In recent years, high-throughput sequencing technologies provide unprecedented opportunity to depict cancer samples at multiple molecular levels. The integration and analysis of these multi-omics ...datasets is a crucial and critical step to gain actionable knowledge in a precision medicine framework. This paper explores recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing. We systematically retrieved peer-reviewed journals published from 2014 to 2019, select and thoroughly describe the tools presenting the most promising innovations regarding the integration of heterogeneous data, the machine learning methodologies that successfully tackled the complexity of multi-omics data, and the frameworks to deliver actionable results for clinical practice. The review is organized according to the applied methods: Deep learning, Network-based methods, Clustering, Features Extraction, and Transformation, Factorization. We provide an overview of the tools available in each methodological group and underline the relationship among the different categories. Our analysis revealed how multi-omics datasets could be exploited to drive precision oncology, but also current limitations in the development of multi-omics data integration.
The integration of data and knowledge from heterogeneous sources can be a key success factor in drug design, drug repurposing and multi-target therapies. In this context, biological networks provide ...a useful instrument to highlight the relationships and to model the phenomena underlying therapeutic action in cancer. In our work, we applied network-based modeling within a novel bioinformatics pipeline to identify promising multi-target drugs. Given a certain tumor type/subtype, we derive a disease-specific Protein-Protein Interaction (PPI) network by combining different data-bases and knowledge repositories. Next, the application of suitable graph-based algorithms allows selecting a set of potentially interesting combinations of drug targets. A list of drug candidates is then extracted by applying a recent data fusion approach based on matrix tri-factorization. Available knowledge about selected drugs mechanisms of action is finally exploited to identify the most promising candidates for planning in vitro studies. We applied this approach to the case of Triple Negative Breast Cancer (TNBC), a subtype of breast cancer whose biology is poorly understood and that lacks of specific molecular targets. Our "in-silico" findings have been confirmed by a number of in vitro experiments, whose results demonstrated the ability of the method to select candidates for drug repurposing.
Developmental and cognitive psychology recently started to take an interest in the sports domain, exploring the role of either cognitive functions or emotions in youth sport. However, to the extent ...that cognition and emotions are inextricably linked, studying them jointly from a developmental perspective could inform on their interplay in determining performance in different sports. This research examined the role of general cognitive abilities, attentional style, and emotions (controlling for age and experience), in predicting performance in youth volleyball and artistic gymnastics. A total of 218 female participants, of which 114 volleyball players and 104 artistic gymnasts (11–17 years old) were administered two measures of working memory and six measures of executive functions (namely inhibition, updating, and shifting). They also completed an attentional style and an emotion-related questionnaire. For each volleyball player, an individual performance index based on every gesture performed during the games and controlled for the team performance was computed. As a measure of gymnasts’ performance, scores in 2017–2018 competitions were used. Regression analysis showed that the main predictor of the volleyball players’ performance (R
2
= 0.23) was a working memory-updating factor (ß = 0.45,
p
= 0.001), together with experience (ß = 0.29,
p
= 0.030) and high-arousal unpleasant emotions (ß = 0.30,
p
= 0.029), which positively predicted performance. Experience (ß = 0.30,
p
= 0.011), age (ß = −0.036,
p
= 0.005) and high-arousal unpleasant emotions (ß = −0.27,
p
= 0.030) were the predictors of gymnasts’ performance (R
2
= 0.25). These results represent a first step in understanding if and how youth female athletes of open- and closed-skills sports rely on different psychological abilities. This line of research could offer insight to practitioners regarding which psychological abilities could be more relevant to train depending on the type of sport.
Defective cell migration causes delayed wound healing (WH) and chronic skin lesions. Autologous micrograft (AMG) therapies have recently emerged as a new effective and affordable treatment able to ...improve wound healing capacity. However, the precise molecular mechanism through which AMG exhibits its beneficial effects remains unrevealed. Herein we show that AMG improves skin re-epithelialization by accelerating the migration of fibroblasts and keratinocytes. More specifically, AMG-treated wounds showed improvement of indispensable events associated with successful wound healing such as granulation tissue formation, organized collagen content, and newly formed blood vessels. We demonstrate that AMG is enriched with a pool of WH-associated growth factors that may provide the starting signal for a faster endogenous wound healing response. This work links the increased cell migration rate to the activation of the extracellular signal-regulated kinase (ERK) signaling pathway, which is followed by an increase in matrix metalloproteinase expression and their extracellular enzymatic activity. Overall we reveal the AMG-mediated wound healing transcriptional signature and shed light on the AMG molecular mechanism supporting its potential to trigger a highly improved wound healing process. In this way, we present a framework for future improvements in AMG therapy for skin tissue regeneration applications.
Reverse engineering of transcriptional regulatory networks (TRN) from genomics data has always represented a computational challenge in System Biology. The major issue is modeling the complex ...crosstalk among transcription factors (TFs) and their target genes, with a method able to handle both the high number of interacting variables and the noise in the available heterogeneous experimental sources of information.
In this work, we propose a data fusion approach that exploits the integration of complementary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-scale transcriptional networks. We develop a hybrid structure-learning algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia to reconstruct TRNs in a genome-wide perspective. Applying our method to high-throughput data, we verified its ability to deal with the complexity of a genomic TRN, providing a snapshot of the synergistic TFs regulatory activity. Given the noisy nature of data-driven prior knowledge, which potentially contains incorrect information, we also tested the method's robustness to false priors on a benchmark dataset, comparing the proposed approach to other regulatory network reconstruction algorithms. We demonstrated the effectiveness of our framework by evaluating structural commonalities of our learned genomic network with other existing networks inferred by different DNA binding information-based methods.
This Bayesian omics-data fusion based methodology allows to gain a genome-wide picture of the transcriptional interplay, helping to unravel key hierarchical transcriptional interactions, which could be subsequently investigated, and it represents a promising learning approach suitable for multi-layered genomic data integration, given its robustness to noisy sources and its tailored framework for handling high dimensional data.
Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is an immune-mediated disorder affecting the peripheral nervous system. Despite the established diagnostic criteria, monitoring ...disease activity and treatment remains challenging. To address this limitation, we investigated serum neurofilament light chain (sNfL) and serum free light chains (sFLCs) as potential biomarkers. A total of 32 CIDP patients undergoing immunoglobulin therapy and 32 healthy controls enrolled in the present study, and agreed to have their blood plasma sNfL and sFLCs analyzed, while CIDP severity was assessed through the modified Rankin Scale (mRS) and the Overall Neuropathy Limitations Scale (ONLS). In line with the immunoglobulin treatment aimed at limiting neuronal damage administered to the majority of patients, sNfL levels did not exhibit significant differences between the two groups. However, CIDP patients showed significantly elevated sFLC and sFLC ratios, while the marker levels did not correlate with the clinical scores. The study confirms the potential of sFLCs as a sensitive biomarker of inflammatory processes in CIDP. Additionally, the present study results regarding neurofilaments strengthen the role of sNfL in monitoring CIDP treatments, confirming the effectiveness of immunoglobulin therapy. Overall, our results demonstrate how combining these markers can lead to better patient characterization for improved treatment.
In the sport context, imagery has been described as the condition in which persons imagine themselves while executing skills to deal with the upcoming task or enhance performance. Systematic reviews ...have shown that mental imagery improves performance in motor tasks.
The aim of the present study was to explore whether imagery vividness (i.e., the clarity or realism of the imagery experience) and controllability (i.e., the ease and accuracy with which an image can be manipulated mentally) differ by sport types (team vs. individual and contact vs. non-contact). Participants were athletes from team contact and non-contact sports (rugby and volleyball, respectively), and individual contact and non-contact sports (karate and tennis, respectively) between the ages of 20 and 33 years (
= 24.37,
= 2.85). The participants completed the Vividness of Visual Imagery Questionnaire, the Vividness of Movement Imagery Questionnaire-2, and the Mental Image Transformation Tasks.
A 2 ×2 × 2 (gender × 2 contact-no-contact × 2 sport type) between groups MANOVA showed differences in imagery ability by sport type. Practical indications deriving from the findings of this study can help coaches and athletes to develop mental preparation programs using sport-specific imagery.
Mix, click, cyclize: Conformationally constrained organo–peptide hybrids can be constructed by a tandem chemoselective reaction between a synthetic molecule and a recombinant protein. Diverse ...macrocyclic structures were obtained in cyclic, lariat, and protein‐tethered configurations by varying the nature of the synthetic and biosynthetic precursors.
Multiple sclerosis (MS), the most common neurological disease that causes disability in youth, does not only affect physical functions but is also associated with cognitive impairment, fatigue, ...depression, and anxiety and can significantly impact health-related quality of life (HRQoL). Since MS is generally diagnosed at a young age-a period of great significance for personal, relational, and professional development-adaptation can become highly challenging. Therefore, enhancing the competence of young people to adaptively cope with these potential challenges is of utmost importance in order to promote their potentialities and talents. It has been shown that psychological interventions targeting MS patients can enhance resilience and HRQoL and that regular physical activity (PA) and social engagement can improve psychological well-being. However, literature on the development of global interventions based on the bio-psycho-social model of the disease is missing. Even less attention has been paid to interventions dedicated to young adults with MS (YawMS) and to the involvement of patients in the development of such programs.
In collaboration with MS patients, this study aims to develop a bio-psycho-social intervention (ESPRIMO) for YawMS, aiming to improve their HRQoL and to explore its feasibility, acceptability, and effects.
To tailor the intervention to the specific needs of YawMS, "patient engagement principles" will be adopted in the co-creation phase, performing a web survey and focus groups with patients and healthcare professionals. In the intervention phase, a pilot sample of 60 young adults with MS will be enrolled. The co-created intervention, composed of group sessions over a 12-week period, will cover psycho-social strategies and include physical activities. Adopting a longitudinal, pre-post evaluation design, self-report questionnaires measuring HRQoL and other bio-psycho-social features (e.g., resilience, well-being, mindfulness traits, self-efficacy, perceived social support, psychological symptoms, illness perception, committed action, fatigue, attitudes, subjective norms, perceived behavioral control, motivation, perception of autonomy support for PA, barriers and intentions to PA) will be administered, the quantity and quality of PA will be measured, and a questionnaire developed by the authors will be used to evaluate the feasibility and acceptability of the ESPRIMO intervention.
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