Study on imbibition during the CO2 enhanced oil recovery in fractured tight sandstone reservoirs

CO2 enhanced oil recovery (CO2-EOR) is a key technology for improving the oil recovery of fractured tight reservoirs, and imbibition has been recognized as an important mechanism for oil recovery in low-permeability reservoirs. To clarify the imbibition role and influencing factors during the CO2-EOR process in fractured tight oil reservoirs and also improve the EOR mechanism, a high-temperature and high-pressure CO2 imbibition experiment was performed based on the nuclear magnetic resonance technology. The results show that high pressure and high permeability are beneficial to imbibition efficiency. The salinity of the imbibition fluid is not very sensitive to the imbibition recovery. In addition, the CO2 increases the imbibition speed and can also significantly improve the production rate and oil recovery. It is beneficial to increase the CO2 concentration to shorten the imbibition equilibrium time and enhance oil recovery. According to the results of the nuclear magnetic resonance study, although the nanopore can provide a greater imbibition force, the oil flow resistance is also larger, but CO2 can reduce the flow resistance of oil and be conducive to oil production in smaller pores. The inclusion of imbibition into the research category of CO2-EOR mechanism will be more in line with field practice and more scientific in fractured tight reservoirs, thus providing theoretical support for the development and improvement of the CO2-EOR technology.Document Type: Original articleCited as: Wang, Y., Shang, Q., Guo, J., Zhou, L. Study on imbibition during the CO2 enhanced oil recovery in fractured tight sandstone reservoirs. Capillarity, 2023, 7(3): 47-56. https://doi.org/10.46690/capi.2023.06.0

Draft genome sequence of the mulberry tree Morus notabilis

Human utilization of the mulberry–silkworm interaction started at least 5,000 years ago and greatly influenced world history through the Silk Road. Complementing the silkworm genome sequence, here we describe the genome of a mulberry species Morus notabilis. In the 330-Mb genome assembly, we identify 128 Mb of repetitive sequences and 29,338 genes, 60.8% of which are supported by transcriptome sequencing. Mulberry gene sequences appear to evolve ~3 times faster than other Rosales, perhaps facilitating the species’ spread worldwide. The mulberry tree is among a few eudicots but several Rosales that have not preserved genome duplications in more than 100 million years; however, a neopolyploid series found in the mulberry tree and several others suggest that new duplications may confer benefits. Five predicted mulberry miRNAs are found in the haemolymph and silk glands of the silkworm, suggesting interactions at molecular levels in the plant–herbivore relationship. The identification and analyses of mulberry genes involved in diversifying selection, resistance and protease inhibitor expressed in the laticifers will accelerate the improvement of mulberry plants

Identification of the mulberry genes involved in ethylene biosynthesis and signaling pathways and the expression of MaERF-B2-1 and MaERF-B2-2 in the response to flooding stress

The phytohormone ethylene is essential to plant growth and development. It plays crucial roles in responses to biotic and abiotic stress. The mulberry tree is an important crop plant in countries in which people rear silkworms for silk production. The availability of the mulberry genome has made it possible to identify mulberry genes involved in ethylene biosynthesis and signal pathways. A total of 145 mulberry genes were identified by both homology-based and hidden Markov model (HMM) search, including 29 genes associated with ethylene biosynthesis and 116 genes in the AP2/ERF family. Studies on gene structure have provided a genetic basis for understanding the functions of these genes. The differences in gene expression were also observed in different tissues. The expression of two mulberry genes in the AP2/ERF family, MaERF-B2-1 and MaERF-B2-2, was found to be associated with the response to flooding stress. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10142-014-0403-2) contains supplementary material, which is available to authorized users

DataSheet_1_sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq.docx

Accurately identifying immune cell types in single-cell RNA-sequencing (scRNA-Seq) data is critical to uncovering immune responses in health or disease conditions. However, the high heterogeneity and sparsity of scRNA-Seq data, as well as the similarity in gene expression among immune cell types, poses a great challenge for accurate identification of immune cell types in scRNA-Seq data. Here, we developed a tool named sc-ImmuCC for hierarchical annotation of immune cell types from scRNA-Seq data, based on the optimized gene sets and ssGSEA algorithm. sc-ImmuCC simulates the natural differentiation of immune cells, and the hierarchical annotation includes three layers, which can annotate nine major immune cell types and 29 cell subtypes. The test results showed its stable performance and strong consistency among different tissue datasets with average accuracy of 71-90%. In addition, the optimized gene sets and hierarchical annotation strategy could be applied to other methods to improve their annotation accuracy and the spectrum of annotated cell types and subtypes. We also applied sc-ImmuCC to a dataset composed of COVID-19, influenza, and healthy donors, and found that the proportion of monocytes in patients with COVID-19 and influenza was significantly higher than that in healthy people. The easy-to-use sc-ImmuCC tool provides a good way to comprehensively annotate immune cell types from scRNA-Seq data, and will also help study the immune mechanism underlying physiological and pathological conditions.</p

DataSheet_2_sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq.xlsx

Accurately identifying immune cell types in single-cell RNA-sequencing (scRNA-Seq) data is critical to uncovering immune responses in health or disease conditions. However, the high heterogeneity and sparsity of scRNA-Seq data, as well as the similarity in gene expression among immune cell types, poses a great challenge for accurate identification of immune cell types in scRNA-Seq data. Here, we developed a tool named sc-ImmuCC for hierarchical annotation of immune cell types from scRNA-Seq data, based on the optimized gene sets and ssGSEA algorithm. sc-ImmuCC simulates the natural differentiation of immune cells, and the hierarchical annotation includes three layers, which can annotate nine major immune cell types and 29 cell subtypes. The test results showed its stable performance and strong consistency among different tissue datasets with average accuracy of 71-90%. In addition, the optimized gene sets and hierarchical annotation strategy could be applied to other methods to improve their annotation accuracy and the spectrum of annotated cell types and subtypes. We also applied sc-ImmuCC to a dataset composed of COVID-19, influenza, and healthy donors, and found that the proportion of monocytes in patients with COVID-19 and influenza was significantly higher than that in healthy people. The easy-to-use sc-ImmuCC tool provides a good way to comprehensively annotate immune cell types from scRNA-Seq data, and will also help study the immune mechanism underlying physiological and pathological conditions.</p

Detecting Potentially Adaptive Mutations from the Parallel and Fixed Patterns in SARS-CoV-2 Evolution

Early identification of adaptive mutations could provide timely help for the control and prevention of the COVID-19 pandemic. The fast accumulation of SARS-CoV-2 sequencing data provides important support, while also raising a great challenge for the recognition of adaptive mutations. Here, we proposed a computational strategy to detect potentially adaptive mutations from their fixed and parallel patterns in the phylogenetic trajectory. We found that the biological meanings of fixed substitution and parallel mutation are highly complementary, and can reasonably be integrated as a fixed and parallel (paraFix) mutation, to identify potentially adaptive mutations. Tracking the dynamic evolution of SARS-CoV-2, 37 sites in spike protein were identified as having experienced paraFix mutations. Interestingly, 70% (26/37) of them have already been experimentally confirmed as adaptive mutations. Moreover, most of the mutations could be inferred as paraFix mutations one month earlier than when they became regionally dominant. Overall, we believe that the concept of paraFix mutations will help researchers to identify potentially adaptive mutations quickly and accurately, which will provide invaluable clues for disease control and prevention

seq-ImmuCC: Cell-Centric View of Tissue Transcriptome Measuring Cellular Compositions of Immune Microenvironment From Mouse RNA-Seq Data

The RNA sequencing approach has been broadly used to provide gene-, pathway-, and network-centric analyses for various cell and tissue samples. However, thus far, rich cellular information carried in tissue samples has not been thoroughly characterized from RNA-Seq data. Therefore, it would expand our horizons to better understand the biological processes of the body by incorporating a cell-centric view of tissue transcriptome. Here, a computational model named seq-ImmuCC was developed to infer the relative proportions of 10 major immune cells in mouse tissues from RNA-Seq data. The performance of seq-ImmuCC was evaluated among multiple computational algorithms, transcriptional platforms, and simulated and experimental datasets. The test results showed its stable performance and superb consistency with experimental observations under different conditions. With seq-ImmuCC, we generated the comprehensive landscape of immune cell compositions in 27 normal mouse tissues and extracted the distinct signatures of immune cell proportion among various tissue types. Furthermore, we quantitatively characterized and compared 18 different types of mouse tumor tissues of distinct cell origins with their immune cell compositions, which provided a comprehensive and informative measurement for the immune microenvironment inside tumor tissues. The online server of seq-ImmuCC are freely available at http://wap-lab.org:3200/immune/

Suppression of the METTL3-m6A-integrin β1 axis by extracellular acidification impairs T cell infiltration and antitumor activity

Summary: The acidic metabolic byproducts within the tumor microenvironment (TME) hinder T cell effector functions. However, their effects on T cell infiltration remain largely unexplored. Leveraging the comprehensive The Cancer Genome Atlas dataset, we pinpoint 16 genes that correlate with extracellular acidification and establish a metric known as the “tumor acidity (TuAci) score” for individual patients. We consistently observe a negative association between the TuAci score and T lymphocyte score (T score) across various human cancer types. Mechanistically, extracellular acidification significantly impedes T cell motility by suppressing podosome formation. This phenomenon can be attributed to the reduced expression of methyltransferase-like 3 (METTL3) and the modification of RNA N6-methyladenosine (m6A), resulting in a subsequent decrease in the expression of integrin β1 (ITGB1). Importantly, enforced ITGB1 expression leads to enhanced T cell infiltration and improved antitumor activity. Our study suggests that modulating METTL3 activity or boosting ITGB1 expression could augment T cell infiltration within the acidic TME, thereby improving the efficacy of cell therapy