Big Data Analyses最新文献

{"title":"BD-05 Discovery of SERPINA3 as a candidate urinary biomarker of lupus nephritis chronicity","authors":"Jessica L. Turnier, H. Brunner, M. Bennett, A. AlE'ed, G. Gulati, W. Haffey, S. Thornton, M. Wagner, P. Devarajan, D. Witte, K. Greis, B. Aronow","doi":"10.1136/LUPUS-2018-LSM.29","DOIUrl":"https://doi.org/10.1136/LUPUS-2018-LSM.29","url":null,"abstract":"Background Non-invasive biomarkers of lupus nephritis (LN) damage are needed to guide treatment decisions. Urinary proteomics has advanced as a tool for novel biomarker discovery in recent years. Specifically, isobaric tags for relative and absolute quantification (iTRAQ) is an advanced proteomics technique that quantifies and compares protein expression among samples by mass spectrometry in a single experiment. We used an unbiased proteomics approach to identify candidate urine biomarkers (CUBMs) predictive of LN chronicity and further pursued their validation in a larger cohort. Methods In this cross-sectional pilot study, we selected urine collected at kidney biopsy from 20 children with varying levels of LN damage (discovery cohort) and performed proteomic analysis using iTRAQ. We identified differentially excreted proteins based on degree of LN chronicity and sought to distinguish markers exhibiting different relative expression patterns using hierarchically-clustered log10-normalized relative abundance data with linked and distinct functions by biological network analyses. For each CUBM, we performed specific enzyme-linked immunosorbent assays (ELISAs) on urine from a validation cohort (n=41) and analysis of variance (ANOVA) to detect differences between LN chronicity, with LN activity adjustment. We evaluated for CUBM expression in LN biopsies with immunohistochemistry. Results iTRAQ detected 112 proteins from urine samples in the discovery cohort, 51 of which were quantifiable in all replicates. Simple ANOVA revealed four differentially expressed, chronicity-correlated proteins (p-values Conclusions Using advanced proteomic techniques followed by confirmation using specific ELISAs, we identified SERPINA3, a known inhibitor of neutrophil cathepsin G and angiotensin II production, as a potential urine biomarker to help quantify LN damage. SERPINA3 expression may be a protective mechanism from further kidney damage. Further validation of SERPINA3 as an LN damage biomarker in an independent cohort is needed to determine its ability to guide treatment and predict prognosis. Acknowledgements This study was supported by grants from the National Institutes of Health [P50 DK 096418, U01 AR065098, T32 AR069512, P30 AR070549] and a Lupus Foundation Career Development Award to Dr. Turnier. Mass spectrometry data were collected on a system funded through an NIH shared instrumentation grant (S10 RR027015–01; KD Greis-PI).","PeriodicalId":117843,"journal":{"name":"Big Data Analyses","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122899531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BD-10 The distribution of insurance in a population-based cohort of SLE: georgians organized against lupus cohort","authors":"S. S. Lim, G. Bao, C. Drenkard","doi":"10.1136/LUPUS-2018-LSM.34","DOIUrl":"https://doi.org/10.1136/LUPUS-2018-LSM.34","url":null,"abstract":"Background Having health insurance coverage is important for people with chronic conditions. Those with systemic lupus erythematosus (SLE) are particularly vulnerable given the disproportionate impact on young minorities and women. This is the first description of health insurance changes over time overall and by sociodemographic groups on a population level. Methods The Georgians Organized Against Lupus (GOAL) is a cohort of validated patients with SLE living in Atlanta, predominantly derived from the population-based and Centers for Disease Control and Prevention (CDC) funded Georgia Lupus Registry. Participants have been surveyed annually, including sociodemographics, health insurance, disease activity (Systemic Lupus Activity Questionnaire), and damage (Self-Administered Brief Index of Lupus Damage). Self-reported health insurance was categorized into no insurance, private, Medicare, Medicaid, and Medicare/Medicaid. Those reported being in a different category the year before were classified as having changed insurance. Results An average of 642 individuals were surveyed annually from 2012 to 2016. At baseline, the average age was 46.4±13.4 and disease duration was 13.6±9.2 years. 93.6% were female and 78.5% black. 35.1% had a high school educational level or less, 45.8% were at or below the Federal poverty level, 34.6% were married or with a partner, and 35% were employed. Figure 1 shows the distribution of insurance categories from 2012 through 2016. Compared to the year before, 23.8% changed insurance in 2013, 22.2% in 2014, 24.1% in 2015, and 26.8% in 2016. Those who changed insurance tended to be black, lower in educational attainment, poorer, unemployed, and have greater disease activity and damage. Conclusions In a population-based cohort in Georgia, the majority with SLE have private insurance and Medicare, which has grown over time while those uninsured have dropped. This is in line, but greater in magnitude, with the decrease in uninsured seen with the Affordable Care Act. Georgia is one of the states that has not expanded Medicaid resulting in flat enrollment. There also appears to be an increase in those who switch insurance categories. In this era of fluctuating health care policy, it is important to learn how types of and changes in coverage affect health care utilization, disease treatment and outcomes, self-reported health, and mortality in SLE, particularly given the disproportionate impact on vulnerable groups. Studies utilizing administrative data should also be aware of medical coverage distributions and regional variations in policy that impact these distributions. Acknowledgements Supported by 3U58DP001487–05 W2 and U01DP005119 (CDC).","PeriodicalId":117843,"journal":{"name":"Big Data Analyses","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126029092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BD-08 A novel approach to analyze single cell RNA-Seq data from lupus nephritis samples","authors":"Brian Kegerreis, A. Grammer, P. Lipsky","doi":"10.1136/LUPUS-2018-LSM.32","DOIUrl":"https://doi.org/10.1136/LUPUS-2018-LSM.32","url":null,"abstract":"Background Single-cell RNA-Seq (scRNA-seq) has the potential to increase our understanding of cell populations in lupus. Recently, kidney scRNA-Seq data from lupus nephritis (LN) patients has provided the opportunity to determine the heterogeneity of cells within the affected kidney. However, since individual cells were not identified phenotypically, it is necessary to identify populations computationally. The unique technical challenges of scRNA-Seq data make it difficult to approach this analysis with conventional unsupervised bioinformatics techniques. The implementation of natural language processing (NLP) -inspired techniques, however, makes it possible to identify meaningful clusters of cells without prior knowledge of the cell types present in the sample. Methods We have developed a recursive, unsupervised, heuristic technique (StarShipTM) to dynamically perform top-down, divisive clustering on scRNA-Seq data. First, the cells are mapped onto an n-dimensional unit sphere, where n is the number of available genes. The angles between all cells are used to construct a cosine distance metric: 1-cos(θ). The cosine distance is used to carry out k-means or k-medoids clustering, with k set to 2 for each iteration. At each split of the data, the algorithm evaluates whether it has sorted the remaining cells into meaningful populations and stops making splits when a user-defined criterion is met. Once all clusters are finalized, a Mann-Whitney U test determines genes that distinguish clusters or groups of clusters from other cells. This algorithm was validated using publicly available peripheral blood mononuclear cell (PBMC) scRNA-Seq data from 10X Genomics and tested in scRNA-Seq data from LN patients from the NIAMS AMP RA/SLE initiative. Adjusted Rand Index (ARI) was used to compare generated partitions to known cell types in the PBMC data. Results StarShipTM was used to classify 250 PBMC (50 each of CD14 monocytes, CD19 B cells, CD4 helper T cells, CD8 T cells, and CD56 NK cells). Using dynamic spherical k-means, 6 clusters were generated that closely corresponded to the known cell types (figure 1). For comparison, hierarchical clustering and one-off spherical k-means with k set to 5 were carried out. Hierarchical clustering had an ARI of 0.45, one-off spherical k-means had an ARI of 0.89, and dynamic spherical k-means had an ARI of 0.86. Conclusions This method can effectively partition unknown cells from scRNA-Seq data sets into biologically relevant clusters without prior knowledge of the number of cell types present. The similarity between the performance of the StarShipTM algorithm and one-off k-means, which does incorporate this prior knowledge, highlights the value of this dynamic technique. A full analysis of the AMP LN data is forthcoming. Acknowledgments Research supported by the RILITE Foundation.","PeriodicalId":117843,"journal":{"name":"Big Data Analyses","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122570533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}