BioGraph: Data Model for Linking and Querying Diverse Biological Metadata

Studying the association of gene function, diseases, and regulatory gene network reconstruction demands data compatibility. Data from different databases follow distinct schemas and are accessible in heterogenic ways. Although the experiments differ, data may still be related to the same biological entities. Some entities may not be strictly biological, such as geolocations of habitats or paper references, but they provide a broader context for other entities. The same entities from different datasets can share similar properties, which may or may not be found within other datasets. Joint, simultaneous data fetching from multiple data sources is complicated for the end-user or, in many cases, unsupported and inefficient due to differences in data structures and ways of accessing the data. We propose BioGraph-a new model that enables connecting and retrieving information from the linked biological data that originated from diverse datasets. We have tested the model on metadata collected from five diverse public datasets and successfully constructed a knowledge graph containing more than 17 million model objects, of which 2.5 million are individual biological entity objects. The model enables the selection of complex patterns and retrieval of matched results that can be discovered only by joining the data from multiple sources.

ZBTB7A as a novel vulnerability in neuroendocrine prostate cancer

Neuroendocrine prostate cancer (NEPC) is a highly aggressive subtype of prostate cancer. NEPC is characterized by the loss of androgen receptor (AR) signaling and transdifferentiation toward small-cell neuroendocrine (SCN) phenotypes, which results in resistance to AR-targeted therapy. NEPC resembles other SCN carcinomas clinically, histologically and in gene expression. Here, we leveraged SCN phenotype scores of various cancer cell lines and gene depletion screens from the Cancer Dependency Map (DepMap) to identify vulnerabilities in NEPC. We discovered ZBTB7A, a transcription factor, as a candidate promoting the progression of NEPC. Cancer cells with high SCN phenotype scores showed a strong dependency on RET kinase activity with a high correlation between RET and ZBTB7A dependencies in these cells. Utilizing informatic modeling of whole transcriptome sequencing data from patient samples, we identified distinct gene networking patterns of ZBTB7A in NEPC versus prostate adenocarcinoma. Specifically, we observed a robust association of ZBTB7A with genes promoting cell cycle progression, including apoptosis regulating genes. Silencing ZBTB7A in a NEPC cell line confirmed the dependency on ZBTB7A for cell growth via suppression of the G1/S transition in the cell cycle and induction of apoptosis. Collectively, our results highlight the oncogenic function of ZBTB7A in NEPC and emphasize the value of ZBTB7A as a promising therapeutic strategy for targeting NEPC tumors.

Selective sweeps linked to the colonization of novel habitats and climatic changes in a wild tomato species

Positive selection is the driving force underpinning local adaptation and leaves footprints of selective sweeps on the underlying major genes. Quantifying the timing of selection and revealing the genetic bases of adaptation in plant species occurring in steep and varying environmental gradients are crucial to predict a species' ability to colonize new niches. We use whole-genome sequence data from six populations across three different habitats of the wild tomato species Solanum chilense to infer the past demographic history and search for genes under strong positive selection. We then correlate current and past climatic projections with the demographic history, allele frequencies, the age of selection events and distribution shifts. Several selective sweeps occur at regulatory networks involved in root-hair development in low altitude and response to photoperiod and vernalization in high-altitude populations. These sweeps appear to occur in a concerted fashion in a given regulatory gene network at particular periods of substantial climatic change. Using a unique combination of genome scans and modelling of past climatic data, we quantify the timing of selection at genes likely underpinning local adaptation to semiarid habitats.

A molecular study of pediatric pilomyxoid and pilocytic astrocytomas: Genome-wide copy number screening, retrospective analysis of clinicopathological features and long-term clinical outcome

Background

Pilocytic Astrocytoma (PA) is the most common pediatric brain tumors. PAs are slow-growing tumors with high survival rates. However, a distinct subgroup of tumors defined as pilomyxoid astrocytoma (PMA) presents unique histological characteristics and have more aggressive clinical course. The studies on genetics of PMA are scarce.

Methods

In this study, we report one of the largest cohort of pediatric patients with pilomyxoid (PMA) and pilocytic astrocytomas (PA) in Saudi population providing a comprehensive clinical picture, retrospective analysis with long-term follow-up, genome-wide copy number changes, and clinical outcome of these pediatric tumors. We examined and compared genome-wide copy number aberrations (CNAs) and the clinical outcome of the patients with PA and PMA.

Results

The median progression free survival for the whole cohort was 156 months and it was 111 months for the PMA, however, not statistically significantly different between the groups (log-rank test, P = 0.726). We have identified 41 CNAs (34 gains and 7 losses) in all tested patients. Our study yielded the previously reported KIAA1549-BRAF Fusion gene in over 88% of the tested patients (89% and 80% in PMA and PA, respectively). Besides the fusion gene, twelve patients had additional genomic CNAs. Furthermore, pathway and gene network analyses of genes in the fusion region revealed alterations in retinoic acid mediated apoptosis and MAPK signaling pathways and key hub genes that may potentially be involved in tumor growth and progression, including BRAF, LUC7L2, MKRN1, RICTOR, TP53, HIPK2, HNF4A, POU5F, and SOX4.

Conclusion

Our study is the first report of a large cohort of patients with PMA and PA in the Saudi population that provides detailed clinical features, genomic copy number changes, and outcome of these pediatric tumors and may help better diagnosis and characterization of PMA.

In-Depth Understanding of the Genetic Control of Stripe Rust Resistance (Puccinia striiformis f. sp. tritici) Induced in Wheat (Triticum aestivum) by Trichoderma asperellum T34

Wheat stripe rust (caused by Puccinia striiformis f. tritici Erikss.) causes severe yield losses worldwide. Due to the continuous appearance of new stripe rust races, resistance has been broken in most of the highly resistant genotypes in Egypt and worldwide. Therefore, looking for new ways to resist such a severe disease is urgently needed. Trichoderma asperellum strain T34 has been known as an effective bioagent against many crop diseases. It exists naturally in Egyptian fields. Therefore, in our study, the effectiveness of strain T34 was tested as a bioagent against wheat stripe rust. For this purpose, 198 spring wheat genotypes were tested for their resistance against two different P. striiformis f. tritici populations collected from the Egyptian fields. The most highly aggressive P. striiformis f. tritici population was used to test the effectiveness of strain T34. Highly significant differences were found between strain T34 and stripe rust, suggesting the effectiveness of strain T34 in stripe rust resistance. A genome-wide association study identified 48 gene models controlling resistance under normal conditions and 46 gene models controlling strain T34-induced resistance. Of these gene models, only one common gene model was found, suggesting the presence of two different genetic systems controlling resistance under each condition. The pathways of the biological processes were investigated under both conditions. This study provided in-depth understanding of genetic control and, hence, will accelerate the future of wheat breeding programs for stripe rust resistance.

scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data

The sparse nature of single-cell omics data makes it challenging to dissect the wiring and rewiring of the transcriptional and signaling drivers that regulate cellular states. Many of the drivers, referred to as "hidden drivers", are difficult to identify via conventional expression analysis due to low expression and inconsistency between RNA and protein activity caused by post-translational and other modifications. To address this issue, we developed scMINER, a mutual information (MI)-based computational framework for unsupervised clustering analysis and cell-type specific inference of intracellular networks, hidden drivers and network rewiring from single-cell RNA-seq data. We designed scMINER to capture nonlinear cell-cell and gene-gene relationships and infer driver activities. Systematic benchmarking showed that scMINER outperforms popular single-cell clustering algorithms, especially in distinguishing similar cell types. With respect to network inference, scMINER does not rely on the binding motifs which are available for a limited set of transcription factors, therefore scMINER can provide quantitative activity assessment for more than 6,000 transcription and signaling drivers from a scRNA-seq experiment. As demonstrations, we used scMINER to expose hidden transcription and signaling drivers and dissect their regulon rewiring in immune cell heterogeneity, lineage differentiation, and tissue specification. Overall, activity-based scMINER is a widely applicable, highly accurate, reproducible and scalable method for inferring cellular transcriptional and signaling networks in each cell state from scRNA-seq data. The scMINER software is publicly accessible via: https://github.com/jyyulab/scMINER.

scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data

The sparse nature of single-cell omics data makes it challenging to dissect the wiring and rewiring of the transcriptional and signaling drivers that regulate cellular states. Many of the drivers, referred to as "hidden drivers", are difficult to identify via conventional expression analysis due to low expression and inconsistency between RNA and protein activity caused by post-translational and other modifications. To address this issue, we developed scMINER, a mutual information (MI)-based computational framework for unsupervised clustering analysis and cell-type specific inference of intracellular networks, hidden drivers and network rewiring from single-cell RNA-seq data. We designed scMINER to capture nonlinear cell-cell and gene-gene relationships and infer driver activities. Systematic benchmarking showed that scMINER outperforms popular single-cell clustering algorithms, especially in distinguishing similar cell types. With respect to network inference, scMINER does not rely on the binding motifs which are available for a limited set of transcription factors, therefore scMINER can provide quantitative activity assessment for more than 6,000 transcription and signaling drivers from a scRNA-seq experiment. As demonstrations, we used scMINER to expose hidden transcription and signaling drivers and dissect their regulon rewiring in immune cell heterogeneity, lineage differentiation, and tissue specification. Overall, activity-based scMINER is a widely applicable, highly accurate, reproducible and scalable method for inferring cellular transcriptional and signaling networks in each cell state from scRNA-seq data. The scMINER software is publicly accessible via: https://github.com/jyyulab/scMINER.

Identification of New Candidate Genes Related to Semen Traits in Duroc Pigs through Weighted Single-Step GWAS

Semen traits play a key role in the pig industry because boar semen is widely used in purebred and crossbred pigs. The production of high-quality semen is crucial to ensuring a good result in artificial insemination. With the wide application of artificial insemination in the pig industry, more and more attention has been paid to the improvement of semen traits by genetic selection. The purpose of this study was to identify the genetic regions and candidate genes associated with semen traits of Duroc boars. We used weighted single-step GWAS to identify candidate genes associated with sperm motility, sperm progressive motility, sperm abnormality rate and total sperm count in Duroc pigs. In Duroc pigs, the three most important windows for sperm motility-sperm progressive motility, sperm abnormality rate, and total sperm count-explained 12.45%, 9.77%, 15.80%, and 12.15% of the genetic variance, respectively. Some genes that are reported to be associated with spermatogenesis, testicular function and male fertility in mammals have been detected previously. The candidate genes CATSPER1, STRA8, ZSWIM7, TEKT3, UBB, PTBP2, EIF2B2, MLH3, and CCDC70 were associated with semen traits in Duroc pigs. We found a common candidate gene, STRA8, in sperm motility and sperm progressive motility, and common candidate genes ZSWIM7, TEKT3 and UBB in sperm motility and sperm abnormality rate, which confirms the hypothesis of gene pleiotropy. Gene network enrichment analysis showed that STRA8, UBB and CATSPER1 were enriched in the common biological process and participated in male meiosis and spermatogenesis. The SNPs of candidate genes can be given more weight in genome selection to improve the ability of genome prediction. This study provides further insight into the understanding the genetic structure of semen traits in Duroc boars.

Targeted next-generation sequencing for genetic variants of left ventricular mass status among community-based adults in Taiwan

Background: Left ventricular mass is a highly heritable disease. Previous studies have suggested common genetic variants to be associated with left ventricular mass; however, the roles of rare variants are still unknown. We performed targeted next-generation sequencing using the TruSight Cardio panel, which provides comprehensive coverage of 175 genes with known associations to 17 inherited cardiac conditions. Methods: We conducted next-generation sequencing using the Illumina TruSight Cardiomyopathy Target Genes platform using the 5% and 95% extreme values of left ventricular mass from community-based participants. After removing poor-quality next-generation sequencing subjects, including call rate <98% and Mendelian errors, 144 participants were used for the analysis. We performed downstream analysis, including quality control, alignment, coverage length, and annotation; after setting filtering criteria for depths more than 60, we found a total of 144 samples and 165 target genes for further analysis. Results: Of the 12,287 autosomal variants, most had minor allele frequencies of <1% (rare frequency), and variants had minor allele frequencies ranging from 1% to 5%. In the multi-allele variant analyses, 16 loci in 15 genes were significant using the false discovery rate of less than .1. In addition, gene-based analyses using continuous and binary outcomes showed that three genes (CASQ2, COL5A1, and FXN) remained to be associated with left ventricular mass status. One single-nucleotide polymorphism (rs7538337) was enriched for the CASQ2 gene expressed in aorta artery (p = 4.6 × 10-18), as was another single-nucleotide polymorphism (rs11103536) for the COL5A1 gene expressed in aorta artery (p = 2.0 × 10-9). Among the novel genes discovered, CASQ2, COL5A1, and FXN are within a protein-protein interaction network with known cardiovascular genes. Conclusion: We clearly demonstrated candidate genes to be associated with left ventricular mass. Further studies to characterize the target genes and variants for their functional mechanisms are warranted.

Mechanism of CAV and CAVIN Family Genes in Acute Lung Injury based on DeepGENE

BACKGROUND

The fatality rate of acute lung injury (ALI) is as high as 40% to 60%. Although various factors, such as sepsis, trauma, pneumonia, burns, blood transfusion, cardiopulmonary bypass, and pancreatitis, can induce ALI, patients with these risk factors will eventually develop ALI. The rate of developing ALI is not high, and the outcomes of ALI patients vary, indicating that it is related to genetic differences between individuals. In a previous study, we found multiple functions of cavin-2 in lung function. In addition, many other studies have revealed that CAV1 is a critical regulator of lung injury. Due to the strong relationship between cavin-2 and CAV1, we suspect that cavin-2 is also associated with ALI. Furthermore, we are curious about the role of the CAV family and cavin family genes in ALI.

METHODS

To reveal the mechanism of CAV and CAVIN family genes in ALI, we propose DeepGENE to predict whether CAV and CAVIN family genes are associated with ALI. This method constructs a gene interaction network and extracts gene expression in 84 tissues. We divided these features into two groups and used two network encoders to encode and learn the features.

RESULTS

Compared with DNN, GBDT, RF and KNN, the AUC of DeepGENE increased by 7.89%, 16.84%, 20.19% and 32.01%, respectively. The AUPR scores increased by 8.05%, 15.58%, 22.56% and 23.34%. DeepGENE shows that CAVIN-1, CAVIN-2, CAVIN-3 and CAV2 are related to ALI.

CONCLUSION

DeepGENE is a reliable method for identifying acute lung injury-related genes. Multiple CAV and CAVIN family genes are associated with acute lung injury-related genes through multiple pathways and gene functions.

Comprehensive genomic identification of cotton starch synthase genes reveals that GhSS9 regulates drought tolerance

Introduction

Starch metabolism is involved in the stress response. Starch synthase (SS) is the key enzyme in plant starch synthesis, which plays an indispensable role in the conversion of pyrophosphoric acid to starch. However, the SS gene family in cotton has not been comprehensively identified and systematically analyzed.

Result

In our study, a total of 76 SS genes were identified from four cotton genomes and divided into five subfamilies through phylogenetic analysis. Genetic structure analysis proved that SS genes from the same subfamily had similar genetic structure and conserved sequences. A cis-element analysis of the SS gene promoter showed that it mainly contains light response elements, plant hormone response elements, and abiotic stress elements, which indicated that the SS gene played key roles not only in starch synthesis but also in abiotic stress response. Furthermore, we also conducted a gene interaction network for SS proteins. Silencing GhSS9 expression decreased the resistance of cotton to drought stress. These findings suggested that SS genes could be related to drought stress in cotton, which provided theoretical support for further research on the regulation mechanism of SS genes on abiotic starch synthesis and sugar levels.

Gene Expression Network and Circ_0008012 Promote Progression in MLL/AF4 Positive Acute Lymphoblastic Leukemia

BACKGROUND

Acute lymphoblastic leukemia with MLL/AF4 rearrangement remains a major hurdle to improving outcomes. Gene network and circRNAs have been found to participate in tumorigenesis, while their roles in leukemia still need to be explored. Recent patents have shown that circRNAs exhibit the markers for the children ALL, although the target and related mechanism remain to be elucidated.

OBJECTIVE

This study aims to explore the possible targets and mechanisms of ALL with MLLAF4 rearrangement.

METHODS

We first generated a gene network focusing on MLL-AF4 rearrangement. Cell viability was determined with Cell Counting Kit-8 assay. The cell apoptosis was tested by the Annexin V/PI assay. The RNA-protein complexes were analyzed by qRT-PCR, and the pathway proteins were analyzed by western blot.

RESULTS

This gene network was associated with biological processes, such as nucleic acid metabolism and immunity, indicating its key role in inflammation. We found that circ_0008012 was upregulated in MLL/AF4 ALL cells and regulated cell proliferation and apoptosis. Further computed simulation and RIP showed that IKKβ was the strongest protein in the NF-κB pathway binding with circ_0008012. As a result, possible regulation of circ_0008012 is suggested by binding IKKβ in the IKKα:IKKβ:IKKγ compound, which then phosphorylates IκB and activates NF- κB:p65:p300 compound in cell nucleus, thereby leading to leukemia.

CONCLUSION

We identified a gene network for MLL/AF4 ALL. Moreover, circ_0008012 may be a therapeutic target for this subtype of ALL.

Growth rate-associated transcriptome reorganization in response to genomic, environmental, and evolutionary interruptions

The genomic, environmental, and evolutionary interruptions caused the changes in bacterial growth, which were stringently associated with changes in gene expression. The growth and gene expression changes remained unclear in response to these interruptions that occurred combinative. As a pilot study, whether and how bacterial growth was affected by the individual and dual interruptions of genome reduction, environmental stress, and adaptive evolution were investigated. Growth assay showed that the presence of the environmental stressors, i.e., threonine and chloramphenicol, significantly decreased the growth rate of the wild-type Escherichia coli, whereas not that of the reduced genome. It indicated a canceling effect in bacterial growth due to the dual interruption of the genomic and environmental changes. Experimental evolution of the reduced genome released the canceling effect by improving growth fitness. Intriguingly, the transcriptome architecture maintained a homeostatic chromosomal periodicity regardless of the genomic, environmental, and evolutionary interruptions. Negative epistasis in transcriptome reorganization was commonly observed in response to the dual interruptions, which might contribute to the canceling effect. It was supported by the changes in the numbers of differentially expressed genes (DEGs) and the enriched regulons and functions. Gene network analysis newly constructed 11 gene modules, one out of which was correlated to the growth rate. Enrichment of DEGs in these modules successfully categorized them into three types, i.e., conserved, responsive, and epistatic. Taken together, homeostasis in transcriptome architecture was essential to being alive, and it might be attributed to the negative epistasis in transcriptome reorganization and the functional differentiation in gene modules. The present study directly connected bacterial growth fitness with transcriptome reorganization and provided a global view of how microorganisms responded to genomic, environmental, and evolutionary interruptions for survival from wild nature.

Bioinformatic Analysis Revealed the Essential Regulatory Genes and Pathways of Early and Advanced Atherosclerotic Plaque in Humans

Atherosclerosis (AS) is a lipid-induced, chronic inflammatory, autoimmune disease affecting multiple arteries. Although much effort has been put into AS research in the past decades, it is still the leading cause of death worldwide. The complex genetic network regulation underlying the pathogenesis of AS still needs further investigation to provide effective targeted therapy for AS. We performed a bioinformatic microarray data analysis at different atherosclerotic plaque stages from the Gene Expression Omnibus database with accession numbers GSE43292 and GSE28829. Using gene set enrichment analysis, we further confirmed the immune-related pathways that play an important role in the development of AS. We are reporting, for the first time, that the metabolism of the three branched-chain amino acids (BCAAs; leucine, isoleucine, and valine) and short-chain fatty acids (SCFA; propanoate, and butanoate) are involved in the progression of AS using microarray data of atherosclerotic plaque tissue. Immune and muscle system-related pathways were further confirmed as highly regulated pathways during the development of AS using gene expression pattern analysis. Furthermore, we also identified four modules mainly involved in histone modification, immune-related processes, macroautophagy, and B cell activation with modular differential connectivity in the dataset of GSE43292, and three modules related to immune-related processes, B cell activation, and nuclear division in the dataset of GSE28829 also display modular differential connectivity based on the weighted gene co-expression network analysis. Finally, we identified eight key genes related to the pathways of immune and muscle system function as potential therapeutic biomarkers to distinguish patients with early or advanced stages in AS, and two of the eight genes were validated using the gene expression dataset from gene-deficient mice. The results of the current study will improve our understanding of the molecular mechanisms in the progression of AS. The key genes and pathways identified could be potential biomarkers or new drug targets for AS management.

Multi-omics integration reveals a core network involved in host defence and hyperkeratinization in psoriasis

OBJECTIVES

The precise pathogenesis of psoriasis remains incompletely explored. We aimed to better understand the underlying mechanisms of psoriasis, using a systems biology approach based on transcriptomics and microbiome profiling.

METHODS

We collected the skin tissue biopsies and swabs in both lesional and non-lesional skin of 13 patients with psoriasis, 15 patients with psoriatic arthritis and healthy skin from 12 patients with ankylosing spondylitis. To study the similarities and differences in the molecular profiles between these three conditions, and the associations between the host defence and microbiota composition, we performed high-throughput RNA-sequencing to quantify the gene expression profile in tissues. The metagenomic composition of 16S on local skin sites was quantified by clustering amplicon sequences and counted into operational taxonomic units. We further analysed associations between the transcriptome and microbiome profiling.

RESULTS

We found that lesional and non-lesional samples were remarkably different in terms of their transcriptome profiles. The functional annotation of differentially expressed genes showed a major enrichment in neutrophil activation. By using co-expression gene networks, we identified a gene module that was associated with local psoriasis severity at the site of biopsy. From this module, we found a 'core' set of genes that was functionally involved in neutrophil activation, epidermal cell differentiation and response to bacteria. Skin microbiome analysis revealed that the abundances of Enhydrobacter, Micrococcus and Leptotrichia were significantly correlated with the genes in core network.

CONCLUSIONS

We identified a core gene network that associated with local disease severity and microbiome composition, involved in the inflammation and hyperkeratinization in psoriatic skin.

A Genome-Wide Association Study Reveals a BDNF-Centered Molecular Network Associated with Alcohol Dependence and Related Clinical Measures

At least 50% of factors predisposing to alcohol dependence (AD) are genetic and women affected with this disorder present with more psychiatric comorbidities, probably indicating different genetic factors involved. We aimed to run a genome-wide association study (GWAS) followed by a bioinformatic functional annotation of associated genomic regions in patients with AD and eight related clinical measures. A genome-wide significant association of rs220677 with AD (p-value = 1.33 × 10^-8 calculated with the Yates-corrected χ² test under the assumption of dominant inheritance) was discovered in female patients. Associations of AD and related clinical measures with seven other single nucleotide polymorphisms listed in previous GWASs of psychiatric and addiction traits were differently replicated in male and female patients. The bioinformatic analysis showed that regulatory elements in the eight associated linkage disequilibrium blocks define the expression of 80 protein-coding genes. Nearly 68% of these and of 120 previously published coding genes associated with alcohol phenotypes directly interact in a single network, where BDNF is the most significant hub gene. This study indicates that several genes behind the pathogenesis of AD are different in male and female patients, but implicated molecular mechanisms are functionally connected. The study also reveals a central role of BDNF in the pathogenesis of AD.

In silico strategies to identify protein-protein interaction modulator in cell-to-cell transmission of SARS CoV2

RNA sequence data from SARS CoV2 patients helps to construct a gene network related to this disease. A detailed analysis of the human host response to SARS CoV2 with expression profiling by high-throughput sequencing has been accomplished with primary human lung epithelial cell lines. Using this data, the clustered gene annotation and gene network construction are performed with the help of the String database. Among the four clusters identified, only 1 with 44 genes could be annotated. Interestingly, this corresponded to basal cells with p = 1.37e - 05, which is relevant for respiratory tract infection. Functional enrichment analysis of genes present in the gene network has been completed using the String database and the Network Analyst tool. Among three types of cell-cell communication, only the anchoring junction between the basal cell membrane and the basal lamina in the host cell is involved in the virus transmission. In this junction point, a hemidesmosome structure plays a vital role in virus spread from one cell to basal lamina in the respiratory tract. In this protein complex structure, different integrin protein molecules of the host cell are used to promote the spread of virus infection into the extracellular matrix. So, small molecular blockers of different anchoring junction proteins, such as integrin alpha 3, integrin beta 1, can provide efficient protection against this deadly viral disease. ORF8 from SARS CoV2 virus can interact with both integrin proteins of human host. By using molecular docking technique, a ternary complex of these three proteins is modelled. Several oligopeptides are predicted as modulators for this ternary complex. In silico analysis of these modulators is very important to develop novel therapeutics for the treatment of SARS CoV2.

Integrative metabolomic and transcriptomic analyses reveal the mechanisms of Tibetan hulless barley grain coloration

Cereal grains accumulate anthocyanin during developmental process. The anthocyanin content increases at grain filling stages to develop grain coloration in cereals. However, anthocyanin biosynthesis responsible for grain coloring and its regulatory mechanisms controlled by structural and functional genes remain unclear. Therefore, this study aimed to explore the global map of metabolic changes linked to grain coloration of Tibetan hulless barley (qingke) using an integrative metabolome and transcriptome approach. Grains from three colored qingke cultivars at different developmental stages were considered for molecular and metabolic investigations. A total of 120 differentially accumulated metabolites (DAMs) and 8,327 differentially expressed genes (DEGs) were filtered. DEGs were mainly enriched in the phenylpropanoid and flavonoid pathways. The transcript levels of anthocyanin biosynthesis genes (PAL, C4H, 4CL, CHS, FLS, F3H, F3'H, DFR, ANS, GT, OMT, and MAT) significantly upregulate in colored qingke compared to the non-colored variety. During grain development and maturation, the strong correlation of HvMYC2 expression with anthocyanin contents and anthocyanin biosynthesis genes suggested it as a critical gene in anthocyanin accumulation. Further results confirmed that HvMYC2 could be activated by HvMYB and be a positive regulator of UV-B and cold tolerance in qingke. In addition, verification based on enzymatic assays indicated that six key modifier enzymes could catalyze glycosylation, malonylation, and methylation of anthocyanins, thereby dissecting the major anthocyanin modification pathway in colored qingke. Overall, our study provides global insight into anthocyanin accumulation and the mechanism underlying grain coloration in qingke.

Transcriptome Analysis of Developing Grains from Wheat Cultivars TAM 111 and TAM 112 Reveal Cultivar-Specific Regulatory Networks

Wheat flour's end-use quality is tightly linked to the quantity and composition of storage proteins in the endosperm. TAM 111 and TAM 112 are two popular cultivars grown in the Southern US Great Plains with significantly different protein content. To investigate regulatory differences, transcriptome data were analyzed from developing grains at early- and mid-filling stages. At the mid-filling stage, TAM 111 preferentially upregulated starch metabolism-related pathways compared to TAM 112, whereas amino acid metabolism and transporter-related pathways were over-represented in TAM 112. Elemental analyses also indicated a higher N percentage in TAM 112 at the mid-filling stage. To explore the regulatory variation, weighted correlation gene network was constructed from publicly available RNAseq datasets to identify the modules differentially regulated in TAM 111 and TAM 112. Further, the potential transcription factors (TFs) regulating those modules were identified using graphical least absolute shrinkage and selection operator (GLASSO). Homologs of the OsNF-Y family members with known starch metabolism-related functions showed higher connectivities in TAM 111. Multiple TFs with high connectivity in TAM 112 had predicted functions associated with ABA response in grain. These results will provide novel targets for breeders to explore and further our understanding in mechanisms regulating grain development.

Transcriptome alterations in chicken HD11 cells with steady knockdown and overexpression of RIPK2 gene

Receptor interacting protein kinase 2 (RIPK2) is involved in a variety of signaling pathway to produce a series of inflammatory cytokines in response to a diverse of bacterial, viral and protozoal pathogens. However, the underlying regulating of RIPK2 remain unknown. Transcriptome alterations in chicken HD11 cells following RIPK2 overexpression or silencing by shRNA were analyzed by next-generation sequencing. Both overexpression and knockdown of the RIPK2 gene caused wide-spread changes in gene expression in chicken HD11 cells. Differentially expressed genes (DEGs) caused by altered RIPK2 gene expression were associated with multiple biological processes linked with biological regulation, response to stimulus, cell communication, and signal transduction etc. KEGG analysis revealed that many of the DEGs were enriched in VEGF signaling pathway, ECM-receptor interaction, Focal adhesion, TGF-beta signaling pathway etc. Moreover, we show that initiation genes, TGFB1 and TGFB3, in the TGF-beta signaling pathway are biological targets regulated by RIPK2 in chicken HD11 cells. This is the first transcriptome-wide study in which RIPK2-regulated genes in chicken cells have been screened. Our findings elucidate the molecular events associated with RIPK2 in chicken HD11 cells.

Epigenetic factor competition reshapes the EMT landscape

The emergence of and transitions between distinct phenotypes in isogenic cells can be attributed to the intricate interplay of epigenetic marks, external signals, and gene-regulatory elements. These elements include chromatin remodelers, histone modifiers, transcription factors, and regulatory RNAs. Mathematical models known as gene-regulatory networks (GRNs) are an increasingly important tool to unravel the workings of such complex networks. In such models, epigenetic factors are usually proposed to act on the chromatin regions directly involved in the expression of relevant genes. However, it has been well-established that these factors operate globally and compete with each other for targets genome-wide. Therefore, a perturbation of the activity of a regulator can redistribute epigenetic marks across the genome and modulate the levels of competing regulators. In this paper, we propose a conceptual and mathematical modeling framework that incorporates both local and global competition effects between antagonistic epigenetic regulators, in addition to local transcription factors, and show the counterintuitive consequences of such interactions. We apply our approach to recent experimental findings on the epithelial-mesenchymal transition (EMT). We show that it can explain the puzzling experimental data, as well as provide verifiable predictions.

Comparative transcriptomic profiling in the pulp and peel of pitaya fruit uncovers the gene networks regulating pulp color formation

Pitaya (genus Hylocereus) is a popular fruit. To develop pitaya fruit with greater marketability and high nutritional value, it is important to elucidate the roles of candidate genes and key metabolites that contribute to the coloration of the pitaya pulp and peel. By combining transcriptome and biochemical analyses, we compared and analyzed the dynamic changes in the peel and pulp of H. undatus (white pulp) and H. polyrhizus (red pulp) fruits at four key time points during ripening. Differential expression analysis and temporal analysis revealed the difference regulation in pathways of plant hormone signal transduction, phenylpropanoid biosynthesis, and betalain biosynthesis. Our results suggest that color formation of purple-red peel and pulp of pitaya is influenced by betalains. Increased tyrosine content and fluctuation in acylated betalain content may be responsible for pulp color formation, while some of the key genes in this network showed differential expression patterns during ripening between white pulp and red pulp fruits. The data and analysis results of this study provide theoretical basis for the red color formation mechanism of pitaya, which will facilitate future work to improve pitaya fruit physical appearance and marketability.

The Community Coevolution Model with Application to the Study of Evolutionary Relationships between Genes based on Phylogenetic Profiles

Organismal traits can evolve in a coordinated way, with correlated patterns of gains and losses reflecting important evolutionary associations. Discovering these associations can reveal important information about the functional and ecological linkages among traits. Phylogenetic profiles treat individual genes as traits distributed across sets of genomes and can provide a fine-grained view of the genetic underpinnings of evolutionary processes in a set of genomes. Phylogenetic profiling has been used to identify genes that are functionally linked, and to identify common patterns of lateral gene transfer in microorganisms. However, comparative analysis of phylogenetic profiles and other trait distributions should take into account the phylogenetic relationships among the organisms under consideration. Here we propose the Community Coevolution Model (CCM), a new coevolutionary model to analyze the evolutionary associations among traits, with a focus on phylogenetic profiles. In the CCM, traits are considered to evolve as a community with interactions, and the transition rate for each trait depends on the current states of other traits. Surpassing other comparative methods for pairwise trait analysis, CCM has the additional advantage of being able to examine multiple traits as a community to reveal more dependency relationships. We also develop a simulation procedure to generate phylogenetic profiles with correlated evolutionary patterns that can be used as benchmark data for evaluation purposes. A simulation study demonstrates that CCM is more accurate than other methods including the Jaccard Index and three tree-aware methods. The parameterization of CCM makes the interpretation of the relations between genes more direct, which leads to Darwin's scenario being identified easily based on the estimated parameters. We show that CCM is more efficient and fits real data better than other methods resulting in higher likelihood scores with fewer parameters. An examination of 3786 phylogenetic profiles across a set of 659 bacterial genomes highlights linkages between genes with common functions, including many patterns that would not have been identified under a non-phylogenetic model of common distribution. We also applied the CCM to 44 proteins in the well-studied Mitochondrial Respiratory Complex I and recovered associations that mapped well onto the structural associations that exist in the complex.

Identification of the Key Pathways and Genes Involved in the Wax Biosynthesis of the Chinese White Wax Scale Insect (Ericerus pela Chavannes) by Integrated Weighted Gene Coexpression Network Analysis

The white wax secreted by the male insects of the Chinese white wax scale (CWWS) is a natural high-molecular-weight compound with important economic value. However, its regulatory mechanism of wax biosynthesis is still unclear. In this study, a weighted gene coexpression network analysis (WGCNA) was used to analyze transcriptome data of first- and second-instar females, early and late female adults, and first- and second-instar males. A total of 19 partitioned modules with different topological overlaps were obtained, and three modules were identified as highly significant for wax secretion (p < 0.05). A total of 30 hub genes were obtained through screening, among which elongation of very-long-chain fatty acids protein (ELOVL) and fatty acyl-CoA reductase (FAR) are important catalytic enzymes of fatty acid metabolism. Furthermore, their metabolic catalytic products are involved in the synthesis of wax biosynthesis. The results demonstrate that WGCNA can be used for insect transcriptome analysis and effectively screen out the key genes related to wax biosynthesis.