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Identification of differentially expressed genes (DEGs) is well recognized to be variable across independent replications of genome-wide transcriptional studies. These are often employed to characterize disease state early in the process of discovery and prioritize novel targets aimed at addressing unmet medical need. Increasing reproducibility of biological findings from these studies could potentially positively impact the success rate of new clinical interventions. This work demonstrates that statistically sound combination of gene expression data with prior knowledge about biology in the form of large protein interaction networks can yield quantitatively more reproducible observations from studies characterizing human disease. The novel concept of Well-Associated Proteins (WAPs) introduced herein—gene products significantly associated on protein interaction networks with the differences in transcript levels between control and disease—does not require choosing a differential expression threshold and can be computed efficiently enough to enable false discovery rate estimation via permutation. Reproducibility of WAPs is shown to be on average superior to that of DEGs under easily-quantifiable conditions suggesting that they can yield a significantly more robust description of disease. Enhanced reproducibility of WAPs versus DEGs is first demonstrated with four independent data sets focused on systemic sclerosis. This finding is then validated over thousands of pairs of data sets obtained by random partitions of large studies in several other diseases. Conditions that individual data sets must satisfy to yield robust WAP scores are examined. Reproducible identification of WAPs can potentially benefit drug target selection and precision medicine studies., Author summary Gene expression studies are commonly used to characterize biological systems. Genes identified in such experiments as expressed at different levels between conditions (e.g. healthy vs. disease) can indicate biological functions that are important in this context. However, it is well-recognized that such findings can vary substantially across independent investigations. We quantified reproducibility here under a conservative control scenario that partitions a given data set in two, independently of experimental conditions, for multiple data sets characterizing several diseases in humans. Furthermore, we have shown that it is possible to obtain more reproducible findings than DEGs, which we term Well-Associated Proteins, characterizing differences in gene expression between healthy and disease states. This was accomplished by combining gene expression and prior knowledge of functional relationships between gene products accumulated over many studies and publications. Resulting Well-Associated Proteins can be computed efficiently enough to enable permutation controls and demonstrate on average higher reproducibility than differentially expressed genes, both within and across data sets. This suggests that Well-Associated Proteins may better reflect differences in biology when comparing disease and healthy states than DEGs, thus representing an important step towards identification of key disease drivers.

Heparan sulfate (HS), a glycosaminoglycan present on the surface of cells, has been postulated to have important roles in driving both normal and pathological physiologies. The chemical structure and sulfation pattern (domain structure) of HS is believed to determine its biological function, to vary across tissue types, and to be modified in the context of disease. Characterization of HS requires isolation and purification of cell surface HS as a complex mixture. This process may introduce additional chemical modification of the native residues. In this study, we describe an approach towards thorough characterization of bovine kidney heparan sulfate (BKHS) that utilizes a variety of orthogonal analytical techniques (e.g. NMR, IP-RPHPLC, LC-MS). These techniques are applied to characterize this mixture at various levels including composition, fragment level, and overall chain properties. The combination of these techniques in many instances provides orthogonal views into the fine structure of HS, and in other instances provides overlapping / confirmatory information from different perspectives. Specifically, this approach enables quantitative determination of natural and modified saccharide residues in the HS chains, and identifies unusual structures. Analysis of partially digested HS chains allows for a better understanding of the domain structures within this mixture, and yields specific insights into the non-reducing end and reducing end structures of the chains. This approach outlines a useful framework that can be applied to elucidate HS structure and thereby provides means to advance understanding of its biological role and potential involvement in disease progression. In addition, the techniques described here can be applied to characterization of heparin from different sources. Electronic supplementary material The online version of this article (doi:10.1007/s10719-016-9734-7) contains supplementary material, which is available to authorized users.

We present an analytical framework to analyze lists of proteins with large undirected graphs representing their known functional relationships. We consider edge-count variables such as the number of interactions between a protein and a list, the size of a subgraph induced by a list, and the number of interactions bridging two lists. We derive approximate analytical expressions for the probability distributions of these variables in a model of a random graph with given expected degrees. Probabilities obtained with the analytical expressions are used to mine a protein interaction network for functional modules, characterize the connectedness of protein functional categories, and measure the strength of relations between modules.

In the presence of 1 mM phosphate, 2-amino-6-mercapto-7-methylpurine ribonucleoside (MESG) is a well-behaved substrate for calf spleen purine nucleoside phosphorylase (PNP). In the absence of phosphate, calf spleen PNP catalyzes a slow hydrolysis of MESG, which is accompanied by inactivation of the enzyme, analogous to the previously observed PNP-catalyzed hydrolysis of inosine and guanosine with formation, in the former case, of a stable PNP·hypoxan thine complex (P. C. Kline and V. L. Schramm (1992) Biochemistry 31 , 5964–5973). Qualitative and semiquantitative features of calf spleen PNP-catalyzed hydrolysis of MESG are accounted for by the following model. First, in the absence of phosphate and at pH 7.4, the enzyme exists as an equilibrium mixture of monomer and trimer with a dissociation constant for the trimer of 3 × 10 14 M 2 . Second, a stoichiometric reaction between three molecules of MESG and the PNP trimer results in the formation of a stable PNP·purinethiol complex. Third, the PNP·purinethiol complex initially formed with the monomeric enzyme partitions between product release and formation of a stable complex with 55 turnovers per inactivation event. Fourth, the stable PNP·purinethiol complexes are rapidly dissociated by phosphate to regenerate active enzyme. This dissociation is accompanied by an increase in absorbance at 356 nm consistent with a p K a for the purinethiol base on the enzyme of 8.1, compared to a corresponding value of 8.8 in aqueous solution.

We present a computational approach based on a local search strategy that discovers sets of proteins that preferentially interact with each other. Such sets are referred to as protein communities and are likely to represent functional modules. Preferential interaction between module members is quantified via an analytical framework based on a network null model known as the random graph with given expected degrees. Based on this framework, the concept of local protein community is generalized to that of community of communities. Protein communities and higher-level structures are extracted from two yeast protein interaction data sets and a network of published interactions between human proteins. The high level structures obtained with the human network correspond to broad biological concepts such as signal transduction, regulation of gene expression, and intercellular communication. Many of the obtained human communities are enriched, in a statistically significant way, for proteins having no clear orthologs in lower organisms. This indicates that the extracted modules are quite coherent in terms of function.

Earlier a three-dimensional model for a new unusual DNA conformation referred to as Slipped Loop Structure (SLS) has been suggested by us (1). The same type of folding could occur with RNA as well which means that one must use the A-form of the double helix rather than the B-one. The present paper discusses the creation of an all-atom stereochemically sound model for SLS-RNA. This calculated model, while possessing the same folding topology as the SLS-DNA, differs dramatically from the SLS-DNA by an overall folding geometry. It also differs radically from the RNA-pseudoknot and can thus be regarded as a new type of an RNA folding.