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Background Aberrant protein kinase regulation leading to abnormal substrate phosphorylation is associated with several human diseases. Despite the promise of therapies targeting kinases, many human kinases remain understudied. Most existing computational tools predicting phosphorylation cover less than 50% of known human kinases. They utilize local feature selection based on protein sequences, motifs, domains, structures, and/or functions, and do not consider the heterogeneous relationships of the proteins. In this work, we present KSFinder, a tool that predicts kinase-substrate links by capturing the inherent association of proteins in a network comprising 85% of the known human kinases. We also postulate the potential role of two understudied kinases based on their substrate predictions from KSFinder. Methods KSFinder learns the semantic relationships in a phosphoproteome knowledge graph using a knowledge graph embedding algorithm and represents the nodes in low-dimensional vectors. A multilayer perceptron (MLP) classifier is trained to discern kinase-substrate links using the embedded vectors. KSFinder uses a strategic negative generation approach that eliminates biases in entity representation and combines data from experimentally validated non-interacting protein pairs, proteins from different subcellular locations, and random sampling. We assess KSFinder’s generalization capability on four different datasets and compare its performance with other state-of-the-art prediction models. We employ KSFinder to predict substrates of 68 “dark” kinases considered understudied by the Illuminating the Druggable Genome program and use our text-mining tool, RLIMS-P along with manual curation, to search for literature evidence for the predictions. In a case study, we performed functional enrichment analysis for two dark kinases - HIPK3 and CAMKK1 using their predicted substrates. Results KSFinder shows improved performance over other kinase-substrate prediction models and generalized prediction ability on different datasets. We identified literature evidence for 17 novel predictions involving an understudied kinase. All of these 17 predictions had a probability score ≥0.7 (nine at >0.9, six at 0.8–0.9, and two at 0.7–0.8). The evaluation of 93,593 negative predictions (probability ≤0.3) identified four false negatives. The top enriched biological processes of HIPK3 substrates relate to the regulation of extracellular matrix and epigenetic gene expression, while CAMKK1 substrates include lipid storage regulation and glucose homeostasis. Conclusions KSFinder outperforms the current kinase-substrate prediction tools with higher kinase coverage. The strategically developed negatives provide a superior generalization ability for KSFinder. We predicted substrates of 432 kinases, 68 of which are understudied, and hypothesized the potential functions of two dark kinases using their predicted substrates.

Abstract Chinese hamster ovary (CHO) cell lines are widely used to manufacture biopharmaceuticals. However, CHO cells are not an optimal expression host due to the intrinsic plasticity of the CHO genome. Genome plasticity can lead to chromosomal rearrangements, transgene exclusion, and phenotypic drift. A poorly understood genomic element of CHO cell line instability is extrachromosomal circular DNA (eccDNA) in gene expression and regulation. EccDNA can facilitate ultra-high gene expression and are found within many eukaryotes including humans, yeast, and plants. EccDNA confers genetic heterogeneity, providing selective advantages to individual cells in response to dynamic environments. In CHO cell cultures, maintaining genetic homogeneity is critical to ensuring consistent productivity and product quality. Understanding eccDNA structure, function, and microevolutionary dynamics under various culture conditions could reveal potential engineering targets for cell line optimization. In this study, eccDNA sequences were investigated at the beginning and end of two-week fed-batch cultures in an ambr®250 bioreactor under control and lactate-stressed conditions. This work characterized structure and function of eccDNA in a CHO-K1 clone. Gene annotation identified 1551 unique eccDNA genes including cancer driver genes and genes involved in protein production. Furthermore, RNA-seq data is integrated to identify transcriptionally active eccDNA genes.

Chinese hamster ovary (CHO) cells are widely used for mass production of therapeutic proteins in the pharmaceutical industry. With the growing need in optimizing the performance of producer CHO cell lines, research on CHO cell line development and bioprocess continues to increase in recent decades. Bibliographic mapping and classification of relevant research studies will be essential for identifying research gaps and trends in literature. To qualitatively and quantitatively understand the CHO literature, we have conducted topic modeling using a CHO bioprocess bibliome manually compiled in 2016, and compared the topics uncovered by the Latent Dirichlet Allocation (LDA) models with the human labels of the CHO bibliome. The results show a significant overlap between the manually selected categories and computationally generated topics, and reveal the machine-generated topic-specific characteristics. To identify relevant CHO bioprocessing papers from new scientific literature, we have developed supervized models using Logistic Regression to identify specific article topics and evaluated the results using three CHO bibliome datasets, Bioprocessing set, Glycosylation set, and Phenotype set. The use of top terms as features supports the explainability of document classification results to yield insights on new CHO bioprocessing papers.

Abstract Background Sustainable production of high-quality feedstock has been of great interest in bioenergy research. Despite the economic importance, high temperatures and water deficit are limiting factors for the successful cultivation of switchgrass in semi-arid areas. There are limited reports on the molecular basis of combined abiotic stress tolerance in switchgrass, particularly the combination of drought and heat stress. We used transcriptomic approaches to elucidate the changes in the response of switchgrass to drought and high temperature simultaneously. Results We conducted solely drought treatment in switchgrass plant Alamo AP13 by withholding water after 45 days of growing. For the combination of drought and heat effect, heat treatment (35 °C/25 °C day/night) was imposed after 72 h of the initiation of drought. Samples were collected at 0 h, 72 h, 96 h, 120 h, 144 h, and 168 h after treatment imposition, total RNA was extracted, and RNA-Seq conducted. Out of a total of 32,190 genes, we identified 3912, as drought (DT) responsive genes, 2339 and 4635 as, heat (HT) and drought and heat (DTHT) responsive genes, respectively. There were 209, 106, and 220 transcription factors (TFs) differentially expressed under DT, HT and DTHT respectively. Gene ontology annotation identified the metabolic process as the significant term enriched in DTHT genes. Other biological processes identified in DTHT responsive genes included: response to water, photosynthesis, oxidation-reduction processes, and response to stress. KEGG pathway enrichment analysis on DT and DTHT responsive genes revealed that TFs and genes controlling phenylpropanoid pathways were important for individual as well as combined stress response. For example, hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyl transferase (HCT) from the phenylpropanoid pathway was induced by single DT and combinations of DTHT stress. Conclusion Through RNA-Seq analysis, we have identified unique and overlapping genes in response to DT and combined DTHT stress in switchgrass. The combination of DT and HT stress may affect the photosynthetic machinery and phenylpropanoid pathway of switchgrass which negatively impacts lignin synthesis and biomass production of switchgrass. The biological function of genes identified particularly in response to DTHT stress could further be confirmed by techniques such as single point mutation or RNAi.

Abstract Background Epithelial to mesenchymal transition (EMT) plays a crucial role in cancer propagation. It can be orchestrated by the activation of multiple signaling pathways, which have been found to be highly coordinated with many epigenetic regulators. Although the mechanism of EMT has been studied over decades, cross talk between signaling and epigenetic regulation is not fully understood. Results Here, we present a time-resolved multi-omics strategy, which featured the identification of the correlation between protein changes (proteome), signaling pathways (phosphoproteome) and chromatin modulation (histone modifications) dynamics during TGF-β-induced EMT. Our data revealed that Erk signaling was activated in 5-min stimulation and structural proteins involved in cytoskeleton rearrangement were regulated after 1-day treatment, constituting a detailed map of systematic changes. The comprehensive profiling of histone post-translational modifications identified H3K27me3 as the most significantly up-regulated mark. We thus speculated and confirmed that a combined inhibition of Erk signaling and Ezh2 (H3K27me3 methyltransferase) was more effective in blocking EMT progress than individual inhibitions. Conclusions In summary, our data provided a more detailed map of cross talk between signaling pathway and chromatin regulation comparing to previous EMT studies. Our findings point to a promising therapeutic strategy for EMT-related diseases by combining Erk inhibitor (singling pathway) and Ezh2 inhibitor (epigenetic regulation).

Abstract Background The Gene Ontology (GO) is a resource that supplies information about gene product function using ontologies to represent biological knowledge. These ontologies cover three domains: Cellular Component (CC), Molecular Function (MF), and Biological Process (BP). GO annotation is a process which assigns gene functional information using GO terms to relevant genes in the literature. It is a common task among the Model Organism Database (MOD) groups. Manual GO annotation relies on human curators assigning gene functional information using GO terms by reading the biomedical literature. This process is very time-consuming and labor-intensive. As a result, many MODs can afford to curate only a fraction of relevant articles. Methods GO terms from the CC domain can be essentially divided into two sub-hierarchies: subcellular location terms, and protein complex terms. We cast the task of gene annotation using GO terms from the CC domain as relation extraction between gene and other entities: (1) extract cases where a protein is found to be in a subcellular location, and (2) extract cases where a protein is a subunit of a protein complex. For each relation extraction task, we use an approach based on triggers and syntactic dependencies to extract the desired relations among entities. Results We tested our approach on the BC4GO test set, a publicly available corpus for GO annotation. Our approach obtains a F1-score of 71%, a precision of 91% and a recall of 58% for predicting GO terms from CC Domain for given genes. Conclusions We have described a novel approach of treating gene annotation with GO terms from CC domain as two relation extraction subtasks. Evaluation results show that our approach achieves a F1-score of 71% for predicting GO terms for given genes. Thereby our approach can be used to accelerate the process of GO annotation for the bio-annotators.

Abstract Background Although hatching is perhaps the most abrupt and profound metabolic challenge that a chicken must undergo; there have been no attempts to functionally map the metabolic pathways induced in liver during the embryo-to-hatchling transition. Furthermore, we know very little about the metabolic and regulatory factors that regulate lipid metabolism in late embryos or newly-hatched chicks. In the present study, we examined hepatic transcriptomes of 12 embryos and 12 hatchling chicks during the peri-hatch period—or the metabolic switch from chorioallantoic to pulmonary respiration. Results Initial hierarchical clustering revealed two distinct, albeit opposing, patterns of hepatic gene expression. Cluster A genes are largely lipolytic and highly expressed in embryos. While, Cluster B genes are lipogenic/thermogenic and mainly controlled by the lipogenic transcription factor THRSPA. Using pairwise comparisons of embryo and hatchling ages, we found 1272 genes that were differentially expressed between embryos and hatchling chicks, including 24 transcription factors and 284 genes that regulate lipid metabolism. The three most differentially-expressed transcripts found in liver of embryos were MOGAT1, DIO3 and PDK4, whereas THRSPA, FASN and DIO2 were highest in hatchlings. An unusual finding was the “ectopic” and extremely high differentially expression of seven feather keratin transcripts in liver of 16 day embryos, which coincides with engorgement of liver with yolk lipids. Gene interaction networks show several transcription factors, transcriptional co-activators/co-inhibitors and their downstream genes that exert a ‘ying-yang’ action on lipid metabolism during the embryo-to-hatching transition. These upstream regulators include ligand-activated transcription factors, sirtuins and Kruppel-like factors. Conclusions Our genome-wide transcriptional analysis has greatly expanded the hepatic repertoire of regulatory and metabolic genes involved in the embryo-to-hatchling transition. New knowledge was gained on interactive transcriptional networks and metabolic pathways that enable the abrupt switch from ectothermy (embryo) to endothermy (hatchling) in the chicken. Several transcription factors and their coactivators/co-inhibitors appear to exert opposing actions on lipid metabolism, leading to the predominance of lipolysis in embryos and lipogenesis in hatchlings. Our analysis of hepatic transcriptomes has enabled discovery of opposing, interconnected and interdependent transcriptional regulators that provide precise ying-yang or homeorhetic regulation of lipid metabolism during the critical embryo-to-hatchling transition.

Abstract Background Protein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of cellular and intracellular processes. Recently, an increasing number of computational methods have shifted from pair-wise prediction to network level prediction. Many of the existing network level methods predict PPIs under the assumption that the training network should be connected. However, this assumption greatly affects the prediction power and limits the application area because the current golden standard PPI networks are usually very sparse and disconnected. Therefore, how to effectively predict PPIs based on a training network that is sparse and disconnected remains a challenge. Results In this work, we developed a novel PPI prediction method based on deep learning neural network and regularized Laplacian kernel. We use a neural network with an autoencoder-like architecture to implicitly simulate the evolutionary processes of a PPI network. Neurons of the output layer correspond to proteins and are labeled with values (1 for interaction and 0 for otherwise) from the adjacency matrix of a sparse disconnected training PPI network. Unlike autoencoder, neurons at the input layer are given all zero input, reflecting an assumption of no a priori knowledge about PPIs, and hidden layers of smaller sizes mimic ancient interactome at different times during evolution. After the training step, an evolved PPI network whose rows are outputs of the neural network can be obtained. We then predict PPIs by applying the regularized Laplacian kernel to the transition matrix that is built upon the evolved PPI network. The results from cross-validation experiments show that the PPI prediction accuracies for yeast data and human data measured as AUC are increased by up to 8.4 and 14.9% respectively, as compared to the baseline. Moreover, the evolved PPI network can also help us leverage complementary information from the disconnected training network and multiple heterogeneous data sources. Tested by the yeast data with six heterogeneous feature kernels, the results show our method can further improve the prediction performance by up to 2%, which is very close to an upper bound that is obtained by an Approximate Bayesian Computation based sampling method. Conclusions The proposed evolution deep neural network, coupled with regularized Laplacian kernel, is an effective tool in completing sparse and disconnected PPI networks and in facilitating integration of heterogeneous data sources.

The extracellular matrix (ECM) is thought to play a critical role in the progression of breast cancer. In this work, we have designed a photopolymerizable, biomimetic synthetic matrix for the controlled, 3D culture of breast cancer cells and, in combination with imaging and bioinformatics tools, utilized this system to investigate the breast cancer cell response to different matrix cues. Specifically, hydrogel-based matrices of different densities and modified with receptor-binding peptides derived from ECM proteins [fibronectin/vitronectin (RGDS), collagen (GFOGER), and laminin (IKVAV)] were synthesized to mimic key aspects of the ECM of different soft tissue sites. To assess the breast cancer cell response, the morphology and growth of breast cancer cells (MDA-MB-231 and T47D) were monitored in three dimensions over time, and differences in their transcriptome were assayed using next generation sequencing. We observed increased growth in response to GFOGER and RGDS, whether individually or in combination with IKVAV, where binding of integrin β1 was key. Importantly, in matrices with GFOGER, increased growth was observed with increasing matrix density for MDA-MB-231s. Further, transcriptomic analyses revealed increased gene expression and enrichment of biological processes associated with cell-matrix interactions, proliferation, and motility in matrices rich in GFOGER relative to IKVAV. In sum, a new approach for investigating breast cancer cell-matrix interactions was established with insights into how microenvironments rich in collagen promote breast cancer growth, a hallmark of disease progression in vivo, with opportunities for future investigations that harness the multidimensional property control afforded by this photopolymerizable system.

The PIRSF protein classification system (http://pir.georgetown.edu/pirsf/) reflects evolutionary relationships of full-length proteins and domains. The primary PIRSF classification unit is the homeomorphic family, whose members are both homologous (evolved from a common ancestor) and homeomorphic (sharing full-length sequence similarity and a common domain architecture). PIRSF families are curated systematically based on literature review and integrative sequence and functional analysis, including sequence and structure similarity, domain architecture, functional association, genome context, and phyletic pattern. The results of classification and expert annotation are summarized in PIRSF family reports with graphical viewers for taxonomic distribution, domain architecture, family hierarchy, and multiple alignment and phylogenetic tree. The PIRSF system provides a comprehensive resource for bioinformatics analysis and comparative studies of protein function and evolution. Domain or fold-based searches allow identification of evolutionarily related protein families sharing domains or structural folds. Functional convergence and functional divergence are revealed by the relationships between protein classification and curated family functions. The taxonomic distribution allows the identification of lineage-specific or broadly conserved protein families and can reveal horizontal gene transfer. Here we demonstrate, with illustrative examples, how to use the web-based PIRSF system as a tool for functional and evolutionary studies of protein families.

Chondrichthyan fishes are a diverse class of gnathostomes that provide a valuable perspective on fundamental characteristics shared by all jawed and limbed vertebrates. Studies of phylogeny, species diversity, population structure, conservation, and physiology are accelerated by genomic, transcriptomic and protein sequence data. These data are widely available for many sarcopterygii (coelacanth, lungfish and tetrapods) and actinoptergii (ray-finned fish including teleosts) taxa, but limited for chondrichthyan fishes. In this study, we summarize available data for chondrichthyes and describe resources for one of the largest projects to characterize one of these fish, Leucoraja erinacea, the little skate. SkateBase (http://skatebase.org) serves as the skate genome project portal linking data, research tools, and teaching resources.

The PIRSF protein classification system ( http://pir.georgetown.edu/pirsf/ ) reflects evolutionary relationships of full-length proteins and domains. The primary PIRSF classification unit is the homeomorphic family, whose members are both homologous (evolved from a common ancestor) and homeomorphic (sharing full-length sequence similarity and a common domain architecture). PIRSF families are curated systematically based on literature review and integrative sequence and functional analysis, including sequence and structure similarity, domain architecture, functional association, genome context, and phyletic pattern. The results of classification and expert annotation are summarized in PIRSF family reports with graphical viewers for taxonomic distribution, domain architecture, family hierarchy, and multiple alignment and phylogenetic tree. The PIRSF system provides a comprehensive resource for bioinformatics analysis and comparative studies of protein function and evolution. Domain or fold-based searches allow identification of evolutionarily related protein families sharing domains or structural folds. Functional convergence and functional divergence are revealed by the relationships between protein classification and curated family functions. The taxonomic distribution allows the identification of lineage-specific or broadly conserved protein families and can reveal horizontal gene transfer. Here we demonstrate, with illustrative examples, how to use the web-based PIRSF system as a tool for functional and evolutionary studies of protein families.