CN106795480A - Biomarker of rheumatoid arthritis and application thereof - Google Patents
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Abstract
There is provided for predicting the disease relevant with micropopulation, the particularly biomarker and method of rheumatoid arthritis (RA).
Description
Cross reference to related applications
Nothing
Technical field
The present invention relates to biomedical sector, and the prediction disease relevant with micropopulation is particularly directed to, especially
It is the biomarker and method of the risk of rheumatoid arthritis (RA).
Background technology
Rheumatoid arthritis (RA) is to influence the debilitating autoimmune disease of global tens million of populations and increase
Add the death rate of the patient with its cardiovascular and other systemic complications, but the cause of disease of RA is still unclear.Spreading venereal diseases
Substance always with RA implications.However, the feature of RA related pathogen and it is pathogenic be largely unclear, and most
It is near to determine that human body is that beneficial and harmful microbe super organism (super-organism) of the boarding number in terms of trillion makes
Problem is further complicated.Although successfully alleviating the state of many RA patients using disease-modifying antirheumatic drug (DMARD),
But the insufficient understanding to triggering or promoting the factor of disease hinders the specific and more effective treatment method of exploitation.To micro-
Biological investigation also reveal that prevention or mitigate the probiotics of RA.
Think that other body parts are initiated and hidden the several years at some in arthritis premorbid RA.Intestinal microbiota
It is the critical environments factor of human health, there is the effect for determining in obesity, diabetes, colon cancer etc..Except in nutrition and
Different biological metabolism aspect work it is outer, microorganism in the enteron aisle of end also with nerve-immune-internal system and blood flow phase interaction
It is used to influence whole human body.Intestines micropopulation is related to given individual stabilization, increased its value in disease correlation studies.
The heterogeneity of intestinal microbiota shows in crowd, and the treatment of disease should be according to intestinal microbiota individuation, and it is in medicine
Effect in activation or inactivation, immunological regulation etc. is largely still unclear.Compared with road micropopulation, oral microorganism
Over the ground in research, wherein human microbial group's plan (HMP) only acquires about 100 healthy individuals for WGS to faciation
(Human Microbiome Project Consortium.A framework for human microbiome
Research.Nature 486,215-21 (2012), is incorporated herein by reference).Despite the fact that upper teeth and saliva sample exist
It is easier than fecal sample to obtain in treat-and-release, but lacks acted in disease oral cavity micropopulation grand all the time
Genome analysis excrement.Also unknown is oral cavity consistent in their characteristic or function aspects with enteric microorganism disease markers
To which kind of degree.
The content of the invention
Embodiment of the present disclosure be intended at least to solve to a certain extent problems of the prior art at least it
One.
The present invention is the following discovery based on the present inventor：
The assessment of enteric microorganism and sign have become the main of the human diseases including rheumatoid arthritis (RA)
Research field.In order to the enteric microorganism content to RA patient is analyzed, the present inventor is based on to from 212 individualities
Microbial DNA carries out depth shotgun sequencing and has carried out grand genome association analysis (Metagenome-Wide Association
Study, MGWAS) (Qin, J. et al. .A metagenome-wide association study of gut microbiota
In type 2diabetes.Nature 490,55-60 (2012), are incorporated herein by reference) scheme.The present inventor is based on
RA- related gene marker is identified by Random Forest model and confirms enteron aisle/tooth/saliva label group (29
Enteron aisle MLG 28 tooth MLG 19 saliva MLG).In order to be based on these 29 enteron aisle MLG 28 tooth MLG 19 salivas
MLG intuitively assesses the risk of RA diseases, and the present inventor is based on the relative abundance spectrum of the MLG labels in training set by random
Forest model calculates the probability of disease respectively.The data of the present inventor provide pair enteron aisle/tooth/saliva related to RA risks
The deep understanding of the feature of the grand genome of liquid, to research in future enteron aisle/grand genome of tooth/saliva in other relevant diseases
Pathological Physiology effect provides example, and there is provided individual risky with this disease for assessing based on micropopulation
The potential use of the method for disease.
It is thought that due to the fact that, RA- related intestinal microbiota (29 enteron aisle MLG 28 tooth MLG 19
Individual saliva MLG) detect it is valuable to increasing RA in early stage.First, label of the invention has specificity and spirit
Quick property.Second, the analysis of excrement ensures accuracy, security, affordability and patient compliance.And the sample of excrement is can
Transport.Experiment based on polymerase chain reaction (PCR) is comfortable and noninvasive, so people can be easier to participate in given screening journey
Sequence.3rd, label of the invention is also used as carrying out the instrument of Treatment monitoring to RA patient to detect to treatment
Response.
On the one hand, there is provided the biomarker group for predicting subject's disease relevant with micropopulation, and according to
Embodiment of the present disclosure, the biomarker group is by enteron aisle biomarker, tooth biomarker, saliva biomarker
Or with including SEQ ID NO:The microorganism group of the genomic DNA of 1 to 15843 at least part of sequence is into wherein enteron aisle is given birth to
Substance markers thing includes bifidobacterium dentium (Bifidobacterium dentium), RA-2633, enterococcus spp (Enterococcus
Sp.), RA-781, Gordonibacter pamelaeae, RA-3396, RA-6638, RA-2441, RA-527, fusiform gemma bar
Pseudomonas (Clostridium sp.), RA-2637, Citrobacter (Citrobacter sp.), Eubacterium
(Eubacterium sp.), Citrobacter, RA-3215, Con-1722, Con-4360, Con-4212, Con-1261, two
Discrimination Bifidobacterium (Bifidobacterium bifidum), Klebsiella Pneumoniae (Klebsiella pneumoniae), Con-
1423rd, veillonellasp category (Veillonella sp.), Con-4095, Con-4103, Con-1735, Con-1710, Con-
1832nd, Con-1170,
Tooth biomarker include RA-10848, RA-9842, RA-9941, RA-9938, RA-10684, RA-9998,
Con-7913、Con-20702、Con-11、Con-8169、Con-1708、Con-7847、Con-5233、Con-791、Con-
5566、Con-4455、Con-13169、Con-6088、Con-5554、Con-14781、Con-2466、Con-483、Con-
2562nd, Con-4701, Con-4824, Con-5030, Con-757, Con-530, and
Saliva biomarker includes RA-27683, RA-9651, RA-13621, RA-27616, Con-6908, Con-
305th, Con-1559, Con-1374, Con-6746, Campylobacter (Campylobacter rectus), Con-1141,
Con-20, streptococcus (Streptococcus sp.), Con-1238, Con-1073, Con-636, Con-1, porphyromonas list
Born of the same parents bacterium (Porphyromonas gingivalis), lactococcus (Lactococcus sp.),
Or genomic DNA includes SEQ ID NO:The microorganism of 1 to 15843 at least part of sequence.
Alternatively, biomarker group is made up of at least one kind being listed in the kind in table 3-2, preferably by extremely
Few 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least
90%th, at least 100% kind being listed in table 3-2 composition.
According to embodiment of the present disclosure, tooth biomarker includes the SEQ ID NO as described in table 6:1 to
15843 at least part of sequence.
According to embodiment of the present disclosure, enteron aisle biomarker includes bifidobacterium dentium JCVIHMP022, general Salmonella CB7
It is (Prevotella copri CB7), DSM 18205, VREF E980 (Enterococcus faecium E980), avette
Ruminococcus A2-162 (Ruminococcus obeum A2-162), Gordonibacter pamelaeae 7-10-1-bT,
DSM 19378, Ruminococcus bromii L2-63 (Ruminococcus bromii L2-63), Eubacterium ventriosum ATCC 27560
(Eubacterium ventriosum ATCC 27560), (the Klebsiella oxytoca of Klebsiella oxytoca KCTC 1686
KCTC 1686), Clostridium asparagiforme DSM 15981, general Salmonella CB7 (Prevotella copri
CB7), DSM 18205, citrobacter freundii 4_7_47CFAA (Citrobacter freundii 4_7_47CFAA), true bar
Pseudomonas 3_1_31 (Eubacterium sp.3_1_31), Citrobacter 30_2 (Citrobacter sp.30_2), fusiform
Bacillus 7_2_43FAA (Clostridium sp.7_2_43FAA), Roche vibrios M50/1 (Roseburia
intestinalis M50/1)、Dialister invisus DSM 15470、Bacteroides plebeius M12、DSM
17135th, bifidobacterium bifidum S17 (Bifidobacterium bifidum S17), Klebsiella Pneumoniae NTUH-K2044
(Klebsiella pneumoniae NTUH-K2044), veillonellasp belong to oral cavity taxon 158F0412 (Veillonella
Sp.oral taxon 158F0412), Comamonas testosteroni KF-1 (Comamonas testosteroni KF-1), lung
Scorching klebsiella NTUH-K2044 (Klebsiella pneumoniae NTUH-K2044), Veillonella atypica ACS-134-
V-Col7a (Veillonella atypica ACS-134-V-Col7a), Australian streptococcus ATCC 700641
(Streptococcus australis ATCC 700641), Parabacteroides merdae ATCC 43184,
Tooth biomarker includes actinomyces oral cavity taxon 180F0310 (Actinomyces sp.oral
Taxon 180F0310), stick-slip Ross bacterium DY-18 (Rothia mucilaginosa DY-18), Actinomyces
Graevenitzii C83, actinomyces dentocariosus ATCC 17982 (Actinomyces odontolyticus ATCC 17982),
Veillonella atypica ACS-134-V-Col7a (Veillonella atypica ACS-134-V-Col7a), actinomyces
F0384 (Actinomyces sp.F0384), actinomyces oral cavity taxon 848F0332 (Actinomyces sp.oral
Taxon 848F0332), neisseria mucosa M26 (Neisseria mucosa M26), ATCC 25996, actinomyces oral cavity point
Monoid 448F0400 (Actinomycessp.oral taxon 448F0400), tannerella ATCC 43037
(Tannerella forsythensis ATCC 43037), actinomyces oral cavity taxon 448F0400 (Actinomyces
Sp.oral taxon 448F0400), shaft-like Neisseria ATCC BAA-1200 (Neisseria bacilliformis ATCC
BAA-1200), mutual bacteria door bacterium SGP1 (Synergistetesbacterium SGP1), the dynamic bacterium ATCC 51599 of unusual mouth
(Lautropia mirabilis ATCC 51599), Capnocytophaga gingivalis ATCC 33624
(Capnocytophaga gingivalis ATCC 33624), (Cardiobacterium of cardiobacterium hominis ATCC 15826
Hominis ATCC 15826), (the Capnocytophaga gingivalis of Capnocytophaga gingivalis ATCC 33624
ATCC 33624), dynamic bacterium ATCC 51599 (Lautropia mirabilis ATCC 51599) of unusual mouth, lazy Claes Johanson bacterium
ATCC 51276 (Johnsonella ignava ATCC 51276), P. freudenreichii ssp CIRM-BIA1
(Propionibacterium freudenreichii shermanii CIRM-BIA1), treponema denticola ATCC 35405
(the Treponema denticola ATCC 35405), F0437 (Fusobacterium of Fusobacterium oral cavity taxon 370
Sp.oral taxon 370F0437), dynamic bacterium ATCC 51599 (Lautropia mirabilis ATCC 51599) of unusual mouth,
Corrode Aitken bacterium ATCC 23834 (Eikenella corrodens ATCC 23834), noxia ATCC
43541 (Selenomonas noxia ATCC 43541), (Porphyromonas of Li Shi Detection of Porphyromonas DSM 23370
Levii DSM 23370), Bulleidia extructa W1219,
Saliva biomarker includes (the Gemella haemolysans ATCC of gemella haemolysans ATCC 10379
10379), Veillonella atypica ACS-049-V-Sch6 (Veillonella atypica ACS-049-V-Sch6), carious tooth
Actinomyces ATCC 17982 (Actinomyces odontolyticus ATCC 17982), actinomyces dentocariosus ATCC 17982
(Actinomyces odontolyticus ATCC 17982), (Treponema of treponema denticola ATCC 35405
Denticola ATCC 35405), actinomyces oral cavity taxon 448F0400 (Actinomyces sp.oral taxon
448F0400), treponema vincentii ATCC 35580 (Treponema vincentii ATCC 35580), Australian hammer
Bacterium ATCC 700641 (Streptococcus australis ATCC 700641), Campylobacter RM3267
(Campylobacter rectus RM3267), CCUG 20446, actinomyces oral cavity taxon 171F0337
(Actinomyces sp.oral taxon 171F0337), (Treponema of treponema denticola ATCC 35405
Denticola ATCC 35405), Streptococcus sanguis VMC66 (Streptococcus sanguinis VMC66), actinomyces mouthful
Chamber taxon 448F0400 (Actinomyces sp.oral taxon 448F0400), actinomyces oral cavity taxon
448F0400 (Actinomyces sp.oral taxon 448F0400), shaft-like Neisseria ATCC BAA-1200
(Neisseria bacilliformis ATCC BAA-1200), Burkholderia mallei PRL-20 (Burkholderia
Mallei PRL-20), porphyromonas gingivalis TDC60 (Porphyromonas gingivalis TDC60), Lactococcus lactis
Newborn subspecies KF147 (Lactococcus lactis lactis KF147).
In another aspect of the present disclosure, there is provided the biomarker for predicting subject's disease relevant with micropopulation
Thing group, according to embodiment of the present disclosure, the biomarker group is by enteron aisle biomarker, tooth biomarker and saliva
Label is constituted, wherein
Tooth biomarker includes SEQ ID NO:1 to 15843 at least part of sequence.
According to embodiment of the present disclosure, disease is rheumatoid arthritis or relevant disease.
In another aspect of the present disclosure, there is provided the kit for determining said gene label group, including for PCR
Amplification and the primer designed according to the DNA sequence dna being listed below：
Tooth biomarker includes SEQ ID NO:1 to 15843 at least part of sequence.
In another aspect of the present disclosure, there is provided the kit for determining said gene label group, including one kind with
On the probe that is designed according to gene as listed below：Tooth biomarker includes SEQ ID NO:1 to 15843 at least portion
Sub-sequence.
In another aspect of the present disclosure, there is provided said gene label group is used to predict that subject's rheumatoid to be measured is closed
The purposes of the risk of section inflammation or relevant disease, including：
(1) collecting sample from subject to be measured；
(2) biomarker according to any one of claim 1 to 5 in the sample obtained in step (1) is determined
The relative abundance information of each biomarker of group；
(3) by using multivariate statistical model is by the relative abundance information of each biomarker of subject to be measured and instructs
Practice data set and be compared the probability for obtaining rheumatoid arthritis,
Wherein the probability of rheumatoid arthritis shows that subject to be measured suffers from rheumatoid arthritis or phase more than threshold value
Related disorders or risky development rheumatoid arthritis or relevant disease.
According to embodiment of the present disclosure, training dataset is to be based on multiple with rheumatoid using multivariate statistical model
The relative abundance information architecture of each biomarker of arthritic subject and multiple normal subjectses, it is alternatively, many
First statistical model is Random Forest model.
According to embodiment of the present disclosure, training dataset is matrix, wherein each row is represented according in claim 1 to 5
Each biomarker of biomarker group described in any one, each list sample sheet, unit represents the life in sample
The relative abundance spectrum of substance markers thing, and sample morbid state is vector, wherein 1 represents rheumatoid arthritis and 0 expression control.
According to embodiment of the present disclosure, RA-10848, RA-9842, RA-9941, RA-9938, RA-10684, RA-
9998、Con-7913、Con-20702、Con-11、Con-8169、Con-1708、Con-7847、Con-5233、Con-791、
Con-5566、Con-4455、Con-13169、Con-6088、Con-5554、Con-14781、Con-2466、Con-483、Con-
2562nd, the relative abundance information of each in Con-4701, Con-4824, Con-5030, Con-757 and Con-530, for example
Actinomyces oral cavity taxon 180F0310, stick-slip Ross bacterium DY-18, Actinomyces graevenitzii C83, carious tooth
Actinomyces ATCC 17982, Veillonella atypica ACS-134-V-Col7a, actinomyces F0384, the classification of actinomyces oral cavity
Group 848F0332, neisseria mucosa M26, ATCC 25996, actinomyces oral cavity taxon 448F0400, tannerella
ATCC 43037, actinomyces oral cavity taxon 448F0400, shaft-like Neisseria ATCC BAA-1200, mutual bacteria door bacterium
The dynamic bacterium ATCC 51599, Capnocytophaga gingivalis ATCC 33624 of SGP1, unusual mouth, cardiobacterium hominis ATCC 15826,
Capnocytophaga gingivalis ATCC 33624, unusual mouth move bacterium ATCC 51599, lazy Claes Johanson bacterium ATCC 51276, take
It is family name's freudenreichii ssp CIRM-BIA1, treponema denticola ATCC 35405, Fusobacterium oral cavity taxon 370F0437, strange
The dynamic bacterium ATCC 51599 of different mouth, erosion Aitken bacterium ATCC 23834, noxia ATCC 43541, Li Shi porphyrin lists
Born of the same parents bacterium DSM 23370, the relative abundance of Bulleidia extructa W1219 are according to SEQ ID NO:1 to 15843 phase
Abundance messages are obtained.
According to embodiment of the present disclosure, training dataset is at least one of table 9-1 and table 9-2, and rheumatoid is closed
It is at least 0.5 to show subject to be measured with rheumatoid arthritis or relevant disease or risky development class to save scorching probability
Rheumatic arthritis or relevant disease.
In another aspect of the present disclosure, there is provided said gene label is being prepared for predicting subject's rheumatoid to be measured
The purposes of the kit of the risk of property arthritis or relevant disease, including：
(1) collecting sample from subject to be measured；
(2) biomarker according to any one of claim 1 to 5 in the sample obtained in step (1) is determined
The relative abundance information of each biomarker of group；
(3) by using multivariate statistical model is by the relative abundance information of each biomarker of subject to be measured and instructs
Practice data set and be compared the probability for obtaining rheumatoid arthritis,
Wherein the probability of rheumatoid arthritis shows that subject to be measured suffers from rheumatoid arthritis or phase more than threshold value
Related disorders or risky development rheumatoid arthritis or relevant disease.
According to embodiment of the present disclosure, training dataset is to be based on multiple with rheumatoid using multivariate statistical model
The relative abundance information architecture of each biomarker of arthritic subject and multiple normal subjectses, it is alternatively, many
First statistical model is Random Forest model.
According to embodiment of the present disclosure, training dataset is matrix, wherein each row is represented according in claim 1 to 5
Each biomarker of biomarker group described in any one, each list sample sheet, unit represents the life in sample
The relative abundance spectrum of substance markers thing, and sample morbid state is vector, wherein 1 represents rheumatoid arthritis and 0 expression control.
According to embodiment of the present disclosure, wherein RA-10848, RA-9842, RA-9941, RA-9938, RA-10684,
RA-9998、Con-7913、Con-20702、Con-11、Con-8169、Con-1708、Con-7847、Con-5233、Con-
791、Con-5566、Con-4455、Con-13169、Con-6088、Con-5554、Con-14781、Con-2466、Con-483、
The relative abundance information of each in Con-2562, Con-4701, Con-4824, Con-5030, Con-757 and Con-530,
Such as actinomyces oral cavity taxon 180F0310, stick-slip Ross bacterium DY-18, Actinomyces graevenitzii C83,
Actinomyces dentocariosus ATCC 17982, Veillonella atypica ACS-134-V-Col7a, actinomyces F0384, actinomyces oral cavity
Taxon 848F0332, neisseria mucosa M26, ATCC 25996, actinomyces oral cavity taxon 448F0400, bacterioides forsythus are smooth receives
Bacterium ATCC 43037, actinomyces oral cavity taxon 448F0400, shaft-like Neisseria ATCC BAA-1200, mutual bacteria door bacterium
The dynamic bacterium ATCC 51599, Capnocytophaga gingivalis ATCC 33624 of SGP1, unusual mouth, cardiobacterium hominis ATCC 15826,
Capnocytophaga gingivalis ATCC 33624, unusual mouth move bacterium ATCC 51599, lazy Claes Johanson bacterium ATCC 51276, take
It is family name's freudenreichii ssp CIRM-BIA1, treponema denticola ATCC 35405, Fusobacterium oral cavity taxon 370F0437, strange
The dynamic bacterium ATCC 51599 of different mouth, erosion Aitken bacterium ATCC 23834, noxia ATCC 43541, Li Shi porphyrin lists
Born of the same parents bacterium DSM 23370, the relative abundance of Bulleidia extructa W1219 are according to SEQ ID NO:1 to 15843 phase
Abundance messages are obtained.
According to embodiment of the present disclosure, training dataset is at least one of table 9-1 and table 9-2, and rheumatoid is closed
It is at least 0.5 to show subject to be measured with rheumatoid arthritis or relevant disease or risky development class to save scorching probability
Rheumatic arthritis or relevant disease.
In another aspect of the present disclosure, there is provided diagnosis subject whether have the abnormality relevant with micropopulation or
The method of the risky development of the person abnormality relevant with micropopulation, including：
It is determined that the relative abundance of the above-mentioned biomarker in the sample from subject, and
Determine whether subject has the abnormality or risky hair relevant with micropopulation based on the relative abundance
The exhibition abnormality relevant with micropopulation.
According to embodiment of the present disclosure, the method includes：
(1) collecting sample from subject to be measured；
(2) biomarker according to any one of claim 1 to 5 in the sample obtained in step (1) is determined
The relative abundance information of each biomarker of group；
(3) by using multivariate statistical model is by the relative abundance information of each biomarker of subject to be measured and instructs
Practice data set and be compared the probability for obtaining rheumatoid arthritis,
Wherein the probability of rheumatoid arthritis shows that subject to be measured suffers from rheumatoid arthritis or phase more than threshold value
Related disorders or risky development rheumatoid arthritis or relevant disease.
According to embodiment of the present disclosure, training dataset is to be based on multiple with rheumatoid using multivariate statistical model
The relative abundance information architecture of each biomarker of arthritic subject and multiple normal subjectses, it is alternatively, many
First statistical model is Random Forest model.
According to embodiment of the present disclosure, training dataset is matrix, wherein each row is represented according in claim 1 to 5
Each biomarker of biomarker group described in any one, each list sample sheet, unit represents the life in sample
The relative abundance spectrum of substance markers thing, and sample morbid state is vector, wherein 1 represents rheumatoid arthritis and 0 expression control.
According to embodiment of the present disclosure, wherein RA-10848, RA-9842, RA-9941, RA-9938, RA-10684,
RA-9998、Con-7913、Con-20702、Con-11、Con-8169、Con-1708、Con-7847、Con-5233、Con-
791、Con-5566、Con-4455、Con-13169、Con-6088、Con-5554、Con-14781、Con-2466、Con-483、
The relative abundance information of each in Con-2562, Con-4701, Con-4824, Con-5030, Con-757 and Con-530,
Such as actinomyces oral cavity taxon 180F0310, stick-slip Ross bacterium DY-18, Actinomyces graevenitzii C83,
Actinomyces dentocariosus ATCC 17982, Veillonella atypica ACS-134-V-Col7a, actinomyces F0384, actinomyces oral cavity
Taxon 848F0332, neisseria mucosa M26, ATCC 25996, actinomyces oral cavity taxon 448F0400, bacterioides forsythus are smooth receives
Bacterium ATCC 43037, actinomyces oral cavity taxon 448F0400, shaft-like Neisseria ATCC BAA-1200, mutual bacteria door bacterium
The dynamic bacterium ATCC 51599, Capnocytophaga gingivalis ATCC 33624 of SGP1, unusual mouth, cardiobacterium hominis ATCC 15826,
Capnocytophaga gingivalis ATCC 33624, unusual mouth move bacterium ATCC 51599, lazy Claes Johanson bacterium ATCC 51276, take
It is family name's freudenreichii ssp CIRM-BIA1, treponema denticola ATCC 35405, Fusobacterium oral cavity taxon 370F0437, strange
The dynamic bacterium ATCC 51599 of different mouth, erosion Aitken bacterium ATCC 23834, noxia ATCC 43541, Li Shi porphyrin lists
Born of the same parents bacterium DSM 23370, the relative abundance of Bulleidia extructa W1219 are according to SEQ ID NO:1 to 15843 phase
Abundance messages are obtained.
According to embodiment of the present disclosure, training dataset is at least one of table 9-1 and table 9-2, and rheumatoid is closed
It is at least 0.5 to show subject to be measured with rheumatoid arthritis or relevant disease or risky development class to save scorching probability
Rheumatic arthritis or relevant disease.
Brief description of the drawings
The these and other aspect and advantage of the disclosure will be apparent from following description with reference to the accompanying drawings and more hold
It is readily understood, wherein：
Fig. 1 enteron aisles or oral cavity MLG allow the RA patient that classifies from normal healthy controls.(a, d, f) by untreated RA cases and
The training set of the excrement (a), tooth (d) and saliva (f) of unrelated normal control composition ROC curve (for excrement, tooth and
Saliva sample, respectively n=157,100,94).Round dot marked the false positive rate and True Positive Rate of optimal threshold probability.(b)
Excrement test set by having genetic connection or 17 without genetic connection controls and 17 RA cases to constitute each other is entered
Row classification.(c, e, g) is classified (for excrement to the RA samples of excrement (c), tooth (e) and saliva (g) after DMARD treatments
Just, tooth and saliva sample, respectively n=40,38,24).According to European antirheumatic alliance (EULAR) standard, DAS28 ＜ 2.6
Show remission.The classification results of all samples are listed in table 12.
Specific embodiment
Embodiment
Term as used herein has the implication that the those of ordinary skill of relevant art is generally understood that.Term,
As " one ", " one " and " being somebody's turn to do " are not intended to only refer to singular entity, but it is general comprising what is illustrated using specific embodiment
Classification.In addition to such as summarizing in the claims, term herein is used to describe specific embodiment of the invention, but
It is that their usage does not limit the present invention.
The present invention is further illustrated in following non-limiting example.Unless otherwise indicated, number and percentage
Than by weight and the number of degrees for degree Celsius.Such as this area one, those of ordinary skill understands, these embodiments, although show
The preferred embodiment of the present invention, but be only given by way of illustration, and reagent is commercially available obtainable.
Embodiment 1. differentiates and verifies the biomarker for assessing rheumatoid arthritis risk
1. material and method
1.1 sample collections and DNA are extracted
The present inventor acquires 212 fecal samples of individuality (table 1-1, fecal sample, bacterial plaque sample and saliva altogether
Sample), comprising training set (n=157, (being through treatment) the RA cases of 77 treatment blank and 80 normal healthy controls) and test set
(right for relevant case-control, case-control pair that n=34, i.e., 8 have relationship by blood and 9 are without genetic connection
Case-control pair；For the RA patient that DMARD- is treated, n=21).
Fecal sample is in BJ Union Hospital's collection, refrigeration transportation and as previously described in BGI- Shenzhen (Shenzhen Hua Da base
Cause) extracted (Qin, J. et al. .A metagenome-wide association study of gut microbiota
In type 2diabetes.Nature 490,55-60 (2012), are incorporated herein by reference).Bacterial plaque is to use ophthalmology tweezers
From the through volume with 3 μ l of dental surface scraping.Transfer samples to 200 μ l contain 10mM Tris, 1mM EDTA,
1 × the lysis buffer of 0.5% polysorbas20 and 200 μ g/ml Proteinase Ks (Fermentas) is simultaneously incubated 2 hours at 55 DEG C.It is logical
Cross to be incubated at 95 DEG C 10 minutes and terminate cracking, and sample is frozen in -80 DEG C before shipping.According to the side for fecal sample
Case carries out DNA extractions.For saliva, 100 μ l salivas are added in the 2 × lysis buffer of 100 μ l, wipe posterior pharyngeal wall and add
Enter in same test tube, then sample is cracked and extracted as tooth samples.
RA is diagnosed in BJ Union Hospital according to 2010ACR/EULAR criteria for classifications.According to standardization program, receiving
All phenotypic informations are gathered when examination person is to hospital's first visit.Between recruiting 18 to 65 years old, disease duration at least 6 weeks, at least 1
The RA patient of articular pain at arthroncus and 3.If patient has chronic severe infections history, any current infection or any class
The cancer of type, then foreclose them.Pregnant woman or women breast-feeding their children are foreclosed.Inform that all patients have infertile wind
Danger simultaneously forecloses the patient of desired child.Although some patients have suffered from RA many years, they are unused DMARD, because
For they were not diagnosed with RA before medical BJ Union Hospital in local hospital, and they only take anodyne
Alleviate RA symptoms.
According to standardization program, all phenotypic informations are gathered when subject is to hospital's first visit.212 are used for enteric microorganism
Only have 21 fecal samples of the patient from DMARD- treatments in the sample that gene catalogue builds and do not have in this article
It is analyzed.
This research has obtained the approval of the institutional review board of BJ Union Hospital and Shenzhen Hua Da gene.
Table 1-1. is used for the sample that gene catalogue builds
1.2 grand gene order-checkings and assembling
As previously described (Qin et al. .2012, supra), the grand gene order-checking in double ends is carried out on Illumina platforms
(Insert Fragment 350bp, sequence length 100bp), quality control is carried out to sequencing read and use SOAPdenovo v2.04 will
Sequencing read is reassembled into contig (Luo, R. et al. .SOAPdenovo2:an empirically improved
The of memory-efficient short-read de novo assembler.Gigascience 1,18 (2012), by drawing
With being incorporated herein).The average rate of host's pollution is 0.37% for fecal sample, is 5.55% for tooth samples, right
Saliva sample is 40.85%.
1.3 gene catalogues build
The gene of the contig by assembling is predicted using GeneMark v2.7d.Using BLAT (Kent,
W.J.BLAT--the BLAST-like alignment tool.Genome Res.12,656-64 (2002), by quoting simultaneously
Enter herein) redundancy gene is removed with the threshold value of 90% overlap and 95% homogeneity (not allowing the presence in hole), for 212 excrement
Sample (containing 21 samples of DMARD- treatments) forms 3,800,011 nonredundancy gene catalogue of gene, for 203 mouths
Chamber sample (105 bacterial plaque samples and 98 saliva samples) forms 3,234,997 catalogues of gene.Using BLAT (95%
Homogeneity, 90% overlap) the gene catalogue from fecal sample is incorporated to the existing micro- life of the enteron aisle comprising 4,300,000 genes
In thing reference list (Qin et al. .2012, supra), formed comprising 5,900,000 final catalogues of gene.Using with publish
Identical program refers to base by the way that high-quality is sequenced into read with enteron aisle or oral cavity in T2D papers (Qin et al., 2012, ibid)
Compare to determine the relative abundance of gene because of catalogue.
1.4 classification annotations and abundance are calculated
The internal process (pipeline) (Qin et al., 2012, ibid) described in detail using previous is according to IMG databases
(v400) classification distribution is carried out to predicted gene, wherein for distributing to door, 70% and homogeneity 65% are overlapped, for dividing
It is assigned to for category, homogeneity 85%, for distributing to class, homogeneity 95%.Calculated from the relative abundance of taxon gene
The relative abundance of taxon.
By Wilcoxon rank tests (wherein p<0.05) the relatively rich of taxon between patient and normal healthy controls is determined
The significant difference of degree.
1.5 grand genome associations analysis (MGWAS)
Case-control for fecal microorganism group compares, the base that removal is detected in less than 6 samples (n=157)
Because causing that there are 3,110,085 collection of gene.83,858 genes show between control and case in terms of relative abundance
Go out difference (p<0.01, Wilcoxon rank test, FDR=0.3285).According to these marker genes in all samples
They are clustered into MLG (Qin et al., 2012, ibid) by Plantago fengdouensis.For building tooth MLG, from 2,247,835 genes
209820 marker gene (p of selection in (being present at least 6 samples, n=105)<0.01, Wilcoxon rank test,
FDR=0.072).For saliva MLG, the present inventor from 2,404,726 genes (being present at least 6 samples, n=98)
206399 marker gene (p of middle selection<0.01, Wilcoxon rank test, FDR=0.088).
As discussed previously (Qin et al., 2012, ibid), the relative abundance according to taxology and their constitutivegene is entered
Row classification distribution and enrichment analysis.In short, being assigned to the gene and the genome ratio planted kind needed in MLG more than 90%
Pair when with the homogeneity more than 95%, 70% inquiry is overlapped.MLG is distributed to category and requires its gene and base more than 80%
Because of a group comparison, wherein having 85% homogeneity in DNA and protein sequence.Show and calculated from all genes and genome
Average homogeneity be only used for reference.According to the Kendall correlations between abundance of the MLG in all samples but regardless of disease
Example-control state further clusters MLG, and co-occurrence network is visualized by Cytoscape 3.0.2.
1.6 graders based on MLG
Composed to Random Forest model (R.2.14, randomForest4.6-7 using the MLG abundance of group (table 1-2) is trained
Software kit) (Liaw, Andy＆Wiener, Matthew.Classification and Regression by
RandomForest, R News (2002), the 2/3rd phase, page 18, are incorporated herein by reference) it is trained to select MLG to mark
Remember the best set of thing.Predicated error is tested the model and calculated on more than one test set.
On Random Forest model, using " the random forest 4.6-7 software kits " packed in the R of 2.14 versions, it is input into and is
Training dataset (the relative abundance spectrum of the MLG selected i.e. in training sample), sample morbid state (the sample disease of training sample
Shape is vector, and 1 represents RA, and 0 represents control) and test set (the relative abundance spectrum of the MLG for simply being selected in test set).Then originally
Inventor builds classification using the random forest function of the random forest software kit from R softwares, and using anticipation function come pre-
Survey test set.Being output as predicting the outcome, (P, threshold value is 0.5, and if P >=0.5, then subject is risky
With RA).
The sample information (selected from the sample built for gene catalogue in table 1-1) of table 1-2. training sets
2. result
The identification and checking of the RA patient based on micropopulation
In order to further illustrate diagnosis or the prognostic value of the related micropopulations of RA, the present inventor is primarily based on enteron aisle
MLG builds random forest classification of diseases device.Using 85 enteron aisle MLG labels (at least 100 bases from control and case
Cause) in the model of 29 enteron aisle MLG labels give training set (n=157) (Fig. 1 a, table 2-1, table 2-2, table 5, table 8-
1st, table 8-2) in minimum predicated error and recipient's operating characteristics (ROC) TG-AUC (AUC) be 0.977.On by having
The test that the case-control pair and the case-control without genetic connection having relationship by blood are constituted to (n=34, table 1-3)
Collection, overall error rate is 32% (Fig. 1 b, table 11) and AUC is 0.706.Therefore, the model based on enteron aisle MLG is to training set and fits
In the case of to the efficiency of test set comparable to or more than the existing grader based on RA serum markerses efficiency (Van der
Helm-van Mil,A.H.M.Risk estimation in rheumatoid arthritis-from bench to
bedside.Nat.Rev.Rheumatol.(2014).doi:10.1038/nrrheum.2013.215, is incorporated by reference into this
Text).
Similarly, selected from 28 MLG (table 3-1, table 3-2, table 6, the table 9- of 171 tooth MLG (at least 100 genes)
1, table 9-2) be given in training set 0.864 AUC (Fig. 1 d).Selected from 19 of 142 saliva MLG (at least 100 genes)
MLG (table 4-1, table 4-2, table 7, table 10-1, table 10-2) provides 0.898 AUC (Fig. 1 f).These results show excrement, tooth
It is all highly useful to diagnosis RA with saliva microbial biomarker.
Additionally, the clinical samples (table 1-3) to being treated through DMARD test enteron aisle and tooth MLG graders still by them
In major part be accredited as RA patient, and the tooth samples (DAS28) with low disease activity are more often classified as the (figure of health
1c, 1e, table 12), illustrate that tooth micropopulation faithfully indicates the effect of DMARD treatments.Additionally, come DMARD treatments of hanging oneself
The saliva sample of patient be typically categorized into control, it may be possible to because DMARD is to the direct regulation (figure of saliva micropopulation
1g, table 12).In a word, as a result show that enteron aisle and oral cavity MLG can distinguish effective and futile treatment and promote to therapeutic strategy
Assessment.
The sample information of table 1-3 test sets
5. 29 SEQ ID of enteron aisle optimum mark thing of table
| MLG ID | SEQ ID NO: | Gene number |
| mlg_id:2441 | 1~159 | 159 |
| mlg_id:4103 | 160~304 | 145 |
| mlg_id:4212 | 305~709 | 405 |
| mlg_id:1047 | 710~856 | 147 |
| mlg_id:1735 | 857~1536 | 680 |
| mlg_id:4360 | 1537~1646 | 110 |
| mlg_id:1796 | 1647~1798 | 152 |
| mlg_id:3396 | 1799~2071 | 273 |
| mlg_id:2472 | 2072~2309 | 238 |
| mlg_id:1261 | 2310~2991 | 682 |
| mlg_id:1832 | 2992~3093 | 102 |
| mlg_id:6638 | 3094~3214 | 121 |
| mlg_id:1722 | 3215~3353 | 139 |
| mlg_id:1423 | 3354~3455 | 102 |
| mlg_id:1170 | 3456~3558 | 103 |
| mlg_id:3215 | 3559~3739 | 181 |
| mlg_id:4095 | 3740~4381 | 642 |
| mlg_id:2637 | 4382~4754 | 373 |
| mlg_id:905 | 4755~4885 | 131 |
| mlg_id:4111 | 4886~6743 | 1858 |
| mlg_id:1710 | 6744~6862 | 119 |
| mlg_id:2633 | 6863~7113 | 251 |
| mlg_id:819 | 7114~7425 | 312 |
| mlg_id:4158 | 7426~7736 | 311 |
| mlg_id:527 | 7737~7854 | 118 |
| mlg_id:784 | 7855~8048 | 194 |
| mlg_id:2473 | 8049~8758 | 710 |
| mlg_id:781 | 8759~8869 | 111 |
| mlg_id:5 | 8870~9319 | 450 |
6. 28 SEQ ID of tooth optimum mark thing of table
7. 19 SEQ ID of saliva optimum mark thing of table
| MLG ID | SEQ ID NO: | Gene number |
| mlg_id:1238 | 1~126 | 126 |
| mlg_id:1559 | 127~231 | 105 |
| mlg_id:6908 | 232~360 | 129 |
| mlg_id:1141 | 361~519 | 159 |
| mlg_id:6746 | 520~697 | 178 |
| mlg_id:1 | 698~5680 | 4983 |
| mlg_id:27683 | 5681~5851 | 171 |
| mlg_id:1374 | 5852~6032 | 181 |
| mlg_id:13 | 6033~8482 | 2450 |
| mlg_id:1073 | 8483~9597 | 1115 |
| mlg_id:29 | 9598~10469 | 872 |
| mlg_id:636 | 10470~11246 | 777 |
| mlg_id:9651 | 11247~11383 | 137 |
| mlg_id:305 | 11384~11485 | 102 |
| mlg_id:12 | 11486~14228 | 2743 |
| mlg_id:20 | 14229~16239 | 2011 |
| mlg_id:2831 | 16240~17605 | 1366 |
| mlg_id:13621 | 17606~18115 | 510 |
| mlg_id:27616 | 18116~15843 | 123 |
Therefore, the present inventor has been identified and has been verified to based on the related gene markers of RA by Random Forest model
Label group (29 enteron aisle MLG 28 tooth MLG 19 saliva MLG).And the present inventor is had been built up out based on this
A little RA related intestinal microbiota assesses the RA graders of the risk of RA diseases.
While exemplary embodiments have been shown and described, it should be appreciated to those skilled in the art that above-mentioned implementation
Example is not construed to limit the disclosure, and can be in the case where spirit, principle and the scope of the disclosure is not departed to reality
Apply example be changed, substitutions and modifications.
Claims (23)
1. a kind of biomarker group for predicting subject's disease relevant with micropopulation, by tooth biomarker group
Into, the tooth biomarker include RA-10848, RA-9842, RA-9941, RA-9938, RA-10684, RA-9998,
Con-7913、Con-20702、Con-11、Con-8169、Con-1708、Con-7847、Con-5233、Con-791、Con-
5566、Con-4455、Con-13169、Con-6088、Con-5554、Con-14781、Con-2466、Con-483、Con-
2562、Con-4701、Con-4824、Con-5030、Con-757、Con-530。
2. the biomarker group for predicting subject's disease relevant with micropopulation according to claim 1, its
Described in tooth biomarker include SEQ ID NO:1 to 15843 at least part of sequence.
3. it is used to predict the biomarker group of subject's disease relevant with micropopulation, wherein the tooth biomarker
Including actinomyces oral cavity taxon 180F0310 (Actinomyces sp.oral taxon 180F0310), stick-slip Ross bacterium
DY-18 (Rothia mucilaginosa DY-18), Actinomyces graevenitzii C83, actinomyces dentocariosus ATCC
17982 (Actinomyces odontolyticus ATCC 17982), Veillonella atypica ACS-134-V-Col7a
(Veillonella atypica ACS-134-V-Col7a), actinomyces F0384 (Actinomyces sp.F0384), put
The F0332 of line Pseudomonas oral cavity taxon 848 (F0332 of Actinomyces sp.oral taxon 848), neisseria mucosa M26
(Neisseria mucosa M26), ATCC 25996, the F0400 (Actinomyces of actinomyces oral cavity taxon 448
The F0400 of sp.oral taxon 448), (the Tannerella forsythensis ATCC of tannerella ATCC 43037
43037), the F0400 of actinomyces oral cavity taxon 448 (F0400 of Actinomyces sp.oral taxon 448), shaft-like
Neisseria ATCC BAA-1200 (Neisseria bacilliformis ATCC BAA-1200), mutual bacteria door bacterium SGP1
Dynamic (the Lautropia mirabilis ATCC of bacterium ATCC 51599 of (Synergistetes bacterium SGP1), unusual mouth
51599), Capnocytophaga gingivalis ATCC 33624 (Capnocytophaga gingivalis ATCC 33624),
Cardiobacterium hominis ATCC 15826 (Cardiobacterium hominis ATCC 15826), Capnocytophaga gingivalis
The dynamic bacterium ATCC 51599 of ATCC 33624 (Capnocytophaga gingivalis ATCC 33624), unusual mouth
(Lautropia mirabilis ATCC 51599), lazy (the Johnsonella ignava of Claes Johanson bacterium ATCC 51276
ATCC 51276), P. freudenreichii ssp CIRM-BIA1 (Propionibacterium freudenreichii
Shermanii CIRM-BIA1), treponema denticola ATCC 35405 (Treponema denticola ATCC 35405),
Fusobacterium oral cavity taxon 370F0437 (Fusobacterium sp.oral taxon 370F0437), the dynamic bacterium of unusual mouth
ATCC 51599 (Lautropia mirabilis ATCC 51599), (Eikenella of erosion Aitken bacterium ATCC 23834
Corrodens ATCC 23834), (the Selenomonas noxia ATCC of noxia ATCC 43541
43541), Li Shi Detection of Porphyromonas DSM 23370 (Porphyromonas levii DSM 23370), Bulleidia
extructa W1219。
4. a kind of biomarker group for predicting subject's disease relevant with micropopulation, by including SEQ ID NO:1
To the tooth biomarker composition of 15843 at least part of sequence.
5. it is used to predict the biomarker group of subject's disease relevant with micropopulation, wherein the disease is rheumatoid
Arthritis or relevant disease.
6. a kind of kit for determining the gene marker group any one of claim 1 to 5, including for PCR
Amplification and the primer designed according to the DNA sequence dna as described in claim 4.
7. a kind of kit for determining the gene marker group any one of claim 1 to 5, including more than one
According to the probe that the gene as described in claim 4 is designed.
8. the gene marker group any one of claim 1 to 5 is used to predict subject's rheumatoid arthritis to be measured
Or the purposes of the risk of relevant disease, including：
(1) from subject's collecting sample to be measured；
(2) biomarker according to any one of claim 1 to 5 in the sample obtained in step (1) is determined
The relative abundance information of each biomarker of group；
(3) by using multivariate statistical model is by the relative abundance information of each biomarker of subject to be measured and instructs
Practice data set and be compared the probability for obtaining rheumatoid arthritis,
The probability of wherein described rheumatoid arthritis shows that the subject to be measured is closed with the rheumatoid more than threshold value
Section inflammation or relevant disease or the risky development rheumatoid arthritis or relevant disease.
9. purposes according to claim 8, wherein the training dataset be based on using the multivariate statistical model it is many
The relative abundance information structure of each biomarker of the individual subject with rheumatoid arthritis and multiple normal subjectses
Build, alternatively, the multivariate statistical model is Random Forest model.
10. purposes according to claim 9, wherein the training dataset is matrix, wherein each row is represented according to right
It is required that each biomarker of the biomarker group any one of 1 to 5, each list sample sheet, unit is represented
The relative abundance spectrum of the biomarker in the sample, and sample morbid state is vector, wherein 1 represents rheumatoid joint
Scorching and 0 expression control.
11. purposes according to claim 9, wherein RA-10848, RA-9842, RA-9941, RA-9938, RA-10684,
RA-9998、Con-7913、Con-20702、Con-11、Con-8169、Con-1708、Con-7847、Con-5233、Con-
791、Con-5566、Con-4455、Con-13169、Con-6088、Con-5554、Con-14781、Con-2466、Con-483、
The relative abundance information of each in Con-2562, Con-4701, Con-4824, Con-5030, Con-757 and Con-530
It is according to SEQ ID NO:1 to 15843 relative abundance information acquisition.
12. purposes according to claim 9, wherein the training dataset is at least one of table 9-1 and table 9-2, and
The probability of the rheumatoid arthritis be at least 0.5 show the subject to be measured suffer from the rheumatoid arthritis or phase
Related disorders or the risky development rheumatoid arthritis or relevant disease.
Gene marker group any one of 13. claims 1 to 5 is being prepared for predicting subject's rheumatoid to be measured
The purposes of the kit of the risk of arthritis or relevant disease, including：
(1) from subject's collecting sample to be measured；
(2) biomarker according to any one of claim 1 to 5 in the sample obtained in step (1) is determined
The relative abundance information of each biomarker of group；
(3) by using multivariate statistical model is by the relative abundance information of each biomarker of subject to be measured and instructs
Practice data set and be compared the probability for obtaining rheumatoid arthritis,
The probability of wherein described rheumatoid arthritis shows that the subject to be measured is closed with the rheumatoid more than threshold value
Section inflammation or relevant disease or the risky development rheumatoid arthritis or relevant disease.
14. purposes according to claim 13, wherein the training dataset is based on using the multivariate statistical model
The relative abundance information of each biomarker of subject and multiple normal subjects of the multiple with rheumatoid arthritis
Build, alternatively, the multivariate statistical model is Random Forest model.
15. purposes according to claim 14, wherein the training dataset is matrix, wherein each row is represented according to right
It is required that each biomarker of the biomarker group any one of 1 to 5, each list sample sheet, unit is represented
The relative abundance spectrum of the biomarker in the sample, and sample morbid state is vector, wherein 1 represents rheumatoid joint
Scorching and 0 expression control.
16. purposes according to claim 14, wherein RA-10848, RA-9842, RA-9941, RA-9938, RA-
10684、RA-9998、Con-7913、Con-20702、Con-11、Con-8169、Con-1708、Con-7847、Con-5233、
Con-791、Con-5566、Con-4455、Con-13169、Con-6088、Con-5554、Con-14781、Con-2466、Con-
483rd, the relative abundance of each in Con-2562, Con-4701, Con-4824, Con-5030, Con-757 and Con-530
Information is according to SEQ ID NO:1 to 15843 relative abundance information acquisition.
17. purposes according to claim 14, wherein the training dataset is at least one of table 9-1 and table 9-2, and
The probability of the rheumatoid arthritis be at least 0.5 show the subject to be measured suffer from the rheumatoid arthritis or phase
Related disorders or the risky development rheumatoid arthritis or relevant disease.
Whether a kind of 18. diagnosis subjects have the abnormality relevant with micropopulation or risky development and micropopulation
The method of relevant abnormality, including：
It is determined that the phase of the biomarker according to any one of claim 1 to 5 in the sample from the subject
To abundance, and
Determine whether subject has the abnormality relevant with micropopulation or risky development based on the relative abundance
The abnormality relevant with micropopulation.
19. methods according to claim 18, including：
(1) from subject's collecting sample to be measured；
(2) biomarker according to any one of claim 1 to 5 in the sample obtained in step (1) is determined
The relative abundance information of each biomarker of group；
(3) by using multivariate statistical model is by the relative abundance information of each biomarker of subject to be measured and instructs
Practice data set and be compared the probability for obtaining rheumatoid arthritis,
The probability of wherein described rheumatoid arthritis shows that the subject to be measured is closed with the rheumatoid more than threshold value
Section inflammation or relevant disease or the risky development rheumatoid arthritis or relevant disease.
20. methods according to claim 19, wherein the training dataset is based on using the multivariate statistical model
The relative abundance information of each biomarker of subject and multiple normal subjects of the multiple with rheumatoid arthritis
Build, alternatively, the multivariate statistical model is Random Forest model.
21. methods according to claim 20, wherein the training dataset is matrix, wherein each row is represented according to right
It is required that each biomarker of the biomarker group any one of 1 to 5, each list sample sheet, unit is represented
The relative abundance spectrum of the biomarker in the sample, and sample morbid state is vector, wherein 1 represents rheumatoid joint
Scorching and 0 expression control.
22. methods according to claim 20, wherein RA-10848, RA-9842, RA-9941, RA-9938, RA-
10684、RA-9998、Con-7913、Con-20702、Con-11、Con-8169、Con-1708、Con-7847、Con-5233、
Con-791、Con-5566、Con-4455、Con-13169、Con-6088、Con-5554、Con-14781、Con-2466、Con-
483rd, the relative abundance of each in Con-2562, Con-4701, Con-4824, Con-5030, Con-757 and Con-530
Information is according to SEQ ID NO:1 to 15843 relative abundance information acquisition.
23. methods according to claim 20, wherein the training dataset is at least one of table 9-1 and table 9-2, and
The probability of the rheumatoid arthritis be at least 0.5 show the subject to be measured suffer from the rheumatoid arthritis or phase
Related disorders or the risky development rheumatoid arthritis or relevant disease.
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| CN109266765B (en) * | 2018-09-28 | 2022-06-28 | 人和未来生物科技（长沙）有限公司 | Microbial flora for predicting precancerous lesion risk of oral cavity and application |
Also Published As
| Publication number | Publication date |
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| CN106795480B (en) | 2021-05-07 |
| WO2016049937A1 (en) | 2016-04-07 |
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