Taxonomy 2: Molecular Classification

Historically the phylogeny of different organisms was based on readily observable characteristics. But the advent of molecular genetics in the late 20th century revealed other ways to organise phylogenetic trees. Genetic methods allow for a standardised way to compare all living organisms without relying on observable characteristics that can often be subjective. Modern taxonomy relies heavily on comparing the nucleic acids (deoxyribonucleic acid [DNA] or ribonucleic acid [RNA]) or proteins from different organisms. The more similar the nucleic acids and proteins are between two organisms, the more closely related they are considered to be.

Scientists continue to use analysis of RNA, DNA, and proteins to determine how organisms are related. One interesting, and complicating, discovery is that of horizontal gene transfer — when a gene of one species is absorbed into another organism’s genome. Horizontal gene transfer is especially common in microorganisms and can make it difficult to determine how organisms are evolutionarily related. Consequently, some scientists now think in terms of “webs of life” rather than “trees of life.”

Molecular advances impacting classification methods

With the advancement of DNA technology, the area of molecular systematics, which describes the use of information on the molecular level including DNA sequencing, has blossomed. New analysis of molecular characteristics not only confirms many earlier classifications, but also uncovers previously made errors. Molecular characteristics can include differences in the amino-acid sequence of a protein, differences in the individual nucleotide sequence of a gene, or differences in the arrangements of genes. Phylogenies based on molecular characteristics assume that the more similar the sequences are in two organisms, the more closely related they are. Different genes change evolutionarily at different rates and this affects the level at which they are useful at identifying relationships. Rapidly evolving sequences are useful for determining the relationships among closely related species. More slowly evolving sequences are useful for determining the relationships between distantly related species. To determine the relationships between very different species such as Eukarya and Archaea, the genes used must be very ancient, slowly evolving genes that are present in both groups, such as the genes for ribosomal RNA. Comparing phylogenetic trees using different sequences and finding them similar helps to build confidence in the inferred relationships.

Sometimes two segments of DNA in distantly related organisms randomly share a high percentage of bases in the same locations, causing these organisms to appear closely related when they are not. For example, the fruit fly (Drosophila species) shares 60 percent of its DNA with humans. In this situation, computer-based statistical algorithms have been developed to help identify the actual relationships, and ultimately, the coupled use of both morphologic and molecular information is more effective in determining phylogeny.

Molecular techniques for taxonomic classification

Phylogeny is a reference to the development of an organism evolutionarily. Molecular techniques allow for the evolutionary assessment of organisms using genomes or ribosomal RNA (rRNA) nucleotide sequences, generally believed to provide the most accurate information about the relatedness of microbes. Sequencing, or determining the base order of an organism’s DNA or RNA, is often one of the first steps to finding out detailed information about an organism. A bacterial genome can range from 130 kilobase pairs (kbp) to over 14 Megabase pairs (Mbp), while a viral genome ranges from 0.859 to 2473 kbp. For comparison, the human genome contains about 3 billion base pairs.

Given that the length of a single, individual sequencing read is somewhere between 45bp and 700bp, determining the sequence of longer fragments, such as the chromosomes in an entire genome of humans (3 x10⁹ bp), is more difficult. To determine a longer sequence, the genome needs to be broken into smaller fragments and reconstructed during analysis. There are two different strategies for doing this:

1. clone-by-clone sequencing, which relies on the creation of a physical map first then sequencing, and
2. whole genome shotgun sequencing, which sequences first and does not require a physical map.

The technology of molecular genetics and data processing and storage are maturing to the point where cataloguing the planet’s species in an accessible way is close to feasible. DNA barcoding is one molecular genetic method, which takes advantage of a specialised structures inside of some cells called mitochondria (Figure 4.1). Mitochondria contain DNA that is separate from the rest of the cell, and one of the genes in mitochondrial DNA changes more quickly through the process of evolution than regular DNA. While plants contain mitochondria, DNA from their chloroplasts, the specialised structures in which photosynthesis occurs, are more often barcoded.

Rapid DNA sequencing technology means the molecular genetics portion of the work is relatively inexpensive and quick. Computer resources store and make available the large volumes of data. Projects are currently underway to use DNA barcoding to catalogue museum specimens, which have already been named and studied, as well as testing the method on less studied groups. As of mid-2012, close to 150,000 named species had been barcoded. Early studies suggest there are significant numbers of undescribed species that looked too much like sibling species to previously be recognised as different. These now can be identified with DNA barcoding.

DNA Sequencing

DNA polymerases incorporate nucleotides (dNTPs) into a growing strand of DNA, based on the sequence of a template strand. DNA polymerases add a new base only to the 3’-OH group of an existing strand of DNA; this is why primers are required in natural DNA synthesis and in techniques such as PCR. Sanger sequencing relies on the random incorporation of modified nucleotides called terminators. Examples of terminators are the dideoxy nucleotides (ddNTPs), which lack a 3’-OH group and therefore cannot serve as an attachment site for the addition of new bases to a growing strand of DNA (Figure 4.2). After a ddNTP is incorporated into a strand of DNA, no further elongation can occur. Terminators are labelled with one of four fluorescent dyes, each specific for one the four nucleotide bases.

To sequence a DNA fragment, many copies of that fragment are required (Figure 4.3). Unlike PCR, DNA sequencing does not amplify the target sequence and only one primer is used. This primer is hybridised to the denatured template DNA and determines where on the template strand the sequencing reaction will begin. A mixture of dNTPs, fluorescently labelled terminators, and DNA polymerase is added to a tube containing the primer-template hybrid. The DNA polymerase will then synthesise a new strand of DNA until a fluorescently labelled nucleotide is incorporated, at which point extension is terminated. Because the reaction contains millions of template molecules, a corresponding number of shorter molecules is synthesised, each ending in a fluorescent label that corresponds to the last base incorporated. 

The newly synthesised strands can be denatured from the template and then separated using electrophoresis based on their length (Figure 4.4). Since each band differs in length by one nucleotide, and the identity of that nucleotide is known from its fluorescence, the DNA sequence can be read simply from the order of the colours in successive bands. In practice, the maximum length of sequence that can be read from a single sequencing reaction is about 700 bp.

A particularly sensitive electrophoresis method used in the analysis of DNA sequencing reactions is called capillary electrophoresis (Figure 4.5). In this method, a current pulls the sequencing products through a gel-like matrix that is encased in a fine tube of clear plastic. As in conventional electrophoresis, the smallest fragments move through the capillary the fastest. As they pass through a point near the end of the capillary, the fluorescent intensity of each dye is read. This produces a graph called a chromatogram. The sequence is determined by identifying the highest peak (i.e. the dye with the most intense fluorescent signal) at each position. 

A physical map is a representation of a genome, comprised of cloned fragments of DNA. The map is therefore made from physical entities (pieces of DNA) rather than abstract concepts such as the linkage frequencies and genes that make up a genetic map (Figure 4.6). It is usually possible to correlate genetic and physical maps, for example by identifying the clone that contains a particular molecular marker. The connection between physical and genetic maps allows the genes underlying particular mutations to be identified through a process call map-based cloning.

To create a physical map, large fragments of the genome are cloned into plasmid vectors, or into larger vectors called bacterial artificial chromosomes (BACs). BACs can contain approximately 100kb fragments. The set of BACs produced in a cloning reaction will be redundant, meaning that different clones will contain DNA from the same part of the genome. Because of this redundancy, it is useful to select the minimum set of clones that represent the entire genome, and to order these clones respective to the sequence of the original chromosome. Note that this is all to be done without knowing the complete sequence of each BAC. Making a physical map may therefore rely on techniques related to Southern blotting: DNA from the ends of one BAC is used as a probe to find clones that contain the same sequence. These clones are then assumed to overlap each other. A set of overlapping clones is called a contig.

Clone-by-clone sequencing

Physical mapping of cloned sequences was once considered a pre-requisite for genome sequencing. The process would begin by breaking the genome into BAC-sized pieces, arranging these BACs into a map, then breaking each BAC up into a series of smaller clones, which were usually then also mapped. Eventually, a minimum set of smaller clones would be identified, each of which was small enough to be sequenced. Because the order of clones relative to the complete chromosome was known prior to sequencing, the resulting sequence information could be easily assembled into one complete chromosome at the end of the project. Clone-by-clone sequencing therefore minimises the number of sequencing reactions that must be performed and makes sequence assembly straightforward and reliable. However, a drawback of this strategy is the tedious process of building physical map prior to any sequencing.

Whole genome shotgun sequencing

Shotgun sequencing initially involves construction of a genomic library, where the genome is broken into randomly sized fragments that are inserted into vectors to produce a library of clones. The fragments are sequenced and then analysed by a computer, which searches for overlapping regions to form a longer stretch of sequence (Figure 4.7). Eventually all the sequences are aligned to give the complete genome sequence. Errors are reduced because many of the clones contain identical or near identical sequences, resulting in good “coverage” of the genome.

 

Shotgun sequencing avoids the laborious process of making a physical map (Figure 4.8). However, it requires many more sequencing reactions than the clone-by-clone method, because, in the shotgun approach, there is no way to avoid sequencing redundant fragments. There is also a question of the feasibility of assembling complete chromosomes based simply on the sequence overlaps of many small fragments. This is particularly a problem when the size of the fragments is smaller than the length of a repetitive region of DNA. Nevertheless, this method has now been successfully demonstrated in the nearly complete sequencing of many large genomes (rice, human, and many others). It is the current standard methodology.

However, shotgun assemblies are rarely able to complete entire genomes. The human genome, for example, relied on a combination of shotgun sequence and physical mapping to produce contiguous sequence for the length of each arm of each chromosome. Note that because of the highly repetitive nature of centromeric and telomeric DNA, sequencing projects rarely include these heterochromatic, gene poor regions.

 

Second-generation DNA sequencing uses massively parallel methods, where multiple samples are sequenced side-by-side. DNA fragments of a few hundred bases each are amplified by PCR and then attached to small beads, so that each bead carries several copies of the same section of DNA. The beads are put into a plate containing more than a million wells, each with one bead, and the DNA fragments are sequenced. Third-generation DNA sequencing involves the sequencing of single molecules of DNA. Fourth-generation DNA sequencing, also known as “post light sequencing,” utilises methods other than optical detection for sequencing.

DNA-DNA hybridisation

A single molecule provides only a narrow window for glimpsing evolutionary relationships. The technique of DNA-DNA hybridisation provides a way of comparing the total genome of two species.

To assess the evolutionary relationship between species A and species B, DNA hybridisation can be used. To do this, total DNA is extracted and purified from both species. The samples are denatured using heat into single strands (ssDNA) and subsequently cooled to allow repetitive sequences to rehybridise into double-stranded DNA (dsDNA). This mixture of ssDNA (representing single genes) and dsDNA (representing repetitive DNA) is passed through a hydroxyapatite column, which retains dsDNA while allowing ssDNA to flow through. To enable tracking, the ssDNA from species A is made radioactive. This labelled DNA is then hybridised separately with non-radioactive ssDNA from species A (A/A) and species B (A/B). After hybridisation, both mixtures are gradually heated in small temperature increments (2°–3°C). At each step, samples are passed through hydroxyapatite to separate any radioactive ssDNA (A) that have dissociated from the duplexes. The amount of radioactivity is measured to determine the proportion of ssDNA at each temperature. A graph is plotted to show this relationship, and the temperature at which 50% of the DNA duplexes (dsDNA) have denatured (T₅₀H) is identified (Figure 4.9).

Typically, the A/B hybrid denatures at a lower temperature than the A/A hybrid, indicating that the sequences in species B are not perfectly complementary to those in species A. This difference in thermal stability reflects evolutionary divergence, as mismatches between sequences reduce the number of hydrogen bonds, making the duplex easier to separate.

Thus DNA-DNA hybridisation provides genetic comparisons integrated over the entire genome. Its use has cleared up several puzzling taxonomic relationships. DNA-DNA hybridisation can also be used to compare genomes of mixed populations of organisms. For example, when all the bacteria are extracted from 10 g of uncontaminated soil (about 1010 cells), the DNA extracted and purified from the bacteria and subjected to DNA-DNA hybridisation analysis, the resulting curves indicate that there are over a million different species in the soil sample, although the population is dominated by only a few of these.

Chromosome painting

Another way to compare entire genomes is to attach a fluorescent label to the DNA of individual chromosomes of one species and expose the chromosomes of another species to it. Regions of gene homology will hybridise taking up the fluorescent label and the "painted" chromosomes can be examined under the microscope. The method is a modification of fluorescence in situ hybridisation (FISH) and is also called Zoo-FISH.

Chromosome painting has shown, for example, that large sections of human chromosome 6 (which includes hundreds of genes in the major histocompatibility complex (MHC) have their counterpart; i.e. homologous genes, in chromosome 5 of the chimpanzee, chromosome B2 of the domestic cat, chromosome 7 of the pig and chromosome 23 of the cow.

Protein sequences

The study of the proteins of an organism (or the proteome) is referred to as proteomics. Much of the interest focuses on functional proteomics, which examines the functions of the cellular proteins and the ways in which they interact with one another. One common technique used in the study of proteins is two-dimensional gel electrophoresis, which first separates proteins based on their isoelectric points. This is accomplished by using a pH gradient, which separates the proteins based on their amino acid content. The separated proteins are then run through a polyacrylamide gel, providing the second dimension as proteins are separated by size.

Structural proteomics focuses on the three-dimensional structure of proteins, which is often determined by protein modelling, using computer algorithms to predict the most likely folding of the protein based on amino acid information and known protein patterns. Protein sequencing provides a tool for establishing homologies from which genealogies can be constructed and phylogenetic trees drawn, for example haemoglobin and cytochrome c.

Human beta chain	0
Gorilla	1
Gibbon	2
Rhesus monkey	8
Dog	15
Horse, cow	25
Mouse	27
Gray kangaroo	38
Chicken	45
Frog	67
Lamprey	125
Sea slug (a mollusc)	127
Soybean (leghemoglobin)	124

Table 1. Haemoglobins: example of molecular biology. Source: Taxonomy In Biology by John W. Kimball, used under a Creative Commons Attribution 4.0 International Licence.

The numbers (Table 1) represent the number of amino acid differences between the beta chain of human haemoglobin and the haemoglobins of the other species. In general, the number is inversely proportional to the closeness of kinship. All the values listed are for the beta chain except for the last three, in which the distinction between alpha and beta chains does not occur. The human beta chain contains 146 amino acid residues, as do most of the others.

Cytochrome c is part of the electron transport chain down which electrons are passed to oxygen during cellular respiration. Cytochrome c is found in the mitochondria of every aerobic eukaryote - animal, plant, and protist. The amino acid sequences of many of these have been determined and comparing them shows that they are related. Human cytochrome c contains 104 amino acids, and 37 of these have been found at equivalent positions in every cytochrome c that has been sequenced. It’s assumed that each of these molecules has descended from a precursor cytochrome in a primitive microbe that existed over 2 billion years ago. In other words, these molecules are homologous.

The first step in comparing cytochrome c sequences is to align them to find the maximum number of positions that have the same amino acid. Sometimes gaps are introduced to maximise the number of identities in the alignment (none was needed in this table). Gaps correct for insertions and deletions that occurred during the evolution of the molecule.

Table 2 shows the N-terminal 22 amino acid residues of human cytochrome c with the corresponding sequences from six other organisms aligned beneath. A dash indicates that the amino acid is the same one found at that position in the human molecule. All the vertebrate cytochromes (the first four) start with glycine (Gly). The Drosophila, wheat, and yeast cytochromes have several amino acids that precede the sequence shown here (indicated by <<<). In every case, the heme group of the cytochrome is attached to Cys-14. and Cys-17 (human numbering). In addition to the two Cys residues, Gly-1, Gly-6, Phe-10, and His-18 are found at the equivalent positions in every cytochrome c that has been sequenced.

		1					6				10				14						20
Human		Gly	Asp	Val	Glu	Lys	Gly	Lys	Lys	Ile	Phe	Ile	Met	Lys	Cys	Ser	Gln	Cys	His	Thr	Val	Glu	Lys
Pig		-	-	-	-	-	-	-	-	-	-	Val	Gln	-	-	Ala	-	-	-	-	-	-	-
Chicken		-	-	Ile	-	-	-	-	-	-	-	Val	Gln	-	-	-	-	-	-	-	-	-	-
Dogfish		-	-	-	-	-	-	-	-	Val	-	Val	Gln	-	-	Ala	-	-	-	-	-	-	Asn
Drosophilia	<<<	-	-	-	-	-	-	-	-	Leu		Val	Gln	Arg		Ala	-	-	-	-	-	-	Ala
Wheat	<<<	-	Asn	Pro	Asp	Ala	-	Ala	-	-	-	Lys	Thr	-	-	Ala	-	-	-	-	-	Asp	Ala
Yeast	<<<	Ser	Ala	Lys	-	-	Ala	Thr	Leu	-	Lys	Thr	Arg	-	Glu	Leu	-	-	-	-	-	-	-

Table 2. Molecular homology of Cytochrome c. Source: Taxonomy In Biology by John W. Kimball, used under a Creative Commons Attribution 4.0 International Licence.

We assume that the more identities there are between two molecules, the more recently they have evolved from a common ancestral molecule and thus the closer the kinship of their owners. Thus, the cytochrome c of the rhesus monkey is identical to that of humans except for one amino acid, whereas yeast cytochrome c differs from that of humans at 44 positions. (There are no differences between the cytochrome c of humans and that of chimpanzees).

With such information, one can reconstruct an evolutionary history of the molecule and thus of their respective owners. This requires using the genetic code to determine the minimum number of nucleotide substitutions in the DNA of the gene needed to derive one protein from another, and a powerful computer program to search for the shortest paths linking the molecules together.

Figure 4.10 Phylogenetic tree. Source: Taxonomy In Biology by John W. Kimball, used under a Creative Commons Attribution 4.0 International Licence.

The result is a phylogenetic tree (Figure 4.10). This one (the work of Walter M. Fitch and Emanuel Margoliash) shows the relationship between 20 species of eukaryotes. The numbers represent the minimum number of nucleotide substitutions in the gene for cytochrome c needed to produce these 20 proteins from a series of hypothetical ancestral genes at the various branching points (nodes).

The tree corresponds quite well to what we have long believed to be the evolutionary relationships among the vertebrates. But there are some anomalies. It indicates, for example, that the primates (humans and monkeys) split off before the split separating the kangaroo, a marsupial, from the other placental mammals. This is certainly wrong. But sequence analysis of other proteins can resolve such discrepancies.

Cytochrome c is an ancient molecule, and it has evolved very slowly. Even after more than 2 billion years, one-third of its amino acids are unchanged. This conservatism is a great help in working out the evolutionary relationships between distantly related creatures like fish and humans. But what of humans and the great apes? Their cytochrome c molecules are identical and can tell us nothing about evolutionary relationships. However, some proteins have evolved much more rapidly than cytochrome c, and these can be used to decipher recent evolutionary events. During blood clotting, short peptides are cut from fibrinogen converting it into insoluble fibrin. Once removed, these fibrinopeptides have no further function. They have been pretty much free from the rigors of natural selection and have, consequently, diverged rapidly during evolution. So, proteins such as these provide data useful in sorting out the twigs of phylogenetic trees of mammals.

Bioinformatics and data analysis

An assembled genome is a string of millions of A’s,C’s,G’s,T’s, and does not highlight which of these represent nucleotides that encode proteins, and which of these represent other features of genes and their regulatory elements. The field of bioinformatics combines many fields together (i.e. biology, computer science, statistics) to use the power of computers to analyse information contained in the genomic sequence. The process of genome annotation relies on computers to define features such a start and stop codons, introns, exons, and splice sites. However, few of the predictions made by these programs is entirely accurate, and most must be verified experimentally for any gene of particular importance or interest.

An open reading frame (ORF) denotes a possible protein-coding gene. For double-stranded DNA, there are six reading frames to be analysed, since the DNA is read in sets of three bases at a time and there are two strands of DNA. An ORF typically has at least 100 codons before a stop codon, with 3’ terminator sequences. A functional ORF is one that is actually used by the organism to encode a protein. Computers are used to search the DNA sequence looking for ORFs, with those presumed to encode protein further analysed by a bioinformatician.

It is often helpful for the sequence to be compared against a database of sequences coding for known proteins. GenBank is a database of over 200 billion base pairs of sequences that scientists can access, to try and find matches to the sequence of interest.  The database search tool BLAST (basic local alignment search tool) has programs for comparing both nucleotide sequences and amino acid sequences, providing a ranking of results in order of decreasing similarity.

 

 

Once the sequences of organisms have been obtained, meaningful information can be gathered using comparative genomics. For this, genomes are assessed for information regarding size, organisation, and gene content.

Comparison of the genome of microbial strains has given scientists a better picture regarding the genes that organisms pick up. A group of multiple strains share a core genome, genes coding for essential cellular functions that they all have in common. The pan genome represents all the genes found in all the members of species, so provides a good idea of the diversity of a group. Most of these “extra” genes are probably picked up by horizontal gene transfer.

Comparative genomics also shows that many genes are derived as a result of gene duplication. Genes within a single organism that likely came about because of gene duplication are referred to as paralogs. In many cases one of the genes might be altered to take on a new function.  It is also possible for gene duplication to be found in different organisms, as a result of acquiring the original gene from a common ancestor. These genes are called orthologs.

The sequence of a genome and the location of genes provide part of the picture, but in order to fully understand an organism we need an idea of what the cell is doing with its genes. In other words, what happens when the genes are expressed? This is where functional genomics  comes in – placing the genomic information in context.

The first step in gene expression is transcription or the manufacture of RNA. Transcriptome refers to the entire complement of RNA that a cell can make from its genome, while proteome refers to all the proteins encoded by an organisms’ genome, in the final step of gene expression.

\Microarrays or gene chips are solid supports upon which multiple spots of DNA are placed, in a grid-like fashion. Each spot of DNA represents a single gene or ORF. Known fragments of nucleic acid are labelled and used as probes, with a signal produced if binding occurs. Microarrays can be used to determine what genes might be turned on or off under particular conditions, such as comparing the growth of a bacterial pathogen inside the host versus outside of the host.

Microarrays can measure the abundance of mRNA for hundreds or thousands of genes at once. The abundance of mRNA of a particular gene is usually correlated with the activity of that gene. For example, genes that are involved in neuronal development likely produce more mRNA in brain tissue than in heart tissue. We can therefore learn about the relationship between particular genes and particular processes by comparing transcript abundance under different conditions. This can identify tissue specific expression (e.g. the nerve/heart example above), as well as differences in temporal expression (development), or exposure to external agents (eg, disease, hormones, drugs, etc.).

Metabolomics strives to identify the complete set of metabolic intermediates produced by an organism. This can be extremely complicated, since many metabolites are used by cells in multiple pathways. Metagenomicsorenvironmental genomics refers to the extraction of pooled DNA directly from a specific environment, without the initial isolation and identification of organisms within that environment. Since many microbial species are difficult to culture in the laboratory, studying the metagenome of an environment allows scientists to consider all organisms that might be present. Taxa can even be identified in the absence of organism isolation using nucleic acid sequences alone, where the taxon is known as phylotype.

Nucleic acid hybridisation or DNA-DNA hybridisation is a commonly used tool for molecular phylogeny, comparing the similarities between genomes. Nucleic acid sequencing, typically using the rRNAs from small ribosomal subunits, allows for direct comparison of sequences. The ribosomal sequence is seen as ideal because the genes encoding it do not change very much over time, nor does it appear to be strongly influenced by horizontal gene transfer. This makes it an excellent “molecular chronometer”, or way to track genetic changes over a long period of time, even between closely related organisms.

Traditionally, microbiology has been taught with the view that microorganisms are best studied under pure culture conditions, which involves isolating a single type of cell and culturing it in the laboratory. Because microorganisms can go through several generations in a matter of hours, their gene expression profiles adapt to the new laboratory environment very quickly. On the other hand, many species resist being cultured in isolation. Most microorganisms do not live as isolated entities, but in microbial communities known as biofilms. For all of these reasons, pure culture is not always the best way to study microorganisms. Metagenomics is the study of the collective genomes of multiple species that grow and interact in an environmental niche. Metagenomics can be used to identify new species more rapidly and to analyse the effect of pollutants on the environment (Figure 4.12). Metagenomics techniques can now also be applied to communities of higher eukaryotes, such as fish.

Comparison of total DNA versus coding sequences

Proteins are the expression of genes, so it might seem logical to compare actual gene sequences instead. There are several advantages to this approach. Firstly, DNA is much easier to sequence than protein. Additionally, genes contain regions that are more flexible and prone to change during evolution compared to protein sequences. These include nucleotides that produce synonymous codons—meaning that even if the amino acid at a particular position in two proteins is the same, the codons encoding that amino acid might differ between species. Genes also contain introns and flanking sequences, which can vary significantly without affecting the final protein product. These regions are under less selective pressure, making them useful for evolutionary studies. Another advantage is that DNA is more stable than protein in environmental conditions, allowing for the possibility of sequencing DNA from extinct organisms. For example, DNA has been successfully extracted and sequenced from Neanderthal remains over 38,000 years old.

Some of the most informative comparative DNA sequencing studies have focused on rDNA genes, which encode ribosomal RNA molecules—typically the small subunit (18S in eukaryotes and 16S in bacteria) of the ribosome—and genes found on mitochondrial DNA (mtDNA). In both cases, these genes are present in multiple copies, making their isolation and analysis more straightforward.

Applying molecular techniques to phylogenetic classification

To determine the relationships between very different species such as Eukarya and Archaea, the genes used must be very ancient, slowly evolving genes that are present in both groups, such as the genes for ribosomal RNA. Comparing phylogenetic trees using different sequences and finding them similar helps to build confidence in the inferred relationships.

Sometimes two segments of DNA in distantly related organisms randomly share a high percentage of bases in the same locations, causing these organisms to appear closely related when they are not. For example, the fruit fly shares 60 percent of its DNA with humans. These similar sections are those that make fruit flies and humans both eukaryotes and animals. This 60 percent is important but does not tell us as much about why we are different. In this situation, computer-based statistical algorithms have been developed to help identify how far back our common ancestor was, which is difficult, if not impossible with morphology alone. Ultimately, the coupled use of both morphologic and molecular information is most effective in determining phylogeny.

Scientists use phylogenetic trees to show the evolutionary pathways and relationships between organisms. The hierarchical classification of groups nested within more inclusive groups is reflected in diagrams. Scientists consider phylogenetic trees to be a hypothesis of the evolutionary past because one cannot go back through time to confirm the proposed relationships. Unlike taxonomy, a phylogenetic tree can be read like a map of evolutionary history. Scientists apply the concept of maximum parsimony, which states that the likeliest order of events is the simplest shortest path. For evolutionary events, this would be the path with the least number of major divergences that correlate with the evidence.

Recent genetic analysis and other advancements have found that some earlier taxonomic classifications do not reflect actual evolutionary relationships, and therefore, changes and updates must be made as new discoveries take place. Phylogenetic trees are hypotheses and are modified as data becomes available. In addition, classification historically has focused on grouping organisms mainly by shared characteristics and does not necessarily illustrate how the various groups relate to each other from an evolutionary perspective. For example, despite the fact that a hippopotamus resembles a pig more than a whale, the hippopotamus may be the closest living relative to the whale. One dramatic and recent example was the breaking apart of prokaryotic species which, until the 1970s, were all classified as 'bacteria'. Their division into Archaea and Bacteria came about after the recognition that their large genetic differences warranted their separation into two of three fundamental Domains of life.

Phylogenetic Trees

Many phylogenetic trees have a single branch point at the base representing a common ancestor of all the branches in the tree. This is known as a rooted tree. The branching from that root indicates evolutionary relationships (Figure 4.13). The point where a split occurs, called a branch point or "node", represents where a single lineage evolved into distinct new ones.

A lineage that evolved early from the root and remains unbranched is called basal taxon. When two lineages stem from the same node, they are called sister taxa. A node with more than two lineages is called a polytomy and serves to illustrate where scientists have not definitively determined all relationships. It is important to note that while sister taxa or polytomy share an ancestor, this does not mean that one taxon evolved from the other. The branch point, or split, represents a common ancestor that existed in the past, but that no longer exists. Organisms in two taxa may have split apart at a specific node, but neither taxon gave rise to the other.

Rooted phylogenetic trees can serve as a pathway to understanding evolutionary history. The pathway can be traced from the origin of life to any individual species by navigating through the evolutionary branches between the two points. Also, by starting with a single species and tracing back towards the “trunk” of the tree, one can discover that species’ ancestors, as well as where lineages share a common ancestry. In addition, the tree can be used to study entire groups of organisms.

Understanding Phylogenetic Trees

One of the most common forms of a phylogenetic tree is a cladogram (Figure 4.14). To begin this diagram, scientists often organise the homologous and analogous traits of various organisms using a system called cladistics. This system sorts organisms into clades: groups of organisms that descended from a single ancestor. For example, all organisms in the orange region in Figure 4.14 evolved from a single ancestor that had amniotic eggs. Consequently, all these organisms also have amniotic eggs and make a single clade, also called a monophyletic group. Clades must include all descendants from a branch point.

 

Clades can vary in size depending on which branch point is being referenced. The important factor is that all organisms in the clade or monophyletic group stem from a single point on the tree. This can be remembered because monophyletic breaks down into “mono,” meaning one, and “phyletic,” meaning evolutionary relationship. Notice in the examples of clades how each clade comes from a single point, whereas the non-clade groups show branches that do not share a single point.

Cladistics and Shared Characteristics

Organisms evolve from common ancestors and then diversify. Scientists use the phrase “descent with modification” because even though related organisms have many of the same characteristics and genetic codes, changes occur.

This pattern repeats as one goes through the phylogenetic tree of life:

1. A change in the genetic makeup of an organism leads to a new trait that becomes prevalent in the group.
2. Many organisms descend from this point and have this trait.
3. New variations continue to arise, some are adaptive and persist, leading to new traits.
4. With new traits, a new branch point is determined (go back to step 1 and repeat).

Cladistics rests on three assumptions.

1. Living things are related by descent from a common ancestor, which is a general assumption of evolution.
2. Speciation occurs by splits of one species into two, never more than two at a time, and at one point in time. This is a simplification but is accepted by most biologists.
3. Traits change enough over time to be considered to be in a different state.

It is also assumed that one can identify the direction of change for an adaptation. In other words, we assume that an amniotic egg is a later character state than non-amniotic eggs. We know this by reference to a group outside the clade: for example, insects have non-amniotic eggs; therefore, this is the older or ancestral character state.

Cladistics compares ingroups and outgroups. An ingroup (lizard, rabbit and human in our example) is the group of taxa being analysed in Figure 4.14. An outgroup (lancelet, lamprey and fish in our example) is a species or group of species that diverged before the lineage containing the group(s) of interest. By comparing ingroup members to each other and to the outgroup members, we can determine which characteristics are evolutionary adaptations determining the branch points of the ingroup’s phylogeny.

If a characteristic is found in all members of a group, it is a shared ancestral character: there has been no change in the adaptation during the descent of each member of the clade. Although these traits appear interesting because they unify the clade, in cladistics they are considered not helpful when we are trying to determine the relationships of the members of the clade because every member is the same. In contrast, consider the amniotic egg characteristic of Figure 4.14. Only some of the organisms have this trait; to those that do, it is called a shared-derived character  because this trait derived at some point but does not include all of the ancestors in the tree. This character tells us about the relationships among the members of the clade; it tells us that lizards, rabbits, and humans group more closely together than any of these organisms do with fish, lampreys, and lancelets.

The tricky aspect to shared-ancestral and shared-derived characters is the fact that these terms are relative. The same trait could be either ancestral or derived depending on the diagram being used and the organisms being compared. Scientists find these terms useful when distinguishing between clades during the building of phylogenetic trees, but it is important to remember that their meaning depends on context.

Choosing the Right Relationships

Constructing a phylogenetic tree, or cladogram, from the character data is a monumental task that is usually left up to a computer. The computer draws a tree such that all clades share the same list of derived characters. However, there are decisions to be made. For example, what if a species present in a clade is supported by all of the shared derived characters for that clade except one? One conclusion is that the trait evolved in the ancestor but then changed back in that one species. Also, a character state that appears in two clades must be assumed to have evolved independently in those clades. These inconsistencies are common in trees drawn from character data and complicate the decision-making process about which tree most closely represents the real relationships among the taxa.

To aid in the tremendous task of choosing the best tree, scientists use maximum parsimony, which means they assume that events occurred in the simplest, most obvious way. This means that the “best” tree is the one with the fewest number of character reversals, the fewest number of independent character changes, and the fewest number of character changes throughout the tree. Computer programs search through all of the possible trees to find the small number of trees with the simplest evolutionary pathways.

Why Does Phylogeny Matter?

In addition to enhancing our understanding of the evolutionary history of species, our own included, phylogenetic analysis has numerous practical applications. Two of those applications include understanding the evolution and transmission of disease and making decisions about conservation efforts. A 2010 study of MRSA (methicillin-resistant Staphylococcus aureus), an antibiotic-resistant pathogenic bacterium, traced the origin and spread of the strain in the past 40 years (Harris, Feil, Holden, Quail, Nickerson, Chantratita, Gardette, Tavares, Day, & Bentley). The study uncovered the timing and patterns in which the resistant strain moved from its point of origin in Europe to centers of infection and evolution in South America, Asia, North America, and Australasia. The study suggested that introductions of the bacteria to new populations occurred very few times, perhaps only once, and then spread from that limited number of individuals. This contrasts with the possibility that many individuals had carried the bacteria from one place to another. This result suggests that public health officials should concentrate on quickly identifying the contacts of individuals infected with a new strain of bacteria to control its spread.

A second area of usefulness for phylogenetic analysis is in conservation. Biologists argue that it is important to protect species throughout a phylogenetic tree rather than just those from one branch of the tree. Doing this preserves more of the variation produced by evolution. This is important because genetic variation makes a species more robust to disruption by being more likely to have some individuals with the traits necessary to survive a change in the environment. For example, conservation efforts should focus on a single species without sister species rather than other species that have a cluster of close sister species that recently evolved. If a single evolutionarily distinct species goes extinct, a disproportionate amount of variation from the tree will be lost compared to one species in the cluster of closely related species.

A study published in 2007 (Isaac, Turvey, Collen, Waterman, & Baillie) made recommendations for the conservation of mammal species worldwide based on how evolutionarily distinct and at risk of extinction they are. The study found that their recommendations differed from priorities based on simply the level of extinction threat to the species. The study recommended protecting some threatened and valued large mammals such as the orangutans, the giant and lesser pandas, and the African and Asian elephants. But they also found that some much lesser-known species should be protected based on how evolutionary distinct they are. These include several rodents, bats, shrews, and hedgehogs. In addition, there are some critically endangered species that did not rate as very important in evolutionary distinctiveness including species of deer mice and gerbils. While many criteria affect conservation decisions, preserving phylogenetic diversity provides an objective way to protect the full range of diversity generated by evolution.

References

Harris, S.R. et al. 2010. Evolution of MRSA during hospital transmission and intercontinental spread. Science 327:469–474. doi:10.1126/science.1182395

Isaac NJ, Turvey ST, Collen B, Waterman C, Baillie JE (2007) Mammals on the EDGE: Conservation Priorities Based on Threat and Phylogeny. PLoS ONE 2(3): e296. doi:10.1371/journal.pone.0000296