SHP-2 and PD-1-SHP-2 signaling regulate myeloid cell differentiation and antitumor responses.

The inhibitory receptor PD-1 suppresses T cell activation by recruiting the phosphatase SHP-2. However, mice with a T-cell-specific deletion of SHP-2 do not have improved antitumor immunity. Here we showed that mice with conditional targeting of SHP-2 in myeloid cells, but not in T cells, had diminished tumor growth. RNA sequencing (RNA-seq) followed by gene set enrichment analysis indicated the presence of polymorphonuclear myeloid-derived suppressor cells and tumor-associated macrophages (TAMs) with enriched gene expression profiles of enhanced differentiation, activation and expression of immunostimulatory molecules. In mice with conditional targeting of PD-1 in myeloid cells, which also displayed diminished tumor growth, TAMs had gene expression profiles enriched for myeloid differentiation, activation and leukocyte-mediated immunity displaying >50% overlap with enriched profiles of SHP-2-deficient TAMs. In bone marrow, GM-CSF induced the phosphorylation of PD-1 and recruitment of PD-1-SHP-2 to the GM-CSF receptor. Deletion of SHP-2 or PD-1 enhanced GM-CSF-mediated phosphorylation of the transcription factors HOXA10 and IRF8, which regulate myeloid differentiation and monocytic-moDC lineage commitment, respectively. Thus, SHP-2 and PD-1-SHP-2 signaling restrained myelocyte differentiation resulting in a myeloid landscape that suppressed antitumor immunity.

Organ Mapping Antibody Panels: a community resource for standardized multiplexed tissue imaging.

Multiplexed antibody-based imaging enables the detailed characterization of molecular and cellular organization in tissues. Advances in the field now allow high-parameter data collection (>60 targets); however, considerable expertise and capital are needed to construct the antibody panels employed by these methods. Organ mapping antibody panels are community-validated resources that save time and money, increase reproducibility, accelerate discovery and support the construction of a Human Reference Atlas.

Prediction of disease-free survival for precision medicine using cooperative learning on multi-omic data.

In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.

PanomiR: a systems biology framework for analysis of multi-pathway targeting by miRNAs.

Charting microRNA (miRNA) regulation across pathways is key to characterizing their function. Yet, no method currently exists that can quantify how miRNAs regulate multiple interconnected pathways or prioritize them for their ability to regulate coordinate transcriptional programs. Existing methods primarily infer one-to-one relationships between miRNAs and pathways using differentially expressed genes. We introduce PanomiR, an in silico framework for studying the interplay of miRNAs and disease functions. PanomiR integrates gene expression, mRNA-miRNA interactions and known biological pathways to reveal coordinated multi-pathway targeting by miRNAs. PanomiR utilizes pathway-activity profiling approaches, a pathway co-expression network and network clustering algorithms to prioritize miRNAs that target broad-scale transcriptional disease phenotypes. It directly resolves differential regulation of pathways, irrespective of their differential gene expression, and captures co-activity to establish functional pathway groupings and the miRNAs that may regulate them. PanomiR uses a systems biology approach to provide broad but precise insights into miRNA-regulated functional programs. It is available at https://bioconductor.org/packages/PanomiR.

DIANA-miTED: a microRNA tissue expression database.

microRNAs (miRNAs) are short (∼23nt) single-stranded non-coding RNAs that act as potent post-transcriptional gene expression regulators. Information about miRNA expression and distribution across cell types and tissues is crucial to the understanding of their function and for their translational use as biomarkers or therapeutic targets. DIANA-miTED is the most comprehensive and systematic collection of miRNA expression values derived from the analysis of 15 183 raw human small RNA-Seq (sRNA-Seq) datasets from the Sequence Read Archive (SRA) and The Cancer Genome Atlas (TCGA). Metadata quality maximizes the utility of expression atlases, therefore we manually curated SRA and TCGA-derived information to deliver a comprehensive and standardized set, incorporating in total 199 tissues, 82 anatomical sublocations, 267 cell lines and 261 diseases. miTED offers rich instant visualizations of the expression and sample distributions of requested data across variables, as well as study-wide diagrams and graphs enabling efficient content exploration. Queries also generate links towards state-of-the-art miRNA functional resources, deeming miTED an ideal starting point for expression retrieval, exploration, comparison, and downstream analysis, without requiring bioinformatics support or expertise. DIANA-miTED is freely available at http://www.microrna.gr/mited.

The causality measure of partial mutual information from mixed embedding (PMIME) revisited.

The measure of partial mutual information from mixed embedding (PMIME) is an information theory-based measure to accurately identify the direct and directional coupling, termed Granger causality or simply causality, between the observed variables or subsystems of a high-dimensional dynamical and complex system, without any a priori assumptions about the nature of the coupling relationship. In its core, it is a forward selection procedure that aims to iteratively identify the lag-dependence structure of a given observed variable (response) to all the other observed variables (candidate drivers). This model-free approach is capable of detecting nonlinear interactions, abundantly present in real-world complex systems, and it was shown to perform well on multivariate time series of moderately high dimension. However, the PMIME presents some inefficiencies in its performance mainly when applied on strongly stochastic (linear or nonlinear) systems as it may falsely detect non-existent relationships. Moreover, and by construction, the measure cannot extract purely synergetic relationships present in a system. In the current work, the issue of false detections is addressed by introducing an improved resampling significance test and a procedure of rechecking the identified drivers (backward revision). Regarding the inability to detect synergetic relationships, the PMIME is further enhanced by checking pairs as candidate drivers for the response variable after having considered all drivers individually. The effects of these modifications are investigated in a systematic simulation study on properly designed systems involving strong stochasticity, regressor terms with synergetic effects, and a system dimension ranging from 3 to 30. The overall results of the simulations indicate that these modifications indeed improve the performance of PMIME and alleviate to a significant degree the issues of the original algorithm. Guidelines for balancing between accuracy and computational efficiency are also given, particularly relevant for real-world applications. Finally, the measure performance is investigated in the study of futures of various government bonds and stock market indices in the period around COVID-19 pandemic.

Neutrophil extracellular traps contribute to liver damage and increase defective low-density neutrophils in alcohol-associated hepatitis.

BACKGROUND & AIMS: In alcohol-associated hepatitis (AH), inflammation and neutrophil counts correlate with poor clinical outcomes. Here, we investigated how neutrophils contribute to liver damage in AH. METHODS: We isolated blood neutrophils from individuals with AH to examine neutrophil extracellular traps (NETs) and performed RNA sequencing to explore their unique characteristics. RESULTS: We observed a significant increase in NET production in AH. We also observed a unique low-density neutrophil (LDN) population in individuals with AH and alcohol-fed mice that was not present in healthy controls. Transcriptome analysis of peripheral LDNs and high-density neutrophils (HDNs) from individuals with AH revealed that LDNs exhibit a functionally exhausted phenotype, while HDNs are activated. Indeed, AH HDNs exhibited increased resting reactive oxygen species (ROS) production and produced more ROS upon lipopolysaccharide stimulation than control HDNs, whereas AH LDNs failed to respond to lipopolysaccharide. We show that LDNs are generated from HDNs after alcohol-induced NET release in vitro, and this LDN subset has decreased functionality, including reduced phagocytic capacity. Moreover, LDNs showed reduced homing capacity and clearance by macrophage efferocytosis; therefore, dysfunctional neutrophils could remain in the circulation and liver. Depletion of both HDNs and LDNs in vivo prevented alcohol-induced NET production and liver damage in mice. Granulocyte-colony stimulating factor treatment also ameliorated alcohol-induced liver injury in mice. CONCLUSION: Neutrophils contribute to liver damage through increased NET formation which increases defective LDNs in AH. Alcohol induces phenotypic changes in neutrophils; HDNs are activated whereas LDNs are defective. Our findings provide mechanistic insights that could guide the development of therapeutic interventions for AH. IMPACT AND IMPLICATIONS: In this study we discovered heterogeneity of neutrophils in alcohol-associated hepatitis, including high-density and low-density neutrophils that show hyper-activated or exhausted transcriptomic profiles, respectively. We found that alcohol induces neutrophil extracellular trap (NET) formation, which contributes to liver damage. NET release by high-density neutrophils resulted in low-density neutrophils that reside in the liver and escape clean-up by macrophages. Our findings help to understand the opposing neutrophil phenotypes observed in individuals with alcohol-associated hepatitis and provide mechanistic insights that could guide therapeutic strategies targeting neutrophils.

Toker Cell Hyperplasia in the Nipple-Areolar Complex of Transmasculine Individuals.

We previously reported breast histopathologic features associated with testosterone therapy in transmasculine chest-contouring surgical specimens. During that study, we observed a high frequency of intraepidermal glands in the nipple-areolar complex (NAC) formed by Toker cells. This study reports Toker cell hyperplasia (TCH)-the presence of clusters of Toker cells consisting of at least 3 contiguous cells and/or glands with lumen formation-in the transmasculine population. Increased numbers of singly dispersed Toker cells were not considered TCH. Among the 444 transmasculine individuals, 82 (18.5%) had a portion of their NAC excised and available for evaluation. We also reviewed the NACs from 55 cisgender women who were aged <50 years old and had full mastectomies. The proportion of transmasculine cases with TCH (20/82; 24.4%) was 1.7-fold higher than cisgender women (8/55; 14.5%) but did not achieve significance (P = .20). However, in cases with TCH, the rate of gland formation is 2.4-fold higher in transmasculine cases, achieving borderline significance (18/82 vs 5/55; P = .06). Among transmasculine individuals, TCH was significantly more likely to be present in those with higher body mass index (P = .03). A subset of 5 transmasculine and 5 cisgender cases were stained for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), androgen receptor (AR), cytokeratin 7, and Ki67. All 10 cases were cytokeratin 7+ and Ki67-; 9 out of 10 cases were AR+. Toker cells in transmasculine cases demonstrated variable expression of ER, PR, and HER2. For cisgender cases, Toker cells were consistently ER+, PR-, and HER2-. In conclusion, there is a higher rate of TCH in the transmasculine than cisgender population, particularly among transmasculine individuals with high body mass index and taking testosterone. To our knowledge, this is the first study to demonstrate that Toker cells are AR+. Toker cell features display variable ER, PR, and HER2 immunoreactivity. The clinical significance of TCH in the transmasculine population remains to be elucidated.

Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis.

BACKGROUND: Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking. AIMS: To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models. METHODS: A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t-test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models). RESULTS: A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI-based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74-0.85) versus 0.61 (95% CI: 0.54-0.68), respectively (p < .001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination. CONCLUSION: The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models.

MicroRNA-22 Is a Key Regulator of Lipid and Metabolic Homeostasis.

Obesity is a growing public health problem associated with increased risk of type 2 diabetes, cardiovascular disease, nonalcoholic fatty liver disease (NAFLD) and cancer. Here, we identify microRNA-22 (miR-22) as an essential rheostat involved in the control of lipid and energy homeostasis as well as the onset and maintenance of obesity. We demonstrate through knockout and transgenic mouse models that miR-22 loss-of-function protects against obesity and hepatic steatosis, while its overexpression promotes both phenotypes even when mice are fed a regular chow diet. Mechanistically, we show that miR-22 controls multiple pathways related to lipid biogenesis and differentiation. Importantly, genetic ablation of miR-22 favors metabolic rewiring towards higher energy expenditure and browning of white adipose tissue, suggesting that modulation of miR-22 could represent a viable therapeutic strategy for treatment of obesity and other metabolic disorders.

Detecting Nonlinear Interactions in Complex Systems: Application in Financial Markets.

Emerging or diminishing nonlinear interactions in the evolution of a complex system may signal a possible structural change in its underlying mechanism. This type of structural break may exist in many applications, such as in climate and finance, and standard methods for change-point detection may not be sensitive to it. In this article, we present a novel scheme for detecting structural breaks through the occurrence or vanishing of nonlinear causal relationships in a complex system. A significance resampling test was developed for the null hypothesis (H0) of no nonlinear causal relationships using (a) an appropriate Gaussian instantaneous transform and vector autoregressive (VAR) process to generate the resampled multivariate time series consistent with H0; (b) the modelfree Granger causality measure of partial mutual information from mixed embedding (PMIME) to estimate all causal relationships; and (c) a characteristic of the network formed by PMIME as test statistic. The significance test was applied to sliding windows on the observed multivariate time series, and the change from rejection to no-rejection of H0, or the opposite, signaled a non-trivial change of the underlying dynamics of the observed complex system. Different network indices that capture different characteristics of the PMIME networks were used as test statistics. The test was evaluated on multiple synthetic complex and chaotic systems, as well as on linear and nonlinear stochastic systems, demonstrating that the proposed methodology is capable of detecting nonlinear causality. Furthermore, the scheme was applied to different records of financial indices regarding the global financial crisis of 2008, the two commodity crises of 2014 and 2020, the Brexit referendum of 2016, and the outbreak of COVID-19, accurately identifying the structural breaks at the identified times.

Serine and arginine rich splicing factor 1 deficiency alters pathways involved in IL-17A expression and is implicated in human psoriasis.

Serine and Arginine Rich Splicing Factor 1 (SRSF1) is a splicing factor that binds to exonic enhancers and stimulates splicing and is previously implicated with autoimmunity. Herein, we investigate the role of SRSF1 in regulating innate immune functions that are pertinent in the pathogenesis of auto-inflammatory diseases. Specifically, we show that conditional deletion of SRSF1 in mature lymphocytes resulted in higher expression of il-17a and il-17 f and an expansion of IL17A+ CD8 T cells. Mechanistically, the aberrant expression of IL-17A in SRSF1 cKO mice could not be attributed to alternative splicing of il-17a or il-17 f genes but possibly to defective CD11B+LY6C+ myeloid derived suppressor function in the spleen. Finally, meta-analysis of RNA-Seq collected from psoriasis patients demonstrate a clear correlation between SRSF1 and psoriasis that suggests a putative role of SRSF1 in IL-17A-induced psoriasis.

Genetic Variation in the Mitochondrial Glycerol-3-Phosphate Acyltransferase Is Associated With Liver Injury.

BACKGROUND AND AIMS: Most of the genetic basis of chronic liver disease remains undiscovered. APPROACH AND RESULTS: To identify genetic loci that modulate the risk of liver injury, we performed genome-wide association studies on circulating levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and total bilirubin across 312,671 White British participants in the UK Biobank. We focused on variants associated with elevations in all four liver biochemistries at genome-wide significance (P < 5 × 10-8 ) and that replicated using Mass General Brigham Biobank in 19,323 European ancestry individuals. We identified a genetic locus in mitochondrial glycerol-3-phosphate acyltransferase (GPAM rs10787429) associated with increased levels of ALT (P = 1.4 × 10-30 ), AST (P = 3.6 × 10-10 ), ALP (P = 9.5 × 10-30 ), and total bilirubin (P = 2.9 × 10-12 ). This common genetic variant was also associated with an allele dose-dependent risk of alcohol-associated liver disease (odd ratio [OR] = 1.34, P = 2.6 × 10-5 ) and fatty liver disease (OR = 1.18, P = 5.8 × 10-4 ) by International Classification of Diseases, 10th Revision codes. We identified significant interactions between GPAM rs10787429 and elevated body mass index in association with ALT and AST (P = 7.1 × 10-9 and 3.95 × 10-8 , respectively), as well as between GPAM rs10787429 and weekly alcohol consumption in association with ALT, AST, and alcohol-associated liver disease (P = 4.0 × 10-2 , 1.6 × 10-2 , and 1.3 × 10-2 , respectively). Unlike previously described genetic variants that are associated with an increased risk of liver injury but confer a protective effect on circulating lipids, GPAM rs10787429 was associated with an increase in total cholesterol (P = 2.0 × 10-17 ), LDL cholesterol (P = 2.0 × 10-10 ), and HDL cholesterol (P = 6.6 × 10-37 ). Single-cell RNA-sequencing data demonstrated hepatocyte-predominant expression of GPAM in cells that co-express genes related to VLDL production (P = 9.4 × 10-103 ). CONCLUSIONS: Genetic variation in GPAM is associated with susceptibility to liver injury. GPAM may represent a therapeutic target in chronic liver disease.

Phase-based causality analysis with partial mutual information from mixed embedding.

Instantaneous phases extracted from multivariate time series can retain information about the relationships between the underlying mechanisms that generate the series. Although phases have been widely used in the study of nondirectional coupling and connectivity, they have not found similar appeal in the study of causality. Herein, we present a new method for phase-based causality analysis, which combines ideas from the mixed embedding technique and the information-theoretic approach to causality in coupled oscillatory systems. We then use the introduced method to investigate causality in simulated datasets of bivariate, unidirectionally paired systems from combinations of Rössler, Lorenz, van der Pol, and Mackey-Glass equations. We observe that causality analysis using the phases can capture the true causal relation for coupling strength smaller than the analysis based on the amplitudes can capture. On the other hand, the causality estimation based on the phases tends to have larger variability, which is attributed more to the phase extraction process than the actual phase-based causality method. In addition, an application on real electroencephalographic data from an experiment on elicited human emotional states reinforces the usefulness of phases in causality identification.

CanVaS: Documenting the genetic variation spectrum of Greek cancer patients.

National genetic variation registries vastly increase the level of detail for the relevant population, while directly affecting patient management. Herein, we report CanVaS, a Cancer Variation reSource aiming to document the genetic variation of cancer patients in Greece. CanVaS comprises germline genetic data from 7,363 Greek individuals with a personal and/or family history of malignancy. The data set incorporates approximately 24,000 functionally annotated rare variants in 97 established or suspected cancer susceptibility genes. For each variant, allele frequency for the Greek population, interpretation for clinical significance, anonymized family and segregation information, as well as phenotypic traits of the carriers, are included. Moreover, information on the geographic distribution of the variants across the country is provided, enabling the study of Greek population isolates. Direct comparisons between Greek (sub)populations with relevant genetic resources are supported, allowing fine-grain localized adjustment of guidelines and clinical decision-making. Most importantly, anonymized data are available for download, while the Leiden Open Variation Database schema is adopted, enabling integration/interconnection with central resources. CanVaS could become a stepping-stone for a countrywide effort to characterize the cancer genetic variation landscape, concurrently supporting national and international cancer research. The database can be accessed at: http://ithaka.rrp.demokritos.gr/CanVaS.

Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API.

MOTIVATION: MicroRNAs (miRNAs) are small RNA molecules (∼22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, the diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods. RESULTS: To overcome this situation, we present here a community-based project, miRNA Transcriptomic Open Project (miRTOP) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream isomiR analysis tools that are compatible with existing detection and quantification tools. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to create and manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, isomiR-SEA, sRNAbench, Prost! as well as BAM files. Some tools have also incorporated the mirGFF3 format directly into their code, such as, miRge2.0, IsoMIRmap and OptimiR. Its open architecture enables any tool or pipeline to output or convert results into mirGFF3. Collectively, this isomiR categorization system, along with the accompanying mirGFF3 and mirtop API, provide a comprehensive solution for the standardization of miRNA and isomiR annotation, enabling data sharing, reporting, comparative analyses and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps of miRNA detection, annotation and quantification. AVAILABILITY AND IMPLEMENTATION: https://github.com/miRTop/mirGFF3/ and https://github.com/miRTop/mirtop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

One in three highly selected Greek patients with breast cancer carries a loss-of-function variant in a cancer susceptibility gene.

BACKGROUND: Gene panel testing has become the norm for assessing breast cancer (BC) susceptibility, but actual cancer risks conferred by genes included in panels are not established. Contrarily, deciphering the missing hereditability on BC, through identification of novel candidates, remains a challenge. We aimed to investigate the mutation prevalence and spectra in a highly selected cohort of Greek patients with BC, questioning an extensive number of genes, implicated in cancer predisposition and DNA repair, while calculating gene-specific BC risks that can ultimately lead to important associations. METHODS: To further discern BC susceptibility, a comprehensive 94-cancer gene panel was implemented in a cohort of 1382 Greek patients with BC, highly selected for strong family history and/or very young age (<35 years) at diagnosis, followed by BC risk calculation, based on a case-control analysis. RESULTS: Herein, 31.5% of patients tested carried pathogenic variants (PVs) in 28 known, suspected or candidate BC predisposition genes. In total, 24.8% of the patients carried BRCA1/2 loss-of-function variants. An additional 6.7% carried PVs in additional genes, the vast majority of which can be offered meaningful clinical changes. Significant association to BC predisposition was observed for ATM, PALB2, TP53, RAD51C and CHEK2 PVs. Primarily, compared with controls, RAD51C PVs and CHEK2 damaging missense variants were associated with high (ORs 6.19 (Exome Aggregation Consortium (ExAC)) and 12.6 (Fabulous Ladies Over Seventy (FLOSSIES)), p<0.01) and moderate BC risk (ORs 3.79 (ExAC) and 5.9 (FLOSSIES), p<0.01), respectively. CONCLUSION: Studying a large and unique cohort of highly selected patients with BC, deriving from a population with founder effects, provides important insight on distinct associations, pivotal for patient management.

DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions.

DIANA-TarBase v8 (http://www.microrna.gr/tarbase) is a reference database devoted to the indexing of experimentally supported microRNA (miRNA) targets. Its eighth version is the first database indexing >1 million entries, corresponding to ∼670 000 unique miRNA-target pairs. The interactions are supported by >33 experimental methodologies, applied to ∼600 cell types/tissues under ∼451 experimental conditions. It integrates information on cell-type specific miRNA-gene regulation, while hundreds of thousands of miRNA-binding locations are reported. TarBase is coming of age, with more than a decade of continuous support in the non-coding RNA field. A new module has been implemented that enables the browsing of interactions through different filtering combinations. It permits easy retrieval of positive and negative miRNA targets per species, methodology, cell type and tissue. An incorporated ranking system is utilized for the display of interactions based on the robustness of their supporting methodologies. Statistics, pie-charts and interactive bar-plots depicting the database content are available through a dedicated result page. An intuitive interface is introduced, providing a user-friendly application with flexible options to different queries.

Utilization of New Technologies in the Production of Pharmaceutical Olive Oil.

Olive oil is a key ingredient in the Mediterranean diet and offers many health benefits. However, many factors affect the quality and quantity of olive oil such as olive tree diseases and olive-related pests. Unfortunately, the procedure of identifying pests or the outbreak of a disease is time-consuming, and it depends heavily on the size of the olive grove. Through the use of ICT, remote monitoring of the olive grove can be achieved, by collecting environment-related data and having an overview of the olive grove's overall health. In this paper we propose a low-cost dense network of sensors that collects daily data regarding the olive grove, thus, providing the possibility to prevent infestation of olive fruit fly and/or the outbreak of olive tree-related disease.

Defective synaptic connectivity and axonal neuropathology in a human iPSC-based model of familial Parkinson's disease.

α-Synuclein (αSyn) is the major gene linked to sporadic Parkinson's disease (PD), whereas the G209A (p.A53T) αSyn mutation causes a familial form of PD characterized by early onset and a generally severe phenotype, including nonmotor manifestations. Here we generated de novo induced pluripotent stem cells (iPSCs) from patients harboring the p.A53T mutation and developed a robust model that captures PD pathogenic processes under basal conditions. iPSC-derived mutant neurons displayed novel disease-relevant phenotypes, including protein aggregation, compromised neuritic outgrowth, and contorted or fragmented axons with swollen varicosities containing αSyn and Tau. The identified neuropathological features closely resembled those in brains of p.A53T patients. Small molecules targeting αSyn reverted the degenerative phenotype under both basal and induced stress conditions, indicating a treatment strategy for PD and other synucleinopathies. Furthermore, mutant neurons showed disrupted synaptic connectivity and widespread transcriptional alterations in genes involved in synaptic signaling, a number of which have been previously linked to mental disorders, raising intriguing implications for potentially converging disease mechanisms.