Mechanism of Calcium Lactate Facilitating Phytic Acid Degradation in Soybean during Germination

Calcium lactate facilitates the growth and phytic acid degradation of soybean sprouts, but the mechanism is unclear. In this study, calcium lactate (Ca) and calcium lactate with lanthanum chloride (Ca+La) were used to treat soybean sprouts to reveal the relevant mechanism. Results showed that the phytic acid content decreased and the availability of phosphorus increased under Ca treatment. This must be due to the enhancement of enzyme activity related to phytic acid degradation. In addition, the energy metabolism was accelerated by Ca treatment. The energy status and energy metabolism-associated enzyme activity also increased. However, the transmembrane transport of calcium was inhibited by La³⁺ and concentrated in intercellular space or between the cell wall and cell membrane; thus, Ca+La treatment showed reverse results compared with those of Ca treatment. Interestingly, gene expression did not vary in accordance with their enzyme activity. These results demonstrated that calcium lactate increased the rate of phytic acid degradation by enhancing growth, phosphorus metabolism, and energy metabolism

Table_1_An anoikis-related gene signature for prediction of the prognosis in prostate cancer.docx

PurposeThis study presents a novel approach to predict postoperative biochemical recurrence (BCR) in prostate cancer (PCa) patients which involves constructing a signature based on anoikis-related genes (ARGs).MethodsIn this study, we utilised data from TCGA-PARD and GEO databases to identify specific ARGs in prostate cancer. We established a signature of these ARGs using Cox regression analysis and evaluated their clinical predictive efficacy and immune-related status through various methods such as Kaplan-Meier survival analysis, subject work characteristics analysis, and CIBERSORT method. Our findings suggest that these ARGs may have potential as biomarkers for prostate cancer prognosis and treatment. To investigate the biological pathways of genes associated with anoikis, we utilised GSVA, GO, and KEGG. The expression of ARGs was confirmed by the HPA database. Furthermore, we conducted PPI analysis to identify the core network of ARGs in PCa.ResultsBased on analysis of the TCGA database, a set of eight ARGs were identified as prognostic signature genes for prostate cancer. The reliability and validity of this signature were well verified in both the TCGA and GEO codifications. Using this signature, patients were classified into two groups based on their risk for developing BCR. There was a significant difference in BCR-free time between the high and low risk groups (P ConclusionThis signature suggests the potential role of ARGs in the development and progression of PCa and can effectively predict the risk of BCR in PCa patients after surgery. It also provides a basis for further research into the mechanism of ARGs in PCa and for the clinical management of patients with PCa.</p

MOESM1 of Serum and thyroid tissue level of let-7b and their correlation with TRAb in Graves’ disease

Additional file 1: Table S1. The predicted candidate gene, pathway and related other inflammatory disease of the selected microRNA candidates

DataSheet1_Evaluation of strategies for identification of infants with pathogenic glucose-6-phosphate dehydrogenase variants in China.docx

Glucose-6-phosphate dehydrogenase (G6PD) deficiency, which is caused by pathogenic variants of G6PD that result in decreased G6PD activity, is an X-linked inherited inborn error of metabolism that occurs worldwide. Individuals with G6PD deficiency and heterozygous females with normal G6PD activity (i.e., all individuals with pathogenic G6PD variants) are at risk of developing hemolytic anemia under increased oxidative challenge. However, this risk can be minimized by timely diagnosis. Currently, two assays are used to diagnose G6PD deficiency in China: evaluation of enzymatic activity and targeted genotyping. In terms of identification of all individuals with pathogenic G6PD variants, the performance and cost of different diagnostic strategies (isolated or combined evaluation of G6PD activity and G6PD genotyping) can vary, and these factors should be comprehensively evaluated. In this study, we examined 555 infants (437 males and 118 females) who were positive for the newborn screening of G6PD deficiency. We first evaluated the diagnostic performances of enzymatic testing and targeted genotyping. Both assays attained 100% specificities and positive predictive values for both male and female infants. In contrast, the sensitivities and negative predictive values (NPVs) of the diagnostic tests were different for male and female infants. For male infants, the sensitivities were 99.8 and 98.3%, and the NPVs were 94.1% and 69.6%, for enzymatic testing and targeted genotyping, respectively. For female infants, the sensitivities were 62.5% and 97.9%, and the NPVs were 37.9% and 91.7%, for enzymatic testing and targeted genotyping, respectively. We also evaluated the cost of the five different diagnostic strategies. The combination of G6PD activity testing of all infants, followed by genotyping of female infants with normal G6PD activity, attained high diagnostic sensitivity (99.8%) at a low cost (8.60 USD per diagnosed case). In the future, simultaneous examination of G6PD activity and whole-exon or whole-gene G6PD sequencing could become a standard clinical practice. Our data provide references for clinical practice on the standardization of current and future interventions for G6PD deficiency in China.</p

Additional file 12: of Germline and somatic variations influence the somatic mutational signatures of esophageal squamous cell carcinomas in a Chinese population

Figure S7 Correlation of the somatic events with the genetic burdens of the risk-associated genes in ESCC. The genetic burdens of CHEK2 and HECTD4 are associated with the frequencies of C > G substitution (a). The genetic burdens of HEATR3 are associated with the frequencies of C > T substitution (b). The genetic burdens of CHEK2, HEATR3 and SMG6 are associated with the “AID/APOBEC-1” signature (c). The genetic burdens of DNAH11, HAP1, HECTD4 and HLA-DQA1 are associated with the “AID/APOBEC-2” signature (d). FDR is based on the adjusted SKAT P values, in which the age, the clinical stage and ancestry are considered as covariates. (PDF 407 kb

Additional file 2: of Comparative expression analysis identifies the respiratory transition-related miRNAs and their target genes in tissues of metamorphosing Chinese giant salamander (Andrias davidianus)

Figure S1. Size distribution of the assembled unigenes. Horizontal axis gives different size intervals of the assembled unigenes, vertical axis gives the number of unigenes located in the specific size interval. (PNG 44 kb

Additional file 1: of Comparative expression analysis identifies the respiratory transition-related miRNAs and their target genes in tissues of metamorphosing Chinese giant salamander (Andrias davidianus)

Supplementary Tables S1 to S11. (DOC 122 kb

Additional file 8: of Germline and somatic variations influence the somatic mutational signatures of esophageal squamous cell carcinomas in a Chinese population

Figure S3. Population stratification of 302 ESCC patients. Two hundred eight genotyped reference individuals are obtained from TGP including 103 CHB and 105 CHS. After filtering 20 outliers, the remaining 282 samples are classified into CHB and CHS at a threshold level of 0 for PC2. (PDF 165 kb

Additional file 4: of Comparative expression analysis identifies the respiratory transition-related miRNAs and their target genes in tissues of metamorphosing Chinese giant salamander (Andrias davidianus)

Figure S3. Distribution of the top BLASTX hits for unigenes in the Nr databases. (PNG 64 kb

Additional file 3: of Comparative expression analysis identifies the respiratory transition-related miRNAs and their target genes in tissues of metamorphosing Chinese giant salamander (Andrias davidianus)

Figure S2. Frequency distribution of the putative cSSRs observed. C is the number of SSRs present in compound formation; C* is the number of sequences containing more than one SSR; p1 – p5 represent the numbers of mono-, di-, tri-, tetra- and penta-nucleotides respectively. (PNG 21 kb