WO2022238515A1

WO2022238515A1 - Rna markers for tuberculosis and methods of detecting thereof

Info

Publication number: WO2022238515A1
Application number: PCT/EP2022/062854
Authority: WO
Inventors: Carolina CORREIA; Stephen Gordon; Eamonn Gormley; David Machugh; Kirsten MCLOUGHLIN
Original assignee: University College Dublin,
Priority date: 2021-05-11
Filing date: 2022-05-11
Publication date: 2022-11-17

Abstract

The present invention relates to RNA biomarkers for the detection of tuberculosis and methods of detecting the same. In particular, the present invention relates to methods and kits for detection of bovine tuberculosis caused by the Mycobacterium tuberculosis complex comprised of Mycobacterium bovis (M. bovis) and other intracellular mycobacterial pathogens. In particular the invention relates to a panel of biomarkers which indicate increased gene expression that is indicative of tuberculosis infection in a subject. Suitably such a panel may allow advantageous characterisation of an infection status of a subject, for example an animal, for example cattle.

Description

RNA MARKERS FOR TUBERCULOSIS AND METHODS OF DETECTING THEREOF

The present invention relates to RNA biomarkers for the detection of tuberculosis and methods of detecting the same. In particular, the present invention relates to methods and kits for detection of bovine tuberculosis caused by the Mycobacterium tuberculosis complex comprised of Mycobacterium bovis {M. bovis) and other intracellular mycobacterial pathogens.

BACKGROUND

Bovine tuberculosis (BTB) is an infectious disease of cattle, caused by bacteria within the Mycobacterium tuberculosis complex, which includes M. bovis, the most common cause of BTB. As BTB is a zoonosis, the infection has important implications for other mammals including humans, where tuberculosis caused by M. bovis have been reported. In addition to the impact on animal and human health, bovine tuberculosis also has a large economic impact within the livestock industry. Outbreaks of the disease have significant financial impact upon farmers with infected livestock, with BTB costing the farming industry an estimated $3 billion annually. Despite stringent surveillance protocols and ‘test and slaughter programmes, BTB remains an endemic livestock disease in countries throughout the world.

The bacteria which cause BTB, are spread between subjects primarily by inhalation of aerosol droplets. Host alveolar macrophages phagocytose the droplets and, consequently, infection is normally initiated within the lungs.

Tuberculous mycobacteria have evolved a wide range of mechanisms to modulate, supress and manipulate specific host immune mechanisms including inhibition of phagosomal maturation, detoxification of reactive oxygen and nitrogen species (ROS and RNS), repair of ROS- and RNS-induced cellular damage, resistance to antimicrobial and cytokine defences, modulation of antigen presentation, and induction of cellular necrosis and inhibition of apoptosis. Tuberculosis disease is characterised by lesions located at the site of infection, which are formed when alveolar macrophages and other immune cells engage and eliminate most of the bacilli. The remaining intact mycobacterial cells are confined in granulomas that act to contain the infection, but may, under certain conditions, facilitate expansion and dissemination of mycobacteria to spread.

The availability of high-throughput sequencing techniques has enabled more in- depth analyses of gene expression patterns at the transcriptome level. The advent of this technology provides the opportunity to identify RNA biomarkers for the detection and diagnosis of infectious diseases such as BTB.

Currently, testing for BTB includes use of the single intradermal comparative tuberculin test (SICTT) alone, or in conjunction with an in vitro ELISA-based interferon-gamma release assay. Both of these diagnostic methods are limited in their sensitivity or specificity, such that they do not allow for early and accurate detection of BTB. Expediency of diagnosis is crucial to allow for removal of infected cattle from the herd, so as to prevent subsequent transmission of BTB. Therefore, there is a pressing need for more sensitive diagnostic assays capable of earlier detection of BTB.

The present invention serves to provide a more sensitive method of identifying differential gene expression from peripheral blood samples which can be used to diagnose infection of a subject with bacteria from the Mycobacterium tuberculosis complex.

SUMMARY OF THE INVENTION

Previous work on BTB caused by M. bovis has shown that immune responses in peripheral blood reflect immune responses at the site of infection. It was determined that peripheral blood samples may be used to identify biomarkers for changes happening elsewhere in the body in response to BTB infection, obfuscating the need for a more invasive tissue biopsy. In particular, the inventors consider the leukocytes present in peripheral blood provide valuable information about the status of the immune system and which provide biomarkers / biosignatures of infection. Advantageously, peripheral blood is considered to be easily accessible, can be stabilised and processed for high throughput analysis using gene expression technologies, for example RNA-seq.

Through their studies, the inventors have generated data in support of a group of messenger RNA (mRNA) biomarkers, which show differential gene expression in bovine whole peripheral blood samples before and after infection with bacteria from the Mycobacterium tuberculosis complex. Using RNA-seq to measure mRNA expression in bovine whole peripheral blood samples, a 19-gene transcriptional biosignature was identified by the inventors as differentially expressed in cows at +1 week, +2 weeks, +6 weeks, +10 weeks and +12 weeks post-infection as compared to -1 week pre-infection with bacteria M. bovis.

Whilst a number of previous studies have implicated a number of genes as differentially expressed in samples after infection with M. bovis, these studies from a range of tissue samples including monocytes and alveolar tissue samples have identified different genes within the biosignature as differentially expressed in a variety of mammals including cattle, mice and humans.

Importantly, the inventors have supplied the first evidence of a cohort of 19 differentially expressed genes that can be used to diagnose subjects with bacteria from the Mycobacterium tuberculosis complex and BTB caused by infection with M. bovis in a subject from a whole peripheral blood sample.

Accordingly there is provided a method for use in the diagnosis of subjects with bacteria from the Mycobacterium tuberculosis complex comprising the step of:

Determining in a sample from a subject to be tested at least one biomarker of expression or a combination of biomarkers of expression of a gene(s) listed in Table 1. Suitably the sample is a whole peripheral blood sample.

Suitably the method detects gene expression associated with M. bovis infection. Without wishing to be bound by theory, it is considered the peripheral blood transcriptome constitutes a source of gene expression biomarkers for BTB caused by M. bovis in cattle. Suitably the gene expression patterns, suitably the time dependent patterns of gene expression in peripheral blood may reflect parthogenesis of early BTB disease with concomitant host cellular responses to M. bovis infection, disruption of homoeostasis, and changing cellular, tissue and organismal energy requirements.

Suitably the determining step may be of least one biomarker determination of a RNA transcript of a gene of Table 1. Suitably the determining step may use reverse transcription quantitative real-time PCR (RT-qPCR) to determine if the one or more genes are differentially expressed. Suitably the method comprises detecting in a whole peripheral blood sample from a subject to be tested at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19 biomarkers wherein the biomarkers are selected from a group consisting of biomarkers of a gene listed in Table 1. Suitably the method comprises detecting in a whole peripheral blood sample from a subject to be tested at least 10 biomarkers wherein the biomarkers are selected from a group comprising of biomarkers of genes listed in Table 1.

Suitably the method comprises detecting in a whole peripheral blood sample from a subject to be tested expression of each of the genes listed in Table 1. Suitably a panel of biomarkers to detect increased gene expression, for example a panel comprising 19 biomarkers as provided in Table 1 to consider increased expression, for example by determination of RNA transcripts, provides a test of increased sensitivity and sensitivity. Suitably such a panel as provided herein may allow advantageous characterisation of an infection status of a subject, suitably an animal, for example where a subject, for example an animal, suitably cattle may also be infected with other viral and / or bacterial pathogens.

Suitably the method provides for the diagnosis of infection of a subject with bacteria from the Mycobacterium tuberculosis complex.

Suitably the method comprises the step: determining differentially expressed genes selected as listed in Table 1 in a whole peripheral blood sample from a subject to be tested.

Suitably the method may further comprise the step: comparing the differentially expressed genes against a control signature.

Suitably at least a subset of the biomarkers of expression, for example the RNA transcript biomarkers are upregulated during infection.

A person of skill in the art will be aware of a number of methods that can be used to isolate RNA from a sample for analysis. Moreover, persons of skill in the art will be aware of several methods of detecting and quantifying the level of RNA transcripts within a sample.

Suitably the RNA transcript biomarker may be identified using a method selected from PCR, RT-PCR, qPCR, transcript sequencing, microarray, hybridisation assay, nucleic acid detection assay or a lateral flow assay. Levels of biomarkers in the sample may be detected so as to determine whether the subject has bovine TB or does not have TB. For example a level of biomarkers as listed in Table 1 in a test sample may equate to the level of biomarkers in a control sample, for example the control sample can be the expression level of a set of housekeeping genes in the subject or the control sample can be a set of genes from a non-infected control sample, to indicate the test sample is from a non- infected subject. As will be appreciated, where the control sample is from an infected control sample and levels of biomarker in a test sample equate to the level of biomarkers in a control sample this is indicative the test sample is from an infected subject.

Suitably the level of an RNA transcript biomarker of expression at least one gene or a combination of genes as listed in Table 1 may be determined and the expression level(s) used to provide a gene signature. The gene signature may be normalised relative to a reference expression level. Suitably the normalised expression levels may be used to generate a score for the gene signature. Suitably the generated score may be compared to reference score and the sample classified as being from an infected or non-infected subject.

Suitably the RNA transcript biomarker may be converted into a DNA probe, such as for example double stranded (dsDNA), single stranded DNA (ssDNA), and / or a hybrid double stranded DNA (dsDNA) probe. Suitably such conversion may be by reverse transcription recombinase polymerase reaction (RT-RPA). Suitably the RT- RPA may be provided by an isothermal RT-RPA reaction, wherein said DNA probe can be detected by reporter probe, for example wherein the reporter probe may be complementary to the DNA probe. Suitably the reporter probe may be detected, for example by aggregation and /or binding of the reporter probe in a detection zone which allows detection of a signal from the reporter.

Suitably the level of biomarker may be detected by a reverse transcription recombinase polymerase amplification reaction. This may be provided in a pre prepared reaction module. This pre-prepared reaction module may include a sample receiving portion. Suitably RNA transcripts of the biomarkers listed in Table 1 may be amplified in the reaction module to form a DNA product. Suitably the DNA products may be provided with adaptor sequences and or 5’ modifications for downstream hybridisation. Suitably an indicator may be coupled to the DNA product. Suitably an indicator may allow binding of a DNA product provided by the RNA transcript biomarker.

Suitably RNA may be extracted from sample provided from a potentially exposed or infected subject and the RNA may undergo a qRT-PCR reaction to determine the levels of RNA transcript in the sample.

Suitably RNA-Seq may be used to provide measurement of the levels of RNA transcript biomarkers in the sample.

Suitably, a plurality of samples may be taken from one or more subjects to generate a time course of infection. The time course of infection may show the relative levels of biomarkers over time.

Suitably the level of RNA transcript biomarkers may be used to track infection relative to treatment of the infection. For example the presence, absence or level of a RNA transcript biomarker may be provided relative to treatment with a therapeutic or prophylactic therapeutic.

The present invention includes a method of using differential gene expression to detect infection with bacteria from the Mycobacterium tuberculosis complex which can cause BTB disease. For instance, the present invention includes a method which can be used to detect the changes in gene expression as a result of an immune response, such as determining if there is differential expression of at least one gene from a subset of identified transcriptional biomarkers. The method may comprise the step of extracting the RNA from a biological sample, testing for the RNA expression level(s) of at least one gene or combination of genes and comparing the RNA expression level(s) to RNA expression level(s) from a control signature. The control signature may be provided by a control sample from a non- infected subject. Suitably a control signature may be provided by a control sample from an infected subject. Suitably a control signature may be provided by a control sample of housekeeping gene(s). As will be appreciated, when the control sample is from a non-infected subject then when the signature of a whole peripheral blood sample from a subject to be tested is similar to the control, the subject will be considered to be free from bovine tuberculosis infection. Suitably when the control sample is from an infected subject then when the signature of a whole peripheral blood sample from a subject to be tested is similar to the control, the subject will be considered to have bovine tuberculosis infection. 7

WO 2022/238515 PCT/EP2022/062854

Suitably, the differentially expressed genes in a whole peripheral blood sample from a subject to be tested and compared against a control sample are one or more genes as listed in Table 1.

Suitably the method may comprise the steps of detecting bovine tuberculosis in a subject, the method comprising providing an RNA sample from a subject, assaying the RNA sample to determine the expression levels of RNA transcript biomarkers for one or more genes, wherein the one or more genes are genes listed in Table 1, and determining if one or more genes are differentially expressed in the subject when compared to a control RNA sample. Table 1 - as shown in figure 4.

Suitably the differentially expressed genes detected in a whole peripheral blood sample may be a subset as recited in Table 1 , for example a subset may comprise RNA expression levels of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen genes.

Suitably the method may consist of detecting the RNA expression levels of a subset of genes as recited in Table 1. Suitably the subset may consist of two or more genes from Table 1. Suitably the subset of genes may consist of three or more genes from Table 1. Suitably the subset of genes may consist of four or more genes from Table 1. Suitably the subset of genes may consist of five or more genes from T able 1. Suitably the subset of genes may consist of ten or more genes from Table 1. Suitably a subset may show increased expression or a signature of expression relative to other genes. Suitably the subset of genes may consist of twelve or more genes from Table 1. Suitably the subset of genes may consist of fifteen or more genes from T able 1.

Suitably the first aspect of the invention may involve detecting the RNA expression levels of gene ENSBTAG00000035224. Suitably the first aspect of the invention may involve detecting the presence of a plurality of genes, wherein the plurality of genes includes ENSBTAG00000035224 and any other gene or combinations of genes from Table 1.

Suitably the first aspect of the invention may involve detecting the RNA expression levels of gene EVI2A. Suitably the first aspect of the invention may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes EVI2A and any other gene or combinations of genes from Table 1. Suitably the first aspect of the invention may involve detecting the RNA expression levels of gene ITK. Suitably the first aspect of the invention may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes ITK and any other gene or combinations of genes from Table 1. Suitably the first aspect of the invention may involve detecting the RNA expression levels of genes EIV2A and ITK. Suitably the first aspect of the invention may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes EIV2A and ITK, and any other gene or combinations of genes from Table 1.

Suitably the first aspect of the invention may involve detecting the RNA expression levels of gene FOSB. Suitably the first aspect of the invention may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes FOSB and any other gene or combinations of genes from Table 1. Suitably the first aspect of the invention may involve detecting the RNA expression levels of gene FRMD6. Suitably the first aspect may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes FRMD6 and any other gene or combinations of genes from Table 1. Suitably the first aspect of the invention may involve detecting the RNA expression levels of genes FMRD6 and FOSB. Suitably the first aspect may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes FMRD6 and FOSB, and any other gene or combinations of genes from Table 1. Suitably the first aspect of the invention may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes SERPINB4 and any other gene or combinations of genes from Table 1. Suitably the first aspect of the invention may involve detecting the RNA expression levels of genes SERPINB4 and FOSB. Suitably the first aspect of the invention may involve detecting the RNA expression levels of genes SERPINB4 and FRMD6. Suitably the first aspect may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes FMRD6, FOSB and SERPINB4. Suitably the first aspect may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes FMRD6, FOSB and SERPINB4, and any other gene or combinations of genes from T able 1.

Suitably the first aspect of the invention may involve detecting the RNA expression levels of gene FOSB. Suitably the first aspect of the invention may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes FOSB and any other gene or combinations of genes from Table 1. Suitably the first aspect of the invention may involve detecting the RNA expression levels of gene PLAUR. Suitably the first aspect may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes PLAUR and any other gene or combinations of genes from Table 1. Suitably the first aspect of the invention may involve detecting the RNA expression levels of genes PLAUR and FOSB. Suitably the first aspect may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes PLAUR and FOSB, and any other gene or combinations of genes from Table 1. Suitably the first aspect of the invention may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes CXCL8 and any other gene or combinations of genes from Table 1. Suitably the first aspect of the invention may involve detecting the RNA expression levels of genes CXCL8 and FOSB. Suitably the first aspect of the invention may involve detecting the RNA expression levels of genes CXCL8 and PLAUR. Suitably the first aspect may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes PLAUR, FOSB and CXCL8. Suitably the first aspect may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes PLAUR, FOSB and CXCL8, and any other gene or combinations of genes from Table 1.

Suitably the first aspect of the invention may involve detecting the RNA expression levels of any one of the following genes: NR4A1, CXCR4, THBD, NR4A2, RGS16, KRT17, CDKN1A, PLAUR, CXCL8 or FRMD6. Suitably the first aspect of the invention may involve detecting the RNA expression levels of a plurality of genes, wherein the plurality of genes includes any two, three, four, five, six, seven, eight or nine of the following genes: NR4A1, CXCR4, THBD, NR4A2, RGS16, KRT17, CDKN1A, PLAUR, CXCL8 or FRMD6, wherein the two, three, four, five, six, seven, eight or nine genes may be any combination of these genes. Suitably the first aspect of the invention may involve detecting the RNA expression levels of NR4A1, CXCR4, THBD, NR4A2,

RGS16, KRT17, CDKN1A, PLAUR, CXCL8 and FRMD6.

Suitably the first aspect of the present invention may involve a method of detecting bovine tuberculosis in a RNA sample from a subject, wherein the RNA is isolated from peripheral blood taken from a subject that has Mycobacterium tuberculosis colonisation at the site of infection (usually the lungs). Suitably the Mycobacterium tuberculosis complex may include bacterium Mycobacterium bovis {M. bovis). Suitably, the Mycobacterium tuberculosis complex may include Mycobacterium caprae {M. caprae). Suitably, the Mycobacterium tuberculosis complex may include Mycobacterium orygis {M. orygis). Suitably the Mycobacterium tuberculosis complex may further include other intracellular mycobacterial pathogens. Suitably, the method of detecting BTB in a subject may further comprise normalising the RNA expression levels of the one or more genes in Table 1 against one or more housekeeping genes. Suitably the RNA expression levels of the one or more genes in Table 1 in the control sample will also have been normalised against one or more housekeeping genes.

Suitably the first aspect of the present invention may involve a method of detecting BTB in a subject, the method further comprising determining the differential expression of one or more genes from T able 1 , wherein the one or more genes are increased in expression.

Suitably the increased expression of the one or more genes may be statistically significant. Suitably the increased expression of the one or more genes may be greater than 1-fold. Suitably the increased expression of the one or more genes may be greater than 2-fold. Suitably the increased expression of the one or more genes may be greater than 5-fold.

Suitably the first aspect of the present invention may involve a method of detecting BTB in a subject, comprising determining the differential expression of two or more genes from Table 1 , wherein the two or more genes express a combination of up- regulation and down-regulation.

Suitably the first aspect of the present invention may involve a method of detecting BTB in a subject, wherein the RNA sample provided by the subject has been provided at least one week after the subject is infected with mycobacteria from the Mycobacterium tuberculosis complex. Suitably the first aspect of the present invention may involve a method of detecting BTB in a subject, wherein the RNA sample provided by the subject has been provided at least two weeks after the subject is infected with mycobacteria from the Mycobacterium tuberculosis complex. Suitably the first aspect of the present invention may involve a method of detecting BTB in a subject, wherein the RNA sample provided by the subject has been provided at least six weeks after the subject is infected with mycobacteria from the Mycobacterium tuberculosis complex. Suitably the first aspect of the present invention may involve a method of detecting BTB in a subject, wherein the RNA sample provided by the subject has been provided at least ten weeks after the subject is infected with mycobacteria from the Mycobacterium tuberculosis complex. Suitably the first aspect of the present invention may involve a method of detecting BTB in a subject, wherein the RNA sample provided by the subject has been provided at least twelve weeks after the subject is infected with mycobacteria from the Mycobacterium tuberculosis complex.

Suitably the first aspect of the invention may include a method of detecting BTB in a subject, wherein the subject providing the RNA sample is a mammalian subject. Suitably the first aspect of the invention may include a method of detecting BTB in a subject, wherein the subject providing the RNA sample is a bovine animal.

Suitably the first aspect of the invention may include a method of detecting BTB in a subject, wherein the degree of gene differential expression in the subject serves to determine the prognostic outcome of the subject.

Suitably the first aspect of the invention may include a method of detecting BTB in a subject, wherein the control RNA sample is obtained from a healthy control subject. Suitably, the first aspect may include a method of detecting BTB in a subject, wherein the control RNA sample is a RNA sample wherein the expression levels of the one or more genes from Table 1 is known. Suitably the first aspect of the invention may include a method of detecting BTB in a subject, wherein the control RNA sample is obtained from a subject with BTB disease.

Suitably the first aspect of the invention may include a method of detecting BTB in a subject, wherein the method allows for the detection of bacteria from the Mycobacterium tuberculosis complex in an asymptomatic subject. Suitably the method may provide for detection of latent BTB.

Suitably the first aspect of the invention may include a method of detecting BTB in a subject, wherein the method allows for detecting differential expression in one or more genes, wherein the one or more genes is one or more genes recited in Table 1 , wherein the method allows for the differentiation between BTB and other diseases of similar pathology in a symptomatic subject.

Suitably the first aspect of the invention may include a method of detecting BTB, wherein the RNA expression levels detected are mRNA. Suitably the first aspect of the invention may include a method of detecting BTB, wherein the method of detecting RNA expression levels is RNA-seq. Suitably the first aspect of the invention may include a method of detecting bovine tuberculosis, wherein the method of detecting RNA expression levels is reverse transcription quantitative real-time PCR (RT-qPCR) or other low-plex or mid-plex technologies such as the Nanostring nCounter^® gene expression platform.

The method may comprise detecting the presence of differentially expressed genes, wherein the method further comprises providing an RNA sample from a subject, assaying the RNA sample to determine the expression levels of RNA transcripts for one or more genes, wherein the one or more genes are genes listed in Table 1 and determining if the one or more genes are differentially expressed in the subject when compared to a control RNA sample.

The method may consist of detecting the RNA expression levels of a subset of genes as recited in T able 1. Suitably the subset may consist of two or more genes from Table 1. Suitably the subset of genes may consist of three or more genes from T able 1. Suitably the subset of genes may consist of four or more genes from T able 1. Suitably the subset of genes may consist of five or more genes from T able 1. Suitably the subset of genes may consist of ten or more genes from Table 1. Suitably the subset of genes may consist of twelve or more genes from Table 1. Suitably the subset of genes may consist of fifteen or more genes from T able 1.

The one or more genes may be encoded by the sequences in Figures 6 to 24. The one or more genes may encode any of the alternative RNA transcripts encoded in Figures 6 to 24. The method may include detecting any one or more RNA transcripts depicted in Figures 6 to 24. The method may include detecting any one or more RNA transcripts encoded by the genes in table 1, including RNA transcripts not depicted in Figures 6 to 24.

According to a second aspect of the invention there is provided a device for diagnosing tuberculosis according to the method described above, the device comprising: a means for receiving a blood sample from a subject; capture agents for determining the level of at least one or a combination of the biomarkers of expression of genes listed in Table 1.

Suitably, the device may comprise at least one indicator which indicates when the level of at least one biomarker in the sample. Suitably the device may comprise a plurality of indicators to indicate the level of at least two or more biomarkers, suitably each of the biomarkers of expression of genes listed in Table 1 in the sample. Suitably the device may comprise a microarray to bind to at least one RNA transcript biomarker of a gene as listed in Table 1 or a combination thereof. Suitably the microarray may comprise a solid support, for example a support formed from glass, plastics, polymers, metals, metalloids, ceramics, organics, etc. Suitably, using chip masking technologies and photoprotective chemistry an array of nucleic acids capable of binding to at least one, at least two or more, at least each of the RNA transcript biomarkers of a gene as listed in Table 1 may be provided. To compare expression levels, labelled nucleic acids may be contacted with the array under conditions sufficient for binding between an RNA transcript biomarker or amplified product thereof and a probe bound to the solid support of the array. Factors to allow binding are well known to those of skill in the art and could be tested in assays using the arrays.

Suitably the device may be an assay device comprising a sample receiving portion configured to receive a sample and to generate an amplified sample through a reverse transcription recombinase amplification reaction, and a detection zone configured to receive the amplified sample and allow determination of the level of RNA transcript biomarker in the sample. Optionally the device may further include a control zone to allow determination of non-RNA transcript biomarker in the sample.

Suitably the device may comprise measuring means for measuring the levels of the detected biomarkers. Suitably the device may be a hand-held point-of-care device, and may further include amplifying means for increasing the sensitivity of the detection of the biomarkers. The method of the invention can be performed using a diagnostic device which detects and indicates the presence of the biomarkers in the sample. The device can have a means for receiving the sample from the subject, such as a loading or receiving area onto or into which the sample is placed. A capture agent(s) and indicator(s) may be present in the device, and once the sample has been loaded onto or received into the device, the sample can be brought into contact with the capture agents, which are allowed to bind to the biomarkers if present. The indicator will signify that binding has occurred.

Embodiments of the second aspect of the invention may include one or more features of the first aspect of the invention.

According to a third aspect of the invention there is provided a kit for detecting the presence of BTB in a subject using a method according to the first or second aspects of the invention. Suitably the kit may comprise one or more reagent suitable for analysing the level of an RNA sample. Suitably the one or more reagent is suitable for determining the expression level of RNA transcripts for one or more genes, wherein the one or more genes are genes listed in Table 1 , and determining if the one or more genes are differentially expressed in the subject when compared to an RNA sample from an uninfected subject. Suitably the kit may comprise an oligonucleotide which can specifically hybridise to an RNA transcript biomarker or cDNA generated therefrom. By specific hybridisation is meant the oligonucleotide will form an anti-parallel double stranded structure with a target region of the RNA transcript biomarker or cDNA generated therefrom under hybridising conditions, while failing to form such a structure with non-target regions when incubated with a polynucleotide under the same hybridizing conditions.

Suitably the device or kit may be a portable device. Suitably the portable device may be a microfluidics device.

According to a further aspect of the invention there is provided a method for detecting the presence of BTB in a subject, the method comprising sending a sample from a test sample to a laboratory with a request to test the sample for the presence of at least one RNA transcript biomarker of a gene as listed in Table 1 receiving a report from the laboratory that states whether the sample is biomarker positive or negative wherein the sample is classified as being biomarker positive or negative using a method according to any of the methods as described herein.

Optionally the method may further comprise administering a treatment to the subject wherein the subject is considered to be biomarker or infection positive. Optionally the method may further comprise instructing an action against the subject, for example quarantine of the subject or removal of the subject, wherein the subject is considered to be biomarker or infection positive.

Embodiments of the third aspect of the invention may include one or more features of the first or second aspects of the invention or a combination thereof.

Embodiments of the present invention are described with reference to the accompanying figures in which: Figure 1 shows a schedule for the M. bovis infection time course experiment. Sampling time points for the ten non-vaccinated control cattle used are indicated by arrows above the figure (week abbreviated to ‘wk’ in the figure).

Figure 2 shows statistically significant differentially expressed genes. Five post infection time points are shown relative to the -1 week pre-infection time point (B-H FDR adjusted P-value < 0.05). A) Bar graph showing numbers of genes with increased and decreased expression; and B) Venn diagram showing the overlaps of differentially expressed genes for every multiple time point comparison.

Figure 3 shows a heat map which displays linear fold change values for the panel of 19 consistently differentially expressed genes across the M. bovis infection course. Linear fold-change values for the post-infection time points are shown relative to the -1 week pre-infection time point.

Figure 4 shows a table of the nineteen genes that exhibited statistically significant differential expression for each of the five post-infection time points versus the -1 week pre-infection control time point. Linear mean fold-change values are shown for each gene at each post-infection time point versus the -1 week pre-infection control time point

Figure 5 shows the bioinformatics workflow and computational pipeline used for the RNA-seq differential gene expression and downstream analyses.

Figure 6 shows the sequence for gene ITK (SEQ ID NO: 1) (6A) and associated RNA transcript (6B) (SEQ ID NO: 20).

Figure 7 shows the sequence for gene NR4A1 (SEQ ID NO: 2) (7A) and associated RNA transcripts (7B - 7G) (SEQ ID NOs: 21 to 26).

Figure 8 shows the sequence for gene CXCR4 (SEQ ID NO: 3) (8A) and associated RNA transcript (8B) (SEQ ID NO: 27).

Figure 9 shows the sequence for gene THBD (SEQ ID NO: 4) (9A) and associated RNA transcript (9B) (SEQ ID NO: 28).

Figure 10 shows the sequence for gene ZFP36L2 (SEQ ID NO: 5) and associated RNA transcript (10B) (SEQ ID NO: 29). Figure 11 shows the sequence for gene NR4A2 (SEQ ID NO: 6) (11 A) and associated RNA transcripts (11 B - 111) (SEQ ID NOs: 30 to 37).

Figure 12 shows the sequence for gene RGS16 (SEQ ID NO: 7) (12A) and associated RNA transcript (12B) (SEQ ID NO: 38).

Figure 13 shows the sequence for gene KRT17 (SEQ ID NO: 8) (13A) and associated RNA transcripts (13B - 13C) (SEQ ID NOs: 39 to 40).

Figure 14 shows the sequence for gene FOSB (SEQ ID NO: 9) (14A) and associated RNA transcripts (14B - 14C) (SEQ ID NOs: 41 to 42).

Figure 15 shows the sequence for gene CDKN1A (SEQ ID NO: 10) (15A) and associated RNA transcripts (15B - 15C) (SEQ ID NOs: 43 to 44).

Figure 16 shows the sequence for gene EVI2A (SEQ ID NO: 11) (16A) and associated RNA transcript (16B) (SEQ ID NO: 45)./

Figure 17 shows the gene sequence for PLAUR (SEQ ID NO: 12) (17A) and associated RNA transcript (17B) (SEQ ID NO: 46).

Figure 18 shows the gene sequence for OSM (SEQ ID NO: 13) (18A) and associated RNA transcript (18B) (SEQ ID NO: 47).

Figure 19 shows the gene sequence for CXCL8 (SEQ ID NO: 14) (19A) and associated RNA transcript (19B) (SEQ ID NO: 48).

Figure 20 shows the gene sequence for HBEGF (SEQ ID NO: 15) (20A) and associated RNA transcript (20B) (SEQ ID NO: 49).

Figure 21 shows the gene sequence for FRMD6 (SEQ ID NO: 16) (21A) and associated RNA transcript (21 B - 21H) (SEQ ID NOs: 50 to 56).

Figure 22 shows the cDNA sequence for ENSBTAG00000035224 (SEQ ID NO: 17) (22k) and associated RNA transcript (22B) (SEQ ID NO: 57).

Figure 23 shows the cDNA sequence for ENSBTAG00000037608 (SEQ ID NO: 18) (23A) and associated RNA transcript (23B) (SEQ ID NO: 58).

Figure 24 shows the cDNA sequence for SERPINB4 (SEQ ID NO: 19) (24A) and associated RNA transcripts (24B - 24C) (SEQ ID NOs: 59 to 60). DETAILED DESCRIPTION OF THE INVENTION

In the context of this invention, the following definitions are discussed.

The term bovine is used to encompass ruminant vertebrates, which are members of the Bovinae sub-family. Specifically, the term may be used to encompass cows, cattle and oxen.

The term transcriptome is used to define all RNA transcripts expressed at a given time point in a cell or population of cells. In this instance, transcriptome may be used to define all coding and non-coding RNA transcripts in a cell or population of cells. In this instance, transcriptome may be used to define all mRNA transcripts in a cell or population of cells.

The term RNA sequencing (RNA-seq) is used to reference any sequencing technique, which uses next-generation sequencing to analyse the transcriptome of a sample.

The term RT-qPCR refers to quantitative reverse transcription PCR and encompasses any technique involving the reverse transcription of total RNA or mRNA to complementary DNA and subsequent qPCR analysis.

The term qPCR refers to quantitative PCR and encompasses any real-time polymerase chain reaction, which measures the amplification of targeted DNA molecules (encoding a specific gene(s)) during PCR.

The term biomarker is an identifiable biological marker, the presence of which indicates a biological state or process.

The term biosignature relates to a plurality of biomarkers, the collective presence of which indicates a biological state or process.

The term B-H FDR refers to the statistical method of correcting for multiple comparisons termed the Benjamini-Hochberg False Discovery Rate.

The term housekeeping gene refers to any constitutive gene that are required for the maintenance of basal cellular functions that are essential for the existence of a cell. The term may also be used to encompass any gene(s), which display consistent expression under normal and patho-physiological conditions, such that their expression levels can be used to normalise gene expression in a biological sample. The inventors have identified a 19-gene biosignature, which displays increased expression of mRNA in cows, which have been infected with M. bovis, particularly at distinct time points of infection. This up-regulation was identified in the subjects at all time-points during the experiment (+1 week, +2 weeks, +6 weeks, +10 weeks and +12 weeks post-infection) when compared to -1 week infection levels (pre infection mRNA levels).

Figure 1 demonstrates the time course of the experiment, highlighting the sampling time points at which whole peripheral blood samples were taken from the cows for analysis.

Statistical analysis of the RNA-seq gene expression data with a B-H FDR adjusted P-value < 0.05, demonstrated that differential gene expression was evident at each of the five post-infection time points (Figure 2A). Relatively small numbers of differentially expressed genes were detected at +1 week (37 exhibited increased and 20 exhibited decreased expression) and +2 weeks (83 increased and 10 decreased); however, the numbers of differentially expressed genes were substantially greater at +6 weeks (415 increased and 272 decreased), +10 week (1 ,278 increased and 1 ,305 decreased) and +12 weeks (222 increased and 116 decreased).

Figure 2B also shows a Venn diagram for the significant differentially expressed genes at each of the five post-infection time points (relative to the -1 week pre infection control point). 19 putative candidate genes, which identified as differentially expressed at all time-points (Figure 2B).

Analysis of the expression values for each of these 19 putative candidate genes, as highlighted in Figure 4, shows persistent increased expression over all time points when compared to -1 week pre-infection levels. The results identify candidate genes, the differential expression of which could be used to determine the presence of mycobacteria from the Mycobacterium tuberculosis complex infection and BTB disease.

It is considered that this provides a biosignature of M. bovis infection from transcriptomics data from cattle with early- and later-stage BTB. The 19 gene biosignature demonstrated increased expression at all time-points post-infection. This result provides a biosignature, a subset of which could be used to determine the presence of BTB at all time-points. Diagnostic biosignature development focusing on smaller panels of transcriptomics- based biomarkers has been used with notable success for human TB. In this case, research work has focused on specificity and differentiating active TB from latent TB and also TB disease from non-infected controls and diseases with similar pathology but distinct aetiology such as sarcoidosis, pneumonia and lung cancer (Berry et al. , 2010; Maertzdorf et al. , 2012; Blankley et al. , 2014; Cliff et al. , 2015; Blankley et al., 2016; Maertzdorf et al., 2016; Sweeney et al., 2016; Leong et al., 2018; Estevez et al., 2020).

The present results have demonstrated that mRNA transcriptional changes can be detected in whole peripheral blood samples, therefore providing a method of diagnosis which allows for detection of BTB without invasive tissue biopsy.

Further, it is to be considered that the method of determining differential gene expression, wherein the method is for use in the detection of bovine tuberculosis can be used to detect such differential gene expression from an early time point post-infection. The inventors have, therefore, provided a more sensitive method which can be for use in the detection of bovine tuberculosis.

Methods

Peripheral blood collection and total RNA extraction

Approximately 3 ml of ex vivo peripheral blood was sampled from all ten naive control animals at -1 week pre-infection and then at +1 week, +2 weeks, +6 weeks, +10 weeks and +12 weeks post-infection (Figure 1). All blood samples were obtained during the morning (between 7:00 and 10:00) of each collection day and directly collected into Tempus™ blood RNA tubes (Applied Biosystems^®/Thermo Fisher Scientific, Warrington, UK). Immediately after blood collection at each time point, Tempus™ tube samples for each animal were vortexed for approximately 10s to ensure complete red blood cell lysis. Tempus™ tube blood lysate samples for animals at each of the nine time points were then stored at -80°C until they were used for total RNA extraction and purification.

The Tempus™ Spin RNA Isolation Kit (Applied Biosystems^®/Thermo Fisher Scientific) was used for total RNA extraction and purification using the following protocol provided by the manufacturer. Tempus™ tube blood lysate samples were thawed at room temperature prior to RNA extraction and purification. Once thawed, for each sample, approximately 3 ml of blood lysate was transferred to a 50 ml plastic centrifuge tube and PBS was added to a final volume of 12 ml. Each sample was then mixed by vortexing for 30s and then centrifuged at 3,000 *g for 30 min at 4°C. The supernatant was then removed, and the remaining RNA-containing pellet was re-suspended with a brief vortex in 400pl of the proprietary RNA Purification Resuspension Solution. Following this, the re-suspended RNA sample was pipetted into the RNA purification filter inserted into a 1.5 ml microcentrifuge tube for waste collection. The RNA purification filter/microcentrifuge tube was then centrifuged at 16,000 *g for 30s and the liquid waste and microcentrifuge tube discarded. The RNA purification filter was then placed in a clean microcentrifuge tube, 500mI of proprietary RNA Purification Wash Solution 1 was added, followed by another centrifugation step at 16,000

for 30s and disposal of the liquid waste and microcentrifuge tube. This step was then repeated using 500mI of proprietary RNA Purification Wash Solution 2 with a centrifugation step at 16,000 *g for 30s. A final wash step was then performed with 500mI of RNA Purification Wash Solution 2 and centrifugation at 16,000 *g for 30s followed by disposal of the liquid waste and microcentrifuge tube. The RNA purification filter was then placed in a clean microcentrifuge tube and centrifuged at 16,000 *g for 30s to dry the membrane. The RNA purification filter was then inserted into a clean RNase-free collection microcentrifuge tube and 100mI of Nucleic Acid Purification Elution Solution was added and incubated for 2 min followed by centrifugation at 16,000 *g for 30s; the RNA eluate was then pipetted back onto the filter membrane and the centrifugation step was repeated. Approximately 90mI of the final RNA eluate was then pipetted (avoiding particulate material) into a new labelled RNase-free collection microcentrifuge for long-term storage at -80°C.

RNA quality checking and quantification

RNA quantity and quality checking were performed using a NanoDrop™ 1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and an Agilent 2100 Bioanalyzer using an RNA 6000 Nano LabChip kit (Agilent Technologies, Cork, Ireland).

Strand-specific RNA-seq library preparation and sequencing

For RNA-seq library preparation, 1 pg of total RNA from each sample was used to prepare individually barcoded strand-specific RNA-seq libraries. Two rounds of poly(A)+ RNA purification were performed for all RNA samples using the Dynabeads^® mRNA DIRECT™ Micro Kit (Ambion^®/Thermo Fisher Scientific, Austin, TX, USA) according to the manufacturer’s instructions. The purified poly(A)+ RNA was then used to generate strand-specific RNA-seq libraries using the ScriptSeq™ v2 RNA-Seq Library Preparation Kit, the ScriptSeq™ Index PCR Primers (Sets 1 to 4) and the FailSafe™ PCR enzyme system (all sourced from Epicentre®/lllumina® Inc., Madison, Wl, USA) according to the manufacturer’s instructions. RNA-seq libraries were purified using the Agencourt^® AM Pure^® XP system (Beckman Coulter Genomics, Danvers, MA, USA) according to the manufacturer’s instructions for double size selection (198 0.75* followed by 1.0* ratio). RNA-seq libraries were quantified using a Qubit^® fluorometer and Qubit^® dsDNA HS Assay Kit (lnvitrogen™/Thermo Fisher Scientific, Carlsbad, CA, USA), while library quality checks were performed using an Agilent 2100 Bioanalyzer and High Sensitivity DNA Kit (Agilent Technologies Ltd.). Individually barcoded RNA-seq libraries were pooled in equimolar quantities, and the quantity and quality of the final pooled libraries (three pools in total) were assessed as described above.

Prior to high-throughput sequencing, the content of several RNA-seq libraries was validated using conventional Sanger dideoxy sequencing. Library inserts from 16 libraries were cloned using the Zero Blunt^® TOPO^® PCR Cloning Kit according to the manufacturer’s instructions (lnvitrogen™/ThermoFisher Scientific). Sanger sequencing of 36 plasmid inserts from these selected libraries confirmed that the RNA-seq libraries contained inserts derived from bovine mRNA. Plasmid sequencing was outsourced (Source Bioscience Ltd., Dublin, Ireland) and sequences generated were validated using BLAST-searching of the DNA sequence database (Johnson et al., 2008). Cluster generation and high throughput sequencing of the pooled RNA-seq libraries were performed using an lllumina® HiSeq™ 2000 Sequencing System at the MSU Research Technology Support Facility (RTSF) Genomics Core (https://rtsf.natsci.msu.edu/genomics; Michigan State University, Ml, USA). Each of the three pooled libraries was sequenced independently on five lanes split across multiple lllumina^® flow cells. The pooled libraries were sequenced as paired-end 2 ^c 100 nucleotide reads using lllumina^® version 5.0 sequencing kits. Additionally, after exploratory data analysis it was decided to remove animal ID 6522 completely from the analysis and proceed with 52 RNA-seq sample data sets. All RNA-seq data generated for this study have been deposited in the European Nucleotide database with experiment series accession numbers (PRJEB27764 and PRJEB44470). Bioinformatics analyses of RNA-seq data

Except where indicated, bioinformatics procedures and analyses were performed on a 32-core. Compute Server running Linux Ubuntu [version 12.04.2] hosted at the UCD Research IT Data Centre (stampede. ucd.ie) and administered by the UCD Animal Genomics Group. Figure 5 shows a schematic of the complete RNA-seq bioinformatics workflow and the downstream tools used for time series analysis, and various systems biology methods.

Deconvolution (filtering and segregation of sequence reads based on the unique RNA-seq library barcode index sequences) was performed by the MSU RTSF Genomics Core using a pipeline that simultaneously demultiplexed and converted pooled sequence reads to discrete FASTQ files for each RNA-seq sample with no barcode index mismatches permitted. The RNA-seq FASTQ sequence read data were then downloaded from the MSU RTSF. Genomics Core FTP server and a custom Perl script was used to filter out paired-end reads containing adapter sequence contamination (with up to three mismatches allowed) and to remove poor quality paired-end reads (i.e., one or both reads containing 25% of bases with a Phred quality score below 20). The quality of individual RNA-seq sample library files was then reassessed post-filtering using the FastQC software package [version 0.10.1] (Andrews, 2016).

Paired-end reads, from each filtered individual library, were aligned to the B. taurus reference genome (UMD3.1.73) (Zimin et al. , 2009) using the STAR aligner software package [version 2.3.0] (Dobin et al., 2013). For each library, raw counts for each gene based on the sense strand data were obtained using the featureCounts software from the Subread package [version 1.3.5-p4] (Liao et al. ,2014). The featureCounts parameters were set to unambiguously assign uniquely aligned paired-end reads in a stranded manner to the exons of genes within the UMD3.1.73 B. taurus reference genome annotation. The gene count outputs were then used to perform differential 248 gene expression analysis using the edgeR Bioconductor package [version 3.2.4] (Robinson et al., 2010) within an R-based pipeline that was customised to perform the following functions:

1. Use biomaRt (Durinck et al., 2005) to generate a detailed bovine gene annotation for downstream analyses, then filter out all bovine rRNA genes. 2. Filter out genes displaying expression levels below a minimal detection threshold of one count per million (CPM) in at least n = 9 individual libraries (where n = smallest group of biological replicates).

3. Calculate normalisation factors for each library using the trimmed mean of M values method (Robinson and Oshlack, 2010).

4. Identify differentially expressed (DE) genes between the pre-infection animal group (-1 week) and each of the post-infection animal groups (+1 week, +2 weeks, +6 weeks, +10 weeks and +12 weeks) using a paired-sample approach with the edgeR package. Differential expression was evaluated by fitting a negative binomial generalised linear model for each gene.

5. Correct for multiple testing using the Benjamini-Hochberg method (Benjamini and Hochberg, 1995) with a false discovery rate (FDR) threshold of < 0.05.

Claims

25 WO 2022/238515 PCT/EP2022/062854 Claims

1. A method of diagnosing the presence of infection with bacteria from the Mycobacterium tuberculosis complex and optionally bovine tuberculosis in a bovine subject, the method comprising: determining in a peripheral blood sample from a bovine subject to be tested at least one biomarker of expression of a gene listed in Table 1

2. The method of claim 1 , wherein the method comprises detecting the presence of differentially expressed genes, wherein the method further comprises:

(i) providing an RNA sample from a subject;

(ii) assaying the RNA sample to determine the expression levels of RNA transcripts for one or more genes, wherein the one or more genes are genes listed in Table 1; and

(iii) determining if the one or more genes are differentially expressed in the subject when compared to a control RNA sample.

3. The method according to any preceding claim, wherein one of the genes is ENSBTAG00000035224.

4. The method according to claim 1 or 2, wherein the method of determining further comprises assaying the RNA sample to determine the expression levels of RNA transcripts for five or more genes, wherein the five or more genes are genes listed in Table 1.

5. The method according to claim 1 or 2, wherein the method further comprises assaying the RNA sample to determine the expression levels of RNA transcripts for at least ten or more of the genes listed in Table 1.

6. The method according to claim 1 or 2, wherein the method further comprises assaying the RNA sample to determine the expression levels of RNA transcripts for each of the genes listed in Table 1.

7. The method according to any preceding claim, wherein the RNA sample provided by the subject is derived from a peripheral blood sample.

8. The method of claims 3 to 7, wherein the at least two genes are EVI2A and ITK.

9. The method of claims 3 to 7 wherein the least two genes are selected from FOSB, FRMD6 and SERPINB4, optionally wherein at least one of the genes is FOSB. 27

WO 2022/238515 PCT/EP2022/062854

10. The method of claims 4 to 9, wherein the at least three genes are FOSB, PLAUR and CXCL8.

11. The method of claims 3 to 9, wherein the at least two or more genes are any combination of NR4A1, CXCR4, THBD, NR4A2, RGS16, KRT17, CDKN1A, PLAUR,

CXCL8 or FRMD6.

12. The method of any of claims 1, 2, 4 to 7 wherein in the step of determining, at least 16 biomarkers from Table 1 are determined wherein the biomarkers are selected from a gene as indicated by SEQ ID NO: 1, SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 10, SEQ ID NO: 12, SEQ ID NO: 13, SEQ ID NO: 14, SEQ ID NO: 15, SEQ ID NO: 18, SEQ ID NO: 19

13. The method of any of claims 1, 2, 4 to 7 wherein in the step of determining at least 10 biomarkers selected from Table 1 wherein the biomarkers are selected from SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO:6, SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 10, SEQ ID NO: 12, SEQ ID NO: 14, SEQ ID NO: 16

14. The method of any preceding claim wherein in the step of determining all 19 biomarkers from Table 1 are determined.

15. The method of any preceding claim wherein in the step of determining, the biomarkers are determined and correlated with temporal information during an infection period.

16. The method of claim 15 wherein the temporal information is determined over an infection period from the beginning of infection to the end of infection.

17. The method of claim 15 or claim 16 wherein the infection period is up to 14 weeks post the beginning of infection.

18. The method of any preceding claim wherein the biomarkers are determined and correlated with temporal information from the beginning of infection up to 14 weeks post infection.

19. The method of any preceding claim wherein the step of determining biomarkers is undertaken for a plurality of biomarkers at a plurality of time points during infection and optionally during infection and post infection.

20. The method of any preceding claim wherein the step of determining biomarkers is undertaken for a plurality of biomarkers at a plurality of time points.

21. The method of any preceding claim wherein the step of determining biomarkers is undertaken for a plurality of biomarkers at a plurality of time points selected from +1 week, +2 weeks, + 6 weeks, +10 weeks, +12 weeks after infection.

22. The method of any of the preceding claims wherein the step of determining biomarkers is undertaken for a plurality of biomarkers at a plurality of time points selected from at least two of, at least three of, at least four of, at least five of +1 week, +2 weeks, + 6 weeks, +10 weeks, +12 weeks after infection.

23. The method of any of the preceding claims wherein the step of determining biomarkers is undertaken for a plurality of biomarkers at a plurality of time points selected from at least two of, at least three of, at least four of, at least five of +1 week, +2 weeks, + 6 weeks, +10 weeks, +12 weeks after infection wherein the biomarkers are selected from those indicated at claims 12 to 14.

24. The method according to any preceding claim, wherein the differential expression of the one or more genes are increased in expression when compared to the control RNA sample.

25. The method according claims 1 to 24, wherein the differential expression of the one or more genes is detected at least one week post-infection.

26. The method according to claim 1 to 25, wherein the tuberculosis infection is caused by infection with bacteria from the Mycobacterium tuberculosis complex, optionally Mycobacterium bovis.

27. The method according to any preceding claim, wherein the method further comprises determining between tuberculosis and other diseases with a similar pathology.

28. A kit for detecting the presence of bovine tuberculosis, in a subject using a method according to claims 1 to 27, the kit comprising one or more reagent suitable for analysing the RNA expression levels of an RNA sample, optionally wherein the one or more reagent is suitable for:

(i) determining the presence of RNA transcripts for one or more genes, wherein the one or more genes are genes listed in Table 1; and

(ii) determining if the one or more genes are differentially expressed in the subject when compared to an RNA sample from a control RNA sample.