Gil Alterovitz

Serum proteome profiling detects myelodysplastic syndromes and identifies CXC chemokine ligands 4 and 7 as markers for advanced disease

Myelodysplastic syndromes (MDS) are among the most frequent hematologic malignancies. Patients have a short survival and often progress to acute myeloid leukemia. The diagnosis of MDS can be difficult; there is a paucity of molecular markers, and the pathophysiology is largely unknown. Therefore, we conducted a multicenter study investigating whether serum proteome profiling may serve as a noninvasive platform to discover novel molecular markers for MDS. We generated serum proteome profiles from 218 individuals by MS and identified a profile that distinguishes MDS from non-MDS cytopenias in a learning sample set. This profile was validated by testing its ability to predict MDS in a first independent validation set and a second, prospectively collected, independent validation set run 5 months apart. Accuracy was 80.5% in the first and 79.0% in the second validation set. Peptide mass fingerprinting and quadrupole TOF MS identified two differential proteins: CXC chemokine ligands 4 (CXCL4) and 7 (CXCL7), both of which had significantly decreased serum levels in MDS, as confirmed with independent antibody assays. Western blot analyses of platelet lysates for these two platelet-derived molecules revealed a lack of CXCL4 and CXCL7 in MDS. Subtype analyses revealed that these two proteins have decreased serum levels in advanced MDS, suggesting the possibility of a concerted disturbance of transcription or translation of these chemokines in advanced MDS.

Gene expression analysis in pregnant women and their infants identifies unique fetal biomarkers that circulate in maternal blood

The discovery of fetal mRNA transcripts in the maternal circulation holds great promise for noninvasive prenatal diagnosis. To identify potential fetal biomarkers, we studied whole blood and plasma gene transcripts that were common to 9 term pregnant women and their newborns but absent or reduced in the mothers postpartum. RNA was isolated from peripheral or umbilical blood and hybridized to gene expression arrays. Gene expression, paired Students t test, and pathway analyses were performed. In whole blood, 157 gene transcripts met statistical significance. These fetal biomarkers included 27 developmental genes, 5 sensory perception genes, and 22 genes involved in neonatal physiology. Transcripts were predominantly expressed or restricted to the fetus, the embryo, or the neonate. Real-time RT-PCR amplification confirmed the presence of specific gene transcripts; SNP analysis demonstrated the presence of 3 fetal transcripts in maternal antepartum blood. Comparison of whole blood and plasma samples from the same pregnant woman suggested that placental genes are more easily detected in plasma. We conclude that fetal and placental mRNA circulates in the blood of pregnant women. Transcriptional analysis of maternal whole blood identifies a unique set of biologically diverse fetal genes and has a multitude of clinical applications.

SMART on FHIR Genomics: Facilitating standardized clinico-genomic apps

BACKGROUND Supporting clinical decision support for personalized medicine will require linking genome and phenome variants to a patients electronic health record (EHR), at times on a vast scale. Clinico-genomic data standards will be needed to unify how genomic variant data are accessed from different sequencing systems. METHODS A specification for the basis of a clinic-genomic standard, building upon the current Health Level Seven International Fast Healthcare Interoperability Resources (FHIR®) standard, was developed. An FHIR application protocol interface (API) layer was attached to proprietary sequencing platforms and EHRs in order to expose gene variant data for presentation to the end-user. Three representative apps based on the SMART platform were built to test end-to-end feasibility, including integration of genomic and clinical data. RESULTS Successful design, deployment, and use of the API was demonstrated and adopted by HL7 Clinical Genomics Workgroup. Feasibility was shown through development of three apps by various types of users with background levels and locations. CONCLUSION This prototyping work suggests that an entirely data (and web) standards-based approach could prove both effective and efficient for advancing personalized medicine.

Optimization and evaluation of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) with reversed-phase protein arrays for protein profiling.

Abstract Surface-enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry with protein arrays has facilitated the discovery of disease-specific protein profiles in serum. Such results raise hopes that protein profiles may become a powerful diagnostic tool. To this end, reliable and reproducible protein profiles need to be generated from many samples, accurate mass peak heights are necessary, and the experimental variation of the profiles must be known. We adapted the entire processing of protein arrays to a robotics system, thus improving the intra-assay coefficients of variation (CVs) from 45.1% to 27.8% (p<0.001). In addition, we assessed up to 16 technical replicates, and demonstrated that analysis of 2–4 replicates significantly increases the reliability of the protein profiles. A recent report on limited long-term reproducibility seemed to concord with our initial inter-assay CVs, which varied widely and reached up to 56.7%. However, we discovered that the inter-assay CV is strongly dependent on the drying time before application of the matrix molecule. Therefore, we devised a standardized drying process and demonstrated that our optimized SELDI procedure generates reliable and long-term reproducible protein profiles with CVs ranging from 25.7% to 32.6%, depending on the signal-to-noise ratio threshold used.

The challenges of informatics in synthetic biology: from biomolecular networks to artificial organisms

The field of synthetic biology holds an inspiring vision for the future; it integrates computational analysis, biological data and the systems engineering paradigm in the design of new biological machines and systems. These biological machines are built from basic biomolecular components analogous to electrical devices, and the information flow among these components requires the augmentation of biological insight with the power of a formal approach to information management. Here we review the informatics challenges in synthetic biology along three dimensions: in silico, in vitro and in vivo. First, we describe state of the art of the in silico support of synthetic biology, from the specific data exchange formats, to the most popular software platforms and algorithms. Next, we cast in vitro synthetic biology in terms of information flow, and discuss genetic fidelity in DNA manipulation, development strategies of biological parts and the regulation of biomolecular networks. Finally, we explore how the engineering chassis can manipulate biological circuitries in vivo to give rise to future artificial organisms.

Accelerating wrapper-based feature selection with K-nearest-neighbor

We propose to accelerate wrapper-based feature selection with a KNN classifier.We construct a classifier distance matrix to evaluate the quality of a feature.The proposed approach can apply to three types of wrapper-based feature selectors.Theoretical time complexity analysis proves the efficiency of the proposed approach.Experimental results demonstrate its effectiveness and efficiency. Wrapper-based feature subset selection (FSS) methods tend to obtain better classification accuracy than filter methods but are considerably more time-consuming, particularly for applications that have thousands of features, such as microarray data analysis. Accelerating this process without degrading its high accuracy would be of great value for gene expression analysis. In this study, we explored how to reduce the time complexity of wrapper-based FSS with an embedded K-Nearest-Neighbor (KNN) classifier. Instead of considering KNN as a black box, we proposed to construct a classifier distance matrix and incrementally update the matrix to accelerate the calculation of the relevance criteria in evaluating the quality of the candidate features. Extensive experiments on eight publicly available microarray datasets were first conducted to demonstrate the effectiveness of the wrapper methods with KNN for selecting informative features. To demonstrate the performance gain in terms of time cost reduction, we then conducted experiments on the eight microarray datasets with the embedded KNN classifiers and analyzed the theoretical time/space complexity. Both the experimental results and theoretical analysis demonstrated that the proposed approach markedly accelerates the wrapper-based feature selection process without degrading the high classification accuracy, and the space complexity analysis indicated that the additional space overhead is affordable in practice.

Network Biology of Tumor Stem-like Cells Identified a Regulatory Role of CBX5 in Lung Cancer

Mounting evidence links cancers possessing stem-like properties with worse prognosis. Network biology with signal processing mechanics was explored here using expression profiles of a panel of tumor stem-like cells (TSLCs). The profiles were compared to their parental tumor cells (PTCs) and the human embryonic stem cells (hESCs), for the identification of gene chromobox homolog 5, CBX5, as a potential target for lung cancer. CBX5 was found to regulate the stem-like properties of lung TSLCs and was predictive of lung cancer prognosis. The investigation was facilitated by finding target genes based on modeling epistatic signaling mechanics via a predictive and scalable network-based survival model. Topologically-weighted measurements of CBX5 were synchronized with those of BIRC5, DNMT1, E2F1, ESR1, MLH1, MSH2, RB1, SMAD1 and TAF5. We validated our findings in another Taiwanese lung cancer cohort, as well as in knockdown experiments using sh-CBX5 RNAi both in vitro and in vivo.

An artificial intelligence tool to predict fluid requirement in the intensive care unit: a proof-of-concept study

IntroductionThe goal of personalised medicine in the intensive care unit (ICU) is to predict which diagnostic tests, monitoring interventions and treatments translate to improved outcomes given the variation between patients. Unfortunately, processes such as gene transcription and drug metabolism are dynamic in the critically ill; that is, information obtained during static non-diseased conditions may have limited applicability. We propose an alternative way of personalising medicine in the ICU on a real-time basis using information derived from the application of artificial intelligence on a high-resolution database. Calculation of maintenance fluid requirement at the height of systemic inflammatory response was selected to investigate the feasibility of this approach.MethodsThe Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) is a database of patients admitted to the Beth Israel Deaconess Medical Center ICU in Boston. Patients who were on vasopressors for more than six hours during the first 24 hours of admission were identified from the database. Demographic and physiological variables that might affect fluid requirement or reflect the intravascular volume during the first 24 hours in the ICU were extracted from the database. The outcome to be predicted is the total amount of fluid given during the second 24 hours in the ICU, including all the fluid boluses administered.ResultsWe represented the variables by learning a Bayesian network from the underlying data. Using 10-fold cross-validation repeated 100 times, the accuracy of the model in predicting the outcome is 77.8%. The network generated has a threshold Bayes factor of seven representing the posterior probability of the model given the observed data. This Bayes factor translates into p < 0.05 assuming a Gaussian distribution of the variables.ConclusionsBased on the model, the probability that a patient would require a certain range of fluid on day two can be predicted. In the presence of a larger database, analysis may be limited to patients with identical clinical presentation, demographic factors, co-morbidities, current physiological data and those who did not develop complications as a result of fluid administration. By better predicting maintenance fluid requirements based on the previous days physiological variables, one might be able to prevent hypotensive episodes requiring fluid boluses during the course of the following day.

Knowledge-Based Bioinformatics: From analysis to interpretation

There is an increasing need throughout the biomedical sciences for a greater understanding of knowledge-based systems and their application to genomic and proteomic research. This book discusses knowledge-based and statistical approaches, along with applications in bioinformatics and systems biology. The text emphasizes the integration of different methods for analysing and interpreting biomedical data. This, in turn, can lead to breakthrough biomolecular discoveries, with applications in personalized medicine. Key Features: Explores the fundamentals and applications of knowledge-based and statistical approaches in bioinformatics and systems biology. Helps readers to interpret genomic, proteomic, and metabolomic data in understanding complex biological molecules and their interactions. Provides useful guidance on dealing with large datasets in knowledge bases, a common issue in bioinformatics. Written by leading international experts in this field. Students, researchers, and industry professionals with a background in biomedical sciences, mathematics, statistics, or computer science will benefit from this book. It will also be useful for readers worldwide who want to master the application of bioinformatics to real-world situations and understand biological problems that motivate algorithms.

Introduction to the Special Issue on Information Theory in Molecular Biology and Neuroscience

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