Use of Integrated Metabolomics, Transcriptomics, and Signal Protein Profile to Characterize the Effector Function and Associated Metabotype of Polarized Macrophage Phenotypes
Project description:MΦs display remarkable plasticity and the ability to activate diverse responses to a host of intracellular and external stimuli. Despite extensive characterization of M1 MΦs and a broad set of M2 MΦs, comprehensive characterization of functional phenotype and associated metabotype driving this diverse MΦ activation remains. Herein, an ex vivo model was utilized to produce 6 MΦ functional phenotypes. Isolated CD14+ PBMCs were differentiated into resting M0 MΦs, and then polarized into M1 (IFN-γ/LPS), M2a (IL-4/IL-13), M2b (IC/LPS), M2c (IL-10), and M2d (IL-6/LIF) MΦs. The MΦs were profiled using a bioanalyte matrix of 4 cell surface markers, ∼50 secreted proteins, ∼800 expressed myeloid genes, and ∼450 identified metabolites relative to M0 MΦs. Signal protein and expressed gene profiles grouped the MΦs into inflammatory (M1 and M2b) and wound resolution (M2a, M2c, and M2d) phenotypes; however, each had a unique metabolic profile. While both M1 and M2b MΦs shared metabotype profiles consistent with an inflammatory signature; key differences were observed in the TCA cycle, FAO, and OXPHOS. Additionally, M2a, M2c, and M2d MΦs all profiled as tissue repair MΦs; however, metabotype differences were observed in multiple pathways including hexosamine, polyamine, and fatty acid metabolism. These metabolic and other key functional distinctions suggest phagocytic and proliferative functions for M2a MΦs, and angiogenesis and ECM assembly capabilities for M2b, M2c, and M2d MΦs. By integrating metabolomics into a systems analysis of MΦ phenotypes, we provide the most comprehensive map of MΦ diversity to date, along with the global metabolic shifts that correlate to MΦ functional plasticity in these phenotypes.
Project description:Macrophages are dynamic immune cells that can adopt several activation states. Fundamental to these functional activation states is the regulation of cellular metabolic processes. Especially in mice, metabolic alterations underlying pro-inflammatory or homeostatic phenotypes have been assessed using various techniques. However, researchers new to the field may encounter ambiguity in choosing which combination of techniques is best suited to profile immunometabolism. To address this need, we have developed a toolbox to assess cellular metabolism in a semi-high-throughput 96-well-plate-based format. Application of the toolbox to activated mouse and human macrophages enables fast metabolic pre-screening and robust measurement of extracellular fluxes, mitochondrial mass and membrane potential, and glucose and lipid uptake. Moreover, we propose an application of SCENITH technology for ex vivo metabolic profiling. We validate established activation-induced metabolic rewiring in mouse macrophages and report new insights into human macrophage metabolism. By thoroughly discussing each technique, we hope to guide readers with practical workflows for investigating immunometabolism.
Project description:High-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the "-omics" family. For this work, we focus on subsets that interact with one another and represent these "pathways" as graphs. Observed pathways often have disjoint components, i.e., nodes or sets of nodes (metabolites, etc.) not connected to any other within the pathway, which notably lessens testing power. In this paper we propose the Pathway Integrated Regression-based Kernel Association Test (PaIRKAT), a new kernel machine regression method for incorporating known pathway information into the semi-parametric kernel regression framework. This work extends previous kernel machine approaches. This paper also contributes an application of a graph kernel regularization method for overcoming disconnected pathways. By incorporating a regularized or "smoothed" graph into a score test, PaIRKAT can provide more powerful tests for associations between biological pathways and phenotypes of interest and will be helpful in identifying novel pathways for targeted clinical research. We evaluate this method through several simulation studies and an application to real metabolomics data from the COPDGene study. Our simulation studies illustrate the robustness of this method to incorrect and incomplete pathway knowledge, and the real data analysis shows meaningful improvements of testing power in pathways. PaIRKAT was developed for application to metabolomic pathway data, but the techniques are easily generalizable to other data sources with a graph-like structure.
Project description:Serotonin is an important neurotransmitter that broadly participates in various biological processes. While serotonin deficiency has been associated with multiple pathological conditions such as depression, schizophrenia, Alzheimer's disease and Parkinson's disease, the serotonin-dependent mechanisms remain poorly understood. This study therefore aimed to identify novel biomarkers and metabolic pathways perturbed by serotonin deficiency using metabolomics approach in order to gain new metabolic insights into the serotonin deficiency-related molecular mechanisms. Serotonin deficiency was achieved through pharmacological inhibition of tryptophan hydroxylase (Tph) using p-chlorophenylalanine (pCPA) or genetic knockout of the neuronal specific Tph2 isoform. This dual approach improved specificity for the serotonin deficiency-associated biomarkers while minimizing nonspecific effects of pCPA treatment or Tph2 knockout (Tph2-/-). Non-targeted metabolic profiling and a targeted pCPA dose-response study identified 21 biomarkers in the pCPA-treated mice while 17 metabolites in the Tph2-/- mice were found to be significantly altered compared with the control mice. These newly identified biomarkers were associated with amino acid, energy, purine, lipid and gut microflora metabolisms. Oxidative stress was also found to be significantly increased in the serotonin deficient mice. These new biomarkers and the overall metabolic pathways may provide new understanding for the serotonin deficiency-associated mechanisms under multiple pathological states.
Project description:AimIdentifying the critical genes that differentiate gall bladder cancer from a normal gall bladder and the related biological terms was the aim of this study.BackgroundThe molecular mechanism underlying gall bladder cancer (GBC) trigger and development still requires investigations. Potential therapeutic biomarkers can be identified through protein-protein interaction network prediction of proteome as a complementary study.MethodsHere, a literature review of proteomics studies of gall bladder cancer from 2010 to 2019 was undertaken to screen differentially expressed proteins in this cancer. A network of 27 differentially expressed proteins (DEPs) via Cytoscape 3.7.1 and its plug-ins was constructed and analyzed.ResultsTen proteins were introduced as hub-bottlenecks among which four were from DEPs. The gene ontology analysis also indicated that positive regulation of multi-organism process and regulation of response to biotic stimulus are the most disrupted biological processes of GBC considering their relationships with the DEPs.ConclusionACTG, ALB, GGH, and DYNC1H1, and relative biological terms were introduced as drug targets and possible diagnostic biomarkers.
Project description:Loss-of-function mutations in filamin A (FLNA) cause an X-linked dominant disorder with multiple organ involvement. Affected females present with periventricular nodular heterotopia (PVNH), cardiovascular complications, thrombocytopenia and Ehlers-Danlos syndrome. These mutations are typically lethal to males, and rare male survivors suffer from failure to thrive, PVNH, and severe cardiovascular and gastrointestinal complications. Here we report two surviving male siblings with a loss-of-function mutation in FLNA. They presented with multiple complications, including valvulopathy, intestinal malrotation and chronic intestinal pseudo-obstruction (CIPO). However, these siblings had atypical clinical courses, such as a lack of PVNH and a spontaneous improvement of CIPO. Trio-based whole-exome sequencing revealed a 4-bp deletion in exon 40 that was predicted to cause a lethal premature protein truncation. However, molecular investigations revealed that the mutation induced in-frame skipping of the mutated exon, which led to the translation of a mutant FLNA missing an internal region of 41 amino acids. Functional analyses of the mutant protein suggested that its binding affinity to integrin, as well as its capacity to induce focal adhesions, were comparable to those of the wild-type protein. These results suggested that exon skipping of FLNA partially restored its protein function, which could contribute to amelioration of the siblings' clinical courses. This study expands the diversity of the phenotypes associated with loss-of-function mutations in FLNA.
Project description:BACKGROUND:Few risk factors have been identified for the development of calcinosis among patients with Juvenile Dermatomyositis, and currently no clinical phenotype has been associated with its development. We analyzed a large database of patients to further elucidate any relationships among patients with and without calcinosis. METHOD:The CARRA legacy registry recruited pediatric rheumatology patients from 55 centers across North America from 2010 through 2014, including over 650 subjects with Juvenile Dermatomyositis. We compared the demographic characteristics, clinical disease features and treatment histories of those with and without calcinosis using univariate and multivariate logistic regression. RESULTS:Of the 631 patients included in the analysis, 84 (13%) had a current or prior history of calcinosis. These patients were statistically more likely to have longer durations of disease prior to diagnosis and treatment, have lipodystrophy and joint contractures, and to have received intravenous immune globulin or rituximab as treatments. CONCLUSIONS:Calcinosis is found more often in patients with prolonged active disease, severe disease, and certain clinical features such as lipodystrophy and joint contractures. When these factors are combined with other known associations and predictors, groups of at-risk patients can be more effectively identified, treated and studied to improve overall outcomes.
Project description:The aim of this study was to explore colorectal tumor-associated macrophage (TAM) biomarkers for early diagnosis and surveillance of colorectal cancer (CRC). We used bioinformatic methods to screen array expression data of CRC-related macrophages (GEO: GSE29214) to detect the differentially expressed genes (DEGs) between CRC-related macrophages and normal control cells. We found 431 DEGs in TAMs compared with the control group; 399 were up-regulated and 32 were down-regulated. A functional enrichment analysis showed that the DEGs were involved in positive regulation of the ERK1 and ERK2 cascade, cell activation involved in the immune response, cytokine-mediated signaling pathway, and receptor activity, all of which were significantly enriched. We constructed a protein-protein interaction (PPI) network to identify hub genes. We identified 10 hub genes: ITGB2, ITGAM, C3AR1, PTAFR, C3, CYBB, FCER1G, PLAU, STOM, and GPR84, in the PPI network. We verified the results using array expression data of peripheral blood mononuclear cells (GEO: GSE47756). The results showed that the expression trends of CYBB, PLAU, and STOM were consistent with those found in the GSE29214 dataset. Further verification with The Cancer Genome Atlas and Human Protein Atlas showed that the high expression of PLAU in TAMs was statistically significant (P<0.05). We concluded that PLAU may be a biomarker of CRC-associated macrophages and may have prognostic and predictive significance for clinical utility in CRC management.
Project description:Although numerous studies provide general support for the importance of genetic factors in the risk for alcohol use disorders (AUDs), candidate gene and genome-wide studies have yet to identify a set of genetic variations that explain a significant portion of the variance in AUDs. One reason is that alcohol-related phenotypes used in genetic studies are typically based on highly heterogeneous diagnostic categories. Therefore, identifying neurobiological phenotypes related to neuroadaptations that drive the development of AUDs is critical for the future success of genetic and epigenetic studies. One such neurobiological phenotype is the degree to which exposure to alcohol taste cues recruits the basal ganglia, prefrontal cortex, and motor areas, all of which have been shown to have a critical role in addictive behaviors in animal studies. To that end, this study was designed to examine whether cue-elicited responses of these structures are associated with AUD severity in a large sample (n=326) using voxelwise and functional connectivity measures. Results suggested that alcohol cues significantly activated dorsal striatum, insula/orbitofrontal cortex, anterior cingulate cortex, and ventral tegmental area. AUD severity was moderately correlated with regions involved in incentive salience such as the nucleus accumbens and amygdala, and stronger relationships with precuneus, insula, and dorsal striatum. The findings indicate that AUDs are related to neuroadaptations in these regions and that these measures may represent important neurobiological phenotypes for subsequent genetic studies.
Project description:During protein synthesis, organisms detect translation defects that induce ribosome stalling and result in protein aggregation. The Ribosome-associated Quality Control (RQC) complex, comprising TCF25, LTN1, and NEMF, is responsible for identifying incomplete protein products from unproductive translation events, targeting them for degradation. Although RQC disruption causes adverse effects on vertebrate neurons, data regarding mRNA/protein expression and regulation across tissues are lacking. Employing high-throughput methods, we analyzed public datasets to explore RQC gene expression and phenotypes. Our findings revealed widespread expression of RQC components in human tissues; however, silencing of RQC yielded only mild negative effects on cell growth. Notably, TCF25 exhibited elevated mRNA levels that were not reflected in the protein content. We experimentally demonstrated that this disparity arose from post-translational protein degradation by the proteasome. Additionally, we observed that cellular aging marginally influenced RQC expression, leading to reduced mRNA levels in specific tissues. Our results suggest the necessity of RQC expression in all mammalian tissues. Nevertheless, when RQC falters, alternative mechanisms seem to compensate, ensuring cell survival under nonstress conditions.