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Isovaleric acidemia: an integrated approach toward predictive laboratory medicine
Dercksen, M.
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Download date: 08 Sep 2018
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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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.
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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
pter
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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