Improving the Data Quality of Untargeted Metabolomics through a Targeted Data-Dependent Acquisition Based on an Inclusion List of Differential and Preidentified Ions

Metabolomics based on high-resolution mass spectrometry has become a powerful technique in biomedical research. The development of various analytical tools and online libraries has promoted the identification of biomarkers. However, how to make mass spectrometry collect more data information is an important but underestimated research topic. Herein, we combined full-scan and data-dependent acquisition (DDA) modes to develop a new targeted DDA based on the inclusion list of differential and preidentified ions (dpDDA). In this workflow, the MS1 datasets for statistical analysis and metabolite preidentification were first obtained using full-scan, and then, the MS/MS datasets for metabolite identification were obtained using targeted DDA of quality control samples based on the inclusion list. Compared with the current methods (DDA, data-independent acquisition, targeted DDA with time-staggered precursor ion list, and iterative exclusion DDA), dpDDA showed better stability, higher characteristic ion coverage, higher differential metabolites’ MS/MS coverage, and higher quality MS/MS spectra. Moreover, the same trend was verified in the analysis of large-scale clinical samples. More surprisingly, dpDDA can distinguish patients with different severities of coronary heart disease (CHD) based on the Canadian Cardiovascular Society angina classification, which we cannot distinguish through conventional metabolomics data collection. Finally, dpDDA was employed to differentiate CHD from healthy control, and targeted metabolomics confirmed that dpDDA could identify a more complete metabolic pathway network. At the same time, four unreported potential CHD biomarkers were identified, and the area under the receiver operating characteristic curve was greater than 0.85. These results showed that dpDDA would expand the discovery of biomarkers based on metabolomics, more comprehensively explore the key metabolites and their association with diseases, and promote the development of precision medicine

Improving the Data Quality of Untargeted Metabolomics through a Targeted Data-Dependent Acquisition Based on an Inclusion List of Differential and Preidentified Ions

Metabolomics based on high-resolution mass spectrometry has become a powerful technique in biomedical research. The development of various analytical tools and online libraries has promoted the identification of biomarkers. However, how to make mass spectrometry collect more data information is an important but underestimated research topic. Herein, we combined full-scan and data-dependent acquisition (DDA) modes to develop a new targeted DDA based on the inclusion list of differential and preidentified ions (dpDDA). In this workflow, the MS1 datasets for statistical analysis and metabolite preidentification were first obtained using full-scan, and then, the MS/MS datasets for metabolite identification were obtained using targeted DDA of quality control samples based on the inclusion list. Compared with the current methods (DDA, data-independent acquisition, targeted DDA with time-staggered precursor ion list, and iterative exclusion DDA), dpDDA showed better stability, higher characteristic ion coverage, higher differential metabolites’ MS/MS coverage, and higher quality MS/MS spectra. Moreover, the same trend was verified in the analysis of large-scale clinical samples. More surprisingly, dpDDA can distinguish patients with different severities of coronary heart disease (CHD) based on the Canadian Cardiovascular Society angina classification, which we cannot distinguish through conventional metabolomics data collection. Finally, dpDDA was employed to differentiate CHD from healthy control, and targeted metabolomics confirmed that dpDDA could identify a more complete metabolic pathway network. At the same time, four unreported potential CHD biomarkers were identified, and the area under the receiver operating characteristic curve was greater than 0.85. These results showed that dpDDA would expand the discovery of biomarkers based on metabolomics, more comprehensively explore the key metabolites and their association with diseases, and promote the development of precision medicine

Association of T-Cell Immunoglobulin and Mucin Domain-Containing Molecule 3 (Tim-3) Polymorphisms with Susceptibility and Disease Progression of HBV Infection

<div><p>Purpose</p><p>T-cell immunoglobulin and mucin domain-containing molecule 3 (Tim-3) plays an important role in regulating T cells in hepatitis B virus (HBV) infection and hepatocellular carcinoma (HCC). However, few researches have reported the association of Tim-3 genetic variants with susceptibility and progression of HBV infection. In this study, we focused on the association of Tim-3 polymorphisms with HBV infection, HBsAg seroclearance and hepatocellular carcinoma.</p><p>Methods</p><p>A total of 800 subjects were involved in this study. Four groups were studied here, including HBV, HBsAg seroclearance, HBV-associated HCC and healthy controls. Three single-nucleotide polymorphisms (SNPs) of Tim-3, rs246871, rs25855 and rs31223 were genotyped to analyze the association of Tim-3 polymorphisms with susceptibility and disease progression of HBV infection.</p><p>Results</p><p>Our study found that rs31223 and rs246871 were associated with disease progression of HBV infection, while none of the three SNPs was relevant to HBV susceptibility. The minor allele “C” of rs31223 was found to be associated with an increased probability of HBsAg seroclearance (P = 0.033) and genotype “CC” of rs246871 to be associated with an increased probability of HBV-associated HCC (P = 0.007). In accordance, haplotypic analysis of the three polymorphisms also showed that the haplotype block CGC* and TGC* were significantly associated with HBsAg seroclearance (P<0.05) while haplotype block CAT*, CGT*, TAC* and TGT* were significantly associated with HBV-associated HCC (all P<0.05).</p><p>Conclusions</p><p>Genetic variants of Tim-3 have an important impact on disease progression of HBV infection. With specific Tim-3 polymorphisms, patients infected with HBV could be potential candidates of HCC and HBsAg seroclearance.</p></div

Hardy-Weinberg Calculations for All Three Polymorphisms in All Groups.

<p>Genotypic distribution was considered in Hardy-Weinberg equilibrium when P>0.05</p

Multivariate logistic regression of HBV prevalence adjusted by age and gender.

§<p>age and gender were adjusted.</p

Clinical Demographics of the Four Patient Groups.

a<p>P₁ value was calculated between HBV and healthy controls.</p>b<p>P₂ value was calculated between HBV and HBsAg Seroclearance.</p>c<p>P₃ value was calculated between HBV and HCC. P<0.05 was considered statistically significant.</p><p>Abbreviations: HBV, Hepatitis B virus; HCC, Hepatocellular Carcinoma; ALT, alanine aminotransferase; AST, aspartate aminotransferase.</p

Association of Tim-3 Polymorphisms with Susceptibility of HBV Infection, HBsAg Seroclearance and HBV-Associated HCC.

a<p>Allelic: major allele “A” vs. minor allele “B”; dominant gene mode: AA vs. AB + BB; recessive gene mode: AA + AB vs. BB. Additive gene mode: AA vs. AB vs. BB.</p>b<p>P* value was calculated between HBV and healthy controls.</p>c<p>P** value was calculated between HBV and HBsAg Seroclearance.</p>d<p>P*** value was calculated between HBV and HCC. All results were adjusted for age and sex. P<0.05 was considered statistically significant.</p><p>Abbreviations: SNP, single-nucleotide polymorphism; CI, confidence interval; OR, odds ratio.</p

Univariable logistic regression analysis of TP prevalence.

<p>Univariable logistic regression analysis of TP prevalence.</p

Haplotype Analysis of Tim-3 Polymorphisms with HBV Infection, HBsAg Seroclearance and HBV-Associated HCC.

a<p>P* value was calculated between healthy controls and HBV Infection.</p>b<p>P** value was calculated between HBV Infection and HBsAg Seroclearance.</p>c<p>P*** value was calculated between HBV Infection and HBV-Associated HCC. P<0.05 was considered statistically significant.</p>d<p>Order of haplotype block: rs246871 (minor allele “C”), rs25855 (minor allele “A”), rs31223 (minor allele “C”).</p

Age, gender and category of eye patients^*.

*<p>26386 patients were tested for HBsAg, 26385 for anti-HCV and TP, 26379 for anti-HIV.</p