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Lignans are widely produced by various plant species; they are a class of natural products that share structural similarity. They usually contain a core scaffold that is formed by two or more phenylpropanoid units. Lignans possess diverse pharmacological properties, including their antiviral activities that have been reported in recent years. This review discusses the distribution of lignans in nature according to their structural classification, and it provides a comprehensive summary of their antiviral activities. Among them, two types of antiviral lignans—podophyllotoxin and bicyclol, which are used to treat venereal warts and chronic hepatitis B (CHB) in clinical, serve as examples of using lignans for antivirals—are discussed in some detail. Prospects of lignans in antiviral drug discovery are also discussed.

A disturbance of reactive oxygen species (ROS) homeostasis may cause the pathogenesis of many diseases. Inspired by natural photosynthesis, this work proposes a photo-driven H 2 -evolving liposomal nanoplatform (Lip NP) that comprises an upconversion nanoparticle (UCNP) that is conjugated with gold nanoparticles (AuNPs) via a ROS-responsive linker, which is encapsulated inside the liposomal system in which the lipid bilayer embeds chlorophyll a (Chl a ). The UCNP functions as a transducer, converting NIR light into upconversion luminescence for simultaneous imaging and therapy in situ. Functioning as light-harvesting antennas, AuNPs are used to detect the local concentration of ROS for FRET biosensing, while the Chl a activates the photosynthesis of H 2 gas to scavenge local excess ROS. The results thus obtained indicate the potential of using the Lip NPs in the analysis of biological tissues, restoring their ROS homeostasis, possibly preventing the initiation and progression of diseases. Hydrogen can be used to reduce the concentration of reactive oxygen species (ROS), but its delivery to diseased tissues is challenging due to its low solubility. Here the authors develop a photosynthesis-inspired FRET nanocomplex to detect and scavenge local excess of ROS in the tissue using photocatalytic hydrogen production.

Background Small open reading frame (smORF) is open reading frame with a length of less than 100 codons. Microproteins, translated from smORFs, have been found to participate in a variety of biological processes such as muscle formation and contraction, cell proliferation, and immune activation. Although previous studies have collected and annotated a large abundance of smORFs, functions of the vast majority of smORFs are still unknown. It is thus increasingly important to develop computational methods to annotate the functions of these smORFs. Results In this study, we collected 617,462 unique smORFs from three studies. The expression of smORF RNAs was estimated by reannotated microarray probes. Using a speed-optimized correlation algorism, the functions of smORFs were predicted by their correlated genes with known functional annotations. After applying our method to 5 known microproteins from literatures, our method successfully predicted their functions. Further validation from the UniProt database showed that at least one function of 202 out of 270 microproteins was predicted. Conclusions We developed a method, smORFunction, to provide function predictions of smORFs/microproteins in at most 265 models generated from 173 datasets, including 48 tissues/cells, 82 diseases (and normal). The tool can be available at https://www.cuilab.cn/smorfunction .

Certain cancer genes contribute to tumorigenesis in a manner of either co-occurring or mutually exclusive (anti-co-occurring) mutations; however, the global picture of when, where and how these functional interactions occur remains unclear. This study presents a systems biology approach for this purpose. After applying this method to cancer gene mutation data generated from large-scale and whole genome sequencing of cancer samples, a network of cancer genes with co-occurring and anti-co-occurring mutations was constructed. Analysis of this network revealed that genes with co-occurring mutations prefer direct signaling transductions and that the interaction relations among cancer genes in the network are related with their functional similarity. It was also revealed that genes with co-occurring mutations tend to have similar mutation frequencies, whereas genes with anti-co-occurring mutations tend to have different mutation frequencies. Moreover, genes with more exons tend to have more co-occurring mutations with other genes, and genes having lower local coherent network structures tend to have higher mutation frequency. The network showed two complementary modules that have distinct functions and have different roles in tumorigenesis. This study presented a framework for the analysis of cancer genome sequencing outputs. The presented data and uncovered patterns are helpful for understanding the contribution of gene mutations to tumorigenesis and valuable in the identification of key biomarkers and drug targets for cancer.

The overall survival of hepatocellular carcinoma (HCC) remains poor, highlighting the need for better prognostic tools. Nucleotide metabolism fuels tumor progression, while the immune microenvironment dictates therapy response, but integrated models combining both features are lacking. Using TCGA-LIHC transcriptomic/clinical data, we identified nucleotide metabolism and immune-related differentially expressed genes (NMIRGs), which stratified HCC patients into two subtypes via non-negative matrix factorization. A nine-gene prognostic risk signature was constructed through LASSO/Cox regression and validated using independent GEO datasets, and the NMIRG signature was further validated experimentally via RT-qPCR in HCC cell lines and independently using the HPA database for protein-level evidence. As evaluated by our risk signature, high-risk patients exhibited altered immune profiles (T cells increasing, neutrophils decreasing), elevated tumor mutation burden and microsatellite instability, and worse predicted immunotherapy response. Gene set enrichment analysis linked high-risk genes to immune pathways and low-risk genes to metabolic processes. Our risk signature predicted HCC prognosis independent of demographic features and outperformed existing signatures with superior C-index accuracy, effectively predicting immune microenvironment status and therapy benefits. Together, this integrated NMIRG signature offers enhanced prognostication and identifies promising biomarkers for personalized HCC management.

The hemagglutinin (HA) and neuraminidase (NA) of influenza A virus possess antagonistic activities on interaction with sialic acid (SA), which is the receptor for virus attachment. HA binds SA through its receptor-binding sites, while NA is a receptor-destroying enzyme by removing SAs. The function of HA during virus entry has been extensively investigated, however, examination of NA has long been focused to its role in the exit of progeny virus from infected cells, and the role of NA in the entry process is still under-appreciated. This review summarizes the current understanding of the roles of HA and NA in relation to each other during virus entry.

Background 2′-O-methylation (2′-O-me or Nm) is a post-transcriptional RNA methylation modified at 2′-hydroxy, which is common in mRNAs and various non-coding RNAs. Previous studies revealed the significance of Nm in multiple biological processes. With Nm getting more and more attention, a revolutionary technique termed Nm-seq, was developed to profile Nm sites mainly in mRNA with single nucleotide resolution and high sensitivity. In a recent work, supported by the Nm-seq data, we have reported a method in silico for predicting Nm sites, which relies on nucleotide sequence information, and established an online server named NmSEER. More recently, a more confident dataset produced by refined Nm-seq was available. Therefore, in this work, we redesigned the prediction model to achieve a more robust performance on the new data. Results We redesigned the prediction model from two perspectives, including machine learning algorithm and multi-encoding scheme combination. With optimization by 5-fold cross-validation tests and evaluation by independent test respectively, random forest was selected as the most robust algorithm. Meanwhile, one-hot encoding, together with position-specific dinucleotide sequence profile and K-nucleotide frequency encoding were collectively applied to build the final predictor. Conclusions The predictor of updated version, named NmSEER V2.0, achieves an accurate prediction performance (AUROC = 0.862) and has been settled into a brand-new server, which is available at http://www.rnanut.net/nmseer-v2/ for free.

It has been reported that increasingly microRNAs are associated with diseases. However, the patterns among the microRNA-disease associations remain largely unclear. In this study, in order to dissect the patterns of microRNA-disease associations, we performed a comprehensive analysis to the human microRNA-disease association data, which is manually collected from publications. We built a human microRNA associated disease network. Interestingly, microRNAs tend to show similar or different dysfunctional evidences for the similar or different disease clusters, respectively. A negative correlation between the tissue-specificity of a microRNA and the number of diseases it associated was uncovered. Furthermore, we observed an association between microRNA conservation and disease. Finally, we uncovered that microRNAs associated with the same disease tend to emerge as predefined microRNA groups. These findings can not only provide help in understanding the associations between microRNAs and human diseases but also suggest a new way to identify novel disease-associated microRNAs.

We previously revealed the relationships of human tissue microRNAs (miRNAs) with those from various body fluids including plasma, bile, urine, serum, and feces, which provided valuable clues for discovering miRNA-based biomarkers for specific diseases. However, it remains unknown for the relationships of human tissue miRNAs with those from other body fluids. Here, by analyzing miRNA expression data from 39 tissue types from healthy humans and various body fluids including cerebrospinal fluid, tear, sweat, semen, and saliva, we have uncovered the relationships of human healthy tissues with those body fluids. All miRNA expression data from body fluids were significantly positively correlated with total expression levels of miRNAs in 39 healthy tissues, and body fluids from different sources exhibited distinct patterns in their relationship with healthy tissues. Moreover, body fluids from different sources showed the highest correlation with different healthy tissues. Notably, saliva samples from patients with oral cavity squamous cell carcinoma showed higher correlations with all tissues than those from healthy controls. These findings together provide evidence for the application of body fluid miRNAs as biomarkers for the diagnosis and treatment of relevant human diseases.

Background Recently, the potential role of gut microbiome in metabolic diseases has been revealed, especially in cardiovascular diseases. Hypertension is one of the most prevalent cardiovascular diseases worldwide, yet whether gut microbiota dysbiosis participates in the development of hypertension remains largely unknown. To investigate this issue, we carried out comprehensive metagenomic and metabolomic analyses in a cohort of 41 healthy controls, 56 subjects with pre-hypertension, 99 individuals with primary hypertension, and performed fecal microbiota transplantation from patients to germ-free mice. Results Compared to the healthy controls, we found dramatically decreased microbial richness and diversity, Prevotella -dominated gut enterotype, distinct metagenomic composition with reduced bacteria associated with healthy status and overgrowth of bacteria such as Prevotella and Klebsiella , and disease-linked microbial function in both pre-hypertensive and hypertensive populations. Unexpectedly, the microbiome characteristic in pre-hypertension group was quite similar to that in hypertension. The metabolism changes of host with pre-hypertension or hypertension were identified to be closely linked to gut microbiome dysbiosis. And a disease classifier based on microbiota and metabolites was constructed to discriminate pre-hypertensive and hypertensive individuals from controls accurately. Furthermore, by fecal transplantation from hypertensive human donors to germ-free mice, elevated blood pressure was observed to be transferrable through microbiota, and the direct influence of gut microbiota on blood pressure of the host was demonstrated. Conclusions Overall, our results describe a novel causal role of aberrant gut microbiota in contributing to the pathogenesis of hypertension. And the significance of early intervention for pre-hypertension was emphasized.