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Results for “systems biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2016

Network inference and hypotheses-generation from single-cell transcriptomic data using multivariate information measures

While single-cell gene expression experiments present new challenges for data processing, the cell-to-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks from three experimental single-cell data sets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available here: https://github.com/Tchanders/network_inference_tutorials. PIDC should facilitate the identification of putative functional relationships and mechanistic hypotheses from single-cell transcriptomic data.

systems biology↗

bioRxiv2016

Inferability of transcriptional networks from large scalegene deletion studies

Generating a comprehensive map of molecular interactions in living cells is difficult and great efforts are undertaken to infer molecular interactions from large scale perturbation experiments. Here, we develop the analytical and numerical tools to quantify the fundamental limits for inferring transcriptional networks from gene knockout screens and introduce a network inference method that is unbiased and scalable to large network sizes. We show that it is possible to infer gene regulatory interactions with high statistical significance, even if prior knowledge about potential regulators is absent.

systems biology↗

bioRxiv2016

A compound that directly and selectively stalls PCSK9 translation

Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) plays a key role in regulating the levels of plasma low density lipoprotein cholesterol (LDL-C). Here we demonstrate that the compound PF-06446846 inhibits translation of PCSK9 by inducing the ribosome to stall around codon 34, mediated by the sequence of the nascent chain within the exit tunnel. We further show that PF-06446846 reduces plasma PCSK9 and total cholesterol levels in rats following oral dosing. Using ribosome profiling, we demonstrate that PF-06446846 is highly selective for the inhibition of PCSK9 translation. The mechanism of action employed by PF-06446846 reveals a previously unexpected tunability of the human ribosome, which allows small molecules to specifically block translation of individual transcripts.\n\nOne Sentence SummaryA small-molecule PCSK9 inhibitor targets the human ribosome and selectively prevents PCSK9 synthesis.

systems biology↗

bioRxiv2016

Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome

The ability to quantify differentiation potential of single cells is a task of critical importance for single-cell studies. So far however, there is no robust general molecular correlate of differentiation potential at the single cell level. Here we show that differentiation potency of a single cell can be approximated by computing the signaling promiscuity, or entropy, of a cells transcriptomic profile in the context of a cellular interaction network, without the need for model training or feature selection. We validate signaling entropy in over 7,000 single cell RNA-Seq profiles, representing all main differentiation stages, including time-course data. We develop a novel algorithm called Single Cell Entropy (SCENT), which correctly identifies known cell subpopulations of varying potency, enabling reconstruction of cell-lineage trajectories. By comparing bulk to single cell data, SCENT reveals that expression heterogeneity within single cell populations is regulated, pointing towards the importance of cell-cell interactions. In the context of cancer, SCENT can identify drug resistant cancer stem-cell phenotypes, including those obtained from circulating tumor cells. In summary, SCENT can directly estimate the differentiation potency and plasticity of single-cells, allowing unbiased quantification of intercellular heterogeneity, and providing a means to identify normal and cancer stem cell phenotypes.\n\nSoftware AvailabilitySCENT is freely available as an R-package from github: https://github.com/aet21/SCENT

systems biology↗

bioRxiv2016

Modeling trophic dependencies and exchanges among insects’ bacterial symbionts in a host-simulated environment

Individual organisms are linked to their communities and ecosystems via metabolic activities. Metabolic exchanges and co-dependencies have long been suggested to have a pivotal role in determining community structure. Metabolic interactions with bacteria have been key drivers in the evolution of sap-feeding insects, enabling complementation of their deprived nutrition. The sap-feeding whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) harbors an obligatory symbiotic bacterium, as well as varying combinations of facultative symbionts. We took advantage of the well-defined bacterial community in B. tabaci as a case study for a comprehensive and systematic survey of metabolic interactions within the bacterial community and their associations with documented frequency of bacterial combinations. We first reconstructed the metabolic networks of five common B. tabaci symbionts (Portiera, Rickettsia, Hamiltonella, Cardinium and Wolbachia), and then used network analysis approaches to predict: (1) species-specific metabolic capacities in a simulated bacteriocyte-like environment; (2) metabolic capacities of the corresponding species combinations, and (3) dependencies of each species on different media components.\n\nThe automatic-based predictions for metabolic capacities of the symbionts in the host environment were in general agreement with previously reported genome analyses, each focused on the single-species level. The analysis suggested several previously un-reported routes for complementary interactions. Highly abundant symbiont combinations were found to have the potential to produce a diverse set of complementary metabolites, in comparison to un-detected combinations. No clear association was detected between metabolic codependencies and co-occurrence patterns. The findings indicate a potential key role for metabolic exchanges as key determinants shaping community structure in this system.

systems biology↗

bioRxiv2016

Impact of Zygosity in Bimodal Phenotype Distributions

Allele number, or zygosity, is a clear determinant of gene expression in diploid cells. But the relationship between the number of copies of a gene and its expression can be hard to anticipate, especially when the gene in question is embedded in a regulatory circuit that contains feedbacks. Here we study this question making use of the natural genetic variability of human populations, which allows us to compare the expression profiles of a receptor protein in natural killer cells between donors infected with human cytomegalovirus (HCMV) with one or two copies of the allele. Crucially, the distribution of gene expression in many of the donors is bimodal, indicative of the presence of a positive feedback somewhere in the regulatory environment of the gene. Three separate gene-circuit models differing in the location of the positive feedback with respect to the gene can all reproduce well the homozygous data. However, when the resulting fitted models are applied to the hemizygous donors, only one model (the one with the positive feedback located at the level of gene transcription) reproduces the experimentally observed gene-expression profile. In that way, our work shows that zygosity can help us relate structure and function of gene regulatory networks.\n\nAuthor SummaryNearly all mammalian cells, including human cells, have two copies of each chromosome, and thus possess two potentially different copies of each gene (which might be in some cases non-functional or even absent). Naiively one might expect that two identical copies of the gene would lead to the protein being expressed at twice the rate, but many factors can alter this simple calculation. One of these factors is the existence of feedback mechanisms affecting in one way or another the regulatory circuit in which our gene of interest is embedded. Here we study the relationship between the number of gene copies and the expression of a receptor protein that plays a crucial role in the recognition of pathogens by natural killer cells, which are important elements of the innate immune system. Experimental data of virus-infected donors reveals a bimodal expression profile of this receptor, typical of a positive feedback, and a clear difference between donors with one or two copies of the gene. Mathematical modeling allows us to find the likely location of the feedback loop within the genes regulatory circuit, by requiring the correct model to reproduce the expression profiles of both types of donors.

systems biology↗

bioRxiv2016

Genome-scale transcriptional regulatory network models for the mouse and human striatum predict roles for SMAD3 and other transcription factors in Huntington’s disease

Transcriptional changes occur presymptomatically and throughout Huntingtons Disease (HD), motivating the study of transcriptional regulatory networks (TRNs) in HD. We reconstructed a genome-scale model for the target genes of 718 TFs in the mouse striatum by integrating a model of the genomic binding sites with transcriptome profiling of striatal tissue from HD mouse models. We identified 48 differentially expressed TF-target gene modules associated with age- and Htt allele-dependent gene expression changes in the mouse striatum, and replicated many of these associations in independent transcriptomic and proteomic datasets. Strikingly, many of these predicted target genes were also differentially expressed in striatal tissue from human disease. We experimentally validated a key model prediction that SMAD3 regulates HD-related gene expression changes using chromatin immunoprecipitation and deep sequencing (ChIP-seq) of mouse striatum. We found Htt allele-dependent changes in the genomic occupancy of SMAD3 and confirmed our models prediction that many SMAD3 target genes are down-regulated early in HD. Importantly, our study provides a mouse and human striatal-specific TRN and prioritizes a hierarchy of transcription factor drivers in HD.

systems biology↗

bioRxiv2016

Estimating Drivers of Cell State Transitions Using Gene Regulatory Network Models

Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. Our results demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.

systems biology↗

bioRxiv2016

SINCERITIES: Inferring gene regulatory networks fromtime-stamped single cell transcriptional expression profiles

Recent advances in single cell transcriptional profiling open up a new avenue in studying the functional role of cell-to-cell variability in physiological processes such as stem cell differentiation. In this work, we developed a novel algorithm called SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS), for the inference of gene regulatory networks (GRNs) from single cell transcriptional expression data. In particular, we focused on time-stamped cross-sectional expression data, a common type of dataset generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers the regulatory (causal) relationships among genes by employing regularized linear regression, particularly ridge regression, using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using simulated time-stamped in silico single cell expression data and single transcriptional profiling of THP-1 monocytic human leukemia cell differentiation. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Meanwhile, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality.

systems biology↗

bioRxiv2016

History of Antibiotic Adaptation Influences Microbial Evolutionary Dynamics During Subsequent Treatment

Antibiotic regimens often include the sequential changing of drugs to limit development and evolution of resistance of bacterial pathogens. It remains unclear how history of adaptation to one antibiotic can influence the resistance profiles when bacteria subsequently adapt to a different antibiotic. Here, we experimentally evolved Pseudomonas aeruginosa to six two-drug sequences. We observed drug order-specific effects whereby: adaptation to the first drug can limit subsequent adaptation to the second drug, adaptation to the second drug can restore susceptibility to the first drug, or final resistance levels depend on the order of the two-drug sequence. These findings demonstrate how resistance not only depends on the current drug regimen but also history of past regimens. These order-specific effects have profound clinical implications and provide support for the need to consider history of past drug exposure when designing strategies to mitigate resistance and combat bacterial infections.

systems biology↗

bioRxiv2016

Investigating side effect modules in the interactome and their use in drug adverse effect discovery

One of the biggest challenges in drug development is increasing costs of bringing new drugs to the market. Many candidate drugs fail during phase II and III trials due to unexpected side effects and experimental methods remain cost ineffective for large scale discovery of adverse effects. Alternatively, computational methods are used to characterize drug side effects, but they often rely on training predictors based on drug and side effect similarity. Moreover, these methods are typically tailored to the underlying data set and provide little mechanistic insights on the predicted associations. In this study, we investigate the role of network topology in explaining observed side effects of drugs. We find that drug targets are closer in the interactome to the proteins inducing the known side effects of the drug compared to the proteins associated with the rest of the side effects. We show that the interactome based proximity can be used to identify side effects and we highlight a use case in which interactome-based side effect prediction can give insights on drug side effects observed in the clinic.

systems biology↗

bioRxiv2016

A synthesis of over 9,000 mass spectrometry experiments reveals the core set of human protein complexes

Macromolecular protein complexes carry out many of the essential functions of cells, and many genetic diseases arise from disrupting the functions of such complexes. Currently there is great interest in defining the complete set of human protein complexes, but recent published maps lack comprehensive coverage. Here, through the synthesis of over 9,000 published mass spectrometry experiments, we present hu.MAP, the most comprehensive and accurate human protein complex map to date, containing >4,600 total complexes, >7,700 proteins and >56,000 unique interactions, including thousands of confident protein interactions not identified by the original publications. hu.MAP accurately recapitulates known complexes withheld from the learning procedure, which was optimized with the aid of a new quantitative metric (k-cliques) for comparing sets of sets. The vast majority of complexes in our map are significantly enriched with literature annotations and the map overall shows improved coverage of many disease-associated proteins, as we describe in detail for ciliopathies. Using hu.MAP, we predicted and experimentally validated candidate ciliopathy disease genes in vivo in a model vertebrate, discovering CCDC138, WDR90, and KIAA1328 to be new cilia basal body/centriolar satellite proteins, and identifying ANKRD55 as a novel member of the intraflagellar transport machinery. By offering significant improvements to the accuracy and coverage of human protein complexes, hu.MAP (http://proteincomplexes.org) serves as a valuable resource for better understanding the core cellular functions of human proteins and helping to determine mechanistic foundations of human disease.

systems biology↗

bioRxiv2016

Genomic determinants of protein abundance variation in colorectal cancer cells

Assessing the extent to which genomic alterations compromise the integrity of the proteome is fundamental in identifying the mechanisms that shape cancer heterogeneity. We have used isobaric labelling and tribrid mass spectrometry to characterize the proteomic landscapes of 50 colorectal cancer cell lines and to decipher the relationships between genomic and proteomic variation. The robust quantification of 12,000 proteins and 27,000 phosphopeptides revealed how protein symbiosis translates to a co-variome which is subjected to a hierarchical order and exposes the collateral effects of somatic mutations on protein complexes. Targeted depletion of key chromatin modifiers confirmed the transmission of variation and the directionality as characteristics of protein interactions. Protein level variation was leveraged to build drug response predictive models towards a better understanding of pharmacoproteomic interactions in colorectal cancer. Overall, we provide a deep integrative view of the molecular structure underlying the variation of colorectal cancer cells.\n\nHighlightsO_LIThe cancer cell functional \"co-variome\" is a strong attribute of the proteome.\nC_LIO_LIMutations can have a direct impact on protein levels of chromatin modifiers.\nC_LIO_LITransmission of genomic variation is a characteristic of protein interactions.\nC_LIO_LIPharmacoproteomic models are strong predictors of response to DNA damaging agents.\nC_LI\n\nAbbreviations

systems biology↗

bioRxiv2016

Cell-to-cell variability in the yeast pheromone response: high throughput screen identifiesgenes with different effects on transmitted signal and response

Populations of isogenic cells often respond coherently to signals despite differences in protein abundance and cell state. Our previous work in the Saccharomyces cerevisiae pheromone response system (PRS) uncovered processes that reduced cell-to-cell variation in signal and response. To understand these and other processes that controlled variation, we generated a whole-genome collection of haploid strains with deletions in non-essential genes and used high-throughput flow cytometry to screen more than 1000. We identified 50 \"variation genes\" required for normal cell-to-cell variability in signal and response. Some genes affected only signal variability, signal strength, or system output, defining these quantities as separable \"axes\" of system behavior. Two genes affected cytoplasmic microtubule function.

systems biology↗

bioRxiv2016

Cell-to-cell variability in the yeast pheromone response: Cytoplasmic microtubule functionstabilizes signal generation and promotes accurate fate choice

In a companion paper, we carried out a high-throughput screen to identify genes that suppressed cell-to-cell variability in signaling in yeast. Two genes affected cytoplasmic microtubules that can connect the nucleus to a signaling site on the membrane. Here, we show that microtubule perturbations that affected polymerization and depolymerization, membrane attachment, and force generation increased variability. For some perturbations, \"outlier\" cells drove the increased variability. Bypass experiments that activated the PRS ectopically at downstream points indicated that microtubule-dependent processes might stabilize the membrane-recruited scaffold protein Ste5. The variability caused by microtubule perturbations required the MAP kinase Fus3. Microtubule perturbations hindered stable scaffold formation and decreased the accuracy of a polarity-dependent fate choice. Our experiments suggest that membrane-attached microtubules stabilize signaling by scaffold-bound Fus3, and are consistent with a model in which signaling irregularities from changes in microtubule function are amplified by cross-stimulatory feedbacks among PRS proteins. The fact that microtubule perturbations also cause aberrant fate and polarity decisions during embryonic development and cancer initiation suggests that similar variation-reducing processes might also operate in metazoans.

systems biology↗

bioRxiv2016

A network theory of the origin of life

The origin of life may have been a low probability event that involved both RNA and polypeptides. While such an event would occur with low probability, the probability is not too low in the context of the number of nucleation opportunities on the primitive Earth. The probability for the nucleation event is too small if needed components occur as frequently as disruptive components do, without specific interactions between the two classes. However, if many of the needed components contribute by inhibiting otherwise disruptive components, a high probability for a complex nucleation as the beginning of life event emerges. RNA interference and long non-coding RNAs are ascribed roles in inhibiting components that would otherwise be disruptive in the context of a nucleation event. The theory likewise provides an inhibitory role for the large fraction of proteins (35% to 40%) that have no known function in both prokaryotes and eukaryotes. It is suggested that volcanic ash could provide the large number of random shapes needed as the basis for the nucleation event. Experiments based on that hypothesis are proposed.

systems biology↗

bioRxiv2017

The evolution of p53 network behavior

We study the evolution of the p53 core regulation network across the taxonomic span of humans to protozoans and nematodes. We introduce a new model for the core regulation network in mammalian cells, and conduct a formal analysis of the different network configurations that emerge in the evolutionary path to complexity. Solving the high dimensional equations associated with this model is typically challenging, and we develop a novel algorithm to overcome this problem. A key technical tool used is the representation of the distinct pathways in the core regulation networks as \"modules\", such that the behavior of the composite of two or more modules can be inferred from the characteristics of each of the individual modules. Apart from simplifying the complexity of the algorithm, this modular representation also allows us to qualitatively compare the distinct types of switching behaviors each network can exhibit. This then allows us to demonstrate how our model for the core regulation network in mammalian cells matches experimentally observed phenomena, and contrast this with the plausible behaviors admitted by the network configurations in putative primordial organisms. We show that the complexity of the p53 core regulation network in vertebrates permits a range of behaviors that can bring about distinct cell fate decisions not possible in the putative primordial organisms.\n\nSignificance StatementThe p53 protein has been protecting organisms from tumors for a billion years. We study the link between the evolution of the p53 network structure and its corresponding tumor suppression strategies. We compare the dynamical behaviors in putative primordial organisms with simple networks with the vertebrate network that contains multiple feedback loops. We show that the vertebrate network, but not the ancestral network, can both repair moderate damage and induce apoptosis if too much damage accumulates, balancing the risk of cancer with the cost of too much cell death. Moreover, the complexity of the vertebrate network allows for adaptation, for example to increase p53 network sensitivity, which is consistent with recent research on large mammals.

systems biology↗

bioRxiv2017

Noise suppression in cell-size control

Diverse cell types employ mechanisms to maintain size homeostasis and minimize aberrant fluctuations in cell size. It is well known that exponential cellular growth can drive unbounded intercellular variations in cell size, if the timing of cell division is size independent. Hence coupling of division timing to size is an essential feature of size control. We formulate a stochastic model, where exponential cellular growth is coupled with random cell division events, and the rate at which division events occur increases as a power function of cell size. Interestingly, in spite of nonlinearities in the stochastic dynamical model, statistical moments of the newborn cell size can be determined in closed form, providing fundamental limits to suppression of size fluctuations. In particular, formulas reveal that the magnitude of fluctuations in the newborn size is determined by the inverse of the size exponent in the division rate, and this relationship is independent of other model parameters, such as the growth rate. We further expand these results to consider randomness in the partitioning of mother cell size among daughters at the time of division. The sensitivity of newborn size fluctuations to partitioning noise is found to monotonically decrease, and approach a non-zero value, with increasing size exponent in the division rate. Finally, we discuss how our analytical results provide limits on noise control in commonly used models for cell size regulation.

systems biology↗