Introduction: This study was aimed at deciphering the secretome of adipose-derived mesenchymal stromal cells (ADSCs) cultured in standard and hypoxic conditions to reveal proteins, which may be responsible for regenerative action of these cells. Methods: Human ADSCs were isolated from 10 healthy donors and cultured for 3–4 passages. Cells were serum deprived and cell purity was assessed using multiple cell surface markers. Conditioned media was collected and analyzed using LC-MS with a focus on characterizing secreted proteins. Results: Purity of the ADSC assessed as CD90+/CD73+/CD105+/CD45-/CD31- cells was greater than 99 % and viability was greater than 97 %. More than 600 secreted proteins were detected in conditioned media of ADSCs. Of these 100 proteins were common to all cultures and included key molecules involved in tissue regeneration such as collagens and collagen maturation enzymes, matrix metalloproteases, matricellular proteins, macrophage-colony stimulating factor and pigment epithelium derived factor. Common set of proteins also included molecules, which contribute to regenerative processes but were not previously associated with ADSCs. These included olfactomedin-like 3, follistatin-like 1 and prosaposin. In addition, ADSCs from the different subjects secreted proteins, which were variable between different cultures. These included proteins with neurotrophic activities, which were not previously associated with ADSCs, such as mesencephalic astrocyte-derived neurotrophic factor, meteorin and neuron derived neurotrophic factor. Hypoxia resulted in secretion of 6 proteins, the most prominent included EGF-like repeats and discoidin I-like domains 3, adrenomedullin and ribonuclease 4 of RNase A family. It also caused the disappearance of 8 proteins, including regulator of osteogenic differentiation cartilage-associated protein. Conclusions: Human ADSCs with CD90+/CD73+/CD105+/CD45-/CD31-/PDGFRβ+/NG2+/CD146+(−) immunophenotype secrete a large array of proteins, the most represented group is comprised of extracellular matrix components. Number of secreted proteins is largely unaffected by prolonged hypoxia. Variability in the secretion of several proteins from cultured ADSCs of individual subjects suggests that these cells exist as a heterogeneous population containing functionally distinct subtypes, which differ in numbers between donors.

A calibrated mathematical model of antiviral immune response to SARS-CoV-2 infection is developed. The model considers the innate and antigen-specific responses to SARS-CoV-2 infection. Recently published data sets from human challenge studies with SARS-CoV-2 were used for parameter evaluation. The calibration of the mathematical model of SARS-CoV-2 infection is based on combining the parameter guesses from our earlier study of influenza A virus infection, some recent quantitative models of SARS-CoV-2 infection and clinical data-based parameter estimation of a subset of the model parameters. Hence, the calibrated mathematical model represents a theoretical exploration type of study, i.e., ‘in silico patient’ with mild-to-moderate severity phenotype, rather than a completely validated quantitative model of COVID-19 with respect to all its state-space variables. Understanding the regulation of multiple intertwined reaction components of the immune system is necessary for linking the kinetics of immune responses with the clinical phenotypes of COVID-19. Consideration of multiple immune reaction components in a single calibrated mathematical model allowed us to address some fundamental issues related to the pathogenesis of COVID-19, i.e., the sensitivity of the peak viral load to the parameters characterizing the antiviral specific response components, the kinetic coordination of the individual innate and adaptive immune responses, and the factors favoring a prolonged viral persistence. The model provides a tool for predicting the infectivity of patients, i.e., the amount of virus which is transmitted via droplets from the person infected with SARS-CoV-2, depending on the time of infection. The thresholds for variations of the innate and adaptive response parameters which lead to a prolonged persistence of SARS-CoV-2 due to the loss of a kinetic response synchrony/coordination between them were identified.

A calibrated mathematical model of antiviral immune response to SARS-CoV-2 infection is developed. The model considers the innate and antigen-specific responses to SARS-CoV-2 infection. Recently published data sets from human challenge studies with SARS-CoV-2 were used for parameter estimation. Understanding the regulation of multiple intertwined reaction components of the immune system is necessary for linking the clinical phenotypes of COVID-19 with the kinetics of immune responses. Consideration of multiple immune reaction components in a single calibrated mathematical model allowed us to address some fundamental issues related to pathogenesis of COVID-19, i.e. sensitivity of the peak viral load to parameters characterizing the specific response components, the kinetic coordination of the individual responses, and the factors favoring a prolonged viral persistence. The model provides a tool for predicting the infectivity of patients, i.e. the amount of virus which is transmitted via droplets from the person infected with SARS-CoV-2, depending on the time of infection. The thresholds in the relative unbalance between innate and adaptive response parameters which lead to a prolonged persistence of SARS-CoV-2 due to the loss of a kinetic response synchrony/coordination were identified.

Autoimmune diabetes encompasses rapidly progressive type 1 diabetes mellitus (T1D) and indolent latent autoimmune diabetes in adults (LADA), which represent distinct inflammatory set points along a shared autoimmune spectrum. Yet, the immunological mechanisms that determine these divergent inflammatory states remain unresolved. We performed single-cell RNA sequencing with paired T and B cell receptor profiling on over 400,000 PBMCs from patients with LADA, newly diagnosed T1D, and individuals acting as healthy controls. PBMC composition was comparable across cohorts, indicating that qualitative rather than quantitative immune differences underlie disease heterogeneity. In T1D, pan-lineage activation of NF-κB, EGFR, MAPK, and hypoxia pathways, coupled with a TNF-centered communication hub, enhanced MHC signaling, disrupted adhesion, and promoted systemic inflammation. LADA, by contrast, exhibited global suppression of NF-κB/EGFR activity, retention of moderate JAK/STAT tone, reinforced NK cell inhibitory checkpoints via HLA-C–KIR2DL3/3DL1 interaction, and stabilized CD8 ⁺ T cell synapses through HLA-C–CD8 binding, collectively restraining effector activation. Single-cell V(D)J analysis revealed multiclonal, patient-unique adaptive repertoires, emphasizing the primacy of signaling context over receptor convergence. These findings define autoimmune diabetes as an inflammatory-inhibitory set-point continuum, positioning the NF-κB/EGFR–JAK/STAT gradient and HLA-C–KIR axis as potential therapeutic targets to preserve residual β cell function.

Background: Inflammatory responses that accompany the progression of type 1 (T1D) and type 2 diabetes mellitus (T2D) are fundamentally distinct in their underlying nature. T1D is predominantly driven by autoimmune-mediated inflammation, whereas T2D is characterized by a chronic, low-grade metabolic inflammation. A growing body of evidence has highlighted the involvement of natural killer (NK) cells in pathophysiology of both forms of diabetes; nevertheless, the precise mechanisms and the roles played by specific NK cell subsets remain incompletely understood. Methods: Multicolor flow cytometry was used to identify several NK and innate-like T cell subpopulations in peripheral blood of patients with adult-onset T1D (n=23) and T2D (n=14) in comparison to healthy volunteers (n=24). Subset identification was based on expression of functional antigens (CD16 and CD56), co-receptors (CD8 and CD38), inhibitory receptors (NKG2A and CD161), and transcription factor EOMES. Quantitative analysis using Spearman’s rank correlation coefficients was performed to identify possible association between immune and clinical parameters and to rank the clinical parameters with respect to the number of connections with immune cell populations. Results: T1D was accompanied by a reduction in overall NK cells and their dominant cytolytic CD56 ^dim CD16 ^bright subpopulation. Also a 50% increase in frequency of CD8+ NK cells was observed together with a growth of CD8+CD38+, CD8+CD161+, CD8+NKG2A+, and CD8+EOMES+ NK subsets. T2D was associated with a more than twofold decrease in the frequency of MAIT cell subset within CD8+ T cell population. Correlation analysis of the obtained data set revealed quantitative relationships between immune status parameters and clinical indicators, highlighting the involvement of NK cell subsets and innate-like T cells in shaping the phenotype of the pathological process and their complex role in regulating the immunopathogenesis of T1D and T2D. Conclusion: In this pilot study, we provide evidence that different subpopulations of NK cells and innate-like T cells are involved in the immunopathogenesis of T1D and T2D, through a decline in the control of immune reactivity in T1D, while sustaining chronic metainflammatory responses in T2D. A critical elevation of CD8+ NK cells was associated with T1D, while loss of circulating MAIT cells was a hallmark of T2D.

Of the many clinical phenotypes of obesity, the most prevalent are metabolically “healthy” (MHO) and metabolically “unhealthy” (MUO) obesities, the latter being associated with a range of comorbidities, including type 2 diabetes mellitus (T2DM). The underlying causes of different obesity phenotypes and the mechanisms of conversion of one phenotype into another have yet to be fully elucidated. However, increasing evidence suggests the key role of low-grade metabolic inflammation (metaflammation) in the pathogenesis of obesity and metabolic dysfunction. The review presents a comprehensive description of changes in immune cell populations and pro-inflammatory mediators, as well as a detailed comparative mapping of the adipose tissue immune landscape during MHO/MUO transition. Based upon a conceptual model for the intensification of metaflammation during MHO progression and conversion to MUO, a pattern of dynamical changes that accompany MHO/MUO transition is described. Though many parameters demonstrate significant differences in multiple cross-sectional and some longitudinal studies, only a few of them (CRP, IL-6, IL-17A, absolute counts of leukocytes and neutrophils) meet the criteria of a validated biomarker in clinical setting. A lack of standardization in MHO definition and heterogeneity in the severity of MUO make the search for predictive biomarkers a challenge. The review also discusses the mechanisms underlying metabolic memory and the incomplete reversibility of metabolic disturbances after bariatric surgery.