Mitochondrial-Associated Cell Death Mechanisms Are Reset to an Embryonic-Like State in Aged Donor-Derived iPS Cells Harboring Chromosomal Aberrations

Somatic cells reprogrammed into induced pluripotent stem cells (iPSCs) acquire features of human embryonic stem cells (hESCs) and thus represent a promising source for cellular therapy of debilitating diseases, such as age-related disorders. However, reprogrammed cell lines have been found to harbor various genomic alterations. In addition, we recently discovered that the mitochondrial DNA of human fibroblasts also undergoes random mutational events upon reprogramming. Aged somatic cells might possess high susceptibility to nuclear and mitochondrial genome instability. Hence, concerns over the oncogenic potential of reprogrammed cells due to the lack of genomic integrity may hinder the applicability of iPSC-based therapies for age-associated conditions. Here, we investigated whether aged reprogrammed cells harboring chromosomal abnormalities show resistance to apoptotic cell death or mitochondrial-associated oxidative stress, both hallmarks of cancer transformation. Four iPSC lines were generated from dermal fibroblasts derived from an 84-year-old woman, representing the oldest human donor so far reprogrammed to pluripotency. Despite the presence of karyotype aberrations, all aged-iPSCs were able to differentiate into neurons, re-establish telomerase activity, and reconfigure mitochondrial ultra-structure and functionality to a hESC-like state. Importantly, aged-iPSCs exhibited high sensitivity to drug-induced apoptosis and low levels of oxidative stress and DNA damage, in a similar fashion as iPSCs derived from young donors and hESCs. Thus, the occurrence of chromosomal abnormalities within aged reprogrammed cells might not be sufficient to over-ride the cellular surveillance machinery and induce malignant transformation through the alteration of mitochondrial-associated cell death. Taken together, we unveiled that cellular reprogramming is capable of reversing aging-related features in somatic cells from a very old subject, despite the presence of genomic alterations. Nevertheless, we believe it will be essential to develop reprogramming protocols capable of safeguarding the integrity of the genome of aged somatic cells, before employing iPSC-based therapy for age-associated disorders

DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach

<p>Abstract</p> <p>Background</p> <p>The analysis of massive high throughput data via clustering algorithms is very important for elucidating gene functions in biological systems. However, traditional clustering methods have several drawbacks. Biclustering overcomes these limitations by grouping genes and samples simultaneously. It discovers subsets of genes that are co-expressed in certain samples. Recent studies showed that biclustering has a great potential in detecting marker genes that are associated with certain tissues or diseases. Several biclustering algorithms have been proposed. However, it is still a challenge to find biclusters that are significant based on biological validation measures. Besides that, there is a need for a biclustering algorithm that is capable of analyzing very large datasets in reasonable time.</p> <p>Results</p> <p>Here we present a fast biclustering algorithm called DeBi (Differentially Expressed BIclusters). The algorithm is based on a well known data mining approach called frequent itemset. It discovers maximum size homogeneous biclusters in which each gene is strongly associated with a subset of samples. We evaluate the performance of DeBi on a yeast dataset, on synthetic datasets and on human datasets.</p> <p>Conclusions</p> <p>We demonstrate that the DeBi algorithm provides functionally more coherent gene sets compared to standard clustering or biclustering algorithms using biological validation measures such as Gene Ontology term and Transcription Factor Binding Site enrichment. We show that DeBi is a computationally efficient and powerful tool in analyzing large datasets. The method is also applicable on multiple gene expression datasets coming from different labs or platforms.</p

Recurrent somatic mutations in POLR2A define a distinct subset of meningiomas

RNA polymerase II mediates the transcription of all protein-coding genes in eukaryotic cells, a process that is fundamental to life. Genomic mutations altering this enzyme have not previously been linked to any pathology in humans, which is a testament to its indispensable role in cell biology. On the basis of a combination of next-generation genomic analyses of 775 meningiomas, we report that recurrent somatic p.Gln403Lys or p.Leu438_His439del mutations in POLR2A, which encodes the catalytic subunit of RNA polymerase II (ref. 1), hijack this essential enzyme and drive neoplasia. POLR2A mutant tumors show dysregulation of key meningeal identity genes including WNT6 and ZIC1/ZIC4. In addition to mutations in POLR2A, NF2, SMARCB1, TRAF7, KLF4, AKT1, PIK3CA, and SMO4 we also report somatic mutations in AKT3, PIK3R1, PRKAR1A, and SUFU in meningiomas. Our results identify a role for essential transcriptional machinery in driving tumorigenesis and define mutually exclusive meningioma subgroups with distinct clinical and pathological features

Biclusteranalyse für große Dateien

High-Throughput-Technologien stellen einen Durchbruch in der experimentellen Molekularbiologie dar. Sie ermöglichen eine Einsicht in die molekularen Mechanismen der Zelle, die mit traditionellen Ansätzen nicht zu erforschen sind. Mithilfe von differenzierten statistischen und computergestützen Methoden können wertvolle Informationen aus diesen Datensätzen gezogen werden. Clustering ist der am häufigsten gebrauchte Ansatz, um in solchen Hochdurchsatzdaten Gensätze mit verwandten Funktionen zu entdecken. Traditionelle Clustering-Methoden wie das hierarchische Clustering und k-means haben jedoch ihre Grenzen. Erstens basieren sie auf der Annahme, dass sich ein Gencluster in allen Proben gleich verhält. Es ist aber auch möglich, dass ein zellulärer Prozess nur eine Teilmenge der Gene beeinflußt oder dass er nur unter bestimmten Bedingungen seine Wirkung entfaltet. Zweitens wird in traditionellen Clusteriung-Methoden jedes einzelne Gen einem einzigen Cluster zugeteilt, obwohl manche Gene in bestimmten Proben nicht aktiv, andere dagegen in mehrere Prozesse involviert sind. Biclustering überwindet diese Schwierigkeiten, weil dabei Gene und Proben gleichzeitig gruppiert werden. Neue Studien haben gezeigt, dass Biclustering ein großes Potential für die Entdeckung von Markergenen hat, die mit bestimmten Geweben oder Krankheiten assoziiert sind. Mehrere Biclustering-Algorithmen existieren, aber es ist immer noch schwierig, Bicluster zu finden, deren Signifikanz biologisch validiert ist. Zusätzlich ist es nötig, einen Biclustering-Algorithmus zu finden, der in der Lage ist, sehr große Datensätze innerhalb kurzer Zeit zu analysieren. Der erste Teil dieser Doktorarbeit beschäftigt sich mit Biclustering-Algorithmen. Wir schlagen einen neuen, schnellen Biclustering- Algorithmus speziell für die Analyse von großen Datensätzen vor. Der Algorithmus findet Bicluster, in denen jedes Gen im Vergleich zu den übrigen Proben in allen Biclusterproben hoch oder niedrig exprimiert ist. Im Gegensatz zu anderen Algorithmen muss die Anzahl der Bicluster nicht a priori definiert werden. Anhand synthetischer und biologischer Datensätze vergleichen wir unsere Methode mit andere Biclustering-Algorithmen. GO term und TFBS- Anreicherung zeigen, dass der DeBi-Algorithmus biologisch signifikante Bicluster identifiziert. Wir zeigen auch, dass der Algorithmus nützlich und leistungsstark in der Analyse großer Datensätze ist. Die Methode kann auf Expressionsdatensätze aus verschiedenen Laboren und von unterschiedlichen Plattformen angewandt werden. Wir hoffen, dass unsere Methode die Entwicklung des Biclustering als Werkzeug für die Datenanalyse in der funktionellen Genomik vorantreiben und unterstützen wird. Der zweite Teil der Doktorarbeit beschäftigt sich mit der Aufklärung von molekularen Mechanismen in der traditionellen chinesischen Medizin (TCM), sowie mit der Identifi¬kation neuer Kandidaten aus traditionellen chinesischen Heilmitteln für die Entwicklung neuer Medikamente. Für diese Zwecke werden gene set enrichment tools und Biclustering eingesetzt. Weiterhin wurde Datenbank namens Medicinal Connectivity of TCM, MecoTCM, etabliert, welches systematische Verbindungen zwischen Genexpression, Krankheitsstatus und biochemischer Aktivität aufbaut. Dadurch gelang es, biologisch relevante Informationen über die TCM-Theorie zu finden, zum Beispiel über die Pflanze Ginseng. Unsere Ergebnisse wurden mit Chip-Seq-Experimenten validiert. Künftig soll dieser Datensatz durch die Integration von Tumorgenexpressionsprofilen verbessert werden.High throughput technologies are the latest breakthroughs in experimental molecular biology. These technologies provide insight into the molecular mechanism of the cell which was impossible to study with traditional approaches. However, sophisticated statistical and computational methods are required to extract useful information from these datasets. The most common approach for detecting functionally related gene sets from such high throughput data is clustering. Traditional clustering methods like hierarchical clustering and k-means, have several limitations. Firstly, they are based on the assumption that a cluster of genes behaves similarly in all samples. However, a cellular process may affect a subset of genes, only under certain conditions. Secondly, clustering assigns each gene or sample to a single cluster. However, some genes may not be active in any of the samples and some genes may participate in multiple processes. Biclustering overcomes these limitations by grouping genes and samples simultaneously. Recent studies showed that biclustering has a great potential in detecting marker genes that are associated with certain tissues or diseases. Several biclustering algorithms have been proposed. However, it is still a challenge to find biclusters that are significant based on biological validation measures. Additionally, there is a need for a biclustering algorithm that is capable of analyzing very large datasets in reasonable time. The first part of the thesis focuses on biclustering algorithms. We propose a novel fast biclustering algorithm especially for analyzing large data sets. Our algorithm aims to find biclusters where each gene in a bicluster should be highly or lowly expressed over all the bicluster samples compared to the rest of the samples. Unlike other algorithms, it is not required to define the number of biclusters apriori. We compare our method with other biclustering algorithms using synthetic data and biological data. It is shown that the DeBi algorithm provides biologically significant biclusters using GO term and TFBS enrichment. We also present the computational efficiency of our algorithm. It is a useful and powerful tool in analyzing large data sets. The method is also applicable on multiple gene expression datasets coming from different coming from different labs or platforms. The hope is that our method will further contribute to establishing biclustering as a general purpose tool for data analysis in functional genomics. In the second part of the thesis, we aim to contribute to: (1) the elucidation of the molecular mechanism of Traditional Chinese Medicine (TCM) functions, (2) the identification of new drug candidates from TCM against different human disease, by using gene set enrichment tools and biclustering. In this regard, we establish a research network called Medicinal Connectivity of TCM, MecoTCM, in which the systematic connections is created among gene expression, disease status, and bioactive chemicals. Using our approach, we discovered biologically significant findings about TCM theory, for example on herb Ginseng. We further validated our results using Chip-Seq experiments. We plan to further upgrade this dataset by comparisons to existing tumor gene expression profiles, in an attempt to match tumor profiles and TCM treatment profiles in terms of the affected pathways

METAP1mutation is a novel candidate for autosomal recessive intellectual disability

Intellectual disability (ID) is a genetic and clinically heterogeneous common disease and underlying molecular pathogenesis can frequently not be identified by whole-exome/genome testing. Here, we report four siblings born to a consanguineous union who presented with intellectual disability and discuss theMETAP1pathway as a novel etiology of ID. Genomic analyses demonstrated that patients harbor a novel homozygous nonsense mutation in the geneMETAP1.METAP1codes for methionine aminopeptidase 1 (MetAP1) which oversees the co-translational excision of the first methionine remnants in eukaryotes. The loss-of-function mutations to this gene may result in a defect in the translation of many essential proteins within a cell. Improper neuronal function resulting from this loss of essential proteins could lead to neurologic impairment and ID

ALPK3 gene mutation in a patient with congenital cardiomyopathy and dysmorphic features

Primary cardiomyopathy is one of the most common inherited cardiac diseases and harbors significant phenotypic and genetic heterogeneity. Because of this, genetic testing has become standard in treatment of this disease group. Indeed, in recent years, next-generation DNA sequencing has found broad applications in medicine, both as a routine diagnostic tool for genetic disorders and as a high-throughput discovery tool for identifying novel disease-causing genes. We describe a male infant with primary dilated cardiomyopathy who was diagnosed using intrauterine echocardiography and found to progress to hypertrophic cardiomyopathy after birth. This proband was born to a nonconsanguineous family with a past history of a male fetus that died because of cardiac abnormalities at 30 wk of gestation. Using whole-exome sequencing, a novel homozygous frameshift mutation (c.2018delC; p.GIn675SerfsX30) in ALPK3 was identified and confirmed with Sanger sequencing. Heterozygous family members were normal with echocardiographic examination. To date, only two studies have reported homozygous pathogenic variants of ALPK3, with a total of seven affected individuals with cardiomyopathy from four unrelated consanguineous families. We include a discussion of the patient's phenotypic features and a review of relevant literature findings

NGLY1 mutation causes neuromotor impairment, intellectual disability, and neuropathy

N-glycanase 1 (NGLY1) is a conserved enzyme that is responsible for the deglycosylation of misfolded N-glycosylated proteins in the cytoplasm prior to their proteasome-mediated degradation. Disruption of this degradation process has been associated with various neurologic diseases including amyotrophic lateral sclerosis and Parkinson's disease. Here, we describe two siblings with neuromotor impairment, apparent intellectual disability, corneal opacities, and neuropathy who were found to possess a novel homozygous frame-shift mutation due to a 4 base pair deletion in NGLY1 (c.1533_1536delTCAA. p.Asn511LysfsX51). We hypothesize that this mutation likely limits the capability of neuronal cells to respond to stress due to accumulation of misfolded proteins, thereby impairing their survival and resulting in progressive loss of neurological function. (C) 2014 Elsevier Masson SAS. All rights reserved