## 5 Conclusions  
This study provides a detailed metabolite analysis in response to TPP and its vectored pathogen Lso in insect-susceptible and resistant tomato plants. We were able to identify metabolic changes in various classes of metabolites, including volatiles, hormones, and phenolics. Different volatile and non-volatile profiles were identified in susceptible and resistant genotypes in response to the tomato-potato psyllid vectoring or not, and its transmitted pathogen Lso. While some differentially regulated metabolites may be implicated in plant defensive signaling, further functional analysis (e.g., the use of hormone-deficient genotypes or exogenous applications) need to be performed to test the effect on insect mortality and fecundity. Moreover, upon validation, the identified metabolites could be used to screen and select breeding lines with enhanced resistance to TPP-Lso vector-pathogen complexes, which may reduce economic losses associated with the TPP insect and its transmitted disease.  
The following are available online at [[http://www.mdpi.com/2223-7747/9/9/1154/s1](http://www.mdpi.com/2223-7747/9/9/1154/s1)]([http://www.mdpi.com/2223-7747/9/9/1154/s1](http://www.mdpi.com/2223-7747/9/9/1154/s1)), Figure S1: The concentration of volatile metabolites according to the impact of the genotype and treatment, and the asterisk indicates the significant difference based on the student t-test and posthoc [PERSON]'s test (*, \(p<0.05\); **, \(p<0.01\); and ***, \(p<0.001\)). n.s. indicates no significance. Figure S2: Multivariate analysis using a gas chromatography-mass spectrometry (GC-MS) dataset to explore the different effects of the mock control, tomato-potato psyllid (TPP) without _Candidatus_ Libberiateror solanacearum (Lso) (TPP-Lso (-)), and TPP carrying Lso (TPP-Lso (+)) inoculations on the insect-susceptible and -resistant tomato varieties, CastleMart (CM) and recombinant inbred lines LA3952 (LA), respectively. Score plots of (A) principal component analysis (PCA), (B) partial least squares discriminant analysis (PLS-DA), and (C) sparse PLS-DA indicate the discrimination between studied test groups. (D) Variable importance for projection (VIP) scores derived from a PLS-DA model to examine and filter the variables (VIP > 1.0) having influence on the PLS-DA scores plot. (E) The loading plot illustrates the variables responsible for the separating pattern in the PLS-DA model. Table S1: Identification of plant phenolics and serotonin in tomatoes by an ultra-performance liquid chromatography coupled to electrospray ionization high resolution quadrupole time-of flight mass spectrometry (UPLC/ESI-HR-QTOFMS) with diode array detector (DAD) in ESI positive ionization mode. Table S2: Identification of plant hormones and melatonin using an ultra-performance liquid chromatography coupled to electrospray ionization high resolution quadrupole time-of flight mass spectrometry (UPLC/ESI-HR-QTOFMS).  
[PERSON] and [PERSON] drafted the manuscript; [PERSON] and [PERSON] performed metabolic extractions and quantification; [PERSON] performed insect rearing, infestation, and tissue collection; [PERSON], [PERSON], and [PERSON] designed and supervised all experiments. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This study was supported in part by funds from Texas A&M AgriLife Research Vegetable Seed Grant FY2016-2017 to C.A.A.  
**Acknowledgments:** The authors thank [PERSON] and [PERSON] (Texas A&M AgriLife Research, Weslaco, TX, USA) for their help in insect colony rearing and [PERSON] (Texas A&M AgriLife Research, Weslaco, TX, USA) for experiment maintenance.  
**Conflicts of Interest:** The authors declare no conflict of interest.