Prenatal depression marker

Abstract

This invention relates to the field of microbial technology, and particularly to a biomarker for prenatal depression. The biomarker comprises gut microbiota, including *Clostridium*, *Enterobacter*, *Eubacterium*, *Taizei*, *UCG-005* (family Oscillatoriaceae), *Family XIII AD3011* (family Anaerobic Phagocytaceae), *Unclassified* (family Trichophytonceae), *UCG-004* (family Trichophytonceae), *Family XIII UCG-001* (family Anaerobic Phagocytaceae), and *Lactobacillus*. Quantitative detection of the content or activity level of this biomarker in pregnant women's fecal samples can effectively predict and screen for the risk of prenatal depression during pregnancy. It has advantages such as convenient operation, non-invasive safety, controllable cost, and low diagnostic bias. It can provide objective reference for the development of individualized and precise clinical intervention plans and open up new avenues for understanding the intrinsic molecular regulatory pathways of prenatal depression, demonstrating promising application prospects.

Description

Technical Field

[0001] This invention relates to the field of microbial technology, and more specifically, this application relates to a biomarker for prenatal depression. Background Technology

[0002] Prenatal depression is a common mental health disorder during pregnancy, seriously endangering maternal and infant health. The occurrence and development of prenatal depression in pregnant women are influenced by multiple complex factors, and currently there is no mandatory or recommended prenatal depression risk screening mechanism for pregnant women. Based on the "gut microbiota-gut-brain" axis theory proposed in recent years, the gut microbiota may participate in regulating host brain function and behavior by participating in a bidirectional communication system between the gut and brain; however, research on the mechanisms between gut microbiota and prenatal depression is very limited.

[0003] Therefore, research and utilization of gut microbiota characteristics in relation to prenatal depression are yet to be developed. Summary of the Invention

[0004] This invention aims to address some of the technical problems existing in the prior art by proposing a prenatal depression biomarker. It utilizes fecal samples from pregnant women to quantitatively detect the content or activity level of this biomarker, effectively enabling the prediction and screening of the risk of prenatal depression during pregnancy. This invention has the advantages of convenient operation, non-invasive safety, controllable economic costs, and low diagnostic bias. It can provide objective reference for the development of individualized and precise clinical intervention plans and also open up new avenues for understanding the intrinsic molecular regulatory pathways of prenatal depression, demonstrating promising application prospects.

[0005] It should be noted that this invention is based on the inventors' discovery and understanding of the following facts and problems: prenatal depression can pose adverse risks and threats to the physical and mental health of both pregnant women and fetuses, but currently there are no effective diagnostic mechanisms or methods. In view of this, the inventors of this application obtained stool samples from two groups of pregnant women, one with prenatal depression and the other without, and used 16S rRNA high-throughput sequencing technology to detect gut microbiota data. Based on differences in microbiota abundance, bacterial biomarkers were screened for the diagnosis of prenatal depression. The diagnostic model constructed by combining gut microbiota biomarkers with machine learning algorithms can further accurately assess the risk of prenatal depression in the tested individuals, providing an effective and reliable means for the screening and intervention of prenatal depression.

[0006] Firstly, this invention proposes a biomarker for prenatal depression. According to embodiments of the invention, the biomarker comprises gut microbiota, which includes at least one of the following: *Enterobacter*, *Eubacterium* siraeum group, *Oscillospiraceae* UCG-005, *Lachnospiraceae* UCG-004, *Family XIII* UCG-001, and *Lactonifactor*. By detecting the abundance of the above-mentioned microbiota biomarkers in fecal samples from pregnant women, the risk of prenatal depression in pregnant women can be effectively diagnosed, facilitating timely intervention and relief of prenatal depression, improving maternal and infant health, and providing precise intervention guidance for clinical practice. Furthermore, maternal samples are easy to collect, have low medical costs, and are suitable for widespread clinical application.

[0007] According to embodiments of the present invention, the marker further includes at least one of the following: Clostridium, Tyzzerella, Family_XIII_AD3011_group of anaerobic phagocytic bacteria, and norank_f_Lachnospiraceae.

[0008] In a second aspect, the present invention provides a kit comprising: a reagent for detecting the markers described in the first aspect.

[0009] Thirdly, the present invention proposes the application of reagents for detecting the biomarkers described in the first aspect in the preparation of a kit for predicting prenatal depression.

[0010] Fourthly, the present invention proposes the use of formulations that regulate the activity and / or abundance of the biomarkers described in the first aspect in the preparation of a drug for the treatment or relief of prenatal depression.

[0011] Fifthly, the present invention proposes a computer-implemented method for predicting the risk of prenatal depression. According to an embodiment of the present invention, the method includes: inputting abundance data of the biomarkers described in the first aspect in a biological sample to be tested into a prenatal depression diagnostic model to obtain a result indicating whether prenatal depression is present; and outputting a result indicating whether prenatal depression is present.

[0012] Sixthly, the present invention proposes an apparatus for diagnosing prenatal depression. According to an embodiment of the present invention, the apparatus includes: an acquisition unit for acquiring microbial data, the microbial data being derived from fecal samples of pregnant women with prenatal depression and / or pregnant women without prenatal depression; a detection unit for detecting the abundance of the biomarkers described in the first aspect; an analysis unit for obtaining a result indicating whether prenatal depression is present using a model obtained through the method for constructing a prenatal depression diagnostic model as described in the third aspect embodiment; and an output unit for outputting the analysis results of the analysis unit.

[0013] According to an embodiment of the present invention, the output result of the output unit includes the probability of having a risk of prenatal depression, wherein a probability > 0.5 indicates a risk of prenatal depression.

[0014] In a seventh aspect, the present invention provides an electronic device. According to an embodiment of this application, the electronic device includes: a processor and a memory; the memory for storing a computer program; and the processor for executing the computer program to implement the computer-implemented method for predicting the risk of prenatal depression as described in the fifth aspect.

[0015] Eighthly, the present invention provides a computer-readable storage medium. According to embodiments of this application, the computer-readable storage medium stores computer instructions or programs that, when executed on a computer, are used to implement the computer-implemented method for predicting the risk of prenatal depression as described in the fifth aspect.

[0016] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0017] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 To identify the different gut microbiota species between the prenatal depression group and the control group using the rank-sum test; Figure 2 The training and testing results of the diagnostic model constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) were evaluated. Figure 3 The training and testing results of the diagnostic model constructed using linear kernel support vector machine (linear kernel SVM); Figure 4 The training and testing results of the diagnostic model constructed using radial kernel support vector machine (radial kernel SVM) are presented. Figure 5 The training and testing results of the diagnostic model constructed using Random Forest (RF) are presented. Detailed Implementation

[0018] The embodiments of the present invention are described in detail below. These embodiments are exemplary and are only used to explain the present invention, and should not be construed as limiting the invention. Where specific techniques or conditions are not specified in the embodiments, they are performed according to the techniques or conditions described in the literature in the art or according to the product instructions. Reagents or instruments used, unless otherwise specified, are all commercially available conventional products.

[0019] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0020] In this embodiment of the invention, the terms "comprising" or "including" are open-ended expressions, meaning they include the content specified in this invention, but do not exclude other aspects.

[0021] In embodiments of the present invention, the terms “optionally,” “optionally,” or “optionally” generally refer to events or conditions described subsequently that may but may not occur, and the description includes both cases in which the event or condition occurs and cases in which the event or condition does not occur.

[0022] In this embodiment of the invention, the term "V3-V4 region" refers to the 3rd and 4th variable regions of the bacterial 16S rRNA gene, which are among the most commonly used molecular marker regions in microbial community structure analysis. The 16S rRNA gene has a total of 9 variable regions (V1-V9), and the V3-V4 region is a combination of these regions with moderate length and high variability, typically ranging from 400 bp to 500 bp, suitable for the sequencing length requirements of high-throughput sequencing platforms.

[0023] In this embodiment of the invention, the term "data sparsity" refers to the characteristic that the vast majority of elements in a dataset, matrix, or vector are zero, missing values, or default values, while only a few elements are non-zero or valid values.

[0024] In this embodiment of the invention, the term "sequencing depth" refers to the average number of times each base site of the genome or target region is covered by the sequencing read during the sequencing process. It is an important indicator for evaluating the amount of sequencing data and data reliability.

[0025] In this embodiment of the invention, the term "fixed random seed" refers to setting a fixed initial value (seed value) for a random number generator (RNG) in programming, data analysis, or machine learning, so that the random number generation process is repeatable and the results are reproducible. It ensures the reproducibility of the random process and can improve the reliability, verifiability, and debugging efficiency of data analysis.

[0026] In this embodiment of the invention, the term "CSS" refers to Accumulation and Standardization, which is a standardization method for microbial sequencing data.

[0027] In this embodiment of the invention, the term "operational taxonomic unit" is short for Operational Taxonomic Unit, abbreviated as OTU. It is an artificially defined clustering unit and statistical counting unit in microbiome research. It is usually generated based on the clustering of amplicon sequencing sequences such as 16S rRNA, ITS, and 18S rRNA according to a set similarity threshold. It is the smallest analytical unit commonly used in amplicon data analysis.

[0028] In this embodiment of the invention, the term "internal standard" is a pure substance with known concentration and properties that is close to the target analyte. It is quantitatively added at the very beginning of sample pretreatment to correct for pretreatment losses, injection errors, and instrument fluctuations, and is ultimately used to accurately calculate the true content of the analyte.

[0029] This application discloses a biomarker for prenatal depression, a reagent kit, preparation and application of the reagent kit, a method and device for predicting prenatal depression, and a device and medium, which will be described in detail below.

[0030] In one aspect of the invention, a biomarker for prenatal depression is proposed. According to an embodiment of the invention, the biomarker comprises gut microbiota, which includes at least one of the following: *Enterobacter*, *Eubacterium* siraeum group, *Oscillospiraceae* UCG-005, *Lachnospiraceae* UCG-004, *Family XIII* UCG-001, and *Lactonifactor*. Preferably, the markers include *Intestinibacter*, *Eubacterium* siraeum group, *Oscillospiraceae* UCG-005, *Lachnospiraceae* UCG-004, *Family XIII* UCG-001, and *Lactonifactor*.

[0031] According to embodiments of the present invention, the markers further include at least one of the following: Clostridium, Tyzzerella, Family_XIII_AD3011_group of anaerobic phagocytic bacteria, and norank_f_Lachnospiraceae.

[0032] In some embodiments, the markers include *Intestinibacter*, *Eubacterium* siraeum group, *Oscillospiraceae* UCG-005, *Lachnospiraceae* UCG-004, *Family XIII* UCG-001, *Lactonifactor*, *Clostridium*, *Tyzzerella*, *Family XIII* AD3011 group, and *norank* spp. of *Lachnospiraceae*.

[0033] It should be noted that the classification of each biomarker genus was obtained by high-throughput sequencing of 16S rRNA and then alignment and annotation based on the SILVA138.2 database. Among them, the operational taxonomic unit (OTU) of the genus [Eubacterium]_siraeum_group includes at least one of the sequences shown in SEQ ID NO: 1 or 2 or sequences with 97% similarity to the aforementioned sequences.

[0034] TGGGGGATATTGGACAATGGGGGCAACCCTGATCCAGCGACGCCGTGAGGGAAGAAGGTTTTCGGATTGTAAACCTCTGTTGACGGAGAAAAAAATGATGGTATCCGTTTAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGTGTAGGC GGGATATCAAGTCAGAAGTGAAAATTACGGGCTCAACTCGTAACCTGCTTTTGAAACTGACATTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTTACTGACGCTGAGGCTCGAAAGCGTGGGGAGCAAACAGG (SEQ ID NO: 1).

[0035] TGGGGGATATTGGACAATGGGGGAAACCCTGATCCAGCGATGCCGCGTGAGGGAAGAAGGTTTTCGGATTGTAAACCTCTGTGGACAGGGACGATAATGACGGTACCTGTTTAGAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCGAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGTGTAGGCGGGAAGGCAAGTCAGAAGTGAAAATTATGGGCTTAACCCATAACCTGCTTTTGAAACTGTTTTTCTTGAGTGAGGCAGAGGCAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCCTTTACTGACGCTGAGGCTCGAAAGCGTGGGGAGCAAACAGG (SEQ ID NO: 2).

[0036] The operational taxonomic unit (OTU) of the Oscillospiraceae_UCG-005 genus in the Oscillospiraceae family comprises at least one of the sequences shown in SEQ ID NO: 3, 4, 5, 6, 7, 8, 9, 10 or 11, or sequences having 97% similarity to the aforementioned sequences.

[0037] TGGGGAATATTGGGCAATGGGGGAAACCCTGACCCAGCAACGCCGCGTGAAGGAAGAAGGCTTTCGGGTTGTAAACTTCTTTTACCAGGGACGAAGAAAGTGACGGTACCTGGAGAAAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGAGATGCAAGTCAGATGTGAAATCCTCGGGCTTAACCCGGGAACTGCATTTGAAACTGTATCCCTTGAGTATCGGAGAGGCAGGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG (SEQ ID NO: 3).

[0038] TGGGGAATATTGGGCAATGGGGGAAACCCTGACCCAGCAACGCCGCGTGAGGGAAGAAGGTCTTCGGATTGTAAACCTCTTTTACCAGGGAAGAAGAAAGTGACGGTACCTGGAGAAAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGGAAGACAGGTCAGATGTGAAATGCCGGGGCTCAACTCCGGAGCTGCATTTGAAACCGTTTTTCTTGAGTATCGGAGAGGCAGGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:4)。

[0039] TGGGGAATATTGGGCAATGGGGGAAACCCTGACCCAGCAACGCCGCGTGAAGGAAGAAGGCTTTCGGGTTGTAAACTTCTTTTACCAGGGACGAAACAAATGACGGTACCTGGAGAAAAAGCTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGAGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGAGATGCAAGTCAGATGTGAAATCTGGGGGCTCAACCCCCAAACTGCATTTGAAACTGTATCCCTTGAGTATCGGAGAGGCAGGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:5)。

[0040] TGGGGAATATTGGGCAATGGGGGAAACCCTGACCCAGCAACGCCGCGTGAAGGAAGAAGGCCTTCGGGTTGTAAACTTCTTTTACCAGGGACGAAGGACGTGACGGTACCTGGAGAAAAAGCAACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTTGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGAGATGCAAGTTGGGAGTGAAATCCATGGGCTCAACCCATGAACTGCTCTCAAAACTGTATCCCTTGAGTATCGGAGAGGCAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:6)。

[0041] TGGGGAATATTGGGCAATGGGGGAAACCCTGACCCAGCAACGCCGCGTGAAGGAAGAAGGCCTTCGGGTTGTAAACTTCTTTTAGCAGGGACGAAGGACGTGACGGTACCTGCAGAAAAAGCAACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTTGCAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGCGTGTAGGCGGAGCTGCAAGTCAGATGTGAAATCCCGGGGCTCAACCCCGGAACTGCATTTGAAACTGTAGCCCTTGAGTATCGGAGAGGCAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:7)。

[0042] TGGGGAATATTGGGCAATGGGGGAAACCCTGACCCAGCAACGCCGCGTGAAGGAAGAAGGCCCTCGGGTTGTAAACTTCTTTTACCAGGGACGAAGGAAGTGACGGTACCTGGAGAAAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGGACTGCAAGTCTGATGTGTAATCTGGTGGCTCAACCACCAAACTGCATTGGAAACTGTAGTTCTTGAGTATCGGAGAGGCAGGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGGCCTGCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:8)。

[0043] TGGGGAATATTGGGCAATGGGGGAAACCCTGACCCAGCAACGCCGCGTGAAGGAAGAAGGCCTTCGGGTTGTAAACTTCTTTTACCAGGGACGAAGGACGTGACGGTACCTGGAGAAAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGAGCTGCAAGTCAGATGTGAAATCTCCGGGCTTAACCCGGAAACTGCATTTGAAACTGTAGTCCTTGAGTATCGGAGAGGCAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:9)。

[0044] TGGGGAATATTGGGCAATGGGCGCAAGCCTGACCCAGCAACGCCGCGTGAAGGAAGAAGGCTTTCGGGTTGTAAACTTCTTTTACCAGGGACGAAGGACGTGACGGTACCTGGAGAAAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGGAGAGCAAGTCAGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAGGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:10)。

[0045] TGGGGAATATTGGGCAATGGGGGAAACCCTGACCCAGCAACGCCGCGTGAAGGAAGAAGGCTTTCGGGTTGTAAACTTCTTTTACCAGGGACGAAGGACGTGACGGTACCTGGAGAAAAAGCCACGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGAGAAGCAAGTCAGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTTGAAACTGTTTCCCTTGAGTATCGGAGAGGCAGGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:11)。

[0046] The operational taxonomic unit (OTU) of the genus UCG-001 of family XIII of the family Anaerobic phagocytic bacteria includes at least one of the sequences shown in SEQ ID NO: 12, 13, 14, 15, 16, 17, 18 or 19 or sequences having 97% similarity to the aforementioned sequences.

[0047] TGGGGAATATTGCACAATGGGCGAAAGCCTGATGCAGCAACGCCGCGTGAAGGAAGAAGGTCTTCGGATCGTAAACTTCTGTCCTATGGGAAGATAGTGACGGTACCATAGGAGGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGAGTGCGTAGG TGGTAACTTAAGCGCGGGGTTTAAGGCAATGGCTTAACCATTGTTCGCCCTGCGAACTGGGTTACTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGAAACTGACACTGAGGCACGAAAGTGTGGGGAGCAAACAGG (SEQ ID NO: 12).

[0048] TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCAACGCCGCGTGAGTGAAGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGATAGTGACGGTACCTTAGGAGCAAGTCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGAGTACGTAGGTGGTTTTCTAAGCACGGGGTTTAAGGCAATGGCTTAACCATTGTTCGCCTTGTGAACTGGAAGACTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACACTGAGGTACGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:13)。

[0049] TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCAACGCCGCGTGAAGGAAGAAGGCCTTTGGGTCGTAAACTTCTGTTCTAAGGGAAGATAGTGACGGTACCTTAGGAGAAAGTTCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGTACGTAGGTGGTTTCTTAAGCGCAGGGTATAAGGCAATGGCTTAACCATTGTTCGCCTTGCGAACTGGGGGACTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACACTGAGGTACGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:14)。

[0050] TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCAACGCCGCGTGAAGGAAGAAGGCCTTTGGGTCGTAAACTTCTGTTCTAAGGGAAGATAATGACGGTACCTTAGGAGCAAGTCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGAGTACGTAGGTGGTTACCTAAGCACGAGGTATAAGGCAATGGCTTAACCATTGTTCGCCTTGTGAACTGGGCTACTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:15)。

[0051] TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCAACGCCGCGTGAAGGATGAAGGCCTTTGGGTCGTAAACTTCTGTTCTAAGGGAAGATAGTGACGGTACCTTAGGAGCAAGTCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGAGTACGTAGGTGGTTTTCTAAGCACGGGGTTTAAGGCAATGGCTTAACCATTGTTCGCCTTGTGAACTGGAAGACTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACACTGAGGTACGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:16)。

[0052] TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCAACGCCGCGTGAAGGATGAAGGCCCTTGGGTCGTAAACTTCTGTTCTGGGGGAAGATAGTGACGGTACCTTAGGAGCAAGTCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGAGTACGTAGGTGGTTACCTAAGCAAGGGGTTTAAGGCAATGGCTTAACTATTGTTCGCCCCTTGAACTGGGCTACTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACACTGAGGTACGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:17)。

[0053] TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCAACGCCGCGTGAAGGATGAAGGCCTTTGGGTCGTAAACTTCTGTTCTAAGGGAAGATAATGACTGTACCTTAGGAGCAAGTCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGTACGTAGGTGGTTACCTAAGCACAGGGTATAAGGCAATAGCTTAACTATTGTTCGCCTTGTGAACTGGGCTACTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACACTGAGGTACGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:18)。

[0054] TGGGGAATATTGCACAATGGGCGAAAGCCTGATGCAGCAACGCCGCGTGAAGGAAGAAGGTCTTCGGATCGTAAACTTCTGTCCTTGGGGAAGATAATGACGGTACCCTTGGAGGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGAGTGCGTAGGTGGTTACCTAAGCAGGGGGTGAAAGGCACTGGCTTAACCAATGTCAGCCCCCTGAACTGGGTACCTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTTACTGACACTGAGGCACGAAAGTGTGGGGAGCAAACAGG (SEQ ID NO: 19).

[0055] The operational taxonomic unit (OTU) of the Family_XIII_UCG-001 genus belonging to the Anaerovoracaceae family comprises at least one of the sequence set forth in SEQ ID NO: 20 or a sequence having 97% similarity to the aforementioned sequence.

[0056] TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCAACGCCGCGTGAAGGAAGAAGGCCTTTGGGTCGTAAACTTCTGTTCTGAGGGAAGAAGAAAGTGACGGTACCTCAGGAGAAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGAGTACGTAGGCGGTTTGCTAAGCGCAAGGTGAAAGGCAGTGGCTTAACCATTGTAAGCCTTGCGAACTGACAGACTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCGGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGTACGAAAGCGTGGGGAGCAAACAGG (SEQ ID NO: 20).

[0057] Operational taxonomic units (OTUs) of the genus Lachnospiraceae_UCG-004 include at least one of the sequences shown in SEQ ID NO: 21, 22 or 23 or sequences having 97% similarity to the aforementioned sequences.

[0058] TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCGACGCCGTGAAGGAAGAAGTATCTCGGTATGTAAACTTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGAC GGCGACGCAAGTCTGAAGTGAAATACCCGGGCTCAACCTGGGAACTGCTTTGGAAACTGTGTTGCTAGAGTGCTGGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG (SEQ ID NO: 21).

[0059] TGAGGAATATTGGTCAATGGACGAGAGTCTGAACCAGCCAAGTAGCGTGAAGGATGACTGCCCTATGGGTTGTAAACTTCTTTTATACGGGAATAAAGTGAGGCACGTGTGCCTTTTTGTATGTACCGTATGAATAAGGATCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCAAGGCAAGTCTGATGTGAAAACCCAGGGCTTAACCCTGGGACTGCATTGGAAACTGTCTGGCTCGAGTGCCGGAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:22)。

[0060] TGGGGGATATTGCACAATGGAGGAAACTCTGATGCAGCGACGCCGCGTGAGTGAAGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGACGCAAGTCTGAAGTGAAATACCCGGGCTCAACCTGGGAACTGCTTTGGAAACTGTGTTGCTAGAGTGCTGGAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG(SEQ ID NO:23)。

[0061] The operational taxonomic units (OTUs) of the unclassified genus in the family Lachnospiraceae (norank_f_Lachnospiraceae) include at least one of the sequences set forth in SEQ ID NO: 24, 25 or 26, or sequences having 97% similarity to the aforementioned sequences.

[0062] TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCAACGCCGCGTGAAGGAAGACGGTTTTCGGATTGTAAACTTCTATCAATAGGGACGAAGAAAGTGACGGTACCTAAATAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGAATTACTGGGTGTAAAGGGTGAGTAGGCGGCGTGACAAGTAAGATGTGAAAGCCCGTGGCTTAACCACGGGATTGCATTTTAAACTGTTGAGCTAGAGTACAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGAAACTGACGCTGAGGCACGAAAGCGTGGGGAGCAAACAGG (SEQ ID NO: 24).

[0063] TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCAACGCCGCGTGAGTGAAGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAGTGACGGTACCTGACTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCACAGCAAGTCTGAAGTGAAATCCCCGGGCTCAACCCGGGAACTGCTTTGGAAACTGCTGGGCTGGAGTGCTGGAGAGGCAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG (SEQ ID NO: 25).

[0064] TGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCAACGCCGCGTGAGTGAAGAAGTATTTCGGTATGTAAAGCTCTATCAGCAGGGAAGAAAATGACGGTACCTGACTAAGAAGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGAC GGCGAAGCAAGTCTGAAGTGAAAGCCCGGGGCTTAACCCCGGGACTGCTTTGGAAACTGTTTTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACTGAAACTGACACTGAGGCACGAAAGCGTGGGGAGCAAACAGG (SEQ ID NO: 26).

[0065] It should be noted that in the SILVA 138.2 database, both the genus *norank_f__Lachnospiraceae* and the genus *Lachnospiraceae_UCG-004* belong to the phylum Firmicutes, class Clostridia, order Lachnospirales, and family Lachnospiraceae, but are different taxonomic units. *Norank_f__Lachnospiraceae* is a recognized and standardized taxonomic label; it belongs to the family Lachnospiraceae but cannot be matched with any known genera within Lachnospiraceae in existing databases. These microorganisms constitute a taxonomically defined, reproducibly identifiable cluster with stable taxonomic boundaries and biological characteristics. It represents a highly sequence-homologically related microbial group, not an arbitrary or uncertain microorganism. *UCG-004* is a specific genus within the family Lachnospiraceae. "UCG" is an abbreviation for "Uncultured Genus". It is a genus of bacteria that has been discovered, has unique sequence characteristics, and belongs to an independent evolutionary branch, but has not yet been successfully isolated and cultured in pure form.

[0066] In another aspect of the invention, a kit is provided comprising reagents for detecting the aforementioned biomarkers. In some embodiments, the aforementioned reagents include reagents for detecting antibodies, mass spectrometry, chromatography, PCR, and enzyme-linked immunosorbent assay (ELISA).

[0067] In some embodiments, the kit further includes at least one of sample pretreatment reagents, internal standards, and data analysis software. It should be noted that the pretreatment reagents include, but are not limited to, at least one of the following: sample preservation reagents, bacterial lysis reagents, nucleic acid extraction and purification reagents, PCR library preparation reagents, and inhibitor removal reagents. Exemplarily, sample preservation reagents include sterile physiological saline, 70% anhydrous ethanol, or PBS buffer; bacterial lysis reagents include CTAB buffer, lysozyme, or proteinase K; nucleic acid extraction and purification reagents include anhydrous ethanol, sodium acetate, phenolform isoamyl alcohol, universal column extraction reagents, or magnetic bead extraction reagents; PCR library preparation reagents include RNA-degrading enzymes, nucleic acid separation electrophoresis reagents, or nucleic acid quantitative buffers; and inhibitor removal reagents include polyvinylpyrrolidone or bovine serum albumin.

[0068] In another aspect of the invention, the invention proposes the use of reagents for detecting the aforementioned biomarkers in the preparation of a kit for predicting prenatal depression. In some embodiments, the aforementioned reagents include reagents for detecting antibodies, mass spectrometry, chromatography, PCR, and enzyme-linked immunosorbent assay (ELISA).

[0069] In another aspect of the invention, the invention proposes the use of formulations that regulate the activity and / or abundance of the aforementioned biomarkers in the preparation of a medicament for the treatment or relief of prenatal depression.

[0070] In some embodiments, the formulations that modulate the activity and / or abundance of the aforementioned biomarkers include: upregulating Clostridium, Intestinibacter, Eubacterium siraeum group, Oscillospiraceae UCG-005, norank f Lachnospiraceae, and family XIII of the anaerobic phagocytic bacteria family UCG. A formulation containing the activity and / or abundance of at least one genus of bacteria in Family_XIII_UCG-001, and / or a formulation that downregulates the activity and / or abundance of at least one genus of bacteria in the family Tyzzerella, Family_XIII_AD3011_group of anaerobic phagocytic bacteria, family Lachnospiraceae_UCG-004, and genus Lactonifactor.

[0071] In some embodiments, the medicament further includes pharmaceutically acceptable excipients, carriers, or mediators. Pharmaceutically acceptable excipients, carriers, or mediators may include any solvent, including but not limited to pharmaceutically acceptable carriers, stabilizers, dispersants, solubilizers, plasticizers, solid excipients, diluents, or other liquid excipients, etc., suitable for a specific target dosage form. The use of any conventional excipients is also within the scope of consideration for this application, except for any range of incompatibilities with the compounds, pharmaceutical compositions, or medicaments containing them, such as any adverse biological effects or harmful interactions with any other component of the pharmaceutically acceptable composition.

[0072] In another aspect, the present invention proposes a computer-implemented method for predicting the risk of prenatal depression. According to an embodiment of the invention, the method includes: inputting abundance data of the aforementioned biomarkers in a biological sample to be tested into a prenatal depression diagnostic model to obtain a result indicating whether prenatal depression is present; and outputting a result indicating whether prenatal depression is present.

[0073] In some embodiments, the abundance data of the biomarkers includes stool samples from pregnant women with prenatal depression and / or those without prenatal depression. The prenatal depression diagnostic model is constructed by using the abundance of biomarkers in the pregnant woman's stool samples as features and whether the pregnant woman has prenatal depression as a label to obtain the diagnostic model. Exemplarily, the prenatal depression diagnostic model is constructed based on machine learning algorithms, including at least one of the following: Least Absolute Shrinkage and Selection Operator (LASSO), Linear Kernel Support Vector Machine (Linear Kernel SVM), Radial Kernel Support Vector Machine (Radial Kernel SVM), and Random Forest (RF).

[0074] In some embodiments, the input data of the prenatal depression diagnostic model is preprocessed. Exemplarily, the preprocessing includes: screening the input data, with screening criteria including: 1) removing bacterial genera not detected in 10% or less of the samples to eliminate noise and reduce data sparsity; 2) using CSS to standardize the characteristic bacterial genera data to eliminate the influence of differences in sequencing depth between samples.

[0075] In some embodiments, after screening out bacterial communities with different abundances, the data is standardized by log2(x+1) transformation to reduce data skewness and improve data normality.

[0076] In some embodiments, the Wilcoxon rank-sum test is used to identify differentially expressed bacterial genera associated with prenatal depression, which are then used to construct a prenatal depression diagnostic model or to predict prenatal depression based on the constructed prenatal depression diagnostic model.

[0077] In some embodiments, the prenatal depression diagnostic model is constructed using a training set and validated using a test set, including: setting a fixed random seed and dividing the selected data into a training set and a test set in an 8:2 ratio. The training set comprises 80% of the differentially expressed bacterial data, and the test set comprises 20% of the differentially expressed bacterial data.

[0078] In one aspect of the invention, an apparatus for diagnosing prenatal depression is provided. According to an embodiment of the invention, the apparatus includes: an acquisition unit for acquiring microbial data from fecal samples of pregnant women with prenatal depression and / or those without; a detection unit for detecting the abundance of the aforementioned biomarkers; an analysis unit for using a prenatal depression diagnostic model to determine whether prenatal depression is present; and an output unit for outputting the analysis results from the analysis unit.

[0079] According to an embodiment of the present invention, the output result of the output unit includes the probability of having a risk of prenatal depression, and a probability > 0.5 indicates that there is a risk of prenatal depression.

[0080] In another aspect, the present invention provides an electronic device. According to an embodiment of the invention, the electronic device includes: a processor and a memory; the memory for storing a computer program; and the processor for executing the computer program to implement the method for predicting the risk of prenatal depression as described above.

[0081] In some embodiments of this application, the aforementioned processor may include, but is not limited to: General-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0082] In some embodiments of the present invention, the aforementioned memory includes, but is not limited to: Volatile memory and / or non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0083] In another aspect, the present invention provides a computer-readable storage medium. According to embodiments of the invention, the computer-readable storage medium stores computer instructions or programs that, when executed on a computer, are used to implement the method for predicting the risk of prenatal depression as described above.

[0084] The present invention will be explained below with reference to embodiments. Those skilled in the art will understand that the following embodiments are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0085] Example 1: Screening of gut biomarkers for prenatal depression In this embodiment, markers are screened according to the following steps: (1) Sample collection Inclusion criteria were as follows: 1) Age ≥ 20 years; 2) Pregnant women residing in Wuhan and in late pregnancy; 3) Self-reported absence of cognitive impairment. Exclusion criteria were as follows: 1) History of mental illness; 2) History of cold and fever within one week prior to assessment and sample collection. Based on these criteria, a total of 353 pregnant women in late pregnancy were ultimately included.

[0086] The Edinburgh Postnatal Depression Scale (EPDS) was used to assess prenatal depression in pregnant women in late pregnancy. Developed by Cox, the EPDS is widely used for screening for prenatal depression. This self-report scale consists of 10 items, each rated on a 4-point scale (0-3 points), with a total score of 0-30 points and a cutoff of 10 points. Pregnant women with an EPDS score ≥10 were included in the prenatal depression group, while those with a score <10 were included in the control group. Stool samples were collected from pregnant women in late pregnancy and stored at -80 ℃.

[0087] (2) Microbial community detection The gut microbiota of pregnant women in late pregnancy was determined using 16S rRNA high-throughput sequencing technology. The specific measurements are as follows: 1) Total DNA extraction Sample preparation: Add 0.5 g fecal sample, 250 mg magnetic beads and 700 µL SLX solution to a 2 mL centrifuge tube and homogenize at 45 Hz for 5 min to fully break the sample.

[0088] Lysis and incubation: Add 70 µL DS Buffer, gently invert to mix, and incubate the centrifuge tube in a 70 °C water bath for 10 min, followed by incubation at 95 °C for 2 min to fully lyse the cells and release DNA.

[0089] Centrifugation and transfer: Centrifuge at 13000 g for 5 min at room temperature, transfer 500 µL of supernatant to a new 1.5 mL centrifuge tube and discard the precipitate.

[0090] Magnetic bead treatment: Add 170 µL P2 Buffer and 170 µL HTR, gently invert to mix, and incubate in an ice bath (-20 °C) for 5 min; centrifuge at 13000 rpm for 5 min at room temperature, and transfer 450 µL of supernatant to a new 1.5 mL centrifuge tube; add 450 µL XP5 Buffer and 40 µL magnetic beads, and invert to mix for 8 min; place the mixture on a magnetic rack for adsorption, discard the residue, remove the centrifuge tube, add 500 µL XP5 Buffer and mix; place on a magnetic rack for adsorption again, discard the residue, remove the centrifuge tube, add 600 µL PHB and mix; repeat the magnetic rack adsorption step, discard the residue, remove the centrifuge tube, add 600 µL SPW and mix; perform the fourth magnetic rack adsorption, discard the residue, remove the centrifuge tube, add 600 µL SPW and mix; repeat the magnetic rack adsorption step for the fifth time, discard the residue, and centrifuge at 13000 rpm at room temperature. The centrifuge tube was run at rpm for several seconds; it was placed on the magnetic rack for adsorption for the sixth time, and the residual liquid was removed using a 10 µL pipette tip. It was then air-dried for 8 min; 100 µL of solution buffer was added and mixed well, and the mixture was allowed to stand at room temperature for 5 min; it was placed on the magnetic rack for adsorption for the last time, and the supernatant was transferred to a new 1.5 mL centrifuge tube to obtain total genomic DNA.

[0091] 2) DNA testing The purity and concentration of DNA were determined using a NanoDrop2000 spectrophotometer, and DNA integrity was assessed by 1% agarose gel electrophoresis at 5 V / cm for 20 min.

[0092] 3) PCR amplification The V3-V4 region of DNA was amplified using specific primers. The upstream primer sequence 338F is shown in SEQ ID NO: 27, and the downstream primer sequence 806R is shown in SEQ ID NO: 28. (This primer pair is referenced from: Liu C, Zhao D, Ma W, et al. Denitrifying sulfide removal process on high-salinity wastewaters in the presence of Halomonas sp. Applied Microbiology and Biotechnology, 2016, 100(3): 1421-1426.) ACTCCTACGGGAGGCAGCA (SEQ ID NO: 27); GGACTACHVGGGTWTCTAAT (SEQ ID NO: 28).

[0093] The PCR amplification system had a total volume of 20 µL, including: 4 µL of 5× FastPfu buffer, 2 µL of 2.5 mmol / L dNTPs, 0.8 µL each of 5 mmol / L upstream and downstream primers, 0.4 µL of FastPfu DNA polymerase, 0.2 µL of BSA, 10 ng of genomic DNA, and ddH2O added to bring the volume to 20 µL.

[0094] PCR amplification conditions: initial denaturation at 95°C for 3 min; 30 amplification cycles, each cycle consisting of 30 s of denaturation at 95°C, 30 s of annealing at 55°C, and 45 s of extension at 72°C; final extension at 72°C for 10 min; storage at 10°C after completion.

[0095] 4) Identification, purification and quantification of PCR products Three PCR replicates were set up for each sample. The PCR products from the three replicates were mixed and detected by 2% agarose gel electrophoresis. The qualified PCR products were purified using a PCR purification kit to remove unbound primers, dNTPs, enzymes and other impurities. The purified PCR products were quantified using a Quantus™ Fluorometer instrument. According to the sequencing requirements, the quantified PCR products were mixed in the appropriate proportions for subsequent sequencing.

[0096] 5) Constructing PE libraries and performing Illumina sequencing Library construction using the NEXTFLEX® Rapid DNA-Seq Kit involves the following steps: first, adapter ligation is performed, followed by screening with magnetic beads to remove self-ligated adapter fragments; then, the library template is enriched using PCR amplification technology, and finally, the PCR products are recovered using magnetic beads to obtain the final library.

[0097] Sequencing was performed using the PE300 platform. The specific steps included: one end of the DNA fragment was paired and fixed with a linker embedded in the chip, while the other end was randomly paired with another embedded linker nearby to form a "bridge" structure; DNA clusters were generated by PCR amplification, and then the DNA amplicon was linearized into single strands; a modified DNA polymerase and dNTPs with four fluorescent labels were added, synthesizing only one base per cycle; the surface of the reaction plate was scanned with a laser to read the type of nucleotides polymerized in the first round of the reaction for each template sequence; the "fluorescent group" and "terminator group" were removed by chemical cutting to restore the 3' end adhesion and continue polymerizing the next nucleotide; finally, the sequence information of the template DNA fragment was determined by statistically analyzing the fluorescence signal results of each round.

[0098] (3) Clustering and annotation of gut microbiota The raw sequencing sequences were quality controlled using Fastp software and assembled using Flash software. OTU clustering was performed using USEARCH 11 software with the uparse algorithm based on 97% similarity (see: http: / / drive5.com / uparse / for details). To obtain the species classification information corresponding to each OTU, the RDPclassifier Bayesian algorithm was used to perform taxonomic comparison between representative OTU sequences with 97% similarity and the SILVA 138.2 database to obtain the specific genus and species annotations.

[0099] (4) Screening of differentially expressed microbiota Using the Wilcoxon rank-sum test, we screened out characteristic gut microbiota genera that showed significant differences between pregnant women with and without prenatal depression, thus obtaining biomarkers for prenatal depression.

[0100] Reference Appendix Figure 1As shown, the rank-sum test indicated that, compared with the non-pregnant depression pregnant women (control group), the abundance of Tyzzerella, Family XIII AD3011 group of anaerobic phagocytic bacteria, Lachnospiraceae UCG-004, and Lactonifactor was increased in the prenatal depression group, while the abundance of Clostridium, Intestinibacter, [Eubacterium] siraeum group, Oscillospiraceae UCG-005, norank f Lachnospiraceae, and Family XIII UCG-001 was decreased.

[0101] Example 2: Construction and Validation of a Diagnostic Model for Prenatal Depression 1. The 353 pregnant women in late pregnancy from Example 1 were randomly divided into a training set and a test set in a ratio of 8:2. 80% of the training set (283 pregnant women in late pregnancy) was used to train the model, and 20% of the test set (70 pregnant women in late pregnancy) was used to evaluate the model's performance.

[0102] 2. Data Preprocessing The abundance data of the 10 biomarker bacterial communities in Example 1 were preprocessed, including: 1) removing bacterial genera that were not detected in 10% or less of the samples; 2) standardizing the characteristic bacterial genera data using CSS; and 3) standardizing the data using log2(x+1) transformation.

[0103] 2. Model Building and Validation Reference Appendix Figures 2-5 As shown, four machine learning methods were used to construct a prenatal depression diagnostic model: Least Absolute Contraction and Selection Operator (LASSO), Linear Kernel Support Vector Machine (Linear Kernel SVM), Radial Kernel Support Vector Machine (Radial Kernel SVM), and Random Forest (RF). The predictive efficacy of the models was evaluated using Receiver Operating Characteristic (ROC) curves and the Area Under the ROC Curve (AUC). 1-Specificity represents the false positive rate, which is the proportion of samples that were actually negative but were misclassified as positive out of all actual negative samples; Sensitivity refers to the proportion of correctly identified positive samples out of all actual positive samples.

[0104] Reference Appendix Figure 2As shown, in the diagnostic model based on LASSO analysis, seven bacterial genera—Intestinibacter, Tyzzerella, UCG_005, norank_f__Lachnospiraceae, Lachnospiraceae_UCG_004, Family_XIII_UCG_001, and Lactonifactor—were ultimately included in the diagnostic model. The AUC value for the training set was 0.64, with a 95% confidence interval of 0.58–0.71, and the AUC value for the test set was 0.78, with a 95% confidence interval of 0.65–0.91.

[0105] Reference Appendix Figure 3 As shown, in the diagnostic model based on linear kernel SVM, all 10 bacterial genera were included in the model. The AUC value of the training set was 0.55, with a 95% confidence interval of 0.48-0.62, and the AUC value of the test set was 0.62, with a 95% confidence interval of 0.47-0.77.

[0106] Reference Appendix Figure 4 As shown, in the diagnostic model based on radial kernel SVM, all 10 bacterial genera were included in the model, with the training set AUC value as high as 0.83, 95% confidence interval of 0.78-0.89, and the test set AUC value of 0.68, 95% confidence interval of 0.54-0.81.

[0107] Reference Appendix Figure 5 As shown, in the RF-based diagnostic model, all 10 bacterial genera were included in the model, with the AUC value of 0.99 in the training set (95% confidence interval 0.98-1.00) and the AUC value of 0.80 in the test set (95% confidence interval 0.69-0.91).

[0108] The results above demonstrate that the 10 bacterial biomarkers mentioned above have high specificity and sensitivity as detection variables to predict the risk of prenatal depression, and can be used as a safe, non-invasive and accurate prediction method.

[0109] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0110] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A biomarker for prenatal depression, characterized in that, The biomarker includes gut microbiota, which includes at least one of the following: Enterobacter, Eubacterium siraeum group, Oscillospiraceae UCG-005, Lachnospiraceae UCG-004, Family XIII UCG-001, and Lactonifactor.

2. The marker according to claim 1, characterized in that, Further including at least one of the following: Clostridium, Tyzzerella, Family_XIII_AD3011_group of anaerobic phagocytic bacteria, and norank_f_Lachnospiraceae.

3. A reagent kit, characterized in that, include: A reagent for detecting the markers described in any one of claims 1 to 2.

4. The use of the reagent for detecting the marker according to any one of claims 1 to 2 in the preparation of a kit, characterized in that, The kit is used to predict prenatal depression.

5. The use of a formulation for regulating the activity and / or abundance of the biomarker according to any one of claims 1 to 2 in the preparation of a drug, characterized in that, The medication is used to treat or alleviate prenatal depression.

6. A computer-implemented method for predicting the risk of prenatal depression, characterized in that, include: Abundance data of the biomarkers described in any one of claims 1 to 2 in the biological sample to be tested are input into the prenatal depression diagnostic model to obtain the result of whether prenatal depression is present; Output whether there is a result indicating prenatal depression.

7. A device for diagnosing prenatal depression, characterized in that, include: An acquisition unit is used to acquire microbial data, which is obtained from fecal samples of pregnant women with prenatal depression and / or pregnant women without prenatal depression. The detection unit is used to detect the abundance of the markers described in any one of claims 1 to 2; The analysis unit is used to obtain the result of whether or not the patient has prenatal depression using a prenatal depression diagnostic model; The output unit is used to output the analysis results of the analysis unit.

8. The apparatus according to claim 7, characterized in that, The output of the output unit is the probability of having prenatal depression, wherein a probability > 0.5 indicates a risk of prenatal depression.

9. An electronic device, characterized in that, include: Processor and memory; The memory is used to store computer programs; The processor is configured to execute the computer program to implement the computer-implemented method for predicting the risk of prenatal depression as described in claim 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions or programs that, when executed on a computer, are used to implement the computer-implemented method for predicting the risk of prenatal depression as described in claim 6.

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