Statistical distribution of blood serotonin as a predictor of early autistic brain abnormalities

Background  

A wide range of abnormalities has been reported in autistic brains, but these abnormalities may be the result of an earlier underlying developmental alteration that may no longer be evident by the time autism is diagnosed. The most consistent biological finding in autistic individuals has been their statistically elevated levels of 5-hydroxytryptamine (5-HT, serotonin) in blood platelets (platelet hyperserotonemia). The early developmental alteration of the autistic brain and the autistic platelet hyperserotonemia may be caused by the same biological factor expressed in the brain and outside the brain, respectively. Unlike the brain, blood platelets are short-lived and continue to be produced throughout the life span, suggesting that this factor may continue to operate outside the brain years after the brain is formed. The statistical distributions of the platelet 5-HT levels in normal and autistic groups have characteristic features and may contain information about the nature of this yet unidentified factor.     

Novel metabolic biomarkers related to sulfur-dependent detoxification pathways in autistic patients of Saudi Arabia

Background  

Xenobiotics are neurotoxins that dramatically alter the health of the child. In addition, an inefficient detoxification system leads to oxidative stress, gut dysbiosis, and immune dysfunction. The consensus among physicians who treat autism with a biomedical approach is that those on the spectrum are burdened with oxidative stress and immune problems. In a trial to understand the role of detoxification in the etiology of autism, selected parameters related to sulfur-dependent detoxification mechanisms in plasma of autistic children from Saudi Arabia will be investigated compared to control subjects.     

The evaluation of food allergy on behavior in autistic children

Background:

Despite many efforts, the etiology of autism remains unknown. Food allergy has been suggested as a pathogenic factor in Autism Spectrum Disorder (ASD). Our aim in this study was to determine whether food allergy could be considered as a risk factor for autistic children.

Methods:

Thirty-nine autistic children were examined by the skin prick test (SPT), and total serum IgE was evaluated by ELISA. SPTs were performed for egg whites, oranges, peanuts, tomatoes, tuna fish, walnuts, aubergines, melons, grapes, and cow milk. Parents and teachers were then asked to exclude these items from the childrens’ diets for six months. After the treatment period, the autistic children who tested positive for food allergies were re-assessed by a standard questionnaire to obtain further information about their medical histories.

Results:

Three of the study’s 39 autistic children (7.7%) tested positive on the SPT. Total serum IgE levels were elevated in 56.4% of the subjects (mean=164±24.5, cut-off >155 IU/ml). The results showed a decreased mean in the childrens’ autistic behaviors on the Children Autism Rating Scale (CARS) after both eight weeks and six months; however, this decrease was not statistically significant.

Conclusion:

Food allergy may play a role in the pathophysiology of autism. We conclude that avoidance of certain foods benefits the behavior of autistic children.Key Words: Autism, Food allergy, Skin prick test     

VANLO - Interactive visual exploration of aligned biological networks

Background  

Protein-protein interaction (PPI) is fundamental to many biological processes. In the course of evolution, biological networks such as protein-protein interaction networks have developed. Biological networks of different species can be aligned by finding instances (e.g. proteins) with the same common ancestor in the evolutionary process, so-called orthologs. For a better understanding of the evolution of biological networks, such aligned networks have to be explored. Visualization can play a key role in making the various relationships transparent.     

WordCloud: a Cytoscape plugin to create a visual semantic summary of networks

Background  

When biological networks are studied, it is common to look for clusters, i.e. sets of nodes that are highly inter-connected. To understand the biological meaning of a cluster, the user usually has to sift through many textual annotations that are associated with biological entities.     

Autistic disorder in patients with Williams-Beuren syndrome: a reconsideration of the Williams-Beuren syndrome phenotype

Background

Williams-Beuren syndrome (WBS), a rare developmental disorder caused by deletion of contiguous genes at 7q11.23, has been characterized by strengths in socialization (overfriendliness) and communication (excessive talkativeness). WBS has been often considered as the polar opposite behavioral phenotype to autism. Our objective was to better understand the range of phenotypic expression in WBS and the relationship between WBS and autistic disorder.

Methodology

The study was conducted on 9 French individuals aged from 4 to 37 years old with autistic disorder associated with WBS. Behavioral assessments were performed using Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) scales. Molecular characterization of the WBS critical region was performed by FISH.

Findings

FISH analysis indicated that all 9 patients displayed the common WBS deletion. All 9 patients met ADI-R and ADOS diagnostic criteria for autism, displaying stereotypies and severe impairments in social interaction and communication (including the absence of expressive language). Additionally, patients showed improvement in social communication over time.

Conclusions

The results indicate that comorbid autism and WBS is more frequent than expected and suggest that the common WBS deletion can result in a continuum of social communication impairment, ranging from excessive talkativeness and overfriendliness to absence of verbal language and poor social relationships. Appreciation of the possible co-occurrence of WBS and autism challenges the common view that WBS represents the opposite behavioral phenotype of autism, and might lead to improved recognition of WBS in individuals diagnosed with autism.     

Relationships among body mass, brain size, gut length, and blood tryptophan and serotonin in young wild-type mice

Background  

The blood hyperserotonemia of autism is one of the most consistent biological findings in autism research, but its causes remain unclear. A major difficulty in understanding this phenomenon is the lack of information on fundamental interactions among the developing brain, gut, and blood in the mammalian body. We therefore investigated relationships among the body mass, the brain mass, the volume of the hippocampal complex, the gut length, and the whole-blood levels of tryptophan and 5-hydroxytryptamine (5-HT, serotonin) in young, sexually immature wild-type mice.     

Kavosh: a new algorithm for finding network motifs

Background  

Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs.     

MetNetGE: interactive views of biological networks and ontologies

Background  

Linking high-throughput experimental data with biological networks is a key step for understanding complex biological systems. Currently, visualization tools for large metabolic networks often result in a dense web of connections that is difficult to interpret biologically. The MetNetGE application organizes and visualizes biological networks in a meaningful way to improve performance and biological interpretability.     

Comparative analysis of neurological disorders focuses genome-wide search for autism genes

The behaviors of autism overlap with a diverse array of other neurological disorders, suggesting common molecular mechanisms. We conducted a large comparative analysis of the network of genes linked to autism with those of 432 other neurological diseases to circumscribe a multi-disorder subcomponent of autism. We leveraged the biological process and interaction properties of these multi-disorder autism genes to overcome the across-the-board multiple hypothesis corrections that a purely data-driven approach requires. Using prior knowledge of biological process, we identified 154 genes not previously linked to autism of which 42% were significantly differentially expressed in autistic individuals. Then, using prior knowledge from interaction networks of disorders related to autism, we uncovered 334 new genes that interact with published autism genes, of which 87% were significantly differentially regulated in autistic individuals. Our analysis provided a novel picture of autism from the perspective of related neurological disorders and suggested a model by which prior knowledge of interaction networks can inform and focus genome-scale studies of complex neurological disorders.     

Walk-weighted subsequence kernels for protein-protein interaction extraction

Background  

The construction of interaction networks between proteins is central to understanding the underlying biological processes. However, since many useful relations are excluded in databases and remain hidden in raw text, a study on automatic interaction extraction from text is important in bioinformatics field.     

Detecting multivariate differentially expressed genes

Background  

Gene expression is governed by complex networks, and differences in expression patterns between distinct biological conditions may therefore be complex and multivariate in nature. Yet, current statistical methods for detecting differential expression merely consider the univariate difference in expression level of each gene in isolation, thus potentially neglecting many genes of biological importance.     

Indel PDB: A database of structural insertions and deletions derived from sequence alignments of closely related proteins

Background  

Insertions and deletions (indels) represent a common type of sequence variations, which are less studied and pose many important biological questions. Recent research has shown that the presence of sizable indels in protein sequences may be indicative of protein essentiality and their role in protein interaction networks. Examples of utilization of indels for structure-based drug design have also been recently demonstrated. Nonetheless many structural and functional characteristics of indels remain less researched or unknown.     

Transforming growth factor beta 1 869T/C and 915G/C polymorphisms and risk of autism spectrum disorders

Background:

Transforming growth factor-β1 (TGF-β1) has been found to play a crucial role in early central nervous system development. Several studies have illustrated decreased TGF-β1 levels in sera and brains of autistic children. Two point mutations in the TGF-β1 signal peptide at 869T/C and 915G/C have been reported to influence TGF-β1 expression. The aim of the present study was to investigate the correlation of TGF-β1 polymorphisms and their haplotypes with autism.

Methods:

This study was performed on 39 autistic patients and 35 age- and sex-matched normal controls in an Iranian population, using the sequence specific primed-polymerase chain reaction (PCR-SSP) technique. Patients were divided into mild-to-moderate and severe groups according to the childhood autism rating scale.

Results:

No significant differences were observed for allele, genotype, or haplotype frequencies between the autistics and controls. Only a slight difference was observed in GC25 between the controls and all children with autism.

Conclusion:

Thus, these results indicate that the polymorphisms in TGF-β1 gene may not play an important role in the development of autism.Key Words: Autism spectrum disorders, Development, Polymorphism, Transforming Growth Factor beta 1     

Usefulness and limitations of dK random graph models to predict interactions and functional homogeneity in biological networks under a pseudo-likelihood parameter estimation approach

Background  

Many aspects of biological functions can be modeled by biological networks, such as protein interaction networks, metabolic networks, and gene coexpression networks. Studying the statistical properties of these networks in turn allows us to infer biological function. Complex statistical network models can potentially more accurately describe the networks, but it is not clear whether such complex models are better suited to find biologically meaningful subnetworks.     

Examining the Genetic and Environmental Associations between Autistic Social and Communication Deficits and Psychopathic Callous-Unemotional Traits

Background

Difficulties in appropriate social interaction are characteristic of both children with autism spectrum disorders and children with callous-unemotional traits (who are at risk of developing psychopathy). Extant experimental studies suggest that the nature of atypical social cognition that characterises these two profiles is not identical. However, ‘empathizing’ difficulties have been hypothesised for both groups, raising questions about the degree of aetiological separation between social impairments that characterize each disorder. This study explored the relative contribution of independent vs. shared aetiological influences to social and communication impairments associated with autistic traits and callous-unemotional traits, indexed by parent-report in a population-based cohort of twins.

Methods

Participants were over 5,000 twin pairs from a UK cohort (the Twins Early Development Study; TEDS), assessed for callous-unemotional traits at 7 years and autistic social and communication impairments at 8 years. Multivariate model-fitting was used to explore the relative contribution of independent vs. overlapping genetic/environmental influences on these traits.

Results

Both social and communication impairments and callous-unemotional traits were highly heritable, although the genetic and environmental influences accounting for individual differences on each domain were predominantly independent.

Conclusions

Extant evidence from experimental and neuro-imaging studies has suggested that, despite some superficially overlapping behaviours, the social difficulties seen in children with autism spectrum disorders and callous-unemotional traits are largely distinct. The current study is the first to demonstrate considerable aetiological independence of the social interaction difficulties seen in children with autism spectrum disorders and those with callous-unemotional traits.     

Statistical inference of the time-varying structure of gene-regulation networks

Background  

Biological networks are highly dynamic in response to environmental and physiological cues. This variability is in contrast to conventional analyses of biological networks, which have overwhelmingly employed static graph models which stay constant over time to describe biological systems and their underlying molecular interactions.     

Zooming of states and parameters using a lumping approach including back-translation

Background  

Systems biology models tend to become large since biological systems often consist of complex networks of interacting components, and since the models usually are developed to reflect various mechanistic assumptions of those networks. Nevertheless, not all aspects of the model are equally interesting in a given setting, and normally there are parts that can be reduced without affecting the relevant model performance. There are many methods for model reduction, but few or none of them allow for a restoration of the details of the original model after the simplified model has been simulated.     

On the architecture of cell regulation networks

Background  

With the rapid development of high-throughput experiments, detecting functional modules has become increasingly important in analyzing biological networks. However, the growing size and complexity of these networks preclude structural breaking in terms of simplest units. We propose a novel graph theoretic decomposition scheme combined with dynamics consideration for probing the architecture of complex biological networks.     

Algorithms for effective querying of compound graph-based pathway databases

Background  

Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools.