Characterization of Genomic Variation from Lotus (Nelumbo Adans.) Mutants with Wide and Narrow Tepals

Compared with rose, chrysanthemum, and water lily, the absence of short-wide and long-narrow tepals of ornamental lotus (Nelumbo Adans.) limits the commercial value of flowers. In this study, the genomes of two groups of lotus mutants with wide-short and narrow-long tepals were resequenced to uncover the genomic variation and candidate genes associated with tepal shape. In group NL (short for N. lutea, containing two mutants and one control of N. lutea), 716,656 single nucleotide polymorphisms (SNPs) and 221,688 insertion-deletion mutations (Indels) were obtained, while 639,953 SNPs and 134,6118 Indels were obtained in group WSH (short for ‘Weishan Hong’, containing one mutant and two controls of N. nucifera ‘Weishan Hong’). Only a small proportion of these SNPs and Indels was mapped to exonic regions of genome: 1.92% and 0.47%, respectively, in the NL group, and 1.66% and 0.48%, respectively, in the WSH group. Gene Ontology (GO) analysis showed that out of 4890 (NL group) and 1272 (WSH group) annotated variant genes, 125 and 62 genes were enriched (Q < 0.05), respectively. Additionally, in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, 104 genes (NL group) and 35 genes (WSH group) were selected (p < 0.05). Finally, there were 306 candidate genes that were sieved to determine the development of tepal shape in lotus plants. It will be an essential reference for future identification of tepal-shaped control genes in lotus plants. This is the first comprehensive report of genomic variation controlling tepal shape in lotus, and the mutants in this study are promising materials for breeding novel lotus cultivars with special tepals.

Probe-mining of endo-1,4-beta-xylanase from goats-rumen bacterial metagenomic DNA data

Endo-1,4-beta-xylanases (xylanases) are classified into 9 glycoside hydrolase families, GH5, 8, 10, 11, 30, 43, 51, 98, and 141 based on the CAZy database. The probe sequences representing the enzymes were constructed from published sequences of actual experimental studies with xylan decomposition activity. From online databases, we found one sequence belonging to the GH5 family, 6 sequences belonging to the GH8 family and 5 sequences belonging to the GH30 family exhibiting xylanase activity. Thus specific probes for xylanase GH8 and GH30 families were designed with the length of 351 and 425 amino acids respectively. The reference values for the probe of the GH8 family were defined as the sequences with maximum score greater than 168, the lowest coverage was 84%, the lowest similarity was 36%; for the probe GH30, the maximum score was greater than 316, the coverage was greater than 98%, the similarity was greater than 41%. Using the built probes, including the probe of the two GH10 and GH11 families, we found 41 xylanase-encoding sequences from the metagenomic DNA data of bacteria in Vietnamese goats’rumen. Of the 41 exploited sequences, 19 were identical to the BGI company's annotation result based on KEGG database, whereas there were 16 sequences that are not annotated by the BGI company. Total 28 of 41 exploited sequences were complete open reading frames, of which the predicted ternary structure was highly similar to the published structures of xylanase.

Predicting metabolic pathway membership with deep neural networks by integrating sequential and ontology information

Background Inference of protein’s membership in metabolic pathways has become an important task in functional annotation of protein. The membership information can provide valuable context to the basic functional annotation and also aid reconstruction of incomplete pathways. Previous works have shown success of inference by using various similarity measures of gene ontology. Results In this work, we set out to explore integrating ontology and sequential information to further improve the accuracy. Specifically, we developed a neural network model with an architecture tailored to facilitate the integration of features from different sources. Furthermore, we built models that are able to perform predictions from pathway-centric or protein-centric perspectives. We tested the classifiers using 5-fold cross validation for all metabolic pathways reported in KEGG database. Conclusions The testing results demonstrate that by integrating ontology and sequential information with a tailored architecture our deep neural network method outperforms the existing methods significantly in the pathway-centric mode, and in the protein-centric mode, our method either outperforms or performs comparably with a suite of existing GO term based semantic similarity methods.

Modularisation of published and novel models toward a complex KIR2DL4 pathway in pbNK cell

KIR2DL4 is an interesting receptor expressed on the peripheral blood natural killer (pbNK) cell as it can be either activating or inhibitory depending on the amino acid residues in the domain. This model uses mathematical modelling to investigate the downstream effects of natural killer cells’ activation (KIR2DL4) receptor after stimulation by key ligand (HLA-G) on pbNK cells. Development of this large pathway is based on a comprehensive qualitative description of pbNKs’ intracellular signalling pathways leading to chemokine and cytotoxin secretion, obtained from the KEGG database (https://www.genome.jp/kegg-bin/show pathway?hsa04650). From this qualitative description we built a quantitative model for the pathway, reusing existing curated models where possible and implementing new models as needed. This large pathway consists of two published sub-models; the Ca2+ model and the NFAT model, and a newly built FCeRIγ sub-model. The full pathway was fitted to HLA-G-KIR2DL4 pathway published dataset and the model that we developed fitted well to one of two secreted cytokines. The model can be used to predict the production of IFNγ and TNFα cytokines.

Antibiotic Biosynthesis Pathways from Endophytic Streptomyces SUK 48 through Metabolomics and Genomics Approaches

Streptomyces sp. has been known to be a major antibiotic producer since the 1940s. As the number of cases related to resistance pathogens infection increases yearly, discovering the biosynthesis pathways of antibiotic has become important. In this study, we present the streamline of a project report summary; the genome data and metabolome data of newly isolated Streptomyces SUK 48 strain are also analyzed. The antibacterial activity of its crude extract is also determined. To obtain genome data, the genomic DNA of SUK 48 was extracted using a commercial kit (Promega) and sent for sequencing (Pac Biosciences technology platform, Menlo Park, CA, USA). The raw data were assembled and polished using Hierarchical Genome Assembly Process 4.0 (HGAP 4.0). The assembled data were structurally predicted using tRNAscan-SE and rnammer. Then, the data were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) database and antiSMASH analysis. Meanwhile, the metabolite profile of SUK 48 was determined using liquid chromatography-mass spectrophotometry (LC-MS) for both negative and positive modes. The results showed that the presence of kanamycin and gentamicin, as well as the other 11 antibiotics. Nevertheless, the biosynthesis pathways of aurantioclavine were also found. The cytotoxicity activity showed IC50 value was at 0.35 ± 1.35 mg/mL on the cell viability of HEK 293. In conclusion, Streptomyces sp. SUK 48 has proven to be a non-toxic antibiotic producer such as auranticlavine and gentamicin.

Classification of potential pathways affecting the VEGF pathway in glioblastoma multiforme (GBM) recurrent: A bioinformatics study based on differentially expressed- microRNAs

Glioblastoma multiforme (GBM) resistance to anti-angiogenesis drugs results in recurrence of the disease which leads to death. The resistance to anti-angiogenesis drugs that target the VEGF pathway is due to the influence of other pathways. This study aimed to identify and classify the pathways that are related to the VEGF pathway in GBM recurrent. The identification of differentially expressed miRNAs (DEmiRNAs) based on GBM GSE profiles (GSE32466) were carried out using a LIMMA R package and VEGF pathway genes in the KEGG database. Pathways related to DEmiRNAs and VEGF pathway genes were discovered by DIANA-miRPath v3.0, NetworkAnalyst and ToppGene databases, respectively. Inhibitory or activity affecting pathways relating to VEGF pathway were obtained based on XTalkDB database. The classification was determined by the KEGG database. There were 1014 genes that were found to have interaction with VEGF signaling pathway genes. One hundred ninteen pathways were achieved which have overlapping genes with the VEGF pathway genes. The MAPK pathway had the most in common genes with the VEGF pathway (39 genes). A total of 91 pathways were identified in 24 different classes. Several pathways significantly affect the VEGF pathway. Hence, it seems necessary to achieve new targets for combination therapies for GBM.

Comparative Transcriptomic and Metagenomic Analysis Reveal the Key Factors on the Growth Rate in Red Swamp Crayfish (Procambarus Clarkii)

Background: Although red swamp crayfish (Procambarus clarkii) is one of the most important species in aquaculture, the factors that influence differences in growth and development between individual siblings are still not fully understood. To address this lack of knowledge, we designed experiments to elucidate factors contributing to individual differences by comparing the hepatopancreatic transcriptome and gut flora structure of individuals with differences in sibling crayfish size under the same rearing conditions and attempted to find links between gut flora and host transcriptome information. Result: In total, 300691028 high-quality reads were obtained that were used to assemble 60637958 unigenes. Comparison of the expression profiles of the hepatopancreas of crayfish of different sizes revealed 497 differentially expressed genes (P<0.05). A total of 32 KEGG signaling pathways were found to be enriched after the KEGG database and GO functional annotation analyses, with the highest number of unigene enrichments related to organismal metabolism. Additionally, we found that proteobacteria, tenericutes, actinobacteria, and bacteroidetes made up a larger portion of the microbiome of larger individuals, suggesting that crayfish microbiota adapts to rapid growth, which may promote accelerated development by regulating the expression level of relevant genes.Conclusions: This work has accumulated data to support the potential impact of structural alterations in gut flora under equivalent feeding conditions in explaining the differential expression of transcriptomic genes among differentially developing individuals of sibling crayfish. These results further elucidate how the intestinal environment affects the rapid development of invertebrate crustaceans and provide a reference for further understanding of the regulatory mechanisms of the host's in vivo environment.

Pathway size matters: the influence of pathway granularity on over-representation (enrichment analysis) statistics

Background Enrichment or over-representation analysis is a common method used in bioinformatics studies of transcriptomics, metabolomics, and microbiome datasets. The key idea behind enrichment analysis is: given a set of significantly expressed genes (or metabolites), use that set to infer a smaller set of perturbed biological pathways or processes, in which those genes (or metabolites) play a role. Enrichment computations rely on collections of defined biological pathways and/or processes, which are usually drawn from pathway databases. Although practitioners of enrichment analysis take great care to employ statistical corrections (e.g., for multiple testing), they appear unaware that enrichment results are quite sensitive to the pathway definitions that the calculation uses. Results We show that alternative pathway definitions can alter enrichment p-values by up to nine orders of magnitude, whereas statistical corrections typically alter enrichment p-values by only two orders of magnitude. We present multiple examples where the smaller pathway definitions used in the EcoCyc database produces stronger enrichment p-values than the much larger pathway definitions used in the KEGG database; we demonstrate that to attain a given enrichment p-value, KEGG-based enrichment analyses require 1.3–2.0 times as many significantly expressed genes as does EcoCyc-based enrichment analyses. The large pathways in KEGG are problematic for another reason: they blur together multiple (as many as 21) biological processes. When such a KEGG pathway receives a high enrichment p-value, which of its component processes is perturbed is unclear, and thus the biological conclusions drawn from enrichment of large pathways are also in question. Conclusions The choice of pathway database used in enrichment analyses can have a much stronger effect on the enrichment results than the statistical corrections used in these analyses.

Differential gene expression analysis of palbociclib-resistant TNBC via RNA-seq

Purpose The management of triple-negative breast cancer (TNBC) remains a significant clinical challenge due to the lack of effective targeted therapies. Inhibitors of the cyclin-dependent kinases 4 and 6 (CDK4/6) are emerging as promising therapeutic agents against TNBC; however, cells can rapidly acquire resistance through multiple mechanisms that are yet to be identified. Therefore, determining the mechanisms underlying resistance to CDK4/6 inhibition is crucial to develop combination therapies that can extend the efficacy of the CDK4/6 inhibitors or delay resistance. This study aims to identify differentially expressed genes (DEG) associated with acquired resistance to palbociclib in ER− breast cancer cells. Methods We performed next-generation transcriptomic sequencing (RNA-seq) and pathway analysis in ER− MDA-MB-231 palbociclib-sensitive (231/pS) and palbociclib-resistant (231/pR) cells. Results We identified 2247 up-regulated and 1427 down-regulated transcripts in 231/pR compared to 231/pS cells. DEGs were subjected to functional analysis using Gene Ontology (GO) and the KEGG database which identified many transduction pathways associated with breast cancer, including the PI3K/AKT, PTEN and mTOR pathways. Additionally, Ingenuity Pathway Analysis (IPA) revealed that resistance to palbociclib is closely associated with altered cholesterol and fatty acid biosynthesis suggesting that resistance to palbociclib may be dependent on lipid metabolic reprograming. Conclusion This study provides evidence that lipid metabolism is altered in TNBC with acquired resistance to palbociclib. Further studies are needed to determine if the observed lipid metabolic rewiring can be exploited to overcome therapy resistance in TNBC.