CompNet: a GUI based tool for comparison of multiple biological interaction networks

Time-Varying Gene Expression Network Analysis Reveals Conserved Transition States in Hematopoietic Differentiation between Human and Mouse

(1) Background: analyses of gene networks can elucidate hematopoietic differentiation from single-cell gene expression data, but most algorithms generate only a single, static network. Because gene interactions change over time, it is biologically meaningful to examine time-varying structures and to capture dynamic, even transient states, and cell-cell relationships. (2) Methods: a transcriptomic atlas of hematopoietic stem and progenitor cells was used for network analysis. After pseudo-time ordering with Monocle 2, LOGGLE was used to infer time-varying networks and to explore changes of differentiation gene networks over time. A range of network analysis tools were used to examine properties and genes in the inferred networks. (3) Results: shared characteristics of attributes during the evolution of differentiation gene networks showed a “U” shape of network density over time for all three branches for human and mouse. Differentiation appeared as a continuous process, originating from stem cells, through a brief transition state marked by fewer gene interactions, before stabilizing in a progenitor state. Human and mouse shared hub genes in evolutionary networks. (4) Conclusions: the conservation of network dynamics in the hematopoietic systems of mouse and human was reflected by shared hub genes and network topological changes during differentiation.

The Fungal Gut Microbiome Exhibits Reduced Diversity and Increased Relative Abundance of Ascomycota in Severe COVID-19 Illness and Distinct Interconnected Communities in SARS-CoV-2 Positive Patients

Clinical and experimental studies indicate that the bacterial and fungal gut microbiota modulates immune responses in distant organs including the lungs. Immune dysregulation is associated with severe SARS-CoV-2 infection, and several groups have observed gut bacterial dysbiosis in SARS-CoV-2 infected patients, while the fungal gut microbiota remains poorly defined in these patients. We analyzed the fungal gut microbiome from rectal swabs taken prior to anti-infective treatment in 30 SARS-CoV-2 positive (21 non-severe COVID-19 and 9 developing severe/critical COVID-19 patients) and 23 SARS-CoV-2 negative patients by ITS2-sequencing. Pronounced but distinct interconnected fungal communities distinguished SARS-CoV-2 positive and negative patients. Fungal gut microbiota in severe/critical COVID-19 illness was characterized by a reduced diversity, richness and evenness and by an increase of the relative abundance of the Ascomycota phylum compared with non-severe COVID-19 illness. A dominance of a single fungal species with a relative abundance of >75% was a frequent feature in severe/critical COVID-19. The dominating fungal species were highly variable between patients even within the groups. Several fungal taxa were depleted in patients with severe/critical COVID-19.The distinct compositional changes of the fungal gut microbiome in SARS-CoV-2 infection, especially in severe COVID-19 illness, illuminate the necessity of a broader approach to investigate whether the differences in the fungal gut microbiome are consequences of SARS-CoV-2 infection or a predisposing factor for critical illness.

Differential Network Testing Reveals Diverging Dynamics of Organ System Interactions for Survivors and Non-survivors in Intensive Care Medicine

Statistical network analyses have become popular in many scientific disciplines, where an important task is to test for differences between two networks. We describe an overall framework for differential network testing procedures that vary regarding (1) the network estimation method, typically based on specific concepts of association, and (2) the network characteristic employed to measure the difference. Using permutation-based tests, our approach is general and applicable to various overall, node-specific or edge-specific network difference characteristics. The methods are implemented in our freely available R software package DNT, along with an R Shiny application. In a study in intensive care medicine, we compare networks based on parameters representing main organ systems to evaluate the prognosis of critically ill patients in the intensive care unit (ICU), using data from the surgical ICU of the University Medical Centre Mannheim, Germany. We specifically consider both cross-sectional comparisons between a non-survivor and a survivor group and longitudinal comparisons at two clinically relevant time points during the ICU stay: first, at admission, and second, at an event stage prior to death in non-survivors or a matching time point in survivors. The non-survivor and the survivor networks do not significantly differ at the admission stage. However, the organ system interactions of the survivors then stabilize at the event stage, revealing significantly more network edges, whereas those of the non-survivors do not. In particular, the liver appears to play a central role for the observed increased connectivity in the survivor network at the event stage.

Exploring the Microbiome Analysis and Visualization Landscape

Research on the microbiome has boomed recently, which resulted in a wide range of tools, packages, and algorithms to analyze microbiome data. Here we investigate and map currently existing tools that can be used to perform visual analysis on the microbiome, and associate the including methods, visual representations and data features to the research objectives currently of interest in microbiome research. The analysis is based on a combination of a literature review and workshops including a group of domain experts. Both the reviewing process and workshops are based on domain characterization methods to facilitate communication and collaboration between researchers from different disciplines. We identify several research questions related to microbiomes, and describe how different analysis methods and visualizations help in tackling them.

NetCoMi: network construction and comparison for microbiome data in R

MOTIVATION

Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analysing and comparing microbial association networks from high-throughput sequencing data.

RESULTS

Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analysing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi's wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children's rooms between samples from two study centers (Ulm and Munich).

AVAILABILITY

R scripts used for producing the examples shown in this manuscript are provided as supplementary data. The NetCoMi package, together with a tutorial, is available at https://github.com/stefpeschel/NetCoMi.

CONTACT

Tel:+49 89 3187 43258; stefanie.peschel@mail.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Briefings in Bioinformatics online.

Host population size is linked to orchid mycorrhizal fungal communities in roots and soil, which are shaped by microenvironment

Interaction with orchid mycorrhizal fungi (OMF) is essential to all members of the Orchidaceae, yet we know little about whether or how OMF abundances in substrates shape orchid populations. While root-associated OMF diversity is catalogued frequently, technological constraints have impeded the assessments of OMF communities in substrates until recently, thereby limiting the ability to link OMF communities in a habitat to population responses. Furthermore, there is some evidence that edaphic and microclimatic conditions impact OMF in soil, yet we lack an understanding of the coupled influences of abiotic environment and OMF structure on orchid population dynamics. To discover the linkages between abiotic environment, OMF community structure, and population size, we characterized the microclimatic conditions, soil physicochemistry, and OMF communities hosted by roots and soil across large and small populations of a terrestrial orchid endemic to California Floristic Province in North America. By using high-throughput sequencing of the ITS2 region of nrDNA amplified from root and soil DNAs, we determined that both roots and soil of larger populations, which were high in phosphorus but low in zinc, organic matter, and silt, were dominated by Tulasnellaceae OTUs. In comparison, roots and soil from smaller populations of the orchid hosted higher relative abundances of the Ceratobasidiaceae. In this multiyear, range-wide study that simultaneously measured habitat environmental conditions, and soil and root OMF communities, our results suggest that soil chemistry is clearly linked to soil and root OMF communities, which then likely alter and shape orchid populations.

Exploring the Multi-Tissue Crosstalk Relevant to Insulin Resistance Through Network-Based Analysis

Insulin resistance (IR) is a precursor event that occurs in multiple organs and underpins many metabolic disorders. However, due to the lack of effective means to systematically explore and interpret disease-related tissue crosstalk, the tissue communication mechanism in pathogenesis of IR has not been elucidated yet. To solve this issue, we profiled all proteins in white adipose tissue (WAT), liver, and skeletal muscle of a high fat diet induced IR mouse model via proteomics. A network-based approach was proposed to explore IR related tissue communications. The cross-tissue interface was constructed, in which the inter-tissue connections and also their up and downstream processes were particularly inspected. By functional quantification, liver was recognized as the only organ that can output abnormal carbohydrate metabolic signals, clearly highlighting its central role in regulation of glucose homeostasis. Especially, the CD36-PPAR axis in liver and WAT was identified and verified as a potential bridge that links cross-tissue signals with intracellular metabolism, thereby promoting the progression of IR through a PCK1-mediated lipotoxicity mechanism. The cross-tissue mechanism unraveled in this study not only provides novel insights into the pathogenesis of IR, but also is conducive to development of precision therapies against various IR associated diseases. With further improvement, our network-based cross-tissue analytic method would facilitate other disease-related tissue crosstalk study in the near future.

Time-Varying Gene Network Analysis of Human Prefrontal Cortex Development

The prefrontal cortex (PFC) constitutes a large part of the human central nervous system and is essential for the normal social affection and executive function of humans and other primates. Despite ongoing research in this region, the development of interactions between PFC genes over the lifespan is still unknown. To investigate the conversion of PFC gene interaction networks and further identify hub genes, we obtained time-series gene expression data of human PFC tissues from the Gene Expression Omnibus (GEO) database. A statistical model, loggle, was used to construct time-varying networks and several common network attributes were used to explore the development of PFC gene networks with age. Network similarity analysis showed that the development of human PFC is divided into three stages, namely, fast development period, deceleration to stationary period, and recession period. We identified some genes related to PFC development at these different stages, including genes involved in neuronal differentiation or synapse formation, genes involved in nerve impulse transmission, and genes involved in the development of myelin around neurons. Some of these genes are consistent with findings in previous reports. At the same time, we explored the development of several known KEGG pathways in PFC and corresponding hub genes. This study clarified the development trajectory of the interaction between PFC genes, and proposed a set of candidate genes related to PFC development, which helps further study of human brain development at the genomic level supplemental to regular anatomical analyses. The analytical process used in this study, involving the loggle model, similarity analysis, and central analysis, provides a comprehensive strategy to gain novel insights into the evolution and development of brain networks in other organisms.

Correction to: Visual exploration of microbiome data

Correction to: J Biosci (2019) 44:119 https://doi.org/10.1007/s12038-019-9933-z In the October 2019 Special Issue of the Journal of Biosciences on Current Trends in Microbiome Research, in the Review article titled "Visual exploration of microbiome data" by Bhusan K. Kuntal and Sharmila S. Mande (DOI: 10.1007/s12038-019-9933-z; Vol. 44, Article No. 119), affiliation 3 for Bhusan K. Kuntal was incorrectly mentioned as "Academy of Scientific and Innovative Research, CSIR-National Chemical Laboratory Campus, Pune 411008, India''. The correct affiliation should read as ''Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201 002, India".

Whole transcriptomic network analysis using Co-expression Differential Network Analysis (CoDiNA)

Biological and medical sciences are increasingly acknowledging the significance of gene co-expression-networks for investigating complex-systems, phenotypes or diseases. Typically, complex phenotypes are investigated under varying conditions. While approaches for comparing nodes and links in two networks exist, almost no methods for the comparison of multiple networks are available and—to best of our knowledge—no comparative method allows for whole transcriptomic network analysis. However, it is the aim of many studies to compare networks of different conditions, for example, tissues, diseases, treatments, time points, or species. Here we present a method for the systematic comparison of an unlimited number of networks, with unlimited number of transcripts: Co-expression Differential Network Analysis (CoDiNA). In particular, CoDiNA detects links and nodes that are common, specific or different among the networks. We developed a statistical framework to normalize between these different categories of common or changed network links and nodes, resulting in a comprehensive network analysis method, more sophisticated than simply comparing the presence or absence of network nodes. Applying CoDiNA to a neurogenesis study we identified candidate genes involved in neuronal differentiation. We experimentally validated one candidate, demonstrating that its overexpression resulted in a significant disturbance in the underlying gene regulatory network of neurogenesis. Using clinical studies, we compared whole transcriptome co-expression networks from individuals with or without HIV and active tuberculosis (TB) and detected signature genes specific to HIV. Furthermore, analyzing multiple cancer transcription factor (TF) networks, we identified common and distinct features for particular cancer types. These CoDiNA applications demonstrate the successful detection of genes associated with specific phenotypes. Moreover, CoDiNA can also be used for comparing other types of undirected networks, for example, metabolic, protein-protein interaction, ecological and psychometric networks. CoDiNA is publicly available as an R package in CRAN (https://CRAN.R-project.org/package=CoDiNA).

NetConfer: a web application for comparative analysis of multiple biological networks

Background

Most biological experiments are inherently designed to compare changes or transitions of state between conditions of interest. The advancements in data intensive research have in particular elevated the need for resources and tools enabling comparative analysis of biological data. The complexity of biological systems and the interactions of their various components, such as genes, proteins, taxa, and metabolites, have been inferred, represented, and visualized via graph theory-based networks. Comparisons of multiple networks can help in identifying variations across different biological systems, thereby providing additional insights. However, while a number of online and stand-alone tools exist for generating, analyzing, and visualizing individual biological networks, the utility to batch process and comprehensively compare multiple networks is limited.

Results

Here, we present a graphical user interface (GUI)-based web application which implements multiple network comparison methodologies and presents them in the form of organized analysis workflows. Dedicated comparative visualization modules are provided to the end-users for obtaining easy to comprehend, insightful, and meaningful comparisons of various biological networks. We demonstrate the utility and power of our tool using publicly available microbial and gene expression data.

Conclusion

NetConfer tool is developed keeping in mind the requirements of researchers working in the field of biological data analysis with limited programming expertise. It is also expected to be useful for advanced users from biological as well as other domains (working with association networks), benefiting from provided ready-made workflows, as they allow to focus directly on the results without worrying about the implementation. While the web version allows using this application without installation and dependency requirements, a stand-alone version has also been supplemented to accommodate the offline requirement of processing large networks.

Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

Investigating host-bacterial interactions among enteric pathogens

Background

In 2017, World Health Organization (WHO) published a catalogue of 12 families of antibiotic-resistant “priority pathogens” that are posing the greatest threats to human health. Six of these dreaded pathogens are known to infect the human gastrointestinal system. In addition to causing gastrointestinal and systemic infections, these pathogens can also affect the composition of other microbes constituting the healthy gut microbiome. Such aberrations in gut microbiome can significantly affect human physiology and immunity. Identifying the virulence mechanisms of these enteric pathogens are likely to help in developing newer therapeutic strategies to counter them.

Results

Using our previously published in silico approach, we have evaluated (and compared) Host-Pathogen Protein-Protein Interaction (HPI) profiles of four groups of enteric pathogens, namely, different species of Escherichia, Shigella, Salmonella and Vibrio. Results indicate that in spite of genus/ species specific variations, most enteric pathogens possess a common repertoire of HPIs. This core set of HPIs are probably responsible for the survival of these pathogen in the harsh nutrient-limiting environment within the gut. Certain genus/ species specific HPIs were also observed.

Conslusions

The identified bacterial proteins involved in the core set of HPIs are expected to be helpful in understanding the pathogenesis of these dreaded gut pathogens in greater detail. Possible role of genus/ species specific variations in the HPI profiles in the virulence of these pathogens are also discussed. The obtained results are likely to provide an opportunity for development of novel therapeutic strategies against the most dreaded gut pathogens.

Visual exploration of microbiome data

A dramatic increase in large-scale cross-sectional and temporal-level metagenomic experiments has led to an improved understanding of the microbiome and its role in human well-being. Consequently, a plethora of analytical methods has been developed to decipher microbial biomarkers for various diseases, cluster different ecosystems based on microbial content, and infer functional potential of the microbiome as well as analyze its temporal behavior. Development of user-friendly visualization methods and frameworks is necessary to analyze this data and infer taxonomic and functional patterns corresponding to a phenotype. Thus, new methods as well as application of pre-existing ones has gained importance in recent times pertaining to the huge volume of the generated microbiome data. In this review, we present a brief overview of some useful visualization techniques that have significantly enriched microbiome data analytics.

Meta-network: optimized species-species network analysis for microbial communities

Background

The explosive growth of microbiome data provides ample opportunities to gain a better understanding of the microbes and their interactions in microbial communities. Given these massive data, optimized data mining methods become important and necessary to perform deep and comprehensive analysis. Among the various priorities for microbiome data mining, the examination of species-species co-occurrence patterns becomes one of the key themes in urgent need.

Results

Hence, in this work, we propose the Meta-Network framework to lucubrate the microbial communities. Rooted in loose definitions of network (two species co-exist in a certain samples rather than all samples) as well as association rule mining (mining more complex forms of correlations like indirect correlation and mutual information), this framework outperforms other methods in restoring the microbial communities, based on two cohorts of microbial communities: (a) the loose definition strategy is capable to generate more reasonable relationships among species in the species-species co-occurrence network; (b) important species-species co-occurrence patterns could not be identified by other existing approaches, but could successfully generated by association rule mining.

Conclusions

Results have shown that the species-species co-occurrence network we generated are much more informative than those based on traditional methods. Meta-Network has consistently constructed more meaningful networks with biologically important clusters, hubs, and provides a general approach towards deciphering the species-species co-occurrence networks.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5471-1) contains supplementary material, which is available to authorized users.

BayesPiles

We address the problem of exploring, combining, and comparing large collections of scored, directed networks for understanding inferred Bayesian networks used in biology. In this field, heuristic algorithms explore the space of possible network solutions, sampling this space based on algorithm parameters and a network score that encodes the statistical fit to the data. The goal of the analyst is to guide the heuristic search and decide how to determine a final consensus network structure, usually by selecting the top-scoring network or constructing the consensus network from a collection of high-scoring networks. BayesPiles, our visualisation tool, helps with understanding the structure of the solution space and supporting the construction of a final consensus network that is representative of the underlying dataset. BayesPiles builds upon and extends MultiPiles to meet our domain requirements. We developed BayesPiles in conjunction with computational biologists who have used this tool on datasets used in their research. The biologists found our solution provides them with new insights and helps them achieve results that are representative of the underlying data.

'NetShift': a methodology for understanding 'driver microbes' from healthy and disease microbiome datasets

The combined effect of mutual association within the co-inhabiting microbes in human body is known to play a major role in determining health status of individuals. The differential taxonomic abundance between healthy and disease are often used to identify microbial markers. However, in order to make a microbial community based inference, it is important not only to consider microbial abundances, but also to quantify the changes observed among inter microbial associations. In the present study, we introduce a method called 'NetShift' to quantify rewiring and community changes in microbial association networks between healthy and disease. Additionally, we devise a score to identify important microbial taxa which serve as 'drivers' from the healthy to disease. We demonstrate the validity of our score on a number of scenarios and apply our methodology on two real world metagenomic datasets. The 'NetShift' methodology is also implemented as a web-based application available at https://web.rniapps.net/netshift.

Metabolite-Centric Reporter Pathway and Tripartite Network Analysis of Arabidopsis Under Cold Stress

The study of plant resistance to cold stress and the metabolic processes underlying its molecular mechanisms benefit crop improvement programs. Here we investigate the effects of cold stress on the metabolic pathways of Arabidopsis when directly inferred at system level from transcriptome data. A metabolite-centric reporter pathway analysis approach enabled the computation of metabolites associated with transcripts at four time points of cold treatment. Tripartite networks of gene-metabolite-pathway connectivity outlined the response of metabolites and pathways to cold stress. Our metabolome-independent analysis revealed stress-associated metabolites in pathway routes of the cold stress response, including amino acid, carbohydrate, lipid, hormone, energy, photosynthesis, and signaling pathways. Cold stress first triggered the mobilization of energy from glycolysis and ethanol degradation to enhance TCA cycle activity via acetyl-CoA. Interestingly, tripartite networks lacked power law behavior and scale free connectivity, favoring modularity. Network rewiring explicitly involved energetics, signal, carbon and redox metabolisms and membrane remodeling.

Understanding the role of interactions between host and Mycobacterium tuberculosis under hypoxic condition: an in silico approach

Background

Mycobacterium tuberculosis infection in humans is often associated with extended period of latency. To adapt to the hostile hypoxic environment inside a macrophage, M. tuberculosis cells undergo several physiological and metabolic changes. Previous studies have mostly focused on inspecting individual facets of this complex process. In order to gain deeper insights into the infection process and to understand the coordination among different regulatory/ metabolic pathways in the pathogen, the current in silico study investigates three aspects, namely, (i) host-pathogen interactions (HPIs) between human and M. tuberculosis proteins, (ii) gene regulatory network pertaining to adaptation of M. tuberculosis to hypoxia and (iii) alterations in M. tuberculosis metabolism under hypoxic condition. Subsequently, cross-talks between these components have been probed to evaluate possible gene-regulatory events as well as HPIs which are likely to drive metabolic changes during pathogen’s adaptation to the intra-host hypoxic environment.

Results

The newly identified HPIs suggest the pathogen’s ability to subvert host mediated reactive oxygen intermediates/ reactive nitrogen intermediates (ROI/ RNI) stress as well as their potential role in modulating host cell cycle and cytoskeleton structure. The results also indicate a significantly pronounced effect of HPIs on hypoxic metabolism of M. tuberculosis. Findings from the current study underscore the necessity of investigating the infection process from a systems-level perspective incorporating different facets of intra-cellular survival of the pathogen.

Conclusions

The comprehensive host-pathogen interaction network, a Boolean model of M. tuberculosis H37Rv (Mtb) hypoxic gene-regulation, as well as a genome scale metabolic model of Mtb, built for this study are expected to be useful resources for future studies on tuberculosis infection.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4947-8) contains supplementary material, which is available to authorized users.

Alterations in the gut bacterial microbiome in fungal Keratitis patients

Dysbiosis in the gut microbiome has been implicated in several diseases including auto-immune diseases, inflammatory diseases, cancers and mental disorders. Keratitis is an inflammatory disease of the eye significantly contributing to corneal blindness in the developing world. It would be worthwhile to investigate the possibility of dysbiosis in the gut microbiome being associated with Keratitis. Here, we have analyzed fungal and bacterial populations in stool samples through high-throughput sequencing of the ITS2 region for fungi and V3-V4 region of 16S rRNA gene for bacteria in healthy controls (HC, n = 31) and patients with fungal keratitis (FK, n = 32). Candida albicans (2 OTUs), Aspergillus (1 OTU) and 3 other denovo-OTUs were enriched in FK samples and an unclassified denovo-OTU was enriched in HC samples. However, the overall abundances of these ‘discriminatory’ OTUs were very low (< 0.001%) and not indicative of significant dysbiosis in the fungal community inhabiting the gut of FK patients. In contrast, the gut bacterial richness and diversity in FK patients was significantly decreased when compared to HC. 52 OTUs were significantly enriched in HC samples whereas only 5 OTUs in FK. The OTUs prominently enriched in HC were identified as Faecalibacterium prausnitzii, Bifidobacterium adolescentis, Lachnospira, Mitsuokella multacida, Bacteroides plebeius, Megasphaera and Lachnospiraceae. In FK samples, 5 OTUs affiliated to Bacteroides fragilis, Dorea, Treponema, Fusobacteriaceae, and Acidimicrobiales were significantly higher in abundance. The functional implications are that Faecalibacterium prausnitzii, an anti-inflammatory bacterium and Megasphaera, Mitsuokella multacida and Lachnospira are butyrate producers, which were enriched in HC patients, whereas Treponema and Bacteroides fragilis, which are pathogenic were abundant in FK patients, playing a potential pro-inflammatory role. Heatmap, PCoA plots and functional profiles further confirm the distinct patterns of gut bacterial composition in FK and HC samples. Our study demonstrates dysbiosis in the gut bacterial microbiomes of FK patients compared to HC. Further, based on inferred functions, it appears that dysbiosis in the gut of FK subjects is strongly associated with the disease phenotype with decrease in abundance of beneficial bacteria and increase in abundance of pro-inflammatory and pathogenic bacteria.

Computational Analysis of Host-Pathogen Protein Interactions between Humans and Different Strains of Enterohemorrhagic Escherichia coli

Serotype O157:H7, an enterohemorrhagic Escherichia coli (EHEC), is known to cause gastrointestinal and systemic illnesses ranging from diarrhea and hemorrhagic colitis to potentially fatal hemolytic uremic syndrome. Specific genetic factors like ompA, nsrR, and LEE genes are known to play roles in EHEC pathogenesis. However, these factors are not specific to EHEC and their presence in several non-pathogenic strains indicates that additional factors are involved in pathogenicity. We propose a comprehensive effort to screen for such potential genetic elements, through investigation of biomolecular interactions between E. coli and their host. In this work, an in silico investigation of the protein–protein interactions (PPIs) between human cells and four EHEC strains (viz., EDL933, Sakai, EC4115, and TW14359) was performed in order to understand the virulence and host-colonization strategies of these strains. Potential host–pathogen interactions (HPIs) between human cells and the “non-pathogenic” E. coli strain MG1655 were also probed to evaluate whether and how the variations in the genomes could translate into altered virulence and host-colonization capabilities of the studied bacterial strains. Results indicate that a small subset of HPIs are unique to the studied pathogens and can be implicated in virulence. This subset of interactions involved E. coli proteins like YhdW, ChuT, EivG, and HlyA. These proteins have previously been reported to be involved in bacterial virulence. In addition, clear differences in lineage and clade-specific HPI profiles could be identified. Furthermore, available gene expression profiles of the HPI-proteins were utilized to estimate the proportion of proteins which may be involved in interactions. We hypothesized that a cumulative score of the ratios of bound:unbound proteins (involved in HPIs) would indicate the extent of colonization. Thus, we designed the Host Colonization Index (HCI) measure to determine the host colonization potential of the E. coli strains. Pathogenic strains of E. coli were observed to have higher HCIs as compared to a non-pathogenic laboratory strain. However, no significant differences among the HCIs of the two pathogenic groups were observed. Overall, our findings are expected to provide additional insights into EHEC pathogenesis and are likely to aid in designing alternate preventive and therapeutic strategies.

A global view of regulatory networks in lung cancer: An approach to understand homogeneity and heterogeneity

A number of new biotechnologies are used to identify potential biomarkers for the early detection of lung cancer, enabling a personalized therapy to be developed in response. The combinatorial cross-regulation of hundreds of biological function-specific transcription factors (TFs) is defined as the understanding of regulatory networks of molecules within the cell. Here we integrated global databases with 537 patients with lung adenocarcinoma (ADC), 140 with lung squamous carcinoma (SCC), 9 with lung large-cell carcinoma (LCC), 56 with small-cell lung cancer (SCLC), and 590 without cancer with the understanding of TF functions. The present review aims at the homogeneity or heterogeneity of gene expression profiles among subtypes of lung cancer. About 5, 136, 52, or 16 up-regulated or 19, 24, 122, or 97down-regulated type-special TF genes were identified in ADC, SCC, LCC or SCLC, respectively. DNA-binding and transcription regulator activity associated genes play a dominant role in the differentiation of subtypes in lung cancer. Subtype-specific TF gene regulatory networks with elements should be an alternative for diagnostic and therapeutic targets for early identification of lung cancer and can provide insightful clues to etiology and pathogenesis.