Project description:Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95% confidence interval (CI) [0.79 to 0.89], P = 1.2 × 10-40, N = 53, training set; R = 0.81, 95% CI [0.61 to 0.91], P = 3.9 × 10-7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies.
Project description:Gestational Diabetes Mellitus (GDM), which is correlated with changes in the gut microbiota, is a risk factor for neonatal inborn errors of metabolism (IEMs). Maternal hyperglycemia exerts epigenetic effects on genes that encode IEM-associated enzymes, resulting in changes in the neonatal blood metabolome. However, the relationship between maternal gut microbiota and the neonatal blood metabolome remains poorly understood. This study aimed at understanding the connections between maternal gut microbiota and the neonatal blood metabolome in GDM. 1H-NMR-based untargeted metabolomics was performed on maternal fecal samples and targeted metabolomics on the matched neonatal dry blood spots from a cohort of 40 pregnant women, including 22 with GDM and 18 controls. Multi-omic association methods (including Co-Inertia Analysis and Procrustes Analysis) were applied to investigate the relationship between maternal fecal metabolome and the neonatal blood metabolome. Both maternal fecal metabolome and the matched neonatal blood metabolome could be separated along the vector of maternal hyperglycemia. A close relationship between the maternal and neonatal metabolomes was observed by multi-omic association approaches. Twelve out of thirty-two maternal fecal metabolites with altered abundances from 872 1H- NMR features (Bonferroni-adjusted P < 0.05) in women with GDM and the controls were identified, among which 8 metabolites contribute (P < 0.05 in a 999-step permutation test) to the close connection between maternal and the neonatal metabolomes in GDM. Four of these eight maternal fecal metabolites, including lysine, putrescine, guanidinoacetate, and hexadecanedioate, were negatively associated (Spearman rank correlation, coefficient value < -0.6, P < 0.05) with maternal hyperglycemia. Biotin metabolism was enriched (Bonferroni-adjusted P < 0.05 in the hypergeometric test) with the four-hyperglycemia associated fecal metabolites. The results of this study suggested that maternal fecal metabolites contribute to the connections between maternal fecal metabolome and the neonatal blood metabolome and may further affect the risk of IEMs.
Project description:Colorectal cancer (CRC) is currently the third most common cancer with a high mortality rate. The underlying molecular mechanism of CRC, especially advanced CRC, remains poorly understood, resulting in few available therapeutic plans. To expand our knowledge of the molecular characteristics of advanced CRC and explore possible new therapeutic strategies, we herein conducted integrated proteomics and metabolomics analyses of 40 serum samples collected from 20 advanced CRC patients before and after treatment. The mass spectrometry-based proteomics analysis was performed under data-independent acquisition (DIA), and the metabolomics analysis was performed by ultra-performance liquid chromatography coupled with time-of-flight tandem mass spectrometry (UPLC-TOF-MS/MS). Trace elements including Mg, Zn, and Fe were measured by inductively coupled plasma spectrometry (ICP-MS) analysis. Four of the 20 patients had progressive disease (PD) after treatment, and clinical test results indicated that they all had impaired liver functions. In the proteomics analysis, 64 proteins were discovered to be significantly altered after treatment. These proteins were enriched in cancer-related pathways and pathways participating immune responses, such as MAPK signaling pathway and complement/coagulation cascades. In the metabolomics analysis, 128 metabolites were found to be significantly changed after treatment, and most of them are enriched in pathways associated with lipid metabolism. The cholesterol metabolism pathway was significantly enriched in both the proteomics and metabolomics pathway enrichment analyses. The concentrations of Mg in the serums of CRC patients were significantly lower than those in healthy individuals, which returned to the normal range after treatment. Correlation analysis linked key lipids, proteins, and Mg as immune modulators in the development of advanced CRC. The results of this study not only extended our knowledge on the molecular basis of advanced CRC but also provided potential novel therapeutic targets for CRC treatment.
Project description:The remobilization of stored assimilates from stems to seeds plays a pivotal role in augmenting barley yield, particularly under water stress conditions. This study examines the molecular mechanisms underlying stem reserve utilization by conducting a comparative analysis of the proteome and metabolome across three barley contrasting genotypes: Yousef, Morocco, and PBYT17. Evaluations were performed at 21 and 28 days after anthesis (DAA) under both water stress and control conditions. The results indicate that the Yousef genotype exhibits superior remobilization of stem reserves, thereby demonstrating its potential to thrive even in adverse environmental conditions. Utilizing advanced quantitative proteomics and targeted metabolomics, this investigation identified a significant number of metabolites and proteins exhibiting differential accumulation across the genotypes. Specifically, 17 metabolites and 1580 proteins were catalogued, highlighting the intricate biochemical responses to water stress. Noteworthy enzymes such as sucrose synthase, inositol monophosphatase 3, and galactokinase were found to be closely associated with remobilization efficiency. In the drought-tolerant genotype, these enzymes maintained stable levels, in stark contrast to the decline observed in the susceptible genotype. This stability is crucial for promoting seed development through ascorbic acid synthesis and for mitigating oxidative stress, which is exacerbated by drought conditions. The elevated levels of certain metabolites, including glucose 6-phosphate, and UDP-glucose, in the drought-tolerant genotype suggest a robust mechanism for maintaining signalling molecules for carbon availability, which is then instrumental in regulating plant growth and seed size development. The findings from this study strongly imply that the drought-tolerant genotype, through enhanced antioxidant capacity, can effectively produce energy-rich storage compounds, thereby optimizing carbon allocation under water stress. Such insights are invaluable for future breeding strategies aimed at improving barley resilience in the face of climate variability.
Project description:Tandem high-throughput proteomics and metabolomics were employed to functionally characterize natural microbial biofilm communities. Distinct molecular signatures exist for each analyzed sample. Deconvolution of the high-resolution molecular data demonstrates that identified proteins and detected metabolites exhibit organism-specific correlation patterns. These patterns are reflective of the functional differentiation of two bacterial species that share the same genus and that co-occur in the sampled microbial communities. Our analyses indicate that the two species have similar niche breadths and are not in strong competition with one another.
Project description:Changes in cellular metabolism contribute to the development and progression of tumors, and can render tumors vulnerable to interventions. However, studies of human cancer metabolism remain limited due to technical challenges of detecting and quantifying small molecules, the highly interconnected nature of metabolic pathways, and the lack of designated tools to analyze and integrate metabolomics with other âomics data. Our study generates the largest comprehensive metabolomics dataset on a single cancer type, and provides a significant advance in integration of metabolomics with sequencing data. Our results highlight the massive re-organization of cellular metabolism as tumors progress and acquire more aggressive features. The results of our work are made available through an interactive public data portal for cancer research community. 10 RNA samples from human ccRCC tumors analyzed from the high glutathione cluster
Project description:Sharing of research data in public repositories has become best practice in academia. With the accumulation of massive data, network bandwidth and storage requirements are rapidly increasing. The ProteomeXchange (PX) consortium implements a mode of centralized metadata and distributed raw data management, which promotes effective data sharing. To facilitate open access of proteome data worldwide, we have developed the integrated proteome resource iProX (http://www.iprox.org) as a public platform for collecting and sharing raw data, analysis results and metadata obtained from proteomics experiments. The iProX repository employs a web-based proteome data submission process and open sharing of mass spectrometry-based proteomics datasets. Also, it deploys extensive controlled vocabularies and ontologies to annotate proteomics datasets. Users can use a GUI to provide and access data through a fast Aspera-based transfer tool. iProX is a full member of the PX consortium; all released datasets are freely accessible to the public. iProX is based on a high availability architecture and has been deployed as part of the proteomics infrastructure of China, ensuring long-term and stable resource support. iProX will facilitate worldwide data analysis and sharing of proteomics experiments.
Project description:C2C12 myoblast is a model that has been used extensively to study the process of skeletal muscle differentiation. Proteomics has advanced our understanding of skeletal muscle biology and the process of myogenesis. However, there is still no deep coverage of C2C12 myoblast proteome, which is important for the understanding of key drivers for the differentiation of skeletal muscle cells. Here, we conducted a multi-dimensional proteome profiling with TiSH strategy to get a comprehensive analysis of proteome, phosphoproteome and N-linked sialylated glycoproteome of C2C12 myoblasts. A total of 8313 protein groups were identified in C2C12 myoblasts, including 7827 protein groups from non-modified peptides, 3803 phosphoproteins and 977 formerly N-linked sialylated glycoproteins.
Project description:Proteomics and metabolomics are essential in systems biology, and simultaneous proteo-metabolome liquid-liquid extraction (SPM-LLE) allows isolation of the metabolome and proteome from the same sample. Since the proteome is present as a pellet in SPM-LLE, it must be solubilized for quantitative proteomics. Solubilization and proteome extraction are critical factors in the information obtained at the proteome level. In this study, we investigated the performance of two surfactants (sodium deoxycholate (SDC), sodium dodecyl sulfate (SDS)) and urea in terms of proteome coverage and extraction efficiency of an interphase proteome pellet generated by methanol-chloroform based SPM-LLE. We also investigated how the performance differs when the proteome is extracted from the interphase pellet or by direct cell lysis. We quantified 12 lipids covering triglycerides and various phospholipid classes, and 25 polar metabolites covering central energy metabolism in chloroform and methanol extracts. Our study reveals that the proteome coverages between the two surfactants and urea for the SPM-LLE interphase pellet were similar, but the extraction efficiencies differed significantly. While SDS led to enrichment of basic proteins, which were mainly ribosomal and ribonuclear proteins, urea was the most efficient extraction agent for simultaneous proteo-metabolome analysis. The results of our study also show that the performance of surfactants for quantitative proteomics is better when the proteome is extracted through direct cell lysis rather than an interphase pellet. In contrast, the performance of urea for quantitative proteomics was significantly better when the proteome was extracted from an interphase pellet than by direct cell lysis. We demonstrated that urea is superior to surfactants for proteome extraction from SPM-LLE interphase pellets, with a particularly good performance for the extraction of proteins associated with metabolic pathways. Data are available via ProteomeXchange with identifier PXD027338.
Project description:To expand the understanding of aging in the model organism Caenorhabditis elegans, global quantification of metabolite and protein levels in young and aged nematodes was performed using mass spectrometry. With age, there was a decreased abundance of proteins functioning in transcription termination, mRNA degradation, mRNA stability, protein synthesis, and proteasomal function. Furthermore, there was altered S-adenosyl methionine metabolism as well as a decreased abundance of the S-adenosyl methionine synthetase (SAMS-1) protein. Other aging-related changes included alterations in free fatty acid levels and composition, decreased levels of ribosomal proteins, decreased levels of NADP-dependent isocitrate dehydrogenase (IDH1), a shift in the cellular redox state, an increase in sorbitol content, alterations in free amino acid levels, and indications of altered muscle function and sarcoplasmic reticulum Ca(2+) homeostasis. There were also decreases in pyrimidine and purine metabolite levels, most markedly nitrogenous bases. Supplementing the culture medium with cytidine (a pyrimidine nucleoside) or hypoxanthine (a purine base) increased lifespan slightly, suggesting that aging-induced alterations in ribonucleotide metabolism affect lifespan. An age-related increase in body size, lipotoxicity from ectopic yolk lipoprotein accumulation, a decline in NAD(+) levels, and mitochondrial electron transport chain dysfunction may explain many of these changes. In addition, dietary restriction in aged worms resulting from sarcopenia of the pharyngeal pump likely decreases the abundance of SAMS-1, possibly leading to decreased phosphatidylcholine levels, larger lipid droplets, and ER and mitochondrial stress. The complementary use of proteomics and metabolomics yielded unique insights into the molecular processes altered with age in C. elegans.