The occurrence of two members of the genus Hypsugo , namely H. alaschanicus and H. savii caucasicus , have been reported for Mongolia in the literature. Due to various taxonomic reassignments within and between genera, the number of records for the genus Hypsugo in Mongolia is quite scarce and sometimes not resolved at species or subspecies level. Despite recognition of the two above-mentioned species, recent reports based on genetic analyses describe only new and further records of H. alaschanicus . Thus, it exists a large uncertainty regarding the occurrence and distribution of H. savii caucasicus in Mongolia. Here, our efforts in gaining a deeper understanding towards the occurrence and distribution of Hypsugo species in Mongolia are described. A combination of genetic and morphological analyses of collected material from Hypsugo specimens revealed the existence of a genetically largely distant Hypsugo clade. Therefore, a new and cryptic Hypsugo species is proposed which is named after Prof. Dr. Michael Stubbe for his continuous, long-standing and significant contributions into the biological exploration of Mongolia. Hypsugo stubbei sp. nov. differs by at least 8.4 % and 9 % to the closest Western Palearctic distributed H . cf. darwinii and H. savii as well as at least 11.3 % to the Easter Palearctic (including Mongolia) distributed H. alaschanicus based on the first 798 nucleotides of the gene encoding the mitochondrial ND1 (subunit one of NADH dehydrogenase). Neither a close proximity species based on the gene encoding the mitochondrial COI (cytochrome oxidase subunit one) could be found in publicly accessible nucleotide databases. While the cryptic H. stubbei sp. nov. reveals no obvious cranial and morphological differences, few external characteristics are dissimilar to both H. alaschanicus and H. savii ( caucasicus ). Currently, Hypsugo stubbei sp. nov. was found at four different locations in Mongolia. Among the 11 specimens captured, six facilitated a genetic assignment. Based on the current scarce data records, the species seems to occur mainly in the far west of Mongolia inhabiting semi-deserts and steppes up to high mountain areas. An overlapping distribution with H. alaschanicus cannot be excluded based on the limited data currently available.

Several metabolic processes tightly regulate growth and biomass accumulation. A highly conserved protein complex containing the target of rapamycin (TOR) kinase is known to integrate intra- and extracellular stimuli controlling nutrient allocation and hence cellular growth. Although several functions of TOR have been described in various heterotrophic eukaryotes, our understanding lags far behind in photosynthetic organisms. In the present investigation, we used the model alga Chlamydomonas reinhardtii to conduct a time-resolved analysis of molecular and physiological features throughout the diurnal cycle after TOR inhibition. Detailed examination of the cell cycle phases revealed that growth is not only repressed by 50%, but also that significant, non-linear delays in the progression can be observed. By using metabolomics analysis, we elucidated that the growth repression was mainly driven by differential carbon partitioning between anabolic and catabolic processes. Accordingly, the time-resolved analysis illustrated that metabolic processes including amino acid-, starch- and triacylglycerol synthesis, as well RNA degradation, were redirected within minutes of TOR inhibition. Here especially the high accumulation of nitrogen-containing compounds indicated that an active TOR kinase controls the carbon to nitrogen balance of the cell, which is responsible for biomass accumulation, growth and cell cycle progression.

Metabolites and lipids are the final products of enzymatic processes, distinguishing the different cellular functions and activities of single cells or whole tissues. Understanding these cellular functions within a well-established model system requires a systemic collection of molecular and physiological information. In the current report, the green alga Chlamydomonas reinhardtii was selected to establish a comprehensive workflow for the detailed multi-omics analysis of a synchronously growing cell culture system. After implementation and benchmarking of the synchronous cell culture, a two-phase extraction method was adopted for the analysis of proteins, lipids, metabolites and starch from a single sample aliquot of as little as 10-15 million Chlamydomonas cells. In a proof of concept study, primary metabolites and lipids were sampled throughout the diurnal cell cycle. The results of these time-resolved measurements showed that single compounds were not only coordinated with each other in different pathways, but that these complex metabolic signatures have the potential to be used as biomarkers of various cellular processes. Taken together, the developed workflow, including the synchronized growth of the photoautotrophic cell culture, in combination with comprehensive extraction methods and detailed metabolic phenotyping has the potential for use in in-depth analysis of complex cellular processes, providing essential information for the understanding of complex biological systems.

Microcystis is a freshwater cyanobacterium frequently forming nuisance blooms in the summer months. The genus belongs to the predominant producers of the potent hepatotoxin microcystin. The success of Microcystis and its remarkable resistance to high light conditions are not well understood. Here, we have compared the metabolic response of Microcystis aeruginosa PCC7806, its microcystin-deficient ΔmcyB mutant (Mut) and the cyanobacterial model organism Synechocystis PCC6803 to high light exposure of 250 μmol photons m(-2)  s(-1) using GC/MS-based metabolomics. Microcystis wild type and Mut show pronounced differences in their metabolic reprogramming upon high light. Seventeen per cent of the detected metabolites showed significant differences between the two genotypes after high light exposure. Whereas the microcystin-producing wild type shows a faster accumulation of glycolate upon high light illumination, loss of microcystin leads to an accumulation of general stress markers such as trehalose and sucrose. The study further uncovers differences in the high light adaptation of the bloom-forming cyanobacterium Microcystis and the model cyanobacterium Synechocystis. Most notably, Microcystis invests more into carbon reserves such as glycogen after high light exposure. Our data shed new light on the lifestyle of bloom-forming cyanobacteria, the role of the widespread toxin microcystin and the metabolic diversity of cyanobacteria.

Lipids are prominent components of the nervous system. Here we performed a large-scale mass spectrometry-based analysis of the lipid composition of three brain regions as well as kidney and skeletal muscle of humans, chimpanzees, rhesus macaques, and mice. The human brain shows the most distinct lipid composition: 76% of 5,713 lipid compounds examined in our study are either enriched or depleted in the human brain. Concentration levels of lipids enriched in the brain evolve approximately four times faster among primates compared with lipids characteristic of non-neural tissues and show further acceleration of change in human neocortical regions but not in the cerebellum. Human-specific concentration changes are supported by human-specific expression changes for corresponding enzymes. These results provide the first insights into the role of lipids in human brain evolution.

Metabolite concentrations reflect the physiological states of tissues and cells. However, the role of metabolic changes in species evolution is currently unknown. Here, we present a study of metabolome evolution conducted in three brain regions and two non-neural tissues from humans, chimpanzees, macaque monkeys, and mice based on over 10,000 hydrophilic compounds. While chimpanzee, macaque, and mouse metabolomes diverge following the genetic distances among species, we detect remarkable acceleration of metabolome evolution in human prefrontal cortex and skeletal muscle affecting neural and energy metabolism pathways. These metabolic changes could not be attributed to environmental conditions and were confirmed against the expression of their corresponding enzymes. We further conducted muscle strength tests in humans, chimpanzees, and macaques. The results suggest that, while humans are characterized by superior cognition, their muscular performance might be markedly inferior to that of chimpanzees and macaque monkeys.

Every biological organism relies for its proper function on interactions between a multitude of molecular entities like RNA, proteins, and metabolites. The comprehensive measurement and the analysis of all these entities would therefore provide the basis for our functional and mechanistic understanding of most biological processes. Next to their amount and identity, it is most crucial to also gain information about the subcellular distribution and the flux of the measured compounds between the cellular compartments. That is, we want to understand not only the individual functions of cellular components but also their functional implications within the whole organism. While the analysis of macromolecules like DNA, RNA, and proteins is quite established and robust, analytical techniques for small metabolites, which are prone to diffusion and degradation processes, provide a host of unsolved challenges. The major limitations here are the metabolite conversion and relocation processes. In this protocol we describe a methodological workflow which includes a nonaqueous fractionation method, a fractionated two-phase liquid/liquid extraction protocol, and a software package, which together allow extracting and analyzing starch, proteins, and especially polar and lipophilic metabolites from a single sample towards the estimation of their subcellular distributions.

Mitochondria are tightly linked to cellular nutrient sensing, and provide not only energy, but also intermediates for the de novo synthesis of cellular compounds including amino acids. Mitochondrial metabolic enzymes as generators and/or targets of signals are therefore important players in the distribution of intermediates between catabolic and anabolic pathways. The highly regulated 2-oxoglutarate dehydrogenase complex (OGDHC) participates in glucose oxidation via the tricarboxylic acid cycle. It occupies an amphibolic branch point in the cycle, where the energy-producing reaction of the 2-oxoglutarate degradation competes with glutamate (Glu) synthesis via nitrogen incorporation into 2-oxoglutarate. To characterize the specific impact of the OGDHC inhibition on amino acid metabolism in both plant and animal mitochondria, a synthetic analog of 2-oxoglutarate, namely succinyl phosphonate (SP), was applied to living systems from different kingdoms, both in situ and in vivo. Using a high-throughput mass spectrometry-based approach, we showed that organisms possessing OGDHC respond to SP by significantly changing their amino acid pools. By contrast, cyanobacteria which lack OGDHC do not show perturbations in amino acids following SP treatment. Increases in Glu, 4-aminobutyrate and alanine represent the most universal change accompanying the 2-oxoglutarate accumulation upon OGDHC inhibition. Other amino acids were affected in a species-specific manner, suggesting specific metabolic rearrangements and substrate availability mediating secondary changes. Strong perturbation in the relative abundance of amino acids due to the OGDHC inhibition was accompanied by decreased protein content. Our results provide specific evidence of a considerable role of OGDHC in amino acid metabolism.

A tight coordination of biological processes between cellular compartments and organelles is crucial for the survival of any eukaryotic organism. According to cellular requirements, signals can be generated within organelles, such as chloroplasts and mitochondria, modulating the nuclear gene expression in a process called retrograde signaling. Whilst many research efforts have been focused on dissecting retrograde signaling pathways using biochemical and genetics approaches, metabolomics and systems biology driven studies have illustrated their great potential for hypotheses generation and for dissecting signaling networks in a rather unbiased or untargeted fashion. Recently, integrative genomics approaches, in which correlation analysis has been applied on transcript and metabolite profiling data of Arabidopsis thaliana, revealed the identification of metabolites which are putatively acting as mediators of nuclear gene expression. Complimentary, the continuous technological developments in the field of metabolomics per se has further demonstrated its potential as a very suitable readout to unravel metabolite-mediated signaling processes. As foundation for these studies here we outline and discuss recent advances in elucidating retrograde signaling molecules and pathways with an emphasis on metabolomics and systems biology driven approaches.

As a fundamental energy-conserving process common to all living organisms, respiration is responsible for the oxidation of respiratory substrates to drive ATP synthesis. Accordingly, it has long been accepted that a complete tricarboxylic acid (TCA) cycle is necessary for respiratory energy production. Cyanobacteria, similar to some other prokaryotes, appeared to have an incomplete TCA cycle because they lack the enzyme 2-oxoglutarate dehydrogenase (OGDH). However, it has recently been reported that the cycle can be completed by the action of two alternative enzymes. In this opinion article, we discuss the progress being made to elucidate the nature of the TCA cycles in cyanobacteria and plants and outline open questions concerning the functional significance of this unusual metabolic feature in a broader evolutionary context.

The main goal of metabolomics is the comprehensive qualitative and quantitative analysis of the time- and space-resolved distribution of all metabolites present in a given biological system. Because metabolite structures, in contrast to transcript and protein sequences, are not directly deducible from the genomic DNA sequence, the massive increase in genomic information is only indirectly of use to metabolomics, leaving compound annotation as a key problem to be solved by the available analytical techniques. Furthermore, as metabolites vary widely in both concentration and chemical behavior, there is no single analytical procedure allowing the unbiased and comprehensive structural elucidation and determination of all metabolites present in a given biological system. In this review the different approaches for targeted and non-targeted metabolomics analysis will be described with special emphasis on mass spectrometry-based techniques. Particular attention is given to approaches which can be employed for the annotation of unknown compounds. In the second part, the different experimental approaches aimed at tissue-specific or subcellular analysis of metabolites are discussed including a range of non-mass spectrometry based technologies.

While recent years have witnessed dramatic advances in our capacity to identify and quantify an ever-increasing number of plant metabolites, our understanding of how metabolism is spatially regulated is still far from complete. In an attempt to partially address this question, we studied the storage metabolome of the barley (Hordeum vulgare) vacuole. For this purpose, we used highly purified vacuoles isolated by silicon oil centrifugation and compared their metabolome with that found in the mesophyll protoplast from which they were derived. Using a combination of gas chromatography-mass spectrometry and Fourier transform-mass spectrometry, we were able to detect 59 (primary) metabolites for which we know the exact chemical structure and a further 200 (secondary) metabolites for which we have strong predicted chemical formulae. Taken together, these metabolites comprise amino acids, organic acids, sugars, sugar alcohols, shikimate pathway intermediates, vitamins, phenylpropanoids, and flavonoids. Of the 259 putative metabolites, some 12 were found exclusively in the vacuole and 34 were found exclusively in the protoplast, while 213 were common in both samples. When analyzed on a quantitative basis, however, there is even more variance, with more than 60 of these compounds being present above the detection limit of our protocols. The combined data were also analyzed with respect to the tonoplast proteome in an attempt to infer specificities of the transporter proteins embedded in this membrane. Following comparison with recent observations made using nonaqueous fractionation of Arabidopsis (Arabidopsis thaliana), we discuss these data in the context of current models of metabolic compartmentation in plants.

BACKGROUND:The extensive subcellular compartmentalization of metabolites and metabolism in eukaryotic cells is widely acknowledged and represents a key factor of metabolic activity and functionality. In striking contrast, the knowledge of actual compartmental distribution of metabolites from experimental studies is surprisingly low. However, a precise knowledge of, possibly all, metabolites and their subcellular distributions remains a key prerequisite for the understanding of any cellular function.METHODOLOGY/PRINCIPAL FINDINGS:Here we describe results for the subcellular distribution of 1,117 polar and 2,804 lipophilic mass spectrometric features associated to known and unknown compounds from leaves of the model plant Arabidopsis thaliana. Using an optimized non-aqueous fractionation protocol in conjunction with GC/MS- and LC/MS-based metabolite profiling, 81.5% of the metabolic data could be associated to one of three subcellular compartments: the cytosol (including the mitochondria), vacuole, or plastids. Statistical analysis using a marker-'free' approach revealed that 18.5% of these metabolites show intermediate distributions, which can either be explained by transport processes or by additional subcellular compartments.CONCLUSION/SIGNIFICANCE:Next to a functional and conceptual workflow for the efficient, highly resolved metabolite analysis of the fractionated Arabidopsis thaliana leaf metabolome, a detailed survey of the subcellular distribution of several compounds, in the graphical format of a topological map, is provided. This complex data set therefore does not only contain a rich repository of metabolic information, but due to thorough validation and testing by statistical methods, represents an initial step in the analysis of metabolite dynamics and fluxes within and between subcellular compartments.

With the development of high-throughput metabolic technologies, a plethora of primary and secondary compounds have been detected in the plant cell. However, there are still major gaps in our understanding of the plant metabolome. This is especially true with regards to the compartmental localization of these identified metabolites. Non-aqueous fractionation (NAF) is a powerful technique for the determination of subcellular metabolite distributions in eukaryotic cells, and it has become the method of choice to analyze the distribution of a large number of metabolites concurrently. However, the NAF technique produces a continuous gradient of metabolite distributions, not discrete assignments. Resolution of these distributions requires computational analyses based on marker molecules to resolve compartmental localizations. In this article we focus on expanding the computational analysis of data derived from NAF. Along with an experimental workflow, we describe the critical steps in NAF experiments and how computational approaches can aid in assessing the quality and robustness of the derived data. For this, we have developed and provide a new version (v1.2) of the BestFit command line tool for calculation and evaluation of subcellular metabolite distributions. Furthermore, using both simulated and experimental data we show the influence on estimated subcellular distributions by modulating important parameters, such as the number of fractions taken or which marker molecule is selected. Finally, we discuss caveats and benefits of NAF analysis in the context of the compartmentalized metabolome.

Activities of 28 enzymes from central carbon metabolism were measured in pericarp tissue of ripe tomato fruits from field trials with an introgression line (IL) population generated by introgressing segments of the genome of the wild relative Solanum pennellii (LA0716) into the modern tomato cultivar Solanum lycopersicum M82. Enzyme activities were determined using a robotized platform in optimized conditions, where the activities largely reflect the level of the corresponding proteins. Two experiments were analyzed from years with markedly different climate conditions. A total of 27 quantitative trait loci were shared in both experiments. Most resulted in increased enzyme activity when a portion of the S. lycopersicum genome was substituted with the corresponding portion of the genome of S. pennellii. This reflects the change in activity between the two parental genotypes. The mode of inheritance was studied in a heterozygote IL population. A similar proportion of quantitative trait loci (approximately 30%) showed additive, recessive, and dominant modes of inheritance, with only 5% showing overdominance. Comparison with the location of putative genes for the corresponding proteins indicates a large role of trans-regulatory mechanisms. These results point to the genetic control of individual enzyme activities being under the control of a complex program that is dominated by a network of trans-acting genes.

The time-resolved response of Arabidopsis thaliana towards changing light and/or temperature at the transcriptome and metabolome level is presented. Plants grown at 21°C with a light intensity of 150 μE m⁻² sec⁻¹ were either kept at this condition or transferred into seven different environments (4°C, darkness; 21°C, darkness; 32°C, darkness; 4°C, 85 μE m⁻² sec⁻¹; 21 °C, 75 μE m⁻² sec⁻¹; 21°C, 300 μE m⁻² sec⁻¹ ; 32°C, 150 μE m⁻² sec⁻¹). Samples were taken before (0 min) and at 22 time points after transfer resulting in (8×) 22 time points covering both a linear and a logarithmic time series totaling 177 states. Hierarchical cluster analysis shows that individual conditions (defined by temperature and light) diverge into distinct trajectories at condition-dependent times and that the metabolome follows different kinetics from the transcriptome. The metabolic responses are initially relatively faster when compared with the transcriptional responses. Gene Ontology over-representation analysis identifies a common response for all changed conditions at the transcriptome level during the early response phase (5-60 min). Metabolic networks reconstructed via metabolite-metabolite correlations reveal extensive environment-specific rewiring. Detailed analysis identifies conditional connections between amino acids and intermediates of the tricarboxylic acid cycle. Parallel analysis of transcriptional changes strongly support a model where in the absence of photosynthesis at normal/high temperatures protein degradation occurs rapidly and subsequent amino acid catabolism serves as the main cellular energy supply. These results thus demonstrate the engagement of the electron transfer flavoprotein system under short-term environmental perturbations.

Here we describe an integrative protocol for metabolite extraction and the measurement of three cellular constituents, chlorophyll a, total protein, and glycogen from the same small volume of cyanobacterial cultures that can be used as alternative sample amount parameters for data adjustment in comparative metabolome studies. We conducted recovery experiments to assess the robustness and reproducibility of the measurements obtained for the cellular constituents. Also, we have chosen three profile-intrinsic parameters derived from gas chromatography-mass spectrometry (GC/MS) data in order to test their utility for spectral data adjustment. To demonstrate the relevance of these six parameters, we analyzed three cyanobacteria with greatly different morphologies, comprising a unicellular, a filamentous, and a filamentous biofilm-forming strain. Comparative analysis of GC/MS data from cultures grown under standardized conditions indicated that adjustment of the corresponding metabolite profiles by any of the measured cellular constituents or chosen intrinsic parameters led to similar results with respect to sample cohesion and strain separation. Twenty-one metabolites significantly enriched for the carbohydrate and amine superclasses are mainly responsible for strain separation, with a majority of the remaining metabolites contributing to sample group cohesion. Therefore, we conclude that any of the parameters tested in this study can be used for spectral data adjustment of cyanobacterial strains grown under controlled conditions. However, their use for the differentiation between different stresses or physiological states within a strain remains to be shown. Interestingly, both the adjustment approaches and statistical tests applied effected the detection of metabolic differences and their patterns among the analyzed strains.