The overall goal of the following experiment is to discover and characterize novel gene function using gene annotation via combined analysis of metabolite profiling and co-expression analysis. This is achieved by consideration of experimental design with a focus on particular metabolic pathways and plant species. As a second step, analytical approaches are used to construct the whole metabolic pathway based on the chemical structures of detected compounds.
Next, the structure of the predicted pathway is integrated with co-expression analysis for narrowing down candidate genes, which putatively indicate key genes of metabolism. Ultimately, results are obtained by performing minimum sets of experiments with efficient use of several bio resources in order to characterize functional identification of genes. We first had the idea for this method when we found a very high correlation of Abosi OID bio cystic genes in the co-expression database named ate.
We considered that this strategy is one of the fastest way to find the novogene functions with ins datas publicly available database and efficient use of bio resources. To begin the extraction, harvest the plant materials and freeze immediately in liquid nitrogen. Once frozen powder, the plant materials using a mixer mill, aliquot the frozen plant material into a two milliliter EOR tube, and then add a metal ball to the tube.
Add 250 microliters of extraction buffer per 50 milligrams of sample. The extraction buffer contains the internal standard for normalization. After that, homogenize the frozen powder with the mixer mill for two minutes.
At a frequency of 25, centrifuge the sample at 12, 000 RPM for 10 minutes. Then transfer the supernatant to a centrifugal filter. After performing a second round of centrifugation, transfer the supernatant to a new einor tube.
Next, set up the HPLC by first checking that the temperature of the column oven is 35 degrees Celsius, and that of the sample tray is 10 degrees Celsius. Set up the mass spectrometry condition and check the state of vacuum and heating capillary. Perform a mass overcharge calibration of the MS detector by infusion injection of the calibration solution mixture.
Using the automatic calibration application in the software. Then transfer 50 microliters of the extracts to a glass vial. For HPLC, perform at least five injections of extraction buffer before performing the analysis for the first sample as described in the written procedure.
After configuring a mass spectrometry analysis tool, select the data to be processed. Prepare a table of the detected peaks of interest in accordance with the compound class shown in the written protocol. Identify peaks by co-evolution with standard compounds, annotate the detected peaks using Ms.MS analysis, literature, survey and metabolite database search At this step.
Prediction on pathway should be based on the chemical structures by linking the enzymatic function in the metabolic pathway. So these steps should be conducted by precise peak ation of detected compounds. Databases of general metabolic pathways such as keg database and plant psych are very effective for prediction of the metabolic pathway of interest.
To prepare a gene list with Arabidopsis orthologs gene, ID first download a gene ID list from the genomic database. Add the arabidopsis gene ID of orthologs gene in case the target plant is not arabidopsis. Then prepare a list of genes in the pathway of interest.
Annotation of Arabidopsis pathway data and gene family data are available on the TAAR website. Lists of Arabidopsis Orthologs genes can subsequently be combined. Perform co-expression network analysis by using the prepared gene ID list to search the best database for the predicted pathway.
Checking the correlation of well-known gene pairs in the pathway of interest. If the co-expression database or gene expression database are not available. In the plan of interest, the Arabidopsis co-expression database should be used with a list of arabidopsis autologous genes.
Next, construct the framework for the target co-expression network. Based on the connections of the well-known genes in the pathway of interest. Add correlated candidate genes and check their gene annotation in the predicted families to the connections of this network for finding the best candidate.
Genes threshold of coefficient value should be coordinated according to network structure and density of correlated genes. Now make a list of genes which can be narrowed down as being specialized to the target pathway. Using this list, check gene expression of the organ specificities and stress responses of the candidate genes.
Begin integration of the information by adding well-characterized genes that have been used for query of co-expression analysis to the predicted metabolic pathway. Perform a literature search to check if the uncharacterized parts of this pathway are novel, including uncharacterized, enzymatic steps, transport proteins, and transcription factors. At this point, predict the most suitable gene annotation for these missing uncharacterized steps.
Combining the results of metabolite profiling and candidate genes of silico gene expression based on the predicted pathway. Next, arrange the candidate genes on the predicted pathway according to gene function. For example, acetyl transferase for acetylated metabolite, glycosyl transferase for glycoside and P four 50 for oxidized compound.
Check the consistency of tissue specificities or stress responses between metabolite accumulation and gene expression level of candidate genes. Also check the connections to other metabolism for providing substrate and stress responsive genes. As a final step, perform an experiment for identification of gene function using bioresources such as knockout mutant library and full length CD NA library.Shown.
Here is an example of co-regulation network analysis of the anthocyanin pathway. Co-expression analyses were performed using the PRAM co-expression index based on the dataset of added version three with the PX program displayed as red nodes 12 anthocyanin enzymatic genes and two transcription factors for anthocyanin production were used for searching candidate genes. A co-expression network including 68 correlated candidate genes and 14 query genes, was reconstructed by an interconnection of set search using the prime database resulting in T net output files that were drawn using PX software.
The blue nodes indicate the candidate genes which correlated with anthocyanin genes After its development. This strategy will gave us a good hint to predicts and novel gene function in plant metabolisms.
