Automated nuclear magnetic resonance fingerprinting of mixtures.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for qualitative and quantitative analysis. However, for complex mixtures, determining the speciation from NMR spectra can be tedious and sometimes even unfeasible. On the other hand, identifying and quantifying structural groups in a mixture from NMR spectra is much easier than doing the same for components. We call this group-based approach "NMR fingerprinting." In this work, we show that NMR fingerprinting can even be performed in an automated way, without expert knowledge, based only on standard NMR spectra, namely, 13C, 1H, and 13C DEPT NMR spectra. Our approach is based on the machine-learning method of support vector classification (SVC), which was trained here on thousands of labeled pure-component NMR spectra from open-source data banks. We demonstrate the applicability of the automated NMR fingerprinting using test mixtures, of which spectra were taken using a simple benchtop NMR spectrometer. The results from the NMR fingerprinting agree remarkably well with the ground truth, which was known from the gravimetric preparation of the samples. To facilitate the application of the method, we provide an interactive website (https://nmr-fingerprinting.de), where spectral information can be uploaded and which returns the NMR fingerprint. The NMR fingerprinting can be used in many ways, for example, for process monitoring or thermodynamic modeling using group-contribution methods-or simply as a first step in species analysis.

Rational method for defining and quantifying pseudo-components based on NMR spectroscopy.

Poorly specified mixtures, whose composition is unknown, are ubiquitous in chemical and biochemical engineering. In the present work, we propose a rational method for defining and quantifying pseudo-components in such mixtures that is free of ad hoc assumptions. The new method requires only standard nuclear magnetic resonance (NMR) experiments and can be fully automated. In the first step, the method analyzes the composition of the poorly specified mixture in terms of structural groups, which is much easier than obtaining the component speciation. The structural groups are then clustered into pseudo-components based on information on the self-diffusion coefficients measured by pulsed-field gradient (PFG) NMR spectroscopy. We demonstrate the performance of the new method on several aqueous mixtures. The method is broadly applicable and provides a sound basis for modeling and simulation of processes with poorly specified mixtures, without the need for tedious and expensive structure elucidation. It is also attractive for process monitoring.

Plate Fixation for Irreducible Proximal Humeral Fractures in Children and Adolescents-A Single-Center Case Series of Six Patients.

BACKGROUND: Recommended treatment for severely displaced proximal humeral fractures in children is the closed reduction and percutaneous fixation by K-wires or intramedullary nailing. METHODS: From January 2016 to January 2017 6, 21 children/adolescents (range 8 to 16 years) with proximal humeral fractures were treated surgically for severe displacement. In these six patients, several attempts of closed reduction were unsuccessful, and an open reduction was performed. The humeral head was fixed with a 3.5 mm T-plate without affecting the growth plate. Plate removal was performed at a mean interval of 132 days after initial surgery. Two years after initial surgery, the clinical outcome was assessed by the Constant-Murley score and QuickDASH score (including sport/music and work) and the shoulder joint was evaluated with a standardized sonographic examination for the rotator cuff and the conjoint tendon. RESULTS: In all six patients, dorsal displacement of the fracture was irreducible due to the interposition of tendinous or osseous structures. Intraoperatively, the interposed structures were the long biceps tendon in two, periosteal tissue in two, a bony fragment in one, and the long biceps tendon together with the conjoint tendon in one case. At mean follow-up of 26 months (range 22 months to 29 months), patients showed very good clinical results with an excellent mean Constant-Murley score of 97.5 (range 91 to 100) and mean QuickDASH score (including sport/music and work) of 5.5 (range 0-20.8). An X-ray follow-up 6 weeks after surgery demonstrated early consolidation and correct alignment in all patients. A sonographic evaluation at 2 years post injury showed that the biceps and the conjoined tendon were intact in all patients. CONCLUSIONS: If a proximal humeral fracture is not reducible by closed means, a tissue entrapment (most likely biceps tendon) should be considered. Treatment with an open reduction and plate fixation yields very good clinical and radiological results and preserves interposed structures as the biceps and conjoint tendon.

Automated Methods for Identification and Quantification of Structural Groups from Nuclear Magnetic Resonance Spectra Using Support Vector Classification.

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for elucidating the structure of unknown components and the composition of liquid mixtures. However, these tasks are often tedious and challenging, especially if complex samples are considered. In this work, we introduce automated methods for the identification and quantification of structural groups in pure components and mixtures from NMR spectra using support vector classification. As input, a 1H NMR spectrum and a 13C NMR spectrum of the liquid sample (pure component or mixture) that is to be analyzed is needed. The first method, called group-identification method, yields qualitative information on the structural groups in the sample. The second method, called group-assignment method, provides the basis for a quantitative analysis of the sample by identifying the structural groups and assigning them to signals in the 13C NMR spectrum of the sample; quantitative information can then be obtained with readily available tools by simple integration. We demonstrate that both methods, after being trained to NMR spectra of nearly 1000 pure components, yield excellent predictions for pure components that were not part of the training set as well as mixtures. The structural group-specific information obtained with the presented methods can, e.g., be used in combination with thermodynamic group-contribution methods to predict fluid properties of unknown samples.

Prediction of flow effects in quantitative NMR measurements.

A method for the prediction of the magnetization in flow NMR experiments is presented, which can be applied to mixtures. It enables a quantitative evaluation of NMR spectra of flowing liquid samples even in cases in which the magnetization is limited by the flow. A transport model of the nuclei's magnetization, which is based on the Bloch-equations, is introduced into a computational fluid dynamics (CFD) code. This code predicts the velocity field and relative magnetization of different nuclei for any chosen flow cell geometry, fluid and flow rate. The prediction of relative magnetization is used to correct the observed reduction of signal intensity caused by incomplete premagnetization in fast flowing liquids. By means of the model, quantitative NMR measurements at high flow rates are possible. The method is predictive and enables calculating correction factors for any flow cell design and operating condition based on simple static T1 time measurements. This makes time-consuming calibration measurements for assessing the influence of flow effects obsolete, which otherwise would have to be carried out for each studied condition. The new method is especially interesting for flow measurements with compact medium field NMR spectrometers, which have small premagnetization volumes. In the present work, experiments with three different flow cells in a medium field NMR spectrometer were carried out. Acetonitrile, water, and mixtures of these components were used as model fluids. The experimental results for the magnetization were compared to the predictions from the CFD model and good agreement was observed.

Genome Sequence and Annotation of Acremonium chrysogenum, Producer of the ß-Lactam Antibiotic Cephalosporin C.

The filamentous fungus Acremonium chrysogenum is the industrial producer of the ß-lactam antibiotic cephalosporin C. Here, we present the genome sequence of strain ATCC 11550, which contains genes for 8,901 proteins, 127 tRNAs, and 22 rRNAs. Genome annotation led to the prediction of 42 gene clusters for secondary metabolites.

Complete Sequencing and Chromosome-Scale Genome Assembly of the Industrial Progenitor Strain P2niaD18 from the Penicillin Producer Penicillium chrysogenum.

Penicillium chrysogenum is the major industrial producer of the ß-lactam antibiotic penicillin. Here, we report the complete genome sequence of the industrial progenitor strain P. chrysogenum P2niaD18 in a chromosome-scale genome assembly. P2niaD18 is distinguished from the recently sequenced P. chrysogenum Wisconsin 54-1255 strain by major chromosomal rearrangements leading to a modified chromosomal architecture.

An endogenous promoter for conditional gene expression in Acremonium chrysogenum: the xylan and xylose inducible promoter xyl1(P.).

Acremonium chrysogenum is the natural producer of the beta-lactam antibiotic cephalosporin C and therefore of significant biotechnological importance. Here we identified and characterized the xylanase-encoding xyl1 gene and demonstrate that its promoter, xyl1(P), is suitable for conditional expression of heterologous genes in A. chrysogenum. This was shown by xylose and xylan-inducible xyl1(P)-driven expression of genes encoding green fluorescence protein and phleomycin resistance. Moreover, we demonstrate the potential of the xyl1(P) promoter for selection marker recycling. Taken together, these finding will help to overcome the limitation in genetic tools in this important filamentous fungus.

Among developmental regulators, StuA but not BrlA is essential for penicillin V production in Penicillium chrysogenum.

In filamentous fungi, secondary metabolism is often linked with developmental processes such as conidiation. In this study we analyzed the link between secondary metabolism and conidiation in the main industrial producer of the ß-lactam antibiotic penicillin, the ascomycete Penicillium chrysogenum. Therefore, we generated mutants defective in two central regulators of conidiation, the transcription factors BrlA and StuA. Inactivation of either brlA or stuA blocked conidiation and altered hyphal morphology during growth on solid media, as shown by light and scanning electron microscopy, but did not affect biomass production during liquid-submerged growth. Genome-wide transcriptional profiling identified a complex StuA- and BrlA-dependent regulatory network, including genes previously shown to be involved in development and secondary metabolism. Remarkably, inactivation of stuA, but not brlA, drastically downregulated expression of the penicillin biosynthetic gene cluster during solid and liquid-submerged growth. In agreement, penicillin V production was wild-type-like in brlA-deficient strains but 99% decreased in stuA-deficient strains during liquid-submerged growth, as shown by high-performance liquid chromatography (HPLC) analysis. Thus, among identified regulators of penicillin V production StuA has the most severe influence. Overexpression of stuA increased the transcript levels of brlA and abaA (another developmental regulator) and derepressed conidiation during liquid-submerged growth but did not affect penicillin V productivity. Taken together, these data demonstrate an intimate but not exclusive link between regulation of development and secondary metabolism in P. chrysogenum.

Morphology and development in Aspergillus nidulans: a complex puzzle.

Like other filamentous fungi, Aspergillus nidulans forms a multitude of cell types that facilitate colonization and development. The molecular basis of cellular morphogenesis in A. nidulans is not well understood.Here, we summarize results obtained from detailed annotation of the A. nidulans genome sequence for genes with predicted roles in morphogenesis, with primary focus on polarized growth, calcium signaling, and development. We draw three broad conclusions from our results. First, the components of the signal transduction pathways and morphogenetic machinery as defined in the model yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe are largely conserved in A. nidulans. Second,A. nidulans possesses many additional genes implicated in morphogenesis that are not conserved in these yeasts. Third, the number of A. nidulans genes involved in morphogenesis is likely to be rather large;based on our annotation, we estimate that as many as 2000 A. nidulans genes encode proteins that may participate at some level in morphogenesis during vegetative growth and development.

Expression profiling of microdissected matched prostate cancer samples reveals CD166/MEMD and CD24 as new prognostic markers for patient survival.

In order to screen for differentially expressed genes that might be useful in diagnosis or therapy of prostate cancer we have used a custom made Affymetrix GeneChip containing 3950 cDNA fragments. Expression profiles were obtained from 42 matched pairs of mRNAs isolated from microdissected malignant and benign prostate tissues. Applying three different bioinformatic approaches to define differential gene expression, we found 277 differentially expressed genes, of which 98 were identified by all three methods. Fourteen per cent of these genes were not found in other expression studies, which were based on bulk tissue. Resultant candidate genes were further validated by quantitative RT-PCR, mRNA in situ hybridization and immunohistochemistry. AGR2 was over-expressed in 89% of prostate carcinomas, but did not have prognostic significance. Immunohistologically detected over-expression of MEMD and CD24 was identified in 86% and 38.5% of prostate carcinomas respectively, and both were predictive of PSA relapse. Combined marker analysis using MEMD and CD24 expression proved to be an independent prognostic factor (RR = 4.7, p = 0.006) in a Cox regression model, and was also superior to conventional markers. This combination of molecular markers thus appears to allow improved prediction of patient prognosis, but should be validated in larger studies.

Identification and validation of commonly overexpressed genes in solid tumors by comparison of microarray data.

Cancers originating from epithelial cells are the most common malignancies. No common expression profile of solid tumors compared to normal tissues has been described so far. Therefore we were interested if genes differentially expressed in the majority of carcinomas could be identified using bioinformatic methods. Complete data sets were downloaded for carcinomas of the prostate, breast, lung, ovary, colon, pancreas, stomach, bladder, liver, and kidney, and were subjected to an expression analysis using SAM. In each experiment, a gene was scored as differentially expressed if the q value was below 25%. Probe identifiers were unified by comparing the respective probe sequences to the Unigene build 155 using BlastN. To obtain differentially expressed genes within the set of analyzed carcinomas, the number of experiments in which differential expression was observed was counted. Differential expression was assigned to genes if they were differentially expressed in at least eight experiments of tumors from different origin. The identified candidate genes ADRM1, EBNA1BP2, FDPS, FOXM1, H2AFX, HDAC3, IRAK1, and YY1 were subjected to further validation. Using this comparative approach, 100 genes were identified as upregulated and 21 genes as downregulated in the carcinomas.

Systematic isolation of genes differentially expressed in normal and cancerous tissue of the pancreas.

BACKGROUND: There is increasing knowledge about the genetic basis of pancreatic cancer (PaCa). Tumor suppressor genes (TSGs; e.g. p53 and DPC4) and oncogenes (e.g. K-ras) have been shown to be involved in the development of PaCa. However, the extent of chromosomal changes (gains and losses) implicates that many more genes may be involved in the multistep progression of PaCa. Identification of these genes is essential for understanding the molecular events in the development of PaCa. METHODS: We assembled public and proprietary libraries of more than 4 million expressed sequence tags using newly developed software tools. RESULTS: We identified a total of 249 genes with specific expression patterns in normal and cancerous tissue of the pancreas. Of these, 27 genes were found to be preferentially expressed in normal tissue of the pancreas, while 222 genes showed significant upregulation of expression in PaCa. Of the 249 genes, 232 (93.2%) were found to represent known human genes or putative human homologues of genes characterized previously in other species, while 17 (6.8%) represent putative new genes. CONCLUSION: These genes may represent a valuable source to identify novel TSGs and oncogenes involved in the carcinogenesis of PaCa.