Systems biology of SNPs

1. Systems biology of SNPs Neema Jamshidi and Bernhard Palsson Presentation : Haseong Kim BIBS SNU 2006. 12. 11

2. Introduction • High-throughput technologies • Measure thousands of interdependent biological variables • Numerous methods are used to reduce the complexity of HT data sets • Determine dependencies among variables • Correlate the variables with biological functions • Dimensionality reduction • Identify patterns of correlation within data • Identify sets of co-regulated genes in mRNA expression profiles (Reymond et al., 2002) • Perfect proxy sets : sets of perfectly correlated SNPs (Altshuler et al., 2005)

3. Introduction : Perfect proxy sets • HapMap project • Sets of perfectly correlated SNPs • Power of ‘perfect proxy sets’ (Altshuler et al., 2005) • As segments of the human chromosomes • Suggest their utility in identifying differences in individual genomes

4. Introduction • Systems biology aims reconstruct networks of cellular interactions mathematically and to compute their functional states (Price et al., 2004) • Correlated sets of reactions (co-sets) • In the context of biochemical networks, groups of reactions that always or often function togeter in metabolic networks under the constraints of mass conservation, charge conservation, and thermodynamic considerations. • Groups of enzymatic reactions that are perfectly correlated (correlation=1) • Open nonobvious, may not be adjacent. • Represent mathematically functional modules of a network • Identify genes whose products are collectively required to achieve physiological states • Perturbations affecting genes belonging to the same co-set are expected to lead to similar functional consequences.

5. Classifying SNPs and co-sets • Use co-sets to seek dependencies among SNPs with causal implications on metabolic function • By grouping SNPs in proteins that catalyze different reactions but are shared within the same co-set • Not all SNPs will affect protein function; However, as the goal of this systems-based analysis is to study functional consequences of causal SNPs. • Although the SNPs may affect different proteins with different catalytic activities, if the reactions are in the same co-set, the phenotypic consequences of such SNPs are expected to be similar.

6. Classifying SNPs and co-sets • Classifying a group of genes that encode members of a co-set into three functional types • Type A • Multimeric enzyme where an SNP in any subunit of the multimer can thus result in the same phenotype • Type B • A co-set of reactions in a contiguous pathway • Type C • Co-sets that formed by non-contiguous reactions

9. Disease-associated SNP co-sets in the mitochondria • Map the human mitochondrial metabolic co-sets to various diseases in the OMIM and identified those cases in which SNPs have been described in the literature • Mitochondria • 호흡 : C6H12O6 해당과정 2C3H4O3+2NADH2+2ATP TCA cycle (활성아세트산시트르산알파케토글루타르산숙신산말산)  6CO2+8NADH2+2FADH2+2ATP  전자 전달계

10. Disease-associated SNP co-sets in the mitochondria • Succinate dehydrogenase (SDH) – type A co-set • 숙신산에서 수소 2원자를 빼앗아서 푸마르산을 생성하는 반응을 가역적으로 촉매 하는 효소 • A series of SNPs in the different subunits of SDH have been found to have similar phenotypic consequences. • Heme biosynthesis – type B co-set • 헤모글로빈의 구성 성분 중(헴+글로빈) 헴이 만들어지는 과정에서 여덟 개의 생합성단계로 이루어짐. 각 단계마다 8가지 종류의 효소가 관여. 그 과정에서 어느 특정 효소가 부족하게 되면 그 효소가 관여하는 생화학반응의 전 단계 포피린이 몸에 축적되게 되며 그 결과 포피리아라는 병이 생김 • Many SNPs result in various manifestations of porphyria (range of severity and symptoms for a given enzyme) • Attributable to the specific location of particular SNPs (present of absence of other SNPs across the genome, differential tissue expression, the specific metabolic by-products that accumulate or diminish based on the specific reaction)

11. Disease-associated SNP co-sets in the mitochondria • Urea cycle (orithine cycle) – type C co-set • 단백질을 섭취 하였을 때 분해되는 과정에서 몸에 유해한 암모니아가 만들어지는데 이 암모니아를 효소로 전환시켜주는 회로 • Clinical hoherence between SNOs in three of the four reactions in this set • Most non-obvious and have the greatest effect on revising previous views of interactions and classifications of disease. • Citrulline/ornithine co-set • There is only SNP-related disease information for one of the two reactions in the co-set, the SLC25A15 transporter, whose deficiency results in the HHH. • Overexpression of SLC25A2 rescue patients with HHH (Camacho et al., 2003) • If two reactions with overlapping substrate utilization are in the same co-set, then overexpression of one enzyme can compensate for the deficiency or lack of expression of the other

13. Implications of SNP co-set network analysis • The approach taken to network reconstruction is a ‘bottom-up’ approach. • Network reconstruction is based on documented physical interactions and biochemical knowledge, rather than inferred interactions from HT data sets. • The co-set predictions made are a direct result of network-wide analysis reflecting fundamental properties of the reconstructed biochemical network. • This type of analysis of bottom-up networks can be used in conjunction with top-down analysis of HT data sets to help elucidate functional biological relationships. • The ability to map similarly causal SNPs to co-sets represents a new dimension in SNP analysis that is enabled by systems biology. • Classification of diseases, the mechanistic understanding of the genotype-phenotype relationship, and the potential identification of therapeutic targets and strategies for disease treatment.