Biomarkers in the Diagnosis of Mycobacterium tuberculosis Complex Infections 195
have demonstrated increased CNA levels in
blood via apoptotic or necrotic pathways
( Fournie et al., 1995; Jiang et al., 2003; Jiang
and Pisetsky, 2005). Increased CNA levels have
been documented in plasma as a result of
apoptosis- induced oxidative stress on placental
tissue (Tjoa et al., 2006). Evidence for the active
release of CNA from activated lymphocytes or
other nucleated cells (Anker et al., 1975; Stroun
et al., 2001) and lysis of tumour cells has also
been reported (Sorenson, 2000). Apoptosis and
necrosis are also associated with TB pathogene-
sis and are critical for mycobacterial killing,
granuloma formation and chronic inflamma-
tory condition induced by the pathogen (Cosivi
et al 1997). Thus, an increase in CNA of
M. bovis-infected animals is expected to occur as
the disease progresses.
Methodologies used for CNA discoveryAlthough there has been a controversy about
the use of serum or plasma for CNA discoveries,
most of the CNA discoveries have been applied to
serum (Leon et al., 1977; Sorenson et al., 1994;
Nawroz et al., 1996; Kopreski et al., 1997; Lo
et al., 1997). It has been reported that CNA
recovered from serum is several-fold higher than
that in plasma. The difference in CNA levels have
been considered due to the in vitro lysis of white
blood cells during the process of clotting (Chen
et al., 1999; Lui et al., 2002). Lui et al.’s study
concluded that the serum CNA might not be a
true representation of the biological condition
of the patient.
Several groups have measured the levels of
CNA in different diseases in search of diagnostic
or prognostic markers (Ziegler et al., 2002).
Multiple techniques have been used in different
studies for quantitative analysis of CNA post
discovery using de novo sequencing of total
circulating nucleic acids. These include radio-
immunoassay (Leon et al., 1977; Shapiro et al.,
1983), competitive PCR (Jahr et al., 2001),
quantitative real-time PCR (Thijssen et al.,
2002), fluorimetric quantification (Thijssen
et al., 2002), spectrophotometric determination
(Shao et al., 2001) and visual comparison with
known standards (Sozzi et al., 2001). To date, all
the studies into CNA demonstrated a significant
increase of CNA levels in the diseased condition
irrespective of the use of serum or plasma.
13.3.2 Proteomics approachThe term proteome is derived from ‘protein and
genome’ and was first coined by Marc Wilkin in- Proteome refers to all the proteins
expressed by a genome at a given time within a
given environment (Solassol et al., 2006). With
the completion of the genome sequences for
many prokaryotic and eukaryotic organisms,
researchers had to assign cellular and molecular
functions to thousands of newly predicted gene
products and explain how these products coop-
erate in complex physiological processes. This
led to the emergence of a new field of research
termed ‘proteomics’ that aims to characterize
biological mechanisms by identifying different
proteins involved.
In the last decade, proteomics has provided
us with an ability to rapidly identify novel pro-
tein biomarkers for various cancerous and non-
cancerous diseases. Several researchers have
reported that not a single biomarker, but a
battery of biomarkers is required to show the
specificity and sensitivity for the detection
or monitoring of most cancerous diseases
( Petricoin et al., 2002a, 2002b; Tirumalai et al.,
2003; Zhang et al., 2004; Stone et al., 2005).
While substantial research on biomarker discov-
ery exists in fields such as oncology, very few
studies have investigated the utility in using
the proteomic approach to understanding the
pathogenesis of infectious diseases (Gravett
et al., 2004; Poon et al., 2004; Yip et al., 2005;
Agranoff et al., 2006; Pang et al., 2006).
Nonetheless, several studies have looked at
the diagnostic potential of proteomic finger-
printing to determine different disease states as
well as monitor the treatment response in TB
(Haas et al., 2016). Early proteomic research
showed that a combination of four biomarkers
(serum amyloid A, transthyretin, neopterin and
C-reactive protein) could distinguish between
active pulmonary TB and non-TB disease and
healthy controls (Agranoff et al., 2006; Seth
et al., 2009). It was speculated that targeting
specific protein variants rather than the total
protein would improve the accuracy of the diag-
nosis. The translation of proteomic biomarkers
into diagnostic tests has been faced with some
challenges: the protein biomarker candidates
reported by independent studies vary consider-
ably and a universal proteomic profile of TB is