A metabolomics approach to understand mechanism of heat stress response in rat

There are a few examples of metabolic profiling applications in medical field such as in drug metabolism in animal systems, but reports investigating effects of heat stress with a metabo

Heat stress is one of the leading cause for concern amongst defense personnel

in the tropics since the performance of soldiers on the battlefield is greatly influenced

by environmental factors such as ambient temperature Fatigue, resulting from prolonged heat exposure, causes a decline in coordination, alertness, and performance Understanding the physiology of heat stress, mechanisms of heat tolerance and methods to alleviate damage due to heat stress, is the main motivation for our studies

All organisms respond to a hyperthermic stress by synthesizing a highly conserved set of proteins known as heat-shock proteins (HSPs) An important feature

of HSPs is their role in cryoprotection and repair of cells and tissues against the harmful effects of stress and trauma Extensive studies have been done on HSPs and their role in heat stress tolerance in diverse species from bacteria to humans Study of HSP’s as biomarkers of heat stress has been the traditional approach to investigate heat related illnesses so far

A biomarker is a cellular or molecular entity found in increased amounts in blood, urine or tissues that can be used as an indicator of disease, susceptibility to disease or exposure to any externally applied perturbation Biomarkers are measurements thought to be directly related to clinical outcomes Depending on the specific characteristic, biomarkers can be used to identify the risk of developing an illness (antecedent biomarkers), aid in identifying disease (diagnostic biomarkers), or predict future disease course, including response to therapy (prognostic biomarkers) Biomarkers are nowadays routinely identified using RNA- (microarray) or protein- (proteomics) based platforms Both these types of markers provide possibilities that the cell may behave in a specified manner, but they are not the endpoints of the cellular biochemical responses In contrast, metabolites provide several advantages Firstly, metabolite markers are most closely related to the cell’s final endpoint its biochemical phenotype Secondly, they provide more stable and longer term markers than RNA or proteins Thirdly, metabolome is extremely sensitive to exogenous stimulation, hence it responds quickly and in a stable manner Fourthly, metabolome changes reflect the cumulative responses of cells, from signaling and transportation to regulation; hence they show an amplification effect in the response, leading to easier detection of changes Since these changes also arise from convergence of multiple

signals to common metabolic pathways, they make the metabolite markers more robust and representative of broader range of signalling responses Lastly, having a small mass, instrumentation needs for metabolite detection are more established and less expensive than for protein-based (proteomics) methods These reasons make metabolite-based approaches highly desirable for monitoring purposes In spite of these advantages, only specific metabolite markers (those detectable by biochemical assays) of heat stress have been extensively studied This is mainly due to lack of standardized or commercially available reagents or kits as compared to expression profiling approaches

The general aim of metabolomics is to identify, measure and interpret the complex time-related concentration, activity and flux of endogenous metabolites in cells, tissues, and other biosamples such as blood, urine, and saliva without any bias for the class of molecules Metabolic responses of cells provide the final steps of cellular adaptation to stresses or other perturbations to the tissues or individuals Multiparallel techniques, allowing analyses of the levels of low molecular weight compounds, have only just begun to be established during the past decade This is especially true in mammalian systems There are a few examples of metabolic profiling applications in medical field such as in drug metabolism in animal systems, but reports investigating effects of heat stress with a metabolomics approach are almost absent

Recent advances have made it possible to carry out an unbiased, simultaneous, and rapid determination of metabolites in various organisms based on metabolic profiling Thus, metabolic profiling appears to be one of key additional tools in

multiparallel system analysis and plays an important role in functional genomics The focus of my research is to develop a metabolomics platform for understanding heat stress response in a model animal, rat, and identify marker metabolites (intermediates and end-points of metabolic pathways) responsible for this response This will ultimately help in monitoring performance and recovery of military personnel under heat stress

1.2 Objectives

The overall aim of this project was to understand cellular responses to hyperthermia in multiple organs using a metabolomics approach The specific objectives of this study were as follows:

1) Establish a metabolomics platform for application in animal model

2) Identify metabolites from plasma and organs using a statistical approach 3) Identify metabolic pathways affected by heat stress and their regulation 4) Identify a comprehensive set of biomarkers from biochemical entities, specific to heat stress

In this thesis, the first chapter gives a brief introduction and the major objectives of this research work Chapter 2 is the literature review section, which provides background information and previous as well as current research carried in the field of heat stress effects and acclimation in mammalian systems and metabolomics Chapter

3 provides details of the materials and methods that were used during the entire study Chapter 4 focuses on the extensive optimization studies of metabolomics methods, performed using organ tissue of Rattus norvegicus (model animal, rat) It establishes a

metabolomics platform for ideal sample preparation and data processing techniques using a data driven approach This involved the use of various homogenization buffers, ionization methods and solid phase extraction methods and their combinations In Chapter 5, a non-targeted approach to identify perturbational effects

is focused upon Metabolic profiling results of the heat stressed animals after different times of recovery, in plasma were reported and differential metabolites were identified Chapter 6 compares the differential expression of metabolites in various organs at different time-points during heat stress and after times of recovery This chapter explores the effects of heat stress on a systems level and identifies the target pathways Statistical methods like t-test, ANOVA, and log base2 ratio and database searches were used for identification of early as well as late responding markers of heat stress and the pathways involved Lastly, in Chapter 7, a summary of this whole research work and scope for future research potential from the current study are described

CHAPTER 2 REVIEW OF LITERATURE

The literature reviewed here has been categorized under three parts The first part of the review includes heat stress metabolism, the effects of heat stress in animals, heat acclimation, markers of heat stress and other common stressors The second part of this review deals with metabolomic overview, metabolomics technology platforms, its applications and metabolomic data handling and knowledge extraction In the third part, metabolic pathways and networks and the applications of pathway analysis have been highlighted

2.1 Heat Stress Metabolism

2.1.1 Heat stress and homeostasis

Homeothermic animals must keep their body temperature within narrow limits for optimal function While heat is constantly generated in the body due to metabolism and due to external factors like the air, radiant temperature, as well as humidity, the body is equipped with adaptive mechanisms that enable a person to preserve a constant core temperature (Tc) The hypothalamus plays a vital role in controlling body temperature by coordinating thermal information from all body areas and directing the efferent signals to the appropriate heat production and heat conservation systems Because both thermal and several non-thermal factors will be present at all times, it may not be appropriate to dismiss the contribution of either when discussing the regulation of body temperature in mammals

The sources of heat gain and heat loss to and from the body are, principally: (1)

Bodily heat production, or the heat of metabolism, which can vary depending on the amount of physical activity undertaken, (2) convection and (3) radiation, either of which may result in heat gain or heat loss depending on whether the skin temperature

is respectively below or above the ambient temperature, and (4) evaporation of sweat from the surface of the skin, which can only result in the loss of heat from the body Non-thermal factors influencing heat loss and heat production responses are exercise (Kenny et al., 1997, Thoden et al., 1994), blood glucose (Passias et al., 1993), hydration/plasma osmolality (Ekbolm et al., 1970; Turlejska et al., 1986), sleep (Aschoff et al., 1974), motion sickness (Mekjavic et al., 2001; Nobel et al., 2005), fever (Bligh J., 1998), inert gas narcosis (Meklavic et al., 1992; Passias et al., 1992; Washington et al., 1993)

Fig 2.1 is a representation of the pathology of heat stress leading to heat stroke in mammals (Leithead, 1978)

2.1.2 Heat stress in animals

All living creatures suffer from excessive heat and the effects of heat stress on various species of bacteria, plants and animals have been extensively studied in the past few decades Metabolic adaptation of E coli to a higher temperature via production of heat stress proteins was reported (Weber et al., 2002) Eukaryotes like yeasts have been known to produce heat stress proteins (Chen et al., 2003), though sphingolipids too seem to be relevant for heat stress adaptation (Dickson et al., 1997; Jenkins et al., 1997)

Figure 2.1: Schematic representation of the factors leading to heat stress and

heat stroke (Redrawn based on Leithead, 1978)

Effects of heat stress on plants has been widely investigated in a variety of plant

species (Hong et al., 2001; Locato et al., 2009; Meiri et al., 2009), including

commercially important species like rice (Wang et al., 2009), lettuce (Oh et al., 2009)

and tomato (Qu et al., 2009)

Chronic exposure to environmental heat is known to improve tolerance via heat

acclimation even in lower animals like Caenorhabditis elegans (Treinin et al., 2003)

Cell lines have also been subjected to heat stress and their effects been studied

(Zimmerman et al., 1991; Gibbs et al., 2009) Effects of heat stress on mammalian

cell cultures (CHOK1, P19 and NIH 3T3) include changes in the cellular architecture, and the synthesis and degradation rates, of specific proteins and during recovery from hyperthermic shock (Roobol et al., 2009) To define better the subcellular mechanism

of heat shock induced cardioprotection, the selective expression of individual heat stress proteins (HSPs) has been investigated (Wei et al., 2006)

Heat tolerance in higher animals is mediated by activation of the pituitary-adrenocortical (HPA) axis (Michel et al., 2007) Some studies have shown that marked accumulation of either dopamine, serotonin or IL-1 in brain occurs in heatstroke-induced cerebral ischemia and neuronal damage in rats The survival of such animals can be increased by inhibition of IL-1 receptors or monoamine system in brain as well as by induction of heat shock proteins (Lin et al., 1997)

hypothalamo-2.1.3 Factors affecting the outcome of heat stress

Other than environmental conditions of temperature, humidity, air movement, insulation and clothing that may affect heat tolerance, there are several physiological conditions that make certain individuals more vulnerable to heat stress These personal factors include, age, sex, obesity, sleep deprivation and diabetes Ageing has been shown to increase protein nitration, causing a decline in HSP induction (Oberley

et al., 2008) Studies have also shown that mitochondria in old rats are more vulnerable to and less able to repair oxidative damage that occurs in response to physiologically relevant heat stress (Zhang et al., 2002) Aging alters stress-induced expression of heme oxygenase-1 in a cell-specific manner, which may contribute to the diminished stress tolerance observed in older organisms (Bloomer et al., 2009) Mitochondria in old animals are more vulnerable to incurring and less able to repair

oxidative damage that occurs in response to a physiologically relevant heat stress (Haak et al.,2009)

The thermoregulatory capacities of men and women are mostly dependent on the number and activity of sweat glands, sex hormones and the distribution of subcutaneous fat A variety of chronic pain conditions are more prevalent for females, and psychological stress is implicated in development and maintenance of these conditions (Kawahata, A 1960) Understanding relationships between gender differences in stress and pain sensitivity and sympathetic activation could shed light

on mechanisms for some varieties of chronic stress (Vierck et al., 2008)

Diabetes impairs the ability to activate the stress response partly due to, the selective atrophy of certain muscles or muscle fiber types (Najemnikova et al., 2007) It is also known to cause aortic stiffness and this may contribute to the increase in mortality and morbidity associated with diabetes in rats (Ugurlucan et al., 2009) Neural differentiation is associated with a decreased induction of the heat shock response and

an increased vulnerability to stress induced pathologies and death (Yang et al., 2008)

The activation of the hypothalamus-pituitary-adrenal axis by stress depends mainly on the characteristics of the stressor Moreover, the response of this axis to stress also depends on the time of day in which the stressor is applied (Retana-Márquez et al., 2003)

2.1.4 Heat stress response and heat acclimation

Among the variety of predisposing factors that affect thermal tolerance, only two adaptations are directly invoked to combat heat stress: 1) the rapid heat shock response (HSR); and 2) heat acclimation (Moseley P.L., 1997; Sawka et al., 1985)

Heat acclimation is a long-term developing process leading to an expanded dynamic body temperature regulatory range due to left and right shifts in the temperature thresholds for heat dissipation and thermal injury, respectively (Horowitz M., 2001)

In contrast, the HSR is a rapid molecular cytoprotective mechanism and involves the production of heat shock proteins (HSP) A rise in body temperature increases transcription of the heat shock genes, leading to rapid augmentation of their expression Under normothermic conditions, the resting cellular 72-kDa HSP (HSP72) level is low However, heat acclimation leads to a marked upregulation of the basal level of HSP72, an inducible member of the HSP72 family that is considered the most responsive to heat stress, and to a faster HSR (Maloyan et al., 1999)

ET-1A receptor antagonism can alleviate symptoms of heatstroke, like hyperthermia, arterial hypotension, decreased cardiac output, increment of tumor necrosis factor-γ, and increment of cerebral ischemia (e.g., glutamate and lactate/pyruvate ratio) and injury (e.g., glycerol) markers in rat (Chang et al., 2004) When rats were exposed to high environmental temperature (e.g., 42 or 43°C), hyperthermia, hypotension, and cerebral ischemia and damage occurred during heat stroke were associated with increased production of free radicals (specifically hydroxyl radicals and superoxide anions), higher lipid peroxidation, lower enzymatic antioxidant defenses, and higher enzymatic pro-oxidants in the brain of heat stroke-affected rats (Chang et al., 2007)

The breaching of the blood-cerebrospinal fluid barrier in hyperthermia significantly contributes to cell and tissue injuries in the CNS (Sharma et al., 2007) and induction

of heat shock protein, antagonism of interleukin-1 or N-methyl-D-aspartate receptors

or depletion of brain monoamines protects against the heatstroke-induced arterial hypotension and cerebral ischemic injury (Lin et al., 1999)

Heat stress has been shown to influence normal bodily functions like digestion, vision, smell and sleep depending on the intensity, duration and the mode of exposure

to heat (Sinha et al., 2006; Maloiy et al., 2008) Some studies even suggest that sleep may be necessary for effective thermoregulation (Rechtschaffen et al.,2002)

2.1.5 Markers of Heat Stress

Although referred to as heat shock or stress proteins, we now know that most of these proteins are expressed constitutively in normal unstressed cell and participate in several important biological pathways A select few of these stress proteins however, are expressed only in times of trouble, and hence their appearance is often diagnostic

of some stress or trauma undergone by the cell Over the years, heat shock proteins, especially the HSP 70 family, have been the biomarkers of stress in diverse species

In fact, HSP 72 over expression protects against hyperthermia, circulatory shock, and cerebral ischemia during heatstroke (Yang et al., 1998; Maloyan et al., 2002; Lee et al., 2006) It has also been shown that after the onset of heatstroke, the hypotension and altered protein profiles displayed by animals can be reversed by whole body cooling (Cheng et al., 2008)

There were significant differences in the concentrations of glucose, blood urea nitrogen, sodium, potassium, calcium, inorganic phosphorus, triiodothyronine (T3) and thyroxine (T4) and the activities of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), creatine kinase (CK) and lactate dehydrogenase (LD) in heat stressed camels (Gheisari et al., 1999), quails (Yenisey et al., 2004), rats (Lee et al., 2008)

Presently, more than 100 genes (including HSPs) have been found to be affected by heat stress Expression profiling has contributed to this effort by identifying many elements not previously known to be involved in the cellular response to thermal stress Changes in gene expression represent only a part of the overall response to thermal stress A full understanding of the cellular physiology of stress requires an integrative approach that includes understanding the function and interactions of all of the involved elements, proteins and others

2.1.6 Heat stress management

Simultaneous measurement of heart rate, blood pressure and temperature, has not previously been reported in unrestrained heat-acclimated rats Measurement of these variables without the confounding effects of restraint or handling has increased the validity of the rat as a model for human heat acclimation Better understanding of heat stress mechanism and metabolism in rat can eventually help in better management of heat related disorders

Studies have already shown that pretreatment with anti-inflammatory dose of aspirin can provide protection against heat stroke in rats, which may be associated with the inhibition of elevation of plasma IL-1beta levels by aspirin (Song et al., 2004) Also, dietary supplementation of chromium as chromium nanoparticles significantly decreased serum concentrations of insulin and cortisol, increased sera levels of insulin-like growth factor I and immunoglobulin G, and enhanced the lymphoproliferative response and phagocytic activity of peritoneal macrophages in heat-stressed rats (Zha et al., 2008) In a similar study, it was suggested that hyper-HAES (hydroxyethyl starch) is superior to 7.2% NaCl or HAES alone in resuscitation

of heatstroke The benefit of hyper-HAES during heatstroke is related to restoration of normal multi-organ function (Liu et al., 2009) Discovery of early markers of heat stress will lead to early diagnosis and treatment of heat stress and can even aid in monitoring physiological status of target groups like defense personnel, miners, foundry workers etc

2.1.7 Other common stressors

The biochemical manifestations of stress in mammals is often similar, irrespective of the type of stress For example, in response to short-term treadmill running, rats show signs of systemic stress including increase in serum corticosterone and HSP72 (Brown et al.,2007) Immobilization is a severe stressor that elicits extremely large elevations of plasma epinephrine (EPI), norepinephrine (NE), and corticosterone levels (Mravec et al., 2008) Combined biochemical, proteomic and histological evidence suggests that the effects of spaceflight on the liver may be similar to mild cold stress or fasting (Baba et al., 2008; Luo et al., 2008)

General anesthesia is a major stressor and it causes suppression of thermoregulatory mechanisms Some research indicates that isoflurane anesthesia significantly increases the esophageal temperature triggering thermoregulatory sweating, but that the sensitivity and maximum sweating rate are maintained at normal levels relatively well (Washington et al., 1993)

Ischemia followed by reperfusion presents a stress in mammalian tissue, that is very similar to heat stress in its metabolic outcome and like in heat stressed animals, the ischemia-reperfusion tolerance can be improved by mild exercise Wang et al., 2001)

or presence of reactive oxygen species scavengers (Lee et al., 1999)

2.2 Metabolomics

2.2.1 Metabolism and metabolomics

Since the systematic genome sequencing of the first microbe, we have seen the advent

of the ‘omics’ technologies, in which investigators seek to understand complex biological systems on a large scale The macromolecular omics (especially the transcriptomics and proteomics) were the first to gain widespread attention However, metabolomics, is one of the more recently introduced ‘omics’ technologies The general aim of metabolomics is to identify, measure and interpret the complex time-related concentration, activity and flux of endogenous metabolites in cells, tissues, and other biosamples such as blood, urine, and saliva In the last few years, there has been an increased focus on the application of metabolomics for functional genomics The application of metabolomics for functional genomics was first discussed (Oliver

et al., 1998) In the same year, the term metabolome analysis was mentioned in the context of analysis of metabolites for phenotypic profiling of Escherichia coli cells (Tweeddale et al., 1998) A clear definition for metabolome analysis and terms for other approaches to measure cellular metabolites was later introduced (Fiehn et al., 2001)

The metabolome is the complete set of metabolites in a cell or tissue (Fiehn et al., 2001; Goodacre et al., 2004), consists of low-molecular weight chemical intermediates (Oliver et al., 1998), which can be considered to be the end products of gene expression It is a well-established fact that while change in gene (protein) expression levels will have only small effects on metabolic fluxes, they must have large effects on metabolite concentrations Moreover, metabolic responses of cells provide the final steps of cellular adaptation to stresses or other perturbations to the tissues or individuals Metabolomics thus represents an ideal level at which to

analyse change in biological system sensitively (Goodacre et al, 2004), under conditions in which there may be negligible effects on the gross phenotype (Cornish-Bowden and Ca´rdenas, 2001; Raamsdonk et al., 2001) Qualitative and quantitative metabolome analyses also provide a view of the biochemical status of an organism under specific conditions For this reason, in the context of functional genomics, metabolomics is now regarded as a viable counterpart to proteomics and transcriptomics

2.2.2 Metabolic profiling and metabolomics technology platforms

As the definition of the metabolome above suggests, in a metabolomics experiment one would like to quantify all the metabolites in a cellular system, which could be cells, tissues or biofluids in a given state, at a particular time point For the analysis of mRNA and proteins one ‘only’ needs to know the genome sequence of the organism and exploit this information using nucleic acid hybridization or protein separation followed by mass spectrometry (although post-translational modifications are problematic) However, the analysis of metabolites is not as straightforward Whilst triple quadrupole MS instruments can be calibrated for accurate quantification of specific metabolites of known structure, in general, for unknown analytes there is a lack of simple automated analytical techniques that can measure hundreds to thousands of metabolites quantitatively in a reproducible and robust way In contrast with transcriptome analysis (but in common with protein analysis) methods are not available for amplification of metabolites and therefore sensitivity is a major issue Metabolites are generally labile species, by their nature are chemically very diverse, and often present in a wide dynamic range All of these challenges need to be adequately addressed by the analysis strategy employed This is currently a very

active area within metabolomics and in particular is presenting opportunities for novel analytical instrument manufacture Finally, in contrast to transcripts or protein identification, metabolites are not organism specific (that is to say, sequence dependent), thus when one has learnt how to measure the metabolite once, the analytical protocol is equally applicable to prokaryotes, fungi, plants and animals (Goodacre et al.,2007)

The strategies of sampling and sample preparation are diverse Both invasive (blood, intra-cellular metabolites in plants and microbes) and non-invasive (urine, volatile components) sampling can be performed Extra-cellular metabolites from urine (metabolic footprint), depict a picture over a period of metabolic activity and are normally stored at low temperatures to inhibit metabolic reactions The extraction of intra-cellular metabolites provides a snapshot of the metabolome, but can be time consuming, and is subject to certain difficulties when compared to other sampling strategies

Metabolic processes are rapid (reaction times less than 1 second), hence rapid inhibition of enzymatic processes is required and subsequent storage at -80°C For unicellular organisms or biofluids this is usually achieved by spraying the biomass into very cold (<-40°C) 60% buffered methanol (Tweeddale et al., 1998) Whilst for animal and plant tissues, liquid nitrogen is used to snap freeze the sample with subsequent storage at -80°C (Whittmann et al., 2004) The storage of samples is important, as the continued freeze/thawing of samples can be detrimental to stability and composition (Wilson et al., 1997)

For the extraction of the metabolites from the matrix, there are many different methods The most common ones are: acid extraction using perchloric acid or nitric acid, followed by freeze thawing, then neutralization with potassium hydroxide; alkali extraction typically using sodium hydroxide, followed by heating (80°C); and ethanolic extraction by boiling the sampling in ethanol at 80°C (Buchholz et al., 2001; Nielsen et al., 2005) However, these approaches can result in a severe reduction in the number of metabolites detected and degradation compounds not stable at extreme pH Polar/non-polar extractions are the most frequently applied method and are performed by physical/chemical disruption of the cells, removal of the cell pellet by centrifugation and distribution of metabolites to polar (methanol/water) and non-polar (chloroform) solvents Recently, in microbial metabolomics metabolites naturally excreted from intra-cellular volumes to the extra-cellular supernatant are analysed (Tava et al., 2000) Sampling and collection of volatile compounds from plants has also been performed (Weckwerth et al., 2003), and a procedure for extraction and separation of metabolome, proteome and transcriptome has also been reported (Rossi et al., 2002) Depending on nature of sample and downstream metabolomic application, further sample preparation maybe necessary This may involve protein precipitation with organic solvents (Xu et al., 2008) and further isolation from the sample matrix by solid phase extraction (SPE) or liquid– liquid extraction (LLE) (Repetto et al., 2001) Indeed, with complex matrices like mammalian tissue, sample preparation becomes a critical step before metabolomic analysis actually begins Tissue homogenization in appropriate buffers, protein precipitation, centrifugation and sample filtration are steps that ultimately determine the level of chemical information obtained from the sample

The extract once ready for analysis, there are many different methods and approaches that one could use The choice of analytical technology is based on the level of chemical information required from the metabolites, keeping in mind the speed and resolution of the analysis Realistically, no single technology is ‘all encompassing’ and there are currently no ‘set’ protocols to study metabolomics In metabolomics (also termed metabonomics for NMR- based clinical applications), NMR spectroscopy provides a rapid, unbiased, reproducible, non-destructive, high-throughput method that requires minimal sample preparation (Lindon et al., 2004; Reo et al., 2002) The technique, especially 1H NMR is used extensively in clinical and pharmaceutical applications since it provides detailed structural information of small organic molecules and, as such, has enabled a large number of biofluid constituents to be identified and catalogued or listed (Lentner C., 1981; Lindon et al., 2004)

Although not as sensitive as other techniques, such as mass spectrometry, useful data can still be generated by NMR, from small sample volumes Typically, biofluids such

as urine, bile, and blood plasma have been investigated, but also tissue extracts (Coen

et al., 2003) Studies as varied as toxicology (Keun et al., 2002), disease progression (Makinen et al.,2008), drug efficacy monitoring (Griffin et al., 2004) and biomarker discovery (Williams et al., 2005; Kim et al., 2003) have been successfully carried out using NMR However, this approach has limited sensitivity, resolution, and dynamic range, resulting in only the most abundant components being observed

Mass spectrometry is the most widely applied technology in metabolomics, as it provides a blend of rapid, sensitive and selective qualitative and quantitative analyses

with the ability to identify metabolites This approach, coupled with gas chromatography (GC/MS) is the method of choice for plant metabolomics (Hall et al., 2002) And owing to its success in plant metabolomics, mass spectrometry has been adapted to mammalian (Welthagen et al., 2005) and yeast (Moheler et al., 2007) metabolomics Although GC/MS is biased against non-volatile, high-MW metabolites, all metabolites can be analyzed after chemical derivatisation at elevated temperatures prior to analysis Due to hard ionization energies, results are highly consistent between laboratories and datasets from different laboratories can be shared, leading to construction of standard databases However, the limitations as to the size and types of metabolites that can be analyzed and the extensive preparation and derivatization required is a big concern

Liquid chromatography mass spectrometry (LC/MS) is ideal for metabolite profiling

as biofluids such as urine can be directly injected whereas samples such as plasma need minimal pretreatment (protein precipitation) LC/MS is also capable of moderate

to high throughput, has a reasonable dynamic range combined with good potential for biomarker identification (based on the spectral data generated), is not specific to particular classes of compounds and can be extremely sensitive The electrospray ionization (ESI) technique has made polar molecules accessible to direct analysis by

MS Quantification of multiple compounds in crude extracts can, in principle, be achieved in the same way as described for GC/MS, although automation of the procedure presents greater practical difficulties LC/MS/MS provides additional structural information that can be a very useful aid in the identification of new or unusual metabolites, or in the characterization of known metabolites in cases where ambiguity exists

Fourier transform ion cyclotron resonance (FT-ICR) MS analysis has become popular in the recent years because it enables the rapid, non-destructive, reagentless and high-throughput analysis of a diverse range of sample types It is a sensitive technique and, with its high mass resolution (>106) coupled to software that can exploit the information in isotope patterns, can produce the empirical formulae for metabolites directly (Aharoni et al., 2002) Due to its holistic nature, FT-IR spectroscopy is a valuable metabolic fingerprinting/ footprinting tool owing to its ability to analyse carbohydrates, amino acids, lipids and fatty acids as well as proteins and polysaccharides simultaneously (Harrigan G and Goodacre R., 2003) Relative quantitation could also be achieved by comparing the absolute intensities of each mass using internal calibration Whilst selectivity is not as high as the other methods, the rapidity of spectral collection and the fact that FT-IR readily lends itself to high-throughput analyses is highly advantageous

Some metabolomics work has also been carried out using Raman spectroscopy This

is an emerging technology with significant potential for monitoring metabolites (Mahadevan-Jansen et al., 19984 The metabolic fingerprinting potential of near IR (NIR) spectroscopy should also be recognised, and studies undertaken using NIR include measurement of lactate in human blood (Lafrance et al., 2003) as well as the investigation of metabolites in faeces (Nakamura et al., 1998)

The application of liquid chromatography-mass spectrometry in the field of metabolomics in recent years has been increased further by the development of higher pressure systems such as Ultra Performance Liquid Chromatography-Mass

Spectrometry (UPLC-MS) (Plumb et al., 2005; Wilson et al., 2005; Kind et al., 2007) UPLC-Tof MS has also been extensively used for toxicity, drug metabolism and biomarkers studies (Crockford et al., 2006; Plumb et al., 2004; Yin et al., 2006)

Detection and quantitation of high molecular weight biomolecules from biofluids and intact tissue (plant and animal) by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-ToFMS) shows promise as a metabolomics technology for several applications (Bucknall et al., 2002; Fraser et al., 2007) In studies where ESI-MS would present complex spectra due to multiple charging effects, quantitative MALDI-ToFMS can prove useful The predominance of the singly charged species in a complex mixture enables easier interpretation of spectra, and the ability of MALDI-ToFMS to analyze complex biological samples aids in eliminating the need for chromatographic steps

Successful de novo identifications of biomarker metabolites have already been demonstrated in animals exposed to various perturbations and stresses (Soga et al.,2006; Loftus et al., 2008) coupled to database queries Most recently, high-throughput profiling of metabolic snapshots has been demonstrated in various rat tissues (Ding et al., 2004; Chu et al., 2004) The future challenge will be to integrate the observed metabolic alterations into hypotheses about changes in biochemical pathways and gene expression levels

2.2.3 Data handling and knowledge extraction

As with all functional analyses, a typical metabolomics experiment can generate mountains of data (samples times variables times metabolites) and critical steps must

be taken to turn these data (information) into knowledge In particular, we need curated databases, very good data to populate them, and even better algorithms to turn these metabolite data into knowledge

well-Irrespective of the analytical technique used, the analysis of the data is essentially performed in three stages Initially the raw data need to be preprocessed to convert them to a suitable form Secondly it may be useful to subject these modified data to data reduction so that only the most relevant input variables are used in the subsequent data analysis The objective of the third stage of the data analysis is to find patterns within the data which give useful biological information that can be used to generate hypotheses that can be further tested and refined

For the metabolome, because the biological differences between samples sometimes arises from comparatively small differences in many metabolite concentrations, recognizing the pattern and interpreting it is not straightforward The methods available for metabolome analysis can be placed in four main (and partly overlapping) categories – univariate and multivariate statistical, unsupervised learning (which looks at the overall pattern or structure of the data), supervised learning (which uses known information to help guide the classification of the data (Yeang et al., 2001; Hastie et al., 2001), and system-based analyses which use theories such as MCA (Thomas et al., 1997) to help interpret the data in terms of the biological networks that generated them (Kell et al., 2004) Many unsupervised learning methods are equivalent to clustering methods and are often statistically based, while supervised methods come in many varieties (Weiss and Kulikowski 1991; Mitchell, 1997), including statistical, neural, rule-based, evolutionary and so on

The concept of multivariate biomarker profiles has become reality (van der Greef J., 2004) and hence, more powerful supervised learning multivariate analysis methods are needed (Hastie et al., 2001) In supervised learning an algorithm (vide infra) is used to transform the multivariate data from metabolite profiles into something of biological interest, usually of much lower dimensionality, which as discussed above can be categorical (diseased vs healthy) or quantitative (severity of disease) Discriminant analysis (DA) is a particularly popular algorithm, which is a cluster analysis-based method and involves projection of test data into cluster space (Manly B.F.J., 1994) This is a categorical method and loadings matrices can give an indication of important inputs (metabolites) Partial least squares (PLS) is a very popular linear regression-based method (Martens et al., 1989) The algorithm can be programmed in a quantitative way (PLS1) or categorical (PLS2 or PLS-DA), and as for DA, loadings matrices can give an indication of important metabolites Artificial neural networks (ANN) are very popular based machine learning methods, which in contrast to DA and PLS can learn non-linear as well as linear mappings (Bishop C.M., 1995)

2.2.4 Application of metabolomics in animal studies

Metabolic profiling has been in use from the early 1970s (Farreet et al., 2001) It has been extensively used in medical applications such as the screening of blood and urine samples The use of NMR spectroscopy was the first step in the development of

‘metabolic fingerprinting’ technologies NMR spectroscopic analysis of biofluids, cells or tissues enables generation of spectral profiles of a wide range of low molecular weights (MW) metabolites that reflect the metabolic status of an organism

Nowadays a variety of metabolomics technologies are employed to several applications for studying mammalian systems By far the most extensive application

of metabolomics is in the medical/clinical field ‘Clinical metabolomics’ aims at evaluating and predicting health and disease risk in an individual by investigating metabolic signatures in body fluids or tissues, which are influenced by genetics, epigenetics, environmental exposures, diet, and behaviour (Ceglarek et al., 2008) Metabolic signatures of cancer (Spratlin et al.,2009), Celiac disease (Bertini et al., 2009), gut microbiota (Jacobs et al., 2009) have been investigated using this platform Most metabolomics studies in rats involve toxicology studies (Stokvis et al., 2004) and drug metabolism (Tong et al., 2006) One study even attempted to determine hepatopathy signatures in dogs (Whitfield et al., 2005) Zuker rats have been used for obesity related research using metabolomics consistently in the past few years (Welthagen et al.,2005; Loftus et al., 2008) Of late, attempts are being made to integrate metabolomic and transcriptomic data for a systems level perspective (Li et al., 2009; Weckwerth et al., 2008)

2.2.5 Metabolite biomarkers

A biomarker is a molecule or a set of molecules that indicate an alteration of the physiological state of an individual in relation to health or disease state, drug treatment, toxins, and other challenges of the environment Biomarkers are routinely identified using RNA (microarray) or protein (proteomics) based platforms Although, both these types of markers provide possibilities that the cell may behave in a specified manner, they are not the endpoints of the cellular biochemical responses In contrast, metabolites provide several advantages Firstly, metabolite markers are most

closely related to the cell’s final endpoint- its biochemical phenotype Secondly, they provide more stable and long term markers than RNA or proteins Thirdly, metabolome is extremely sensitive to exogenous stimulation, hence it responds quickly and in a stable manner Fourthly, metabolome changes reflect the cumulative responses of cells at both RNA and protein levels; hence they show an amplification effect in the response, leading to easier detection of changes These changes also arise from convergence of multiple signals to common metabolic pathways, hence making the metabolite markers more robust and representative of broader range of signalling responses Lastly, being small in size, instrumentation needs for metabolite detection are more established and less expensive than for protein-based (proteomics) methods These reasons make metabolite-based markers highly desirable as targets for monitoring purposes

Biomarker discovery using metabolomics is a very active research area Metabolite profiling is used to generate quantitative lists of metabolites from control populations and test subjects that are diseased Data analysis is then used to mine the metabolites and determine which are discriminatory for the disease and which of these could be used in predictive medicine To begin with the detection of biomarkers one can use differential profiling where the average metabolite profile from the diseased subjects

is subtracted from the average metabolite profile from healthy individuals Finally, simple univariate analyses of ANOVA (analysis of variance), Student’s t-test or non-parametric equivalents can be used to ascertain if there is any statistically significant difference between individual metabolites for healthy versus diseased individuals

Biomarkers for diseases like intestinal fistulas (Yin et al., 2006), pre-eclampsia

(Crocker et al., 2005), coronary heart disease (Jia et al., 2008), renal failure (Jia et al., 2008), heart failure (Dunn et al., 2007), encephalopathy (Kawashima et al., 2006) have been investigated Metabolite biomarkers are fast replacing proteomic biomarkers in cancer detection (Ransohoff D., 2004; Liu et al., 2005) The integration

of methods based on gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS) for the comprehensive identification and, particularly, the accurate quantification of metabolites has attained a technical robustness that is comparable or even better than conventional mRNA or protein profiling technologies (Weckwerth, 2003)

2.3 Metabolic networks

2.3.1 Metabolic Pathways and Networks

Understanding the biochemical pathways that comprise human metabolism represents one of the major achievements of research in the biological sciences over the past 100 years The enzymes, cofactors, substrates, products and intermediates throughout the multitude of molecular pathways are more completely understood than almost any other aspect of human biology Biological networks are highly interconnected chains

of metabolic pathways that encompass heterogeneous biological entities including DNA (genes), mRNA, proteins and metabolites The networks are a reflection of biological processes that take place in the cells such as metabolism, transcription and translation It gives an overview of the regulation by proteins, activation or inactivation of enzymes by posttranslational processes as well as feed back loops A holistic network would be more dynamic giving the status of each enzyme’s activity

in the network based on its kinetics, the turn over of substrates which is represented

by flux values The global topological properties and local structural characteristics,

network motifs, network comparison and query, detection of functional modules and network motifs, function prediction from network analysis, inferring molecular networks from biological data as well as representative databases and software tools has been reviewed with respect to all molecular networks (Zhang et al., 2007) In this review more focus is on metabolic networks and their properties The structure and properties of metabolic networks especially the topology have been described in detail and methods for the reconstruction of metabolic networks have been highlighted in a book by Palsson (2006)

Study of the structure and dynamics of metabolic networks is a strenuous task considering the fact that it is very tedious to identify metabolites from complex mixtures such as crude extracts and lack of any one technique that could identify biochemical relationships among them Large-scale sequencing of complete genomes and projects for their annotations has paved way to experiments that could be integrated in several axes to obtain multidimensional correlations High throughput technologies like protein arrays, microarrays, interaction arrays, and large-scale biochemical assays have widened the scope of data presented for developing networks The advent of mass spectrometry for use in biological samples has proved

to be a boon in analyzing, identifying and quantifying both proteins and metabolites The use of the new generation of mass spectrometers to identify metabolites and their connection in developing metabolic networks has been reviewed (Breitling et al., 2006) The use of UPLC-MS-based methods to enable functional genomic discrimination of metabolic phenotypes in three strains and two genders of mice has been demonstrated (Wilson et al., 2005) This metabolomic strategy, was shown to facilitate interpretation and validation of metabolic interactions as well as revealing

the identity of potential markers for known or novel pathways This multivariate approach of addressing metabolism studies will hopefully help to increase the understanding of the integrative biology behind, as well as unravel new mechanistic explanations in relation to the metabolic pathways that are studied Data from other studies such as perturbational analysis, flux and kinetic studies and isotope-tracking experiments complement the metabolic data along with data from transcriptomics, genomics and proteomics to create metabolic maps

2.3.2 Applications of Pathway Analysis

Understanding the metabolic networks has several applications like, identifying unknown pathways and missing metabolic enzymes (Villas-Boas et al., 2005) Pathway analysis can be applied for the identification of dysfunction in the system by comparison between normal and diseased conditions as demonstrated by Wang et al., (2006) in case of gastrointestinal diseases and Yu et al., (2007) in liver disease Understanding the metabolic pathways involved can aid in better management of the disease The effects of different perturbations on the system can be studied including various types of stresses like heat, cold, dehydration (Allen et al., 2003; Cook et al., 2004; Wang et al., 2006)

Comparison of metabolic networks of one organism with another will help in understanding the similarity in the pathways and regulatory mechanisms that are common to these metabolic networks An approach for comparing cellular-biological networks and finding conserved regions in two or more such networks has been described (Chor and Tuller, 2007) They have made use of the KEGG's metabolic networks as the starting point Networks were developed that could identify biologically relevant conserved regions among more than a dozen metabolic

networks, and among two protein interaction networks Such comparison between networks also gives insight into functional relationships under different physiological conditions

A good understanding of metabolic networks is necessary for metabolic engineering

of pathways or products that are economically important whether its dietary supplements or medicinally valuable metabolites Knowledge of pathways and networks is valuable for improvement of microbial strains for various purposes like fermentation, bioremediation etc (Castrillo et al., 2003) Identifying modules in networks, can be helpful in designing new subsystems and aid in synthetic biology applications (Weber et al., 2002; Hoffmann et al., 2002)

With a solid conceptual framework developed and a growing list of applications for the study of metabolic pathways, the time is ripe to develop further applications and analytical techniques to better characterize the relation between an organism’s metabolic genotype and phenotype

CHAPTER 3 MATERIALS AND METHODS

3.2 Method development and optimization

3.2.1 Animal handling and tissue processing

For this phase of experiments twelve male Sprague-Dawley rats aged 6-7 weeks and weighing 250-300 gm were used They were sacrificed by carbon-dioxide inhalation and tissues of approximately 0.2 gm was immediately excised from the left lateral lobe of the liver These were collected from the same location in each animal Tissues were immediately washed in ice-cold saline to free them of any blood or other fluids

The tissues were then snap frozen in liquid nitrogen and stored at –80°C until further processing

The tissue samples were homogenized using a tissue homogenizer (Heidolph DIAX900, Sigma-Aldrich, Bellefonte, USA) in 100% methanol or phosphate buffered saline (pH7.0) After extraction, the homogenate was centrifuged at 14,000 g for 10 min The supernatant was diluted with 0.1M formic acid or 0.1N hydrochloric acid till

pH of the sample reached 3 The samples were re-centrifuged at 14,000g for 10 min and filtered twice through syringe filters (25mm id, 0.2µm, Sartorius) The samples were then subjected to solid phase extraction

3.2.2 Solid phase extraction

3.2.2.1 Types of cartridges

Solid phase extraction was performed using cartridges of varying specificities from three different manufacturers; the first set of SPE cartridges were from WATERS (Milford, MA, USA) OASIS MAX cartridges (60mg, 3ml), with an anion exchange sorbent that has selectivity for acidic compounds (Cat No 186000368), MCX cartridges (60mg, 3ml) with a cation exchange sorbent that has selectivity for basic compounds (Cat No 186000253) and HLB cartridges (30mg, 1ml) that capture hydrophilic as well as lipophyllic analytes (Cat No WAT094225) Second type of cartridges used were from Supelco, Sigma-Aldrich (Bellefonte, USA) and included the Discovery DSC-18 (50mg, 1ml), DSC-SAX (50mg, 1ml) and DSC-SCX (50mg, 1ml) cartridges DSC-18 is a reversed phase C-18 column that retains most organic analytes from aqueous matrices DSC-SAX and DSC-SCX

are both silica based ion exchange cartridges containing a quaternary amine with Cland a benzene sulfonic acid functional group with H+ as counter ion, respectively The

-last set of cartridges used were the Phenomenex (Torrance, USA) StrataX

(30mg,1ml), SAX (100mg, 1ml) and SCX (100mg, 1ml) StrataX is useful for general screening of acidic, basic and neutral compounds by reversed phase and can

be used for polar and non-polar compounds SAX and SCX are anion and cation exchange cartridges, respectively20,21,22 A Waters Vacuum Manifold was used to perform SPE

3.2.2.2 SPE protocols

All SPE procedures were handled according to the manufacturers recommendations The individual protocols are briefly described here All three cartridges from Waters were conditioned with 2ml 100% methanol followed by 2ml water after which 2ml of filtered sample was loaded For MAX cartridges, the first wash was with 2ml of 2% ammonium hydroxide followed by 100% methanol and the sample was eluted with 2ml of 2% formic acid in methanol For MCX cartridges, washing was carried out with 2ml 0.1N HCl followed by 100% methanol and the sample was eluted with 2ml

of 5% ammonium hydroxide in methanol For HLB cartridges, washing with 1ml 5% methanol in water was followed by elution with 1ml of 100% methanol For Supelco DSC-18, DSC-SAX and DSC-SCX cartridges the conditioning was done with 1ml of methanol followed by 1ml of water, after which 1ml of sample was applied The sample in DSC-18 was washed with 2ml of 5% methanol and eluted with 1ml of 100% methanol The sample in DSC-SAX was washed with 2ml of water followed by elution with 1ml of 2% acetic acid in methanol For DSC-SCX cartridges, samples were washed with 2ml of water followed by elution with 1ml of 2% ammonium hydroxide in methanol The Phenomenex cartridges were also conditioned with 1ml 100% methanol, and 1ml water, after which 1ml of sample was

applied The sample in StrataX cartridge was washed with 1ml of 5% methanol in water and eluted with 1ml of 100% methanol The sample in SAX cartridge was washed with 50% methanol and eluted with 5% ammonium hydroxide in methanol The sample in SCX cartridge was washed with 50% methanol and eluted with 2% acetic acid in methanol The final elution step of SPE with all these cartridges was repeated thrice in order to compare the efficiency of the elution solutions The eluates were then dried under rotary vacuum evaporator, (Eppendorf concentrator 5301, Hamburg, Germany) and reconstituted in 300µl of the 100% methanol After clean-up

by SPE, the samples were transferred to the autosampler for mass spectrometry A schematic representation of overall strategy used for this study is shown in Chapter 4, Figure 4.1

3.2.3 Liquid chromatography-mass spectrometry

3.2.3.1 Liquid chromatography: The online LC experiments were carried out on a Agilent 1100 series LC gradient system consisting of an LC-10AD pump and series

1200 autosampler (Agilent Technologies, Palo Alto, USA) Water acidified with glacial acetic acid to pH 3 was used as mobile phase A and 100% acetonitrile was mobile phase B Isocratic elution was performed with flow rate of the mobile phase adjusted to 60 µL/min

3.2.3.2 Mass spectrometry: An Applied Biosystems MDS Sciex API 4000 QTrap

LC/MS/MS System (PE Sciex, Concord, Canada) was used for mass spectrometric analysis This model consists of a hybrid triple quadrupole-linear ion trap instrument equipped with a Turbo V ion source Sample injection was performed by the Agilent 1100 autosampler (Agilent Technologies, Palo Alto, CA, USA) with a 10 µL

sample volume injected for every acquisition The sample compartment of the autosampler was maintained at 4°C

3.2.3.3 Mass spectrometric set-up: All samples were prepared in triplicates, each of which was injected three times into the mass spectrometer to obtain machine replicates Each experiment was repeated three times to check the reproducibility of data A blank was run in between each sample to prevent carry over Data was acquired in both, positive and negative ionization modes The optimum conditions of the interface were: ring voltage 250V; orifice voltage 150V and electro-ion spray voltage 4450V Mass spectra were acquired over the scan range of m/z 50 –1200, using a step size of 0.5 amu and a dwell time of 3 mins Acquisition of the spectra was performed at 60 scans per minute The nebulizer and turbo gas (both compressed air) flow rates were set at 15 µL per min Those of the curtain gas and collision gas (both Nitrogen) were 1.2 L per min and 1.8 x 1015 molecules per cm, respectively The ion-spray voltage was kept at 4,500 V, with a source temperature of 300 °C Operation and data acquisition of the resulting Q1 chromatograms were performed using the Analyst® version 1.4 software, Sciex Both, isotopic and adduct peaks were treated as separate metabolite peaks, thus contributing to the total number of MS data points Hence the actual number of biologically relevant ions are much less than the number of ions detected as peaks with different m/z values

3.3 Heat stress experiments

3.3.1 Sedation and Cannulation

The experimental workflow is illustrated in Figure 3.1

Figure 3.1: Workflow of Heat Stress Experiments

Prior to the experiment, the body weight of all animals was recorded, following which, the area of surgical incision was shaved Rats were sedated with inspired isofluorane gas (1-3%) Sedation was confirmed by the absence of response to toe pinch and was tested every 15 minutes during the experiment Dosage of anesthetic used was adjusted to keep the animal well sedated throughout the experiment Once sedation was confirmed, a rectal probe was inserted 7-8 cm past the anal sphincter for monitoring of core temperature (Tc) An open incision in the lower abdomen, between the top of the thigh and the urethral opening was made The femoral artery was then located and cannulated for blood collection and blood volume replacement with saline (Fig 3.3) Following cannulation, the first blood draw (1ml) will be taken (P1) A similar volume of saline (1ml) will be replaced through the cannula after each

blood sampling Once the cannula was anchored, the incision was covered with standard surgical gauze until the end of the experiment The rats were allowed to rest and recover at room temperature for 2 hours

Fig 3.2: Location of incisions and insertions made on ventral side

of the rat for various monitoring processes and tissue excision

Fig 3.3: Arterial network of the rat, with the site of cannulation highlighted in red

3.3.2 Heat stress

After 2 hours of rest, infrared heating lamps were used for increasing the core temperature of the rat The lamp was placed at a height of 45 cm above the animal Heat was applied till the core temperature reached 40°C At this point, 1 ml of blood was collected (P2) Heating was continued till the core temperature reached 41°C This is maintained for 10 minutes At the end of 10 minutes, 1 ml of blood was drawn

(P3) After this, the heating lamp was switched off and the animal allowed to recover

at room temperature Animals were divided into 6 groups (5 animals each) based of the amount of time they were allowed to recover Five animals were selected for each time-point since all the animals in the same set showed excellent consistency Very little variation between the animals in terms of detected ions and their relative intensities was observed and the decision was made to not use any more animals for the study

One set of animals that did not undergo heat stress was the control group This group went through the exact same procedures except that they remained under room temperature Their body temperature was maintained at about 37 °C Blood collection

of the non-heat stressed animals was matched with those of the heat stressed animals when the latter achieve the respective heat stress core temperatures Core temperature was recorded every 15 minutes throughout the experiment and every 2 minutes during heat stress (DSO/IACUC/05/17)

3.3.3 Animal sacrifice and tissue excision

After the recovery period, just prior to sacrifice, 1 ml of blood was drawn (P4) The animals were sacrificed at the following time-points; zero time (just after heat stress),

30 minutes, 1 hr, 2hr and 4 hr after the initial 10 min exposure at core temperature 41°C The animals are sacrificed using 100% carbon dioxide introduced at the rate of 10-20% of the chamber volume per minute to minimize distress to the animals All the blood was collected in pyrogen-free EDTA tubes Plasma was extracted immediately from the blood, snap frozen in liquid nitrogen and later transferred to a permanent freezer (-80°C) Once the animal was sacrificed, a midline incision was

made to expose the viscera of the rat Parts of the liver, heart, lungs and kidney weighing approximately 0.2 gm were immediately removed, washed in ice-cold saline and snap frozen in liquid nitrogen The tissue samples were collected from the same location in each animal (Fig 3.2 and Fig 3.4) They were later stored at –80°C for subsequent metabolomics analysis

Fig 3.4: Organs from which tissue was excised: Lung, Heat, Liver and Kidney, with sampling locations highlighted in red

3.3.4 Tissue processing and solid phase extraction

The tissue samples were homogenized using a tissue homogenizer (Heidolph DIAX900, Sigma-Aldrich, Bellefonte, USA) in phosphate buffered saline (pH7.0)