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– Acute lung injury (ALI) is a type of serious clinical syndrome leading to morbidity and mortality. However, the precise pathogenesis of ALI remains elusive. Here, we implemented an integrative meta-analysis of six GEO microarray studies with 76 samples in the ALI mouse model. A total of 958 differentially expressed genes (DEGs) were identified in LPS relative to normal samples. Then, a network-based meta-analysis was used to mine core DEGs and to unfold the interactions among these genes. We found that Ebi3 was the top upregulated genes in the LPS-induced ALI. GO, KEGG, and GSEA analyses were performed for functional annotation. qRT-PCR revealed augmented expression of six candidate genes (Stat1, Syk, Jak3, Rac2, Ripk1, and Traf6) in the established ALI mouse model with LPS exposure. Taken together, our study investigated comprehensively hub DEGs and their networks for LPS-stimulated ALI, which might afford an additional approach to determine biomarkers and therapeutic targets and explore the molecular pathophysiology toward ALI. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s10753-021-01518-8.

Background The number of panicles per plant, number of grains per panicle, and 1000-grain weight are important factors contributing to the grain yield per plant in rice. The Rice Diversity Panel 1 (RDP1) contains a total of 421 purified, homozygous rice accessions representing diverse genetic variations within O. sativa. The release of High-Density Rice Array (HDRA, 700 k SNPs) dataset provides a new opportunity to discover the genetic variants of panicle architectures in rice. Results In this report, a new method genome-phenome wide association study (GPWAS) was performed with 391 individuals and 27 traits derived from RDP1 to scan the relationship between the genes and multi-traits. A total of 1985 gene models were linked to phenomic variation with a p-value cutoff of 4.49E-18. Besides, 406 accessions derived from RDP1 with 411,066 SNPs were used to identify QTLs associated with the total spikelets number per panicle (TSNP), grain number per panicle (GNP), empty grain number per panicle (EGNP), primary branch number (PBN), panicle length (PL), and panicle number per plant (PN) by GLM, MLM, FarmCPU, and BLINK models for genome-wide association study (GWAS) analyses. A total of 18, 21, 18, 17, 15, and 17 QTLs were identified tightly linked with TSNP, GNP, EGNP, PBN, PL, and PN, respectively. Then, a total of 23 candidate genes were mapped simultaneously using both GWAS and GPWAS methods, composed of 6, 4, 5, 4, and 4 for TSNP, GNP, EGNP, PBN, and PL. Notably, one overlapped gene (Os01g0140100) were further investigated based on the haplotype and gene expression profile, indicating this gene might regulate the TSNP or panicle architecture in rice. Conclusions Nearly 30 % (30/106) QTLs co-located with the previous published genes or QTLs, indicating the power of GWAS. Besides, GPWAS is a new method to discover the relationship between genes and traits, especially the pleiotropy genes. Through comparing the results from GWAS and GPWAS, we identified 23 candidate genes related to panicle architectures in rice. This comprehensive study provides new insights into the genetic basis controlling panicle architectures in rice, which lays a foundation in rice improvement.

Additional file 1: Figure S1. The kinship plot of 406 accessions from RDP1. Figure S2. The distribution of SNP numbers in genes. Figure S3. Genome-wide association analysis for total spikelets number per panicle (TSNP) with general linear model (GLM), mixed linear models (MLM), Fixed and random model Circulating Probability Unification (FarmCPU), and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) methods (left). Quantile-quantile plot of each model (right). Red texts indicate reported genes, blue texts indicate candidate genes. The horizontal solid red line indicates the Bonferroni-corrected significance threshold at -log10(P) = 6.91. Figure S4. Genome-wide association analysis for grain number per panicle (GNP) with general linear model (GLM), mixed linear models (MLM), Fixed and random model Circulating Probability Unification (FarmCPU), and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) methods (left). Red texts indicate reported genes. The horizontal solid red line indicates the Bonferroni-corrected significance threshold and -log10(P) = 6.91. Figure S5. Genome-wide association analysis for EGNP with general linear model (GLM), mixed linear models (MLM), Fixed and random model Circulating Probability Unification (FarmCPU), and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) methods (left). Quantile-quantile plot of each model (right). Red texts indicate reported genes. The horizontal solid red line indicates the Bonferroni-corrected significance threshold at -log10(P) = 6.91. Figure S6. Genome-wide association analysis for PBN with general linear model (GLM), mixed linear models (MLM), Fixed and random model Circulating Probability Unification (FarmCPU), and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) methods (left). Quantile-quantile plot of each model (right). Red texts indicate reported genes. The horizontal solid red line indicates the Bonferroni-corrected significance threshold at -log10(P) = 6.91. Figure S7. Genome-wide association analysis for PL with general linear model (GLM), mixed linear models (MLM), Fixed and random model Circulating Probability Unification (FarmCPU), and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) methods (left). Quantile-quantile plot of each model (right). Red texts indicate reported genes. The horizontal solid red line indicates the Bonferroni-corrected significance threshold at -log10(P) = 6.91. Figure S8. Genome-wide association analysis for PN with general linear model (GLM), mixed linear models (MLM), Fixed and random model Circulating Probability Unification (FarmCPU), and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) methods (left). Quantile-quantile plot of each model (right). Red texts indicate reported genes. The horizontal solid red line indicates the Bonferroni-corrected significance threshold at -log10(P) = 6.91. Figure S9. Expression pattern of Os01g0140100 in different tissues. Anther: Anther; Embryo-25 DAP: Embryo 25 Days After Pollination; Endosperm-25 DAP: Endosperm 25 Days After Pollination; Leaves-20 days: 20 Day Leaves; Pistil: Pistil; Pre-emergence inflorescence: Early Inflorescence; Post-emergence inflorescence: Emerging Inflorescence; Seed-5 DAP: Seed 5 Days After Pollination; Seed-10 DAP: Seed 10 Days After Pollination.

The number of panicles per plant, number of grains per panicle, and 1000-grain weight are important factors contributing to the grain yield per plant in rice. The Rice Diversity Panel 1 (RDP1) contains a total of 421 purified, homozygous rice accessions representing diverse genetic variations within O. sativa. The release of High-Density Rice Array (HDRA, 700 k SNPs) dataset provides a new opportunity to discover the genetic variants of panicle architectures in rice.In this report, a new method genome-phenome wide association study (GPWAS) was performed with 391 individuals and 27 traits derived from RDP1 to scan the relationship between the genes and multi-traits. A total of 1985 gene models were linked to phenomic variation with a p-value cutoff of 4.49E-18. Besides, 406 accessions derived from RDP1 with 411,066 SNPs were used to identify QTLs associated with the total spikelets number per panicle (TSNP), grain number per panicle (GNP), empty grain number per panicle (EGNP), primary branch number (PBN), panicle length (PL), and panicle number per plant (PN) by GLM, MLM, FarmCPU, and BLINK models for genome-wide association study (GWAS) analyses. A total of 18, 21, 18, 17, 15, and 17 QTLs were identified tightly linked with TSNP, GNP, EGNP, PBN, PL, and PN, respectively. Then, a total of 23 candidate genes were mapped simultaneously using both GWAS and GPWAS methods, composed of 6, 4, 5, 4, and 4 for TSNP, GNP, EGNP, PBN, and PL. Notably, one overlapped gene (Os01g0140100) were further investigated based on the haplotype and gene expression profile, indicating this gene might regulate the TSNP or panicle architecture in rice.Nearly 30 % (30/106) QTLs co-located with the previous published genes or QTLs, indicating the power of GWAS. Besides, GPWAS is a new method to discover the relationship between genes and traits, especially the pleiotropy genes. Through comparing the results from GWAS and GPWAS, we identified 23 candidate genes related to panicle architectures in rice. This comprehensive study provides new insights into the genetic basis controlling panicle architectures in rice, which lays a foundation in rice improvement.