Methods for identifying fungi

Patent metadata

Field	Value
Publication number	US-10465223-B2
Application number	US-201415026400-A
Country	US
Kind code	B2
Filing date	Oct 9, 2014
Priority date	Oct 9, 2013
Publication date	Nov 5, 2019
Grant date	Nov 5, 2019

Abstract

Methods for identifying fungal species by analysis of fungal membrane lipids, such as glycerophospholipids, sphingolipids and sterols, using mass spectrometry ionization patterns are disclosed.

First claim

We claim:

1. A method for identifying fungi by species in a sample, comprising
(a) obtaining precursor ion mass spectra (PIMS) data on precursor ions for one or more lipids selected from the group consisting of (i) a fungal glycerophospholipid, (ii) a fungal sphingolipid, (iii) a fungal sterol, and (iv) precursors molecules thereof, from a sample containing fungi of interest;
(b) comparing the PIMS data to a counterpart database of (i) fungal glycerophospholipid PIMS data, (ii) fungal sphingolipid PIMS data, (iii) fungal sterol PIMS data, and/or (iv) precursor molecule PIMS data;
wherein the comparing is used to identify fungi by species in the sample.

2. The method of claim 1 , wherein the comparing comprises comparing precursor ion m/z values and relative abundance of the precursor ions to the database of glycerophospholipid, sphingolipid, sterol, or precursor molecule PIMS data.

3. The method of claim 1 , further comprising fragmenting all or a subset of the precursor ions to produce a multiplexed set of ions, and obtaining mass spectra on all or a subset of the multiplexed set of ions (multiplexed mass spectra data), and wherein the comparing further comprises comparing the multiplexed mass spectra data to one or more of fungal glycerophospholipid, sphingolipid, sterol, or precursor molecule multiplexed mass spectra data in the database to assist in identifying fungi by species in the sample.

4. The method of claim 1 , further comprising fragmenting all or a subset of the precursor ions to produce a set of derived fragment ions, and obtaining mass spectra on all or a subset of the derived fragment ions (MS n data), and wherein the comparing further comprises sequentially comparing the MS n data to one or more of fungal glycerophospholipid, sphingolipid, sterol, or precursor molecule MS n data in the database to assist in identifying fungi by species in the sample.

5. The method of claim 4 , further comprising searching the precursor ion and/or MS n data against a database of fungal glycerophospholipid, sphingolipid, sterol, and precursor molecule signature ions to identify signature ions in the precursor ion and/or MS n data.

6. The method of claim 5 , further comprising
(i) searching neutral losses of signature ions in the MS n data against a theoretical neutral loss database to identify dissociation formulae;
(ii) proposing glycerophospholipid, sphingolipid and/or sterol candidate structures from fungi in the sample based on the dissociation formulae and the signature ions in the MS n data;
(iii) assigning a score to each glycerophospholipid, sphingolipid and/or sterol candidate structure based on correlation between theoretical and acquired MS n data, wherein candidate structures that meet or exceed a user-defined threshold are considered as accurate assignments.

7. The method of claim 6 , wherein step (i) comprises
(A) determining a neutral loss of every MS n spectrum's precursor ion in the corresponding MS n-1 spectrum and searching against the theoretical neutral loss database; and
(B) iteratively repeating step (A) until level MS n is reached; and
wherein step (ii) comprises proposing the glycerophospholipid, sphingolipid and/or sterol structures from the fungi in the sample based on the integrating data from each MS n level.

8. The method of claim 6 , wherein step (iii) comprises
(A) fragmenting the glycerophospholipid, sphingolipid and/or sterol candidate structures by direct bond cleavage to produce fragmentations;
(B) combining the fragmentations into a reconstructed mass spectra representing the theoretical dissociation of the glycerophospholipid, sphingolipid and/or sterol candidate structures; and
(C) assigning the score to each of the glycerophospholipid, sphingolipid and/or sterol candidate structure based on correlation between theoretical MS n spectra and the reconstructed mass spectra.

9. The method of claim 1 , further comprising
(c) obtaining mass spectra data on precursor ions for fungal proteins in the sample;
(d) comparing the protein mass spectra data to a database of fungal protein precursor ion mass spectra data;
wherein the comparing is used to help identify fungi by species in the sample.

10. The method of claim 1 , wherein the fungal glycerophospholipid is a fungal membrane glycerophospholipid, wherein the fungal sphingolipid is a fungal membrane sphingolipid, and wherein the fungal sterol is a fungal membrane sterol.

11. The method of claim 1 , wherein the sample contains a single fungal species.

12. The method of claim 1 , wherein the sample contains two or more fungal species.

13. The method of claim 1 , wherein the fungi is a species of a fungal genera selected from the group consisting of Candida, Aspergillus, Rhyzopus, Cryptococcus, Histoplasma, Pneumocystis, Stachybotrys, Sporothrix, Trichophyton, Microsporum, Blastomyces, Mucoromycotina, Coccidioides, Exserohilum, Cladosporium, Coccoides, Encephalitozoon, Encephalitozoon, Fusarium, Lichtheimia, Mortierella, Malassezia, Prototheca, Pythium, Rhodotorula, Fusarium, Thielaviopsis, Verticillium, Magnaporthe, Sclerotinia, Ustilago, Rhizoctonia, Puccinia, Armillaria, Botrytis, Blumeria, Mycosphaerella, Colletotrichum, Melampsora, Saprolegniasis, Ichthyosporidium, Exophiala, Branchiomycosis, and Penicillium.

14. The method of claim 1 , wherein the fungi is a fungal species selected from the group consisting of Histoplasma capsulatum, Blastomyces dermatitidis, Coccidioides immitis, Paracoccidioides brasiliensis, Aspergillus fumigatus, Candida albicans, Cryptococcus neoformans, Magnaporthe grisea, Sclerotinia sclerotiorum, Phakospora pachyrhizi and Botrytis cinerea.

Assignees

• Univ Maryland

Inventors

• Goodlett David
• Ernst Robert

Classifications

• G16C20/60

  In silico combinatorial chemistry · CPC title

• G16B35/00

  ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides · CPC title

• G01N33/56961

  Plant cells or fungi · CPC title

• C12Q1/04 Primary

  Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor {(C12Q1/6897 takes precedence)} · CPC title

• G01N2333/37

  from fungi · CPC title