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Review| March 28 2017
Living in an adaptive world: Genomic dissection of the genus Homo and its immune response
Hélène Quach,
Hélène Quach
Human Evolutionary Genetics Unit, Department of Genomes and Genetics, Institut Pasteur, 75015 Paris, France
Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, 75015 Paris, France
Centre National de la Recherche Scientifique, URA3012, 75015 Paris, France
Lluis Quintana-Murci
Correspondence to Lluis Quintana-Murci: quintana@pasteur.fr
Agence Nationale de la Recherche (13-BSV4-0002-01)
Centre National de la Recherche Scientifique (NO AWARD)
Institut Pasteur (NO AWARD)
Seventh Framework Programme (NO AWARD)
© 2017 Quach and Quintana-Murci
This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/).
J Exp Med (2017) 214 (4): 877–894.
https://doi.org/10.1084/jem.20161942
Hélène Quach, Lluis Quintana-Murci; Living in an adaptive world: Genomic dissection of the genus Homo and its immune response. J Exp Med 3 April 2017; 214 (4): 877–894. doi: https://doi.org/10.1084/jem.20161942
More than a decade after the sequencing of the human genome, a deluge of genome-wide population data are generating a portrait of human genetic diversity at an unprecedented level of resolution. Genomic studies have provided new insight into the demographic and adaptive history of our species, Homo sapiens, including its interbreeding with other hominins, such as Neanderthals, and the ways in which natural selection, in its various guises, has shaped genome diversity. These studies, combined with functional genomic approaches, such as the mapping of expression quantitative trait loci, have helped to identify genes, functions, and mechanisms of prime importance for host survival and involved in phenotypic variation and differences in disease risk. This review summarizes new findings in this rapidly developing field, focusing on the human immune response. We discuss the importance of defining the genetic and evolutionary determinants driving immune response variation, and highlight the added value of population genomic approaches in settings relevant to immunity and infection.
Human disease genetics, Infectious disease and host defense, Innate immunity and inflammation
Studies of the history of the genus Homo have entered a golden age with the advent of genome-wide, deep-sequencing approaches. Surveys of genetic variation, such as the Exome Variant Server (Fu et al., 2013), the Exome Aggregation Consortium (Lek et al., 2016), the 1,000 Genomes Project (Auton et al., 2015), and the Simons Genome Diversity Project (Mallick et al., 2016), provide invaluable resources for the clinical interpretation of genetic variants identified in disease patients, and for dissections of the genetic architecture of morphological and physiological traits (Fig. 1, A and B). Genomic studies have also increased our understanding of the demographic history of our species (Veeramah and Hammer, 2014; Nielsen et al., 2017), including the contribution of interbreeding with archaic hominins, such as Neanderthals and Denisovans (Racimo et al., 2015), and the extent to which natural selection has acted on the human genome (Fan et al., 2016). This work has, in turn, provided insight into the mechanisms by which deleterious variants are removed from the population (Lohmueller, 2014; Henn et al., 2015) and the potential of humans to adapt to the broad range of environments they occupy (Jeong and Di Rienzo, 2014; Fan et al., 2016).
Over the last decade, genomic scans of natural selection in the human genome have consistently identified genes and functions relating to immunity and host defense as recurrent targets of selection (Barreiro and Quintana-Murci, 2010; Quintana-Murci and Clark, 2013; Fumagalli and Sironi, 2014; Karlsson et al., 2014). There is also growing evidence to suggest that admixture with ancient hominins introduced advantageous immune variants into the modern human population (Racimo et al., 2015). Dissection of the action of selection on immune genes in settings relevant to immunity and infection, and investigations of the effects of genetic variants on responses to immune activation, through the mapping of expression quantitative trait loci (eQTL), are the key to identifying evolutionarily important determinants of host immune responsiveness and of the mechanisms underlying the diversity of immune phenotypes and disease risk (Fig. 1, C and D; Casanova et al., 2013; Fairfax and Knight, 2014).
This review focuses on the crucial contribution of genome-wide approaches to the understanding of human phenotypic diversity, by improving our knowledge of the demographic and adaptive history of our species, particularly as concerns pathogen pressures, and the admixture of Homo sapiens with archaic, now-extinct hominins, leading to the acquisition of advantageous variants. We will specially highlight recent advances in studies of the genetic, nongenetic, environmental, and evolutionary factors driving the inter-individual and inter-population phenotypic variability of immune responses, providing insight into the mechanisms underlying host immunity to infection and susceptibility to immune-related diseases.
Reconstructing the genomic history of Homo sapiens
Genomic surveys support an origin of modern humans in sub-Saharan Africa ∼200,000 yr ago, with subsequent dispersal out of Africa ∼40,000–80,000 yr ago, followed by the rapid colonization of South Asia, Australia, Europe, and East Asia (Novembre and Ramachandran, 2011; Veeramah and Hammer, 2014; Nielsen et al., 2017). Humans ultimately reached distant locations, such as the Americas, ∼15,000–35,000 yr ago, and the remote islands of Oceania, which were settled as recently as ∼1,000–4,000 yr ago (Fig. 2). African populations present the highest levels of diversity worldwide, and the diversity of non-African populations decreases with increasing distance from Africa (Abecasis et al., 2010, 2012; Campbell et al., 2014), attesting to the occurrence of bottlenecks and founder events during their migrations across the globe (Jakobsson et al., 2008; Henn et al., 2012). Recent studies based on whole-genome sequencing have shown that all contemporary non-Africans are descended from a single population that left Africa less than 60,000 yr ago (Malaspinas et al., 2016; Mallick et al., 2016). Modern Papua New Guineans present genomic traces of an ancient dispersal out of Africa ∼120,000 yr ago (Pagani et al., 2016), a dispersal that remains to be confirmed, but which clearly made little contribution to modern Eurasians.
Genetic studies have also revealed substantial population structure (i.e., subdivision) within continents. In sub-Saharan Africa, population structure is correlated with geography, language family, and mode of subsistence (Henn et al., 2012; Campbell et al., 2014; Patin et al., 2014), whereas, in Europe, genetic diversity is strongly driven by geography, and population structure is detectable within even very small geographic areas (Novembre et al., 2008). The quantification of population structure has important implications for studies of disease, as it must be carefully controlled for in genome-wide association studies (GWAS). Genomic studies can also quantify major admixture events between populations, such as those between the ancestors of African-Americans (Bryc et al., 2010; Montinaro et al., 2015). Decomposing the genomes of admixed populations into segments of different ancestry facilitates the identification of variants associated with disease risk, particularly for traits with different prevalence in the parental populations of admixed groups (Smith and O'Brien, 2005).
Natural selection has driven human adaptation to environmental cues
During their spread across the globe, humans encountered a highly diverse set of climatic, nutritional, and pathogenic conditions, to which they had to adapt. Phenotypic traits increasing their chances to survive and reproduce in such environments are due, at least in part, to genetic variation and thus, transmitted across generations. The dissection of the legacy of past selection in the human genome has proved crucial for identification of genes underlying the broad morphological and physiological diversity observed across human populations, and for increasing our understanding of the genetic architecture of adaptive phenotypes (Vitti et al., 2013; Jeong and Di Rienzo, 2014; Key et al., 2014b; Fan et al., 2016).
Forms of natural selection
Although natural selection acts on phenotypes, it is primarily the genetics underlying the phenotype that is inherited, so genotypes will be acted on by selection. Selection can be classified according to whether an allele is disfavored (purifying selection) or favored (positive selection). Purifying selection removes deleterious alleles and is the most pervasive form of selection (Eyre-Walker and Keightley, 1999; Kryukov et al., 2007). Conversely, positive selection acts upon advantageous mutations after different evolutionary trends (Pritchard et al., 2010). According to the classic sweep model, positive selection acts upon a newly arising advantageous mutation that will increase in frequency, whereas selection on standing variation involves the selection of an allele that is already segregating in the population, which therefore increases in frequency, and polygenic adaptation involves the simultaneous selection of variants at many loci, each of which makes a small contribution to fitness. Genetic adaptation may also occur through balancing selection, which preserves functional diversity through heterozygote advantage, frequency-dependent selection, or pleiotropy (Charlesworth, 2006; Key et al., 2014b). Each selection form leaves specific molecular signatures around the selected region (Fig. 3 A), which are detected by an ever-increasing number of statistical methods that, depending on the nature of genetic data analyzed, can inform the time range at which selection occurred (Box 1).
Methods to detect natural selection at different time scales
An increasing number of statistical tests are available to detect the occurrence, intensity, and time frame of selection acting on genomes. Frequency-based methods measure nucleotide diversity at a given locus, as well as the deviations of the distribution of its allele frequency spectrum from standard neutral expectations. These methods, including Tajima's D and derivatives, generally detect selection events that occurred in the past ∼250,000 years: negative values of these tests indicate an excess of rare alleles, compatible with negative or positive selection, whereas positive values reflect an excess of intermediate frequency alleles, a pattern compatible with balancing selection (Kreitman, 2000; Nielsen, 2005).
Methods based on population genetic differences, such as FST and its derivatives, allow the identification of variants that have been favored locally (i.e., frequency increase of the advantageous allele in a population-specific manner) in the past ∼75,000 years by pinpointing marked population differences in frequency of these alleles (Holsinger and Weir, 2009). In turn, methods based on linkage disequilibrium generally measure the level of haplotype homozygosity associated with the selected allele, with respect to that associated with the corresponding neutral allele at a specific site. These include the long-range haplotype (LRH), the integrated haplotype score (iHS), and the cross population extended haplotype homozygosity (XP-EHH) tests; significant values of these tests highlight the rapid increase in frequency of the causal advantageous mutation, and their associated variants, supporting an event of positive selection occurring in the past ∼25,000 years (Sabeti et al., 2002; Voight et al., 2006; Wang et al., 2006). In the context of recent selection, a novel method, the singleton density score, allows the detection of adaptive events within the last ∼2,000 years, based on the low number of rare mutations present on the haplotype carrying the advantageous allele (Field et al., 2016).
Due to the moderate to high false discovery rate of some of these methods, together with the difficulty in identifying the causal selected variants, composite methods that combine multiple statistics into a single score, such as the composite likelihood ratio (CLR) and the composite of multiple signals (CMS), have been developed to increase power and minimize the detection of false-positive signals (Nielsen et al., 2005; Grossman et al., 2010, 2013).
More generally, the performance of methods to detect selection depends on several parameters, such as the age of the advantageous mutation, its selection coefficient, and the demographic history of the population studied, which affect allele frequencies and other aspects of the genetic data to different extents, thus impacting the interpretation of the results and the significance of the statistical tests. The extent to which currently used methods to detect selection are empowered and robust to some of these factors remains an active field of research in population genetics. Further details of these intraspecies methods, together with methods analyzing interspecies divergence to detect negative and positive selection, such as the ratio of substitution rates at non-synonymous and synonymous sites (dN/dS), Hudson-Kreitman-Aguadé (HKA), and McDonald-Kreitman tests, have been extensively reviewed elsewhere (Kreitman, 2000; Nielsen, 2005; Sabeti et al., 2006; Nielsen et al., 2007; Vitti et al., 2013).
Removal of deleterious mutations by purifying selection
Recent studies have shed new light on the ways in which population history can modify the mode in which selection removes deleterious variants (Lohmueller, 2014; Henn et al., 2015; Simons and Sella, 2016). Non-African populations have a higher proportion of deleterious variants of essential genes than Africans, a pattern consistent with less efficient purifying selection to purge deleterious alleles from small populations (Lohmueller et al., 2008; Gutenkunst et al., 2009; Tennessen et al., 2012). Population history appears to have a negligible impact on the mean burden of individual deleterious variants (Fu et al., 2014; Simons et al., 2014; Do et al., 2015), but it has been consistently shown that genetic drift affects the frequency of weakly deleterious mutations more strongly in bottlenecked than in large populations (Fig. 3 B). Indeed, the number of homozygous deleterious genotypes carried by individuals increases with distance from Africa (Henn et al., 2016), and founder populations, such as French-Canadians, and bottlenecked populations, such as the Finns, have larger proportions of deleterious variants, including loss-of-function variants and complete gene knockouts, than expanding populations, such as French or other European populations (Casals et al., 2013; Lim et al., 2014).
Population genetic adaptation to local environments
Genome-wide approaches have identified several hundreds of genomic regions as candidates for positive selection, based on molecular signatures such as the degree of population differentiation or haplotype homozygosity (Fig. 3 C; Voight et al., 2006; Sabeti et al., 2007; Barreiro et al., 2008; Grossman et al., 2013; Scheinfeldt and Tishkoff, 2013; Vitti et al., 2013). Iconic cases of positive selection include the lactase (LCT) gene, responsible for the persistence of lactose tolerance in adults (Bersaglieri et al., 2004; Tishkoff et al., 2007; Itan et al., 2009); genes involved in the variation of skin pigmentation and hair morphology (Lamason et al., 2005; Miller et al., 2007; Norton et al., 2007; Beleza et al., 2013); genes involved in adaptation to diverse habitats, from the rainforest to hypoxic conditions at high altitude (Bigham et al., 2010; Simonson et al., 2010; Yi et al., 2010; Jarvis et al., 2012); and genes related to infectious disease resistance and the immune response (Barreiro and Quintana-Murci, 2010; Quintana-Murci and Barreiro, 2010; Quintana-Murci and Clark, 2013; Fumagalli and Sironi, 2014; Karlsson et al., 2014; Siddle and Quintana-Murci, 2014). Table 1 presents a subset of strongly supported cases of positive selection occurring at different time scales, ranging from 45,000 to as recent as 1,500 yr, of an ever-growing number of genes and functions having participated in human adaptation to environmental cues.
Genomic traces of admixture between archaic and modern humans
Genomic studies of ancient DNA have revealed that our species, Homo sapiens, interbred with other human forms, such as Neanderthals and Denisovans, present in Eurasia from ∼30,000–50,000 yr ago. This interbreeding resulted in a phenomenon known as archaic introgression; the legacy of this ancient admixture is observed in the genomes of modern human populations (Figs. 2 and 3 A; Kelso and Prüfer, 2014; Vattathil and Akey, 2015). Whole-genome sequences from ancient specimens have revealed the presence of DNA of Neanderthal ancestry accounting for ∼2% of the genome in Europeans to ∼4% in Asians (Green et al., 2010; Prüfer et al., 2014). DNA introgressed into modern humans from Denisovans is found mostly in Australo-Melanesians, in whom it may account for up to 6% of their genomes, and, to a lesser extent, in South East Asians (Reich et al., 2010, 2011; Meyer et al., 2012). These estimates are averages across the modern human genome, and specific regions of the genome may have degrees of Neanderthal ancestry as high as 64% in Europeans and 62% in Asians (Sankararaman et al., 2014).
However, there has been strong selection against archaic introgression, particularly among protein-coding genes, probably caused by its deleterious effects in modern humans (Sankararaman et al., 2014; Vernot and Akey, 2014; Fu et al., 2016). Identifying regions in the human genome strongly depleted of archaic ancestry can thus identify functions that may have contributed to the uniqueness of some modern human traits (Vernot et al., 2016). For example, a region devoid of Neanderthal ancestry has been identified around the forkhead box protein P2 (FOXP2) gene, mutations of which are associated with language disorders (Konopka and Roberts, 2016). The AMY1 gene, encoding an amylase enzyme responsible for starch digestion, also lies in a Neanderthal desert. Unlike modern humans, who carry multiple copies of AMY1, Neanderthals and Denisovans had only one copy of this gene, suggesting that the production of larger amounts of salivary amylase for starch digestion has been of great benefit to modern humans (Perry et al., 2015).
Despite the widespread signature of purifying selection against archaic alleles, modern humans have also acquired advantageous alleles via admixture with ancient hominins, through adaptive introgression (Kelso and Prüfer, 2014; Racimo et al., 2015; Vattathil and Akey, 2015). Genes involved in functions relating to keratin filaments, sugar metabolism, muscle contraction, and oocyte meiosis have been targeted by adaptive introgression from Neanderthals (Sankararaman et al., 2014). For example, EPAS1, associated with hemoglobin concentration and response to hypoxia, displays a high degree of Denisovan ancestry in Tibetans, suggesting that this population acquired advantageous alleles for life at high altitude through ancient admixture (Huerta-Sánchez et al., 2014). Whatever the potential benefits of archaic introgression in the past, alleles of Neanderthal origin have been shown to be associated with several neurological, dermatological, and immunological phenotypes, indicating an influence of ancient admixture on current disease risk in humans (Simonti et al., 2016). These studies show how the detection of introgression from archaic hominins can shed light on the impact of ancient hybridization on the morphological and physiological variability of modern human populations.
Evolutionary genetic dissection of human immunity
Population genetic studies have clearly indicated that immunity and host defense functions are among the functions most frequently subject to natural selection, whether purifying, positive or balancing (Bustamante et al., 2005; Voight et al., 2006; Sabeti et al., 2007; Barreiro et al., 2008; Grossman et al., 2013; Leffler et al., 2013). Identifying the causal agents of selection remains a daunting task, with the most supported cases of selection being primarily restricted to Plasmodium, the causative agent of malaria, which appears to be the true selective factor driving the variability of genes such as HBB, DARC, G6PD, or CD36 (Kwiatkowski, 2005; Siddle and Quintana-Murci, 2014). More generally, although pathogens appear to be the underlying pressure leading to selection signatures at immune defense genes (Fumagalli et al., 2011), it is difficult to rule out the contribution of nonpathogenic microbes, such as those present in the microbiota, and of other biological processes exerting pressure of host genes and pathways. Regardless of the true selective agent, the dissection of the form of selection acting on immunity genes allows to determine the biological relevance of these genes in natura—essentiality, redundancy or adaptability—and their potential contribution to variable immune traits and diseases (Quintana-Murci et al., 2007; Alcaïs et al., 2010; Casanova et al., 2013; Quintana-Murci and Clark, 2013).
Purifying selection and essentiality for immune responses
Genes evolving under purifying selection, and thus under strong constraints, are generally involved in mechanisms essential for host defense, and the variation of these genes can lead to severe disorders. Microbial sensors, such as endosomal TLRs and many NLRs, adaptors, such as MYD88 and TRIF, and effectors, such as some type-I IFNs and IFN-γ, have been shown to evolve under strong purifying selection, attesting to the unique, essential nature of the mechanisms in which they are involved (Quintana-Murci and Clark, 2013). Consequently, rare mutations of highly constrained genes, such as TLR3, TRIF, MYD88, STAT1, TRAF3 and the genes of the IFN-γ pathway, are associated with life-threatening diseases, such as HSV-1 encephalitis, pyogenic bacterial infections, and Mendelian susceptibility to mycobacterial disease (Pérez de Diego et al., 2010; Boisson-Dupuis et al., 2012; Casanova et al., 2013). More generally, a recent genome-wide study has shown that innate immunity genes have evolved under stronger purifying selection than the rest of the genome, with those associated with autosomal-dominant forms of primary immunodeficiencies subject to the strongest degree of purifying selection (Deschamps et al., 2016). These findings support the hypothesis that constrained genes are of major biological relevance in host survival and should be given priority in genetic studies aiming to identify new genetic etiologies of severe, infectious disease phenotypes.
Genetic adaptation and immune diversity
Genes undergoing adaptive evolution through positive selection provide evidence of the functional variability that has been beneficial to specific human populations. The list of immune system genes known to be subject to positive selection is continually growing (Barreiro and Quintana-Murci, 2010; Quintana-Murci and Clark, 2013; Brinkworth and Barreiro, 2014; Fumagalli and Sironi, 2014; Karlsson et al., 2014). Relevant cases of host defense and immune genes harboring advantageous variants include HBB, CD36, CR1, G6PD, DARC, and CD40LG, which are involved in malaria resistance in Africans; TLR1 and TLR5, which are involved in decreases in NF-κB signaling in Europeans and Africans, respectively (Barreiro et al., 2009; Grossman et al., 2013; Deschamps et al., 2016); type III IFN genes, implicated in the higher levels of spontaneous viral clearance in Europeans and Asians (Manry et al., 2011; Key et al., 2014a); and LARGE and IL21, which have been implicated in Lassa fever immunity in West Africans (Andersen et al., 2012). The evidence for balancing selection in humans is largely circumstantial, but host defense genes, such as those encoding several membrane glycoproteins, the KIR regions that coevolve with HLA ligands, and other genes encoding proteins involved in cell migration or innate immunity, are subject to this otherwise rare selective regimen (Norman et al., 2007; Single et al., 2007; Andrés et al., 2009; Leffler et al., 2013; DeGiorgio et al., 2014).
A recent study has estimated the time frame of selection targeting innate immunity genes, and found that most selective events occurred ∼6,000–13,000 yr ago (Deschamps et al., 2016), corresponding to the period at which human societies adopted agriculture, a major transition in human lifestyle that likely modified human exposure to pathogens and led to genetic adaptation. The detection of immune genes targeted by local adaptation can increase our understanding of the mechanisms involved in host resistance to infectious challenges at different time scales, the nature of which may also differ between human populations.
Present deleterious effects of past selection events
Environmental factors may change over time, and genetic variants inherited from our ancestors and that have been favored by past selection can become detrimental in modern societies, increasing the risk of inflammation or autoimmunity (Barreiro and Quintana-Murci, 2010; Sironi and Clerici, 2010; Brinkworth and Barreiro, 2014). For example, the prevalence of celiac disease, an autoimmune disorder caused by gluten intolerance, differs considerably between human populations, with individuals of European ancestry particularly affected (Kang et al., 2013). Population genetic analyses have shown that the high frequency of several risk alleles of genes associated with celiac disease, such as IL12A, IL18RAP, and SH2B3 (Hunt et al., 2008), in Europeans results from past positive selection events (Barreiro and Quintana-Murci, 2010; Zhernakova et al., 2010). Functional analyses of the SH2B3 risk variant have suggested that this otherwise deleterious variant was subject to positive selection in the past because it confers greater protection against infection (Zhernakova et al., 2010). However, the pleiotropic nature of SH2B3 (i.e., regulating the structural organization and development of platelets and endothelial cells), like that of many immune genes, suggests that alleles that currently increase the risk of inflammatory or autoimmune disorders have in the past conferred greater reproductive success via a broader range of beneficial phenotypes (Brinkworth and Barreiro, 2014). The case of celiac disease neatly illustrates the tradeoff between past selection and current maladaptation, and highlights the value of population genetics for elucidating the mechanisms contributing to pathological inflammation and its heterogeneous distribution across populations.
The immune legacy of archaic hominins in our genomes
There is increasing evidence to suggest that immune genes have not only been an important substrate of selection, but also that their diversity in modern humans has been affected by archaic introgression (Racimo et al., 2015). Innate immunity genes generally display a higher degree of Neanderthal ancestry than the rest of the genome, and genes encoding sensors, such the OAS and the TLR6-1-10 clusters, transcription factors, such as IRF6, and effector molecules, such as the restriction factors IFITM1-3 and the cytokines IL17A and IL17F, present the highest Neanderthal introgression scores at the genome-wide level (Fig. 3 D; Deschamps et al., 2016). In some cases, there is also evidence for selection acting on the archaic introgressed segments (i.e., adaptive introgression). For example, the HLA region, which evolves under balancing selection in humans, harbors functional variants that were probably introgressed from Neanderthals and Denisovans (Abi-Rached et al., 2011). Other candidates for adaptive introgression include STAT2, which is involved in the interferon response to viral infection; OAS1, which has been implicated in innate immune responses to viruses; and the TLR6-1-10 cluster, encoding proteins responsible for the sensing of microbial components on the cell surface (Mendez et al., 2012, 2013; Sankararaman et al., 2014; Dannemann et al., 2016; Deschamps et al., 2016; Sams et al., 2016).
The signals of adaptive introgression detected suggest an advantage for modern human survival, but recent findings have suggested that there may be a tradeoff for archaic introgression. For example, a nonsynonymous variant of the ZNF365D gene present in ∼26% of Europeans and absent from Africans was inherited from Neanderthals and is associated with a higher risk of Crohn's disease (Sankararaman et al., 2014). Likewise, variants of TLR6-1-10 inherited from Neanderthals and Denisovans and present in Europeans and Asians have been associated with greater susceptibility to allergies (Dannemann et al., 2016). These studies collectively demonstrate that admixture with archaic hominins introduced variants into the gene pool of modern Eurasians some 30,000–50,000 yr ago, increasing the diversity of the modern human immune repertoire. These variants were initially neutral or advantageous for modern humans, but some are today associated with disease phenotypes.
Understanding immune response phenotypes through regulatory variation
Studies of the effects of selection on diversity at immune gene loci increase our understanding of the biological relevance of the functions concerned, complementing immunological, clinical, and epidemiological genetic studies (Casanova et al., 2013). Nevertheless, little is known about the relationship between genetic variation and immune phenotype diversity, and the nature of the immunological mechanisms under selection remains largely unexplored. The contribution of host genetic variants to variation in immune phenotypes, including those related to disease, is increasingly being documented by GWAS (Vannberg et al., 2011; Parkes et al., 2013; Abel et al., 2014). However, the multiple variants associated with immune phenotypes tend to have small individual effect sizes, and the identification of causal functional variants remains challenging (Manolio et al., 2009). In recent years, analyses of regulatory variants of gene expression (eQTL) have proved to be of considerable biomedical value (Montgomery and Dermitzakis, 2011), establishing links between intermediate phenotypes, such as gene expression, and organism traits, such as immunity to infection (Fig. 4 A; Fairfax and Knight, 2014). Furthermore, GWAS have shown that susceptibility to common immune diseases is primarily controlled by noncoding, probably regulatory, variants (Hindorff et al., 2009; Nicolae et al., 2010; Schaub et al., 2012; Fraser, 2013; Pickrell, 2014). However, eQTL studies have also shown that the genetic control of gene expression varies considerably across tissues and cell types (Dimas et al., 2009; Price et al., 2011; Powell et al., 2012), reinforcing the need to consider the appropriate cell context specificity of eQTL in studies aiming to increase knowledge on the genetic bases of phenotypic traits or diseases.
Mapping eQTL for the immune response
Several studies have mapped eQTL in cell types relevant to immunity, such as primary monocytes and T lymphocytes, focusing on gene expression measurements at the basal state (Dimas et al., 2009; Fairfax et al., 2012; Raj et al., 2014), while others have more recently explored the genetic determinants of gene expression in the presence of external, immune stimuli (i.e., response eQTL), thereby increasing our understanding of gene–environment interactions in the context of infection (Fig. 4 B; Barreiro et al., 2012; Fairfax et al., 2014; Lee et al., 2014; Çalışkan et al., 2015; Nédélec et al., 2016; Quach et al., 2016). For example, a pioneering study, in which human dendritic cells from healthy individuals were infected with Mycobacterium tuberculosis (MTB), showed that immune response variation to MTB was often associated with genetic factors (Barreiro et al., 2012). Some of the regulatory variants detected overlapped genes already shown to be associated with susceptibility to pulmonary tuberculosis, including the MAPK phosphatase gene DUSP14 and the gene encoding the RIPK2 adapter of the NOD2-signaling pathway. A subsequent study in this experimental setting identified two loci, miR-326 and miR-1260, as controlling miRNA expression in response to MTB infection, providing the first assessment of the impact of genotype–infection interactions on miRNA molecular phenotypes (Siddle et al., 2014).
Two subsequent seminal eQTL studies provided mechanistic insights into the genetic basis of immune variation (Fairfax et al., 2014; Lee et al., 2014). The expression of hundreds of genes after the stimulation of monocytes from individuals of European descent with LPS or IFN-γ was shown to be under genetic control. The genes concerned include nodal genes and effector molecules involved in pathways such as those involving the products of the TLR4-related TIRAP, TRAF6, FADD, and MAP kinase genes; the inflammasome-related CASP1 and PYCARD genes; genes encoding downstream cytokines, such as IL6, CXCL9, and IFNB1; and many IFN-γ pathway genes (Fairfax et al., 2014). The other study focused on responses to LPS, influenza, or IFN-β in dendritic cells from individuals of various ethnic backgrounds (Lee et al., 2014). The authors identified a large number of response eQTL, and showed that some of the regulatory variants involved altered the binding of transcription factors released only upon immune activation, such as STAT1 and STAT2, which are released upon IFN-β treatment, providing some insight into the mechanisms underlying the gene–environment interactions detected. In some cases, response eQTL overlap variants associated with susceptibility to infectious and autoimmune diseases, as illustrated by CARD9 and Crohn's disease, NOD2 and leprosy, or IRF7 and systemic lupus erythematosus (Fairfax et al., 2014; Lee et al., 2014). These findings support the notion that disease-risk alleles can display activity only after some sort of immune activation (Gaulton et al., 2010; Gregory et al., 2012), and highlight the value of response eQTL mapping for investigations of the mechanisms underlying complex disease phenotypes.
Genetic determinants of population variation in immune responses
Analyses of eQTL on lymphoblastoid cell lines from various human populations worldwide provided proof of concept that genetic variation accounts for differences in gene expression among ethnic groups (Spielman et al., 2007; Stranger et al., 2012). Using primary monocytes and T lymphocytes, it has been shown that cis-eQTL are largely shared across populations, with only a small number of them being population specific (Raj et al., 2014). Although this study focused on gene expression at the steady state, two recent studies have determined the degree, and underlying genetic control, of population differences in the response to immune stimulation (Nédélec et al., 2016; Quach et al., 2016). They defined transcriptional responses to immune challenges through RNA sequencing, and mapped eQTL in macrophages from African-Americans and European-Americans exposed to Listeria monocytogenes and Salmonella Typhimurium (Nédélec et al., 2016), and in monocytes from Africans and Europeans exposed to TLR ligands (activating TLR4, TLR1/2, and TLR7/8) and influenza A virus (Quach et al., 2016). Despite differences in the experimental settings, both these studies found that the genes for which the response to immune stimulation differed strongly between populations were enriched in genetic control, with the regulatory variants concerned presenting different allele frequencies in populations of different ancestries.
Individuals of African descent generally display a stronger response to bacterial infection, particularly for inflammatory response genes, than individuals of European descent (Nédélec et al., 2016). A large fraction of European individuals has a mutation at the TLR1 locus that impairs NK-κB activity (Barreiro et al., 2009) and acts as a population-specific trans-eQTL (Fig. 4 C). This TLR1 mutation is associated with a large network of genes displaying decreased levels of expression in response to immune activation only in Europeans (Quach et al., 2016). Altogether, these studies identified a large number of genes displaying considerable differences between populations, especially in the context of their responses to immune activation, revealing mechanisms that might account for differences in the clinical manifestations of immune-related diseases between individuals or ethnic groups.
Selection and archaic admixture have shaped immune response regulation
There is growing evidence to suggest that regulatory variants play a major role in population adaptation (Schaub et al., 2012; Fraser, 2013; Pickrell, 2014). The enrichment of immune-responsive regulatory variants in population-specific signals of positive selection thus facilitates the identification of genes and phenotypes of evolutionary importance that have contributed to human survival. Examples of population differences in immune responses resulting from local adaptation driven by regulatory variants include the expression of HLA-DQA1, associated with susceptibility to celiac disease; ERAP2, involved in susceptibility to Crohn's disease; CCR1, limiting leukocyte recruitment and preventing inflammatory responses; and TLR1, associated with markedly lower levels of inflammatory response gene expression (Nédélec et al., 2016; Quach et al., 2016). The positive selection signature detected at the TLR1 trans-eQTL is consistent with attenuated TLR1-mediated signaling being advantageous to Europeans, highlighting the benefits of avoiding strong inflammatory responses that may be harmful to the host (Quach et al., 2016).
Introgression from Neanderthals also contributed to the diversification of transcriptional responses to infection in human populations. The genetic segments introgressed appear to have preferentially introduced regulatory variants into European genomes, with effects on steady-state expression and responses to TLR7/8 stimulation and influenza virus (Quach et al., 2016). Furthermore, several eQTL have been identified as potential candidates for adaptive introgression, in which the archaic variants confer greater adaptation through the regulation of gene expression. Examples include genes such as DARS, which is associated with neuroinflammatory and white matter disorders (Nédélec et al., 2016); the OAS locus, at which archaic variants appear to be associated with diverse flavivirus resistance phenotypes (Sams et al., 2016); and PNMA1, which harbors a response eQTL for influenza virus and encodes a protein that interacts with the viral protein PB2 and stimulate interferon production (Quach et al., 2016).
All these studies support the notion that variation in gene expression has been an important vehicle for human adaptation (Fraser, 2013), and clearly demonstrate that selection and archaic admixture have had a significant impact on present-day inter-population differences in immune responses, at least in terms of transcriptional variability. Only a partial understanding of the factors driving immune response variability can be gleaned from analyses based solely on eQTL, as these studies provide no information about the effects of genetic variants on other molecular phenotypes, such as protein production (protein quantitative trait loci, pQTL). Indeed, it has been shown that while a substantial fraction of genetic variants influence gene expression at all levels, from mRNA to steady-state protein abundance, the effects of eQTL are attenuated at the protein level, and some pQTL have little or no effect at the mRNA level suggesting that they affect posttranscriptional gene regulation (Battle et al., 2015). Likewise, one recent study identified genetic variants regulating cytokine production in response to various microbial stimuli, and showed that some of these variants co-localize with regions presenting signatures of positive selection (Li et al., 2016). Further investigations are required, but, by and large, the studies published to date have provided invaluable resources, by identifying genetic variants affecting intermediate molecular phenotypes in response to immune challenges in different cell types. They have also increased our understanding of the molecular and cellular mechanisms underlying host immunity to infection and susceptibility to disease.
Nongenetic and environmental factors driving immune response variation
Exploration of the epigenetic and environmental factors affecting the diversity of immune responses, across individuals and populations, is currently a major challenge, as a large fraction of the variation in immune responses cannot be attributed to genetic factors (Barreiro et al., 2012; Fairfax et al., 2014; Lee et al., 2014; Çalışkan et al., 2015; Nédélec et al., 2016; Quach et al., 2016). In this context, a recent study has shown a major impact of host nongenetic factors, such as age and gender, and environmental variables, such as annual seasonality, on the production of inflammatory cytokines in healthy donors upon cellular activation by 19 (non-) microbial stimuli; for example, the production of IFN-γ and IL-22 was significantly lower in elderly individuals (Ter Horst et al., 2016). Likewise, it has been shown that the composition and function of gut microbial communities also contribute to interindividual variation in cytokine responses to immune stimulation, providing a set of microbial-derived mediators that influence immune phenotypes in healthy individuals (Schirmer et al., 2016). Furthermore, there is growing evidence to suggest that other factors, such as social environment, can also affect immune response variation and, therefore, individual physiology and disease risk (Tung et al., 2012). In this context, a recent study has sought to decipher the influence of social status on immune system functions, using female rhesus macaques as a model (Snyder-Mackler et al., 2016). They showed that social rank affects immune cell proportions, gene expression levels and responses to immune stimuli, with a stronger proinflammatory and antibacterial response in low-status individuals. Together, these studies highlight the need to further investigate, using integrative approaches, the respective effects of host genetic and nongenetic factors, together with environmental parameters and lifestyle variables, on immune response variation, to improve our understanding of the different factors contributing to health disparities between individuals and populations.
The collection of sequencing data for thousands of individuals from different populations worldwide, including some archaic hominins, has provided new insight into the demographic and selection history of our species, revealing the admixture of early Eurasians with other types of humans, such as Neanderthals and Denisovans, from whom they acquired advantageous alleles. Interestingly, genomic traces of archaic introgression have also been detected in Africans (Hammer et al., 2011; Lachance et al., 2012), raising the exciting possibility that other unknown archaic groups may have contributed to human genetic diversity. Sequencing additional samples from ancient hominins and from multiple human populations, and the development of robust statistical models and computational methods for detecting selection, will further deepen our knowledge of the contribution of archaic hominins to the diversity of human traits and complex diseases and will help to identify the functions and mechanisms that have contributed to human adaptation and survival over time.
The study of genetic variants with regulatory effects on gene expression (eQTL) has also provided insight into the genetic and evolutionary determinants of population phenotypic diversity, particularly for immune-related diversity (Fairfax and Knight, 2014). However, because the regulation of gene expression is highly dependent on cellular context (Dimas et al., 2009; Nica et al., 2011; Fairfax et al., 2012), it is essential to map eQTL, under basal conditions or after challenge, in a vast array of innate and adaptive cell types, to obtain a complete portrait of the genetic control of immune responses. In this context, the GTEx project explores the landscape of gene expression across 54 different tissues, providing the richest catalog of tissue-specific and shared eQTL (GTEx Consortium, 2015). The extension of this across-tissue rationale to multiple populations from different ethnic backgrounds will, together with population genetic analyses, help to provide a comprehensive picture of the immunological mechanisms underlying host adaptation to pathogen pressure and the maintenance of homeostasis.
An expansion of current efforts to map the genetics of gene expression onto other immune phenotypes, such as protein production, metabolite levels, and heterogeneity in immune cell populations, together with the influence of the microbiome, is increasing our understanding of the genetic, epigenetic, and environmental determinants of variation in immune responses and immunity to infection (Liston et al., 2016; Brodin and Davis, 2017). For example, studies analyzing twin cohorts, as well as a population from Sardinia, have dissected the heritable and nonheritable status of immune phenotypes, such as cell population frequencies and protein expression levels (Orrù et al., 2013; Brodin et al., 2015; Roederer et al., 2015), and found, for example, that age increases interindividual variation of these parameters, owing to increased exposure to pathogens, nonpathogenic microbes, or vaccination history (Brodin et al., 2015). Likewise, a recent study has characterized the genetic, epigenetic, and transcriptomic landscape of several major immune cell types and found that transcriptional variation at the majority of genes is a result of the presence of cis-regulatory genetic variants, but epigenetic influences were also observed for a small subset of biologically relevant genes (Chen et al., 2016). In this line, ongoing efforts, such as those of the Immune Variation (ImmVar) Project, the Human Functional Genomics Project, and the Milieu Intérieur Consortium, are focusing on the dissection of how interactions between host genetic and epigenetic variation and a large number of variables, including age, sex, commensal microbiota, nutrition, latent infections, and history of vaccination, drive the variation of human immune responses. Collectively, these studies constitute useful, comprehensive resources to investigate the interplay between genetic and nongenetic factors in modulating the plasticity of the immune system (Liston et al., 2016; Brodin and Davis, 2017).
In the context of how evolution has acted on immune phenotypes, it is important to keep in mind that potentially advantageous immune traits can be transmitted across generations not only through genetics but also via some environmental and cultural factors. These include, for example, nutritional and physical activity behaviors, smoking, mate choice, and access to medical care. Thus, another way to explore the evolution of human populations and the influence of selection is to use phenotypic approaches in the natural setting (Linnen and Hoekstra, 2009; Stearns et al., 2010). Focusing on phenotypic data collection, it has been reported that traits related to morphology (e.g., weight and height), physiology (e.g., cholesterol and systolic blood pressure), and life history (e.g., age at first and last births) have been targets of selection (Stearns et al., 2010). Future studies should develop comprehensive phenotypic databases as a complement to genetic studies to assess how the environmental and cultural changes operated by human populations can affect the evolution of our species at the genetic and phenotypic levels over time.
In conclusion, the integration of all of these datasets into a clinical, epidemiological, and population genetics framework will provide new insight, and probably a few surprises, concerning the history and immune responses of the genus Homo, and the ways in which our genetic and nongenetic makeup, together with changes in our environment and cultural behaviors, influence phenotypic variation in both health and disease.
We wish to thank the members of the Human Evolutionary Genetics Laboratory for discussions and critical reading of the manuscript.
Research in the Quintana-Murci laboratory was supported by the Institut Pasteur, the Centre National de la Recherche Scientifique (CNRS), the French Government's Investissement d'Avenir program, Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases (grant no. ANR-10-LABX-62-IBEID), the Agence Nationale de la Recherche (ANR) grants IEIHSEER (ANR-14-CE14-0008-02) and TBPATHGEN (ANR-14-CE14-0007-02), and the European Research Council under the European Union's Seventh Framework Program (FP/2007–2013)/ERC Grant Agreement No. 281297.
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Diavatopoulos
Lemmers
Host and environmental factors influencing individual human cytokine responses
Varkonyi
Cahinhinan
Argyropoulos
Destro-Bisol
Drousiotou
Loiselet
Haplotype diversity and linkage disequilibrium at human G6PD: recent origin of alleles that confer malarial resistance
Voight
Convergent adaptation of human lactase persistence in Africa and Europe
Michopoulos
Toufexis
Social environment is associated with gene regulatory variation in the rhesus macaque immune system
Human genetic susceptibility to intracellular pathogens
Immunol. Rev.
https://doi.org/10.1111/j.1600-065X.2010.00996.x
Vattathil
Small amounts of archaic admixture provide big insights into human history
Veeramah
K.R.
The impact of whole-genome sequencing on the reconstruction of human population history
Resurrecting surviving Neandertal lineages from modern human genomes
Excavating Neandertal and Denisovan DNA from the genomes of Melanesian individuals
Detecting natural selection in genomic data
https://doi.org/10.1146/annurev-genet-111212-133526
Kudaravalli
A map of recent positive selection in the human genome
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Moyzis
Global landscape of recent inferred Darwinian selection for Homo sapiens
Huerta-Sanchez
Z.X.
Cuo
Korneliussen
Sequencing of 50 human exomes reveals adaptation to high altitude
Trynka
Barisani
Finnish Celiac Disease Study Group
Evolutionary and functional analysis of celiac risk loci reveals SH2B3 as a protective factor against bacterial infection
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Approaches to dissect the genetic basis of phenotypic traits and diseases. (A) Clinical genetics aims to identify rare mutations associated with severe clinical phenotypes. The genealogy illustrates the Mendelian inheritance of an autosomal recessive disorder, where one copy of the segregating disease mutation has no phenotype (gray), whereas two copies lead to the manifestation of the severe disorder (black). (B) Genetic epidemiology aims to dissect the genetic and environmental factors contributing to complex disease phenotypes by using population-based approaches. The Manhattan plot shows six loci associated with susceptibility to leprosy by a GWAS in the Chinese population (Liu et al., 2015). (C) Population genetics explores how past human demography, including admixture with archaic hominins, and natural selection have affected the genetic diversity of human populations. It represents a powerful approach to identify common variants associated with phenotypic traits that have played a major role in human survival and adaptation, including resistance to infectious diseases. (D) Cellular genomics allows the investigation of the genetic basis of intermediate molecular phenotypes, such as gene expression, which can be linked to ultimate organismal traits. The identification of genetic variants affecting gene expression in cis or trans, through the mapping of eQTL, helps to pinpoint cellular and molecular mechanisms involved in complex phenotypic traits, including immunity to infection.
Major human migrations across the globe and presence of archaic ancestry in the genomes of modern human populations. Red arrows indicate the major migrations of Homo sapiens to colonize the world after the out of Africa exodus. Blue arrows indicate some more recent migratory events (<5,000 yr ago), and dashed arrows represent the historical migrations related to the Trans-Atlantic slave trade. Approximate geographic areas of modern human populations presenting Neanderthal or Denisovan ancestry are shaded in blue and orange, respectively, according to the Simons Genome Diversity Project (Mallick et al., 2016). This map does not present a comprehensive evaluation of the populations presenting archaic ancestry, as only populations for which genetic profiles are available are plotted. The Neanderthal ancestry observed in American populations does not reflect in situ admixture with Neanderthals but, instead, their varying levels of European ancestry.
Natural selection and archaic introgression affect human genome diversity. (A) Schematic representation of how different selective regimes and archaic introgression affect population genetic diversity. Purifying selection removes deleterious alleles (black dots) from the population. In turn, genetic variants conferring a selective advantage (e.g., disease resistance) increase in frequency, or are maintained, in the population though positive and balancing selection, respectively. Different models of positive selection are represented: the classic sweep, where a new mutation rapidly increases in frequency until fixation (red dots); selection on standing variation, where selection acts on a preexisting genetic variant in the population (orange dots); and polygenic adaptation, where multiple genetic variants located in different genomic regions simultaneously increase in frequency (green dots). Balancing selection is represented here by heterozygote advantage (blue dots). Archaic introgression illustrates ancient gene flow between archaic hominins (purple segments) and, in this case, the ancestors of modern Europeans. The traces of this archaic introgression are today detectable in the genomes of Europeans. (B) Genetic diversity and proportion of deleterious alleles in populations with different demographic regimes. A population that underwent a past expansion (left) presents higher levels of genetic diversity, i.e., higher absolute numbers of benign (white dots) and deleterious (black dots) variants, than a population that experienced a bottleneck followed by an expansion (right). However, the proportion of deleterious variants, with respect to those that are benign is lower in the expanding population due to the increased efficiency of purifying selection in removing deleterious alleles. (C) Some molecular signatures of positive selection. The FST statistic (top) measures genetic distances between populations; positive selection, which acts locally, tends to increase the genetic differentiation of particular loci, resulting in high FST values. The distribution of FST values, calculated for genome-wide SNPs across all chromosomes, is presented here. The dashed red line corresponds to the 99th percentile of the distribution, and loci above this line should be considered as strong candidates for positive selection. The degree of haplotype homozygosity (bottom) is another feature commonly used to detect advantageous variants. Positive selection can lead to an excess of homozygosity (green line) of the haplotypes carrying the positively selected allele (red line), with respect to the other haplotypes (blue line). This pattern is the result of the rapid increase in frequency of the advantageous allele, with recombination not having enough time to break down the haplotype on which the selected mutation arose. (D) Comparison of archaic introgression scores between two classes of genes (top); class B genes (e.g., innate immunity genes) present higher archaic ancestry than class A genes (e.g., the rest of the genome). The TLR1-6-10 gene cluster presents a high degree of archaic ancestry (bottom); archaic haplotypes (blue lines) surrounding this genomic region are represented here in European individuals (adapted from Deschamps et al., 2016).
Principles of eQTL mapping for investigating immune response variation. (A) General workflow to map the genetic basis of immune response variation in human populations; transcriptional responses, to various infections or immune stimulations, of primary immune cells from healthy donors are defined to subsequently assess the correlation between genotype variation and gene expression phenotypes, through eQTL mapping. (B) Plots representing eQTL; in contrast with SNP1, which does not behave as an eQTL (left), SNP2 is detected as an eQTL because its genotypes correlate with gene expression variation (middle). SNP3 behaves as a response eQTL; genotypes are correlated with gene expression variation only in stimulated conditions, indicating gene–environment interactions (right). (C) The fine mapping of the TLR1 genomic region (left) detected the SNP rs5743618 as the best trans-eQTL in Europeans (Quach et al., 2016), whose derived C allele is associated with the expression patterns of multiple genes upon Pam3CSK4 monocyte stimulation (right). The C allele presents signatures of local adaptation in Europeans (EUR), where it is present at very high frequency, whereas it is virtually absent in African (AFR) and East-Asian (EAS) populations.
Some iconic cases of positive selection in the human genome
Gene .
Function .
Associated phenotype .
Main selection method .
Population .
Date .
Putative selective factor .
References .
LCT Glycoside hydrolase Lactose tolerance Haplotype homozygosity Europeans ∼7,000–11,000 Dairy farming Enattah et al., 2002; Bersaglieri et al., 2004; Tishkoff et al., 2007; Itan et al., 2009; Ranciaro et al., 2014; Mathieson et al., 2015
Africans ∼3,000–7,000
EDAR Cell surface receptor for ectodysplasin A Hair straightness, changes of epidermal appendages Haplotype homozygosity East Asians ∼10,000 Thermo-regulation Sabeti et al., 2007; Barreiro et al., 2008; Bryk et al., 2008; Tan et al., 2013
SLC24A5 Potassium-dependent sodium/calcium exchanger Light skin color Haplotype homozygosity Europeans ∼18,700 Low environmental UV radiation exposure Lamason et al., 2005; Barreiro et al., 2008; Beleza et al., 2013
EPAS1 Hypoxia-inducible factor Response to hypoxic challenge Haplotype homozygosity Tibetans ∼18,200 Altitude-induced hypoxia Simonson et al., 2010; Yi et al., 2010; Peng et al., 2011
G6PD Pentose monophosphate shunt of glycolysis Resistance to malaria Haplotype homozygosity Africans ∼3,800–11,700 Plasmodium falciparum/vivax Ruwende et al., 1995; Tishkoff et al., 2001; Saunders et al., 2002; Louicharoen et al., 2009
Southeast Asians ∼1,500
DARC Duffy antigen receptor for chemokines Resistance to malaria Population differentiation Africans ∼33,000 Plasmodium vivax Hamblin and Di Rienzo, 2000; Hamblin et al., 2002
CD36 Membrane protein Resistance to malaria Composite statistics; population differentiation Africans ∼3,900 Plasmodium falciparum Newbold et al., 1999; Ayodo et al., 2007; Fry et al., 2009; Bhatia et al., 2011; Deschamps et al., 2016
TLR1 Pathogen recognition receptor Lower inflammatory response Composite statistics; haplotype homozygosity Europeans ∼11,000 ? Barreiro et al., 2009; Deschamps et al., 2016


patients, relatives and hospital staff.
We have Catholic Chaplains in all hospitals and someone is available to the patients at all times – 24/7. Our chaplains are there to listen to you, hold your hand, bring the Sacraments to you or to be a comforting presence when your loved one is in hospital. Chaplains are there to celebrate life with you as well as to journey with you as you enter that place of everlasting life.
If you or your loved one is admitted in hospital, it is essential that you fill in 'Roman Catholic' on the admission form. This puts you on a list that is forwarded to the Catholic Chaplain. If for some reason you have not received a visit from a chaplain and would like one, please do not hesitate to telephone the hospital and ask for the chaplain to be paged or let the hospital staff know of your need for a Catholic Chaplain.
Hospital Chaplains support by being there!


text:
https://apnews.com/article/fact-checking-741499781117
TikTok video spreads unsupported claim of human trafficking trap
By ANGELO FICHERAOctober 15, 2021 GMT
CLAIM: Car seats are being left in public places as a sex trafficking ploy to kidnap women.
AP'S ASSESSMENT: False. There is no evidence that this is a ploy being used by traffickers, officials say. A viral TikTok video references a North Carolina police department's announcement but ignores what it actually said: that, contrary to rumors, a car seat left in a Walmart parking lot was not found to be associated with trafficking.
THE FACTS: A TikTok video viewed more than 13 million times and shared on other social media platforms is raising alarms about a purported sex trafficking ploy that officials say is not rooted in evidence.
In the video, a woman shows a Facebook post from the police department in Wilkesboro, North Carolina, with a photo of a pink and black car seat in a parking lot. She asks viewers if they've ever seen "this type of car seat just out of nowhere."
"That's not an ordinary car seat," she tells viewers. "That's actually a trap. Please do not go near that car seat. That's actually a sex trafficking car seat where they're just waiting for you to walk up, you as a woman to walk up, where they can grab you."
The woman claims that local officials in her town, which isn't specified, "didn't know that that was a trap at all." She encourages women who "see a random car seat" to call the National Human Trafficking Hotline.
But the police department's Facebook post shown at the start of the video was actually refuting — not supporting — the idea that the car seat in the photo was left as part of a human trafficking trap.
"The Wilkesboro Police Department has investigated this incident and discovered the circumstances of how the seat was left in the parking lot," the department wrote. "Two customers walked out of Wal-Mart after purchasing a new child car seat. An older seat was removed from their car and placed on the ground and the new child seat was installed. The customers then left the parking lot leaving the old child seat behind on the ground. At no time was this incident deemed to be involved in any criminal activity."
Tommy Rhodes, interim chief of the department, said in a phone interview that police reviewed security footage and watched the events unfold to determine what actually happened.
"If we for one second thought that was a sex trafficking ploy, we would be all over that," he said.
Attempts to reach the woman who made the TikTok video were unsuccessful.
The Polaris Project, a nonprofit organization that operates that National Human Trafficking Hotline, said it was aware of the TikTok video and that it had received calls about it.
"Rumors like this one have been proven to be false," Megan Cutter, director of the hotline, said in an email statement.
"One of the most pervasive myths about human trafficking is that it always — or often — involves kidnapping or otherwise physically forcing someone into a situation," Cutter's statement said. "In reality, most human traffickers use psychological means such as tricking, defrauding, manipulating or threatening victims into providing commercial sex or exploitative labor."
Cutter also said that survivors of human trafficking were typically trafficked by people they know, such as romantic partners or family members. The hotline takes all calls seriously, she said, but those stemming from false information or conspiracy theories take resources away from assisting actual victims and survivors.
The TikTok video isn't alone in advancing the unsubstantiated rumor about car seats being used to ensnare women.
A similar claim about the supposed car seat ploy appeared in a September Facebook post shared by nearly 300,000 users. The post showed photos of a car seat in a Nashville shopping center parking lot.
"HUMAN TRAFFICKING WARNING," the post reads. "Ladies be aware empty baby car seats are being used as a lure to get you out of your car so you can be abducted or followed. DO NOT get out of your car."
A spokesperson for the Metropolitan Nashville Police Department told The Associated Press that the agency was not aware of any reports about such incidents.
This is part of AP's effort to address widely shared misinformation, including work with outside companies and organizations to add factual context to misleading content that is circulating online. Learn more about fact-checking at AP.

redpajama_set_name: RedPajamaCommonCrawl


Reboot SOBR Extent while in the middle of evacuation?
Can you reboot an extent that is in the middle of an evacuation? I mean I know I can, but will it recover nicely or will that mess up the evacuation?
Re: Reboot SOBR Extent while in the middle of evacuation?
it must recover nicely - a power outage or whatever can happen at every time. If you faced any issues, please open a support case and let us know the case number.


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After the last 2 runs being much more challenging than anticipated I am happy to say I thoroughly enjoyed this one.
Someone said to me that it can be harder stopping for the walking intervals then starting up again and I definitely believe that to be true.
My advice for anyone with this run coming up is to take it SUPER slow. Certainly up until the last 5 minutes. I also had a coffee and a small bowl of porridge about 45 minutes before which seemed to give me some energy to burn.
Oh and change your music. I downloaded the Get Some Happy playlist from Spotify and loved not knowing which song was going to be next.
Well done. I'll be running run 1 of week 9 on Saturday at my local Parkrun too. You have just 3 weeks to go, enjoy the rest of your runs.
Thank you. Only 3 runs for you 😊 Enjoy!
Well done. Much more satisfying than W5R3. You get to consolidate this with the same run for all of W7. I loved this as it felt nice and consistent after W5 & 6 doing different runs each time you go out.
Well done. See told you that you could do it, now what was all the worry about?
Very well done 👍 it's a slow and steady progression to graduation now!
OMG - My Motivation has Returned!
Love, love, love my running club!
My love of running is well and truly resurrected!
This is how to return to running!
Lovely post graduation running, plus proud of my son😀!


text:
You Are Here: Home » News » Taipower Will Submit Nuclear Plant Proposal in June
Taipower Will Submit Nuclear Plant Proposal in June
Posted date: February 25, 2013 In: News, Nuclear | comment : 0
ITaiwan Power Co (Taipower) chairman Hwang Jung-chiou yesterday said the company plans to submit its proposal for the last additional budget allocation for finishing the construction of the Fourth Nuclear Power Plant — the Lungmen Nuclear Power Plant in New Taipei City's Gongliao District — to the Legislative Yuan in June.nu
At Taipower's group offering event for the arrival of the Lunar New Year yesterday, Hwang responded to media questions about the controversial issue of seeking extra funds for the ongoing construction project in the next legislative session.
He said the power plant is in its final stage of construction and has begun pre-operational tests already, and the specific additional budget and construction schedule are expected to be finalized by June. Responding to a question about how much the estimated supplementary budget will be, Hwang said: "We have not made a final decision yet."
Taipower has greatly improved its management of nuclear power operations and controls in the past couple of years, and will make certain that the plant is safe, he said, adding that the Atomic Energy Council (AEC) safeguards the plants' nuclear safety, so Taipower will only move forward after gaining consent from specialists in Taiwan and overseas.
Hwang also responded to last week's media reports about an AEC official that said that Taipower has continuously postponed the submission date of a requested report on the possible safety threats to the two current operating nuclear plants in northern Taiwan in the event of a massive earthquake.
He said Taipower has already asked geologists to look into the issue and make evaluations of the possible effects, and that the company would deal with the issue very cautiously.
In addition, Taipower will continue to improve the company's results this year, he said, adding that it hopes the increase in electricity prices can help solve the problem of financial losses caused by the increased cost of fuel last year.


We've decided to jump on the #vanlife bandwagon! We purchased a van, fit it with a bed, and are hitting the road, going coast to coast across the United States. I have broken up the trip into three segments. The first segment of the trip runs from August through October and will take us from Florida to California (3,000+ miles). The major stops along the way are scheduled to include Atlanta, Nashville, Chicago, the national parks of South Dakota, and Yellowstone National Park.
We are still trying to stick our $200 per week budget. Without decent public transit or easy access to cheap accommodations, this could be pretty challenging in the United States. The average cost of a hotel in the USA is $130 USD per night. This compares with about $40 in Malaysia, $30 in China, and $58 in Australia. The cheapest options seem to be couch surfing which is free-ish and plentiful, but potentially awkward. Another option is house sitting, which you all know I am a huge fan of. Finally, there is the option of camping. Camp sites can be found for as little as $10 per night, and often have electricity, internet, and showers. So we'll be doing our best to jump between these three options as we make our way across the country.
Then there is transportation. Budget airlines in other countries are also much cheaper than in the states; several of our one-way tickets were in the $30 range. In America, not so much. Trains are just as expensive and the destinations are quite limited. There is the option of taking the bus, but traveling with two passengers starts to really reduce the savings. The average price of gas is $2.54 per gallon ($0.67 per liter). Based on this price, the estimated number of miles, and the gas mileage of the van, I should be able to stay within budget. Fingers crossed.
We found our van on Craigslist in Melbourne, Florida. It is a 2013 Dodge Ram Passenger Van with 187k miles and no back seats since it used to be a delivery van. But we didn't want the back seats anyway, so that was kind of a win. There are several dings and dents and obviously the mileage is a bit high, but it seems to be in working order. After purchasing it, we took it to the Dodge dealership to get it a check up. They said it looked good, minus a bit of a transmission problem when backing up, a faulty tire pressure gauge, and old brake pads. We got the pads changed and called it good.
We are still below my arbitrary goal of $7,000 USD, but Jeez-Louise is this van getting expensive! Thankfully, the selling price was way lower than it deserves. If we can make it around the country without needing any major repairs, we may be able to come out of this without losing much money. Wish us luck!
Once we got the van home, and I had a chance to scream anxiety screams into my pillow for a little while, we started to get pretty excited. The van just needed a few things to get her road trip ready. So, I shopped Mom's house.
Thankfully, Mom had just about everything we needed. I grabbed a three inch mattress pad, two sets of sheets, three pillows, and a blanket. We pimped out the storage area using milk crates she had in her garage and grabbed some camping gear left over from when my sister and I were kids. I even grabbed an ice cream tub from out of the recycling bin to use as a garbage can. We did end up purchasing a phone charger for $7, but otherwise decided not to purchase anything until we knew exactly what we needed. Minimalism is the name of the game!
We're hitting the road tomorrow. It feels like the night before Christmas. I can't wait to get on the road and start exploring again. I am not sure how I am going to handle the long drives nor how Josh is going to handle sleeping in a van, but hey, that's half the fun!
All the best for the trip!
Your spirit of adventure lives on! You might say you'll be RVING. We are very happy for you and hope it goes according to plan. We'll be in Az. for 4 or 5 months this winter…come see us too.
Absolutely! We'll probably be heading that way after Christmas.


There are many options for your feet. The questions to ask are: Do you need a solution that doesn't compromise your cycling efficiency? Are you going for long rides or just short 1-2 hour rides? Are you only going to ride in mildly cold weather?
For mildly cold weather, you can probably get by with some booties for your current mountain bike/road shoes. They work okay but they're a pain to put on, they don't always work well if you want to hike-a-bike in the show, and they don't always last. It also means you'll be using your current shoes which probably aren't big enough for thicker socks. If you're going to commit yourself to winter riding, you really should just buy a winter boot solution.
First off, you can always forgo the clipless pedals, throw on some platforms, and wear snow boots. It's not he most efficient for cycling, but it works and you won't have to deal with ice buildup on clipless pedals. Besides, riding on snow is pretty inefficient to begin with. I'm not sure how much this really hurts.I originally did this and used Powerstraps, the extra long ones made for boots. I'm not sure those are even available anymore.
If you want to use boots and clipless pedals, you can follow these directions. I did and had good success. I used some Thinsulate-lined L.L. Bean Snow Sneakers and added the SPD cleats using a Syntace SPD-to-Look converter plate. Snow sneakers work well because they don't have laces that can get caught in your gearing. One problem is Snow Sneakers allow a little wind through the front toe box. And, unlike mountain bike shoes, the sole was very flexible, which made pedaling less efficient.
Remember that with these winter boots, you'll probably need to raise your seat up since there's more sole between your foot and the pedal.
The solution I'm using today are the Lake winter mountain bike boots. I own two pair, but neither are the latest models. The first is a size 10, one size larger than my normal shoe. I can wear one thick sock with it. I'm not sure if Lake has fixed this with later models, but I find the boots runs small which is the opposite of what you want. Anyway, I rarely use this boot except in mild winter temperatures from freezing to 45F.
My other Lake boots are size 13.5. Yep. I normally wear a size 9. I can wear 2 thick socks and a thin one or two.
First off, the key is don't wear so many socks that your foot is too snug within the shoe. It will constrict blood flow and make your feet colder faster.
I basically have two sock strategies. For shorter (1 hour) rides or rides in temperatures not far below freezing, I simply wear thick socks. Don't wear cotton socks since they absorb water and become a much less effective insulator. Wool or synthetics are fine. Fleece socks aren't as fitted so you need a larger shoe to wear them.
The best socks I have are for XC skiing. They have a thicker knit on the bottom (where your feet get cold first) and a thinner layer on top.
For longer rides and/or colder rides, you need to manage your foot moisture. Even in the cold, your skin sweats in order to maintain a 70% humidity level. As your feet sweat, the moisture accumulates in your socks and shoes. Damp insulation doesn't insulate as well.
The solution is to wear a vapor barrier that keeps the moisture against your skin. Your feet will stop sweating and your insulation remains dry. Currently I'm using SealSkinz socks which work well, although they're bulky. I find nylon-coated vapor barriers are better, but they're a little tougher to find. The poor man method is to use Subway sandwich bags.
For the Iditasport 350 in 2001 I wore a thin liner sock, a nylon vapor barrier, a thick wool sock, and a fleece sock. All this fit in my big Lake boots. Since we were hiking through deep snow, I duct taped the top of my boot to keep the snow out.
I recommend them, but I almost never use them. They work really well so long as they're dry, which is another reason to use a vapor barrier. The drawbacks? They cost money and in a long race, the last thing you want to do is stop, take your boots off, and replace your warmers.
As mentioned before, your feet may not be 100% toasty for your ride. Mine are usually in a slight state of chill. I don't get concerned until I feel them drift towards serious cold and numbness. I'll often revert to curling my toes with each pedal stroke. If that doesn't work, you can get off the bike and run beside it. Of course there's always the option of stopping at the local coffee shop, if you have that option.


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