Showing papers in "Molecular Systems Biology in 2016"

Deep learning for computational biology

Abstract: Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.

Gene‐specific correlation of RNA and protein levels in human cells and tissues

Abstract: An important issue for molecular biology is to establish whether transcript levels of a given gene can be used as proxies for the corresponding protein levels. Here, we have developed a targeted proteomics approach for a set of human non-secreted proteins based on parallel reaction monitoring to measure, at steady-state conditions, absolute protein copy numbers across human tissues and cell lines and compared these levels with the corresponding mRNA levels using transcriptomics. The study shows that the transcript and protein levels do not correlate well unless a gene-specific RNA-to-protein (RTP) conversion factor independent of the tissue type is introduced, thus significantly enhancing the predictability of protein copy numbers from RNA levels. The results show that the RTP ratio varies significantly with a few hundred copies per mRNA molecule for some genes to several hundred thousands of protein copies per mRNA molecule for others. In conclusion, our data suggest that transcriptome analysis can be used as a tool to predict the protein copy numbers per cell, thus forming an attractive link between the field of genomics and proteomics.

Proteomics reveals the effects of sustained weight loss on the human plasma proteome.

Abstract: Sustained weight loss is a preferred intervention in a wide range of metabolic conditions, but the effects on an individual's health state remain ill-defined. Here, we investigate the plasma proteomes of a cohort of 43 obese individuals that had undergone 8 weeks of 12% body weight loss followed by a year of weight maintenance. Using mass spectrometry-based plasma proteome profiling, we measured 1,294 plasma proteomes. Longitudinal monitoring of the cohort revealed individual-specific protein levels with wide-ranging effects of losing weight on the plasma proteome reflected in 93 significantly affected proteins. The adipocyte-secreted SERPINF1 and apolipoprotein APOF1 were most significantly regulated with fold changes of -16% and +37%, respectively (P < 10-13), and the entire apolipoprotein family showed characteristic differential regulation. Clinical laboratory parameters are reflected in the plasma proteome, and eight plasma proteins correlated better with insulin resistance than the known marker adiponectin. Nearly all study participants benefited from weight loss regarding a ten-protein inflammation panel defined from the proteomics data. We conclude that plasma proteome profiling broadly evaluates and monitors intervention in metabolic diseases.

Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress

Abstract: The relative importance of regulation at the mRNA versus protein level is subject to ongoing debate. To address this question in a dynamic system, we mapped proteomic and transcriptomic changes in mammalian cells responding to stress induced by dithiothreitol over 30 h. Specifically, we estimated the kinetic parameters for the synthesis and degradation of RNA and proteins, and deconvoluted the response patterns into common and unique to each regulatory level using a new statistical tool. Overall, the two regulatory levels were equally important, but differed in their impact on molecule concentrations. Both mRNA and protein changes peaked between two and eight hours, but mRNA expression fold changes were much smaller than those of the proteins. mRNA concentrations shifted in a transient, pulse-like pattern and returned to values close to pre-treatment levels by the end of the experiment. In contrast, protein concentrations switched only once and established a new steady state, consistent with the dominant role of protein regulation during misfolding stress. Finally, we generated hypotheses on specific regulatory modes for some genes.

Bacterial persistence is an active σS stress response to metabolic flux limitation

Abstract: While persisters are a health threat due to their transient antibiotic tolerance, little is known about their phenotype and what actually causes persistence. Using a new method for persister generation and high-throughput methods, we comprehensively mapped the molecular phenotype of Escherichia coli during the entry and in the state of persistence in nutrient-rich conditions. The persister proteome is characterized by σ(S)-mediated stress response and a shift to catabolism, a proteome that starved cells tried to but could not reach due to absence of a carbon and energy source. Metabolism of persisters is geared toward energy production, with depleted metabolite pools. We developed and experimentally verified a model, in which persistence is established through a system-level feedback: Strong perturbations of metabolic homeostasis cause metabolic fluxes to collapse, prohibiting adjustments toward restoring homeostasis. This vicious cycle is stabilized and modulated by high ppGpp levels, toxin/anti-toxin systems, and the σ(S)-mediated stress response. Our system-level model consistently integrates past findings with our new data, thereby providing an important basis for future research on persisters.

Transcriptomics resources of human tissues and organs

Abstract: Quantifying the differential expression of genes in various human organs, tissues, and cell types is vital to understand human physiology and disease. Recently, several large-scale transcriptomics studies have analyzed the expression of protein-coding genes across tissues. These datasets provide a framework for defining the molecular constituents of the human body as well as for generating comprehensive lists of proteins expressed across tissues or in a tissue-restricted manner. Here, we review publicly available human transcriptome resources and discuss body-wide data from independent genome-wide transcriptome analyses of different tissues. Gene expression measurements from these independent datasets, generated using samples from fresh frozen surgical specimens and postmortem tissues, are consistent. Overall, the different genome-wide analyses support a distribution in which many proteins are found in all tissues and relatively few in a tissue-restricted manner. Moreover, we discuss the applications of publicly available omics data for building genome-scale metabolic models, used for analyzing cell and tissue functions both in physiological and in disease contexts.

Antisense transcription as a tool to tune gene expression

Abstract: A surprise that has emerged from transcriptomics is the prevalence of genomic antisense transcription, which occurs counter to gene orientation. While frequent, the roles of antisense transcription in regulation are poorly understood. We built a synthetic system in Escherichia coli to study how antisense transcription can change the expression of a gene and tune the response characteristics of a regulatory circuit. We developed a new genetic part that consists of a unidirectional terminator followed by a constitutive antisense promoter and demonstrate that this part represses gene expression proportionally to the antisense promoter strength. Chip-based oligo synthesis was applied to build a large library of 5,668 terminator–promoter combinations that was used to control the expression of three repressors (PhlF, SrpR, and TarA) in a simple genetic circuit (NOT gate). Using the library, we demonstrate that antisense promoters can be used to tune the threshold of a regulatory circuit without impacting other properties of its response function. Finally, we determined the relative contributions of antisense RNA and transcriptional interference to repressing gene expression and introduce a biophysical model to capture the impact of RNA polymerase collisions on gene repression. This work quantifies the role of antisense transcription in regulatory networks and introduces a new mode to control gene expression that has been previously overlooked in genetic engineering.

Diverse mechanisms of metaeffector activity in an intracellular bacterial pathogen, Legionella pneumophila.

Abstract: Pathogens deliver complex arsenals of translocated effector proteins to host cells during infection, but the extent to which these proteins are regulated once inside the eukaryotic cell remains poorly defined. Among all bacterial pathogens, Legionella pneumophila maintains the largest known set of translocated substrates, delivering over 300 proteins to the host cell via its Type IVB, Icm/Dot translocation system. Backed by a few notable examples of effector–effector regulation in L. pneumophila , we sought to define the extent of this phenomenon through a systematic analysis of effector–effector functional interaction. We used Saccharomyces cerevisiae , an established proxy for the eukaryotic host, to query > 108,000 pairwise genetic interactions between two compatible expression libraries of ~330 L. pneumophila‐ translocated substrates. While capturing all known examples of effector–effector suppression, we identify fourteen novel translocated substrates that suppress the activity of other bacterial effectors and one pair with synergistic activities. In at least nine instances, this regulation is direct—a hallmark of an emerging class of proteins called metaeffectors, or “effectors of effectors”. Through detailed structural and functional analysis, we show that metaeffector activity derives from a diverse range of mechanisms, shapes evolution, and can be used to reveal important aspects of each cognate effector9s function. Metaeffectors, along with other, indirect, forms of effector–effector modulation, may be a common feature of many intracellular pathogens—with unrealized potential to inform our understanding of how pathogens regulate their interactions with the host cell.

Determinants of RNA metabolism in the Schizosaccharomyces pombe genome

Abstract: To decrypt the regulatory code of the genome, sequence elements must be defined that determine the kinetics of RNA metabolism and thus gene expression. Here, we attempt such decryption in an eukaryotic model organism, the fission yeast S. pombe. We first derive an improved genome annotation that redefines borders of 36% of expressed mRNAs and adds 487 non-coding RNAs (ncRNAs). We then combine RNA labeling in vivo with mathematical modeling to obtain rates of RNA synthesis and degradation for 5,484 expressed RNAs and splicing rates for 4,958 introns. We identify functional sequence elements in DNA and RNA that control RNA metabolic rates and quantify the contributions of individual nucleotides to RNA synthesis, splicing, and degradation. Our approach reveals distinct kinetics of mRNA and ncRNA metabolism, separates antisense regulation by transcription interference from RNA interference, and provides a general tool for studying the regulatory code of genomes.

Environment-induced epigenetic reprogramming in genomic regulatory elements in smoking mothers and their children

Abstract: Epigenetic mechanisms have emerged as links between prenatal environmental exposure and disease risk later in life. Here, we studied epigenetic changes associated with maternal smoking at base pair resolution by mapping DNA methylation, histone modifications, and transcription in expectant mothers and their newborn children. We found extensive global differential methylation and carefully evaluated these changes to separate environment associated from genotype‐related DNA methylation changes. Differential methylation is enriched in enhancer elements and targets in particular “commuting” enhancers having multiple, regulatory interactions with distal genes. Longitudinal whole‐genome bisulfite sequencing revealed that DNA methylation changes associated with maternal smoking persist over years of life. Particularly in children prenatal environmental exposure leads to chromatin transitions into a hyperactive state. Combined DNA methylation, histone modification, and gene expression analyses indicate that differential methylation in enhancer regions is more often functionally translated than methylation changes in promoters or non‐regulatory elements. Finally, we show that epigenetic deregulation of a commuting enhancer targeting c‐Jun N‐terminal kinase 2 (JNK2) is linked to impaired lung function in early childhood. ![][1] Genome‐wide epigenomic and transcriptomic data reveal that maternal smoking induces important changes in the methylome of mothers and their children that affect in particular enhancer regions. These changes are stably maintained for years in the epigenome of the child. Mol Syst Biol. (2016) 12: 861 [1]: /embed/graphic-1.gif

Pooled-matrix protein interaction screens using Barcode Fusion Genetics.

Abstract: High-throughput binary protein interaction mapping is continuing to extend our understanding of cellular function and disease mechanisms. However, we remain one or two orders of magnitude away from a complete interaction map for humans and other major model organisms. Completion will require screening at substantially larger scales with many complementary assays, requiring further efficiency gains in proteome-scale interaction mapping. Here, we report Barcode Fusion Genetics-Yeast Two-Hybrid (BFG-Y2H), by which a full matrix of protein pairs can be screened in a single multiplexed strain pool. BFG-Y2H uses Cre recombination to fuse DNA barcodes from distinct plasmids, generating chimeric protein-pair barcodes that can be quantified via next-generation sequencing. We applied BFG-Y2H to four different matrices ranging in scale from ~25 K to 2.5 M protein pairs. The results show that BFG-Y2H increases the efficiency of protein matrix screening, with quality that is on par with state-of-the-art Y2H methods.

A population-based temporal logic gate for timing and recording chemical events.

Abstract: Engineered bacterial sensors have potential applications in human health monitoring, environmental chemical detection, and materials biosynthesis. While such bacterial devices have long been engineered to differentiate between combinations of inputs, their potential to process signal timing and duration has been overlooked. In this work, we present a two‐input temporal logic gate that can sense and record the order of the inputs, the timing between inputs, and the duration of input pulses. Our temporal logic gate design relies on unidirectional DNA recombination mediated by bacteriophage integrases to detect and encode sequences of input events. For an E. coli strain engineered to contain our temporal logic gate, we compare predictions of Markov model simulations with laboratory measurements of final population distributions for both step and pulse inputs. Although single cells were engineered to have digital outputs, stochastic noise created heterogeneous single‐cell responses that translated into analog population responses. Furthermore, when single‐cell genetic states were aggregated into population‐level distributions, these distributions contained unique information not encoded in individual cells. Thus, final differentiated sub‐populations could be used to deduce order, timing, and duration of transient chemical events.

An Atlas of human kinase regulation

Abstract: The coordinated regulation of protein kinases is a rapid mechanism that integrates diverse cues and swiftly determines appropriate cellular responses. However, our understanding of cellular decision-making has been limited by the small number of simultaneously monitored phospho-regulatory events. Here, we have estimated changes in activity in 215 human kinases in 399 conditions derived from a large compilation of phosphopeptide quantifications. This atlas identifies commonly regulated kinases as those that are central in the signaling network and defines the logic relationships between kinase pairs. Co-regulation along the conditions predicts kinase-complex and kinase-substrate associations. Additionally, the kinase regulation profile acts as a molecular fingerprint to identify related and opposing signaling states. Using this atlas, we identified essential mediators of stem cell differentiation, modulators of Salmonella infection, and new targets of AKT1. This provides a global view of human phosphorylation-based signaling and the necessary context to better understand kinase-driven decision-making.

Single-cell sequencing maps gene expression to mutational phylogenies in PDGF- and EGF-driven gliomas.

Abstract: Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor. Epidermal growth factor (EGF) and platelet‐derived growth factor (PDGF) receptors are frequently amplified and/or possess gain‐of‐function mutations in GBM. However, clinical trials of tyrosine‐kinase inhibitors have shown disappointing efficacy, in part due to intra‐tumor heterogeneity. To assess the effect of clonal heterogeneity on gene expression, we derived an approach to map single‐cell expression profiles to sequentially acquired mutations identified from exome sequencing. Using 288 single cells, we constructed high‐resolution phylogenies of EGF‐driven and PDGF‐driven GBMs, modeling transcriptional kinetics during tumor evolution. Descending the phylogenetic tree of a PDGF‐driven tumor corresponded to a progressive induction of an oligodendrocyte progenitor‐like cell type, expressing pro‐angiogenic factors. In contrast, phylogenetic analysis of an EGFR ‐amplified tumor showed an up‐regulation of pro‐invasive genes. An in‐frame deletion in a specific dimerization domain of PDGF receptor correlates with an up‐regulation of growth pathways in a proneural GBM and enhances proliferation when ectopically expressed in glioma cell lines. In‐frame deletions in this domain are frequent in public GBM data. ![][1] An approach is presented for mapping single‐cell expression profiles to sequentially acquired mutations identified by exome sequencing. Phylogenies of EGF‐driven and PDGF‐driven gliomas are constructed, modeling transcriptional kinetics during tumor evolution. Mol Syst Biol. (2016) 12: 889 [1]: /embed/graphic-1.gif

Chemogenomics and orthology-based design of antibiotic combination therapies

Abstract: Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical-genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug-interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less-studied pathogens by leveraging chemogenomics data in model organisms.

Pervasive isoform‐specific translational regulation via alternative transcription start sites in mammals

Abstract: Transcription initiated at alternative sites can produce mRNA isoforms with different 5'UTRs, which are potentially subjected to differential translational regulation. However, the prevalence of such isoform-specific translational control across mammalian genomes is currently unknown. By combining polysome profiling with high-throughput mRNA 5' end sequencing, we directly measured the translational status of mRNA isoforms with distinct start sites. Among 9,951 genes expressed in mouse fibroblasts, we identified 4,153 showed significant initiation at multiple sites, of which 745 genes exhibited significant isoform-divergent translation. Systematic analyses of the isoform-specific translation revealed that isoforms with longer 5'UTRs tended to translate less efficiently. Further investigation of cis-elements within 5'UTRs not only provided novel insights into the regulation by known sequence features, but also led to the discovery of novel regulatory sequence motifs. Quantitative models integrating all these features explained over half of the variance in the observed isoform-divergent translation. Overall, our study demonstrated the extensive translational regulation by usage of alternative transcription start sites and offered comprehensive understanding of translational regulation by diverse sequence features embedded in 5'UTRs.

Positional proteomics reveals differences in N-terminal proteoform stability

Abstract: To understand the impact of alternative translation initiation on a proteome, we performed a proteome-wide study on protein turnover using positional proteomics and ribosome profiling to distinguish between N-terminal proteoforms of individual genes. By combining pulsed SILAC with N-terminal COFRADIC, we monitored the stability of 1,941 human N-terminal proteoforms, including 147 N-terminal proteoform pairs that originate from alternative translation initiation, alternative splicing or incomplete processing of the initiator methionine. N-terminally truncated proteoforms were less abundant than canonical proteoforms and often displayed altered stabilities, likely attributed to individual protein characteristics, including intrinsic disorder, but independent of N-terminal amino acid identity or truncation length. We discovered that the removal of initiator methionine by methionine aminopeptidases reduced the stability of processed proteoforms, while susceptibility for N-terminal acetylation did not seem to influence protein turnover rates. Taken together, our findings reveal differences in protein stability between N-terminal proteoforms and point to a role for alternative translation initiation and co-translational initiator methionine removal, next to alternative splicing, in the overall regulation of proteome homeostasis.

A gene-centered C. elegans protein-DNA interaction network provides a framework for functional predictions

Abstract: Transcription factors (TFs) play a central role in controlling spatiotemporal gene expression and the response to environmental cues. A comprehensive understanding of gene regulation requires integrating physical protein-DNA interactions (PDIs) with TF regulatory activity, expression patterns, and phenotypic data. Although great progress has been made in mapping PDIs using chromatin immunoprecipitation, these studies have only characterized ~10% of TFs in any metazoan species. The nematode C. elegans has been widely used to study gene regulation due to its compact genome with short regulatory sequences. Here, we delineated the largest gene-centered metazoan PDI network to date by examining interactions between 90% of C. elegans TFs and 15% of gene promoters. We used this network as a backbone to predict TF binding sites for 77 TFs, two-thirds of which are novel, as well as integrate gene expression, protein-protein interaction, and phenotypic data to predict regulatory and biological functions for multiple genes and TFs.

Dynamical compensation in physiological circuits.

Abstract: Biological systems can maintain constant steady‐state output despite variation in biochemical parameters, a property known as exact adaptation. Exact adaptation is achieved using integral feedback, an engineering strategy that ensures that the output of a system robustly tracks its desired value. However, it is unclear how physiological circuits also keep their output dynamics precise—including the amplitude and response time to a changing input. Such robustness is crucial for endocrine and neuronal homeostatic circuits because they need to provide a precise dynamic response in the face of wide variation in the physiological parameters of their target tissues; how such circuits compensate their dynamics for unavoidable natural fluctuations in parameters is unknown. Here, we present a design principle that provides the desired robustness, which we call dynamical compensation (DC). We present a class of circuits that show DC by means of a nonlinear feedback loop in which the regulated variable controls the functional mass of the controlling endocrine or neuronal tissue. This mechanism applies to the control of blood glucose by insulin and explains several experimental observations on insulin resistance. We provide evidence that this mechanism may also explain compensation and organ size control in other physiological circuits. ![][1] Physiological circuits must keep their output dynamics precise despite wide variation in circuit parameters. We present a design principle for this robustness, find that it explains experimental observations on glucose homeostasis, and show that it may apply to other physiological circuits. Mol Syst Biol. (2016) 12: 886 [1]: /embed/graphic-1.gif

Orthogonal intercellular signaling for programmed spatial behavior

Abstract: Bidirectional intercellular signaling is an essential feature of multicellular organisms, and the engineering of complex biological systems will require multiple pathways for intercellular signaling with minimal crosstalk. Natural quorum-sensing systems provide components for cell communication, but their use is often constrained by signal crosstalk. We have established new orthogonal systems for cell-cell communication using acyl homoserine lactone signaling systems. Quantitative measurements in contexts of differing receiver protein expression allowed us to separate different types of crosstalk between 3-oxo-C6- and 3-oxo-C12-homoserine lactones, cognate receiver proteins, and DNA promoters. Mutating promoter sequences minimized interactions with heterologous receiver proteins. We used experimental data to parameterize a computational model for signal crosstalk and to estimate the effect of receiver protein levels on signal crosstalk. We used this model to predict optimal expression levels for receiver proteins, to create an effective two-channel cell communication device. Establishment of a novel spatial assay allowed measurement of interactions between geometrically constrained cell populations via these diffusible signals. We built relay devices capable of long-range signal propagation mediated by cycles of signal induction, communication and response by discrete cell populations. This work demonstrates the ability to systematically reduce crosstalk within intercellular signaling systems and to use these systems to engineer complex spatiotemporal patterning in cell populations.

Multistability and dynamic transitions of intracellular Min protein patterns.

Abstract: Cells owe their internal organization to self-organized protein patterns, which originate and adapt to growth and external stimuli via a process that is as complex as it is little understood. Here, we study the emergence, stability, and state transitions of multistable Min protein oscillation patterns in live Escherichia coli bacteria during growth up to defined large dimensions. De novo formation of patterns from homogenous starting conditions is observed and studied both experimentally and in simulations. A new theoretical approach is developed for probing pattern stability under perturbations. Quantitative experiments and simulations show that, once established, Min oscillations tolerate a large degree of intracellular heterogeneity, allowing distinctly different patterns to persist in different cells with the same geometry. Min patterns maintain their axes for hours in experiments, despite imperfections, expansion, and changes in cell shape during continuous cell growth. Transitions between multistable Min patterns are found to be rare events induced by strong intracellular perturbations. The instances of multistability studied here are the combined outcome of boundary growth and strongly nonlinear kinetics, which are characteristic of the reaction-diffusion patterns that pervade biology at many scales.

Tristetraprolin binding site atlas in the macrophage transcriptome reveals a switch for inflammation resolution.

Abstract: Precise regulation of mRNA decay is fundamental for robust yet not exaggerated inflammatory responses to pathogens. However, a global model integrating regulation and functional consequences of inflammation-associated mRNA decay remains to be established. Using time-resolved high-resolution RNA binding analysis of the mRNA-destabilizing protein tristetraprolin (TTP), an inflammation-limiting factor, we qualitatively and quantitatively characterize TTP binding positions in the transcriptome of immunostimulated macrophages. We identify pervasive destabilizing and non-destabilizing TTP binding, including a robust intronic binding, showing that TTP binding is not sufficient for mRNA destabilization. A low degree of flanking RNA structuredness distinguishes occupied from silent binding motifs. By functionally relating TTP binding sites to mRNA stability and levels, we identify a TTP-controlled switch for the transition from inflammatory into the resolution phase of the macrophage immune response. Mapping of binding positions of the mRNA-stabilizing protein HuR reveals little target and functional overlap with TTP, implying a limited co-regulation of inflammatory mRNA decay by these proteins. Our study establishes a functionally annotated and navigable transcriptome-wide atlas (http://ttp-atlas.univie.ac.at) of cis-acting elements controlling mRNA decay in inflammation.

Non‐genetic diversity modulates population performance

Abstract: Biological functions are typically performed by groups of cells that express predominantly the same genes, yet display a continuum of phenotypes. While it is known how one genotype can generate such non-genetic diversity, it remains unclear how different phenotypes contribute to the performance of biological function at the population level. We developed a microfluidic device to simultaneously measure the phenotype and chemotactic performance of tens of thousands of individual, freely swimming Escherichia coli as they climbed a gradient of attractant. We discovered that spatial structure spontaneously emerged from initially well-mixed wild-type populations due to non-genetic diversity. By manipulating the expression of key chemotaxis proteins, we established a causal relationship between protein expression, non-genetic diversity, and performance that was theoretically predicted. This approach generated a complete phenotype-to-performance map, in which we found a nonlinear regime. We used this map to demonstrate how changing the shape of a phenotypic distribution can have as large of an effect on collective performance as changing the mean phenotype, suggesting that selection could act on both during the process of adaptation.

Distinct cellular states determine calcium signaling response.

Abstract: The heterogeneity in mammalian cells signaling response is largely a result of pre-existing cell-to-cell variability. It is unknown whether cell-to-cell variability rises from biochemical stochastic fluctuations or distinct cellular states. Here, we utilize calcium response to adenosine trisphosphate as a model for investigating the structure of heterogeneity within a population of cells and analyze whether distinct cellular response states coexist. We use a functional definition of cellular state that is based on a mechanistic dynamical systems model of calcium signaling. Using Bayesian parameter inference, we obtain high confidence parameter value distributions for several hundred cells, each fitted individually. Clustering the inferred parameter distributions revealed three major distinct cellular states within the population. The existence of distinct cellular states raises the possibility that the observed variability in response is a result of structured heterogeneity between cells. The inferred parameter distribution predicts, and experiments confirm that variability in IP3R response explains the majority of calcium heterogeneity. Our work shows how mechanistic models and single-cell parameter fitting can uncover hidden population structure and demonstrate the need for parameter inference at the single-cell level.

Exploiting native forces to capture chromosome conformation in mammalian cell nuclei

Abstract: Mammalian interphase chromosomes fold into a multitude of loops to fit the confines of cell nuclei, and looping is tightly linked to regulated function. Chromosome conformation capture (3C) technology has significantly advanced our understanding of this structure-to-function relationship. However, all 3C-based methods rely on chemical cross-linking to stabilize spatial interactions. This step remains a "black box" as regards the biases it may introduce, and some discrepancies between microscopy and 3C studies have now been reported. To address these concerns, we developed "i3C", a novel approach for capturing spatial interactions without a need for cross-linking. We apply i3C to intact nuclei of living cells and exploit native forces that stabilize chromatin folding. Using different cell types and loci, computational modeling, and a methylation-based orthogonal validation method, "TALE-iD", we show that native interactions resemble cross-linked ones, but display improved signal-to-noise ratios and are more focal on regulatory elements and CTCF sites, while strictly abiding to topologically associating domain restrictions.

Translation elicits a growth rate-dependent, genome-wide, differential protein production in Bacillus subtilis.

Abstract: Complex regulatory programs control cell adaptation to environmental changes by setting condition‐specific proteomes. In balanced growth, bacterial protein abundances depend on the dilution rate, transcript abundances and transcript‐specific translation efficiencies. We revisited the current theory claiming the invariance of bacterial translation efficiency. By integrating genome‐wide transcriptome datasets and datasets from a library of synthetic gfp ‐reporter fusions, we demonstrated that translation efficiencies in Bacillus subtilis decreased up to fourfold from slow to fast growth. The translation initiation regions elicited a growth rate‐dependent, differential production of proteins without regulators, hence revealing a unique, hard‐coded, growth rate‐dependent mode of regulation. We combined model‐based data analyses of transcript and protein abundances genome‐wide and revealed that this global regulation is extensively used in B. subtilis . We eventually developed a knowledge‐based, three‐step translation initiation model, experimentally challenged the model predictions and proposed that a growth rate‐dependent drop in free ribosome abundance accounted for the differential protein production.

Strand-specific, high-resolution mapping of modified RNA polymerase II

Abstract: Reversible modification of the RNAPII C-terminal domain links transcription with RNA processing and surveillance activities. To better understand this, we mapped the location of RNAPII carrying the five types of CTD phosphorylation on the RNA transcript, providing strand-specific, nucleotide-resolution information, and we used a machine learning-based approach to define RNAPII states. This revealed enrichment of Ser5P, and depletion of Tyr1P, Ser2P, Thr4P, and Ser7P in the transcription start site (TSS) proximal ~150 nt of most genes, with depletion of all modifications close to the poly(A) site. The TSS region also showed elevated RNAPII relative to regions further 3', with high recruitment of RNA surveillance and termination factors, and correlated with the previously mapped 3' ends of short, unstable ncRNA transcripts. A hidden Markov model identified distinct modification states associated with initiating, early elongating and later elongating RNAPII. The initiation state was enriched near the TSS of protein-coding genes and persisted throughout exon 1 of intron-containing genes. Notably, unstable ncRNAs apparently failed to transition into the elongation states seen on protein-coding genes.

An inter-species protein-protein interaction network across vast evolutionary distance.

Abstract: In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical-versus-functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an "inter-interactome" approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter-interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra-species networks. Although substantially reduced relative to intra-species networks, the levels of functional overlap in the yeast-human inter-interactome network uncover significant remnants of co-functionality widely preserved in the two proteomes beyond human-yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co-functionality. Such non-functional interactions, however, represent a reservoir from which nascent functional interactions may arise.

Predictive features of ligand-specific signaling through the estrogen receptor.

Abstract: Some estrogen receptor-α (ERα)-targeted breast cancer therapies such as tamoxifen have tissue-selective or cell-specific activities, while others have similar activities in different cell types. To identify biophysical determinants of cell-specific signaling and breast cancer cell proliferation, we synthesized 241 ERα ligands based on 19 chemical scaffolds, and compared ligand response using quantitative bioassays for canonical ERα activities and X-ray crystallography. Ligands that regulate the dynamics and stability of the coactivator-binding site in the C-terminal ligand-binding domain, called activation function-2 (AF-2), showed similar activity profiles in different cell types. Such ligands induced breast cancer cell proliferation in a manner that was predicted by the canonical recruitment of the coactivators NCOA1/2/3 and induction of the GREB1 proliferative gene. For some ligand series, a single inter-atomic distance in the ligand-binding domain predicted their proliferative effects. In contrast, the N-terminal coactivator-binding site, activation function-1 (AF-1), determined cell-specific signaling induced by ligands that used alternate mechanisms to control cell proliferation. Thus, incorporating systems structural analyses with quantitative chemical biology reveals how ligands can achieve distinct allosteric signaling outcomes through ERα.

Negative frequency‐dependent interactions can underlie phenotypic heterogeneity in a clonal microbial population

Abstract: Genetically identical cells in microbial populations often exhibit a remarkable degree of phenotypic heterogeneity even in homogenous environments. Such heterogeneity is commonly thought to represent a bet-hedging strategy against environmental uncertainty. However, evolutionary game theory predicts that phenotypic heterogeneity may also be a response to negative frequency-dependent interactions that favor rare phenotypes over common ones. Here we provide experimental evidence for this alternative explanation in the context of the well-studied yeast GAL network. In an environment containing the two sugars glucose and galactose, the yeast GAL network displays stochastic bimodal activation. We show that in this mixed sugar environment, GAL-ON and GAL-OFF phenotypes can each invade the opposite phenotype when rare and that there exists a resulting stable mix of phenotypes. Consistent with theoretical predictions, the resulting stable mix of phenotypes is not necessarily optimal for population growth. We find that the wild-type mixed strategist GAL network can invade populations of both pure strategists while remaining uninvasible by either. Lastly, using laboratory evolution we show that this mixed resource environment can directly drive the de novo evolution of clonal phenotypic heterogeneity from a pure strategist population. Taken together, our results provide experimental evidence that negative frequency-dependent interactions can underlie the phenotypic heterogeneity found in clonal microbial populations.