What Changed? a Short Tutorial

A Brief Overview

Note the Show functions matching keywords link. It allows you to search for specific functional roles and then pursue relevant events. We will come back to it later. Similarly, for now, please just skip the Genes that Distinguishing Families (Genes as Signatures) link. For now, let's peruse the tree a bit. If you click on Mycobacterium: Gains/losses of Gene Families, you should get a phylogentic tree estimating the evolutionary history of the Mycobacteria. The root of the tree was based on a crude estimate based on what is called "midpoint rooting". It was chosen by computing the maximum distance between any two leaves and placing the root at exactly halfway down the sequence of arcs connecting the two leaves. The tree itself was computed by looking for a set of genes that appeared to occur in all of the representative set of genomes we selected (call these core genes). Then a set of alignments were computed using these sets of core genes, the alignments were concatenated, and then a tree was estimated. A tutorial is not the place to dwell on the details, but you should realize that the tree is just an approximation.

Exploring the Functionality Gained or Lost on Each Arc

To speak of "gaining or losing a gene" we need to first form gene families, so that we can reasonably think about "corresponding genes". WC? uses an unpublished algorithm to construct its gene/protein families. We think that it is pretty good, but the reader needs to be aware that there are many errors in the families (in the sense that the genes in the family do not always encode isofunctional homologs). For that matter, the reader must be aware that some genes are truncated, some had assembly errors, and so forth. Noise and error pervade the effort.

Let's talk about Mycobacterium tuberculosis

What Are the Gained/Lost Tables Trying to Show?

1. The first gives a link that you can click on to see where members of family 4722 occur in the tree. Look carefully and note that the family does occur outside the TB genomes, and a few TB genomes fail to include the family.
2. The seccond column, PEGs can be used to see exactly what protein-encoding genes were included in the family, a link that can be used to construct a phylogenetic treee from the members of the family (useful in evaluating potential horizontal transfer) and a link to construct an alignment of the members (useful to see if the family really is a coherent group).
3. The third column contains the family id, which is just an integer.
4. The fourth column gives the function assigned to the family (in this case DNA primase (EC 2.7.7.-)). It is very important that you realize that there may be multiple families that have the same assigned function. This will occur when there are paralogs floating around. If you click on this function, it will take you to a page showing which families exist with the same function. In this case there are three such families -- 327, 4722, and 19383. These families may cover non-overlapping sections of the tree, or they may intersect. The point you need to be aware of is that the formation of such families is error-prone. If you wish to see which genomes in the tree include a DNA primase, click on Family on Tree with union of families, which will show all three families, and they totally cover the tree.

   Does this mean that the families are too error-prone to be worth pursuing? We believe not. As we proceed with our analysis, we will note that in many cases we are characterizing transposition events. In these events, the machinery is often similar, but the events are distinguishable. That is, we will end up arguing, based largely on analysis of chromosomal regions that we are, in fact, seeing the effects of an ancestral event (or perhaps several), and these events are (at least largely) distinguishable.

5. The fifth column will contain a link if clustering on the chromosome seems to be occurring, and it will take you to a page that shows potentially clustered genes (relating to this family) in each of the genomes.
6. The sixth column contains "coupled families", which in this case includes 12 distinct families. These families often occur within 5 kilobases on the chromosome from the family represented by the row. The twelve coupled families suggests that we are looking at a phiRv1 prophage event that has been inherited by at least a number of the TB genomes. The question immediately arises "Are these apparently conserved chromosomal clustering really that, or are they multiple copies of the same mobil element?" The only way that I know of to answer this is to compare the chromosomal regions seeking the point where recognizable conservation is detected.

Studying the Chromosomal Context

First, you should begin by trying to get instances of each family from a common genome. This is what the fifth column in the gained/lost tables (the link to show clusters) is used for. By clicking on it, you can see potential clusters in many of the genomes. If you were to click on clusters for family 4722, you would get a description of relevant clusters in all genomes. This will include a fair number of clusters, if we are, in fact, looking at a mobile element.

I was interested in the gain of family 4722, and I decided to look for it in genome 83332.1: Mycobacterium tuberculosis H37Rv. If you search through the clusters containing family 4722, you will find that there are just two in genome 83332.1:

Cluster for 83332.1: Mycobacterium tuberculosis H37Rv

Family	Function	PEG
6453	hypothetical protein	fig|83332.1.peg.1576
2645	Phage major capsid protein	fig|83332.1.peg.1578
3062	PhiRv1 phage, prohead protease, HK97 family	fig|83332.1.peg.1579
2917	Probable phiRv1 phage protein	fig|83332.1.peg.1585

Let's try to summarize this first example:

• WC? attempts to predict the arcs in the tree in which events occurred, where an event is the acquisition or loss of a protein family (i.e., the gene that codes for a member of the family).
• Events often relate to sets of genes (and, by extrapalation, sets of families). Clues to what happened can often be gained by surveying clusters of genes.
• Our implicit belief is that some of these events relate directly to the phenotype associated with sets of closely-related organisms -- virulence, in particular.
• Figuring out which of the events are actually significant, and how to understand what happened is aided by the sort of phylogentic comparative analysis attempted in WC?

Using the "search" field

Some More Examples to Look at

Signature Families

1. You have an alignment. Suppose that it is an rRNA alignment (which it was).
2. You have a tree. A subtree contains a set of genomes (which correspond to rows in the alignment) which we call the "in group".
3. The genomes that occur around the nested "in group" we call the "out group".
4. The question then becomes "Which columns in the alignment best distinguish the "in group" from the "out group".

Using this approach, a user species two sets of genomes. Let us call one the out group and the second the in group. Sergei's computation produces a score, the details of which are beyond the scope of a tutorial. Suffice it to say that a score is produced, and scores in the range 1.5 to 2.0 are pretty good, and those are the only ones we display.

So, the basic idea is to define two sets of genomes, compute scores for all families and then create a table for you to peruse.

Go back to the Mycobacterium entry page and look for the link Genes that Distinguishing Families (Genes as Signatures). If you click on this, you will get a tree that should be similar to those we have already looked at, but you should see sets of 3 radio buttons associate with each node. The middle setting is the default value in each set, and those should be visible to you. You define two sets of genomes. As an example, let us pick the Mycobacterium massiliense under n77 as "set 2". This is done by selecting the rightmost button associated with "n77. Now for set 1, let us pick all descendants of node n76 that are not in set 2. We would do this by clicking the leftmost button of the three associated with n76. The way this works is that all of the selected genomes in set 1 are marked, then set 2 is marked and the set 2 choices overwrite the set 1 choices. I am not explaining this well, but it actually is pretty convenient once you get the hang of it. So if I set the two nodes (n76 as defining set 1 and n77 as defining set 2) and go the the bottom of the page, you can click on Compute Gene Signatures to see what appears. You will get a large number of sequences with perfect scores (which implies that there are not hits in both set 1 and set 2).

Anyway, we suggest you play with it. We suspect that it might be useful when looking at sets of virulent and novirulent organisms that are phylogenticallly mixed (due to, for example, horizontal transfer).

Summary