Arne Kutzner

Bio: Arne Kutzner is an academic researcher from Hanyang University. The author has contributed to research in topics: Asymptotically optimal algorithm & SRGAP2. The author has an hindex of 9, co-authored 38 publications receiving 213 citations. Previous affiliations of Arne Kutzner include Goethe University Frankfurt & Seokyeong University.

A cancer tissue-specific FAM72 expression profile defines a novel glioblastoma multiform (GBM) gene-mutation signature.

Abstract: Glioblastoma multiform (GBM) is a neural stem cell (NSC)-derived malignant brain tumor with complex genetic alterations challenging clinical treatments. FAM72 is a NSC-specific protein comprised of four paralogous genes (FAM72 A-D) in the human genome, but its functional tumorigenic significance is unclear. We conducted an in-depth expression and somatic mutation data analysis of FAM72 (A-D) in GBM using the comprehensive human clinical cancer study database cBioPortal [including The Cancer Genome Atlas (TCGA)]. We established a FAM72 transcription profile across TCGA correlated with the expression of the proliferative marker MKI67 and a tissue-specific gene-mutation signature represented by pivotal genes involved in driving the cell cycle. FAM72 paralogs are overexpressed in cancer cells, specifically correlating with the mitotic cell cycle genes ASPM, KIF14, KIF23, CENPE, CENPE, CEP55, SGO1, and BUB1, thereby contributing to centrosome and mitotic spindle formation. FAM72 expression correlation identifies a novel GBM-specific gene set (SCN9A, MXRA5, ADAM29, KDR, LRP1B, and PIK3C2G) in the de novo pathway of primary GBM predestined as viable targets for therapeutics. Our newly identified primary GBM-specific gene-mutation signature, along with FAM72, could thus provide a new basis for prognostic biomarkers for diagnostics of GBM and could serve as potential therapeutic targets.

A non-deterministic call-by-need lambda calculus

Abstract: In this paper we present a non-deterministic call-by-need (untyped) lambda calculus λnd with a constant choice and a let-syntax that models sharing. Our main result is that λnd has the nice operational properties of the standard lambda calculus: confluence on sets of expressions, and normal order reduction is sufficient to reach head normal form. Using a strong contextual equivalence we show correctness of several program transformations. In particular of lambda-lifting using deterministic maximal free expressions. These results show that λnd is a new and also natural combination of non-determinism and lambda-calculus, which has a lot of opportunities for parallel evaluation.An intended application of λnd is as a foundation for compiling lazy functional programming languages with I/O based on direct calls. The set of correct program transformations can be rigorously distinguished from non-correct ones. All program transformations are permitted with the slight exception that for transformations like common subexpression elimination and lambda-lifting with maximal free expressions the involved subexpressions have to be deterministic ones.

Lead discovery and in silico 3D structure modeling of tumorigenic FAM72A (p17)

Abstract: FAM72A (p17) is a novel neuronal protein that has been linked to tumorigenic effects in non-neuronal tissue. Using state of the art in silico physicochemical analyses (e.g., I-TASSER, RaptorX, and Modeller), we determined the three-dimensional (3D) protein structure of FAM72A and further identified potential ligand-protein interactions. Our data indicate a Zn(2+)/Fe(3+)-containing 3D protein structure, based on a 3GA3_A model template, which potentially interacts with the organic molecule RSM ((2s)-2-(acetylamino)-N-methyl-4-[(R)-methylsulfinyl] butanamide). The discovery of RSM may serve as potential lead for further anti-FAM72A drug screening tests in the pharmaceutical industry because interference with FAM72A's activities via RSM-related molecules might be a novel option to influence the tumor suppressor protein p53 signaling pathways for the treatment of various types of cancers.

Patterns of Somatic Mutation in Human Cancer Genomes

Abstract: AACR Centennial Conference: Translational Cancer Medicine-- Nov 4-8, 2007; Singapore PL02-05 All cancers are due to abnormalities in DNA. The availability of the human genome sequence has led to the proposal that resequencing of cancer genomes will reveal the full complement of somatic mutations and hence all the cancer genes. To explore the nature of the information that will be derived from cancer genome sequencing we have sequenced the coding exons of the family of 518 protein kinases, ~1.3Mb DNA per cancer sample, in 210 cancers of diverse histological types. Despite the screen being directed toward the coding regions of a gene family that has previously been strongly implicated in oncogenesis, the results indicate that the majority of somatic mutations detected are “passengers”. There is considerable variation in the number and pattern of these mutations between individual cancers, indicating substantial diversity of processes of molecular evolution between cancers. The imprints of exogenous mutagenic exposures, mutagenic treatment regimes and DNA repair defects can all be seen in the distinctive mutational signatures of individual cancers. This systematic mutation screen and others have previously yielded a number of cancer genes that are frequently mutated in one or more cancer types and which are now anticancer drug targets (for example BRAF , PIK3CA , and EGFR ). However, detailed analyses of the data from our screen additionally suggest that there exist a large number of additional “driver” mutations which are distributed across a substantial number of genes. It therefore appears that cells may be able to utilise mutations in a large repertoire of potential cancer genes to acquire the neoplastic phenotype. However, many of these genes are employed only infrequently. These findings may have implications for future anticancer drug development.

The somatic genomic landscape of glioblastoma

Abstract: We describe the landscape of somatic genomic alterations based on multidimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors, including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer.

Integrative Genomics Viewer

Abstract: Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Evolutionary Dynamics of Gene and Isoform Regulation in Mammalian Tissues

Abstract: Most mammalian genes produce multiple distinct messenger RNAs through alternative splicing, but the extent of splicing conservation is not clear. To assess tissue-specific transcriptome variation across mammals, we sequenced complementary DNA from nine tissues from four mammals and one bird in biological triplicate, at unprecedented depth. We find that while tissue-specific gene expression programs are largely conserved, alternative splicing is well conserved in only a subset of tissues and is frequently lineage-specific. Thousands of previously unknown, lineage-specific, and conserved alternative exons were identified; widely conserved alternative exons had signatures of binding by MBNL, PTB, RBFOX, STAR, and TIA family splicing factors, implicating them as ancestral mammalian splicing regulators. Our data also indicate that alternative splicing often alters protein phosphorylatability, delimiting the scope of kinase signaling.

Ecotoxicological and health concerns of persistent coloring pollutants of textile industry wastewater and treatment approaches for environmental safety

Abstract: Textile industry wastewater (TIWW) is considered as one of the worst polluters of our precious water and soil ecologies. It causes carcinogenic, mutagenic, genotoxic, cytotoxic and allergenic threats to living organisms. TIWW contains a variety of persistent coloring pollutants (dyes), formaldehyde, phthalates, phenols, surfactants, perfluorooctanoic acid (PFOA), pentachlorophenol and different heavy metals like lead (Pb), cadmium (Cd), arsenic (As), chromium (Cr), zinc (Zn) and nickel (Ni) etc. TIWW is characterized by high dye content, high pH, chemical oxygen demand (COD), biochemical oxygen demand (BOD), total dissolved solids (TDS), total suspended solids (TSS), total organic carbon (TOC), chlorides and sulphates. Thus, requires adequate treatment before its final discharge into the water bodies to protect public health and environment. The treatment of TIWW is a major challenge as there is no particular economically feasible treatment method capable to adequately treat TIWW. Therefore, there is a need to develop a novel, cost-effective and eco-friendly technology for the effective treatment of TIWW. This review paper emphasizes on the different textile industry processes, wastewater generation, its nature and chemical composition, environmental impacts and health hazards and treatment approaches available for TIWW treatment. It also presents various analytical techniques used to detect and characterize TIWW pollutants and their metabolites, challenges, key issues and future prospectives.