In order to develop a further understanding of the evolutionary relationships between different Chlorella algal strains and related species, methods of algal DNA extraction, PCR, sequencing, and analysis were tailored to our project. With the goal of understanding algal relationships and an intent of affecting algal biofuel production, DNA from multiple regions of algal cells was targeted. Ribosomal DNA including the 18S, ITS1, 5.8S, ITS2, and 28S regions; Chloroplastic DNA (the RuBisCO large subunit coding region); Genomic DNA (the RuBisCO small subunit coding region); and Mitochondrial DNA (Cytochrome C Oxidase subunit I coding region) were picked to be analyzed from each of our algal strains. The comparison of the phylogenetic trees is then being used to determine relationship grouping patterns with increasingly robust trees as more sequencing data is obtained. Further analysis and sequencing of the four regions should give a better understanding of relationships and help to determine the proximity of certain chlorella‐like algal species to one another. Furthermore, extended distance between two strains of the same species provides ground for questioning the nomenclature of certain strains with comparison to their actual genetic composition. Funding provided by NSF‐EPSCoR: 1004–094.
Using the 18S (partial), ITS1 (Internal Transcribed Spacer 1), 5.8S, ITS2 (Internal Transcribed Spacer 2) regions of the genomic SSU (small subunit) DNA, we are able to use basic bioinformatics tools to create an effective phylogenetic tree. Sanger sequencing produces a 1.2–1.5 kb region for each individual species that is annotated into four elements, (partial 18S, ITS1, 5.8S, and ITS2) and is formatted for stand‐alone MAFFT v6.903b (Multiple Alignment using Fast Fourier Transform). The multiple alignment is then carried out on each element and concatenated to remove the potential overlap between the elements. Stand‐alone PhyML v3.0 (Phylogenetic estimation using Maximum Likelihood) is then used on the multiple alignment data, and a tree can be viewed using FigTree v1.3.1 (a phylogenetic tree editor and viewer program). The results of this method gives well‐defined clades that are now being used in genera and even species level determination. Using our simplistic method is an effective way to engaging the problem of green algae identification.
The object of this project is to identify changes in soil microbial community DNA and Fatty Acid profiles caused by storage, using capillary electrophoresis single‐strand conformation polymorphism (CE‐SSCP) and fatty acid methyl ester (FAME) analysis.There were six different storage treatments tested (−80°C, −20°C, 4°C, freeze dried, air dried, oven dried) on soil samples collected from 4 different locations in Nebraska. Samples were taken at a depth of 0.0 cm to 5.0 cm from three biological replicates at each collection site. After collection, soils were sieved and subsamples were placed into each storage treatment for five weeks then analyzed, with the exception of one subsample (fresh) which had DNA and fatty acids extracted within 36 hours of collection. Additional fresh samples were collected and processed two weeks after initial collection and seasonally.The CE‐SSCP and FAME analysis will illustrate the microbial community and its diversity for an individual soil sample through the number of peaks, molecular size of peaks and relative peak heights. Using statistical analysis it can be determined which storage treatments altered the soil microbial community profile when compared to the fresh subsample. Results will be presented to show the effectiveness of these two methods to detect small variations in the soil microbial community. Funded by National Institute of Justice (25‐6228‐0159‐001).
Capillary electrophoresis can separate fragments of nucleic acids using single strand conformation polymorphism (SSCP) This method has been increasingly used in microbial ecology to develop fingerprints of the microbial community within soil. SSCP detects mutations in DNA fragments due to changes in the secondary structure of single‐stranded DNA fragments. Molecular fingerprinting can have a wide application in Forensics. It is important to find if a soil fingerprint from a criminal's shoe can be matched to the fingerprint found in the database. This is a blind study comparing the possible use of CESSCP in Forensics. The soil samples are collected from two locations by two people. Each individual acted as a “criminal” by digging the soil, walking over the soil and buried a small object. One shoe was removed at the “crime scene” and the other shoe was worn home. The soil was collected from the shovel and both of the shoes for each individual. All the soil samples were air dried for 48 hours before DNA was extracted. The samples were then run through polymerase chain reaction (PCR) to amplify and fluorescently tag the genes of interest. The samples will be processed in the Genetic Analyzer using the CE‐SSCP. The results can very well show that CE‐SSCP will not produce similar fingerprints for the shoe worn home and the database, thus inapplicable to Forensics.Supported by National Institute of Justice (25‐6228‐0159‐001).
The purpose of this project is to identify the time interval during storage in which the greatest change in microbial community within soil samples of varying soil types occur. Three storage conditions, air drying, 4° C, and −20°C will be tested, and DNA extracted from stored soil at time intervals of 0, 1, 2, 4, 7, and 14 days. the extracted DNA will undergo PCR to amplify the v3 region of the 16S rDNA. The PCR product will be processed using Capillary Electrophoresis‐ Single Stranded Conformational Polymorphism (CE‐SSCP) on a genetic analyzer. The resulting microbial community fingerprints will be compared using multi‐variant statistical analysis using the StatFingerprint component of the R Software package. Comparisons will be made to determine the time interval that undergoes the most change as well as the storage condition that produced the most change overall. Detailed results will be presented. This project is funded by The NASA Nebraska Fellowship Grant.
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