An R-Package for the Deconvolution and Integration of 1D NMR Data: MetaboDecon1D

NMR spectroscopy is a widely used method for the detection and quantification of metabolites in complex biological fluids. However, the large number of metabolites present in a biological sample such as urine or plasma leads to considerable signal overlap in one-dimensional NMR spectra, which in turn hampers both signal identification and quantification. As a consequence, we have developed an easy to use R-package that allows the fully automated deconvolution of overlapping signals in the underlying Lorentzian line-shapes. We show that precise integral values are computed, which are required to obtain both relative and absolute quantitative information. The algorithm is independent of any knowledge of the corresponding metabolites, which also allows the quantitative description of features of yet unknown identity

Robust Metabolite Quantification from J-Compensated 2D 1H-13C-HSQC Experiments

The spectral resolution of 2D H-1-C-13 heteronuclear single quantum coherence (H-1-C-13-HSQC) nuclear magnetic resonance (NMR) spectra facilitates both metabolite identification and quantification in nuclear magnetic resonance-based metabolomics. However, quantification is complicated by variations in magnetization transfer, which among others originate mainly from scalar coupling differences. Methods that compensate for variation in scalar coupling include the generation of calibration factors for individual signals or the use of additional pulse sequence schemes such as quantitative HSQC (Q-HSQC) that suppress the J(CH)-dependence by modulating the polarization transfer delays of HSQC or, additionally, employ a pure-shift homodecoupling approach in the 1(H) dimension, such as Quantitative, Perfected and Pure Shifted HSQC (QUIPU-HSQC). To test the quantitative accuracy of these three methods, employing a 600 MHz NMR spectrometer equipped with a helium cooled cryoprobe, a Latin-square design that covered the physiological concentration ranges of 10 metabolites was used. The results show the suitability of all three methods for the quantification of highly abundant metabolites. However, the substantially increased residual water signal observed in QUIPU-HSQC spectra impeded the quantification of low abundant metabolites located near the residual water signal, thus limiting its utility in high-throughput metabolite fingerprinting studies

Regulation and function of the atypical cadherin FAT1 in hepatocellular carcinoma

Giant cadherin FAT1 is increased in HCC and is a protumorigenic factors in HCC cell

Bucket Fuser: Statistical Signal Extraction for 1D 1H NMR Metabolomic Data

Untargeted metabolomics is a promising tool for identifying novel disease biomarkers and unraveling underlying pathomechanisms. Nuclear magnetic resonance (NMR) spectroscopy is particularly suited for large-scale untargeted metabolomics studies due to its high reproducibility and cost effectiveness. Here, one-dimensional (1D) 1H NMR experiments offer good sensitivity at reasonable measurement times. Their subsequent data analysis requires sophisticated data preprocessing steps, including the extraction of NMR features corresponding to specific metabolites. We developed a novel 1D NMR feature extraction procedure, called Bucket Fuser (BF), which is based on a regularized regression framework with fused group LASSO terms. The performance of the BF procedure was demonstrated using three independent NMR datasets and was benchmarked against existing state-of-the-art NMR feature extraction methods. BF dynamically constructs NMR metabolite features, the widths of which can be adjusted via a regularization parameter. BF consistently improved metabolite signal extraction, as demonstrated by our correlation analyses with absolutely quantified metabolites. It also yielded a higher proportion of statistically significant metabolite features in our differential metabolite analyses. The BF algorithm is computationally efficient and it can deal with small sample sizes. In summary, the Bucket Fuser algorithm, which is available as a supplementary python code, facilitates the fast and dynamic extraction of 1D NMR signals for the improved detection of metabolic biomarker

Biological and clinical significance of tryptophan-catabolizing enzymes in cutaneous T-cell lymphomas

Indoleamine 2,3-deoxygenase 1 (IDO1) induces immune tolerance in the tumor microenvironment (TME) and is recognized as a potential therapeutic target. We studied the expression of both IDO1 and the related tryptophan 2,3-dioxygenase (TDO) in several different subtypes of cutaneous T-cell lymphoma (CTCL), and evaluated the kynurenine (KYN) pathway in the local TME and in patient sera. Specimens from the total of 90 CTCL patients, including mycosis fungoides (MF, n = 37), lymphomatoid papulosis (LyP, n = 36), primary cutaneous anaplastic large cell lymphoma (pcALCL, n = 4), subcutaneous panniculitis-like T-cell lymphoma (SPTCL n = 13), and 10 patients with inflammatory lichen ruber planus (LRP), were analyzed by immunohistochemistry (IHC), immunofluorescence (IF), quantitative PCR, and/or liquid chromatography-tandem mass spectrometry (LC-MS/MS). Three CTCL cell lines also were studied. Expression of both IDO1 and TDO was upregulated in CTCL. In MF specimens and in the MF cell line MyLa2000, IDO1 expression exceeded that of TDO, whereas the opposite was true for LyP, ALCL, and corresponding Mac1/2A cell lines. The spectrum of IDO1-expressing cell types differed among CTCL subtypes and was reflected in the clinical behavior. In MF, SPTCL, and LyP, IDO1 was expressed by malignant cells and by CD33(+) myeloid-derived suppressor cells, whereas in SPTCL CD163(+) tumor-associated macrophages also expressed IDO1. Significantly elevated serum KYN/Trp ratios were found in patients with advanced stages of MF. Epacadostat, an IDO1 inhibitor, induced a clear decrease in KYN concentration in cell culture. These results show the importance of IDO1/TDO-induced immunosuppression in CTCL and emphasize its role as a new therapeutic target.Peer reviewe

Platform independent protein-based cell-of-origin subtyping of diffuse large B-cell lymphoma in formalin-fixed paraffin-embedded tissue

Diffuse large B-cell lymphoma (DLBCL) is commonly classified by gene expression profiling according to its cell of origin (COO) into activated B-cell (ABC)-like and germinal center B-cell (GCB)-like subgroups. Here we report the application of label-free nano-liquid chromatography - Sequential Window Acquisition of all THeoretical fragment-ion spectra - mass spectrometry (nanoLC-SWATH-MS) to the COO classification of DLBCL in formalin-fixed paraffin-embedded (FFPE) tissue. To generate a protein signature capable of predicting Affymetrix-based GCB scores, the summed log(2)-transformed fragment ion intensities of 780 proteins quantified in a training set of 42 DLBCL cases were used as independent variables in a penalized zero-sum elastic net regression model with variable selection. The eight-protein signature obtained showed an excellent correlation (r=0.873) between predicted and true GCB scores and yielded only 9 (21.4%) minor discrepancies between the three classifications: ABC, GCB, and unclassified. The robustness of the model was validated successfully in two independent cohorts of 42 and 31 DLBCL cases, the latter cohort comprising only patients aged >75 years, with Pearson correlation coefficients of 0.846 and 0.815, respectively, between predicted and NanoString nCounter based GCB scores. We further show that the 8-protein signature is directly transferable to both a triple quadrupole and a Q Exactive quadrupole-Orbitrap mass spectrometer, thus obviating the need for proprietary instrumentation and reagents. This method may therefore be used for robust and competitive classification of DLBCLs on the protein level

Adjusting microbiome profiles for differences in microbial load by spike-in bacteria

Background: Next-generation 16S ribosomal RNA gene sequencing is widely used to determine the relative composition of the mammalian gut microbiomes. However, in the absence of a reference, this does not reveal alterations in absolute abundance of specific operational taxonomic units if microbial loads vary across specimens. Results: Here we suggest the spiking of exogenous bacteria into crude specimens to quantify ratios of absolute bacterial abundances. We use the 16S rDNA read counts of the spike-in bacteria to adjust the read counts of endogenous bacteria for changes in total microbial loads. Using a series of dilutions of pooled faecal samples from mice containing defined amounts of the spike-in bacteria Salinibacter ruber, Rhizobium radiobacter and Alicyclobacillus acidiphilus, we demonstrate that spike-in-based calibration to microbial loads allows accurate estimation of ratios of absolute endogenous bacteria abundances. Applied to stool specimens of patients undergoing allogeneic stem cell transplantation, we were able to determine changes in both relative and absolute abundances of various phyla, especially the genus Enterococcus, in response to antibiotic treatment and radio-chemotherapeutic conditioning. Conclusion: Exogenous spike-in bacteria in gut microbiome studies enable estimation of ratios of absolute OTU abundances, providing novel insights into the structure and the dynamics of intestinal microbiomes

Isotope Ratio Outlier Analysis (IROA) for HPLC–TOFMS-Based Metabolomics of Human Urine

Metabolic fingerprinting by mass spectrometry aims at the comprehensive, semiquantitative analysis of metabolites. Isotope dilution, if successfully implemented, may provide a more reliable, relative quantification. Therefore, the 13C labeled yeast extract of the IROA TruQuant kit was added as an internal standard (IS) to human urine samples measured in full-scan mode on a high-performance liquid chromatography-time-of-flight mass spectrometer (HPLC–TOFMS) system. The isotope ratio approach enabled the analysis of 112 metabolites. The correlation with reference data did not improve significantly using 12C/13C ratios compared to absolute 12C peak areas. Moreover, using an intricate 13C-labeled standard increased the complexity of the mass spectra, which made correct signal annotation more challenging. On the positive side, the ratio approach helps to reduce batch effects, but it does not perform better than computational methods such as the “removebatcheffect” function in the R package Limma