No abstract

Statistical analysis of alignments of large numbers of protein sequences has revealed “sectors” of collectively coevolving amino acids in several protein families. Here, we show that selection acting on any functional property of a protein, represented by an additive trait, can give rise to such a sector. As an illustration of a selected trait, we consider the elastic energy of an important conformational change within an elastic network model, and we show that selection acting on this energy leads to correlations among residues. For this concrete example and more generally, we demonstrate that the main signature of functional sectors lies in the small-eigenvalue modes of the covariance matrix of the selected sequences. However, secondary signatures of these functional sectors also exist in the extensively-studied large-eigenvalue modes. Our simple, general model leads us to propose a principled method to identify functional sectors, along with the magnitudes of mutational effects, from sequence data. We further demonstrate the robustness of these functional sectors to various forms of selection, and the robustness of our approach to the identification of multiple selected traits.

Energy flows in biomolecular motors and machines are vital to their function. Yet experimental observations are often limited to a small subset of variables that participate in energy transport and dissipation. Here we show, through a solvable Langevin model, that the seemingly hidden entropy production is measurable through the violation spectrum of the fluctuation-response relation of a slow observable. For general Markov systems with time scale separation, we prove that the violation spectrum exhibits a characteristic plateau in the intermediate frequency region. Despite its vanishing height, the plateau can account for energy dissipation over a broad time scale. Our findings suggest a general possibility to probe hidden entropy production in nanosystems without direct observation of fast variables.

A goal of single cell genome-wide profiling is to reconstruct dynamic transitions during cell differentiation, disease onset, and drug response. Single cell assays have recently been integrated with lineage tracing, a set of methods that identify cells of common ancestry to establish bona fide dynamic relationships between cell states. These integrated methods have revealed unappreciated cell dynamics, but their analysis faces recurrent challenges arising from noisy, dispersed lineage data. Here, we develop coherent, sparse optimization (CoSpar) as a robust computational approach to infer cell dynamics from single-cell genomics integrated with lineage tracing. CoSpar is robust to severe down-sampling and dispersion of lineage data, which enables simpler, lower-cost experimental designs and requires less calibration. In datasets representing hematopoiesis, reprogramming, and directed differentiation, CoSpar identifies fate biases not previously detected, predicting transcription factors and receptors implicated in fate choice. Documentation and detailed examples for common experimental designs are available at https://cospar.readthedocs.io/.Recently, a number of efforts from us and others have integrated lineage-tracing with single-cell genome-wide profiling (hereafter LT-scSeq) using unique, heritable, and expressed DNA barcodes 2,13-21 . These technologies identify cells that share a common ancestor and define their genomic state in an unbiased manner. LT-scSeq experiments have been used to successfully identify when fate decisions occur 13,14 , novel markers for stem cells 16 , and pathways which control cell fate choice 14,16 . The simplest of these methods labels cells at one time point 13 (Fig. 1b); more complex methods allow the accumulation of barcodes over successive cell divisions to reveal the substructure of clones 2,13-21 (Fig. 1c).Emerging LT-scSeq methods have been successful at revealing novel regulators of cell fate 14,16 and the fate potential of early progenitors 13,14 , but they also present challenges that may limit their utility in practice. We identified at least five technical and biological challenges that affect experimental design and interpretation (Fig. 1f). These include stochastic differentiation and variable expansion of clones 22 (Fig. 1f-i), cell loss during analysis (Fig. 1f-ii), barcode homoplasy wherein cells acquire the same barcode despite not having a lineage relationship 2 (Fig. 1f-iii), access to clones only at a single time point 23,, and clonal dispersion due to a lag time between labeling cells and the first sampling (Fig. 1f-v). Addressing these problems should greatly simplify the design and interpretation of LT-scSeq assays and put them in the hands of a wider research community. To our knowledge, there is not yet an analysis method that systematically overcomes these problems.

Abstract.The ability to monitor nutrient and other environmental conditions with high sensitivity is crucial for cell growth and survival. Sensory adaptation allows a cell to recover its sensitivity after a transient response to a shift in the strength of extracellular stimulus. The working principles of adaptation have been established previously based on rate equations which do not consider fluctuations in a thermal environment. Recently, G. Lan et al. (Nature Phys., 8:422-8, 2012) performed a detailed analysis of a stochastic model for the E. coli sensory network. They showed that accurate adaptation is possible only when the system operates in a nonequilibrium steady-state (NESS). They further proposed an energy-speed-accuracy (ESA) tradeoff relation. We present here analytic results on the NESS of the model through a mapping to a one-dimensional birth-death process. An exact expression for the entropy production rate is also derived. Based on these results, we are able to discuss the ESA relation in a more general setting. Our study suggests that the adaptation error can be reduced exponentially as the methylation range increases. Finally, we show that a nonequilibrium phase transition exists in the infinite methylation range limit, despite the fact that the model contains only two discrete variables.

BackgroundBlood culture contamination in emergency departments (ED) that experience a high volume of patients has negative impacts on optimal patient care. It is therefore important to identify risk factors associated with blood culture contamination in EDs.Methodology/Principal FindingsA prospectively observational study in a university-affiliated hospital were conducted between August 2011 and December 2012. Positive monomicrobial and negative blood cultures drawn from adult patients in the ED were analyzed to evaluate the possible risk factors for contamination. A total of 1,148 positive monomicrobial cases, 391 contamination cases, and 13,689 cases of negative blood culture were identified. Compared to patients with negative blood cultures, patients in triage levels 1 and 2 (Incidence Rate Ratio, IRR = 2.24), patients with end-stage renal disease (ESRD) (IRR = 2.05), and older patients (IRR: 1.02 per year) were more likely to be associated with ED blood culture contamination.Conclusions/SignificanceCritical patients (triage levels 1 and 2), ESRD patients, and older patients were more commonly associated with blood culture contamination in the ED. Further studies to evaluate whether the characteristics of skin commensals contribute to blood culture contamination is warranted, especially in hospitals populated with high-risk patients.

Collective oscillations of cells in a population appear under diverse biological contexts. Here, we establish a set of common principles by categorising the response of individual cells against a time-varying signal. A positive intracellular signal relay of sufficient gain from participating cells is required to sustain the oscillations, together with phase matching. The two conditions yield quantitative predictions for the onset cell density and frequency in terms of measured single-cell and signal response functions. Through mathematical constructions, we show that cells that adapt to a constant stimulus fulfil the phase requirement by developing a leading phase in an active frequency window that enables cell-to-signal energy flow. Analysis of dynamical quorum sensing in several cellular systems with increasing biological complexity reaffirms the pivotal role of adaptation in powering oscillations in an otherwise dissipative cell-to-cell communication channel. The physical conditions identified also apply to synthetic oscillatory systems.

The Harada-Sasa equality elegantly connects the energy dissipation rate of a moving object with its measurable violation of the Fluctuation-Dissipation Theorem (FDT). Although proven for Langevin processes, its validity remains unclear for discrete Markov systems whose forward and backward transition rates respond asymmetrically to external perturbation. A typical example is a motor protein called kinesin. Here we show generally that the FDT violation persists surprisingly in the high-frequency limit due to the asymmetry, resulting in a divergent FDT violation integral and thus a complete breakdown of the Harada-Sasa equality. A renormalized FDT violation integral still well predicts the dissipation rate when each discrete transition produces a small entropy in the environment. Our study also suggests a way to infer this perturbation asymmetry based on the measurable high-frequency-limit FDT violation.