Systematic single-cell analysis reveals dynamic control of transposable element activity orchestrating the endothelial-to-hematopoietic transition.

BACKGROUND: The endothelial-to-hematopoietic transition (EHT) process during definitive hematopoiesis is highly conserved in vertebrates. Stage-specific expression of transposable elements (TEs) has been detected during zebrafish EHT and may promote hematopoietic stem cell (HSC) formation by activating inflammatory signaling. However, little is known about how TEs contribute to the EHT process in human and mouse. RESULTS: We reconstructed the single-cell EHT trajectories of human and mouse and resolved the dynamic expression patterns of TEs during EHT. Most TEs presented a transient co-upregulation pattern along the conserved EHT trajectories, coinciding with the temporal relaxation of epigenetic silencing systems. TE products can be sensed by multiple pattern recognition receptors, triggering inflammatory signaling to facilitate HSC emergence. Interestingly, we observed that hypoxia-related signals were enriched in cells with higher TE expression. Furthermore, we constructed the hematopoietic cis-regulatory network of accessible TEs and identified potential TE-derived enhancers that may boost the expression of specific EHT marker genes. CONCLUSIONS: Our study provides a systematic vision of how TEs are dynamically controlled to promote the hematopoietic fate decisions through transcriptional and cis-regulatory networks, and pre-train the immunity of nascent HSCs.

HALD, a human aging and longevity knowledge graph for precision gerontology and geroscience analyses.

Human aging is a natural and inevitable biological process that leads to an increased risk of aging-related diseases. Developing anti-aging therapies for aging-related diseases requires a comprehensive understanding of the mechanisms and effects of aging and longevity from a multi-modal and multi-faceted perspective. However, most of the relevant knowledge is scattered in the biomedical literature, the volume of which reached 36 million in PubMed. Here, we presented HALD, a text mining-based human aging and longevity dataset of the biomedical knowledge graph from all published literature related to human aging and longevity in PubMed. HALD integrated multiple state-of-the-art natural language processing (NLP) techniques to improve the accuracy and coverage of the knowledge graph for precision gerontology and geroscience analyses. Up to September 2023, HALD had contained 12,227 entities in 10 types (gene, RNA, protein, carbohydrate, lipid, peptide, pharmaceutical preparations, toxin, mutation, and disease), 115,522 relations, 1,855 aging biomarkers, and 525 longevity biomarkers from 339,918 biomedical articles in PubMed. HALD is available at https://bis.zju.edu.cn/hald .

Genomic insight into domestication of rubber tree.

Understanding the genetic basis of rubber tree (Hevea brasiliensis) domestication is crucial for further improving natural rubber production to meet its increasing demand worldwide. Here we provide a high-quality H. brasiliensis genome assembly (1.58 Gb, contig N50 of 11.21 megabases), present a map of genome variations by resequencing 335 accessions and reveal domestication-related molecular signals and a major domestication trait, the higher number of laticifer rings. We further show that HbPSK5, encoding the small-peptide hormone phytosulfokine (PSK), is a key domestication gene and closely correlated with the major domestication trait. The transcriptional activation of HbPSK5 by myelocytomatosis (MYC) members links PSK signaling to jasmonates in regulating the laticifer differentiation in rubber tree. Heterologous overexpression of HbPSK5 in Russian dandelion (Taraxacum kok-saghyz) can increase rubber content by promoting laticifer formation. Our results provide an insight into target genes for improving rubber tree and accelerating the domestication of other rubber-producing plants.

Chromosome-level wild Hevea brasiliensis genome provides new tools for genomic-assisted breeding and valuable loci to elevate rubber yield.

The rubber tree (Hevea brasiliensis) is grown in tropical regions and is the major source of natural rubber. Using traditional breeding approaches, the latex yield has increased by sixfold in the last century. However, the underlying genetic basis of rubber yield improvement is largely unknown. Here, we present a high-quality, chromosome-level genome sequence of the wild rubber tree, the first report on selection signatures and a genome-wide association study (GWAS) of its yield traits. Population genomic analysis revealed a moderate population divergence between the Wickham clones and wild accessions. Interestingly, it is suggestive that H. brasiliensis and six relatives of the Hevea genus might belong to the same species. The selective sweep analysis found 361 obvious signatures in the domesticated clones associated with 245 genes. In a 15-year field trial, GWAS identified 155 marker-trait associations with latex yield, in which 326 candidate genes were found. Notably, six genes related to sugar transport and metabolism, and four genes related to ethylene biosynthesis and signalling are associated with latex yield. The homozygote frequencies of the causal nonsynonymous SNPs have been greatly increased under selection, which may have contributed to the fast latex yield improvement during the short domestication history. Our study provides insights into the genetic basis of the latex yield trait and has implications for genomic-assisted breeding by offering valuable resources in this new domesticated crop.

Characterization of the complete chloroplast genome of Hevea pauciflora (Euphorbiaceae), an important wild relative of the rubber tree.

Hevea pauciflora belongs to the Euphorbiaceae family, an important wild relative of the rubber tree. This study sequenced, assembled, and annotated the complete chloroplast genome of H. pauciflora. The complete chloroplast genome is 161,123 bp with a canonical quadripartite structure containing a large single-copy (LSC) region (89,109 bp), a small single-copy (SSC) region (18,376 bp), and two inverted repeat regions (IRa and IRb) (26,819 bp, each). A total of 134 genes were annotated, including 86 protein-coding genes, four pseudogenes, 36 tRNA genes, and eight rRNA genes. The 134 genes include four major groups: 'self-replication', 'photosynthesis', 'unknown function', and 'others'. A phylogenetic analysis clustered H. pauciflora, H. brasiliensis, H. camargoana, and H. benthamiana into one clade, consistent with traditional taxonomy. This study provides useful data for further studies of Hevea genus and the phylogenetic relationships of Euphorbiaceae species.

CoVM2: Molecular Biological Data Integration of SARS-CoV-2 Proteins in a Macro-to-Micro Method.

The COVID-19 pandemic has been a major public health event since 2020. Multiple variant strains of SARS-CoV-2, the causative agent of COVID-19, were detected based on the mutation sites in their sequences. These sequence mutations may lead to changes in the protein structures and affect the binding states of SARS-CoV-2 and human proteins. Experimental research on SARS-CoV-2 has accumulated a large amount of structural data and protein-protein interactions (PPIs), but the studies on the SARS-CoV-2-human PPI networks lack integration of physical associations with possible protein docking information. In addition, the docking structures of variant viral proteins with human receptor proteins are still insufficient. This study constructed SARS-CoV-2-human protein-protein interaction network with data integration methods. Crystal structures were collected to map the interaction pairs. The pairs of direct interactions and physical associations were selected and analyzed for variant docking calculations. The study examined the structures of spike (S) glycoprotein of variants Delta B.1.617.2, Omicron BA.1, and Omicron BA.2. The calculated docking structures of S proteins and potential human receptors were obtained. The study integrated binary protein interactions with 3D docking structures to fulfill an extended view of SARS-CoV-2 proteins from a macro- to micro-scale.

LBD: a manually curated database of experimentally validated lymphoma biomarkers.

Lymphoma is a heterogeneous disease caused by malignant proliferation of lymphocytes, resulting in significant mortality worldwide. While more and more lymphoma biomarkers have been identified with the advent and development of precision medicine, there are currently no databases dedicated to systematically gathering these scattered treasures. Therefore, we developed a lymphoma biomarker database (LBD) to curate experimentally validated lymphoma biomarkers in this study. LBD consists of 793 biomarkers extracted from 978 articles covering diverse subtypes of lymphomas, including 715 single and 78 combined biomarkers. These biomarkers can be categorized into molecular, cellular, image, histopathological, physiological and other biomarkers with various functions such as prognosis, diagnosis and treatment. As a manually curated database that provides comprehensive information about lymphoma biomarkers, LBD is helpful for personalized diagnosis and treatment of lymphoma. Database URL http://bis.zju.edu.cn/LBD.

CoGO: a contrastive learning framework to predict disease similarity based on gene network and ontology structure.

MOTIVATION: Quantifying the similarity of human diseases provides guiding insights to the discovery of micro-scope mechanisms from a macro scale. Previous work demonstrated that better performance can be gained by integrating multiview data sources or applying machine learning techniques. However, designing an efficient framework to extract and incorporate information from different biological data using deep learning models remains unexplored. RESULTS: We present CoGO, a Contrastive learning framework to predict disease similarity based on Gene network and Ontology structure, which incorporates the gene interaction network and gene ontology (GO) domain knowledge using graph deep learning models. First, graph deep learning models are applied to encode the features of genes and GO terms from separate graph structure data. Next, gene and GO features are projected to a common embedding space via a nonlinear projection. Then cross-view contrastive loss is applied to maximize the agreement of corresponding gene-GO associations and lead to meaningful gene representation. Finally, CoGO infers the similarity between diseases by the cosine similarity of disease representation vectors derived from related gene embedding. In our experiments, CoGO outperforms the most competitive baseline method on both AUROC and AUPRC, especially improves 19.57% in AUPRC (0.7733). The prediction results are significantly comparable with other disease similarity studies and thus highly credible. Furthermore, we conduct a detailed case study of top similar disease pairs which is demonstrated by other studies. Empirical results show that CoGO achieves powerful performance in disease similarity problem. AVAILABILITY AND IMPLEMENTATION: https://github.com/yhchen1123/CoGO.

The complete chloroplast genome of Hevea camargoana.

Hevea camargoana is a natural latex producing tropical plant and a close relative of H. brasiliensis, the primary commercial source of natural rubber. This study sequenced and analyzed the chloroplast genome of H. camargoana. The circular chloroplast genome of H. camargoana contains 161,291 bp with a GC content of 35.72%. This region contains two inverted repeat regions (26,819 bp), a large single-copy region (89,281 bp), and a small single-copy (18,372 bp) region in the complete chloroplast genome. A total of 134 genes were annotated, including 86 protein-coding genes, 36 transfer RNA genes, 8 ribosomal RNA genes, and 4 pseudogenes. The results showed that H. camargoana and H. brasiliensis were closely related, suggesting that H. camargoana may be used for the future variety improvement of rubber trees.

PncStress: a manually curated database of experimentally validated stress-responsive non-coding RNAs in plants.

Non-coding RNAs (ncRNAs) are recognized as key regulatory molecules in many biological processes. Accumulating evidence indicates that ncRNA-related mechanisms play important roles in plant stress responses. Although abundant plant stress-responsive ncRNAs have been identified, these experimentally validated results have not been gathered into a single public domain archive. Therefore, we established PncStress by curating experimentally validated stress-responsive ncRNAs in plants, including microRNAs, long non-coding RNAs and circular RNAs. The current version of PncStress contains 4227 entries from 114 plants covering 48 biotic and 91 abiotic stresses. For each entry, PncStress has biological information and network visualization. Serving as a manually curated database, PncStress will become a valuable resource in support of plant stress response research.

Complete chloroplast genome of Hevea benthamiana, a SALB-resistant relative wild species of rubber tree.

Hevea benthamiana is a SALB-resistant wild species of H. brasiliensis, the only source of mass production of high quality natural rubber. This study sequenced and analyzed the chloroplast genome of H. benthamiana. The chloroplast genome of H. benthamiana contains 161,124 bp and consists of 51,495 bp of A (31.96%), 52,022 bp of T (32.29%), 28,915 bp of G (17.95%), and 28,692 bp of C (17.81%). The ring-shaped genome includes four regions: a large single-copy region (LSC, 89,110 bp), a small single copy (SSC, 18,376 bp) region, and two inverted repeat regions (IRs, 26,819 bp). A total of 134 genes were annotated, of which 86 encode proteins; four are pseudogenes; 36 are tRNA genes, and eight are rRNA genes. Phylogenetic analyses showed that H. benthamiana is very closely related to H. Brasiliensis, this result indicates that H. benthamiana is highly valuable for the breeding of SALB-resistant varieties of rubber trees.

The inflammatory cytokine IL-6 induces FRA1 deacetylation promoting colorectal cancer stem-like properties.

Colorectal cancer (CRC) has long been known for its tight association with chronic inflammation, thought to play a key role in tumor onset and malignant progression through the modulation of cancer stemness. However, the underlying molecular and cellular mechanisms are still largely elusive. Here we show that the IL-6/STAT3 inflammatory signaling axis induces the deacetylation of FRA1 at the Lys-116 residue located within its DNA-binding domain. The HDAC6 deacetylase underlies this key modification leading to the increase of FRA1 transcriptional activity, the subsequent transactivation of NANOG expression, and the acquisition of stem-like cellular features. As validated in a large (n = 123) CRC cohort, IL-6 secretion was invariably accompanied by increased FRA1 deacetylation at K116 and an overall increase in its protein levels, coincident with malignant progression and poor prognosis. Of note, combined treatment with the conventional cytotoxic drug 5-FU together with Tubastatin A, a HDAC6-specific inhibitor, resulted in a significant in vivo synergistic inhibitory effect on tumor growth through suppression of CRC stemness. Our results reveal a novel transcriptional and posttranslational regulatory cross-talk between inflammation and stemness signaling pathways that underlie self-renewal and maintenance of CRC stem cells and promote their malignant behavior. Combinatorial treatment aimed at the core regulatory mechanisms downstream of IL-6 may offer a novel promising approach for CRC treatment.

Comparative transcriptome analysis reveals an early gene expression profile that contributes to cold resistance in Hevea brasiliensis (the Para rubber tree).

The rubber tree (Hevea brasiliensis Muell. Arg) is a tropical, perennial, woody plant that is susceptible to cold stress. In China, cold stress has been found to severely damage rubber plants in plantations in past decades. Although several Hevea clones that are resistant to cold have been developed, their cold hardiness mechanism has yet to be elucidated. For the study reported herein, we subjected the cold-resistant clone CATAS93-114 and the cold-sensitive clone Reken501 to chilling stress, and characterized their transcriptomes at 0, 2, 8 and 24 h after the start of chilling. We found that 7870 genes were differentially expressed in the transcriptomes of the two clones. In CATAS93-114, a greater number of genes were found to be up- or downregulated between 2 h and 8 h than in Reken501, which indicated a more rapid and intensive response by CATAS93-114 than by Reken501. The differentially expressed genes were grouped into seven major clusters, according to their Gene Ontology terms. The expression profiles for genes involved in abscisic acid metabolism and signaling, in an abscisic acid-independent pathway, and in early signal perception were found to have distinct expression patterns for the transcriptomes of the two clones. The differential expression of 22 genes that appeared to have central roles in response to chilling was confirmed by quantitative real-time PCR.

Detecting pathway relationship in the context of human protein-protein interaction network and its application to Parkinson's disease.

In human physiological conditions like complex diseases, a large number of genes/proteins, as well as their interactions, are involved. Thus, detecting the biochemical pathways enriched in these genes/proteins and identifying the pathway relationships is critical to understand the molecular mechanisms underlying a disease and can also be valuable in selecting the potential molecular targets for further exploration. In this study, we proposed a method to measure the relationship between pathways based on their distribution in the human PPI network. By representing each pathway as a gene module in the PPI network, a distance was calculated to measure the closeness of two pathways. For the pathways in the KEGG database, a total of 2143 pathway pairs with close connections were identified. Additional evaluations indicated the pathway relationship built via such approach was consistent with available evidence. Further, based on the genes and pathways potentially associated with the pathogenesis of Parkinson's disease (PD), we analyzed the pathway relationship and identified the major pathways related to this disorder via the new method. Also, by analyzing the pathway interaction network constructed by the identified major pathways, we explored the potential pathway targets that may be important in the etiology and development of PD. In summary, we proposed an approach to measure the relationship between pathways, which could provide a more systematic profile on pathways involved in a phenotype, and may also help to improve the result of pathway enrichment analysis.

Analyzing the genes related to Alzheimer's disease via a network and pathway-based approach.

BACKGROUND: Our understanding of the molecular mechanisms underlying Alzheimer's disease (AD) remains incomplete. Previous studies have revealed that genetic factors provide a significant contribution to the pathogenesis and development of AD. In the past years, numerous genes implicated in this disease have been identified via genetic association studies on candidate genes or at the genome-wide level. However, in many cases, the roles of these genes and their interactions in AD are still unclear. A comprehensive and systematic analysis focusing on the biological function and interactions of these genes in the context of AD will therefore provide valuable insights to understand the molecular features of the disease. METHOD: In this study, we collected genes potentially associated with AD by screening publications on genetic association studies deposited in PubMed. The major biological themes linked with these genes were then revealed by function and biochemical pathway enrichment analysis, and the relation between the pathways was explored by pathway crosstalk analysis. Furthermore, the network features of these AD-related genes were analyzed in the context of human interactome and an AD-specific network was inferred using the Steiner minimal tree algorithm. RESULTS: We compiled 430 human genes reported to be associated with AD from 823 publications. Biological theme analysis indicated that the biological processes and biochemical pathways related to neurodevelopment, metabolism, cell growth and/or survival, and immunology were enriched in these genes. Pathway crosstalk analysis then revealed that the significantly enriched pathways could be grouped into three interlinked modules-neuronal and metabolic module, cell growth/survival and neuroendocrine pathway module, and immune response-related module-indicating an AD-specific immune-endocrine-neuronal regulatory network. Furthermore, an AD-specific protein network was inferred and novel genes potentially associated with AD were identified. CONCLUSION: By means of network and pathway-based methodology, we explored the pathogenetic mechanism underlying AD at a systems biology level. Results from our work could provide valuable clues for understanding the molecular mechanism underlying AD. In addition, the framework proposed in this study could be used to investigate the pathological molecular network and genes relevant to other complex diseases or phenotypes.

Network and Pathway-Based Analyses of Genes Associated with Parkinson's Disease.

Parkinson's disease (PD) is a major neurodegenerative disease influenced by both genetic and environmental factors. Although previous studies have provided insights into the significant impacts of genetic factors on PD, the molecular mechanism underlying PD remains largely unclear. Under such situation, a comprehensive analysis focusing on biological function and interactions of PD-related genes will provide us valuable information to understand the pathogenesis of PD. In the current study, by reviewing the literatures deposited in PUBMED, we identified 242 genes genetically associated with PD, referred to as PD-related genes gene set (PDgset). Functional analysis revealed that biological processes and biochemical pathways related to neurodevelopment, metabolism, and immune system were enriched in PDgset. Then, pathway crosstalk analysis indicated that the enriched pathways could be grouped into two modules, with one module consisted of pathways mainly involved in neuronal signaling and another in immune response. Further, based on a global human interactome, we found that PDgset tended to have more moderate degree compared with cancer-related genes. Moreover, PD-specific molecular network was inferred using Steiner minimal tree algorithm and some potential related genes associated with PD were identified. In summary, by using network- and pathway-based methods to explore pathogenetic mechanism underlying PD, results from our work may have important implications for understanding the molecular mechanism underlying PD. Also, the framework proposed in our current work can be used to infer pathological molecular network and genes related to a specific disease.

GRONS: a comprehensive genetic resource of nicotine and smoking.

Nicotine, the primary psychoactive component in tobacco, can exert a broad impact on both the central and peripheral nervous systems. During the past years, a tremendous amount of efforts has been put to exploring the molecular mechanisms underlying tobacco smoking related behaviors and diseases, and many susceptibility genes have been identified via various genomic approaches. For many human complex diseases, there is a trend towards collecting and integrating the data from genetic studies and the biological information related to them into a comprehensive resource for further investigation, but we have not found such an effort for nicotine addiction or smoking-related phenotypes yet. To collect, curate, and integrate cross-platform genetic data so as to make them interpretable and easily accessible, we developed Genetic Resources Of Nicotine and Smoking (GRONS), a comprehensive database for genes related to biological response to nicotine exposure, tobacco smoking related behaviors or diseases. GRONS deposits genes from nicotine addiction studies in the following four categories, i.e. association study, genome-wide linkage scan, expression analysis on genes/proteins via high-throughput technologies, as well as single gene/protein-based experimental studies via literature search. Moreover, GRONS not only provides tools for data browse, search and graphical presentation of gene prioritization, but also presents the results from comprehensive bioinformatics analyses for the prioritized genes associated with nicotine addiction. With more and more genetic data and analysis tools integrated, GRONS will become a useful resource for studies focusing on nicotine addiction or tobacco smoking. Database URL: http://bioinfo.tmu.edu.cn/GRONS/.

Functional Characterization of Hevea brasiliensis CRT/DRE Binding Factor 1 Gene Revealed Regulation Potential in the CBF Pathway of Tropical Perennial Tree.

Rubber trees (Hevea brasiliensis) are susceptible to low temperature and therefore are only planted in the tropical regions. In the past few decades, although rubber trees have been successfully planted in the northern margin of tropical area in China, they suffered from cold injury during the winter. To understand the physiological response under cold stress, we isolated a C-repeat binding factor 1 (CBF1) gene from the rubber tree. This gene (HbCBF1) was found to respond to cold stress but not drought or ABA stress. The corresponding HbCBF1 protein showed CRT/DRE binding activity in gel shift experiment. To further characterize its molecular function, the HbCBF1 gene was overexpressed in Arabidopsis. The HbCBF1 over expression (OE) line showed enhanced cold resistance and relatively slow dehydration, and the expression of Arabidopsis CBF pathway downstream target genes, e.g. AtCOR15a and AtRD29a, were significantly activated under non-acclimation condition. These data suggest HbCBF1 gene is a functional member of the CBF gene family, and may play important regulation function in rubber tree.