Live-animal imaging of native haematopoietic stem and progenitor cells.

The biology of haematopoietic stem cells (HSCs) has predominantly been studied under transplantation conditions1,2. It has been particularly challenging to study dynamic HSC behaviour, given that the visualization of HSCs in the native niche in live animals has not, to our knowledge, been achieved. Here we describe a dual genetic strategy in mice that restricts reporter labelling to a subset of the most quiescent long-term HSCs (LT-HSCs) and that is compatible with current intravital imaging approaches in the calvarial bone marrow3-5. We show that this subset of LT-HSCs resides close to both sinusoidal blood vessels and the endosteal surface. By contrast, multipotent progenitor cells (MPPs) show greater variation in distance from the endosteum and are more likely to be associated with transition zone vessels. LT-HSCs are not found in bone marrow niches with the deepest hypoxia and instead are found in hypoxic environments similar to those of MPPs. In vivo time-lapse imaging revealed that LT-HSCs at steady-state show limited motility. Activated LT-HSCs show heterogeneous responses, with some cells becoming highly motile and a fraction of HSCs expanding clonally within spatially restricted domains. These domains have defined characteristics, as HSC expansion is found almost exclusively in a subset of bone marrow cavities with bone-remodelling activity. By contrast, cavities with low bone-resorbing activity do not harbour expanding HSCs. These findings point to previously unknown heterogeneity within the bone marrow microenvironment, imposed by the stages of bone turnover. Our approach enables the direct visualization of HSC behaviours and dissection of heterogeneity in HSC niches.

CREDO: a friendly Customizable, REproducible, DOcker file generator for bioinformatics applications.

BACKGROUND: The analysis of large and complex biological datasets in bioinformatics poses a significant challenge to achieving reproducible research outcomes due to inconsistencies and the lack of standardization in the analysis process. These issues can lead to discrepancies in results, undermining the credibility and impact of bioinformatics research and creating mistrust in the scientific process. To address these challenges, open science practices such as sharing data, code, and methods have been encouraged. RESULTS: CREDO, a Customizable, REproducible, DOcker file generator for bioinformatics applications, has been developed as a tool to moderate reproducibility issues by building and distributing docker containers with embedded bioinformatics tools. CREDO simplifies the process of generating Docker images, facilitating reproducibility and efficient research in bioinformatics. The crucial step in generating a Docker image is creating the Dockerfile, which requires incorporating heterogeneous packages and environments such as Bioconductor and Conda. CREDO stores all required package information and dependencies in a Github-compatible format to enhance Docker image reproducibility, allowing easy image creation from scratch. The user-friendly GUI and CREDO's ability to generate modular Docker images make it an ideal tool for life scientists to efficiently create Docker images. Overall, CREDO is a valuable tool for addressing reproducibility issues in bioinformatics research and promoting open science practices.

Surface protein and functional analyses identify CD4+CD39+ TCR αß+ and activated TCR Vδ1+ cells with distinct pro-inflammatory functions in Crohn's disease lesions.

Crohn's disease (CD) is a chronic immune-mediated disorder of the gastrointestinal tract. Extensive screening studies have revealed the accumulation of immune cell subsets with unique plasticity and immunoregulatory properties in patients with CD. We performed phenotypic and functional studies on inflamed and non-inflamed bioptic tissue to investigate the presence of distinct T cells in the intestinal mucosa of CD patients. We analysed hundreds of surface molecules expressed on cells isolated from the intestinal tissue of CD patients using anti-CD45 mAbs-based barcoding. A gene ontology enrichment analysis showed that proteins that regulate the activation of T cells were the most enriched group. We, therefore, designed T-cell focused multicolour flow-cytometry panels and performed clustering analysis which revealed an accumulation of activated TEM CD4+CD39+ T cells producing IL-17 and IL-21 and increased frequency of terminally differentiated TCR Vδ1+ cells producing TNF-α and IFN-γ in inflamed tissue of CD patients. The different functional capacities of CD4+ and TCR Vδ1+ cells in CD lesions indicate their non-overlapping contribution to inflammation. The abnormally high number of terminally differentiated TCR Vδ1+ cells suggests that they are continuously activated in inflamed tissue, making them a potential target for novel therapies.

CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system.

MOTIVATION: The transition from evaluating a single time point to examining the entire dynamic evolution of a system is possible only in the presence of the proper framework. The strong variability of dynamic evolution makes the definition of an explanatory procedure for data fitting and clustering challenging. RESULTS: We developed CONNECTOR, a data-driven framework able to analyze and inspect longitudinal data in a straightforward and revealing way. When used to analyze tumor growth kinetics over time in 1599 patient-derived xenograft growth curves from ovarian and colorectal cancers, CONNECTOR allowed the aggregation of time-series data through an unsupervised approach in informative clusters. We give a new perspective of mechanism interpretation, specifically, we define novel model aggregations and we identify unanticipated molecular associations with response to clinically approved therapies. AVAILABILITY AND IMPLEMENTATION: CONNECTOR is freely available under GNU GPL license at https://qbioturin.github.io/connector and https://doi.org/10.17504/protocols.io.8epv56e74g1b/v1.

Clonal analysis of lineage fate in native haematopoiesis.

Haematopoiesis, the process of mature blood and immune cell production, is functionally organized as a hierarchy, with self-renewing haematopoietic stem cells and multipotent progenitor cells sitting at the very top. Multiple models have been proposed as to what the earliest lineage choices are in these primitive haematopoietic compartments, the cellular intermediates, and the resulting lineage trees that emerge from them. Given that the bulk of studies addressing lineage outcomes have been performed in the context of haematopoietic transplantation, current models of lineage branching are more likely to represent roadmaps of lineage potential than native fate. Here we use transposon tagging to clonally trace the fates of progenitors and stem cells in unperturbed haematopoiesis. Our results describe a distinct clonal roadmap in which the megakaryocyte lineage arises largely independently of other haematopoietic fates. Our data, combined with single-cell RNA sequencing, identify a functional hierarchy of unilineage- and oligolineage-producing clones within the multipotent progenitor population. Finally, our results demonstrate that traditionally defined long-term haematopoietic stem cells are a significant source of megakaryocyte-restricted progenitors, suggesting that the megakaryocyte lineage is the predominant native fate of long-term haematopoietic stem cells. Our study provides evidence for a substantially revised roadmap for unperturbed haematopoiesis, and highlights unique properties of multipotent progenitors and haematopoietic stem cells in situ.

YAP Drives Growth by Controlling Transcriptional Pause Release from Dynamic Enhancers.

The Hippo/YAP signaling pathway is a crucial regulator of tissue growth, stem cell activity, and tumorigenesis. However, the mechanism by which YAP controls transcription remains to be fully elucidated. Here, we utilize global chromatin occupancy analyses to demonstrate that robust YAP binding is restricted to a relatively small number of distal regulatory elements in the genome. YAP occupancy defines a subset of enhancers and superenhancers with the highest transcriptional outputs. YAP modulates transcription from these elements predominantly by regulating promoter-proximal polymerase II (Pol II) pause release. Mechanistically, YAP interacts and recruits the Mediator complex to enhancers, allowing the recruitment of the CDK9 elongating kinase. Genetic and chemical perturbation experiments demonstrate the requirement for Mediator and CDK9 in YAP-driven phenotypes of overgrowth and tumorigenesis. Our results here uncover the molecular mechanisms employed by YAP to exert its growth and oncogenic functions, and suggest strategies for intervention.

YAP-TEAD signaling promotes basal cell carcinoma development via a c-JUN/AP1 axis.

The mammalian Hippo signaling pathway, through its effectors YAP and TAZ, coerces epithelial progenitor cell expansion for appropriate tissue development or regeneration upon damage. Its ability to drive rapid tissue growth explains why many oncogenic events frequently exploit this pathway to promote cancer phenotypes. Indeed, several tumor types including basal cell carcinoma (BCC) show genetic aberrations in the Hippo (or YAP/TAZ) regulators. Here, we uncover that while YAP is dispensable for homeostatic epidermal regeneration, it is required for BCC development. Our clonal analyses further demonstrate that the few emerging Yap-null dysplasia have lower fitness and thus are diminished as they progress to invasive BCC Mechanistically, YAP depletion in BCC tumors leads to effective impairment of the JNK-JUN signaling, a well-established tumor-driving cascade. Importantly, in this context, YAP does not influence canonical Wnt or Hedgehog signaling. Overall, we reveal Hippo signaling as an independent promoter of BCC pathogenesis and thereby a viable target for drug-resistant BCC.

Laniakea@ReCaS: exploring the potential of customisable Galaxy on-demand instances as a cloud-based service.

BACKGROUND: Improving the availability and usability of data and analytical tools is a critical precondition for further advancing modern biological and biomedical research. For instance, one of the many ramifications of the COVID-19 global pandemic has been to make even more evident the importance of having bioinformatics tools and data readily actionable by researchers through convenient access points and supported by adequate IT infrastructures. One of the most successful efforts in improving the availability and usability of bioinformatics tools and data is represented by the Galaxy workflow manager and its thriving community. In 2020 we introduced Laniakea, a software platform conceived to streamline the configuration and deployment of "on-demand" Galaxy instances over the cloud. By facilitating the set-up and configuration of Galaxy web servers, Laniakea provides researchers with a powerful and highly customisable platform for executing complex bioinformatics analyses. The system can be accessed through a dedicated and user-friendly web interface that allows the Galaxy web server's initial configuration and deployment. RESULTS: "Laniakea@ReCaS", the first instance of a Laniakea-based service, is managed by ELIXIR-IT and was officially launched in February 2020, after about one year of development and testing that involved several users. Researchers can request access to Laniakea@ReCaS through an open-ended call for use-cases. Ten project proposals have been accepted since then, totalling 18 Galaxy on-demand virtual servers that employ ~ 100 CPUs, ~ 250 GB of RAM and ~ 5 TB of storage and serve several different communities and purposes. Herein, we present eight use cases demonstrating the versatility of the platform. CONCLUSIONS: During this first year of activity, the Laniakea-based service emerged as a flexible platform that facilitated the rapid development of bioinformatics tools, the efficient delivery of training activities, and the provision of public bioinformatics services in different settings, including food safety and clinical research. Laniakea@ReCaS provides a proof of concept of how enabling access to appropriate, reliable IT resources and ready-to-use bioinformatics tools can considerably streamline researchers' work.

Application of the Euro Clonality next-generation sequencing-based marker screening approach to detect immunoglobulin heavy chain rearrangements in mantle cell lymphoma patients: first data from the Fondazione Italiana Linfomi MCL0208 trial.

Minimal residual disease (MRD) determined by classic polymerase chain reaction (PCR) methods is a powerful outcome predictor in mantle cell lymphoma (MCL). Nevertheless, some technical pitfalls can reduce the rate of of molecular markers. Therefore, we applied the EuroClonality-NGS IGH (next-generation sequencing immunoglobulin heavy chain) method (previously published in acute lymphoblastic leukaemia) to 20 MCL patients enrolled in an Italian phase III trial sponsored by Fondazione Italiana Linfomi. Results from this preliminary investigation show that EuroClonality-NGS IGH method is feasible in the MCL context, detecting a molecular IGH target in 19/20 investigated cases, allowing MRD monitoring also in those patients lacking a molecular marker for classical screening approaches.

Molecular and functional characterization of urine-derived podocytes from patients with Alport syndrome.

Alport syndrome (AS) is a genetic disorder involving mutations in the genes encoding collagen IV α3, α4 or α5 chains, resulting in the impairment of glomerular basement membrane. Podocytes are responsible for production and correct assembly of collagen IV isoforms; however, data on the phenotypic characteristics of human AS podocytes and their functional alterations are currently limited. The evident loss of viable podocytes into the urine of patients with active glomerular disease enables their isolation in a non-invasive way. Here we isolated, immortalized, and subcloned podocytes from the urine of three different AS patients for molecular and functional characterization. AS podocytes expressed a typical podocyte signature and showed a collagen IV profile reflecting each patient's mutation. Furthermore, RNA-sequencing analysis revealed 348 genes differentially expressed in AS podocytes compared with control podocytes. Gene Ontology analysis underlined the enrichment in genes involved in cell motility, adhesion, survival, and angiogenesis. In parallel, AS podocytes displayed reduced motility. Finally, a functional permeability assay, using a podocyte-glomerular endothelial cell co-culture system, was established and AS podocyte co-cultures showed a significantly higher permeability of albumin compared to control podocyte co-cultures, in both static and dynamic conditions under continuous perfusion. In conclusion, our data provide a molecular characterization of immortalized AS podocytes, highlighting alterations in several biological processes related to extracellular matrix remodelling. Moreover, we have established an in vitro model to reproduce the altered podocyte permeability observed in patients with AS. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland..

Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis.

BACKGROUND: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell. METHODS: Since single cell technologies provide many sample measurements, they are the ideal environment for the application of Deep Learning and Machine Learning approaches. An autoencoder is composed of an encoder and a decoder sub-model. An autoencoder is a very powerful tool in data compression and noise removal. However, the decoder model remains a black box from which is impossible to depict the contribution of the single input elements. We have recently developed a new class of autoencoders, called Sparsely Connected Autoencoders (SCA), which have the advantage of providing a controlled association among the input layer and the decoder module. This new architecture has the benefit that the decoder model is not a black box anymore and can be used to depict new biologically interesting features from single cell data. RESULTS: Here, we show that SCA hidden layer can grab new information usually hidden in single cell data, like providing clustering on meta-features difficult, i.e. transcription factors expression, or not technically not possible, i.e. miRNA expression, to depict in single cell RNAseq data. Furthermore, SCA representation of cell clusters has the advantage of simulating a conventional bulk RNAseq, which is a data transformation allowing the identification of similarity among independent experiments. CONCLUSIONS: In our opinion, SCA represents the bioinformatics version of a universal "Swiss-knife" for the extraction of hidden knowledgeable features from single cell omics data.

MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning.

BACKGROUND: Disruption of alternative splicing (AS) is frequently observed in cancer and might represent an important signature for tumor progression and therapy. Exon skipping (ES) represents one of the most frequent AS events, and in non-small cell lung cancer (NSCLC) MET exon 14 skipping was shown to be targetable. METHODS: We constructed neural networks (NN/CNN) specifically designed to detect MET exon 14 skipping events using RNAseq data. Furthermore, for discovery purposes we also developed a sparsely connected autoencoder to identify uncharacterized MET isoforms. RESULTS: The neural networks had a Met exon 14 skipping detection rate greater than 94% when tested on a manually curated set of 690 TCGA bronchus and lung samples. When globally applied to 2605 TCGA samples, we observed that the majority of false positives was characterized by a blurry coverage of exon 14, but interestingly they share a common coverage peak in the second intron and we speculate that this event could be the transcription signature of a LINE1 (Long Interspersed Nuclear Element 1)-MET (Mesenchymal Epithelial Transition receptor tyrosine kinase) fusion. CONCLUSIONS: Taken together, our results indicate that neural networks can be an effective tool to provide a quick classification of pathological transcription events, and sparsely connected autoencoders could represent the basis for the development of an effective discovery tool.

Different miRNA Profiles in Plasma Derived Small and Large Extracellular Vesicles from Patients with Neurodegenerative Diseases.

Identifying biomarkers is essential for early diagnosis of neurodegenerative diseases (NDs). Large (LEVs) and small extracellular vesicles (SEVs) are extracellular vesicles (EVs) of different sizes and biological functions transported in blood and they may be valid biomarkers for NDs. The aim of our study was to investigate common and different miRNA signatures in plasma derived LEVs and SEVs of Alzheimer's disease (AD), Parkinson's disease (PD), Amyotrophic Lateral Sclerosis (ALS) and Fronto-Temporal Dementia (FTD) patients. LEVs and SEVs were isolated from plasma of patients and healthy volunteers (CTR) by filtration and differential centrifugation and RNA was extracted. Small RNAs libraries were carried out by Next Generation Sequencing (NGS). MiRNAs discriminate all NDs diseases from CTRs and they can provide a signature for each NDs. Common enriched pathways for SEVs were instead linked to ubiquitin mediated proteolysis and Toll-like receptor signaling pathways and for LEVs to neurotrophin signaling and Glycosphingolipid biosynthesis pathway. LEVs and SEVs are involved in different pathways and this might give a specificity to their role in the spreading of the disease. The study of common and different miRNAs transported by LEVs and SEVs can be of great interest for biomarker discovery and for pathogenesis studies in neurodegeneration.

Apoptotic Cell Exclusion and Bias-Free Single-Cell Selection Are Important Quality Control Requirements for Successful Single-Cell Sequencing Applications.

Single-cell sequencing experiments are a new mainstay in biology and have been advancing science especially in the biomedical field. The high pressure to integrate the technology into daily laboratory live requires solid knowledge with respect to potential limitations and precautions to be taken care of before applying it to complex research questions. In the past, we have identified two issues with quality measures neglected by the growing community involving SmartSeq and droplet or micro-well-based scRNASeq methods (1) how to ensure that only single cells are introduced without biasing on light scattering when handling complex cell mixtures and organ preparations or (2) how best to control for (pro-)apoptotic cell contaminations in single-cell sequencing approaches. Sighting of concurrent literature involving single-cell sequencing technologies revealed that these topics are generally neglected or simply approached in silico but not at the bench before generating single-cell data sets. We fear that those important quality aspects are overlooked due to reduced awareness of their importance for guaranteeing the quality of experiments. In this Cytometry rigor issue, we provide experimentally supported guidance on how to circumvent those critical shortcomings in order to promote a better use of the fantastic single-cell sequencing toolbox in biology. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

BITS2018: the fifteenth annual meeting of the Italian Society of Bioinformatics.

This preface introduces the content of the BioMed Central Bioinformatics journal Supplement related to the 15th annual meeting of the Bioinformatics Italian Society, BITS2018. The Conference was held in Torino, Italy, from June 27th to 29th, 2018.

A computational approach based on the colored Petri net formalism for studying multiple sclerosis.

BACKGROUND: Multiple Sclerosis (MS) is an immune-mediated inflammatory disease of the Central Nervous System (CNS) which damages the myelin sheath enveloping nerve cells thus causing severe physical disability in patients. Relapsing Remitting Multiple Sclerosis (RRMS) is one of the most common form of MS in adults and is characterized by a series of neurologic symptoms, followed by periods of remission. Recently, many treatments were proposed and studied to contrast the RRMS progression. Among these drugs, daclizumab (commercial name Zinbryta), an antibody tailored against the Interleukin-2 receptor of T cells, exhibited promising results, but its efficacy was accompanied by an increased frequency of serious adverse events. Manifested side effects consisted of infections, encephalitis, and liver damages. Therefore daclizumab has been withdrawn from the market worldwide. Another interesting case of RRMS regards its progression in pregnant women where a smaller incidence of relapses until the delivery has been observed. RESULTS: In this paper we propose a new methodology for studying RRMS, which we implemented in GreatSPN, a state-of-the-art open-source suite for modelling and analyzing complex systems through the Petri Net (PN) formalism. This methodology exploits: (a) an extended Colored PN formalism to provide a compact graphical description of the system and to automatically derive a set of ODEs encoding the system dynamics and (b) the Latin Hypercube Sampling with PRCC index to calibrate ODE parameters for reproducing the real behaviours in healthy and MS subjects.To show the effectiveness of such methodology a model of RRMS has been constructed and studied. Two different scenarios of RRMS were thus considered. In the former scenario the effect of the daclizumab administration is investigated, while in the latter one RRMS was studied in pregnant women. CONCLUSIONS: We propose a new computational methodology to study RRMS disease. Moreover, we show that model generated and calibrated according to this methodology is able to reproduce the expected behaviours.

SeqBox: RNAseq/ChIPseq reproducible analysis on a consumer game computer.

Summary: Short reads sequencing technology has been used for more than a decade now. However, the analysis of RNAseq and ChIPseq data is still computational demanding and the simple access to raw data does not guarantee results reproducibility between laboratories. To address these two aspects, we developed SeqBox, a cheap, efficient and reproducible RNAseq/ChIPseq hardware/software solution based on NUC6I7KYK mini-PC (an Intel consumer game computer with a fast processor and a high performance SSD disk), and Docker container platform. In SeqBox the analysis of RNAseq and ChIPseq data is supported by a friendly GUI. This allows access to fast and reproducible analysis also to scientists with/without scripting experience. Availability and implementation: Docker container images, docker4seq package and the GUI are available at http://www.bioinformatica.unito.it/reproducibile.bioinformatics.html. Contact: beccuti@di.unito.it. Supplementary information: Supplementary data are available at Bioinformatics online.

Oxidative stress controls the choice of alternative last exons via a Brahma-BRCA1-CstF pathway.

Alternative splicing of terminal exons increases transcript and protein diversity. How physiological and pathological stimuli regulate the choice between alternative terminal exons is, however, largely unknown. Here, we show that Brahma (BRM), the ATPase subunit of the hSWI/SNF chromatin-remodeling complex interacts with BRCA1/BARD1, which ubiquitinates the 50 kDa subunit of the 3' end processing factor CstF. This results in the inhibition of transcript cleavage at the proximal poly(A) site and a shift towards inclusion of the distal terminal exon. Upon oxidative stress, BRM is depleted, cleavage inhibition is released, and inclusion of the proximal last exon is favoored. Our findings elucidate a novel regulatory mechanism, distinct from the modulation of transcription elongation by BRM that controls alternative splicing of internal exons.

Docker4Circ: A Framework for the Reproducible Characterization of circRNAs from RNA-Seq Data.

Recent improvements in cost-effectiveness of high-throughput technologies has allowed RNA sequencing of total transcriptomes suitable for evaluating the expression and regulation of circRNAs, a relatively novel class of transcript isoforms with suggested roles in transcriptional and post-transcriptional gene expression regulation, as well as their possible use as biomarkers, due to their deregulation in various human diseases. A limited number of integrated workflows exists for prediction, characterization, and differential expression analysis of circRNAs, none of them complying with computational reproducibility requirements. We developed Docker4Circ for the complete analysis of circRNAs from RNA-Seq data. Docker4Circ runs a comprehensive analysis of circRNAs in human and model organisms, including: circRNAs prediction; classification and annotation using six public databases; back-splice sequence reconstruction; internal alternative splicing of circularizing exons; alignment-free circRNAs quantification from RNA-Seq reads; and differential expression analysis. Docker4Circ makes circRNAs analysis easier and more accessible thanks to: (i) its R interface; (ii) encapsulation of computational tasks into docker images; (iii) user-friendly Java GUI Interface availability; and (iv) no need of advanced bash scripting skills for correct use. Furthermore, Docker4Circ ensures a reproducible analysis since all its tasks are embedded into a docker image following the guidelines provided by Reproducible Bioinformatics Project.

Reproducible bioinformatics project: a community for reproducible bioinformatics analysis pipelines.

BACKGROUND: Reproducibility of a research is a key element in the modern science and it is mandatory for any industrial application. It represents the ability of replicating an experiment independently by the location and the operator. Therefore, a study can be considered reproducible only if all used data are available and the exploited computational analysis workflow is clearly described. However, today for reproducing a complex bioinformatics analysis, the raw data and the list of tools used in the workflow could be not enough to guarantee the reproducibility of the results obtained. Indeed, different releases of the same tools and/or of the system libraries (exploited by such tools) might lead to sneaky reproducibility issues. RESULTS: To address this challenge, we established the Reproducible Bioinformatics Project (RBP), which is a non-profit and open-source project, whose aim is to provide a schema and an infrastructure, based on docker images and R package, to provide reproducible results in Bioinformatics. One or more Docker images are then defined for a workflow (typically one for each task), while the workflow implementation is handled via R-functions embedded in a package available at github repository. Thus, a bioinformatician participating to the project has firstly to integrate her/his workflow modules into Docker image(s) exploiting an Ubuntu docker image developed ad hoc by RPB to make easier this task. Secondly, the workflow implementation must be realized in R according to an R-skeleton function made available by RPB to guarantee homogeneity and reusability among different RPB functions. Moreover she/he has to provide the R vignette explaining the package functionality together with an example dataset which can be used to improve the user confidence in the workflow utilization. CONCLUSIONS: Reproducible Bioinformatics Project provides a general schema and an infrastructure to distribute robust and reproducible workflows. Thus, it guarantees to final users the ability to repeat consistently any analysis independently by the used UNIX-like architecture.