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IntroductionThe molecular mechanisms underlying pressure adaptation remain largely unexplored, despite their significance for understanding biological adaptation and improving sterilization methods in the food and beverage industry. The heat shock response leads to a global stabilization of the proteome. Prior research suggested that the heat shock regulon may exhibit a transcriptional response to high-pressure stress. MethodsIn this study, we investigated the pressure-dependent heat shock response inE. colistrains using plasmid-borne green fluorescent protein (GFP) promoter fusions and fluorescence fluctuation microscopy. ResultsWe quantitatively confirm that key heat shock genes-rpoH,rpoE,dnaK, andgroEL- are transcriptionally upregulated following pressure shock in both piezosensitiveEscherichia coliand a more piezotolerant laboratory-evolved strain, AN62. Our quantitative imaging results provide the first single cell resolution measurements for both the heat shock and pressure shock transcriptional responses, revealing not only the magnitude of the responses, but also the biological variance involved. Moreover, our results demonstrate distinct responses in the pressure-adapted strain. Specifically,P_groELis upregulated more thanP_dnaKin AN62, while the reverse is true in the parental strain. Furthermore, unlike in the parental strain, the pressure-induced upregulation ofP_rpoEis highly stochastic in strain AN62, consistent with a strong feedback mechanism and suggesting that RpoE could act as a pressure sensor. DiscussionDespite its capacity to grow at pressures up to 62 MPa, the AN62 genome shows minimal mutations, with notable single nucleotide substitutions in genes of the transcriptionally importantbsubunit of RNA polymerase and the Rho terminator. In particular, the mutation in RNAP is one of a cluster of mutations known to confer rifampicin resistance toE. colivia modification of RNAP pausing and termination efficiency. The observed differences in the pressure and heat shock responses between the parental MG1655 strain and the pressure-adapted strain AN62 could arise in part from functional differences in their RNAP molecules. 

Conformational dynamics play essential roles in RNA function. However, detailed structural characterization of excited states of RNA remains challenging. Here, we apply high hydrostatic pressure (HP) to populate excited conformational states of tRNA Lys3 , and structurally characterize them using a combination of HP 2D-NMR, HP-SAXS (HP-small-angle X-ray scattering), and computational modeling. HP-NMR revealed that pressure disrupts the interactions of the imino protons of the uridine and guanosine U–A and G–C base pairs of tRNA Lys3 . HP-SAXS profiles showed a change in shape, but no change in overall extension of the transfer RNA (tRNA) at HP. Configurations extracted from computational ensemble modeling of HP-SAXS profiles were consistent with the NMR results, exhibiting significant disruptions to the acceptor stem, the anticodon stem, and the D-stem regions at HP. We propose that initiation of reverse transcription of HIV RNA could make use of one or more of these excited states. 

Commitment to cell division at the end of G1 phase, termed Start in the budding yeast Saccharomyces cerevisiae , is strongly influenced by nutrient availability. To identify new dominant activators of Start that might operate under different nutrient conditions, we screened a genome-wide ORF overexpression library for genes that bypass a Start arrest caused by absence of the G1 cyclin Cln3 and the transcriptional activator Bck2. We recovered a hypothetical gene YLR053c , renamed NRS1 for Nitrogen-Responsive Start regulator 1, which encodes a poorly characterized 108 amino acid microprotein. Endogenous Nrs1 was nuclear-localized, restricted to poor nitrogen conditions, induced upon TORC1 inhibition, and cell cycle-regulated with a peak at Start. NRS1 interacted genetically with SWI4 and SWI6 , which encode subunits of the main G1/S transcription factor complex SBF. Correspondingly, Nrs1 physically interacted with Swi4 and Swi6 and was localized to G1/S promoter DNA. Nrs1 exhibited inherent transactivation activity, and fusion of Nrs1 to the SBF inhibitor Whi5 was sufficient to suppress other Start defects. Nrs1 appears to be a recently evolved microprotein that rewires the G1/S transcriptional machinery under poor nitrogen conditions. 

Synopsis Many biological systems across scales of size and complexity exhibit a time-varying complex network structure that emerges and self-organizes as a result of interactions with the environment. Network interactions optimize some intrinsic cost functions that are unknown and involve for example energy efficiency, robustness, resilience, and frailty. A wide range of networks exist in biology, from gene regulatory networks important for organismal development, protein interaction networks that govern physiology and metabolism, and neural networks that store and convey information to networks of microbes that form microbiomes within hosts, animal contact networks that underlie social systems, and networks of populations on the landscape connected by migration. Increasing availability of extensive (big) data is amplifying our ability to quantify biological networks. Similarly, theoretical methods that describe network structure and dynamics are being developed. Beyond static networks representing snapshots of biological systems, collections of longitudinal data series can help either at defining and characterizing network dynamics over time or analyzing the dynamics constrained to networked architectures. Moreover, due to interactions with the environment and other biological systems, a biological network may not be fully observable. Also, subnetworks may emerge and disappear as a result of the need for the biological system to cope with for example invaders or new information flows. The confluence of these developments renders tractable the question of how the structure of biological networks predicts and controls network dynamics. In particular, there may be structural features that result in homeostatic networks with specific higher-order statistics (e.g., multifractal spectrum), which maintain stability over time through robustness and/or resilience to perturbation. Alternative, plastic networks may respond to perturbation by (adaptive to catastrophic) shifts in structure. Here, we explore the opportunity for discovering universal laws connecting the structure of biological networks with their function, positioning them on the spectrum of time-evolving network structure, i.e. dynamics of networks, from highly stable to exquisitely sensitive to perturbation. If such general laws exist, they could transform our ability to predict the response of biological systems to perturbations—an increasingly urgent priority in the face of anthropogenic changes to the environment that affect life across the gamut of organizational scales. 

Sampling and genomic efforts over the past decade have revealed an enormous quantity and diversity of life in Earth's extreme environments. This new knowledge of life on Earth poses the challenge of understandingits molecular basis in such inhospitable conditions, given that such conditions lead to loss of structure and of function in biomolecules from mesophiles. In this review, we discuss the physicochemical properties of extreme environments. We present the state of recent progress in extreme environmental genomics. We then present an overview of our current understanding of the biomolecular adaptation to extreme conditions. As our current and future understanding of biomolecular structure–function relationships in extremophiles requires methodologies adapted to extremes of pressure, temperature, and chemical composition, advances in instrumentation for probing biophysical properties under extreme conditions are presented. Finally, we briefly discuss possible future directions in extreme biophysics. 

In budding yeast, the transcription factors SBF and MBF activate a large program of gene expression in late G1 phase that underlies commitment to cell division, termed Start. SBF/MBF are limiting with respect to target promoters in small G1 phase cells and accumulate as cells grow, raising the questions of how SBF/MBF are dynamically distributed across the G1/S regulon and how this impacts the Start transition. Super-resolution Photo-Activatable Localization Microscopy (PALM) mapping of the static positions of SBF/MBF subunits in fixed cells revealed each transcription factor was organized into discrete clusters containing approximately eight copies regardless of cell size and that the total number of clusters increased as cells grew through G1 phase. Stochastic modeling using reasonable biophysical parameters recapitulated growth-dependent SBF/MBF clustering and predicted TF dynamics that were confirmed in live cell PALM experiments. This spatio-temporal organization of SBF/MBF may help coordinate activation of G1/S regulon and the Start transition.