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.

Testing alternative hypotheses on the origin and speciation of Hawaiian katydids.

BACKGROUND: Hawaiian Islands offer a unique and dynamic evolutionary theatre for studying origin and speciation as the islands themselves sequentially formed by erupting undersea volcanos, which would subsequently become dormant and extinct. Such dynamics have not been used to resolve the controversy surrounding the origin and speciation of Hawaiian katydids in the genus Banza, whose ancestor could be from either the Old-World genera Ruspolia and Euconocephalus, or the New World Neoconocephalus. To address this question, we performed a chronophylogeographic analysis of Banza species together with close relatives from the Old and New Worlds. RESULTS: Based on extensive dated phylogeographic analyses of two mitochondrial genes (COX1 and CYTB), we show that our data are consistent with the interpretation that extant Banza species resulted from two colonization events, both by katydids from the Old World rather than from the New World. The first event was by an ancestral lineage of Euconocephalus about 6 million years ago (mya) after the formation of Nihoa about 7.3 mya, giving rise to B. nihoa. The second colonization event was by a sister lineage of Ruspolia dubia. The dating result suggests that this ancestral lineage first colonized an older island in the Hawaiian-Emperor seamount chain before the emergence of Hawaii Islands, but colonized Kauai after its emergence in 5.8 mya. This second colonization gave rise to the rest of the Banza species in two major lineages, one on the older northwestern islands, and the other on the newer southwestern islands. CONCLUSION: Chronophylogeographic analyses with well-sampled taxa proved crucial for resolving phylogeographic controversies on the origin and evolution of species colonizing a new environment.

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.

Identifying the drivers of computationally detected correlated evolution among sites under antibiotic selection.

The ultimate causes of correlated evolution among sites in a genome remain difficult to tease apart. To address this problem directly, we performed a high-throughput search for correlated evolution among sites associated with resistance to a fluoroquinolone antibiotic using whole-genome data from clinical strains of Pseudomonas aeruginosa, before validating our computational predictions experimentally. We show that for at least two sites, this correlation is underlain by epistasis. Our analysis also revealed eight additional pairs of synonymous substitutions displaying correlated evolution underlain by physical linkage, rather than selection associated with antibiotic resistance. Our results provide direct evidence that both epistasis and physical linkage among sites can drive the correlated evolution identified by high-throughput computational tools. In other words, the observation of correlated evolution is not by itself sufficient evidence to guarantee that the sites in question are epistatic; such a claim requires additional evidence, ideally coming from direct estimates of epistasis, based on experimental evidence.

Swift evolutionary response of microbes to a rise in anthropogenic mercury in the Northern Hemisphere.

Anthropogenic mercury remobilization has considerably increased since the Industrial Revolution in the late 1700s. The Minamata Convention on Mercury is a United Nations treaty (2017) aiming at curbing mercury emissions. Unfortunately, evaluating the effectiveness of such a global treaty is hampered by our inability to determine the lag in aquatic ecosystem responses to a change in atmospheric mercury deposition. Whereas past metal concentrations are obtained from core samples, there are currently no means of tracking historical metal bioavailability or toxicity. Here, we recovered DNA from nine dated sediment cores collected in Canada and Finland, and reconstructed the past demographics of microbes carrying genes coding for the mercuric reductase (MerA)-an enzyme involved in Hg detoxification-using Bayesian relaxed molecular clocks. We found that the evolutionary dynamics of merA exhibited a dramatic increase in effective population size starting from 1783.8 ± 3.9 CE, which coincides with both the Industrial Revolution, and with independent measurements of atmospheric Hg concentrations. We show that even low levels of anthropogenic mercury affected the evolutionary trajectory of microbes in the Northern Hemisphere, and that microbial DNA encoding for detoxification determinants stored in environmental archives can be used to track historical pollutant toxicity.

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.

A Not-So-Long Introduction to Computational Molecular Evolution.

In this chapter, we give a not-so-long and self-contained introduction to computational molecular evolution. In particular, we present the emergence of the use of likelihood-based methods, review the standard DNA substitution models, and introduce how model choice operates. We also present recent developments in inferring absolute divergence times and rates on a phylogeny, before showing how state-of-the-art models take inspiration from diffusion theory to link population genetics, which traditionally focuses at a taxonomic level below that of the species, and molecular evolution. Although this is not a cookbook chapter, we try and point to popular programs and implementations along the way.

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.

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.

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.

Predicting the Reasons of Customer Complaints: A First Step Toward Anticipating Quality Issues of In Vitro Diagnostics Assays with Machine Learning.

BACKGROUND: Vendors in the health care industry produce diagnostic systems that, through a secured connection, allow them to monitor performance almost in real time. However, challenges exist in analyzing and interpreting large volumes of noisy quality control (QC) data. As a result, some QC shifts may not be detected early enough by the vendor, but lead a customer to complain. OBJECTIVE: The aim of this study was to hypothesize that a more proactive response could be designed by utilizing the collected QC data more efficiently. Our aim is therefore to help prevent customer complaints by predicting them based on the QC data collected by in vitro diagnostic systems. METHODS: QC data from five select in vitro diagnostic assays were combined with the corresponding database of customer complaints over a period of 90 days. A subset of these data over the last 45 days was also analyzed to assess how the length of the training period affects predictions. We defined a set of features used to train two classifiers, one based on decision trees and the other based on adaptive boosting, and assessed model performance by cross-validation. RESULTS: The cross-validations showed classification error rates close to zero for some assays with adaptive boosting when predicting the potential cause of customer complaints. Performance was improved by shortening the training period when the volume of complaints increased. Denoising filters that reduced the number of categories to predict further improved performance, as their application simplified the prediction problem. CONCLUSIONS: This novel approach to predicting customer complaints based on QC data may allow the diagnostic industry, the expected end user of our approach, to proactively identify potential product quality issues and fix these before receiving customer complaints. This represents a new step in the direction of using big data toward product quality improvement.

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.

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.