The TBC1D31/praja2 complex controls primary ciliogenesis through PKA-directed OFD1 ubiquitylation.

The primary cilium is a microtubule-based sensory organelle that dynamically links signalling pathways to cell differentiation, growth, and development. Genetic defects of primary cilia are responsible for genetic disorders known as ciliopathies. Orofacial digital type I syndrome (OFDI) is an X-linked congenital ciliopathy caused by mutations in the OFD1 gene and characterized by malformations of the face, oral cavity, digits and, in the majority of cases, polycystic kidney disease. OFD1 plays a key role in cilium biogenesis. However, the impact of signalling pathways and the role of the ubiquitin-proteasome system (UPS) in the control of OFD1 stability remain unknown. Here, we identify a novel complex assembled at centrosomes by TBC1D31, including the E3 ubiquitin ligase praja2, protein kinase A (PKA), and OFD1. We show that TBC1D31 is essential for ciliogenesis. Mechanistically, upon G-protein-coupled receptor (GPCR)-cAMP stimulation, PKA phosphorylates OFD1 at ser735, thus promoting OFD1 proteolysis through the praja2-UPS circuitry. This pathway is essential for ciliogenesis. In addition, a non-phosphorylatable OFD1 mutant dramatically affects cilium morphology and dynamics. Consistent with a role of the TBC1D31/praja2/OFD1 axis in ciliogenesis, alteration of this molecular network impairs ciliogenesis in vivo in Medaka fish, resulting in developmental defects. Our findings reveal a multifunctional transduction unit at the centrosome that links GPCR signalling to ubiquitylation and proteolysis of the ciliopathy protein OFD1, with important implications on cilium biology and development. Derangement of this control mechanism may underpin human genetic disorders.

MOViDA: multiomics visible drug activity prediction with a biologically informed neural network model.

MOTIVATION: The process of drug development is inherently complex, marked by extended intervals from the inception of a pharmaceutical agent to its eventual launch in the market. Additionally, each phase in this process is associated with a significant failure rate, amplifying the inherent challenges of this task. Computational virtual screening powered by machine learning algorithms has emerged as a promising approach for predicting therapeutic efficacy. However, the complex relationships between the features learned by these algorithms can be challenging to decipher. RESULTS: We have engineered an artificial neural network model designed specifically for predicting drug sensitivity. This model utilizes a biologically informed visible neural network, thereby enhancing its interpretability. The trained model allows for an in-depth exploration of the biological pathways integral to prediction and the chemical attributes of drugs that impact sensitivity. Our model harnesses multiomics data derived from a different tumor tissue sources, as well as molecular descriptors that encapsulate the properties of drugs. We extended the model to predict drug synergy, resulting in favorable outcomes while retaining interpretability. Given the imbalanced nature of publicly available drug screening datasets, our model demonstrated superior performance to state-of-the-art visible machine learning algorithms. AVAILABILITY AND IMPLEMENTATION: MOViDA is implemented in Python using PyTorch library and freely available for download at https://github.com/Luigi-Ferraro/MOViDA. Training data, RIS score and drug features are archived on Zenodo https://doi.org/10.5281/zenodo.8180380.

8-oxodG accumulation within super-enhancers marks fragile CTCF-mediated chromatin loops.

8-Oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG), a major product of the DNA oxidization process, has been proposed to have an epigenetic function in gene regulation and has been associated with genome instability. NGS-based methodologies are contributing to the characterization of the 8-oxodG function in the genome. However, the 8-oxodG epigenetic role at a genomic level and the mechanisms controlling the genomic 8-oxodG accumulation/maintenance have not yet been fully characterized. In this study, we report the identification and characterization of a set of enhancer regions accumulating 8-oxodG in human epithelial cells. We found that these oxidized enhancers are mainly super-enhancers and are associated with bidirectional-transcribed enhancer RNAs and DNA Damage Response activation. Moreover, using ChIA-PET and HiC data, we identified specific CTCF-mediated chromatin loops in which the oxidized enhancer and promoter regions physically associate. Oxidized enhancers and their associated chromatin loops accumulate endogenous double-strand breaks which are in turn repaired by NHEJ pathway through a transcription-dependent mechanism. Our work suggests that 8-oxodG accumulation in enhancers-promoters pairs occurs in a transcription-dependent manner and provides novel mechanistic insights on the intrinsic fragility of chromatin loops containing oxidized enhancers-promoters interactions.

A review of COVID-19 biomarkers and drug targets: resources and tools.

The stratification of patients at risk of progression of COVID-19 and their molecular characterization is of extreme importance to optimize treatment and to identify therapeutic options. The bioinformatics community has responded to the outbreak emergency with a set of tools and resource to identify biomarkers and drug targets that we review here. Starting from a consolidated corpus of 27 570 papers, we adopt latent Dirichlet analysis to extract relevant topics and select those associated with computational methods for biomarker identification and drug repurposing. The selected topics span from machine learning and artificial intelligence for disease characterization to vaccine development and to therapeutic target identification. Although the way to go for the ultimate defeat of the pandemic is still long, the amount of knowledge, data and tools generated so far constitutes an unprecedented example of global cooperation to this threat.

A screening test proposal for congenital defects based on maternal serum metabolomics profile.

BACKGROUND: Historically, noninvasive techniques are only able to identify chromosomal anomalies that accounted for <50% of all congenital defects; the other congenital defects are diagnosed via ultrasound evaluations in the later stages of pregnancy. Metabolomic analysis may provide an important improvement, potentially addressing the need for novel noninvasive and multicomprehensive early prenatal screening tools. A growing body of evidence outlines notable metabolic alterations in different biofluids derived from pregnant women carrying fetuses with malformations, suggesting that such an approach may allow the discovery of biomarkers common to most fetal malformations. In addition, metabolomic investigations are inexpensive, fast, and risk-free and often generate high performance screening tests that may allow early detection of a given pathology. OBJECTIVE: This study aimed to evaluate the diagnostic accuracy of an ensemble machine learning model based on maternal serum metabolomic signatures for detecting fetal malformations, including both chromosomal anomalies and structural defects. STUDY DESIGN: This was a multicenter observational retrospective study that included 2 different arms. In the first arm, a total of 654 Italian pregnant women (334 cases with fetuses with malformations and 320 controls with normal developing fetuses) were enrolled and used to train an ensemble machine learning classification model based on serum metabolomics profiles. In the second arm, serum samples obtained from 1935 participants of the New Zealand Screening for Pregnancy Endpoints study were blindly analyzed and used as a validation cohort. Untargeted metabolomics analysis was performed via gas chromatography-mass spectrometry. Of note, 9 individual machine learning classification models were built and optimized via cross-validation (partial least squares-discriminant analysis, linear discriminant analysis, naïve Bayes, decision tree, random forest, k-nearest neighbor, artificial neural network, support vector machine, and logistic regression). An ensemble of the models was developed according to a voting scheme statistically weighted by the cross-validation accuracy and classification confidence of the individual models. This ensemble machine learning system was used to screen the validation cohort. RESULTS: Significant metabolic differences were detected in women carrying fetuses with malformations, who exhibited lower amounts of palmitic, myristic, and stearic acids; N-α-acetyllysine; glucose; L-acetylcarnitine; fructose; para-cresol; and xylose and higher levels of serine, alanine, urea, progesterone, and valine (P<.05), compared with controls. When applied to the validation cohort, the screening test showed a 99.4%±0.6% accuracy (specificity of 99.9%±0.1% [1892 of 1894 controls correctly identified] with a sensitivity of 78%±6% [32 of 41 fetal malformations correctly identified]). CONCLUSION: This study provided clinical validation of a metabolomics-based prenatal screening test to detect the presence of congenital defects. Further investigations are needed to enable the identification of the type of malformation and to confirm these findings on even larger study populations.

On the Fidelity Robustness of CHSH-Bell Inequality via Filtered Random States.

The theorem developed by John Bell constituted the starting point of a revolution that translated a philosophical question about the nature of reality into the broad and intense field of research of the quantum information technologies. We focus on a system of two qubits prepared in a random, mixed state, and we study the typical behavior of their nonlocality via the CHSH-Bell inequality. Afterward, motivated by the necessity of accounting for inefficiency in the state preparation, we address to what extent states close enough to one with a high degree of nonclassicality can violate local realism with a previously chosen experimental setup.

What is Nonclassical about Uncertainty Relations?

Uncertainty relations express limits on the extent to which the outcomes of distinct measurements on a single state can be made jointly predictable. The existence of nontrivial uncertainty relations in quantum theory is generally considered to be a way in which it entails a departure from the classical worldview. However, this perspective is undermined by the fact that there exist operational theories which exhibit nontrivial uncertainty relations but which are consistent with the classical worldview insofar as they admit of a generalized-noncontextual ontological model. This prompts the question of what aspects of uncertainty relations, if any, cannot be realized in this way and so constitute evidence of genuine nonclassicality. We here consider uncertainty relations describing the tradeoff between the predictability of a pair of binary-outcome measurements (e.g., measurements of Pauli X and Pauli Z observables in quantum theory). We show that, for a class of theories satisfying a particular symmetry property, the functional form of this predictability tradeoff is constrained by noncontextuality to be below a linear curve. Because qubit quantum theory has the relevant symmetry property, the fact that its predictability tradeoff describes a section of a circle is a violation of this noncontextual bound, and therefore constitutes an example of how the functional form of an uncertainty relation can witness contextuality. We also deduce the implications for a selected group of operational foils to quantum theory and consider the generalization to three measurements.

Metabolomics signatures of acutely ill and short-term weight recovered women with anorexia nervosa.

Although metabolomics studies are recently spreading and have allowed the characterization of putative biomarkers in many diseases, they are relatively scanty in anorexia nervosa (AN). In this explorative study we analyzed the fecal metabolomics profiles of women with AN in the underweight phase (n = 24) and after short-term weight restoration (n = 16) and compared them with 20 healthy women. An untargeted metabolomic procedure allowed the characterization of 224 metabolites involved in energy, lipid, and amino acid metabolism. A partial least square discriminant analysis identified 14 metabolites with a variable importance in projection score >1.5 that clearly differentiated underweight from weight-restored patients from healthy women. Compared with healthy women, fecal concentrations of valeric acid and 3-methyl,2-ketobutyric acid were increased in both underweight and weight-restored patients; fecal concentrations of propionic acid, stearic acid, linolenic acid, methyl-galactoside, coprosterol, cycloserine, and lauric acid were increased while fecal levels of xylose, fucose, and rhamnose were decreased in underweight patients and normalized after weight-restoration; fecal concentrations of piperine, phenylalanine, butyric acid, and meso-erythritol-1 were decreased while fecal levels of hydroxystearic acid were increased in weight-restored but normal in underweight AN patients. All these changes point to peculiar fecal metabolomics profiles of acute and short-term weight restored AN patients. The value of these changes to improve our understanding of the pathophysiology of AN and to characterize potential biomarker targets for developing new treatment strategies needs further studies to be clarified.

Genome-wide mapping of genomic DNA damage: methods and implications.

Exposures from the external and internal environments lead to the modification of genomic DNA, which is implicated in the cause of numerous diseases, including cancer, cardiovascular, pulmonary and neurodegenerative diseases, together with ageing. However, the precise mechanism(s) linking the presence of damage, to impact upon cellular function and pathogenesis, is far from clear. Genomic location of specific forms of damage is likely to be highly informative in understanding this process, as the impact of downstream events (e.g. mutation, microsatellite instability, altered methylation and gene expression) on cellular function will be positional-events at key locations will have the greatest impact. However, until recently, methods for assessing DNA damage determined the totality of damage in the genomic location, with no positional information. The technique of "mapping DNA adductomics" describes the molecular approaches that map a variety of forms of DNA damage, to specific locations across the nuclear and mitochondrial genomes. We propose that integrated comparison of this information with other genome-wide data, such as mutational hotspots for specific genotoxins, tumour-specific mutation patterns and chromatin organisation and transcriptional activity in non-cancerous lesions (such as nevi), pre-cancerous conditions (such as polyps) and tumours, will improve our understanding of how environmental toxins lead to cancer. Adopting an analogous approach for non-cancer diseases, including the development of genome-wide assays for other cellular outcomes of DNA damage, will improve our understanding of the role of DNA damage in pathogenesis more generally.

The genomic landscape of 8-oxodG reveals enrichment at specific inherently fragile promoters.

8-Oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) is the most common marker of oxidative stress and its accumulation within the genome has been associated with major human health issues such as cancer, aging, cardiovascular and neurodegenerative diseases. The characterization of the different genomic sites where 8-oxodG accumulates and the mechanisms underlying its formation are still poorly understood. Using OxiDIP-seq, we recently derived the genome-wide distribution of 8-oxodG in human non-tumorigenic epithelial breast cells (MCF10A). Here, we identify a subset of human promoters that accumulate 8-oxodG under steady-state condition. 8-oxodG nucleotides co-localize with double strand breaks (DSBs) at bidirectional and CG skewed promoters and their density correlate with RNA Polymerase II co-occupancy and transcription. Furthermore, by performing OxiDIP-seq in quiescent (G0) cells, we found a strong reduction of oxidatively-generated damage in the majority of 8-oxodG-positive promoters in the absence of DNA replication. Overall, our results suggest that the accumulation of 8-oxodG at gene promoters occurs through DNA replication-dependent or -independent mechanisms, with a possible contribution to the formation of cancer-associated translocation events.

CpGmotifs: a tool to discover DNA motifs associated to CpG methylation events.

BACKGROUND: The investigation of molecular alterations associated with the conservation and variation of DNA methylation in eukaryotes is gaining interest in the biomedical research community. Among the different determinants of methylation stability, the DNA composition of the CpG surrounding regions has been shown to have a crucial role in the maintenance and establishment of methylation statuses. This aspect has been previously characterized in a quantitative manner by inspecting the nucleotidic composition in the region. Research in this field still lacks a qualitative perspective, linked to the identification of certain sequences (or DNA motifs) related to particular DNA methylation phenomena. RESULTS: Here we present a novel computational strategy based on short DNA motif discovery in order to characterize sequence patterns related to aberrant CpG methylation events. We provide our framework as a user-friendly, shiny-based application, CpGmotifs, to easily retrieve and characterize DNA patterns related to CpG methylation in the human genome. Our tool supports the functional interpretation of deregulated methylation events by predicting transcription factors binding sites (TFBS) encompassing the identified motifs. CONCLUSIONS: CpGmotifs is an open source software. Its source code is available on GitHub https://github.com/Greco-Lab/CpGmotifs and a ready-to-use docker image is provided on DockerHub at https://hub.docker.com/r/grecolab/cpgmotifs .

Multi-walled carbon nanotubes elicit concordant changes in DNA methylation and gene expression following long-term pulmonary exposure in mice.

Pulmonary exposure to multi-walled carbon nanotubes (MWCNTs) causes inflammation and fibrosis. Our previous work has shown that industrially produced MWCNTs trigger specific changes in gene expression in the lungs of exposed animals. To elucidate whether epigenetic effects play a role for these gene expression changes, we performed whole genome bisulphite sequencing to assess DNA methylation patterns in the lungs 56 days after exposure to MWCNTs. Lung tissues were also evaluated with respect to histopathological changes and cytokine profiling of bronchoalveolar lavage (BAL) fluid was conducted using a multi-plex array. Integrated analysis of transcriptomics data and DNA methylation data revealed concordant changes in gene expression. Functional analysis showed that the muscle contraction, immune system/inflammation, and extracellular matrix pathways were the most affected pathways. Taken together, the present study revealed that MWCNTs exert epigenetic effects in the lungs of exposed animals, potentially driving the subsequent gene expression changes.

Noninvasive screening for congenital heart defects using a serum metabolomics approach.

OBJECTIVE: Heart anomalies represent nearly one-third of all congenital anomalies. They are currently diagnosed using ultrasound. However, there is a strong need for a more accurate and less operator-dependent screening method. Here we report a metabolomics characterization of maternal serum in order to describe a metabolomic fingerprint representative of heart congenital anomalies. METHODS: Metabolomic profiles were obtained from serum of 350 mothers (280 controls and 70 cases). Nine classification models were built and optimized. An ensemble model was built based on the results from the individual models. RESULTS: The ensemble machine learning model correctly classified all cases and controls. Malonic, 3-hydroxybutyric and methyl glutaric acid, urea, androstenedione, fructose, tocopherol, leucine, and putrescine were determined as the most relevant metabolites in class separation. CONCLUSION: The metabolomic signature of second trimester maternal serum from pregnancies affected by a fetal heart anomaly is quantifiably different from that of a normal pregnancy. Maternal serum metabolomics is a promising tool for the accurate and sensitive screening of such congenital defects. Moreover, the revelation of the associated metabolites and their respective biochemical pathways allows a better understanding of the overall pathophysiology of affected pregnancies.

Genome-wide mapping of 8-oxo-7,8-dihydro-2'-deoxyguanosine reveals accumulation of oxidatively-generated damage at DNA replication origins within transcribed long genes of mammalian cells.

8-Oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) is one of the major DNA modifications and a potent pre-mutagenic lesion prone to mispair with 2'-deoxyadenosine (dA). Several thousand residues of 8-oxodG are constitutively generated in the genome of mammalian cells, but their genomic distribution has not yet been fully characterized. Here, by using OxiDIP-Seq, a highly sensitive methodology that uses immuno-precipitation with efficient anti-8-oxodG antibodies combined with high-throughput sequencing, we report the genome-wide distribution of 8-oxodG in human non-tumorigenic epithelial breast cells (MCF10A), and mouse embryonic fibroblasts (MEFs). OxiDIP-Seq revealed sites of 8-oxodG accumulation overlapping with γH2AX ChIP-Seq signals within the gene body of transcribed long genes, particularly at the DNA replication origins contained therein. We propose that the presence of persistent single-stranded DNA, as a consequence of transcription-replication clashes at these sites, determines local vulnerability to DNA oxidation and/or its slow repair. This oxidatively-generated damage, likely in combination with other kinds of lesion, might contribute to the formation of DNA double strand breaks and activation of DNA damage response.

DNA Methylation Levels in Mononuclear Leukocytes from the Mother and Her Child Are Associated with IgE Sensitization to Allergens in Early Life.

DNA methylation changes may predispose becoming IgE-sensitized to allergens. We analyzed whether DNA methylation in peripheral blood mononuclear cells (PBMC) is associated with IgE sensitization at 5 years of age (5Y). DNA methylation was measured in 288 PBMC samples from 74 mother/child pairs from the birth cohort ALADDIN (Assessment of Lifestyle and Allergic Disease During INfancy) using the HumanMethylation450BeadChip (Illumina). PBMCs were obtained from the mothers during pregnancy and from their children in cord blood, at 2 years and 5Y. DNA methylation levels at each time point were compared between children with and without IgE sensitization to allergens at 5Y. For replication, CpG sites associated with IgE sensitization in ALADDIN were evaluated in whole blood DNA of 256 children, 4 years old, from the BAMSE (Swedish abbreviation for Children, Allergy, Milieu, Stockholm, Epidemiology) cohort. We found 34 differentially methylated regions (DMRs) associated with IgE sensitization to airborne allergens and 38 DMRs associated with sensitization to food allergens in children at 5Y (Sidak p ≤ 0.05). Genes associated with airborne sensitization were enriched in the pathway of endocytosis, while genes associated with food sensitization were enriched in focal adhesion, the bacterial invasion of epithelial cells, and leukocyte migration. Furthermore, 25 DMRs in maternal PBMCs were associated with IgE sensitization to airborne allergens in their children at 5Y, which were functionally annotated to the mTOR (mammalian Target of Rapamycin) signaling pathway. This study supports that DNA methylation is associated with IgE sensitization early in life and revealed new candidate genes for atopy. Moreover, our study provides evidence that maternal DNA methylation levels are associated with IgE sensitization in the child supporting early in utero effects on atopy predisposition.

Carbon Nanomaterials Promote M1/M2 Macrophage Activation.

Toxic effects of certain carbon nanomaterials (CNM) have been observed in several exposure scenarios both in vivo and in vitro. However, most of the data currently available has been generated in a high-dose/acute exposure setup, limiting the understanding of their immunomodulatory mechanisms. Here, macrophage-like THP-1 cells, exposed to ten different CNM for 48 h in low-cytotoxic concentration of 10 µg mL-1 , are characterized by secretion of different cytokines and global transcriptional changes. Subsequently, the relationships between cytokine secretion and transcriptional patterns are modeled, highlighting specific pathways related to alternative macrophage activation. Finally, time- and dose-dependent activation of transcription and secretion of M1 marker genes IL-1ß and tumor necrosis factor, and M2 marker genes IL-10 and CSF1 is confirmed among the three most responsive CNM, with concentrations of 5, 10, and 20 µg mL-1 at 24, 48, and 72 h of exposure. These results underline CNM effects on the formation of cell microenvironment and gene expression leading to specific patterns of macrophage polarization. Taken together, these findings imply that, instead of a high and toxic CNM dose, a sub-lethal dose in controlled exposure setup can be utilized to alter the cell microenvironment and program antigen presenting cells, with fascinating implications for novel therapeutic strategies.

Immune-microbiota interaction in Finnish and Russian Karelia young people with high and low allergy prevalence.

BACKGROUND: After the Second World War, the population living in the Karelian region was strictly divided by the "iron curtain" between Finland and Russia. This resulted in different lifestyle, standard of living, and exposure to the environment. Allergic manifestations and sensitization to common allergens have been much more common on the Finnish compared to the Russian side. OBJECTIVE: The remarkable allergy disparity in the Finnish and Russian Karelia calls for immunological explanations. METHODS: Young people, aged 15-20 years, in the Finnish (n = 69) and Russian (n = 75) Karelia were studied. The impact of genetic variation on the phenotype was studied by a genome-wide association analysis. Differences in gene expression (transcriptome) were explored from the blood mononuclear cells (PBMC) and related to skin and nasal epithelium microbiota and sensitization. RESULTS: The genotype differences between the Finnish and Russian populations did not explain the allergy gap. The network of gene expression and skin and nasal microbiota was richer and more diverse in the Russian subjects. When the function of 261 differentially expressed genes was explored, innate immunity pathways were suppressed among Russians compared to Finns. Differences in the gene expression paralleled the microbiota disparity. High Acinetobacter abundance in Russians correlated with suppression of innate immune response. High-total IgE was associated with enhanced anti-viral response in the Finnish but not in the Russian subjects. CONCLUSIONS AND CLINICAL RELEVANCE: Young populations living in the Finnish and Russian Karelia show marked differences in genome-wide gene expression and host contrasting skin and nasal epithelium microbiota. The rich gene-microbe network in Russians seems to result in a better-balanced innate immunity and associates with low allergy prevalence.

Gain-of-function CEBPE mutation causes noncanonical autoinflammatory inflammasomopathy.

BACKGROUND: CCAAT enhancer-binding protein epsilon (C/EBPε) is a transcription factor involved in late myeloid lineage differentiation and cellular function. The only previously known disorder linked to C/EBPε is autosomal recessive neutrophil-specific granule deficiency leading to severely impaired neutrophil function and early mortality. OBJECTIVE: The aim of this study was to molecularly characterize the effects of C/EBPε transcription factor Arg219His mutation identified in a Finnish family with previously genetically uncharacterized autoinflammatory and immunodeficiency syndrome. METHODS: Genetic analysis, proteomics, genome-wide transcriptional profiling by means of RNA-sequencing, chromatin immunoprecipitation (ChIP) sequencing, and assessment of the inflammasome function of primary macrophages were performed. RESULTS: Studies revealed a novel mechanism of genome-wide gain-of-function that dysregulated transcription of 464 genes. Mechanisms involved dysregulated noncanonical inflammasome activation caused by decreased association with transcriptional repressors, leading to increased chromatin occupancy and considerable changes in transcriptional activity, including increased expression of NLR family, pyrin domain-containing 3 protein (NLRP3) and constitutively expressed caspase-5 in macrophages. CONCLUSION: We describe a novel autoinflammatory disease with defective neutrophil function caused by a homozygous Arg219His mutation in the transcription factor C/EBPε. Mutated C/EBPε acts as a regulator of both the inflammasome and interferome, and the Arg219His mutation causes the first human monogenic neomorphic and noncanonical inflammasomopathy/immunodeficiency. The mechanism, including widely dysregulated transcription, is likely not unique for C/EBPε. Similar multiomics approaches should also be used in studying other transcription factor-associated diseases.

FunMappOne: a tool to hierarchically organize and visually navigate functional gene annotations in multiple experiments.

BACKGROUND: Functional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molecular pathways. Annotating results from omics experiments into functional categories is essential not only to understand the underlying regulatory dynamics but also to compare multiple experimental conditions at a higher level of abstraction. Several tools are already available to the community to represent and compare functional profiles of omics experiments. However, when the number of experiments and/or enriched functional terms is high, it becomes difficult to interpret the results even when graphically represented. Therefore, there is currently a need for interactive and user-friendly tools to graphically navigate and further summarize annotations in order to facilitate results interpretation also when the dimensionality is high. RESULTS: We developed an approach that exploits the intrinsic hierarchical structure of several functional annotations to summarize the results obtained through enrichment analyses to higher levels of interpretation and to map gene related information at each summarized level. We built a user-friendly graphical interface that allows to visualize the functional annotations of one or multiple experiments at once. The tool is implemented as a R-Shiny application called FunMappOne and is available at https://github.com/grecolab/FunMappOne . CONCLUSION: FunMappOne is a R-shiny graphical tool that takes in input multiple lists of human or mouse genes, optionally along with their related modification magnitudes, computes the enriched annotations from Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, or Reactome databases, and reports interactive maps of functional terms and pathways organized in rational groups. FunMappOne allows a fast and convenient comparison of multiple experiments and an easy way to interpret results.

INfORM: Inference of NetwOrk Response Modules.

Summary: Detecting and interpreting responsive modules from gene expression data by using network-based approaches is a common but laborious task. It often requires the application of several computational methods implemented in different software packages, forcing biologists to compile complex analytical pipelines. Here we introduce INfORM (Inference of NetwOrk Response Modules), an R shiny application that enables non-expert users to detect, evaluate and select gene modules with high statistical and biological significance. INfORM is a comprehensive tool for the identification of biologically meaningful response modules from consensus gene networks inferred by using multiple algorithms. It is accessible through an intuitive graphical user interface allowing for a level of abstraction from the computational steps. Availability and implementation: INfORM is freely available for academic use at https://github.com/Greco-Lab/INfORM. Supplementary information: Supplementary data are available at Bioinformatics online.