Microsporidium Nosema ceranae is well known for exerting a negative impact on honey bee health, including down-regulation of immunoregulatory genes. Protein nutrition has been proven to have beneficial effects on bee immunity and other aspects of bee health. Bearing this in mind, the aim of our study was to evaluate the potential of a dietary amino acid and vitamin complex “BEEWELL AminoPlus” to protect honey bees from immunosuppression induced by N. ceranae. In a laboratory experiment bees were infected with N. ceranae and treated with supplement on first, third, sixth and ninth day after emergence. The expression of genes for immune-related peptides (abaecin, apidaecin, hymenoptaecin, defensin and vitellogenin) was compared between groups. The results revealed significantly lower (p<0.01 or p<0.001) numbers of Nosema spores in supplemented groups than in the control especially on day 12 post infection. With the exception of abacein, the expression levels of immune-related peptides were significantly suppressed (p<0.01 or p<0.001) in control group on the 12^th day post infection, compared to bees that received the supplement. It was supposed that N. ceranae had a negative impact on bee immunity and that the tested amino acid and vitamin complex modified the expression of immune-related genes in honey bees compromised by infection, suggesting immune-stimulation that reflects in the increase in resistance to diseases and reduced bee mortality. The supplement exerted best efficacy when applied simultaneously with Nosema infection, which can help us to assume the most suitable period for its application in the hive.

Personalized medicine is focused on research disciplines which contribute to the individualization of therapy, like pharmacogenomics and pharmacotranscriptomics. Acute lymphoblastic leukemia (ALL) is the most common malignancy of childhood. It is one of the pediatric malignancies with the highest cure rate, but still a lethal outcome due to therapy accounts for 1–3% of deaths. Further improvement of treatment protocols is needed through the implementation of pharmacogenomics and pharmacotranscriptomics. Emerging high-throughput technologies, including microarrays and next-generation sequencing, have provided an enormous amount of molecular data with the potential to be implemented in childhood ALL treatment protocols. In the current review, we summarized the contribution of these novel technologies to the pharmacogenomics and pharmacotranscriptomics of childhood ALL. We have presented data on molecular markers responsible for the efficacy, side effects, and toxicity of the drugs commonly used for childhood ALL treatment, i.e., glucocorticoids, vincristine, asparaginase, anthracyclines, thiopurines, and methotrexate. Big data was generated using high-throughput technologies, but their implementation in clinical practice is poor. Research efforts should be focused on data analysis and designing prediction models using machine learning algorithms. Bioinformatics tools and the implementation of artificial i Lack of association of the CEP72 rs924607 TT genotype with intelligence are expected to open the door wide for personalized medicine in the clinical practice of childhood ALL.

Background: This study analyzed poorly understood relationship of two overlapping conditions: metabolic syndrome (MeS) and inflammatory bowel disease (IBD), both associated with inflammation in the visceral adipose tissue. Methods: Newly diagnosed 104 IBD patients, of which 50 Crohn's disease (CD) and 54 ulcerative colitis (UC), and 45 non-IBD controls were examined for MeS-related obesity and lipid markers. Th-17 immune genes IL17A, IL17F, IL23A, and TLR9 mRNAs were measured in intestinal mucosa by qRT-PCR. Subjects were genotyped for obesity-associated FTO variant rs9939609 by polymerase chain reaction–amplification refractory mutation system. Results: CD was associated with MeS (P = 0.01), while both CD and UC were associated with central obesity (P = 10⁻⁵, P = 0.002, respectively) and low levels of high-density lipoprotein (HDL) cholesterol (P = 5 × 10⁻⁶, P = 6 × 10⁻⁶, respectively). IBD lipid profile was characterized by decreased total and HDL cholesterol, while low-density lipoprotein cholesterol was reduced only in CD. Negative correlations were found between total cholesterol and CD activity index (P = 0.005), waist circumference and IL17A as well as IL17F mRNA levels in inflamed CD colon (P = 0.003, P = 0.001, respectively). Carriers of FTO rs9939609 AA genotype showed increased risk of CD (OR 2.6, P = 0.01). Conclusions: MeS, central obesity, and dyslipidemia could be important for IBD pathogenesis. This could influence therapeutic approaches and prevention strategies in high-risk groups.

Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.

Methotrexate (MTX) is one of the staples of pediatric acute lymphoblastic leukemia (ALL) treatment. MTX targets the folate metabolic pathway (FMP). Abnormal function of the enzymes in FMP, due to genetic aberrations, leads to adverse drug reactions. The aim of this study was to investigate variants in pharmacogenes involved in FMP and their association with MTX pharmacokinetics (MTX elimination profile) and toxicity in the consolidation therapy phase of pediatric ALL patients. Eleven variants in the thymidylate synthetase (TYMS), methylenetetrahydrofolate reductase (MTHFR), dihydrofolate reductase (DHFR), SLC19A1 and SLCO1B genes were analyzed in 148 patients, using PCR- and sequencing-based methodology. For the Serbian and European control groups, data on allele frequency distribution were extracted from in-house and public databases. Our results show that the A allele of SLC19A1 c.80 variant contributes to slow MTX elimination. Additionally, the AA genotype of the same variant is a predictor of MTX-related hepatotoxicity. Patients homozygous for TYMS 6bp deletion were more likely to experience gastrointestinal toxicity. No allele frequency dissimilarity was found for the analyzed variants between Serbian and European populations. Statistical modelling did not show a joint effect of analyzed variants. Our results indicate that SLC19A1 c.80 variant and TYMS 6bp deletion are the most promising pharmacogenomic markers of MTX response in pediatric ALL patients.