Trung Nghia Vu

1H NMR based metabolomics of CSF and blood serum: A metabolic profile for a transgenic rat model of Huntington disease

Huntington disease (HD) is a hereditary brain disease. Although the causative gene has been found, the exact mechanisms of the pathogenesis are still unknown. Recent investigations point to metabolic and energetic dysfunctions in HD neurons. Both univariate and multivariate analyses were used to compare proton nuclear magnetic resonance spectra of serum and cerebrospinal fluid (CSF) taken from presymptomatic HD transgenic rats and their wild-type littermates. N-acetylaspartate (NAA), was found to be significantly decreased in the serum of HD rats compared to wild-type littermates. Moreover, in the serum their levels of glutamine, succinic acid, glucose and lactate are significantly increased as well. An increased concentration of lactate and glucose is also found in CSF. There is a 1:1 stoichiometry coupling glucose utilization and glutamate cycling. The observed increase in the glutamine concentration, which indicates a shutdown in the neuronal-glial glutamate-glutamine cycling, results therefore in an increased glucose concentration. The elevated succinic acid concentration might be due to an inhibition of succinate dehydrogenase, an enzyme linked to the mitochondrial respiratory chain and TCA cycle. Moreover, reduced levels of NAA may reflect an impairment of mitochondrial energy production. In addition, the observed difference in lactate supports a deficiency of oxidative energy metabolism in rats transgenic for HD as well. The observed metabolic alterations seem to be more profound in serum than in CSF in presymptomatic rats. All findings suggest that even in presymptomatic rats, a defect in energy metabolism is already apparent. These results support the hypothesis of mitochondrial energy dysfunction in HD.

A primer to frequent itemset mining for bioinformatics

Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences.

Getting your peaks in line: a review of alignment methods for NMR spectral data.

One of the most significant challenges in the comparative analysis of Nuclear Magnetic Resonance (NMR) metabolome profiles is the occurrence of shifts between peaks across different spectra, for example caused by fluctuations in pH, temperature, instrument factors and ion content. Proper alignment of spectral peaks is therefore often a crucial preprocessing step prior to downstream quantitative analysis. Various alignment methods have been developed specifically for this purpose. Other methods were originally developed to align other data types (GC, LC, SELDI-MS, etc.), but can also be applied to NMR data. This review discusses the available methods, as well as related problems such as reference determination or the evaluation of alignment quality. We present a generic alignment framework that allows for comparison and classification of different alignment approaches according to their algorithmic principles, and we discuss their performance.

Beta-Poisson model for single-cell RNA-seq data analyses

MOTIVATION Single-cell RNA-sequencing technology allows detection of gene expression at the single-cell level. One typical feature of the data is a bimodality in the cellular distribution even for highly expressed genes, primarily caused by a proportion of non-expressing cells. The standard and the over-dispersed gamma-Poisson models that are commonly used in bulk-cell RNA-sequencing are not able to capture this property. RESULTS We introduce a beta-Poisson mixture model that can capture the bimodality of the single-cell gene expression distribution. We further integrate the model into the generalized linear model framework in order to perform differential expression analyses. The whole analytical procedure is called BPSC. The results from several real single-cell RNA-seq datasets indicate that ∼90% of the transcripts are well characterized by the beta-Poisson model; the model-fit from BPSC is better than the fit of the standard gamma-Poisson model in > 80% of the transcripts. Moreover, in differential expression analyses of simulated and real datasets, BPSC performs well against edgeR, a conventional method widely used in bulk-cell RNA-sequencing data, and against scde and MAST, two recent methods specifically designed for single-cell RNA-seq data. AVAILABILITY AND IMPLEMENTATION An R package BPSC for model fitting and differential expression analyses of single-cell RNA-seq data is available under GPL-3 license at https://github.com/nghiavtr/BPSC CONTACT: yudi.pawitan@ki.se or mattias.rantalainen@ki.se SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Regulation of EMT in Colorectal Cancer: A Culprit in Metastasis

Epithelial to mesenchymal transition (EMT) is a process during which cells lose their epithelial characteristics, for instance cell polarity and cell–cell contact, and gain mesenchymal properties, such as increased motility. In colorectal cancer (CRC), EMT is associated with an invasive or metastatic phenotype. In this review, we discuss recent studies exploring novel regulation mechanisms of EMT in CRC, including the identification of new CRC EMT regulators. Upregulation of inducers can promote EMT, leading to increased invasiveness and metastasis in CRC. These inducers can downregulate E-cadherin and upregulate N-cadherin and vimentin (VIM) through modulating EMT-related signaling pathways, for instance WNT/β-catenin and TGF-β, and EMT transcription factors, such as zinc finger E-box binding homeobox 1 (ZEB1) and ZEB2. In addition, several microRNAs (miRNAs), including members of the miR-34 and miR-200 families, are found to target mRNAs of EMT-transcription factors, for example ZEB1, ZEB2, or SNAIL. Downregulation of these miRNAs is associated with distant metastasis and advanced stage tumors. Furthermore, the role of EMT in circulating tumor cells (CTCs) is also discussed. Mesenchymal markers on the surface of EMT CTCs were found to be associated with metastasis and could serve as potential biomarkers for metastasis. Altogether, these studies indicate that EMT is orchestrated by a complicated network, involving regulators of different signaling pathways. Further studies are required to understand the mechanisms underlying EMT in CRC.

Comprehensive landscape of subtype-specific coding and non-coding RNA transcripts in breast cancer

Molecular classification of breast cancer into clinically relevant subtypes helps improve prognosis and adjuvant-treatment decisions. The aim of this study is to provide a better characterization of the molecular subtypes by providing a comprehensive landscape of subtype-specific isoforms including coding, long non-coding RNA and microRNA transcripts. Isoform-level expression of all coding and non-coding RNAs is estimated from RNA-sequence data of 1168 breast samples obtained from The Cancer Genome Atlas (TCGA) project. We then search the whole transcriptome systematically for subtype-specific isoforms using a novel algorithm based on a robust quasi-Poisson model. We discover 5451 isoforms specific to single subtypes. A total of 27% of the subtype-specific isoforms have better accuracy in classifying the intrinsic subtypes than that of their corresponding genes. We find three subtype-specific miRNA and 707 subtype-specific long non-coding RNAs. The isoforms from long non-coding RNAs also show high performance for separation between Luminal A and Luminal B subtypes with an AUC of 0.97 in the discovery set and 0.90 in the validation set. In addition, we discover 1500 isoforms preferentially co-expressed in two subtypes, including 369 isoforms co-expressed in both Normal-like and Basal subtypes, which are commonly considered to have distinct ER-receptor status. Finally, analyses at protein level reveal four subtype-specific proteins and two subtype co-expression proteins that successfully validate results from the isoform level.

Efficient reduction of candidate matches in peptide spectrum library searching using the top k most intense peaks.

Spectral library searching is a popular approach for MS/MS-based peptide identification. Because the size of spectral libraries continues to grow, the performance of searching algorithms is an important issue. This technical note introduces a strategy based on a minimum shared peak count between two spectra to reduce the set of admissible candidate spectra when issuing a query. A theoretical validation through time complexity analysis and an experimental validation based on an implementation of the candidate reduction strategy show that the approach can achieve a reduction of the set of candidate spectra by (at least) an order of magnitude, resulting in a significant improvement in the speed of the search. Meanwhile, more than 99% of the positive search results is retained. This efficient strategy to drastically improve the speed of spectral library searching with a negligible loss of sensitivity can be applied to any current spectral library search tool, irrespective of the employed similarity metric.

Accumulation of potential driver genes with genomic alterations predicts survival of high-risk neuroblastoma patients

BackgroundNeuroblastoma is the most common pediatric malignancy with heterogeneous clinical behaviors, ranging from spontaneous regression to aggressive progression. Many studies have identified aberrations related to the pathogenesis and prognosis, broadly classifying neuroblastoma patients into high- and low-risk groups, but predicting tumor progression and clinical management of high-risk patients remains a big challenge.ResultsWe integrate gene-level expression, array-based comparative genomic hybridization and functional gene-interaction network of 145 neuroblastoma patients to detect potential driver genes. The drivers are summarized into a driver-gene score (DGscore) for each patient, and we then validate its clinical relevance in terms of association with patient survival. Focusing on a subset of 48 clinically defined high-risk patients, we identify 193 recurrent regions of copy number alterations (CNAs), resulting in 274 altered genes whose copy-number gain or loss have parallel impact on the gene expression. Using a network enrichment analysis, we detect four common driver genes, ERCC6, HECTD2, KIAA1279, EMX2, and 66 patient-specific driver genes. Patients with high DGscore, thus carrying more copy-number-altered genes with correspondingly up- or down-regulated expression and functional implications, have worse survival than those with low DGscore (P = 0.006). Furthermore, Cox proportional-hazards regression analysis shows that, adjusted for age, tumor stage and MYCN amplification, DGscore is the only significant prognostic factor for high-risk neuroblastoma patients (P = 0.008).ConclusionsIntegration of genomic copy number alteration, expression and functional interaction-network data reveals clinically relevant and prognostic putative driver genes in high-risk neuroblastoma patients. The identified putative drivers are potential drug targets for individualized therapy.ReviewersThis article was reviewed by Armand Valsesia, Susmita Datta and Aleksandra Gruca.

Isoform-level gene expression patterns in single-cell RNA-sequencing data

Motivation: RNA sequencing of single cells enables characterization of transcriptional heterogeneity in seemingly homogeneous cell populations. Single‐cell sequencing has been applied in a wide range of researches fields. However, few studies have focus on characterization of isoform‐level expression patterns at the single‐cell level. In this study, we propose and apply a novel method, ISOform‐Patterns (ISOP), based on mixture modeling, to characterize the expression patterns of isoform pairs from the same gene in single‐cell isoform‐level expression data. Results: We define six principal patterns of isoform expression relationships and describe a method for differential‐pattern analysis. We demonstrate ISOP through analysis of single‐cell RNA‐sequencing data from a breast cancer cell line, with replication in three independent datasets. We assigned the pattern types to each of 16 562 isoform‐pairs from 4929 genes. Among those, 26% of the discovered patterns were significant (P<0.05), while remaining patterns are possibly effects of transcriptional bursting, drop‐out and stochastic biological heterogeneity. Furthermore, 32% of genes discovered through differential‐pattern analysis were not detected by differential‐expression analysis. Finally, the effects of drop‐out events and expression levels of isoforms on ISOPs performances were investigated through simulated datasets. To conclude, ISOP provides a novel approach for characterization of isoform‐level preference, commitment and heterogeneity in single‐cell RNA‐sequencing data. Availability and implementation: The ISOP method has been implemented as a R package and is available at https://github.com/nghiavtr/ISOP under a GPL‐3 license. Supplementary information: Supplementary data are available at Bioinformatics online.

Molecular characterization of pro-metastatic functions of β4-integrin in colorectal cancer

The β4-integrin subunit has been implicated in development and progression of several epithelial tumor types. However, its role in metastases of colorectal cancer (CRC) remains elusive. To study CRC metastasis, we generated a highly invasive, metastatic cell line MC38-LM10 (LM10) by passaging mouse CRC MC38 cells ten times, using a splenic injection model of liver metastasis. Affymetrix microarray analyses of LM10 and MC38 cell lines and their corresponding liver metastases generated a gene signature for CRC metastasis. This signature shows strong upregulation of β4-integrin in LM10 cells and corresponding metastases. Upregulation of β4-integrin in highly aggressive LM10 cells is associated with increased migration, invasion, and liver metastases. Furthermore, stable knockdown of β4-integrin in human CRC SW620 cells reduces Bcl-2 expression, increases apoptosis, and decreases invasion, tumorigenicity, and liver metastasis, thus resulting in significantly increased survival of mice (hazard ratio = 0.32, 95% confidence interval = 0.15-0.66, P<0.01). Patients with CRC tumors display higher β4-integrin levels in stages 1-4 and significantly lower survival rate. Collectively, β4-integrin plays a critical role in CRC progression, invasion, and metastasis, suggesting that it could be a potential therapeutic target for CRC patients.