Surfactant Protein A and B Gene Polymorphisms and Risk of Respiratory Distress Syndrome in Late-Preterm Neonates.

BACKGROUND AND OBJECTIVES: Newborns delivered late-preterm (between 340/7 and 366/7 weeks of gestation) are at increased risk of respiratory distress syndrome (RDS). Polymorphisms within the surfactant protein (SP) A and B gene have been shown to predispose to RDS in preterm neonates. The aim of this study was to investigate whether specific SP-A and/or SP-B genetic variants are also associated with RDS in infants born late-preterm. METHODS: This prospective cross-sectional study included 56 late-preterm infants with and 60 without RDS. Specific SP-A1/SP-A2 haplotypes and SP-B Ile131Thr polymorphic alleles were determined in blood specimens using polymerase-chain-reaction and DNA sequencing. RESULTS: The SP-A1 6A4 and the SP-A2 1A5 haplotypes were significantly overrepresented in newborns with RDS compared to controls (OR 2.86, 95%CI 1.20-6.83 and OR 4.68, 95%CI 1.28-17.1, respectively). The distribution of the SP-B Ile131Thr genotypes was similar between the two late-preterm groups. Overall, the SP-A1 6A4 or/and SP-A2 1A5 haplotype was present in 20 newborns with RDS (35.7%), resulting in a 4.2-fold (1.60-11.0) higher probability of RDS in carriers. Multivariable regression analysis revealed that the effect of SP-A1 6A4 and SP-A2 1A5 haplotypes was preserved when adjusting for known risk or protective factors, such as male gender, smaller gestational age, smaller weight, complications of pregnancy, and administration of antenatal corticosteroids. CONCLUSIONS: Specific SP-A genetic variants may influence the susceptibility to RDS in late-preterm infants, independently of the effect of other perinatal factors.

Composite macroH2A/NRF-1 Nucleosomes Suppress Noise and Generate Robustness in Gene Expression.

The histone variant macroH2A (mH2A) has been implicated in transcriptional repression, but the molecular mechanisms that contribute to global mH2A-dependent genome regulation remain elusive. Using chromatin immunoprecipitation sequencing (ChIP-seq) coupled with transcriptional profiling in mH2A knockdown cells, we demonstrate that singular mH2A nucleosomes occupy transcription start sites of subsets of both expressed and repressed genes, with opposing regulatory consequences. Specifically, mH2A nucleosomes mask repressor binding sites in expressed genes but activator binding sites in repressed genes, thus generating distinct chromatin landscapes that limit genetic or extracellular inductive signals. We show that composite nucleosomes containing mH2A and NRF-1 are stably positioned on gene regulatory regions and can buffer transcriptional noise associated with antiviral responses. In contrast, mH2A nucleosomes without NRF-1 bind promoters weakly and mark genes with noisier gene expression patterns. Thus, the strategic position and stabilization of mH2A nucleosomes in human promoters defines robust gene expression patterns.

Supervised method for construction of microRNA-mRNA networks: application in cardiac tissue aging dataset.

MicroRNAs play an important role in regulation of gene expression, but still detection of their targets remains a challenge. In this work we present a supervised regulatory network inference method with aim to identify potential target genes (mRNAs) of microRNAs. Briefly, the proposed method exploiting mRNA and microRNA expression trains Random Forests on known interactions and subsequently it is able to predict novel ones. In parallel, we incorporate different available data sources, such as Gene Ontology and ProteinProtein Interactions, to deliver biologically consistent results. Application in both benchmark data and an experiment studying aging showed robust performance.

Multi-scale modeling of gene regulatory networks via integration of temporal and topological biological data.

Regulome is the dynamic network representation of the regulatory interplay among genes, proteins and other cellular components that control cellular processes. Reconstruction of gene regulatory networks (GRN) delineates one of the main objectives of Systems Biology towards understanding the organization of regulome. Significant progress has been reported the last years regarding GRN reconstruction methods, but the majority of them either consider information originating solely from gene expression data or/and are applied on a small fraction of the experimental dataset. In this paper, we will describe an integrative method, utilizing both temporal information arriving from time-series gene expression profiles, as well as topological properties of protein networks. The proposed methodology detects relations among either groups of genes or specific genes depending on the level of abstraction or resolution requested. Application on real data proved the ability of the method to extract relations in accordance with current biological knowledge as well as discriminate between different experimental conditions.

Revealing the dynamic modularity of composite biological networks in breast cancer treatment.

A major challenge in modern breast cancer treatment is to unravel the effect of drug activity through the systematic rewiring of cellular networks over time. Here, we illustrate the efficacy and discriminative power of our integrative approach in detecting modules that represent the regulatory effect of tamoxifen, widely used in anti-estrogen treatment, on transcriptome and proteome and serve as dynamic sub-network signatures. Initially, composite networks, after integrating protein interaction and time series gene expression data between two conditions (estradiol and estradiol plus tamoxifen), were constructed. Further, the Detect Module from Seed Protein (DMSP) algorithm elaborated on the graphs and constructed modules, with specific 'seed' proteins used as starting points. Our findings provide evidence about the way drugs perturb and rewire the high-order organization of interactome in time.

Gene regulatory networks modelling using a dynamic evolutionary hybrid.

BACKGROUND: Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type. RESULTS: The recurrent, self-organizing structure and evolutionary training of our network yield an optimized pool of regulatory relations, while its fuzzy nature avoids noise-related problems. Furthermore, we are able to assign scores for each regulation, highlighting the confidence in the retrieved relations. The approach was tested by applying it to several benchmark datasets of yeast, managing to acquire biologically validated relations among genes. CONCLUSIONS: The results demonstrate the effectiveness of the ENFRN in retrieving biologically valid regulatory relations and providing meaningful insights for better understanding the dynamics of gene regulatory networks. The algorithms and methods described in this paper have been implemented in a Matlab toolbox and are available from: http://bioserver-1.bioacademy.gr/DataRepository/Project_ENFRN_GRN/.

Computational models reconstruct gene regulatory networks.

The post-genomic era is flooded with data from high-throughput techniques such as cDNA microarrays. In the field of systems biology the reconstruction of gene regulatory networks from gene expression data is one of the major problems in understanding complex cell functions. Drawing conclusions from microarray data requires sophisticated computational analyses that will explore causal genetic relations. In this paper we provide a brief summary of some of the most recent and promising computational models and mathematical frameworks used to reconstruct, model and infer gene regulatory networks from data.

An in silico method for detecting overlapping functional modules from composite biological networks.

BACKGROUND: The ever-increasing flow of gene expression and protein-protein interaction (PPI) data has assisted in understanding the dynamics of the cell. The detection of functional modules is the first step in deciphering the apparent modularity of biological networks. However, most network-partitioning algorithms consider only the topological aspects and ignore the underlying functional relationships. RESULTS: In the current study we integrate proteomics and microarray data of yeast, in the form of a weighted PPI graph. We partition the enriched PPI network with the novel DetMod algorithm and we identify 335 modules. One of the main advantages of DetMod is that it manages to capture the inter-module cross-talk by allowing a controlled degree of overlap among the detected modules. The obtained modules are densely connected in terms of protein interactions, while their members share up to a high degree similar biological process GO terms.Moreover, known protein complexes are largely incorporated in the assessed modules. Finally, we display the prevalence of our method against modules resulting from other computational approaches. CONCLUSION: The successful integration of heterogeneous data and the concept of the proposed algorithm provide confident functional modules. We also proved that our approach is superior to methods restricted to PPI data only.

Growing functional modules from a seed protein via integration of protein interaction and gene expression data.

BACKGROUND: Nowadays modern biology aims at unravelling the strands of complex biological structures such as the protein-protein interaction (PPI) networks. A key concept in the organization of PPI networks is the existence of dense subnetworks (functional modules) in them. In recent approaches clustering algorithms were applied at these networks and the resulting subnetworks were evaluated by estimating the coverage of well-established protein complexes they contained. However, most of these algorithms elaborate on an unweighted graph structure which in turn fails to elevate those interactions that would contribute to the construction of biologically more valid and coherent functional modules. RESULTS: In the current study, we present a method that corroborates the integration of protein interaction and microarray data via the discovery of biologically valid functional modules. Initially the gene expression information is overlaid as weights onto the PPI network and the enriched PPI graph allows us to exploit its topological aspects, while simultaneously highlights enhanced functional association in specific pairs of proteins. Then we present an algorithm that unveils the functional modules of the weighted graph by expanding a kernel protein set, which originates from a given 'seed' protein used as starting-point. CONCLUSION: The integrated data and the concept of our approach provide reliable functional modules. We give proofs based on yeast data that our method manages to give accurate results in terms both of structural coherency, as well as functional consistency.