Large-scale analysis of genome and transcriptome alterations in multiple tumors unveils novel cancer-relevant splicing networks

Alternative splicing is regulated by multiple RNA-binding proteins and influences the expression of most eukaryotic genes. However, the role of this process in human disease, and particularly in cancer, is only starting to be unveiled. We systematically analyzed mutation, copy number, and gene expression patterns of 1348 RNA-binding protein (RBP) genes in 11 solid tumor types, together with alternative splicing changes in these tumors and the enrichment of binding motifs in the alternatively spliced sequences. Our comprehensive study reveals widespread alterations in the expression of RBP genes, as well as novel mutations and copy number variations in association with multiple alternative splicing changes in cancer drivers and oncogenic pathways. Remarkably, the altered splicing patterns in several tumor types recapitulate those of undifferentiated cells. These patterns are predicted to be mainly controlled by MBNL1 and involve multiple cancer drivers, including the mitotic gene NUMA1. We show that NUMA1 alternative splicing induces enhanced cell proliferation and centrosome amplification in nontumorigenic mammary epithelial cells. Our study uncovers novel splicing networks that potentially contribute to cancer development and progression.Alternative splicing alterations are emerging as important signatures to further understand tumor formation and to develop new therapeutic strategies (Grosso and Carmo-Fonseca 2014). Specific alternative splicing changes that confer tumor cells with a selective advantage may be caused by mutations in splicing regulatory sequences (Dorman et al. 2014) and/or regulatory factors (Brooks et al. 2014). Various splicing factors have been described to be mutated in cancer, including SF3B1, SRSF2, ZRSR2, and U2AF1 in myelodysplastic syndromes and lymphoid leukemias (Yoshida et al. 2011), RBM10 and U2AF1 in lung tumors (Imielinski et al. 2012; Brooks et al. 2014), and SF3B1 in breast tumors (Ellis et al. 2012; Maguire et al. 2015). These mutations generally impair the recognition of regulatory sites, thereby affecting the splicing of multiple genes, including oncogenes and tumor suppressors (Kim et al. 2015). On the other hand, increasing evidence shows that changes in the relative concentration of splicing factors can also trigger oncogenic processes. For instance, splicing factors from the SR and hnRNP families are overexpressed in multiple tumor types and induce splicing changes that contribute to cell proliferation (Karni et al. 2007; Golan-Gerstl et al. 2011). Similarly, down-regulation of splicing factors that act as tumor suppressors has also been observed (Wang et al. 2014; Zong et al. 2014).Importantly, specific alternative splicing events can substantially recapitulate cancer-associated phenotypes linked to mutations or expression alterations of splicing factors. This is the case of NUMB, for which the reversal of the splicing change induced by RBM10 mutations in lung cancer cells can revert the proliferative phenotype (Bechara et al. 2013). Events that contribute to cancer are often controlled by multiple factors, like the exon skipping event of MST1R involved in cell invasion, which is controlled by SRSF1 (Ghigna et al. 2005), HNRNPA2B1 (Golan-Gerstl et al. 2011), HNRNPH1, and SRSF2 (Moon et al. 2014). Furthermore, some events may be affected by both mutations and expression changes in splicing factors. For instance, mutations in RBM10 or down-regulation of QKI lead to the same splicing change in NUMB that promotes cell proliferation (Bechara et al. 2013; Zong et al. 2014). Alternative splicing changes that characterize and contribute to the pathophysiology of cancer (Sebestyén et al. 2015) are thus potentially triggered by alterations in a complex network of RNA binding proteins, which remain to be comprehensively described. To elucidate the complete set of alterations in these factors and how they globally affect alternative splicing that may contribute to cancer, we analyzed RNA and DNA sequencing data from The Cancer Genome Atlas (TCGA) project for 11 solid tumor types.     

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.     

Evolutionary assembly patterns of prokaryotic genomes

Evolutionary innovation must occur in the context of some genomic background, which limits available evolutionary paths. For example, protein evolution by sequence substitution is constrained by epistasis between residues. In prokaryotes, evolutionary innovation frequently happens by macrogenomic events such as horizontal gene transfer (HGT). Previous work has suggested that HGT can be influenced by ancestral genomic content, yet the extent of such gene-level constraints has not yet been systematically characterized. Here, we evaluated the evolutionary impact of such constraints in prokaryotes, using probabilistic ancestral reconstructions from 634 extant prokaryotic genomes and a novel framework for detecting evolutionary constraints on HGT events. We identified 8228 directional dependencies between genes and demonstrated that many such dependencies reflect known functional relationships, including for example, evolutionary dependencies of the photosynthetic enzyme RuBisCO. Modeling all dependencies as a network, we adapted an approach from graph theory to establish chronological precedence in the acquisition of different genomic functions. Specifically, we demonstrated that specific functions tend to be gained sequentially, suggesting that evolution in prokaryotes is governed by functional assembly patterns. Finally, we showed that these dependencies are universal rather than clade-specific and are often sufficient for predicting whether or not a given ancestral genome will acquire specific genes. Combined, our results indicate that evolutionary innovation via HGT is profoundly constrained by epistasis and historical contingency, similar to the evolution of proteins and phenotypic characters, and suggest that the emergence of specific metabolic and pathological phenotypes in prokaryotes can be predictable from current genomes.A fundamental question in evolutionary biology is how present circumstances affect future adaptation and phenotypic change (Gould and Lewontin 1979). Studies of specific proteins, for example, indicate that epistasis between sequence residues limits accessible evolutionary trajectories and thereby renders certain adaptive paths more likely than others (Weinreich et al. 2006; Gong et al. 2013; de Visser and Krug 2014; Harms and Thornton 2014). Similarly, both phenotypic characters (Ord and Summers 2015) and specific genetic adaptations (Conte et al. 2012; Christin et al. 2015) show strong evidence of parallel evolution rather than convergent evolution. That is, a given adaptation is more likely to repeat in closely related organisms than in distantly related ones. This inverse relationship between the repeatability of evolution and taxonomic distance implies a strong effect of lineage-specific contingency on evolution, also potentially mediated by epistasis (Orr 2005).Such observations suggest that genetic adaptation is often highly constrained, and the present state of an evolving system can impact future evolution. Yet, the aforementioned studies are limited to small data sets and specific genetic pathways, and a more principled understanding of the rules by which future evolutionary trajectories are governed by the present state of the system is still lacking. For example, it is not known whether such adaptive constraints are a feature of genome-scale evolution or whether they are limited to finer scales. Moreover, the mechanisms that underlie observed constraints are often completely unknown. Addressing these questions is clearly valuable for obtaining a more complete theory of evolutionary biology, but more pressingly, is essential for tackling a variety of practical concerns, including our ability to combat evolving infectious diseases or engineer complex biological systems.Here, we address this challenge by analyzing horizontal gene transfer (HGT) in prokaryotes. HGT is an ideal system to systematically study genome-wide evolutionary constraints because it involves gene-level innovation, occurs at very high rates relative to sequence substitution (Nowell et al. 2014; Puigbò et al. 2014), and is a principal source of evolutionary novelty in prokaryotes (Gogarten et al. 2002; Jain et al. 2003; Lerat et al. 2005; Puigbò et al. 2014). Clearly, many or most acquired genes are rapidly lost due to fitness costs (van Passel et al. 2008; Baltrus 2013; Soucy et al. 2015), indicating that genes retained in the long term are likely to provide a selective advantage. Moreover, not all genes are equally transferrable (Jain et al. 1999; Sorek et al. 2007; Cohen et al. 2011), and not all species are equally receptive to the same genes (Smillie et al. 2011; Soucy et al. 2015). However, differences in HGT among species have been attributed not only to ecology (Smillie et al. 2011) or to phylogenetic constraints (Popa et al. 2011; Nowell et al. 2014), but also to interactions with the host genome (Jain et al. 1999; Cohen et al. 2011; Popa et al. 2011). Indeed, studies involving single genes or single species support the influence of genome content on the acquisition and retention of transferred genes (Pal et al. 2005; Sorek et al. 2007; Iwasaki and Takagi 2009; Chen et al. 2011; Press et al. 2013; Johnson and Grossman 2014). For example, it has been demonstrated that the presence of specific genes facilitates integration of others into genetic networks (Chen et al. 2011), and genes are more commonly gained in genomes already containing metabolic genes in the same pathway (Pal et al. 2005; Iwasaki and Takagi 2009). However, to date, a systematic, large-scale analysis of such dependencies has not been presented. In this paper, we therefore set out to characterize a comprehensive collection of genome-wide HGT-based dependencies among prokaryotic genes, analyze the obtained set of epistatic interactions, and identify patterns in the evolution of prokaryotic genomes.     

The clustering of functionally related genes contributes to CNV-mediated disease

Clusters of functionally related genes can be disrupted by a single copy number variant (CNV). We demonstrate that the simultaneous disruption of multiple functionally related genes is a frequent and significant characteristic of de novo CNVs in patients with developmental disorders (P = 1 × 10⁻³). Using three different functional networks, we identified unexpectedly large numbers of functionally related genes within de novo CNVs from two large independent cohorts of individuals with developmental disorders. The presence of multiple functionally related genes was a significant predictor of a CNV''s pathogenicity when compared to CNVs from apparently healthy individuals and a better predictor than the presence of known disease or haploinsufficient genes for larger CNVs. The functionally related genes found in the de novo CNVs belonged to 70% of all clusters of functionally related genes found across the genome. De novo CNVs were more likely to affect functional clusters and affect them to a greater extent than benign CNVs (P = 6 × 10⁻⁴). Furthermore, such clusters of functionally related genes are phenotypically informative: Different patients possessing CNVs that affect the same cluster of functionally related genes exhibit more similar phenotypes than expected (P < 0.05). The spanning of multiple functionally similar genes by single CNVs contributes substantially to how these variants exert their pathogenic effects.Proteins rarely act in isolation; they participate in large interacting networks. Genes and their protein products can interact in a variety of ways: Proteins physically interact, regulate gene expression, modify the activity of other proteins, or catalyze sequential metabolic reactions. Genes encoding functionally related proteins tend to be located close together in the genomes of human (Caron et al. 2001; Lee and Sonnhammer 2003; Fukuoka et al. 2004; Singer et al. 2005; Sémon and Duret 2006; Makino and McLysaght 2008; Michalak 2008; Al-Shahrour et al. 2010), yeast (Cohen et al. 2000; Pal and Hurst 2003; Poyatos and Hurst 2006), mouse (Li et al. 2005; Singer et al. 2005), fly (Spellman and Rubin 2002; Mezey et al. 2008; Weber and Hurst 2011), worm (Kamath et al. 2003), and zebrafish (Ng et al. 2009). Significant clustering of functionally related genes in the genome (hereafter termed “functional clustering”) has been identified using protein–protein interactions (Poyatos and Hurst 2006; Makino and McLysaght 2009), KEGG pathways (Lee and Sonnhammer 2003), Gene Ontology terms (Al-Shahrour et al. 2010), and phenotypes exhibited from gene knockdowns (Kamath et al. 2003). Clusters of broadly expressed housekeeping genes (Lercher et al. 2002; Singer et al. 2005; Michalak 2008; Weber and Hurst 2011), and clusters of coexpressed or tissue-specific genes (Cohen et al. 2000; Caron et al. 2001; Fukuoka et al. 2004; Li et al. 2005; Mezey et al. 2008; Ng et al. 2009; Weber and Hurst 2011) have previously been reported in humans and other eukaryotes. However, the extent of functional clustering in the genome varies according to the methodology used (Lercher et al. 2002; Michalak 2008; Weber and Hurst 2011). All previous studies of functional clustering have been limited by their dependence on a single source of functional information with which to identify functional clusters. Each source of functional information, however, captures only a subset of possible functional relationships and thus will be incomplete. By combining multiple sources of information, functional predictions are improved (Troyanskaya et al. 2003; Deng et al. 2004; Lee et al. 2004), but this technique has yet to be applied when examining functional clustering within the genome.The importance of functional clustering in human disease has not yet been demonstrated. Mutations that affect multiple genes close together in the genome may incur compounding deleterious effects if the affected genes participate in the same biological process. Recent studies of copy number variants (CNVs; deletions or duplications >1 kb in size) have revealed instances in which multiple functionally related candidate genes are affected by a single variant (Boulding and Webber 2012; Golzio et al. 2012; Doelken et al. 2013). Boulding and Webber (2012) and Doelken et al. (2013) each found multiple genes within single CNVs that when individually knocked out in model organisms cause phenotypes similar to those observed in the respective patient (Boulding and Webber 2012; Doelken et al. 2013). Golzio et al. (2012) found that KCTD13, MVP, and MAPK3, which are present within the 16p11.2 CNV locus, interact to produce microcephaly or macrocephaly when their orthologs were concurrently overexpressed or underexpressed, respectively, in zebrafish (Golzio et al. 2012).In light of the existence of functional clusters, we examine how the simultaneous copy change of multiple functionally related genes contributes to the pathogenic effects of CNVs. We investigated the prevalence and extent of functional clustering within de novo CNVs in individuals with developmental disorders and identify similar clusters present throughout the genome. In addition, we examined the functional clusters for the presence of known disease genes and tested their ability to distinguish pathogenic CNVs from those found in control individuals. Finally, we considered whether patients with CNVs affecting the same functional cluster exhibit similar phenotypes.