Biology subjects

Luo, H.

Publications and source records attributed to Luo, H..

168 records · Page 10Linked to original sources

bioRxiv2018

Interleukin-17 regulates neuron-glial communications, inhibitory synaptic transmission and neuropathic pain after chemotherapy

The proinflammatory cytokine Interleukin-17 (IL-17) is produced mainly by Th17 cells and has been implicated in pain regulation. However, synaptic mechanisms by which IL-17 regulates pain transmission are unknown. Here we report that glia-produced IL-17 suppresses inhibitory synaptic transmission in spinal cord pain circuit and drives chemotherapy-induced neuropathic pain. We observed respective expression of IL-17 and its receptor IL-17R in spinal cord astrocytes and neurons. Patch clamp recording in spinal cord slices revealed that IL-17 not only enhanced EPSCs but also suppressed IPSCs and GABA-induced currents in lamina IIo somatostatin-expressing neurons. Spinal IL-17 was upregulated after paclitaxel treatment, and intrathecal IL-17R blockade reduced paclitaxel-induced neuropathic pain. In dorsal root ganglia, respective IL-17 and IL-17R expression in satellite glial cells and neurons was sufficient and required for inducing neuronal hyperexcitability after paclitaxel. Together, our data show that IL-17/IL-17R mediate both central and peripheral neuron-glial interactions in chemotherapy-induced peripheral neuropathy.

neuroscience↗

bioRxiv2018

HIV protease inhibitor Saquinavir inhibits toll-like receptor 4 activation by targeting receptor dimerization

Toll like receptor 4 (TLR4) is crucial in induction of innate immune response through recognition of invading pathogens or endogenous alarming molecules.Ligand-induced dimerization of TLR4 is required for the activation of downstream signaling pathways. TLR4 dimerization induces the activation of NF-kB and IRF3 through MyD88- or TRIF-dependent pathways. Saquinavir (SQV), a FDA-approved HIV protease inhibitor, has been shown to suppress the activation of NF-kB induced by HMGB1 by blocking TLR4-MyD88 association in proteasome-independent pathway. However, it remains nknown whether SQV is a HMGB1-specific and MyD88-dependent TLR4 signaling inhibitor and which precise signaling element of TLR4 is targeted by SQV. Our results showed that SQV inhibits both MyD88- and TRIF-dependent pathways in response to LPS, a critical sepsis inducer and TLR4 agonist, leading to downregulation of NF-kB and IRF3. SQV did not suppress MyD88-dependent pathway triggered by TLR1/2 agonist Pam3csk4. In the only TRIF-dependent pathway, SQV did not attenuate IRF3 activation induced by TLR3 agonist Poly(I:C). Furthermore, dimerization of TLR4 induced by LPS and HMGB1 was decreased by SQV. These results suggest that TLR4 receptor complex is the molecular target of SQV and shed light on that TLR4-mediated inmune responses and consequent risk for uncontrolled inflammation could be modulated by FDA-approved drug SQV.

immunology↗

bioRxiv2018

Optimum Search Schemes for Approximate String Matching Using Bidirectional FM-Index

Finding approximate occurrences of a pattern in a text using a full-text index is a central problem in bioinformatics and has been extensively researched. Bidirectional indices have opened new possibilities in this regard allowing the search to start from anywhere within the pattern and extend in both directions. In particular, use of search schemes (partitioning the pattern and searching the pieces in certain orders with given bounds on errors) can yield significant speed-ups. However, finding optimal search schemes is a difficult combinatorial optimization problem.\n\nHere for the first time, we propose a mixed integer program (MIP) capable to solve this optimization problem for Hamming distance with given number of pieces. Our experiments show that the optimal search schemes found by our MIP significantly improve the performance of search in bidirectional FM-index upon previous ad-hoc solutions. For example, approximate matching of 101-bp Illumina reads (with two errors) becomes 35 times faster than standard backtracking. Moreover, despite being performed purely in the index, the running time of search using our optimal schemes (for up to two errors) is comparable to the best state-of-the-art aligners, which benefit from combining search in index with in-text verification using dynamic programming. As a result, we anticipate a full-fledged aligner that employs an intelligent combination of search in the bidirectional FM-index using our optimal search schemes and in-text verification using dynamic programming that will outperform todays best aligners. The development of such an aligner, called FAMOUS (Fast Approximate string Matching using OptimUm search Schemes), is ongoing as our future work.

bioinformatics↗

bioRxiv2017

Ennet: exert enhaner-only somatic mutations to discover potential cancer-driving biological networks

Whole genome sequencing technology has facilitated the discovery of a large number of somatic mutations in enhancers (SMEs), whereas the utility of SMEs in tumorigenesis has not been fully explored. Here we present Ennet, a method to comprehensively investigate SMEs enriched networks (SME-networks) in cancer by integrating SMEs, enhancer-gene interactions and gene-gene interactions. Using Ennet, we performed a pan-cancer analysis in 2004 samples from 8 cancer types and found many well-known cancer drivers were involved in the SME-networks, including ESR1, SMAD3, MYC, EGFR, BCL2 and PAX5. Meanwhile, Ennet also identified many new networks with less characterization but have potentially important roles in cancer, including a large SME-network in medulloblastoma (MB), which contains genes enriched in the glutamate receptor and neural development pathways. Interestingly, SME-networks are specific across cancer types, and the vast majority of the genes identified by Ennet have few mutations in gene bodies. Collectively, our work suggests that using enhancer-only somatic mutations can be an effective way to discover potential cancer-driving networks. Ennet provides a new perspective to explore new mechanisms for tumor progression from SMEs.

bioinformatics↗

bioRxiv2017

Fluctuations of fMRI activation patterns reveal theta-band dynamics of visual object priming

The brain dynamically creates predictions about upcoming stimuli to guide perception efficiently. Recent behavioral results suggest theta-band oscillations contribute to this prediction process, however litter is known about the underlying neural mechanism. Here, we combine fMRI and a time-resolved psychophysical paradigm to access fine temporal-scale profiles of the fluctuations of brain activation patterns corresponding to visual object priming. Specifically, multi-voxel activity patterns in the fusiform face area (FFA) and the parahippocampal place area (PPA) show temporal fluctuations at a theta-band (~5 Hz) rhythm. Importantly, the theta-band power in the FFA negatively correlates with reaction time, further indicating the critical role of the observed cortical theta oscillations. Moreover, alpha-band (~10 Hz) shows a dissociated spatial distribution, mainly linked to the occipital cortex. These findings, to our knowledge, are the first fMRI study that indicates temporal fluctuations of multi-voxel activity patterns and that demonstrates theta and alpha rhythms in relevant brain areas.

neuroscience↗

bioRxiv2016

Accurate prediction of human essential genes using only nucleotide composition and association information

Three groups recently identified essential genes in human cancer cell lines using wet experiments, and these genes are of high values. Herein, we improved the widely used Z curve method by creating a {lambda}-interval Z curve, which considered interval association information. With this method and recursive feature elimination technology, a computational model was developed to predict human gene essentiality. The 5-fold cross-validation test based on our benchmark dataset obtained an area under the receiver operating characteristic curve (AUC) of 0.8814. For the rigorous jackknife test, the AUC score was 0.8854. These results demonstrated that the essentiality of human genes could be reliably reflected by only sequence information. However, previous classifiers in three eukaryotes can gave satisfactory prediction only combining sequence with other features. It is also demonstrated that although the information contributed by interval association is less than adjacent nucleotides, this information can still play an independent role. Integrating the interval information into adjacent ones can significantly improve our classifiers prediction capacity. We re-predicted the benchmark negative dataset by Pheg server (https://cefg.uestc.edu.cn/Pheg), and 118 genes were additionally predicted as essential. Among them, 21 were found to be homologues in mouse essential genes, indicating that at least a part of the 118 genes were indeed essential, however previous experiments overlooked them. As the first available server, Pheg could predict essentiality for anonymous gene sequences of human. It is also hoped the {lambda}-interval Z curve method could be effectively extended to classification issues of other DNA elements.

bioinformatics↗