The Application of Aptamer and Research Progress in Liver Disease.

Aptamers, as a kind of small-molecule nucleic acid, have attracted much attention since their discovery. Compared with biological reagents such as antibodies, aptamers have the advantages of small molecular weight, low immunogenicity, low cost, and easy modification. At present, aptamers are mainly used in disease biomarker discovery, disease diagnosis, treatment, and targeted drug delivery vectors. In the process of screening and optimizing aptamers, it is found that there are still many problems need to be solved such as the design of the library, optimization of screening conditions, the truncation of screened aptamer, and the stability and toxicity of the aptamer. In recent years, the incidence of liver-related diseases is increasing year by year and the treatment measures are relatively lacking, which has attracted the people's attention in the application of aptamers in liver diseases. This article mainly summarizes the research status of aptamers in disease diagnosis and treatment, especially focusing on the application of aptamers in liver diseases, showing the crucial significance of aptamers in the diagnosis and treatment of liver diseases, and the use of Discovery Studio software to find the binding target and sequence of aptamers, and explore their possible interaction sites.

BMP-ACVR1 Axis is Critical for Efficacy of PRC2 Inhibitors in B-Cell Lymphoma.

EZH2 is the catalytic subunit of the histone methyltransferase Polycomb Repressive Complex 2 (PRC2), and its somatic activating mutations drive lymphoma, particularly the germinal center B-cell type. Although PRC2 inhibitors, such as tazemetostat, have demonstrated anti-lymphoma activity in patients, the clinical efficacy is not limited to EZH2-mutant lymphoma. In this study, Activin A Receptor Type 1 (ACVR1), a type I Bone Morphogenetic Protein (BMP) receptor, is identified as critical for the anti-lymphoma efficacy of PRC2 inhibitors through a whole-genome CRISPR screen. BMP6, BMP7, and ACVR1 are repressed by PRC2-mediated H3K27me3, and PRC2 inhibition upregulates their expression and signaling in cell and patient-derived xenograft models. Through BMP-ACVR1 signaling, PRC2 inhibitors robustly induced cell cycle arrest and B cell lineage differentiation in vivo. Remarkably, blocking ACVR1 signaling using an inhibitor or genetic depletion significantly compromised the in vitro and in vivo efficacy of PRC2 inhibitors. Furthermore, high levels of BMP6 and BMP7, along with ACVR1, are associated with longer survival in lymphoma patients, underscoring the clinical relevance of this study. Altogether, BMP-ACVR1 exhibits anti-lymphoma function and represents a critical PRC2-repressed pathway contributing to the efficacy of PRC2 inhibitors.

THEMIS is a substrate and allosteric activator of SHP1, playing dual roles during T cell development.

THEMIS plays an indispensable role in T cells, but its mechanism of action has remained highly controversial. Using the systematic proximity labeling methodology PEPSI, we identify THEMIS as an uncharacterized substrate for the phosphatase SHP1. Saturated mutagenesis assays and mass spectrometry analysis reveal that phosphorylation of THEMIS at the evolutionally conserved Tyr34 residue is oppositely regulated by SHP1 and the kinase LCK. Similar to THEMIS-/- mice, THEMISY34F/Y34F knock-in mice show a significant decrease in CD4 thymocytes and mature CD4 T cells, but display normal thymic development and peripheral homeostasis of CD8 T cells. Mechanistically, the Tyr34 motif in THEMIS, when phosphorylated upon T cell antigen receptor activation, appears to act as an allosteric regulator, binding and stabilizing SHP1 in its active conformation, thus ensuring appropriate negative regulation of T cell antigen receptor signaling. However, cytokine signaling in CD8 T cells fails to elicit THEMIS Tyr34 phosphorylation, indicating both Tyr34 phosphorylation-dependent and phosphorylation-independent roles of THEMIS in controlling T cell maturation and expansion.

Integrated multi-omics profiling to dissect the spatiotemporal evolution of metastatic hepatocellular carcinoma.

Comprehensive molecular analyses of metastatic hepatocellular carcinoma (HCC) are lacking. Here, we generate multi-omic profiling of 257 primary and 176 metastatic regions from 182 HCC patients. Primary tumors rich in hypoxia signatures facilitated polyclonal dissemination. Genomic divergence between primary and metastatic HCC is high, and early dissemination is prevalent. The remarkable neoantigen intratumor heterogeneity observed in metastases is associated with decreased T cell reactivity, resulting from disruptions to neoantigen presentation. We identify somatic copy number alterations as highly selected events driving metastasis. Subclones without Wnt mutations show a stronger selective advantage for metastasis than those with Wnt mutations and are characterized by a microenvironment rich in activated fibroblasts favoring a pro-metastatic phenotype. Finally, metastases without Wnt mutations exhibit higher enrichment of immunosuppressive B cells that mediate terminal exhaustion of CD8+ T cells via HLA-E:CD94-NKG2A checkpoint axis. Collectively, our results provide a multi-dimensional dissection of the complex evolutionary process of metastasis.

Phylogenomics Reveals High Levels of Incomplete Lineage Sorting at the Ancestral Nodes of the Macaque Radiation.

The genus Macaca includes 23 species assigned into 4 to 7 groups. It exhibits the largest geographic range and represents the most successful example of adaptive radiation of nonhuman primates. However, intrageneric phylogenetic relationships among species remain controversial and have not been resolved so far. In this study, we conducted a phylogenomic analysis on 16 newly generated and 8 published macaque genomes. We found strong evidence supporting the division of this genus into 7 species groups. Incomplete lineage sorting (ILS) was the primary factor contributing to the discordance observed among gene trees; however, we also found evidence of hybridization events, specifically between the ancestral arctoides/sinica and silenus/nigra lineages that resulted in the hybrid formation of the fascicularis/mulatta group. Combined with fossil data, our phylogenomic data were used to establish a scenario for macaque radiation. These findings provide insights into ILS and potential ancient introgression events that were involved in the radiation of macaques, which will lead to a better understanding of the rapid speciation occurring in nonhuman primates.

Structural basis of gRNA stabilization and mRNA recognition in trypanosomal RNA editing.

In Trypanosoma brucei, the editosome, composed of RNA-editing substrate-binding complex (RESC) and RNA-editing catalytic complex (RECC), orchestrates guide RNA (gRNA)-programmed editing to recode cryptic mitochondrial transcripts into messenger RNAs (mRNAs). The mechanism of information transfer from gRNA to mRNA is unclear owing to a lack of high-resolution structures for these complexes. With cryo-electron microscopy and functional studies, we have captured gRNA-stabilizing RESC-A and gRNA-mRNA-binding RESC-B and RESC-C particles. RESC-A sequesters gRNA termini, thus promoting hairpin formation and blocking mRNA access. The conversion of RESC-A into RESC-B or -C unfolds gRNA and allows mRNA selection. The ensuing gRNA-mRNA duplex protrudes from RESC-B, likely exposing editing sites to RECC-catalyzed cleavage, uridine insertion or deletion, and ligation. Our work reveals a remodeling event facilitating gRNA-mRNA hybridization and assembly of a macromolecular substrate for the editosome's catalytic modality.

Genome-wide coancestry reveals details of ancient and recent male-driven reticulation in baboons.

Baboons (genus Papio) are a morphologically and behaviorally diverse clade of catarrhine monkeys that have experienced hybridization between phenotypically and genetically distinct phylogenetic species. We used high-coverage whole-genome sequences from 225 wild baboons representing 19 geographic localities to investigate population genomics and interspecies gene flow. Our analyses provide an expanded picture of evolutionary reticulation among species and reveal patterns of population structure within and among species, including differential admixture among conspecific populations. We describe the first example of a baboon population with a genetic composition that is derived from three distinct lineages. The results reveal processes, both ancient and recent, that produced the observed mismatch between phylogenetic relationships based on matrilineal, patrilineal, and biparental inheritance. We also identified several candidate genes that may contribute to species-specific phenotypes.

Genome-wide coancestry reveals details of ancient and recent male-driven reticulation in baboons.

Baboons (genus Papio ) are a morphologically and behaviorally diverse clade of catarrhine monkeys that have experienced hybridization between phenotypically and genetically distinct phylogenetic species. We used high coverage whole genome sequences from 225 wild baboons representing 19 geographic localities to investigate population genomics and inter-species gene flow. Our analyses provide an expanded picture of evolutionary reticulation among species and reveal novel patterns of population structure within and among species, including differential admixture among conspecific populations. We describe the first example of a baboon population with a genetic composition that is derived from three distinct lineages. The results reveal processes, both ancient and recent, that produced the observed mismatch between phylogenetic relationships based on matrilineal, patrilineal, and biparental inheritance. We also identified several candidate genes that may contribute to species-specific phenotypes. One-Sentence Summary: Genomic data for 225 baboons reveal novel sites of inter-species gene flow and local effects due to differences in admixture.

Point mutations of homologs as an adaptive solution to the gene loss.

Gene loss is common and influences genome evolution trajectories. Multiple adaptive strategies to compensate for gene loss have been observed, including copy number gain of paralogous genes and mutations in genes of the same pathway. By using the Ubl-specific protease 2 (ULP2) eviction model, we identify compensatory mutations in the homologous gene ULP1 by laboratory evolution and find that these mutations are capable of rescuing defects caused by the loss of ULP2. Furthermore, bioinformatics analysis of genomes of yeast gene knockout library and natural yeast isolate datasets suggests that point mutations of a homologous gene might be an additional mechanism to compensate for gene loss.

CoNet: Efficient Network Regression for Survival Analysis in Transcriptome-Wide Association Studies-With Applications to Studies of Breast Cancer.

Transcriptome-wide association studies (TWASs) aim to detect associations between genetically predicted gene expression and complex diseases or traits through integrating genome-wide association studies (GWASs) and expression quantitative trait loci (eQTL) mapping studies. Most current TWAS methods analyze one gene at a time, ignoring the correlations between multiple genes. Few of the existing TWAS methods focus on survival outcomes. Here, we propose a novel method, namely a COx proportional hazards model for NEtwork regression in TWAS (CoNet), that is applicable for identifying the association between one given network and the survival time. CoNet considers the general relationship among the predicted gene expression as edges of the network and quantifies it through pointwise mutual information (PMI), which is under a two-stage TWAS. Extensive simulation studies illustrate that CoNet can not only achieve type I error calibration control in testing both the node effect and edge effect, but it can also gain more power compared with currently available methods. In addition, it demonstrates superior performance in real data application, namely utilizing the breast cancer survival data of UK Biobank. CoNet effectively accounts for network structure and can simultaneously identify the potential effecting nodes and edges that are related to survival outcomes in TWAS.

Extending Ag Nanoparticles as Colorimetric Sensor to Industrial Zinc Electrolyte for Cobalt Ion Detection.

The direct and rapid determination of trace cobalt ion (Co2+) in the electrolyte of zinc smelting plants is urgently needed but is impeded by the severe interference of extremely high-concentration zinc ions in the solution. Herein, colorimetric detection of Co2+ by the polyvinylpyrrolidone functionalized silver nanoparticles (PVP-AgNPs) is realized in solutions with the Zn/Co ratio being high, up to (0.8-5) × 104, which is located within the ratio range in industrial solution. The high concentration of Zn2+ induces a strong attenuation of Co2+-related signals in ultraviolet-visible (UV-vis) extinction spectra; nevertheless, a good linear range for detecting 1-6 mg/L Co2+ in 50 g/L Zn2+ solution is still acquired. The strong anti-interference toward other metal ions and the mechanism understanding for trace Co2+ detection in such a high-concentration Zn2+ solution are also revealed by systematic analysis techniques. The results extend the AgNPs as colorimetric sensors to industrial solutions, providing a new strategy for detecting trace-metal ions in industrial plants.

The roles of ETS transcription factors in liver fibrosis.

E26 transformation specific or E twenty-six (ETS) protein family consists of 28 transcription factors, five of which, named ETS1/2, PU.1, ERG and EHF, are known to involve in the development of liver fibrosis, and are expected to become diagnostic markers or therapeutic targets of liver fibrosis. In recent years, some small molecule inhibitors of ETS protein family have been discovered, which might open up a new path for the liver fibrosis therapy targeting ETS. This article reviews the research progress of ETS family members in the development liver fibrosis as well as their prospect of clinical application.

Hypothalamic warm-sensitive neurons require TRPC4 channel for detecting internal warmth and regulating body temperature in mice.

Precise monitoring of internal temperature is vital for thermal homeostasis in mammals. For decades, warm-sensitive neurons (WSNs) within the preoptic area (POA) were thought to sense internal warmth, using this information as feedback to regulate body temperature (Tcore). However, the cellular and molecular mechanisms by which WSNs measure temperature remain largely undefined. Via a pilot genetic screen, we found that silencing the TRPC4 channel in mice substantially attenuated hypothermia induced by light-mediated heating of the POA. Loss-of-function studies of TRPC4 confirmed its role in warm sensing in GABAergic WSNs, causing additional defects in basal temperature setting, warm defense, and fever responses. Furthermore, TRPC4 antagonists and agonists bidirectionally regulated Tcore. Thus, our data indicate that TRPC4 is essential for sensing internal warmth and that TRPC4-expressing GABAergic WSNs function as a novel cellular sensor for preventing Tcore from exceeding set-point temperatures. TRPC4 may represent a potential therapeutic target for managing Tcore.

PRAGAN: Progressive Recurrent Attention GAN with Pretrained ViT Discriminator for Single-Image Deraining.

Images captured in bad weather are not conducive to visual tasks. Rain streaks in rainy images will significantly affect the regular operation of imaging equipment; to solve this problem, using multiple neural networks is a trend. The ingenious integration of network structures allows for full use of the powerful representation and fitting abilities of deep learning to complete low-level visual tasks. In this study, we propose a generative adversarial network (GAN) with multiple attention mechanisms for image rain removal tasks. Firstly, to the best of our knowledge, we propose a pretrained vision transformer (ViT) as the discriminator in GAN for single-image rain removal for the first time. Secondly, we propose a neural network training method that can use a small amount of data for training while maintaining promising results and reliable visual quality. A large number of experiments prove the correctness and effectiveness of our method. Our proposed method achieves better results on synthetic and real image datasets than multiple state-of-the-art methods, even when using less training data.

Characterization of the evolution trajectory and immune profiling of new histologic patterns in lung adenocarcinoma.

In lung adenocarcinoma (LUAD), the appearance of morphologically diverse tumor regions, termed histological patterns, is closely associated with disease progression and lymph node metastasis. However, the molecular characteristics of the histological patterns in LUAD and the underlying molecular evolutionary mechanisms between the histological patterns in primary tumors and lymph node metastases are poorly understood. Here, we re-analyzed the large TCGA-LUAD dataset and depicted a comprehensive profiling of the genome and transcriptome across the histological patterns in LUAD. Tumor phylogenetic trajectory analysis suggested that the complex glands is more apt to metastasize to the lymph node. Further deconvolution of the tumor microenvironment demonstrated that the complex glands had a higher infiltration of cancer-associated fibroblasts (CAFs). Single-cell transcriptome profiling of complex glands pattern identified a novel CAF subtype co-expressing fibroblast activation protein-alpha (FAP) and stimulator of interferon genes (STING). Moreover, our data demonstrated that FAP is an important downstream effector of STING in CAFs. In summary, our results provide the basis for the development of innovative therapeutic guidelines and intervention strategies for LUAD patients.

Network regression analysis for binary and ordinal categorical phenotypes in transcriptome-wide association studies.

Transcriptome-wide association studies aim to integrate genome-wide association studies and expression quantitative trait loci mapping studies for exploring the gene regulatory mechanisms underlying diseases. Existing transcriptome-wide association study methods primarily focus on 1 gene at a time. However, complex diseases are seldom resulted from the abnormality of a single gene, but from the biological network involving multiple genes. In addition, binary or ordinal categorical phenotypes are commonly encountered in biomedicine. We develop a proportional odds logistic model for network regression in transcriptome-wide association study, Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study, to detect the association between a network and binary or ordinal categorical phenotype. Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study relies on 2-stage transcriptome-wide association study framework. It first adopts the distribution-robust nonparametric Dirichlet process regression model in expression quantitative trait loci study to obtain the SNP effect estimate on each gene within the network. Then, Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study uses pointwise mutual information to represent the general relationship among the network nodes of predicted gene expression in genome-wide association study, followed by the association analysis with all nodes and edges involved in proportional odds logistic model. A key feature of Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study is its ability to simultaneously identify the disease-related network nodes or edges. With extensive realistic simulations including those under various between-node correlation patterns, we show Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study can provide calibrated type I error control and yield higher power than other existing methods. We finally apply Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study to analyze bipolar and major depression status and blood pressure from UK Biobank to illustrate its benefits in real data analysis.

Joint Estimation for Time Delay and Direction of Arrival in Reconfigurable Intelligent Surface with OFDM.

Recently, the joint estimation for time delay (TD) and direction of arrival (DOA) has suffered from the high complexity of processing multi-dimensional signal models and the ineffectiveness of correlated/coherent signals. In order to improve this situation, a joint estimation method using orthogonal frequency division multiplexing (OFDM) and a uniform planar array composed of reconfigurable intelligent surface (RIS) is proposed. First, the time-domain coding function of the RIS is combined with the multi-carrier characteristic of the OFDM signal to construct the coded channel frequency response in tensor form. Then, the coded channel frequency response covariance matrix is decomposed by CANDECOMP/PARAFAC (CPD) to separate the signal subspaces of TD and DOA. Finally, we perform a one-dimensional (1D) spectral search for TD values and a two-dimensional (2D) spectral search for DOA values. Compared to previous efforts, this algorithm not only enhances the adaptability of coherent signals, but also greatly decreases the complexity. Simulation results indicate the robustness and effectiveness for the proposed algorithm in independent, coherent, and mixed multipath environments and low signal-to-noise ratio (SNR) conditions.

Network regression analysis in transcriptome-wide association studies.

BACKGROUND: Transcriptome-wide association studies (TWASs) have shown great promise in interpreting the findings from genome-wide association studies (GWASs) and exploring the disease mechanisms, by integrating GWAS and eQTL mapping studies. Almost all TWAS methods only focus on one gene at a time, with exception of only two published multiple-gene methods nevertheless failing to account for the inter-dependence as well as the network structure among multiple genes, which may lead to power loss in TWAS analysis as complex disease often owe to multiple genes that interact with each other as a biological network. We therefore developed a Network Regression method in a two-stage TWAS framework (NeRiT) to detect whether a given network is associated with the traits of interest. NeRiT adopts the flexible Bayesian Dirichlet process regression to obtain the gene expression prediction weights in the first stage, uses pointwise mutual information to represent the general between-node correlation in the second stage and can effectively take the network structure among different gene nodes into account. RESULTS: Comprehensive and realistic simulations indicated NeRiT had calibrated type I error control for testing both the node effect and edge effect, and yields higher power than the existed methods, especially in testing the edge effect. The results were consistent regardless of the GWAS sample size, the gene expression prediction model in the first step of TWAS, the network structure as well as the correlation pattern among different gene nodes. Real data applications through analyzing systolic blood pressure and diastolic blood pressure from UK Biobank showed that NeRiT can simultaneously identify the trait-related nodes as well as the trait-related edges. CONCLUSIONS: NeRiT is a powerful and efficient network regression method in TWAS.

Joint analysis of scATAC-seq datasets using epiConv.

BACKGROUND: Technical improvement in ATAC-seq makes it possible for high throughput profiling the chromatin states of single cells. However, data from multiple sources frequently show strong technical variations, which is referred to as batch effects. In order to perform joint analysis across multiple datasets, specialized method is required to remove technical variations between datasets while keep biological information. RESULTS: Here we present an algorithm named epiConv to perform joint analyses on scATAC-seq datasets. We first show that epiConv better corrects batch effects and is less prone to over-fitting problem than existing methods on a collection of PBMC datasets. In a collection of mouse brain data, we show that epiConv is capable of aligning low-depth scATAC-Seq from co-assay data (simultaneous profiling of transcriptome and chromatin) onto high-quality ATAC-seq reference and increasing the resolution of chromatin profiles of co-assay data. Finally, we show that epiConv can be used to integrate cells from different biological conditions (T cells in normal vs. germ-free mouse; normal vs. malignant hematopoiesis), which reveals hidden cell populations that would otherwise be undetectable. CONCLUSIONS: In this study, we introduce epiConv to integrate multiple scATAC-seq datasets and perform joint analysis on them. Through several case studies, we show that epiConv removes the batch effects and retains the biological signal. Moreover, joint analysis across multiple datasets improves the performance of clustering and differentially accessible peak calling, especially when the biological signal is weak in single dataset.