Addressing false discoveries in network inference.

MOTIVATION: Experimentally determined gene regulatory networks can be enriched by computational inference from high-throughput expression profiles. However, the prediction of regulatory interactions is severely impaired by indirect and spurious effects, particularly for eukaryotes. Recently, published methods report improved predictions by exploiting the a priori known targets of a regulator (its local topology) in addition to expression profiles. RESULTS: We find that methods exploiting known targets show an unexpectedly high rate of false discoveries. This leads to inflated performance estimates and the prediction of an excessive number of new interactions for regulators with many known targets. These issues are hidden from common evaluation and cross-validation setups, which is due to Simpson's paradox. We suggest a confidence score recalibration method (CoRe) that reduces the false discovery rate and enables a reliable performance estimation. CONCLUSIONS: CoRe considerably improves the results of network inference methods that exploit known targets. Predictions then display the biological process specificity of regulators more correctly and enable the inference of accurate genome-wide regulatory networks in eukaryotes. For yeast, we propose a network with more than 22 000 confident interactions. We point out that machine learning approaches outside of the area of network inference may be affected as well. AVAILABILITY AND IMPLEMENTATION: Results, executable code and networks are available via our website http://www.bio.ifi.lmu.de/forschung/CoRe. CONTACT: robert.kueffner@helmholtz-muenchen.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A commonly used rumen-protected conjugated linoleic acid supplement marginally affects fatty acid distribution of body tissues and gene expression of mammary gland in heifers during early lactation.

BACKGROUND: Conjugated linoleic acids (CLA) in general, and in particular the trans-10,cis-12 (t10,c12-CLA) isomer are potent modulators of milk fat synthesis in dairy cows. Studies in rodents, such as mice, have revealed that t10,c12-CLA is responsible for hepatic lipodystrophy and decreased adipose tissue with subsequent changes in the fatty acid distribution. The present study aimed to investigate the fatty acid distribution of lipids in several body tissues compared to their distribution in milk fat in early lactating cows in response to CLA treatment. Effects in mammary gland are further analyzed at gene expression level. METHODS: Twenty-five Holstein heifers were fed a diet supplemented with (CLA groups) or without (CON groups) a rumen-protected CLA supplement that provided 6 g/d of c9,t11- and t10,c12-CLA. Five groups of randomly assigned cows were analyzed according to experimental design based on feeding and time of slaughter. Cows in the first group received no CLA supplement and were slaughtered one day postpartum (CON0). Milk samples were taken from the remaining cows in CON and CLA groups until slaughter at 42 (period 1) and 105 (period 2) days in milk (DIM). Immediately after slaughter, tissue samples from liver, retroperitoneal fat, mammary gland and M. longissimus (13th rib) were obtained and analyzed for fatty acid distribution. Relevant genes involved in lipid metabolism of the mammary gland were analyzed using a custom-made microarray platform. RESULTS: Both supplemented CLA isomers increased significantly in milk fat. Furthermore, preformed fatty acids increased at the expense of de novo-synthesized fatty acids. Total and single trans-octadecenoic acids (e.g., t10-18:1 and t11-18:1) also significantly increased. Fatty acid distribution of the mammary gland showed similar changes to those in milk fat, due mainly to residual milk but without affecting gene expression. Liver fatty acids were not altered except for trans-octadecenoic acids, which were increased. Adipose tissue and M. longissimus were only marginally affected by CLA supplementation. CONCLUSIONS: Daily supplementation with CLA led to typical alterations usually observed in milk fat depression (reduction of de novo-synthesized fatty acids) but only marginally affected tissue lipids. Gene expression of the mammary gland was not influenced by CLA supplementation.

Factors influencing the efficiency of generating genetically engineered pigs by nuclear transfer: multi-factorial analysis of a large data set.

BACKGROUND: Somatic cell nuclear transfer (SCNT) using genetically engineered donor cells is currently the most widely used strategy to generate tailored pig models for biomedical research. Although this approach facilitates a similar spectrum of genetic modifications as in rodent models, the outcome in terms of live cloned piglets is quite variable. In this study, we aimed at a comprehensive analysis of environmental and experimental factors that are substantially influencing the efficiency of generating genetically engineered pigs. Based on a considerably large data set from 274 SCNT experiments (in total 18,649 reconstructed embryos transferred into 193 recipients), performed over a period of three years, we assessed the relative contribution of season, type of genetic modification, donor cell source, number of cloning rounds, and pre-selection of cloned embryos for early development to the cloning efficiency. RESULTS: 109 (56%) recipients became pregnant and 85 (78%) of them gave birth to offspring. Out of 318 cloned piglets, 243 (76%) were alive, but only 97 (40%) were clinically healthy and showed normal development. The proportion of stillborn piglets was 24% (75/318), and another 31% (100/318) of the cloned piglets died soon after birth. The overall cloning efficiency, defined as the number of offspring born per SCNT embryos transferred, including only recipients that delivered, was 3.95%. SCNT experiments performed during winter using fetal fibroblasts or kidney cells after additive gene transfer resulted in the highest number of live and healthy offspring, while two or more rounds of cloning and nuclear transfer experiments performed during summer decreased the number of healthy offspring. CONCLUSION: Although the effects of individual factors may be different between various laboratories, our results and analysis strategy will help to identify and optimize the factors, which are most critical to cloning success in programs aiming at the generation of genetically engineered pig models.

Detection and correction of probe-level artefacts on microarrays.

BACKGROUND: A recent large-scale analysis of Gene Expression Omnibus (GEO) data found frequent evidence for spatial defects in a substantial fraction of Affymetrix microarrays in the GEO. Nevertheless, in contrast to quality assessment, artefact detection is not widely used in standard gene expression analysis pipelines. Furthermore, although approaches have been proposed to detect diverse types of spatial noise on arrays, the correction of these artefacts is mostly left to either summarization methods or the corresponding arrays are completely discarded. RESULTS: We show that state-of-the-art robust summarization procedures are vulnerable to artefacts on arrays and cannot appropriately correct for these. To address this problem, we present a simple approach to detect artefacts with high recall and precision, which we further improve by taking into account the spatial layout of arrays. Finally, we propose two correction methods for these artefacts that either substitute values of defective probes using probeset information or filter corrupted probes. We show that our approach can identify and correct defective probe measurements appropriately and outperforms existing tools. CONCLUSIONS: While summarization is insufficient to correct for defective probes, this problem can be addressed in a straightforward way by the methods we present for identification and correction of defective probes. As these methods output CEL files with corrected probe values that serve as input to standard normalization and summarization procedures, they can be easily integrated into existing microarray analysis pipelines as an additional pre-processing step. An R package is freely available from http://www.bio.ifi.lmu.de/artefact-correction.

Inferring gene regulatory networks by ANOVA.

MOTIVATION: To improve the understanding of molecular regulation events, various approaches have been developed for deducing gene regulatory networks from mRNA expression data. RESULTS: We present a new score for network inference, η(2), that is derived from an analysis of variance. Candidate transcription factor:target gene (TF:TG) relationships are assumed more likely if the expression of TF and TG are mutually dependent in at least a subset of the examined experiments. We evaluate this dependency by η(2), a non-parametric, non-linear correlation coefficient. It is fast, easy to apply and does not require the discretization of the input data. In the recent DREAM5 blind assessment, the arguably most comprehensive evaluation of inference methods, our approach based on η(2) was rated the best performer on real expression compendia. It also performs better than methods tested in other recently published comparative assessments. About half of our predicted novel predictions are true interactions as estimated from qPCR experiments performed for DREAM5. CONCLUSIONS: The score η(2) has a number of interesting features that enable the efficient detection of gene regulatory interactions. For most experimental setups, it is an interesting alternative to other measures of dependency such as Pearson's correlation or mutual information.

Experiment specific expression patterns.

The differential analysis of genes between microarrays from several experimental conditions or treatments routinely estimates which genes change significantly between groups. As genes are never regulated individually, observed behavior may be a consequence of changes in other genes. Existing approaches like co-expression analysis aim to resolve such patterns from a wide range of experiments. The knowledge of such a background set of experiments can be used to compute expected gene behavior based on known links. It is particularly interesting to detect previously unseen specific effects in other experiments. Here, a new method to spot genes deviating from expected behavior (PAttern DEviation SCOring--Padesco) is devised. It uses linear regression models learned from a background set to arrive at gene specific prediction accuracy distributions. For a given experiment, it is then decided whether each gene is predicted better or worse than expected. This provides a novel way to estimate the experiment specificity of each gene. We propose a validation procedure to estimate the detection of such specific candidates and show that these can be identified with an average accuracy of about 85%.

Coevolution of ABC transporters and two-component regulatory systems as resistance modules against antimicrobial peptides in Firmicutes Bacteria.

In Firmicutes bacteria, ATP-binding cassette (ABC) transporters have been recognized as important resistance determinants against antimicrobial peptides. Together with neighboring two-component systems (TCSs), which regulate their expression, they form specific detoxification modules. Both the transport permease and sensor kinase components show unusual domain architecture: the permeases contain a large extracellular domain, while the sensor kinases lack an obvious input domain. One of the best-characterized examples is the bacitracin resistance module BceRS-BceAB of Bacillus subtilis. Strikingly, in this system, the ABC transporter and TCS have an absolute mutual requirement for each other in both sensing of and resistance to bacitracin, suggesting a novel mode of signal transduction in which the transporter constitutes the actual sensor. We identified over 250 such BceAB-like ABC transporters in the current databases. They occurred almost exclusively in Firmicutes bacteria, and 80% of the transporters were associated with a BceRS-like TCS. Phylogenetic analyses of the permease and sensor kinase components revealed a tight evolutionary correlation. Our findings suggest a direct regulatory interaction between the ABC transporters and TCSs, mediating communication between both components. Based on their observed coclustering and conservation of response regulator binding sites, we could identify putative corresponding two-component systems for transporters lacking a regulatory system in their immediate neighborhood. Taken together, our results show that these types of ABC transporters and TCSs have coevolved to form self-sufficient detoxification modules against antimicrobial peptides, widely distributed among Firmicutes bacteria.

Petri Nets with Fuzzy Logic (PNFL): reverse engineering and parametrization.

BACKGROUND: The recent DREAM4 blind assessment provided a particularly realistic and challenging setting for network reverse engineering methods. The in silico part of DREAM4 solicited the inference of cycle-rich gene regulatory networks from heterogeneous, noisy expression data including time courses as well as knockout, knockdown and multifactorial perturbations. METHODOLOGY AND PRINCIPAL FINDINGS: We inferred and parametrized simulation models based on Petri Nets with Fuzzy Logic (PNFL). This completely automated approach correctly reconstructed networks with cycles as well as oscillating network motifs. PNFL was evaluated as the best performer on DREAM4 in silico networks of size 10 with an area under the precision-recall curve (AUPR) of 81%. Besides topology, we inferred a range of additional mechanistic details with good reliability, e.g. distinguishing activation from inhibition as well as dependent from independent regulation. Our models also performed well on new experimental conditions such as double knockout mutations that were not included in the provided datasets. CONCLUSIONS: The inference of biological networks substantially benefits from methods that are expressive enough to deal with diverse datasets in a unified way. At the same time, overly complex approaches could generate multiple different models that explain the data equally well. PNFL appears to strike the balance between expressive power and complexity. This also applies to the intuitive representation of PNFL models combining a straightforward graphical notation with colloquial fuzzy parameters.