A multisite study to develop and validate first trimester, circulating microparticle biomarkers for tiered risk stratification of spontaneous preterm birth in nulliparas.

BACKGROUND: Despite much research, advances in early prediction of spontaneous preterm birth (sPTB) has been slow. The evolving field of circulating microparticle (CMP) biology may identify novel blood-based, and clinically useful, biomarkers. OBJECTIVE: To test the ability of a previously identified, 7-marker set of CMP-derived proteins from the first trimester of pregnancy, in the form of an in vitro diagnostic multivariate index assay (IVDMIA), to stratify pregnant patients according to their risk for sPTB. STUDY DESIGN: We employed a previously validated set of CMP protein biomarkers, utilizing mass spectrometry assays and a nested case-control design in a subset of participants from the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (nuMoM2b). We evaluated these biomarkers in the form of an IVDMIA to predict risk for sPTB at different gestational ages. Plasma samples collected at 9- to 13-weeks' gestation were analyzed. The IVDMIA assigned subjects to 1 of 3 sPTB risk categories: low risk (LR), moderate risk (MR), or high risk (HR). Independent validation on a set-aside set confirmed the IVDMIA's performance in risk stratification. RESULTS: Samples from 400 participants from the nuMoM2b cohort were used for the study; of these, 160 delivered<37 weeks and 240 delivered at term. Through Monte Carlo simulation in which the validation results were adjusted based on actual weekly sPTB incidence rates in the nuMoM2b cohort, the IVDMIA stratifications demonstrated statistically significant differences among the risk groups in time-to-event (birth) analysis (P<.0001). The incidence-rate adjusted cumulative risks of sPTB at ≤32 weeks' gestation were 0.4%, 1.6%, and 7.5%, respectively for the LR, MR, and HR groups, respectively. Compared to the LR group, the corresponding risk ratios of the IVDMIA assigned MR and HR group were 4.25 (95% confidence interval [CI] 2.2-7.9) and 19.92 (95% CI 10.4-37.4), respectively. CONCLUSION: A first trimester CMP protein biomarker panel can be used to stratify risk for sPTB at different gestational ages. Such a multitiered stratification tool could be used to assess risk early in pregnancy to enable timely clinical management and interventions, and, ultimately, to enable the development of tailored care pathways for sPTB prevention.

Circulating microparticle proteins predict pregnancies complicated by placenta accreta spectrum.

Placenta accreta spectrum (PAS) is characterized by abnormal attachment of the placenta to the uterus, and attempts at placental delivery can lead to catastrophic maternal hemorrhage and death. Multidisciplinary delivery planning can significantly improve outcomes; however, current diagnostics are lacking as approximately half of pregnancies with PAS are undiagnosed prior to delivery. This is a nested case-control study of 35 cases and 70 controls with the primary objective of identifying circulating microparticle (CMP) protein panels that identify pregnancies complicated by PAS. Size exclusion chromatography and liquid chromatography with tandem mass spectrometry were used for CMP protein isolation and identification, respectively. A two-step iterative workflow was used to establish putative panels. Using plasma sampled at a median of 26 weeks' gestation, five CMP proteins distinguished PAS from controls with a mean area under the curve (AUC) of 0.83. For a separate sample taken at a median of 35 weeks' gestation, the mean AUC was 0.78. In the second trimester, canonical pathway analyses demonstrate over-representation of processes related to iron homeostasis and erythropoietin signaling. In the third trimester, these analyses revealed abnormal immune function. CMP proteins classify PAS well prior to delivery and have potential to significantly reduce maternal morbidity and mortality.

Klotho Protects Dopaminergic Neuron Oxidant-Induced Degeneration by Modulating ASK1 and p38 MAPK Signaling Pathways.

Klotho transgenic mice exhibit resistance to oxidative stress as measured by their urinal levels of 8-hydroxy-2-deoxyguanosine, albeit this anti-oxidant defense mechanism has not been locally investigated in the brain. Here, we tested the hypothesis that the reactive oxygen species (ROS)-sensitive apoptosis signal-regulating kinase 1 (ASK1)/p38 MAPK pathway regulates stress levels in the brain of these mice and showed that: 1) the ratio of free ASK1 to thioredoxin (Trx)-bound ASK1 is relatively lower in the transgenic brain whereas the reverse is true for the Klotho knockout mice; 2) the reduced p38 activation level in the transgene corresponds to higher level of ASK1-bound Trx, while the KO mice showed elevated p38 activation and lower level of-bound Trx; and 3) that 14-3-3ζ is hyper phosphorylated (Ser-58) in the transgene which correlated with increased monomer forms. In addition, we evaluated the in vivo robustness of the protection by challenging the brains of Klotho transgenic mice with a neurotoxin, MPTP and analyzed for residual neuron numbers and integrity in the substantia nigra pars compacta. Our results show that Klotho overexpression significantly protects dopaminergic neurons against oxidative damage, partly by modulating p38 MAPK activation level. Our data highlight the importance of ASK1/p38 MAPK pathway in the brain and identify Klotho as a possible anti-oxidant effector.

Prostate cancer biomarker discovery using high performance mass spectral serum profiling.

Prostate-specific antigen (PSA) is the most widely used serum biomarker for early detection of prostate cancer (PCA). Nevertheless, PSA level can be falsely elevated due to prostatic enlargement, inflammation or infection, which limits the PSA test specificity. The objective of this study is to use a machine learning approach for the analysis of mass spectrometry data to discover more reliable biomarkers that distinguish PCA from benign specimens. Serum samples from 179 prostate cancer patients and 74 benign patients were analyzed. These samples were processed using ProXPRESSION Biomarker Enrichment Kits (PerkinElmer). Mass spectra were acquired using a prOTOF 2000 matrix-assisted laser desorption/ionization orthogonal time-of-flight (MALDI-O-TOF) mass spectrometer. In this study, we search for potential biomarkers using our feature selection method, the Extended Markov Blanket (EMB). From the new marker selection algorithm, a panel of 26 peaks achieved an accuracy of 80.7%, a sensitivity of 83.5%, a specificity of 74.4%, a positive predictive value (PPV) of 87.9%, and a negative predictive value (NPV) of 68.2%. On the other hand, when PSA alone was used (with a cutoff of 4.0ng/ml), a sensitivity of 66.7%, a specificity of 53.6%, a PPV of 73.5%, and a NPV of 45.4% were obtained.

An extended Markov blanket approach to proteomic biomarker detection from high-resolution mass spectrometry data.

High-resolution matrix-assisted laser desorption/ionization time-of-flight mass spectrometry has recently shown promise as a screening tool for detecting discriminatory peptide/protein patterns. The major computational obstacle in finding such patterns is the large number of mass/charge peaks (features, biomarkers, data points) in a spectrum. To tackle this problem, we have developed methods for data preprocessing and biomarker selection. The preprocessing consists of binning, baseline correction, and normalization. An algorithm, extended Markov blanket, is developed for biomarker detection, which combines redundant feature removal and discriminant feature selection. The biomarker selection couples with support vector machine to achieve sample prediction from high-resolution proteomic profiles. Our algorithm is applied to recurrent ovarian cancer study that contains platinum-sensitive and platinum-resistant samples after treatment. Experiments show that the proposed method performs better than other feature selection algorithms. In particular, our algorithm yields good performance in terms of both sensitivity and specificity as compared to other methods.

Biomarker selection and sample prediction for multi-category disease on MALDI-TOF data.

MOTIVATION: Diseases normally progress through several stages. Therefore, biomarkers corresponding to each stage may exist. To deal with such a multi-category problem, including sample stage prediction and biomarker selection, we propose methods for classification and feature selection. The proposed classification method is based on two schemes: error-correcting output coding (ECOC) and pairwise coupling (PWC). The final decision for a test sample prediction is an integration of these two schemes. The biomarker pattern for distinguishing each disease category from another one is achieved by the development of an extended Markov blanket (EMB) feature selection method. RESULTS: In this study, a liver cancer matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) dataset was used, which comprises hepatocellular carcinoma (HCC), cirrhosis, and healthy spectra. Peak patterns were discovered for distinguishing pairwise categories among the three classes. Importance and reliability of individual peaks were presented by the measurements of certain weight values and frequencies. The classification capability of the proposed approach was compared with classical ECOC, random forest, Naive Bayes, and J48 methods. AVAILABILITY: Supplementary materials are available at http://visionlab.uta.edu/biomarker/bioinfo.htm.

Serum biomarkers for Alzheimer's disease: proteomic discovery.

For Alzheimer's disease (AD), the most common neurodegenerative disease, there is no simple, cost-effective biomarker for disease identification. Using novel mass spectrometry (MS)-based techniques, and analysis of the albumin-enriched low molecular weight proteome, minute amounts of human serum were analyzed for the measurement of thousands of peptides and proteins in parallel. The mass spectrograms were then evaluated with a novel computer algorithm to identify spectral peaks that discriminate between samples from patients with and without AD. There are four peaks that distinguish AD from control subjects and AD subjects from those with Parkinson's disease (PD). Additionally, after analyzing data from a recently published study of AD and control subjects, we found three discriminating peaks in common with the four from our patient serum samples. The identification of these peptides/proteins, and their direct measurement in patient serum, may allow the development of a simple, cost-effective test for AD.

A novel, high-throughput workflow for discovery and identification of serum carrier protein-bound peptide biomarker candidates in ovarian cancer samples.

BACKGROUND: Most cases of ovarian cancer are detected at later stages when the 5-year survival is approximately 15%, but 5-year survival approaches 90% when the cancer is detected early (stage I). To use mass spectrometry (MS) of serum proteins for early detection, a seamless workflow is needed that provides an opportunity for rapid profiling along with direct identification of the underpinning ions. METHODS: We used carrier protein-bound affinity enrichment of serum samples directly coupled with MALDI orthagonal TOF MS profiling to rapidly search for potential ion signatures that contained discriminatory power. These ions were subsequently directly subjected to tandem MS for sequence identification. RESULTS: We discovered several biomarker panels that enabled differentiation of stage I ovarian cancer from unaffected (age-matched) patients with no evidence of ovarian cancer, with positive results in >93% of samples from patients with disease-negative results and in 97% of disease-free controls. The carrier protein-based approach identified additional protein fragments, many from low-abundance proteins or proteins not previously seen in serum. CONCLUSIONS: This workflow system using a highly reproducible, high-resolution MALDI-TOF platform enables rapid enrichment and profiling of large numbers of clinical samples for discovery of ion signatures and integration of direct sequencing and identification of the ions without need for additional offline, time-consuming purification strategies.

Fluorescence-based analysis of cellular protein lysate arrays using quantum dots.

Reverse-phase protein microarrays (RPPMAs) enable heterogeneous mixtures of proteins from cellular extracts to be directly spotted onto a substrate (such as a protein biochip) in minute volumes (nanoliter-to-picoliter volumes). The protein spots can then be probed with primary antibodies to detect important posttranslational modifications such as phosphorylations that are important for protein activation and the regulation of cellular signaling. Previously, we relied on chromogenic signals for detection. However, quantum dots (QDs) represent a more versatile detection system because the signals can be time averaged and the narrow-emission spectra enable multiple protein targets to be quantified within the same spot. We found that commercially available pegylated, streptavidin-conjugated QDs are effective detection agents, with low-background binding to heterogeneous protein mixtures. This type of test, the RPPMAs, is at the forefront of an exciting, clinically-oriented discipline that is emerging, namely tissue or clinical proteomics.

Proteomic biomarker identification for diagnosis of early relapse in ovarian cancer.

Ovarian cancer recurs at the rate of 75% within a few months or several years later after therapy. Early recurrence, though responding better to treatment, is difficult to detect. Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry has showed the potential to accurately identify disease biomarkers to help early diagnosis. A major challenge in the interpretation of SELDI-TOF data is the high dimensionality of the feature space. To tackle this problem, we have developed a multi-step data processing method composed of t-test, binning and backward feature selection. A new algorithm, support vector machine-Markov blanket/recursive feature elimination (SVM-MB/RFE) is presented for the backward feature selection. This method is an integration of minimum weight feature elimination by SVM-RFE and information theory based redundant/irrelevant feature removal by Markov Blanket. Subsequently, SVM was used for classification. We conducted the biomarker selection algorithm on 113 serum samples to identify early relapse from ovarian cancer patients after primary therapy. To validate the performance of the proposed algorithm, experiments were carried out in comparison with several other feature selection and classification algorithms.

Regulation of oxidative stress by the anti-aging hormone klotho.

klotho is an aging suppressor gene and extends life span when overexpressed in mice. Klotho protein was recently demonstrated to function as a hormone that inhibits insulin/insulin-like growth factor-1 (IGF-1) signaling. Here we show that Klotho protein increases resistance to oxidative stress at the cellular and organismal level in mammals. Klotho protein activates the FoxO forkhead transcription factors that are negatively regulated by insulin/IGF-1 signaling, thereby inducing expression of manganese superoxide dismutase. This in turn facilitates removal of reactive oxygen species and confers oxidative stress resistance. Thus, Klotho-induced inhibition of insulin/IGF-1 signaling is associated with increased resistance to oxidative stress, which potentially contributes to the anti-aging properties of klotho.

Suppression of aging in mice by the hormone Klotho.

A defect in Klotho gene expression in mice accelerates the degeneration of multiple age-sensitive traits. Here, we show that overexpression of Klotho in mice extends life span. Klotho protein functions as a circulating hormone that binds to a cell-surface receptor and represses intracellular signals of insulin and insulin-like growth factor 1 (IGF1), an evolutionarily conserved mechanism for extending life span. Alleviation of aging-like phenotypes in Klotho-deficient mice was observed by perturbing insulin and IGF1 signaling, suggesting that Klotho-mediated inhibition of insulin and IGF1 signaling contributes to its anti-aging properties. Klotho protein may function as an anti-aging hormone in mammals.

Pegylated, steptavidin-conjugated quantum dots are effective detection elements for reverse-phase protein microarrays.

Protein microarray technologies provide a means of investigating the proteomic content of clinical biopsy specimens in order to determine the relative activity of key nodes within cellular signaling pathways. A particular kind of protein microarray, the reverse-phase microarray, is being evaluated in clinical trials because of its potential to utilize limited amounts of cellular material obtained through biopsy. Using this approach, cellular lysates are arrayed in dilution curves on nitrocellulose substrates for subsequent probing with antibodies. To improve the sensitivity and utility of reverse-phase microarrays, we tested whether a new reporter technology as well as a new detection instrument could enhance microarray performance. We describe the use of an inorganic fluorescent nanoparticle conjugated to streptavidin, Qdot 655 Sav, in a reverse-phase protein microarray format for signal pathway profiling. Moreover, a pegylated form of this bioconjugate, Qdot 655 Sav, is found to have superior detection characteristics in assays performed on cellular protein extracts over the nonpegylated form of the bioconjugate. Hyperspectral imaging of the quantum dot microarray enabled unamplified detection of signaling proteins within defined cellular lysates, which indicates that this approach may be amenable to multiplexed, high-throughput reverse-phase protein microarrays in which numerous analytes are measured in parallel within a single spot.

Diagnosis of early relapse in ovarian cancer using serum proteomic profiling.

Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry data has been increasingly analyzed for identifying biomarkers to help early detection of the disease. Ovarian cancer commonly recurs at the rate of 75% within a few months or several years later after standard treatment. Since recurrent ovarian cancer is relatively difficult to be diagnosed and small tumors generally respond better to treatment, new methods for the detection of early relapse in ovarian cancer are urgently needed. Here, we propose a new algorithm SVM-MB/RFE (SVM-Markov Blanket/Recursive Feature Elimination) based on SVM-RFE, which identifies biomarkers for predicting the early recurrence of ovarian cancer. In this approach, we first apply t-test for feature pruning and then binning using 5-fold cross validation. Finally, 58 peaks are obtained from 27,000 of the raw data. Such dramatically reduced features relax the computational burden in the next step of our algorithm. We compare the performance of three feature selection algorithms and demonstrate that SVM-MB/RFE outperforms other methods.

Validation of a near-infrared probe for detection of thin intracranial white matter structures.

OBJECT: The authors have developed an intracranial near-infrared (NIR) probe that analyzes the scattering of light emitted from its tip to measure the optical properties of cerebral tissue. Despite its success in distinguishing graymatter from white matter in humans during stereotactic surgery, the limits of this instrument's resolution remain unclear. In this study, the authors determined the spatial resolution of this new probe by using a rodent model supplemented with phantom measurements and computer simulation. METHODS: A phantom consisting of Intralipid and gelatin was constructed to resemble a layer of white matter overlying a layer of gray matter. Near-infrared measurements were obtained as the probe was inserted through the gray-white matter transition. A computer simulation of NIR measurements through a gray-white matter transition was also performed using Monte Carlo techniques. The NIR probe was then used to study 19 tracks from the cortical surface through the corpus callosum in an in vivo rodent preparation. The animals were killed and histological sections through the tracks were obtained. Data from the phantom models and computer simulations showed that the NIR probe samples a volume of tissue extending 1 to 1.5 mm in front of the probe tip (this distance is termed the "lookthrough" distance). Measurements obtained from an NIR probe passing through a thin layer of white matter consisted of an initial segment of increasing values, a maximum (peak) value, and a trailing segment of decreasing values. The length of the initial segment is the lookthrough distance, the position of the peak indicates the location of the superficial white matter boundary, and the length of the trailing segment is the thickness of the layer. These considerations were confirmed in experiments with rodents. All tracks passed through the corpus callosum, which was demonstrated as a broad peak on each NIR graph. The position of the dorsal boundary of the corpus callosum and its width (based on histological measurements) correlated well with the peak of the NIR curve and its trailing segment, respectively. The initial segments correlated well with estimates of the lookthrough distance. Five of the tracks transected the smaller anterior commissure (diameter 0.2 mm), producing a narrow NIR peak at the correct depth. CONCLUSIONS: Data in this study confirm that the NIR probe can reliably detect and measure the thickness of layers of white matter as thin as 0.2 mm. Such resolution should be adequate to detect larger structures of interest encountered during stereotactic surgery in humans.