Echinococcosis is a worldwide zoonotic parasitic disease of humans and various herbivorous domestic animals (intermediate hosts) transmitted by the contact with wild and domestic carnivores (definitive hosts), mainly foxes and dogs. Recently, a vaccine was developed showing high levels of protection against one parasite haplotype (G1) of Echinococcus granulosus, and its potential efficacy against distinct parasite variants or species is still unclear. Interestingly, the EG95 vaccine antigen is a secreted glycosylphosphatydilinositol (GPI)-anchored protein containing a fibronectin type III domain, which is ubiquitous in modular proteins involved in cell adhesion. EG95 is highly expressed in oncospheres, the parasite life cycle stage which actively invades the intermediate hosts. After amplifying and sequencing the complete CDS of 57 Echinococcus isolates belonging to 7 distinct species, we uncovered a large amount of genetic variability, which may influence protein folding. Two positively selected sites are outside the vaccine epitopes, but are predicted to alter protein conformation. Moreover, phylogenetic analyses indicate that EG95 isoform evolution is convergent with regard to the number of beta-sheets and alpha-helices. We conclude that having a variety of EG95 isoforms is adaptive for Echinococcus parasites, in terms of their ability to invade different hosts, and we propose that a mixture of isoforms could possibly maximize vaccine efficacy.

The dramatic increase in heterogeneous types of biological data--in particular, the abundance of new protein sequences--requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity--GPCRs and kinases from humans, and the crotonase superfamily of enzymes--we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships.

Unlabelled

Unraveling the drivers controlling the response and adaptation of biological communities to environmental change, especially anthropogenic activities, is a central but poorly understood issue in ecology and evolution. Comparative genomics studies suggest that lateral gene transfer (LGT) is a major force driving microbial genome evolution, but its role in the evolution of microbial communities remains elusive. To delineate the importance of LGT in mediating the response of a groundwater microbial community to heavy metal contamination, representative Rhodanobacter reference genomes were sequenced and compared to shotgun metagenome sequences. 16S rRNA gene-based amplicon sequence analysis indicated that Rhodanobacter populations were highly abundant in contaminated wells with low pHs and high levels of nitrate and heavy metals but remained rare in the uncontaminated wells. Sequence comparisons revealed that multiple geochemically important genes, including genes encoding Fe(2+)/Pb(2+) permeases, most denitrification enzymes, and cytochrome c553, were native to Rhodanobacter and not subjected to LGT. In contrast, the Rhodanobacter pangenome contained a recombinational hot spot in which numerous metal resistance genes were subjected to LGT and/or duplication. In particular, Co(2+)/Zn(2+)/Cd(2+) efflux and mercuric resistance operon genes appeared to be highly mobile within Rhodanobacter populations. Evidence of multiple duplications of a mercuric resistance operon common to most Rhodanobacter strains was also observed. Collectively, our analyses indicated the importance of LGT during the evolution of groundwater microbial communities in response to heavy metal contamination, and a conceptual model was developed to display such adaptive evolutionary processes for explaining the extreme dominance of Rhodanobacter populations in the contaminated groundwater microbiome.

Importance

Lateral gene transfer (LGT), along with positive selection and gene duplication, are the three main mechanisms that drive adaptive evolution of microbial genomes and communities, but their relative importance is unclear. Some recent studies suggested that LGT is a major adaptive mechanism for microbial populations in response to changing environments, and hence, it could also be critical in shaping microbial community structure. However, direct evidence of LGT and its rates in extant natural microbial communities in response to changing environments is still lacking. Our results presented in this study provide explicit evidence that LGT played a crucial role in driving the evolution of a groundwater microbial community in response to extreme heavy metal contamination. It appears that acquisition of genes critical for survival, growth, and reproduction via LGT is the most rapid and effective way to enable microorganisms and associated microbial communities to quickly adapt to abrupt harsh environmental stresses.