High‐throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples from a variety of sources, including fresh‐frozen tissue, FFPE tissue, or blood. To reach this goal, we have implemented single‐pot solid‐phase‐enhanced sample preparation (SP3) on a liquid handling robot for automated processing (autoSP3) of tissue lysates in a 96‐well format. AutoSP3 performs unbiased protein purification and digestion, and delivers peptides that can be directly analyzed by LCMS, thereby significantly reducing hands‐on time, reducing variability in protein quantification, and improving longitudinal reproducibility. We demonstrate the distinguishing ability of autoSP3 to process low‐input samples, reproducibly quantifying 500–1,000 proteins from 100 to 1,000 cells. Furthermore, we applied this approach to a cohort of clinical FFPE pulmonary adenocarcinoma (ADC) samples and recapitulated their separation into known histological growth patterns. Finally, we integrated autoSP3 with AFA ultrasonication for the automated end‐to‐end sample preparation and LCMS analysis of 96 intact tissue samples. Collectively, this constitutes a generic, scalable, and cost‐effective workflow with minimal manual intervention, enabling reproducible tissue proteomics in a broad range of clinical and non‐clinical applications.

Dynamic mechanistic models, that is, those that can simulate behavior over time courses, are a cornerstone of molecular systems biology. They are being used to model molecular mechanisms with varying degrees of granularity—from elementary reactions to causal links—and to describe these systems by various dynamic mathematical frameworks, such as Boolean networks or systems of differential equations. The models can be based exclusively on experimental data, or on prior knowledge of the underlying biological processes. The latter are typically generic, but can be adapted to a certain context, such as a particular cell type, after training with context‐specific data. Dynamic mechanistic models that are based on biological knowledge have great potential for modeling specific systems, because they require less data for training to provide biological insight in particular into causal mechanisms, and to extrapolate to scenarios that are outside the conditions they have been trained on.

Arrayed CRISPR‐based screens emerge as a powerful alternative to pooled screens making it possible to investigate a wide range of cellular phenotypes that are typically not amenable to pooled screens. Here, we describe a solid‐phase transfection platform that enables CRISPR‐based genetic screens in arrayed format with flexible readouts. We demonstrate efficient gene knockout upon delivery of guide RNAs and Cas9/guide RNA ribonucleoprotein complexes into untransformed and cancer cell lines. In addition, we provide evidence that our platform can be easily adapted to high‐throughput screens and we use this approach to study oncogene addiction in tumor cells. Finally demonstrating that the human primary cells can also be edited using this method, we pave the way for rapid testing of potential targeted therapies.

While prokaryotic promoters controlled by signal‐responding regulators typically display a range of input/output ratios when exposed to cognate inducers, virtually no naturally occurring cases are known to have an OFF state of zero transcription—as ideally needed for synthetic circuits. To overcome this problem, we have modelled and implemented a simple digitalizer module that completely suppresses the basal level of otherwise strong promoters in such a way that expression in the absence of induction is entirely impeded. The circuit involves the interplay of a translation‐inhibitory sRNA with the translational coupling of the gene of interest to a repressor such as LacI. The digitalizer module was validated with the strong inducible promoters Pm (induced by XylS in the presence of benzoate) and PalkB (induced by AlkS/dicyclopropyl ketone) and shown to perform effectively in both Escherichia coli and the soil bacterium Pseudomonas putida. The distinct expression architecture allowed cloning and conditional expression of, e.g. colicin E3, one molecule of which per cell suffices to kill the host bacterium. Revertants that escaped ColE3 killing were not found in hosts devoid of insertion sequences, suggesting that mobile elements are a major source of circuit inactivation in vivo.

Recent developments in proteomics have enabled signaling studies where > 10,000 phosphosites can be routinely identified and quantified. Yet, current analyses are limited in throughput, reproducibility, and robustness, hampering experiments that involve multiple perturbations, such as those needed to map kinase–substrate relationships, capture pathway crosstalks, and network inference analysis. To address these challenges, we introduce rapid‐robotic phosphoproteomics (R2‐P2), an end‐to‐end automated method that uses magnetic particles to process protein extracts to deliver mass spectrometry‐ready phosphopeptides. R2‐P2 is rapid, robust, versatile, and high‐throughput. To showcase the method, we applied it, in combination with data‐independent acquisition mass spectrometry, to study signaling dynamics in the mitogen‐activated protein kinase (MAPK) pathway in yeast. Our results reveal broad and specific signaling events along the mating, the high‐osmolarity glycerol, and the invasive growth branches of the MAPK pathway, with robust phosphorylation of downstream regulatory proteins and transcription factors. Our method facilitates large‐scale signaling studies involving hundreds of perturbations opening the door to systems‐level studies aiming to capture signaling complexity.

Metabolic heterogeneity between individual cells of a population harbors significant challenges for fundamental and applied research. Identifying metabolic heterogeneity and investigating its emergence require tools to zoom into metabolism of individual cells. While methods exist to measure metabolite levels in single cells, we lack capability to measure metabolic flux, i.e., the ultimate functional output of metabolic activity, on the single‐cell level. Here, combining promoter engineering, computational protein design, biochemical methods, proteomics, and metabolomics, we developed a biosensor to measure glycolytic flux in single yeast cells. Therefore, drawing on the robust cell‐intrinsic correlation between glycolytic flux and levels of fructose‐1,6‐bisphosphate (FBP), we transplanted the B. subtilis FBP‐binding transcription factor CggR into yeast. With the developed biosensor, we robustly identified cell subpopulations with different FBP levels in mixed cultures, when subjected to flow cytometry and microscopy. Employing microfluidics, we were also able to assess the temporal FBP/glycolytic flux dynamics during the cell cycle. We anticipate that our biosensor will become a valuable tool to identify and study metabolic heterogeneity in cell populations.

Loss‐of‐function (LoF) mutations associated with disease do not manifest equally in different individuals. The impact of the genetic background on the consequences of LoF mutations remains poorly characterized. Here, we systematically assessed the changes in gene deletion phenotypes for 3,786 gene knockouts in four Saccharomyces cerevisiae strains and 38 conditions. We observed 18.5% of deletion phenotypes changing between pairs of strains on average with a small fraction conserved in all four strains. Conditions causing higher wild‐type growth differences and the deletion of pleiotropic genes showed above‐average changes in phenotypes. In addition, we performed a genome‐wide association study (GWAS) for growth under the same conditions for a panel of 925 yeast isolates. Gene–condition associations derived from GWAS were not enriched for genes with deletion phenotypes under the same conditions. However, cases where the results were congruent indicate the most likely mechanism underlying the GWAS signal. Overall, these results show a high degree of genetic background dependencies for LoF phenotypes.

During embryogenesis, differentiation of pluripotent cells into somatic cell types depends both on signaling cues and intrinsic gene expression programs. While the molecular underpinnings of pluripotency are well mapped, much less is known on how mouse embryonic stem cells (mESCs) differentiate. Using RNA‐Seq profiling during specification to the three germ layers, we showed that mESCs switched on condition‐specific gene expression programs from the onset of the differentiation procedure and that primed pluripotency did not constitute an obligatory intermediate state. After inferring the gene network controlling mESC differentiation, we tested the role of the highly connected nodes by deleting them in a triple knock‐in Sox1‐Brachyury‐Eomes mESC line reporting on ectoderm, mesoderm, and endoderm fates. This led to the identification of regulators of mESC differentiation that acted at several levels: Sp1 as a global break on differentiation, Nr5a2 controlling ectoderm specification, and notably Fos:Jun and Zfp354c as opposite switches between ectoderm and mesendoderm fate.

Molecular Systems Biology warmly welcomes its new academic Chief Editor, M. Madan Babu. Madan shared his thoughts on the evolution of the field and the importance of bridging disciplines to enable next generation systems biology.

Biology is reaching a convergence point of its historic reductionist and modern holistic approaches to understanding the living system. Structural biology has historically taken the reductionist approach to deeply probe the inner workings of complex molecular machines. In contrast, systems biology and genome‐scale modeling have organically grown out of the wealth of data now being generated by diverse omics measurements. In the late 2000s, a proposed interdisciplinary field of structural systems biology pitched the merger of these two approaches, with widespread applications in pharmacology, disease modeling, protein engineering, and evolutionary studies. In this commentary, we highlight the challenges of integrating these two fields, with a focus on genome‐scale metabolic modeling, and the novel findings that are made possible from such a merger.

Multiple sequence alignments are fundamental to many sequence analysis methods. Most alignments are computed using the progressive alignment heuristic. These methods are starting to become a bottleneck in some analysis pipelines when faced with data sets of the size of many thousands of sequences. Some methods allow computation of larger data sets while sacrificing quality, and others produce high‐quality alignments, but scale badly with the number of sequences. In this paper, we describe a new program called Clustal Omega, which can align virtually any number of protein sequences quickly and that delivers accurate alignments. The accuracy of the package on smaller test cases is similar to that of the high‐quality aligners. On larger data sets, Clustal Omega outperforms other packages in terms of execution time and quality. Clustal Omega also has powerful features for adding sequences to and exploiting information in existing alignments, making use of the vast amount of precomputed information in public databases like Pfam.

We have systematically made a set of precisely defined, single‐gene deletions of all nonessential genes in Escherichia coli K‐12. Open‐reading frame coding regions were replaced with a kanamycin cassette flanked by FLP recognition target sites by using a one‐step method for inactivation of chromosomal genes and primers designed to create in‐frame deletions upon excision of the resistance cassette. Of 4288 genes targeted, mutants were obtained for 3985. To alleviate problems encountered in high‐throughput studies, two independent mutants were saved for every deleted gene. These mutants—the ‘Keio collection’—provide a new resource not only for systematic analyses of unknown gene functions and gene regulatory networks but also for genome‐wide testing of mutational effects in a common strain background, E. coli K‐12 BW25113. We were unable to disrupt 303 genes, including 37 of unknown function, which are candidates for essential genes. Distribution is being handled via GenoBase (http://ecoli.aist‐nara.ac.jp/).

Although a great deal is known about the signaling events that promote nuclear translocation of NF-κB, how cellular biophysics and the microenvironment might regulate the dynamics of this pathway is poorly understood. In this study, we used high-content image analysis and Bayesian network modeling to ask whether cell shape and context features influence NF-κB activation using the inherent variability present in unperturbed populations of breast tumor and non-tumor cell lines. Cell–cell contact, cell and nuclear area, and protrusiveness all contributed to variability in NF-κB localization in the absence and presence of TNFα. Higher levels of nuclear NF-κB were associated with mesenchymal-like versus epithelial-like morphologies, and RhoA-ROCK-myosin II signaling was critical for mediating shape-based differences in NF-κB localization and oscillations. Thus, mechanical factors such as cell shape and the microenvironment can influence NF-κB signaling and may in part explain how different phenotypic outcomes can arise from the same chemical cues.

Elucidating functions of commensal microbial genes in the mammalian gut is challenging because many commensals are recalcitrant to laboratory cultivation and genetic manipulation. We present Temporal FUnctional Metagenomics sequencing (TFUMseq), a platform to functionally mine bacterial genomes for genes that contribute to fitness of commensal bacteria in vivo. Our approach uses metagenomic DNA to construct large-scale heterologous expression libraries that are tracked over time in vivo by deep sequencing and computational methods. To demonstrate our approach, we built a TFUMseq plasmid library using the gut commensal Bacteroides thetaiotaomicron (Bt) and introduced Escherichia coli carrying this library into germfree mice. Population dynamics of library clones revealed Bt genes conferring significant fitness advantages in E. coli over time, including carbohydrate utilization genes, with a Bt galactokinase central to early colonization, and subsequent dominance by a Bt glycoside hydrolase enabling sucrose metabolism coupled with co-evolution of the plasmid library and E. coli genome driving increased galactose utilization. Our findings highlight the utility of functional metagenomics for engineering commensal bacteria with improved properties, including expanded colonization capabilities in vivo.

High-throughput omics have proven invaluable in studying human disease, and yet day-to-day clinical practice still relies on physiological, non-omic markers. The metabolic syndrome, for example, is diagnosed and monitored by blood and urine indices such as blood cholesterol levels. Nevertheless, the association between the molecular and the physiological manifestations of the disease, especially in response to treatment, has not been investigated in a systematic manner. To this end, we studied a mouse model of diet-induced dyslipidemia and atherosclerosis that was subject to various drug treatments relevant to the disease in question. Both physiological data and gene expression data (from the liver and white adipose) were analyzed and compared. We find that treatments that restore gene expression patterns to their norm are associated with the successful restoration of physiological markers to their baselines. This holds in a tissue-specific manner—treatments that reverse the transcriptomic signatures of the disease in a particular tissue are associated with positive physiological effects in that tissue. Further, treatments that introduce large non-restorative gene expression alterations are associated with unfavorable physiological outcomes. These results provide a sound basis to in silico methods that rely on omic metrics for drug repurposing and drug discovery by searching for compounds that reverse a disease’s omic signatures. Moreover, they highlight the need to develop drugs that restore the global cellular state to its healthy norm rather than rectify particular disease phenotypes.

Liver regeneration is a tightly controlled process mainly achieved by proliferation of usually quiescent hepatocytes. The specific molecular mechanisms ensuring cell division only in response to proliferative signals such as hepatocyte growth factor (HGF) are not fully understood. Here, we combined quantitative time-resolved analysis of primary mouse hepatocyte proliferation at the single cell and at the population level with mathematical modeling. We showed that numerous G1/S transition components are activated upon hepatocyte isolation whereas DNA replication only occurs upon additional HGF stimulation. In response to HGF, Cyclin:CDK complex formation was increased, p21 rather than p27 was regulated, and Rb expression was enhanced. Quantification of protein levels at the restriction point showed an excess of CDK2 over CDK4 and limiting amounts of the transcription factor E2F-1. Analysis with our mathematical model revealed that T160 phosphorylation of CDK2 correlated best with growth factor-dependent proliferation, which we validated experimentally on both the population and the single cell level. In conclusion, we identified CDK2 phosphorylation as a gate-keeping mechanism to maintain hepatocyte quiescence in the absence of HGF.

Bacterial growth environment strongly influences the efficacy of antibiotic treatment, with slow growth often being associated with decreased susceptibility. Yet in many cases, the connection between antibiotic susceptibility and pathogen physiology remains unclear. We show that for ribosome-targeting antibiotics acting on Escherichia coli, a complex interplay exists between physiology and antibiotic action; for some antibiotics within this class, faster growth indeed increases susceptibility, but for other antibiotics, the opposite is true. Remarkably, these observations can be explained by a simple mathematical model that combines drug transport and binding with physiological constraints. Our model reveals that growth-dependent susceptibility is controlled by a single parameter characterizing the ‘reversibility’ of ribosome-targeting antibiotic transport and binding. This parameter provides a spectrum classification of antibiotic growth-dependent efficacy that appears to correspond at its extremes to existing binary classification schemes. In these limits, the model predicts universal, parameter-free limiting forms for growth inhibition curves. The model also leads to nontrivial predictions for the drug susceptibility of a translation mutant strain of E. coli, which we verify experimentally. Drug action and bacterial metabolism are mechanistically complex; nevertheless, this study illustrates how coarse-grained models can be used to integrate pathogen physiology into drug design and treatment strategies.

Mammalian chromosomes fold into arrays of megabase-sized topologically associating domains (TADs), which are arranged into compartments spanning multiple megabases of genomic DNA. TADs have internal substructures that are often cell type specific, but their higher-order organization remains elusive. Here, we investigate TAD higher-order interactions with Hi-C through neuronal differentiation and show that they form a hierarchy of domains-within-domains (metaTADs) extending across genomic scales up to the range of entire chromosomes. We find that TAD interactions are well captured by tree-like, hierarchical structures irrespective of cell type. metaTAD tree structures correlate with genetic, epigenomic and expression features, and structural tree rearrangements during differentiation are linked to transcriptional state changes. Using polymer modelling, we demonstrate that hierarchical folding promotes efficient chromatin packaging without the loss of contact specificity, highlighting a role far beyond the simple need for packing efficiency.

Numerous molecular components have been identified that regulate the directed migration of eukaryotic cells toward sources of chemoattractant. However, how the components of this system are wired together to coordinate multiple aspects of the response, such as directionality, speed, and sensitivity to stimulus, remains poorly understood. Here we developed a method to shape chemoattractant gradients optically and analyze cellular chemotaxis responses of hundreds of living cells per well in 96-well format by measuring speed changes and directional accuracy. We then systematically characterized migration and chemotaxis phenotypes for 285 siRNA perturbations. A key finding was that the G-protein Giα subunit selectively controls the direction of migration while the receptor and Gβ subunit proportionally control both speed and direction. Furthermore, we demonstrate that neutrophils chemotax persistently in response to gradients of fMLF but only transiently in response to gradients of ATP. The method we introduce is applicable for diverse chemical cues and systematic perturbations, can be used to measure multiple cell migration and signaling parameters, and is compatible with low- and high-resolution fluorescence microscopy.

In Arabidopsis, lateral roots originate from pericycle cells deep within the primary root. New lateral root primordia (LRP) have to emerge through several overlaying tissues. Here, we report that auxin produced in new LRP is transported towards the outer tissues where it triggers cell separation by inducing both the auxin influx carrier LAX3 and cell-wall enzymes. LAX3 is expressed in just two cell files overlaying new LRP. To understand how this striking pattern of LAX3 expression is regulated, we developed a mathematical model that captures the network regulating its expression and auxin transport within realistic three-dimensional cell and tissue geometries. Our model revealed that, for the LAX3 spatial expression to be robust to natural variations in root tissue geometry, an efflux carrier is required--later identified to be PIN3. To prevent LAX3 from being transiently expressed in multiple cell files, PIN3 and LAX3 must be induced consecutively, which we later demonstrated to be the case. Our study exemplifies how mathematical models can be used to direct experiments to elucidate complex developmental processes.

Rate control analysis defines the in vivo control map governing yeast protein synthesis and generates an extensively parameterized digital model of the translation pathway. Among other non-intuitive outcomes, translation demonstrates a high degree of functional modularity and comprises a non-stoichiometric combination of proteins manifesting functional convergence on a shared maximal translation rate. In exponentially growing cells, polypeptide elongation (eEF1A, eEF2, and eEF3) exerts the strongest control. The two other strong control points are recruitment of mRNA and tRNA(i) to the 40S ribosomal subunit (eIF4F and eIF2) and termination (eRF1; Dbp5). In contrast, factors that are found to promote mRNA scanning efficiency on a longer than-average 5'untranslated region (eIF1, eIF1A, Ded1, eIF2B, eIF3, and eIF5) exceed the levels required for maximal control. This is expected to allow the cell to minimize scanning transition times, particularly for longer 5'UTRs. The analysis reveals these and other collective adaptations of control shared across the factors, as well as features that reflect functional modularity and system robustness. Remarkably, gene duplication is implicated in the fine control of cellular protein synthesis.

The interpretation of morphogen gradients is a pivotal concept in developmental biology, and several mechanisms have been proposed to explain how gene regulatory networks (GRNs) achieve concentration-dependent responses. However, the number of different mechanisms that may exist for cells to interpret morphogens, and the importance of design features such as feedback or local cell-cell communication, is unclear. A complete understanding of such systems will require going beyond a case-by-case analysis of real morphogen interpretation mechanisms and mapping out a complete GRN 'design space.' Here, we generate a first atlas of design space for GRNs capable of patterning a homogeneous field of cells into discrete gene expression domains by interpreting a fixed morphogen gradient. We uncover multiple very distinct mechanisms distributed discretely across the atlas, thereby expanding the repertoire of morphogen interpretation network motifs. Analyzing this diverse collection of mechanisms also allows us to predict that local cell-cell communication will rarely be responsible for the basic dose-dependent response of morphogen interpretation networks.

An important question in plant biology is how genes influence the crosstalk between hormones to regulate growth. In this study, we model POLARIS (PLS) gene function and crosstalk between auxin, ethylene and cytokinin in Arabidopsis. Experimental evidence suggests that PLS acts on or close to the ethylene receptor ETR1, and a mathematical model describing possible PLS-ethylene pathway interactions is developed, and used to make quantitative predictions about PLS-hormone interactions. Modelling correctly predicts experimental results for the effect of the pls gene mutation on endogenous cytokinin concentration. Modelling also reveals a role for PLS in auxin biosynthesis in addition to a role in auxin transport. The model reproduces available mutants, and with new experimental data provides new insights into how PLS regulates auxin concentration, by controlling the relative contribution of auxin transport and biosynthesis and by integrating auxin, ethylene and cytokinin signalling. Modelling further reveals that a bell-shaped dose-response relationship between endogenous auxin and root length is established via PLS. This combined modelling and experimental analysis provides new insights into the integration of hormonal signals in plants.

Cellular senescence--the permanent arrest of cycling in normally proliferating cells such as fibroblasts--contributes both to age-related loss of mammalian tissue homeostasis and acts as a tumour suppressor mechanism. The pathways leading to establishment of senescence are proving to be more complex than was previously envisaged. Combining in-silico interactome analysis and functional target gene inhibition, stochastic modelling and live cell microscopy, we show here that there exists a dynamic feedback loop that is triggered by a DNA damage response (DDR) and, which after a delay of several days, locks the cell into an actively maintained state of 'deep' cellular senescence. The essential feature of the loop is that long-term activation of the checkpoint gene CDKN1A (p21) induces mitochondrial dysfunction and production of reactive oxygen species (ROS) through serial signalling through GADD45-MAPK14(p38MAPK)-GRB2-TGFBR2-TGFbeta. These ROS in turn replenish short-lived DNA damage foci and maintain an ongoing DDR. We show that this loop is both necessary and sufficient for the stability of growth arrest during the establishment of the senescent phenotype.

It has recently been suggested that the use of optimal codons limits mistranslation-induced protein misfolding, yet evidence for this remains largely circumstantial. In contrast, molecular chaperones have long been recognized to play crucial roles in misfolding prevention and remedy. We propose that putative error limitation in cis can be elucidated by examining the interaction between codon usage and chaperoning processes. Using Escherichia coli as a model system, we find that codon optimality covaries with dependency on the chaperonin GroEL. Sporadic but not obligate substrates of GroEL exhibit higher average codon adaptation and are conspicuously enriched for optimal codons at structurally sensitive sites. Further, codon optimality of sporadic clients is more conserved in the E. coli clone Shigella dysenteriae. We suggest that highly expressed genes cannot routinely use GroEL for error control so that codon usage has evolved to provide complementary error limitation. These findings provide independent evidence for a role of misfolding in shaping gene evolution and highlight the need to co-characterize adaptations in cis and trans to unravel the workings of integrated molecular systems.

Although several studies have provided important insights into the general principles of biological networks, the link between network organization and the genome-scale dynamics of the underlying entities (genes, mRNAs, and proteins) and its role in systems behavior remain unclear. Here we show that transcription factor (TF) dynamics and regulatory network organization are tightly linked. By classifying TFs in the yeast regulatory network into three hierarchical layers (top, core, and bottom) and integrating diverse genome-scale datasets, we find that the TFs have static and dynamic properties that are similar within a layer and different across layers. At the protein level, the top-layer TFs are relatively abundant, long-lived, and noisy compared with the core- and bottom-layer TFs. Although variability in expression of top-layer TFs might confer a selective advantage, as this permits at least some members in a clonal cell population to initiate a response to changing conditions, tight regulation of the core- and bottom-layer TFs may minimize noise propagation and ensure fidelity in regulation. We propose that the interplay between network organization and TF dynamics could permit differential utilization of the same underlying network by distinct members of a clonal cell population.

Biochemical networks are characterized by recurrent patterns and motifs, but the design principles underlying the dynamics of the mammalian intracellular signalling network remain unclear. We systematically analysed decay rates of 134 signalling proteins and investigated their gene expression profiles in response to stimulation to get insights into transcriptional feedback regulation. We found a clear separation of the signalling pathways into flexible and static parts: for each pathway a subgroup of unstable signal inhibitors is transcriptionally induced upon stimulation, while the other constitutively expressed signalling proteins are long-lived. Kinetic modelling suggests that this design principle allows for swift feedback regulation and establishes latency phases after signalling, and that it might be an optimal design due to a trade-off between energy efficiency and flexibility.

The log-rank test statistic is very broadly used in biology. Unfortunately, P-values based on the popular chi-square approximation are often inaccurate and can be misleading.

A recent study by Haapaniemi et al (2018) reported that intact p53 signaling hampers CRISPR-based functional genomic screens. Brown et al report good performance of genome-scale screens in TP53 wild-type cells and reiterate best practices for CRISPR screening.

Haapaniemi et al address the issues raised by Brown et al and discuss several differences between the analyses performed by the two groups.

Seven-transmembrane receptors (7TMRs) are involved in nearly all aspects of chemical communications and represent major drug targets. 7TMRs transmit their signals not only via heterotrimeric G proteins but also through β-arrestins, whose recruitment to the activated receptor is regulated by G protein-coupled receptor kinases (GRKs). In this paper, we combined experimental approaches with computational modeling to decipher the molecular mechanisms as well as the hidden dynamics governing extracellular signal-regulated kinase (ERK) activation by the angiotensin II type 1A receptor (AT(1A)R) in human embryonic kidney (HEK)293 cells. We built an abstracted ordinary differential equations (ODE)-based model that captured the available knowledge and experimental data. We inferred the unknown parameters by simultaneously fitting experimental data generated in both control and perturbed conditions. We demonstrate that, in addition to its well-established function in the desensitization of G-protein activation, GRK2 exerts a strong negative effect on β-arrestin-dependent signaling through its competition with GRK5 and 6 for receptor phosphorylation. Importantly, we experimentally confirmed the validity of this novel GRK2-dependent mechanism in both primary vascular smooth muscle cells naturally expressing the AT(1A)R, and HEK293 cells expressing other 7TMRs.

Circadian clocks synchronise biological processes with the day/night cycle, using molecular mechanisms that include interlocked, transcriptional feedback loops. Recent experiments identified the evening complex (EC) as a repressor that can be essential for gene expression rhythms in plants. Integrating the EC components in this role significantly alters our mechanistic, mathematical model of the clock gene circuit. Negative autoregulation of the EC genes constitutes the clock's evening loop, replacing the hypothetical component Y. The EC explains our earlier conjecture that the morning gene Pseudo-Response Regulator 9 was repressed by an evening gene, previously identified with Timing Of CAB Expression1 (TOC1). Our computational analysis suggests that TOC1 is a repressor of the morning genes Late Elongated Hypocotyl and Circadian Clock Associated1 rather than an activator as first conceived. This removes the necessity for the unknown component X (or TOC1mod) from previous clock models. As well as matching timeseries and phase-response data, the model provides a new conceptual framework for the plant clock that includes a three-component repressilator circuit in its complex structure.

In this work, we present a proof-of-principle experiment that extends advanced live cell microscopy to the scale of pool-generated strain libraries. We achieve this by identifying the genotypes for individual cells in situ after a detailed characterization of the phenotype. The principle is demonstrated by single-molecule fluorescence time-lapse imaging of Escherichia coli strains harboring barcoded plasmids that express a sgRNA which suppresses different genes in the E. coli genome through dCas9 interference. In general, the method solves the problem of characterizing complex dynamic phenotypes for diverse genetic libraries of cell strains. For example, it allows screens of how changes in regulatory or coding sequences impact the temporal expression, location, or function of a gene product, or how the altered expression of a set of genes impacts the intracellular dynamics of a labeled reporter.

Quantification of gene expression has become a central tool for understanding genetic networks. In many systems, the only viable way to measure protein levels is by immunofluorescence, which is notorious for its limited accuracy. Using the early Drosophila embryo as an example, we show that careful identification and control of experimental error allows for highly accurate gene expression measurements. We generated antibodies in different host species, allowing for simultaneous staining of four Drosophila gap genes in individual embryos. Careful error analysis of hundreds of expression profiles reveals that less than ∼20% of the observed embryo-to-embryo fluctuations stem from experimental error. These measurements make it possible to extract not only very accurate mean gene expression profiles but also their naturally occurring fluctuations of biological origin and corresponding cross-correlations. We use this analysis to extract gap gene profile dynamics with ∼1 min accuracy. The combination of these new measurements and analysis techniques reveals a twofold increase in profile reproducibility owing to a collective network dynamics that relays positional accuracy from the maternal gradients to the pair-rule genes.

Time-series analysis of whole-genome expression data during Drosophila melanogaster development indicates that up to 86% of its genes change their relative transcript level during embryogenesis. By applying conservative filtering criteria and requiring 'sharp' transcript changes, we identified 1534 maternal genes, 792 transient zygotic genes, and 1053 genes whose transcript levels increase during embryogenesis. Each of these three categories is dominated by groups of genes where all transcript levels increase and/or decrease at similar times, suggesting a common mode of regulation. For example, 34% of the transiently expressed genes fall into three groups, with increased transcript levels between 2.5-12, 11-20, and 15-20 h of development, respectively. We highlight common and distinctive functional features of these expression groups and identify a coupling between downregulation of transcript levels and targeted protein degradation. By mapping the groups to the protein network, we also predict and experimentally confirm new functional associations.

Translating a graded morphogen distribution into tight response borders is central to all developmental processes. Yet, the molecular mechanisms generating such behavior are poorly understood. During patterning of the Drosophila embryonic ventral ectoderm, a graded mitogen-activated protein kinase (MAPK) activation is converted into an all-or-none degradation switch of the Yan transcriptional repressor. Replacing the cardinal phosphorylated amino acid of Yan by a phosphomimetic residue allowed its degradation in a MAPK-independent manner, consistent with Yan phosphorylation being the critical event in generating the switch. Several alternative threshold mechanisms that could, in principle, be realized by this phosphorylation, including first order, cooperativity, positive feedback and zero-order ultrasensitivity, were analyzed. We found that they can be distinguished by their kinetics and steady-state responses to Yan overexpression. In agreement with the predictions for zero-order kinetics, an increase in Yan levels did not shift the degradation border, but significantly elevated the time required to reach steady state. We propose that a reversible loop of Yan phosphorylation implements a zero-order ultrasensitivity-like threshold mechanism, with the capacity to form sharp thresholds that are independent of the level of Yan.

Phosphorylation of the mitogen-activated protein kinase (MAPK) is essential for its enzymatic activity and ability to control multiple substrates inside a cell. According to the current models, control of MAPK phosphorylation is independent of its substrates, which are viewed as mere sensors of MAPK activity. Contrary to this modular view of MAPK signaling, our studies in the Drosophila embryo demonstrate that substrates can regulate the level of MAPK phosphorylation in vivo. We demonstrate that a twofold change in the gene dosage of a single substrate can induce a significant change in the phosphorylation level of MAPK and in the conversion of other substrates. Our results support a model where substrates of MAPK counteract its dephosphorylation by phosphatases. Substrate-dependent control of MAPK phosphorylation is a manifestation of a more general retroactive effect that should be intrinsic to all networks with covalent modification cycles.

Gene expression patterns can diverge between species due to changes in a gene's regulatory DNA or changes in the proteins, e.g., transcription factors (TFs), that regulate the gene. We developed a modeling framework to uncover the sources of expression differences in blastoderm embryos of three Drosophila species, focusing on the regulatory circuit controlling expression of the hunchback (hb) posterior stripe. Using this framework and cellular-resolution expression measurements of hb and its regulating TFs, we found that changes in the expression patterns of hb's TFs account for much of the expression divergence. We confirmed our predictions using transgenic D. melanogaster lines, which demonstrate that this set of orthologous cis-regulatory elements (CREs) direct similar, but not identical, expression patterns. We related expression pattern differences to sequence changes in the CRE using a calculation of the CRE's TF binding site content. By applying this calculation in both the transgenic and endogenous contexts, we found that changes in binding site content affect sensitivity to regulating TFs and that compensatory evolution may occur in circuit components other than the CRE.

Morphogen gradients infer cell fate as a function of cellular position. Experiments in Drosophila embryos have shown that the Bicoid (Bcd) gradient is precise and exhibits some degree of scaling. We present experimental results on the precision of Bcd target genes for embryos with a single, double or quadruple dose of bicoid demonstrating that precision is highest at mid-embryo and position dependent, rather than gene dependent. This confirms that the major contribution to precision is achieved already at the Bcd gradient formation. Modeling this dynamic process, we investigate precision for inter-embryo fluctuations in different parameters affecting gradient formation. Within our modeling framework, the observed precision can only be achieved by a transient Bcd profile. Studying different extensions of our modeling framework reveals that scaling is generally position dependent and decreases toward the posterior pole. Our measurements confirm this trend, indicating almost perfect scaling except for anterior most expression domains, which overcompensate fluctuations in embryo length.

Genome-wide association studies (GWAS) have identified numerous loci associated with blood pressure (BP). The molecular mechanisms underlying BP regulation, however, remain unclear. We investigated BP-associated molecular mechanisms by integrating BP GWAS with whole blood mRNA expression profiles in 3,679 individuals, using network approaches. BP transcriptomic signatures at the single-gene and the coexpression network module levels were identified. Four coexpression modules were identified as potentially causal based on genetic inference because expression-related SNPs for their corresponding genes demonstrated enrichment for BP GWAS signals. Genes from the four modules were further projected onto predefined molecular interaction networks, revealing key drivers. Gene subnetworks entailing molecular interactions between key drivers and BP-related genes were uncovered. As proof-of-concept, we validated SH2B3, one of the top key drivers, using Sh2b3(-/-) mice. We found that a significant number of genes predicted to be regulated by SH2B3 in gene networks are perturbed in Sh2b3(-/-) mice, which demonstrate an exaggerated pressor response to angiotensin II infusion. Our findings may help to identify novel targets for the prevention or treatment of hypertension.

The transcription factor POU5f1/OCT4 controls pluripotency in mammalian ES cells, but little is known about its functions in the early embryo. We used time-resolved transcriptome analysis of zebrafish pou5f1 MZspg mutant embryos to identify genes regulated by Pou5f1. Comparison to mammalian systems defines evolutionary conserved Pou5f1 targets. Time-series data reveal many Pou5f1 targets with delayed or advanced onset of expression. We identify two Pou5f1-dependent mechanisms controlling developmental timing. First, several Pou5f1 targets are transcriptional repressors, mediating repression of differentiation genes in distinct embryonic compartments. We analyze her3 gene regulation as example for a repressor in the neural anlagen. Second, the dynamics of SoxB1 group gene expression and Pou5f1-dependent regulation of her3 and foxD3 uncovers differential requirements for SoxB1 activity to control temporal dynamics of activation, and spatial distribution of targets in the embryo. We establish a mathematical model of the early Pou5f1 and SoxB1 gene network to demonstrate regulatory characteristics important for developmental timing. The temporospatial structure of the zebrafish Pou5f1 target networks may explain aspects of the evolution of the mammalian stem cell networks.

Inactivation of β-lactam antibiotics by resistant bacteria is a 'cooperative' behavior that may allow sensitive bacteria to survive antibiotic treatment. However, the factors that determine the fraction of resistant cells in the bacterial population remain unclear, indicating a fundamental gap in our understanding of how antibiotic resistance evolves. Here, we experimentally track the spread of a plasmid that encodes a β-lactamase enzyme through the bacterial population. We find that independent of the initial fraction of resistant cells, the population settles to an equilibrium fraction proportional to the antibiotic concentration divided by the cell density. A simple model explains this behavior, successfully predicting a data collapse over two orders of magnitude in antibiotic concentration. This model also successfully predicts that adding a commonly used β-lactamase inhibitor will lead to the spread of resistance, highlighting the need to incorporate social dynamics into the study of antibiotic resistance.

T-cell receptor (TCR) signaling is essential for the function of T cells and negatively regulated by the E3 ubiquitin-protein ligases CBL and CBLB Here, we combined mouse genetics and affinity purification coupled to quantitative mass spectrometry to monitor the dynamics of the CBL and CBLB signaling complexes that assemble in normal T cells over 600 seconds of TCR stimulation. We identify most previously known CBL and CBLB interacting partners, as well as a majority of proteins that have not yet been implicated in those signaling complexes. We exploit correlations in protein association with CBL and CBLB as a function of time of TCR stimulation for predicting the occurrence of direct physical association between them. By combining co-recruitment analysis with biochemical analysis, we demonstrated that the CD5 transmembrane receptor constitutes a key scaffold for CBL- and CBLB-mediated ubiquitylation following TCR engagement. Our results offer an integrated view of the CBL and CBLB signaling complexes induced by TCR stimulation and provide a molecular basis for their negative regulatory function in normal T cells.

Caulobacter crescentus is a model organism for the integrated circuitry that runs a bacterial cell cycle. Full discovery of its essential genome, including non-coding, regulatory and coding elements, is a prerequisite for understanding the complete regulatory network of a bacterial cell. Using hyper-saturated transposon mutagenesis coupled with high-throughput sequencing, we determined the essential Caulobacter genome at 8 bp resolution, including 1012 essential genome features: 480 ORFs, 402 regulatory sequences and 130 non-coding elements, including 90 intergenic segments of unknown function. The essential transcriptional circuitry for growth on rich media includes 10 transcription factors, 2 RNA polymerase sigma factors and 1 anti-sigma factor. We identified all essential promoter elements for the cell cycle-regulated genes. The essential elements are preferentially positioned near the origin and terminus of the chromosome. The high-resolution strategy used here is applicable to high-throughput, full genome essentiality studies and large-scale genetic perturbation experiments in a broad class of bacterial species.

Meta-analyses combining gene expression microarray experiments offer new insights into the molecular pathophysiology of disease not evident from individual experiments. Although the established technical reproducibility of microarrays serves as a basis for meta-analysis, pathophysiological reproducibility across experiments is not well established. In this study, we carried out a large-scale analysis of disease-associated experiments obtained from NCBI GEO, and evaluated their concordance across a broad range of diseases and tissue types. On evaluating 429 experiments, representing 238 diseases and 122 tissues from 8435 microarrays, we find evidence for a general, pathophysiological concordance between experiments measuring the same disease condition. Furthermore, we find that the molecular signature of disease across tissues is overall more prominent than the signature of tissue expression across diseases. The results offer new insight into the quality of public microarray data using pathophysiological metrics, and support new directions in meta-analysis that include characterization of the commonalities of disease irrespective of tissue, as well as the creation of multi-tissue systems models of disease pathology using public data.

To elucidate the molecular mechanisms underlying non-alcoholic fatty liver disease (NAFLD), we recruited 86 subjects with varying degrees of hepatic steatosis (HS). We obtained experimental data on lipoprotein fluxes and used these individual measurements as personalized constraints of a hepatocyte genome-scale metabolic model to investigate metabolic differences in liver, taking into account its interactions with other tissues. Our systems level analysis predicted an altered demand for NAD+ and glutathione (GSH) in subjects with high HS Our analysis and metabolomic measurements showed that plasma levels of glycine, serine, and associated metabolites are negatively correlated with HS, suggesting that these GSH metabolism precursors might be limiting. Quantification of the hepatic expression levels of the associated enzymes further pointed to altered de novo GSH synthesis. To assess the effect of GSH and NAD+ repletion on the development of NAFLD, we added precursors for GSH and NAD+ biosynthesis to the Western diet and demonstrated that supplementation prevents HS in mice. In a proof-of-concept human study, we found improved liver function and decreased HS after supplementation with serine (a precursor to glycine) and hereby propose a strategy for NAFLD treatment.

Large-scale molecular annotation of epithelial ovarian cancer (EOC) indicates remarkable heterogeneity in the etiology of that disease. This diversity presents a significant obstacle against intervention target discovery. However, inactivation of miRNA biogenesis is commonly associated with advanced disease. Thus, restoration of miRNA activity may represent a common vulnerability among diverse EOC oncogenotypes. To test this, we employed genome-scale, gain-of-function, miRNA mimic toxicity screens in a large, diverse spectrum of EOC cell lines. We found that all cell lines responded to at least some miRNA mimics, but that the nature of the miRNA mimics provoking a response was highly selective within the panel. These selective toxicity profiles were leveraged to define modes of action and molecular response indicators for miRNA mimics with tumor-suppressive characteristics in vivo. A mechanistic principle emerging from this analysis was sensitivity of EOC to miRNA-mediated release of cell fate specification programs, loss of which may be a prerequisite for development of this disease.

Morphogenesis in animal tissues is largely driven by actomyosin networks, through tensions generated by an active contractile process. Although the network components and their properties are known, and networks can be reconstituted in vitro, the requirements for contractility are still poorly understood. Here, we describe a theory that predicts whether an isotropic network will contract, expand, or conserve its dimensions. This analytical theory correctly predicts the behavior of simulated networks, consisting of filaments with varying combinations of connectors, and reveals conditions under which networks of rigid filaments are either contractile or expansile. Our results suggest that pulsatility is an intrinsic behavior of contractile networks if the filaments are not stable but turn over. The theory offers a unifying framework to think about mechanisms of contractions or expansion. It provides the foundation for studying a broad range of processes involving cytoskeletal networks and a basis for designing synthetic networks.

Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry (MS)-based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous "triangular strategies" aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a "rectangular" plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision-making, and we discuss some first steps in this direction.

Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.

The arrangement of proteins into complexes is a key organizational principle for many cellular functions. Although the topology of many complexes has been systematically analyzed in isolation, their molecular sociology in situ remains elusive. Here, we show that crude cellular extracts of a eukaryotic thermophile, Chaetomium thermophilum, retain basic principles of cellular organization. Using a structural proteomics approach, we simultaneously characterized the abundance, interactions, and structure of a third of the C. thermophilum proteome within these extracts. We identified 27 distinct protein communities that include 108 interconnected complexes, which dynamically associate with each other and functionally benefit from being in close proximity in the cell. Furthermore, we investigated the structure of fatty acid synthase within these extracts by cryoEM and this revealed multiple, flexible states of the enzyme in adaptation to its association with other complexes, thus exemplifying the need for in situ studies. As the components of the captured protein communities are known-at both the protein and complex levels-this study constitutes another step forward toward a molecular understanding of subcellular organization.