The initial peopling of the Americas: A growing number of founding mitochondrial genomes from Beringia

Pan-American mitochondrial DNA (mtDNA) haplogroup C1 has been recently subdivided into three branches, two of which (C1b and C1c) are characterized by ages and geographical distributions that are indicative of an early arrival from Beringia with Paleo-Indians. In contrast, the estimated ages of C1d—the third subset of C1—looked too young to fit the above scenario. To define the origin of this enigmatic C1 branch, we completely sequenced 63 C1d mitochondrial genomes from a wide range of geographically diverse, mixed, and indigenous American populations. The revised phylogeny not only brings the age of C1d within the range of that of its two sister clades, but reveals that there were two C1d founder genomes for Paleo-Indians. Thus, the recognized maternal founding lineages of Native Americans are at least 15, indicating that the overall number of Beringian or Asian founder mitochondrial genomes will probably increase extensively when all Native American haplogroups reach the same level of phylogenetic and genomic resolution as obtained here for C1d.While debate is still ongoing among scientists from several disciplines regarding the number of migratory events, their timing, and entry routes into the Americas (Wallace and Torroni 1992; Torroni et al. 1993; Forster et al. 1996; Kaufman and Golla 2000; Goebel et al. 2003, 2008; Schurr and Sherry 2004; Wang et al. 2007; Waters and Stafford 2007; Dillehay et al. 2008; Gilbert et al. 2008a; O''Rourke and Raff 2010), the general consensus is that modern Native American populations ultimately trace their gene pool to Asian groups who colonized northeast Siberia, including parts of Beringia, prior to the last glacial period. These ancestral population(s) probably retreated into refugial areas during the Last Glacial Maximum (LGM), where their genetic variation was reshaped by drift. Thus, pre-LGM haplotypes of Asian ancestry were differently preserved and lost in Beringian enclaves, but at the same time, novel haplotypes and alleles arose in situ due to new mutations, often becoming predominant because of major founder events (Tamm et al. 2007; Achilli et al. 2008; Bourgeois et al. 2009; Perego et al. 2009; Schroeder et al. 2009). The scenario of a temporally important differentiation stage in Beringia explains the predominance in Native Americans of private alleles and haplogroups such as the autosomal 9-repeat at microsatellite locus D9S1120 (Phillips et al. 2008; Schroeder et al. 2009), the Y chromosome haplogroup Q1a3a-M3 (Bortolini et al. 2003; Karafet et al. 2008; Rasmussen et al. 2010), and the pan-American mtDNA haplogroups A2, B2, C1b, C1c, C1d, D1, and D4h3a (Tamm et al. 2007; Achilli et al. 2008; Fagundes et al. 2008; Perego et al. 2009).In the millennia after the initial Paleo-Indian migrations, other groups from Beringia or eastern Siberia expanded into North America. If the gene pool of the source population(s) had in the meantime partially changed, not only because of drift, but also due to the admixture with population groups newly arrived from regions located west of Beringia, this would have resulted in the entry of additional Asian lineages into North America. This scenario, sometimes invoked to explain the presence of certain mtDNA haplogroups such as A2a, A2b, D2a, D3, and X2a only in populations of northern North America (Torroni et al. 1992; Brown et al. 1998; Schurr and Sherry 2004; Helgason et al. 2006; Achilli et al. 2008; Gilbert et al. 2008b; Perego et al. 2009), has recently received support from nuclear and morphometric data showing that some native groups from northern North America harbor stronger genetic similarities with some eastern Siberian groups than with Native American groups located more in the South (González-José et al. 2008; Bourgeois et al. 2009; Wang et al. 2009; Rasmussen et al. 2010).As for the pan-American mtDNA haplogroups, when analyzed at the highest level of molecular resolution (Bandelt et al. 2003; Tamm et al. 2007; Fagundes et al. 2008; Perego et al. 2009), they all reveal, with the exception of C1d, entry times of 15–18 thousand years ago (kya), which are suggestive of a (quasi) concomitant post-LGM arrival from Beringia with early Paleo-Indians. A similar entry time is also shown for haplogroup X2a, whose restricted geographical distribution in northern North America appears to be due not to a later arrival, but to its entry route through the ice-free corridor (Perego et al. 2009). Despite its continent-wide distribution, C1d was hitherto characterized by an expansion time of only 7.6–9.7 ky (Perego et al. 2009). This major discrepancy has been attributed to a poor and possibly biased representation of complete C1d mtDNA sequences (only 10) in the available data sets (Achilli et al. 2008; Malhi et al. 2010). To clarify the issue of the age of haplogroup C1d and its role as a founding Paleo-Indian lineage, we sequenced and analyzed 63 C1d mtDNAs from populations distributed over the entire geographical range of the haplogroup.     

Two new insulator proteins,Pita and ZIPIC,target CP190 to chromatin

Insulators are multiprotein–DNA complexes that regulate the nuclear architecture. The Drosophila CP190 protein is a cofactor for the DNA-binding insulator proteins Su(Hw), CTCF, and BEAF-32. The fact that CP190 has been found at genomic sites devoid of either of the known insulator factors has until now been unexplained. We have identified two DNA-binding zinc-finger proteins, Pita, and a new factor named ZIPIC, that interact with CP190 in vivo and in vitro at specific interaction domains. Genomic binding sites for these proteins are clustered with CP190 as well as with CTCF and BEAF-32. Model binding sites for Pita or ZIPIC demonstrate a partial enhancer-blocking activity and protect gene expression from PRE-mediated silencing. The function of the CTCF-bound MCP insulator sequence requires binding of Pita. These results identify two new insulator proteins and emphasize the unifying function of CP190, which can be recruited by many DNA-binding insulator proteins.Insulators in the Drosophila and vertebrate genomes have been identified based on their ability to disrupt the communication between an enhancer and a promoter when inserted between them (Raab and Kamakaka 2010; Ghirlando et al. 2012; Herold et al. 2012; Matzat and Lei 2013; Chetverina et al. 2014; Kyrchanova and Georgiev 2014). The growing amount of data show that insulator proteins fulfil an architectural function in mediating inter- and intrachromosomal interactions and in contacting regulatory elements such as promoters or enhancers (Maksimenko and Georgiev 2014).The best studied Drosophila insulator proteins, dCTCF (homolog of vertebrate insulator protein CTCF) and Su(Hw) are DNA-binding zinc-finger proteins (Herold et al. 2012; Matzat and Lei 2013; Kyrchanova and Georgiev 2014). Binding sites for dCTCF have been identified in the insulators that separate functional regulatory domains of the bithorax complex and in many promoter regions (Moon et al. 2005; Holohan et al. 2007; Mohan et al. 2007; Nègre et al. 2010, 2011; Ni et al. 2012). The Su(Hw) protein more frequently associates with intergenic sites (Adryan et al. 2007; Bushey et al. 2009; Nègre et al. 2010, 2011; Soshnev et al. 2012, 2013). As shown in a transgenic assay, dCTCF and Su(Hw) binding sites can support specific distant interactions (Kyrchanova et al. 2008a,b), which suggests a key role for these proteins in organizing chromatin architecture.The Su(Hw), dCTCF, and BEAF-32 proteins interact with Centrosomal Protein 190 kD, named CP190 (Pai et al. 2004; Gerasimova et al. 2007; Mohan et al. 2007; Bartkuhn et al. 2009; Oliver et al. 2010; Liang et al. 2014). CP190 (1096 amino acids) contains an N-terminal BTB/POZ domain, an aspartic-acid-rich D-region, four C2H2 zinc-finger motifs, and a C-terminal E-rich domain (Oliver et al. 2010; Ahanger et al. 2013). The BTB domain of CP190 forms stable homodimers that may be involved in protein–protein interactions (Oliver et al. 2010; Bonchuk et al. 2011). In addition to these motifs, CP190 also contains a centrosomal targeting domain (M) responsible for its localization to centrosomes during mitosis (Butcher et al. 2004). It has been shown that CP190 is recruited to chromatin via its interaction with the Su(Hw) and dCTCF proteins (Pai et al. 2004; Mohan et al. 2007). Inactivation of CP190 affects the activity of the dCTCF-dependent insulator Fab-8 from the bithorax complex (Gerasimova et al. 2007; Mohan et al. 2007; Moshkovich et al. 2011) and the gypsy insulator, which contains 12 binding sites for the Su(Hw) protein (Pai et al. 2004). Binding of Su(Hw) and CP190 at gypsy-like sites is mutually dependent, indicating a stabilizing role of CP190 in these cases (Schwartz et al. 2012).Recent genome-wide ChIP-chip studies provide evidence for an extensive overlap of the CP190 distribution pattern with dCTCF, BEAF-32, and Su(Hw) insulator proteins and the promoters of active genes (Bartkuhn et al. 2009; Bushey et al. 2009; Nègre et al. 2010, 2011; Schwartz et al. 2012; Soshnev et al. 2012). Very recently, it has been demonstrated that CP190 bridges DNA-bound insulator factors with promoters (Liang et al. 2014). These data support the model that CP190 has a global role in the function of insulator proteins. However, there are a number of sites in the Drosophila genome where CP190 does not colocalize with any known insulator DNA binding protein (IBP), suggesting that there may be some other proteins that recruit CP190 to chromatin (Schwartz et al. 2012).To identify new factors that associate with CP190, we purified the FLAG-tagged CP190 protein from S2 cells and identified two zinc-finger proteins, CG7928 and Pita, which were shown to interact with CP190 in vivo and in vitro. Genome-wide identification of binding sites for Pita and CG7928 in S2 cells revealed their extensive colocalization with CP190, providing evidence for direct interactions between these proteins, which was supported by binding and in vivo functional assays. Based on these results we termed CG7928 the “zinc-finger protein interacting with CP190” (ZIPIC).     

High-resolution mapping and analysis of copy number variations in the human genome: A data resource for clinical and research applications

We present a database of copy number variations (CNVs) detected in 2026 disease-free individuals, using high-density, SNP-based oligonucleotide microarrays. This large cohort, comprised mainly of Caucasians (65.2%) and African-Americans (34.2%), was analyzed for CNVs in a single study using a uniform array platform and computational process. We have catalogued and characterized 54,462 individual CNVs, 77.8% of which were identified in multiple unrelated individuals. These nonunique CNVs mapped to 3272 distinct regions of genomic variation spanning 5.9% of the genome; 51.5% of these were previously unreported, and >85% are rare. Our annotation and analysis confirmed and extended previously reported correlations between CNVs and several genomic features such as repetitive DNA elements, segmental duplications, and genes. We demonstrate the utility of this data set in distinguishing CNVs with pathologic significance from normal variants. Together, this analysis and annotation provides a useful resource to assist with the assessment of CNVs in the contexts of human variation, disease susceptibility, and clinical molecular diagnostics.Copy number variation (CNV) in the human genome significantly influences human diversity and predisposition to disease (Sebat et al. 2004, 2007; Sharp et al. 2005; Conrad et al. 2006; Feuk et al. 2006; Hinds et al. 2006; McCarroll et al. 2006; Redon et al. 2006; Kidd et al. 2008; Perry et al. 2008; Walsh et al. 2008). CNVs arise from genomic rearrangements, primarily owing to deletion, duplication, insertion, and unbalanced translocation events. The pathogenic role of CNVs in genetic disorders has been well documented (Lupski and Stankiewicz 2005), yet the extent to which CNVs contribute to phenotypic variation and complex disease predisposition remains poorly understood. CNVs have been known to contribute to genetic disease through different mechanisms, resulting in either imbalance of gene dosage or gene disruption in most cases. In addition to their direct correlation with genetic disorders, CNVs are known to mediate phenotypic changes that can be deleterious (Feuk et al. 2006; Freeman et al. 2006). Recently, several studies have reported an increased burden of rare or de novo CNVs in complex disorders such as Autism, ADHD, and schizophrenia as compared to normal controls, highlighting the potential pathogenicity of rare or unique CNVs (Sebat et al. 2007; International Schizophrenia Consortium 2008; Stefansson et al. 2008; Walsh et al. 2008; Xu et al. 2008; Elia et al. 2009). Thus, more thorough analysis of genomic CNVs is necessary in order to determine their role in conveying disease risk.Several approaches have been used to examine CNVs in the genome, including array CGH and genotyping microarrays (Albertson and Pinkel 2003; Iafrate et al. 2004; Sebat et al. 2004; Sharp et al. 2005; Redon et al. 2006; Wong et al. 2007). Results from more than 30 studies comprising 21,000 CNVs have been reported in public repositories (Iafrate et al. 2004). However, a majority of these studies have been performed on limited numbers of individuals using a variety of nonuniform technologies, reporting methods, and disease states. In addition, these data are both substantially reiterative and enriched in CNV events that are frequently observed in one or more populations. Thus, extreme care is needed in determining whether a particular structural variant plays a role in disease susceptibility or progression. To address these challenges, we identified and characterized the constellation of CNVs observed in a large cohort of healthy children and their parents, when available. This study uses uniform measures to detect and assess CNVs within the context of genomic and functional annotations, as well as to demonstrate the utility of this information in assessing their impact on abnormal phenotypes. Our analysis and annotation provide a useful resource to assist with the assessment of structural variants in the contexts of human variation, disease susceptibility, and clinical molecular diagnostics.