Inferring genetic interactions via a nonlinear model and an optimization algorithm

Spinocerebellar ataxia 17: full phenotype in a 41 CAG/CAA repeats carrier

Background

Spinocerebellar ataxia 17 (SCA17) is one of the most heterogeneous forms of autosomal dominant cerebellar ataxias with a large clinical spectrum which can mimic other movement disorders such as Huntington disease (HD), dystonia and parkinsonism. SCA17 is caused by an expansion of CAG/CAA repeat in the Tata binding protein (TBP) gene. Normal alleles contain 25 to 40 CAG/CAA repeats, alleles with 50 or greater CAG/CAA repeats are pathological with full penetrance. Alleles with 43 to 49 CAG/CAA repeats were also reported and their penetrance is estimated between 50 and 80%. Recently few symptomatic individuals having 41 and 42 repeats were reported but it is still unclear whether CAG/CAA repeats of 41 or 42 are low penetrance disease-causing alleles. Thus, phenotypic variability like the disease course in subject with SCA17 locus restricted expansions remains to be fully understood.

Case presentation

The patients was a 63-year-old woman who, at 54 years, showed personality changes and increased frequency of falls. At 55 years of age neuropsychological tests showed executive attention and visuospatial deficit. At the age of 59 the patient developed dysarthria and a progressive cognitive deficit. The neurological examination showed moderate gait ataxia, dysdiadochokinesia and dysmetria, dysphagia, dysarthria and abnormal saccadic pursuit, severe axial asynergy during postural changes, choreiform dyskinesias. Molecular analysis of the TBP gene demonstrated an allele with 41 repeat suggesting that 41 CAG/CCG TBP repeats could be an allele associated with the full clinical spectrum of SCA17.

Conclusions

The described case with the other similar cases described in the literature suggests that 41 CAG/CAA trinucleotides should be considered as critical threshold in SCA17. We suggest that SCA17 diagnosis should be suspected in patients presenting with movement disorders associated with other neurodegenerative signs and symptoms.

Inferring microbial interaction network from microbiome data using RMN algorithm

Background

Microbial interactions are ubiquitous in nature. Recently, many similarity-based approaches have been developed to study the interaction in microbial ecosystems. These approaches can only explain the non-directional interactions yet a more complete view on how microbes regulate each other remains elusive. In addition, the strength of microbial interactions is difficult to be quantified by only using correlation analysis.

Results

In this study, a rule-based microbial network (RMN) algorithm, which integrates regulatory OTU-triplet model with parametric weighting function, is being developed to construct microbial regulatory networks. The RMN algorithm not only can extrapolate the cooperative and competitive relationships between microbes, but also can infer the direction of such interactions. In addition, RMN algorithm can theoretically characterize the regulatory relationship composed of microbial pairs with low correlation coefficient in microbial networks. Our results suggested that Bifidobacterium, Streptococcus, Clostridium XI, and Bacteroides are essential for causing abundance changes of Veillonella in gut microbiome. Furthermore, we inferred some possible microbial interactions, including the competitive relationship between Veillonella and Bacteroides, and the cooperative relationship between Veillonella and Clostridium XI.

Conclusions

The RMN algorithm provides the reconstruction of gut microbe networks, and can shed light on the dynamical interactions of microbes in the infant intestinal tract.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0199-2) contains supplementary material, which is available to authorized users.

Modeling the zebrafish segmentation clock's gene regulatory network constrained by expression data suggests evolutionary transitions between oscillating and nonoscillating transcription

During segmentation of vertebrate embryos, somites form in accordance with a periodic pattern established by the segmentation clock. In the zebrafish (Danio rerio), the segmentation clock includes six hairy/enhancer of split-related (her/hes) genes, five of which oscillate due to negative autofeedback. The nonoscillating gene hes6 forms the hub of a network of 10 Her/Hes protein dimers, which includes 7 DNA-binding dimers and 4 weak or non-DNA-binding dimers. The balance of dimer species is critical for segmentation clock function, and loss-of-function studies suggest that the her genes have both unique and redundant functions within the clock. However, the precise regulatory interactions underlying the negative feedback loop are unknown. Here, we combine quantitative experimental data, in silico modeling, and a global optimization algorithm to identify a gene regulatory network (GRN) designed to fit measured transcriptional responses to gene knockdown. Surprisingly, we find that hes6, the clock gene that does not oscillate, responds to negative feedback. Consistent with prior in silico analyses, we find that variation in transcription, translation, and degradation rates can mediate the gain and loss of oscillatory behavior for genes regulated by negative feedback. Extending our study, we found that transcription of the nonoscillating Fgf pathway gene sef responds to her/hes perturbation similarly to oscillating her genes. These observations suggest a more extensive underlying regulatory similarity between the zebrafish segmentation clock and the mouse and chick segmentation clocks, which exhibit oscillations of her/hes genes as well as numerous other Notch, Fgf, and Wnt pathway genes.

MORE: mixed optimization for reverse engineering--an application to modeling biological networks response via sparse systems of nonlinear differential equations

Reverse engineering is the problem of inferring the structure of a network of interactions between biological variables from a set of observations. In this paper, we propose an optimization algorithm, called MORE, for the reverse engineering of biological networks from time series data. The model inferred by MORE is a sparse system of nonlinear differential equations, complex enough to realistically describe the dynamics of a biological system. MORE tackles separately the discrete component of the problem, the determination of the biological network topology, and the continuous component of the problem, the strength of the interactions. This approach allows us both to enforce system sparsity, by globally constraining the number of edges, and to integrate a priori information about the structure of the underlying interaction network. Experimental results on simulated and real-world networks show that the mixed discrete/continuous optimization approach of MORE significantly outperforms standard continuous optimization and that MORE is competitive with the state of the art in terms of accuracy of the inferred networks.

Qualitative reasoning for biological network inference from systematic perturbation experiments

The systematic perturbation of the components of a biological system has been proven among the most informative experimental setups for the identification of causal relations between the components. In this paper, we present Systematic Perturbation-Qualitative Reasoning (SPQR), a novel Qualitative Reasoning approach to automate the interpretation of the results of systematic perturbation experiments. Our method is based on a qualitative abstraction of the experimental data: for each perturbation experiment, measured values of the observed variables are modeled as lower, equal or higher than the measurements in the wild type condition, when no perturbation is applied. The algorithm exploits a set of IF-THEN rules to infer causal relations between the variables, analyzing the patterns of propagation of the perturbation signals through the biological network, and is specifically designed to minimize the rate of false positives among the inferred relations. Tested on both simulated and real perturbation data, SPQR indeed exhibits a significantly higher precision than the state of the art.

Inferring Genetic Interactions via a Data-Driven Second Order Model

Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al. (2002) proposed to model this coregulation by a fixed second order response surface (called the RS algorithm), in which T is a function of A, R, and AR. Unfortunately, the RS algorithm did not result in a sufficient number of genetic interactions (GIs) when it was applied to a group of 51 yeast genes in a pilot study. Thus, we propose a data-driven second order model (DDSOM), an approximation to the non-linear transcriptional interactions, to infer genetic and transcriptional regulatory interactions. For each triplet of genes of interest (A, R, and T), we regress the expression of T at time t + 1 on the expression of A, R, and AR at time t. Next, these well-fitted regression models (viewed as points in R³) are collected, and the center of these points is used to identify triples of genes having the A-R-T relationship or GIs. The DDSOM and RS algorithms are first compared on inferring transcriptional compensation interactions of a group of yeast genes in DNA synthesis and DNA repair using microarray gene expression data; the DDSOM algorithm results in higher modified true positive rate (about 75%) than that of the RS algorithm, checked against quantitative RT-polymerase chain reaction results. These validated GIs are reported, among which some coincide with certain interactions in DNA repair and genome instability pathways in yeast. This suggests that the DDSOM algorithm has potential to predict pathway components. Further, both algorithms are applied to predict transcriptional regulatory interactions of 63 yeast genes. Checked against the known transcriptional regulatory interactions queried from TRANSFAC, the proposed also performs better than the RS algorithm.

Optimal structural inference of signaling pathways from unordered and overlapping gene sets

MOTIVATION

A plethora of bioinformatics analysis has led to the discovery of numerous gene sets, which can be interpreted as discrete measurements emitted from latent signaling pathways. Their potential to infer signaling pathway structures, however, has not been sufficiently exploited. Existing methods accommodating discrete data do not explicitly consider signal cascading mechanisms that characterize a signaling pathway. Novel computational methods are thus needed to fully utilize gene sets and broaden the scope from focusing only on pairwise interactions to the more general cascading events in the inference of signaling pathway structures.

RESULTS

We propose a gene set based simulated annealing (SA) algorithm for the reconstruction of signaling pathway structures. A signaling pathway structure is a directed graph containing up to a few hundred nodes and many overlapping signal cascades, where each cascade represents a chain of molecular interactions from the cell surface to the nucleus. Gene sets in our context refer to discrete sets of genes participating in signal cascades, the basic building blocks of a signaling pathway, with no prior information about gene orderings in the cascades. From a compendium of gene sets related to a pathway, SA aims to search for signal cascades that characterize the optimal signaling pathway structure. In the search process, the extent of overlap among signal cascades is used to measure the optimality of a structure. Throughout, we treat gene sets as random samples from a first-order Markov chain model. We evaluated the performance of SA in three case studies. In the first study conducted on 83 KEGG pathways, SA demonstrated a significantly better performance than Bayesian network methods. Since both SA and Bayesian network methods accommodate discrete data, use a 'search and score' network learning strategy and output a directed network, they can be compared in terms of performance and computational time. In the second study, we compared SA and Bayesian network methods using four benchmark datasets from DREAM. In our final study, we showcased two context-specific signaling pathways activated in breast cancer.

AVAILABILITY

Source codes are available from http://dl.dropbox.com/u/16000775/sa_sc.zip.

PathCase-SB architecture and database design

Background

Integration of metabolic pathways resources and regulatory metabolic network models, and deploying new tools on the integrated platform can help perform more effective and more efficient systems biology research on understanding the regulation in metabolic networks. Therefore, the tasks of (a) integrating under a single database environment regulatory metabolic networks and existing models, and (b) building tools to help with modeling and analysis are desirable and intellectually challenging computational tasks.

Description

PathCase Systems Biology (PathCase-SB) is built and released. The PathCase-SB database provides data and API for multiple user interfaces and software tools. The current PathCase-SB system provides a database-enabled framework and web-based computational tools towards facilitating the development of kinetic models for biological systems. PathCase-SB aims to integrate data of selected biological data sources on the web (currently, BioModels database and KEGG), and to provide more powerful and/or new capabilities via the new web-based integrative framework. This paper describes architecture and database design issues encountered in PathCase-SB's design and implementation, and presents the current design of PathCase-SB's architecture and database.

Conclusions

PathCase-SB architecture and database provide a highly extensible and scalable environment with easy and fast (real-time) access to the data in the database. PathCase-SB itself is already being used by researchers across the world.