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Isovaleric acidemia: an integrated approach toward predictive laboratory medicine

Dercksen, M.

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Download date: 08 Sep 2018

165

Chapter 8

General discussion and future prospects

Chapter 8

166

1. Context

Investigations of Mendelian disorders caused by defects in single genes, such as

isovaleric acidemia (IVA), have progressively revealed the existence of non-specific

genetic and phenotypic variations. These findings paved the way for genome-wide

association studies (McCarthy et al., 2008), resulting in the assessment of underlying

health risks and the disclosure of global metabolite profiles (Wikoff et al., 2007) for

both rare and common diseases. The comprehensive investigations of genes,

proteins and metabolic pathways in diseased and healthy individuals through

powerful new applications, such as the "-omics" technologies, may well contribute to

the development of optimized therapeutic strategies focusing on the individual,

commonly known as personalized laboratory medicine. The success of personalized

medicine depends not only on having accurate diagnostic tests consisting of state-of-

the-art laboratory practices, but ideally also requires communication between

physicians/paediatricians, clinical chemists/biochemists and geneticists/molecular

biologists on their combined findings, interpretation and knowledge, which may

become paramount for innovative approaches towards clinical and laboratory

practice (Hall et al., 2004; Hamburg and Collins, 2010).

Against this brief background on contemporary tendencies with regard to IEMs, a

primary goal of this thesis was to follow such an integrated approach by using a

monogenetic disorder, IVA, as a model to investigate. The extensive knowledge of

IVA (Vockley et al., 2012), the first organic acidemia to be described in literature

(Tanaka et al., 1966), makes it an ideal model for the evaluation of an IEM through

an integrated approach. The evolving characterization of IVA patients has steadily

revealed the genetic and phenotypic heterogeneity of this disease, which further

motivated the need for a personalized understanding of this disease (Vockley and

Ensenhauer, 2006). The availability of biological material from a small IVA cohort in

South Africa, with limited information about the clinical and biochemical features of

these patients as well as no genotypical characterization of the IVD gene, provided a

unique opportunity to understand the underlying genetics in relation to the disease.

The intention of this study was that such understanding will ultimately lead to a

laboratory protocol for the early identification and subsequent individualized

treatment of conditions such as IVA. A general review of these topics, with a focus on

IVA, is presented in Chapter 1, which includes the outline and aims of this study.

What follows in this chapter is the emphasis on the link between the observations

from the integrated approach to monogenic disorders (subsection 2) leading to

perspectives on predictive laboratory medicine (subsection 3).

General discussion and future prospects

167

2. The integrated approach

The integrated approach adopted in this study consisted first of standard diagnostic

protocols, which included clinical, biochemical and genetic characterization of the

individual patients which formed part of the South African IVA cohort (Chapter 2). The

biochemical study was subsequently expanded to include a metabolomics approach

(Chapter 4) inspired by the high expectations among the clinical fraternity on the

potential contributions of metabolomics towards predictive laboratory medicine

(McCabe, 2010; Mamas et al., 2011) and personalized health in paediatric care

(Baraldi et al., 2009). The outcomes of Chapters 2 and 4 prompted the examination

of an aberrant pathophysiological event – hyperammonemia – as well as the

nutritional status of treated IVA patients (Chapters 5 and 7) respectively. This study

also demanded new methodologies, which are described in Chapters 2 and 6

(enzymological applications) as well as in Chapter 3 (a bioinformatics improvement).

The first aim of this study (refer to Chapter 1) dealt with the biochemical and

molecular analyses on biological material of South African IVA patients, the

outcomes of which are presented in Chapter 2. Most importantly, the investigation

also revealed a conspicuous genetic and biochemical homogeneity within the cohort.

First, all patients turned out to be homozygous for the c.367G > A mutation, which is

responsible for the substitution of glycine at position 123 by arginine (Chapter 2, Fig.

1). The genetic aberration in the IVD gene from the patient group was the first of its

kind to be described for IVA (Dercksen et al., 2012). Studies on many IVA cases in

populations around the world have revealed a wide array of mutations in the IVD

gene, but no other example of the unique homogeneity of the IVA variant seen in the

South African patients has been reported to date. Comparative results in this regard

are presented in Table 1, which summarizes the IVD mutational studies reported in

family groups from particular populations and further shows that these findings are

unprecedented.

The observed genetic homogeneity of the South African IVA cohort strongly suggests

a founder effect. It remains to be established, however, whether the 10 different IVA

patients with the same homozygous mutation, but coming from 7 different families,

can be traced back to the same ancestor as would be expected for a founder

mutation. The use of bio-genealogy has been successfully applied in the laboratory

for inborn errors of metabolism in Potchefstroom, South Africa, confirming a founder

effect of osteogenesis imperfecta in South African cases (De Vries an De Wet, 1986;

Knoll et al., 1988) and may well allow the determination of the inheritance pattern of

this specific IVA cohort. Eventually, haplotype studies as well as population-genetic

testing, which include determining the frequency of the mutation in the specific

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168

population, as described for common genetic diseases in Ashkenazi Jews, for

example, are required to confirm a founder effect (Slatkin, 2004).

Table 1: Summary of IVD gene mutations reported in various populations compared to the South African cohort.

Countries

Number of

patients Gene/protein abnormalities References

Germany/US

12 German, 7 US patients (family

relations not specified)

5 mild homozygotes (p.A314V) 8 mild heterozygotes (p.A314V)

6 heterozygotes (without p.A313V)

Ensenauer et al., 2004

Taiwan 6 (5 families) 1 homozygote and 5 different

heterozygotes Lin et al., 2007

Korea 8 (6 families) 2 (x2) different homozygotes and

4 different heterozygotes Lee et al., 2007; Cho et al., 2013

China 3 (3 families) 3 different heterozygotes Qiu et al., 2008; Lee et al., 2010; Bei et al., 2013

Thailand 5 (5 families) 4 different heterozygotes and 1

homozygote Vatanavicharn et al., 2011

United Arab Emirates

6 (3 families) 3 different homozygotes Hertecant et al., 2012

Saudi Arabia 3 (1 family) 1 common homozygote Kaya et al., 2013 South

Africa 10 (7 families)

1 common homozygote

(p.G123R)

Dercksen et al.,

2012

* Note, only populations, with known mutations as well as more than one patient in an affected family, is presented in

this table

Furthermore, homogeneity on a biochemical level was established by means of

immunoblot analyses in fibroblasts from the IVA cohort, indicating the absence of the

IVD protein (Chapter 2, Fig. 2). Moreover, the activity of IVD in fibroblasts from the

patients was confirmed to be deficient (Chapter 2, Table 3). The activity of the IVD

protein was not restored through the use of improved protein folding techniques

(Chapter 2, Table 3). Therefore a treatment option with potential chaperones (Bernier

et al., 2004) for improved folding of the tertiary structure of IVD will probably not be of

any benefit for this IVA cohort.

In addition, it was thought to be desirable to provide further insight with regards to

results presented in Chapter 2 Fig. 2, by using computational investigative tools to

predict stability and consequently pathogenicity of a mutant protein. The use of in

silico analyses to predict the stability of mutant IVD proteins has been informative in

recent IVA case reports (Hertecant et al., 2012; Kaya et al., 2013; Bei et al., 2013).

Consequently, an in silico analysis was conducted on the p.G123R IVD mutant with

the computational application, PolyPhen-2 (Adzhubei et al., 2010). Fig. 1 illustrates

the high degree of destabilisation within the corresponding p.G123R IVD protein. In

General discussion and future prospects

169

conclusion, the in silico analyse with PolyPhen-2 supports the observation of faulty

structural formation of the IVD protein.

Fig.1: An illustration of the use of an in silico application i.e. PolyPhen-2, to predict the effect of mutation on the IVD protein structure. The HumVar result (specifically applied to Mendelian diseases) gives a score of 0.989 (indicated by the black line in the coloured spectrum), which suggests that the mutation has a severely damaging effect on the structure of the IVD protein. The high specificity of 0.94 supports pathogenicity of this mutation. The somewhat low sensitivity of 0.54 indicates that the probability of the correct classification may be affected due to different amino acid alignment criteria (Flanagan et al., 2010).

In contrast to the irrefutable genetic and protein homogeneity, the IVA cohort studied

showed wide variability in terms of clinical symptoms (Chapter 2, Table 1) as well as

varied metabolite levels associated with individual IVA patients (Chapter 2, Table 3).

These findings were attributed to delayed diagnosis, the limited accessibility to NBS

in South Africa, inconsistent treatment regimens and in some cases inadequate

clinical follow-up sessions to assess the progression of the disease. In addition, a

study of a larger cohort may disclose reasons for phenotypical as well as individual

metabolite variation. Furthermore, other contributing factors, which were not

investigated in this thesis, may complicate the clinical presentation of IVA. Such

factors, which may inform on IVA heterogeneity, include acute or chronic bacterial or

viral infections, environmental influences on additional modifier genes (epistasis),

non-allelic mutations (polygenetic traits) and epigenetics (Budd et al., 1967; Dipple

and McCabe 2000; Flatcher et al., 2012). Nevertheless, the protocol which was

followed in this study provided evidence to successfully characterize the molecular

and biochemical features of this specific IVA group and thereby satisfied the first aim

set for this thesis.

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170

The informative value of metabolomics prompted the metabolomic investigation of

IVA, which became the second aim of this thesis (Chapter 1). The outcomes of this

study are presented in Chapters 3 and 4. Three aspects of the metabolomics

approach are highlighted in this discussion chapter: first, methodological aspects not

covered in Chapter 3; second the relationship between the outcomes in the context

of systems biology; and third, new biochemical insights that emanated from the

metabolomics data obtained for untreated and treated patients.

• The methodological aspect

A semi-targeted approach (with focus on the organic acid components of the

metabolome) was followed in three experimental groups affected by IVA – untreated,

treated patients and adult carriers of IVA (obligate heterozygotes) – and compared to

appropriate control groups (infants and children as well as adults, respectively) (Fig

2A). A condensed form of the four main components of the pipeline, which was

followed in Chapters 3 and 4, is illustrated in Fig. 2 of this chapter for discussion

purposes.

Fig 2: A simplified representation of the experimental design followed in the IVA metabolomics study. The four main components of the pipeline are defined and illustrated in panels A, B, C and E. Drop-line representations of the original data and of the remaining metabolites after data reduction are shown in the figures linked to panels D and F, respectively. Metabolites indicated by I (N-isovalerylglycine) and II (a carbohydrate metabolite due to dietary treatment of the IVA cases) in the two drop-line figures are highlighted to illustrate the characteristic dynamic range of the metabolomics data, as well as the biological importance of metabolites with very different concentrations, as discussed in the text.

General discussion and future prospects

171

The analytical aspects of this study generated unique challenges:

First, approaches had to be devised to compare five different groups (See Fig. 2A).

Dr. G. Koekemoer, expert on bioinformatics and part of the IVA multidisciplinary

team, suggested the development of a new method to compare the global metabolite

profiles of these groups, resulting in the development of the "concurrent class

analysis" (CONCA), presented in Chapter 3. CONCA is a novel approach that is

similar to a traditional principal component analysis (PCA) model with the added

benefit of providing further information concerning each individual group and an

ability to identify which variables have discriminatory power and are responsible for

group separation. This benefit is clearly illustrated by the results from three groups

(untreated IVA patients, treated IVA patients and children controls), which were used

to evaluate the CONCA method, as shown in Chapter 3 (Table 1), and were

successfully applied in the comparison of the five different case groups, which

included the original 3 groups as well as the obligate heterozygotes and adult

controls, in Chapter 4 (Fig. 2).

Second, the metabolite profiles of IVA were found to exhibit an extensive dynamic

range (illustrated in Figs. 2D and 2F), dominated by N-isovalerylglycine (e.g.

exceeding 1 000 mmol/mol creatinine, and at position I in Fig. 2D and 2F). The

protocol designed for the GC-MS measurement of samples (Fig. 2B) can be

summarized as: B-B-B-[S1-B]….[Sn-B]. S1 to Sn were randomly selected samples

from all five experimental groups, in each case separated by a blank (B) (hexane, the

carrier solvent). This measurement design was chosen to exclude the carryover

effect of subsequent samples separated on the GC column.

Third, the samples analysed presented with many features1 at a very low level, which

may include noise or potentially important indicators (e.g. drop-lines shown at

position II in Fig. 2). In addition, metabolites derived from medication were also

identified in some samples of patients as well as controls. Taken together, this

resulted in an extensive number of features (more than 200) shown in the original

data set (drop-lines below panel D in Fig. 2). The combination of zero reduction - a

bioinformatics approach, as used in Reinecke et al. (2012) - and filtering of

exogenous substances by manual curation - a biological approach, described in

1 Features and noise: In metabolomics, features indicate uncharacterized or unclassified chemical substances (as shown in Figure 2D and 2F above) or even a peak in a mass-spectrum, having a signal to noise ratio of 10 to 1. Noise will be those areas in raw metabolomics spectra of very low signal points. While the goal of the global metabolomics approach is to identify as many as possible features in a biological sample, the targeted approach differs in that the analytical method is designed to highlight metabolites of a particular class in an unbiased fashion (Griffiths et al., 2007).

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Williams et al. (2012) - was used to produce reduced 2 normalized 3 data (drop-lines

below panel E in Fig. 2). A [n x p]-matrix could subsequently be constructed from the

reduced data, consisting of 86 cases (n) and 52 metabolites (p). This matrix was well

suited for multivariate (PCA and PLS-DA) and univariate (effect size and Mann-

Whitney tests) analysis. The bioinformatics analysis identified the important

biomarkers for IVA (Chapter 4, Table 1) and identified metabolites associated with

various treatment regimens (Chapter 4, Table 3).

The above overview of the metabolomics approach was included in this chapter to

illustrate how it contrasts with the traditional approach to identify biomarkers for IEMs,

articulated by Duran et al. (1982), as the "continuous search for 'new' metabolites

[that] may eventually lead to a better understanding of the relationship between the

clinical conditions and their biochemical abnormalities". In the case of IVA, the

identification of the 11 biomarkers summarized in Table 1 of Chapter 4 embraced

metabolites obtained from research conducted over a period of 40 years – from the

first abnormal metabolites for IVA identified using packed-GC columns (Tanaka et al.,

1966) to the description of N-isovaleryl leucine identified through ESI-MS/MS

analysis (Loots et al., 2005). These markers were traditionally identified by extensive

and time-consuming analytical procedures and based on samples from a single or a

few individuals. In contrast, by using contemporary high resolution instrumentation

and bioinformatics analysis, the metabolomics approach revealed an extensive

complement of metabolites in biological samples, illustrating the holistic nature of

metabolite profiles. The availability of technologically highly sensitive analytical

equipment [e.g. NMR and 'hyphenated'-MS platforms (GC, LC or CE linked to various

MS configurations, e.g. TOF-MS, QQQ or MSn)], software for spectral analysis (e.g.

AMDIS or CHROMATOF for deconvolution) and several commercial (e.g. AMDIS and

NIST) and customised metabolite libraries for metabolite identification lend

substantial impetus to innovative ideas for further development and refinements in

predictive laboratory medicine, as described below.

• The context of systems biology

Systems biology has been defined by Hood et al. (2004) as "...the scientific discipline

that endeavours to quantify all of the molecular elements of a biological system to

assess their interactions and to integrate that information into a comprehensive

profile that serves as predictive hypotheses to explain emergent behaviours". With

2 Reduced metabolomics data refers to date from which either or both outlier features or cases have been removed, often by application of a standardized outlier removing bioinformatics technique, like a Mahalanobis analysis (Hadi, 1992). 3 Normalization of metabolomics data means an approach of adjusting values measured on different scales to a notionally common scale, e.g. the expression of metabolite concentrations in this thesis adjusted to the creatinine concentration in the urine samples (De Livera et al., 2012).

General discussion and future prospects

173

this in mind, the global metabolite profile of the IVA model revealed biomarkers for

the disease and secondly, indicated significant metabolic changes which strongly

resemble the systems approach of the definition. Consequently, the unique potential

of metabolomics and its use in IEMs is clearly illustrated through our study of IVA.

In this regard, a model of secondary metabolic pathways and the involvement of

different cellular compartments was identified during this study (Chapter 4, Fig. 4)

which subsequently contributes to the understanding of the IVA biological system.

Indeed, the statistically significant presence of 2-hydroxyisovaleric acid in untreated

IVA patients points to the involvement of peroxisomes with phytanoyl-CoA

hydroxylase as the most likely enzyme involved. More importantly, primary

mitochondrial dysfunction was evident through the absence and presence of free

carnitine and carnitine conjugation in untreated and treated patients, respectively.

Consequently, detoxification pathways, involving carnitine conjugation and glycine

acylation, were shown to serve as important "rescue" mechanisms which enable IVA

patients to be treated. The carbohydrate-related biomarkers identified, mostly due to

the therapeutic diet, are also described in Chapters 3 and 4. In addition,

pathophysiological aberrations (discussed in Chapter 5) as well as possible

nutritional deficiencies in treated IVA patients due to the dietary regimen (addressed

in Chapter 7), was also disclosed. In summary, the results obtained with the use of

metabolomics as a data-driven, informative and even predictive tool supports the

initial aim that was set for this aspect of the thesis.

Evidently, future prospects and potential investigations should be kept in mind if IVA

is studied (as well as other IEMs) in relation to systems biology. The induced

secondary pathways and associated metabolites (as shown in Chapter 1, Fig. 2)

brought about by the IVD deficiency, may lead to the discovery of new

pathophysiological biochemical mechanisms which in turn uniquely affect the

homeostasis in the cells of the individual IVA patient. An example of the existing as

well as potential role of IVA metabolites in a biological system is comprehensively

portrayed in Fig. 3, which illustrates the effect of accumulating isovaleryl-CoA and

isovaleric acid (as a secondary product formed by a yet to be identified thioesterase)

in the cell. This thesis has addressed some of the effects (marked in red in the figure)

on the cell, but it may well be that novel avenues (correspondingly marked in blue)

should be investigated in vitro as well as in vivo, which subsequently may lead to

greater understanding of phenotypical variation within IVA groups.

Chapter 8

174

Fig. 3: A representation of the potential effect of isovaleryl-CoA and isovaleric acid on several biochemical processes leading to the disruption of cellular homeostasis. This thesis has addressed some of the biochemical consequences of IVA (marked in red). Additional events (marked in blue) have the potential to disclose more information with regard to heterogeneity observed in IVA (figure adapted from Hunt and Alexson, 2002).

In addition, various catabolic intermediates of the leucine pathway (Chapter 1, Fig. 1)

as well as IVA-related metabolites (Chapter 1, Fig. 2) have been implicated in

signalling pathways. Leucine and to a lesser extent α-ketoisocaproic acid, act as

secondary regulators of glucose utilization (especially in the muscle and brain), lipid

and cholesterol metabolism, protein anabolism and the inflammatory response in

adipose tissue, among other functions (Gao et al., 2003; Lynch et al., 2003,

Yoshizawa, 2004; Macotela et al., 2011; Su et al., 2012). Furthermore, the presence

of metabolic decompensation in IVA leads to the subsequent activation of a catabolic

state associated with hypoglycemia and elevated cellular AMP. This

pathophysiological effect triggers a cascade of several signal transduction pathways

(e.g. activation of AMPK and mTORC) (Howell and Manning, 2011; Laplante and

Sabatini, 2012) that tend to induce recovery of cellular homeostasis.

A further consequence of a catabolic state which occurs in IVA patients, is

proteolysis. It has been shown that leucine and more importantly isovalerylcarnitine

(elevated in IVA patients) may regulate proteolysis in the liver (Miotto et al., 1998).

The latter may play a role as an anti-proteolytic agent, through signalling pathways,

during acute metabolic decompensation. In addition, the presence of 3-

hydroxyisovaleric acid (elevated in some IVA patients) has been shown to play an

important role in several signal transduction mechanisms, which indicates its

multifaceted function in biological systems (Eley et al., 2007; Pimentel et al., 2011;

Wilson et al., 2008). Moreover, certain PUFAs, e.g. EPA (found to be reduced in IVA

patients undergoing treatment; Chapter 7) have been shown to be linked to signal

transduction processes, important in protein synthesis (Whitehouse et al., 2001; Eley

et al., 2007). In conclusion, these metabolic variations and mechanisms should be

General discussion and future prospects

175

taken into account from a systems biology perspective and merit attention in the

future investigation of the pathophysiology of disease. These complex cellular

mechanisms have to our knowledge not been investigated in IVA specifically and

may help to explain the phenotypical variation in this disease.

• New biochemical insights

The metabolomics approach was instrumental in providing new biochemical insights

with regard to untreated and treated IVA patients. Several pathophysiological and

therapeutic features of this specific IVA cohort were identified through metabolomics

and prompted further investigations. Consequently, the final aim of this thesis was

to focus on one pathophysiological aberration i.e. hyperammonemia, as well as the

nutritional status of treated IVA patients.

The metabolomics finding of a significant elevation of N-isovalerylglutamate in the

urine of IVA patients coincided with our study of the mechanism of secondary

hyperammonemia and the consequent formation of N-isovalerylglutamate in IVA. To

this end, an in vitro determination of the activity of N-acetylglutamate synthase

(NAGS), one of the vital steps in the urea cycle which plays a key role in ammonia

elimination, was developed (Chapter 5). Subsequently the mechanism of secondary

hyperammonemia present in IVA and several other short-chain and short/branched

chain organic acidemias was studied. Short-chain and short/branched chain acyl-

CoAs, including isovaleryl-CoA, had various effects as substrates for and inhibitors of

NAGS, as described in Chapter 5. Consequently, the inhibition of NAGS was found to

be a contributing factor to secondary hyperammonemia, as described in Chapter 5.

The final finding of this in vitro investigation of secondary hyperammonemia in IVA

and other short-chain related organic acidemias did, however, raise additional

questions concerning the factors responsible for the secondary hyperammonemia.

Several contributing mechanisms on a biochemical level may include:

• The varied substrate specificity of several acyl-CoA dehydrogenases and

acylases, which may limit accumulation of acyl-CoAs.

• The availability of acetyl-CoA and free CoA in the mitochondrion.

• The potential variation in the activity of urea cycle enzymes in individual patients.

• The activation of secondary and detoxification pathways, which consequently

eliminate accumulated acyl-CoAs.

• The subsequent effect (activation or inhibition) of alternative N-acylglutamate

conjugates on carbamyl phosphate synthetase 1 (CPS).

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176

In conclusion, the function of the urea cycle which is potentially influenced by

described biochemical variation should be considered in the treatment of

hyperammonemia. Consequently, the benefits of the administration of

pharmaceutical agents such as N-carbamylglutamate, may be of use in certain

patients who are more prone to attack of acute hyperammonemia, as discussed in

Chapter 5. These considerations in treatment of pathophysiological effects, for

example hyperammonemia, again emphasize a need for a personalized approach to

treatment of IEMs.

The study of NAGS inhibition led to the development of an improved UPLC-MS/MS

activity assay applicable to a small amount of liver homogenates obtained by needle

biopsy as described in Chapter 6. The measurement of NAGS for primary as well as

secondary NAGS deficiencies (due to organic acidemias and drug-induced incidents)

has proved to be problematic as explained in Chapter 6. We succeeded in setting up

a reproducible and sensitive NAGS assay in liver tissue to investigate

hyperammonemia on an enzymatic level, thereby contributing to the identification of

patients having a primary NAGS disorder and in addition being able to investigate

secondary hyperammonemia as a pathophysiological complication.

Finally, we addressed some aspects of the nutritional status of treated IVA patients.

Chapter 1 section 4 emphasizes the importance of dietary adjustments and the

advent of "detoxifiers" as well as therapeutic support of patients with IVA. In this

regard, the IVA metabolomics study (Chapter 4) showed moderately increased

methylmalonic acid in urine of treated IVA, which may be potentially associated with

functional vitamin B12 deficiency (Herrmann et al., 2003). Consequently, functional

vitamin B12 markers were measured but results in Chapter 7 showed no clear

evidence of vitamin B12 deficiency on biochemical as well as symptomatic levels

(e.g. absence of macrocytic anemia). We suggest that future studies, which include a

larger cohort of treated patients as well as improved biochemical assessment of

functional vitamin B12 status through the measurement of holotranscobalamine, as

proposed by Heil et al. (2012), should be considered to confidently exclude a vitamin

B12 deficiency. Furthermore, the inhibition of succinyl-CoA ligase by isovaleryl-CoA,

subsequently resulting in elevated methylmalonic acid levels, has been reported by

Berger et al. (1982) and consequently mimics a functional vitamin B12 deficiency.

The latter is unlikely in the case of our study, for significantly elevated methylmalonic

acid levels were observed in treated patients and not in untreated patients (Chapter

4). The influence of isovaleryl-CoA on succinyl-CoA ligase activity should however be

reinvestigated to obtain a more convincing prospective.

The results in Chapter 7 irrefutably indicated an overwhelming omega-3- and

omega-6 fatty acid depletion, which was attributed to dietary intervention. The

General discussion and future prospects

177

depletion of essential fatty acids can have a profound effect on the neurological

development of a young child as well as possible immunological complications and

need to be investigated further (Vlaardingerbroek et al., 2006). Moreover, the

incorporation of accumulated isovaleryl-CoA into the fatty acid synthesis pathway has

been mentioned shortly in a report by Malins et al. (1972), but was not investigated in

our study. Consequently, it should be noted that limited production of essential fatty

acids may be caused by the direct biochemical influence of isovaleryl-CoA on the

fatty acid biosynthesis pathway and warrants further investigation. In summary,

Chapter 7 reports the nutritional insufficiencies of treated IVA patients and therefore

concludes that their nutritional status needs continuous monitoring of several

essential biomolecules required for vital biochemical processes.

3. Future prospects: Towards predictive laboratory medicine

In general, current clinical and laboratory practices in Western society still focus

predominantly on symptomology and/or a correlated diagnosis after the onset of

disease, which are the foundation of specific therapies. To some degree, this

approach has led to ever increasing healthcare costs, yet the prediction, precise

diagnosis, prevention and treatment of both acute and chronic diseases remains

incomprehensive. In addition, the limited understanding of the underlying

mechanisms involved in the onset, progression and therapeutic intervention of many

contemporary diseases has led to the growing awareness of a need to transform

current diagnostic views in health and disease (Naylor and Chen, 2010). A special

feature of a biological system is its diversity and corresponding variability. Personal

medicine addresses this characteristic and is consequently paramount in our

understanding of disease and the treatment of the individual patient at the

appropriate time in a cost-effective manner (Zhang et al., 2012).

Furthermore, personalized medicine may bring about the understanding of

pathophysiological effects on a molecular level and may even lead to the diagnosis of

diseases or disorders prior to clinical onset. It ultimately seeks to cautiously predict

diagnosis and consequently classify the illness in order to optimize therapy

appropriate to a patient's unique molecular profile and biological make-up. Current

ideas on personalized medicine are thus not just enthusiastic views on innovations in

future health-care, but aspire to investigate human complexity and variability through

systems biology, a growing scientific movement leading to an integrated diagnostic

and therapeutic approach (Naylor and Chen, 2010). Moreover, even within the realm

of healthcare costs, Aspinall and Hamermesh in the Harvard Business Review (2007)

recognized personalized medicine as a breakthrough of targeted therapies that could

save many lives as well as a great deal of money.

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178

Van der Greef et al. (2007) and Ginsburg and Willard (2009) suggest that

personalized medicine forms part of an integrated, coordinated evidence-based

approach within the context of systems biology which includes the use of current "-

omics" technologies to understand disease in relation to health. This philosophy is

also evident from the present study described in this thesis. A range of current and

novel laboratory and diagnostic protocols, which are reproducible as well as

repeatable are essential in order to understand several aspects of a biological

system. This evidence-based approach is summarized in the model shown in Fig. 4.

It is a more elaborate and sophisticated representation of the model presented in

Chapter 1, Fig. 4, but it was also inspired by a related model proposed for a

personalised approach to the treatment of cancer (Chin et al., 2011). The model

emphasizes the following:

• The focus on the variability in IEMs, covering patients presenting with

conditions ranging from acute symptoms (as in IVA) to chronic or virtually

asymptomatic states and/or a characteristic phenotype (as described by

Ensenauer et al., 2004). This pipeline, however, still retains the important

general protocol followed for the diagnosis of IEMs.

• The requirement of different interventions and treatment regimens:

emergent symptomatic interventions in patients presenting with acute

symptoms (often in intensive care) towards well-founded and rationalized

sophisticated interventions, including treatment and monitoring of progress.

• The importance of input from clinicians (especially in acute cases), but also

the use of more specialized sources and protocols such as whole genome

sequencing (WGS), whole exome sequencing (WES) and metabolomics are

expected to define the resources required for the clinical and analytical

pipeline as well as the costs involved in implementing the sophisticated

mode of personalized health.

Despite the medical advances which continue up to the present time, numerous

disease states can be explained only by complex "traits" rather than by alteration of a

single gene, gene product or metabolite (Dipple and McCabe, 2000). Thus, in order

to gather a more thorough and relevant understanding of disease, one must obtain a

comprehensive perspective of the biological system, thereby uncovering the

interdependent and dynamic pathway, network and cellular events that undergo

change, resulting in disease predisposition, onset and progression. Hence, as noted

recently by Lemberger, the application of systems biology to human biology and

medicine should provide "...a deeper understanding of the genotype–phenotype

relationship; impact of the interactions between environmental conditions and

genotype; novel mechanistic and functional insights based on global unbiased

General discussion and future prospects

179

approaches; and elaboration of powerful predictive models capturing the intricacies of

physiological states" (Lemberger, 2007; Naylor and Chen, 2010). Application of the

clinical-analytical pipeline (Fig. 4) could hopefully contribute to this deeper

understanding of the genotype–phenotype relationship in inherited diseases.

Chapter 8

180

Fig

. 4: A

put

ativ

e m

odel

des

crib

ing

the

rout

e to

war

ds im

prov

ed p

erso

naliz

ed m

edic

ine.

The

pro

pose

d m

odel

indi

cate

s th

e tw

o m

ajor

res

earc

h ar

eas

whi

ch i

nclu

de t

radi

tiona

l di

agno

stic

pro

toco

ls (

to t

he l

eft

side

of

the

pipe

line)

as

wel

l as

im

prov

ed a

nd n

ovel

bio

chem

ical

and

bi

oinf

orm

atic

app

licat

ions

(m

iddl

e an

d rig

ht s

ide

of th

e pi

pelin

e). T

he lo

wer

pan

el in

dica

tes

poss

ible

poi

nts

of e

ntry

in th

e cl

inic

al a

nd a

naly

tical

pi

pelin

e, a

nd t

he u

pper

pan

el p

rese

nts

poss

ible

mod

es o

f di

agno

sis,

tre

atm

ent

or o

ther

int

erve

ntio

ns.

Rou

tine

diag

nosi

s th

roug

h ta

rget

ed

met

abol

ite i

dent

ifica

tion,

enz

ymat

ic a

nd I

VD

gen

e sp

ecifi

c an

alys

es (

Cha

pter

2)

as w

ell

as t

hera

peut

ic i

nter

vent

ion

incl

udin

g nu

triti

onal

in

suffi

cien

cies

(C

hapt

er 7

) w

ere

addr

esse

d in

thi

s st

udy.

In

addi

tion,

the

use

of

met

abol

omic

s (C

hapt

er 3

and

4)

and

the

inve

stig

atio

n of

the

pa

thop

hysi

olog

ical

mec

hani

sm o

f hy

pera

mm

onem

ia (

Cha

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5)

furt

her

illus

trat

e th

e im

port

ance

of

asse

ssin

g di

seas

e in

a b

iolo

gica

l sys

tem

. T

he r

esea

rch

done

on

IVA

em

phas

izes

that

furt

her

scie

ntifi

c ex

plor

atio

n is

ulti

mat

ely

need

ed to

impr

ove

the

qual

ity o

f life

in tr

eata

ble

IEM

s an

d co

nseq

uent

ly le

ads

tow

ards

pre

dict

ive

labo

rato

ry m

edic

ine.

General discussion and future prospects

181

It is thus suggested that three research settings are required to potentially

understand the phenotypical variation in IVA and to fully address personalized

medicine. The use of untargeted metabolomics as well as proteomics may point to

the involvement of secondary pathways and pathophysiological mechanisms

including changes in signalling pathways (Collins et al., 2006). The application of

next-generation sequencing (NGS), including WES (Yang et al., 2013) and/or WGS

(Brunham and Hayden, 2012), may well become paramount in exploring phenotypical

variation of IVA. The latter may lead to the identification of polymorphisms,

consequently resulting in "inadequate" enzyme expression and function, which are

involved in secondary pathways in individual IVA patients. Lastly, an IVA animal

model may provide various biological samples, in which pathological mechanism of

IEMs may be investigated by "-omics" technologies (Wajner and Goodman, 2011).

However, the development of such an animal model for IVA is not yet available for

these comprehensive studies (personal communication with Professor J. Vockley,

and Professor R. Ensenauer). In summary, results obtained from this work may yield

new options for investigating IVA and help to resolve unexplained phenotypic

variations as observed in the patients and thereby contribute to the development of

personalized medicine to use in everyday healthcare in the future.

In conclusion, given the lack of genotype-phenotype correlation for single gene

disorders, personalized medicine is clearly gaining importance in the field of IEMs. In

this regard, as a biochemist, I can only endorse the view expressed in the

Presidential Address at the opening of the 11th International Congress of Inborn

Errors of Metabolism in 2010 (McCabe, 2010). "The metabolome is our world. We

have explored and exploited the metabolome to identify new diseases, to examine

disease pathogenesis, to treat metabolic diseases, and to develop and expand NBS.

..... Those investigators who are focused on genomics, transcriptomics and

proteomics are seeking to move toward the metabolome". The integrated approach

followed in this thesis, even by using a well-studied disease such as IVA as model,

clearly emphasized the important inputs that we as biochemists could make towards

predictive laboratory medicine: as experts in the field of metabolism, in understanding

and interpretation of metabolomics data on a systems level and as members of a

multidisciplinary team of professionals, committed to the well-being of patients

suffering from an IEM.

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