The evolutionary success of an organism is a testament to its inherent capacity to keep pace with environmental conditions that change over short and long periods. Mechanisms underlying adaptive processes are being investigated with renewed interest and excitement. This revival is partly fueled by powerful technologies that can probe molecular phenomena at a systems scale. Such studies provide spectacular insight into the mechanisms of adaptation, including rewiring of regulatory networks via natural selection of horizontal gene transfers, gene duplication, deletion, readjustment of kinetic parameters, and myriad other genetic reorganizational events. Here, we will discuss advances in prokaryotic systems biology from the perspective of evolutionary principles that have shaped regulatory networks for dynamic adaptation to environmental change.Microorganisms experience myriad environmental factors over their evolutionary history, including those that remain essentially constant over long periods (e.g. geological epochs), change slowly (e.g. general increase in annual temperatures), fluctuate periodically (e.g. day-night cycles and seasonal variations), or change frequently and somewhat randomly (e.g. unpredictable nutrient loading). These changes occur over diverse timescales,

Microbes can tailor transcriptional responses to diverse environmental challenges despite having streamlined genomes and a limited number of regulators. Here, we present data-driven models that capture the dynamic interplay of the environment and genome-encoded regulatory programs of two types of prokaryotes: Escherichia coli (a bacterium) and Halobacterium salinarum (an archaeon). The models reveal how the genome-wide distributions of cis-acting gene regulatory elements and the conditional influences of transcription factors at each of those elements encode programs for eliciting a wide array of environment-specific responses. We demonstrate how these programs partition transcriptional regulation of genes within regulons and operons to re-organize gene–gene functional associations in each environment. The models capture fitness-relevant co-regulation by different transcriptional control mechanisms acting across the entire genome, to define a generalized, system-level organizing principle for prokaryotic gene regulatory networks that goes well beyond existing paradigms of gene regulation. An online resource (http://egrin2.systemsbiology.net) has been developed to facilitate multiscale exploration of conditional gene regulation in the two prokaryotes.

Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of information and communication technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity – particularly activity of the human brain – with a phenomenon we call “intelligence.” Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as “human” and “brain” out of the defining features of “intelligence,” all forms of life – from microbes to humans – exhibit some or all characteristics consistent with “intelligence.” We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo.

Microbes are diverse and extremely versatile organisms that play vital roles in all ecological niches. Understanding and harnessing microbial systems will be key to the sustainability of our planet. One approach to improving our knowledge of microbial processes is through data-driven and mechanism-informed computational modeling. Individual models of biological networks (such as metabolism, transcription, and signaling) have played pivotal roles in driving microbial research through the years. These networks, however, are highly interconnected and function in concert—a fact that has led to the development of a variety of approaches aimed at simulating the integrated functions of two or more network types. Though the task of integrating these different models is fraught with new challenges, the large amounts of high-throughput data sets being generated, and algorithms being developed, means that the time is at hand for concerted efforts to build integrated regulatory-metabolic networks in a data-driven fashion. In this perspective, we review current approaches for constructing integrated regulatory-metabolic models and outline new strategies for future development of these network models for any microbial system.

BackgroundExpansion of transcription factors is believed to have played a crucial role in evolution of all organisms by enabling them to deal with dynamic environments and colonize new environments. We investigated how the expansion of the Feast/Famine Regulatory Protein (FFRP) or Lrp-like proteins into an eight-member family in Halobacterium salinarum NRC-1 has aided in niche-adaptation of this archaeon to a complex and dynamically changing hypersaline environment.ResultsWe mapped genome-wide binding locations for all eight FFRPs, investigated their preference for binding different effector molecules, and identified the contexts in which they act by analyzing transcriptional responses across 35 growth conditions that mimic different environmental and nutritional conditions this organism is likely to encounter in the wild. Integrative analysis of these data constructed an FFRP regulatory network with conditionally active states that reveal how interrelated variations in DNA-binding domains, effector-molecule preferences, and binding sites in target gene promoters have tuned the functions of each FFRP to the environments in which they act. We demonstrate how conditional regulation of similar genes by two FFRPs, AsnC (an activator) and VNG1237C (a repressor), have striking environment-specific fitness consequences for oxidative stress management and growth, respectively.ConclusionsThis study provides a systems perspective into the evolutionary process by which gene duplication within a transcription factor family contributes to environment-specific adaptation of an organism.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-014-0122-2) contains supplementary material, which is available to authorized users.

The Sc2.0 project is building a eukaryotic synthetic genome from scratch, incorporating thousands of designer features. A major milestone has been achieved with the assembly of all individual Sc2.0 chromosomes. Here, we describe the consolidation of multiple synthetic chromosomes using endoreduplication intercross to generate a strain with 6.5 synthetic chromosomes. Genome-wide chromosome conformation capture and long-read direct RNA sequencing were performed on this strain to evaluate the effects of designer modifications, such as loxPsym site insertion, tRNA relocation, and intron deletion, on 3D chromosome organization and transcript isoform profiles. To precisely map "bugs", we developed a method, CRISPR Directed Biallelic URA3-assisted Genome Scan, or CRISPR D-BUGS, exploiting directed mitotic recombination in heterozygous diploids. Using this method, we first fine-mapped a synII defect resulting from two loxPsym sites in the 3′ UTR of SHM1. This approach was also used to map a combinatorial bug associated with synIII and synX, revealing a highly unexpected genetic interaction that links transcriptional regulation, inositol metabolism and tRNASerCGA abundance. "Starvation" for tRNASerCGA leads to insufficient levels of the key positive inositol biosynthesis regulator, Swi3, which contains tandem UCG codons. Finally, to further expedite consolidation, we employed a new method, chromosome swapping, to incorporate the largest chromosome (synIV), thereby consolidating more than half of the Sc2.0 genome in a single strain.

Here we report the design, construction and characterization of a tRNA neochromosome, a designer chromosome that functions as an additional, de novo counterpart to the native complement of Saccharomyces cerevisiae. Intending to address one of the central design principles of the Sc2.0 project, the ~190 kb tRNA neochromosome houses all 275 relocated nuclear tRNA genes. To maximize stability, the design incorporated orthogonal genetic elements from non-S. cerevisiae yeast species. Furthermore, the presence of 283 rox recombination sites enable an orthogonal SCRaMbLE system capable of adjusting tRNA abundance. Following construction, we obtained evidence of a potent selective force once the neochromosome was introduced into yeast cells, manifesting as a spontaneous doubling in cell ploidy. Furthermore, tRNA sequencing, transcriptomics, proteomics, nucleosome mapping, replication profiling, FISH and Hi-C were undertaken to investigate questions of tRNA neochromosome behavior and function. Its construction demonstrates the remarkable tractability of the yeast model and opens up new opportunities to directly test hypotheses surrounding these essential non-coding RNAs.

Sequence features of genes and their flanking regulatory regions are determinants of RNA transcript isoform expression and have been used as context-independent plug-and-play modules in synthetic biology. However, genetic context—including the adjacent transcriptional environment—also influences transcript isoform expression levels and boundaries. We used synthetic yeast strains with stochastically repositioned genes to systematically disentangle the effects of sequence and context. Profiling 120 million full-length transcript molecules across 612 genomic perturbations, we observed sequence-independent alterations to gene expression levels and transcript isoform boundaries that were influenced by neighboring transcription. We identified features of transcriptional context that could predict these alterations and used these features to engineer a synthetic circuit where transcript length was controlled by neighboring transcription. This demonstrates how positional context can be leveraged in synthetic genome engineering.