Biomedical Informatics

Background: An increasing number of genetic components are available in several depositories of such components to facilitate synthetic biology research, but picking out those that will allow a designed circuit to achieve the specified function still requires multiple cycles of testing. Here, we addressed this problem by developing a computational pipeline to mathematically simulate a gene circuit for a comprehensive range and combination of the kinetic parameters of the biological components that constitute the gene circuit. Results: We showed that, using a well-studied transcriptional repression cascade as an example, the sets of kinetic parameters that could produce the specified system dynamics of the gene circuit formed clusters of recurrent combinations, referred to as kinetic motifs, which appear to be associated with both the specific topology and specified dynamics of the circuit. Furthermore, the use of the resulting "handbook" of performance-ranked kinetic motifs in finding suitable circuit components was illustrated in two application scenarios. Conclusions: These results show that the computational pipeline developed here can provide a rational-based guide to aid in the design and improvement of synthetic gene circuits.

Proteome-scale bioinformatics research is increasingly conducted as the number of completely sequenced genomes increases, but analysis of protein domains (PDs) usually relies on similarity in their amino acid sequences and/or three-dimensional structures. Here, we present results from a bi-clustering analysis on presence/absence data for 6,580 unique PDs in 2,134 species with a sequenced genome, thus covering a complete set of proteins, for the three superkingdoms of life, Bacteria, Archaea, and Eukarya. Our analysis revealed eight distinctive PD clusters, which, following an analysis of enrichment of Gene Ontology functions and CATH classification of protein structures, were shown to exhibit structural and functional properties that are taxa-characteristic. For examples, the largest cluster is ubiquitous in all three superkingdoms, constituting a set of 1,472 persistent domains created early in evolution and retained in living organisms and characterized by basic cellular functions...

Background A major challenge in mathematical modeling of biological systems is to determine how model parameters contribute to systems dynamics. As biological processes are often complex in nature, it is desirable to address this issue using a systematic approach. Here, we propose a simple methodology that first performs an enrichment test to find patterns in the values of globally profiled kinetic parameters with which a model can produce the required system dynamics; this is then followed by a statistical test to elucidate the association between individual parameters and different parts of the system’s dynamics. Results We demonstrate our methodology on a prototype biological system of perfect adaptation dynamics, namely the chemotaxis model for Escherichia coli. Our results agreed well with those derived from experimental data and theoretical studies in the literature. Using this model system, we showed that there are motifs in kinetic parameters and that these motifs are govern...

Background: An increasing number of genetic components are available in several depositories of such components to facilitate synthetic biology research, but picking out those that will allow a designed circuit to achieve the specified function still requires multiple cycles of testing. Here, we addressed this problem by developing a computational pipeline to mathematically simulate a gene circuit for a comprehensive range and combination of the kinetic parameters of the biological components that constitute the gene circuit. Results: We showed that, using a well-studied transcriptional repression cascade as an example, the sets of kinetic parameters that could produce the specified system dynamics of the gene circuit formed clusters of recurrent combinations, referred to as kinetic motifs, which appear to be associated with both the specific topology and specified dynamics of the circuit. Furthermore, the use of the resulting "handbook" of performance-ranked kinetic motifs in finding suitable circuit components was illustrated in two application scenarios. Conclusions: These results show that the computational pipeline developed here can provide a rational-based guide to aid in the design and improvement of synthetic gene circuits.

High-throughput transcriptomic experiments have made it possible to classify genes that are ubiquitously expressed as housekeeping (HK) genes and those expressed only in selective tissues as tissue-specific (TS) genes. Although partitioning a transcriptome into HK and TS genes is conceptually problematic owing to the lack of precise definitions and gene expression profile criteria for the two, information whether a gene is an HK or a TS gene can provide an initial clue to its cellular and/or functional role. Consequently, the development of new and novel HK (TS) classification methods has been a topic of considerable interest in post-genomics research. Here, we report such a development. Our method, called HKera, differs from the others by utilizing a novel property of HK genes that we have previously uncovered, namely that the ranking order of their expression levels, as opposed to the expression levels themselves, tends to be preserved from one tissue to another. Evaluated against multiple benchmark sets of human HK genes, including one recently derived from second generation sequencing data, HKera was shown to perform significantly better than five other classifiers that use different methodologies. An enrichment analysis of pathway and gene ontology annotations showed that HKera-predicted HK and TS genes have distinct functional roles and, together, cover most of the ontology categories. These results show that HKera is a good transcriptome partitioner that can be used to search for, and obtain useful expression and functional information for, novel HK (TS) genes.

The functions of proteins are usually determined by domains, and the sequential order in which domains are connected to make up a protein chain is known as the domain architecture. Here, we constructed evolutionary networks of protein domain architectures in species from three major life lineages (bacteria, fungi, and metazoans) by connecting any two architectures between which an evolutionary event could be inferred by a model that assumes maximum parsimony. We found that proteins with domain architectures with a higher level of evolvability, indicated by a greater number of connections in the evolutionary network, are present in a wider range of species. However, these proteins tend to be less essential to the organism, are duplicated more often during evolution, have more isoforms, and, intriguingly, tend to be associated with functional categories important for organismal adaptation. These results reveal the presence, in many genomes, of genes coding for a core set of nonessenti...

Diverse glycans on proteins help impact cell and organism physiology along with drug activity. Since many protein-based biotherapeutics are glycosylated and these glycans have biological activity, there is a desire to engineer glycosylation for recombinant protein-based biotherapeutics. Engineered glycosylation can impact the recombinant protein efficacy and also influence many cell pathways by first changing glycan-protein interactions and consequently modulating disease physiologies. However, its complexity is enormous. Due to recent advances in glycoengineering, modulating protein-glycan interactions become more amenable to therapeutic approaches. Here, we discuss how engineered glycans contribute to therapeutic monoclonal antibodies (mAbs) in the treatment of cancers, how these glycoengineered therapeutic mAbs affect the transformed phenotypes and downstream cell pathways, and how systems biology can help in the next generation mAb glycoengineering process by aiding in data analysis and guiding engineering efforts to tailor mAb glycan and ultimately drug efficacy, safety and affordability.

To meet the ever-growing demand for effective, safe, and affordable protein therapeutics, decades of intense efforts have aimed to maximize the quantity and quality of recombinant proteins produced in CHO cells. Bioprocessing innovations and cell engineering efforts have improved product titer; however, uncharacterized cellular processes and gene regulatory mechanisms still hinder cell growth, specific productivity, and protein quality. Herein, we summarize recent advances in systems biology and data-driven approaches aiming to unravel how molecular pathways, cellular processes, and extrinsic factors (e.g. media supplementation) influence recombinant protein production. In particular, as the available omics data for CHO cells continue to grow, predictive models and screens will be increasingly used to unravel the biological drivers of protein production, which can be used with emerging genome editing technologies to rationally engineer cells to further control the quantity, quality ...

RATIONALE: Eosinophilic esophagitis (EoE) is a chronic, T cellmediated disease that is triggered by specific foods and results in progressive esophageal dysfunction. However, we have limited knowledge of the antigen specificity and TCR diversity of T cells in this condition. Identifying disease-causing antigen/TCR clonotypes in EoE patients will provide useful information to aid the development of novel diagnostic and therapeutic approaches. METHODS: We isolated peripheral blood mononuclear cells from patients with EoE or healthy controls and cultured them in vitro in the absence or presence of five milk antigens. T cell expansion and type 2 cytokine production were measured. Single cell TCR sequencing with linked RNA sequencing was performed to examine clonotype expansion and transcriptional response in response to antigen stimulation. RESULTS: In contrast to peripheral CD4 + T cells from non-allergic controls, cells from patients with milk-triggered EoE divide and produce type 2 cytokines in response to milk stimulation. Single-cell TCR sequencing reveals milk-induced clonal expansion of T cell subsets, and identifies specific T cell clonotypes that are enriched upon milk stimulation. Milk-enriched T cell clonotypes shared transcriptional features with T cells from inflamed EoE esophageal mucosa. CONCLUSIONS: Food-specific T cells circulate in the blood of EoE patients, and are detectable after stimulation with food allergens. TCR repertoire analysis reveals expansion of CDR3s with high probability for allergen epitope specificity. This work improves our understanding of the pathophysiology of EoE, and is an important step towards developing efficient, definitive, and minimally invasive diagnostic tools for identification of EoE-causal foods.

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Resting-state fMRI (RS-fMRI) has been drawing more and more attention in recent years. However, a publicly available, systematically integrated and easy-to-use tool for RS-fMRI data processing is still lacking. We developed a toolkit for the analysis of RS-fMRI data, namely the RESting-state fMRI data analysis Toolkit (REST). REST was developed in MATLAB with graphical user interface (GUI). After data preprocessing with SPM or AFNI, a few analytic methods can be performed in REST, including functional connectivity analysis based on linear correlation, regional homogeneity, amplitude of low frequency fluctuation (ALFF), and fractional ALFF. A few additional functions were implemented in REST, including a DICOM sorter, linear trend removal, bandpass filtering, time course extraction, regression of covariates, image calculator, statistical analysis, and slice viewer (for result visualization, multiple comparison correction, etc.). REST is an open-source package and is freely available at http://www.restfmri.net.

The Signal-to-Noise-Ratio (SNR) is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs). These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of individual gene and 2-D Sammon's projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems.

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single-and multipleclass specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases.

Microbiota plays an important role in regulating immune responses associated with atopic diseases. We sought to evaluate relationships among airway microbiota, serum IgE levels, allergic sensitization and their relevance to rhinitis and asthma. Microbial characterization was performed using Illumina-based 16S rRNA gene sequencing of 87 throat swabs collected from children with asthma (n = 32) and rhinitis (n = 23), and from healthy controls (n = 32). Data analysis was performed using QIIME (Quantitative Insights Into Microbial Ecology) v1.8. Significantly higher abundance of Proteobacteria was found in children with rhinitis than in the healthy controls (20.1% vs. 16.1%, P = 0.009). Bacterial species richness (Chao1 index) and diversity (Shannon index) were significantly reduced in children with mite sensitization but not in those with food or IgE sensitization. Compared with healthy children without mite sensitization, the mite-sensitized children with rhinitis and asthma showed si...

Adrenoleukodystrophy (ALD) is a rare and progressive neurogenetic disease that may manifest disparate symptoms. The present study aims at investigating the role of ataxic variant of ALD (AVALD) in patients with adult-onset cerebellar ataxia, as well as characterizing their clinical features that distinguish AVALD from other cerebellar ataxias. Mutations in the ATP binding cassette subfamily D member 1 gene (ABCD1) were ascertained in 516 unrelated patients with ataxia. The patients were categorized into three groups: molecularly unassigned hereditary ataxia (n = 118), sporadic ataxia with autonomic dysfunctions (n = 296), and sporadic ataxia without autonomic dysfunctions (n = 102). Brain MRIs were scrutinized for white matter hyperintensity (WMH) in the parieto-occipital lobes, frontal lobes, corticospinal tracts, pons, middle cerebellar peduncles and cerebellar hemispheres. Two ABCD1 mutations (p.S108L and p.P623fs) previously linked to cerebral ALD and adrenomyeloneuropathy but n...

Lymphoid enhancer factor 1 (LEF1) activity is associated with progression of several types of cancers. The role of LEF1 in progression of hepatocellular carcinoma (HCC) remains poorly known. We investigated LEF1 expression in HCC and its interactions with epithelial-mesenchymal transition (EMT) regulators (e.g., Snail, Slug, Twist) and stemness genes (e.g., octamer-binding transcription factor 4 [Oct4], sex determining region Y-box 2 [Sox2], Nanog homeobox [Nanog]). Microarray analysis was performed on resected tumor samples from patients with HCC with or without postoperative recurrence. LEF1 expression was associated with postoperative recurrence as validated by immunohistochemical staining in another HCC cohort. Among 74 patients, 44 displayed a relatively high percentage of LEF1 staining (>30% of HCC cells), which was associated with a reduced recurrence-free interval (P < 0.001) and overall survival (P = 0.009). In multivariate analysis, a high percentage of LEF1 staining was significantly associated with low albumin level (P = 0.035), Twist overexpression (P = 0.018), Snail overexpression (P = 0.064), co-expression of Twist and Snail (P = 0.054), and multinodular tumors (P = 0.025). Down-regulation of LEF1 by short hairpin RNA decreased tumor sphere formation, soft agar colony formation, and transwell invasiveness of HCC cell lines Mahlavu and PLC. Xenotransplant and tail vein injection experiments revealed that LEF1 down-regulation in Mahlavu reduced tumor size and metastasis. LEF1 up-regulation in Huh7 increased sphere formation, soft agar colony formation, and transwell invasiveness. LEF1 was shown to physically interact with and transcriptionally activate promoter regions of Oct4, Snail, Slug, and Twist. Furthermore, Oct4, Snail, and Twist transactivated LEF1 to form a regulatory positive-feedback loop. Conclusion: LEF1 plays a pivotal role in HCC progression through transcriptional regulation of Oct4 and EMT regulators. (Hepatalogy Communications 2018;2:1392-1407).

Objective: Charcot-Marie-Tooth disease (CMT) is a clinically and genetically heterogeneous group of inherited neuropathies. Mutations in more than 90 genes have been implicated in CMT; however, the mutational spectrum of CMT in Chinese population remains obscure. This study aims to provide a comprehensive overview of the frequency of mutations in Taiwanese patients with CMT and look for genotype-phenotype correlations. Methods: Mutational analyses were performed on 427 unrelated Taiwanese patients with CMT by polymorphic microsatellite markers analysis or real-time fluorescent PCR for PMP22 duplication, Sanger sequencing for GJB1 mutations, and targeted sequencing covering 124 genes causing or relevant to inherited neuropathies. We also correlated the genotypes with the phenotypic features, such as age at disease onset and ulnar motor nerve conduction velocity. Results: Pathogenic mutations were identified in 312 patients (73.1%; 312/427), including 208 patients with a PMP22 duplication, 40 patients with a GJB1 mutation, and 64 patients with a mutation in one of other 18 CMT genes. A confirmed molecular diagnosis was achieved in 84.4% (266/315) of the patients with demyelinating CMT and 41.1% (46/112) of the patients with axonal CMT. Mutations in MPZ, MFN2, or NEFL are the most frequent disease causes in patients with infantileonset CMT (≤2 years), while PMP22 duplications and mutations in GJB1, MFN2, or MPZ are the frequent causes among patients with childhood-or adolescence-onset CMT (3-9 years). Interpretation: This study provides a genotype-phenotype landscape of CMT in Taiwan and highlights the unique spectrum of CMT genes frequencies among patients of Chinese origin.

Objectives: To investigate the clinical, electrophysiological, neuroimaging characteristics and genetic features of SPG5 in Taiwan. Methods: Mutational analysis of the coding regions of CYP7B1 was performed by utilizing targeted resequencing analysis of the 187 unrelated Taiwanese HSP patients. The diagnosis of SPG5 was ascertained by the presence of biallelic CYP7B1 mutations. The SPG5 patients received clinical, electrophysiological, and neuroimaging evaluations. Disease severity was assessed by using the Spastic Paraplegia Rating Scale (SPRS) and the disability score. Two microsatellite markers as well as 18 singlenucleotide polymorphism (SNP) markers flanking CYP7B1 were genotyped to assess the founder effect of the CYP7B1 p.R112* mutation. Results: Nineteen SPG5 patients from 17 families were identified. They typically presented an insidious onset progressive spastic paraparesis with proprioception involvement beginning at age 8 to 40 years. Their MRIs often showed white matter abnormalities in bilateral occipito-parietal regions, spinal cord atrophy, and mild cerebellar atrophy. Six different mutations in CYP7B1 were recognized, including three novel ones (p.N131Ifs*4, p.A295V, and p.L439R). CYP7B1 p.R112* was the most common mutation and present in 88.2% of the 17 SPG5 pedigrees. The patients with homozygous CYP7B1 p.R112* mutations had a milder clinical severity. Detailed haplotype analyses demonstrated a shared haplotype in the 25 individuals carrying at least one single allele of CYP7B1 p.R112*, suggesting a founder effect. Interpretation: This study delineates the distinct clinical and genetic features of SPG5 in Taiwan and provides useful information for the diagnosis and management of SPG5, especially in patients of Chinese descent.

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an in...