Other methods for identification

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What's the point?

  1. To list and describe some of the traditional non-sequence-based approaches to identifying organisms
  2. To draw from experience to give examples of how they, and others, have used or continue to use some of these methods to anakyse or identify organisms

ssu-rRNA cannot be used to distinguish closely-related organisms

Although ssu-rRNA is the standard method for general phylogenetic analysis, other methods are often needed for fine-scale analyses or identifications. In many cases, alternative sequences are useful, as described in the module on alternative sequences for phylogenetic analysis. In other cases, entirely different approaches are used.

Let's touch (just touch) on a few of these, especially those you are either already familiar with or are likely to run in to in reference materials (e.g. Bergey's Manual).

DNA:DNA hybridization

This is a method that was commonly used in the past, and is still often required when defining new species. The extent that the genomic DNAs of two species will hybridize is a general measure of how much sequence similarity there is between them, and therefore how closely related the organisms are. This method was traditionally used to define bacterial species; in general, two organisms were considered to be the same species if the DNA:DNA hybridization was ca. 70% or greater, or different species of the same genus if they have measurable hybridization less than 70%.

DNA base composition

This is also an older technology. Because every G is paired to a C, and every A to a T, the ratios of G and C are always the same, and likewise for A and T. And because the sum of all 4 is 100%, there is only one degree of freedom in base composition, and it is usually expressed as %G+C. The %G+C in most organisms genomes are somewhere between 40 and 65, averaging about 55. Two closely related strains are considered to be in the same species if their DNA base composition (%G+C) are very close.

There are two commonly-used methods for determining the relative amounts of A, G, C and T in an organisms DNA. DNA can be hydrolysed to individual nucleosides, which can be separated (typically by 2D thin-layer chromatography) and quatitated. An alternative method is to carefully measure the denaturation midpoint of the DNA; because G=C pairs are more resistant to denaturation than A=T pairs, melting point can be related more-or-less directly to the ratio of G=C/A=T basepairs.

However, DNA base composition is rarely used any more - it turns out that DNA base compositions can change very rapidly and unpredictably in evolution. Nevertheless, differences in %G+C content have traditionally been used to distinguish different species within a genus where the species are phenotypically very similar, and %G+C is one of the standard values listed for species in their formal descriptions.

Serology

Serology is used primarily to identify very closely-related clinical isolates, usually different strains of a single species. This method uses antisera developed from various strains of Bacteria to identify which strain a new isolate is. For example, when Salmonella is isolated from a patient, a bank of antisera is used to determine which of the hundreds of serotypes that particular isolate is. A rapid serological test is used to test for virulent strains of Streptococcus pyogenes in throat swabs, to see if a patient has Strep throat. This is an old but still widely used method, since the antisera are easy to make and the assay is very quick, easily automated, & reliable. Perhaps as important, both the virulence and the serological type of pathogens are dependent on variable surface proteins. This means that the serotype of a strain can be a good predictor of virulence. The commonly-used ELISA is a serological method.

Strep test kit
iScreen Stept A test kit, CLIA-Waived Inc.

Lipid profiling

FAME (fatty acid methyl ester) analysis is also a fairly quick & easy method. A sample of growing culture is treated with extracted with a base to lyse the cells and saponify the fatty acids, then treated with a strong acid and methanol to convert these to fatty acid methyl esters (FAMEs). FAMEs are extracted with an organic solvent, and a sample of this is analyzed by gas chromatography. The FAME profile (both the identity of the FAMEs and their relative ratios) is compared to a database of standard profiles for identification, and can also be analyzed using tree-building methods. This method is very fast (just over 24 hours), standardized, and automatable. Amongst medically-relevant organisms, the resolution of FAME analysis is better than ssu-rRNA analysis; species within a genus are routinely distinguishable. However, cultures have to be grown under strictly controlled conditions, since organisms alter their membrane lipids in response to growth conditions. The analysis is fundamentally limited to the quality and coverage of the standards database - little information is derived from organisms not related to those in the FAME database. As a result, this approach is most commonly used in medical microbiology.

FAME tracing
Example FAME profile : Mycobacterium szulgai
K.-Dieter Müller, EN Schmid, RM Kroppenstedt 1998 J Clinical Microbiol. 36:2477-2480


RFLP methods

Restriction fragment length polymorphism (RFLP) analysis is a widely used group of technologies, commonly used by animal and plant geneticists to determine paternity, to identify the source of forensic tissue samples, etc. When a layman hears about “DNA testing”, it is almost certainly some form of RFLP analysis. When used for humans or other animals it is primarily to distinguish or identify individuals, but in the microbial world is more often used to differentiate strains of the same species.

In traditional RFLP, genomic DNA from the organism is digested with restriction enzymes and separated by size on a gel. The gel is then transferred to a membrane and hybridized with a set of oligonucleotide probes complementary to variable regions in the genome. Variation in these sequences results in differences in presence or absence of these restriction sites, or the lengths of the fragments that hybridize to the probes. If the probes are carefully designed, the RFLP banding patterns are unique genetic fingerprints of the individual or strain. The patterns of bands are compared to other strains (or parents, suspects, etc) to test identity or specific genetic relationships. With primers or probes designed to yield very complex banding patterns, it is possible to generate trees based on these patterns.

A very commonly-used RFLP method is to use PCR to amplify a specific variable region of genomic DNA (this is often the ssu-rRNA, or some other region of the rRNA operon such as the intergenic spacer), then use restriction enzymes to digest this DNA fragment into several smaller fragments. These are separated by electrophoresis, and the number and size of the resulting DNA bands are diagnostic for specific kinds of organisms.

Although not strictly speaking RFLP, related methods are now more common in which PCR amplification, rather than restriction digestion, is used to generate a complex banding patterns. This method often uses many arbitrary or “random” primers together in a complex PCR mix; good primer sets are identified empirically. This is more formally celled "AFLP" (Amplified Fragment Length polymorphism).

RFLP
AFLP analysis of Clostridium botulinum type I strains
R. Keto-Timonen, M Nevas, H Korkeala 2005 Appl Environ Microbiol 71:1148-1154

Notice in the image above that the serotype and the AFLP groups do not correspond - this is the rule, not the exception. How you divide species into strains is directly related to how you distinguish them.

Phenotype

Phenotypic markers are what most people think of when they want to compare different microorganisms. What carbon sources can it use? Is it motile or non-motile? Is it aerobic, anaerobic, or facultative? What is it's shape and size? What is it's optimal growth temperature? Is it Gram-negative or Gram-positive?

Although gross phenotypic markers aren't always very useful in determining phylogeny, they are still perfectly viable markers for taxonomies. Until relatively recently, phenotype was all microbiologists could go by!

You are familiar with these sorts of tests and markers from General Microbiology - you used them to identify your "unknown". In most research or clinical environments you wouldn't be using giant tubes of broth, but more rapid systems that let you measure a number of phenotypic properties at the same time. A classic is the API strip, by BioMereiux in Durham, NC. The API strip is a plastic strip with little wells of dehydrated media, into which you add a specified dilution of your inoculum. These grow up, change color (or some other property), and you read off the identification of the organism from the pattern of results on the strip.

Because different kinds of tests are required to distinguish organisms in different phylogenetic groups, this approach usually starts with a preliminary set of tests to determine very generally what kind of organism is in hand: enterics, non-enteric Gram-negative rods, Staphylococci, Streptococci, yeast, &c. Then the appropriate specific API strip is used for the identification.

For example, the API 20E test strip is used to identify enterics. Organisms are first tested by the Gram stain (enterics are Gram-negative rods), and cytochrome oxidase test (enterics are negative). Once confirmed to be enterics, they can test tsted on he API 20E strip, which contains tests for (reading in order from left to right on the strip): ONPG, arginine decarboxylase, lysine decarboxylase, ornithine decarboxylase, citrate utilization, H2S production, urease, tryptophane deaminase, indole production, Voges-Proskauer test, gelatinase, and fermentation/oxidation tests for glucose, mannitol, inositol, sorbitol, rhamnose, saccharose, melibiose, amygdalin, and arabinose.

API strip
API-20E strip, BioMereiux - this result identifies the culture as E. coli

Organisms are identified by scoring the strip using the “Analytic Profile Index “ (thus “API”). The pattern above decodes into + - + + - - - - + - / - + + - + + - + - + , which can be looked up to identify this organism: Escherichia coli.