Effect of Fungicide Treatment on Multi-Mycotoxin Occurrence in French Wheat during a 4-Year Period.

Wheat represents one of the most widely consumed cereals worldwide. Cultivated in winter and spring, it is vulnerable to an array of different pathogens, including fungi, which are managed largely through the in-field application of fungicides. During this study, a 4-year field investigation (2018-2021) was performed in France, aiming to assess the efficacy of fungicide treatment to reduce mycotoxin contamination in common and durum wheat. Several different commercially available fungicides were applied via sprayers. Concentrations of mycotoxins and fungal metabolites in wheat were determined using a multi-analyte liquid-chromatography-tandem-mass-spectrometry-based method. The highest contamination levels and strongest effects of fungicides were observed in 2018, followed by 2021. A significant fungicide-mediated reduction was observed for the trichothecenes deoxynivalenol, deoxynivalenol-3-glucoside, nivalenol, and nivalenol-3-glucoside. Furthermore, fungicide treatment also reduced levels of culmorin and its hydroxy metabolites 5- and 15-hydroxy-culmorin, as well as aurofusarin. Interestingly, the Alternaria metabolite infectopyron was increased following fungicide treatment. In conclusion, fungicide treatment was effective in reducing mycotoxin levels in wheat. However, as complete prevention of mycotoxin contamination was not achieved, fungicide treatment should always be combined with other pre- and post-harvest mycotoxin mitigation strategies to improve food and feed safety.

TNFR2 is critical for TNF-induced rheumatoid arthritis fibroblast-like synoviocyte inflammation.

OBJECTIVES: TNF-induced activation of fibroblast-like synoviocytes (FLS) is a critical determinant for synovial inflammation and joint destruction in RA. The detrimental role of TNF-receptor 1 (TNFR1) has thoroughly been characterized. The contributions of TNFR2, however, are largely unknown. This study was performed to delineate the role of TNFR2 in human FLS activation. METHODS: TNFR2 expression in synovial tissue samples was determined by immunohistochemistry. Expression of TNFR2 was silenced using RNAi or CRISPR/Cas9 technologies. Global transcriptional changes were determined by RNA-seq. QPCR, ELISA and immunoblotting were used to validate RNA-seq results and to uncover pathways operating downstream of TNFR2 in FLS. RESULTS: TNFR2 expression was increased in RA when compared with OA synovial tissues. In particular, RA-FLS demonstrated higher levels of TNFR2 when compared with OA-FLS. TNFR2 expression in RA-FLS correlated with RA disease activity, synovial T- and B-cell infiltration. TNF and IL1ß were identified as inflammatory mediators that upregulate TNFR2 in RA-FLS. Silencing of TNFR2 in RA-FLS markedly diminished the TNF-induced expression of inflammatory cytokines and chemokines, including CXCR3-binding chemokines and the B-cell activating factor TNFSF13B. Immunobiochemical analyses revealed that TNFR2-mediated expression of inflammatory mediators critically depends on STAT1. CONCLUSION: Our results define a critical role for TNFR2 in FLS-driven inflammation and unfold its participation in the unresolved course of synovial inflammation in RA.

Trajectory clusters of radiographic progression in patients with rheumatoid arthritis: associations with clinical variables.

OBJECTIVES: Identification of trajectories of radiographic damage in rheumatoid arthritis (RA) by clustering patients according to the shape of their curve of Sharp-van der Heijde scores (SHSs) over time. Developing models to predict their progression cluster from baseline characteristics. METHODS: Patient-level data over a 2-year period from five large randomised controlled trials on tumour necrosis factor inhibitors in RA were used. SHSs were clustered in a shape-respecting manner to identify distinct clusters of radiographic progression. Characteristics of patients within different progression clusters were compared at baseline and over time. Logistic regression models were developed to predict trajectory of radiographic progression using information at baseline. RESULTS: In total, 1887 patients with 7738 X-rays were used for cluster analyses. We identified four distinct clusters with characteristic shapes of radiographic progression: one with a stable SHS over the whole 2-year period (C0/lowChange; 86%); one with relentless progression (C1/rise; 5.8%); one with decreasing SHS (C2/improvement; 6.9%); one going up and down (C3/bothWays; 1.4%) of the SHS. Robustness of clusters were confirmed using different clustering methods. Regression models identified disease duration, baseline C-reactive protein (CRP) and SHS and treatment status as predictors for cluster assignment. CONCLUSIONS: We were able to identify and partly characterise four different clusters of radiographic progression over time in patients with RA, most remarkably one with relentless progression and another one with amelioration of joint damage over time, suggesting the existence of distinct patterns of joint damage accrual in RA.

Histone deacetylase 1 (HDAC1): A key player of T cell-mediated arthritis.

Rheumatoid Arthritis (RA) represents a chronic T cell-mediated inflammatory autoimmune disease. Studies have shown that epigenetic mechanisms contribute to the pathogenesis of RA. Histone deacetylases (HDACs) represent one important group of epigenetic regulators. However, the role of individual HDAC members for the pathogenesis of arthritis is still unknown. In this study we demonstrate that mice with a T cell-specific deletion of HDAC1 (HDAC1-cKO) are resistant to the development of Collagen-induced arthritis (CIA), whereas the antibody response to collagen type II was undisturbed, indicating an unaltered T cell-mediated B cell activation. The inflammatory cytokines IL-17 and IL-6 were significantly decreased in sera of HDAC1-cKO mice. IL-6 treated HDAC1-deficient CD4+ T cells showed an impaired upregulation of CCR6. Selective inhibition of class I HDACs with the HDAC inhibitor MS-275 under Th17-skewing conditions inhibited the upregulation of chemokine receptor 6 (CCR6) in mouse and human CD4+ T cells. Accordingly, analysis of human RNA-sequencing (RNA-seq) data and histological analysis of synovial tissue samples from human RA patients revealed the existence of CD4+CCR6+ cells with enhanced HDAC1 expression. Our data indicate a key role for HDAC1 for the pathogenesis of CIA and suggest that HDAC1 and other class I HDACs might be promising targets of selective HDAC inhibitors (HDACi) for the treatment of RA.

Regional and gender differences in population-based oral health insurance data.

OBJECTIVE: Early dental monitoring contributes substantially to good oral health in children. However, little is known on whether children from different geographical regions and gender are equally reached with current preventive and curative oral health strategies. The aim of our study therefore was to explore regional and gender differences in a population-based oral health dataset of Austrian children up to the age of 14. MATERIALS AND METHODS: We extracted the first electronically available health insurance data of children aged up to 14 years on dental services within a 4-year observation period in Austria and performed a separate analysis in up to 6-year-old children. In addition, we used a smaller randomly selected sample dataset of 3000 children as the large numbers would result in significant, but very small effects. RESULTS: In a total of 130,895 children, of whom 77,173 children (59%) were up to the age of six, we detected an east-west gradient: The eastern regions of Austria showed an older age at first contact and a higher number of dental services. A child aged up to 6 years who needed more than four dental services had a likelihood of 40% to be from Vienna, Austria's capital city located in the east. The smaller random sample did not show significant gender differences. CONCLUSIONS: Even in regions with a high density of dentists, such as Vienna, we obviously did not reach young children in the same extent as in other regions. CLINICAL RELEVANCE: Stratified interventions could be developed to overcome regional inequalities.

IRF1 is critical for the TNF-driven interferon response in rheumatoid fibroblast-like synoviocytes : JAKinibs suppress the interferon response in RA-FLSs.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by persistent synovial inflammation. The major drivers of synovial inflammation are cytokines and chemokines. Among these molecules, TNF activates fibroblast-like synoviocytes (FLSs), which leads to the production of inflammatory mediators. Here, we show that TNF regulates the expression of the transcription factor interferon regulatory factor 1 (IRF1) in human FLSs as well as in a TNF transgenic arthritis mouse model. Transcriptomic analyses of IRF1-deficient, TNF-stimulated FLSs define the interferon (IFN) pathway as a major target of IRF1. IRF1 expression is associated with the expression of IFNß, which leads to the activation of the JAK-STAT pathway. Blocking the JAK-STAT pathway with the Janus kinase inhibitor (JAKinib) baricitinib or tofacitinib reduces the expression of IFN-regulated genes (IRGs) in TNF-activated FLSs. Therefore, we conclude that TNF induces a distinct inflammatory cascade, in which IRGs are key elements, in FLSs. The IFN-signature might be a promising biomarker for the efficient and personalized use of new treatment strategies for RA, such as JAKinibs.

Analysis of gene expression in rheumatoid arthritis and related conditions offers insights into sex-bias, gene biotypes and co-expression patterns.

The era of next-generation sequencing has mounted the foundation of many gene expression studies. In rheumatoid arthritis research, this has led to the discovery of important candidate genes which offered novel insights into mechanisms and their possible roles in the cure of the disease. In the last years, data generation has outstripped data analysis and while many studies focused on specific aspects of the disease, a global picture of the disease is not yet accomplished. Here, we analyzed and compared a collection of gene expression information from healthy individuals and from patients suffering under different arthritis conditions from published studies containing the following clinical conditions: early and established rheumatoid arthritis, osteoarthritis and arthralgia. We show comprehensive overviews of this data collection and give new insights specifically on gene expression in the early stage, into sex-dependent gene expression, and we describe general differences in expression of different biotypes of genes. Many genes that are related to cytoskeleton changes (actin filament related genes) are differently expressed in early rheumatoid arthritis in comparison to healthy subjects; interestingly, eight of these genes reverse their expression ratio significantly between men and women compared early rheumatoid arthritis and healthy subjects. There are some slighter changes between men and woman between the conditions early and established rheumatoid arthritis. Another aspect are miRNAs and other gene biotypes which are not only promising candidates for diagnoses but also change their expression grossly in average at rheumatoid arthritis and arthralgia compared to the healthy condition. With a selection of intersecting genes, we were able to generate simple classification models to distinguish between healthy and rheumatoid arthritis as well as between early rheumatoid arthritis to other arthritides based on gene expression.

FOXO3 is involved in the tumor necrosis factor-driven inflammatory response in fibroblast-like synoviocytes.

Fibroblast-like synoviocytes (FLS) are major contributors to joint inflammation in rheumatoid arthritis (RA). Forkhead box O 3 (FOXO3) perturbations in immune cells are increasingly linked to RA pathogenesis. Here, we show that FOXO3 is distinctly inactivated/phosphorylated in the FLS of rheumatoid synovitis. In vitro, stimulation of FLS with tumor necrosis factor-alpha α (TNFα) induced a rapid and sustained inactivation of FOXO3. mRNA profiling revealed that the inactivation of FOXO3 is important for the sustained pro-inflammatory interferon response to TNFα (CXCL9, CXCL10, CXCL11, and TNFSF18). Mechanistically, our studies demonstrate that the inactivation of FOXO3 results from TNF-induced downregulation of phosphoinositide-3-kinase-interacting protein 1 (PIK3IP1). Thus, we identified FOXO3 and its modulator PIK3IP1 as a critical regulatory circuit for the inflammatory response of the resident mesenchymal cells to TNFα and contribute insight into how the synovial tissue brings about chronic inflammation that is driven by TNFα.

Eco-Metabolomics and Metabolic Modeling: Making the Leap From Model Systems in the Lab to Native Populations in the Field.

Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.

BioSankey: Visualization of Microbial Communities Over Time.

Metagenomics provides quantitative measurements for microbial species over time. To obtain a global overview of an experiment and to explore the full potential of a given dataset, intuitive and interactive visualization tools are needed. Therefore, we established BioSankey to visualize microbial species in microbiome studies over time as a Sankey diagram. These diagrams are embedded into a project-specific webpage which depends only on JavaScript and Google API to allow searches of interesting species without requiring a web server or connection to a database. BioSankey is a valuable tool to visualize different data elements from single or dual RNA-seq datasets and additionally enables a straightforward exchange of results among collaboration partners.

Determination of Autoantibody Isotypes Increases the Sensitivity of Serodiagnostics in Rheumatoid Arthritis.

Anti-citrullinated protein antibodies (ACPA) and rheumatoid factor (RF) are the most commonly used diagnostic markers of rheumatoid arthritis (RA). These antibodies are predominantly of the immunoglobulin (Ig) M (RF) or IgG (ACPA) isotype. Other subtypes of both antibodies-particularly IgA isotypes and other autoantibodies-such as RA33 antibodies-have been repeatedly reported but their diagnostic value has still not been fully elucidated. Here, we investigated the prevalence of IgA, IgG, and IgM subtypes of RF, ACPA, and RA33 antibodies in patients with RA. To determine the diagnostic specificity and sensitivity sera from 290 RA patients (165 early and 125 established disease), 261 disease controls and 100 healthy subjects were tested for the presence of IgA, IgG, and IgM isotypes of RF, ACPA, and RA33 by EliA™ platform (Phadia AB, Uppsala, Sweden). The most specific antibodies were IgG-ACPA, IgA-ACPA, and IgG-RF showing specificities >98%, closely followed by IgG- and IgA-RA33 while IgM subtypes were somewhat less specific, ranging from 95.8% (RA33) to 90% (RF). On the other hand, IgM-RF was the most sensitive subtype (65%) followed by IgG-ACPA (59.5%) and IgA-RF (50.7%). Other subtypes were less sensitive ranging from 35 (IgA-ACPA) to 6% (IgA-RA33). RA33 antibodies as well as IgA-RF and IgA-ACPA were found to increase the diagnostic sensitivity of serological testing since they were detected also in seronegative patients reducing their number from 109 to 85. Moreover, analyzing IgM-RF by EliA™ proved more sensitive than measuring RF by nephelometry and further reduced the number of seronegative patients to 76 individuals. Importantly, among antibody positive individuals, RA patients were found having significantly more antibodies (≥3) than disease controls which generally showed one or two antibody species. Thus, increasing the number of autoantibodies in serological routine testing provides valuable additional information allowing to better distinguish between RA and other rheumatic disorders, also in patients not showing antibodies in current routine diagnostics. In conclusion, testing for multiple autoantibody specificities increases the diagnostic power of autoimmune diagnostics and could further support physicians in clinical decision-making.

Patterns of Polymorphism at the Self-Incompatibility Locus in 1,083 Arabidopsis thaliana Genomes.

Although the transition to selfing in the model plant Arabidopsis thaliana involved the loss of the self-incompatibility (SI) system, it clearly did not occur due to the fixation of a single inactivating mutation at the locus determining the specificities of SI (the S-locus). At least three groups of divergent haplotypes (haplogroups), corresponding to ancient functional S-alleles, have been maintained at this locus, and extensive functional studies have shown that all three carry distinct inactivating mutations. However, the historical process of loss of SI is not well understood, in particular its relation with the last glaciation. Here, we took advantage of recently published genomic resequencing data in 1,083 Arabidopsis thaliana accessions that we combined with BAC sequencing to obtain polymorphism information for the whole S-locus region at a species-wide scale. The accessions differed by several major rearrangements including large deletions and interhaplogroup recombinations, forming a set of haplogroups that are widely distributed throughout the native range and largely overlap geographically. "Relict" A. thaliana accessions that directly derive from glacial refugia are polymorphic at the S-locus, suggesting that the three haplogroups were already present when glacial refugia from the last Ice Age became isolated. Interhaplogroup recombinant haplotypes were highly frequent, and detailed analysis of recombination breakpoints suggested multiple independent origins. These findings suggest that the complete loss of SI in A. thaliana involved independent self-compatible mutants that arose prior to the last Ice Age, and experienced further rearrangements during postglacial colonization.

Germline replications and somatic mutation accumulation are independent of vegetative life span in Arabidopsis.

In plants, gametogenesis occurs late in development, and somatic mutations can therefore be transmitted to the next generation. Longer periods of growth are believed to result in an increase in the number of cell divisions before gametogenesis, with a concomitant increase in mutations arising due to replication errors. However, there is little experimental evidence addressing how many cell divisions occur before gametogenesis. Here, we measured loss of telomeric DNA and accumulation of replication errors in Arabidopsis with short and long life spans to determine the number of replications in lineages leading to gametes. Surprisingly, the number of cell divisions within the gamete lineage is nearly independent of both life span and vegetative growth. One consequence of the relatively stable number of replications per generation is that older plants may not pass along more somatically acquired mutations to their offspring. We confirmed this hypothesis by genomic sequencing of progeny from young and old plants. This independence can be achieved by hierarchical arrangement of cell divisions in plant meristems where vegetative growth is primarily accomplished by expansion of cells in rapidly dividing meristematic zones, which are only rarely refreshed by occasional divisions of more quiescent cells. We support this model by 5-ethynyl-2'-deoxyuridine retention experiments in shoot and root apical meristems. These results suggest that stem-cell organization has independently evolved in plants and animals to minimize mutations by limiting DNA replication.

ConsPred: a rule-based (re-)annotation framework for prokaryotic genomes.

MOTIVATION: The rapidly growing number of available prokaryotic genome sequences requires fully automated and high-quality software solutions for their initial and re-annotation. Here we present ConsPred, a prokaryotic genome annotation framework that performs intrinsic gene predictions, homology searches, predictions of non-coding genes as well as CRISPR repeats and integrates all evidence into a consensus annotation. ConsPred achieves comprehensive, high-quality annotations based on rules and priorities, similar to decision-making in manual curation and avoids conflicting predictions. Parameters controlling the annotation process are configurable by the user. ConsPred has been used in the institutions of the authors for longer than 5 years and can easily be extended and adapted to specific needs. SUMMARY: The ConsPred algorithm for producing a consensus from the varying scores of multiple gene prediction programs approaches manual curation in accuracy. Its rule-based approach for choosing final predictions avoids overriding previous manual curations. AVAILABILITY AND IMPLEMENTATION: ConsPred is implemented in Java, Perl and Shell and is freely available under the Creative Commons license as a stand-alone in-house pipeline or as an Amazon Machine Image for cloud computing, see https://sourceforge.net/projects/conspred/. CONTACT: thomas.rattei@univie.ac.atSupplementary information: Supplementary data are available at Bioinformatics online.

EffectiveDB--updates and novel features for a better annotation of bacterial secreted proteins and Type III, IV, VI secretion systems.

Protein secretion systems play a key role in the interaction of bacteria and hosts. EffectiveDB (http://effectivedb.org) contains pre-calculated predictions of bacterial secreted proteins and of intact secretion systems. Here we describe a major update of the database, which was previously featured in the NAR Database Issue. EffectiveDB bundles various tools to recognize Type III secretion signals, conserved binding sites of Type III chaperones, Type IV secretion peptides, eukaryotic-like domains and subcellular targeting signals in the host. Beyond the analysis of arbitrary protein sequence collections, the new release of EffectiveDB also provides a 'genome-mode', in which protein sequences from nearly complete genomes or metagenomic bins can be screened for the presence of three important secretion systems (Type III, IV, VI). EffectiveDB contains pre-calculated predictions for currently 1677 bacterial genomes from the EggNOG 4.0 database and for additional bacterial genomes from NCBI RefSeq. The new, user-friendly and informative web portal offers a submission tool for running the EffectiveDB prediction tools on user-provided data.

Massive genomic variation and strong selection in Arabidopsis thaliana lines from Sweden.

Despite advances in sequencing, the goal of obtaining a comprehensive view of genetic variation in populations is still far from reached. We sequenced 180 lines of A. thaliana from Sweden to obtain as complete a picture as possible of variation in a single region. Whereas simple polymorphisms in the unique portion of the genome are readily identified, other polymorphisms are not. The massive variation in genome size identified by flow cytometry seems largely to be due to 45S rDNA copy number variation, with lines from northern Sweden having particularly large numbers of copies. Strong selection is evident in the form of long-range linkage disequilibrium (LD), as well as in LD between nearby compensatory mutations. Many footprints of selective sweeps were found in lines from northern Sweden, and a massive global sweep was shown to have involved a 700-kb transposition.

Visualization of SNPs with t-SNE.

BACKGROUND: Single Nucleotide Polymorphisms (SNPs) are one of the largest sources of new data in biology. In most papers, SNPs between individuals are visualized with Principal Component Analysis (PCA), an older method for this purpose. PRINCIPAL FINDINGS: We compare PCA, an aging method for this purpose, with a newer method, t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of large SNP datasets. We also propose a set of key figures for evaluating these visualizations; in all of these t-SNE performs better. SIGNIFICANCE: To transform data PCA remains a reasonably good method, but for visualization it should be replaced by a method from the subfield of dimension reduction. To evaluate the performance of visualization, we propose key figures of cross-validation with machine learning methods, as well as indices of cluster validity.

TE-Locate: A Tool to Locate and Group Transposable Element Occurrences Using Paired-End Next-Generation Sequencing Data.

Transposable elements (TEs) are common mobile DNA elements present in nearly all genomes. Since the movement of TEs within a genome can sometimes have phenotypic consequences, an accurate report of TE actions is desirable. To this end, we developed TE-Locate, a computational tool that uses paired-end reads to identify the novel locations of known TEs. TE-Locate can utilize either a database of TE sequences, or annotated TEs within the reference sequence of interest. This makes TE-Locate useful in the search for any mobile sequence, including retrotransposed gene copies. One major concern is to act on the correct hierarchy level, thereby avoiding an incorrect calling of a single insertion as multiple events of TEs with high sequence similarity. We used the (super)family level, but TE-Locate can also use any other level, right down to the individual transposable element. As an example of analysis with TE-Locate, we used the Swedish population in the 1,001 Arabidopsis genomes project, and presented the biological insights gained from the novel TEs, inducing the association between different TE superfamilies. The program is freely available, and the URL is provided in the end of the paper.

Metabolic profiling reveals key metabolic features of renal cell carcinoma.

Recent evidence suggests that metabolic changes play a pivotal role in the biology of cancer and in particular renal cell carcinoma (RCC). Here, a global metabolite profiling approach was applied to characterize the metabolite pool of RCC and normal renal tissue. Advanced decision tree models were applied to characterize the metabolic signature of RCC and to explore features of metastasized tumours. The findings were validated in a second independent dataset. Vitamin E derivates and metabolites of glucose, fatty acid, and inositol phosphate metabolism determined the metabolic profile of RCC. α-tocopherol, hippuric acid, myoinositol, fructose-1-phosphate and glucose-1-phosphate contributed most to the tumour/normal discrimination and all showed pronounced concentration changes in RCC. The identified metabolic profile was characterized by a low recognition error of only 5% for tumour versus normal samples. Data on metastasized tumours suggested a key role for metabolic pathways involving arachidonic acid, free fatty acids, proline, uracil and the tricarboxylic acid cycle. These results illustrate the potential of mass spectroscopy based metabolomics in conjunction with sophisticated data analysis methods to uncover the metabolic phenotype of cancer. Differentially regulated metabolites, such as vitamin E compounds, hippuric acid and myoinositol, provide leads for the characterization of novel pathways in RCC.

Characterization of protein-interaction networks in tumors.

BACKGROUND: Analyzing differential-gene-expression data in the context of protein-interaction networks (PINs) yields information on the functional cellular status. PINs can be formally represented as graphs, and approximating PINs as undirected graphs allows the network properties to be characterized using well-established graph measures. This paper outlines features of PINs derived from 29 studies on differential gene expression in cancer. For each study the number of differentially regulated genes was determined and used as a basis for PIN construction utilizing the Online Predicted Human Interaction Database. RESULTS: Graph measures calculated for the largest subgraph of a PIN for a given differential-gene-expression data set comprised properties reflecting the size, distribution, biological relevance, density, modularity, and cycles. The values of a distinct set of graph measures, namely Closeness Centrality, Graph Diameter, Index of Aggregation, Assortative Mixing Coefficient, Connectivity, Sum of the Wiener Number, modified Vertex Distance Number, and Eigenvalues differed clearly between PINs derived on the basis of differential gene expression data sets characterizing malignant tissue and PINs derived on the basis of randomly selected protein lists. CONCLUSION: Cancer PINs representing differentially regulated genes are larger than those of randomly selected protein lists, indicating functional dependencies among protein lists that can be identified on the basis of transcriptomics experiments. However, the prevalence of hub proteins was not increased in the presence of cancer. Interpretation of such graphs in the context of robustness may yield novel therapies based on synthetic lethality that are more effective than focusing on single-action drugs for cancer treatment.