Reference point insensitive molecular data analysis

Motivation: In biomedicine, every molecular measurement is relative to a reference point, like a fixed aliquot of RNA extracted from a tissue, a defined number of blood cells, or a defined volume of biofluid. Reference points are often chosen for practical reasons. For example, we might want to assess the metabolome of a diseased organ but can only measure metabolites in blood or urine. In this case, the observable data only indirectly reflects the disease state. The statistical implications of these discrepancies in reference points have not yet been discussed. Results: Here, we show that reference point discrepancies compromise the performance of regression models like the LASSO. As an alternative, we suggest zero-sum regression for a reference point insensitive analysis. We show that zero-sum regression is superior to the LASSO in case of a poor choice of reference point both in simulations and in an application that integrates intestinal microbiome analysis with metabolomics. Moreover, we describe a novel coordinate descent based algorithm to fit zero-sum elastic nets

Metabolomics methods for the synthetic biology of secondary metabolism

Many microbial secondary metabolites are of high biotechnological value for medicine, agriculture, and the food industry. Bacterial genome mining has revealed numerous novel secondary metabolite biosynthetic gene clusters, which encode the potential to synthesize a large diversity of compounds that have never been observed before. The stimulation or “awakening” of this cryptic microbial secondary metabolism has naturally attracted the attention of synthetic microbiologists, who exploit recent advances in DNA sequencing and synthesis to achieve unprecedented control over metabolic pathways. One of the indispensable tools in the synthetic biology toolbox is metabolomics, the global quantification of small biomolecules. This review illustrates the pivotal role of metabolomics for the synthetic microbiology of secondary metabolism, including its crucial role in novel compound discovery in microbes, the examination of side products of engineered metabolic pathways, as well as the identification of major bottlenecks for the overproduction of compounds of interest, especially in combination with metabolic modeling. We conclude by highlighting remaining challenges and recent technological advances that will drive metabolomics towards fulfilling its potential as a cornerstone technology of synthetic microbiology

Separating the wheat from the chaff: a prioritisation pipeline for the analysis of metabolomics datasets

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful and widely applied method for the study of biological systems, biomarker discovery and pharmacological interventions. LC-MS measurements are, however, significantly complicated by several technical challenges, including: (1) ionisation suppression/enhancement, disturbing the correct quantification of analytes, and (2) the detection of large amounts of separate derivative ions, increasing the complexity of the spectra, but not their information content. Here we introduce an experimental and analytical strategy that leads to robust metabolome profiles in the face of these challenges. Our method is based on rigorous filtering of the measured signals based on a series of sample dilutions. Such data sets have the additional characteristic that they allow a more robust assessment of detection signal quality for each metabolite. Using our method, almost 80% of the recorded signals can be discarded as uninformative, while important information is retained. As a consequence, we obtain a broader understanding of the information content of our analyses and a better assessment of the metabolites detected in the analyzed data sets. We illustrate the applicability of this method using standard mixtures, as well as cell extracts from bacterial samples. It is evident that this method can be applied in many types of LC-MS analyses and more specifically in untargeted metabolomics

Captive breeding of European freshwater mussels as aconservation tool: A review

1. Freshwater mussels are declining throughout their range. Their importantecological functions along with insufficient levels of natural recruitment haveprompted captive breeding for population augmentation and questions about the usefulness and applicability of such measures. 2. This article reviews the current state of captive breeding and rearing programmes for freshwater mussels in Europe. It considers the various species, strategies, andtechniques of propagation, as well as the different levels of effort requiredaccording to rearing method, highlighting the key factors of success. 3. Within the last 30 years, 46 breeding activities in 16 European countries have been reported, mainly of Margaritifera margaritifera and Unio crassus. Some facilities propagate species that are in a very critical situation, such as Pseudunio auricularius, Unio mancus, and Unio ravoisieri, or multiple species concurrently. Insome streams, the number of released captive-bred mussels already exceeds the size of the remaining natural population. 4. Rearing efforts range from highly intensive laboratory incubation to lowerintensity methods using in-river mussel cages or silos. Most breeding efforts are funded by national and EU LIFE(+) grants, are well documented, and consider the genetic integrity of the propagated mussels. Limited long-term funding perspectives, the availability of experienced staff, water quality, and feeding/survival during early life stages are seen as the most important challenges. 5. Successful captive breeding programmes need to be combined with restoration ofthe habitats into which the mussels are released. This work will benefit from anevidence-based approach, knowledge exchange among facilities, and an overall breeding strategy comprising multiple countries and conservation units. aquaculture, captive breeding, conservation translocation, freshwater mussel culturing, Margaritifera margaritifera, propagation, reintroduction, Unio crassusCaptive breeding of European freshwater mussels as aconservation tool: A reviewpublishedVersio

Cortisol-mediated adhesion of synovial fibroblasts is dependent on the degradation of anandamide and activation of the endocannabinoid system

OBJECTIVE: In rheumatoid arthritis (RA) synovial fluid, levels of the endocannabinoids anandamide (AEA) and 2-arachidonylglycerol are elevated. Since synovial fibroblasts (SFs) possess all of the enzymes necessary for endocannabinoid synthesis, it is likely that these cells contribute significantly to elevated endocannabinoid levels. While glucocorticoids initiate endocannabinoid synthesis in neurons, this study was undertaken to test whether cortisol also regulates endocannabinoid levels in mesenchymal cells such as SFs, and whether this interferes with integrin-mediated adhesion. METHODS: Adhesion was determined in 1-minute intervals over 60 minutes using an xCELLigence system. Slopes from individual treatment groups were averaged and compared to the control. Fatty acid amide hydrolase (FAAH) and cyclooxygenase 2 (COX-2) were detected by immunocytochemistry, and AEA was detected by mass spectrometry. RESULTS: Cortisol increased the adhesion of RASFs and osteoarthritis SFs with a maximum of 200% at both 10(-7) M and 10(-8) M. When cortisol was administered together with either cannabinoid receptor 1 (CB(1) ) antagonist (rimonabant; 100 nM), CB(2) antagonist (JTE907; 100 nM), transient receptor potential vanilloid channel 1 (TRPV-1) antagonist (capsazepine; 1 μM), FAAH inhibitor, or COX-2 inhibitor, adhesion was reduced below the level in controls. Concomitant inhibition of FAAH and COX-2 reversed these effects. Mass spectrometry revealed the presence of AEA in SFs. CONCLUSION: Our findings indicate that glucocorticoid-induced adhesion is dependent on CB(1) /CB(2) /TRPV-1 activation. Since AEA is produced in SFs, this endocannabinoid is the most likely candidate to mediate these effects. Since AEA levels are regulated by COX-2 and FAAH, inhibition of both enzymes along with low-dose glucocorticoids may provide a therapeutic option to maximally boost the endocannabinoid system in RA, with possible beneficial effects

Correcting for natural isotope abundance and tracer impurity in MS-, MS/MS- and high-resolution-multiple-tracer-data from stable isotope labeling experiments with IsoCorrectoR

Abstract Experiments with stable isotope tracers such as 13C and 15N are increasingly used to gain insights into metabolism. However, mass spectrometric measurements of stable isotope labeling experiments should be corrected for the presence of naturally occurring stable isotopes and for impurities of the tracer substrate. Here, we analyzed the effect that such correction has on the data: omitting correction or performing invalid correction can result in largely distorted data, potentially leading to misinterpretation. IsoCorrectoR is the first R-based tool to offer said correction capabilities. It is easy-to-use and comprises all correction features that comparable tools can offer in a single solution: correction of MS and MS/MS data for natural stable isotope abundance and tracer impurity, applicability to any tracer isotope and correction of multiple-tracer data from high-resolution measurements. IsoCorrectoR’s correction performance agreed well with manual calculations and other available tools including Python-based IsoCor and Perl-based ICT. IsoCorrectoR can be downloaded as an R-package from: http://bioconductor.org/packages/release/bioc/html/IsoCorrectoR.html

Integrative normalization and comparative analysis for metabolic fingerprinting by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry

Comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC x GC-TOF-MS) was applied to the comparative metabolic fingerprinting of a wild-type versus a double mutant strain of Escherichia coli lacking the transhydrogenases UdhA and PntAB. Using peak lists generated with the Leco ChromaTOF software as input, we developed retention time correction and data alignment tools (INCA). The accuracy of peak alignment and detection of 1.1- to 4-fold changes in metabolite concentration was validated by a spike-in experiment with 20 standard compounds. A list of 48 significant features that differentiated the two E. coli strains was obtained with an estimated false discovery rate (FDR) of <0.05. A total of 27 metabolites, mainly from the citrate cycle, were identified. That the signal intensity of the m/z 73 trace of the trimethylsilyl (TMS) group reflected true differences in metabolite abundance was confirmed by quantification of pyruvate, fumarate, malate, succinate, alpha-ketoglutarate, citrate, cis-aconitate, myo-inositol, and glucose-6-phosphate using compound specific fragment ions and stable isotope labeled standards. Relative standard deviations for metabolite extraction and GC x GC-TOF-MS analysis of those analytes ranged from 13.2 to 26.3% for the universal m/z 73 trace and 7.4 to 24.5% for the analyte specific fragment ion trace

Anomaly detection in mixed high dimensional molecular data

Motivation Mixed molecular data combines continuous and categorical features of the same samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high dimensional molecular data it is prone to incorrect values that can stem from various sources as for example the technical limitations of the measurement devices, errors in the sample preparation or contamination. Most anomaly detection algorithms identify complete samples as outliers or anomalies. However, in most cases, not all measurements of those samples are erroneous but only a few one-dimensional features within the samples are incorrect. These one-dimensional data errors are continuous measurements that are either located outside or inside the normal ranges of their features but in both cases show atypical values given all other continuous and categorical features in the sample. Additionally, categorical anomalies can occur for example when the genotype or diagnosis was submitted wrongly. Results We introduce ADMIRE (Anomaly Detection using MIxed gRaphical modEls), a novel approach for the detection and correction of anomalies in mixed high dimensional data. Hereby, we focus on the detection of single (one-dimensional) data errors in the categorical and continuous features of a sample. For that the joint distribution of continuous and categorical features is learned by Mixed Graphical Models, anomalies are detected by the difference between measured and model-based estimations and are corrected using imputation. We evaluated ADMIRE in simulation and by screening for anomalies in one of our own metabolic data sets. In simulation experiments ADMIRE outperformed the state-of-the-art methods Local Outlier Factor, stray and Isolation Forest. Availability All data and code is available at https://github.com/spang-lab/adadmire. ADMIRE is implemented in a python package called adadmire which can be found at https://pypi.org/project/adadmire

Anomaly detection in mixed high dimensional molecular data

Motivation Mixed molecular data combines continuous and categorical features of the same samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high dimensional molecular data it is prone to incorrect values that can stem from various sources as for example the technical limitations of the measurement devices, errors in the sample preparation or contamination. Most anomaly detection algorithms identify complete samples as outliers or anomalies. However, in most cases, not all measurements of those samples are erroneous but only a few one-dimensional features within the samples are incorrect. These one-dimensional data errors are continuous measurements that are either located outside or inside the normal ranges of their features but in both cases show atypical values given all other continuous and categorical features in the sample. Additionally, categorical anomalies can occur for example when the genotype or diagnosis was submitted wrongly. Results We introduce ADMIRE (Anomaly Detection using MIxed gRaphical modEls), a novel approach for the detection and correction of anomalies in mixed high dimensional data. Hereby, we focus on the detection of single (one-dimensional) data errors in the categorical and continuous features of a sample. For that the joint distribution of continuous and categorical features is learned by Mixed Graphical Models, anomalies are detected by the difference between measured and model-based estimations and are corrected using imputation. We evaluated ADMIRE in simulation and by screening for anomalies in one of our own metabolic data sets. In simulation experiments ADMIRE outperformed the state-of-the-art methods Local Outlier Factor, stray and Isolation Forest. Availability All data and code is available at https://github.com/spang-lab/adadmire. ADMIRE is implemented in a python package called adadmire which can be found at https://pypi.org/project/adadmire

Low urinary indoxyl sulfate levels early after transplantation reflect a disrupted microbiome and are associated with poor outcome

Urinary 3-IS levels predict outcome after ASCT and are associated with antibiotics and NOD2/CARD15 variants.Publisher’s Note: There is an Inside Blood Commentary on this article in this issue.Indole, which is produced from l-tryptophan by commensal bacteria expressing tryptophanase, not only is an important intercellular signal in microbial communities, but also modulates mucosal barrier function and expression of pro- and anti-inflammatory genes by intestinal epithelial cells. Here, we hypothesized that decreased urinary excretion of 3-indoxyl sulfate (3-IS), the major conjugate of indole found in humans, may be a marker of gut microbiota disruption and increased risk of developing gastrointestinal (GI) graft-versus-host-disease. Using liquid chromatography/tandem mass spectrometry, 3-IS was determined in urine specimens collected weekly within the first 28 days after allogeneic stem cell transplantation (ASCT) in 131 patients. Low 3-IS levels within the first 10 days after ASCT were associated with significantly higher transplant-related mortality (P = .017) and worse overall survival (P = .05) 1 year after ASCT. Least absolute shrinkage and selection operator regression models trained on log-normalized counts of 763 operational taxonomic units derived from next-generation sequencing of the hypervariable V3 region of the 16S ribosomal RNA gene showed members of the families of Lachnospiraceae and Ruminococcaceae of the class of Clostridia to be associated with high urinary 3-IS levels, whereas members of the class of Bacilli were associated with low 3-IS levels. Risk factors of early suppression of 3-IS levels were the type of GI decontamination (P = .01), early onset of antibiotic treatment (P = .001), and recipient NOD2/CARD15 genotype (P = .04). In conclusion, our findings underscore the relevance of microbiota-derived indole and metabolites thereof in mucosal integrity and protection from inflammation