Identification and antibiotic characterization of pathogens in metagenomic sample

Patent metadata

Field	Value
Publication number	US-11749381-B2
Application number	US-201716342017-A
Country	US
Kind code	B2
Filing date	Oct 12, 2017
Priority date	Oct 13, 2016
Publication date	Sep 5, 2023
Grant date	Sep 5, 2023

Abstract

A method for identifying a pathogen contained in a metagenomic sample and for identifying pathogenic markers in the genome of the pathogen includes: processing the sample to extract DNA from pathogens, sequencing the extracted DNA, thereby producing a set of reads, comparing the reads to a database of genomes of known pathogens to assign reads to the pathogens; producing a pool of reads and assembling them to produce contigs, comparing the contigs to a second database of markers to check whether they contain a marker. The method further includes the step of comparing the reads to the second database to assign reads to the markers, a read being assigned to a marker if it falls entirely into or is astride the marker, and the pool also includes the reads assigned to the markers, the contigs thereby being assembled from reads assigned to a pathogen and reads assigned to markers.

First claim

The invention claimed is:

1. A method of analyzing a metagenomic sample, comprising:
processing the metagenomic sample to extract DNA from one or more pathogens if present in the metagenomic sample;
sequencing the extracted DNA to produce a set of digital nucleic acid sequences, i.e., “reads”, that have an average length of L bp in which L>100;
assigning a first subset of reads to a known pathogen by mapping the first subset of reads against genomic data of the known pathogen using a first database comprising genomic data for a plurality of known pathogens;
assigning a second subset of reads to a sequence of a known antibiotic resistance determinant or virulence genetic determinant of a length greater than that of the average length of the reads by mapping the second subset of reads against sequence data of the known antibiotic resistance determinant or virulence genetic determinant using a second database comprising sequence data for a plurality of antibiotic resistance determinants and/or virulence genetic determinants such that there are (i) reads within the second subset of reads that fall entirely within the sequence of the known antibiotic resistance determinant or virulence genetic determinant and (ii) reads within the second subset of reads that are astride the sequence of the known antibiotic resistance determinant or virulence genetic determinant;
producing a pool of reads comprising the first subset of reads and the second subset of reads; and
assembling the reads in the pool in order to produce at least one assembled digital nucleic acid, i.e., “contig”,
wherein the reads that are astride the sequence of the known antibiotic resistance determinant or virulence genetic determinant each have (i) a first portion falling inside of the sequence and (ii) a second portion falling outside of the sequence in the range [1; L-50] bp.

2. The method of claim 1 , wherein the antibiotic resistance determinant or virulence genetic determinant is a resistance gene or virulence gene, respectively.

3. The method of claim 1 , further comprising annotating the contig after assembly of the contig using the second database or another database comprising sequence data for a plurality of antibiotic resistance determinants and/or virulence genetic determinants.

4. The method of claim 1 , wherein:
the second database is an antibiotic resistance determinant (ARD) database comprising sequence data for a plurality of antibiotic resistance determinants; and
the second portion has a probability of right assignment against the ARD database that is greater than or equal to 70 %.

5. The method of claim 1 , wherein the reads of the second subset are mapped against the sequence data of the known antibiotic resistance determinant or virulence genetic determinant independently from each other.

6. The method of claim 1 , wherein the metagenomic sample is a brochoalveolar lavage sample, urine sample, or blood sample.

7. The method of claim 1 , wherein the metagenomic sample is a brochoalveolar lavage sample collected from a patient with ventilarory acquired pneumonia (VAP).

8. The method of claim 1 , wherein the extracted DNA is sequenced by high throughput sequencing (HTS).

9. The method of claim 1 , wherein the first portion falling inside of the sequence of the known antibiotic resistance determinant or virulence genetic determinant has a length that is at least 20 bp.

10. The method of claim 1 , wherein the sequencing is a paired-end sequencing and, if a read assigned to the known antibiotic resistance determinant or virulence genetic determinant has a complementary read, the complementary read is also included within the pool of reads.

11. The method of claim 1 , wherein the known antibiotic resistance determinant or virulence genetic determinant is determined to be part of the known pathogen's genome when:

D
ARD

∈

[

1
3

×

D
path

;

3
×

D
path

]

where D ARD is a median sequencing depth of the reads assigned to the known antibiotic resistance determinant or virulence genetic determinant and D path is a median sequencing depth of the reads assigned to the known pathogen.

12. The method of claim 1 , wherein the genomic data for the plurality of known pathogens comprises 16SrDNA sequence and/or metaphlan2 data.

13. The method of claim 1 , wherein the metagenomic sample is taken from a human or an animal, the first database further comprises flora and host genomic data, and reads mapped to the flora and host genomic data are filtered out.

14. A non-transitory computer readable medium storing instructions for executing a method performed by a computer, the method comprising:
assigning a first subset of reads to a known pathogen by mapping the first subset of reads against genomic data of the known pathogen using a first database comprising genomic data for a plurality of known pathogens;
assigning a second subset of reads to a sequence of a known antibiotic resistance determinant or virulence genetic determinant by mapping the second subset of reads against sequence data of the known antibiotic resistance determinant or virulence genetic determinant using a second database comprising sequence data for a plurality of antibiotic resistance determinants and/or virulence genetic determinants such that there are (i) reads within the second subset of reads that fall entirely within the sequence of the known antibiotic resistance determinant or virulence genetic determinant and (ii) reads within the second subset of reads that are astride the sequence of the known antibiotic resistance determinant or virulence genetic determinant;
producing a pool of reads comprising the first subset of reads and the second subset of reads; and
assembling the reads in the pool in order to produce at least one assembled digital nucleic acid, i.e., “contig”, wherein:
the metagenomic sample was processed to extract DNA from one or more pathogens if present in the metagenomic sample and the extracted DNA was sequenced to produce a set of digital nucleic acid sequences, i.e., “reads”, that have an average length of L bp in which L>100;
the sequence of the known antibiotic resistance determinant or virulence genetic determinant has a length that is greater than that of the average length of the reads;
and the reads that are astride the sequence of the known antibiotic resistance determinant or virulence genetic determinant each have (i) a first portion falling inside of the sequence and (ii) a second portion falling outside of the sequence in the range [1; L-50] bp.

15. The non-transitory computer readable medium of claim 14 , wherein the antibiotic resistance determinant or virulence genetic determinant is a resistance gene or virulence gene, respectively.

16. The non-transitory computer readable medium of claim 14 , wherein the method further comprises annotating the contig after assembly of the contig using the second database or an

Assignees

• Biomerieux Sa

Inventors

• Mahe Pierre
• Tournoud Maud
• Schicklin Stéphane
• Guigon Ghislaine
• Ruppe Etienne

Classifications

• G16B40/20 Primary

  Supervised data analysis · CPC title

• C12Q1/689

  for bacteria · CPC title

• G16B20/00

  ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations · CPC title

• G16B30/00 Primary

  ICT specially adapted for sequence analysis involving nucleotides or amino acids · CPC title

• C12Q1/6888

  for detection or identification of organisms · CPC title