Project description:MicroRNAs are important negative regulators of protein coding gene expression, and have been studied intensively over the last few years. To this purpose, different measurement platforms to determine their RNA abundance levels in biological samples have been developed. In this study, we have systematically compared 12 commercially available microRNA expression platforms by measuring an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples, and synthetic spikes from homologous microRNA family members. We developed novel quality metrics in order to objectively assess platform performance of very different technologies such as small RNA sequencing, RT-qPCR and (microarray) hybridization. We assessed reproducibility, sensitivity, quantitative performance, and specificity. The results indicate that each method has its strengths and weaknesses, which helps guiding informed selection of a quantitative microRNA gene expression platform in function of particular study goals.
Project description:Metabolite structure identification remains a significant challenge in nontargeted metabolomics research. One commonly used strategy relies on searching biochemical databases using exact mass. However, this approach fails when the database does not contain the unknown metabolite (i.e., for unknown-unknowns). For these cases, constrained structure generation with combinatorial structure generators provides a potential option. Here we evaluated structure generation constraints based on the specification of: (1) substructures required (i.e., seed structures); (2) substructures not allowed; and (3) filters to remove incorrect structures. Our approach (database assisted structure identification, DASI) used predictive models in MolFind to find candidate structures with chemical and physical properties similar to the unknown. These candidates were then used for seed structure generation using eight different structure generation algorithms. One algorithm was able to generate correct seed structures for 21/39 test compounds. Eleven of these seed structures were large enough to constrain the combinatorial structure generator to fewer than 100,000 structures. In 35/39 cases, at least one algorithm was able to generate a correct seed structure. The DASI method has several limitations and will require further experimental validation and optimization. At present, it seems most useful for identifying the structure of unknown-unknowns with molecular weights <200 Da.
Project description:Cancer-associated fibroblasts (CAFs) have been recognized as important contributors to cancer development and progression. However, opposing evidence has been published whether CAFs, in addition to epigenetic, also undergo somatic genetic alterations and whether these changes contribute to carcinogenesis and tumour progression. We combined multiparameter DNA flow cytometry, flow-sorting and 6K SNP-arrays to study DNA aneuploidy, % S-phase, loss of heterozygosity (LOH) and copy number alterations (CNAs) to study somatic genetic alterations in cervical cancer-associated stromal cell fractions (n = 58) from formalin-fixed, paraffin-embedded (FFPE) samples. Tissue sections were examined for the presence of CAFs. Microsatellite analysis was used to study LOH. By flow cytometry we found no proof for DNA aneuploidy in the vimentin-positive stromal cell fractions of any samples (CV G0G1 population 3.7% +/- 1.2; S-phase 1.4% +/- 1.8). The genotype concordance between the stromal cells and the paired normal endometrium samples was > 99.9%. No evidence for CNAs or LOH was found in the stromal cell fractions. In contrast, high frequencies of DNA content abnormalities (43/57), a significant higher S-phase (14.6% +/- 8.5)(p = 0.0001) and substantial numbers of CNAs and LOH were identified in the keratin-positive epithelial cell fractions (CV G0G1 population 4.1% +/- 1.0). Smooth muscle actin and vimentin immunohistochemistry verified the presence of CAFs in all cases tested. LOH hot-spots on chromosomes 3p, 4p and 6p were confirmed by microsatellite analysis but the stromal cell fractions showed retention of heterozygosity only. From our study we conclude that stromal cell fractions from cervical carcinomas are DNA diploid, have a genotype undistinguishable from patient-matched normal tissue and are genetically stable. Stromal genetic changes do not seem to play a role during cervical carcinogenesis and progression. In addition, the stromal cell fraction of cervical carcinomas can be used as reference allowing large retrospective studies of archival FFPE tissues for which no normal reference tissue is available. Paired experiment, Endometrial (non-tumor) cells vs stromal cells from cervical tumors. Biological replicates: 58 patients. From 5 tumors also the tumor fraction was profiled.
Project description:Recurrent non-medullary thyroid carcinoma (NMTC) is a rare disease. We initially characterized 27 recurrent NMTC: 13 papillary thyroid cancers (PTC), 10 oncocytic follicular carcinomas (FTC-OV), and 4 non-oncocytic follicular carcinomas (FTC). A validation cohort composed of benign and malignant (both recurrent and non-recurrent) thyroid tumours was subsequently analysed (n = 20). Methods Data from genome-wide SNP arrays and flow cytometry were combined to determine the chromosomal dosage (allelic state) in these tumours, including mutation analysis of components of PIK3CA/AKT and MAPK pathways. Results All FTC-OVs showed a very distinct pattern of genomic alterations. Ten out of 10 FTC-OV cases showed near-haploidisation with or without subsequent genome endoreduplication. Near-haploidisation was seen in 5/10 as extensive chromosome-wide monosomy (allelic state [A]) with near-haploid DNA indices and retention of especially chromosome 7 (seen as a heterozygous allelic state [AB]). In the remaining 5/10 chromosomal allelic states AA with near diploid DNA indices were seen with allelic state AABB of chromosome 7, suggesting endoreduplication after preceding haploidisation. The latter was supported by the presence of both near-haploid and endoreduplicated tumour fractions in some of the cases. Results were confirmed using FISH analysis. Relatively to FTC-OV limited numbers of genomic alterations were identified in other types of recurrent NMTC studied, except for chromosome 22q which showed alterations in 6 of 13 PTCs. Only two HRAS, but no mutations of EGFR or BRAF were found in FTC-OV. The validation cohort showed two additional tumours with the distinct pattern of genomic alterations (both with oncocytic features and recurrent). Conclusions We demonstrate that recurrent FTC-OV is frequently characterised by genome-wide DNA haploidisation, heterozygous retention of chromosome 7, and endoreduplication of a near-haploid genome. Whether normal gene dosage on especially chromosome 7 (containing EGFR, BRAF, cMET) is crucial for FTC-OV tumour survival is an important topic for future research. 28 thyroid tumors from 27 patients were profiled by SNP array. Comparisons between different types were made.
Project description:Infantile hemangioma (IH) is the most common benign tumor in children. However, the exact pathogenesis of IH remains unclear. Integrated nontargeted and targeted metabolic analyses were performed to obtain insight into the possible pathogenic mechanism of IH. The results of nontargeted metabolic analysis showed that 216 and 128 differential metabolites (DMs) were identified between hemangioma-derived endothelial cells (HemECs) and HUVECs in positive-ion and negative-ion models, respectively. In both models, these DMs were predominantly enriched in pathways related to amino acid metabolism, including aminoacyl-tRNA biosynthesis and arginine and proline metabolism. Then, targeted metabolic analysis of amino acids was further performed to further clarify HemEC metabolism. A total of 22 amino acid metabolites were identified, among which only 16 metabolites, including glutamine, arginine and asparagine, were significantly differentially expressed between HemECs and HUVECs. These significant amino acids were significantly enriched in 10 metabolic pathways, including 'alanine, aspartate and glutamate metabolism', 'arginine biosynthesis', 'arginine and proline metabolism', and 'glycine, serine and threonine metabolism'. The results of our study revealed that amino acid metabolism is involved in IH. Key differential amino acid metabolites, including glutamine, asparagine and arginine, may play an important role in regulating HemEC metabolism.
Project description:BackgroundNovel potential small molecular biomarkers for sepsis were analyzed with nontargeted metabolite profiling to find biomarkers for febrile neutropenia after intensive chemotherapy for hematological malignancies.MethodsAltogether, 85 patients were included into this prospective study at the start of febrile neutropenia after intensive chemotherapy for acute myeloid leukemia or after autologous stem cell transplantation. The plasma samples for the nontargeted metabolite profiling analysis by liquid chromatography-mass spectrometry were taken when fever rose over 38° and on the next morning.ResultsAltogether, 90 differential molecular features were shown to explain the differences between patients with complicated (bacteremia, severe sepsis, or fatal outcome) and noncomplicated courses of febrile neutropenia. The most differential compounds were an androgen hormone, citrulline, and phosphatidylethanolamine PE(18:0/20:4). The clinical relevance of the findings was evaluated by comparing them with conventional biomarkers like C-reactive protein and procalcitonin.ConclusionThese results hold promise to find out novel biomarkers for febrile neutropenia, including citrulline. Furthermore, androgen metabolism merits further studies.
Project description:Current methods of structure identification in mass-spectrometry-based nontargeted metabolomics rely on matching experimentally determined features of an unknown compound to those of candidate compounds contained in biochemical databases. A major limitation of this approach is the relatively small number of compounds currently included in these databases. If the correct structure is not present in a database, it cannot be identified, and if it cannot be identified, it cannot be included in a database. Thus, there is an urgent need to augment metabolomics databases with rationally designed biochemical structures using alternative means. Here we present the In Vivo/In Silico Metabolites Database (IIMDB), a database of in silico enzymatically synthesized metabolites, to partially address this problem. The database, which is available at http://metabolomics.pharm.uconn.edu/iimdb/, includes ~23,000 known compounds (mammalian metabolites, drugs, secondary plant metabolites, and glycerophospholipids) collected from existing biochemical databases plus more than 400,000 computationally generated human phase-I and phase-II metabolites of these known compounds. IIMDB features a user-friendly web interface and a programmer-friendly RESTful web service. Ninety-five percent of the computationally generated metabolites in IIMDB were not found in any existing database. However, 21,640 were identical to compounds already listed in PubChem, HMDB, KEGG, or HumanCyc. Furthermore, the vast majority of these in silico metabolites were scored as biological using BioSM, a software program that identifies biochemical structures in chemical structure space. These results suggest that in silico biochemical synthesis represents a viable approach for significantly augmenting biochemical databases for nontargeted metabolomics applications.
Project description:Many marine algae occupy habitats that are dark, deep, or encrusted on other organisms and hence are frequently overlooked by natural product chemists. However, exploration of less-studied organisms can lead to new opportunities for drug discovery. Genetic variation at the individual, species, genus, and population levels as well as environmental influences on gene expression enable expansion of the chemical repertoire associated with a taxonomic group, enabling natural product exploration using innovative analytical methods. A nontargeted LC-MS and 1H NMR spectroscopy-based metabolomic study of 32 collections of representatives of the calcareous red algal genus Peyssonnelia from coral reef habitats in Fiji and the Solomon Islands revealed significant correlations between natural products' chemistry, phylogeny, and biomedically relevant biological activity. Hierarchical cluster analysis (HCA) of LC-MS data in conjunction with NMR profiling and MS/MS-based molecular networking revealed the presence of at least four distinct algal chemotypes within the genus Peyssonnelia. Two Fijian collections were prioritized for further analysis, leading to the isolation of three novel sulfated triterpene glycosides with a rearranged isomalabaricane carbon skeleton, guided by the metabolomic data. The discovery of peyssobaricanosides A-C (15-17) from two Fijian Peyssonnelia collections, but not from closely related specimens collected in the Solomon Islands that were otherwise chemically and phylogenetically very similar, alludes to population-level variation in secondary metabolite production. Our study reinforces the significance of exploring unusual ecological niches and showcases marine red algae as a chemically rich treasure trove.