Identification of specificity determining residues in peptide recognition domains using an information theoretic approach applied to large-scale binding maps

<p>Abstract</p> <p>Background</p> <p>Peptide Recognition Domains (PRDs) are commonly found in signaling proteins. They mediate protein-protein interactions by recognizing and binding short motifs in their ligands. Although a great deal is known about PRDs and their interactions, prediction of PRD specificities remains largely an unsolved problem.</p> <p>Results</p> <p>We present a novel approach to identifying these Specificity Determining Residues (SDRs). Our algorithm generalizes earlier information theoretic approaches to coevolution analysis, to become applicable to this problem. It leverages the growing wealth of binding data between PRDs and large numbers of random peptides, and searches for PRD residues that exhibit strong evolutionary covariation with some positions of the statistical profiles of bound peptides. The calculations involve only information from sequences, and thus can be applied to PRDs without crystal structures. We applied the approach to PDZ, SH3 and kinase domains, and evaluated the results using both residue proximity in co-crystal structures and verified binding specificity maps from mutagenesis studies.</p> <p>Discussion</p> <p>Our predictions were found to be strongly correlated with the physical proximity of residues, demonstrating the ability of our approach to detect physical interactions of the binding partners. Some high-scoring pairs were further confirmed to affect binding specificity using previous experimental results. Combining the covariation results also allowed us to predict binding profiles with higher reliability than two other methods that do not explicitly take residue covariation into account.</p> <p>Conclusions</p> <p>The general applicability of our approach to the three different domain families demonstrated in this paper suggests its potential in predicting binding targets and assisting the exploration of binding mechanisms.</p

Associations between Longer Leukocyte Telomere Length and Increased Lung Cancer Risk among Never Smokers in Urban China

BACKGROUND: The complex relationship between measured leukocyte telomere length (LTL), genetically predicted LTL (gTL), and carcinogenesis is exemplified by lung cancer. We previously reported associations between longer pre-diagnostic LTL, gTL, and increased lung cancer risk among European and East Asian populations. However, we had limited statistical power to examine the associations among never smokers by gender and histology. METHODS: To investigate further, we conducted nested case-control analyses on an expanded sample of never smokers from the prospective Shanghai Women\u27s Health Studies (798 cases and 792 controls) and Shanghai Men\u27s Health Studies (161 cases and 162 controls). We broke the case-control matching and used multivariable unconditional logistic regression models to estimate the ORs and 95% confidence intervals (CI) of incident lung cancer and adenocarcinoma (LUAD), in relation to LTL measured using quantitative PCR and gTL determined using a polygenic score. In addition, we conducted Mendelian randomization (MR) using MR-PRESSO. RESULTS: We found striking dose-response relationships between longer LTL and gTL, and increased lung cancer risk among never-smoking women (P trendLTL = 4×10-6; P trendgTL = 3×10-4). Similarly, among never-smoking men, longer measured LTL was associated with over triple the risk compared with those with the shortest (OR, 3.48; 95% CI, 1.85-6.57). The overall results were similar for LUAD among women and men. MR analyses supported causal associations with LUAD among women (OR1 SD gTL, 1.19; 95% CI, 1.03-1.37; P = 0.03). CONCLUSIONS: Longer pre-diagnostic LTL is associated with increased lung cancer risk among never smokers. IMPACT: Our findings firmly support the role of longer telomeres in lung carcinogenesis

Asymmetric 3D Elasticâ Plastic Strainâ Modulated Electron Energy Structure in Monolayer Graphene by Laser Shocking

Graphene has a great potential to replace silicon in prospective semiconductor industries due to its outstanding electronic and transport properties; nonetheless, its lack of energy bandgap is a substantial limitation for practical applications. To date, straining graphene to break its lattice symmetry is perhaps the most efficient approach toward realizing bandgap tunability in graphene. However, due to the weak lattice deformation induced by uniaxial or inâ plane shear strain, most strained graphene studies have yielded bandgaps <1 eV. In this work, a modulated inhomogeneous local asymmetric elasticâ plastic straining is reported that utilizes GPaâ level laser shocking at a high strain rate (dε/dt) â 106â 107 sâ 1, with excellent formability, inducing tunable bandgaps in graphene of up to 2.1 eV, as determined by scanning tunneling spectroscopy. Highâ resolution imaging and Raman spectroscopy reveal strainâ induced modifications to the atomic and electronic structure in graphene and firstâ principles simulations predict the measured bandgap openings. Laser shock modulation of semimetallic graphene to a semiconducting material with controllable bandgap has the potential to benefit the electronic and optoelectronic industries.Both the bandgap structure and the Fermi level of monolayer graphene are modulated using an easy and effective optomechanical method. Laserâ shockâ induced 3D nanoshaping enables an asymmetric elasticâ plastic straining of graphene, resulting in a wide graphene bandgap of over 2.1 eV and a wide Fermi level adjustment range of 0.6 eV.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149335/1/adma201900597.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149335/2/adma201900597-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149335/3/adma201900597_am.pd

Magnetic Resonance Imaging Is More Sensitive Than PET for Detecting Treatment-Induced Cell Death-Dependent Changes in Glycolysis.

Metabolic imaging has been widely used to measure the early responses of tumors to treatment. Here, we assess the abilities of PET measurement of [18F]FDG uptake and MRI measurement of hyperpolarized [1-13C]pyruvate metabolism to detect early changes in glycolysis following treatment-induced cell death in human colorectal (Colo205) and breast adenocarcinoma (MDA-MB-231) xenografts in mice. A TRAIL agonist that binds to human but not mouse cells induced tumor-selective cell death. Tumor glycolysis was assessed by injecting [1,6-13C2]glucose and measuring 13C-labeled metabolites in tumor extracts. Injection of hyperpolarized [1-13C]pyruvate induced rapid reduction in lactate labeling. This decrease, which correlated with an increase in histologic markers of cell death and preceded decrease in tumor volume, reflected reduced flux from glucose to lactate and decreased lactate concentration. However, [18F]FDG uptake and phosphorylation were maintained following treatment, which has been attributed previously to increased [18F]FDG uptake by infiltrating immune cells. Quantification of [18F]FDG uptake in flow-sorted tumor and immune cells from disaggregated tumors identified CD11b+/CD45+ macrophages as the most [18F]FDG-avid cell type present, yet they represented <5% of the cells present in the tumors and could not explain the failure of [18F]FDG-PET to detect treatment response. MRI measurement of hyperpolarized [1-13C]pyruvate metabolism is therefore a more sensitive marker of the early decreases in glycolytic flux that occur following cell death than PET measurements of [18F]FDG uptake. SIGNIFICANCE: These findings demonstrate superior sensitivity of MRI measurement of hyperpolarized [1-13C]pyruvate metabolism versus PET measurement of 18F-FDG uptake for detecting early changes in glycolysis following treatment-induced tumor cell death

Improving object segmentation by using EEG signals and rapid serial visual presentation

This paper extends our previous work on the potential of EEG-based brain computer interfaces to segment salient objects in images. The proposed system analyzes the Event Related Potentials (ERP) generated by the rapid serial visual presentation of windows on the image. The detection of the P300 signal allows estimating a saliency map of the image, which is used to seed a semi-supervised object segmentation algorithm. Thanks to the new contributions presented in this work, the average Jaccard index was improved from $0.47$ to $0.66$ when processed in our publicly available dataset of images, object masks and captured EEG signals. This work also studies alternative architectures to the original one, the impact of object occupation in each image window, and a more robust evaluation based on statistical analysis and a weighted F-score

Calmodulin-like proteins localized to the conoid regulate motility and cell invasion by Toxoplasma gondii

Toxoplasma gondii contains an expanded number of calmodulin (CaM)-like proteins whose functions are poorly understood. Using a combination of CRISPR/Cas9-mediated gene editing and a plant-like auxin-induced degron (AID) system, we examined the roles of three apically localized CaMs. CaM1 and CaM2 were individually dispensable, but loss of both resulted in a synthetic lethal phenotype. CaM3 was refractory to deletion, suggesting it is essential. Consistent with this prediction auxin-induced degradation of CaM3 blocked growth. Phenotypic analysis revealed that all three CaMs contribute to parasite motility, invasion, and egress from host cells, and that they act downstream of microneme and rhoptry secretion. Super-resolution microscopy localized all three CaMs to the conoid where they overlap with myosin H (MyoH), a motor protein that is required for invasion. Biotinylation using BirA fusions with the CaMs labeled a number of apical proteins including MyoH and its light chain MLC7, suggesting they may interact. Consistent with this hypothesis, disruption of MyoH led to degradation of CaM3, or redistribution of CaM1 and CaM2. Collectively, our findings suggest these CaMs may interact with MyoH to control motility and cell invasion

Outcomes in patients with gunshot wounds to the brain.

Introduction:Gunshot wounds to the brain (GSWB) confer high lethality and uncertain recovery. It is unclear which patients benefit from aggressive resuscitation, and furthermore whether patients with GSWB undergoing cardiopulmonary resuscitation (CPR) have potential for survival or organ donation. Therefore, we sought to determine the rates of survival and organ donation, as well as identify factors associated with both outcomes in patients with GSWB undergoing CPR. Methods:We performed a retrospective, multicenter study at 25 US trauma centers including dates between June 1, 2011 and December 31, 2017. Patients were included if they suffered isolated GSWB and required CPR at a referring hospital, in the field, or in the trauma resuscitation room. Patients were excluded for significant torso or extremity injuries, or if pregnant. Binomial regression models were used to determine predictors of survival/organ donation. Results:825 patients met study criteria; the majority were male (87.6%) with a mean age of 36.5 years. Most (67%) underwent CPR in the field and 2.1% (n=17) survived to discharge. Of the non-survivors, 17.5% (n=141) were considered eligible donors, with a donation rate of 58.9% (n=83) in this group. Regression models found several predictors of survival. Hormone replacement was predictive of both survival and organ donation. Conclusion:We found that GSWB requiring CPR during trauma resuscitation was associated with a 2.1% survival rate and overall organ donation rate of 10.3%. Several factors appear to be favorably associated with survival, although predictions are uncertain due to the low number of survivors in this patient population. Hormone replacement was predictive of both survival and organ donation. These results are a starting point for determining appropriate treatment algorithms for this devastating clinical condition. Level of evidence:Level II

Reconstructing Disease Histories in Huge Discrete State Spaces

Many progressive diseases develop unnoticed and insidiously at the beginning. This leads to an observational gap, since the first data on the disease can only be obtained after diagnosis. Mutual Hazard Networks address this gap by reconstructing latent disease dynamics. They model the disease as a Markov chain on the space of all possible combinations of progression events. This space can be huge: Given a set of events, its size exceeds the number of atoms in the universe. Mutual Hazard Networks combine time-to-event modeling with generalized probabilistic graphical models, regularization, and modern numerical tensor formats to enable efficient calculations in large state spaces using compressed data formats. Here we review Mutual Hazard Networks and put them in the context of machine learning theory. We describe how the Mutual Hazard assumption leads to a compact parameterization of the models and show how modern tensor formats allow for efficient computations in large state spaces. Finally, we show how Mutual Hazard Networks reconstruct the most likely history of glioblastomas

Genome sequence of an Australian kangaroo, Macropus eugenii, provides insight into the evolution of mammalian reproduction and development.

BACKGROUND: We present the genome sequence of the tammar wallaby, Macropus eugenii, which is a member of the kangaroo family and the first representative of the iconic hopping mammals that symbolize Australia to be sequenced. The tammar has many unusual biological characteristics, including the longest period of embryonic diapause of any mammal, extremely synchronized seasonal breeding and prolonged and sophisticated lactation within a well-defined pouch. Like other marsupials, it gives birth to highly altricial young, and has a small number of very large chromosomes, making it a valuable model for genomics, reproduction and development. RESULTS: The genome has been sequenced to 2 × coverage using Sanger sequencing, enhanced with additional next generation sequencing and the integration of extensive physical and linkage maps to build the genome assembly. We also sequenced the tammar transcriptome across many tissues and developmental time points. Our analyses of these data shed light on mammalian reproduction, development and genome evolution: there is innovation in reproductive and lactational genes, rapid evolution of germ cell genes, and incomplete, locus-specific X inactivation. We also observe novel retrotransposons and a highly rearranged major histocompatibility complex, with many class I genes located outside the complex. Novel microRNAs in the tammar HOX clusters uncover new potential mammalian HOX regulatory elements. CONCLUSIONS: Analyses of these resources enhance our understanding of marsupial gene evolution, identify marsupial-specific conserved non-coding elements and critical genes across a range of biological systems, including reproduction, development and immunity, and provide new insight into marsupial and mammalian biology and genome evolution