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.

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.

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.

The detection and removal of volatile organic compounds (VOCs) are of great importance to reduce the risk of indoor air quality concerns. This study reports the rational synthesis of a dual-functional Janus nanostructure and its feasibility for simultaneous detection and removal of VOCs. The Janus nanostructure was synthesized via an anisotropic growth method, composed of plasmonic nanoparticles, semiconductors, and metal organic frameworks (e.g., Au@ZnO@ZIF-8). It exhibits excellent selective detection to formaldehyde (HCHO, as a representative VOC) at room temperature over a wide range of concentrations (from 0.25 to 100 ppm), even in the presence of water and toluene molecules as interferences. In addition, HCHO was also found to be partially oxidized into non-toxic formic acid simultaneously with detection. The mechanism underlying this technology was unraveled by both experimental measurements and theoretical calculations: ZnO maintains the conductivity, while ZIF-8 improves the selective gas adsorption; the plasmonic effect of Au nanorods enhances the visible-light-driven photocatalysis of ZnO at room temperature.

Stimuli-responsive behaviors of flexible metal-organic frameworks (MOFs) make these materials promising in a wide variety of applications such as gas separation, drug delivery, and molecular sensing. Considerable efforts have been made over the last decade to understand the structural changes of flexible MOFs in response to external stimuli. Uniform pore deformation has been used as the general description. However, recent advances in synthesizing MOFs with non-uniform porous structures, i.e. with multiple types of pores which vary in size, shape, and environment, challenge the adequacy of this description. Here, we demonstrate that the CO2-adsorption-stimulated structural change of a flexible MOF, ZIF-7, is induced by CO2 migration in its non-uniform porous structure rather than by the proactive opening of one type of its guest-hosting pores. Structural dynamics induced by guest migration in non-uniform porous structures is rare among the enormous number of MOFs discovered and detailed characterization is very limited in the literature. The concept presented in this work provides new insights into MOF flexibility.

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.

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.

An important mechanism of endocrine activity is chemicals entering target cells via transport proteins and then interacting with hormone receptors such as the estrogen receptor (ER). α-Fetoprotein (AFP) is a major transport protein in rodent serum that can bind and sequester estrogens, thus preventing entry to the target cell and where they could otherwise induce ER-mediated endocrine activity. Recently, we reported rat AFP binding affinities for a large set of structurally diverse chemicals, including 53 binders and 72 non-binders. However, the lack of three-dimensional (3D) structures of rat AFP hinders further understanding of the structural dependence for binding. Therefore, a 3D structure of rat AFP was built using homology modeling in order to elucidate rat AFP-ligand binding modes through docking analyses and molecular dynamics (MD) simulations.

The phenome represents a distinct set of information in the human population. It has been explored particularly in its relationship with the genome to identify correlations for diseases. The phenome has been also explored for drug repositioning with efforts focusing on the search space for the most similar candidate drugs. For a comprehensive analysis of the phenome, we assumed that all phenotypes (indications and side effects) were inter-connected with a probabilistic distribution and this characteristic may offer an opportunity to identify new therapeutic indications for a given drug. Correspondingly, we employed Latent Dirichlet Allocation (LDA), which introduces latent variables (topics) to govern the phenome distribution.

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/.

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.

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 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.

To construct short hairpin RNAs (shRNAs) and miR30-based shRNAs against heparanase (HPSE) to compare their safety and their effects on HPSE down-modulation in vitro and in vivo to develop a more ideal therapeutic RNA interference (RNAi) vector targeting HPSE.

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.

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.

ArrayTrack is a Food and Drug Administration (FDA) bioinformatics tool that has been widely adopted by the research community for genomics studies. It provides an integrated environment for microarray data management, analysis and interpretation. Most of its functionality for statistical, pathway and gene ontology analysis can also be applied independently to data generated by other molecular technologies. ArrayTrack has been undergoing active development and enhancement since its inception in 2001. This review summarises its key functionalities, with emphasis on the most recent extensions in support of the evolving needs of FDA's research programmes. ArrayTrack has added capability to manage, analyse and interpret proteomics and metabolomics data after quantification of peptides and metabolites abundance, respectively. Annotation information about single nucleotide polymorphisms and quantitative trait loci has been integrated to support genetics-related studies. Other extensions have been added to manage and analyse genomics data related to bacterial food-borne pathogens.

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.

Nucleotide-binding leucine-rich repeat (NLR) proteins play critical roles in plant immunity. However, how NLRs are regulated and activate defense signaling is not fully understood. The rice (Oryza sativa) NLR receptor Piz-t confers broad-spectrum resistance to the fungal pathogen Magnaporthe oryzae and the RING-type E3 ligase AVRPIZ-T INTERACTING PROTEIN 10 (APIP10) negatively regulates Piz-t accumulation. In this study, we found that APIP10 interacts with two rice transcription factors, VASCULAR PLANT ONE-ZINC FINGER 1 (OsVOZ1) and OsVOZ2, and promotes their degradation through the 26S proteasome pathway. OsVOZ1 displays transcriptional repression activity while OsVOZ2 confers transcriptional activation activity in planta. The osvoz1 and osvoz2 single mutants display modest but opposite M. oryzae resistance in the non-Piz-t background. However, the osvoz1 osvoz2 double mutant exhibits strong dwarfism and cell death, and silencing of both genes via RNA interference also leads to dwarfism, mild cell death, and enhanced resistance to M. oryzae in the non-Piz-t background. Both OsVOZ1 and OsVOZ2 interact with Piz-t. Double silencing of OsVOZ1 and OsVOZ2 in the Piz-t background decreases Piz-t protein accumulation and transcription, reactive oxygen species-dependent cell death, and resistance to M. oryzae containing AvrPiz-t. Taken together, these results indicate that OsVOZ1 and OsVOZ2 negatively regulate basal defense but contribute positively to Piz-t-mediated immunity.

With the rapid advancement of sequencing technologies, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples and generated whole-genome (WGS) and whole-exome sequencing (WES) data using sixteen library protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.