Biology subjects

Zhang, C.

Publications and source records attributed to Zhang, C..

At least 991 records · Page 55Linked to original sources

bioRxiv2018

Trends and projections of universal health coverage indicators in Ghana, 1995-2030: A national and subnational study

Ghana has made significant stride towards universal health coverage (UHC) by implementing the National Health Insurance Scheme (NHIS) in 2003. This paper investigates the progress of UHC indicators in Ghana from 1995 to 2030 and makes future predictions up to 2030 to assess the probability of achieving UHC targets. National representative surveys of Ghana were used to assess health service coverage and financial risk protection. The analysis estimated the coverage of 13 prevention and four treatment service indicators at the national level and across wealth quintiles. In addition, this analysis calculated catastrophic health payments and impoverishment to assess financial hardship and used a Bayesian regression model to estimate trends and future projections as well as the probabilities of achieving UHC targets by 2030. Wealth-based inequalities and regional disparities were also assessed. At the national level, 14 out of the 17 health service indicators are projected to reach the target of 80% coverage by 2030. Across wealth quintiles, inequalities were observed amongst most indicators with richer groups obtaining more coverage than their poorer counterparts. Subnational analysis revealed while all regions will achieve the 80% coverage target with high probabilities for prevention services, the same cannot be applied to treatment services. In 2015, the proportion of households that suffered catastrophic health payments and impoverishment at a threshold of 25% non-food expenditure were 1.9% (95%CrI: 0.9-3.5) and 0.4% (95%CrI: 0.2-0.8), respectively. These are projected to reduce to less than 0.5% by 2030. Inequality measures and subnational assessment revealed that catastrophic expenditure experienced by wealth quintiles and regions are not equal. Significant improvements were seen in both health service coverage and financial risk protection as a result of NHIS. However, inequalities across wealth quintiles and at the subnational level continue to be cause of concerns. Further efforts are needed to narrow these inequality gaps.

scientific communication and education↗

bioRxiv2018

BEAST 2.5: An Advanced Software Platform for Bayesian Evolutionary Analysis

Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments.\n\nDeveloping such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.\n\nAuthor summaryBayesian phylogenetic inference methods have undergone considerable development in recent years, and joint modelling of rich evolutionary data, including genomes, phenotypes and fossil occurrences is increasingly common. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing scientific software is increasingly crucial to advancement in many fields of biology. The challenges range from practical software development and engineering, distributed team coordination, conceptual development and statistical modelling, to validation and testing. BEAST 2 is one such computational software platform for phylogenetics, population genetics and phylodynamics, and was first announced over 4 years ago. Here we describe the full range of new tools and models available on the BEAST 2.5 platform, which expand joint evolutionary inference in many new directions, especially for joint inference over multiple data types, non-tree models and complex phylodynamics.

evolutionary biology↗

bioRxiv2018

Inherited defects in natural killer cells shape tumor immune microenvironment, clinical outcome and immunotherapy response

Tumor immune microenvironment (TIME) plays an important role in metastasis and immunotherapy. However, it has been not much known how to classify TIMEs and how TIMEs are genetically regulated. Here we showed that tumors were classified into TIME-rich, -intermediate and -poor subtypes which had significant differences in clinical outcomes, abundances of tumor-infiltrating lymphocytes (TILs), the degree of key immune programs activation, and immunotherapy response across 13 common cancer types (n= [~]6,000). Furthermore, TIME-intermediate/-poor patients had significantly more inherited genetic defects (i.e., functional germline variants) in natural killer (NK) cells, antigen processing and presentation (APP) and Wnt signaling pathways than TIME-rich patients, and so did cancer patients than non-cancer individuals (n=4,500). These results suggested that individuals who had more inherited defects in NK cells, APP and Wnt pathways had a higher risk of developing cancers. Moreover, in the 13 common cancers the number of inheritably defected genes of NK cells was significantly negative-correlated with patients survival, TILs abundance in TIMEs and immunotherapy response, suggesting that inherited defects in NK cells alone were sufficient to shape TILs recruitment, clinical outcome, and immunotherapy response, highlighting that NK cell activation was required in the 13 cancer types to drive the recruitment of immune troops into TIMEs. Thus, we proposed that cancer was a disease of NK cell inherited deficiencies. These results had implications in identifying of high-risk individuals based on germline genomes, implementing precision cancer prevention by adoptive transfer of healthy NK cells, and improving existing immunotherapies by combining of adoptive NK cell transfer (i.e., converting TIME-intermediate/-poor tumors into TIME-rich tumors) and anti-PD-1 or CAR-T therapy.\n\nContactEW (edwin.wang@ucalgary.ca)

cancer biology↗

bioRxiv2018

Characterization and Variation of the Rhizosphere Fungal Community Structure of Cultivated Tetraploid Cotton

Rhizosphere fungal communities exert important influential forces on plant growth and health. However, information on the dynamics of the rhizosphere fungal community structure of the worldwide economic crop, cotton (Gossypium spp.), is limited. Next-generation sequencing of nuclear ribosomal internal transcribed spacer-1 (ITS1) was used to characterize the rhizosphere fungal communities of worldwide cultivated tetraploid cotton using G. hirsutum cv. TM-1 (upland cotton) and G. barbadense cv. Hai 7124 (island cotton). Plants were grown in field soil (FS) that had been continuously cropped with cotton and nutrient-rich soil (NS) that had not been cropped. Fungal species richness, diversity, and community composition were analyzed and compared among soil resources, cotton genotypes, and developmental stages. We found that the fungal community structure between the rhizosphere and bulk soil of cotton were different and the rhizosphere fungal communities were significantly varied between FS and NS. These results suggest that cotton rhizosphere fungal community structure variation was primarily determined by the interaction of cotton roots with different soil resources. We also found that the community composition of cotton rhizosphere fungi varied significantly during different developmental stages, suggesting that developmental stages were also important factors in the dynamics of rhizosphere fungal communities for the varying dominant fungal genera of the rhizosphere. In addition, we also observed that fungal pathogens were clearly increased at certain developmental stages, suggesting a higher infection rate and a high incidence of corresponding soil-borne disease in each stage. This research illustrates the characteristics of cotton rhizosphere fungal communities and provides important information for understanding the potential influences of rhizosphere fungal communities on cotton growth and health.

ecology↗

bioRxiv2018

Hypothalamic transcriptome analysis reveals the neuroendocrine mechanisms in controlling broodiness of Muscovy duck (Cairina moschata)

Broodiness, one of the maternal behaviors and instincts for natural breeding in birds, is an interesting topic in reproductive biology. Broodiness in poultry is characterized by persistent nesting, usually associated with cessation of egg laying. The study of avian broodiness is essential for bird conservation breeding and commercial poultry industry. In this study, we examined the hypothalamus transcriptome of Muscovy duck in three reproductive stages, including egg-laying anaphase (LA), brooding prophase (BP) and brooding metaphase (BM). Differences in gene expression during the transition from egg-laying to broodiness were examined, and 155, 379, 292 differently expressed genes (DEGs) were obtained by pairwise comparisons of LA-vs-BP, LA-vs-BM and BP-vs-BM, respectively (fold change[≥] 1.5, P < 0.05). Gene Ontology Term (GO) enrichment analysis suggested a possible role of oxidative stress in the hypothalamus might invoke reproductive costs that potentially change genes expression. KEGG analysis revealed glutamatergic synapse, dopaminergic synapse, serotonergic synapse and GABAergic synapse pathway were significantly enriched, and regulator genes were identified. Eight gene expression patterns were illustrated by trend analysis and further clustered into three clusters. Additional six hub genes were identified through combining trend analysis and protein-protein interaction (PPI) analysis. Our results suggested that the cyclical mechanisms of reproductive function conversion include effects of oxidative stress, biosynthesis of neurotransmitters or their receptors, and interactions between glucocorticoids and thyroid hormones and regulatory genes. These candidate genes and biological pathways may be used as targets for artificial manipulation and marker-assisted breeding in the reproductive behavior.

animal behavior and cognition↗

bioRxiv2018

A genetically encoded fluorescent sensor for rapid and specific in vivo detection ofnorepinephrine

Norepinephrine (NE) and epinephrine (Epi), two key biogenic monoamine neurotransmitters, are involved in a wide range of physiological processes. However, their precise dynamics and regulation remain poorly characterized, in part due to limitations of available techniques for measuring these molecules in vivo. Here, we developed a family of GPCR Activation-Based NE/Epi (GRABNE) sensors with a 230% peak {Delta}F/F0 response to NE, good photostability, nanomolar-to-micromolar sensitivities, sub-second rapid kinetics, high specificity to NE vs. dopamine. Viral- or transgenic- mediated expression of GRABNE sensors were able to detect electrical-stimulation evoked NE release in the locus coeruleus (LC) of mouse brain slices, looming-evoked NE release in the midbrain of live zebrafish, as well as optogenetically and behaviorally triggered NE release in the LC and hypothalamus of freely moving mice. Thus, GRABNE sensors are a robust tool for rapid and specific monitoring of in vivo NE/Epi transmission in both physiological and pathological processes.

neuroscience↗

bioRxiv2018

Revealing S-layer Functions in the Hyperthermophilic Crenarchaeon Sulfolobus islandicus

The crystalline surface layer (S-layer), consisting of two glycoproteins SlaA and SlaB, is considered to be the exclusive component of the cell envelope outside of the cytoplasmic membrane in Sulfolobus species. Although biochemically and structurally characterized, the S-layer in vivo functions remain largely elusive in Archaea. Here, we investigate how the S-layer genes contribute to the S-layer architecture and affect cellular physiology in a crenarchaeal model, Sulfolobus islandicus M.16.4. Electron micrographs of mutant cells lacking slaA or both slaA and slaB confirm the absence of the outermost layer (SlaA), whereas cells with intact, partially, or completely detached SlaA are observed for the {triangleup}slaB mutant. Importantly, we identify a novel S-layer-associated protein M164_1049, which does not functionally replace its homolog SlaB but likely assists SlaB to stabilize SlaA. Additionally, we find that mutants deficient in SlaA form large cell aggregates and the individual cell size varies significantly. The slaB gene deletion also causes noticeable cellular aggregation, but the size of those aggregates is smaller when compared to {triangleup}slaA and {triangleup}slaAB mutants. We further show the {triangleup}slaA mutant cells exhibit more sensitivity to hyperosmotic stress but are not reduced to wild-type cell size. Finally, we demonstrate that the {triangleup}slaA mutant contains aberrant chromosome copy numbers not seen in wild-type cells where the cell cycle is tightly regulated. Together these data suggest that the lack of slaA results in either cell fusion or irregularities in cell division. Our studies provide novel insights into the physiological and cellular functions of the S-layer in Archaea.\n\nSignificanceRediscovery of the ancient evolutionary relationship between archaea and eukaryotes has revitalized interest in archaeal cell biology. Key to understanding the archaeal cell is the S-layer which is ubiquitous in Archaea but whose in vivo function is unknown. In this study, we genetically dissect how the two well-known S-layer genes as well as a newly identified S-layer-associated-protein-encoding gene contribute to the S-layer architecture in a hyperthermophilic crenarchaeal model S. islandicus. We provide genetic evidence for the first time showing that the slaA gene is a key cell morphology determinant and may play a role in Sulfolobus cell division or cell fusion.

microbiology↗

bioRxiv2018

A Simulation-Based Evaluation of Total-Evidence Dating Under the Fossilized Birth-Death Process

Bayesian molecular dating is widely used to study evolutionary timescales. This procedure usually involves phylogenetic analysis of nucleotide sequence data, with fossil-based calibrations applied as age constraints on internal nodes of the tree. An alternative approach is Bayesian total-evidence dating, which involves the joint analysis of molecular data from present-day taxa and morphological data from both extant and fossil taxa. Part of its appeal stems from the fossilized birth-death process, which provides a model of lineage diversification for the prior on the tree topology and node times. However, total-evidence dating faces a number of considerable challenges, especially those associated with fossil sampling and evolutionary models for morphological characters. We conducted a simulation study to evaluate the performance of total-evidence dating with the fossilized birth-death model. We simulated fossil occurrences and the evolution of nucleotide sequences and morphological characters under a wide range of conditions. Our analyses show that fossil occurrences have a greater influence than the degree of among-lineage rate variation or the number of morphological characters on estimates of node times and the tree topology. Total-evidence dating generally performs well in recovering the relationships among extant taxa, but has difficulties in correctly placing fossil taxa in the tree and identifying the number of sampled ancestors. The method yields accurate estimates of the origin time of the fossilized birth-death process and the ages of the root and crown group, although the precision of these estimates varies with the probability of fossil occurrence. The exclusion of morphological characters results in a slight overestimation of node times, whereas the exclusion of nucleotide sequences has a negative impact on inference of the tree topology. Overall, our results provide a detailed view of the performance of total-evidence dating, which will inform further development of the method and its application to key questions in evolutionary biology.

evolutionary biology↗

bioRxiv2018

LTMG (Left truncated mixture Gaussian) based modeling of transcriptional regulatory heterogeneities in single cell RNA-seq data - a perspective from the kinetics of mRNA metabolism

A key challenge in modeling single-cell RNA-seq (scRNA-seq) data is to capture the diverse gene expression states regulated by different transcriptional regulatory inputs across single cells, which is further complicated by a large number of observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model that stems from the kinetic relationships between the transcriptional regulatory inputs and metabolism of mRNA and gene expression abundance in a cell. LTMG infers the expression multi-modalities across single cell entities, representing a genes diverse expression states; meanwhile the dropouts and low expressions are treated as left truncated, specifically representing an expression state that is under suppression. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of single-cell data sets, comparing to three other state of the art models. In addition, our systems kinetic approach of handling the low and zero expressions and correctness of the identified multimodality are validated on several independent experimental data sets. Application on data of complex tissues demonstrated the capability of LTMG in extracting varied expression states specific to cell types or cell functions. Based on LTMG, a differential gene expression test and a co-regulation module identification method, namely LTMG-DGE and LTMG-GCR, are further developed. We experimentally validated that LTMG-DGE is equipped with higher sensitivity and specificity in detecting differentially expressed genes, compared with other five popular methods, and that LTMG-GCR is capable to retrieve the gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

bioinformatics↗

bioRxiv2018

AhRLK1, a CLAVATA1-like leucine-rich repeat receptor-like kinase of peanut, confers increased resistance to bacterial wilt in tobacco

Bacterial wilt caused by Ralstonia solanacearum is a devastating disease that infects hundreds of plant species. Host factors involved in disease resistance and pathogenesis remain poorly characterized. An up regulated and leucine-rich repeat receptor-like kinase characterized as CLAVATA1 and named AhRLK1 was obtained by microarray analysis in response to R. solanacearum in peanut. AhRLK1 contained presumably, a signal peptide, ten leucine-rich repeat (LRR) domains and conserved motifs of intracellular kinases. For subcellular localization, the AhRLK1 protein was visualized only in the plasma membrane. After inoculation with R. solanacearum, AhRLK1 was constantly up regulated in the susceptible variety Xinhuixiaoli but showed little changed in the resistant cultivar Yueyou92. Different hormones, including salicylic acid, abscisic acid, methyl jasmonate, and ethephon, induced expression, but expression was completely down regulated under cold and drought treatments. Transient overexpression provoked a hypersensitive response (HR) in Nicotiana benthamiana following agro-infiltration. Furthermore, in transgenic tobacco with overexpression of the gene, the resistance to R. solanacearum increased significantly. By contrast, most representative defense-responsive genes in HR, SA, JA and ET signal pathways such as NtHIN1, NtPR2, NtLOX1, and NtACS6, among others, were considerably up regulated in the AhRLK1 transgenic lines. Additionally, the EDS1 and PAD4 in the R gene signal were also up regulated in transgenic plants, but the NDR1 and NPR1 genes were down regulated. Accordingly, we suggest that AhRLK1 increases the defense response to R. solanacearum via HR and hormone defense signalling, associated with the EDS1 pathway of R gene signalling. The results provide new understanding of CLV1 function and will contribute to genetic enhancement of peanut.

plant biology↗

bioRxiv2018

ICTD: Inference of cell types and deconvolution -- a next-generation deconvolution method for accurate assess cell population and activities in tumor microenvironment.

We developed a novel deconvolution method, namely Inference of Cell Types and Deconvolution (ICTD) that addresses the fundamental issue of identifiability and robustness in current tissue data deconvolution problem. ICTD provides substantially new capabilities for omics data based characterization of a tissue microenvironment, including (1) maximizing the resolution in identifying resident cell and sub types that truly exists in a tissue, (2) identifying the most reliable marker genes for each cell type, which are tissue and data set specific, (3) handling the stability problem with co-linear cell types, (4) co-deconvoluting with available matched multi-omics data, and (5) inferring functional variations specific to one or several cell types. ICTD is empowered by (i) rigorously derived mathematical conditions of identifiable cell type and cell type specific functions in tissue transcriptomics data and (ii) a semi supervised approach to maximize the knowledge transfer of cell type and functional marker genes identified in single cell or bulk cell data in the analysis of tissue data, and (iii) a novel unsupervised approach to minimize the bias brought by training data. Application of ICTD on real and single cell simulated tissue data validated that the method has consistently good performance for tissue data coming from different species, tissue microenvironments, and experimental platforms. Other than the new capabilities, ICTD outperformed other state-of-the-art devolution methods on prediction accuracy, the resolution of identifiable cell, detection of unknown sub cell types, and assessment of cell type specific functions. The premise of ICTD also lies in characterizing cell-cell interactions and discovering cell types and prognostic markers that are predictive of clinical outcomes.

bioinformatics↗

bioRxiv2018

Modeling RNA-binding protein specificity in vivo by precisely registering protein-RNA crosslink sites

RNA-binding proteins (RBPs) regulate post-transcriptional gene expression by recognizing short and degenerate sequence elements in their target transcripts. Despite the expanding list of RBPs with in vivo binding sites mapped genomewide using crosslinking and immunoprecipitation (CLIP), defining precise RBP binding specificity remains challenging. We previously demonstrated that the exact protein-RNA crosslink sites can be mapped using CLIP data at single-nucleotide resolution and observed that crosslinking frequently occurs at specific positions in RBP motifs. Here we have developed a computational method, named mCross, to jointly model RBP binding specificity while precisely registering the crosslinking position in motif sites. We applied mCross to 112 RBPs using ENCODE eCLIP data and validated the reliability of the resulting motifs by genome-wide analysis of allelic binding sites also detected by CLIP. We found that the prototypical SR protein SRSF1 recognizes GGA clusters to regulate splicing in a much larger repertoire of transcripts than previously appreciated.

molecular biology↗

bioRxiv2018

Gut Microbiota in male patients with chronic traumatic complete spinal cord injury

This study examined the diversity and structure of gut microbiota in healthy adults and chronic traumatic complete spinal cord injury (SCI) patients, documented neurogenic bowel management of SCI patients. The V3-V4 region of 16S rRNA gene from DNA of 91 fecal samples of 48 healthy and 43 diseased subjects was amplified and sequenced. There was difference in gut microbiota between healthy adult males and females. Neurogenic bowel dysfunction (NBD) was common in patients with chronic traumatic complete SCI, patients with quadriplegia have longer time to defecate than paraplegic patients, with higher NBD scores and heavier neurogenic bowel symptoms. Gut microbiota dysbiosis existed in SCI patients. The abundance of Veillonellaceae and Prevotellaceae increased while Bacteroidaceae and Bacteroides decreased in SCI group. The abundance of Bacteroidaceae, Bacteroides in quadriplegia group and Acidaminococcaceae, Blautia in paraplegia group were significant high than the health male group. Serum biomarkers GLU, HDL, CR and NBD symptoms defecation time, COURSE had significant correlation with microbial community structure. This study presents a comprehensive landscape of gut microbiota in adult male patients with chronic traumatic complete SCI and documents their neurogenic bowel management. The gut microbiota dysbiosis of SCI patients was correlation with serum biomarkers and NBD symptoms.\n\nIMPORTANCENeurogenic bowel dysfunction is a major physical and psychological problem in patients with spinal cord injury, which can seriously affect the quality life of them. Gut dysbiosis are highly likely to occur in spinal cord injury patients There are few studies on intestinal microecology after spinal cord injury, and the clinical studies are fewer. It is importance to document their neurogenic bowel management and present a landscape of gut microbiota in them. We found the gut microbiota dysbiosis of spinal cord injury patients was correlation with serum biomarkers and neurogenic bowel dysfunction symptoms. These results may have implications in the next study about metagenomics and precision treatment of neurogenic bowel dysfunction in spinal cord injury patients.

neuroscience↗

bioRxiv2018

Degradation of the extracellular matrix is part of the pathology of ulcerative colitis

The scientific value of re-analyzing existing datasets is often proportional to the complexity of the data. Proteomics data are inherently complex and can be analyzed at many levels, including proteins, peptides, and post-translational modifications to verify and/or develop new hypotheses. In this paper, we present our re-analysis of a previously published study comparing colon biopsy samples from ulcerative colitis (UC) patients to non-affected controls. In addition to confirming and reinforcing the original finding of upregulation of neutrophil extracellular traps (NETs), we report novel findings, including that Extracellular Matrix (ECM) degradation and neutrophil maturation are involved in the pathology of UC. The pharmaceutically most relevant differential protein expressions were confirmed using immunohistochemistry as an orthogonal method. As part of this study, we also compared proteomics data to previously published mRNA expression data. These comparisons indicated compensatory regulation at transcription levels of the ECM proteins we identified and open possible new venues for drug discovery.

molecular biology↗

bioRxiv2018

QUBIC2: A novel biclustering algorithm for large-scale bulk RNA-sequencing and single-cell RNA-sequencing data analysis

The combination of biclustering and large-scale gene expression data holds a promising potential for inference of the condition specific functional pathways/networks. However, existing biclustering tools do not have satisfied performance on high-resolution RNA-sequencing (RNA-Seq) data, majorly due to the lack of (i) a consideration of high sparsity of RNA-Seq data, e.g., the massive zeros or lowly expressed genes in the data, especially for single-cell RNA-Seq (scRNA-Seq) data, and (ii) an understanding of the underlying transcriptional regulation signals of the observed gene expression values. Here we presented a novel biclustering algorithm namely QUBIC2, for the analysis of large-scale bulk RNA-Seq and scRNA-Seq data. Key novelties of the algorithm include (i) used a truncated model to handle the unreliable quantification of genes with low or moderate expression, (ii) adopted the mixture Gaussian distribution and an information-divergency objective function to capture shared transcriptional regulation signals among a set of genes, (iii) utilized a Core-Dual strategy to identify biclusters and optimize relevant parameters, and (iv) developed a size-based P-value framework to evaluate the statistical significances of all the identified biclusters. Our method validation on comprehensive data sets of bulk and single cell RNA-seq data suggests that QUBIC2 had superior performance in functional modules detection and cell type classification compared with the other five widely-used biclustering tools. In addition, the applications of temporal and spatial data demonstrated that QUBIC2 can derive meaningful biological information from scRNA-Seq data. The source code for QUBIC2 can be freely accessed at https://github.com/maqin2001/qubic2.

bioinformatics↗

bioRxiv2018

The essential genome of the crenarchaeal model Sulfolobus islandicus

Sulfolobus islandicus is a model experimental system in the TACK superphylum of the Archaea, a key lineage in the evolutionary history of cell biology. Here we report a genome-wide identification of the repertoire of genes essential to S. islandicus growth in culture. We confirm previous targeted gene knockouts, uncover the non-essentiality of functions assumed to be essential to the Sulfolobus cell, including the proteinaceous S-layer, and highlight key essential genes whose functions are yet to be determined. Phyletic distributions illustrate the potential transitions that have occurred during the evolution of this contemporary archaeal cell and highlight the sets of genes that may have been associated with each transition. We use this comparative context as a lens to focus future research on archaea-specific uncharacterized essential genes for which future functional data would provide valuable insights into the evolutionary history of the contemporary cell.

microbiology↗

bioRxiv2018

Inhibition of HtrA2 alleviated colitis by preventing necroptosis of intestinal epithelial cells

Necroptosis of intestinal epithelial cells has been indicated to play an important role in the pathogenesis of inflammatory bowel disease (IBD). The identification of dysregulated proteins that can regulate necroptosis in dextran sulfate sodium (DSS)-induced colitis is the key to the rational design of therapeutic strategies for colitis. Through Tandem Mass Tag (TMT)-based quantitative proteomics, HtrA2 was found to be downregulated in the colon of DSS-treated mice. UCF-101, a specific serine protease inhibitor of HtrA2, significantly alleviated DSS-induced colitis as indicated by prevention of body weight loss and decreased mortality. UCF-101 decreased DSS-induced colonic inflammation, prevented intestinal barrier function loss and inhibited necroptosis of intestinal epithelial cells. In vitro, UCF-101 or silencing of HtrA2 decreased necroptosis of HT-29 and L929 cells. UCF-101 decreased phosphorylation of RIPK1 and subsequent phosphorylation of RIPK3 and MLKL during necroptosis. HtrA2 directly interacted with RIPK1 and promoted its degradation during a specific time phase of necroptosis. Our findings highlight the importance of HtrA2 in regulating colitis by modulation of necroptosis and suggest HtrA2 as an attractive target for anti-colitis treatment.

molecular biology↗

bioRxiv2018

Generation of Highly Diverse Peptide Library by Linear-double-stranded DNA Based AND Logic Computation in Mammalian Cells

DNA-encoded peptide libraries are ideal functional peptide discovery platforms for their extremely large capacity. However, its still difficult to build high content peptide library in intact mammalian cells, which offer advantages associated with appropriate protein modification, proper protein folding, and natural status of membrane protein. Our previous work established linear-double-stranded DNAs (ldsDNAs) as innovative biological parts to implement AND gate genetic circuits in mammalian cell line. In the current study, we employ ldsDNA with terminal NNK degenerate codons as AND gate input to build highly diverse peptide library in mammalian cells. This ldsDNA-based AND gate (LBAG) peptide strategy is easy to conduct, only PCR reaction and cell transfection experiments are needed. High-throughput sequencing (HTS) results reveal that our new LBAG strategy could generate peptide library with both amino acid sequence and peptide length diversities. Moreover, by a mammalian cell two-hybrid system, we pan an MDM2 protein interacting peptide through the LBAG peptide library. Our work establishes ldsDNA as biological parts for building highly diverse peptide library in mammalian cells.

bioengineering↗