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.

It is unclear what causes variation in cell death in response to chemotherapy. Here, the authors show that cellular mitochondrial content modulates apoptotic protein levels, which in turn regulates response to agents such as TRAIL.

An experimental and computational framework for engineering multifunctional nanoparticles: designing selective anticancer therapies

A key challenge in the treatment of cancer with nanomedicine is to engineer and select nanoparticle formulations that lead to the desired selectivity between tumorigenic and non-tumorigenic cells. To this aim, novel designed nanomaterials, deep biochemical understanding of the mechanisms of interaction between nanomaterials and cells, and computational models are emerging as very useful tools to guide the design of efficient and selective nanotherapies. This works shows, using a combination of detailed experimental approaches and simulations, that the specific targeting of cancer cells in comparison to non-tumorigenic cells can be achieved through the custom design of multivalent nanoparticles. A theoretical model that provides simple yet quantitative predictions to tune the nanoparticles targeting and cytotoxic properties by their degree of functionalization is developed. As a case study, a system that included a targeting agent and a drug and is amenable to controlled experimental manipulation and theoretical analysis is used. This study shows how at defined functionalization levels multivalent nanoparticles can selectively kill tumor cells, while barely affecting non-tumorigenic cells. This work opens a way to the rational design of multifunctionalized nanoparticles with defined targeting and cytotoxic properties for practical applications.

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.

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.

The current importance of cell surface receptors as primary targets for drug treatment explains the increasing interest in a mathematical and quantitative description of the process of ligand-receptor interaction. Recently, a new generation of synthetic chimeric ligands has been developed to selectively target unhealthy cells, without harming healthy tissue. To understand these and other types of complex ligand-receptor systems, conventional chemical interaction models often rely on simplifications and assumptions about the spatial characteristics of the system, while full atomistic molecular dynamics simulations are too computationally demanding to perform many particle statistical analysis. In this paper, we describe a novel approach to model the interaction between ligands and receptors based on a coarse grained approximation that includes explicitly both spatial and kinetic details of the interaction, while allowing many-particle simulations and therefore, statistical analysis at biologically relevant time scales. The model is used to study the binding properties of generic chimeric ligands to understand how cell specificity is achieved, its dependence on receptor concentration and the influence of the distance between subunits of the chimera. Overall, this approach proves optimal to study other ligand-receptor systems with complex spatial regulation, such as receptor clustering, multimerization and multivalent asymmetric ligand binding.

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.

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.

Periodic variations in protein abundances are at the heart of important cellular processes, with common examples being the circadian rhythms or the cell cycle. What is the molecular basis of this behavior? Recent reports showed how simple architectures, based on the interaction of a few molecular components, are capable of inducing oscillatory dynamics. These structures are also of interest for the understanding of complex cellular oscillators, as they appear to be core constituents of them. The authors carefully analyze one of these architectures and uncover how different genetic implementations of such structures strikingly influence its dynamical behavior. They consider two genetic implementations of a repressor and activator element combined in a simple logical motif. While activation is transcriptionally implemented in both cases, repression may act either transcriptionally or post-translationally. These differences in design originate drastic changes in the way oscillations are produced, in the tolerance to molecular noise, or in the circuit response to external stimuli. Similar aspects have been discussed in relation to neural dynamics; therefore this work is able to connect two very different biological scenarios. Thus, simple genetic motifs exploit not only their connectivity pattern but their design to act as information processing units within living cells.

Variability in noise-driven integrator neurons

Neural variability in the presence of noise has been studied mainly in resonator neurons, such as Hodgkin-Huxley or FitzHugh-Nagumo models. Here we investigate this variability for integrator neurons, whose excitability is due to a saddle-node bifurcation of the rest state instead of a Hopf bifurcation. Using simple theoretical expressions for the interspike times distributions, we obtain coefficients of variation in good agreement with numerical calculations in realistic neuron models. The main features of this coefficient as a function of noise depend on the refractory period and on the presence of bistability. The bistability is responsible for the existence of two different time scales in the spiking behavior giving an antiresonance effect.

Hamiltonian theory for vibrational line shapes of atoms adsorbed on surfaces

The vibrational motions of atomic adsorbates on surfaces can be probed by helium atom scattering. The experimental observable is the dynamic structure factor, which shows an inelastic peak around the vibrational frequency of the isolated adsorbates known as the frustrated translational or T-mode peak. In this paper we develop a theory for the line shape of this peak, as well as for its temperature-dependent shift and broadening, based on a Hamiltonian equivalent of the generalized Langevin equation. The theory can be used to infer physical parameters of the adatom-surface interaction, such as the friction coefficient, the barrier height to diffusion, and the anharmonicity parameter. Numerical simulations are used to ascertain the range of validity of the theory, which is also generalized to describe multidimensional systems and to include quantum corrections. We compare the theoretical predictions for the shift and broadening with experimental results for the Na/Cu(001) system, showing quantitative agreement within experimental resolution.

Collisional line shapes for low frequency vibrations of adsorbates on a metal surface

The dynamics of atoms or molecules adsorbed on a metal surface, and excited by collisions with an atomic beam, are treated within a theory that includes energy dissipation into lattice vibrations by means of a frequency and temperature dependent friction function. The theory provides dynamic structure factors for energy transfer derived from collisional time correlation functions. It describes the relaxation of a vibrationally excited atom or molecule within a model of a damped quantum harmonic oscillator bilinearly coupled to a bath of lattice oscillators. The collisional time correlation function is generalized to include friction effects and is applied to the vibrational relaxation of the frustrated translation mode of Na adsorbed on a Cu(001) surface, CO on Cu(001), and CO on Pt(111), following excitation by collisions with He atoms. Results for the frequency shift and width of line shapes versus surface temperature are in very good agreement with experimental measurements of inelastic He atom scattering. Our interpretation of the experimental results provides insight on the relative role of phonon versus electron-hole relaxation.

Chaotic transport of particles in two-dimensional periodic potentials driven by ac forces

The diffusive and directed transport of particles in a two-dimensional periodic potential subjected to frictional and time-periodic forces is analyzed in detail. The model represents diffusion of atoms adsorbed on metal surfaces under an applied ac electric field (surface electromigration) in the low-temperature limit. The second dimension and the potential energy coupling are shown to play an important role on both diffusion and net currents, depending on the direction of the drive. A properly chosen biharmonic field is able to control the directed ratchetlike dynamics of atoms on symmetric surfaces, since current reversals take place by different stabilization of attractors. Reversals identified with hysteresis loops between periodic running attractors are robust against an increase of the second harmonic amplitude, and against temperature effects inside the experimental range for measurements of surface diffusion.