Induction of apoptosis in HeLa cells by chloroform fraction of seed extracts of Nigella sativa

<p>Abstract</p> <p>Background</p> <p>Cancer remains one of the most dreaded diseases causing an astonishingly high death rate, second only to cardiac arrest. The fact that conventional and newly emerging treatment procedures like chemotherapy, catalytic therapy, photodynamic therapy and radiotherapy have not succeeded in reverting the outcome of the disease to any drastic extent, has made researchers investigate alternative treatment options. The extensive repertoire of traditional medicinal knowledge systems from various parts of the world are being re-investigated for their healing properties. This study progresses in the direction of identifying component(s) from <it>Nigella sativa </it>with anti cancer acitivity. In the present study we investigated the efficacy of Organic extracts of <it>Nigella sativa </it>seed powder for its clonogenic inhibition and induction of apoptosis in HeLa cancer cell.</p> <p>Results</p> <p>Methanolic, n-Hexane and chloroform extracts of <it>Nigella sativa </it>seedz effectively killed HeLa cells. The IC₅₀values of methanolic, n-hexane, and chloroform extracts of <it>Nigella sativa </it>were 2.28 μg/ml, 2.20 μg/ml and 0.41 ng/ml, respectively. All three extracts induced apoptosis in HeLa cells. Apoptosis was confirmed by DNA fragmentation, western blot and terminal transferase-mediated dUTP-digoxigenin-end labeling (TUNEL) assay.</p> <p>Conclusion</p> <p>Western Blot and TUNEL results suggested that <it>Nigella sativa </it>seed extracts regulated the expression of pro- and anti- apoptotic genes, indicating its possible development as a potential therapeutic agent for cervical cancer upon further investigation.</p

Differential Expression Profile and Genetic Variants of MicroRNAs Sequences in Breast Cancer Patients

The technology available for cancer diagnosis and prognosis is not yet satisfactory at the molecular level, and requires further improvements. Micro RNAs (miRNAs) have been recently reported as useful biomarkers in diseases including cancer. We performed a miRNA expression profiling study using peripheral blood from breast cancer patients to detect and identify characteristic patterns. A total of 100 breast cancer patients and 89 healthy patients were recruited for miRNA genotyping and expression profiling. We found that hs-miR-196a2 in premenopausal patients, and hs-miR-499, hs-miR-146a and hs-miR-196a2 in postmenopausal patients, may discriminate breast cancer patients from healthy individuals. In addition, we found a significant association between two microRNA polymorphisms (hs-miR-196a2 and hs-miR-499) and breast cancer risk. However, no significant association between the hs-miR-146a gene and breast cancer risk was found. In summary, the study demonstrates that peripheral blood miRNAs and their expression and genotypic profiles can be developed as biomarkers for early diagnosis and prognosis of breast cancer

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

Affinity of estrogens for human progesterone receptor A and B monomers and risk of breast cancer: a comparative molecular modeling study

Tarique N Hasan1,4, Leena Grace B2, Tariq A Masoodi3,5, Gowhar Shafi4 , Ali A. Alshatwi4, P Sivashanmugham31Department of Biotechnology, Bharathiar University, Coimbator, TN, India; 2Department of Biotechnology, V. M. K. V. College of Engineering, Salem, TN, India; 3Department of Bioinformatics, Jamal Mohammed College, Bharathidasan University, Tiruchirappalli, India; 4Molecular Cancer Biology Laboratory, Department of Food Science and Nutrition, College of Food and Agricultural Sciences; 5Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Saudi ArabiaBackground: The human progesterone receptor (hPR) belongs to the steroid receptor family. It may be found as monomers (A and B) and or as a dimer (AB). hPR is regarded as the prognostic biomarker for breast cancer. In a cellular dimer system, AB is the dominant species in most cases. However, when a cell coexpresses all three isoforms of hPR, the complexity of the action of this receptor increases. For example, hPR A suppresses the activity of hPR B, and the ratio of hPR A to hPR B may determine the physiology of a breast tumor. Also, persistent exposure of hPRs to nonendogenous ligands is a common risk factor for breast cancer. Hence we aimed to study progesterone and some nonendogenous ligand interactions with hPRs and their molecular docking.Methods and results: A pool of steroid derivatives, namely, progesterone, cholesterol, testosterone, testolectone, estradiol, estrone, norethindrone, exemestane, and norgestrel, was used for this in silico study. Dockings were performed on AutoDock 4.2. We found that estrogens, including estradiol and estrone, had a higher affinity for hPR A and B monomers in comparison with the dimer, hPR AB, and that of the endogenous progesterone ligand. hPR A had a higher affinity to all the docked ligands than hPR B.Conclusion: This study suggests that the exposure of estrogens to hPR A as well as hPR B, and more particularly to hPR A alone, is a risk factor for breast cancer.Keywords: human progesterone receptor, breast cancer, steroid derivatives, estrogens, molecular dockin

Identification of functional SNPs in BARD1 gene and in silico analysis of damaging SNPs: based on data procured from dbSNP database.

The BARD1 gene encodes for the BRCA1-associated RING domain (BARD1) protein. Germ line and somatic mutations in BARD1 are found in sporadic breast, ovarian and uterine cancers. There is a plethora of single nucleotide polymorphisms (SNPs) which may or may not be involved in the onset of female cancers. Hence, before planning a larger population study, it is advisable to sort out the possible functional SNPs. To accomplish this goal, data available in the dbSNP database and different computer programs can be used. To the best of our knowledge, until now there has been no such study on record for the BARD1 gene. Therefore, this study was undertaken to find the functional nsSNPs in BARD1.2.85% of all SNPs in the dbSNP database were present in the coding regions. SIFT predicted 11 out of 50 nsSNPs as not tolerable and PolyPhen assessed 27 out of 50 nsSNPs as damaging. FastSNP revealed that the rs58253676 SNP in the 3' UTR may have splicing regulator and enhancer functions. In the 5' UTR, rs17489363 and rs17426219 may alter the transcriptional binding site. The intronic region SNP rs67822872 may have a medium-high risk level. The protein structures 1JM7, 3C5R and 2NTE were predicted by PDBSum and shared 100% similarity with the BARD1 amino acid sequence. Among the predicted nsSNPs, rs4986841, rs111367604, rs13389423 and rs139785364 were identified as deleterious and damaging by the SIFT and PolyPhen programs. Additionally, I-Mutant showed a decrease in stability for these nsSNPs upon mutation. Finally, the ExPASy-PROSIT program revealed that the predicted deleterious mutations are contained in the ankyrin ring and BRCT domains.Using the available bioinformatics tools and the data present in the dbSNP database, the four nsSNPs, rs4986841, rs111367604, rs13389423 and rs139785364, were identified as deleterious, reducing the protein stability of BARD1. Hence, these SNPs can be used for the larger population-based studies of female cancers

The available PDB structure for the BARD1 gene with a similarity (100%) with BARD1 FASTA sequence at PDBsum.

<p>The available PDB structure for the BARD1 gene with a similarity (100%) with BARD1 FASTA sequence at PDBsum.</p