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Human Distal Gut Microbiome

healthy adults surveyed was dominated by just two bacterial divisions, the Bacteroidetes and

the Firmicutes, which made up >99% of the identified phylogenetic types (phylotypes), and

by one prominent methanogenic archaeon, Methanobrevibacter smithii (2). The human

distal gut microbiome is estimated to contain ≥100 times as many genes as our 2.85–billion

base pair (bp) human genome (1). Therefore, superorganismal view of our genetic landscape

should include genes embedded in our human genome and the genes in our affiliated

microbiome, whereas a comprehensive view of our metabolome would encompass the

metabolic networks based in our microbial communities.

Progress made with 16S rDNA-based enumerations has disclosed significant differences in

community membership between healthy adults (2,3), differences that may contribute to

variations in normal physiology between individuals or that may predispose to disease. For

example, studies of humans and gnotobiotic mouse models indicate that our mutualistic

relations with the gut microbiota influence maturation of the immune system (4), modulate

responses to epithelial cell injury (5), affect energy balance (6), and support

biotransformations that we are ill-equipped to perform on our own, including processing of

xenobiotics (7). However, we are limited by our continued inability to cultivate the majority

of our indigenous microbial community members, biases introduced by preferential

polymerase chain reaction (PCR) amplification of 16S rDNA genes and by our limited

ability to infer organismal function from these gene sequences.

As with soil (8) and ocean (9), metagenomic analysis of complex communities offers an

opportunity to examine in a comprehensive manner how ecosystems respond to

environmental perturbations, and in the case of humans, how our microbial ecosystems

contribute to health and disease. In the current study, we use a metagenomics approach to

reveal microbial genomic and genetic diversity and to identify some of the distinctive

functional attributes encoded in our distal gut microbiome.Sequencing the microbiome

Although whole-genome shotgun sequencing and assembly have historically been applied to

the study of single organisms, recent reports from Venter et al . (9) and Baker et al . (10) have

demonstrated the utility of this approach for studying mixed microbial communities.

Variations in the relative abundance of each member of the microbial community and their

respective genome sizes determine the final depth of sequence coverage for any organism at

a particular level of sequencing. This means that the genome sequences of abundant species

will be well represented in a set of random shotgun reads, whereas lower abundance species

may be represented by a small number of sequences. In fact, the size and depth of coverage

(computed as the ratio between the total length of the reads placed into contigs and the total

size of the contigs) of genome assemblies generated from a metagenomics project can

provide information on relative species abundance.

A total of 65,059 and 74,462 high-quality sequence reads were generated from random DNA

libraries created with fecal specimens of two healthy humans (subjects 7 and 8). These two

subjects, ages 28 and 37, female and male, respectively, had not used antibiotics or any other

medications during the year before specimen collection (11). The combined sequenced distal

gut “microbiome” of subjects 7 and 8 consisted of 17,668 contigs that assembled into 14,572

scaffolds, totaling 33,753,108 bp. The scaffolds ranged in size from 1000 to 57,894 bp and

the contigs from 92 to 44,747 bp. The average depth of sequence coverage in contigs was

2.13-fold. Forty percent of the reads (56,292 total) could not be assembled into contigs, most

likely because of a combination of low depth of coverage and low abundance of some

organisms within the specimens. Together, these singletons accounted for an additional

45,078,063 bp of DNA.

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A total of 50,164 open reading frames (ORFs) were predicted from the data set (25,077 for

subject 7 and 25,087 for subject 8). These ORFs correspond to 19,866 unique database

matches (13,293 for subject 7; 12,273 for subject 8; 5700 that were present in both). ORF-

based alignments against public databases identified 259 contigs in subject 7 and 330 in

subject 8 that could be assigned to members of Archaea, plus 5992 contigs from subject 7

and 7138 from subject 8 assignable to members of Bacteria (table S1). The remaining

contigs either did not match any known ORFs or were ambiguously assigned.

Insight into the diversity within our samples was obtained by comparison of a subset of the

shotgun reads to the completed sequence of Bifidobacterium longum, a member of the lactic

acid bacteria present in the distal gut of healthy humans (12). A total of 1965 reads from the

combined data set from subjects 7 and 8 could be aligned to the genome sequence of B.

longum . These reads represented a total of 1,617,706 bp of DNA sequence, which

corresponds to ~0.7-fold coverage of the B. longum genome. There was a great deal of

heterogeneity in nucleotide sequence in the 1965 reads that aligned with the B. longum

genome sequence (80 to 100% identity) with 52% of the reads aligned at less than 95%

identity (Fig. 1A). These data suggest that these reads are not derived from a single discrete

strain of B. longum in subjects 7 and 8, but instead, reflect the presence of multiple strains,

as well as other Bifidobacterium phylotypes in the distal gut microbiota.

Previous work (2) has shown that archaeal species, in particular M. smithii , are also major

players in the human distal gut ecosystem. M. smithii was represented in our data set at ~3.5-

fold coverage, as indicated by the 7955 shotgun reads that matched this draft assembly (Fig.

1B). The presence of M. smithii is also supported by the identification of eight partial-length

16S rDNA sequences with 99.65 to 100% identity to M. smithii . Unlike B. longum , the

majority (89%) of alignments to M. smithii had 95% or better sequence identity to the draft

assembly, indicating low divergence between Methanobrevibacter strains present in our

samples. More than half of the archaeal contigs in our data set had significant similarity to

M. smithii : 145 of 259 archaeal contigs in subject 7 and 174 of 330 archaeal contigs from

subject 8 had matches ≥100 bases, and ≥80% identity to a deep draft assembly of this

genome (13), consistent with previous reports on the abundance of this species in the human

gut.Identifying phylotypes

We explored bacterial diversity in both stool samples with analysis of 16S rDNA sequences

from the random shotgun assemblies and from libraries of cloned, PCR-amplified 16S

rDNA. Phylogenetic assessments of the local microbial community census provide a

benchmark for interpreting the functional predictions from metagenomic data. Of the 237

partial bacterial-length 16S rDNA sequences identified in the shotgun assemblies, we

selected 132 bacterial sequences for further analysis (2,11). Using a 97% minimum pair-

wise similarity definition, 72 bacterial phylotypes were identified. Only one archaeal

phylotype was identified (i.e., M. smithii ). Sixteen bacterial phylotypes (22.2%) were novel,

and 60 (83.3%) represented uncultivated species. The bacterial phylotypes were assigned to

only two divisions, the Firmicutes (62 phylotypes, 105 sequences) and the Actinobacteria

(10 phylotypes, 27 sequences). Sixty of the Firmicute phylotypes belonged to the class

Clostridia , including Clostridia cluster XIV and Faecalibacteria . Analysis of 2062 near–

full-length PCR-amplified 16S rDNA sequences (1024 from subject 7 and 1038 from subject

8) revealed a similar phylogenetic distribution among higher-order taxa, but a more diverse

population at the species level. Using a ≥97% similarity phylotype threshold, 151

phylotypes were identified (23% novel; 150 Firmicutes ; 1 Actinobacteria ) (fig. S1A).

Similar analyses based on a ≥99% similarity threshold are provided (11).

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Although there were no Bacteroidetes 16S rDNA sequences identified in the random

assemblies and clone libraries, amplification with species-specific 16S rDNA primers

yielded sequences from Bacteroides fragilis and Bacteroides uniformis . This relative paucity

of Bacteroidetes sequences is in conflict with data from other studies (2,3). This discrepancy

may have been caused by the known biases associated with the fecal lysis and DNA

extraction methods used in the current study with respect to Bacteroides spp. (14); although

less likely, it is also possible that members of the Bacteroidetes division are less abundant in

the feces of subjects 7 and 8. In addition, with respect to the PCR-amplified 16S rDNA

sequence data, there may be biases associated with the primers or PCR reaction conditions.

Similar arguments may apply to other underrepresented taxa as well, such as the

Actinobacteria and Proteobacteria phyla. Estimates of diversity indicated that at least 300

unique bacterial phylotypes would be detected with continued sequencing from these stool

samples (fig. S1, B to D).Comparative functional analysis of the distal gut microbiome To delineate how the human distal gut microbiome endows us with physiological properties that we have not had to evolve on our own, we explored the metabolic potential of the microbiota in subjects 7 and 8 using KEGG (Kyoto Encyclopedia of Genes and Genomes,version 37) pathways and COGs (Clusters of Orthologous Groups) (15,16). Both annotation schemes contain categories of metabolic functions organized in multiple hierarchical levels:KEGG analysis maps enzymes onto known metabolic pathways; COG analysis uses evolutionary relations (orthologs) to group functionally related genes. Odds ratios were used to rank the relative enrichment or underrepresentation of COG and KEGG categories. An odds ratio of one indicates that the community DNA has the same proportion of hits to a given category as the comparison data set; an odds ratio greater than one indicates enrichment (more hits to a given category than expected), whereas an odds ratio less than one indicates underrepresentation (fewer hits to a given category than expected). Odds ratios

for the KEGG pathway involved in biosynthesis of peptidoglycan (table S3), a major

component of the bacterial cell wall, are consistent with expectations: The human gut

microbiome is highly enriched relative to the human genome (77.88), similar to all

sequenced bacteria (1.83), and moderately enriched relative to all sequenced Archaea (7.06).

Because we have not obtained saturation (see below), we cannot be confident that a given

COG or KEGG pathway component is not present in the human distal gut microbiome.

Therefore, we have focused our analysis on identified functional categories that are enriched

relative to previously sequenced genomes.

BLAST comparisons of all sequences yielded 62,036 hits to the COG database,

corresponding to 2407 unique COGs. ACE and Chao1 estimates of community richness

were 2558 and 2553 COGs, respectively. This observed degree of community COG

diversity is greater than that described for an acid mine drainage (1824 COGs), but less than

that described for whale fall (3332), soil (3394), and Sargasso Sea samples (3714) (17). The

number of KEGG pathways and COG terms enriched in the human distal gut microbiomes

of subjects 7 and 8 is listed in table S2. KEGG maps and COG assignments can be found at

(11,18,19).

The metabolome of the human distal gut microbiota

Both human subjects showed similar patterns of enrichment for each COG (Fig. 2) and

KEGG (Fig. 3) category involved in metabolism. However, compared with subject 7,

subject 8 was enriched for energy production and conversion; carbohydrate transport and

metabolism; amino acid transport and metabolism; coenzyme transport and metabolism; and

secondary metabolites biosynthesis, transport, and catabolism (Fig. 2). At this time, it is not

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clear whether these differences reflect limited coverage of their microbiomes or other factors such as host diet, genotype, and life-style. The analysis presented below combines the genes identified in the fecal microbiotas of both subjects to create an aggregate “human distal gut microbiome.”The plant polysaccharides that we commonly consume are rich in xylan-, pectin-, and arabinose-containing carbohydrate structures. The human genome lacks most of the enzymes required for degrading these glycans (20). However, the distal gut microbiome provides us with this capacity (1) (Fig. 3 and tables S3 and S4). The human gut microbiome is enriched for genes involved in starch and sucrose metabolism (fig. S2) plus the metabolism of glucose, galactose, fructose, arabinose, mannose, and xylose (table S4). At least 81 different glycoside hydrolase families are represented in the microbiome, many of which are not present in the human “glycobiome” (table S5).Host mucus provides a consistent reservoir of glycans for the microbiota and thus, in principle, can serve to mitigate the effects of marked changes in the availability of dietary polysaccharides (1). Gnotobiotic mouse models of the human gut microbiota have indicated that α-linked terminal fucose in host glycans is an attractive and accessible source of energy for members of the microbiota such as the Bacteroidetes (1,6). Several COGs responsible for fucose utilization are enriched in the human gut microbiome relative to all microbial genomes (table S4).Fermentation of dietary fiber or host-derived glycans requires cooperation of groups of microorganisms linked in a trophic chain. Primary fermenters process glycans to short-chain fatty acids (SCFAs), mainly acetate, propionate, and butyrate, plus gases (i.e., H 2 and CO 2).The bulk of SCFAs are absorbed by the host: Together, they account for ~10% of calories extracted from a Western diet each day (21). COG analyses demonstrated enrichment of key genes involved in generating acetate, butyrate, lactate, and succinate in the gut microbiome compared with all microbial genomes in the COG database (table S6). The most enriched

COG was related to butyrate kinase (odds ratio of 9.30), an enzyme that facilitates formation

of butyryl-coenzyme A by phosphorylating butyrate. This enrichment underscores the

important commitment of the distal gut microbiota to generating this biologically significant

SCFA, which serves as the principal energy source for colonocytes and may fortify the

intestinal mucosal barrier by stimulating their growth (22).

Accumulation of H 2, an end product of bacterial fermentation, reduces the efficiency of

processing of dietary polysaccharides (23). Production of methane by mesophilic

methanogenic archaeons is a major pathway for removing H 2 from the human distal gut

(23), although sulfate reduction and homoacetogenesis serve as alternate pathways.

Enhancement of bacterial growth rates, fermentation of polysaccharides, and SCFA

production have been observed when bacteria (e.g., Fibrobacter succinogenes and

Ruminococcus flavefaciens ) are cocultured with a Methanobrevibacter species (24). The

distal gut microbiome is enriched for many COGs representing key genes in the

methanogenic pathway (Fig. 4, C and D), consistent with the importance of H 2 removal

from the distal gut ecosystem via methanogenesis.

The distal gut microbiome is enriched for a variety of COGs involved in synthesis of

essential amino acids and vitamins (tables S7 and S8). COGs representing enzymes in the

MEP (2-methyl-D -erythritol 4-phosphate) pathway, used for biosynthesis of deoxyxylulose

5-phosphate (DXP) and isopenteryl pyrophosphate (IPP), are notably enriched (P < 0.0001;

relative to all sequenced microbes) (Fig. 4, A and B). DXP is a precursor in the biosynthesis

of vitamins essential for human health, including B 1 (thiamine) and B 6 (pyridoxal form)

(25). IPP is found in all known prokaryotic and eukaryotic cells and can give rise to at least

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25,000 known derivatives, including archaeal membrane lipids (26), carotenoids (27), and

cholesterol (28). Together, these results indicate that the MEP pathway is much better

represented in the distal human gut microbiome than was previously known. The MEP

pathway has been proposed as a target for developing new antibiotics, because some

pathogenic bacteria use the MEP pathway instead of the mevanolate pathway for IPP

biosynthesis (29). However, our metagenomic study indicates that this approach may be

detrimental to the microbiota and, in turn, the host.

Detoxification of xenobiotics could impact the host in a variety of ways, ranging from

susceptibility to cancer to the efficiency of drug metabolism. Dietary plant–derived

phenolics, such as flavonoids and cinnamates, have pronounced effects on mammalian cells

(30–32). Hydrolysis of phenolic glycosidic or ester linkages occurs in the distal gut by

microbial β-glucosidases, β-rhamnosidases, and esterases (33). The human distal gut

microbiome is enriched for β-glucosidase (COG1472, COG2723 in table S4; P < 0.0005;

glycosidase families GH3 and GH9 in table S5). Glucuronide conjugates of xenobiotics and

bile salts induce microbial β-glucuronidase activity (34). The microbiome is enriched in this

enzyme activity (i.e., COG3250; table S4). KEGG analysis also indicates enrichment for

pathways involved in degradation of tetrachloroethene, dichloroethane, caprolactam, and

benzoate (table S3).Conclusion This metagenomics analysis begins to define the gene content and encoded functional attributes of the gut microbiome in healthy humans. Future studies are needed to provide deeper coverage of the microbiome and to assess the effects of age, diet, and pathologic states (e.g., inflammatory bowel diseases, obesity, and cancer) on the distal gut microbiome of humans living in different environments. Periodic sampling of the distal gut microbiome (and of our other microbial communities) may provide insights into the effects of

environmental change on our “microevolution.” The results should provide a broader view

of human biology, including new biomarkers for defining our health; new ways for

optimizing our personal nutrition; new ways for predicting the bio-availability of orally

administered drugs; and new ways to forecast our individual and societal predispositions to

disorders such as infections with pathogens, obesity, and misdirected or maladapted host

immune responses of the gut.

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

References and Notes

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8. Rondon MR, et al. Appl. Environ. Microbiol 2000;66:2541. [PubMed: 10831436]

9. Venter JC, et al. Science 2004;304:66. [PubMed: 15001713]

10. Tyson GW, et al. Nature 2004;428:25. [PubMed: 14999264]

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11. Materials and methods are available as supporting material on Science Online.12. Schell MA, et al. Proc. Natl. Acad. Sci. U.S.A 2002;99:14422. [PubMed: 12381787]13. (https://www.sodocs.net/doc/2712018646.html,/supplemental/Gill/Msmithii/draftgenome/)14. McOrist AL, Jackson M, Bird AR. J. Microbiol. Methods 2002;50:131. [PubMed: 11997164]15. Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. Nucleic Acids Res 2004;32:D277.[PubMed: 14681412]16. Tatusov RL, et al. BMC Bioinformatics 2003;4:41. [PubMed: 12969510]17. Tringe SG, et al. Science 2005;308:554. [PubMed: 15845853]18. (https://www.sodocs.net/doc/2712018646.html,/supplemental/Gill)19. Database available as supporting material on Science Online.20. URL. (https://www.sodocs.net/doc/2712018646.html,rs-mrs.fr/CAZY/)21. McNeil NI. Am. J. Clin. Nutr 1984;39:338. [PubMed: 6320630]22. Topping DL, Clifton PM. Physiol. Rev 2001;81:1031. [PubMed: 11427691]23. Stams AJ. Antonie Van Leeuwenhoek 1994;66:271. [PubMed: 7747937]24. Rychlik JL, May T. Curr. Microbiol 2000;40:176. [PubMed: 10679049]25. Rodriguez-Concepcion M, Boronat A. Plant Physiol 2002;130:1079. [PubMed: 12427975]26. Pereto J, Lopez-Garcia P, Moreira D. Trends Biochem. Sci 2004;29:469. [PubMed: 15337120]27. Umeno D, Tobias AV, Arnold FH. Microbiol. Mol. Biol. Rev 2005;69:51. [PubMed: 15755953]28. Wang KC, Ohnuma S. Biochim. Biophys. Acta 2000;1529:33. [PubMed: 11111076]29. Begley M, et al. FEBS Lett 2004;561:99. [PubMed: 15013758]30. Reiners JJ Jr, Clift R, Mathieu P. Carcinogenesis 1999;20:1561. [PubMed: 10426807]31. Rice-Evans CA, Miller NJ, Paganga G. Free Radic. Biol. Med 1996;20:933. [PubMed: 8743980]32. Williamson G, Plumb GW, Uda Y, Price KR, Rhodes MJ. Carcinogenesis 1996;17:2385.[PubMed: 8968052]33. Schneider H, Schwiertz A, Collins MD, Blaut M. Arch. Microbiol 1999;171:81. [PubMed:9914304]

34. Mallett AK, Bearne CA, Rowland IR. Appl. Environ. Microbiol 1983;46:591. [PubMed: 6416166]

35. We thank W. Nelson and I. Hance (The Institute for Genomic Research), L. Dethlefsen and E. Bik

(Stanford), and D. Leip (Washington University) for their valuable assistance. This work was

supported by Defense Advanced Research Projects Agency (DARPA) and the Office of Naval

Research grant no. ONR-N00014-02-1-1002 (S.R.G., K.E.N.), the W. M. Keck Foundation

(J.I.G.), the Ellison Medical Foundation (D.A.R., J.I.G.), and NIH grants AI51259 (D.A.R.) and

DK70977 (J.I.G.). B.S.S. is a recipient of a graduate research fellowship from the NSF

(DGE-0202737). This whole-genome shotgun project has been deposited at the DNA Data Bank

of Japan (DDBJ), European Molecular Biology Laboratory (EMBL), and GenBank under the

project accession AAQK00000000 (subject 7) and AAQL00000000 (subject 8). The version

described in this paper is the first version, AAQK01000000 and AAQL01000000. All near–full-

length 16S rDNA sequences were deposited at DDBJ/EMBL/GenBank under the accessions

DQ325545 to DQ327606.

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Fig. 1.

Comparison of random metagenome reads with completed genome of Bifidobacterium

longum and Methanobrevibacter smithii . (A ) Percent identity plot (PIP) of alignments of

shotgun reads along the genome of B. longum strain NCC2705. The x axis represents the

coordinate along the genome, and the y axis represents the percent identity of the match. (B )

Percent identity plot (PIP) of the alignment of shotgun reads along the draft genome of M.

smithii . The x axis represents the coordinate along a pseudomolecule formed by

concatenating all contigs in the M. smithii draft assembly. The y axis represents the percent

identity of the match. The variation in the percent identity of the matches between the

shotgun reads from subjects 7 and 8 as compared with the genome sequences of B. longum

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NCC2705 suggests considerable diversity among Bifidobacterium-like organisms within our

samples. Alignments of the reads to the draft genome of M. smithii exhibit a much narrower

range of percent identity (89% of alignments were at 95% or better identity as compared NIH-PA Author Manuscript

with 48% for B. longum), consistent with lower levels of diversity among archaeal members

of the gastrointestinal tract. NIH-PA Author Manuscript

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Fig. 2.

COG analysis reveals metabolic functions that are enriched or underrepresented in the

human distal gut microbiome (relative to all sequenced microbes). Color code: black,

subject 7; gray, subject 8. Bars above both dashed lines indicate enrichment, and bars below

both lines indicate underrepresentation (P < 0.05). Asterisks indicate categories that are

significantly different between the two subjects (P < 0.05). Secondary metabolites

biosynthesis includes antibiotics, pigments, and nonribosomal peptides. Inorganic ion

transport and metabolism includes phosphate, sulfate, and various cation transporters. NIH-PA Author Manuscript

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Fig. 3.KEGG pathway reconstructions reveal metabolic functions that are enriched or underrepresented in the human distal gut microbiome as follows: both samples compared with all sequenced bacterial genomes in KEGG (blue), the human genome (red), and all sequenced archaeal genomes in KEGG (yellow). Asterisks indicate enrichment (odds ratio >1, P < 0.05) or underrepresentation (odds ratio < 1, P < 0.05). The KEGG category,“metabolism of other amino acids,” includes amino acids that are not incorporated into proteins, such as β-alanine, taurine, and glutathione. Odds ratios are a measure of relative gene content based on the number of independent hits to enzymes present in a given KEGG category.

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Fig. 4.

Isoprenoid biosynthesis via the MEP pathway and methanogenesis are highly enriched in the

distal gut microbiome. (A) MEP pathway for isoprenoid biosynthesis. (B) Odds ratio for

each COG in the MEP pathway. All enzymes necessary to convert DXP to IPP and thiamine

are enriched (P < 0.0001 relative to all sequenced microbes). (C) Location and role of key

enzymes in methanogenesis. (D) Odds ratio for each COG highlighted in (C). NIH-PA Author Manuscript

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PEP六年级上册英语教案全册

Unit 1How can I get there? 第一课时一、教学内容 Part A Let's try & Let's talk 二、教学目标 1．能够听、说、读、写句子：“Where is the museum shop？”“It's near the door.”。 2．能够听、说、认读单词ask、sir和句型“Is there a…？”“I want to…”“What a great museum！”。三、教学重难点 1．学习句子“Where is the museum shop？”“It's near the door.”。 2．正确使用方位介词。四、教学准备单词卡、录音机、磁带。五、教学过程 Step 1 热身(Warming－up) Let's do Go to the bookstore.Buy some books. Go to the post office.Send a letter. Go to the hospital.See the doctor. Go to the cinema.See a film.

Go to the museum.See some robots. Step 2 新课呈现(Presentation) 1．学习Let's try (1)打开课本读一读Let's try中呈现的问题和选项。 (2)播放录音，让学生听完后勾出正确的选项。 (3)全班核对答案。 2．学习Let's talk (1)播放Let's talk的录音，学生带着问题听录音：Where is the museum shop？Where is the post office？听完录音后让学生回答这两个问题，教师板书：It's near the door.It's next to the museum.教师讲解：near表示“在附近”，next to表示“与……相邻”，它的范围比near小。最后让学生用near和next to来讲述学校周围的建筑物。 (2)讲解“A talking robot！What a great museum！”，让学生说说这两个感叹句的意思。 (3)再次播放录音，学生一边听一边跟读。 (4)分角色朗读课文。 Step 3 巩固与拓展(Consolidation and extension) 1．三人一组分角色练习Let's talk的对话，然后请一些同学到台前表演。 2．教学Part A：Talk about the places in your city/town/village.

人教版六年级上册英语知识点总结

人教版六年级英语上册各单元知识点汇总 Unit 1 How do you go to school？一、重点短语： by plane 坐飞机 by ship 坐轮船 on foot 步行 by bike 骑自行车 by bus 坐公共汽车 by train 坐火车 traffic lights 交通灯 traffic rules 交通规则 go to school 去上学 get to 到达 get on 上车 get off 下车Stop at a red light. 红灯停Wait at a yellow light. 黄灯等Go at a green light. 绿灯行二、重点句型： 1.How do you go to school？你怎么去上学？ https://www.sodocs.net/doc/2712018646.html,ually I go to school on foot. Sometimes I go by bus. 通常我步行去上学。有时候骑自行车去。 3.How can I get to Zhongshan Park ？我怎么到达中山公园？ 4.You can go by the No. 15 bus. 你可以坐 15 路公共汽车去。三、重点语法： 1、There are many ways to go somewhere.到一个地方去有许多方法。这里的 ways 一定要用复数。因为 there are 是There be 句型的复数形式。 2、on foot 步行乘坐其他交通工具大都可以用介词by…，但是步行只能用介词 on 。 4、go to school 的前面绝对不能加 the，这里是固定搭配。 5、USA 和 US 都是美国的意思。另外America 也是美国的意思。 6、go to the park 前面一定要加the. 如果要去的地方有具体的名字，就不能再加 the ，如果要去的地方没有具体名字，都要在前面加 the. （ go to school 除外。） 7、How do you go to …？你怎样到达某个地方？如果要问的是第三人称单数，则要用： How does he/she…go to …？ 8、反义词： get on（上车）---get off（下车） near（近的）—far（远的） fast（快的）—slow（慢的） because（因为）—why（为什么） same（相同的）—different（不同的） 9、近义词： see you---goodbye sure---certainly---of course 10、频度副词： always 总是，一直 usually 通常 often 经常 sometimes 有时候 never 从来不 Unit 2 Where is the science museum？一、重点短语： library 图书馆 post office 邮局 hospital 医院 cinema 电影院

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（）（）（） ( ) ( ) ( ) 四、听录音，判断下列句子与你所听到的内容是否相符，相符的写“√”，不相符的写“×”。（5分） ( ) l. Mike should see a doctor in the morning. ( ) 2. Amy’s father works at sea. ( ) 3. Mike’s brother is a postman. ( ) 4. Peter likes doing kung fu and swimming. ( ) 5. We are going to Beijing next Tuesday. 五、听录音，判断下列图片与你所听到的内容是否相符，相符的打“√”，不相符的打“×”。（6分） 1. ( ) 2. ( ) 3. ( ) 4. ( ) 5. ( ) 6. ( ) 六、听录音，选择相应的答语，将其序号填入题前括号里。（7分） ( ) 1. A. He’s over there. B. Thank you. C. Great! ( ) 2. A. Here you are. B. Sure. C. Thanks. ( ) 3. A. Please turn left. B. It’s near the door.

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PEP7最新六年级下册Unit4-Unit6英语课文翻译 Unit4 I have a pen pal.单词表：学习的三单形式，迷，远足，笔友，业余爱好，茉莉，想法主意，堪培拉，令人惊奇的，表示征求意见，射门，加入，俱乐部，分享 P38Let’s talk奥利弗：彼得的爱好是什么？张鹏：他喜欢读故事。他住在一个农场上，所以有时他读给那些奶牛！奥利弗：那是有趣的。张鹏：他喜欢练功夫和游泳。奥利弗：真的吗？我也是。张鹏：他也喜欢唱歌。奥利弗：哦，你也喜欢唱歌。张鹏：是的。我打算教他中国歌曲《茉莉花》！奥利弗：好主意！P39Let’s learn彼得：嘿，张鹏，你的爱好是什么？张鹏：我喜欢读故事。我也喜欢唱歌和练功夫。跳舞唱歌读故事踢足球练功夫 P40Let’s talk约翰：嘿，一。你正在干什么？（现在进行时）吴一凡：我正在写一份电子邮件给我的朋友在澳大利亚。约翰：他住在悉尼吗？吴一凡：不，他不是。他住在堪培拉。他的名字是约翰，也。约翰：真的吗？他喜欢猜字谜和去远足吗？吴一凡:是的，他是。约翰：太神奇了！我也喜欢这些。我也能成为他的笔友吗？吴一凡：当然。为什么不呢？约翰：酷！ P41Let’s learn约翰：过来并且看看我的新的笔友。他的名字也是约翰！兄弟：真的吗？他也住在中国吗？约翰：不，他不在。他住在澳大利亚，但是他学语文。烧中国食物学语文猜字谜去远足 P42let’s read布告栏：我们能跳舞吗？有一个跳舞课在星期天在下午一点。我喜欢跳舞,兵器我需要一个搭档。打电话给艾米：3345567射门！射门！射门让我们一起读。你的爱好是什么？你喜欢阅读吗？我有很棒的书。我们能分享！打电话给迈克：4435678 科学俱乐部，你的俱乐部。你想学习关于机器人吗？来到科学房间。见见罗宾。他教学生制作机器人。robin Unit 5 What does he do?单词表：工厂，工人，邮递员，商人企业家，警察，渔民科学家，飞行员，教练，国家，校长，大海，保持，大学，体育馆，如果，记者，使用，打字，快速地，秘书 P48Let’s talk莎拉：你的爸爸是在这里今天吗？奥利弗：不，他在澳大利亚。莎拉：他是干什么的？奥利弗：他是一个商人。他经常去其它的城市。莎拉：并且你的妈妈是干什么的？奥利弗：她是一个班主任。莎拉：那是好的。奥利弗：是的，她将要是在这里今天。莎拉：你想成为一个班主任吗？奥利弗：不，我想成为一个出租车司机。 P49Let’s learn职业日工厂工人邮递员商人警察张鹏：你的爸爸是一个邮递员吗？奥利弗：不，他不是。张鹏：他是干什么的？：奥利弗：他是一个商人。 P50Let’s talk迈克：我的叔叔是一个渔夫。小雨：他在哪里工作？迈克：他工作在海上。他看到很多鱼每天。小雨：我知道了。他怎么去工作的？乘小船？迈克：不，他工作在小船上。他骑自行车去工作。小雨：他有一个很健康的生活。迈克：是的。他工作非常努力并且保持健康。小雨：我们也应该努力学习并且保持健康。 P51Let’s learn渔民科学家飞行员教练奥利弗：我的叔叔是科学家。吴一凡：他在哪里工作？奥利弗：他工作在一个大学。 P52let’s read胡斌喜欢运动。他擅长足球，乒乓球和篮球。他经常去跑步放学后。他想工作在一个体育馆。提示：

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人教版六年级英语上册期末重点知识复习资料六年级上册复习要点 Unit1 How can I get there? 一、重点单词：地点：science museum科学博物馆post office 邮局bookstore 书店 cinema 电影院hospital 医院动作：go straight 直走turn left/right 左转、右转方位：in front of :在···前面behind 在···后面near在…旁边next to 紧挨着beside 在旁边 over 在…上方on the left 在左边on the right 在右边二、重点句型：（1）Is / Are there…？某处有某物吗？肯定回答：Yes, there is/are. 否定回答：No, there isn’t/aren’t. （2）Where is the + 地点？... ... 在哪里？ It’s + 表示地点的名词. 它... ... 例句：Where is the cinema? 电影院在哪？ It’s next to the bookstore. 在书店的旁边。（3）How can + 主语+get（to）+ 地点? ... ...怎么到... ...? （如果get后面接的词为副词，则要省略介词to.）例句：我们怎么到那儿？同义句型：Can you tell me the way to + 地点？ ( 4 )Where is + 地点？Which is the way to + 地点？到书店左转。 Unit 2 Ways to go to school? 一、重点单词/短语：交通方式：by bike /bus /plane /subway /train /ship /taxi /ferry 骑自行车/乘公共汽车/飞机/地铁/火车/船/出租汽车/轮渡take the No.57 bus 乘57路公共汽车 on foot 步行其他：slow down慢下来pay attention to 注意traffic lights 交通信号灯look right 向右看cross the road 横穿马路at home 在家二、重点句型：（1）How do you come (to) + 地点？你们怎么来... 的？我通常/经常/有时... How do you go(to) + 地点？你们怎么去...的？ How do you get (to) + 地点？你们怎么到达...的？

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