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Background: Schizophrenia is a persistent incurable mental disorder and is characterized by the manifestation of negative emotions and behaviors with anxiety and depression, fear and insecurity, self-harm and social withdrawal. The intricate molecular mechanisms underlying this phenomenon remain largely elusive. Accumulating evidence points towards the gut microbiota exerting an influence on brain function via the gut-brain axis, potentially contributing to the development of schizophrenia. Therefore, the objective of this study is to delineate the gut microbial composition and metabolic profile of fecal samples from individuals with schizophrenia. Methods: Liquid chromatography-mass spectrometry (LC-MS) and 16S ribosomal RNA (16S rRNA) gene sequencing were employed to analyze fecal metabolites and gut microbiota profiles in a cohort of 29 patients diagnosed with schizophrenia and 30 normal controls. The microbial composition of fecal samples was determined through the 16S rRNA gene sequencing, and microbial α-diversity and β-diversity indices were calculated. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to analyze the distribution of samples. The metabolites and gut microbiota exhibiting differential expression were identified through the application of biological variance criteria. Co-occurrence analysis of bacteria and metabolites was conducted using the spearman's rank correlation coefficient and visualized in a circular layout with the Cytoscape software. Results: The findings of the study indicated a lack of substantial evidence supporting significant disparities in α-diversity and β-diversity between individuals with schizophrenia and normal controls. In terms of metabolomics, a discernible pattern in sample distribution between the two groups was observed. Our analysis has revealed 30 bacterial species and 45 fecal metabolites that exhibited notable differences in abundance between individuals diagnosed with schizophrenia and normal controls. These alterations in multilevel omics have led to the development of a co-expression network associated with schizophrenia. The perturbed microbial genes and fecal metabolites consistently demonstrated associations with amino acid and lipid metabolism, which play essential roles in regulating the central nervous system. Conclusion: Our results offered profound insights into the impact of imbalanced gut microbiota and metabolism on brain function in individuals with schizophrenia. [ABSTRACT FROM AUTHOR]

Background: Schizophrenia is a persistent incurable mental disorder and is characterized by the manifestation of negative emotions and behaviors with anxiety and depression, fear and insecurity, self-harm and social withdrawal. The intricate molecular mechanisms underlying this phenomenon remain largely elusive. Accumulating evidence points towards the gut microbiota exerting an influence on brain function via the gut-brain axis, potentially contributing to the development of schizophrenia. Therefore, the objective of this study is to delineate the gut microbial composition and metabolic profile of fecal samples from individuals with schizophrenia. Methods: Liquid chromatography-mass spectrometry (LC-MS) and 16S ribosomal RNA (16S rRNA) gene sequencing were employed to analyze fecal metabolites and gut microbiota profiles in a cohort of 29 patients diagnosed with schizophrenia and 30 normal controls. The microbial composition of fecal samples was determined through the 16S rRNA gene sequencing, and microbial a-diversity and b-diversity indices were calculated. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to analyze the distribution of samples. The metabolites and gut microbiota exhibiting differential expression were identified through the application of biological variance criteria. Co-occurrence analysis of bacteria and metabolites was conducted using the spearman's rank correlation coefficient and visualized in a circular layout with the Cytoscape software. Results: The findings of the study indicated a lack of substantial evidence supporting significant disparities in a-diversity and b-diversity between individuals with schizophrenia and normal controls. In terms of metabolomics, a discernible pattern in sample distribution between the two groups was observed. Our analysis has revealed 30 bacterial species and 45 fecal metabolites that exhibited notable differences in abundance between individuals diagnosed with schizophrenia and normal controls. These alterations in multilevel omics have led to the development of a co-expression network associated with schizophrenia. The perturbed microbial genes and fecal metabolites consistently demonstrated associations with amino acid and lipid metabolism, which play essential roles in regulating the central nervous system. Conclusion: Our results offered profound insights into the impact of imbalanced gutmicrobiota and metabolismon brain function in individuals with schizophrenia. [ABSTRACT FROM AUTHOR]

Background: Schizophrenia is a persistent incurable mental disorder and is characterized by the manifestation of negative emotions and behaviors with anxiety and depression, fear and insecurity, self-harm and social withdrawal. The intricate molecular mechanisms underlying this phenomenon remain largely elusive. Accumulating evidence points towards the gut microbiota exerting an influence on brain function via the gut-brain axis, potentially contributing to the development of schizophrenia. Therefore, the objective of this study is to delineate the gut microbial composition and metabolic profile of fecal samples from individuals with schizophrenia. Methods: Liquid chromatography-mass spectrometry (LC-MS) and 16S ribosomal RNA (16S rRNA) gene sequencing were employed to analyze fecal metabolites and gut microbiota profiles in a cohort of 29 patients diagnosed with schizophrenia and 30 normal controls. The microbial composition of fecal samples was determined through the 16S rRNA gene sequencing, and microbial a-diversity and b-diversity indices were calculated. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to analyze the distribution of samples. The metabolites and gut microbiota exhibiting differential expression were identified through the application of biological variance criteria. Co-occurrence analysis of bacteria and metabolites was conducted using the spearman’s rank correlation coefficient and visualized in a circular layout with the Cytoscape software. Results: The findings of the study indicated a lack of substantial evidence supporting significant disparities in a-diversity and b-diversity between individuals with schizophrenia and normal controls. In terms of metabolomics, a discernible pattern in sample distribution between the two groups was observed. Our analysis has revealed 30 bacterial species and 45 fecal metabolites that exhibited notable differences in abundance between individuals diagnosed with schizophrenia and normal controls. These alterations in multilevel omics have led to the development of a co-expression network associated with schizophrenia. The perturbed microbial genes and fecal metabolites consistently demonstrated associations with amino acid and lipid metabolism, which play essential roles in regulating the central nervous system. Conclusion: Our results offered profound insights into the impact of imbalanced gut microbiota and metabolism on brain function in individuals with schizophrenia. [ABSTRACT FROM AUTHOR]

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Brassica rapa is an important Chinese vegetable crop that is beneficial to human health. The primary factor affecting B. rapa yield is low temperature, which promotes bolting and flowering, thereby lowering its commercial value. However, quickened bolting and flowering can be used for rapid breeding. Therefore, studying the underlying molecular mechanism of vernalization in B. rapa is crucial for solving production-related problems. Here, the transcriptome of two B. rapa accessions were comprehensively analyzed during different vernalization periods. During vernalization, a total of 974,584,022 clean reads and 291.28 Gb of clean data were obtained. Compared to the reference genome of B. rapa, 44,799 known genes and 2280 new genes were identified. A self-organizing feature map analysis of 21,035 differentially expressed genes was screened in two B. rapa accessions, ‘Jin Wawa’ and ‘Xiao Baojian’. The analysis indicated that transcripts related to the plant hormone signal transduction, starch and sucrose metabolism, photoperiod and circadian clock, and vernalization pathways changed notably at different vernalization periods. Moreover, different expression patterns of TPS, UGP, CDF, VIN1, and seven hormone pathway genes were observed during vernalization between the two accessions. The transcriptome results of this study provide a new perspective on the changes that occur during B. rapa vernalization, as well as serve as an excellent reference for B. rapa breeding. [ABSTRACT FROM AUTHOR]