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Slides for Lecture 18 in EVE 161 Course by Jonathan Eisen at UC Davis

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  • 1. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Lecture 18: EVE 161:
 Microbial Phylogenomics ! Lecture #18: Era IV: Metagenomics Case Study ! UC Davis, Winter 2014 Instructor: Jonathan Eisen !1
  • 2. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 • Next week Student Presentations • Each student gets 10 minutes total • Eight minutes to present and 2 minutes for questions • Possible presentation timing ! 2 minutes Overview and Methods ! 4 minutes R & D ! 2 minutes Conclusions and Future Ideas ! 2 minutes Questions • Contact Holly Ganz hhganz@ucdavis.edu for non Eisen guidance
  • 3. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 ARTICLES A human gut microbial gene catalogue established by metagenomic sequencing Junjie Qin1 *, Ruiqiang Li1 *, Jeroen Raes2,3 , Manimozhiyan Arumugam2 , Kristoffer Solvsten Burgdorf4 , Chaysavanh Manichanh5 , Trine Nielsen4 , Nicolas Pons6 , Florence Levenez6 , Takuji Yamada2 , Daniel R. Mende2 , Junhua Li1,7 , Junming Xu1 , Shaochuan Li1 , Dongfang Li1,8 , Jianjun Cao1 , Bo Wang1 , Huiqing Liang1 , Huisong Zheng1 , Yinlong Xie1,7 , Julien Tap6 , Patricia Lepage6 , Marcelo Bertalan9 , Jean-Michel Batto6 , Torben Hansen4 , Denis Le Paslier10 , Allan Linneberg11 , H. Bjørn Nielsen9 , Eric Pelletier10 , Pierre Renault6 , Thomas Sicheritz-Ponten9 , Keith Turner12 , Hongmei Zhu1 , Chang Yu1 , Shengting Li1 , Min Jian1 , Yan Zhou1 , Yingrui Li1 , Xiuqing Zhang1 , Songgang Li1 , Nan Qin1 , Huanming Yang1 , Jian Wang1 , Søren Brunak9 , Joel Dore´6 , Francisco Guarner5 , Karsten Kristiansen13 , Oluf Pedersen4,14 , Julian Parkhill12 , Jean Weissenbach10 , MetaHIT Consortium{, Peer Bork2 , S. Dusko Ehrlich6 & Jun Wang1,13 To understand the impact of gut microbes on human health and well-being it is crucial to assess their genetic potential. Here we describe the Illumina-based metagenomic sequencing, assembly and characterization of 3.3 million non-redundant microbial genes, derived from 576.7 gigabases of sequence, from faecal samples of 124 European individuals. The gene set, ,150 times larger than the human gene complement, contains an overwhelming majority of the prevalent (more frequent) microbial genes of the cohort and probably includes a large proportion of the prevalent human intestinal microbial genes. The genes are largely shared among individuals of the cohort. Over 99% of the genes are bacterial, indicating that the entire cohort harbours between 1,000 and 1,150 prevalent bacterial species and each individual at least 160 such species, which are also largely shared. We define and describe the minimal gut metagenome and the minimal gut bacterial genome in terms of functions present in all individuals and most bacteria, respectively. Vol 464|4 March 2010|doi:10.1038/nature08821
  • 4. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 ARTICLES A human gut microbial gene catalogue established by metagenomic sequencing Junjie Qin1 *, Ruiqiang Li1 *, Jeroen Raes2,3 , Manimozhiyan Arumugam2 , Kristoffer Solvsten Burgdorf4 , Chaysavanh Manichanh5 , Trine Nielsen4 , Nicolas Pons6 , Florence Levenez6 , Takuji Yamada2 , Daniel R. Mende2 , Junhua Li1,7 , Junming Xu1 , Shaochuan Li1 , Dongfang Li1,8 , Jianjun Cao1 , Bo Wang1 , Huiqing Liang1 , Huisong Zheng1 , Yinlong Xie1,7 , Julien Tap6 , Patricia Lepage6 , Marcelo Bertalan9 , Jean-Michel Batto6 , Torben Hansen4 , Denis Le Paslier10 , Allan Linneberg11 , H. Bjørn Nielsen9 , Eric Pelletier10 , Pierre Renault6 , Thomas Sicheritz-Ponten9 , Keith Turner12 , Hongmei Zhu1 , Chang Yu1 , Shengting Li1 , Min Jian1 , Yan Zhou1 , Yingrui Li1 , Xiuqing Zhang1 , Songgang Li1 , Nan Qin1 , Huanming Yang1 , Jian Wang1 , Søren Brunak9 , Joel Dore´6 , Francisco Guarner5 , Karsten Kristiansen13 , Oluf Pedersen4,14 , Julian Parkhill12 , Jean Weissenbach10 , MetaHIT Consortium{, Peer Bork2 , S. Dusko Ehrlich6 & Jun Wang1,13 To understand the impact of gut microbes on human health and well-being it is crucial to assess their genetic potential. Here we describe the Illumina-based metagenomic sequencing, assembly and characterization of 3.3 million non-redundant microbial genes, derived from 576.7 gigabases of sequence, from faecal samples of 124 European individuals. The gene set, ,150 times larger than the human gene complement, contains an overwhelming majority of the prevalent (more frequent) microbial genes of the cohort and probably includes a large proportion of the prevalent human intestinal microbial genes. The genes are largely shared among individuals of the cohort. Over 99% of the genes are bacterial, indicating that the entire cohort harbours between 1,000 and 1,150 prevalent bacterial species and each individual at least 160 such species, which are also largely shared. We define and describe the minimal gut metagenome and the minimal gut bacterial genome in terms of functions present in all individuals and most bacteria, respectively. Vol 464|4 March 2010|doi:10.1038/nature08821 THAT”S A LOT OF AUTHORS
  • 5. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 METHODS
  • 6. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Human faecal samples were collected, frozen immediately and DNA was purified by standard methods22. For all 124 individuals, paired-end libraries were constructed with different clone insert sizes and subjected to Illumina GA sequencing. All reads were assembled using SOAPdenovo19, with specific parameter ‘2M 3’ for metagenomics data. MetaGene was used for gene prediction. A non-redundant gene set was constructed by pair-wise comparison of all genes, using BLAT36 under the criteria of identity .95% and overlap .90%. Gene taxonomic assignments were made on the basis of BLASTP37 search (e-value ,1 3 1025) of the NCBI-NR database and 126 known gut bacteria genomes. Gene functional annotations were made by BLASTP search (e-value ,1 3 1025) with eggNOG and KEGG (v48.2) databases. The total and shared number of orthologous groups and/or gene families were computed using a random combination of n individuals (with n 5 2 to 124, 100 replicates per bin).
  • 7. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 As part of the MetaHIT (Metagenomics of the Human Intestinal Tract) project, we collected faecal specimens from 124 healthy, over- weight and obese individual human adults, as well as inflammatory bowel disease (IBD) patients, from Denmark and Spain (Supplementary Table 1)
  • 8. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 As part of the MetaHIT (Metagenomics of the Human Intestinal Tract) project, we collected faecal specimens from 124 healthy, over- weight and obese individual human adults, as well as inflammatory bowel disease (IBD) patients, from Denmark and Spain (Supplementary Table 1)
  • 9. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Supplementary Tables Table 1 | DNA sample information. All Danish individuals in the present subsample were originally recruited from a larger population-based sample of middle-aged people living in the northern part of Copenhagen region and sampled from the centralized personal number register. At the original recruitment the individuals included in the present study had normal fasting plasma glucose and normal 2 hour plasma glucose following an oral glucose tolerance test. At the time of fecal sampling all were examined in the fasting state and had non-diabetic fasting plasma glucose levels below 7,0 mmol/l. All of the IBD patients were in clinical remission at the time of fecal sampling. N refers to no IBD, CD & UC to Crohn’s disease and ulcerative colitis, respectively. Sample Name Country Gender Age BMI IBD MH0001 Denmark female 49 25.55 N MH0002 Denmark female 59 27.28 N MH0003 Denmark male 69 33.19 N MH0004 Denmark male 59 31.18 N MH0005 Denmark male 64 21.68 N MH0006 Denmark female 59 22.38 N MH0007 Denmark male 69 33.60 N MH0008 Denmark male 59 24.35 N MH0009 Denmark male 64 29.04 N MH0010 Denmark male 64 33.27 N MH0011 Denmark female 0 22.31 N MH0012 Denmark female 42 32.10 N MH0013 Denmark male 54 20.46 N MH0014 Denmark female 54 38.49 N MH0015 Denmark male 59 25.47 N MH0016 Denmark female 49 30.50 N MH0017 Denmark male 64 21.81 N MH0018 Denmark male 49 31.37 N MH0019 Denmark female 44 20.01 N MH0020 Denmark female 63 33.23 N MH0021 Denmark female 49 25.42 N MH0022 Denmark male 64 24.42 N MH0023 Denmark male 69 31.74 N MH0024 Denmark female 59 22.72 N MH0025 Denmark female 49 34.20 N MH0026 Denmark female 49 37.32 N MH0027 Denmark female 59 23.07 N MH0028 Denmark female 44 22.70 N MH0030 Denmark male 59 35.21 N MH0031 Denmark male 69 22.34 N MH0032 Denmark male 69 35.28 N MH0033 Denmark female 59 31.95 N
  • 10. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 METHODS! & ! RESULTS! (mixed)
  • 11. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Total DNA was extracted from the faecal specimens18 and an average of 4.5 Gb (ranging between 2 and 7.3 Gb) of sequence was generated for each sample, allowing us to capture most of the novelty (see Methods and Supplementary Table 2). In total, we obtained 576.7 Gb of sequence (Supplementary Table 3). !
  • 12. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Total DNA was extracted from the faecal specimens18 and an average of 4.5 Gb (ranging between 2 and 7.3 Gb) of sequence was generated for each sample, allowing us to capture most of the novelty (see Methods and Supplementary Table 2). In total, we obtained 576.7 Gb of sequence (Supplementary Table 3). !
  • 13. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Table 2 | Summary of Sanger reads. The reads were sequenced by 3730xl. Low-quality sequences at both ends with phred score less than 20 were trimmed. Very short reads with length less than 100 bp were filtered. Sample ID # Sanger reads Average length (bp) Total length (bp) MH0006 237,567 660.65 156,949,306 MH0012 230,768 670.26 154,675,458
  • 14. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Table 3 | Summary of Illumina GA reads. We constructed libraries with three different insert sizes of about 135 bp, 200 bp, and 400 bp. The insert sizes of each library were estimated by re-aligning the paired-end reads on the assembled contigs. Sample ID Paired-end insert size (bp) Read length (bp) # of reads Data (Gb) human reads, % # of high quality reads MH0047 136/378 75 35,355,400 2.65 0.18 26,932,064 MH0021 134/354 75 36,454,400 2.73 0.12 26,258,326 MH0079 135/360 75 38,011,600 2.85 0.40 27,418,899 MH0078 146/373 75 38,038,200 2.85 1.56 26,051,537 MH0052 141/367 75 39,538,000 2.97 0.08 28,575,036 MH0049 134/343 75 40,444,200 3.03 0.06 30,654,842 MH0076 134/409 75 40,697,000 3.05 0.42 30,650,106 MH0051 143/374 75 41,911,800 3.14 0.32 25,963,104 MH0048 143/349 75 42,923,600 3.22 0.26 26,972,970 O2.UC-14 141/355 75 43,343,000 3.25 0.06 26,942,750 MH0015 235 44 44,671,400 1.97 0.04 33,014,675 MH0018 233 44 45,081,400 1.98 2.14 36,609,695 MH0027 238 44 45,190,000 1.99 0.09 32,377,390 MH0017 223 44 45,557,200 2.00 0.04 36,154,362 MH0022 256 44 46,415,000 2.04 0.21 37,112,508 MH0023 237 44 48,598,400 2.14 0.04 37,782,998 MH0019 249 44 49,229,400 2.17 0.06 38,856,780 MH0026 156/398 75 49,812,000 3.74 0.05 37,484,066 MH0013 238 44 50,257,200 2.21 1.63 40,028,120 MH0005 237 44 50,704,800 2.23 0.23 39,407,333 MH0007 195 44 50,719,800 2.23 0.31 36,956,284 MH0008 219 44 51,411,000 2.26 0.10 38,156,496 V1.UC-7 141/356 75 51,911,400 3.89 14.67 36,788,540 MH0010 220 44 52,218,200 2.30 0.08 39,169,850 V1.CD-12 148/361 75 53,519,400 4.01 0.02 40,609,134 O2.UC-20 141/362 75 53,637,200 4.02 0.03 38,376,747 V1.CD-15 143/351 75 53,938,600 4.05 2.85 40,560,446 O2.UC-19 133/352 75 54,537,600 4.09 0.01 38,459,550 MH0004 218 44 55,829,800 2.46 0.95 40,288,492 MH0062 144/357 75 57,128,400 4.28 14.32 36,809,224 MH0066 147/429 75 57,234,200 4.29 0.05 36,114,997 O2.UC-21 142/362 75 57,856,000 4.34 0.03 34,832,308 V1.CD-13 139/352 75 58,145,800 4.36 0.04 42,560,831 MH0080 140/376 75 58,220,800 4.37 0.13 46,590,749 V1.UC-13 131/352 75 58,381,400 4.38 9.77 38,553,580 MH0032 142/370 75 58,822,400 3.93 0.39 50,110,067 O2.UC-12 153/384 75 58,927,800 4.42 0.12 36,908,526 MH0001 214 44 59,239,200 2.61 0.06 45,016,612 V1.UC-6 142/376 75 59,270,800 4.45 0.41 43,150,856 MH0060 142/367 75 60,156,000 4.51 0.07 41,112,227 MH0053 137/416 75 60,788,600 4.56 0.07 43,283,564 MH0002 139/370 75 61,077,000 4.58 0.15 46,570,095 O2.UC-11 142/377 75 61,253,800 4.59 0.14 38,507,042 MH0059 136/370 75 61,574,600 4.62 0.18 41,025,606
  • 15. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Wanting to generate an extensive catalogue of microbial genes from the human gut, we first assembled the short Illumina reads into longer contigs, which could then be analysed and annotated by standard methods. Using SOAPdenovo19, a de Bruijn graph-based tool specially designed for assembling very short reads, we performed de novo assembly for all of the Illumina GA sequence data. Because a high diversity between individuals is expected8,16,17, we first assembled each sample independently (Supplementary Fig. 3). As much as 42.7% of the Illumina GA reads was assembled into a total of 6.58 million contigs of a length .500 bp, giving a total contig length of 10.3 Gb, with an N50 length of 2.2 kb (Supplementary Fig. 4) and the range of 12.3 to 237.6 Mb (Supplementary Table 4). Almost 35% of reads from any one sample could be mapped to contigs from other samples, indicating the existence of a common sequence core.
  • 16. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Wanting to generate an extensive catalogue of microbial genes from the human gut, we first assembled the short Illumina reads into longer contigs, which could then be analysed and annotated by standard methods. Using SOAPdenovo19, a de Bruijn graph-based tool specially designed for assembling very short reads, we performed de novo assembly for all of the Illumina GA sequence data. Because a high diversity between individuals is expected8,16,17, we first assembled each sample independently (Supplementary Fig. 3). As much as 42.7% of the Illumina GA reads was assembled into a total of 6.58 million contigs of a length .500 bp, giving a total contig length of 10.3 Gb, with an N50 length of 2.2 kb (Supplementary Fig. 4) and the range of 12.3 to 237.6 Mb (Supplementary Table 4). Almost 35% of reads from any one sample could be mapped to contigs from other samples, indicating the existence of a common sequence core.
  • 17. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Figure 3 | Flowchart of human gut microbiome data analysis process. We performed de novo short reads assembly for each sample independently, then all the unassembled reads were pooled for another round of assembly. ORFs were predicted in each of the contig set, and were merged by removing redundancy. The non-redundant gene set was used in all further analysis.
  • 18. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Figure 4 | Length distribution of assembled contigs. The number of contigs in different length bins for each individual was computed, and the data from all 124 individuals were pooled. Boxes denote 25% and 75% percentiles, the red line corresponds to the median, and the “whiskers” indicate interquartile range from either or both ends of the box
  • 19. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Table 4 | Summary of de novo assembly results. Assembled sequences with length below 500 bp were excluded from the contig set. Sample ID # of contigs Contig N50 (bp) Total length (Mb) % reads assembled Unassembled reads (Gb) MH0001 14,301 1,618 19.69 46.34 1.06 MH0002 65,392 1,680 88.77 45.31 1.91 MH0003 68,658 2,640 119.59 54.40 1.72 MH0004 23,793 1,681 31.92 41.54 1.05 MH0005 14,339 1,684 19.62 40.22 1.04 MH0006 144,440 2,025 217.77 52.39 5.24 MH0007 28,108 1,270 32.00 29.15 1.16 MH0008 26,506 1,768 37.24 43.53 0.95 MH0009 70,014 2,440 112.96 44.14 2.45 MH0010 25,674 1,815 36.52 48.77 0.88 MH0011 86,201 2,158 134.25 46.09 2.37 MH0012 140,991 2,478 237.58 42.77 7.99 MH0013 20,495 2,332 32.20 41.22 1.05 MH0014 66,724 2,957 120.54 50.90 2.08 MH0015 25,933 1,645 34.46 35.53 0.94 MH0016 64,124 2,915 114.03 53.89 1.88 MH0017 24,948 1,679 34.06 39.57 0.96 MH0018 13,247 1,619 17.73 35.23 1.07 MH0019 28,786 1,977 41.95 46.76 0.91 MH0020 44,930 4,708 98.78 56.81 1.49 MH0021 54,101 1,608 70.67 46.49 1.06 MH0022 21,872 1,773 30.00 35.04 1.06 MH0023 16,214 2,100 25.57 35.80 1.07 MH0024 43,145 1,512 54.45 33.21 2.41 MH0025 76,287 1,968 111.48 42.24 2.11 MH0026 33,408 3,769 69.38 43.97 1.58 MH0027 20,369 985 19.72 19.96 1.14 MH0028 61,004 2,630 104.54 49.65 1.80 MH0030 39,267 2,828 66.60 36.73 2.15 MH0031 53,292 1,878 75.84 36.41 2.33 MH0032 37,287 1,921 54.46 20.72 2.60 MH0033 61,782 2,616 102.04 49.87 1.69 MH0034 24,508 2,107 37.63 14.53 2.40 MH0035 68,287 2,075 102.94 48.22 1.93 MH0036 58,690 2,330 94.35 49.44 1.81 MH0037 48,356 2,526 80.44 48.21 1.63 MH0038 50,381 2,921 90.35 47.75 1.79 MH0039 66,509 2,087 104.17 47.66 1.68 MH0040 73,068 2,225 115.15 49.53 1.68
  • 20. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 To assess the quality of the Illumina GA-based assembly we mapped the contigs of samples MH0006 and MH0012 to the Sanger reads from the same samples (Supplementary Table 2). A total of 98.7% of the contigs that map to at least one Sanger read were collinear over 99.6% of the mapped regions. This is comparable to the contigs that were generated by 454 sequencing for one of the two samples (MH0006) as a control, of which 97.9% were collinear over 99.5% of the mapped regions. We estimate assembly errors to be 14.2 and 20.7 per megabase (Mb) of Illumina- and 454-based contigs, respectively (see Methods and Supplementary Fig. 5), indicating that the short- and long-read- based assemblies have comparable accuracies.
  • 21. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 To assess the quality of the Illumina GA-based assembly we mapped the contigs of samples MH0006 and MH0012 to the Sanger reads from the same samples (Supplementary Table 2). A total of 98.7% of the contigs that map to at least one Sanger read were collinear over 99.6% of the mapped regions. This is comparable to the contigs that were generated by 454 sequencing for one of the two samples (MH0006) as a control, of which 97.9% were collinear over 99.5% of the mapped regions. We estimate assembly errors to be 14.2 and 20.7 per megabase (Mb) of Illumina- and 454-based contigs, respectively (see Methods and Supplementary Fig. 5), indicating that the short- and long-read- based assemblies have comparable accuracies.
  • 22. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Table 2 | Summary of Sanger reads. The reads were sequenced by 3730xl. Low-quality sequences at both ends with phred score less than 20 were trimmed. Very short reads with length less than 100 bp were filtered. Sample ID # Sanger reads Average length (bp) Total length (bp) MH0006 237,567 660.65 156,949,306 MH0012 230,768 670.26 154,675,458
  • 23. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Figure 5 | Validating Illumina contigs using Sanger reads. Illumina/454 contigs from samples MH0006 and MH0012 were mapped to Sanger reads from the same samples. Aligned regions were scanned for breakage of collinearity, and each unique break is counted as an error. a. number of errors per Mb of Illumina/454 contigs mapped to Sanger reads. b. percentage of collinear Illumina/454 contigs and collinear basepairs in those contigs. b
  • 24. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 To complete the contig set we pooled the unassembled reads from all 124 samples, and repeated the de novo assembly process. About 0.4 million additional contigs were thus generated, having a length of 370 Mb and an N50 length of 939 bp. The total length of our final contig set was thus 10.7 Gb. Some 80% of the 576.7 Gb of Illumina GA sequence could be aligned to the contigs at a threshold of 90% identity, allowing for accommodation of sequencing errors and strain variability in the gut (Fig. 1), almost twice the 42.7% of sequence that was assembled into contigs by SOAPdenovo, because assembly uses more stringent criteria. This indicates that a vast majority of the Illumina sequence is represented by our contigs.
  • 25. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 To complete the contig set we pooled the unassembled reads from all 124 samples, and repeated the de novo assembly process. About 0.4 million additional contigs were thus generated, having a length of 370 Mb and an N50 length of 939 bp. The total length of our final contig set was thus 10.7 Gb. Some 80% of the 576.7 Gb of Illumina GA sequence could be aligned to the contigs at a threshold of 90% identity, allowing for accommodation of sequencing errors and strain variability in the gut (Fig. 1), almost twice the 42.7% of sequence that was assembled into contigs by SOAPdenovo, because assembly uses more stringent criteria. This indicates that a vast majority of the Illumina sequence is represented by our contigs.
  • 26. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 the 90% identity threshold. A total of 70.1% and 85.9% of the reads from the Japanese and US samples, respectively, could be aligned to (the highest indicates tha Although th that the catal genes of the Each indiv genes (Supp gene pool m found in on 20%, wherea term these ‘ depth; seque catalogue ge Nevertheless harboured 2 cating that a Interestingly than the ind consistent w diversity tha Common ba Deep metag the existence 100 50 0 Assembled contig set Known human gut bacteria GenBank bacteria Coverageofsequencingreads(%) Figure 1 | Coverage of human gut microbiome. The three human microbial sequencing read sets—Illumina GA reads generated from 124 individuals in this study (black; n 5 124), Roche/454 reads from 18 human twins and their mothers (grey; n 5 18) and Sanger reads from 13 Japanese individuals (white; n 5 13)—were aligned to each of the reference sequence sets. Mean values 6 s.e.m. are plotted. 60
  • 27. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 To compare the representation of the human gut microbiome in our contigs with that from previous work, we aligned them to the reads from the two largest published gut metagenome studies (1.83Gb of Roche/454 sequencing reads from 18 US adults8, and 0.79 Gb of Sanger reads from 13 Japanese adults and infants17), using the 90% identity threshold. A total of 70.1% and 85.9% of the reads from the Japanese and US samples, respectively, could be aligned to our contigs (Fig. 1), showing that the contigs include a high fraction of sequences from previous studies. In contrast, 85.7% and 69.5% of our contigs were not covered by the reads from the Japanese and US samples, respectively, highlighting the novelty we captured. !
  • 28. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 To compare the representation of the human gut microbiome in our contigs with that from previous work, we aligned them to the reads from the two largest published gut metagenome studies (1.83Gb of Roche/454 sequencing reads from 18 US adults8, and 0.79 Gb of Sanger reads from 13 Japanese adults and infants17), using the 90% identity threshold. A total of 70.1% and 85.9% of the reads from the Japanese and US samples, respectively, could be aligned to our contigs (Fig. 1), showing that the contigs include a high fraction of sequences from previous studies. In contrast, 85.7% and 69.5% of our contigs were not covered by the reads from the Japanese and US samples, respectively, highlighting the novelty we captured. !
  • 29. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 the 90% identity threshold. A total of 70.1% and 85.9% of the reads from the Japanese and US samples, respectively, could be aligned to (the highest indicates tha Although th that the catal genes of the Each indiv genes (Supp gene pool m found in on 20%, wherea term these ‘ depth; seque catalogue ge Nevertheless harboured 2 cating that a Interestingly than the ind consistent w diversity tha Common ba Deep metag the existence 100 50 0 Assembled contig set Known human gut bacteria GenBank bacteria Coverageofsequencingreads(%) Figure 1 | Coverage of human gut microbiome. The three human microbial sequencing read sets—Illumina GA reads generated from 124 individuals in this study (black; n 5 124), Roche/454 reads from 18 human twins and their mothers (grey; n 5 18) and Sanger reads from 13 Japanese individuals (white; n 5 13)—were aligned to each of the reference sequence sets. Mean values 6 s.e.m. are plotted. 60
  • 30. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Only 31.0–48.8% of the reads from the two previous studies and the present study could be aligned to 194 public human gut bacterial genomes (Supplementary Table 5), and 7.6–21.2% to the bacterial genomes deposited in GenBank (Fig. 1). This indicates that the reference gene set obtained by sequencing genomes of isolated bac- terial strains is still of a limited scale.
  • 31. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Only 31.0–48.8% of the reads from the two previous studies and the present study could be aligned to 194 public human gut bacterial genomes (Supplementary Table 5), and 7.6–21.2% to the bacterial genomes deposited in GenBank (Fig. 1). This indicates that the reference gene set obtained by sequencing genomes of isolated bacterial strains is still of a limited scale.
  • 32. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 the 90% identity threshold. A total of 70.1% and 85.9% of the reads from the Japanese and US samples, respectively, could be aligned to (the highest indicates tha Although th that the catal genes of the Each indiv genes (Supp gene pool m found in on 20%, wherea term these ‘ depth; seque catalogue ge Nevertheless harboured 2 cating that a Interestingly than the ind consistent w diversity tha Common ba Deep metag the existence 100 50 0 Assembled contig set Known human gut bacteria GenBank bacteria Coverageofsequencingreads(%) Figure 1 | Coverage of human gut microbiome. The three human microbial sequencing read sets—Illumina GA reads generated from 124 individuals in this study (black; n 5 124), Roche/454 reads from 18 human twins and their mothers (grey; n 5 18) and Sanger reads from 13 Japanese individuals (white; n 5 13)—were aligned to each of the reference sequence sets. Mean values 6 s.e.m. are plotted. 60
  • 33. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 A gene catalogue of the human gut microbiome To establish a non-redundant human gut microbiome gene set we first used the MetaGene20 program to predict ORFs in our contigs and found 14,048,045 ORFs longer than 100bp (Supplementary Table 6). They occupied 86.7% of the contigs, comparable to the value found for fully sequenced genomes (,86%). Two-thirds of the ORFs appeared incomplete, possibly due to the size of our contigs (N50 of 2.2 kb). We next removed the redundant ORFs, by pair-wise comparison, using a very stringent criterion of 95% identity over 90% of the shorter ORF length, which can fuse orthologues but avoids inflation of the data set due to possible sequencing errors (see Methods). Yet, the final non-redundant gene set contained as many as 3,299,822 ORFs with an average length of 704 bp (Supplementary Table 7).
  • 34. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 A gene catalogue of the human gut microbiome To establish a non-redundant human gut microbiome gene set we first used the MetaGene20 program to predict ORFs in our contigs and found 14,048,045 ORFs longer than 100bp (Supplementary Table 6). They occupied 86.7% of the contigs, comparable to the value found for fully sequenced genomes (,86%). Two-thirds of the ORFs appeared incomplete, possibly due to the size of our contigs (N50 of 2.2 kb). We next removed the redundant ORFs, by pair-wise comparison, using a very stringent criterion of 95% identity over 90% of the shorter ORF length, which can fuse orthologues but avoids inflation of the data set due to possible sequencing errors (see Methods). Yet, the final non-redundant gene set contained as many as 3,299,822 ORFs with an average length of 704 bp (Supplementary Table 7).
  • 35. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Table 7 | Non-redundant genes. Genes were compared at 95 % identity cut-off. Those that were overlapped over 90% length were considered redundant and removed. Common and rare genes were present in >50% and < 20% of individuals, respectively. # of genes Total length (bp) Mean length (bp) Non-redundant gene set 3,299,822 2,323,171,095 704.03 Common 294,110 292,960,308 996.09 Rare 2,375,655 1,510,527,924 635.84
  • 36. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 We term the genes of the non-redundant set ‘prevalent genes’, as they are encoded on contigs assembled from the most abundant reads (see Methods). The minimal relative abundance of the prevalent genes was , 631027, as estimated from the minimum sequence coverage of the unique genes (close to 3), and the total Illumina sequence length generated for each individual (on average, 4.5 Gb), assuming the average gene length of 0.85 kb (that is, 3 3 0.85 3 103/ 4.5 3 109).
  • 37. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 We term the genes of the non-redundant set ‘prevalent genes’, as they are encoded on contigs assembled from the most abundant reads (see Methods). The minimal relative abundance of the prevalent genes was , 63x-7, as estimated from the minimum sequence coverage of the unique genes (close to 3), and the total Illumina sequence length generated for each individual (on average, 4.5 Gb), assuming the average gene length of 0.85 kb (that is, 3 3 0.85 3 103/ 4.5 3 109).
  • 38. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 We mapped the 3.3 million gut ORFs to the 319,812 genes (target genes) of the 89 frequent reference microbial genomes in the human gut. At a 90% identity threshold, 80% of the target genes had at least 80% of their length covered by a single gut ORF (Fig. 2b). This indicates that the gene set includes most of the known human gut bacterial genes.
  • 39. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 We mapped the 3.3 million gut ORFs to the 319,812 genes (target genes) of the 89 frequent reference microbial genomes in the human gut. At a 90% identity threshold, 80% of the target genes had at least 80% of their length covered by a single gut ORF (Fig. 2b). This indicates that the gene set includes most of the known human gut bacterial genes.
  • 40. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 We examined the number of prevalent genes identified across all individuals as a function of the extent of sequencing, demanding at least two supporting reads for a gene call (Fig. 2a). The incidence- based coverage richness estimator (ICE), determined at 100 individuals (the highest number the EstimateS21 program could accommodate), indicates that our catalogue captures 85.3% of the prevalent genes. Although this is probably an underestimate, it nevertheless indicates that the catalogue contains an overwhelming majority of the prevalent genes of the cohort.
  • 41. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 We examined the number of prevalent genes identified across all individuals as a function of the extent of sequencing, demanding at least two supporting reads for a gene call (Fig. 2a). The incidence- based coverage richness estimator (ICE), determined at 100 individuals (the highest number the EstimateS21 program could accommodate), indicates that our catalogue captures 85.3% of the prevalent genes. Although this is probably an underestimate, it nevertheless indicates that the catalogue contains an overwhelming majority of the prevalent genes of the cohort.
  • 42. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014the cohort. For this purpose, we used a non-redundant set of 650 A complex pattern of species relatedness, character Relative abundance Blautia hansenii Clostridium scindens Enterococcus faecalis TX0104 Clostridium asparagiforme Bacteroides fragilis 3_1_12 Bacteroides intestinalis Ruminococcus gnavus Anaerotruncus colihominis Bacteroides pectinophilus Clostridium nexile Clostridium sp. L2−50 Parabacteroides johnsonii Bacteroides finegoldii Butyrivibrio crossotus Bacteroides eggerthii Clostridium sp. M62 1 Coprococcus eutactus Bacteroides stercoris Holdemania filiformis Clostridium leptum Streptococcus thermophilus LMD−9 Bacteroides capillosus Subdoligranulum variabile Ruminococcus obeum A2−162 Bacteroides dorei Eubacterium ventriosum Bacteroides sp. D4 Bacteroides sp. D1 Coprococcus comes SL7 1 Bacteriodes xylanisolvens XB1A Eubacterium rectale M104 1 Bacteroides sp. 2_2_4 Bacteroides sp. 4_3_47FAA Bacteroides ovatus Bacteroides sp. 9_1_42FAA Parabacteroides distasonis ATCC 8503 Eubacterium siraeum 70 3 Bacteroides sp. 2_1_7 Roseburia intestinalis M50 1 Bacteroides vulgatus ATCC 8482 Dorea formicigenerans Collinsella aerofaciens Ruminococcus lactaris Faecalibacterium prausnitzii SL3 3 Ruminococcus sp. SR1 5 Unknown sp. SS3 4 Ruminococcus torques L2−14 Eubacterium hallii Bacteroides thetaiotaomicron VPI−5482 Clostridium sp. SS2−1 Bacteroides caccae Ruminococcus bromii L2−63 Dorea longicatena Parabacteroides merdae Alistipes putredinis Bacteroides uniformis –4 –3 Figure 3 | Relative abundance of 57 frequent microbial ge individuals of the cohort. See Fig. 2c for definition of box See Methods for computation. 1 Number of individuals sampled Numberoforthologousgroups/genefamilies(×103 ) 25 50 75 100 124 a b c 320,000 280, 000 240,000 200,000 160,000 1.0 0.8 0.6 0.4 0.2 0 0.6 0.7 0.8 0.9 1.0 0.5 85% 90% 95% 0 5 10 15 20 0 1 2 3 4 1 20 40 60 80 100 OGs + novel gene families Known + unknown OGs Known OGs Numberofnon-redundant genes(×106 ) Numberoftargetgenescovered Fractionoftargetgenes covered Number of samples Fraction of gene length covered Figure 2 | Predicted ORFs in the human gut microbiome. a, Number of uniquegenes as a function ofthe extent ofsequencing. The gene accumulation curve corresponds to the Sobs (Mao Tau) values (number of observed genes), calculated using EstimateS21 (version 8.2.0) on randomly chosen 100 samples (due to memory limitation). b, Coverage of genes from 89 frequent gut microbial species (Supplementary Table 12). c, Number of functions captured by number of samples investigated, based on known (well characterized) orthologous groups (OGs; bottom), known plus unknown orthologous groups (including, for example, putative, predicted, conserved hypothetical functions; middle) and orthologous groups plus novel gene families (.20 proteins) recovered from the metagenome (top). Boxes denote the interquartile range (IQR) between the first and third quartiles (25th and 75th percentiles, respectively) and the line inside denotes the median. Whiskers denote the lowest and highest values within 1.5 times IQR from the first and third quartiles, respectively. Circles denote outliers beyond the whiskers. NATURE|Vol 464|4 March 2010
  • 43. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 ) a b c 0. 0. 0. 0. 1. 0. 20 0 1 2 3 4 1 20 40 60 80 100 Numberofnon-redundant genes(×106 ) Fractionoftargetgenes covered Number of samples
  • 44. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Each individual carried 536,112 ±12,167 (mean 6 s.e.m.) prevalent genes (Supplementary Fig. 6b), indicating that most of the 3.3 million gene pool must be shared. However, most of the prevalent genes were found in only a few individuals: 2,375,655 were present in less than 20%, whereas 294,110 were found in at least 50% of individuals (we term these ‘common’ genes). These values depend on the sampling depth; sequencing of MH0006 and MH0012 revealed more of the catalogue genes, present at a low abundance (Supplementary Fig. 7). Nevertheless, even at our routine sampling depth, each individual harboured 204,05663,603 (mean ± s.e.m.) common genes, indi- cating that about 38% of an individual’s total gene pool is shared. Interestingly, the IBD patients harboured, on average, 25% fewer genes than the individuals not suffering from IBD (Supplementary Fig. 8), consistent with the observation that the former have lower bacterial diversity than the latter22.
  • 45. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Each individual carried 536,112 ±12,167 (mean 6 s.e.m.) prevalent genes (Supplementary Fig. 6b), indicating that most of the 3.3 million gene pool must be shared. However, most of the prevalent genes were found in only a few individuals: 2,375,655 were present in less than 20%, whereas 294,110 were found in at least 50% of individuals (we term these ‘common’ genes). These values depend on the sampling depth; sequencing of MH0006 and MH0012 revealed more of the catalogue genes, present at a low abundance (Supplementary Fig. 7). Nevertheless, even at our routine sampling depth, each individual harboured 204,056 ± 3,603 (mean ± s.e.m.) common genes, indi- cating that about 38% of an individual’s total gene pool is shared. Interestingly, the IBD patients harboured, on average, 25% fewer genes than the individuals not suffering from IBD (Supplementary Fig. 8), consistent with the observation that the former have lower bacterial diversity than the latter22.
  • 46. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Figure 7 | Number of unique genes identified with increase of sequencing depth in sample MH0006 and MH0012.
  • 47. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Figure 8 | Distribution of nonredundant bacterial genes in IBD patients and healthy controls. The proportion of individuals having a given number of genes (classes of 100 thousand genes were used) is shown.The average gene number for IBD patients and individuals not suffering from IBD was425,397 + 126,685 (s.d.; n=25) and 564,070 + 121,962 (s.d.; n=99), respectively; p<10-6 (one-tailed Student t test).
  • 48. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Common bacterial core Deep metagenomic sequencing provides the opportunity to explore the existence of a common set of microbial species (common core) in the cohort. For this purpose, we used a non-redundant set of 650 sequenced bacterial and archaeal genomes (see Methods). We aligned the Illumina GA reads of each human gut microbial sample onto the genome set, using a 90% identity threshold, and determined the proportion of the genomes covered by the reads that aligned onto only a single position in the set. At a 1% coverage, which for a typical gut bacterial genome corresponds to an average length of about 40 kb, some 25-fold more than that of the 16S gene generally used for species identification, we detected 18 species in all individuals, 57 in ≥90% and 75 in ≥50% of individuals (Supplementary Table 8). At 10% coverage, requiring ,10-fold higher abundance in a sample, we still found 13 of the above species in ≥90% of individuals and 35 in ≥50%.
  • 49. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Common bacterial core Deep metagenomic sequencing provides the opportunity to explore the existence of a common set of microbial species (common core) in the cohort. For this purpose, we used a non-redundant set of 650 sequenced bacterial and archaeal genomes (see Methods). We aligned the Illumina GA reads of each human gut microbial sample onto the genome set, using a 90% identity threshold, and determined the proportion of the genomes covered by the reads that aligned onto only a single position in the set. At a 1% coverage, which for a typical gut bacterial genome corresponds to an average length of about 40 kb, some 25-fold more than that of the 16S gene generally used for species identification, we detected 18 species in all individuals, 57 in ≥90% and 75 in ≥50% of individuals (Supplementary Table 8). At 10% coverage, requiring ,10- fold higher abundance in a sample, we still found 13 of the above species in ≥90% of individuals and 35 in ≥50%.
  • 50. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 When the cumulated sequence length increased from 3.96 Gb to 8.74 Gb and from 4.41 Gb to 11.6 Gb, for samples MH0006 and MH0012, respectively, the number of strains common to the two at the 1% coverage threshold increased by 25%, from 135 to 169. This indicates the existence of a significantly larger common core than the one we could observe at the sequence depth routinely used for each individual.
  • 51. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 When the cumulated sequence length increased from 3.96 Gb to 8.74 Gb and from 4.41 Gb to 11.6 Gb, for samples MH0006 and MH0012, respectively, the number of strains common to the two at the 1% coverage threshold increased by 25%, from 135 to 169. This indicates the existence of a significantly larger common core than the one we could observe at the sequence depth routinely used for each individual.
  • 52. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 The variability of abundance of microbial species in individuals can greatly affect identification of the common core. To visualize this variability, we compared the number of sequencing reads aligned to different genomes across the individuals of our cohort. Even for the most common 57 species present in ≥90% of individuals with genome coverage .1% (Supplementary Table 8), the inter-individual variability was between 12- and 2,187-fold (Fig. 3). As expected10,23, Bacteroidetes and Firmicutes had the highest abundance.
  • 53. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 The variability of abundance of microbial species in individuals can greatly affect identification of the common core. To visualize this variability, we compared the number of sequencing reads aligned to different genomes across the individuals of our cohort. Even for the most common 57 species present in ≥90% of individuals with genome coverage .1% (Supplementary Table 8), the inter-individual variability was between 12- and 2,187-fold (Fig. 3). As expected10,23, Bacteroidetes and Firmicutes had the highest abundance.
  • 54. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014pose, we used a non-redundant set of 650 A complex pattern of species relatedness, characterized by clusters Relative abundance (log10 ) Blautia hansenii Clostridium scindens Enterococcus faecalis TX0104 Clostridium asparagiforme Bacteroides fragilis 3_1_12 Bacteroides intestinalis Ruminococcus gnavus Anaerotruncus colihominis Bacteroides pectinophilus Clostridium nexile Clostridium sp. L2−50 Parabacteroides johnsonii Bacteroides finegoldii Butyrivibrio crossotus Bacteroides eggerthii Clostridium sp. M62 1 Coprococcus eutactus Bacteroides stercoris Holdemania filiformis Clostridium leptum Streptococcus thermophilus LMD−9 Bacteroides capillosus Subdoligranulum variabile Ruminococcus obeum A2−162 Bacteroides dorei Eubacterium ventriosum Bacteroides sp. D4 Bacteroides sp. D1 Coprococcus comes SL7 1 Bacteriodes xylanisolvens XB1A Eubacterium rectale M104 1 Bacteroides sp. 2_2_4 Bacteroides sp. 4_3_47FAA Bacteroides ovatus Bacteroides sp. 9_1_42FAA Parabacteroides distasonis ATCC 8503 Eubacterium siraeum 70 3 Bacteroides sp. 2_1_7 Roseburia intestinalis M50 1 Bacteroides vulgatus ATCC 8482 Dorea formicigenerans Collinsella aerofaciens Ruminococcus lactaris Faecalibacterium prausnitzii SL3 3 Ruminococcus sp. SR1 5 Unknown sp. SS3 4 Ruminococcus torques L2−14 Eubacterium hallii Bacteroides thetaiotaomicron VPI−5482 Clostridium sp. SS2−1 Bacteroides caccae Ruminococcus bromii L2−63 Dorea longicatena Parabacteroides merdae Alistipes putredinis Bacteroides uniformis –4 –3 –2 –1 Figure 3 | Relative abundance of 57 frequent microbial genomes among individuals of the cohort. See Fig. 2c for definition of box and whisker plot. See Methods for computation. Number of individuals sampled 50 75 100 124 b 320,000 280, 000 240,000 200,000 160,000 1.0 0.8 0.6 0.4 0.2 0 0.6 0.7 0.8 0.9 1.0 0.5 85% 90% 95% 80 100 OGs + novel gene families Known + unknown OGs Known OGs Numberoftargetgenescovered Fractionoftargetgenes covered Fraction of gene length covered n the human gut microbiome. a, Number of fthe extent ofsequencing. The gene accumulation bs (Mao Tau) values (number of observed genes), (version 8.2.0) on randomly chosen 100 samples . b, Coverage of genes from 89 frequent gut ntary Table 12). c, Number of functions captured tigated, based on known (well characterized) ottom), known plus unknown orthologous ple, putative, predicted, conserved hypothetical ologous groups plus novel gene families (.20 e metagenome (top). Boxes denote the tween the first and third quartiles (25th and 75th d the line inside denotes the median. Whiskers st values within 1.5 times IQR from the first and Circles denote outliers beyond the whiskers. 2010 ARTICLES
  • 55. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 A complex pattern of species relatedness, characterized by clusters at the genus and family levels, emerges from the analysis of the net- work based on the pair-wise Pearson correlation coefficients of 155 species present in at least one individual at$ ≥1% coverage (Supplementary Fig. 9). Prominent clusters include some of the most abundant gut species, such as members of the Bacteroidetes and Dorea/Eubacterium/ Ruminococcus groups and also bifidobacteria, Proteobacteria and streptococci/lactobacilli groups. These observa- tions indicate that similar constellations of bacteria may be present in different individuals of our cohort, for reasons that remain to be established.
  • 56. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 A complex pattern of species relatedness, characterized by clusters at the genus and family levels, emerges from the analysis of the network based on the pair-wise Pearson correlation coefficients of 155 species present in at least one individual at ≥1% coverage (Supplementary Fig. 9). Prominent clusters include some of the most abundant gut species, such as members of the Bacteroidetes and Dorea/Eubacterium/Ruminococcus groups and also bifidobacteria, Proteobacteria and streptococci/ lactobacilli groups. These observa- tions indicate that similar constellations of bacteria may be present in different individuals of our cohort, for reasons that remain to be established.
  • 57. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Figure 9 | Relations between the most abundant bacterial species. The network was deduced from the analysis of 155 bacterial species present in at least 1 individual at a genome coverage of 1%. Size of the nodes (circles) indicates species abundance over the cohort, width of the edges (lines connecting the circles) indicates the value of the Pearson correlation coefficient (only the 342 values above 0.4 or below -0.4 out of a total of 11,935 were used for the network).
  • 58. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 The above result indicates that the Illumina-based bacterial profiling should reveal differences between the healthy individuals and patients. To test this hypothesis we compared the IBD patients and healthy controls (Supplementary Table 1), as it was previously reported that the two have different microbiota22. The principal component analysis, based on the same 155 species, clearly separates patients from healthy individuals and the ulcerative colitis from the Crohn’s disease patients (Fig. 4), confirming our hypothesis.
  • 59. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 The above result indicates that the Illumina-based bacterial profiling should reveal differences between the healthy individuals and patients. To test this hypothesis we compared the IBD patients and healthy controls (Supplementary Table 1), as it was previously reported that the two have different microbiota22. The principal component analysis, based on the same 155 species, clearly separates patients from healthy individuals and the ulcerative colitis from the Crohn’s disease patients (Fig. 4), confirming our hypothesis.
  • 60. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Almost all (99.96%) of the phylogenetically assigned genes belonged were within t This suggests t (Supplementa functions imp We found t required in all 40 30 20 10 0 Cluster(%) 1 Figure 5 | Clust were ranked by length and copy clusters with th groups of 100 c that contains 86 • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Healthy Crohn’s disease Ulcerative colitis P value: 0.031 PC2 PC1 Figure 4 | Bacterial species abundance differentiates IBD patients and healthy individuals. Principal component analysis with health status as instrumental variables, based on the abundance of 155 species with $1% genome coverage by the Illumina reads in at least 1 individual of the cohort, was carried out with 14 healthy individuals and 25 IBD patients (21 ulcerative colitis and 4 Crohn’s disease) from Spain (Supplementary Table 1). Two first components (PC1 and PC2) were plotted and represented 7.3% of whole inertia. Individuals (represented by points) were clustered and centre of gravity computed for each class; P-value of the link between health status and species abundance was assessed using a Monte-Carlo test (999 replicates). ARTICLES
  • 61. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Functions encoded by the prevalent gene set We classified the predicted genes by aligning them to the integrated NCBI-NR database of non- redundant protein sequences, the genes in the KEGG (Kyoto Encyclopedia of Genes and Genomes)24 pathways, and COG (Clusters of Orthologous Groups)25 and eggNOG26 data- bases. There were 77.1% genes classified into phylotypes, 57.5% to eggNOG clusters, 47.0% to KEGG orthology and 18.7% genes assigned to KEGG pathways, respectively (Supplementary Table 9). Almost all (99.96%) of the phylogenetically assigned genes belonged to the Bacteria and Archaea, reflecting their predominance in the gut. Genes that were not mapped to orthologous groups were clustered into gene families (see Methods). To investigate the functional con- tent of the prevalent gene set we computed the total number of orthologous groups and/or gene families present in any combination of n individuals (with n 5 2–124; see Fig. 2c). This rarefaction ana- lysis shows that the ‘known’ functions (annotated in eggNOG or KEGG) quickly saturate (a value of 5,569 groups was observed): when sampling any subset of 50 individuals, most have been detected. However, three-quarters of the prevalent gut functionalities consists of uncharacterized orthologous groups and/or completely novel gene families (Fig. 2c). When including these groups, the rarefaction curve only starts to plateau at the very end, at a much higher level (19,338 groups were detected), confirming that the extensive sampling of a large number of individuals was necessary to capture this considerable amount of novel/unknown functionality.
  • 62. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Functions encoded by the prevalent gene set We classified the predicted genes by aligning them to the integrated NCBI-NR database of non- redundant protein sequences, the genes in the KEGG (Kyoto Encyclopedia of Genes and Genomes)24 pathways, and COG (Clusters of Orthologous Groups)25 and eggNOG26 data- bases. There were 77.1% genes classified into phylotypes, 57.5% to eggNOG clusters, 47.0% to KEGG orthology and 18.7% genes assigned to KEGG pathways, respectively (Supplementary Table 9). Almost all (99.96%) of the phylogenetically assigned genes belonged to the Bacteria and Archaea, reflecting their predominance in the gut. Genes that were not mapped to orthologous groups were clustered into gene families (see Methods). To investigate the functional con- tent of the prevalent gene set we computed the total number of orthologous groups and/or gene families present in any combination of n individuals (with n 5 2–124; see Fig. 2c). This rarefaction ana- lysis shows that the ‘known’ functions (annotated in eggNOG or KEGG) quickly saturate (a value of 5,569 groups was observed): when sampling any subset of 50 individuals, most have been detected. However, three-quarters of the prevalent gut functionalities consists of uncharacterized orthologous groups and/or completely novel gene families (Fig. 2c). When including these groups, the rarefaction curve only starts to plateau at the very end, at a much higher level (19,338 groups were detected), confirming that the extensive sampling of a large number of individuals was necessary to capture this considerable amount of novel/unknown functionality.
  • 63. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 1 Number of individuals sampled Numberoforthologousgroups/genefamilies(×103 ) 25 50 75 100 124 c 160,000 1.0 0.8 0.6 0.4 0.2 0 0.5 95% 0 5 10 15 20 0 1 20 40 60 80 100 OGs + novel gene families Known + unknown OGs Known OGs Nu covered Fra Number of samples Fraction of gene length covered Figure 2 | Predicted ORFs in the human gut microbiome. a, Number of
  • 64. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Bacterial functions important for life in the gut The extensive non-redundant catalogue of the bacterial genes from the human intestinal tract provides an opportunity to identify bacterial functions important for life in this environment. There are functions necessary for a bacterium to thrive in a gut context (that is, the ‘minimal gut genome’) and those involved in the homeostasis of the whole ecosystem, encoded across many species (the ‘minimal gut metagenome’). The first set of functions is expected to be present in most or all gut bacterial species; the second set in most or all individuals’ gut samples.
  • 65. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 To identify the functions encoded by the minimal gut genome we use the fact that they should be present in most or all gut bacterial species and therefore appear in the gene catalogue at a frequency above that of the functions present in only some of the gut bacterial species. The relative frequency of different functions can be deduced from the number of genes recruited to different eggNOG clusters, after normalization for gene length and copy number (Supplementary Fig. 10a, b). We ranked all the clusters by gene frequencies and determined the range that included the clusters specifying well- known essential bacterial functions, such as those determined experi-mentally for a well- studied firmicute, Bacillus subtilis27, hypothesizing that additional clusters in this range are equally important. As expected, the range that included most of B. subtilis essential clusters (86%) was at the very top of the ranking order (Fig. 5). Some 76% of the clusters with essential genes of Escherichia coli28 were within this range, confirming the validity of our approach. This suggests that 1,244 metagenomic clusters found within the range (Supplementary Table 10; termed ‘range clusters’ hereafter) specify functions important for life in the gut.
  • 66. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 To identify the functions encoded by the minimal gut genome we use the fact that they should be present in most or all gut bacterial species and therefore appear in the gene catalogue at a frequency above that of the functions present in only some of the gut bacterial species. The relative frequency of different functions can be deduced from the number of genes recruited to different eggNOG clusters, after normalization for gene length and copy number (Supplementary Fig. 10a, b). We ranked all the clusters by gene frequencies and determined the range that included the clusters specifying well- known essential bacterial functions, such as those determined experi-mentally for a well- studied firmicute, Bacillus subtilis27, hypothesizing that additional clusters in this range are equally important. As expected, the range that included most of B. subtilis essential clusters (86%) was at the very top of the ranking order (Fig. 5). Some 76% of the clusters with essential genes of Escherichia coli28 were within this range, confirming the validity of our approach. This suggests that 1,244 metagenomic clusters found within the range (Supplementary Table 10; termed ‘range clusters’ hereafter) specify functions important for life in the gut.
  • 67. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 40 30 20 10 0 Cluster(%) 1 2,001 4,001 6,001 8,001 10,001 Cluster rank Range Figure 5 | Clusters that contain the B. subtilis essential genes. The clusters were ranked by the number of genes they contain, normalized by average length and copy number (see Supplementary Fig. 10), and the proportion of clusters with the essential B. subtilis genes was determined for successive groups of 100 clusters. Range indicates the part of the cluster distribution that contains 86% of the B. subtilis essential genes. and us as $1% cohort, cerative Two first NATURE|Vol 464|4 March 2010
  • 68. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 We found two types of functions among the range clusters: those required in all bacteria (housekeeping) and those potentially specific for the gut. Among many examples of the first category are the functions that are part of main metabolic pathways (for example, central carbon metabolism, amino acid synthesis), and important protein complexes (RNA and DNA polymerase, ATP synthase, general secretory apparatus). Not surprisingly, projection of the range clusters on the KEGG metabolic pathways gives a highly integrated picture of the global gut cell metabolism (Fig. 6a). The putative gut-specific functions include those involved in adhe- sion to the host proteins (collagen, fibrinogen, fibronectin) or in harvesting sugars of the globoseries glycolipids, which are carried on blood and epithelial cells. Furthermore, 15% of range clusters encode functions that are present in ,10% of the eggNOG genomes (see Supplementary Fig. 11) and are largely (74.3%) not defined (Fig. 6b). Detailed studies of these should lead to a deeper compre- hension of bacterial life in the gut.
  • 69. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 We found two types of functions among the range clusters: those required in all bacteria (housekeeping) and those potentially specific for the gut. Among many examples of the first category are the functions that are part of main metabolic pathways (for example, central carbon metabolism, amino acid synthesis), and important protein complexes (RNA and DNA polymerase, ATP synthase, general secretory apparatus). Not surprisingly, projection of the range clusters on the KEGG metabolic pathways gives a highly integrated picture of the global gut cell metabolism (Fig. 6a). The putative gut-specific functions include those involved in adhe-sion to the host proteins (collagen, fibrinogen, fibronectin) or in harvesting sugars of the globoseries glycolipids, which are carried on blood and epithelial cells. Furthermore, 15% of range clusters encode functions that are present in ,10% of the eggNOG genomes (see Supplementary Fig. 11) and are largely (74.3%) not defined (Fig. 6b). Detailed studies of these should lead to a deeper comprehension of bacterial life in the gut.
  • 70. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 c a b map00565 map00350 map00629 resistance β-Lactam map00643 map00620 map00780 map00260 map00940 map00512 map00670 map00513 map00220 map00628 Limonene and pinene degradation map00040 map00632 map00563 map00920 biosynthesis II Alkaloid map00600 map00625 map00400 map00941 map00520 map00790 map00330 map00621 map00271 map00591 map00072 map00480 map00031 map00460 map00910 map00604 map00631 map00900 map00010 map00331 map00290 map00240 map00300 map00561 map00196 map00053 map00071 map00660 map00860 map00440 map00601 Tetracycline biosynthesis map00641 map00642 map00750 map00710 map00195 map00251 map00052 map00531 map00051 map00410 map00540 map00140 map00120 map00252 map00380 map00627 biosynthesis Penicillins and cephalosporins map00830 map00623 Monoterpenoid biosynthesis map00360 map00472 map00562 map00530 map00650 map00770 map00062 map00640 map00730 map00473 map00130 map00760 map00950 map00510 map00272 map00622 map00363 map00680 Diterpenoid biosynthesis Streptomycin biosynthesis map00340 map00791 map00564 map00020 map00500 map00720 map00362 map00310 map00230 map00550 map00630 map00603 map00471 map00901 map00602 map00590 map00351 map00626 map00030 map00534 map00532 map00190 map00740 map00430 map00624 map00061 map00150 biosynthesis Novobiocin map00280 map00906 map00100 map00361 map00930 map00450 Carbohydrate metabolism and metabolism Glycan biosynthesis metabolism Amino acid metabolism Energy Lipid metabolism xenobiotics Biodegradation of Metabolism of other amino acids metabolism Nucleotide Metabolism of cofactors and vitamins Biosynthesis of secondary metabolites 0.0% 10.0% 20.0% 30.0% General function - R Translation - J Amino acids - E DNA - L Unknown - S Envelope - M Carbohydrates - G Energy - C Transcription - K Coenzymes - H Nucleotides - F Inorganic - P Protein turnover - O Lipids - I Signal transduction - T Secretion - U Cell cycle - D Defence- V Second metabolites - Q Cell motility - N RNA - A Chromatin - B Extracellular - W Nuclear structure - Y Cytoskeleton - Z Rare minimal Rare minimal Frequent min Frequent min Projection of the minimal gut genome on the KEGG pathways using the iPath tool38. b, Functional composition of the minimal gut genome and metagenome. Rare and frequent refer to the presence in sequenced eggNOG genomes. c, Estimation of the minimal gut metagenome size. Known orthologous groups (red), known plus unknown orthologous groups (blue) and orthologous groups plus novel gene families (>20 proteins; grey) are shown (see Fig. 2c for definition of box and whisker plot). The inset shows composition of the gut minimal microbiome. Large circle: classification in the minimal metagenome according to orthologous group occurrence in STRING739 bacterial genomes. Common (25%), uncommon (35%) and rare (45%) refer to functions that are present in >50%, <50% but >10%, and <10% of STRING bacteria genomes, respectively. Small circle: composition of the rare orthologous groups. Unknown (80%) have no annotation or are poorly characterized, whereas known bacterial (19%) and phage-related (1%) orthologous groups have functional description.
  • 71. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 b map00350 resistance β-Lactam map00780 map00260 map00670 map00220 map00628 map00632 biosynthesis II Alkaloid map00400 map00790 map00330 map00271 map00910 map00290 map00240 map00300 map00860 Tetracycline biosynthesis map00750 map00251 map00380 biosynthesis Penicillins and cephalosporins map00830 map00623 map00360 map00472 map00770 map00730 map00130 map00760 map00950 map00791 map00362 map00310 map00230 map00351 map00626 map00740 0 map00624 biosynthesis Novobiocin map00930 metabolism Amino acid sm Metabolism of other amino acids bolism eotide Metabolism of cofactors and vitamins 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% General function - R Translation - J Amino acids - E DNA - L Unknown - S Envelope - M Carbohydrates - G Energy - C Transcription - K Coenzymes - H Nucleotides - F Inorganic - P Protein turnover - O Lipids - I Signal transduction - T Secretion - U Cell cycle - D Defence- V Second metabolites - Q Cell motility - N RNA - A Chromatin - B Extracellular - W Nuclear structure - Y Cytoskeleton - Z Rare minimal genome Rare minimal metagenome Frequent minimal genome Frequent minimal metagenome
  • 72. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Common Uncommon Rare Unknown Known Phage-associated 2,000 4,000 6,000 8,000 10,000 12,000 14,000 Number of individuals sampled Minimalmetagenomesize 1 25 50 75 100 c map00629 map00940 map00628 Limonene and pinene degradation map00941 map00621 map00331 map00627 map00623 Monoterpenoid biosynthesis map00622 map00363Diterpenoid biosynthesis map00791 map00362 map00901 map00351 map00626 map00190 map00361 map00930 xenobiotics Biodegradation of Biosynthesis of secondary metabolites 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% Cytoskeleton - Z acterization of the minimal gut genome and metagenome. the minimal gut genome on the KEGG pathways using the composition of the gut minimal microbiome. Large circle: c the minimal metagenome according to orthologous group o
  • 73. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 To identify the functions encoded by the minimal gut metagenome, we computed the orthologous groups that are shared by individuals of our cohort. This minimal set, of 6,313 functions, is much larger than the one estimated in a previous study8. There are only 2,069 functionally annotated orthologous groups, showing that they gravely underesti- mate the true size of the common functional complement among indi- viduals (Fig. 6c). The minimal gut metagenome includes a considerable fraction of functions (,45%) that are present in ,10% of the sequenced bacterial genomes (Fig. 6c, inset). These otherwise rare func- tionalities that are found in each of the 124 individuals may be necessary for the gut ecosystem. Eighty per cent of these orthologous groups contain genes with at best poorly characterized function, underscoring our limited knowledge of gut functioning.
  • 74. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 To identify the functions encoded by the minimal gut metagenome, we computed the orthologous groups that are shared by individuals of our cohort. This minimal set, of 6,313 functions, is much larger than the one estimated in a previous study8. There are only 2,069 functionally annotated orthologous groups, showing that they gravely underesti- mate the true size of the common functional complement among indi- viduals (Fig. 6c). The minimal gut metagenome includes a considerable fraction of functions (,45%) that are present in ,10% of the sequenced bacterial genomes (Fig. 6c, inset). These otherwise rare func- tionalities that are found in each of the 124 individuals may be necessary for the gut ecosystem. Eighty per cent of these orthologous groups contain genes with at best poorly characterized function, underscoring our limited knowledge of gut functioning.
  • 75. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Of the known fraction, about 5% codes for (pro)phage-related proteins, implying a universal presence and possible important eco- logical role of bacteriophages in gut homeostasis. The most striking secondary metabolism that seems crucial for the minimal metage- nome relates, not unexpectedly, to biodegradation of complex sugars and glycans harvested from the host diet and/or intestinal lining. Examples include degradation and uptake pathways for pectin (and its monomer, rhamnose) and sorbitol, sugars which are omni- present in fruits and vegetables, but which are not or poorly absorbed by humans. As some gut microorganisms were found to degrade both of them29,30, this capacity seems to be selected for by the gut ecosystem as a non-competitive source of energy. Besides these, capacity to ferment, for example, mannose, fructose, cellulose and sucrose is also part of the minimal metagenome. Together, these emphasize the strong dependence of the gut ecosystem on complex sugar degrada- tion for its functioning. !
  • 76. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Of the known fraction, about 5% codes for (pro)phage-related proteins, implying a universal presence and possible important eco- logical role of bacteriophages in gut homeostasis. The most striking secondary metabolism that seems crucial for the minimal metage- nome relates, not unexpectedly, to biodegradation of complex sugars and glycans harvested from the host diet and/or intestinal lining. Examples include degradation and uptake pathways for pectin (and its monomer, rhamnose) and sorbitol, sugars which are omni- present in fruits and vegetables, but which are not or poorly absorbed by humans. As some gut microorganisms were found to degrade both of them29,30, this capacity seems to be selected for by the gut ecosystem as a non- competitive source of energy. Besides these, capacity to ferment, for example, mannose, fructose, cellulose and sucrose is also part of the minimal metagenome. Together, these emphasize the strong dependence of the gut ecosystem on complex sugar degrada- tion for its functioning. !
  • 77. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Functional complementarities of the genome and metagenome Detailed analysis of the complementarities between the gut metage- nome and the human genome is beyond the scope of the present work. To provide an overview, we considered two factors: conservation of the functions in the minimal metagenome and presence/absence of func- tions in one or the other (Supplementary Table 11). Gut bacteria use mostly fermentation to generate energy, converting sugars, in part, to short-chain fatty acid, that are used by the host as energy source. Acetate is important for muscle, heart and brain cells31, propionate is used in host hepatic neoglucogenic processes, whereas, in addition, butyrate is important for enterocytes32. Beyond short-chain fatty acid, a number of amino acids are indispensable to humans33 and can be provided by bacteria34. Similarly, bacteria can contribute certain vitamins3 (for example, biotin, phylloquinone) to the host. All of the steps of biosyn- thesis of these molecules are encoded by the minimal metagenome
  • 78. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Gut bacteria seem to be able to degrade numerous xenobiotics, including non-modified and halogenated aromatic compounds (Sup- plementary Table 11), even if the steps of most pathways are not part of the minimal metagenome and are found in a fraction of individuals only. A particularly interesting example is that of benzoate, which is a common food supplement, known as E211. Its degradation by the coenzyme-A ligation pathway, encoded in the minimal metagenome, leads to pimeloyl-coenzyme-A, which is a precursor of biotin, indi- cating that this food supplement can have a potentially beneficial role for human health.
  • 79. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 DISCUSSION
  • 80. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Discussion We have used extensive Illumina GA short-read-based sequencing of total faecal DNA from a cohort of 124 individuals of European (Nordic and Mediterranean) origin to establish a catalogue of non- redundant human intestinal microbial genes. The catalogue contains 3.3 million microbial genes, 150-fold more than the human gene complement, and includes an overwhelming majority (.86%) of prevalent genes harboured by our cohort. The catalogue probably contains a large majority of prevalent intestinal microbial genes in the human population, for the following reasons: (1) over 70% of the metagenomic reads from three previous studies, including American and Japanese individuals8,16,17, can be mapped on our contigs; (2) about 80% of the microbial genes from 89 frequent gut reference genomes are present in our set. This result represents a proof of principle that short-read sequencing can be used to characterize complex microbiomes.
  • 81. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 The full bacterial gene complement of each individual was not sampled in our work. Nevertheless, we have detected some 536,000 prevalent unique genes in each, out of the total of 3.3 million carried by our cohort. Inevitably, the individuals largely share the genes of the common pool. At the present depth of sequencing, we found that almost 40% of the genes from each individual are shared with at least half of the individuals of the cohort. Future studies of world-wide span, envisaged within the International Human Microbiome Consortium, will complete, as necessary, our gene catalogue and establish boundaries to the proportion of shared genes.
  • 82. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Essentially all (99.1%) of the genes of our catalogue are of bacterial origin, the remainder being mostly archaeal, with only 0.1% of eukar- yotic and viral origins. The gene catalogue is therefore equivalent to that of some 1,000 bacterial species with an average-sized genome, encoding about 3,364 non-redundant genes. We estimate that no more than 15% of prevalent genes of our cohort may be missing from the catalogue, and suggest that the cohort harbours no more than ,1,150 bacterial species abundant enough to be detected by our sampling. Given the large overlap between microbial sequences in this and previous studies we suggest that the number of abundant intestinal bacterial species may be not much higher than that observed in our cohort. Each individual of our cohort harbours at least 160 such bacterial species, as estimated by the average prevalent gene number, and many must thus be shared.
  • 83. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 ` We assigned about 12% of the reference set genes (404,000) to the 194 sequenced intestinal bacterial genomes, and can thus associate them with bacterial species. Sequencing of at least 1,000 human- associated bacterial genomes is foreseen within the International Human Microbiome Consortium, via the Human Microbiome Project and MetaHIT. This is commensurate with the number of dominant species in our cohort and expected more broadly in human gut, and should enable a much more extensive gene to species assign- ment. Nevertheless, we used the presently available sequenced genomes to explore further the concept of largely shared species among our cohort and identified 75 species common to .50% of individuals and 57 species common to .90%. These numbers are likely to increase with the number of sequenced reference strains and a deeper sampling. Indeed, a 2–3-fold increase in sequencing depth raised by 25% the number of species that we could detect as shared between two individuals. A large number of shared species supports the view that the prevalent human microbiome is of a finite and not overly large size.
  • 84. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 How can this view be reconciled with that of a considerable inter- personal diversity of innumerable bacterial species in the gut, arising from most previous studies using the 16S RNA marker gene4,8,10,11? Possibly the depth of sampling of these studies was insufficient to reveal common species when present at low abundance, and empha- sized the difference in the composition of a relatively few dominant species. We found a very high variability of abundance (12- to 2,200- fold) for the 57 most common species across the individuals of our cohort. Nevertheless, a recent 16S rRNA-based study concluded that a common bacterial species ‘core’, shared among at least 50% of individuals under study, exists35 !
  • 85. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Detailed comparisons of bacterial genes across the individuals of our cohort will be carried out in the future, within the context of the ongoing MetaHIT clinical studies of which they are part. Nevertheless, clustering of the genes in families allowed us to capture a virtually full functional potential of the prevalent gene set and revealed a considerable novelty, extending the functional categories by some 30% in regard to previous work8. Similarly, this analysis has revealed a functional core, conserved in each individual of the cohort, which reflects the full minimal human gut metagenome, encoded across many species and probably required for the proper functioning of the gut ecosystem. The size of this minimal metagenome exceeds several-fold that of the core metagenome reported previously8. It includes functions known to be important to the host–bacterial inter- action, such as degradation of complex polysaccharides, synthesis of short-chain fatty acids, indispensable amino acids and vitamins. Finally, we also identified functions that we attribute to a minimal gut bacterial genome, likely to be required by any bacterium to thrive in this ecosystem. Besides general housekeeping functions, the minimal genome encompasses many genes of unknown function, rare in sequenced genomes and possibly specifically required in the gut.
  • 86. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Beyond providing the global view of the human gut microbiome, the extensive gene catalogue we have established enables future studies of association of the microbial genes with human phenotypes and, even more broadly, human living habits, taking into account the environment, including diet, from birth to old age. We anticipate that these studies will lead to a much more complete understanding of human biology than the one we presently have.
  • 87. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 LETTERS A core gut microbiome in obese and lean twins Peter J. Turnbaugh1 , Micah Hamady3 , Tanya Yatsunenko1 , Brandi L. Cantarel5 , Alexis Duncan2 , Ruth E. Ley1 , Mitchell L. Sogin6 , William J. Jones7 , Bruce A. Roe8 , Jason P. Affourtit9 , Michael Egholm9 , Bernard Henrissat5 , Andrew C. Heath2 , Rob Knight4 & Jeffrey I. Gordon1 The human distal gut harbours a vast ensemble of microbes (the microbiota) that provide important metabolic capabilities, includ- ing the ability to extract energy from otherwise indigestible dietary polysaccharides1–6 . Studies of a few unrelated, healthy adults have revealed substantial diversity in their gut communities, as mea- sured by sequencing 16S rRNA genes6–8 , yet how this diversity relates to function and to the rest of the genes in the collective genomes of the microbiota (the gut microbiome) remains obscure. Studies of lean and obese mice suggest that the gut microbiota affects energy balance by influencing the efficiency of calorie har- vest from the diet, and how this harvested energy is used and stored3–5 . Here we characterize the faecal microbial communities of adult female monozygotic and dizygotic twin pairs concordant for leanness or obesity, and their mothers, to address how host genotype, environmental exposure and host adiposity influence the gut microbiome. Analysis of 154 individuals yielded 9,920 near full-length and 1,937,461 partial bacterial 16S rRNA sequences, plus 2.14 gigabases from their microbiomes. The results reveal that the human gut microbiome is shared among family members, but that each person’s gut microbial community varies in the specific bacterial lineages present, with a comparable degree of co-variation between adult monozygotic and dizygotic twin pairs. However, there was a wide array of shared microbial genes among sampled individuals, comprising an extensive, identifiable ‘core micro- biome’ at the gene, rather than at the organismal lineage, level. Obesity is associated with phylum-level changes in the microbiota, reduced bacterial diversity and altered representation of bacterial genes and metabolic pathways. These results demonstrate that a diversity of organismal assemblages can nonetheless yield a core microbiome at a functional level, and that deviations from this core are associated with different physiological states (obese compared leanness (BMI 5 18.5–24.9 kg m22 ) (one twin pair was lean/over- weight (overweight defined as BMI $ 25 and , 30) and six pairs were overweight/obese). They had not taken antibiotics for at least 5.49 6 0.09 months. Each participant completed a detailed medical, lifestyle and dietary questionnaire: study enrolees were broadly representative of the overall Missouri population for BMI, parity, education and marital status (see Supplementary Results). Although all were born in Missouri, they currently live throughout the USA: 29% live in the same house, but some live more than 800 km apart. Because faecal samples are readily attainable and representative of interpersonal differences in gut microbial ecology7 , they were col- lected from each individual and frozen immediately. The collection procedure was repeated again with an average interval between sampling of 57 6 4 days. To characterize the bacterial lineages present in the faecal micro- biotas of these 154 individuals, we performed 16S rRNA sequencing, targeting the full-length gene with an ABI 3730xl capillary sequencer. Additionally, we performed multiplex pyrosequencing with a 454 FLX instrument to survey the gene’s V2 variable region13 and its V6 hypervariable region14 (Supplementary Tables 1–3). Complementary phylogenetic and taxon-based methods were used to compare 16S rRNA sequences among faecal communities (see Methods). No matter which region of the gene was examined, individuals from the same family (a twin and her co-twin, or twins and their mother) had a more similar bacterial community structure than unrelated individuals (Fig. 1a and Supplementary Fig. 1a, b), and shared significantly more species-level phylotypes (16S rRNA sequences with $97% identity comprise each phylotype) (G 5 55.2, P , 10212 (V2); G 5 12.3, P , 0.001 (V6); G 5 11.3, P , 0.001 (full-length)). No significant correlation was seen between the degree of physical separation of family members’ current homes Vol 457|22 January 2009|doi:10.1038/nature07540
  • 88. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 in coverage, all analyses were performed on an equal number of randomly selected sequences (200 full-length, 1,000 V2 and 10,000 V6). At this level of coverage, there was little overlap between the sampled faecal communities. Moreover, the number of 16S rRNA gene sequences belonging to each phylotype varied greatly between Analysis of 16S rRNA data sets produced by the three P methods, plus shotgun sequencing of community DNA (se revealed a lower proportion of Bacteroidetes and a higher p of Actinobacteria in obese compared with lean individua ancestries (Supplementary Table 9). Combining the ind values across these independent analyses using Fisher’s me closed significantly fewer Bacteroidetes (P 5 0.003 Actinobacteria (P 5 0.002) but no significant diffe Firmicutes (P 5 0.09). These findings agree with previ showing comparable differences in both taxa in mice2 and a ive increase in the representation of Bacteroidetes when 12 u obese humans lost weight after being placed on one of two calorie diets6 . Across all methods, obesity was associated with a s decrease in the level of diversity (Fig. 1b and Suppleme 1c–f). This reduced diversity suggests an analogy: the microbiota is not like a rainforest or reef, which are adapte energy flux and are highly diverse; rather, it may be m fertilizer runoff where a reduced-diversity microbial co blooms with abnormal energy input16 . We subsequently characterized the microbial lineage content of the faecal microbiomes of 18 individuals rep six of the families (three lean and three obese European monozygotic twin pairs and their mothers) through shotg sequencing (Supplementary Tables 4 and 5) and BLASTX isons against several databases (KEGG17 (version 44) and S plus a custom database of 44 reference human gut microbia (Supplementary Figs 7–10 and Supplementary Results). Ou parameters were validated using control data sets compr domly fragmented microbial genes with annotations in t database17 (Supplementary Fig. 11 and Supplementary M We also tested how technical advances that produce lon might improve these assignments by sequencing faecal co samples from one twin pair using Titanium pyrosequencing (average read length of 341 6 134 nucleotides (s.d.) versus nucleotides for the standard FLX method). Supplementa shows that the frequency and quality of sequence assign improved as read length increases from 200 to 350 nucleo The 18 microbiomes were searched to identify sequences domains from experimentally validated carbohydr enzymes (CAZymes). Sequences matching 156 total CAZ were found within at least one human gut microbiome, inc glycoside hydrolase, 21 carbohydrate-binding module, 35 transferase, 12 polysaccharide lyase and 11 carbohydrat families (Supplementary Table 10). On average, 2.62 6 b a * 0.66 0.68 0.70 0.72 0.74 0.76 0.78 0.80 0.82 Self Twin–twin Mono- zygotic Twin–mother Unrelated UniFracdistance Dizygotic Mono- zygotic Dizygotic 2 22 42 62 82 102 122 0 2,000 4,000 6,000 8,000 1,0000 Number of sequences Phylogeneticdiversity Lean Obese * *** MoresimilarMoredifferent * *** ** ** ns Figure 1 | 16S rRNA gene surveys reveal familial similarity and reduced diversity of the gut microbiota in obese individuals. a, Average unweighted UniFrac distance (a measure of differences in bacterial community structure) between individuals over time (self), twin pairs, twins and their mother, and unrelated individuals (1,000 sequences per V2 data set; Student’s t-test with Monte Carlo; *P , 1025 ; **P , 10214 ; ***P , 10241 ; mean 6 s.e.m.). b, Phylogenetic diversity curves for the microbiota of lean and obese individuals (based on 1–10,000 sequences per V6 data set; mean 6 95% confidence intervals shown). NATURE|Vol 457|22 January 2009 L
  • 89. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 and taxonomic similarity (see Supplementary Methods) disclosed a significant association: individuals with similar taxonomic profiles of b-diversity with respect to the relative abundance of bacte (Fig. 3a), analysis of the relative abundance of broad functi egories of genes and metabolic pathways (KEGG) revealed a consistent pattern regardless of the sample surveyed (Fig Supplementary Table 11): the pattern is also consistent wi we obtained from a meta-analysis of previously published gu biome data sets from nine adults20,21 (Supplementary Fig. consistency is not simply due to the broad level of these ann as a similar analysis of Bacteroidetes and Firmicutes refere omes revealed substantial variation in the relative abundanc category (see Supplementary Fig. 17). Furthermore, pairw parisons of metabolic profiles obtained from the 18 microb this study revealed an average value of R2 of 0.97 6 0.002 indicating a high level of functional similarity. Overall functional diversity was compared using the index22 , a measurement that combines diversity (the numb ferent metabolic pathways) and evenness (the relative abun each pathway). The human gut microbiomes surveyed had and high Shannon index value (4.63 6 0.01), close to the m possible level of functional diversity (5.54; see Suppl Methods). Despite the presence of a small number of abunda bolic pathways (listed in Supplementary Table 11), the ove tional profile of each gut microbiome is quite even (Shannon of 0.846 0.001 on a scale of 0–1), demonstrating that most m pathways are found at a similar level of abundance. Interest level of functional diversity in each microbiome was sig linked to the relative abundance of the Bacteroidetes (R P , 1026 ); microbiomes enriched for Firmicutes/Actinobac a lowerleveloffunctionaldiversity.Thisobservation isconsis an analysis of simulated metagenomic reads generated from e Bacteroidetes and Firmicutes genomes (Supplementary Fig average, the Bacteroidetes genomes have a significantly high both functional diversity and evenness (Mann–Whitne P , 0.01). At a finer level, 26–53% of ‘enzyme’-level functiona (KEGG/CAZy/STRING) were shared across all 18 micr whereas 8–22% of the groups were unique to a single mic (Supplementary Fig. 19a–c). The ‘core’ functional groups p all microbiomes were also highly abundant, representing 93 thetotal sequences. Given thehigherrelative abundance of th groups, more than 95% were found after 26.11 6 2.02 meg sequence were collected from a given microbiome, whereas able’ groups continued to increase substantially with each a megabase of sequence. Of course, any estimate of the total s core microbiome will depend on sequencing effort, espe 0.990.980.97R2 value: PC1 (20%) Bacteroidetes(%) a b c High Firmicutes/ActinobacteriaHigh Bacteroidetes Twin versus twin Twin versus mother Unrelated pairs Functionalsimilarity(R2) * 0.94 0.95 0.96 0.97 0.98 0.99 1 R2 = 0.96 0 20 40 60 80 100 –0.6 –0.4 –0.2 0 0.2 0.4 0.6 1.00 0.98 0.98 0.99 0.99 0.99 0.98 0.98 0.99 0.98 0.98 1.00 0.99 0.99 0.97 0.97 0.98 0.98 0.98 0.97 0.98 0.99 0.98 0.99 1.00 1.00 0.98 0.98 0.98 0.98 0.99 0.99 0.99 0.98 0.98 0.99 0.99 0.99 1.00 1.00 0.99 0.99 0.99 0.97 0.99 1.00 1.00 0.99 0.98 0.98 0.99 0.99 0.98 0.99 1.00 0.99 0.98 0.97 0.99 0.99 0.99 0.99 0.99 0.97 0.98 0.99 0.99 1.00 0.97 0.98 0.99 0.99 0.99 0.97 0.98 0.99 0.98 0.97 1.00 0.97 0.98 0.99 0.99 0.98 0.98 0.97 0.98 0.97 1.00 0.99 0.98 0.98 0.97 0.99 0.98 0.97 0.99 1.00 0.99 0.99 0.98 0.98 0.98 0.99 0.99 0.99 0.98 0.98 0.99 1.00 0.99 0.99 0.99 0.98 0.99 1.00 0.99 0.99 0.99 0.99 0.99 1.00 0.99 0.98 0.97 0.97 0.98 0.98 0.98 0.99 1.00 0.99 0.99 0.99 0.98 0.99 0.99 1.00 0.98 0.98 0.97 0.98 0.98 0.99 0.99 0.98 0.98 0.98 1.00 0.99 0.97 0.98 0.99 0.99 0.97 0.98 0.98 0.98 0.97 0.98 0.99 1.00 0.98 0.99 0.99 0.99 0.97 0.98 1.00 1.00 0.99 0.98 0.98 0.99 1.00 1.00 0.99 0.98 0.98 0.98 0.98 0.97 0.97 0.97 0.99 0.99 0.99 0.99 1.00 0.99 0.99 0.99 0.99 0.98 0.98 0.98 0.99 0.99 0.98 0.98 0.99 1.00 F1T1Le F1T2Le F1MOv F2T1Le F2T2Le F2MOb F3T1Le F3T2Le F3MOv F4T1Ob F4T2Ob F4MOb F5T1Ob F5T2Ob F5MOv F6T1Ob F6T2Ob F6MOb F1T1Le F1T2Le F1MOv F2T1Le F2T2Le F2MOb F3T1Le F3T2Le F3MOv F4T1Ob F4T2Ob F4MOb F5T1Ob F5T2Ob F5MOv F6T1Ob F6T2Ob F6MOb <0.97 Figure 2 | Metabolic-pathway-based clustering and analysis of the human gut microbiome of monozygotic twins. a, Clustering of functional profiles based on the relative abundance of KEGG metabolic pathways. All pairwise comparisons were made of the profiles by calculating each R2 value. Sample identifier nomenclature: family number, twin number or mother, and BMI category (Le, lean; Ov, overweight; Ob, obese; for example, F1T1Le stands for family 1, twin 1, lean). b, The relative abundance of Bacteroidetes as a function of the first principal component derived from an analysis of KEGG metabolic profiles. c, Comparisons of functional similarity between twin pairs, between twins and their mother, and between unrelated individuals. Asterisk indicates significant differences (Student’s t-test with Monte Carlo; P , 0.01; mean 6 s.e.m.). a COG categoriesBacterial phylum b LETTERS NATURE|Vol 457|22 Janu
  • 90. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 ds) disclosed a nomic profiles el test). s representing icutes and 14 clustering by analyses of the biome-derived d 26 pathways upplementary eral carbohyd- were enriched our CAZyme abundance of glycosyltrans- erases in the n Microbiome ntifiable ‘core al capabilities y of humans1 . ed a high level able’ groups continued to increase substantially with each additional megabase of sequence. Of course, any estimate of the total size of the core microbiome will depend on sequencing effort, especially for h Monte Carlo; [Q] [P] [I] [H] [F] [E] [G] [C] [S] [R] [O] [U] [W] [Z] [N] [M] [T] [V] [Y] [D] [B] [L] [K] [A] [J] 0 20 40 60 80 100 Relativeabundance(%) OtherProteobacteria F1T1Le F1T2Le F1MOv F2T1Le F2T2Le F2MOb F3T1Le F3T2Le F3MOv F4T1Ob F4T2Ob F4MOb F5T1Ob F5T2Ob F5MOv F6T1Ob F6T2Ob F6MOb F1T1Le F1T2Le F1MOv F2T1Le F2T2Le F2MOb F3T1Le F3T2Le F3MOv F4T1Ob F4T2Ob F4MOb F5T1Ob F5T2Ob F5MOv F6T1Ob F6T2Ob F6MOb a COG categoriesBacterial phylum b ActinobacteriaBacteroidetesFirmicutes Figure 3 | Comparison of taxonomic and functional variations in the human gut microbiome. a, Relative abundance of major phyla across 18 faecal microbiomes from monozygotic twins and their mothers, based on BLASTX comparisons of microbiomes and the National Center for Biotechnology Information non-redundant database. b, Relative abundance of categories of genes across each sampled gut microbiome (letters correspond to categories in the COG database). millan Publishers Limited. All rights reserved
  • 91. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 including those involved in transcription and translation (Fig. 4). Metabolic profile-based clustering indicated that the representation of ‘core’ functional groups was highly consistent across samples (Supplementary Fig. 20), and included several pathways that are To identify metabolic pathways associated with obesity, o core associated (variable) functional groups were included i parison of the gut microbiomes of lean versus obese twin bootstrap analysis23 was used to identify metabolic pathw were enriched or depleted in the variable obese gut micr For example, similar to a mouse model of diet-induced the obese human gut microbiome was enriched for phospho ase systems involved in microbial processing of carbo (Supplementary Table 12). All gut microbiome sequences w pared with the custom database of 44 human gut genomes: ratio analysis revealed 383 genes that were significantly between the obese and lean gut microbiome (q value , 0 enriched and 110 depleted in the obese micr Supplementary Tables 13 and 14). By contrast, only 49 ge consistently enriched or depleted between all twin p Supplementary Methods). These obesity-associated genes were representative of t nomic differences described above: 75% of the obesity- genes were from Actinobacteria (compared with 0% of lean- genes; the other 25% are from Firmicutes) whereas 42% of enriched genes were from Bacteroidetes (compared with 0 obesity-enriched genes). Their functional annotation indic many are involved in carbohydrate, lipid and amino-acid ism (Supplementary Tables 13 and 14). Together, they com initial set of microbial biomarkers of the obese gut microbi Our finding that the gut microbial community structures monozygotic twin pairs had a degree of similarity that was able to that of dizygotic twin pairs, and only slightly mor than that of their mothers, is consistent with an earlier finger study of adult twins24 , and with a recent microarray-based which revealed that gut community assembly during the fir life followed a more similar pattern in a pair of dizygotic tw 12 unrelated infants25 . Intriguingly, another fingerprinting monozygotic and dizygotic twins in childhood showed a reduced similarity profile in dizygotic twins26 . Thus, compr time-course studies, comparing monozygotic and dizygo pairs from birth through adulthood, as well as intergen analyses of their families’ microbiotas, will be key to dete the relative contributions of host genotype and environmen sures to (gut) microbial ecology. The hypothesis that there is a core human gut microbiom able by a set of abundant microbial organismal lineages th share, may be incorrect: by adulthood, no single bacterial p was detectable at an abundant frequency in the guts o sampled humans. Instead, it appears that a core gut mic exists at the level of shared genes, including an important com involved in various metabolic functions. This conservation s high degree of redundancy in the gut microbiome and sup ecological view of each individual as an ‘island’ inhabited b 0 2 4 6 8 10 12 14 Transcription Translation Nucleotide metabolism Amino-acid metabolism Biosynthesis of secondary metabolites Replication and repair Metabolism of other amino acids Glycan biosynthesis and metabolism Carbohydrate metabolism Lipid metabolism Biosynthesis of polyketides Cell growth and death Metabolism of cofactors and vitamins Energy metabolism Xenobiotics biodegradation and metabolism Genetic information processing protein families Metabolism protein families Metabolism unclassified Membrane transport Folding, sorting, and degradation Cellular processes and signalling protein families Cellular processes and signalling unclassified Signal transduction Poorly characterized unclassified Genetic information processing unclassified Cell motility Signalling molecules and interaction Relative abundance (percentage of KEGG assignments) KEGG category Core Variable *** * ** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** Figure 4 | KEGG categories enriched or depleted in the core versus variable components of the gut microbiome. Sequences from each of the 18 faecal microbiomes were binned into the ‘core’ or ‘variable’ microbiome based on the co-occurrence of KEGG orthologous groups (core groups were found in all 18 microbiomes whereas variable groups were present in fewer (,18) microbiomes; see Supplementary Fig. 19a). Asterisks indicate significant differences (Student’s t-test, *P , 0.05, **P , 0.001, ***P , 1025 ; mean 6 s.e.m.). Macmillan Publishers Limited. All rights reserved©2009
  • 92. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 ARTICLE doi:10.1038/nature11234 Structure, function and diversity of the healthy human microbiome The Human Microbiome Project Consortium* Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome. A total of 4,788 specimens from 242 screened and phenotyped adults1 (129 males, 113 females) were available for this study, representing the majority of the target Human Microbiome Project (HMP) cohort of 300 individuals. Adult subjects lacking evidence of disease were recruited based on a lengthy list of exclusion criteria; we will refer to them here as ‘healthy’, as defined by the consortium clinical sampling criteria (K. Aagaard et al., manuscript submitted). Women were sampled at 18 body habitats, men at 15 (excluding three vaginal sites), distributed among fivemajor bodyareas.Ninespecimens were collected from the oral cavity and oropharynx: saliva; buccal involving microbiome samples collected from healthy volunteers at two distinct geographic locations in the United States, we have defined themicrobial communitiesat each body habitat, encountering 81–99% of predicted genera and saturating the range of overall community configurations (Fig. 1, Supplementary Fig. 1 and Supplementary Table 1; see also Fig. 4). Oral and stool communities were especially diverse in terms of community membership, expanding prior observa- tions5 , and vaginal sites harboured particularly simple communities (Fig. 1a). This study established that these patterns of alpha diversity (within samples) differed markedly from comparisons between The Human Microbiome Project Consortium Curtis Huttenhower1,2 *, Dirk Gevers2 *, Rob Knight3,4 , Sahar Abubucker5 , Jonathan H. Badger6 , Asif T. Chinwalla5 , Heather H. Creasy7 , Ashlee M. Earl2 , Michael G. FitzGerald2 , Robert S. Fulton5 , Michelle G. Giglio7 , Kymberlie Hallsworth-Pepin5 , Elizabeth A. Lobos5 , Ramana Madupu6 , Vincent Magrini5 , John C. Martin5 , Makedonka Mitreva5 , Donna M. Muzny8 , Erica J. Sodergren5 , James Versalovic9,10 , Aye M. Wollam5 , Kim C. Worley8 , Jennifer R.Wortman7 , Sarah K. Young2 , Qiandong Zeng2 , Kjersti M.Aagaard11 , Olukemi O. Abolude7 , Emma Allen-Vercoe12 , Eric J. Alm13,2 , Lucia Alvarado2 , Gary L. Andersen14 , Scott Anderson2 , Elizabeth Appelbaum5 , Harindra M. Arachchi2 , Gary Armitage15 , Cesar A. Arze7 , Tulin Ayvaz16 , Carl C. Baker17 , Lisa Begg18 , Tsegahiwot Belachew19 , Veena Bhonagiri5 , Monika Bihan6 , Martin J. Blaser20 , Toby Bloom2 , Vivien Bonazzi21 , J. Paul Brooks22,23 , Gregory A. Buck23,24 , Christian J. Buhay8 , Dana A. Busam6 , Joseph L. Campbell21,19 , Shane R. Canon25 , Brandi L. Cantarel7 , Patrick S. G. Chain26,27 , I-Min A. Chen28 , Lei Chen5 , Shaila Chhibba21 , Ken Chu28 , Dawn M. Ciulla2 , Jose C. Clemente3 , Sandra W. Clifton5 , Sean Conlan79 , Jonathan Crabtree7 , Mary A. Cutting29 , Noam J. Davidovics7 , Catherine C. Davis30 , Todd Z. DeSantis31 , Carolyn Deal19 , Kimberley D. Delehaunty5 , Floyd E. Dewhirst32,33 , Elena Deych34 , Yan Ding8 , David J. Dooling5 , Shannon P. Dugan8 , Wm Michael Dunne35,36 , A. Scott Durkin6 , Robert C. Edgar37 , Rachel L. Erlich2 , Candace N. Farmer5 , Ruth M. Farrell38 , Karoline Faust39,40 , Michael Feldgarden2 , Victor M. Felix7 , Sheila Fisher2 , Anthony A. Fodor41 , Larry J. Forney42 , Leslie Foster6 , Valentina Di Francesco19 , Jonathan Friedman43 , Dennis C. Friedrich2 , Catrina C. Fronick5 , Lucinda L. Fulton5 , Hongyu Gao5 , Nathalia Garcia44 , Georgia Giannoukos2 , Christina Giblin19 , Maria Y. Giovanni19 , Jonathan M. Goldberg2 , Johannes Goll6 , Antonio Gonzalez45 , Allison Griggs2 , Sharvari Gujja2 , Susan Kinder Haake46 , Brian J. Haas2 , Holli A. Hamilton29 , Emily L. Harris29 , Theresa A. Hepburn2 , Brandi Herter5 , Diane E. Hoffmann47 , Michael E. Holder8 , Clinton Howarth2 , Katherine H. Huang2 , Susan M. Huse48 , Jacques Izard32,33 , Janet K. Jansson49 , Huaiyang Jiang8 , Catherine Jordan7 , Vandita Joshi8 , James A. Katancik50 , Wendy A. Keitel16 , Scott T. Kelley51 , Cristyn Kells2 , Nicholas B. King52 , Dan Knights45 , Heidi H. Kong53 , Omry Koren54 , Sergey Koren55 , Karthik C. Kota5 , Christie L. Kovar8 , Nikos C. Kyrpides27 , Patricio S. La Rosa34 , Sandra L. Lee8 , Katherine P. Lemon32,56 , Niall Lennon2 , Cecil M. Lewis57 , Lora Lewis8 , Ruth E. Ley54 , Kelvin Li6 , Konstantinos Liolios27 , Bo Liu55 , Yue Liu8 , Chien-Chi Lo26 , Catherine A. Lozupone3 , R. Dwayne Lunsford29 , Tessa Madden58 , Anup A. Mahurkar7 , Peter J. Mannon59 , Elaine R. Mardis5 , Victor M. Markowitz27,28 , Konstantinos Mavromatis27 , Jamison M. McCorrison6 , Daniel McDonald3 , Jean McEwen21 , Amy L. McGuire60 , Pamela McInnes29 , Teena Mehta2 , Kathie A. Mihindukulasuriya5 , Jason R. Miller6 , Patrick J. Minx5 , Irene Newsham8 , Chad Nusbaum2 , Michelle O’Laughlin5 , Joshua Orvis7 , Ioanna Pagani27 , Krishna Palaniappan28 , Shital M. Patel61 , Matthew Pearson2 , Jane Peterson21 , Mircea Podar62 , Craig Pohl5 , Katherine S. Pollard63,64,65 , Mihai Pop55,66 , Margaret E. Priest2 , Lita M. Proctor21 , Xiang Qin8 , Jeroen Raes39,40 , Jacques Ravel7 , Jeffrey G. Reid8 , Mina Rho67 , Rosamond Rhodes68 , Kevin P. Riehle69 , Maria C. Rivera23,24 , Beltran Rodriguez-Mueller51 , Yu-Hui Rogers6 , Matthew C. Ross16 , Carsten Russ2 , Ravi K. Sanka6 , Pamela Sankar70 , J. Fah Sathirapongsasuti1 , Jeffery A. Schloss21 , Patrick D. Schloss71 , Thomas M. Schmidt72 , Matthew Scholz26 , Lynn Schriml7 , Alyxandria M. Schubert71 , NicolaSegata1 , JuliaA. Segre79 , WilliamD. Shannon34 , Richard R. Sharp38 , Thomas J.Sharpton63 , Narmada Shenoy2 , NiharU. Sheth23 , GinaA. Simone73 , Indresh Singh6 , Christopher S. Smillie43 , Jack D. Sobel74 , Daniel D. Sommer55 , Paul Spicer57 , GrangerG.Sutton6 , SeanM.Sykes2 , DianaG.Tabbaa2 , Mathangi Thiagarajan6 , ChadM. Tomlinson5 , Manolito Torralba6 , Todd J. Treangen75 , Rebecca M. Truty63 , Tatiana A. Vishnivetskaya62 , Jason Walker5 , Lu Wang21 , Zhengyuan Wang5 , Doyle V. Ward2 , Wesley Warren5 , Mark A. Watson35 , Christopher Wellington21 , Kris A. Wetterstrand21 , James R. White7 , Katarzyna Wilczek-Boney8 , YuanQing Wu8 , Kristine M. Wylie5 , Todd Wylie5 , Chandri Yandava2 , Liang Ye5 , Yuzhen Ye67 , Shibu Yooseph76 , Bonnie P. Youmans16 , Lan Zhang8 , Yanjiao Zhou5 , Yiming Zhu8 , Laurie Zoloth77 , Jeremy D. Zucker2 , Bruce W. Birren2 , Richard A. Gibbs8 , Sarah K. Highlander8,16 , Barbara A. Methe´6 , Karen E. Nelson6 , Joseph F. Petrosino8,78,16 , George M. Weinstock5 , Richard K. Wilson5 & Owen White7 1 Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA. 2 The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA. 3 Department of Chemistry and Biochemistry, University of Colorado, Boulder, Colorado 80309, USA. 4 Howard Hughes Medical Institute, Boulder, Colorado 80309, USA. 5 The Genome Institute, Washington University School of Medicine, St. Louis, Missouri 63108, USA. 6 J. Craig Venter Institute, Rockville, Maryland 20850, USA. 7 Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA. 8 17 H N Al U N of O U R U En B Al In Te La C U C B C Im 34 Sc Im 36 C C 10 U G 42 43 C Lo U Sp C B La Sc U D U 36 C U B In M N W of Al M M La C G 65 94 M B Yo Te Pe RESEARCH ARTICLE
  • 93. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Abstract Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.
  • 94. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Abstract Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome. ! We did a big big study - bigger than anyone!
  • 95. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Abstract Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome. ! We did a big big study - bigger than anyone! Lots of variation w/in and between people
  • 96. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Abstract Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome. ! We did a big big study - bigger than anyone! Lots of variation w/in and between people We covered a lot of diversity
  • 97. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Abstract Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome. ! We did a big big study - bigger than anyone! Lots of variation w/in and between people We covered a lot of diversity Functions varied less than taxa
  • 98. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Abstract Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome. ! We did a big big study - bigger than anyone! Lots of variation w/in and between people We covered a lot of diversity Functions varied less than taxa Good reference data set
  • 99. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 a Within-sample alpha diversity Between-sample beta diversity log2(relativealphadiversity) d Vaginalintroitus Posteriorfornix Mid-vagina Stool Supragingivalplaque Subgingivalplaque Tonguedorsum Throat Saliva Palatinetonsils Hardpalate Keratinizedgingiva Buccalmucosa Rretroauricularcrease Lretroauricularcrease Rantecubitalfossa Lantecubitalfossa Anteriornares PC2(4.4%) PC1 (13%) Urogenital Skin Nasal Technical replicates (16S) Between visits (16S) Between subjects ( Phylotypes (16S) Reference genomes (WGS) Metabolic modules (WGS) Gene index (WGS) OTUs (16S) c b log2 (relativebetadiversity) Gastrointestinal Urogenital Skin Nasal 4 2 0 –2 –4 0.6 0.4 0.2 0.0 –0.2 –0.4 –0.6 log2 (relativediversity) 5 4 3 2 1 0 –1 –2 –3 –4 Figure 1 | Diversity of the human microbiome is concordant among alpha- and beta-diversity are not directly comparable RESEARCH ARTICLE
  • 100. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 d PC2(4.4%) PC1 (13%) Technical replicates (16S) Between visits (16S) Between visits (WGS) ylotypes (16S) ference genomes (WGS) etabolic modules (WGS) ene index (WGS) TUs (16S) c Gastrointestinal Urogenital Skin Nasal 5 Oral
  • 101. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 d Vaginalintroitus PC1 (13%) Urogenital Skin Nasal Technical replicates (16S) Between visits (16S) Between subjects (16S) Between visits (WGS) Between subjects (WGS) Gastrointestinal Oral Nasal log2 (relativediversity) 5 4 3 2 1 0 –1 –2 –3 –4
  • 102. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 Phyla Firmicutes Actinobacteria Bacteroidetes Proteobacteria Fusobacteria Tenericutes Spirochaetes Cyanobacteria Verrucomicrobia TM7 Metabolic pathways Central carbohydrate metabolism Cofactor and vitamin biosynthesis Oligosaccharide and polyol transport system Purine metabolism ATP synthesis Phosphate and amino acid transport system Aminoacyl tRNA Pyrimidine metabolism Ribosome Aromatic amino acid metabolism a b Anterior nares RC Buccal mucosa Supragingival plaque Tongue dorsum Stool Posterior fornix Figure 2 | Carriage of microbial taxa varies while metabolic pathways remain stable within a healthy population. a, b, Vertical bars represent microbiome samples by body habitat in the seven locations with both shotgun and 16S data; bars indicate relative abundances colored by microbial phyla from binned OTUs (a) and metabolic modules (b). Legend indicates most abundant phyla/pathways by average within one or more body habitats; RC, retroauricular crease. A plurality of most communities’ memberships consists of a single dominant phylum (and often genus; see Supplementary Fig. 2), but this is universal neither to all body habitats nor to all individuals. Conversely, most metabolic pathways are evenly distributed and prevalent across both individuals and body habitats. Abundant species (metagenomic data) Abundant genera (16S data) Mean non-zero abundance (size) and population prevalence (intensity) of microbial clades a b ARTICLE RESEARCH
  • 103. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 abundant phyla/pathways by average within one or more body habitats; RC, Anterior nares Antecubital fossa Retroauricular crease Buccal m ucosa Keratinized gingiva Hard palate Saliva Throat Tongue dorsum Subgingival plaque Supragingival plaque Stool M id-vagina Posterior fornix Vaginal introitus Veillonella Prevotella Haemophilus Moraxella Staphylococcus Corynebacterium Bacteroides Streptococcus Propionibacterium Lactobacillus Anterior nares Retroauricular crease Buccal m ucosa Tongue dorsum Supragingival plaque Stool Rothia mucilaginosa Gardnerella vaginalis Bacteroides vulgatus Alistipes putredinis Bifidobacterium dentium Staphylococcus epidermidis Staphylococcus aureus Corynebacterium matruchotii Streptococcus mitis Propionibacterium acnes Corynebacterium accolens Corynebacterium kroppenstedtii Prevotella copri Lactobacillus jensenii Prevotella amnii Lactobacillus gasseri Lactobacillus iners Streptococcus mitis Propionibacterium acnes Lactobacillus crispatus Abundant species (metagenomic data) Abundant genera (16S data) Mean non-zero abundance (size) and population prevalence (intensity) of microbial clades a b c Beta-diversity added by sampled microbial communities OTUs (16S data)Enzyme classes (metagenomic data) Diversity(Bray–Curtis) Actinobacteria|Actinobacteria Bacteroidetes|Bacteroidia Firmicutes|Bacilli Firmicutes|Negativicutes Proteobacteria|Gammaproteobacteria Prevalence (%) d 100% Abundance 0% Diversity(weightedUniFrac) Samples e Abundant PATRIC ‘pathogens’ (metagenomic data) Samples Posterior fornix 0.3 0.2 0.1 0.0 0 20 40 60 80 100 Anterior nares Right retroauricular crease Left retroauricular crease Buccal mucosa Posterior fornix Stool Supragingival plaque Tongue dorsum 0.5 0.4 0.3 0.2 0.1 0.0 0 50 100 150 200 250 300 Subgingival plaque Saliva Supragingival plaque Palatine tonsils Stool Tongue dorsum Throat Hard palate Buccal mucosa Anterior nares Attached keratinized gingiva Right antecubital fossa Left antecubital fossa Right retroauricular crease Left retroauricular crease Vaginal introitus Mid-vagina Posterior fornix Anterior nares Retroauricular crease Buccal m ucosa Tongue dorsum Supragingival plaque Stool Posterior fornix 0 100 Figure 3 | Abundant taxa in the human microbiome that have been metagenomically and taxonomically well defined in the HMP population. a–c, Prevalence (intensity, colour denoting phylum/class)and abundancewhen present (size) of clades in the healthy microbiome. The most abundant metagenomically-identified species (a), 16S-identified genera (b) and PATRIC12 pathogens (metagenomic) (c) are shown. d, e, The population size and sequencing depths of the HMP have well defined the microbiome at all assayed body sites, as assessed by saturation of added community metabolic configurations (rarefaction of minimum Bray–Curtis beta-diversity of metagenomic enzyme class abundances to nearest neighbour, inter-quartile range over 100 samples) (d) and phylogenetic configurations (minimum 16S OTU weighted UniFrac distance to nearest neighbour) (e). 1 4 J U N E 2 0 1 2 | V O L 4 8 6 | N A T U R E | 2 0 9 Macmillan Publishers Limited. All rights reserved©2012
  • 104. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 St Co S Prop Corynebacterium accolens Corynebacterium kroppenstedtii Prevotella copri Lactobacillus jensenii Prevotella amnii Lactobacillus gasseri Lactobacillus iners Streptococcus mitis Propionibacterium acnes Lactobacillus crispatus Abundant species (metagenomic data) Mean non-zero abundance a b Actinobacteria|Actinobacteria Prevalence (%) Anterior nares Retroauricular crease Buccal m ucosa Tongue dorsum Supragingival plaque Stool Posterior fornix 0 10
  • 105. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 body habitats; RC, Anterior nares Antecubital fossa Retroauricular crease Buccal m ucosa Keratinized gingiva Hard palate Saliva Throat Tongue dorsum Subgingival plaque Supragingival plaque Stool M id-vagina Posterior fornix Vaginal introitus Veillonella Prevotella Haemophilus Moraxella Staphylococcus Corynebacterium Bacteroides Streptococcus Propionibacterium Lactobacillus Abundant genera (16S data) ndance (size) and population prevalence (intensity) of microbial clades b Beta-diversity added by sampled microbial communities OTUs (16S data)Enzyme classes (metagenomic data) eria idia acilli utes eria (%) 100% Abundance 0% rnix 0 100
  • 106. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 be due to selective pressures acting on pathways differentially present among Streptococcus species or strains (Fig. 4b). Indeed, we observed extensive strain-level genomic variation within microbial species in this population, enriched for host-specific structural variants around genomic islands (Fig. 4c). Even with respect to the single Streptococcus mitis strain B6, gene losses associated with these events were common, responses in animal models via the capsular polysaccharide A18 , and in the HMP stool samples this taxon was carried at a level of at least 0.1% in 16% of samples (over 1% abundance in 3%). Bacteroides thetaiotaomicron has been studied for its effect on host gastrointestinal metabolism19 andwaslikewisecommonat46%prevalence.Ontheskin, S. aureus, of particular interest as the cause of methicillin-resistant 0 10 20 30 40 50 60 Other S. sanguinis S. gordonii S. oralis S. thermophilus S. mitis S. mitis S. peroris S. vestibularis S. australis S. infantis S. salivarius S. parasanguinis RelativeStreptococcusspeciesabundance(%) 127 tongue dorsum samples Average relative Streptococcus abundance 1 500 1000 1500 2000 kb log(RPKM) Choline-binding proteins V-type H+ ATPase subunits 127 tongue dorsum samples Streptococcus mitis B6 Genomic islands Streptococcus mitis V CH S. gordonii Challis S. mitis B6 S. mutans UA159 S. pneumoniae TIGR4 S. pyogenes SF370 S. sanguinis SK36 S. suis 05ZYH33 S. thermophilus LMD9 M00283:PTSsystem,ascorbate-specificIIcpnt M00280:PTSsystem,glucitol/sorbitol-specificIIcpnt M00279:PTSsystem,galactitol-specificIIcpnt M00277:PTSsystem,N-acetylgalactosamine−specificIIcpnt M00274:PTSsystem,mannitol-specificIIcpnt M00270:PTSsystem,trehalose-specificIIcpnt M00269:PTSsystem,sucrose-specificIIcpnt 00265:PTSsystem,glucose-specificIIcpnt M00159:V-typeATPase,prokaryotes a b c d 1 0 –1 log(RPKM) –2 –1 0 0.5 1 Figure 4 | Microbial carriage varies between subjects down to the species and strain level. Metagenomic reads from 127 tongue samples spanning 90 subjects were processed with MetaPhlAn to determine relative abundances for each species. a, Relativeabundances of 11 distinct Streptococcus spp. In addition to variation in broader clades (see Fig. 2), individual species within a single habitat demonstrate a wide range of compositional variation. Inset illustrates average tongue sample composition. b, Metabolic modules present/absent (grey/white) in KEGG24 reference genomes of tongue streptococci denote selected areas of strain-specific functional differentiation. cpnt, component. c, Comparative genomic coverage for the single Streptococcus mitis B6 strain. Grey dots are median reads per kilobase per million reads (RPKM) for 1-kb windows, grey bars are the 25th to 75th percentiles across all samples, red line the LOWESS-smoothed average. Red bars at the bottom highlight predicted genomic islands27 . Large, discrete, and highly variable islands are commonly under-represented. d, Two islands are highlighted, V (V-type H1 ATPase subunits I, K, E, C, F, A and B) and CH (choline-binding proteins cbp6 and cbp12), indicating functional cohesion of strain-specific gene loss within individual human hosts. 2 1 0 | N A T U R E | V O L 4 8 6 | 1 4 J U N E 2 0 1 2 Macmillan Publishers Limited. All rights reserved©2012
  • 107. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014 metadata, and other potentially important factors such as short- and long-term diet, daily cycles, founder effects such as mode of delivery, variabilityinthehumanmicrobiometoaskhowandwhythesemicrobial communities vary so extensively. Asian Black Mexican Puerto Rican White Race/ethnicity Norm.rel.abundance a M00028: ornithine biosynthesis, glutamate => ornithine (tongue dorsum) M00026: histidine biosynthesis, PRPP => histidine (tongue dorsum) Proteobacteria|Gammaproteobacteria|Enterobacteriales|Enterobacteriaceae|Klebsiella (anterior nares) Proteobacteria|Gammaproteobacteria|Pseudomonadales (antecubital fossa) Vaginal pH (posterior fornix) 3.5 4.0 4.5 M00222: Phosphate transport system, posterior fornix b 3.5 4.0 4.5 5.0 Actinobacteria, mid-vagina Age 20 25 30 35 M00012: Glyoxylate cycle, retroauricular crease c 20 25 30 35 40 Firmicutes, retroauricular crease BMI 20 25 30 M00004: Pentose phosphate pathway, tongue dorsum d 20 25 30 Pseudomonadaceae, throat Figure 5 | Microbial community membership and function correlates with host phenotype and sample metadata. a–d, The pathway and clade abundances most significantly associated (all FDR q , 0.2) using a multivariate linear model with subject race or ethnicity (a), vaginal posterior fornix pH (b), subject age (c) and BMI (d). Scatter plots of samples are shown with lines indicating best simple linear fit. Race/ethnicity and vaginal pH are particularly strong associations; age and BMI are more representative of typically modest phenotypic associations (Supplementary Table 3), suggesting that variation in the healthy microbiota may correspond to other host or environmental factors. RESEARCH ARTICLE
  • 108. Slides for UC Davis EVE161 Course Taught by Jonathan Eisen Winter 2014

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