A complete Neandertal mitochondrial genome sequence determined by high-throughput sequencing.

A complete mitochondrial (mt) genome sequence was reconstructed from a 38,000 year-old Neandertal individual with 8341 mtDNA sequences identified among 4.8 Gb of DNA generated from approximately 0.3 g of bone. Analysis of the assembled sequence unequivocally establishes that the Neandertal mtDNA falls outside the variation of extant human mtDNAs, and allows an estimate of the divergence date between the two mtDNA lineages of 660,000 +/- 140,000 years. Of the 13 proteins encoded in the mtDNA, subunit 2 of cytochrome c oxidase of the mitochondrial electron transport chain has experienced the largest number of amino acid substitutions in human ancestors since the separation from Neandertals. There is evidence that purifying selection in the Neandertal mtDNA was reduced compared with other primate lineages, suggesting that the effective population size of Neandertals was small.

Dynamics and turnover of memory CD8 T cell responses following yellow fever vaccination.

Understanding how immunological memory lasts a lifetime requires quantifying changes in the number of memory cells as well as how their division and death rates change over time. We address these questions by using a statistically powerful mixed-effects differential equations framework to analyze data from two human studies that follow CD8 T cell responses to the yellow fever vaccine (YFV-17D). Models were first fit to the frequency of YFV-specific memory CD8 T cells and deuterium enrichment in those cells 42 days to 1 year post-vaccination. A different dataset, on the loss of YFV-specific CD8 T cells over three decades, was used to assess out of sample predictions of our models. The commonly used exponential and bi-exponential decline models performed relatively poorly. Models with the cell loss following a power law (exactly or approximately) were most predictive. Notably, using only the first year of data, these models accurately predicted T cell frequencies up to 30 years post-vaccination. Our analyses suggest that division rates of these cells drop and plateau at a low level (0.1% per day, ∼ double the estimated values for naive T cells) within one year following vaccination, whereas death rates continue to decline for much longer. Our results show that power laws can be predictive for T cell memory, a finding that may be useful for vaccine evaluation and epidemiological modeling. Moreover, since power laws asymptotically decline more slowly than any exponential decline, our results help explain the longevity of immune memory phenomenologically.

Population structure, intergroup interaction, and human contact govern infectious disease impacts in mountain gorilla populations.

Infectious zoonotic diseases are a threat to wildlife conservation and global health. They are especially a concern for wild apes, which are vulnerable to many human infectious diseases. As ecotourism, deforestation, and great ape field research increase, the threat of human-sourced infections to wild populations becomes more substantial and could result in devastating population declines. The endangered mountain gorillas (Gorilla beringei beringei) of the Virunga Massif in east-central Africa suffer periodic disease outbreaks and are exposed to infections from human-sourced pathogens. It is important to understand the possible risks of disease introduction and spread in this population and how human contact may facilitate disease transmission. Here we present and evaluate an individual-based, stochastic, discrete-time disease transmission model to predict epidemic outcomes and better understand health risks to the Virunga mountain gorilla population. To model disease transmission we have derived estimates for gorilla contact, interaction, and migration rates. The model shows that the social structure of gorilla populations plays a profound role in governing disease impacts with subdivided populations experiencing less than 25% of the outbreak levels of a single homogeneous population. It predicts that gorilla group dispersal and limited group interactions are strong factors in preventing widespread population-level outbreaks of infectious disease after such diseases have been introduced into the population. However, even a moderate amount of human contact increases disease spread and can lead to population-level outbreaks.

Linking high GC content to the repair of double strand breaks in prokaryotic genomes.

Genomic GC content varies widely among microbes for reasons unknown. While mutation bias partially explains this variation, prokaryotes near-universally have a higher GC content than predicted solely by this bias. Debate surrounds the relative importance of the remaining explanations of selection versus biased gene conversion favoring GC alleles. Some environments (e.g. soils) are associated with a high genomic GC content of their inhabitants, which implies that either high GC content is a selective adaptation to particular habitats, or that certain habitats favor increased rates of gene conversion. Here, we report a novel association between the presence of the non-homologous end joining DNA double-strand break repair pathway and GC content; this observation suggests that DNA damage may be a fundamental driver of GC content, leading in part to the many environmental patterns observed to-date. We discuss potential mechanisms accounting for the observed association, and provide preliminary evidence that sites experiencing higher rates of double-strand breaks are under selection for increased GC content relative to the genomic background.

Exploring the functional composition of the human microbiome using a hand-curated microbial trait database.

BACKGROUND: Even when microbial communities vary wildly in their taxonomic composition, their functional composition is often surprisingly stable. This suggests that a functional perspective could provide much deeper insight into the principles governing microbiome assembly. Much work to date analyzing the functional composition of microbial communities, however, relies heavily on inference from genomic features. Unfortunately, output from these methods can be hard to interpret and often suffers from relatively high error rates. RESULTS: We built and analyzed a domain-specific microbial trait database from known microbe-trait pairs recorded in the literature to better understand the functional composition of the human microbiome. Using a combination of phylogentically conscious machine learning tools and a network science approach, we were able to link particular traits to areas of the human body, discover traits that determine the range of body areas a microbe can inhabit, and uncover drivers of metabolic breadth. CONCLUSIONS: Domain-specific trait databases are an effective compromise between noisy methods to infer complex traits from genomic data and exhaustive, expensive attempts at database curation from the literature that do not focus on any one subset of taxa. They provide an accurate account of microbial traits and, by limiting the number of taxa considered, are feasible to build within a reasonable time-frame. We present a database specific for the human microbiome, in the hopes that this will prove useful for research into the functional composition of human-associated microbial communities.

Selection influences naive CD8+ TCR-ß repertoire sharing.

Within each individual, the adaptive immune system generates a repertoire of cells expressing receptors capable of recognizing diverse potential pathogens. The theoretical diversity of the T-cell receptor (TCR) repertoire exceeds the actual size of the T-cell population in an individual by several orders of magnitude - making the observation of identical TCRs in different individuals extremely improbable if all receptors were equally likely. Despite this disparity between the theoretical and the realized diversity of the repertoire, these 'public' receptor sequences have been identified in autoimmune, cancer and pathogen interaction contexts. Biased generation processes explain the presence of public TCRs in the naive repertoire, but do not adequately explain the different abundances of these public TCRs. We investigate and characterize the distribution of genomic TCR-ß sequences of naive CD8+ T cells from three genetically identical mice, comparing non-productive (non-functional sequences) and productive sequences. We find public TCR-ß sequences at higher abundances compared with unshared sequences in the productive, but not in the non-productive, repertoire. We show that neutral processes such as recombination biases, codon degeneracy and generation probability do not fully account for these differences, and conclude that thymic or peripheral selection plays an important role in increasing the abundances of public TCR-ß sequences.

Genome sequence of a 45,000-year-old modern human from western Siberia.

We present the high-quality genome sequence of a ∼45,000-year-old modern human male from Siberia. This individual derives from a population that lived before-or simultaneously with-the separation of the populations in western and eastern Eurasia and carries a similar amount of Neanderthal ancestry as present-day Eurasians. However, the genomic segments of Neanderthal ancestry are substantially longer than those observed in present-day individuals, indicating that Neanderthal gene flow into the ancestors of this individual occurred 7,000-13,000 years before he lived. We estimate an autosomal mutation rate of 0.4 × 10(-9) to 0.6 × 10(-9) per site per year, a Y chromosomal mutation rate of 0.7 × 10(-9) to 0.9 × 10(-9) per site per year based on the additional substitutions that have occurred in present-day non-Africans compared to this genome, and a mitochondrial mutation rate of 1.8 × 10(-8) to 3.2 × 10(-8) per site per year based on the age of the bone.

The complete genome sequence of a Neanderthal from the Altai Mountains.

We present a high-quality genome sequence of a Neanderthal woman from Siberia. We show that her parents were related at the level of half-siblings and that mating among close relatives was common among her recent ancestors. We also sequenced the genome of a Neanderthal from the Caucasus to low coverage. An analysis of the relationships and population history of available archaic genomes and 25 present-day human genomes shows that several gene flow events occurred among Neanderthals, Denisovans and early modern humans, possibly including gene flow into Denisovans from an unknown archaic group. Thus, interbreeding, albeit of low magnitude, occurred among many hominin groups in the Late Pleistocene. In addition, the high-quality Neanderthal genome allows us to establish a definitive list of substitutions that became fixed in modern humans after their separation from the ancestors of Neanderthals and Denisovans.

Recalibrating Equus evolution using the genome sequence of an early Middle Pleistocene horse.

The rich fossil record of equids has made them a model for evolutionary processes. Here we present a 1.12-times coverage draft genome from a horse bone recovered from permafrost dated to approximately 560-780 thousand years before present (kyr BP). Our data represent the oldest full genome sequence determined so far by almost an order of magnitude. For comparison, we sequenced the genome of a Late Pleistocene horse (43 kyr BP), and modern genomes of five domestic horse breeds (Equus ferus caballus), a Przewalski's horse (E. f. przewalskii) and a donkey (E. asinus). Our analyses suggest that the Equus lineage giving rise to all contemporary horses, zebras and donkeys originated 4.0-4.5 million years before present (Myr BP), twice the conventionally accepted time to the most recent common ancestor of the genus Equus. We also find that horse population size fluctuated multiple times over the past 2 Myr, particularly during periods of severe climatic changes. We estimate that the Przewalski's and domestic horse populations diverged 38-72 kyr BP, and find no evidence of recent admixture between the domestic horse breeds and the Przewalski's horse investigated. This supports the contention that Przewalski's horses represent the last surviving wild horse population. We find similar levels of genetic variation among Przewalski's and domestic populations, indicating that the former are genetically viable and worthy of conservation efforts. We also find evidence for continuous selection on the immune system and olfaction throughout horse evolution. Finally, we identify 29 genomic regions among horse breeds that deviate from neutrality and show low levels of genetic variation compared to the Przewalski's horse. Such regions could correspond to loci selected early during domestication.

Initial viral load determines the magnitude of the human CD8 T cell response to yellow fever vaccination.

CD8 T cells are a potent tool for eliminating intracellular pathogens and tumor cells. Thus, eliciting robust CD8 T-cell immunity is the basis for many vaccines under development. However, the relationship between antigen load and the magnitude of the CD8 T-cell response is not well-described in a human immune response. Here we address this issue by quantifying viral load and the CD8 T-cell response in a cohort of 80 individuals immunized with the live attenuated yellow fever vaccine (YFV-17D) by sampling peripheral blood at days 0, 1, 2, 3, 5, 7, 9, 11, 14, 30, and 90. When the virus load was below a threshold (peak virus load < 225 genomes per mL, or integrated virus load < 400 genome days per mL), the magnitude of the CD8 T-cell response correlated strongly with the virus load (R(2) ∼ 0.63). As the virus load increased above this threshold, the magnitude of the CD8 T-cell responses saturated. Recent advances in CD8 T-cell-based vaccines have focused on replication-incompetent or single-cycle vectors. However, these approaches deliver relatively limited amounts of antigen after immunization. Our results highlight the requirement that T-cell-based vaccines should deliver sufficient antigen during the initial period of the immune response to elicit a large number of CD8 T cells that may be needed for protection.

Genome-wide ancestry of 17th-century enslaved Africans from the Caribbean.

Between 1500 and 1850, more than 12 million enslaved Africans were transported to the New World. The vast majority were shipped from West and West-Central Africa, but their precise origins are largely unknown. We used genome-wide ancient DNA analyses to investigate the genetic origins of three enslaved Africans whose remains were recovered on the Caribbean island of Saint Martin. We trace their origins to distinct subcontinental source populations within Africa, including Bantu-speaking groups from northern Cameroon and non-Bantu speakers living in present-day Nigeria and Ghana. To our knowledge, these findings provide the first direct evidence for the ethnic origins of enslaved Africans, at a time for which historical records are scarce, and demonstrate that genomic data provide another type of record that can shed new light on long-standing historical questions.

Genetic history of an archaic hominin group from Denisova Cave in Siberia.

Using DNA extracted from a finger bone found in Denisova Cave in southern Siberia, we have sequenced the genome of an archaic hominin to about 1.9-fold coverage. This individual is from a group that shares a common origin with Neanderthals. This population was not involved in the putative gene flow from Neanderthals into Eurasians; however, the data suggest that it contributed 4-6% of its genetic material to the genomes of present-day Melanesians. We designate this hominin population 'Denisovans' and suggest that it may have been widespread in Asia during the Late Pleistocene epoch. A tooth found in Denisova Cave carries a mitochondrial genome highly similar to that of the finger bone. This tooth shares no derived morphological features with Neanderthals or modern humans, further indicating that Denisovans have an evolutionary history distinct from Neanderthals and modern humans.

Peripheral selection rather than thymic involution explains sudden contraction in naive CD4 T-cell diversity with age.

A diverse array of T cells is required for defense against pathogens. The naive CD4 T-cell repertoire reaches its peak diversity by early human adulthood and is maintained until older age. Surprisingly, around age 70, this diversity appears to plummet abruptly. A similar qualitative pattern holds for the CD4 T memory-cell population. We used mathematical models to explore different hypotheses for how such a loss of diversity might occur. The prevailing hypotheses suggest that the loss of diversity is due to a decline in emigration of cells from the thymus or a contraction in total number of cells. Our models reject these mechanisms because they yield only a gradual and minimal decline in the repertoire instead of the observed sudden and profound decrease later in life. We propose that an abrupt decline in the repertoire could be caused by the accumulation of mutations (defined here as any cell-intrinsic heritable event) that provide a short-term fitness advantage to a small number of T-cell clones (e.g., by an increased division rate or decreased death rate), with the person as a whole incurring the long-term cost of a decreased ability to fight infections.

A population biological approach to understanding the maintenance and loss of the T-cell repertoire during aging.

The adaptive immune system requires a diverse T-cell repertoire to be able to respond to a wide variety of pathogens. Worryingly, the repertoire diversity declines dramatically in old age. As thymic output generates novel T cells, the conventional view holds that a decrease in this output with age is responsible for the loss in the repertoire. However, many additional factors affect the repertoire such as homeostatic turnover and antigen-dependent expansion in response to infection. Mathematical models taking a population biology perspective are important tools for understanding how the interplay between these factors affects the immune repertoire. These models suggest that thymic decline is not a major factor but rather that some combination of virus-induced proliferation and T-cell-intrinsic genetic or epigenetic changes gives rise to the oligoclonal expansions that cause the decline in T-cell diversity. We also discuss consequences for strategies to rejuvenate the immune repertoire in old age.

mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters.

MOTIVATION: Ancient DNA (aDNA) molecules in fossilized bones and teeth, coprolites, sediments, mummified specimens and museum collections represent fantastic sources of information for evolutionary biologists, revealing the agents of past epidemics and the dynamics of past populations. However, the analysis of aDNA generally faces two major issues. Firstly, sequences consist of a mixture of endogenous and various exogenous backgrounds, mostly microbial. Secondly, high nucleotide misincorporation rates can be observed as a result of severe post-mortem DNA damage. Such misincorporation patterns are instrumental to authenticate ancient sequences versus modern contaminants. We recently developed the user-friendly mapDamage package that identifies such patterns from next-generation sequencing (NGS) sequence datasets. The absence of formal statistical modeling of the DNA damage process, however, precluded rigorous quantitative comparisons across samples. RESULTS: Here, we describe mapDamage 2.0 that extends the original features of mapDamage by incorporating a statistical model of DNA damage. Assuming that damage events depend only on sequencing position and post-mortem deamination, our Bayesian statistical framework provides estimates of four key features of aDNA molecules: the average length of overhangs (λ), nick frequency (ν) and cytosine deamination rates in both double-stranded regions ( ) and overhangs ( ). Our model enables rescaling base quality scores according to their probability of being damaged. mapDamage 2.0 handles NGS datasets with ease and is compatible with a wide range of DNA library protocols. AVAILABILITY: mapDamage 2.0 is available at ginolhac.github.io/mapDamage/ as a Python package and documentation is maintained at the Centre for GeoGenetics Web site (geogenetics.ku.dk/publications/mapdamage2.0/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ecological drivers of CRISPR immune systems.

CRISPR-Cas is the only known adaptive immune system of prokaryotes. It is a powerful defense system against mobile genetic elements such as bacteriophages. While CRISPR-Cas systems can be found throughout the prokaryotic tree of life, they are distributed unevenly across taxa and environments. Since adaptive immunity is more useful in environments where pathogens persist or reoccur, the density and/or diversity of the host/pathogen community may drive the uneven distribution of CRISPR system. We directly tested hypotheses connecting CRISPR incidence with prokaryotic density/diversity by analyzing 16S rRNA and metagenomic data from publicly available environmental sequencing projects. In terms of density, we found that CRISPR systems are significantly favored in lower abundance (less dense) taxa and disfavored in higher abundance taxa, at least in marine environments. When we extended this work to compare taxonomic diversity between samples, we found CRISPR system incidence strongly correlated with diversity in human oral environments. Together, these observations confirm that, at least in certain types of environments, the prokaryotic ecological context indeed plays a key role in selecting for CRISPR immunity. Importance: 2Microbes must constantly defend themselves against viral pathogens, and a large proportion of prokaryotes do so using the highly effective CRISPR-Cas adaptive immune system. However, many prokaryotes do not. We investigated the ecological factors behind this uneven distribution of CRISPR-Cas immune systems in natural microbial populations. We found strong patterns linking CRISPR-Cas systems to prokaryotic density within ocean environments and to prokaryotic diversity within human oral environments. Our study validates previous within-lab experimental results that suggested these factors might be important and confirms that local environment and ecological context interact to select for CRISPR immunity.

Evolution of the Growth Hormone Gene Duplication in Passerine Birds.

Birds of the order Passeriformes represent the most speciose order of land vertebrates. Despite strong scientific interest in this super-radiation, genetic traits unique to passerines are not well characterized. A duplicate copy of growth hormone (GH) is the only gene known to be present in all major lineages of passerines, but not in other birds. GH genes plausibly influence extreme life history traits that passerines exhibit, including the shortest embryo-to-fledging developmental period of any avian order. To unravel the implications of this GH duplication, we investigated the molecular evolution of the ancestral avian GH gene (GH or GH1) and the novel passerine GH paralog (GH2), using 497 gene sequences extracted from 342 genomes. Passerine GH1 and GH2 are reciprocally monophyletic, consistent with a single duplication event from a microchromosome onto a macrochromosome in a common ancestor of extant passerines. Additional chromosomal rearrangements have changed the syntenic and potential regulatory context of these genes. Both passerine GH1 and GH2 display substantially higher rates of nonsynonymous codon change than non-passerine avian GH, suggesting positive selection following duplication. A site involved in signal peptide cleavage is under selection in both paralogs. Other sites under positive selection differ between the two paralogs, but many are clustered in one region of a 3D model of the protein. Both paralogs retain key functional features and are actively but differentially expressed in two major passerine suborders. These phenomena suggest that GH genes may be evolving novel adaptive roles in passerine birds.

How do antigenically varying pathogens avoid cross-reactive responses to invariant antigens?

Pathogens such as trypanosomes and malaria use antigenic variation to evade immune responses and prolong the duration of infections. As pathogens typically express more than one antigen, even relatively rare conserved antigens might be expected to trigger cross-reactive immune responses capable of clearing the infection. We use simple mathematical models that explicitly consider the dynamic interplay between the replicating pathogen, immune responses to different antigens and immune exhaustion to explore how pathogens can escape the responses to both variable and invariant (conserved) antigens. Our results suggest two hypotheses. In the first, limited quantities of invariant antigens on each pathogen may lead to saturation in killing by cross-reactive responses. In the second, antigenic variation of the dominant antigens prolongs the duration of infection sufficiently to allow for exhaustion of the cross-reactive responses to subdominant, invariant epitopes prior to their being able to control the infection. These hypotheses make distinct predictions: the former predicts that cross-reactive responses will always be ineffective while the latter predicts that appropriately timed treatment could, by preventing exhaustion, lead to the generation of long-lasting protective cross-reactive immunity and thus act similarly to a vaccine.

Vaccination alters the balance between protective immunity, exhaustion, escape, and death in chronic infections.

While T cell-based vaccines have the potential to provide protection against chronic virus infections, they also have the potential to generate immunopathology following subsequent virus infection. We develop a mathematical model to investigate the conditions under which T cells lead to protection versus adverse pathology. The model illustrates how the balance between virus clearance and immune exhaustion may be disrupted when vaccination generates intermediate numbers of specific CD8 T cells. Surprisingly, our model suggests that this adverse effect of vaccination is largely unaffected by the generation of mutant viruses that evade T cell recognition and cannot be avoided by simply increasing the quality (affinity) or diversity of the T cell response. These findings should be taken into account when developing vaccines against persistent infections.