Chronic obstructive pulmonary disease (COPD) is a multifactorial disease of the respiratory system which develops as a result of a complex interaction of genetic and environmental factors closely related to lifestyle. We aimed to assess the combined effect of the PI3K/AKT/mTOR signaling pathway (PIK3R1, AKT1, MTOR, PTEN) and sirtuin (SIRT1, SIRT3, SIRT6) genes to COPD risk. SNPs of SIRT1 (rs3758391, rs3818292), SIRT3 (rs3782116, rs536715), SIRT6 (rs107251), AKT1 (rs2494732), PIK3R1 (rs10515070, rs831125, rs3730089), MTOR (rs2295080, rs2536), PTEN (rs701848, rs2735343) genes were genotyped by real-time polymerase chain reaction (PCR) among 1245 case and control samples. Logistic regression was used to detect the association of SNPs in different models. Linear regression analyses were performed to estimate the relationship between SNPs and lung function parameters and smoking pack-years. Significant associations with COPD were identified for SIRT1 (rs3818292) (P = 0.001, OR = 1.51 for AG), SIRT3 (rs3782116) (P = 0.0055, OR = 0.69) and SIRT3 (rs536715) (P = 0.00001, OR = 0.50) under the dominant model, SIRT6 (rs107251) (P = 0.00001, OR = 0.55 for СT), PIK3R1: (rs10515070 (P = 0.0023, OR = 1.47 for AT), rs831125 (P = 0.00001, OR = 2.28 for AG), rs3730089 (P = 0.0007, OR = 1.73 for GG)), PTEN: (rs701848 (P = 0.0015, OR = 1.35 under the log-additive model), and rs2735343 (P = 0.0001, OR = 1.64 for GC)). A significant genotype-dependent variation of lung function parameters was observed for SIRT1 (rs3818292), SIRT3 (rs3782116), PIK3R1 (rs3730089), and MTOR (rs2536). Gene-gene combinations that remained significantly associated with COPD were obtained; the highest risk of COPD was conferred by a combination of G allele of the PIK3R1 (rs831125) gene and GG of SIRT3 (rs536715) (OR = 3.45). The obtained results of polygenic analysis indicate the interaction of genes encoding sirtuins SIRT3, SIRT2, SIRT6 and PI3KR1, PTEN, MTOR and confirm the functional relationship between sirtuins and the PI3K/AKT/mTOR signaling pathway.

Obesity affects over 700 million people worldwide and its prevalence keeps growing steadily. The problem is particularly relevant due to the increased risk of COVID-19 complications and mortality in obese patients. Obesity prevalence increase is often associated with the influence of environmental and behavioural factors, leading to stigmatization of people with obesity due to beliefs that their problems are caused by poor lifestyle choices. However, hereditary predisposition to obesity has been established, likely polygenic in nature. Morbid obesity can result from rare mutations having a significant effect on energy metabolism and fat deposition, but the majority of patients does not present with monogenic forms. Microbiome low diversity significantly correlates with metabolic disorders (inflammation, insulin resistance), and the success of weight loss (bariatric) surgery. However, data on the long-term consequences of bariatric surgery and changes in the microbiome composition and genetic diversity before and after surgery are currently lacking. In this review, we summarize the results of studies of the genetic characteristics of obesity patients, molecular mechanisms of obesity, contributing to the unfavourable course of coronavirus infection, and the evolution of their microbiome during bariatric surgery, elucidating the mechanisms of disease development and creating opportunities to identify potential new treatment targets and design effective personalized approaches for the diagnosis, management, and prevention of obesity.

The poultry industry, a globally fast growing agricultural sector, provides affordable animal protein due to high efficiency. Gallus gallus domesticus are the most common domestic birds. Hybrid chicken breeds (crosses) are widely used to achieve high productivity. Maintaining industry competitiveness requires constant genetic selection of parent stock to improve performance traits. Genetic studies, which are essential in modern breeding programs, help identify genome variants linked to economically important traits and preserve population health. Next-generation sequencing (NGS) has identified millions of single nucleotide polymorphisms (SNPs) and insertions/deletions (INDELs), enabling detection of genome-wide regions associated with selection traits. Recent studies have pinpointed such regions using broiler lines, laying hen lines, or pooled genomic data. This review discusses advances in chicken genomic and transcriptomic research focused on traits enhancing meat breed performance and reproductive abilities. Special attention is given to transcriptome studies revealing regulatory mechanisms and key signaling pathways involved in artificial molting, as well as metagenome studies investigating resistance to infectious diseases and climate adaptation. Finally, a dedicated section highlights CRISPR/Cas genomic editing techniques for targeted genome modification in chicken genomics.

We tested associations between 13 established genetic variants and type 2 diabetes (T2D) in 1371 study participants from the Volga-Ural region of the Eurasian continent, and evaluated the predictive ability of the model containing polygenic scores for the variants associated with T2D in our dataset, alone and in combination with other risk factors such as age and sex. Using logistic regression analysis, we found associations with T2D for the CCL20 rs6749704 (OR = 1.68, P_FDR = 3.40 × 10⁻⁵), CCR5 rs333 (OR = 1.99, P_FDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, P_FDR = 2.64 × 10⁻⁴), TCF7L2 rs114758349 (OR = 1.77, P_FDR = 9.37 × 10⁻⁵), and CCL2 rs1024611 (OR = 1.38, P_FDR = 0.033) polymorphisms. We showed that the most informative prognostic model included weighted polygenic scores for these five loci, and non-genetic factors such as age and sex (AUC 85.8%, 95%CI 83.7–87.8%). Compared to the model containing only non-genetic parameters, adding the polygenic score for the five T2D-associated loci showed improved net reclassification (NRI = 37.62%, 1.39 × 10⁻⁶). Inclusion of all 13 tested SNPs to the model with age and sex did not improve the predictive ability compared to the model containing five T2D-associated variants (NRI = −17.86, p = 0.093). The five variants associated with T2D in people from the Volga-Ural region are linked to inflammation (CCR5, CCL2, CCL20) and glucose metabolism regulation (TCF7L, ADIPOQ2). Further studies in independent groups of T2D patients should validate the prognostic value of the model and elucidate the molecular mechanisms of the disease development.