1.Introduction
The objectives of this study were to determine the correct identity of the plant Sarracenia leucophylla using a series of molecular biology techniques. Phylogenetic analysis and genetic sequencing alignment was used to further confirm its identity in addition to construct a maximum parsimony tree. Sarracenia leucophylla, or the crimson pitcher plant, is a carnivorous plant in the Family Sarraceniaceae. The species is endemic to the Southeastern region of the United States and resides in moist, low-nutrient habitats (3 from wiki). Although the species within the genera are closely related, their geographic regions are scattered across North and South America with few current overlapping regions. Beyond determining species identity,
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leucophylla DNA was added to the master mix in rbcL and ITS2 tubes. The PCR was set to start with (1) 2 minutes at 94 ℃, (2) 30 seconds at 94 ℃ (3) 30 seconds at 51 ℃, (4) 90 seconds at 71℃ (5) 10 minutes at 71 ℃ and held at 4℃ until gel electrophoresis can be conducted. Steps 2-4 will be repeated a total of 35 times.
3.2.2 Gel Electrophoresis
To determine if our DNA sequences were properly extracted and amplified, we used gel electrophoresis. A 1% agarose gel was made using 1 gram of agarose powder per 100 ml of 1x TBE buffer. The solidified gel was placed in the electrophoresis tank containing 1x TBE buffer. Our samples were prepared by mixing gel loading solution with our PCR products (3 µL gel-loading solution, 5 µL PCR product). The samples were loaded in a selected order (table 2) and the gel was run between 30 minutes to an hour at 70-80 V (Lab 8, 2017)
Lane
Group Number
1
KB Ladder
2
(+) control
3
(-) control
4
10
5
11
6
12
7
13
8
14
9
15
10
16
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It is to be noted that for ITS2 we received the reverse primer and had to take that into account when performing sequence alignment. Both regions were aligned using MEGA 6 software with 15 other species, two outgroups, and an already defined region of rbcL and ITS2 of S. leucophylla. Alignment was using using ClustalW* (lab 9, 2017). Default parameters can be found in Taylor Dodge’s lab notebook under DNA sequencing section. The ITS region of our species was 282 bp long with 193 bp minimum overlap. The rbcL region was 496 bp long with 484 bp minimum overlap. With an adequate sequence alignment we were able to construct maximum parsimony trees for both sequences.
3.4 Phylogenetic Analysis *a more detailed protocol can be found in Lab handout 9, (lab 9, 2017).*
Using our final alignment we using MEGA to search for the most parsimonious tree for our species. ITS2 used an MP search method of tree bisection reconnection, or TBR, while rbcL used maximum branch and bound. The tree was rooted using our outgroup species seen below in figures 2 and 3. For ITS2 and rbcL, a total of 73 and 270 trees were generated, respectively. ITS2 has 325 steps and rbcL has 90 steps with all of the trees for both sequences being equally
The Bayesian inference analysis of the COI barcode sequences included 45 L. sericata sequences and 42 L. cuprina sequences. Despite the number of sequences, L. sericata was poorly resolved, which explains the poor node support (0.61) of the L. sericata + L. cuprina + L. taiyuanensis clade. Lucilia taiyuanensis is represented by only one sequence
C. jejuni is such a diverse species that it has been questioned if it should be contained in one genus (Gulbronson et al, 2016). It is also
Analysis of DNA from practicals 1 and 2 using the technique of agarose gel electrophoresis and analysis of transfomed E. coli from practical 2 (part B)
After analyzing my gels using a UV light, I was able to conclude that my PCR was indeed successful. Then, I proceeded to isolate my DNA sample in order to send it for sequencing. I accomplished this by cutting it from the gel, dissolving it into a solution, and centrifuging the solution many times between a myriad of washes. When my sequences came back, I analyzed the results using FinchTV. Overall, my sequence was clear and “noise” was minimal. Furthermore, the computer did not have much trouble identifying my peaks as specific nucleotides. For the most part, I had very few “N’s” in my sequence. Next, I copied and pasted my individual sequence into BLAST in order to compare my results with other sequences within the NCBI database. My sequence matched a myriad of petunia integrifolia samples. My maximum score was 833 and my E value was 0 which indicated an excellent match. My BLAST results did indeed match the observations I made during the field trip; thus, I had correctly identified my plant in the beginning steps. Next, I used a program called Cluster Omega to align my sequence with other students sequences in order to isolate my 5.8S rRNA gene.
Next, they filtered out any CNEs that were less than twelve base pairs in length due to the fact that sequences twelve or more base pairs in length are expected to be found at relatively low rates simply due to chance. The sequences remaining were then collapsed into groups based on sequence similarity, and consensus sequences (termed motifs) were generated for each group. These consensus sequences were generated by calculating a position weight matrix (PWM), which is generally done by looking at each position within a sequence, determining how often each of the four possible nucleotides is present at that position, and assigning higher weights to those nucleotides which occur more frequently at that position. With this newly generated catalog of sequences, the authors further characterized each motif based on conservation.
(Ferriol, Pico et al. 2004) analysed 47 accessions of C. moschata featuring the morphological characters which revealed considerable variability, comparable to that found in different C. moschata centers of diversity. Molecular analysis through AFLP (amplified fragment length polymorphism) markers which analyze neutral genetic diversity and SRAP (sequence-related amplified polymorphism) markers, which amplify gene regions both showed a genetic diversity in accordance with the morphological variability. With both markers clustered the genotypes according to their geographical origin: Central America, South America, and Spain, which suggested the existence of two independent domestications in both American areas, and/or introgressions from related species.
The samples that were run containing the DNA isolates from the precipitation method, (lane 2) and the column method (lane 3), both failed to yield physical evidence that DNA was present since no bands appeared in either lane. The DNA mass loaded into each of the wells containing the isolated DNA samples was 349.25 ng for the precipitation sample and 0.39 ng for the column
In addition, these segments of newly exchanged genes are very brief, so that there is little co-transfer of niche specifying genes with the transfer of the new niche-transcending adaptations (Zawadski, 124). Most importantly, the astronomical sizes of bacterial populations increase the rate of adaptive mutations and recombinations occurring at the population level (Bergstrom and Levin, 6982). However this theory that even the closest related bacteria may be ecologically distinct, has not been steadily evaluated. Most previous work focused only comparing members of varying bacterial species taxa (Cohan, 521) but not the genomic contents members of close relatives within a certain species. If differences are found between close relatives, then these differences must show ecological significance in terms of adaptation to the varying environment these relatives inhabit. This study attempted to define these phylogenetic groups of close relatives that are very similar ecologically by sharing genetic adaptations that confer a fitness advantage to a particular environment they inhabit as an “ecotype”. This defied the current scholarly landscape which defined an ecotype only conferring ecological distinctness, but no other
After the sequences of the eight species were aligned, they were used to create a “Maximum Likelihood Estimate of
“Now knowing that focusing on a single reference genome leads to incomplete and biased estimates of genetic diversity and ignores genes potentially important for breeding applications, we should better incorporate multiple references in future studies of natural diversity.”
The common gene for sequencing bacterial communities is 16S rRNA, a mitochondrial gene, although other genes such as cpn60 (chaperonin) can be used for bacterial sequencing and for phylogenetic studies (Hill et al, 2004; Janda and Abott, 2007; Mignard and Flandrois, 2006). Mitochondrial genes cytb (cytochrome b gene) and COI (cytochrome oxydase I), also mitochondrial genes, are used for a wide range of vertebrates and invertebrates. Mitochondrial genes are most suitable for identification of taxonomic group end evolutionary relationship due to high mutation events (http://www2.le.ac.uk), and the additional gene for eukaryotes identifications is mitochondrial 18S rRNA gene. Plastid genes rbcl (ribulose1,5-bisphosphate carboxylase gene) and matk (maturase K gene) which have similarities with mitochondrial genes such are their own DNA and
After base calling, the all analyzed events are compiled into a single complete sequence. This sequence is then compared to sequences in databases such as What’s in My Pot (WIMP) or 16S, which match the sequenced DNA to a specific species [8]. Because of the high speed of the input and base calling, usually species identification is done in real-time [8].
Next step involve DNA sequencing of the cambial-region cDNA inserts. It was performed using PCR products as templates from the 59 end. Microtiter plates were loaded onto a robotic worktable. This worktable is the place where the PCRs, quality control and sequencing reactions performed automatically. How do PCRs performed? By using general vector primers and standard PCR controls
In the 1990s, M. eumusae was discovered and it seems to be a close relative to the two species described above. The
The knowledge of genetic variability is a pre requisite to study the evolutionary history of a species, as well as for other intraspecific variation, genetic resource conservation etc. (Islam et al. 2007). Hence, genetic diversity and gene differentiation through molecular marker analysis are essential for their taxonomic relationship evaluation, conservation and sustainable utilization. For proper conservation programme it is essential to characterize the plants genetically. Number of molecular markers is being regularly used for studying genetic relations, population genetics, genetic characterizations in different plant groups and cultivars. The molecular markers are not influenced by the external environmental factor unlike that of morphological markers and hence accurately testify the genetic relationship between and among plant groups. Molecular markers like RAPD, ISSR and SSR are being used regularly for genetic diversity assessment as a thorough knowledge of the level and distribution of genetic variation is essential for conservation (Dreisigacker et al. 2005; Sharma et al. 2008; Naik et al. 2010; Das et al. 2011). PCR-based DNA fingerprinting techniques like RAPD, ISSR and SSR are proven to be very informative and cost-effective