Surviving in the cold: yeast mutants with extended hibernating lifespan are oxidant sensitive

Metabolic activity generates oxidizing molecules throughout life, but it is still debated if the resulting damage of macromolecules is a causality, or consequence, of the aging process. This problem demands for studying growth- and longevity phenotypes separately. Here, we assayed a complete collection of haploid Saccharomyces cerevisiae knock-out strains for their capacity to endure long periods at low metabolic rates. Deletion of 93 genes, predominantly factors of primary metabolism, allowed yeast to survive for more than 58 months in the cold. The majority of these deletion strains were not resistant against oxidants or reductants, but many were hypersensitive. Hence, survival at low metabolic rates has limiting genetic components, and correlates with stress resistance inversely. Indeed, maintaining the energy consuming anti-oxidative machinery seems to be disadvantageous under coldroom conditions

The application of massively parallel sequencing technologies in diagnostics

Massively parallel sequencing (MPS) is rapidly evolving and is starting to be utilized by the clinical field as well as diagnostics. We describe major recent advances that have come about as a result of the application of MPS in the biomedical field and the first approaches in medical genetics that have made use of MPS. Without any doubt, MPS has proven to be a very powerful technique. To unravel the capabilities of MPS for patient care, the most important aspect for the acceptance of MPS within clinics and diagnostics is to guarantee that the large amount of data undergoes vitally important analyses and interpretation and is securely managed

A European focus on proteomics

A report on the First International Symposium of the Austrian Proteomics Platform, Seefeld, Austria, 26-29 January 2004

Cancer stem cells in solid tumors: elusive or illusive?

During the past years in vivo transplantation experiments and in vitro colony-forming assays indicated that tumors arise only from rare cells. These cells were shown to bear self-renewal capacities and the ability to recapitulate all cell types within an individual tumor. Due to their phenotypic resemblance to normal stem cells, the term "cancer stem cells" is used. However, some pieces of the puzzle are missing: (a) a stringent definition of cancer stem cells in solid tumors (b) specific markers that only target cells that meet the criteria for a cancer stem cell in a certain type of tumor. These missing parts started an ongoing debate about which is the best method to identify and characterize cancer stem cells, or even if their mere existence is just an artifact caused by the experimental procedures. Recent findings query the cancer stem cell hypothesis for solid tumors itself since it was shown in xenograft transplantation experiments that under appropriate conditions tumor-initiating cells are not rare

d-matrix – database exploration, visualization and analysis

BACKGROUND: Motivated by a biomedical database set up by our group, we aimed to develop a generic database front-end with embedded knowledge discovery and analysis features. A major focus was the human-oriented representation of the data and the enabling of a closed circle of data query, exploration, visualization and analysis. RESULTS: We introduce a non-task-specific database front-end with a new visualization strategy and built-in analysis features, so called d-matrix. d-matrix is web-based and compatible with a broad range of database management systems. The graphical outcome consists of boxes whose colors show the quality of the underlying information and, as the name suggests, they are arranged in matrices. The granularity of the data display allows consequent drill-down. Furthermore, d-matrix offers context-sensitive categorization, hierarchical sorting and statistical analysis. CONCLUSIONS: d-matrix enables data mining, with a high level of interactivity between humans and computer as a primary factor. We believe that the presented strategy can be very effective in general and especially useful for the integration of distinct data types such as phenotypical and molecular data

In silico identification of a core regulatory network of OCT4 in human embryonic stem cells using an integrated approach

<p>Abstract</p> <p>Background</p> <p>The transcription factor OCT4 is highly expressed in pluripotent embryonic stem cells which are derived from the inner cell mass of mammalian blastocysts. Pluripotency and self renewal are controlled by a transcription regulatory network governed by the transcription factors OCT4, SOX2 and NANOG. Recent studies on reprogramming somatic cells to induced pluripotent stem cells highlight OCT4 as a key regulator of pluripotency.</p> <p>Results</p> <p>We have carried out an integrated analysis of high-throughput data (ChIP-on-chip and RNAi experiments along with promoter sequence analysis of putative target genes) and identified a core OCT4 regulatory network in human embryonic stem cells consisting of 33 target genes. Enrichment analysis with these target genes revealed that this integrative analysis increases the functional information content by factors of 1.3 – 4.7 compared to the individual studies. In order to identify potential regulatory co-factors of OCT4, we performed a <it>de novo </it>motif analysis. In addition to known validated OCT4 motifs we obtained binding sites similar to motifs recognized by further regulators of pluripotency and development; e.g. the heterodimer of the transcription factors C-MYC and MAX, a prerequisite for C-MYC transcriptional activity that leads to cell growth and proliferation.</p> <p>Conclusion</p> <p>Our analysis shows how heterogeneous functional information can be integrated in order to reconstruct gene regulatory networks. As a test case we identified a core OCT4-regulated network that is important for the analysis of stem cell characteristics and cellular differentiation. Functional information is largely enriched using different experimental results. The <it>de novo </it>motif discovery identified well-known regulators closely connected to the OCT4 network as well as potential new regulators of pluripotency and differentiation. These results provide the basis for further targeted functional studies.</p

Adaptive nanopores: A bioinspired label-free approach for protein sequencing and identification

AbstractSingle molecule protein sequencing would tremendously impact in proteomics and human biology and it would promote the development of novel diagnostic and therapeutic approaches. However, its technological realization can only be envisioned, and huge challenges need to be overcome. Major difficulties are inherent to the structure of proteins, which are composed by several different amino-acids. Despite long standing efforts, only few complex techniques, such as Edman degradation, liquid chromatography and mass spectroscopy, make protein sequencing possible. Unfortunately, these techniques present significant limitations in terms of amount of sample required and dynamic range of measurement. It is known that proteins can distinguish closely similar molecules. Moreover, several proteins can work as biological nanopores in order to perform single molecule detection and sequencing. Unfortunately, while DNA sequencing by means of nanopores is demonstrated, very few examples of nanopores able to perform reliable protein-sequencing have been reported so far. Here, we investigate, by means of molecular dynamics simulations, how a re-engineered protein, acting as biological nanopore, can be used to recognize the sequence of a translocating peptide by sensing the "shape" of individual amino-acids. In our simulations we demonstrate that it is possible to discriminate with high fidelity, 9 different amino-acids in a short peptide translocating through the engineered construct. The method, here shown for fluorescence-based sequencing, does not require any labelling of the peptidic analyte. These results can pave the way for a new and highly sensitive method of sequencing