Anatoly Sorokin

Systems biology reveals new strategies for personalizing cancer medicine and confirms the role of PTEN in resistance to trastuzumab

Resistance to targeted cancer therapies such as trastuzumab is a frequent clinical problem not solely because of insufficient expression of HER2 receptor but also because of the overriding activation states of cell signaling pathways. Systems biology approaches lend themselves to rapid in silico testing of factors, which may confer resistance to targeted therapies. Inthis study, we aimed to develop a new kinetic model that could be interrogated to predict resistance to receptor tyrosine kinase (RTK) inhibitor therapies and directly test predictions in vitro and in clinical samples. The new mathematical model included RTK inhibitor antibody binding, HER2/HER3 dimerization and inhibition, AKT/mitogen-activated protein kinase cross-talk, and the regulatory properties of PTEN. The model was parameterized using quantitative phosphoprotein expression data from cancer cell lines using reverse-phase protein microarrays. Quantitative PTEN protein expression was found to be the key determinant of resistance to anti-HER2 therapy in silico, which was predictive of unseen experiments in vitro using the PTEN inhibitor bp(V). When measured in cancer cell lines, PTEN expression predicts sensitivity to anti-HER2 therapy; furthermore, this quantitative measurement is more predictive of response (relative risk, 3.0; 95% confidence interval, 1.6-5.5; P < 0.0001) than other pathway components taken in isolation and when tested by multivariate analysis in a cohort of 122 breast cancers treated with trastuzumab. For the first time, a systems biology approach has successfully been used to stratify patients for personalized therapy in cancer and is further compelling evidence that PTEN, appropriately measured in the clinical setting, refines clinical decision making in patients treated with anti-HER2 therapies.

Software support for SBGN maps

Motivation: LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner. Availability and implementation: Milestone 2 was released in December 2011. Source code, example files and binaries are freely available under the terms of either the LGPL v2.1+ or Apache v2.0 open source licenses from http://libsbgn.sourceforge.net. Contact: sbgn-libsbgn@lists.sourceforge.net

Inertinite-rich tertiary coals from the Zeya-Bureya Basin, Far Eastern Russia

Selected Tertiary coals from the Zeya–Buryea Basin, Far Eastern Russia, were investigated for aspects of their coal type, rank, depositional environment and post-depositional history. The coals have been examined in outcrop (lithotype logging), microscopically (maceral, reflectance and fluorescence), and geochemically (proximate analysis). Two laterally extensive coal-bearing horizons occur: one of Palaeocene age and the other of early Miocene age. The Palaeocene coals were investigated in active open-cut mines at Raichikhinsk and Yerkovtsi and the early Miocene deposit in an abandoned open-cut mine at Cergeyevka. Palaeocene coals at Raichikhinsk and Yerkovtsi were indistinguishable from each other macroscopically, microscopically, and geochemically. The deposits were sufficiently coalified that brightness logging could be undertaken. Dull coals, with numerous fusainous wisps, were dominant. Four dulling-up sequences, which represent stacked peat deposits, were observed at Raichikhinsk. At Yerkovtsi, only a small section of the middle of the seam, which was mostly dull and muddy coal, was investigated. Petrographically, these coals were dominated by inertinite group macerals, which is unusual in non-Gondwanan coals and rare in the Tertiary. Rank classification was problematic with volatile matter (VM) content of vitrain (daf), macroscopic appearance, and microscopic textures suggesting subbituminous B rank, but carbon content, moisture content and specific energy indicating a lignite rank. Notwithstanding complications of rank, estimates of the maximum-range burial depths were calculated. Taking the VM (daf) content of vitrain as 48%, burial depth estimates range from 900 m for a high geothermal gradient and long heating time to a maximum of 3300 m for a low geothermal gradient and short heating time. These estimates are maxima as the coal rank may be lower than implied by the VM. The Cergeyevka deposit is a soft brown coal. Limited sampling of the upper-most portion indicated a high moisture content (75% daf) and an unusual, hydrogen-rich geochemistry. Lack of identifiable liptinites using either reflected light or fluorescence microscopy suggested a significant bituminite component. Otherwise, the coals appear to be typical for the Tertiary. An estimate of 125 m maximum burial depth was obtained using the bed-moisture content of the coal, which is around the present burial depth. Comparison of present-day thicknesses with inferred burial depths suggests that at least 500 m of section is missing between the Palaeocene coals and the early Miocene coals. Palaeoenvironmental considerations suggest that fire played a significant role in the accumulation of the peats at Raichikhinsk and Yerkovtsi. At Cergeyevka, peat accumulation ended by drowning of the mire. Two tuff beds were recognised within the seam at Raichikhinsk and one in the seam at Yerkovtsi. Correlation of the tuff beds is uncertain but they should prove useful in regional coal seam correlation and interpreting coal depositional environments. Geochemical analysis by XRF was complicated by high loss-on-ignition (LOI) values. Despite extensive alteration, an acid igneous source is implied from the presence of free quartz and TiO2/Al2O3 ratios of 0.02 to 0.05.

Wiring diagrams in biology: towards the standardized representation of biological information

The growing complexity of experimental datasets requires a uniform representation of knowledge for accurate and efficient information exchange. The Systems Biology Graphical Notation (SBGN) [1] standardizes the graphical representation of biological networks. We introduce ‘SBGN bricks’ as a way to represent typical biological patterns in networks. SBGN bricks should serve as a tool for biologists to draw networks quickly that can then easily be interpreted, used, and altered by other scientists.

Comparative Metagenomic Analysis of Electrogenic Microbial Communities in Differentially Inoculated Swine Wastewater-Fed Microbial Fuel Cells

Bioelectrochemical systems such as microbial fuel cells (MFCs) are promising new technologies for efficient removal of organic compounds from industrial wastewaters, including that generated from swine farming. We inoculated two pairs of laboratory-scale MFCs with sludge granules from a beer wastewater-treating anaerobic digester (IGBS) or from sludge taken from the bottom of a tank receiving swine wastewater (SS). The SS-inoculated MFC outperformed the IGBS-inoculated MFC with regard to COD and VFA removal and electricity production. Using a metagenomic approach, we describe the microbial diversity of the MFC planktonic and anodic communities derived from the different inocula. Proteobacteria (mostly Deltaproteobacteria) became the predominant phylum in both MFC anodic communities with amplification of the electrogenic genus Geobacter being the most pronounced. Eight dominant and three minor species of Geobacter were found in both MFC anodic communities. The anodic communities of the SS-inoculated MFCs had a higher proportion of Clostridium and Bacteroides relative to those of the IGBS-inoculated MFCs, which were enriched with Pelobacter. The archaeal populations of the SS- and IGBS-inoculated MFCs were dominated by Methanosarcina barkeri and Methanothermobacter thermautotrophicus, respectively. Our results show a long-term influence of inoculum type on the performance and microbial community composition of swine wastewater-treating MFCs.

COMBINED CHEMICAL-BENEFICIATION PROCESSES OF VALUABLE COMPONENTS EXTRACTION FROM COAL BURNING WASTES

This paper demonstrates the variety of the composition and properties of the ash and slag combustion products depending on coal type, grade, and conditions of combustion. Typical integrated processing methods of coal ash from Russia’s different regions are presented based on concentration technology: collection of aluminosilicate microspheres; flotation of the unburnt coal organic mass; magnetic separation to produce iron concentrate; flotation of the aluminosilicate product of non-magnetic fraction; gravity concentration recovering valuable trace elements into the heavy fraction and the aluminosilicate product into the light fraction; chemical treatment to recover various metals (germanium, gallium, scandium, gold, etc.). To recover gold from stack emissions and ash, an experimental process is proposed for liquid-phase condensation of volatile gold from stack emissions by live steaming followed by purification of the solution and sorption of gold by activated carbon. Process for gold capture from stack emissions is proposed using solid-phase condensation.

Conceptual model of E. coli transcriptional machinery

References: 1. Freyre-González et al. Functional architecture of Escherichia coli: new insights provided by a natural decomposition approach. Genome Biol (2008) vol. 9 (10) pp. R154 2. Klumpp and Hwa. Growth-rate-dependent partitioning of RNA polymerases in bacteria. Proc Natl Acad Sci USA (2008) vol. 105 (51) pp. 20245-50 3. Barker et al. Mechanism of regulation of transcription initiation by ppGpp. II. Models for positive control based on properties of RNAP mutants and competition for RNAP. J Mol Biol (2001) vol. 305 (4) pp. 689-702 4. Le Novère et al. The systems biology graphical notation. Nature biotechnology (2009) vol. 27 (8) pp. 735-41 5. Record MT Jr, Reznikoff W, Craig M, McQuade K, Schlax P. 1996. Escherichia coli RNA polymerase (Eσ70), promoters, and the kinetics of the steps of transcription initiation. In Escherichia coli and Salmonella: Cellular and Molecular Biology, ed. FC Neidhart, 1:792–820. Washington, DC: Am. Soc. Microbiol. Press 6. Socorro Gama-Castro; Heladia Salgado; Martin Peralta-Gil; Alberto Santos-Zavaleta; Luis Muniz-Rascado; Hilda Solano-Lira; Veronica Jimenez-Jacinto; Verena Weiss; Jair S. Garcia-Sotelo; Alejandra Lopez-Fuentes; Liliana Porron-Sotelo; Shirley Alquicira-Hernandez; Alejandra Medina-Rivera; Irma Martinez-Flores; Kevin AlquiciraHernandez; Ruth Martinez-Adame; Cesar Bonavides-Martinez; Juan Miranda-Rios; Araceli M. Huerta; Alfredo Mendoza-Vargas; Leonardo Collado-Torres; Blanca Taboada; Leticia Vega-Alvarado; Maricela Olvera; Leticia Olvera; Ricardo Grande; Enrique Morett; Julio Collado-Vides RegulonDB (version 7.0): transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units)Nucleic Acids Research 2010 The research has been supported by RFBR (11-04-01436-a) ER diagram of promoter specific regulation ER diagram of transcription initiation