B-cell receptor signaling activity identifies patients with mantle cell lymphoma at higher risk of progression

Mantle cell lymphoma (MCL) is an incurable B-cell malignancy characterized by a high clinical variability. Therefore, there is a critical need to define parameters that identify high-risk patients for aggressive disease and therapy resistance. B-cell receptor (BCR) signaling is crucial for MCL initiation and progression and is a target for therapeutic intervention. We interrogated BCR signaling proteins (SYK, LCK, BTK, PLCγ2, p38, AKT, NF-κB p65, and STAT5) in 30 primary MCL samples using phospho-specific flow cytometry. Anti-IgM modulation induced heterogeneous BCR signaling responses among samples allowing the identification of two clusters with differential responses. The cluster with higher response was associated with shorter progression free survival (PFS) and overall survival (OS). Moreover, higher constitutive AKT activity was predictive of inferior response to the Bruton's tyrosine kinase inhibitor (BTKi) ibrutinib. Time-to-event analyses showed that MCL international prognostic index (MIPI) high-risk category and higher STAT5 response were predictors of shorter PFS and OS whilst MIPI high-risk category and high SYK response predicted shorter OS. In conclusion, we identified BCR signaling properties associated with poor clinical outcome and resistance to ibrutinib, thus highlighting the prognostic and predictive significance of BCR activity and advancing our understanding of signaling heterogeneity underlying clinical behavior of MCL.

Identifying the joint signature of brain atrophy and gene variant scores in Alzheimer's Disease

The joint modeling of genetic data and brain imaging information allows for determining the pathophysiological pathways of neurodegenerative diseases such as Alzheimer's disease (AD). This task has typically been approached using mass-univariate methods that rely on a complete set of Single Nucleotide Polymorphisms (SNPs) to assess their association with selected image-derived phenotypes (IDPs). However, such methods are prone to multiple comparisons bias and, most importantly, fail to account for potential cross-feature interactions, resulting in insufficient detection of significant associations. Ways to overcome these limitations while reducing the number of traits aim at conveying genetic information at the gene level and capturing the integrated genetic effects of a set of genetic variants, rather than looking at each SNP individually. Their associations with brain IDPs are still largely unexplored in the current literature, though they can uncover new potential genetic determinants for brain modulations in the AD continuum. In this work, we explored an explainable multivariate model to analyze the genetic basis of the grey matter modulations, relying on the AD Neuroimaging Initiative (ADNI) phase 3 dataset. Cortical thicknesses and subcortical volumes derived from T1-weighted Magnetic Resonance were considered to describe the imaging phenotypes. At the same time the genetic counterpart was represented by gene variant scores extracted by the Sequence Kernel Association Test (SKAT) filtering model. Moreover, transcriptomic analysis was carried on to assess the expression of the resulting genes in the main brain structures as a form of validation. Results highlighted meaningful genotype-phenotype interactionsas defined by three latent components showing a significant difference in the projection scores between patients and controls. Among the significant associations, the model highlighted EPHX1 and BCAS1 gene variant scores involved in neurodegenerative and myelination processes, hence relevant for AD. In particular, the first was associated with decreased subcortical volumes and the second with decreasedtemporal lobe thickness. Noteworthy, BCAS1 is particularly expressed in the dentate gyrus. Overall, the proposed approach allowed capturing genotype-phenotype interactions in a restricted study cohort that was confirmed by transcriptomic analysis, offering insights into the underlying mechanisms of neurodegeneration in AD in line with previous findings and suggesting new potential disease biomarkers.

Emissions from modern engines induce distinct effects in human olfactory mucosa cells, depending on fuel and aftertreatment

Ultrafine particles (UFP) with a diameter of ≤0.1 μm, are contributors to ambient air pollution and derived mainly from traffic emissions, yet their health effects remain poorly characterized. The olfactory mucosa (OM) is located at the rooftop of the nasal cavity and directly exposed to both the environment and the brain. Mounting evidence suggests that pollutant particles affect the brain through the olfactory tract, however, the exact cellular mechanisms of how the OM responds to air pollutants remain poorly known. Here we show that the responses of primary human OM cells are altered upon exposure to UFPs and that different fuels and engines elicit different adverse effects. We used UFPs collected from exhausts of a heavy-duty-engine run with renewable diesel (A0) and fossil diesel (A20), and from a modern diesel vehicle run with renewable diesel (Euro6) and compared their health effects on the OM cells by assessing cellular processes on the functional and transcriptomic levels. Quantification revealed all samples as UFPs with the majority of particles being ≤0.1 μm by an aerodynamic diameter. Exposure to A0 and A20 induced substantial alterations in processes associated with inflammatory response, xenobiotic metabolism, olfactory signaling, and epithelial integrity. Euro6 caused only negligible changes, demonstrating the efficacy of aftertreatment devices. Furthermore, when compared to A20, A0 elicited less pronounced effects on OM cells, suggesting renewable diesel induces less adverse effects in OM cells. Prior studies and these results suggest that PAHs may disturb the inflammatory process and xenobiotic metabolism in the OM and that UFPs might mediate harmful effects on the brain through the olfactory route. This study provides important information on the adverse effects of UFPs in a human-based in vitro model, therefore providing new insight to form the basis for mitigation and preventive actions against the possible toxicological impairments caused by UFP exposure.

PanDelos-frags: A methodology for discovering pangenomic content of incomplete microbial assemblies

Pangenomics was originally defined as the problem of comparing the composition of genes into gene families within a set of bacterial isolates belonging to the same species. The problem requires the calculation of sequence homology among such genes. When combined with metagenomics, namely for human microbiome composition analysis, gene-oriented pangenome detection becomes a promising method to decipher ecosystem functions and population-level evolution. Established computational tools are able to investigate the genetic content of isolates for which a complete genomic sequence is available. However, there is a plethora of incomplete genomes that are available on public resources, which only a few tools may analyze. Incomplete means that the process for reconstructing their genomic sequence is not complete, and only fragments of their sequence are currently available. However, the information contained in these fragments may play an essential role in the analyses. Here, we present PanDelos-frags, a computational tool which exploits and extends previous results in analyzing complete genomes. It provides a new methodology for inferring missing genetic information and thus for managing incomplete genomes. PanDelos-frags outperforms state-of-the-art approaches in reconstructing gene families in synthetic benchmarks and in a real use case of metagenomics. PanDelos-frags is publicly available at https://github.com/InfOmics/PanDelos-frags.

APDB: a database on air pollutant characterization and similarity prediction

The World Health Organization estimates that 9 out of 10 people worldwide breathe air containing high levels of pollutants. Long-term and chronic exposure to high concentrations of air pollutants is associated with deleterious effects on vital organs, including increased inflammation in the lungs, oxidative stress in the heart and disruption of the blood-brain barrier. For this reason, in an effort to find an association between exposure to pollutants and the toxicological effects observable on human health, an online resource collecting and characterizing in detail pollutant molecules could be helpful to investigate their properties and mechanisms of action. We developed a database, APDB, collecting air-pollutant-related data from different online resources, in particular, molecules from the US Environmental Protection Agency, their associated targets and bioassays found in the PubChem chemical repository and their computed molecular descriptors and quantum mechanics properties. A web interface allows (i) to browse data by category, (ii) to navigate the database by querying molecules and targets and (iii) to visualize and download molecule and target structures as well as computed descriptors and similarities. The desired data can be freely exported in textual/tabular format and the whole database in SQL format. Database URL http://apdb.di.univr.it.

Esearch3D: propagating gene expression in chromatin networks to illuminate active enhancers

Most cell type-specific genes are regulated by the interaction of enhancers with their promoters. The identification of enhancers is not trivial as enhancers are diverse in their characteristics and dynamic in their interaction partners. We present Esearch3D, a new method that exploits network theory approaches to identify active enhancers. Our work is based on the fact that enhancers act as a source of regulatory information to increase the rate of transcription of their target genes and that the flow of this information is mediated by the folding of chromatin in the three-dimensional (3D) nuclear space between the enhancer and the target gene promoter. Esearch3D reverse engineers this flow of information to calculate the likelihood of enhancer activity in intergenic regions by propagating the transcription levels of genes across 3D genome networks. Regions predicted to have high enhancer activity are shown to be enriched in annotations indicative of enhancer activity. These include: enhancer-associated histone marks, bidirectional CAGE-seq, STARR-seq, P300, RNA polymerase II and expression quantitative trait loci (eQTLs). Esearch3D leverages the relationship between chromatin architecture and transcription, allowing the prediction of active enhancers and an understanding of the complex underpinnings of regulatory networks. The method is available at: https://github.com/InfOmics/Esearch3D and https://doi.org/10.5281/zenodo.7737123.

A survey on algorithms to characterize transcription factor binding sites

Transcription factors (TFs) are key regulatory proteins that control the transcriptional rate of cells by binding short DNA sequences called transcription factor binding sites (TFBS) or motifs. Identifying and characterizing TFBS is fundamental to understanding the regulatory mechanisms governing the transcriptional state of cells. During the last decades, several experimental methods have been developed to recover DNA sequences containing TFBS. In parallel, computational methods have been proposed to discover and identify TFBS motifs based on these DNA sequences. This is one of the most widely investigated problems in bioinformatics and is referred to as the motif discovery problem. In this manuscript, we review classical and novel experimental and computational methods developed to discover and characterize TFBS motifs in DNA sequences, highlighting their advantages and drawbacks. We also discuss open challenges and future perspectives that could fill the remaining gaps in the field.

Dynamic release of neuronal extracellular vesicles containing miR-21a-5p is induced by hypoxia

Hypoxia induces changes in the secretion of extracellular vesicles (EVs) in several non-neuronal cells and pathological conditions. EVs are packed with biomolecules, such as microRNA(miR)-21-5p, which respond to hypoxia. However, the true EV association of miR-21-5p, and its functional or biomarker relevance, are inadequately characterised. Neurons are extremely sensitive cells, and it is not known whether the secretion of neuronal EVs and miR-21-5p are altered upon hypoxia. Here, we characterised the temporal EV secretion profile and cell viability of neurons under hypoxia. Hypoxia induced a rapid increase of miR-21a-5p secretion in the EVs, which preceded the elevation of hypoxia-induced tissue or cellular miR-21a-5p. Prolonged hypoxia induced cell death and the release of morphologically distinct EVs. The EVs protected miR-21a-5p from enzymatic degradation but a remarkable fraction of miR-21a-5p remained fragile and non-EV associated. The increase in miR-21a-5p secretion may have biomarker potential, as high blood levels of miR-21-5p in stroke patients were associated with significant disability at hospital discharge. Our data provides an understanding of the dynamic regulation of EV secretion from neurons under hypoxia and provides a candidate for the prediction of recovery from ischemic stroke.

Human genetic diversity alters off-target outcomes of therapeutic gene editing

CRISPR gene editing holds great promise to modify DNA sequences in somatic cells to treat disease. However, standard computational and biochemical methods to predict off-target potential focus on reference genomes. We developed an efficient tool called CRISPRme that considers single-nucleotide polymorphism (SNP) and indel genetic variants to nominate and prioritize off-target sites. We tested the software with a BCL11A enhancer targeting guide RNA (gRNA) showing promise in clinical trials for sickle cell disease and β-thalassemia and found that the top candidate off-target is produced by an allele common in African-ancestry populations (MAF 4.5%) that introduces a protospacer adjacent motif (PAM) sequence. We validated that SpCas9 generates strictly allele-specific indels and pericentric inversions in CD34+ hematopoietic stem and progenitor cells (HSPCs), although high-fidelity Cas9 mitigates this off-target. This report illustrates how genetic variants should be considered as modifiers of gene editing outcomes. We expect that variant-aware off-target assessment will become integral to therapeutic genome editing evaluation and provide a powerful approach for comprehensive off-target nomination.

Stardust: improving spatial transcriptomics data analysis through space-aware modularity optimization-based clustering

Background

Spatial transcriptomics (ST) combines stained tissue images with spatially resolved high-throughput RNA sequencing. The spatial transcriptomic analysis includes challenging tasks like clustering, where a partition among data points (spots) is defined by means of a similarity measure. Improving clustering results is a key factor as clustering affects subsequent downstream analysis. State-of-the-art approaches group data by taking into account transcriptional similarity and some by exploiting spatial information as well. However, it is not yet clear how much the spatial information combined with transcriptomics improves the clustering result.

Results

We propose a new clustering method, Stardust, that easily exploits the combination of space and transcriptomic information in the clustering procedure through a manual or fully automatic tuning of algorithm parameters. Moreover, a parameter-free version of the method is also provided where the spatial contribution depends dynamically on the expression distances distribution in the space. We evaluated the proposed methods results by analyzing ST data sets available on the 10x Genomics website and comparing clustering performances with state-of-the-art approaches by measuring the spots' stability in the clusters and their biological coherence. Stability is defined by the tendency of each point to remain clustered with the same neighbors when perturbations are applied.

Conclusions

Stardust is an easy-to-use methodology allowing to define how much spatial information should influence clustering on different tissues and achieving more stable results than state-of-the-art approaches.

Microglial amyloid beta clearance is driven by PIEZO1 channels

Background

Microglia are the endogenous immune cells of the brain and act as sensors of pathology to maintain brain homeostasis and eliminate potential threats. In Alzheimer's disease (AD), toxic amyloid beta (Aβ) accumulates in the brain and forms stiff plaques. In late-onset AD accounting for 95% of all cases, this is thought to be due to reduced clearance of Aβ. Human genome-wide association studies and animal models suggest that reduced clearance results from aberrant function of microglia. While the impact of neurochemical pathways on microglia had been broadly studied, mechanical receptors regulating microglial functions remain largely unexplored.

Methods

Here we showed that a mechanotransduction ion channel, PIEZO1, is expressed and functional in human and mouse microglia. We used a small molecule agonist, Yoda1, to study how activation of PIEZO1 affects AD-related functions in human induced pluripotent stem cell (iPSC)-derived microglia-like cells (iMGL) under controlled laboratory experiments. Cell survival, metabolism, phagocytosis and lysosomal activity were assessed using real-time functional assays. To evaluate the effect of activation of PIEZO1 in vivo, 5-month-old 5xFAD male mice were infused daily with Yoda1 for two weeks through intracranial cannulas. Microglial Iba1 expression and Aβ pathology were quantified with immunohistochemistry and confocal microscopy. Published human and mouse AD datasets were used for in-depth analysis of PIEZO1 gene expression and related pathways in microglial subpopulations.

Results

We show that PIEZO1 orchestrates Aβ clearance by enhancing microglial survival, phagocytosis, and lysosomal activity. Aβ inhibited PIEZO1-mediated calcium transients, whereas activation of PIEZO1 with a selective agonist, Yoda1, improved microglial phagocytosis resulting in Aβ clearance both in human and mouse models of AD. Moreover, PIEZO1 expression was associated with a unique microglial transcriptional phenotype in AD as indicated by assessment of cellular metabolism, and human and mouse single-cell datasets.

Conclusion

These results indicate that the compromised function of microglia in AD could be improved by controlled activation of PIEZO1 channels resulting in alleviated Aβ burden. Pharmacological regulation of these mechanoreceptors in microglia could represent a novel therapeutic paradigm for AD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12974-022-02486-y.

Biometal Dyshomeostasis in Olfactory Mucosa of Alzheimer's Disease Patients

Olfactory function, orchestrated by the cells of the olfactory mucosa at the rooftop of the nasal cavity, is disturbed early in the pathogenesis of Alzheimer's disease (AD). Biometals including zinc and calcium are known to be important for sense of smell and to be altered in the brains of AD patients. Little is known about elemental homeostasis in the AD patient olfactory mucosa. Here we aimed to assess whether the disease-related alterations to biometal homeostasis observed in the brain are also reflected in the olfactory mucosa. We applied RNA sequencing to discover gene expression changes related to metals in olfactory mucosal cells of cognitively healthy controls, individuals with mild cognitive impairment and AD patients, and performed analysis of the elemental content to determine metal levels. Results demonstrate that the levels of zinc, calcium and sodium are increased in the AD olfactory mucosa concomitantly with alterations to 17 genes related to metal-ion binding or metal-related function of the protein product. A significant elevation in alpha-2-macroglobulin, a known metal-binding biomarker correlated with brain disease burden, was observed on the gene and protein levels in the olfactory mucosa cells of AD patients. These data demonstrate that the olfactory mucosa cells derived from AD patients recapitulate certain impairments of biometal homeostasis observed in the brains of patients.

PANPROVA: PANgenomic PROkaryotic eVolution of full Assemblies

MOTIVATION

Computational tools for pangenonic analysis have gained increasing interest over the past two decades in various applications such as evolutionary studies and vaccine development. Synthetic benchmarks are essential for the systematic evaluation of their performance. Currently, benchmarking tools represent a genome as a set of genetic sequences and fail to simulate the complete information of the genomes, which is essential for evaluating pangenomic detection between fragmented genomes.

RESULTS

We present PANPROVA, a benchmark tool to simulate prokaryotic pangenomic evolution by evolving the complete genomic sequence of an ancestral isolate. In this way the possibility of operating in the pre-assembly phase is enabled. Gene set variations, sequence variation and horizontal acquisition from a pool of external genomes are the evolutionary features of the tool.

AVAILABILITY AND IMPLEMENTATION

PANPROVA is publicly available at https://github.com/InfOmics/PANPROVA.

Interrupting the nitrosative stress fuels tumor-specific cytotoxic T lymphocytes in pancreatic cancer

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest tumors owing to its robust desmoplasia, low immunogenicity, and recruitment of cancer-conditioned, immunoregulatory myeloid cells. These features strongly limit the success of immunotherapy as a single agent, thereby suggesting the need for the development of a multitargeted approach. The goal is to foster T lymphocyte infiltration within the tumor landscape and neutralize cancer-triggered immune suppression, to enhance the therapeutic effectiveness of immune-based treatments, such as anticancer adoptive cell therapy (ACT).

Methods

We examined the contribution of immunosuppressive myeloid cells expressing arginase 1 and nitric oxide synthase 2 in building up a reactive nitrogen species (RNS)-dependent chemical barrier and shaping the PDAC immune landscape. We examined the impact of pharmacological RNS interference on overcoming the recruitment and immunosuppressive activity of tumor-expanded myeloid cells, which render pancreatic cancers resistant to immunotherapy.

Results

PDAC progression is marked by a stepwise infiltration of myeloid cells, which enforces a highly immunosuppressive microenvironment through the uncontrolled metabolism of L-arginine by arginase 1 and inducible nitric oxide synthase activity, resulting in the production of large amounts of reactive oxygen and nitrogen species. The extensive accumulation of myeloid suppressing cells and nitrated tyrosines (nitrotyrosine, N-Ty) establishes an RNS-dependent chemical barrier that impairs tumor infiltration by T lymphocytes and restricts the efficacy of adoptive immunotherapy. A pharmacological treatment with AT38 ([3-(aminocarbonyl)furoxan-4-yl]methyl salicylate) reprograms the tumor microenvironment from protumoral to antitumoral, which supports T lymphocyte entrance within the tumor core and aids the efficacy of ACT with telomerase-specific cytotoxic T lymphocytes.

Conclusions

Tumor microenvironment reprogramming by ablating aberrant RNS production bypasses the current limits of immunotherapy in PDAC by overcoming immune resistance.

TUBE Project: Transport-Derived Ultrafines and the Brain Effects

The adverse effects of air pollutants on the respiratory and cardiovascular systems are unquestionable. However, in recent years, indications of effects beyond these organ systems have become more evident. Traffic-related air pollution has been linked with neurological diseases, exacerbated cognitive dysfunction, and Alzheimer's disease. However, the exact air pollutant compositions and exposure scenarios leading to these adverse health effects are not known. Although several components of air pollution may be at play, recent experimental studies point to a key role of ultrafine particles (UFPs). While the importance of UFPs has been recognized, almost nothing is known about the smallest fraction of UFPs, and only >23 nm emissions are regulated in the EU. Moreover, the role of the semivolatile fraction of the emissions has been neglected. The Transport-Derived Ultrafines and the Brain Effects (TUBE) project will increase knowledge on harmful ultrafine air pollutants, as well as semivolatile compounds related to adverse health effects. By including all the major current combustion and emission control technologies, the TUBE project aims to provide new information on the adverse health effects of current traffic, as well as information for decision makers to develop more effective emission legislation. Most importantly, the TUBE project will include adverse health effects beyond the respiratory system; TUBE will assess how air pollution affects the brain and how air pollution particles might be removed from the brain. The purpose of this report is to describe the TUBE project, its background, and its goals.

TEDAR: Temporal dynamic signal detection of adverse reactions

Computational approaches to detect the signals of adverse drug reactions are powerful tools to monitor the unattended effects that users experience and report, also preventing death and serious injury. They apply statistical indices to affirm the validity of adverse reactions reported by users. The methodologies that scan fixed duration intervals in the lifetime of drugs are among the most used. Here we present a method, called TEDAR, in which ranges of varying length are taken into account. TEDAR has the advantage to detect a greater number of true signals without significantly increasing the number of false positives, which are a major concern for this type of tools. Furthermore, early detection of signals is a key feature of methods to prevent the safety of the population. The results show that TEDAR detects adverse reactions many months earlier than methodologies based on a fixed interval length.

GRAFIMO: Variant and haplotype aware motif scanning on pangenome graphs

Transcription factors (TFs) are proteins that promote or reduce the expression of genes by binding short genomic DNA sequences known as transcription factor binding sites (TFBS). While several tools have been developed to scan for potential occurrences of TFBS in linear DNA sequences or reference genomes, no tool exists to find them in pangenome variation graphs (VGs). VGs are sequence-labelled graphs that can efficiently encode collections of genomes and their variants in a single, compact data structure. Because VGs can losslessly compress large pangenomes, TFBS scanning in VGs can efficiently capture how genomic variation affects the potential binding landscape of TFs in a population of individuals. Here we present GRAFIMO (GRAph-based Finding of Individual Motif Occurrences), a command-line tool for the scanning of known TF DNA motifs represented as Position Weight Matrices (PWMs) in VGs. GRAFIMO extends the standard PWM scanning procedure by considering variations and alternative haplotypes encoded in a VG. Using GRAFIMO on a VG based on individuals from the 1000 Genomes project we recover several potential binding sites that are enhanced, weakened or missed when scanning only the reference genome, and which could constitute individual-specific binding events. GRAFIMO is available as an open-source tool, under the MIT license, at https://github.com/pinellolab/GRAFIMO and https://github.com/InfOmics/GRAFIMO.

Transcription factors (TFs) are key regulatory proteins and mutations occurring in their binding sites can alter the normal transcriptional landscape of a cell and lead to disease states. Pangenome variation graphs (VGs) efficiently encode genomes from a population of individuals and their genetic variations. GRAFIMO is an open-source tool that extends the traditional PWM scanning procedure to VGs. By scanning for potential TBFS in VGs, GRAFIMO can simultaneously search thousands of genomes while accounting for SNPs, indels, and structural variants. GRAFIMO reports motif occurrences, their statistical significance, frequency, and location within the reference or alternative haplotypes in a given VG. GRAFIMO makes it possible to study how genetic variation affects the binding landscape of known TFs within a population of individuals.

Exon-Intron Differential Analysis Reveals the Role of Competing Endogenous RNAs in Post-Transcriptional Regulation of Translation

Stressful conditions induce the cell to save energy and activate a rescue program modulated by mammalian target of rapamycin (mTOR). Along with transcriptional and translational regulation, the cell relies also on post-transcriptional modulation to quickly adapt the translation of essential proteins. MicroRNAs play an important role in the regulation of protein translation, and their availability is tightly regulated by RNA competing mechanisms often mediated by long noncoding RNAs (lncRNAs). In our paper, we simulated the response to growth adverse condition by bimiralisib, a dual PI3K/mTOR inhibitor, in diffuse large B cell lymphoma cell lines, and we studied post-transcriptional regulation by the differential analysis of exonic and intronic RNA expression. In particular, we observed the upregulation of a lncRNA, lncTNK2-2:1, which correlated with the stabilization of transcripts involved in the regulation of translation and DNA damage after bimiralisib treatment. We identified miR-21-3p as miRNA likely sponged by lncTNK2-2:1, with consequent stabilization of the mRNA of p53, which is a master regulator of cell growth in response to DNA damage.

Peripheral inflammation preceeding ischemia impairs neuronal survival through mechanisms involving miR-127 in aged animals

Ischemic stroke, the third leading cause of death in the Western world, affects mainly the elderly and is strongly associated with comorbid conditions such as atherosclerosis or diabetes, which are pathologically characterized by increased inflammation and are known to influence the outcome of stroke. Stroke incidence peaks during influenza seasons, and patients suffering from infections such as pneumonia prior to stroke exhibit a worse stroke outcome. Earlier studies have shown that comorbidities aggravate the outcome of stroke, yet the mediators of this phenomenon remain obscure. Here, we show that acute peripheral inflammation aggravates stroke‐induced neuronal damage and motor deficits specifically in aged mice. This is associated with increased levels of plasma proinflammatory cytokines, rather than with an increase of inflammatory mediators in the affected brain parenchyma. Nascent transcriptomics data with mature microRNA sequencing were used to identify the neuron‐specific miRNome, in order to decipher dysregulated miRNAs in the brains of aged animals with stroke and co‐existing inflammation. We pinpoint a previously uninvestigated miRNA in the brain, miR‐127, that is highly neuronal, to be associated with increased cell death in the aged, LPS‐injected ischemic mice. Target prediction tools indicate that miR‐127 interacts with several basally expressed neuronal genes, and of these we verify miR‐127 binding to Psmd3. Finally, we report reduced expression of miR‐127 in human stroke brains. Our results underline the impact of peripheral inflammation on the outcome of stroke in aged subjects and pinpoint molecular targets for restoring endogenous neuronal capacity to combat ischemic stroke.

netDx: Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks

Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data – a common problem in real-world data – without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features.

The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data.

Hypoxia and extracellular vesicles: A review on methods, vesicular cargo and functions

Hypoxia is an essential hallmark of several serious diseases such as cardiovascular and metabolic disorders and cancer. A decline in the tissue oxygen level induces hypoxic responses in cells which strive to adapt to the changed conditions. A failure to adapt to prolonged or severe hypoxia can trigger cell death. While some cell types, such as neurons, are highly vulnerable to hypoxia, cancer cells take advantage of a hypoxic environment to undergo tumour growth, angiogenesis and metastasis. Hypoxia-induced processes trigger complex intercellular communication and there are now indications that extracellular vesicles (EVs) play a fundamental role in these processes. Recent developments in EV isolation and characterization methodology have increased the awareness of the importance of EV purity in functional and cargo studies. Cell death, a hallmark of severe hypoxia, is a known source of intracellular contaminants in isolated EVs. In this review, methodological aspects of studies investigating hypoxia-induced EVs are critically evaluated. Key concerns and gaps in the current knowledge are highlighted and future directions for studies are set. To accelerate and advance research, an in-depth analysis of the functions and cargo of hypoxic EVs, compared to normoxic EVs, is provided with the focus on the altered microRNA contents of the EVs.

Telemedicine and Virtual Reality for Cognitive Rehabilitation: A Roadmap for the COVID-19 Pandemic

The current COVID-19 pandemic presents unprecedented new challenges to public health and medical care delivery. To control viral transmission, social distancing measures have been implemented all over the world, interrupting the access to routine medical care for many individuals with neurological diseases. Cognitive disorders are common in many neurological conditions, e.g., stroke, traumatic brain injury, Alzheimer's disease, and other types of dementia, Parkinson's disease and parkinsonian syndromes, and multiple sclerosis, and should be addressed by cognitive rehabilitation interventions. To be effective, cognitive rehabilitation programs must be intensive and prolonged over time; however, the current virus containment measures are hampering their implementation. Moreover, the reduced access to cognitive rehabilitation might worsen the relationship between the patient and the healthcare professional. Urgent measures to address issues connected to COVID-19 pandemic are, therefore, needed. Remote communication technologies are increasingly regarded as potential effective options to support health care interventions, including neurorehabilitation and cognitive rehabilitation. Among them, telemedicine, virtual reality, augmented reality, and serious games could be in the forefront of these efforts. We will briefly review current evidence-based recommendations on the efficacy of cognitive rehabilitation and offer a perspective on the role of tele- and virtual rehabilitation to achieve adequate cognitive stimulation in the era of social distancing related to COVID-19 pandemic. In particular, we will discuss issues related to their diffusion and propose a roadmap to address them. Methodological and technological improvements might lead to a paradigm shift to promote the delivery of cognitive rehabilitation to people with reduced mobility and in remote regions.

Challenges in gene-oriented approaches for pangenome content discovery

Given a group of genomes, represented as the sets of genes that belong to them, the discovery of the pangenomic content is based on the search of genetic homology among the genes for clustering them into families. Thus, pangenomic analyses investigate the membership of the families to the given genomes. This approach is referred to as the gene-oriented approach in contrast to other definitions of the problem that takes into account different genomic features. In the past years, several tools have been developed to discover and analyse pangenomic contents. Because of the hardness of the problem, each tool applies a different strategy for discovering the pangenomic content. This results in a differentiation of the performance of each tool that depends on the composition of the input genomes. This review reports the main analysis instruments provided by the current state of the art tools for the discovery of pangenomic contents. Moreover, unlike previous works, the presented study compares pangenomic tools from a methodological perspective, analysing the causes that lead a given methodology to outperform other tools. The analysis is performed by taking into account different bacterial populations, which are synthetically generated by changing evolutionary parameters. The benchmarks used to compare the pangenomic tools, in addition to the computational pipeline developed for this purpose, are available at https://github.com/InfOmics/pangenes-review. Contact: V. Bonnici, R. Giugno Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

Intracerebral overexpression of miR-669c is protective in mouse ischemic stroke model by targeting MyD88 and inducing alternative microglial/macrophage activation

Background

Ischemic stroke is a devastating disease without a cure. The available treatments for ischemic stroke, thrombolysis by tissue plasminogen activator, and thrombectomy are suitable only to a fraction of patients and thus novel therapeutic approaches are urgently needed. The neuroinflammatory responses elicited secondary to the ischemic attack further aggravate the stroke-induced neuronal damage. It has been demonstrated that these responses are regulated at the level of non-coding RNAs, especially miRNAs.

Methods

We utilized lentiviral vectors to overexpress miR-669c in BV2 microglial cells in order to modulate their polarization. To detect whether the modulation of microglial activation by miR-669c provides protection in a mouse model of transient focal ischemic stroke, miR-669c overexpression was driven by a lentiviral vector injected into the striatum prior to induction of ischemic stroke.

Results

Here, we demonstrate that miR-669c-3p, a member of chromosome 2 miRNA cluster (C2MC), is induced upon hypoxic and excitotoxic conditions in vitro and in two different in vivo models of stroke. Rather than directly regulating the neuronal survival in vitro, miR-669c is capable of attenuating the microglial proinflammatory activation in vitro and inducing the expression of microglial alternative activation markers arginase 1 (Arg1), chitinase-like 3 (Ym1), and peroxisome proliferator-activated receptor gamma (PPAR-γ). Intracerebral overexpression of miR-669c significantly decreased the ischemia-induced cell death and ameliorated the stroke-induced neurological deficits both at 1 and 3 days post injury (dpi). Albeit miR-669c overexpression failed to alter the overall Iba1 protein immunoreactivity, it significantly elevated Arg1 levels in the ischemic brain and increased colocalization of Arg1 and Iba1. Moreover, miR-669c overexpression under cerebral ischemia influenced several morphological characteristics of Iba1 positive cells. We further demonstrate the myeloid differentiation primary response gene 88 (MyD88) transcript as a direct target for miR-669c-3p in vitro and show reduced levels of MyD88 in miR-669c overexpressing ischemic brains in vivo.

Conclusions

Collectively, our data provide the evidence that miR-669c-3p is protective in a mouse model of ischemic stroke through enhancement of the alternative microglial/macrophage activation and inhibition of MyD88 signaling. Our results accentuate the importance of controlling miRNA-regulated responses for the therapeutic benefit in conditions of stroke and neuroinflammation.

CRISPRitz: rapid, high-throughput and variant-aware in silico off-target site identification for CRISPR genome editing

MOTIVATION

Clustered regularly interspaced short palindromic repeats (CRISPR) technologies allow for facile genomic modification in a site-specific manner. A key step in this process is the in silico design of single guide RNAs to efficiently and specifically target a site of interest. To this end, it is necessary to enumerate all potential off-target sites within a given genome that could be inadvertently altered by nuclease-mediated cleavage. Currently available software for this task is limited by computational efficiency, variant support or annotation, and assessment of the functional impact of potential off-target effects.

RESULTS

To overcome these limitations, we have developed CRISPRitz, a suite of software tools to support the design and analysis of CRISPR/CRISPR-associated (Cas) experiments. Using efficient data structures combined with parallel computation, we offer a rapid, reliable, and exhaustive search mechanism to enumerate a comprehensive list of putative off-target sites. As proof-of-principle, we performed a head-to-head comparison with other available tools on several datasets. This analysis highlighted the unique features and superior computational performance of CRISPRitz including support for genomic searching with DNA/RNA bulges and mismatches of arbitrary size as specified by the user as well as consideration of genetic variants (variant-aware). In addition, graphical reports are offered for coding and non-coding regions that annotate the potential impact of putative off-target sites that lie within regions of functional genomic annotation (e.g. insulator and chromatin accessible sites from the ENCyclopedia Of DNA Elements [ENCODE] project).

AVAILABILITY AND IMPLEMENTATION

The software is freely available at: https://github.com/pinellolab/CRISPRitzhttps://github.com/InfOmics/CRISPRitz.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrated analysis of transcriptome, methylome and copy number aberrations data of marginal zone lymphoma and follicular lymphoma in dog

Marginal zone lymphoma (MZL) and follicular lymphoma (FL) are classified as indolent B-cell lymphomas in dogs. Aside from the clinical and histopathological similarities with the human counterpart, the molecular pathogenesis remains unclear. We integrated transcriptome, genome-wide DNA methylation and copy number aberration analysis to provide insights on the pathogenesis of canine MZL (n = 5) and FL (n = 7), also comparing them with diffuse large B-cell lymphoma (DLBCL). Transcriptome profiling highlighted the presence of similar biological processes affecting both histotypes, including BCR and TLR signalling pathways. However, FLs showed an enrichment of E2F targets, whereas MZLs were characterized by MYC-driven transcriptional activation signatures. FLs showed a distinctive loss on chr1 containing CEACAM23 and 24, conversely MZLs presented multiple recurrent gains on chr13, where MYC is located. The distribution of methylation peaks was similar between the two histotypes. Integrating data from the three omics, FLs resulted clearly separated from MZLs and DLBCL dataset. MZLs showed the enrichment of FoxM1 network and TLR associated TICAM1-dependent IRFs activation pathway. However, no specific signatures differentiated MZLs from DLBCLs. In conclusion, our study presents the first comprehensive analysis of molecular and epigenetic pathogenesis of canine FL and MZL.

Long Non-Coding RNAs as Molecular Signatures for Canine B-Cell Lymphoma Characterization

Background: Diffuse large B-cell lymphoma (DLBCL), marginal zone lymphoma (MZL) and follicular lymphoma (FL) are the most common B-cell lymphomas (BCL) in dogs. Recent investigations have demonstrated overlaps of these histotypes with the human counterparts, including clinical presentation, biologic behavior, tumor genetics, and treatment response. The molecular mechanisms that underlie canine BCL are still unknown and new studies to improve diagnosis, therapy, and the utilization of canine species as spontaneous animal tumor models are undeniably needed. Recent work using human DLBCL transcriptomes has suggested that long non-coding RNAs (lncRNAs) play a key role in lymphoma pathogenesis and pinpointed a restricted number of lncRNAs as potential targets for further studies. Results: To expand the knowledge of non-coding molecules involved in canine BCL, we used transcriptomes obtained from a cohort of 62 dogs with newly-diagnosed multicentric DLBCL, MZL and FL that had undergone complete staging work-up and were treated with chemotherapy or chemo-immunotherapy. We developed a customized R pipeline performing a transcriptome assembly by multiple algorithms to uncover novel lncRNAs, and delineate genome-wide expression of unannotated and annotated lncRNAs. Our pipeline also included a new package for high performance system biology analysis, which detects high-scoring network biological neighborhoods to identify functional modules. Moreover, our customized pipeline quantified the expression of novel and annotated lncRNAs, allowing us to subtype DLBCLs into two main groups. The DLBCL subtypes showed statistically different survivals, indicating the potential use of lncRNAs as prognostic biomarkers in future studies. Conclusions: In this manuscript, we describe the methodology used to identify lncRNAs that differentiate B-cell lymphoma subtypes and we interpreted the biological and clinical values of the results. We inferred the potential functions of lncRNAs to obtain a comprehensive and integrative insight that highlights their impact in this neoplasm.

Immunosuppression by monocytic myeloid-derived suppressor cells in patients with pancreatic ductal carcinoma is orchestrated by STAT3

Background

Pancreatic ductal adenocarcinoma (PDAC) is a highly devastating disease with an overall 5-year survival rate of less than 8%. New evidence indicates that PDAC cells release pro-inflammatory metabolites that induce a marked alteration of normal hematopoiesis, favoring the expansion and accumulation of myeloid-derived suppressor cells (MDSCs). We report here that PDAC patients show increased levels of both circulating and tumor-infiltrating MDSC-like cells.

Methods

The frequency of MDSC subsets in the peripheral blood was determined by flow cytometry in three independent cohorts of PDAC patients (total analyzed patients, n = 117). Frequency of circulating MDSCs was correlated with overall survival of PDAC patients. We also analyzed the frequency of tumor-infiltrating MDSC and the immune landscape in fresh biopsies. Purified myeloid cell subsets were tested in vitro for their T-cell suppressive capacity.

Results

Correlation with clinical data revealed that MDSC frequency was significantly associated with a shorter patients’ overall survival and metastatic disease. However, the immunosuppressive activity of purified MDSCs was detectable only in some patients and mainly limited to the monocytic subset. A transcriptome analysis of the immunosuppressive M-MDSCs highlighted a distinct gene signature in which STAT3 was crucial for monocyte re-programming. Suppressive M-MDSCs can be characterized as circulating STAT3/arginase1-expressing CD14⁺ cells.

Conclusion

MDSC analysis aids in defining the immune landscape of PDAC patients for a more appropriate diagnosis, stratification and treatment.

PSEN1ΔE9, APPswe, and APOE4 Confer Disparate Phenotypes in Human iPSC-Derived Microglia

Here we elucidate the effect of Alzheimer disease (AD)-predisposing genetic backgrounds, APOE4, PSEN1ΔE9, and APPswe, on functionality of human microglia-like cells (iMGLs). We present a physiologically relevant high-yield protocol for producing iMGLs from induced pluripotent stem cells. Differentiation is directed with small molecules through primitive erythromyeloid progenitors to re-create microglial ontogeny from yolk sac. The iMGLs express microglial signature genes and respond to ADP with intracellular Ca²⁺ release distinguishing them from macrophages. Using 16 iPSC lines from healthy donors, AD patients and isogenic controls, we reveal that the APOE4 genotype has a profound impact on several aspects of microglial functionality, whereas PSEN1ΔE9 and APPswe mutations trigger minor alterations. The APOE4 genotype impairs phagocytosis, migration, and metabolic activity of iMGLs but exacerbates their cytokine secretion. This indicates that APOE4 iMGLs are fundamentally unable to mount normal microglial functionality in AD.

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APOE4 genotype has a profound impact on several functions of microglia-like cells

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Inflammatory responses are aggravated in cells with APOE4 genotype

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Metabolism, phagocytosis, and migration are decreased in APOE4 microglia-like cells

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Familial mutations APPswe and PSEN1ΔE9 have only minor effects on functionality

miRandola 2017: a curated knowledge base of non-invasive biomarkers

miRandola (http://mirandola.iit.cnr.it/) is a database of extracellular non-coding RNAs (ncRNAs) that was initially published in 2012, foreseeing the relevance of ncRNAs as non-invasive biomarkers. An increasing amount of experimental evidence shows that ncRNAs are frequently dysregulated in diseases. Further, ncRNAs have been discovered in different extracellular forms, such as exosomes, which circulate in human body fluids. Thus, miRandola 2017 is an effort to update and collect the accumulating information on extracellular ncRNAs that is spread across scientific publications and different databases. Data are manually curated from 314 articles that describe miRNAs, long non-coding RNAs and circular RNAs. Fourteen organisms are now included in the database, and associations of ncRNAs with 25 drugs, 47 sample types and 197 diseases. miRandola also classifies extracellular RNAs based on their extracellular form: Argonaute2 protein, exosome, microvesicle, microparticle, membrane vesicle, high density lipoprotein and circulating. We also implemented a new web interface to improve the user experience.

The 2017 Network Tools and Applications in Biology (NETTAB) workshop: aims, topics and outcomes

The 17th International NETTAB workshop was held in Palermo, Italy, on October 16-18, 2017. The special topic for the meeting was "Methods, tools and platforms for Personalised Medicine in the Big Data Era", but the traditional topics of the meeting series were also included in the event. About 40 scientific contributions were presented, including four keynote lectures, five guest lectures, and many oral communications and posters. Also, three tutorials were organised before and after the workshop. Full papers from some of the best works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.

Extracellular Vesicles Mediate Mesenchymal Stromal Cell-Dependent Regulation of B Cell PI3K-AKT Signaling Pathway and Actin Cytoskeleton

Mesenchymal stromal cells (MSCs) are adult, multipotent cells of mesodermal origin representing the progenitors of all stromal tissues. MSCs possess significant and broad immunomodulatory functions affecting both adaptive and innate immune responses once MSCs are primed by the inflammatory microenvironment. Recently, the role of extracellular vesicles (EVs) in mediating the therapeutic effects of MSCs has been recognized. Nevertheless, the molecular mechanisms responsible for the immunomodulatory properties of MSC-derived EVs (MSC-EVs) are still poorly characterized. Therefore, we carried out a molecular characterization of MSC-EV content by high-throughput approaches. We analyzed miRNA and protein expression profile in cellular and vesicular compartments both in normal and inflammatory conditions. We found several proteins and miRNAs involved in immunological processes, such as MOES, LG3BP, PTX3, and S10A6 proteins, miR-155-5p, and miR-497-5p. Different in silico approaches were also performed to correlate miRNA and protein expression profile and then to evaluate the putative molecules or pathways involved in immunoregulatory properties mediated by MSC-EVs. PI3K-AKT signaling pathway and the regulation of actin cytoskeleton were identified and functionally validated in vitro as key mediators of MSC/B cell communication mediated by MSC-EVs. In conclusion, we identified different molecules and pathways responsible for immunoregulatory properties mediated by MSC-EVs, thus identifying novel therapeutic targets as safer and more useful alternatives to cell or EV-based therapeutic approaches.

18F-Florbetaben PET/CT to Assess Alzheimer's Disease: A new Analysis Method for Regional Amyloid Quantification

BACKGROUND AND PURPOSE

While AD can be definitively confirmed by postmortem histopathologic examination, in vivo imaging may improve the clinician's ability to identify AD at the earliest stage. The aim of the study was to test the performance of amyloid PET using new processing imaging algorithm for more precise diagnosis of AD.

METHODS

Amyloid PET results using a new processing imaging algorithm (MRI-Less and AAL Atlas) were correlated with clinical, cognitive status, CSF analysis, and other imaging. The regional SUVR using the white matter of cerebellum as reference region and scores from clinical and cognitive tests were used to create ROC curves. Leave-one-out cross-validation was carried out to validate the results.

RESULTS

Forty-four consecutive patients with clinical evidence of dementia, were retrospectively evaluated. Amyloid PET scan was positive in 26/44 patients with dementia. After integration with 18F-FDG PET, clinical data and CSF protein levels, 22 of them were classified as AD, the remaining 4 as vascular or frontotemporal dementia. Amyloid and FDG PET, CDR 1, CSF Tau, and p-tau levels showed the best true positive and true negative rates (amyloid PET: AUC = .85, sensitivity .91, specificity .79). A SUVR value of 1.006 in the inferior frontal cortex and of 1.03 in the precuneus region was the best cutoff SUVR value and showed a good correlation with the diagnosis of AD. Thirteen of 44 amyloid PET positive patients have been enrolled in clinical trials using antiamyloid approaches.

CONCLUSIONS

Amyloid PET using SPM-normalized SUVR analysis showed high predictive power for the differential diagnosis of AD.

Construction and Analysis of miRNA Regulatory Networks

This chapter is devoted to illustrate the usage of state-of-the-art methodologies for miRNA regulatory network construction and analysis. Advantages in understanding the role of miRNAs in regulating gene expression are increasing the possibility of developing targeted therapies and drugs. This new possibility can be exploited by gaining new knowledge through analyzing interactions between a specific miRNA and a targeted gene.

PanDelos: a dictionary-based method for pan-genome content discovery

Background

Pan-genome approaches afford the discovery of homology relations in a set of genomes, by determining how some gene families are distributed among a given set of genomes. The retrieval of a complete gene distribution among a class of genomes is an NP-hard problem because computational costs increase with the number of analyzed genomes, in fact, all-against-all gene comparisons are required to completely solve the problem. In presence of phylogenetically distant genomes, due to the variability introduced in gene duplication and transmission, the task of recognizing homologous genes becomes even more difficult. A challenge on this field is that of designing fast and adaptive similarity measures in order to find a suitable pan-genome structure of homology relations.

Results

We present PanDelos, a stand alone tool for the discovery of pan-genome contents among phylogenetic distant genomes. The methodology is based on information theory and network analysis. It is parameter-free because thresholds are automatically deduced from the context. PanDelos avoids sequence alignment by introducing a measure based on k-mer multiplicity. The k-mer length is defined according to general arguments rather than empirical considerations. Homology candidate relations are integrated into a global network and groups of homologous genes are extracted by applying a community detection algorithm.

Conclusions

PanDelos outperforms existing approaches, Roary and EDGAR, in terms of running times and quality content discovery. Tests were run on collections of real genomes, previously used in analogous studies, and in synthetic benchmarks that represent fully trusted golden truth. The software is available at https://github.com/GiugnoLab/PanDelos.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2417-6) contains supplementary material, which is available to authorized users.

cuRnet: an R package for graph traversing on GPU

BACKGROUND

R has become the de-facto reference analysis environment in Bioinformatics. Plenty of tools are available as packages that extend the R functionality, and many of them target the analysis of biological networks. Several algorithms for graphs, which are the most adopted mathematical representation of networks, are well-known examples of applications that require high-performance computing, and for which classic sequential implementations are becoming inappropriate. In this context, parallel approaches targeting GPU architectures are becoming pervasive to deal with the execution time constraints. Although R packages for parallel execution on GPUs are already available, none of them provides graph algorithms.

RESULTS

This work presents cuRnet, a R package that provides a parallel implementation for GPUs of the breath-first search (BFS), the single-source shortest paths (SSSP), and the strongly connected components (SCC) algorithms. The package allows offloading computing intensive applications to GPU devices for massively parallel computation and to speed up the runtime up to one order of magnitude with respect to the standard sequential computations on CPU. We have tested cuRnet on a benchmark of large protein interaction networks and for the interpretation of high-throughput omics data thought network analysis.

CONCLUSIONS

cuRnet is a R package to speed up graph traversal and analysis through parallel computation on GPUs. We show the efficiency of cuRnet applied both to biological network analysis, which requires basic graph algorithms, and to complex existing procedures built upon such algorithms.

INBIA: a boosting methodology for proteomic network inference

Background

The analysis of tissue-specific protein interaction networks and their functional enrichment in pathological and normal tissues provides insights on the etiology of diseases. The Pan-cancer proteomic project, in The Cancer Genome Atlas, collects protein expressions in human cancers and it is a reference resource for the functional study of cancers. However, established protocols to infer interaction networks from protein expressions are still missing.

Results

We have developed a methodology called Inference Network Based on iRefIndex Analysis (INBIA) to accurately correlate proteomic inferred relations to protein-protein interaction (PPI) networks. INBIA makes use of 14 network inference methods on protein expressions related to 16 cancer types. It uses as reference model the iRefIndex human PPI network. Predictions are validated through non-interacting and tissue specific PPI networks resources. The first, Negatome, takes into account likely non-interacting proteins by combining both structure properties and literature mining. The latter, TissueNet and GIANT, report experimentally verified PPIs in more than 50 human tissues. The reliability of the proposed methodology is assessed by comparing INBIA with PERA, a tool which infers protein interaction networks from Pathway Commons, by both functional and topological analysis.

Conclusion

Results show that INBIA is a valuable approach to predict proteomic interactions in pathological conditions starting from the current knowledge of human protein interactions.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2183-5) contains supplementary material, which is available to authorized users.

Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification

Motivation

Prediction of phenotypes from high-dimensional data is a crucial task in precision biology and medicine. Many technologies employ genomic biomarkers to characterize phenotypes. However, such elements are not sufficient to explain the underlying biology. To improve this, pathway analysis techniques have been proposed. Nevertheless, such methods have shown lack of accuracy in phenotypes classification.

Results

Here we propose a novel methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the analysis of signaling pathways, which extends the work of Tarca et al., 2009. MITHrIL augments pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their degree of deregulation, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA.

Availability

MITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril/

Current Knowledge and Computational Techniques for Grapevine Meta-Omics Analysis

Growing grapevine (Vitis vinifera) is a key contribution to the economy of many countries. Tools provided by genomics and bioinformatics did help researchers in obtaining biological knowledge about the different cultivars. Several genetic markers for common diseases were identified. Recently, the impact of microbiome has been proved to be of fundamental importance both in humans and in plants for its ability to confer protection or induce diseases. In this review we report current knowledge about grapevine microbiome, together with a description of the available computational methodologies for meta-omics analysis.

On the Variable Ordering in Subgraph Isomorphism Algorithms

Graphs are mathematical structures to model several biological data. Applications to analyze them require to apply solutions for the subgraph isomorphism problem, which is NP-complete. Here, we investigate the existing strategies to reduce the subgraph isomorphism algorithm running time with emphasis on the importance of the order with which the graph vertices are taken into account during the search, called variable ordering, and its incidence on the total running time of the algorithms. We focus on two recent solutions, which are based on an effective variable ordering strategy. We discuss their comparison both with the variable ordering strategies reviewed in the paper and the other algorithms present in the ICPR2014 contest on graph matching algorithms for pattern search in biological databases.