Automated characterization of cell shape changes during amoeboid motility by skeletonization

<p>Abstract</p> <p>Background</p> <p>The ability of a cell to change shape is crucial for the proper function of many cellular processes, including cell migration. One type of cell migration, referred to as amoeboid motility, involves alternating cycles of morphological expansion and retraction. Traditionally, this process has been characterized by a number of parameters providing global information about shape changes, which are insufficient to distinguish phenotypes based on local pseudopodial activities that typify amoeboid motility.</p> <p>Results</p> <p>We developed a method that automatically detects and characterizes pseudopodial behavior of cells. The method uses skeletonization, a technique from morphological image processing to reduce a shape into a series of connected lines. It involves a series of automatic algorithms including image segmentation, boundary smoothing, skeletonization and branch pruning, and takes into account the cell shape changes between successive frames to detect protrusion and retraction activities. In addition, the activities are clustered into different groups, each representing the protruding and retracting history of an individual pseudopod.</p> <p>Conclusions</p> <p>We illustrate the algorithms on movies of chemotaxing <it>Dictyostelium </it>cells and show that our method makes it possible to capture the spatial and temporal dynamics as well as the stochastic features of the pseudopodial behavior. Thus, the method provides a powerful tool for investigating amoeboid motility.</p

The Escherichia coli K-12 ORFeome: a resource for comparative molecular microbiology

<p>Abstract</p> <p>Background</p> <p>Systems biology and functional genomics require genome-wide datasets and resources. Complete sets of cloned open reading frames (ORFs) have been made for about a dozen bacterial species and allow researchers to express and study complete proteomes in a high-throughput fashion.</p> <p>Results</p> <p>We have constructed an open reading frame (ORFeome) collection of 3974 or 94% of the known <it>Escherichia coli </it>K-12 ORFs in Gateway^®entry vector pENTR/Zeo. The collection has been used for protein expression and protein interaction studies. For example, we have compared interactions among YgjD, YjeE and YeaZ proteins in <it>E. coli</it>, <it>Streptococcus pneumoniae</it>, and <it>Staphylococcus aureus</it>. We also compare this ORFeome with other Gateway-compatible bacterial ORFeomes and show its utility for comparative functional genomics.</p> <p>Conclusions</p> <p>The <it>E. coli </it>ORFeome provides a useful resource for functional genomics and other areas of protein research in a highly flexible format. Our comparison with other ORFeomes makes comparative analyses straighforward and facilitates direct comparisons of many proteins across many genomes.</p

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

An investigation of the role of the Frat oncogene in Wnt signalling and glycogen synthase kinase-3 regulation

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

2010 Automated characterization of cell shape changes during amoeboid motility by skeletonization

Abstract Background: The ability of a cell to change shape is crucial for the proper function of many cellular processes, including cell migration. One type of cell migration, referred to as amoeboid motility, involves alternating cycles of morphological expansion and retraction. Traditionally, this process has been characterized by a number of parameters providing global information about shape changes, which are insufficient to distinguish phenotypes based on local pseudopodial activities that typify amoeboid motility. Results: We developed a method that automatically detects and characterizes pseudopodial behavior of cells. The method uses skeletonization, a technique from morphological image processing to reduce a shape into a series of connected lines. It involves a series of automatic algorithms including image segmentation, boundary smoothing, skeletonization and branch pruning, and takes into account the cell shape changes between successive frames to detect protrusion and retraction activities. In addition, the activities are clustered into different groups, each representing the protruding and retracting history of an individual pseudopod. Conclusions: We illustrate the algorithms on movies of chemotaxing Dictyostelium cells and show that our method makes it possible to capture the spatial and temporal dynamics as well as the stochastic features of the pseudopodial behavior. Thus, the method provides a powerful tool for investigating amoeboid motility

Frat is dispensable for canonical Wnt signaling in mammals

Wnt-signal transduction through β-catenin is thought to require the inhibition of GSK3 by Frat/GBP. To investigate the role of Frat in mammalian development, we have generated mice with targeted mutations in all three murine Frat homologs. We show that Frat is normally expressed at sites of active Wnt signaling. Surprisingly, Frat-deficient mice do not display gross abnormalities. Moreover, canonical Wnt signaling in primary cells is unaffected by the loss of Frat. These studies show that Frat is not an essential component of the canonical Wnt pathway in higher organisms, despite the strict requirement of Frat/GBP for maternal Wnt signaling in Xenopus

tsunami, the Dictyostelium homolog of the Fused kinase, is required for polarization and chemotaxis

In a forward genetic screen for chemotaxis mutants in Dictyostelium discoideum, we identified a loss-of-function mutation, designated tsunami, encoding a homolog of the Fused kinase. Cells lacking tsuA function could not effectively perform chemotaxis and were unable to become polarized or correctly orient pseudopods in chemotactic gradients. While tsuA− cells were able to couple receptor occupancy to phosphatidylinositol (3,4,5) trisphosphate (PIP3) production and actin polymerization, the PIP3 response was prolonged and basal F-actin levels were increased. Interestingly, TsuA localizes to the microtubule network and puncta mainly found at the cell periphery. Analysis of the gene uncovered a novel C-terminal domain that we designated the Tsunami Homology (TH) domain. Both the kinase domain and the TH domain are required to rescue the phenotypic defects of tsuA− cells. While kinase activity is not required for localization to microtubules, the TH domain is essential. Thus, localization of kinase activity to microtubules is critical for TsuA function. We propose that functions in association with the microtubule network may underlie the divergent roles of Fused kinase proteins in different organisms

Identification of the Axin and Frat binding region of glycogen synthase kinase-3

Glycogen synthase kinase-3 (GSK-3) is a key component of several signaling pathways including those regulated by Wnt and insulin ligands. Specificity in GSK-3 signaling is thought to involve interactions with scaffold proteins that localize GSK-3 regulators and substrates. This report shows that GSK-3 forms a low affinity homodimer that is disrupted by binding to Axin and Frat. Based on the crystal structure of GSK-3, we have used surface-scanning mutagenesis to identify residues that differentially affect GSK-3 interactions. Mutations that disrupt Frat and Axin cluster at the dimer interface explaining their effect on homodimer formation. Loss of the Axin binding site blocks the ability of dominant negative GSK-3 to cause axis duplication in Xenopus embryos. The Axin binding site is conserved within all GSK-3 proteins, and its loss affects both cell motility and gene expression in the nonmetazoan,Dictyostelium. Surprisingly, we find no genetic interaction between a non-Axin-binding GSK-3 mutant and T-cell factor activity, arguing that Axin interactions alone cannot explain the regulation of T-cell factor-mediated gene expression