Biology subjects

Yu, R.

Publications and source records attributed to Yu, R..

85 records · Page 5Linked to original sources

bioRxiv2020

A Single Chromosome Strain of S. cerevisiae Exhibits Diminished Ethanol Metabolism and Tolerance.

This study characterized the growth, metabolism, and transcriptional profile of a S. cerevisiae strain with a single large chromosome that was constructed via successive chromosomal fusions. The single chromosome strain exhibited a longer lag phase, increased doubling time, and lower final biomass concentration compared with a wildtype strain when grown on YPD. These phenotypes were amplified when ethanol was added to the medium or used as the sole carbon source. RNAseq analysis showed diminished induction of genes involved in diauxic shift, ethanol metabolism, fatty-acid {beta}-oxidation, and methylglyoxal catabolism during growth on ethanol compared to the reference strain. Enzyme-constrained metabolic modeling predicted that decreased flux through these poorly induced enzymes results in diminished ATP formation and decreased biomass accumulation observed. Together, these observations suggest that switch-like control of carbon source dependent gene expression in S. cerevisiae requires genome arrangement into multiple chromosomes.

systems biology↗

bioRxiv2020

Impacts of Crenarchaeota and Halobacterota on sulfate reduction in the subtropical mangrove ecosystem as revealed by SMDB analysis

Sulfate reduction is an important process in the sulfur cycle. However, the relationship between this process and the genotype of microorganisms involved in subtropical mangrove ecosystems is poorly understood. A dedicated and efficient gene integration database of sulfur metabolism has not been established yet. In this study, a sulfur metabolism gene integrative database (SMDB) had been constructed successfully. The database achieved high coverage, fast retrieval, and low false positives. Then the sulfate reduction by microorganisms in subtropical mangroves ecosystem had been evaluated quickly and accurately with SMDB database. Due to the environmental factors, dissimilatory sulfite reductase was significantly high in mangrove samples. Sulfide, Fe, and available sulfur were found to be the key environmental factors that influence the dissimilatory sulfate reduction, in which sulfur compounds could be utilized as potential energy sources for microorganism. Taxonomic assignment of dissimilatory sulfate-reduction genes revealed that Desulfobacterales and Chromatiales are completely responsible for this process. Sulfite reductase can help the community cope with the toxic sulfite produced by these Bacteria order. Collectively, these findings demonstrated that Desulfobacterales and Chromatiales play essential roles in dissimilatory sulfate reduction.

bioinformatics↗

bioRxiv2020

Snipe: Highly sensitive pathogen detection from metagenomic sequencing data

Metagenomics data provides rich information for the detection of foodborne pathogens from food and environmental samples that are mixed with complex background bacteria strains. While pathogen detection from metagenomic sequencing data has become an activity of increasing interest, shotgun sequencing of uncultured food samples typically produces data that contains reads from many different organisms, making accurate strain typing a challenging task. Particularly, as many pathogens may contain a common set of genes that are highly similar to those from normal bacteria in food samples, traditional strain-level abundance profiling approaches do not perform well at detecting pathogens of very low abundance levels. To overcome this limitation, we propose an abundance correction method based on species-specific genomic regions to achieve high sensitivity and high specificity in target pathogen detection at low abundance.

microbiology↗

bioRxiv2020

Ratio of the interferon-γ signature to the immunosuppression signature predicts anti-PD-1 therapy response in melanoma

Immune checkpoint inhibitor (ICI) treatments produce clinical benefit in many patients. However, better pretreatment predictive biomarkers for ICI are still needed to help match individual patients to the treatment most likely to be of benefit. Existing gene expression profiling (GEP)-based biomarkers for ICI are primarily focused on measuring a T cell-inflamed tumour microenvironment that contributes positively to the response to ICI. Here, we identified an immunosuppression signature (IMS) through analysing RNA sequencing data from a combined discovery cohort (n = 120) consisting of three publicly available melanoma datasets. Using the ratio of an established IFN-{gamma} signature and IMS led to consistently better prediction of the ICI therapy outcome compared to a collection of nine published GEP signatures from the literature on a newly generated internal validation cohort (n = 55) and three published datasets of metastatic melanoma treated with anti-PD-1 (n = 48) and anti-CTLA-4 (n = 42) as well as in patients with gastric cancer treated with antiPD-1 (n = 45), demonstrating the potential utility of IMS as a predictive/prognostic biomarker that complements existing GEP signatures for immunotherapy.

immunology↗

bioRxiv2020

DAISM-DNN: Highly accurate cell type proportion estimation with in silico data augmentation and deep neural networks

Understanding the immune cell abundance of cancer and other disease-related tissues has an important role in guiding disease treatments. Computational cell type proportion estimation methods have been previously developed to derive such information from bulk RNA sequencing (RNA-seq) data. Unfortunately, our results show that the performance of these methods can be seriously plagued by the mismatch between training data and real-world data. To tackle this issue, we propose the DAISM-DNNXMBD1 pipeline that trains a deep neural network (DNN) with dataset-specific training data populated from a small number of calibrated samples using DAISM, a novel Data Augmentation method with an In Silico Mixing strategy. The evaluation results demonstrate that the DAISM-DNN pipeline outperforms other existing methods consistently and substantially for all the cell types under evaluation on real-world datasets.

bioinformatics↗

bioRxiv2020

Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae

Protein quantification via label-free mass spectrometry (MS) has become an increasingly popular method for determining genome-wide absolute protein abundances. A known caveat of this approach is the poor technical reproducibility, i.e. how consistent the estimations are when the same sample is measured repeatedly. Here, we measured proteomics data for Saccharomyces cerevisiae with both biological and inter-batch technical triplicates, to analyze both accuracy and precision of protein quantification via MS. Moreover, we analyzed how these metrics vary when applying different methods for converting MS intensities to absolute protein abundances. We found that a simple normalization and rescaling approach performs as accurately yet more precisely than methods that rely on external standards. Additionally, we show that inter-batch reproducibility is worse than biological reproducibility for all evaluated methods. These results subsequently serve as a benchmark for assessing MS data quality for protein quantification, whilst also underscoring current limitations in this approach.

bioinformatics↗

bioRxiv2020

ScaleQC: A Scalable Lossy to Lossless Solution for NGS Sequencing Data Compression

MotivationPer-base quality values in NGS sequencing data take a significant portion of storage even after compression. Lossy compression technologies could further reduce the space used by quality values. However, in many applications lossless compression is still desired. Hence, sequencing data in multiple file formats have to be prepared for different applications. ResultsWe developed a scalable lossy to lossless compression solution for quality values named ScaleQC. ScaleQC is able to provide bit-stream level scalability. More specifically, the losslessly compressed bit-stream by ScaleQC can be further truncated to lower data rates without re-encoding. Despite its scalability, ScaleQC still achieves same or better compression performance at both lossless and lossy data rates compared to the state-of-the-art lossless or lossy compressors. AvailabilityScaleQC has been integrated with SAMtools as a special quality value encoding mode for CRAM. Its source codes can be obtained from our integrated SAMtools (https://github.com/xmuyulab/samtools) with dependency on integrated HTSlib (https://github.com/xmuyulab/htslib).

bioinformatics↗

bioRxiv2020

Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines

With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. How-ever, it remains unclear whether such integrated analysis would be biased if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performances in terms of running time, computational resource consumption, and data processing consistency using nine public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performances on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

bioinformatics↗

bioRxiv2019

Complementary Performances of Convolutional and Capsule Neural Networks on Classifying Microfluidic Images of Dividing Yeast Cells

Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapsed images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had the most robust performance at detecting one specific category of cell images. An ensemble of three best-fitted single-architecture models achieves the highest overall accuracy, precision, and recall due to complementary performances. In addition, extending classification classes and augmentation of the training dataset can improve the predictions of the biological categories in our study. This work lays a useful framework for sophisticated deep-learning processing of microfluidics-based assays of yeast replicative aging.

bioinformatics↗

bioRxiv2019

Plasma non-esterified fatty acids contribute to increased coagulability in type-2 diabetes through altered plasma zinc speciation

Zn2+ is an essential regulator of coagulation and its availability in plasma is fine-tuned through buffering by human serum albumin (HSA). Non-esterified fatty acids (NEFAs) transported by HSA reduce its ability to bind/buffer Zn2+. This is important as plasma NEFA levels are elevated in type-2 diabetes mellitus (T2DM) and other diseases with an increased risk of developing thrombotic complications. The presence of 5 mol. eq. of myristate, palmitate, stearate, palmitoleate and palmitelaidate reduced Zn2+ binding to HSA. Addition of myristate and Zn2+ increased thrombin-induced platelet aggregation in platelet-rich plasma and increased fibrin clot density and clot time in a purified protein system. The concentrations of key saturated (myristate, palmitate, stearate) and monounsaturated (oleate, vaccinate) NEFAs positively correlated with clot density in subjects with T2DM (and controls). Collectively, these data strongly support the concept that elevated NEFA levels contribute to an increased thrombotic risk in T2DM through dysregulation of plasma zinc speciation.

biochemistry↗

bioRxiv2019

The effects of biodegradable mulch film on the growth, yield, and water use efficiency of cotton and maize in an arid region

Plastic residual film pollution in China is serious, and the use of degradable mulch film instead of plastic mulch can effectively alleviate this situation. The substitution of common polyethylene plastic mulch film with biodegradable mulch film in the agricultural production of cotton and maize in an arid region was investigated in the present study. Using bare soil as the control, we compared the effects of common polyethylene plastic film and biodegradable mulch film on crop growth, yield, and water use efficiency (WUE) in maize and cotton. The results indicated that: (1) the biodegradable mulch film in this region remained intact for 60 days after being laid down, significantly degrading after 120 days, and was associated with increased soil temperature, moisture conservation, and degradability in comparison to a bare soil control. (2) Both the biodegradable mulch film and the polyethylene plastic film significantly increased various physiological parameters, such as crop height, stalk diameter, and leaf area. (3) The biodegradable mulch film significantly increased maize and cotton crop yield by 69.4-76.2% and 65.2-71.9%, respectively, compared to the bare soil control. (4) Compared to the bare soil control, the biodegradable mulch film effectively increased WUE in the crops by 64.5-73.1%. In summary, biodegradable mulch film had comparable results to the common polyethylene plastic film in increasing soil temperature, moisture conservation, crop growth, yield, and WUE. As the biodegradable mulch film causes no residual pollution, it is thus preferable to common plastic mulch film for agricultural applications in arid regions and supports the sustainable development of agroecosystems.

ecology↗

bioRxiv2019

Rapid discovery of synthetic DNA sequences to rewrite endogenous T cell circuits

Genetically-engineered immune cell therapies have been in development for decades1-3 and recently have proven effective to treat some types of cancer4. CRISPR-based genome editing methods, enabling more flexible and targeted sequence integrations than viral transduction, have the potential to extend the clinical utility of cell therapies5,6. Realization of this potential depends on improved knowledge of how coding and non-coding sites throughout the genome can be modified efficiently and on improved methods to discover novel synthetic DNA sequences that can be introduced at targeted sites to enhance critical immune cell functions. Here, we developed improved guidelines for non-viral genome targeting in human T cells and a pooled discovery platform to identify synthetic genome modifications that enhance therapeutically-relevant cell functions. We demonstrated the breadth of targetable genomic loci by performing large knock-ins at 91 different genomic sites in primary human T cells, and established the power of flexible genome targeting by generating cells with Genetically Engineered Endogenous Proteins (GEEPs) that seamlessly integrate synthetic and endogenous genetic elements to alter signaling input, output, or regulatory control of genes encoding key immune receptors. Motivated by success in introducing synthetic circuits into endogenous sites, we then developed a platform to facilitate discovery of novel multi-gene sequences that reprogram both T cell specificity and function. We knocked in barcoded pools of large DNA sequences encoding polycistronic gene programs. High-throughput pooled screening of targeted knock-ins to the endogenous T cell receptor (TCR) locus revealed a transcriptional regulator and novel protein chimeras that combined with a new TCR specificity to enhance T cell responses in the presence of suppressive conditions in vitro and in vivo. Overall, these pre-clinical studies provide flexible tools to discover complex synthetic gene programs that can be written into targeted genome sites to generate more effective therapeutic cells.

immunology↗

bioRxiv2017

Correction of autoimmune IL2RA mutations in primary human T cells using non-viral genome targeting

Human T cells are central to physiological immune homeostasis, which protects us from pathogens without collateral autoimmune inflammation. They are also the main effectors in most current cancer immunotherapy strategies1. Several decades of work have aimed to genetically reprogram T cells for therapeutic purposes2-5, but as human T cells are resistant to most standard methods of large DNA insertion these approaches have relied on recombinant viral vectors, which do not target transgenes to specific genomic sites6, 7. In addition, the need for viral vectors has slowed down research and clinical use as their manufacturing and testing is lengthy and expensive. Genome editing brought the promise of specific and efficient insertion of large transgenes into target cells through homology-directed repair (HDR), but to date in human T cells this still requires viral transduction8, 9. Here, we developed a non-viral, CRISPR-Cas9 genome targeting system that permits the rapid and efficient insertion of individual or multiplexed large (>1 kilobase) DNA sequences at specific sites in the genomes of primary human T cells while preserving cell viability and function. We successfully tested the potential therapeutic use of this approach in two settings. First, we corrected a pathogenic IL2RA mutation in primary T cells from multiple family members with monogenic autoimmune disease and demonstrated enhanced signalling function. Second, we replaced the endogenous T cell receptor (TCR) locus with a new TCR redirecting T cells to a cancer antigen. The resulting TCR-engineered T cells specifically recognized the tumour antigen, with concomitant cytokine release and tumour cell killing. Taken together, these studies provide preclinical evidence that non-viral genome targeting will enable rapid and flexible experimental manipulation and therapeutic engineering of primary human immune cells.

genetics↗