The study of changing sequence composition of DNA, RNA and proteins over time has
offered some of the most fundamental insights into the evolutionary process to date.
From understanding how populations and ultimately species diverge to the study of
how particular selection pressures affect changes in genotype and phenotype, our knowledge
of evolution would be a fraction of what it is now without the major advances made
in the field of molecular evolution. Recent technological and bioinformatical improvements
have continued to expand these insights, and have also offered key applications such
as the ability to model and predict pathogen evolution, monitor the effective population
size of threatened species, and help understand what constitutes a healthy microbiome.
Two recent studies, both led by Nora Besansky and published in Science, emphasize
the power and challenges of comparative genomics when working to understand the evolution
of disease vectors. First, Daniel Neafsey and colleagues report the sequencing, assembly,
and comparison of genomes from 16 Anopheles mosquito species (Neafsey et al. 2014).
As 11 of these species are considered major disease vectors, comparison among the
genomes allowed the researchers to examine underlying genes that may be associated
with vectoring capacity. The results suggest that, relative to the Drosophila genus,
the Anopholes' genomes are remarkably flexible, with rapid rates of gene loss/gain,
increased loss of introns, and shuffling of genes on the X chromosome. The data suggest
a mechanism for the observed functional diversity across the species, especially in
those traits such as chemosensory ability that are associated with adaptation to host
feeding and therefore disease vectoring. However, comparison among genomes was hampered
by what are most likely high levels of interspecific gene flow, or introgression,
as described in a separate paper by Michael Fontaine and coauthors (Fontaine et al.
2014). Depending on which genomic segment the authors used to build phylogenetic trees,
a remarkably different pattern emerged; trees based on autosomal sequences tended
to group the three major vectors of malaria together, while those built using the
X chromosome suggest early radiation of these three species and persistent introgression
on the autosomes. Together, these studies offer tantalizing hypotheses for the adaptive
significance of among-species gene flow and genomic plasticity in allowing the Anopholes
genus to act as vectors for a wide array of pathogens.
In addition to the increasing power of genomics and phylogenomics, the use of transcriptional
profiling has also proven invaluable to the field. A recent review of novel insights
gained through transcriptomic analyses of natural populations by Mariano Alvarez and
collaborators highlights the utility of this approach in testing how genotype translates
to phenotype, and how this translation is influenced by environment-specific gene
expression (Alvarez et al. 2014). Such variation can have dramatic implications for
the process of adaptation as well as our ability to predict the response of populations
to rapid environmental changes such as those resulting from pathogens, pollutants,
or climate change. More recent advancement in transcriptomics includes the ability
to profile gene expression of single cells, as discussed by Nicola Crosetto and coauthors
in a new paper reviewing recent progress in spatiotemporal transcriptomics (Crosetto
et al. 2015). Among the many applications of this powerful approach to unravelling
among-cell expression differences is the ability to examine heterogeneity of tumour
cells to predict drug sensitivity of various cancers.
The use of sequence data to infer evolutionary processes is not limited to single
species. Indeed, the use of metagenomics to infer the composition of species from
environmental samples has greatly enhanced our understanding of microbial diversity.
In its simplest form, metagenomic analysis allows for a culture-independent characterization
of microbial community composition. This type of analysis has gained much recent attention
for its application in understanding the microbiomes of eukaryotic species. For example,
recent work by Julia Goodrich and colleagues examined how human genetics shapes the
relative abundances of various gut bacteria by comparing microbiotas across 416 pairs
of twins (Goodrich et al. 2014). The authors first discovered a clear heritability
for a subset of bacterial taxa, most notably those from the family Christensenellaceae,
which were also correlated with low host body-mass index (BMI). The authors then went
a step further by adding a particular species of Christensenellaceae into an obese-associated
microbiome and inoculating sterile mice with either the unaltered or altered microbial
community. In this way, they were able to demonstrate not only correlation with host
metabolism in humans but also to infer causation, as mice supplemented with this species
showed reduced weight gain relative to those not receiving the supplement.
The simultaneous analysis of multiple genomes within a single environmental sample
also allows for assessment of selection acting on genes shared by members of the community.
A terrific example of this comes from recent work by Molly Gibson and collaborators
who examined the so-called ‘resistome’ of microbial communities from soil and the
human gut, in this case focusing on the genes conferring resistance against 18 antibiotics
typically used in clinical settings (Gibson et al. 2015). The authors used a new database
of protein families to assign antibiotic resistance functions to each metagenomic
segment, and were able to demonstrate that the antibiotic resistance genes found in
environmental versus human-associated microbiota were functionally different, perhaps
suggesting less gene flow among these communities than previously thought.
Overall, the recent advancements in both omics and bioinformatics have been game-changing
for the field of molecular evolution, and the application of such new approaches and
technologies have only begun to surface. The potential for advancement in clinical
and agricultural settings is already being realized, and application to the management
of natural populations, including the spread of disease, is already following.