A Systematic Review of Lipid-Focused Cardiovascular Disease Research: Trends and Opportunities.

Lipids are important modifiers of protein function, particularly as parts of lipoproteins, which transport lipophilic substances and mediate cellular uptake of circulating lipids. As such, lipids are of particular interest as blood biological markers for cardiovascular disease (CVD) as well as for conditions linked to CVD such as atherosclerosis, diabetes mellitus, obesity and dietary states. Notably, lipid research is particularly well developed in the context of CVD because of the relevance and multiple causes and risk factors of CVD. The advent of methods for high-throughput screening of biological molecules has recently resulted in the generation of lipidomic profiles that allow monitoring of lipid compositions in biological samples in an untargeted manner. These and other earlier advances in biomedical research have shaped the knowledge we have about lipids in CVD. To evaluate the knowledge acquired on the multiple biological functions of lipids in CVD and the trends in their research, we collected a dataset of references from the PubMed database of biomedical literature focused on plasma lipids and CVD in human and mouse. Using annotations from these records, we were able to categorize significant associations between lipids and particular types of research approaches, distinguish non-biological lipids used as markers, identify differential research between human and mouse models, and detect the increasingly mechanistic nature of the results in this field. Using known associations between lipids and proteins that metabolize or transport them, we constructed a comprehensive lipid-protein network, which we used to highlight proteins strongly connected to lipids found in the CVD-lipid literature. Our approach points to a series of proteins for which lipid-focused research would bring insights into CVD, including Prostaglandin G/H synthase 2 (PTGS2, a.k.a. COX2) and Acylglycerol kinase (AGK). In this review, we summarize our findings, putting them in a historical perspective of the evolution of lipid research in CVD.

Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputation.

MOTIVATION: Single-cell Chromatin ImmunoPrecipitation DNA-Sequencing (scChIP-seq) analysis is challenging due to data sparsity. High degree of sparsity in biological high-throughput single-cell data is generally handled with imputation methods that complete the data, but specific methods for scChIP-seq are lacking. We present SIMPA, a scChIP-seq data imputation method leveraging predictive information within bulk data from the ENCODE project to impute missing protein-DNA interacting regions of target histone marks or transcription factors. RESULTS: Imputations using machine learning models trained for each single cell, each ChIP protein target, and each genomic region accurately preserve cell type clustering and improve pathway-related gene identification on real human data. Results on bulk data simulating single cells show that the imputations are single-cell specific as the imputed profiles are closer to the simulated cell than to other cells related to the same ChIP protein target and the same cell type. Simulations also show that 100 input genomic regions are already enough to train single-cell specific models for the imputation of thousands of undetected regions. Furthermore, SIMPA enables the interpretation of machine learning models by revealing interaction sites of a given single cell that are most important for the imputation model trained for a specific genomic region. The corresponding feature importance values derived from promoter-interaction profiles of H3K4me3, an activating histone mark, highly correlate with co-expression of genes that are present within the cell-type specific pathways in 2 real human and mouse datasets. The SIMPA's interpretable imputation method allows users to gain a deep understanding of individual cells and, consequently, of sparse scChIP-seq datasets. AVAILABILITY AND IMPLEMENTATION: Our interpretable imputation algorithm was implemented in Python and is available at https://github.com/salbrec/SIMPA.

Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality.

BACKGROUND: The constant evolving and development of next-generation sequencing techniques lead to high throughput data composed of datasets that include a large number of biological samples. Although a large number of samples are usually experimentally processed by batches, scientific publications are often elusive about this information, which can greatly impact the quality of the samples and confound further statistical analyzes. Because dedicated bioinformatics methods developed to detect unwanted sources of variance in the data can wrongly detect real biological signals, such methods could benefit from using a quality-aware approach. RESULTS: We recently developed statistical guidelines and a machine learning tool to automatically evaluate the quality of a next-generation-sequencing sample. We leveraged this quality assessment to detect and correct batch effects in 12 publicly available RNA-seq datasets with available batch information. We were able to distinguish batches by our quality score and used it to correct for some batch effects in sample clustering. Overall, the correction was evaluated as comparable to or better than the reference method that uses a priori knowledge of the batches (in 10 and 1 datasets of 12, respectively; total = 92%). When coupled to outlier removal, the correction was more often evaluated as better than the reference (comparable or better in 5 and 6 datasets of 12, respectively; total = 92%). CONCLUSIONS: In this work, we show the capabilities of our software to detect batches in public RNA-seq datasets from differences in the predicted quality of their samples. We also use these insights to correct the batch effect and observe the relation of sample quality and batch effect. These observations reinforce our expectation that while batch effects do correlate with differences in quality, batch effects also arise from other artifacts and are more suitably  corrected statistically in well-designed experiments.

Physiological and comparative proteomic analyzes reveal immune defense response of the king scallop Pecten maximus in presence of paralytic shellfish toxin (PST) from Alexandrium minutum.

The king scallop, Pecten maximus is a highly valuable seafood in Europe. Over the last few years, its culture has been threatened by toxic microalgae during harmful algal blooms, inducing public health concerns. Indeed, phycotoxins accumulated in bivalves can be harmful for human, especially paralytic shellfish toxins (PST) synthesized by the microalgae Alexandrium minutum. Deleterious effects of these toxic algae on bivalves have also been reported. However, its impact on bivalves such as king scallop is far from being completely understood. This study combined ecophysiological and proteomic analyzes to investigate the early response of juvenile king scallops to a short term exposure to PST producing A. minutum. Our data showed that all along the 2-days exposure to A. minutum, king scallops exhibited transient lower filtration and respiration rates and accumulated PST. Significant inter-individual variability of toxin accumulation potential was observed among individuals. Furthermore, we found that ingestion of toxic algae, correlated to toxin accumulation was driven by two factors: 1/ the time it takes king scallop to recover from filtration inhibition and starts to filtrate again, 2/ the filtration level to which king scallop starts again to filtrate after inhibition. Furthermore, at the end of the 2-day exposure to A. minutum, proteomic analyzes revealed an increase of the killer cell lectin-like receptor B1, involved in adaptative immune response. Proteins involved in detoxification and in metabolism were found in lower amount in A. minutum exposed king scallops. Proteomic data also showed differential accumulation in several structure proteins such as ß-actin, paramyosin and filamin A, suggesting a remodeling of the mantle tissue when king scallops are subjected to an A. minutum exposure.

Annotation and Analysis of 3902 Odorant Receptor Protein Sequences from 21 Insect Species Provide Insights into the Evolution of Odorant Receptor Gene Families in Solitary and Social Insects.

The gene family of insect olfactory receptors (ORs) has expanded greatly over the course of evolution. ORs enable insects to detect volatile chemicals and therefore play an important role in social interactions, enemy and prey recognition, and foraging. The sequences of several thousand ORs are known, but their specific function or their ligands have only been identified for very few of them. To advance the functional characterization of ORs, we have assembled, curated, and aligned the sequences of 3902 ORs from 21 insect species, which we provide as an annotated online resource. Using functionally characterized proteins from the fly Drosophila melanogaster, the mosquito Anopheles gambiae and the ant Harpegnathos saltator, we identified amino acid positions that best predict response to ligands. We examined the conservation of these predicted relevant residues in all OR subfamilies; the results showed that the subfamilies that expanded strongly in social insects had a high degree of conservation in their binding sites. This suggests that the ORs of social insect families are typically finely tuned and exhibit sensitivity to very similar odorants. Our novel approach provides a powerful tool to exploit functional information from a limited number of genes to study the functional evolution of large gene families.

Posttranslational modifications by ADAM10 shape myeloid antigen-presenting cell homeostasis in the splenic marginal zone.

The spleen contains phenotypically and functionally distinct conventional dendritic cell (cDC) subpopulations, termed cDC1 and cDC2, which each can be divided into several smaller and less well-characterized subsets. Despite advances in understanding the complexity of cDC ontogeny by transcriptional programming, the significance of posttranslational modifications in controlling tissue-specific cDC subset immunobiology remains elusive. Here, we identified the cell-surface-expressed A-disintegrin-and-metalloproteinase 10 (ADAM10) as an essential regulator of cDC1 and cDC2 homeostasis in the splenic marginal zone (MZ). Mice with a CD11c-specific deletion of ADAM10 (ADAM10ΔCD11c) exhibited a complete loss of splenic ESAMhi cDC2A because ADAM10 regulated the commitment, differentiation, and survival of these cells. The major pathways controlled by ADAM10 in ESAMhi cDC2A are Notch, signaling pathways involved in cell proliferation and survival (e.g., mTOR, PI3K/AKT, and EIF2 signaling), and EBI2-mediated localization within the MZ. In addition, we discovered that ADAM10 is a molecular switch regulating cDC2 subset heterogeneity in the spleen, as the disappearance of ESAMhi cDC2A in ADAM10ΔCD11c mice was compensated for by the emergence of a Clec12a+ cDC2B subset closely resembling cDC2 generally found in peripheral lymph nodes. Moreover, in ADAM10ΔCD11c mice, terminal differentiation of cDC1 was abrogated, resulting in severely reduced splenic Langerin+ cDC1 numbers. Next to the disturbed splenic cDC compartment, ADAM10 deficiency on CD11c+ cells led to an increase in marginal metallophilic macrophage (MMM) numbers. In conclusion, our data identify ADAM10 as a molecular hub on both cDC and MMM regulating their transcriptional programming, turnover, homeostasis, and ability to shape the anatomical niche of the MZ.

LipiDisease: associate lipids to diseases using literature mining.

SUMMARY: Lipids exhibit an essential role in cellular assembly and signaling. Dysregulation of these functions has been linked with many complications including obesity, diabetes, metabolic disorders, cancer and more. Investigating lipid profiles in such conditions can provide insights into cellular functions and possible interventions. Hence the field of lipidomics is expanding in recent years. Even though the role of individual lipids in diseases has been investigated, there is no resource to perform disease enrichment analysis considering the cumulative association of a lipid set. To address this, we have implemented the LipiDisease web server. The tool analyzes millions of records from the PubMed biomedical literature database discussing lipids and diseases, predicts their association and ranks them according to false discovery rates generated by random simulations. The tool takes into account 4270 diseases and 4798 lipids. Since the tool extracts the information from PubMed records, the number of diseases and lipids will be expanded over time as the biomedical literature grows. AVAILABILITY AND IMPLEMENTATION: The LipiDisease webserver can be freely accessed at http://cbdm-01.zdv.uni-mainz.de:3838/piyusmor/LipiDisease/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Statistical guidelines for quality control of next-generation sequencing techniques.

More and more next-generation sequencing (NGS) data are made available every day. However, the quality of this data is not always guaranteed. Available quality control tools require profound knowledge to correctly interpret the multiplicity of quality features. Moreover, it is usually difficult to know if quality features are relevant in all experimental conditions. Therefore, the NGS community would highly benefit from condition-specific data-driven guidelines derived from many publicly available experiments, which reflect routinely generated NGS data. In this work, we have characterized well-known quality guidelines and related features in big datasets and concluded that they are too limited for assessing the quality of a given NGS file accurately. Therefore, we present new data-driven guidelines derived from the statistical analysis of many public datasets using quality features calculated by common bioinformatics tools. Thanks to this approach, we confirm the high relevance of genome mapping statistics to assess the quality of the data, and we demonstrate the limited scope of some quality features that are not relevant in all conditions. Our guidelines are available at https://cbdm.uni-mainz.de/ngs-guidelines.

DiseaseLinc: Disease Enrichment Analysis of Sets of Differentially Expressed LincRNAs.

Long intergenic non-coding RNAs (LincRNAs) are long RNAs that do not encode proteins. Functional evidence is lacking for most of them. Their biogenesis is not well-known, but it is thought that many lincRNAs originate from genomic duplication of coding material, resulting in pseudogenes, gene copies that lose their original function and can accumulate mutations. While most pseudogenes eventually stop producing a transcript and become erased by mutations, many of these pseudogene-based lincRNAs keep similarity to the parental gene from which they originated, possibly for functional reasons. For example, they can act as decoys for miRNAs targeting the parental gene. Enrichment analysis of function is a powerful tool to discover the functional effects of a treatment producing differential expression of transcripts. However, in the case of lincRNAs, since their function is not easy to define experimentally, such a tool is lacking. To address this problem, we have developed an enrichment analysis tool that focuses on lincRNAs exploiting their functional association, using as a proxy function that of the parental genes and has a focus on human diseases.

seqQscorer: automated quality control of next-generation sequencing data using machine learning.

Controlling quality of next-generation sequencing (NGS) data files is a necessary but complex task. To address this problem, we statistically characterize common NGS quality features and develop a novel quality control procedure involving tree-based and deep learning classification algorithms. Predictive models, validated on internal and external functional genomics datasets, are to some extent generalizable to data from unseen species. The derived statistical guidelines and predictive models represent a valuable resource for users of NGS data to better understand quality issues and perform automatic quality control. Our guidelines and software are available at https://github.com/salbrec/seqQscorer .

Linking individual and population patterns of rocky-shore mussels.

Individual traits and population parameters can be used as proxies of processes taking place within a range of scales, thus improving the way we can evaluate species response to environmental variability. In intertidal rocky shores, patterns at the within-site scale, i.e., between centimeters to hundreds of meters, are important for understanding the population response into these highly variable environments. Here, we studied a rocky-shore mussel population at the within-site spatial scale (1) to test how intertidal height and orientation of the shore affect individual traits and population parameters, (2) to infer the link between individual and population level features, and (3) to explore the upscaling mechanisms driving population structure and processes. We analyzed the patterns of six population parameters: density, biomass, crowding, median individual size, recruitment and mortality rate, and four individual traits: growth rate, spawning phenology, size and condition index. Crowding was defined as the degree of overlapping of individuals within a given area, for which we created a "crowding index". Mussels were studied along the intertidal height gradient in two rocky shores with contrasted orientation at one site over a full year. Our results showed a significant effect of intertidal height and shore orientation on most of individual traits and population parameters studied. In contrast, biomass contained in a full covered surface did not vary in space nor in time. This pattern likely results from relatively constant crowding and a trade-off between median individuals' size and density. We hypothesize that growth, mortality and recruitment rates may all play roles in the stability of the crowding structure of mussel aggregations. Variation in spawning phenology between the two shores in the study site was also observed, suggesting different temporal dynamics of microclimate conditions. Interestingly, despite the different population size distribution between the two shores, our estimates indicate similar potential reproductive output. We hypothesize that the structure of the patches would tend to maintain or carry a maximum of biomass due to trade-offs between density and size while maintaining and maximizing the reproductive output. The patterns of spatial variability of individual traits and population parameters in our study site suggest that heterogeneous within-site conditions influence variation in individual performance and population processes. These results provide insights about the relationship between individual traits and how these relationships make patterns at the population level emerge. They provide baseline information necessary to improve models of metapopulation with spatially explicit processes.

RNA Sequencing of Human Peripheral Blood Cells Indicates Upregulation of Immune-Related Genes in Huntington's Disease.

Huntington's disease (HD) is an autosomal dominantly inherited neurodegenerative disorder caused by a trinucleotide repeat expansion in the Huntingtin gene. As disease-modifying therapies for HD are being developed, peripheral blood cells may be used to indicate disease progression and to monitor treatment response. In order to investigate whether gene expression changes can be found in the blood of individuals with HD that distinguish them from healthy controls, we performed transcriptome analysis by next-generation sequencing (RNA-seq). We detected a gene expression signature consistent with dysregulation of immune-related functions and inflammatory response in peripheral blood from HD cases vs. controls, including induction of the interferon response genes, IFITM3, IFI6 and IRF7. Our results suggest that it is possible to detect gene expression changes in blood samples from individuals with HD, which may reflect the immune pathology associated with the disease.

Interactome Mapping Provides a Network of Neurodegenerative Disease Proteins and Uncovers Widespread Protein Aggregation in Affected Brains.

Interactome maps are valuable resources to elucidate protein function and disease mechanisms. Here, we report on an interactome map that focuses on neurodegenerative disease (ND), connects ∼5,000 human proteins via ∼30,000 candidate interactions and is generated by systematic yeast two-hybrid interaction screening of ∼500 ND-related proteins and integration of literature interactions. This network reveals interconnectivity across diseases and links many known ND-causing proteins, such as α-synuclein, TDP-43, and ATXN1, to a host of proteins previously unrelated to NDs. It facilitates the identification of interacting proteins that significantly influence mutant TDP-43 and HTT toxicity in transgenic flies, as well as of ARF-GEP100 that controls misfolding and aggregation of multiple ND-causing proteins in experimental model systems. Furthermore, it enables the prediction of ND-specific subnetworks and the identification of proteins, such as ATXN1 and MKL1, that are abnormally aggregated in postmortem brains of Alzheimer's disease patients, suggesting widespread protein aggregation in NDs.

Computational identification of cell-specific variable regions in ChIP-seq data.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is used to identify genome-wide DNA regions bound by proteins. Given one ChIP-seq experiment with replicates, binding sites not observed in all the replicates will usually be interpreted as noise and discarded. However, the recent discovery of high-occupancy target (HOT) regions suggests that there are regions where binding of multiple transcription factors can be identified. To investigate ChIP-seq variability, we developed a reproducibility score and a method that identifies cell-specific variable regions in ChIP-seq data by integrating replicated ChIP-seq experiments for multiple protein targets on a particular cell type. Using our method, we found variable regions in human cell lines K562, GM12878, HepG2, MCF-7 and in mouse embryonic stem cells (mESCs). These variable-occupancy target regions (VOTs) are CG dinucleotide rich, and show enrichment at promoters and R-loops. They overlap significantly with HOT regions, but are not blacklisted regions producing non-specific binding ChIP-seq peaks. Furthermore, in mESCs, VOTs are conserved among placental species suggesting that they could have a function important for this taxon. Our method can be useful to point to such regions along the genome in a given cell type of interest, to improve the downstream interpretative analysis before follow-up experiments.

TAF-ChIP: an ultra-low input approach for genome-wide chromatin immunoprecipitation assay.

Chromatin immunoprecipitation (ChIP) followed by next generation sequencing (ChIP-Seq) is a powerful technique to study transcriptional regulation. However, the requirement of millions of cells to generate results with high signal-to-noise ratio precludes it in the study of small cell populations. Here, we present a tagmentation-assisted fragmentation ChIP (TAF-ChIP) and sequencing method to generate high-quality histone profiles from low cell numbers. The data obtained from the TAF-ChIP approach are amenable to standard tools for ChIP-Seq analysis, owing to its high signal-to-noise ratio. The epigenetic profiles from TAF-ChIP approach showed high agreement with conventional ChIP-Seq datasets, thereby underlining the utility of this approach.

Literature optimized integration of gene expression for organ-specific evaluation of toxicogenomics datasets.

The study of drug toxicity in human organs is complicated by their complex inter-relations and by the obvious difficulty to testing drug effects on biologically relevant material. Animal models and human cell cultures offer alternatives for systematic and large-scale profiling of drug effects on gene expression level, as typically found in the so-called toxicogenomics datasets. However, the complexity of these data, which includes variable drug doses, time points, and experimental setups, makes it difficult to choose and integrate the data, and to evaluate the appropriateness of one or another model system to study drug toxicity (of particular drugs) of particular human organs. Here, we define a protocol to integrate drug-wise rankings of gene expression changes in toxicogenomics data, which we apply to the TG-GATEs dataset, to prioritize genes for association to drug toxicity in liver or kidney. Contrast of the results with sets of known human genes associated to drug toxicity in the literature allows to compare different rank aggregation approaches for the task at hand. Collectively, ranks from multiple models point to genes not previously associated to toxicity, notably, the PCNA clamp associated factor (PCLAF), and genes regulated by the master regulator of the antioxidant response NFE2L2, such as NQO1 and SRXN1. In addition, comparing gene ranks from different models allowed us to evaluate striking differences in terms of toxicity-associated genes between human and rat hepatocytes or between rat liver and rat hepatocytes. We interpret these results to point to the different molecular functions associated to organ toxicity that are best described by each model. We conclude that the expected production of toxicogenomics panels with larger numbers of drugs and models, in combination with the ongoing increase of the experimental literature in organ toxicity, will lead to increasingly better associations of genes for organism toxicity.

Individual-based simulation of the spatial and temporal dynamics of macroinvertebrate functional groups provides insights into benthic community assembly mechanisms.

The complexity and scales of the processes that shape communities of marine benthic macroinvertebrates has limited our understanding of their assembly mechanisms and the potential to make projections of their spatial and temporal dynamics. Individual-based models can shed light on community assembly mechanisms, by allowing observed spatiotemporal patterns to emerge from first principles about the modeled organisms. Previous work in the Rance estuary (Brittany, France) revealed the principal functional components of its benthic macroinvertebrate communities and derived a set of functional relationships between them. These elements were combined here for the development of a dynamic and spatially explicit model that operates at two spatial scales. At the fine scale, modeling each individual's life cycle allowed the representation of recruitment, inter- and intra-group competition, biogenic habitat modification and predation mortality. Larval dispersal and environmental filtering due to the tidal characteristics of the Rance estuary were represented at the coarse scale. The two scales were dynamically linked and the model was parameterized on the basis of theoretical expectations and expert knowledge. The model was able to reproduce some patterns of α- and ß-diversity that were observed in the Rance estuary in 1995. Model analysis demonstrated the role of local and regional processes, particularly early post-settlement mortality and spatially restricted dispersal, in shaping marine benthos. It also indicated biogenic habitat modification as a promising area for future research. The combination of this mechanism with different substrate types, along with the representation of physical disturbances and more trophic categories, could increase the model's realism. The precise parameterization and validation of the model is expected to extend its scope from the exploration of community assembly mechanisms to the formulation of predictions about the responses of community structure and functioning to environmental change.

Evaluation of in vivo and in vitro models of toxicity by comparison of toxicogenomics data with the literature.

Toxicity affecting humans is studied by observing the effects of chemical substances in animal organisms (in vivo) or in animal and human cultivated cell lines (in vitro). Toxicogenomics studies collect gene expression profiles and histopathology assessment data for hundreds of drugs and pollutants in standardized experimental designs using different model systems. These data are an invaluable source for analyzing genome-wide drug response in biological systems. However, a problem remains that is how to evaluate the suitability of heterogeneous in vitro and in vivo systems to model the many different aspects of human toxicity. We propose here that a given model system (cell type or animal organ) is supported to appropriately describe a particular aspect of human toxicity if the set of compounds associated in the literature with that aspect of toxicity causes a change in expression of genes with a particular function in the tested model system. This approach provides candidate genes to explain the toxicity effect (the differentially expressed genes) and the compounds whose effect could be modeled (the ones producing both the change of expression in the model system and that are associated with the human phenotype in the literature). Here we present an application of this approach using a computational pipeline that integrates compound-induced gene expression profiles (from the Open TG-GATEs database) and biomedical literature annotations (from the PubMed database) to evaluate the suitability of (human and rat) in vitro systems as well as rat in vivo systems to model human toxicity.

Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas.

This study was designed to assess the contribution of feeding behavior to inter-individual variability of paralytic shellfish toxin (PST) accumulation in the Pacific oyster Crassostrea gigas. For this purpose 42 oysters were exposed for 2 days to non-toxic algae and then for 2 other days to the PST producer Alexandrium minutum. Individual clearance rate (CR) of oysters was continuously monitored over the 4 days using an ecophysiological measurement system. Comparison of CR values when exposed to toxic and non toxic algae allowed to estimate a clearance rate inhibition index (CRII). Toxin concentration of oysters was quantified at the end of the experiment. These data allowed to estimate the toxin accumulation efficiency (TAE) as the ratio of toxin accumulated on toxin consumed. Changes of clearance rate during the experiment indicated that all individuals stopped feeding immediately after being exposed to A. minutum for at least 7 h. This fast response likely corresponded to a behavioral mechanism of avoidance rather to a toxin-induced response. Individuals also showed high inter-variability in their recovery of filtration after this period. Most of the inter-individual variability (78%) in PST accumulation in C. gigas could be explained by the consumption of A. minutum cells, thus emphasizing the importance of the feeding behavior in accumulation. Based on the toxin concentration in their tissues, oysters were clustered in 3 groups showing contrasted patterns of PST accumulation: the high accumulation group was characterized by high feeding rates both on non-toxic and toxic diet and subsequently a low CRII and high TAE. Inversely, the low accumulation group was characterized by low filtration rates, high CRII and low TAE. Both filtration capacity and sensitivity of oysters to toxins may account for the differences in their accumulation. The contribution of TAE in PST accumulation is discussed and might result from differences in assimilation and detoxification abilities among individuals.

Defining Human Tyrosine Kinase Phosphorylation Networks Using Yeast as an In Vivo Model Substrate.

Systematic assessment of tyrosine kinase-substrate relationships is fundamental to a better understanding of cellular signaling and its profound alterations in human diseases such as cancer. In human cells, such assessments are confounded by complex signaling networks, feedback loops, conditional activity, and intra-kinase redundancy. Here we address this challenge by exploiting the yeast proteome as an in vivo model substrate. We individually expressed 16 human non-receptor tyrosine kinases (NRTKs) in Saccharomyces cerevisiae and identified 3,279 kinase-substrate relationships involving 1,351 yeast phosphotyrosine (pY) sites. Based on the yeast data without prior information, we generated a set of linear kinase motifs and assigned ∼1,300 known human pY sites to specific NRTKs. Furthermore, experimentally defined pY sites for each individual kinase were shown to cluster within the yeast interactome network irrespective of linear motif information. We therefore applied a network inference approach to predict kinase-substrate relationships for more than 3,500 human proteins, providing a resource to advance our understanding of kinase biology.