Showing papers in "Molecular Systems Biology in 2014"

Potential of fecal microbiota for early‐stage detection of colorectal cancer

Abstract: Several bacterial species have been implicated in the development of colorectal carcinoma (CRC), but CRC-associated changes of fecal microbiota and their potential for cancer screening remain to be explored. Here, we used metagenomic sequencing of fecal samples to identify taxonomic markers that distinguished CRC patients from tumor-free controls in a study population of 156 participants. Accuracy of metagenomic CRC detection was similar to the standard fecal occult blood test (FOBT) and when both approaches were combined, sensitivity improved > 45% relative to the FOBT, while maintaining its specificity. Accuracy of metagenomic CRC detection did not differ significantly between early- and late-stage cancer and could be validated in independent patient and control populations (N = 335 )f rom different countries. CRC-associated changes in the fecal microbiome at least partially reflected microbial community composition at the tumor itself, indicating that observed gene pool differences may reveal tumor-related host–microbe interactions. Indeed, we deduced a metabolic shift from fiber degradation in controls to utilization of host carbohydrates and amino acids in CRC patients, accompanied by an increase of lipopolysaccharide metabolism.

Ultrasensitive proteome analysis using paramagnetic bead technology

Abstract: In order to obtain a systems-level understanding of a complex biological system, detailed proteome information is essential. Despite great progress in proteomics technologies, thorough interrogation of the proteome from quantity-limited biological samples is hampered by inefficiencies during processing. To address these challenges, here we introduce a novel protocol using paramagnetic beads, termed Single-Pot Solid-Phase-enhanced Sample Preparation (SP3). SP3 provides a rapid and unbiased means of proteomic sample preparation in a single tube that facilitates ultrasensitive analysis by outperforming existing protocols in terms of efficiency, scalability, speed, throughput, and flexibility. To illustrate these benefits, characterization of 1,000 HeLa cells and single Drosophila embryos is used to establish that SP3 provides an enhanced platform for profiling proteomes derived from sub-microgram amounts of material. These data present a first view of developmental stage-specific proteome dynamics in Drosophila at a single-embryo resolution, permitting characterization of inter-individual expression variation. Together, the findings of this work position SP3 as a superior protocol that facilitates exciting new directions in multiple areas of proteomics ranging from developmental biology to clinical applications.

Emergence of robust growth laws from optimal regulation of ribosome synthesis

Abstract: Bacteria must constantly adapt their growth to changes in nutrient availability; yet despite large-scale changes in protein expression associated with sensing, adaptation, and processing different environmental nutrients, simple growth laws connect the ribosome abundance and the growth rate. Here, we investigate the origin of these growth laws by analyzing the features of ribosomal regulation that coordinate proteome-wide expression changes with cell growth in a variety of nutrient conditions in the model organism Escherichia coli. We identify supply-driven feedforward activation of ribosomal protein synthesis as the key regulatory motif maximizing amino acid flux, and autonomously guiding a cell to achieve optimal growth in different environments. The growth laws emerge naturally from the robust regulatory strategy underlying growth rate control, irrespective of the details of the molecular implementation. The study highlights the interplay between phenomenological modeling and molecular mechanisms in uncovering fundamental operating constraints, with implications for endogenous and synthetic design of microorganisms.

Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling

Abstract: Genome-scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype–phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task-driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type-specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty-two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.

Measuring error rates in genomic perturbation screens: gold standards for human functional genomics

Abstract: Technological advancement has opened the door to systematic genetics in mammalian cells. Genome-scale loss-of-function screens can assay fitness defects induced by partial gene knockdown, using RNA interference, or complete gene knockout, using new CRISPR techniques. These screens can reveal the basic blueprint required for cellular proliferation. Moreover, comparing healthy to cancerous tissue can uncover genes that are essential only in the tumor; these genes are targets for the development of specific anticancer therapies. Unfortunately, progress in this field has been hampered by off-target effects of perturbation reagents and poorly quantified error rates in large-scale screens. To improve the quality of information derived from these screens, and to provide a framework for understanding the capabilities and limitations of CRISPR technology, we derive gold-standard reference sets of essential and nonessential genes, and provide a Bayesian classifier of gene essentiality that outperforms current methods on both RNAi and CRISPR screens. Our results indicate that CRISPR technology is more sensitive than RNAi and that both techniques have nontrivial false discovery rates that can be mitigated by rigorous analytical methods.

Causal signals between codon bias, mRNA structure, and the efficiency of translation and elongation

Abstract: Ribosome profiling data report on the distribution of translating ribosomes, at steady-state, with codon-level resolution. We present a robust method to extract codon translation rates and protein synthesis rates from these data, and identify causal features associated with elongation and translation efficiency in physiological conditions in yeast. We show that neither elongation rate nor translational efficiency is improved by experimental manipulation of the abundance or body sequence of the rare AGG tRNA. Deletion of three of the four copies of the heavily used ACA tRNA shows a modest efficiency decrease that could be explained by other rate-reducing signals at gene start. This suggests that correlation between codon bias and efficiency arises as selection for codons to utilize translation machinery efficiently in highly translated genes. We also show a correlation between efficiency and RNA structure calculated both computationally and from recent structure probing data, as well as the Kozak initiation motif, which may comprise a mechanism to regulate initiation.

Metabolic shifts toward glutamine regulate tumor growth, invasion and bioenergetics in ovarian cancer

Abstract: Glutamine can play a critical role in cellular growth in multiple cancers. Glutamine‐addicted cancer cells are dependent on glutamine for viability, and their metabolism is reprogrammed for glutamine utilization through the tricarboxylic acid (TCA) cycle. Here, we have uncovered a missing link between cancer invasiveness and glutamine dependence. Using isotope tracer and bioenergetic analysis, we found that low‐invasive ovarian cancer (OVCA) cells are glutamine independent, whereas high‐invasive OVCA cells are markedly glutamine dependent. Consistent with our findings, OVCA patients’ microarray data suggest that glutaminolysis correlates with poor survival. Notably, the ratio of gene expression associated with glutamine anabolism versus catabolism has emerged as a novel biomarker for patient prognosis. Significantly, we found that glutamine regulates the activation of STAT3, a mediator of signaling pathways which regulates cancer hallmarks in invasive OVCA cells. Our findings suggest that a combined approach of targeting high‐invasive OVCA cells by blocking glutamine9s entry into the TCA cycle, along with targeting low‐invasive OVCA cells by inhibiting glutamine synthesis and STAT3 may lead to potential therapeutic approaches for treating OVCAs.

Acetylation dynamics and stoichiometry in Saccharomyces cerevisiae.

Abstract: Lysine acetylation is a frequently occurring posttranslational modification; however, little is known about the origin and regulation of most sites. Here we used quantitative mass spectrometry to analyze acetylation dynamics and stoichiometry in Saccharomyces cerevisiae. We found that acetylation accumulated in growth-arrested cells in a manner that depended on acetyl-CoA generation in distinct subcellular compartments. Mitochondrial acetylation levels correlated with acetyl-CoA concentration in vivo and acetyl-CoA acetylated lysine residues nonenzymatically in vitro. We developed a method to estimate acetylation stoichiometry and found that the vast majority of mitochondrial and cytoplasmic acetylation had a very low stoichiometry. However, mitochondrial acetylation occurred at a significantly higher basal level than cytoplasmic acetylation, consistent with the distinct acetylation dynamics and higher acetyl-CoA concentration in mitochondria. High stoichiometry acetylation occurred mostly on histones, proteins present in histone acetyltransferase and deacetylase complexes, and on transcription factors. These data show that a majority of acetylation occurs at very low levels in exponentially growing yeast and is uniformly affected by exposure to acetyl-CoA.

Phenotypic bistability in Escherichia coli's central carbon metabolism

Abstract: Fluctuations in intracellular molecule abundance can lead to distinct, coexisting phenotypes in isogenic populations. Although metabolism continuously adapts to unpredictable environmental changes, and although bistability was found in certain substrate-uptake pathways, central carbon metabolism is thought to operate deterministically. Here, we combine experiment and theory to demonstrate that a clonal Escherichia coli population splits into two stochastically generated phenotypic subpopulations after glucose-gluconeogenic substrate shifts. Most cells refrain from growth, entering a dormant persister state that manifests as a lag phase in the population growth curve. The subpopulation-generating mechanism resides at the metabolic core, overarches the metabolic and transcriptional networks, and only allows the growth of cells initially achieving sufficiently high gluconeogenic flux. Thus, central metabolism does not ensure the gluconeogenic growth of individual cells, but uses a population-level adaptation resulting in responsive diversification upon nutrient changes.

Multi-input CRISPR/Cas genetic circuits that interface host regulatory networks

Abstract: Genetic circuits require many regulatory parts in order to implement signal processing or execute algorithms in cells. A potentially scalable approach is to use dCas9, which employs small guide RNAs (sgRNAs) to repress genetic loci via the programmability of RNA:DNA base pairing. To this end, we use dCas9 and designed sgRNAs to build transcriptional logic gates and connect them to perform computation in living cells. We constructed a set of NOT gates by designing five synthetic Escherichia coli σ70 promoters that are repressed by corresponding sgRNAs, and these interactions do not exhibit crosstalk between each other. These sgRNAs exhibit high on-target repression (56- to 440-fold) and negligible off-target interactions (< 1.3-fold). These gates were connected to build larger circuits, including the Boolean-complete NOR gate and a 3-gate circuit consisting of four layered sgRNAs. The synthetic circuits were connected to the native E. coli regulatory network by designing output sgRNAs to target an E. coli transcription factor (malT). This converts the output of a synthetic circuit to a switch in cellular phenotype (sugar utilization, chemotaxis, phage resistance).

The functional diversity of protein lysine methylation

Abstract: Large-scale characterization of post-translational modifications (PTMs), such as phosphorylation, acetylation and ubiquitination, has highlighted their importance in the regulation of a myriad of signaling events. While high-throughput technologies have tremendously helped cataloguing the proteins modified by these PTMs, the identification of lysine-methylated proteins, a PTM involving the transfer of one, two or three methyl groups to the e-amine of a lysine side chain, has lagged behind. While the initial findings were focused on the methylation of histone proteins, several studies have recently identified novel non-histone lysine-methylated proteins. This review provides a compilation of all lysine methylation sites reported to date. We also present key examples showing the impact of lysine methylation and discuss the circuitries wired by this important PTM.

Efficient search, mapping, and optimization of multi‐protein genetic systems in diverse bacteria

Abstract: Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs.

Rapid neurogenesis through transcriptional activation in human stem cells

Abstract: Advances in cellular reprogramming and stem cell differentiation now enable ex vivo studies of human neuronal differentiation. However, it remains challenging to elucidate the underlying regulatory programs because differentiation protocols are laborious and often result in low neuron yields. Here, we overexpressed two Neurogenin transcription factors in human-induced pluripotent stem cells and obtained neurons with bipolar morphology in 4 days, at greater than 90% purity. The high purity enabled mRNA and microRNA expression profiling during neurogenesis, thus revealing the genetic programs involved in the rapid transition from stem cell to neuron. The resulting cells exhibited transcriptional, morphological and functional signatures of differentiated neurons, with greatest transcriptional similarity to prenatal human brain samples. Our analysis revealed a network of key transcription factors and microRNAs that promoted loss of pluripotency and rapid neurogenesis via progenitor states. Perturbations of key transcription factors affected homogeneity and phenotypic properties of the resulting neurons, suggesting that a systems-level view of the molecular biology of differentiation may guide subsequent manipulation of human stem cells to rapidly obtain diverse neuronal types.

Non-enzymatic glycolysis and pentose phosphate pathway-like reactions in a plausible Archean ocean

Abstract: The reaction sequences of central metabolism, glycolysis and the pentose phosphate pathway provide essential precursors for nucleic acids, amino acids and lipids. However, their evolutionary origins are not yet understood. Here, we provide evidence that their structure could have been fundamentally shaped by the general chemical environments in earth’s earliest oceans. We reconstructed potential scenarios for oceans of the prebiotic Archean based on the composition of early sediments. We report that the resultant reaction milieu catalyses the interconversion of metabolites that in modern organisms constitute glycolysis and the pentose phosphate pathway. The 29 observed reactions include the formation and/or interconversion of glucose, pyruvate, the nucleic acid precursor ribose-5-phosphate and the amino acid precursor erythrose-4-phosphate, antedating reactions sequences similar to that used by the metabolic pathways. Moreover, the Archean ocean mimetic increased the stability of the phosphorylated intermediates and accelerated the rate of intermediate reactions and pyruvate production. The catalytic capacity of the reconstructed ocean milieu was attributable to its metal content. The reactions were particularly sensitive to ferrous iron Fe(II), which is understood to have had high concentrations in the Archean oceans. These observations reveal that reaction sequences that constitute central carbon metabolism could have been constrained by the iron-rich oceanic environment of the early Archean. The origin of metabolism could thus date back to the prebiotic world.

Robust synchronization of coupled circadian and cell cycle oscillators in single mammalian cells

Abstract: Circadian cycles and cell cycles are two fundamental periodic processes with a period in the range of 1 day. Consequently, coupling between such cycles can lead to synchronization. Here, we estimated the mutual interactions between the two oscillators by time-lapse imaging of single mammalian NIH3T3 fibroblasts during several days. The analysis of thousands of circadian cycles in dividing cells clearly indicated that both oscillators tick in a 1:1 mode-locked state, with cell divisions occurring tightly 5 h before the peak in circadian Rev-Erbα-YFP reporter expression. In principle, such synchrony may be caused by either unidirectional or bidirectional coupling. While gating of cell division by the circadian cycle has been most studied, our data combined with stochastic modeling unambiguously show that the reverse coupling is predominant in NIH3T3 cells. Moreover, temperature, genetic, and pharmacological perturbations showed that the two interacting cellular oscillators adopt a synchronized state that is highly robust over a wide range of parameters. These findings have implications for circadian function in proliferative tissues, including epidermis, immune cells, and cancer.

Quantitative analysis of mammalian translation initiation sites by FACS-seq.

Abstract: An approach combining fluorescence-activated cell sorting and high-throughput DNA sequencing (FACS-seq) was employed to determine the efficiency of start codon recognition for all possible translation initiation sites (TIS) utilizing AUG start codons. Using FACS-seq, we measured translation from a genetic reporter library representing all 65,536 possible TIS sequences spanning the −6 to +5 positions. We found that the motif RYMRMVAUGGC enhanced start codon recognition and translation efficiency. However, dinucleotide interactions, which cannot be conveyed by a single motif, were also important for modeling TIS efficiency. Our dataset combined with modeling allowed us to predict genome-wide translation initiation efficiency for all mRNA transcripts. Additionally, we screened somatic TIS mutations associated with tumorigenesis to identify candidate driver mutations consistent with known tumor expression patterns. Finally, we implemented a quantitative leaky scanning model to predict alternative initiation sites that produce truncated protein isoforms and compared predictions with ribosome footprint profiling data. The comprehensive analysis of the TIS sequence space enables quantitative predictions of translation initiation based on genome sequence.

A ‘resource allocator’ for transcription based on a highly fragmented T7 RNA polymerase

Abstract: Synthetic genetic systems share resources with the host, including machinery for transcription and translation. Phage RNA polymerases (RNAPs) decouple transcription from the host and generate high expression. However, they can exhibit toxicity and lack accessory proteins (r factors and activators) that enable switching between different promoters and modulation of activity. Here, we show that T7 RNAP (883 amino acids) can be divided into four fragments that have to be co-expressed to function. The DNA-binding loop is encoded in a C-terminal 285-aa ‘r fragment’, and fragments with different specificity can direct the remaining 601-aa ‘core fragment’ to different promoters. Using these parts, we have built a resource allocator that sets the core fragment concentration, which is then shared by multiple r fragments. Adjusting the concentration of the core fragment sets the maximum transcriptional capacity available to a synthetic system. Further, positive and negative regulation is implemented using a 67-aa N-terminal ‘a fragment’ and a null (inactivated) r fragment, respectively. The a fragment can be fused to recombinant proteins to make promoters responsive to their levels. These parts provide a toolbox to allocate transcriptional resources via different schemes, which we demonstrate by building a system which adjusts promoter activity to compensate for the difference in copy number of two plasmids.

Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases

Abstract: Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non-demented controls, we investigated global disruptions in the co-regulation of genes in two neurodegenerative diseases, late-onset Alzheimer’s disease (AD) and Huntington’s disease (HD). We identified networks of differentially co-expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former dominant for both AD and HD and both patterns replicating in independent human cohorts of AD and aging. When aligning networks of DC patterns and physical interactions, we identified a 242-gene subnetwork enriched for independent AD/HD signatures. This subnetwork revealed a surprising dichotomy of gained/lost correlations among two inter-connected processes, chromatin organization and neural differentiation, and included DNA methyltransferases, DNMT1 and DNMT3A, of which we predicted the former but not latter as a key regulator. To validate the inter-connection of these two processes and our key regulator prediction, we generated two brain-specific knockout (KO) mice and show that Dnmt1 KO signature significantly overlaps with the subnetwork (P = 3.1 × 10 � 12 ), while Dnmt3a KO signature does not (P = 0.017).

A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration

Abstract: Over the last decade, the field of cancer metabolism has mainly focused on studying the role of tumorigenic metabolic rewiring in supporting cancer proliferation. Here, we perform the first genome-scale computational study of the metabolic underpinnings of cancer migration. We build genome-scale metabolic models of the NCI-60 cell lines that capture the Warburg effect (aerobic glycolysis) typically occurring in cancer cells. The extent of the Warburg effect in each of these cell line models is quantified by the ratio of glycolytic to oxidative ATP flux (AFR), which is found to be highly positively associated with cancer cell migration. We hence predicted that targeting genes that mitigate the Warburg effect by reducing the AFR may specifically inhibit cancer migration. By testing the anti-migratory effects of silencing such 17 top predicted genes in four breast and lung cancer cell lines, we find that up to 13 of these novel predictions significantly attenuate cell migration either in all or one cell line only, while having almost no effect on cell proliferation. Furthermore, in accordance with the predictions, a significant reduction is observed in the ratio between experimentally measured ECAR and OCR levels following these perturbations. Inhibiting anti-migratory targets is a promising future avenue in treating cancer since it may decrease cytotoxic-related side effects that plague current anti-proliferative treatments. Furthermore, it may reduce cytotoxic-related clonal selection of more aggressive cancer cells and the likelihood of emerging resistance.

Protein acetylation affects acetate metabolism, motility and acid stress response in Escherichia coli

Abstract: Although protein acetylation is widely observed, it has been associated with few specific regulatory functions making it poorly understood. To interrogate its functionality, we analyzed the acetylome in Escherichia coli knockout mutants of cobB, the only known sirtuin-like deacetylase, and patZ, the best-known protein acetyltransferase. For four growth conditions, more than 2,000 unique acetylated peptides, belonging to 809 proteins, were identified and differentially quantified. Nearly 65% of these proteins are related to metabolism. The global activity of CobB contributes to the deacetylation of a large number of substrates and has a major impact on physiology. Apart from the regulation of acetyl-CoA synthetase, we found that CobB-controlled acetylation of isocitrate lyase contributes to the fine-tuning of the glyoxylate shunt. Acetylation of the transcription factor RcsB prevents DNA binding, activating flagella biosynthesis and motility, and increases acid stress susceptibility. Surprisingly, deletion of patZ increased acetylation in acetate cultures, which suggests that it regulates the levels of acetylating agents. The results presented offer new insights into functional roles of protein acetylation in metabolic fitness and global cell regulation.

Integrated systems analysis reveals a molecular network underlying autism spectrum disorders

Abstract: Autism is a complex disease whose etiology remains elusive. We integrated previously and newly generated data and developed a systems framework involving the interactome, gene expression and genome sequencing to identify a protein interaction module with members strongly enriched for autism candidate genes. Sequencing of 25 patients confirmed the involvement of this module in autism, which was subsequently validated using an independent cohort of over 500 patients. Expression of this module was dichotomized with a ubiquitously expressed subcomponent and another subcomponent preferentially expressed in the corpus callosum, which was significantly affected by our identified mutations in the network center. RNA-sequencing of the corpus callosum from patients with autism exhibited extensive gene mis-expression in this module, and our immunochemical analysis showed that the human corpus callosum is predominantly populated by oligodendrocyte cells. Analysis of functional genomic data further revealed a significant involvement of this module in the development of oligodendrocyte cells in mouse brain. Our analysis delineates a natural network involved in autism, helps uncover novel candidate genes for this disease and improves our understanding of its molecular pathology.

Cell cycle population effects in perturbation studies

Abstract: Growth condition perturbation or gene function disruption are commonly used strategies to study cellular systems. Although it is widely appreciated that such experiments may involve indirect effects, these frequently remain uncharacterized. Here, analysis of functionally unrelated Saccharyomyces cerevisiae deletion strains reveals a common gene expression signature. One property shared by these strains is slower growth, with increased presence of the signature in more slowly growing strains. The slow growth signature is highly similar to the environmental stress response (ESR), an expression response common to diverse environmental perturbations. Both environmental and genetic perturbations result in growth rate changes. These are accompanied by a change in the distribution of cells over different cell cycle phases. Rather than representing a direct expression response in single cells, both the slow growth signature and ESR mainly reflect a redistribution of cells over different cell cycle phases, primarily characterized by an increase in the G1 population. The findings have implications for any study of perturbation that is accompanied by growth rate changes. Strategies to counter these effects are presented and discussed.

Alternative polyadenylation diversifies post-transcriptional regulation by selective RNA-protein interactions

Abstract: Recent research has uncovered extensive variability in the boundaries of transcript isoforms, yet the functional consequences of this variation remain largely unexplored. Here, we systematically discriminate between the molecular phenotypes of overlapping coding and non-coding transcriptional events from each genic locus using a novel genome-wide, nucleotide-resolution technique to quantify the half-lives of 3' transcript isoforms in yeast. Our results reveal widespread differences in stability among isoforms for hundreds of genes in a single condition, and that variation of even a single nucleotide in the 3' untranslated region (UTR) can affect transcript stability. While previous instances of negative associations between 3' UTR length and transcript stability have been reported, here, we find that shorter isoforms are not necessarily more stable. We demonstrate the role of RNA-protein interactions in conditioning isoform-specific stability, showing that PUF3 binds and destabilizes specific polyadenylation isoforms. Our findings indicate that although the functional elements of a gene are encoded in DNA sequence, the selective incorporation of these elements into RNA through transcript boundary variation allows a single gene to have diverse functional consequences.

Digital cell quantification identifies global immune cell dynamics during influenza infection

Abstract: Hundreds of immune cell types work in coordination to maintain tissue homeostasis. Upon infection, dramatic changes occur with the localization, migration, and proliferation of the immune cells to first alert the body of the danger, confine it to limit spreading, and finally extinguish the threat and bring the tissue back to homeostasis. Since current technologies can follow the dynamics of only a limited number of cell types, we have yet to grasp the full complexity of global in vivo cell dynamics in normal developmental processes and disease. Here, we devise a computational method, digital cell quantification (DCQ), which combines genome‐wide gene expression data with an immune cell compendium to infer in vivo changes in the quantities of 213 immune cell subpopulations. DCQ was applied to study global immune cell dynamics in mice lungs at ten time points during 7 days of flu infection. We find dramatic changes in quantities of 70 immune cell types, including various innate, adaptive, and progenitor immune cells. We focus on the previously unreported dynamics of four immune dendritic cell subtypes and suggest a specific role for CD103+ CD11b− DCs in early stages of disease and CD8+ pDC in late stages of flu infection. ![][1] A method is presented to infer the changes in the quantities of 213 immune cell types within a complex in vivo cell population. High‐resolution temporal analysis during flu infection reveals specific roles of dendritic cell subtypes in early and late disease phases. Mol Syst Biol. (2014) 10: 720 [1]: /embed/graphic-1.gif

Periodic mRNA synthesis and degradation co-operate during cell cycle gene expression

Abstract: During the cell cycle, the levels of hundreds of mRNAs change in a periodic manner, but how this is achieved by alterations in the rates of mRNA synthesis and degradation has not been studied systematically. Here, we used metabolic RNA labeling and comparative dynamic transcriptome analysis (cDTA) to derive mRNA synthesis and degradation rates every 5 min during three cell cycle periods of the yeast Saccharomyces cerevisiae. A novel statistical model identified 479 genes that show periodic changes in mRNA synthesis and generally also periodic changes in their mRNA degradation rates. Peaks of mRNA degradation generally follow peaks of mRNA synthesis, resulting in sharp and high peaks of mRNA levels at defined times during the cell cycle. Whereas the timing of mRNA synthesis is set by upstream DNA motifs and their associated transcription factors (TFs), the synthesis rate of a periodically expressed gene is apparently set by its core promoter.

An organ boundary-enriched gene regulatory network uncovers regulatory hierarchies underlying axillary meristem initiation.

Abstract: Gene regulatory networks (GRNs) control development via cell type-specific gene expression and interactions between transcription factors (TFs) and regulatory promoter regions. Plant organ boundaries separate lateral organs from the apical meristem and harbor axillary meristems (AMs). AMs, as stem cell niches, make the shoot a ramifying system. Although AMs have important functions in plant development, our knowledge of organ boundary and AM formation remains rudimentary. Here, we generated a cellular-resolution genomewide gene expression map for low-abundance Arabidopsis thaliana organ boundary cells and constructed a genomewide protein–DNA interaction map focusing on genes affecting boundary and AM formation. The resulting GRN uncovers transcriptional signatures, predicts cellular functions, and identifies promoter hub regions that are bound by many TFs. Importantly, further experimental studies determined the regulatory effects of many TFs on their targets, identifying regulators and regulatory relationships in AM initiation. This systems biology approach thus enhances our understanding of a key developmental process.

Pathway connectivity and signaling coordination in the yeast stress-activated signaling network

Abstract: Stressed cells coordinate a multi-faceted response spanning many levels of physiology. Yet knowledge of the complete stressactivated regulatory network as well as design principles for signal integration remains incomplete. We developed an experimental and computational approach to integrate available protein interaction data with gene fitness contributions, mutant transcriptome profiles, and phospho-proteome changes in cells responding to salt stress, to infer the salt-responsive signaling network in yeast. The inferred subnetwork presented many novel predictions by implicating new regulators, uncovering unrecognized crosstalk between known pathways, and pointing to previously unknown ‘hubs’ of signal integration. We exploited these predictions to show that Cdc14 phosphatase is a central hub in the network and that modification of RNA polymerase II coordinates induction of stressdefense genes with reduction of growth-related transcripts. We find that the orthologous human network is enriched for cancercausing genes, underscoring the importance of the subnetwork’s predictions in understanding stress biology.

Specificity, propagation, and memory of pericentric heterochromatin

Abstract: The cell establishes heritable patterns of active and silenced chromatin via interacting factors that set, remove, and read epigenetic marks. To understand how the underlying networks operate, we have dissected transcriptional silencing in pericentric heterochromatin (PCH) of mouse fibroblasts. We assembled a quantitative map for the abundance and interactions of 16 factors related to PCH in living cells and found that stably bound complexes of the histone methyltransferase SUV39H1/2 demarcate the PCH state. From the experimental data, we developed a predictive mathematical model that explains how chromatin-bound SUV39H1/2 complexes act as nucleation sites and propagate a spatially confined PCH domain with elevated histone H3 lysine 9 trimethylation levels via chromatin dynamics. This “nucleation and looping” mechanism is particularly robust toward transient perturbations and stably maintains the PCH state. These features make it an attractive model for establishing functional epigenetic domains throughout the genome based on the localized immobilization of chromatin-modifying enzymes.

An integrative, multi‐scale, genome‐wide model reveals the phenotypic landscape of Escherichia coli

Abstract: Given the vast behavioral repertoire and biological complexity of even the simplest organisms, accurately predicting phenotypes in novel environments and unveiling their biological organization is a challenging endeavor. Here, we present an integrative modeling methodology that unifies under a common framework the various biological processes and their interactions across multiple layers. We trained this methodology on an extensive normalized compendium for the gram-negative bacterium Escherichia coli, which incorporates gene expression data for genetic and environmental perturbations, transcriptional regulation, signal transduction, and metabolic pathways, as well as growth measurements. Comparison with measured growth and high-throughput data demonstrates the enhanced ability of the integrative model to predict phenotypic outcomes in various environmental and genetic conditions, even in cases where their underlying functions are under-represented in the training set. This work paves the way toward integrative techniques that extract knowledge from a variety of biological data to achieve more than the sum of their parts in the context of prediction, analysis, and redesign of biological systems.

A chromatin structure-based model accurately predicts DNA replication timing in human cells

Abstract: The metazoan genome is replicated in precise cell lineage-specific temporal order. However, the mechanism controlling this orchestrated process is poorly understood as no molecular mechanisms have been identified that actively regulate the firing sequence of genome replication. Here, we develop a mechanistic model of genome replication capable of predicting, with accuracy rivaling experimental repeats, observed empirical replication timing program in humans. In our model, replication is initiated in an uncoordinated (time-stochastic) manner at well-defined sites. The model contains, in addition to the choice of the genomic landmark that localizes initiation, only a single adjustable parameter of direct biological relevance: the number of replication forks. We find that DNase-hypersensitive sites are optimal and independent determinants of DNA replication initiation. We demonstrate that the DNA replication timing program in human cells is a robust emergent phenomenon that, by its very nature, does not require a regulatory mechanism determining a proper replication initiation firing sequence.