A Bioinformatics-Assisted Review on Iron Metabolism and Immune System to Identify Potential Biomarkers of Exercise Stress-Induced Immunosuppression

Techniques, procedures, and applications in host genetic analysis

This chapter overviews genetic techniques' fundamentals and methodological features, including different approaches, analyses, and applications that have contributed to advancing health and disease. The aim is to describe laboratory methodologies and analyses employed to understand the genetic landscape of different biological contexts, from conventional techniques to cutting-edge technologies. Besides describing detailed aspects of the polymerase chain reaction (PCR) and derived types as one of the principles for many novel techniques, we also discuss microarray analysis, next-generation sequencing, and genome editing technologies such as transcription activator-like effector nucleases (TALENs) and the clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) systems. These techniques study several phenotypes, ranging from autoimmune disorders to viral diseases. The significance of integrating diverse genetic methodologies and tools to understand host genetics comprehensively and addressing the ethical, legal, and social implications (ELSI) associated with using genetic information is highlighted. Overall, the methods, procedures, and applications in host genetic analysis provided in this chapter furnish researchers and practitioners with a roadmap for navigating the dynamic landscape of host-genome interactions.

Genes and Pathways Involved in the Progression of Malignant Pleural Mesothelioma: A Meta-analysis of Genome-Wide Expression Studies

Malignant pleural mesothelioma (MPM) is a rare and aggressive neoplasm of the pleural tissue that lines the lungs and is mainly associated with long latency from asbestos exposure. This tumor has no effective therapeutic opportunities nowadays and has a very low five-year survival rate. In this sense, identifying molecular events that trigger the development and progression of this tumor is highly important to establish new and potentially effective treatments. We conducted a meta-analysis of genome-wide expression studies publicly available at the Gene Expression Omnibus (GEO) and ArrayExpress databases. The differentially expressed genes (DEGs) were identified, and we performed functional enrichment analysis and protein-protein interaction networks (PPINs) to gain insight into the biological mechanisms underlying these genes. Additionally, we constructed survival prediction models for selected DEGs and predicted the minimum drug inhibition concentration of anticancer drugs for MPM. In total, 115 MPM tumor transcriptomes and 26 pleural tissue controls were analyzed. We identified 1046 upregulated DEGs in the MPM samples. Cellular signaling categories in tumor samples were associated with the TNF, PI3K-Akt, and AMPK pathways. The inflammatory response, regulation of cell migration, and regulation of angiogenesis were overrepresented biological processes. Expression of SOX17 and TACC1 were associated with reduced survival rates. This meta-analysis identified a list of DEGs in MPM tumors, cancer-related signaling pathways, and biological processes that were overrepresented in MPM samples. Some therapeutic targets to treat MPM are suggested, and the prognostic potential of key genes is shown.

Integrated procedures for accelerating, deepening, and leading genetic inquiry: A first application on human muscle secretome

PURPOSE

Beyond classical procedures, bioinformatic-assisted approaches and computational biology offer unprecedented opportunities for scholars. However, these amazing possibilities still need epistemological criticism, as well as standardized procedures. Especially those topics with a huge body of data may benefit from data science (DS)-assisted methods. Therefore, the current study dealt with the combined expert-assisted and DS-assisted approaches to address the broad field of muscle secretome. We aimed to apply DS tools to fix the literature research, suggest investigation targets with a data-driven approach, predict possible scenarios, and define a workflow.

METHODS

Recognized scholars with expertise on myokines were invited to provide a list of the most important myokines. GeneRecommender, GeneMANIA, HumanNet, and STRING were selected as DS tools. Networks were built on STRING and GeneMANIA. The outcomes of DS tools included the top 5 recommendations. Each expert-led discussion has been then integrated with an DS-led approach to provide further perspectives.

RESULTS

Among the results, 11 molecules had already been described as bona-fide myokines in literature, and 11 molecules were putative myokines. Most of the myokines and the putative myokines recommended by the DS tools were described as present in the cargo of extracellular vesicles.

CONCLUSIONS

Including both supervised and unsupervised learning methods, as well as encompassing algorithms focused on both protein interaction and gene represent a comprehensive approach to tackle complex biomedical topics. DS-assisted methods for reviewing existent evidence, recommending targets of interest, and predicting original scenarios are worth exploring as in silico recommendations to be integrated with experts' ideas for optimizing molecular studies.

Evaluation of protein's interaction and the regulatory network of some drought-responsive genes in Canola under drought and re-watering conditions

Drought stress is one of the most important environmental stresses that severely limits the growth and yield of Canola. The re-watering can compensate for the damage caused by drought stress. Investigation of protein's interaction of genes involved in important drought-responsive pathways and their regulatory network by microRNAs (miRNAs) under drought and re-watering conditions are helpful approaches to discovering drought-stress tolerance and recovery mechanisms. In this study, the protein's interaction and functional enrichment analyses of glycolysis, pentose phosphate, glyoxylate cycle, fatty acid biosynthesis, heat shock factor main genes, and the regulatory network of key genes by miRNAs were investigated by in silico analysis. Then, the relative expression of key genes and their related miRNAs were investigated in tolerant and susceptible genotypes of Canola under drought and re-watering conditions by Real-time PCR technique. The bna-miR156b/c/g, bna-miR395d/e/f, bna-miR396a, and all the studied key genes except HSFA1E and PK showed changes in expression levels in one or both genotypes after re-watering. The PPC1 and HSFB2B expression decreased, whereas the MLS and CAC3 expression increased in both genotypes under re-watering treatment after drought stress. It could cause the regulation of oxaloacetate production, the increase of the glyoxylate cycle, lipid biosynthesis, and the reduction of the negative regulation of HSFs under re-watering conditions. It seems that PPC1, G6PD2, MLS, CAC3, and HSFB2B were involved in the recovery mechanisms after drought stress of Canola. They were regulated by drought-responsive miRNAs to respond appropriately to drought stress. Therefore, regulating these genes could be important in plant recovery mechanisms.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-023-01345-1.

Adaptogens on Depression-Related Outcomes: A Systematic Integrative Review and Rationale of Synergism with Physical Activity

Depression is considered the most important disorder affecting mental health. The aim of this systematic integrative review was: (i) to describe the effects of supplementation with adaptogens on variables related to depression in adults; and (ii) to discuss the potential combination with physical exercise to aid planning and commissioning future clinical research. An integrative review was developed complementing the Preferred Reporting Items for Systematic reviews and Meta-Analyses statement (PROSPERO registration: CRD42021249682). A total of 41 articles met the inclusion criteria. With a Price index of 46.4%, we found that: (i) Hypericum perforatum (St. John's Wort) is the most studied and supported adaptogen (17/41 [41.46%], three systematic reviews with meta-analysis) followed by Crocus sativus L. or saffron (6/41 [14.63%], three systematic reviews with meta-analysis and two systematic reviews); (ii) it is possible that the significantly better performance of adaptogens over placebo is due to the reduction of allostatic load via the action of secondary metabolites on BDNF regulation; and, (iii) the number of studies reporting physical activity levels is limited or null for those that combine an exercise program with the consumption of adaptogens. Aware of the need for a multidisciplinary approach for depression treatment, this systematic integrative review provides an up-to-date view for supporting the use of St. John's Wort and saffron as non-pharmacological strategies while also help commissioning future research on the efficacy of other adaptogens. It also contributes to the design of future clinical research studies that evaluate the consumption of herbal extracts plus physical exercise, mainly resistance training, as a potentially safe and powerful strategy to treat depression.

Nutritional Strategies in the Rehabilitation of Musculoskeletal Injuries in Athletes: A Systematic Integrative Review

It is estimated that three to five million sports injuries occur worldwide each year. The highest incidence is reported during competition periods with mainly affectation of the musculoskeletal tissue. For appropriate nutritional management and correct use of nutritional supplements, it is important to individualize based on clinical effects and know the adaptive response during the rehabilitation phase after a sports injury in athletes. Therefore, the aim of this PRISMA in Exercise, Rehabilitation, Sport Medicine and Sports Science PERSiST-based systematic integrative review was to perform an update on nutritional strategies during the rehabilitation phase of musculoskeletal injuries in elite athletes. After searching the following databases: PubMed/Medline, Scopus, PEDro, and Google Scholar, a total of 18 studies met the inclusion criteria (Price Index: 66.6%). The risk of bias assessment for randomized controlled trials was performed using the RoB 2.0 tool while review articles were evaluated using the AMSTAR 2.0 items. Based on the main findings of the selected studies, nutritional strategies that benefit the rehabilitation process in injured athletes include balanced energy intake, and a high-protein and carbohydrate-rich diet. Supportive supervision should be provided to avoid low energy availability. The potential of supplementation with collagen, creatine monohydrate, omega-3 (fish oils), and vitamin D requires further research although the effects are quite promising. It is worth noting the lack of clinical research in injured athletes and the higher number of reviews in the last 10 years. After analyzing the current quantitative and non-quantitative evidence, we encourage researchers to conduct further clinical research studies evaluating doses of the discussed nutrients during the rehabilitation process to confirm findings, but also follow international guidelines at the time to review scientific literature.

Exercise Selection and Common Injuries in Fitness Centers: A Systematic Integrative Review and Practical Recommendations

Weight resistance training (RT) is an essential component of physical conditioning programs to improve the quality of life and physical fitness in different ages and populations. This integrative review aimed to analyze the scientific evidence on the relationship between exercise selection and the appearance of musculoskeletal injuries in physical fitness centers (PFC). The PubMed or Medline, EMBASE or Science Direct, Google Scholar and PEDro databases were selected to examine the available literature using a Boolean algorithm with search terms. The review process was performed using the five-stage approach for an integrative review and it was reported according to the PRISMA in Exercise, Rehabilitation, Sport Medicine and Sports Science (PERSiST) guidelines. A total of 39 peer-reviewed articles (Price index = 71.7%) met the inclusion criteria and evaluated the link between exercise selection and the incidence of injuries in exercisers who regularly attend PFC. Most injuries occur to the shoulders, elbows, vertebrae of the spine, and knees. Although the injury etiologies are multifactorial, the findings of the reviewed articles include the impacts of overuse, short post-exercise recovery periods, poor conditioning in the exercised body areas, frequent use of heavy loads, improper technique in certain exercises, and the abuse of performance- and image-enhancing drugs. Practical recommendations addressed to clinical exercise physiologists, exercise professionals, and health professionals are given in this paper. The exercise selection in RT programs requires professional supervision and adhering to proper lifting techniques and training habits that consider the anatomical and biomechanical patterns of the musculoskeletal structures, as well as genetic, pedagogical, and methodological aspects directly related to the stimulus-response process to mitigate the occurrence of RT-related injuries in PFC.

Discovering Thematically Coherent Biomedical Documents Using Contextualized Bidirectional Encoder Representations from Transformers-Based Clustering

The increasing expansion of biomedical documents has increased the number of natural language textual resources related to the current applications. Meanwhile, there has been a great interest in extracting useful information from meaningful coherent groupings of textual content documents in the last decade. However, it is challenging to discover informative representations and define relevant articles from the rapidly growing biomedical literature due to the unsupervised nature of document clustering. Moreover, empirical investigations demonstrated that traditional text clustering methods produce unsatisfactory results in terms of non-contextualized vector space representations because that neglect the semantic relationship between biomedical texts. Recently, pre-trained language models have emerged as successful in a wide range of natural language processing applications. In this paper, we propose the Gaussian Mixture Model-based efficient clustering framework that incorporates substantially pre-trained (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining) BioBERT domain-specific language representations to enhance the clustering accuracy. Our proposed framework consists of main three phases. First, classic text pre-processing techniques are used biomedical document data, which crawled from the PubMed repository. Second, representative vectors are extracted from a pre-trained BioBERT language model for biomedical text mining. Third, we employ the Gaussian Mixture Model as a clustering algorithm, which allows us to assign labels for each biomedical document. In order to prove the efficiency of our proposed model, we conducted a comprehensive experimental analysis utilizing several clustering algorithms while combining diverse embedding techniques. Consequently, the experimental results show that the proposed model outperforms the benchmark models by reaching performance measures of Fowlkes mallows score, silhouette coefficient, adjusted rand index, Davies-Bouldin score of 0.7817, 0.3765, 0.4478, 1.6849, respectively. We expect the outcomes of this study will assist domain specialists in comprehending thematically cohesive documents in the healthcare field.