Symbiotic properties and first analyses of the genomic sequence of the fast growing model strain Sinorhizobium fredii HH103 nodulating soybean

Glycine max (soybean) plants can be nodulated by fast-growing rhizobial strains of the genus Sinorhizobium as well as by slow-growing strains clustered in the genus Bradyrhizobium. Fast-growing rhizobia strains with different soybean cultivar specificities have been isolated from Chinese soils and from other geographical regions. Most of these strains have been clustered into the species Sinorhizobium fredii. The S. fredii strain HH103 was isolated from soils of Hubei province, Central China and was first described in 1985. This strain is capable to nodulate American and Asiatic soybean cultivars and many other different legumes and is so far the best studied fast-growing soybean-nodulating strain. Additionally to the chromosome S. fredii HH103 carries five indigenous plasmids. The largest plasmid (pSfrHH103e) harbours genes for the production of diverse surface polysaccharides, such as exopolysaccharides (EPS), lipopolysaccharides (LPS), and capsular polysaccharides (KPS). The second largest plasmid (pSfrHH103d) is a typical symbiotic plasmid (pSym), carrying nodulation and nitrogen fixation genes. The present mini review focuses on symbiotic properties of S. fredii HH103, in particular on nodulation and surface polysaccharides aspects. The model strain S. fredii HH103 was chosen for genomic sequencing, which is currently in progress. First analyses of the draft genome sequence revealed an extensive synteny between the chromosomes of S. fredii HH103 and Rhizobium sp. NGR234.     

The complete genome sequence of the dominant Sinorhizobium meliloti field isolate SM11 extends the S. meliloti pan-genome

Isolates of the symbiotic nitrogen-fixing species Sinorhizobium meliloti usually contain a chromosome and two large megaplasmids encoding functions that are absolutely required for the specific interaction of the microsymbiont with corresponding host plants leading to an effective symbiosis. The complete genome sequence, including the megaplasmids pSmeSM11c (related to pSymA) and pSmeSM11d (related to pSymB), was established for the dominant, indigenous S. meliloti strain SM11 that had been isolated during a long-term field release experiment with genetically modified S. meliloti strains. The chromosome, the largest replicon of S. meliloti SM11, is 3,908,022 bp in size and codes for 3785 predicted protein coding sequences. The size of megaplasmid pSmeSM11c is 1,633,319 bp and it contains 1760 predicted protein coding sequences whereas megaplasmid pSmeSM11d is 1,632,395 bp in size and comprises 1548 predicted coding sequences. The gene content of the SM11 chromosome is quite similar to that of the reference strain S. meliloti Rm1021. Comparison of pSmeSM11c to pSymA of the reference strain revealed that many gene regions of these replicons are variable, supporting the assessment that pSymA is a major hot-spot for intra-specific differentiation. Plasmids pSymA and pSmeSM11c both encode unique genes. Large gene regions of pSmeSM11c are closely related to corresponding parts of Sinorhizobium medicae WSM419 plasmids. Moreover, pSmeSM11c encodes further novel gene regions, e.g. additional plasmid survival genes (partition, mobilisation and conjugative transfer genes), acdS encoding 1-aminocyclopropane-1-carboxylate deaminase involved in modulation of the phytohormone ethylene level and genes having predicted functions in degradative capabilities, stress response, amino acid metabolism and associated pathways. In contrast to Rm1021 pSymA and pSmeSM11c, megaplasmid pSymB of strain Rm1021 and pSmeSM11d are highly conserved showing extensive synteny with only few rearrangements. Most remarkably, pSmeSM11b contains a new gene cluster predicted to be involved in polysaccharide biosynthesis. Compilation of the S. meliloti SM11 genome sequence contributes to an extension of the S. meliloti pan-genome.     

Protein Sets Define Disease States and Predict In Vivo Effects of Drug Treatment

Gaining understanding of common complex diseases and their treatments are the main drivers for life sciences. As we show here, comprehensive protein set analyses offer new opportunities to decipher functional molecular networks of diseases and assess the efficacy and side-effects of treatments in vivo. Using mass spectrometry, we quantitatively detected several thousands of proteins and observed significant changes in protein pathways that were (dys-) regulated in diet-induced obesity mice. Analysis of the expression and post-translational modifications of proteins in various peripheral metabolic target tissues including adipose, heart, and liver tissue generated functional insights in the regulation of cell and tissue homeostasis during high-fat diet feeding and medication with two antidiabetic compounds. Protein set analyses singled out pathways for functional characterization, and indicated, for example, early-on potential cardiovascular complication of the diabetes drug rosiglitazone. In vivo protein set detection can provide new avenues for monitoring complex disease processes, and for evaluating preclinical drug candidates.The application of reductionism and experimental manipulation in the 20^th century biological research has generated important insights into functional processes of life. Based on this successful paradigm, researchers rationally dissected multiple underlying molecular mechanisms of “living systems” and efficiently developed drugs. However, drugs or dietary interventions can interfere with numerous proteins in hundreds of different cell types in various tissues, not to mention potential crosstalk on various levels of biological organization. Not surprisingly, conventional in vitro and lengthy preclinical studies that target only specific marker molecules often missed out important but unexpected physiological effects of drug treatment. Although complex biological phenomena such as physiological outcomes of disease treatment depend on various individual molecules, they are based on in vivo network properties, which cannot be adequately described or explained by “parts of the sum” of mechanistic events.Soft-ionization mass spectrometry (MS) has been widely validated as a tool for precise quantitative analysis of biomolecules (1, 2), and isotope-labeling procedures were introduced to detect protein expression, primarily in cell culture models (3, 4). Previous attempts of using mass spectrometry for protein quantification in mammalian disease models were limited to analysis of a small number of usually abundant proteins, which made comprehensive pathway analysis and physiological outcome prediction impossible (5, 6). Recent technical pilot studies provided extensive information on the protein inventories of different mouse tissues (7, 8), and isotope-labeled mice have been introduced as a resource for accurate protein quantification (9).The development of diet-induced obesity and diabetes is a complex pathophysiological process involving a number of interacting organs, in which chronic hyperglycemia and hyperlipidemia lead to cumulative damaging effects on metabolic tissues such as skeletal muscle, liver, and adipose tissues. As we show here, disease processes and in particular physiological effects of drug treatment are largely determined by the actual cellular protein expression levels and post-translational modifications of proteins. Whereas analyses of single protein changes were mostly uninformative, quantitative protein set enrichment analysis was an efficient tool to monitor tissue-specific responses of anti-diabetic treatments. This approach allows for investigation of interacting molecular and physiological processes that occur on the pathway level, and enables sensitive, unbiased and robust diagnostic detection of treatments in vivo.In this pilot study, we compared the effects of the drug rosiglitazone (RSG)¹, which has been associated with a number of undesirable side effects (10), and the plant-derived amorfrutin A1 (A1) (11) in diet-induced obesity (DIO) mice. Both compounds'' antidiabetic effects appear to be derived from activation of the peroxisome proliferator-activated receptor gamma (PPARγ).     

Comprehensive proteomic datasets for studying adipocyte–macrophage cell–cell communication

Cellular communication is a fundamental process in biology. The interaction of adipocytes with macrophages is a key event in the development of common diseases such as type 2 diabetes. We applied an established bilayer cell coculture system and comprehensive MS detection to analyse on a proteome‐wide scale the paracrine interaction of murine adipocytes and macrophages. Altogether, we identified 4486 proteins with at least two unique peptides, of which 2392 proteins were informative for 3T3‐L1 adipocytes and 2957 proteins for RAW 264.7 macrophages. Further, we observed over 12000 phosphorylation sites, of which we could assign 3200 informative phosphopeptides with a single phosphosite for adipocytes and 4514 for macrophages. Using protein set enrichment and phosphosite analyses, we deciphered regulatory protein pathways involved in cellular stress and inflammation, which can contribute to metabolic impairment of cells including insulin resistance and other disorders. The generated datasets provide a holistic, molecular pathway‐centric view on the interplay of adipocytes and macrophages in disease processes and a resource for further studies.     

Proteomic analysis of response to long-term continuous stress in roots of germinating soybean seeds

Germination is a complex process, highly dependent on various environmental factors, including temperature and water availability. Germinating soybean seeds are especially vulnerable to unfavorable environmental conditions and exposure to long-term abiotic stresses may result in diminishing much of the yield and most importantly – restrained germination. In the present study, a proteomic approach was employed to analyze influence of cold and osmotic stress on roots of germinated soybean (Glycine max, L.) seeds. Seeds were germinating under continuous conditions of cold stress (+10 °C/H₂O), osmotic stress (+25 °C/−0.2 MPa) as well as cold and osmotic stress combined (+10 °C/−0.2 MPa). Proteome maps established for control samples and stress-treated samples displayed 1272 CBB-stained spots. A total of 59 proteins, present in both control and stress-treated samples and showing significant differences in volume, were identified with LC/nanoESI-MS. Identified proteins divided into functional categories, revealed 9 proteins involved in plant defense, 8 proteins responsible for plant destination and storage and 10 proteins involved in various tracks of carbohydrate metabolism. Furthermore, a number of proteins were assigned to electron transport, range of metabolic pathways, secondary metabolism, protein synthesis, embryogenesis and development, signal transduction, cellular transport, translocation and storage. By analyzing differences in expression patterns, it was possible to trace the soybean response to long-term abiotic stress as well as to distinguish similarities and differences between response to cold and osmotic stress.     

Host Gut Motility Promotes Competitive Exclusion within a Model Intestinal Microbiota

The gut microbiota is a complex consortium of microorganisms with the ability to influence important aspects of host health and development. Harnessing this “microbial organ” for biomedical applications requires clarifying the degree to which host and bacterial factors act alone or in combination to govern the stability of specific lineages. To address this issue, we combined bacteriological manipulation and light sheet fluorescence microscopy to monitor the dynamics of a defined two-species microbiota within a vertebrate gut. We observed that the interplay between each population and the gut environment produces distinct spatiotemporal patterns. As a consequence, one species dominates while the other experiences sudden drops in abundance that are well fit by a stochastic mathematical model. Modeling revealed that direct bacterial competition could only partially explain the observed phenomena, suggesting that a host factor is also important in shaping the community. We hypothesized the host determinant to be gut motility, and tested this mechanism by measuring colonization in hosts with enteric nervous system dysfunction due to a mutation in the ret locus, which in humans is associated with the intestinal motility disorder known as Hirschsprung disease. In mutant hosts we found reduced gut motility and, confirming our hypothesis, robust coexistence of both bacterial species. This study provides evidence that host-mediated spatial structuring and stochastic perturbation of communities can drive bacterial population dynamics within the gut, and it reveals a new facet of the intestinal host–microbe interface by demonstrating the capacity of the enteric nervous system to influence the microbiota. Ultimately, these findings suggest that therapeutic strategies targeting the intestinal ecosystem should consider the dynamic physical nature of the gut environment.