Narrow Results

Angiogenesis is a regulated process integral to many physiological and pathological situations, including carcinogenesis and tumor growth. The majority of the angiogenic processes are related to inflammation. The interplay is not only important in the case of pathogen entry but also influential in chronic inflammatory diseases, tumor growth and tissue regeneration. Modulating the interaction between inflammation and angiogenesis could be an important target for cancer treatment and wound healing alike. Ginseng has a wide range of pharmacological effects, including anti-inflammatory and angiogenesis-modulating activities. This paper presents the recent research progresses on the inhibition of angiogenesis by ginseng and its active constituents, with a particular focus on processes mediated by inflammation. The modulatory role of ginseng compounds in inflammation-mediated angiogenesis involving hypoxia and microRNAs are also discussed. With the potential to modulate the angiogenesis at the transcriptional, translational and protein signaling level via various different mechanisms, ginseng could prove to be effective in cancer therapeutics.

While a number of coding genes have explained the anticancer activity of ginsenoside Rh2, little is known about noncoding RNAs. This study was performed to elucidate the regulatory activity of long noncoding RNA (lncRNA) CFAP20DC-AS1, which is known to be downregulated by Rh2. MiR-3614-3p, which potentially binds CFAP20DC-AS1, was screened using the LncBase Predicted program, and the binding was verified by assaying the luciferase activity of a luciferase/lncRNA recombinant plasmid construct. The competitive endogenous RNA (ceRNA) relationship of the two RNAs was further validated by quantitative PCR after deregulation of each RNA using siRNA. The effect of miRNA and target genes on the MCF-7 cancer cell growth was determined by monitoring proliferation and apoptosis in the presence of Rh2 after deregulating the corresponding gene. The miRNA decreased the luciferase activity of the luciferase/CFAP20DC-AS1 fusion vector, confirming the binding. SiRNA-based deregulation of CFAP20DC-AS1 attenuated the expression of miR-3614-3p and vice versa. In contrast to CFAP20DC-AS1, miR-3614-3p was upregulated by Rh2, inhibiting proliferation but stimulating apoptosis of the MCF-7 cells. Target genes of miR-3614-3p, BBX and TNFAIP3, were downregulated by Rh2 and the miRNA but upregulated by the lncRNA. Rh2 inhibits CFAP20DC-AS1, which obscures the association of the lncRNA with miR-3614-3p, resulting in the suppression of oncogenic BBX and TNFAIP3. Taken together, the Rh2/CFAP20DC-AS1/miR-3614-3p/target gene axis contributes to the antiproliferation activity of Rh2 in cancer cells.

MicroRNAs (miRNAs) interact with 3′untranslated region (UTR) elements of target genes to regulate mRNA stability or translation, and play a crucial role in regulating many different biological processes. bantam, a conserved miRNA, is involved in several functions, such as regulating Drosophila growth and circadian rhythm. Recently, it has been discovered that bantam plays a crucial role in the core circadian pacemaker. In this paper, based on experimental observations, a detailed dynamical model of bantam-regulated circadian clock system is developed to show the post-transcriptional behaviors in the modulation of Drosophila circadian rhythm, in which the regulation of bantam is incorporated into a classical model. The dynamical behaviors of the model are consistent with the experimental observations, which shows that bantam is an important regulator of Drosophila circadian rhythm. The sensitivity analysis of parameters demonstrates that with the regulation of bantam the system is more sensitive to perturbations, indicating that bantam regulation makes it easier for the organism to modulate its period against the environmental perturbations. The effectiveness in rescuing locomotor activity rhythms of mutated flies shows that bantam is necessary for strong and sustained rhythms. In addition, the biological mechanisms of bantam regulation are analyzed, which may help us more clearly understand Drosophila circadian rhythm regulated by other miRNAs.

In this paper, Myc/E2F/miR-17-92 network under Gaussian white noise is studied. Taking the time delay as the parameter, the Hopf bifurcation of the system is obtained, which causes the protein concentration to oscillate periodically. Under the influence of time delay and noise, the stochastic D-bifurcation of the system is obtained. It is worth noting that the occurrence of stochastic P-bifurcation is successfully captured. Thus a pattern of coexistence of high and low protein concentrations is founded in the network. The specific research methods of this paper are as follows: firstly, the system is reduced to a finite dimensional system by using stochastic center manifold and normal form theory. Then, using the stochastic averaging method, the Fokker–Planck–Kolmogorov equation of the system is constructed in which the statistical response in the stationary state is the probability density. Finally, the stochastic bifurcation analysis and numerical simulation are carried out. The agreements between the analytical method and those obtained numerically validate the effectiveness of the analytical investigations.

Exploring the Role of Glutathione in the Regulation of Immune Cell Function.

Does Oxidative Damage Cause Poor Healing?

Pathogenesis of Atopic Dermatitis in Singapore.

Proteomics and Colorectal Cancer Metastasis: Bird's-Eye View on Current Scenario and Our Contribution.

Zebrafish: A Small Fish Model for a Big Human Disease.

The Reign of a New Dictator: Circulating MicroRNA in Diabetes.

Engineering Artificial Vascularized Bone Grafts for the Repair of Large Bone Defects.

A 'Nano' Era for Blood Glucose Sensing.

Ancient Medicine with Newer Roles: Potential Role of Celastrol in the Treatment of Multiple Myeloma.

Proteins, Proteome and Proteomics.

A Novel Promising Biomarker and Therapy Target of Liver Cancer.

Innovation in Academic Medicine.

Merck Serono awards Grant for Growth Innovation (GGI) for the first time.

National Cancer Institute awards KIYATEC nearly $2M to fund 3D breast cancer, 3D brain cancer micro-tumor development.

Frost & Sullivan honors 2014 Growth, Innovation & Leadership Award winners.

Abcam licenses novel Ceefourin inhibitors of multidrug resistance protein 4 from Australia's Children's Cancer Institute.

Daiichi Sankyo further expands TaNeDS collaborative drug discovery program in Europe.

Veolia Water Technologies South-East Asia to provide advanced water solutions for Tun Razak Exchange Project in Malaysia.

Biocept and Rosetta Genomics collaborate to evaluate microRNAs from circulating tumor cells.

Biocept expands collaboration with MD Anderson Cancer Center.

Shenzhen biotech companies building the world’s largest cell bank in Guizhou.

China’s cancer researcher shares 2018’s Sjoberg Prize of Sweden.

Breakthrough may help with earlier detection of heart attacks and cancer.

New dialyzer to isolate bacteria from unprocessed blood.

Chinese scientists develop new protocols for DNA-free genome editing in wheat.

Synthesized herbs to treat cardio-cerebrovascular disease.

Chinese scientists find antidote to centipede venom.

Grünenthal and Mundipharma enter commercial partnership in China.

Tencent and Medopad to cooperate on medical AI.

Majority of oncology clinical trials in China failed to meet enrolment targets.

WuXi STA and Regulus announce microRNA development and manufacturing collaboration.

ASIA-PACIFIC – A microRNA study might cast new light on ALS treatment.

ASIA-PACIFIC – New standard of care for advanced nose cancer patients.

ASIA-PACIFIC – Dormant neural stem cells in fruit flies activate to generate new brain cells.

REST OF THE WORLD – A wearable that’s not to be laughed at.

REST OF THE WORLD – Junk food could be responsible for the food allergy epidemic.

The following topics are under this section:

• Combination Immunotherapy for Hepatocellular Carcinoma
• Sustainability in Healthcare Financing
• MiRXES – A Game Changer in MicroRNA Applications

The following topics are under this section:

• Placing Singapore at the forefront of global microRNA research and application
• Azelis India acquires S. Zhaveri Pharmakem, increasing market presence
• First Clinical Study on benefits of nicotinamide riboside (NR) in older adults with hypertension
• Agreement to accelerate commercialization of Point-Of-Care kit for Ovarian Cancer Detection
• The Research of Today for the Health Care of Tomorrow
• Providing a Global Roadmap for Healthy Longevity
• Merger to Spark Transformation in Aquaculture Industry
• Innovative Haemostat to Improve Operating Efficiency
• Accelerating Health Through Innovation with Strategic Partnerships

The following topics are under this section:

• ASIA-PACIFIC — Identification of Therapeutic Points for Genetically Diverse and Fatal Leukaemia
• ASIA-PACIFIC — Novel Microfluidic Processes for Drug Development
• ASIA-PACIFIC — Development of Anti-Microbial Coating against COVID-19
• ASIA-PACIFIC — Partnership between Arcturus Therapeutics and Duke-NUS Medical School to develop COVID-19 Vaccine
• ASIA-PACIFIC — Nanoscopic Insights to Proteins against Huntington’s Disease
• ASIA-PACIFIC — Unravelling the Impact of Marine Heatwave on Coral Reef Fishes
• ASIA-PACIFIC — Regulation of Plant Pores by MicroRNAs
• ASIA-PACIFIC — Discovering Clues to Longevity in Our Genome
• ASIA-PACIFIC — Repurposing Nature’s Products to Viable Materials
• ASIA-PACIFIC — Reverse Conversion of Oestrogens to Androgens
• REST OF THE WORLD — HER2-targeted Antibody Drug Conjugate Shows Promise in Patients with Non-Breast-Gastric Cancers
• REST OF THE WORLD — New Research finds Teeth as a Biological Archive
• REST OF THE WORLD — Finding Treatment for Muscular Dystrophy using CRISPR

MicroRNAs are associated with multiple cellular processes and diseases. Here, we designed a highly sensitive, magnetically retrievable biosensor using magnetic beads (MBs) as a model RNA sensor. The assay utilized two biotinylated probes, which were hybridized to the complementary target miRNA in a sandwich assay format. One of the biotinylated ends of the hybridization complex was immobilized onto the surface of a NeutrAvidin (NAV) coated MB and the other biotinylated end was conjugated to HRP via NAV-biotin interaction. The results were presented by colorimetric absorbance of the resorufin product from amplex red oxidation. We show that by combining the use of MBs as well as bio-specific immobilization, the sensitivity of miRNA detection is down to 100 pM. This model HRP-MBs system can be used for simple, rapid colorimetric quantification of low level DNA/RNA or other small molecules.

Background: Current miRNA target prediction tools have the common problem that their false positive rate is high. This renders identification of co-regulating groups of miRNAs and target genes unreliable. In this study, we describe a procedure to identify highly probable co-regulating miRNAs and the corresponding co-regulated gene groups. Our procedure involves a sequence of statistical tests: (1) identify genes that are highly probable miRNA targets; (2) determine for each such gene, the minimum number of miRNAs that co-regulate it with high probability; (3) find, for each such gene, the combination of the determined minimum size of miRNAs that co-regulate it with the lowest p-value; and (4) discover for each such combination of miRNAs, the group of genes that are co-regulated by these miRNAs with the lowest p-value computed based on GO term annotations of the genes. Results: Our method identifies 4, 3 and 2-term miRNA groups that co-regulate gene groups of size at least 3 in human. Our result suggests some interesting hypothesis on the functional role of several miRNAs through a "guilt by association" reasoning. For example, miR-130, miR-19 and miR-101 are known neurodegenerative diseases associated miRNAs. Our 3-term miRNA table shows that miR-130/19/101 form a co-regulating group of rank 22 (p-value =1.16 × 10^-2). Since miR-144 is co-regulating with miR-130, miR-19 and miR-101 of rank 4 (p-value = 1.16 × 10^-2) in our 4-term miRNA table, this suggests hsa-miR-144 may be neurodegenerative diseases related miRNA. Conclusions: This work identifies highly probable co-regulating miRNAs, which are refined from the prediction by computational tools using (1) signal-to-noise ratio to get high accurate regulating miRNAs for every gene, and (2) Gene Ontology to obtain functional related co-regulating miRNA groups. Our result has partly been supported by biological experiments. Based on prediction by TargetScanS, we found highly probable target gene groups in the Supplementary Information. This result might help biologists to find small set of miRNAs for genes of interest rather than huge amount of miRNA set. Supplementary Information:.

Many recent studies have shown that access of animal microRNAs (miRNAs) to their complementary sites in target mRNAs is determined by several sequence-specific determinants beyond the seed regions in the 5′ end of miRNAs. These factors have been related to the repressive power of miRNAs and used in some programs to predict the efficacy of miRNA complementary sites. However, these factors have not been systematically examined regarding their capacities for improving miRNA target prediction. We develop a new miRNA target prediction algorithm, called Hitsensor, by incorporating many sequence-specific features that determine complementarities between miRNAs and their targets, in addition to the canonical seed regions in the 5′ ends of miRNAs. We evaluate the performance of our algorithm on 720 known animal miRNA:target pairs in four species, Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans. Our experimental results show that Hitsensor outperforms five popular existing algorithms, indicating that our unique scheme for quantifying the determinants of complementary sites is effective in improving the performance of a miRNA target prediction algorithm. We also examine the effectiveness of miRNA-mediated repression for the predicted targets by using a published quantitative protein expression dataset of miR-223 knockout in mouse neutrophils. Hitsensor identifies more targets than the existing algorithms, and the predicted targets of Hitsensor show comparable protein level changes to those of the existing algorithms.

Secondary structure remains the most exploitable feature for noncoding RNA (ncRNA) gene finding in genomes. However, methods based on secondary structure prediction may generate superfluous amount of candidates for validation and have yet to deliver the desired performance that can complement experimental efforts in ncRNA gene finding. This paper investigates a novel method, unpaired structural entropy (USE) as a measurement for the structure fold stability of ncRNAs. USE proves to be effective in identifying from the genome background a class of ncRNAs, such as precursor microRNAs (pre-miRNAs) that contains a long stem hairpin loop. USE correlates well and performs better than other measures on pre-miRNAs, including the previously formulated structural entropy. As an SVM classifier, USE outperforms existing pre-miRNA classifiers. A long stem hairpin loop is common for a number of other functional RNAs including introns splicing hairpins loops and intrinsic termination hairpin loops. We believe USE can be further applied in developing ab initio prediction programs for a larger class of ncRNAs.

Alteration of gene expression in response to regulatory molecules or mutations could lead to different diseases. MicroRNAs (miRNAs) have been discovered to be involved in regulation of gene expression and a wide variety of diseases. In a tripartite biological network of human miRNAs, their predicted target genes and the diseases caused by altered expressions of these genes, valuable knowledge about the pathogenicity of miRNAs, involved genes and related disease classes can be revealed by co-clustering miRNAs, target genes and diseases simultaneously. Tripartite co-clustering can lead to more informative results than traditional co-clustering with only two kinds of members and pass the hidden relational information along the relation chain by considering multi-type members. Here we report a spectral co-clustering algorithm for k-partite graph to find clusters with heterogeneous members. We use the method to explore the potential relationships among miRNAs, genes and diseases. The clusters obtained from the algorithm have significantly higher density than randomly selected clusters, which means members in the same cluster are more likely to have common connections. Results also show that miRNAs in the same family based on the hairpin sequences tend to belong to the same cluster. We also validate the clustering results by checking the correlation of enriched gene functions and disease classes in the same cluster. Finally, widely studied miR-17-92 and its paralogs are analyzed as a case study to reveal that genes and diseases co-clustered with the miRNAs are in accordance with current research findings.

Drug repurposing is a new method for disease treatments, which accelerates the identification of new uses for existing drugs with minimal side effects for patients. MicroRNA-based therapeutics are a class of drugs that have been used in gene therapy following the FDA’s approval of the first anti-sense therapy. This study examines the effects of oxLDL on vascular smooth muscle cells (VSMCs) and identifies potential drugs and antimiRs for treating VSMC-associated diseases. The Connectivity Map (cMap) database is utilized to identify potential new uses of existing drugs. The success of the identifications was supported by MTT assay, clonogenic assay and clinical trial data. Specifically, 37 drugs, some of which are undergoing clinical trials, were identified. Three of the identified drugs exhibit IC50 activities. Among the 37 drugs’ targets, three differentially expressed genes (DEGs) are identified as drug targets by using both the DrugBank and the NCBI PubChem Compound databases. Also, one DEG, DNMT1, which is regulated by 17 miRNAs, where these miRNAs are potential targets for developing antimiR-based miRNA therapy, is found.

MicroRNAs are known to play an essential role in gene regulation in plants and animals. The standard method for understanding microRNA–gene interactions is randomized controlled perturbation experiments. These experiments are costly and time consuming. Therefore, use of computational methods is essential. Currently, several computational methods have been developed to discover microRNA target genes. However, these methods have limitations based on the features that are used for prediction. The commonly used features are complementarity to the seed region of the microRNA, site accessibility, and evolutionary conservation. Unfortunately, not all microRNA target sites are conserved or adhere to exact seed complementary, and relying on site accessibility does not guarantee that the interaction exists. Moreover, the study of regulatory interactions composed of the same tissue expression data for microRNAs and mRNAs is necessary to understand the specificity of regulation and function. We developed MicroTarget to predict a microRNA–gene regulatory network using heterogeneous data sources, especially gene and microRNA expression data. First, MicroTarget employs expression data to learn a candidate target set for each microRNA. Then, it uses sequence data to provide evidence of direct interactions. MicroTarget scores and ranks the predicted targets based on a set of features. The predicted targets overlap with many of the experimentally validated ones. Our results indicate that using expression data in target prediction is more accurate in terms of specificity and sensitivity. Available at: https://bioinformatics.cs.vt.edu/~htorkey/microTarget.

MicroRNAs are single-stranded noncoding RNAs known to down-regulate target genes at the protein or mRNA level. Computational prediction of targets is essential for elucidating the detailed functions of microRNA. However, prediction specificity and sensitivity of the existing algorithms still need to be improved to generate useful hypotheses for subsequent experimental testing. A new microRNA binding-site representation method was developed, which uses four symbols “$|$”, “:”, “$\sim$”, and “${}^{{}^{\wedge }}$” (indicating paired, unpaired, insertion, and bulge, respectively) to represent the status of each nucleotide base pair in the microRNA binding site. New features were established with the information of every two adjacent symbols. There are 12 possible combinations and the frequency of each defines a set of novel and useful features. A comprehensive training dataset is constructed for mammalian microRNAs with positive targets obtained from the microRNA target depository in the miRTarbase, while negative targets were derived from pseudo-microRNA bindings. An SVM model was established using the training dataset and a new software called Min3 was developed. Performance of Min3 was assessed with intensively studied examples of miR-155 and miR-92a. Prediction results showed that Min3 can discover 47% of experimental conformed targets on average. The overlapping is above 20% on average when compared with TargetScan and miRanda. Annotations of the public microRNA datasets showed that there is a negative effect (up-regulation) of the Min3 targets for the knock out/down of miR-155 and miR-92a. Six top ranked targets were selected for validation by wet-lab experiments, and five of them showed a regulation effect. The Min3 can be a good alternative to current microRNA target discovery software. This tool is available at https://sourceforge.net/projects/mirt3.

Recent advances in RNA studies show that the well-ordered, structured RNAs perform a broad functions in various biological mechanisms. Included among these functions are regulations of gene expression at multiple levels by diversified ribozymes and various RNA regulatory elements. The discovered microRNAs (miRNAs) with a distinct stem-loops are a new class of RNA regulatory elements. The prediction of those well-ordered folding sequences (WFS) associated with the RNA regulatory elements in genomic sequences is very helpful for our understandings of RNA-based gene regulations. We present here a new computational method in searching for the conserved WFS in genomes. In the method, the WFS is assessed by a quantitative measure E_diff that is defined as the difference of free energies between the computed optimal structure (OS) and its corresponding optimal restrained structure where all the previous base pairings in the OS are forbidden. From those WFS with high E_diff scores, the conserved WFS is determined by computing the maximal similarity score (MSS) between the two compared structures. In practice, we first search for those distinct WFS with high statistical significance in genomic sequences and then seek for those conserved WFS with high MSS. The potential and implications of our discoveries in the genome of Caenorhabditis elegans are discussed.