Autistic gut microbial markers in adolescents and uses thereof

Abstract

This invention discloses a gut microbial biomarker for adolescent autism and its application, belonging to the field of biomedicine. The microbial biomarker includes one or more of the following: active rumenococci, thermophilic streptococci, salivarius streptococci, Enterobacter bartleeri, Corynebacterium maxima, and Prevotella faecalis. This invention provides a product (such as a kit) comprising detection reagents for detecting the relative abundance of the aforementioned microbial biomarkers. The product provided by this invention has good feasibility and accuracy, and can effectively assess the risk of autism in adolescents, providing a new tool for clinical diagnosis.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine, specifically relating to a gut microbiota biomarker for adolescent autism and its application. Background Technology

[0002] Autism in adolescents has become a global public health focus. The World Health Organization (WHO) estimates that 5%-8% of adolescents suffer from depressive disorders, with the first onset often occurring during adolescence. The causes can be attributed to genetic predisposition, childhood trauma, school bullying, and gut-brain axis dysbiosis, which together trigger abnormal development of the prefrontal-limbic system. This can easily lead to persistent low mood, cognitive decline, and increased risk of suicide.

[0003] To address the aforementioned issues, the applicant of this application filed an invention patent application on October 31, 2023, entitled "Application of Gut Microbiota Markers in the Diagnosis and Treatment of Autism," which discovered microorganisms including Bacteroides, Lachnoclostridium, and Parabacteroides. Bifidobacterium, Blautia, and Collinsella Related to autism.

[0004] While the aforementioned gut microbiota biomarkers can be used to assess the risk of autism in adolescents, the healthy samples used are raw data downloaded from the NCBI database, which has the problem of insufficiently strict sample selection criteria. As a result, the above-mentioned microbiota biomarkers are not accurate enough in diagnosing whether the adolescents being tested have autism. Therefore, it is necessary to improve them to lay a good foundation for subsequent early warning and mechanism intervention. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a gut microbiota biomarker for adolescent autism and its application, which can more accurately diagnose whether the adolescent being tested has autism.

[0006] The technical solution provided by this invention is as follows: In a first aspect, the application of a reagent for quantitatively detecting microbial markers in the preparation of products related to adolescent autism, wherein the microbial markers include active rumenococcus gnavus and / or Streptococcus thermophilus.

[0007] In the above technical solution, the microbial markers also include Streptococcus salivarius and / or Enterobacter bartlettii.

[0008] In the above technical solution, the microbial markers also include Anaerostipeshadrus and Faecalibacterium prausnitzii.

[0009] In the above technical solution, the microbial marker is a combination of markers composed of active rumenococcus gnavus, thermophilic streptococcus, salivarius streptococcus, Intestinibacter bartlettii, Anaerostipes hadrus, and Faecalibacterium prausnitzii.

[0010] In the above technical solution, if, after continuous observation over multiple time periods, a significant increasing trend is found in the presence of Streptococcus thermophilus and / or Ruminococcus gnavus in the stool sample of the adolescent being tested, then the adolescent being tested is more likely to be healthy; if a significant increasing trend is found in the presence of Streptococcus salivarius in the stool sample, then the adolescent being tested is more likely to be an adolescent with autism.

[0011] Secondly, a microbial biomarker associated with adolescent autism is provided, the microbial biomarker including Anaerostipes hadrus, Streptococcus salivarius, Intestinibacter bartlettii, and Faecalibacterium prausnitzii.

[0012] In the above technical solution, the microbial markers also include active rumenococcus gnavus and / or thermophilic streptococcus thermophilus.

[0013] Thirdly, a product for diagnosing or predicting autism in adolescents is provided, the product comprising one or more of reagents, test strips, aptamers, and chips, the product being specific to the microbial biomarkers and used for quantitative detection of the microbial biomarkers.

[0014] Fourthly, a kit containing reagents for the quantitative detection of the microbial markers.

[0015] It should be noted that this invention newly discovered and verified a strong correlation between the aforementioned gut microbiota and adolescent autism, which can be used to diagnose whether a adolescent under testing has autism. Based on this, although the embodiments of this invention only list how to achieve quantitative detection of the sample through relative abundance values, other methods for quantitative detection of microorganisms are also feasible (such as absolute abundance or total microbial load information), and can also be used to assist in the diagnosis of whether an adolescent under testing has autism. Users can choose according to their own needs, and these will not be elaborated upon here.

[0016] The beneficial effects of this invention are as follows: 1. This invention newly discovers six gut microbiota associated with adolescent autism, including: Ruminococcus gnavus, Streptococcus thermophilus, Streptococcus salivarius, Intestinibacter bartlettii, Anaerostipes hadrus, and Faecalibacterium prausnitzii.

[0017] Research and verification have shown that one or more of the above six gut microbiota can be used to diagnose whether a adolescent with autism has autism.

[0018] 2. The present invention also provides a reagent and / or kit that can use one or more of the above 6 bacterial species as detection markers (i.e. microbial markers) to diagnose whether the adolescent under test has autism. It is completely non-invasive and highly accurate.

[0019] Metagenomic sequencing provides higher resolution, enabling the analysis of microbial communities to reach the species and even strain level, thereby improving the accuracy and reliability of diagnosis. The six species mentioned can also serve as target microorganisms for developing these systems, filling a gap in this field.

[0020] 3. This invention also provides a product and / or prediction system for diagnosing autism in adolescents. This product and / or prediction system can diagnose the risk of autism in a tested adolescent based on the relative abundance values ​​of various bacterial species. The entire diagnostic process is safe, non-invasive, and efficient, demonstrating good feasibility and accuracy. It can effectively assess the risk of autism in a tested adolescent (from a fecal sample), with relatively accurate assessment results. Attached Figure Description

[0021] Figure 1 This is a graph showing the results of the linear discriminant analysis; Figure 2 Box plots showing the relative abundance of autism in individuals with autism and healthy individuals; Figure 3 ROC curve for predicting scores. Detailed Implementation

[0022] The present invention will now be described in further detail with reference to specific embodiments, so that those skilled in the art can understand it.

[0023] The following description of adolescent autism follows the structure you provided, but with a similar approach, incorporating the background issue of "sufficient sample size but insufficient screening": Autism spectrum disorder (ASD) in adolescents is a complex neurodevelopmental disorder caused by abnormal neurodevelopment accompanied by synaptic plasticity and an imbalance between excitation and inhibition. It has become one of the most serious public health problems affecting children worldwide. Its harm lies not only in its continuously rising prevalence, but also in the lifelong social communication impairments, stereotyped behaviors, and comorbid intellectual developmental delays it causes, placing a heavy burden on patients, families, and society.

[0024] Autism in adolescents has become a global public health focus: the World Health Organization (WHO) estimates that about 1% of children and adolescents worldwide have autism spectrum disorder, with core symptoms often appearing before the age of 3. However, many mild or atypical cases are not diagnosed until adolescence. Compared to those diagnosed in adulthood, the adolescent brain is still in a critical period of remodeling, and the interaction of genetic, epigenetic, and environmental factors is more complex, making symptoms more likely to be accompanied by anxiety, depression, and self-harm. The causes can be attributed to high genetic susceptibility (such as hundreds of risk genes like CHD8 and SHANK3), perinatal immune activation, early sensory deprivation, and gut-immune-central nervous system axis disorders, all of which lead to abnormal synaptic pruning and long-range connectivity dysfunction in early cortical development, resulting in a lack of social motivation, narrow interests, and sensory abnormalities. If behavioral intervention is not implemented before adolescence, the ability to live independently and work in adulthood is severely impaired, and the risk of comorbid mental disorders increases by 3 to 5 times.

[0025] For adolescents with autism, the main treatment options currently include behavioral intervention and symptomatic drug therapy. Behavioral interventions include Applied Behavior Analysis (ABA) and Social Skills Training (SST), which can improve core social deficits and adaptive behaviors. However, the efficacy is limited by the density of therapists, the level of family involvement, and the child's baseline function. Approximately 40% to 50% of adolescents still do not respond well to high-intensity behavioral interventions. In terms of drugs, the US FDA has only approved risperidone and aripiprazole for relieving irritability and stereotyped behaviors. However, they are ineffective for core social communication impairments and may cause significant weight gain, metabolic abnormalities, and extrapyramidal reactions. In addition, there is a lack of precise drugs that target the neurodevelopmental characteristics of adolescence, and clinical drug use is often off-label, making it difficult to control the risks.

[0026] In response to the above-mentioned problems, the applicant of this application filed an invention patent on October 31, 2023, entitled "Application of Gut Microbiota Markers in the Diagnosis and Treatment of Autism," which discovered the microorganisms Bacteroides, Lachnoclostridium, and Parabacteroides. Bifidobacterium, Blautia, and Collinsella Related to autism.

[0027] While the aforementioned gut microbiota biomarkers can be used to assess the risk of autism in adolescents, the healthy human samples used are raw data downloaded from the NCBI database, which has the problem of insufficiently strict sample selection criteria. Therefore, it is necessary to improve these criteria to lay a good foundation for subsequent early warning and mechanism intervention.

[0028] This invention provides a microbial biomarker related to adolescent autism and its application. It not only has a larger sample size and stricter sample screening criteria, but also provides more accurate and representative test results. It can be used as a predictive factor for adolescent autism, thereby diagnosing whether a adolescent being tested has autism. Specifically, the technical concept of this invention is as follows: To address the clinical needs for the diagnosis and detection of autism in adolescents, this invention collects samples from adolescent autism patients and healthy individuals, and through a standardized experimental testing procedure (specific experimental methods are described in Examples 1-3), screens out six microorganisms associated with adolescent autism, including: Ruminococcus gnavus, Streptococcus thermophilus, Streptococcus salivarius, Intestinibacter bartlettii, Anaerostipes hadrus, and Faecalibacterium prausnitzii.

[0029] ROC curve analysis showed that the above six biomarkers have high specificity and sensitivity as detection variables, and these six bacterial species can be used as detection biomarkers for the prediction and diagnosis of adolescent autism.

[0030] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. The following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods not specifically described in the embodiments are generally performed under conventional conditions.

[0031] Example 1: Sample Collection

[0032] The sample sources and inclusion criteria for adolescents with autism are as follows: a. Age 4-10 years; b. Diagnosed with autism spectrum disorder by two or more child psychiatrists based on DSM-5 or ICD-11; c. Assessment results of the Diagnostic Observational Scale for Autism Spectrum II (ADOS-2) or the Diagnostic Interview-Revised Scale for Autism Spectrum (ADI-R) reaching the diagnostic threshold for autism spectrum disorder; d. Excluded subjects who do not meet the criteria, such as those with Rett syndrome, childhood disintegrative disorder, selective mutism, schizophrenia, bipolar disorder, substance abuse, serious physical illness, or who are pregnant or lactating.

[0033] Exclusion criteria for the autism group: a. Received systemic medication or physical therapy (such as transcranial magnetic stimulation) targeting core autism symptoms within 1 month prior; b. Taken antibiotics, probiotics, or prebiotics within the past 3 months; c. Suffers from any chronic disease that may significantly affect brain function or metabolism (including uncontrolled frequent seizures, liver or kidney dysfunction, severe endocrine disorders, etc.); d. Received medication affecting gastrointestinal motility within 1 week; e. History of functional dyspepsia, aerophagia, or abdominal migraine pain; f. Demonstrates growth retardation (e.g., height or weight below the 3rd percentile for age and sex); g. Suffers from gastrointestinal obstruction or stenosis; h. History of abdominal surgery, peptic ulcer, or inflammatory bowel disease in the family; i. Known presence of a specific genetic syndrome clearly associated with autism (e.g., Fragile X syndrome, tuberous sclerosis, 15q11-q13 repeat syndrome, etc.) that may introduce an independent pathogenic mechanism; j. The researchers deem the patient unsuitable for inclusion in this study.

[0034] The sample sources and inclusion criteria for healthy adolescents are as follows: a. Source: The selected candidates are from non-urbanized areas with stable living environments and no record of major infectious disease outbreaks in the past 5 years; the content of heavy metals and organic pollutants in the soil and water must meet the risk control requirements; areas with high-risk environmental factors such as industrial pollution sources and excessive use of agricultural chemicals are excluded.

[0035] b. Information solicitation: 2.1 Age 4-10 years old.

[0036] 2.2 Investigate family medical history. If there is no family medical history, pay special attention to hereditary diseases (including autism, intellectual disability, Fragile X syndrome, etc.), digestive system diseases, infectious diseases, and any family history of mental illness or tumors; confirm the average lifespan of the immediate family members of the target family for human gut microbiota screening.

[0037] 2.3 Investigate personal medical history: No history of any neurodevelopmental disorders (including autism spectrum disorder, attention deficit hyperactivity disorder, tic disorders, specific learning disabilities, intellectual developmental disorders, etc.), no gastrointestinal diseases (inflammatory bowel disease, irritable bowel syndrome, chronic constipation or diarrhea, malignant tumors or known polyposis, celiac disease, congenital or chronic liver disease, rectal bleeding, major surgery), no autoimmune diseases, atopic diseases (asthma, atopic dermatitis, eczema, eosinophilic gastrointestinal diseases), cardiovascular or metabolic diseases (such as diabetes, hypertension, heart disease, etc.), neurological diseases (anxiety disorders, multiple sclerosis, Parkinson's disease, etc.), immunosuppression, chronic pain, infectious diseases, community-acquired pneumonia, etc.

[0038] 2.4 The screening subjects should have good on-site language expression ability, mental vitality (no significant abnormalities in social communication or stereotyped behavior), no tattoos, punctures or injuries, and no history of blood transfusions or other high-risk behaviors.

[0039] c. Scale assessment: 3.1 Interviews with child psychiatrists or psychologists indicate that the selected individuals have normal psychological state and social behavior development, and no suspicious signs of autism.

[0040] 3.2 The scores of the Autism Spectrum Quotient (AQ) Adolescent Version, Social Response Scale (SRS), and behavioral assessment systems (such as BASC-3) are all within the normal range; at the same time, the scores of the Self-Rating Mental Health Scale (SCL-90), Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS), and Pittsburgh Sleep Quality Index (PSQI) are all normal.

[0041] d. Health check-up: 4.1 A comprehensive physical examination conducted by a professional medical examination institution is passed. Blood routine tests, liver and kidney function tests, hepatitis A / E tests, cytomegalovirus tests, EB virus (IgM + IgG), hepatitis B tests (HBsAg + anti-HBcore), HCV tests, hepatitis C tests, HIV tests (anti-HIV), syphilis tests, C13 breath tests, and endocrine indicator tests all meet relevant requirements. 4.2 Test the exons related to hereditary diseases of the target population, screen for single-gene hereditary diseases (pay special attention to known pathogenic genes related to autism such as CHD8, SHANK3, NLGN3, NRXN1, etc.), and exclude pathogenic and suspected pathogenic mutation sites.

[0042] 4.3 Sensitivity testing for common allergens. Screening subjects had no obvious history of allergic reactions and the test results showed no positive reaction to common allergens.

[0043] e. Stool testing: 5.1 The fecal characteristics of the subjects to be screened should conform to the Bristol fecal classification type III and type IV.

[0044] 5.2 Screening subjects should exclude those carrying pathogenic bacteria, drug-resistant bacteria, and potential pathogenic microorganisms. This includes Clostridium difficile, Salmonella (which easily causes bacterial gastroenteritis), Campylobacter, Yersinia, and Shiga toxin-producing Escherichia coli; antibiotic-resistant bacteria such as vancomycin-resistant enterococci (VRE), extended-spectrum β-lactamase (ESBL), and methicillin-resistant Staphylococcus aureus (MRSA); and viral pathogens such as norovirus (types I and II), enteroviruses, and hepatitis E virus.

[0045] 5.3 The feces of the selected subjects should be free of parasitic infections. Exclude parasites such as Clonorchis sinensis, Clonorchis sinensis, Balantidium coli, Hookworm, Giardia lamblia, Cyclospora cayetta, Trichodina, Strongyloides stercoralis, Intestinal nematodes, Meliostomatella asiatica, Taenia spp., Cryptosporidium spp., Ascaris spp., Entamoeba histolytica, and Entamoeba histolytica.

[0046] Exclusion criteria for healthy adolescents: a. Received any form of mental or psychological treatment (including behavioral intervention, drug treatment, etc.) within the past month; b. Taken antibiotics or probiotics / prebiotics within the past three months; c. Having any chronic illness, including neurobehavioral disorders (especially those with a history or diagnosis of autism, ADHD, Tourette syndrome, etc.); d. Having received medication that affects gastrointestinal motility within the past week; e. Having a history of functional dyspepsia, aerophagia, or abdominal migraine pain; f. Exhibiting growth retardation; g. Having gastrointestinal obstruction or stenosis; h. Having a history of abdominal surgery, peptic ulcer, or a family history of inflammatory bowel disease; i. Having a first-degree relative diagnosed with autism spectrum disorder or other pervasive developmental disorder; j. The researchers deem the subject unsuitable for inclusion in this study.

[0047] In summary, both the adolescents with autism and the healthy adolescents obtained written informed consent from themselves and their legal guardians. The fecal samples used in this invention were collected from Hubei Province, totaling 156 adolescents with autism and 153 healthy adolescents. The statistical results are as follows: .

[0048] Example 2: DNA extraction, library construction, and sequencing

[0049] 1. Use the Hi Pure Stool DNA Mini Kit to perform DNA extraction experiments on the collected intestinal samples.

[0050] 2. After extraction, the DNA concentration was detected using Qubit, and the integrity of the extracted genomic DNA was detected by 1.5% agarose gel electrophoresis. The extracted genomic DNA was then subjected to quality control, and qualified genomic DNA samples were selected (DNA concentration ≥20ng / μL, volume ≥20 μL, total amount ≥400 ng).

[0051] 3. For qualified DNA samples, random fragmentation, end repair, A base ligation, adapter and index are added. After adapter ligation, purification and library amplification are performed. After amplification, the DNA concentration is detected (DNA concentration ≥40 ng / μL).

[0052] 4. After the libraries pass the testing, different libraries are pooled according to the effective concentration and target data volume requirements before sequencing. The metagenomic sequencing platform is BGI T7, and the sequencing strategy is PE150.

[0053] Example 3: LEfSe analysis for screening microbial biomarkers

[0054] 1. Split the dataset KneadData software was used for quality control (based on Trimmomatic) and host removal (based on Bowtie2) of the raw data. Kraken2 alignment was used to calculate the sequence number of each species in the sample, and Bracken was used to estimate the actual abundance of species in the sample. 80% of the participants (including those in the disease and healthy groups) were randomly selected as the training set, and the remaining 20% ​​of the samples were used as the validation set. The abundance data of each sample in the training set were then analyzed using LEfSe software, with the default LDA Score filter value set to 2. The sample information is shown in Table 1. The results are as follows Figure 1 As shown, the researchers screened out five biomarkers that were significantly reduced in the autism group, including Streptococcus thermophilus, Anaerostipes hadrus, Ruminococcus gnavus, Faecalibacterium prausnitzii, and Intestinibacter bartlettii; and one biomarker that was significantly increased in the autism group, including Streptococcus salivarius.

[0055] Example 4: Verifying the reliability of the above 6 microbial biomarkers

[0056] 1. First, the remaining 20% ​​of the test subjects in Example 1 (including the adolescent autism group and the healthy group) were used as the validation set. The abundance data of each sample in the validation set were first subjected to binary logistic regression, and then the receiver operating function (ROC curve) was analyzed to obtain the cutoff value (optimal cutoff value).

[0057] 2. Use R language statistical software to calculate specificity and sensitivity and plot ROC curves. The software first calculates the threshold of the actual measurement value, and then calculates the number of true positive cases (TP), false positive cases (FP), true negative cases (TN), and false negative cases (FN) corresponding to the threshold. Specificity (true negative rate) = TN / (TN + FP) Sensitivity (true positive rate) = TP / (TP + FN) 3. The ROC curve can be constructed using 1 minus specificity and sensitivity. The integral of the ROC curve is the AUC. To calculate the specificity and sensitivity of a certain indicator, first calculate the Youden coefficient (Youden index = sensitivity + specificity - 1). The specificity and sensitivity corresponding to the maximum value of the Youden coefficient are the specificity and sensitivity of the certain indicator.

[0058] 4. The relative abundance values ​​of microbial biomarkers for single strains were directly analyzed using receiver operating characteristic (ROC) curve testing to determine the cutoff value. The ROC curve for predictive scoring is shown below. Figure 3 As shown in Table 2, the AUC, optimal cutoff value, sensitivity, and specificity of the predicted mimicry markers (markers formed by the combination of 6 single bacterial species) and individual bacteria are shown in Table 2.

[0059] .

[0060] According to Table 2 and Figure 3 As described, for single bacterial species, *Ruminococcus gnavus* had the highest AUC value (approximately 0.908), while *Corynebacterium salivarius* had the lowest (approximately 0.774). The combined AUC value of the four single bacterial species—*Ruminococcus gnavus*, *Streptococcus thermophilus*, *Streptococcus salivarius*, and *Intestinibacter bartlettii*—was 0.973. The AUC value of the mimicry marker (a marker formed by the combination of six single bacterial species) (approximately 0.963) was higher than that of the single bacterial species.

[0061] As can be seen from the above, one or more of the six newly discovered bacterial species in this application can be used as detection variables, and they all have high specificity and sensitivity. Moreover, the AUC of the six microbial markers is greater than 75%. Therefore, one or more of the six microbial markers can be used as detection markers for the diagnosis of adolescent autism.

[0062] Example 5: Establishing a Logistic Regression Model Based on the microbial biomarkers selected above and the relative abundance value of each metabolic biomarker, the binary logistic regression algorithm in RStudio software was used to calculate the first disease probability (also known as the logarithm of dominance y) for each sample. Then, the disease probability optimization formula Z=exp(y) / {1+exp(y)} was used to calculate the disease probability Z of the sample under test. Finally, this disease probability Z was compared with the actual disease status (e.g., severity) of each sample to verify the accuracy of the disease probability calculation equation. Specifically: a. Establishing a model Based on the biomarkers identified above, and considering the proportion of adolescents with autism and patients in the training set, the relative abundance values ​​of the six detected bacterial species were further used as single variables. The linear relationship between the relative abundance values ​​of the six single bacteria and the probability of disease in the samples was then discussed. The logarithm y (also known as the first probability value y) of the subject's dominance was calculated using a binary logistic regression equation. y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5+B6×x6 Where A is the intercept term, B1 to B7 are the regression coefficients of the independent variables; x1 is the relative abundance value of *Streptococcus salivarius*, x2 is the relative abundance value of *Streptococcus thermophilus*, x3 is the relative abundance value of *Anaerostipes hadrus*, x4 is the relative abundance value of *Ruminococcus gnavus*, x5 is the relative abundance value of *Faecalibacterium prausnitzii*, and x6 is the relative abundance value of *Intestinibacter bartlettii*.

[0063] b. Determine the values ​​of A and B1 to B7 above. After statistical analysis of the sample data, the values ​​were: A = -0.5175, B1 = 45.2045, B2 = -10.6914, B3 = -45.7213, B4 = -23.2247, B5 = -0.2721, and B6 = -27.796. At this point, after rearrangement, the formula for calculating the logarithm y of the dominance is: y=-0.5175+45.2045×X1-10.6914×X2-45.7213×X3-23.2247×X4-0.2721×X5-27.796×X6; c. Calculate the disease probability Z of the subjects to be tested. Substitute the first probability value y into the following formula to calculate the probability Z that the subject is a patient: Z = exp(y) / {1 + exp(y)}; where Z is the probability value that the subject is a patient, and exp(y) is the natural exponential function of the first probability value y.

[0064] After processing, the formula for calculating the probability Z of disease is:

[0065] d. Validation set data calculation and statistical analysis Based on the validation set data, the relative abundance of each single bacterial species in the disease group and the healthy group for each sample was obtained. Then, the first probability value y was obtained using the aforementioned binary logistic regression method. The probability Z of the test sample being a patient was then calculated using the formula. The results are shown in Tables 3 and 4, where "patient" refers to a patient with irritable bowel syndrome.

[0066] Table 4 shows the relative abundance mean and standard deviation for each bacterial species. The relative abundance mean determines the central location of the data distribution, while the standard deviation reflects the dispersion of the data relative to the mean. The p-value is a statistic calculated using the rank-sum test formula. The lower the p-value, the greater the difference between the disease group and the healthy group.

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] Note: In Table 3, E represents powers of 10. For example, 6.78E-05 means 6.78 * 10^6. -5 .

[0073]

[0074] Note: In Table 4, the mean refers to the relative abundance mean, and the standard deviation is similarly defined. E represents a power of 10. For example, 5.25E-10 means 5.25 * 10^10. -10 .

[0075] e. Results and Analysis Based on the results of Examples 4 and 5, it can be seen that for single bacterial species, *Ruminococcus virens* has the highest AUC value (approximately 0.908), while *Corynebacterium praecox* has the lowest AUC value (approximately 0.774). For mimicry markers, the AUC value is approximately 0.963, the optimal cutoff value is approximately 0.521, the sensitivity is approximately 0.935, and the specificity is 0.967.

[0076] The AUC of the six microbial biomarkers identified in this application is greater than 75%. One or more of the six microbial biomarkers can be used as detection biomarkers for the diagnosis of adolescent autism. At the same time, since this invention only requires the collection of fecal samples from the test subjects, this invention is not only completely non-invasive, but also highly accurate, and can be used to diagnose whether the test adolescents (fecal samples) have autism.

[0077] Based on the results obtained from the description in Table 3 above and the calculation formula for the probability of disease Z, it can be seen that the calculation formula for calculating the probability of disease in the sample to be tested, which is summarized in this application, is basically correct and can be used to diagnose the risk and probability of disease in the sample to be tested. The probability of disease in Table 3 above may not fully meet the diagnostic criteria. This is because the intestinal sample of the person to be tested may produce false positive or false negative results. Further testing using other methods is required, including blood routine tests, diagnostic physical signs, etc.

[0078] Example 6: Examining and verifying the probability of autism in patients and adolescents.

[0079] Based on the product and method of Example 5, the probability of autism in healthy individuals and adolescent patients with autism was examined and verified. The specific steps are as follows: S1. Collect intestinal samples from the individuals to be tested and detect the relative abundance of each individual strain in the intestine; among which, the individual strains include active rumenococcus gnavus, thermophilic streptococcus, salivarius streptococcus, Intestinibacter bartlettii, Anaerostipes hadrus, and Faecalibacterium prausnitzii; S2. Calculate the logarithm y of the strength of the object under test based on the binary logistic regression equation; y=A+B1×x1+B2×x2+B3×x3+B4×x4+B5×x5+B6×x6 Where A is the intercept term, B1 to B4 are the regression coefficients of the independent variables; x1 is the relative abundance value of Streptococcus salivarius, x2 is the relative abundance value of Ruminococcus gnavus, x3 is the relative abundance value of Anaerostipes hadrus, x4 is the relative abundance value of Intestinibacter bartlettii, x5 is the relative abundance value of Faecalibacterium prausnitzii, and x6 is the relative abundance value of Streptococcus thermophilus. S3. Calculate the probability Z of the subject being an adolescent with autism based on y, Z = exp(y) / {1 + exp(y)}; exp(y) is the natural exponential function of y; The probability of disease Z can also be expressed as:

[0080] S4. Based on the comparison between the patient's probability Z-score and the reference value, diagnose or predict the risk of the subject having adolescent autism.

[0081] In practice, a Z-score greater than 0.5 indicates a higher probability that the subject has adolescent autism; a Z-score less than 0.5 indicates a lower probability; and a Z-score of 0.5 suggests the subject may be a patient or an adolescent with autism, requiring further testing using methods such as mental health assessments and medication. Furthermore, the closer the Z-score is to 0.5, the more necessary it is to utilize additional testing methods.

[0082] It should be noted that although this embodiment only lists the methods and means of quantitative detection of the sample to be tested through relative abundance value, other means of quantitative detection of microorganisms are also feasible for the present invention (such as absolute abundance or total microbial load information, etc.), and can also be used to assist in the diagnosis of whether the sample to be tested has adolescent autism. People can choose the appropriate microbial quantitative detection means according to their own needs, which will not be elaborated here.

[0083] Example 7: Detection Reagent

[0084] Based on the description of embodiments 1 to 5 above, it can be seen that the biomarkers selected in this application have good predictive effects. Medical staff can use a single bacterial species as a biomarker to detect and diagnose the sample to be tested, so as to diagnose whether the sample to be tested has adolescent autism. Medical staff can also combine multiple single bacterial species together as biomarkers to detect and diagnose the sample to be tested, so as to diagnose whether the sample to be tested has adolescent autism.

[0085] Therefore, this embodiment also provides a reagent for detecting microbial markers, which can be used in the preparation of products for diagnosing adolescent autism to diagnose whether the sample to be tested has adolescent autism; at the same time, the microbial markers can be selected from the six single bacterial species related to adolescent autism discovered in this application, that is, the microbial markers in the detection reagent can include one or more of Ruminococcus gnavus, Streptococcus thermophilus, Streptococcus salivarius, Intestinibacter bartlettii, Anaerostipes hadrus, and Faecalibacterium prausnitzii.

[0086] Example 8: Reagent Kit

[0087] This embodiment also provides a reagent kit, which may contain the detection reagent described in Embodiment 7, to diagnose whether a sample to be tested has adolescent autism. The limitations and technical solutions of this application regarding the reagent kit can be found in the description of Embodiment 7 above, and will not be repeated here. Similarly, the above reagent kit can also be used in the preparation of products for detecting adolescent autism, and will not be repeated here.

[0088] Example 9: Products for diagnosing autism in adolescents

[0089] This application also provides a product for diagnosing adolescent autism, to diagnose whether a sample to be tested has adolescent autism; the product is specific to one or more of the six single bacterial species found in this application that are associated with adolescent autism, and the product includes primers, probes, antibodies, test strips, aptamers or chips, wherein primers, probes and antibodies can be used to make reagents and kits, which are conventional methods in the art and will not be described in detail here.

[0090] As can be seen from the descriptions of Examples 1-5 and Example 9, when one or more of the six bacterial species described in this application are used as detection markers (i.e., microbial markers) to diagnose whether a adolescent under test has autism, it is a technical solution that those skilled in the art can implement to produce corresponding reagents, test strips, aptamers, and chips. That is, reagents, test strips, aptamers, and chips can also be used to assist in the diagnosis of whether an adolescent under test has autism. People can choose appropriate products according to their own needs, which will not be elaborated here.

[0091] The method for diagnosing autism in adolescents is completely non-invasive and highly accurate. Using the six newly discovered single bacterial species as microbial biomarkers, the creation of corresponding, specific products (primers, probes, antibodies, aptamers, or chips, etc.) should be feasible for those skilled in the art and will not be elaborated upon here.

[0092] Example 10: Diagnosing whether a test sample is a adolescent with autism

[0093] If it is necessary to diagnose whether a person under testing is an adolescent with autism, in addition to conventional testing methods such as ultrasound examination, serum liver enzyme test, and liver tissue biopsy, medical staff can also use the methods or products described in Examples 1 to 11 above to diagnose the person under testing, in order to assist medical staff in making a more accurate judgment: (1) If, after continuous observation over multiple time periods, the content of active rumenococcus gnavus, Streptococcus thermophilus, Intestinibacter bartlettii, Anaerostipes hadrus, and Faecalibacterium prausnitzii is found to be high (compared to the mean and standard deviation of the autism group in Table 4), or even shows a significant increasing trend, then the adolescent being tested is more likely to be an autistic patient.

[0094] (2) If the content of Streptococcus salivarius in the test subject is found to be high after continuous observation over multiple time periods (compared with the mean and standard deviation of the healthy group in Table 4), then the test subject adolescent is more likely to be a healthy person.

[0095] (3) If, after observation over multiple consecutive periods, the person being tested does not exhibit the patterns described in (1) and (2) above, then medical staff can combine [the above information with further details]. Figure 1 According to Table 4, a preliminary judgment is made on whether the adolescent under test is autistic. (4) If medical staff want to more accurately determine whether the person to be tested is an adolescent autistic patient, they can calculate the probability that the adolescent to be tested is an autistic patient based on the quantitative detection results of 6 single bacterial species (such as relative abundance values) and the technical solutions described in Examples 5 to 7.

[0096] Conclusion and explanation: 1. By Figures 1-3 As shown in Table 2, any one of the six newly discovered single bacterial species in this application can be used as a microbial biomarker for adolescent autism. Each single bacterial species has sensitivity and specificity for adolescent autism. Therefore, the microbial biomarker for adolescent autism can be selected from any one or more of the six newly discovered single bacterial species in this application.

[0097] 2. As shown in Table 3, it is normal for only one or a few species of bacteria to be detected when testing intestinal samples. This is because there are individual differences. The probability Z of disease in this application is calculated. Therefore, even if a sample contains only a single species of bacteria, this application can still calculate the probability that the sample to be tested has adolescent autism.

[0098] 3. Predictive effect: The AUC value of the mimicry marker (a marker formed by the combination of 6 single bacterial species) (approximately 0.963) is higher than that of the single bacteria. The AUC value of the four single bacterial species Ruminococcus gnavus, Streptococcus thermophilus, Streptococcus salivarius, and Intestinibacter bartlettii combined together is 0.973.

[0099] 4. All six newly discovered bacterial species in this application can be used as detection variables. They all have high specificity and sensitivity, and the AUC of all six microbial markers is greater than 75%. One or more of the six microbial markers can be used as detection markers for the diagnosis of adolescent autism.

[0100] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the invention.

Claims

1. Use of a reagent for quantitatively detecting a microbial marker in the manufacture of a product associated with autism in adolescents, characterized in that, The microbial marker comprises Ruminococcus gnavus and / or Streptococcus thermophilus.

2. Use according to claim 1, characterized in that, The microbial marker further comprises Streptococcus salivarius and / or Intestinibacter bartlettii.

3. Use according to claim 1 or 2, characterized in that, The microbial marker further comprises Anaerostipes hadrus and / or Faecalibacterium prausnitzii.

4. Use according to claim 3, characterized in that, The microbial marker is a marker combination comprising Ruminococcus gnavus, Streptococcus thermophilus, Streptococcus salivarius, Intestinibacter bartlettii, Anaerostipes hadrus and Faecalibacterium prausnitzii.

5. The use according to claim 1, characterized in that, After continuous observation for multiple time periods, if it is found that the number of Streptococcus thermophilus and / or Ruminococcus gnavus in the fecal sample of the to-be-tested teenager shows a significant increasing trend, the to-be-tested teenager is more likely to be a healthy person; if it is found that the number of Streptococcus salivarius in the fecal sample of the to-be-tested teenager shows a significant increasing trend, the to-be-tested teenager is more likely to be a teenager with autism.

6. A microbial marker associated with autism in adolescents, characterized in that, The microbial marker comprises Anaerostipes hadrus, Streptococcus salivarius, Intestinibacter bartlettii and Faecalibacterium prausnitzii.

7. The microbial marker of claim 6, wherein, The microbial marker comprises Ruminococcus gnavus and / or Streptococcus thermophilus.

8. A product for diagnosing or predicting autism in a young child, characterized by: The product comprises one or more of reagents, test paper, aptamer and chip, the product is specific to the microbial marker of claim 6 or 7, and is used for quantitative detection of the microbial marker of claim 6 or 7.

9. A kit characterized in that: The kit contains reagents for quantitative detection of the microbial marker of claim 6 or 7. The kit contains reagents for quantitative detection of the microbial marker of claim 6 or 7.

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