24 research outputs found
Comprehensive analysis of temporal alterations in cellular proteome of bacillus subtilis under curcumin treatment
Curcumin is a natural dietary compound with antimicrobial activity against various gram positive and negative bacteria. This study aims to investigate the proteome level alterations in Bacillus subtilis due to curcumin treatment and identification of its molecular/cellular targets to understand the mechanism of action. We have performed a comprehensive proteomic analysis of B. subtilis AH75 strain at different time intervals of curcumin treatment (20, 60 and 120 min after the drug exposure, three replicates) to compare the protein expression profiles using two complementary quantitative proteomic techniques, 2D-DIGE and iTRAQ. To the best of our knowledge, this is the first comprehensive longitudinal investigation describing the effect of curcumin treatment on B. subtilis proteome. The proteomics analysis revealed several interesting targets such UDP-N-acetylglucosamine 1-carboxyvinyltransferase 1, putative septation protein SpoVG and ATP-dependent Clp protease proteolytic subunit. Further, in silico pathway analysis using DAVID and KOBAS has revealed modulation of pathways related to the fatty acid metabolism and cell wall synthesis, which are crucial for cell viability. Our findings revealed that curcumin treatment lead to inhibition of the cell wall and fatty acid synthesis in addition to differential expression of many crucial proteins involved in modulation of bacterial metabolism. Findings obtained from proteomics analysis were further validated using 5-cyano-2,3-ditolyl tetrazolium chloride (CTC) assay for respiratory activity, resazurin assay for metabolic activity and membrane integrity assay by potassium and inorganic phosphate leakage measurement. The gene expression analysis of selected cell wall biosynthesis enzymes has strengthened the proteomics findings and indicated the major effect of curcumin on cell division
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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
Understanding HIV-Mycobacteria synergism through comparative proteomics of intra-phagosomal mycobacteria during mono- and HIV co-infection
Mycobacterium tuberculosis (Mtb) is the most common co-infection in HIV patients and a serious co-epidemic. Apart from increasing the risk of reactivation of latent tuberculosis (TB), HIV infection also permits opportunistic infection of environmental non-pathogenic mycobacteria. To gain insights into mycobacterial survival inside host macrophages and identify mycobacterial proteins or processes that influence HIV propagation during co-infection, we employed proteomics approach to identify differentially expressed intracellular mycobacterial proteins during mono- and HIV co-infection of human THP-1 derived macrophage cell lines. Of the 92 proteins identified, 30 proteins were upregulated during mycobacterial mono-infection and 40 proteins during HIV-mycobacteria co-infection. We observed down-regulation of toxin-antitoxin (TA) modules, up-regulation of cation transporters, Type VII (Esx) secretion systems, proteins involved in cell wall lipid or protein metabolism, glyoxalate pathway and branched chain amino-acid synthesis during co-infection. The bearings of these mycobacterial factors or processes on HIV propagation during co-infection, as inferred from the proteomics data, were validated using deletion mutants of mycobacteria. The analyses revealed mycobacterial factors that possibly via modulating the host environment, increased viral titers during co-infection. The study provides new leads for investigations towards hitherto unknown molecular mechanisms explaining HIV-mycobacteria synergism, helping address diagnostics and treatment challenges for effective co-epidemic management
Comprehensive analysis of temporal alterations in cellular proteome of Bacillus subtilis under curcumin treatment.
Curcumin is a natural dietary compound with antimicrobial activity against various gram positive and negative bacteria. This study aims to investigate the proteome level alterations in Bacillus subtilis due to curcumin treatment and identification of its molecular/cellular targets to understand the mechanism of action. We have performed a comprehensive proteomic analysis of B. subtilis AH75 strain at different time intervals of curcumin treatment (20, 60 and 120 min after the drug exposure, three replicates) to compare the protein expression profiles using two complementary quantitative proteomic techniques, 2D-DIGE and iTRAQ. To the best of our knowledge, this is the first comprehensive longitudinal investigation describing the effect of curcumin treatment on B. subtilis proteome. The proteomics analysis revealed several interesting targets such UDP-N-acetylglucosamine 1-carboxyvinyltransferase 1, putative septation protein SpoVG and ATP-dependent Clp protease proteolytic subunit. Further, in silico pathway analysis using DAVID and KOBAS has revealed modulation of pathways related to the fatty acid metabolism and cell wall synthesis, which are crucial for cell viability. Our findings revealed that curcumin treatment lead to inhibition of the cell wall and fatty acid synthesis in addition to differential expression of many crucial proteins involved in modulation of bacterial metabolism. Findings obtained from proteomics analysis were further validated using 5-cyano-2,3-ditolyl tetrazolium chloride (CTC) assay for respiratory activity, resazurin assay for metabolic activity and membrane integrity assay by potassium and inorganic phosphate leakage measurement. The gene expression analysis of selected cell wall biosynthesis enzymes has strengthened the proteomics findings and indicated the major effect of curcumin on cell division
Mass spectrometry and bioinformatics analysis data
2DE and 2D-DIGE based proteomics analysis of serum from women with endometriosis revealed several proteins to be dysregulated. A complete list of these proteins along with their mass spectrometry data and subsequent bioinformatics analysis are presented here. The data is related to “Investigation of serum proteome alterations in human endometriosis” by Dutta et al. [1]
Investigation of serum proteome alterations in human endometriosis
Endometriosis is a common benign gynecological disease, characterized by proliferation of functional endometrial glands and stroma outside the uterine cavity. The present study involves investigation of alterations in the serum proteome of endometriosis patients compared to healthy controls using 2DE and 2D-DIGE combined with MALDI TOF/TOF-MS. Comparison of serum proteome of endometriosis patients and healthy subjects revealed 25 significant differentially expressed proteins. Gene ontology and network analysis, performed using PANTHER, DAVID, WebGestalt and STRING, revealed that the differentially expressed proteins are majorly involved in response to stimulus, immune system, metabolic, localization and cellular processes. For serum diagnostic marker identification, several robust statistical screening procedures were applied to identify the set of the most significant proteins responsible for successful diagnosis of different endometriosis stages. Partial least squares (PLS) based marker selection tool and orthogonal partial least squares-discriminant analysis (OPLS-DA) were used to identify the most significant proteins for disease prediction. Western blotting validation in a separate cohort of patients revealed that haptoglobin (HP), Ig kappa chain C region (IGKC), alpha-1B-glycoprotein (A1BG) can be considered effective serum protein markers for the diagnosis of Stage II, III and IV endometriosis. For diagnosis of Stage I, only IGKC and HP seemed promising
Quantitative profiles of the differentially expressed proteins involved in diverse biological processes identified in iTRAQ-based quantitative proteomics analysis using LTQ-Orbitrap.
<p><b>(A)</b> Peptidoglycan biosynthesis, <b>(B)</b> Fatty acid synthesis, <b>(C)</b> Cell division and sporulation, <b>(D)</b> TCA cycle, <b>(E)</b> Stress response and <b>(F)</b> Nucleotide biosynthesis. Data from QTOF is provided in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120620#pone.0120620.s002" target="_blank">S2 Fig</a>.</p
(A) CTC staining and flow cytometric analysis for respiratory activity.
<p>Graphical representation of CTC mean intensity (PE-Texas Red-A) <i>vs</i>. FSC-A obtained in the FACS analysis of control, 20, 60 and 120 min curcumin treated samples and negative control. Both dot plot and histogram representations are displayed for each sample. <b>(B) & (C)</b> Potassium and phosphorus leakage assay; curcumin (20 and 40 μM) was added to the <i>B</i>. <i>subtilis</i> in HEPES-glucose medium and K<sup>+</sup> and P levels were measured at 20, 60, 90 and 120 min time intervals, and also in the untreated control and positive control (heated at 70° C for 30 min) samples using ICP-AES and data was normalized with baseline HEPES-glucose medium. <b>(D)</b> Metabolic activity assay using resazurin. 20 min curcumin treatment has showed lower metabolic activity whereas the metabolic activity increased as time of exposure increased to 60 min and 120 min as compared to control. * indicates <i>p</i> < 0.05. <b>(E)</b> Gene expression analysis using RT-PCR for <i>murAA</i>, <i>spoVG</i> and <i>ftsH</i> genes and the relative expression was calculated by taking mean C<sub>t</sub> values from triplicate runs. * indicates <i>p</i> < 0.05 and ** indicates <i>p</i> < 0.001. <b>(F)</b> Physical interaction analysis of curumin with <i>B</i>. <i>subtilis</i> FtsZ immobilized on CM-5 sensor chip. The interaction was monitored by measuring the response unit and the response unit was increased as the concentration of curcumin increased. Both sensorgram and the bar diagram showing the binding to FtsZ was displayed.</p
List of differentially expressed proteins in <i>B</i>. <i>subtilis</i> due to curcumin treatment obtained from DIGE analysis and its comparison with iTRAQ analysis<sup>$</sup>.
<p> This is a partial list having selected candidates with >1.5 fold change and complete list is provided in S2 Table
#: Proteins unique in DIGE, Bold: Same trend in both DIGE and iTRAQ (Orbitrap data);
* or NS: No significant change in iTRAQ in Orbitrap data (less than 1.2 fold up and down);
NI- Not identified in iTRAQ analysis.
List of differentially expressed proteins in B. subtilis due to curcumin treatment obtained from DIGE analysis and its comparison with iTRAQ analysis
</sup></a>.</p