A multi-omics data analysis workflow packaged as a FAIR Digital Object.

BACKGROUND: Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. FINDINGS: We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. CONCLUSIONS: Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.

Ten quick tips for building FAIR workflows.

Research data is accumulating rapidly and with it the challenge of fully reproducible science. As a consequence, implementation of high-quality management of scientific data has become a global priority. The FAIR (Findable, Accesible, Interoperable and Reusable) principles provide practical guidelines for maximizing the value of research data; however, processing data using workflows-systematic executions of a series of computational tools-is equally important for good data management. The FAIR principles have recently been adapted to Research Software (FAIR4RS Principles) to promote the reproducibility and reusability of any type of research software. Here, we propose a set of 10 quick tips, drafted by experienced workflow developers that will help researchers to apply FAIR4RS principles to workflows. The tips have been arranged according to the FAIR acronym, clarifying the purpose of each tip with respect to the FAIR4RS principles. Altogether, these tips can be seen as practical guidelines for workflow developers who aim to contribute to more reproducible and sustainable computational science, aiming to positively impact the open science and FAIR community.

Computational approaches for network-based integrative multi-omics analysis.

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

Biotechnologically produced chitosans with nonrandom acetylation patterns differ from conventional chitosans in properties and activities.

Chitosans are versatile biopolymers with multiple biological activities and potential applications. They are linear copolymers of glucosamine and N-acetylglucosamine defined by their degree of polymerisation (DP), fraction of acetylation (FA), and pattern of acetylation (PA). Technical chitosans produced chemically from chitin possess defined DP and FA but random PA, while enzymatically produced natural chitosans probably have non-random PA. This natural process has not been replicated using biotechnology because chitin de-N-acetylases do not efficiently deacetylate crystalline chitin. Here, we show that such enzymes can partially N-acetylate fully deacetylated chitosan in the presence of excess acetate, yielding chitosans with FA up to 0.7 and an enzyme-dependent non-random PA. The biotech chitosans differ from technical chitosans both in terms of physicochemical and nanoscale solution properties and biological activities. As with synthetic block co-polymers, controlling the distribution of building blocks within the biopolymer chain will open a new dimension of chitosan research and exploitation.

Tackling the translational challenges of multi-omics research in the realm of European personalised medicine: A workshop report.

Personalised medicine (PM) presents a great opportunity to improve the future of individualised healthcare. Recent advances in -omics technologies have led to unprecedented efforts characterising the biology and molecular mechanisms that underlie the development and progression of a wide array of complex human diseases, supporting further development of PM. This article reflects the outcome of the 2021 EATRIS-Plus Multi-omics Stakeholder Group workshop organised to 1) outline a global overview of common promises and challenges that key European stakeholders are facing in the field of multi-omics research, 2) assess the potential of new technologies, such as artificial intelligence (AI), and 3) establish an initial dialogue between key initiatives in this space. Our focus is on the alignment of agendas of European initiatives in multi-omics research and the centrality of patients in designing solutions that have the potential to advance PM in long-term healthcare strategies.

Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies.

Population-scale expression profiling studies can provide valuable insights into biological and disease-underlying mechanisms. The availability of phenotypic traits is essential for studying clinical effects. Therefore, missing, incomplete, or inaccurate phenotypic information can make analyses challenging and prevent RNA-seq or other omics data to be reused. A possible solution are predictors that infer clinical or behavioral phenotypic traits from molecular data. While such predictors have been developed based on different omics data types and are being applied in various studies, metabolomics-based surrogates are less commonly used than predictors based on DNA methylation profiles.In this study, we inferred 17 traits, including diabetes status and exposure to lipid medication, using previously trained metabolomic predictors. We evaluated whether these metabolomic surrogates can be used as an alternative to reported information for studying the respective phenotypes using expression profiling data of four population cohorts. For the majority of the 17 traits, the metabolomic surrogates performed similarly to the reported phenotypes in terms of effect sizes, number of significant associations, replication rates, and significantly enriched pathways.The application of metabolomics-derived surrogate outcomes opens new possibilities for reuse of multi-omics data sets. In studies where availability of clinical metadata is limited, missing or incomplete information can be complemented by these surrogates, thereby increasing the size of available data sets. Additionally, the availability of such surrogates could be used to correct for potential biological confounding. In the future, it would be interesting to further investigate the use of molecular predictors across different omics types and cohorts.

The non-sulfated ulvanobiuronic acid of ulvans is the smallest active unit able to induce an oxidative burst in dicot cells.

Ulvans from green algae are promising compounds for plant protection because they are environmentally friendly and induce plant defense responses. We analyzed the structure-function relationship of ulvan polymers and oligomers for their elicitor activity in suspension-cultured cells of three dicot species. The polysaccharide from Ulva fasciata was characterized regarding its monosaccharide composition, degree of sulfation, and molecular mass. The polymer was partially depolymerized using acid hydrolysis, and the oligomers were separated using size exclusion chromatography. The oligomeric fractions were analyzed revealing mostly sulfated and de-sulfated ulvan dimers. Both the polymer and the oligomer fractions induced an NADPH oxidase-dependent oxidative burst in plant cells. The elicitor activity of the ulvan dimers did not require sulfation. By identifying the smallest elicitor-active unit, HexA-Rha, we took an important next step to understand how the structure influences ulvan elicitor responses. The desulfated ulvan dimer is discussed as a promising agro-biologic for sustainable agriculture.

A seven-membered cell wall related transglycosylase gene family in Aspergillus niger is relevant for cell wall integrity in cell wall mutants with reduced α-glucan or galactomannan.

Chitin is an important fungal cell wall component that is cross-linked to ß-glucan for structural integrity. Acquisition of chitin to glucan cross-links has previously been shown to be performed by transglycosylation enzymes in Saccharomyces cerevisiae, called Congo Red hypersensitive (Crh) enzymes. Here, we characterized the impact of deleting all seven members of the crh gene family (crhA-G) in Aspergillus niger on cell wall integrity, cell wall composition and genome-wide gene expression. In this study, we show that the seven-fold crh knockout strain shows slightly compact growth on plates, but no increased sensitivity to cell wall perturbing compounds. Additionally, we found that the cell wall composition of this knockout strain was virtually identical to that of the wild type. In congruence with these data, genome-wide expression analysis revealed very limited changes in gene expression and no signs of activation of the cell wall integrity response pathway. However, deleting the entire crh gene family in cell wall mutants that are deficient in either galactofuranose or α-glucan, mainly α-1,3-glucan, resulted in a synthetic growth defect and an increased sensitivity towards Congo Red compared to the parental strains, respectively. Altogether, these results indicate that loss of the crh gene family in A. niger does not trigger the cell wall integrity response, but does play an important role in ensuring cell wall integrity in mutant strains with reduced galactofuranose or α-glucan.

Robust enzymatic-mass spectrometric fingerprinting analysis of the fraction of acetylation of chitosans.

We developed a rapid and precise method to determine the fraction of acetylation (FA) of unknown chitosan samples using a combination of enzymatic sample hydrolysis, isotopic labeling, and HILIC-ESI-MS analysis. Chitosans are ß-(1,4)-linked, partially N-acetylated and linear polyglucosamines representing an interesting group of functional biopolymers with a broad range of applications. For a better understanding of their structure-function relationships, it is key to have sensitive, accurate structural analysis tools available to determine parameters like the degree of polymerization (DP), fraction of acetylation (FA), or pattern of acetylation (PA). Here, we describe an improved enzymatic/mass spectrometric method for FA analysis of chitosan polymers. In contrast to the original chitinosanase-based mass spectrometric fingerprinting analysis of FA, the new method is independent of the PA and the intermolecular variation in FA (DFA) of the chitosan sample. This allows accurate analysis of heterogeneously de-N-acetylated samples representing the majority of commercially available chitosans.

Enzymatic Production and Enzymatic-Mass Spectrometric Fingerprinting Analysis of Chitosan Polymers with Different Nonrandom Patterns of Acetylation.

Chitosans, a family of ß-(1,4)-linked, partially N-acetylated polyglucosamines, are considered to be among the most versatile and most promising functional biopolymers. Chemical analysis and bioactivity studies revealed that the functionalities of chitosans strongly depend on the polymers' degree of polymerization and fraction of acetylation. More recently, the pattern of acetylation ( PA) has been proposed as another important parameter to influence functionalities of chitosans. We therefore carried out studies on the acetylation pattern of chitosan polymers produced by three recombinant fungal chitin deacetylases (CDAs) originating from different species, namely, Podospora anserina, Puccinia graminis f. sp. tritici, and Pestalotiopsis sp. We analyzed the chitosans by 1H NMR, 13C NMR, and SEC-MALS and established new methods for PA analysis based on enzymatic mass spectrometric fingerprinting and in silico simulations. Our studies strongly indicate that the different CDAs indeed produce chitosans with different PA. Finally, Zimm plot analysis revealed that enzymatically treated polymers differ with respect to their second virial coefficient and radius of gyration indicating an influence of PA on polymer-solvent interactions.

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.

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 (F A ). Conflicts with the recent classification are observed at higher F A , 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).

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

Chitosans, ß-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.

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.

pyQms enables universal and accurate quantification of mass spectrometry data.

Quantitative mass spectrometry (MS) is a key technique in many research areas (1), including proteomics, metabolomics, glycomics, and lipidomics. Because all of the corresponding molecules can be described by chemical formulas, universal quantification tools are highly desirable. Here, we present pyQms, an open-source software for accurate quantification of all types of molecules measurable by MS. pyQms uses isotope pattern matching that offers an accurate quality assessment of all quantifications and the ability to directly incorporate mass spectrometer accuracy. pyQms is, due to its universal design, applicable to every research field, labeling strategy, and acquisition technique. This opens ultimate flexibility for researchers to design experiments employing innovative and hitherto unexplored labeling strategies. Importantly, pyQms performs very well to accurately quantify partially labeled proteomes in large scale and high throughput, the most challenging task for a quantification algorithm.

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.

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 redox(II) 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.

pyGCluster, a novel hierarchical clustering approach.

SUMMARY: pyGCluster is a clustering algorithm focusing on noise injection for subsequent cluster validation. The reproducibility of a large amount of clusters obtained with agglomerative hierarchical clustering is assessed. Furthermore, a multitude of different distance-linkage combinations are evaluated. Finally, highly reproducible clusters are meta-clustered into communities. Graphical illustration of the results as node and expression maps is implemented. AVAILABILITY AND IMPLEMENTATION: pyGCluster requires Python 2.7, it is freely available at http://pyGCluster.github.io and published under MIT license. Dependencies are NumPy, SciPy and optionally fastcluster and rpy2. CONTACT: christan@fufezan.net SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online and at http://pyGCluster.github.io.

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.

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.