Functional metagenomics to mine the human gut microbiome for dietary fiber catabolic enzymes

The human gut microbiome is a complex ecosystem composed mainly of uncultured bacteria. It plays an essential role in the catabolism of dietary fibers, the part of plant material in our diet that is not metabolized in the upper digestive tract, because the human genome does not encode adequate carbohydrate active enzymes (CAZymes). We describe a multi-step functionally based approach to guide the in-depth pyrosequencing of specific regions of the human gut metagenome encoding the CAZymes involved in dietary fiber breakdown. High-throughput functional screens were first applied to a library covering 5.4 × 10⁹ bp of metagenomic DNA, allowing the isolation of 310 clones showing beta-glucanase, hemicellulase, galactanase, amylase, or pectinase activities. Based on the results of refined secondary screens, sequencing efforts were reduced to 0.84 Mb of nonredundant metagenomic DNA, corresponding to 26 clones that were particularly efficient for the degradation of raw plant polysaccharides. Seventy-three CAZymes from 35 different families were discovered. This corresponds to a fivefold target-gene enrichment compared to random sequencing of the human gut metagenome. Thirty-three of these CAZy encoding genes are highly homologous to prevalent genes found in the gut microbiome of at least 20 individuals for whose metagenomic data are available. Moreover, 18 multigenic clusters encoding complementary enzyme activities for plant cell wall degradation were also identified. Gene taxonomic assignment is consistent with horizontal gene transfer events in dominant gut species and provides new insights into the human gut functional trophic chain.The human intestinal microbiome is the dense and complex ecosystem that resides in the distal part of our digestive tract. Its role in metabolizing dietary constituents (Sonnenburg et al. 2005; Flint et al. 2008; Ley et al. 2008) and in protecting the host against pathogens (Rakoff-Nahoum et al. 2004) is crucial to human health (Macdonald and Monteleone 2005; McGarr et al. 2005; Manichanh et al. 2006; Turnbaugh and Gordon 2009). It is mainly composed of commensal bacteria from the Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria phyla (five), and of several archaeal and eukaryotic species. With up to 10¹² cells per gram of feces, the bacterial abundance is estimated to reach 1000 operational taxonomic units (OTUs) per individual, 70% to 80% of the most dominant ones being subject-specific (Zoetendal et al. 1998; Tap et al. 2009). However, only 20% of the bacterial species have been successfully cultured so far (Eckburg et al. 2005). Large-scale analyses of genomic and metagenomic sequences have provided gene catalogs and statistical evidence on protein families involved in the predominant functions of the human gut microbiome (Gill et al. 2006; Kurokawa et al. 2007; Flint et al. 2008; Turnbaugh et al. 2009; Qin et al. 2010), among which the catabolism of dietary fibers is of particular interest in human nutrition and health. Dietary fibers are the components of vegetables, cereals, leguminous seeds, and fruits that are not digested in the stomach or in the small intestine, but are fermented in the colon by the gut microbiome and/or excreted in feces (Grabitske and Slavin 2008). Chemically, dietary fibers are mainly composed of complex plant cell wall polysaccharides and their associated lignin (Selvendran 1984), along with storage polysaccharides such as fructans and resistant starch (Institute of Medicine 2005). Dietary fibers have been identified as a strong positive dietary factor in the prevention of obesity, diabetes, and cardiovascular diseases (World Health Organization 2003). Because of the wide structural diversity of dietary fibers, the human gut bacteria produce a huge panel of carbohydrate active enzymes (CAZymes), with widely different substrate specificities, to degrade these compounds into metabolizable monosaccharides and disaccharides. The functions and the evolutionary relationships of CAZyme-encoding genes of the human gut microbiome are being extensively studied through functional and structural genomics investigations (Flint et al. 2008; Lozupone et al. 2008; Mahowald et al. 2009; Martens et al. 2009), which are nevertheless restricted to cultivated bacterial species. CAZyme diversity has also been described in three metagenomics studies focused on this microbiome (Gill et al. 2006; Turnbaugh et al. 2009, 2010), and these revealed the presence of at least 81 families of glycoside-hydrolases, making the human gut metagenome one of the richest source of CAZymes (Li et al. 2009). However, the proof of function of annotated genes issued from metagenomes still constitutes a goal for enzyme discovery. This can be addressed by functional screening of metagenomic libraries, in order to retrieve genes of interest. Numerous studies have provided conclusive evidence on the potential of such an approach for the identification of novel glycoside-hydrolases from various ecosystems such as soil (Rondon et al. 2000; Richardson et al. 2002; Voget et al. 2003; Pang et al. 2009), lakes (Rees et al. 2003), hot springs (Tang et al. 2006, 2008), rumen (Ferrer et al. 2005; Guo et al. 2008; Liu et al. 2008; Duan et al. 2009), rabbit (Feng et al. 2007), and insect guts (Brennan et al. 2004; for review, see Ferrer et al. 2009; Li et al. 2009; Simon and Daniel 2009; Uchiyama and Miyazaki 2009). In all cases, the identification of the gene responsible for the screened activity was carried out by sequencing only a few kilobases of metagenomic DNA. Collectively these studies have established an experimental proof of function for 35 glycoside hydrolases (from eight families) issued from metagenomes (data from the CAZy database; http://www.cazy.org/), a number that is very small considering the known CAZy diversity. Here, we examined the potential of high-throughput functional screening of large insert libraries to guide in-depth pyrosequencing of specific regions of the human gut metagenome that encode the enzymatic machinery involved in dietary fiber catabolism.     

Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival

Somatic missense mutations can initiate tumorogenesis and, conversely, anti-tumor cytotoxic T cell (CTL) responses. Tumor genome analysis has revealed extreme heterogeneity among tumor missense mutation profiles, but their relevance to tumor immunology and patient outcomes has awaited comprehensive evaluation. Here, for 515 patients from six tumor sites, we used RNA-seq data from The Cancer Genome Atlas to identify mutations that are predicted to be immunogenic in that they yielded mutational epitopes presented by the MHC proteins encoded by each patient’s autologous HLA-A alleles. Mutational epitopes were associated with increased patient survival. Moreover, the corresponding tumors had higher CTL content, inferred from CD8A gene expression, and elevated expression of the CTL exhaustion markers PDCD1 and CTLA4. Mutational epitopes were very scarce in tumors without evidence of CTL infiltration. These findings suggest that the abundance of predicted immunogenic mutations may be useful for identifying patients likely to benefit from checkpoint blockade and related immunotherapies.The accumulation of somatic mutations underlies the initiation and progression of most cancers by conferring upon tumor cells unrestricted proliferative capacity (Hanahan and Weinberg 2011). The analysis of cancer genomes has revealed that tumor mutational landscapes (Vogelstein et al. 2013) are extremely variable among patients, among different tumors from the same patient, and even among the different regions of a single tumor (Gerlinger et al. 2012). There is a need for personalized strategies for cancer therapy that are compatible with mutational heterogeneity, and in this regard, immune interventions that aim to initiate or enhance anti-tumor immune responses hold much promise. Therapeutic antibodies and chimeric antigen receptor (CAR) technologies have shown anti-cancer efficacy (Fox et al. 2011), but such antibody-based approaches are limited to cell surface target antigens (Slamon et al. 2001; Coiffier et al. 2002; Yang et al. 2003; Cunningham et al. 2004; Kalos et al. 2011). In contrast, most tumor mutations are point mutations in genes encoding intracellular proteins. Short peptide fragments of these proteins, after intracellular processing and presentation at the cell surface as MHC ligands, can elicit T cell immunoreactivity. Further, the presence of tumor infiltrating lymphocytes (TIL), in particular, CD8⁺ T cells, has been associated with increased survival (Sato et al. 2005; Nelson 2008; Oble et al. 2009; Yamada et al. 2010; Gooden et al. 2011; Hwang et al. 2012), suggesting that the adaptive immune system can mount protective anti-tumor responses in many cancer patients (Kim et al. 2007; Fox et al. 2011). The antigen specificities of tumor-infiltrating T cells remain almost completely undefined (Andersen et al. 2012), but there are numerous examples of cytotoxic T cells recognizing single amino acid coding changes originating from somatic tumor mutations (Lennerz et al. 2005; Matsushita et al. 2012; Heemskerk et al. 2013; Lu et al. 2013; Robbins et al. 2013; van Rooij et al. 2013; Wick et al. 2014). Thus, the notion that tumor mutations are reservoirs of exploitable neo-antigens remains compelling (Heemskerk et al. 2013). For a mutation to be recognized by CD8⁺ T cells, the mutant peptide must be presented by MHC I molecules on the surface of the tumor cell. The ability of a peptide to bind a given MHC I molecule with sufficient affinity for the peptide-MHC complex to be stabilized at the cell surface is the single most limiting step in antigen presentation and T cell activation (Yewdell and Bennink 1999). Recently, several algorithms have been developed that can predict which peptides will bind to given MHC molecules (Nielsen et al. 2003; Bui et al. 2005; Peters and Sette 2005; Vita et al. 2010; Lundegaard et al. 2011), thereby providing guidance into which mutations are immunogenic.The Cancer Genome Atlas (TCGA) (http://cancergenome.nih.gov/) is an initiative of the National Institutes of Health that has created a comprehensive catalog of somatic tumor mutations identified using deep sequencing. As a member of The Cancer Genome Atlas Research Network, our center has generated extensive tumor RNA-seq data. Here, we have used public TCGA RNA-seq data to explore the T cell immunoreactivity of somatic missense mutations across six tumor sites. This type of analysis is challenged not only by large numbers of mutations unique to individual patients, but also by the complexity of personalized antigen presentation by MHC arising from the extreme HLA allelic diversity in the outbred human population. Previous studies have explored the potential immunogenicity of tumor mutations (Segal et al. 2008; Warren and Holt 2010; Khalili et al. 2012), but these have been hampered by small sample size and the inability to specify autologous HLA restriction. Recently, we described a method of HLA calling from RNA-seq data that shows high sensitivity and specificity (Warren et al. 2012). Here, we have obtained matched tumor mutational profiles and HLA-A genotypes from TCGA subjects and used these data to predict patient-specific mutational epitope profiles. The evaluation of these data together with RNA-seq-derived markers of T cell infiltration and overall patient survival provides the first comprehensive view of the landscape of potentially immunogenic mutations in solid tumors.     

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).