Genome-wide identification, expression profiling, and protein interaction analysis of the CCoAOMT gene family in the tea plant (Camellia sinensis)

BACKGROUND

The caffeoyl-CoA-O methyltransferase (CCoAOMT) family plays a crucial role in the oxidative methylation of phenolic substances and is involved in various plant processes, including growth, development, and stress response. However, there is a limited understanding of the interactions among CCoAOMT protein members in tea plants.

RESULTS

In this study, we identified 10 members of the CsCCoAOMT family in the genome of Camellia sinensis (cultivar 'HuangDan'), characterized by conserved gene structures and motifs. These CsCCoAOMT members were located on six different chromosomes (1, 2, 3, 4, 6, and 14). Based on phylogenetic analysis, CsCCoAOMT can be divided into two groups: I and II. Notably, the CsCCoAOMT members of group Ia are likely to be candidate genes involved in lignin biosynthesis. Moreover, through the yeast two-hybrid (Y2H) assay, we established protein interaction networks for the CsCCoAOMT family, revealing 9 pairs of members with interaction relationships.

CONCLUSIONS

We identified the CCoAOMT gene family in Camellia sinensis and conducted a comprehensive analysis of their classifications, phylogenetic and synteny relationships, gene structures, protein interactions, tissue-specific expression patterns, and responses to various stresses. Our findings shed light on the evolution and composition of CsCCoAOMT. Notably, the observed interaction among CCoAOMT proteins suggests the potential formation of the O-methyltransferase (OMT) complex during the methylation modification process, expanding our understanding of the functional roles of this gene family in diverse biological processes.

Effect of altitude and spatial heterogeneity on the host-parasite relationship in anurans from a remnant humid forest in the brazilian semiarid

In the present study, we investigated the effect of habitat heterogeneity, elevation gradient, and phylogenetic distance of host species on the abundance and richness of anuran endoparasites, assuming that parasites follow the distribution of their hosts independently of environmental variation. We collected 192 anurans distributed in three altitude ranges: 100-200 m, 400-500 m, and 700-800 m. We performed discriminant principal component analysis to analyze the interrelationships between environmental heterogeneity and the distribution of parasite and host species in the formation of species groups in each altitude range. We estimated the niche width and parasite overlap, using host species as a variable, and assessed whether parasite abundance is more influenced by historical (distance host phylogeny) or ecological effects in each altitude category and overall. Finally, we use network analyses to understand how interactions between parasites and hosts are formed along the altitude gradient. We found 22 parasite species, and the overall prevalence of infection was 74%. In our study, we did not identify environmental (altitude gradients and heterogeneity) or phylogenetic effects acting on the parasite species diversity. Overall, our results suggest that the parasites are distributed following the dispersal of their hosts and are dispersed among most anuran species.

ListiWiki: A database for the foodborne pathogen Listeria monocytogenes

Listeria monocytogenes is a Gram positive foodborne pathogen that regularly causes outbreaks of systemic infectious diseases. The bacterium maintains a facultative intracellular lifestyle; it thrives under a variety of environmental conditions and is able to infect human host cells. L. monocytogenes is genetically tractable and therefore has become an attractive model system to study the mechanisms employed by facultative intracellular bacteria to invade eukaryotic cells and to replicate in their cytoplasm. Besides its importance for basic research, L. monocytogenes also serves as a paradigmatic pathogen in genomic epidemiology, where the relative stability of its genome facilitates successful outbreak detection and elucidation of transmission chains in genomic pathogen surveillance systems. In both terms, it is necessary to keep the annotation of the L. monocytogenes genome up to date. Therefore, we have created the database ListiWiki (http://listiwiki.uni-goettingen.de/) which stores comprehensive information on the widely used L. monocytogenes reference strain EDG-e. ListiWiki is designed to collect information on genes, proteins and RNAs and their relevant functional characteristics, but also further information such as mutant phenotypes, available biological material, and publications. In its present form, ListiWiki combines the most recent annotation of the EDG-e genome with published data on gene essentiality, gene expression and subcellular protein localization. ListiWiki also predicts protein-protein interactions networks based on protein homology to Bacillus subtilis proteins, for which detailed interaction maps have been compiled in the sibling database SubtiWiki. Furthermore, crystallographic information of proteins is made accessible through integration of Protein Structure Database codes and AlphaFold structure predictions. ListiWiki is an easy-to-use web interface that has been developed with a focus on an intuitive access to all information. Use of ListiWiki is free of charge and its content can be edited by all members of the scientific community after registration. In our labs, ListiWiki has already become an important and easy to use tool to quickly access genome annotation details that we can keep updated with advancing knowledge. It also might be useful to promote the comprehensive understanding of the physiology and virulence of an important human pathogen.

Occurrence of OCPs & PCBs and their effects on multitrophic biological communities in riparian groundwater of the Beiluo River, China

Persistent Organic Pollutants (POPs) may exert adverse effects on human and ecosystem health. However, as an ecologically fragile zone with strong interaction between river and groundwater, the POPs pollution in the riparian zone has received little attention. The goal of this research is to examine the concentrations, spatial distribution, potential ecological risks, and biological effects of organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in the riparian groundwater of the Beiluo River, China. The results showed that the pollution level and ecological risk of OCPs in riparian groundwater of the Beiluo River were higher than PCBs. The presence of PCBs (Penta-CBs, Hexa-CBs) and CHLs, respectively, may have reduced the richness of bacteria (Firmicutes) and fungi (Ascomycota). Furthermore, the richness and Shannon's diversity index of algae (Chrysophyceae and Bacillariophyta) decreased, which could be linked to the presence of OCPs (DDTs, CHLs, DRINs), and PCBs (Penta-CBs, Hepta-CBs), while for metazoans (Arthropoda) the tendency was reversed, presumably as a result of SULPHs pollution. In the network analysis, core species belonging to bacteria (Proteobacteria), fungi (Ascomycota), and algae (Bacillariophyta) played essential roles in maintaining community function. Burkholderiaceae and Bradyrhizobium can be considered biological indicators of PCBs pollution in the Beiluo River. Note that the core species of interaction network, playing a fundamental role in community interactions, are strongly affected by POPs pollutants. This work provides insights into the functions of multitrophic biological communities in maintaining the stability of riparian ecosystems through the response of core species to riparian groundwater POPs contamination.

Microscale dynamics of dark zone alterations in anthropized karstic cave shows abrupt microbial community switch

Strong anthropization of karstic caves may result in formation of various wall alterations including dark zones, whose microbial community differs from that of non-altered surfaces nearby. Dark zones grow quickly and without gradual visual changes, leading to the hypothesis of a simple process rather than complex microbial successions, but this is counter-intuitive as underground microbial changes are typically slow and dark zones are microbiologically very distinct from unmarked surfaces. We tested this hypothesis in Paleolithic Lascaux Cave, across two years of microscale sampling. Indeed, Illumina MiSeq metabarcoding evidenced only three community stages for bacteria, fungi and all microeukaryotes together (i.e. unmarked surfaces, newly-formed dark zones and intermediate/old dark zones) and just two stages for archaea (unmarked surfaces vs dark zones), indicating abrupt community changes. The onset of dark zone formation coincided with the development of Ochroconis fungi, Bacteroidota and the bacterial genera Labrys, Nonomuraea and Sphingomonas, in parallel to Pseudomonas counter-selection. Modeling of community assembly processes highlighted that the dynamics of rare taxa in unmarked surfaces adjacent to dark zones and in newly-formed dark zones were governed in part by deterministic processes. This suggests that cooperative relationships between these taxa might be important to promote dark zone formation. Taken together, these findings indicate an abrupt community switch as these new alterations form on Lascaux cave walls.

Building, Visualizing, and Analyzing Glycosaminoglycan-Protein Interaction Networks

This chapter describes how to generate, visualize, and analyze interaction networks of glycosaminoglycans (GAGs), which are linear polyanionic polysaccharides mostly located at the cell surface and in the extracellular matrix. The protocol is divided into three major steps: (1) the collection of GAG-mediated interaction data, (2) the visualization of GAG interaction networks, and (3) the computational enrichment analyses of these networks to identify their overrepresented features (e.g., protein domains, location, molecular functions, and biological pathways) compared to a reference proteome. These analyses are critical to interpret GAG interactomic datasets, decipher their specificities and functions, and ultimately identify GAG-protein interactions to target for therapeutic purpose.

Comparative transcriptional analysis uncovers molecular processes in early and mature somatic cyst cells of Drosophila testes

The tight interaction between somatic and germline cells is conserved in animal spermatogenesis. The testes of Drosophila melanogaster are the model of choice to identify processes responsible for mature gamete production. However, processes of differentiation and soma-germline interactions occurring in somatic cyst cells are currently understudied. Here we focused on the comparison of transcriptome expression patterns of early and mature somatic cyst cells to find out the developmental changes taking place in them. We employed a FACS-based approach for the isolation of early and mature somatic cyst cells from fly testes, subsequent preparation of RNA-Seq libraries, and analysis of gene differential expression in the sorted cells. We found increased expression of genes involved in cell cycle-related processes in early cyst cells, which is necessary for the proliferation and self-renewal of a crucial population of early cyst cells, cyst stem cells. Genes proposedly required for lamellipodium-like projection organization for proper cyst formation were also detected among the upregulated ones in early cyst cells. Gene Ontology and interactome analyses of upregulated genes in mature cyst cells revealed a striking over-representation of gene categories responsible for metabolic and catabolic cellular processes, as well as genes supporting the energetic state of the cells provided by oxidative phosphorylation that is carried out in mitochondria. Our comparative analyses of differentially expressed genes revealed major peculiarities in early and mature cyst cells and provide novel insight into their regulation, which is important for male fertility.

Recent advances in structural studies of 14-3-3 protein complexes

Being phosphopeptide-binding hubs, 14-3-3 proteins coordinate multiple cellular processes in eukaryotes, including the regulation of apoptosis, cell cycle, ion channels trafficking, transcription, signal transduction, and hormone biosynthesis. Forming constitutive α-helical dimers, 14-3-3 proteins predominantly recognize specifically phosphorylated Ser/Thr sites within their partners; this generally stabilizes phosphotarget conformation and affects its activity, intracellular distribution, dephosphorylation, degradation and interactions with other proteins. Not surprisingly, 14-3-3 complexes are involved in the development of a range of diseases and are considered promising drug targets. The wide interactome of 14-3-3 proteins encompasses hundreds of different phosphoproteins, for many of which the interaction is well-documented in vitro and in vivo but lack the structural data that would help better understand underlying regulatory mechanisms and develop new drugs. Despite obtaining structural information on 14-3-3 complexes is still lagging behind the research of 14-3-3 interactions on a proteome-wide scale, recent works provided some advances, including methodological improvements and accumulation of new interesting structural data, that are discussed in this review.

The glycosaminoglycan interactome 2.0

Glycosaminoglycans (GAGs) are complex linear polysaccharides, which are covalently attached to core proteins (except for hyaluronan) to form proteoglycans. They play key roles in the organization of the extracellular matrix, and at the cell surface where they contribute to the regulation of cell signaling and of cell adhesion. To explore the mechanisms and pathways underlying their functions, we have generated an expanded dataset of 4290 interactions corresponding to 3464 unique GAG-binding proteins, four times more than the first version of the GAG interactome (Vallet and Ricard-Blum, 2021 J Histochem Cytochem 69:93-104). The increased size of the GAG network is mostly due to the addition of GAG-binding proteins captured from cell lysates and biological fluids by affinity chromatography and identified by mass spectrometry. We review here the interaction repertoire of natural GAGs and of synthetic sulfated hyaluronan, the specificity and molecular functions of GAG-binding proteins, and the biological processes and pathways they are involved in. This dataset is also used to investigate the differences between proteins binding to iduronic acid-containing GAGs (dermatan sulfate and heparin/heparan sulfate) and those interacting with GAGs lacking iduronic acid (chondroitin sulfate, hyaluronan, and keratan sulfate).

Leishmania diversity in bats from an endemic area for visceral and cutaneous leishmaniasis in Southeastern Brazil

This study aimed to determine the occurrence of Leishmania infection in bats in urban and wild areas in an endemic municipality for visceral and cutaneous leishmaniasis in Minas Gerais, Brazil. Between April 2014 to April 2015, 247 bats were captured and classified into 26 species belonging to Phyllostomidae (90.7%), Vespertilionidae (8.1%) and Molossidae (1.2%) families. Blood samples from 247 bats were collected and submitted to nested-PCR, targeting the variable V7-V8 region of the SSU rRNA gene, followed by sequencing of the PCR product. The overall infection rate of Leishmania spp. in bats was 4.4%. Of the eleven bats infected, ten were frugivorous bats: Artibeus planirostris (8/11), Artibeus lituratus (1/11) and Artibeus cinereus (1/11) and one a nectarivorous bat (Glossophaga soricina). None of the individuals exhibited macroscopic alterations in the skin, spleen or liver. Phylogenetic analysis separated Leishmania species in clades corresponding to the subgenera Viannia, Leishmania, and Mundinia, and supported that the isolates characterized in the present study clustered closely with Leishmania (Viannia) sp., Leishmania (Leishmania) infantum and Leishmania (Leishmania) amazonensis. Here we report for the first time the bat Artibeus cinereus as a host of Leishmania (Leishmania) amazonensis. In the study we found that the mean abundance of bats did not differ in wild habitats and urban areas and that bat-parasite interactions were similarly distributed in the two environments. On the other hand, further studies should be conducted in more recent times to verify whether there have been changes in these parameters.

NMR Analysis of Protein Folding Interaction Networks

Theory and experimental evidence unequivocally indicate that protein folding is far more complex than the two-state (all-or-none) model that is usually assumed in the analysis of folding experiments. Proteins tend to fold hierarchically by forming secondary structure elements, followed by supersecondary arrangements, and other intermediate states that ultimately adopt the native tertiary fold as a result of a delicate balance between interatomic interactions and entropic contributions. However, small proteins with simple folds typically follow downhill folding, characterized by very small energetic barriers (<3 RT) that allow multiple protein conformations to be populated along the folding path down the free energy landscape, reaching the native fold at the lowest energy level.Here we describe the use of solution-state nuclear magnetic resonance (NMR) for the analysis of protein folding interaction networks at atomic resolution. The assignment of NMR spectra acquired at different unfolding conditions provides hundreds of atomic unfolding curves that are analyzed to infer the network of folding interactions. The method is particularly useful to study small proteins that fold autonomously in the sub-millisecond timescale. The information obtained from the application of this method can potentially unveil the basic relationships between protein structure and folding.

Responses of the macrobenthic community to the Dalian Bay oil spill based on co-occurrence patterns and interaction networks

On July 16, 2010, a pipeline explosion spilled 1500 tons of crude oil into the Port of Dalian, China. To identify taxa responses to the spill, we exploited seven years of monitoring data to examine the co-occurrence of taxon pairs and the variation of the macrobenthic community. Non-parametric correlation analysis was used to construct interaction networks of relationships between oil spill contaminants and macrobenthic taxa. We observed that the impacted macrobenthic community not restored before 2016. The tolerance/sensitivity of taxa was inconsistent with the studies of oil impacts in other locations. We suggest revision of the ecological group classification of Sabellidae, Lumbrineridae, Terebellidae, Sternaspidae, and Spionidae. The variation in the frequency of coexistence indicates the potential impact of oil spill pollution on resource occupation. The interaction network involving macrobenthic families and stressors associated with the oil spill highlights how different macrobenthic families respond to different combinations of stressors.

Forbidden links, trait matching and modularity in plant-hummingbird networks: Are specialized modules characterized by higher phenotypic floral integration?

Background

Plant-pollinator mutualistic networks show non-random structural properties that promote species coexistence. However, these networks show high variability in the interacting species and their connections. Mismatch between plant and pollinator attributes can prevent interactions, while trait matching can enable exclusive access, promoting pollinators' niche partitioning and, ultimately, modularity. Thus, plants belonging to specialized modules should integrate their floral traits to optimize the pollination function. Herein, we aimed to analyze the biological processes involved in the structuring of plant-hummingbird networks by linking network morphological constraints, specialization, modularity and phenotypic floral integration.

Methods

We investigated the understory plant-hummingbird network of two adjacent habitats in the Lacandona rainforest of Mexico, one characterized by lowland rainforest and the other by savanna-like vegetation. We performed monthly censuses to record plant-hummingbird interactions for 2 years (2018-2020). We also took hummingbird bill measurements and floral and nectar measurements. We summarized the interactions in a bipartite matrix and estimated three network descriptors: connectance, complementary specialization (H₂'), and nestedness. We also analyzed the modularity and average phenotypic floral integration index of each module.

Results

Both habitats showed strong differences in the plant assemblage and network dynamics but were interconnected by the same four hummingbird species, two Hermits and two Emeralds, forming a single network of interaction. The whole network showed low levels of connectance (0.35) and high specialization (H₂' = 0.87). Flower morphologies ranged from generalized to specialized, but trait matching was an important network structurer. Modularity was associated with morphological specialization. The Hermits Phaethornis longirostris and P. striigularis each formed a module by themselves, and a third module was formed by the less-specialized Emeralds: Chlorestes candida and Amazilia tzacatl. The floral integration values were higher in specialized modules but not significantly higher than that formed by generalist species.

Conclusions

Our findings suggest that biological processes derived from both trait matching and "forbidden" links, or nonmatched morphological attributes, might be important network drivers in tropical plant-hummingbird systems while morphological specialization plays a minor role in the phenotypic floral integration. The broad variety of corolla and bill shapes promoted niche partitioning, resulting in the modular organization of the assemblage according to morphological specialization. However, more research adding larger datasets of both the number of modules and pollination networks for a wider region is needed to conclude whether phenotypic floral integration increases with morphological specialization in plant-hummingbird systems.

In-silico network-based analysis of drugs used against COVID-19: Human well-being study

INTRODUCTION

Researchers worldwide with great endeavor searching and repurpose drugs might be potentially useful in fighting newly emerged coronavirus. These drugs show inhibition but also show side effects and complications too. On December 27, 2020, 80,926,235 cases have been reported worldwide. Specifically, in Pakistan, 471,335 has been reported with inconsiderable deaths.

PROBLEM STATEMENT

Identification of COVID-19 drugs pathway through drug-gene and gene-gene interaction to find out the most important genes involved in the pathway to deal with the actual cause of side effects beyond the beneficent effects of the drugs.

METHODOLOGY

The medicines used to treat COVID-19 are retrieved from the Drug Bank. The drug-gene interaction was performed using the Drug Gene Interaction Database to check the relation between the genes and the drugs. The networks of genes are developed by Gene MANIA, while Cytoscape is used to check the active functional association of the targeted gene. The developed systems cross-validated using the EnrichNet tool and identify drug genes' concerned pathways using Reactome and STRING.

RESULTS

Five drugs Azithromycin, Bevacizumab, CQ, HCQ, and Lopinavir, are retrieved. The drug-gene interaction shows several genes that are targeted by the drug. Gene MANIA interaction network shows the functional association of the genes like co-expression, physical interaction, predicted, genetic interaction, co-localization, and shared protein domains.

CONCLUSION

Our study suggests the pathways for each drug in which targeted genes and medicines play a crucial role, which will help experts in-vitro overcome and deal with the side effects of these drugs, as we find out the in-silico gene analysis for the COVID-19 drugs.

Network Analysis of Integrin Adhesion Complexes

Cell-surface adhesion receptors mediate interactions with the extracellular matrix (ECM) to control many fundamental aspects of cell behavior, including cell migration, survival, and proliferation. Integrin adhesion receptors recruit structural and signaling proteins to form multimolecular adhesion complexes that link the plasma membrane to the actomyosin cytoskeleton. The assembly and turnover of adhesion complexes are tightly regulated, governed in part by the networks of physical protein interactions and functional signaling associations between components of the adhesome. Proteomic profiling of adhesion complexes has begun to reveal their molecular complexity and diversity. To interrogate the composition of cell-ECM adhesions, we detail herein an approach for the network analysis of adhesion complex proteomes. Integration of these proteomic data with adhesome databases in the context of predicted protein interactions enables the mapping of experimentally defined adhesion complex networks. Computational analysis of resultant network models can identify subnetworks of putative functionally linked adhesion protein communities. This approach provides a framework to predict functional adhesion protein relationships and generate new mechanistic hypotheses for further experimental testing.

Functional Bioinformatics Analyses of the Matrisome and Integrin Adhesome

The extracellular matrix (ECM) is the noncellular compartment of living organisms and is formed of a complex network of cross-linked proteins, which is collectively known as the matrisome. Apart from providing the structure for an organism, cells interact and thereby communicate with the ECM. Cells interact with their surrounding ECM using cell-surface receptors, such as integrins. Upon integrin engagement with the ECM, cytoskeletal proteins are recruited to integrins and form a molecular protein complex known as the integrin adhesome. Global descriptions of the matrisome and integrin adhesome have been proposed using in silico bioinformatics approaches, as well as through biochemical enrichment of matrisome and adhesome fractions coupled with mass spectrometry-based proteomic analyses, providing inventories of their compositions in different contexts. Here, methods are described for the computational downstream analyses of matrisome and adhesome mass spectrometry datasets that are accessible to wet lab biologists, which include comparing datasets to in silico descriptions, generating interaction networks and performing functional ontological analyses.

Cell-surface heparan sulfate proteoglycans as multifunctional integrators of signaling in cancer

Proteoglycans (PGs) represent a large proportion of the components that constitute the extracellular matrix (ECM). They are a diverse group of glycoproteins characterized by a covalent link to a specific glycosaminoglycan type. As part of the ECM, heparan sulfate (HS)PGs participate in both physiological and pathological processes including cell recruitment during inflammation and the promotion of cell proliferation, adhesion and motility during development, angiogenesis, wound repair and tumor progression. A key function of HSPGs is their ability to modulate the expression and function of cytokines, chemokines, growth factors, morphogens, and adhesion molecules. This is due to their capacity to act as ligands or co-receptors for various signal-transducing receptors, affecting pathways such as FGF, VEGF, chemokines, integrins, Wnt, notch, IL-6/JAK-STAT3, and NF-κB. The activation of those pathways has been implicated in the induction, progression, and malignancy of a tumor. For many years, the study of signaling has allowed for designing specific drugs targeting these pathways for cancer treatment, with very positive results. Likewise, HSPGs have become the subject of cancer research and are increasingly recognized as important therapeutic targets. Although they have been studied in a variety of preclinical and experimental models, their mechanism of action in malignancy still needs to be more clearly defined. In this review, we discuss the role of cell-surface HSPGs as pleiotropic modulators of signaling in cancer and identify them as promising markers and targets for cancer treatment.

Navigating the Global Protein-Protein Interaction Landscape Using iRefWeb

iRefWeb is a resource that provides web interface to a large collection of protein-protein interactions aggregated from major primary databases. The underlying data-consolidation process, called iRefIndex, implements a rigorous methodology of identifying redundant protein sequences and integrating disparate data records that reference the same peptide sequences, despite many potential differences in data identifiers across various source databases. iRefWeb offers a unified user interface to all interaction records and associated information collected by iRefIndex, in addition to a number of data filters and visual features that present the supporting evidence. Users of iRefWeb can explore the consolidated landscape of protein-protein interactions, establish the provenance and reliability of each data record, and compare annotations performed by different data curator teams. The iRefWeb portal is freely available at http://wodaklab.org/iRefWeb .

Secreted breast tumor interstitial fluid microRNAs and their target genes are associated with triple-negative breast cancer, tumor grade, and immune infiltration

Background

Studies on tumor-secreted microRNAs point to a functional role of these in cellular communication and reprogramming of the tumor microenvironment. Uptake of tumor-secreted microRNAs by neighboring cells may result in the silencing of mRNA targets and, in turn, modulation of the transcriptome. Studying miRNAs externalized from tumors could improve cancer patient diagnosis and disease monitoring and help to pinpoint which miRNA-gene interactions are central for tumor properties such as invasiveness and metastasis.

Methods

Using a bioinformatics approach, we analyzed the profiles of secreted tumor and normal interstitial fluid (IF) microRNAs, from women with breast cancer (BC). We carried out differential abundance analysis (DAA), to obtain miRNAs, which were enriched or depleted in IFs, from patients with different clinical traits. Subsequently, miRNA family enrichment analysis was performed to assess whether any families were over-represented in the specific sets. We identified dysregulated genes in tumor tissues from the same cohort of patients and constructed weighted gene co-expression networks, to extract sets of co-expressed genes and co-abundant miRNAs. Lastly, we integrated miRNAs and mRNAs to obtain interaction networks and supported our findings using prediction tools and cancer gene databases.

Results

Network analysis showed co-expressed genes and miRNA regulators, associated with tumor lymphocyte infiltration. All of the genes were involved in immune system processes, and many had previously been associated with cancer immunity. A subset of these, BTLA, CXCL13, IL7R, LAMP3, and LTB, was linked to the presence of tertiary lymphoid structures and high endothelial venules within tumors. Co-abundant tumor interstitial fluid miRNAs within this network, including miR-146a and miR-494, were annotated as negative regulators of immune-stimulatory responses. One co-expression network encompassed differences between BC subtypes. Genes differentially co-expressed between luminal B and triple-negative breast cancer (TNBC) were connected with sphingolipid metabolism and predicted to be co-regulated by miR-23a. Co-expressed genes and TIF miRNAs associated with tumor grade were BTRC, CHST1, miR-10a/b, miR-107, miR-301a, and miR-454.

Conclusion

Integration of IF miRNAs and mRNAs unveiled networks associated with patient clinicopathological traits, and underlined molecular mechanisms, specific to BC sub-groups. Our results highlight the benefits of an integrative approach to biomarker discovery, placing secreted miRNAs within a biological context.

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.

Temporal dynamics of the hummingbird-plant interaction network of a dry forest in Chamela, Mexico: a 30-year follow-up after two hurricanes

Background

Interactions among species are a driving force of community structure. The species composition of animal-plant interaction networks can be highly dynamic on a temporal scale, even though the general network structure is usually not altered. However, few studies have examined how interaction networks change over long periods of time, particularly after extreme natural events. We analyzed herein the structure of the hummingbird-plant interaction network in a dry forest of Chamela, Mexico, comparing the structure in 1985–1986 with that in 2016–2017 following the passage of two hurricanes (category 2 Jova in 2011 and category 4 Patricia in 2015).

Methods

The fieldwork was carried out in the Chamela-Cuixmala Biosphere Reserve in Jalisco, Mexico. In the last 30 years, three severe drought events and two hurricanes have affected this region. Previously, from 1985–1986, hummingbird-plant interactions were recorded monthly for one year in the study area. Then, from 2016–2017, we replicated the sampling in the same localities. We compared the network parameters describing the plant-hummingbird interactions of each period using adjacency matrices.

Results

We found differences in the number and identity of interacting species, especially plants. The plant species missing in 2016–2017 were either the least connected in the original network (1985–1986) or belonged to groups such as cacti, epiphytes, or trees. The new plant species incorporated in the 2016–2017 network were herbs, vines, and shrubs, or other species barely connected. These changes in the composition are consistent with reports on vegetation damage after strong hurricanes at other study sites. Conversely, all hummingbird species remained in the network, with the exception of Heliomaster constantii, which was primarily connected to a plant species absent in the 2016–2017 network. Migratory and habitat generalist species (i.e., Archilochus spp.) showed higher abundances following the disturbance events.

Conclusions

Most of the parameters describing the hummingbird-plant network structure remained unchanged after 30 years, with the exception of an increase in plant robustness and hummingbird niche overlap. However, the network’s generalist core was affected by the loss of some species. Also, core plant species such as Ipomoea bracteata, Combretum farinosum, and Justicia candicans were found to be important for maintaining the hummingbird-plant interaction network. The temporal perspective of this study provides unique insights into the conservation of plant-hummingbird networks across time and extreme natural events.

Forest and connectivity loss simplify tropical pollination networks

Mutualistic interactions between plants and pollinators play an essential role in the organization and persistence of biodiversity. The structure of interaction networks mediates the resilience of local communities and ecosystem functioning to environmental changes. Hence, network structure conservation may be more critical for maintaining biodiversity and ecological services than the preservation of isolated species in changing landscapes. Here, we intensively surveyed seven 36 km² landscapes to empirically investigate the effects of forest loss and landscape configuration on the structure of plant-pollinator networks in understory vegetation of Brazilian Atlantic Forest. Our results indicate that forest loss and isolation affect the structure of the plant-pollinator networks, which were smaller in deforested landscapes, and less specialized as patch isolation increased. Lower nestedness and degree of specialization (H'₂) indicated that the remaining plant and bee species tend to be generalists, and many of the expected specialized interactions in the network were already lost. Because generalist species generate a cohesive interaction core in these networks, these simplified networks might be resistant to loss of peripheral species, but may be susceptible to the extinction of the most generalist species. We suggest that such a network pattern is an outcome of landscapes with a few remaining isolated patches of natural habitat. Our results add a new perspective to studies of plant-pollinator networks in fragmented landscapes, showing that those interaction networks might also be used to indicate how changes in natural habitat affect biodiversity and biotic interactions.

Informed Use of Protein-Protein Interaction Data: A Focus on the Integrated Interactions Database (IID)

Protein-protein interaction data is fundamental in molecular biology, and numerous online databases provide access to this data. However, the huge quantity, complexity, and variety of PPI data can be overwhelming, and rather than helping to address research problems, the data may add to their complexity and reduce interpretability. This protocol focuses on solutions for some of the main challenges of using PPI data, including accessing data, ensuring relevance by integrating useful annotations, and improving interpretability. While the issues are generic, we highlight how to perform such operations using Integrated Interactions Database (IID; http://ophid.utoronto.ca/iid ).

MicroRNAs and their regulatory networks in Chinese Gushi chicken abdominal adipose tissue during postnatal late development

Background

Abdominal fat is the major adipose tissue in chickens. The growth status of abdominal fat during postnatal late development ultimately affects meat yield and quality in chickens. MicroRNAs (miRNAs) are endogenous small noncoding RNAs that regulate gene expression at the post-transcriptional level. Studies have shown that miRNAs play an important role in the biological processes involved in adipose tissue development. However, few studies have investigated miRNA expression profiles and their interaction networks associated with the postnatal late development of abdominal adipose tissue in chickens.

Results

We constructed four small RNA libraries from abdominal adipose tissue obtained from Chinese domestic Gushi chickens at 6, 14, 22, and 30 weeks. A total of 507 known miRNAs and 53 novel miRNAs were identified based on the four small RNA libraries. Fifty-one significant differentially expressed (SDE) miRNAs were identified from six combinations by comparative analysis, and the expression patterns of these SDE miRNAs were divided into six subclusters by cluster analysis. Gene ontology enrichment analysis showed that the SDE miRNAs were primarily involved in the regulation of fat cell differentiation, regulation of lipid metabolism, regulation of fatty acid metabolism, and unsaturated fatty acid metabolism in the lipid metabolism- or deposition-related biological process categories. In addition, we constructed differentially expressed miRNA–mRNA interaction networks related to abdominal adipose development. The results showed that miRNA families, such as mir-30, mir-34, mir-199, mir-8, and mir-146, may have key roles in lipid metabolism, adipocyte proliferation and differentiation, and cell junctions during abdominal adipose tissue development in chickens.

Conclusions

This study determined the dynamic miRNA transcriptome and characterized the miRNA–mRNA interaction networks in Gushi chicken abdominal adipose tissue for the first time. The results expanded the number of known miRNAs in abdominal adipose tissue and provide novel insights and a valuable resource to elucidate post-transcriptional regulation mechanisms during postnatal late development of abdominal adipose tissue in chicken.

Looking beyond the flowers: associations of stingless bees with sap-sucking insects

The main sources of food for stingless bees are the nectar and pollen harvested from flowers, whereas one important kind of nesting material (i.e. wax) is produced by their own abdominal glands. Stingless bees can, nonetheless, obtain alternative resources of food and wax from exudates released by sap-sucking insects as honeydew and waxy cover, respectively. To date, there are no comprehensive studies investigating how diversified and structured the network interactions between stingless bees and sap-sucking insects are. Here, we conducted a survey of the data on relationship between stingless bees and sap-sucking insects to evaluate: (1) which resources are collected by which stingless bee species; (2) how diverse the interaction network is, using species degree and specialisation index as a proxy; and if (3) there would be any phylogenetic signal in the species degree and specialisation indices. Our findings demonstrate that approximately 21 stingless bee species like Trigona spp. and Oxytrigona spp. have been observed interacting with 11 sap-sucking species, among which Aethalion reticulatum is the main partner. From ca. 50 records, Brazil is the country with most observations (n = 38) of this type of ecological interaction. We found also that stingless bees harvest fivefold more honeydew than waxy covers on sap-sucking insects. However, we did not find any phylogenetic signal for the occurrence of this interaction, considering species degree and specialisation indices, suggesting that both traits apparently evolved independently among stingless bee species. We suggest that specific ecological demands may drive this opportunistic behaviour exhibited by stingless bees, because major sources of food are obtained from flowers and these bees produce their own wax.

Network-Based Drug Repositioning: Approaches, Resources, and Research Directions

The wealth of knowledge and omic data available in drug research allowed the rising of several computational methods in drug discovery field yielding a novel and exciting application called drug repositioning. Several computational methods try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter we present an in-depth review of data resources and computational models for drug repositioning.

Studying how genetic variants affect mechanism in biological systems

Genetic variants are currently a major component of system-wide investigations into biological function or disease. Approaches to select variants (often out of thousands of candidates) that are responsible for a particular phenomenon have many clinical applications and can help illuminate differences between individuals. Selecting meaningful variants is greatly aided by integration with information about molecular mechanism, whether known from protein structures or interactions or biological pathways. In this review we discuss the nature of genetic variants, and recent studies highlighting what is currently known about the relationship between genetic variation, biomolecular function, and disease.

Flower-mediated plant-butterfly interactions in an heterogeneous tropical coastal ecosystem

BACKGROUND

Interspecific interactions play an important role in determining species richness and persistence in a given locality. However at some sites, the studies, especially for interaction networks on adult butterflies are scarce. The present study aimed the following objectives: (1) determine butterfly species richness and diversity that visit flowering plants, (2) compare species richness and diversity in butterfly-plant interactions among six different vegetation types and (3) analyze the structure of butterfly-flowering plant interaction networks mediated by flowers.

METHODS

The study was developed in six vegetation types within the natural reserve of La Mancha, located in Veracruz, Mexico. In each vegetation type, we recorded the frequency of flower visits by butterflies monthly in round plots (of radius 5 m) for 12 months. We calculated Shannon diversity for butterfly species and diversity of interactions per vegetation type. We determined the classic Jaccard similarity index among vegetation types and estimated parameters at network and species-level.

RESULTS

We found 123 species of butterflies belonging to 11 families and 87 genera. The highest number of species belonged to Hesperiidae (46 species), followed by Nymphalidae (28) and Pieridae (14). The highest butterfly diversity and interaction diversity was observed in pioneer dune vegetation (PDV), coastal dune scrub (CDS) and tropical deciduous flooding forest and wetland (TDF-W). The same order of vegetation types was found for interaction diversity. Highest species similarity was found between PDV-CDS and PDV-TDF. The butterfly-plant interaction network showed a nested structure with one module. The species Ascia monuste, Euptoieta hegesia and Leptotes cassius were the most generalist in the network, while Horama oedippus, E. hegesia, and L. cassius were the species with highest dependencies per plant species.

DISCUSSION

Our study is important because it constitutes a pioneer study of butterfly-plant interactions in this protected area, at least for adult butterflies; it shows the diversity of interactions among flowering plants and butterflies. Our research constitutes the first approach (at a community level) to explore the functional role of pollination services that butterflies provide to plant communities. We highlighted that open areas show a higher diversity and these areas shared a higher number of species that shaded sites. In the interaction networks parameters, our results highlighted the higher dependence of butterflies by the flowers on which they feed than vice versa. In conclusion, the plant species (as a feeding resource) seem to limit the presence of butterfly species. Thus, this protected area is highly relevant for Lepidoptera diversity and the interaction between these insects and flowering plants. We suggest that studying plant and butterfly diversity in tropical habitats will provide insight into their interspecific interactions and community structure.

Consensus dynamics in online collaboration systems

BACKGROUND

In this paper, we study the process of opinion dynamics and consensus building in online collaboration systems, in which users interact with each other following their common interests and their social profiles. Specifically, we are interested in how users similarity and their social status in the community, as well as the interplay of those two factors, influence the process of consensus dynamics.

METHODS

For our study, we simulate the diffusion of opinions in collaboration systems using the well-known Naming Game model, which we extend by incorporating an interaction mechanism based on user similarity and user social status. We conduct our experiments on collaborative datasets extracted from the Web.

RESULTS

Our findings reveal that when users are guided by their similarity to other users, the process of consensus building in online collaboration systems is delayed. A suitable increase of influence of user social status on their actions can in turn facilitate this process.

CONCLUSIONS

In summary, our results suggest that achieving an optimal consensus building process in collaboration systems requires an appropriate balance between those two factors.

Proteomic Profiling of Integrin Adhesion Complex Assembly

Cell adhesion to components of the cellular microenvironment via cell-surface adhesion receptors controls many aspects of cell behavior in a range of physiological and pathological processes. Multimolecular complexes of scaffolding and signaling proteins are recruited to the intracellular domains of adhesion receptors such as integrins, and these adhesion complexes tether the cytoskeleton to the plasma membrane and compartmentalize cellular signaling events. Integrin adhesion complexes are highly dynamic, and their assembly is tightly regulated. Comprehensive, unbiased, quantitative analyses of the composition of different adhesion complexes over the course of their formation will enable better understanding of how the dynamics of adhesion protein recruitment influence the functions of adhesion complexes in fundamental cellular processes. Here, a pipeline is detailed integrating biochemical isolation of integrin adhesion complexes during a time course, quantitative proteomic analysis of isolated adhesion complexes, and computational analysis of temporal proteomic data. This approach enables the characterization of adhesion complex composition and dynamics during complex assembly.

Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools

Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks.

Predicting drug-drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge

Drug–drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our assumption was that a query drug (Dq) and a drug to be examined (De) likely have DDI if the drugs in the interaction network of De are structurally similar to Dq. A network of De describes the associations between the drugs and the proteins relating to PK and PD for De. These include target proteins, proteins interacting with target proteins, enzymes, and transporters for De. We constructed logistic regression models for DDI prediction using only 2D structural similarities between each Dq and the drugs in the network of De. The results indicated that our models could effectively predict DDIs. It was found that integrating structural similarity scores of the drugs relating to both PK and PD of De was crucial for model performance. In particular, the combination of the target- and enzyme-related scores provided the largest increase of the predictive power.Graphical abstract

Identifying Gene Interaction Networks

In this chapter, we introduce interaction networks by describing how they are generated, where they are stored, and how they are shared. We focus on publicly available interaction networks and describe a simple way of utilizing these resources. This chapter features two case studies, both of which utilize Cytoscape, an open source and user-friendly network visualization and analysis tool. In the first example, we demonstrate the basic functionalities of Cytoscape by building an interaction network from a publicly available database, analyzing its topological features, and performing gene ontology enrichment. For the second section, we constructed a network from scratch starting with an experimental gene expression dataset. From there, we implement more advanced visual annotations of the network and perform subnetwork enrichment. The methods described are applicable to larger networks that can be collected from various resources.

Profiling Phosphopeptide-Binding Domain Recognition Specificity Using Peptide Microarrays

Cellular organization and response to internal and external stimuli are mediated by an intricate web of protein interactions. Some of these interactions are regulated by covalent posttranslational modifications such as phosphorylation and acetylation. These modifications can change the chemical nature of the interaction interfaces and modulate the binding affinity of the interacting partners. In signal transduction, the most frequent modification is reversible phosphorylation of tyrosine, serine or threonine residues. Protein phosphorylation may modulate the activity of enzymes by modifying their conformation, or regulate the formation of complexes by creating docking sites to recruit downstream effectors. Families of modular domains, such as SH2, PTB, and 14-3-3, act as "readers" of the modification event. Specificity between closely related domains of the same family is mediated by the chemical properties of the domain binding surface that, aside from offering a hydrophilic pocket for the phosphorylated residue, shows preference for specific sequences. Although the protein structure and the cell context are also important to ensure specificity, the amino acid sequence flanking the phosphorylation site defines the accuracy of the recognition process, and it is therefore essential to define the binding specificity of phosphopeptide binding domains in order to understand and to infer the interaction web mediated by phosphopeptides. Methods commonly used to discover new interactions (such as yeast two hybrid and phage display) are not suited to study interactions with phosphorylated proteins. On the other hand, peptide arrays are a powerful approach to precisely identify the binding preference of phosphopeptide recognition domains. Here we describe a detailed protocol to assemble arrays of hundreds to thousands phospho-peptides and to screen them with any modular domain of interest.

Clustering and Network Analysis of Reverse Phase Protein Array Data

Molecular profiling of proteins and phosphoproteins using a reverse phase protein array (RPPA) platform, with a panel of target-specific antibodies, enables the parallel, quantitative proteomic analysis of many biological samples in a microarray format. Hence, RPPA analysis can generate a high volume of multidimensional data that must be effectively interrogated and interpreted. A range of computational techniques for data mining can be applied to detect and explore data structure and to form functional predictions from large datasets. Here, two approaches for the computational analysis of RPPA data are detailed: the identification of similar patterns of protein expression by hierarchical cluster analysis and the modeling of protein interactions and signaling relationships by network analysis. The protocols use freely available, cross-platform software, are easy to implement, and do not require any programming expertise. Serving as data-driven starting points for further in-depth analysis, validation, and biological experimentation, these and related bioinformatic approaches can accelerate the functional interpretation of RPPA data.

Glycosaminoglycanomics: where we are

Glycosaminoglycans regulate numerous physiopathological processes such as development, angiogenesis, innate immunity, cancer and neurodegenerative diseases. Cell surface GAGs are involved in cell-cell and cell-matrix interactions, cell adhesion and signaling, and host-pathogen interactions. GAGs contribute to the assembly of the extracellular matrix and heparan sulfate chains are able to sequester growth factors in the ECM. Their biological activities are regulated by their interactions with proteins. The structural heterogeneity of GAGs, mostly due to chemical modifications occurring during and after their synthesis, makes the development of analytical techniques for their profiling in cells, tissues, and biological fluids, and of computational tools for mining GAG-protein interaction data very challenging. We give here an overview of the experimental approaches used in glycosaminoglycomics, of the major GAG-protein interactomes characterized so far, and of the computational tools and databases available to analyze and store GAG structures and interactions.

Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes

Background

Alzheimer’s disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer’s is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer’s towards development of effective AD therapeutics.

Results

In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings.

Conclusions

To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3108-1) contains supplementary material, which is available to authorized users.

The probability of speciation on an interaction network with unequal substitution rates

Speciation is characterized by the development of reproductive isolating barriers between diverging groups. A seminal paper of a mathematical model of speciation was published by Orr (1995), extended by Livingstone et al. (2012) to incorporate interaction networks. Here, we further develop the model to take into account the possibility of different substitution rates for network nodes of different connectivity. Mathematically, this amounts to sampling nodes from an undirected graph where the inclusion probability for a given node depends on its degree (number of connecting edges). We establish formulas for the rate of speciation and identify a crucial parameter that is a measure of the deviation from simple random sampling.

Global investigation of composition and interaction networks in gut microbiomes of individuals belonging to diverse geographies and age-groups

BACKGROUND

Factors like ethnicity, diet and age of an individual have been hypothesized to play a role in determining the makeup of gut microbiome. In order to investigate the gut microbiome structure as well as the inter-microbial associations present therein, we have performed a comprehensive global comparative profiling of the structure (composition, relative heterogeneity and diversity) and the inter-microbial networks in the gut microbiomes of 399 individuals of eight different nationalities.

RESULTS

The study identified certain geography-specific trends with respect to composition, intra-group heterogeneity and diversity of the gut microbiomes. Interestingly, the gut microbial association/mutual-exlusion networks were observed to exhibit several cross-geography trends. It was seen that though the composition of gut microbiomes of the American and European individuals were similar, there were distinct patterns in their microbial interaction networks. Amongst European gut-microbiomes, the co-occurrence network obtained for the Danish population was observed to be most dense. Distinct patterns were also observed within Chinese, Japanese and Indian datasets. While performing an age-wise comparison, it was observed that the microbial interactions increased with the age of individuals. Furthermore, certain bacterial groups were identified to be present only in the older age groups.

CONCLUSIONS

The trends observed in gut microbial networks could be due to the inherent differences in the diet of individuals belonging to different nationalities. For example, the higher number of microbial associations in the Danish population as compared to the Spanish population, may be attributed to the evenly distributed diet of the later. This is in line with previously reported findings which indicate an increase in functional interdependency of microbes in individuals with higher nutritional status. To summarise, the present study identifies geography and age specific patterns in the composition as well as microbial interactions in gut microbiomes.

Towards a functional hypothesis relating anti-islet cell autoimmunity to the dietary impact on microbial communities and butyrate production

BACKGROUND

The development of anti-islet cell autoimmunity precedes clinical type 1 diabetes and occurs very early in life. During this early period, dietary factors strongly impact on the composition of the gut microbiome. At the same time, the gut microbiome plays a central role in the development of the infant immune system. A functional model of the association between diet, microbial communities, and the development of anti-islet cell autoimmunity can provide important new insights regarding the role of the gut microbiome in the pathogenesis of type 1 diabetes.

RESULTS

A novel approach was developed to enable the analysis of the microbiome on an aggregation level between a single microbial taxon and classical ecological measures analyzing the whole microbial population. Microbial co-occurrence networks were estimated at age 6 months to identify candidates for functional microbial communities prior to islet autoantibody development. Stratification of children based on these communities revealed functional associations between diet, gut microbiome, and islet autoantibody development. Two communities were strongly associated with breast-feeding and solid food introduction, respectively. The third community revealed a subgroup of children that was dominated by Bacteroides abundances compared to two subgroups with low Bacteroides and increased Akkermansia abundances. The Bacteroides-dominated subgroup was characterized by early introduction of non-milk diet, increased risk for early autoantibody development, and by lower abundances of genes for the production of butyrate via co-fermentation of acetate. By combining our results with information from the literature, we provide a refined functional hypothesis for a protective role of butyrate in the pathogenesis of type 1 diabetes.

CONCLUSIONS

Based on functional traits of microbial communities estimated from co-occurrence networks, we provide evidence that alterations in the composition of mucin degrading bacteria associate with early development of anti-islet cell autoimmunity. We hypothesize that lower levels of Bacteroides in favor of increased levels of Akkermansia lead to a competitive advantage of acetogens compared to sulfate reducing bacteria, resulting in increased butyrate production via co-fermentation of acetate. This hypothesis suggests that butyrate has a protective effect on the development of anti-islet cell autoantibodies.

Analysis of differentially expressed genes between endometrial carcinosarcomas and endometrioid endometrial carcinoma by bioinformatics

PURPOSE

This study aimed to explore the underlying molecular mechanisms of endometrial carcinosarcomas (ECS) and endometrioid endometrial carcinoma (EEC) by bioinformatics analysis.

METHODS

Gene expression profile GSE33723 was downloaded from the Gene Expression Omnibus. A total of 15 ECS and 23 EEC samples were used to identify the differentially expressed genes (DEGs) by significance analysis of microarrays. After construction of protein-protein interaction (PPI) network, Gene Ontology (GO) functional and pathway enrichment analyses of DEGs were performed, followed by network module analysis.

RESULTS

A total of 49 DEGs were identified between EEC and ECS samples. In the PPI network, TP53 (tumor protein p53) was selected as the highest degree, hub centrality and betweenness. The top 10 enriched GO terms including regulation of cell death and top 10 significant pathways including cell cycle were selected. After network module analysis, PIK3R1 (phosphoinositide-3-kinase, regulatory subunit 1) and AKT2 (v-akt murine thymoma viral oncogene homolog 2) were selected as the co-expressed genes in the states of ECS while STAT3 (signal transducer and activator of transcription 3) and JAZF (JAZF zinc finger 1) were selected as the co-expressed genes in the states of EEC.

CONCLUSIONS

The DEGs, such as TP53, PIK3R1 and AKT2 may be used for targeted diagnosis and treatment of ECS while STAT3 and JAZF1 may be served as a target for EEC.

FunRich: An open access standalone functional enrichment and interaction network analysis tool

As high-throughput techniques including proteomics become more accessible to individual laboratories, there is an urgent need for a user-friendly bioinformatics analysis system. Here, we describe FunRich, an open access, standalone functional enrichment and network analysis tool. FunRich is designed to be used by biologists with minimal or no support from computational and database experts. Using FunRich, users can perform functional enrichment analysis on background databases that are integrated from heterogeneous genomic and proteomic resources (>1.5 million annotations). Besides default human specific FunRich database, users can download data from the UniProt database, which currently supports 20 different taxonomies against which enrichment analysis can be performed. Moreover, the users can build their own custom databases and perform the enrichment analysis irrespective of organism. In addition to proteomics datasets, the custom database allows for the tool to be used for genomics, lipidomics and metabolomics datasets. Thus, FunRich allows for complete database customization and thereby permits for the tool to be exploited as a skeleton for enrichment analysis irrespective of the data type or organism used. FunRich (http://www.funrich.org) is user-friendly and provides graphical representation (Venn, pie charts, bar graphs, column, heatmap and doughnuts) of the data with customizable font, scale and color (publication quality).

Structural network analysis of biological networks for assessment of potential disease model organisms

Model organisms provide opportunities to design research experiments focused on disease-related processes (e.g., using genetically engineered populations that produce phenotypes of interest). For some diseases, there may be non-obvious model organisms that can help in the study of underlying disease factors. In this study, an approach is presented that leverages knowledge about human diseases and associated biological interactions networks to identify potential model organisms for a given disease category. The approach starts with the identification of functional and interaction patterns of diseases within genetic pathways. Next, these characteristic patterns are matched to interaction networks of candidate model organisms to identify similar subsystems that have characteristic patterns for diseases of interest. The quality of a candidate model organism is then determined by the degree to which the identified subsystems match genetic pathways from validated knowledge. The results of this study suggest that non-obvious model organisms may be identified through the proposed approach.