Systems-level Approach Research Articles

Identification of key regulators of pancreatic cancer progression through multidimensional systems-level analysis.

BackgroundPancreatic cancer is an aggressive cancer with dismal prognosis, urgently necessitating better biomarkers to improve therapeutic options and early diagnosis. Traditional approaches of biomarker detection that consider only one aspect of the biological continuum like gene expression alone are limited in their scope and lack robustness in identifying the key regulators of the disease. We have adopted a multidimensional approach involving the cross-talk between the omics spaces to identify key regulators of disease progression.MethodsMultidimensional domain-specific disease signatures were obtained using rank-based meta-analysis of individual omics profiles (mRNA, miRNA, DNA methylation) related to pancreatic ductal adenocarcinoma (PDAC). These domain-specific PDAC signatures were integrated to identify genes that were affected across multiple dimensions of omics space in PDAC (genes under multiple regulatory controls, GMCs). To further pin down the regulators of PDAC pathophysiology, a systems-level network was generated from knowledge-based interaction information applied to the above identified GMCs. Key regulators were identified from the GMC network based on network statistics and their functional importance was validated using gene set enrichment analysis and survival analysis.ResultsRank-based meta-analysis identified 5391 genes, 109 miRNAs and 2081 methylation-sites significantly differentially expressed in PDAC (false discovery rate ≤ 0.05). Bimodal integration of meta-analysis signatures revealed 1150 and 715 genes regulated by miRNAs and methylation, respectively. Further analysis identified 189 altered genes that are commonly regulated by miRNA and methylation, hence considered GMCs. Systems-level analysis of the scale-free GMCs network identified eight potential key regulator hubs, namely E2F3, HMGA2, RASA1, IRS1, NUAK1, ACTN1, SKI and DLL1, associated with important pathways driving cancer progression. Survival analysis on individual key regulators revealed that higher expression of IRS1 and DLL1 and lower expression of HMGA2, ACTN1 and SKI were associated with better survival probabilities.ConclusionsIt is evident from the results that our hierarchical systems-level multidimensional analysis approach has been successful in isolating the converging regulatory modules and associated key regulatory molecules that are potential biomarkers for pancreatic cancer progression.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0282-3) contains supplementary material, which is available to authorized users.

Post-transcriptional and translational regulation modulates gene co-expression behavior in more synchronized pace to carry out molecular function in the cell

MotivationBiological processes involve much complex interplay between cellular molecules at different molecular levels, and this interplay may exhibit various co-expression patterns explicitly representing the cellular inner regulation mechanism. Whereas, coexpression patterns cannot be necessarily conserved across the different molecular levels for complex regulation processes involved even after transcripts being produced. Investigation of co-expression propagation from transcript level to protein level will reflect inner regulation effects in function states of cells. ResultsIn this study, we perform a comparative analysis of gene coexpression patterns in Plasmodium falciparum. We investigate coexpression patterns propagation from transcript level to protein level to reveal the underlying biological meaning of post-transcriptional and translational mechanism. Our systems-level approach shows after posttranscriptional and translational regulation gene co-expression pace at protein level is mechanistically adjusted to higher synchronicity. Moreover, co-expression patterns at protein level are more linked to function categories, such as co-expression at the same time point is more related with binding categories, and co-expression delayed by several time points is more related with activity categories. Therefore, posttranscriptional and translational regulation modulates co-expression relationships between molecules for meeting the function demands.

Systems biology of lens development: A paradigm for disease gene discovery in the eye

Over the past several decades, the biology of the developing lens has been investigated using molecular genetics-based approaches in various vertebrate model systems. These efforts, involving target gene knockouts or knockdowns, have led to major advances in our understanding of lens morphogenesis and the pathological basis of cataracts, as well as of other lens related eye defects. In particular, we now have a functional understanding of regulators such as Pax6, Six3, Sox2, Oct1 (Pou2f1), Meis1, Pnox1, Zeb2 (Sip1), Mab21l1, Foxe3, Tfap2a (Ap2-alpha), Pitx3, Sox11, Prox1, Sox1, c-Maf, Mafg, Mafk, Hsf4, Fgfrs, Bmp7, and Tdrd7 in this tissue. However, whether these individual regulators interact or their targets overlap, and the significance of such interactions during lens morphogenesis, is not well defined. The arrival of high-throughput approaches for gene expression profiling (microarrays, RNA-sequencing (RNA-seq), etc.), which can be coupled with chromatin immunoprecipitation (ChIP) or RNA immunoprecipitation (RIP) assays, along with improved computational resources and publically available datasets (e.g. those containing comprehensive protein-protein, protein-DNA information), presents new opportunities to advance our understanding of the lens tissue on a global systems level. Such systems-level knowledge will lead to the derivation of the underlying lens gene regulatory network (GRN), defined as a circuit map of the regulator-target interactions functional in lens development, which can be applied to expedite cataract gene discovery. In this review, we cover the various systems-level approaches such as microarrays, RNA-seq, and ChIP that are already being applied to lens studies and discuss strategies for assembling and interpreting these vast amounts of high-throughput information for effective dispersion to the scientific community. In particular, we discuss strategies for effective interpretation of this new information in the context of the rich knowledge obtained through the application of traditional single-gene focused experiments on the lens. Finally, we discuss our vision for integrating these diverse high-throughput datasets in a single web-based user-friendly tool iSyTE (integrated Systems Tool for Eye gene discovery) – a resource that is already proving effective in the identification and characterization of genes linked to lens development and cataract. We anticipate that application of a similar approach to other ocular tissues such as the retina and the cornea, and even other organ systems, will significantly impact disease gene discovery.

D-Map: Random Walking on Gene Network Inference Maps Towards differential Avenue Discovery.

Differential rewiring of cellular interaction networks between disease and healthy state is of great importance. Through a systems level approach, malfunctioned mechanisms that are absent in the normal cases, may enlighten the key-players in terms of genes and their interaction chains related to disease. We have developed D-Map, a publicly available user-friendly web application, capable of generating and manipulating advanced differential networks by combining state-of-the-art inference reconstruction methods with random walk simulations. The inputs are expression profiles obtained from the Gene Expression Omnibus and a gene list under investigation. Differential networks may be visualized and interpreted through the use of D-Map interface, where display of the disease, the normal and the common state can be performed, interactively. A case study scenario concerning Alzheimer's disease, as well as breast, lung, and bladder cancer was conducted in order to demonstrate the usefulness of the proposed methodology to different disease types. Findings were consistent with the current bibliography, and the provided interaction lists may be further explored towards novel biological insights of the investigated diseases. The DMap web-application is available at: http://bioserver-3.bioacademy.gr/Bioserver/DMap/index.php.

Early-branching gut fungi possess a large, comprehensive array of biomass-degrading enzymes.

The fungal kingdom is the source of almost all industrial enzymes in use for lignocellulose bioprocessing. We developed a systems-level approach that integrates transcriptomic sequencing, proteomics, phenotype, and biochemical studies of relatively unexplored basal fungi. Anaerobic gut fungi isolated from herbivores produce a large array of biomass-degrading enzymes that synergistically degrade crude, untreated plant biomass and are competitive with optimized commercial preparations from Aspergillus and Trichoderma. Compared to these model platforms, gut fungal enzymes are unbiased in substrate preference due to a wealth of xylan-degrading enzymes. These enzymes are universally catabolite-repressed and are further regulated by a rich landscape of noncoding regulatory RNAs. Additionally, we identified several promising sequence-divergent enzyme candidates for lignocellulosic bioprocessing.

Quantitative Profiling of Protein Tyrosine Kinases in Human Cancer Cell Lines by Multiplexed Parallel Reaction Monitoring Assays

Protein tyrosine kinases (PTKs) play key roles in cellular signal transduction, cell cycle regulation, cell division, and cell differentiation. Dysregulation of PTK-activated pathways, often by receptor overexpression, gene amplification, or genetic mutation, is a causal factor underlying numerous cancers. In this study, we have developed a parallel reaction monitoring-based assay for quantitative profiling of 83 PTKs. The assay detects 308 proteotypic peptides from 54 receptor tyrosine kinases and 29 nonreceptor tyrosine kinases in a single run. Quantitative comparisons were based on the labeled reference peptide method. We implemented the assay in four cell models: 1) a comparison of proliferating versus epidermal growth factor-stimulated A431 cells, 2) a comparison of SW480Null (mutant APC) and SW480APC (APC restored) colon tumor cell lines, and 3) a comparison of 10 colorectal cancer cell lines with different genomic abnormalities, and 4) lung cancer cell lines with either susceptibility (11-18) or acquired resistance (11-18R) to the epidermal growth factor receptor tyrosine kinase inhibitor erlotinib. We observed distinct PTK expression changes that were induced by stimuli, genomic features or drug resistance, which were consistent with previous reports. However, most of the measured expression differences were novel observations. For example, acquired resistance to erlotinib in the 11-18 cell model was associated not only with previously reported up-regulation of MET, but also with up-regulation of FLK2 and down-regulation of LYN and PTK7. Immunoblot analyses and shotgun proteomics data were highly consistent with parallel reaction monitoring data. Multiplexed parallel reaction monitoring assays provide a targeted, systems-level profiling approach to evaluate cancer-related proteotypes and adaptations. Data are available through Proteome eXchange Accession PXD002706.

Mining integrated semantic networks for drug repositioning opportunities.

Current research and development approaches to drug discovery have become less fruitful and more costly. One alternative paradigm is that of drug repositioning. Many marketed examples of repositioned drugs have been identified through serendipitous or rational observations, highlighting the need for more systematic methodologies to tackle the problem. Systems level approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but requires an integrative approach to biological data. Integrated networks can facilitate systems level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person is able to identify portions of the graph (semantic subgraphs) that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated approaches are required to systematically mine integrated networks for these subgraphs and bring them to the attention of the user. We introduce a formal framework for the definition of integrated networks and their associated semantic subgraphs for drug interaction analysis and describe DReSMin, an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. We demonstrate the utility of our approach by mining an integrated drug interaction network built from 11 sources. This work identified and ranked 9,643,061 putative drug-target interactions, showing a strong correlation between highly scored associations and those supported by literature. We discuss the 20 top ranked associations in more detail, of which 14 are novel and 6 are supported by the literature. We also show that our approach better prioritizes known drug-target interactions, than other state-of-the art approaches for predicting such interactions.

Oncogenic KRAS activates an embryonic stem cell-like program in human colon cancer initiation

Colorectal cancer is the third most frequently diagnosed cancer worldwide. Prevention of colorectal cancer initiation represents the most effective overall strategy to reduce its associated morbidity and mortality. Activating KRAS mutation (KRASmut) is the most prevalent oncogenic driver in colorectal cancer development, and KRASmut inhibition represents an unmet clinical need. We apply a systems-level approach to study the impact of KRASmut on stem cell signaling during human colon cancer initiation by performing gene set enrichment analysis on gene expression from human colon tissues. We find that KRASmut imposes the embryonic stem cell-like program during human colon cancer initiation from colon adenoma to stage I carcinoma. Expression of miR145, an embryonic SC program inhibitor, promotes cell lineage differentiation marker expression in KRASmut colon cancer cells and significantly suppresses their tumorigenicity. Our data support an in vivo plasticity model of human colon cancer initiation that merges the intrinsic stem cell properties of aberrant colon stem cells with the embryonic stem cell-like program induced by KRASmut to optimize malignant transformation. Inhibition of the embryonic SC-like program in KRASmut colon cancer cells reveals a novel therapeutic strategy to programmatically inhibit KRASmut tumors and prevent colon cancer.

Low carbon fuels and commodity chemicals from waste gases – systematic approach to understand energy metabolism in a model acetogen

Insight into energy metabolism of gas-fermenting acetogens using a systems level approach for sustainable production of fuels and chemicals.

A Systems Level Approach for Identification of Molecular Targets for Antimicrobial Intervention against Pseudomonas Aeruginosa, while Predicting Biofilm Formation

In this case study, we aimed at evaluating the suitability of genome-scale metabolic models to identify molecular targets that can potentially enhance antimicrobial effects of chemical preservatives against P. aeruginosa, while minimizing biofilm formation. For the case study, isothiazolinones were selected as a group of microbicides where their mechanism of action is well described in scientific literature. Target identification was carried out in several steps. First, we developed a computational model of P. aeruginosa metabolism under action of isothiazolinones. Action of sub-inhibitory concentrations of isothiazolinones was simulated based on extensive information on their mechanisms of action. Then, simulations of single and double gene deletion(s) were performed in silico to identify genes or combinations of genes that could be targeted to induce further reduction of bacterial growth rate. Finally, we assessed whether total or partial inhibition of these genes might activate biofilm formation.

Systems Biology Approaches for Understanding Genome Architecture.

The linear and three-dimensional arrangement and composition of chromatin in eukaryotic genomes underlies the mechanisms directing gene regulation. Understanding this organization requires the integration of many data types and experimental results. Here we describe the approach of integrating genome-wide protein-DNA binding data to determine chromatin states. To investigate spatial aspects of genome organization, we present a detailed description of how to run stochastic simulations of protein movements within a simulated nucleus in 3D. This systems level approach enables the development of novel questions aimed at understanding the basic mechanisms that regulate genome dynamics.

Correlation between Obesity, Diet, Physical Activity and Sedentary Behaviour among School-aged Children and Young Adolescents in Macedonia

Diet and physical activity education in school-aged children and young adolescents are in the focus of the modern healthcare and education system. The aims of our cross-section study were to estimate the prevalence of obesity in youth, and the significant correlation between nutrition status, health behavioural and biological markers. The sample size was 400 randomly selected school-aged children and adolescent from 14 to 18 years old. They were divided into two population groups of school-aged children (12-13 years old) and young adolescent (17-18 years). Standardized, non-quantitative Food Frequency Questionnaire (FFQ) and the International Questionnaire for assessment of Physical Activity (IPAQ-short version) were used as well as the anthropometric measurements: body weight and height, body mass index (BMI kg/m2) presented in percentile using the WHO Child Growth Standards. Statistical analysis was included descriptive statistics, Pearson Chi-square test and Logistic regression analysis using SPSS Statistics 17.0. In accordance with the distribution of Pearson Chi-square=16.94 (p<0.001), there was statistical significance between nutritional status of study participants in both, first and second study groups. Also, there was significant difference in high intensity sports activity between two groups (Pearson Chi-square=26.59; p<0.001). Sedentary behaviour had the highest influence on development of childhood obesity (Wald=2.81; p=0.09). Significant correlation was estimated between obesity and sweetness food (pasta, rise and potatoes), soft drinks, but drinking water had a protective effect. Prevention and treatment of pediatric overweight and obesity require systems-level approaches that include the skills of registered dietitians, sports coaches as well as consistent and integrated messages and environmental support across all sectors of society, especially educational sector. The next step is to develop an innovative “Skills for health” model including nutrition and physical activity education program in Macedonia.

Lysine Fermentation: History and Genome Breeding.

Lysine fermentation by Corynebacterium glutamicum was developed in 1958 by Kyowa Hakko Kogyo Co. Ltd. (current Kyowa Hakko Bio Co. Ltd.) and is the second oldest amino acid fermentation process after glutamate fermentation. The fundamental mechanism of lysine production, discovered in the early stages of the process's history, gave birth to the concept known as "metabolic regulatory fermentation," which is now widely applied to metabolite production. After the development of rational metabolic engineering, research on lysine production first highlighted the need for engineering of the central metabolism from the viewpoints of precursor supply and NADPH regeneration. Furthermore, the existence of active export systems for amino acids was first demonstrated for lysine in C. glutamicum, and this discovery has resulted in the current recognition of such exporters as an important consideration in metabolite production. Lysine fermentation is also notable as the first process to which genomics was successfully applied to improve amino acid production. The first global "genome breeding" strategy was developed using a lysine producer as a model; this has since led to new lysine producers that are more efficient than classical industrial producers. These advances in strain development technology, combined with recent systems-level approaches, have almost achieved the optimization of entire cellular systems as cell factories for lysine production. In parallel, the continuous improvement of the process has resulted not only in fermentation processes with reduced load on downstream processing but also in commercialization of various product forms according to their intended uses. Nowadays lysine fermentation underpins a giant lysine demand of more than 2 million metric tons per year.

Third-Kind Encounters in Biomedicine: Immunology Meets Mathematics and Informatics to Become Quantitative and Predictive.

The understanding of the immune response is right now at the center of biomedical research. There are growing expectations that immune-based interventions will in the midterm provide new, personalized, and targeted therapeutic options for many severe and highly prevalent diseases, from aggressive cancers to infectious and autoimmune diseases. To this end, immunology should surpass its current descriptive and phenomenological nature, and become quantitative, and thereby predictive.Immunology is an ideal field for deploying the tools, methodologies, and philosophy of systems biology, an approach that combines quantitative experimental data, computational biology, and mathematical modeling. This is because, from an organism-wide perspective, the immunity is a biological system of systems, a paradigmatic instance of a multi-scale system. At the molecular scale, the critical phenotypic responses of immune cells are governed by large biochemical networks, enriched in nested regulatory motifs such as feedback and feedforward loops. This network complexity confers them the ability of highly nonlinear behavior, including remarkable examples of homeostasis, ultra-sensitivity, hysteresis, and bistability. Moving from the cellular level, different immune cell populations communicate with each other by direct physical contact or receiving and secreting signaling molecules such as cytokines. Moreover, the interaction of the immune system with its potential targets (e.g., pathogens or tumor cells) is far from simple, as it involves a number of attack and counterattack mechanisms that ultimately constitute a tightly regulated multi-feedback loop system. From a more practical perspective, this leads to the consequence that today's immunologists are facing an ever-increasing challenge of integrating massive quantities from multi-platforms.In this chapter, we support the idea that the analysis of the immune system demands the use of systems-level approaches to ensure the success in the search for more effective and personalized immune-based therapies.

Nanotechnology in agriculture: Next steps for understanding engineered nanoparticle exposure and risk

The potential uses and benefits of nanotechnology in agriculture are significant, including producing greater quantities of food with lower cost, energy, and waste. However, many questions regarding the risk of these approaches in food production remain unanswered. A robust literature assessing the toxicity of engineered nanomaterials to terrestrial\\agricultural plant species has begun to develop. However, much of this literature has focused on short term, high dose exposure scenarios often conducted in model media. Although important to determining inherent nanomaterial hazard, these studies are inadequate for assessing the actual risk posed to agricultural systems, including for sensitive receptors such as humans. Although the existing literature is somewhat contradictory, it is notable that the overall findings seem to suggest low to moderate toxicity to terrestrial plant species. However, what is now needed is a systems-level approach investigating more subtle yet potentially more significant impacts of nanomaterial exposure in agricultural systems, including the use of a range of more sensitive endpoints that can mechanistically characterize toxicity. This article will identify these and other key knowledge gaps and also highlight critical next steps for understanding the balance between nanotechnology applications and implications in agriculture and food production.

Integrative omics reveals MYCN as a global suppressor of cellular signalling and enables network-based therapeutic target discovery in neuroblastoma.

Despite intensive study, many mysteries remain about the MYCN oncogene's functions. Here we focus on MYCN's role in neuroblastoma, the most common extracranial childhood cancer. MYCN gene amplification occurs in 20% of cases, but other recurrent somatic mutations are rare. This scarcity of tractable targets has hampered efforts to develop new therapeutic options. We employed a multi-level omics approach to examine MYCN functioning and identify novel therapeutic targets for this largely un-druggable oncogene. We used systems medicine based computational network reconstruction and analysis to integrate a range of omic techniques: sequencing-based transcriptomics, genome-wide chromatin immunoprecipitation, siRNA screening and interaction proteomics, revealing that MYCN controls highly connected networks, with MYCN primarily supressing the activity of network components. MYCN's oncogenic functions are likely independent of its classical heterodimerisation partner, MAX. In particular, MYCN controls its own protein interaction network by transcriptionally regulating its binding partners.Our network-based approach identified vulnerable therapeutically targetable nodes that function as critical regulators or effectors of MYCN in neuroblastoma. These were validated by siRNA knockdown screens, functional studies and patient data. We identified β-estradiol and MAPK/ERK as having functional cross-talk with MYCN and being novel targetable vulnerabilities of MYCN-amplified neuroblastoma. These results reveal surprising differences between the functioning of endogenous, overexpressed and amplified MYCN, and rationalise how different MYCN dosages can orchestrate cell fate decisions and cancerous outcomes. Importantly, this work describes a systems-level approach to systematically uncovering network based vulnerabilities and therapeutic targets for multifactorial diseases by integrating disparate omic data types.

Immune modulators in disease: integrating knowledge from the biomedical literature and gene expression

Cytokines play a central role in both health and disease, modulating immune responses and acting as diagnostic markers and therapeutic targets. This work takes a systems-level approach for integration and examination of immune patterns, such as cytokine gene expression with information from biomedical literature, and applies it in the context of disease, with the objective of identifying potentially useful relationships and areas for future research. We present herein the integration and analysis of immune-related knowledge, namely, information derived from biomedical literature and gene expression arrays. Cytokine-disease associations were captured from over 2.4 million PubMed records, in the form of Medical Subject Headings descriptor co-occurrences, as well as from gene expression arrays. Clustering of cytokine-disease co-occurrences from biomedical literature is shown to reflect current medical knowledge as well as potentially novel relationships between diseases. A correlation analysis of cytokine gene expression in a variety of diseases revealed compelling relationships. Finally, a novel analysis comparing cytokine gene expression in different diseases to parallel associations captured from the biomedical literature was used to examine which associations are interesting for further investigation. We demonstrate the usefulness of capturing Medical Subject Headings descriptor co-occurrences from biomedical publications in the generation of valid and potentially useful hypotheses. Furthermore, integrating and comparing descriptor co-occurrences with gene expression data was shown to be useful in detecting new, potentially fruitful, and unaddressed areas of research. Using integrated large-scale data captured from the scientific literature and experimental data, a better understanding of the immune mechanisms underlying disease can be achieved and applied to research.

Interpreting Heterogeneity in Response of Cells Expressing a Fluorescent Hydrogen Peroxide Biosensor

Fluorescent, genetically encoded sensors of hydrogen peroxide have enabled visualization of perturbations to the intracellular level of this signaling molecule with subcellular and temporal resolution. Ratiometric sensors hold the additional promise of meaningful quantification of intracellular hydrogen peroxide levels as a function of time, a longstanding goal in the field of redox signaling. To date, studies that have connected the magnitudes of observed ratios with peroxide concentrations have either examined suspensions of cells or small numbers of adherent cells (∼10). In this work, we examined the response of all cells in several microscopic fields of view to an identical perturbation and observed a striking degree of heterogeneity of fluorescence ratios from individual cells. The expression level of the probe and phase within the cell cycle were each examined as potential contributors to the observed heterogeneity. Higher ratiometric responses correlated with greater expression levels of the probe and phase in the cell cycle were also shown to influence the magnitude of response. To aid in the interpretation of experimental observations, we incorporated the reaction of the reduced probe with peroxide and the reactions of the oxidized probe with glutathione and glutaredoxin into a larger kinetic model of peroxide metabolism. The predictions of the kinetic model suggest possible explanations for the experimental observations. This work highlights the importance of a systems-level approach to understanding the output of genetically encoded sensors that function via redox reactions involving thiol and disulfide groups.

High-level iron mitigates fusaricidin-induced membrane damage and reduces membrane fluidity leading to enhanced drug resistance in Bacillus subtilis.

Fusaricidins are a class of cyclic lipopeptide antibiotics that have strong antifungal activities against plant pathogenic fungi and excellent bactericidal activities against Gram-positive bacteria. The mechanism through which fusaricidin exerts its action is not yet entirely clear. To investigate the mode of action of fusaricidin, we determined the physiological and transcriptional responses of Bacillus subtilis to fusaricidin treatment by using a systems-level approach. Our data show that fusaricidin rapidly induced the expression of σ(W) regulon and caused membrane damage in B. subtilis. We further demonstrated that ferric ions play multiple roles in the action of fusaricidin on B. subtilis. Iron deprivation blocked the formation of hydroxyl radical in the cells and significantly inhibited the bactericidal activity of fusaricidin. Conversely, high levels of iron (>2 mM) repressed the expression of BkdR regulon, resulting in a smaller cellular pool of branched-chain precursors for iso- and anteiso-branched fatty acids, which in turn led to a decrease in the proportion of branched-chain fatty acids in the membrane of B. subtilis. This change in membrane composition reduced its bilayer fluidity and increased its resistance to antimicrobial agents. In conclusion, our experiments uncovered some novel interactions and a synergism between cellular iron levels and drug resistance in Gram-positive bacteria.

Gene expression profiling of the human natural killer cell response to Fc receptor activation: unique enhancement in the presence of interleukin-12.

BackgroundTraditionally, the CD56dimCD16+ subset of Natural Killer (NK) cells has been thought to mediate cellular cytotoxicity with modest cytokine secretion capacity. However, studies have suggested that this subset may exert a more diverse array of immunological functions. There exists a lack of well-developed functional models to describe the behavior of activated NK cells, and the interactions between signaling pathways that facilitate effector functions are not well understood. In the present study, a combination of genome-wide microarray analyses and systems-level bioinformatics approaches were utilized to elucidate the transcriptional landscape of NK cells activated via interactions with antibody-coated targets in the presence of interleukin-12 (IL-12).MethodsWe conducted differential gene expression analysis of CD56dimCD16+ NK cells following FcR stimulation in the presence or absence of IL-12. Next, we functionally characterized gene sets according to patterns of gene expression and validated representative genes using RT-PCR. IPA was utilized for biological pathway analysis, and an enriched network of interacting genes was generated using GeneMANIA. Furthermore, PAJEK and the HITS algorithm were employed to identify important genes in the network according to betweeness centrality, hub, and authority node metrics.ResultsAnalyses revealed that CD56dimCD16+ NK cells co-stimulated via the Fc receptor (FcR) and IL-12R led to the expression of a unique set of genes, including genes encoding cytotoxicity receptors, apoptotic proteins, intracellular signaling molecules, and cytokines that may mediate enhanced cytotoxicity and interactions with other immune cells within inflammatory tissues. Network analyses identified a novel set of connected key players, BATF, IRF4, TBX21, and IFNG, within an integrated network composed of differentially expressed genes in NK cells stimulated by various conditions (immobilized IgG, IL-12, or the combination of IgG and IL-12).ConclusionsThese results are the first to address the global mechanisms by which NK cells mediate their biological functions when encountering antibody-coated targets within inflammatory sites. Moreover, this study has identified a set of high-priority targets for subsequent investigation into strategies to combat cancer by enhancing the anti-tumor activity of CD56dimCD16+ NK cells.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-015-0142-9) contains supplementary material, which is available to authorized users.