Use of Fatty Acid Profiles To Identify White-Rot Wood Decay Fungi

Chapter 17

Use of Fatty Acid Profiles To Identify White-Rot Wood Decay Fungi

Downloaded by CORNELL UNIV on September 30, 2016 | http://pubs.acs.org Publication Date: March 31, 2003 | doi: 10.1021/bk-2003-0845.ch017

Susan V. Diehl, M . Lynn Prewitt, and Fatima Moore Shmulsky Forest Products Laboratory, Mississippi State University, Mississippi State, M S 39762

Identification libraries have been created for the white-rot fungi Phanerochaete chrysosporium, P. sordida , P. sanguinea, Trametes versicolor, T. hirsuta, and T. pubescens. Libraries were compiled using five to ten isolates of each species with each isolate cultured four times, extracted in duplicate and analyzed by gas chromatography for its fatty acid methyl ester (FAME) profile. Single libraries were created for T. versicolor and T. pubescens, indicating that all isolates of these species clustered as a single species. More than one library was created for P. chrysosporium, P. sordida, and T. hirsuta, indicating that not all isolates of these species clustered as a single species. The P. chrysosporium libraries provided a good identification of all but one blind sample. Analysis of that sample identified P. sordida as first choice, but the second choice of P. chrysosporium was too close for a good species match. The genus Phanerochaete was correctly identified in all blind samples for both species tested. Blind samples of T. pubescens and T. hirsuta were not distinguished at the species level. There appears to be considerable variability within T. hirsuta. It will be interesting in the future to compare the D N A sequences of these outliers to determine if the genetic information supports the separations seen in the F A M E profiles.

© 2003 American Chemical Society Goodell et al.; Wood Deterioration and Preservation ACS Symposium Series; American Chemical Society: Washington, DC, 2003.

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Introduction Classification of wood decay fiingi is currently in a state of revision and review. Recent phylogenetic studies of the Homobasidiomycetes based on nuclear and mitochondrial small subunit r D N A tentatively divided this group into eight major clades (1). Many of the estimated 1,568 species of wood-decay homobasidiomycetes fall within the polypore and corticoid groups (2, 3). Only 6-7% of the wood-decay homobasidiomycetes are brown-rot species, with the remainder being white-rot species (2). Recent molecular studies have suggested that many groups are polyphyletic and that the evolution of the brown-rot decay mode has repeatedly evolved from the ancestral white-rot modes (4). Most phylogenetic analyses are based on a single isolate of a species and, in many cases, a single species of a given genus. These types of studies do not consider the variation that occurs among isolates of a given species and among species. Tanesaka et al. (5) found that variation among species accounted for 63% of the total variation of 17 genera analyzed. Many wood decay isolates show wide ranges of variability in physiological characteristics, appearances and abilities. The identification of wood decay fungi is typically accomplished by isolation and observation of growth characteristics on different culture media and spore formation. Identification keys are used to identify the fungi in question (e.g. 6 and 7). This process is time consuming and can be difficult due to the similar cultural and morphological characteristics of many different fimgi and poor (or lack of) sporulation in culture of many decay isolates. Other methods are being sought to identify unknown fungi more quickly, with higher accuracy, and requiring less expertise. Currently, serological detection, comparisons of cellular fatty acid methyl ester (FAME) profiles, and identification of unique sequences of fungal D N A are being developed for wood decay fungi. Each method has its advantages and disadvantages. The focus of this chapter is the application of F A M E profiles for identification of the wood decay fungi. Identification using unique D N A sequences is discussed in a separate chapter of this book (8). Microorganisms synthesize over 200 different fatty acids and the presence of specific fatty acids and their relative amounts are constant for a particular species (9). The type and amount of fatty acids produced are used to identify a particular genus, species, or strain in bacteria and yeasts. Since the early 1960's, analysis of fatty acids (derivatized to methyl esters) by gas chromatography has been used for identification of bacteria, and more recently, fimgi (10-14). MIDI, Inc. (Newark, DE) has developed a database of F A M E profiles called the Sherlock Microbial

Goodell et al.; Wood Deterioration and Preservation ACS Symposium Series; American Chemical Society: Washington, DC, 2003.


315 Identification System using over 5,000 strains of microorganisms including sixty common fungi. Although this system does not contain profiles of wood decay fungi, it has the capability for the creation of new libraries of fatty acid profiles as deemed necessary by the user. This system of F A M E profiles combined with multivariate discriminate analysis was used to speciate glomalean endomycorrhizal fungi for taxonomic purposes (10) and seven species of Penicillium were correctly identified with a 98% accuracy rate (14). Muller et al. (13) compared the fatty acids of 42 different fimgi including 11 genera and 16 species and found that not all genera could be separated by fatty acid profiles alone, but identification improved when sterols were added to the profiles of the slow-growing fungi. A thesis project in the Department of Forest Products, Mississippi State University evaluated if a MIDI Sherlock System library built for select wood decay fimgi could be used for taxonomic purposes (15). The wood decay fungi, Gloeophyllum trabeum, G. sepiarium, Trametes hirsuta, and T. versicolor, were extracted and analyzed for their F A M E profiles and libraries of these profiles were created. Ten isolates of each species were tested. Each species had its own unique fatty acid profile, however, there was poor extraction of the fatty acids from slowgrowing isolates. Poor extraction efficiency led to low peak area counts on the gas chromatogram, and low percent area counts "lost" fatty acids that were present in low concentrations. In comparison, high extraction efficiency led to high percent area counts and numerous trace fatty acids "appeared". Since the library is based on the presence and percent amounts of fatty acid in a given species, differences between the high and low area counts contributed to a weak match for two species. In addition, the MIDI software selected the data to be included in the library, which led to an omission of some of the trace fatty acids of a given species from the library. Given the problems encountered, we felt that the system could distinguish among the different wood decay fungi, but, certain culture, extraction and library generation variables would need to be optimized. The data given in this paper are a result of tightening a number of variables and generating a more accurate identification system.

Research Methods Preparation of Fungal Cultures The isolates in this study were purchased from the American Type Culture Collection, or provided by the Forest Products Laboratory, Madison, WI or Dr. Dana Richter, Michigan Technological University, Houghton, MI. Fresh cultures of seven to ten isolates of T. versicolor, T. hirsuta, and T. pubescens, eight isolates ofPhanerochaete chrysosporium, five isolates of P. sordida and seven isolates of



316 P.sanguinea were started on Sabouraud dextrose agar (SDA) plates (Difco Laboratories) and incubated at 28 C for three to seven days. One 0.5 cm plug was removed from the leading edge of the culture, placed on a fresh SDA plate, and incubated at 28 C for four days. Four 0.5 cm plugs from the leading edge of the culture were placed into 100 ml of Sabouraud dextrose broth (Difco Laboratories) and incubated for seven days in a Lab-Line environmental shaker at 28 C and 150 revolutions per minute (rpm). This culture procedure was replicated four times for each isolate. Mycelia were filtered through a Whatman 541 (4.7 cm) filter paper, rinsed three times with distilled water, and excess water was blotted from the mycelia. The mycelia were separated into 0.05 g and 0.10 g weights and placed into 16 mm x 125 mm culture tubes. Each weight (g) was duplicated for each isolate replicate. Culture tubes were capped with Teflon-lined caps and frozen at -70 C overnight. Aspergillus fumigatus (ATCC 36607) was extracted and run as a positive control and a reagent blank served as a negative control for every set of samples analyzed.

Extraction and Esterification of the Fatty Acids To each culture, 2 ml of Extraction Solution A (45 g sodium hydroxide, 150 ml methanol and 150 ml deionized water) was added, the mixture vortexed for 10 - 1 5 s, heated in a boiling water bath for 5 min, re-vortexed for 10 - 15 s, and heated an additonal 25 min in the boiling water bath. After cooling to room temperature, 4 ml of Extraction Solution B (325 ml 6.0 N hydrochloric acid and 275 ml methanol) was added to each tube, and the mixture vortexed for 10 -15 s, heated in a water bath for 10 min at 80 C, then cooled to room temperature. The fatty acid methyl esters were extracted with 1.2 ml of Extraction Solution C (50:50 solution of methyl tert-butyl ether and hexane) and 10 min of rotation at 20 rpm on a Thermolyne Vari-Mix. The top layer was transferred to a 13 mm x 100 mm culture tube with Teflon-lined cap, 3 ml of Extraction Solution D (5.4 g sodium hydroxide in 450 ml deionized water) was added to this layer, and the mixture rotated for an additional 5 min. A small portion of this top layer was transferred to a 2 m l autosampler vial containing a 100 pd glass insert. Vials were sealed with a Teflon-lined cap for analysis by gas chromatography.

Establishment of the Fatty Acid Profiles by Gas Chromatography Fatty acid methyl esters were identified and quantified using an Agilent Technologies, Inc., (formerly Hewlett-Packard) 6890 Gas Chromatograph (GC) equipped with an automatic injector and flame ionization detector, and connected


317


to a computer containing ChemStation Data Acquisition and Sherlock Microbial Identification System software packages. G C conditions were: injector 250 C; detector at 300 C; and oven-initial temperature 170 C, 5 C/min to 260 C, 40 C/min to 310 C then hold 1.5 minute. The column used for analysis was the Agilent Ultra2 capillary column, (25m,0.22mm,0.33^im) with hydrogen as the carrier gas. The fatty acid profile for each isolate was analyzed by G C approximately 16 times. There were a few slow-growing isolates that were analyzed fewer times. Since two mycelial weights (0.05 and 0.1 g) were extracted for each analysis, only isolates that produced area counts between 100,000 and 600,000 were used to generate the library. This provided approximately 8 GC profiles per isolate for generation of a library.

Creation of the Wood Decay Fungal Identification Libraries The samples were cataloged in each library using the Sample Catalogue feature of the Sherlock Microbial Identification System software package which searches all the data files and creates a master alpha-numeric index. After cataloging the samples, comparisons among the samples were made using the 2-D plot feature of the software. This program determines relationships among a large number of organisms by generating a 2-D plot of Principle Component 1 on the x axis and Principle Component 2 on the y axis. Closely related organisms will form clusters. A line is drawn around the cluster and perpendicular lines are drawn from the minimum and maximum x and y values to the corresponding x and y intercept. The difference in the minimum and maximum x and y values are multiplied to give the Euclidean Distance Squared (ED ). For bacterial speciation, samples clustered in a group with E D of about 110 or less are assumed to be of the same species; about 60 or less are of the same subspeices; and about 30 or less are of the same strain. There are no suggested guidelines for speciation of fungi. This program is helpful in determining contaminated samples and samples that are outliers. Samples that are outliers or questionable or contain small total area ( 400,000) are flagged either by the software or manually and then eliminated from the database. After each analysis, a report is generated which gives the Similarity Index. The Similarity Index is a numeral value which expresses how closely related the fatty acid composition of an unknown is to the library entry. A n exact match would result in a similarity index of 1.000 and will decrease in proportion to the cumulative variance between the composition of the unknown and the library entry. Similarity indexes of 0.500 or higher with a separation of at least 0.100 between the first and second choice are considered to be a good match. 2

2


318

Results and Discussion The Phanerochaete chrysosporium Library This paper will discuss the creation of the P. chrysosporium (PC) library in detail to illustrate how the library selections were made. The libraries of the other species will not be reported in as much detail due to space constraints. A summary of pertinent information will be provided for the other libraries. The PC library was a compilation of 96 data points, with each data point representing a F A M E profile. The 2-D plot for all 96 points is given in Figure 1. The E D calculated for Figure 1 is 394 (Table I). This number is greater than 110, indicating that some of the isolates tested may not be the same species, therefore, sub-groups were created. It is obvious from Figure 1 that some isolates were outside of the main cluster. For PC, there were 4 sub-group libraries (A - D), with the largest library (A) composed mainly of 5 isolates, B composed of 2 isolates, C composed of 1 isolate, and D composed of 1 isolate. The 2-D plot for library B is shown in Figure 2. Separate F A M E profiles for a given isolate are circled. These same data points are also circled in Figure 1. The E D for PC library B is 75.2, indicating these isolates are the same species but not the same subspecies. Table I lists the E D for all libraries created for all species thus far. When an unknown is analyzed and its identification sought using the wood decay libraries, the identification reported distinguishes among the sub-group libraries of a given species. Thus an organism will be identified as P. chrysosporium A or B, not simply as P. chrysosporium. "Blind" samples were run against the PC library to test the accuracy of the identification system. The purpose of the sub-grouping is to strengthen the accuracy of the library and to establish relationships (or similarities) among isolates. Table II gives the similarity index for the first and second choice when screened against the newly created PC libraries. When tested against the four sub group libraries, three of four isolates were correctly identified as P. chrysosporium with P. sordida as the second choice. These three identifications would be considered a "good" match. The fourth isolate selected P. sordida as its first choice and P. chrysosporium as its second choice. The distance between the first and second choice is small, indicating that this identification is "too close to call". Interestingly, the F A M E profiles for the isolate with poorer identification is located in the larger A library. The better matches came from the smaller B and D libraries.


2

2

2


Goodell et al.; Wood Deterioration and Preservation ACS Symposium Series; American Chemical Society: Washington, DC, 2003. 2

Figure 1. 2-D plot of all 96 data points generatedfor the Phanerochaete chrysosporium library. The two isolates which make up the P. chrysosporium library B are circled. X-axis is Principal Component 1 andy-axis is Principal Component 2. The ED is 394.


Goodell et al.; Wood Deterioration and Preservation ACS Symposium Series; American Chemical Society: Washington, DC, 2003. 2

Figure 2. 2-D plot of Phanerochaete chrysosporium library B. The data for each of the two isolates that comprise this library are circled. X-axis is Principal Component 1 andy-axis is Principal Component 2. The ED is 75.


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Table I. Details of the identification libraries created for Phanerochaete and Trametes. Number of Isolates Used

Number of Profiles in Library

ED

P. chrysosporium

96

394.2

9

A

61

58.2

5

B

18

75.2

2

C

10

18.5

1

D

7

7.2

1

P. sordida

40

558.5

5

A

5

18.7

1

B

12

52.2

1

C

23

52.9

3

37

398.9

7

A

4

22.4

1

B

8

8.4

1

C

25

86.3

5

T. versicolor

49

106.0

7

T. hirsuta

71

736.5

10

A

7

19.5

1

B

9

24.6

1

C

48

62.7

7

D

7

24.5

1

94

94.5

8

P. sanguinea

T. pubescens

2

2

ED is the Euclidean Distance Squared



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Table II. Similarity Indices for the identification of blind P. chrysosporium samples screened against the newly created libraries. Similarity Index Similarity Index First Choice Second Choice

Difference between 1 and 2

st

nd

34541 (Located in Library B)

P. chrysosporium 0.893

P. sordida 0.804

0.089

62778 (Located in Library D)


P. sordida 0.785

0.100

32629 (Located in Library B)


P. sordida 0.822

0.078

48747 (Located in Library A )

P. sordida 0.899


0.041


323

The Phanerochaete and Trametes Libraries 2

The total library E D for P. sordida (Table I) was 558, indicating that some of these isolates may not be same species. Three sub-group libraries were created. Blind samples of P. sordida run against the libraries correctly identified P. sordida as the first choice, but the second choice of P. chrysosporium was too close (0.03 3) for a good species match. The genus Phanerochaete was correctly identified in all blind samples for both species tested. Three sub-group libraries were created for P. sanguinea (Table I). The total library E D was 399. Two of the sub-group libraries contained a single isolate, with the remaining five isolates in the third sub group library. The only sample of Phanerochaete analyzed by recent molecular systematic studies (3, 4) was a single isolate of P. chrysosporium, which was unfortunately not used in this study. These studies reclassified P. chrysosporium with two genera it had never been classified with before (Bjerkandera and Pulcherricium) (3) and was widely separated from the other corticoid fungus used in that study. The molecular relationships within the genus Phanerochaete and individual species is yet to be determined.


2

A single library was created for both T. versicolor and T. pubescens since their E D were only 106 and 94, respectively (Table I). This indicates that all isolates tested for those species are the same species. Four sub-group libraries were created for T. hirsuta. The E D for the total group of 736 was the largest seen thus far. Single isolates composed sub-group libraries A , B , and D. The remaining isolates were grouped in library C. One blind sample of T. versicolor was run against the library and identified correctly with a very good match (difference of 0.292) with T. pubescens a distant second choice. F A M E analysis of a different isolate of T. versicolor choose T. hirsuta first. Analysis of this isolate and its location on a dendogram showed this isolate clustered separately from other T. versicolor isolates, which suggests it should be pooled into its own sub-group. Blind samples of T. pubescens and T. hirsuta could not distinguish between these two species. Molecular systematic studies found T. versicolor and T. sauveolens to be closely related, however, neither T. pubescens nor T. hirsuta were evaluated (3, 4). Based on the high E D there appears to be a lot of variability within T. hirsuta samples. This may be the first series of isolates we attempt to compare by D N A sequencing. There were some isolates that were very distinct outliers, and thus were not included in the library profiles. It will be interesting in the future to compare the D N A sequences of these outliers and the sub-group libraries to determine i f the genetic information supports the separations seen in the F A M E profiles. The authors are currently in the process of creating identification libraries for the brown-rot fimgi Gloeophyllum trabeum, G. sepiarium, and G. striatum. 2

2

2

ACKNOWLEDGMENTS The authors wish to acknowledge Dr. Dana Richter of Michigan Technological University and Rita Rentmeester of the USDA-FS Forest Products Laboratory for their generous contribution of some of the fungal isolates used in this study. Goodell et al.; Wood Deterioration and Preservation ACS Symposium Series; American Chemical Society: Washington, DC, 2003.

324 This work was supported by a Department of Forest Products W U R Grant 9634158-2557 and the State of Mississippi. This manuscript is approved for publication as F W R C Publication No. FP233.

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Hibbett, D.S.; Thorn, R.G. In, The Mycota VII Part B; McLaughlin, D.J.; McLaughlin, E.G.; Lemke, P.A., Eds., Springer-Verlag: Berlin, 2001; pp 121-168. Gilbertson, R.L. Mycologia, 1980, 72,1. Hibbett, D.S.; Donoghue, M.J. Can. J. Bot.. 1994, 73(Suppl1),S853. Hibbett, D.S.; Donoghue, M.J. Syst. Biol., 2001, 50, 215. Tanesaka, E.; Masuda, H.; Kinugawa, K . Mycologia, 1993, 85, 347. Davidson, R.W.; Campbell, W.A.; Vaughn, D.B. Fungi causing decay of living oaks in Eastern United States and their cultural identification. U S D A Tech. Bull. No. 785, Washington, D C , 1942. Nobles, M . K . Can. J. Bot., 1948, 26, 281. Jellison, J; Jasalavich, C.; Ostrofsky, A . Chapter XX, THIS B O O K 2002 Böttger, E.C. ASM News, 1996, 62, 247. Bentivenga S.P.; Morton, J.B. Mycolog. Res., 1994, 98, 1419. Bernard, K . A . ; Bellefeuille, M.; Ewan, E.P. J. Clin. Microbiol., 1991, 29, 83. Moss, C.W. J. Chromatography, 1981, 203, 337. Müller, M.M,; Kantola, R.; Kitunen, V . Mycolog. Res., 1994, 98, 593. Stahl, P.D.; Klug, M.J. Appl. Environ. Microbiol, 1996, 62, 4136. Moore, F.W. Master's Thesis, Mississippi State University, Mississippi State, M S , 1997.


Use of Fatty Acid Profiles To Identify White-Rot Wood Decay Fungi

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