Protein complex-based analysis framework for high-throughput data sets

Circadian regulation of macromolecular complex turnover and proteome renewal

Although costly to maintain, protein homeostasis is indispensable for normal cellular function and long-term health. In mammalian cells and tissues, daily variation in global protein synthesis has been observed, but its utility and consequences for proteome integrity are not fully understood. Using several different pulse-labelling strategies, here we gain direct insight into the relationship between protein synthesis and abundance proteome-wide. We show that protein degradation varies in-phase with protein synthesis, facilitating rhythms in turnover rather than abundance. This results in daily consolidation of proteome renewal whilst minimising changes in composition. Coupled rhythms in synthesis and turnover are especially salient to the assembly of macromolecular protein complexes, particularly the ribosome, the most abundant species of complex in the cell. Daily turnover and proteasomal degradation rhythms render cells and mice more sensitive to proteotoxic stress at specific times of day, potentially contributing to daily rhythms in the efficacy of proteasomal inhibitors against cancer. Our findings suggest that circadian rhythms function to minimise the bioenergetic cost of protein homeostasis through temporal consolidation of protein turnover.

The MYCN oncoprotein is an RNA-binding accessory factor of the nuclear exosome targeting complex

The MYCN oncoprotein binds active promoters in a heterodimer with its partner protein MAX. MYCN also interacts with the nuclear exosome, a 3'-5' exoribonuclease complex, suggesting a function in RNA metabolism. Here, we show that MYCN forms stable high-molecular-weight complexes with the exosome and multiple RNA-binding proteins. MYCN binds RNA in vitro and in cells via a conserved sequence termed MYCBoxI. In cells, MYCN associates with thousands of intronic transcripts together with the ZCCHC8 subunit of the nuclear exosome targeting complex and enhances their processing. Perturbing exosome function results in global re-localization of MYCN from promoters to intronic RNAs. On chromatin, MYCN is then replaced by the MNT(MXD6) repressor protein, inhibiting MYCN-dependent transcription. RNA-binding-deficient alleles show that RNA-binding limits MYCN's ability to activate cell growth-related genes but is required for MYCN's ability to promote progression through S phase and enhance the stress resilience of neuroblastoma cells.

Comparative interactome analysis of α-arrestin families in human and Drosophila

The α-arrestins form a large family of evolutionally conserved modulators that control diverse signaling pathways, including both G-protein-coupled receptor (GPCR)-mediated and non-GPCR-mediated pathways, across eukaryotes. However, unlike β-arrestins, only a few α-arrestin targets and functions have been characterized. Here, using affinity purification and mass spectrometry, we constructed interactomes for 6 human and 12 Drosophila α-arrestins. The resulting high-confidence interactomes comprised 307 and 467 prey proteins in human and Drosophila, respectively. A comparative analysis of these interactomes predicted not only conserved binding partners, such as motor proteins, proteases, ubiquitin ligases, RNA splicing factors, and GTPase-activating proteins, but also those specific to mammals, such as histone modifiers and the subunits of V-type ATPase. Given the manifestation of the interaction between the human α-arrestin, TXNIP, and the histone-modifying enzymes, including HDAC2, we undertook a global analysis of transcription signals and chromatin structures that were affected by TXNIP knockdown. We found that TXNIP activated targets by blocking HDAC2 recruitment to targets, a result that was validated by chromatin immunoprecipitation assays. Additionally, the interactome for an uncharacterized human α-arrestin ARRDC5 uncovered multiple components in the V-type ATPase, which plays a key role in bone resorption by osteoclasts. Our study presents conserved and species-specific protein-protein interaction maps for α-arrestins, which provide a valuable resource for interrogating their cellular functions for both basic and clinical research.

Finding information about uncharacterized Drosophila melanogaster genes

Genes that have been identified in the genome but remain uncharacterized with regards to function offer an opportunity to uncover novel biological information. Novelty is exciting but can also be a barrier. If nothing is known, how does one start planning and executing experiments? Here, we provide a recommended information-mining workflow and a corresponding guide to accessing information about uncharacterized Drosophila melanogaster genes, such as those assigned only a systematic coding gene identifier. The available information can provide insights into where and when the gene is expressed, what the function of the gene might be, whether there are similar genes in other species, whether there are known relationships to other genes, and whether any other features have already been determined. In addition, available information about relevant reagents can inspire and facilitate experimental studies. Altogether, mining available information can help prioritize genes for further study, as well as provide starting points for experimental assays and other analyses.

Protein phosphatase 1 regulates core PCP signaling

Planar cell polarity (PCP) signaling polarizes epithelial cells within the plane of an epithelium. Core PCP signaling components adopt asymmetric subcellular localizations within cells to both polarize and coordinate polarity between cells. Achieving subcellular asymmetry requires additional effectors, including some mediating post-translational modifications of core components. Identification of such proteins is challenging due to pleiotropy. We used mass spectrometry-based proximity labeling proteomics to identify such regulators in the Drosophila wing. We identified the catalytic subunit of protein phosphatase1, Pp1-87B, and show that it regulates core protein polarization. Pp1-87B interacts with the core protein Van Gogh and at least one serine/threonine kinase, Dco/CKIε, that is known to regulate PCP. Pp1-87B modulates Van Gogh subcellular localization and directs its dephosphorylation in vivo. PNUTS, a Pp1 regulatory subunit, also modulates PCP. While the direct substrate(s) of Pp1-87B in control of PCP is not known, our data support the model that cycling between phosphorylated and unphosphorylated forms of one or more core PCP components may regulate acquisition of asymmetry. Finally, our screen serves as a resource for identifying additional regulators of PCP signaling.

Proteomic association with age-dependent sex differences in Wisconsin Card Sorting Test performance in healthy Thai subjects

Sex differences in cognitive function exist, but they are not stable and undergo dynamic change during the lifespan. However, our understanding of how sex-related neural information transmission evolves with age is still in its infancy. This study utilized the Wisconsin Card Sorting Test (WCST) and the label-free proteomics method with bioinformatic analysis to investigate the molecular mechanisms underlying age-related sex differences in cognitive performance in 199 healthy Thai subjects (aged 20-70 years), as well as explore the sex-dependent protein complexes for predicting cognitive aging. The results showed that males outperformed females in two of the five WCST sub-scores: %Corrects and %Errors. Sex differences in these scores were related to aging, becoming noticeable in those over 60. At the molecular level, differently expressed individual proteins and protein complexes between both sexes are associated with the potential N-methyl-D-aspartate type glutamate receptor (NMDAR)-mediated excitotoxicity, with the NMDAR complex being enriched exclusively in elderly female samples. These findings provided a preliminary indication that healthy Thai females might be more susceptible to such neurotoxicity, as evidenced by their cognitive performance. NMDAR protein complex enrichment in serum could be proposed as a potential indication for predicting cognitive aging in healthy Thai females.

Comprehensive interrogation of gene lists from genome-scale cancer screens with oncoEnrichR

Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichR, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichR differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichR through interrogation of two candidate lists from proteomic and CRISPR screens. oncoEnrichR is freely available as a web-based service hosted by the Galaxy platform (https://oncotools.elixir.no), and can also be accessed as a stand-alone R package (https://github.com/sigven/oncoEnrichR).

Protein phosphatase 1 regulates core PCP signaling

PCP signaling polarizes epithelial cells within the plane of an epithelium. Core PCP signaling components adopt asymmetric subcellular localizations within cells to both polarize and coordinate polarity between cells. Achieving subcellular asymmetry requires additional effectors, including some mediating post-translational modifications of core components. Identification of such proteins is challenging due to pleiotropy. We used mass spectrometry-based proximity labeling proteomics to identify such regulators in the Drosophila wing. We identified the catalytic subunit of Protein Phosphatase1, Pp1-87B, and show that it regulates core protein polarization. Pp1-87B interacts with the core protein Van Gogh and at least one Serine/Threonine kinase, Dco/CKIε, that is known to regulate PCP. Pp1-87B modulates Van Gogh subcellular localization and directs its dephosphorylation in vivo. PNUTS, a Pp1 regulatory subunit, also modulates PCP. While the direct substrate(s) of Pp1-87B in control of PCP is not known, our data support the model that cycling between phosphorylated and unphosphorylated forms of one or more core PCP components may regulate acquisition of asymmetry. Finally, our screen serves as a resource for identifying additional regulators of PCP signaling.

Global and tissue-specific aging effects on murine proteomes

Maintenance of protein homeostasis degrades with age, contributing to aging-related decline and disease. Previous studies have primarily surveyed transcriptional aging changes. To define the effects of age directly at the protein level, we perform discovery-based proteomics in 10 tissues from 20 C57BL/6J mice, representing both sexes at adult and late midlife ages (8 and 18 months). Consistent with previous studies, age-related changes in protein abundance often have no corresponding transcriptional change. Aging results in increases in immune proteins across all tissues, consistent with a global pattern of immune infiltration with age. Our protein-centric data reveal tissue-specific aging changes with functional consequences, including altered endoplasmic reticulum and protein trafficking in the spleen. We further observe changes in the stoichiometry of protein complexes with important roles in protein homeostasis, including the CCT/TriC complex and large ribosomal subunit. These data provide a foundation for understanding how proteins contribute to systemic aging across tissues.

PANGEA: a new gene set enrichment tool for Drosophila and common research organisms

Gene set enrichment analysis (GSEA) plays an important role in large-scale data analysis, helping scientists discover the underlying biological patterns over-represented in a gene list resulting from, for example, an 'omics' study. Gene Ontology (GO) annotation is the most frequently used classification mechanism for gene set definition. Here we present a new GSEA tool, PANGEA (PAthway, Network and Gene-set Enrichment Analysis; https://www.flyrnai.org/tools/pangea/), developed to allow a more flexible and configurable approach to data analysis using a variety of classification sets. PANGEA allows GO analysis to be performed on different sets of GO annotations, for example excluding high-throughput studies. Beyond GO, gene sets for pathway annotation and protein complex data from various resources as well as expression and disease annotation from the Alliance of Genome Resources (Alliance). In addition, visualizations of results are enhanced by providing an option to view network of gene set to gene relationships. The tool also allows comparison of multiple input gene lists and accompanying visualisation tools for quick and easy comparison. This new tool will facilitate GSEA for Drosophila and other major model organisms based on high-quality annotated information available for these species.

A phosphate-sensing organelle regulates phosphate and tissue homeostasis

Inorganic phosphate (Pi) is one of the essential molecules for life. However, little is known about intracellular Pi metabolism and signalling in animal tissues1. Following the observation that chronic Pi starvation causes hyperproliferation in the digestive epithelium of Drosophila melanogaster, we determined that Pi starvation triggers the downregulation of the Pi transporter PXo. In line with Pi starvation, PXo deficiency caused midgut hyperproliferation. Interestingly, immunostaining and ultrastructural analyses showed that PXo specifically marks non-canonical multilamellar organelles (PXo bodies). Further, by Pi imaging with a Förster resonance energy transfer (FRET)-based Pi sensor2, we found that PXo restricts cytosolic Pi levels. PXo bodies require PXo for biogenesis and undergo degradation following Pi starvation. Proteomic and lipidomic characterization of PXo bodies unveiled their distinct feature as an intracellular Pi reserve. Therefore, Pi starvation triggers PXo downregulation and PXo body degradation as a compensatory mechanism to increase cytosolic Pi. Finally, we identified connector of kinase to AP-1 (Cka), a component of the STRIPAK complex and JNK signalling3, as the mediator of PXo knockdown- or Pi starvation-induced hyperproliferation. Altogether, our study uncovers PXo bodies as a critical regulator of cytosolic Pi levels and identifies a Pi-dependent PXo-Cka-JNK signalling cascade controlling tissue homeostasis.

The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

PANGEA: A New Gene Set Enrichment Tool for Drosophila and Common Research Organisms

Gene set enrichment analysis (GSEA) plays an important role in large-scale data analysis, helping scientists discover the underlying biological patterns over-represented in a gene list resulting from, for example, an 'omics' study. Gene Ontology (GO) annotation is the most frequently used classification mechanism for gene set definition. Here we present a new GSEA tool, PANGEA (PAthway, Network and Gene-set Enrichment Analysis; https://www.flyrnai.org/tools/pangea/ ), developed to allow a more flexible and configurable approach to data analysis using a variety of classification sets. PANGEA allows GO analysis to be performed on different sets of GO annotations, for example excluding high-throughput studies. Beyond GO, gene sets for pathway annotation and protein complex data from various resources as well as expression and disease annotation from the Alliance of Genome Resources (Alliance). In addition, visualisations of results are enhanced by providing an option to view network of gene set to gene relationships. The tool also allows comparison of multiple input gene lists and accompanying visualisation tools for quick and easy comparison. This new tool will facilitate GSEA for Drosophila and other major model organisms based on high-quality annotated information available for these species.

Next-generation large-scale binary protein interaction network for Drosophila melanogaster

Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster. Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the 'FlyBi' dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings (dwg) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function.

Identification of two pathways mediating protein targeting from ER to lipid droplets

Pathways localizing proteins to their sites of action are essential for eukaryotic cell organization and function. Although mechanisms of protein targeting to many organelles have been defined, how proteins, such as metabolic enzymes, target from the endoplasmic reticulum (ER) to cellular lipid droplets (LDs) is poorly understood. Here we identify two distinct pathways for ER-to-LD protein targeting: early targeting at LD formation sites during formation, and late targeting to mature LDs after their formation. Using systematic, unbiased approaches in Drosophila cells, we identified specific membrane-fusion machinery, including regulators, a tether and SNARE proteins, that are required for the late targeting pathway. Components of this fusion machinery localize to LD–ER interfaces and organize at ER exit sites. We identified multiple cargoes for early and late ER-to-LD targeting pathways. Our findings provide a model for how proteins target to LDs from the ER either during LD formation or by protein-catalysed formation of membrane bridges.

Song et al. identify two protein-targeting pathways from the endoplasmic reticulum to (1) early lipid droplets (LDs) and (2) mature lipid droplets. They define key factors mediating the second, late pathway and its many cargoes.

Regulation of protein abundance in genetically diverse mouse populations

Genetically diverse mouse populations are powerful tools for characterizing the regulation of the proteome and its relationship to whole-organism phenotypes. We used mass spectrometry to profile and quantify the abundance of 6,798 proteins in liver tissue from mice of both sexes across 58 Collaborative Cross (CC) inbred strains. We previously collected liver proteomics data from the related Diversity Outbred (DO) mice and their founder strains. We show concordance across the proteomics datasets despite being generated from separate experiments, allowing comparative analysis. We map protein abundance quantitative trait loci (pQTLs), identifying 1,087 local and 285 distal in the CC mice and 1,706 local and 414 distal in the DO mice. We find that regulatory effects on individual proteins are conserved across the mouse populations, in particular for local genetic variation and sex differences. In comparison, proteins that form complexes are often co-regulated, displaying varying genetic architectures, and overall show lower heritability and map fewer pQTLs. We have made this resource publicly available to enable quantitative analyses of the regulation of the proteome.

mTORC1-chaperonin CCT signaling regulates m6A RNA methylation to suppress autophagy

Mechanistic Target of Rapamycin Complex 1 (mTORC1) is a central regulator of cell growth and metabolism that senses and integrates nutritional and environmental cues with cellular responses. Recent studies have revealed critical roles of mTORC1 in RNA biogenesis and processing. Here, we find that the m6A methyltransferase complex (MTC) is a downstream effector of mTORC1 during autophagy in Drosophila and human cells. Furthermore, we show that the Chaperonin Containing Tailless complex polypeptide 1 (CCT) complex, which facilitates protein folding, acts as a link between mTORC1 and MTC. The mTORC1 activates the chaperonin CCT complex to stabilize MTC, thereby increasing m6A levels on the messenger RNAs encoding autophagy-related genes, leading to their degradation and suppression of autophagy. Altogether, our study reveals an evolutionarily conserved mechanism linking mTORC1 signaling with m6A RNA methylation and demonstrates their roles in suppressing autophagy.

Cross-species identification of PIP5K1-, splicing- and ubiquitin-related pathways as potential targets for RB1-deficient cells

The RB1 tumor suppressor is recurrently mutated in a variety of cancers including retinoblastomas, small cell lung cancers, triple-negative breast cancers, prostate cancers, and osteosarcomas. Finding new synthetic lethal (SL) interactions with RB1 could lead to new approaches to treating cancers with inactivated RB1. We identified 95 SL partners of RB1 based on a Drosophila screen for genetic modifiers of the eye phenotype caused by defects in the RB1 ortholog, Rbf1. We validated 38 mammalian orthologs of Rbf1 modifiers as RB1 SL partners in human cancer cell lines with defective RB1 alleles. We further show that for many of the RB1 SL genes validated in human cancer cell lines, low activity of the SL gene in human tumors, when concurrent with low levels of RB1 was associated with improved patient survival. We investigated higher order combinatorial gene interactions by creating a novel Drosophila cancer model with co-occurring Rbf1, Pten and Ras mutations, and found that targeting RB1 SL genes in this background suppressed the dramatic tumor growth and rescued fly survival whilst having minimal effects on wild-type cells. Finally, we found that drugs targeting the identified RB1 interacting genes/pathways, such as UNC3230, PYR-41, TAK-243, isoginkgetin, madrasin, and celastrol also elicit SL in human cancer cell lines. In summary, we identified several high confidence, evolutionarily conserved, novel targets for RB1-deficient cells that may be further adapted for the treatment of human cancer.

FlyRNAi.org-the database of the Drosophila RNAi screening center and transgenic RNAi project: 2021 update

The FlyRNAi database at the Drosophila RNAi Screening Center and Transgenic RNAi Project (DRSC/TRiP) provides a suite of online resources that facilitate functional genomics studies with a special emphasis on Drosophila melanogaster. Currently, the database provides: gene-centric resources that facilitate ortholog mapping and mining of information about orthologs in common genetic model species; reagent-centric resources that help researchers identify RNAi and CRISPR sgRNA reagents or designs; and data-centric resources that facilitate visualization and mining of transcriptomics data, protein modification data, protein interactions, and more. Here, we discuss updated and new features that help biological and biomedical researchers efficiently identify, visualize, analyze, and integrate information and data for Drosophila and other species. Together, these resources facilitate multiple steps in functional genomics workflows, from building gene and reagent lists to management, analysis, and integration of data.

STRAP regulates alternative splicing fidelity during lineage commitment of mouse embryonic stem cells

Alternative splicing (AS) is involved in cell fate decisions and embryonic development. However, regulation of these processes is poorly understood. Here, we have identified the serine threonine kinase receptor-associated protein (STRAP) as a putative spliceosome-associated factor. Upon Strap deletion, there are numerous AS events observed in mouse embryoid bodies (EBs) undergoing a neuroectoderm-like state. Global mapping of STRAP-RNA binding in mouse embryos by enhanced-CLIP sequencing (eCLIP-seq) reveals that STRAP preferably targets transcripts for nervous system development and regulates AS through preferred binding positions, as demonstrated for two neuronal-specific genes, Nnat and Mark3. We have found that STRAP involves in the assembly of 17S U2 snRNP proteins. Moreover, in Xenopus, loss of Strap leads to impeded lineage differentiation in embryos, delayed neural tube closure, and altered exon skipping. Collectively, our findings reveal a previously unknown function of STRAP in mediating the splicing networks of lineage commitment, alteration of which may be involved in early embryonic lethality in mice.

A pooled genome-wide screening strategy to identify and rank influenza host restriction factors in cell-based vaccine production platforms

Cell-derived influenza vaccines provide better protection and a host of other advantages compared to the egg-derived vaccines that currently dominate the market, but their widespread use is hampered by a lack of high yield, low cost production platforms. Identification and knockout of innate immune and metabolic restriction factors within relevant host cell lines used to grow the virus could offer a means to substantially increase vaccine yield. In this paper, we describe and validate a novel genome-wide pooled CRISPR/Cas9 screening strategy that incorporates a reporter virus and a FACS selection step to identify and rank restriction factors in a given vaccine production cell line. Using the HEK-293SF cell line and A/PuertoRico/8/1934 H1N1 influenza as a model, we identify 64 putative influenza restriction factors to direct the creation of high yield knockout cell lines. In addition, gene ontology and protein complex enrichment analysis of this list of putative restriction factors offers broader insights into the primary host cell determinants of viral yield in cell-based vaccine production systems. Overall, this work will advance efforts to address the public health burden posed by influenza.

Identifying drug targets in tissues and whole blood with thermal-shift profiling

Monitoring drug-target interactions with methods such as the cellular thermal-shift assay (CETSA) is well established for simple cell systems but remains challenging in vivo. Here we introduce tissue thermal proteome profiling (tissue-TPP), which measures binding of small-molecule drugs to proteins in tissue samples from drug-treated animals by detecting changes in protein thermal stability using quantitative mass spectrometry. We report organ-specific, proteome-wide thermal stability maps and derive target profiles of the non-covalent histone deacetylase inhibitor panobinostat in rat liver, lung, kidney and spleen and of the B-Raf inhibitor vemurafenib in mouse testis. In addition, we devised blood-CETSA and blood-TPP and applied it to measure target and off-target engagement of panobinostat and the BET family inhibitor JQ1 directly in whole blood. Blood-TPP analysis of panobinostat confirmed its binding to known targets and also revealed thermal stabilization of the zinc-finger transcription factor ZNF512. These methods will help to elucidate the mechanisms of drug action in vivo.

The role of translationally controlled tumor protein in proliferation of Drosophila intestinal stem cells

Translationally controlled tumor protein (TCTP) is a highly conserved protein functioning in multiple cellular processes, ranging from growth to immune responses. To explore the role of TCTP in tissue maintenance and regeneration, we employed the adult Drosophila midgut, where multiple signaling pathways interact to precisely regulate stem cell division for tissue homeostasis. Tctp levels were significantly increased in stem cells and enteroblasts upon tissue damage or activation of the Hippo pathway that promotes regeneration of intestinal epithelium. Stem cells with reduced Tctp levels failed to proliferate during normal tissue homeostasis and regeneration. Mechanistically, Tctp forms a complex with multiple proteins involved in translation and genetically interacts with ribosomal subunits. In addition, Tctp increases both Akt1 protein abundance and phosphorylation in vivo. Altogether, Tctp regulates stem cell proliferation by interacting with key growth regulatory signaling pathways and the translation process in vivo.

Dynamics of activating and repressive histone modifications in Drosophila neural stem cell lineages and brain tumors

During central nervous system development, spatiotemporal gene expression programs mediate specific lineage decisions to generate neuronal and glial cell types from neural stem cells (NSCs). However, little is known about the epigenetic landscape underlying these highly complex developmental events. Here, we perform ChIP-seq on distinct subtypes of Drosophila FACS-purified NSCs and their differentiated progeny to dissect the epigenetic changes accompanying the major lineage decisions in vivo By analyzing active and repressive histone modifications, we show that stem cell identity genes are silenced during differentiation by loss of their activating marks and not via repressive histone modifications. Our analysis also uncovers a new set of genes specifically required for altering lineage patterns in type II neuroblasts (NBs), one of the two main Drosophila NSC identities. Finally, we demonstrate that this subtype specification in NBs, unlike NSC differentiation, requires Polycomb-group-mediated repression.

Molecular profiling of stem cell-like female germ line cells in Drosophila delineates networks important for stemness and differentiation

Stem cells can self-renew and produce daughter cells destined for differentiation. The precise control of the balance between these two outcomes is essential to ensure tissue homeostasis and to prevent uncontrolled proliferation resulting in tumor formation. As self-renewal and differentiation are likely to be controlled by different gene expression programs, unraveling the underlying gene regulatory networks is crucial for understanding the molecular logic of this system. In this study, we have characterized by next generation RNA sequencing (RNA-seq) the transcriptome of germline stem cell (GSC)-like cells isolated from bag of marbles (bam) mutant Drosophila ovaries and compared it to the transcriptome of germ line cells isolated from wild-type ovaries. We have complemented this dataset by utilizing an RNA-immunoprecipitation strategy to identify transcripts bound to the master differentiation factor Bam. Protein complex enrichment analysis on these combined datasets allows us to delineate known and novel networks essential for GSC maintenance and differentiation. Further comparative transcriptomics illustrates similarities between GSCs and primordial germ cells and provides a molecular footprint of the stem cell state. Our study represents a useful resource for functional studies on stem cell maintenance and differentiation.

Summary: Fruit fly germline stem cell differentiation is accompanied by major changes of the transcriptome that may be regulated at the post-transcriptional level.

Large-scale RNAi screen identified Dhpr as a regulator of mitochondrial morphology and tissue homeostasis

Mitochondria are highly dynamic organelles. Through a large-scale in vivo RNA interference (RNAi) screen that covered around a quarter of the Drosophila melanogaster genes (4000 genes), we identified 578 genes whose knockdown led to aberrant shapes or distributions of mitochondria. The complex analysis revealed that knockdown of the subunits of proteasomes, spliceosomes, and the electron transport chain complexes could severely affect mitochondrial morphology. The loss of Dhpr, a gene encoding an enzyme catalyzing tetrahydrobiopterin regeneration, leads to a reduction in the numbers of tyrosine hydroxylase neurons, shorter lifespan, and gradual loss of muscle integrity and climbing ability. The affected mitochondria in Dhpr mutants are swollen and have fewer cristae, probably due to lower levels of Drp1 S-nitrosylation. Overexpression of Drp1, but not of S-nitrosylation-defective Drp1, rescued Dhpr RNAi-induced mitochondrial defects. We propose that Dhpr regulates mitochondrial morphology and tissue homeostasis by modulating S-nitrosylation of Drp1.

Molecular Interaction Search Tool (MIST): an integrated resource for mining gene and protein interaction data

Model organism and human databases are rich with information about genetic and physical interactions. These data can be used to interpret and guide the analysis of results from new studies and develop new hypotheses. Here, we report the development of the Molecular Interaction Search Tool (MIST; http://fgrtools.hms.harvard.edu/MIST/). The MIST database integrates biological interaction data from yeast, nematode, fly, zebrafish, frog, rat and mouse model systems, as well as human. For individual or short gene lists, the MIST user interface can be used to identify interacting partners based on protein–protein and genetic interaction (GI) data from the species of interest as well as inferred interactions, known as interologs, and to view a corresponding network. The data, interologs and search tools at MIST are also useful for analyzing ‘omics datasets. In addition to describing the integrated database, we also demonstrate how MIST can be used to identify an appropriate cut-off value that balances false positive and negative discovery, and present use-cases for additional types of analysis. Altogether, the MIST database and search tools support visualization and navigation of existing protein and GI data, as well as comparison of new and existing data.

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.

CAPRI enables comparison of evolutionarily conserved RNA interacting regions

RNA-protein complexes play essential regulatory roles at nearly all levels of gene expression. Using in vivo crosslinking and RNA capture, we report a comprehensive RNA-protein interactome in a metazoan at four levels of resolution: single amino acids, domains, proteins and multisubunit complexes. We devise CAPRI, a method to map RNA-binding domains (RBDs) by simultaneous identification of RNA interacting crosslinked peptides and peptides adjacent to such crosslinked sites. CAPRI identifies more than 3000 RNA proximal peptides in Drosophila and human proteins with more than 45% of them forming new interaction interfaces. The comparison of orthologous proteins enables the identification of evolutionary conserved RBDs in globular domains and intrinsically disordered regions (IDRs). By comparing the sequences of IDRs through evolution, we classify them based on the type of motif, accumulation of tandem repeats, conservation of amino acid composition and high sequence divergence.

Comprehensive characterisation of RNA-protein interactions requires different levels of resolution. Here, the authors present an integrated mass spectrometry-based approach that allows them to define the Drosophila RNA-protein interactome from the level of multisubunit complexes down to the RNA-binding amino acid.

Understanding Human-Virus Protein-Protein Interactions Using a Human Protein Complex-Based Analysis Framework

Although human protein complexes have been reported to be directly related to viral infection, previous studies have not systematically investigated human-virus PPIs from the perspective of human protein complexes. To the best of our knowledge, we have presented here the most comprehensive and in-depth analysis of human-virus PPIs in the context of VTCs. Our findings confirm that human protein complexes are heavily involved in viral infection. The observed preferences of virally targeted subunits within complexes reflect the mechanisms used by viruses to manipulate host protein complexes. The identified periodic expression patterns of the VTCs and the corresponding candidates could increase our understanding of how viruses manipulate the host cell cycle. Finally, our proposed conceptual application framework of VTCs and the developed VTcomplex could provide new hints to develop antiviral drugs for the clinical treatment of viral infections.

Computational analysis of human-virus protein-protein interaction (PPI) data is an effective way toward systems understanding the molecular mechanism of viral infection. Previous work has mainly focused on characterizing the global properties of viral targets within the entire human PPI network. In comparison, how viruses manipulate host local networks (e.g., human protein complexes) has been rarely addressed from a computational perspective. By mainly integrating information about human-virus PPIs, human protein complexes, and gene expression profiles, we performed a large-scale analysis of virally targeted complexes (VTCs) related to five common human-pathogenic viruses, including influenza A virus subtype H1N1, human immunodeficiency virus type 1, Epstein-Barr virus, human papillomavirus, and hepatitis C virus. We found that viral targets are enriched within human protein complexes. We observed in the context of VTCs that viral targets tended to have a high within-complex degree and to be scaffold and housekeeping proteins. Complexes that are essential for viral propagation were simultaneously targeted by multiple viruses. We characterized the periodic expression patterns of VTCs and provided the corresponding candidates that may be involved in the manipulation of the host cell cycle. As a potential application of the current analysis, we proposed a VTC-based antiviral drug target discovery strategy. Finally, we developed an online VTC-related platform known as VTcomplex (http://zzdlab.com/vtcomplex/index.php or http://systbio.cau.edu.cn/vtcomplex/index.php). We hope that the current analysis can provide new insights into the global landscape of human-virus PPIs at the VTC level and that the developed VTcomplex will become a vital resource for the community.

IMPORTANCE Although human protein complexes have been reported to be directly related to viral infection, previous studies have not systematically investigated human-virus PPIs from the perspective of human protein complexes. To the best of our knowledge, we have presented here the most comprehensive and in-depth analysis of human-virus PPIs in the context of VTCs. Our findings confirm that human protein complexes are heavily involved in viral infection. The observed preferences of virally targeted subunits within complexes reflect the mechanisms used by viruses to manipulate host protein complexes. The identified periodic expression patterns of the VTCs and the corresponding candidates could increase our understanding of how viruses manipulate the host cell cycle. Finally, our proposed conceptual application framework of VTCs and the developed VTcomplex could provide new hints to develop antiviral drugs for the clinical treatment of viral infections.

PPARγ Interaction with UBR5/ATMIN Promotes DNA Repair to Maintain Endothelial Homeostasis

Using proteomic approaches, we uncovered a DNA damage response (DDR) function for peroxisome proliferator activated receptor γ (PPARγ) through its interaction with the DNA damage sensor MRE11-RAD50-NBS1 (MRN) and the E3 ubiquitin ligase UBR5. We show that PPARγ promotes ATM signaling and is essential for UBR5 activity targeting ATM interactor (ATMIN). PPARγ depletion increases ATMIN protein independent of transcription and suppresses DDR-induced ATM signaling. Blocking ATMIN in this context restores ATM activation and DNA repair. We illustrate the physiological relevance of PPARγ DDR functions by using pulmonary arterial hypertension (PAH) as a model that has impaired PPARγ signaling related to endothelial cell (EC) dysfunction and unresolved DNA damage. In pulmonary arterial ECs (PAECs) from PAH patients, we observed disrupted PPARγ-UBR5 interaction, heightened ATMIN expression, and DNA lesions. Blocking ATMIN in PAH PAEC restores ATM activation. Thus, impaired PPARγ DDR functions may explain the genomic instability and loss of endothelial homeostasis in PAH.

Whole genome screen reveals a novel relationship between Wolbachia levels and Drosophila host translation

Wolbachia is an intracellular bacterium that infects a remarkable range of insect hosts. Insects such as mosquitos act as vectors for many devastating human viruses such as Dengue, West Nile, and Zika. Remarkably, Wolbachia infection provides insect hosts with resistance to many arboviruses thereby rendering the insects ineffective as vectors. To utilize Wolbachia effectively as a tool against vector-borne viruses a better understanding of the host-Wolbachia relationship is needed. To investigate Wolbachia-insect interactions we used the Wolbachia/Drosophila model that provides a genetically tractable system for studying host-pathogen interactions. We coupled genome-wide RNAi screening with a novel high-throughput fluorescence in situ hybridization (FISH) assay to detect changes in Wolbachia levels in a Wolbachia-infected Drosophila cell line JW18. 1117 genes altered Wolbachia levels when knocked down by RNAi of which 329 genes increased and 788 genes decreased the level of Wolbachia. Validation of hits included in depth secondary screening using in vitro RNAi, Drosophila mutants, and Wolbachia-detection by DNA qPCR. A diverse set of host gene networks was identified to regulate Wolbachia levels and unexpectedly revealed that perturbations of host translation components such as the ribosome and translation initiation factors results in increased Wolbachia levels both in vitro using RNAi and in vivo using mutants and a chemical-based translation inhibition assay. This work provides evidence for Wolbachia-host translation interaction and strengthens our general understanding of the Wolbachia-host intracellular relationship.

Time-resolved transcriptomics in neural stem cells identifies a v-ATPase/Notch regulatory loop

Drosophila melanogaster neural stem cells (neuroblasts) divide asymmetrically by differentially segregating protein determinants into their daughter cells. Wissel et al. use time-resolved transcriptional profiling to identify a v-ATPase/Notch regulatory loop that acts in multiple stem cell lineages both during nervous system development and in the adult gut.

Drosophila melanogaster neural stem cells (neuroblasts [NBs]) divide asymmetrically by differentially segregating protein determinants into their daughter cells. Although the machinery for asymmetric protein segregation is well understood, the events that reprogram one of the two daughter cells toward terminal differentiation are less clear. In this study, we use time-resolved transcriptional profiling to identify the earliest transcriptional differences between the daughter cells on their way toward distinct fates. By screening for coregulated protein complexes, we identify vacuolar-type H⁺–ATPase (v-ATPase) among the first and most significantly down-regulated complexes in differentiating daughter cells. We show that v-ATPase is essential for NB growth and persistent activity of the Notch signaling pathway. Our data suggest that v-ATPase and Notch form a regulatory loop that acts in multiple stem cell lineages both during nervous system development and in the adult gut. We provide a unique resource for investigating neural stem cell biology and demonstrate that cell fate changes can be induced by transcriptional regulation of basic, cell-essential pathways.

Proteomic and Metabolomic Characterization of a Mammalian Cellular Transition from Quiescence to Proliferation

There exist similarities and differences in metabolism and physiology between normal proliferative cells and tumor cells. Once a cell enters the cell cycle, metabolic machinery is engaged to facilitate various processes. The kinetics and regulation of these metabolic changes have not been properly evaluated. To correlate the orchestration of these processes with the cell cycle, we analyzed the transition from quiescence to proliferation of a non-malignant murine pro-B lymphocyte cell line in response to IL-3. Using multiplex mass-spectrometry-based proteomics, we show that the transition to proliferation shares features generally attributed to cancer cells: upregulation of glycolysis, lipid metabolism, amino-acid synthesis, and nucleotide synthesis and downregulation of oxidative phosphorylation and the urea cycle. Furthermore, metabolomic profiling of this transition reveals similarities to cancer-related metabolic pathways. In particular, we find that methionine is consumed at a higher rate than that of other essential amino acids, with a potential link to maintenance of the epigenome.

Zinc Detoxification: A Functional Genomics and Transcriptomics Analysis in Drosophila melanogaster Cultured Cells

Cells require some metals, such as zinc and manganese, but excess levels of these metals can be toxic. As a result, cells have evolved complex mechanisms for maintaining metal homeostasis and surviving metal intoxication. Here, we present the results of a large-scale functional genomic screen in Drosophila cultured cells for modifiers of zinc chloride toxicity, together with transcriptomics data for wild-type or genetically zinc-sensitized cells challenged with mild zinc chloride supplementation. Altogether, we identified 47 genes for which knockdown conferred sensitivity or resistance to toxic zinc or manganese chloride treatment, and >1800 putative zinc-responsive genes. Analysis of the 'omics data points to the relevance of ion transporters, glutathione (GSH)-related factors, and conserved disease-associated genes in zinc detoxification. Specific genes identified in the zinc screen include orthologs of human disease-associated genes CTNS, PTPRN (also known as IA-2), and ATP13A2 (also known as PARK9). We show that knockdown of red dog mine (rdog; CG11897), a candidate zinc detoxification gene encoding an ABCC-type transporter family protein related to yeast cadmium factor (YCF1), confers sensitivity to zinc intoxication in cultured cells, and that rdog is transcriptionally upregulated in response to zinc stress. As there are many links between the biology of zinc and other metals and human health, the 'omics data sets presented here provide a resource that will allow researchers to explore metal biology in the context of diverse health-relevant processes.

Pooled genome-wide CRISPR screening for basal and context-specific fitness gene essentiality in Drosophila cells

Genome-wide screens in Drosophila cells have offered numerous insights into gene function, yet a major limitation has been the inability to stably deliver large multiplexed DNA libraries to cultured cells allowing barcoded pooled screens. Here, we developed a site-specific integration strategy for library delivery and performed a genome-wide CRISPR knockout screen in Drosophila S2R+ cells. Under basal growth conditions, 1235 genes were essential for cell fitness at a false-discovery rate of 5%, representing the highest-resolution fitness gene set yet assembled for Drosophila, including 407 genes which likely duplicated along the vertebrate lineage and whose orthologs were underrepresented in human CRISPR screens. We additionally performed context-specific fitness screens for resistance to or synergy with trametinib, a Ras/ERK/ETS inhibitor, or rapamycin, an mTOR inhibitor, and identified key regulators of each pathway. The results present a novel, scalable, and versatile platform for functional genomic screens in invertebrate cells.

The Notch Interactome: Complexity in Signaling Circuitry

The Notch pathway controls a very broad spectrum of cell fates in metazoans during development, influencing proliferation, differentiation and cell death. Given its central role in normal development and homeostasis, misregulation of Notch signals can lead to various disorders including cancer. How the Notch pathway mediates such pleiotropic and differential effects is of fundamental importance. It is becoming increasingly clear through a number of large-scale genetic and proteomic studies that Notch interacts with a staggeringly large number of other genes and pathways in a context-dependent, complex, and highly regulated network, which determines the ultimate biological outcome. How best to interpret and analyze the continuously increasing wealth of data on Notch interactors remains a challenge. Here we review the current state of genetic and proteomic data related to the Notch interactome.

Toward Large-Scale Computational Prediction of Protein Complexes

Cellular functions are often performed by multiprotein structures called protein complexes. These complexes are dynamic structures that evolve during the cell cycle or in response to external and internal stimuli, and are tightly regulated by protein expression in different tissues resulting in quantitative and qualitative variation of protein complexes. Advances in high-throughput techniques, such as mass-spectrometry and yeast two-hybrid provided a large amount of data on protein-protein interactions. This sparked the development of computational methods able to predict protein complex formation under a variety of biological and clinical conditions. However, the challenges that need to be addressed for successful computational protein complex prediction are highly complex.The post-genomic era saw an emerging number of algorithms and software, which are able to predict protein complexes from protein-protein interaction networks and a variety of other sources. Despite the high capacity of these methods to qualitatively predict protein complexes, they could provide only limited or no quantitative information of the predicted complexes. Recently, a new large-scale simulation of protein complexes was able to achieve this task by simulating protein complex formation on the proteome scale.In this chapter, we review representative methods that can predict multiple protein complexes at different scales and discuss how these can be combined with emerging sources of data in order to improve protein complex characterization.

Design and Methods of Large-Scale RNA Interference Screens in Drosophila

Drosophila is an ideal model system for addressing important questions in biology. The use of RNA interference (RNAi) to knockdown gene expression in fly tissues is both very effective and relatively simple. In the past few decades, genome-wide UAS-RNAi transgenic libraries and thousands of Gal4 strains have been generated and have facilitated large-scale in vivo RNAi screening. Here, we discuss methods for the design and performance of a large-scale in vivo RNAi screen in Drosophila. Furthermore, methods for the validation of results and analysis of data will be introduced.

An Integrative Analysis of the InR/PI3K/Akt Network Identifies the Dynamic Response to Insulin Signaling

Insulin regulates an essential conserved signaling pathway affecting growth, proliferation, and metabolism. To expand our understanding of the insulin pathway, we combine biochemical, genetic, and computational approaches to build a comprehensive Drosophila InR/PI3K/Akt network. First, we map the dynamic protein-protein interaction network surrounding the insulin core pathway using bait-prey interactions connecting 566 proteins. Combining RNAi screening and phospho-specific antibodies, we find that 47% of interacting proteins affect pathway activity, and, using quantitative phosphoproteomics, we demonstrate that ∼10% of interacting proteins are regulated by insulin stimulation at the level of phosphorylation. Next, we integrate these orthogonal datasets to characterize the structure and dynamics of the insulin network at the level of protein complexes and validate our method by identifying regulatory roles for the Protein Phosphatase 2A (PP2A) and Reptin-Pontin chromatin-remodeling complexes as negative and positive regulators of ribosome biogenesis, respectively. Altogether, our study represents a comprehensive resource for the study of the evolutionary conserved insulin network.

Characterization of the Optical Properties of Turbid Media by Supervised Learning of Scattering Patterns

Fabricated tissue phantoms are instrumental in optical in-vitro investigations concerning cancer diagnosis, therapeutic applications, and drug efficacy tests. We present a simple non-invasive computational technique that, when coupled with experiments, has the potential for characterization of a wide range of biological tissues. The fundamental idea of our approach is to find a supervised learner that links the scattering pattern of a turbid sample to its thickness and scattering parameters. Once found, this supervised learner is employed in an inverse optimization problem for estimating the scattering parameters of a sample given its thickness and scattering pattern. Multi-response Gaussian processes are used for the supervised learning task and a simple setup is introduced to obtain the scattering pattern of a tissue sample. To increase the predictive power of the supervised learner, the scattering patterns are filtered, enriched by a regressor, and finally characterized with two parameters, namely, transmitted power and scaled Gaussian width. We computationally illustrate that our approach achieves errors of roughly 5% in predicting the scattering properties of many biological tissues. Our method has the potential to facilitate the characterization of tissues and fabrication of phantoms used for diagnostic and therapeutic purposes over a wide range of optical spectrum.

A Human Tyrosine Phosphatase Interactome Mapped by Proteomic Profiling

Tyrosine phosphatases play a critical role in many cellular processes and pathogenesis, yet comprehensive analysis of their functional interacting proteins in the cell is limited. By utilizing a proteomic approach, here we present an interaction network of 81 human tyrosine phosphatases built on 1884 high-confidence interactions of which 85% are unreported. Our analysis has linked several phosphatases with new cellular processes and unveiled protein interactions genetically linked to various human diseases including cancer. We validated the functional importance of an identified interaction network by characterizing a distinct novel interaction between PTPN5 and Mob1a. PTPN5 dephosphorylates Mob1a at Y26 residue. Further, we identify that PTPN5 is required for proper midbody abscission during cytokinesis through regulation of Mob1a dephosphorylation. In conclusion, our study provides a valuable resource of tyrosine phosphatase interactions, which can be further utilized to dissect novel cellular functions of these enzymes.

AML1-ETO requires enhanced C/D box snoRNA/RNP formation to induce self-renewal and leukaemia

Leukaemogenesis requires enhanced self-renewal, which is induced by oncogenes. The underlying molecular mechanisms remain incompletely understood. Here, we identified C/D box snoRNAs and rRNA 2'-O-methylation as critical determinants of leukaemic stem cell activity. Leukaemogenesis by AML1-ETO required expression of the groucho-related amino-terminal enhancer of split (AES). AES functioned by inducing snoRNA/RNP formation via interaction with the RNA helicase DDX21. Similarly, global loss of C/D box snoRNAs with concomitant loss of rRNA 2'-O-methylation resulted in decreased leukaemia self-renewal potential. Genomic deletion of either C/D box snoRNA SNORD14D or SNORD35A suppressed clonogenic potential of leukaemia cells in vitro and delayed leukaemogenesis in vivo. We further showed that AML1-ETO9a, MYC and MLL-AF9 all enhanced snoRNA formation. Expression levels of C/D box snoRNAs in AML patients correlated closely with in vivo frequency of leukaemic stem cells. Collectively, these findings indicate that induction of C/D box snoRNA/RNP function constitutes an important pathway in leukaemogenesis.

Identification of protein complexes by integrating multiple alignment of protein interaction networks

MOTIVATION

Protein complexes are one of the keys to studying the behavior of a cell system. Many biological functions are carried out by protein complexes. During the past decade, the main strategy used to identify protein complexes from high-throughput network data has been to extract near-cliques or highly dense subgraphs from a single protein-protein interaction (PPI) network. Although experimental PPI data have increased significantly over recent years, most PPI networks still have many false positive interactions and false negative edge loss due to the limitations of high-throughput experiments. In particular, the false negative errors restrict the search space of such conventional protein complex identification approaches. Thus, it has become one of the most challenging tasks in systems biology to automatically identify protein complexes.

RESULTS

In this study, we propose a new algorithm, NEOComplex ( NE CC- and O rtholog-based Complex identification by multiple network alignment), which integrates functional orthology information that can be obtained from different types of multiple network alignment (MNA) approaches to expand the search space of protein complex detection. As part of our approach, we also define a new edge clustering coefficient (NECC) to assign weights to interaction edges in PPI networks so that protein complexes can be identified more accurately. The NECC is based on the intuition that there is functional information captured in the common neighbors of the common neighbors as well. Our results show that our algorithm outperforms well-known protein complex identification tools in a balance between precision and recall on three eukaryotic species: human, yeast, and fly. As a result of MNAs of the species, the proposed approach can tolerate edge loss in PPI networks and even discover sparse protein complexes which have traditionally been a challenge to predict.

AVAILABILITY AND IMPLEMENTATION

http://acolab.ie.nthu.edu.tw/bionetwork/NEOComplex.

CONTACT

bab@csail.mit.edu or csliao@ie.nthu.edu.tw.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Investigation of coordination and order in transcription regulation of innate and adaptive immunity genes in type 1 diabetes

Background

Type 1 diabetes (T1D) is an autoimmune disease and extensive evidence has indicated a critical role of both the innate and the adaptive arms of immune system in disease development. To date most clinical trials of immunomodulation therapies failed to show efficacy. A number of gene expression studies of T1D have been carried out. However, a systems analysis of the expression variations of the innate and adaptive immunity gene sets, or their co-expression network structures in cohorts at different disease states or of different disease risks, is not available till now.

Methods

We utilized data from a large gene expression study that included transcription profiles of control peripheral blood mononuclear cells (PBMC) exposed to plasma of 148 human subjects from four cohorts that included unrelated healthy controls (uHC), recent onset T1D patients (RO-T1D), and healthy siblings of probands that possess high (HRS, High Risk Sibling) or low (LRS, Low Risk Sibling) risk HLA haplotypes. Both weighted and non-weighted co-expression networks were constructed in each cohort separately, and edge weight distribution and the activation of known protein complexes were examined. The co-expression networks of the innate and adaptive immunity genes were further examined in more detail through a number of network measures that included network density, Shannon entropy, h-index, and the scaling exponent γ of degree distribution. Pathway analysis was carried out using CoGA, a tool for detecting significant network structural changes of a gene set.

Results

Weighted network edge distribution revealed a globally weakened co-expression network induced by the RO-T1D cohort as compared to that by the uHC, suggesting a broad spectrum loss of transcriptional coordination. The two healthy T1D family cohorts (HRS and LRS) induced more active but heterogeneous transcription coordination globally, and among both the innate and the adaptive immunity genes, than the uHC. This finding is consistent with our previous report of these cohorts sharing a heightened innate inflammatory state. The spike-in of IL-1RA to RO-T1D sera improved co-expression network strength of both the innate and the adaptive immunity genes, and enabled a global order recovery in transcription regulation that resulted in significantly increased number of activated protein complexes. Many of the top pathways that showed significant difference in co-expression network structures and order between RO-T1D and uHC have strong links to T1D.

Conclusions

Network level analysis of the innate and adaptive immunity genes, and the whole genome, revealed striking cohort-dependent differences in co-expression network structural measures, suggesting their potential in cohort classification and disease-relevant pathway identification. The results demonstrated the advantages of systems analysis in defining molecular signatures as well as in predicting targets in future research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-017-0243-8) contains supplementary material, which is available to authorized users.

Alignment of PPI Networks Using Semantic Similarity for Conserved Protein Complex Prediction

Network alignment is a computational technique to identify topological similarity of graph data by mapping link patterns. In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionarily conserved substructures at the system level. In particular, local network alignment of PPI networks searches for conserved functional components between species and predicts unknown protein complexes and signaling pathways. In this article, we present a novel approach of local network alignment by semantic mapping. While most previous methods find protein matches between species by sequence homology, our approach uses semantic similarity. Given Gene Ontology (GO) and its annotation data, we estimate functional closeness between two proteins by measuring their semantic similarity. We adopted a new semantic similarity measure, simVICD, which has the best performance for PPI validation and functional match. We tested alignment between the PPI networks of well-studied yeast protein complexes and the genome-wide PPI network of human in order to predict human protein complexes. The experimental results demonstrate that our approach has higher accuracy in protein complex prediction than graph clustering algorithms, and higher efficiency than previous network alignment algorithms.

Loss of Nardilysin, a Mitochondrial Co-chaperone for α-Ketoglutarate Dehydrogenase, Promotes mTORC1 Activation and Neurodegeneration

We previously identified mutations in Nardilysin (dNrd1) in a forward genetic screen designed to isolate genes whose loss causes neurodegeneration in Drosophila photoreceptor neurons. Here we show that NRD1 is localized to mitochondria, where it recruits mitochondrial chaperones and assists in the folding of α-ketoglutarate dehydrogenase (OGDH), a rate-limiting enzyme in the Krebs cycle. Loss of Nrd1 or Ogdh leads to an increase in α-ketoglutarate, a substrate for OGDH, which in turn leads to mTORC1 activation and a subsequent reduction in autophagy. Inhibition of mTOR activity by rapamycin or partially restoring autophagy delays neurodegeneration in dNrd1 mutant flies. In summary, this study reveals a novel role for NRD1 as a mitochondrial co-chaperone for OGDH and provides a mechanistic link between mitochondrial metabolic dysfunction, mTORC1 signaling, and impaired autophagy in neurodegeneration.

Improving protein complex prediction by reconstructing a high-confidence protein-protein interaction network of Escherichia coli from different physical interaction data sources

BACKGROUND

Although different protein-protein physical interaction (PPI) datasets exist for Escherichia coli, no common methodology exists to integrate these datasets and extract reliable modules reflecting the existing biological process and protein complexes. Naïve Bayesian formula is the highly accepted method to integrate different PPI datasets into a single weighted PPI network, but detecting proper weights in such network is still a major problem.

RESULTS

In this paper, we proposed a new methodology to integrate various physical PPI datasets into a single weighted PPI network in a way that the detected modules in PPI network exhibit the highest similarity to available functional modules. We used the co-expression modules as functional modules, and we shown that direct functional modules detected from Gene Ontology terms could be used as an alternative dataset. After running this integrating methodology over six different physical PPI datasets, orthologous high-confidence interactions from a related organism and two AP-MS PPI datasets gained high weights in the integrated networks, while the weights for one AP-MS PPI dataset and two other datasets derived from public databases have converged to zero. The majority of detected modules shaped around one or few hub protein(s). Still, a large number of highly interacting protein modules were detected which are functionally relevant and are likely to construct protein complexes.

CONCLUSIONS

We provided a new high confidence protein complex prediction method supported by functional studies and literature mining.