Here we describe an update of the Therapeutic Target Database (http://bidd.nus.edu.sg/group/ttd/ttd.asp) for better serving the bench-to-clinic communities and for enabling more convenient data access, processing and exchange. Extensive efforts from the research, industry, clinical, regulatory and management communities have been collectively directed at the discovery, investigation, application, monitoring and management of targeted therapeutics. Increasing efforts have been directed at the development of stratified and personalized medicines. These efforts may be facilitated by the knowledge of the efficacy targets and biomarkers of targeted therapeutics. Therefore, we added search tools for using the International Classification of Disease ICD-10-CM and ICD-9-CM codes to retrieve the target, biomarker and drug information (currently enabling the search of almost 900 targets, 1800 biomarkers and 6000 drugs related to 900 disease conditions). We added information of almost 1800 biomarkers for 300 disease conditions and 200 drug scaffolds for 700 drugs. We significantly expanded Therapeutic Target Database data contents to cover >2300 targets (388 successful and 461 clinical trial targets), 20 600 drugs (2003 approved and 3147 clinical trial drugs), 20,000 multitarget agents against almost 400 target-pairs and the activity data of 1400 agents against 300 cell lines.

With the emergence of genome editing technologies and synthetic biology, it is now possible to engineer genetic circuits driving a cell's phenotypic response to a stressor. However, capturing a continuous response, rather than simply a binary 'on' or 'off' response, remains a bioengineering challenge. No tools currently exist to identify gene candidates responsible for predetermining and fine-tuning cell response phenotypes. To address this gap, we devised a novel Regulostat Inferelator (RSI) algorithm to decipher intrinsic molecular devices or networks that predetermine cellular phenotypic responses. The RSI algorithm is designed to extract gene expression patterns from basal transcriptomic data in order to identify 'regulostat' constituent gene pairs, which exhibit rheostat-like mode-of-cooperation capable of fine-tuning cellular response. Our proof-of-concept study provides computational evidence for the existence of regulostats and that these networks predetermine cellular response prior to exposure to a stressor or drug. In addition, our work, for the first time, provides evidence of context-specific, drug-regulostat interactions in predetermining drug response phenotypes in cancer cells. Given RSI-inferred regulostat networks offer insights for prioritizing gene candidates capable of rendering a resistant phenotype sensitive to a given drug, we envision that this tool will be of great value in bioengineering and medicine.

Understanding the molecular and cellular mechanisms of human primordial germ cells (hPGCs) is essential in studying infertility and germ cell tumorigenesis. Many RNA-binding proteins (RBPs) and non-coding RNAs are specifically expressed and functional during hPGC developments. However, the roles and regulatory mechanisms of these RBPs and non-coding RNAs, such as microRNAs (miRNAs), in hPGCs remain elusive. In this study, we reported a new regulatory function of DAZL, a germ cell-specific RBP, in miRNA biogenesis and cell proliferation. First, DAZL co-localized with miRNA let-7a in human PGCs and up-regulated the levels of >100 mature miRNAs, including eight out of nine let-7 family, miR21, miR22, miR125, miR10 and miR199. Purified DAZL directly bound to the loops of precursor miRNAs with sequence specificity of GUU. The binding of DAZL to the precursor miRNA increased the maturation of miRNA by enhancing the cleavage activity of DICER. Furthermore, cell proliferation assay and cell cycle analysis confirmed that DAZL inhibited the proliferation of in vitro PGCs by promoting the maturation of these miRNAs. Evidently, the mature miRNAs up-regulated by DAZL silenced cell proliferation regulators including TRIM71. Moreover, DAZL inhibited germline tumor cell proliferation and teratoma formation. These results demonstrate that DAZL regulates hPGC proliferation by enhancing miRNA processing.

Understanding the function of non-coding genomic sequence variants represents a challenge for biomedicine. Many diseases are products of gene-by-environment interactions with complex mechanisms. This study addresses these themes by mechanistic characterization of non-coding variants that influence gene expression only after drug or hormone exposure. Using glucocorticoid signaling as a model system, we integrated genomic, transcriptomic, and epigenomic approaches to unravel mechanisms by which variant function could be revealed by hormones or drugs. Specifically, we identified cis-regulatory elements and 3D interactions underlying ligand-dependent associations between variants and gene expression. One-quarter of the glucocorticoid-modulated variants that we identified had already been associated with clinical phenotypes. However, their affected genes were 'unmasked' only after glucocorticoid exposure and often with function relevant to the disease phenotypes. These diseases involved glucocorticoids as risk factors or therapeutic agents and included autoimmunity, metabolic and mood disorders, osteoporosis and cancer. For example, we identified a novel breast cancer risk gene, MAST4, with expression that was repressed by glucocorticoids in cells carrying the risk genotype, repression that correlated with MAST4 expression in breast cancer and treatment outcomes. These observations provide a mechanistic framework for understanding non-coding genetic variant-chemical environment interactions and their role in disease risk and drug response.

Ribosome assembly is an essential and conserved cellular process in eukaryotes that requires numerous assembly factors. The six-subunit UTPB complex is an essential component of the 90S precursor of the small ribosomal subunit. Here, we analyzed the molecular architecture of UTPB using an integrative structural biology approach. We mapped the major interactions that associate each of six UTPB proteins. Crystallographic studies showed that Utp1, Utp21, Utp12 and Utp13 are evolutionarily related and form a dimer of dimers (Utp1-Utp21, Utp12-Utp13) through their homologous helical C-terminal domains. Molecular docking with crosslinking restraints showed that the WD domains of Utp12 and Utp13 are associated, as are the WD domains of Utp1, Utp21 and Utp18. Electron microscopy images of the entire UTPB complex revealed that it predominantly adopts elongated conformations and possesses internal flexibility. We also determined crystal structures of the WD domain of Utp18 and the HAT and deviant HAT domains of Utp6. A structural model of UTPB was derived based on these data.

Recently, due to the dual roles of DNA and enzyme, DNAzyme has been widely used in the field of DNA circuit, which has a wide range of applications in bio-engineered system, information processing and biocomputing. In fact, the activity of DNAzymes was regulated by subunits assembly, pH control and metal ions triggers. However, those regulations required to change the sequences of whole DNAzyme, as separating parts and inserting extra DNA sequence. Inspired by the allosteric regulation of proteins in nature, a new allosteric strategy is proposed to regulate the activity of DNAzyme without DNA sequences changes. In this strategy, DNA strand displacement was used to regulate the DNAzyme structure, through which the activity of DNAzyme was well controlled. The strategy was applied to E6-type DNAzymes, and the operations of DNA logic circuit (YES, OR, AND, cascading and feedback) were established and simulated with the dynamic analyses. The allosteric regulation has potential to construct more complicated molecular systems, which can be applied to bio-sensing and detection.

Hepatocellular carcinoma (HCC) has a high mortality rate and early detection of HCC is crucial for the application of effective treatment strategies. HCC is typically caused by either viral hepatitis infection or by fatty liver disease. To diagnose and treat HCC it is necessary to elucidate the underlying molecular mechanisms. As a major cause for development of HCC is fatty liver disease, we here investigated anomalies in regulation of lipid metabolism in the liver. We applied a tailored network-based approach to identify signaling hubs associated with regulation of this part of metabolism. Using transcriptomics data of HCC patients, we identified significant dysregulated expressions of lipid-regulated genes, across many different lipid metabolic pathways. Our findings, however, show that viral hepatitis causes HCC by a distinct mechanism, less likely involving lipid anomalies. Based on our analysis we suggest signaling hub genes governing overall catabolic or anabolic pathways, as novel drug targets for treatment of HCC that involves lipid anomalies.

Recent developments of single cell RNA-sequencing technologies lead to the exponential growth of single cell sequencing datasets across different conditions. Combining these datasets helps to better understand cellular identity and function. However, it is challenging to integrate different datasets from different laboratories or technologies due to batch effect, which are interspersed with biological variances. To overcome this problem, we have proposed Single Cell Integration by Disentangled Representation Learning (SCIDRL), a domain adaption-based method, to learn low-dimensional representations invariant to batch effect. This method can efficiently remove batch effect while retaining cell type purity. We applied it to thirteen diverse simulated and real datasets. Benchmark results show that SCIDRL outperforms other methods in most cases and exhibits excellent performances in two common situations: (i) effective integration of batch-shared rare cell types and preservation of batch-specific rare cell types; (ii) reliable integration of datasets with different cell compositions. This demonstrates SCIDRL will offer a valuable tool for researchers to decode the enigma of cell heterogeneity.

Biological networks provide new opportunities for understanding the cellular biology in both health and disease states. We generated tissue specific integrated networks (INs) for liver, muscle and adipose tissues by integrating metabolic, regulatory and protein-protein interaction networks. We also generated human co-expression networks (CNs) for 46 normal tissues and 17 cancers to explore the functional relationships between genes as well as their relationships with biological functions, and investigate the overlap between functional and physical interactions provided by CNs and INs, respectively. These networks can be employed in the analysis of omics data, provide detailed insight into disease mechanisms by identifying the key biological components and eventually can be used in the development of efficient treatment strategies. Moreover, comparative analysis of the networks may allow for the identification of tissue-specific targets that can be used in the development of drugs with the minimum toxic effect to other human tissues. These context-specific INs and CNs are presented in an interactive website http://inetmodels.com without any limitation.