HELP: A computational framework for labelling and predicting human common and context-specific essential genes.

Machine learning-based approaches are particularly suitable for identifying essential genes as they allow the generation of predictive models trained on features from multi-source data. Gene essentiality is neither binary nor static but determined by the context. The databases for essential gene annotation do not permit the personalisation of the context, and their update can be slower than the publication of new experimental data.

Identification of Molecular Markers Associated with Prostate Cancer Subtypes: An Integrative Bioinformatics Approach.

Prostate cancer (PCa) is characterised by androgen dependency. Unfortunately, under anti-androgen treatment pressure, castration-resistant prostate cancer (CRPC) emerges, characterised by heterogeneous cell populations that, over time, lead to the development of different androgen-dependent or -independent phenotypes. Despite important advances in therapeutic strategies, CRPC remains incurable.

Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience.

Gene essentiality is a genetic concept crucial for a comprehensive understanding of life and evolution. In the last decade, many essential genes (EGs) have been determined using different experimental and computational approaches, and this information has been used to reduce the genomes of model organisms. A growing amount of evidence highlights that essentiality is a property that depends on the context.

Results and lessons learned from the sbv IMPROVER metagenomics diagnostics for inflammatory bowel disease challenge.

A growing body of evidence links gut microbiota changes with inflammatory bowel disease (IBD), raising the potential benefit of exploiting metagenomics data for non-invasive IBD diagnostics. The sbv IMPROVER metagenomics diagnosis for inflammatory bowel disease challenge investigated computational metagenomics methods for discriminating IBD and nonIBD subjects. Participants in this challenge were given independent training and test metagenomics data from IBD and nonIBD subjects, which could be wither either raw read data (sub-challenge 1, SC1) or processed Taxonomy- and Function-based profiles (sub-challenge 2, SC2).

TumorMet: A repository of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models.

Studies about the metabolic alterations during tumorigenesis have increased our knowledge of the underlying mechanisms and consequences, which are important for diagnostic and therapeutic investigations. In this scenario and in the era of systems biology, metabolic networks have become a powerful tool to unravel the complexity of the cancer metabolic machinery and the heterogeneity of this disease. Here, we present TumorMet, a repository of tumor metabolic networks extracted from context-specific Genome-Scale Metabolic Models, as a benchmark for graph machine learning algorithms and network analyses.

Early anteroposterior regionalisation of human neural crest is shaped by a pro-mesodermal factor.

The neural crest (NC) is an important multipotent embryonic cell population and its impaired specification leads to various developmental defects, often in an anteroposterior (A-P) axial level-specific manner. The mechanisms underlying the correct A-P regionalisation of human NC cells remain elusive. Recent studies have indicated that trunk NC cells, the presumed precursors of childhood tumour neuroblastoma, are derived from neuromesodermal-potent progenitors of the postcranial body.

SRSF1-dependent inhibition of C9ORF72-repeat RNA nuclear export: genome-wide mechanisms for neuroprotection in amyotrophic lateral sclerosis.

Background: Loss of motor neurons in amyotrophic lateral sclerosis (ALS) leads to progressive paralysis and death. Dysregulation of thousands of RNA molecules with roles in multiple cellular pathways hinders the identification of ALS-causing alterations over downstream changes secondary to the neurodegenerative process. How many and which of these pathological gene expression changes require therapeutic normalisation remains a fundamental question.

Netpro2vec: A Graph Embedding Framework for Biomedical Applications.

The ever-increasing importance of structured data in different applications, especially in the biomedical field, has driven the need for reducing its complexity through projections into a more manageable space. The latest methods for learning features on graphs focus mainly on the neighborhood of nodes and edges. Methods capable of providing a representation that looks beyond the single node neighborhood are kernel graphs.

Defining the signalling determinants of a posterior ventral spinal cord identity in human neuromesodermal progenitor derivatives.

The anteroposterior axial identity of motor neurons (MNs) determines their functionality and vulnerability to neurodegeneration. Thus, it is a crucial parameter in the design of strategies aiming to produce MNs from human pluripotent stem cells (hPSCs) for regenerative medicine/disease modelling applications. However, the generation of posterior MNs corresponding to the thoracic/lumbosacral spinal cord has been challenging.

Learning from Metabolic Networks: Current Trends and Future Directions for Precision Medicine.

Background: Systems biology and network modeling represent, nowadays, the hallmark approaches for the development of predictive and targeted-treatment based precision medicine. The study of health and disease as properties of the human body system allows the understanding of the genotype-phenotype relationship through the definition of molecular interactions and dependencies. In this scenario, metabolism plays a central role as its interactions are well characterized and it is considered an important indicator of the genotype- phenotype associations.

Duodenal Metatranscriptomics to Define Human and Microbial Functional Alterations Associated with Severe Obesity: A Pilot Study.

Obesity is a multifactorial disorder, and the gut microbiome has been suggested to contribute to its onset. In order to better clarify the role of the microbiome in obesity, we evaluated the metatranscriptome in duodenal biopsies from a cohort of 23 adult severely obese and lean control subjects using next generation sequencing. Our aim was to provide a general picture of the duodenal metatranscriptome associated with severe obesity.

Correction to: Clustering analysis of tumor metabolic networks.

An amendment to this paper has been published and can be accessed via the original article.

Clustering analysis of tumor metabolic networks.

Background: Biological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes.

Characterization of the Duodenal Mucosal Microbiome in Obese Adult Subjects by 16S rRNA Sequencing.

The gut microbiota may have an impact on obesity. To date, the majority of studies in obese patients reported microbiota composition in stool samples. The aim of this study was to investigate the duodenal mucosa dysbiosis in adult obese individuals from Campania, a region in Italy with a very high percentage of obese people, to highlight microbial taxa likely associated with obesity.

Novel Aptamers Selected on Living Cells for Specific Recognition of Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is a high heterogeneous group of tumors with a distinctly aggressive nature and high rates of relapse. So far, the lack of any known targetable proteins has not allowed a specific anti-tumor treatment. Therefore, the identification of novel agents for specific TNBC targeting and treatment is desperately needed.

Setup of Quantitative PCR for Oral spp. Evaluation in Celiac Disease Diagnosis.

Coeliac disease (CD) is a multifactorial autoimmune disorder and gut dysbiosis contributes to its pathogenesis. We previously profiled by 16S rRNA sequencing duodenal and oropharyngeal microbiomes in active CD (a-CD), gluten-free diet (GFD) patients, and controls (CO) and found significantly higher levels of spp., with pro-inflammatory activities, in a-CD patients than in the other two groups.

Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer.

Background: Obesity is a complex disorder associated with an increased risk of developing several comorbid chronic diseases, including postmenopausal breast cancer. Although many studies have investigated this issue, the link between body weight and either risk or poor outcome of breast cancer is still to characterize. Systems biology approaches, based on the integration of multiscale models and data from a wide variety of sources, are particularly suitable for investigating the underlying molecular mechanisms of complex diseases.

From trash to treasure: detecting unexpected contamination in unmapped NGS data.

Background: Next Generation Sequencing (NGS) experiments produce millions of short sequences that, mapped to a reference genome, provide biological insights at genomic, transcriptomic and epigenomic level. Typically the amount of reads that correctly maps to the reference genome ranges between 70% and 90%, leaving in some cases a consistent fraction of unmapped sequences. This 'misalignment' can be ascribed to low quality bases or sequence differences between the sample reads and the reference genome.

Exploiting single-cell RNA sequencing data to link alternative splicing and cancer heterogeneity: A computational approach.

Cell heterogeneity studies using single-cell sequencing are gaining great significance in the era of personalized medicine. In particular, characterization of tumor heterogeneity is an emergent issue to improve clinical oncology, since both inter- and intra-tumor level heterogeneity influence the utility and application of molecular classifications through specific biomarkers. Majority of studies have exploited gene expression to discriminate cell types.

Human axial progenitors generate trunk neural crest cells in vitro.

The neural crest (NC) is a multipotent embryonic cell population that generates distinct cell types in an axial position-dependent manner. The production of NC cells from human pluripotent stem cells (hPSCs) is a valuable approach to study human NC biology. However, the origin of human trunk NC remains undefined and current in vitro differentiation strategies induce only a modest yield of trunk NC cells.

Oropharyngeal microbiome evaluation highlights Neisseria abundance in active celiac patients.

We previously profiled duodenal microbiome in active (a-), gluten-free diet (GFD) celiac disease (CD) patients and controls finding higher levels of the Proteobacterium Neisseria flavescens in a-CD patients than in the other two groups. Here, we investigate the oropharyngeal microbiome in CD patients and controls to evaluate whether this niche share microbial composition with the duodenum. We characterized by 16S rRNA gene sequencing the oropharyngeal microbiome in 14 a-CD, 22 GFD patients and 20 controls.

A hnRNP K⁻AR-Related Signature Reflects Progression toward Castration-Resistant Prostate Cancer.

The major challenge in castration-resistant prostate cancer (CRPC) remains the ability to predict the clinical responses to improve patient selection for appropriate treatments. The finding that androgen deprivation therapy (ADT) induces alterations in the androgen receptor (AR) transcriptional program by AR coregulators activity in a context-dependent manner, offers the opportunity for identifying signatures discriminating different clinical states of prostate cancer (PCa) progression. Gel electrophoretic analyses combined with western blot showed that, in androgen-dependent PCa and CRPC in vitro models, the subcellular distribution of spliced and serine-phosphorylated heterogeneous nuclear ribonucleoprotein K (hnRNP K) isoforms can be associated with different AR activities.