ZASP: A Highly Compatible and Sensitive ZnCl2 Precipitation-Assisted Sample Preparation Method for Proteomic Analysis.

Universal sample preparation for proteomic analysis that enables unbiased protein manipulation, flexible reagent use, and low protein loss is required to ensure the highest sensitivity of downstream liquid chromatography-mass spectrometry (LC-MS) analysis. To address these needs, we developed a ZnCl2 precipitation-assisted sample preparation method (ZASP) that depletes harsh detergents and impurities in protein solutions prior to trypsin digestion via 10 min of ZnCl2 and methanol-induced protein precipitation at room temperature (RT). ZASP can remove trypsin digestion and LC-MS incompatible detergents such as SDS, Triton X-100, and urea at high concentrations in solution and unbiasedly recover proteins independent of the amount of protein input. We demonstrated the sensitivity and reproducibility of ZASP in an analysis of samples with 1 µg to 1000 µg of proteins. Compared to commonly used sample preparation methods such as SDC-based in-solution digestion, acetone precipitation, FASP, and SP3, ZASP has proven to be an efficient approach. Here, we present ZASP, a practical, robust, and cost-effective proteomic sample preparation method that can be applied to profile different types of samples.

CLIPPER 2.0: Peptide-Level Annotation and Data Analysis for Positional Proteomics.

Positional proteomics methodologies have transformed protease research, and have brought mass spectrometry (MS)-based degradomics studies to the forefront of protease characterization and system-wide interrogation of protease signaling. Considerable advancements in both sensitivity and throughput of liquid chromatography (LC)-MS/MS instrumentation enable the generation of enormous positional proteomics datasets of natural and protein termini and neo-termini of cleaved protease substrates. However, concomitant progress has not been observed to the same extent in data analysis and post-processing steps, arguably constituting the largest bottleneck in positional proteomics workflows. Here, we present a computational tool, CLIPPER 2.0, that builds on prior algorithms developed for MS-based protein termini analysis, facilitating peptide-level annotation and data analysis. CLIPPER 2.0 can be used with several sample preparation workflows and proteomics search algorithms and enables fast and automated database information retrieval, statistical and network analysis, as well as visualization of terminomic datasets. We demonstrate the applicability of our tool by analyzing GluC and MMP9 cleavages in HeLa lysates. CLIPPER 2.0 is available at https://github.com/UadKLab/CLIPPER-2.0.

An Automated and Fast Sample Preparation Workflow for Laser Microdissection Guided Ultrasensitive Proteomics.

Spatial tissue proteomics integrating whole-slide imaging, laser microdissection, and ultrasensitive mass spectrometry is a powerful approach to link cellular phenotypes to functional proteome states in (patho)physiology. To be applicable to large patient cohorts and low sample input amounts, including single-cell applications, loss-minimized and streamlined end-to-end workflows are key. We here introduce an automated sample preparation protocol for laser microdissected samples utilizing the cellenONE robotic system, which has the capacity to process 192 samples in 3 h. Following laser microdissection collection directly into the proteoCHIP LF 48 or EVO 96 chip, our optimized protocol facilitates lysis, formalin de-crosslinking, and tryptic digest of low-input archival tissue samples. The seamless integration with the Evosep ONE LC system by centrifugation allows 'on-the-fly' sample clean-up, particularly pertinent for laser microdissection workflows. We validate our method in human tonsil archival tissue, where we profile proteomes of spatially-defined B-cell, T-cell, and epithelial microregions of 4000 µm2 to a depth of ∼2000 proteins and with high cell type specificity. We finally provide detailed equipment templates and experimental guidelines for broad accessibility.

Integrated multi-omics unveil the impact of H-phosphinic analogs of glutamate and α-ketoglutarate on Escherichia coli metabolism.

Desmethylphosphinothricin (L-Glu-γ-PH) is the H-phosphinic analog of glutamate with carbon-phosphorus-hydrogen (C-P-H) bonds. In L-Glu-γ-PH the phosphinic group acts as a bioisostere of the glutamate γ-carboxyl group allowing the molecule to be a substrate of Escherichia coli glutamate decarboxylase, a pyridoxal 5'-phosphate-dependent α-decarboxylase. In addition, the L-Glu-γ-PH decarboxylation product, GABA-PH, is further metabolized by bacterial GABA-transaminase, another pyridoxal 5'-phosphate-dependent enzyme, and succinic semialdehyde dehydrogenase, a NADP+-dependent enzyme. The product of these consecutive reactions, the so-called GABA shunt, is succinate-PH, the H-phosphinic analog of succinate, a tricarboxylic acid cycle intermediate. Notably, L-Glu-γ-PH displays antibacterial activity in the same concentration range of well-established antibiotics in E. coli. The dipeptide L-Leu-Glu-γ-PH was shown to display an even higher efficacy, likely as a consequence of an improved penetration into the bacteria. Herein, to further understand the intracellular effects of L-Glu-γ-PH, 1H NMR-based metabolomics, and LC-MS-based shotgun proteomics were used. This study included also the keto-derivative of L-Glu-γ-PH, α-ketoglutarate-γ-PH (α-KG-γ-PH), which also exhibits antimicrobial activity. L-Glu-γ-PH and α-KG-γ-PH are found to similarly impact bacterial metabolism, although the overall effect of α-KG-γ-PH is more pervasive. Notably, α-KG-γ-PH is converted intracellularly into L-Glu-γ-PH, but the opposite was not found. In general, both molecules impact the pathways where aspartate, glutamate, and glutamine are used as precursors for the biosynthesis of related metabolites, activate the acid stress response, and deprive cells of nitrogen. This work highlights the multi-target drug potential of L-Glu-γ-PH and α-KG-γ-PH and paves the way for their exploitation as antimicrobials.

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data.

BACKGROUND: Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices. RESULTS: We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

Advances in targeted liquid chromatography-tandem mass spectrometry methods for endocannabinoid and N-acylethanolamine quantification in biological matrices: A systematic review.

Liquid chromatography paired with tandem mass spectrometry (LC-MS/MS) is the gold standard in measurement of endocannabinoid concentrations in biomatrices. We conducted a systematic review of literature to identify advances in targeted LC-MS/MS methods in the period 2017-2024. We found that LC-MS/MS methods for endocannabinoid quantification are relatively consistent both across time and across biomatrices. Recent advances have primarily been in three areas: (1) sample preparation techniques, specific to the chosen biomatrix; (2) the range of biomatrices tested, recently favoring blood matrices; and (3) the breadth of endocannabinoid and endocannabinoid-like analytes incorporated into assays. This review provides a summary of the recent literature and a guide for researchers looking to establish the best methods for quantifying endocannabinoids in a range of biomatrices.

HowDirty: An R package to evaluate molecular contaminants in LC-MS experiments.

Contaminants derived from consumables, reagents, and sample handling often negatively affect LC-MS data acquisition. In proteomics experiments, they can markedly reduce identification performance, reproducibility, and quantitative robustness. Here, we introduce a data analysis workflow combining MS1 feature extraction in Skyline with HowDirty, an R-markdown-based tool, that automatically generates an interactive report on the molecular contaminant level in LC-MS data sets. To facilitate the interpretation of the results, the HTML report is self-contained and self-explanatory, including plots that can be easily interpreted. The R package HowDirty is available from https://github.com/DavidGZ1/HowDirty. To demonstrate a showcase scenario for the application of HowDirty, we assessed the impact of ultrafiltration units from different providers on sample purity after filter-assisted sample preparation (FASP) digestion. This allowed us to select the filter units with the lowest contamination risk. Notably, the filter units with the lowest contaminant levels showed higher reproducibility regarding the number of peptides and proteins identified. Overall, HowDirty enables the efficient evaluation of sample quality covering a wide range of common contaminant groups that typically impair LC-MS analyses, facilitating corrective or preventive actions to minimize instrument downtime.

Lipidomics and mass spectrometry imaging unveil alterations in mice hippocampus lipid composition exposed to hypoxia.

Lipids are components of cytomembranes that are involved in various biochemical processes. High-altitude hypoxic environments not only affect the body's energy metabolism, but these environments can also cause abnormal lipid metabolism involved in the hypoxia-induced cognitive impairment. Thus, comprehensive lipidomic profiling of the brain tissue is an essential step toward understanding the mechanism of cognitive impairment induced by hypoxic exposure. In the present study, mice showed reduced new-object recognition and spatial memory when exposed to hypobaric hypoxia for 1 day. Histomorphological staining revealed significant morphological and structural damage to the hippocampal tissue, along with prolonged exposure to hypobaric hypoxia. Dynamic lipidomics of the mouse hippocampus showed a significant shift in both the type and distribution of phospholipids, as verified by spatial lipid mapping. Collectively, a diverse and dynamic lipid composition in mice hippocampus was uncovered, which deepens our understanding of biochemical changes during sustained hypoxic exposure and could provide new insights into the cognitive decline induced by high-altitude hypoxia exposure.

Altered Metabolism during the Dark Period in Drosophila Short Sleep Mutants.

Sleep is regulated via circadian mechanisms, but effects of sleep disruption on physiological rhythms, in particular metabolic cycling, remain unclear. To examine this question, we probed diurnal metabolic alterations of two Drosophila short sleep mutants, fumin and sleepless. Samples were collected with high temporal sampling (every 2 h) over 24 h under a 12:12 light:dark cycle, and profiling was done using an ion-switching LCMS/MS method. Fewer metabolites with 24 h oscillations were noted with short sleep (50 and 46 in fumin and sleepless, BH. Q < 0.2 by RAIN analysis) compared to a wild-type control (iso31, 63 with BH. Q < 0.2), and peak phases of the sleep mutants were consolidated into two major phase peaks at mid-day and middle of night. Overall, altered nicotinate/nicotinamide, alanine/aspartate/glutamate, acetylcholine, glyoxylate/dicarboxylate, and TCA cycle metabolism were observed in the short sleep mutants, indicative of increased energetic demand and oxidative stress compared to wild type. Both changes in cycling and discriminant models suggest unique alterations in the dark period indicative of constrained metabolic networks. Thus, we conclude that sleep loss alters metabolic function uniquely throughout the day, and further examination of specific mechanisms is warranted.

Multicenter Collaborative Study to Optimize Mass Spectrometry Workflows of Clinical Specimens.

The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography-mass spectrometry (LC-MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six study partners were provided with two clinically relevant sample matrices: plasma and cerebrospinal fluid (CSF). In the first round, each laboratory applied their current best practice protocol for the respective matrix. Based on the achieved results and following a transparent exchange of all lab-specific protocols within the consortium, each laboratory could advance their methods before measuring the same samples in the second acquisition round. Both time points are compared with respect to identifications (IDs), data completeness, and precision, as well as reproducibility. As a result, the individual performances of participating study centers were improved in the second measurement, emphasizing the effect and importance of the expert-driven exchange of best practices for direct practical improvements.

Online Mixed-Bed Ion Exchange Chromatography for Native Top-Down Proteomics of Complex Mixtures.

Native top-down mass spectrometry (nTDMS) allows characterization of protein structure and noncovalent interactions with simultaneous sequence mapping and proteoform characterization. The majority of nTDMS studies utilize purified recombinant proteins, with significant challenges hindering application to endogenous systems. To perform native top-down proteomics (nTDP), where endogenous proteins from complex biological systems are analyzed by nTDMS, it is essential to separate proteins under nondenaturing conditions. However, it remains difficult to achieve high resolution with MS-compatible online chromatography while preserving protein tertiary structure and noncovalent interactions. Herein, we report the use of online mixed-bed ion exchange chromatography (IEC) to enable separation of endogenous proteins from complex mixtures under nondenaturing conditions, preserving noncovalent interactions for nTDP analysis. We have successfully detected large proteins (>146 kDa) and identified endogenous metal-binding and oligomeric protein complexes in human heart tissue lysate. The use of a mixed-bed stationary phase allowed retention and elution of proteins over a wide range of isoelectric points without altering the sample or mobile phase pH. Overall, our method provides a simple online IEC-MS platform that can effectively separate proteins from complex mixtures under nondenaturing conditions and preserve higher-order structure for nTDP applications.

Offline Two-Dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome Following Fecal Microbiota Transplantation.

Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C. difficile infection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.

Multiomics Characterization of a Less Invasive Microfluidic-Based Cell Sorting Technique.

Fluorescence-activated cell sorting (FACS) is a specialized technique to isolate specific cell subpopulations with a high level of recovery and accuracy. However, the cell sorting procedure can impact the viability and metabolic state of cells. Here, we performed a comparative study and evaluated the impact of traditional high-pressure charged droplet-based and microfluidic chip-based sorting on the metabolic and phosphoproteomic profile of different cell types. While microfluidic chip-based sorted cells more closely resembled the unsorted control group for most cell types tested, the droplet-based sorted cells showed significant metabolic and phosphoproteomic alterations. In particular, greater changes in redox and energy status were present in cells sorted with the droplet-based cell sorter along with larger shifts in proteostasis. 13C-isotope tracing analysis on cells recovering postsorting revealed that the sorter-induced suppression of mitochondrial TCA cycle activity recovered faster in the microfluidic chip-based sorted group. Apart from this, amino acid and lipid biosynthesis pathways were suppressed in sorted cells, with minimum impact and faster recovery in the microfluidic chip-based sorted group. These results indicate microfluidic chip-based sorting has a minimum impact on metabolism and is less disruptive compared to droplet-based sorting.

Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS).

To ensure biological validity in metabolic phenotyping, findings must be replicated in independent sample sets. Targeted workflows have long been heralded as ideal platforms for such validation due to their robust quantitative capability. We evaluated the capability of liquid chromatography-mass spectrometry (LC-MS) assays targeting organic acids and bile acids to validate metabolic phenotypes of SARS-CoV-2 infection. Two independent sample sets were collected: (1) Australia: plasma, SARS-CoV-2 positive (n = 20), noninfected healthy controls (n = 22) and COVID-19 disease-like symptoms but negative for SARS-CoV-2 infection (n = 22). (2) Spain: serum, SARS-CoV-2 positive (n = 33) and noninfected healthy controls (n = 39). Multivariate modeling using orthogonal projections to latent structures discriminant analyses (OPLS-DA) classified healthy controls from SARS-CoV-2 positive (Australia; R2 = 0.17, ROC-AUC = 1; Spain R2 = 0.20, ROC-AUC = 1). Univariate analyses revealed 23 significantly different (p < 0.05) metabolites between healthy controls and SARS-CoV-2 positive individuals across both cohorts. Significant metabolites revealed consistent perturbations in cellular energy metabolism (pyruvic acid, and 2-oxoglutaric acid), oxidative stress (lactic acid, 2-hydroxybutyric acid), hypoxia (2-hydroxyglutaric acid, 5-aminolevulinic acid), liver activity (primary bile acids), and host-gut microbial cometabolism (hippuric acid, phenylpropionic acid, indole-3-propionic acid). These data support targeted LC-MS metabolic phenotyping workflows for biological validation in independent sample sets.

Analysis of early-pregnancy metabolome in early- and late-onset gestational diabetes reveals distinct associations with maternal overweight.

AIMS/HYPOTHESIS: It is not known whether the early-pregnancy metabolome differs in patients with early- vs late-onset gestational diabetes mellitus (GDM) stratified by maternal overweight. The aims of this study were to analyse correlations between early-pregnancy metabolites and maternal glycaemic and anthropometric characteristics, and to identify early-pregnancy metabolomic alterations that characterise lean women (BMI <25 kg/m2) and women with overweight (BMI ≥25 kg/m2) with early-onset GDM (E-GDM) or late-onset GDM (L-GDM). METHODS: We performed a nested case-control study within the population-based prospective Early Diagnosis of Diabetes in Pregnancy cohort, comprising 210 participants with GDM (126 early-onset, 84 late-onset) and 209 normoglycaemic control participants matched according to maternal age, BMI class and primiparity. Maternal weight, height and waist circumference were measured at 8-14 weeks' gestation. A 2 h 75 g OGTT was performed at 12-16 weeks' gestation (OGTT1), and women with normal results underwent repeat testing at 24-28 weeks' gestation (OGTT2). Comprehensive metabolomic profiling of fasting serum samples, collected at OGTT1, was performed by untargeted ultra-HPLC-MS. Linear models were applied to study correlations between early-pregnancy metabolites and maternal glucose concentrations during OGTT1, fasting insulin, HOMA-IR, BMI and waist circumference. Early-pregnancy metabolomic features for GDM subtypes (participants stratified by maternal overweight and gestational timepoint at GDM onset) were studied using linear and multivariate models. The false discovery rate was controlled using the Benjamini-Hochberg method. RESULTS: In the total cohort (n=419), the clearest correlation patterns were observed between (1) maternal glucose concentrations and long-chain fatty acids and medium- and long-chain acylcarnitines; (2) maternal BMI and/or waist circumference and long-chain fatty acids, medium- and long-chain acylcarnitines, phospholipids, and aromatic and branched-chain amino acids; and (3) HOMA-IR and/or fasting insulin and L-tyrosine, certain long-chain fatty acids and phospholipids (q<0.001). Univariate analyses of GDM subtypes revealed significant differences (q<0.05) for seven non-glucose metabolites only in overweight women with E-GDM compared with control participants: linolenic acid, oleic acid, docosapentaenoic acid, docosatetraenoic acid and lysophosphatidylcholine 20:4/0:0 abundances were higher, whereas levels of specific phosphatidylcholines (P-16:0/18:2 and 15:0/18:2) were lower. However, multivariate analyses exploring the early-pregnancy metabolome of GDM subtypes showed differential clustering of acylcarnitines and long-chain fatty acids between normal-weight and overweight women with E- and L-GDM. CONCLUSIONS/INTERPRETATION: GDM subtypes show distinct early-pregnancy metabolomic features that correlate with maternal glycaemic and anthropometric characteristics. The patterns identified suggest early-pregnancy disturbances of maternal lipid metabolism, with most alterations observed in overweight women with E-GDM. Our findings highlight the importance of maternal adiposity as the primary target for prevention and treatment.

Variability of human Alpha-1-acid glycoprotein N-glycome in a Caucasian population.

AIM: Alpha-1-acid glycoprotein (AGP) is a highly glycosylated protein in human plasma and one of the most abundant acute phase proteins in humans. Glycosylation plays a crucial role in its biological functions, and alterations in AGP N-glycome have been associated with various diseases and inflammatory conditions. However, large-scale studies of AGP N-glycosylation in the general population are lacking. METHODS: Using recently developed high-throughput glycoproteomic workflow for site-specific AGP N-glycosylation analysis, 803 individuals from the Croatian island of Korcula were analyzed and their AGP N-glycome data associated with biochemical and physiological traits, as well as different environmental factors. RESULTS: After regression analysis, we found that AGP N-glycosylation is strongly associated with sex, somewhat less with age, along with multiple biochemical and physiological traits (e.g. BMI, triglycerides, uric acid, glucose, smoking status, fibrinogen). CONCLUSION: For the first time we have extensively explored the inter-individual variability of AGP N-glycome in a general human population, demonstrating its changes with sex, age, biochemical, and physiological status of individuals, providing the baseline for future population and clinical studies.

Changes in the amount of nucleotide sugars in aged mouse tissues.

Aging affects tissue glycan profiles, which may alter cellular functions and increase the risk of age-related diseases. Glycans are biosynthesized by glycosyltransferases using the corresponding nucleotide sugar, and the availability of nucleotide sugars affects glycosylation efficiency. However, the effects of aging on nucleotide sugar profiles and contents are yet to be elucidated. Therefore, this study aimed to investigate the effects of aging on nucleotide sugars using a new LC-MS/MS method. Specifically, the new method was used to determine the nucleotide sugar contents of various tissues (brain, liver, heart, skeletal muscle, kidney, lung, and colon) of male C57BL/6NCr mice (7- or 26-month-old). Characteristic age-associated nucleotide sugar changes were observed in each tissue sample. Particularly, there was a significant decrease in UDP-glucuronic acid content in the kidney of aged mice and a decrease in the contents of several nucleotide sugars, including UDP-N-acetylgalactosamine, in the brain of aged mice. Additionally, there were variations in nucleotide sugar profiles among the tissues examined regardless of the age. The kidneys had the highest concentration of UDP-glucuronic acid among the seven tissues. In contrast, the skeletal muscle had the lowest concentration of total nucleotide sugars among the tissues; however, CMP-N-acetylneuraminic acid and CDP-ribitol were relatively enriched. Conclusively, these findings may contribute to the understanding of the roles of glycans in tissue aging.

Impact of glycan depletion, glycan debranching and increased glycan charge on HIV-1 neutralization sensitivity and immunogenicity.

Broadly neutralizing antibodies (bNAbs) isolated from HIV-1 infected donors are vaccine paradigms. These bNAbs recognize envelope glycoprotein trimers that carry 75-90 oligomannose and complex-type glycans. Although bNAbs and their precursors must navigate past glycans, they usually also make some glycan contacts. Glycan-modified vaccines may therefore be useful to initiate and guide bNAb development. Here, we describe two ways to modify Env glycans for possible vaccine use: 1) using a cocktail of glycosidases (termed "NGAF3" (Neuraminidase, ß-Galactosidase, N-Acetylglucosaminidase, endoglycosidase F3 (endo F3)) to deplete complex glycans to try to minimize bNAb-glycan clashes and 2) co-expressing ß-1,4-galactosyltransferase 1 (B4G) and ß-galactoside α-2,6 sialyltransferase 1 (ST6) during Env biosynthesis, creating bNAb-preferred glycan structures. Mass spectrometry revealed that NGAF3 removed glycan heads at 3/7 sites occupied by complex glycans. B4G overexpression resulted in hybrid glycan development whenever complex glycans were closely spaced. The glycan at position 611 in of Env's gp41 transmembrane subunit was uniquely isolated from the effects of both endo F3 and B4G. B4G and ST6 co-expression increased hybrid and sialylated glycan abundance, reducing glycan complexity. In rabbit vaccinations, B4G + ST6 virus-like particles (VLPs) induced less frequent, weaker titer NAbs, implying that ST6-mediated increased Env charge dampens vaccine antibodies. In some cases, vaccine sera preferentially neutralized B4G + ST6-modified pseudovirus. HIV-1+ donor plasma NAbs were generally more effective against B4G + ST6 modified pseudovirus, suggesting a preference for less complex and/or α-2,6 sialylated Env trimers. Collectively, our data suggest that B4G and ST6 Env modifications are best suited for intermediate or late vaccine shots.

Salivary metabolomic biomarkers for esophageal and gastric cancers by liquid chromatography-mass spectrometry.

Early detection of esophageal and gastric cancers is essential for patients' prognosis; however, optimal noninvasive screening tests are currently not available. Saliva is a biofluid that is readily available, allowing for frequent screening tests. Thus, we explored salivary diagnostic biomarkers for esophageal and gastric cancers using metabolomic analyses. Saliva samples were collected from patients with esophageal (n = 50) and gastric cancer (n = 63), and patients without cancer as controls (n = 20). Salivary metabolites were analyzed by liquid chromatography-mass spectrometry to identify salivary biomarkers. We also examined the metabolic profiles of gastric cancer tissues and compared them with the salivary biomarkers. The sensitivity of the diagnostic models based on salivary biomarkers was assessed by comparing it with that of serum tumor markers. Additionally, using postoperative saliva samples collected from patients with gastric cancer, we analyzed the changes in the biomarkers' concentrations before and after surgery. Cytosine was detected as a salivary biomarker for gastric cancer, and cytosine, 2-oxoglutarate, and arginine were detected as salivary biomarkers for esophageal cancer. Cytidine, a cytosine nucleotide, showed decreased concentrations in gastric cancer tissues. The sensitivity of the diagnostic models for esophageal and gastric cancers was 66.0% and 47.6%, respectively, while that of serum tumor markers was 40%. Salivary cytosine concentration increased significantly postoperatively relative to the preoperative value. In summary, we identified salivary biomarkers for esophageal and gastric cancers, which showed diagnostic sensitivity at least comparable to that of serum tumor markers. Salivary metabolomic tests could be promising screening tests for these types of cancer.

Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics.

PURPOSE: Preoperative chemotherapy is a critical component of breast cancer management, yet its effectiveness is not uniform. Moreover, the adverse effects associated with chemotherapy necessitate the identification of a patient subgroup that would derive the maximum benefit from this treatment. This study aimed to establish a method for predicting the response to neoadjuvant chemotherapy in breast cancer patients utilizing a metabolomic approach. METHODS: Plasma samples were obtained from 87 breast cancer patients undergoing neoadjuvant chemotherapy at our facility, collected both before the commencement of the treatment and before the second treatment cycle. Metabolite analysis was conducted using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS). We performed comparative profiling of metabolite concentrations by assessing the metabolite profiles of patients who achieved a pathological complete response (pCR) against those who did not, both in initial and subsequent treatment cycles. RESULTS: Significant variances were observed in the metabolite profiles between pCR and non-pCR cases, both at the onset of preoperative chemotherapy and before the second cycle. Noteworthy distinctions were also evident between the metabolite profiles from the initial and the second neoadjuvant chemotherapy courses. Furthermore, metabolite profiles exhibited variations associated with intrinsic subtypes at all assessed time points. CONCLUSION: The application of plasma metabolomics, utilizing CE-MS and LC-MS, may serve as a tool for predicting the efficacy of neoadjuvant chemotherapy in breast cancer in the future after all necessary validations have been completed.