Final answer:
The most suitable method for identifying a microorganism found in muddy pond water in the Santa Monica Mountains is DNA sequencing. This approach allows for precise genetic identification and is particularly helpful when dealing with a complex environmental sample containing a diversity of microorganisms. The correct answer is option (a)
Step-by-step explanation:
To identify a microorganism found in muddy pond water in the Santa Monica Mountains, DNA sequencing would be the most suitable method. Unlike electron microscopy, which provides detailed images of microorganism structures, DNA sequencing allows for the precise identification of the organism at the genetic level, which is especially useful when distinguishing between organisms that have similar physical characteristics. While Gram staining can provide information on the bacteria's cell wall composition, it is not as definitive as genetic methods for identification, especially for environmental samples that may contain a diversity of microorganisms. Lastly, gas chromatography, which is used to analyze the lipid profile of microbes, is not typically used for primary identification.
Given the complexity of microbial classification, molecular biology techniques like polymerase chain reaction (PCR) and sequencing of ribosomal RNA (rRNA) genes are crucial in detecting and identifying microorganisms that are difficult or impossible to culture in a laboratory setting. These techniques align with the methodologies suggested in Bergey's Manual of Systematic Bacteriology, which is a key reference in classifying different prokaryotes. For definitive identification, 16S rRNA sequencing is often used to compare a sample's gene sequence to known sequences in databases.
This approach has even been successful in identifying bacterial strains from historical samples, as in the case where the 16S rRNA sequence from 14th-century skeletons provided a close match to modern strains of particular bacteria, demonstrating the power of this technique in identifying bacteria across diverse and complex samples.