pyPept: a python library to generate atomistic 2D and 3D representations of peptides

We present pyPept, a set of executables and underlying python-language classes to easily create, manipulate, and analyze peptide molecules using the FASTA, HELM, or recently-developed BILN notations. The framework enables the analysis of both pure proteinogenic peptides as well as those with non-natural amino acids, including support to assemble a customizable monomer library, without requiring programming. From line notations, a peptide is transformed into a molecular graph for 2D depiction tasks, the calculation of physicochemical properties, and other systematic analyses or processing pipelines. The package includes a module to rapidly generate approximate peptide conformers by incorporating secondary structure restraints either given by the user or predicted via pyPept, and a wrapper tool is also provided to automate the generation and output of 2D and 3D representations of a peptide directly from the line notation. HELM and BILN notations that include circular, branched, or stapled peptides are fully supported, eliminating errors in structure creation that are prone during manual drawing and connecting. The framework and common workflows followed in pyPept are described together with illustrative examples. pyPept has been released at: https://github.com/Boehringer-Ingelheim/pyPept .

Mutation burden-orthogonal tumor genomic subtypes delineate responses to immune checkpoint therapy

Background

In cancer therapy, higher-resolution tumor-agnostic biomarkers that predict response to immune checkpoint inhibitor (ICI) therapy are needed. Mutation signatures reflect underlying oncogenic processes that can affect tumor immunogenicity, and thus potentially delineate ICI treatment response among tumor types.

Methods

Based on mutational signature analysis, we developed a stratification for all solid tumors in The Cancer Genome Atlas (TCGA). Subsequently, we developed a new software (Genomic Subtyping and Predictive Response Analysis for Cancer Tumor ICi Efficacy, GS-PRACTICE) to classify new tumors submitted to whole-exome sequencing. Using existing data from 973 pan-cancer ICI-treated cases with outcomes, we evaluated the subtype-response predictive performance.

Results

Systematic analysis on TCGA samples identified eight tumor genomic subtypes, which were characterized by features represented by smoking exposure, ultraviolet light exposure, APOBEC enzyme activity, POLE mutation, mismatch repair deficiency, homologous recombination deficiency, genomic stability, and aging. The former five subtypes were presumed to form an immune-responsive group acting as candidates for ICI therapy because of their high expression of immune-related genes and enrichment in cancer types with FDA approval for ICI monotherapy. In the validation cohort, the samples assigned by GS-PRACTICE to the immune-reactive subtypes were significantly associated with ICI response independent of cancer type and TMB high or low status.

Conclusions

The new tumor subtyping method can serve as a tumor-agnostic biomarker for ICI response prediction and will improve decision making in cancer treatment.

Utility of Homologous Recombination Deficiency Biomarkers Across Cancer Types

Homologous recombination DNA repair deficiency (HRD) is associated with sensitivity to platinum and poly (ADP-ribose) polymerase inhibitors in certain cancer types, including breast, ovarian, pancreatic, and prostate. In these cancers, BRCA1/2 alterations and genomic scar signatures are useful indicators for assessing HRD. However, alterations in other homologous recombination repair (HRR)-related genes and their clinical significance in other cancer types have not been adequately and systematically investigated.

A comprehensive pan-cancer analysis on the clinical significance of homologous recombination deficiency

Pilot-scale field demonstration of a hybrid nanofiltration and UV-sulfite treatment train for groundwater contaminated by per- and polyfluoroalkyl substances (PFASs)

Previous laboratory scale studies indicate nanofiltration (NF) and UV-sulfite photochemical treatments as promising technologies for the removal and destruction, respectively, of per- and polyfluoroalkyl substances (PFASs) from contaminated water. This study reports on a field demonstration of a pilot-scale hybrid NF and UV-sulfite treatment train for the remediation of 12 PFASs detected in groundwater impacted by aqueous film-forming foam (AFFF) at a U.S. Department of Defense installation. For most of the detected PFASs, NF rejection was consistently ≥ 95% over a 30-day field trial when operating at 90% total permeate recovery. Rejection of short-chain perfluorosulfonic acids (PFSAs) by NF decreased when recoveries increased from 90 to 97%; tests with a reverse osmosis (RO) membrane showed ≥ 99% rejection of all PFASs regardless of increasing recovery. UV treatment of the NF reject following 90% permeate recovery resulted in variable destruction of individual PFASs, with rates also being dependent on pH and the identity and concentration of UV photosensitizer. Rates of perfluorocarboxylic acid (PFCA) degradation were greater than those measured for PFSAs and perfluoroalkyl acid (PFAA) precursors and were independent of perfluoroalkyl chain length. In contrast, rates of PFSA degradation increased with increasing chain length. Consistent levels of PFAS degradation by UV-sulfite were observed during a 30-day demonstration experiment in NF reject water amended with 10 mM sulfite and adjusted to pH 11.2. Collectively, > 75% of the detected PFAS mass in the NF reject was destroyed after 4 h of UV treatment, increasing to > 90% after 8 h of treatment. An analysis of electrical energy inputs for the hybrid NF/UV-sulfite treatment train showed energy per order magnitude (EE/O) requirements ranging from ≤ 13.1 kWh/m³ for PFCAs and 14.1 kWh/m³ for PFOS to values > 100 kWh/m³ for more recalcitrant short-chain PFSA analogues. The UV reactor and water-cooling system were the major contributors to overall energy requirements and represent the greatest opportunities for improving efficiency of the technology.

ADMET Predictability at Boehringer Ingelheim: State-of-the-Art, and Do Bigger Datasets or Algorithms Make a Difference?

Computational methods assisting drug discovery and development are routine in the pharmaceutical industry. Digital recording of ADMET assays has provided a rich source of data for development of predictive models. Despite the accumulation of data and the public availability of advanced modeling algorithms, the utility of prediction in ADMET research is not clear. Here, we present a critical evaluation of the relationships between data volume, modeling algorithm, chemical representation and grouping, and temporal aspect (time sequence of assays) using an in-house ADMET database. We find no large difference in prediction algorithms nor any systemic and substantial gain from increasingly large datasets. Temporal-based data enlargement led to performance improvement in only in a limited number of assays, and with fractional improvement at best. Assays that are well-, intermediately-, or poorly-suited for ADMET predictions and reasons for such behavior are systematically identified, generating realistic expectations for areas in which computational models can be used to guide decision making in molecular design and development.

Abstract 366: Identification of a clinically significant pan-cancer biomarker for homologous recombination deficiency

[Background] Genomic alterations in BRCA1/2 and the ‘genomic scar' signatures are associated with homologous recombination DNA repair deficiency (HRD) and serve as therapeutic biomarkers for sensitivity to platinum and PARP inhibitors in breast and ovarian cancers. However, the clinical significance of these biomarkers in other homologous recombination repair (HRR) pathway genes or in other cancer types is not fully understood.

[Method] From the TCGA and the AACR GENIE project databases, we obtained clinical and genomic data of patients with multiple solid cancers, evaluated both locus-specific LOH in HRR pathway gene mutations and two genomic scar signature scores (HRD score and mutational signature 3), identified potential HRD cases, and examined the efficacy of DNA-damaging drugs in these HRD cases.

[Results] Germline and somatic gene mutations in the HRR pathway genes with locus-specific LOH were significantly enriched in cases having high genomic scar scores, whereas those without the LOH were not enriched. Somatic mutations without the LOH were remarkably enriched in hypermutated cases and considered to be passenger mutations. The mutation rate and frequency with LOH in the HRR pathway genes varied widely by cancer type. When stratifying patients by gender and presence of TP53 mutation, the correlation between the two genomic scar scores and the resulting optimal cutoff values for biallelic HRR pathway gene alterations differed greatly. The potential HRD cases that were selected by the two genomic scar cutoffs and biallelic HRR pathway gene alterations showed significantly high gene expression-based HRD scores. In Cox proportional hazard multivariate analyses with covariates of age, stage, gender, HRD, and TP53 mutation, HRD was a good prognostic factor in the group with DNA-damaging agents, but a poor one in the group without the agents.

[Conclusion] Increased attention on locus-specific LOH in HRR pathway gene alterations and the differences in genomic scar signatures stratified by gender and TP53 mutation is newly warranted in order to properly assess HRD in a pan-cancer manner. The results in the present study proved the clinical significance of HRD across cancer types and may contribute to the development of personalized medicine based on HRD.

Citation Format: Shiro Takamatsu, JB Brown, Koji Yamanoi, Ken Yamaguchi, Junzo Hamanishi, Takashi Kobayashi, Masaki Mandai, Noriomi Matsumura. Identification of a clinically significant pan-cancer biomarker for homologous recombination deficiency [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 366.

Assessing Human Health Risks from Per- and Polyfluoroalkyl Substance (PFAS)-Impacted Vegetable Consumption: A Tiered Modeling Approach

Irrigation water or soil contaminated with per- and polyfluoroalkyl substances (PFASs) raises concerns among regulators tasked with protecting human health from potential PFAS-contaminated food crops, with several studies identifying crop uptake as an important exposure pathway. We estimated daily dietary exposure intake of individual PFASs in vegetables for children and adults using Monte Carlo simulation in a tiered stochastic modeling approach: exposures were the highest for young children (1-2 years > adults > 3-5 years > 6-11 years > 12-19 years). Using the lowest available human health toxicity reference values (RfDs) and no additional exposure, estimated fifth percentile risk-based threshold concentrations in irrigation water were 38 ng/L (median 180 ng/L) for perfluorooctanoate (PFOA) and 140 ng/L (median 850 ng/L) for perfluorooctane sulfonate (PFOS). Thus, consumption of vegetables irrigated with PFAS-impacted water that meets the current 70 ng/L of PFOA and PFOS U.S. Environmental Protection Agency's lifetime health advisory for drinking water may or may not be protective of vegetable exposures to these contaminants. Hazard analyses using real-world PFAS-contaminated groundwater data for a hypothetical farm showed estimated exposures to most PFASs exceeding available or derived RfDs, indicating water-to-crop transfer is an important exposure pathway for communities with PFAS-impacted irrigation water.

Identification of Qk as a Glial Precursor Cell Marker that Governs the Fate Specification of Neural Stem Cells to a Glial Cell Lineage

During brain development, neural stem cells (NSCs) initially produce neurons and change their fate to generate glias. While the regulation of neurogenesis is well characterized, specific markers for glial precursor cells (GPCs) and the master regulators for gliogenesis remain unidentified. Accumulating evidence suggests that RNA-binding proteins (RBPs) have significant roles in neuronal development and function, as they comprehensively regulate the expression of target genes in a cell-type-specific manner. We systematically investigated the expression profiles of 1,436 murine RBPs in the developing mouse brain and identified quaking (Qk) as a marker of the putative GPC population. Functional analysis of the NSC-specific Qk-null mutant mouse revealed the key role of Qk in astrocyte and oligodendrocyte generation and differentiation from NSCs. Mechanistically, Qk upregulates gliogenic genes via quaking response elements in their 3′ untranslated regions. These results provide crucial directions for identifying GPCs and deciphering the regulatory mechanisms of gliogenesis from NSCs.

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Differential expression analysis identified Qk as a glial precursor cell marker

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Loss of Qk ablated both astrocyte and OL production from neural stem cells

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Qk^−/− NSCs failed to become glia and aberrantly expressed neural genes

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Qk comprehensively upregulates essential genes for gliogenesis as regulons via QREs

Active learning effectively identifies a minimal set of maximally informative and asymptotically performant cytotoxic structure-activity patterns in NCI-60 cell lines

The NCI-60 cancer cell line screening panel has provided insights for development of subtype-specific chemical therapies and repurposing. By extracting chemical structure and cytotoxicity patterns, virtual screening potentially complements the availability of high-throughput assay platforms and improves bioactive compound discovery rates by computational prefiltering of candidate compound libraries. Many groups report high prediction performances in computational models of NCI-60 data when using cross-validation or similar techniques, yet prospective therapy development in novel cancers may have little to no such data and further may not have the resources to perform hit identification using large compound libraries. In contrast to bulk screening and analysis, the active learning methodology has demonstrated how to identify compounds for screening in small batches and update computational models iteratively, leading to predictive models with a minimum number of compounds, and importantly clarifying data volumes at which limits in predictive ability are achieved. Here, in replicate per-cell line experiments using 50% of data (∼20 000 compounds) as the external prediction target, predictive limits are reproducibly demonstrated at the stage of systematic selection of 10-30% of the incorporable half. The pattern was consistent across all 60 cell lines. Limits of predictability are found to be correlated to the doubling times of cell lines and the number of cellular response discontinuities (activity cliffs) present per cell line. Organization into chemical scaffolds delineated degrees of predictive challenge. These results provide key insights for strategies in developing new inhibitors in existing cell lines or for future automated therapy selection in personalized oncotherapy.

Loss of Sfpq Causes Long-Gene Transcriptopathy in the Brain

Genes specifically expressed in neurons contain members with extended long introns. Longer genes present a problem with respect to fulfilment of gene length transcription, and evidence suggests that dysregulation of long genes is a mechanism underlying neurodegenerative and psychiatric disorders. Here, we report the discovery that RNA-binding protein Sfpq is a critical factor for maintaining transcriptional elongation of long genes. We demonstrate that Sfpq co-transcriptionally binds to long introns and is required for sustaining long-gene transcription by RNA polymerase II through mediating the interaction of cyclin-dependent kinase 9 with the elongation complex. Phenotypically, Sfpq disruption caused neuronal apoptosis in developing mouse brains. Expression analysis of Sfpq-regulated genes revealed specific downregulation of developmentally essential neuronal genes longer than 100 kb in Sfpq-disrupted brains; those genes are enriched in associations with neurodegenerative and psychiatric diseases. The identified molecular machinery yields directions for targeted investigations of the association between long-gene transcriptopathy and neuronal diseases.

Reconnaissance of Mixed Organic and Inorganic Chemicals in Private and Public Supply Tapwaters at Selected Residential and Workplace Sites in the United States

Safe drinking water at the point-of-use (tapwater, TW) is a United States public health priority. Multiple lines of evidence were used to evaluate potential human health concerns of 482 organics and 19 inorganics in TW from 13 (7 public supply, 6 private well self-supply) home and 12 (public supply) workplace locations in 11 states. Only uranium (61.9 μg L-1, private well) exceeded a National Primary Drinking Water Regulation maximum contaminant level (MCL: 30 μg L-1). Lead was detected in 23 samples (MCL goal: zero). Seventy-five organics were detected at least once, with median detections of 5 and 17 compounds in self-supply and public supply samples, respectively (corresponding maxima: 12 and 29). Disinfection byproducts predominated in public supply samples, comprising 21% of all detected and 6 of the 10 most frequently detected. Chemicals designed to be bioactive (26 pesticides, 10 pharmaceuticals) comprised 48% of detected organics. Site-specific cumulative exposure-activity ratios (∑EAR) were calculated for the 36 detected organics with ToxCast data. Because these detections are fractional indicators of a largely uncharacterized contaminant space, ∑EAR in excess of 0.001 and 0.01 in 74 and 26% of public supply samples, respectively, provide an argument for prioritized assessment of cumulative effects to vulnerable populations from trace-level TW exposures.

Fundamental Bioinformatic and Chemoinformatic Data Processing

In order to execute more advanced computational chemogenomic workflows, it is essential to understand the basic data formats and options for processing them. In this chapter, de facto standards for compound and protein representation are explained, with procedures for processing them given. A walkthrough demonstrates the step-by-step processes of downloading a ligand-target database, parsing the bioactivity in the database, automatically retrieving its chemical structures and protein sequences from a command line, and finally converting the structures and sequences into representative machine-ready formats. A basic protocol to visualize the parsed database and look for patterns is also given.

Classifiers and their Metrics Quantified

Molecular modeling frequently constructs classification models for the prediction of two‐class entities, such as compound bio(in)activity, chemical property (non)existence, protein (non)interaction, and so forth. The models are evaluated using well known metrics such as accuracy or true positive rates. However, these frequently used metrics applied to retrospective and/or artificially generated prediction datasets can potentially overestimate true performance in actual prospective experiments. Here, we systematically consider metric value surface generation as a consequence of data balance, and propose the computation of an inverse cumulative distribution function taken over a metric surface. The proposed distribution analysis can aid in the selection of metrics when formulating study design. In addition to theoretical analyses, a practical example in chemogenomic virtual screening highlights the care required in metric selection and interpretation.

The Future of Computational Chemogenomics

Following the elucidation of the human genome, chemogenomics emerged in the beginning of the twenty-first century as an interdisciplinary research field with the aim to accelerate target and drug discovery by making best usage of the genomic data and the data linkable to it. What started as a systematization approach within protein target families now encompasses all types of chemical compounds and gene products. A key objective of chemogenomics is the establishment, extension, analysis, and prediction of a comprehensive SAR matrix which by application will enable further systematization in drug discovery. Herein we outline future perspectives of chemogenomics including the extension to new molecular modalities, or the potential extension beyond the pharma to the agro and nutrition sectors, and the importance for environmental protection. The focus is on computational sciences with potential applications for compound library design, virtual screening, hit assessment, analysis of phenotypic screens, lead finding and optimization, and systems biology-based prediction of toxicology and translational research.

Selection of Informative Examples in Chemogenomic Datasets

High-throughput and high-content screening campaigns have resulted in the creation of large chemogenomic matrices. These matrices form the training data which is used to build ligand-target interaction models for pharmacological and chemical biology research. While academic, government, and industrial efforts continuously add to the ligand-target data pairs available for modeling, major research efforts are devoted to improving machine learning techniques to cope with the sparseness, heterogeneity, and size of available datasets as well as inherent noise and bias. This "race of arms" has led to the creation of algorithms to generate highly complex models with high prediction performance at the cost of training efficiency as well as interpretability.In contrast, recent studies have challenged the necessity for "big data" in chemogenomic modeling and found that models built on larger numbers of examples do not necessarily result in better predictive abilities. Automated adaptive selection of the training data (ligand-target instances) used for model creation can result in considerably smaller training sets that retain prediction performance on par with training using hundreds of thousands of data points. In this chapter, we describe the protocols used for one such iterative chemogenomic selection technique, including model construction and update as well as possible techniques for evaluations of constructed models and analysis of the iterative model construction.

Association between UGT1A1*28*28 genotype and lung cancer in the Japanese population

BACKGROUND

Lung cancer is the leading cause of cancer death and is closely linked to tobacco smoking. Genetic polymorphisms in genes that encode enzymes involved in metabolizing tobacco carcinogens could affect an individual's risk for lung cancer. While polymorphism of UDP-glucuronosyltransferase1A1 (UGT1A1) is involved in detoxification of benzo(a)pyrene-7,8-dihydrodiol(-), a major tobacco carcinogen, the association between UGT1A1 genotype and lung cancer has not been examined.

METHODS

We retrieved the clinical data of 5,285 patients who underwent systemic chemotherapy at Kyoto University Hospital. A total of 765 patients (194 lung cancer patients and 671 patients with other malignancies) with UGT1A1 genotyping data were included in this analysis. We used logistic regression with recessive, dominant, and additive models to identify differences in genotype frequencies between lung cancer and other malignancies.

RESULTS

In the recessive model, UGT1A1*28*28 genotype was significantly associated with lung cancer compared to other malignancies (odds ratio 5.3, P = 0.0083). Among lung cancer patients with a smoking history, squamous cell carcinoma was significantly predominant in patients with UGT1A1*28*28 compared to those with other UGT1A1 genotypes (P = 0.024).

CONCLUSION

This is the first study to demonstrate a significant association between the homozygous UGT1A1*28 genotype and lung cancer.

A framework for developing rural academic general practices: a qualitative case study in rural Victoria

INTRODUCTION

There is increasing pressure for Australian rural general practices to engage in educational delivery as a means of addressing workforce issues and accommodating substantial increases in learners. For practices that have now developed a strong focus on education, there is the challenge to complement this by engaging in research activity. This study develops a rural academic general practice framework to assist rural practices in developing both comprehensive educational activity and a strong research focus thus moving towards functioning as mature academic units.

METHODS

A case study research design was used with the unit of analysis at the level of the rural general practice. Purposively sampled practices were recruited and individual interviews conducted with staff (supervisors, practice managers, nurses), learners (medical students, interns and registrars) and patients. Three practices hosted 'multi-level learners', two practices hosted one learner group and one had no learners. Forty-four individual interviews were conducted with staff, learners and patients. Audio recordings were transcribed for thematic analysis. After initial inductive coding, deductive analysis was undertaken with reference to recent literature and the expertise of the research team resulting in the rural academic general practice framework.

RESULTS

Three key themes emerged with embedded subthemes. For the first theme, organisational considerations, subthemes were values/vision/culture, patient population and clinical services, staffing, physical infrastructure/equipment, funding streams and governance. For the second theme, educational considerations, subthemes were processes, clinical supervision, educational networks and learner presence. Third, for research considerations, there were the subthemes of attitude to research and research activity. The framework maps the development of a rural academic practice across these themes in four progressive stages: beginning, emerging, consolidating and established.

CONCLUSIONS

The data enabled a framework to be constructed to map rural general practice activity with respect to activity characteristic of an academic general practice. The framework offers guidance to practices seeking to transition towards becoming a mature academic practice. The framework also offers guidance to educational institutions and funding bodies to support the development of academic activity in rural general practices. The strengths and limitations of the study design are outlined.

Antipsychotics-associated serious adverse events in children: an analysis of the FAERS database

OBJECTIVE

The reports submitted to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) from 1997 to 2011 were reviewed to assess serious adverse events induced by the administration of antipsychotics to children.

METHODS

Following pre-processing of FAERS data by elimination of duplicated records as well as adjustments to standardize drug names, reports involving haloperidol, olanzapine, quetiapine, clozapine, ziprasidone, risperidone, and aripiprazole were analyzed in children (age 0-12). Signals in the data that signified a drug-associated adverse event were detected via quantitative data mining algorithms. The algorithms applied to this study include the empirical Bayes geometric mean, the reporting odds ratio, the proportional reporting ratio, and the information component of a Bayesian confidence propagation neural network. Neuroleptic malignant syndrome (NMS), QT prolongation, leukopenia, and suicide attempt were focused on as serious adverse events.

RESULTS

In regard to NMS, the signal scores for haloperidol and aripiprazole were greater than for other antipsychotics. Significant signals of the QT prolongation adverse event were detected only for ziprasidone and risperidone. With respect to leukopenia, the association with clozapine was noteworthy. In the case of suicide attempt, signals for haloperidol, olanzapine, quetiapine, risperidone, and aripiprazole were detected.

CONCLUSIONS

It was suggested that there is a level of diversity in the strength of the association between various first- and second-generation antipsychotics with associated serious adverse events, which possibly lead to fatal outcomes. We recommend that research be continued in order to gather a large variety and quantity of related information, and that both available and newly reported data be placed in the context of multiple medical viewpoints in order to lead to improved levels of care.

Reliable pre-eclampsia pathways based on multiple independent microarray data sets

Pre-eclampsia is a multifactorial disorder characterized by heterogeneous clinical manifestations. Gene expression profiling of preeclamptic placenta have provided different and even opposite results, partly due to data compromised by various experimental artefacts. Here we aimed to identify reliable pre-eclampsia-specific pathways using multiple independent microarray data sets. Gene expression data of control and preeclamptic placentas were obtained from Gene Expression Omnibus. Single-sample gene-set enrichment analysis was performed to generate gene-set activation scores of 9707 pathways obtained from the Molecular Signatures Database. Candidate pathways were identified by t-test-based screening using data sets, GSE10588, GSE14722 and GSE25906. Additionally, recursive feature elimination was applied to arrive at a further reduced set of pathways. To assess the validity of the pre-eclampsia pathways, a statistically-validated protocol was executed using five data sets including two independent other validation data sets, GSE30186, GSE44711. Quantitative real-time PCR was performed for genes in a panel of potential pre-eclampsia pathways using placentas of 20 women with normal or severe preeclamptic singleton pregnancies (n = 10, respectively). A panel of ten pathways were found to discriminate women with pre-eclampsia from controls with high accuracy. Among these were pathways not previously associated with pre-eclampsia, such as the GABA receptor pathway, as well as pathways that have already been linked to pre-eclampsia, such as the glutathione and CDKN1C pathways. mRNA expression of GABRA3 (GABA receptor pathway), GCLC and GCLM (glutathione metabolic pathway), and CDKN1C was significantly reduced in the preeclamptic placentas. In conclusion, ten accurate and reliable pre-eclampsia pathways were identified based on multiple independent microarray data sets. A pathway-based classification may be a worthwhile approach to elucidate the pathogenesis of pre-eclampsia.

Chemical genomics approach for GPCR-ligand interaction prediction and extraction of ligand binding determinants

Chemical genomics research has revealed that G-protein coupled receptors (GPCRs) interact with a variety of ligands and that a large number of ligands are known to bind GPCRs even with low transmembrane (TM) sequence similarity. It is crucial to extract informative binding region propensities from large quantities of bioactivity data. To address this issue, we propose a machine learning approach that enables identification of both chemical substructures and amino acid properties that are associated with ligand binding, which can be applied to virtual ligand screening on a GPCR-wide scale. We also address the question of how to select plausible negative noninteraction pairs based on a statistical approach in order to develop reliable prediction models for GPCR-ligand interactions. The key interaction sites estimated by our approach can be of great use not only for screening of active compounds but also for modification of active compounds with the aim of improving activity or selectivity.

Systems biology and systems chemistry: new directions for drug discovery

Improvements in drug design have historically been centered around structure-based optimization of molecule specificity for a targeted protein, in an effort to reduce unintentional binding to other proteins and off-target effects. Although the "one-to-one" drug design strategy has been successful in impairing function of targets associated with a number of diseases, recent reports of drug promiscuity, which are a potential source of adverse reactions in patients, make a case to refine the drug design strategy such that it includes an awareness of multiple interactions from both ligand and protein perspectives. Polypharmacology and chemical biology studies are amassing interaction data at rapid rates, and the integration of such data into an interpretable model requires zooming our perspective out from the single ligand-target level to the larger network-wide level. We review some of the recent developments in systems-level research for drug design and discovery, and discuss the directions that some drug design efforts are heading toward.

Adverse event profiles of 5-fluorouracil and capecitabine: data mining of the public version of the FDA Adverse Event Reporting System, AERS, and reproducibility of clinical observations

OBJECTIVE

The safety profiles of oral fluoropyrimidines were compared with 5-fluorouracil (5-FU) using adverse event reports (AERs) submitted to the Adverse Event Reporting System, AERS, of the US Food and Drug Administration (FDA).

METHODS

After a revision of arbitrary drug names and the deletion of duplicated submissions, AERs involving 5-FU and oral fluoropyrimidines were analyzed. Standardized official pharmacovigilance tools were used for the quantitative detection of signals, i.e., drug-associated adverse events, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean.

RESULTS

Based on 22,017,956 co-occurrences, i.e., drug-adverse event pairs, found in 1,644,220 AERs from 2004 to 2009, it was suggested that leukopenia, neutropenia, and thrombocytopenia were more frequently accompanied by the use of 5-FU than capecitabine, whereas diarrhea, nausea, vomiting, and hand-foot syndrome were more frequently associated with capecitabine. The total number of co-occurrences was not large enough to compare tegafur, tegafur-uracil (UFT), tegafur-gimeracil-oteracil potassium (S-1), or doxifluridine to 5-FU.

CONCLUSION

The results obtained herein were consistent with clinical observations, suggesting the usefulness of the FDA's AERS database and data mining methods used, but the number of co-occurrences is an important factor in signal detection.

[Splice sites are overrepresented in Pasilla binding motif clusters in D. melanogaster genes]

For RNA-binding protein Pasilla, which has been shown to play a role in alternative splicing regulation, binding sites and clusters of binding sites are found in silico in the whole genome of D. melanogaster. The current study analyzes the occurrence of splice sites in binding site clusters. Several hundred thousand binding site motifs and thousands of significant motif clusters were identified. It was discovered that exon-intron borders in D. melanogaster genes are reliably found within Pasilla binding motif clusters, with a higher frequency than could be otherwise expected based on a random model. Additionally, donor splice sites are found in Pasilla clusters twice as often as acceptor sites. This phenomena is observed both for exons annotated as alternatively spliced and for exons annotated as constitutive. These observations support the hypothesis that Pasilla plays a functional role in splicing regulation of D. melanogaster.

Hypersensitivity reactions to anticancer agents: data mining of the public version of the FDA adverse event reporting system, AERS

Background

Previously, adverse event reports (AERs) submitted to the US Food and Drug Administration (FDA) database were reviewed to confirm platinum agent-associated hypersensitivity reactions. The present study was performed to confirm whether the database could suggest the hypersensitivity reactions caused by anticancer agents, paclitaxel, docetaxel, procarbazine, asparaginase, teniposide, and etoposide.

Methods

After a revision of arbitrary drug names and the deletion of duplicated submissions, AERs involving candidate agents were analyzed. The National Cancer Institute Common Terminology Criteria for Adverse Events version 4.0 was applied to evaluate the susceptibility to hypersensitivity reactions, and standardized official pharmacovigilance tools were used for quantitative detection of signals, i.e., drug-associated adverse events, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean.

Results

Based on 1,644,220 AERs from 2004 to 2009, the signals were detected for paclitaxel-associated mild, severe, and lethal hypersensitivity reactions, and docetaxel-associated lethal reactions. However, the total number of adverse events occurring with procarbazine, asparaginase, teniposide, or etoposide was not large enough to detect signals.

Conclusions

The FDA's adverse event reporting system, AERS, and the data mining methods used herein are useful for confirming drug-associated adverse events, but the number of co-occurrences is an important factor in signal detection.

Nutrient Sources and Transport in the Missouri River Basin, with Emphasis on the Effects of Irrigation and Reservoirs

SPAtially Referenced Regressions On Watershed attributes (SPARROW) models were used to relate instream nutrient loads to sources and factors influencing the transport of nutrients in the Missouri River Basin. Agricultural inputs from fertilizer and manure were the largest nutrient sources throughout a large part of the basin, although atmospheric and urban inputs were important sources in some areas. Sediment mobilized from stream channels was a source of phosphorus in medium and larger streams. Irrigation on agricultural land was estimated to decrease the nitrogen load reaching the Mississippi River by as much as 17%, likely as a result of increased anoxia and denitrification in the soil zone. Approximately 16% of the nitrogen load and 33% of the phosphorus load that would have otherwise reached the Mississippi River was retained in reservoirs and lakes throughout the basin. Nearly half of the total attenuation occurred in the eight largest water bodies. Unlike the other major tributary basins, nearly the entire instream nutrient load leaving the outlet of the Platte and Kansas River subbasins reached the Mississippi River. Most of the larger reservoirs and lakes in the Platte River subbasin are upstream of the major sources, whereas in the Kansas River subbasin, most of the source inputs are in the southeast part of the subbasin where characteristics of the area and proximity to the Missouri River facilitate delivery of nutrients to the Mississippi River.

UNSATURATED FATTY ACIDS IN THE DIETARY DESTRUCTION OF N,N-DIMETHYLAMINOAZOBENZENE (BUTTER YELLOW) AND IN THE PRODUCTION OF ANEMIA IN RATS

Crude linoleic acid incorporated with or without butter yellow in a synthetic diet proved to be toxic for rats. The toxic effect manifested itself in loss of weight, progressive anemia of the secondary type, leucopenia, and pediculosis. It could be neutralized preventively and therapeutically by administration of yeast. The toxicity of the diet containing linoleic acid appears to be due to oxidative break-down products of the unsaturated fatty acid. The color of the same diet when it contained crude linoleic acid supplemented with butter yellow faded progressively in the presence of air (O₂), even at room temperature. Purified preparations of linoleic acid and, to a less degree, purified preparations of arachidonic and oleic acids have shown the same destructive effect on butter yellow in vitro. Brown (unpolished) or white rice contains a stabilizer (antioxidant) for the preservation of butter yellow. In experiments on the production of hepatoma in rats following the ingestion of butter yellow, rice on one hand and crisco or butter fat on the other hand have proved to be procarcinogenic. These results would seem to be correlated with the preservation of butter yellow in the diet and in the intestine, because of the antioxidant in rice and the low supply of unsaturated fatty acids,respectively.

How to obtain excellent response rates when surveying physicians

This paper outlines ways to maximize response rates to surveys by summarizing the most relevant literature to date and demonstrating how these techniques have resulted in consistently high rates of return in family practice research. We describe the methodology used in recent surveys of physicians conducted by the Centre for Studies in Family Medicine through its Thames Valley Family Practice Research Unit, located in London, Ontario, Canada and funded by the Ontario Ministry of Health and Long-Term Care. The identification and implementation of these techniques to maximize response rates is critical, as primary health care researchers often rely on information gathered through questionnaires to study physicians' practice profiles, experiences and attitudes. Four separate and distinct mailed surveys of physicians using a modified Dillman approach were conducted from 2001 to 2004. The sampling strategies, topics, types of questions and response formats of these surveys varied. The first survey did not use any incentives or recorded delivery/registered mail and received a response rate of 48%. In sharp contrast, the other three surveys obtained responses rates of 76%, 74%, 74%, respectively, achieved through the use of gift certificates and recorded delivery/registered mail. Sending a survey by recorded delivery/registered mail tends to result in the survey package being given priority in the physicians' incoming mail at the practice. Gift certificates partially compensate physicians for time spent completing the survey and recognition of the time required is appreciated. The response rates achieved provide strong evidence to support the use of monetary incentives and recorded delivery/registered mail (along with the Dillman approach) in survey research. It is anticipated that this evidence will be used by other researchers to justify requests for funding to cover the costs associated with incentives and recorded delivery/registered mail. We recommend the use of these strategies to maximize response rates and improve the quality of this type of primary health care research.

Identification of novel DNA repair proteins via primary sequence, secondary structure, and homology

BACKGROUND

DNA repair is the general term for the collection of critical mechanisms which repair many forms of DNA damage such as methylation or ionizing radiation. DNA repair has mainly been studied in experimental and clinical situations, and relatively few information-based approaches to new extracting DNA repair knowledge exist. As a first step, automatic detection of DNA repair proteins in genomes via informatics techniques is desirable; however, there are many forms of DNA repair and it is not a straightforward process to identify and classify repair proteins with a single optimal method. We perform a study of the ability of homology and machine learning-based methods to identify and classify DNA repair proteins, as well as scan vertebrate genomes for the presence of novel repair proteins. Combinations of primary sequence polypeptide frequency, secondary structure, and homology information are used as feature information for input to a Support Vector Machine (SVM).

RESULTS

We identify that SVM techniques are capable of identifying portions of DNA repair protein datasets without admitting false positives; at low levels of false positive tolerance, homology can also identify and classify proteins with good performance. Secondary structure information provides improved performance compared to using primary structure alone. Furthermore, we observe that machine learning methods incorporating homology information perform best when data is filtered by some clustering technique. Analysis by applying these methodologies to the scanning of multiple vertebrate genomes confirms a positive correlation between the size of a genome and the number of DNA repair protein transcripts it is likely to contain, and simultaneously suggests that all organisms have a non-zero minimum number of repair genes. In addition, the scan result clusters several organisms' repair abilities in an evolutionarily consistent fashion. Analysis also identifies several functionally unconfirmed proteins that are highly likely to be involved in the repair process. A new web service, INTREPED, has been made available for the immediate search and annotation of DNA repair proteins in newly sequenced genomes.

CONCLUSION

Despite complexity due to a multitude of repair pathways, combinations of sequence, structure, and homology with Support Vector Machines offer good methods in addition to existing homology searches for DNA repair protein identification and functional annotation. Most importantly, this study has uncovered relationships between the size of a genome and a genome's available repair repertoire, and offers a number of new predictions as well as a prediction service, both which reduce the search time and cost for novel repair genes and proteins.

Multiple methods for protein side chain packing using maximum weight cliques

In this paper, we present several methods for computing a solution to the protein side chain packing problem, with all methods having a common solution approach of breaking the polymer into subpolymers and using maximum edge weight cliques to prune the search space for the optimal side chain packing. We characterize the graph sizes generated for each method and compare their prediction accuracies. These methods are demonstrated for computing proteins up to approximately 8000 residues. In addition, we update a result published previously.

Effects of weight gain on medical care costs

OBJECTIVE

To examine in middle-aged adults the effect of medical care costs of large, rapid weight gain compared to weight maintenance.

DESIGN

: Retrospective cohort study for a 3-y time period.

SETTING AND PARTICIPANTS

Population-based sample (N=15174) of men and women members of a large managed care organization, aged 35-65 y, with a body mass index (BMI) >25 kg/m(2) at baseline. Health-care utilization and costs were measured at baseline and over the 3-y follow-up period.

RESULTS

Mean age at baseline was 49.7 y and mean BMI was 31.5 kg/m(2). During the 3-y follow-up period, 40.8% were classified as weight maintainers (+/-4 pounds), 45.3% gained 5-19 pounds, and 13.9% gained >/=20 pounds. A weight gain of >/=20 pounds was significantly associated with increased total medical care costs in all subgroups evaluated. Among all subjects, for those who gained >/=20 pounds compared to those who maintained weight, the adjusted 3-y increase in costs was 561 dollars. Among the subgroup with baseline comorbidities, the adjusted 3-y change in total medical care costs was 711 dollars. Multivariate analyses showed no significant differences between those who gained 5-19 pounds and those who maintained weight. Baseline BMI and comorbidities were also significant predictors of change in medical care costs, independent of weight gain.

CONCLUSION

A large 3-y weight gain (>/=20 lb) in middle-aged overweight and obese adults is associated with a correspondingly larger increase in total medical care costs compared to weight maintainers. The prevention of large weight gains holds promise for significantly reducing future medical care costs. Future studies should examine the causes of rapid weight gain and evaluate approaches to prevent and reverse such weight gain.