Internal reference sequence design method for microbial quantification in metagenome detection

Abstract

The invention discloses an internal reference sequence design method for microbial quantification in metagenome detection. According to the internal reference sequence design method provided by the invention, DNA sequences with different GC contents in nature are taken as input, and the sequence-specific internal reference sequence is obtained by a random disruption method, so that the problem of homology interference possibly caused by direct screening of natural sequences is avoided; and the problem that a specific sequence with extreme GC content is difficult to directly screen from a natural sequence is solved. The 10 designed internal reference sequences have different GC contents, that is, the preference of different GC contents introduced in the library building and sequencing process and the influence on the quantitative result of the microorganisms are considered, the microorganisms with different GC contents can be accurately quantified, and the application prospect is wide.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and in particular relates to an internal reference sequence design method for quantitative detection of microorganisms by metagenomics. Background Art

[0002] Metagenomic testing has great advantages in identifying pathogens of emerging and recurrent infections, as well as mixed infection pathogens that are difficult to identify by conventional culture. However, its clinical application is limited by the lack of microbial quantitative performance in most marketed metagenomic tests.

[0003] Most of the existing methods for quantifying microorganisms in metagenomes are to add single-molecule or multi-molecule internal reference sequences to the sample to be tested, and then perform high-throughput sequencing on the sample with the added internal reference sequences, and calculate the microbial content in the sample based on the number of internal reference detection sequences, the amount of internal reference added, the number of microbial detection sequences, the size of the microbial genome, etc. However, in addition to the difference in genome size, another important difference between different microorganisms is the difference in GC content. Since the GC content will affect the efficiency of PCR amplification, and ultimately make the microorganisms with different GC contents in the genome, even if the load in the sample is the same, the number of sequences detected by sequencing and the results of quantification using internal references are different. Existing microbial quantification methods do not consider the use of internal reference sequences with different GC contents, which may affect the accuracy of quantification of microorganisms with extreme GC contents. Therefore, it is of great importance in this field to provide a method for designing internal reference sequences for the quantification of microorganisms in metagenomic detection and a corresponding internal reference sequence. Summary of the invention

[0004] To make up for the deficiencies of the prior art, the present invention provides an internal reference sequence design method for quantitative detection of microorganisms by metagenomics and corresponding internal reference sequences.

[0005] To achieve the above purpose, the present invention adopts the following technical solution.

[0006] A first aspect of the present invention provides a method for designing an internal reference sequence for quantitative detection of microorganisms by metagenomics, the method comprising:

[0007] 1) Obtain sequences with different GC contents;

[0008] 2) Randomly shuffle the sequence obtained in step 1);

[0009] 3) cutting the fragments from step 2) and performing homology screening;

[0010] 4) screening the sequences without microsatellite sequences and consecutive base numbers in step 3);

[0011] 5) performing pairwise comparison of the sequences obtained in step 4) to obtain sequence-specific sequences;

[0012] 6) Select sequences with different GC contents and design primers. The amplicon sequence is the internal reference sequence.

[0013] Furthermore, the sequence of step 1) is a sequence with a length of 1200 to 2000 bp.

[0014] Furthermore, the sequence of step 1) is a sequence with a length of 1500 bp.

[0015] Furthermore, the GC content in step 1) ranges from 20% to 70%.

[0016] Furthermore, the specific GC content is 20% to 30%, the GC content is 30% to 40%, the GC content is 40% to 50%, the GC content is 50% to 60%, and the GC content is 60% to 70%.

[0017] Furthermore, there are 2 sequences with different GC contents.

[0018] Furthermore, the random shuffling in step 2) is to randomly shuffle the bases in the sequence.

[0019] Furthermore, 80% of the bases in the sequence were randomly shuffled.

[0020] Furthermore, the length of the cut fragment in step 3) is 50 bp.

[0021] Furthermore, the fragments were cut into 50 bp fragments with a window of 50 bp and a step of 5 bp.

[0022] Furthermore, in step 3), the homology screening is to screen sequences that have no homology with the human genome sequence and all pathogenic microorganism genome sequences.

[0023] Furthermore, the sequence in step 4) is a sequence without microsatellite sequences and with a number of consecutive bases less than 8.

[0024] Furthermore, in step 5), the sequences obtained in step 4) are aligned pairwise in a sliding manner with a k-mer=15 window.

[0025] Furthermore, in step 6), two sequences with different GC contents are selected.

[0026] Furthermore, the GC content ranges from 20% to 70%.

[0027] Furthermore, the specific GC content is 20% to 30%, the GC content is 30% to 40%, the GC content is 40% to 50%, the GC content is 50% to 60%, and the GC content is 60% to 70%.

[0028] The second aspect of the present invention provides the use of the method described in the first aspect of the present invention in preparing an internal reference sequence for quantitative metagenomic detection of microorganisms.

[0029] The third aspect of the present invention provides an internal reference sequence screened by the method described in the first aspect of the present invention.

[0030] Furthermore, the internal reference sequence has at least 80% sequence identity with the sequence shown in SEQ ID NO: 1-10.

[0031] Furthermore, the internal reference sequence is shown in SEQ ID NO: 1-10.

[0032] The fourth aspect of the present invention provides the use of the internal reference sequence described in the third aspect of the present invention in metagenomic microbial detection.

[0033] The fifth aspect of the present invention provides a method for detecting microorganisms using metagenomics, the method comprising using the internal reference sequence described in the third aspect of the present invention.

[0034] A sixth aspect of the present invention provides an electronic device for screening internal reference sequences for quantitative detection of microorganisms by metagenomics, the electronic device comprising:

[0035] Memory: The memory is used to store program instructions;

[0036] Processor: The processor is used to call program instructions, and when the program instructions are executed, it is used to execute the method described in the first aspect of the present invention.

[0037] A seventh aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method described in the first aspect of the present invention are implemented.

[0038] An eighth aspect of the present invention provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the method described in the first aspect of the present invention.

[0039] Advantages and beneficial effects of the present invention:

[0040] The internal reference sequence design method provided in the present application uses DNA sequences with different GC contents existing in nature as input, and obtains sequence-specific internal reference sequences by random shuffling, thereby avoiding the problem of homology interference that may occur when directly screening natural sequences, and solving the problem that specific sequences with extreme GC contents are difficult to obtain directly from natural sequences. The 10 designed internal reference sequences have different GC contents, that is, the preferences introduced by different GC contents in the library construction and sequencing process and the influence on the quantitative results of microorganisms are taken into account, and microorganisms with different GC contents can be accurately quantified, which has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a correlation analysis diagram between the number of internal reference detection sequences and the internal reference concentration;

[0042] Figure 2 It is a correlation analysis chart between the internal reference quantitative results and the actual content of microorganisms. DETAILED DESCRIPTION

[0043] The definitions of some terms used in this specification are provided below. Unless otherwise defined, all technical and scientific terms used herein generally have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0044] The present invention provides a method for designing an internal reference sequence for quantitative detection of microorganisms by metagenomics, the method comprising:

[0045] 1) Obtain sequences with different GC contents;

[0046] 2) Randomly shuffle the sequence obtained in step 1);

[0047] 3) cutting the fragments from step 2) and performing homology screening;

[0048] 4) screening the sequences without microsatellite sequences and consecutive base numbers in step 3);

[0049] 5) performing pairwise comparison of the sequences obtained in step 4) to obtain sequence-specific sequences;

[0050] 6) Select sequences with different GC contents and design primers. The amplicon sequence is the internal reference sequence.

[0051] In some embodiments, homology refers to the relationship between branches that originate from the same ancestor during evolution. This means that if two or more structures or sequences have the same ancestor, they are considered to be homologous. In bioinformatics, homology mainly refers to homology in sequence, that is, two or more proteins or DNA sequences have the same ancestor. In contrast to homology, no homology refers to the absence of this common ancestral relationship between two or more biological sequences. In other words, they originated independently during evolution and did not share a common ancestral sequence.

[0052] In some embodiments, microsatellite sequences, also known as microsatellite DNA or simple repeat sequences, are DNA sequences that exist in the genome and are repeated in tandem in units of a few nucleotides (mostly 1 to 6).

[0053] In some embodiments, the number of consecutive bases refers to the number of consecutive occurrences of a particular base (such as A, T, C, G) in a DNA sequence. For example, in the sequence "AAAAA", the number of consecutive bases of the base A is 5.

[0054] In some embodiments, the GC content is the ratio of guanine (G) to cytosine (C) among the four bases of DNA. The GC content is usually expressed as a percentage, and the calculation formula is: GC content (%) = (G+C) / (A+T+G+C)×100%, wherein A represents adenine and T represents thymine. In double-stranded DNA, the ratio of adenine to thymine (A / T) and the ratio of guanine to cytosine (G / C) are both 1, but the ratio of (A+T) / (G+C) varies with the type of DNA.

[0055] In some embodiments, primers are short, single-stranded DNA or RNA sequences in DNA replication or PCR reactions, which guide DNA polymerase or reverse transcriptase to synthesize a new DNA chain starting from the 3' end of the primer.

[0056] In some embodiments, the amplicon sequence is obtained by PCR reaction, using primers as the starting point, and replicating along the target DNA template chain under the action of DNA polymerase. They usually include the primer binding site and the target DNA sequence between the primers.

[0057] The length of the cut fragment in step 3) is 50 bp.

[0058] The fragments were cut into 50 bp fragments with a window of 50 bp and a step size of 5 bp.

[0059] In some embodiments, the window refers to a fixed-length segment sliding on a DNA sequence, which is used to intercept a portion of the sequence for analysis. In the present application, the length of the window is 50 bp (base pairs).

[0060] In some embodiments, step length refers to the distance that the window moves each time on the DNA sequence. In the present application, step length is 5bp.

[0061] Step 5) The sequences obtained in step 4) are aligned pairwise using a sliding window of k-mer=15.

[0062] In some embodiments, k-mer refers to a DNA fragment of length k, and the k-mer sequence is composed of four bases (A, T, C, G) in DNA, and these bases are arranged in a specific order to form different k-mers. k-mer=15, that is, each k-mer fragment contains 15 consecutive bases.

[0063] The present invention provides a method for detecting microorganisms using a metagenomic approach, the method comprising using the above-mentioned internal reference sequence.

[0064] In some embodiments, the method described in this application prepares a certain gradient of specific internal reference sequences, adds them to the sample, and constructs an internal standard curve through analysis by a bioinformatics system to quantify the DNA of the microorganism. This makes up for the weakness of metagenomic sequencing, effectively quantifies the copy number of pathogenic microorganisms, unifies the interpretation standards, and presents better results to physicians. In addition, the metagenomic sequencing results are quality controlled to increase the credibility of the experimental results.

[0065] The present invention provides an electronic device for screening internal reference sequences for quantitative detection of microorganisms by metagenome, the electronic device comprising:

[0066] Memory: The memory is used to store program instructions;

[0067] Processor: The processor is used to call program instructions, and when the program instructions are executed, it is used to execute the above method.

[0068] In some embodiments, the device is a computer device, which may be a terminal, including a processor and a memory connected via a system bus; and also including a network interface, a display screen, and an input device. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0069] The above-mentioned devices are merely partial structures related to the present application scheme and do not constitute a limitation on the computer devices to which the present application scheme is applied.

[0070] The present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0071] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface storage, optical disk, or CD-ROM; or may be various devices including one or any combination of the above memories.

[0072] In some embodiments, executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0073] As an example, executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0074] By way of example, executable instructions may be deployed to be executed on one computing device, or on multiple computing devices located at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0075] The invention will be further described below in conjunction with specific examples. It should be understood that the specific embodiments described herein are presented by way of example and are not intended to limit the invention. The main features of the invention may be used in a variety of embodiments without departing from the scope of the invention.

[0076] Example

[0077] The computer was used to generate 10 sequence-specific internal reference sequences with a GC content of 25% to 75% and a length of 1000 bp. The specific steps included:

[0078] 1. From the cotton genome GCF_007990345.1 and the rice genome GCF_034140825.1, 10 sequences with a length of 1500bp, 2 sequences with a GC content of 30% to 40%, 2 sequences with a GC content of 40% to 50%, 2 sequences with a GC content of 50% to 60%, and 2 sequences with a GC content of 60% to 70% were screened.

[0079] Second, randomly shuffle each of the 10 sequences screened out in step 1: use the python random module, set the random seed, randomly select 80% of the bases in a sequence for shuffling, and obtain 50 randomly shuffled candidate sequences for each original sequence for the next specific alignment.

[0080] 3. Compare the candidate sequence obtained in step 2 with the human genome sequence and all pathogenic microorganism genome sequences in public databases such as NCBI and BV-BRC to obtain a sequence that has no homology with the human genome sequence and all pathogenic microorganism genome sequences.

[0081] Fourth, from the sequences obtained in step three, sequences without microsatellite sequences and with a continuous base number of less than 8 (ie, a specific base, such as A, G, C, T, appears continuously less than 8 times) are screened out.

[0082] 5. Perform pairwise comparison of the sequences obtained in step 4 in a sliding k-mer=15 window: break a sequence into 15 bp k-mer segments and compare them with other sequences. If all 15 bp segments generated by the break are inconsistent with other sequences, the sequence is considered specific. Otherwise, it is considered non-specific and is eliminated.

[0083] 6. Select 10 sequences from the sequence obtained in step 5 (two sequences for each GC content), design PCR primers for each of these 10 sequences so that the length of the amplicon is ∼1000 bp. The amplicon sequence is the final internal reference sequence.

[0084] 7. The 10 internal reference sequences with a length of ∼1000 bp obtained in step 6 were compared with the human genome sequence and all pathogenic microorganism genome sequences to ensure their sequence specificity. The 10 internal reference sequences are shown in Table 1.

[0085] The designed internal reference sequence is directly synthesized or constructed into a plasmid, and the internal reference sequence for the experiment is obtained by PCR amplification. In order to verify that the internal reference sequence designed in this application can accurately quantify microorganisms with different GC contents, the following experiment (Experiment 1) was conducted: 1) 10 internal reference sequences were prepared into internal reference solutions according to a 10-fold concentration gradient (1 ng / μL, 0.1 ng / μL, 0.01 ng / μL, 0.0001 ng / μL, 0.00001 ng / μL, each concentration corresponding to two internal reference sequences); 2) 1 μL of internal reference solution and a total mass of 0.01 ng of microbial DNA were added to the negative matrix nucleic acid, and the microbial DNA consisted of 6 types of microbial genomic DNA with different GC contents (30% to 70%); 3) The DNA mixture prepared in step 2) was used for library construction and sequencing, and the number of sequences detected for each internal reference sequence and each microorganism was analyzed; 4) A linear equation was constructed based on the number of internal reference detected sequences and the internal reference concentration; 5) The amount of microorganisms was calculated using the slope and intercept of the linear equation in step 4), the number of microbial detected sequences and the length of the microbial genome; 6) The correlation analysis was performed between the internal reference quantitative microbial results and the actual content of the microorganisms.

[0086] Experiment 1

[0087] 1. Experimental Materials

[0088] Library construction kit, Novozymes.

[0089] Table 1 Internal reference sequences

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] Table 2 Mixture of genomic DNA of six microorganisms

[0097] name GC content (%) Copy number ratio (%) Cryptococcus neoformans 48.3 0.8 Enterococcus faecalis 37.5 32.0 Salmonella enterica 52.2 19.1 Saccharomyces cerevisiae 38.3 1.3 Staphylococcus aureus 32.9 33.4 Pseudomonas aeruginosa 66.2 13.4

[0098] 2. Experimental Methods

[0099] 1) Prepare an internal reference sequence mixture, as shown in Table 3.

[0100] Table 3 Internal reference sequence mixture

[0101] name GC content (%) Concentration (ng / μL) Sequence 1 30 1 Sequence 10 70 1 Sequence 2 30 0.0001 Sequence 9 70 0.0001 Sequence 3 40 0.1 Sequence 8 60 0.1 Sequence 4 40 0.001 Sequence 7 60 0.001 Sequence 5 50 0.01 Sequence 6 50 0.01

[0102] 2) Add 0.01 ng of microbial genomic DNA and 0.1 ng of internal reference sequence mixture to 1 ng of negative matrix (human genomic DNA).

[0103] 3) The negative matrix samples supplemented with microbial genomic DNA and internal reference sequences are used for library construction and sequencing.

[0104] 4) Analyze the number of internal reference detection sequences and the number of microbial detection sequences.

[0105] 5) Construct a correlation equation based on the number of detected sequences of the 10 internal reference sequences and the concentration of the corresponding internal reference sequences: y=a+bx.

[0106] 6) Using the intercept (a) and slope (b) of the correlation equation in step 5, as well as the number of sequences detected per million (RPM_m) of microorganism m and the genome size of microorganism m (L_m, bp), calculate the copy number of microorganism m (C_m) and the proportion of this microorganism in all added microorganisms, C_m = ((a+b×RPM_m)×6.022×10 23 ) / (L_m×650).

[0107] 7) Carry out correlation analysis between the internal reference quantitative results and the actual content of microorganisms.

[0108] 3. Experimental results

[0109] The results of the correlation analysis between the number of detected sequences of the 10 internal references and the concentration of the corresponding internal reference sequences are shown in the figure below. Figure 1 shown.

[0110] Using the formula C_m=((-4.1718+1.3036×RPM_m)×6.022×10 23 ) / (L_m×650) was used to calculate the copy numbers of the six microorganisms, and the proportion of each microorganism in all the added microorganisms was calculated. The results are shown in Table 4.

[0111] Table 4 The proportion of 6 added microorganisms in all microorganisms

[0112] microorganism GC content (%) Quantitative results (%) Actual proportion (%) Cryptococcus neoformans 48.3 0.7 0.8 Enterococcus faecalis 37.5 30.8 32.0 Salmonella enterica 52.2 21.9 19.1 Saccharomyces cerevisiae 38.3 0.3 1.3 Staphylococcus aureus 32.9 37.3 33.4 Pseudomonas aeruginosa 66.2 8.9 13.4

[0113] Correlation analysis between internal reference quantitative results and actual microbial content Figure 2 As shown, the results show that the correlation coefficient between the quantitative result and the actual content of the microorganism is >0.96, indicating that the internal reference sequence designed in the present application can accurately quantify microorganisms with different genomic GC contents.

[0114] The description of the above embodiments is only used to understand the method and core idea of ​​the present invention. It should be pointed out that, for those skilled in the art, several improvements and modifications can be made to the present invention without departing from the principle of the present invention, and these improvements and modifications will also fall within the scope of protection of the claims of the present invention.

Claims

1. A method for designing internal reference sequences for quantitative detection of microorganisms by metagenomics, characterized in that: The method comprises: 1) Obtain sequences with different GC contents; 2) Randomly shuffle the sequence obtained in step 1); 3) cutting the fragments from step 2) and performing homology screening; 4) screening the sequences without microsatellite sequences and consecutive base numbers in step 3); 5) performing pairwise comparison of the sequences obtained in step 4) to obtain sequence-specific sequences; 6) Select sequences with different GC contents and design primers. The amplicon sequence is the internal reference sequence.

2. The method according to claim 1, characterized in that The sequence of step 1) is a sequence with a length of 1200 to 2000 bp; Preferably, the sequence of step 1) is a sequence with a length of 1500 bp; Preferably, the GC content in step 1) ranges from 20% to 70%; Preferably, the specific GC content is 20% to 30%, the GC content is 30% to 40%, the GC content is 40% to 50%, the GC content is 50% to 60%, and the GC content is 60% to 70%; Preferably, there are 2 sequences of each different GC content; Preferably, the random shuffling in step 2) is to randomly shuffle the bases in the sequence; Preferably, 80% of the bases in the sequence are randomly shuffled; Preferably, the length of the cut fragment in step 3) is 50 bp; Preferably, the fragments are cut into 50 bp fragments with a window of 50 bp and a step of 5 bp; Preferably, the homology screening in step 3) is to screen for sequences that have no homology with human genome sequences and all pathogenic microorganism genome sequences; Preferably, the sequence in step 4) is a sequence without microsatellite sequences and having a continuous base number of less than 8; Preferably, in step 5), the sequences obtained in step 4) are aligned pairwise in a sliding manner with a k-mer=15 window; Preferably, in step 6), two sequences with different GC contents are selected; Preferably, the GC content ranges from 20% to 70%; Preferably, the specific GC content is 20% to 30%, the GC content is 30% to 40%, the GC content is 40% to 50%, the GC content is 50% to 60%, and the GC content is 60% to 70%.

3. Use of the method according to claim 1 or 2 in preparing an internal reference sequence for quantitative detection of microorganisms by metagenomics.

4. The internal reference sequence obtained by screening according to the method of claim 1 or 2.

5. The internal reference sequence according to claim 4, characterized in that The internal reference sequence has at least 80% sequence identity with the sequence shown in SEQ ID NO: 1-10; Preferably, the internal reference sequence is shown as SEQ ID NO: 1-10.

6. Use of the internal reference sequence described in claim 4 or 5 in metagenomic microbial detection.

7. A method for detecting microorganisms using metagenomics, characterized in that: The method comprises using the internal reference sequence of claim 4 or 5.

8. An electronic device for screening internal reference sequences for quantitative detection of microorganisms by metagenomics, characterized in that: The electronic device comprises: Memory: The memory is used to store program instructions; Processor: The processor is used to call program instructions, and when the program instructions are executed, it is used to execute the method described in claim 1 or 2.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to claim 1 or 2 are implemented.

10. A computer program product comprising computer instructions, which implement the steps of the method according to claim 1 or 2 when executed by a processor.

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