Virus-host protein co-expression networks reveal temporal organization and strategies of viral infection.

Viral replication is a complex dynamical process involving the global remodeling of the host cellular machinery across several stages. In this study, we provide a unified view of the virus-host interaction at the proteome level reconstructing protein co-expression networks from quantitative temporal data of four large DNA viruses. We take advantage of a formal framework, the theory of competing networks, to describe the viral infection as a dynamical system taking place on a network of networks where perturbations induced by viral proteins spread to hijack the host proteome for the virus benefit. Our methodology demonstrates how the viral replication cycle can be effectively examined as a complex interaction between protein networks, providing useful insights into the viral and host's temporal organization and strategies, key protein nodes targeted by the virus and dynamical bottlenecks during the course of the infection.

Seasonal changes dominate long-term variability of the urban air microbiome across space and time.

Compared to soil or aquatic ecosystems, the atmosphere is still an underexplored environment for microbial diversity. In this study, we surveyed the composition, variability and sources of microbes (bacteria and fungi) in the near surface atmosphere of a highly populated area, spanning ~ 4,000 Km2 around the city center of Madrid (Spain), in different seasonal periods along two years. We found a core of abundant bacterial genera robust across space and time, most of soil origin, while fungi were more sensitive to environmental conditions. Microbial communities showed clear seasonal patterns driven by variability of environmental factors, mainly temperature and accumulated rain, while local sources played a minor role. We also identified taxa in both groups characteristic of seasonal periods, but not of specific sampling sites or plant coverage. The present study suggests that the near surface atmosphere of urban environments contains an ecosystem stable across relatively large spatial and temporal scales, with a rather homogenous composition, modulated by climatic variations. As such, it contributes to our understanding of the long-term changes associated to the human exposome in the air of highly populated areas.

Mitochondrial levels determine variability in cell death by modulating apoptotic gene expression.

Fractional killing is the main cause of tumour resistance to chemotherapy. This phenomenon is observed even in genetically identical cancer cells in homogeneous microenvironments. To understand this variable resistance, here we investigate the individual responses to TRAIL in a clonal population of HeLa cells using live-cell microscopy and computational modelling. We show that the cellular mitochondrial content determines the apoptotic fate and modulates the time to death, cells with higher mitochondrial content are more prone to die. We find that all apoptotic protein levels are modulated by the mitochondrial content. Modelling the apoptotic network, we demonstrate that these correlations, and especially the differential control of anti- and pro-apoptotic protein pairs, confer mitochondria a powerful discriminatory capacity of apoptotic fate. We find a similar correlation between the mitochondria and apoptotic proteins in colon cancer biopsies. Our results reveal a different role of mitochondria in apoptosis as the global regulator of apoptotic protein expression.

Validation of the Hirst-Type Spore Trap for Simultaneous Monitoring of Prokaryotic and Eukaryotic Biodiversities in Urban Air Samples by Next-Generation Sequencing.

Pollen, fungi, and bacteria are the main microscopic biological entities present in outdoor air, causing allergy symptoms and disease transmission and having a significant role in atmosphere dynamics. Despite their relevance, a method for monitoring simultaneously these biological particles in metropolitan environments has not yet been developed. Here, we assessed the use of the Hirst-type spore trap to characterize the global airborne biota by high-throughput DNA sequencing, selecting regions of the 16S rRNA gene and internal transcribed spacer for the taxonomic assignment. We showed that aerobiological communities are well represented by this approach. The operational taxonomic units (OTUs) of two traps working synchronically compiled >87% of the total relative abundance for bacterial diversity collected in each sampler, >89% for fungi, and >97% for pollen. We found a good correspondence between traditional characterization by microscopy and genetic identification, obtaining more-accurate taxonomic assignments and detecting a greater diversity using the latter. We also demonstrated that DNA sequencing accurately detects differences in biodiversity between samples. We concluded that high-throughput DNA sequencing applied to aerobiological samples obtained with Hirst spore traps provides reliable results and can be easily implemented for monitoring prokaryotic and eukaryotic entities present in the air of urban areas.IMPORTANCE Detection, monitoring, and characterization of the wide diversity of biological entities present in the air are difficult tasks that require time and expertise in different disciplines. We have evaluated the use of the Hirst spore trap (an instrument broadly employed in aerobiological studies) to detect and identify these organisms by DNA-based analyses. Our results showed a consistent collection of DNA and a good concordance with traditional methods for identification, suggesting that these devices can be used as a tool for continuous monitoring of the airborne biodiversity, improving taxonomic resolution and characterization together. They are also suitable for acquiring novel DNA amplicon-based information in order to gain a better understanding of the biological particles present in a scarcely known environment such as the air.

Transcription factor levels enable metabolic diversification of single cells of environmental bacteria.

Transcriptional noise is a necessary consequence of the molecular events that drive gene expression in prokaryotes. However, some environmental microorganisms that inhabit polluted sites, for example, the m-xylene degrading soil bacterium Pseudomonas putida mt-2 seem to have co-opted evolutionarily such a noise for deploying a metabolic diversification strategy that allows a cautious exploration of new chemical landscapes. We have examined this phenomenon under the light of deterministic and stochastic models for activation of the main promoter of the master m-xylene responsive promoter of the system (Pu) by its cognate transcriptional factor (XylR). These analyses consider the role of co-factors for Pu activation and determinants of xylR mRNA translation. The model traces the onset and eventual disappearance of the bimodal distribution of Pu activity along time to the growth-phase dependent abundance of XylR itself, that is, very low in exponentially growing cells and high in stationary. This tenet was validated by examining the behaviour of a Pu-GFP fusion in a P. putida strain in which xylR expression was engineered under the control of an IPTG-inducible system. This work shows how a relatively simple regulatory scenario (for example, growth-phase dependent expression of a limiting transcription factor) originates a regime of phenotypic diversity likely to be advantageous in competitive environmental settings.

Mitochondria and the non-genetic origins of cell-to-cell variability: More is different.

Gene expression activity is heterogeneous in a population of isogenic cells. Identifying the molecular basis of this variability will improve our understanding of phenomena like tumor resistance to drugs, virus infection, or cell fate choice. The complexity of the molecular steps and machines involved in transcription and translation could introduce sources of randomness at many levels, but a common constraint to most of these processes is its energy dependence. In eukaryotic cells, most of this energy is provided by mitochondria. A clonal population of cells may show a large variability in the number and functionality of mitochondria. Here, we discuss how differences in the mitochondrial content of each cell contribute to heterogeneity in gene products. Changes in the amount of mitochondria can also entail drastic alterations of a cell's gene expression program, which ultimately leads to phenotypic diversity. Also watch the Video Abstract.

Global variability in gene expression and alternative splicing is modulated by mitochondrial content.

Noise in gene expression is a main determinant of phenotypic variability. Increasing experimental evidence suggests that genome-wide cellular constraints largely contribute to the heterogeneity observed in gene products. It is still unclear, however, which global factors affect gene expression noise and to what extent. Since eukaryotic gene expression is an energy demanding process, differences in the energy budget of each cell could determine gene expression differences. Here, we quantify the contribution of mitochondrial variability (a natural source of ATP variation) to global variability in gene expression. We find that changes in mitochondrial content can account for ∼50% of the variability observed in protein levels. This is the combined result of the effect of mitochondria dosage on transcription and translation apparatus content and activities. Moreover, we find that mitochondrial levels have a large impact on alternative splicing, thus modulating both the abundance and type of mRNAs. A simple mathematical model in which mitochondrial content simultaneously affects transcription rate and splicing site choice can explain the alternative splicing data. The results of this study show that mitochondrial content (and/or probably function) influences mRNA abundance, translation, and alternative splicing, which ultimately affects cellular phenotype.

Controlled nanometric fibers of self-assembled designed protein scaffolds.

The use of biological molecules as platforms for templating and nanofabrication is an emerging field. Here, we use designed protein building blocks based on small repetitive units (consensus tetratricopeptide repeat - CTPR) to generate fibrillar linear nanostructures by controlling the self-assembly properties of the units. We fully characterize the kinetics and thermodynamics of the assembly and describe the polymerization process by a simple model that captures the features of the structures formed under defined conditions. This work, together with previously established functionalization potential, sets up the basis for the application of these blocks in the fabrication and templating of complex hybrid nanostructures.

A tunable coarse-grained model for ligand-receptor interaction.

Cell-surface receptors are the most common target for therapeutic drugs. The design and optimization of next generation synthetic drugs require a detailed understanding of the interaction with their corresponding receptors. Mathematical approximations to study ligand-receptor systems based on reaction kinetics strongly simplify the spatial constraints of the interaction, while full atomistic ligand-receptor models do not allow for a statistical many-particle analysis, due to their high computational requirements. Here we present a generic coarse-grained model for ligand-receptor systems that accounts for the essential spatial characteristics of the interaction, while allowing statistical analysis. The model captures the main features of ligand-receptor kinetics, such as diffusion dependence of affinity and dissociation rates. Our model is used to characterize chimeric compounds, designed to take advantage of the receptor over-expression phenotype of certain diseases to selectively target unhealthy cells. Molecular dynamics simulations of chimeric ligands are used to study how selectivity can be optimized based on receptor abundance, ligand-receptor affinity and length of the linker between both ligand subunits. Overall, this coarse-grained model is a useful approximation in the study of systems with complex ligand-receptor interactions or spatial constraints.

Dynamic and structural constraints in signal propagation by regulatory networks.

Regulatory networks are able to process complex signals and respond appropriately to the cellular context. Thus, an increasing effort by systems biology researchers is being focused on understanding which interactions are responsible for a given functional response. When translated into specific mathematical models, however, it has been repeatedly shown that this mapping between topology and function is not one-to-one, even for the simplest networks. Moreover, dynamical behavior may play an important role which is necessary to integrate in the general picture. We propose a unified theoretical/statistical approach to characterize the structure-function relationship in molecular networks when temporal features of both input signal and output response are important. The theory allows fast computation of network responses in terms of interaction strengths irrespective of molecular details, while statistical analysis identifies constraints between structural and dynamical features and network function. Investigating different feedback and feedforward loop architectures, we find that processing of temporal signals is strongly correlated to certain combinations of structural and dynamical characteristics, rather than to individual interactions. Our analysis offers new insight into the structure-function relationship in network motifs, quantifying how much the tuning of specific interactions affects network outcome, identifying key structural parameters for a given response and relating dynamics to network topology and function. This kind of analyses can be especially useful for synthetic biology approaches, where promoter libraries with a range of inputs and outputs can be engineered, and one has to choose the correct component needed to produce the desired network function.

Positive regulatory dynamics by a small noncoding RNA: speeding up responses under temperature stress.

Recent discoveries of noncoding regulatory RNAs have led to further understanding of the elements controlling genetic expression. In E. coli, most of those ncRNAs for which functional knowledge is available were shown to be dependent on the Hfq RNA chaperone and to act as inhibitors of translation by base pairing with their mRNA target. Nevertheless, there are also some examples where the sRNA plays a role of a translational activator, structurally enhancing ribosome binding to mRNA. In this work, we seek to understand the dynamics of DsrA-based positive regulation of rpoS mRNA, encoding the σ(S) RNA polymerase subunit, and to understand how it helps to mitigate environmental stress in bacteria. Our analysis is based on the first absolute quantification of the copy number of both the sRNA and of its corresponding mRNA in combination with mathematical models for post-transcriptional regulation. We show that on average, DsrA is present at a ratio of 3 to 24 copies per cell, while an rpoS transcript is present at a level of 1 to 4 copies per cell, both levels increasing when temperature is decreased. Our analysis supports the idea that temperature dependency of DsrA degradation is not a crucial condition for the attainment of observed DsrA steady levels, but highlights that this may have a marked influence on the dynamics of the regulation, notably to speed up the time of recovery to normal RNA levels after ending the stress signal. Further, our analysis also reveals how reversibility of RNA complex formation and σ(S)-regulated degradation act to reduce intrinsic noise in σ(S) induction. Taking into account the importance of this master regulator, which allows E. coli as well as other important pathogens to survive their environment, the present work contributes to complete the panel of multiple signals used to regulate bacterial transcription.

Trade-offs and noise tolerance in signal detection by genetic circuits.

Genetic circuits can implement elaborated tasks of amplitude or frequency signal detection. What type of constraints could circuits experience in the performance of these tasks, and how are they affected by molecular noise? Here, we consider a simple detection process-a signal acting on a two-component module-to analyze these issues. We show that the presence of a feedback interaction in the detection module imposes a trade-off on amplitude and frequency detection, whose intensity depends on feedback strength. A direct interaction between the signal and the output species, in a type of feed-forward loop architecture, greatly modifies these trade-offs. Indeed, we observe that coherent feed-forward loops can act simultaneously as good frequency and amplitude noise-tolerant detectors. Alternatively, incoherent feed-forward loop structures can work as high-pass filters improving high frequency detection, and reaching noise tolerance by means of noise filtering. Analysis of experimental data from several specific coherent and incoherent feed-forward loops shows that these properties can be realized in a natural context. Overall, our results emphasize the limits imposed by circuit structure on its characteristic stimulus response, the functional plasticity of coherent feed-forward loops, and the seemingly paradoxical advantage of improving signal detection with noisy circuit components.

Multistable decision switches for flexible control of epigenetic differentiation.

It is now recognized that molecular circuits with positive feedback can induce two different gene expression states (bistability) under the very same cellular conditions. Whether, and how, cells make use of the coexistence of a larger number of stable states (multistability) is however largely unknown. Here, we first examine how autoregulation, a common attribute of genetic master regulators, facilitates multistability in two-component circuits. A systematic exploration of these modules' parameter space reveals two classes of molecular switches, involving transitions in bistable (progression switches) or multistable (decision switches) regimes. We demonstrate the potential of decision switches for multifaceted stimulus processing, including strength, duration, and flexible discrimination. These tasks enhance response specificity, help to store short-term memories of recent signaling events, stabilize transient gene expression, and enable stochastic fate commitment. The relevance of these circuits is further supported by biological data, because we find them in numerous developmental scenarios. Indeed, many of the presented information-processing features of decision switches could ultimately demonstrate a more flexible control of epigenetic differentiation.

Sodium pumps adapt spike bursting to stimulus statistics.

Pump activity is a homeostatic mechanism that maintains ionic gradients. Here we examined whether the slow reduction in excitability induced by sodium-pump activity that has been seen in many neuronal types is also involved in neuronal coding. We took intracellular recordings from a spike-bursting sensory neuron in the leech Hirudo medicinalis in response to naturalistic tactile stimuli with different statistical distributions. We show that regulation of excitability by sodium pumps is necessary for the neuron to make different responses depending on the statistical context of the stimuli. In particular, sodium-pump activity allowed spike-burst sizes and rates to code not for stimulus values per se, but for their ratio with the standard deviation of the stimulus distribution. Modeling further showed that sodium pumps can be a general mechanism of adaptation to statistics on the time scale of 1 min. These results implicate the ubiquitous pump activity in the adaptation of neural codes to statistics.

Dynamical principles of two-component genetic oscillators.

Genetic oscillators based on the interaction of a small set of molecular components have been shown to be involved in the regulation of the cell cycle, the circadian rhythms, or the response of several signaling pathways. Uncovering the functional properties of such oscillators then becomes important for the understanding of these cellular processes and for the characterization of fundamental properties of more complex clocks. Here, we show how the dynamics of a minimal two-component oscillator is drastically affected by its genetic implementation. We consider a repressor and activator element combined in a simple logical motif. While activation is always exerted at the transcriptional level, repression is alternatively operating at the transcriptional (Design I) or post-translational (Design II) level. These designs display differences on basic oscillatory features and on their behavior with respect to molecular noise or entrainment by periodic signals. In particular, Design I induces oscillations with large activator amplitudes and arbitrarily small frequencies, and acts as an "integrator" of external stimuli, while Design II shows emergence of oscillations with finite, and less variable, frequencies and smaller amplitudes, and detects better frequency-encoded signals ("resonator"). Similar types of stimulus response are observed in neurons, and thus this work enables us to connect very different biological contexts. These dynamical principles are relevant for the characterization of the physiological roles of simple oscillator motifs, the understanding of core machineries of complex clocks, and the bio-engineering of synthetic oscillatory circuits.