Structural insight into molecular mechanism of poly (ethylene terephthalate) degradation

Plastics, including poly(ethylene terephthalate) (PET), possess many desirable characteristics and thus are widely used in daily life. However, non-biodegradability, once thought to be an advantage offered by plastics, is causing major environmental problem. Recently, a PET-degrading bacterium, Ideonella sakaiensis, was identified and suggested for possible use in degradation and/or recycling of PET. However, the molecular mechanism of PET degradation is not known. Here we report the crystal structure of I. sakaiensis PETase (IsPETase) at 1.5 angstrom resolution. IsPETase has a Ser-His-Asp catalytic triad at its active site and contains an optimal substrate binding site to accommodate four monohydroxyethyl terephthalate (MHET) moieties of PET. Based on structural and site-directed mutagenesis experiments, the detailed process of PET degradation into MHET, terephthalic acid, and ethylene glycol is suggested. Moreover, other PETase candidates potentially having high PET-degrading activities are suggested based on phylogenetic tree analysis of 69 PETase-like proteins

Predicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell lines

<p>Abstract</p> <p>Background</p> <p>Drugs can influence the whole metabolic system by targeting enzymes which catalyze metabolic reactions. The existence of interactions between drugs and metabolic reactions suggests a potential way to discover drug targets.</p> <p>Results</p> <p>In this paper, we present a computational method to predict new targets for approved anti-cancer drugs by exploring drug-reaction interactions. We construct a Drug-Reaction Network to provide a global view of drug-reaction interactions and drug-pathway interactions. The recent reconstruction of the human metabolic network and development of flux analysis approaches make it possible to predict each metabolic reaction's cell line-specific flux state based on the cell line-specific gene expressions. We first profile each reaction by its flux states in NCI-60 cancer cell lines, and then propose a kernel k-nearest neighbor model to predict related metabolic reactions and enzyme targets for approved cancer drugs. We also integrate the target structure data with reaction flux profiles to predict drug targets and the area under curves can reach 0.92.</p> <p>Conclusions</p> <p>The cross validations using the methods with and without metabolic network indicate that the former method is significantly better than the latter. Further experiments show the synergism of reaction flux profiles and target structure for drug target prediction. It also implies the significant contribution of metabolic network to predict drug targets. Finally, we apply our method to predict new reactions and possible enzyme targets for cancer drugs.</p

Lysosomes in iron metabolism, ageing and apoptosis

The lysosomal compartment is essential for a variety of cellular functions, including the normal turnover of most long-lived proteins and all organelles. The compartment consists of numerous acidic vesicles (pH ∼4 to 5) that constantly fuse and divide. It receives a large number of hydrolases (∼50) from the trans-Golgi network, and substrates from both the cells’ outside (heterophagy) and inside (autophagy). Many macromolecules contain iron that gives rise to an iron-rich environment in lysosomes that recently have degraded such macromolecules. Iron-rich lysosomes are sensitive to oxidative stress, while ‘resting’ lysosomes, which have not recently participated in autophagic events, are not. The magnitude of oxidative stress determines the degree of lysosomal destabilization and, consequently, whether arrested growth, reparative autophagy, apoptosis, or necrosis will follow. Heterophagy is the first step in the process by which immunocompetent cells modify antigens and produce antibodies, while exocytosis of lysosomal enzymes may promote tumor invasion, angiogenesis, and metastasis. Apart from being an essential turnover process, autophagy is also a mechanism by which cells will be able to sustain temporary starvation and rid themselves of intracellular organisms that have invaded, although some pathogens have evolved mechanisms to prevent their destruction. Mutated lysosomal enzymes are the underlying cause of a number of lysosomal storage diseases involving the accumulation of materials that would be the substrate for the corresponding hydrolases, were they not defective. The normal, low-level diffusion of hydrogen peroxide into iron-rich lysosomes causes the slow formation of lipofuscin in long-lived postmitotic cells, where it occupies a substantial part of the lysosomal compartment at the end of the life span. This seems to result in the diversion of newly produced lysosomal enzymes away from autophagosomes, leading to the accumulation of malfunctioning mitochondria and proteins with consequent cellular dysfunction. If autophagy were a perfect turnover process, postmitotic ageing and several age-related neurodegenerative diseases would, perhaps, not take place

An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves.

Patients with cancer have been shown to have increased risk of COVID-19 severity. We previously built and validated the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to predict the likely severity of COVID-19 in patients with active cancer who present to hospital. We assessed the differences in presentation and outcomes of patients with cancer and COVID-19, depending on the wave of the pandemic. We examined differences in features at presentation and outcomes in patients worldwide, depending on the waves of the pandemic: wave 1 D614G (n = 1430), wave 2 Alpha (n = 475), and wave 4 Omicron variant (n = 63, UK and Spain only). The performance of CORONET was evaluated on 258, 48, and 54 patients for each wave, respectively. We found that mortality rates were reduced in subsequent waves. The majority of patients were vaccinated in wave 4, and 94% were treated with steroids if they required oxygen. The stages of cancer and the median ages of patients significantly differed, but features associated with worse COVID-19 outcomes remained predictive and did not differ between waves. The CORONET tool performed well in all waves, with scores in an area under the curve (AUC) of &gt;0.72. We concluded that patients with cancer who present to hospital with COVID-19 have similar features of severity, which remain discriminatory despite differences in variants and vaccination status. Survival improved following the first wave of the pandemic, which may be associated with vaccination and the increased steroid use in those patients requiring oxygen. The CORONET model demonstrated good performance, independent of the SARS-CoV-2 variants

An integrated analysis of molecular aberrations in NCI-60 cell lines

<p>Abstract</p> <p>Background</p> <p>Cancer is a complex disease where various types of molecular aberrations drive the development and progression of malignancies. Large-scale screenings of multiple types of molecular aberrations (e.g., mutations, copy number variations, DNA methylations, gene expressions) become increasingly important in the prognosis and study of cancer. Consequently, a computational model integrating multiple types of information is essential for the analysis of the comprehensive data.</p> <p>Results</p> <p>We propose an integrated modeling framework to identify the statistical and putative causal relations of various molecular aberrations and gene expressions in cancer. To reduce spurious associations among the massive number of probed features, we sequentially applied three layers of logistic regression models with increasing complexity and uncertainty regarding the possible mechanisms connecting molecular aberrations and gene expressions. Layer 1 models associate gene expressions with the molecular aberrations on the same loci. Layer 2 models associate expressions with the aberrations on different loci but have known mechanistic links. Layer 3 models associate expressions with nonlocal aberrations which have unknown mechanistic links. We applied the layered models to the integrated datasets of NCI-60 cancer cell lines and validated the results with large-scale statistical analysis. Furthermore, we discovered/reaffirmed the following prominent links: (1)Protein expressions are generally consistent with mRNA expressions. (2)Several gene expressions are modulated by composite local aberrations. For instance, CDKN2A expressions are repressed by either frame-shift mutations or DNA methylations. (3)Amplification of chromosome 6q in leukemia elevates the expression of MYB, and the downstream targets of MYB on other chromosomes are up-regulated accordingly. (4)Amplification of chromosome 3p and hypo-methylation of PAX3 together elevate MITF expression in melanoma, which up-regulates the downstream targets of MITF. (5)Mutations of TP53 are negatively associated with its direct target genes.</p> <p>Conclusions</p> <p>The analysis results on NCI-60 data justify the utility of the layered models for the incoming flow of cancer genomic data. Experimental validations on selected prominent links and application of the layered modeling framework to other integrated datasets will be carried out subsequently.</p

Does the unification of health financing affect the distribution pattern of out‐of‐pocket health expenses in Turkey?

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149529/1/ijsw12389_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149529/2/ijsw12389.pd

State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand

Serum-Dependent Selective Expression of EhTMKB1-9, a Member of Entamoeba histolytica B1 Family of Transmembrane Kinases

Entamoeba histolytica transmembrane kinases (EhTMKs) can be grouped into six distinct families on the basis of motifs and sequences. Analysis of the E. histolytica genome revealed the presence of 35 EhTMKB1 members on the basis of sequence identity (≥95%). Only six homologs were full length containing an extracellular domain, a transmembrane segment and an intracellular kinase domain. Reverse transcription followed by polymerase chain reaction (RT-PCR) of the kinase domain was used to generate a library of expressed sequences. Sequencing of randomly picked clones from this library revealed that about 95% of the clones were identical with a single member, EhTMKB1-9, in proliferating cells. On serum starvation, the relative number of EhTMKB1-9 derived sequences decreased with concomitant increase in the sequences derived from another member, EhTMKB1-18. The change in their relative expression was quantified by real time PCR. Northern analysis and RNase protection assay were used to study the temporal nature of EhTMKB1-9 expression after serum replenishment of starved cells. The results showed that the expression of EhTMKB1-9 was sinusoidal. Specific transcriptional induction of EhTMKB1-9 upon serum replenishment was further confirmed by reporter gene (luciferase) expression and the upstream sequence responsible for serum responsiveness was identified. EhTMKB1-9 is one of the first examples of an inducible gene in Entamoeba. The protein encoded by this member was functionally characterized. The recombinant kinase domain of EhTMKB1-9 displayed protein kinase activity. It is likely to have dual specificity as judged from its sensitivity to different kinase inhibitors. Immuno-localization showed EhTMKB1-9 to be a surface protein which decreased on serum starvation and got relocalized on serum replenishment. Cell lines expressing either EhTMKB1-9 without kinase domain, or EhTMKB1-9 antisense RNA, showed decreased cellular proliferation and target cell killing. Our results suggest that E. histolytica TMKs of B1 family are functional kinases likely to be involved in serum response and cellular proliferation

Quantifying unpredictability: A multiple-model approach based on satellite imagery data from Mediterranean ponds.

Fluctuations in environmental parameters are increasingly being recognized as essential features of any habitat. The quantification of whether environmental fluctuations are prevalently predictable or unpredictable is remarkably relevant to understanding the evolutionary responses of organisms. However, when characterizing the relevant features of natural habitats, ecologists typically face two problems: (1) gathering long-term data and (2) handling the hard-won data. This paper takes advantage of the free access to long-term recordings of remote sensing data (27 years, Landsat TM/ETM+) to assess a set of environmental models for estimating environmental predictability. The case study included 20 Mediterranean saline ponds and lakes, and the focal variable was the water-surface area. This study first aimed to produce a method for accurately estimating the water-surface area from satellite images. Saline ponds can develop salt-crusted areas that make it difficult to distinguish between soil and water. This challenge was addressed using a novel pipeline that combines band ratio water indices and the short near-infrared band as a salt filter. The study then extracted the predictable and unpredictable components of variation in the water-surface area. Two different approaches, each showing variations in the parameters, were used to obtain the stochastic variation around a regular pattern with the objective of dissecting the effect of assumptions on predictability estimations. The first approach, which is based on Colwell's predictability metrics, transforms the focal variable into a nominal one. The resulting discrete categories define the relevant variations in the water-surface area. In the second approach, we introduced General Additive Model (GAM) fitting as a new metric for quantifying predictability. Both approaches produced a wide range of predictability for the studied ponds. Some model assumptions-which are considered very different a priori-had minor effects, whereas others produced predictability estimations that showed some degree of divergence. We hypothesize that these diverging estimations of predictability reflect the effect of fluctuations on different types of organisms. The fluctuation analysis described in this manuscript is applicable to a wide variety of systems, including both aquatic and nonaquatic systems, and will be valuable for quantifying and characterizing predictability, which is essential within the expected global increase in the unpredictability of environmental fluctuations. We advocate that a priori information for organisms of interest should be used to select the most suitable metrics estimating predictability, and we provide some guidelines for this approach