The Spartina alterniflora genome sequence provides insights into the salt-tolerance mechanisms of exo-recretohalophytes.

Spartina alterniflora is an exo-recretohalophyte Poaceae species that is able to grow well in seashore, but the genomic basis underlying its adaptation to salt tolerance remains unknown. Here, we report a high-quality, chromosome-level genome assembly of S. alterniflora constructed through PacBio HiFi sequencing, combined with high-throughput chromosome conformation capture (Hi-C) technology and Illumina-based transcriptomic analyses. The final 1.58 Gb genome assembly has a contig N50 size of 46.74 Mb. Phylogenetic analysis suggests that S. alterniflora diverged from Zoysia japonica approximately 21.72 million years ago (MYA). Moreover, whole-genome duplication (WGD) events in S. alterniflora appear to have expanded gene families and transcription factors relevant to salt tolerance and adaptation to saline environments. Comparative genomics analyses identified numerous species-specific genes, significantly expanded genes and positively selected genes that are enriched for 'ion transport' and 'response to salt stress'. RNA-seq analysis identified several ion transporter genes including the high-affinity K+ transporters (HKTs), SaHKT1;2, SaHKT1;3 and SaHKT1;8, and high copy number of Salt Overly Sensitive (SOS) up-regulated under high salt conditions, and the overexpression of SaHKT2;4 in Arabidopsis thaliana conferred salt tolerance to the plant, suggesting specialized roles for S. alterniflora to adapt to saline environments. Integrated metabolomics and transcriptomics analyses revealed that salt stress activate glutathione metabolism, with differential expressions of several genes such as γ-ECS, GSH-S, GPX, GST and PCS in the glutathione metabolism. This study suggests several adaptive mechanisms that could contribute our understanding of evolutional basis of the halophyte.

Genomic basis determining root system architecture in maize.

KEY MESSAGE: A total of 389 and 344 QTLs were identified by GWAS and QTL mapping explaining accumulatively 32.2-65.0% and 23.7-63.4% of phenotypic variation for 14 shoot-borne root traits using more than 1300 individuals across multiple field trails. Efficient nutrient and water acquisition from soils depends on the root system architecture (RSA). However, the genetic determinants underlying RSA in maize remain largely unexplored. In this study, we conducted a comprehensive genetic analysis for 14 shoot-borne root traits using 513 inbred lines and 800 individuals from four recombinant inbred line (RIL) populations at the mature stage across multiple field trails. Our analysis revealed substantial phenotypic variation for these 14 root traits, with a total of 389 and 344 QTLs identified through genome-wide association analysis (GWAS) and linkage analysis, respectively. These QTLs collectively explained 32.2-65.0% and 23.7-63.4% of the trait variation within each population. Several a priori candidate genes involved in auxin and cytokinin signaling pathways, such as IAA26, ARF2, LBD37 and CKX3, were found to co-localize with these loci. In addition, a total of 69 transcription factors (TFs) from 27 TF families (MYB, NAC, bZIP, bHLH and WRKY) were found for shoot-borne root traits. A total of 19 genes including PIN3, LBD15, IAA32, IAA38 and ARR12 and 19 GWAS signals were overlapped with selective sweeps. Further, significant additive effects were found for root traits, and pyramiding the favorable alleles could enhance maize root development. These findings could contribute to understand the genetic basis of root development and evolution, and provided an important genetic resource for the genetic improvement of root traits in maize.

Mining genes regulating root system architecture in maize based on data integration analysis.

KEY MESSAGE: A new strategy that integrated multiple public data resources was established to construct root gene co-expression network and mine genes regulating root system architecture in maize. A root gene co-expression network, containing 13,874 genes, was constructed. A total of 53 root hub genes and 16 priority root candidate genes were identified. One priority root candidate was further functionally verified using overexpression transgenic maize lines. Root system architecture (RSA) is crucial for crops productivity and stress tolerance. In maize, few RSA genes are functionally cloned, and effective discovery of RSA genes remains a great of challenge. In this work, we established a strategy to mine maize RSA genes by integrating functionally characterized root genes, root transcriptome, weighted gene co-expression network analysis (WGCNA) and genome-wide association analysis (GWAS) of RSA traits based on public data resources. A total of 589 maize root genes were collected by searching well-characterized root genes in maize or homologous genes of other species. We performed WGCNA to construct a maize root gene co-expression network containing 13874 genes based on public available root transcriptome data, and further discovered the 53 hub genes related to root traits. In addition, by the prediction function of obtained root gene co-expression network, a total of 1082 new root candidate genes were explored. By further overlapping the obtained new root candidate gene with the root-related GWAS of RSA candidate genes, 16 priority root candidate genes were identified. Finally, a priority root candidate gene, Zm00001d023379 (encodes pyruvate kinase 2), was validated to modulate root open angle and shoot-borne roots number using its overexpression transgenic lines. Our results develop an integration analysis method for effectively exploring regulatory genes of RSA in maize and open a new avenue to mine the candidate genes underlying complex traits.

Plasticity of root anatomy during domestication of a maize-teosinte derived population.

Maize (Zea mays L.) has undergone profound changes in root anatomy for environmental adaptation during domestication. However, the genetic mechanism of plasticity of maize root anatomy during the domestication process remains unclear. In this study, high-resolution mapping was performed for nine root anatomical traits using a maize-teosinte population (mexicana × Mo17) across three environments. Large genetic variations were detected for different root anatomical traits. The cortex, stele, aerenchyma areas, xylem vessel number, and cortical cell number had large variations across three environments, indicating high plasticity. Sixteen quantitative trait loci (QTL) were identified, including seven QTL with QTL × environment interaction (EIQTL) for high plasticity traits and nine QTL without QTL × environment interaction (SQTL). Most of the root loci were consistent with shoot QTL depicting domestication signals. Combining transcriptome and genome-wide association studies revealed that AUXIN EFFLUX CARRIER PIN-FORMED LIKE 4 (ZmPILS4) serves as a candidate gene underlying a major QTL of xylem traits. The near-isogenic lines (NILs) with lower expression of ZmPILS4 had 18-24% more auxin concentration in the root tips and 8-15% more xylem vessels. Nucleotide diversity values analysis in the promoter region suggested that ZmPILS4 was involved in maize domestication and adaptation. These results revealed the potential genetic basis of root anatomical plasticity during domestication.

Genetic characterization of inbred lines from Shaan A and B groups for identifying loci associated with maize grain yield.

BACKGROUND: Increasing grain yield is a primary objective of maize breeding. Dissecting the genetic architecture of grain yield furthers genetic improvements to increase yield. Presented here is an association panel composed of 126 maize inbreds (AM126), which were genotyped by the genotyping-by-sequencing (tGBS) method. We performed genetic characterization and association analysis related to grain yield in the association panel. RESULTS: In total, 46,046 SNPs with a minor allele frequency (MAF) ≥0.01 were used to assess genetic diversity and kinship in AM126. The results showed that the average MAF and polymorphism information content (PIC) were 0.164 and 0.198, respectively. The Shaan B group, with 11,284 unique SNPs, exhibited greater genetic diversity than did the Shaan A group, with 2644 SNPs. The 61.82% kinship coefficient in AM126 was equal to 0, and only 0.15% of that percentage was greater than 0.7. A total of 31,983 SNPs with MAF ≥0.05 were used to characterize population structure, LD decay and association mapping. Population structure analysis suggested that AM126 can be divided into 6 subgroups, which is consistent with breeding experience and pedigree information. The LD decay distance in AM126 was 150 kb. A total of 51 significant SNPs associated with grain yield were identified at P < 1 × 10- 3 across two environments (Yangling and Yulin). Among those SNPs, two loci displayed overlapping regions in the two environments. Finally, 30 candidate genes were found to be associated with grain yield. CONCLUSIONS: These results contribute to the genetic characterization of this breeding population, which serves as a reference for hybrid breeding and population improvement, and demonstrate the genetic architecture of maize grain yield, potentially facilitating genetic improvement.

Environmental endocrine disruptor-induced mitochondrial dysfunction: a potential mechanism underlying diabetes and its complications.

Diabetes and its complications significantly affect individuals' quality of life. The etiology of diabetes mellitus and its associated complications is complex and not yet fully understood. There is an increasing emphasis on investigating the effects of endocrine disruptors on diabetes, as these substances can impact cellular processes, energy production, and utilization, ultimately leading to disturbances in energy homeostasis. Mitochondria play a crucial role in cellular energy generation, and any impairment in these organelles can increase susceptibility to diabetes. This review examines the most recent epidemiological and pathogenic evidence concerning the link between endocrine disruptors and diabetes, including its complications. The analysis suggests that endocrine disruptor-induced mitochondrial dysfunction-characterized by disruptions in the mitochondrial electron transport chain, dysregulation of calcium ions (Ca2+), overproduction of reactive oxygen species (ROS), and initiation of signaling pathways related to mitochondrial apoptosis-may be key mechanisms connecting endocrine disruptors to the development of diabetes and its complications.

Exploring salt tolerance mechanisms using machine learning for transcriptomic insights: case study in Spartina alterniflora.

Salt stress poses a significant threat to global cereal crop production, emphasizing the need for a comprehensive understanding of salt tolerance mechanisms. Accurate functional annotations of differentially expressed genes are crucial for gaining insights into the salt tolerance mechanism. The challenge of predicting gene functions in under-studied species, especially when excluding infrequent GO terms, persists. Therefore, we proposed the use of NetGO 3.0, a machine learning-based annotation method that does not rely on homology information between species, to predict the functions of differentially expressed genes under salt stress. Spartina alterniflora, a halophyte with salt glands, exhibits remarkable salt tolerance, making it an excellent candidate for in-depth transcriptomic analysis. However, current research on the S. alterniflora transcriptome under salt stress is limited. In this study we used S. alterniflora as an example to investigate its transcriptional responses to various salt concentrations, with a focus on understanding its salt tolerance mechanisms. Transcriptomic analysis revealed substantial changes impacting key pathways, such as gene transcription, ion transport, and ROS metabolism. Notably, we identified a member of the SWEET gene family in S. alterniflora, SA_12G129900.m1, showing convergent selection with the rice ortholog SWEET15. Additionally, our genome-wide analyses explored alternative splicing responses to salt stress, providing insights into the parallel functions of alternative splicing and transcriptional regulation in enhancing salt tolerance in S. alterniflora. Surprisingly, there was minimal overlap between differentially expressed and differentially spliced genes following salt exposure. This innovative approach, combining transcriptomic analysis with machine learning-based annotation, avoids the reliance on homology information and facilitates the discovery of unknown gene functions, and is applicable across all sequenced species.

MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm.

Identifying the potential associations between microbes and diseases is the first step for revealing the pathological mechanisms of microbe-associated diseases. However, traditional culture-based microbial experiments are expensive and time-consuming. Thus, it is critical to prioritize disease-associated microbes by computational methods for further experimental validation. In this study, we proposed a novel method called MNNMDA, to predict microbe-disease associations (MDAs) by applying a Matrix Nuclear Norm method into known microbe and disease data. Specifically, we first calculated Gaussian interaction profile kernel similarity and functional similarity for diseases and microbes. Then we constructed a heterogeneous information network by combining the integrated disease similarity network, the integrated microbe similarity network and the known microbe-disease bipartite network. Finally, we formulated the microbe-disease association prediction problem as a low-rank matrix completion problem, which was solved by minimizing the nuclear norm of a matrix with a few regularization terms. We tested the performances of MNNMDA in three datasets including HMDAD, Disbiome, and Combined Data with small, medium and large sizes respectively. We also compared MNNMDA with 5 state-of-the-art methods including KATZHMDA, LRLSHMDA, NTSHMDA, GATMDA, and KGNMDA, respectively. MNNMDA achieved area under the ROC curves (AUROC) of 0.9536 and 0.9364 respectively on HDMAD and Disbiome, better than the AUCs of compared methods under the 5-fold cross-validation for all microbe-disease associations. It also obtained a relatively good performance with AUROC 0.8858 in the combined data. In addition, MNNMDA was also better than other methods in area under precision and recall curve (AUPR) under the 5-fold cross-validation for all associations, and in both AUROC and AUPR under the 5-fold cross-validation for diseases and the 5-fold cross-validation for microbes. Finally, the case studies on colon cancer and inflammatory bowel disease (IBD) also validated the effectiveness of MNNMDA. In conclusion, MNNMDA is an effective method in predicting microbe-disease associations. Availability: The codes and data for this paper are freely available at Github https://github.com/Haiyan-Liu666/MNNMDA.

Genome-wide dissection of changes in maize root system architecture during modern breeding.

Appropriate root system architecture (RSA) can improve maize yields in densely planted fields, but little is known about its genetic basis in maize. Here we performed root phenotyping of 14,301 field-grown plants from an association mapping panel to study the genetic architecture of maize RSA. A genome-wide association study identified 81 high-confidence RSA-associated candidate genes and revealed that 28 (24.3%) of known root-related genes were selected during maize domestication and improvement. We found that modern maize breeding has selected for a steeply angled root system. Favourable alleles related to steep root system angle have continuously accumulated over the course of modern breeding, and our data pinpoint the root-related genes that have been selected in different breeding eras. We confirm that two auxin-related genes, ZmRSA3.1 and ZmRSA3.2, contribute to the regulation of root angle and depth in maize. Our genome-wide identification of RSA-associated genes provides new strategies and genetic resources for breeding maize suitable for high-density planting.

Lipoprotein Lipase Gene Polymorphisms Are Associated with Myocardial Infarction Risk: A Meta-Analysis.

Aims: Many studies and researchers have reported on the genetic association between lipoprotein lipase (LPL) gene polymorphisms and myocardial infarction (MI). The results, however, have been inconclusive. Therefore, we assessed the relationship of LPL gene polymorphisms and MI risk by performing a meta-analysis. Methods: Literature was retrieved through PubMed, Web of Science, the Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), and Embase databases. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to evaluate the genetic associations between LPL gene polymorphisms and MI risk. A total of nine studies, with 10 individual groups, comprising 2785 cases and 4317 controls were used for this meta-analysis. Results: The allelic (p = 0.0003, OR [95% CI] = 0.86 [0.79-0.93]) and dominant models (p = 0.001, OR [95% CI] = 0.83 [0.73-0.93]), but not the recessive model (p > 0.05) of LPL gene showed that the HindIII variant significantly decreased the risk of MI. In addition, the allelic model (p = 0.04, OR [95% CI] = 0.71 [0.50-0.99]) for the S447X variant showed a significant decrease in the risk of MI. No association was observed between the PvuII variant and MI (p > 0.05). A subgroup analysis based on ethnicity revealed that all of the genetic models (allelic model: p < 0.00001, OR [95% CI] = 0.62 [0.51-0.77]; dominant model: p = 0.003, OR [95% CI] = 0.66 [0.50-0.87]; recessive model (p = 0.02, OR [95% CI] = 0.47 [0.25-0.88]) found an association of the HindIII polymorphism with MI in the Asian, but not in the Caucasian population (p > 0.05). Under the dominant model the HindIII SNP was also shown to be associated with MI risk in the Caucasian population (p = 0.03, OR [95% CI] = 0.87 [0.76-0.99]). In addition, the allelic (p = 0.02, OR [95% CI] = 0.75 [0.59-0.95]) and dominant models (p = 0.02, OR [95% CI] = 0.51 [0.29-0.90]) for S447X demonstrated a significantly decreased MI risk in the Caucasian, but not in the Asian population (p > 0.05). Conclusions: LPL HindIII and S447X polymorphisms, but not PvuII might be the protective factors for MI. To confirm these results, case-control studies with larger numbers of subjects need to be conducted.