Presentation_1_Rhizosphere element circling, multifunctionality, aboveground productivity and trade-offs are better predicted by rhizosphere rare taxa.zip

Microbes, especially abundant microbes in bulk soils, form multiple ecosystem functions, which is relatively well studied. However, the role of rhizosphere microbes, especially rhizosphere rare taxa vs. rhizosphere abundant taxa in regulating the element circling, multifunctionality, aboveground net primary productivity (ANPP) and the trade-offs of multiple functions remains largely unknown. Here, we compared the multiple ecosystem functions, the structure and function of rhizosphere soil bacterial and fungal subcommunities (locally rare, locally abundant, regionally rare, regionally abundant, and entire), and the role of subcommunities in the Zea mays and Sophora davidii sole and Z. mays/S. davidii intercropping ecosystems in subtropical China. Results showed that intercropping altered multiple ecosystem functions individually and simultaneously. Intercropped Z. mays significantly decreased the trade-off intensity compared to sole Z. mays, the trade-off intensity under intercropped S. davidii was significantly higher than under intercropped Z. mays. The beta diversities of bacterial and fungal communities, and fungal functions in each subcommunity significantly differed among groups. Network analysis showed intercropping increased the complexity and positive links of rare bacteria in Z. mays rhizosphere, but decreased the complexity and positive links of rare bacteria in S. davidii rhizosphere and the complexity and positive links of fungi in both intercropped plants rhizosphere. Mantel test showed significant changes in species of locally rare bacteria were most strongly related to nitrogen-cycling multifunctionality, ANPP and trade-offs intensity, significant changes in species of locally rare fungus were most strongly related to carbon-cycling multifunctionality, phosphorus-cycling multifunctionality, and average ecosystem multifunctionality. This research highlights the potential and role of rare rhizosphere microorganisms in predicting and regulating system functions, productivity, and trade-offs.</p

Image_1_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.TIF

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Image_2_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.TIF

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Data_Sheet_2_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.docx

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Image_4_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.TIF

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Image_5_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.TIF

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Image_3_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.TIF

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Data_Sheet_1_Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.docx

Herpes zoster (HZ) is a localized, painful cutaneous eruption that occurs upon reactivation of the herpes virus. Postherpetic neuralgia (PHN) is the most common chronic complication of HZ. In this study, we examined the metabolomic and proteomic signatures of disease progression in patients with HZ and PHN. We identified differentially expressed metabolites (DEMs), differentially expressed proteins (DEPs), and key signaling pathways that transition from healthy volunteers to the acute or/and chronic phases of herpetic neuralgia. Moreover, some specific metabolites correlated with pain scores, disease duration, age, and pain in sex dimorphism. In addition, we developed and validated three optimal predictive models (AUC > 0.9) for classifying HZ and PHN from healthy individuals based on metabolic patterns and machine learning. These findings may reveal the overall metabolomics and proteomics landscapes and proposed the optimal machine learning predictive models, which provide insights into the mechanisms of HZ and PHN.</p

Additional file 2: Figure S1. of miR-589-5p inhibits MAP3K8 and suppresses CD90+ cancer stem cells in hepatocellular carcinoma

CD90 is predominantly expressed in a small population in HCC cell lines. Figure S2. CD90+ HCC cells possess CSC characteristics. Figure S3. Overexpression of miR-589-5p has no impact on the regulation of MAP3K8 and stemness in CD90- HCC cells. Figure S4. Suppression of miR-589-5p fails to alter the CD90+ population in HCC cells. Figure S5. CD90- tumor xenograft contains CD90+ cells (DOCX 16 kb

Additional file 1: Table S1. of miR-589-5p inhibits MAP3K8 and suppresses CD90+ cancer stem cells in hepatocellular carcinoma

Antibodies used in this study. Table S2. The primers have been used for quantitative real-time PCR. Table S3. All differentially expressed microRNAs in MHCC97H and MHCC97L CD90+ cells. Table S4. The relationship of CD90 and miR-589-5p expression to clinical parameters in human HCC (n=40). (DOCX 21 kb