Explore the vast range of titles available.

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

The initial genome‐scale reconstruction of the metabolic network of Escherichia coli K‐12 MG1655 was assembled in 2000. It has been updated and periodically released since then based on new and curated genomic and biochemical knowledge. An update has now been built, named i JO1366, which accounts for 1366 genes, 2251 metabolic reactions, and 1136 unique metabolites. i JO1366 was (1) updated in part using a new experimental screen of 1075 gene knockout strains, illuminating cases where alternative pathways and isozymes are yet to be discovered, (2) compared with its predecessor and to experimental data sets to confirm that it continues to make accurate phenotypic predictions of growth on different substrates and for gene knockout strains, and (3) mapped to the genomes of all available sequenced E. coli strains, including pathogens, leading to the identification of hundreds of unannotated genes in these organisms. Like its predecessors, the i JO1366 reconstruction is expected to be widely deployed for studying the systems biology of E. coli and for metabolic engineering applications.

Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism–environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation. Synopsis A new computational framework integrating network component analysis and model selection is combined with transcriptomic datasets and generates an expanded and more accurate transcriptional regulatory network (TRN) for Bacillus subtilis . A global TRN is inferred for B. subtilis and contains 3,086 protein‐coding genes, 215 transcription factors (TFs) and predicts 4,516 interactions (2,258 novel). Previously known interactions are recalled at high proportion (74%) and experimental support is provided for 1,289 TF–gene interactions (out of 1,841 tested) in transcriptional profiling data with KO strains, including 391 (out of 635) novel interactions. The inferred TRN provides novel functional insights even for well‐studied pathways, such as spore formation. Graphical Abstract A new computational framework integrating network component analysis and model selection is combined with transcriptomic datasets and generates an expanded and more accurate transcriptional regulatory network (TRN) for Bacillus subtilis .

Growth is a fundamental process of life. Growth requirements are well‐characterized experimentally for many microbes; however, we lack a unified model for cellular growth. Such a model must be predictive of events at the molecular scale and capable of explaining the high‐level behavior of the cell as a whole. Here, we construct an ME‐Model for Escherichia coli —a genome‐scale model that seamlessly integrates metabolic and gene product expression pathways. The model computes ∼80% of the functional proteome (by mass), which is used by the cell to support growth under a given condition. Metabolism and gene expression are interdependent processes that affect and constrain each other. We formalize these constraints and apply the principle of growth optimization to enable the accurate prediction of multi‐scale phenotypes, ranging from coarse‐grained (growth rate, nutrient uptake, by‐product secretion) to fine‐grained (metabolic fluxes, gene expression levels). Our results unify many existing principles developed to describe bacterial growth. A constraint‐based approach for integrative modeling of metabolism and gene expression is developed. New constraints on molecular catalysis increase both the accuracy and scope of computable phenotypes corresponding to optimal microbial growth. Synopsis A constraint‐based approach for integrative modeling of metabolism and gene expression is developed. New constraints on molecular catalysis increase both the accuracy and scope of computable phenotypes corresponding to optimal microbial growth. An integrated network of metabolic and gene expression pathways is built for E. coli . A growth model is developed by adding demands and constraints on molecular catalysis. Model yields accurate predictions of growth phenotypes from molecules to whole cell. A few basic principles underlie growth rate optimization at the systems level.

The availability and utility of genome‐scale metabolic reconstructions have exploded since the first genome‐scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high‐throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis‐driven discovery, (4) interrogation of multi‐species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome‐scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology.

Type 2 diabetes (T2D) can be prevented in pre‐diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre‐diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population‐based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre‐diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P ‐values ranging from 2.4 × 10 −4 to 2.1 × 10 −13 . Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)‐Potsdam cohort. Using metabolite–protein network analysis, we identified seven T2D‐related genes that are associated with these three IGT‐specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D. A targeted metabolomics approach was used to identify candidate biomarkers of pre‐diabetes. The relevance of the identified metabolites is further corroborated with a protein‐metabolite interaction network and gene expression data. Synopsis A targeted metabolomics approach was used to identify candidate biomarkers of pre‐diabetes. The relevance of the identified metabolites is further corroborated with a protein‐metabolite interaction network and gene expression data. Three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine C2) were found with significantly altered levels in pre‐diabetic individuals compared with normal controls. Lower levels of glycine and LPC (18:2) were found to predict risks for pre‐diabetes and type 2 diabetes (T2D). Seven T2D‐related genes ( PPARG , TCF7L2 , HNF1A , GCK , IGF1 , IRS1 and IDE ) are functionally associated with the three identified metabolites. The unique combination of methodologies, including prospective population‐based and nested case–control, as well as cross‐sectional studies, was essential for the identification of the reported biomarkers.

Various post‐translational modifications (PTMs) fine‐tune the functions of almost all eukaryotic proteins, and co‐regulation of different types of PTMs has been shown within and between a number of proteins. Aiming at a more global view of the interplay between PTM types, we collected modifications for 13 frequent PTM types in 8 eukaryotes, compared their speed of evolution and developed a method for measuring PTM co‐evolution within proteins based on the co‐occurrence of sites across eukaryotes. As many sites are still to be discovered, this is a considerable underestimate, yet, assuming that most co‐evolving PTMs are functionally associated, we found that PTM types are vastly interconnected, forming a global network that comprise in human alone >50 000 residues in about 6000 proteins. We predict substantial PTM type interplay in secreted and membrane‐associated proteins and in the context of particular protein domains and short‐linear motifs. The global network of co‐evolving PTM types implies a complex and intertwined post‐translational regulation landscape that is likely to regulate multiple functional states of many if not all eukaryotic proteins. This study is the first large‐scale comparative analysis of multiple types of post‐translational modifications in different eukaryotic species. The resulting network of co‐evolving and functionally associated modifications reveals the global landscape of post‐translational regulation. Synopsis This study is the first large‐scale comparative analysis of multiple types of post‐translational modifications in different eukaryotic species. The resulting network of co‐evolving and functionally associated modifications reveals the global landscape of post‐translational regulation. In all, 115 149 non‐redundant post‐translational modifications (PTMs) of 13 different types were collected from 8 eukaryotes. Comparison of evolution speed reveals that carboxylation is the most conserved while SUMOylation is the fastest evolving PTM type. Co‐evolution of PTM pairs that co‐occur within proteins reveals a vastly interconnected global network of functionally associated PTM types in eukaryotes. Central to the network of functionally associated PTM types appear phosphorylation, acetylation, ubiquitination and O‐linked glycosylation that control both temporal events and processes that govern protein localization.

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‐TGFβ. 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. Synopsis The phenomenon of cellular ‘senescence’—the permanent arrest of division in normally proliferating mammalian cells such as fibroblasts—is thought to be a central component of the ageing process. Senescence contributes both to age‐related loss of tissue homeostasis, as the loss of division capacity leads to impaired cell renewal, and also to protect against cancer, because it acts to block the uncontrolled proliferation of cells that may give rise to a malignant tumour. Replicative senescence is triggered by uncapped telomeres or by ‘unrepairable’ non‐telomeric DNA damage. Both lesions initiate the same canonical DNA damage response (DDR) (d'Adda di Fagagna, 2008 ). This response is characterized by activation of sensor kinases (ATM/ATR, DNA‐PK), formation of DNA damage foci containing activated H2A.X (γH2A.X) and ultimately induction of cell cycle arrest through activation of checkpoint proteins, notably p53 (TP53) and the CDK inhibitor p21 (CDKN1A). This signalling pathway continues to contribute actively to the stability of the G0 arrest in fully senescent cells long after induction of senescence (d'Adda di Fagagna et al , 2003 ). However, senescence is more complex than mere CDKI‐mediated growth arrest. Senescent cells alter their expression of literally hundreds of genes (Shelton et al , 1999 ), prominent among these being pro‐inflammatory secretory genes (Coppe et al , 2008 ) and marker genes for a retrograde response induced by mitochondrial dysfunction (Passos et al , 2007a ). There is a growing evidence that multiple mechanisms interact to underpin ageing at the cellular level (Kirkwood, 2005 ; Passos et al , 2007b ) necessitating a systems biology approach if the complex mechanisms of ageing are to be understood (Kirkwood, 2008 ). With respect to cell senescence, the two major unanswered questions are (i) How does a DNA lesion that can be repaired, at least in principle, induce and maintain irreversible growth arrest? and (ii) How does a growth arrest trigger a completely different cellular phenotype as soon as it becomes irreversible? To understand those questions, we performed a kinetic analysis of the establishment phase of senescence initiated by DNA damage or telomere dysfunction, focussing on pathways downstream of the classical DDR. Using an approach that combined (i) in‐silico interactome analysis, (ii) functional target gene inhibition, (iii) stochastic modelling, and (iv) live cell microscopy, we identified a positive feedback loop between DDR and mitochondrial production of reactive oxygen species (ROS) as necessary and sufficient for long‐term maintenance of growth arrest. Using pathway log likelihood scores calculated by a quantitative in‐silico interactome analysis to guide siRNA and small molecule inhibition experiments, and using results of sequential and combined inhibition experiments to refine the predictions from the interactome analysis, we found that DDR triggered mitochondrial dysfunction leading to enhanced ROS activation through a linear signal transduction through TP53, CDKN1A, GADD45A, p38 (MAPK14), GRB2, TGFBR2 and TGFβ(Figure 2D ). We hypothesized that these ROS stochastically generate novel DNA damage in the nucleus, thus forming a positive feedback loop contributing to the long‐term maintenance of DDR (Figure 3A ). First confirmation came from static inhibitor experiments as before, showing that nuclear DNA damage foci frequencies in senescent cells were reduced if feedback signalling was suppressed. To formally establish the existence of a feedback loop and its relevance for senescence, we used live cell microscopy in combination with quantitative modelling. We transformed the conceptual model shown in Figure 3A into a stochastic mechanistic model of the DDR feedback loop by extending the previously published model of the TP53/Mdm2 circuit (Proctor and Gray, 2008 ) to include reactions for synthesis/activation and degradation/deactivation/repair of CDKN1A, GADD45, MAPK14, ROS and DNA damage. The model replicated very precisely the kinetic behaviour of activated TP53, CDKN1A, ROS and DNA damage foci after initiation of senescence by irradiation. Having established its concordance with the experimental data, the model was then used to predict the effects of intervening in the feedback loop. The model predicted that any intervention reducing ROS levels by about half would decrease average DNA damage foci frequencies from six to four foci/nucleus within about 15 h. It further predicted that this would be sufficient to reduce CDKN1A to basal levels continuously for at least 6 h in about 20% of the treated cells, thus allowing a significant fraction of cells to escape from growth arrest and to resume proliferation. This should happen even if the intervention into the feedback loop was started at a late time point (e.g. 6 days) after induction of senescence. To analyse DNA damage foci dynamics we used a reporter construct (AcGFP–53BP1c) that quantitatively reports single DNA damage foci kinetics in time‐resolved live cell microscopy (Nelson et al , 2009 ). Foci frequency measurements quantitatively confirmed the prediction from the stochastic model. More importantly, we found that many individual foci in both telomere‐ and stress‐dependent senescence had short lifespans with half‐lives below 15 h. Feedback loop inhibition reduced only the frequencies of short‐lived DNA damage foci in accordance with the hypothesis that ROS production contributed to DDR by constant replenishment of short‐lived DNA damage foci. Finally, we inhibited signalling through the loop at different time points after induction of senescence by ionizing radiation and measured ROS levels, DNA damage foci frequencies and proliferation markers. Treatments with the MAPK14 inhibitor SB203580 or the free radical scavenger PBN were used to block the loop. The results quantitatively confirmed the model prediction and indicated that the feedback loop between DDR and ROS production was both necessary and sufficient to maintain cell cycle arrest for at least 6–10 days after induction of senescence. Interestingly, the loop was still active at later time points and in deep senescence, but proliferation arrest was then stabilized by additional factor(s). This indicated that certain features of the senescent phenotype‐like ROS production that might be responsible for the negative impact of senescent cells into their tissue environment can be successfully inhibited even in deep senescence. This may prove relevant for novel therapeutic studies aiming to modulate intracellular ROS levels in both aging and cancer. The sustained activation of CDKN1A (p21/Waf1/Cip1) by a DNA damage response induces mitochondrial dysfunction and reactive oxygen species (ROS) production via signalling through CDKN1A‐GADD45A‐MAPK14‐ GRB2‐TGFBR2‐TGFbeta in senescing primary human and mouse cells in vitro and in vivo. Enhanced ROS production in senescing cells generates additional DNA damage. Although this damage is repairable and transient, it elevates the average levels of DNA damage response permanently, thus forming a positive feedback loop. This loop is necessary and sufficient to maintain the stability of growth arrest until a ‘point of no return’ is reached during establishment of senescence.

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. Recent findings are incorporated into a new mathematical model of the plant circadian clock, revealing a complex circuit structure comprised of multiple negative feedback loops, and predicting a repressive role for a key regulator, TOC1, which the authors confirm experimentally. Synopsis Recent findings are incorporated into a new mathematical model of the plant circadian clock, revealing a complex circuit structure comprised of multiple negative feedback loops, and predicting a repressive role for a key regulator, TOC1, which the authors confirm experimentally. The feedback structure of the plant clock's evening loop was reconstructed based on multiple data, and is now represented by the evening complex (ELF3‐ELF4‐LUX), which represses transcription from the ELF4 and LUX promoters. Computational analysis of timeseries data from mutant plants predicts that TOC1 is a repressor of the key morning genes LHY and CCA1 , not an activator. Analysis of LHY and CCA1 expression in TOC1 gain‐ and loss‐of‐function plants confirms this prediction. Light induction of LHY and CCA1 expression is predicted to determine the clock's response to brief light pulses, matching the observed phase‐response curve. The evening complex controls LHY and CCA1 expression by a double‐negative connection, rather than direct activation, forming part of a three‐component repressilator circuit, which is itself only part of the more complex circuit of the clock system.

The growth rate‐dependent regulation of cell size, ribosomal content, and metabolic efficiency follows a common pattern in unicellular organisms: with increasing growth rates, cell size and ribosomal content increase and a shift to energetically inefficient metabolism takes place. The latter two phenomena are also observed in fast growing tumour cells and cell lines. These patterns suggest a fundamental principle of design. In biology such designs can often be understood as the result of the optimization of fitness. Here we show that in basic models of self‐replicating systems these patterns are the consequence of maximizing the growth rate. Whereas most models of cellular growth consider a part of physiology, for instance only metabolism, the approach presented here integrates several subsystems to a complete self‐replicating system. Such models can yield fundamentally different optimal strategies. In particular, it is shown how the shift in metabolic efficiency originates from a tradeoff between investments in enzyme synthesis and metabolic yields for alternative catabolic pathways. The models elucidate how the optimization of growth by natural selection shapes growth strategies. A basic model of a self‐replicating cell, when optimized for growth rate,displays regulation of of properties, like the ribosomal content and the surface/volume ratio, similar to those observed in real cells An extension of this model predicts that a shift from the use of high‐efficiency to low‐efficiency central metabolic pathways should take place when the substrate concentration, and concomitantly the growth rate, increase