Mutagenesis of protein-encoding sequences occurs ubiquitously; it enables evolution, accumulates during aging, and is associated with disease. Many biotechnological methods exploit random mutations to evolve novel proteins. To quantitate protein tolerance to random change, it is vital to understand the probability that a random amino acid replacement will lead to a protein's functional inactivation. We define this probability as the ''x factor.'' Here, we develop a broadly applicable approach to calculate x factors and demonstrate this method using the human DNA repair enzyme 3-methyladenine DNA glycosylase (AAG). Three gene-wide mutagenesis libraries were created, each with 10 5 diversity and averaging 2.2, 4.6, and 6.2 random amino acid changes per mutant. After determining the percentage of functional mutants in each library using high-stringency selection (>19,000-fold), the x factor was found to be 34% ؎ 6%. Remarkably, reanalysis of data from studies of diverse proteins reveals similar inactivation probabilities. To delineate the nature of tolerated amino acid substitutions, we sequenced 244 surviving AAG mutants. The 920 tolerated substitutions were characterized by substitutability index and mapped onto the AAG primary, secondary, and known tertiary structures. Evolutionarily conserved residues show low substitutability indices. In AAG, ␤ strands are on average less substitutable than ␣ helices; and surface loops that are not involved in DNA binding are the most substitutable. Our results are relevant to such diverse topics as applied molecular evolution, the rate of introduction of deleterious alleles into genomes in evolutionary history, and organisms' tolerance of mutational burden.A fundamental aspect of evolution is that mutations generate novel alleles that are then favored by selection. However, new coding mutations can be deleterious, neutral, or beneficial. Mutations can result from environmental and endogenous damage to DNA and from errors during DNA synthetic processes. In humans, random mutations produce inherited diseases and accumulate with aging and cancer (1). Conversely, targeted hypermutagenesis by immune defenses helps to generate antibody diversity and was recently shown to inactivate retroviral genomes (2). John Maynard Smith (3) proposed more than 30 years ago that the occurrence of functional mutant proteins that differ from wild type by one residue is likely frequent for evolution to be possible. Since then, numerous evolutionary and mutagenesis studies have led to the assertion that proteins are highly plastic in tolerating amino acid changes (4, 5). However, to date, we lack a quantitative measure of the degree of proteins' tolerance for random amino acid changes that occur at a random position in the protein. If a rigorous measure of proteins' degree of tolerance of random amino acid changes can be defined, then such fundamental calculations as the steepness of protein fitness landscapes or the rate of introduction of deleterious mutations into coding genomes can be more clearl...

Molecular analysis of the mutation status for EGFR and KRAS are now routine in the management of non-small cell lung cancer. Radiogenomics, the linking of medical images with the genomic properties of human tumors, provides exciting opportunities for non-invasive diagnostics and prognostics. We investigated whether EGFR and KRAS mutation status can be predicted using imaging data. To accomplish this, we studied 186 cases of NSCLC with preoperative thin-slice CT scans. A thoracic radiologist annotated 89 semantic image features of each patient’s tumor. Next, we built a decision tree to predict the presence of EGFR and KRAS mutations. We found a statistically significant model for predicting EGFR but not for KRAS mutations. The test set area under the ROC curve for predicting EGFR mutation status was 0.89. The final decision tree used four variables: emphysema, airway abnormality, the percentage of ground glass component and the type of tumor margin. The presence of either of the first two features predicts a wild type status for EGFR while the presence of any ground glass component indicates EGFR mutations. These results show the potential of quantitative imaging to predict molecular properties in a non-invasive manner, as CT imaging is more readily available than biopsies.

Advances in precision molecular imaging promise to transform our ability to detect, diagnose and treat disease. Here, we describe the engineering and validation of a new cystine knot peptide (knottin) that selectively recognizes human integrin αvβ6 with single-digit nanomolar affinity. We solve its 3D structure by NMR and x-ray crystallography and validate leads with 3 different radiolabels in pre-clinical models of cancer. We evaluate the lead tracer’s safety, biodistribution and pharmacokinetics in healthy human volunteers, and show its ability to detect multiple cancers (pancreatic, cervical and lung) in patients at two study locations. Additionally, we demonstrate that the knottin PET tracers can also detect fibrotic lung disease in idiopathic pulmonary fibrosis patients. Our results indicate that these cystine knot PET tracers may have potential utility in multiple disease states that are associated with upregulation of integrin αvβ6.

Human alkyladenine DNA glycosylase (hAAG) excises alkylated purines, hypoxanthine and etheno bases from DNA to form abasic (AP) sites. Surprisingly, elevated expression of hAAG increases spontaneous frameshift mutagenesis. By random mutagenesis of eight active site residues, we isolated hAAG-Y127I/H136L double mutant that induces even higher rates of frameshift mutation than the wild-type hAAG; the Y127I mutation accounts for the majority of the hAAG-Y127I/H136L induced mutator phenotype. The hAAG-Y127I/H136L and hAAG-Y127I mutants increased the rate of spontaneous frameshifts by up to 120-fold in S. cerevisiae, and also induced high rates of microsatellite instability (MSI) in human cells. hAAG and its mutants bind DNA containing 1 and 2 base pair-loops with significant affinity, thus shielding them from mismatch repair; the strength of such binding correlates with their ability to induce the mutator phenotype. This study provides important insights into the mechanism of hAAG-induced genomic instability.

Background: There is intense interest and speculation in the application of artificial intelligence (AI) to radiology. The goals of this investigation were (1) to assess thoracic radiologists’ perspectives on the role and expected impact of AI in radiology, and (2) to compare radiologists’ perspectives with those of computer science (CS) experts working in the AI development. Methods: An online survey was developed and distributed to chest radiologists and CS experts at leading academic centers and societies, comparing their expectations of AI’s influence on radiologists’ jobs, job satisfaction, salary, and role in society. Results: A total of 95 radiologists and 45 computer scientists responded. Computer scientists reported having read more scientific journal articles on AI/machine learning in the past year than radiologists (mean [95% confidence interval]=17.1 [9.01-25.2] vs. 7.3 [4.7-9.9], P=0.0047). The impact of AI in radiology is expected to be high, with 57.8% and 73.3% of computer scientists and 31.6% and 61.1% of chest radiologists predicting radiologists’ job will be dramatically different in 5 to 10 years, and 10 to 20 years, respectively. Although very few practitioners in both fields expect radiologists to become obsolete, with 0% expecting radiologist obsolescence in 5 years, in the long run, significantly more computer scientists (15.6%) predict radiologist obsolescence in 10 to 20 years, as compared with 3.2% of radiologists reporting the same (P=0.0128). Overall, both chest radiologists and computer scientists are optimistic about the future of AI in radiology, with large majorities expecting radiologists’ job satisfaction to increase or stay the same (89.5% of radiologists vs. 86.7% of CS experts, P=0.7767), radiologists’ salaries to increase or stay the same (83.2% of radiologists vs. 73.4% of CS experts, P=0.1827), and the role of radiologists in society to improve or stay the same (88.4% vs. 86.7%, P=0.7857). Conclusions: Thoracic radiologists and CS experts are generally positive on the impact of AI in radiology. However, a larger percentage, but still small minority, of computer scientists predict radiologist obsolescence in 10 to 20 years. As the future of AI in radiology unfolds, this study presents a historical timestamp of which group of experts’ perceptions were closer to eventual reality.

Simultaneous cardiac PET/MRI is feasible in the evaluation of cardiac sarcoidosis and myocarditis achieving diagnostic image quality.

No abstract

The morphometry of the large conducting airways is presumed to have a strong effect on the regional deposition of inhaled aerosol particles. Nevertheless, sex-based differences have not been fully quantified and are still largely ignored in designing inhalation therapies. To this end, we retrospectively analyzed high-resolution computer-tomography scans for 185 individuals (90 women, 95 men) in the age range of 12−89 years to determine airway luminal areas, airway lengths and bifurcation angles. Only subjects free of chronic airway disease were considered. In men, luminal areas of the upper conducting airways were on the average ~ 30-50% larger when compared to those in women, with the largest differences found in the trachea (289.72±54.25mm2 vs. 193.50±42.37mm2 for men/women respectively). The ratio of the largest luminal area in men to the smallest luminal area in women (in any given segment) ranged between 4.5 and 8.6, the largest differences being found in the lobar bronchi. Sex-based differences were minor in the case of bifurcation angles (e.g. average main bifurcation angle: 93.04±9.58o vs. 91.03±9.81o for men/women respectively), but large inter-subject variability was found irrespective of sex (e.g. range of main bifurcation angle: 65.04−122.01o vs. 69.46−113.94o for men/women respectively). Bronchial segments were shorter by ~ 5-20% in women relative to men, the largest differences being located in the upper lobes. False discovery rate (FDR) analysis revealed statistically significant associations among morphometric measures of the right lung in women (but not in men) suggesting two phenotypes among women that we attribute to the smaller female thoracic volume.