RIPK3 and RIPK1 gene expression in pterygium: unveiling molecular insights into pathogenesis.

BACKGROUND: Pterygium, characterized by the abnormal proliferation of epithelial cells, matrix remodeling, vascularization, and lesion migration, is a prevalent ocular surface disease involving the growth of fibrovascular tissue on the cornea. Despite the unclear underlying causes of pterygium, numerous investigations have indicated the involvement of cell death pathways in the regulation of cell cycle dynamics. Consequently, the objective of this study was to assess the expression levels of necroptosis markers in individuals diagnosed with pterygium, aiming to shed light on the potential role of necroptosis in the pathogenesis of this condition. METHODS: This study aimed to investigate the expression patterns of receptor-interacting serine/threonine kinase 3 (RIPK3) and receptor-interacting serine/threonine kinase 1 (RIPK1) genes in pterygium tissues. 41 patients undergoing pterygium excision surgery were recruited. Resected pterygium samples and normal conjunctival tissues were collected, and RIPK3 and RIPK1 mRNA levels were measured using quantitative real-time PCR. RESULTS: Our findings reveal that the expression of RIPK3 is significantly increased in samples obtained from individuals with pterygium. However, no significant alterations were observed in the expression of RIPK1 in these samples. Results showed significantly higher RIPK3 expression in pterygium tissues compared to controls. Moreover, increased RIPK3 levels correlated negatively with pterygium recurrence rates. CONCLUSIONS: These findings suggest RIPK3 may play a protective role against pterygium recurrence through necroptosis.

Data-driven detection of age-related arbitrary monotonic changes in single-cell gene expression distributions.

Identification of genes whose expression increases or decreases with age is central to understanding the mechanisms behind aging. Recent scRNA-seq studies have shown that changes in single-cell expression profiles with aging are complex and diverse. In this study, we introduce a novel workflow to detect changes in the distribution of arbitrary monotonic age-related changes in single-cell expression profiles. Since single-cell gene expression profiles can be analyzed as probability distributions, our approach uses information theory to quantify the differences between distributions and employs distance matrices for association analysis. We tested this technique on simulated data and confirmed that potential parameter changes could be detected in a set of probability distributions. Application of the technique to a public scRNA-seq dataset demonstrated its potential utility as a straightforward screening method for identifying aging-related cellular features.

Tissue-specific and cis-regulatory changes underlie parallel, adaptive gene expression evolution in house mice.

Changes in gene regulation have long been appreciated as a driving force of adaptive evolution, however the relative contributions of cis- and trans-acting changes to gene regulation over short evolutionary timescales remain unclear. Instances of recent, parallel phenotypic evolution provide an opportunity to assess whether parallel patterns are seen at the level of gene expression, and to assess the relative contribution of cis- and trans- changes to gene regulation in the early stages of divergence. Here, we studied gene expression in liver and brown adipose tissue in two wild-derived strains of house mice that independently adapted to cold, northern environments, and we compared them to a strain of house mice from a warm, tropical environment. To investigate gene regulatory evolution, we studied expression in parents and allele-specific expression in F1 hybrids of crosses between warm-adapted and cold-adapted strains. First, we found that the different cold-adapted mice showed both unique and shared changes in expression, but that the proportion of shared changes (i.e. parallelism) was greater than expected by chance. Second, we discovered that expression evolution occurred largely at tissue-specific and cis-regulated genes, and that these genes were over-represented in parallel cases of evolution. Finally, we integrated the expression data with scans for selection in natural populations and found substantial parallelism in the two northern populations for genes under selection. Furthermore, selection outliers were associated with cis-regulated genes more than expected by chance; cis-regulated genes under selection influenced phenotypes such as body size, immune functioning, and activity level. These results demonstrate that parallel patterns of gene expression in mice that have independently adapted to cold environments are driven largely by tissue-specific and cis-regulatory changes, providing insight into the mechanisms of adaptive gene regulatory evolution at the earliest stages of divergence.

Relationship between vitamin D receptor genotypes (FOK1rs2228570) and IL18 gene expression in sample of multiple sclerosis Iraqi patients.

BACKGROUND: Multiple Sclerosis known as MS, this chronic inflammatory demyelinating condition affects the nervous system. It is a heterogenic and multifactorial disease. The goal of the current study was to investigate the relationship between MS patients' IL18 gene expression and the vitamin D receptor gene polymorphism (FOK1rs2228570). OBJECTIVE: The aim of the study to investigate the association of vitamin D receptor (FOK1rs2228570) gene polymorphism and pro inflammatory cytokine (IL18) gene expression among multiple sclerosis Iraqi patients. Detection VDR polymorphism and determine whether this SNP is involved in susceptibility to multiple sclerosis and estimation IL18 gene expression and explore its relation with multiple sclerosis susceptibility. METHODS: Blood samples were taken from 75 MS patients in Iraq (30 men and 45 women), as well as from 75 volunteers who seemed to be in a favorable state of health and fell within the age range of 20 to 50 years. Tetra-ARMS Polymerase Chain Reaction (Tetra-ARMS PCR) was used to find polymorphisms in the vitamin D receptor (VDR) gene, and Real-time Polymerase Chain Reaction (RT-PCR) was used to measure IL18 gene expression. RESULTS: The findings from the analysis of VDR gene polymorphism in patients with MS indicated that the wild-type genotype T/T was present in 8 individuals, accounting for 10.6%, the heterogeneous genotype TC was 36 (48%), and the homogeneous genotype CC was 31 (41.3%), whilst T allele frequency was 52(34.6%) and C allele was 98(65.3%) with (P⩽ 0.01) significant difference and even as in control T/T genotype was 49(65.3%), TC genotype was 21(28%), CC genotype was 5(6.66%), T allele frequency was 119(79.3%) and C allele was 31(20.6%) with significant difference (P⩽ 0.001). While estimation of IL18 expression showed high elevation in patients' group (2.59 ± 0.51 fold) by significance difference (P⩽ 0.5) when compared to control group (1.35 ± 0.14 fold). The relationship between IL18 gene expression with VDR variant in MS patients demonstrated a significant rise (2.9 ± 0.51 fold) at CC genotype patients in IL18 folding gene expression, followed by (4.6 ± 0.17 fold) in TC genotype patients and finally (1.4 ± 0.08 fold) in TT genotype patients with highly significant (P⩽ 0.01). CONCLUSION: The VDR(FOK1rs2228570) genotype was significantly correlated with IL18 expression in MS patients from Iraq.

Implications of stress-induced gene expression for hematopoietic stem cell aging studies.

A decline in hematopoietic stem cell (HSC) function is believed to underlie hematological shortcomings with age; however, a comprehensive molecular understanding of these changes is currently lacking. Here we provide evidence that a transcriptional signature reported in several previous studies on HSC aging is linked to stress-induced changes in gene expression rather than aging. Our findings have strong implications for the design and interpretation of HSC aging studies.

The human Y and inactive X chromosomes similarly modulate autosomal gene expression.

Somatic cells of human males and females have 45 chromosomes in common, including the "active" X chromosome. In males the 46th chromosome is a Y; in females it is an "inactive" X (Xi). Through linear modeling of autosomal gene expression in cells from individuals with zero to three Xi and zero to four Y chromosomes, we found that Xi and Y impact autosomal expression broadly and with remarkably similar effects. Studying sex chromosome structural anomalies, promoters of Xi- and Y-responsive genes, and CRISPR inhibition, we traced part of this shared effect to homologous transcription factors-ZFX and ZFY-encoded by Chr X and Y. This demonstrates sex-shared mechanisms by which Xi and Y modulate autosomal expression. Combined with earlier analyses of sex-linked gene expression, our studies show that 21% of all genes expressed in lymphoblastoid cells or fibroblasts change expression significantly in response to Xi or Y chromosomes.

FOXS1 is increased in liver fibrosis and regulates TGFß responsiveness and proliferation pathways in human hepatic stellate cells.

Liver fibrosis commences with liver injury stimulating transforming growth factor beta (TGFß) activation of hepatic stellate cells (HSCs), causing scarring and irreversible damage. TGFß induces expression of the transcription factor Forkhead box S1 (FOXS1) in hepatocytes and may have a role in the pathogenesis of hepatocellular carcinoma (HCC). To date, no studies have determined how it affects HSCs. We analyzed human livers with cirrhosis, HCC, and a murine fibrosis model and found that FOXS1 expression is significantly higher in fibrotic livers but not in HCC. Next, we treated human LX2 HSC cells with TGFß to activate fibrotic pathways, and FOXS1 mRNA was significantly increased. To study TGFß-FOXS1 signaling, we developed human LX2 FOXS1 CRISPR KO and scrambled control HSCs. To determine differentially expressed gene transcripts controlled by TGFß-FOXS1, we performed RNA-seq in the FOXS1 KO and control cells and over 400 gene responses were attenuated in the FOXS1 KO HSCs with TGFß-activation. To validate the RNA-seq findings, we used our state-of-the-art PamGene PamStation kinase activity technology that measures hundreds of signaling pathways nonselectively in real time. Using our RNA-seq data, kinase activity data, and descriptive measurements, we found that FOXS1 controls pathways mediating TGFß responsiveness, protein translation, and proliferation. Our study is the first to identify that FOXS1 may serve as a biomarker for liver fibrosis and HSC activation, which may help with early detection of hepatic fibrosis or treatment options for end-stage liver disease.

Optimal Reference Genes for RT-qPCR Experiments in Hippocampus and Cortex of Rats Chronically Exposed to Excessive Fluoride.

Normalization of the quantitative real-time PCR (RT-qPCR) data to the stably expressed reference genes is critically important for obtaining reliable results. However, all previous studies focused on F- toxicity for brain tissues used a single, non-validated reference gene, what might be a cause of contradictory or false results. The present study was designed to analyze the expression of a series of reference genes to select optimal ones for RT-qPCR analysis in cortex and hippocampus of rats chronically exposed to excessive fluoride (F-) amounts. Six-week-old male Wistar rats randomly assigned to four groups consumed regular tap water with 0.4 (control), 5, 20, and 50 ppm F- (NaF) for 12 months. The expression of six genes (Gapdh, Pgk1, Eef1a1, Ppia, Tbp, Helz) was compared by RT-qPCR in brain tissues from control and F--exposed animals. The stability of candidate reference genes was evaluated by coefficient of variation (CV) analysis and RefFinder online program summarizing the results of four well-acknowledged statistical methods (Delta-Ct, BestKeeper, NormFinder, and GeNorm). In spite of some discrepancies in gene ranking between these algorisms, Pgk1, Eef1a1, and Ppia were found to be most valid in cortex, while Ppia, Eef1a1, and Helz showed the greatest expression stability in hippocampus. Tbp and Helz were identified as the least stable genes in cortex, whereas Gapdh and Tbp are unsuitable for hippocampus. These data indicate that reliable mRNA quantification in the cortex and hippocampus of F--poisoned rats is possible using normalization to geometric mean of Pgk1+Eef1a1 or Ppia+Eef1a1 expression, respectively.

Overexpression of AMPKγ2 increases AMPK signaling to augment human T cell metabolism and function.

Cellular therapies are currently employed to treat a variety of disease processes. For T cell-based therapies, success often relies on the metabolic fitness of the T cell product, where cells with enhanced metabolic capacity demonstrate improved in vivo efficacy. AMP-activated protein kinase (AMPK) is a cellular energy sensor which combines environmental signals with cellular energy status to enforce efficient and flexible metabolic programming. We hypothesized that increasing AMPK activity in human T cells would augment their oxidative capacity, creating an ideal product for adoptive cellular therapies. Lentiviral transduction of the regulatory AMPKγ2 subunit stably enhanced intrinsic AMPK signaling and promoted mitochondrial respiration with increased basal oxygen consumption rates, higher maximal oxygen consumption rate, and augmented spare respiratory capacity. These changes were accompanied by increased proliferation and inflammatory cytokine production, particularly within restricted glucose environments. Introduction of AMPKγ2 into bulk CD4 T cells decreased RNA expression of canonical Th2 genes, including the cytokines interleukin (IL)-4 and IL-5, while introduction of AMPKγ2 into individual Th subsets universally favored proinflammatory cytokine production and a downregulation of IL-4 production in Th2 cells. When AMPKγ2 was overexpressed in regulatory T cells, both in vitro proliferation and suppressive capacity increased. Together, these data suggest that augmenting intrinsic AMPK signaling via overexpression of AMPKγ2 can improve the expansion and functional potential of human T cells for use in a variety of adoptive cellular therapies.

Experiences of racial discrimination and adverse gene expression among black individuals in a level 1 trauma center sample.

Up to 40 % of individuals who sustain traumatic injuries are at risk for posttraumatic stress disorder (PTSD) and the conditional risk for developing PTSD is even higher for Black individuals. Exposure to racial discrimination, including at both interpersonal and structural levels, helps explain this health inequity. Yet, the relationship between racial discrimination and biological processes in the context of traumatic injury has yet to be fully explored. The current study examined whether racial discrimination is associated with a cumulative measure of biological stress, the gene expression profile conserved transcriptional response to adversity (CTRA), in Black trauma survivors. Two-weeks (T1) and six-months (T2) post-injury, Black participants (N = 94) provided a blood specimen and completed assessments of lifetime racial discrimination and PTSD symptoms. Mixed effect linear models evaluated the relationship between change in CTRA gene expression and racial discrimination while adjusting for age, gender, body mass index (BMI), smoking history, heavy alcohol use history, and trauma-related variables (mechanism of injury, lifetime trauma). Results revealed that for individuals exposed to higher levels of lifetime racial discrimination, CTRA significantly increased between T1 and T2. Conversely, CTRA did not increase significantly over time in individuals exposed to lower levels of lifetime racial discrimination. Thus, racial discrimination appeared to lead to a more sensitized biological profile which was further amplified by the effects of a recent traumatic injury. These findings replicate and extend previous research elucidating the processes by which racial discrimination targets biological systems.

Affective reactivity to daily stressors and immune cell gene expression in the MIDUS study.

Affective reactivity to stress is a person-level measurement of how well an individual copes with daily stressors. A common method of measuring affective reactivity entails the estimation of within-person differences of either positive or negative affect on days with and without stressors present. Individuals more reactive to common stressors, as evidenced by affective reactivity measurements, have been shown to have increased levels of circulating pro-inflammatory markers. While affective reactivity has previously been associated with inflammatory markers, the upstream mechanistic links underlying these associations are unknown. Using data from the Midlife in the United States (MIDUS) Refresher study (N = 195; 52% female; 84% white), we quantified daily stress processes over 10 days and determined individuals' positive and negative affective reactivities to stressors. We then examined affective reactivity association with peripheral blood mononuclear cell (PBMC) gene expression of the immune-related conserved transcriptional response to adversity. Results indicated that individuals with a greater decrease in positive affect to daily stressors exhibited heightened PBMC JUNB expression after Bonferroni corrections (p-adjusted < 0.05). JUNB encodes a protein that acts as a transcription factor which regulates many aspects of the immune response, including inflammation and cell proliferation. Due to its critical role in the activation of macrophages and maintenance of CD4+ T-cells during inflammation, JUNB may serve as a potential upstream mechanistic target for future studies of the connection between affective reactivity and inflammatory processes. Overall, our findings provide evidence that affective reactivity to stress is associated with levels of immune cell gene expression.

A New Approach for Multimodal Usage of Gene Expression and Its Image Representation for the Detection of Alzheimer's Disease.

Alzheimer's disease (AD) is a complex neurodegenerative disorder and the multifaceted nature of it requires innovative approaches that integrate various data modalities to enhance its detection. However, due to the cost of collecting multimodal data, multimodal datasets suffer from an insufficient number of samples. To mitigate the impact of a limited sample size on classification, we introduce a novel deep learning method (One2MFusion) which combines gene expression data with their corresponding 2D representation as a new modality. The gene vectors were first mapped to a discriminative 2D image for training a convolutional neural network (CNN). In parallel, the gene sequences were used to train a feed forward neural network (FNN) and the outputs of the FNN and CNN were merged, and a joint deep network was trained for the binary classification of AD, normal control (NC), and mild cognitive impairment (MCI) samples. The fusion of the gene expression data and gene-originated 2D image increased the accuracy (area under the curve) from 0.86 (obtained using a 2D image) to 0.91 for AD vs. NC and from 0.76 (obtained using a 2D image) to 0.88 for MCI vs. NC. The results show that representing gene expression data in another discriminative form increases the classification accuracy when fused with base data.

Atlas of primary cell-type-specific sequence models of gene expression and variant effects.

Human biology is rooted in highly specialized cell types programmed by a common genome, 98% of which is outside of genes. Genetic variation in the enormous noncoding space is linked to the majority of disease risk. To address the problem of linking these variants to expression changes in primary human cells, we introduce ExPectoSC, an atlas of modular deep-learning-based models for predicting cell-type-specific gene expression directly from sequence. We provide models for 105 primary human cell types covering 7 organ systems, demonstrate their accuracy, and then apply them to prioritize relevant cell types for complex human diseases. The resulting atlas of sequence-based gene expression and variant effects is publicly available in a user-friendly interface and readily extensible to any primary cell types. We demonstrate the accuracy of our approach through systematic evaluations and apply the models to prioritize ClinVar clinical variants of uncertain significance, verifying our top predictions experimentally.

Gene expression alterations in the postmortem hippocampus from older patients with bipolar disorder - A hypothesis generating study.

Bipolar disorder (BD) presents with a progressive course in a subset of patients. However, our knowledge of molecular changes in older BD is limited. In this study, we examined gene expression changes in the hippocampus of BD from the Biobank of Aging Studies to identify genes of interest that warrant further exploration. RNA was extracted from the hippocampus from 11 subjects with BD and 11 age and sex-matched controls. Gene expression data was generated using the SurePrint G3 Human Gene Expression v3 microarray. Rank feature selection was performed to identify a subset of features that can optimally differentiate BD and controls. Genes ranked in the top 0.1% with log2 fold change >1.2 were identified as genes of interest. Average age of the subjects was 64 years old; duration of disease was 21 years and 82% were female. Twenty-five genes were identified, of which all but one was downregulated in BD. Of these, CNTNAP4, MAP4, SLC4A1, COBL, and NEURL4 had been associated with BD and other psychiatric conditions in previous studies. We believe our findings have identified promising targets to inform future studies aiming to understand the pathophysiology of BD in later life.

Social network position is a major predictor of ant behavior, microbiota composition, and brain gene expression.

The physiology and behavior of social organisms correlate with their social environments. However, because social environments are typically confounded by age and physical environments (i.e., spatial location and associated abiotic factors), these correlations are usually difficult to interpret. For example, associations between an individual's social environment and its gene expression patterns may result from both factors being driven by age or behavior. Simultaneous measurement of pertinent variables and quantification of the correlations between these variables can indicate whether relationships are direct (and possibly causal) or indirect. Here, we combine demographic and automated behavioral tracking with a multiomic approach to dissect the correlation structure among the social and physical environment, age, behavior, brain gene expression, and microbiota composition in the carpenter ant Camponotus fellah. Variations in physiology and behavior were most strongly correlated with the social environment. Moreover, seemingly strong correlations between brain gene expression and microbiota composition, physical environment, age, and behavior became weak when controlling for the social environment. Consistent with this, a machine learning analysis revealed that from brain gene expression data, an individual's social environment can be more accurately predicted than any other behavioral metric. These results indicate that social environment is a key regulator of behavior and physiology.

Tissue heterogeneity of mitochondrial activity, biogenesis and mitochondrial protein gene expression in buffalo.

BACKGROUND: Cellular metabolism is most invariant process, occurring in all living organisms, which involves mitochondrial proteins from both nuclear and mitochondrial genomes. The mitochondrial DNA (mtDNA) copy number, protein-coding genes (mtPCGs) expression, and activity vary between various tissues to fulfill specific energy demands across the tissues. METHODS AND RESULTS: In present study, we investigated the OXPHOS complexes and citrate synthase activity in isolated mitochondria from various tissues of freshly slaughtered buffaloes (n = 3). Further, the evaluation of tissue-specific diversity based on the quantification of mtDNA copy numbers was performed and also comprised an expression study of 13 mtPCGs. We found that the functional activity of individual OXPHOS complex I was significantly higher in the liver compared to muscle and brain. Additionally, OXPHOS complex III and V activities was observed significantly higher levels in liver compared to heart, ovary, and brain. Similarly, CS-specific activity differs between tissues, with the ovary, kidney, and liver having significantly greater. Furthermore, we revealed the mtDNA copy number was strictly tissue-specific, with muscle and brain tissues exhibiting the highest levels. Among 13 PCGs expression analyses, mRNA abundances in all genes were differentially expressed among the different tissue. CONCLUSIONS: Overall, our results indicate the existence of a tissue-specific variation in mitochondrial activity, bioenergetics, and mtPCGs expression among various types of buffalo tissues. This study serves as a critical first stage in gathering vital comparable data about the physiological function of mitochondria in energy metabolism in distinct tissues, laying the groundwork for future mitochondrial based diagnosis and research.

The Nature of Prefrontal Cortical GABA Neuron Alterations in Schizophrenia: Markedly Lower Somatostatin and Parvalbumin Gene Expression Without Missing Neurons.

OBJECTIVE: In schizophrenia, somatostatin (SST) and parvalbumin (PV) mRNA levels are lower in the dorsolateral prefrontal cortex (DLPFC), but it remains unclear whether these findings reflect lower transcript levels per neuron, fewer neurons, or both. Distinguishing among these alternatives has implications for understanding the pathogenesis of, and developing new treatments for, DLPFC dysfunction in schizophrenia. METHODS: To identify SST and PV neurons in postmortem human DLPFC, the authors used fluorescent in situ hybridization to label cells expressing two transcripts not altered in schizophrenia: vesicular GABA transporter (VGAT; a marker of all GABA neurons) and SOX6 (a marker of only SST and PV neurons). In cortical layers 2 and 4, where SST and PV neurons, respectively, are differentially enriched, levels of SST and PV mRNA per neuron and the relative densities of SST-, PV-, and VGAT/SOX6-positive neurons were quantified. RESULTS: In individuals with schizophrenia, mRNA levels per positive neuron were markedly and significantly lower for SST in both layers (effect sizes >1.48) and for PV only in layer 4 (effect size=1.14) relative to matched unaffected individuals. In contrast, the relative densities of all SST-, PV-, or VGAT/SOX6-positive neurons were unaltered in schizophrenia. CONCLUSIONS: Novel multiplex fluorescent in situ hybridization techniques permit definitive distinction between cellular levels of transcripts and the presence of neurons expressing those transcripts. In schizophrenia, pronounced SST and PV mRNA deficits are attributable to lower levels of each transcript per neuron, not fewer neurons, arguing against death or abnormal migration of these neurons. Instead, these neurons appear to be functionally altered and thus amenable to therapeutic interventions.

OutSingle: a novel method of detecting and injecting outliers in RNA-Seq count data using the optimal hard threshold for singular values.

MOTIVATION: Finding outliers in RNA-sequencing (RNA-Seq) gene expression (GE) can help in identifying genes that are aberrant and cause Mendelian disorders. Recently developed models for this task rely on modeling RNA-Seq GE data using the negative binomial distribution (NBD). However, some of those models either rely on procedures for inferring NBD's parameters in a nonbiased way that are computationally demanding and thus make confounder control challenging, while others rely on less computationally demanding but biased procedures and convoluted confounder control approaches that hinder interpretability. RESULTS: In this article, we present OutSingle (Outlier detection using Singular Value Decomposition), an almost instantaneous way of detecting outliers in RNA-Seq GE data. It uses a simple log-normal approach for count modeling. For confounder control, it uses the recently discovered optimal hard threshold (OHT) method for noise detection, which itself is based on singular value decomposition (SVD). Due to its SVD/OHT utilization, OutSingle's model is straightforward to understand and interpret. We then show that our novel method, when used on RNA-Seq GE data with real biological outliers masked by confounders, outcompetes the previous state-of-the-art model based on an ad hoc denoising autoencoder. Additionally, OutSingle can be used to inject artificial outliers masked by confounders, which is difficult to achieve with previous approaches. We describe a way of using OutSingle for outlier injection and proceed to show how OutSingle outperforms its competition on 16 out of 18 datasets that were generated from three real datasets using OutSingle's injection procedure with different outlier types and magnitudes. Our methods are applicable to other types of similar problems involving finding outliers in matrices under the presence of confounders. AVAILABILITY AND IMPLEMENTATION: The code for OutSingle is available at https://github.com/esalkovic/outsingle.

Upregulation of peripheral blood mononuclear cells resistin gene expression in severe obstructive sleep apnea and obstructive sleep apnea with coexisting type 2 diabetes mellitus.

PURPOSE: Obstructive sleep apnea (OSA) is characterised by increased systemic inflammation, and is often accompanied with type 2 diabetes mellitus (T2DM) and cardiovascular disease. The aim of this investigation was to evaluate gene expression of resistin, its receptor CAP1 and CD36 as the indicators of the inflammatory changes in PBMCs in relation to the severity of OSA, and the presence of type 2 diabetes mellitus (T2DM) in OSA. METHODS: Severity of OSA was defined by the apnea/hypopnea index (AHI): AHI < 30: mild to moderate OSA (MM-OSA), AHI ≥ 30: severe OSA (S-OSA). Presence of T2DM was captured: OSA with T2DM (OSA + T2DM), OSA without T2DM (OSA-T2DM). PBMC resistin, CAP1, and CD36 mRNA were determined by real-time PCR. RESULTS: Resistin mRNA was significantly upregulated in S-OSA (N = 54) compared to the MM-OSA (N = 52, P = 0.043); CAP1 and CD36 mRNA levels did not differ between the groups (P = 0.302; P = 0.166, respectively). Resistin mRNA was significantly upregulated in OSA + T2DM (N = 29) compared to the OSA-T2DM (N = 77, P = 0.029); CAP1 and CD36 mRNA levels did not differ between the groups (P = 0.662; P = 0.108, respectively). AHI and T2DM were independent predictors of resistin mRNA above the 75th percentile (OR = 3.717 [1.152-11.991]; OR = 3.261 [1.000-10.630], P = 0.042 respectively). CONCLUSION: Resistin gene upregulation in S-OSA indicates its possible contribution to increased inflammation in S-OSA and makes it a possible marker of the disease severity. Resistin gene upregulation in OSA + T2DM suggests that a joint effect of these two comorbidities may have a major contribution to increased inflammation and complications that arise from this state.

Baseline gene expression in subcutaneous adipose tissue predicts diet-induced weight loss in individuals with obesity.

Background: Weight loss effectively reduces cardiometabolic health risks among people with overweight and obesity, but inter-individual variability in weight loss maintenance is large. Here we studied whether baseline gene expression in subcutaneous adipose tissue predicts diet-induced weight loss success. Methods: Within the 8-month multicenter dietary intervention study DiOGenes, we classified a low weight-losers (low-WL) group and a high-WL group based on median weight loss percentage (9.9%) from 281 individuals. Using RNA sequencing, we identified the significantly differentially expressed genes between high-WL and low-WL at baseline and their enriched pathways. We used this information together with support vector machines with linear kernel to build classifier models that predict the weight loss classes. Results: Prediction models based on a selection of genes that are associated with the discovered pathways 'lipid metabolism' (max AUC = 0.74, 95% CI [0.62-0.86]) and 'response to virus' (max AUC = 0.72, 95% CI [0.61-0.83]) predicted the weight-loss classes high-WL/low-WL significantly better than models based on randomly selected genes (P < 0.01). The performance of the models based on 'response to virus' genes is highly dependent on those genes that are also associated with lipid metabolism. Incorporation of baseline clinical factors into these models did not noticeably enhance the model performance in most of the runs. This study demonstrates that baseline adipose tissue gene expression data, together with supervised machine learning, facilitates the characterization of the determinants of successful weight loss.