Moving beyond the van Krevelen Diagram: A New Stoichiometric Approach for Compound Classification in Organisms

van Krevelen diagrams (O/C vs H/C ratios of elemental formulas) have been widely used in studies to obtain an estimation of the main compound categories present in environmental samples. However, the limits defining a specific compound category based solely on O/C and H/C ratios of elemental formulas have never been accurately listed or proposed to classify metabolites in biological samples. Furthermore, while O/C vs H/C ratios of elemental formulas can provide an overview of the compound categories, such classification is inefficient because of the large overlap among different compound categories along both axes. We propose a more accurate compound classification for biological samples analyzed by high-resolution mass spectrometry based on an assessment of the C/H/O/N/P stoichiometric ratios of over 130 000 elemental formulas of compounds classified in 6 main categories: lipids, peptides, amino sugars, carbohydrates, nucleotides, and phytochemical compounds (oxy-aromatic compounds). Our multidimensional stoichiometric compound classification (MSCC) constraints showed a highly accurate categorization of elemental formulas to the main compound categories in biological samples with over 98% of accuracy representing a substantial improvement over any classification based on the classic van Krevelen diagram. This method represents a signficant step forward in environmental research, especially ecological stoichiometry and eco-metabolomics studies, by providing a novel and robust tool to improve our understanding of the ecosystem structure and function through the chemical characterization of biological samples

Moving beyond the van Krevelen Diagram: A New Stoichiometric Approach for Compound Classification in Organisms

van Krevelen diagrams (O/C vs H/C ratios of elemental formulas) have been widely used in studies to obtain an estimation of the main compound categories present in environmental samples. However, the limits defining a specific compound category based solely on O/C and H/C ratios of elemental formulas have never been accurately listed or proposed to classify metabolites in biological samples. Furthermore, while O/C vs H/C ratios of elemental formulas can provide an overview of the compound categories, such classification is inefficient because of the large overlap among different compound categories along both axes. We propose a more accurate compound classification for biological samples analyzed by high-resolution mass spectrometry based on an assessment of the C/H/O/N/P stoichiometric ratios of over 130 000 elemental formulas of compounds classified in 6 main categories: lipids, peptides, amino sugars, carbohydrates, nucleotides, and phytochemical compounds (oxy-aromatic compounds). Our multidimensional stoichiometric compound classification (MSCC) constraints showed a highly accurate categorization of elemental formulas to the main compound categories in biological samples with over 98% of accuracy representing a substantial improvement over any classification based on the classic van Krevelen diagram. This method represents a signficant step forward in environmental research, especially ecological stoichiometry and eco-metabolomics studies, by providing a novel and robust tool to improve our understanding of the ecosystem structure and function through the chemical characterization of biological samples

Phylogenetic and comparative gene expression analysis of barley WRKY transcription factor family reveals putatively retained functions between monocots and dicots-4

The expression trajectories of genes relative to its controls over three different organs in three plant species. RT-PCR analysis (lower panels) with mRNA of barley, Arabidopsis and rice isolated from homologous organs (roots, left; leaves, middle; infructescence, right).<p><b>Copyright information:</b></p><p>Taken from "Phylogenetic and comparative gene expression analysis of barley () WRKY transcription factor family reveals putatively retained functions between monocots and dicots"</p><p>http://www.biomedcentral.com/1471-2164/9/194</p><p>BMC Genomics 2008;9():194-194.</p><p>Published online 28 Apr 2008</p><p>PMCID:PMC2390551.</p><p></p

Normalized signal intensities of 23 probesets representing genes are displayed for microarray experiments BB4 and BB7 (according to BarleyBase 27, 28)

Experimental samples and timepoints are indicated on the x-axis. Mlo, mlo5 and Mla1 represent different barley genotypes, Bgh_5874 represents a particular strain of powdery mildew (see BarleyBase [27, 28] for experimental details). Fold changes compared to the control (BB7) or timepoint zero (BB4) are color coded as indicated. gene probesets are arranged according to WRKY groups 1 to 3. Several probesets representing the same gene are named _a to _c.<p><b>Copyright information:</b></p><p>Taken from "Phylogenetic and comparative gene expression analysis of barley () WRKY transcription factor family reveals putatively retained functions between monocots and dicots"</p><p>http://www.biomedcentral.com/1471-2164/9/194</p><p>BMC Genomics 2008;9():194-194.</p><p>Published online 28 Apr 2008</p><p>PMCID:PMC2390551.</p><p></p

Phylogenetic and comparative gene expression analysis of barley WRKY transcription factor family reveals putatively retained functions between monocots and dicots-5

N bold letters, the amino acids forming the zinc-finger motif are displayed in grey, gaps are marked with dashes.<p><b>Copyright information:</b></p><p>Taken from "Phylogenetic and comparative gene expression analysis of barley () WRKY transcription factor family reveals putatively retained functions between monocots and dicots"</p><p>http://www.biomedcentral.com/1471-2164/9/194</p><p>BMC Genomics 2008;9():194-194.</p><p>Published online 28 Apr 2008</p><p>PMCID:PMC2390551.</p><p></p

Psychobiological stress response to a lung cancer diagnosis: a prospective study of patients in Iceland and Sweden

A diagnostic work-up leading to a lung cancer diagnosis is a severely stressful experience that may impact tumor progression. Yet, prospective data are scarce on psychological and biological components of stress at the time of lung cancer diagnosis. The aim of this study was to assess pre-to-post diagnosis change in psychological distress and urinary excretion of catecholamines in patients with suspected lung cancer. Participants were 167 patients within the LUCASS study, recruited at referral for suspected lung cancer to University Hospitals in Iceland and Sweden. Patients completed questionnaires on perceived distress (Hospital Anxiety and Depression Scale, HADS) before and after diagnosis of lung cancer or a non-malignant origin. A subpopulation of 85 patients also provided overnight urine for catecholamine analysis before and at a median of 24 days after diagnosis but before treatment. A lung cancer diagnosis was confirmed in 123 (73.7%) patients, with a mean age of 70.1 years. Patients diagnosed with lung cancer experienced a post-diagnosis increase in psychological distress (p = 0.010), while patients with non-malignant lung pathology showed a reduction in distress (p = 0.070). Both urinary epinephrine (p = 0.001) and norepinephrine (p = 0.032) levels were higher before the diagnosis among patients eventually diagnosed with lung cancer compared to those with non-malignant lung pathology. We observed indications of associations between pre-to-post diagnosis changes in perceived distress and changes in urinary catecholamine levels. Receiving a lung cancer diagnosis is associated with an increase in psychological distress, while elevated catecholamine levels are evident already before lung cancer diagnosis.</p