Biology subjects

Cheng, Z.

Publications and source records attributed to Cheng, Z..

8 recordsLinked to original sources

bioRxiv2018

Using pan RNA-seq analysis to reveal the ubiquitous existence of 5′ end and 3′ end small RNAs

In this study, we used pan RNA-seq analysis to reveal the ubiquitous existence of 5 end and 3 end small RNAs. 5 and 3 sRNAs alone can be used to annotate mitochondrial with 1-bp resolution and nuclear non-coding genes and identify new steady-state RNAs, which are usually from functional genes. Using 5, 3 and intronic sRNAs, we revealed that the enzymatic dsRNA cleavage and RNAi could involve in the RNA degradation and gene expression regulation of U1 snRNA in human. The further study of 5, 3 and intronic sRNAs help rediscover double-stranded RNA (dsRNA) cleavage, RNA interference (RNAi) and the regulation of gene expression, which challenges the classical theories. In this study, we provided a simple and cost effective way for the annotation of mitochondrial and nuclear non-coding genes and the identification of new steady-state RNAs, particularly long non-coding RNAs (lncRNAs). We also provided a different point of view for cancer and virus, based on the new discoveries of dsRNA cleavage, RNAi and the regulation of gene expression.

bioinformatics↗

bioRxiv2018

Using QC-Blind for quality control and contamination screening of bacteria DNA sequencing data without reference genome

Quality control in next generation sequencing has become increasingly important as the technique becomes widely used. Tools have been developed for filtering possible contaminants in the sequencing data of species with known reference genome. Unfortunately, reference genomes for all the species involved, including the contaminants, are required for these tools to work. This precludes many real-life samples that have no information about the complete genome of the target species, and are contaminated with unknown microbial species.\n\nIn this work we propose QC-Blind, a novel quality control pipeline for removing contaminants without any use of reference genomes. The pipeline requires only very little information from the marker genes of the target species. The entire pipeline consists of unsupervised read assembly, contig binning, read clustering and marker gene assignment.\n\nWhen evaluated on in silico, ab initio and in vivo datasets, QC-Blind proved effective in removing unknown contaminants with high specificity and accuracy, while preserving most of the genomic information of the target bacterial species. Therefore, QC-Blind could serve well in situations where limited information is available for both target and contamination species.\n\nIMPORTANCEAt present, many sequencing projects are still performed on potentially contaminated samples, which bring into question their accuracies. However, current reference-based quality control method are limited as they need either the genome of target species or contaminations. In this work we propose QC-Blind, a novel quality control pipeline for removing contaminants without any use of reference genomes. When evaluated on in silico, ab initio and in vivo datasets, QC-Blind proved effective in removing unknown contaminants with high specificity and accuracy, while preserving most of the genomic information of the target bacterial species. Therefore, QC-Blind is suitable for real-life samples where limited information is available for both target and contamination species.

bioinformatics↗

bioRxiv2018

FT/FD-GRF5 repression loop directs growth to increase soybean yield

Major advances in crop yield are eternally needed to cope with population growth. To balance vegetative and reproductive growth plays an important role in agricultural yield. To extend vegetative phase can increase crop yield, however, this strategy risks loss of yield in the field as crops may not mature in time before winter come. Here, we identified a repression feedback loop between GmFTL/GmFDL and GmGRF5-1 (Glycine-max-Flowering-Locus-T/Glycine-max-FDL and Glycine-max-GROWTH-REGULATING-FACTOR5-1), which functions as a pivotal regulator in balancing vegetative and reproductive phases in soybean. GmFTL/GmFDL and GmGRF5-1 directly repress gene expression each other. Additionally, GmGRF5-1 enhances vegetative growth by directly enhancing expression of photosynthesis- and auxin synthesis-related genes. To modulate the loop, such as fine-tuning GmFTL expression to trade-off vegetative and reproductive growth, increases substantially soybean yield in the field. Our findings not only uncover the mechanism balancing vegetative and reproductive growth, but open a new window to improve crop yield.

plant biology↗

bioRxiv2018

Developmental heterogeneity of microglia and brain myeloid cells revealed by deep single-cell RNA sequencing

Microglia are increasingly recognized for their major contributions during brain development and neurodegenerative disease. It is currently unknown if these functions are carried out by subsets of microglia during different stages of development and adulthood or within specific brain regions. Here, we performed deep single-cell RNA sequencing (scRNA-seq) of microglia and related myeloid cells sorted from various regions of embryonic, postnatal, and adult mouse brains. We found that the majority of adult microglia with homeostatic signatures are remarkably similar in transcriptomes, regardless of brain region. By contrast, postnatal microglia represent a more heterogeneous population. We discovered that postnatal white matter-associated microglia (WAM) are strikingly different from microglia in other regions and express genes enriched in degenerative disease-associated microglia. These postnatal WAM have distinct amoeboid morphology, are metabolically active, and phagocytose newly formed oligodendrocytes. This scRNA-seq atlas will be a valuable resource for dissecting innate immune functions in health and disease.\n\nHighlightsO_LIMyeloid scRNA-seq atlas across brain regions and developmental stages\nC_LIO_LILimited transcriptomic heterogeneity of homeostatic microglia in the adult brain\nC_LIO_LIPhase-specific gene sets of proliferating microglia along cell cycle pseudotime\nC_LIO_LIPhagocytic postnatal white matter-associated microglia sharing DAM gene signatures\nC_LI

neuroscience↗

bioRxiv2018

A non-canonical metal center drives activity of the Sediminispirochaeta smaragdinae metallo-β- lactamase SPS-1

In an effort to evaluate whether a recently reported putative metallo-{beta}-lactamase (M{beta}L) contains a novel M{beta}L active site, SPS-1 from Sediminispirochaeta smaragdinae was over-expressed, purified, and characterized using spectroscopic and crystallographic studies. Metal analyses demonstrate that recombinant SPS-1 binds nearly 2 equivalents of Zn (II), and steady-state kinetic studies show that the enzyme hydrolyzes carbapenems and certain cephalosporins but not {beta}-lactam substrates with bulky substituents in the 6-7 position. Spectroscopic studies on Co (II)-substituted SPS-1 suggest a novel metal center in SPS-1, with reduced spin coupling between the metal ions and a novel Zn1 metal binding site. This site was confirmed with a crystal structure of the enzyme. The structure shows a Zn2 site that is similar that that in NDM-1 and other subclass B1 M{beta}Ls; however, the Zn1 metal ion is coordinated by 2 histidine residues and a water molecule, which is held in position by a hydrogen bond network. The Zn1 metal is displaced nearly 1 [A] from the position reported in other M{beta}Ls. The structure also shows extended helices above the active site, which create a binding pocket that precludes the binding of substrates with large, bulky substituents in the 6/7 position of {beta}-lactam antibiotics. This study reveals a novel metal binding site in M{beta}Ls, and suggests that the targeting of metal binding sites in M{beta}Ls with inhibitors is now more challenging with the identification of this new M{beta}L.

biochemistry↗

bioRxiv2018

Quantifying multi-layered expression regulation in response to stress of the endoplasmic reticulum

The mammalian response to endoplasmic reticulum (ER) stress dynamically affects all layers of gene expression regulation. We quantified transcript and protein abundance along with footprints of ribosomes and non-ribosomal proteins for thousands of genes in cervical cancer cells responding to treatment with tunicamycin or hydrogen peroxide over an eight hour time course. We identify shared and stress-specific significant regulatory events at the transcriptional and post-transcriptional level and at different phases of the experiment. ER stress regulators increase transcription and translation at different times supporting an adaptive response. ER stress also induces translation of genes from serine biosynthesis and one-carbon metabolism indicating a shift in energy production. Discordant regulation of DNA repair genes suggests transcriptional priming in which delayed translation fine-tunes the early change in the transcriptome. Finally, case studies on stress-dependent alternative splicing and protein-mRNA binding demonstrate the ability of this resource to generate hypotheses for new regulatory mechanisms.

systems biology↗

bioRxiv2017

A Neural Network Framework For The Orbitofrontal Cortex During Model-Based Reinforcement Learning

Reinforcement learning has been widely used in explaining animal behavior. In reinforcement learning, the agent learns the value of the states in the task, collectively constituting the task state space, and use the knowledge to choose actions and acquire desired outcomes. It has been proposed that the orbitofrontal cortex (OFC) encodes the task state space during reinforcement learning. However, it is not well understood how the OFC acquires and stores task state information. Here, we propose a neural network model based on reservoir computing. Reservoir networks exhibit heterogeneous and dynamic activity patterns that are suitable to encode task states. The information can be extracted by a linear readout trained with reinforcement learning. We demonstrate how the network acquires and stores the task structures. The network exhibits reinforcement learning behavior and its aspects resemble experimental findings of the OFC. Our study provides a theoretical explanation of how the OFC may contribute to reinforcement learning and a new approach to understanding the neural mechanism underlying reinforcement learning.

neuroscience↗

bioRxiv2017

Gene expression profiling reveals U1 snRNA regulates cancer gene expression

U1 small nuclear RNA (U1 snRNA), as one of the most abundant noncoding RNA in eukaryotic cells plays an important role in splicing of pre-mRNAs. Compared to other studies which have focused on the primary function of U1 snRNA and the neurodegenerative diseases caused by the abnormalities of U1 snRNA, this study is to investigate how the U1 snRNA over-expression affects the expression of genes on a genome-wide scale. In this study, we built a model of U1 snRNA over-expression in a rat cell line. By comparing the gene expression profiles of U1 snRNA over-expressed cells with those of their controls using the microarray experiments, 916 genes or loci were identified significantly differentially expressed. These 595 up-regulated genes and 321 down-regulated genes were further analyzed using the annotations from the GO terms and the KEGG database. As a result, three of 12 enriched pathways are well-known cancer pathways, while nine of them were associated to cancers in previous studies. The further analysis of 73 genes involved in 12 pathways suggests that U1 snRNA regulates cancer gene expression. The microarray data with ID GSE84304 is available in the NCBI GEO database.

cancer biology↗