Current Genomics - Volume 14, Issue 2, 2013

The prominence of the human mismatch repair (MMR) pathway is clearly reflected by the causal link between MMR gene mutations and the occurrence of Lynch syndrome (or HNPCC). The MMR family of proteins also carries out a plethora of diverse cellular functions beyond its primary role in MMR and homologous recombination. In fact, members of the MMR family of proteins are being increasingly recognized as critical mediators between DNA damage repair and cell survival. Thus, a better functional understanding of MMR proteins will undoubtedly aid the development of strategies to effectively enhance apoptotic signaling in response to DNA damage induced by anti-cancer therapeutics. Among the five known human MutS homologs, hMSH4 and hMSH5 form a unique heterocomplex. However, the expression profiles of the two genes are not correlated in a number of cell types, suggesting that they may function independently as well. Consistent with this, these two proteins are promiscuous and thought to play distinct roles through interacting with different binding partners. Here, we describe the gene and protein structures of eukaryotic MSH4 and MSH5 with a particular emphasis on their human homologues, and we discuss recent findings of the roles of these two genes in DNA damage response and repair. Finally, we delineate the potential links of single nucleotide polymorphism (SNP) loci of these two genes with several human diseases.

Until recently, understanding the regulatory behavior of cells has been pursued through independent analysis of the transcriptome or the proteome. Based on the central dogma, it was generally assumed that there exist a direct correspondence between mRNA transcripts and generated protein expressions. However, recent studies have shown that the correlation between mRNA and Protein expressions can be low due to various factors such as different half lives and post transcription machinery. Thus, a joint analysis of the transcriptomic and proteomic data can provide useful insights that may not be deciphered from individual analysis of mRNA or protein expressions. This article reviews the existing major approaches for joint analysis of transcriptomic and proteomic data. We categorize the different approaches into eight main categories based on the initial algorithm and final analysis goal. We further present analogies with other domains and discuss the existing research problems in this area.

Stripe rust of wheat, caused by Puccinia striiformis f. sp. tritici, continues to cause severe damage worldwide. Durable resistance is necessary for sustainable control of the disease. High-temperature adult-plant (HTAP) resistance, which expresses when the weather becomes warm and plants grow older, has been demonstrated to be durable. We conducted numerous studies to understand the molecular mechanisms of different types of stripe rust resistance using a transcriptomics approach. Through comparing gene expression patterns with race-specific, all-stage resistance controlled by various genes, we found that a greater diversity of genes is involved in HTAP resistance than in all-stage resistance. The genes involved in HTAP resistance are induced more slowly and their expression induction is less dramatic than genes involved in all-stage resistance. The high diversity of genes and less dramatic induction may explain durability and the incomplete expression level of HTAP resistance. Identification of transcripts may be helpful in identifying resistance controlled by different genes and in selecting better combinations of genes to combine for achieving adequate and durable resistance.

MicroRNAs (miRNAs) comprise a recently discovered class of small, non-coding RNA molecules of 21-25 nucleotides in length that regulate the gene expression by base-pairing with the transcripts of their targets i.e. proteincoding genes, leading to down-regulation or repression of the target genes. However, target gene activation has also been described. miRNAs are involved in diverse regulatory pathways, including control of developmental timing, apoptosis, cell proliferation, cell differentiation, modulation of immune response to macrophages, and organ development and are associated with many diseases, such as cancer. Computational prediction of miRNA targets is much more challenging in animals than in plants, because animal miRNAs often perform imperfect base-pairing with their target sites, unlike plant miRNAs which almost always bind their targets with near perfect complementarity. In the past years, a large number of target prediction programs and databases on experimentally validated information have been developed for animal miRNAs to fulfil the need of experimental scientists conducting miRNA research. In this review we first succinctly describe the prediction criteria (rules or principles) adapted by prediction algorithms to generate possible miRNA binding site interactions and introduce most relevant algorithms, and databases. We then summarize their applications with the help of some previously published studies. We further provide experimentally validated functional binding sites outside 3’-UTR region of target mRNAs and the resources which offer such predictions. Finally, the issue of experimental validation of miRNA binding sites will be briefly discussed.

Over the two past decades, a significant number of studies have observed animal growth traits to examine animal genetic mechanisms due to their ease of measurement and high heritability. Chicken which has a significant impact on fundamental biology is a major source of protein worldwide, making it an ideal model for examining animal growth trait development. The genetic mechanisms of chicken growth traits have been studied using quantitative trait loci mapping through genome-scan and candidate gene approaches, genome-wide association studies (GWAS), comparative genomic strategies, microRNA (miRNA) regulation of growth development analysis, and epigenomic analysis. This review focuses on chicken GWAS and miRNA regulation of growth traits. Several recently published GWAS reports showed that most genome-wide significant single nucleotide polymorphisms are located on chromosomes 1 and 4 in chickens. Chicken growth, particularly skeletal muscle growth and development, is greatly regulated by miRNA. Using dwarf and normal chickens, let-7b was found to be involved in determining chicken dwarf phenotypes by regulating growth hormone receptor gene expression.

Late-onset Alzheimer's disease (LOAD) is the most common form of dementia in the elderly. LOAD has a complex and largely unknown etiology with strong genetic determinants. Genetics of LOAD is known to involve several genetic risk factors among which the Apolipoprotein E (APOE) gene seems to be the major recognized genetic determinant. Recent efforts have been made to identify other genetic factors involved in the pathophysiology of LOAD such as genes associated with a deficit of neurotrophic factors in the AD brain. Genetic variations of neurotrophic factors, such as brain-derived neurotrophic factor (BDNF), and transforming-growth-factor-β1 (TGF-β1) are known to increase the risk to develop LOAD and have also been related to depression susceptibility in LOAD. Transforming-Growth-Factor-β1 (TGF- β1) is a neurotrophic factor that exerts neuroprotective effects against β-amyloid-induced neurodegeneration. Recent evidence suggests that a specific impairment in the signaling of TGF-β is an early event in the pathogenesis of AD. TGF-β1 protein levels are predominantly under genetic control, and the TGF-β1 gene, located on chromosome 19q13.1–3, contains several single nucleotide polymorphisms (SNPs) upstream and in the transcript region, such as the SNP at codon +10 (T/C) and +25 (G/C), which is known to influence the level of expression of TGF-β1. In the present review, we summarize the current literature on genetic risk factors for LOAD, focusing on the role of the TGF-β1 gene, finally discussing the possible implications of these genetic studies for the selection of patients eligible for neuroprotective strategies in AD.