Metabolomic Analysis of Alfalfa (Medicago sativa L.) Root-Symbiotic Rhizobia Responses under Alkali Stress

Alkaline salts (e.g., NaHCO3 and Na2CO3) causes more severe morphological and physiological damage to plants than neutral salts (e.g., NaCl and Na2SO4) due to differences in pH. The mechanism by which plants respond to alkali stress is not fully understood, especially in plants having symbotic relationships such as alfalfa (Medicago sativa L.). Therefore, a study was designed to evaluate the metabolic response of the root-nodule symbiosis in alfalfa under alkali stress using comparative metabolomics. Rhizobium-nodulized (RI group) and non-nodulized (NI group) alfalfa roots were treated with 200 mmol/L NaHCO3 and, roots samples were analyzed for malondialdehydyde (MDA), proline, glutathione (GSH), superoxide dismutase (SOD), and peroxidase (POD) content. Additionally, metabolite profiling was conducted using gas chromatography combined with time-of-flight mass spectrometry (GC/TOF-MS). Phenotypically, the RI alfalfa exhibited a greater resistance to alkali stress than the NI plants examined. Physiological analysis and metabolic profiling revealed that RI plants accumulated more antioxidants (SOD, POD, GSH), osmolytes (sugar, glycols, proline), organic acids (succinic acid, fumaric acid, and alpha-ketoglutaric acid), and metabolites that are involved in nitrogen fixation. Our pairwise metabolomics comparisons revealed that RI alfalfa plants exhibited a distinct metabolic profile associated with alkali putative tolerance relative to NI alfalfa plants. Data provide new information about the relationship between non-nodulized, rhizobium-nodulized alfalfa and alkali resistance

Genetic variation in selenoprotein S influences inflammatory response

Chronic inflammation has a pathological role in many common diseases and is influenced by both genetic and environmental factors. Here we assess the role of genetic variation in selenoprotein S (SEPS1, also called SELS or SELENOS), a gene involved in stress response in the endoplasmic reticulum and inflammation control. After resequencing SEPS1, we genotyped 13 SNPs in 522 individuals from 92 families. As inflammation biomarkers, we measured plasma levels of IL-6, IL-1b and TNF-a. Bayesian quantitative trait nucleotide analysis identified associations between SEPS1 polymorphisms and all three proinflammatorycytokines. One promoter variant, 105G-A, showed strong evidence for an association with each cytokine (multivariate P = 0.0000002). Functional analysis of this polymorphism showed that the A variant significantly impaired SEPS1 expression after exposure to endoplasmic reticulum stress agents (P = 0.00006). Furthermore, suppression of SEPS1 by short interfering RNA in macrophage cells increased the release of IL-6 and TNF-a. To investigate further the significance of the observed associations, we genotyped 105G-A in 419 Mexican American individuals from 23 families for replication. This analysis confirmed a significantassociation with both TNF-a (P = 0.0049) and IL-1b (P = 0.0101). These results provide a direct mechanistic link between SEPS1 and the production of inflammatory cytokines and suggest that SEPS1 has a role in mediating inflammation.<br /

Localization of chromosomal regions influencing the phenotypes of the metabolic syndrome

textThe goal of this project was to study the genetic structure of the metabolic syndrome. The first objective was to locate chromosomal regions influencing insulin resistance in Mexican Americans of the San Antonio Family Heart Study (SAFHS). Two studies were performed to achieve this objective using a genomewide scan. In the first study using data from the first visit of the SAFHS, we detected significant linkage evidence on chromosome 8p between marker D8S1130 and D8S1106 and on chromosome 13q between marker D13S787 and D13S252. In the second study that used data from the second visit of the SAFHS, markers D1S1663 on chromosome 1 and D2S436 on chromosome 2 were found to be linked to insulin sensitivity indices. Candidate genes on detected locations were proposed. The significant findings in both studies duplicate those of previous investigations. The second objective of this project was to identify the genetic locations related to the quantitative traits that constitute the metabolic syndrome in the same population of Mexican Americans. Principal component factor analysis (PCFA) was conducted, and significant and suggestive evidence for linkage of lipid (factor 4) and body size/adiposity (factor 1) were found on chromosome 4 near marker D4S403 and chromosome 1 near marker D1S1597, respectively. The third objective of this project was to explore the genetic pleiotropy between insulin resistance and adiposity, especially visceral obesity using the baboon as a model. The present study is the first to use omental tissue to investigate gene pleiotropy between visceral fat and insulin resistance. The results from the baboon study in this thesis, coupled with research in humans, suggest that a common set of genes contribute to insulin resistance and obesity in both species. It is also plausible that those two groups of genes completely overlap each other. At present, the variance decomposition based, multipoint linkage analysis is a mathematical model that can provide useful information for susceptible gene mapping. Future finemapping and the positional candidate gene approach will be helpful to further our understanding of the genetic structure of this complex disease.Nutritional Science

FV-MViT: Mobile Vision Transformer for Finger Vein Recognition

In addressing challenges related to high parameter counts and limited training samples for finger vein recognition, we present the FV-MViT model. It serves as a lightweight deep learning solution, emphasizing high accuracy, portable design, and low latency. The FV-MViT introduces two key components. The Mul-MV2 Block utilizes a dual-path inverted residual connection structure for multi-scale convolutions, extracting additional local features. Simultaneously, the Enhanced MobileViT Block eliminates the large-scale convolution block at the beginning of the original MobileViT Block. It converts the Transformer’s self-attention into separable self-attention with linear complexity, optimizing the back end of the original MobileViT Block with depth-wise separable convolutions. This aims to extract global features and effectively reduce parameter counts and feature extraction times. Additionally, we introduce a soft target center cross-entropy loss function to enhance generalization and increase accuracy. Experimental results indicate that the FV-MViT achieves a recognition accuracy of 99.53% and 100.00% on the Shandong University (SDU) and Universiti Teknologi Malaysia (USM) datasets, with equal error rates of 0.47% and 0.02%, respectively. The model has a parameter count of 5.26 million and exhibits a latency of 10.00 milliseconds from the sample input to the recognition output. Comparison with state-of-the-art (SOTA) methods reveals competitive performance for FV-MViT

Genome-Wide Identification and Analysis of the NF-Y Transcription Factor Family in <i>Medicago sativa</i> L.

The nuclear factor Y (NF-Y) gene family is an important transcription factor family consisting of three subfamilies, NF-YA, NF-YB and NF-YC, which are widely involved in plant growth and development, stress responses and other processes. In this study, we identified 64 members of the NF-Y gene family in the M. sativa L. (Xinjiang Daye) genome, including 11 MsNF-YAs, 33 MsNF-YBs and 20 MsNF-YCs. Analysis of conserved motifs indicated that each unit included unique compounds of motifs, although certain members lost some motifs. Conserved functional domain analysis showed that each subunit contained a specific set of functional domains. Analysis of cis-acting elements in the promoter region of the MsNF-Y genes identified a series of cis-acting elements associated with stress responses. In addition, the transcriptome data and qRT-PCR analysis showed that MsNF-Y genes were significantly induced or downregulated by alkali treatment. The results of this study may help to establish a basis for further cloning and functional studies of NF-Y genes in Medicago sativa and other related legume species

Principal Component for Metabolic Syndrome Risk Maps to Chromosome 4p in Mexican Americans: The San Antonio Family Heart Study

Metabolic syndrome refers to the clustering of disease conditions such as insulin resistance, hyperinsulinemia, dyslipidemia, hypertension, and obesity. To explore the genetic predispositions of this complex syndrome, we conducted a principal components analysis using data on 14 phenotypes related to the risk of developing metabolic syndrome. The subjects were 566 nondiabetic Mexican Americans, distributed in 41 extended families from the San Antonio Family Heart Study. The factor scores obtained from these 14 phenotypes were used in multipoint linkage analysis using SOLAR. Factors were identified that accounted for 73% of the total variance of the original variables: body size–adiposity, insulin–glucose, blood pressure, and lipid levels. Each factor exhibited evidence for either significant or suggestive linkage involving four factor-specific chromosomal regions relating to chromosomes 1, 3, 4, and 6. Significant evidence for linkage of the lipid factor was found on chromosome 4 near marker D4S403 (LOD 3.52), where the cholecystokinin A receptor (CCKAR) and ADPribosyl cyclase 1 (CD38) genes are located. Suggestive evidence for linkage of the body size–adiposity factor to chromosome 1 near marker D1S1597 (LOD 2.53) in the region containing the nuclear receptor subfamily 0, group B, member 2 gene (NROB2) also was observed. The insulin–glucose and blood pressure factors were linked suggestively to regions on chromosome 3 near marker D3S1595 (LOD 2.20) and on chromosome 6 near marker D6S1031 (LOD 2.08), respectively. In summary, our findings suggest that the factor structures for the risk of metabolic syndrome are influenced by multiple distinct genes across the genome

Effect of Non-Esterified Fatty Acids on Fatty Acid Metabolism-Related Genes in Calf Hepatocytes Cultured in Vitro

Background: NEFA plays numerous roles in the metabolism of glucose, lipids, and proteins. A number of experimental studies have shown that NEFA may have an important role in fatty acid metabolism in the liver, especially in dairy cows that experience negative energy balance (NEB) during early lactation. Methods: In this study, using fluorescent quantitative RT-PCR, ELISA, and primary hepatocytes cultured in vitro, we examined the effect of NEFA (0, 0.2, 0.4, 0.8, 1.6, and 3.2 mmol/L) on fatty acid metabolism by monitoring the mRNA and protein expression of the following key enzymes: long chain acyl-CoA synthetase (ACSL), carnitine palmitoyltransferase IA (CPT IA), long chain acyl-CoA dehydrogenase (ACADL), and acetyl-CoA carboxylase (ACC). Results: The mRNA and protein expression levels of ACSL and ACADL markedly increased as the concentration of NEFA in the media was increased. The mRNA and protein expression levels of CPT IA were enhanced significantly when the NEFA concentrations increased from 0 to 1.6 mmol/L and decreased significantly when the NEFA concentrations increased from 1.6 to 3.2 mmol/L. The mRNA and protein expression of ACC decreased gradually with increasing concentrations of NEFA. Conclusion: These findings indicate that increased NEFA significantly promote the activation and β-oxidation of fatty acids, but very high NEFA concentrations may inhibit the translocation of fatty acids into mitochondria of hepatocytes. This may explain the development of ketosis or liver lipidosis in dairy cows. CPT IA might be the key control enzyme of the fatty acid oxidation process in hepatocytes