Most mammals have hair which can vary greatly in color, length and texture (Pough et al., 1989). However, there are several so-called ‘hairless’ species – such as whales, walruses, elephants, and humans – that have considerably less hair than other mammals.
Although certain genes connected to the development of hair have been identified, the genetic mechanisms that led to the loss of hair in certain mammals were unknown. One way to uncover candidate genes is to investigate their relative evolutionary rate (RER) in hairless versus haired mammals (Kowalczyk et al., 2019). In theory, the parts of the genome that once contributed to hair growth are free to accumulate more mutations once hairlessness arises. This is because in hairless mammals these regions are no longer used, so mutations in them are not selected against and amass faster.
Now, in eLife, Amanda Kowalczyk, Maria Chikina and Nathan Clark report on the identification of genetic regions that show accelerated evolution in hairless mammals using a computational approach called RERconverge (Kowalczyk et al., 2022). The team (who are based at Carnegie-Mellon University, the University of Pittsburgh and the University of Utah) compared the genomes of 62 mammalian species (including several hairless species) with phylogenetic relationships that indicated hair was lost at least nine times independently. Kowalczyk et al. focused their analyses on around 20,000 protein coding and around 350,000 noncoding regions that were shared among these genomes. Noncoding regions are potentially important players in the regulation of nearby genes.
The analyses of Kowalczyk et al. revealed that 20 protein coding regions and almost 2,000 noncoding regions had evolved significantly faster in hairless versus haired species. Some of the genes identified were known from other research to be involved in hair development, supporting the idea that the RERconverge approach had indeed uncovered regions of the genome relevant to hairlessness. However, most of the genomic sites detected had no known connection to hair growth, adding to the list of genomic regions that may be responsible for hair loss.
By comparing protein coding and noncoding regions, Kowalczyk et al. were able to yield three important insights. First, around ten keratin genes showed accelerated evolution in both coding and noncoding regions, suggesting that both the expression and structure of some proteins have changed in response to hairlessness.
Second, many accelerated noncoding regions did not have rapidly-evolving protein coding sequences nearby. This suggests that the protein coding sequences near these noncoding regions undergo shifts in expression that can affect hair growth, but likely also have roles unrelated to hair growth. This would explain why these protein coding sequences have not undergone accelerated evolution: the proteins they encode need to maintain their structure to perform their other roles, preventing these sequences from rapidly accumulating mutations.
Third, genes with accelerated evolution tended to be expressed in the hair shaft itself, while accelerated noncoding regions were often found near genes expressed in basal cells that give rise to hair (Figure 1). Together, these results show that hairlessness likely evolved through a complex combination of protein coding and noncoding mechanisms.
Evolution has tested countless mutations across individuals and generations, in sample sizes that could never be achieved in laboratory settings. Approaches like RERconverge exploit these evolutionary experiments to identify genetic sequences connected to specific traits by analyzing the speed at which these genomic regions evolve in different species. This is particularly useful to identify regions of the genome involved in the loss a trait (such as the loss of hair), because they will accumulate mutations faster in species that have lost the trait. Indeed, the work by Kowalczyk et al. joins a growing list of studies into the evolutionary genetics of trait loss, which include research into adaptations to aquatic and subterranean lifestyle in mammals (Chikina et al., 2016; Partha et al., 2017). One exciting next step in the study of hair loss will be to genetically modify model organisms like mice to test whether the genomic regions identified by Kowalczyk et al. affect hair growth in the laboratory.
Hundreds of genes experienced convergent shifts in selective pressure in marine mammalsMolecular Biology and Evolution 33:2182–2192.https://doi.org/10.1093/molbev/msw112
BookVertebrate LifeMacmillan Publishing Company.
Vertebrate limb morphology often reflects the environment due to variation in locomotor requirements. However, proximal and distal limb segments may evolve differently from one another, reflecting an anatomical gradient of functional specialization that has been suggested to be impacted by the timing of development. Here we explore whether the temporal sequence of bone condensation predicts variation in the capacity of evolution to generate morphological diversity in proximal and distal forelimb segments across more than 600 species of mammals. Distal elements not only exhibit greater shape diversity, but also show stronger within-element integration and, on average, faster evolutionary responses than intermediate and upper limb segments. Results are consistent with the hypothesis that late developing distal bones display greater morphological variation than more proximal limb elements. However, the higher integration observed within the autopod deviates from such developmental predictions, suggesting that functional specialization plays an important role in driving within-element covariation. Proximal and distal limb segments also show different macroevolutionary patterns, albeit not showing a perfect proximo-distal gradient. The high disparity of the mammalian autopod, reported here, is consistent with the higher potential of development to generate variation in more distal limb structures, as well as functional specialization of the distal elements.
We have focused on the mushroom bodies (MB) of Drosophila to determine how the larval circuits are formed and then transformed into those of the adult at metamorphosis. The adult MB has a core of thousands of Kenyon neurons; axons of the early-born g class form a medial lobe and those from later-born a'b' and ab classes form both medial and vertical lobes. The larva, however, hatches with only g neurons and forms a vertical lobe 'facsimile' using larval-specific axon branches from its g neurons. Computations by the MB involves MB input (MBINs) and output (MBONs) neurons that divide the lobes into discrete compartments. The larva has 10 such compartments while the adult MB has 16. We determined the fates of 28 of the 32 types of MBONs and MBINs that define the 10 larval compartments. Seven larval compartments are eventually incorporated into the adult MB; four of their larval MBINs die, while 12 MBINs/MBONs continue into the adult MB although with some compartment shifting. The remaining three larval compartments are larval specific, and their MBIN/MBONs trans-differentiate at metamorphosis, leaving the MB and joining other adult brain circuits. With the loss of the larval vertical lobe facsimile, the adult vertical lobes, are made de novo at metamorphosis, and their MBONs/MBINs are recruited from the pool of adult-specific cells. The combination of cell death, compartment shifting, trans-differentiation, and recruitment of new neurons result in no larval MBIN-MBON connections persisting through metamorphosis. At this simple level, then, we find no anatomical substrate for a memory trace persisting from larva to adult. For the neurons that trans-differentiate, our data suggest that their adult phenotypes are in line with their evolutionarily ancestral roles while their larval phenotypes are derived adaptations for the larval stage. These cells arise primarily within lineages that also produce permanent MBINs and MBONs, suggesting that larval specifying factors may allow information related to birth-order or sibling identity to be interpreted in a modified manner in these neurons to cause them to adopt a modified, larval phenotype. The loss of such factors at metamorphosis, though, would then allow these cells to adopt their ancestral phenotype in the adult system.