Genomic analysis of non-NF2 meningiomas reveals mutations in TRAF7, KLF4, AKT1, and SMO.

We report genomic analysis of 300 meningiomas, the most common primary brain tumors, leading to the discovery of mutations in TRAF7, a proapoptotic E3 ubiquitin ligase, in nearly one-fourth of all meningiomas. Mutations in TRAF7 commonly occurred with a recurrent mutation (K409Q) in KLF4, a transcription factor known for its role in inducing pluripotency, or with AKT1(E17K), a mutation known to activate the PI3K pathway. SMO mutations, which activate Hedgehog signaling, were identified in ~5% of non-NF2 mutant meningiomas. These non-NF2 meningiomas were clinically distinctive-nearly always benign, with chromosomal stability, and originating from the medial skull base. In contrast, meningiomas with mutant NF2 and/or chromosome 22 loss were more likely to be atypical, showing genomic instability, and localizing to the cerebral and cerebellar hemispheres. Collectively, these findings identify distinct meningioma subtypes, suggesting avenues for targeted therapeutics.

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.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.