Essay: Protein-Protein Interaction In Human Diseases - Key Importance

Overview of Presentation

The Interactome The Department Of Surgery And The Unit Of Analytic And Translational Genetics

This is basically the introductory portion of your presentation where you greet the audience, state your name, and introduce the title of your research paper and to which department it is under. Also, state what you will be speaking of in which case it is the research the paper and when it was completed and submitted.

This is basically an outline of the presentation. Take the audience through the overview by reading through it to give them an idea and a guide of how the presentation is going proceed and also to help easily arrange the ideas of the paper.

States the main ideas of the research paper that are centered on the importance of the protein-protein interaction, how these interactions are measured, collected and the quality control of the accuracy of these interactions (Kasper, 2014). Also, it is determined to decipher how this data on protein-protein interaction is used computationally along-side the GWAS (Genome-Wide Association Studies) databases and Exome-Sequence Data (ESP) to provide understanding of the molecular processes involved in human diseases (Kasper, 2014).

Why your audience should read your research paper?

The research paper is designed to provide an understanding of how the available, identified genomic variants affect complex biological mechanisms (E.S. Lander, et al., 2001). These variants are explored in terms of proteins coded by the various genes in the human genome (J.C. Venter, et al., 2001). These proteins that are expressed are then evaluated for the type of other proteins they interact with to provide the functional effect they produce through expressed phenotypes.

These protein-protein interactions are then recorded in databases after measurement, collection and quality control. These interaction databases are cross referenced with other databases including GWAS and Exome-Sequence Data to provide biological understanding of human diseases and the appropriate therapeutic interventions. All this is done by use of computerized analysis (M.D. Mailman, et al., (2007)

Genes are the functional unit of a genome and produce the genetic information and function by the use of proteins that are coded for in their codon (J.E. Krebs et al., 2013). The genetic codon, codes for specific proteins that are translated in the ribosomes with the help of mRNAs (Knud and Wilson, 2009). These proteins undergo various post-translational modifications to attain their functional structure (Walsh, 2006). These modifications include the interactions with other proteins, for example, molecular chaperons to facilitate proper protein folding (Stuart, 2009). The folded proteins are hence the functional proteins. These functional proteins further interact with other proteins to effectively carry out their roles. The study of these protein-protein interactions is therefore important in helping scientist decipher the molecular basis of various genetic disorders, help them to understand the appropriate therapeutic interventions that best curtail the complex pathways that result in disorders and help in the understanding of the basis behind complex human phenotypes.

Importance of Protein-Protein Interaction in Understanding Human Diseases

Diagram illustrating how protein-protein interactions are used to identify the disease modules. The protein complexes are used to prioritize genes in linkage interval as depicted in the diagram.

The protein-protein interactions can be generated in vitro by use of two major, complementary methods; Yeast-two-Hybrid (Y2H) method and the Affinity Purification followed by mass spectrometry (AP-MS) method. In the Y2H method, functional assay is used to stimulate the potential interaction between two proteins (Yiyi, 2009) while in the affinity purification method, a bait protein is used to probe the interaction between proteins using direct affinity with other proteins whose identity can then be identified using a mass spectrometry (Richard, 2013). Therefore these two methods are different in their mechanisms of action and the data they produce.

Protein-protein interactions can be measured by various methods such as, Mass spectroscopy, Optical spectroscopy, surface Plasmon resonance biosensors and Micro-calorimetry. The optical spectroscopy, particularly fluorescence, is the most flexible technique (Lakey and Regott, 1998). Mass spectrometry is a data collection and interpretation tool that uses the principle of gaseous ion mobility (B. T. Routolo et al, 2008).

The collection of protein-protein interactions is done in systematic mapping of protein-protein interactions, or ‘interactome’ mapping (J.F. Rual et all, 2005).

The accuracy, potential, biases, strength and weaknesses of the protein-protein interactions are determined through Statistical testing and comparative analysis using large-scale approaches which have been used to predicted many new protein interactions in yeast (C. V. Mering et all, 2002)

Properties of protein-protein networks generated as illustrated in the diagram depicts that the distribution of the interaction followed specific graph and theoretical laws with many proteins having a small amount of interaction, and a few proteins having up to hundreds of interactions.

This slide seeks to evaluate how the data obtained from the collection of protein-protein interactions, can be used to expand the knowledge on the molecular processes involved in human diseases.

The Genome-Wide Association Studies is a database that has led to the discovery of many single nucleotide polymorphisms (SNPs) that are associated with various phenotypic states (Swanson, 2013). It compares the frequency of SNP alleles between cases and controls and the result is the association of SNPs that represent different genomic regions (if any) that are likely to be the underlying biological cause of the various complex genetic disorders. Comparing the information in this database with those available in the protein-protein interaction databases leads to the identification of the pathways that result in these genetic disorders and can prompt the development of the appropriate interventions to curtail or reverse the progression these pathways, hence providing a permanent remedy for these genetic disorders.

Measurement, Collection, and Quality Control of Protein-Protein Interactions

It contains an illustration of how the data from the GWAS database can be augmented and interpreted using protein-protein interaction networks. This is a classic example of a type-2- diabetes individual. The GWAS has identified the diabetes related genes and gene loci and using the protein interactions from the identified genes, the shown CREBBP network (A) and Adipocytokine network (B) have been revealed. This points out to the related proteins and possible pathways that are involved in the manifestation of diabetes type-2.

The human genome consists of the coding and the non-coding regions. The coding regions result in protein and these are referred to as the exome while the non-coding regions do not result in any proteins and are referred to as the introns. There exists a number of variance in the protein-coding portion of the genome. The Exome-Sequencing Data is a database that contains information on these exome variants (C.V Alexander et al., 2013). It points to specific genes harboring the identified genomic variants. However, there is rarely a single genetic variant that is significantly associated to the phenotype being analyzed. Therefore, protein networks identified in the protein-protein interaction help in interpreting the exome-sequencing data through analyzing the rates and patterns of exonic de novo mutations.

An illustration of the colored proteins relating to and interacting with the de novo mutated proteins is attached. This illustration shows a significant chromatin remodeling network in autism disorders. The genes with the de novo mutation in patients with sporadic autism spectrum disorders interact at the level of proteins, revealing a chromatin remodeling network as depicted in the illustration.

Scientists have over the years used these protein-protein interactions in the study of human phenotypes that are of a genetic origin. This is done by evaluating the genetic dependencies of these interactions and the manifestation of the genetic information in protein formation and function.

It also includes an illustration of the topologies of a protein in the gene loci, as a functional unit and in the disease state as is viewed InWeb to facilitate the understanding of the proteins involved in the manifestation of the disease and be able to trace the changes in its normal functional structure and the altered structure that leads to the development of the disease.

This portion analyses the limitations of the available systems used in the study of the molecular basis of the human disorders.

Looking at the protein-protein interaction databases, they fall short of the exact number of protein interactions that exists for the identified number of proteins that are coded for in the human genome and the accuracy of these interactions. The difficulty in assessing the protein interaction numbers and their accuracy arises from a number of issues such as how to combine information from Y2H and AP–MS experiments, as well as the knowledge that interactions of a protein can be highly dynamic and depend on the growth condition of the cell in question (A.C. Gavin, et al., (2006), and the specific tissue being analyzed (K. Lage, et al., 2008). For this reason the estimates of the amounts of interactions in humans have also varied.

Use of Protein-Protein Interactions in Genome-wide Association Studies and Exome Sequencing Data

The other limitation is in the heterogeneity of the information open to the public. This provides a lot of varying combination of information that makes it hard to determine the origin of the information and relevant answers to questions such as the source of the information, the organism it was derived from and its ideals.

This the conclusion part of the presentation that acknowledges the importance and the need of protein-protein interaction databases that play a key role in understanding the molecular basis of the human diseases. It also states the controls put in place to ensure uniformity of these databases and states the limitations, that is, the setbacks that still exist in the molecular level understanding of many diseases and the effect of the specific disease alleles in human population.

These are the recommendations as per the loopholes discovered in the course of the research. The implementation of devices and strategies that address these loopholes will move the molecular understanding of the human diseases to the next level. This includes the intuition of statistical testing, development of statistical tools and methods that are reliable and the development of standards for identifying statistically relevant finding in the protein networks.

References

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E.S. Lander, et al., (2001) Initial sequencing and analysis of the human genome, Nature 409 860 – 921.

J.C. Venter, et al. (2001). The sequence of the human genome. Science 291. 1304–1351.

(M.D. Mailman, et al., (2007) The NCBI dbGaP database of genotypes and phenotypes, Nat. Genet.39 1181–1186.

J.E. Krebs, B. Lewin and S.G. Elliot. (2013). Lewin’s Essential Genes. 625

H.N. Knud and D. Wilson (2009). Protein Systensis and Ribosome Structure. 391.

C. Walsh. (2006). Posttranslational Modification of Proteins: Expanding Nature’s Inventory.

Stuart K. C. (2009). Cell Stress Proteins.

S. Yiyi (2009). Theory of Random Networks and their Applications. 63

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J.H.Lakey and E.M.Regott.(1998) Current Opinion in Standard Biology

B.T.Ruotolo, J.L.P.Benesch, A.M.Sandercock, C.V. Robinsonand S.J.Hyung (2008). Ion Mobility-Mass spectrometry Annalysis of Large Protein Complexes

J.F.Ruall, K.Venkatesan,T.Hao and T.H-Kshikawa (2005). Towards a Proteome-Scale Map of the Human Protein-Ptotein Interaction Network.

C.V.Mering, R.Krause, B.Snel,C.Michael, S.G.Oliver,S.Fields and P.Bork (2002)Comparative Assessment of Large Scale Data Sets of Protein-Protein Interactions.

Swanson D. M. (2013). Hypothesis Testing in GWAS and Statistical Issues with Compensation in Clinical Trials.

C.V Alexander, M.A Pervaiz and A. Husami. (2013). Next Generation Sequencing Technologies in Medical Genetics

A.C. Gavin, et al., (2006) Proteome survey reveals modularity of the yeast cell machinery, Nature 440. 631–636.

K. Lage, et al., (2008). A large-scale analysis of tissue-specific pathology, and gene expression of human disease genes and complexes, Proc. Natl. Acad. Sci. U. S. A. 105. 20870–20875.