Xiaogang Zhong

Plasma phospholipids identify antecedent memory impairment in older adults

Alzheimers disease causes a progressive dementia that currently affects over 35 million individuals worldwide and is expected to affect 115 million by 2050 (ref. 1). There are no cures or disease-modifying therapies, and this may be due to our inability to detect the disease before it has progressed to produce evident memory loss and functional decline. Biomarkers of preclinical disease will be critical to the development of disease-modifying or even preventative therapies. Unfortunately, current biomarkers for early disease, including cerebrospinal fluid tau and amyloid-β levels, structural and functional magnetic resonance imaging and the recent use of brain amyloid imaging or inflammaging, are limited because they are either invasive, time-consuming or expensive. Blood-based biomarkers may be a more attractive option, but none can currently detect preclinical Alzheimers disease with the required sensitivity and specificity. Herein, we describe our lipidomic approach to detecting preclinical Alzheimers disease in a group of cognitively normal older adults. We discovered and validated a set of ten lipids from peripheral blood that predicted phenoconversion to either amnestic mild cognitive impairment or Alzheimers disease within a 2–3 year timeframe with over 90% accuracy. This biomarker panel, reflecting cell membrane integrity, may be sensitive to early neurodegeneration of preclinical Alzheimers disease.

Plasma 24-metabolite Panel Predicts Preclinical Transition to Clinical Stages of Alzheimer’s Disease

We recently documented plasma lipid dysregulation in preclinical late-onset Alzheimer’s disease (LOAD). A 10 plasma lipid panel, predicted phenoconversion and provided 90% sensitivity and 85% specificity in differentiating an at-risk group from those that would remain cognitively intact. Despite these encouraging results, low positive predictive values limit the clinical usefulness of this panel as a screening tool in subjects aged 70–80 years or younger. In this report, we re-examine our metabolomic data, analyzing baseline plasma specimens from our group of phenoconverters (n = 28) and a matched set of cognitively normal subjects (n = 73), and discover and internally validate a panel of 24 plasma metabolites. The new panel provides a classifier with receiver operating characteristic area under the curve for the discovery and internal validation cohort of 1.0 and 0.995 (95% confidence intervals of 1.0–1.0, and 0.981–1.0), respectively. Twenty-two of the 24 metabolites were significantly dysregulated lipids. While positive and negative predictive values were improved compared to our 10-lipid panel, low positive predictive values provide a reality check on the utility of such biomarkers in this age group (or younger). Through inclusion of additional significantly dysregulated analyte species, our new biomarker panel provides greater accuracy in our cohort but remains limited by predictive power. Unfortunately, the novel metabolite panel alone may not provide improvement in counseling and management of at-risk individuals but may further improve selection of subjects for LOAD secondary prevention trials. We expect that external validation will remain challenging due to our stringent study design, especially compared with more diverse subject cohorts. We do anticipate, however, external validation of reduced plasma lipid species as a predictor of phenoconversion to either prodromal or manifest LOAD.

Liver Metabolomics Reveals Increased Oxidative Stress and Fibrogenic Potential in Gfrp Transgenic Mice in Response to Ionizing Radiation

Although radiation-induced tissue-specific injury is well documented, the underlying molecular changes resulting in organ dysfunction and the consequences thereof on overall metabolism and physiology have not been elucidated. We previously reported the generation and characterization of a transgenic mouse strain that ubiquitously overexpresses Gfrp (GTPH-1 feedback regulatory protein) and exhibits higher oxidative stress, which is a possible result of decreased tetrahydrobiopterin (BH4) bioavailability. In this study, we report genotype-dependent changes in the metabolic profiles of liver tissue after exposure to nonlethal doses of ionizing radiation. Using a combination of untargeted and targeted quantitative mass spectrometry, we report significant accumulation of metabolites associated with oxidative stress, as well as the dysregulation of lipid metabolism in transgenic mice after radiation exposure. The radiation stress seems to exacerbate lipid peroxidation and also results in higher expression of genes that facilitate liver fibrosis, in a manner that is dependent on the genetic background and post-irradiation time interval. These findings suggest the significance of Gfrp in regulating redox homeostasis in response to stress induced by ionizing radiation affecting overall physiology.

Conditionally reprogrammed normal and primary tumor prostate epithelial cells: a novel patient-derived cell model for studies of human prostate cancer

Our previous study demonstrated that conditional reprogramming (CR) allows the establishment of patient-derived normal and tumor epithelial cell cultures from a variety of tissue types including breast, lung, colon and prostate. Using CR, we have established matched normal and tumor cultures, GUMC-29 and GUMC-30 respectively, from a patients prostatectomy specimen. These CR cells proliferate indefinitely in vitro and retain stable karyotypes. Most importantly, only tumor-derived CR cells (GUMC-30) produced tumors in xenografted SCID mice, demonstrating maintenance of the critical tumor phenotype. Characterization of cells with DNA fingerprinting demonstrated identical patterns in normal and tumor CR cells as well as in xenografted tumors. By flow cytometry, both normal and tumor CR cells expressed basal, luminal, and stem cell markers, with the majority of the normal and tumor CR cells expressing prostate basal cell markers, CD44 and Trop2, as well as luminal marker, CD13, suggesting a transit-amplifying phenotype. Consistent with this phenotype, real time RT-PCR analyses demonstrated that CR cells predominantly expressed high levels of basal cell markers (KRT5, KRT14 and p63), and low levels of luminal markers. When the CR tumor cells were injected into SCID mice, the expression of luminal markers (AR, NKX3.1) increased significantly, while basal cell markers dramatically decreased. These data suggest that CR cells maintain high levels of proliferation and low levels of differentiation in the presence of feeder cells and ROCK inhibitor, but undergo differentiation once injected into SCID mice. Genomic analyses, including SNP and INDEL, identified genes mutated in tumor cells, including components of apoptosis, cell attachment, and hypoxia pathways. The use of matched patient-derived cells provides a unique in vitro model for studies of early prostate cancer.

Metabolomic biomarkers of pancreatic cancer: a meta-analysis study

Pancreatic cancer (PC) is an aggressive disease with high mortality rates, however, there is no blood test for early detection and diagnosis of this disease. Several research groups have reported on metabolomics based clinical investigations to identify biomarkers of PC, however there is a lack of a centralized metabolite biomarker repository that can be used for meta-analysis and biomarker validation. Furthermore, since the incidence of PC is associated with metabolic syndrome and Type 2 diabetes mellitus (T2DM), there is a need to uncouple these common metabolic dysregulations that may otherwise diminish the clinical utility of metabolomic biosignatures. Here, we attempted to externally replicate proposed metabolite biomarkers of PC reported by several other groups in an independent group of PC subjects. Our study design included a T2DM cohort that was used as a non-cancer control and a separate cohort diagnosed with colorectal cancer (CRC), as a cancer disease control to eliminate possible generic biomarkers of cancer. We used targeted mass spectrometry for quantitation of literature-curated metabolite markers and identified a biomarker panel that discriminates between normal controls (NC) and PC patients with high accuracy. Further evaluation of our model with CRC, however, showed a drop in specificity for the PC biomarker panel. Taken together, our study underscores the need for a more robust study design for cancer biomarker studies so as to maximize the translational value and clinical implementation.

Thermodynamically consistent Bayesian analysis of closed biochemical reaction systems

BackgroundEstimating the rate constants of a biochemical reaction system with known stoichiometry from noisy time series measurements of molecular concentrations is an important step for building predictive models of cellular function. Inference techniques currently available in the literature may produce rate constant values that defy necessary constraints imposed by the fundamental laws of thermodynamics. As a result, these techniques may lead to biochemical reaction systems whose concentration dynamics could not possibly occur in nature. Therefore, development of a thermodynamically consistent approach for estimating the rate constants of a biochemical reaction system is highly desirable.ResultsWe introduce a Bayesian analysis approach for computing thermodynamically consistent estimates of the rate constants of a closed biochemical reaction system with known stoichiometry given experimental data. Our method employs an appropriately designed prior probability density function that effectively integrates fundamental biophysical and thermodynamic knowledge into the inference problem. Moreover, it takes into account experimental strategies for collecting informative observations of molecular concentrations through perturbations. The proposed method employs a maximization-expectation-maximization algorithm that provides thermodynamically feasible estimates of the rate constant values and computes appropriate measures of estimation accuracy. We demonstrate various aspects of the proposed method on synthetic data obtained by simulating a subset of a well-known model of the EGF/ERK signaling pathway, and examine its robustness under conditions that violate key assumptions. Software, coded in MATLAB®, which implements all Bayesian analysis techniques discussed in this paper, is available free of charge at http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.html.ConclusionsOur approach provides an attractive statistical methodology for estimating thermodynamically feasible values for the rate constants of a biochemical reaction system from noisy time series observations of molecular concentrations obtained through perturbations. The proposed technique is theoretically sound and computationally feasible, but restricted to quantitative data obtained from closed biochemical reaction systems. This necessitates development of similar techniques for estimating the rate constants of open biochemical reaction systems, which are more realistic models of cellular function.

OPTIMIZED CROSS-STUDY ANALYSIS OF MICROARRAY-BASED PREDICTORS

Background: Microarray-based gene expression analysis is widely used in cancer research to discover molecular signatures for cancer classification and prediction. In addition to numerous independent profiling projects, a number of investigators have analyzed multiple published data sets for purposes of cross-study validation. However, the diverse microarray platforms and technical approaches make direct comparisons across studies difficult, and without means to identify aberrant data patterns, less than optimal. To address this issue, we previously developed an integrative correlation approach to systematically address agreement of gene expression measurements across studies, providing a basis for cross-study validation analysis. Here we generalize this methodology to provide a metric for evaluating the overall efficacy of preprocessing and cross-referencing, and explore optimal combinations of filtering and cross-referencing strategies. We operate in the context of validating prognostic breast cancer gene expression signatures on data reported by three different groups, each using a different platform. Results: To evaluate overall cross-platform reproducibility in the context of a specific prediction problem, we suggest integrative association, that is the the cross-study correlation of gene-specific measure of association with the phenotype predicted. Specifically, in this paper we use the correlation among the Cox proportional

Biomarker validation: Methods and matrix matter

We read with interest the research article from Casanova et al. for an upcoming edition of Alzheimer’s & Dementia (2016, article in press). We appreciate the efforts that these investigators have expended in attempting to replicate our plasma lipidomic findings in preclinical Alzheimer’s disease [1]. We enthusiastically endorse independent validation of new findings in science but assert that valid replication requires identical methods including those related to clinical characterization and biospecimen collection, processing, and analysis. The methodology used by Casanova et al., in particular, metabolomic analysis of serum rather than plasma makes their study distinct but renders direct comparison with our study invalid. A brief delineation of these critical differences is important for the reader as they effectively preclude comparison of results. Casanova et al. attempted to replicate our findings using samples of convenience from two well-established longitudinal studies, the Baltimore Longitudinal Study on Aging (BLSA) and the Age, Gene/Environment Susceptibility— Reykjavik Study (AGES-RS). These studies do not use many of the methods we used in our study that was specifically designed to discover blood-based biomarkers for preclinical Alzheimer’s disease. As a result, Casanova et al. were restricted by the methodologies used by these extant studies rather than replicating the methodology used in our study. In these terms, their study cannot be considered a replication of ours. Several of the methodological differences are significant. First and foremost, we used plasma. Casanova et al. used serum. Whether this was a conscious decision or a matter of sample availability is not clear, but it is clear that partitioning of metabolites is nonuniform and the matrix can have a profound effect on the findings. Recent studies have shown that levels of certain metabolites can differ greatly between serum and plasma in the same individuals [2], and these differences may be even more significant when measuring glycerophospholipids [3]. Of note, two metabolites in our panel shown in Table 4 of Casanova et al. were excluded from the original study by Yu et al. [4] because of low-measurement stability.

Primary cancer cell culture: mammary-optimized vs conditional reprogramming

The impact of different culture conditions on biology of primary cancer cells is not always addressed. Here, conditional reprogramming (CRC) was compared with mammary-optimized EpiCult-B (EpiC) for primary mammary epithelial cell isolation and propagation, allograft generation, and genome-wide transcriptional consequences using cancer and non-cancer mammary tissue from mice with different dosages of Brca1 and p53 Selective comparison to DMEM was included. Primary cultures were established with all three media, but CRC was most efficient for initial isolation (P<0.05). Allograft development was faster using cells grown in EpiC compared with CRC (P<0.05). Transcriptome comparison of paired CRC and EpiC cultures revealed 1700 differentially expressed genes by passage 20. CRC promoted Trp53 gene family upregulation and increased expression of epithelial differentiation genes, whereas EpiC elevated expression of epithelial-mesenchymal transition genes. Differences did not persist in allografts where both methods yielded allografts with relatively similar transcriptomes. Restricting passage (<7) reduced numbers of differentially expressed genes below 50. In conclusion, CRC was most efficient for initial cell isolation but EpiC was quicker for allograft generation. The extensive culture-specific gene expression patterns that emerged with longer passage could be limited by reducing passage number when both culture transcriptomes were equally similar to that of the primary tissue. Defining impact of culture condition and passage on the transcriptome of primary cells could assist experimental design and interpretation. For example, differences that appear with passage and culture condition are potentially exploitable for comparative studies targeting specific biological networks in different transcriptional environments.

Hierarchical Bayesian analysis of somatic mutation data in cancer

Identifying genes underlying cancer development is critical to cancer biology and has important implications across prevention, diagnosis and treatment. Cancer sequencing studies aim at discovering genes with high frequencies of somatic mutations in specific types of cancer, as these genes are potential driving factors (drivers) for cancer development. We introduce a hierarchical Bayesian methodology to estimate gene-specific mutation rates and driver probabilities from somatic mutation data and to shed light on the overall proportion of drivers among sequenced genes. Our methodology applies to different experimental designs used in practice, including one-stage, two-stage and candidate gene designs. Also, sample sizes are typically small relative to the rarity of individual mutations. Via a shrinkage method borrowing strength from the whole genome in assessing individual genes, we reinforce inference and address the selection effects induced by multistage designs. Our simulation studies show that the posterior driver probabilities provide a nearly unbiased false discovery rate estimate. We apply our methods to pancreatic and breast cancer data, contrast our results to previous estimates and provide estimated proportions of drivers for these two types of cancer.