Identification and Validation of Urinary Metabolite Biomarkers for Major Depressive Disorder

Major depressive disorder (MDD) is a widespread and debilitating mental disorder. However, there are no biomarkers available to aid in the diagnosis of this disorder. In this study, a nuclear magnetic resonance spectroscopy–based metabonomic approach was employed to profile urine samples from 82 first-episode drug-naïve depressed subjects and 82 healthy controls (the training set) in order to identify urinary metabolite biomarkers for MDD. Then, 44 unselected depressed subjects and 52 healthy controls (the test set) were used to independently validate the diagnostic generalizability of these biomarkers. A panel of five urinary metabolite biomarkers—malonate, formate, N-methylnicotinamide, m-hydroxyphenylacetate, and alanine—was identified. This panel was capable of distinguishing depressed subjects from healthy controls with an area under the receiver operating characteristic curve (AUC) of 0.81 in the training set. Moreover, this panel could classify blinded samples from the test set with an AUC of 0.89. These findings demonstrate that this urinary metabolite biomarker panel can aid in the future development of a urine-based diagnostic test for MDD.Major depressive disorder (MDD)¹ is a debilitating mental disorder affecting up to 15% of the general population and accounting for 12.3% of the global burden of disease (1, 2). Currently, the diagnosis of MDD still relies on the subjective identification of symptom clusters rather than empirical laboratory tests. The current diagnostic modality results in a considerable error rate (3), as the clinical presentation of MDD is highly heterogeneous and the current symptom-based method is not capable of adequately characterizing this heterogeneity (4). An approach that can be used to circumvent these limitations is to identify disease biomarkers to support objective diagnostic laboratory tests for MDD.Metabonomics, which can measure the small molecules in given biosamples such as plasma and urine without bias (5), has been extensively used to characterize the metabolic changes of diseases and thus facilitate the identification of novel disease-specific signatures as putative biomarkers (6–10). Nuclear magnetic resonance (NMR) spectroscopy–based metabonomic approaches characterized by sensitive, high-throughput molecular screening have been employed previously in identifying novel biomarkers for a variety of neuropsychiatric disorders, including stroke, bipolar disorder, and schizophrenia (11–13).Specifically with regard to MDD, several animal studies have already characterized the metabolic changes in the blood and urine (14–19). These studies provide valuable clues as to the pathophysiological mechanism of MDD. However, no study has been designed with the aim of diagnosing this disease. Recently, using an NMR-based metabonomic approach, this research group identified a unique plasma metabolic signature that enables the discrimination of MDD from healthy controls with both high sensitivity and specificity (20). These findings motivated further study on urinary diagnostic metabolite biomarkers for MDD, which would be more valuable from a clinical applicability standpoint, as urine can be more non-invasively collected. Moreover, previous studies have also demonstrated the feasibility of identifying diagnostic metabolite biomarkers of psychiatric disorders in the urine. For example, using an NMR-based metabonomics approach, Yap et al. (21) identified a unique urinary metabolite signature that clearly discriminated autism patients from healthy controls. As systemic metabolic disturbances have been observed in the urine of a depressed animal model, it is likely that diagnostic metabolite markers for MDD can be detected in human urine.Therefore, in this study, NMR spectroscopy combined with multivariate pattern recognition techniques were used to profile 82 first-episode drug-naïve MDD subjects and 82 healthy controls (the training set) in order to identify potential metabolite biomarkers for MDD. Furthermore, 44 unselected MDD subjects and 52 healthy controls (the test set) were employed to independently validate the diagnostic performance of these urinary metabolite biomarkers.     

Quantitative Measurement of Phosphoproteome Response to Osmotic Stress in Arabidopsis Based on Library-Assisted eXtracted Ion Chromatogram (LAXIC)

Global phosphorylation changes in plants in response to environmental stress have been relatively poorly characterized to date. Here we introduce a novel mass spectrometry-based label-free quantitation method that facilitates systematic profiling plant phosphoproteome changes with high efficiency and accuracy. This method employs synthetic peptide libraries tailored specifically as internal standards for complex phosphopeptide samples and accordingly, a local normalization algorithm, LAXIC, which calculates phosphopeptide abundance normalized locally with co-eluting library peptides. Normalization was achieved in a small time frame centered to each phosphopeptide to compensate for the diverse ion suppression effect across retention time. The label-free LAXIC method was further treated with a linear regression function to accurately measure phosphoproteome responses to osmotic stress in Arabidopsis. Among 2027 unique phosphopeptides identified and 1850 quantified phosphopeptides in Arabidopsis samples, 468 regulated phosphopeptides representing 497 phosphosites have shown significant changes. Several known and novel components in the abiotic stress pathway were identified, illustrating the capability of this method to identify critical signaling events among dynamic and complex phosphorylation. Further assessment of those regulated proteins may help shed light on phosphorylation response to osmotic stress in plants.Phosphorylation plays a pivotal role in the regulation of a majority of cellular processes via signaling transduction pathways. During the last decade, quantitative phosphoproteomics has become a powerful and versatile platform to profile signaling pathways at a system-wide scale. Multiple signaling networks in different organisms have been characterized through global phosphorylation profiling (1–3), which has evolved over the years with highly optimized procedures for sample preparation and phosphopeptide enrichment, high resolution mass spectrometry, and data analysis algorithms to identify and quantify thousands of phosphorylation events (4–8).Quantitative phosphoproteomics can be achieved mainly by two major techniques, stable isotope labeling and label-free quantitation. Isotope labeling prior to liquid chromatography-mass spectrometry (LC-MS)¹ has been widely used, including metabolic labeling such as stable isotope labeling by amino acids in cell culture (SILAC), chemical labeling such as multiplexed isobaric tags for relative and absolute quantification (iTRAQ) and isotope-coded affinity tags (ICAT) (9–12). On the other hand, label-free quantitation has gained momentum in recent years (13–15), as no additional chemistry or sample preparation steps are required. Compared with stable isotope labeling, label-free quantitation has higher compatibility with the source of the samples, the number of samples for comparison, and the instrument choice.Many label-free approaches, in particular to phosphoproteomics, are based on ion intensity (16, 17), but they are relatively error-prone because of run-to-run variations in LC/MS performance (18). In theory, such systematic errors can be corrected by spiking an internal standard into every sample to be compared. Several methods based on internal standard spiking have been reported so far. Absolute quantification peptide technology (AQUA) (19), for example, uses synthetic peptides with isotope labeling for absolute quantitation. For a global quantitative proteomics study, it is unrealistic to spike-in all reference peptides. Another labeling reference method, spike-in SILAC appears to be a promising technique to quantify the proteome in vivo with multiplex capability and it can be extended to clinical samples (20). One solution to large-scale quantitation without any isotope labeling is pseudo internal standard approach (21), which selects endogenous house-keeping proteins as the internal standard so that no spike-in reagent is required. However, finding a good pseudo internal standard remains a challenge for phosphoproteome samples. Spike-in experiments are an alternative way to improve normalization profile. Some internal standard peptides such as MassPREP^TM (Waters) were already widely used. Other groups spiked an identical amount of standard protein into samples prior to protein digestion (22–24). There are two major normalization procedures. In one approach, sample peptides were normalized to the total peak intensity of spike-in peptides (25). Alternatively, the digested peptides were compared at first and the normalization factor was determined in different ways such as the median (26) or average of ratios (27). However, spiking an identical amount of standard proteins into phosphoproteomic samples before protein digestion may not be compatible with phosphoproteomic analyses which typically require a phosphopeptide enrichment step. Spectral counting has been extensively applied in large sets of proteomic samples because of its simplicity but the method is often not reliable for the quantitation of phosphoproteins, which are typically identified by single phosphopeptides with few spectra (28–30). Many software packages have been implemented to support the wide variety of those quantitation techniques, including commercial platforms such as Progenesis LC-MS^TM, Mascot Distiller^TM, PEAKS Q^TM, etc., as well as open-source software packages including MaxQuant (31), PEPPeR (32), Skyline (33), etc.In this study, we have devised a novel label-free quantitation strategy termed Library Assisted eXtracted Ion Chromatogram (LAXIC) for plant phosphoproteomic analyses with high accuracy and consistency (Fig. 1). The approach employs synthetic peptide libraries as the internal standard. These peptides were prepared to have proper properties for quality control assessments and mass spectrometric measurements. In particular, peptides were designed to elute sequentially over an entire LC gradient and to have suitable ionization efficiency and m/z values within the normally scanned mass range. Local normalization of peak intensity is performed using Loess Procedure, a data treatment adopted from cDNA microarray data analysis (34). To monitor the diverse ion suppression effect across retention time, the local normalization factors (LNFs) are determined by internal standard pairs in individual time windows. Finally, samples will be quantified with LNFs in order to correct variance of LC-MS conditions. This quantification occurs in a small time frame centered to each target peptide.Open in a separate windowFig. 1.Work flow for the LAXIC strategy to quantify the phosphorylation change in response to osmotic stress. A, Schematic representation of the LAXIC algorithm. First, all the chromatographic peaks were aligned and the ratios were calculated. Second, the normalization factors which equal to ratios of library peptide peaks between MS runs were chosen to construct normalization curve. Third, sample peptide peak ratios were normalized against predicted normalization factor corresponding to certain retention time. B, Schematic representation of quantitative phosphoproteomics. Plants either treated with mannitol or PBS were lysed and mixed proportionally at first. Following peptide digestion and enrichment, phosphopeptides were identified and further quantified through LAXIC incorporated with self-validating process using thelinear regression model to analyze the fold change (fold), linearity (R²) and accuracy (%Acc).Water deficit and salinity causes osmotic stress, which is a major environmental factor limiting plant agricultural productivity. Osmotic stress rapidly changes the metabolism, gene expression and development of plant cells by activating several signaling pathways. Several protein kinases have been characterized as key components in osmotic stress signaling. Arabidopsis histidine kinase AHK1 can complement the histidine kinase mutant yeast, which can act as the osmosensor in yeast (35). Overexpression of AHK1 gene increases the drought tolerance of transgenic plants in Arabidopsis (36). Similar to yeast, the MAPK kinase cascade is also involved in osmotic stress response in plants. It is reported that AtMPK3, AtMPK6, and tobacco SIPK can be activated by NaCl or mannitol, and play positive roles in osmotic signaling (37, 38). MKK7 and MKKK20 may act as the up-stream kinase in the kinase cascade (39). Involvement of some calcium-dependent protein kinases, such as AtCPK21, AtCPK6, and OsCPK7 (CDPK) in osmotic stress signaling has also been reported (40–42). Another kinase family, SNF1-related protein kinase (SnRK) 2, also participates in osmotic stress response. In Arabidopsis, there are ten members in the SnRK2 family. Five from the ten SnRK2s, SnRK2, 3, 6, 7, and 8, can be activated by abscisic acid (ABA) and play central roles in ABA-receptor coupled signaling (43, 44). Furthermore, all SnRK2s except SnRK2.9 can be activated by NaCl or mannitol treatment (43). The decuple mutant of SnRK2 showed a strong osmotic hypersensitive phenotype (45). It is proposed that protein kinases including MAPK and SnRK2s have a critical function in osmotic stress (46), but the detailed mechanism and downstream substrates or target signal components are not yet clarified. We applied, therefore, the LAXIC approach with a self-validating method (47) to profile the osmotic stress-dependent phosphoproteome in Arabidopsis by quantifying phosphorylation events before and after mannitol treatment. Among a total of over 2000 phosphopeptides, more than 400 of them are dependent on osmotic stress. Those phosphoproteins are present on enzymes participating in signaling networks that are involved in many processes such as signal transduction, cytoskeleton development, and apoptosis. Overall, LAXIC represents a powerful tool for label-free quantitative phosphoproteomics.     

Identification of Direct Tyrosine Kinase Substrates Based on Protein Kinase Assay-Linked Phosphoproteomics

Protein kinases are implicated in multiple diseases such as cancer, diabetes, cardiovascular diseases, and central nervous system disorders. Identification of kinase substrates is critical to dissecting signaling pathways and to understanding disease pathologies. However, methods and techniques used to identify bona fide kinase substrates have remained elusive. Here we describe a proteomic strategy suitable for identifying kinase specificity and direct substrates in high throughput. This approach includes an in vitro kinase assay-based substrate screening and an endogenous kinase dependent phosphorylation profiling. In the in vitro kinase reaction route, a pool of formerly phosphorylated proteins is directly extracted from whole cell extracts, dephosphorylated by phosphatase treatment, after which the kinase of interest is added. Quantitative proteomics identifies the rephosphorylated proteins as direct substrates in vitro. In parallel, the in vivo quantitative phosphoproteomics is performed in which cells are treated with or without the kinase inhibitor. Together, proteins phosphorylated in vitro overlapping with the kinase-dependent phosphoproteome in vivo represents the physiological direct substrates in high confidence. The protein kinase assay-linked phosphoproteomics was applied to identify 25 candidate substrates of the protein-tyrosine kinase SYK, including a number of known substrates and many novel substrates in human B cells. These shed light on possible new roles for SYK in multiple important signaling pathways. The results demonstrate that this integrated proteomic approach can provide an efficient strategy to screen direct substrates for protein tyrosine kinases.Protein phosphorylation plays a pivotal role in regulating biological events such as protein–protein interactions, signal transduction, subcellular localization, and apoptosis (1). Deregulation of kinase-substrate interactions often leads to disease states such as human malignancies, diabetes, and immune disorders (2). Although a number of kinases are being targeted to develop new drugs, our understanding of the precise relationships between protein kinases and their direct substrates is incomplete for the majority of protein kinases (3). Thus, mapping kinase–substrate relationships is essential for the understanding of biological signaling networks and the discovery and development of drugs for targeted therapies (4). Toward this goal, various in vitro kinase assays using synthetic peptide libraries (5), phage expression libraries (6), protein arrays (7–9), or cell extracts (10, 11) have been explored for the screening of kinase substrates.Besides classical biochemical and genetic methods, mass spectrometry-based high throughput approaches have become increasingly attractive because they are capable of sequencing proteins and localizing phosphorylation sites at the same time. Mass spectrometry-based proteomic methods have been extensively applied to kinase-substrate interaction mapping (12) and global phosphorylation profiling (13–15). Although thousands of phosphorylation events can be inspected simultaneously (16, 17), large-scale phosphoproteomics does not typically reveal direct relationships between protein kinases and their substrates.Recently, several mass spectrometry-based proteomic strategies have been introduced for identifying elusive kinase substrates (7, 18, 19). Taking advantage of recent advances of high speed and high-resolution mass spectrometry, these methods used purified, active kinases to phosphorylate cell extracts in vitro, followed by mass spectrometric analysis to identify phosphoproteins. These approaches commonly face the major challenge of distinguishing phosphorylation events triggered by the kinase reaction from background signals introduced by endogenous kinase activities (20). To dissect the phosphorylation cascade, Shokat and colleagues developed an approach named Analog-Sensitive Kinase Allele (ASKA)¹ (21). In their approach, a kinase is engineered to accept a bulky-ATP analog exclusively so that direct phosphorylation caused by the analog-sensitive target kinase can be differentiated from that of wild type kinases. As a result, indirect effects caused by contaminating kinases during the in vitro kinase assay are largely eliminated. ASKA has recently been coupled with quantitative proteomics, termed Quantitative Identification of Kinase Substrates (QIKS) (12), to identify substrate proteins of Mek1. Recently, one extension of the ASKA technique is for the analog ATP to carry a γ-thiophosphate group so that in vitro thiophosphorylated proteins can be isolated for mass spectrometric detection (22–24). In addition to ASKA, radioisotope labeling using [γ-³²P]ATP (10), using concentrated purified kinase (25), inactivating endogenous kinase activity by an additional heating step (11), and quantitative proteomics (26, 27) are alternative means aimed to address the same issues. All of these methods, however, have been limited to the identification of in vitro kinase substrates.To bridge the gap between in vitro phosphorylation and physiological phosphorylation events, we have recently introduced an integrated strategy termed Kinase Assay-Linked Phosphoproteomics (KALIP) (28). By combining in vitro kinase assays with in vivo phosphoproteomics, this method was demonstrated to have exceptional sensitivity for high confidence identification of direct kinase substrates. The main drawback for the KALIP approach is that the kinase reaction is performed at the peptide stage to eliminate any problems related to contamination by endogenous kinases. However, the KALIP method may not be effective for kinases that require a priming phosphorylation event (i.e. a previous phosphorylation, on substrate or kinase, has effect on following phosphorylation) (29), additional interacting surfaces (30), or a docking site on the protein (31). For example, basophilic kinases require multiple basic resides for phosphorylation and tryptic digestion will abolish these motifs, which are needed for effective kinase reactions.We address the shortcoming by introducing an alternative strategy termed Protein Kinase Assay-Linked Phosphoproteomics (proKALIP). The major difference between this method and the previous KALIP method is the utilization of protein extracts instead of digested peptides as the substrate pool. The major issue is how to reduce potential interference by endogenous kinase activities. One effective solution is to use a generic kinase inhibitor, 5′-(4-fluorosulfonylbenzoyl)adenosine (FSBA), which was widely used for covalent labeling of kinases (32, 33), kinase isolation (34), kinase activity exploration (35, 36), and more recently kinase substrate identification by Kothary and co-workers (37). However, an extra step is required to effectively remove the inhibitor before the kinase reaction, which may decrease the sensitivity. ProKALIP addresses the issue by carrying out the kinase reaction using formerly in vivo phosphorylated proteins as candidates. This step efficiently improves the sensitivity and specificity of the in vitro kinase reaction. Coupled with in vivo phosphoproteomics, proKALIP has gained a high sensitivity and provided physiologically relevant substrates with high confidence.To demonstrate the proKALIP strategy, the protein-tyrosine kinase SYK was used as our target kinase. SYK is known to play a crucial role in the adaptive immune response, particularly in B cells, by facilitating the antigen induced B-cell receptor (BCR) signaling pathways and modulating cellular responses to oxidative stress in a receptor-independent manner (38, 39). SYK also has diverse biological functions such as innate immune recognition, osteoclast maturation, cellular adhesion, platelet activation, and vascular development (38). In addition, the expression of SYK is highly correlated to tumorigenesis by promoting cell–cell adhesion and inhibiting the motility, growth, and invasiveness of certain cancer cells (40). In this study, we attempt to identify bona fide substrates of SYK in human B cells using the proKALIP approach and demonstrate the specificity and sensitivity of this strategy.     

Isolation and molecular analysis of genes Stpk-V2 and Stpk-V3 homologous to powdery mildew resistance gene Stpk-V in a Dasypyrum villosum accession and its derivatives

Wheat-Dasypyrum villosum translocated chromosomes T6V#2S?6AL and T6V#4S?6DL are known to confer excellent resistance to wheat powdery mildew (PM). However, it is difficult to distinguish the two sources of PM resistance genes through multi-pathotype testing because to date no virulence for them has been found. To reveal the relationship between the PM resistance genes from the two translocations, the sequence of the Stpk-V gene, a key member of powdery mildew resistance locus Pm21, was used as a reference to isolate homologous genes from a D. villosum accession No.1026 and its derivatives 6V#4(6D) disomic substitution (DS) line RW15 and T6V#4S?6DL translocation line Pm97033. Two genes Stpk-V2 and Stpk-V3 were cloned from No.1026. Sequence alignment showed that Stpk-V2 and Stpk-V3 shared 98.2 % and 96.2 % of their DNA and 99.3 % and 100 % of their amino acids in identity with Stpk-V. Compared with Stpk-V, a 22-bp direct sequence repeat and a miniature inverted-repeat transposable element (MITE) were found in the intron 4 of Stpk-V2 and Stpk-V3, respectively. However, Stpk-V2 was not present in DS line RW15 and translocation line Pm97033 based on the PCR result, indicating that Stpk-V2 did not contribute to the PM resistance of RW15 and Pm97033. In the promoter region, a 78-bp insertion was found not only in Stpk-V2 and Stpk-V3, but also in its orthologous gene Stpk-A of wheat. In addition, there was a 17 bp/8 bp deletion/insertion in the putative promoter of Stpk-V3 in comparison with that of Stpk-V/Stpk-V2. Real-time quantitative RT-PCR analysis indicated that the expression levels of Stpk-V and Stpk-V3 genes in the translocation lines were induced by the pathogen, but Stpk-V had a higher expression level than Stpk-V3 at 12 h after inoculation with Bgt. The diversity of Stpk-V gene will help to explore new resistance genes to PM in D. villosum for wheat breeding.     

Evaluation of urinary 1-hydroxypyrene,S-phenylmercapturic acid,trans,trans-muconic acid, 3-methyladenine, 3-ethyladenine, 8-hydroxy-2′-deoxyguanosine and thioethers as biomarkers of exposure to cigarette smoke

The objective was to evaluate the utility of urinary 1-hydroxypyrene (1-OHP), S-phenylmercapturic acid (S-PMA), trans,trans-muconic acid (t,t-MA), 3-methyladenine (3-MeAd), 3-ethyladenine (3-EtAd), 8-hydroxy-2′-deoxyguanosine (8-OHdG) and thioethers as biomarkers for assessing the exposure in adult smokers who switched from smoking conventional cigarettes to candidate potential reduced exposure products (PREP) or who stopped smoking. Two electrically heated smoking systems (EHCSS) were used as prototype cigarettes that have significant reductions in a number of mainstream smoke constituents as measured by smoking machines relative to those from conventional cigarettes. Urine samples were collected from a randomized, controlled, forced-switching study in which 110 adult smokers of a conventional cigarette brand (CC1) were randomly assigned to five study groups. The groups included the CC1 smoking group, a lower-tar conventional cigarette (CC2) smoking group, EHCSS1 group, EHCSS2 group and a no smoking group that were monitored for 8 days. Biomarkers were measured at baseline and day 8. The daily excretion levels of these biomarkers were compared among the groups before and after switching, and the relationships between the daily excretion levels of these biomarkers and cigarette smoking-related exposure were investigated using Pearson product-moment correlation and multiple regression analyses. It was concluded that under controlled study conditions: (1) 1-OHP, S-PMA and t,t-MA are useful biomarkers that could differentiate exposure between smoking conventional and EHCSS cigarettes or between smoking conventional cigarettes and no smoking; between S-PMA and t,t-MA, the former appeared to be more sensitive; (2) 3-MeAd could only differentiate between smoking conventional cigarettes and no smoking; the results for 3-EtAd were not conclusive because contradictory results were observed; (3) 8-OHdG had a questionable association with smoking and therefore the utility of this biomarker for smoking-related exposure could not be established; and (4) urinary excretion of thioethers as a biomarker lacked sensitivity to demonstrate a clear dose–response relationship in conventional cigarette smokers, although it could differentiate the excretion levels between those subjects who smoked a conventional cigarette and those who stopped smoking.