Mining-impacted rice paddies select for Archaeal methylators and reveal a putative (Archaeal) regulator of mercury methylation

Methylmercury (MeHg) is a microbially produced neurotoxin derived from inorganic mercury (Hg), which accumulation in rice represents a major health concern to humans. However, the microbial control of MeHg dynamics in the environment remains elusive. Here, leveraging three rice paddy fields with distinct concentrations of Hg (Total Hg (THg): 0.21-513 mg kg-1 dry wt. soil; MeHg: 1.21-6.82 ng g-1 dry wt. soil), we resorted to metagenomics to determine the microbial determinants involved in MeHg production under contrasted contamination settings. We show that Hg methylating Archaea, along with methane-cycling genes, were enriched in severely contaminated paddy soils. Metagenome-resolved Genomes of novel putative Hg methylators belonging to Nitrospinota (UBA7883), with poorly resolved taxonomy despite high completeness, showed evidence of facultative anaerobic metabolism and adaptations to fluctuating redox potential. Furthermore, we found evidence of environmental filtering effects that influenced the phylogenies of not only hgcA genes under different THg concentrations, but also of two housekeeping genes, rpoB and glnA, highlighting the need for further experimental validation of whether THg drives the evolution of hgcAB. Finally, assessment of the genomic environment surrounding hgcAB suggests that this gene pair may be regulated by an archaeal toxin-antitoxin (TA) system, instead of the more frequently found arsR-like genes in bacterial methylators. This suggests the presence of distinct hgcAB regulation systems in bacteria and archaea. Our results support the emerging role of Archaea in MeHg cycling under mining-impacted environments and shed light on the differential control of the expression of genes involved in MeHg formation between Archaea and Bacteria.

Long-Term Tillage and Crop Rotation Regimes Reshape Soil-Borne Oomycete Communities in Soybean, Corn, and Wheat Production Systems

Soil-borne oomycetes include devastating plant pathogens that cause substantial losses in the agricultural sector. To better manage this important group of pathogens, it is critical to understand how they respond to common agricultural practices, such as tillage and crop rotation. Here, a long-term field experiment was established using a split-plot design with tillage as the main plot factor (conventional tillage (CT) vs. no till (NT), two levels) and rotation as the subplot factor (monocultures of soybean, corn, or wheat, and corn-soybean-wheat rotation, four levels). Post-harvest soil oomycete communities were characterized over three consecutive years (2016-2018) by metabarcoding the Internal Transcribed Spacer 1 (ITS1) region. The community contained 292 amplicon sequence variants (ASVs) and was dominated by Globisporangium spp. (85.1% in abundance, 203 ASV) and Pythium spp. (10.4%, 51 ASV). NT decreased diversity and community compositional structure heterogeneity, while crop rotation only affected the community structure under CT. The interaction effects of tillage and rotation on most oomycetes species accentuated the complexity of managing these pathogens. Soil and crop health represented by soybean seedling vitality was lowest in soils under CT cultivating soybean or corn, while the grain yield of the three crops responded differently to tillage and crop rotation regimes.

Machine Learning Algorithms Associate Case Numbers with SARS-CoV-2 Variants Rather Than with Impactful Mutations

During the SARS-CoV-2 pandemic, much effort has been geared towards creating models to predict case numbers. These models typically rely on epidemiological data, and as such overlook viral genomic information, which could be assumed to improve predictions, as different variants show varying levels of virulence. To test this hypothesis, we implemented simple models to predict future case numbers based on the genomic sequences of the Alpha and Delta variants, which were co-circulating in Texas and Minnesota early during the pandemic. Sequences were encoded, matched with case numbers at a future time based on collection date, and used to train two algorithms: one based on random forests and one based on a feed-forward neural network. While prediction accuracies were ≥93%, explainability analyses showed that the models were not associating case numbers with mutations known to have an impact on virulence, but with individual variants. This work highlights the necessity of gaining a better understanding of the data used for training and of conducting explainability analysis to assess whether model predictions are misleading.

Viral spillover risk increases with climate change in High Arctic lake sediments

The host spectrum of viruses is quite diverse, as they can sustainedly infect a few species to several phyla. When confronted with a new host, a virus may even infect it and transmit sustainably in this new host, a process called ‘viral spillover’. However, the risk of such events is difficult to quantify. As climate change is rapidly transforming environments, it is becoming critical to quantify the potential for spillovers. To address this issue, we resorted to a metagenomics approach and focused on two environments, soil and lake sediments from Lake Hazen, the largest High Arctic freshwater lake in the world. We used DNA and RNA sequencing to reconstruct the lake’s virosphere in both its sediments and soils, as well as its range of eukaryotic hosts. We then estimated the spillover risk by measuring the congruence between the viral and the eukaryotic host phylogenetic trees, and show that spillover risk increases with runoff from glacier melt, a proxy for climate change. Should climate change also shift species range of potential viral vectors and reservoirs northwards, the High Arctic could become fertile ground for emerging pandemics.

Geolocators link marine mercury with levels in wild seabirds throughout their annual cycle: Consequences for trans-ecosystem biotransport

Seabirds are widely used as indicators of marine pollution, including mercury (Hg), because they track contaminant levels across space and time. However, many seabirds are migratory, and it is difficult to understand the timing and location of their Hg accumulation. Seabirds may obtain Hg thousands of kilometers away, during their non-breeding period, and deposit that Hg into their terrestrial breeding colonies. We predicted that Hg concentration in rectrices reflects exposure during the previous breeding season, in body feathers reflects non-breeding exposure, and in blood collected during breeding reflects exposure during current breeding. To test this hypothesis, we measured total Hg concentration in these three tissues, which reflect different timepoints during the annual cycle of rhinoceros auklets (Cerorhinca monocerata) breeding on both sides of the North Pacific (Middleton Island in Alaska and Teuri Island in Hokkaido), and tracked their wintering movement patterns with biologging devices. We (i) identify the wintering patterns of both populations, (ii) examine Hg levels in different tissues representing exposure at different time periods, (iii) test how environmental Hg exposure during the non-breeding season affects bird contamination, and (iv) assess whether variation in Hg levels during the non-breeding season influences levels accumulated in terrestrial plants. Individuals from both populations followed a figure-eight looping migration pattern. We confirm the existence of a pathway from environmental Hg to plant roots via avian tissues, as Hg concentrations were higher in plants within the auklet colonies than at control sites. Hg concentrations of breast feathers were higher in Alaskan than in Japanese auklets, but Hg concentrations in rectrices and blood were similar. Moreover, we found evidence that tissues with different turnover rates could record local anthropogenic Hg emission rates of areas visited during winter. In conclusion, Hg was transported across thousands of kilometers by seabirds and transferred to local plants.

Warming Climate Is Reducing the Diversity of Dominant Microbes in the Largest High Arctic Lake

Temperatures in the Arctic are expected to increase dramatically over the next century, and transform high latitude watersheds. However, little is known about how microbial communities and their underlying metabolic processes will be affected by these environmental changes in freshwater sedimentary systems. To address this knowledge gap, we analyzed sediments from Lake Hazen, NU Canada. Here, we exploit the spatial heterogeneity created by varying runoff regimes across the watershed of this uniquely large high-latitude lake to test how a transition from low to high runoff, used as one proxy for climate change, affects the community structure and functional potential of dominant microbes. Based on metagenomic analyses of lake sediments along these spatial gradients, we show that increasing runoff leads to a decrease in taxonomic and functional diversity of sediment microbes. Our findings are likely to apply to other, smaller, glacierized watersheds typical of polar or high latitude ecosystems; we can predict that such changes will have far reaching consequences on these ecosystems by affecting nutrient biogeochemical cycling, the direction and magnitude of which are yet to be determined.

Direct Evidence of an Increasing Mutational Load in Humans

The extent to which selection has shaped present-day human populations has attracted intense scrutiny, and examples of local adaptations abound. However, the evolutionary trajectory of alleles that, today, are deleterious has received much less attention. To address this question, the genomes of 2,062 individuals, including 1,179 ancient humans, were reanalyzed to assess how frequencies of risk alleles and their homozygosity changed through space and time in Europe over the past 45,000 years. Although the overall deleterious homozygosity has consistently decreased, risk alleles have steadily increased in frequency over that period of time. Those that increased most are associated with diseases such as asthma, Crohn disease, diabetes, and obesity, which are highly prevalent in present-day populations. These findings may not run against the existence of local adaptations but highlight the limitations imposed by drift and population dynamics on the strength of selection in purging deleterious mutations from human populations.

Viral Long-Term Evolutionary Strategies Favor Stability over Proliferation

Viruses are known to have some of the highest and most diverse mutation rates found in any biological replicator, with single-stranded (ss) RNA viruses evolving the fastest, and double-stranded (ds) DNA viruses having rates approaching those of bacteria. As mutation rates are tightly and negatively correlated with genome size, selection is a clear driver of viral evolution. However, the role of intragenomic interactions as drivers of viral evolution is still unclear. To understand how these two processes affect the long-term evolution of viruses infecting humans, we comprehensively analyzed ssRNA, ssDNA, dsRNA, and dsDNA viruses, to find which virus types and which functions show evidence for episodic diversifying selection and correlated evolution. We show that selection mostly affects single stranded viruses, that correlated evolution is more prevalent in DNA viruses, and that both processes, taken independently, mostly affect viral replication. However, the genes that are jointly affected by both processes are involved in key aspects of their life cycle, favoring viral stability over proliferation. We further show that both evolutionary processes are intimately linked at the amino acid level, which suggests that it is the joint action of selection and correlated evolution, and not just selection, that shapes the evolutionary trajectories of viruses—and possibly of their epidemiological potential.

Flight costs in volant vertebrates: A phylogenetically-controlled meta-analysis of birds and bats

Flight costs play an important role in determining the behavior, ecology, and physiology of birds and bats. Mechanical flight costs can be estimated from aerodynamics. However, measured metabolic flight costs (oxygen consumption rate) are less accurately predicted by flight theory, either because of (1) variation in flight efficiency across species, (2) variation in how basal costs interact with flight costs or (3) methodological biases. To tease apart these three hypotheses, we conducted a phylogenetically-controlled meta-analysis based on data from birds and bats. Birds doing short flights in a lab had higher metabolic rates than those with sustained flapping flight. In turn, species that used sustained flapping flight had a higher metabolic rate than those that flew primarily via gliding. Models accounting for relatedness (phylogeny) explained the data better than those that did not, which is congruent with the idea that several different flight Bauplans have evolved within birds and bats. Focusing on species with sustained flapping flight, for which more data are currently available, we found that flight cost estimates were not affected by measurement methods in both birds and bats. However, efficiency increased with body mass and decreased with flight speed in both birds and bats. Basal metabolic rate was additive to flight metabolic rate in bats but not birds. We use these results to derive an equation for estimating metabolic flight costs of birds and bats that includes variation in whole animal efficiency with flight speed and body mass.

Identifying genetic determinants of complex phenotypes from whole genome sequence data

BACKGROUND

A critical goal in biology is to relate the phenotype to the genotype, that is, to find the genetic determinants of various traits. However, while simple monofactorial determinants are relatively easy to identify, the underpinnings of complex phenotypes are harder to predict. While traditional approaches rely on genome-wide association studies based on Single Nucleotide Polymorphism data, the ability of machine learning algorithms to find these determinants in whole proteome data is still not well known.

RESULTS

To better understand the applicability of machine learning in this case, we implemented two such algorithms, adaptive boosting (AB) and repeated random forest (RRF), and developed a chunking layer that facilitates the analysis of whole proteome data. We first assessed the performance of these algorithms and tuned them on an influenza data set, for which the determinants of three complex phenotypes (infectivity, transmissibility, and pathogenicity) are known based on experimental evidence. This allowed us to show that chunking improves runtimes by an order of magnitude. Based on simulations, we showed that chunking also increases sensitivity of the predictions, reaching 100% with as few as 20 sequences in a small proteome as in the influenza case (5k sites), but may require at least 30 sequences to reach 90% on larger alignments (500k sites). While RRF has less specificity than random forest, it was never <50%, and RRF sensitivity was significantly higher at smaller chunk sizes. We then used these algorithms to predict the determinants of three types of drug resistance (to Ciprofloxacin, Ceftazidime, and Gentamicin) in a bacterium, Pseudomonas aeruginosa. While both algorithms performed well in the case of the influenza data, results were more nuanced in the bacterial case, with RRF making more sensible predictions, with smaller errors rates, than AB.

CONCLUSIONS

Altogether, we demonstrated that ML algorithms can be used to identify genetic determinants in small proteomes (viruses), even when trained on small numbers of individuals. We further showed that our RRF algorithm may deserve more scrutiny, which should be facilitated by the decreasing costs of both sequencing and phenotyping of large cohorts of individuals.

Biotransport of metallic trace elements from marine to terrestrial ecosystems by seabirds

Physical systems, such as currents and winds, have traditionally been considered responsible for transporting contaminants. Although evidence is mounting that animals play a role in this process through their movements, we still know little about how such contaminant biotransport occurs and the extent of effects at deposition sites. In the present study, we address this question by studying how rhinoceros auklets (Cerorhinca monocerata), a seabird that occurs in immense colonies (∼300 000 pairs at our study site, Teuri Island), affect contaminant levels at their colony and at nearby sites. More specifically, we hypothesize that contaminants are transported and deposited by seabirds at their colony and that these contaminants are passed on locally to the terrestrial ecosystem. To test this hypothesis, we analyzed the concentration of 9 heavy metal and metalloids, as well as δ¹³ C and δ¹⁵ N stable isotopes, in bird tissues, plants, and soil, both within and outside of the colony. The results show that rhinoceros auklets transport marine-derived mercury (Hg), possibly from their wintering location, and deposit Hg via their feces at their breeding site, thereby contaminating plants and soils within the breeding colony. The present study confirms not only that animals can transport contaminants from marine to terrestrial ecosystems, potentially over unexpectedly long distances, but also that bird tissues contribute locally to plant contamination. Environ Toxicol Chem 2019;38:106-114. © 2018 SETAC.

How the Central American Seaway and an Ancient Northern Passage Affected Flatfish Diversification

While the natural history of flatfish has been debated for decades, the mode of diversification of this biologically and economically important group has never been elucidated. To address this question, we assembled the largest molecular data set to date, covering > 300 species (out of ca. 800 extant), from 13 of the 14 known families over nine genes, and employed relaxed molecular clocks to uncover their patterns of diversification. As the fossil record of flatfish is contentious, we used sister species distributed on both sides of the American continent to calibrate clock models based on the closure of the Central American Seaway (CAS), and on their current species range. We show that flatfish diversified in two bouts, as species that are today distributed around the equator diverged during the closure of CAS, whereas those with a northern range diverged after this, hereby suggesting the existence of a postCAS closure dispersal for these northern species, most likely along a trans-Arctic northern route, a hypothesis fully compatible with paleogeographic reconstructions.

Physicochemical Drivers of Microbial Community Structure in Sediments of Lake Hazen, Nunavut, Canada

The Arctic is undergoing rapid environmental change, potentially affecting the physicochemical constraints of microbial communities that play a large role in both carbon and nutrient cycling in lacustrine environments. However, the microbial communities in such Arctic environments have seldom been studied, and the drivers of their composition are poorly characterized. To address these gaps, we surveyed the biologically active surface sediments in Lake Hazen, the largest lake by volume north of the Arctic Circle, and a small lake and shoreline pond in its watershed. High-throughput amplicon sequencing of the 16S rRNA gene uncovered a community dominated by Proteobacteria, Bacteroidetes, and Chloroflexi, similar to those found in other cold and oligotrophic lake sediments. We also show that the microbial community structure in this Arctic polar desert is shaped by pH and redox gradients. This study lays the groundwork for predicting how sediment microbial communities in the Arctic could respond as climate change proceeds to alter their physicochemical constraints.

Viral outbreaks involve destabilized evolutionary networks: evidence from Ebola, Influenza and Zika

Recent history has provided us with one pandemic (Influenza A/H1N1) and two severe viral outbreaks (Ebola and Zika). In all three cases, post-hoc analyses have given us deep insights into what triggered these outbreaks, their timing, evolutionary dynamics, and phylogeography, but the genomic characteristics of outbreak viruses are still unclear. To address this outstanding question, we searched for a common denominator between these recent outbreaks, positing that the genome of outbreak viruses is in an unstable evolutionary state, while that of non-outbreak viruses is stabilized by a network of correlated substitutions. Here, we show that during regular epidemics, viral genomes are indeed stabilized by a dense network of weakly correlated sites, and that these networks disappear during pandemics and outbreaks when rates of evolution increase transiently. Post-pandemic, these evolutionary networks are progressively re-established. We finally show that destabilization is not caused by substitutions targeting epitopes, but more likely by changes in the environment sensu lato. Our results prompt for a new interpretation of pandemics as being associated with evolutionary destabilized viruses.

Widespread Historical Contingency in Influenza Viruses

In systems biology and genomics, epistasis characterizes the impact that a substitution at a particular location in a genome can have on a substitution at another location. This phenomenon is often implicated in the evolution of drug resistance or to explain why particular "disease-causing" mutations do not have the same outcome in all individuals. Hence, uncovering these mutations and their locations in a genome is a central question in biology. However, epistasis is notoriously difficult to uncover, especially in fast-evolving organisms. Here, we present a novel statistical approach that replies on a model developed in ecology and that we adapt to analyze genetic data in fast-evolving systems such as the influenza A virus. We validate the approach using a two-pronged strategy: extensive simulations demonstrate a low-to-moderate sensitivity with excellent specificity and precision, while analyses of experimentally validated data recover known interactions, including in a eukaryotic system. We further evaluate the ability of our approach to detect correlated evolution during antigenic shifts or at the emergence of drug resistance. We show that in all cases, correlated evolution is prevalent in influenza A viruses, involving many pairs of sites linked together in chains; a hallmark of historical contingency. Strikingly, interacting sites are separated by large physical distances, which entails either long-range conformational changes or functional tradeoffs, for which we find support with the emergence of drug resistance. Our work paves a new way for the unbiased detection of epistasis in a wide range of organisms by performing whole-genome scans.

Breeding phenology and winter activity predict subsequent breeding success in a trans-global migratory seabird

Inter-seasonal events are believed to connect and affect reproductive performance (RP) in animals. However, much remains unknown about such carry-over effects (COEs), in particular how behaviour patterns during highly mobile life-history stages, such as migration, affect RP. To address this question, we measured at-sea behaviour in a long-lived migratory seabird, the Manx shearwater (Puffinus puffinus) and obtained data for individual migration cycles over 5 years, by tracking with geolocator/immersion loggers, along with 6 years of RP data. We found that individual breeding and non-breeding phenology correlated with subsequent RP, with birds hyperactive during winter more likely to fail to reproduce. Furthermore, parental investment during one year influenced breeding success during the next, a COE reflecting the trade-off between current and future RP. Our results suggest that different life-history stages interact to influence RP in the next breeding season, so that behaviour patterns during winter may be important determinants of variation in subsequent fitness among individuals.

Foraging flexibility and search patterns are unlinked during breeding in a free-ranging seabird

In order to maximize foraging efficiency in a varying environment, predators are expected to optimize their search strategy. Environmental conditions are one important factor affecting these movement patterns, but variations in breeding constraints (self-feeding vs. feeding young and self-feeding) during different breeding stages (incubation vs. chick-rearing) are often overlooked, so that the mechanisms responsible for such behavioral shifts are still unknown. Here, to test how search patterns are affected at different breeding stages and to explore the proximate causes of these variations, we deployed data loggers to record both position (global positioning system) and dive activity (time-depth recorders) of a colonial breeding seabird, the razorbill Alca torda. Over a period of 3 years, our recordings of 56 foraging trips from 18 breeders show that while there is no evidence for individual route fidelity, razorbills exhibit higher foraging flexibility during incubation than during chick rearing, when foraging becomes more focused on an area of high primary productivity. We further show that this behavioral shift is not due to a shift in search patterns, as reorientations during foraging are independent of breeding stage. Our results suggest that foraging flexibility and search patterns are unlinked, perhaps because birds can read cues from their environment, including conspecifics, to optimize their foraging efficiency.

Automatic detection of rate change in large data sets with an unsupervised approach: the case of influenza viruses

Influenza viruses evolve at such a high rate that vaccine recommendations need to be changed, but not quite on a regular basis. This observation suggests that the rate of evolution of these viruses is not constant through time, which begs the question as to when such rate changes occur, if they do so independently of the host in which they circulate and (or) independently of their subtype. To address these outstanding questions, we introduce a novel heuristics, Mclust*, that is based on a two-tier clustering approach in a phylogenetic context to estimate (i) absolute rates of evolution and (ii) when rate change occurs. We employ the novel approach to compare the two influenza surface proteins, hemagglutinin and neuraminidase, that circulated in avian, human, and swine hosts between 1960 and 2014 in two subtypes: H3N2 and H1N1. We show that the algorithm performs well in most conditions, accounting for phylogenetic uncertainty by means of bootstrapping and scales up to analyze very large data sets. Our results show that our approach is robust to the time-dependent artifact of rate estimation, and confirm pervasive punctuated evolution across hosts and subtypes. As such, the novel approach can potentially detect when vaccine composition needs to be updated.

Dual foraging and pair coordination during chick provisioning by Manx shearwaters: empirical evidence supported by a simple model

The optimal allocation of time and energy between one's own survival and offspring survival is critical for iteroparous animals, but creates a conflict between what maximises the parent's fitness and what maximises fitness of the offspring. For central-place foragers, provisioning strategies may reflect this allocation, while the distance between central-places and foraging areas may influence the decision. Nevertheless, few studies have explored the link between life history and foraging in the context of resource allocation. Studying foraging behaviour alongside food load rates to chicks provides a useful system for understanding the foraging decisions made during parent-offspring conflict. Using simultaneously deployed GPS and time-depth recorders, we examined the provisioning strategies in free-living Manx shearwaters Puffinus puffinus, which were caring for young. Our results showed a bimodal pattern, where birds alternate short and long trips. Short trips were associated with higher feeding frequency and larger meals than long trips, suggesting that long trips were performed for self-feeding. Furthermore, most foraging was carried out within 100 km of sea fronts. A simple model based on patch quality and travel time shows that for Manx shearwaters combining chick feeding and self-maintenance, bimodal foraging trip durations optimise feeding rates.

Genome analyses suggest the presence of polyploidy and recent human-driven expansions in eight global populations of the honeybee pathogen Nosema ceranae

Nosema ceranae is a microsporidian pathogen whose infections have been associated with recent global declines in the populations of western honeybees (Apis mellifera). Despite the outstanding economic and ecological threat that N. ceranae may represent for honeybees worldwide, many aspects of its biology, including its mode of reproduction, propagation and ploidy, are either very unclear or unknown. In the present study, we set to gain knowledge in these biological aspects by re-sequencing the genome of eight isolates (i.e. a population of spores isolated from one single beehive) of this species harvested from eight geographically distant beehives, and by investigating their level of polymorphism. Consistent with previous analyses performed using single gene sequences, our analyses uncovered the presence of very high genetic diversity within each isolate, but also very little hive-specific polymorphism. Surprisingly, the nature, location and distribution of this genetic variation suggest that beehives around the globe are infected by a population of N. ceranae cells that may be polyploid (4n or more), and possibly clonal. Lastly, phylogenetic analyses based on genome-wide single-nucleotide polymorphism data extracted from these parasites and mitochondrial sequences from their hosts all failed to support the current geographical structure of our isolates.

Dual foraging and pair coordination during chick provisioning by Manx shearwaters: empirical evidence supported by a simple model

The optimal allocation of time and energy between one's own survival and offspring survival is critical for iteroparous animals, but creates a conflict between what maximises the parent's fitness and what maximises fitness of the offspring. For central-place foragers, provisioning strategies may reflect this allocation, while the distance between central-places and foraging areas may influence the decision. Nevertheless, few studies have explored the link between life history and foraging in the context of resource allocation. Studying foraging behaviour alongside food load rates to chicks provides a useful system for understanding the foraging decisions made during parent-offspring conflict. Using simultaneously deployed GPS and time-depth recorders, we examined the provisioning strategies in free-living Manx shearwaters Puffinus puffinus, which were caring for young. Our results showed a bimodal pattern, where birds alternate short and long trips. Short trips were associated with higher feeding frequency and larger meals than long trips, suggesting that long trips were performed for self-feeding. Furthermore, most foraging was carried out within 100 km of sea fronts. A simple model based on patch quality and travel time shows that for Manx shearwaters combining chick feeding and self-maintenance, bimodal foraging trip durations optimise feeding rates.

Microbial community structure in lake and wetland sediments from a high Arctic polar desert revealed by targeted transcriptomics

While microbial communities play a key role in the geochemical cycling of nutrients and contaminants in anaerobic freshwater sediments, their structure and activity in polar desert ecosystems are still poorly understood, both across heterogeneous freshwater environments such as lakes and wetlands, and across sediment depths. To address this question, we performed targeted environmental transcriptomics analyses and characterized microbial diversity across three depths from sediment cores collected in a lake and a wetland, located on Cornwallis Island, NU, Canada. Microbial communities were characterized based on 16S rRNA and two functional gene transcripts: mcrA, involved in archaeal methane cycling and glnA, a bacterial housekeeping gene implicated in nitrogen metabolism. We show that methane cycling and overall bacterial metabolic activity are the highest at the surface of lake sediments but deeper within wetland sediments. Bacterial communities are highly diverse and structured as a function of both environment and depth, being more diverse in the wetland and near the surface. Archaea are mostly methanogens, structured by environment and more diverse in the wetland. McrA transcript analyses show that active methane cycling in the lake and wetland corresponds to distinct communities with a higher potential for methane cycling in the wetland. Methanosarcina spp., Methanosaeta spp. and a group of uncultured Archaea are the dominant methanogens in the wetland while Methanoregula spp. predominate in the lake.

Inferring influenza global transmission networks without complete phylogenetic information

Influenza is one of the most severe respiratory infections affecting humans throughout the world, yet the dynamics of its global transmission network are still contentious. Here, I describe a novel combination of phylogenetics, time series, and graph theory to analyze 14.25 years of data stratified in space and in time, focusing on the main target of the human immune response, the hemagglutinin gene. While bypassing the complete phylogenetic inference of huge data sets, the method still extracts information suggesting that waves of genetic or of nucleotide diversity circulate continuously around the globe for subtypes that undergo sustained transmission over several seasons, such as H3N2 and pandemic H1N1/09, while diversity of prepandemic H1N1 viruses had until 2009 a noncontinuous transmission pattern consistent with a source/sink model. Irrespective of the shift in the structure of H1N1 diversity circulation with the emergence of the pandemic H1N1/09 strain, US prevalence peaks during the winter months when genetic diversity is at its lowest. This suggests that a dominant strain is generally responsible for epidemics and that monitoring genetic and/or nucleotide diversity in real time could provide public health agencies with an indirect estimate of prevalence.

Predicting the emergence of H3N2 influenza viruses reveals contrasted modes of evolution of HA and NA antigens

Vaccine design for rapidly changing viruses is based on empirical surveillance of strains circulating in a given season to assess those that will most likely spread during the next season. The choice of which strains to include in the vaccine is critical, as an erroneous decision can lead to a nonimmunized human population that will then be at risk in the face of an epidemic or, worse, a pandemic. Here, we present the first steps toward a very general phylogenetic approach to predict the emergence of novel viruses. Our genomic model builds upon natural features of viral evolution such as selection and recombination / reassortment, and incorporates episodic bursts of evolution and or of recombination. As a proof-of-concept, we assess the performance of this model in a retrospective study, focusing: (i) on the emergence of an unexpected H3N2 influenza strain in 2007, and (ii) on a longitudinal design. Based on the analysis of hemagglutinin (HA) and neuraminidase (NA) genes, our results show a lack of predictive power in both experimental designs, but shed light on the mode of evolution of these two antigens: (i) supporting the lack of significance of recombination in the evolution of this influenza virus, and (ii) showing that HA evolves episodically while NA changes gradually.

Habitat-specific type I polyketide synthases in soils and street sediments

Actinomycetes produce many pharmaceutically useful compounds through type I polyketide biosynthetic pathways. Soil has traditionally been an important source for these actinomycete-derived pharmaceuticals. As the rate of antibiotic discovery has decreased and the incidence of antibiotic resistance has increased, researchers have looked for alternatives to soil for bioprospecting. Street sediment, where actinomycetes make up a larger fraction of the bacterial population than in soil, is one such alternative environment. To determine if these differences in actinomycetal community structure are reflected in type I polyketide synthases (PKSI) distribution, environmental DNA from soils and street sediments was characterized by sequencing amplicons of PKSI-specific PCR primers. Amplicons covered two domains: the last 80 amino acids of the ketosynthase (KS) domain and the first 240 amino acids of the acyltransferase (AT) domain. One hundred and ninety clones from ten contrasting soils from six regions and nine street sediments from six cities were sequenced. Twenty-five clones from two earthworm-affected samples were also sequenced. UniFrac lineage-specific analysis identified two clades that clustered with actinomycetal GenBank matches that were street sediment-specific, one similar to the PKSI segment of the mycobactin siderophore involved in mycobacterial virulence. A clade of soil-specific sequences clustered with GenBank matches from the ambruticin and jerangolid pathways of Sorangium cellulosum. All three of these clades were found in sites >700 km apart. Street sediments are enriched in actinomycetal PKSIs. Non-actinomycetal PKSI pathways may be more chemically diverse than actinomycetal PKSIs. Common soil and street sediment PKIs are globally distributed.

Comparative dynamics and distribution of influenza drug resistance acquisition to protein m2 and neuraminidase inhibitors

Although efficient influenza vaccines are designed on a regular basis, the only protection of human populations against an unforeseen virus such as during the H1N1 pandemic in 2009 might be antiviral drugs. Adamantanes and neuraminidase inhibitors (Oseltamivir) represent two classes of such drugs that target the viral matrix protein 2 and neuraminidase, respectively. Although the emergence of resistance to both drugs has been described, the timing and spread of the acquisition of either single or dual resistances by different hosts is still unclear. Using a multilayered phylogenetic approach based on relaxed molecular clocks and large-scale maximum likelihood approaches, we show that Adamantane resistance evolved multiple times in various subtypes and hosts, possibly in breeding contexts (swine); and Oseltamivir resistance was also found in different subtypes and hosts, but its transmission is only sustained in humans. Furthermore, the dynamics of the emergence of antiviral resistance were examined for each drug. This showed that although the first mutations conferring resistance to Adamantanes precede US Food and Drug Administration (FDA) approval, general resistance emerged 15-38 years post-drug approval. This is in contrast to Oseltamivir resistance mutations that emerged at most 7 years after FDA approval of the drug. This study demonstrates the power of large-scale analyses to uncover and monitor the emergence dynamics of drug resistance.

Adaptive evolution of HIV at HLA epitopes is associated with ethnicity in Canada

Host immune selection pressure influences the development of mutations that allow for HIV escape. Mutation patterns induced in HIV by the human leukocyte antigen (HLA) are HLA-allele specific. As ethnic groups have distinct and characteristic HLA allele frequencies, we can expect divergent viral evolution within ethnicities. Here, we have sequenced and analyzed the HIV pol gene from 1248 subtype B infected, treatment-naïve individuals in Canada. Phylogenetic analysis showed no separation between pol sequences from five self-identified ethnic groups, yet fixation index (F_ST) values showed significant divergence between ethnicities. A total of 17 amino acid sites showed an ethnic-specific fixation pattern (0.015<F_ST <0.060, p<0.01), and 27 codons were inferred to be under positive selection (p<0.01), with each set of sites strongly associated with HLA sites (p = 1.78×10⁻⁶ and p = 1.91×10⁻⁷, respectively). Within the pol gene, eight sites under HLA selective pressure were correlated with ethnicity, indicating ‘adaptive divergence’ between the groups studied. Our findings highlight challenges in HIV vaccine design in ethnically diverse countries with subtype B epidemics.

The metabolic consequences of hepatic AMP-kinase phosphorylation in rainbow trout

AMP-activated protein kinase (AMPK), a phylogenetically conserved serine/threonine protein kinase, is proposed to function as a "fuel gauge" to monitor cellular energy status in response to nutritional environmental variations. However, in fish, few studies have addressed the metabolic consequences related to the activation of this kinase. This study demonstrates that the rainbow trout (Oncorhynchus mykiss) possesses paralogs of the three known AMPK subunits that co-diversified, that the AMPK protein is present in the liver and in isolated hepatocytes, and it does change in response to physiological (fasting-re-feeding cycle) and pharmacological (AICAR and metformin administration and incubations) manipulations. Moreover, the phosphorylation of AMPK results in the phosphorylation of acetyl-CoA carboxylase, a main downstream target of AMPK in mammals. Other findings include changes in hepatic glycogen levels and several molecular actors involved in hepatic glucose and lipid metabolism, including mRNA transcript levels for glucokinase, glucose-6-phosphatase and fatty acid synthase both in vivo and in vitro. The fact that most results presented in this study are consistent with the recognized role of AMPK as a master regulator of energy homeostasis in living organisms supports the idea that these functions are conserved in this piscine model.

Incubation patterns in a central-place forager affect lifetime reproductive success: scaling of patterns from a foraging bout to a lifetime

BACKGROUND

Long-lived seabirds face a conflict between current and lifelong reproductive success. During incubation shifts, egg neglect is sometimes necessary to avoid starvation, but may compromise the current reproductive attempt. However, factors underlying this decision process are poorly understood. We focus on the ancient murrelet, Synthliboramphus antiquus, an alcid with exceptionally long incubation shift lengths, and test the impact of environmental factors on incubation shift length in relation to reproductive success.

METHODOLOGY/PRINCIPAL FINDINGS

Using an information theoretic approach, we show that incubation shift length was a strong predictor of reproductive success for ancient murrelets at Reef Island, Haida Gwaii, British Columbia, Canada during the 2007 and 2008 breeding seasons. The most important factors explaining an individual's shift length were egg size, wind speed and the length of the mate's previous shift. Wind speed and tide height were the two most important factors for determining foraging behavior, as measured by dive frequency and depth.

CONCLUSIONS/SIGNIFICANCE

Our study demonstrates that (i) species-specific reproductive strategies interact with environmental conditions such as wind speed to form multiple incubation patterns and (ii) maintaining regular incubation shifts is an essential component of reproductive success.

The nonadaptive nature of the H1N1 2009 Swine Flu pandemic contrasts with the adaptive facilitation of transmission to a new host

BACKGROUND

The emergence of the 2009 H1N1 Influenza pandemic followed a multiple reassortment event from viruses originally circulating in swines and humans, but the adaptive nature of this emergence is poorly understood.

RESULTS

Here we base our analysis on 1180 complete genomes of H1N1 viruses sampled in North America between 2000 and 2010 in swine and human hosts. We show that while transmission to a human host might require an adaptive phase in the HA and NA antigens, the emergence of the 2009 pandemic was essentially nonadaptive. A more detailed analysis of the NA protein shows that the 2009 pandemic sequence is characterized by novel epitopes and by a particular substitution in loop 150, which is responsible for a nonadaptive structural change tightly associated with the emergence of the pandemic.

CONCLUSIONS

Because this substitution was not present in the 1918 H1N1 pandemic virus, we posit that the emergence of pandemics is due to epistatic interactions between sites distributed over different segments. Altogether, our results are consistent with population dynamics models that highlight the epistatic and nonadaptive rise of novel epitopes in viral populations, followed by their demise when the resulting virus is too virulent.

Characterization and functional divergence of the alpha 1-adrenoceptor gene family: insights from rainbow trout (Oncorhynchus mykiss)

Presently, three alpha(1)-adrenoceptor (AR) types are recognized in vertebrates: alpha(1A)-, alpha(1B)-, and alpha(1D)-ARs. These alpha(1)-subtypes have distinct pharmacology and molecular profiles, play crucial roles in metabolic and vascular control, and are the targets for numerous pharmaceuticals, especially those affecting blood pressure and vascular resistance. To better understand the functional divergence within the alpha(1)-AR gene family, we sequenced these alpha(1)-AR paralogs in the rainbow trout and performed an extensive phylogenetic analysis. We show that these AR genes evolved by duplication events just before the origin of the jawed vertebrates. Our computational analyses suggest that the differences between the three alpha(1)-AR subtypes may affect their tissue specificity, ligand specificity, and possibly signal transduction processes and desensitization. We also show that, within each subtype, differences exist between fish and mammalian receptors, both at the transcriptional and at the physiological level. These differences, however, suggest that the role of alpha(1)-ARs in fish is more complex than previously thought. Our integrated analysis of the alpha(1)-AR gene family suggests that these receptors evolved these distinct features very early within vertebrates.

Early evolution of ionotropic GABA receptors and selective regimes acting on the mammalian-specific theta and epsilon subunits

Background

The amino acid neurotransmitter GABA is abundant in the central nervous system (CNS) of both invertebrates and vertebrates. Receptors of this neurotransmitter play a key role in important processes such as learning and memory. Yet, little is known about the mode and tempo of evolution of the receptors of this neurotransmitter. Here, we investigate the phylogenetic relationships of GABA receptor subunits across the chordates and detail their mode of evolution among mammals.

Principal Findings

Our analyses support two major monophyletic clades: one clade containing GABA_A receptor α, γ, and ε subunits, and another one containing GABA_A receptor ρ, β, δ, θ, and π subunits. The presence of GABA receptor subunits from each of the major clades in the Ciona intestinalis genome suggests that these ancestral duplication events occurred before the divergence of urochordates. However, while gene divergence proceeded at similar rates on most receptor subunits, we show that the mammalian-specific subunits θ and ε experienced an episode of positive selection and of relaxed constraints, respectively, after the duplication event. Sites putatively under positive selection are placed on a three-dimensional model obtained by homology-modeling.

Conclusions

Our results suggest an early divergence of the GABA receptor subunits, before the split from urochordates. We show that functional changes occurred in the lineages leading to the mammalian-specific subunit θ, and we identify the amino acid sites putatively responsible for the functional divergence. We discuss potential consequences for the evolution of mammals and of their CNS.

Dating phylogenies with hybrid local molecular clocks

Background

Because rates of evolution and species divergence times cannot be estimated directly from molecular data, all current dating methods require that specific assumptions be made before inferring any divergence time. These assumptions typically bear either on rates of molecular evolution (molecular clock hypothesis, local clocks models) or on both rates and times (penalized likelihood, Bayesian methods). However, most of these assumptions can affect estimated dates, oftentimes because they underestimate large amounts of rate change.

Principal Findings

A significant modification to a recently proposed ad hoc rate-smoothing algorithm is described, in which local molecular clocks are automatically placed on a phylogeny. This modification makes use of hybrid approaches that borrow from recent theoretical developments in microarray data analysis. An ad hoc integration of phylogenetic uncertainty under these local clock models is also described. The performance and accuracy of the new methods are evaluated by reanalyzing three published data sets.

Conclusions

It is shown that the new maximum likelihood hybrid methods can perform better than penalized likelihood and almost as well as uncorrelated Bayesian models. However, the new methods still tend to underestimate the actual amount of rate change. This work demonstrates the difficulty of estimating divergence times using local molecular clocks.

Identifying sites under positive selection with uncertain parameter estimates

Codon-based substitution models are routinely used to measure selective pressures acting on protein-coding genes. To this effect, the nonsynonymous to synonymous rate ratio (dN/dS = ω) is estimated. The proportion of amino-acid sites potentially under positive selection, as indicated by ω > 1, is inferred by fitting a probability distribution where some sites are permitted to have ω > 1. These sites are then inferred by means of an empirical Bayes or by a Bayes empirical Bayes approach that, respectively, ignores or accounts for sampling errors in maximum-likelihood estimates of the distribution used to infer the proportion of sites with ω > 1. Here, we extend a previous full-Bayes approach to include models with high power and low false-positive rates when inferring sites under positive selection. We propose some heuristics to alleviate the computational burden, and show that (i) full Bayes can be superior to empirical Bayes when analyzing a small data set or small simulated data, (ii) full Bayes has only a small advantage over Bayes empirical Bayes with our small test data, and (iii) Bayesian methods appear relatively insensitive to mild misspecifications of the random process generating adaptive evolution in our simulations, but in practice can prove extremely sensitive to model specification. We suggest that the codon model used to detect amino acids under selection should be carefully selected, for instance using Akaike information criterion (AIC).Key words: codon substitution models, empirical Bayes, Bayes empirical Bayes, full Bayes, ROC curves, AIC.

Large-scale analyses of synonymous substitution rates can be sensitive to assumptions about the process of mutation

A popular approach to examine the roles of mutation and selection in the evolution of genomes has been to consider the relationship between codon bias and synonymous rates of molecular evolution. A significant relationship between these two quantities is taken to indicate the action of weak selection on substitutions among synonymous codons. The neutral theory predicts that the rate of evolution is inversely related to the level of functional constraint. Therefore, selection against the use of non-preferred codons among those coding for the same amino acid should result in lower rates of synonymous substitution as compared with sites not subject to such selection pressures. However, reliably measuring the extent of such a relationship is problematic, as estimates of synonymous rates are sensitive to our assumptions about the process of molecular evolution. Previous studies showed the importance of accounting for unequal codon frequencies, in particular when synonymous codon usage is highly biased. Yet, unequal codon frequencies can be modeled in different ways, making different assumptions about the mutation process. Here we conduct a simulation study to evaluate two different ways of modeling uneven codon frequencies and show that both model parameterizations can have a dramatic impact on rate estimates and affect biological conclusions about genome evolution. We reanalyze three large data sets to demonstrate the relevance of our results to empirical data analysis.

Determinants of Adaptive Evolution at the Molecular Level: the Extended Complexity Hypothesis

To explain why informational genes (i.e., those involved in transcription, translation, and related processes) are less likely than housekeeping genes to be horizontally transferred, Jain and coworkers proposed the complexity hypothesis. The underlying idea is that informational genes belong to large, complex systems of coevolving genes. Consequently, the likelihood of the successful horizontal transfer of a single gene from such an integrated system is expected to be low. Here, this hypothesis is extended to explain some of the determinants of the mode of evolution of coding sequences. It is proposed that genes belonging to complex systems are relatively less likely to be under adaptive evolution. To evaluate this "extended complexity hypothesis," 2,428 families and protein domains were analyzed. This analysis found that genes whose products are highly connected, located in intracellular components, and involved in complex processes and functions were more conserved and less likely to be under adaptive evolution than are other gene products. The extended complexity hypothesis suggests that both the mode and the rate of evolution of a protein are influenced by its gene ontology (localization, biological process, and molecular function) and by its connectivity.

Least and most powerful phylogenetic tests to elucidate the origin of the seed plants in the presence of conflicting signals under misspecified models

Several tests of molecular phylogenies have been proposed over the last decades, but most of them lead to strikingly different P-values. I propose that such discrepancies are principally due to different forms of null hypotheses. To support this hypothesis, two new tests are described. Both consider the composite null hypothesis that all the topologies are equidistant from the true but unknown topology. This composite hypothesis can either be reduced to the simple hypothesis at the least favorable distribution (frequentist significance test [FST]) or to the maximum likelihood topology (frequentist hypothesis test [FHT]). In both cases, the reduced null hypothesis is tested against each topology included in the analysis. The tests proposed have an information-theoretic justification, and the distribution of their test statistic is estimated by a nonparametric bootstrap, adjusting P-values for multiple comparisons. I applied the new tests to the reanalysis of two chloroplast genes, psaA and psbB, and compared the results with those of previously described tests. As expected, the FST and the FHT behaved approximately like the Shimodaira-Hasegawa test and the bootstrap, respectively. Although the tests give overconfidence in a wrong tree when an overly simple nucleotide substitution model is assumed, more complex models incorporating heterogeneity among codon positions resolve some conflicts. To further investigate the influence of the null hypothesis, a power study was conducted. Simulations showed that FST and the Shimodaira-Hasegawa test are the least powerful and FHT is the most powerful across the parameter space. Although the size of all the tests is affected by misspecification, the two new tests appear more robust against misspecification of the model of evolution and consistently supported the hypothesis that the Gnetales are nested within gymnosperms.

How Bayes tests of molecular phylogenies compare with frequentist approaches

MOTIVATION

The desire to compare molecular phylogenies has stimulated the design of numerous tests. Most of these tests are formulated in a frequentist framework, and it is not known how they compare with Bayes procedures. I propose here two new Bayes tests that either compare pairs of trees (Bayes hypothesis test, BHT), or test each tree against an average of the trees included in the analysis (Bayes significance test, BST).

RESULTS

The algorithm, based on a standard Metropolis-Hastings sampler, integrates nuisance parameters out and estimates the probability of the data under each topology. These quantities are used to estimate Bayes factors for composite vs. composite hypotheses. Based on two data sets, the BHT and BST are shown to construct similar confidence sets to the bootstrap and the Shimodaira Hasegawa test, respectively. This suggests that the known difference among previous tests is mainly due to the null hypothesis considered.

Effects of models of rate evolution on estimation of divergence dates with special reference to the metazoan 18S ribosomal RNA phylogeny

The molecular clock, i.e., constancy of the rate of evolution over time, is commonly assumed in estimating divergence dates. However, this assumption is often violated and has drastic effects on date estimation. Recently, a number of attempts have been made to relax the clock assumption. One approach is to use maximum likelihood, which assigns rates to branches and allows the estimation of both rates and times. An alternative is the Bayes approach, which models the change of the rate over time. A number of models of rate change have been proposed. We have extended and evaluated models of rate evolution, i.e., the lognormal and its recent variant, along with the gamma, the exponential, and the Ornstein-Uhlenbeck processes. These models were first applied to a small hominoid data set, where an empirical Bayes approach was used to estimate the hyperparameters that measure the amount of rate variation. Estimation of divergence times was sensitive to these hyperparameters, especially when the assumed model is close to the clock assumption. The rate and date estimates varied little from model to model, although the posterior Bayes factor indicated the Ornstein-Uhlenbeck process outperformed the other models. To demonstrate the importance of allowing for rate change across lineages, this general approach was used to analyze a larger data set consisting of the 18S ribosomal RNA gene of 39 metazoan species. We obtained date estimates consistent with paleontological records, the deepest split within the group being about 560 million years ago. Estimates of the rates were in accordance with the Cambrian explosion hypothesis and suggested some more recent lineage-specific bursts of evolution.

The impact of population expansion and mutation rate heterogeneity on DNA sequence polymorphism

In order to study the effect of mutation rate heterogeneity on patterns of DNA polymorphism, we simulated samples of DNA sequences with gamma-distributed nucleotide substitution rates in stationary and expanding populations. We find that recent population expansions and mutation rate heterogeneity have similar effects on several polymorphism indicators, like the shape and the mean of the observed pairwise difference distribution, or the number of segregating sites. The inferred size of population expansion thus appears overestimated if nucleotides have dissimilar substitution rates. Interestingly, population expansion and uneven mutation rates have contrasting effects on Tajima's D statistic when acting separately, and the consequence on the associated test of selective neutrality is investigated. The patterns of polymorphism of several human populations analyzed for the mitochondrial control region are examined, mainly showing the difficulty in quantifying the respective contribution of past demographic history and uneven mutation rates from a single sampled evolutionary process. However, substitution rates appear more heterogeneous in the second hypervariable segment of the control region than in the first segment.