High-throughput mRNA sequencing of human placenta shows sex differences across gestation

INTRODUCTION

Fetal sex affects fetal and maternal health outcomes in pregnancy, but this connection remains poorly understood. As the placenta is the route of fetomaternal communication and derives from the fetal genome, placental gene expression sex differences may explain these outcomes.

OBJECTIVES

We utilized next generation sequencing to study the normal human placenta in both sexes in first and third trimester to generate a normative transcriptome based on sex and gestation.

STUDY DESIGN

We analyzed 124 first trimester (T1, 59 female and 65 male) and 43 third trimester (T3, 18 female and 25 male) samples for sex differences within each trimester and sex-specific gestational differences.

RESULTS

Placenta shows more significant sexual dimorphism in T1, with 94 T1 and 26 T3 differentially expressed genes (DEGs). The sex chromosomes contributed 60.6% of DEGs in T1 and 80.8% of DEGs in T3, excluding X/Y pseudoautosomal regions. There were 6 DEGs from the pseudoautosomal regions, only significant in T1 and all upregulated in males. The distribution of DEGs on the X chromosome suggests genes on Xp (the short arm) may be particularly important in placental sex differences. Dosage compensation analysis of X/Y homolog genes shows expression is primarily contributed by the X chromosome. In sex-specific analyses of first versus third trimester, there were 2815 DEGs common to both sexes upregulated in T1, and 3263 common DEGs upregulated in T3. There were 7 female-exclusive DEGs upregulated in T1, 15 female-exclusive DEGs upregulated in T3, 10 male-exclusive DEGs upregulated in T1, and 20 male-exclusive DEGs upregulated in T3.

DISCUSSION

This is the largest cohort of placentas across gestation from healthy pregnancies defining the normative sex dimorphic gene expression and sex common, sex specific and sex exclusive gene expression across gestation. The first trimester has the most sexually dimorphic transcripts, and the majority were upregulated in females compared to males in both trimesters. The short arm of the X chromosome and the pseudoautosomal region is particularly critical in defining sex differences in the first trimester placenta. As pregnancy is a dynamic state, sex specific DEGs across gestation may contribute to sex dimorphic changes in overall outcomes.

EndoGenius: Optimized Neuropeptide Identification from Mass Spectrometry Datasets

Neuropeptides represent a unique class of signaling molecules that have garnered much attention but require special consideration when identifications are gleaned from mass spectra. With highly variable sequence lengths, neuropeptides must be analyzed in their endogenous state. Further, neuropeptides share great homology within families, differing by as little as a single amino acid residue, complicating even routine analyses and necessitating optimized computational strategies for confident and accurate identifications. We present EndoGenius, a database searching strategy designed specifically for elucidating neuropeptide identifications from mass spectra by leveraging optimized peptide-spectrum matching approaches, an expansive motif database, and a novel scoring algorithm to achieve broader representation of the neuropeptidome and minimize reidentification. This work describes an algorithm capable of reporting more neuropeptide identifications at 1% false-discovery rate than alternative software in five Callinectes sapidus neuronal tissue types.

Precursor deconvolution error estimation: The missing puzzle piece in false discovery rate in top-down proteomics

Top-down proteomics (TDP) directly analyzes intact proteins and thus provides more comprehensive qualitative and quantitative proteoform-level information than conventional bottom-up proteomics (BUP) that relies on digested peptides and protein inference. While significant advancements have been made in TDP in sample preparation, separation, instrumentation, and data analysis, reliable and reproducible data analysis still remains one of the major bottlenecks in TDP. A key step for robust data analysis is the establishment of an objective estimation of proteoform-level false discovery rate (FDR) in proteoform identification. The most widely used FDR estimation scheme is based on the target-decoy approach (TDA), which has primarily been established for BUP. We present evidence that the TDA-based FDR estimation may not work at the proteoform-level due to an overlooked factor, namely the erroneous deconvolution of precursor masses, which leads to incorrect FDR estimation. We argue that the conventional TDA-based FDR in proteoform identification is in fact protein-level FDR rather than proteoform-level FDR unless precursor deconvolution error rate is taken into account. To address this issue, we propose a formula to correct for proteoform-level FDR bias by combining TDA-based FDR and precursor deconvolution error rate.

Assessing Multiple Evidence Streams to Decide on Confidence for Identification of Post-Translational Modifications, within and Across Data Sets

Phosphorylation is a post-translational modification of great interest to researchers due to its relevance in many biological processes. LC-MS/MS techniques have enabled high-throughput data acquisition, with studies claiming identification and localization of thousands of phosphosites. The identification and localization of phosphosites emerge from different analytical pipelines and scoring algorithms, with uncertainty embedded throughout the pipeline. For many pipelines and algorithms, arbitrary thresholding is used, but little is known about the actual global false localization rate in these studies. Recently, it has been suggested to use decoy amino acids to estimate global false localization rates of phosphosites, among the peptide-spectrum matches reported. Here, we describe a simple pipeline aiming to maximize the information extracted from these studies by objectively collapsing from peptide-spectrum match to the peptidoform-site level, as well as combining findings from multiple studies while maintaining track of false localization rates. We show that the approach is more effective than current processes that use a simpler mechanism for handling phosphosite identification redundancy within and across studies. In our case study using eight rice phosphoproteomics data sets, 6368 unique sites were confidently identified using our decoy approach compared to 4687 using traditional thresholding in which false localization rates are unknown.

HyPep: An Open-Source Software for Identification and Discovery of Neuropeptides Using Sequence Homology Search

Neuropeptides are a class of endogenous peptides that have key regulatory roles in biochemical, physiological, and behavioral processes. Mass spectrometry analyses of neuropeptides often rely on protein informatics tools for database searching and peptide identification. As neuropeptide databases are typically experimentally built and comprised of short sequences with high sequence similarity to each other, we developed a novel database searching tool, HyPep, which utilizes sequence homology searching for peptide identification. HyPep aligns de novo sequenced peptides, generated through PEAKS software, with neuropeptide database sequences and identifies neuropeptides based on the alignment score. HyPep performance was optimized using LC-MS/MS measurements of peptide extracts from various Callinectes sapidus neuronal tissue types and compared with a commercial database searching software, PEAKS DB. HyPep identified more neuropeptides from each tissue type than PEAKS DB at 1% false discovery rate, and the false match rate from both programs was 2%. In addition to identification, this report describes how HyPep can aid in the discovery of novel neuropeptides.

Heavy Methyl SILAC Metabolic Labeling of Human Cell Lines for High-Confidence Identification of R/K-Methylated Peptides by High-Resolution Mass Spectrometry

Protein methylation is a widespread post-translational modification (PTM) involved in several important biological processes including, but not limited to, RNA splicing, signal transduction, translation, and DNA repair. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is considered today the most versatile and accurate technique to profile PTMs with high precision and proteome-wide depth; however, the identification of protein methylations by MS is still prone to high false discovery rates. In this chapter, we describe the heavy methyl SILAC metabolic labeling strategy that allows high-confidence identification of in vivo methyl-peptides by MS-based proteomics. We provide a general protocol that covers the steps of heavy methyl labeling of cultured cells, protein sample preparation, LC-MS/MS analysis, and downstream computational analysis of the acquired MS data.

fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS

BACKGROUND

Genome-wide association studies (GWAS) are limited in power to detect associations that exceed the stringent genome-wide significance threshold. This limitation can be alleviated by leveraging relevant auxiliary data, such as functional genomic data. Frameworks utilising the conditional false discovery rate have been developed for this purpose, and have been shown to increase power for GWAS discovery whilst controlling the false discovery rate. However, the methods are currently only applicable for continuous auxiliary data and cannot be used to leverage auxiliary data with a binary representation, such as whether SNPs are synonymous or non-synonymous, or whether they reside in regions of the genome with specific activity states.

RESULTS

We describe an extension to the cFDR framework for binary auxiliary data, called "Binary cFDR". We demonstrate FDR control of our method using detailed simulations, and show that Binary cFDR performs better than a comparator method in terms of sensitivity and FDR control. We introduce an all-encompassing user-oriented CRAN R package ( https://annahutch.github.io/fcfdr/ ; https://cran.r-project.org/web/packages/fcfdr/index.html ) and demonstrate its utility in an application to type 1 diabetes, where we identify additional genetic associations.

CONCLUSIONS

Our all-encompassing R package, fcfdr, serves as a comprehensive toolkit to unite GWAS and functional genomic data in order to increase statistical power to detect genetic associations.

False discovery rate: the Achilles' heel of proteogenomics

Proteogenomics refers to the integrated analysis of the genome and proteome that leverages mass-spectrometry (MS)-based proteomics data to improve genome annotations, understand gene expression control through proteoforms and find sequence variants to develop novel insights for disease classification and therapeutic strategies. However, proteogenomic studies often suffer from reduced sensitivity and specificity due to inflated database size. To control the error rates, proteogenomics depends on the target-decoy search strategy, the de-facto method for false discovery rate (FDR) estimation in proteomics. The proteogenomic databases constructed from three- or six-frame nucleotide database translation not only increase the search space and compute-time but also violate the equivalence of target and decoy databases. These searches result in poorer separation between target and decoy scores, leading to stringent FDR thresholds. Understanding these factors and applying modified strategies such as two-pass database search or peptide-class-specific FDR can result in a better interpretation of MS data without introducing additional statistical biases. Based on these considerations, a user can interpret the proteogenomics results appropriately and control false positives and negatives in a more informed manner. In this review, first, we briefly discuss the proteogenomic workflows and limitations in database construction, followed by various considerations that can influence potential novel discoveries in a proteogenomic study. We conclude with suggestions to counter these challenges for better proteogenomic data interpretation.

A comprehensive landscape of 60S ribosome biogenesis factors

Eukaryotic ribosome biogenesis is facilitated and regulated by numerous ribosome biogenesis factors (RBFs). High-resolution cryoelectron microscopy (cryo-EM) maps have defined the molecular interactions of RBFs during maturation, but many transient and dynamic interactions, particularly during early assembly, remain uncharacterized. Using quantitative proteomics and crosslinking coupled to mass spectrometry (XL-MS) data from an extensive set of pre-ribosomal particles, we derive a comprehensive and time-resolved interaction map of RBF engagement during 60S maturation. We localize 22 previously unmapped RBFs to specific biogenesis intermediates and validate our results by mapping the catalytic activity of the methyltransferases Bmt2 and Rcm1 to their predicted nucleolar 60S intermediates. Our analysis reveals the interaction sites for the RBFs Noc2 and Ecm1 and elucidates the interaction map and timing of 60S engagement by the DEAD-box ATPases Dbp9 and Dbp10. Our data provide a powerful resource for future studies of 60S ribosome biogenesis.

In this study, Sailer et al. generate a comprehensive and precise timeline of ribosome biogenesis factor (RBF) engagement during 60S maturation and localize previously unmapped RBFs in the yeast Saccharomyces cerevisiae. Overall, their data represent an essential resource for future structural studies of large subunit ribosome biogenesis.

Leveraging crosslinking mass spectrometry in structural and cell biology

Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.

Revisiting the genome-wide significance threshold for common variant GWAS

Over the last decade, GWAS meta-analyses have used a strict P-value threshold of 5 × 10-8 to classify associations as significant. Here, we use our current understanding of frequently studied traits including lipid levels, height, and BMI to revisit this genome-wide significance threshold. We compare the performance of studies using the P = 5 × 10-8 threshold in terms of true and false positive rate to other multiple testing strategies: (1) less stringent P-value thresholds, (2) controlling the FDR with the Benjamini-Hochberg and Benjamini-Yekutieli procedure, and (3) controlling the Bayesian FDR with posterior probabilities. We applied these procedures to re-analyze results from the Global Lipids and GIANT GWAS meta-analysis consortia and supported them with extensive simulation that mimics the empirical data. We observe in simulated studies with sample sizes ∼20,000 and >120,000 that relaxing the P-value threshold to 5 × 10-7 increased discovery at the cost of 18% and 8% of additional loci being false positive results, respectively. FDR and Bayesian FDR are well controlled for both sample sizes with a few exceptions that disappear under a less stringent definition of true positives and the two approaches yield similar results. Our work quantifies the value of using a relaxed P-value threshold in large studies to increase their true positive discovery but also show the excess false positive rates due to such actions in modest-sized studies. These results may guide investigators considering different thresholds in replication studies and downstream work such as gene-set enrichment or pathway analysis. Finally, we demonstrate the viability of FDR-controlling procedures in GWAS.

The functional false discovery rate with applications to genomics

The false discovery rate (FDR) measures the proportion of false discoveries among a set of hypothesis tests called significant. This quantity is typically estimated based on p-values or test statistics. In some scenarios, there is additional information available that may be used to more accurately estimate the FDR. We develop a new framework for formulating and estimating FDRs and q-values when an additional piece of information, which we call an "informative variable", is available. For a given test, the informative variable provides information about the prior probability a null hypothesis is true or the power of that particular test. The FDR is then treated as a function of this informative variable. We consider two applications in genomics. Our first application is a genetics of gene expression (eQTL) experiment in yeast where every genetic marker and gene expression trait pair are tested for associations. The informative variable in this case is the distance between each genetic marker and gene. Our second application is to detect differentially expressed genes in an RNA-seq study carried out in mice. The informative variable in this study is the per-gene read depth. The framework we develop is quite general, and it should be useful in a broad range of scientific applications.

Major Depressive Disorder Is Associated With Differential Expression of Innate Immune and Neutrophil-Related Gene Networks in Peripheral Blood: A Quantitative Review of Whole-Genome Transcriptional Data From Case-Control Studies

BACKGROUND

Whole-genome transcription has been measured in peripheral blood samples as a candidate biomarker of inflammation associated with major depressive disorder.

METHODS

We searched for all case-control studies on major depressive disorder that reported microarray or RNA sequencing measurements on whole blood or peripheral blood mononuclear cells. Primary datasets were reanalyzed, when openly accessible, to estimate case-control differences and to evaluate the functional roles of differentially expressed gene lists by technically harmonized methods.

RESULTS

We found 10 eligible studies (N = 1754 depressed cases and N = 1145 healthy controls). Fifty-two genes were called significant by 2 of the primary studies (published overlap list). After harmonization of analysis across 8 accessible datasets (n = 1706 cases, n = 1098 controls), 272 genes were coincidentally listed in the top 3% most differentially expressed genes in 2 or more studies of whole blood or peripheral blood mononuclear cells with concordant direction of effect (harmonized overlap list). By meta-analysis of standardized mean difference across 4 studies of whole-blood samples (n = 1567 cases, n = 954 controls), 343 genes were found with false discovery rate <5% (standardized mean difference meta-analysis list). These 3 lists intersected significantly. Genes abnormally expressed in major depressive disorder were enriched for innate immune-related functions, coded for nonrandom protein-protein interaction networks, and coexpressed in the normative transcriptome module specialized for innate immune and neutrophil functions.

CONCLUSIONS

Quantitative review of existing case-control data provided robust evidence for abnormal expression of gene networks important for the regulation and implementation of innate immune response. Further development of white blood cell transcriptional biomarkers for inflamed depression seems warranted.

DEqMS: A Method for Accurate Variance Estimation in Differential Protein Expression Analysis

Quantitative proteomics by mass spectrometry is widely used in biomarker research and basic biology research for investigation of phenotype level cellular events. Despite the wide application, the methodology for statistical analysis of differentially expressed proteins has not been unified. Various methods such as t test, linear model and mixed effect models are used to define changes in proteomics experiments. However, none of these methods consider the specific structure of MS-data. Choices between methods, often originally developed for other types of data, are based on compromises between features such as statistical power, general applicability and user friendliness. Furthermore, whether to include proteins identified with one peptide in statistical analysis of differential protein expression varies between studies. Here we present DEqMS, a robust statistical method developed specifically for differential protein expression analysis in mass spectrometry data. In all data sets investigated there is a clear dependence of variance on the number of PSMs or peptides used for protein quantification. DEqMS takes this feature into account when assessing differential protein expression. This allows for a more accurate data-dependent estimation of protein variance and inclusion of single peptide identifications without increasing false discoveries. The method was tested in several data sets including E. coli proteome spike-in data, using both label-free and TMT-labeled quantification. Compared with previous statistical methods used in quantitative proteomics, DEqMS showed consistently better accuracy in detecting altered protein levels compared with other statistical methods in both label-free and labeled quantitative proteomics data. DEqMS is available as an R package in Bioconductor.

Protein Probability Model for High-Throughput Protein Identification by Mass Spectrometry-Based Proteomics

Shotgun proteomics is the method of choice for high-throughput protein identification; however, robust statistical methods are essential to automatize this task while minimizing the number of false identifications. The standard method for estimating the false discovery rate (FDR) of individual identifications and keeping it below a threshold (typically 1%) is the target-decoy approach. However, numerous works have shown that FDR at the protein level may become much larger than FDR at the peptide level. The development of an appropriate scoring model to identify proteins from their peptides using high-throughput shotgun proteomics is highly needed. In this study, we present a novel protein-level scoring algorithm that uses the scores of the identified peptides and maintains all of the properties expected for a true protein probability. We also present a refinement of the picked method to calculate FDR at the protein level. These algorithms can be used together as a robust identification workflow suitable for large-scale proteomics, and we show that the identification performance of this workflow is superior to that of other widely used methods in several samples and using different search engines. Our protein probability model offers the scientific community an algorithm that is easy to integrate into protein identification workflows for the automated analysis of shotgun proteomics data.

Thorough Performance Evaluation of 213 nm Ultraviolet Photodissociation for Top-down Proteomics

Top-down proteomics studies intact proteoform mixtures and offers important advantages over more common bottom-up proteomics technologies, as it avoids the protein inference problem. However, achieving complete molecular characterization of investigated proteoforms using existing technologies remains a fundamental challenge for top-down proteomics. Here, we benchmark the performance of ultraviolet photodissociation (UVPD) using 213 nm photons generated by a solid-state laser applied to the study of intact proteoforms from three organisms. Notably, the described UVPD setup applies multiple laser pulses to induce ion dissociation, and this feature can be used to optimize the fragmentation outcome based on the molecular weight of the analyzed biomolecule. When applied to complex proteoform mixtures in high-throughput top-down proteomics, 213 nm UVPD demonstrated a high degree of complementarity with the most employed fragmentation method in proteomics studies, higher-energy collisional dissociation (HCD). UVPD at 213 nm offered higher average proteoform sequence coverage and degree of proteoform characterization (including localization of post-translational modifications) than HCD. However, previous studies have shown limitations in applying database search strategies developed for HCD fragmentation to UVPD spectra which contains up to nine fragment ion types. We therefore performed an analysis of the different UVPD product ion type frequencies. From these data, we developed an ad hoc fragment matching strategy and determined the influence of each possible ion type on search outcomes. By paring down the number of ion types considered in high-throughput UVPD searches from all types down to the four most abundant, we were ultimately able to achieve deeper proteome characterization with UVPD. Lastly, our detailed product ion analysis also revealed UVPD cleavage propensities and determined the presence of a product ion produced specifically by 213 nm photons. All together, these observations could be used to better elucidate UVPD dissociation mechanisms and improve the utility of the technique for proteomic applications.

A Seven-Rod Dielectric Sensor for Determination of Soil Moisture in Well-Defined Sample Volumes

This paper presents a novel seven-rod sensor used for time-domain reflectometry (TDR) and frequency-domain reflectometry (FDR) measurements of soil water content in a well-defined sample volume. The probe directly measures the complex dielectric permittivity spectrum and for this purpose requires three calibration media: air, water, and ethanol. Firstly, electromagnetic simulations were used to study the influence of the diameter of a container on the sensitivity zone of the probe with respect to the measured calibration media and isopropanol as a verification liquid. Next, the probe was tested in three soils-sandy loam and two silt loams-with six water contents from air-dry to saturation. The conversion from S 11 parameters to complex dielectric permittivity from vector network analyzer (VNA) measurements was obtained using an open-ended liquid procedure. The simulation and measurement results for the real part of the isopropanol dielectric permittivity obtained from four containers with different diameters were in good agreement with literature data up to 200 MHz. The real part of the dielectric permittivity was extracted and related to the moisture of the tested soil samples. Relations between the volumetric water content and the real part of the dielectric permittivity (by FDR) and apparent dielectric permittivity (by TDR) were compared with Topp's equation. It was concluded that the best fit to Topp's equation was observed in the case of a sandy loam. Data calculated according to the equation proposed by Malicki, Plagge, and Roth gave results closer to Topp's calibration. The obtained results indicated that the seven-rod probe can be used to accurately measure of the dielectric permittivity spectrum in a well-defined sample volume of about 8 cm³ in the frequency range from 20 MHz to 200 MHz.

ROC Curves for the Statistical Analysis of Microarray Data

A receiver operating characteristic (ROC) curve is a graphical plot that illustrates the diagnostic ability of a binary classifier as a function of its discrimination threshold. This chapter is an overview on the use of ROC curves for microarray data. The notion of ROC curve and its motivation is introduced in Subheading 1. Relevant scientific contributions concerning the use of ROC curves for microarray data are briefly reviewed in Subheading 2. The special case with covariates is considered in Subheading 3. Two relevant aspects are reviewed in this section: the use of LASSO techniques for selecting and combining relevant markers and how to correct for multiple testing when a large number of markers are available. Finally, some conclusions are included.

Propensity Score-Based Approaches in High Dimension for Pharmacovigilance Signal Detection: an Empirical Comparison on the French Spontaneous Reporting Database

Classical methods used for signal detection in pharmacovigilance rely on disproportionality analysis of counts aggregating spontaneous reports of a given adverse drug reaction. In recent years, alternative methods have been proposed to analyze individual spontaneous reports such as penalized multiple logistic regression approaches. These approaches address some well-known biases resulting from disproportionality methods. However, while penalization accounts for computational constraints due to high-dimensional data, it raises the issue of determining the regularization parameter and eventually that of an error-controlling decision rule. We present a new automated signal detection strategy for pharmacovigilance systems, based on propensity scores (PS) in high dimension. PSs are increasingly used to assess a given association with high-dimensional observational healthcare databases in accounting for confusion bias. Our main aim was to develop a method having the same advantages as multiple regression approaches in dealing with bias, while relying on the statistical multiple comparison framework as regards decision thresholds, by considering false discovery rate (FDR)-based decision rules. We investigate four PS estimation methods in high dimension: a gradient tree boosting (GTB) algorithm from machine-learning and three variable selection algorithms. For each (drug, adverse event) pair, the PS is then applied as adjustment covariate or by using two kinds of weighting: inverse proportional treatment weighting and matching weights. The different versions of the new approach were compared to a univariate approach, which is a disproportionality method, and to two penalized multiple logistic regression approaches, directly applied on spontaneous reporting data. Performance was assessed through an empirical comparative study conducted on a reference signal set in the French national pharmacovigilance database (2000–2016) that was recently proposed for drug-induced liver injury. Multiple regression approaches performed better in detecting true positives and false positives. Nonetheless, the performances of the PS-based methods using matching weights was very similar to that of multiple regression and better than with the univariate approach. In addition to being able to control FDR statistical errors, the proposed PS-based strategy is an interesting alternative to multiple regression approaches.

A novel bayesian multiple testing approach to deregulated miRNA discovery harnessing positional clustering

MicroRNAs (miRNAs) are small non-coding RNAs that function as regulators of gene expression. In recent years, there has been a tremendous interest among researchers to investigate the role of miRNAs in normal as well as in disease processes. To investigate the role of miRNAs in oral cancer, we analyse expression levels of miRNAs to identify miRNAs with statistically significant differential expression in cancer tissues. In this article, we propose a novel Bayesian hierarchical model of miRNA expression data. Compelling evidence has demonstrated that the transcription process of miRNAs in the human genome is a latent process instrumental for the observed expression levels. We take into account positional clustering of the miRNAs in the analysis and model the latent transcription phenomenon nonparametrically by an appropriate Gaussian process. For the purpose of testing, we employ a novel Bayesian multiple testing method where we mainly focus on utilizing the dependence structure between the hypotheses for better results, while also ensuring optimality in many respects. Indeed, our non-marginal method yielded results in accordance with the underlying scientific knowledge which are found to be missed by the very popular Benjamini-Hochberg method.

A new estimation of protein-level false discovery rate

Background

In mass spectrometry-based proteomics, protein identification is an essential task. Evaluating the statistical significance of the protein identification result is critical to the success of proteomics studies. Controlling the false discovery rate (FDR) is the most common method for assuring the overall quality of the set of identifications. Existing FDR estimation methods either rely on specific assumptions or rely on the two-stage calculation process of first estimating the error rates at the peptide-level, and then combining them somehow at the protein-level. We propose to estimate the FDR in a non-parametric way with less assumptions and to avoid the two-stage calculation process.

Results

We propose a new protein-level FDR estimation framework. The framework contains two major components: the Permutation+BH (Benjamini–Hochberg) FDR estimation method and the logistic regression-based null inference method. In Permutation+BH, the null distribution of a sample is generated by searching data against a large number of permuted random protein database and therefore does not rely on specific assumptions. Then, p-values of proteins are calculated from the null distribution and the BH procedure is applied to the p-values to achieve the relationship of the FDR and the number of protein identifications. The Permutation+BH method generates the null distribution by the permutation method, which is inefficient for online identification. The logistic regression model is proposed to infer the null distribution of a new sample based on existing null distributions obtained from the Permutation+BH method.

Conclusions

In our experiment based on three public available datasets, our Permutation+BH method achieves consistently better performance than MAYU, which is chosen as the benchmark FDR calculation method for this study. The null distribution inference result shows that the logistic regression model achieves a reasonable result both in the shape of the null distribution and the corresponding FDR estimation result.

The effect of interaction between EtOH dosage and exposure time on gene expression in DPSC

Alcohol (EtOH) dosage and exposure time can affect gene expression. However, whether there exists synergistic effect is unknown. Here, we analyzed the hDPSC gene microarray dataset GSE57255 downloaded from Gene Expression Omnibus and found that the interaction between EtOH dosage and exposure time on gene expression are statistically significant for two probes: 201917_s_at near gene SLC25A36 and 217649_at near gene ZFAND5. GeneMania showed that SLC25A36 and ZFAND5 were related to 20 genes, three of which had alcohol-related functions. WebGestalt revealed that the 22 genes were enriched in 10 KEGG pathways, four of which are related to alcoholic diseases. We explored the possible nonlinear interaction effect and got 172 gene probes with significant p-values. However, no significantly enriched pathways based on the 172 probes were detected. Our analyses indicated a possible molecular mechanism that could help explain why alcohol consumption has both deleterious and beneficial effects on human health.

Germline Genetic Features of Young Individuals With Colorectal Cancer

BACKGROUND & AIMS

The incidence of colorectal cancer (CRC) in individuals younger than 50 years is increasing. We sought to ascertain the proportion of young CRC cases associated with genetic predisposition.

METHODS

We performed a retrospective study of individuals diagnosed with CRC at an age younger than 50 years, evaluated by the clinical genetics service at a single tertiary care cancer center from 1998 through 2015. We collected data on patient histories, tumor phenotypes, and results of germline DNA sequencing. For subjects with uninformative clinical evaluations, germline DNA samples were (re)sequenced using a research-based next-generation sequencing multigene panel. The primary outcome was identification of a pathogenic germline mutation associated with cancer predisposition.

RESULTS

Of 430 young CRC cases, 111 (26%) had a first-degree relative with CRC. Forty-one of the subjects with CRC (10%) had tumors with histologic evidence for mismatch repair deficiency. Of 315 subjects who underwent clinical germline sequencing, 79 had mutations associated with a hereditary cancer syndrome and 21 had variants of uncertain significance. Fifty-six subjects had pathogenic variants associated with Lynch syndrome (25 with mutations in MSH2, 24 with mutations in MLH1, 5 with mutations in MSH6, and 2 with mutations in PMS2) and 10 subjects had pathogenic variants associated with familial adenomatous polyposis. Thirteen subjects had mutations in other cancer-associated genes (8 in MUTYH, 2 in SMAD4, 1 in BRCA1, 1 in TP53, and 1 in CHEK2), all identified through multigene panel tests. Among 117 patients with uninformative clinical evaluations, next-generation sequence analysis using a multigene panel detected actionable germline variants in 6 patients (5%). Only 43 of the 85 subjects with germline mutations associated with a hereditary cancer syndrome (51%) reported a CRC diagnosis in a first-degree relative.

CONCLUSIONS

Approximately 1 in 5 individuals diagnosed with CRC at age younger than 50 years carries a germline mutation associated with cancer; nearly half of these do not have clinical histories typically associated with the identified syndrome. Germline testing with multigene cancer panels should be considered for all young patients with CRC.

Discrete False-Discovery Rate Improves Identification of Differentially Abundant Microbes

DS-FDR can achieve higher statistical power to detect significant findings in sparse and noisy microbiome data compared to the commonly used Benjamini-Hochberg procedure and other FDR-controlling procedures.

Differential abundance testing is a critical task in microbiome studies that is complicated by the sparsity of data matrices. Here we adapt for microbiome studies a solution from the field of gene expression analysis to produce a new method, discrete false-discovery rate (DS-FDR), that greatly improves the power to detect differential taxa by exploiting the discreteness of the data. Additionally, DS-FDR is relatively robust to the number of noninformative features, and thus removes the problem of filtering taxonomy tables by an arbitrary abundance threshold. We show by using a combination of simulations and reanalysis of nine real-world microbiome data sets that this new method outperforms existing methods at the differential abundance testing task, producing a false-discovery rate that is up to threefold more accurate, and halves the number of samples required to find a given difference (thus increasing the efficiency of microbiome experiments considerably). We therefore expect DS-FDR to be widely applied in microbiome studies.

IMPORTANCE DS-FDR can achieve higher statistical power to detect significant findings in sparse and noisy microbiome data compared to the commonly used Benjamini-Hochberg procedure and other FDR-controlling procedures.

Multiple testing corrections in quantitative proteomics: A useful but blunt tool

Multiple testing corrections are a useful tool for restricting the FDR, but can be blunt in the context of low power, as we demonstrate by a series of simple simulations. Unfortunately, in proteomics experiments low power can be common, driven by proteomics-specific issues like small effects due to ratio compression, and few replicates due to reagent high cost, instrument time availability and other issues; in such situations, most multiple testing corrections methods, if used with conventional thresholds, will fail to detect any true positives even when many exist. In this low power, medium scale situation, other methods such as effect size considerations or peptide-level calculations may be a more effective option, even if they do not offer the same theoretical guarantee of a low FDR. Thus, we aim to highlight in this article that proteomics presents some specific challenges to the standard multiple testing corrections methods, which should be employed as a useful tool but not be regarded as a required rubber stamp.

Bon-EV: an improved multiple testing procedure for controlling false discovery rates

BACKGROUND

Stability of multiple testing procedures, defined as the standard deviation of total number of discoveries, can be used as an indicator of variability of multiple testing procedures. Improving stability of multiple testing procedures can help to increase the consistency of findings from replicated experiments. Benjamini-Hochberg's and Storey's q-value procedures are two commonly used multiple testing procedures for controlling false discoveries in genomic studies. Storey's q-value procedure has higher power and lower stability than Benjamini-Hochberg's procedure. To improve upon the stability of Storey's q-value procedure and maintain its high power in genomic data analysis, we propose a new multiple testing procedure, named Bon-EV, to control false discovery rate (FDR) based on Bonferroni's approach.

RESULTS

Simulation studies show that our proposed Bon-EV procedure can maintain the high power of the Storey's q-value procedure and also result in better FDR control and higher stability than Storey's q-value procedure for samples of large size(30 in each group) and medium size (15 in each group) for either independent, somewhat correlated, or highly correlated test statistics. When sample size is small (5 in each group), our proposed Bon-EV procedure has performance between the Benjamini-Hochberg procedure and the Storey's q-value procedure. Examples using RNA-Seq data show that the Bon-EV procedure has higher stability than the Storey's q-value procedure while maintaining equivalent power, and higher power than the Benjamini-Hochberg's procedure.

CONCLUSIONS

For medium or large sample sizes, the Bon-EV procedure has improved FDR control and stability compared with the Storey's q-value procedure and improved power compared with the Benjamini-Hochberg procedure. The Bon-EV multiple testing procedure is available as the BonEV package in R for download at https://CRAN.R-project.org/package=BonEV .

Controlling the Rate of GWAS False Discoveries

With the rise of both the number and the complexity of traits of interest, control of the false discovery rate (FDR) in genetic association studies has become an increasingly appealing and accepted target for multiple comparison adjustment. While a number of robust FDR-controlling strategies exist, the nature of this error rate is intimately tied to the precise way in which discoveries are counted, and the performance of FDR-controlling procedures is satisfactory only if there is a one-to-one correspondence between what scientists describe as unique discoveries and the number of rejected hypotheses. The presence of linkage disequilibrium between markers in genome-wide association studies (GWAS) often leads researchers to consider the signal associated to multiple neighboring SNPs as indicating the existence of a single genomic locus with possible influence on the phenotype. This a posteriori aggregation of rejected hypotheses results in inflation of the relevant FDR. We propose a novel approach to FDR control that is based on prescreening to identify the level of resolution of distinct hypotheses. We show how FDR-controlling strategies can be adapted to account for this initial selection both with theoretical results and simulations that mimic the dependence structure to be expected in GWAS. We demonstrate that our approach is versatile and useful when the data are analyzed using both tests based on single markers and multiple regression. We provide an R package that allows practitioners to apply our procedure on standard GWAS format data, and illustrate its performance on lipid traits in the North Finland Birth Cohort 66 cohort study.

Multiple confidence intervals for selected parameters adjusted for the false coverage rate in monotone dose-response microarray experiments

Benjamini and Yekutieli () introduced the concept of the false coverage-statement rate (FCR) to account for selection when the confidence intervals (CIs) are constructed only for the selected parameters. Dose-response analysis in dose-response microarray experiments is conducted only for genes having monotone dose-response relationship, which is a selection problem. In this paper, we consider multiple CIs for the mean gene expression difference between the highest dose and control in monotone dose-response microarray experiments for selected parameters adjusted for the FCR. A simulation study is conducted to study the performance of the method proposed. The method is applied to a real dose-response microarray experiment with 16, 998 genes for illustration.

Weighted false discovery rate controlling procedures for clinical trials

Having identified that the lack of replicability of results in earlier phases of clinical medical research stems largely from unattended selective inference, we offer a new hierarchical weighted false discovery rate controlling testing procedure alongside the single-level weighted procedure. These address the special structure of clinical research, where the comparisons of treatments involve both primary and secondary endpoints, by assigning weights that reflect the relative importance of the endpoints in the error being controlled. In the hierarchical method, the primary endpoints and a properly weighted intersection hypothesis that represents all secondary endpoints are tested. Should the intersection hypothesis be among the rejected, individual secondary endpoints are tested. We identify configurations where each of the two procedures has the advantage. Both offer higher power than competing hierarchical (gatekeeper) familywise error-rate controlling procedures being used for drug approval. By their design, the advantage of the proposed methods is the increased power to discover effects on secondary endpoints, without giving up the rigor of addressing their multiplicity.

An improved cumulative sum-based procedure for prospective disease surveillance for count data in multiple regions

We present an improved procedure for detecting outbreaks in multiple spatial regions using count data. We combine well-known methods for disease surveillance with recent developments from other areas to provide a more powerful procedure that is still relatively simple and fast to implement. Disease counts from neighboring regions are aggregated to compute a Poisson cumulative sum statistic for each region of interest. Instead of controlling the average run length criterion in the monitoring process, we instead utilize the FDR, which is more appropriate in a public health context. Additionally, p-values are used to make decisions instead of traditional critical values. The use of the FDR and p-values in testing allows us to utilize recently developed multiple testing methodologies, greatly increasing the power of this procedure. This is verified using a simulation experiment. The simplicity and rapid detection ability of this procedure make it useful in disease surveillance settings. The procedure is successfully applied in detecting the 2011 Salmonella Newport outbreak in 16 German federal states. Copyright © 2016 John Wiley & Sons, Ltd.

Single-pollen genotyping to estimate mode of unreduced pollen formation in Citrus tamurana cv. Nishiuchi Konatsu

2n pollen formed by FDR in citrus. The Japanese local citrus cultivar, Nishiuchi Konatsu (Citrus tamurana hort. ex Tanaka; NK hereafter), has the ability to produce unreduced 2n pollen grains, allowing generation of polyploid progenies via sexual polyploidization. In this study, we developed a method of single-pollen genotyping for citrus and applied it to the analysis of transmission of heterozygosity in NK 2n pollen grains. Heterozygosity transmission was expressed as the percentage inheritance of a set of heterozygous alleles from the parent to the 2n gamete. The pathway of 2n pollen development was investigated by applying the observed heterozygosity transmission and genetic distance to two different map functions, for first division restitution (FDR) and second division restitution (SDR). The fit of the values observed for both functions was calculated, while virtually moving the centromere position. We screened for six heterozygous SSR (codominant microsatellite marker loci) in NK, all of which were expected to lie within the same linkage group. Pollen germination prior to DNA extraction was essential for this work, and 6-h incubation proved to be optimal for subsequent PCR amplification. Single-pollen genotyping unreduced NK 2n pollen grains revealed that heterozygosity transmission exceeded 50 % in all six alleles, and fitness tests indicated that the FDR map function better fitted the heterozygosity transmission observed rather than the SDR function. Our data thus strongly indicate that 2n pollen in NK is a result of first division restitution.

Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments

Background

RNA-Sequencing (RNA-seq) experiments have been popularly applied to transcriptome studies in recent years. Such experiments are still relatively costly. As a result, RNA-seq experiments often employ a small number of replicates. Power analysis and sample size calculation are challenging in the context of differential expression analysis with RNA-seq data. One challenge is that there are no closed-form formulae to calculate power for the popularly applied tests for differential expression analysis. In addition, false discovery rate (FDR), instead of family-wise type I error rate, is controlled for the multiple testing error in RNA-seq data analysis. So far, there are very few proposals on sample size calculation for RNA-seq experiments.

Results

In this paper, we propose a procedure for sample size calculation while controlling FDR for RNA-seq experimental design. Our procedure is based on the weighted linear model analysis facilitated by the voom method which has been shown to have competitive performance in terms of power and FDR control for RNA-seq differential expression analysis. We derive a method that approximates the average power across the differentially expressed genes, and then calculate the sample size to achieve a desired average power while controlling FDR. Simulation results demonstrate that the actual power of several popularly applied tests for differential expression is achieved and is close to the desired power for RNA-seq data with sample size calculated based on our method.

Conclusions

Our proposed method provides an efficient algorithm to calculate sample size while controlling FDR for RNA-seq experimental design. We also provide an R package ssizeRNA that implements our proposed method and can be downloaded from the Comprehensive R Archive Network (http://cran.r-project.org).

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0994-9) contains supplementary material, which is available to authorized users.

microRNA-449a functions as a tumor suppressor in neuroblastoma through inducing cell differentiation and cell cycle arrest

microRNA-449a (miR-449a) has been identified to function as a tumor suppressor in several types of cancers. However, the role of miR-449a in neuroblastoma has not been intensively investigated. We recently found that the overexpression of miR-449a significantly induces neuroblastoma cell differentiation, suggesting its potential tumor suppressor function in neuroblastoma. In this study, we further investigated the mechanisms underlying the tumor suppressive function of miR-449a in neuroblastoma. We observed that miR-449a inhibits neuroblastoma cell survival and growth through 2 mechanisms--inducing cell differentiation and cell cycle arrest. Our comprehensive investigations on the dissection of the target genes of miR-449a revealed that 3 novel targets- MFAP4, PKP4 and TSEN15 -play important roles in mediating its differentiation-inducing function. In addition, we further found that its function in inducing cell cycle arrest involves down-regulating its direct targets CDK6 and LEF1. To determine the clinical significance of the miR-449a-mediated tumor suppressive mechanism, we examined the correlation between the expression of these 5 target genes in neuroblastoma tumor specimens and the survival of neuroblastoma patients. Remarkably, we noted that high tumor expression levels of all the 3 miR-449a target genes involved in regulating cell differentiation, but not the target genes involved in regulating cell cycle, are significantly correlated with poor survival of neuroblastoma patients. These results suggest the critical role of the differentiation-inducing function of miR-449a in determining neuroblastoma progression. Overall, our study provides the first comprehensive characterization of the tumor-suppressive function of miR-449a in neuroblastoma, and reveals the potential clinical significance of the miR-449a-mediated tumor suppressive pathway in neuroblastoma prognosis.

DecoyPyrat: Fast Non-redundant Hybrid Decoy Sequence Generation for Large Scale Proteomics

Accurate statistical evaluation of sequence database peptide identifications from tandem mass spectra is essential in mass spectrometry based proteomics experiments. These statistics are dependent on accurately modelling random identifications. The target-decoy approach has risen to become the de facto approach to calculating FDR in proteomic datasets. The main principle of this approach is to search a set of decoy protein sequences that emulate the size and composition of the target protein sequences searched whilst not matching real proteins in the sample. To do this, it is commonplace to reverse or shuffle the proteins and peptides in the target database. However, these approaches have their drawbacks and limitations. A key confounding issue is the peptide redundancy between target and decoy databases leading to inaccurate FDR estimation. This inaccuracy is further amplified at the protein level and when searching large sequence databases such as those used for proteogenomics. Here, we present a unifying hybrid method to quickly and efficiently generate decoy sequences with minimal overlap between target and decoy peptides. We show that applying a reversed decoy approach can produce up to 5% peptide redundancy and many more additional peptides will have the exact same precursor mass as a target peptide. Our hybrid method addresses both these issues by first switching proteolytic cleavage sites with preceding amino acid, reversing the database and then shuffling any redundant sequences. This flexible hybrid method reduces the peptide overlap between target and decoy peptides to about 1% of peptides, making a more robust decoy model suitable for large search spaces. We also demonstrate the anti-conservative effect of redundant peptides on the calculation of q-values in mouse brain tissue data.

Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI): A framework for single-subject analysis in diffusion tensor imaging

The purpose of this work is to develop a framework for single-subject analysis of diffusion tensor imaging (DTI) data. This framework is termed Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI) because it is capable of testing whether an individual tract as represented by the major eigenvector of the diffusion tensor and its corresponding angular dispersion are significantly different from a group of tracts on a voxel-by-voxel basis. This work develops two complementary statistical tests based on the elliptical cone of uncertainty, which is a model of uncertainty or dispersion of the major eigenvector of the diffusion tensor. The orientation deviation test examines whether the major eigenvector from a single subject is within the average elliptical cone of uncertainty formed by a collection of elliptical cones of uncertainty. The shape deviation test is based on the two-tailed Wilcoxon-Mann-Whitney two-sample test between the normalized shape measures (area and circumference) of the elliptical cones of uncertainty of the single subject against a group of controls. The False Discovery Rate (FDR) and False Non-discovery Rate (FNR) were incorporated in the orientation deviation test. The shape deviation test uses FDR only. TOADDI was found to be numerically accurate and statistically effective. Clinical data from two Traumatic Brain Injury (TBI) patients and one non-TBI subject were tested against the data obtained from a group of 45 non-TBI controls to illustrate the application of the proposed framework in single-subject analysis. The frontal portion of the superior longitudinal fasciculus seemed to be implicated in both tests (orientation and shape) as significantly different from that of the control group. The TBI patients and the single non-TBI subject were well separated under the shape deviation test at the chosen FDR level of 0.0005. TOADDI is a simple but novel geometrically based statistical framework for analyzing DTI data. TOADDI may be found useful in single-subject, graph-theoretic and group analyses of DTI data or DTI-based tractography techniques.

A new genome-wide method to track horizontally transferred sequences: application to Drosophila

Because of methodological breakthroughs and the availability of an increasing amount of whole-genome sequence data, horizontal transfers (HTs) in eukaryotes have received much attention recently. Contrary to similar analyses in prokaryotes, most studies in eukaryotes usually investigate particular sequences corresponding to transposable elements (TEs), neglecting the other components of the genome. We present a new methodological framework for the genome-wide detection of all putative horizontally transferred sequences between two species that requires no prior knowledge of the transferred sequences. This method provides a broader picture of HTs in eukaryotes by fully exploiting complete-genome sequence data. In contrast to previous genome-wide approaches, we used a well-defined statistical framework to control for the number of false positives in the results, and we propose two new validation procedures to control for confounding factors. The first validation procedure relies on a comparative analysis with other species of the phylogeny to validate HTs for the nonrepeated sequences detected, whereas the second one built upon the study of the dynamics of the detected TEs. We applied our method to two closely related Drosophila species, Drosophila melanogaster and D. simulans, in which we discovered 10 new HTs in addition to all the HTs previously detected in different studies, which underscores our method’s high sensitivity and specificity. Our results favor the hypothesis of multiple independent HTs of TEs while unraveling a small portion of the network of HTs in the Drosophila phylogeny.

Distinct effects of duration of untreated psychosis on brain cortical activities in different treatment phases of schizophrenia: a multi-channel near-infrared spectroscopy study

BACKGROUND

Duration of untreated psychosis (DUP) has been shown to be associated with both poor short-term and long-term outcomes in schizophrenia. Even so, few studies have used functional neuroimaging to investigate DUP in schizophrenia. In the present study, we used near-infrared spectroscopy (NIRS) to investigate the influence of DUP on brain functions during a verbal fluency test (VFT) in patients with schizophrenia.

METHODS

A total of 62 patients with schizophrenia were included. They were categorized into either short treatment (≤6months, n=33) or long treatment (>6months, n=29) groups based on their duration of treatment. Hemodynamic changes over the frontotemporal regions during a VFT were measured using multi-channel NIRS. We examined the associations between DUP and hemodynamic changes in each group to explore if there were different effects of DUP on brain cortical activity at different treatment durations.

RESULTS

In the long treatment group, we found significant associations between a longer DUP and decreased cortical activity approximately at the left inferior frontal gyrus, left middle frontal gyrus, left postcentral gyrus, right precentral gyrus, bilateral superior temporal gyrus, and bilateral middle temporal gyrus, whereas no associations between DUP and brain cortical activity were observed in the short treatment group.

CONCLUSIONS

Our results indicated that longer DUP may be associated with decreased level of cortical activities over the frontotemporal regions in the long-term. Early detection and intervention of psychosis that shortens DUP might help to improve the long-term outcomes in patients with schizophrenia.

Transcriptome and metabolome analysis of citrus fruit to elucidate puffing disorder

A systems-level analysis reveals details of molecular mechanisms underlying puffing disorder in Citrus fruit. Flavedo, albedo and juice sac tissues of normal fruits and fruits displaying symptoms of puffing disorder were studied using metabolomics at three developmental stages. Microarrays were used to compare normal and puffed fruits for each of the three tissues. A protein-protein interaction network inferred from previous work on Arabidopsis identified hub proteins whose transcripts show significant changes in expression. Glycolysis, the backbone of primary metabolism, appeared to be severely affected by the disorder, based on both transcriptomic and metabolomic results. Significantly less citric acid was observed consistently in puffed fruits. Gene set enrichment analysis suggested that glycolysis and carbohydrate metabolism were significantly altered in puffed samples in both albedo and flavedo. Expression of invertases and genes for sucrose export, amylose-starch and starch-maltose conversion was higher in puffed fruits. These changes may significantly alter source-sink communications. Genes associated with gibberellin and cytokinin signaling were downregulated in symptomatic albedo tissues, suggesting that these hormones play key roles in the disorder. Findings may be applied toward the development of early diagnostic methods based on host response genes and metabolites (i.e. citric acid), and toward therapeutics based on hormones.

The increasing incidence of young-onset colorectal cancer: a call to action

In the United States, colorectal cancer (CRC) is the third most common and second most lethal cancer. More than one-tenth of CRC cases (11% of colon cancers and 18% of rectal cancers) have a young onset (ie, occurring in individuals younger than 50 years). The CRC incidence and mortality rates are decreasing among all age groups older than 50 years, yet increasing in younger individuals for whom screening use is limited and key symptoms may go unrecognized. Familial syndromes account for approximately 20% of young-onset CRCs, and the remainder are typically microsatellite stable cancers, which are more commonly diploid than similar tumors in older individuals. Young-onset CRCs are more likely to occur in the distal colon or rectum, be poorly differentiated, have mucinous and signet ring features, and present at advanced stages. Yet, stage-specific survival in patients with young-onset CRC is comparable to that of patients with later-onset cancer. Primary care physicians have an important opportunity to identify high-risk young individuals for screening and to promptly evaluate CRC symptoms. Risk modification, targeted screening, and prophylactic surgery may benefit individuals with a predisposing hereditary syndrome or condition (eg, inflammatory bowel disease) or a family history of CRC or advanced adenomatous polyps. When apparently average-risk young adults present with CRC-like symptoms (eg, unexplained persistent rectal bleeding, anemia, and abdominal pain), endoscopic work-ups can expedite diagnosis. Early screening in high-risk individuals and thorough diagnostic work-ups in symptomatic young adults may improve young-onset CRC trends.

Molecular polymorphism related to flowering trait variation in a Phaseolus vulgaris L. collection

The aim of this study was to investigate the flowering variation and the molecular polymorphism in key regulatory genes that control flowering in a Phaseolus vulgaris L. collection of 94 accessions from Europe and the Americas. The analysis of variance revealed that the difference in days-to-flowering between accessions was significant, with European accessions characterized by flowering precocity. Population structure analysis corroborated previous data on the genetic distinction between the Andean and Mesoamerican gene pools. A low level of admixture was detected. Genomic sequences of 15 gene fragments were obtained. About 7.0 kb per accession were sequenced and a total of 48 nucleotide substitutions identified. A Mixed Linear Model analysis, including population structure and kinship, was used to identify marker-trait associations. Haplotype tagging single nucleotide polymorphisms (htSNPs) associated with the studied traits were detected: in PvVRN1 and PvPHYB with days-to-flowering, in PvMYB29 with number of flower buds per inflorescence and in PvTFL1z and PvFCA with inflorescence length. The two genes associated with days-to-flowering control belong to the photoperiod and vernalization pathways. In particular, the PvVRN1 gene appears to play an important role in regulating the adaptation process of common bean.

Correspondence of executive function related functional and anatomical alterations in aging brain

Neurocognitive aging studies have focused on age-related changes in neural activity or neural structure but few studies have focused on relationships between the two. The present study quantitatively reviewed 24 studies of age-related changes in fMRI activation across a broad spectrum of executive function tasks using activation likelihood estimation (ALE) and 22 separate studies of age-related changes in gray matter using voxel-based morphometry (VBM). Conjunction analyses between functional and structural alteration maps were constructed. Overlaps were only observed in the conjunction of dorsolateral prefrontal cortex (DLPFC) gray matter reduction and functional hyperactivation but not hypoactivation. It was not evident that the conjunctions between gray matter and activation were related to task performance. Theoretical implications of these results are discussed.

Psychosis effect on hippocampal reduction in schizophrenia

INTRODUCTION

In schizophrenia, disruption of the neurodevelopmental processes may lead to brain changes and subsequent clinical manifestations of the illness. Reports of the progressive nature of these morphological brain changes raise questions about their causes. The possible toxic effects of repeated stressful psychotic episodes may contribute to the disease progression.

OBJECTIVES

To analyze the influence of illness duration and previous psychotic episodes on hippocampal gray matter volume (GMV) in schizophrenia.

METHODS

We performed an analysis of hippocampal GMV correlations with illness duration, number of previous psychotic episodes, and age in 24 schizophrenia patients and 24 matched healthy controls.

RESULTS

We found a cluster of GMV voxels in the left hippocampal tail that negatively correlated with the number of previous psychotic episodes, independent from the effect of age. On the other hand we found no effect of illness duration independent of age on the hippocampal GMV. Finally, we found a cluster of significant group-by-age interaction in the left hippocampal head.

CONCLUSIONS

We found an additive adverse effect of psychotic episodes on hippocampal morphology in schizophrenia. Our findings support toxicity of psychosis concept, together with etiological heterogeneity of brain changes in schizophrenia.

Genetic variations in VDR associated with prostate cancer risk and progression in a Korean population

Low levels of vitamin D are implicated as a potential risk factor for prostate cancer, and the vitamin D receptor (VDR) gene may be important in the onset and progression of prostate cancer. In this study, sequence variants in the VDR gene were investigated in a Korean study cohort to determine whether they are associated with prostate cancer risk. We evaluated the association between 47 single nucleotide polymorphisms (SNPs) in the VDR gene and prostate cancer risk as well as clinical characteristics (prostate-specific antigen level, clinical stage, pathological stage and Gleason score) in Korean men (272 prostate cancer patients and 173 benign prostatic hyperplasia patient who underwent a prostate biopsy, which was negative for malignancy) using unconditional logistic regression. The statistical analysis suggested that two VDR sequence variants (rs2408876 and rs2239182) had a significant association with prostate cancer risk (odds ratio [OR]. 1.41; p=0.03; OR, 0.73; p=0.05, respectively). Logistic analyses of the VDR polymorphisms with several prostate cancer related factors showed that several SNPs were significant; nine SNPs to PSA level, three to clinical stage, two to pathological stage, and three SNPs to the Gleason score. The results suggest that some VDR gene polymorphisms in Korean men might not only be associated with prostate cancer risk but also significantly related to prostate cancer-related risk factors such as PSA level, tumor stage, and Gleason score. However, current limitation for small cohort with not-healthy control group might have false positive effects; therefore it should be overcome via further large-scale validating studies.

Open source libraries and frameworks for mass spectrometry based proteomics: a developer's perspective

Data processing, management and visualization are central and critical components of a state of the art high-throughput mass spectrometry (MS)-based proteomics experiment, and are often some of the most time-consuming steps, especially for labs without much bioinformatics support. The growing interest in the field of proteomics has triggered an increase in the development of new software libraries, including freely available and open-source software. From database search analysis to post-processing of the identification results, even though the objectives of these libraries and packages can vary significantly, they usually share a number of features. Common use cases include the handling of protein and peptide sequences, the parsing of results from various proteomics search engines output files, and the visualization of MS-related information (including mass spectra and chromatograms). In this review, we provide an overview of the existing software libraries, open-source frameworks and also, we give information on some of the freely available applications which make use of them. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Maternal-fetal metabolic gene-gene interactions and risk of neural tube defects

Single-gene analyses indicate that maternal genes associated with metabolic conditions (e.g., obesity) may influence the risk of neural tube defects (NTDs). However, to our knowledge, there have been no assessments of maternal-fetal metabolic gene-gene interactions and NTDs. We investigated 23 single nucleotide polymorphisms among 7 maternal metabolic genes (ADRB3, ENPP1, FTO, LEP, PPARG, PPARGC1A, and TCF7L2) and 2 fetal metabolic genes (SLC2A2 and UCP2). Samples were obtained from 737 NTD case-parent triads included in the National Birth Defects Prevention Study for birth years 1999-2007. We used a 2-step approach to evaluate maternal-fetal gene-gene interactions. First, a case-only approach was applied to screen all potential maternal and fetal interactions (n = 76), as this design provides greater power in the assessment of gene-gene interactions compared to other approaches. Specifically, ordinal logistic regression was used to calculate the odds ratio (OR) and 95% confidence interval (CI) for each maternal-fetal gene-gene interaction, assuming a log-additive model of inheritance. Due to the number of comparisons, we calculated a corrected p-value (q-value) using the false discovery rate. Second, we confirmed all statistically significant interactions (q < 0.05) using a log-linear approach among case-parent triads. In step 1, there were 5 maternal-fetal gene-gene interactions with q < 0.05. The "top hit" was an interaction between maternal ENPP1 rs1044498 and fetal SLC2A2 rs6785233 (interaction OR = 3.65, 95% CI: 2.32-5.74, p = 2.09×10(-8), q=0.001), which was confirmed in step 2 (p = 0.00004). Our findings suggest that maternal metabolic genes associated with hyperglycemia and insulin resistance and fetal metabolic genes involved in glucose homeostasis may interact to increase the risk of NTDs.

Spinal gene expression profiling and pathways analysis of a CB2 agonist (MDA7)-targeted prevention of paclitaxel-induced neuropathy

AIMS

Patients receiving paclitaxel often develop peripheral neuropathies. We found that a novel selective cannabinoid CB2 receptor agonist (MDA7) prevents paclitaxel-induced mechanical allodynia in rats and mice. Here we investigated gene expression profiling in the lumbar spinal cord after 14-day treatment of MDA7 in paclitaxel animals and analyzed possible signaling pathways underlying the preventive effect of MDA7 on paclitaxel-induced neuropathy.

METHODS

Peripheral mechanical allodynia was induced in rats or mice receiving intraperitoneal (i.p.) injection of paclitaxel at a dose of 1mg/kg daily for four consecutive days. MDA7 was administered at a dose of 15mg/kg 15min before paclitaxel and then continued daily for another 10days. Whole-genome gene expression profiling in the lumbar spinal cord of MDA7 and paclitaxel-treated rats was investigated using microarray analysis. The Ingenuity pathway analysis was performed to determine the potential relevant canonical pathways responsible for the effect of MDA7 on paclitaxel-induced peripheral neuropathy.

RESULTS

We observed that the inflammatory molecular networks including tumor necrosis factor (TNF), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), transforming growth factor beta (TGFβ), and mitogen-activated protein kinases (MAPK) signaling are most relevant to the preventive effect of MDA7 on paclitaxel-induced peripheral neuropathy. In addition, genes encoding molecules that are important in central sensitization such as glutamate transporters and N-methyl-d-aspartate receptor 2B (NMDAR2B), and neuro-immune-related genes such as neuronal nitric oxide synthase (nNOS1), chemokine CX3CL1 (a mediator for microglial activation), toll-like receptor 2 (TLR2), and leptin were differentially modulated by MDA7.

CONCLUSION

The preventive effect of MDA7 on paclitaxel-induced peripheral allodynia in rats may be associated with genes involved in signal pathways in central sensitization, microglial activation, and neuroinflammation in the spinal cord.

Lack of BDNF expression through promoter IV disturbs expression of monoamine genes in the frontal cortex and hippocampus

Brain-derived neurotrophic factor (BDNF) is implicated in the pathophysiology of psychiatric conditions including major depression and schizophrenia. Mice lacking activity-driven BDNF expression through promoter IV (knock-in promoter IV: KIV) exhibit depression-like behavior, inflexible learning, and impaired response inhibition. Monoamine systems (serotonin, dopamine, and noradrenaline) are suggested to be involved in depression and schizophrenia since many of the current antidepressants and antipsychotics increase the brain levels of monoamines and/or act on monoamine receptors. To elucidate the impact of activity-driven BDNF on the monoamine systems, we examined mRNA levels for 30 monoamine-related genes, including receptors, transporters, and synthesizing enzymes, in KIV and control wild-type mice by using quantitative reverse-transcription polymerase chain reaction (qRT-PCR). mRNA levels were measured in the frontal cortex and hippocampus, which are regions related to depression and schizophrenia and where promoter IV is active. The frontal cortex of KIV mice showed reduced levels of mRNA expression for serotonin receptors 1b, 2a, and 5b (5HTR1b, 5HTR2a, 5HTR5b), dopamine D2 receptors (DRD2), and adrenergic receptors alpha 1a and 1d (AdRα1a and AdRα1b), but increased levels for serotonin synthesizing enzyme, tryptophan hydroxylase (TPH), and dopamine D4 receptor (DRD4) when compared to control wild-type mice. The hippocampus of KIV mice showed decreased levels of 5HTR5b. Our results provide causal evidence that lack of promoter IV-driven BDNF disturbs expression of monoaminergic genes in the frontal cortex and hippocampus. These disturbed expression changes in the monoamine systems may mediate the depression- and schizophrenia-like behavior of KIV mice. Our results also suggest that antidepressant and antipsychotic treatments may actually interfere with and normalize the disturbed monoamine systems caused by reduced activity-dependent BDNF, while the treatment responses to these drugs may differ in the subject with reduced BDNF levels caused by stress and lack of neuronal activity.

Serological autoantibody profiling of type 1 diabetes by protein arrays

The need for biomarkers that illuminate the pathophysiology of type 1 diabetes (T1D), enhance early diagnosis and provide additional avenues for therapeutic intervention is well recognized in the scientific community. We conducted a proteome-scale, two-stage serological AAb screening followed by an independent validation study. In the first stage, the immunoreactivity was compared between T1D cases and healthy controls against ~6000 human proteins using the nucleic acid programmable protein array (NAPPA). Genes identified with higher signal intensities in patients were challenged with a larger sample set during the second stage. Statistical analysis revealed 26 novel autoantigens and a known T1D-associated autoantigen. During validation, we verified the presence of AAbs to dual specificity tyrosine-phosphorylation-regulated kinase 2 (DYRK2) using the Luciferase ImmunoPrecipitation System (LIPS) assay (36% sensitivity, 98% specificity). The AUC for a combination of DYRK2A and the classical T1D AAb IA-2A was 0.90 compared to 0.72 for DYRK2A and 0.64 for IA-2A alone. This is the first systematic screening for seroreactivity against a large number of human proteins in T1D patients. We demonstrated the application of protein microarrays to identify novel autoantigens in T1D, expanded the current T1D "autoantigenome" and help fulfill the goal of searching for novel biomarker candidates for T1D.

BIOLOGICAL SIGNIFICANCE

Protein microarrays provide a high-throughput platform that enables the profiling of serum antibodies to a large number of protein antigens. The value of AAb biomarkers in diagnosis, prognosis and treatment is well recognized in autoimmune diseases including T1D. We performed a systematic screening for new T1D-associated autoantigens by adapting the innovative protein array platform NAPPA. We believe that the discovery in this study will add information on candidate autoantigens that could potentially improve the diagnosis and help uncover the pathophysiology of T1D. The successful use of NAPPA for T1D AAb profiling will open the window for larger studies including more human antigen genes and other autoimmune diseases.

Regional brain activity change predicts responsiveness to treatment for stuttering in adults

Developmental stuttering is known to be associated with aberrant brain activity, but there is no evidence that this knowledge has benefited stuttering treatment. This study investigated whether brain activity could predict progress during stuttering treatment for 21 dextral adults who stutter (AWS). They received one of two treatment programs that included periodic H2(15)O PET scanning (during oral reading, monologue, and eyes-closed rest conditions). All participants successfully completed an initial treatment phase and then entered a phase designed to transfer treatment gains; 9/21 failed to complete this latter phase. The 12 pass and 9 fail participants were similar on speech and neural system variables before treatment, and similar in speech performance after the initial phase of their treatment. At the end of the initial treatment phase, however, decreased activation within a single region, L. putamen, in all 3 scanning conditions was highly predictive of successful treatment progress.

Transcriptomic analysis of incised leaf-shape determination in birch

Plant researchers have focused much attention on leaf shape because of its importance in the identification. To evaluate the impact of intraspecies leaf-shape variation on the transcriptome, a series of Betula pendula 'Dalecarlica' and B. pendula saplings were generated through tissue culture. The leaf shapes and transcriptomes of B. pendula 'Dalecarlica' clones were compared with those of B. pendula clones. The leaf shape of B. pendula 'Dalecarlica' was incised and that of B. pendula was ovate. Transcriptome data revealed numerous changes in gene expression between B. pendula 'Dalecarlica' and B. pendula, including upregulation of 8767 unigenes and downregulation of 8379 unigenes in B. pendula 'Dalecarlica'. A pathway analysis revealed that the transport and signal transduction of auxin were altered in 'Dalecarlica', which may have contributed to its altered leaf shape. These results shed light on variation in birch leaf shape and help identify important genes for the genetic engineering of birch trees.