NRT1.1 Regulates Nitrate Allocation and Cadmium Tolerance in Arabidopsis

Abiotic stress induces nitrate (NO3-) allocation to roots, which increases stress tolerance in plants. NRT1.1 is broadly involved in abiotic stress tolerance in plants, but the relationship between NRT1.1 and NO3- allocation under stress conditions is unclear. In this study, we found that Arabidopsis wild-type Col-0 was more cadmium (Cd2+)-tolerant than the nrt1.1 mutant at 20 μM CdCl2. Cd2+ exposure repressed NRT1.5 but upregulated NRT1.8 in roots of Col-0 plants, resulting in increased NO3- allocation to roots and higher [NO3-] root-to-shoot (R:S) ratios. Interestingly, NITRATE REGULATORY GENE2 (NRG2) was upregulated by Cd2+ stress in Col-0 but not in nrt1.1. Under Cd2+ stress, nrg2 and nrg2-3chl1-13 mutants exhibited similar phenotypes and NO3- allocation patterns as observed in the nrt1.1 mutant, but overexpression of NRG2 in Col-0 and nrt1.1 increased the [NO3-] R:S ratio and restored Cd2+ stress tolerance. Our results indicated that NRT1.1 and NRG2 regulated Cd2+ stress-induced NO3- allocation to roots and that NRG2 functioned downstream of NRT1.1. Cd2+ uptake did not differ between Col-0 and nrt1.1, but Cd2+ allocation to roots was higher in Col-0 than in nrt1.1. Stressed Col-0 plants increased Cd2+ and NO3- allocation to root vacuoles, which reduced their cytosolic allocation and transport to the shoots. Our results suggest that NRT1.1 regulates NO3- allocation to roots by coordinating Cd2+ accumulation in root vacuoles, which facilitates Cd2+ detoxification

MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study

Objectives: To develop and validate an MRI radiomics-based decision support tool for the automated grading of cervical disc degeneration.Methods: The retrospective study included 2,610 cervical disc samples of 435 patients from two hospitals. The cervical magnetic resonance imaging (MRI) analysis of patients confirmed cervical disc degeneration grades using the Pfirrmann grading system. A training set (1,830 samples of 305 patients) and an independent test set (780 samples of 130 patients) were divided for the construction and validation of the machine learning model, respectively. We provided a fine-tuned MedSAM model for automated cervical disc segmentation. Then, we extracted 924 radiomic features from each segmented disc in T1 and T2 MRI modalities. All features were processed and selected using minimum redundancy maximum relevance (mRMR) and multiple machine learning algorithms. Meanwhile, the radiomics models of various machine learning algorithms and MRI images were constructed and compared. Finally, the combined radiomics model was constructed in the training set and validated in the test set. Radiomic feature mapping was provided for auxiliary diagnosis.Results: Of the 2,610 cervical disc samples, 794 (30.4%) were classified as low grade and 1,816 (69.6%) were classified as high grade. The fine-tuned MedSAM model achieved good segmentation performance, with the mean Dice coefficient of 0.93. Higher-order texture features contributed to the dominant force in the diagnostic task (80%). Among various machine learning models, random forest performed better than the other algorithms (p < 0.01), and the T2 MRI radiomics model showed better results than T1 MRI in the diagnostic performance (p < 0.05). The final combined radiomics model had an area under the receiver operating characteristic curve (AUC) of 0.95, an accuracy of 89.51%, a precision of 87.07%, a recall of 98.83%, and an F1 score of 0.93 in the test set, which were all better than those of other models (p < 0.05).Conclusion: The radiomics-based decision support tool using T1 and T2 MRI modalities can be used for cervical disc degeneration grading, facilitating individualized management

The relationship between endogenous androgens and body fat distribution in early and late postmenopausal women.

To investigate the relationship between endogenous androgens and body fat distribution in early and late postmenopausal women.We enrolled postmenopausal women consisting of an early group (≤ 5 years since menopause, n = 105) and a late group (≥ 10 years since menopause, n = 107). Each group was subdivided into normal weight (BMI <24 kg/m(2)) group, overweight and obese (BMI ≥ 24 kg/m(2)) group. Fasting total testosterone (T), dehydroepiandrosterone-sulfate (DHEA-S) and sex hormone-binding globulin (SHBG) levels were measured. Body fat distribution was evaluated by dual-energy X-ray absorptiometry (DEXA).Late postmenopausal women had a higher proportion of body fat than early postmenopausal women. The body fat of the overweight and obese women had a greater tendency to accumulate in the abdomen compared with the normal weight women both in early and late postmenopausal groups. The overweight and obese women had a higher free testosterone (FT) than the normal weight women in early postmenopausal women (P<0.05). In late postmenopausal women, the overweight and obese women had higher DHEA-S levels than normal weight women (P<0.05). No direct relationship was observed between the T levels and body fat distribution both in early and late postmenopausal groups (P>0.05).The FT in early postmenopausal women and the DHEA-S levels in late postmenopausal women correlated positively with the trunk/leg fat ratio (T/L) and the proportion of android fat whereas correlated negatively with the proportion of gynoid fat in the partial correlation and multiple linear regression analyses (all P<0.05).Serum T levels do not correlate directly with body fat distribution, the FT in early postmenopausal women and DHEA-S levels in late postmenopausal women correlate positively with abdominal fat accumulation

LncRNA SMARCD3-OT1 Promotes Muscle Hypertrophy and Fast-Twitch Fiber Transformation via Enhancing SMARCD3X4 Expression

Long noncoding RNA (lncRNA) plays a crucial part in all kinds of life activities, especially in myogenesis. SMARCD3 (SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily d, member 3) is a member of the SWI/SNF protein complex and was reported to be required for cell proliferation and myoblast differentiation. In this study, we identified a new lncRNA named SMARCD3-OT1 (SMARCD3overlappinglncRNA), which strongly regulated the development of myogenesis by improving the expression of SMARCD3X4 (SMARCD3transcripts4). We overexpressed and knockdown the expression of SMARCD3-OT1 and SMARCD3X4 to investigate their function on myoblast proliferation and differentiation. Cell experiments proved that SMARCD3-OT1 and SMARCD3X4 promoted myoblast proliferation through the CDKN1A pathway and improved differentiation of differentiated myoblasts through the MYOD pathway. Moreover, they upregulated the fast-twitch fiber-related genes and downregulated the slow-twitch fiber-related genes, which indicated that they facilitated the slow-twitch fiber to transform into the fast-twitch fiber. The animals&rsquo; experiments supported the results above, demonstrating that SMARCD3-OT1 could induce muscle hypertrophy and fast-twitch fiber transformation. In conclusion, SMARCD3-OT1 can improve the expression of SMARCD3X4, thus inducing muscle hypertrophy. In addition, SMARCD3-OT1 can facilitate slow-twitch fibers to transform into fast-twitch fibers

PPARGC1A Is a Moderator of Skeletal Muscle Development Regulated by miR-193b-3p

Meat production performance is one of the most important factors in determining the economic value of poultry. Myofiber is the basic unit of skeletal muscle, and its physical and chemical properties determine the meat quality of livestock and poultry to a certain extent. Peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PPARGC1A) as a transcriptional coactivator has been found to be widely involved in a series of biological processes. However, PPARGC1A is still poorly understood in chickens. In this manuscript, we reported that PPARGC1A was highly expressed in slow-twitch myofibers. PPARGC1A facilitated mitochondrial biogenesis and regulated skeletal muscle metabolism by mediating the flux of glycolysis and the TCA cycle. Gain- and loss-of-function analyses revealed that PPARGC1A promoted intramuscular fatty acid oxidation, drove the transformation of fast-twitch to slow-twitch myofibers, and increased chicken skeletal muscle mass. Mechanistically, the expression level of PPARGC1A is regulated by miR-193b-3p. Our findings help to understand the genetic regulation of skeletal muscle development and provide a molecular basis for further research on the antagonism of skeletal muscle development and fat deposition in chickens

LncRNA-TBP mediates TATA-binding protein recruitment to regulate myogenesis and induce slow-twitch myofibers

Abstract Background Skeletal muscle is comprised of heterogeneous myofibers that differ in their physiological and metabolic parameters. Of these, slow-twitch (type I; oxidative) myofibers have more myoglobin, more mitochondria, and higher activity of oxidative metabolic enzymes compared to fast-twitch (type II; glycolytic) myofibers. Methods In our previous study, we found a novel LncRNA-TBP (for “LncRNA directly binds TBP transcription factor”) is specifically enriched in the soleus (which has a higher proportion of slow myofibers). The primary myoblast cells and animal model were used to assess the biological function of the LncRNA-TBP in vitro or in vivo. Meanwhile, we performed a RNA immunoprecipitation (RIP) and pull-down analysis to validate this interaction between LncRNA-TBP and TBP. Results Functional studies demonstrated that LncRNA-TBP inhibits myoblast proliferation but promotes myogenic differentiation in vitro. In vivo, LncRNA-TBP reduces fat deposition, activating slow-twitch muscle phenotype and inducing muscle hypertrophy. Mechanistically, LncRNA-TBP acts as a regulatory RNA that directly interacts with TBP protein to regulate the transcriptional activity of TBP-target genes (such as KLF4, GPI, TNNI2, and CDKN1A). Conclusion Our findings present a novel model about the regulation of LncRNA-TBP, which can regulate the transcriptional activity of TBP-target genes by recruiting TBP protein, thus modulating myogenesis progression and inducing slow-twitch fibers. Video Abstrac

Myogenic Determination and Differentiation of Chicken Bone Marrow-Derived Mesenchymal Stem Cells under Different Inductive Agents

Poultry plays an important role in the meat consumer market and is significant to further understanding the potential mechanism of muscle development in the broiler. Bone marrow-derived mesenchymal stem cells (BM-MSCs) can provide critical insight into muscle development due to their multi-lineage differentiation potential. To our knowledge, chicken BM-MSCs demonstrate limited myogenic differentiation potential under the treatment with dexamethasone (DXMS) and hydrocortisone (HC). 5-azacytidine (5-Aza), a DNA demethylating agent, which has been widely used in the myogenic differentiation of BM-MSCs in other species. There is no previous report that applies 5-Aza to myogenic-induced differentiation of chicken BM-MSCs. In this study, we evaluated the myogenic determination and differentiation effect of BM-MSCs under different inductive agents. BM-MSCs showed better differentiation potential under the 5-Aza-treatment. Transcriptome sequence analysis identified 2402 differentially expressed DEGs including 28 muscle-related genes after 5-Aza-treatment. The DEGs were significantly enriched in Gene Ontology database terms, including in the cell plasma membrane, molecular binding, and cell cycle and differentiation. KEGG pathway analysis revealed that DEGs were enriched in myogenic differentiation-associated pathways containing the PI3K-Akt signaling pathway, the TGF-&beta; signaling pathway, Arrhythmogenic right ventricular cardiomyopathy, dilated cardiomyopathy, and hypertrophic cardiomyopathy, which suggested that BM-MSCs differentiated into a muscle-like phenotype under 5-Aza-treatment. Although BM-MSCs have not formed myotubes in our study, it is worthy of further study. In summary, our study lays the foundation for constructing a myogenic determination and differentiation model in chicken BM-MSCs

Associations of androgens with body fat distribution by multiple linear regressions analysis in early and late postmenopausal groups.

<p>Results are reported as standardized regression coefficient (β). The Model¹ used in analysis included T, DHEA-S, SHBG, BMI and age. The Model^1a used in analysis included T, FT, DHEA-S, SHBG, BMI and age. The Model² used in analysis included DHEA-S, FT, BMI and age. The Model^2a used in analysis included DHEA-S, SHBG, FT, BMI and age.BMI and age are not shown in the table. Regression coefficient is significant at the 0.05 level (P<0.05).</p

Body fat distribution and androgens after adjustment for BMI.

*<p>p<0.05, overweight women vs. normal weight women in the early postmenopausal group.</p>**<p>p<0.05, overweight women vs. normal weight women in the late postmenopausal group.</p