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bioRxiv2018

Harnessing the Anti-Cancer Natural Product Nimbolide for Targeted Protein Degradation

Nimbolide, a terpenoid natural product derived from the Neem tree, impairs cancer pathogenicity across many types of human cancers; however, the direct targets and mechanisms by which nimbolide exerts its effects are poorly understood. Here, we used activity-based protein profiling (ABPP) chemoproteomic platforms to discover that nimbolide reacts with a novel functional cysteine crucial for substrate recognition in the E3 ubiquitin ligase RNF114. Nimbolide impairs breast cancer cell proliferation by disrupting RNF114 substrate recognition, leading to inhibition of ubiquitination and degradation of the tumor-suppressor p21, resulting in its rapid stabilization. We further demonstrate that nimbolide can be harnessed to recruit RNF114 as an E3 ligase in targeted protein degradation applications and show that synthetically simpler scaffolds are also capable of accessing this unique reactive site. Our study highlights the utility of ABPP platforms in uncovering unique druggable modalities accessed by natural products for cancer therapy and drug discovery applications.

cancer biology↗

bioRxiv2018

Hydrophobicity drives the systemic distribution of lipid-conjugated siRNAs via lipid transport pathways

Efficient delivery of therapeutic RNA is the fundamental obstacle preventing its clinical utility. Lipid conjugation improves plasma half-life, tissue accumulation, and cellular uptake of small interfering RNAs (siRNAs). However, the impact of conjugate structure and hydrophobicity on siRNA pharmacokinetics is unclear, impeding the design of clinically relevant lipid-siRNAs. Using a panel of biologically-occurring lipids, we show that lipid conjugation modulates siRNA hydrophobicity and governs spontaneous partitioning into distinct plasma lipoprotein classes in vivo. Lipoprotein binding influences siRNA distribution by delaying renal excretion and promoting uptake into lipoprotein receptor-enriched tissues. Lipid-siRNAs elicit mRNA silencing without causing toxicity in a tissue-specific manner. Lipid-siRNA internalization occurs independently of lipoprotein endocytosis, and is mediated by siRNA phosphorothioate modifications. Although biomimetic lipoprotein nanoparticles have been considered for the enhancement of siRNA delivery, our findings suggest that hydrophobic modifications can be leveraged to incorporate therapeutic siRNA into endogenous lipid transport pathways without the requirement for synthetic formulation.

pharmacology and toxicology↗

bioRxiv2015

Targeted reduction of highly abundant transcripts with pseudo-random primers

Transcriptome studies based on quantitative sequencing estimate gene expression levels by measuring the abundance of target RNAs in libraries of sequence reads. The sequencing cost is proportional to the total number of sequenced reads. Therefore, in order to cover rare RNAs, considerable quantities of abundant and identical reads have to be sequenced. This major limitation can be lifted by strategies used to deplete the library from some of the most abundant sequences. However, these strategies involve either an extra handling of the input RNA sample, or the use of a large number of reverse-transcription primers (termed \"not-so-random primers\"), which are costly to synthetize and customize. Here, we demonstrate that with a precise selection of only 40 \"pseudo-random\" reverse-transcription primers, it is possible to decrease the rate of undesirable abundant sequences within a library without affecting the transcriptome diversity. \"Pseudo-random\" primers are simple to design, and therefore are a flexible tool for enriching transcriptome libraries in rare transcripts sequences.

Molecular Biology↗

bioRxiv2018

Gsmodutils: A python based framework for test-driven genome scale metabolic model development

MotivationGenome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for the continued management of curated large scale models. For example, when genome annotations are updated or new understanding regarding behaviour of is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build and test cycle.\n\nResultsAs part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimising error between model versions.\n\nAvailabilityThe software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils

bioinformatics↗

bioRxiv2018

MOSim: Multi-Omics Simulation in R

As multi-omics sequencing technologies advance, the need for simulation tools capable of generating realistic and diverse (bulk and single-cell) multi-omics datasets for method testing and benchmarking becomes increasingly important. We present MOSim, an R package that simulates both bulk (via mosim function) and single-cell (via sc_mosim function) multi-omics data. The mosim function generates bulk transcriptomics data (RNA-seq) and additional regulatory omics layers (ATAC-seq, miRNA-seq, ChIP-seq, Methyl-seq and Transcription Factors), while sc_mosim simulates single-cell transcriptomics data (scRNA-seq) with scATAC-seq and Transcription Factors as regulatory layers. The tool supports various experimental designs, including simulation of gene co-expression patterns, biological replicates, and differential expression between conditions. MOSim enables users to generate quantification matrices for each simulated omics data type, capturing the heterogeneity and complexity of bulk and single-cell multi-omics datasets. Furthermore, MOSim provides differentially abundant features within each omics layer and elucidates the active regulatory relationships between regulatory omics and gene expression data at both bulk and single-cell levels. By leveraging MOSim, researchers will be able to generate realistic and customizable bulk and single-cell multi-omics datasets to benchmark and validate analytical methods specifically designed for the integrative analysis of diverse regulatory omics data. Key PointsO_LIMOSim is capable of generating synthetic datasets for a broad spectrum of omics types, supporting bulk RNA-seq, ChIP-seq, ATAC-seq, miRNA-seq, Methyl-seq, and transcription factor data, as well as single-cell omics, including scRNA-seq, scATAC-seq, and transcription factors. C_LIO_LIMOSim enables the robust simulation of complex, many-to-many regulatory relationships across molecular layers, faithfully capturing intricate regulatory patterns. C_LIO_LIOffering extensive options for customization, MOSims flexible experimental design and parameterization empowers users to simulate count matrices and multilayer regulatory networks, tailoring simulations to diverse experimental scenarios and omics types. C_LI

bioinformatics↗

bioRxiv2017

CRISPR/Cas9 Mutation Of A Gonad-Expressed Opsin Prevents Jellyfish Light-Induced Spawning

Across the animal kingdom, environmental light cues are widely involved in regulating gamete release, but the molecular and cellular bases of the photoresponsive mechanisms are poorly understood. In hydrozoan jellyfish, spawning is triggered by dark-light or light-dark transitions acting on the gonad, and is mediated by oocyte maturation-inducing neuropeptide hormones (MIHs) released from the ectoderm. We determined in Clytia hemisphaerica that blue-cyan light triggers spawning in isolated gonads. A candidate opsin (Opsin9) was found co-expressed with MIH within specialised ectodermal cells. Opsin9 knockout jellyfish generated by CRISPR/Cas9 failed to undergo oocyte maturation and spawning, a phenotype reversible by synthetic MIH. Gamete maturation and release in Clytia is thus regulated by gonadal photosensory-neurosecretory cells that secrete MIH in response to light via Opsin9. Similar cells in ancestral eumetazoans may have allowed tissue-level photo-regulation of diverse behaviours, a feature elaborated in cnidarians in parallel with expansion of the opsin gene family.

evolutionary biology↗

bioRxiv2016

Transcription factor loads tend to decrease the robustness of stable gene transcription networks

Robustness of a systems behavior to changes in parameter values is a remarkable property of natural systems and especially desirable when designing de novo synthetic gene circuits. Loads on transcription factors resulting from binding to target promoters have been shown to significantly affect the effective time constants of gene transcription networks and to thus alter the overall systems behavior. Here, we employ models that explicitly account for load effects to investigate how these impact the robustness of a stable gene transcription network to parameter perturbations. By employing a combination of sampling-based methods and analytical tools from control theory, we demonstrate that the presence of loading shrinks the region of parameter space where a gene circuit performs the desired function. A number of multi-module synthetic gene circuits are analyzed to demonstrate this point, including an event detector and a molecular signature classifier. These results indicate that for designing genetic circuits that are robust to parameter uncertainty it is highly desirable to find ways to mitigate the effects of transcription factor loading.\n\nAuthor SummaryEfforts to understand how loads affect gene transcription networks have been underway in the past decade. Here we perform a numerical investigation on three synthetic gene circuits to show that loads tend to decrease the robustness of stable gene transcriptional networks. We complement the numerical findings with analytical derivations that employ the stability radius to compare the robustness of different networks to parameter perturbations near an equilibrium point. Consistent with the numerical finding, the analytical results support that systems with substantial transcription factor loading have smaller stability radius (less robustness) than systems without loading.

Systems Biology↗

bioRxiv2016

Psip1/p52 regulates distal Hoxa genes through activation of lncRNA Hottip

Long noncoding RNAs (lncRNAs) have been implicated in various biological functions including regulation of gene expression, X-inactivation, imprinting, cell proliferation and differentiation. However, the functionality of lncRNAs is not clearly understood and conflicting conclusions have often been reached when comparing different methods to investigate them. Moreover, little is known about the upstream regulation of lncRNAs. Here we show that a transcriptional co activator - PC4 and SF2 interacting protein (Psip1)/p52, which is involved in linking transcription to RNA processing, regulates the expression of the lncRNA Hottip. Using complementary approaches - knockdown, Cas9 mediated lncRNA deletion, analysis of lncRNA binding by Chromatin isolation by RNA purification (ChIRP) - we demonstrate that Hottip binds to the 5 Hoxa genes located in cis, which leads to their upregulation. Moreover, the synthetic activation of Hottip is sufficient to induce the expression ofpolycomb repressed Hox genes in mouse embryonic stem cells (mESCs).

Genetics↗

bioRxiv2018

Plate-based assay of AP-3 traffic in budding yeast

SYNOPSISThe AP-3 (adaptor complex 3) mediates traffic from the late Golgi or early endosomes to late endosomal compartments. Here, a synthetic reporter is presented that allows convenient monitoring of AP-3 traffic, and direct screening or selection for mutants with defects in the pathway. The reporter can be assayed by fluorescence microscopy or in liquid or agar plate formats and is adaptable to high-throughput screening. SUMMARYAP-3 (adaptor complex 3) mediates traffic from the late Golgi or early endosomes to late endosomal compartments. In mammals, mutations in AP-3 cause Hermansky-Pudlak Syndrome type 2, cyclic neutropenias, and a form of epileptic encephalopathy. In budding yeast, AP-3 carries cargo directly from the trans-Golgi to the lysosomal vacuole. Despite the pathways importance and its discovery two decades ago, rapid screens and selections for AP-3 mutants have not been available. We now report GNSI, a synthetic, genetically encoded reporter that allows rapid plate-based assessment of AP-3 functional deficiency, using either chromogenic or growth phenotype readouts. This system identifies defects in both the formation and consumption of AP-3 carrier vesicles and is adaptable to high-throughput screening or selection in both plate array and liquid batch culture formats. Episomal and integrating plasmids encoding GNSI have been submitted to the Addgene repository.

cell biology↗

bioRxiv2018

Identification and characterisation of serotonin signalling in the potato cyst nematode Globodera pallida reveals new targets for crop protection

Plant parasitic nematodes are microscopic pests that invade plant roots and cause extensive damage to crops worldwide. To investigate mechanisms underpinning their parasitic behaviour we used a chemical biology approach: We discovered that reserpine, a plant alkaloid known for its antagonism of the mammalian vesicular monoamine transporter VMAT and ability to impart a global depletion of synaptic biogenic amines in the nervous system, potently impairs the ability of the potato cyst nematode Globodera pallida to enter the host plant root. We show that this effect of reserpine is mediated by an inhibition of serotonergic signalling that is essential for activation of the stylet, a lance-like organ that protrudes from the mouth of the worm and which is used to pierce the host root to gain access. Prompted by this we identified core molecular components of G. pallida serotonin signalling encompassing the target of reserpine, VMAT; the synthetic enzyme for serotonin, tryptophan hydroxylase; the G protein coupled receptor SER-7 and the serotonin-gated chloride channel MOD-1. We found that inhibitors of tryptophan hydroxylase, SER-7 and MOD-1 phenocopy the plant protecting action of reserpine. Thus targeting the serotonin signalling pathway presents a promising new route to control plant parasitic nematodes.\n\nSummaryIndian snakeroot, an herbal medicine prepared from the roots of the shrub Rauwolfia serpentina, has been used for centuries for its calming action. The major active constituent is reserpine which works by depleting a specific class of mood regulating chemical in the brain, the biogenic amines. We have discovered a remarkable effect of reserpine on a pest of global concern, the plant parasitic nematodes. These microscopic worms invade the roots of crops presenting a severe threat to food production. We show that reserpine disables serotonin signalling in the worms brain that regulates the rhythmic thrusting of the stylet: a lance-like structure that protrudes from its mouth to pierce the plant root and which is essential to its parasitic lifecycle. Thus, reserpine joins nicotine as another intriguing example of Nature evolving its own protection against pests. We have identified key components of the serotonin signalling pathway in the potato cyst nematode Globodera pallida and show that chemicals that target these sites inhibit the ability of the nematode to invade its host plant. We conclude that biogenic amine transmitters are intimately involved in the worms parasitic behaviour and provide a new discrete route to crop protection.

pharmacology and toxicology↗

bioRxiv2018

MYCN Amplification and ATRX Mutations are Incompatible in Neuroblastoma

Aggressive cancers often have activating mutations in growth-controlling oncogenes and inactivating mutations in tumor-suppressor genes. In neuroblastoma, amplification of the MYCN oncogene and inactivation of the ATRX tumor-suppressor gene correlate with high-risk disease and poor prognosis. Here we show that ATRX mutations and MYCN amplification are mutually exclusive across all ages and stages in neuroblastoma. Using human cell lines and mouse models, we found that elevated MYCN expression and ATRX mutations are incompatible. Elevated MYCN levels promote metabolic reprogramming, mitochondrial dysfunction, reactive-oxygen species generation, and DNA-replicative stress. The combination of replicative stress caused by defects in the ATRX-histone chaperone complex and that induced by MYCN-mediated metabolic reprogramming leads to synthetic lethality. Therefore, ATRX and MYCN represent an unusual example, where inactivation of a tumor-suppressor gene and activation of an oncogene are incompatible. This synthetic lethality may eventually be exploited to improve outcomes for patients with high-risk neuroblastoma.

cancer biology↗

bioRxiv2018

DNA Methylation Network Estimation with Sparse Latent Gaussian Graphical Model

Inferring molecular interaction networks from genomics data is important for advancing our understanding of biological processes. Whereas considerable research effort has been placed on inferring such networks from gene expression data, network estimation from DNA methylation data has received very little attention due to the substantially higher dimensionality and complications with result interpretation for non-genic regions. To combat these challenges, we propose here an approach based on sparse latent Gaussian graphical model (SLGGM). The core idea is to perform network estimation on q latent variables as opposed to d CpG sites, with q<<d. To impose a correspondence between the latent variables and genes, we use the distance between CpG sites and transcription starting sites of the genes to generate a prior on the CpG sites latent class membership. We evaluate this approach on synthetic data, and show on real data that the gene network estimated from DNA methylation data significantly explains gene expression patterns in unseen datasets.

genomics↗

bioRxiv2018

Chimeric antigen receptors that trigger phagocytosis

Chimeric antigen receptors (CARs) are synthetic receptors that reprogram T cells to kill cancer. The success of CAR-T cell therapies highlights the promise of programmed immunity, and suggests that applying CAR strategies to other immune cell lineages may be beneficial. Here, we engineered a family of Chimeric Antigen Receptors for Phagocytosis (CAR-Ps) that direct macrophages to engulf specific targets, including cancer cells. CAR-Ps consist of an extracellular antibody fragment, which can be modified to direct CAR-P activity towards specific antigens. By screening a panel of engulfment receptor intracellular domains, we found that the cytosolic domains from Megf10 and FcR{gamma} robustly triggered engulfment independently of their native extracellular domain. We show that CAR-Ps drive specific engulfment of antigen-coated synthetic particles and whole cancer cells. Addition of a tandem PI3K recruitment domain increased cancer cell engulfment. Finally, we show that CAR-P expressing macrophages reduce cancer cell number in co-culture by over 40%.\n\nSummaryWe report the first Chimeric Antigen Receptors for Phagocytosis (CAR-Ps) that promote engulfment of antigen-coated particles and cancer cells.

cell biology↗

bioRxiv2016

Using a structural root system model for an in-depth assessment of root image analysis pipeline

Root system analysis is a complex task, often performed with fully automated image analysis pipelines. However, the outcome is rarely verified by ground-truth data, which might lead to underestimated biases.\n\nWe have used a root model, ArchiSimple, to create a large and diverse library of ground-truth root system images (10,000). For each image, three levels of noise were created. This library was used to evaluate the accuracy and usefulness of several image descriptors classically used in root image analysis softwares.\n\nOur analysis highlighted that the accuracy of the different traits is strongly dependent on the quality of the images and the type, size and complexity of the root systems analysed. Our study also demonstrated that machine learning algorithms can be trained on a synthetic library to improve the estimation of several root system traits.\n\nOverall, our analysis is a call to caution when using automatic root image analysis tools. If a thorough calibration is not performed on the dataset of interest, unexpected errors might arise, especially for large and complex root images. To facilitate such calibration, both the image library and the different codes used in the study have been made available to the community.

Plant Biology↗

bioRxiv2018

Reversion of the photoperiod dependence of flowering in rice with synthetic Hd1-microProteins

Rice (Oryza sativa) is a facultative short-day plant that flowers very late when grown in non-inductive long day conditions. Photoperiod-dependent flowering in rice is regulated by heading date (Hd1) which acts as both an activator and repressor of flowering in a day length-dependent manner. In order to regulate flowering of rice in long days (LD), overexpression of a synthetic Hd1miP, which is capable of interacting with Hd1, was employed. Transgenic Hd1miP rice plants flowered significantly earlier when grown in LD compared to SD, showing that synthetic microProteins can be used to revert short-day plants to long-day plants. Yield analysis revealed that although the OX-Hd1miP grains are comparable to WT in terms of the size of the grains produced, OX-Hd1miP plants like hd1 knockout plants, are compromised in the number of grains produced and the grain maturity rate, suggesting an additional unrecognized role of Hd1 in grain maturity.

plant biology↗

bioRxiv2018

A giant amphipathic helix from a perilipin that is adapted for coating lipid droplets

How proteins are targeted to lipid droplets (LDs) and distinguish the LD surface from the surfaces of other organelles is poorly understood, but many contain predicted amphipathic helices (AHs) that are involved in targeting. We have focused on human perilipin 4 (Plin4), which contains an AH that is exceptional in terms of length and repetitiveness. Using model cellular systems, we show that AH length, hydrophobicity, and charge are important for AH targeting to LDs and that these properties can compensate for one another, albeit at a loss of targeting specificity. Using synthetic lipids, we show that purified Plin4 AH binds poorly to lipid bilayers but strongly interacts with pure triglycerides, acting as a coat and forming small oil droplets. Because Plin4 overexpression alleviates LD instability under conditions where their coverage by phospholipids is limiting, we propose that the Plin4 AH replaces the LD lipid monolayer, for example during LD growth.

cell biology↗

bioRxiv2018

Harnessing Notch signaling to decode mechanisms of proteolytic regulation in diverse cell-surface receptors

Proteolysis of transmembrane receptors is a critical cellular communication mechanism dysregulated in many diseases, yet decoding proteolytic regulation mechanisms of the estimated 400 receptors shed from the cell surface has been hindered by difficulties in controlling stimuli and unknown fates of cleavage products. Notch proteolytic regulation is a notable exception, where decades of study have revealed that intercellular forces drive exposure of a cryptic protease site within a juxtamembrane \"proteolytic switch\" domain to activate transcriptional programs inside the cell. Thus, we created a Synthetic Notch Assay for Proteolytic Switches (SNAPS) that exploits the modularity and unequivocal input/response of Notch proteolysis to screen surface receptors for other putative proteolytic switches. Here, we identify several new proteolytic switches among receptors with structural homology to Notch. We demonstrate that SNAPS can detect shedding in chimeras of diverse cell surface receptors, leading to new, testable hypotheses. Finally, we establish that the assay can be used to measure modulation of proteolysis by potential therapeutics.

systems biology↗

bioRxiv2026

Intelligent differential ion mobility spectrometry (iDMS): A deep neural network that predicts optimal space-resolved ion mobility parameters for isomeric monoglycosphingolipids

Simultaneous quantification of monoglycosphingolipid stereoisomers is required to monitor changes in defective enzymatic pathways linked to diseases such as Gaucher Disease, Parkinson's Disease, and Krabbe Disease. Resolution of beta-glucosyl and beta-galactosyl epimers cannot be achieved by standard liquid chromatography, electrospray ionization, tandem mass spectrometry (LC-ESI-MS/MS). Separation becomes possible when field asymmetric ion mobility spectrometry (FAIMS), also known as differential mobility mass spectrometry (DMS), is added as an orthogonal separation technique to LC. FAIMS/DMS separates epimeric ion clusters in a high versus low electric field (separation voltage, SV) then redirects the target epimeric ions to the mass spectrometer through the application of a direct current (compensation voltage, CoV). Resolving SVs and CoVs must be manually determined for each lipid. Manual derivation is a labour-intensive process that requires pure synthetic standards, limiting the number of stereoisomers a user can include in an assay. To address this problem, we introduce here intelligent DMS (iDMS). iDMS is an in silico supervised neural network model that learns the ion mobility relationships between SV and CoV and the monoglycosphingolipid structural features of sugar headgroup, N-acyl chain length, and N-acyl degree of unsaturation. iDMS predicts the SV and CoV combinations capable of resolving any stereoisomer pair from a training dataset of composed of measured signal intensities across a range of SVs and CoVs of 12 lipids. This machine learning alternative to manual DMS optimization promises to accelerate the deployment of multiple-reaction-monitoring mode (MRM) RPLC-ESI-DMS-MS/MS assays for the routine and rapid quantification of biologically relevant monoglycosphingolipid stereoisomers.

bioinformatics↗