Identification and Analysis of Chemical Constituents and Rat Serum Metabolites in Gushuling Using UPLC-Q-TOF/MS Coupled with Novel Informatics UNIFI Platform

Gushuling (GSL), a well-known hospital preparation composed of traditional Chinese medicine (TCM), has been widely used in the clinical treatment of osteoporosis (OP) for decades due to its remarkable therapeutic effect. However, the chemical constituents of GSL are still unclear so far, which limits the in-depth study of its pharmacodynamic material basis and further restricts its clinical application. In this study, we developed a strategy for qualitative analysis of the chemical constituents of GSL in vitro and in vivo. Based on the results of ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS) and the UNIFI informatics platform, the chemical constituents of GSL can be determined quickly and effectively. By comparing the retention time, accurate mass, and fragmentation spectrum of the compounds in GSL, a total of 93 compounds were identified or preliminarily identified, including flavonoids, terpenoids, phenylpropanoids, steroids, etc. Among them, nine compounds have been confirmed by standard substances, namely epimedin A, epimedin B, epimedin C, icariin, ecdysterone, calycosin, calycosin-7-glucoside, ononin, and ginsenoside Ro. Fragment patterns and characteristic ions of representative compounds with different chemical structure types were analyzed. At the same time, 20 prototype compounds and 42 metabolites were detected in rat serum. Oxidation, hydration, reduction, dehydration, glutathione S-conjugation, and acetylcysteine conjugation were the main transformation reactions of GSL in rat serum. In this research, the rapid method to characterize the in vitro and in vivo chemical constituents of GSL can not only be used for the standardization and quality control of GSL but also be helpful for further research on its pharmacodynamic material basis.

ionbot: a novel, innovative and sensitive machine learning approach to LC-MS/MS peptide identification

Mass spectrometry-based proteomics generates vast amounts of signal data that require computational interpretation to obtain peptide identifications. Dozens of algorithms for this task exist, but all exploit only part of the acquired data to judge a peptide-to-spectrum match (PSM), ignoring important information such as the observed retention time and fragment ion peak intensity pattern. Moreover, only few identification algorithms allow open modification searches that can substantially increase peptide identifications. We here therefore introduce ionbot, a novel open modification search engine that is the first to fully merge machine learning with peptide identification. This core innovation brings the ability to include a much larger range of experimental data into PSM scoring, and even to adapt this scoring to the specifics of the data itself. As a result, ionbot substantially increases PSM confidence for open searches, and even enables a further increase in peptide identification rate of up to 30% by also considering highly plausible, lower-ranked, co-eluting matches for a fragmentation spectrum. Moreover, the exclusive use of machine learning for scoring also means that any future improvements to predictive models for peptide behavior will also result in more sensitive and accurate peptide identification.

ionbot: a novel, innovative and sensitive machine learning approach to LC-MS/MS peptide identification

Mass spectrometry-based proteomics generates vast amounts of signal data that require computational interpretation to obtain peptide identifications. Dozens of algorithms for this task exist, but all exploit only part of the acquired data to judge a peptide-to-spectrum match (PSM), ignoring important information such as the observed retention time and fragment ion peak intensity pattern. Moreover, only few identification algorithms allow open modification searches that can substantially increase peptide identifications. We here therefore introduce ionbot, a novel open modification search engine that is the first to fully merge machine learning with peptide identification. This core innovation brings the ability to include a much larger range of experimental data into PSM scoring, and even to adapt this scoring to the specifics of the data itself. As a result, ionbot substantially increases PSM confidence for open searches, and even enables a further increase in peptide identification rate of up to 30% by also considering highly plausible, lower-ranked, co-eluting matches for a fragmentation spectrum. Moreover, the exclusive use of machine learning for scoring also means that any future improvements to predictive models for peptide behavior will also result in more sensitive and accurate peptide identification.

The fragmentation spectrum from space-time reciprocity

Analyzing the single inclusive annihilation spectrum of charged hadrons in e+e− collisions, I confront the hadronization hypothesis of local parton-hadron duality with a systematic resummation of the dependence on the small energy fraction. This resummation is based on the reciprocity between time-like and space-like splitting processes in 4 − 2ϵ-dimensions, which I extend to resum all the soft terms of the cross-section for inclusive jet production. Under the local-parton-hadron duality hypothesis, the resulting distribution of jets essentially determines the spectrum of hadrons as the jet radius goes to zero. Thus I take the resummed perturbative jet function as the non-perturbative fragmentation function with an effective infra-red coupling. I find excellent agreement with data, and comment on the mixed leading log approximation previously used to justify local parton-hadron duality.

Improving power while controlling the false discovery rate when only a subset of peptides are relevant

The standard proteomics database search strategy involves searching spectra against a peptide database and estimating the false discovery rate (FDR) of the resulting set of peptide-spectrum matches. One assumption of this protocol is that all the peptides in the database are relevant to the hypothesis being investigated. However, in settings where researchers are interested in a subset of peptides, alternative search and FDR control strategies are needed. Recently, two methods were proposed to address this problem: subset-search and all-sub. We show that both methods fail to control the FDR. For subset-search, this failure is due to the presence of “neighbor” peptides, which are defined as irrelevant peptides with a similar precursor mass and fragmentation spectrum as a relevant peptide. Not considering neighbors compromises the FDR estimate because a spectrum generated by an irrelevant peptide can incorrectly match well to a relevant peptide. Therefore, we have developed a new method, “filter then subsetneighbor search” (FSNS), that accounts for neighbor peptides. We show evidence that FSNS properly controls the FDR when neighbors are present and that FSNS outperforms group-FDR, the only other method able to control the FDR relative to a subset of relevant peptides.

Progress in the Smoothed Particle Hydrodynamics Method to Simulate and Post-process Numerical Simulations of Annular Airblast Atomizers

This paper illustrates recent progresses in the development of the smoothed particle hydrodynamics (SPH) method to simulate and post-process liquid spray generation. The simulation of a generic annular airblast atomizer is presented, in which a liquid sheet is fragmented by two concentric counter swirling air streams. The accent is put on how the SPH method can bridge the gap between the CAD geometry of a nozzle and its characterization, in terms of spray characteristics and dynamics. In addition, the Lagrangian nature of the SPH method allows to extract additional data to give further insight in the spraying process. First, the sequential breakup events can be tracked from one large liquid blob to very fine stable droplets. This is herein called the tree of fragmentation. From this tree of fragmentation, abstract quantities can be drawn such as the breakup activity and the fragmentation spectrum. Second, the Lagrangian coherent structures in the turbulent flow can be determined easily with the finite-time Lyapunov exponent (FTLE). The extraction of the FTLE is particularly feasible in the SPH framework. Finally, it is pointed out that there is no universal and ultimate non-dimensional number that can characterize airblast primary breakup. Depending on the field of interest, a non-dimensional number (e.g. Weber number) might be more appropriate than another one (e.g. momentum flux ratio) to characterize the regime, and vice versa.

Air-Assisted Atomization at Constant Mass and Momentum Flow Rate: Investigation into the Ambient Pressure Influence With the Smoothed Particle Hydrodynamics Method

A twin-fluid atomizer configuration is simulated by means of the two-dimensional (2D) weakly compressible smoothed particle hydrodynamics (SPH) method and compared to experiments. The gas-to-liquid ratio (GLR), the momentum flux ratio, and the velocity ratio are set constant for different ambient pressures, which lead to different gaseous flow sections. The objectives of this study are (i) to investigate the effect of ambient pressure at constant global parameters and (ii) to verify the capability of 2D SPH to qualitatively predict the proper disintegration mechanism and to recover the correct evolution of the spray characteristics. The setup consists of an axial liquid jet of water fragmented by a coflowing high-speed air stream (Ug = 80 m/s) in a pressurized atmosphere up to 16 bar. The results are compared to the experiment and presented in terms of (i) mean velocity profiles, (ii) drop size distributions, and (iii) Sauter mean diameter (SMD) of the spray. It is found that there exists an optimal pressure to minimize the mean size of the spray droplets. Finally, two new quantities related to atomization are presented: (i) the breakup activity that quantifies the number of breakup events per time and volume unit and (ii) the fragmentation spectrum of the whole breakup chain, which characterize the cascade phenomenon in terms of probability. The breakup activity confirms the presence of the optimal pressure, and the fragmentation spectrum gives information on the type of breakup, depending on the ambient pressure.

MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools

Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR), and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.

Air-Assisted Atomization at Constant Mass and Momentum Flow Rate: Investigation of the Ambient Pressure Influence With the SPH Method

A twin-fluid atomizer configuration is simulated by means of the 2D weakly-compressible Smooth Particle Hydrodynamics method, and compared to experiments. The Gas-to-Liquid-Ratio, the momentum flux ratio and the velocity ratio are set constant for different ambient pressures, which leads to different gaseous flow sections. The objectives of this study are to (i) investigate the effect of ambient pressure at constant global parameters, and (ii) to verify the capability of 2D SPH to qualitatively predict the proper disintegration mechanism and to recover the correct evolution of the spray characteristics. The setup consists of an axial liquid jet of water fragmented by a co-flowing high-speed air stream (Ug = 80 m/s) in a pressurized atmosphere up to 16 bar. The results are compared to the experiment, and presented in terms of (i) mean velocity profiles, (ii) drop size distributions and (iii) Sauter Mean Diameter of the spray. It is found that there exists an optimal pressure to minimize the mean size of the spray droplets. Finally, two new quantities related to atomization are presented: (i) the breakup activity that quantifies the number of breakup events per time and volume unit and (ii) the fragmentation spectrum of the whole breakup chain, which characterizes the cascade phenomenon in terms of probability. The breakup activity confirms the presence of the optimal pressure and the fragmentation spectrum gives information on the type of breakup, depending on the ambient pressure.

MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools

Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR) and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.