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A Proteome-Scale Map of the Human Interactome Network

Resource A Proteome-Scale Map

of the Human Interactome Network

Thomas Rolland,1,2,19Murat Tas x an,1,3,4,5,19Benoit Charloteaux,1,2,19Samuel J.Pevzner,1,2,6,7,19Quan Zhong,1,2,8,19 Nidhi Sahni,1,2,19Song Yi,1,2,19Irma Lemmens,9Celia Fontanillo,10Roberto Mosca,11Atanas Kamburov,1,2

Susan D.Ghiassian,1,12Xinping Yang,1,2Lila Ghamsari,1,2Dawit Balcha,1,2Bridget E.Begg,1,2Pascal Braun,1,2

Marc Brehme,1,2Martin P.Broly,1,2Anne-Ruxandra Carvunis,1,2Dan Convery-Zupan,1,2Roser Corominas,13

Jasmin Coulombe-Huntington,1,14Elizabeth Dann,1,2Matija Dreze,1,2Ame′lie Dricot,1,2Changyu Fan,1,2Eric Franzosa,1,14 Fana Gebreab,1,2Bryan J.Gutierrez,1,2Madeleine F.Hardy,1,2Mike Jin,1,2Shuli Kang,13Ruth Kiros,1,2Guan Ning Lin,13 Katja Luck,1,2Andrew MacWilliams,1,2Jo¨rg Menche,1,12Ryan R.Murray,1,2Alexandre Palagi,1,2Matthew M.Poulin,1,2 Xavier Rambout,1,2,15John Rasla,1,2Patrick Reichert,1,2Viviana Romero,1,2Elien Ruyssinck,9Julie M.Sahalie,1,2 Annemarie Scholz,1,2Akash A.Shah,1,2Amitabh Sharma,1,12Yun Shen,1,2Kerstin Spirohn,1,2Stanley Tam,1,2 Alexander O.Tejeda,1,2Shelly A.Trigg,1,2Jean-Claude Twizere,1,2,15Kerwin Vega,1,2Jennifer Walsh,1,2

Michael E.Cusick,1,2Yu Xia,1,14Albert-La′szlo′Baraba′si,1,12,16Lilia M.Iakoucheva,13Patrick Aloy,11,17

Javier De Las Rivas,10Jan Tavernier,9Michael A.Calderwood,1,2,20David E.Hill,1,2,20Tong Hao,1,2,20

Frederick P.Roth,1,3,4,5,18,*and Marc Vidal1,2,*

1Center for Cancer Systems Biology(CCSB)and Department of Cancer Biology,Dana-Farber Cancer Institute,Boston,MA02215,USA

2Department of Genetics,Harvard Medical School,Boston,MA02115,USA

3Departments of Molecular Genetics and Computer Science,University of Toronto,Toronto,ON M5S3E1,Canada

4Donnelly Centre,University of Toronto,Toronto,ON M5S3E1,Canada

5Lunenfeld-Tanenbaum Research Institute,Mount Sinai Hospital,Toronto,ON M5G1X5,Canada

6Department of Biomedical Engineering,Boston University,Boston,MA02215,USA

7Boston University School of Medicine,Boston,MA02118,USA

8Department of Biological Sciences,Wright State University,Dayton,OH45435,USA

9Department of Medical Protein Research,VIB,9000Ghent,Belgium

10Cancer Research Center(Centro de Investigacio′n del Cancer),University of Salamanca and Consejo Superior de Investigaciones

Cient?′?cas,Salamanca37008,Spain

11Joint IRB-BSC Program in Computational Biology,Institute for Research in Biomedicine(IRB Barcelona),Barcelona08028,Spain

12Center for Complex Network Research(CCNR)and Department of Physics,Northeastern University,Boston,MA02115,USA

13Department of Psychiatry,University of California,San Diego,La Jolla,CA92093,USA

14Department of Bioengineering,McGill University,Montreal,QC H3A0C3,Canada

15Protein Signaling and Interactions Lab,GIGA-R,University of Liege,4000Liege,Belgium

16Department of Medicine,Brigham and Women’s Hospital,Harvard Medical School,Boston,MA02115,USA

17Institucio′Catalana de Recerca i Estudis Avanc?ats(ICREA),Barcelona08010,Spain

18Canadian Institute for Advanced Research,Toronto M5G1Z8,Canada

19Co-?rst author

20Co-senior author

*Correspondence:fritz.roth@utoronto.ca(F.P.R.),marc_vidal@https://www.wendangku.net/doc/b716413431.html,(M.V.)

https://www.wendangku.net/doc/b716413431.html,/10.1016/j.cell.2014.10.050

SUMMARY

Just as reference genome sequences revolutionized human genetics,reference maps of interactome networks will be critical to fully understand geno-type-phenotype relationships.Here,we describe a systematic map of$14,000high-quality human bi-nary protein-protein interactions.At equal quality, this map is$30%larger than what is available from small-scale studies published in the literature in the last few decades.While currently available informa-tion is highly biased and only covers a relatively small portion of the proteome,our systematic map appears strikingly more homogeneous,revealing a‘‘broader’’human interactome network than currently appre-ciated.The map also uncovers signi?cant inter-connectivity between known and candidate cancer gene products,providing unbiased evidence for an expanded functional cancer landscape,while demon-strating how high-quality interactome models will help‘‘connect the dots’’of the genomic revolution. INTRODUCTION

Since the release of a high-quality human genome sequence a decade ago(International Human Genome Sequencing Con-sortium,2004),our ability to assign genotypes to phenotypes has exploded.Genes have been identi?ed for most Mendelian dis-orders(Hamosh et al.,2005)and over100,000alleles have been implicated in at least one disorder(Stenson et al.,2014).Hundreds of susceptibility loci have been uncovered for numerous complex traits(Hindorff et al.,2009)and the genomes of a few thousand hu-man tumors have been nearly fully sequenced(Chin et al.,2011). This genomic revolution is poised to generate a complete descrip-tion of all relevant genotypic variations in the human

population.

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Genomic sequencing will,however,if performed in isolation, leave fundamental questions pertaining to genotype-phenotype relationships unresolved(Vidal et al.,2011).The causal changes that connect genotype to phenotype remain generally unknown, especially for complex trait loci and cancer-associated mu-tations.Even when identi?ed,it is often unclear how a causal mu-tation perturbs the function of the corresponding gene or gene product.To‘‘connect the dots’’of the genomic revolution,func-tions and context must be assigned to large numbers of geno-typic changes.

Complex cellular systems formed by interactions among genes and gene products,or interactome networks,appear to underlie most cellular functions(Vidal et al.,2011).Thus,a full understand-ing of genotype-phenotype relationships in human will require mechanistic descriptions of how interactome networks are per-turbed as a result of inherited and somatic disease susceptibil-ities.This,in turn,will require high-quality and extensive genome and proteome-scale maps of macromolecular interactions such as protein-protein interactions(PPIs),protein-nucleic acid inter-actions,and posttranslational modi?ers and their targets.

First-generation human binary PPI interactome maps(Rual et al.,2005;Stelzl et al.,2005)have already provided network-based explanations for some genotype-phenotype relation-ships,but they remain incomplete and of insuf?cient quality to derive accurate global interpretations(Figure S1A available on-line).There is a dire need for empirically-controlled(Venkatesan et al.,2009)high-quality proteome-scale interactome reference maps,reminiscent of the high-quality reference genome sequence that revolutionized human genetics.

The challenges are manifold.Even considering only one splice variant per gene,approximately20,000protein-coding genes (Kim et al.,2014;Wilhelm et al.,2014)must be handled and $200million protein pairs tested to generate a comprehensive bi-nary reference PPI map.Whether such a comprehensive network could ever be mapped by the collective efforts of small-scale studies remains https://www.wendangku.net/doc/b716413431.html,putational predictions of protein interactions can generate information at proteome scale(Zhang et al.,2012)but are inherently limited by biases in currently avail-able knowledge used to infer such interactome models.Should in-teractome maps be generated for all individual human tissues us-ing biochemical cocomplex association data,or would‘‘context-free’’information on direct binary biophysical interaction for all possible PPIs be preferable?To what extent would these ap-proaches be complementary?Even with nearly complete,high-quality reference interactome maps of biophysical interactions, how can the biological relevance of each interaction be evaluated under physiological conditions?Here,we begin to address these questions by generating a proteome-scale map of the human bi-nary interactome and comparing it to alternative network maps.

RESULTS

Vast Uncharted Interactome Zone in Literature

To investigate whether small-scale studies described in the liter-ature are adequate to qualitatively and comprehensively map the human binary PPI network,we assembled all binary pairs identi-?ed in such studies and available as of2013from seven public databases(Figure S1B,see Extended Experimental Procedures,

Section1).Out of the33,000lit erature b inary pairs extracted,two thirds were reported in only a s ingle publication and detected by only a single method(Lit-BS pairs),thus potentially presenting higher rates of curation errors than binary pairs supported by m ul-tiple pieces of evidence(Lit-BM pairs;Tables S1A,S1B,and S1C) (Cusick et al.,2009).Testing representative samples from both of these sets using the mammalian protein-protein interaction trap (MAPPIT)(Eyckerman et al.,2001)and yeast two-hybrid(Y2H) (Dreze et al.,2010)assays,we observed that Lit-BS pairs were recovered at rates that were only slightly higher than the ran-domly selected protein pairs used as negative control(random reference set;RRS)and signi?cantly lower than Lit-BM pairs(Fig-ure1A and Table S2A;see Extended Experimental Procedures, Section2).Lit-BS pairs co-occurred in the literature signi?cantly less often than Lit-BM pairs as indicated by STRING literature mining scores(Figure1A and Figure S1C;see Extended Experi-mental Procedures,Section2)(von Mering et al.,2003),suggest-ing that these pairs were less thoroughly studied.Therefore,use of binary PPI information from public databases should be restricted to interactions with multiple pieces of evidence in the literature.In2013,this corresponded to11,045high-quality pro-tein pairs(Lit-BM-13),more than an order of magnitude below current estimates of the number of PPIs in the full human interac-tome(Stumpf et al.,2008;Venkatesan et al.,2009).

The relatively low number of high-quality binary literature PPIs may re?ect inspection biases inherent to small-scale studies. Some genes such as RB1are described in hundreds of publi-cations while most have been mentioned only in a few(e.g., the unannotated C11orf21gene).To investigate the effect of such biases on the current coverage of the human interactome network,we organized the interactome search space by ranking proteins according to the number of publications in which they are mentioned(Figure1B).Interactions between highly studied proteins formed a striking‘‘dense zone’’in contrast to a large sparsely populated zone,or‘‘sparse zone,’’involving poorly studied proteins.Candidate gene products identi?ed in genome-wide association studies(GWAS)or associated with Mendelian disorders distribute homogeneously across the pub-lication-ranked interactome space(Figure1B and Figure S1D), demonstrating a need for unbiased systematic PPI mapping to cover this uncharted territory.

A Proteome-wide Binary Interactome Map

Based on literature-curated information,the human interactome appears to be restricted to a narrow dense zone,suggesting that half of the human proteome participates only rarely in the inter-actome network.Alternatively,the zone that appears sparse in the literature could actually be homogeneously populated by PPIs that have been overlooked due to sociological or experi-mental biases.

To distinguish between these possibilities and address other fundamental questions outlined above,we generated a new pro-teome-scale binary interaction map.By acting on all four param-eters of our empirically-controlled framework(Venkatesan et al., 2009),we increased the coverage of the human binary interac-tome with respect to our previous h uman i nteractome data set obtained by investigating a search space de?ned by$7,000pro-tein-coding genes(‘‘Space I’’)and published in2005(HI-I-05) Cell159,1212–1226,November20,2014a2014Elsevier Inc.1213

Figure 1.Vast Uncharted Interactome Zone in Literature and Generation of a Systematic Binary Data Set

(A)Validation of binary literature pairs extracted from public databases (Bader et al.,2003;Berman et al.,2000;Chatr-Aryamontri et al.,2013;Kerrien et al.,2012;Licata et al.,2012;Keshava Prasad et al.,2009;Salwinski et al.,2004).Fraction of pairs recovered by MAPPIT at increasing RRS recovery rates (top left)and at 1%RRS recovery rate (bottom left),found to co-occur in the literature as reported in the STRING database (upper right),and recovered by Y2H (lower right).Shading and error bars indicate standard error of the proportion.p values,two-sided Fisher’s exact tests.For n values,see Table S6.

(B)Adjacency matrix showing Lit-BM-13interactions,with proteins in bins of $350and ordered by number of publications along both axes.Upper and right histograms show the median number of publications per bin.The color intensity of each square re?ects the total number of interactions between proteins for the corresponding bins.Total number of interactions per bin (lower histogram).Number of products from GWAS loci (Hindorff et al.,2009),Mendelian disease (Hamosh et al.,2005),and Sanger Cancer Gene Census (Cancer Census)(Futreal et al.,2004)genes per bin (circles).

(legend continued on next page)

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(Rual et al.,2005)(Figures1C and1D;see Extended Experi-mental Procedures,Section3).A search space consisting of all pairwise combinations of proteins encoded by$13,000genes (‘‘Space II’’;Table S2B)was systematically probed,representing a3.1-fold increase with respect to the HI-I-05search space.To gain in sensitivity,we performed the Y2H assay in different strain backgrounds that showed increased detection of pairs of a pos-itive reference set(PRS)composed of high-quality pairs from the literature without increasing the detection rate of RRS pairs.To increase our sampling,the entire search space was screened twice independently.Pairs identi?ed in this?rst pass were sub-sequently tested pairwise in quadruplicate starting from fresh yeast colonies.To ensure reproducibility,only pairs testing pos-itive at least three times out of the four attempts and with con?rmed identity were considered interacting pairs,resulting in$14,000distinct interacting protein pairs.

We validated these binary interactions using three binary pro-tein interaction assays that rely on different sets of conditions than the Y2H assay:(1)reconstituting a membrane-bound recep-tor complex in mammalian cells using MAPPIT,(2)in vitro using the well-based nucleic acid programmable protein array (wNAPPA)assay(Braun et al.,2009;Ramachandran et al., 2008),and(3)reconstituting a?uorescent protein in Chinese hamster ovary cells using a protein-fragment complementation assay(PCA)(Nyfeler et al.,2005)(see Extended Experimental Procedures,Section4).The Y2H pairs exhibited validation rates that were statistically indistinguishable from a PRS of$500Lit-BM interactions while signi?cantly different from an RRS of $700pairs with all three orthogonal assays and over a large range of score thresholds(Figure1D,Tables S2A and S2C),demon-strating the quality of the entire data https://www.wendangku.net/doc/b716413431.html,ing three-dimensional cocrystal structures available for protein complexes in the Protein Data Bank(Berman et al.,2000)and for domain-domain interac-tions(Stein et al.,2011)(Figure S2and Tables S2D,S2E,and S2F; see Extended Experimental Procedures,Sections5and6),we also demonstrated that our binary interactions re?ect direct bio-physical contacts,a conclusion in stark contrast to a previous report suggesting that Y2H interactions are inconsistent with structural data(Edwards et al.,2002).Our results also suggested that Y2H sensitivity correlates with the number of residue-residue contacts and thus presumably with interaction af?nity.The corresponding h uman i nteractome data set covering Space II and reported in2014(HI-II-14;Table S2G)is the largest experi-mentally-determined binary interaction map yet reported,with 13,944interactions among4,303distinct proteins.

Overall Biological Signi?cance

To assess the overall functional relevance of HI-II-14,we combined computational analyses with a large-scale experi-mental approach.We?rst measured enrichment for shared Gene Ontology(GO)terms and phenotypic annotations and observed that HI-II-14shows signi?cant enrichments that are similar to those of Lit-BM-13(Figures2A and2B;see Extended Experimental Procedures,Section7).Second,we measured how much binary interactions from HI-II-14re?ect membership in larger protein complexes as annotated in CORUM(Ruepp et al.,2010)or reported in a cocomplex association map (Woodsmith and Stelzl,2014).In both cases,we observed a signi?cant enrichment for binary interactions between protein pairs that belong to a common complex(p<0.001;Figure2B). Third,we performed a similar analysis using tissue-speci?c mRNA expression data across the16human tissues of the Illu-mina Human Body Map2.0project as well as cellular compart-ment localization annotations from the GO Slim terms.Again, HI-II-14was enriched for interactions mediated by protein pairs present in at least one common compartment or cell type(Fig-ures2C and2D).Finally,we measured the overlap of HI-II-14 with speci?c biochemical relationships,as represented by kinase-substrate interactions.Both HI-II-14and Lit-BM-13 contained signi?cantly more PPIs re?ecting known kinase-substrate relationships(Hornbeck et al.,2012)than the corre-sponding degree-controlled randomized networks(Figure2E). In addition,HI-II-14tended to connect tyrosine and serine/ threonine kinases(Manning et al.,2002)to proteins with tyro-sine or serine/threonine phospho-sites(Hornbeck et al.,2012; Olsen et al.,2010),respectively(Figure S3A),pointing to the corresponding interactions being genuine kinase-sub-strate interactions.In short,our systematic interactome map, which was generated independently from any pre-existing biological information,reveals functional relationships at levels comparable to those seen for the literature-based interaction map.

To further investigate the overall biological relevance of HI-II-14,we used an experimental approach that compares the impact of mutations associated with human disorders to that of common variants with no reported phenotypic consequences on biophysical interactions(Figure3).Our rationale is that a set of interactions corresponding to genuine functional relationships should more likely be perturbed by disease-associated mu-tations than by common variants.The following example will illustrate this concept.Mutations R24C and R24H in CDK4are clearly associated with melanoma by conferring resistance to CDKN2A inhibition(Wo¨lfel et al.,1995),whereas N41S and S52N mutations are of less clear clinical signi?cance(Zhong et al.,2009)and have remained functionally uncharacterized. HI-II-14contains?ve CDK4interactors:two inhibitors(CDKN2C and CDKN2D),two cyclins(CCND1and CCND3),and HOOK1,a novel interacting partner and a potential phosphorylation target

(C)Improvements from?rst-generation to second-generation interactome mapping based on an empirically-controlled framework(Venkatesan et al.,2009). Completeness:fraction of all pairwise protein combinations tested;Assay sensitivity:fraction of all true biophysical interactions that are identi?able by a given assay;Sampling sensitivity:fraction of identi?able interactions that are detected in the experiment;Precision:fraction of reported pairs that are true positives.PRS:positive reference set;RRS:random reference set.

(D)Experimental pipeline for identifying high-quality binary protein-protein interactions(left).ORF:open reading frame.Fraction of HI-II-14,PRS,and RRS pairs (right)recovered by MAPPIT,PCA,and wNAPPA at increasing assay stringency.Shading indicates standard error of the proportion.p>0.05for all assays when comparing PRS and HI-II-14at1%RRS,two-sided Fisher’s exact tests.For n values,see Table S6.

See also Figures S1and S2and Tables S1and S2.

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of CDK4(Figure S3B).In agreement with previous reports,the comparative interaction pro?le shows that R24C and R24H,but not N41S and S52N,speci?cally perturb CDK4binding to CDKN2C (Figure 3).In total,we identi?ed 32human genes for which:(1)the cor-responding gene product is reported to have binary interactors in HI-II-14,(2)germline disease-associated missense mutations have been reported,and (3)common coding missense variants

unlikely to be involved in any disease have been identi?ed in the 1000Genomes Project (1000Genomes Project Consortium,2012).To avoid overrepresentation of certain genes,we sele-cted a total of 115variants,testing up to four disease and four common variants per disease gene for their impact on the ability of the corresponding proteins to interact with known interaction partners (see Extended Experimental Procedures ,Section 8).Disease variants were 10-fold more likely to perturb interactions than nondisease variants (Figure 3and Table S3).Strikingly,more than 55%of the 107HI-II-14interactions tested were perturbed by at least one disease-associated variant,and the same trend was observed when considering only mutants with evidence of expression in yeast as indicated by their ability to mediate at least one interaction (Figure S3C).Examples of novel speci?cally perturbed interactions include

AANAT-

Prediction

e n e O n t o l

o g y M

o u s e p h e n o t y p e s U n i o n B P M F C

C O R U

M M S

1101001,000E n r i c h m e n t o d d s r a t i o

101001,000C o m p l e x e s 0

150300868890920150300

0250500

250500F r e q u e n c y i n r a n d o m i z e d n e t w o r k s

Fraction in same compartment (%)Fraction in same

cell type (%)

Shared GO terms Shared phenotypes

Cocomplex membership Coexpressed Colocalized

Kinase-substrate relationship

Number of known kinase-substrate interactions

510

04008000

150300A

Figure 2.Overall Biological Signi?cance

(A)Schematic of the method to assess biological relevance of binary maps.

(B)Enrichment of binary interactome maps for functional relationships (left)and cocomplex memberships (right).Error bars indicate 95%con?dence intervals.BP:Biological process;MF:Molecular function;CC:Cellular component.Mouse phenotypes:Shared phenotypes in mouse models by orthology mapping.MS:Mass-spec-trometry-based map.Enrichments:p %0.05for all annotations and maps,two-sided Fisher’s exact tests.For n values,see Table S6.

(C)and (D)Fraction of binary interactions between proteins localized in a common cellular compart-ment and proteins copresent in at least one cell type (arrows)compared to those in 1,000degree-controlled randomized networks.Empirical p values.For n values,see Table S6.

(E)Number of known kinase-substrate interactions found in binary maps (arrows)compared to those in 1,000randomized networks.Empirical p values are shown.

See also Figure S3.

BHLHE40and RAD51D-IKZF1(Figure 3).In the ?rst case,the A129T mutation in AANAT is known to be associated with

a delayed sleeping phase syndrome

and speci?cally perturbs an interaction

between AANAT and BHLHE40,the

product of a gene reported to function in circadian rhythm regulation (Naka-shima et al.,2008).In the second case,the breast-cancer-associated RAD51D E233G mutation perturbs interactions

with a number of partners,including the known cancer gene product IKZF1(Futreal et al.,2004).Altogether these computational and experimental results pro-vide strong evidence that HI-II-14pairs correspond to biologi-cally relevant interactions and represent a valuable resource to further our understanding of the human interactome and its per-turbations in human disease.A ‘‘Broader’’Interactome

Unlike literature-curated interactions,HI-II-14protein pairs are distributed homogeneously across the interactome space (Fig-ure 4A),indicating that sociological biases,and not fundamental biological properties,underlie the existence of a densely popu-lated zone in the literature.Since 1994,the number of high-qual-ity binary literature PPIs has grown roughly linearly to reach $11,000interactions in 2013(Figure 4B),while systematic data sets are punctuated by a few large-scale releases.Although the sparse territory of the literature map gradually gets popu-lated,interaction density in this zone continues to lag behind that of the dense zone (Figure 4B).In terms of proteome coverage,the expansion rate is faster for systematic maps than for literature maps,especially in the sparse territory (Fig-ure 4C and Figure S4A;see Extended Experimental Procedures ,

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Section 9).While Lit-BM-13provides more information in the dense zone,HI-II-14reveals interactions for more than 2,000proteins absent from Lit-BM-13.These observations are likely due to a tendency of the literature map to expand from already connected proteins (Figure 4D).

To more deeply explore the heterogeneous coverage of the human interactome,we compared HI-II-14and Lit-BM-13to a collection of $25,000pre dicted binary PPI s of h igh-c on?dence (PrePPI-HC)(Zhang et al.,2012)and a co-frac tionation map of $14,000potentially binary interactions (Co-Frac)(Havugimana

Interactions:Maintained Perturbed

Common variant Proteins:Wild-type

Disease variant

Biological relevance

Disease variant

Common variant

Wild type Common variant Disease variant Gene

Protein

Missense mutation HI-II-14 interaction

C E P 57L 1A M

O T L 2K I F C 3I K Z F 1I K Z F 3L N X 1K R T 15L Z T S 2X R C C 2

1o r f 94M D F I B H

L H E 40

AANAT

T3M A129T

C C N

D 3C D K N 2D H O O K 1

C N

D 1C D K N 2C CDK4R24C R24H

N41S S52N

RAD51D

R165Q E233G

Wild type Common variant Disease variant WT WT WT Disease variants (n = 67)Common variants (n = 48)

F r a c t i o n o f i n t e r a c t i o n s p e r t u r b e d (%)

P = 9 × 10-16

20406080Putative Figure 3.Perturbations of Protein Interactions by Disease and Common Variants

Predicted effect of mutations on PPIs as a function of their biological relevance (top left).Fraction of interactions of the wild-type gene product lost by mutants bearing the disease-associated or common variants (top right,error bars indicate standard error of the proportion).p value,two-sided Fisher’s exact https://www.wendangku.net/doc/b716413431.html,parison of interaction pro?le of wild-type CDK4,AANAT,and RAD51D to the interaction pro?le of mutants bearing disease or common variants (bottom).Yeast growth phenotypes on SC-Leu-Trp-His+3AT media in quadruplicate experiments are shown.See also Figure S3and Table S3.

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et al.,2012).We tested the extent to which these two data sets contain binary interactions (see Extended Experimental Proce-dures ,Section 10).Representative samples from both Co-Frac and PrePPI-HC were recovered by Y2H at a much lower rate than a sample of Lit-BM-13and appeared statistically indi-stinguishable from random pairs (Figure 5A and Table S4A).A lit erature n on -b inary data set (Lit-NB-13)performed similarly.However,Co-Frac and PrePPI-HC,like Lit-NB-13,were both signi?cantly enriched for functionally relevant relationships.Thus,although these data sets represent potentially valuable

Number of publications

HI-II-14Number of interactions

Time

N u m

b e r o f b i n a r y i n t e r a

c t i o n s

5,000

10,00015,00020,000N u m b e r o f p u b l i c a t i o n

Systematic binary maps

Binary literature maps

Number of interactions

100

10B

? 19981998 ? 20022002 ? 20062006 ? 20102010 ? 2013

F r a c t i o n o f i n t e r a c t i o n s c o n n e c t i n g t w o n e w p r o t e i n s (%)

20406080100Observed

Expected

2040600

2040

60800

10203040Y ears 199420022010199820062013

Systematic binary maps Binary literature maps Union of binary maps

F r a c t i o n o f p r o t e o m e i n n e t w o r k (%)

199420022010199820062013

Time t + 4 years Newly connected protein

New binary interaction Fraction of interactions

connecting two new proteins

100%

? 19981998 ? 20022002 ? 20062006 ? 20102010 ? 2013

Time intervals

Full

≥100

Figure 4.A ‘‘Broader’’Interactome

(A)Adjacency matrices showing Lit-BM-13(blue)and HI-II-14(purple)interactions,with proteins in bins of $350and ordered by number of publications along both axes.The color intensity of each square re?ects the total number of interactions for the corresponding bins.

(B)Total number of binary interactions in literature and systematic interactome maps over the past 20years (top),with years re?ecting either date of public release of systematic binary data sets or date of publication that resulted in inclusion of interactions in Lit-BM-13.Adjacency matrices (bottom)as in Figure 4A.

(C)Fraction of the human proteome present in binary interactome maps at selected time points since 1994,considering the full interactome space (left)or only dense (middle)and sparse (right)zones of Lit-BM-13with respect to number of publications.

(D)Fraction of new interactions connecting two proteins that were both absent from the map at the previous time point (four years interval;middle)compared to the average in 1,000randomized networks (right).Error bars indicate standard deviation.

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resources,both Co-Frac and PrePPI-HC appear to be more comparable to nonbinary than to binary data sets.Surprisingly, even though PrePPI-HC and Co-Frac systematically surveyed the full human proteome and map different portions of the inter-actome(Figures S4B),both exhibit a strong tendency to report interactions among well-studied proteins(Figure5B).This bias is likely due to the integration of functional annotations in the generation of both data sets.

Because coverage might depend on gene expression levels, we also examined interactome maps for expression-related sparse versus dense zones.Co-Frac shows a strong bias toward interactions involving proteins encoded by genes highly ex-pressed in the cell lines used(Figure5B).This expression-depen-dent bias is echoed in the literature map,perhaps re?ecting a general tendency to study highly expressed proteins.In contrast, both HI-II-14and PrePPI-HC exhibit a uniform interaction density across the full spectrum of expression levels,likely explained by the standardized expression of proteins tested in Y2H and by the independence of homology-based predictions from expression levels.

We more broadly explored the intrinsic biases that might in?u-ence the appearance of sparsely populated zones by examining 21protein or gene properties,roughly classi?ed as expression-, sequence-,or knowledge-based(Figures5B and5C,Tables S4B and S4C;see Extended Experimental Procedures,Section9). For example,PrePPI-HC is virtually devoid of interactions be-tween proteins lacking Pfam domains,consistent with conserved domains forming the basis of the prediction method.HI-II-14 appears depleted of interactions among proteins containing pre-dicted transmembrane helices,consistent with expected limita-tions of the Y2H assay(Stagljar and Fields,2002).Co-Frac is similarly depleted in interactions involving proteins with trans-membrane helices,which may result from membrane-bound proteins being?ltered out during biochemical fractionations. Compared to HI-II-14,HI-I-05presented a less homogenous coverage of the space with respect to abundance and knowl-edge properties,likely re?ecting the content of early versions of the hORFeome(Figure S4C).Importantly,no single map appeared unbiased in all21examined properties.A combined map presented a slightly increased homogeneity although intrinsic knowledge biases of the three maps using literature-derived evidence were still predominant.

To con?rm that HI-II-14interactions found in the sparse zones of the three other maps are of as high quality as those found in dense zones,we compared MAPPIT validation rates and func-tional enrichment across these zones for all protein properties examined.MAPPIT validation rates of dense and sparse zone pairs were consistent for nearly all properties(Figures5D and S4D),indicating that HI-II-14interactions are of similar biophysical quality throughout the full interactome space.Functional enrich-ment within the sparse zone was statistically indistinguishable from that of the dense zone(Figures5D and S4E),demonstrating the functional importance of HI-II-14biophysical interactions in zones covered sparsely by other types of interactome maps. Considering all current maps,more than half of the proteome is now known to participate in the interactome network.Our sys-tematic exploration of previously uncharted territories dramati-cally expands the interactome landscape,suggesting that the

human interactome network is broader in scope than previously observed and that the entire proteome may be represented within a fully mapped interactome.

Interactome Network and Cancer Landscape

Genes associated with the same disease are believed to be preferentially interconnected in interactome networks(Baraba′si et al.,2011;Vidal et al.,2011).However,in many cases,these observations were made with interactome maps that are composites of diverse evidence,e.g.,binary PPIs,cocomplex memberships,and functional associations,a situation further complicated by the uneven quality and sociological biases described https://www.wendangku.net/doc/b716413431.html,ing HI-II-14,we revisited this concept for cancer gene products.Our goal was to investigate whether the cancer genomic landscape is limited to the known cancer genes curated in the Sanger Cancer Gene Census(‘‘Cancer Census’’) (Futreal et al.,2004),or if,alternatively,it might extend to some of the hundreds of additional candidate genes enriched in so-matic mutations uncovered by systematic cancer genome sequencing(‘‘SM genes’’)(Chin et al.,2011)and/or identi?ed by functional genomic strategies such as Sleeping Beauty trans-poson-based screens in mice(‘‘SB genes’’)(Copeland and Jen-kins,2010)or global investigations on DNA tumor virus targets (‘‘VT genes’’)(Rozenblatt-Rosen et al.,2012).

Given our homogeneous coverage of the space for known (Cancer Census)and candidate(SB,SM,and VT)cancer genes (Figure6A),we?rst tested the postulated central role of cancer gene products in biological networks(Baraba′si et al.,2011)and veri?ed that both sets tend to have more interactions and to be more central in the systematic map than proteins not associ-ated with cancer(Figure6B).We then examined the intercon-nectivity of known cancer proteins and showed that Cancer Census gene products interact with each other more frequently than expected by chance,a trend not apparent in HI-I-05(Fig-ure6C).We sought to use this topological property as the basis for novel cancer gene discovery in the large lists of cancer can-didates from genomic and functional genomic screens.

We examined whether products of candidate cancer genes identi?ed by GWAS(Table S5A)tend to be connected to Cancer Census proteins,and observed signi?cant connectivity in all four maps(Figure S5A;see Extended Experimental Procedures,Sec-tion11).When loci containing a known cancer gene were excluded,only HI-II-14showed such connectivity,supporting its unique value to identify cancer candidate genes beyond those already well demonstrated(Figures7A and S5A).In further support of their association with cancer,genes in cancer GWAS loci prioritized by‘‘guilt-by-association’’in HI-II-14tend to correspond to cancer candidates from systematic cancer studies(Figures7B and7C).These results suggest that can-cer-associated proteins tend to form subnetworks perturbed in tumorigenesis,and that HI-II-14provides new context to priori-tize cancer genes from genome-wide studies.

The following example illustrates the power of our combined approach.C-terminal Binding Protein2(CTBP2)is encoded at a locus associated with prostate cancer susceptibility (Thomas et al.,2008)and belongs to both SB and VT gene lists (Mann et al.,2012;Rozenblatt-Rosen et al.,2012).Two Cancer Census genes,IKZF1and FLI1,encode interacting partners of Cell159,1212–1226,November20,2014a2014Elsevier Inc.1219

mRNA

abundance

HI-II-14

Lit-BM-13

Co-Frac

PrePPI-HC

HI-II-14

Lit-BM-13

Co-Frac

Number of

publications

Fraction of

sequence

in Pfam

domains

Fraction of

sequence

in transmembrane

(

)

M-

?0.600.6

Highest density imbalance

Time since first publication

Number of publications

Number of linear motifs

Fraction of sequence in Pfam domains

Fraction of sequence in transmembrane helices

Fraction of sequence in disordered regions

Number of binding sites

ORF length

Number of protein complexes

Number of pathways

Number of GO associations

mRNA abundance in HEK cells

mRNA abundance in HeLa cells

Protein abundance in HEK cells

Protein abundance in HeLa cells

?0.30.3

Depletion

Gene products ordered by decreasing

Fraction of sequence in polar uncharged amino-acids

Fraction of sequence in basic amino-acids

Fraction of sequence in charged amino-acids

Fraction of sequence in polar amino-acids

Fraction of sequence in acidic amino-acids

Fraction of sequence in hydrophobic amino-acids

Mirrored zones

100

(

)

mRNA

abundance

in HEK cells

from

Co-Frac

Number of

publications

from

Lit-BM-13

Fraction of

sequence

in Pfam

domains

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PrePPI-HC

100

1,000

100

1,000

100

1,000

https://www.wendangku.net/doc/b716413431.html,parison of Interaction Mapping Approaches

(A)Evaluation of the quality of Co-Frac(orange),PrePPI-HC(red),and pairs from small-scale experiments in the literature with no binary evidence(Lit-NB-13, grey).Fraction of pairs recovered by Y2H as compared to pairs from Lit-BM-13and pairs of randomly selected proteins(RRS)(left).Error bars indicate standard error of the proportion.Enrichment in functional interactions and cocomplex memberships(right).Legend as in Figure2B.For n values,see Table S6.

(B)Adjacency matrices for HI-II-14,Lit-BM-13,Co-Frac,and PrePPI-HC maps,with proteins per bins of$350and ordered by number of publications,mRNA abundance in HEK cells,fraction of protein sequence covered by Pfam domains,or fraction of protein sequence in transmembrane helices.Figure legend as in Figure1B.

(C)Highest interaction density imbalances(observed minus expected)in the four maps,the union of all four maps,and our previous binary map(HI-I-05)for21 protein properties.

(legend continued on next page) 1220Cell159,1212–1226,November20,2014a2014Elsevier Inc.

CTBP2in HI-II-14.These are transcription factors with tumor suppressor(Payne and Dovat,2011)and proto-oncogene(Korn-blau et al.,2011)roles,respectively,in lymphoid tumors.Given its interactions with IKZF1and FLI1,we investigated the poten-tial role of CTBP2in lymphoid tumorigenesis.In the Cancer Cell Line Encyclopedia(Barretina et al.,2012),FLI1was signi?cantly more often ampli?ed in lymphoid than in other cell lines(Fig-ure7D),consistent with its proposed proto-oncogenic role in these tumors.In contrast,both CTBP2and IKZF1,but not CTBP1,were deleted signi?cantly more often in lymphoid cancer cell lines.Notably,deletion of CTBP2or IKZF1and ampli?cation of FLI1were mostly nonoverlapping in the different cell lines, suggesting that either event may be suf?cient to affect tumori-genesis(Figure S5B).Altogether,these results suggest a role for CTBP2in suppressing lymphoid tumors by direct repression of FLI1function,potentially involving IKZF1.

Finally,we assessed how HI-II-14interactions can be inte-grated with genomic and functional genomic data sets.Going beyond the‘‘guilt-by-pro?ling’’concept,we also used these gene sets in‘‘guilt-by-association’’predictions in a combined model(Figure S6A),which leads to substantially improved can-cer gene rankings over those found using either predictive strat-egy alone(Figures7E,S6B,and S6C and Table S5B;see Extended Experimental Procedures,Section12).In contrast,a similar analysis using HI-I-05interactions showed that its limited size prevented inclusion of any guilt-by-association terms (Figure S6D).Genes signi?cantly mutated in cancer patients from recent TCGA pan-cancer mutation screens(Table S5C) (Lawrence et al.,2014)were enriched among highly ranked pre-dictions from the combined model(p=6310à3,one-sided Wil-coxon rank test),supporting the validity of our integrated cancer gene predictions.Our top-ranked prediction was the cyclin-dependent kinase4(CDK4),a well-known cancer gene product. Four other genes from the Cancer Census list appeared among the top25ranked genes.Strikingly,STAT3,which ranked third, was added to the Cancer Census after our training set was es-tablished,highlighting the ability of this approach to identify novel cancer gene products.

To characterize the biological processes in which the candi-date cancer genes predicted by the combined model are likely to be involved,we identi?ed binary interactions linking them to each other or to Cancer Census proteins in the12‘‘pathways of cancer’’relevant to cancer development and progression (Table S5D)(Vogelstein et al.,2013).Of our top100candidates, 60mapped to at least one cancer pathway(Figures7F and S7), twice as many as would be expected from predictions using either the guilt-by-pro?ling or guilt-by-association approach alone.We propose that many novel cancer candidates can be an-notated to speci?c processes based on their interactions with Cancer Census gene products and known participation in cellular pathways.For example,the candidate protein ID3,a DNA-bind-ing inhibitor,interacts with the two Cancer Census transcription factors TCF12and TCF3,suggesting a role for ID3in the regula-tion of transcription by inhibiting binding of speci?c transcription factors to DNA(Loveys et al.,1996;Richter et al.,2012).CTBP2, which we identi?ed as a potential suppressor in lymphoid tumors, represents another example(Figures5E and S7).

In summary,the increased and uniform coverage of HI-II-14 demonstrates that known and candidate cancer gene products are highly connected in the interactome network,which in turn provides unbiased evidence for an expanded functional cancer landscape.

DISCUSSION

By systematically screening half of the interactome space with minimal inspection bias,we more than doubled the number of high-quality binary PPIs available from the literature.Covering zones of the human interactome landscape that have been weakly charted by other approaches,our systematic binary map provides deeper functional context to thousands of pro-teins,as demonstrated for candidates identi?ed in unbiased cancer genomic screens.Systematic binary mapping therefore stands as a powerful approach to‘‘connect the dots’’of the genomic revolution.

Combining high-quality binary pairs from the literature with systematic binary maps,30,000high-con?dence interactions are now available.It is likely that a large proportion of the human interactome can soon be mapped by taking advantage of the emergence of reference proteome maps(Kim et al.,2014; Wilhelm et al.,2014),a combination of nearly complete clone col-lections(Yang et al.,2011),rapid improvements in Y2H assay sensitivity,and emerging interaction-mapping technologies that drastically reduce cost(Cau?eld et al.,2012;Stagljar and Fields,2002;Yu et al.,2011).

Reference binary interactome maps of increased coverage and quality will be required to interpret condition-speci?c inter-actions and to characterize the effects of splicing and genetic variation on interactions(Zhong et al.,2009).While protein-pro-tein interactions represent an important class of interactions be-tween macromolecules,future efforts integrating this information with protein-DNA,protein-RNA,RNA-RNA or protein-metabolite interactions will provide a uni?ed view of the molecular inter-actions governing cell behavior.Just as a reference genome enabled detailed maps of human genetic variation(1000Ge-nomes Project Consortium,2012),completion of a reference interactome network map will enable deeper insight into geno-type-phenotype relationships in human.

EXPERIMENTAL PROCEDURES

Extraction of the Literature-Based Data Sets

Human PPIs annotated with tractable publication records were extracted from seven databases through https://www.wendangku.net/doc/b716413431.html,rge-scale systematic data sets and

(D)Precision at1%RRS recovery in the MAPPIT assay(top,error bars indicate standard error of the proportion)and functional enrichment(bottom,union of Gene Ontology and mouse phenotypes based annotations,error bars indicate95%con?dence intervals)of HI-II-14pairs found in dense and sparse zones mirrored from Lit-BM-13,Co-Frac,and PrePPI-HC.p>0.05for all pairwise comparisons of dense and sparse zones,two-sided Fisher’s exact tests.For n values,see Table S6.

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.

成语,俗语,歇后语摘抄

熟语汇总――成语，俗语，歇后语1 成语篇日常生活中极容易用错的成语知识 1 曾几何时：表示“过去没有多久”。常误用为“曾经”“不知何时”。 2 不学无术：指“没有学问才能”。不能在其前加上“整天”“整月”等修饰词语。“学”为名词，常误作动词。 3 不可理喻：指“无法用道理使之明白”，常误用为“不可思议”。 4 不以为然：指“不认为是正确的”。常误用为“不以为意”，表示“不放在心上”“无所谓”。 5 守株待兔：贬义词，指“不主动努力，心存侥幸，希望得到意外的收获”，常误来形容公安干警的机智。 6 耳提面命：褒义词，形容师长殷切教导。常误用为贬义词。 7 不忍卒读：“不忍心读完”，形容文章的“悲”。常误用为形容文章写得不好。 8 鼎力相助；敬词，指对别人对自己的帮助。常误用为表示自己对他人的帮助。 9 蹉跎岁月：指虚度光阴。常误用来形容“岁月艰难、艰苦”。 10 名噪一时：指在当时很有名声。常误用为贬义词。 11 始作俑者：指某种坏风气的创始者。常误用为贬指。 12 不胜其烦：贬义词，指不能忍受其烦琐。常误用为“不厌其烦”。 13 脑无城府：指为人坦率，褒义词。常误用来形容“贬义词”，贬义词。 14 身无长物：指人贫困。常误用来形容没有特长。 15 目无全牛：指人的技艺高超，得心应手，易误作缺乏整体观念。 16 鬼斧神工：形容人的制作技艺高超，常误用为形容自然景观。相类似的还有“巧夺天工”。 17 求全责备：指对人苛求完善，后面不能带宾语，与此类似的还有“漠不关心”。 18 充耳不闻：塞住耳朵不听，形容不愿听取别人的意见。易误用为形容人专心，没有听到。 19 瓜田李下：形容容易引起嫌疑的地方。易误用为形容田园生活。 20 卓尔不群：形容非常优秀，超出常人。易误用为形容人的性格。 21 出神入化：形容技艺高超。易误用为形容听得出神。 22 登堂入室：比喻学问技能由浅入深，循序渐进，达到更高的水平。易误用为“进入”。 23 如坐春风：形容受到良好的教化。与“景物”无关。 24 对簿公堂：在公堂上受到审问。易误解为“争论，明辨是非”。 25 相敬如宾：特指夫妻相敬相爱。另有“举案齐眉”。 26 走马观花：比喻粗略地观察事物，强调过程，易和“浮光掠影”相混。后者指印象不深刻，强调结果。 27 师心自用：形容固执已见，自以为是。易误用为“善于学习借鉴，为我所用”。 28 安土重迁：安居故土，不愿随便迁往别处。易理解相反。 29 罪不容诛：形容罪大恶极，与“死有余辜”同义。易误解为罪行还没有达到被杀的程度。 30 屡试不爽：屡次试验都没有差错。易误解为“没有成功”。

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.wendangku.net/doc/b716413431.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

关于合作的成语俗语

关于合作的成语俗语导语：人心齐，泰山移。团结合作是团队工作取得成功的前提。下面是语文小编收集整理的关于团结合作的成语，欢迎大家阅读参考! 关于团结合作的成语【1】风雨同舟和衷共济精诚团结齐心协力群策群力同心协力同舟共济万众一心患难与共众志成城爱人以德助人为乐煮粥焚须忠信乐易亲去骨肉亲如手足情同手足情深潭水雪中送炭解衣推食奔走之友不分彼此布衣之交同心协力称兄道弟赤心相待打成一片打得火热道义之交分甘共苦甘苦与共肝胆相照高义薄云管鲍之交患难之交金龟换酒金兰之友兰友瓜戚民胞物与莫逆之交契若金兰人扶人兴仁民爱物仁义道德如兄如弟深情厚谊负荆请罪胜友如云式好之情视同手足视为知己手足之情同窗之情同舟共济推襟送抱刎颈之交四海之内皆兄弟关于团结合作的成语【2】? ? ? ? ? 1、同舟共济tóng zhōu gòng jì 【解释】：舟：船;济：渡，过洒。坐一条船，共同渡河。比喻团结互助，同心协力，战胜困难。也比喻利害相同。【出自】：《孙子·九地》：“夫吴人与越人相恶也，当其同舟而济，遇风，其相救也如左右手。” 【示例】：通常在林海里人与人相遇，和在远洋航海中～一样，为什么他竟这样势不两立。◎曲波《林海雪原》二十七【近义词】：患难与共、风雨同舟【反义词】：反目成仇、各行其事【语法】：联合式;作谓语、定语;含褒义 2、万众一心wàn zhòng yī xīn 【解释】：千万人一条心。形容团结一致。【出自】：《后汉书·朱擕传》：“万人一心，犹不可当，况十万乎!” 【示例】：～，同声呼吁，力抗汉奸，唤醒政府。◎蔡东藩、许廑父《民国通俗演义》第一0八回【近义词】：万全之策、同心同德【反义词】：挂一漏万、各自为政【语法】：主谓式;作谓语、定语;含褒义 3、患难与共huàn nàn yǔ gòng 【解释】：共同承担危险和困难。指彼此关系密切，利害一致。【出自】：《礼记·儒行》：“儒有闻善以相告也，见善以礼相示也，爵位相先也，患难相死也。” 【示例】：高桂英毕竟是他的～的结发妻子和好帮手。◎姚雪垠《李自成》第一卷第十三章【近义词】：休戚与共、同甘共苦、同舟共济【反义词】：貌合神离、同床异梦

新版pep六年级上册英语-各单元知识点总结

Unit 1 How can I get there? 一：重点单词和短语 Science科学, museum博物馆, post office, bookstore, cinema, hospital ，tasty, buy, London Eye伦敦银, stomach胃,crossing十字路口, turn left, turn right, go straight=walk straight直走.next to紧挨着/与。。。相邻, far from(离。。。远), near在。。。附近,behind（在。。。后面）, in front of（在。。。前面）, between…and…（在。。。和。。。之间）二：按要求写单词： hot(反义词)cold , cool(反义词)warm，too（同音词）to/two can not(缩写)can’t right(反义词)left/wrong buy(同音词)by/bye sea(同音词)see first（基数词）one four(序数词)fourth did (原形)do /does three(序数词)third give(过去式)gave 三：重点句型分析 1. Where is the museum shop? 此问句是由特殊疑问词where 引导的一个特殊疑问句，where意为“在哪里，到哪里”，用来询问地点，放在句子的开头。询问“某人或某物在哪里”的基本句型是：“ Where +is/are+ 主语？”，where is 后接名词或代词的单数形式，where are 后接名词或代词的复数形式。表示地点的词：museum博物馆, post office邮局, bookstore书店, cinema电影院, hospital医院restaurant餐馆bank银行bus stop 公交车站lake湖library图书馆zoo动物园school学校park公园garden花园hotel旅馆 2. It’s nea r the door. 此句中near是表示位置的介词，意为“旁边，附近” ，其同义句是：It’s next to the door.它在门的旁边。表示位置的短语：next to the bookstore挨着书店near the hospital在医院附近near the post office在邮局附近over there 在那边on Dongfang Street在东方大街上in front of the school在学校前面 3. How can we get there? 此句用来询问“怎样去某地”，后面直接跟地点。回答时，可以用“ Turn left, turn right, go straight.”等句来回答。同义句：1.Can you tell me the way to+地点？ 2.Where is the +地点？ 3.Which is the way to +地点 4. Turn left at the bookstore. Then turn right at the hospital. 此句是指路的句型。常用到的句型有：turn left, 向左转turn right, 向右转go straight直着走。同时表示在某处的介词用at. 5.Is the Thames far from here? No, it isn’t. 此句是个be动词开头的一般疑问句，其回答要用Yes或No. 句中的far from意为“离……远”。反义词组为next to.

成语俗语复习

1.俗话说：“（人非圣贤，孰能无过）”，生活中，我们难免会犯一些错误，不过我们要从中吸取教训。 2.俗话说：“（要想人不知，除非己莫为）”经常干小偷小摸的事，终究会有被人发现的时候。 3我走进教室，发现同学们用异样的眼光看着我，弄得我：“（丈二的和尚---摸不着头脑）.”一时间，我不知所措。 4有人嘲笑我小小年纪，心得比天高，我想对他说：“燕雀安知鸿鹄之志哉。” 5.同学遭到打击，灰心丧气时，我想对他说：“失败是成功之母。” 6.小组合作时，王明处处小瞧他人，我想对他说：“众人拾柴火焰高。” 7张嫂十分抠门，每次别人借东西，她总是推三阻四说没有。人们都说她是“铁公鸡---一毛不拔” 8.语文老师正在抽查昨天要求背诵的课文。这时,我在座位上是“十五个吊水桶---七上八下”，生怕老师点到我的名字。 9.古人说：“条条大路通罗马”，是说答案不只一个，是丰富多彩的。这就是创新，我国诗人苏轼早在宋代就写了这样的诗句是：“横看成岭侧成峰，远近高低各不同。”当局者迷，旁观者清，苏轼也写过这样的诗句：“不识庐山真面目，只缘身在此山中。”告诉我们看待事物，不要光看到局部，还要看到整体。

10.现在是新时代，社会需要各种人才，（三百六时行，行行出状元），请你用一句诗来表达怎样选人才最好：（“我劝天公重抖擞，不拘一格降人才。” 11.清晨，我漫步在校园里，看到花园里的景色真美。草坪上、银杏树园里都树立着一块块保护花草的标语，我最喜欢那句：（保护花和草，阳光空气好）我来到阅览室，看到同学们正在专心地读书，我想到了与读书有关的名言（书山有路勤为径，学海无涯苦作舟）。我也想用（聚精会神、专心致志）这两个成语来形容大家遨游书海。漫步在这充满生机而熟悉的校园，想到再过一段时间，我们即将离开亲爱的母校，我不禁想用（春色满园、书声朗朗、井然有序、热闹欢腾）等成语来形容母校，我想对母校说：“（母校万岁）（别了，母校）“12.教育人要积极向上，好上加好：（欲穷千里目，更上一层楼）。 13、教育节约粮食，珍惜农民劳动成果：（谁知盘中餐，粒粒皆辛苦） 14小明读书很不认真，班长想帮他，可他却对班长说：“狗拿耗子—多管闲事”；这真是“狗咬吕洞宾---不识好人心”。（填歇后语） 15在期末写评语时，老师会祝我们来年百尺竿头------更进一步。（歇后语） 16写一句关于严守规则、遵纪守法的名言警句没有规矩，不成方圆。

成语谚语俗语

成语故事一、含有历史故事或人物的成语 1、破釜沉舟破釜沉舟表示下定决心,义无返顾。典故出自秦末的巨鹿之战。项羽为报叔父项梁之仇,亲率二万精兵进攻章邯。渡江前命令兵士打破煮食用的锅,渡江后凿破船只,只带三天干粮.最后在巨鹿大败秦。 2、卧薪尝胆卧薪尝胆表示刻苦自励，发愤图强，不敢安逸。春秋时期，吴国打败越国，越王勾践被押送到吴国做奴隶，勾践忍受屈辱伺候夫差三年后，夫差才把他送回越国。其实勾践并没有放弃报仇之心，他表面上对吴王服从，但暗中训练精兵，等待时机反击吴国。勾践害怕自己会贪图眼前的安逸，所以他晚上睡觉不用褥，只铺些柴草，又在屋里挂了一只苦胆，他不时会尝苦胆的味道，为的就是不忘过去的耻辱。最终越国强大了起来，灭了吴国。 3、负荆请罪负荆请罪.用于赔礼道歉的场合，典出自史记。讲的是廉颇觉得自己身为赵国第一武将，出生入死，战功显赫，见蔺相如并没有任何功劳，却职位比自己高，很不服气，就总想找机会羞辱蔺相如，但是蔺相如知道后故意躲着他。别人问他是不是害怕廉颇。蔺相如就说自己面对秦王都毫不惧怕，又怎么会怕廉颇。只是担心将相失和，会影响到国家，所以才躲着走。后来廉颇知道了，觉得羞愧。就赤裸上身，背负荆条去请求蔺相如的原谅。结果自然是两人化敌为友，共同辅佐赵王，为赵国赢得几十年的安定，成为一段佳话。 4、指鹿为马指鹿为马形容一个人是非不分，颠倒黑白，出自《史记·秦始皇本纪》。相传赵高试图要谋朝篡位，为了试验朝廷中有哪些大臣顺从他的意愿，特地呈上一只鹿给秦二世，并说这是马。秦二世不信，赵高便问各位大臣。不敢违背赵高意愿的大臣都说是马，而敢于反对赵高的人则说是鹿。后来说是鹿的大臣都被赵高用各种手段害死了。 5、胸有成竹胸有成竹指比喻熟练有把握，心中有数。北宋时候，有一个著名的画家是当时画竹子的高手。他为了画好竹子，无论春夏秋冬，还是刮风下雨，都常年不断地在竹林子里头钻来钻去。正是因为对竹子形态等有了细致入微的了解和认识，所以画竹子的时候不用草图，可以直接画好。

PEP小学英语六年级上册各单元知识点复习资料

六年级上复习资料 Unit1 How Do You Go There? (你怎样去那里？) 重点单词： on foot= walk走路by bike骑自行车by bus坐公车by train坐火车by plane坐飞机by ship坐轮船by subway坐地铁near近的far远的usually通常sometimes有时候easy简单的the fifth floor第五层楼traffic lights交通灯traffic rules交通规则stop停止wait等待get to到达same相同的mean意思是driver司机right 右边left左边must必须know知道重点句子： 1. How do you go to school? 你是怎样去上学的？ 2. I go to school by bus.我是坐公交车去上学的。 3. Usually I go to school on foot. 我通常走路去上学。 4. Sometimes I go by bike. 有时候我骑自行车去。、 5. My home is near/ far. 我的家是近的。/ 远的。 6. Look at the traffic lights, remember the traffic rules. 看着交通灯，记住交通规则。 7. Stop at a red light. Wait at a yellow light. Go at a green light. 红灯停。黄灯等一等。绿灯行。 8. Red means stop, yellow means wait, green means go. 红色的意思是停止，黄色的意思是等待，绿色的意思是通行。 9. How can I get to the park? 我该怎样到达公园呢？ 10. You can go by the No. 15 bus. 你可以坐15路公交车去。重点知识： 1.坐某种交通工具用by，例如：by bike, by train。而走路用“on”例如on foot. 2.国家名字，地方名字第一个字母要大些：例如：Canada加拿大, China中国, America美国，England英国，Australia澳大利亚 3.国家名缩写前面加the，缩写字母都要大写。例如：the USA=the US美国，the UK英国，the CAN加拿大，the PRC中国。 4.频度副词是表示做的次数多少的词语。从多到少依次排列为：always总是，usually通常，often经常，sometimes有时候，never从不。频度副词可以放在句首，也可以放在人称后面。例如：Usually I go to school by bus. = I usually go to school by bus. 5.near近的，far远的。这两个词是一对反义词。注意：not near= far, not far = near. 6.时间前面用at. 例如：在三点钟：at 3 o’ clock. 一段时间前面用for 7.表达第几层楼的时候我们要用序数词，前面还要有the。例如：第一、二、三、四、五层楼分别是：the first floor. the second floor. the third floor. the fourth floor. the fifth floor. 8.交通灯traffic lights，交通规则：traffic rules。这两个词后面都一定要加s, 绝对不能少。因为交通灯有红黄绿三盏，一定是复数，交通规则不可能只有一条，所以都一定要加s，考试的时候千万别忘了加s哦！ 9.大部分的国家都是靠右行驶的：drivers drive on the right side of the road. 记住England and Australia, drivers drive on the left side of the road.英国和澳大利亚，司机是靠左行驶的。

成语俗语趣例

成语俗语趣例一、成语“吉尼斯记录” 1、最长的腿——一步登天 2、最坏的人——无恶不作 3、最长的寿命——万寿无疆 4、最短的季节——一日三秋 5、最长的一天——度日如年 6、最高的人——顶天立地 7、最吝啬的人——一毛不拔 8、最快的速度——风驰电掣 9、最残暴的人——杀人如麻 10、最大的胆量——胆大包天11、最远的距离——天各一方 12、最富的资源——取之不尽13、最高的瀑布——一落千丈 14、最深的门——侯门似海 15、最反常的气候——晴天霹雳 16、最大的变化——沧海桑田 17、最怪的人——三头六臂二、成语新解铁杵成针——浪费资源水滴石穿——浪费时间无理取闹——藐视法律口若悬河——自我推荐世外桃源——度假胜地信手涂鸦——影响市容斩草除根——破坏植被乌烟瘴气——污染空气披发文身——追赶时髦七嘴八舌——太吵赤膊上阵——太热斩钉截铁——刀快改头换面——整容特立独行——酷毙了豪放不羁——帅呆了放虎归山——拯救濒危动物只见树木，不见森林——乱砍滥伐鹿死谁手——猎杀国家保护动物三、反义俗语 1、癞蛤蟆想吃天鹅肉——燕雀安知鸿鹄之志哉 2、穷在闹市无人问，富在深山有远亲——有福同享，有难同当

3、留得青山在，不怕没柴烧——宁为玉碎，不为瓦全 4、万般皆下品，惟有读书高——三百六十行，行行出状元 5、有钱能使鬼推磨——君子爱财，取之有道 6、人为财死，鸟为食亡——钱财如粪土，仁义值千金 7、有奶便是娘——士为知己者死（良禽择木而栖） 8、英雄无用武之地——天生我才必有用 9、富贵声淫欲，饥寒起盗心——富贵不能淫，贫贱不能移 10、各人自扫门前雪，莫管他人瓦上霜——路见不平，拔刀相助 11、人不犯我，我不犯人，人若犯我，我必犯人——忍一时风平浪静，退一步海阔天空 12、顺我者昌，逆我者亡——宰相肚里能撑船 13、人在屋檐下，不得不低头——威武不能屈 14、龙生龙，凤生凤，老鼠生儿回打洞——王侯将相宁有种乎 15、常在河边走，哪能不湿鞋——出淤泥而不染，濯清涟而不妖 16、鸡犬之声相闻，老死不相往来——远亲不如近邻 17、逢人且说三句话，不可全抛一片心——知无不言，言无不尽 18、嘴是两块皮，说话有改的——一言既出，驷马难追四、近义俗语 1、木秀于林，风必摧之——枪打出头鸟 2、不入虎穴，焉得虎子——舍不得孩子套不着狼 3、放下屠刀，立地成佛——浪子回头金不换 4、塞翁失马，焉知非福——福兮祸所倚，祸兮福所伏 5、不到黄河心不死——不见棺材不掉泪 6、人心隔肚皮——知人知面不知心

六年级上册英语知识点总结归纳(PEP新版)

PEP新版六年级上册英语知识点归纳总结目录 Unit1How can I get there? (2) Unit2Ways to go to school (3) Unit3My weekend plan (4) Unit4I have a pen pal (5) Unit5What does he do? (6) Unit6How do you feel? (7)

Unit1How can I get there? library图书馆north(北) post office邮局 hospital医院turn left左转turn right右转places:cinema电影院 (地点)bookstore书店(东)east west(西) science museum科学博物馆 pet hospital宠物医院crossing十字路口 school学校south(南) shoe store/shop鞋店 supermarket超市go straight直行一、问路 1．Where is the cinema,please?请问电影院在哪儿？ next to the hospital.在医院的旁边。 in front of the school.在学校的前面. behind the park在公园的后面 It’s near the zoo.在动物园的附近. on the right/left of the bookstore.在书店的左/右边. east of the bank.在银行的东边. far from here.离这儿很远. 2.Excuse me,is there a cinema near here请问这附近有电影院吗？ Yes,there is./No,there isn’t.有./没有。 3.How can I get to the hospital?我该怎样到达医院呢？ Take the No.57bus.乘坐57路公汽。二、指引路 1．You can take the No.312bus.你可乘坐312路公交车去那儿. 2.Go straight for three minutes.向前直走3分钟. 3.Turn right/left at the…在…地方向右/左转. 4.Walk east/west/south/north for…minutes.朝东/西/南/北/走…分钟. 三、Is it far from here?离这儿远吗? Yes,it is./No,it isn’t.是的，很远/不是，很远。

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