Biology subjects

He, D.

Publications and source records attributed to He, D..

3 recordsLinked to original sources

bioRxiv2018

Conservation of the structural and functional architecture of encapsulated ferritins in bacteria and archaea

Iron is an essential element for many biological processes; however, due to its high reactivity iron can also be very toxic, producing reactive oxygen species through Fenton chemistry. Ferritins protect the cell from oxidative stress by catalytically converting Fe(II) into less toxic Fe(III) and storing the resulting iron minerals within their core. Encapsulated ferritins (EncFtn) are a sub-family of ferritin-like proteins, which are widely distributed in all bacterial and archaeal phyla. We recently characterised the Rhodospirillum rubrum EncFtn, showing that although enzymatically active, due to its open structure it requires the association with an encapsulin nanocage in order to act as an iron store. Given the wide distribution of the EncFtn family in organisms with diverse environmental niches, a question arises as to whether the structure and catalytic activity is conserved across the family. Here we structurally characterise two EncFtn members from the halophile Haliangium ochraceum and the thermophile Pyrococcus furiosus, which show the same distinct annular decamer topology observed in R. rubrum EncFtn, with the ferroxidase centre (FOC) formed between one of the dimer interfaces. Solution and native mass spectrometr analyses show that the stability of the protein quaternary structure differs between EncFtn proteins from different species. The catalytic role of the EncFtn proteins was confirmed by biochemical assays, and we show that Zn(II) ions inhibit the ferroxidase activity of the EncFtn proteins to varying degrees. Our results represent a further step in the characterisation of the recently discovered EncFtn ferritin-like sub-family, indicating a common structural organisation and catalytic activity, despite diverse host environments.

biochemistry↗

bioRxiv2018

IFN-γ induces epithelial-to-mesenchymal transition of cancer cells via an unique microRNA processing

Interferon-{gamma} (IFN{gamma}) is a potent cytokine in modulating tumor immunity and tumoricidal effects. We demonstrate a new function of IFN{gamma} in inducing epithelial-to-mesenchymal transition (EMT) in normal and cancer cells from different cell types. IFN{gamma} activates JAK-STAT signaling pathway leading to the transcription of IFN-stimulated genes (ISGs), such as interferon-induced tetratricopeptide repeat 5 (IFIT5). We unveil a new function of IFIT5 complex in degrading precursor microRNAs (pre-miRNA) that include pre-miR-363 from the miR-106a-363 cluster, as well as pre-miR-101 and pre-miR-128 with a similar 5-end structure with pre-miR-363. Noticeably, these suppressive miRNAs have similar functions by targeting EMT transcription factors in prostate cancer (PCa) cells. We further demonstrated that IFIT5 plays a critical role in IFN{gamma}-induced cell invasiveness in vitro and lung metastasis in vivo. Clinically, IFIT5 is highly elevated in high-grade PCa and its expression inversely correlates with these suppressive miRNAs. Altogether, this study unveils pro-tumorigenic role of the IFN pathway via a new mechanism of action, which certainly raises concern about its clinical application.

cancer biology↗

bioRxiv2017

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

MotivationA complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application.\n\nResultsWe propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data.\n\nAvailabilityR scripts available at https://github.com/jijiadong/JDINAC\n\nContactlxie@iscb.org\n\nSupplementary informationSupplementary data are available at bioRxiv online.

systems biology↗