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Disease gene prediction is to date one of the main computational challenges of precision medicine. It is still uncertain if disease genes have unique functional properties that distinguish them from other non-disease genes or, from a network perspective, if they are located randomly in the interactome or show specific patterns in the network topology. In this study, we propose a new method for disease gene prediction based on the use of biological knowledge-bases (gene-disease associations, genes functional annotations, etc.) and interactome network topology. The proposed algorithm called MOSES is based on the definition of two somewhat opposing sets of genes both disease-specific from different perspectives: warm seeds (i.e., disease genes obtained from databases) and cold seeds (genes far from the disease genes on the interactome and not involved in their biological functions). The application of MOSES to a set of 40 diseases showed that the suggested putative disease genes are significantly enriched in their reference disease. Reassuringly, known and predicted disease genes together, tend to form a connected network module on the human interactome, mitigating the scattered distribution of disease genes which is probably due to both the paucity of disease-gene associations and the incompleteness of the interactome.

Purpose: Several studies have shown that different tumour types sharing a driver gene mutation do not respond uniformly to the same targeted agent. Our aim was to use an unbiased network-based approach to investigate this fundamental issue using BRAF mutant tumours and the BRAF inhibitor vemurafenib. Methods: We applied SWIM, a software able to identify putative regulatory (switch) genes involved in drastic changes to the cell phenotype, to gene expression profiles of different BRAF mutant cancers and their normal counterparts in order to identify the switch genes that could potentially explain the heterogeneity of these tumours' responses to vemurafenib. Results: We identified lung adenocarcinoma as the tumour with the highest number of switch genes (298) compared to its normal counterpart. By looking for switch genes encoding for kinases with homology sequences similar to known vemurafenib targets, we found that thyroid cancer and lung adenocarcinoma have a similar number of putative targetable switch gene kinases (5 and 6, respectively) whereas colorectal cancer has just one. Conclusions: We are persuaded that our network analysis may aid in the comprehension of molecular mechanisms underlying the different responses to vemurafenib in BRAF mutant tumours.

Cancer stem-like cells (CSCs) have self-renewal abilities responsible for cancer progression, therapy resistance, and metastatic growth. The glioblastoma stem-like cells are the most studied among CSC populations. A recent study identified four transcription factors (SOX2, SALL2, OLIG2, and POU3F2) as the minimal core sufficient to reprogram differentiated glioblastoma (GBM) cells into stem-like cells. Transcriptomic data of GBM tissues and cell lines from two different datasets were then analyzed by the SWItch Miner (SWIM), a network-based software, and FOSL1 was identified as a putative regulator of the previously identified minimal core. Herein, we selected NTERA-2 and HEK293T cells to perform an in vitro study to investigate the role of FOSL1 in the reprogramming mechanisms. We transfected the two cell lines with a constitutive FOSL1 cDNA plasmid. We demonstrated that FOSL1 directly regulates the four transcription factors binding their promoter regions, is involved in the deregulation of several stemness markers, and reduces the cells’ ability to generate aggregates increasing the extracellular matrix component FN1. Although further experiments are necessary, our data suggest that FOSL1 reprograms the stemness by regulating the core of the four transcription factors.

In this study, we integrate the outcomes of co-expression network analysis with the human interactome network to predict novel putative disease genes and modules. We first apply the SWItch Miner (SWIM) methodology, which predicts important (switch) genes within the co-expression network that regulate disease state transitions, then map them to the human protein–protein interaction network (PPI, or interactome) to predict novel disease–disease relationships (i.e., a SWIM-informed diseasome). Although the relevance of switch genes to an observed phenotype has been recently assessed, their performance at the system or network level constitutes a new, potentially fascinating territory yet to be explored. Quantifying the interplay between switch genes and human diseases in the interactome network, we found that switch genes associated with specific disorders are closer to each other than to other nodes in the network, and tend to form localized connected subnetworks. These subnetworks overlap between similar diseases and are situated in different neighborhoods for pathologically distinct phenotypes, consistent with the well-known topological proximity property of disease genes. These findings allow us to demonstrate how SWIM-based correlation network analysis can serve as a useful tool for efficient screening of potentially new disease gene associations. When integrated with an interactome-based network analysis, it not only identifies novel candidate disease genes, but also may offer testable hypotheses by which to elucidate the molecular underpinnings of human disease and reveal commonalities between seemingly unrelated diseases.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous and complex syndrome. Network-based analysis implemented by SWIM software can be exploited to identify key molecular switches - called “switch genes” - for disease. Genes contributing to common biological processes or define given cell types are frequently co-regulated and co-expressed, giving rise to expression network modules. Consistently, we found that the COPD correlation network built by SWIM consists of three well-characterized modules: one populated by switch genes, all up-regulated in COPD cases and related to the regulation of immune response, inflammatory response, and hypoxia (like TIMP1, HIF1A, SYK, LY96, BLNK and PRDX4); one populated by well-recognized immune signature genes, all up-regulated in COPD cases; one where the GWAS genes AGER and CAVIN1 are the most representative module genes, both down-regulated in COPD cases. Interestingly, 70% of AGER negative interactors are switch genes including PRDX4, whose activation strongly correlates with the activation of known COPD GWAS interactors SERPINE2, CD79A, and POUF2AF1. These results suggest that SWIM analysis can identify key network modules related to complex diseases like COPD.

MicroRNAs (miRNAs) are small noncoding RNAs (ncRNAs) involved in several biological processes and diseases. MiRNAs regulate gene expression at the posttranscriptional level, mostly downregulating their targets by binding specific regions of transcripts through imperfect sequence complementarity. Prediction of miRNA-binding sites is challenging, and target prediction algorithms are usually based on sequence complementarity. In the last years, it has been shown that by adding miRNA and protein coding gene expression, we are able to build tissue-, cell line-, or disease-specific networks improving our understanding of complex biological scenarios. In this chapter, we present an application of a recently published software named SWIM, that allows to identify key genes in a network of interactions by defining appropriate "roles" of genes according to their local/global positioning in the overall network. Furthermore, we show how the SWIM software can be used to build miRNA-disease networks, by applying the approach to tumor data obtained from The Cancer Genome Atlas (TCGA).

Broader comprehension of gene expression regulatory mechanisms can be gained from a global analysis of how transcription and degradation are coordinated to orchestrate complex cell responses. The role of messenger RNA (mRNA) turnover modulation in gene expression levels has become increasingly recognized. From such perspective, in this review we briefly illustrate how a simple but effective mathematical model of mRNA turnover and some experimental findings, may together shed light on the molecular mechanisms underpinning the major role of mRNA decay rates in shaping the kinetics of gene activation and repression.

SWItchMiner (SWIM) is a wizard-like software implementation of a procedure, previously described, able to extract information contained in complex networks. Specifically, SWIM allows unearthing the existence of a new class of hubs, called “fight-club hubs”, characterized by a marked negative correlation with their first nearest neighbors. Among them, a special subset of genes, called “switch genes”, appears to be characterized by an unusual pattern of intra- and inter-module connections that confers them a crucial topological role, interestingly mirrored by the evidence of their clinic-biological relevance. Here, we applied SWIM to a large panel of cancer datasets from The Cancer Genome Atlas, in order to highlight switch genes that could be critically associated with the drastic changes in the physiological state of cells or tissues induced by the cancer development. We discovered that switch genes are found in all cancers we studied and they encompass protein coding genes and non-coding RNAs, recovering many known key cancer players but also many new potential biomarkers not yet characterized in cancer context. Furthermore, SWIM is amenable to detect switch genes in different organisms and cell conditions, with the potential to uncover important players in biologically relevant scenarios, including but not limited to human cancer.

The codon composition of coding sequences plays an important role in the regulation of gene expression. Herein, we report systematic differences in the usage of synonymous codons among Arabidopsis thaliana genes that are expressed specifically in distinct tissues. Although we observed that both regionally and transcriptionally associated mutational biases were associated significantly with codon bias, they could not explain the observed differences fully. Similarly, given that transcript abundances did not account for the differences in codon usage, it is unlikely that selection for translational efficiency can account exclusively for the observed codon bias. Thus, we considered the possible evolution of codon bias as an adaptive response to the different abundances of tRNAs in different tissues. Our analysis demonstrated that in some cases, codon usage in genes that were expressed in a broad range of tissues was influenced primarily by the tissue in which the gene was expressed maximally. On the basis of this finding we propose that genes that are expressed in certain tissues might show a tissue-specific compositional signature in relation to codon usage. These findings might have implications for the design of transgenes in relation to optimizing their expression.

We developed a genome-wide transcriptomic atlas of grapevine (Vitis vinifera) based on 54 samples representing green and woody tissues and organs at different developmental stages as well as specialized tissues such as pollen and senescent leaves. Together, these samples expressed ∼91% of the predicted grapevine genes. Pollen and senescent leaves had unique transcriptomes reflecting their specialized functions and physiological status. However, microarray and RNA-seq analysis grouped all the other samples into two major classes based on maturity rather than organ identity, namely, the vegetative/green and mature/woody categories. This division represents a fundamental transcriptomic reprogramming during the maturation process and was highlighted by three statistical approaches identifying the transcriptional relationships among samples (correlation analysis), putative biomarkers (O2PLS-DA approach), and sets of strongly and consistently expressed genes that define groups (topics) of similar samples (biclustering analysis). Gene coexpression analysis indicated that the mature/woody developmental program results from the reiterative coactivation of pathways that are largely inactive in vegetative/green tissues, often involving the coregulation of clusters of neighboring genes and global regulation based on codon preference. This global transcriptomic reprogramming during maturation has not been observed in herbaceous annual species and may be a defining characteristic of perennial woody plants.

Self-sustained oscillations are perhaps the most studied objects in science. The accomplishment of such a task reliably and accurately requires the presence of specific control mechanisms to face the presence of variable and largely unpredictable environmental stimuli and noise. Self-sustained oscillations of transcript abundance are, in fact, widespread and are not limited to the reproductive cycle but are also observed during circadian rhythms, metabolic cycles, developmental cycles and so on. To date, much of the literature has focused on the transcriptional machinery underlying control of the basic timing of transcript abundance. However, mRNA abundance is known to be regulated at the post-transcriptional level also and the relative contribution of the two mechanisms to gene-expression programmes is currently a major challenge in molecular biology. Here, we review recent results showing the relevance of the post-transcriptional regulation layer and present a statistical reanalysis of the yeast metabolic cycle using publicly available gene-expression and RNA-binding data. Taken together, the recent theoretical and experimental developments reviewed and the results of our reanalysis strongly indicate that regulation of mRNA stability is a widespread, phase-specific and finely tuned mechanism for the multi-layer control of gene expression needed to achieve high flexibility and adaptability to external and internal signals.

Background miRNAs are the most abundant class of small non-coding RNAs, and they are involved in post-transcriptional regulations, playing a crucial role in the refinement of genetic programming during plant development. Here we present a comprehensive picture of miRNA regulation in Vitis vinifera L. plant during its complete life cycle. Furthering our knowledge about the post-transcriptional regulation of plant development is fundamental to understand the biology of such an important crop. Results We analyzed 70 small RNA libraries, prepared from berries, inflorescences, tendrils, buds, carpels, stamens and other samples at different developmental stages. One-hundred and ten known and 175 novel miRNAs have been identified and a wide grapevine expression atlas has been described. The distribution of miRNA abundance reveals that 22 novel miRNAs are specific to stamen, and two of them are, interestingly, involved in ethylene biosynthesis, while only few miRNAs are highly specific to other organs. Thirty-eight miRNAs are present in all our samples, suggesting a role in key regulatory circuit. On the basis of miRNAs abundance and distribution across samples and on the estimated correlation, we suggest that miRNA expression define organ identity. We performed target prediction analysis and focused on miRNA expression analysis in berries and inflorescence during their development, providing an initial functional description of the identified miRNAs. Conclusions Our findings represent a very extensive miRNA expression atlas in grapevine, allowing the definition of how the spatio-temporal distribution of miRNAs defines organ identity. We describe miRNAs abundance in specific tissues not previously described in grapevine and contribute to future targeted functional analyses. Finally, we present a deep characterization of miRNA involvement in berry and inflorescence development, suggesting a role for miRNA-driven hormonal regulation. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1610-5) contains supplementary material, which is available to authorized users.

Background Non-coding RNAs (ncRNAs) are emerging as key regulators of many cellular processes in both physiological and pathological states. Moreover, the constant discovery of new non-coding RNA species suggests that the study of their complex functions is still in its very early stages. This variegated class of RNA species encompasses the well-known microRNAs (miRNAs) and the most recently acknowledged long non-coding RNAs (lncRNAs). Interestingly, in the last couple of years, a few studies have shown that some lncRNAs can act as miRNA sponges, i.e. as competing endogenous RNAs (ceRNAs), able to reduce the amount of miRNAs available to target messenger RNAs (mRNAs). Results We propose a computational approach to explore the ability of lncRNAs to act as ceRNAs by protecting mRNAs from miRNA repression. A seed match analysis was performed to validate the underlying regression model. We built normal and cancer networks of miRNA-mediated sponge interactions (MMI-networks) using breast cancer expression data provided by The Cancer Genome Atlas. Conclusions Our study highlights a marked rewiring in the ceRNA program between normal and pathological breast tissue, documented by its “on/off” switch from normal to cancer, and vice-versa. This mutually exclusive activation confers an interesting character to ceRNAs as potential oncosuppressive, or oncogenic, protagonists in cancer. At the heart of this phenomenon is the lncRNA PVT1, as illustrated by both the width of its antagonist mRNAs in normal-MMI-network, and the relevance of the latter in breast cancer. Interestingly, PVT1 revealed a net binding preference towards the mir-200 family as the bone of contention with its rival mRNAs.

Phenotypic plasticity refers to the range of phenotypes a single genotype can express as a function of its environment. These phenotypic variations are attributable to the effect of the environment on the expression and function of genes influencing plastic traits. We investigated phenotypic plasticity in grapevine by comparing the berry transcriptome in a single clone of the vegetatively-propagated common grapevine species Vitis vinifera cultivar Corvina through 3 consecutive growth years cultivated in 11 different vineyards in the Verona area of Italy. Most of the berry transcriptome clustered by year of growth rather than common environmental conditions or viticulture practices, and transcripts related to secondary metabolism showed high sensitivity towards different climates, as confirmed also by metabolomic data obtained from the same samples. When analyzed in 11 vineyards during 1 growth year, the environmentally-sensitive berry transcriptome comprised 5% of protein-coding genes and 18% of the transcripts modulated during berry development. Plastic genes were particularly enriched in ontology categories such as transcription factors, translation, transport, and secondary metabolism. Specific plastic transcripts were associated with groups of vineyards sharing common viticulture practices or environmental conditions, and plastic transcriptome reprogramming was more intense in the year characterized by extreme weather conditions. We also identified a set of genes that lacked plasticity, showing either constitutive expression or similar modulation in all berries. Our data reveal candidate genes potentially responsible for the phenotypic plasticity of grapevine and provide the first step towards the characterization of grapevine transcriptome plasticity under different agricultural systems.

The transcriptome in a cell is finely regulated by a large number of molecular mechanisms able to control the balance between mRNA production and degradation. Recent experimental findings have evidenced that fine and specific regulation of degradation is needed for proper orchestration of a global cell response to environmental conditions. We developed a computational technique based on stochastic modeling, to infer condition-specific individual mRNA half-lives directly from gene expression time-courses. Predictions from our method were validated by experimentally measured mRNA decay rates during the intraerythrocytic developmental cycle of Plasmodium falciparum. We then applied our methodology to publicly available data on the reproductive and metabolic cycle of budding yeast. Strikingly, our analysis revealed, in all cases, the presence of periodic changes in decay rates of sequentially induced genes and co-ordination strategies between transcription and degradation, thus suggesting a general principle for the proper coordination of transcription and degradation machinery in response to internal and/or external stimuli., Author Summary The amount of a given transcript in a cell is determined by a fine tuned balance of production and degradation in a complex regulatory network. Regulation of transcription controls when transcription occurs and how much mRNA is created, whereas regulation of degradation controls the rate at which messengers are destroyed. The latter mechanism has recently gained attention due to the increasing evidence of its key role in the overall co-ordination of gene expression. A long lifetime of mRNA enables a cell to produce more proteins from that mRNA. By contrast, a short lifetime rapidly alters protein levels in response to changing needs. Measuring mRNA stability is a complex and expensive experiment and, given the condition-specific response of the degradation pathway, it would be desirable to take advantage of the large variety of expression experiments stored in public databases. To this end, we developed a stochastic model to infer each specific mRNA lifetime from gene expression data. Predictions were validated using malaria data. We then applied our methodology to the reproductive and metabolic cycle of budding yeast and found, in all cases, the presence of a general principle for the proper coordination of transcription and degradation machinery.

International audience; The assumption that cells are temporally organized systems, i.e. showing relevant dynamics of their state variables such as gene expression or protein and metabolite concentration, while tacitly given for granted at the molecular level, is not explicitly taken into account when interpreting biological experimental data. This conundrum stems from the (undemonstrated) assumption that a cell culture, the actual object of biological experimentation, is a population of billions of independent oscillators (cells) randomly experiencing different phases of their cycles and thus not producing relevant coordinated dynamics at the population level. Moreover the fact of considering reproductive cycle as by far the most important cyclic process in a cell resulted in lower attention given to other rhythmic processes. Here we demonstrate that growing yeast cells show a very repeatable and robust cyclic variation of the concentration of proteins with different cellular functions. We also report experimental evidence that the mechanism governing this basic oscillator and the cellular entrainment is resistant to external chemical constraints. Finally, cell growth is accompanied by cyclic dynamics of medium pH. These cycles are observed in batch cultures, different from the usual continuous cultures in which yeast metabolic cycles are known to occur, and suggest the existence of basic, spontaneous, collective and synchronous behaviors of the cell population as a whole.

The composition of culture media and the microbial cell mass influence on secondary metabolites production in fermentative procedures. The effect of two process variables, namely glucose and inoculum concentrations on biomass formation, bioactivity and surface tension of extract obtained during the cultivation of endophytic Paenibacillus polymyxa RNC-D was evaluated. Assays were performed using glucose and inoculum concentrations varying from 5.0 to 40.0 g.l −1 and from 2.5% to 5.0% (v/v), respectively, according to a 2 2 full factorial design. The microorganism was cultivated in orbital shaker (30 ◦ C, 180 rpm) for 96 h. Cell growth was estimated by optical density (600 nm) vs dry weight calibration curve. Bioassays were performed using two-fold serially diluted extract displayed in 96-well plates. Escherichia coli ATCC 25923 and Staphylococcus aureus ATCC 25922 were used as indicator strains. The minimal inhibitory concentration (MIC) was expressed in g.ml −1 . Surface tension (mN.m −1 ) of extracts was measured using a tensiometer. A significant (p

Background In yeast, genome-wide periodic patterns associated with energy-metabolic oscillations have been shown recently for both short (approx. 40 min) and long (approx. 300 min) periods. Results The dynamical regulation due to mRNA stability is found to be an important aspect of the genome-wide coordination of the long-period yeast metabolic cycle. It is shown that for periodic genes, arranged in classes according either to expression profile or to function, the pulses of mRNA abundance have phase and width which are directly proportional to the corresponding turnover rates. Conclusion The cascade of events occurring during the yeast metabolic cycle (and their correlation with mRNA turnover) reflects to a large extent the gene expression program observable in other dynamical contexts such as the response to stresses/stimuli.

Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R2 that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R2 for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R2 value among genes coding for a transient complex., Author Summary Microarrays provide snapshots of the transcriptional state of the cell at some point in time. Multiple snapshots can be taken sequentially in time, thus providing insight into the dynamics of change. Since genome-wide expression data report on the abundance of mRNA, not on the underlying activity of genes, we developed a novel method to relate the expression pattern of genes, detected in a time-series experiment, using a similarity measure that incorporates mRNA decay and called lead-lag R2. We used the lead-lag R2 similarity measure to predict the presence of common transcription factors between gene pairs using an integrated dataset consisting of 13 yeast cell-cycles. The method was benchmarked against six well-established similarity measures and obtained the best true positive rate result, around 95%. We believe that the lead-lag analysis can be successfully used also to predict the presence of a common mechanism able to modulate the degradation rate of specific transcripts. Finally, we envisage the possibility to extend our analysis to different experimental conditions and organisms, thus providing a simple off-the-shelf computational tool to support the understanding of the transcriptional and post-transcriptional regulation layer and its role in many diseases, such as cancer.

The topological bases of essentiality in the yeast metabolic network from the perspective of double mutations are the subject of this study. A strong relationship between essentiality and the ‘missing alternative’ topological property is shown in terms of the presence of multiple genes synthesizing the same enzyme, supplementary enzymes participating in the same metabolic reaction, and availability of other pathways in the graph connecting the separated nodes after the knockouts.We demonstrate that the ‘missing alternative’ paradigm is sufficient to explain the generation of essentiality for double mutations in which each single deleted element is non-essential.

The ergodic hypothesis, which assumes the independence of each cell of the ensemble from all the others, is a necessary prerequisite to attach single cell based explanations to the grand averages taken from population data. This was the prevailing view about the interpretation of cellular biology experiments that typically are performed on colonies of billions of cells. By analysing gene expression data of different cells going from yeast to mammalian cell cultures, we demonstrate that cell cultures display a sort of "ecology-in-a-plate" giving rise to a rich dynamics of gene expression that are independent from reproductive cycles, hence contradicting simple ergodic assumptions The aspecific character of the observed coordinated gene expression activity inhibits any simple mechanistic hypothesis and highlights the need to consider population effects in the interpretation of data coming from cell cultures.

Gene expression is to a large extent controlled at the level of mRNA accumulation. Genes whose products function together are likely under a common regulatory system ( e. g. signal transduction pathways, sets of regulatory proteins) such that they are expressed in a coordinated manner. This property has been frequently used in the analysis of genome-wide expression data, as the experimental observation that a group of genes is co-expressed frequently implies that the genes share a common regulatory mechanism. The authors have investigated the situation in which dissimilarity in gene-expression time profiles may still result from the presence of the same regulatory signal, as in the case of common transcription factors. To this aim, a dynamic model that takes into account the effect of specific mRNA degradation on the shape of gene-expression time series has been developed, and the concept of 'dynamically co-regulated' genes has accordingly been introduced as the goodness-of-fit to such a model ( called dynamic R 2). The statistical analysis of dynamic R 2 over a number of different experimental data sets and organisms shows that the presence of dynamically co-regulated genes is by far more significant than that expected from the randomised data. Furthermore, as an example of the usefulness of the proposed method, genome-wide yeast measurements such as cell-cycle time series and transcription factors targets data, were used to prove that dynamic co-regulation is statistically related to the presence of common transcription factor(s). This latter property is very useful when trying to infer computational indications of co-regulation for not-yet annotated genes that do not display a co-expression pattern.