Coverage profile correction of shallow-depth circulating cell-free DNA sequencing via multidistance learning.

Shallow-depth whole-genome sequencing (WGS) of circulating cell-free DNA (ccfDNA) is a popular approach for non-invasive genomic screening assays, including liquid biopsy for early detection of invasive tumors as well as non-invasive prenatal screening (NIPS) for common fetal trisomies. In contrast to nuclear DNA WGS, ccfDNA WGS exhibits extensive inter- and intra- sample coverage variability that is not fully explained by typical sources of variation in WGS, such as GC content. This variability may inflate false positive and false negative screening rates of copy-number alterations and aneuploidy, particularly if these features are present at a relatively low proportion of total sequenced content. Herein, we propose an empirically-driven coverage correction strategy that leverages prior annotation information in a multi-distance learning context to improve within-sample coverage profile correction. Specifically, we train a weighted k-nearest neighbors-style method on non-pregnant female donor ccfDNA WGS samples, and apply it to NIPS samples to evaluate coverage profile variability reduction. We additionally characterize improvement in the discrimination of positive fetal trisomy cases relative to normal controls, and compare our results against a more traditional regression-based approach to profile coverage correction based on GC content and mappability. Under cross-validation, performance measures indicated benefit to combining the two feature sets relative to either in isolation. We also observed substantial improvement in coverage profile variability reduction in leave-out clinical NIPS samples, with variability reduced by 26.5-53.5% relative to the standard regression-based method as quantified by median absolute deviation. Finally, we observed improvement discrimination for screening positive trisomy cases reducing ccfDNA WGS coverage variability while additionally improving NIPS trisomy screening assay performance. Overall, our results indicate that machine learning approaches can substantially improve ccfDNA WGS coverage profile correction and downstream analyses.

A comparison of adult rhabdomyosarcoma and high-grade neuroendocrine carcinoma of the urinary bladder reveals novel PPP1R12A fusions in rhabdomyosarcoma.

Some rhabdomyosarcomas and sarcomatoid carcinomas with heterologous rhabdomyosarcomatous elements resemble high-grade neuroendocrine carcinoma, creating a diagnostic difficulty. The purpose of this study was to characterize the overlap of adult genitourinary rhabdomyosarcomas, excluding those occurring at paratesticular sites, with high-grade neuroendocrine carcinoma and identify features helpful in their separation. Seventeen cases of rhabdomyosarcoma (11 from the urinary bladder and 3 each from kidney and prostate) were compared to 10 cases of high-grade neuroendocrine carcinoma from the urinary bladder. These tumors were analyzed by immunohistochemistry for desmin, MyoD1, myogenin, chromogranin, synaptophysin, CD56, TTF1, and ASCL1, and RNA sequencing was performed on 4 cases of bladder rhabdomyosarcoma (2 rhabdomyosarcomas and 2 sarcomatoid-rhabdomyosarcoma) and 10 cases of bladder high-grade neuroendocrine carcinoma. This was compared to public data from 414 typical urothelial carcinomas from The Cancer Genome Atlas dataset. Morphologic and immunophenotypic overlap with high-grade neuroendocrine carcinoma was seen in half of the bladder tumors, which included 4 rhabdomyosarcomas and 2 sarcomatoid rhabdomyosarcomas. RNA sequencing confirmed expression of neuroendocrine markers in these cases (2 rhabdomyosarcomas and 2 sarcomatoid rhabdomyosarcomas). Differential neuroendocrine differentiation was highlighted by ASCL1 protein expression only in high-grade neuroendocrine carcinoma. Moreover, both a pure alveolar rhabdomyosarcoma and sarcomatoid rhabdomyosarcoma of the urinary bladder demonstrated a fusion involving PPP1R12A. In summary, adult rhabdomyosarcomas of the urinary bladder are molecularly distinct from high-grade neuroendocrine carcinomas based on specific patterns of expression of myogenic and epithelial to mesenchymal transition-related transcription factors as well as the presence of a novel PPP1R12A fusion which is seen in a subset of cases.

ZBTB24 is a transcriptional regulator that coordinates with DNMT3B to control DNA methylation.

The interplay between transcription factors and epigenetic writers like the DNA methyltransferases (DNMTs), and the role of this interplay in gene expression, is being increasingly appreciated. ZBTB24, a poorly characterized zinc-finger protein, or the de novo methyltransferase DNMT3B, when mutated, cause Immunodeficiency, Centromere Instability, and Facial anomalies (ICF) syndrome, suggesting an underlying mechanistic link. Chromatin immunoprecipitation coupled with loss-of-function approaches in model systems revealed common loci bound by ZBTB24 and DNMT3B, where they function to regulate gene body methylation. Genes coordinately regulated by ZBTB24 and DNMT3B are enriched for molecular mechanisms essential for cellular homeostasis, highlighting the importance of the ZBTB24-DNMT3B interplay in maintaining epigenetic patterns required for normal cellular function. We identify a ZBTB24 DNA binding motif, which is contained within the promoters of most of its transcriptional targets, including CDCA7, AXIN2, and OSTC. Direct binding of ZBTB24 at the promoters of these genes targets them for transcriptional activation. ZBTB24 binding at the promoters of RNF169 and CAMKMT, however, targets them for transcriptional repression. The involvement of ZBTB24 targets in diverse cellular programs, including the VDR/RXR and interferon regulatory pathways, suggest that ZBTB24's role as a transcriptional regulator is not restricted to immune cells.

Whole Blood mRNA Expression-Based Prognosis of Metastatic Renal Cell Carcinoma.

The Memorial Sloan Kettering Cancer Center (MSKCC) prognostic score is based on clinical parameters. We analyzed whole blood mRNA expression in metastatic clear cell renal cell carcinoma (mCCRCC) patients and compared it to the MSKCC score for predicting overall survival. In a discovery set of 19 patients with mRCC, we performed whole transcriptome RNA sequencing and selected eighteen candidate genes for further evaluation based on associations with overall survival and statistical significance. In an independent validation of set of 47 patients with mCCRCC, transcript expression of the 18 candidate genes were quantified using a customized NanoString probeset. Cox regression multivariate analysis confirmed that two of the candidate genes were significantly associated with overall survival. Higher expression of BAG1 [hazard ratio (HR) of 0.14, p < 0.0001, 95% confidence interval (CI) 0.04-0.36] and NOP56 (HR 0.13, p < 0.0001, 95% CI 0.05-0.34) were associated with better prognosis. A prognostic model incorporating expression of BAG1 and NOP56 into the MSKCC score improved prognostication significantly over a model using the MSKCC prognostic score only (p < 0.0001). Prognostic value of using whole blood mRNA gene profiling in mCCRCC is feasible and should be prospectively confirmed in larger studies.

Human Analysts at Superhuman Scales: What Has Friendly Software To Do?

As analysts are expected to process a greater amount of information in a shorter amount of time, creators of big data software are challenged with the need for improved efficiency. Ray, our group's usable, scalable genome assembler, addresses big data problems by using optimal resources and producing one, correct and conservative, timely solution. Only by abstracting the size of the data from both the computers and the humans can the real scientific question, often complex in itself, eventually be solved. To draw a curtain over the specific computational machinery of big data, we developed RayPlatform, a programming framework that allows users to concentrate on their domain-specific problems. RayPlatform is a parallel message-passing software framework that runs on clouds, supercomputers, and desktops alike. Using established technologies such as C++ and MPI (message-passing interface), we handle the genomes of hundreds of species, from viruses to plants, using machines ranging from desktop computers to supercomputers. From this experience, we present insights on making computer time more useful-and user time much more valuable.

Multilevel X-Pol: a fragment-based method with mixed quantum mechanical representations of different fragments.

The explicit polarization (X-Pol) method is a fragment-based quantum mechanical model, in which a macromolecular system or other large or complex system in solution is partitioned into monomeric fragments. The present study extends the original X-Pol method, where all fragments are treated using the same electronic structure theory, to multilevel representations, called multilevel X-Pol, in which different electronic structure methods are used to describe different fragments. The multilevel X-Pol method has been implemented into a locally modified version of Gaussian 09. A key ingredient that is used to couple interfragment electrostatic interactions at different levels of theory is the use of the response density for the post-self-consistent-field energy. (The response density is also called the generalized density.) The method is useful for treating fragments in a small region of the system such as a solute molecule or the substrate and amino acids in the active site of an enzyme with a high-level theory, and the fragments in the rest of the system by a lower-level and computationally more efficient method. Multilevel X-Pol is illustrated here by applications to hydrogen bonding complexes in which one fragment is treated with the hybrid M06 density functional, Møller-Plesset perturbation theory, or coupled cluster theory, and the other fragments are treated by Hartree-Fock theory or the B3LYP or M06 hybrid density functionals.

Quercetin-3-methyl ether suppresses proliferation of mouse epidermal JB6 P+ cells by targeting ERKs.

Chemoprevention has been acknowledged as an important and practical strategy for the management of skin cancer. Quercetin-3-methyl ether, a naturally occurring compound present in various plants, has potent anticancer-promoting activity. We identified this compound by in silico virtual screening of the Traditional Chinese Medicine Database using extracellular signal-regulated kinase 2 (ERK2) as the target protein. Here, we showed that quercetin-3-methyl ether inhibited proliferation of mouse skin epidermal JB6 P+ cells in a dose- and time-dependent manner by inducing cell cycle G(2)-M phase accumulation. It also suppressed 12-O-tetradecanoylphorbol-13-acetate-induced neoplastic cell transformation in a dose-dependent manner. Its inhibitory effect was greater than quercetin. The activation of activator protein-1 was dose-dependently suppressed by quercetin-3-methyl ether treatment. Western blot and kinase assay data revealed that quercetin-3-methyl ether inhibited ERKs kinase activity and attenuated phosphorylation of ERKs. Pull-down assays revealed that quercetin-3-methyl ether directly binds with ERKs. Furthermore, a loss-of-function ERK2 mutation inhibited the effectiveness of the quercetin-3-methyl ether. Overall, these results indicated that quercetin-3-methyl ether exerts potent chemopreventive activity by targeting ERKs.

Norathyriol suppresses skin cancers induced by solar ultraviolet radiation by targeting ERK kinases.

Ultraviolet (UV) irradiation is the leading factor in the development of skin cancer, prompting great interest in chemopreventive agents for this disease. In this study, we report the discovery of norathyriol, a plant-derived chemopreventive compound identified through an in silico virtual screening of the Chinese Medicine Library. Norathyriol is a metabolite of mangiferin found in mango, Hypericum elegans, and Tripterospermum lanceolatum and is known to have anticancer activity. Mechanistic investigations determined that norathyriol acted as an inhibitor of extracellular signal-regulated kinase (ERK)1/2 activity to attenuate UVB-induced phosphorylation in mitogen-activated protein kinases signaling cascades. We confirmed the direct and specific binding of norathyriol with ERK2 through a cocrystal structural analysis. The xanthone moiety in norathyriol acted as an adenine mimetic to anchor the compound by hydrogen bonds to the hinge region of the protein ATP-binding site on ERK2. Norathyriol inhibited in vitro cell growth in mouse skin epidermal JB6 P+ cells at the level of G(2)-M phase arrest. In mouse skin tumorigenesis assays, norathyriol significantly suppressed solar UV-induced skin carcinogenesis. Further analysis indicated that norathyriol mediates its chemopreventive activity by inhibiting the ERK-dependent activity of transcriptional factors AP-1 and NF-κB during UV-induced skin carcinogenesis. Taken together, our results identify norathyriol as a safe new chemopreventive agent that is highly effective against development of UV-induced skin cancer.

Parallelization of Nullspace Algorithm for the computation of metabolic pathways.

Elementary mode analysis is a useful metabolic pathway analysis tool in understanding and analyzing cellular metabolism, since elementary modes can represent metabolic pathways with unique and minimal sets of enzyme-catalyzed reactions of a metabolic network under steady state conditions. However, computation of the elementary modes of a genome- scale metabolic network with 100-1000 reactions is very expensive and sometimes not feasible with the commonly used serial Nullspace Algorithm. In this work, we develop a distributed memory parallelization of the Nullspace Algorithm to handle efficiently the computation of the elementary modes of a large metabolic network. We give an implementation in C++ language with the support of MPI library functions for the parallel communication. Our proposed algorithm is accompanied with an analysis of the complexity and identification of major bottlenecks during computation of all possible pathways of a large metabolic network. The algorithm includes methods to achieve load balancing among the compute-nodes and specific communication patterns to reduce the communication overhead and improve efficiency.

Multilevel Parallelization of AutoDock 4.2.

BACKGROUND: Virtual (computational) screening is an increasingly important tool for drug discovery. AutoDock is a popular open-source application for performing molecular docking, the prediction of ligand-receptor interactions. AutoDock is a serial application, though several previous efforts have parallelized various aspects of the program. In this paper, we report on a multi-level parallelization of AutoDock 4.2 (mpAD4). RESULTS: Using MPI and OpenMP, AutoDock 4.2 was parallelized for use on MPI-enabled systems and to multithread the execution of individual docking jobs. In addition, code was implemented to reduce input/output (I/O) traffic by reusing grid maps at each node from docking to docking. Performance of mpAD4 was examined on two multiprocessor computers. CONCLUSIONS: Using MPI with OpenMP multithreading, mpAD4 scales with near linearity on the multiprocessor systems tested. In situations where I/O is limiting, reuse of grid maps reduces both system I/O and overall screening time. Multithreading of AutoDock's Lamarkian Genetic Algorithm with OpenMP increases the speed of execution of individual docking jobs, and when combined with MPI parallelization can significantly reduce the execution time of virtual screens. This work is significant in that mpAD4 speeds the execution of certain molecular docking workloads and allows the user to optimize the degree of system-level (MPI) and node-level (OpenMP) parallelization to best fit both workloads and computational resources.

Identification of dynamical hinge points of the L1 ligase molecular switch.

The L1 ligase is an in vitro selected ribozyme that uses a noncanonically base-paired ligation site to catalyze regioselectively and regiospecifically the 5' to 3' phosphodiester bond ligation, a reaction relevant to origin of life hypotheses that invoke an RNA world scenario. The L1 ligase crystal structure revealed two different conformational states that were proposed to represent the active and inactive forms. It remains an open question as to what degree these two conformers persist as stable conformational intermediates in solution, and along what pathway are they able to interconvert. To explore these questions, we have performed a series of molecular dynamics simulations in explicit solvent of the inactive-active conformational switch in L1 ligase. Four simulations were performed departing from both conformers in both the reactant and product states, in addition to a simulation where local unfolding in the active state was induced. From these simulations, along with crystallographic data, a set of four virtual torsion angles that span two evolutionarily conserved and restricted regions were identified as dynamical hinge points in the conformational switch transition. The ligation site visits three distinct states characterized by hydrogen bond patterns that are correlated with the formation of specific contacts that may promote catalysis. The insights gained from these simulations contribute to a more detailed understanding of the coupled catalytic/conformational switch mechanism of L1 ligase that may facilitate the design and engineering of new catalytic riboswitches.

Threshold occupancy and specific cation binding modes in the hammerhead ribozyme active site are required for active conformation.

The relationship between formation of active in-line attack conformations and monovalent (Na(+)) and divalent (Mg(2+)) metal ion binding in hammerhead ribozyme (HHR) has been explored with molecular dynamics simulations. To stabilize repulsions between negatively charged groups, different requirements of the threshold occupancy of metal ions were observed in the reactant and activated precursor states both in the presence and in the absence of a Mg(2+) in the active site. Specific bridging coordination patterns of the ions are correlated with the formation of active in-line attack conformations and can be accommodated in both cases. Furthermore, simulation results suggest that the HHR folds to form an electronegative recruiting pocket that attracts high local concentrations of positive charge. The present simulations help to reconcile experiments that probe the metal ion sensitivity of HHR catalysis and support the supposition that Mg(2+), in addition to stabilizing active conformations, plays a specific chemical role in catalysis.

Computational classification of classically secreted proteins.

The ability to identify classically secreted proteins is an important component of targeted therapeutic studies and the discovery of circulating biomarkers. Here, we review some of the most recent programs available for the in silico prediction of secretory proteins, the performance of which is benchmarked with an independent set of annotated human proteins. The description of these programs and the results of this benchmarking provide insights into the most recently developed prediction programs, which will enable investigators to make more informed decisions about which program best addresses their research needs.

Ab initio conformational space study of model compounds of O-glycosides of serine diamide.

Relative stabilities of rotamers of the N-acetyl-O-(2-acetamido-2-deoxy-alpha-D-galactopyranosyl)-L-seryl-N'-methyl amide (1) and eleven analogous molecules containing beta-galactose, alpha- and beta-mannose, alpha- and beta-glucose, and L-threonine were calculated to learn whether they could explain the natural preference for 1 in linkages between the carbohydrate and protein in glycoproteins. The lowest energy rotamers of four O-glycoside models of serine diamide were identified with a Monte Carlo search coupled with molecular mechanics (MM2*). These rotamers were further optimized with an ab initio level of theory (HF/6-31G(d)). Subsequently, B3LYP/6-31 + G(d) single point energies were calculated for the most stable HF structures. The most favorable interactions are present in 1 and its glucose analogue. The monosaccharide for the carbohydrate antenna is anchored to the serine residue with an AcNH...O=C-NHMe hydrogen bond in the most stable rotamers. The mannose analogue and the beta-anomers are considerably less stable according to the MM2* and especially to the ab inito energy values. The three analogues have HF/6-31 G(d) energies which are 4-6 kcal mol-1 higher; the single point B3LYP/6-31 + G(d)//HF/6-31 G(d) calculations yield preferences of 3-5 kcal mol-1 for 1. The most stable L-threonine analogues show a behaviour very similarly to the corresponding serine analogues. The ZPE and thermal correction components of the calculated delta H298 and delta G298 values are relatively small (< 0.4 kcal mol-1). However, the T delta S298 term can be as large as 2.6 kcal mol-1. The entropy terms stabilize the alpha-anomers relative to beta-anomers, and ManNAc relative to GalNAc. The largest stabilization effect is observed for one of the rotamers of the alpha-anomer of ManNAc.