Agus Salim

FunRich: An open access standalone functional enrichment and interaction network analysis tool

As high‐throughput techniques including proteomics become more accessible to individual laboratories, there is an urgent need for a user‐friendly bioinformatics analysis system. Here, we describe FunRich, an open access, standalone functional enrichment and network analysis tool. FunRich is designed to be used by biologists with minimal or no support from computational and database experts. Using FunRich, users can perform functional enrichment analysis on background databases that are integrated from heterogeneous genomic and proteomic resources (>1.5 million annotations). Besides default human specific FunRich database, users can download data from the UniProt database, which currently supports 20 different taxonomies against which enrichment analysis can be performed. Moreover, the users can build their own custom databases and perform the enrichment analysis irrespective of organism. In addition to proteomics datasets, the custom database allows for the tool to be used for genomics, lipidomics and metabolomics datasets. Thus, FunRich allows for complete database customization and thereby permits for the tool to be exploited as a skeleton for enrichment analysis irrespective of the data type or organism used. FunRich (http://www.funrich.org) is user‐friendly and provides graphical representation (Venn, pie charts, bar graphs, column, heatmap and doughnuts) of the data with customizable font, scale and color (publication quality).

Statistical challenges associated with detecting copy number variations with next-generation sequencing

MOTIVATION Analysing next-generation sequencing (NGS) data for copy number variations (CNVs) detection is a relatively new and challenging field, with no accepted standard protocols or quality control measures so far. There are by now several algorithms developed for each of the four broad methods for CNV detection using NGS, namely the depth of coverage (DOC), read-pair, split-read and assembly-based methods. However, because of the complexity of the genome and the short read lengths from NGS technology, there are still many challenges associated with the analysis of NGS data for CNVs, no matter which method or algorithm is used. RESULTS In this review, we describe and discuss areas of potential biases in CNV detection for each of the four methods. In particular, we focus on issues pertaining to (i) mappability, (ii) GC-content bias, (iii) quality control measures of reads and (iv) difficulty in identifying duplications. To gain insights to some of the issues discussed, we also download real data from the 1000 Genomes Project and analyse its DOC data. We show examples of how reads in repeated regions can affect CNV detection, demonstrate current GC-correction algorithms, investigate sensitivity of DOC algorithm before and after quality control of reads and discuss reasons for which duplications are harder to detect than deletions.

Comparison of data analysis strategies for intent-to-treat analysis in pre-test-post-test designs with substantial dropout rates

The pre-test-post-test design (PPD) is predominant in trials of psychotherapeutic treatments. Missing data due to withdrawals present an even bigger challenge in assessing treatment effectiveness under the PPD than under designs with more observations since dropout implies an absence of information about response to treatment. When confronted with missing data, often it is reasonable to assume that the mechanism underlying missingness is related to observed but not to unobserved outcomes (missing at random, MAR). Previous simulation and theoretical studies have shown that, under MAR, modern techniques such as maximum-likelihood (ML) based methods and multiple imputation (MI) can be used to produce unbiased estimates of treatment effects. In practice, however, ad hoc methods such as last observation carried forward (LOCF) imputation and complete-case (CC) analysis continue to be used. In order to better understand the behaviour of these methods in the PPD, we compare the performance of traditional approaches (LOCF, CC) and theoretically sound techniques (MI, ML), under various MAR mechanisms. We show that the LOCF method is seriously biased and conclude that its use should be abandoned. Complete-case analysis produces unbiased estimates only when the dropout mechanism does not depend on pre-test values even when dropout is related to fixed covariates including treatment group (covariate-dependent: CD). However, CC analysis is generally biased under MAR. The magnitude of the bias is largest when the correlation of post- and pre-test is relatively low.

Diabetes with hypertension as risk factors for adult dengue hemorrhagic fever in a predominantly dengue serotype 2 epidemic: a case control study.

Background Dengue hemorrhagic fever (DHF) is a severe form of dengue, characterized by bleeding and plasma leakage. A number of DHF risk factors had been suggested. However, these risk factors may not be generalized to all populations and epidemics for screening and clinical management of patients at risk of developing DHF. This study explored demographic and comorbidity risk factors for DHF in adult dengue epidemics in Singapore in year 2006 (predominantly serotype 1) and in year 2007–2008 (predominantly serotype 2). Methods A retrospective case-control study was conducted with 149 DHF and 326 dengue fever (DF) patients from year 2006, and 669 DHF and 1,141 DF patients from year 2007–2008. Demographic and reported comorbidity data were collected from patients previously. We performed multivariate logistic regression to assess the association between DHF and demographic and co-morbidities for year 2006 and year 2007–2008, respectively. Results Only Chinese (adjusted odds ratio [AOR] = 1.90; 95% confidence interval [CI]: 1.01–3.56) was independently associated with DHF in year 2006. In contrast, age groups of 30–39 years (AOR = 1.41; 95% CI:1.09–1.81), 40–49 years (AOR = 1.34; 95% CI:1.09–1.81), female (AOR = 1.57; 95% CI:1.28–1.94), Chinese (AOR = 1.67; 95% CI:1.24–2.24), diabetes (AOR = 1.78; 95% CI:1.06–2.97), and diabetes with hypertension (AOR = 2.16; 95%CI:1.18–3.96) were independently associated with DHF in year 2007–2008. Hypertension was proposed to have effect modification on the risk of DHF outcome in dengue patients with diabetes. Chinese who had diabetes with hypertension had 2.1 (95% CI:1.07–4.12) times higher risk of DHF compared with Chinese who had no diabetes and no hypertension. Conclusions Adult dengue patients in Singapore who were 30–49 years, Chinese, female, had diabetes or diabetes with hypertension were at greater risk of developing DHF during epidemic of predominantly serotype 2. These risk factors can be used to guide triaging of patients who require closer clinical monitoring and early hospitalization in Singapore, when confirmed in more studies.

The discovery of human genetic variations and their use as disease markers: past, present and future

The field of human genetic variations has progressed rapidly over the past few years. It has added much information and deepened our knowledge and understanding of the diversity of genetic variations in the human genome. This significant progress has been driven mainly by the developments of microarray and next generation sequencing technologies. The array-based methods have been widely used for large-scale copy number variation (CNV) detection in the human genome. The arrival of next generation sequencing technologies, which enabled the completion of several whole genome resequencing studies, has also resulted in a massive discovery of genetic variations. These studies have identified several hundred thousand short indels and a total of thousands of CNVs and other structural variations in the human genome. The discovery of these ‘newer’ types of genetic variations, indels, CNVs and copy neutral variations (inversions and translocations) has also widened the scope of genetic markers in human genetic and disease gene mapping studies. The aim of this review article is to summarize the latest developments in the discovery of human genetic variations and address the issue of inadequate coverage of genetic variations in the current genome-wide association studies, which mainly focuses on common SNPs. Finally, we also discuss the future directions in the field and their impacts on next generation genome-wide association studies.

Accessory subunits are integral for assembly and function of human mitochondrial complex I

Complex I (NADH:ubiquinone oxidoreductase) is the first enzyme of the mitochondrial respiratory chain and is composed of 45 subunits in humans, making it one of the largest known multi-subunit membrane protein complexes. Complex I exists in supercomplex forms with respiratory chain complexes III and IV, which are together required for the generation of a transmembrane proton gradient used for the synthesis of ATP. Complex I is also a major source of damaging reactive oxygen species and its dysfunction is associated with mitochondrial disease, Parkinson’s disease and ageing. Bacterial and human complex I share 14 core subunits that are essential for enzymatic function; however, the role and necessity of the remaining 31 human accessory subunits is unclear. The incorporation of accessory subunits into the complex increases the cellular energetic cost and has necessitated the involvement of numerous assembly factors for complex I biogenesis. Here we use gene editing to generate human knockout cell lines for each accessory subunit. We show that 25 subunits are strictly required for assembly of a functional complex and 1 subunit is essential for cell viability. Quantitative proteomic analysis of cell lines revealed that loss of each subunit affects the stability of other subunits residing in the same structural module. Analysis of proteomic changes after the loss of specific modules revealed that ATP5SL and DMAC1 are required for assembly of the distal portion of the complex I membrane arm. Our results demonstrate the broad importance of accessory subunits in the structure and function of human complex I. Coupling gene-editing technology with proteomics represents a powerful tool for dissecting large multi-subunit complexes and enables the study of complex dysfunction at a cellular level.

Low Level Primary Blast Injury in Rodent Brain

The incidence of blast attacks and resulting traumatic brain injuries has been on the rise in recent years. Primary blast is one of the mechanisms in which the blast wave can cause injury to the brain. The aim of this study was to investigate the effects of a single sub-lethal blast over pressure (BOP) exposure of either 48.9 kPa (7.1 psi) or 77.3 kPa (11.3 psi) to rodents in an open-field setting. Brain tissue from these rats was harvested for microarray and histopathological analyses. Gross histopathology of the brains showed that cortical neurons were “darkened” and shrunken with narrowed vasculature in the cerebral cortex day 1 after blast with signs of recovery at day 4 and day 7 after blast. TUNEL-positive cells were predominant in the white matter of the brain at day 1 after blast and double-labeling of brain tissue showed that these DNA-damaged cells were both oligodendrocytes and astrocytes but were mainly not apoptotic due to the low caspase-3 immunopositivity. There was also an increase in amyloid precursor protein immunoreactive cells in the white matter which suggests acute axonal damage. In contrast, Iba-1 staining for macrophages or microglia was not different from control post-blast. Blast exposure altered the expression of over 5786 genes in the brain which occurred mostly at day 1 and day 4 post-blast. These genes were narrowed down to 10 overlapping genes after time-course evaluation and functional analyses. These genes pointed toward signs of repair at day 4 and day 7 post-blast. Our findings suggest that the BOP levels in the study resulted in mild cellular injury to the brain as evidenced by acute neuronal, cerebrovascular, and white matter perturbations that showed signs of resolution. It is unclear whether these perturbations exist at a milder level or normalize completely and will need more investigation. Specific changes in gene expression may be further evaluated to understand the mechanism of blast-induced neurotrauma.

Patterns of physical activity in different domains and implications for intervention in a multi-ethnic Asian population: a cross-sectional study

BackgroundThe benefits of regular physical activity for quality of life and disease prevention have been well documented. Identification of low activity groups would facilitate interventional programs. Many studies have focussed on leisure time activity, which may not capture the spectrum of physical activity relevant to disease prevention. Furthermore, few studies have been conducted in urban Asian settings.MethodsWe evaluated physical activity in different domains (leisure time, occupational, household and transportation) and its sociodemographic determinants in 4750 adult Chinese, Malay, and Asian Indian Singaporeans. Physical activity was assessed using locally validated questionnaires.ResultsOccupational and household activity contributed substantially more to total physical activity than leisure time or transportation activity. However, when only activity of at least moderate intensity was considered leisure time activity contributed most to total physical activity. Higher socio-economic status was associated with more leisure time activity, but less total physical activity due to reduced activity in the other domains. Chinese ethnicity was also associated with less total physical activity as a result of less activity in non-leisure time domains.ConclusionsIn assessing levels of physical activity and recommending changes, it is important to consider physical activity in different domains. Focus on leisure-time physical activity alone could identify the wrong groups for intervention and miss opportunities for increasing physical activity in populations.

Clinicopathological significance of calreticulin in breast invasive ductal carcinoma

Calreticulin is a chaperone protein located in the lumen of the endoplasmic reticulum. The association of calreticulin with pathological conditions such as autoimmune disorders and certain types of cancer have been reported. However, little is known about its role in the pathogenesis of breast cancer. The aim of this study was to determine the expression of calreticulin in vitro and correlate its expression levels in breast cancer tissue samples with clinicopathological parameters. Calreticulin expression was evaluated in MCF-7 and MDA-MB-231 breast cancer cells by real-time RT–PCR, Western blot, immunohistochemistry, and immunofluorescence staining. Patient tissue microarrays were constructed from 228 breast cancer specimens for immunohistochemical analysis. The in vitro study showed a higher calreticulin expression in more aggressive MDA-MB-231 cells as compared with MCF-7 cells at both mRNA and protein levels. In all, 227 out of 228 breast cancer samples exhibited calreticulin staining in at least 5% of the cancer cells. Calreticulin immunostaining was observed to be localized to the cytoplasm of the cancer cells. Regression analysis of calreticulin immunostaining in the tissue microarrays revealed that its expression was positively correlated to logarithm of (log) tumor size (P=0.046) and development of distant metastasis (P=0.017). Multivariate analysis confirmed calreticulin expression as an independent predictor of log tumor size and occurrence of distant metastasis. The data suggest that calreticulin expression is associated with more advanced tumors and is a potential prognostic biomarker.

Follow-up of mild cognitive impairment and related disorders over four years in adults in their sixties: the PATH Through Life Study.

Aims: The study aimed to estimate incidence rates of mild cognitive impairment and related disorders, and conversion to dementia. Methods: The data are drawn from the PATH Through Life Study. Baseline assessment in 2001–2002 included 2,551 participants 60–64 years old with 2,222 participating in a 4-year follow-up. Those screened positive with a cognitive assessment received clinical assessment for diagnoses of mild cognitive disorders (MCD) or dementia using established clinical criteria. Prevalence and incidence rates for the cohort were estimated with predictive regression models. Results: Annual incidence of dementia was 0.25%. Prevalence of mild cognitive impairment was 4.2%, age-associated memory impairment was 2.4%, age-associated cognitive decline was 7.6%, mild neurocognitive disorders occurred in 12.9% and other cognitive disorders in 7.3%. Prevalence of any diagnosis of any MCD (Any-MCD) was 29.5% and the annual incidence rate for Any-MCD was 5.7%. Agreement for specific diagnoses between waves 1 and 2 was fair to poor (0–47.0%), but agreement for Any-MCD over 4 years was 89.0%. Conclusion: MCD diagnoses do not predict dementia at a 4-year follow-up in young-old adults. Prevalence rates for MCD vary greatly depending on the criteria and time of assessment.