Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect coregulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1 was not uploaded but is available by contacting the author. 27 pages, 5 figures, 15 supplementary file

Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions

Genetic regulatory networks (GRNs) have been widely studied, yet there is a lack of understanding with regards to the final size and properties of these networks, mainly due to no network currently being complete. In this study, we analyzed the distribution of GRN structural properties across a large set of distinct prokaryotic organisms and found a set of constrained characteristics such as network density and number of regulators. Our results allowed us to estimate the number of interactions that complete networks would have, a valuable insight that could aid in the daunting task of network curation, prediction, and validation. Using state-of-the-art statistical approaches, we also provided new evidence to settle a previously stated controversy that raised the possibility of complete biological networks being random and therefore attributing the observed scale-free properties to an artifact emerging from the sampling process during network discovery. Furthermore, we identified a set of properties that enabled us to assess the consistency of the connectivity distribution for various GRNs against different alternative statistical distributions. Our results favor the hypothesis that highly connected nodes (hubs) are not a consequence of network incompleteness. Finally, an interaction coverage computed for the GRNs as a proxy for completeness revealed that high-throughput based reconstructions of GRNs could yield biased networks with a low average clustering coefficient, showing that classical targeted discovery of interactions is still needed.Comment: 28 pages, 5 figures, 12 pages supplementary informatio

Data Mining a Medieval Medical Text Reveals Patterns in Ingredient Choice That Reflect Biological Activity against Infectious Agents

We used established methodologies from network science to identify patterns in medicinal ingredient combinations in a key medieval text, the 15th-century Lylye of Medicynes, focusing on recipes for topical treatments for symptoms of microbial infection. We conducted experiments screening the antimicrobial activity of selected ingredients. These experiments revealed interesting examples of ingredients that potentiated or interfered with each other’s activity and that would be useful bases for future, more detailed experiments. Our results highlight (i) the potential to use methodologies from network science to analyze medieval data sets and detect patterns of ingredient combination, (ii) the potential of interdisciplinary collaboration to reveal different aspects of the ethnopharmacology of historical medical texts, and (iii) the potential development of novel therapeutics inspired by premodern remedies in a time of increased need for new antibiotics.The pharmacopeia used by physicians and laypeople in medieval Europe has largely been dismissed as placebo or superstition. While we now recognize that some of the materia medica used by medieval physicians could have had useful biological properties, research in this area is limited by the labor-intensive process of searching and interpreting historical medical texts. Here, we demonstrate the potential power of turning medieval medical texts into contextualized electronic databases amenable to exploration by the use of an algorithm. We used established methodologies from network science to reveal patterns in ingredient selection and usage in a key text, the 15th-century Lylye of Medicynes, focusing on remedies to treat symptoms of microbial infection. In providing a worked example of data-driven textual analysis, we demonstrate the potential of this approach to encourage interdisciplinary collaboration and to shine a new light on the ethnopharmacology of historical medical texts

Graph Theory and Networks in Biology

In this paper, we present a survey of the use of graph theoretical techniques in Biology. In particular, we discuss recent work on identifying and modelling the structure of bio-molecular networks, as well as the application of centrality measures to interaction networks and research on the hierarchical structure of such networks and network motifs. Work on the link between structural network properties and dynamics is also described, with emphasis on synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape

Duplex-specific nuclease efficiently removes rRNA for prokaryotic RNA-seq

Next-generation sequencing has great potential for application in bacterial transcriptomics. However, unlike eukaryotes, bacteria have no clear mechanism to select mRNAs over rRNAs; therefore, rRNA removal is a critical step in sequencing-based transcriptomics. Duplex-specific nuclease (DSN) is an enzyme that, at high temperatures, degrades duplex DNA in preference to single-stranded DNA. DSN treatment has been successfully used to normalize the relative transcript abundance in mRNA-enriched cDNA libraries from eukaryotic organisms. In this study, we demonstrate the utility of this method to remove rRNA from prokaryotic total RNA. We evaluated the efficacy of DSN to remove rRNA by comparing it with the conventional subtractive hybridization (Hyb) method. Illumina deep sequencing was performed to obtain transcriptomes from Escherichia coli grown under four growth conditions. The results clearly showed that our DSN treatment was more efficient at removing rRNA than the Hyb method was, while preserving the original relative abundance of mRNA species in bacterial cells. Therefore, we propose that, for bacterial mRNA-seq experiments, DSN treatment should be preferred to Hyb-based methods.

Computer-aided whole-cell design:taking a holistic approach by integrating synthetic with systems biology

Computer-aided design for synthetic biology promises to accelerate the rational and robust engineering of biological systems; it requires both detailed and quantitative mathematical and experimental models of the processes to (re)design, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. Computer-aided design strategies require quantitative representations of cells, able to capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems, and we discuss several challenges for the realization of our vision. The possibility to describe and build in silico whole-cells offers an opportunity to develop increasingly automatized, precise and accessible computer-aided design tools and strategies throughout novel interdisciplinary collaborations

Understanding the paradox of genetic diversity in uropathogenic E. coli: the uncommon evolution of a common pathogen

Urinary tract infections (UTIs) are the second most common bacterial infection of people in the U.S.A and are frequently recurrent, as an initial UTI is quickly followed by a second episode in 30-35% of cases despite appropriate antibiotic treatment and clearance of the bacteria from the urine. The vast majority, \u3e80%, of UTIs are caused by uropathogenic Escherichia coli (UPEC). UPEC that colonize the bladder are thought to originate in the gut, where they live as commensal organisms. UPEC can be shed in feces to colonize the vagina and/or periurethral area, and then can ascend into the bladder to start a UTI. E. coli strains, including UPEC, have been sub-divided into clades (e.g., clades A, B1, B2 and D) based on their genetic relatedness. In the U.S.A, most (50-75%) UPEC fall into clade B2 while the rest (25-50%) are spread through clades A, B1, and D. Many UPEC encode a variety of putative urovirulence factor genes that are thought to enable bladder colonization and whose carriage in has been correlated with both UTI and recurrence in humans. However, in contrast to many other E. coli pathotypes and despite decades of research, a clear, genetic definition of UPEC remains elusive. Towards this goal, I pursued a research strategy integrating multiple fields of study, including large-scale bioinformatic analysis, in vitro and in vivo modeling of pathogenesis, and structural biology, within a holistic view of the UPEC evolutionary history that incorporates their residence in both the gut and the bladder. Thus, I have shown that clinical UPEC are genetically heterogeneous and that gene carriage alone is not a robust predictor of UPEC’s ability to colonize the bladder in mouse models of cystitis. Instead, I have found the transcriptional regulation of core genes shared by all E. coli strains can be used to predict the outcome of bladder infections in mice. Further, I have found that evolution has stringently conserved bacterial behaviors that are critical to both bladder and gut colonization by E. coli, namely the tension and unwinding of the type 1 pilus rod in response to shear stress. The type 1 pilus is found in the vast majority of E. coli strains and nearly every UPEC isolate and has been shown to be critical in bladder colonization in animal models of cystitis, thus underscoring the fact that bacterial features enabling uropathogenicity are common and conserved across many E. coli strains. Finally, I have shown that clade B2 UPEC have adopted genetic tools from other gut bacteria that provide them with a selective advantage in gut colonization and persistence, potentially enhancing their ability to cause recurrent UTIs. This may explain why B2 strains are enriched in UPEC overall, especially in those strains causing recurrent UTI, despite the fact that both B2 and non-B2 strains can be robust colonizers of the bladder. Taken together, these findings indicate the bladder pathogenesis may be a “core feature” of most E. coli and that the definition of UPEC may be related more to the core bacterial behaviors enabling persistence and survival in multiple body sites than any one specific virulence mechanism or carriage of certain genes. These findings extend beyond UPEC to other bacterial diseases, such as respiratory infections caused by Klebsiella or Pneumocococcus, where bacteria transition from commensal lifestyles in one habitat to pathogenic lifestyles in another body site and further work is needed to understand how conserved bacterial features may be coopted for pathogenicity in the new environment