BIOINFORMATICS DISCOVERY NOTE Vol . 20 no. 17 2004 , pages 2911 2917 doi : 10.1093 / bioinformatics/bth374 Evidence that microRNA precursors , unlike other non-coding RNAs , have lower folding free energies than random sequences Eric Bonnet1 , Jan Wuyts1 , Pierre Rouz 2 and Yves Van de Peer1 , 1 Department of Plant Systems Biology , Flanders Interuniversity Institute for Biotechnology -LRB- VIB -RRB- , Ghent University , Technologiepark 927 , B-9052 Ghent , Belgium and 2 Laboratoire associ de l'INRA , France Received on January 21 , 2004 ; revised on June 2 , 2004 ; accepted on June 17 , 2004 Advance Access publication June 23 , 2004 ABSTRACT Motivation : Most non-coding RNAs are characterized by a specific secondary and tertiary structure that determines their function . Here , we investigate the folding energy of the secondary structure of non-coding RNA sequences , such as microRNA precursors , transfer RNAs and ribosomal RNAs in several eukaryotic taxa . Statistical biases are assessed by a randomization test , in which the predicted minimum free energy of folding is compared with values obtained for structures inferred from randomly shuffling the original sequences . Results : In contrast with transfer RNAs and ribosomal RNAs , the majority of the microRNA sequences clearly exhibit a folding free energy that is considerably lower than that for shuffled sequences , indicating a high tendency in the sequence towards a stable secondary structure . A possible usage of this statistical test in the framework of the detection of genuine miRNA sequences is discussed . Availability : The dataset , software and additional data files are freely available as supplementary information on our Website . Contact : yves.vandepeer@psb.ugent.be Supplementary information : http://www.psb.ugent.be/ bioinformatics / INTRODUCTION It is generally accepted that the functional activity of many RNA molecules is determined by their specific secondary -LRB- and tertiary -RRB- structure , which is , in turn , determined by Watson Crick , wobble and other non-canonical base pairings . As a consequence , the stability of an RNA with a specific secondary structure and involved in functional processes in the cell is expected to be higher than that of RNAs which do not have such a structure , or for which the secondary structure is of less functional importance . Starting from this hypothesis , Maizel and co-workers -LRB- Le et al. , 1988 , 1989 ; Chen To et al. , 1990 -RRB- proposed assessing thermodynamic stability and statistical significance of secondary structure features using a method that combines minimum free energy -LRB- MFE -RRB- values and Monte Carlo simulations . Following this pioneer work , several authors have tried to find significant secondary structures in messenger RNAs -LRB- Seffens and Digby , 1999 ; Workman and Krogh , 1999 ; Katz and Burge , 2003 -RRB- . For other classes of RNA sequences , Rivas and Eddy -LRB- 2000 -RRB- demonstrated that , for most of the non-coding RNAs examined , the stability of the secondary structure was not sufficiently different from the predicted stability of a random sequence to be useful as a general gene-finding approach . MicroRNAs -LRB- miRNAs -RRB- are a class of small regulatory noncoding RNAs that have generated much excitement recently -LRB- Eddy , 2001 ; Bartel and Bartel , 2003 ; Carrington and Ambros , 2003 -RRB- . Analysis of miRNAs is leading to new paradigms for control of gene expression during development in plants and animals . MiRNAs arise from larger precursor molecules that can fold into a stable stem loop structure -LRB- Lee et al. , 1993 ; Lagos-Quintana et al. , 2001 ; Lau et al. , 2001 ; Llave et al. , 2002 ; Reinhart et al. , 2002 -RRB- . Those structures are processed by ribonuclease III-like nuclease -LRB- Dicer in animals and Dicerlike in plants -LRB- Hutvagner and Zamore , 2002 ; Schauer et al. , 2002 -RRB- and all have a typical stem loop shape -LRB- Lee et al. , 1993 ; Reinhart et al. , 2000 , 2002 ; Lagos-Quintana et al. , 2001 ; Lau et al. , 2001 ; Llave et al. , 2002 -RRB- . Here , we present a study on the MFE values of secondary structures in miRNAs and other non-coding RNAs . Original MFE values of the secondary structure are compared with values obtained by randomly shuffling the original sequences . MATERIALS AND METHODS Non-coding RNA sequences A total of 506 miRNA precursor sequences were downloaded from the RFAM database -LRB- Ambros et al. , 2003 ; Griffiths-Jones et al. , 2003 ; Griffiths-Jones , 2004 -RRB- . The available sequences whom correspondence should be addressed . Bioinformatics vol . 20 issue 17 Oxford University Press 2004 ; all rights reserved . 2911 E.Bonnet et al. . Table 1 . Basic statistic values for the sequences used in the study % A tRNA A.thaliana C.elegans D.melanogaster H.sapiens miRNA C.briggsae C.elegans D.melanogaster H.sapiens M.musculus A.thaliana rRNA % T % G % C Count Length % GC % Paired bases MFE 20 3 18 3 19 3 20 4 25 4 26 5 26 4 23 5 23 5 26 4 21 7 24 3 23 3 23 3 24 4 29 4 29 5 32 4 28 5 29 5 31 3 26 4 32 3 33 3 33 3 31 4 23 3 23 4 22 3 26 4 26 5 22 3 30 4 24 3 25 3 25 3 24 4 22 4 21 5 20 3 23 6 22 5 21 4 22 6 125 125 125 125 51 107 78 158 69 43 581 75 5 75 7 75 8 75 5 92 7 97 5 88 13 89 14 69 5 135 49 88 25 55 4 58 4 58 4 55 6 45 5 44 7 42 5 49 8 48 9 43 5 52 10 30 3 30 3 31 3 30 3 36 2 34 4 35 3 36 3 37 3 37 2 32 4 -27.5 3.7 -27.5 5 - 29 5 - 26 5 - 37 6 - 36 9 - 32 6 - 40 9 - 32 5 - 57 17 - 33 12 Values are expressed as the mean SD . Base pairs represent the number of base pairs in the secondary structure relative to the total number of nucleotides in the sequence -LRB- the maximum value is thus 50 % -RRB- . The results for rRNAs are showed globally , since the number of different species selected was very high . cover five animal genomes and one plant genome : Homo sapiens , Drosophila melanogaster , Caenorhabdbitis elegans , C.briggsae , Mus musculus and Arabidopsis thaliana . Most of the sequences were recently isolated using direct cloning techniques , but some were predicted using computational tools . However , this set should not be considered as complete and is the subject of very active research -LRB- Carrington and Ambros , 2003 ; Bartel , 2004 -RRB- . Transfer RNAs were downloaded from the genomic tRNA database -LRB- Lowe and Eddy , 1997 -RRB- . In order to have a comparable number of sequences , 500 sequences were selected at random from four eukaryotic genomes , namely H.sapiens , A.thaliana , C.elegans and D.melanogaster . Ribosomal RNA sequences are much longer -LRB- 1500 3000 nt -RRB- and have a much more complex secondary and tertiary structure than the other types of non-coding RNAs . A selection of regions that are similar in length compared with miRNAs and tRNAs was made . From the European ribosomal RNA database , 120 randomly chosen eukaryotic rRNA sequences from the small ribosomal subunit were selected -LRB- Wuyts et al. , 2004 -RRB- . From each of these sequences , five areas were extracted that fold into a secondary structure pattern that does not include any significant base pairing with other parts of the molecule . These areas correspond to helices 12 13 , 23/e1 23/e3 , 24 , 42 44 and 49 -LRB- Wuyts et al. , 2001 -RRB- . Some basic statistics about nucleotide composition , sequences length and MFE values are displayed in Table 1 . decreased by the stacking energy of successive base pairs or increased by the destabilizing energy associated with noncomplementary bases -LRB- Zuker and Stiegler , 1981 ; Schuster et al. , 1994 -RRB- . For this reason , the dinucleotide bias in RNA sequences is considered to be important for the predicted MFE and several authors have pointed out the importance of taking into account the dinucleotide distributions when randomizing sequences -LRB- Workman and Krogh , 1999 ; Rivas and Eddy , 2000 -RRB- . In our study , only the results of the optimal folding were used . The biologically correct structure for tRNAs and rRNAs is often not the calculated optimal structure but one that is within a percent of the optimal structure -LRB- suboptimal structure -RRB- -LRB- Zuker and Stiegler , 1981 ; Durbin et al. , 1998 -RRB- . Therefore , the results should be very close for the biologically active structure . Unfortunately , the real in vivo secondary structure of miRNA is not yet known . Mononucleotide shuffling This type of shuffling is trivial and is done simply by permuting the nucleotides of the sequence at random . The dinucleotide frequencies are completely distorted using this method . Dinucleotide shuffling The goal here is to shuffle a sequence while keeping the dinucleotide distribution -LRB- or frequencies -RRB- constant . An implementation of the algorithm as described by Workman and Krogh -LRB- 1999 -RRB- was used . The dinucleotide and mononucleotide frequencies are exactly preserved . RNA secondary structure Methods to predict the secondary structure from the primary sequence information are based on minimizing the free energy of the molecule by maximizing the number of favorable base pairing interactions -LRB- Zuker and Stiegler , 1981 ; Schuster et al. , 1994 -RRB- . MFE is estimated by considering the minimum energy values obtained by complementary base pairs First-order Markov model We also used the DiShuffle program -LRB- Katz and Burge , 2003 -RRB- . This program derives a first-order Markov model from the conditional probabilities found in the initial sequence . 2912 MicroRNA precursors have lower folding free energy Number of sequences A random nucleotide is chosen as a seed for a new sequence after which nucleotides will be added according to the conditional probabilities . The process stops when the sequence has the same length as the original . This method produces shuffled sequences with dinucleotide frequencies close to the original sequences but that do not have exactly the same values . Mononucleotide frequencies are not preserved . a 250 C. Elegans - let7 miRNA A variant of the Z-score procedure -LRB- Le et al. , 1988 , 1989 ; Workman and Krogh , 1999 ; Katz and Burge , 2003 -RRB- was used to determine if the MFE value is significantly different from that of random sequences . The MFE value of the sequence is compared with the distribution of MFE values obtained by randomly reshuffling the order of the nucleotides in the native sequence . This procedure is known as Monte Carlo and randomization tests , and is used in many fields of biology -LRB- Efron , 1979 ; Eddington , 1995 ; Manly , 1997 -RRB- . A high number of permutations -LRB- minimum 1000 -RRB- are required to have an accurate probability -LRB- Edwards , 1985 ; Jockel , 1986 ; Eddington , 1995 -RRB- , but as the length of the sequences used here is rather small , this is not a computational issue . Under this model , no assumption is made upon the nature of the distribution and the probability is straightforward to compute , as detailed below . The randomization test can be summarized as follows : -LRB- 1 -RRB- Compute MFE of the secondary structure inferred from the original sequence . -LRB- 2 -RRB- Randomize the order of the nucleotides in the original sequence and compute MFE for the inferred structure based on the shuffled sequence . -LRB- 3 -RRB- Repeat step 2 a great number of times -LRB- 1000 -RRB- in order to build the distribution of MFE values . -LRB- 4 -RRB- If N is the number of iterations and R the number of randomized sequences that have a MFE value less or equal to the original value , then p is defined here as : R . N +1 To fold sequences into their secondary structure , we used the ViennaRNA fold package -LRB- Hofacker et al. , 1994 -RRB- that implements Zucker 's energy minimization algorithm . The software that implements the randomization test of secondary structure MFE , called randfold , is available under the terms of the General Public License -LRB- see supplementary data -RRB- . p = 0 -45 50 100 Randomization test 150 200 -40 -35 -30 -25 -20 -15 Minimum free energy value b 250 C. Elegans - tRNA39 Number of sequences 0 50 100 150 200 -35 -30 -25 -20 Minimum free energy value Fig. 1 . Distribution of MFE values for randomized sequences -LRB- dinucleotide shuffling -RRB- for miRNA gene let-7 -LRB- a -RRB- and for a tRNA gene -LRB- b -RRB- . The black arrow indicates the MFE for the original structure . RESULTS AND DISCUSSION Two examples of randomized distributions are given in Figure 1 . Figure 1a shows the distribution of MFE values for secondary structures inferred from randomized sequences of the let7 miRNA precursor . Let-7 and Lin-4 genes are the founding members of the miRNAs and control developmental timing in C.elegans -LRB- Lee et al. , 1993 ; Reinhart et al. , 2000 -RRB- . The MFE value for the original structure of let7 lies clearly in the extreme tail of the distribution , which corresponds with a low P - value of 0.001 . This implies that the MFE value of the original structure of the let7 miRNA gene is significantly different from that of a random sequence . Figure 1b shows the distribution of MFE values for structures inferred from randomized sequences of a tRNA sequence , Ce-chr4 . trna39-ArgACG -LRB- Lowe and Eddy , 1997 -RRB- of C.elegans . In this case , the MFE value for the original tRNA structure falls in the middle of the distribution giving a P - value of 0.345 . Therefore , the null hypothesis can not be rejected and the MFE value for the original structure is not significantly different from that of a random sequence . A graph showing the distribution of P - values for all the sequences used in this study is available as supplementary data . Figure 2 shows the results of the randomization tests for miRNAs , rRNAs and tRNAs sequences -LRB- the complete list of 2913 E.Bonnet et al. . miRNA 100 90 80 70 60 p < 0.05 % 50 40 30 20 10 0 p < 0.01 p 0.001 Mononucleotide Markov one Dinucleotide rRNA 100 90 80 70 60 p < 0.05 % 50 40 30 20 10 0 p < 0.01 p 0.001 Mononucleotide Markov one Dinucleotide tRNA 100 90 80 70 60 p < 0.05 % 50 40 30 20 10 0 p < 0.01 p 0.001 Mononucleotide Markov one Dinucleotide Fig. 2 . Proportion of sequences -LRB- given in % -RRB- that have a P - value below 0.05 , 0.01 and 0.001 , for each type of shuffling method used and for each of the three types of non-coding RNAs used in this study . 2914 MicroRNA precursors have lower folding free energy MFE values and their associated probabilities is available as supplementary data -RRB- . The proportion of miRNA sequences that have a structure with a lower MFE than structures based on randomized miRNA sequences is very high for the three levels of significance defined . For example , more than 90 % of the miRNA sequences have a P - value < 0.01 with the mononucleotide and dinucleotide shuffling . There is also a high proportion of sequences with a P - value of 0.001 , based on the three types of randomization methods -LRB- 70 % for the mononucleotide and dinucleotide shuffling and > 40 % with the first-order Markov -RRB- . Ribosomal RNA sequences show a less high proportion of sequences for each P - value level compared to miRNA . For example , for P < 0.01 , only 50 % of the sequences show an MFE value that is lower than MFE values for structures inferred from randomized sequences . For tRNA sequences , < 8 % of the sequences have a P - value < 0.01 . This result is in agreement with previous findings -LRB- Rivas and Eddy , 2000 -RRB- where 26 tRNA sequences out of 1415 -LRB- 1.8 % -RRB- were found to have a P - value of 0.01 . The values obtained with the first-order Markov type of shuffling are always slightly smaller than the values obtained with the other types of shuffling . This is due to fluctuations in the energies for the Markov sequences . Those fluctuations arose due to higher variations in GC content while shuffling sequences maintain a constant GC : AT ratio -LRB- Workman and Krogh , 1999 -RRB- . This could artificially increase the number of low values for the MFE of randomized sequences . Thermodynamic stability of the secondary structure does not seem to be of major importance for tRNAs and rRNAs . For rRNAs , it is well known that the stability is enhanced by interactions with protein structures in the ribosome -LRB- Xing and Draper , 1995 -RRB- . For tRNAs , Workman and Krogh -LRB- 1999 -RRB- already suggested that the tertiary structure , which is not taken into account in secondary structure predictions , might be of major importance for this class of molecules . For miRNAs , precursor sequences do not have tight interactions with proteins before their cleavage by Dicer -LRB- or Dicer-like -RRB- enzymes -LRB- Lee et al. , 2002 -RRB- . Therefore , a stable secondary structure might be needed for this class of non-coding RNAs to avoid early degradation . Indeed , the great majority of the miRNA precursors have an MFE value significantly smaller than MFE values found for structures inferred from randomized sequences . We believe that this observation can be used for the more successful annotation of plant miRNA genes . Usually , in order to identify miRNA genes , the MFE of the sequence is calculated . Once this MFE is lower than a specified cutoff value the precursor sequence is accepted . Some authors also suggested to use a threshold based on the MFE normalized by the length of the sequence -LRB- Pervouchine et al. , 2003 -RRB- . This kind of approach has been proven to be very successful , especially in animal genomes , where all known miRNA genes are of comparable length and contain few unpaired bases -LRB- Lai et al. , 2003 ; Lim et al. , 2003a , b -RRB- . However , in plant genomes the miRNA precursor sequences are often much longer -LRB- Llave et al. , 2002 ; Reinhart et al. , 2002 -RRB- . It is poorly understood how this property would affect the cutoff value that is to be used in miRNA annotation . In our approach the MFE value is compared to that of random sequences with the same -LRB- di - -RRB- nucleotide composition , leading to a less error-prone annotation of miRNAs in plants -LRB- Bonnet et al. , 2004 -RRB- . The miRNAs used in our study have heterogeneous origins , and were obtained both by direct cloning methods and by pure computational prediction . In order to avoid a possible bias introduced by considering predicted miRNA precursors , we analyzed a subset of miRNA precursor sequences that were validated by northern blot experiments -LRB- LagosQuintana et al. , 2001 ; Lau et al. , 2001 ; Lee and Ambros , 2001 ; Calin et al. , 2002 ; Lee et al. , 2002 ; Schmittgen et al. , 2004 -RRB- . The set is composed of 47 sequences covering three different animal taxa , namely C.elegans , D.melanogaster and H.sapiens . Unfortunately , no plant miRNA precursor sequence has been verified experimentally up to now -LRB- Reinhart et al. , 2002 -RRB- . Table 2 shows the MFE values and their associated P - values computed using 1000 repeats and a dinucleotide randomization method . The results show clearly that most of the P - values are very low , namely 45 sequences out of 47 -LRB- 96 % -RRB- have a P - value < 0.07 . Only two sequences -LRB- C.elegans mir-52 and mir-69 -RRB- have very high P - values -LRB- 0.113 and 0.201 -RRB- . The fact that both those sequences are U rich and that mir-69 have an unusual high MFE value of -18 kcal/mol might explain why they have such high P - values . Based on these results , the level of sensitivity of the test appears to be high . However , the fact that a relative high number of rRNA sequences that also have a low P - value clearly suggest that the MFE randomization test alone can not be used as a valid miRNA sequence detection tool . It should be used in association with other type of evidence such as the absolute MFE value , comparative genomics or other secondary structure characteristics in order to reliably detect genuine miRNA sequences -LRB- Bonnet et al. , 2004 -RRB- . In this respect , for the integration of the MFE randomization test for validating potential miRNA precursor sequences , a cutoff level must be defined . Preferably , this value should be evaluated on a set of precursor sequences that have been characterized experimentally . The cutoff value will be a trade-off between discarding ` true ' sequences -LRB- false negatives -RRB- and accepting ` false ' ones -LRB- false positives -RRB- . For example , if we consider the results of Table 2 for H.sapiens , a cutoff value set to P = 0.05 would discard only two sequences from this set . Our results also strengthen what is known from the biogenesis of miRNA precursors : a stable secondary structure with specific properties is probably needed for a correct processing of those molecules by specific enzymes . Mature miRNAs are processed from their stem loop precursors by double-stranded specific nucleases of the so-called Dicer 2915 E.Bonnet et al. . Table 2 . MFE and computed P - values for experimentally validated miRNA precursor sequences -LRB- see text for details -RRB- miRNA C.elegans cel-mir-35 cel-mir-36 cel-mir-37 cel-mir-38 cel-mir-39 cel-mir-40 cel-mir-1 cel-mir-42 cel-mir-43 cel-mir-44 cel-mir-45 cel-mir-80 cel-mir-60 cel-mir-34 cel-mir-84 cel-mir-52 cel-mir-69 dme-mir-1 dme-mir-2b-2 dme-mir-6-2 dme-mir-6-3 dme-mir-2b-1 dme-mir-6-1 hsa-let-7d hsa-mir-16 hsa-mir-20 hsa-mir-21 hsa-mir-28 hsa-mir-29 hsa-mir-30a hsa-mir-33 hsa-mir-92-1 hsa-mir-93-1 hsa-mir-101 hsa-mir-105-1 hsa-mir-224 hsa-mir-30d hsa-let-7d hsa-mir-219 hsa-mir-27a hsa-mir-15a hsa-mir-24-1 hsa-mir-216 hsa-mir-220 hsa-mir-107 hsa-mir-18 hsa-mir-24-2 MFE -54 -50 -45 -50 -45 -46 -41 -42 -47 -42 -41 -31 -35 -37 -23 -26 -18 -36 -37 -27 -32 -28 -26 -43 -38 -31 -36 -51 -25 -37 -37 -36 -45 -36 -43 -37 -28 -43 -55 -38 -31 -26 -41 -43 -30 -22 -27 P 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.003 0.113 0.201 0.001 0.001 0.001 0.001 0.002 0.006 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.002 0.003 0.003 0.005 0.011 0.016 0.055 0.069 this question , it would be important to precisely determine the structures of miRNA precursors and RNase III proteins . 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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energy bioinformatics rna free structure mirna


The human disease network Kwang-Il Goh * , Michael E. Cusick , David Valle , Barton Childs , Marc Vidal ** , and Albert-Laszlo Barabasi *** * Center for Complex Network Research and Department of Physics , University of Notre Dame , Notre Dame , IN 46556 ; Center for Cancer Systems Biology -LRB- CCSB -RRB- and Department of Cancer Biology , Dana Farber Cancer Institute , 44 Binney Street , Boston , MA 02115 ; Department of Genetics , Harvard Medical School , 77 Avenue Louis Pasteur , Boston , MA 02115 ; Department of Physics , Korea University , Seoul 136-713 , Korea ; and Department of Pediatrics and the McKusick Nathans Institute of Genetic Medicine , Johns Hopkins University School of Medicine , Baltimore , MD 21205 Edited by H. Eugene Stanley , Boston University , Boston , MA , and approved April 3 , 2007 -LRB- received for review February 14 , 2007 -RRB- biological networks complex networks biology diseasome human genetics systems D ecades-long efforts to map human disease loci , at first genetically and later physically -LRB- 1 -RRB- , followed by recent positional cloning of many disease genes -LRB- 2 -RRB- and genome-wide association studies -LRB- 3 -RRB- , have generated an impressive list of disorder gene association pairs -LRB- 4 , 5 -RRB- . In addition , recent efforts to map the protein protein interactions in humans -LRB- 6 , 7 -RRB- , together with efforts to curate an extensive map of human metabolism -LRB- 8 -RRB- and regulatory networks offer increasingly detailed maps of the relationships between different disease genes . Most of the successful studies building on these new approaches have focused , however , on a single disease , using network-based tools to gain a better understanding of the relationship between the genes implicated in a selected disorder -LRB- 9 -RRB- . Here we take a conceptually different approach , exploring whether human genetic disorders and the corresponding disease genes might be related to each other at a higher level of cellular and organismal organization . Support for the validity of this approach is provided by examples of genetic disorders that arise from mutations in more than a single gene -LRB- locus heterogeneity -RRB- . For example , Zellweger syndrome is caused by mutations in any of at least 11 genes , all associated with peroxisome biogenesis -LRB- 10 -RRB- . Similarly , there are many examples of different mutations in the same gene -LRB- allelic heterogeneity -RRB- giving rise to phenotypes currently classified as different disorders . For example , mutations in TP53 have been linked to 11 clinically distinguishable cancerrelated disorders -LRB- 11 -RRB- . Given the highly interlinked internal organization of the cell -LRB- 12 17 -RRB- , it should be possible to improve the single gene single disorder approach by developing a conceptual framework to link systematically all genetic disorders -LRB- the human `` disease phenome '' -RRB- with the complete list of disease genes -LRB- the `` disease genome '' -RRB- , resulting in a global view of the `` diseasome , '' the combined set of all known disorder/disease gene associations . Results Properties of the HDN . If each human disorder tends to have a distinct and unique genetic origin , then the HDN would be disconnected into many single nodes corresponding to specific disorders or grouped into small clusters of a few closely related disorders . In contrast , the obtained HDN displays many connections between both individual disorders and disorder classes -LRB- Fig. 2a -RRB- . Of 1,284 disorders , 867 have at least one link to other disorders , and 516 disorders form a giant component , suggesting that the genetic origins of most diseases , to some extent , are shared with other diseases . The number of genes associated with a disorder , s , has a broad distribution -LRB- see SI Fig. 6a -RRB- , indicating that most disorders relate to a few disease genes , whereas a handful of phenotypes , such as deafness -LRB- s 41 -RRB- , leukemia -LRB- s 37 -RRB- , and colon cancer -LRB- s 34 -RRB- , relate to dozens of genes -LRB- Fig. 2a -RRB- . The degree -LRB- k -RRB- distribution of HDN -LRB- SI Fig. 6b -RRB- indicates that most disorders are linked to only Author contributions : D.V. , B.C. , M.V. , and A.-L.B. designed research ; K.-I.G. and M.E.C. performed research ; K.-I.G. and M.E.C. analyzed data ; and K.-I.G. , M.E.C. , D.V. , M.V. , and A.-L.B. wrote the paper . The authors declare no conflict of interest . This article is a PNAS Direct Submission . Abbreviations : DGN , disease gene network ; HDN , human disease network ; GO , Gene Ontology ; OMIM , Online Mendelian Inheritance in Man ; PCC , Pearson correlation coefficient . ** To whom correspondence may be addressed . E-mail : alb@nd.edu or marc vidal @ dfci.harvard.edu . This article contains supporting information online at www.pnas.org/cgi/content/full/ 0701361104/DC1 . 2007 by The National Academy of Sciences of the USA Construction of the Diseasome . We constructed a bipartite graph consisting of two disjoint sets of nodes . One set corresponds to all www.pnas.org cgi doi 10.1073 pnas .0701361104 PNAS May 22 , 2007 vol . 104 no. 21 8685 8690 APPLIED PHYSICAL SCIENCES A network of disorders and disease genes linked by known disorder gene associations offers a platform to explore in a single graphtheoretic framework all known phenotype and disease gene associations , indicating the common genetic origin of many diseases . Genes associated with similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts , supporting the existence of distinct disease-specific functional modules . We find that essential human genes are likely to encode hub proteins and are expressed widely in most tissues . This suggests that disease genes also would play a central role in the human interactome . In contrast , we find that the vast majority of disease genes are nonessential and show no tendency to encode hub proteins , and their expression pattern indicates that they are localized in the functional periphery of the network . A selection-based model explains the observed difference between essential and disease genes and also suggests that diseases caused by somatic mutations should not be peripheral , a prediction we confirm for cancer genes . known genetic disorders , whereas the other set corresponds to all known disease genes in the human genome -LRB- Fig. 1 -RRB- . A disorder and a gene are then connected by a link if mutations in that gene are implicated in that disorder . The list of disorders , disease genes , and associations between them was obtained from the Online Mendelian Inheritance in Man -LRB- OMIM ; ref . 18 -RRB- , a compendium of human disease genes and phenotypes . As of December 2005 , this list contained 1,284 disorders and 1,777 disease genes . OMIM initially focused on monogenic disorders but in recent years has expanded to include complex traits and the associated genetic mutations that confer susceptibility to these common disorders -LRB- 18 -RRB- . Although this history introduces some biases , and the disease gene record is far from complete , OMIM represents the most complete and up-todate repository of all known disease genes and the disorders they confer . We manually classified each disorder into one of 22 disorder classes based on the physiological system affected -LSB- see supporting information -LRB- SI -RRB- Text , SI Fig. 5 , and SI Table 1 for details -RSB- . Starting from the diseasome bipartite graph we generated two biologically relevant network projections -LRB- Fig. 1 -RRB- . In the `` human disease network '' -LRB- HDN -RRB- nodes represent disorders , and two disorders are connected to each other if they share at least one gene in which mutations are associated with both disorders -LRB- Figs. 1 and 2a -RRB- . In the `` disease gene network '' -LRB- DGN -RRB- nodes represent disease genes , and two genes are connected if they are associated with the same disorder -LRB- Figs. 1 and 2b -RRB- . Next , we discuss the potential of these networks to help us understand and represent in a single framework all known disease gene and phenotype associations . DISEASOME disease phenome disease genome AR ATM BRCA1 HEXB BRCA2 Prostate cancer Silver spastic paraplegia syndrome Sandhoff disease Ovarian cancer Lymphoma CDH1 VAPB Amyotrophic lateral sclerosis GARS HEXB KRAS Androgen insensitivity Prostate cancer Perineal hypospadias MSH2 Pancreatic cancer Lymphoma Wilms tumor Pancreatic cancer Papillary serous carcinoma Fanconi anemia T-cell lymphoblastic leukemia Breast cancer Ovarian cancer Wilms tumor Spinal muscular atrophy MAD1L1 Sandhoff disease Lipodystrophy Charcot-Marie-Tooth disease Ataxia-telangiectasia Amyotrophic lateral sclerosis Silver spastic paraplegia syndrome Spastic ataxia/paraplegia Fanconi anemia RAD54L VAPB CHEK2 BSCL2 ALS2 BRIP1 CDH1 MAD1L1 PIK3CA MSH2 CHEK2 PIK3CA KRAS TP53 RAD54L TP53 BRCA1 Breast cancer LMNA BRIP1 AR ATM BRCA2 GARS ALS2 LMNA BSCL2 Human Disease Network -LRB- HDN -RRB- Charcot-Marie-Tooth disease Lipodystrophy Spastic ataxia/paraplegia Ataxia-telangiectasia Perineal hypospadias Androgen insensitivity T-cell lymphoblastic leukemia Papillary serous carcinoma Disease Gene Network -LRB- DGN -RRB- Spinal muscular atrophy Fig. 1 . Construction of the diseasome bipartite network . -LRB- Center -RRB- A small subset of OMIM-based disorder disease gene associations -LRB- 18 -RRB- , where circles and rectangles correspond to disorders and disease genes , respectively . A link is placed between a disorder and a disease gene if mutations in that gene lead to the specific disorder . The size of a circle is proportional to the number of genes participating in the corresponding disorder , and the color corresponds to the disorder class to which the disease belongs . -LRB- Left -RRB- The HDN projection of the diseasome bipartite graph , in which two disorders are connected if there is a gene that is implicated in both . The width of a link is proportional to the number of genes that are implicated in both diseases . For example , three genes are implicated in both breast cancer and prostate cancer , resulting in a link of weight three between them . -LRB- Right -RRB- The DGN projection where two genes are connected if they are involved in the same disorder . The width of a link is proportional to the number of diseases with which the two genes are commonly associated . A full diseasome bipartite map is provided as SI Fig. 13 . a few other disorders , whereas a few phenotypes such as colon cancer -LRB- linked to k 50 other disorders -RRB- or breast cancer -LRB- k 30 -RRB- represent hubs that are connected to a large number of distinct disorders . The prominence of cancer among the most connected disorders arises in part from the many clinically distinct cancer subtypes tightly connected with each other through common tumor repressor genes such as TP53 and PTEN . Although the HDN layout was generated independently of any knowledge on disorder classes , the resulting network is naturally and visibly clustered according to major disorder classes . Yet , there are visible differences between different classes of disorders . Whereas the large cancer cluster is tightly interconnected due to the many genes associated with multiple types of cancer -LRB- TP53 , KRAS , ERBB2 , NF1 , etc. -RRB- and includes several diseases with strong predisposition to cancer , such as Fanconi anemia and ataxia telangiectasia , metabolic disorders do not appear to form a single distinct cluster but are underrepresented in the giant component and overrepresented in the small connected components -LRB- Fig. 2a -RRB- . To quantify this difference , we measured the locus heterogeneity of each disorder class and the fraction of disorders that are connected to each other in the HDN -LRB- see SI Text -RRB- . We find that cancer and neurological disorders show high locus heterogeneity and also represent the most connected disease classes , in contrast with metabolic , skeletal , and multiple disorders that have low genetic heterogeneity and are the least connected -LRB- SI Fig. 7 -RRB- . Properties of the DGN . In the DGN , two disease genes are connected if they are associated with the same disorder , providing a comple8686 www.pnas.org cgi doi 10.1073 pnas .0701361104 mentary , gene-centered view of the diseasome . Given that the links signify related phenotypic association between two genes , they represent a measure of their phenotypic relatedness , which could be used in future studies , in conjunction with protein protein interactions -LRB- 6 , 7 , 19 -RRB- , transcription factor-promoter interactions -LRB- 20 -RRB- , and metabolic reactions -LRB- 8 -RRB- , to discover novel genetic interactions . In the DGN , 1,377 of 1,777 disease genes are connected to other disease genes , and 903 genes belong to a giant component -LRB- Fig. 2b -RRB- . Whereas the number of genes involved in multiple diseases decreases rapidly -LRB- SI Fig. 6d ; light gray nodes in Fig. 2b -RRB- , several disease genes -LRB- e.g. , TP53 , PAX6 -RRB- are involved in as many as 10 disorders , representing major hubs in the network . Functional Clustering of HDN and DGN . To probe how the topology of the HDN and GDN deviates from random , we randomly shuffled the associations between disorders and genes , while keeping the number of links per each disorder and disease gene in the bipartite network unchanged . Interestingly , the average size of the giant component of 104 randomized disease networks is 643 16 , significantly larger than 516 -LRB- P 10 4 ; for details of statistical analyses of the results reported hereafter , see SI Text -RRB- , the actual size of the HDN -LRB- SI Fig. 6c -RRB- . Similarly , the average size of the giant component from randomized gene networks is 1,087 20 genes , significantly larger than 903 -LRB- P 10 4 -RRB- , the actual size of the DGN -LRB- SI Fig. 6e -RRB- . These differences suggest important pathophysiological clustering of disorders and disease genes . Indeed , in the actual networks disorders -LRB- genes -RRB- are more likely linked to disorders -LRB- genes -RRB- of the same disorder class . For example , in the HDN there Goh et al. . Bethlem myopathy Heinz Zellweger body syndrome anemia Trichothiodystrophy a Human Disease Network Cataract Node size Myopathy Deafness Retinitis pigmentosa Epidermolysis bullosa 41 34 Muscular dystrophy Leigh syndrome Cardiomyopathy Stroke Charcot-Marie-Tooth disease Diabetes mellitus Ataxiatelangiectasia Gastric cancer Prostate cancer Fanconi anemia Obesity Hypertension Atheroscierosis Breast cancer Lymphoma Colon cancer Asthma Pseudohypoaldosteronism Myocardial infarction Alzheimer disease 30 25 21 15 Epilepsy Mental retardation Blood group Spherocytosis Spinocereballar ataxia Hemolytic anemia Complement_component deficiency b Disease Gene Network FBN1 PAX6 LRP5 SCN4A Disorder Class COL2A1 GJB2 GNAS ARX ACE FGFR2 MSH2 ERBB2 APC FGFR3 PTEN TP53 KRAS NF1 KI T MEN1 Bone Cancer Cardiovascular Connective tissue Dermatological Developmental Ear , Nose , Throat Endocrine Gastrointestinal Hematological Immunological Metabolic Muscular Neurological Nutritional Ophthamological Psychiatric Renal Respiratory Skeletal multiple Unclassified Fig. 2 . The HDN and the DGN . -LRB- a -RRB- In the HDN , each node corresponds to a distinct disorder , colored based on the disorder class to which it belongs , the name of the 22 disorder classes being shown on the right . A link between disorders in the same disorder class is colored with the corresponding dimmer color and links connecting different disorder classes are gray . The size of each node is proportional to the number of genes participating in the corresponding disorder -LRB- see key -RRB- , and the link thickness is proportional to the number of genes shared by the disorders it connects . We indicate the name of disorders with 10 associated genes , as well as those mentioned in the text . For a complete set of names , see SI Fig. 13 . -LRB- b -RRB- In the DGN , each node is a gene , with two genes being connected if they are implicated in the same disorder . The size of each node is proportional to the number of disorders in which the gene is implicated -LRB- see key -RRB- . Nodes are light gray if the corresponding genes are associated with more than one disorder class . Genes associated with more than five disorders , and those mentioned in the text , are indicated with the gene symbol . Only nodes with at least one link are shown . Goh et al. . PNAS May 22 , 2007 vol . 104 no. 21 8687 APPLIED PHYSICAL SCIENCES Thyroid carcinoma Leukemia Parkinson disease Hirschprung disease 10 5 1 a Probability density 0.1 b Normalized histogram Observed 0.7 0.6 0.08 0.5 0.4 0.3 0.2 0.1 0.06 0.04 0.02 0 10 20 30 40 50 60 290 0 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Number of interaction overlap Tissue homogeneity c Probability density 2 d Probability density 3 2.5 1.5 2 1.5 1 0.5 1 0.5 0 -0.75 -0.5 -0.25 0 0 ij 0.25 0.5 0.75 1 -1 -0.75 -0.5 -0.25 disease 0 0.25 0.5 0.75 1 Fig. 3 . Characterizing the disease modules . -LRB- a -RRB- Number of observed physical interactions between the products of genes within the same disorder -LRB- red arrow -RRB- and the distribution of the expected number of interactions for the random control -LRB- blue -RRB- -LRB- P 10 6 -RRB- . -LRB- b -RRB- Distribution of the tissue-homogeneity of a disorder -LRB- red -RRB- . Random control -LRB- blue -RRB- with the same number of genes chosen randomly is shown for comparison . -LRB- c -RRB- The distribution of PCC ij values of the expression profiles of each disease gene pair that belongs to the same disorder -LRB- red -RRB- and the control -LRB- blue -RRB- , representing the PCC distribution between all gene pairs -LRB- P 10 6 -RRB- . -LRB- d -RRB- Distribution of the average PCC between expression profiles of all genes associated with the same disorder -LRB- red -RRB- is also shifted toward higher values than the random control -LRB- blue -RRB- with the same number of genes chosen randomly -LRB- P 10 6 -RRB- . are 812 links between disorders of the same class , an 8-fold enrichment with respect to 107 10 links obtained between the same set of nodes in the randomized networks . This local functional clustering accounts for the small size of the giant components observed in the actual networks . Disease-Associated Genes Identify Distinct Functional Modules . For several disorders known to arise from mutations in any one of a few distinct genes , the corresponding protein products have been shown to participate in the same cellular pathway , molecular complex , or functional module -LRB- 21 , 22 -RRB- . For example , Fanconi anemia arises from mutations in a set of genes encoding proteins involved in DNA repair , many of them forming a single heteromeric complex -LRB- 23 -RRB- . Yet , the extent to which most disorders and disorder classes correspond to distinct functional modules in the cellular network has remained largely unclear . If genes linked by disorder associations encode proteins that interact in functionally distinguishable modules , then the proteins within such disease modules should more likely interact with one another than with other proteins . To test this hypothesis , we overlaid the DGN on a network of physical protein protein interactions derived from high-quality systematic interactome mapping -LRB- 6 , 7 -RRB- and literature curation -LRB- 6 -RRB- . We found that 290 interactions overlap between the two networks , a 10-fold increase relative to random expectation -LRB- P 10 6 ; Fig. 3a -RRB- . Genes associated with the same disorder share common cellular and functional characteristics , as annotated in the Gene Ontology -LRB- GO -RRB- -LRB- 24 -RRB- . If the HDN shows modular organization , then a group of genes associated with the same common disorder should share similar cellular and functional characteristics , as annotated in GO . To investigate the validity of this hypothesis , we measured the GO homogeneity of each disorder -LRB- see SI Text -RRB- separately for each branch of GO , biological process , molecular function , and cellular 8688 www.pnas.org cgi doi 10.1073 pnas .0701361104 component , finding significant elevation of GO homogeneity with respect to random controls in all three branches -LRB- SI Fig. 8 -RRB- . Disease genes encoding proteins that interact within common functional modules should tend to be expressed in the same tissue . To measure this , we introduced the tissue-homogeneity coefficient of a disorder , defined as the maximum fraction of genes among those belonging to a common disorder that are expressed in a specific tissue in a microarray data set obtained for 10,594 genes across 36 healthy tissues -LRB- 25 -RRB- . We found that 68 % of disorders exhibited almost perfect tissue-homogeneity -LRB- Fig. 3b -RRB- , compared with 51 % expected by chance -LRB- P 10 5 -RRB- . Finally , disease genes that participate in a common functional module should also show high expression profiling correlation -LRB- 26 -RRB- . The distribution of Pearson correlation coefficients -LRB- PCCs -RRB- for the coexpression profiles of pairs of genes associated with the same disorder was shifted toward higher values compared with that of a random control -LRB- Fig. 3c ; P 10 6 , 2 test -RRB- . Similarly , the average PCC over all pairs of genes within a given disorder shows a significant shift from the random reference -LRB- Fig. 3d -RRB- , with a small but clearly distinguishable peak in the distribution around PCC 0.75 . This peak corresponds to 33 disorders with average PCC 0.6 for which all genes are highly coexpressed in most tissues , including Heinz body anemia -LRB- PCC 0.935 -RRB- , Bethlem myopathy -LRB- PCC 0.835 -RRB- , and spherocytosis -LRB- PCC 0.656 -RRB- . In summary , genes that contribute to a common disorder -LRB- i -RRB- show an increased tendency for their products to interact with each other through protein protein interactions , -LRB- ii -RRB- have a tendency to be expressed together in specific tissues , -LRB- iii -RRB- tend to display high coexpression levels , -LRB- iv -RRB- exhibit synchronized expression as a group , and -LRB- v -RRB- tend to share GO terms . Together , these findings support the hypothesis of a global functional relatedness for disease genes and their products and offer a network-based model for the diseasome . Cellular networks are modular , consisting of groups of highly interconnected proteins responsible for specific cellular functions -LRB- 21 , 22 -RRB- . A disorder then represents the perturbation or breakdown of a specific functional module caused by variation in one or more of the components producing recognizable developmental and/or physiological abnormalities . This model offers a network-based explanation for the emergence of complex or polygenic disorders : a phenotype often correlates with the inability of a particular functional module to carry out its basic functions . For extended modules , many different combinations of perturbed genes could incapacitate the module , as a result of which mutations in different genes will appear to lead to the same phenotype . This correlation between disease and functional modules can also inform our understanding of cellular networks by helping us to identify which genes are involved in the same cellular function or network module -LRB- 21 , 22 -RRB- . Centrality and Peripherality . An early indication of the connection between the structure of a cellular network and its functional properties was the finding that in Saccharomyces cerevisiae highly connected proteins or `` hubs '' are more likely encoded by essential genes -LRB- 15 , 16 -RRB- . This prompted a number of recent studies -LRB- 27 , 28 -RRB- to formulate the hypothesis that human disease genes should also have a tendency to encode hubs . Yet , previous measurements found only a weak correlation between disease genes and hubs -LRB- 29 -RRB- , resulting in an important mystery : what is the role , if any , of the cellular network in human diseases ? Are disease genes more likely to encode hubs in the cellular network ? Our initial analysis appears to support the hypothesis that disease genes , given their impact on the organism , display a tendency to encode hubs in the interactome -LRB- 27 , 28 -RRB- , finding that disease related proteins have a 32 % larger number of interactions -LRB- 6 , 7 -RRB- with other proteins -LRB- average degree -RRB- than the nondisease proteins -LRB- see SI Fig 9 -RRB- and that high-degree proteins are more likely to be encoded by genes associated with diseases than proteins with few interactions -LRB- P 1.6 10 17 ; Fig. 4a -RRB- . Next , we show , however , that despite this Goh et al. . a fDisease genes 1 0.8 0.6 0.4 0.2 0 1 2 4 8 b P-value = 1.6x10-17 All human genes ~ 25,000 Essential 1,267 Non-essential disease 1,379 Essential disease 398 k 16 32 64 128 256 c fLethal mouse phenotype 1 0.8 0.6 0.4 0.2 d fNon-essential disease genes 1 0.8 P-value = 0.015 0.6 0.4 0.2 0 P-value = 1.3 x10 0 1 2 4 8 16 -17 k 32 64 128 256 1 2 4 8 k 16 32 64 128 256 e fLethal mouse phenotype 1 0.8 0.6 0.4 0.2 0 -0.1 0 0.1 -4 fNon-essential disease genes 0.25 0.2 0.15 0.1 0.05 0 P-value = 2.6 x10 -8 P-value = 1.7 x10 0.2 0.3 0.4 -0.1 0 0.1 0.2 0.3 0.4 g fLethal mouse phenotype 1 0.8 0.6 0.4 0.2 0 0 5 10 15 20 25 30 35 -16 h fNon-essential disease genes P-value = 2.8 x10 0.2 P-value = 1.4x10-6 0.15 0.1 0.05 0 0 5 10 15 20 25 30 35 nT nT Fig. 4 . Functional characteristics of disease and essential genes . -LRB- a -RRB- The fraction of disease genes among those whose protein products that interact with k other proteins . -LRB- b -RRB- Venn diagram showing the relationship between the human genes studied in this work . -LRB- c -RRB- The fraction of genes with lethal mouse phenotypes -LRB- essential genes -RRB- among those with mouse phenotypes that interact with k other proteins . -LRB- d -RRB- The same as in a , but only for nonessential disease genes , i.e. , excluding 398 proteins with lethal mouse phenotypes . -LRB- e and f -RRB- The fraction of essential genes -LRB- e -RRB- and nonessential disease genes -LRB- f -RRB- among those whose average PCC with other genes is . -LRB- g and h -RRB- The fraction of essential genes -LRB- g -RRB- and nonessential disease genes -LRB- h -RRB- among those whose transcript is expressed in nT tissues . Gray horizontal lines in a and c h indicate the global average . Error bars represent standard errors . Note that for some data points the error bars are smaller than the symbol size , and thus are not visible . In a , c , and d gray symbols are the linearly binned data points , whereas color corresponds to the statistically more uniform log-binned data . For details of the significance analysis , see SI Text . apparent correlation , the relationship between diseases and hubs hides deep differences between various disease genes . When exploring whether disease genes encode hubs , we , and authors of other earlier studies -LRB- 27 29 -RRB- , ignored the fact that some human genes are essential in early development and functional changes in these contribute to the high rate of first-trimester spontaneous abortions , which might be as much as 20 % of recognized pregnancies . One strategy to explore the impact of this in utero essential segment of human disease is to consider human Goh et al. . PNAS May 22 , 2007 vol . 104 no. 21 8689 APPLIED PHYSICAL SCIENCES f 0.3 orthologs of mouse genes that result in embryonic or postnatal lethality when disrupted by homologous recombination -LRB- Mouse Genome Informatics ; www.informatics.jax.org -RRB- . All together , we find 1,267 such mouse lethal orthologs of human genes , of which 398 are associated with human diseases , representing 22 % of all known human disease genes . This allows us to distinguish between two classes of human genes : 1,267 `` essential genes '' and 1,379 `` nonessential disease genes , '' the latter obtained by removing from the full list of 1,777 OMIM disease genes the 398 that are also essential -LRB- Fig. 4b -RRB- . Next , we show that these two classes of genes play quite different roles in the human interactome . First , we find that essential proteins show a tendency to be associated with hubs -LRB- P 1.3 10 17 ; Fig. 4c -RRB- , displaying a much stronger trend than the one observed for all disease proteins -LRB- Fig. 4a -RRB- . This raises an important question : Could the observed correlation between disease genes and hubs -LRB- Fig. 4a -RRB- be the sole consequence of the fact that a small fraction -LRB- 22 % -RRB- of disease genes is also essential ? To address this question we measured the degree dependence of the nonessential disease proteins -LRB- Fig. 4d -RRB- . Surprisingly , the correlation between hubs and disease proteins entirely disappears . Thus , the vast majority of disease genes -LRB- 78 % -RRB- , those that are nonessential , do not show a tendency to encode hubs , indicating that the observed weak correlations between hubs and disease genes -LRB- Fig. 4a -RRB- was entirely due to the few essential genes within the disease gene class . To carry on its basic functions , the cell needs to maintain the coordinated activity of important functional modules , driving in a relatively synchronized manner the expression patterns of the most important genes . Therefore , one expects that the expression pattern of both essential and disease genes will be synchronized with a significant number of other genes . To test this , we determined the average gene coexpression coefficient i jPCCij between an essential -LRB- or nonessential disease -RRB- gene i and all other genes in the cell , calculating the PCCij values from healthy human tissue microarray measurements -LRB- 25 -RRB- . Confirming our expectation , for essential genes we find that genes that display high average coexpression with all other genes are more likely to be essential than those that show small or negative -LRB- P 1.7 10 4 ; Fig. 4e -RRB- . Surprisingly , however , nonessential disease genes show the opposite effect , being associated with genes whose expression pattern is anticorrelated or not-correlated with other genes , and underrepresented among the genes that are highly synchronized -LRB- 0.2 -RRB- -LRB- P 2.6 10 8 ; Fig. 4f -RRB- . Thus , the expression pattern of nonessential disease genes appears to be decoupled from the overall expression pattern of all other genes , whereas essential genes have a tendency to be coupled to the rest of the cell . Finally , we asked whether housekeeping genes , expressed in all tissues , have a tendency to encode disease genes . We find that the more tissues in which a gene is expressed , the higher the likelihood that it will be essential -LRB- P 2.8 10 16 ; Fig. 4g -RRB- . The opposite is true for nonessential disease genes : they have a tendency to be expressed in a few tissues -LRB- P 1.4 10 6 ; Fig. 4h -RRB- . Similarly , we found that only 9.9 % of housekeeping genes correspond to disease genes , compared with 13.5 % of nonhousekeeping genes , a significant 36 % difference -LRB- P 3.6 10 6 -RRB- . In contrast , 59.8 % of housekeeping genes annotated with mouse phenotype were essential , compared with 40.5 % for nonhousekeeping genes -LRB- P 10 4 -RRB- . These results support the somewhat unexpected conclusion that nonessential disease genes are not associated with hubs -LRB- 27 , 28 -RRB- , show smaller correlation in their expression pattern with the rest of the genes in the cell than expected from random , and have a tendency to be expressed in only a few tissues . Therefore , contrary to earlier hypotheses and our expectations , the vast majority of nonessential disease genes occupy functionally peripheral and topologically neutral positions in the cellular network . In stark contrast , essential genes are likely to encode hubs , show highly synchronized expression with the rest of the genes , and are expressed in most tissues , being overrepresented among housekeep - ing genes . Thus , essential genes are topologically and functionally central . This unexpected peripherality of most disease genes can be best explained by using an evolutionary argument . Mutations in topologically central , widely expressed genes are more likely to result in severe impairment of normal developmental and/or physiological function , leading to lethality in utero or early extrauterine life and to eventual deletion from the population . Only mutations compatible with survival into the reproductive years are likely to be maintained in a population . Therefore , disease-related mutations in the functionally and topologically peripheral regions of the cell give a higher chance of viability . Disease genes whose mutations are somatic should not be subject to the selective pressure discussed above . Instead , somatic mutations that lead to severe disease phenotypes should more likely affect the functional center . To test the predictive power of this selection-based argument , we studied separately the properties of somatic cancer genes -LRB- Cancer Genome Census ; www.sanger.ac.uk / genetics/CGP/Census -RRB- and found that they -LRB- i -RRB- are more likely to encode hubs , -LRB- ii -RRB- show higher coexpression with the rest of the genes in the cell , and -LRB- iii -RRB- are more represented among housekeeping genes -LRB- SI Fig. 10 -RRB- . The observed functional and topological centrality of somatic cancer genes fits well with our current understanding that many cancer genes play critical roles in cellular development and growth -LRB- 11 -RRB- . Discussion Throughout history , clinicians and medical researchers have focused on a few disorder -LRB- s -RRB- sharing commonalities in etiology or pathology . Recent progress in genetics and genomics has led to an appreciation of the effects of gene mutations in virtually all disorders and provides the opportunity to study human diseases all at once rather than one at a time -LRB- 4 , 30 -RRB- . This unique approach offers the possibility of discerning general patterns and principles of human disease not readily apparent from the study of individual disorders . 1 . Pasternak JJ -LRB- 2005 -RRB- An Introduction to Human Molecular Genetics -LRB- Wiley , Hoboken , NJ -RRB- , 2nd Ed . 2 . Broeckel U , Schork NJ -LRB- 2004 -RRB- J Physiol 554:40 45 . 3 . Hirschhorn JN , Daly MJ -LRB- 2005 -RRB- Nat Rev Genet 6:95 108 . 4 . Jimenez-Sanchez G , Childs B , Valle D -LRB- 2001 -RRB- Nature 409:853 855 . 5 . Peltonen L , McKusick VA -LRB- 2001 -RRB- Science 291:1224 1229 . 6 . 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Albert R -LRB- 2005 -RRB- J Cell Sci 118:4947 4957 . 15 . Jeong H , Mason SP , Barabasi A-L , Oltvai ZN -LRB- 2001 -RRB- Nature 411:41 42 . An important tool in this quest is the HDN that represents a genome-wide roadmap for future studies on disease associations . The accompanying detailed diseasome map -LRB- SI Fig. 13 -RRB- , showing all disorders and the genes associated with different disorders , offers a rapid visual reference of the genetic links between disorders and disease genes , a valuable global perspective for physicians , genetic counselors , and biomedical researchers alike . To test whether the conclusions obtained in this work are robust to the incompleteness of the OMIM coverage , we expanded our study to include not only genes with identified mutations linked to the specific disease phenotype , but also those that satisfy the less stringent criterion that the phenotype has not been mapped to a specific locus -LRB- 18 -RRB- . This expansion increased the number of diseaseassociated genes from 1,777 to 2,765 , but also introduced noise in the data , because the link between many of the newly added genes and diseases is less stringent . Yet , the overall organization of the expanded diseasome map remains largely unaltered -LRB- SI Fig. 11 -RRB- , and none of the trends uncovered in Fig. 4 are affected by this extension -LRB- SI Fig. 12 -RRB- , supporting the robustness of our findings to further expansion of the OMIM database . Thus , although the maps shown in Fig. 2 and SI Fig. 13 will inevitably undergo local changes with the discovery of new disease genes , this will not change the overall organization and layout of the HDN significantly , because the HDN reflects the underlying cellular network-based relationship between genes and functional modules . We thank Victor McKusick , Ada Hamosh , Joanna Amberger , and the rest of the OMIM team for their hard work and dedication and Tom Deisboeck , Zoltan Oltvai , Joanna Amberger , Todd Golub , Gerardo Jimenez-Sanchez and the members of the M.V. laboratory and the Center for Cancer Systems Biology , especially David E. Hill , for useful discussions . K.-I.G. and A.-L.B. were supported by National Institutes of Health -LRB- NIH -RRB- Grants IH U01 A1070499-01 and U56 CA113004 and National Science Foundation Grant ITR DMR-0926737 IIS-0513650 . This work was supported by the Dana Farber Cancer Institute -LRB- DFCI -RRB- Strategic Initiative -LRB- M.V. -RRB- and grants from the W. M. Keck Foundation -LRB- to M.V. -RRB- and the NIH/National Human Genome Research Institute and NIH/National Institute of General Medical Sciences -LRB- to M.V. -RRB- . 16 . 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O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O B O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O

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