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bioRxiv2018

CausalTab: PSI-MITAB 2.8 updated format for signaling data representation and dissemination

Combining multiple layers of information underlying biological complexity into a structured framework, and in particular deciphering the molecular mechanisms behind cellular phenotypes, represent two challenges in systems biology. A key task is the formalisation of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signaling information, such as SIGNOR, SignaLink and IntAct, focus on capturing, organising and displaying signaling interactions by representing them as binary, causal relationships between biological entities. The curation efforts that build these individual databases demand a concerted effort to ensure interoperability among resources, through the development of a standardized exchange format, ontologies and controlled vocabularies supporting the domain of causal interactions. Aware of the enormous benefits of standardization efforts in the molecular interaction research field, representatives of the signalling network community agreed to extend the PSI-MI controlled vocabulary to include additional terms representing aspects of causal interactions. Here, we present a common standard for the representation and dissemination of signaling information: the PSI Causal Interaction tabular format (CausalTAB) which is an extension of the existing PSI-MI tab-delimited format, now designated MITAB2.8. We define the new term \"causal interaction\", and related child terms, which are children of the PSI-MI \"molecular interaction\" term. The new vocabulary terms in this extended PSI-MI format will enable systems biologists to model large-scale signaling networks more precisely and with higher coverage than before.

systems biology↗

bioRxiv2017

Symmetry broken and rebroken during the ATP hydrolysis cycle of the mitochondrial Hsp90 TRAP1

Hsp90 is a homodimeric ATP-dependent molecular chaperone that remodels its substrate \"client\" proteins, facilitating their folding and activating them for biological function. Despite decades of research, the mechanism connecting ATP hydrolysis and chaperone function remains elusive. Particularly puzzling has been the apparent lack of cooperativity in hydrolysis of the ATP in each protomer. A crystal structure of the mitochondrial Hsp90, TRAP1, revealed that the catalytically active state is closed in a highly-strained asymmetric conformation. This asymmetry, unobserved in other Hsp90 homologs, is due to buckling of one of the protomers and is most pronounced at the broadly conserved client-binding region. Here, we show that rather than being cooperative or independent, ATP hydrolysis on the two protomers is sequential and deterministic. Moreover, dimer asymmetry sets up differential hydrolysis rates for each protomer, such that the buckled conformation favors ATP hydrolysis. Remarkably, after the first hydrolysis, the dimer undergoes a flip in the asymmetry while remaining in a closed state for the second hydrolysis. From these results, we propose a model where direct coupling of ATP hydrolysis and conformational flipping rearranges client-binding sites, providing a paradigm of how energy from ATP hydrolysis can be used for client remodeling.

molecular biology↗

bioRxiv2016

DynOmics to identify delays and co-expression patterns across time course experiments

Dynamic changes in biological systems can be captured by measuring molecular expression from different levels (e.g., genes and proteins) across time. Integration of such data aims to identify molecules that show similar expression changes over time; such molecules may be co-regulated and thus involved in similar biological processes. Combining data sources presents a systematic approach to study molecular behaviour. It can compensate for missing data in one source, and can reduce false positives when multiple sources highlight the same pathways. However, integrative approaches must accommodate the challenges inherent in omics data, including high-dimensionality, noise, and timing differences in expression. As current methods for identification of co-expression cannot cope with this level of complexity, we developed a novel algorithm called DynOmics. DynOmics is based on the fast Fourier transform, from which the difference in expression initiation between trajectories can be estimated. This delay can then be used to realign the trajectories and identify those which show a high degree of correlation. Through extensive simulations, we demonstrate that DynOmics is efficient and accurate compared to existing approaches. We consider two case studies highlighting its application, identifying regulatory relationships across omics data within an organism and for comparative gene expression analysis across organisms.

Bioinformatics↗

bioRxiv2026

High-Resolution Subtyping of Pediatric Low-Grade Glioma Using an Integrated Meta-Clustering Framework

Pediatric low-grade glioma (pLGG) is the most common type of brain tumor in children, accounting for approximately 30% of all central nervous system tumors in children. pLGG has multiple molecular subtypes that differ in disease progression, recurrence patterns, and treatment responses. Conventional wet lab approaches including molecular profiling and histopathological studies for pLGG characterization are time consuming, costly, and laborious. Recently, methods based on artificial intelligence (AI) or machine learning (ML) have been widely used for pLGG molecular categorization, but most of them can only identify two or three pLGG subtypes. To more comprehensively characterize the molecular subtypes of pLGG and their potential biological and therapeutic significance, we develop an integrated meta-clustering approach, namely Meta-pLGG, that can explore high resolution molecular subtypes and their transcriptional heterogeneity for pLGG. Specifically, we first performed multiple rounds of random projection (RP) to generate dimension-reduced feature vectors from pLGG transcriptomics data, each of which was subsequently clustered by different clustering algorithms including hierarchical clustering, K-means, Self-Organizing Maps (SOM), Non-negative Matrix Factorization (NMF), Gaussian Mixture Model (GMM), and Spectral Clustering, as base clustering methods. Then, to yield robust clustering performance, we integrated the clustering results of these RP based individual clustering algorithms by adopting a weighted meta-clustering (wMetaC) approach. Results based on 532 pLGG patients suggested that our proposed approach demonstrated superior stability and discriminative powers for higher resolution pLGG subtyping compared to conventional approaches. Based on consensus matrix analysis, we identified two major pLGG mega-subtypes, with one further subdivided into three subgroups and the other into two. Then, we performed cluster specific differential gene expression analysis, molecular pathway analysis, and gene-drug-disease association analysis. The results showed that the identified five subgroups exhibited significant subtype-specific transcriptomic heterogeneity. In summary, our meta-clustering approach demonstrated much higher performance and robustness in identifying higher resolution molecular subtypes of pLGG, revealing the molecular heterogeneity within pLGG and potentially providing new insights for more precise molecular subtyping and precision therapy.

bioinformatics↗

bioRxiv2016

DIABLO - an integrative, multi-omics, multivariate method for multi-group classification

Systems biology approaches, leveraging multi-omics measurements, are needed to capture the complexity of biological networks while identifying the key molecular drivers of disease mechanisms. We present DIABLO, a novel integrative method to identify multi-omics biomarker panels that can discriminate between multiple phenotypic groups. In the multi-omics analyses of simulated and real-world datasets, DIABLO resulted in superior biological enrichment compared to other integrative methods, and achieved comparable predictive performance with existing multi-step classification schemes. DIABLO is a versatile approach that will benefit a diverse range of research areas, where multiple high dimensional datasets are available for the same set of specimens. DIABLO is implemented along with tools for model selection, and validation, as well as graphical outputs to assist in the interpretation of these integrative analyses (http://mixomics.org/).

Bioinformatics↗

bioRxiv2017

RNA-binding activity of TRIM25 is mediated by its PRY/SPRY domain and is required for ubiquitination

TRIM25 is a novel RNA-binding protein and a member of the Tripartite Motif (TRIM) family of E3 ubiquitin ligases, which plays a pivotal role in the innate immune response. Almost nothing is known about its RNA-related roles in cell biology. Furthermore, its RNA-binding domain has not been characterized. Here, we reveal that RNA-binding activity of TRIM25 is mediated by its PRY/SPRY domain, which we postulate to be a novel RNA-binding domain. Using CLIP-seq and SILAC-based co-immunoprecipitation assays, we uncover TRIM25s endogenous RNA targets and protein binding partners. Finally, we show that the RNA-binding activity of TRIM25 is important for its ubiquitin ligase function. These results reveal new insights into the molecular roles and characteristics of RNA-binding E3 ubiquitin ligases and demonstrate that RNA could be an essential factor for their biological functions.

molecular biology↗

bioRxiv2015

TESS: Bayesian inference of lineage diversification rates from (incompletely sampled) molecular phylogenies in R

SummaryMany fundamental questions in evolutionary biology entail estimating rates of lineage diversification (speciation - extinction). We develop a flexible Bayesian framework for specifying an effectively infinite array of diversification models--where rates are constant, vary continuously, or change episodically through time--and implement numerical methods to estimate parameters of these models from molecular phylogenies, even when species sampling is incomplete. Additionally we provide robust methods for comparing the relative and absolute fit of competing branching-process models to a given tree, thereby providing rigorous tests of biological hypotheses regarding patterns and processes of lineage diversification.\n\nAvailability and implementationthe source code for TESS is freely available at http://cran.r-project.org/web/packages/TESS/.\n\nContactSebastian.Hoehna@gmail.com

Bioinformatics↗

bioRxiv2018

Bacteria: A novel source for potent mosquito feeding-deterrents

Antibiotic and insecticidal bioactivities of the extracellular secondary metabolites produced by entomopathogenic bacteria belonging to genus Xenorhabdus have been identified; however, their novel applications such as mosquito feeding-deterrence have not been reported. Here, we show that a mixture of compounds isolated from Xenorhabdus budapestensis in vitro cultures exhibits potent feeding-deterrent activity against three deadly mosquito vectors: Aedes aegypti, Anopheles gambiae and Culex pipiens. We further demonstrate that the deterrent-active fraction isolated from replicate bacterial cultures is consistently highly enriched in two modified peptides identical to the previously described fabclavines, strongly suggesting that these are molecular species responsible for feeding-deterrence. The mosquito feeding-deterrent activity in the fabclavines-rich fraction is comparable to or better than that of N, N-diethyl-3-methylbenzamide (also known as Deet) or picaridin in side-by-side assays. Our unique discovery lays the groundwork for research into biologically derived, peptide-based low molecular weight compounds isolated from bacteria for exploitation as mosquito repellents and feeding-deterrents.

microbiology↗

bioRxiv2018

A two-step probing method to compare lysine accessibility across macromolecular complex conformations

Structural models of multi-megadalton molecular complexes are appearing in increasing numbers, in large part because of technical advances in cryo-electron microscopy realized over the last decade. However, the inherent complexity of large biological assemblies comprising dozens of components often limits the resolution of structural models. Furthermore, multiple functional configurations of a complex can leave a puzzle as to how one intermediate moves to the next stage. Orthogonal biochemical information is crucial to understanding the molecular interactions that drive those rearrangements. We present a two-step method for chemical probing detected by tandem mass-spectrometry to globally assess the reactivity of lysine residues within purified macromolecular complexes. Because lysine side chains often balance the negative charge of RNA in ribonucleoprotein complexes, the method is especially powerful for detecting changes in protein-RNA interactions. Probing the E. coli 30S ribosome subunit showed that the reactivity pattern of lysine residues quantitatively reflects structure models from X-ray crystallography. We assessed differences in two conformations of purified human spliceosomes. Our results demonstrate that this method supplies powerful biochemical information that aids in functional interpretation of atomic models of macromolecular complexes at the intermediate resolution often provided by cryo-electron microscopy.

molecular biology↗

bioRxiv2018

Locoregional Radiogenomic Models Capture Gene Expression Heterogeneity in Glioblastoma

Radiogenomics mapping noninvasively determines important relationships between the molecular genotype and imaging phenotype of various tumors, allowing advances in both clinical care and cancer research. While early work has shown its technical feasibility, here we extend radiogenomic mapping to a locoregional level that can account for the molecular heterogeneity of tumors. To achieve this, our data processing pipeline relies on three main steps: 1) the use of multi-omics data fusion to generate a set of 100 interpretable gene modules, 2) the use of patch-based image analysis (specifically of contrast-enhanced T1-weighted weighted MR images) combined with Generalized Linear Models (GLM) to establish potential links between module expressions and local MR signal, and 3) the use of expression heatmaps based on GLMs decision values to explore visualization of tumor molecular heterogeneity. The performance of the proposed approach was evaluated using a leave-one-patient-out crossvalidation method as well as a separate validation data set. The top performing models were based on a small set of 20 features and yielded Area Under the receiver operating characteristic Curve (AUC) above 0.65 on the validation cohort for eight modules. Next, we demonstrate the clinical and biological interpretation of four modules using molecular expression heatmaps superimposed on clinical radiographic images, showing the potential for assessing tumor molecular heterogeneity and the utility of this method for precision treatment in clinical decision making and imaging surveillance.

bioinformatics↗

bioRxiv2018

Logarithmic molecular sampling for next-generation sequencing

Next-generation sequencing enables measurement of chemical and biological signals at high throughput and falling cost. Conventional sequencing requires increasing sampling depth to improve signal to noise discrimination, a costly procedure that is also impossible when biological material is limiting. We introduce a new general sampling theory, Molecular Entropy encodinG (MEG), which uses biophysical principles to functionally encode molecular abundance before sampling. SeQUential DepletIon and enriCHment (SQUICH) is a specific example of MEG that, in theory and simulation, enables sampling at a logarithmic or better rate to achieve the same precision as attained with conventional sequencing. In proof-of-principle experiments, SQUICH reduces sequencing depth by a factor of 10. MEG is a general solution to a fundamental problem in molecular sampling and enables a new generation of efficient, precise molecular measurement at logarithmic or better sampling depth.

genomics↗

bioRxiv2016

The E. coli molecular phenotype under different growth conditions

Modern systems biology requires extensive, carefully curated measurements of cellular components in response to different environmental conditions. While high-throughput methods have made transcriptomics and proteomics datasets widely accessible and relatively economical to generate, systematic measurements of both mRNA and protein abundances under a wide range of different conditions are still relatively rare. Here we present a detailed, genome-wide transcriptomics and proteomics dataset of E. coli grown under 34 different conditions. We manipulate concentrations of sodium and magnesium in the growth media, and we consider four different carbon sources glucose, gluconate, lactate, and glycerol. Moreover, samples are taken both in exponential and stationary phase, and we include two extensive time-courses, with multiple samples taken between 3 hours and 2 weeks. We find that exponential-phase samples systematically differ from stationary-phase samples, in particular at the level of mRNA. Regulatory responses to different carbon sources or salt stresses are more moderate, but we find numerous differentially expressed genes for growth on gluconate and under salt and magnesium stress. Our data set provides a rich resource for future computational modeling of E. coli gene regulation, transcription, and translation.

bioinformatics↗

bioRxiv2018

Untargeted Mass Spectrometry-Based Metabolomics Tracks Molecular Changes in Raw and Processed Foods and Beverages

A major aspect of our daily lives is the need to acquire, store and prepare our food. Storage and preparation can have drastic effects on the compositional chemistry of our foods, but we have a limited understanding of the temporal nature of processes such as storage, spoilage, fermentation and brewing on the chemistry of the foods we eat. Here, we performed a temporal analysis of the chemical changes in foods during common household preparations using untargeted mass spectrometry and novel data analysis approaches. Common treatments of foods such as home fermentation of yogurt, brewing of tea, spoilage of meats and ripening of tomatoes altered the chemical makeup through time, through both chemical and biological processes. For example, brewing tea altered its composition by increasing the diversity of molecules, but this change was halted after 4 min of brewing. The results indicate that this is largely due to differential extraction of the material from the tea and not modification of the molecules during the brewing process. This is in contrast to the preparation of yogurt from milk, spoilage of meat and the ripening of tomatoes where biological transformations directly altered the foods molecular composition. Comprehensive assessment of chemical changes using multivariate statistics showed the varied impacts of the different food treatments, while analysis of individual chemical changes show specific alterations of chemical families in the different food types. The methods developed here represent novel approaches to studying the changes in food chemistry that can reveal global alterations in chemical profiles and specific transformations at the chemical level.\n\nO_LSTHighlightsC_LSTO_LIWe created a reference data set for tomato, milk to yogurt, tea, coffee, turkey and beef.\nC_LIO_LIWe show that normal preparation and handling affects the molecular make-up.\nC_LIO_LITea preparation is largely driven by differential extraction.\nC_LIO_LIFormation of yogurt involves chemical transformations.\nC_LIO_LIThe majority of meat molecules are not altered in 5 days at room temperature.\nC_LI

biochemistry↗

bioRxiv2018

Homogenous subgroups of atypical meningiomas defined using oncogenic signatures: basis for a new grading system?

Meningiomas are the most common brain tumor with a prevalence of 3% in the population. Histological grading of meningiomas (1 through 3) has a major role in determining treatment choice and predicting outcome. While largely indolent grade 1 and the highly aggressive grade 3 meningiomas as considered mostly homogenous in clinical behavior, atypical or grade 2 meningiomas have highly diverse biological properties. Our aim was to identify homogenous subgroups of atypical meningiomas with the working hypothesis that these subgroups would share features with grade 1 and grade 3 counterparts. We carried out systems level analysis by gene module discovery using co-expression networks on the transcriptomics of 212 meningiomas. The newly identified subgroups were characterized in terms of recurrence rate and overlapping biological processes in gene ontology. We were able to reclassify 33 of 46 atypical meningiomas (72%) into a benign \"grade 1-like\" (14/46) and malignant \"grade 3-like\" (19/46) subgroup based on oncogenic signatures. Recurrence rates of \"Grade 1-like\" and \"grade 3-like\" tumors was 0% and 72% respectively. These two new subgroups showed similar recurrence rates and concordant biological processes with the respected grades. Our findings help resolve the heterogeneity/uncertainty around atypical meningioma biology and identify subgroups more homogenous than in prior studies. These results may help reshape prediction, follow-up planning, treatment decisions and recruitment protocols for future and ongoing clinical trials. The findings demonstrate the conceptual advantage of systems biology approaches and underpin the utility of molecular signatures as complements to the current histological grading system.

cancer biology↗

bioRxiv2018

Immunomodulatory activity of Ganoderma lucidum immunomodulatory protein via PI3K/Akt and MAPK signaling pathways in macrophage RAW264.7 cells

Ganoderma lucidum, a traditional edible and medicinal fungus, holds an important status in health care systems in China and other Asian countries. Fungal immunomodulatory protein (FIP), one of the active ingredients isolated from G. lucidum, is a class of naturally occurring proteins and possesses potential biological functions. This study was conducted to explore the molecular mechanism of its immunomodulatory potency in immune responses of macrophages. In vitro assays of biological activity indicated that rFIP-glu significantly activated macrophage RAW264.7 cells, and possessed the ability of pro-and anti-inflammation the cells. RNA sequencing analysis showed that macrophage activation involved Toll-like receptors and mitogen-activated protein kinases pathways. Furthermore, qRT-PCR indicated that phosphoinositide 3 kinase inhibitor LY294002 blocked the mRNA levels of MCP-1, MEK1/2 inhibitor U0126 reduced the mRNA levels of TNF- and MCP-1, and JNK inhibitor SP600125 prevented the up-regulation of iNOS mRNA in the rFIP-glu-induced cells. FIP-glu mediated these inflammatory effects not through a general pathway, instead through a different pathway for different inflammatory mediator. These data indicate the possibility that rFIP-glu has an important immune-regulation function and thus has potential therapeutic uses.

immunology↗

bioRxiv2018

An integrative systems biology and experimental approach identifies convergence of epithelial plasticity, metabolism, and autophagy to promote chemoresistance

The evolution of therapeutic resistance is a major cause of death for patients with solid tumors. The development of therapy resistance is shaped by the ecological dynamics within the tumor microenvironment and the selective pressure induced by the host immune system. These ecological and selective forces often lead to evolutionary convergence on one or more pathways or hallmarks that drive progression. These hallmarks are, in turn, intimately linked to each other through gene expression networks. Thus, a deeper understanding of the evolutionary convergences that occur at the gene expression level could reveal vulnerabilities that could be targeted to treat therapy-resistant cancer. To this end, we used a combination of phylogenetic clustering, systems biology analyses, and wet-bench molecular experimentation to identify convergences in gene expression data onto common signaling pathways. We applied these methods to derive new insights about the networks at play during TGF-{beta}-mediated epithelial-mesenchymal transition in a lung cancer model system. Phylogenetics analyses of gene expression data from TGF-{beta} treated cells revealed evolutionary convergence of cells toward amine-metabolic pathways and autophagy during TGF-{beta} treatment. Using high-throughput drug screens, we found that knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to cancer therapies following TGF-{beta}-induced resistance, implicating autophagy as a TGF-{beta}-mediated chemoresistance mechanism. Analysis of publicly-available clinical data sets validated the adverse prognostic importance of ATG16L expression in multiple cancer types including kidney, lung, and colon cancer patients. These analyses reveal the usefulness of combining evolutionary and systems biology methods with experimental validation to illuminate new therapeutic vulnerabilities.

cancer biology↗

bioRxiv2018

Photoacoustic pigment relocalization sensor

Photoacoustic (optoacoustic) imaging can extract molecular information with deeper tissue penetration than possible by fluorescence microscopy techniques. However, there is currently still a lack of robust genetically controlled contrast agents and molecular sensors that can dynamically detect biological analytes of interest with photoacoustics. In this biomimetic approach, we took inspiration from cuttlefish who can change their color by relocalizing pigment-filled organelles in so-called chromatophore cells under neurohumoral control. Analogously, we tested the use of melanophore cells from Xenopus laevis, containing compartments (melanosomes) filled with strongly absorbing melanin, as whole-cell sensors for optoacoustic imaging. Our results show that pigment relocalization in these cells, which is dependent on binding of a ligand of interest to a specific G protein-coupled receptor (GPCR), can be monitored in vitro and in vivo using photoacoustic mesoscopy. In addition to changes in the photoacoustic signal amplitudes, we could furthermore detect the melanosome aggregation process by a change in the frequency content of the photoacoustic signals. Using bioinspired engineering, we thus introduce a photoacoustic pigment relocalization sensor (PaPiReS) for molecular photoacoustic imaging of GPCR-mediated signaling molecules.

bioengineering↗

bioRxiv2017

Clinically Important sex differences in GBM biology revealed by analysis of male and female imaging, transcriptome and survival data

Sex differences in the incidence and outcome of human disease are broadly recognized but in most cases not adequately understood to enable sex-specific approaches to treatment. Glioblastoma (GBM), the most common malignant brain tumor, provides a case in point. Despite well-established differences in incidence, and emerging indications of differences in outcome, there are few insights that distinguish male and female GBM at the molecular level, or allow specific targeting of these biological differences. Here, using a quantitative imaging-based measure of response, we found that temozolomide chemotherapy is more effective in female compared to male GBM patients. We then applied a novel computational algorithm to linked GBM transcriptome and outcome data, and identified novel sex-specific molecular subtypes of GBM in which cell cycle and integrin signaling were identified as the critical determinants of survival for male and female patients, respectively. The clinical utility of cell cycle and integrin signaling pathway signatures was further established through correlations between gene expression and in vitro chemotherapy sensitivity in a panel of male and female patient-derived GBM cell lines. Together these results suggest that greater precision in GBM molecular subtyping can be achieved through sex-specific analyses, and that improved outcome for all patients might be accomplished via tailoring treatment to sex differences in molecular mechanisms.\n\nOne Sentence SummaryMale and female glioblastoma are biologically distinct and maximal chances for cure may require sex-specific approaches to treatment.

cancer biology↗