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Arbuscular mycorrhiza/

Molecular genetic analyses of arbuscular mycorrhizal fungi
In the past ten years there have been spectacular advances in molecular genetic technologies and tools. These advances allow microbial and mycorrhizal ecologists to ask new and exciting questions about the ecological and evolutionary roles of arbuscular mycorrhizal (AM) fungi as individuals, in communities and ecosystems. Genetic analyses of AM fungi have been used to explore the genetic structure of single spores using multilocus genotyping, AM fungal diversity and adaptation across multiple grassland communities , all the way up to a global investigation of AM fungal diverstiy, which greatly increased the described molecular diversity within the phylum Glomeromycota.

All the recent advances in molecular genetics clearly permit the analysis of microbial communities at much finer and functional scales and potentially with more confidence than previous methods. The classical AM fungal identification method of spore extraction from soil and further spore morphological analysis is fraught with complicating issues due to the various strategies and forms of AM fungi, e.g., lack of sporulation in certain species, seasonality, high unculturability, possible misidentification (human error), and new evidence of multi-nucleate spores and high genetic variation within clonal AM species. Because of these various problems, in the past researchers likely misreprestened the true composition of AM fungal communities present at any one point in time or place. Additionally, by following the traditional extraction, culture and microscopic identification methods, there is no way to determine active/functioning AM fungal populations, which are likely the most important when attempting to relate plant-AM symbiotic interactions and mechanisms to ecological or ecosystem function. This is especially true in the case of root colonization analyses, which can determine percentage of roots colonized by AM fungi. The major problem with this analysis is in field soils, which contain multiple species of AM fungi in association with a target plant at the same time (see Ecology of AM). The identification of the associated fungal symbionts is impossible without the use of molecular methods. Though genetic analysis of AM fungal communities has advanced a great deal in the past decade, the methodology is not yet completely refined. Below is an overview of the methods used in molecular genetic analyses of AM fungi, along with applications to research, future directions and some of their problems.

DNA/RNA
Genetic analyses of AM fungi from soil and root samples range in their applicability to answer ecological or phylogenetic questions. DNA analyses utilize various nuclear markers to describe AM fungi and represent different regions of the nuclear ribosomal operon (18S rRNA) found in all eukaryotic organisms. The DNA analysis of AM fungi using these markers began in the early 1990s and are continuing to be developed today. The small subunit (SSU) rRNA gene, the internal transcribed spacer (ITS) gene, and the large subunit (LSU) rRNA gene are currently the most common DNA markers used. The SSU region has been used most frequently in ecological studies, while the ITS and LSU regions have been predominantly used in taxonomic constructions of the phylum Glomeromycota.

General procedure
The first step of all molecular genetic analyses is the preparation and/or preservation of a sample. In the case of AM fungi, samples typically come in the form of soil or roots that will contain AM spores, hyphae and/or various AM colonization structures. Sample preservation will vary depending on the desired analysis (DNA or RNA). For analysis of DNA, samples should either be processed immediately or kept frozen prior to nucleic acid extraction. For analysis of RNA, samples should be cryogenically frozen (−196 °C) almost immediately upon collection, or stored in an RNA stabilization and preservation reagent (e.g. RNAlater). The next step is to extract the desired nucleic acids from the sample, which can be performed manually using various published extraction methods or by using one of the many commercially available DNA/RNA extraction kits. Due to the labile nature of RNA, synthesis of complementary DNA (cDNA) using extracted RNA as a template is performed for further analysis. For most molecualr genetic sequencing methods of AM fungi a PCR step is required to increase the total amount of target DNA/RNA/cDNA. There are many PCR conditions proposed for analysis of AM fungi and some of the most accessible are breifly summarized below.

PCR methods
From Öpik et al. :
 * Reaction mixture:
 * 20 μl Qiagen HotStarTaq Master mix
 * 0.23 μM of each primer (NS31 and AM1, more on AM fungal specific primers below)
 * 2 μl template DNA


 * PCR:
 * Run on an MWG AG Biotech Primus 96 Plus thermocycler
 * 15 minutes at 99°C
 * 5 cycles of 30 seconds at 42°C
 * 60 seconds at 72°C
 * 45 seconds at 92°C
 * 35 cycles of 30 seconds at 65°C
 * 60 seconds at 72°C
 * 45 seconds at 92°C
 * 30 seconds at 65°C
 * 10 minutes at 72°C
 * PCR products then separated by gel electrophoresis on 1.5% agarose gel in 0.5 x TBE
 * Separated PCR products were then purified using the Qiagen QIAquick Gel Extraction kit

From Krüger et al. :
 * Reaction mixture:
 * 0.02 U μl-1 Phusion polymerase
 * 1X Phusion buffer with 1.5 mM MgCl2
 * 200 μM of each dNTP
 * 0.5 μM of each primer : SSUmAf-LSUmAr and SSUmCf-LSUmBr


 * PCR:
 * Thermal cycling was performed in an Eppendorf Mastercycler Gradient
 * 5 minute initial denaturation at 99°C
 * 40 cycles of 10 second denaturation at 99°C
 * 30 seconds annealing at 60°C
 * 1 minute elongation at 72°C
 * 10 minute final elongation
 * To visualize PCR product, load onto 1% agarose gel with 1x sodium borate buffer at 220 V, and stain with ethidium bromide (1 μg ml-1)

Primer selection for arbuscular mycorrhizal fungi
One difficulty with the genetic analysis of arbuscular mycorrhizal fungi has been the selection of ideal, comprehensive, and repeatable primers or primer sets. Currently there are four common AM fungal specific markers/primers used in genetic sequencing to describe AM fungal communities in a sample, ideally to species level identification. These sequence markers are deisgned for the nuclear ribosomal RNA (rRNA) in the 18S region and are either used individually or in some combination. The partial small subunit (SSU), the partial large subunit (LSU), and the internal transcribed spacer (ITS1, 5.8S, ITS2) are the regions used for genetic sequencing of AMF. Additionally, there are 'primer sets' that incorporate a combination of these different regions into one target primer for AMF, these include the "Krüger " and the "Redecker " primers. The "Krüger" primer utilizes the partial SSU, the ITS, and the partial LSU regions, while the "Redecker" primer utilizes the partial SSU and the ITS.

Currently, there is no consensus as to which primers or primer sets, being used with varying degrees of success, repeatability and species-level resolution, are best for molecular genetic analysis of AMF. Additionally, the current advances and coming changes in genetic sequencing technology, e.g. Sanger, to 454 pyrosequencing, to Illumina HiSeq/MiSeq, can force researchers to only use certain primers. The large size of the "Krüger" (~1500bp) and "Redecker" (~900bp) primer sets prohibit use with newer sequencing technology (e.g. Illumina MiSeq) as opposed to 454 pyrosequencing that is capable of these long read lengths. Though Roche Diagnostics has announced the discontinuation of the 454 platform for 2016, it is still commonly used in genetic analyses. Perhaps new 'all-inclusive' AM specific primers should be created to support the new technologies for as descriptive a molecular analysis from the "Kruger" primer set using 454 pyrosqeuncing, as shown below. The reverse may also be true, where molecular technologies should be developed with both long read lengths (which would allow for large primer sets) as well as sequencing depth.

Kohout et al. present a study using all of the aforementioned primers/primer sets on identical plant samples using 454 sequencing analysis. Results of their experiment are summarized below.


 * "Kruger" primers yieled relatively higher diversity parameters than other comparable primers (LSU, ITS2)
 * "Kruger" primers showed significantly higher Shannon diversity measures than did SSU primer
 * "Redecker" primers yieled the most different, but maybe most descriptive community composition of all primers tested. This may be explained by the ability of the "Redecker" primers to find less abundant AMF lineages such as the Claroideoglomeraceae or the Paraglomeraceae
 * LSU primers had a strong bias towards Glomeraceae, excluding other families
 * SSU primers had a bias towards Glomeraceae and underestimated the presence of differenct families within the Glomeromycota, including the Claroideoglomeraceae, Diversisporaceae and Paraglomeraceae

MOTU = Molecular operationaly taxonomic unit, synonymous with OTU or phylotype.

qPCR and qRT-PCR
Real-time PCR or quantitative PCR (qPCR), is becoming a well-established method to quickly amplify and simultaneously quantify targeted AM fungal DNA from biological samples (plant roots or soils). Fairly recent developments in qPCR markers allow researchers to explore the relative abundance of AM fungal species within roots in greenhouse experiments as well as in the field to identifty local AM fungal communities.

qPCR markers for arbuscular mycorrhizal fungi will consist of AM specific primers and flourescently labelled hydrolysis probes. These AM specific primers (discussed above) can be chosen by the researcher and this decision is typically guided by the question at hand, resources available, and willingness to troubleshoot in the lab.

Microarray
DNA microarray analysis is currently being used in AM fungal research to simultaneously measure the expression of many genes from target species or experimental samples. The most common tool or method is to use functional gene array (FGA) technology, a specialized microarray that contains probes for genes that are functionally important in microbial processes such as carbon, nitrogen or phosphorus cycling. FGAs have the ability to simultaneously examine many functional genes. This technique is typically used for general analysis of functional microbial genes, but when complemented with genetic sequencing, inferences can be made about the connection between fungal community composition and microbial functionality.

PLFA/NLFA
Specific organismal chemical signatures can be used to detect biomass of more cryptic organisms, such as AM fungi or soil bacteria. Lipids, more specifically phospholipids and neutral lipids, contain fatty acids connected to a glycerol backbone. The fatty acid composition of organisms varies, and the proportions of specific fatty acids can be organism specific. For example, in AM fungi the proportion of the fatty acids, 16:1ω5 and 18:1ω7, in the phospholipid portion account for approximately 58% of total fatty acid composition. The fatty acid, 16:1ω5 is the most commonly used acid to characterize AM fungi in soils and can be used as a strong indicator of mycelial biomass in soil sample.

Neutral lipid fatty acid analysis of AM fungi is typically looked upon as a method to indicate energy storage, but most importantly, the ratio of NLFA (16:1ω5) to PLFA (16:1ω5) can potentially be used to indicate nutritional status of AM fungi in soils. Energy is mainly stored in AM fungi as neutral lipids in storage structures like spores and vesicles. Because of this NLFA correlates quite well with the number of spores in a given volume of soil. The ratio of NLFA concentration to PLFA concentration (active mycelia) can then give the proportion of carbon allocated to storage structures (spores, measured as NLFA).

Problems with lipid fatty acid analyses include the incomplete specificity of fatty acids to AM fungi, the species- or genera-specific variation in fatty acid composition can complicte analysis in systems with multiple AM fungal species (e.g. field soil), the high background levels of certain fatty acid concentration in soils, and that phospholipids are correlated to an organism's membrane area, and the surface to volume ratio can vary widely between organisms such as bacteria and fungi. More work must be done to identify the efficacy of this method in field soils with many genera and species of AM fungi to discern the methods ability to discriminate between many varying fatty acid compositions.

Future research directions with AM fungi
An exciting prospect for future analysis of AM fungi is the use of stable isotope probes. Stable isotope probing (SIP) is a technique that can be used to determine the active metabolic function of individual taxa within a complex system of microbes. This level of specificity, linking microbial function and phylogentics, has not been acheived previously in microbial ecology. This method can also be used independently of classical culture methods in microbial ecology, allowing for in situ analysis of functional microbes.

SIP Method
SIP, more explicitly DNA/RNA-based SIP, uses stable-isotope enriched substrates, such as 13C, 15N, or H218O, and then analyzes the 'labeled' markers using species specific DNA or RNA markers. The analysis of labelled DNA is performed by separating unlabeled and labeled DNA on a cesium chloride gradient formed in an ultra centrifuge. Because all microbial organisms are capable of importing water into their cells, the use of H218O stable isotope probing is a very exciting new method that can shed light on questions microbial ecologists and biologists have struggled with answering for years, in partilcular, what are the active microbial organisms in my system? The H218O, or heavy water method will target all organisms that are actively growing, and induce little influence on growth itself. This would be especially true with most greenhouse experiments with arbuscular mycorrhizas because plants must be watered anyway, and water does not directly select for organisms with specific metabolic pathways, as would happen when using 13C and15N.

Little has been done with this method in arbuscular mycorrhizal experiments, but if proven to work in a controlled experiment, and with further refinement of DNA/RNA fungal community analyses techniques, this may be a viable option to very specifically determine the actively growing portion of AM fungal species across growing seasons, with different plant hosts or treatments, and in the face of climate change.