Metabolomics of sorghum roots during nitrogen stress reveals compromised metabolic capacity for salicylic acid biosynthesis

Sorghum (Sorghum bicolor [L.] Moench) is the fifth most productive cereal crop worldwide with some hybrids having high biomass yield traits making it promising for sustainable, economical biofuel production. To maximize biofuel feedstock yields, a more complete understanding of metabolic responses to low nitrogen (N) will be useful for incorporation in crop improvement efforts. In this study, 10 diverse sorghum entries (including inbreds and hybrids) were field-grown under low and full N conditions and roots were sampled at two time points for metabolomics and 16S amplicon sequencing. Roots of plants grown under low N showed altered metabolic profiles at both sampling dates including metabolites important in N storage and synthesis of aromatic amino acids. Complementary investigation of the rhizosphere microbiome revealed dominance by a single operational taxonomic unit (OTU) in an early sampling that was taxonomically assigned to the genus Pseudomonas. Abundance of this Pseudomonas OTU was significantly greater under low N in July and was decreased dramatically in September. Correlation of Pseudomonas abundance with root metabolites revealed a strong negative association with the defense hormone salicylic acid (SA) under full N but not under low N, suggesting reduced defense response. Roots from plants with N stress also contained reduced phenylalanine, a precursor for SA, providing further evidence for compromised metabolic capacity for defense response under low N conditions. Our findings suggest that interactions between biotic and abiotic stresses may affect metabolic capacity for plant defense and need to be concurrently prioritized as breeding programs become established for biofuels production on marginal soils

P63 targeted deletion under the FOXN1 promoter disrupts pre-and post-natal thymus development, function and maintenance as well as induces severe hair loss

Progressive immune deficiency of aging is characterized by severe thymic atrophy, contracted T cell repertoire, and poor immune function. p63 is critical for the proliferative potential of embryonic and adult stem cells, as well as thymic epithelial cells (TECs). Because p63 null mice experience rapid post-natal lethality due to epidermal and limb morphogenesis defects, studies to define a role for p63 expression in TEC biology focused on embryonic thymus development and in vitro experiments. Since post-natal thymic stromal development and function differs from that of the embryo, we assessed the impact of lineage-restricted p63 loss on pre- and post-natal murine TEC function by generating mice with a loss of p63 function targeted to TEC, termed p63 TECko mice. In adult p63 TECko mice, severe thymic hypoplasia was observed with a lack in a discernable segregation into medullary and cortical compartments and peripheral T cell lymphopenia. This profound thymic defect was seen in both neonatal as well as embryonic p63 TECko mice. In addition to TECs, p63 also plays in important role in the development of stratified epithelium of the skin; lack of p63 results in defects in skin epidermal stratification and differentiation. Interestingly, all adult p63 TECko mice lacked hair follicles despite having normal p63 expression in the skin. Together our results show a critical role of TEC p63 in thymic development and maintenance and show that p63 expression is critical for hair follicle formation

Multiple M. tuberculosis Phenotypes in Mouse and Guinea Pig Lung Tissue Revealed by a Dual-Staining Approach

A unique hallmark of tuberculosis is the granulomatous lesions formed in the lung. Granulomas can be heterogeneous in nature and can develop a necrotic, hypoxic core which is surrounded by an acellular, fibrotic rim. Studying bacilli in this in vivo microenvironment is problematic as Mycobacterium tuberculosis can change its phenotype and also become acid-fast negative. Under in vitro models of differing environments, M. tuberculosis alters its metabolism, transcriptional profile and rate of replication. In this study, we investigated whether these phenotypic adaptations of M. tuberculosis are unique for certain environmental conditions and if they could therefore be used as differential markers. Bacilli were studied using fluorescent acid-fast auramine-rhodamine targeting the mycolic acid containing cell wall, and immunofluorescence targeting bacterial proteins using an anti-M. tuberculosis whole cell lysate polyclonal antibody. These techniques were combined and simultaneously applied to M. tuberculosis in vitro culture samples and to lung sections of M. tuberculosis infected mice and guinea pigs. Two phenotypically different subpopulations of M. tuberculosis were found in stationary culture whilst three subpopulations were found in hypoxic culture and in lung sections. Bacilli were either exclusively acid-fast positive, exclusively immunofluorescent positive or acid-fast and immunofluorescent positive. These results suggest that M. tuberculosis exists as multiple populations in most conditions, even within seemingly a single microenvironment. This is relevant information for approaches that study bacillary characteristics in pooled samples (using lipidomics and proteomics) as well as in M. tuberculosis drug development

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019

Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019

Statistical methods for ChIP-seq and microbiome studies using next-generation DNA sequencing data

In this dissertation, we studied two different types of data generated by next-generation sequencing technologies. Chapter 2 is about analysis of ChIP-seq data with biological replicates to identify protein-binding sites. Chapters 3-4 are about analysis of microbiome data to estimate the causal effects of microbiome features on interesting outcomes in presence of confounding variables. ChIP-seq experiments aim to detect DNA-protein binding sites and require biological replication to draw inferential conclusions. However, there is no current consensus on how to analyze ChIP-seq data with biological replicates. Very few methodologies exist for the joint analysis of replicated ChIP-seq data, with approaches ranging from combining the results of analyzing replicates individually to joint modeling of all replicates. Combining the results of individual replicates analyzed separately can lead to reduced peak classification performance compared to joint modeling. Currently available methods for joint analysis may fail to control the false discovery rate at the nominal level. In Chapter 2, we propose BinQuasi, a peak caller for replicated ChIP-seq data, that jointly models biological replicates using a generalized linear model framework and employs a one-sided quasi-likelihood ratio test to detect peaks. When applied to simulated and real data, BinQuasi performs favorably compared to existing methods, including better control of false discovery rate than existing joint modeling approaches. BinQuasi offers a flexible approach to joint modeling of replicated ChIP-seq data which is preferable to combining the results of replicates analyzed individually. We created an R package called BinQuasi that is available at https://cran.r-project.org/package=BinQuasi. Microbiome studies have uncovered associations between microbes and human, animal, and plant health outcomes. This has led to an interest in identifying microbial interventions for treatment of disease and optimization of crop yields which will require the identification of individual relevant microbiome features. That task is challenging because of the high dimensionality of microbiome data and the confounding that results from the complex and dynamic interactions among host, environment, and microbiome. The performance of variable selection and estimation procedures may be unsatisfactory when there are differentially abundant features resulting from a categorical confounding variable. For microbiome studies with such a confounding structure, we propose a standardization approach in Chapter 3 to estimation of population effects of individual microbiome features. Due to the high dimensionality and confounding-induced correlation between features, we propose feature screening, selection, and estimation conditional on each stratum of the confounder. Comprehensive simulation studies are used to demonstrate the advantages of our approach in recovering relevant features. Utilizing a potential-outcomes framework, we outline assumptions required to ascribe causal, rather than associational, interpretations to the identified microbiome effects. We applied the proposed approach to an agricultural study of the rhizosphere microbiome of sorghum in which nitrogen fertilizer application is a confounding variable. We identified microbial taxa that are consistent with biological understanding of potential plant-microbe interactions. In Chapter 4, we present an inverse probability weighting approach to causal analysis of the effects of individual microbiome features in presence of continuous confounding variables. In simulated microbiome data, we show inverse probability weighting in marginal models provides microbiome effect estimates with lower bias and mean squared error than conditional regression adjustment for confounding. Our approach is demonstrated using an agricultural data set for identification of soil microbes with the potential to modulate biomass production in sorghum.</p

Analysis of the impact of indole-3-acetic acid (IAA) on gene expression during leaf senescence in Arabidopsis thaliana

Leaf senescence is an important developmental process for the plant life cycle. It is controlled by endogenous and environmental factors and can be posi- tively or negatively affected by plant growth regulators. It is characterised by major and significant changes in the patterns of gene expression. Auxin, especially indole-3- acetic acid (IAA), is a plant growth hormone that affects plant growth and development. The effect of IAA on leaf senescence is still unclear. In this study, we performed microarray analysis to investigate the role of IAA on gene expression during senescence in Arabidopsis thaliana. We sprayed IAA on plants at 3 different time points (27, 31 or 35 days after sowing). Following spraying, PSII activity of the eighth leaf was evaluated daily by measurement of chlorophyll fluorescence parameters. Our results show that PSII activity decreased following IAA application and the IAA treatment triggered different gene expression respon- ses in leaves of different ages