Parthenolide ameliorates tweak-induced podocytes injury

Molecular Biology Reports - Parthenolide (PTL) is a natural product from the shoots of Tanacetum parthenium, which has immunomodulatory effects in multiply type of diseases. This study aimed to...     

HAP1 Modulates Epileptic Seizures by Regulating GABAAR Function in Patients with Temporal Lobe Epilepsy and in the PTZ-Induced Epileptic Model

Neurochemical Research - The number of γ-aminobutyric acid type A receptors (GABAARs) expressed on the surface membrane and at synaptic sites is implicated in the enhanced excitation of...     

Effect of Early Normobaric Hyperoxia on Blast-Induced Traumatic Brain Injury in Rats

Neurochemical Research - Blast-induced traumatic brain injury (bTBI) is a leading cause of disability and mortality in soldiers during the conflicts in Iraq and Afghanistan. Although substantial...     

Long noncoding RNA LINC01234 promoted cell proliferation and invasion via miR-1284/TRAF6 axis in colorectal cancer

Colorectal cancer is one of the most common and leading malignancies globally. Long noncoding RNAs (lncRNAs) function as potentially critical regulator in colorectal cancer. LINC01234, a novel lncRNA in tumor biology, regulates the progression of various tumors. However, the tumorigenic mechanism of LINC01234 in colorectal cancer is still unclear. This study was performed with the aim to prospectively investigate clinical significance, effect, and mechanism of lncRNA LINC01234 in colorectal cancer. First, we found that LINC01234, localized in the cytoplasm, was increased in both colorectal cancer cell lines and tissues. Subsequent functional assays suggested LINC01234 knockdown suppressed cell proliferation, migration, and invasion of colorectal cancer cells, while blocked cell cycle and induced cell apoptosis. Moreover, we identified that miR-1284 was target of LINC01234, we further demonstrated a negative correlation with LINC01234 in colorectal cancer tissues and cells. Furthermore, miR-1284 targeted and suppressed tumor necrosis factor receptor–associated factor 6 (TRAF6). Loss-of-function assay revealed that LINC01234 silencing suppressed colorectal cancer progression through inhibition of miR-1284. In vivo subcutaneous xenotransplanted tumor model indicated LINC01234 knockdown inhibited in vivo tumorigenic ability of colorectal cancer via downregulation of TRAF6. Collectively, this study clarified the biological significance of LINC01234/miR-1284/TRAF6 axis in colorectal cancer progression, providing insights into LINC01234 as novel potential therapeutic target for colorectal cancer therapeutic from bench to clinic.     

Circular RNA circABCC4 regulates lung adenocarcinoma progression via miR-3186-3p/TNRC6B axis

Lung adenocarcinoma (LUAD), a general kind of bronchogenic malignancy globally, is depicted as one of the most critical factors affecting human health severely. Featured with loop structure, circular RNA (circRNA) has been described as an essential regulator of multiple human malignancies. Nevertheless, knowledge concerning the regulatory function of circRNA in LUAD progression remains limited. Identified as a novel circRNA, circABCC4 has not been studied in LUAD as yet. This is the first time to probe into the underlying role of circABCC4 in LUAD. In this study, a notably elevated expression of circABCC4 was found in LUAD tissues and cells. Besides, circABCC4 is verified to be characterized with a circular structure in LUAD. Functional assays elucidated that knockdown of circABCC4 significantly impaired LUAD cell proliferation, migration as well as accelerated cell apoptosis. Molecular mechanism experiments later revealed that circABCC4 could bind with miR-3186-3p and miR-3186-3p was a tumor suppressor in LUAD. Moreover, TNRC6B was validated to combine with miR-3186-3p, and its expression was respectively negatively and positively regulated by miR-3186-3p and circABCC4 in LUAD. Final rescue experiments further delineated that TNRC6B upregulation partially restored circABCC4 downregulation-mediated effect on LUAD progression. In sum, circABCC4 regulates LUAD progression via miR-3186-3p/TNRC6B axis.     

Flexible Mixture Model Approaches That Accommodate Footprint Size Variability for Robust Detection of Balancing Selection

Long-term balancing selection typically leaves narrow footprints of increased genetic diversity, and therefore most detection approaches only achieve optimal performances when sufficiently small genomic regions (i.e., windows) are examined. Such methods are sensitive to window sizes and suffer substantial losses in power when windows are large. Here, we employ mixture models to construct a set of five composite likelihood ratio test statistics, which we collectively term B statistics. These statistics are agnostic to window sizes and can operate on diverse forms of input data. Through simulations, we show that they exhibit comparable power to the best-performing current methods, and retain substantially high power regardless of window sizes. They also display considerable robustness to high mutation rates and uneven recombination landscapes, as well as an array of other common confounding scenarios. Moreover, we applied a specific version of the B statistics, termed B₂, to a human population-genomic data set and recovered many top candidates from prior studies, including the then-uncharacterized STPG2 and CCDC169–SOHLH2, both of which are related to gamete functions. We further applied B₂ on a bonobo population-genomic data set. In addition to the MHC-DQ genes, we uncovered several novel candidate genes, such as KLRD1, involved in viral defense, and SCN9A, associated with pain perception. Finally, we show that our methods can be extended to account for multiallelic balancing selection and integrated the set of statistics into open-source software named BalLeRMix for future applications by the scientific community.     

SAXSDom: Modeling multidomain protein structures using small-angle X-ray scattering data

Many proteins are composed of several domains that pack together into a complex tertiary structure. Multidomain proteins can be challenging for protein structure modeling, particularly those for which templates can be found for individual domains but not for the entire sequence. In such cases, homology modeling can generate high quality models of the domains but not for the orientations between domains. Small-angle X-ray scattering (SAXS) reports the structural properties of entire proteins and has the potential for guiding homology modeling of multidomain proteins. In this article, we describe a novel multidomain protein assembly modeling method, SAXSDom that integrates experimental knowledge from SAXS with probabilistic Input-Output Hidden Markov model to assemble the structures of individual domains together. Four SAXS-based scoring functions were developed and tested, and the method was evaluated on multidomain proteins from two public datasets. Incorporation of SAXS information improved the accuracy of domain assembly for 40 out of 46 critical assessment of protein structure prediction multidomain protein targets and 45 out of 73 multidomain protein targets from the ab initio domain assembly dataset. The results demonstrate that SAXS data can provide useful information to improve the accuracy of domain-domain assembly. The source code and tool packages are available at https://github.com/jianlin-cheng/SAXSDom .     

ProDCoNN: Protein design using a convolutional neural network

Designing protein sequences that fold to a given three-dimensional (3D) structure has long been a challenging problem in computational structural biology with significant theoretical and practical implications. In this study, we first formulated this problem as predicting the residue type given the 3D structural environment around the C _α atom of a residue, which is repeated for each residue of a protein. We designed a nine-layer 3D deep convolutional neural network (CNN) that takes as input a gridded box with the atomic coordinates and types around a residue. Several CNN layers were designed to capture structure information at different scales, such as bond lengths, bond angles, torsion angles, and secondary structures. Trained on a very large number of protein structures, the method, called ProDCoNN (protein design with CNN), achieved state-of-the-art performance when tested on large numbers of test proteins and benchmark datasets.