16 February / March 2018
Table 1 GCxGC-QTOFMS Instrument Conditions - Rice Blast Fungus Parameter
Setting
GC Conditions Primary Column
Secondary Column Split Ratio
Split Inlet Temperature
Oven Temperature Program Carrier Gas Flow
Modulation Conditions Modulator
Modulation Period Cold Jet Flow
Hot Jet Temperature MS Conditions
Transfer Line Temperature Ionisation Mode
Data Acquisition Rate
310°C EI
50 Hz
the deletion of genes encoding a nitrogen regulator (Δnut1), a carbon regulator (Δmdt1), and an integrator of carbon and nitrogen metabolism (Δtps1) [10, 11]. Three samples were collected for each of the four classes (wt, Δnut1, Δmdt1, Δtps1). Mycelial tissue samples were collected, lyophilised, and ground in liquid nitrogen. The metabolites were extracted using a mixture of methanol:chloroform:water (1:2.5:1, v/v/v). The extracts were dried under vacuum and derivatised by methoximation followed by
silylation with MSTFA + 1% TMCS. The 12 samples were analysed using a GCxGC- QTOFMS system. The GCxGC system (with Model 7890B GC, Agilent Technologies, Santa Clara CA, USA) employed a loop thermal modulator (Model ZX2, Zoex Corporation, Houston TX, USA). The QTOFMS system (7200 Series GC/Q-TOF MS, Agilent Technologies, Inc) acquired high resolution mass spectra of the secondary column effluent at a rate of 50 spectra per second. The instrument conditions are
HP-5MS UI, 15 m × 0.25 mm × 0.25 µm SGE BPX-50, 3.25 m × 0.1 mm × 0.1 µm 15:1
280°C
60°C to 310°C at 3°C/min 1.2 mL/min
Zoex ZX2
6.8 seconds 13 L/min 375°C
summarised in Table 1.
The Investigator framework extracted 159 reliable peaks used for alignment and 572 peak-regions used to create a feature template. The following criteria were used to search:
• Detection Filter: SNR > 10,
• Cross-Sample Significance Check: SNR F Value Threshold = 5,
• Cross-Sample Identification Check: Spectral Match Factor Threshold = 500.
From the total of 572 features, 35 features were found as common and 5 features were found as unique for two fungus types, as shown in Figure 3. On the left, a bubble plot shows all features with F values as bubble sizes. Each feature is assigned with a colour for the class that has the largest FDR value computed with the one-vs-all strategy [12]. Features with a large FDR or multi- class F value can be regarded as potential biomarkers of metabolomic differences. On the right, a bubble plot shows common and unique features with F values as bubble sizes. Each unique feature is assigned the class label of the sample that it belongs to after pruning by the above criteria. Clearly, not all potential markers are also unique makers for a specific class. The most promising markers can be examined more closely. Figure 4 shows one of the distinctive features found for wt samples.
Figure 4. Comparative Results: wt(Guy11) vs. Others. The regions with red outline are compound features found only in wt(Guy11) samples.
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