고찰

본 연구는 마이크로어레이 데이터를 이용한 조건 특이적인 PPIs 예측정도를 확인한 것으로, 이를 위해서 첫째, 조건 특이적인 PPIs 데이터를 구축하고 둘째, 다양한 마이크로어레이 전처리 방법과 유전자 공동발현 측정법을 포괄적으로 비교분석하였다.

실제, ESC 를 대상으로 조건 특이적인 PPIs 사이의 유전자 공동발현 정도가 General PPIs, random pairs 및 NIPs 보다 가장 높게 나왔으며, 이 결과는 Random 과 General PPIs 와 비교했을 시 유의한 차이가 있음을 보여주었다. 특히, ESC 를 Random 과 비교했을 시 General 보다 더 많은 방법조합 (정규화 및 연관성) 사이에서 유의함을 나타내었으며, 가장 유의한 P-value 를 나타낸 것도 random 과의 비교 (Mouse, 29 개 조합, P = 4.2E-15; Human, 20 개 조합, P = 2.7E-06)에서 였다.

위와 같은 결과는 타당함과 동시에 여전히 General PPIs 와 조건 특이적인 PPIs 사이의 유사함을 의미하고 있으며, 이는 General PPIs 에 여러 조건 특이적인 PPIs 가 혼재되어 있음을 말해주고 있다. 실제로 본 연구에서 구축한 조건 특이적인 PPIs 데이터는 General PPIs 에서 유래한 일부에 해당한다.

연관성 측정법 및 정규화 방법을 비교한 결과는 흥미롭게도 MIk 방법이 가장 좋은 성능을 나타낸 공동발현 측정법 (Reshef 등, 2011; Song 등, 2012) 이었으며, Mouse 에 있어서는 추가적인 정규화를 하지 않는 raw 데이터에서 가장 좋은 성능을 보여주었다. 이는 본 연구에서 사용한 데이터가 이미 충분한 표본

간 오차가 교정되어있음을 의미한다. 이어 유의하게 나온 정규화 방법은

데이터에 대하여 Interlog 맵핑을 적용하여 ESC 및 다른 조건에서 추가연구를 진행할 수 있을 것이다. 또한, 마이크로어레이 데이터의 정규화 방법 외에 다양한 이산화 방법을 적용하고, 여러 마이크로어레이 데이터를 통합하여 보다 포괄적인 비교분석을 남겨두고 있다.

V. 결론

본 연구를 통해 마이크로어레이를 통한 조건 특이적인 PPIs 예측정도를 여러 방법들 사이에서 비교하고, 새로운 PPIs 예측을 위한 참고정보를 제공하였다.

기존에 알려진 General PPIs 사이의 유전자 공동발현 결과와 일치하게 Condition-specific PPIs 에서는 General PPIs 보다 더 높은 연관성을 나타내었으며, 이러한 결과를 바탕으로 새로운 Condition-specific PPIs 를 예측함에 있어 적절한 방법을 사용하도록 제안할 수 있다.

또한, 추가연구로 다른 세포조건의 PPI 데이터 및 여러 GEP 데이터셋을 통합하여 보다 일관된 결과를 얻을 필요가 있으며, 해당 결과를 바탕으로 GEP 데이터 상의 전체 유전자쌍에 대하여 공동발현 측정을 통한 Condition-specific PPI 를 예측하고 이를 실험적으로 검증해야할 것이다.

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- ABSTRACT -

Protein-protein interactions (PPIs) are functional core units within the cells that make up the vast networks and specific functions. The functional status of each cell depends on its PPIs network configuration. Most of publicly available PPI databases until now, annotation information about cellular condition is not commented even though individual PPIs experimentally assayed have their own cellular conditions. The absence of cellular conditions for specific PPIs gives limitations to accurately predict near physiological PPIs.

On the other hand, a number of gene expression profiles (GEP) data are available within various cellular conditions, but there are little attempts in predicting condition-specific PPIs using GEP data.

In this study, condition-specific PPIs (on stem cells and tumor cells) are built by integrating public PPI databases and by curating literatures. Using condition-specific PPIs above, several normalization methods and gene co-expression measures are comprehensively compared in terms of predicting embryonic stem cells(ESC)–specific PPIs. This study aims to propose the proper methodology to predict condition-specific PPIs using GEP data.

Abstracts of individual papers derived from generally known PPIs are searched using keywords of stem cells and cancers and curated manually for the details of stem cells and cancer subtype as annotation information. For GEP data, we collected the profiles of human and mouse stem cells from the NCBI GEO. Then we preprocessed the collected GEP data using six different normalization methods and caculated co-expression degree of ESC-specific and general PPIs accordingly. Finally we compared the result of caculated co-expression degree in an unbiased manner and found co-co-expression measures of most

enlarging difference between ESC-specific and general PPIs.

The literature-curated condition-specific PPIs database consists of a total of 4,161 proteins and 8,347 interactions, specially 371 proteins and 603 interactions in the ESC which is the greatest amount of stem cells. In Human, the quantile and MIk measure while not further nomalized and MIk measures in Mouse shown as the most significant methods in terms of ESC-specific PPIs. We found also the Boundary normalized data and MI-based co-expression measures (MI, MIk) shown to have significant difference compared to the general PPIs.

In this paper, we first constructed condition-specific PPIs and comprehensively compared the co-expression mearsures and normalization methods which could assist in predicting genome-wide and condition-specific PPIs directly from GEPs data. Our data also could find a certain conditional PPIs when compared with general and other conditional PPIs and this network may reveal in-depth mechanism of cellular condition of interest. The constructed database of condition-specific PPIs can be used as a reference to predict novel PPIs from GEPs data of stem cells and cancers.

Keywords : Protein-protein interactions (PPIs), Context/Condition-specific PPIs, Gene expression profiles, Gene co-expresion, Pair association, Mutual information, Quantile normalization, Embryonic stem cell (ESC), ESC-specific PPIs