IL6-mediated HCoV-host interactome regulatory network and GO/Pathway enrichment analysis.

During these days of global emergency for the COVID-19 disease outbreak, there is an urgency to share reliable information able to help worldwide life scientists to get better insights and make sense of the large amount of data currently available. In this study we used the results presented in [1] to perform two different Systems Biology analyses on the HCoV-host interactome. In the first one, we reconstructed the interactome of the HCoV-host proteins, integrating it with highly reliable miRNA and drug interactions information. We then added the IL-6 gene, identified in recent publications [2] as heavily involved in the COVID-19 progression and, interestingly, we identified several interactions with the reconstructed interactome. In the second analysis, we performed a Gene Ontology and a Pathways enrichment analysis on the full set of the HCoV-host interactome proteins and on the ones belonging to a significantly dense cluster of interacting proteins identified in the first analysis. Results of the two analyses provide a compact but comprehensive glance on some of the current state-of-the-art regulations, GO, and pathways involved in the HCoV-host interactome, and that could support all scientists currently focusing on SARS-CoV-2 research.

CyTRANSFINDER: a Cytoscape 3.3 plugin for three-component (TF, gene, miRNA) signal transduction pathway construction.

BACKGROUND: Biological research increasingly relies on network models to study complex phenomena. Signal Transduction Pathways are molecular circuits that model how cells receive, process, and respond to information from the environment providing snapshots of the overall cell dynamics. Most of the attempts to reconstruct signal transduction pathways are limited to single regulator networks including only genes/proteins. However, networks involving a single type of regulator and neglecting transcriptional and post-transcriptional regulations mediated by transcription factors and microRNAs, respectively, may not fully reveal the complex regulatory mechanisms of a cell. We observed a lack of computational instruments supporting explorative analysis on this type of three-component signal transduction pathways. RESULTS: We have developed CyTRANSFINDER, a new Cytoscape plugin able to infer three-component signal transduction pathways based on user defined regulatory patterns and including miRNAs, TFs and genes. Since CyTRANSFINDER has been designed to support exploratory analysis, it does not rely on expression data. To show the potential of the plugin we have applied it in a study of two miRNAs that are particularly relevant in human melanoma progression, miR-146a and miR-214. CONCLUSIONS: CyTRANSFINDER supports the reconstruction of small signal transduction pathways among groups of genes. Results obtained from its use in a real case study have been analyzed and validated through both literature data and preliminary wet-lab experiments, showing the potential of this tool when performing exploratory analysis.

An extended gene protein/products Boolean network model including post-transcriptional regulation.

BACKGROUND: Networks Biology allows the study of complex interactions between biological systems using formal, well structured, and computationally friendly models. Several different network models can be created, depending on the type of interactions that need to be investigated. Gene Regulatory Networks (GRN) are an effective model commonly used to study the complex regulatory mechanisms of a cell. Unfortunately, given their intrinsic complexity and non discrete nature, the computational study of realistic-sized complex GRNs requires some abstractions. Boolean Networks (BNs), for example, are a reliable model that can be used to represent networks where the possible state of a node is a boolean value (0 or 1). Despite this strong simplification, BNs have been used to study both structural and dynamic properties of real as well as randomly generated GRNs. RESULTS: In this paper we show how it is possible to include the post-transcriptional regulation mechanism (a key process mediated by small non-coding RNA molecules like the miRNAs) into the BN model of a GRN. The enhanced BN model is implemented in a software toolkit (EBNT) that allows to analyze boolean GRNs from both a structural and a dynamic point of view. The open-source toolkit is compatible with available visualization tools like Cytoscape and allows to run detailed analysis of the network topology as well as of its attractors, trajectories, and state-space. In the paper, a small GRN built around the mTOR gene is used to demonstrate the main capabilities of the toolkit. CONCLUSIONS: The extended model proposed in this paper opens new opportunities in the study of gene regulation. Several of the successful researches done with the support of BN to understand high-level characteristics of regulatory networks, can now be improved to better understand the role of post-transcriptional regulation for example as a network-wide noise-reduction or stabilization mechanisms.

A combined approach for genome wide protein function annotation/prediction.

BACKGROUND: Today large scale genome sequencing technologies are uncovering an increasing amount of new genes and proteins, which remain uncharacterized. Experimental procedures for protein function prediction are low throughput by nature and thus can't be used to keep up with the rate at which new proteins are discovered. On the other hand, proteins are the prominent stakeholders in almost all biological processes, and therefore the need to precisely know their functions for a better understanding of the underlying biological mechanism is inevitable. The challenge of annotating uncharacterized proteins in functional genomics and biology in general motivates the use of computational techniques well orchestrated to accurately predict their functions. METHODS: We propose a computational flow for the functional annotation of a protein able to assign the most probable functions to a protein by aggregating heterogeneous information. Considered information include: protein motifs, protein sequence similarity, and protein homology data gathered from interacting proteins, combined with data from highly similar non-interacting proteins (hereinafter called Similactors). Moreover, to increase the predictive power of our model we also compute and integrate term specific relationships among functional terms based on Gene Ontology (GO). RESULTS: We tested our method on Saccharomyces Cerevisiae and Homo sapiens species proteins. The aggregation of different structural and functional evidence with GO relationships outperforms, in terms of precision and accuracy of prediction than the other methods reported in literature. The predicted precision and accuracy is 100% for more than half of the input set for both species; overall, we obtained 85.38% precision and 81.95% accuracy for Homo sapiens and 79.73% precision and 80.06% accuracy for Saccharomyces Cerevisiae species proteins.

Special Issue: New Advances in Bioinformatics and Biomedical Engineering Using Machine Learning Techniques, IWBBIO-2022.

Bioinformatics is revolutionizing Biomedicine in the way we treat and diagnose pathologies related to biological manifestations resulting from variations or mutations of our DNA [...].

Application of Microsatellites to Trace the Dairy Products Back to the Farm of Origin.

The increasing number of food frauds, mainly targeting high quality products, is a rising concern among producers and authorities appointed to food controls. Therefore, the development or implementation of methods to reveal frauds is desired. The genetic traceability of traditional or high-quality dairy products (i.e., products of protected designation of origin, PDO) represents a challenging issue due to the technical problems that arise. The aim of the study was to set up a genetic tool for the origin traceability of dairy products. We investigated the use of Short Tandem Repeats (STRs) to assign milk and cheese to the corresponding producer. Two farms were included in the study, and the blood of the cows, bulk milk, and derived cheese were sampled monthly for one year. Twenty STRs were selected and Polymerase Chain Reactions for each locus were carried out. The results showed that bulk milk and derived cheese express an STR profile composed of a subset of STRs of the lactating animals. A bioinformatics tool was used for the exclusion analysis. The study allowed the identification of a panel of 20 markers useful for the traceability of milk and cheeses, and its effectiveness in the traceability of dairy products obtained from small producers was demonstrated.

The integration of clinical data in the assessment of multiple sclerosis - A review.

BACKGROUND AND OBJECTIVES: Multiple Sclerosis (MS) is a neurological disease associated with various and heterogeneous clinical characteristics. Given its complex nature and its unpredictable evolution over time, there isn't an established and exhaustive clinical protocol (or tool) for its diagnosis nor for monitoring its progression. Instead, different clinical exams and physical/psychological evaluations need to be taken into account. The Expanded Disability Status Scale (EDSS) is the most used clinical scale, but it suffers from several limitations. Developing computational solutions for the identification of bio-markers of disease progression that overcome the downsides of currently used scales is crucial and is gaining interest in current literature and research. METHODS: This Review focuses on the importance of approaching MS diagnosis and monitoring by investigating correlations between cognitive impairment and clinical data that refer to different MS domains. We review papers that integrate heterogeneous data and analyse them with statistical methods to understand their applicability into more advanced computational tools. Particular attention is paid to the impact that computational approaches can have on personalized-medicine. RESULTS: Personalized medicine for neuro-degenerative diseases is an unmet clinical need which can be addressed using computational approaches able to efficiently integrate heterogeneous clinical data extracted from both private and publicly available electronic health databases. CONCLUSIONS: Reliable and explainable Artificial Intelligence are computational approaches required to understand the complex and demonstrated interactions between MS manifestations as well as to provide reliable predictions on the disease evolution, representing a promising research field.

Building gene expression profile classifiers with a simple and efficient rejection option in R.

BACKGROUND: The collection of gene expression profiles from DNA microarrays and their analysis with pattern recognition algorithms is a powerful technology applied to several biological problems. Common pattern recognition systems classify samples assigning them to a set of known classes. However, in a clinical diagnostics setup, novel and unknown classes (new pathologies) may appear and one must be able to reject those samples that do not fit the trained model. The problem of implementing a rejection option in a multi-class classifier has not been widely addressed in the statistical literature. Gene expression profiles represent a critical case study since they suffer from the curse of dimensionality problem that negatively reflects on the reliability of both traditional rejection models and also more recent approaches such as one-class classifiers. RESULTS: This paper presents a set of empirical decision rules that can be used to implement a rejection option in a set of multi-class classifiers widely used for the analysis of gene expression profiles. In particular, we focus on the classifiers implemented in the R Language and Environment for Statistical Computing (R for short in the remaining of this paper). The main contribution of the proposed rules is their simplicity, which enables an easy integration with available data analysis environments. Since in the definition of a rejection model tuning of the involved parameters is often a complex and delicate task, in this paper we exploit an evolutionary strategy to automate this process. This allows the final user to maximize the rejection accuracy with minimum manual intervention. CONCLUSIONS: This paper shows how the use of simple decision rules can be used to help the use of complex machine learning algorithms in real experimental setups. The proposed approach is almost completely automated and therefore a good candidate for being integrated in data analysis flows in labs where the machine learning expertise required to tune traditional classifiers might not be available.

Nets-within-nets for modeling emergent patterns in ontogenetic processes.

Ontogenesis is the development of an organism from its earliest stage to maturity, including homeostasis maintenance throughout adulthood despite environmental perturbations. Almost all cells of a multicellular organism share the same genomic information. Nevertheless, phenotypic diversity and complex supra-cellular architectures emerge at every level, starting from tissues and organs. This is possible thanks to a robust and dynamic interplay of regulative mechanisms. To study ontogenesis, it is necessary to consider different levels of regulation, both genetic and epigenetic. Each cell undergoes a specific path across a landscape of possible regulative states affecting both its structure and its functions during development. This paper proposes using the Nets-Within-Nets formalism, which combines Petri Nets' simplicity with the capability to represent and simulate the interplay between different layers of regulation connected by non-trivial and context-dependent hierarchical relations. In particular, this work introduces a modeling strategy based on Nets-Within-Nets that can model several critical processes involved in ontogenesis. Moreover, it presents a case study focusing on the first phase of Vulval Precursor Cells specification in C.Elegans. The case study shows that the proposed model can simulate the emergent morphogenetic pattern corresponding to the observed developmental outcome of that phase, in both the physiological case and different mutations. The model presented in the results section is available online at https://github.com/sysbio-polito/NWN_CElegans_VPC_model/.

Severe and moderate seasonal influenza epidemics among Italian healthcare workers: A comparison of the excess of absenteeism.

BACKGROUND: This study aims to quantify the excess of sickness absenteeism among healthcare workers (HCWs), to estimate the impact of a severe versus moderate influenza season and to determine whether the vaccination rates are associated with reduced sickness absence. METHODS: We investigated the excess absenteeism that occurred in a large Italian hospital, 5300 HCWs, during the severe influenza season of 2017/2018 and compared it with three moderate flu seasons (2010/2013). Data on influenza vaccinations and absenteeism were obtained from the hospital's databases. The data were split into two periods: the epidemic, from 42 to 17 weeks, and non-epidemic, defined as 18 to 41 weeks, which was used as the baseline. We stratified the absenteeism among HCWs in multiple variables. RESULTS: Our study showed an increased absenteeism among HCWs during the epidemic period of severe season in comparison with non-epidemic periods, the absolute increase correlated with a relative increase of 70% (from 4.05 to 6.68 days/person). Vaccinated HCWs had less excess of absenteeism in comparison with non-vaccinated HCWs (1.74 vs 2.71 days/person). The comparison with the moderate seasons showed a stronger impact on HCW sick absenteeism in the severe season (+0.747days/person, P = .03), especially among nurses and HCWs in contact with patients (+1.53 P < .01; +1.19 P < .01). CONCLUSIONS: In conclusion, a severe influenza epidemic has greater impacts on the absenteeism among HCWs than a moderate one. Although at a low rate, a positive effect of vaccination on absenteeism is present, it may support healthcare facilities to recommend vaccinations for their workers.

Determinants Associated With the Risk of Emergency Department Visits Among Patients Receiving Integrated Home Care Services: A 6-Year Retrospective Observational Study in a Large Italian Region.

BACKGROUND: Allowing patients to remain at home and decreasing the number of unnecessary emergency room visits have become important policy goals in modern healthcare systems. However, the lack of available literature makes it critical to identify determinants that could be associated with increased emergency department (ED) visits in patients receiving integrated home care (IHC). METHODS: A retrospective observational study was carried out in a large Italian region among patients with at least one IHC event between January 1, 2012 and December 31, 2017. IHC is administered from 8 am to 8 pm by a team of physicians, nurses, and other professionals as needed based on the patient's health conditions. A clinical record is opened at the time a patient is enrolled in IHC and closed after the last service is provided. Every such clinical record was defined as an IHC event, and only ED visits that occurred during IHC events were considered. Sociodemographic, clinical and IHC variables were collected. A multivariate, stepwise logistic analysis was then performed, using likelihood of ED visit as a dependent variable. RESULTS: A total of 29 209 ED visits were recorded during the 66 433 IHC events that took place during the observation period. There was an increased risk of ED visits in males (odds ratio [OR]=1.29), younger patients, those with a family caregiver (OR=1.13), and those with a higher number of cohabitant family members. Long travel distance from patients' residence to the ED reduced the risk of ED visits. The risk of ED visits was higher when patients were referred to IHC by hospitals or residential facilities, compared to referrals by general practitioners. IHC events involving patients with neoplasms (OR=1.91) showed the highest risk of ED visits. CONCLUSION: Evidence of sociodemographic and clinical determinants of ED visits may offer IHC service providers a useful perspective to implement intervention programmes based on appropriate individual care plans and broad-based client assessment.

'One DB to rule them all'-the RING: a Regulatory INteraction Graph combining TFs, genes/proteins, SNPs, diseases and drugs.

In the last decade, genomics data have been largely adopted to sketch, study and better understand the complex mechanisms that underlie biological processes. The amount of publicly available data sources has grown accordingly, and several types of regulatory interactions have been collected and documented in literature. Unfortunately, often these efforts do not follow any data naming/interoperability/formatting standards, resulting in high-quality but often uninteroperable heterogeneous data repositories. To efficiently take advantage of the large amount of available data and integrate these heterogeneous sources of information, we built the RING (Regulatory Interaction Graph), an integrative standardized multilevel database of biological interactions able to provide a comprehensive and unmatched high-level perspective on several phenomena that take place in the regulatory cascade and that researchers can use to easily build regulatory networks around entities of interest.

Cost of Sickness Absenteeism during Seasonal Influenza Outbreaks of Medium Intensity among Health Care Workers.

This study aims to estimate the economic costs of sickness absenteeism of health care workers in a large Italian teaching hospital during the seasonal flu periods. A retrospective observational study was performed. The excess data of hospital's sickness absenteeism during three seasonal influenza periods (2010/2011; 2011/2012; 2012/2013) came from a previous study. The cost of sickness absenteeism was calculated for six job categories: medical doctor, technical executive (i.e., pharmacists); nurses and allied health professionals (i.e., radiographer), other executives (i.e., engineer), non-medical support staff, and administrative staff, and for four age ranges: <39, 40⁻49, 50⁻59, and >59 years. An average of 5401 employees working each year were under study. There were over 11,100 working days/year lost associated with an influenza period in Italy, the costs associated were approximately 1.7 million euros, and the average work loss was valued at € 327/person. The major shares of cost appeared related to nurses and allied health professionals (45% of total costs). The highest costs for working days lost were reported in the 40⁻49 age range, accounting for 37% of total costs. Due to the substantial economic burden of sickness absenteeism, there are clear benefits to be gained from the effective prevention of the influenza.

Modeling antibiotic resistance in the microbiota using multi-level Petri Nets.

BACKGROUND: The unregulated use of antibiotics not only in clinical practice but also in farm animals breeding is causing a unprecedented growth of antibiotic resistant bacterial strains. This problem can be analyzed at different levels, from the antibiotic resistance spreading dynamics at the host population level down to the molecular mechanisms at the bacteria level. In fact, antibiotic administration policies and practices affect the societal system where individuals developing resistance interact with each other and with the environment. Each individual can be seen as a meta-organism together with its associated microbiota, which proves to have a prominent role in the resistance spreading dynamics. Eventually, in each microbiota, bacterial population dynamics and vertical or horizontal gene transfer events activate cellular and molecular mechanisms for resistance spreading that can also be possible targets for its prevention. RESULTS: In this work we show how to use the Nets-Within-Nets formalism to model the dynamics between different antibiotic administration protocols and antibiotic resistance, both at the individuals population and at the single microbiota level. Three application examples are presented to show the flexibility of this approach in integrating heterogeneous information in the same model, a fundamental property when creating computational models complex biological systems. Simulations allow to explicitly take into account timing and stochastic events. CONCLUSIONS: This work demonstrates how the NWN formalism can be used to efficiently model antibiotic resistance population dynamics at different levels of detail. The proposed modeling approach not only provides a valuable tool for investigating causal, quantitative relations between different events and mechanisms, but can be also used as a valid support for decision making processes and protocol development.

Prioritizing single-nucleotide polymorphisms and variants associated with clinical mastitis.

Next-generation sequencing technology has provided resources to easily explore and identify candidate single-nucleotide polymorphisms (SNPs) and variants. However, there remains a challenge in identifying and inferring the causal SNPs from sequence data. A problem with different methods that predict the effect of mutations is that they produce false positives. In this hypothesis, we provide an overview of methods known for identifying causal variants and discuss the challenges, fallacies, and prospects in discerning candidate SNPs. We then propose a three-point classification strategy, which could be an additional annotation method in identifying causalities.

A three-way approach for protein function classification.

The knowledge of protein functions plays an essential role in understanding biological cells and has a significant impact on human life in areas such as personalized medicine, better crops and improved therapeutic interventions. Due to expense and inherent difficulty of biological experiments, intelligent methods are generally relied upon for automatic assignment of functions to proteins. The technological advancements in the field of biology are improving our understanding of biological processes and are regularly resulting in new features and characteristics that better describe the role of proteins. It is inevitable to neglect and overlook these anticipated features in designing more effective classification techniques. A key issue in this context, that is not being sufficiently addressed, is how to build effective classification models and approaches for protein function prediction by incorporating and taking advantage from the ever evolving biological information. In this article, we propose a three-way decision making approach which provides provisions for seeking and incorporating future information. We considered probabilistic rough sets based models such as Game-Theoretic Rough Sets (GTRS) and Information-Theoretic Rough Sets (ITRS) for inducing three-way decisions. An architecture of protein functions classification with probabilistic rough sets based three-way decisions is proposed and explained. Experiments are carried out on Saccharomyces cerevisiae species dataset obtained from Uniprot database with the corresponding functional classes extracted from the Gene Ontology (GO) database. The results indicate that as the level of biological information increases, the number of deferred cases are reduced while maintaining similar level of accuracy.

Estimation of sickness absenteeism among Italian healthcare workers during seasonal influenza epidemics.

OBJECTIVES: To analyze absenteeism among healthcare workers (HCWs) at a large Italian hospital and to estimate the increase in absenteeism that occurred during seasonal flu periods. DESIGN: Retrospective observational study. METHODS: The absenteeism data were divided into three "epidemic periods," starting at week 42 of one year and terminating at week 17 of the following year (2010-2011, 2011-2012, 2012-2013), and three "non-epidemic periods," defined as week 18 to week 41 and used as baseline data. The excess of the absenteeism occurring among HCWs during periods of epidemic influenza in comparison with baseline was estimated. All data, obtained from Hospital's databases, were collected for each of the following six job categories: medical doctors, technical executives (i.e., pharmacists), nurses and allied health professionals (i.e., radiographers), other executives (i.e., engineers), nonmedical support staff, and administrative staff. The HCWs were classified by: in and no-contact; vaccinated and unvaccinated. RESULTS: 5,544, 5,369, and 5,291 workers in three years were studied. The average duration of absenteeism during the epidemic periods increased among all employees by +2.07 days/person (from 2.99 to 5.06), and the relative increase ranged from 64-94% among the different job categories. Workers not in contact with patients experienced a slightly greater increase in absenteeism (+2.28 days/person, from 2.73 to 5.01) than did employees in contact with patients (+2.04, from 3.04 to 5.08). The vaccination rate among HCWs was below 3%, however the higher excess of absenteeism rate among unvaccinated in comparison with vaccinated workers was observed during the epidemic periods (2.09 vs 1.45 days/person). CONCLUSION: The influenza-related absenteeism during epidemic periods was quantified as totaling more than 11,000 days/year at the Italian hospital studied. This result confirms the economic impact of sick leave on healthcare systems and stresses on the necessity of encouraging HCWs to be immunized against influenza.

A Graph Based Framework to Model Virus Integration Sites.

With next generation sequencing thousands of virus and viral vector integration genome targets are now under investigation to uncover specific integration preferences and to define clusters of integration, termed common integration sites (CIS), that may allow to assess gene therapy safety or to detect disease related genomic features such as oncogenes. Here, we addressed the challenge to: 1) define the notion of CIS on graph models, 2) demonstrate that the structure of CIS enters in the category of scale-free networks and 3) show that our network approach analyzes CIS dynamically in an integrated systems biology framework using the Retroviral Transposon Tagged Cancer Gene Database (RTCGD) as a testing dataset.

Identification of miRNAs Potentially Involved in Bronchiolitis Obliterans Syndrome: A Computational Study.

The pathogenesis of Bronchiolitis Obliterans Syndrome (BOS), the main clinical phenotype of chronic lung allograft dysfunction, is poorly understood. Recent studies suggest that epigenetic regulation of microRNAs might play a role in its development. In this paper we present the application of a complex computational pipeline to perform enrichment analysis of miRNAs in pathways applied to the study of BOS. The analysis considered the full set of miRNAs annotated in miRBase (version 21), and applied a sequence of filtering approaches and statistical analyses to reduce this set and to score the candidate miRNAs according to their potential involvement in BOS development. Dysregulation of two of the selected candidate miRNAs-miR-34a and miR-21 -was clearly shown in in-situ hybridization (ISH) on five explanted human BOS lungs and on a rat model of acute and chronic lung rejection, thus definitely identifying miR-34a and miR-21 as pathogenic factors in BOS and confirming the effectiveness of the computational pipeline.