JUMPt: Comprehensive Protein Turnover Modeling of In Vivo Pulse SILAC Data by Ordinary Differential Equations.

Recent advances in mass spectrometry (MS)-based proteomics allow the measurement of turnover rates of thousands of proteins using dynamic labeling methods, such as pulse stable isotope labeling by amino acids in cell culture (pSILAC). However, when applying the pSILAC strategy to multicellular animals (e.g., mice), the labeling process is significantly delayed by native amino acids recycled from protein degradation in vivo, raising a challenge of defining accurate protein turnover rates. Here, we report JUMPt, a software package using a novel ordinary differential equation (ODE)-based mathematical model to determine reliable rates of protein degradation. The uniqueness of JUMPt is to consider amino acid recycling and fit the kinetics of the labeling amino acid (e.g., Lys) and whole proteome simultaneously to derive half-lives of individual proteins. Multiple settings in the software are designed to enable simple to comprehensive data inputs for precise analysis of half-lives with flexibility. We examined the software by studying the turnover of thousands of proteins in the pSILAC brain and liver tissues. The results were largely consistent with the proteome turnover measurements from previous studies. The long-lived proteins are enriched in the integral membrane, myelin sheath, and mitochondrion in the brain. In summary, the ODE-based JUMPt software is an effective proteomics tool for analyzing large-scale protein turnover, and the software is publicly available on GitHub (https://github.com/JUMPSuite/JUMPt) to the research community.

Improved Dried Blood Spot-Based Metabolomics Analysis by a Postcolumn Infused-Internal Standard Assisted Liquid Chromatography-Electrospray Ionization Mass Spectrometry Method.

Dried blood spots (DBSs) have gained increasing attention recently with their growing importance in precision medicine. DBS-based metabolomics analysis provides a powerful tool for investigating new biomarkers. Until now, very few studies have discussed measures for improving analytical accuracy with the consideration of the special characteristics of DBSs. The present study proposed a postcolumn infused-internal standard (PCI-IS) assisted strategy to improve data quality for DBS-based metabolomics studies. An efficient sample preparation protocol with 80% acetonitrile as the extraction solvent was first established to improve the metabolite recovery. The PCI-IS assisted liquid chromatography-electrospray ionization mass spectrometry (LC-ESI-MS) method was used to simultaneously estimate the blood volume and correct the signal change caused by ion source contamination and the matrix effect to evaluate the spot volume effect and hematocrit (Hct) variation effect on target metabolites. Phenylalanine-d8 was selected as the single PCI-IS to correct the matrix effect. For calibration of errors caused by the blood volume difference, 75% of the test metabolites showed good correlation (R2 ≥ 0.9) between the spot volume and the signal intensity after PCI-IS correction compared to less than 50% metabolites with good correlation before calibration. The spot volume was further calibrated by the same PCI-IS. Investigation of the Hct variation effect on target metabolites revealed that it affected the concentrations of metabolites in the DBS samples depending on their abundance in the red blood cell (RBC) or plasma; it is essential to preinvestigate the distribution of metabolites in blood to minimize the comparison bias in metabolomics studies. Finally, the PCI-IS assisted method was applied to study acetaminophen-induced liver toxicity. The results indicated that the proposed PCI-IS strategy could effectively remove analytical errors and improve the data quality, which would make the DBS-based metabolomics more feasible in real-world applications.

High-throughput and Deep-proteome Profiling by 16-plex Tandem Mass Tag Labeling Coupled with Two-dimensional Chromatography and Mass Spectrometry.

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an expanded 16-plex TMT method has been introduced, which further increases the throughput of proteomic studies. In this manuscript, we present an optimized protocol for 16-plex TMT-based deep-proteome profiling, including protein sample preparation, enzymatic digestion, TMT labeling reaction, two-dimensional reverse-phase liquid chromatography (LC/LC) fractionation, tandem mass spectrometry (MS/MS), and computational data processing. The crucial quality control steps and improvements in the process specific for the 16-plex TMT analysis are highlighted. This multiplexed process offers a powerful tool for profiling a variety of complex samples such as cells, tissues, and clinical specimens. More than 10,000 proteins and posttranslational modifications such as phosphorylation, methylation, acetylation, and ubiquitination in highly complex biological samples from up to 16 different samples can be quantified in a single experiment, providing a potent tool for basic and clinical research.

Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer's disease.

BACKGROUND: Based on amyloid cascade and tau hypotheses, protein biomarkers of different Aß and tau species in cerebrospinal fluid (CSF) and blood/plasma/serum have been examined to correlate with brain pathology. Recently, unbiased proteomic profiling of these human samples has been initiated to identify a large number of novel AD biomarker candidates, but it is challenging to define reliable candidates for subsequent large-scale validation. METHODS: We present a comprehensive strategy to identify biomarker candidates of high confidence by integrating multiple proteomes in AD, including cortex, CSF and serum. The proteomes were analyzed by the multiplexed tandem-mass-tag (TMT) method, extensive liquid chromatography (LC) fractionation and high-resolution tandem mass spectrometry (MS/MS) for ultra-deep coverage. A systems biology approach was used to prioritize the most promising AD signature proteins from all proteomic datasets. Finally, candidate biomarkers identified by the MS discovery were validated by the enzyme-linked immunosorbent (ELISA) and TOMAHAQ targeted MS assays. RESULTS: We quantified 13,833, 5941, and 4826 proteins from human cortex, CSF and serum, respectively. Compared to other studies, we analyzed a total of 10 proteomic datasets, covering 17,541 proteins (13,216 genes) in 365 AD, mild cognitive impairment (MCI) and control cases. Our ultra-deep CSF profiling of 20 cases uncovered the majority of previously reported AD biomarker candidates, most of which, however, displayed no statistical significance except SMOC1 and TGFB2. Interestingly, the AD CSF showed evident decrease of a large number of mitochondria proteins that were only detectable in our ultra-deep analysis. Further integration of 4 cortex and 4 CSF cohort proteomes highlighted 6 CSF biomarkers (SMOC1, C1QTNF5, OLFML3, SLIT2, SPON1, and GPNMB) that were consistently identified in at least 2 independent datasets. We also profiled CSF in the 5xFAD mouse model to validate amyloidosis-induced changes, and found consistent mitochondrial decreases (SOD2, PRDX3, ALDH6A1, ETFB, HADHA, and CYB5R3) in both human and mouse samples. In addition, comparison of cortex and serum led to an AD-correlated protein panel of CTHRC1, GFAP and OLFM3. In summary, 37 proteins emerged as potential AD signatures across cortex, CSF and serum, and strikingly, 59% of these were mitochondria proteins, emphasizing mitochondrial dysfunction in AD. Selected biomarker candidates were further validated by ELISA and TOMAHAQ assays. Finally, we prioritized the most promising AD signature proteins including SMOC1, TAU, GFAP, SUCLG2, PRDX3, and NTN1 by integrating all proteomic datasets. CONCLUSIONS: Our results demonstrate that novel AD biomarker candidates are identified and confirmed by proteomic studies of brain tissue and biofluids, providing a rich resource for large-scale biomarker validation for the AD community.

Efficient and flexible implementation of Langevin simulation for gene burst production.

Gene expression involves bursts of production of both mRNA and protein, and the fluctuations in their number are increased due to such bursts. The Langevin equation is an efficient and versatile means to simulate such number fluctuation. However, how to include these mRNA and protein bursts in the Langevin equation is not intuitively clear. In this work, we estimated the variance in burst production from a general gene expression model and introduced such variation in the Langevin equation. Our approach offers different Langevin expressions for either or both transcriptional and translational bursts considered and saves computer time by including many production events at once in a short burst time. The errors can be controlled to be rather precise (<2%) for the mean and <10% for the standard deviation of the steady-state distribution. Our scheme allows for high-quality stochastic simulations with the Langevin equation for gene expression, which is useful in analysis of biological networks.

Noise propagation with interlinked feed-forward pathways.

Functionally similar pathways are often seen in biological systems, forming feed-forward controls. The robustness in network motifs such as feed-forward loops (FFLs) has been reported previously. In this work, we studied noise propagation in a development network that has multiple interlinked FFLs. A FFL has the potential of asymmetric noise-filtering (i.e., it works at either the "ON" or the "OFF" state in the target gene). With multiple, interlinked FFLs, we show that the propagated noises are largely filtered regardless of the states in the input genes. The noise-filtering property of an interlinked FFL can be largely derived from that of the individual FFLs, and with interlinked FFLs, it is possible to filter noises in both "ON" and "OFF" states in the output. We demonstrated the noise filtering effect in the developmental regulatory network of Caenorhabditis elegans that controls the timing of distal tip cell (DTC) migration. The roles of positive feedback loops involving blmp-1 and the degradation regulation of DRE-1 also studied. Our analyses allow for better inference from network structures to noise-filtering properties, and provide insights into the mechanisms behind the precise DTC migration controls in space and time.