Prevalence, Patterns, and Genetic Association Analysis of Modic Vertebral Endplate Changes

Study DesignA prospective genetic association study.PurposeThe etiology of Modic changes (MCs) is unclear. Recently, the role of genetic factors in the etiology of MCs has been evaluated. However, studies with a larger patient subset are lacking, and candidate genes involved in other disc degeneration phenotypes have not been evaluated. We studied the prevalence of MCs and genetic association of 41 candidate genes in a large Indian cohort.Overview of LiteratureMCs are vertebral endplate signal changes predominantly observed in the lumbar spine. A significant association between MCs and lumbar disc degeneration and nonspecific low back pain has been described, with the etiopathogenesis implicating various mechanical, infective, and biochemical factors.MethodsWe studied 809 patients using 1.5-T magnetic resonance imaging to determine the prevalence, patterns, distribution, and type of lumbar MCs. Genetic association analysis of 71 single nucleotide polymorphisms (SNPs) of 41 candidate genes was performed based on the presence or absence of MCs. SNPs were genotyped using the Sequenome platform, and an association test was performed using PLINK software.ResultsThe mean age of the study population (n=809) was 36.7±10.8 years. Based on the presence of MCs, the cohort was divided into 702 controls and 107 cases (prevalence, 13%). MCs were more commonly present in the lower (149/251, 59.4%) than in the upper (102/251, 40.6%) endplates. L4–5 endplates were the most commonly affected levels (30.7%). Type 2 MCs were the most commonly observed pattern (n=206, 82%). The rs2228570 SNP of VDR (p=0.02) and rs17099008 SNP of MMP20 (p=0.03) were significantly associated with MCs.ConclusionsGenetic polymorphisms of SNPs of VDR and MMP20 were significantly associated with MCs. Understanding the etiopathogenetic mechanisms of MCs is important for planning preventive and therapeutic strategies

GBS-based SNP map pinpoints the QTL associated with sorghum downy mildew resistance in maize (Zea mays L.)

Sorghum downy mildew (SDM), caused by the biotrophic fungi Peronosclerospora sorghi, threatens maize production worldwide, including India. To identify quantitative trait loci (QTL) associated with resistance to SDM, we used a recombinant inbred line (RIL) population derived from a cross between resistant inbred line UMI936 (w) and susceptible inbred line UMI79. The RIL population was phenotyped for SDM resistance in three environments [E1-field (Coimbatore), E2-greenhouse (Coimbatore), and E3-field (Mandya)] and also utilized to construct the genetic linkage map by genotyping by sequencing (GBS) approach. The map comprises 1516 SNP markers in 10 linkage groups (LGs) with a total length of 6924.7 cM and an average marker distance of 4.57 cM. The QTL analysis with the phenotype and marker data detected nine QTL on chromosome 1, 2, 3, 5, 6, and 7 across three environments. Of these, QTL namely qDMR1.2, qDMR3.1, qDMR5.1, and qDMR6.1 were notable due to their high phenotypic variance. qDMR3.1 from chromosome 3 was detected in more than one environment (E1 and E2), explaining the 10.3% and 13.1% phenotypic variance. Three QTL, qDMR1.2, qDMR5.1, and qDMR6.1 from chromosomes 1, 5, and 6 were identified in either E1 or E3, explaining 15.2%–18% phenotypic variance. Moreover, genome mining on three QTL (qDMR3.1, qDMR5.1, and qDMR6.1) reveals the putative candidate genes related to SDM resistance. The information generated in this study will be helpful for map-based cloning and marker-assisted selection in maize breeding programs

A Comparative Metabolomic Analysis Reveals the Nutritional and Therapeutic Potential of Grains of the Traditional Rice Variety Mappillai Samba

Rice (Oryza sativa L.) is the staple food of the majority of the population, particularly in Asia and Africa. Enriching rice with nutritional and therapeutic contents can improve its benefits for patients with lifestyle disorders. This study aimed to profile the phytochemical contents of the therapeutically known traditional rice Mappillai Samba against white rice CBMAS 14065 using non-targeted gas chromatography–mass spectrometry (GC-MS/MS). An analysis of the data using a mass spectrometry–data independent analysis (MS-DIAL) and MetaboAnalyst identified 113 metabolites belonging to 21 different classes of metabolites. A partial least square-discriminant analysis (PLS-DA) revealed 43 variable importance in projection (VIP) metabolites. This study identified therapeutically important metabolites, including phenylpropanoids, phytosterols, flavonoids, and polyamines, in the grains of Mappillai Samba. Three significant metabolic pathways, viz., phenylpropanoid biosynthesis, ubiquinone and other terpenoid-quinone biosynthesis, and steroid biosynthesis, were responsible for the grain metabolome variation between CBMAS 14065 and Mappillai Samba. Overall, the results of this study unravelled the biochemical complexity of Mappillai Samba, paving the way for the genetic mapping of the therapeutic compound accumulation in rice and the development of similar therapeutic rice varieties through molecular breeding

Plant Metabolomics: Current Initiatives and Future Prospects

Plant metabolomics is a rapidly advancing field of plant sciences and systems biology. It involves comprehensive analyses of small molecules (metabolites) in plant tissues and cells. These metabolites include a wide range of compounds, such as sugars, amino acids, organic acids, secondary metabolites (e.g., alkaloids and flavonoids), lipids, and more. Metabolomics allows an understanding of the functional roles of specific metabolites in plants’ physiology, development, and responses to biotic and abiotic stresses. It can lead to the identification of metabolites linked with specific traits or functions. Plant metabolic networks and pathways can be better understood with the help of metabolomics. Researchers can determine how plants react to environmental cues or genetic modifications by examining how metabolite profiles change under various crop stages. Metabolomics plays a major role in crop improvement and biotechnology. Integrating metabolomics data with other omics data (genomics, transcriptomics, and proteomics) provides a more comprehensive perspective of plant biology. This systems biology approach enables researchers to understand the complex interactions within organisms

Molecular characterization and SNP identification using genotyping-by-sequencing in high-yielding mutants of proso millet

Proso millet (Panicummiliaceum L.) is a short-duration C4 crop that is drought tolerant and nutritionally rich and can grow well in marginal lands. Though the crop has many climate-resilient traits like tolerance to drought and heat, its yield is lower than that of common cereals like rice, wheat, and maize. Being an underutilized crop, the molecular resources in the crop are limited. The main aim of the present study was to develop and characterize contrasting mutants for yield and generate functional genomic information for the trait in proso millet. Gamma irradiation-induced mutant population was screened to identify high-yielding mutants, which were evaluated up to M4 generation. One mutant with a dense panicle and high yield (ATL_hy) and one with a lax panicle and low yield (ATL_ly) along with the wild type were sequenced using the genotyping-by-sequencing approach. The variants detected as single nucleotide polymorphisms (SNPs) and insertions–deletions (InDels) were annotated against the reference genome of proso millet. Bioinformatic analyses using the National Center for Biotechnology Information (NCBI) and UniProt databases were performed to elucidate genetic information related to the SNP variations. A total of 25,901, 30,335, and 31,488 SNPs, respectively, were detected in the wild type, ATL_hy mutants, and ATL_ly mutants. The total number of functional SNPs identified in high-yielding and low-yielding mutants was 84 and 171, respectively. Two functional SNPs in the high-yielding mutant (ATL_hy) and one in the low-yielding mutant (ATL_ly) corresponded to the gene coding for “E3 ubiquitin-protein ligase UPL7”. Pathway mapping of the functional SNPs identified that two SNPs in ATL_ly were involved in the starch biosynthetic pathway coding for the starch synthase enzyme. This information can be further used in identifying genes responsible for various metabolic processes in proso millet and in designing useful genetic markers

Complete Genome Sequence Analysis of <i>Bacillus subtilis</i> Bbv57, a Promising Biocontrol Agent against Phytopathogens

Plant growth-promoting rhizobacteria (PGPR) are a group of root-associated beneficial bacteria emerging as one of the powerful agents in sustainable plant disease management. Among the PGPR, Bacillus sp. has become a popular biocontrol agent for controlling pests and the diseases of several crops of agricultural and horticultural importance. Understanding the molecular basis of the plant growth-promoting and biocontrol abilities of Bacillus spp. will allow us to develop multifunctional microbial consortia for sustainable agriculture. In our study, we attempted to unravel the genome complexity of the potential biocontrol agent Bacillus subtilis Bbv57 (isolated from the betelvine’s rhizosphere), available at TNAU, Coimbatore. A WGS analysis generated 26 million reads, and a de novo assembly resulted in the generation of 4,302,465 bp genome of Bacillus subtilis Bbv57 containing 4363 coding sequences (CDS), of which 4281 were functionally annotated. An analysis of 16S rRNA revealed its 100% identity to Bacillus subtilis IAM 12118. A detailed data analysis identified the presence of >100 CAZymes and nine gene clusters involved in the production of secondary metabolites that exhibited antimicrobial properties. Further, Bbv57 was found to harbor 282 unique genes in comparison with 19 other Bacillus strains, requiring further exploration

Dynamic Transcriptome Profiling of Mungbean Genotypes Unveil the Genes Respond to the Infection of Mungbean Yellow Mosaic Virus

Yellow mosaic disease (YMD), incited by mungbean yellow mosaic virus (MYMV), is a primary viral disease that reduces mungbean production in South Asia, especially in India. There is no detailed knowledge regarding the genes and molecular mechanisms conferring resistance of mungbean to MYMV. Therefore, disclosing the genetic and molecular bases related to MYMV resistance helps to develop the mungbean genotypes with MYMV resistance. In this study, transcriptomes of mungbean genotypes, VGGRU-1 (resistant) and VRM (Gg) 1 (susceptible) infected with MYMV were compared to those of uninfected controls. The number of differentially expressed genes (DEGs) in the resistant and susceptible genotypes was 896 and 506, respectively. Among them, 275 DEGs were common between the resistant and susceptible genotypes. Functional annotation of DEGs revealed that the DEGs belonged to the following categories defense and pathogenesis, receptor-like kinases; serine/threonine protein kinases, hormone signaling, transcription factors, and chaperons, and secondary metabolites. Further, we have confirmed the expression pattern of several DEGs by quantitative real-time PCR (qRT-PCR) analysis. Collectively, the information obtained in this study unveils the new insights into characterizing the MYMV resistance and paved the way for breeding MYMV resistant mungbean in the future

Dynamic transcriptome profiling of mungbean genotypes unveil the genes respond to the infection of mungbean yellow mosaic virus

Yellow mosaic disease (YMD), incited by mungbean yellow mosaic virus (MYMV), is a primary viral disease that reduces mungbean production in South Asia, especially in India. There is no detailed knowledge regarding the genes and molecular mechanisms conferring resistance of mungbean to MYMV. Therefore, disclosing the genetic and molecular bases related to MYMV resistance helps to develop the mungbean genotypes with MYMV resistance. In this study, transcriptomes of mungbean genotypes, VGGRU-1 (resistant) and VRM (Gg) 1 (susceptible) infected with MYMV were compared to those of uninfected controls. The number of differentially expressed genes (DEGs) in the resistant and susceptible genotypes was 896 and 506, respectively. Among them, 275 DEGs were common between the resistant and susceptible genotypes. Functional annotation of DEGs revealed that the DEGs belonged to the following categories defense and pathogenesis, receptor-like kinases; serine/threonine protein kinases, hormone signaling, transcription factors, and chaperons, and secondary metabolites. Further, we have confirmed the expression pattern of several DEGs by quantitative real-time PCR (qRT-PCR) analysis. Collectively, the information obtained in this study unveils the new insights into characterizing the MYMV resistance and paved the way for breeding MYMV resistant mungbean in the future