Hepatitis B Virus and microRNAs: A Bioinformatics Approach.

In recent decades, microRNAs (miRNAs) have emerged as key regulators of gene expression, and the identification of viral miRNAs (v-miRNAs) within some viruses, including hepatitis B virus (HBV), has attracted significant attention. HBV infections often progress to chronic states (CHB) and may induce fibrosis/cirrhosis and hepatocellular carcinoma (HCC). The presence of HBV can dysregulate host miRNA expression, influencing several biological pathways, such as apoptosis, innate and immune response, viral replication, and pathogenesis. Consequently, miRNAs are considered a promising biomarker for diagnostic, prognostic, and treatment response. The dynamics of miRNAs during HBV infection are multifaceted, influenced by host variability and miRNA interactions. Given the ability of miRNAs to target multiple messenger RNA (mRNA), understanding the viral-host (human) interplay is complex but essential to develop novel clinical applications. Therefore, bioinformatics can help to analyze, identify, and interpret a vast amount of miRNA data. This review explores the bioinformatics tools available for viral and host miRNA research. Moreover, we introduce a brief overview focusing on the role of miRNAs during HBV infection. In this way, this review aims to help the selection of the most appropriate bioinformatics tools based on requirements and research goals.

The Impact of Drug-Drug Interactions on the Toxicity Profile of Combined Treatment with BRAF and MEK Inhibitors in Patients with BRAF-Mutated Metastatic Melanoma.

BACKGROUND: BRAF and MEK inhibition is a successful strategy in managing BRAF-mutant melanoma, even if the treatment-related toxicity is substantial. We analyzed the role of drug-drug interactions (DDI) on the toxicity profile of anti-BRAF/anti-MEK therapy. METHODS: In this multicenter, observational, and retrospective study, DDIs were assessed using Drug-PIN software (V 2/23). The association between the Drug-PIN continuous score or the Drug-PIN traffic light and the occurrence of treatment-related toxicities and oncological outcomes was evaluated. RESULTS: In total, 177 patients with advanced BRAF-mutated melanoma undergoing BRAF/MEK targeted therapy were included. All grade toxicity was registered in 79% of patients. Cardiovascular toxicities occurred in 31 patients (17.5%). Further, 94 (55.9%) patients had comorbidities requiring specific pharmacological treatments. The median Drug-PIN score significantly increased when the target combination was added to the patient's home therapy (p-value < 0.0001). Cardiovascular toxicity was significantly associated with the Drug-PIN score (p-value = 0.048). The Drug-PIN traffic light (p = 0.00821) and the Drug-PIN score (p = 0.0291) were seen to be significant predictors of cardiotoxicity. Patients with low-grade vs. high-grade interactions showed a better prognosis regarding overall survival (OS) (p = 0.0045) and progression-free survival (PFS) (p = 0.012). The survival analysis of the subgroup of patients with cardiological toxicity demonstrated that patients with low-grade vs. high-grade DDIs had better outcomes in terms of OS (p = 0.0012) and a trend toward significance in PFS (p = 0.068). CONCLUSIONS: DDIs emerged as a critical issue for the risk of treatment-related cardiovascular toxicity. Our findings support the utility of DDI assessment in melanoma patients treated with BRAF/MEK inhibitors.

An ontology-based approach for modelling and querying Alzheimer's disease data.

BACKGROUND: The recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being "standardized" so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. This is the case of neurodegenerative diseases and the Alzheimer's Disease (AD) in whose context specialized data collections such as the one by the Alzheimer's Disease Neuroimaging Initiative (ADNI) are maintained. METHODS: Ontologies are controlled vocabularies that allow the semantics of data and their relationships in a given domain to be represented. They are often exploited to aid knowledge and data management in healthcare research. Computational Ontologies are the result of the combination of data management systems and traditional ontologies. Our approach is i) to define a computational ontology representing a logic-based formal conceptual model of the ADNI data collection and ii) to provide a means for populating the ontology with the actual data in the Alzheimer Disease Neuroimaging Initiative (ADNI). These two components make it possible to semantically query the ADNI database in order to support data extraction in a more intuitive manner. RESULTS: We developed: i) a detailed computational ontology for clinical multimodal datasets from the ADNI repository in order to simplify the access to these data; ii) a means for populating this ontology with the actual ADNI data. Such computational ontology immediately makes it possible to facilitate complex queries to the ADNI files, obtaining new diagnostic knowledge about Alzheimer's disease. CONCLUSIONS: The proposed ontology will improve the access to the ADNI dataset, allowing queries to extract multivariate datasets to perform multidimensional and longitudinal statistical analyses. Moreover, the proposed ontology can be a candidate for supporting the design and implementation of new information systems for the collection and management of AD data and metadata, and for being a reference point for harmonizing or integrating data residing in different sources.

A network approach to define the predictive role of immune profile on tumor response and toxicity of anti PD-1 single agent immunotherapy in patients with solid tumors.

Background: The immune profile of each patient could be considered as a portrait of the fitness of his/her own immune system. The predictive role of the immune profile in immune-related toxicities (irAEs) development and tumour response to treatment was investigated. Methods: A prospective, multicenter study evaluating, through a multiplex assay, the soluble immune profile at the baseline of 53 patients with advanced cancer, treated with immunotherapy as single agent was performed. Four connectivity heat maps and networks were obtained by calculating the Spearman correlation coefficients for each group: responder patients who developed cumulative toxicity (R-T), responders who did not develop cumulative toxicity (R-NT), non-responders who developed cumulative toxicity (NR-T), non-responders who did not develop cumulative toxicity (NR-NT). Results: A statistically significant up-regulation of IL-17A, sCTLA4, sCD80, I-CAM-1, sP-Selectin and sEselectin in NR-T was detected. A clear loss of connectivity of most of the soluble immune checkpoints and cytokines characterized the immune profile of patients with toxicity, while an inversion of the correlation for ICAM-1 and sP-selectin was observed in NR-T. Four connectivity networks were built for each group. The highest number of connections characterized the NR-T. Conclusions: A connectivity network of immune dysregulation was defined for each subgroup of patients, regardless of tumor type. In patients with the worst prognosis (NR-T) the peculiar connectivity model could facilitate their early and timely identification, as well as the design of a personalized treatment approach to improve outcomes or prevent irAEs.

Circular RNA mediated gene regulation in human breast cancer: A bioinformatics analysis.

Circular RNAs (circRNAs) are a new acknowledged class of RNAs that has been shown to play a major role in several biological functions both in physiological and pathological conditions, operating as critical part of regulatory processes, like competing endogenous RNA (ceRNA) networks. The ceRNA hypothesis is a recently discovered molecular mechanism that adds a new key layer of post-transcriptional regulation, whereby various types of RNAs can reciprocally influence each other's expression competing for binding the same pool of microRNAs, even affecting disease development. In this study, we build a network of circRNA-miRNA-mRNA interactions in human breast cancer, called CERNOMA, that is a bipartite graph with one class of nodes corresponding to differentially expressed miRNAs (DEMs) and the other one corresponding to differentially expressed circRNAs (DEC) and mRNAs (DEGs). A link between a DEC (or DEG) and DEM is placed if it is predicted to be a target of the DEM and shows an opposite expression level trend with respect to the DEM. Within the CERNOMA, we highlighted an interesting deregulated circRNA-miRNA-mRNA triplet, including the up-regulated hsa_circRNA_102908 (BRCA1 associated RING domain 1), the down-regulated miR-410-3p, and the up-regulated ESM1, whose overexpression has been already shown to promote tumor dissemination and metastasis in breast cancer.

Immune-related toxicity and soluble profile in patients affected by solid tumors: a network approach.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have particular, immune-related adverse events (irAEs), as a consequence of interfering with self-tolerance mechanisms. The incidence of irAEs varies depending on ICI class, administered dose and treatment schedule. The aim of this study was to define a baseline (T0) immune profile (IP) predictive of irAE development. METHODS: A prospective, multicenter study evaluating the immune profile (IP) of 79 patients with advanced cancer and treated with anti-programmed cell death protein 1 (anti-PD-1) drugs as a first- or second-line setting was performed. The results were then correlated with irAEs onset. The IP was studied by means of multiplex assay, evaluating circulating concentration of 12 cytokines, 5 chemokines, 13 soluble immune checkpoints and 3 adhesion molecules. Indoleamine 2, 3-dioxygenase (IDO) activity was measured through a modified liquid chromatography-tandem mass spectrometry using the high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) method. A connectivity heatmap was obtained by calculating Spearman correlation coefficients. Two different networks of connectivity were constructed, based on the toxicity profile. RESULTS: Toxicity was predominantly of low/moderate grade. High-grade irAEs were relatively rare, while cumulative toxicity was high (35%). Positive and statistically significant correlations between the cumulative toxicity and IP10 and IL8, sLAG3, sPD-L2, sHVEM, sCD137, sCD27 and sICAM-1 serum concentration were found. Moreover, patients who experienced irAEs had a markedly different connectivity pattern, characterized by disruption of most of the paired connections between cytokines, chemokines and connections of sCD137, sCD27 and sCD28, while sPDL-2 pair-wise connectivity values seemed to be intensified. Network connectivity analysis identified a total of 187 statistically significant interactions in patients without toxicity and a total of 126 statistically significant interactions in patients with toxicity. Ninety-eight interactions were common to both networks, while 29 were specifically observed in patients who experienced toxicity. CONCLUSIONS: A particular, common pattern of immune dysregulation was defined in patients developing irAEs. This immune serological profile, if confirmed in a larger patient population, could lead to the design of a personalized therapeutic strategy in order to prevent, monitor and treat irAEs at an early stage.

A Network of MicroRNAs and mRNAs Involved in Melanosome Maturation and Trafficking Defines the Lower Response of Pigmentable Melanoma Cells to Targeted Therapy.

BACKGROUND: The ability to increase their degree of pigmentation is an adaptive response that confers pigmentable melanoma cells higher resistance to BRAF inhibitors (BRAFi) compared to non-pigmentable melanoma cells. METHODS: Here, we compared the miRNome and the transcriptome profile of pigmentable 501Mel and SK-Mel-5 melanoma cells vs. non-pigmentable A375 melanoma cells, following treatment with the BRAFi vemurafenib (vem). In depth bioinformatic analyses (clusterProfiler, WGCNA and SWIMmeR) allowed us to identify the miRNAs, mRNAs and biological processes (BPs) that specifically characterize the response of pigmentable melanoma cells to the drug. Such BPs were studied using appropriate assays in vitro and in vivo (xenograft in zebrafish embryos). RESULTS: Upon vem treatment, miR-192-5p, miR-211-5p, miR-374a-5p, miR-486-5p, miR-582-5p, miR-1260a and miR-7977, as well as GPR143, OCA2, RAB27A, RAB32 and TYRP1 mRNAs, are differentially expressed only in pigmentable cells. These miRNAs and mRNAs belong to BPs related to pigmentation, specifically melanosome maturation and trafficking. In fact, an increase in the number of intracellular melanosomes-due to increased maturation and/or trafficking-confers resistance to vem. CONCLUSION: We demonstrated that the ability of pigmentable cells to increase the number of intracellular melanosomes fully accounts for their higher resistance to vem compared to non-pigmentable cells. In addition, we identified a network of miRNAs and mRNAs that are involved in melanosome maturation and/or trafficking. Finally, we provide the rationale for testing BRAFi in combination with inhibitors of these biological processes, so that pigmentable melanoma cells can be turned into more sensitive non-pigmentable cells.

Genomic and Immune Approach in Platinum Refractory HPV-Negative Head and Neck Squamous Cell Carcinoma Patients Treated with Immunotherapy: A Novel Combined Profile.

INTRODUCTION: Only a minority of patients with platinum refractory head and neck squamous cell carcinoma (PR/HNSCC) gain some lasting benefit from immunotherapy. METHODS: The combined role of the comprehensive genomic (through the FoundationOne Cdx test) and immune profiles of 10 PR/HNSCC patients treated with the anti-PD-1 nivolumab was evaluated. The immune profiles were studied both at baseline and at the second cycle of immunotherapy, weighing 20 circulating cytokines/chemokines, adhesion molecules, and 14 soluble immune checkpoints dosed through a multiplex assay. A connectivity map was obtained by calculating the Spearman correlation between the expression profiles of circulating molecules. RESULTS: Early progression occurred in five patients, each of them showing TP53 alteration and three of them showing a mutation/loss/amplification of genes involved in the cyclin-dependent kinase pathway. In addition, ERB2 amplification (1 patient), BRCA1 mutation (1 patient), and NOTCH1 genes alteration (3 patients) occurred. Five patients achieved either stable disease or partial response. Four of them carried mutations in PI3K/AKT/PTEN pathways. In the only two patients, with a long response to immunotherapy, the tumor mutational burden (TMB) was high. Moreover, a distinct signature, in terms of network connectivity of the circulating soluble molecules, characterizing responder and non-responder patients, was evidenced. Moreover, a strong negative and statistically significant (p-value ≤ 0.05) correlation with alive status was evidenced for sE-selectin at T1. CONCLUSIONS: Our results highlighted the complexity and heterogeneity of HNSCCs, even though it was in a small cohort. Molecular and immune approaches, combined in a single profile, could represent a promising strategy, in the context of precision immunotherapy.

Identification of Potential Repurposable Drugs in Alzheimer's Disease Exploiting a Bioinformatics Analysis.

Alzheimer's disease (AD) is a neurologic disorder causing brain atrophy and the death of brain cells. It is a progressive condition marked by cognitive and behavioral impairment that significantly interferes with daily activities. AD symptoms develop gradually over many years and eventually become more severe, and no cure has been found yet to arrest this process. The present study is directed towards suggesting putative novel solutions and paradigms for fighting AD pathogenesis by exploiting new insights from network medicine and drug repurposing strategies. To identify new drug-AD associations, we exploited SAveRUNNER, a recently developed network-based algorithm for drug repurposing, which quantifies the vicinity of disease-associated genes to drug targets in the human interactome. We complemented the analysis with an in silico validation of the candidate compounds through a gene set enrichment analysis, aiming to determine if the modulation of the gene expression induced by the predicted drugs could be counteracted by the modulation elicited by the disease. We identified some interesting compounds belonging to the beta-blocker family, originally approved for treating hypertension, such as betaxolol, bisoprolol, and metoprolol, whose connection with a lower risk to develop Alzheimer's disease has already been observed. Moreover, our algorithm predicted multi-kinase inhibitors such as regorafenib, whose beneficial effects were recently investigated for neuroinflammation and AD pathology, and mTOR inhibitors such as sirolimus, whose modulation has been associated with AD.

Repurposing Histaminergic Drugs in Multiple Sclerosis.

Multiple sclerosis is an autoimmune disease with a strong neuroinflammatory component that contributes to severe demyelination, neurodegeneration and lesions formation in white and grey matter of the spinal cord and brain. Increasing attention is being paid to the signaling of the biogenic amine histamine in the context of several pathological conditions. In multiple sclerosis, histamine regulates the differentiation of oligodendrocyte precursors, reduces demyelination, and improves the remyelination process. However, the concomitant activation of histamine H1-H4 receptors can sustain either damaging or favorable effects, depending on the specifically activated receptor subtype/s, the timing of receptor engagement, and the central versus peripheral target district. Conventional drug development has failed so far to identify curative drugs for multiple sclerosis, thus causing a severe delay in therapeutic options available to patients. In this perspective, drug repurposing offers an exciting and complementary alternative for rapidly approving some medicines already approved for other indications. In the present work, we have adopted a new network-medicine-based algorithm for drug repurposing called SAveRUNNER, for quantifying the interplay between multiple sclerosis-associated genes and drug targets in the human interactome. We have identified new histamine drug-disease associations and predicted off-label novel use of the histaminergic drugs amodiaquine, rupatadine, and diphenhydramine among others, for multiple sclerosis. Our work suggests that selected histamine-related molecules might get to the root causes of multiple sclerosis and emerge as new potential therapeutic strategies for the disease.

SPINNAKER: an R-based tool to highlight key RNA interactions in complex biological networks.

BACKGROUND: Recently, we developed a mathematical model for identifying putative competing endogenous RNA (ceRNA) interactions. This methodology has aroused a broad acknowledgment within the scientific community thanks to the encouraging results achieved when applied to breast invasive carcinoma, leading to the identification of PVT1, a long non-coding RNA functioning as ceRNA for the miR-200 family. The main shortcoming of the model is that it is no freely available and implemented in MATLAB®, a proprietary programming platform requiring a paid license for installing, operating, manipulating, and running the software. RESULTS: Breaking through these model limitations demands to distribute it in an open-source, freely accessible environment, such as R, designed for an ordinary audience of users that are not able to afford a proprietary solution. Here, we present SPINNAKER (SPongeINteractionNetworkmAKER), the open-source version of our widely established mathematical model for predicting ceRNAs crosstalk, that is released as an exhaustive collection of R functions. SPINNAKER has been even designed for providing many additional features that facilitate its usability, make it more efficient in terms of further implementation and extension, and less intense in terms of computational execution time. CONCLUSIONS: SPINNAKER source code is freely available at https://github.com/sportingCode/SPINNAKER.git together with a thoroughgoing PPT-based guideline. In order to help users get the key points more conveniently, also a practical R-styled plain-text guideline is provided. Finally, a short movie is available to help the user to set the own directory, properly.

Comprehensive network medicine-based drug repositioning via integration of therapeutic efficacy and side effects.

Despite advances in modern medicine that led to improvements in cardiovascular outcomes, cardiovascular disease (CVD) remains the leading cause of mortality and morbidity globally. Thus, there is an urgent need for new approaches to improve CVD drug treatments. As the development time and cost of drug discovery to clinical application are excessive, alternate strategies for drug development are warranted. Among these are included computational approaches based on omics data for drug repositioning, which have attracted increasing attention. In this work, we developed an adjusted similarity measure implemented by the algorithm SAveRUNNER to reposition drugs for cardiovascular diseases while, at the same time, considering the side effects of drug candidates. We analyzed nine cardiovascular disorders and two side effects. We formulated both disease disorders and side effects as network modules in the human interactome, and considered those drug candidates that are proximal to disease modules but far from side-effects modules as ideal. Our method provides a list of drug candidates for cardiovascular diseases that are unlikely to produce common, adverse side-effects. This approach incorporating side effects is applicable to other diseases, as well.

A Comparison of Network-Based Methods for Drug Repurposing along with an Application to Human Complex Diseases.

Drug repurposing strategy, proposing a therapeutic switching of already approved drugs with known medical indications to new therapeutic purposes, has been considered as an efficient approach to unveil novel drug candidates with new pharmacological activities, significantly reducing the cost and shortening the time of de novo drug discovery. Meaningful computational approaches for drug repurposing exploit the principles of the emerging field of Network Medicine, according to which human diseases can be interpreted as local perturbations of the human interactome network, where the molecular determinants of each disease (disease genes) are not randomly scattered, but co-localized in highly interconnected subnetworks (disease modules), whose perturbation is linked to the pathophenotype manifestation. By interpreting drug effects as local perturbations of the interactome, for a drug to be on-target effective against a specific disease or to cause off-target adverse effects, its targets should be in the nearby of disease-associated genes. Here, we used the network-based proximity measure to compute the distance between the drug module and the disease module in the human interactome by exploiting five different metrics (minimum, maximum, mean, median, mode), with the aim to compare different frameworks for highlighting putative repurposable drugs to treat complex human diseases, including malignant breast and prostate neoplasms, schizophrenia, and liver cirrhosis. Whilst the standard metric (that is the minimum) for the network-based proximity remained a valid tool for efficiently screening off-label drugs, we observed that the other implemented metrics specifically predicted further interesting drug candidates worthy of investigation for yielding a potentially significant clinical benefit.

SWIMmeR: an R-based software to unveiling crucial nodes in complex biological networks.

SUMMARY: We present SWIMmeR, an open-source version of its predecessor SWIM (SWitchMiner) that is a network-based tool for mining key (switch) genes that are associated with intriguing patterns of molecular co-abundance and may play a crucial role in phenotypic transitions in various biological settings. SWIM was originally written in MATLAB®, a proprietary programming language that requires the purchase of a license to install, manipulate, operate and run the software. Over the last years, SWIM has sparked a widespread interest within the scientific community thanks to the promising results obtained through its application in a broad range of phenotype-specific scenarios, spanning from complex diseases to grapevine berry maturation. This success has created the call for it to be distributed in a freely accessible, open-source, runtime environment, such as R, aimed at a general audience of non-expert users that cannot afford the leading proprietary solution. SWIMmeR is provided as a comprehensive collection of R functions and it also includes several additional features that make it less intensive in terms of computer time and more efficient in terms of usability and further implementation and extension. AVAILABILITY AND IMPLEMENTATION: The SWIMmeR source code is freely available at https://github.com/sportingCode/SWIMmeR.git, along with a practical user guide, including a usage example of its application on breast cancer dataset. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integro-differential approach for modeling the COVID-19 dynamics - Impact of confinement measures in Italy.

The COVID-19 pandemic has overwhelmed the life and security of most of the world countries, and especially of the Western countries, without similar experiences in the recent past. In a first phase, the response of health systems and governments was disorganized, but then incisive, also driven by the fear of a new and dramatic phenomenon. In the second phase, several governments, including Italy, accepted the doctrine of "coexistence with the virus" by putting into practice a series of containment measures aimed at limiting the dramatic sanitary consequences while not jeopardizing the economic and social stability of the country. Here, we present a new mathematical approach to modeling the COVID-19 dynamics that accounts for typical evolution parameters (i.e., virus variants, vaccinations, containment measurements). Reproducing the COVID-19 epidemic spread is an extremely challenging task due to the low reliability of the available data, the lack of recurrent patterns, and the considerable amount and variability of the involved parameters. However, the adoption of fairly uniform criteria among the Italian regions enabled to test and optimize the model in various conditions leading to robust and interesting results. Although the regional variability is quite large and difficult to predict, we have retrospectively obtained reliable indications on which measures were the most appropriate to limit the transmissibility coefficients within detectable ranges for all the regions. To complicate matters further, the rapid spread of the English variant has upset contexts where the propagation of contagion was close to equilibrium conditions, decreeing success or failure of a certain measure. Finally, we assessed the effectiveness of the zone assignment criteria, highlighting how the reactivity of the measures plays a fundamental role in limiting the spread of the infection and thus the total number of deaths, the most important factor in assessing the success of epidemic management.

Transcriptomics and Metabolomics Integration Reveals Redox-Dependent Metabolic Rewiring in Breast Cancer Cells.

Rewiring glucose metabolism toward aerobic glycolysis provides cancer cells with a rapid generation of pyruvate, ATP, and NADH, while pyruvate oxidation to lactate guarantees refueling of oxidized NAD+ to sustain glycolysis. CtPB2, an NADH-dependent transcriptional co-regulator, has been proposed to work as an NADH sensor, linking metabolism to epigenetic transcriptional reprogramming. By integrating metabolomics and transcriptomics in a triple-negative human breast cancer cell line, we show that genetic and pharmacological down-regulation of CtBP2 strongly reduces cell proliferation by modulating the redox balance, nucleotide synthesis, ROS generation, and scavenging. Our data highlight the critical role of NADH in controlling the oncogene-dependent crosstalk between metabolism and the epigenetically mediated transcriptional program that sustains energetic and anabolic demands in cancer cells.

In silico drug repurposing in COVID-19: A network-based analysis.

The SARS-CoV-2 pandemic is a worldwide public health emergency. Despite the beginning of a vaccination campaign, the search for new drugs to appropriately treat COVID-19 patients remains a priority. Drug repurposing represents a faster and cheaper method than de novo drug discovery. In this study, we examined three different network-based approaches to identify potentially repurposable drugs to treat COVID-19. We analyzed transcriptomic data from whole blood cells of patients with COVID-19 and 21 other related conditions, as compared with those of healthy subjects. In addition to conventionally used drugs (e.g., anticoagulants, antihistaminics, anti-TNFα antibodies, corticosteroids), unconventional candidate compounds, such as SCN5A inhibitors and drugs active in the central nervous system, were identified. Clinical judgment and validation through clinical trials are always mandatory before use of the identified drugs in a clinical setting.

Assessing the impact of data-driven limitations on tracing and forecasting the outbreak dynamics of COVID-19.

The availability of the epidemiological data strongly affects the reliability of several mathematical models in tracing and forecasting COVID-19 pandemic, hampering a fair assessment of their relative performance. The marked difference between the lethality of the virus when comparing the first and second waves is an evident sign of the poor reliability of the data, also related to the variability over time in the number of performed swabs. During the early epidemic stage, swabs were made only to patients with severe symptoms taken to hospital or intensive care unit. Thus, asymptomatic people, not seeking medical assistance, remained undetected. Conversely, during the second wave of infection, total infectives included also a percentage of detected asymptomatic infectives, being tested due to close contacts with swab positives and thus registered by the health system. Here, we compared the outcomes of two SIR-type models (the standard SIR model and the A-SIR model that explicitly considers asymptomatic infectives) in reproducing the COVID-19 epidemic dynamic in Italy, Spain, Germany, and France during the first two infection waves, simulated separately. We found that the A-SIR model overcame the SIR model in simulating the first wave, whereas these discrepancies are reduced in simulating the second wave, when the accuracy of the epidemiological data is considerably higher. These results indicate that increasing the complexity of the model is useless and unnecessarily wasteful if not supported by an increased quality of the available data.

The role of FOSL1 in stem-like cell reprogramming processes.

Cancer stem-like cells (CSCs) have self-renewal abilities responsible for cancer progression, therapy resistance, and metastatic growth. The glioblastoma stem-like cells are the most studied among CSC populations. A recent study identified four transcription factors (SOX2, SALL2, OLIG2, and POU3F2) as the minimal core sufficient to reprogram differentiated glioblastoma (GBM) cells into stem-like cells. Transcriptomic data of GBM tissues and cell lines from two different datasets were then analyzed by the SWItch Miner (SWIM), a network-based software, and FOSL1 was identified as a putative regulator of the previously identified minimal core. Herein, we selected NTERA-2 and HEK293T cells to perform an in vitro study to investigate the role of FOSL1 in the reprogramming mechanisms. We transfected the two cell lines with a constitutive FOSL1 cDNA plasmid. We demonstrated that FOSL1 directly regulates the four transcription factors binding their promoter regions, is involved in the deregulation of several stemness markers, and reduces the cells' ability to generate aggregates increasing the extracellular matrix component FN1. Although further experiments are necessary, our data suggest that FOSL1 reprograms the stemness by regulating the core of the four transcription factors.

An Overview of the Computational Models Dealing with the Regulatory ceRNA Mechanism and ceRNA Deregulation in Cancer.

Pools of RNA molecules can act as competing endogenous RNAs (ceRNAs) and indirectly alter their expression levels by competitively binding shared microRNAs. This ceRNA cross talk yields an additional posttranscriptional regulatory layer, which plays key roles in both physiological and pathological processes. MicroRNAs can act as decoys by binding multiple RNAs, as well as RNAs can act as ceRNAs by competing for binding multiple microRNAs, leading to many cross talk interactions that could favor significant large-scale effects in spite of the weakness of single interactions. Identifying and studying these extended ceRNA interaction networks could provide a global view of the fine-tuning gene regulation in a wide range of biological processes and tumor progressions. In this chapter, we review current progress of predicting ceRNA cross talk, by summarizing the most up-to-date databases, which collect computationally predicted and/or experimentally validated miRNA-target and ceRNA-ceRNA interactions, as well as the widespread computational methods for discovering and modeling possible evidences of ceRNA-ceRNA interaction networks. These methods can be grouped in two categories: statistics-based methods exploit multivariate analysis to build ceRNA networks, by considering the miRNA expression levels when evaluating miRNA sponging relationships; mathematical methods build deterministic or stochastic models to analyze and predict the behavior of ceRNA cross talk.