mismatches SEQ ID NO: 10 Firmicutes ACTGACGCTGAGGC-CGAAAGCGTGGGGAGCAAACAG 2 mismatches SEQ ID NO: 11 Fusobacteria ACTGACGCTGAAGCGCGAAAGCGTGGGTAGCAAACAG mismatches SEQ ID NO: 12 Proteobacteria ACTGACGCTGAGGTGCGAAAGCGTGGGGAGCAAACAG 2 mismatches SEQ ID NO: 13 Example probe 3 Primers V4fwd GTGICAGCIGCCGCGGTAA SEQ ID NO: 1 V4rev GACTACIIGGGTATCTAATCC SEQ ID NO: 2 Probe GGAATTCCTAGTGTAGCGGTGAAATGCGTAGATAT SEQ ID NO: 14 Variants Actinobacteria GGAATTCCTGGTGTAGCGGTGAAATGCGCAGATAT 2 mismatches SEQ ID NO: 15 Bacteroidetes GGAATGTGTAGTGTAGCGGTGAAATGCATAGATAT 4 mismatches SEQ ID NO: 16 Firmicutes GGAATTCCTAGTGTAGCGGTGAAATGCGTAGATAT 0 mismatches SEQ ID NO: 17 Fusobacteria GGAACTACAAGTGTAGAGGTGAAATTCGTAGATAT 5 mismatches SEQ ID NO: 18 Proteobacteria GGAATT-CCGGTGTAGCGGTGAAATGCGTAGATAT 2 mismatches SEQ ID NO: 19 Results Figure 4 shows melt peaks obtained directly from saliva using probe design 1. Asymmetric PCR was performed using V4 primers to generate single stranded amplicon. Data shows the difference obtained between fast (0.1˚C / sec) and slow (0.05˚C / sec) melt peak analysis from the probe. 4 peaks positions are identified (Figure 5). The height and distribution of these peaks vary from person to person, indicating different proportions of the different phyla in the sample. Peaks can be quantified at each position for example using AI processing including neural networks. Figure 6 shows data obtained from several individuals. As an example, BC data is taken and deconvoluting the peaks gives the graph shown in Figure 7. The methodology was also tested against a known sample of microbial genome oral mix containing genomic DNA from the following 20 species: # % Species ATCC Catalogue # Phylum 1 5% Acinetobacter baumannii ATCC 17978 Pseudomonadota 2 5% Bacillus pacificus ATCC 10987 Firmicutes (Bacillota) 3 5% Phocaeicola vulgatus ATCC 8482 Bacteroidota 4 5% Bifidobacterium adolescentis ATCC 15703 Actinomycetota 5 5% Clostridium beijerinckii ATCC 35702 Firmicutes (Bacillota) 6 5% Cutibacterium acnes ATCC 11828 Actinomycetota 7 5% Deinococcus radiodurans ATCC BAA-816 Deinococcota 8 5% Enterococcus faecalis ATCC 47077 Firmicutes (Bacillota) 9 5% Escherichia coli ATCC 700926 Pseudomonadota 10 5% Helicobacter pylori ATCC 700392 Campylobacterota 11 5% Lactobacillus gasseri ATCC 33323 Firmicutes (Bacillota) 12 5% Neisseria meningitidis ATCC BAA-335 Pseudomonadota 13 5% Porphyromonas gingivalis ATCC 33277 Bacteroidota 14 5% Pseudomonas paraeruginosa ATCC 9027 Pseudomonadota 15 5% Cereibacter sphaeroides ATCC 17029 Pseudomonadota 16 5% Schaalia odontolytica ATCC 17982 Actinomycetota 17 5% Staphylococcus aureus ATCC BAA-1556 Firmicutes (Bacillota) 18 5% Staphylococcus epidermidis ATCC 12228 Firmicutes (Bacillota) 19 5% Streptococcus agalactiae ATCC BAA-611 Firmicutes (Bacillota) 20 5% Streptococcus mutans ATCC 700610 Firmicutes (Bacillota) These represent six different phyla. The graphs in Figure 8 show amplification plots and a melt curve analysis for negative control (in red), and a dilution series of 1,000, 10,000, and 100,000 copies. There are six peaks visible in the melt curve, indicated by the dotted lines, representing the six different phyla. Detection of all 5 phyla represented by the different peak positions highlighted at 1,000 copies and above. We see some bacterial contamination in the negative control at later cycles and a single low peak at 62˚C possibly corresponding to the Pseudomonadota phylum for E. coli which is the expression vector. It is also possible to use different colour labels to allow 2 probes to be used simultaneously. The graph in Figure 9 shows in more detail the melt curve analysis of the 10,000 copy experiment analysed with the probe, and with the five main peaks (six total) deconvoluted. These can clearly be seen to be distinct; and it is straightforward to determine the melting temperature for a given probe:target duplex, allowing peaks to be assigned to phyla, and relative abundance determined.

Claims

CLAIMS:

1. A method for determining high-level taxonomic microbiome population information across a plurality of taxa of interest in a sample, the method comprising the steps of: a) subjecting the sample to asymmetric nucleic acid amplification of a portion of r16S DNA using primers which are conserved across the taxa of interest; b) detecting said amplified portion using a probe selected to provide varying numbers of mismatched bases across the taxa of interest; c) determining the relative abundance of probe:amplified portion detection events with each of said varying numbers of mismatched bases; and d) determining the relative abundance of each of said taxa of interest based on the determined relative abundance of step c).

2. The method of claim 1 wherein the high-level taxonomic microbiome population information permits distinguishing between different microbial phyla, but no lower classifications.

3. The method of claim 1 or claim 2 wherein amplification is by means of a thermal cycling amplification.

4. The method of claim 1 or claim 2 wherein amplification is isothermal amplification.

5. The method of any preceding claim wherein the V4 variable region of the r16S DNA is amplified.

6. The method of any preceding claim wherein each taxon of interest has a unique number and / or pattern of mismatched bases with respect to the probe.

7. The method of any preceding claim wherein the probe includes one or more universal bases.

8. The method of any preceding claim wherein the determination step of step c) is performed using melt curve analysis.

9. The method of claim 8 wherein the determination of step d) is performed by calculating ratios of peak height from a melt curve, and mapping peak heights to predicted taxa of interest.

10. The method of any preceding claim wherein the sample is obtained from a human subject.

11. The method of any preceding claim wherein the sample is nasal, oral, buccal, gular, vaginal, urinary tract, or gut.

12. The method of any preceding claim wherein the method further comprises the step of e1) comparing the determined high-level taxonomic microbiome population information with predetermined high-level taxonomic microbiome population information indicative of a particular selected condition.

13. The method of claim 12 wherein the selected condition includes a disease state or physiological condition; or a subject population characteristic.

14. The method of any preceding claim wherein the method further comprises the step of e2) repeating steps a) to d) to obtain high-level taxonomic microbiome population information over time.

15. The method of claim 14 further comprising comparing said high-level taxonomic microbiome population information obtained over time, and determining changes and / or trends in population.

16. The method of claim 15 further comprising correlating said determined changes and / or trends with lifestyle information from the subject, and / or further medical tests.

17. The method of claim 15 or 16 further comprising providing the subject with lifestyle advice and / or a therapeutic regimen based on said determined changes and / or trends.

18. The method of any preceding claim, wherein the taxa of interest comprise or consist of: a) the phyla Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria; and optionally also Spirochaetes; b) the phyla Firmicutes, Chlamydiae, Proteobacteria, and Actinobacteria; or c) the phyla Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, and Fusobacteria.

19. A method for monitoring high-level taxonomic microbiome population information across a plurality of taxa of interest in samples over time, the method comprising the steps of: a) subjecting the sample to asymmetric nucleic acid amplification of a portion of r16S DNA using primers which are conserved across the taxa of interest; b) detecting said amplified portion using a probe selected to provide varying numbers of mismatched bases across the taxa of interest; c) determining the relative abundance of probe:amplified portion detection events with each of said varying numbers of mismatched bases; d) determining the relative abundance of each of said taxa of interest based on the determined relative abundance of step c); and e) repeating steps a) to d) over time for a plurality of samples.

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