CRISPR/Cas9 screenings unearth protein arginine methyltransferase 7 as a novel essential gene in prostate cancer metastasis.

Due to the limited effectiveness of current treatments, the survival rate of patients with metastatic castration-resistant prostate cancer (mCRPC) is significantly reduced. Consequently, it is imperative to identify novel therapeutic targets for managing these patients. Since the invasive ability of cells is crucial for establishing and maintaining metastasis, the aim of this study was to identify the essential regulators of invasive abilities of mCRPC cells by conducting two independent high-throughput CRISPR/Cas9 screenings. Furthermore, some of the top hits were validated using siRNA technology, with protein arginine methyltransferase 7 (PRMT7) emerging as the most promising candidate. We demonstrated that its inhibition or depletion via genetic or pharmacological approaches significantly reduces invasive, migratory and proliferative abilities of mCRPC cells in vitro. Moreover, we confirmed that PRMT7 ablation reduces cell dissemination in chicken chorioallantoic membrane and mouse xenograft assays. Molecularly, PRMT7 reprograms the expression of several adhesion molecules by methylating various transcription factors, such as FoxK1, resulting in the loss of adhesion from the primary tumor and increased motility of mCRPC cells. Furthermore, PRMT7 higher expression correlates with tumor aggressivity and poor overall survival in prostate cancer patients. Thus, this study demonstrates that PRMT7 is a potential therapeutic target and potential biomarker for mPCa.

Shotgun Characterization of the Circulating IgM Antigenome of an Infectious Pathogen by Immunocapture-LC-MS/MS from Dried Serum Spots.

One of the main challenges in compiling the complete collection of protein antigens from pathogens for the selection of vaccine candidates or intervention targets is to acquire a broad enough representation of them to be recognized by the highly diversified immunoglobulin repertoire in human populations. Dried serum spot sampling (DSS) retains a large repertoire of circulating immunoglobulins from each individual that can be representative of a population, according to the sample size. In this work, shotgun proteomics of an infectious pathogen based on DSS sampling coupled with IgM immunoprecipitation, liquid chromatography-mass spectrometry (LC-MS/MS), and bioinformatic analyses was combined to characterize the circulating IgM antigenome. Serum samples from a malaria endemic region at different clinical statuses were studied to optimize IgM binding efficiency and antibody leaching by varying serum/immunomagnetic bead ratios and elution conditions. The method was validated using Plasmodium falciparum extracts identifying 110 of its IgM-reactive antigens while minimizing the presence of human proteins and antibodies. Furthermore, the IgM antigen recognition profile differentiated between malaria-infected and noninfected individuals at the time of sampling. We conclude that a shotgun proteomics approach offers advantages in providing a high-throughput, reliable, and clean way to identify IgM-recognized antigens from trace amounts of serum. The mass spectrometry raw data and metadata have been deposited with ProteomeXchange via MassIVE with the PXD identifier PXD043800.

Evaluating the performance of polygenic risk profiling across diverse ancestry populations in Parkinson's disease.

Objective: This study aims to address disparities in risk prediction by evaluating the performance of polygenic risk score (PRS) models using the 90 risk variants across 78 independent loci previously linked to Parkinson's disease (PD) risk across seven diverse ancestry populations. Methods: We conducted a multi-stage study, testing PRS models in predicting PD status across seven different ancestries applying three approaches: 1) PRS adjusted by gender and age; 2) PRS adjusted by gender, age and principal components (PCs); and 3) PRS adjusted by gender, age and percentage of population admixture. These models were built using the largest four population-specific summary statistics of PD risk to date (base data) and individual level data obtained from the Global Parkinson's Genetics Program (target data). We performed power calculations to estimate the minimum sample size required to conduct these analyses. A total of 91 PRS models were developed to investigate cumulative known genetic variation associated with PD risk and age of onset in a global context. Results: We observed marked heterogeneity in risk estimates across non-European ancestries, including East Asians, Central Asians, Latino/Admixed Americans, Africans, African admixed, and Ashkenazi Jewish populations. Risk allele patterns for the 90 risk variants yielded significant differences in directionality, frequency, and magnitude of effect. PRS did not improve in performance when predicting disease status using similar base and target data across multiple ancestries, demonstrating that cumulative PRS models based on current known risk are inherently biased towards European populations. We found that PRS models adjusted by percentage of admixture outperformed models that adjusted for conventional PCs in highly admixed populations. Overall, the clinical utility of our models in individually predicting PD status is limited in concordance with the estimates observed in European populations. Interpretation: This study represents the first comprehensive assessment of how PRS models predict PD risk and age at onset in a multi-ancestry fashion. Given the heterogeneity and distinct genetic architecture of PD across different populations, our assessment emphasizes the need for larger and diverse study cohorts of individual-level target data and well-powered ancestry-specific summary statistics. Our current understanding of PD status unraveled through GWAS in European populations is not generally applicable to other ancestries. Future studies should integrate clinical and *omics level data to enhance the accuracy and predictive power of PRS across diverse populations.

NeuroBooster Array: A Genome-Wide Genotyping Platform to Study Neurological Disorders Across Diverse Populations.

Genome-wide genotyping platforms have the capacity to capture genetic variation across different populations, but there have been disparities in the representation of population-dependent genetic diversity. The motivation for pursuing this endeavor was to create a comprehensive genome-wide array capable of encompassing a wide range of neuro-specific content for the Global Parkinson's Genetics Program (GP2) and the Center for Alzheimer's and Related Dementias (CARD). CARD aims to increase diversity in genetic studies, using this array as a tool to foster inclusivity. GP2 is the first supported resource project of the Aligning Science Across Parkinson's (ASAP) initiative that aims to support a collaborative global effort aimed at significantly accelerating the discovery of genetic factors contributing to Parkinson's disease and atypical parkinsonism by generating genome-wide data for over 200,000 individuals in a multi-ancestry context. Here, we present the Illumina NeuroBooster array (NBA), a novel, high-throughput and cost-effective custom-designed content platform to screen for genetic variation in neurological disorders across diverse populations. The NBA contains a backbone of 1,914,934 variants (Infinium Global Diversity Array) complemented with custom content of 95,273 variants implicated in over 70 neurological conditions or traits with potential neurological complications. Furthermore, the platform includes over 10,000 tagging variants to facilitate imputation and analyses of neurodegenerative disease-related GWAS loci across diverse populations. The NBA can identify low frequency variants and accurately impute over 15 million common variants from the latest release of the TOPMed Imputation Server as of August 2023 (reference of over 300 million variants and 90,000 participants). We envisage this valuable tool will standardize genetic studies in neurological disorders across different ancestral groups, allowing researchers to perform genetic research inclusively and at a global scale.

Posttranslational splicing modifications as a key mechanism in cytarabine resistance in acute myeloid leukemia.

Despite the approval of several drugs for AML, cytarabine is still widely used as a therapeutic approach. However, 85% of patients show resistance and only 10% overcome the disease. Using RNA-seq and phosphoproteomics, we show that RNA splicing and serine-arginine-rich (SR) proteins phosphorylation were altered during cytarabine resistance. Moreover, phosphorylation of SR proteins at diagnosis were significantly lower in responder than non-responder patients, pointing to their utility to predict response. These changes correlated with altered transcriptomic profiles of SR protein target genes. Notably, splicing inhibitors were therapeutically effective in treating sensitive and resistant AML cells as monotherapy or combination with other approved drugs. H3B-8800 and venetoclax combination showed the best efficacy in vitro, demonstrating synergistic effects in patient samples and no toxicity in healthy hematopoietic progenitors. Our results establish that RNA splicing inhibition, alone or combined with venetoclax, could be useful for the treatment of newly diagnosed or relapsed/refractory AML.

Whole-transcriptome analysis reveals virulence-specific pathogen-host interactions at the placenta in bovine neosporosis.

Research on bovine neosporosis has achieved relevant milestones, but the mechanisms underlying the occurrence of foetal death or protection against foetal death remain unclear. In a recent study, placentas from heifers challenged with the high-virulence isolate Nc-Spain7 exhibited focal necrosis and inflammatory infiltrates as soon as 10 days post-infection (dpi), although parasite detection was minimal. These lesions were more frequent at 20 dpi, coinciding with higher rates of parasite detection and the occurrence of foetal death in some animals. In contrast, such lesions were not observed in placentas from animals infected with the low-virulence isolate Nc-Spain1H, where the parasite was detected only in placenta from one animal at 20 dpi. This work aimed to study which mechanisms are triggered in the placentas (caruncles and cotyledons) of these pregnant heifers at early stages of infection (10 and 20 dpi) through whole-transcriptome analysis. In caruncles, infection with the high-virulence isolate provoked a strong proinflammatory response at 10 dpi. This effect was not observed in heifers infected with the low-virulence isolate, where IL-6/JAK/STAT3 signalling and TNF-alpha signalling via NF-κB pathways were down-regulated. Interestingly, the expression of E2F target genes, related to restraining the inflammatory response, was higher in these animals. At 20 dpi, more pronounced proinflammatory gene signatures were detectable in heifers infected with the high-virulence isolate, being more intense in heifers carrying dead fetuses. However, the low-virulence isolate continued without activating the proinflammatory response. In cotyledons, the response to infection with the high-virulence isolate was similar to that observed in caruncles; however, the low-virulence isolate induced mild proinflammatory signals at 20 dpi. Finally, a deconvolutional analysis of gene signatures from both placentome tissues revealed a markedly higher fraction of activated natural killers, M1 macrophages and CD8+ T cells for the high-virulence isolate. Therefore, our transcriptomic analysis supports the hypothesis that an intense immune response probably triggered by parasite multiplication could be a key contributor to abortion. Further studies are required to determine the parasite effectors that govern the distinct interactions of high- and low-virulence isolates with the host, which could help elucidate the molecular processes underlying the pathogenesis of neosporosis in cattle.

GRaNIE and GRaNPA: inference and evaluation of enhancer-mediated gene regulatory networks.

Enhancers play a vital role in gene regulation and are critical in mediating the impact of noncoding genetic variants associated with complex traits. Enhancer activity is a cell-type-specific process regulated by transcription factors (TFs), epigenetic mechanisms and genetic variants. Despite the strong mechanistic link between TFs and enhancers, we currently lack a framework for jointly analysing them in cell-type-specific gene regulatory networks (GRN). Equally important, we lack an unbiased way of assessing the biological significance of inferred GRNs since no complete ground truth exists. To address these gaps, we present GRaNIE (Gene Regulatory Network Inference including Enhancers) and GRaNPA (Gene Regulatory Network Performance Analysis). GRaNIE (https://git.embl.de/grp-zaugg/GRaNIE) builds enhancer-mediated GRNs based on covariation of chromatin accessibility and RNA-seq across samples (e.g. individuals), while GRaNPA (https://git.embl.de/grp-zaugg/GRaNPA) assesses the performance of GRNs for predicting cell-type-specific differential expression. We demonstrate their power by investigating gene regulatory mechanisms underlying the response of macrophages to infection, cancer and common genetic traits including autoimmune diseases. Finally, our methods identify the TF PURA as a putative regulator of pro-inflammatory macrophage polarisation.

Microscopic and submicroscopic infection by Plasmodium falciparum: Immunoglobulin M and A profiles as markers of intensity and exposure.

Assessment of serological Plasmodium falciparum-specific antibodies in highly endemic areas provides valuable information about malaria status and parasite exposure in the population. Although serological evidence of Plasmodium exposure is commonly determined by Plasmodium-specific immunoglobulin G (IgG) levels; IgM and IgA are likely markers of malaria status that remain relatively unexplored. Previous studies on IgM and IgA responses have been based on their affinity for single antigens with shortage of immune responses analysis against the whole Plasmodium proteome. Here, we provide evidence of how P. falciparum infection triggers the production of specific IgM and IgA in plasma and its relationship with parasite density and changes in hematological parameters. A total of 201 individuals attending a hospital in Breman Asikuma, Ghana, were recruited into this study. Total and P. falciparum-specific IgM, IgA, and IgG were assessed by ELISA and examined in relation to age (0-5, 14-49, and ≥50 age ranges); infection (submicroscopic vs. microscopic malaria); pregnancy and hematological parameters. Well-known IgG response was used as baseline control. P. falciparum-specific IgM and IgA levels increased in the population with the age, similarly to IgG. These data confirm that acquired humoral immunity develops by repeated infections through the years endorsing IgM and IgA as exposure markers in endemic malaria regions. High levels of specific IgA and IgM in children were associated with microscopic malaria and worse prognosis, because most of them showed severe anemia. This new finding shows that IgM and IgA may be used as diagnostic markers in this age group. We also found an extremely high prevalence of submicroscopic malaria (46.27% on average) accompanied by IgM and IgA levels indistinguishable from those of uninfected individuals. These data, together with the observed lack of sensitivity of rapid diagnostic tests (RDTs) compared to PCR, invoke the urgent need to implement diagnostic markers for submicroscopic malaria. Overall, this study opens the potential use of P. falciparum-specific IgM and IgA as new serological markers to predict malaria status in children and parasite exposure in endemic populations. The difficulties in finding markers of submicroscopic malaria are highlighted, emphasizing the need to explore this field in depth.

Modulation of Host Immune Response during Leishmania infantum Natural Infection: A Whole-Transcriptome Analysis of the Popliteal Lymph Nodes in Dogs.

Leishmania infantum, the etiological agent of canine leishmaniosis (CanL) in Europe, was responsible of the largest outbreak of human leishmaniosis in Spain. The parasite infects and survives within myeloid lineage cells, causing a potentially fatal disease if left untreated. The only treatment option relies on chemotherapy, although immunotherapy strategies are being considered as novel approaches to prevent progression of the disease. To this aim, a deeper characterization of the molecular mechanisms behind the immunopathogenesis of leishmaniosis is necessary. Thus, we evaluated, for the first time, the host immune response during L. infantum infection through transcriptome sequencing of the popliteal lymph nodes aspirates of dogs with CanL. Differential expression and weighted gene co-expression network analyses were performed, resulting in the identification of 5,461 differentially expressed genes (DEGs) and four key modules in sick dogs, compared to controls. As expected, defense response was the highest enriched biological process in the DEGs, with six genes related to immune response against pathogens (CHI3L1, SLPI, ACOD1, CCL5, MPO, BPI) included among the ten most expressed genes; and two of the key co-expression modules were associated with regulation of immune response, which also positively correlated with clinical stage and blood monocyte concentration. In particular, sick dogs displayed significant changes in the expression of Th1, Th2, Th17 and Tr1 cytokines (e. g. TNF-α, IFN-γ, IL-21, IL-17, IL-15), markers of T cell and NK cell exhaustion (e. g. LAG3, CD244, Blimp-1, JUN), and B cell, monocyte and macrophage disrupted functionality (e. g. CD40LG, MAPK4, IL-1R, NLRP3, BCMA). In addition, we found an overexpression of XBP1 and some other genes involved in endoplasmic reticulum stress and the IRE1 branch of the unfolded protein response, as well as one co-expression module associated with these processes, which could be induced by L. infantum to prevent host cell apoptosis and modulate inflammation-induced lymphangiogenesis at lymph nodes. Moreover, 21 lncRNAs were differentially expressed in sick dogs, and one key co-expression module was associated with chromatin organization, suggesting that epigenetic mechanisms could also contribute to dampening host immune response during natural L. infantum infection in the lymph nodes of dogs suffering from clinical leishmaniosis.

Remodeling of active endothelial enhancers is associated with aberrant gene-regulatory networks in pulmonary arterial hypertension.

Environmental and epigenetic factors often play an important role in polygenic disorders. However, how such factors affect disease-specific tissues at the molecular level remains to be understood. Here, we address this in pulmonary arterial hypertension (PAH). We obtain pulmonary arterial endothelial cells (PAECs) from lungs of patients and controls (n = 19), and perform chromatin, transcriptomic and interaction profiling. Overall, we observe extensive remodeling at active enhancers in PAH PAECs and identify hundreds of differentially active TFs, yet find very little transcriptomic changes in steady-state. We devise a disease-specific enhancer-gene regulatory network and predict that primed enhancers in PAH PAECs are activated by the differentially active TFs, resulting in an aberrant response to endothelial signals, which could lead to disturbed angiogenesis and endothelial-to-mesenchymal-transition. We validate these predictions for a selection of target genes in PAECs stimulated with TGF-ß, VEGF or serotonin. Our study highlights the role of chromatin state and enhancers in disease-relevant cell types of PAH.

Quantification of Differential Transcription Factor Activity and Multiomics-Based Classification into Activators and Repressors: diffTF.

Transcription factors (TFs) regulate many cellular processes and can therefore serve as readouts of the signaling and regulatory state. Yet for many TFs, the mode of action-repressing or activating transcription of target genes-is unclear. Here, we present diffTF (https://git.embl.de/grp-zaugg/diffTF) to calculate differential TF activity (basic mode) and classify TFs into putative transcriptional activators or repressors (classification mode). In basic mode, it combines genome-wide chromatin accessibility/activity with putative TF binding sites that, in classification mode, are integrated with RNA-seq. We apply diffTF to compare (1) mutated and unmutated chronic lymphocytic leukemia patients and (2) two hematopoietic progenitor cell types. In both datasets, diffTF recovers most known biology and finds many previously unreported TFs. It classifies almost 40% of TFs based on their mode of action, which we validate experimentally. Overall, we demonstrate that diffTF recovers known biology, identifies less well-characterized TFs, and classifies TFs into transcriptional activators or repressors.

Hepatic leukemia factor is a novel leukemic stem cell regulator in DNMT3A, NPM1, and FLT3-ITD triple-mutated AML.

FLT3, DNMT3A, and NPM1 are the most frequently mutated genes in cytogenetically normal acute myeloid leukemia (AML), but little is known about how these mutations synergize upon cooccurrence. Here we show that triple-mutated AML is characterized by high leukemia stem cell (LSC) frequency, an aberrant leukemia-specific GPR56 highCD34low immunophenotype, and synergistic upregulation of Hepatic Leukemia Factor (HLF). Cell sorting based on the LSC marker GPR56 allowed isolation of triple-mutated from DNMT3A/NPM1 double-mutated subclones. Moreover, in DNMT3A R882-mutated patients, CpG hypomethylation at the HLF transcription start site correlated with high HLF mRNA expression, which was itself associated with poor survival. Loss of HLF via CRISPR/Cas9 significantly reduced the CD34+GPR56+ LSC compartment of primary human triple-mutated AML cells in serial xenotransplantation assays. HLF knockout cells were more actively cycling when freshly harvested from mice, but rapidly exhausted when reintroduced in culture. RNA sequencing of primary human triple-mutated AML cells after shRNA-mediated HLF knockdown revealed the NOTCH target Hairy and Enhancer of Split 1 (HES1) and the cyclin-dependent kinase inhibitor CDKN1C/p57 as novel targets of HLF, potentially mediating these effects. Overall, our data establish HLF as a novel LSC regulator in this genetically defined high-risk AML subgroup.

Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases.

Copy number variations (CNVs) are genomic structural variations (deletions, duplications, or translocations) that represent the 4.8-9.5% of human genome variation in healthy individuals. In some cases, CNVs can also lead to disease, being the etiology of many known rare genetic/genomic disorders. Despite the last advances in genomic sequencing and diagnosis, the pathological effects of many rare genetic variations remain unresolved, largely due to the low number of patients available for these cases, making it difficult to identify consistent patterns of genotype-phenotype relationships. We aimed to improve the identification of statistically consistent genotype-phenotype relationships by integrating all the genetic and clinical data of thousands of patients with rare genomic disorders (obtained from the DECIPHER database) into a phenotype-patient-genotype tripartite network. Then we assessed how our network approach could help in the characterization and diagnosis of novel cases in clinical genetics. The systematic approach implemented in this work is able to better define the relationships between phenotypes and specific loci, by exploiting large-scale association networks of phenotypes and genotypes in thousands of rare disease patients. The application of the described methodology facilitated the diagnosis of novel clinical cases, ranking phenotypes by locus specificity and reporting putative new clinical features that may suggest additional clinical follow-ups. In this work, the proof of concept developed over a set of novel clinical cases demonstrates that this network-based methodology might help improve the precision of patient clinical records and the characterization of rare syndromes.

Systematic identification of phenotypically enriched loci using a patient network of genomic disorders.

BACKGROUND: Network medicine is a promising new discipline that combines systems biology approaches and network science to understand the complexity of pathological phenotypes. Given the growing availability of personalized genomic and phenotypic profiles, network models offer a robust integrative framework for the analysis of "omics" data, allowing the characterization of the molecular aetiology of pathological processes underpinning genetic diseases. METHODS: Here we make use of patient genomic data to exploit different network-based analyses to study genetic and phenotypic relationships between individuals. For this method, we analyzed a dataset of structural variants and phenotypes for 6,564 patients from the DECIPHER database, which encompasses one of the most comprehensive collections of pathogenic Copy Number Variations (CNVs) and their associated ontology-controlled phenotypes. We developed a computational strategy that identifies clusters of patients in a synthetic patient network according to their genetic overlap and phenotype enrichments. RESULTS: Many of these clusters of patients represent new genotype-phenotype associations, suggesting the identification of newly discovered phenotypically enriched loci (indicative of potential novel syndromes) that are currently absent from reference genomic disorder databases such as ClinVar, OMIM or DECIPHER itself. CONCLUSIONS: We provide a high-resolution map of pathogenic phenotypes associated with their respective significant genomic regions and a new powerful tool for diagnosis of currently uncharacterized mutations leading to deleterious phenotypes and syndromes.

kpath: integration of metabolic pathway linked data.

In the last few years, the Life Sciences domain has experienced a rapid growth in the amount of available biological databases. The heterogeneity of these databases makes data integration a challenging issue. Some integration challenges are locating resources, relationships, data formats, synonyms or ambiguity. The Linked Data approach partially solves the heterogeneity problems by introducing a uniform data representation model. Linked Data refers to a set of best practices for publishing and connecting structured data on the Web. This article introduces kpath, a database that integrates information related to metabolic pathways. kpath also provides a navigational interface that enables not only the browsing, but also the deep use of the integrated data to build metabolic networks based on existing disperse knowledge. This user interface has been used to showcase relationships that can be inferred from the information available in several public databases.

PhenUMA: a tool for integrating the biomedical relationships among genes and diseases.

BACKGROUND: Several types of genetic interactions in humans can be directly or indirectly associated with the causal effects of mutations. These interactions are usually based on their co-associations to biological processes, coexistence in cellular locations, coexpression in cell lines, physical interactions and so on. In addition, pathological processes can present similar phenotypes that have mutations either in the same genomic location or in different genomic regions. Therefore, integrative resources for all of these complex interactions can help us prioritize the relationships between genes and diseases that are most deserving to be studied by researchers and physicians. RESULTS: PhenUMA is a web application that displays biological networks using information from biomedical and biomolecular data repositories. One of its most innovative features is to combine the benefits of semantic similarity methods with the information taken from databases of genetic diseases and biological interactions. More specifically, this tool is useful in studying novel pathological relationships between functionally related genes, merging diseases into clusters that share specific phenotypes or finding diseases related to reported phenotypes. CONCLUSIONS: This framework builds, analyzes and visualizes networks based on both functional and phenotypic relationships. The integration of this information helps in the discovery of alternative pathological roles of genes, biological functions and diseases. PhenUMA represents an advancement toward the use of new technologies for genomics and personalized medicine.

Biocomputational resources useful for drug discovery against compartmentalized targets.

It has been estimated that the cost of bringing a new drug onto the market is 10 years and 0.5-2 billions of dollars, making it a non-profitable project, particularly in the case of low prevalence diseases. The advances in Systems Biology have been absolutely decisive for drug discovery, as iterative rounds of predictions made from in silico models followed by selected experimental validations have resulted in a substantial saving of time and investments. Many diseases have their origins in proteins that are not located in the cytosol but in intracellular compartments (i.e. mitochondria, lysosome, peroxisome and others) or cell membranes. In these cases, biocomputational approaches present limitations to their study. In the present work, we review them and propose new initiatives to advance towards a safer, more efficient and personalized pharmacology. This focus could be especially useful for drug discovery and the reposition of known drugs in rare and emergent diseases associated with compartmentalized proteins.

Correction: Global Analysis of the Human Pathophenotypic Similarity Gene Network Merges Disease Module Components.

[This corrects the article on p. e56653 in vol. 8.].

Global analysis of the human pathophenotypic similarity gene network merges disease module components.

The molecular complexity of genetic diseases requires novel approaches to break it down into coherent biological modules. For this purpose, many disease network models have been created and analyzed. We highlight two of them, "the human diseases networks" (HDN) and "the orphan disease networks" (ODN). However, in these models, each single node represents one disease or an ambiguous group of diseases. In these cases, the notion of diseases as unique entities reduces the usefulness of network-based methods. We hypothesize that using the clinical features (pathophenotypes) to define pathophenotypic connections between disease-causing genes improve our understanding of the molecular events originated by genetic disturbances. For this, we have built a pathophenotypic similarity gene network (PSGN) and compared it with the unipartite projections (based on gene-to-gene edges) similar to those used in previous network models (HDN and ODN). Unlike these disease network models, the PSGN uses semantic similarities. This pathophenotypic similarity has been calculated by comparing pathophenotypic annotations of genes (human abnormalities of HPO terms) in the "Human Phenotype Ontology". The resulting network contains 1075 genes (nodes) and 26197 significant pathophenotypic similarities (edges). A global analysis of this network reveals: unnoticed pairs of genes showing significant pathophenotypic similarity, a biological meaningful re-arrangement of the pathological relationships between genes, correlations of biochemical interactions with higher similarity scores and functional biases in metabolic and essential genes toward the pathophenotypic specificity and the pleiotropy, respectively. Additionally, pathophenotypic similarities and metabolic interactions of genes associated with maple syrup urine disease (MSUD) have been used to merge into a coherent pathological module.Our results indicate that pathophenotypes contribute to identify underlying co-dependencies among disease-causing genes that are useful to describe disease modularity.

What is known on angiogenesis-related rare diseases? A systematic review of literature.

Angiogenesis, the formation of new vessels from pre-existing ones, is essential during ontogenetic development and is related to many important physio-pathological processes in the adult. In fact, a persistent and deregulated angiogenesis is a required event for many diseases and pathological situations, including cancer progression and metastasis. Some rare diseases are also angiogenesis-related pathologies. However, there is a lack of an exhaustive review on the topic. The main purpose of this work is to carry out a systematic review of literature to determine what (and how much) scientific information concerning angiogenesis-related rare diseases can be extracted from available sources. After exhaustive searches in bibliographic databases, preselected data were filtered by selecting only those articles on rare diseases with an Orpha number hosted in the Orphanet web. The selected bibliographic references were further curated manually. With the 187 selected references, a critical reading and analysis was carried out allowing for an identification and classification of angiogenesis-related rare diseases, the involved genes and the drugs available for their treatment, all on the basis of the information available in Orphanet database.