Mano Ram Maurya

A Mouse Macrophage Lipidome

We report the lipidomic response of the murine macrophage RAW cell line to Kdo2-lipid A, the active component of an inflammatory lipopolysaccharide functioning as a selective TLR4 agonist and compactin, a statin inhibitor of cholesterol biosynthesis. Analyses of lipid molecular species by dynamic quantitative mass spectrometry and concomitant transcriptomic measurements define the lipidome and demonstrate immediate responses in fatty acid metabolism represented by increases in eicosanoid synthesis and delayed responses characterized by sphingolipid and sterol biosynthesis. Lipid remodeling of glycerolipids, glycerophospholipids, and prenols also take place, indicating that activation of the innate immune system by inflammatory mediators leads to alterations in a majority of mammalian lipid categories, including unanticipated effects of a statin drug. Our studies provide a systems-level view of lipid metabolism and reveal significant connections between lipid and cell signaling and biochemical pathways that contribute to innate immune responses and to pharmacological perturbations.

Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets

Abstract Recently, Maurya et al. (Ind. Eng. Chem. Res. 42 (2003b, c) 4789,4811) have presented a comprehensive framework for signed directed graph-based analysis of process systems where major theoretical results have been substantiated with simple examples or individual unit-based case studies. In this article, two case studies are presented to illustrate SDG-based analysis of process flowsheets containing many units and control loops. While the literature is replete with single unit examples, flowsheet level analysis as described in this paper is virtually non-existent. The first case study deals with prediction of initial response and its fault diagnostic application in the Tennessee Eastman (TE) flowsheet using a lumped parameter model of the process. The second case study deals with the steady-state analysis and fault diagnosis (FD) of a reaction–separation process. For this case study, the overall signed digraph for the process is developed from the digraphs for individual units and control loops in the flowsheet. It is shown that digraph-based steady-state analysis results in good diagnostic resolution.

Fault diagnosis using dynamic trend analysis: A review and recent developments

Dynamic trend analysis is an important technique for fault detection and diagnosis. Trend analysis involves hierarchical representation of signal trends, extraction of the trends, and their comparison (estimation of similarity) to infer the state of the process. In this paper, an overview of some of the existing methods for trend extraction and similarity estimation is presented. A novel interval-halving method for trend extraction and a fuzzy-matching-based method for similarity estimation and inferencing are also presented. The effectiveness of the interval halving and trend matching is shown through simulation studies on the fault diagnosis of the Tennessee Eastman process. Industrial experiences on the application of trend analysis technique for fault detection and diagnosis is also presented followed by a discussion on outstanding issues and solution approaches.

Bioinformatics and Systems Biology of the Lipidome

Lipids play an important role in physiology and pathophysiology of living systems. Until a few decades ago, the number of lipid molecules that were chemically characterized was a few hundred at most and were catalogued in monographs and compendia.1 Since the advent of the era of the genome and the proteome, there has been increasing recognition that other macromolecules like lipids and polysaccharides in living systems display considerable structural diversity and systematic efforts are underway to identify, characterize and catalog these molecules. With mass spectrometric techniques coming of age, several thousand distinct molecular species have been identified from living species and the roles of several of these are beginning to be characterized.2 Unlike genes and proteins, whose defined alphabets provide the framework for ontologies and classification at the sequence level, lipids and polysaccharides have been characterized for the large part by popular names, with no foundations for systematic classification. The past two decades have witnessed two major advances in lipid biology. In the first, mass spectrometry has enabled the identification of thousands of lipid molecular species from cells and tissues and this has pointed to the important need for developing a systematic ontology that can rationally name and catalog the molecules. Second, the ability to investigate the functional roles of lipid molecules through systematic phenotypic studies has led to the identification of lipids as extremely important players in physiology and pathophysiology of living species.3 In combination with proteins and nucleic acids, lipids are integrally involved in biochemical networks that lead to phenotypes such as homeostasis, differentiation, and death of cells and tissues. Any approach to systems characterization of living systems, of necessity, has to include lipids along with other macromolecules and all complex cellular pathways involving lipid molecular species. Systems biology now extends in its scope to identify biosynthetic and metabolic lipid networks, cellular signaling networks that explicitly include lipid molecules and transcriptional and epigenetic networks where lipids play an integral role.4 Several large scale projects to characterize lipids and their functional roles have been initiated as exemplified by the LIPID MAPS5 effort. The LIPID MAPS is an exemplar systems biology project that measures cell-wide lipid changes in an attempt to reconstruct biochemical pathways associated with lipid processing and signaling. The cell-wide measurements of components of these pathways include mass spectrometric measurements of lipid changes in response to stimulus in mammalian cells, changes in transcription profiles in response to stimulus and in select cases proteomic changes in response to stimulus. Figure 1 shows a schematic of the LIPID MAPS experiments related to different lipid categories/pathways and the subsequent processing of the experimental data generated. Network reconstruction efforts rely on organization, analysis and integration of these data and this requires a strong bioinformatics and systems biology effort. The former has to include development of a systematic and universal classification and nomenclature system, design and development of lipid and lipid-gene, lipid-protein databases with appropriate functional annotations, and efficient query and analysis systems that can be broadly useful to the biology research community. The latter has to include methods for analysis of large scale lipid measurements in cells, reconstruction of lipid metabolic and biosynthetic pathways, and quantitative models of lipid fluxes in cells under varied perturbations. In this review, we will provide a comprehensive summary of extant developments in lipid bioinformatics and systems biology and discuss the outlook for the future integration of lipidomics into cellular and organismic biology. The sections that follow are delineated into the informatics approaches specific to lipid biology followed by an overview and exemplar approach to analysis of large scale lipidomic data towards a systems description of mammalian cells. Figure 1 Overview of the process of performing a quantitative lipid analysis of macrophage cell sample (in this example, a time-course experiment using bone marrow derived macrophages). Extraction methods, LC/GC purification methods, MS acquisition strategies ... 2. Classification, Ontology, Nomenclature and Structure Representation of Lipid Molecules The first step towards classification of lipids is the establishment of an ontology that is extensible, flexible and scalable. One must be able to classify, name and represent these molecules in a logical manner which is amenable to data basing and computational manipulation. Lipids have been loosely defined as biological substances that are generally hydrophobic in nature and in many cases soluble in organic solvents.6 These chemical features are present in a broad range of molecules such as fatty acids, phospholipids, sterols, sphingolipids, terpenes and others. In view of the fact that lipids comprise an extremely heterogeneous collection of molecules from a structural and functional standpoint, it is not surprising that there are significant differences with regard to the scope and organization of current classification schemes. 2.1. Classification, Ontology and Nomenclature In order to address the lack of a consistent classification and nomenclature methodology for lipids, LIPID MAPS consortium members have developed a comprehensive classification system for lipids.7 The consortium has taken a more chemistry-based approach and defines lipids as hydrophobic or amphipathic small molecules that may originate entirely or in part by carbanion based condensations of thioesters (such as fatty acids and polyketides) and/or by carbocation based condensations of isoprene units (such as prenols and sterols). Figure 2 shows the mechanisms of lipid biosynthesis.8 Based on this classification system, lipids have been divided into eight categories: Fatty acyls, Glycerolipids, Glycerophospholipids, Sphingolipids, Sterol lipids, Prenol lipids, Saccharolipids, and Polyketides. Each category is further divided into classes and subclasses. Additionally, following the existing rules and recommendations proposed by the International Union of Biochemistry and Applied Chemists and the International Union of Biochemistry and Molecular Biology (IUPAC-IUBMB) commission on Biochemical Nomenclature, a consistent nomenclature scheme has also been developed to provide systematic names for various classes and subclasses of lipids.7 Figure 2 Mechanisms of lipid biosynthesis. Biosynthesis of ketoacyl- and isoprene-containing lipids proceeds by carbanion and carbocation-mediated chain extension, respectively.8 All lipids in the LIPID MAPS Structure Database (LMSD) are classified and annotated using this comprehensive classification and nomenclature system developed by the LIPID MAPS consortium.

Biomarkers of NAFLD progression: a lipidomics approach to an epidemic

The spectrum of nonalcoholic fatty liver disease (NAFLD) includes steatosis, nonalcoholic steatohepatitis (NASH), and cirrhosis. Recognition and timely diagnosis of these different stages, particularly NASH, is important for both potential reversibility and limitation of complications. Liver biopsy remains the clinical standard for definitive diagnosis. Diagnostic tools minimizing the need for invasive procedures or that add information to histologic data are important in novel management strategies for the growing epidemic of NAFLD. We describe an “omics” approach to detecting a reproducible signature of lipid metabolites, aqueous intracellular metabolites, SNPs, and mRNA transcripts in a double-blinded study of patients with different stages of NAFLD that involves profiling liver biopsies, plasma, and urine samples. Using linear discriminant analysis, a panel of 20 plasma metabolites that includes glycerophospholipids, sphingolipids, sterols, and various aqueous small molecular weight components involved in cellular metabolic pathways, can be used to differentiate between NASH and steatosis. This identification of differential biomolecular signatures has the potential to improve clinical diagnosis and facilitate therapeutic intervention of NAFLD.

A Signed Directed Graph and Qualitative Trend Analysis-Based Framework for Incipient Fault Diagnosis

In this article a combined signed directed graph (SDG) and qualitative trend analysis (QTA) framework for incipient fault diagnosis has been proposed. The SDG is the first level in this framework and provides a possible candidate set of faults based on the incipient response of the process. The search for the actual fault is performed based on a QTA (level 2), which uses the temporal evolution of the sensors for further resolution. Thus, this framework combines the completeness property of SDG with the high diagnostic resolution property of QTA. Methods to address the problem of incorrect diagnosis arising due to incorrect measurement of initial response have also been presented. The proposed approach is tested on the Tennessee Eastman (TE) case study. Correct fault diagnosis is performed in all possible single fault scenarios. It is shown that this framework provides fast, reliable and accurate incipient fault diagnosis.

An Integrated Model of Eicosanoid Metabolism and Signaling Based on Lipidomics Flux Analysis

There is increasing evidence for a major and critical involvement of lipids in signal transduction and cellular trafficking, and this has motivated large-scale studies on lipid pathways. The Lipid Metabolites and Pathways Strategy consortium is actively investigating lipid metabolism in mammalian cells and has made available time-course data on various lipids in response to treatment with KDO(2)-lipid A (a lipopolysaccharide analog) of macrophage RAW 264.7 cells. The lipids known as eicosanoids play an important role in inflammation. We have reconstructed an integrated network of eicosanoid metabolism and signaling based on the KEGG pathway database and the literature and have developed a kinetic model. A matrix-based approach was used to estimate the rate constants from experimental data and these were further refined using generalized constrained nonlinear optimization. The resulting model fits the experimental data well for all species, and simulated enzyme activities were similar to their literature values. The quantitative model for eicosanoid metabolism that we have developed can be used to design experimental studies utilizing genetic and pharmacological perturbations to probe fluxes in lipid pathways.

A framework for on-line trend extraction and fault diagnosis

Qualitative trend analysis (QTA) is a process-history-based data-driven technique that works by extracting important features (trends) from the measured signals and evaluating the trends. QTA has been widely used for process fault detection and diagnosis. Recently, Dash et al. [2004. A novel interval-halving framework for automated identification of process trends. AIChE Journal 50 (1), 149-162] presented an interval-halving-based algorithm for off-line automatic trend extraction from a record of data, a fuzzy-logic based methodology for trend-matching and a fuzzy-rule-based framework for fault diagnosis (FD). In this article, an algorithm for on-line extraction of qualitative trends is proposed. A framework for on-line fault diagnosis using QTA also has been presented. Some of the issues addressed are: (i) development of a robust and computationally efficient QTA-knowledge-base, (ii) fault detection, (iii) estimation of the fault occurrence time, (iv) on-line trend-matching, and (v) updating the QTA-knowledge-base when a novel fault is diagnosed manually. A prototype QTA-based diagnostic system has been developed in Matlab^(R). Results for fault diagnosis of the Tennessee Eastman process using the developed framework are presented.

Integration of lipidomics and transcriptomics data towards a systems biology model of sphingolipid metabolism

BackgroundSphingolipids play important roles in cell structure and function as well as in the pathophysiology of many diseases. Many of the intermediates of sphingolipid biosynthesis are highly bioactive and sometimes have antagonistic activities, for example, ceramide promotes apoptosis whereas sphingosine-1-phosphate can inhibit apoptosis and induce cell growth; therefore, quantification of the metabolites and modeling of the sphingolipid network is imperative for an understanding of sphingolipid biology.ResultsIn this direction, the LIPID MAPS Consortium is developing methods to quantitate the sphingolipid metabolites in mammalian cells and is investigating their application to studies of the activation of the RAW264.7 macrophage cell by a chemically defined endotoxin, Kdo2-Lipid A. Herein, we describe a model for the C16-branch of sphingolipid metabolism (i.e., for ceramides with palmitate as the N-acyl-linked fatty acid, which is selected because it is a major subspecies for all categories of complex sphingolipids in RAW264.7 cells) integrating lipidomics and transcriptomics data and using a two-step matrix-based approach to estimate the rate constants from experimental data. The rate constants obtained from the first step are further refined using generalized constrained nonlinear optimization. The resulting model fits the experimental data for all species. The robustness of the model is validated through parametric sensitivity analysis.ConclusionsA quantitative model of the sphigolipid pathway is developed by integrating metabolomics and transcriptomics data with legacy knowledge. The model could be used to design experimental studies of how genetic and pharmacological perturbations alter the flux through this important lipid biosynthetic pathway.

A Complement–IL-4 Regulatory Circuit Controls Liver Regeneration

The involvement of IL-4 in liver regeneration has not yet been recognized. In this article, we show that IL-4, produced by NKT cells that accumulate in regenerating livers after partial hepatectomy, contributes to this process by regulating the activation of complement after liver resection in mice. The mechanism of this regulation was associated with the maintenance of an appropriate level of IgM in mouse blood, because IgM deposited in liver parenchyma most likely initiated complement activation during liver regeneration. By controlling complement activation, IL-4 regulated the induction of IL-6, thereby influencing a key pathway involved in regenerating liver cell proliferation and survival. Furthermore, the secretion of IL-4 was controlled by complement through the recruitment of NKT cells to regenerating livers. Our study thus reveals the existence of a regulatory feedback mechanism involving complement and IL-4 that controls liver regeneration.