Recent advances in high-throughput genotyping technology are paving the way for research in personalized medicine and nutrition. However, most of the genetic markers identified from association studies account for a small contribution to the total risk/benefit of the studied phenotypic trait. Testing whether the candidate genes identified by association studies are causal is critically important to the development of personalized medicine and nutrition. An efficient data mining strategy and a set of sophisticated tools are necessary to help better understand and utilize the findings from genetic association studies.

In the era of personalized medical practice, understanding the genetic basis of patient-specific adverse drug reaction (ADR) is a major challenge. Clozapine provides effective treatments for schizophrenia but its usage is limited because of life-threatening agranulocytosis. A recent high impact study showed the necessity of moving clozapine to a first line drug, thus identifying the biomarkers for drug-induced agranulocytosis has become important. Here we report a methodology termed as antithesis chemical-protein interactome (CPI), which utilizes the docking method to mimic the differences in the drug-protein interactions across a panel of human proteins. Using this method, we identified HSPA1A, a known susceptibility gene for CIA, to be the off-target of clozapine. Furthermore, the mRNA expression of HSPA1A-related genes (off-target associated systems) was also found to be differentially expressed in clozapine treated leukemia cell line. Apart from identifying the CIA causal genes we identified several novel candidate genes which could be responsible for agranulocytosis. Proteins related to reactive oxygen clearance system, such as oxidoreductases and glutathione metabolite enzymes, were significantly enriched in the antithesis CPI. This methodology conducted a multi-dimensional analysis of drugs' perturbation to the biological system, investigating both the off-targets and the associated off-systems to explore the molecular basis of an adverse event or the new uses for old drugs.

Mitochondria are major players on the production of energy, and host several key reactions involved in basic metabolism and biosynthesis of essential molecules. Currently, the majority of nucleus-encoded mitochondrial proteins are unknown even for model plant Arabidopsis. We reported a computational framework for predicting Arabidopsis mitochondrial proteins based on a probabilistic model, called Naive Bayesian Network, which integrates disparate genomic data generated from eight bioinformatics tools, multiple orthologous mappings, protein domain properties and co-expression patterns using 1,027 microarray profiles. Through this approach, we predicted 2,311 candidate mitochondrial proteins with 84.67% accuracy and 2.53% FPR performances. Together with those experimental confirmed proteins, 2,585 mitochondria proteins (named CoreMitoP) were identified, we explored those proteins with unknown functions based on protein-protein interaction network (PIN) and annotated novel functions for 26.65% CoreMitoP proteins. Moreover, we found newly predicted mitochondrial proteins embedded in particular subnetworks of the PIN, mainly functioning in response to diverse environmental stresses, like salt, draught, cold, and wound etc. Candidate mitochondrial proteins involved in those physiological acitivites provide useful targets for further investigation. Assigned functions also provide comprehensive information for Arabidopsis mitochondrial proteome.

The rat is used extensively by the pharmaceutical, regulatory, and academic communities for safety assessment of drugs and chemicals and for studying human diseases; however, its transcriptome has not been well studied. As part of the SEQC (i.e., MAQC-III) consortium efforts, a comprehensive RNA-Seq data set was constructed using 320 RNA samples isolated from 10 organs (adrenal gland, brain, heart, kidney, liver, lung, muscle, spleen, thymus, and testes or uterus) from both sexes of Fischer 344 rats across four ages (2-, 6-, 21-, and 104-week-old) with four biological replicates for each of the 80 sample groups (organ-sex-age). With the Ribo-Zero rRNA removal and Illumina RNA-Seq protocols, 41 million 50 bp single-end reads were generated per sample, yielding a total of 13.4 billion reads. This data set could be used to identify and validate new rat genes and transcripts, develop a more comprehensive rat transcriptome annotation system, identify novel gene regulatory networks related to tissue specific gene expression and development, and discover genes responsible for disease and drug toxicity and efficacy.

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.

Drug-drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug-human protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user's molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model. It can suggest potential DDIs between the user's molecule and our library of 2515 drug molecules. In cross-validation and independent validation, the server achieved an AUC greater than 0.85. Additionally, by investigating the CPI profiles of predicted DDI, users can explore the PK/PD proteins that might be involved in a particular DDI. A 3D visualization of the drug-protein interaction will be provided as well. The DDI-CPI is freely accessible at http://cpi.bio-x.cn/ddi/.

The discovery of vitamins and clarification of their role in preventing frank essential nutrient deficiencies occurred in the early 1900s. Much vitamin research has understandably focused on public health and the effects of single nutrients to alleviate acute conditions. The physiological processes for maintaining health, however, are complex systems that depend upon interactions between multiple nutrients, environmental factors, and genetic makeup. To analyze the relationship between these factors and nutritional health, data were obtained from an observational, community-based participatory research program of children and teens (age 6-14) enrolled in a summer day camp in the Delta region of Arkansas. Assessments of erythrocyte S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), plasma homocysteine (Hcy) and 6 organic micronutrients (retinol, 25-hydroxy vitamin D3, pyridoxal, thiamin, riboflavin, and vitamin E), and 1,129 plasma proteins were performed at 3 time points in each of 2 years. Genetic makeup was analyzed with 1 M SNP genotyping arrays, and nutrient status was assessed with 24-h dietary intake questionnaires. A pattern of metabolites (met_PC1) that included the ratio of erythrocyte SAM/SAH, Hcy, and 5 vitamins were identified by principal component analysis. Met_PC1 levels were significantly associated with (1) single-nucleotide polymorphisms, (2) levels of plasma proteins, and (3) multilocus genotypes coding for gastrointestinal and immune functions, as identified in a global network of metabolic/protein-protein interactions. Subsequent mining of data from curated pathway, network, and genome-wide association studies identified genetic and functional relationships that may be explained by gene-nutrient interactions. The systems nutrition strategy described here has thus associated a multivariate metabolite pattern in blood with genes involved in immune and gastrointestinal functions.

Millions of human single nucleotide polymorphisms (SNPs) or mutations have been identified so far, and these variants could be strongly correlated with phenotypic variations of traits/diseases. Among these variants, non-synonymous ones can result in amino-acid changes that are called single amino-acid polymorphisms (SAPs). Although some studies have tried to investigate the SAPs, only a small fraction of SAPs have been identified due to inadequately inferred protein variation database and the low coverage of mass spectrometry (MS) experiments. Here, we present the dbSAP database for conveniently accessing the comprehensive information and relationships of spectra, peptides and proteins of SAPs, as well as related genes, pathways, diseases and drug targets. In order to fully explore human SAPs, we built a customized protein database that contained comprehensive variant proteins by integrating and annotating the human SNPs and mutations from eight distinct databases (UniProt, Protein Mutation Database, HPMD, MSIPI, MS-CanProVar, dbSNP, Ensembl and COSMIC). After a series of quality controls, a total of 16 854 SAP peptides involving in 439 537 spectra were identified with large scale MS datasets from various human tissues and cell lines. dbSAP is freely available at http://www.megabionet.org/dbSAP/index.html.

Cellular function and diversity are orchestrated by complex interactions of fundamental biomolecules including DNA, RNA and proteins. Technological advances in genomics, epigenomics, transcriptomics and proteomics have enabled massively parallel and unbiased measurements. Such high-throughput technologies have been extensively used to carry out broad, unbiased studies, particularly in the context of human diseases. Nevertheless, a unified analysis of the genome, epigenome, transcriptome and proteome of a single human cell type to obtain a coherent view of the complex interplay between various biomolecules has not yet been undertaken. Here, we report the first multi-omic analysis of human primary naïve CD4+ T cells isolated from a single individual.

Fulvestrant (Faslodex™) is a pure antiestrogen that is approved to treat hormone receptor-positive metastatic breast cancer in postmenopausal women. Previous studies have demonstrated that fulvestrant metabolism in humans involves cytochromes P450 and UDP-glucuronosyltransferases (UGTs). To date, fulvestrant sulfation has not been characterized. This study examined fulvestrant sulfation with nine recombinant sulfotransferases and found that only SULT1A1 and SULT1E1 displayed catalytic activity toward this substrate, with K(m) of 4.2 ± 0.99 and 0.2 ± 0.16 μM, respectively. In vitro assays of 104 human liver cytosols revealed marked individual variability that was highly correlated with β-naphthol sulfation (SULT1A1 diagnostic substrate; r = 0.98, P < 0.0001), but not with 17β-estradiol sulfation (SULT1E1 diagnostic substrate; r = 0.16, P = 0.10). Fulvestrant sulfation was correlated with both SULT1A1*1/2 genotype (P value = 0.023) and copy number (P < 0.0001). These studies suggest that factors influencing SULT1A1/1E1 tissue expression and/or enzymatic activity could influence the efficacy of fulvestrant therapy.

Schizophrenia (SCZ) and type 2 diabetes mellitus (T2D) are both complex diseases. Accumulated studies indicate that schizophrenia patients are prone to present the type 2 diabetes symptoms, but the potential mechanisms behind their association remain unknown. Here we explored the pathogenetic association between SCZ and T2D based on pathway analysis and protein-protein interaction.

Subsyndromal symptomatic depression (SSD) is a subtype of subthreshold depressive and also lead to significant psychosocial functional impairment as same as major depressive disorder (MDD). Several studies have suggested that SSD is a transitory phenomena in the depression spectrum and is thus considered a subtype of depression. However, the pathophysioloy of depression remain largely obscure and studies on SSD are limited. The present study compared the expression profile and made the classification with the leukocytes by using whole-genome cRNA microarrays among drug-free first-episode subjects with SSD, MDD, and matched controls (8 subjects in each group). Support vector machines (SVMs) were utilized for training and testing on candidate signature expression profiles from signature selection step. Firstly, we identified 63 differentially expressed SSD signatures in contrast to control (P< = 5.0E-4) and 30 differentially expressed MDD signatures in contrast to control, respectively. Then, 123 gene signatures were identified with significantly differential expression level between SSD and MDD. Secondly, in order to conduct priority selection for biomarkers for SSD and MDD together, we selected top gene signatures from each group of pair-wise comparison results, and merged the signatures together to generate better profiles used for clearly classify SSD and MDD sets in the same time. In details, we tried different combination of signatures from the three pair-wise compartmental results and finally determined 48 gene expression signatures with 100% accuracy. Our finding suggested that SSD and MDD did not exhibit the same expressed genome signature with peripheral blood leukocyte, and blood cell-derived RNA of these 48 gene models may have significant value for performing diagnostic functions and classifying SSD, MDD, and healthy controls.

Si-Wu-Tang (SWT), comprising the combination of four herbs, Paeoniae, Angelicae, Chuanxiong and Rehmanniae, is one of the most popular traditional oriental medicines for women's diseases. In our previous study, the microarray gene expression profiles of SWT on breast cancer cell line MCF-7 were found similar to the effect of β-estradiol (E2) on MCF-7 cells in the Connectivity Map database.

The complete and accurate human reference genome is important for functional genomics researches. Therefore, the incomplete reference genome and individual specific sequences have significant effects on various studies.

In their expression process, different genes can generate diverse functional products, including various protein-coding or noncoding RNAs. Here, we investigated the protein-coding capacities and the expression levels of their isoforms for human known genes, the conservation and disease association of long noncoding RNAs (ncRNAs) with two transcriptome sequencing datasets from human brain tissues and 10 mixed cell lines. Comparative analysis revealed that about two-thirds of the genes expressed between brain and cell lines are the same, but less than one-third of their isoforms are identical. Besides those genes specially expressed in brain and cell lines, about 66% of genes expressed in common encoded different isoforms. Moreover, most genes dominantly expressed one isoform and some genes only generated protein-coding (or noncoding) RNAs in one sample but not in another. We found 282 human genes could encode both protein-coding and noncoding RNAs through alternative splicing in the two samples. We also identified more than 1,000 long ncRNAs, and most of those long ncRNAs contain conserved elements across either 46 vertebrates or 33 placental mammals or 10 primates. Further analysis showed that some long ncRNAs differentially expressed in human breast cancer or lung cancer, several of those differentially expressed long ncRNAs were validated by RT-PCR. In addition, those validated differentially expressed long ncRNAs were found significantly correlated with certain breast cancer or lung cancer related genes, indicating the important biological relevance between long ncRNAs and human cancers. Our findings reveal that the differences of gene expression profile between samples mainly result from the expressed gene isoforms, and highlight the importance of studying genes at the isoform level for completely illustrating the intricate transcriptome.

Environmental chemicals exposure is one of the primary factors for liver toxicity and hepatocarcinoma. Thioacetamide (TAA) is a well-known hepatotoxicant and could be a liver carcinogen in humans. The discovery of early and sensitive microRNA (miRNA) biomarkers in liver injury and tumor progression could improve cancer diagnosis, prognosis, and management. To study this, we performed next generation sequencing of the livers of Sprague-Dawley rats treated with TAA at three doses (4.5, 15 and 45 mg/kg) and four time points (3-, 7-, 14- and 28-days). Overall, 330 unique differentially expressed miRNAs (DEMs) were identified in the entire TAA-treatment course. Of these, 129 DEMs were found significantly enriched for the "liver cancer" annotation. These results were further complemented by pathway analysis (Molecular Mechanisms of Cancer, p53-, TGF-β-, MAPK- and Wnt-signaling). Two miRNAs (rno-miR-34a-5p and rno-miR-455-3p) out of 48 overlapping DEMs were identified to be early and sensitive biomarkers for TAA-induced hepatocarcinogenicity. We have shown significant regulatory associations between DEMs and TAA-induced liver carcinogenesis at an earlier stage than histopathological features. Most importantly, miR-34a-5p is the most suitable early and sensitive biomarker for TAA-induced hepatocarcinogenesis due to its consistent elevation during the entire treatment course.

Murine transplantation models are used extensively to research immunological rejection and tolerance. Here we studied both murine heart and liver allograft models using microarray technology. We had difficulty in identifying genes related to acute rejections expressed in both heart and liver transplantation models using two standard methodologies: Student's t test and linear models for microarray data (Limma). Here we describe a new method, standardized fold change (SFC), for differential analysis of microarray data. We estimated the performance of SFC, the t test and Limma by generating simulated microarray data 100 times. SFC performed better than the t test and showed a higher sensitivity than Limma where there is a larger value for fold change of expression. SFC gave better reproducibility than Limma and the t test with real experimental data from the MicroArray Quality Control platform and expression data from a mouse cardiac allograft. Eventually, a group of significant overlapping genes was detected by SFC in the expression data of mouse cardiac and hepatic allografts and further validated with the quantitative RT-PCR assay. The group included genes for important reactions of transplantation rejection and revealed functional changes of the immune system in both heart and liver of the mouse model. We suggest that SFC can be utilized to stably and effectively detect differential gene expression and to explore microarray data in further studies.

Reference genome selection is a prerequisite for successful analysis of next generation sequencing (NGS) data. Current practice employs one of the two most recent human reference genome versions: HG19 or HG38. To date, the impact of genome version on SNV identification has not been rigorously assessed.

Derepression of transcription factors is the key mechanism for triggering plant jasmonate (JA) responses. Unlike regulating certain physiological functions for the majority of transcription factors in JA signaling, MYC2 and EIN3 control more diverse aspects. MYC2 predominantly participates in wounding response, metabolism, and root growth inhibition, while EIN3 (and its closest homolog EIL1) regulates defense gene expression and root hair development. Recently, it was reported that MYC2 and EIN3/EIL1 proteins mutually interact with each other and suppress their interaction partner's transcriptional activities. To understand their contributions in the modulation of transcriptomic network, we initially identified 1,495 differentially expressed jasmonate (JA)-responsive genes in wild-type Arabidopsis through RNA-seq analysis. Among them, 25% or 4.2% were independently regulated by EIN3/EIL1 or MYC2, respectively. Further analysis showed that EIN3/EIL1 and MYC2 interdependently regulate 16.3% of the JA-regulated transcriptome, including downregulation of three auxin-related genes, which might confer JA-inhibited root elongation. Lastly, we found that <30 genes were antagonistically regulated by MYC2 and EIN3/EIL1. We conclude that EIN3/EIL1 play a dominant role while MYC2 largely relies on EIN3/EIL1 for executing its transcriptional activity, either synergistically or antagonistically.

The majority of patients with neuroblastoma due to MYCN oncogene amplification and consequent N-Myc oncoprotein over-expression die of the disease. Here our analyses of RNA sequencing data identify the long noncoding RNA lncNB1 as one of the transcripts most over-expressed in MYCN-amplified, compared with MYCN-non-amplified, human neuroblastoma cells and also the most over-expressed in neuroblastoma compared with all other cancers. lncNB1 binds to the ribosomal protein RPL35 to enhance E2F1 protein synthesis, leading to DEPDC1B gene transcription. The GTPase-activating protein DEPDC1B induces ERK protein phosphorylation and N-Myc protein stabilization. Importantly, lncNB1 knockdown abolishes neuroblastoma cell clonogenic capacity in vitro and leads to neuroblastoma tumor regression in mice, while high levels of lncNB1 and RPL35 in human neuroblastoma tissues predict poor patient prognosis. This study therefore identifies lncNB1 and its binding protein RPL35 as key factors for promoting E2F1 protein synthesis, N-Myc protein stability and N-Myc-driven oncogenesis, and as therapeutic targets.