Biology subjects

Peng, Q.

Publications and source records attributed to Peng, Q..

4 recordsLinked to original sources

bioRxiv2018

The ghr-miR164 and GhNAC100 module participates in cotton plant defence against Verticillium dahliae

Previous reports have shown that many miRNAs were identified at the early induction stage during which Verticillium dahliae localizes at the root surface. In this study, we constructed two sRNA libraries of cotton root responses to this fungus at the later induction stage when the pathogen enters the root vascular tissue. We identified 71 known miRNAs and 378 novel miRNAs from two pathogen-induced sRNAs and the control libraries. Combined with degradome and sRNA sequencing, 178 corresponding miRNA target genes were identified, in which 40 target genes from differentially expressed miRNAs were primarily associated with oxidation-reduction and stress responses. More importantly, we characterized the ghr-miR164-GhNAC100 module in the response of the plant to V dahliae infection. A GUS fusion reporter showed that ghr-miR164 directly cleaved the mRNA of GhNAC100 in the post-transcriptional process. ghr-miR164-silencing increased the resistance of the plant to this fungus, while the knockdown of GhNAC100 elevated the susceptibility of the plant, indicating that ghr-miR164-GhNAC100 modulates plant defence through the post-transcriptional regulation. Our data documented that there are numerous miRNAs at the later induction stage that participate in the plant response to V. dahliae, suggesting that miRNAs play important roles in plant resistance to vascular disease.\n\nHighlightAccording to degradome and sRNA sequencings of cotton root in responses to Verticillium dahliae at the later induction stage, many miRNAs and corresponding targets including ghr-miR164-GhNAC100 module participate plant defence.

molecular biology↗

bioRxiv2018

IMMUNOMODULATORY ROLE OF KERATIN 76 IN ORAL AND GASTRIC CANCER

Keratin 76 (Krt76) is expressed in the differentiated epithelial layers of skin, oral cavity and squamous stomach. Krt76 downregulation in human oral squamous cell carcinomas (OSCC) correlates with poor prognosis. We show that genetic ablation of Krt76 in mice leads to spleen and lymph node enlargement, an increase in regulatory T cells (Tregs) and high levels of pro-inflammatory cytokines. Krt76-/- Tregs have increased suppressive ability correlated with increased CD39 and CD73 expression, while their effector T cells are less proliferative than controls. Loss of Krt76 increases carcinogen-induced tumours in tongue and squamous stomach. Carcinogenesis is further increased when Treg levels are elevated experimentally. The carcinogenesis response includes upregulation of pro-inflammatory cytokines and enhanced accumulation of Tregs in the tumour microenvironment. Tregs also accumulate in human OSCC exhibiting Krt76 loss. Our study highlights the role of epithelial cells in modulating carcinogenesis via communication with cells of the immune system.

cancer biology↗

bioRxiv2018

smCounter2: an accurate low-frequency variant caller for targeted sequencing data with unique molecular identifiers

MotivationLow-frequency DNA mutations are often confounded with technical artifacts from sample preparation and sequencing. With unique molecular identifiers (UMIs), most of the sequencing errors can be corrected. However, errors before UMI tagging, such as DNA polymerase errors during end-repair and the first PCR cycle, cannot be corrected with single-strand UMIs and impose fundamental limits to UMI-based variant calling.\n\nResultsWe developed smCounter2, a UMI-based variant caller for targeted sequencing data and an upgrade from the current version of smCounter. Compared to smCounter, smCounter2 features lower detection limit at 0.5%, better overall accuracy (particularly in non-coding regions), a consistent threshold that can be applied to both deep and shallow sequencing runs, and easier use via a Docker image and code for read pre-processing. We benchmarked smCounter2 against several state-of-the-art UMI-based variant calling methods using multiple datasets and demonstrated smCounter2s superior performance in detecting somatic variants. At the core of smCounter2 is a statistical test to determine whether the allele frequency of the putative variant is significantly above the background error rate, which was carefully modeled using an independent dataset. The improved accuracy in non-coding regions was mainly achieved using novel repetitive region filters that were specifically designed for UMI data.\n\nAvailabilityThe entire pipeline is available at https://github.com/qiaseq/qiaseq-dna under MIT license.

bioinformatics↗

bioRxiv2017

Structural network maturation of the preterm human brain

During the 3rd trimester, large-scale of neural circuits are formed in the human brain, resulting in the adult-like brain networks at birth. However, how the brain circuits develop into a highly efficient and segregated connectome during this period is unknown. We hypothesized that faster increases of connectivity efficiency and strength at the brain hubs and rich-club are critical for emergence of an efficient and segregated brain connectome. Here, using high resolution diffusion MRI of 77 preterm-born and term-born neonates scanned at 31-42 postmenstrual weeks (PMW), we constructed the structural connectivity matrices and performed graph-theory-based analyses. We found faster increases of nodal efficiency mainly at the brain hubs, distributed in primary sensorimotor regions, superior-middle frontal and posterior cingulate gyrus during 31-42PMW. The rich-club and within-module connections were characterized by higher rates of edge strength increases. Edge strength of short-range connections increased faster than that of long-range connections. The nodal efficiencies of the hubs predicted individual postmenstrual ages more accurately than those of non-hubs. Collectively, these findings revealed regionally differentiated maturation in the baby brain structural connectome and more rapid increases of the hub and rich-club connections, which underlie network segregation and differentiated brain function emergence.

neuroscience↗