Sascha Sauer

Mass spectrometry tools for the classification and identification of bacteria

Mass spectrometry has become an important analytical tool in biology in the past two decades. In principle, mass spectrometry offers high-throughput, sensitive and specific analysis for many applications in microbiology, including clinical diagnostics and environmental research. Recently, several mass spectrometry methods for the classification and identification of bacteria and other microorganisms, as well as new software analysis tools, have been developed. In this Review we discuss the application range of these mass spectrometry procedures and their potential for successful transfer into microbiology laboratories.

Classification and identification of bacteria by mass spectrometry and computational analysis

Background In general, the definite determination of bacterial species is a tedious process and requires extensive manual labour. Novel technologies for bacterial detection and analysis can therefore help microbiologists in minimising their efforts in developing a number of microbiological applications. Methodology We present a robust, standardized procedure for automated bacterial analysis that is based on the detection of patterns of protein masses by MALDI mass spectrometry. We particularly applied the approach for classifying and identifying strains in species of the genus Erwinia. Many species of this genus are associated with disastrous plant diseases such as fire blight. Using our experimental procedure, we created a general bacterial mass spectra database that currently contains 2800 entries of bacteria of different genera. This database will be steadily expanded. To support users with a feasible analytical method, we developed and tested comprehensive software tools that are demonstrated herein. Furthermore, to gain additional analytical accuracy and reliability in the analysis we used genotyping of single nucleotide polymorphisms by mass spectrometry to unambiguously determine closely related strains that are difficult to distinguish by only relying on protein mass pattern detection. Conclusions With the method for bacterial analysis, we could identify fire blight pathogens from a variety of biological sources. The method can be used for a number of additional bacterial genera. Moreover, the mass spectrometry approach presented allows the integration of data from different biological levels such as the genome and the proteome.

Phylogenetic classification and identification of bacteria by mass spectrometry

Bacteria are a convenient source of intrinsic marker proteins, which can be detected efficiently by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The patterns of protein masses observed can be used for accurate classification and identification of bacteria. Key to the reliability of the method is a robust and standardized procedure for sample preparations, including bacterial culturing, chemical treatment for bacterial cell wall disruption and for protein extraction, and mass spectrometry analysis. The protocol is an excellent alternative to classical microbiological classification and identification procedures, requiring minimal sample preparation efforts and costs. Without cell culturing, the protocol takes in general <1 h.

Targeted enrichment of genomic DNA regions for next-generation sequencing

In this review, we discuss the latest targeted enrichment methods and aspects of their utilization along with second-generation sequencing for complex genome analysis. In doing so, we provide an overview of issues involved in detecting genetic variation, for which targeted enrichment has become a powerful tool. We explain how targeted enrichment for next-generation sequencing has made great progress in terms of methodology, ease of use and applicability, but emphasize the remaining challenges such as the lack of even coverage across targeted regions. Costs are also considered versus the alternative of whole-genome sequencing which is becoming ever more affordable. We conclude that targeted enrichment is likely to be the most economical option for many years to come in a range of settings.

Amorfrutins are potent antidiabetic dietary natural products

Given worldwide increases in the incidence of obesity and type 2 diabetes, new strategies for preventing and treating metabolic diseases are needed. The nuclear receptor PPARγ (peroxisome proliferator-activated receptor gamma) plays a central role in lipid and glucose metabolism; however, current PPARγ-targeting drugs are characterized by undesirable side effects. Natural products from edible biomaterial provide a structurally diverse resource to alleviate complex disorders via tailored nutritional intervention. We identified a family of natural products, the amorfrutins, from edible parts of two legumes, Glycyrrhiza foetida and Amorpha fruticosa, as structurally new and powerful antidiabetics with unprecedented effects for a dietary molecule. Amorfrutins bind to and activate PPARγ, which results in selective gene expression and physiological profiles markedly different from activation by current synthetic PPARγ drugs. In diet-induced obese and db/db mice, amorfrutin treatment strongly improves insulin resistance and other metabolic and inflammatory parameters without concomitant increase of fat storage or other unwanted side effects such as hepatoxicity. These results show that selective PPARγ-activation by diet-derived ligands may constitute a promising approach to combat metabolic disease.

Miniaturization in functional genomics and proteomics

Proteins are the key components of the cellular machinery responsible for processing changes that are ordered by genomic information. Analysis of most human proteins and nucleic acids is important in order to decode the complex networks that are likely to underlie many common diseases. Significant improvements in current technology are also required to dissect the regulatory processes in high-throughtput and with low cost. Miniaturization of biological assays is an important prerequisite to achieve these goals in the near future.

Association mapping reveals gene action and interactions in the determination of flowering time in barley

The interaction between members of a gene network has an important impact on the variation of quantitative traits, and can influence the outcome of phenotype/genotype association studies. Three genes (Ppd-H1, HvCO1, HvFT1) known to play an essential role in the regulation of flowering time under long days in barley were subjected to an analysis of nucleotide diversity in a collection of 220 spring barley accessions. The coding region of Ppd-H1 was highly diverse, while both HvCO1 and HvFT1 showed a rather limited level of diversity. Within all three genes, the extent of linkage disequilibrium was variable, but on average only moderate. Ppd-H1 is strongly associated with flowering time across four environments, showing a difference of five to ten days between the most extreme haplotypes. The association between flowering time and the variation at HvFT1 and HvCO1 was strongly dependent on the haplotype present at Ppd-H1. The interaction between HvCO1 and Ppd-H1 was statistically significant, but this association disappeared when the analysis was corrected for the geographical origin of the accessions. No association existed between flowering time and allelic variation at HvFT1. In contrast to Ppd-H1, functional variation at both HvCO1 and HvFT1 is limited in cultivated barley.

Association and linkage of allelic variants of the dopamine transporter gene in ADHD

Previously, we had reported a genome-wide scan for attention-deficit/hyperactivity disorder (ADHD) in 102 families with affected sibs of German ancestry; the highest multipoint LOD score of 4.75 was obtained on chromosome 5p13 (parametric HLOD analysis under a dominant model) near the dopamine transporter gene (DAT1). We genotyped 30 single nucleotide polymorphisms (SNPs) in this candidate gene and its 5′ region in 329 families (including the 102 initial families) with 523 affected offspring. We found that (1) SNP rs463379 was significantly associated with ADHD upon correction for multiple testing (P=0.0046); (2) the global P-value for association of haplotypes was significant for block two upon correction for all (n=3) tested blocks (P=0.0048); (3) within block two we detected a nominal P=0.000034 for one specific marker combination. This CGC haplotype showed relative risks of 1.95 and 2.43 for heterozygous and homozygous carriers, respectively; and (4) finally, our linkage data and the genotype-IBD sharing test (GIST) suggest that genetic variation at the DAT1 locus explains our linkage peak and that rs463379 (P<0.05) is the only SNP of the above haplotype that contributed to the linkage signal. In sum, we have accumulated evidence that genetic variation at the DAT1 locus underlies our ADHD linkage peak on chromosome 5; additionally solid association for a single SNP and a haplotype were shown. Future studies are required to assess if variation at this locus also explains other positive linkage results obtained for chromosome 5p.

Identification of novel transcriptional regulators involved in macrophage differentiation and activation in U937 cells

BackgroundMonocytes and macrophages play essential role in innate immunity. Understanding the underlying mechanism of macrophage differentiation and the identification of regulatory mechanisms will help to find new strategies to prevent their harmful effects in chronic inflammatory diseases and sepsis.ResultsMaturation of blood monocytes into tissue macrophages and subsequent inflammatory response was mimicked in U937 cells of human histocytic lymphoma origin. Whole genome array analysis was employed to evaluate gene expression profile to identify underlying transcriptional networks implicated during the processes of differentiation and inflammation. In addition to already known transcription factors (i.e. MAFB, EGR, IRF, BCL6, NFkB, AP1, Nur77), gene expression analysis further revealed novel genes (i.e. MEF2, BRI, HLX, HDAC5, H2AV, TCF7L2, NFIL3) previously uncharacterized to be involved in the differentiation process. A total of 58 selected genes representing cytokines, chemokines, surface antigens, signaling molecules and transcription factors were validated by real time PCR and compared to primary monocyte-derived macrophages. Beside the verification of several new genes, the comparison reveals individual heterogeneity of blood donors.ConclusionUp regulation of MEF2 family, HDACs, and H2AV during cell differentiation and inflammation sheds new lights onto regulation events on transcriptional and epigenetic level controlling these processes. Data generated will serve as a source for further investigation of macrophages differentiation pathways and related biological responses.

A Y2H-seq approach defines the human protein methyltransferase interactome

To accelerate high-density interactome mapping, we developed a yeast two-hybrid interaction screening approach involving short-read second-generation sequencing (Y2H-seq) with improved sensitivity and a quantitative scoring readout allowing rapid interaction validation. We applied Y2H-seq to investigate enzymes involved in protein methylation, a largely unexplored post-translational modification. The reported network of 523 interactions involving 22 methyltransferases or demethylases is comprehensively annotated and validated through coimmunoprecipitation experiments and defines previously undiscovered cellular roles of nonhistone protein methylation.