Download Bacterial Fatty Acids

Vol. 4, No. 4
CLINICAL MICROBIOLOGY REVIEWS, Oct. 1991, p. 422-438
0893-8512/91/040422-17$02.00/0
Copyright © 1991, American Society for Microbiology
Applications of Cellular Fatty Acid Analysis
DAVID F. WELCH
Department of Pediatrics, University of Oklahoma Health Sciences Center, and Clinical Microbiology Laboratories,
Oklahoma Medical Center, P.O. Box 26307, Oklahoma City, Oklahoma 73126
INTRODUCTION
a probe. The main requirement of CFA analysis is proper
instrumentation. Technological development by industry
involved in the manufacture of chromatographs and capillary
columns has made it possible for microbiologists to use
gas-liquid chromatography (GLC) more easily. Improved
microelectronics and computer-aided interpretation of data
have also facilitated numerous applications of GLC in clinical microbiology.
In practice, whole cells of bacteria or yeasts are treated to
cause release of their fatty acids, which are converted to a
methyl ester derivative and then analyzed by GLC. Early
attempts to apply CFA analysis to bacterial identification
were made in the 1950s (62). In 1963, Abel et al. (1) were the
first to present evidence suggesting that CFA analysis by
Medically important microorganisms can be identified in
many ways. Conventional methods rely on the expression of
certain properties that are usually mediated directly by
enzyme activity. Extension of this approach to include
numerical identification (120) or automated systems to analyze results often strengthens conclusions. Immunodiagnostic and nucleotide hybridization techniques have improved
sensitivity, specificity, precision, and ease of testing. Chemotaxonomy is also precise and can result in the definition of
highly discriminatory properties. Cellular fatty acid (CFA)
analysis falls into this category (146). In contrast to antigen
detection and hybridization, this technique does not require
422
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422
INTRODUCTION ...................................................................
423
MOLECULAR BASIS OF CFA ANALYSIS ...................................................................
423
Nomenclature of Fatty Acids ..................................................................
423
Lipid Content of Microbes ...................................................................
INSTRUMENTATION AND DATA PROCESSING ................................................................... 424
METHODS ...................................................................
424
FEASIBILITY OF WIDESPREAD USE OF GLC IN CLINICAL LABORATORIES .......................... 425
Practical Considerations ...................................................................
425
426
Comparison with DNA Probes ...................................................................
427
CHARACTERIZATION OF BACTERIA ...................................................................
427
Identification to Genus or Species ...................................................................
Staphylococcus spp ....................................................................427
427
Streptococcus and Enterococcus spp ..................................................................
427
Gram-positive bacilli ...................................................................
428
Mycobacterium and Nocardia spp ...................................................................
428
Enterobacteriaceae ...................................................................
428
Vibrionaceae ...............................................................................
Neisseria and Moraxella spp ................................................................... 428
Campylobacter and Helicobacter spp ..................................................................429
Pseudomonas spp. and nonfermenters ...................................................................
429
Legionella spp.................................................................... 430
Miscellaneous bacteria ...................................................................
430
430
Spirochetes ...................................................................
430
Anaerobes ..................................................................
Identification to Subspecies and Epidemiologic Typing ............................................................... 431
431
Campylobacter jejuni ...................................................................
Pseudomonas cepacia and Staphylococcus epidermidis .............................................................. 431
CHARACTERIZATION OF OTHER MICROBIAL SPECIES .......................................................431
Rickettsia spp .................................................................431
Chlamydia spp .................................................................432
432
Fungi ................................................................
Blood Parasites ................................................................
432
432
Mycoplasmas and Viruses ................................................................
Unclassified Organisms ................................................................
433
DETECTING MARKERS OF INFECTION IN CLINICAL MATERIAL ..........................................433
MEASURING ANTIMICROBIAL RESISTANCE ................................................................
433
CONCLUSION ................................................................
434
ACKNOWLEDGMENTS ................................................................
434
REFERENCES ................................................................
434
VOL. 4, 1991
APPLICATIONS OF CELLULAR FATTY ACID ANALYSIS
smaller genomes, such as Rochalimaea spp., tend to have
few fatty acids, while other eubacteria, such as Xanthomonas spp., have more than 20 fatty acids. Bacteria contain
some CFAs that are unique, i.e., not generally found in
eukaryotic cells. Branched-chain and cyclopropane-containing CFAs characterize many gram-positive and gram-negative bacteria, respectively, but are not found in fungi (or in
humans). Conversely, the polyunsaturated fatty acids found
in higher organisms tend to be absent in aerobic bacteria.
Gram-negative bacteria generally have a higher proportion
of saturated and monounsaturated CFAs with an evennumbered chain of carbon atoms than gram-positive bacteria. The latter, represented by Bacillus spp. and staphylococci, tend to have saturated, branched-chain CFAs with an
odd-numbered chain of carbon atoms and lower amounts of
straight-chain, saturated CFAs. Coryneforms and streptococci have straight-chain and unsaturated CFAs.
Nomenclature of Fatty Acids
Fatty acids are properly named according to the number of
carbon atoms, the type of functional groups, and the doublebond location(s). The systematic name can be simplified by
writing C followed by the number of carbon atoms to the left
of a colon and the number of double bonds on the right
(Table 1). Different conventions are encountered for locating
the double-bond positions and cis or trans isomers. w, the
lowercase letter omega of the Greek alphabet, indicates the
double-bond position from the hydrocarbon end of the chain,
and c and t indicate the cis and trans configurations of the
hydrogen atoms. It is important to note that, as with other
conventions, numbering of branched-chain, cyclopropanecontaining, and hydroxy fatty acids proceeds from the
carboxyl end of the molecule. To illustrate these principles,
Fig. 1 shows the long form of two fatty acids commonly
found in bacteria. Some fatty acids were at one time given a
common name to reflect the source from which they were
originally identified, e.g., "lactobacillic" acid from Lactobacillus spp. Since not all fatty acids have been given
common names, the use of such terminology can be confusing, and the systematic or simplified form is usually preferred.
Lipid Content of Microbes
MOLECULAR BASIS OF CFA ANALYSIS
The source of fatty acids in microbial cells is lipid,
primarily that of the cell membranes (e.g., phospholipid) or
the lipid A component of LPS in gram-negative bacteria and
lipoteichoic acid in gram-positive bacteria. In addition to
phospholipid, fungi synthesize sterols (e.g., ergosterol) as a
major lipid. The fatty acid content of all lipids is determined
by the particular type of biosynthetic pathway of a given
species. The process is initiated by synthesis of the coenzyme A ester of a fatty acid, with a molecule of acetyl
coenzyme A used as primer. Most bacteria synthesize fatty
acids with chain lengths of 10 to 19 carbon atoms, and the
most prevalent fatty acids are those with 16 or 18 carbon
atoms. The 16-carbon saturated CFA hexadecanoic acid, in
particular, is highly conserved among prokaryotes. The
variable properties that make an organism's CFA composition distinctive include quantitative differences in CFA content and the presence of other CFAs, of which more than 100
have been identified (132). The usual profile features 5 to 15
CFAs in significant amounts. Organisms in genera with
Figure 2 illustrates typical cell membrane structures and
hypothetical examples of commonly found fatty acid moieties in two phospholipids. Figure 3 illustrates the lipid A
molecule of Escherichia coli. The fatty acids constituting E.
coli lipid A are qualitatively and quantitatively reflected in
the results of whole-cell CFA analysis with respect to the
relative proportions of C14:0, C12:0, and 3-OH-C140. While
3-hydroxy fatty acids in general are a marker for gramnegative bacteria, 3-OH-C14:0 is indicative of the type of lipid
A found in E. coli (53). The fatty acid composition of lipid A
in other gram-negative bacteria may be different from that of
E. coli lipid A and similarly is reflected in the CFA analysis
of whole cells. For example, the lipid A of Bacteroides
fragilis contains five fatty acids instead of the six found
in E. coli (161). The CFAs of B. fragilis found in LPS
are iso-C14:0, 3-OH-C15:0s iso-3-OH-C160, 30HC16:0, and
3-OH-C17:0. Besides the lipids and fatty acids typical of other
bacteria, many anaerobes also contain unique lipids called
plasmalogens (56). These are phospholipid analogs with an
ether linkage instead of the usual ester linkage of a fatty acid
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GLC could successfully identify bacteria. Other early studies were aimed at CFA analysis from the standpoint of
bacterial virulence factors. It was recognized that rough
(avirulent) strains of Vibrio cholerae lacked branched-chain
CFAs but that such chains were present in smooth (virulent)
strains (14). An alteration of the lipid A composition of
Salmonella spp. was reported to correlate with a change in
virulence (97). Abel et al. (1) identified different CFA patterns within various members of the family Enterobacteriaceae and in some gram-positive bacteria, but no attempt
was made to identify specifically the CFAs that characterized each genus or species that they tested. Moreover, the
analyses required numerous steps, and the apparatus used
for extraction and esterification was cumbersome and technically complicated by today's standards. Nevertheless,
their report established the potential usefulness of CFA
analysis and established the foundation of further investigation.
This review will discuss the development of CFA analysis
pertaining to microbial identification from the mid-1960s to
the present. For the purpose of this review, CFAs will be
defined as the components of any cellular lipid that have a
carbon chain length of 9 to 20 atoms. This includes the
majority of fatty acids located in the cell membrane as
glycolipid and phospholipid. It includes the fatty acid constituents of lipopolysaccharides (LPS) but does not include
the long-chain (24 to 90 carbon atoms) mycolic acids or the
isoprenoid quinones. Understanding the molecular basis and
development of the analytic instrumentation is central to an
appreciation of the potential use and limitations of this
technology. The CFA composition of most microbes has
been studied, but data to predict the success of applications
in diagnostic microbiology are incomplete in many cases
because of the limited scope of the studies or use of older
instrumentation. Thus, a fair degree of caution must be
exercised in examining the earlier literature and comparing
results of different studies. In addition to changes in technique with respect to chromatographic equipment, the CFA
composition of cells varies, at least quantitatively, according
to culture conditions (46, 72, 90). Nonetheless, the existing
information from these older studies interpreted in light of
what has been reported recently can be used to form
reasonable conclusions about the role of CFA analysis in
clinical microbiology.
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WELCH
424
TABLE 1. Nomenclature of some fatty acids commonly found in bacteria
Name
Type of
fatty acid
Simplified
Systematic
Common
Dodecanoic
Tetradecanoic
Hexadecanoic
Octadecanoic
Eicosanoic
C12:0
C14:0
cis-9-Hexadecenoic
cis-9-Octadecenoic
cis-11-Octadecenoic
C16:1w7c
C18:1w9c
13-Methyltetradecanoic
12-Methyltetradecanoic
10-Methyloctadecanoic
Iso-C15:0
Anteiso-C,5.0
10-Me-C19.0
Tuberculostearic acid
Hydroxy
3-Hydroxytetradecanoic
3-OH-C14:0
P-Hydroxymyristic acid
Cyclopropane
cis-11,12-Methylene-octadecanoic
C19:Oycll,12
Lactobacillic acid
Saturated
Unsaturated
Branched chain
Lauric acid
Myristic acid
Palmitic acid
Stearic acid
Arachidic acid
C16:0
C18:0
C20:0
Palmitoleic acid
Oleic acid
Vaccenic acid
C18 lw7c
to a glycerol carbon. The derivative formed is a dimethylacetal instead of the typical methyl ester derivative.
INSTRUMENTATION AND DATA PROCESSING
The only commercially available GLC system dedicated to
the identification of bacteria and yeasts by CFA analysis is
the Microbial Identification System. It was codeveloped by
the Hewlett-Packard Co. and Microbial ID, Inc., Newark,
Del., and is now marketed by Microbial ID. The system
consists of a gas chromatograph equipped with a flame
ionization detector, 5% methylphenyl silicone fused-silica
capillary column (25 m by 0.2 mm), automatic sampler,
integrator, computer, and printer (163). The original data
base for identification of aerobic bacteria was developed by
Sasser (132). Software libraries for the identification of a
large number of aerobes, anaerobes, mycobacteria, and
yeasts have subsequently been developed and updated.
Equipment designed and dedicated for the purpose of microbiologic identification is not a necessary requirement, but the
Microbial Identification System greatly facilitates the control
of conditions during analysis and the interpretation of results. An element of automation is added by the automatic
sampler, which lets the operator run up to 100 samples
without intervention. Upon injection of a sample into the
column containing a specified flow of hydrogen (carrier) gas,
the fatty acids are separated because of different retention
times under conditions of increasing temperature. A computer-controlled temperature program begins at 170°C and is
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gradually increased to 270°C at 5°C/min. When the methyl
ester derivatives reach the end of the column, a signal from
the flame ionization detector is recorded as a peak by the
integrator. The area under the peak reflects the relative
amount of individual fatty acids. The retention time of a
mixture of known fatty acids is used by the computer, or by
the individual if done manually, to calculate an equivalent
chain length for the molecule. The equivalent chain length is
equal to the number of carbon atoms of a straight-chain
saturated fatty acid or to a number that can be calculated by
interpolation with a mathematical formula for other fatty
acids. The accuracy of naming fatty acid peaks by comparing
retention times with those of a known mixture is high when
the computer is used, but definitive identification can be
made only by mass spectrometry. For most applications,
mass spectrometry is unnecessary. The amounts of CFAs
detected are calculated as a percentage of the total amount,
and a summary can be printed at the end of each run to show
the names and amounts of the CFAs and, optionally, the
most likely identification according to similarity to entries in
the data base (132, 163). The multivariate statistical method
of principal-component analysis is used by the computer as
the basis for interpreting data and matching an unknown
with data base entries. Each CFA quantitatively represents
the objects of the principal-component analysis, resulting in
pattern recognition as the basic means of identifying an
isolate (89). Numerical analysis of CFA data can also be
performed, resulting in a computer-generated dendrogram.
An unweighted pair-matching method may be used to show
similarities at the genus, species, and subspecies levels
(128).
H
FIG. 1. Molecular formulas of cis-li-octadecenoic acid or C18.
1'a7c or vaccenic acid (top) and cis-11,12-methylene-octadecanoic
acid or C19:cyc11,12 or lactobacillic acid (bottom).
METHODS
The usual preparation of samples for CFA analysis consists of hydrolysis of the whole-cell fatty acids to form
sodium salts and then methylation of the CFA esters to make
them volatile in the gas chromatograph. Various procedures
involving acid or base hydrolysis followed by esterification
with methanol have been described before (78, 95, 99, 111,
134). Recent improvements in methods have optimized the
recovery of CFAs that formerly were difficult to identify
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a Adapted from reference 134.
APPLICATIONS OF CELLULAR FATTY ACID ANALYSIS
VOL. 4, 1991
BILAYER.....
CELL MEMBRANE
FIG. 2. Diagram of cell membrane and example of fatty acid composition. Adapted from reference 129.
reliably. Miller (96) described a simple washing procedure
with NaOH that removes free acids and secondarily prevents the tailing of hydroxy acid peaks in fused-silica capillary columns. This and other refinements in the procedures
developed by Lambert and Moss (78, 99) have led to a
relatively simple four-step process for preparation of samples (132, 163).
First, after cells (approximately 50 mg, wet weight) are
harvested from culture plates incubated for 24 to 48 h,
saponification is conducted in a sodium hydroxide-methanol
solution for 30 min at 100'C. This liberates the fatty acid
from cellular lipid. The second step is methylation with HCO
in methanol at 80'C for 10 min. Third, the fatty acid methyl
esters are extracted into a solution of hexane and methyl
tertiary butyl ether (10 min), and finally, the extract is
washed in aqueous NaOH for 5 min and then transferred to
a GLC vial which is capped for injection into the GLC. Thus,
the entire process of sample preparation takes roughly 1 h.
Multiple samples can be processed simultaneously, with the
incremental increase in the time required generally proportional to the number of samples.
Derivatives of CFAs other than methyl esters can be
analyzed. For direct analysis of clinical material in which
increased sensitivity may be required, i.e., requiring electron capture rather than flame ionization detection, halogenated CFAs are prepared. Many of the methods pertaining to
the preparation of trichloroethanol esters have been developed by Brooks and co-workers (20, 21, 33) and have been
most successfully applied to cerebrospinal fluid (CSF) (21)
and diarrheal stool specimens (19). A drawback of these
applications is that the sample preparation steps are more
involved and time-consuming than those for the preparation
of fatty acid methyl esters. In addition, a reversed-phase
column chromatography step is recommended to partition
CFAs of interest from interfering constituents (34).
The idea was ahead of its time, however, because the
technology for making it practical and dependable in the
clinical laboratory was lacking. The developmental status of
most applications reached a plateau in the 1970s and did not
justify widespread use of the method until recently. The
situation was aptly summed up by O'Leary in a 1975 review
(123): "We are still convinced that fast analyses and proper
interpretations of cellular lipid contents can be used to
identify pathogens, but so far it seems that this is an idea
whose time has not yet come, but is close." Interesting
foresight was also contained in the title of a contribution by
Moss to the proceedings of the Third International Symposium on Rapid Methods and Automation in Microbiology:
"Chromatographic Analysis: a New Future for Clinical
Microbiology" (98).
Two major advances that have brought the technology
forward in terms of making it appropriate for use in the
clinical laboratory can be cited. One is the development and
implementation of fused-silica capillary columns (99, 104). In
contrast to packed columns and those of greater width, these
columns allow reproducible recovery of hydroxy fatty acids
and the ability to distinguish several isomers of fatty acids
with the same carbon chain length (99). The second advance
is the efficient data processing afforded by modern microcomputer systems (5, 48). Of considerable importance also
are the contributions of Miller (96) and of Moss and others
(78, 95, 99, 111), who defined practical steps for sample
preparation in the clinical laboratory. A natural extension of
current technology will be further automation and thus
reduced technologist involvement in the saponification,
methylation, and extraction procedures. It also seems possible to concomitantly scale down the process, since the
actual sample requirement is only a few microliters. Thus,
fewer cells or even single colonies from primary cultures
could be analyzed instead of large inocula from pure subcultures.
FEASIBILITY OF WIDESPREAD USE OF GLC IN
CLINICAL LABORATORIES
The prospects for using GLC of bacterial CFAs in diagnostic microbiology have been appreciated for several years.
Practical Considerations
From a practical standpoint, the chief drawback of CFA
analysis mainly involves the preparation of samples. Al-
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LIPID
425
426
CLIN. MICROBIOL. REV.
WELCH
v7
HO
H-G
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OH
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FIG. 3. Diagram of fatty acids constituting E. coli lipid A. Adapted from reference 53.
though the complexity and number of steps have been
minimized by recent methodologic improvements (78, 99,
132), sample preparation is relatively laborious compared
with preparation for other identification methods used in
clinical microbiology. While the process is amenable to
handling one to many samples at a time, it is impractical to
handle very few and laborious to handle very many (.40 to
50) at once. Sufficient technical expertise is necessary to
ensure proper preparation of samples. Complete saponification by proper exposure of cells in a boiling water bath and
careful control of the time and temperature during methylation are critical steps. The quantity of cells that must be
harvested may be achieved easily with a loopful for some
strains but with great difficulty for certain strains that have
different growth characteristics or colonial properties.
Properly functioning instruments require attention to
maintenance. Except for the regular replacement of column
liners and injection port septa, the demands of GLC maintenance are not generally very great. High-purity gases are
required, but their acquisition and storage should not be a
problem for laboratories that maintain CO2 incubators or
anaerobic chambers. The use of gases is economical, as is
the use of other reagents, which can be prepared in large
quantities and stored at room temperature. Before analysis,
samples can be stored in a freezer if desired. The risk of
chemical contamination of samples during preparation must
be controlled by using clean glassware and avoiding the
introduction of interfering substances, especially those such
as particulate rubber or oil matter. In contrast to sample
preparation, the performance of GLC, including data entry,
storage, and retrieval, is economical in terms of manual
labor.
Comparison with DNA Probes
There are similarities as well as differences between the
two approaches of CFA analysis and use of DNA probes (4,
138) in clinical microbiology. Both are widely applicable,
since most pathogens contain fatty acids and all contain
DNA (or RNA). In this context, CFA analysis requires
sophisticated instrumentation, whereas hybridization technology requires a battery of probes. Both methods potentially offer a high level of sensitivity and specificity, although
the ultimate sensitivity and specificity are theoretically
achievable with probes combined with DNA amplification.
The sensitivity of GLC, however, can be made to exceed
that of DNA probes without amplification. By using an
electron capture detector, femtomole (10-15) quantities of a
Mycobacterium tuberculosis CFA can be detected (20). Both
methods can provide results the same day that tests are
begun, both have similar requirements of sample preparation, and both potentially can be adapted to direct detection
of pathogens in clinical material.
Both techniques also share limitations to their widespread
use in clinical laboratories. They are generally unwarranted
for common pathogens such as Staphylococcus aureus that
are easily identified by one or two simple tests. Moreover,
although CFA and DNA probe analyses are relatively rapid,
many simpler tests are more rapid and less expensive.
Though not as significant a problem as that associated with
the use of radiolabeled probes, the disposal of waste in the
form of organic solvents is a minor drawback of GLC. The
use of either approach in smaller laboratories may not be
cost-effective because of a relatively high cost for lowvolume testing. Conversely, laboratories that provide refer-
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HN
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VOL. 4,
1991
APPLICATIONS OF CELLULAR FATTY ACID ANALYSIS
ence services, mycobacteriology, or high-volume testing
may find either technology equally applicable. Characterization of unusual isolates that may be frequently encountered
in a reference laboratory can be more readily achieved by
GLC than by DNA hybridization because of the limited
array of specific probes available. Many laboratories are
beginning to address and evaluate the judicious use of DNA
probe technology and should consider doing the same for
GLC to advance capabilities in the laboratory diagnosis of
infectious diseases.
Identification to Genus or Species
Staphylococcus spp. S. aureus and coagulase-negative species have qualitatively similar CFA compositions (47, 76,
122). They consist mainly of the branched-chain iso-C15:0,
anteiso-C15:0, and anteiso-C170 CFAs and C18:0 and C20:0
CFAs. From the results of early investigation (65), it was
concluded that S. aureus could not be distinguished from S.
epidermidis by CFA patterns but that there was a difference
between Micrococcus and Staphylococcus spp. The latter
species contain iso-C19:0. anteiso-C19:0 and C20:0, whereas
C15:0 is present in Micrococcus spp. but is either absent (65,
139) or present in only very low amounts (47) in Staphylococcus species. With the exception of S. saprophyticus and
S. warneri, other species of coagulase-negative staphylococci are not readily distinguished by CFA analysis. S.
warneri is characterized by a C22 CFA, and S. saprophyticus
contains larger amounts of C16:0 and C20:0 than of other
chains (47). It is interesting that the species of Micrococcus
studied by Jantzen et al. (65) included Micrococcus mucilaginosus (Stomatococcus mucilaginosus), now recognized
with increasing frequency in vascular-catheter-related infections. Their study of five strains revealed the absence of
micrococcal species, and
C16:1, which characterized otherconcentrations
of iso-C14.0
the presence of relatively high
and C16:0 CFAs.
Notwithstanding the reported similarity between the
CFAs of S. aureus and other species, which have probably
been studied in some cases as a heterogeneous group, it
seems that well-characterized species of the coagulasenegative staphylococci deserve further study to determine
the usefulness of CFA analysis in identifying them. The
results of one such study have been reported recently.
Kotilainen et al. (76) found that the CFA compositions of
blood isolates could be used to distinguish S. epidermidis, S.
haemolyticus, S. warneri, S. capitis, S. lugdunensis, S.
simulans, and S. hominis, provided that results were interpreted by cluster analysis. Quantitative differences in the
amounts of individual CFAs were insufficient without computer analysis to distinguish species. These results are not
entirely consistent with those of O'Donnell et al. (122), who
showed that numerical analysis of CFA data separated only
50% of the strains studied into homogeneous clusters. In
view of increased rates of nosocomial infections caused by
coagulase-negative staphylococci, this technique may be
helpful in readily characterizing (i) isolates from a series of
blood cultures in a patient or (ii) paired isolates from
indwelling line and venipuncture sites, thus guiding interpretation as to the clinical significance of positive blood culture
isolates.
Streptococcus and Enterococcus spp. CFA analysis has been
used as an aid in classification of the streptococci. Bosley et
al. (12) compared the CFAs of streptococci (175 cultures)
with those of related genera. Streptococcus spp. contain
C16:1.5, which the genera Aerococcus, Enterococcus, Pediococcus, and Lactococcus do not. Those investigators also
reported the presence of significant quantities of two relatively unusual unsaturated CFAs, C16:1i9c and C16:1.9g, in
Aerococcus spp. Clinically important enterococci contain
the cyclopropane-containing CFA C19:0cyc11,12' which was
noted in an earlier study to be present in reproducibly
different amounts among four species of enterococci (2). The
lowest amount, an average of 1%, occurs in Enterococcus
casseliflavus; intermediate amounts are found in E. faecalis
and E. faecium; and the greatest amount, an average of 17%,
occurs in E. durans (2). Difficulties involving phenotypic
variability at the species level may be overcome to a certain
extent, but CFA profiles also indicated heterogeneity among
certain species. The profile of "S. milleri" (S. anginosus)
differs qualitatively by source of isolates. For example,
vaginal strains reportedly contain C17:0, which is absent in
oral strains (45).
Attempts to identify Lancefield groups by CFA were made
by Drucker (44), who determined that representative strains
of different groups had profiles that could be distinguished
only by computer analysis. Determining the CFA composition may be more helpful among streptococcal species not
identifiable by group carbohydrate antigen. For example,
Streptococcus mutans and S. salivarius are distinguished
from related species by the presence of C20:0 and C20:1 (80,
130). A relatively large amount of C20:1 can be used to
distinguish S. mutans or S. salivarius from a physiologically
similar species such as S. bovis (130). In summary, the
applicability of CFA analysis for Streptococcus spp. is
minimal for those species that can be identified by immunodiagnostic reagents and seems to be greatest for other
species and for differentiation among related genera.
Gram-positive bacilli. The coryneforms are probably distinguishable on the basis of CFAs, but many have been
difficult to identify to the species level by classical means.
Therefore, few studies have applied CFA analysis to this
group. In general, these organisms contain large amounts of
C18:0, C16:0, and C18:1, and some species contain tuberculostearic acid (TBSA), the 10-methyl octadecanoic acid
typical of mycobacteria (6). Corynebacterium diphtheriae
does not contain TBSA, but other species of Corynebacterium contain small amounts and Rhodococcus species contain larger amounts of it. GLC with well-characterized
strains may aid in overcoming the problems of classification
that persist with members of this group. Athalye et al. (6)
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CHARACTERIZATION OF BACTERIA
Analysis of CFA composition can be done in conjunction
with other tests or as the main determinant in identification.
In general, the greater the dependence on CFA analysis, the
greater the need to control variables such as culture media
and growth conditions. These must be controlled as carefully
as possible to arrive at valid conclusions from CFA analysis
as the basis for differentiation at the subspecies level. On the
other hand, medium, length and atmosphere of incubation,
and temperature generally have little impact on the qualitative detection of CFAs. The influence of temperature, for
example, is seen mainly in the relative proportions of CFAs
rather than in loss or gain of major components. In E. coli,
an increase in temperature results in decreased unsaturated
relative to saturated fatty acids (90). For bacteria that form
cyclopropane CFAs from monoenoic acids, cells harvested
during the stationary phase of growth contain larger amounts
of these CFAs than cells harvested during exponential
growth (72, 90).
427
428
WELCH
identified in nine strains of Nocardia asteroides and a CFA
identified by mass spectra as 2-methyl-2-octadecenoic acid
was found in all strains of M. fortuitum, but neither of these
CFAs was detected in M. chelonae or in Nocardia spp. In
addition, M. fortuitum and M. chelonae, but not N. asteroides, reportedly contain C14:0 (49).
Enterobacteriaceae. Typical CFAs are primarily saturated
C17:0cyc, C19:ocyc, and 3-OH-C14:0 Most also contain C16:1 in
variable amounts (9, 88). Clear separation of members on the
basis of CFA composition has not consistently been established, particularly by earlier investigations. Differences
between strains of the same species have been noted, and
similarities may be observed from one genus to the next (88).
As with other groups, the explanation is largely the uncertainty of taxonomic relationships. More recent studies,
however, suggest that the CFA composition of members of
the family Enterobacteriaceae is at least genus specific and
in some cases specific enough for identification to species (9,
154, 163). For example, Morganella spp. can be distinguished from Proteus and Providencia spp. by the presence
of a significant amount of C12:0 (154). Other minor quantitative differences distinguish Proteus vulgaris and Providencia
alcalifaciens from the other species of the Proteus-Providencia group.
Algorithms based on cluster analysis of CFA data were
established by Boe and Gjerde (9) to separate members of
the families Vibrionaceae and Enterobacteriaceae. Within
the latter family, Boe and Gjeide were able to identify two
species of Salmonella, E. coli, Enterobacter cloacae, Morganella spp., and Klebsiella pneumoniae on the basis of
quantitative differences in 17 CFAs. As taxonomic relationship among members of this family continue to become
validated, it will be possible to apply CFA analysis for
comparison with phenotypic characterization. It is not expected, however, that certain species or even genera such as
Escherichia (E. coli) and Shigella that have high DNA
relatedness could ever be wholly distinguished by CFAs,
given the presently accepted classification.
Vibrionaceae. The Vibrionaceae may be distinguished
from the Enterobacteriaceae by the presence of significant
amounts of branched-chain CFAs, especially iso-C16.0 and
iso-C18:0, in addition to greater amounts of C16:1 (9). Various
species of Vibrio and Aeromonas can be distinguished by
CFAs. Urdaci et al. (149) studied Vibrio isolates belonging to
22 species and found that satisfactory separation of most of
them could be achieved on the basis of principal-component
analysis of the CFA data. Isolates identified to species level
included V. vulnificus, V. fluvialis, and V. damsela. However, V. cholerae could not be distinguished from V. mimicus, and V. parahaemolyticus clustered with V. alginolyticus and V. natriegens. Among Aeromonas species, the ratio
of C16:0 to C17:0 was used to separate clinical isolates into the
species designated Aeromonas hydrophila (mean ratio, 8.6),
A. sobria (mean ratio, 22.7), and A. caviae (mean ratio, 42.3)
(26). Low amounts of iso-C15:1 and iso-C17.1 may serve to
distinguish Aeromonas from Vibrio species (149), but these
CFAs were not reported in Aeromonas spp. by other investigators (26).
Neisseria and Moraxella spp. CFA analysis was first used
with members of the genus Neisseria to address problems
pertaining to classification. Consistent with findings from
DNA studies, results of GLC showed a marked difference
between Neisseria (Moraxella) catarrhalis and other species
(86). M. catarrhalis contains significant amounts of C10:0 and
C18:0 but less C14:0 than is found in the other species (66, 86).
The "true" Neisseria spp. are qualitatively similar, with the
Downloaded from cmr.asm.org at Griffith University on May 9, 2008
compared profiles of unidentified pathogenic coryneforms
with those of several reference strains and found similarities
that may provide aids to identification.
Listeria monocytogenes is characterized by branchedchain CFAs 15 and 17 carbons long (both iso and anteiso),
C16:0, and C14:0 (126). The composition of Bacillus species is
generally the most complex in terms of branched-chain
CFAs, with typically seven or more iso or anteiso forms (70).
In addition to large amounts of C16:0, Bacillus cereus is
characterized by iso-C15:0, iso-C13:0, iso-C17:0, iso-C14:0,
anteiso-C15:0,. and anteiso-C13:0 (119). A large study recently
examined 561 isolates encompassing the genera Corynebacterium, Arcanobacterium, Actinomyces, Brevibacterium,
Erysipelothrix, Oerskovia, Propionibacterium, Rothia, Listeria, Kurthia, and Jonesia (7). Strains were assigned to one
of two broad groups on the basis of overall amounts of
branched-chain versus straight-chain CFA. Several but not
all species within the two groups could be precisely identified by further quantitative analysis of the CFA profiles,
resulting in the investigators' conclusion that conventional
tests in conjunction with GLC should be performed for
identification of coryneforms and similar bacteria.
Mycobacterium and Nocardia spp. Mycobacteria have been
chosen frequently for study by CFA analysis because of
their high content of cellular lipid. Because of the abundance
of mycolic acids in mycobacterial species, several studies
have addressed the separation of compounds of both types,
i.e., those with chains of >20 carbons (mycolic acid cleavage
products) and those with chains of <20 carbons (constitutive
fatty acids) (58, 87, 150). Quantitative analysis of fatty acids
belonging to these two groups results in accurate identification to the species level (58, 69, 82, 150, 151). Among isolates
encountered over a 2-year period, Jantzen et al. (69) determined that M. tuberculosis could be identified without
exception, largely on the basis of C26:0 at concentrations of
1 to 13%. Other studies suggest that differentiation of species
may be achieved without establishing special chromatographic parameters to detect mycolic acid cleavage products
as well as constitutive CFAs (82, 93, 124, 143). Tisdall et al.
(144) concluded that GLC was equally accurate as and more
rapid than conventional identification. All isolates of M.
gordonae and M. kansasii and 85% of isolates of M. tuberculosis were correctly identified in a prospective clinical
study. In a subsequent follow-up study, there were no
discrepancies in the identification of 42 isolates of M. tuberculosis. Of 325 strains overall, 320 matching identifications
were obtained by GLC and biochemical profiles (143). The
most frequent discrepancies occurred with M. avium, M.
scrofulaceum, and M. gastri.
All species of mycobacteria are characterized by an abundance of C16:0, Cl8i,:,9, and, with the exception of M.
gordonae, the single-methyl branched constituent TBSA
(69, 82, 144). M. gordonae may also be recognized by the
presence of iso-C14:0 (82, 144). However, the closely related
species M. avium, M. intracellulare, and M. scrofulaceum
cannot be easily differentiated, even on the basis of mycolic
acid cleavage products (69). Differentiation of species belonging to Runyon group I (M. kansasii and M. marinum) is
possible (69), as is differentiation of the less commonly
encountered species M. terrae, M. xenopi, M. flavescens
(93), and M. malmoense (69, 150). Unusual branched-chain
CFAs, i.e., a 14-carbon chain in M. kansasii and a 15-carbon
chain in M. marinum, distinguish these species (141, 144).
Analysis of CFAs may also be useful in differentiating the
group IV rapid growers M. fortuitum and M. chelonae from
Nocardia spp. (49). A C20:1 CFA absent in mycobacteria was
CLIN. MICROBIOL. REV.
APPLICATIONS OF CELLULAR FATLY ACID ANALYSIS
VOL. 4, 1991
exceptions of N. flavescens and N. elongate, which contain
The pathogenic Neisseria spp. contain large
30OHHC16:0.
amounts of C16:0, C16:1, C12:0, 30HC12:0, and C14:0 (107). In
contrast to Moss et al. (107), who reported essentially
identical patterns of CFAs in N. gonorrhoeae and N.
of
ingitidis, Jantzen et al. (66) presented a numerical analysis
data that suggests that minor quantitative differences may be
sufficient to reliably distinguish N. gonorrhoeae and N.
men-
meningitidis.
Campylobacter and Helicobacter spp. With the recognition
of new species of Campylobacter-like organisms, CFA com-
hydroxy CFAs (Table 2).
Nonfermenters associated with rarely encountered but
identified by GLC. For
important infections can be readily
two
other pathogens, a
and
corrodens
Eikenella
example,
which it
Kingella sp. and Cardiobacterium hominis,arewithdistinguishshares certain phenotypic characteristics,
able by CFAs (157). Eikenella corrodens contains less C14:0
than the other two and has C18:1.,7c and C16:0 as the major
CFAs and C16:1, C12:0, C14:0, and C18:0 in smaller amounts
(125, 157).
Cardiobactenium,
hominis is the
only
one
of the
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in
position has become regarded as an important parameter
isolates
Human
members
of
this
true
genus.
characterizing
of various other species such as "Campylobacter cinaedi"
and "C. fenelliae" have CFA profiles that distinguish them
from other campylobacters. The CFAs individually unique
to these organisms suggest that these species may not be
members of the genus Campylobacter. Their assignment to
the genus Helicobacter has been proposed (152). Blaser et
al. (10) first demonstrated that C. jejuni could be differentiated from C. fetus or C. intestinalis by the presence of
C19:Ocyc. Three isolates of another species, C. landis, in
which Cl9:Ocyc is absent, were recognized by CFA analysis
among 23 isolates that were from patients with gastroenteritis and were initially identified as C. jejuni (29). Major
differences in the CFA composition of species were further
documented by Lambert et al. (83). In addition to the
association of Cl9:ocyc with C. jejuni, they found that a group
of
including C. fetus was characterized by the presence
and 3-0H-C16:0. This observation has been con3-0H-C14:0
firmed recently by Brondz and Olsen (17). Unlike Campylobacter spp., Helicobacter pylori (formerly C. pyloridis) is
characterized by unusually low amountsof C16:0 (57). The
major CFAs of Helicobacter spp. are C14:0, C19:ocyc, C18:1,
and C18:0.
Pseudomonas spp. and nonfermenters. The genus Pseudomonas contains a larger number of species that can be
characterized by CFA composition, which greatly facilitates
identification. This is in part due to the fact that typical CFA
which, when
profiles of pseudomonads have several CFAs
of nearly
quantitatively analyzed, are distinctive. Examples branched
all major CFA types, i.e., saturated, hydroxy,
chain, cyclopropane, and unsaturated, are found in this
group (37, 39, 40, 42, 101, 113, 131). The division of
Pseudomonas species into eight GLC groups (101) parallels
RNA homology grouping. The GLC groups I and II described by Moss and Dees (101) in 1976 still correspond to
the respective RNA groups consisting of Pseudomonas
and others
aeruginosa and others (group I) and P. cepacia identify
P.
to
needed
is
CFA
analysis rarely
(group II).
and more
aeruginosa, of course, because much simpler
reliable techniques exist. However, Veys et al. (156) proroutinely,
posed the use of GLC to identify nonfermenters
of 35
total
a
to
other
belonging
pseudomonads
including
distinct GLC groups. Members
species. They identified 19differed
primarily in the types of
belonging to these groups
110
429
430
WELCH
C15:0 (158). Group EF-4 and M-5 organisms contain 3-OHC12:0, and they can be separated on the basis of 2-OH-C16:0,
Canteiso-17:0, and C170cyc' which are all present in group EF-4
organisms. Capnocytophaga spp. have been further characterized and found to contain the uncommon CFAs 3-OH-isoC14:0 and 3-OH-iso-C16:0 (38). These serve to distinguish
Capnocytophaga spp. from other gliding bacteria, but they
also are found in Flavobacterium spp. (102).
The application of CFA analysis in studying another
formerly unclassified group (IVe) proved useful in establishing proper classification of the urinary tract pathogen Ohigella urealytica (35). The phenotypic characteristics of this
organism resemble those of Alcaligenes, Brucella, and Bordetella spp., but the CFA compositions of these organisms
are sharply different. 0. urealytica (IVe) contains C18.1 in
large amounts; Brucella spp., except Brucella canis (36), are
characterized by large amounts of Cl9:ocyc, and Alcaligenes
spp. and Bordetella bronchiseptica contain C17:0cyc (35).
Cyclo CFAs were not detected by Jantzen et al. (68) in
Bordetella pertussis, whereas significant amounts were
found in Bordetella parapertussis and, along with 2-OHC12:0, in Bordetella bronchiseptica. Additional investigations of the CFA compositions of Brucella spp. have shown
specific patterns for Brucella abortus, Brucella melitensis,
Brucella canis, and Brucella ovis (28, 43, 140).
Phenotypically similar members of the Achromobacter
group can be separated by CFA composition. Achromobacter xylosoxidans primarily consists of C16:0, C17:Ocyc' and
hydroxy CFAs, whereas other Achromobacter species contain large amounts of C18j1 and C19 0cyc CFAs (40).
F. tularensis has one of the more distinctive patterns,
being characterized by long-chain CFAs (63, 118) and the
hydroxy CFAs 2-OH-C10:0, 3-0H-C16:0, and 3-OH-C18.0
(63). Because there is little similarity between the CFAs of
F. tularensis and those of other gram-negative bacteria,
GLC is highly specific for the identification of F. tularensis
(61). Yersinia pestis and Y. pseudotuberculosis have been
reported to contain 16- and 18-carbon hydroxy CFAs, but
Yersinia spp. are different in containing C17:0cyc (145).
Spirochetes. Treponema pallidum purified from rabbit testicular tissue has been inconclusively analyzed in regard to
CFA content. The CFAs found (mainly C16:0, C18:1, C18:0,
and C18:2) were present in the bacteria but were also found in
smaller amounts in uninfected tissue (92). Up to 50% of the
total CFAs in Leptospira spp. are in the form C16:0 (74). The
rest are mainly unsaturated C,16:1 C18:1' C18:2, and C14:2
CFAs.
Anaerobes. The most common application of GLC in
identification of anaerobes has been the detection of metabolic end products of glucose fermentation. Analysis of
whole cells also is potentially useful and has been proposed
as offering a practical approach, at least to identification of
Bacteroides spp. (153). The key in a proposed scheme is the
relative amount of the 15-carbon CFAs that predominate in
clinically encountered species (94, 95). The species within
three GLC groups delineated by Mayberry et al. (95) were
identified on the basis of ratios of one 15-carbon CFA to
another.
The clinically important Fusobacterium species are
clearly separated from Bacteroides spp. by CFAs. Fusobacteria are generally characterized by the presence of 3-OHC14:0, C14:0, C16:0, C16:1, 3-OH-C16:0, C18:0, and C18:1 (25, 60,
67). In contrast to Bacteroides spp., Fusobacterium spp.
contain no methyl branched CFAs. The amounts of 3-OHC14:0 vary according to species, and these differences and
the presence of 3-OH-C16:0 aid in the identification of Fuso-
Downloaded from cmr.asm.org at Griffith University on May 9, 2008
three characterized by the absence of 3-OH-C12:0. Another
example is in cystic fibrosis patients, from whom the important pathogen P. cepacia can usually be isolated and identified from respiratory specimens with little difficulty by using
selective media. However, when a closely related species
such as P. gladioli is present, differentiation of P. cepacia
may be problematic. Furthermore, the distinction is likely to
be important, since P. cepacia is a known prognostic factor
in pulmonary disease of cystic fibrosis patients but P.
gladioli is not. The CFA profile of P. gladioli differs from
that of P. cepacia by the presence of 3-OH-C10:0 and
substantially smaller amounts of C16:1 and C17:0cyc (27).
Legionella spp. Legionella species contain large amounts
of branched-chain CFAs (81, 100, 103, 115). The major
component, iso-C16.0, typically accounts for 25 to 50% of the
total CFA and has been used as the basis for dividing isolates
into major CFA groups (81). Quantitative differences in
conjunction with ubiquinone content allow the differentiation of all Legionella species and may permit more rapid
identification than any other means. Certain species may be
identified by CFA composition alone. Legionella longbeachae, for example, is quantitatively different from other
species in the relative amounts of iso-C16:0 and C16:0 it
contains (106). In addition to the branched-chain CFAs,
Legionella spp. also contain small amounts of hydroxy and
cyclopropane CFAs, although the amounts are considerably
smaller than in other gram-negative bacteria (81, 103).
Through generation of the data for entry in a computer data
base such as the Microbial Identification System (81), performance of GLC for identification of Legionella spp. becomes a practical approach for laboratories handling these
isolates. Unrelated pathogens that may be confused with
Legionella spp. because of growth on charcoal-yeast extract
agar (e.g., Francisella tularensis) could also be readily
differentiated by virtue of CFA content.
Miscellaneous bacteria. Members of the family Pasteurellaceae possess similar CFAs (15, 64) and Haemophilus
influenzae (64) or H. aphrophilus (13) closely resemble
Actinobacillus actinomycetemcomitans in CFA composition, but Pasteurella multocida can be separated by its
greater amount of saturated and unsaturated C18 CFAs.
Quantitative differences may also permit differentiation of
H. ducreyi, H. parahaemolyticus, H. paraphrophilus, and
H. suis from other species (64). Gardnerella (formerly Haemophilus) vaginalis does not resemble Haemophilus species
in CFA content. It lacks 3-OH-C14.0 and other hydroxy
CFAs commonly found in gram-negative bacteria (31, 64).
Major CFA constituents of G. vaginalis are C18:1 and C18:0,
with smaller amounts of C14:0, C16:0, and C16:1. Jantzen et al.
(64) reported the absence of C18:2 in G. vaginalis, whereas
this CFA was identified in amounts from 4 to 20% in the
study by Csango et al. (31). This discrepancy illustrates the
limitation in drawing conclusions from separate studies in
which preparation and analysis of cells for CFA may have
been performed by different methods.
Differentiation of the unusual from the more frequently
encountered organisms at certain sites of infection can be
facilitated by CFA analysis. Four organisms that occur along
with Pasteurella multocida in wound infections resulting
from dog bites were studied by Dees et al. (43). Pasteurella
multocida was the only one that contained 3-OH-C14:0.
Flavobacterium spp. (group IIj) and Capnocytophaga canimorsus (DF-2) were similar in possessing large amounts of
iso-C15:0 but could be differentiated from each other by the
presence of iso-C17:1 in Flavobacterium spp. Group DF-3
organisms contain substantially more anteiso-C15:0 than iso-
CLIN. MICROBIOL. REV.
VOL. 4, 1991
APPLICATIONS OF CELLULAR FATTY ACID ANALYSIS
Identification to Subspecies and Epidemiologic Typing
In general, the discriminatory power of CFA analysis is
not great enough to provide useful typing systems. Significant advantages of this analysis, however, include ease,
reproducibility, storage of information when computerized,
and universal typeability. The applicability of GLC for
subgrouping (typing) also depends on the species in question. Those with more complex profiles, i.e., the gramnegative bacilli and especially Pseudomonas species, are
more amenable to GLC typing than are those with simpler
CFA patterns. At least three studies (29, 76, 116) have
attempted to use GLC as a tool for identification to the
subspecies level, and they suggest its use in epidemiologic
typing.
Campylobacterjejuni. Coloe et al. (29) proposed GLC as an
easy method of epidemiologic typing based on CFA analysis
of several human and animal isolates of C. jejuni. The ratio
of C18:1 to C19:Ocyc was used to place isolates into one of
three groups, resulting in 85% of the isolates of human origin
and 56% of the isolates of other animal origin being assigned
to separate groups. It is uncertain whether variations in the
amounts of cyclopropane-containing CFAs and the corresponding precursor (C16: 1.w9c or C18:1) can be relied on as true
indicators of difference in CFA composition. If the amounts
of cyclopropane-containing CFA and its unsaturated precursor are combined, there is actually very little difference in
the groups identified by Coloe et al. (29). For example,
instead of a distribution of 14, 21, and 37% for the amounts
of C18:1 in groups I, II, and III, respectively, of human
isolates, those amounts when added to the amounts of
C19:Ocyc become 41, 43, and 48%, representing questionable
differences. A stronger case could be made for distinction of
types by analyzing differences, if they existed, among other
features.
Pseudomonas cepacia and Staphylococcus epidermidis. Not
only use of the overall CFA composition but also computerassisted numerical analysis greatly enhance our ability to
recognize valid subgroups (116). In a study of P. cepacia
isolates collected from five cystic fibrosis centers, 42 strains
were divided into five subgroups on the basis of overall CFA
compositions. The distribution in subgroups varied from
center to center, but most centers had a predominant subgroup, which suggests that this subgroup was acquired in all
cases from a common source. Furthermore, the subgrouping
was corroborated in part by biotype distribution in that a
relatively unusual biotype of lysine decarboxylase-negative
strains all came from the same center, and six of the seven
strains also fell within the same GLC subgroup.
Correlation analysis has been applied recently to the CFA
data on blood culture isolates from 60 patients (76). Of
Staphylococcus strains serially isolated from the same patient, 97.6% gave a correlation value of >95. The correlation
values of nonidentical strains (isolates from different patients) of S. epidermidis varied from 34.2 to 94.9, and among
different species, the correlation value ranged from 59 to 77.
Consideration of the use of GLC with numerical or correlation analysis for subgrouping strains collected for epidemiologic studies is warranted when an efficient but not highly
discriminating typing method is desired.
CHARACTERIZATION OF OTHER
MICROBIAL SPECIES
A limited number of studies have addressed identification
of microbial pathogens other than bacteria by GLC of CFAs.
Purification of a sufficient quantity of the pathogen in question is the primary challenge for those organisms that are not
cultivable on artificial media. GLC can potentially be used as
an aid to identification of those organisms that can be
prepared in sufficient quantity and purity. Rickettsia and
Chlamydia spp. tend to have CFAs similar to those of other
bacteria, whereas of the various yeasts, several are characterized by large amounts of di- and triunsaturated 18-carbon
CFAs.
Rickettsia spp.
The CFAs of five Rickettsia spp. and Rochalimaea quintana were determined by Tzianabos et al. (148). Rickettsia
rickettsia, R. akari, R. typhi, R. prowazekii, and R. canada
were purified by a standard method from yolk sacs, and all
had similar CFA profiles. Large amounts of C18:1, C16:1, and
C16:0 characterize these species. Quantitative but not qualitative differences in CFA composition were found. The
Downloaded from cmr.asm.org at Griffith University on May 9, 2008
bacterium nucleatum (67). Calhoon et al. (25) used the ratio
of C16:0 to 3-OH-C16:0 to differentiate Fusobacterium nucleatum from other species isolated from the oral cavity.
Propionibacterium species contain anteiso-C15:0 or isoC15:0 as the most abundant CFA (105). However, Propionibacterium acnes is characterized by relatively minor
amounts of anteiso-C15:0 compared with the amounts in
other propionibacteria. Significant amounts of branchedchain C17:0 and C16:0 CFAs are also found in these species.
The anaerobic gram-positive cocci have not been sufficiently studied in the context of a clear understanding of
their taxonomic status (79). Variable amounts of 14- and
15-carbon branched-chain CFAs serve to differentiate several of the species (79, 110). They range from none in
Peptostreptococcus micros to significant quantities of isoC14:0 in Peptostreptococcus anaerobius and iso-C15:0 in
Peptostreptococcus saccharolyticus (79). Similarly, with the
clostridia, studies demonstrating the utility of CFA analysis
in diagnostic microbiology are lacking. Of 41 isolates studied
by Moss and Lewis (112), all were categorized in four
groups. A large amount of C12:0, C14:0, C16:0, and C16.1
characterized group I, which was represented by Clostridium perfringens. Group II lacked C20:0. Groups II, III, and
IV, represented respectively by Clostridium sporogenes, C.
bifermentans, and C. histolyticum, possessed other qualitative and quantitative differences in CFAs, but 10 additional
species were assigned to group IV. Cundy et al. (32) reported
recently that 51 of 52 isolates of C. difficile were accurately
identified by CFA analysis with the Microbial Identification
System. However, in comparison with headspace GLC, the
Microbial Identification System method was thought to be
too complicated. Toxigenic types A, B, and E of C. botulinum were studied by Fugate et al. (52). In contrast to the
results of CFA analysis with toxigenic types of C. perfringens (112), those investigators suggested that quantitative
analysis of the CFAs could serve to distinguish types of C.
botulinum. Additional information is needed to validate the
usefulness of CFA analysis for Clostridium spp. and the
anaerobes in general. Beyond providing an aid to routine
identification, there may be useful applications in special
circumstances, such as the isolation of a Clostridium sp.
suspected to be C. botulinum from a wound infection.
Because it is not be possible to distinguish C. botulinum
from C. sporogenes phenotypically by commonly used tests,
GLC could potentially provide a rapid means of distinguishing the two.
431
432
WELCH
CFAs of Rochalimaea quintana grown on blood agar medium resemble the other rickettsial CFAs except that they
lack the 3-OH-C14:0 found in the others. Additional studies
have examined Rochalimaea quintana and revealed similar
findings (135, 162). That C18l: accounts for such a large
percentage of the total CFAs (40 to 60%) is a distinctive
feature of Rochalimaea quintana. In contrast to other researchers, Westfall et al. (162) analyzed Rochalimaea quintana and related species by electron capture GLC and
reported that it contained C9:0. However, they noted that
complex media may have been the source of this unusual
CFA. Neither of the studies by Tzianabos et al. (148) or
Slater et al. (135) found C9.0 in Rochalimaea quintana.
Coxiella burnetii has a CFA profile markedly different
from that of other rickettsiae (148, 165). Significant amounts
of branched-chain CFAs (anteiso-C15:0, anteiso-cl7:0, and
anteiso-C14:0) are found, along with C16:0, C18:0, C20:0, and
C16:1. The similarity between the CFA patterns of Coxiella
and Legionella spp. is interesting, although DNA hybridization has revealed no relatedness between the two (148).
Fungi
The CFA compositions of various yeasts and a few
filamentous fungi have been investigated. Initial studies of
yeasts by Gangopadhyay et al. (54), Gunasekaran and
Hughes (59), and Moss et al. (114) resulted in somewhat
conflicting data with respect to the CFAs detected. Their
results and those from more recent studies indicate that at
least group-specific and, for some, species-specific CFA
profiles can be identified. Substantial quantitative differences among Saccharomyces spp. (large amounts of C16:1)
and Candida spp. (large amounts of C18.2 and C16:1) separate
these genera. Candida spp. are also characterized by the
presence of C16:0, C18:0, C18,:, and C18.3 (73). Torulopsis
glabrata is characterized by a high ratio of C16:1 to C16:0, and
Cryptococcus species can be identified by the absence or
only trace amounts of C16:1 relative to the amounts in other
yeasts (91, 114). The results of a study recently reported by
Marumo and Aoki (91) show a 95% overall agreement with
standard methods in the identification of Candida spp., T.
glabrata, and Cryptococcus neoformans. These results,
analyzed by discriminate analysis, yielded rates of correct
identification ranging from slightly less than 90% for Candida parapsilosis and Candida albicans to 100% for T.
glabrata, Cryptococcus neoformans, and other Candida
spp.
Brondz and Olsen (16) examined both cellular carbohydrates and CFAs and found that multivariate analyses distinguished Candida albicans from T. glabrata and from
Saccharomyces cerevisiae. In contrast to other investigators, Brondz et al. (18) reported in another study the finding
of significant amounts of C12:0 and C14:0 in Candida, Toru-
lopsis, and Saccharomyces spp. A methodologic variable in
the form of alcoholysis with ethanol, propanol, or butanol
was reported to influence the recovery of these more volatile
CFAs.
Approaches to the identification of filamentous fungi on
the basis of CFA composition have not been described.
Notwithstanding the greater technical difficulty in working
with molds rather than yeasts, CFA analysis may in the
future become a useful aid as a supplement to morphologic
features for identification. Fusarium spp. (137) and Trichophyton rubrum (75, 164) are examples of fungi that have been
analyzed for CFA content. The mycelial mass of growth
from fluid cultures was harvested and subjected to fatty acid
methyl esterification by these investigators. At various
stages of fungal growth, C16:0, C18:0, C18:1, and particularly
large amounts of C18:2 accounted for most of the CFAs of T.
rubrum. C16:1 and C20:0 were also found. The principal CFAs
(C16:0, C18:0, C18:1, and C18:2) of Fusarium oxysporum were
similar to those of T. rubrum, raising the question of whether
there is less diversity of CFAs among molds than among
other microorganisms. Additional studies need to be performed with related species and genera to develop more
experience before GLC can be applied to the identification of
filamentous fungi.
Blood Parasites
Schistosomes, trypanosomes, Leishmania spp., and plasmodia have been analyzed for CFA composition. Schistosoma mansoni purified from infected mice (adult stage of the
parasite) has a complex fatty acid composition (136), including saturated, unsaturated, and branched-chain components
with carbon-chain lengths of 12 to 24 atoms. The fatty acids
in the total lipid fractions in Trypanosoma cruzi consist
mainly of C18:2, C18:1, C18:0, and C18:0 (142). Polyunsaturated
acids are also found.
Promastigotes of Leishmania spp. have been studied to
determine whether CFA composition can be used to differentiate various species. Means other than host specificity are
generally unable to separate members of this genus. Vessal
et al. (155) concluded that distinctive features of the CFA
profiles of Leishmania donovani, L. tropica, and L. enriettii
were helpful in identification. A significant amount of C18:3 in
L. donovani and its complete absence in the other two
species, along with the absence of all unsaturated 18-carbon
CFAs in L. enriettii, were the major differences.
Using Plasmodium lophurae and P. berghei released from
erythrocytes, Wallace (160) determined the CFA composition of lipid fractions. Both species have complex profiles
consisting mainly of C16:0, C18:0, and C18:1. P. berghei was
reported to contain greater amounts of saturated CFAs than
P. lophurae; it was noted that these parasites and the blood
of the hosts share similar compositions.
Mycoplasmas and Viruses
Because of their requirement for exogenous sources of
growth factor from either complex medium or host cells,
mycoplasmas and viruses can have widely variable fatty acid
compositions. The incorporation of several or as few as one
fatty acid into membranes will allow mycoplasmas to grow,
while only Mycoplasma (Acholeplasma) laidlawii is known
to synthesize its own CFAs (127).
Lipid constitutes the envelope of viruses such as influenza
virus. Variable amounts of several fatty acids with chains of
12 to >20 carbons have been shown to occur, suggesting that
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Chlamydia spp.
The lipid elementary bodies of Chlamydia trachomatis
purified from McCoy cells consist largely of C16:0, iso-C15:0,
anteiso-C15:0. and C18:0 (8). Serotypes D and L3 can be
distinguished from serotypes C and G by quantitative differences in the amounts of the branched-chain C15:0 CFAs. C.
psittaci is identifiable and can be similarly differentiated
from the C. trachomatis serotypes (8). The CFAs of Chlamydia spp. were absent in uninfected McCoy cells except for
a minor amount of C18,:, which was common to uninfected
McCoy cells as well as to the Chlamydia serotype.
CLIN. MICROBIOL. REV.
VOL. 4, 1991
APPLICATIONS OF CELLULAR FATTY ACID ANALYSIS
there may be strain differences in the influenza virus (11).
Other investigators have analyzed the fatty acid contents of
uninfected and virus-infected cells and found modification in
the fatty acid compositions. For example, cells persistently
infected by measles virus have more C16.0 and less C18.1
compared with uninfected or lyrically infected monkey kidney cells (3).
DETECTING MARKERS OF INFECTION IN
CLINICAL MATERIAL
The possibility of diagnosing meningitis by GLC of CSF
was reported in 1976 (133). Initial studies focused on the
differences in patterns of infected and uninfected CSF without associating particular derivatized compounds with any
given pathogen. In most cases, derivatives of more than one
class of compounds (acids, alcohols, and amines) were
analyzed. Cryptococcal (30, 133), enteroviral (24, 30, 133),
herpesvirus (30), measles virus, varicella-zoster virus,
Rocky Mountain spotted fever, meningococcal (24), nocardial (22), and tuberculous (21, 30, 51) meningitis have been
studied. Modification of GLC resulting in increased sensitivity is central to the direct analysis of clinical specimens (20,
85). Preparation of samples appropriate for fatty acid analysis by frequency-pulsed electron capture (FPEC) GLC involves production of pentafluorobenzyl (20, 33) or trichloroethyl (21) derivatives. FPEC-GLC alone (20, 21) or mass
spectrometry with selected ion monitoring in addition to
FPEC-GLC (50, 51, 117, 121) is then used to analyze
samples. The latter approach is thought by some investigators to be necessary for optimal resolution of analytes. The
greatly increased sensitivity of FPEC-GLC along with the
heavy background of fatty acids of host origin are thereby
managed. Another approach has been to use reversed-phase
chromatography columns (34), which simplify the removal of
certain components that cause interfering peaks. For computer-assisted interpretation of results, methods that ignore
certain peaks normally encountered in clinical specimens
can be installed.
Another special consideration relative to direct CFA analysis is specificity. In contrast to analysis of cells grown in
cultures for which a profile of CFAs is interpreted qualitatively and quantitatively to arrive at an identification, FPECGLC depends more on detection of selected CFAs as
markers indicating a microbe's presence. Thus, any of the
CFAs uniquely found among prokaryotes could serve as a
marker of infection. In CSF, for example, TBSA has been
studied most extensively in this regard (20, 50, 51, 84, 117).
Such markers are useful for diagnosis when detected in
normally sterile sites and in the setting of a clinical picture
consistent with the agent represented by the CFA marker.
TBSA is also a component of Nocardia spp. (22) and other
coryneforms, however, so detection in specimens such as
sputum must be interpreted cautiously. Despite these limitations, FPEC-GLC promises rapid diagnosis of tuberculosis
because of its good sensitivity. Increasing promise of applicability to the rapid diagnosis of tuberculosis has been
shown by two recent studies (21, 117). Muranishi et al. (117)
tested specimens from the respiratory tract of 223 patients
with active tuberculosis. All of 61 specimens with positive
smears and cultures were found to be positive for TBSA
(100% sensitivity), and 84% of culture-positive, smear-negative patients were detected by TBSA measurement. Of 160
controls, 9.4% were TBSA positive. In 75 patients with
meningitis, Brooks et al. (21) reported 95% sensitivity and
91%'specificity of FPEC-GLC for diagnosis of tuberculous
meningitis on the basis of either the presence of TBSA in
CSF or the PFEC-GLC profile of CSF. FPEC-GLC has also
been applied directly to naturally infected armadillo tissue
for detection of Mycobacterium leprae (77), and attempts
have been made to analyze joint fluid for diagnosing septic
arthritis (23). Results of the latter study, which compared
profiles of gonococcal, streptococcal, and staphylococcal
arthritis with those of traumatic synovitis, were probably
based on an analysis of metabolic products.
Perhaps it would be useful to reexamine FPEC-GLC for
the rapid diagnosis of opportunistic infections as well as of
the rarely encountered, diagnostically difficult infections
such as tuberculous meningitis in the light of current epidemiology. All of the published experience with FPEC-GLC
and diagnosis of cryptococcal meningitis, for example, appeared before recognition of AIDS. Bacteremia due to
Mycobacterium avium-Mycobacterium intracellulare, common in patients with this syndrome, is often of higher
magnitude than bacteremias in immunocompetent adults
(166), suggesting that serum might serve as the source of
detectable amounts of CFAs in FPEC-GLC. It would be
interesting to determine the applicability of the technique to
the diagnosis of other relatively common opportunistic infections such as histoplasmosis and to actual detection of the
human
immunodeficiency virus.
MEASURING ANTIMICROBIAL RESISTANCE
Among the many prospects for future development of
CFA analysis in clinical microbiology are methods of assessing antimicrobial susceptibility or resistance. The basis of
this possibility lies'either in the direct effects of antimicrobial
agents on membrane lipids or on quantitative analysis of the
rate of synthesis and incorporation of fatty acids into lipid.
The latter would be analogous to the adaptation of DNA
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Unclassified Organisms
CFA characterization is useful in initially placing unclassified species into groups. Establishing the similarity of a
CFA profile to that of a recognized species, especially
among the nonfermenters, steers definitive taxonomic studies based on genetic analysis. Examples include determination of the CFA composition of "Flavobacterium-like" (41)
and "Moraxella-like" (108) organisms. Many belonging to
these groups are unusual opportunistic pathogens that may
grow slowly and need to be distinguished from traditional
fastidious pathogens. In characterizing a collection of strains
that resembled Methylobacterium extorquens, Wallace et al.
(159) relied heavily on CFA composition to define groups
with similarities and groups with differences. CFA analysis
may reveal differences not readily apparent by phenotypic
characterization. An unclassified species responsible for
serious infection in association with chronic granulomatous
disease (147) was confirmed to be distinct from Pseudomonas cepacia by virtue of containing C12:0, which did not
characterize other members of the pseudomallei group. The
isolate was phenotypically similar to Pseudomonas cepacia
and Pseudomonas pickettii. Similarly, distinct CFA profiles
have recently been established for the agent of cat scratch
disease (109) and for an unidentified fastidious organism
causing septicemia (135). Of seven CFAs found in the cat
scratch disease bacillus, an unusual component, 11-methyloctadec-12-enoic acid, was detected by GLC, identified by
mass spectrometry, and proposed to serve as a marker of the
cat scratch disease agent (109).
433
434
CLIN. MICROBIOL. REV.
WELCH
CONCLUSION
The technology of CFA analysis using GLC has gained
applicability for several purposes. It offers considerable
power as a tool for microbial identification, because characteristic patterns of CFAs can be defined for several microbes
to the species level and results are achievable rapidly. While
many applications remain highly specialized, laboratories
engaged in mycobacteriology or frequent identification of
unusual isolates should consider GLC as an effective approach to identification of these isolates. Experience with
CFA analysis of these and other microbial groups for purposes of identification is rapidly developing, making the
versatility of the method more fully realized. GLC is presently applicable on a broad scale in conjunction with conventional tests for identification of bacterial pathogens.
Other applications, including characterization at the subspecies level, identification of nonbacterial pathogens, and
direct analysis of clinical material, are enticing as a new
technology for achieving epidemiologically and diagnostically useful information.
ACKNOWLEDGMENTS
Excellent secretarial support was provided by Rose Stursa.
Geoffrey Mukwaya and Denise Pickett provided helpful discussions
and review of the manuscript.
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