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Case Western Reserve School of Medicine scientists have made an extraordinary double discovery. First, they have identified thousands of novel long non-coding ribonucleic acid (lncRNA) transcripts. Second, they have learned that some of them defy conventional wisdom regarding lncRNA transcripts, because they actually do direct the synthesis of proteins in cells.
Both of the breakthroughs are detailed in the June 12 issue of Cell Reports.
Kristian E. Baker, PhD, assistant professor in the Center for RNA Molecular Biology, led the team that applied high throughput gene expression analysis to yield these impressive findings, which ultimately could lead to treatments for cancer and some genetic disorders.
“Our work establishes that lncRNAs in yeast can encode proteins, and we provide evidence that this is probably true also in mammals, including humans,” Baker said. “Our investigation has expanded our knowledge of the genetic coding potential of already well-characterized genomes.”
Collaborating with researchers including Case Western Reserve University graduate and undergraduate students, Baker analyzed yeast and mouse cells, which serve as model organisms because of their functional resemblance to human cells.
Previously, lncRNAs were thought to lack the information and capacity to encode for proteins, distinguishing them from the messenger RNAs that are expressed from known genes and act primarily as templates for the synthesis of proteins. Yet this team demonstrated that a subset of these lncRNAs is engaged by the translation machinery and can function to produce protein products.
In the future, Baker and fellow investigators will continue to look for novel RNA transcripts and also search for a function for these lncRNAs and their protein products in cells.
“Discovery of more transcripts equates to the discovery of new and novel genes,” Baker said. “The significance of this work is that we have discovered evidence for the expression of previously undiscovered genes. Knowing that genes are expressed is the very first step in figuring out what they do in normal cellular function or in dysfunction and disease.”
This investigation was funded by the National Institutes of Health’s National Institute of General Medical Sciences (GM080465 and GM095621) and the National Science Foundation (NSF1253788).
Reference:
Lecture Contents delivered at Koch Institute for Integrative Cancer Research, Summer Symposium 2014: RNA Biology, Cancer and Therapeutic Implications, June 13, 2014 @MIT
ENCODE data reveals important information from Genome Wide Association Studies relevant to understanding complex genetic diseases
Author: Ritu Saxena, Ph.D.
Introduction
“The depth, quality, and diversity of the ENCODE data are unprecedented” is what was stated by John Stamatoyannopoulos, professor of genomic sciences at the University of Washington and one of the many principle investigators of ENCODE project. ENCODE (Encyclopedia of DNA elements), indeed, was an ambitious project launched as a pilot in 2003 and then expanded in 2007 for the whole genome analysis and identification of all the functional elements of the human genome. The findings were striking as they challenged the definition of “gene” and ‘the central dogma of genetics (Gene-mRNA-protein). Infact, the non-coding part that constitutes about 80% of the genome or the so-called “junk DNA” was found to contain elements crucial for gene regulation. The elements, in large part, include RNA transcripts that are not transcribed into proteins but might have a regulatory role. For detailed reading, refer to the findings published in the issue of Nature, The ENCODE Project Consortium Nature 489, 57–74 (2012) An integrated encyclopedia of DNA elements in the human genome
Protein-coding genes — Proteins are molecules made of amino acids linked together in a specific sequence; the amino acid sequence is encoded by the sequence of DNA subunits called nucleotides that make up genes.
Non-coding genes — Stretches of DNA that are read by the cell as if they were genes but do not encode proteins. These appear to help regulate the activity of the genome.
Chromatin structure features — Complex physical structures made from a combination of DNA and binding proteins that make up the contents of the nucleus and affects genome function.
Histone modifications — Histones are the proteins that make up the chromatin structures that help shape and control the genome. In addition, histone proteins can be physically modified by adding chemical groups, such as a methyl molecule, that further regulates genomic activity.
DNA methylation — Just like histones, methyl groups can be added to DNA itself in a process called DNA methylation. Chemically attaching methyl groups to DNA physically changes the ability of enzymes to reach the DNA and thus alters the gene expression pattern in cells. Methylation helps cells “remember what they are doing” or alter levels of gene expression, and it is a crucial part of normal development and cellular differentiation in higher organisms.
Transcription factor binding sites — Transcription factors are proteins that bind to specific DNA sequences, controlling the flow (or transcription) of genetic information from DNA to mRNA. Mapping the binding sites can help researchers understand how genomic activity is controlled.
How could ENCODE be helpful in the study of complex human diseases?
Complex diseases and Genome wide association studies (GWAS)
Coronary artery disease, type 2 diabetes and many forms of cancer are complex human diseases that have a significant genetic component. Unlike mendelian disorders that have defined loci, the genetic component of complex disorders lies in the form of genetic variations in the genome making an individual susceptible to these complex diseases.
Researchers have performed Genome-wide association studies (GWAS) of the human genome, leading to the identification of thousands of DNA variants that could be linked with complex traits and diseases. However, identifying the variants, referred to as SNPs (Single Nucleotide Polymorphisms), that actually contribute to the disease, and understanding how they exert influence on a disease has been more of a mystery.
How would ENCODE solve the puzzle?
The puzzle lies in interpreting how the SNPs found in the genome affect a person’s susceptibility to a particular trait or disease and what is the mechanism behind it. As identified in the GWAS, most variants that are associated with the phenotype of the trait or disease lie in the non-coding region of the genome. Infact, in more than 400 studies compiled in the GWAS catalog only a small minority of the trait/disease-associated SNPs occur in protein-coding regions; the large majority (89%) are in noncoding regions. These variants fall in the gene deserts that lie far from protein-coding region, similar to those where cis-regulatory modules (CRMs) are found. CRMs such as promoters and enhancers are a group of binding sites for transcription factors, and the presence of transcription factors bound to these sites is a good indicator of the potential regulatory regions.
The integrative analysis of ENCODE data has give important insights to the results of GWAS studies. Investigators have employed ENCODE data as an initial guide to discover regulatory regions in which genetic variation is affecting a complex trait. Additionally, ENCODE study when examined the SNPs from GWAS that were associated with the phenotype of the trait, found that these regions are enriched in DNase-sensitive regions i.e, lie in the function-associated DNA region of the genome as it could be bound by transcription factors affecting the regulation of gene expression. Thus, the project demonstrates that non-coding regions must be considered when interpreting GWAS results, and it provides a strong motivation for reinterpreting previous GWAS findings.
Using ENCODE Data to Interpret GWAS Results
ENCODE and predisposition to CANCER:
C-Myc, a proto-oncogene, codes for a transcripton factor, when expressed constitutively leads to uninhibited cell proliferation resulting in cancer. It has been observed that common variants within a ~1 Mb region upstream of c-Myc gene have been associated with cancers of the colon, prostate, and breast. Several SNPs have been reported in this region, that although affect the phenotype, lie in the distal cis-region of the MYC gene. Alignment of the ENCODE data in this region with the significant variants from the GWAS also reveals that key variants are found in the transcription factor occupied DNA segments mapped by this consortium. One variant rs698327, lies within a DNase hypersensitive site that is bound by several transcription factors, enhancer-associated protein p300, and contains histone modifications relative to enhancers (high H3K4me1, low H3K4me3). ENCODE data indicates that non-coding regions in the human chromosome 8q24 loci are associated with cancer and as observed in the case of c-myc gene, similar studies on cancer-related genes could help explain predisposition to cancer.
ENCODE and fetal hemoglobin expression:
Another example of the use of ENCODE data is that of gene regulation of fetal hemoglobin. Several regions were predicted via ENCODE that were involved in the regulation of fetal hemoglobin. It was found that these predicted regions are close to the SNPs in the BLC11A gene that is associated with persistent expression of fetal hemoglobin.
Future perspective
As evident from the above examples, the ENCODE data shows that genetic variants do affect regulated expression of a target gene. Recently, several research groups in the UK performed a large-scale GWAS study to determine the genetic predisposition to fracture risk. The collaborative effort, published in a recent issue of the PLoS journal, was made to identify genetic variants associated with cortical bone thickness (CBT) and bone mineral density (BMD) with data from more than 10,000 subjects. http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002745 The study generated a wealth of data including the result – identification of SNPs in the WNT16 and its adjacent gene, FAM3C were found to be relevant to CBT and BMD. ENCODE data, in this case, could be helpful in interpreting more detailed information including determining additional SNPs, the regulatory information of the genes involved and much more. Thus, it could be concluded that ENCODE data could be immensely useful in interpreting associations between disease and DNA sequences that can vary from person to person.
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