ewinter's blog

When a geneticist is given an unknown DNA sequence and is tasked with finding out its function, there are two approaches.  Ab initio, or “from the beginning” involves using programs that analyze the sequence for known trends in gene expression.  These trends include translation initiation occurring at ATG, and intron boundaries being defined by GT at the beginning and AG at the end.  Stop codons include TAG, TAA, and TGA.  Using these in combination with more complex trends of gene expression, ab initio programs can make a prediction about the coding sequence and protein sequence of a gene.  The other method is homology-based searches.  These include comparing a query sequence to sequences of nucleic acids of a known origin.  One database includes expressed sequence tags (ESTs), which are sequences derived from cDNA clones.  A set of ESTs can be joined together to form a consensus “contig” sequence, which can then be used to find an mRNA for the gene.  In this lab, we begin by building two predictions of the protein our gene encodes: one using ab initio methods and another using homology-based methods.  For the ab initio method, we use the program FGENESH.  For the homology-based searches, we use Phytozome and NCBI BLAST.  Both programs output predicted intron-exon boundaries as well as a predicted protein sequence.  We compare the two predictions and finalize our working map with intron-exon boundaries and a predicted protein sequence, keeping in mind the differences between ab initio and homology-based searched.  We then proceed to research our gene of interest and provide an assessment of function of our gene. 

The ab initio program FGENESH predicted that the mRNA will be 2229 base pairs spanning 7 exons and the protein will be 742 amino acids long.  The homology-based program Phytozome predicted that the mRNA will be x base pairs and 847 amino acids long. The discrepancy between the two peptide sequences lies after amino acid 437 (Figure 2).  FGENESH predicts the following sequence to be KSLQ, while Phytozome predicts the sequence to be a set of 109 amino acids, nowhere containing KSLQ in sequential order. The sequence towards the C terminus of this discrepancy is in agreement.  21 ESTs were found in the Brachypodium distachyon genome with an Ident greater than 95%.  These ESTs were imported into CAP3 software to form contiguous sequences. Two contiguous sequences were created from the ESTs (Figure x and Figure x), and all ESTs contributed to the contigs; there were no singlets.  There is a very slights gap between the ESTs, but other than that they cover the whole cDNA found.

To find the official identity of the RZW gene, we performed a Phytozome BLAST search of the RZW genomic DNA with Brachypodium distachyon v3.1 as the target species.  The best match to the query sequence was Bradi1g25180.1, and the predicted protein sequence was saved. The FGENESH and Phytozome predicted protein sequences were compared and the working map was updated.  From the Phytozome locus page, the functional annotation was saved as a graphic showing domains. The link to Uniprot was followed and there were 16 annotations that contained information about different domains.  We used NCBI BLAST to perform two Standard Nucleotide BLAST searches using the Nucleotide Collection (nr/nt) database and the Somewhat Similar Sequences (blastn) algorithm. Max target sequences was set to 1000, and the expect threshold was set to 1.  The first search was of our genomic RZW sequence and the second was of the Phytozome predicted coding sequence. We used NCBI BLAST to perform a Standard Protein BLAST using the non-redundant protein sequences (nr) database and the protein-protein BLAST (blastp) algorithm.  Max target sequences was set to 1000, and the expect threshold was set to 1.

The National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST) was used as a homology-based method.  We performed a Standard Nucleotide BLAST of the RZW genomic DNA using the Highly Similar Sequences (megablast) program in the Expressed Sequence Tags (EST) database for Brachypodium distachyon (taxid: 15368).  All matches with greater than 95% Ident were saved. All of the saved ESTs were imported into the CAP3 software. CAP3 takes these and generated consensus contiguous sequences bases on the overlap between numerous ESTs.  To find a full length cDNA sequence for our gene, we performed a Standard Nucleotide BLAST of the RZW genomic DNA using the Highly Similar Sequences (megablast) program in the Nucleotide Collection (nr/nt) database for Brachypodium distachyon (taxid: 15368).  We found accession number XM_003562897.4, an mRNA sequence that is predicted to code for the Brachypodium distachyon G-type lectin S-receptor-like serine/threonine-protein kinase B120 (LOC100825184). The two contigs were aligned to the cDNA sequence using the NCBI BLAST.

When a geneticist is given an unknown DNA sequence and is tasked with finding out its function, there are two approaches.  Ab initio, or “from the beginning” involves using programs that analyze the sequence for known trends in gene expression. These trends include translation initiation occuring at ATG, and intron boundaries being defined by GT at the beginning and AG at the end.  Stop codons include TAG, TAA, and TGA. Using these in combination with more complex trends of gene expression, ab initio programs can make a prediction about the coding sequence and protein sequence of a gene. The other method is homology based searches. These include comparing a query sequence to sequences of nucleic acids of a known origin. In this lab, we begin by building two predictions of the protein our gene encodes: one using ab initio methods and another using homology based methods.  For the ab initio method, we use the program FGENESH. For the homology based searches, we use Phytozome and NCBI BLAST. We compare the two predictions and proceed to research our gene of interest.  We then provide an assessment of function of our gene.  

This article is titled “Inhibition of the substantia nigra pars reticulata produces divergent effects on sensorimotor gating in rats and monkeys.”  It piqued my interest because I am a part of Karine Fenelon’s lab here on campus, in which we investigate the neural circuits that modulate sensorimotor gating.  In the study, the GABA_A receptor agonist muscimol was used to test the role of the SNpr in auditory pre-pulse inhibition (PPI), a key component of sensorimotor gating.  From class, we know that GABA is the major inhibitory amino acid neurotransmitter, so muscimol must mimic its inhibitory effects on target neurons.  In the study, it was found that the inhibition of the SNpr using muscimol disrupted PPI in rats, while facilitated PPI in rhesus macaques.  This study discovered these divergent effects.  I wonder what differences in neuron connections contribute to this divergence, because there must be a difference either upstream or downstream of the point of innervation of the SNpr.  I also wonder what the effect of muscimol on the SNpr in mice is, since this is the model organism we use in lab.

https://www.nature.com/articles/s41598-018-27577-w.pdf

I took Statistics 240 in the Fall 2017 semester.  I remember that a normal distribution, or bell curve, is a representation of data around a mean.  68% of data falls within one standard deviation of the mean, 95% within two, and 99% within three.  A chi squared test is used to see if two variables are related. The null is that they are not, but if the p value comes to less than 0.05, then we reject the null and say that the variables are related.  Sampling distributions are used to draw conclusions about an entire population based on data taken from a small subset. When this is done, confidence intervals can be made - generally 95% or 99%. For example, a 95% confidence interval would mean that researchers are 95% confident that the true mean of the entire population lies within the range they set.  When probabilities of events occuring are known and we want to know the probability of something happening based on that repeated event, we use p, the population proportion. We can construct a confidence interval for a population proportion using “p hat.” Z scores are used to test how likely an event is to occur. If a z-score gives us a standard deviation that is not near zero (above 3, for example) we can say that we do not believe the event occurred because the probability is so low.  

The omentum is a sheet of adipose tissue that surrounds the digestive organs.  It is a common site of ovarian cancer metastasis. There are two ways in which ovarian cancer is known to metastasize.  The first is passive dissemination.  In this model, cancer cells detach from the tumor and are transported by the peritoneal fluid and ascites to their metastatic site.  Ascite formation occurs due to VEGF signaling and blocking of lymphatic vessels.  Cancer cells adhere to their metastatic site using matrix metalloproteinase (MMP), which is upregulated in cancer cells.  MMP, along with other proteins, is also responsible for enhanced cell motility.  The tumor cells then release cytokines such as interleukins to cause angiogenesis and a preferential microenvironment.  The second model is hematogenous metastasis.  Cancer cells undergo intravasation at the primary tumor site and extravasation at a distance metastatic site.  The cancer cells target their metastatic site using cancer associated fibroblasts (CAFs).  CAFs at the primary tumor site secrete proteins that upregulate pathways that promote cell motility in ovarian cancer cells.  At the metastatic site, adipocytes and macrophages form a favorable tumor microenvironment.

Yeung TL, Leung CS, Yip KP, Au Yeung CL, Wong ST, Mok SC. Cellular and molecular processes in ovarian cancer metastasis. A Review in the Theme: Cell and Molecular Processes in Cancer Metastasis. Am J Physiol Cell Physiol. 2015;309(7):C444-56.

There are four differences between the contents of the pictures.  First, the figures do not represent the same interspecific interaction.  This is evident because the original contains the forest green scale-like leaves of the juniper (Fig. 1 - B), while the replicate does not (Fig. 2 - B).  Second, the backgrounds of the pictures are not the same. There is no background in the original picture of the juniper only (Fig. 1 - A), while the background of the bush 1 only picture includes a tan structure on the left side (Fig. 2 - A).  The background of forsythia and juniper includes two LSL windows separated by tan brick, with the left window being shown more than the right window (Fig. 1 - B), while the background of bush 1 and bush 2 includes an LSL window in the top right corner and a glass panel that meets the ground on the left side (Fig. 2 - B).  The background of forsythia only is solely tan bricks (Fig. 1 - C), while the background of bush 2 only is snow and a tan brick structure on the left side (Fig. 2 - C). Third, the colors of certain elements that are present in both figures vary between the two. The snow has a bluish tint in the original (Fig. 1 - B) while it resembles true white moreso in the replicate (Fig. 2).  The tan color of the LSL building also has this bluish tint in the original (Fig. 1 - B) while it appears more of an orange-tan in the replicate (Fig. 2 - B). Fourth, the figure components that are specific to one organism show much greater detail in the original (Fig. 1 - A, Fig 1 - C) than in the replicate (Fig. 2 - A, Fig. 2 - C).

The blueish tint that characterizes the elements of Figure 1 as compared to those same elements in Figure 2 is likely due to the pictures being taken at a different time of day.  Time of day was specified in the methods. It is also possible that a filter was added to the pictures comprising Figure 2.

The difference in relative size of each component of Figure 1 vs that in Figure 2 can be attributed to different sizing of images in the figure making programs used.  The image sizes for Figure 1 are specified in the methods. The different labeling of the Figure components is also due to technical differences in figure making. In Figure 2, white boxes were not made and periods were added after each letter.  Also, contrary to Figure 1, the letters in Figure 2 were not centered in the upper-left corner using the program’s tool to do so.