Genome and transcriptome architecture of allopolyploid okra (Abelmoschus esculentus)

We present the first annotated genome assembly of the allopolyploid okra (Abelmoschus esculentus). Analysis of telomeric repeats and gene rich regions suggested we obtained whole chromosome and chromosomal arm scaffolds. Besides long distal blocks we also detected short interstitial TTTAGGG telomeric repeats, possibly representing hallmarks of chromosomal speciation upon polyploidization of okra. Ribosomal RNA genes are organized in 5S clusters separated from the 18S-5.8S-28S units, clearly indicating an S-type rRNA gene arrangement. The assembly is consistent with cytogenetic and cytometry observations, identifying 65 chromosomes and 1.45Gb of expected genome size in a haploid sibling. Approximately 57% of the genome consists of repetitive sequence. BUSCO scores and A50 plot statistics indicated a nearly complete genome. Kmer distribution analysis suggests that approximately 75% has a diploid nature, and at least 15% of the genome is heterozygous. We did not observe aberrant meiotic configurations, suggesting there is no recombination among the sub-genomes. BUSCO configurations pointed to the presence of at least 3 sub-genomes. These observations are indicative for an allopolyploid nature of the okra genome. Structural annotation using gene models derived from mapped transcriptome data, generated over 130,000 putative genes. The discovered genes appeared to be located predominantly at the distal ends of scaffolds, gradually decreasing in abundance toward more centrally positioned scaffold domains. In contrast, LTR retrotransposons were more abundant in centrally located scaffold domains, while less frequently represented in the distal ends. This gene and LTR-retrotransposon distribution is consistent with the observed heterochromatin organization of pericentromeric heterochromatin and distal euchromatin. The derived amino acid queries of putative genes were subsequently used for phenol biosynthesis pathway annotation in okra. Comparison against manually curated reference KEGG pathways from related Malvaceae species revealed the genetic basis for putative enzyme coding genes that likely enable metabolic reactions involved in the biosynthesis of dietary and therapeutic compounds in okra.

Ubiquitin Proteasome Pathway annotation in Diaphorina citri can reveal potential targets for RNAi based pest management

Ubiquitination is an ATP-dependent process that targets proteins for degradation by the proteasome. In this study, we annotated 15 genes from the ubiquitin-proteasome pathway in the Asian citrus psyllid, Diaphorina citri. This psyllid vector has come to prominence in the last decade due to its role in the transmission of the devastating bacterial pathogen, Candidatus Liberibacter asiaticus (CLas). Infection of citrus crops by this pathogen causes Huanglongbing (HLB or citrus greening disease) and results in the eventual death of citrus trees. The identification and correct annotation of these genes in D. citri will be useful for functional genomic studies that aid in the development of RNAi-based management strategies aimed at reducing the spread of HLB. Investigating the effects of CLas infection on the expression of ubiquitin-proteasome pathway genes may provide new information regarding the role that these genes play in the acquisition and transmission of CLas by D. citri.

Use of Network Pharmacology and Molecular Docking Technology to Analyze the Mechanism of Action of Velvet Antler in the Treatment of Postmenopausal Osteoporosis

Deer velvet antlers are the young horns of male deer that are not ossified and densely overgrown. Velvet antler and its preparations have been widely used in the treatment of postmenopausal osteoporosis (PMOP) in recent years, although its mechanism of action in the human body remains unclear. To screen the effective ingredients and targets of velvet antler in the treatment of PMOP using network pharmacology and to explore the potential mechanisms of velvet antler action in such treatments, we screened the active ingredients and targets of velvet antler in the BATMAN-TCM database. We also screened the relevant targets of PMOP in the GeneCards and OMIM databases and then compared the targets at the intersection of both velvet antler and PMOP. We used Cytoscape 3.7.2 software to construct a network diagram of “disease-drug-components-targets” and a protein-protein interaction (PPI) network through the STRING database and screened out the core targets; the R language was then used to analyze the shared targets between antler and PMOP for GO-enrichment analysis and KEGG pathway-annotation analysis. Furthermore, we used the professional software Maestro 11.1 to verify the predictive analysis based on network pharmacology. Hematoxylin-eosin (H&E) staining and micro-CT were used to observe the changes in trabecular bone tissue, further confirming the results of network pharmacological analysis. The potentially effective components of velvet antler principally include 17β-E2, adenosine triphosphate, and oestrone. These components act on key target genes such as AKT1, IL6, MAPK3, TP53, EGFR, SRC, and TNF and regulate the PI3K/Akt-signaling and MAPK-signaling pathways. These molecules participate in a series of processes such as cellular differentiation, apoptosis, metabolism, and inflammation and can ultimately be used to treat PMOP; they reflect the overall regulation, network regulation, and protein interactions.

WormPaths: Caenorhabditis elegans metabolic pathway annotation and visualization

In our group, we aim to understand metabolism in the nematode Caenorhabditis elegans and its relationships with gene expression, physiology and the response to therapeutic drugs. Visualization of the metabolic pathways that comprise the metabolic network is extremely useful for interpreting a wide variety of experiments. Detailed annotated metabolic pathway maps for C. elegans is mostly limited to pan-organismal maps, many with incomplete or inaccurate pathway and enzyme annotations. Here we present WormPaths, which is composed of two parts: 1) the careful manual annotation of metabolic genes into pathways, categories and levels, and 2) 62 pathway maps that include metabolites, metabolite structures, genes, reactions, and pathway connections between maps. These maps are available on the WormFlux website. We show that WormPaths provides easy-to-navigate maps and that the different levels in WormPaths can be used for metabolic pathway enrichment analysis of transcriptomic data. In the future we envision further developing these maps to be more interactive, with an analogy of road maps that are available on mobile devices.

Predicting the Molecular Mechanism of “Angong Niuhuang Pills” in the Treatment of COVID-19 Based on Network Pharmacology

Introduction Angong Niuhuang Pills (AGNH), a Chinese patent medicine recommended in the “Diagnosis and Treatment Plan for COVID-19 (8th Edition),” may be clinically effective in treating COVID-19. The active components and signal pathways of AGNH through network pharmacology have been examined, and its potential mechanisms determined. Methods We screened the components in the Traditional Chinese Medicine Systems Pharmacology (TCMSP) via Drug-like properties (DL) and Oral bioavailability (OB); PharmMapper and GeneCards databases were used to collect components and COVID-19 related targets; KEGG pathway annotation and GO bioinformatics analysis were based on KOBAS3.0 database; “herb-components-targets-pathways” (H-C-T-P) network and protein-protein interaction network (PPI) were constructed by Cytoscape 3.6.1 software and STRING 10.5 database; we utilized virtual molecular docking to predict the binding ability of the active components and key proteins. Results A total of 87 components and 40 targets were screened in AGNH. The molecular docking results showed that the docking scores of the top 3 active components and the targets were all greater than 90. Conclusion Through network pharmacology research, we found that moslosooflavone, oroxylin A, and salvigenin in AGNH can combine with ACE2 and 3CL, and then are involved in the MAPK and JAK-STAT signaling pathways. Finally, it is suggested that AGNH may have a role in the treatment of COVID-19.

Mechanism of Compound Houttuynia Mixture as an Anti-COVID-19 Drug Based on Network Pharmacology and Molecular Docking

Objective To explore the anti-COVID-19 active components and mechanism of Compound Houttuynia mixture by using network pharmacology and molecular docking. Methods First, the main chemical components of Compound Houttuynia mixture were obtained by using the TCMSP database and referring to relevant chemical composition literature. The components were screened for OB ≥30% and DL ≥0.18 as the threshold values. Then Swiss Target Prediction database was used to predict the target of the active components and map the targets of COVID-19 obtained through GeneCards database to obtain the gene pool of the potential target of COVID-19 resistance of the active components of Compound Houttuynia mixture. Next, DAVID database was used for GO enrichment and KEGG pathway annotation of targets function. Cytoscape 3.8.0 software was used to construct a “components-targets-pathways” network. Then String database was used to construct a “protein-protein interaction” network. Finally, the core targets, SARS-COV-2 3 Cl, ACE2 and the core active components of Compound Houttuyna Mixture were imported into the Discovery Studio 2016 Client database for molecular docking verification. Results Eighty-two active compounds, including Xylostosidine, Arctiin, ZINC12153652 and ZINC338038, were screened from Compound Houttuyniae mixture. The key targets involved 128 targets, including MAPK1, MAPK3, MAPK8, MAPK14, TP53, TNF, and IL6. The HIF-1 signaling, VEGF signaling, TNF signaling and another 127 signaling pathways associated with COVID-19 were affected ( P < 0.05). From the results of molecular docking, the binding ability between the selected active components and the core targets was strong. Conclusion Through the combination of network pharmacology and molecular docking technology, this study revealed that the therapeutic effect of Compound Houttuynia mixture on COVID-19 was realized through multiple components, multiple targets and multiple pathways, which provided a certain scientific basis of the clinical application of Compound Houttuynia mixture.

Pharmacological Mechanisms Underlying the Anti-asthmatic Effects of Modified Guomin Decoction Determined by Network Pharmacology and Molecular Docking

BackgroundAsthma is a chronic inflammatory disease characterized by Th2-predominant inflammation and airway remodeling. Modified Guo Min decoction (MGMD) has been an extensive practical strategy for allergic disorders in China. Although its potential anti-asthmatic activity has been reported, the exact mechanism of action of MGMD in asthma remains unexplored.MethodsNetwork pharmacology approach was employed to predict the active components, potential targets, and molecular mechanism of MGMD for asthma treatment, including drug-likeness evaluation, oral bioavailability prediction, protein–protein interaction (PPI) network construction and analysis, Gene Ontology (GO) terms, and Reactome pathway annotation. Molecular docking was carried out to investigate interactions between active compounds and potential targets.ResultsA total of 92 active compounds and 72 anti-asthma targets of MGMD were selected for analysis. The GO enrichment analysis results indicated that the anti-asthmatic targets of MGMD mainly participate in inflammatory and in airway remolding processes. The Reactome pathway analysis showed that MGMD prevents asthma mainly through regulation of the IL-4 and IL-13 signaling and the specialized pro-resolving mediators (SPMs) biosynthesis. Molecular docking results suggest that each bioactive compounds (quercetin, wogonin, luteolin, naringenin, and kaempferol) is capable to bind with STAT3, PTGS2, JUN, VEGFA, EGFR, and ALOX5.ConclusionThis study revealed the active ingredients and potential molecular mechanism by which MGMD treatment is effective against airway inflammation and remodeling in asthma through regulating IL-4 and IL-13 signaling and SPMs biosynthesis.

WormPaths: Caenorhabditis elegans metabolic pathway annotation and visualization

In our group, we aim to understand metabolism in the nematode Caenorhabditis elegans and its relationships with gene expression, physiology and the response to therapeutic drugs. On March 15, 2020, a stay-at-home order was put into effect in the state of Massachusetts, USA, to flatten the curve of the spread of the novel SARS-CoV2 virus that causes COVID-19. For biomedical researchers in our state, this meant putting a hold on experiments for nine weeks until May 18, 2020. To keep the lab engaged and productive, and to enhance communication and collaboration, we embarked on an in-lab project that we all found important but that we never had the time for: the detailed annotation and drawing of C. elegans metabolic pathways. As a result, we present WormPaths, which is composed of two parts: 1) the careful manual annotation of metabolic genes into pathways, categories and levels, and 2) 66 pathway maps that include metabolites, metabolite structures, genes, reactions, and pathway connections between maps. These maps are available on our WormFlux website. We show that WormPaths provides easy-to-navigate maps and that the different levels in WormPaths can be used for metabolic pathway enrichment analysis of transcriptomic data. In the unfortunate event of additional lockdowns, we envision further developing these maps to be more interactive, with an analogy of road maps that are available on mobile devices.

Cortical Transcriptomic Alterations in Association With Appetitive Neuropeptides and Body Mass Index in Posttraumatic Stress Disorder

Background The molecular pathology underlying posttraumatic stress disorder (PTSD) remains unclear mainly due to a lack of human PTSD postmortem brain tissue. The orexigenic neuropeptides ghrelin, neuropeptide Y, and hypocretin were recently implicated in modulating negative affect. Drawing from the largest functional genomics study of human PTSD postmortem tissue, we investigated whether there were molecular changes of these and other appetitive molecules. Further, we explored the interaction between PTSD and body mass index (BMI) on gene expression. Methods We analyzed previously reported transcriptomic data from 4 prefrontal cortex regions from 52 individuals with PTSD and 46 matched neurotypical controls. We employed gene co-expression network analysis across the transcriptomes of these regions to uncover PTSD-specific networks containing orexigenic genes. We utilized Ingenuity Pathway Analysis software for pathway annotation. We identified differentially expressed genes (DEGs) among individuals with and without PTSD, stratified by sex and BMI. Results Three PTSD-associated networks (P &lt; .01) contained genes in signaling families of appetitive molecules: 2 in females and 1 in all subjects. We uncovered DEGs (P &lt; .05) between PTSD and control subjects stratified by sex and BMI with especially robust changes in males with PTSD with elevated vs normal BMI. Further, we identified putative upstream regulators (P &lt; .05) driving these changes, many of which were enriched for involvement in inflammation. Conclusions PTSD-associated cortical transcriptomic modules contain transcripts of appetitive genes, and BMI further interacts with PTSD to impact expression. DEGs and inferred upstream regulators of these modules could represent targets for future pharmacotherapies for obesity in PTSD.

Pathway-specific model estimation for improved pathway annotation by network crosstalk

Pathway enrichment analysis is the most common approach for understanding which biological processes are affected by altered gene activities under specific conditions. However, it has been challenging to find a method that efficiently avoids false positives while keeping a high sensitivity. We here present a new network-based method ANUBIX based on sampling random gene sets against intact pathway. Benchmarking shows that ANUBIX is considerably more accurate than previous network crosstalk based methods, which have the drawback of modelling pathways as random gene sets. We demonstrate that ANUBIX does not have a bias for finding certain pathways, which previous methods do, and show that ANUBIX finds biologically relevant pathways that are missed by other methods.