Uncovering Biological Network Function via Graphlet Degree Signatures

Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassified proteins is large even for simple and well studied organisms such as baker's yeast. Methods for determining protein function have shifted their focus from targeting specific proteins based solely on sequence homology to analyses of the entire proteome based on protein-protein interaction (PPI) networks. Since proteins aggregate to perform a certain function, analyzing structural properties of PPI networks may provide useful clues about the biological function of individual proteins, protein complexes they participate in, and even larger subcellular machines. We design a sensitive graph theoretic method for comparing local structures of node neighborhoods that demonstrates that in PPI networks, biological function of a node and its local network structure are closely related. The method groups topologically similar proteins under this measure in a PPI network and shows that these protein groups belong to the same protein complexes, perform the same biological functions, are localized in the same subcellular compartments, and have the same tissue expressions. Moreover, we apply our technique on a proteome-scale network data and infer biological function of yet unclassified proteins demonstrating that our method can provide valuable guidelines for future experimental research.Comment: First submitted to Nature Biotechnology on July 16, 2007. Presented at BioPathways'07 pre-conference of ISMB/ECCB'07, July 19-20, 2007, Vienna, Austria. Published in full in the Posters section of the Schedule of the RECOMB Satellite Conference on Systems Biology, November 30 - December 1, 2007, University of California, San Diego, US

Optimized Null Model for Protein Structure Networks

Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by comparing various RIG definitions against a series of network models

Dominating Biological Networks

Proteins are essential macromolecules of life that carry out most cellular processes. Since proteins aggregate to perform function, and since protein-protein interaction (PPI) networks model these aggregations, one would expect to uncover new biology from PPI network topology. Hence, using PPI networks to predict protein function and role of protein pathways in disease has received attention. A debate remains open about whether network properties of “biologically central (BC)” genes (i.e., their protein products), such as those involved in aging, cancer, infectious diseases, or signaling and drug-targeted pathways, exhibit some topological centrality compared to the rest of the proteins in the human PPI network.To help resolve this debate, we design new network-based approaches and apply them to get new insight into biological function and disease. We hypothesize that BC genes have a topologically central (TC) role in the human PPI network. We propose two different concepts of topological centrality. We design a new centrality measure to capture complex wirings of proteins in the network that identifies as TC those proteins that reside in dense extended network neighborhoods. Also, we use the notion of domination and find dominating sets (DSs) in the PPI network, i.e., sets of proteins such that every protein is either in the DS or is a neighbor of the DS. Clearly, a DS has a TC role, as it enables efficient communication between different network parts.We find statistically significant enrichment in BC genes of TC nodes and outperform the existing methods indicating that genes involved in key biological processes occupy topologically complex and dense regions of the network and correspond to its “spine” that connects all other network parts and can thus pass cellular signals efficiently throughout the network. To our knowledge, this is the first study that explores domination in the context of PPI networks

Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets

Background: RNA-mediated interference (RNAi)-based functional genomics is a systems-level approach to identify novel genes that control biological phenotypes. Existing computational approaches can identify individual genes from RNAi datasets that regulate a given biological process. However, currently available methods cannot identify which RNAi screen "hits" are novel components of well-characterized biological pathways known to regulate the interrogated phenotype. In this study, we describe a method to identify genes from RNAi datasets that are novel components of known biological pathways. We experimentally validate our approach in the context of a recently completed RNAi screen to identify novel regulators of melanogenesis. Results: In this study, we utilize a PPI network topology-based approach to identify targets within our RNAi dataset that may be components of known melanogenesis regulatory pathways. Our computational approach identifies a set of screen targets that cluster topologically in a human PPI network with the known pigment regulator Endothelin receptor type B (EDNRB). Validation studies reveal that these genes impact pigment production and EDNRB signaling in pigmented melanoma cells (MNT-1) and normal melanocytes. Conclusions: We present an approach that identifies novel components of well-characterized biological pathways from functional genomics datasets that could not have been identified by existing statistical and computational approaches

Diosgenin-caused changes of the adrenal gland histological parameters in a rat model of the menopause

The authors (Vladimir Ajdzanovic, Ivana Jaric, Dragan Milenkovic and Verica Milosevic) are participating in the COSTAction FA 1403 POSITIVe (Inter individual variation in response to consumption of plant food bioactives and determinants involved),supported by COST (European Cooperation in Science and Technology). We are grateful to Mr. Hinko Sauter (“Swiss-ConceptSciences” d.o.o., Belgrade, Serbia) for additional technical support and to Mrs. Maja R. Vojvodi´c for language correction of the manuscriptDiosgenin, a steroidal sapogenin of natural origin, has demonstrated benefits when it comes to the treatment of malignancies, cardiovascular issues and menopausal symptoms. In this study, we investigated the histological changes of the adrenal gland after diosgenin application in a rat model of the menopause. Middle-aged, acyclic female Wistar rats were divided into control (C; n=6) and diosgenin treated (D; n=6) groups. Diosgenin (100mg/kg b.w./day) was orally administered for four weeks, while C group received the vehicle alone. A histological approach included design-based stereology, histochemistry and immunohistochemistry. The adrenal cortex volume decreased in D females by 15% (p<0.05) while the volume of adrenal medulla increased (p<0.05) by 64%, compared to the same parameters in C group. Volume density of the zona glomerulosa (expressed per absolute adrenal gland volume) in D rats increased (p<0.05) by 22% in comparison with C animals. Diosgenin treatment decreased (p<0.05) the volume density of the zona fasciculata (expressed per volume of adrenal cortex) by 15% when compared to C females. Absolute volume of the zona reticularis in D group decreased (p<0.05) by 38% in comparison with the same parameter in C rats. Also, after diosgenin application, the volume density of the zona reticularis (expressed per volume of adrenal cortex) and the zona reticularis cell volume were decreased by 51% and 20% (p<0.05) respectively, compared to C animals. Our results, reflecting a decrease in many stereological parameters of the adrenal cortex, indicate that diosgenin took over the role of corticosteroid precursors and became incorporated into steroidogenesi

Predictors of Mortality in Critically Ill COVID-19 Patients Demanding High Oxygen Flow: A Thin Line between Inflammation, Cytokine Storm, and Coagulopathy

Introduction. Mortality among critically ill COVID-19 patients remains relatively high despite different potential therapeutic modalities being introduced recently. The treatment of critically ill patients is a challenging task, without identified credible predictors of mortality. Methods. We performed an analysis of 160 consecutive patients with confirmed COVID-19 infection admitted to the Respiratory Intensive Care Unit between June 23, 2020, and October 2, 2020, in University Hospital Center Bezanijska kosa, Belgrade, Serbia. Patients on invasive, noninvasive ventilation and high flow oxygen therapy with moderate to severe ARDS, according to the Berlin definition of ARDS, were selected for the study. Demographic data, past medical history, laboratory values, and CT severity score were analyzed to identify predictors of mortality. Univariate and multivariate logistic regression models were used to assess potential predictors of mortality in critically ill COVID-19 patients. Results. The mean patient age was 65.6 years (range, 29–92 years), predominantly men, 68.8%. 107 (66.9%) patients were on invasive mechanical ventilation, 31 (19.3%) on noninvasive, and 22 (13.8%) on high flow oxygen therapy machine. The median total number of ICU days was 10 (25th to 75th percentile: 6–18), while the median total number of hospital stay was 18 (25th to 75th percentile: 12–28). The mortality rate was 60% (96/160). Univariate logistic regression analysis confirmed the significance of age, CRP, and lymphocytes at admission to hospital, serum albumin, D-dimer, and IL-6 at admission to ICU, and CT score. Serum albumin, D-dimer, and IL-6 at admission to ICU were independently associated with mortality in the final multivariate analysis. Conclusion. In the present study of 160 consecutive critically ill COVID-19 patients with moderate to severe ARDS, IL-6, serum albumin, and D-dimer at admission to ICU, accompanied by chest CT severity score, were marked as independent predictors of mortality