Tissue proteomics of the low‐molecular weight proteome using an integrated cLC‐ESI‐QTOFMS approach

Analysis of the protein/peptide composition of tissue has provided meaningful insights into tissue biology and even disease mechanisms. However, little has been published regarding top down methods to investigate lower molecular weight (MW) (500–5000 Da) species in tissue. Here, we evaluate a tissue proteomics approach involving tissue homogenization followed by depletion of large proteins and then cLC‐MS (where c stands for capillary) analysis to interrogate the low MW/low abundance tissue proteome. In the development of this method, sheep heart, lung, liver, kidney, and spleen were surveyed to test our ability to observe tissue differences. After categorical tissue differences were demonstrated, a detailed study of this method's reproducibility was undertaken to determine whether or not it is suitable for analyzing more subtle differences in the abundance of small proteins and peptides. Our results suggest that this method should be useful in exploring the low MW proteome of tissues.     

Combining focused identification of germplasm and core collection strategies to identify genebank accessions for central European soybean breeding

Environmental adaptation of crops is essential for reliable agricultural production and an important breeding objective. Genebanks provide genetic variation for the improvement of modern varieties, but the selection of suitable germplasm is frequently impeded by incomplete phenotypic data. We address this bottleneck by combining a Focused Identification of Germplasm Strategy (FIGS) with core collection methodology to select soybean (Glycine max) germplasm for Central European breeding from a collection of >17,000 accessions. By focussing on adaptation to high-latitude cold regions, we selected an “environmental precore” of 3,663 accessions using environmental data and compared the Donor opulation of Environments (DPE) in Asia and the Target Population of Environments (TPE) in Central Europe in the present and 2070. Using single nucleotide polymorphisms, we reduced the precore into two diverse core collections of 183 and 366 accessions to serve as diversity panels for evaluation in the TPE. Genetic differentiation between precore and non-precore accessions revealed genomic regions that control maturity, and novel candidate loci for environmental adaptation, demonstrating the potential of diversity panels for studying adaptation. Objective-driven core collections have the potential to increase germplasm utilization for abiotic adaptation by breeding for a rapidly changing climate, or de novo adaptation of crops to expand cultivation ranges.     

基于非数据依赖的鞘脂菌蛋白质组学分析方法的建立

【背景】鞘脂菌是一类可高效降解以菲为代表的多环芳烃有机污染物的菌株,其在环境污染治理及生物技术领域具有广阔的应用前景。【目的】为了优化测试方法,获得更完整的鞘脂菌Sphingobium yanoikuyae SJTF-8在菲胁迫下表达差异的蛋白。【方法】利用数据依赖型及数据非依赖型两种蛋白质组学数据采集方法,比较了鞘脂菌SJTF-8在菲胁迫下蛋白质水平的表达变化。【结果】两种技术方法下共得到580个表达差异蛋白,这些蛋白在细胞代谢、转运和调控等方面发挥一定功能。【结论】数据非依赖性采集(data-independent acquisition,DIA)技术在重复性以及低丰度蛋白的检测上明显好于数据依赖型采集(datadependentacquisition,DDA)技术,因此,DIA在实际可用的表达差异蛋白检出方面具备明显优势,为发现菲胁迫下细胞诱导表达的低丰度调控蛋白提供帮助。     

Computational Reproducibility in The Wildlife Society's Flagship Journals

Scientific progress depends upon the accumulation of empirical knowledge via reproducible methodology. Although reproducibility is a main tenet of the scientific method, recent studies have highlighted widespread failures in adherence to this ideal. The goal of this study was to gauge the level of computational reproducibility, or the ability to obtain the same results using the same data and analytic methods as in the original publication, in the field of wildlife science. We randomly selected 80 papers published in the Journal of Wildlife Management and Wildlife Society Bulletin between 1 June 2016 and 1 June 2018. Of those that were suitable for reproducibility review (n = 74), we attempted to obtain study data from online repositories or directly from authors. Forty-two authors did not respond to our requests, and we were further unable to obtain data from authors of 13 other studies. Of the 19 studies for which we were able to obtain data and complete our analysis, we judged that 13 were mostly or fully reproducible. We conclude that the studies with publicly available data or data shared upon request were largely reproducible, but we remain concerned about the difficulty in obtaining data from recently published papers. We recommend increased data-sharing, data organization and documentation, communication, and training to advance computational reproducibility in the wildlife sciences. © 2020 The Authors. The Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.     

Integrated evaluation of targeted and non-targeted therapies in a network meta-analysis

Individualized therapies for patients with biomarkers are moving more and more into the focus of research interest when developing new treatments. Hereby, the term individualized (or targeted) therapy denotes a treatment specifically developed for biomarker-positive patients. A network meta-analysis model for a binary endpoint combining the evidence for a targeted therapy from individual patient data with the evidence for a non-targeted therapy from aggregate data is presented and investigated. The biomarker status of the patients is either available at patient-level in individual patient data or at study-level in aggregate data. Both types of biomarker information have to be included. The evidence synthesis model follows a Bayesian approach and applies a meta-regression to the studies with aggregate data. In a simulation study, we address three treatment arms, one of them investigating a targeted therapy. The bias and the root-mean-square error of the treatment effect estimate for the subgroup of biomarker-positive patients based on studies with aggregate data are investigated. Thereby, the meta-regression approach is compared to approaches applying alternative solutions. The regression approach has a surprisingly small bias even in the presence of few studies. By contrast, the root-mean-square error is relatively greater. An illustrative example is provided demonstrating implementation of the presented network meta-analysis model in a clinical setting.     

Outlier detection methods are still effective even using virtual species created with the probabilistic approach

Liu et al. (Journal of Biogeography, 2018, 45 :164–176) presented an approach to detect outliers in species distribution data by developing virtual species created using the threshold approach. Meynard et al. (Journal of biogeography, 2019, 46 :2141–2144) raised concerns about this approach stating that ‘using a probabilistic approach … may significantly change results’. Here we provide a new series of simulations using the two approaches and demonstrate that the outlier detection approach based on pseudo species distribution models was still effective when using the probabilistic approach, although the detection rate was lower than when using the threshold approach.     

One-step targeted maximum likelihood estimation for time-to-event outcomes

Researchers in observational survival analysis are interested in not only estimating survival curve nonparametrically but also having statistical inference for the parameter. We consider right-censored failure time data where we observe n independent and identically distributed observations of a vector random variable consisting of baseline covariates, a binary treatment at baseline, a survival time subject to right censoring, and the censoring indicator. We assume the baseline covariates are allowed to affect the treatment and censoring so that an estimator that ignores covariate information would be inconsistent. The goal is to use these data to estimate the counterfactual average survival curve of the population if all subjects are assigned the same treatment at baseline. Existing observational survival analysis methods do not result in monotone survival curve estimators, which is undesirable and may lose efficiency by not constraining the shape of the estimator using the prior knowledge of the estimand. In this paper, we present a one-step Targeted Maximum Likelihood Estimator (TMLE) for estimating the counterfactual average survival curve. We show that this new TMLE can be executed via recursion in small local updates. We demonstrate the finite sample performance of this one-step TMLE in simulations and an application to a monoclonal gammopathy data.     

Large scale maximum average power multiple inference on time-course count data with application to RNA-seq analysis

Experiments that longitudinally collect RNA sequencing (RNA-seq) data can provide transformative insights in biology research by revealing the dynamic patterns of genes. Such experiments create a great demand for new analytic approaches to identify differentially expressed (DE) genes based on large-scale time-course count data. Existing methods, however, are suboptimal with respect to power and may lack theoretical justification. Furthermore, most existing tests are designed to distinguish among conditions based on overall differential patterns across time, though in practice, a variety of composite hypotheses are of more scientific interest. Finally, some current methods may fail to control the false discovery rate. In this paper, we propose a new model and testing procedure to address the above issues simultaneously. Specifically, conditional on a latent Gaussian mixture with evolving means, we model the data by negative binomial distributions. Motivated by Storey (2007) and Hwang and Liu (2010), we introduce a general testing framework based on the proposed model and show that the proposed test enjoys the optimality property of maximum average power. The test allows not only identification of traditional DE genes but also testing of a variety of composite hypotheses of biological interest. We establish the identifiability of the proposed model, implement the proposed method via efficient algorithms, and demonstrate its good performance via simulation studies. The procedure reveals interesting biological insights, when applied to data from an experiment that examines the effect of varying light environments on the fundamental physiology of the marine diatom Phaeodactylum tricornutum.     

Partial least squares for functional joint models with applications to the Alzheimer's disease neuroimaging initiative study

Many biomedical studies have identified important imaging biomarkers that are associated with both repeated clinical measures and a survival outcome. The functional joint model (FJM) framework, proposed by Li and Luo in 2017, investigates the association between repeated clinical measures and survival data, while adjusting for both high-dimensional images and low-dimensional covariates based on the functional principal component analysis (FPCA). In this paper, we propose a novel algorithm for the estimation of FJM based on the functional partial least squares (FPLS). Our numerical studies demonstrate that, compared to FPCA, the proposed FPLS algorithm can yield more accurate and robust estimation and prediction performance in many important scenarios. We apply the proposed FPLS algorithm to a neuroimaging study. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.     

A Bayesian multi-risks survival (MRS) model in the presence of double censorings

Semi-competing risks data include the time to a nonterminating event and the time to a terminating event, while competing risks data include the time to more than one terminating event. Our work is motivated by a prostate cancer study, which has one nonterminating event and two terminating events with both semi-competing risks and competing risks present as well as two censoring times. In this paper, we propose a new multi-risks survival (MRS) model for this type of data. In addition, the proposed MRS model can accommodate noninformative right-censoring times for nonterminating and terminating events. Properties of the proposed MRS model are examined in detail. Theoretical and empirical results show that the estimates of the cumulative incidence function for a nonterminating event may be biased if the information on a terminating event is ignored. A Markov chain Monte Carlo sampling algorithm is also developed. Our methodology is further assessed using simulations and also an analysis of the real data from a prostate cancer study. As a result, a prostate-specific antigen velocity greater than 2.0 ng/mL per year and higher biopsy Gleason scores are positively associated with a shorter time to death due to prostate cancer.