Phase stability in nanoscale material systems: extension from bulk phase diagrams

Phase diagrams of multi-component systems are critical for the development and engineering of material alloys for all technological applications. At nano dimensions, surfaces (and interfaces) play a significant role in changing equilibrium thermodynamics and phase stability. In this work, it is shown that these surfaces at small dimensions affect the relative equilibrium thermodynamics of the different phases. The CALPHAD approach for material surfaces (also termed “nano-CALPHAD”) is employed to investigate these changes in three binary systems by calculating their phase diagrams at nano dimensions and comparing them with their bulk counterparts. The surface energy contribution, which is the dominant factor in causing these changes, is evaluated using the spherical particle approximation. It is first validated with the Au–Si system for which experimental data on phase stability of spherical nano-sized particles is available, and then extended to calculate phase diagrams of similarly sized particles of Ge–Si and Al–Cu. Additionally, the surface energies of the associated compounds are calculated using DFT, and integrated into the thermodynamic model of the respective binary systems. In this work we found changes in miscibilities, reaction compositions of about 5 at%, and solubility temperatures ranging from 100–200 K for particles of sizes 5 nm, indicating the importance of phase equilibrium analysis at nano dimensions

Fast index based algorithms and software for matching position specific scoring matrices

BACKGROUND: In biological sequence analysis, position specific scoring matrices (PSSMs) are widely used to represent sequence motifs in nucleotide as well as amino acid sequences. Searching with PSSMs in complete genomes or large sequence databases is a common, but computationally expensive task. RESULTS: We present a new non-heuristic algorithm, called ESAsearch, to efficiently find matches of PSSMs in large databases. Our approach preprocesses the search space, e.g., a complete genome or a set of protein sequences, and builds an enhanced suffix array that is stored on file. This allows the searching of a database with a PSSM in sublinear expected time. Since ESAsearch benefits from small alphabets, we present a variant operating on sequences recoded according to a reduced alphabet. We also address the problem of non-comparable PSSM-scores by developing a method which allows the efficient computation of a matrix similarity threshold for a PSSM, given an E-value or a p-value. Our method is based on dynamic programming and, in contrast to other methods, it employs lazy evaluation of the dynamic programming matrix. We evaluated algorithm ESAsearch with nucleotide PSSMs and with amino acid PSSMs. Compared to the best previous methods, ESAsearch shows speedups of a factor between 17 and 275 for nucleotide PSSMs, and speedups up to factor 1.8 for amino acid PSSMs. Comparisons with the most widely used programs even show speedups by a factor of at least 3.8. Alphabet reduction yields an additional speedup factor of 2 on amino acid sequences compared to results achieved with the 20 symbol standard alphabet. The lazy evaluation method is also much faster than previous methods, with speedups of a factor between 3 and 330. CONCLUSION: Our analysis of ESAsearch reveals sublinear runtime in the expected case, and linear runtime in the worst case for sequences not shorter than | [Formula: see text] |(m )+ m - 1, where m is the length of the PSSM and [Formula: see text] a finite alphabet. In practice, ESAsearch shows superior performance over the most widely used programs, especially for DNA sequences. The new algorithm for accurate on-the-fly calculations of thresholds has the potential to replace formerly used approximation approaches. Beyond the algorithmic contributions, we provide a robust, well documented, and easy to use software package, implementing the ideas and algorithms presented in this manuscript

The Stress Response Factors Yap6, Cin5, Phd1, and Skn7 Direct Targeting of the Conserved Co-Repressor Tup1-Ssn6 in S. cerevisiae

Maintaining the proper expression of the transcriptome during development or in response to a changing environment requires a delicate balance between transcriptional regulators with activating and repressing functions. The budding yeast transcriptional co-repressor Tup1-Ssn6 is a model for studying similar repressor complexes in multicellular eukaryotes. Tup1-Ssn6 does not bind DNA directly, but is directed to individual promoters by one or more DNA-binding proteins, referred to as Tup1 recruiters. This functional architecture allows the Tup1-Ssn6 to modulate the expression of genes required for the response to a variety of cellular stresses. To understand the targeting or the Tup1-Ssn6 complex, we determined the genomic distribution of Tup1 and Ssn6 by ChIP-chip. We found that most loci bound by Tup1-Ssn6 could not be explained by co-occupancy with a known recruiting cofactor and that deletion of individual known Tup1 recruiters did not significantly alter the Tup1 binding profile. These observations suggest that new Tup1 recruiting proteins remain to be discovered and that Tup1 recruitment typically depends on multiple recruiting cofactors. To identify new recruiting proteins, we computationally screened for factors with binding patterns similar to the observed Tup1-Ssn6 genomic distribution. Four top candidates, Cin5, Skn7, Phd1, and Yap6, all known to be associated with stress response gene regulation, were experimentally confirmed to physically interact with Tup1 and/or Ssn6. Incorporating these new recruitment cofactors with previously characterized cofactors now explains the majority of Tup1 targeting across the genome, and expands our understanding of the mechanism by which Tup1-Ssn6 is directed to its targets

Copy Number Analysis Identifies Novel Interactions Between Genomic Loci in Ovarian Cancer

Ovarian cancer is a heterogeneous disease displaying complex genomic alterations, and consequently, it has been difficult to determine the most relevant copy number alterations with the scale of studies to date. We obtained genome-wide copy number alteration (CNA) data from four different SNP array platforms, with a final data set of 398 ovarian tumours, mostly of the serous histological subtype. Frequent CNA aberrations targeted many thousands of genes. However, high-level amplicons and homozygous deletions enabled filtering of this list to the most relevant. The large data set enabled refinement of minimal regions and identification of rare amplicons such as at 1p34 and 20q11. We performed a novel co-occurrence analysis to assess cooperation and exclusivity of CNAs and analysed their relationship to patient outcome. Positive associations were identified between gains on 19 and 20q, gain of 20q and loss of X, and between several regions of loss, particularly 17q. We found weak correlations of CNA at genomic loci such as 19q12 with clinical outcome. We also assessed genomic instability measures and found a correlation of the number of higher amplitude gains with poorer overall survival. By assembling the largest collection of ovarian copy number data to date, we have been able to identify the most frequent aberrations and their interactions

Genome Wide DNA Copy Number Analysis of Serous Type Ovarian Carcinomas Identifies Genetic Markers Predictive of Clinical Outcome

Ovarian cancer is the fifth leading cause of cancer death in women. Ovarian cancers display a high degree of complex genetic alterations involving many oncogenes and tumor suppressor genes. Analysis of the association between genetic alterations and clinical endpoints such as survival will lead to improved patient management via genetic stratification of patients into clinically relevant subgroups. In this study, we aim to define subgroups of high-grade serous ovarian carcinomas that differ with respect to prognosis and overall survival. Genome-wide DNA copy number alterations (CNAs) were measured in 72 clinically annotated, high-grade serous tumors using high-resolution oligonucleotide arrays. Two clinically annotated, independent cohorts were used for validation. Unsupervised hierarchical clustering of copy number data derived from the 72 patient cohort resulted in two clusters with significant difference in progression free survival (PFS) and a marginal difference in overall survival (OS). GISTIC analysis of the two clusters identified altered regions unique to each cluster. Supervised clustering of two independent large cohorts of high-grade serous tumors using the classification scheme derived from the two initial clusters validated our results and identified 8 genomic regions that are distinctly different among the subgroups. These 8 regions map to 8p21.3, 8p23.2, 12p12.1, 17p11.2, 17p12, 19q12, 20q11.21 and 20q13.12; and harbor potential oncogenes and tumor suppressor genes that are likely to be involved in the pathogenesis of ovarian carcinoma. We have identified a set of genetic alterations that could be used for stratification of high-grade serous tumors into clinically relevant treatment subgroups

Mitochondrial physiology

As the knowledge base and importance of mitochondrial physiology to evolution, health and disease expands, the necessity for harmonizing the terminology concerning mitochondrial respiratory states and rates has become increasingly apparent. The chemiosmotic theory establishes the mechanism of energy transformation and coupling in oxidative phosphorylation. The unifying concept of the protonmotive force provides the framework for developing a consistent theoretical foundation of mitochondrial physiology and bioenergetics. We follow the latest SI guidelines and those of the International Union of Pure and Applied Chemistry (IUPAC) on terminology in physical chemistry, extended by considerations of open systems and thermodynamics of irreversible processes. The concept-driven constructive terminology incorporates the meaning of each quantity and aligns concepts and symbols with the nomenclature of classical bioenergetics. We endeavour to provide a balanced view of mitochondrial respiratory control and a critical discussion on reporting data of mitochondrial respiration in terms of metabolic flows and fluxes. Uniform standards for evaluation of respiratory states and rates will ultimately contribute to reproducibility between laboratories and thus support the development of data repositories of mitochondrial respiratory function in species, tissues, and cells. Clarity of concept and consistency of nomenclature facilitate effective transdisciplinary communication, education, and ultimately further discovery