Table_2_Metabolic changes and potential biomarkers in "Candidatus Liberibacter solanacearum"-infected potato psyllids: implications for psyllid-pathogen interactions.xlsx

Psyllid yellows, vein-greening (VG), and zebra chip (ZC) diseases, which are primarily transmitted by potato psyllid (PoP) carrying Candidatus Liberibacter solanacearum (CLso), have caused significant losses in solanaceous crop production worldwide. Pathogens interact with their vectors at the organic and cellular levels, while the potential changes that may occur at the biochemical level are less well reported. In this study, the impact of CLso on the metabolism of PoP and the identification of biomarkers from infected psyllids were examined. Using ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) analysis, metabolomic changes in CLso-infected psyllids were compared to uninfected ones. A total of 34 metabolites were identified as potential biomarkers of CLso infection, which were primarily related to amino acid, carbohydrate, and lipid metabolism. The significant increase in glycerophospholipids is thought to be associated with CLso evading the insect vector’s immune defense. Matrix-assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) was used to map the spatial distribution of these biomarkers, revealing that 15-keto-Prostaglandin E2 and alpha-D-Glucose were highly expressed in the abdomen of uninfected psyllids but down-regulated in infected psyllids. It is speculated that this down-regulation may be due to CLso evading surveillance by immune suppression in the PoP midgut. Overall, valuable biochemical information was provided, a theoretical basis for a better understanding of psyllid-pathogen interactions was offered, and the findings may aid in breaking the transmission cycle of these diseases.</p

Table_3_Metabolic changes and potential biomarkers in "Candidatus Liberibacter solanacearum"-infected potato psyllids: implications for psyllid-pathogen interactions.xlsx

Psyllid yellows, vein-greening (VG), and zebra chip (ZC) diseases, which are primarily transmitted by potato psyllid (PoP) carrying Candidatus Liberibacter solanacearum (CLso), have caused significant losses in solanaceous crop production worldwide. Pathogens interact with their vectors at the organic and cellular levels, while the potential changes that may occur at the biochemical level are less well reported. In this study, the impact of CLso on the metabolism of PoP and the identification of biomarkers from infected psyllids were examined. Using ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) analysis, metabolomic changes in CLso-infected psyllids were compared to uninfected ones. A total of 34 metabolites were identified as potential biomarkers of CLso infection, which were primarily related to amino acid, carbohydrate, and lipid metabolism. The significant increase in glycerophospholipids is thought to be associated with CLso evading the insect vector’s immune defense. Matrix-assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) was used to map the spatial distribution of these biomarkers, revealing that 15-keto-Prostaglandin E2 and alpha-D-Glucose were highly expressed in the abdomen of uninfected psyllids but down-regulated in infected psyllids. It is speculated that this down-regulation may be due to CLso evading surveillance by immune suppression in the PoP midgut. Overall, valuable biochemical information was provided, a theoretical basis for a better understanding of psyllid-pathogen interactions was offered, and the findings may aid in breaking the transmission cycle of these diseases.</p

Table_1_Metabolic changes and potential biomarkers in "Candidatus Liberibacter solanacearum"-infected potato psyllids: implications for psyllid-pathogen interactions.xlsx

Psyllid yellows, vein-greening (VG), and zebra chip (ZC) diseases, which are primarily transmitted by potato psyllid (PoP) carrying Candidatus Liberibacter solanacearum (CLso), have caused significant losses in solanaceous crop production worldwide. Pathogens interact with their vectors at the organic and cellular levels, while the potential changes that may occur at the biochemical level are less well reported. In this study, the impact of CLso on the metabolism of PoP and the identification of biomarkers from infected psyllids were examined. Using ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) analysis, metabolomic changes in CLso-infected psyllids were compared to uninfected ones. A total of 34 metabolites were identified as potential biomarkers of CLso infection, which were primarily related to amino acid, carbohydrate, and lipid metabolism. The significant increase in glycerophospholipids is thought to be associated with CLso evading the insect vector’s immune defense. Matrix-assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) was used to map the spatial distribution of these biomarkers, revealing that 15-keto-Prostaglandin E2 and alpha-D-Glucose were highly expressed in the abdomen of uninfected psyllids but down-regulated in infected psyllids. It is speculated that this down-regulation may be due to CLso evading surveillance by immune suppression in the PoP midgut. Overall, valuable biochemical information was provided, a theoretical basis for a better understanding of psyllid-pathogen interactions was offered, and the findings may aid in breaking the transmission cycle of these diseases.</p

MOESM2 of Transcriptional profiling of skeletal muscle reveals starvation response and compensatory growth in Spinibarbus hollandi

Additional file 2: Table S2. Figure S2. The list of all differentially expressed genes during fasting-refeeding

MOESM1 of Transcriptional profiling of skeletal muscle reveals starvation response and compensatory growth in Spinibarbus hollandi

Additional file 1: Table S1. Primer pairs used for qRT-PCR. Figure S1. Daily changes of growth traits. Figure S2. The expression of genes associated with structure of myofiber

Detection of population structure in four HapMap populations.

<p>The first two principal components from EIGENSTRAT are plotted for all 3 SNP panels (A, SNP panel 93; B, SNP panel 52; C, SNP panel 19). As more AIMs are used in the analysis, the resolution improves. The 52 SNP panel appears to have some overlap between CEU and CHB+JPT though it should be noted that these datapoints are more clearly differentiated by considering the third and fourth principal components (not shown).</p

Estimated odds ratios (OR) from case-control analysis of 3 simulated SNPs within <i>BDNF</i>.

<p>Conventions are the same as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019699#pone-0019699-g002" target="_blank">Figure 2</a> except the y-axis is the average estimated OR from the same analysis as presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019699#pone-0019699-g002" target="_blank">Figure 2</a>.</p

Analysis of case-control study type I error rates from 3 simulated SNPs within <i>BDNF</i>.

<p>The three SNPs show allele frequency differences between CEU and YRI of 0.066 (rs11030108), 0.102 (rs10767658), and 0.233 (rs1013402). The y-axis is estimated type I error rate versus the simulated CEU proportion (x-axis). Panels on the left show data with a difference in disease prevalence ratio of 1.25 while a ratio of 1.5 is shown on the right.</p

Descriptive statistics on the number of identical genotypes between all possible pairing of samples by group.

<p>Descriptive statistics on the number of identical genotypes between all possible pairing of samples by group.</p

Additional file 1 of Comparative genome analysis of plant ascomycete fungal pathogens with different lifestyles reveals distinctive virulence strategies

Additional file 1