Geriatric syndromes (GSs) and aging-associated diseases (AADs) are common side effects of aging. They are affecting the lives of millions of older adults and placing immense pressure on healthcare systems and economies worldwide. It is imperative to study the factors causing these conditions and develop a holistic framework for their management. The so-called long-lived individuals—people over the age of 90 who managed to retain much of their health and functionality—could be holding the key to understanding these factors and their health implications. We analyzed the health status and lifestyle of the long-lived individuals and identified risk factors for GSs. Family history greatly contributes to the health and prevention of cognitive decline in older adults. Lifestyle and certain socioeconomic factors such as education, the age of starting to work and retiring, job type and income level, physical activity, and hobby were also associated with certain GSs. Moreover, the levels of total protein, albumin, alpha-1 globulins, high-density lipoprotein, free triiodothyronine, and 25-hydroxyvitamin D were direct indicators of the current health status. The proposed mathematical model allows the prediction of successful aging based on family history, social and economic factors, and life-long physical activity (f1 score = 0.72, AUC = 0.68, precision = 0.83 and recall = 0.64).

To continue progress in the treatment of cardiovascular disease, there is a need to improve the overall understanding of the processes that contribute to the pathogenesis of cardiovascular disease (CVD). Exploring the role of gut microbiota in various heart diseases is a topic of great interest since it is not so easy to find such reliable connections despite the fact that microbiota undoubtedly affect all body systems. The present study was conducted to investigate the composition of gut microbiota in patients with atherosclerotic cardiovascular disease (ASCVD) and heart failure syndromes with reduced ejection fraction (HFrEF) and HF with preserved EF (HFpEF), and to compare these results with the microbiota of individuals without those diseases (control group). Fecal microbiota were evaluated by three methods: living organisms were determined using bacterial cultures, total DNA taxonomic composition was estimated by next generation sequencing (NGS) of 16S rRNA gene (V3–V4) and quantitative assessment of several taxa was performed using qPCR (quantitative polymerase chain reaction). Regarding the bacterial culture method, all disease groups demonstrated a decrease in abundance of Enterococcus faecium and Enterococcus faecalis in comparison to the control group. The HFrEF group was characterized by an increased abundance of Streptococcus sanguinus and Streptococcus parasanguinis. NGS analysis was conducted at the family level. No significant differences between patient’s groups were observed in alpha-diversity indices (Shannon, Faith, Pielou, Chao1, Simpson, and Strong) with the exception of the Faith index for the HFrEF and control groups. Erysipelotrichaceae were significantly increased in all three groups; Streptococcaceae and Lactobacillaceae were significantly increased in ASCVD and HFrEF groups. These observations were indirectly confirmed with the culture method: two species of Streptococcus were significantly increased in the HFrEF group and Lactobacillus plantarum was significantly increased in the ASCVD group. The latter observation was also confirmed with qPCR of Lactobacillus sp. Acidaminococcaceae and Odoribacteraceae were significantly decreased in the ASCVD and HFrEF groups. Participants from the HFpEF group showed the least difference compared to the control group in all three study methods. The patterns found expand the knowledge base on possible correlations of gut microbiota with cardiovascular diseases. The similarities and differences in conclusions obtained by the three methods of this study demonstrate the need for a comprehensive approach to the analysis of microbiota.

The gut microbiota plays an important role in maintaining human health, as well as in the development of various pathologies, as indicated by a large amount of research. One of the manifestations of an imbalance in the gut microbiome composition is the appearance of various diseases or immune reactions, in particular, atopic dermatitis (AD) and/or food allergies (FA). In this research, using 16S NGS sequencing, it was found that the gut microbiome of children with food allergies and children with atopic dermatitis can be characterized as having higher inflammatory potential. Both groups exhibited an abundance of representatives from the Pasteurellaceae and Erysipelotrichaceae families, as well as a decrease in the relative number of representatives from the Barnesiellaceae family compared to healthy participants. In the group of participants with food allergies, there was a decrease in the relative number of Desulfovibrionaceae representatives and Bifidobacteriaceae family enrichment in relatively healthy participants. In addition, when comparing this group with patients with atopic dermatitis, it was revealed that a number of representatives of such families as Erysipelotrichaceae, Ruminococcaceae and Sutterellaceae prevailed. This information confirms that AD and FA correlate with changes in the composition of the gut microbiota. Further research is needed to determine the cause–effect connections and the effect of compounds derived from the microbiota on the AD and FA development and progression, as well as to create new probiotic drugs to prevent and modulate immune responses, including at an early age.

Background. Currently, it is known that the gut microbiota plays an important role in the functioning of the immune system, and a rebalancing of the bacterial community can arouse complex immune reactions and lead to immune-mediated responses in an organism, in particular, the development of atopic dermatitis (AD). Cytokines and chemokines are regulators of the innate and adaptive immune response and represent the most important biomarkers of the immune system. It is known that changes in cytokine profiles are a hallmark of many diseases, including atopy. However, it remains unclear how the bacterial imbalance disrupts the function of the immune response in AD. Objectives. We attempted to determine the role of gut bacteria in modulating cytokine pathways and their role in atopic inflammation. Methods. We sequenced the 16S rRNA gene from 50 stool samples of children aged 3–12 years who had confirmed atopic dermatitis, and 50 samples from healthy children to serve as a control group. To evaluate the immune status, we conducted a multiplex immunofluorescence assay and measured the levels of 41 cytokines and chemokines in the serum of all participants. Results. To find out whether changes in the composition of the gut microbiota were significantly associated with changes in the level of inflammatory cytokines, a correlation was calculated between each pair of bacterial family and cytokine. In the AD group, 191 correlations were significant (Spearman’s correlation coefficient, p ≤ 0.05), 85 of which were positive and 106 which were negative. Conclusions. It has been demonstrated that intestinal dysbiosis is associated with alterations in cytokine profiles, specifically an increase in proinflammatory cytokine concentrations. This may indicate a systemic impact of these conditions, leading to an imbalance in the immune system’s response to the Th2 type. As a result, atopic conditions may develop. Additionally, a correlation between known AD biomarkers (IL-5, IL-8, IL-13, CCL22, IFN-γ, TNF-α) and alterations in the abundance of bacterial families (Pasteurellaceae, Barnesiellaceae, Eubacteriaceae) was observed.

The coronavirus disease 2019 (COVID-19) is accompanied by a cytokine storm with the release of many proinflammatory factors and development of respiratory syndrome. Several SARS-CoV-2 lineages have been identified, and the Delta variant (B.1.617), linked with high mortality risk, has become dominant in many countries. Understanding the immune responses associated with COVID-19 lineages may therefore aid the development of therapeutic and diagnostic strategies. Multiple single-cell gene expression studies revealed innate and adaptive immunological factors and pathways correlated with COVID-19 severity. Additional investigations covering host–pathogen response characteristics for infection caused by different lineages are required. Here, we performed single-cell transcriptome profiling of blood mononuclear cells from the individuals with different severity of the COVID-19 and virus lineages to uncover variant specific molecular factors associated with immunity. We identified significant changes in lymphoid and myeloid cells. Our study highlights that an abundant population of monocytes with specific gene expression signatures accompanies Delta lineage of SARS-CoV-2 and contributes to COVID-19 pathogenesis inferring immune components for targeted therapy.

The neurobiological systems of maintenance and control of behavioral responses result from natural selection. We have analyzed the selection signatures for single nucleotide variants (SNV) of the genes of oxytocin (OXT, OXTR) and vasopressin (AVP, AVPR1A, AVPR1B) systems, which are associated with the regulation of social and emotional behavior in distinct populations. The analysis was performed using original WGS (whole genome sequencing) data on Eastern Slavs (SlEast), as well as publicly available data from the 1000 Genomes Project on GBR, FIN, IBR, PUR, BEB, CHB, and ACB populations (the latter were taken as reference). To identify selection signatures, we rated the integrated haplotype scores (iHS), the numbers of segregating sites by length (nSl), and the integrated haplotype homozygosity pooled (iHH12) measures; the fixation index Fst was implemented to assess genetic differentiation between populations. We revealed that the strongest genetic differentiation of populations was found with respect to the AVPR1B gene, with the greatest differentiation observed in GRB (Fst = 0.316) and CHB (Fst = 0.325) in comparison to ACB. Also, high Fst values were found for SNVs of the AVPR1B gene rs28499431, rs33940624, rs28477649, rs3883899, and rs28452187 in most of the populations. Selection signatures have also been identified in the AVP, AVPR1A, OXT, and OXTR genes. Our analysis shows that the OXT, OXTR, AVP, AVPR1A, and AVPR1B genes were subject to positive selection in a population-specific process, which was likely contributing to the diversity of adaptive emotional response types and social function realizations.