Lipid and sugar metabolism play an essential role in pollen development and male sterility: a case analysis in Brassica napus.

AIMS: The genic male sterility (GMS) system is an important strategy for generating heterosis in plants. To better understand the essential role of lipid and sugar metabolism and to identify additional candidates for pollen development and male sterility, transcriptome and metabolome analysis of a GMS line of 1205AB in B. napus was used as a case study. DATA RESOURCES GENERATED: To characterize the GMS system, the transcriptome and metabolome profiles were generated for 24 samples and 48 samples of 1205AB in B. napus, respectively. Transcriptome analysis yielded a total of 156.52 Gb of clean data and revealed the expression levels of 109,541 genes and 8,501 novel genes. In addition, a total of 1,353 metabolites were detected in the metabolomic analysis, including 784 in positive ion mode and 569 in negative ion mode. KEY RESULTS: A total of 15,635 differentially expressed genes (DEGs) and 83 differential metabolites (DMs) were identified from different comparison groups, most of which were involved in lipid and sugar metabolism. The combination of transcriptome and metabolome analysis revealed 49 orthologous GMS genes related to lipid metabolism and 46 orthologous GMS genes related to sugar metabolism, as well as 45 novel genes. UTILITY OF THE RESOURCE: The transcriptome and metabolome profiles and their analysis provide useful reference data for the future discovery of additional GMS genes and the development of more robust male sterility breeding systems for use in the production of plant hybrids.

Differentiation and prognostic stratification of acute myeloid leukemia by serum-based spectroscopy coupling with metabolic fingerprints.

Acute myeloid leukemia (AML) is a heterogeneous disease characterized by complex molecular and cytogenetic abnormalities. New approaches to predict the prognosis of AML have increasingly attracted attention. There were 98 non-M3 AML cases and 48 healthy controls were enrolled in the current work. Clinically routine assays for cytogenetic and molecular genetic analyses were performed on the bone marrow samples of patients with AML. Meanwhile, metabolic profiling of these AML subjects was also performed on the serum samples by combining Ag nanoparticle-based surface-enhanced Raman spectroscopy (SERS) with proton nuclear magnetic resonance (NMR) spectroscopy. Although most of the routine biochemical test showed no significant differences between the M0-M2 and M5 groups, the metabolic profiles were significantly different either between AML subtypes or between prognostic risk subgroups. Specific SERS bands were screened to serve as potential markers for AML subtypes. The results demonstrated that the classification models for M0-M2 and M5 shared two bands (i.e., 1328 and 741 cm-1 ), all came from nucleic acid signals. Furthermore, Metabolic profiles provided various differential metabolites responsible for different AML subtypes, and we found altered pathways mainly included energy metabolism like glycolysis, pyruvate metabolism, and metabolisms of nucleic acid bases as well as specific amino acid metabolisms. It is concluded that integration of SERS and NMR provides the rational and could be reliable to reveal AML differentiation, and meanwhile lay the basis for experimental and clinical practice to monitor disease progression and prognostic evaluation.

Subtype discrimination of acute myeloid leukemia based on plasma SERS technique.

Acute myeloid leukemia (AML) is a common hematologic malignancy. To this day, diagnose of AML and its genetic mutation still rely on invasive and time-consuming methods. In this study, 222 plasma samples were collected to discuss the performance of surface-enhanced Raman spectroscopy (SERS) to discriminate AML subtype acute promyelocytic leukemia and acute monocytic leukemia based on plasma. The Ag nanoparticles-based SERS technique was used to explore the biochemical differences among different AML subtypes. With the help of powerful supervised and unsupervised algorithms, the performance using the whole spectra and band intensities was confirmed to identify different subtypes of AML. The results demonstrated the intensities of several bands and band-intensity ratios were significantly different between groups, thus related to the discrimination of several AML subtypes and control. Combining indexes of band-intensity ratios, the result of multi-indexes ROC has excellent performance in differentiating AML patient with healthy control. Our work demonstrated the great potential of SERS technique as a rapid and micro detection method in clinical laboratory field, it's a new and powerful tool for analyzing human blood plasma.

[Evaluation of gingival inflammation related to different retraction agents].

OBJECTIVE: To choose the best retraction agent for the clinic by evaluating the gingival inflammation related to three kinds of retraction agents. METHODS: 40 maxillary premolars were divided into four groups according to the randomized block design: Ferric sulfate group, aluminum chloride group, epinephrine group, sodium chloride group(control group), each 10 teeth, respectively used 25% AlCl3, 15.5% Fe2(SO4)3, 0.1% HCl-epinephrine, sodium chloride as retraction agents. The quantity of gingival crevicular fluid (GCF) and the active level of aspartate amino-transferase (AST) in gingival crevicular fluid were measured before and 1, 3, 5, 7, 9 days after retracting gingiva by four kinds retraction agents. The changes of GCF were calculated. RESULTS: The change of the GCF from the smallest to the largest was sodium chloride, 0.1% HCl-epinephrine, 25% AlCl,, 15.5% Fe2 (SO)3. Compared with sodium chloride, only 15.5% Fe2 (SO)3 in AST was the significant difference in the first day and the third day (P < 0.05). AST of ferric sulfate group after 1, 3 days greater than 800 IU. CONCLUSION: 0.1% HCl-epinephrine is suggested in patient without cardiovascular disease. For patient with cardiovascular disease, the better substitute is 25% AlCl3. 15.5% Fe2 (SO4)3 will not be used until its concentration is fallen.