Anna Niehues

pymzML—Python module for high-throughput bioinformatics on mass spectrometry data

SUMMARY pymzML is an extension to Python that offers (i) an easy access to mass spectrometry (MS) data that allows the rapid development of tools, (ii) a very fast parser for mzML data, the standard data format in MS and (iii) a set of functions to compare or handle spectra. AVAILABILITY AND IMPLEMENTATION pymzML requires Python2.6.5+ and is fully compatible with Python3. The module is freely available on http://pymzml.github.com or pypi, is published under LGPL license and requires no additional modules to be installed. CONTACT christian@fufezan.net.

The Metabolic Status Drives Acclimation of Iron Deficiency Responses in Chlamydomonas reinhardtii as Revealed by Proteomics Based Hierarchical Clustering and Reverse Genetics

Iron is a crucial cofactor in numerous redox-active proteins operating in bioenergetic pathways including respiration and photosynthesis. Cellular iron management is essential to sustain sufficient energy production and minimize oxidative stress. To produce energy for cell growth, the green alga Chlamydomonas reinhardtii possesses the metabolic flexibility to use light and/or carbon sources such as acetate. To investigate the interplay between the iron-deficiency response and growth requirements under distinct trophic conditions, we took a quantitative proteomics approach coupled to innovative hierarchical clustering using different “distance-linkage combinations” and random noise injection. Protein co-expression analyses of the combined data sets revealed insights into cellular responses governing acclimation to iron deprivation and regulation associated with photosynthesis dependent growth. Photoautotrophic growth requirements as well as the iron deficiency induced specific metabolic enzymes and stress related proteins, and yet differences in the set of induced enzymes, proteases, and redox-related polypeptides were evident, implying the establishment of distinct response networks under the different conditions. Moreover, our data clearly support the notion that the iron deficiency response includes a hierarchy for iron allocation within organelles in C. reinhardtii. Importantly, deletion of a bifunctional alcohol and acetaldehyde dehydrogenase (ADH1), which is induced under low iron based on the proteomic data, attenuates the remodeling of the photosynthetic machinery in response to iron deficiency, and at the same time stimulates expression of stress-related proteins such as NDA2, LHCSR3, and PGRL1. This finding provides evidence that the coordinated regulation of bioenergetics pathways and iron deficiency response is sensitive to the cellular and chloroplast metabolic and/or redox status, consistent with systems approach data.

The Interplay of Light and Oxygen in the Reactive Oxygen Stress Response of Chlamydomonas reinhardtii Dissected by Quantitative Mass Spectrometry

Light and oxygen are factors that are very much entangled in the reactive oxygen species (ROS) stress response network in plants, algae and cyanobacteria. The first obligatory step in understanding the ROS network is to separate these responses. In this study, a LC-MS/MS based quantitative proteomic approach was used to dissect the responses of Chlamydomonas reinhardtii to ROS, light and oxygen employing an interlinked experimental setup. Application of novel bioinformatics tools allow high quality retention time alignment to be performed on all LC-MS/MS runs increasing confidence in protein quantification, overall sequence coverage and coverage of all treatments measured. Finally advanced hierarchical clustering yielded 30 communities of co-regulated proteins permitting separation of ROS related effects from pure light effects (induction and repression). A community termed redoxII was identified that shows additive effects of light and oxygen with light as the first obligatory step. Another community termed 4-down was identified that shows repression as an effect of light but only in the absence of oxygen indicating ROS regulation, for example, possibly via product feedback inhibition because no ROS damage is occurring. In summary the data demonstrate the importance of separating light, O2 and ROS responses to define marker genes for ROS responses. As revealed in this study, an excellent candidate is DHAR with strong ROS dependent induction profiles.

Quantitative Mass-Spectrometric Sequencing of Chitosan Oligomers Revealing Cleavage Sites of Chitosan Hydrolases

Partially acetylated chito-oligosaccharides (paCOS) have diverse bioactivities that turn them into promising compounds especially for medical and agricultural applications. These properties likely arise from different acetylation patterns, but determining the sequences of paCOS and producing paCOS with patterns of interest have proven difficult. We present a novel method for sequencing submicrogram amounts of paCOS using quantitative mass spectrometry, allowing one to rapidly analyze the substrate specificities of chitosan hydrolases that can be used to produce paCOS. The method involves four major steps: (i) acetylation of free amino groups in paCOS using a deuterated reagent; (ii) labeling the reducing end with an 18O-tag; (iii) quantifying paCOS using [13C2, 2H3]-labeled isotopologs as internal standards; (iv) sequencing paCOS by tandem MS. Eventually, this method will aid in developing enzymes with cleavage patterns optimized for producing paCOS with defined patterns of acetylation and specific bioactivities.

Reassessment of chitosanase substrate specificities and classification

Chitosanases can be used to produce partially acetylated chitosan oligosaccharides (paCOS) for different applications, provided they are thoroughly characterized. However, recent studies indicate that the established classification system for chitosanases is too simplistic. Here, we apply a highly sensitive method for quantitatively sequencing paCOS to reassess the substrate specificities of the best-characterized class I–III chitosanases. The enzymes’ abilities to cleave bonds at GlcNAc residues positioned at subsite (−1) or (+1), on which the classification system is based, vary especially when the substrates have different fractions of acetylation (FA). Conflicts with the recent classification are observed at higher FA, which were not investigated in prior specificity determinations. Initial analyses of pectin-degrading enzymes reveal that classifications of other polysaccharide-degrading enzymes should also be critically reassessed. Based on our results, we tentatively suggest a chitosanase classification system which is based on specificities and preferences of subsites (−2) to (+2).Chitosanases are classified according to their specificity in cleaving bonds at GlcNAc residues but the current system may be too simplistic. Here, the authors use quantitative mass spectrometry to revisit chitosanase specificity and propose additional determinants for their classification.

Chitinosanase: A fungal chitosan hydrolyzing enzyme with a new and unusually specific cleavage pattern

The biological activities of partially acetylated chitosan oligosaccharides (paCOS) depend on their degree of polymerization (DP), fraction of acetylation (FA), and potentially their pattern of acetylation (PA). Therefore, analyzing structure-function relationships require fully defined paCOS, but these are currently unavailable. A promising approach for obtaining at least partially defined paCOS is using chitosanolytic enzymes. Here we purified and characterized a novel chitosan-hydrolyzing enzyme from the fungus Alternaria alternata possessing an absolute cleavage specificity, yielding fully defined paCOS. It cleaves specifically after GlcN-GlcNAc pairs and is most active towards moderately acetylated chitosans, but shows no activity against fully acetylated or fully deacetylated substrates. These unique properties match neither those of chitinases nor chitosanases. Therefore, the enzyme represents the first member of a new class of chitosanolytic enzymes that will allow for the production of fully defined paCOS. Additionally, it represents a highly valuable tool for fingerprinting analyses of chitosan polymers.

Protein‐engineering of chitosanase from Bacillus sp. MN to alter its substrate specificity

Partially acetylated chitosan oligosaccharides (paCOS) have various potential applications in agriculture, biomedicine, and pharmaceutics due to their suitable bioactivities. One method to produce paCOS is partial chemical hydrolysis of chitosan polymers, but that leads to poorly defined mixtures of oligosaccharides. However, the effective production of defined paCOS is crucial for fundamental research and for developing applications. A more promising approach is enzymatic depolymerization of chitosan using chitinases or chitosanases, as the substrate specificity of the enzyme determines the composition of the oligomeric products. Protein‐engineering of these enzymes to alter their substrate specificity can overcome the limitations associated with naturally occurring enzymes and expand the spectrum of specific paCOS that can be produced. Here, engineering the substrate specificity of Bacillus sp. MN chitosanase is described for the first time. Two muteins with active site substitutions can accept N‐acetyl‐D‐glucosamine units at their subsite (−2), which is impossible for the wildtype enzyme.

Chitosan Analysis by Enzymatic/Mass Spectrometric Fingerprinting and in Silico Predictive Modeling

Chitosans, β-1,4-linked partially N-acetylated linear polyglucosamines, are very versatile and promising functional biopolymers. Understanding their structure-function relationships requires sensitive and accurate structural analyses to determine parameters like degree of polymerization (DP), fraction of acetylation (FA), or pattern of acetylation (PA). NMR, the gold standard for FA analysis, requires large amounts of sample. Here, we describe an enzymatic/mass spectrometric fingerprinting method to analyze the FA of chitosan polymers. The method combines the use of chitinosanase, a sequence-specific hydrolase that cleaves chitosan polymers into oligomeric fingerprints, ultrahigh-performance liquid chromatography-electrospray ionization-mass spectrometry (UHPLC-ESI-MS), and partial least-squares regression (PLSR). We also developed a technique to simulate enzymatic fingerprints in silico that were used to build the PLS models for FA determination. Overall, we found our method to be as accurate as NMR while at the same time requiring only microgram amounts of sample. Thus, the method represents a powerful technique for chitosan analysis.