Data_Sheet_1_Comparative Proteomics Demonstrates Altered Metabolism Pathways in Cotrimoxazole- Resistant and Amikacin-Resistant Klebsiella pneumoniae Isolates.docx

Antibiotic resistance (AMR) has always been a hot topic all over the world and its mechanisms are varied and complicated. Previous evidence revealed the metabolic slowdown in resistant bacteria, suggesting the important role of metabolism in antibiotic resistance. However, the molecular mechanism of reduced metabolism remains poorly understood, which inspires us to explore the global proteome change during antibiotic resistance. Here, the sensitive, cotrimoxazole-resistant, amikacin-resistant, and amikacin/cotrimoxazole -both-resistant KPN clinical isolates were collected and subjected to proteome analysis through liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS). A deep coverage of 2,266 proteins were successfully identified and quantified in total, representing the most comprehensive protein quantification data by now. Further bioinformatic analysis showed down-regulation of tricarboxylic acid cycle (TCA) pathway and up-regulation of alcohol metabolic or glutathione metabolism processes, which may contribute to ROS clearance and cell survival, in drug-resistant isolates. These results indicated that metabolic pathway alteration was directly correlated with antibiotic resistance, which could promote the development of antibacterial drugs from “target” to “network.” Moreover, combined with minimum inhibitory concentration (MIC) of cotrimoxazole and amikacin on different KPN isolates, we identified nine proteins, including garK, uxaC, exuT, hpaB, fhuA, KPN_01492, fumA, hisC, and aroE, which might contribute mostly to the survival of KPN under drug pressure. In sum, our findings provided novel, non-antibiotic-based therapeutics against resistant KPN.</p

Table_1_Comparative Proteomics Demonstrates Altered Metabolism Pathways in Cotrimoxazole- Resistant and Amikacin-Resistant Klebsiella pneumoniae Isolates.xlsx

Antibiotic resistance (AMR) has always been a hot topic all over the world and its mechanisms are varied and complicated. Previous evidence revealed the metabolic slowdown in resistant bacteria, suggesting the important role of metabolism in antibiotic resistance. However, the molecular mechanism of reduced metabolism remains poorly understood, which inspires us to explore the global proteome change during antibiotic resistance. Here, the sensitive, cotrimoxazole-resistant, amikacin-resistant, and amikacin/cotrimoxazole -both-resistant KPN clinical isolates were collected and subjected to proteome analysis through liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS). A deep coverage of 2,266 proteins were successfully identified and quantified in total, representing the most comprehensive protein quantification data by now. Further bioinformatic analysis showed down-regulation of tricarboxylic acid cycle (TCA) pathway and up-regulation of alcohol metabolic or glutathione metabolism processes, which may contribute to ROS clearance and cell survival, in drug-resistant isolates. These results indicated that metabolic pathway alteration was directly correlated with antibiotic resistance, which could promote the development of antibacterial drugs from “target” to “network.” Moreover, combined with minimum inhibitory concentration (MIC) of cotrimoxazole and amikacin on different KPN isolates, we identified nine proteins, including garK, uxaC, exuT, hpaB, fhuA, KPN_01492, fumA, hisC, and aroE, which might contribute mostly to the survival of KPN under drug pressure. In sum, our findings provided novel, non-antibiotic-based therapeutics against resistant KPN.</p

Image_2_Comparative Proteomics Demonstrates Altered Metabolism Pathways in Cotrimoxazole- Resistant and Amikacin-Resistant Klebsiella pneumoniae Isolates.JPEG

Antibiotic resistance (AMR) has always been a hot topic all over the world and its mechanisms are varied and complicated. Previous evidence revealed the metabolic slowdown in resistant bacteria, suggesting the important role of metabolism in antibiotic resistance. However, the molecular mechanism of reduced metabolism remains poorly understood, which inspires us to explore the global proteome change during antibiotic resistance. Here, the sensitive, cotrimoxazole-resistant, amikacin-resistant, and amikacin/cotrimoxazole -both-resistant KPN clinical isolates were collected and subjected to proteome analysis through liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS). A deep coverage of 2,266 proteins were successfully identified and quantified in total, representing the most comprehensive protein quantification data by now. Further bioinformatic analysis showed down-regulation of tricarboxylic acid cycle (TCA) pathway and up-regulation of alcohol metabolic or glutathione metabolism processes, which may contribute to ROS clearance and cell survival, in drug-resistant isolates. These results indicated that metabolic pathway alteration was directly correlated with antibiotic resistance, which could promote the development of antibacterial drugs from “target” to “network.” Moreover, combined with minimum inhibitory concentration (MIC) of cotrimoxazole and amikacin on different KPN isolates, we identified nine proteins, including garK, uxaC, exuT, hpaB, fhuA, KPN_01492, fumA, hisC, and aroE, which might contribute mostly to the survival of KPN under drug pressure. In sum, our findings provided novel, non-antibiotic-based therapeutics against resistant KPN.</p

Image_1_Comparative Proteomics Demonstrates Altered Metabolism Pathways in Cotrimoxazole- Resistant and Amikacin-Resistant Klebsiella pneumoniae Isolates.JPEG

Antibiotic resistance (AMR) has always been a hot topic all over the world and its mechanisms are varied and complicated. Previous evidence revealed the metabolic slowdown in resistant bacteria, suggesting the important role of metabolism in antibiotic resistance. However, the molecular mechanism of reduced metabolism remains poorly understood, which inspires us to explore the global proteome change during antibiotic resistance. Here, the sensitive, cotrimoxazole-resistant, amikacin-resistant, and amikacin/cotrimoxazole -both-resistant KPN clinical isolates were collected and subjected to proteome analysis through liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS). A deep coverage of 2,266 proteins were successfully identified and quantified in total, representing the most comprehensive protein quantification data by now. Further bioinformatic analysis showed down-regulation of tricarboxylic acid cycle (TCA) pathway and up-regulation of alcohol metabolic or glutathione metabolism processes, which may contribute to ROS clearance and cell survival, in drug-resistant isolates. These results indicated that metabolic pathway alteration was directly correlated with antibiotic resistance, which could promote the development of antibacterial drugs from “target” to “network.” Moreover, combined with minimum inhibitory concentration (MIC) of cotrimoxazole and amikacin on different KPN isolates, we identified nine proteins, including garK, uxaC, exuT, hpaB, fhuA, KPN_01492, fumA, hisC, and aroE, which might contribute mostly to the survival of KPN under drug pressure. In sum, our findings provided novel, non-antibiotic-based therapeutics against resistant KPN.</p

Additional file 1 of Targeted sequencing of high-density SNPs provides an enhanced tool for forensic applications and genetic landscape exploration in Chinese Korean ethnic group

Additional file 1. Fig. S1: Sequencing depths and allele coverage ratios of the SNP loci in the Chinese Korean ethnic group. A Distribution of the 1993 SNP loci in the 22 autosomes; B Histogram of the average sequencing depths for 1993 SNP loci in 161 Chinese Korean individuals; C Boxplot of the sequencing depths for SNP loci with a sequencing depth less than 500×; D Histogram of the average allele coverage ratios for the heterozygous SNP loci in the Korean ethnic group. Fig. S2: Forensic efficiencies of the 1946 SNPs in the Chinese Korean ethnic group. A Forensic statistical parameters, encompassing gene diversity (GD), Hobs (Observed heterozygosity), PD (Power of discrimination), PE (Power of exclusion) and PM (Probability of match) of the 1946 SNPs; B Distribution of 1-CPD and 1-CPE values estimated with the increase of SNP loci. Fig. S3: ADMIXTURE results for K = 2 ~10, with the K denoting the pre-assumed ancestry components represented by different colors. The ancestry composition of each population is proportional to the height of different colors. Fig. S4: Principal component analyses (PCA) of the Chinese Korean ethnic group and the reference populations. Each dot represents a single individual and is colored according to its continental origin. A PCA of the Chinese Korean ethnic group and all the reference populations from eight major geographic regions worldwide. The Chinese Korean ethnic group and East Asian populations are marked with black box; B PCA of the Chinese Korean ethnic group and the East Asian reference populations. Fig. S5: Phylogenetic reconstruction of Treemix results for one to seven (except for four) migration events between the Chinese Korean ethnic group and the reference populations. Fig. S6: Pairwise residuals of Treemix results for one to seven (except for four) migration events between the Chinese Korean ethnic group and the reference populations

Additional file 1 of Insights into AIM-InDel diversities in Yunnan Miao and Hani ethnic groups of China for forensic and population genetic purposes

Additional file 1: Supplementary Table 1. List of reference populations and their abbreviations, sample sizes and locations. Supplementary Table 2. r2 values, x2 and their p-values for pairwise loci in the linkage disequilibrium analyses (Yunnan Hani group). Supplementary Table 3. r2 values, x2 and their p-values for pairwise loci in the linkage disequilibrium analyses (Yunnan Miao group). Supplementary Table 4. Forensic parameters of 39 AIM-InDel loci as well as their p values for Hardy-Weinberg equilibrium tests in Yunnan Miao ethnic group of China (n = 203). Supplementary Table 5. Forensic parameters of 39 AIM-InDel loci as well as their p values for Hardy-Weinberg equilibrium tests in Yunnan Hani ethnic group of China (n = 203). Supplementary Table 6. The FST values between the studied groups and reference populations. Supplementary Table 7. Allele frequency differential (δ) of 38 AIM-InDel loci in pairwise intercontinental populations