Retention Time Data Research Articles

Rapid Profiling and Characterization of the Multicomponents from the Root and Rhizome of Salvia miltiorrhiza by Ultra-High Performance Liquid Chromatography/Ion Mobility-Quadrupole Time-of-Flight Mass Spectrometry in Combination with Computational Peak Annotation Workflows

Herbal components characterization represents a challenging task because of the co-existing of multiple classes of naturally occurring compounds with wide spans of polarity, molecular mass, and the ubiquitous isomerism. The root and rhizome of Salvia miltiorrhiza have been utilized as a reputable traditional Chinese medicine Salviae Miltiorrhizae Radix et Rhizoma (Dan-Shen) in the treatment of cardiovascular disease. Herein, a dimension-enhanced ultra-high performance liquid chromatography/ion mobility/quadrupole time-of-flight mass spectrometry approach in combination with intelligent peak annotation workflows was established aimed to rapidly characterize the multicomponents from S. miltiorrhiza. Due to the sufficient optimization, satisfactory chromatography separation was enabled on an HSS T3 column within 33 min using 0.1% formic acid in water (A) and acetonitrile (B) as the mobile phase, while the data-independent HDMSE in both the negative and positive electrospray ionization modes was utilized for the high-coverage MS2 data acquisition. Streamlined automatic peak annotation by searching an in-house library (recording 198 known compounds) followed by the subsequent confirming steps (e.g., comparison with the reference compounds, fragmentation pathways analysis, and retention behavior comparison, etc.), allowed us to identify or tentatively characterize a total of 86 components (including 50 terpenoids, 21 phenolic acids, and 15 others) from S. miltiorrhiza. Importantly, three-dimensional structure information, such as the retention time, MS1 and MS2 data, and collision cross section (CCS), was provided, which can facilitate the more reliable characterization of herbal components.

Set free: an evaluation of two break-away mechanisms for tracking collars

Context One of the welfare and ethical challenges with tracking animals is ensuring that the tracking device is removed from the animal at the conclusion of the study. However, for animals that are not readily re-trapped, the impact of devices and alternatives for their retrieval are rarely examined. Aims We compared the retention time of two types of break-away mechanisms for tracking collars deployed on mainland quokkas (Setonix brachyurus). Methods We tested a cotton thread (CT) weak-link, where the collar was cut and then a looping stitch was made to link the cut ends of the collar. We compared collar retention time of this simple mechanism with a lightweight automatic micro timed-release device (mTRD, Sirtrack). Key results Of the 17 radio collars with CT, the fates of 15 collars contributed to retention time data. Seven collars released: six fell off and were recovered 148 ± 64 (s.d.) days after deployment and another collar fell off 136 days after deployment but could not be recovered. Eight quokkas were recaptured (161 ± 109 days after deployment) and collars removed. Two quokkas were each tracked for over a year but then disappeared. Of the 11 GPS collars fitted with a mTRD, the fates of nine collars contributed to retention time data. Two released early at 16 and 29 days and were recovered. Seven fell off around the scheduled release date. There were two unknown fates. Re-trapped collared quokkas did not show evidence of injuries from wearing collars or any significant change in body mass (P = 0.442). Conclusions The timing of release for the CT weak-link was unpredictable, with a third of the collars releasing within 1 year and 7/15 lasting only about 6 months. Over two-thirds (7/9) of the GPS collars fitted with timed-release device released on schedule while 2/9 released early. Implications Tracking devices equipped with break-away mechanisms are essential for safeguarding animal welfare outcomes for species where the chance of recapture is not certain. For both break-away types examined in this study, the release timing was unpredictable and poor collar recovery rates show the importance of adding camera traps to monitor the outcomes for collared animals.

Deep learning for retention time prediction in reversed-phase liquid chromatography

Retention time prediction in high-performance liquid chromatography (HPLC) is the subject of many studies since it can improve the identification of unknown molecules in untargeted profiling using HPLC coupled with high-resolution mass spectrometry. Lots of approaches were developed for retention time prediction in liquid chromatography for a different number of molecules considering various molecular properties and machine learning algorithms. The recently built large retention time data set of standard compounds from the Metabolite and Chemical Entity Database (METLIN) allows researchers to create a model that can be used for retention time prediction of small molecules with wide varieties of structures and physicochemical properties. The ability to predict retention times using the largest data set was studied for different architectures of deep learning models that were trained on molecular fingerprints, and SMILES (string representation of a molecule) represented as one-hot matrices. The best result was achieved with a one-dimensional convolutional neural network (1D CNN) that uses SMILES as an input. The proposed model reached the mean absolute error and the median absolute error equal to 34.7 and 18.7 s, respectively, which outperformed the results previously obtained for this data set. The pre-trained 1D CNN on the METLIN SMRT data set was transferred on five other data sets to evaluate the generalization ability.

Development of High Performance Liquid Chromatographic Estimation of Domperidone and Lansoprazole

The combination of Domperidone and Lansoprazole is very useful in Gastro-esophageal disinfection (Dyspepsia). These methods provide means to separate the components characterize and quantify the components. An accurate, precise, specific and simple HPLC method was developed for simultaneous estimation of Domperidone and Lansoprazole. By this method retention time, linearity and accuracy data is respectively found for Domperidone and Lansoprazole. Mobile phase was prepared by mixing 51 volume of Acetonitrile and 49 volume of Ammonium Acetate (51:49 V/V) then 25mg each of Domperidone and Lansoprazole was dissolved in small volume of Acetonitrile: Ammonium Acetate (51:49 V/V) separately. Retention time was recorded 4.330 ± 0.003 minute and 5.820 ± 0.003 minute for Domperidone and Lansoprazole with 1.0 ml/min flow rate. The low value of % R.S.D indicates that this method is precise and accurate. Thus it can be concluded that the proposed method was good approach for obtaining reliable result.

Deep Neural Network Pretrained by Weighted Autoencoders and Transfer Learning for Retention Time Prediction of Small Molecules.

Retention time (RT) prediction contributes to identification of small molecules measured by high-performance liquid chromatography coupled with high-resolution mass spectrometry. Deep learning algorithms based on big data can enhance the accuracy of RT prediction. But at different chromatographic conditions, RTs of compounds are different, and the number of compounds with known RTs is small in most cases. Therefore, the transfer of big data is necessary. In this work, a strategy using a deep neural network (DNN) pretrained by weighed autoencoders and transfer learning (DNNpwa-TL) was proposed to efficiently predict RTs of compounds. The loss function in the autoencoders was calculated with features weighted by mutual information. Then, a DNN pretrained by weighted autoencoders (DNNpwa) was produced. For other specific chromatographic methods, the transfer learning model DNNpwa-TLs were built through fine-tuning the DNNpwa with the help of some compounds with known RTs to conduct the RT prediction. With the above strategy, a DNNpwa was first built with the METLIN small molecule retention time data set containing 80 038 small molecule compounds. A median relative error of 3.1% and a mean relative error of 4.9% were achieved. Then, 17 data sets from different chromatographic methods were studied, and the results showed that the performance of DNNpwa-TL was better than those of other deep learning models. Besides, DNNpwa-TL outperformed random forest, gradient boost, least absolute shrinkage and selection operator regression, and DNN for most of the 17 data sets. Therefore, DNNpwa-TL can provide an efficient method to perform RT prediction of small molecule compounds for different chromatographic methods and conditions.

Enhancement of vincristine under in vitro culture of Catharanthus roseus supplemented with Alternaria sesami endophytic fungal extract as a biotic elicitor.

Vincristine, one of the major vinca alkaloid of Catharanthus roseus (L.) G. Don. (Apocynaceae), was enhanced under in vitro callus culture of C. roseus using fungal extract of an endophyte Alternaria sesami isolated from the surface-sterilized root cuttings of C. roseus. Vindoline, a precursor molecule for vincristine production, was detected for the first time in the fungal endophyte A. sesami which was used as a biotic elicitor in this study to enhance vincristine content in the C. roseus callus. It was identified using high-performance liquid chromatography and mass spectroscopy techniques by matching retention time and mass data with reference molecule. Supplementing the heat sterilized A. sesami endophytic fungal culture extract into the callus culture medium of C. roseus resulted in the enhancement of vincristine content in C. roseus callus by 21.717% after 105-day culture.

Retention time prediction in hydrophilic interaction liquid chromatography with graph neural network and transfer learning

The combination of retention time (RT), accurate mass and tandem mass spectra can improve the structural annotation in untargeted metabolomics. However, the incorporation of RT for metabolite identification has received less attention because of the limitation of available RT data, especially for hydrophilic interaction liquid chromatography (HILIC). Here, the Graph Neural Network-based Transfer Learning (GNN-TL) is proposed to train a model for HILIC RTs prediction. The graph neural network was pre-trained using an in silico HILIC RT dataset (pseudo-labeling dataset) with ∼306 K molecules. Then, the weights of dense layers in the pre-trained GNN (pre-GNN) model were fine-tuned by transfer learning using a small number of experimental HILIC RTs from the target chromatographic system. The GNN-TL outperformed the methods in Retip, including the Random Forest (RF), Bayesian-regularized neural network (BRNN), XGBoost, light gradient-boosting machine (LightGBM), and Keras. It achieved the lowest mean absolute error (MAE) of 38.6 s on the test set and 33.4 s on an additional test set. It has the best ability to generalize with a small performance difference between training, test, and additional test sets. Furthermore, the predicted RTs can filter out nearly 60% false positive candidates on average, which is valuable for the identification of compounds complementary to mass spectrometry.

Development of a single retention time prediction model integrating multiple liquid chromatography systems: Application to new psychoactive substances

Database-driven suspect screening has proven to be a useful tool to detect new psychoactive substances (NPS) outside the scope of targeted screening; however, the lack of retention times specific to a liquid chromatography (LC) system can result in a large number of false positives. A singular stream-lined, quantitative structure-retention relationship (QSRR)-based retention time prediction model integrating multiple LC systems with different elution conditions is presented using retention time data (n = 1281) from the online crowd-sourced database, HighResNPS. Modelling was performed using an artificial neural network (ANN), specifically a multi-layer perceptron (MLP), using four molecular descriptors and one-hot encoding of categorical labels. Evaluation of test set predictions (n = 193) yielded coefficient of determination (R2) and mean absolute error (MAE) values of 0.942 and 0.583 min, respectively. The model successfully differentiated between LC systems, predicting 54%, 81% and 97% of the test set within ±0.5, ±1 and ±2 min, respectively. Additionally, retention times for an analyte not previously observed by the model were predicted within ±1 min for each LC system. The developed model can be used to predict retention times for all analytes on HighResNPS for each participating laboratory's LC system to further support suspect screening.

AVB-500, a selective inhibitor of GAS6-AXL, in combination with paclitaxel alters uterine serous cancer cell metabolism

<h3>Objectives:</h3> Even in the presence of oxygen, cancer cells preferentially metabolize glucose to ATP by the less efficient glycolysis pathway over oxidative phosphorylation. This concept, known as the Warburg effect, has been implicated in the rapid proliferation of cancer cells, tumor progression, and resistance to chemotherapy. The receptor tyrosine kinase AXL is highly expressed in uterine serous cancer (USC) and promotes chemo-resistance and metastasis. Our lab has shown that treatment with novel GAS6-AXL inhibitor, AVB-500, in combination with paclitaxel (P) has improved therapeutic efficacy compared to P alone. The role of AXL in cancer cell metabolism has not been elucidated. Our objective was to evaluate if AVB-500+P decreases tumor cell ability to generate energy via glycolysis, thereby providing a mechanism by which this treatment decreases tumor burden in a USC mouse model. <h3>Methods:</h3> In vivo studies were performed using NOD-SCID mice injected intraperitoneal (IP) with 1 x 107 ARK1 uterine serous cells. Treatment groups included vehicle, AVB-500, P, and AVB-500+P. [U-13C] glucose tracer was injected IP one hour before sacrifice. Tumor was collected, snap frozen in liquid nitrogen, and prepared for liquid chromatography/mass spectrometry (LC/MS). Hydrophilic interaction liquid chromatography was performed with an Agilent 6530 QTOF. Metabolites were identified by in-house database with retention time and fragment data. To determine the relative metabolite abundance across samples, the area of the extracted ion current peak for the metabolite of interest was normalized for protein content. <h3>Results:</h3> Treatment with AVB-500+P was found to decrease multiple glycolytic metabolites: glucose-6-phosphate by 2.66 fold (p=0.031), fructose-1,6-bisphosphate by 4.20 fold (p=0.002), 3-phosphoglyceric acid by 7.76 fold (p=0.014), phosphoenolpyruvate by 8.85 fold (p=0.013), lactate by 2.78 fold (p=0.024) compared to P alone. Additionally, TCA cycle intermediates including fumarate and malate were decreased by 1.94 fold (p=0.036) and 2.03 fold (p=0.031) respectively in AVB-500+P treated tumor compared to P alone. Notably, glucose abundance was not significantly different between P and AVB-500+P treated tumor (p=0.440) suggesting that AVB-500 is acting downstream of glucose uptake which correlates with the decrease in the metabolites listed above. <h3>Conclusions:</h3> Innovative [U-13C] glucose tracing <i>in vivo</i> has revealed metabolic changes in tumor cells after treatment. We observed that the novel GAS6-AXL inhibitor AVB-500+P prevents tumors cells from generating adequate energy (ATP) through the preferred glycolysis pathway. These results indicate that GAS6-AXL signaling may play a role in metabolic reprogramming in USC tumors, suggesting a mechanism for the observed decreased tumor burden in AVB-500+P treated mice. Additional studies are ongoing to better define this relationship.

The Pharmacometabodynamics of Gefitinib after Intravenous Administration to Mice: A Preliminary UPLC-IM-MS Study.

The effects of intravenous gefitinib (10 mg/kg), an anilinoquinazoline thymidylate kinase inhibitor (TKI), selective for the epidermal growth factor receptor (EGFR), on the urinary metabotypes of mice were studied. We hypothesized that, in response to the administration of gefitinib, there might be significant changes in the excretion of many endogenous metabolites in the urine, which could be correlated with the plasma pharmacokinetics (PK) of the drug. In order to investigate this conjecture, urine from male C57 BL6 mice was collected before IV dosing (10 mg/kg) and at 0–3, 3–8, and 8–24 h post-dose. The samples were profiled by UPLC/IM/MS and compared with the profiles obtained from undosed control mice with the data analyzed using multivariate statistical analysis (MVA). This process identified changes in endogenous metabolites over time and these were compared with drug and drug metabolite PK and excretion. While the MVA of these UPLC/IM/MS data did indeed reveal time-related changes for endogenous metabolites that appeared to be linked to drug administration, this analysis did not highlight the presence of either the drug or its metabolites in urine. Endogenous metabolites affected by gefitinib administration were identified by comparison of mass spectral, retention time and ion mobility-derived collision cross section data (compared to authentic standards wherever possible). The changes in endogenous metabolites resulting from gefitinib administration showed both increases (e.g., tryptophan, taurocholic acid, and the dipeptide lysyl-arginine) and decreases (e.g., deoxyguanosine, 8-hydroxydeoxyguanosine, and asparaginyl-histidine) relative to the control animals. By 8–24 h, the post-dose concentrations of most metabolites had returned to near control values. From these studies, we conclude that changes in the amounts of endogenous metabolites excreted in the urine mirrored, to some extent, the plasma pharmacokinetics of the drug. This phenomenon is similar to pharmacodynamics, where the pharmacological effects are related to the drug concentrations, and by analogy, we have termed this effect “pharmacometabodynamics”.

Application of Bayesian Multilevel Modeling in the Quantitative Structure-Retention Relationship Studies of Heterogeneous Compounds.

Quantitative structure-retention relationships (QSRRs) are used in the field of chromatography to model the relationship between an analyte structure and chromatographic retention. Such models are typically difficult to build and validate for heterogeneous compounds because of their many descriptors and relatively limited analyte-specific data. In this study, a Bayesian multilevel model is proposed to characterize the isocratic retention time data collected for 1026 heterogeneous analytes. The QSRR considers the effects of the molecular mass and 100 functional groups (substituents) on analyte-specific chromatographic parameters of the Neue model (i.e., the retention factor in water, the retention factor in acetonitrile, and the curvature coefficient). A Bayesian multilevel regression model was used to smooth noisy parameter estimates with too few data and to consider the uncertainties in the model parameters. We discuss the benefits of the Bayesian multilevel model (i) to understand chromatographic data, (ii) to quantify the effect of functional groups on chromatographic retention, and (iii) to predict analyte retention based on various types of preliminary data. The uncertainty of isocratic and gradient predictions was visualized using uncertainty chromatograms and discussed in terms of usefulness in decision making. We think that this method will provide the most benefit in providing a unified scheme for analyzing large chromatographic databases and assessing the impact of functional groups and other descriptors on analyte retention.

Probabilistic framework for integration of mass spectrum and retention time information in small molecule identification.

MotivationIdentification of small molecules in a biological sample remains a major bottleneck in molecular biology, despite a decade of rapid development of computational approaches for predicting molecular structures using mass spectrometry (MS) data. Recently, there has been increasing interest in utilizing other information sources, such as liquid chromatography (LC) retention time (RT), to improve identifications solely based on MS information, such as precursor mass-per-charge and tandem mass spectrometry (MS2).ResultsWe put forward a probabilistic modelling framework to integrate MS and RT data of multiple features in an LC-MS experiment. We model the MS measurements and all pairwise retention order information as a Markov random field and use efficient approximate inference for scoring and ranking potential molecular structures. Our experiments show improved identification accuracy by combining MS2 data and retention orders using our approach, thereby outperforming state-of-the-art methods. Furthermore, we demonstrate the benefit of our model when only a subset of LC-MS features has MS2 measurements available besides MS1.Availability and implementationSoftware and data are freely available at https://github.com/aalto-ics-kepaco/msms_rt_score_integration.Supplementary information Supplementary data are available at Bioinformatics online.

Attribution and identification of absorbed components by HPLC-DAD-ESI-MS after oral administration of Erhuang decoction

To realize the attribution and identification of absorbed components in rat serum after oral administration of Erhuang decoction prepared by semi-bionic enzyme extraction method, the fingerprints of serum samples were established using a HPLC-DAD-ESI-MS method. Thirty-two peaks in Erhuang decoction and 24 peaks in rat serum after oral administration of Erhuang decoction were detected. Among the 24 peaks detected in rat serum, 25 compounds were identified by comparing the retention time and mass spectrometry data with that of reference compounds, or by mass spectrometry analysis and retrieving the reference literatures. Among the identified 25 compounds in vivo, 24 were the original form of compounds absorbed from the detected compounds in vitro, and one was the metabolite compounds of licorice. By analyzing the mass spectrometry or ultraviolet absorption characteristics, other unidentified compounds in vivo were deduced to be the endogenous metabolites in serum or the original form and metabolites of the compounds existed in vivo. Results indicated that HPLC-DAD-ESI-MS is suitable for identifying the bioactive constituents in serum after oral administration of Erhuang decoction, and the findings would be beneficial to further research and development of the pharmacodynamic substance base of Erhuang decoction.

Detection of polyphenols and tanshinones in commercial Danshen by liquid chromatography with UV and mass spectrometry

Danshen, the dried rhizome of Salvia miltiorrhiza Bunge is a widely used herb in traditional Chinese medicinal preparations to treat cardiovascular diseases, renal and liver diseases. Tanshinones and salvianolic acids are used as markers to survey the commercial available products. In this study, a convenient high-performance liquid chromatographic method was developed to simultaneously separate and identify six main polyphenolic components, caffeic acid (1), danshensu (2), lithospermic acid (3), rosmarinic acid (4), salvianolic acid B (5) and salvianolic acid A (6), and four major abietane-type diterpenes, 15,16-dihydrotanshinone I (7), cryptotanshinone (8), tanshinone I (9), and tanshinone IIA (10), with UV and MS detectors by comparing their retention time, MS and MS2 data with those data obtained from the authentic compounds. A good resolution of all the analytes was obtained by choosing methanol and 0.25% aqueous acetic acid (adjusted to pH 2.77 with 1N HCl) as the mobile phase. By using LC-UV (290 nm)-MS method, polyphenols (1-6) and tanshinones (7-10) were easily detected and unambiguously identified in commercial Danshen.

Creating a Reliable Mass Spectral-Retention Time Library for All Ion Fragmentation-Based Metabolomics.

Accurate metabolite identification remains one of the primary challenges in a metabolomics study. A reliable chemical spectral library increases the confidence in annotation, and the availability of raw and annotated data in public databases facilitates the transfer of Liquid chromatography coupled to mass spectrometry (LC–MS) methods across laboratories. Here, we illustrate how the combination of MS2 spectra, accurate mass, and retention time can improve the confidence of annotation and provide techniques to create a reliable library for all ion fragmentation (AIF) data with a focus on the characterization of the retention time. The resulting spectral library incorporates information on adducts and in-source fragmentation in AIF data, while noise peaks are effectively minimized through multiple deconvolution processes. We also report the development of the Mass Spectral LIbrary MAnager (MS-LIMA) tool to accelerate library sharing and transfer across laboratories. This library construction strategy improves the confidence in annotation for AIF data in LC–MS-based metabolomics and will facilitate the sharing of retention time and mass spectral data in the metabolomics community.

UHPLC-HR-MS/MS-Guided Recovery of Bioactive Flavonol Compounds from Greco di Tufo Vine Leaves.

Leaves of Vitis vinifera cv. Greco di Tufo, a precious waste made in the Campania Region (Italy), after vintage harvest, underwent reduction, lyophilization, and ultrasound-assisted maceration in ethanol. The alcoholic extract, as evidenced by a preliminary UHPLC-HR-MS analysis, showed a high metabolic complexity. Thus, the extract was fractionated, obtaining, among others, a fraction enriched in flavonol glycosides and glycuronides. Myricetin, quercetin, kaempferol, and isorhamnetin derivatives were tentatively identified based on their relative retention time and TOF-MS2 data. As the localization of saccharidic moiety in glycuronide compounds proved to be difficult due to the lack of well-established fragmentation pattern and/or the absence of characteristic key fragments, to obtain useful MS information and to eliminate matrix effect redundancies, the isolation of the most abundant extract’s compound was achieved. HR-MS/MS spectra of the compound, quercetin-3-O-glucuronide, allowed us to thoroughly rationalize its fragmentation pattern, and to unravel the main differences between MS/MS behavior of flavonol glycosides and glycuronides. Furthermore, cytotoxicity assessment on the (poly)phenol rich fraction and the pure isolated compound was carried out using central nervous system cell lines. The chemoprotective effect of both the (poly)phenol fraction and quercetin-3-O-glucuronide was evaluated.

Nanaomycin K, a new epithelial–mesenchymal transition inhibitor produced by the actinomycete “Streptomyces rosa subsp. notoensis” OS-3966

A new nanaomycin analog, nanaomycin K, was isolated from a cultured broth of actinomycete strain "Streptomyces rosa subsp. notoensis" OS-3966. Nuclear magnetic resonance (NMR) analyses revealed that the planar structure of nanaomycin K had an ergothioneine moiety. To determine the absolute configuration, nanaomycin K was semisynthesized using standards of nanaomycin E and l-ergothioneine. The natural and semisynthetic nanaomycin K were identified as the same compounds based on retention time, mass spectrometry, 1H NMR, and optical rotation data. Nanaomycin K showed cytotoxicity against Madin-Darby canine kidney (MDCK) cells undergoing transforming growth factor (TGF) β1-induced epithelial-mesenchymal transition.

Ultrahigh-Performance capillary liquid chromatography-mass spectrometry at 35 kpsi for separation of lipids

Improvements in sample preparation, separation, and mass spectrometry continue to expand the coverage in LC-MS based lipidomics. While longer columns packed with smaller particles in theory give higher separation performance compared to shorter columns, the implementation of this technology above commercial limits has been sparse due to difficulties in packing long columns and successfully operating instruments at ultrahigh pressures. In this work, a liquid chromatograph that operates up to 35 kpsi was investigated for the separation and identification of lipid species from human plasma. Capillary columns between 15–50 cm long were packed with 1.7 µm BEH C18 particles and evaluated for their ability to separate lipid isomers and complex lipid extracts from human plasma. Putative lipid class identifications were assigned using accurate mass and relative retention time data of the eluting peaks. Our findings indicate that longer columns packed and operated at 35 kpsi outperform shorter columns packed and run at lower pressures in terms of peak capacity and numbers of features identified. Packing columns with relatively high concentration slurries (200 mg/mL) while sonicating the column resulted in 6–34% increase in peak capacity for 50 cm columns compared to lower slurry concentrations and no sonication. For a given analysis time, 50 cm long columns operated at 35 kpsi provided a 20–95% increase in chromatographic peak capacity compared with 15 cm columns operated at 15 kpsi. Analysis times up to 4 h were evaluated, generating peak capacities up to 410 ± 5 (n = 3, measured at 4σ) and identifying 480 ± 85 lipids (n = 2). Importantly, the results also show a correlation between the peak capacity and the number of lipids identified from a human plasma extract. This correlation indicates that ionization suppression is a limiting factor in obtaining sufficient signal for identification by mass spectrometry. The result also shows that the higher resolution obtained by shallow gradients overcomes possible signal reduction due to broader, more dilute peaks in long gradients for improving detection of lipids in LC-MS. Lastly, longer columns operated at shallow gradients allowed for the best separation of both regional and geometrical isomers. These results demonstrate a system that enables the advantages of using longer columns packed and run at ultrahigh pressure for improving lipid separations and lipidome coverage.

Characterization and quantification of methyl-benzotriazoles and chloromethyl-benzotriazoles produced from disinfection processes in wastewater treatment

Wastewater treatment plants (WWTPs) are one of the major sources of contaminants of emerging concern (CECs) in the environment. Benzotriazole corrosion inhibitors are a class of CECs that are resistant to biodegradation and have been reported in waters varying from WWTP effluent to groundwater and drinking water. This study examined wastewater influent and effluent grab samples over three years using Comprehensive Two-Dimensional Gas Chromatography (GC × GC) to discover six target benzotriazoles, four of which have never been properly characterized in water prior to this work. The six benzotriazoles were two methyl isomers (4-methyl-1H-benzotriazole and 5-methyl-1H-benzotriazole) as well as four chloromethyl isomers (previously unidentified). Using targeted analysis, the benzotriazoles were quantified and semi-quantified in the wastewater. In all seasons sampled but one, the concentration of three of the four chloromethyl-benzotriazoles increased from the influent to effluent waters. For the first time, it was observed that the 4 and 5-methyl-benzotriazoles interact with the sodium hypochlorite in the tertiary treatment step of the WWTP leading to the formation of the four chloromethyl-benzotriazoles. This was confirmed with lab scale synthesis of the reaction where the products were chromatographically analyzed and matched mass spectral and retention time data of the water samples. Assisted by the mass spectral fragmentation information, the four chloromethyl-benzotriazole isomers were tentatively identified as 4-chloromethyl-2H-benzotriazole, 5-chloromethyl-1H-benzotriazole, 4-chloromethyl-1H-benzotriazole, and 5-chloromethyl-2H-benzotriazole, in order of elution. No analytical standards are available for the chloromethyl-benzotriazole compounds and this is the first attempted identification of them in waters. The yearly mass loadings of total benzotriazoles were estimated to average between 148.86 and 394.64 kg/year at this particular facility. The WWTP studied reuses all effluent water for irrigation of crop and forested land so this high value of benzotriazoles entering the environment is concerning and the impacts need to be further studied.

Long seed dispersal distances by an inquisitive flightless rail (Gallirallus australis) are reduced by interaction with humans.

Human presence is becoming increasingly ubiquitous, but the influence this has on the seed dispersal services performed by frugivorous animals is largely unknown. The New Zealand weka (Gallirallus australis) is an inquisitive flightless rail that frequently congregates in areas of high human use. Weka are important seed dispersers, yet the seed dispersal services they provide are still poorly understood. We estimated seed dispersal distances of weka for two plant species (Prumnopitys ferruginea and Elaeocarpus dentatus) and tested how human interaction affected these dispersal distances. We estimated weka seed dispersal distances by combining GPS data from 39 weka over three sites with weka seed retention time data in a mechanistic model. The mean seed retention times were extremely long (38–125 h). Weka were highly effective dispersers, dispersing 93–96% of seeds away from parent canopies, and 1% of seeds over 1 km. However, we found evidence of a significant human impact on the seed dispersal distances of weka, with birds occupying areas of high human use performing 34.8–40.9% shorter distances than their more remote counterparts. This represents an example of cryptic function loss, where although weka are still present in the ecosystem, their seed dispersal services are impaired by human interaction.