Due to concerns surrounding potential large-scale radiological events, there is a need to determine robust radiation signatures for the rapid identification of exposed individuals, which can then be used to guide the development of compact field deployable instruments to assess individual dose. Metabolomics provides a technology to process easily accessible biofluids and determine rigorous quantitative radiation biomarkers with mass spectrometry (MS) platforms. While multiple studies have utilized murine models to determine radiation biomarkers, limited studies have profiled nonhuman primate (NHP) metabolic radiation signatures. In addition, these studies have concentrated on short-term biomarkers (i.e., <72 h). The current study addresses the need for biomarkers beyond 72 h using a NHP model. Urine samples were collected at 7 days postirradiation (2, 4, 6, 7 and 10 Gy) and processed with ultra-performance liquid chromatography (UPLC) quadrupole time-of-flight (QTOF) MS, acquiring global metabolomic radiation signatures. Multivariate data analysis revealed clear separation between control and irradiated groups. Thirteen biomarkers exhibiting a dose response were validated with tandem MS. There was significantly higher excretion of L-carnitine, L-acetylcarnitine, xanthine and xanthosine in males versus females. Metabolites validated in this study suggest perturbation of several pathways including fatty acid β oxidation, tryptophan metabolism, purine catabolism, taurine metabolism and steroid hormone biosynthesis. In this novel study we detected long-term biomarkers in a NHP model after exposure to radiation and demonstrate differences between sexes using UPLC-QTOF-MS-based metabolomics technology.

Introduction Due to dangers associated with potential accidents from nuclear energy and terrorist threats, there is a need for high-throughput biodosimetry to rapidly assess individual doses of radiation exposure. Lipidomics and metabolomics are becoming common tools for determining global signatures after disease or other physical insult and provide a “snapshot” of potential cellular damage. Objectives The current study assesses changes in the nonhuman primate (NHP) serum lipidome and metabolome 7 days following exposure to ionizing radiation (IR). Methods Serum sample lipids and metabolites were extracted using a biphasic liquid-liquid extraction and analyzed by ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry. Global radiation signatures were acquired in data-independent mode. Results Radiation exposure caused significant perturbations in lipid metabolism, affecting all major lipid species, including free fatty acids, glycerolipids, glycerophospholipids and esterified sterols. In particular, we observed a significant increase in the levels of polyunsaturated fatty acids (PUFA)-containing lipids in the serum of NHPs exposed to 10 Gy radiation, suggesting a primary role played by PUFAs in the physiological response to IR. Metabolomics profiling indicated an increase in the levels of amino acids, carnitine, and purine metabolites in the serum of NHPs exposed to 10 Gy radiation, suggesting perturbations to protein digestion/absorption, biological oxidations, and fatty acid β-oxidation. Conclusions This is the first report to determine changes in the global NHP serum lipidome and metabolome following radiation exposure and provides information for developing metabolomic biomarker panels in human-based biodosimetry.

A powerful tool for postgenomic analysis of mammalian gene function is gene targeting in mouse ES cells. We report that homologous recombination using a promoterless gene trap vector (''targeting trapping'') yields targeting frequencies averaging above 50%, a significant increase compared with current approaches. These high frequencies appear to be due to the stringency of selection with promoterless constructs, because most random insertions are silent and eliminated by drug selection. The promoterless design requires that the targeted gene be expressed in ES cells at levels exceeding a certain threshold (which we estimate to be Ϸ1% of the transferrin receptor gene expression level, for the secretory trap vector used here). Analysis of 127 genes that had been trapped by random (nontargeted) gene trapping with the same vector shows that virtually all are expressed in ES cells above this threshold, suggesting that targeted and random trapping share similar requirements for expression levels. In a random sampling of 130 genes encoding secretory proteins, about half were expressed above threshold, suggesting that about half of all secretory genes are accessible by either targeted or random gene trapping. The simplicity and high efficiency of the method facilitate systematic targeting of a large fraction of the genome by individual investigators and large-scale consortia alike.

There is a need for research to rapidly determine an individual’s absorbed dose and its potential health effects after a potential radiological or nuclear event that could expose large portions of a population to ionizing radiation (IR). Studies on biomarker identification after radiation exposure could aid in biodosimetry, identifying individual dose absorbed, as well as biologic response, and administering immediate and proper medical care. Metabolomics on easily accessible biofluids is an emerging field with potential for high-throughput biodosimetry. While tremendous effort has been put into obtaining discovery based global radiation signatures from a number of biofluids and model organisms, quantitative targeted analysis on a subset of known radiation biomarkers is required to develop an optimized panel of biomarkers for future clinical applications. The current study analyzes levels of several known broad chemical groups (acylcarnitines, amino acids, phosphatidylcholines, and biogenic amines) affected by IR in serum from nonhuman primates (NHP) 7 days after exposure through multiple reaction monitoring (MRM) analysis with a triple quadrupole mass spectrometry (MS) platform. We identified several novel metabolites affected by IR exposure through univariate and unsupervised multivariate analyses. Levels of acylcarnitines, amino acids, and phospholipids were perturbed indicating altered protein metabolism, fatty acid β-oxidation, and inflammation. Fold changes in carnitine and short-chain acylcarnitines (acetylcarnitine, propionylcarnitine, butyrylcarnitine, and valerylcarnitine) complement previous global radiation signatures on NHP; notably, the levels of change were lower than previously observed in urine. Decreased levels of glutamate, citrulline, and arginine after IR are biomarkers indicating gastrointestinal syndrome and perturbations to the urea cycle. Sex differences were also assessed and were more prevalent in circulating acylcarnitines and phospholipids after IR exposure. These biomarkers may be combined with previously described compounds from DNA damage to develop a defined metabolomic biodosimetry panel to be analyzed by MS platforms, which are increasingly available in clinical laboratories.

State-sponsored urbanization prevailed in the prereform China. A new track of spontaneous urbanization has emerged since 1978. However, state-sponsored urbanization has continued to be an important process of urbanization in the reform period. The Chinese pattern of urbanization since 1978 is distinguished by dual-track urbanization. This paper uses the dualtrack urbanization approach to examine the urbanization process in Pearl River Delta in the reform period. Whether urbanization takes place in main cities or low-order settlements has been a lasting theme of the urbanization inquiry. This issue is assessed by an analysis of the relative concentration and deconcentration of urban population among four groups of settlements: the primate city, cities with special economic zones, prefecture-level cities and county-level cities/ counties. Contrary to previous research, it is found that both state-sponsored and spontaneous urbanization are driving a dispersed pattern of urbanization in the region. [

Objective: To systematically compare the endometrial microbiota in infertile women with and without chronic endometritis (CE), as diagnosed by a quantitative and reference range-based method. Design: Case-control observational study. Setting: University-affiliated hospital. Patient(s): One hundred and thirty infertile women. Intervention(s): Endometrial biopsy and fluid (uterine lavage, UL) collected precisely 7 days after LH surge, with plasma cell density (PCD) determined based on Syndecan-1 (CD138)-positive cells in the entire biopsy section and culture-independent massively parallel sequencing of the 16S ribosomal RNA gene performed on both the CE and non-CE endometrial fluid samples. Main Outcome Measure(s): Relative abundance of bacterial taxa. Result(s): Chronic endometritis was diagnosed if the PCD was above the 95th percentile (>5.15 cells per 10 mm 2 ) of the reference range in fertile control subjects. With this stringent diagnostic criterion, 12 women (9%) were diagnosed with CE. Sequencing was successfully performed on all endometrial samples obtained by UL) (CE, n ¼ 12; non-CE, n ¼ 118). The median relative abundance of Lactobacillus was 1.89% and 80.7% in the CE and non-CE microbiotas, respectively. Lactobacillus crispatus was less abundant in the CE microbiota (fold-change, range: 2.10-2.30). Eighteen non-Lactobacillus taxa including Dialister, Bifidobacterium, Prevotella, Gardnerella, and Anaerococcus were more abundant in the CE microbiota (fold-change, 2.10-18.9). Of these, Anaerococcus and Gardnerella were negatively correlated in relative abundance with Lactobacillus (SparCC correlation magnitude, range: 0.142-0.177). Conclusion(s): Chronic endometritis was associated with a statistically significantly higher abundance of 18 bacterial taxa in the endometrial cavity.

BACKGROUND A recent study has reported that the microbiota in endometrial fluid of patients receiving in vitro fertilization and embryo transfer (IVF-ET) may predict implantation and pregnancy rates. However, studies are lacking that simultaneously compare the microbiota between endometrial fluid and tissue samples. Whether the microbiota composition in endometrial fluid reflects that in the endometrial tissue remains unclear. METHODS We systematically profiled the microbiota in endometrial fluid and tissue samples of IVF-ET patients using massively parallel sequencing. The bacterial 16S ribosomal RNA gene (V4 region) was PCR-amplified. Sequencing reads with &gt;98% nucleotide identity were clustered as a bacterial taxon. To account for the different number of reads per sample, we normalized the read counts of each taxon before comparing its relative abundances across samples. RESULTS Thirteen taxa, including Verrucomicrobiaceae, Brevundimonas, Achromobacter, Exiguobacterium, and Flavobacterium, were consistently detected only in endometrial tissue samples but not fluid samples. Eight taxa were detected in fluid but not tissue. Twenty-two taxa were differentially abundant between fluid and tissue samples (adjusted P values, 4.1 × 10−25 to 0.025). The numbers of taxa identified per 1000 sequencing reads, diversity, and evenness in fluid samples were smaller than those in tissue samples. CONCLUSIONS Our data suggest that the microbiota composition in endometrial fluid does not fully reflect that in endometrial tissue. Sampling from both endometrial fluid and biopsy allows a more comprehensive view of microbial colonization. Further efforts are needed to identify the preanalytical effects, including sampling sites, methods, and sequencing depth, on profiling endometrial microbiota.

Background: Several high throughput technologies have been employed to identify differentially regulated genes that may be molecular targets for drug discovery. Here we compared the sets of differentially regulated genes discovered using two experimental approaches: a subtracted suppressive hybridization (SSH) cDNA library methodology and Affymetrix GeneChip ® technology. In this "case study" we explored the transcriptional pattern changes during the in vitro differentiation of human monocytes to myeloid dendritic cells (DC), and evaluated the potential for novel gene discovery using the SSH methodology.