Memory in Microbes: Quantifying History-Dependent Behavior in a Bacterium

Memory is usually associated with higher organisms rather than bacteria. However, evidence is mounting that many regulatory networks within bacteria are capable of complex dynamics and multi-stable behaviors that have been linked to memory in other systems. Moreover, it is recognized that bacteria that have experienced different environmental histories may respond differently to current conditions. These “memory” effects may be more than incidental to the regulatory mechanisms controlling acclimation or to the status of the metabolic stores. Rather, they may be regulated by the cell and confer fitness to the organism in the evolutionary game it participates in. Here, we propose that history-dependent behavior is a potentially important manifestation of memory, worth classifying and quantifying. To this end, we develop an information-theory based conceptual framework for measuring both the persistence of memory in microbes and the amount of information about the past encoded in history-dependent dynamics. This method produces a phenomenological measure of cellular memory without regard to the specific cellular mechanisms encoding it. We then apply this framework to a strain of Bacillus subtilis engineered to report on commitment to sporulation and degradative enzyme (AprE) synthesis and estimate the capacity of these systems and growth dynamics to ‘remember’ 10 distinct cell histories prior to application of a common stressor. The analysis suggests that B. subtilis remembers, both in short and long term, aspects of its cell history, and that this memory is distributed differently among the observables. While this study does not examine the mechanistic bases for memory, it presents a framework for quantifying memory in cellular behaviors and is thus a starting point for studying new questions about cellular regulation and evolutionary strategy

Time- and dose dependent actions of cardiotonic steroids on transcriptome and intracellular content of Na+ and K+: a comparative analysis

Recent studies demonstrated that in addition to Na(+),K(+)-ATPase inhibition cardiotonic steroids (CTSs) affect diverse intracellular signaling pathways. This study examines the relative impact of [Na(+)](i)/[K(+)](i)-mediated and -independent signaling in transcriptomic changes triggered by the endogenous CTSs ouabain and marinobufagenin (MBG) in human umbilical vein endothelial cells (HUVEC). We noted that prolongation of incubation increased the apparent affinity for ouabain estimated by the loss of [K(+)](i) and gain of [Na(+)](i). Six hour exposure of HUVEC to 100 and 3,000 nM ouabain resulted in elevation of the [Na(+)](i)/[K(+)](i) ratio by ~15 and 80-fold and differential expression of 258 and 2185 transcripts, respectively. Neither [Na(+)](i)/[K(+)](i) ratio nor transcriptome were affected by 6-h incubation with 30 nM ouabain. The 96-h incubation with 3 nM ouabain or 30 nM MBG elevated the [Na(+)](i)/[K(+)](i) ratio by ~14 and 3-fold and led to differential expression of 880 and 484 transcripts, respectively. These parameters were not changed after 96-h incubation with 1 nM ouabain or 10 nM MBG. Thus, our results demonstrate that elevation of the [Na(+)](i)/[K(+)](i) ratio is an obligatory step for transcriptomic changes evoked by CTS in HUVEC. The molecular origin of upstream [Na(+)](i)/[K(+)](i) sensors involved in transcription regulation should be identified in forthcoming studies

Design principles of biochemical oscillators.

Cellular rhythms are generated by complex interactions among genes, proteins and metabolites. They are used to control every aspect of cell physiology, from signalling, motility and development to growth, division and death. We consider specific examples of oscillatory processes and discuss four general requirements for biochemical oscillations: negative feedback, time delay, sufficient 'nonlinearity' of the reaction kinetics and proper balancing of the timescales of opposing chemical reactions. Positive feedback is one mechanism to delay the negative-feedback signal. Biological oscillators can be classified according to the topology of the positive- and negative-feedback loops in the underlying regulatory mechanism