Network-based approaches to explore complex biological systems towards network medicine

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes

SWIM: A computational tool to unveiling crucial nodes in complex biological networks

SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called "fight-club hubs", characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called "switch genes", appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer

WormBase 2007

WormBase (www.wormbase.org) is the major publicly available database of information about Caenorhabditis elegans, an important system for basic biological and biomedical research. Derived from the initial ACeDB database of C. elegans genetic and sequence information, WormBase now includes the genomic, anatomical and functional information about C. elegans, other Caenorhabditis species and other nematodes. As such, it is a crucial resource not only for C. elegans biologists but the larger biomedical and bioinformatics communities. Coverage of core areas of C. elegans biology will allow the biomedical community to make full use of the results of intensive molecular genetic analysis and functional genomic studies of this organism. Improved search and display tools, wider cross-species comparisons and extended ontologies are some of the features that will help scientists extend their research and take advantage of other nematode species genome sequences

Codon Bias Patterns of E.coliE.coli's Interacting Proteins

Synonymous codons, i.e., DNA nucleotide triplets coding for the same amino acid, are used differently across the variety of living organisms. The biological meaning of this phenomenon, known as codon usage bias, is still controversial. In order to shed light on this point, we propose a new codon bias index, $CompAI$, that is based on the competition between cognate and near-cognate tRNAs during translation, without being tuned to the usage bias of highly expressed genes. We perform a genome-wide evaluation of codon bias for $E.coli$, comparing $CompAI$ with other widely used indices: $tAI$, $CAI$, and $Nc$. We show that $CompAI$ and $tAI$ capture similar information by being positively correlated with gene conservation, measured by ERI, and essentiality, whereas, $CAI$ and $Nc$ appear to be less sensitive to evolutionary-functional parameters. Notably, the rate of variation of $tAI$ and $CompAI$ with ERI allows to obtain sets of genes that consistently belong to specific clusters of orthologous genes (COGs). We also investigate the correlation of codon bias at the genomic level with the network features of protein-protein interactions in $E.coli$. We find that the most densely connected communities of the network share a similar level of codon bias (as measured by $CompAI$ and $tAI$). Conversely, a small difference in codon bias between two genes is, statistically, a prerequisite for the corresponding proteins to interact. Importantly, among all codon bias indices, $CompAI$ turns out to have the most coherent distribution over the communities of the interactome, pointing to the significance of competition among cognate and near-cognate tRNAs for explaining codon usage adaptation

Bio-Communication of Bacteria and its Evolutionary Interrelations to Natural Genome Editing Competences of Viruses

Communicative competences enable bacteria to develop, organise and coordinate rich social life with a great variety of behavioral patterns even in which they organise themselves like multicellular organisms. They have existed for almost four billion years and still survive, being part of the most dramatic changes in evolutionary history such as DNA invention, cellular life, invention of nearly all protein types, partial constitution of eukaryotic cells, vertical colonisation of all eukaryotes, high adaptability through horizontal gene transfer and co-operative multispecies colonisation of all ecological niches. Recent research demonstrates that these bacterial competences derive from the aptitude of viruses for natural genome editing. 
	In contrast to a book which would be the appropriate space to outline in depth all communicative pathways inherent in bacterial life in this current article I want to give an overview for a broader readership over the great variety of bacterial bio-communication: In a first step I describe how they interpret and coordinate, what semiochemical vocabulary they share and which goals they try to reach. In a second stage I describe the main categories of sign-mediated interactions between bacterial and non-bacterial organisms, and between bacteria of the same or related species. In a third stage I will focus on the relationship between bacteria and their obligate settlers, i.e. viruses. We will see that bacteria are important hosts for multiviral colonisation and virally-determined order of nucleic acid sequences.

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Two genetic codes: Repetitive syntax for active non-coding RNAs; non-repetitive syntax for the DNA archives

Current knowledge of the RNA world indicates 2 different genetic codes being present throughout the living world. In contrast to non-coding RNAs that are built of repetitive nucleotide syntax, the sequences that serve as templates for proteins share—as main characteristics—a non-repetitive syntax. Whereas non-coding RNAs build groups that serve as regulatory tools in nearly all genetic processes, the coding sections represent the evolutionarily successful function of the genetic information storage medium. This indicates that the differences in their syntax structure are coherent with the differences of the functions they represent. Interestingly, these 2 genetic codes resemble the function of all natural languages, i.e., the repetitive non-coding sequences serve as appropriate tool for organization, coordination and regulation of group behavior, and the nonrepetitive coding sequences are for conservation of instrumental constructions, plans, blueprints for complex protein-body architecture. This differentiation may help to better understand RNA group behavioral motifs

What is Life?

In searching for life in extraterrestrial space, it is essential to act based on an unequivocal definition of life. In the twentieth century, life was defined as cells that self-replicate, metabolize, and are open for mutations, without which genetic information would remain unchangeable, and evolution would be impossible. Current definitions of life derive from statistical mechanics, physics, and chemistry of the twentieth century in which life is considered to function machine like, ignoring a central role of communication. Recent observations show that context-dependent meaningful communication and network formation (and control) are central to all life forms. Evolutionary relevant new nucleotide sequences now appear to have originated from social agents such as viruses, their parasitic relatives, and related RNA networks, not from errors. By applying the known features of natural languages and communication, a new twenty-first century definition of life can be reached in which communicative interactions are central to all processes of life. A new definition of life must integrate the current empirical knowledge about interactions between cells, viruses, and RNA networks to provide a better explanatory power than the twentieth century narrative