Metabolomic analysis of cold acclimation of arctic mesorhizobium sp. strain N33

Arctic Mesorhizobium sp. N33 isolated from nodules of Oxytropis arctobia in Canada’s eastern Arctic has a growth temperature range from 0°C to 30°C and is a well-known cold-adapted rhizobia. The key molecular mechanisms underlying cold adaptation in Arctic rhizobia remains totally unknown. Since the concentration and contents of metabolites are closely related to stress adaptation, we applied GC-MS and NMR to identify and quantify fatty acids and water soluble compounds possibly related to low temperature acclimation in strain N33. Bacterial cells were grown at three different growing temperatures (4°C, 10°C and 21°C). Cells from 21°C were also cold-exposed to 4°C for different times (2, 4, 8, 60 and 240 minutes). We identified that poly-unsaturated linoleic acids 18∶2 (9, 12) & 18∶2 (6, 9) were more abundant in cells growing at 4 or 10°C, than in cells cultivated at 21°C. The mono-unsaturated phospho/neutral fatty acids myristoleic acid 14∶1(11) were the most significantly overexpressed (45-fold) after 1hour of exposure to 4°C. As reported in the literature, these fatty acids play important roles in cold adaptability by supplying cell membrane fluidity, and by providing energy to cells. Analysis of water-soluble compounds revealed that isobutyrate, sarcosine, threonine and valine were more accumulated during exposure to 4°C. These metabolites might play a role in conferring cold acclimation to strain N33 at 4°C, probably by acting as cryoprotectants. Isobutyrate was highly upregulated (19.4-fold) during growth at 4°C, thus suggesting that this compound is a precursor for the cold-regulated fatty acids modification to low temperature adaptation

The Human Urine Metabolome

Urine has long been a “favored” biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing the complete set of 2651 confirmed human urine metabolite species, their structures (3079 in total), concentrations, related literature references and links to their known disease associations are freely available at http://www.urinemetabolome.ca

The Human Serum Metabolome

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca

Significant changes (P≤0.05, FC ≥2) of fatty acids in <i>Mesorhizobium</i> N₃₃ exposed to cold.

<p>GT4 =  Growth at 4°C; T1 = 2 min, T2 = 4 min, T3 = 8 min, T4 = 60 min, T5 = 240 min exposure to 4°C of cells grown at 21°C; GT10 =  Growth at 10°C. Observed significant changes: A: fatty acids from total lipids; B: fatty acids from neutral lipids; C: fatty acids from glycolipids and D: fatty acids from phospholipids. Arrows indicate change in concentration: green-increase, red-decrease. FC =  fold changes; NA =  no significant change.</p

PCA and PLS-DA of fatty acids from total lipids present in <i>Mesorhizobium</i> N₃₃ growing at constant temperatures or exposed to suboptimal 4°C.

<p>Growth temperatures: GT21 = 21°C (control); GT4 = 4°C; GT10 = 10°C. For both groups of data, row-wise normalization was used by a pooled averaged reference samples (GT21orT0), and data were auto scaled and log transformed. A: PCA analysis was performed on 13 fatty acids total. B: PLS-DA plot of total fatty acids data from GC-MS shows significant trends of the separation of compounds changes (permutation test, P<0.01) at different times of cold treatment conditions T0 = 21°C (reference), T1 = 2 min; T2 = 4 min; T3 = 8 min; T4 = 60 min; T5 = 240 min exposure to 4°C of cells grown at 21°C. The optimal PLS-DA model for fatty acids from total lipids used the top one component with a Q² of 0.31.</p

Significant changes (P≤0.05, FC ≥2) of water soluble metabolites in N₃₃ exposed to cold.

<p>GT4 =  Growth at 4°C; T1 = 2 min, T2 = 4 min, T3 = 8 min, T4 = 60 min, T5 = 240 min exposure to 4°C of cells grown at 21°C, GT10 =  Growth at 10°C. Arrows indicate change in concentration: green-increase, red-decrease. FC =  fold changes; NA =  no significant change.</p

Water soluble metabolites of Arctic <i>Mesorhizobium</i> N₃₃ detected by NMR and by GC-MS.

<p>Compounds indicated in bold were detected by NMR and GC-MS.</p><p>HMDB: Human metabolome database (<a href="http://www.hmdb.ca/" target="_blank">http://www.hmdb.ca/</a>).</p

Fatty acids identified by GC-MS in cold exposed Arctic <i>Mesorhizobium</i> N33.

<p>Fatty acids identified by GC-MS in cold exposed Arctic <i>Mesorhizobium</i> N33.</p

PCA and PLS-DA of water soluble metabolites found in <i>Mesorhizobium</i> N₃₃ growing at constant temperatures or exposed to suboptimal 4°C.

<p>Growth temperatures: GT21 = 21°C (control); GT4 = 4°C; GT10 = 10°C. For all data, row-wise normalization was used by a pooled averaged reference samples (GT21 or T0). Data were auto scaled and log transformed. A: PCA analysis was performed based on 29 water soluble metabolites. B: PLS-DA plot showing grouping of compounds (permutation test, P<0.01) according to the different time of exposure to a suboptimal 4°C temperature. T0 = 21°C (reference), T1 = 2 min; T2 = 4 min; T3 = 8 min; T4 = 60 min; T5 = 240 min exposure to 4°C of cells grown at 21°C. The optimal PLS-DA model for water soluble metabolites data (measured by NMR) used the top four components with a Q² = 0.78.</p