A Keystone for ncRNA

A report on the Keystone symposium 'Non-coding RNAs' held at Snowbird, Utah, USA, 31 March to 5 April 2012.Molecular and Cellular BiologyStem Cell and Regenerative Biolog

Predicting metabolic biomarkers of human inborn errors of metabolism

Early diagnosis of inborn errors of metabolism is commonly performed through biofluid metabolomics, which detects specific metabolic biomarkers whose concentration is altered due to genomic mutations. The identification of new biomarkers is of major importance to biomedical research and is usually performed through data mining of metabolomic data. After the recent publication of the genome-scale network model of human metabolism, we present a novel computational approach for systematically predicting metabolic biomarkers in stochiometric metabolic models. Applying the method to predict biomarkers for disruptions of red-blood cell metabolism demonstrates a marked correlation with altered metabolic concentrations inferred through kinetic model simulations. Applying the method to the genome-scale human model reveals a set of 233 metabolites whose concentration is predicted to be either elevated or reduced as a result of 176 possible dysfunctional enzymes. The method's predictions are shown to significantly correlate with known disease biomarkers and to predict many novel potential biomarkers. Using this method to prioritize metabolite measurement experiments to identify new biomarkers can provide an order of a 10-fold increase in biomarker detection performance

Peptidomic discovery of short open reading frame-encoded peptides in human cells

The amount of the transcriptome that is translated into polypeptides is of fundamental importance. We developed a peptidomic strategy to detect short ORF (sORF)-encoded polypeptides (SEPs) in human cells. We identified 90 SEPs, 86 of which are novel, the largest number of human SEPs ever reported. SEP abundances range from 10-1000 molecules per cell, identical to known proteins. SEPs arise from sORFs in non-coding RNAs as well as multi-cistronic mRNAs, and many SEPs initiate with non-AUG start codons, indicating that non-canonical translation may be more widespread in mammals than previously thought. In addition, coding sORFs are present in a small fraction (8/1866) of long intergenic non-coding RNAs (lincRNAs). Together, these results provide the strongest evidence to date that the human proteome is more complex than previously appreciated

Localization and abundance analysis of human lncRNAs at single-cell and single-molecule resolution

Background: Long non-coding RNAs (lncRNAs) have been implicated in diverse biological processes. In contrast to extensive genomic annotation of lncRNA transcripts, far fewer have been characterized for subcellular localization and cell-to-cell variability. Addressing this requires systematic, direct visualization of lncRNAs in single cells at single-molecule resolution. Results: We use single-molecule RNA-FISH to systematically quantify and categorize the subcellular localization patterns of a representative set of 61 lncRNAs in three different cell types. Our survey yields high-resolution quantification and stringent validation of the number and spatial positions of these lncRNA, with an mRNA set for comparison. Using this highly quantitative image-based dataset, we observe a variety of subcellular localization patterns, ranging from bright sub-nuclear foci to almost exclusively cytoplasmic localization. We also find that the low abundance of lncRNAs observed from cell population measurements cannot be explained by high expression in a small subset of ‘jackpot’ cells. Additionally, nuclear lncRNA foci dissolve during mitosis and become widely dispersed, suggesting these lncRNAs are not mitotic bookmarking factors. Moreover, we see that divergently transcribed lncRNAs do not always correlate with their cognate mRNA, nor do they have a characteristic localization pattern. Conclusions: Our systematic, high-resolution survey of lncRNA localization reveals aspects of lncRNAs that are similar to mRNAs, such as cell-to-cell variability, but also several distinct properties. These characteristics may correspond to particular functional roles. Our study also provides a quantitative description of lncRNAs at the single-cell level and a universally applicable framework for future study and validation of lncRNAs. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0586-4) contains supplementary material, which is available to authorized users

Mutations causing medullary cystic kidney disease type 1 (MCKD1) lie in a large VNTR in MUC1 missed by massively parallel sequencing

While genetic lesions responsible for some Mendelian disorders can be rapidly discovered through massively parallel sequencing (MPS) of whole genomes or exomes, not all diseases readily yield to such efforts. We describe the illustrative case of the simple Mendelian disorder medullary cystic kidney disease type 1 (MCKD1), mapped more than a decade ago to a 2-Mb region on chromosome 1. Ultimately, only by cloning, capillary sequencing, and de novo assembly, we found that each of six MCKD1 families harbors an equivalent, but apparently independently arising, mutation in sequence dramatically underrepresented in MPS data: the insertion of a single C in one copy (but a different copy in each family) of the repeat unit comprising the extremely long (~1.5-5 kb), GC-rich (>80%), coding VNTR in the mucin 1 gene. The results provide a cautionary tale about the challenges in identifying genes responsible for Mendelian, let alone more complex, disorders through MPS

Large intergenic non-coding RNA-RoR modulates reprogramming of human induced pluripotent stem cells

February 17, 2011The conversion of lineage-committed cells to induced pluripotent stem cells (iPSCs) by reprogramming is accompanied by a global remodeling of the epigenome[superscript 1, 2, 3, 4, 5], resulting in altered patterns of gene expression[superscript 2, 6, 7, 8, 9]. Here we characterize the transcriptional reorganization of large intergenic non-coding RNAs (lincRNAs)[superscript 10, 11] that occurs upon derivation of human iPSCs and identify numerous lincRNAs whose expression is linked to pluripotency. Among these, we defined ten lincRNAs whose expression was elevated in iPSCs compared with embryonic stem cells, suggesting that their activation may promote the emergence of iPSCs. Supporting this, our results indicate that these lincRNAs are direct targets of key pluripotency transcription factors. Using loss-of-function and gain-of-function approaches, we found that one such lincRNA (lincRNA-RoR) modulates reprogramming, thus providing a first demonstration for critical functions of lincRNAs in the derivation of pluripotent stem cells

Mutations causing medullary cystic kidney disease type 1 (MCKD1) lie in a large VNTR in MUC1 missed by massively parallel sequencing

While genetic lesions responsible for some Mendelian disorders can be rapidly discovered through massively parallel sequencing (MPS) of whole genomes or exomes, not all diseases readily yield to such efforts. We describe the illustrative case of the simple Mendelian disorder medullary cystic kidney disease type 1 (MCKD1), mapped more than a decade ago to a 2-Mb region on chromosome 1. Ultimately, only by cloning, capillary sequencing, and de novo assembly, we found that each of six MCKD1 families harbors an equivalent, but apparently independently arising, mutation in sequence dramatically underrepresented in MPS data: the insertion of a single C in one copy (but a different copy in each family) of the repeat unit comprising the extremely long (~1.5-5 kb), GC-rich (>80%), coding VNTR in the mucin 1 gene. The results provide a cautionary tale about the challenges in identifying genes responsible for Mendelian, let alone more complex, disorders through MPS

Mutations causing medullary cystic kidney disease type 1 lie in a large VNTR in MUC1 missed by massively parallel sequencing

Although genetic lesions responsible for some mendelian disorders can be rapidly discovered through massively parallel sequencing of whole genomes or exomes, not all diseases readily yield to such efforts. We describe the illustrative case of the simple mendelian disorder medullary cystic kidney disease type 1 (MCKD1), mapped more than a decade ago to a 2-Mb region on chromosome 1. Ultimately, only by cloning, capillary sequencing and de novo assembly did we find that each of six families with MCKD1 harbors an equivalent but apparently independently arising mutation in sequence markedly under-represented in massively parallel sequencing data: the insertion of a single cytosine in one copy (but a different copy in each family) of the repeat unit comprising the extremely long (~1.5–5 kb), GC-rich (>80%) coding variable-number tandem repeat (VNTR) sequence in the MUC1 gene encoding mucin 1. These results provide a cautionary tale about the challenges in identifying the genes responsible for mendelian, let alone more complex, disorders through massively parallel sequencing.National Institutes of Health (U.S.) (Intramural Research Program)National Human Genome Research Institute (U.S.)Charles University (program UNCE 204011)Charles University (program PRVOUK-P24/LF1/3)Czech Republic. Ministry of Education, Youth, and Sports (grant NT13116-4/2012)Czech Republic. Ministry of Health (grant NT13116-4/2012)Czech Republic. Ministry of Health (grant LH12015)National Institutes of Health (U.S.) (Harvard Digestive Diseases Center, grant DK34854

GA4GH: International policies and standards for data sharing across genomic research and healthcare.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits