Chapter 6: Global Gene Expression in Acute and Chronic Hypoxia
6.2.1 Microarray further analysis
In an earlier chapter (4.2.2.1), we observed that hypoxia-induced resistance required chronic hypoxia. Here, we further explore gene expression under acute and chronic hypoxic conditions to elucidate the implication of chronic hypoxia on MB cells.
6.2.1.1Profile Clustering by hypoxic time points
A ‘Profile search’ function was added to the MATLAB code to allow us to obtain an output list of expressed transcripts at a particular time point, which can be used for further functional analysis. Profile clustering was achieved by adding a simple ‘binary code’ within our microarray analysis coding. The output search profile was then grouped by each or a combination of time points to allow better visualisation of genes up- or down- regulated during a given time frame.
The ordering (left to right) of the binary codes corresponds to the hypoxic time points 0, 6, 64 and 96 hours. In the binary code, it is set to be ‘1’, ‘0’ or ‘-1’, where ‘1’ means the data have met the criteria of ≥two fold upregulation; ‘0’ means criteria not met; and ‘-1’ means data met criteria of ≥two fold downregulation. Some possible binary code combinations are illustrated in Figure 6.1. As we are interested to differentiate between the gene expressions in acute and chronic hypoxia, we have manually altered the MATLAB binary codes between each run to generate transcript lists up- and down-regulated at early and late hypoxic time points.
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Figure 6.1 Binary combination for Profile searches. Hypoxic time points are across the top row of the table. Rows underneath are some possible binary combinations for a given profile search. (A) Upregulated transcripts. For example, first line are transcripts, which are not expressed in hypoxia (Figure 5.3); second line for transcripts upregulated at 6 hours only; third line for transcripts upregulated at 64 hours only. (B) Downregulated transcripts. For example, second line for transcripts downregulated at 6 hours only; third line for transcripts down regulated at 64 hours only and fifth line for transcripts downregulated at both 64 and 96 hours hypoxia.
Taking into account that a gene can have more than one transcript, we have here condensed the output transcript lists by grouping them into their corresponding gene names. The gene lists resulting from of all the profiles analysed were used to produce Venn diagrams for both up/down regulated genes (Figure 6.2). We first examined genes, which were expressed at a single time point and at that time point only. We observed an upregulation of 28 genes at 6 hours hypoxia only, the expression of these genes returned to a non-significant expression level for the remaining duration of the hypoxic time course. At 64 hours, 401 genes were upregulated, these genes were not regulated before or after this time point. Lastly, there were 625 genes which were upregulated at the 96 hours hypoxic time point but they are not expressed at 6 or 64 hours (Figure 6.2A). For the hypoxia repressed genes at each individual time point, there were 4 genes which were downregulated at 6 hours, 590 genes at 64 hours only and 551 genes at 96 hours (Figure 6.2B).
When we analysed genes expressed at 2 different time points, interestingly, the highest number of genes regulated was seen at the joint time points of 64 and 96 hours, where
Global Gene Expression in Acute and Chronic Hypoxia ~900 genes were upregulated and over ~1000 genes were downregulated at both of these times (Figure 6.2). Of note, the total number of genes which were upregulated at 96 hours will therefore be the combination value of 625 genes (upregulated at T96) plus 927 genes (upregulated at T64& 96). These results demonstrate that chronic hypoxia has a larger influence in genetic expression than acute hypoxia and this is likely to affect the cellular response of cells cultured under these conditions.
Figure 6.2 Venn diagram showing up/down regulated genes at each hypoxic time point. D283 cells were incubated in 1% O2 for 0, 6, 64 or 96 hours. Data were analysed using MATLAB binary code profile search functions. (A) Upregulated genes. For examples, 28 genes are upregulated at 6 hours only; 401 genes are upregulated at 64 hours only; and 927 genes are upregulated at combined time point of 64 and 96 hours (B) Downregulated transcripts. For examples, 4 genes are downregulated at 6 hours only; 590 genes are downregulated at 64 hours only; and 1012 genes are downregulated at combined time point of 64 and 96 hours.
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6.2.1.2Grouping by expression pattern using k-means clustering
We have demonstrated that the clustering by hypoxic time points results in a large list of genes which are expressed or repressed in chronic hypoxia. In order to resolve this further into smaller clusters, we took the pattern of expression profile into account and performed clustering using another method. This allowed us to better deduce whether there is a correlation between a groups of genes with similar expression dynamics in relation to their biological functions. The significantly expressed transcripts were clustered by their expression pattern using a method called k-means clustering. k-means is a partition method based on actual observations, treating data as an object belonging to a location in space. The data (observations) are separated into k number of cluster using an iterative algorithm. Initially, k means are computationally generated within the data domain, ideally placing the means as far away from each other as possible. Then each observation (objects) in the data are associated with the nearest k mean thus partitioning into the defined number of k clusters. After the initial k clusters partitioning, a new mean is calculated for each of the cluster, this will be the centroid of all the objects within that cluster. The process of associating each object with the nearest k means and recalculating new k cluster centroid is repeated till the sum of distances of every object within one cluster to the centroid is minimised. The final k clusters would then be produced when the algorithm has converged, producing the most possible well-separated compacted clusters.
We have utilised MATLB built in K-means function, “kmeans” algorithm to run a 16 partition clustering using the default settings with the exceptions of parameters listed in Table 6.1.
Parameter Value Description
‘replicates’ 15 Number of times to repeat the clustering, each with a new set of cluster centroid position
‘display’ final Level of display output
Table 6.1 Alterations to k-means clustering parameters. The number of replicates is set to ‘15’, this increase in repeated clustering ensures a good separation of all the transcripts with similar expression patterns. The display is set as final, meaning that only the final output of the clustering analysis is exported.
Global Gene Expression in Acute and Chronic Hypoxia The 16 k-means clusters generated are shown in (Figure 6.3). Each cluster display the expression plot of each of the transcripts grouped to that cluster due to mean normalisation. Although some of the expression trends look similar between clusters, the data actually fall on different scales. For example, to look at upregulated transcripts in hypoxia, candidates from both cluster 8 and 14 should be considered.
Figure 6.3 MATLAB k-mean clustering output. Significantly expressed transcripts are clustered into one of sixteen clusters. Transcripts with similar expression pattern and level of expression in hypoxia are grouped together in the same cluster.
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