Circulating Biomarkers to Identify Responders in Cardiac Cell therapy.

Bone marrow mononuclear cell (BM-MNC) therapy in ST-elevation acute myocardial infarction (STEMI) has no biological inclusion criteria. Here, we analyzed 63 biomarkers and cytokines in baseline plasma samples from 77 STEMI patients treated with BM-MNCs in the TIME and Late-TIME trials as well as 61 STEMI patients treated with placebo. Response to cell therapy was defined by changes in left ventricular ejection fraction, systolic/diastolic volumes, and wall motion indexes. We investigated the clinical value of circulating proteins in outcome prediction using significance testing, partial least squares discriminant analysis, and receiver operating characteristic (ROC) analysis. Responders had higher biomarker levels (76-94% elevated) than non-responders. Several biomarkers had values that differed significantly (P < 0.05) between responders and non-responders including stem cell factor, platelet-derived growth factor, and interleukin-15. We then used these lead candidates for ROC analysis and found multiple biomarkers with values areas under the curve >0.70 including interleukin 15. These biomarkers were not involved in the placebo-treated subjects suggesting that they may have predictive power. We conclude that plasma profiling after STEMI may help identify patients with a greater likelihood of response to cell-based treatment. Prospective trials are needed to assess the predictive value of the circulating biomarkers

Author Correction: Circulating Biomarkers to Identify Responders in Cardiac Cell therapy.

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Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy.

MotivationMultiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.ResultsWe performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementationDatasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary informationSupplementary data are available at Bioinformatics online

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy

Motivation Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia. Results We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified. Availability and implementation Datasets and scripts for reproduction of results are available through: Https://nalab.stanford.edu/multiomics-pregnancy/

The Stanford Data Miner: a novel approach for integrating and exploring heterogeneous immunological data

Abstract Background Systems-level approaches are increasingly common in both murine and human translational studies. These approaches employ multiple high information content assays. As a result, there is a need for tools to integrate heterogeneous types of laboratory and clinical/demographic data, and to allow the exploration of that data by aggregating and/or segregating results based on particular variables (e.g., mean cytokine levels by age and gender). Methods Here we describe the application of standard data warehousing tools to create a novel environment for user-driven upload, integration, and exploration of heterogeneous data. The system presented here currently supports flow cytometry and immunoassays performed in the Stanford Human Immune Monitoring Center, but could be applied more generally. Results Users upload assay results contained in platform-specific spreadsheets of a defined format, and clinical and demographic data in spreadsheets of flexible format. Users then map sample IDs to connect the assay results with the metadata. An OLAP (on-line analytical processing) data exploration interface allows filtering and display of various dimensions (e.g., Luminex analytes in rows, treatment group in columns, filtered on a particular study). Statistics such as mean, median, and N can be displayed. The views can be expanded or contracted to aggregate or segregate data at various levels. Individual-level data is accessible with a single click. The result is a user-driven system that permits data integration and exploration in a variety of settings. We show how the system can be used to find gender-specific differences in serum cytokine levels, and compare them across experiments and assay types. Conclusions We have used the tools and techniques of data warehousing, including open-source business intelligence software, to support investigator-driven data integration and mining of diverse immunological data.</p

A cross‐sectional study comparing the inflammatory profile of menstrual effluent vs. peripheral blood

Abstract Background and Aims Cytokine profiles of peripheral blood and other bodily fluids provide diagnostic indicators for assessing inflammatory processes. Menstrual effluent may provide a noninvasive source of biological material for monitoring cytokine levels in blood and in endometrial tissues. This pilot study investigated the potential of measuring cytokines in menstrual effluent, and compared the cytokine profiles of menstrual versus peripheral blood. Methods Seven healthy donors (aged ≥18 and ≤45 years) collected menstrual effluent on day 2 of menses. Matched peripheral blood samples were collected by venous blood draw on the same day. Levels of 62 cytokines were measured in all samples by 62‐plex Luminex assay. Results Peripheral blood and menstrual effluent cytokine profiles were tenuously correlated (r2 = 0.26, p  0.05) for 53/62 cytokines in menstrual effluent versus peripheral blood. Levels of TGF‐β (r2 = 0.87, p = 0.002) and CCL7 (r2 = 0.63, p = 0.033) were significantly positively correlated between matched menstrual and peripheral blood samples. Conclusion In this group of study participants, the cytokine profile of menstrual effluent was quantitatively distinct from peripheral blood, and also characterized by higher levels of inflammatory signaling. This pattern of comparative menstrual blood cytokine profiles points to a need for further studies to evaluate the relationship between peripheral and menstrual blood cytokines in broader populations including both healthy and diseased states

Azithromycin promotes proliferation, and inhibits inflammation in nasal epithelial cells in primary ciliary dyskinesia

Abstract Primary ciliary dyskinesia (PCD) is a genetic disorder associated with recurrent and chronic respiratory infections due to functional defects of motile cilia. In this study, we aimed to elucidate inflammatory and proliferative responses in PCD respiratory epithelium and evaluate the effect of Azithromycin (AZT) on these responses. Airway basal cells (BCs) were isolated from nasal samples of Wild-type (WT) epitope of healthy donors and PCD donors with bi-allelic mutations in DNAH5, DNAH11 and CCDC39. Cells were expanded in vitro and stimulated with either Lipopolysaccharide (LPS) or vehicle control. Post stimulation, cells were treated with either Azithromycin (AZT) or vehicle control. Cell proliferation was imaged in real-time. Separately, BCs from the same donors were expanded and grown at an air–liquid interface (ALI) to generate a multi-ciliated epithelium (MCE). Once fully mature, cells were stimulated with LPS, AZT, LPS + AZT or vehicle control. Inflammatory profiling was performed on collected media by cytokine Luminex assay. At baseline, there was a significantly higher mean production of pro-inflammatory cytokines by CCDC39 BCs and MCEs when compared to WT, DNAH11 and DNAH5 cells. AZT inhibited production of cytokines induced by LPS in PCD cells. Differences in cell proliferation were noted in PCD and this was also corrected with AZT treatment