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We present the first long-term, highly resolved prokaryotic cell concentration record obtained from a polar ice core. This record, obtained from the West Antarctic Ice Sheet (WAIS) Divide (WD) ice core, spanned from the Last Glacial Maximum (LGM) to the early Holocene (EH) and showed distinct fluctuations in prokaryotic cell concentration coincident with major climatic states. The time series also revealed a ~1,500-year periodicity with greater amplitude during the Last Deglaciation (LDG). Higher prokaryotic cell concentration and lower variability occurred during the LGM and EH than during the LDG. A sevenfold decrease in prokaryotic cell concentration coincided with the LGM/LDG transition and the global 19 ka meltwater pulse. Statistical models revealed significant relationships between the prokaryotic cell record and tracers of both marine (sea-salt sodium [ssNa]) and burning emissions (black carbon [BC]). Collectively, these models, together with visual observations and methanosulfidic acid (MSA) measurements, indicated that the temporal variability in concentration of airborne prokaryotic cells reflected changes in marine/sea-ice regional environments of the WAIS. Our data revealed that variations in source and transport were the most likely processes producing the significant temporal variations in WD prokaryotic cell concentrations. This record provided strong evidence that airborne prokaryotic cell deposition differed during the LGM, LDG, and EH, and that these changes in cell densities could be explained by different environmental conditions during each of these climatic periods. Our observations provide the first ice-core time series evidence for a prokaryotic response to long-term climatic and environmental processes.

Autosomal dominant polycystic kidney disease (ADPKD) is characterized by the formation of numerous fluid-filled cysts that lead to progressive loss of functional nephrons. Currently, there is an unmet need for diagnostic and prognostic indicators of early stages of the disease. Metabolites were extracted from the urine of patients with early-stage ADPKD (n = 48 study participants) and age- and sex-matched normal controls (n = 47) and analyzed by liquid chromatography-mass spectrometry. Orthogonal partial least squares-discriminant analysis was used to generate a global metabolomic profile of early ADPKD for the identification of metabolic pathway alterations and discriminatory metabolites as candidates of diagnostic and prognostic biomarkers. The global metabolomic profile exhibited alterations in steroid hormone biosynthesis and metabolism, fatty acid metabolism, pyruvate metabolism, amino acid metabolism, and the urea cycle. A panel of 46 metabolite features was identified as candidate diagnostic biomarkers. Notable putative identities of candidate diagnostic biomarkers for early detection include creatinine, cAMP, deoxycytidine monophosphate, various androgens (testosterone; 5-α-androstane-3,17,dione; trans-dehydroandrosterone), betaine aldehyde, phosphoric acid, choline, 18-hydroxycorticosterone, and cortisol. Metabolic pathways associated with variable rates of disease progression included steroid hormone biosynthesis and metabolism, vitamin D3 metabolism, fatty acid metabolism, the pentose phosphate pathway, tricarboxylic acid cycle, amino acid metabolism, sialic acid metabolism, and chondroitin sulfate and heparin sulfate degradation. A panel of 41 metabolite features was identified as candidate prognostic biomarkers. Notable putative identities of candidate prognostic biomarkers include ethanolamine, C20:4 anandamide phosphate, progesterone, various androgens (5-α-dihydrotestosterone, androsterone, etiocholanolone, and epiandrosterone), betaine aldehyde, inflammatory lipids (eicosapentaenoic acid, linoleic acid, and stearolic acid), and choline. Our exploratory data support metabolic reprogramming in early ADPKD and demonstrate the ability of liquid chromatography-mass spectrometry-based global metabolomic profiling to detect metabolic pathway alterations as new therapeutic targets and biomarkers for early diagnosis and tracking disease progression of ADPKD. NEW & NOTEWORTHY To our knowledge, this study is the first to generate urinary global metabolomic profiles from individuals with early-stage ADPKD with preserved renal function for biomarker discovery. The exploratory dataset reveals metabolic pathway alterations that may be responsible for early cystogenesis and rapid disease progression and may be potential therapeutic targets and pathway sources for candidate biomarkers. From these results, we generated a panel of candidate diagnostic and prognostic biomarkers of early-stage ADPKD for future validation. [ABSTRACT FROM AUTHOR]

Workshop Statistics: Discovery With Data, A Bayesian Approach, by James H. ALBERT and Allan J ROSSMAN, Emeryville, CA: Key College Publishing 2001, ISBN 1-930190-12-3, xxxv + 532 pp., $49.95. This [...]

Monothetic clustering for multivariate binary data provides a method of identifying variables whose levels identify the different patterns of responses. The default graphical summary of the standard version of this method provides information on the variables used to split the observations into groups and the proportions of the responses in the groups but not the levels of the responses in each cluster. A modified graph is proposed to provide fuller interpretations of the clustering results. The methods are applied to two examples and contrasted with other approaches to clustering multivariate binary responses. One application involves the clustering different bacterial clones presence/absence throughout distinct strata (or sections) in ice cores from Pony Lake, Antarctica, where the interest is in the full patterns of responses in the different clusters. The second is from a test developed to measure elementary education mathematics coaches knowledge of different aspects of mathematics coaching. In this second application, the interest lies more with questions that define distinct groups of individuals than with the entire pattern of responses. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this study was to analyse the metabolomic profiles of rheumatoid arthritis synovial fluid to test the use of global metabolomics by liquid chromatography-mass spectrometry for clinical analysis of synovial fluid., Methods: Metabolites were extracted from rheumatoid arthritis (n=3) and healthy (n=5) synovial fluid samples using 50:50 water: acetonitrile. Metabolite extracts were analysed in positive mode by normal phase liquid chromatography-mass spectrometry for global metabolomics. Statistical analyses included hierarchical clustering analysis, principal component analysis, Student's t-test, and volcano plot analysis. Metabolites were matched with known metabolite identities using METLIN and enriched for relevant pathways using IMPaLA., Results: 1018 metabolites were detected by LC-MS analysis in synovial fluid from rheumatoid arthritis and healthy patients, with 162 metabolites identified as significantly different between diseased and control. Pathways upregulated with disease included ibuprofen metabolism, glucocorticoid and mineralocorticoid metabolism, alpha-linolenic acid metabolism, and steroid hormone biosynthesis. Pathways downregulated with disease included purine and pyrimidine metabolism, biological oxidations, arginine and proline metabolism, the citrulline-nitric oxide cycle, and glutathione metabolism. Receiver operating characteristic analysis identified 30 metabolites as putative rheumatoid arthritis biomarkers including various phospholipids, diol and its derivatives, arsonoacetate, oleananoic acid acetate, docosahexaenoic acid methyl ester, and linolenic acid and eicosatrienoic acid derivatives., Conclusions: This study supports the use of global metabolomic profiling by liquid chromatography-mass spectrometry for synovial fluid analysis to provide insight into the aetiology of disease.