Current Genomics - Volume 8, Issue 1, 2007

High-throughput technologies for genomics provide tens of thousands of genetic measurements, for instance, gene-expression measurements on microarrays, and the availability of these measurements has motivated the use of machine learning (inference) methods for classification, clustering, and gene networks. Generally, a design method will yield a model that satisfies some model constraints and fits the data in some manner. On the other hand, a scientific theory consists of two parts: (1) a mathematical model to characterize relations between variables, and (2) a set of relations between model variables and observables that are used to validate the model via predictive experiments. Although machine learning algorithms are constructed to hopefully produce valid scientific models, they do not ipso facto do so. In some cases, such as classifier estimation, there is a well-developed error theory that relates to model validity according to various statistical theorems, but in others such as clustering, there is a lack of understanding of the relationship between the learning algorithms and validation. The issue of validation is especially problematic in situations where the sample size is small in comparison with the dimensionality (number of variables), which is commonplace in genomics, because the convergence theory of learning algorithms is typically asymptotic and the algorithms often perform in counter-intuitive ways when used with samples that are small in relation to the number of variables. For translational genomics, validation is perhaps the most critical issue, because it is imperative that we understand the performance of a diagnostic or therapeutic procedure to be used in the clinic, and this performance relates directly to the validity of the model behind the procedure. This paper treats the validation issue as it appears in two classes of inference algorithms relating to genomics - classification and clustering. It formulates the problem and reviews salient results.

The accelerated growth of proteomics data presents both opportunities and challenges. Large-scale proteomic profiling of biological samples such as cells, organelles or biological fluids has led to discovery of numerous key and novel proteins involved in many biological/disease processes including cancers, as well as to the identification of novel disease biomarkers and potential therapeutic targets. While proteomic data analysis has been greatly assisted by the many bioinformatics tools developed in recent years, a careful analysis of the major steps and flow of data in a typical highthroughput analysis reveals a few gaps that still need to be filled to fully realize the value of the data. To facilitate functional and pathway discovery for large-scale proteomic data, we have developed an integrated proteomic expression analysis system, iProXpress, which facilitates protein identification using a comprehensive sequence library and functional interpretation using integrated data. With its modular design, iProXpress complements and can be integrated with other software in a proteomic data analysis pipeline. This novel approach to complex biological questions involves the interrogation of multiple data sources, thereby facilitating hypothesis generation and knowledge discovery from the genomic-scale studies and fostering disease diagnosis and drug development.

“Nutrigenomics” refers to the ability of nutrients to alter gene expression. Insulin secreting β-cells exhibit genomic and molecular changes that enhance their function when acutely exposed to physiological concentrations of glucose and fatty acids. However, chronic exposure, such as occurs in the hyperlipidemic, hyperglycemic state of obesity/ prediabetes can exert deleterious effects on β-cell function through alteration of gene expression. The genomic underpinnings of so-called glucolipotoxicity on the β-cell and its relationship to development of type 2 diabetes will be discussed in this review article. For example, free fatty acids influence β-cell gene expression by direct interaction with transcription factors such as peroxisome proliferator-activated receptors. Glucose responsive genes include the insulin gene as well as genes involved in β-cell survival and other functions. In addition, obesity is now recognized as a condition of chronic, low-level inflammation. The adipose tissue secretes multiple circulating hormones termed adipokines, and the relationship between these and the β-cell has been termed adipotoxicity. The influence of adipokines on regulation of insulin secretion and β-cell survival will be reviewed.

The ribosome is essential for protein synthesis. The composition and structure of ribosomes from several organisms have been determined, and it is well documented that ribosomal RNAs (rRNAs) and ribosomal proteins (RPs) constitute this important organelle. Many RPs also fill various roles that are independent of protein biosynthesis, called extraribosomal functions. These functions include DNA replication, transcription and repair, RNA splicing and modification, cell growth and proliferation, regulation of apoptosis and development, and cellular transformation. Previous investigations have revealed that RP regulation in colorectal carcinomas (CRC) differs from that found in colorectal adenoma or normal mucosa, with some RPs being up-regulated while others are down-regulated. The expression patterns of RPs are associated with the differentiation, progression or metastasis of CRC. Additionally, the recent literature has shown that the perturbation of specific RPs may promote certain genetic diseases and tumorigenesis. Because of the implications of RPs in disease, especially malignancy, our review sought to address several questions. Why do expression levels or categories of RPs differ in different diseases, most notably in CRC? Is this a cause or consequence of the diseases? What are their possible roles in the diseases? We review the known extraribosomal functions of RPs and associated changes in colorectal cancer and attempt to clarify the possible roles of RPs in colonic malignancy.

Major histocompatibility complex (MHC) class I and NK cell receptor gene regions are a paradigm of genomic plasticity as they reveal a considerable degree of diversity, exemplified by high allelic polymorphism, genomic duplications and contractions, and formation of gene families. Both genetic components show signs of rapid evolution due to strong selective pressure to combat pathogens. Comparative analyses of these genomic regions in various primates revealed considerable differences, reflecting species-specific adaptations to pathogenic threat or different strategies to combat infections. MHC and NK receptor genomic diversity in populations are important factors that determine susceptibility or resistance to a variety of diseases including autoimmune and infectious diseases as well as reproductive success.

The characterization of the involvement of different histone post-translational modifications (PTMs) and histone variants in chromatin structure has represented one of the most recurrent topics in molecular biology during the last decade (since 1996). The interest in this topic underscores the critical roles played by chromatin in such important processes as DNA packaging, DNA repair and recombination, and regulation of gene expression. The genomic information currently available has pushed the boundaries of this research a step further, from the study of local domains to the genome- wide characterization of the mechanisms governing chromatin dynamics. How the heterchromatin and euchromatin compartmentalization is established has been the subject of recent extensive research. Many PTMs, as well as histone variants have been identified to play a role, including the replacement of histone H2A by the histone variant H2A.Z. Several studies have provided support to a role for H2A.Z (known as Htz1 in yeast) in transcriptional regulation, chromosome structure, DNA repair and heterochromatin formation. Although the mechanisms by which H2A.Z defines different structural regions in the chromatin have long remained elusive, various reports published last year have shed new insight into this process. The present mini review focuses its attention on the genome-wide distribution of H2A.Z, with special attention to the mechanisms involved in its distribution and exchange as well as on the role of its N-terminal acetylation.

The technique of chromosome microdissection and microcloning has been developed for more than 20 years. As a bridge between cytogenetics and molecular genetics, it leads to a number of applications: chromosome painting probe isolation, genetic linkage map and physical map construction, and expressed sequence tags generation. During those 20 years, this technique has not only been benefited from other technological advances but also cross-fertilized with other techniques. Today, it becomes a practicality with extensive uses. The purpose of this article is to review the development of this technique and its application in the field of genomic research. Moreover, a new method of generating ESTs of specific chromosomes developed by our lab is introduced. By using this method, the technique of chromosome microdissection and microcloning would be more valuable in the advancement of genomic research.