Decoding m6A RNA methylome identifies PRMT6-regulated lipid transport promoting AML stem cell maintenance.

N6-methyladenosine (m6A) is a common chemical modification for mammalian mRNA and exhibits high dynamics in various biological processes. However, dynamics of m6A RNA methylome during leukemogenesis remains unknown. Here, we delineate a comprehensive m6A landscape during acute myeloid leukemia (AML) development and identify PRMT6 as a key for maintaining AML stem cells. We observe an obvious change in m6A methylome during leukemogenesis and find that protein arginine methyltransferase PRMT6 and m6A reader IGF2BP2 maintain the function of human and murine leukemia stem cells (LSCs). Genetic deletion or pharmacological inhibition of PRMT6 damages AML development and LSC function. Mechanistically, IGF2BP2 stabilizes PRMT6 mRNA via m6A-mediated manner, which catalyzes H3R2me2a and suppresses lipid transporter MFSD2A expression. PRMT6 loss upregulates MFSD2A expression that increases docosahexaenoic acid levels and impairs LSC maintenance. Collectively, our findings reveal a critical role of PRMT6-MFSD2A signaling axis in AML development and provide a therapeutic strategy for targeting LSCs.

PTIP governs NAD+ metabolism by regulating CD38 expression to drive macrophage inflammation.

NAD+ metabolism is involved in many biological processes. However, the underlying mechanism of how NAD+ metabolism is regulated remains elusive. Here, we find that PTIP governs NAD+ metabolism in macrophages by regulating CD38 expression and is required for macrophage inflammation. Through integrating histone modifications with NAD+ metabolic gene expression profiling, we identify PTIP as a key factor in regulating CD38 expression, the primary NAD+-consuming enzyme in macrophages. Interestingly, we find that PTIP deletion impairs the proinflammatory response of primary murine and human macrophages, promotes their metabolic switch from glycolysis to oxidative phosphorylation, and alters NAD+ metabolism via downregulating CD38 expression. Mechanistically, an intronic enhancer of CD38 is identified. PTIP regulates CD38 expression by cooperating with acetyltransferase p300 in establishing the CD38 active enhancer with enriched H3K27ac. Overall, our findings reveal a critical role for PTIP in fine-tuning the inflammatory responses of macrophages via regulating NAD+ metabolism.

Differential m6A RNA landscapes across hematopoiesis reveal a role for IGF2BP2 in preserving hematopoietic stem cell function.

N6-methyladenosine (m6A) on mRNA plays critical roles in various cellular processes. However, the landscape and dynamics of m6A modification in hematopoietic system remain unknown. Here, we delineate a comprehensive m6A landscape across hematopoietic hierarchy and uncover that IGF2BP2 is required for preserving the function of hematopoietic stem cells (HSCs). Our data reveal a cell-type-specific m6A landscape in hematopoiesis. m6A modifications arise mostly in the early stage of hematopoiesis and prefer to play distinct roles for determining mRNA fates in HSCs and committed progenitors. Mechanistically, increased m6A-IGF2BP2 expression controls transcriptional state and maintenance of HSCs. IGF2BP2 deficiency induces quiescence loss and impairs HSC function. Moreover, IGF2BP2 loss increases mitochondrial activity of HSCs by accelerating Bmi1 mRNA decay, leading to de-repression of mitochondria-related genes. Collectively, our results present a fascinating portrait of m6A modification of hematopoietic hierarchy and reveal a key role of IGF2BP2 in maintaining HSC function by restraining mitochondrial activity.

YBX1 is required for maintaining myeloid leukemia cell survival by regulating BCL2 stability in an m6A-dependent manner.

RNA-binding proteins (RBPs) are critical regulators of transcription and translation that are often dysregulated in cancer. Although RBPs are increasingly recognized as being important for normal hematopoiesis and for hematologic malignancies as oncogenes or tumor suppressors, RBPs that are essential for the maintenance and survival of leukemia remain elusive. Here we show that YBX1 is specifically required for maintaining myeloid leukemia cell survival in an N6-methyladenosine (m6A)-dependent manner. We found that expression of YBX1 is significantly upregulated in myeloid leukemia cells, and deletion of YBX1 dramatically induces apoptosis and promotes differentiation coupled with reduced proliferation and impaired leukemic capacity of primary human and mouse acute myeloid leukemia cells in vitro and in vivo. Loss of YBX1 has no obvious effect on normal hematopoiesis. Mechanistically, YBX1 interacts with insulin-like growth factor 2 messenger RNA (mRNA)-binding proteins (IGF2BPs) and stabilizes m6A-tagged RNA. Moreover, YBX1 deficiency dysregulates the expression of apoptosis-related genes and promotes mRNA decay of MYC and BCL2 in an m6A-dependent manner, which contributes to the defective survival that results from deletion of YBX1. Thus, our findings have uncovered a selective and critical role of YBX1 in maintaining myeloid leukemia survival, which might provide a rationale for the therapeutic targeting of YBX1 in myeloid leukemia.

Coinhibition of activated p38 MAPKα and mTORC1 potentiates stemness maintenance of HSCs from SR1-expanded human cord blood CD34+ cells via inhibition of senescence.

The stemness of ex vivo expanded hematopoietic stem cells (HSCs) is usually compromised by current methods. To explore the failure mechanism of stemness maintenance of human HSCs, which were expanded from human umbilical cord blood (hUCB) CD34+ cells, by differentiation inhibitor Stem Regenin 1 (SR1), an antagonist of aryl hydrocarbon receptor, we investigated the activity of p38 mitogen-activated protein kinase α (p38 MAPKα, p38α) and mammalian target of rapamycin complex 1 (mTORC1), and their effect on SR1-expanded hUCB CD34+ cells. Our results showed that cellular senescence occurred in the SR1-expanded hUCB CD34+ cells in which p38α and mTORC1 were successively activated. Furthermore, their coinhibition resulted in a further decrease in hUCB CD34+ cell senescence without an effect on apoptosis, promoted the maintenance of expanded phenotypic HSCs without differentiation inhibition, increased the hematopoietic reconstitution ability of multiple lineages, and potentiated the long-term self-renewal capability of HSCs from SR1-expanded hUCB CD34+ cells in NOD/Shi-scid/IL-2Rγnull mice. Our mechanistic study revealed that senescence inhibition by our strategy was mainly attributed to downregulation of the splicesome, proteasome formation, and pyrimidine metabolism signaling pathways. These results suggest that coinhibition of activated p38α and mTORC1 potentiates stemness maintenance of HSCs from SR1-expanded hUCB CD34+ cells via senescence inhibition. Thus, we established a new strategy to maintain the stemness of ex vivo differentiation inhibitor-expanded human HSCs via coinhibition of multiple independent senescence initiating signal pathways. This senescence inhibition-induced stemness maintenance of ex vivo expanded HSCs could also have an important role in other HSC expansion systems.

Leukemogenic Chromatin Alterations Promote AML Leukemia Stem Cells via a KDM4C-ALKBH5-AXL Signaling Axis.

N6-methyladenosine (m6A) is a commonly present modification of mammalian mRNAs and plays key roles in various cellular processes. m6A modifiers catalyze this reversible modification. However, the underlying mechanisms by which these m6A modifiers are regulated remain elusive. Here we show that expression of m6A demethylase ALKBH5 is regulated by chromatin state alteration during leukemogenesis of human acute myeloid leukemia (AML), and ALKBH5 is required for maintaining leukemia stem cell (LSC) function but is dispensable for normal hematopoiesis. Mechanistically, KDM4C regulates ALKBH5 expression via increasing chromatin accessibility of ALKBH5 locus, by reducing H3K9me3 levels and promoting recruitment of MYB and Pol II. Moreover, ALKBH5 affects mRNA stability of receptor tyrosine kinase AXL in an m6A-dependent way. Thus, our findings link chromatin state dynamics with expression regulation of m6A modifiers and uncover a selective and critical role of ALKBH5 in AML that might act as a therapeutic target of specific targeting LSCs.

A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations.

Prioritization of cancer-related genes from gene expression profiles and proteomic data is vital to improve the targeted therapies research. Although computational approaches have been complementing high-throughput biological experiments on the understanding of human diseases, it still remains a big challenge to accurately discover cancer-related proteins/genes via automatic learning from large-scale protein/gene expression data and protein-protein interaction data. Most of the existing methods are based on network construction combined with gene expression profiles, which ignore the diversity between normal samples and disease cell lines. In this study, we introduced a deep learning model based on a sparse auto-encoder to learn the specific characteristics of protein interactions in cancer cell lines integrated with protein expression data. The model showed learning ability to identify cancer-related proteins/genes from the input of different protein expression profiles by extracting the characteristics of protein interaction information, which could also predict cancer-related protein combinations. Comparing with other reported methods including differential expression and network-based methods, our model got the highest area under the curve value (>0.8) in predicting cancer-related genes. Our study prioritized ~500 high-confidence cancer-related genes; among these genes, 211 already known cancer drug targets were found, which supported the accuracy of our method. The above results indicated that the proposed auto-encoder model could computationally prioritize candidate proteins/genes involved in cancer and improve the targeted therapies research.

Prediction of Protein-Protein Interactions by Evidence Combining Methods.

Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

Construction and analysis of a genome-scale metabolic network for Bacillus licheniformis WX-02.

We constructed the genome-scale metabolic network of Bacillus licheniformis (B. licheniformis) WX-02 by combining genomic annotation, high-throughput phenotype microarray (PM) experiments and literature-based metabolic information. The accuracy of the metabolic network was assessed by an OmniLog PM experiment. The final metabolic model iWX1009 contains 1009 genes, 1141 metabolites and 1762 reactions, and the predicted metabolic phenotypes showed an agreement rate of 76.8% with experimental PM data. In addition, key metabolic features such as growth yield, utilization of different substrates and essential genes were identified by flux balance analysis. A total of 195 essential genes were predicted from LB medium, among which 149 were verified with the experimental essential gene set of B. subtilis 168. With the removal of 5 reactions from the network, pathways for poly-γ-glutamic acid (γ-PGA) synthesis were optimized and the γ-PGA yield reached 83.8 mmol/h. Furthermore, the important metabolites and pathways related to γ-PGA synthesis and bacterium growth were comprehensively analyzed. The present study provides valuable clues for exploring the metabolisms and metabolic regulation of γ-PGA synthesis in B. licheniformis WX-02.

Network analysis of postharvest senescence process in citrus fruits revealed by transcriptomic and metabolomic profiling.

Citrus (Citrus spp.), a nonclimacteric fruit, is one of the most important fruit crops in global fruit industry. However, the biological behavior of citrus fruit ripening and postharvest senescence remains unclear. To better understand the senescence process of citrus fruit, we analyzed data sets from commercial microarrays, gas chromatography-mass spectrometry, and liquid chromatography-mass spectrometry and validated physiological quality detection of four main varieties in the genus Citrus. Network-based approaches of data mining and modeling were used to investigate complex molecular processes in citrus. The Citrus Metabolic Pathway Network and correlation networks were constructed to explore the modules and relationships of the functional genes/metabolites. We found that the different flesh-rind transport of nutrients and water due to the anatomic structural differences among citrus varieties might be an important factor that influences fruit senescence behavior. We then modeled and verified the citrus senescence process. As fruit rind is exposed directly to the environment, which results in energy expenditure in response to biotic and abiotic stresses, nutrients are exported from flesh to rind to maintain the activity of the whole fruit. The depletion of internal substances causes abiotic stresses, which further induces phytohormone reactions, transcription factor regulation, and a series of physiological and biochemical reactions.

Construction of a genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum provides new strategies for bactericide discovery.

We reconstructed the first genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum subsp. carotovorum PC1 based on its genomic sequence, annotation, and physiological data. Metabolic characteristics were analyzed using flux balance analysis (FBA), and the results were afterwards validated by phenotype microarray (PM) experiments. The reconstructed genome-scale metabolic model, iPC1209, contains 2235 reactions, 1113 metabolites and 1209 genes. We identified 19 potential bactericide targets through a comprehensive in silico gene-deletion study. Next, we performed virtual screening to identify candidate inhibitors for an important potential drug target, alkaline phosphatase, and experimentally verified that three lead compounds were able to inhibit both bacterial cell viability and the activity of alkaline phosphatase in vitro. This study illustrates a new strategy for the discovery of agricultural bactericides.

Prediction and functional analysis of the sweet orange protein-protein interaction network.

BACKGROUND: Sweet orange (Citrus sinensis) is one of the most important fruits world-wide. Because it is a woody plant with a long growth cycle, genetic studies of sweet orange are lagging behind those of other species. RESULTS: In this analysis, we employed ortholog identification and domain combination methods to predict the protein-protein interaction (PPI) network for sweet orange. The K-nearest neighbors (KNN) classification method was used to verify and filter the network. The final predicted PPI network, CitrusNet, contained 8,195 proteins with 124,491 interactions. The quality of CitrusNet was evaluated using gene ontology (GO) and Mapman annotations, which confirmed the reliability of the network. In addition, we calculated the expression difference of interacting genes (EDI) in CitrusNet using RNA-seq data from four sweet orange tissues, and also analyzed the EDI distribution and variation in different sub-networks. CONCLUSIONS: Gene expression in CitrusNet has significant modular features. Target of rapamycin (TOR) protein served as the central node of the hormone-signaling sub-network. All evidence supported the idea that TOR can integrate various hormone signals and affect plant growth. CitrusNet provides valuable resources for the study of biological functions in sweet orange.

Citrus sinensis annotation project (CAP): a comprehensive database for sweet orange genome.

Citrus is one of the most important and widely grown fruit crop with global production ranking firstly among all the fruit crops in the world. Sweet orange accounts for more than half of the Citrus production both in fresh fruit and processed juice. We have sequenced the draft genome of a double-haploid sweet orange (C. sinensis cv. Valencia), and constructed the Citrus sinensis annotation project (CAP) to store and visualize the sequenced genomic and transcriptome data. CAP provides GBrowse-based organization of sweet orange genomic data, which integrates ab initio gene prediction, EST, RNA-seq and RNA-paired end tag (RNA-PET) evidence-based gene annotation. Furthermore, we provide a user-friendly web interface to show the predicted protein-protein interactions (PPIs) and metabolic pathways in sweet orange. CAP provides comprehensive information beneficial to the researchers of sweet orange and other woody plants, which is freely available at http://citrus.hzau.edu.cn/.

Comprehensive insights on how 2,4-dichlorophenoxyacetic acid retards senescence in post-harvest citrus fruits using transcriptomic and proteomic approaches.

Auxin-like 2,4-dichlorophenoxyacetic acid (2,4-D), a high-efficiency anti-stalling agent for the post-harvest fresh fruit industry, has had its use restricted due to environmental concerns. However, no other substitutes for 2,4-D are available to the post-harvest industry. Insights into the molecular mechanism underlying the effects of 2,4-D on fruit quality preservation will provide a theoretical basis for exploring new safe and effective anti-stalling agents. This study comprehensively analysed changes in the peel of Olinda Valencia orange [Citrus sinensis (L.) Osbeck] induced by 500 ppm 2,4-D using 'omic'-driven approaches. Transcriptional profiling revealed that transcriptional factor (mainly AP2/ERF, WRKY, and NAC family members), transport, and hormone metabolism genes were over-represented and up-regulated within 24h post-treatment (HPT). Stress defence genes were up-regulated, while cell wall metabolism genes were down-regulated after 48 HPT. However, secondary metabolism genes, especially phenylpropanoid and lignin biosynthesis-related genes, were over-represented at all the time points. Comparative proteomic analysis indicated that the expression of proteins implicated in stress responses (25%), hormone metabolism, and signal transduction (12%) significantly accumulated at the post-transcriptional level. Hormone levels detected by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) showed that abscisic acid, salicylic acid, and 2,4-D significantly increased, while ethylene production (detected by gas chromatography) decreased after 2,4-D treatment. In addition, lignin and water content in the fruit peel also increased and the epicuticle wax ultrastructure was modified. In conclusion, 2,4-D retarded fruit senescence by altering the levels of many endogenous hormones and by improving stress defence capabilities by up-regulating defence-related genes and proteins.

Prediction and characterization of protein-protein interaction network in Xanthomonas oryzae pv. oryzae PXO99 A.

Xanthomonas oryzae pv. oryzae (Xoo), the causal agent of bacterial blight disease in rice, is one of the most serious plant pathogens worldwide. In the current analysis, we constructed a protein-protein interaction network of Xoo strain PXO99(A) with two computational approaches (interolog method and domain combination method), and verified by K-Nearest Neighbors classification method. The predicted PPI network of Xoo PXO99(A) contains 36,886 interactions among 1988 proteins. KNN verification and GO annotation confirm the reliability of the network. Detailed analysis of flagellar synthesis and chemotaxis system shows that σ factors (especially σ(28), σ(54)) in Xoo PXO99(A) are very important for flagellar synthesis and motility, and transcription factors RpoA, RpoB and RpoC are hubs to connect most σ factors. Furthermore, Xoo PXO99(A) may have both cAMP and c-di-GMP signal transduction system, and the latter is especially important for this plant pathogen. This study therefore provides valuable clues to explore the pathogenicity and metabolic regulation of Xoo PXO99(A).

The draft genome of sweet orange (Citrus sinensis).

Oranges are an important nutritional source for human health and have immense economic value. Here we present a comprehensive analysis of the draft genome of sweet orange (Citrus sinensis). The assembled sequence covers 87.3% of the estimated orange genome, which is relatively compact, as 20% is composed of repetitive elements. We predicted 29,445 protein-coding genes, half of which are in the heterozygous state. With additional sequencing of two more citrus species and comparative analyses of seven citrus genomes, we present evidence to suggest that sweet orange originated from a backcross hybrid between pummelo and mandarin. Focused analysis on genes involved in vitamin C metabolism showed that GalUR, encoding the rate-limiting enzyme of the galacturonate pathway, is significantly upregulated in orange fruit, and the recent expansion of this gene family may provide a genomic basis. This draft genome represents a valuable resource for understanding and improving many important citrus traits in the future.

DIGA--a database of improved gene annotation for phytopathogens.

BACKGROUND: Bacterial plant pathogens are very harmful to their host plants, which can cause devastating agricultural losses in the world. With the development of microbial genome sequencing, many strains of phytopathogens have been sequenced. However, some misannotations exist in these phytopathogen genomes. Our objective is to improve these annotations and store them in a central database DIGAP. DESCRIPTION: DIGAP includes the following improved information on phytopathogen genomes. (i) All the 'hypothetical proteins' were checked, and non-coding ORFs recognized by the Z curve method were removed. (ii) The translation initiation sites (TISs) of 20% approximately 25% of all the protein-coding genes have been corrected based on the NCBI RefSeq, ProTISA database and an ab initio program, GS-Finder. (iii) Potential functions of about 10% 'hypothetical proteins' have been predicted using sequence alignment tools. (iv) Two theoretical gene expression indices, the codon adaptation index (CAI) and the E(g) index, were calculated to predict the gene expression levels. (v) Potential agricultural bactericide targets and their homology-modeled 3D structures are provided in the database, which is of significance for agricultural antibiotic discovery. CONCLUSION: The results in DIGAP provide useful information for understanding the pathogenetic mechanisms of phytopathogens and for finding agricultural bactericides. DIGAP is freely available at http://ibi.hzau.edu.cn/digap/.