Substrate specificities of Escherichia coli ItaT that acetylates aminoacyl-tRNAs.

Escherichia coli ItaT toxin reportedly acetylates the α-amino group of the aminoacyl-moiety of Ile-tRNAIle specifically, using acetyl-CoA as an acetyl donor, thereby inhibiting protein synthesis. The mechanism of the substrate specificity of ItaT had remained elusive. Here, we present functional and structural analyses of E. coli ItaT, which revealed the mechanism of ItaT recognition of specific aminoacyl-tRNAs for acetylation. In addition to Ile-tRNAIle, aminoacyl-tRNAs charged with hydrophobic residues, such as Val-tRNAVal and Met-tRNAMet, were acetylated by ItaT in vivo. Ile-tRNAIle, Val-tRNAVal and Met-tRNAMet were acetylated by ItaT in vitro, while aminoacyl-tRNAs charged with other hydrophobic residues, such as Ala-tRNAAla, Leu-tRNALeu and Phe-tRNAPhe, were less efficiently acetylated. A comparison of the structures of E. coli ItaT and the protein N-terminal acetyltransferase identified the hydrophobic residues in ItaT that possibly interact with the aminoacyl moiety of aminoacyl-tRNAs. Mutations of the hydrophobic residues of ItaT reduced the acetylation activity of ItaT toward Ile-tRNAIlein vitro, as well as the ItaT toxicity in vivo. Altogether, the size and shape of the hydrophobic pocket of ItaT are suitable for the accommodation of the specific aminoacyl-moieties of aminoacyl-tRNAs, and ItaT has broader specificity toward aminoacyl-tRNAs charged with certain hydrophobic amino acids.

Constructing training set using distance between learnt graphical models of time series data on patient physiology, to predict disease scores.

Interventional endeavours in medicine include prediction of a score that parametrises a new subject's susceptibility to a given disease, at the pre-onset stage. Here, for the first time, we provide reliable learning of such a score in the context of the potentially-terminal disease VOD, that often arises after bone marrow transplants. Indeed, the probability of surviving VOD, is correlated with early intervention. In our work, the VOD-score of each patient in a retrospective cohort, is defined as the distance between the (posterior) probability of a random graph variable-given the inter-variable partial correlation matrix of the time series data on variables that represent different aspects of patient physiology-and that given such time series data of an arbitrarily-selected reference patient. Such time series data is recorded from a pre-transplant to a post-transplant time, for each patient in this cohort, though the data available for distinct patients bear differential temporal coverage, owing to differential patient longevities. Each graph is a Soft Random Geometric Graph drawn in a probabilistic metric space, and the computed inter-graph distance is oblivious to the length of the time series data. The VOD-score learnt in this way, and the corresponding pre-transplant parameter vector of each patient in this retrospective cohort, then results in the training data, using which we learn the function that takes VOD-score as its input, and outputs the vector of pre-transplant parameters. We model this function with a vector-variate Gaussian Process, the covariance structure of which is kernel parametrised. Such modelling is easier than if the score variable were the output. Then for any prospective patient, whose pre-transplant variables are known, we learn the VOD-score (and the hyperparameters of the covariance kernel), using Markov Chain Monte Carlo based inference.

Molecular basis of glycyl-tRNAGly acetylation by TacT from Salmonella Typhimurium.

Bacterial toxin-antitoxin modules contribute to the stress adaptation, persistence, and dormancy of bacteria for survival under environmental stresses and are involved in bacterial pathogenesis. In Salmonella Typhimurium, the Gcn5-related N-acetyltransferase toxin TacT reportedly acetylates the α-amino groups of the aminoacyl moieties of several aminoacyl-tRNAs, inhibits protein synthesis, and promotes persister formation during the infection of macrophages. Here, we show that TacT exclusively acetylates Gly-tRNAGlyin vivo and in vitro. The crystal structure of the TacT:acetyl-Gly-tRNAGly complex and the biochemical analysis reveal that TacT specifically recognizes the discriminator U73 and G71 in tRNAGly, a combination that is only found in tRNAGly isoacceptors, and discriminates tRNAGly from other tRNA species. Thus, TacT is a Gly-tRNAGly-specific acetyltransferase toxin. The molecular basis of the specific aminoacyl-tRNA acetylation by TacT provides advanced information for the design of drugs targeting Salmonella.

Nomogram for predicting overall survival in stage II-III colorectal cancer.

PURPOSE: The overall survival (OS) of patients diagnosed with stage II-III colorectal cancer (CRC) can vary greatly, even between patients with the same tumor stage. We aimed to design a nomogram to predict OS in resected, stage II-III CRC and stratify patients with CRC into different risk groups. PATIENTS AND METHODS: Based on data from 873 patients with CRC, we used univariate Cox regression analysis to select the significant prognostic features, which were subjected to the least absolute shrinkage and selection operator (LASSO) regression algorithm for feature selection. Cross-validation was used to confirm suitable tuning parameters (λ) for LASSO logistic regression. Then, the nomogram was used to estimate 3- and 5-year OS based on the multivariable Cox regression model. The survival curves of the two groups were produced using the Kaplan-Meier method. Risk group stratification was performed to assess the predictive capacity of the nomogram. RESULTS: Preoperative mean platelet volume, preoperative platelet distribution width, monocytes, and postoperative adjuvant chemotherapy were identified as independent prognostic factors by LASSO regression and integrated for the construction of the nomogram. The nomogram provided good discrimination, with C-indices of 0.67 and 0.69 for the training and validation sets, respectively. Calibration plots illustrated excellent agreement between the nomogram predictions and actual observations for 3- and 5-year OS. Moreover, a significant difference in OS was shown between patients stratified into different risk groups (P < .001). CONCLUSION: We constructed and validated an original predictive nomogram for OS in patients with CRC after surgery, facilitating physicians to appraise the individual survival of postoperative patients accurately and identify high-risk patients who need more aggressive treatment and follow-up strategies.

A nomogram for preoperative prediction of lymphatic infiltration in colorectal cancer: A personalized approach to clinical decision making.

Lymphatic infiltration (LI) is a key factor affecting the treatment of patients with colorectal cancer (CRC). Thus, the aim of this study was to develop and validate a nomogram for individual preoperative prediction of LI in patients with CRC.We conducted a retrospective analysis of 664 patients who received their initial diagnosis of CRC at our center. Those patients were allocated to a training dataset (n = 468) and a validation dataset (n = 196). The least absolute shrinkage and selection operator regression model was used for data dimension reduction and feature selection. The nomogram was constructed from the training dataset and internally verified using the concordance index (C-index), calibration, area under the receiver operating characteristic curve and decision curve analysis (DCA).The enhancement computed tomography reported N1/N2 classification, preoperative tumor differentiation, elevated carcinoembryonic antigen, and carbohydrate antigen19-9 level were selected as variables for the prediction nomogram. Encouragingly, the nomogram showed favorable calibration with C-index 0.757 in the training cohort and 0.725 in validation cohort. The DCA signified that the nomogram was clinically useful. The Kaplan-Meier survival curve showed that patients with LI had a worse prognosis and could benefit from postoperative adjuvant chemotherapy.Use common clinicopathologic factors, a non-invasive scale for individualized preoperative forecasting of LI was established conveniently. LI prediction has great significance for risk stratification of prognosis and treatment of resectable CRC.

A nomogram to predict vascular invasion before resection of colorectal cancer.

Vascular invasion (VI) is an important feature for systemic recurrence and an indicator for the application of adjuvant therapy in colorectal cancer (CRC). Preoperative knowledge of VI is important in determining whether adjuvant therapy is necessary, as well as the adequacy of surgical resection. In the present study, a predictive nomogram for VI in patients with CRC was constructed. The prediction model consisted of 664 eligible patients with CRC, who were divided into a training set (n=468) and a validation set (n=196). Data were collected between August 2013 and April 2018. The feature selection model was established using the least absolute shrinkage and selection operator regression model. Multivariable logistic regression analysis was used to construct the predictive nomogram. The performance of the nomogram was evaluated by calibration, discrimination and clinical usefulness. Differentiation, computed tomography (CT)-based on N stage (CT N stage), hemameba and tumor distance from the anus (cm) were integrated into the nomogram. The nomogram exhibited good discrimination, with an area under the curve (AUC) of 0.731 and good calibration. Application of the nomogram in the validation cohort showed acceptable discrimination, with an AUC of 0.710 and good calibration. Decision curve analysis revealed that the nomogram was clinically useful. These findings suggests, to the best of our knowledge, that this may be the first nomogram for individual preoperative prediction of VI in patients with CRC, which may promote preoperative optimization strategies for this selected group of patients.

Polymerizable Microsphere-Induced High Mechanical Strength of Hydrogel Composed of Acrylamide.

Polymerizable microspheres are introduced into acrylamide to prepare the high mechanical strength hydrogels with a novel three-dimensional pore structure. Rheological properties, compressive stress⁻strain, tensile property, and compression strength of three different types of hydrogels were investigated. Moreover, a scanning electron microscope (SEM) was adopted to observe the three-dimension network structure of three different types of hydrogels. The test results illustrated that viscous moduli (G″) and elastic moduli (G') of a hydrogel containing polymerizable microspheres (P) reached maximum values, compared to the normal hydrogel (N) and the composite hydrogel containing ordinary microspheres (O). When the hydrogels were squeezed, the N was easily fractured under high strain (99%), whereas the P was not broken, and quickly recovered its initial morphology after the release of load. The P showed excellent tensile properties, with an elongation at break up to 90% and a tensile strength greater than 220 g. The compression strength of the N was 100.44 kPa·m-1, while the resulting strength of P was enhanced to be 248.00 kPa·m-1. Therefore, the various performances of N were improved by adding polymerizable microspheres. In addition, the SEM images indicated that N has a general three-dimensional network structure; the conventional network structure did not exist in the P, which has a novel three-dimensional pore structure in the spherical projection and very dense channels, which led to the compaction of the space between the three-dimensional pore network layers and reduced the flowing of free water wrapped in the network. Therefore, the mechanical strength of hydrogel was enhanced.