Retransformation of a male sterile barnase line with the barstar gene as an efficient alternative method to identify male sterile-restorer combinations for heterosis breeding

We report in this study, an improved method for identifying male sterile-restorer combinations using the barnase-barstar system of pollination control for heterosis breeding in crop plants, as an alternative to the conventional line × tester cross method. In this strategy, a transgenic male sterile barnase line was retransformed with appropriate barstar constructs. Double transformants carrying both the barnase and barstar genes were identified and screened for their male fertility status. Using this strategy, 66-90% of fertile retransformants (restored events) were obtained in Brassica juncea using two different barstar constructs. Restored events were analysed for their pollen viability and copy number of the barstar gene. Around 90% of the restored events showed high pollen viability and ~30% contained single copy integrations of the barstar gene. These observations were significantly different from those made in our earlier studies using line (barnase) × tester (barstar) crosses, wherein only two viable male sterile–restorer combinations were identified by screening 88 different cross-combinations. The retransformation strategy not only generated several independent restorers for a given male sterile line from a single transformation experiment but also identified potential restorers in the T0 generation itself leading to significant savings in time, cost and labour. Single copy restored plants with high pollen viability were selfed to segregate male sterile (barnase) and restorer (barstar) lines in the T1 progeny which could subsequently be diversified into appropriate combiners for heterosis breeding. This strategy will be particularly useful for crop plants where poor transformation frequencies and/or lengthy transformation protocols are a major limitation

QTL Landscape for Oil Content in Brassica juncea: Analysis in Multiple Bi-Parental Populations in High and “0” Erucic Background

Increasing oil content in oilseed mustard (Brassica juncea) is a major breeding objective—more so, in the lines that have “0” erucic acid content (< 2% of the seed oil) as earlier studies have shown negative pleiotropic effect of erucic acid loci on the oil content, both in oilseed mustard and rapeseed. We report here QTL analysis of oil content in eight different mapping populations involving seven different parents—including a high oil content line J8 (~49%). The parental lines of the mapping populations contained wide variation in oil content and erucic acid content. The eight mapping populations were categorized into two sets—five populations with individuals segregating for erucic acid (SE populations) and the remaining three with zero erucic acid segregants (ZE populations). Meta-analysis of QTL mapped in individual SE populations identified nine significant C-QTL, with two of these merging most of the major oil QTL that colocalized with the erucic acid loci on the linkage groups A08 and B07. QTL analysis of oil content in ZE populations revealed a change in the landscape of the oil QTL compared to the SE populations, in terms of altered allelic effects and phenotypic variance explained by ZE QTL at the “common” QTL and observation of “novel” QTL in the ZE background. The important loci contributing to oil content variation, identified in the present study could be used in the breeding programmes for increasing the oil content in high erucic and “0” erucic backgrounds

Comparative mapping of Brassica juncea and Arabidopsis thaliana using Intron Polymorphism (IP) markers: homoeologous relationships, diversification and evolution of the A, B and C Brassica genomes

Background: Extensive mapping efforts are currently underway for the establishment of comparative genomics between the model plant, Arabidopsis thaliana and various Brassica species. Most of these studies have deployed RFLP markers, the use of which is a laborious and time-consuming process. We therefore tested the efficacy of PCR-based Intron Polymorphism (IP) markers to analyze genome-wide synteny between the oilseed crop, Brassica juncea (AABB genome) and A. thaliana and analyzed the arrangement of 24 (previously described) genomic block segments in the A, B and C Brassica genomes to study the evolutionary events contributing to karyotype variations in the three diploid Brassica genomes. Results: IP markers were highly efficient and generated easily discernable polymorphisms on agarose gels. Comparative analysis of the segmental organization of the A and B genomes of B. juncea (present study) with the A and B genomes of B. napus and B. nigra respectively (described earlier), revealed a high degree of colinearity suggesting minimal macro-level changes after polyploidization. The ancestral block arrangements that remained unaltered during evolution and the karyotype rearrangements that originated in the Oleracea lineage after its divergence from Rapa lineage were identified. Genomic rearrangements leading to the gain or loss of one chromosome each between the A-B and A-C lineages were deciphered. Complete homoeology in terms of block organization was found between three linkage groups (LG) each for the A-B and A-C genomes. Based on the homoeology shared between the A, B and C genomes, a new nomenclature for the B genome LGs was assigned to establish uniformity in the international Brassica LG nomenclature code. Conclusion: IP markers were highly effective in generating comparative relationships between Arabidopsis and various Brassica species. Comparative genomics between the three Brassica lineages established the major rearrangements, translocations and fusions pivotal to karyotype diversification between the A, B and C genomes of Brassica species. The inter-relationships established between the Brassica lineages vis-à-vis Arabidopsis would facilitate the identification and isolation of candidate genes contributing to traits of agronomic value in crop Brassicas and the development of unified tools for Brassica genomics

Smoking, alcohol use disorder and tuberculosis treatment outcomes: A dual co-morbidity burden that cannot be ignored

BackgroundMore than 20% of tuberculosis (TB) disease worldwide may be attributable to smoking and alcohol abuse. India is the second largest consumer of tobacco products, a major consumer of alcohol particularly among males, and has the highest burden of TB globally. The impact of increasing tobacco dose, relevance of alcohol misuse and past versus current or never smoking status on TB treatment outcomes remain inadequately defined.MethodsWe conducted a multi-centric prospective cohort study of newly diagnosed adult pulmonary TB patients initiated on TB treatment and followed for a minimum of 6 months to assess the impact of smoking status with or without alcohol abuse on treatment outcomes. Smokers were defined as never smokers, past smokers or current smokers. Alcohol Use Disorder Identification Test (AUDIT) scores were used to assess alcohol misuse. The association between smoking status and treatment outcomes was assessed in univariate and multivariate random effects poisson regression models.ResultsOf 455 enrolled, 129 (28%) had a history of smoking with 94 (20%) current smokers and 35 (8%) past smokers. Unfavourable treatment outcomes were significantly higher among past and current smokers as compared to never smokers. Specifically, the risk of treatment failure was significantly higher among past smokers (aIRR = 2.66, 95% CI: 1.41-4.90, p = 0.002), recurrent TB among current smokers (aIRR = 2.94, 95% CI: 1.30-6.67, p = 0.010) and death among both past (2.63, 95% CI: 1.11-6.24, p = 0.028) and current (aIRR = 2.59, 95% CI: 1.29-5.18, p = 0.007) smokers. Furthermore, the combined effect of alcohol misuse and smoking on unfavorable treatment outcomes was significantly higher among past smokers (aIRR: 4.67, 95% CI: 2.17-10.02, pConclusionPast and current smoking along with alcohol misuse have combined effects on increasing the risk of unfavourable TB treatment outcomes. Innovative interventions that can readily address both co-morbidities are urgently needed

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Identification of the putative cytoplasmic donor of a CMS system in Brassica juncea

Chloroplast (cp) and mitochondrial (mt) DNA restriction profiles of a cytoplasmic male sterile (CMS) line of Brassica juncea and its maintainer line were compared and found to be markedly different. Comparison of cpDNA restriction profiles of fifty different species of genus Brassica and some allied genera showed that the cpDNA profiles of CMS lines were similar to that of B. tournefortii for twenty different restriction endonucleases. This CMS system is thus not of spontaneous origin as reported earlier, but is alloplasmic in nature. Comparison of restriction profiles of mtDNA of B. tournefortii and CMS lines revealed some differences which might either be due to changes in DNA pattern during the transfer, or, due to the cytoplasm coming from a B. tournefortii line different from the one used in this study

Development of transgenic <i>Brassica juncea</i> lines for reduced seed sinapine content by perturbing phenylpropanoid pathway genes - Fig 3

<p>Expression analyses of (a) <i>SGT</i> and (b) <i>SCT</i> gene at different developmental stages of the seeds (10 to 70 DAP) of <i>B</i>. <i>juncea</i> cv. Varuna. The data are average of three biological replicates (±SD).</p

BjuB.CYP79F1 Regulates Synthesis of Propyl Fraction of Aliphatic Glucosinolates in Oilseed Mustard Brassica juncea: Functional Validation through Genetic and Transgenic Approaches.

Among the different types of methionine-derived aliphatic glucosinolates (GS), sinigrin (2-propenyl), the final product in 3C GS biosynthetic pathway is considered very important as it has many pharmacological and therapeutic properties. In Brassica species, the candidate gene regulating synthesis of 3C GS remains ambiguous. Earlier reports of GSL-PRO, an ortholog of Arabidopsis thaliana gene At1g18500 as a probable candidate gene responsible for 3C GS biosynthesis in B. napus and B. oleracea could not be validated in B. juncea through genetic analysis. In this communication, we report the isolation and characterization of the gene CYP79F1, an ortholog of A. thaliana gene At1g16410 that is involved in the first step of core GS biosynthesis. The gene CYP79F1 in B. juncea showed presence-absence polymorphism between lines Varuna that synthesizes sinigrin and Heera virtually free from sinigrin. Using this presence-absence polymorphism, CYP79F1 was mapped to the previously mapped 3C GS QTL region (J16Gsl4) in the LG B4 of B. juncea. In Heera, the gene was observed to be truncated due to an insertion of a ~4.7 kb TE like element leading to the loss of function of the gene. Functional validation of the gene was carried out through both genetic and transgenic approaches. An F2 population segregating only for the gene CYP79F1 and the sinigrin phenotype showed perfect co-segregation. Finally, genetic transformation of a B. juncea line (QTL-NIL J16Gsl4) having high seed GS but lacking sinigrin with the wild type CYP79F1 showed the synthesis of sinigrin validating the role of CYP79F1 in regulating the synthesis of 3C GS in B. juncea

Three dimensional structure and binding of 1-<i>O</i>-sinapoylglucose with SCT enzyme.

<p>(a) Three dimensional structure of SCT enzyme. The core domain is shown in magenta, helical sub-domain in yellow and the maturation sub-domain is highlighted in dark blue. The catalytic triad residues are shown in green sticks. (b) The binding mode of 1-O-sinapoylglucose in deep cavity of SCT enzyme. The substrate 1-<i>O</i>-sinapoylglucose is represented by green stick whereas the cavity of the SCT is shown in cyan colour cartoon. The catalytic triad residues are shown as red sticks. The substrate molecule is hydrogen bonded (yellow dotted lines) with S179, Y180 and G80.</p