@article{amses_simmons_longcore_mondo_seto_jeronimo_bonds_quandt_davis_chang_et al._2022, title={Diploid-dominant life cycles characterize the early evolution of Fungi}, volume={119}, ISSN={["1091-6490"]}, DOI={10.1073/pnas.2116841119}, abstractNote={Most of the described species in kingdom Fungi are contained in two phyla, the Ascomycota and the Basidiomycota (subkingdom Dikarya). As a result, our understanding of the biology of the kingdom is heavily influenced by traits observed in Dikarya, such as aerial spore dispersal and life cycles dominated by mitosis of haploid nuclei. We now appreciate that Fungi comprises numerous phylum-level lineages in addition to those of Dikarya, but the phylogeny and genetic characteristics of most of these lineages are poorly understood due to limited genome sampling. Here, we addressed major evolutionary trends in the non-Dikarya fungi by phylogenomic analysis of 69 newly generated draft genome sequences of the zoosporic (flagellated) lineages of true fungi. Our phylogeny indicated five lineages of zoosporic fungi and placed Blastocladiomycota, which has an alternation of haploid and diploid generations, as branching closer to the Dikarya than to the Chytridiomyceta. Our estimates of heterozygosity based on genome sequence data indicate that the zoosporic lineages plus the Zoopagomycota are frequently characterized by diploid-dominant life cycles. We mapped additional traits, such as ancestral cell-cycle regulators, cell-membrane- and cell-wall-associated genes, and the use of the amino acid selenocysteine on the phylogeny and found that these ancestral traits that are shared with Metazoa have been subject to extensive parallel loss across zoosporic lineages. Together, our results indicate a gradual transition in the genetics and cell biology of fungi from their ancestor and caution against assuming that traits measured in Dikarya are typical of other fungal lineages.}, number={36}, journal={PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA}, author={Amses, Kevin R. and Simmons, D. Rabern and Longcore, Joyce E. and Mondo, Stephen J. and Seto, Kensuke and Jeronimo, Gustavo H. and Bonds, Anne E. and Quandt, C. Alisha and Davis, William J. and Chang, Ying and et al.}, year={2022}, month={Sep} }
 @article{lin_buchler_2022, title={Gene expression noise accelerates the evolution of a biological oscillator}, volume={3}, url={https://doi.org/10.1101/2022.03.21.485207}, DOI={10.1101/2022.03.21.485207}, abstractNote={Gene expression is a biochemical process, where stochastic binding and un-binding events naturally generate fluctuations and cell-to-cell variability in gene dynamics. These fluctuations typically have destructive consequences for proper biological dynamics and function (e.g., loss of timing and synchrony in biological oscillators). Here, we show that gene expression noise counter-intuitively accelerates the evolution of a biological oscillator and, thus, can impart a benefit to living organisms. We used computer simulations to evolve two mechanistic models of a biological oscillator at different levels of gene expression noise. We first show that gene expression noise induces oscillatory-like dynamics in regions of parameter space that cannot oscillate in the absence of noise. We then demonstrate that these noise-induced oscillations generate a fitness landscape whose gradient robustly and quickly guides evolution by mutation towards robust and self-sustaining oscillation. These results suggest that noise can help dynamical systems evolve or learn new behavior by revealing cryptic dynamic phenotypes outside the bifurcation point.}, publisher={Cold Spring Harbor Laboratory}, author={Lin, Yen Ting and Buchler, Nicolas E.}, year={2022}, month={Mar} }
 @article{zhang_ho_buchler_gordan_2021, title={Competition for DNA binding between paralogous transcription factors determines their genomic occupancy and regulatory functions}, volume={31}, ISSN={["1549-5469"]}, url={https://doi.org/10.1101/gr.275145.120}, DOI={10.1101/gr.275145.120}, abstractNote={Most eukaryotic transcription factors (TFs) are part of large protein families, with members of the same family (i.e., paralogous TFs) recognizing similar DNA-binding motifs but performing different regulatory functions. Many TF paralogs are coexpressed in the cell and thus can compete for target sites across the genome. However, this competition is rarely taken into account when studying the in vivo binding patterns of eukaryotic TFs. Here, we show that direct competition for DNA binding between TF paralogs is a major determinant of their genomic binding patterns. Using yeast proteins Cbf1 and Pho4 as our model system, we designed a high-throughput quantitative assay to capture the genomic binding profiles of competing TFs in a cell-free system. Our data show that Cbf1 and Pho4 greatly influence each other's occupancy by competing for their common putative genomic binding sites. The competition is different at different genomic sites, as dictated by the TFs’ expression levels and their divergence in DNA-binding specificity and affinity. Analyses of ChIP-seq data show that the biophysical rules that dictate the competitive TF binding patterns in vitro are also followed in vivo, in the complex cellular environment. Furthermore, the Cbf1-Pho4 competition for genomic sites, as characterized in vitro using our new assay, plays a critical role in the specific activation of their target genes in the cell. Overall, our study highlights the importance of direct TF-TF competition for genomic binding and gene regulation by TF paralogs, and proposes an approach for studying this competition in a quantitative and high-throughput manner.}, number={7}, journal={GENOME RESEARCH}, publisher={Cold Spring Harbor Laboratory}, author={Zhang, Yuning and Ho, Tiffany D. and Buchler, Nicolas E. and Gordan, Raluca}, year={2021}, month={Jul}, pages={1216-+} }
 @article{medina_buchler_2020, title={Chytrid fungi.}, volume={5}, url={http://europepmc.org/abstract/med/32428492}, DOI={10.1016/j.cub.2020.02.076}, abstractNote={Fungi have distinguishing traits, such as hyphae and cell walls, that evolved in a fungal ancestor over one billion years ago. Chytrid fungi are some of the earliest diverging fungal lineages that retained features of the opisthokont ancestor of animals and fungi (Figure 1). For example, chytrids make reproductive cells known as zoospores that swim with a motile cilium or crawl like an amoeba. The aim of this primer is to introduce the reader to the life cycle, biology, and ecology of chytrids and other zoosporic fungi. We highlight how chytrids are well positioned to elucidate both the cell biology of the animal–fungal ancestor and the evolution of derived fungal features. Life first evolved in the ocean and the last eukaryotic common ancestor (LECA) likely swam and engulfed organic matter via phagocytosis. Based on the shared features found across eukaryotes, LECA had a nucleus, mitochondria, an endo-membrane system, actin and tubulin cytoskeleton, and a centriole for building a mitotic spindle and cilium. LECA gave rise to diverse eukaryotes, some of which remained in aquatic environments and others which colonized land over 500 million years ago. Fungi (e.g. chytrids, rusts, molds, mushrooms, and yeast) are a large eukaryotic kingdom found in many environments and ecological niches. These eukaryotes are decomposers that live on organic matter or as parasites of plants and animals. Fungi are also important symbionts: they are partners of algae and cyanobacteria in lichens or they form mycorrhizae that colonize plant roots and extract water and nutrients from soil in exchange for sugars. The successful expansion and colonization of terrestrial environments by the plant and fungal kingdoms is likely the consequence of a symbiotic relationship between early fungi and photosynthetic algae. Fungi are closely related to animals through a common opisthokont ancestor that lived in an aquatic environment over one billion years ago (Figure 1). Chytrids and other early-diverging fungi have persisted in this ancestral habitat and have retained traits that make them well adapted to foraging for resources in water. For example, chytrids produce spores (known as zoospores) that lack a cell wall and swim via a motile cilium and/or crawl on surfaces via amoeboid motion (Figure 1). The presence of a centriole and a motile cilium is unique to chytrids and other zoosporic fungi within the fungal kingdom. The cilium is attached to a basal body that contains a classic centriole with nine circularly arranged triplet microtubules that nucleate the axoneme. Similar to many animal cells, chytrids resorb the cilium and the centriole is repurposed as a centrosome to organize the mitotic spindle for nuclear division cycles. The fungal ancestor evolved new traits (‘derived traits’) that are shared by all fungi including chytrids. For example, the chytrid life cycle includes a vegetative body (‘thallus’) with a cell wall and hyphal-like feeding structure known as a rhizoid (Figure 1). Fungal hyphae are branching, filamentous tubes that penetrate organic matter and secrete digestive enzymes to extract nutrients for cell growth. Hyphae grow into substrates by depositing cell wall materials and remodeling enzymes at the hyphal tip via directed vesicle trafficking on a cytoskeletal network. The cell wall is critical because it holds large, hydrostatic pressures caused by internal osmolytes, which generate the biomechanical forces that drive cell wall expansion at the hyphal tip. As in other fungi, the hyphal-like rhizoid is important for colonizing substrates and extracting nutrients to fuel chytrid cell growth. We use the term zoosporic fungi to describe chytrids and other early diverging fungi that have a zoospore stage during their life cycle (Figure 2A). Meta-genomic sequencing has shown that zoosporic fungi comprise much of the unknown fungal diversity in aquatic environments. Zoosporic fungi span at least three phyla (Cryptomycota, Chytridiomycota, and Blastocladiomycota). The Cryptomycota are the deepest lineage and include the genus Rozella, which parasitizes chytrids (Figure 2B), and other uncultured parasites of fungi, amoeba, oomycetes and algae. The Cryptomycota also include the Microsporidia, which are common animal parasites that have small, fast-evolving eukaryotic genomes and that have lost their cilium. Despite their lack of a zoospore stage, phylogenetic analyses place Microsporidia within the Cryptomycota. The Chytridiomycota and Blastocladiomycota are later-diverging phyla that are better studied than the Cryptomycota. The Chytridiomycota (commonly called ‘chytrids’) are found in aquatic and terrestrial habitats, and are saprotrophs as well as parasites of algae, plants and animals (e.g. the amphibian pathogen Batrachochytrium). These chytrids play an important role in aquatic food webs by infecting large, inedible algae and producing small zoospores (Figure 2C) that are edible to zooplankton. Anaerobic, multi-ciliated Neocallimastigomycota in ruminants (e.g. sheep, cattle) are a well-characterized subgroup of this phylum that have evolved hydrogen-producing organelles known as hydrogenosomes (Figure 2D). The rumen microbiome contains eubacteria, archaea, ciliates, and chytrids that collectively ferment plant material to produce volatile fatty acids and microbial protein for their animal host. The rumen chytrids penetrate plant tissue with their hyphal-like structures, secrete cellulases, and help breakdown highly recalcitrant carbohydrates for the microbiome. The Blastocladiomycota include saprotrophs as well as parasites of fungi, algae, plants and invertebrates (Figure 2E). Although zoosporic, and once classified as Chytridiomycota, the Blastocladiomycota differ from the other chytrids in the complexity of their thallus and life cycle: they can have haplodiplontic alternation of generations (much like land plants) and exhibit multicellular haploid (gametophyte) and multicellular diploid thalli (sporophyte). While asexual reproduction is through zoospores, sexual reproduction involves motile gametes of opposite sexes with different sizes and coloration that attract and swim towards each other through pheromone signaling. The Blastocladiomycota have diverse body plans with some species (e.g. Allomyces) developing true hyphae (nucleated, with pseudo-septa and polarized indeterminate growth with an apical organizing center, similar to the Spitzenkörper found in filamentous fungi). Chytrid species can differ considerably in their life history, morphology, metabolism, and sub-cellular organelles. However, many chytrids exhibit a similar life cycle that progresses from zoospore to thallus to sporangium (Figure 3A). Chytrid zoospores range from 2–10 microns in diameter and have a single posterior motile cilium, although anaerobic chytrids of the rumen can have multiple cilia (Figure 2D). Zoospore ultrastructure (e.g. basal body and associated sub-structures) is diverse and is often used to identify and classify chytrid species. Chytrids swim with a motile cilium and some species can switch to amoeboid crawling when attached to a surface. Zoospores have a single nucleus and are quiescent, i.e. inactive cell division cycle and no growth. They sustain the energetic demands for motility by catabolizing lipids and storage carbohydrates that were maternally provisioned by the chytrid sporangium during zoosporogenesis in the previous life cycle. Lipid droplets are often visible when observing chytrid zoospores by light microscopy (Figure 2E). Although chytrid zoospores are metabolically active, they do not produce new DNA, RNA, or proteins until after germination. Zoospores are translationally inactive and contain inactive ribosomes pre-loaded with maternal mRNAs. In the Blastocladiomycota, inactive mRNA–ribosomes are packaged into an organelle associated with the nucleus called the nuclear cap (Figure 2E). Ribosome activity in the zoospore is blocked in the elongation stage by an inhibitor whose identity remains unknown. It is unclear how universal this mechanism might be across all chytrids; however, it has been established that some Chytridiomycota zoospores are also translationally inactive. Once chytrid zoospores find an appropriate niche, they encyst by retracting their motile cilium and building a fungal cell wall (Figure 3A). The mechanics of ciliary retraction are diverse with at least four scenarios (lash-around, body-twist, straight in, and vesicular) that can vary depending on the species or the environment. In the lash-around retraction, the cilium lashes around the immobile zoospore body resulting in a sheath-less axoneme coiled inside the membrane. In the body-twist retraction, the zoospore body twists or rotates while the cilium remains passive, with the same resulting axoneme coiled under the membrane. In the straight-in retraction, the immobile cilium slowly reduces length, entering the immobile zoospore body at the point of attachment. Finally, for vesicular retraction, the axoneme coils or loops within itself in a vesicle of the cilia membrane, progressively shortening the cilium until the vesicle reaches and fuses into the main zoospore body (Figure 3B). The cell biology and mechanisms used by zoospores for retraction are still an open question, but its diversity of form and plasticity may reflect the structural diversity seen in the chytrid zoospore basal body and associated structures. Much like metazoan cells, retraction of the cilium liberates the centrioles for cell division that occurs during chytrid growth. It may also repurpose ciliary components for germination or other cellular processes until new protein is synthesized in the chytrid. Upon encystment, changes in the regulation of the actin cytoskeleton shift the chytrid from a naked motility specialist (ciliary swimming and crawling) to a foraging specialist with a fungal cell wall and turgor-driven polarized growth. The germinating cyst usually forms a single germ tube that later expands and branches into a hyphal-like rhizoidal system (Figure 3A). Zoospores in some Chytridiomycota crawl using pseudopod-based alpha-motility, which is driven by the expansion of branched-actin filament networks via the Arp2/3 complex. Chytrid species whose zoospores crawl contain activators of branched-actin assembly (WASP, SCAR/WAVE), which are correlated with crawling and alpha-motility in other eukaryotes. Once the zoospores encyst and germinate, there is a shift in actin cytoskeleton organization to actin patches and cables that extend into the germ tube and rhizoids. This architecture is typical of fungi, where actin patches are associated with endocytosis and cell wall deposition, whereas actin cables are pathways for targeted delivery of exocytic vesicles. In some chytrids, the nucleus remains in the cyst during germ tube expansion and the cyst will develop into a spherical reproductive structure called the sporangium (Figure 3A). In other species, the nucleus can migrate into the rhizoid and eventually trigger the growth and formation of a sporangium outside the original cyst. During the formation of the sporangium, a nucleus goes through multiple rounds of nuclear division without cytokinesis to create a shared compartment of nuclei known as a coenocyte. This is later followed by ciliogenesis (i.e. the conversion of centrioles into basal bodies and the building of motile cilia), membrane invagination and the coordinated encapsulation of individual nuclei, cilia and other organelles into single cells to form new zoospores. Membrane cellularization of a coenocytic compartment is an ancestral process that occurs in pre-metazoan lineages and animal embryogenesis. In appropriate environmental conditions, the mature zoospores are released through one or multiple pores (called discharge papillae) that open in the cell wall (Figure 3C). Upon release from a sporangium, chytrid zoospores swim or crawl to find new niches. The zoospores can swim for hours to days with speeds of up to 100 microns per second. The motion consists of swimming in mostly straight lines with rapid changes in direction, interspersed with long breaks of crawling in some species. Zoospores sense both chemical cues and use light cues to locate hosts or substrates. Chemotaxis assays have shown that zoospores will swim towards specific nutrients (e.g. sugars, proteins, fatty acids, amino acids) usually associated with target hosts or substrates. Likewise, phototaxis assays have shown that zoospores of different species will swim towards green or blue light. A type-I opsin (also known as bacteriorhodopsin) fused to guanylate-cyclase drives the phototaxis of zoospores in Blastocladiella emersonii (Blastocladiomycota). The mechanism of phototransduction of this type-I opsin is reminiscent of the phototransduction pathway of animal G-protein-coupled ciliary opsins. This type-I opsin is homologous to the one used by pre-metazoan choanoflagellates to drive ciliary movement and contractility of the multi-cell colony. The diversity of type-I opsins seen in chytrids and choanoflagellates have optogenetic potential. For example, Blastocladiella ‘CyclOp’ was recently developed for sensitive and fast control of cGMP levels in target cells and animals. Chemotaxis also drives sexual reproduction in the Allomyces (Blastocladiomycota) in which motile male and female gametes produced by male and female gametangia (Figure 3D) swim towards each other using pheromone signaling. Although the chemical structure of the male pheromone (parisin) is unknown, the female pheromone (sirenin) is a sesquiterpene. Strikingly, sirenin can activate the human sperm CatSper calcium channel, much like progesterone. CatSper is essential for hyperactivation of the sperm’s cilium and plays a role in chemotaxis towards the egg. Interestingly, chytrids have orthologs of CatSper and calcium signaling is involved in sirenin signaling. The chytrid pheromone receptor and its mechanism of action remain unknown, but we anticipate that chytrids will be useful organisms for understanding the conserved mechanisms of sexual chemotaxis, calcium signaling, and the regulation of swimming motility via CatSper. Basic research in animal and fungal model organisms elucidated conserved mechanisms and regulators of eukaryotic cell biology (e.g. cell cycle). However, these closely related eukaryotes also evolved new features and adaptations over the last one billion years. The common ancestor of most fungi committed to a cellular morphology and sessile life cycle that produces hyphae that grow into their substrates and durable spores that disperse via air currents or ejection. These adaptations are useful for a saprophytic or parasitic lifestyle in a terrestrial environment, but they emerged when the fungal ancestor was still living in aquatic environments. Chytrids are an early-diverging fungal lineage that likely reflect a transitional phase in the evolution of terrestrial fungi, not unlike amphibious animals. Chytrid genomes are also unique because they contain ancestral, animal-like genes and regulatory networks that were lost in most other fungi. For example, the fungal cell cycle was rewired by a viral domain that eventually replaced the ancestral G1/S regulator in most fungi. The ancestral and viral regulators and G1/S pathways still coexist in chytrids. This same viral domain also created a large family of transcription factors that regulate fungal-specific processes, such as hyphal morphogenesis. As such, chytrids are promising organisms to help understand the molecular evolution of derived fungal features and the conservation of ancestral features.}, journal={Current biology : CB}, author={Medina, EM and Buchler, NE}, year={2020}, month={May} }
 @article{medina_robinson_bellingham-johnstun_ianiri_laplante_fritz-laylin_buchler_2020, title={Genetic transformation of Spizellomyces punctatus, a resource for studying chytrid biology and evolutionary cell biology}, url={https://doi.org/10.7554/eLife.52741}, DOI={10.7554/eLife.52741}, abstractNote={Chytrids are early-diverging fungi that share features with animals that have been lost in most other fungi. They hold promise as a system to study fungal and animal evolution, but we lack genetic tools for hypothesis testing. Here, we generated transgenic lines of the chytrid Spizellomyces punctatus, and used fluorescence microscopy to explore chytrid cell biology and development during its life cycle. We show that the chytrid undergoes multiple rounds of synchronous nuclear division, followed by cellularization, to create and release many daughter ‘zoospores’. The zoospores, akin to animal cells, crawl using actin-mediated cell migration. After forming a cell wall, polymerized actin reorganizes into fungal-like cortical patches and cables that extend into hyphal-like structures. Actin perinuclear shells form each cell cycle and polygonal territories emerge during cellularization. This work makes Spizellomyces a genetically tractable model for comparative cell biology and understanding the evolution of fungi and early eukaryotes.}, journal={eLife}, author={Medina, Edgar M and Robinson, Kristyn A and Bellingham-Johnstun, Kimberly and Ianiri, Giuseppe and Laplante, Caroline and Fritz-Laylin, Lillian K and Buchler, Nicolas E}, year={2020}, month={May} }
 @article{chen_lin_gallegos_hazlett_gomez-schiavon_yang_kalmeta_zhou_holtzman_gersbach_et al._2019, title={Enhancer Histone Acetylation Modulates Transcriptional Bursting Dynamics of Neuronal Activity-Inducible Genes}, volume={26}, ISSN={["2211-1247"]}, url={https://publons.com/publon/9042681/}, DOI={10.1016/j.celrep.2019.01.032}, abstractNote={Neuronal activity-inducible gene transcription correlates with rapid and transient increases in histone acetylation at promoters and enhancers of activity-regulated genes. Exactly how histone acetylation modulates transcription of these genes has remained unknown. We used single-cell in situ transcriptional analysis to show that Fos and Npas4 are transcribed in stochastic bursts in mouse neurons and that membrane depolarization increases mRNA expression by increasing burst frequency. We then expressed dCas9-p300 or dCas9-HDAC8 fusion proteins to mimic or block activity-induced histone acetylation locally at enhancers. Adding histone acetylation increased Fos transcription by prolonging burst duration and resulted in higher Fos protein levels and an elevation of resting membrane potential. Inhibiting histone acetylation reduced Fos transcription by reducing burst frequency and impaired experience-dependent Fos protein induction in the hippocampus in vivo. Thus, activity-inducible histone acetylation tunes the transcriptional dynamics of experience-regulated genes to affect selective changes in neuronal gene expression and cellular function.}, number={5}, journal={CELL REPORTS}, author={Chen, Liang-Fu and Lin, Yen Ting and Gallegos, David A. and Hazlett, Mariah F. and Gomez-Schiavon, Mariana and Yang, Marty G. and Kalmeta, Breanna and Zhou, Allen S. and Holtzman, Liad and Gersbach, Charles A. and et al.}, year={2019}, month={Jan}, pages={1174-+} }
 @article{gomez-schiavon_buchler_2019, title={Epigenetic switching as a strategy for quick adaptation while attenuating biochemical noise}, volume={15}, ISSN={["1553-7358"]}, url={https://publons.com/publon/27274058/}, DOI={10.1371/journal.pcbi.1007364}, abstractNote={Epigenetic switches are bistable, molecular systems built from self-reinforcing feedback loops that can spontaneously switch between heritable phenotypes in the absence of DNA mutation. It has been hypothesized that epigenetic switches first evolved as a mechanism of bet-hedging and adaptation, but the evolutionary trajectories and conditions by which an epigenetic switch can outcompete adaptation through genetic mutation remain unknown. Here, we used computer simulations to evolve a mechanistic, biophysical model of a self-activating genetic circuit, which can both adapt genetically through mutation and exhibit epigenetic switching. We evolved these genetic circuits under a fluctuating environment that alternatively selected for low and high protein expression levels. In all tested conditions, the population first evolved by genetic mutation towards a region of genotypes where genetic adaptation can occur faster after each environmental transition. Once in this region, the self-activating genetic circuit can exhibit epigenetic switching, which starts competing with genetic adaptation. We show a trade-off between either minimizing the adaptation time or increasing the robustness of the phenotype to biochemical noise. Epigenetic switching was superior in a fast fluctuating environment because it adapted faster than genetic mutation after an environmental transition, while still attenuating the effect of biochemical noise on the phenotype. Conversely, genetic adaptation was favored in a slowly fluctuating environment because it maximized the phenotypic robustness to biochemical noise during the constant environment between transitions, even if this resulted in slower adaptation. This simple trade-off predicts the conditions and trajectories under which an epigenetic switch evolved to outcompete genetic adaptation, shedding light on possible mechanisms by which bet-hedging strategies might emerge and persist in natural populations.}, number={10}, journal={PLOS COMPUTATIONAL BIOLOGY}, author={Gomez-Schiavon, Mariana and Buchler, Nicolas E.}, year={2019}, month={Oct} }
 @misc{medina_walsh_buchler_2019, title={Evolutionary innovation, fungal cell biology, and the lateral gene transfer of a viral KilA-N domain}, volume={58-59}, ISSN={["1879-0380"]}, url={https://publons.com/publon/34754702/}, DOI={10.1016/j.gde.2019.08.004}, abstractNote={Fungi are found in diverse ecological niches as primary decomposers, mutualists, or parasites of plants and animals. Although animals and fungi share a common ancestor, fungi dramatically diversified their life cycle, cell biology, and metabolism as they evolved and colonized new niches. This review focuses on a family of fungal transcription factors (Swi4/Mbp1, APSES, Xbp1, Bqt4) derived from the lateral gene transfer of a KilA-N domain commonly found in prokaryotic and eukaryotic DNA viruses. These virus-derived fungal regulators play central roles in cell cycle, morphogenesis, sexual differentiation, and quiescence. We consider the possible origins of KilA-N and how this viral DNA binding domain came to be intimately associated with fungal processes.}, journal={CURRENT OPINION IN GENETICS & DEVELOPMENT}, author={Medina, Edgar M. and Walsh, Evan and Buchler, Nicolas E.}, year={2019}, month={Oct}, pages={103–110} }
 @article{lin_buchler_2019, title={Exact and efficient hybrid Monte Carlo algorithm for accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts}, url={https://doi.org/10.1063/1.5110503}, DOI={10.1063/1.5110503}, abstractNote={Abstract}, journal={The Journal of Chemical Physics}, author={Lin, Yen Ting and Buchler, Nicolas E.}, year={2019}, month={Jul} }
 @article{gowans_bridgers_zhang_dronamraju_burnetti_king_thiengmany_shinsky_bhanu_garcia_et al._2019, title={Recognition of Histone Crotonylation by Taf14 Links Metabolic State to Gene Expression}, volume={76}, ISSN={["1097-4164"]}, url={https://publons.com/publon/34754701/}, DOI={10.1016/j.molcel.2019.09.029}, abstractNote={Metabolic signaling to chromatin often underlies how adaptive transcriptional responses are controlled. While intermediary metabolites serve as co-factors for histone-modifying enzymes during metabolic flux, how these modifications contribute to transcriptional responses is poorly understood. Here, we utilize the highly synchronized yeast metabolic cycle (YMC) and find that fatty acid β-oxidation genes are periodically expressed coincident with the β-oxidation byproduct histone crotonylation. Specifically, we found that H3K9 crotonylation peaks when H3K9 acetylation declines and energy resources become limited. During this metabolic state, pro-growth gene expression is dampened; however, mutation of the Taf14 YEATS domain, a H3K9 crotonylation reader, results in de-repression of these genes. Conversely, exogenous addition of crotonic acid results in increased histone crotonylation, constitutive repression of pro-growth genes, and disrupted YMC oscillations. Together, our findings expose an unexpected link between metabolic flux and transcription and demonstrate that histone crotonylation and Taf14 participate in the repression of energy-demanding gene expression.}, number={6}, journal={MOLECULAR CELL}, author={Gowans, Graeme J. and Bridgers, Joseph B. and Zhang, Jibo and Dronamraju, Raghuvar and Burnetti, Anthony and King, Devin A. and Thiengmany, Aline V and Shinsky, Stephen A. and Bhanu, Natarajan V and Garcia, Benjamin A. and et al.}, year={2019}, month={Dec}, pages={909-+} }
 @article{mwimba_karapetyan_liu_marques_mcginnis_buchler_dong_2018, title={Daily humidity oscillation regulates the circadian clock to influence plant physiology}, volume={9}, ISSN={["2041-1723"]}, url={https://publons.com/publon/34754703/}, DOI={10.1038/s41467-018-06692-2}, abstractNote={Abstract Early circadian studies in plants by de Mairan and de Candolle alluded to a regulation of circadian clocks by humidity. However, this regulation has not been described in detail, nor has its influence on physiology been demonstrated. Here we report that, under constant light, circadian humidity oscillation can entrain the plant circadian clock to a period of 24 h probably through the induction of clock genes such as CIRCADIAN CLOCK ASSOCIATED 1 . Under simulated natural light and humidity cycles, humidity oscillation increases the amplitude of the circadian clock and further improves plant fitness-related traits. In addition, humidity oscillation enhances effector-triggered immunity at night possibly to counter increased pathogen virulence under high humidity. These results indicate that the humidity oscillation regulates specific circadian outputs besides those co-regulated with the light-dark cycle.}, journal={NATURE COMMUNICATIONS}, author={Mwimba, Musoki and Karapetyan, Sargis and Liu, Lijing and Marques, Jorge and McGinnis, Erin M. and Buchler, Nicolas E. and Dong, Xinnian}, year={2018}, month={Oct} }
 @article{lin_buchler_2018, title={Efficient analysis of stochastic gene dynamics in the non-adiabatic regime using piecewise deterministic Markov processes}, volume={15}, url={https://doi.org/10.1098/rsif.2017.0804}, DOI={10.1098/rsif.2017.0804}, abstractNote={Single-cell experiments show that gene expression is stochastic and bursty, a feature that can emerge from slow switching between promoter states with different activities. One source of long-lived promoter states is the slow binding and unbinding kinetics of transcription factors to promoters, i.e. the non-adiabatic binding regime. Here, we introduce a simple analytical framework, known as a piecewise deterministic Markov process (PDMP), that accurately describes the stochastic dynamics of gene expression in the non-adiabatic regime. We illustrate the utility of the PDMP on a non-trivial dynamical system by analyzing the properties of a titration-based oscillator in the non-adiabatic limit. We first show how to transform the underlying Chemical Master Equation into a PDMP where the slow transitions between promoter states are stochastic, but whose rates depend upon the faster deterministic dynamics of the transcription factors regulated by these promoters. We show that the PDMP accurately describes the observed periods of stochastic cycles in activator and repressor-based titration oscillators. We then generalize our PDMP analysis to more complicated versions of titration-based oscillators to explain how multiple binding sites lengthen the period and improve coherence. Last, we show how noise-induced oscillation previously observed in a titration-based oscillator arises from non-adiabatic and discrete binding events at the promoter site.}, number={138}, journal={Journal of The Royal Society Interface}, publisher={The Royal Society}, author={Lin, Yen Ting and Buchler, Nicolas E.}, year={2018}, month={Jan}, pages={20170804} }
 @article{hendler_medina_buchler_bruin_aharoni_2018, title={The evolution of a G1/S transcriptional network in yeasts}, volume={64}, url={https://publons.com/publon/2047243/}, DOI={10.1007/S00294-017-0726-3}, abstractNote={The G1-to-S cell cycle transition is promoted by the periodic expression of a large set of genes. In Saccharomyces cerevisiae G1/S gene expression is regulated by two transcription factor (TF) complexes, the MBF and SBF, which bind to specific DNA sequences, the MCB and SCB, respectively. Despite extensive research little is known regarding the evolution of the G1/S transcription regulation including the co-evolution of the DNA binding domains with their respective DNA binding sequences. We have recently examined the co-evolution of the G1/S TF specificity through the systematic generation and examination of chimeric Mbp1/Swi4 TFs containing different orthologue DNA binding domains in S. cerevisiae (Hendler et al. in PLoS Genet 13:e1006778. doi: 10.1371/journal.pgen.1006778 , 2017). Here, we review the co-evolution of G1/S transcriptional network and discuss the evolutionary dynamics and specificity of the MBF-MCB and SBF-SCB interactions in different fungal species.}, number={1}, journal={Current Genetics}, publisher={Springer Nature}, author={Hendler, Adi and Medina, Edgar M. and Buchler, Nicolas E. and Bruin, Robertus A. M. and Aharoni, Amir}, year={2018}, pages={81–86} }
 @article{gómez-schiavon_chen_west_buchler_2017, title={BayFish: Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells}, volume={18}, url={https://doi.org/10.1186/s13059-017-1297-9}, DOI={10.1186/s13059-017-1297-9}, abstractNote={Single-molecule RNA fluorescence in situ hybridization (smFISH) provides unparalleled resolution in the measurement of the abundance and localization of nascent and mature RNA transcripts in fixed, single cells. We developed a computational pipeline (BayFish) to infer the kinetic parameters of gene expression from smFISH data at multiple time points after gene induction. Given an underlying model of gene expression, BayFish uses a Monte Carlo method to estimate the Bayesian posterior probability of the model parameters and quantify the parameter uncertainty given the observed smFISH data. We tested BayFish on synthetic data and smFISH measurements of the neuronal activity-inducible gene Npas4 in primary neurons.}, number={1}, journal={Genome Biology}, publisher={Springer Science and Business Media LLC}, author={Gómez-Schiavon, Mariana and Chen, Liang-Fu and West, Anne E. and Buchler, Nicolas E.}, year={2017}, month={Dec} }
 @article{liban_medina_tripathi_sengupta_henry_buchler_rubin_2017, title={Conservation and divergence of C-terminal domain structure in the retinoblastoma protein family}, volume={114}, url={https://doi.org/10.1073/pnas.1619170114}, DOI={10.1073/pnas.1619170114}, abstractNote={Significance The retinoblastoma (Rb) pocket protein and E2F transcription factor families regulate cell division and are commonly deregulated in proliferating cancer cells. An important question has been what distinguishing molecular features of Rb and its interaction with E2F result in its unique potency as a tumor suppressor relative to its homologous proteins p107 and p130. Here we identify structures in Rb, p107, and E2Fs that determine the specificity in their association. We explain binding preferences with an X-ray crystal structure of a p107–E2F5–DP1 complex, and present phylogenetic analyses that implicate coevolving protein interactions between family members as a key determinant of their evolution.}, number={19}, journal={Proceedings of the National Academy of Sciences}, publisher={Proceedings of the National Academy of Sciences}, author={Liban, Tyler J. and Medina, Edgar M. and Tripathi, Sarvind and Sengupta, Satyaki and Henry, R. William and Buchler, Nicolas E. and Rubin, Seth M.}, year={2017}, month={May}, pages={4942–4947} }
 @article{hendler_medina_kishkevich_abu-qarn_klier_buchler_bruin_aharoni_2017, title={Gene duplication and co-evolution of G1/S transcription factor specificity in fungi are essential for optimizing cell fitness}, volume={13}, url={https://publons.com/publon/2047240/}, DOI={10.1371/journal.pgen.1006778}, abstractNote={Transcriptional regulatory networks play a central role in optimizing cell survival. How DNA binding domains and cis-regulatory DNA binding sequences have co-evolved to allow the expansion of transcriptional networks and how this contributes to cellular fitness remains unclear. Here we experimentally explore how the complex G1/S transcriptional network evolved in the budding yeast Saccharomyces cerevisiae by examining different chimeric transcription factor (TF) complexes. Over 200 G1/S genes are regulated by either one of the two TF complexes, SBF and MBF, which bind to specific DNA binding sequences, SCB and MCB, respectively. The difference in size and complexity of the G1/S transcriptional network across yeast species makes it well suited to investigate how TF paralogs (SBF and MBF) and DNA binding sequences (SCB and MCB) co-evolved after gene duplication to rewire and expand the network of G1/S target genes. Our data suggests that whilst SBF is the likely ancestral regulatory complex, the ancestral DNA binding element is more MCB-like. G1/S network expansion took place by both cis- and trans- co-evolutionary changes in closely related but distinct regulatory sequences. Replacement of the endogenous SBF DNA-binding domain (DBD) with that from more distantly related fungi leads to a contraction of the SBF-regulated G1/S network in budding yeast, which also correlates with increased defects in cell growth, cell size, and proliferation.}, number={5}, journal={PLOS Genetics}, publisher={Public Library of Science (PLoS)}, author={Hendler, Adi and Medina, Edgar M. and Kishkevich, Anastasiya and Abu-Qarn, Mehtap and Klier, Steffi and Buchler, Nicolas E. and Bruin, Robertus A. M. and Aharoni, Amir}, editor={Snyder, MichaelEditor}, year={2017}, month={May}, pages={e1006778} }
 @article{tanouchi_pai_park_huang_buchler_you_2017, title={Long-term growth data of Escherichia coli at a single-cell level}, url={https://doi.org/10.1038/sdata.2017.36}, DOI={10.1038/sdata.2017.36}, abstractNote={Abstract Long-term, single-cell measurement of bacterial growth is extremely valuable information, particularly in the study of homeostatic aspects such as cell-size and growth rate control. Such measurement has recently become possible due to the development of microfluidic technology. Here we present data from single-cell measurements of Escherichia coli growth over 70 generations obtained for three different growth conditions. The data were recorded every minute, and contain time course data of cell length and fluorescent intensity of constitutively expressed yellow fluorescent protein.}, journal={Scientific Data}, author={Tanouchi, Yu and Pai, Anand and Park, Heungwon and Huang, Shuqiang and Buchler, Nicolas E. and You, Lingchong}, year={2017}, month={Mar} }
 @article{burnetti_aydin_buchler_2016, title={Cell cycle Start is coupled to entry into the yeast metabolic cycle across diverse strains and growth rates.}, volume={27}, url={http://europepmc.org/abstract/med/26538026}, DOI={10.1091/mbc.e15-07-0454}, abstractNote={Cells have evolved oscillators with different frequencies to coordinate periodic processes. Here we studied the interaction of two oscillators, the cell division cycle (CDC) and the yeast metabolic cycle (YMC), in budding yeast. Previous work suggested that the CDC and YMC interact to separate high oxygen consumption (HOC) from DNA replication to prevent genetic damage. To test this hypothesis, we grew diverse strains in chemostat and measured DNA replication and oxygen consumption with high temporal resolution at different growth rates. Our data showed that HOC is not strictly separated from DNA replication; rather, cell cycle Start is coupled with the initiation of HOC and catabolism of storage carbohydrates. The logic of this YMC-CDC coupling may be to ensure that DNA replication and cell division occur only when sufficient cellular energy reserves have accumulated. Our results also uncovered a quantitative relationship between CDC period and YMC period across different strains. More generally, our approach shows how studies in genetically diverse strains efficiently identify robust phenotypes and steer the experimentalist away from strain-specific idiosyncrasies.}, number={1}, journal={Molecular Biology of the Cell}, author={Burnetti, AJ and Aydin, M and Buchler, NE}, year={2016}, month={Jan}, pages={64–74,} }
 @article{medina_turner_gordân_skotheim_buchler_2016, title={Punctuated evolution and transitional hybrid network in an ancestral cell cycle of fungi}, volume={5}, url={https://doi.org/10.7554/eLife.09492}, DOI={10.7554/eLife.09492}, abstractNote={Although cell cycle control is an ancient, conserved, and essential process, some core animal and fungal cell cycle regulators share no more sequence identity than non-homologous proteins. Here, we show that evolution along the fungal lineage was punctuated by the early acquisition and entrainment of the SBF transcription factor through horizontal gene transfer. Cell cycle evolution in the fungal ancestor then proceeded through a hybrid network containing both SBF and its ancestral animal counterpart E2F, which is still maintained in many basal fungi. We hypothesize that a virally-derived SBF may have initially hijacked cell cycle control by activating transcription via the cis-regulatory elements targeted by the ancestral cell cycle regulator E2F, much like extant viral oncogenes. Consistent with this hypothesis, we show that SBF can regulate promoters with E2F binding sites in budding yeast.}, journal={eLife}, publisher={eLife Sciences Organisation, Ltd.}, author={Medina, Edgar M and Turner, Jonathan J and Gordân, Raluca and Skotheim, Jan M and Buchler, Nicolas E}, year={2016}, month={May} }
 @article{schaap_barrantes_minx_sasaki_anderson_bénard_biggar_buchler_bundschuh_chen_et al._2016, title={The Physarum polycephalum Genome Reveals Extensive Use of Prokaryotic Two-Component and Metazoan-Type Tyrosine Kinase Signaling}, volume={8}, url={https://publons.com/publon/2047248/}, DOI={10.1093/GBE/EVV237}, abstractNote={Physarum polycephalum is a well-studied microbial eukaryote with unique experimental attributes relative to other experimental model organisms. It has a sophisticated life cycle with several distinct stages including amoebal, flagellated, and plasmodial cells. It is unusual in switching between open and closed mitosis according to specific life-cycle stages. Here we present the analysis of the genome of this enigmatic and important model organism and compare it with closely related species. The genome is littered with simple and complex repeats and the coding regions are frequently interrupted by introns with a mean size of 100 bases. Complemented with extensive transcriptome data, we define approximately 31,000 gene loci, providing unexpected insights into early eukaryote evolution. We describe extensive use of histidine kinase-based two-component systems and tyrosine kinase signaling, the presence of bacterial and plant type photoreceptors (phytochromes, cryptochrome, and phototropin) and of plant-type pentatricopeptide repeat proteins, as well as metabolic pathways, and a cell cycle control system typically found in more complex eukaryotes. Our analysis characterizes P. polycephalum as a prototypical eukaryote with features attributed to the last common ancestor of Amorphea, that is, the Amoebozoa and Opisthokonts. Specifically, the presence of tyrosine kinases in Acanthamoeba and Physarum as representatives of two distantly related subdivisions of Amoebozoa argues against the later emergence of tyrosine kinase signaling in the opisthokont lineage and also against the acquisition by horizontal gene transfer.}, number={1}, journal={Genome Biology and Evolution}, author={Schaap, P and Barrantes, I and Minx, P and Sasaki, N and Anderson, RW and Bénard, M and Biggar, KK and Buchler, NE and Bundschuh, R and Chen, X and et al.}, year={2016}, pages={109–125,} }
 @article{tanouchi_pai_park_huang_stamatov_buchler_you_2015, title={A noisy linear map underlies oscillations in cell size and gene expression in bacteria}, volume={523}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84937414579&partnerID=MN8TOARS}, DOI={10.1038/nature14562}, abstractNote={During bacterial growth, a cell approximately doubles in size before division, after which it splits into two daughter cells. This process is subjected to the inherent perturbations of cellular noise and thus requires regulation for cell-size homeostasis. The mechanisms underlying the control and dynamics of cell size remain poorly understood owing to the difficulty in sizing individual bacteria over long periods of time in a high-throughput manner. Here we measure and analyse long-term, single-cell growth and division across different Escherichia coli strains and growth conditions. We show that a subset of cells in a population exhibit transient oscillations in cell size with periods that stretch across several (more than ten) generations. Our analysis reveals that a simple law governing cell-size control-a noisy linear map-explains the origins of these cell-size oscillations across all strains. This noisy linear map implements a negative feedback on cell-size control: a cell with a larger initial size tends to divide earlier, whereas one with a smaller initial size tends to divide later. Combining simulations of cell growth and division with experimental data, we demonstrate that this noisy linear map generates transient oscillations, not just in cell size, but also in constitutive gene expression. Our work provides new insights into the dynamics of bacterial cell-size regulation with implications for the physiological processes involved.}, number={7560}, journal={Nature}, publisher={Nature Publishing Group}, author={Tanouchi, Yu and Pai, Anand and Park, Heungwon and Huang, Shuqiang and Stamatov, Rumen and Buchler, Nicolas E. and You, Lingchong}, year={2015}, month={Jun}, pages={357–360} }
 @article{rienzo_poveda-huertes_aydin_buchler_pascual-ahuir_proft_2015, title={Different mechanisms confer gradual control and memory at nutrient- and stress-regulated genes in yeast}, volume={35}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84944603437&partnerID=MN8TOARS}, DOI={10.1128/MCB.00729-15}, abstractNote={Cells respond to environmental stimuli by fine-tuned regulation of gene expression. Here we investigated the dose-dependent modulation of gene expression at high temporal resolution in response to nutrient and stress signals in yeast. The GAL1 activity in cell populations is modulated in a well-defined range of galactose concentrations, correlating with a dynamic change of histone remodeling and RNA polymerase II (RNAPII) association. This behavior is the result of a heterogeneous induction delay caused by decreasing inducer concentrations across the population. Chromatin remodeling appears to be the basis for the dynamic GAL1 expression, because mutants with impaired histone dynamics show severely truncated dose-response profiles. In contrast, the GRE2 promoter operates like a rapid off/on switch in response to increasing osmotic stress, with almost constant expression rates and exclusively temporal regulation of histone remodeling and RNAPII occupancy. The Gal3 inducer and the Hog1 mitogen-activated protein (MAP) kinase seem to determine the different dose-response strategies at the two promoters. Accordingly, GAL1 becomes highly sensitive and dose independent if previously stimulated because of residual Gal3 levels, whereas GRE2 expression diminishes upon repeated stimulation due to acquired stress resistance. Our analysis reveals important differences in the way dynamic signals create dose-sensitive gene expression outputs.}, number={21}, journal={Molecular and Cellular Biology}, author={Rienzo, A. and Poveda-Huertes, D. and Aydin, S. and Buchler, N.E. and Pascual-Ahuir, A. and Proft, M.}, year={2015}, pages={3669–3683} }
 @article{zhou_wang_karapetyan_mwimba_marqués_buchler_dong_2015, title={Redox rhythm reinforces the circadian clock to gate immune response}, volume={523}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84936968103&partnerID=MN8TOARS}, DOI={10.1038/nature14449}, abstractNote={Recent studies have shown that in addition to the transcriptional circadian clock, many organisms, including Arabidopsis, have a circadian redox rhythm driven by the organism's metabolic activities. It has been hypothesized that the redox rhythm is linked to the circadian clock, but the mechanism and the biological significance of this link have only begun to be investigated. Here we report that the master immune regulator NPR1 (non-expressor of pathogenesis-related gene 1) of Arabidopsis is a sensor of the plant's redox state and regulates transcription of core circadian clock genes even in the absence of pathogen challenge. Surprisingly, acute perturbation in the redox status triggered by the immune signal salicylic acid does not compromise the circadian clock but rather leads to its reinforcement. Mathematical modelling and subsequent experiments show that NPR1 reinforces the circadian clock without changing the period by regulating both the morning and the evening clock genes. This balanced network architecture helps plants gate their immune responses towards the morning and minimize costs on growth at night. Our study demonstrates how a sensitive redox rhythm interacts with a robust circadian clock to ensure proper responsiveness to environmental stimuli without compromising fitness of the organism.}, number={7561}, journal={Nature}, publisher={Nature Publishing Group}, author={Zhou, Mian and Wang, Wei and Karapetyan, Sargis and Mwimba, Musoki and Marqués, Jorge and Buchler, Nicolas E. and Dong, Xinnian}, year={2015}, month={Jun}, pages={472–476} }
 @article{karapetyan_buchler_e_2015, title={Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators.}, volume={92}, url={http://europepmc.org/abstract/med/26764732}, DOI={10.1103/physreve.92.062712}, abstractNote={Genetic oscillators, such as circadian clocks, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest sources of stochasticity is the binary noise that arises from the binding of a regulatory protein to a promoter in the chromosomal DNA. In this study, we focus on two minimal oscillators based on activator titration and repressor titration to understand the key parameters that are important for oscillations and for overcoming binary noise. We show that the rate of unbinding from the DNA, despite traditionally being considered a fast parameter, needs to be slow to broaden the space of oscillatory solutions. The addition of multiple, independent DNA binding sites further expands the oscillatory parameter space for the repressor-titration oscillator and lengthens the period of both oscillators. This effect is a combination of increased effective delay of the unbinding kinetics due to multiple binding sites and increased promoter ultrasensitivity that is specific for repression. We then use stochastic simulation to show that multiple binding sites increase the coherence of oscillations by mitigating the binary noise. Slow values of DNA unbinding rate are also effective in alleviating molecular noise due to the increased distance from the bifurcation point. Our work demonstrates how the number of DNA binding sites and slow unbinding kinetics, which are often omitted in biophysical models of gene circuits, can have a significant impact on the temporal and stochastic dynamics of genetic oscillators.}, number={6}, journal={Physical Review E}, author={Karapetyan, S. and Buchler, N.E. and E, Physical}, year={2015}, month={Dec}, pages={062712,} }
 @article{mazo-vargas_park_aydin_buchler_2014, title={Measuring fast gene dynamics in single cells with time-lapse luminescence microscopy}, volume={25}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84908583872&partnerID=MN8TOARS}, DOI={10.1091/mbc.E14-07-1187}, abstractNote={Time-lapse fluorescence microscopy is an important tool for measuring in vivo gene dynamics in single cells. However, fluorescent proteins are limited by slow chromophore maturation times and the cellular autofluorescence or phototoxicity that arises from light excitation. An alternative is luciferase, an enzyme that emits photons and is active upon folding. The photon flux per luciferase is significantly lower than that for fluorescent proteins. Thus time-lapse luminescence microscopy has been successfully used to track gene dynamics only in larger organisms and for slower processes, for which more total photons can be collected in one exposure. Here we tested green, yellow, and red beetle luciferases and optimized substrate conditions for in vivo luminescence. By combining time-lapse luminescence microscopy with a microfluidic device, we tracked the dynamics of cell cycle genes in single yeast with subminute exposure times over many generations. Our method was faster and in cells with much smaller volumes than previous work. Fluorescence of an optimized reporter (Venus) lagged luminescence by 15–20 min, which is consistent with its known rate of chromophore maturation in yeast. Our work demonstrates that luciferases are better than fluorescent proteins at faithfully tracking the underlying gene expression.}, number={22}, journal={Molecular Biology of the Cell}, author={Mazo-Vargas, A. and Park, H. and Aydin, M. and Buchler, N.E.}, year={2014}, pages={3699–3708} }
 @article{archambault_buchler_wilmes_jacobson_cross_2014, title={Two-Faced Cyclins with Eyes on the Targets}, volume={4}, url={https://publons.com/publon/2047264/}, DOI={10.4161/CC.4.1.1402}, abstractNote={We recently reported that the 'hydrophobic patch' (HP) of the Saccharomyces cerevisiae S-phase cyclin Clb5 facilitates its interaction with Orc6 (via its Cy or RXL motif), providing a mechanism that helps prevent rereplication from individual origins. This is the first finding of a biological function for an interaction between a cyclin and a cyclin-binding motif (Cy or RXL motif) in a target protein in Saccharomyces cerevisiae. It is also the first such example involving a B-type cyclin in any organism. Yet, some of our observations as well as work from other groups suggest that HP-RXL interactions are functionally important for cyclin-Cdk signaling to other targets. The evolutionary conservation of the HP motif suggests that it allows cyclins to carry out important and specialized functions.}, number={1}, journal={Cell Cycle}, author={Archambault, V. and Buchler, N.E. and Wilmes, G.M. and Jacobson, M.D. and Cross, F.R.}, year={2014}, pages={125–130} }
 @article{tanouchi_pai_buchler_you_2012, title={Programming stress-induced altruistic death in engineered bacteria}, volume={8}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84870835870&partnerID=MN8TOARS}, DOI={10.1038/msb.2012.57}, abstractNote={Article20 November 2012Open Access Programming stress-induced altruistic death in engineered bacteria Yu Tanouchi Yu Tanouchi Department of Biomedical Engineering, Duke University, Durham, NC, USA Search for more papers by this author Anand Pai Anand Pai Department of Biomedical Engineering, Duke University, Durham, NC, USA Search for more papers by this author Nicolas E Buchler Nicolas E Buchler Department of Physics, Duke University, Durham, NC, USA Department of Biology, Duke University, Durham, NC, USA Center for Systems Biology, Duke University, Durham, NC, USA Institute for Genome Sciences and Policy, Duke University, Durham, NC, USA Search for more papers by this author Lingchong You Corresponding Author Lingchong You Department of Biomedical Engineering, Duke University, Durham, NC, USA Center for Systems Biology, Duke University, Durham, NC, USA Institute for Genome Sciences and Policy, Duke University, Durham, NC, USA Search for more papers by this author Yu Tanouchi Yu Tanouchi Department of Biomedical Engineering, Duke University, Durham, NC, USA Search for more papers by this author Anand Pai Anand Pai Department of Biomedical Engineering, Duke University, Durham, NC, USA Search for more papers by this author Nicolas E Buchler Nicolas E Buchler Department of Physics, Duke University, Durham, NC, USA Department of Biology, Duke University, Durham, NC, USA Center for Systems Biology, Duke University, Durham, NC, USA Institute for Genome Sciences and Policy, Duke University, Durham, NC, USA Search for more papers by this author Lingchong You Corresponding Author Lingchong You Department of Biomedical Engineering, Duke University, Durham, NC, USA Center for Systems Biology, Duke University, Durham, NC, USA Institute for Genome Sciences and Policy, Duke University, Durham, NC, USA Search for more papers by this author Author Information Yu Tanouchi1, Anand Pai1, Nicolas E Buchler2,3,4,5 and Lingchong You 1,4,5 1Department of Biomedical Engineering, Duke University, Durham, NC, USA 2Department of Physics, Duke University, Durham, NC, USA 3Department of Biology, Duke University, Durham, NC, USA 4Center for Systems Biology, Duke University, Durham, NC, USA 5Institute for Genome Sciences and Policy, Duke University, Durham, NC, USA *Corresponding author. Department of Biomedical Engineering, Duke University, CIEMAS 2355 101, Science Drive, Box 3382, Durham, NC 27708, USA. Tel.:+1 919 660 8408; Fax:+1 919 668 0795; E-mail: [email protected] Molecular Systems Biology (2012)8:626https://doi.org/10.1038/msb.2012.57 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info Programmed death is often associated with a bacterial stress response. This behavior appears paradoxical, as it offers no benefit to the individual. This paradox can be explained if the death is ‘altruistic’: the killing of some cells can benefit the survivors through release of ‘public goods’. However, the conditions where bacterial programmed death becomes advantageous have not been unambiguously demonstrated experimentally. Here, we determined such conditions by engineering tunable, stress-induced altruistic death in the bacterium Escherichia coli. Using a mathematical model, we predicted the existence of an optimal programmed death rate that maximizes population growth under stress. We further predicted that altruistic death could generate the ‘Eagle effect’, a counter-intuitive phenomenon where bacteria appear to grow better when treated with higher antibiotic concentrations. In support of these modeling insights, we experimentally demonstrated both the optimality in programmed death rate and the Eagle effect using our engineered system. Our findings fill a critical conceptual gap in the analysis of the evolution of bacterial programmed death, and have implications for a design of antibiotic treatment. Synopsis Altruistic death is shown to confer a population level advantage in engineered E. coli. Cost-benefit trade-offs are analyzed and altruistic death is shown to account for the ‘Eagle effect’, whereby bacteria appear to grow better in high antibiotics concentrations. We engineered a synthetic system to program tunable altruistic death in bacteria. Our system demonstrated conditions for a population-level advantage of altruistic death. Cost–benefit trade-off results in emergence of an optimal degree of death that is tunable by rates of public-good synthesis. Altruistic death can cause non-monotonic dose responses in antibiotic treatment. Introduction Programmed death is commonly associated with bacterial response to stressful conditions, such as amino-acid starvation (Aizenman et al, 1996), presence of competitors (Ackermann et al, 2008), and antibiotic treatment (Rice and Bayles, 2003; Hazan et al, 2004). As death offers no benefit to its actor (i.e., a bacterial cell that has activated programmed death will die), its occurrence raises a fundamental, unresolved question with regard to its evolution: how can this trait be selected for? An oft-cited explanation is that the death is ‘altruistic’: the killing of some cells can provide direct or indirect benefits to the survivors, including the actor's kin, through the release of ‘public goods’ (Figure 1A) (West et al, 2007). In other words, death may represent an extreme form of cooperation, analogous to sterile workers in social-insect colonies who give up their personal reproduction but benefit their fertile family members (Gardner and Kümmerli, 2008). By making this assumption, evolution of programmed death in microbes can be analyzed under the general framework of public-good cooperation (Ackermann et al, 2008). Figure 1A summarizes several natural examples that may fit in this framework: Streptococcus pneumoniae (Berry et al, 1989; Hirst et al, 2004) responds to its host environment and releases the virulence factor pneumolysin through cell lysis, which helps its host invasion. Salmonella typhimurim (Stecher et al, 2007; Ackermann et al, 2008) responds to competition in the host's microbiota by causing host inflammation through programmed death. This inflammation kills the microbiota and reduces competition. E. coli (Aizenman et al, 1996) responds to amino-acid starvation by triggering programmed death, which is speculated to help surviving cells by providing nutrients. Colicinogenic E. coli responds to DNA-damaging agent and nutrient depletion by releasing colicin through cell lysis, which kills neighboring competitors (Gardner et al, 2004; Cascales et al, 2007). In response to nutrient limitation, Bacillus subtilis develops spores, an extreme means to withstand the stress. B. subtilis delays the sporulation by killing and feeding on their non-sporulating siblings to prevent unnecessary spore formation in case the environmental condition improves shortly (Ellermeier et al, 2006). Other examples of possible public goods resulting from programmed death include extracellular DNA, a structural component of biofilm in Pseudomonas aeruginosa (Allesen-Holm et al, 2006), Staphylococcus aureus (Rice et al, 2007), and Streptococcus mutants. Figure 1.Altruistic bacterial death in response to stress. (A) Coupling programmed death with public-good production. Some cells undergo programmed death in response to stress (red triangles), leading to generation of a public good (green ovals). The public good removes the stress, allowing survival and recovery of the overall population. Table: examples of natural systems where programmed bacterial death has been proposed to be altruistic by providing direct or indirect benefits to survivors. See text for further details. (B) A synthetic gene circuit to program altruistic death. 6-APA causes murein breakdown and generates aMur-Tp inside the cell; aMur-Tp induces expression of the E gene by activating the PampC promoter through AmpR. A non-secreted form of beta-lactamase (BlaM) is placed under IPTG-inducible promoter, Plac/ara-1. Upon cell lysis owing to E expression, BlaM is released to the extracellular space where it degrades 6-APA. In the conceptual framework described in (A), 6-APA represents the stress, whereas BlaM represents the public good. Download figure Download PowerPoint While plausible, however, the conditions where altruistic death becomes advantageous have not been unequivocally demonstrated in an experimental system. As such, there remains a considerable gap between theoretical models and corresponding experimental validation. For example, a recent study of S. typhimurim used an evolutionary game theory approach to investigate conditions under which evolution of altruistic death is possible (Ackermann et al, 2008). It demonstrated that basic assumptions of the altruistic-death model are consistent with experiments, but the exact nature of cost–benefit relationship of death remains elusive. A major challenge in tackling this problem is the complexity of natural biological processes, where confounding factors could obscure the quantitative analysis and interpretation of the outcome resulting from the trade-off between death and public-good production. These include the severity of initial stress, degree of death, per-cell rate of public-good generation, as well as the growth cycle of the organism. Often times, precise manipulation and even interpretation of basic parameters are nearly impossible. For instance, during S. typhimurium infection, the benefit results from a combination of highly intertwined, host–pathogen–microflora interactions (Stecher et al, 2007; Ackermann et al, 2008) while the system responsible for suicide (type-III secretion system) have multiple roles in pathogenesis (Haraga et al, 2008). Modifying one factor likely has diverse and unintended effects; thus the experimental results are open to alternative explanations (Nedelcu et al, 2011). Owing to these issues, it remains an open question with regard to the specific conditions under which programmed death can pay off at the population level. To address this question, we have taken a synthetic-biology approach to explicitly measure and test the adaptive advantage of programmed bacterial death through the release of public goods. We created synthetic gene circuits in E. coli that respond to environmental stress by exhibiting varying extent of programmed death that releases a public good (Figure 1B). In our circuits, both the degree of programmed death and the rate of public-good production are tunable, which allows us to test the benefits of altruistic death under various conditions in a controllable manner. Such synthetic systems are often simpler than their natural counterparts, have fewer confounding factors, are amenable to modulation of system parameters, and allow clear mapping between experimental manipulation and its effect (Tanouchi et al, 2009). This approach has been successfully adopted to investigate other problems of population and evolutionary biology, and is complementary to directly studying natural systems (Kerr et al, 2002; Shou et al, 2007; Acar et al, 2008; Chuang et al, 2009, 2010; Song et al, 2009). For instance, Chuang et al (2009) created a secretion-based cooperation system in E. coli where ‘producers’ secrete a public good at the cost of reduced growth rate whereas ‘non-producers’ benefit from the public good without paying the cost. Using this system, the authors studied how population structure, cost of being the producer, and degree of benefit affect the outcome of competition between the two strains (Chuang et al, 2009). In this study, we have used a similar approach to address an unresolved, more complex biological phenomenon, namely altruistic death where cells completely give up their reproductive opportunity upon public-good production. Using our synthetic system, we examined whether altruistic death can promote population fitness, and elucidated how this fitness depends on intrinsic (e.g., production rate of the public good and the programmed death rate) and extrinsic (e.g., the stress level, duration, and cell density at which the stress is applied) factors. Our approach also revealed a mechanistic explanation for the ‘Eagle effect’, a counter-intuitive phenomenon where bacteria appear to grow better when treated with higher antibiotic concentrations. This result provides a novel insight that connects two apparently unrelated phenomena, altruistic death and the Eagle effect. Overall, our results fill a conceptual gap in understanding the evolutionary dynamics of programmed bacterial death during stress and have implications for designing intervention strategies for effective treatment of bacterial infections. Results Circuit design, implementation, and parts characterization Our base circuit, termed PAD (programmed altruistic death), consists of a suicide module and a public-good module (Figure 1B). The suicide module expresses the E lysis gene from bacteriophage φX174 under PampC promoter in response to a beta-lactam antibiotic, 6-APA. 6-APA causes partial cell-wall breakdown and accumulation of a cell-wall intermediate, anhMurNAc-tripeptide, which binds and activates AmpR, a transcriptional regulator of PampC promoter (Jacobs et al, 1997). The public-good module expresses a modified, cytoplasmic form of beta-lactamase (BlaM) from an IPTG-inducible promoter, Plac/ara-1 (Lutz and Bujard, 1997). Having suicide function and public-good production in separate modules allows their independent modulation to examine their effects on system dynamics. That is, the BlaM production rate per cell can be modulated without the influence of the E protein production rate. Likewise, the death rate can be modulated by changing the translation rate of the E protein, for a given public-good production rate and stress level. Importantly, these changes can be mapped to separate parameters in our mathematical model (see below) in an unambiguous manner. As a control, we used a cell strain that is identical to PAD except that it lacks the E gene (no programmed death, or NPD). Microscope analysis confirmed significantly greater cell lysis for PAD in comparison with NPD (Supplementary Figure S1). It also suggested a minor growth inhibition effect of E protein for low E gene induction (i.e., low 6-APA concentration; Supplementary Figure S1d, e). We chose BlaM for its two properties that are critical for the designed circuit function. First, because 6-APA recognizes its targets (penicillin-binding proteins) in the periplasm, a cytoplasmic BlaM should not protect cells against 6-APA (Broome-Smith and Spratt, 1986; Everett et al, 1990). We confirmed this idea experimentally by using a reporter circuit (pCSaGFP), where 6-APA-induced cell-wall damage is reported by GFP expression (Figure 2A). Our results showed that BlaM expression did not reduce the damage response in comparison with the negative control (without BlaM induction), indicating lack of 6-APA degradation. In contrast, the native, periplasmic form of beta-lactamase, Bla, reduced the damage response in comparision with the negative control (without Bla induction), indicating its ability to degrade 6-APA. Second, when released to extracellular space by cell lysis, BlaM should degrade 6-APA and offer protection for surviving cells. We tested this idea by using a protection assay (Figure 2B). Supernatant from lysed, BlaM-expressing cells offered full protection for a sensitive strain against 6-APA treatment. In contrast, supernatant from cells expressing BlaM but not lysed or that from lysed cells not expressing BlaM did not offer protection. Figure 2.Characterization of the public-good module. (A) Cytoplasmic BlaM provides negligible protection against 6-APA. PampC induction upon 6-APA treatment was measured using GFP in relative fluorescence unit (RFU) in the presence of BlaM or Bla expression. BlaM expression (blue, induced by 1 mM IPTG) did not reduce GFP expression in comparison with negative control (green, no induction by IPTG), indicating that BlaM did not prevent cell-wall damage by 6-APA. In contrast, expression of the native Bla (blue, induced by 1 mM IPTG) reduced GFP expression in comparison with the negative control (green, no induction by IPTG). In the absence of 6-APA, GFP was not induced for either circuit, with (black) or without (red) 1 mM IPTG. (B) Released BlaM by cell lysis provides protection against 6-APA. Left: growth of a 6-APA-sensitive strain (lacking E gene and BlaM) was assayed in conditioned media containing supernatants prepared from PAD cultures with or without BlaM induction by 1 mM IPTG, and with (lysis) or without (no lysis) 6-APA treatment (400 μg/ml). Right: no lysis, no 6-APA (negative control): growth of the sensitive strain in the absence of 6-APA and in supernatants from unlysed PAD cultures. No lysis, +6-APA: growth of the sensitive strain in supernatants from unlysed PAD cultures with 400 μg/ml 6-APA. Lysis, +6-APA: growth of the sensitive strain in supernatants from PAD cultures lysed by 400 μg/ml 6-APA (i.e., 6-APA is present in the supernatants). Only supernatant from lysed PAD cells expressing BlaM provided significant protection for the sensitive strain against the 6-APA treatment. Error bars are s.d. of six replicates (three technical replicates × two independent experiments) for the growth assay. Download figure Download PowerPoint Therefore, the programmed death is completely altruistic by design in our circuits: the pubic good (BlaM) can only realize its protective function through the killing of its host cell. Also implied in this design is the requirement for cell–cell variability in death, which could arise from stochastic E protein expression or variable sensitivity to E-protein-mediated killing. We note that the first aspect is evident in the GFP expression from the PampC promoter in cells carrying a reporter circuit; the main peak of the GFP expression shows a broad distribution, covering about 50-fold range (Figure 2A). Advantage of PAD at the population level To test the advantage of altruistic death, we first compared growth dynamics of PAD and NPD in response to antibiotic treatment (Figure 3A). Altruistic death is defined to be advantageous when the PAD population outgrows the NPD population. Addition of 400 μg/ml 6-APA caused drastic lysis in the PAD strain but only slight lysis in the NPD strain. The density of the PAD strain remained lower than that of the NPD strain until ∼18 h later when it started to grow faster and eventually reached a higher density than the NPD strain. This growth advantage was due to faster release of BlaM and thus faster degradation of 6-APA (Figure 2B). This result provides a direct experimental demonstration that altruistic death can indeed benefit overall population survival in clonal populations, a minimum requirement for the evolution of altruistic death. The situation, however, is expected to reverse when the two strains are grown in a mixture. Social evolution theory predicts that a public-good producer (e.g., PAD) decreases in frequency when cocultured with a strain with no or less public-good production (e.g., NPD). Using fluorescence-tagged versions of PAD and NPD, we confirmed this prediction in our synthetic system (Supplementary Figure S2), thus reinforcing the idea that BlaM release by E-mediated cell lysis is altruistic and that BlaM is indeed a public good. Figure 3.Emergence of growth advantage by PAD. (A) Growth dynamics of PAD (red) and the NPD (blue) strains with 1 mM IPTG after addition of 400 μg/ml 6-APA at time 0 (solid lines). Control cultures received no 6-APA (dashed lines). As another control, NPD without IPTG is also shown (black). (B) Simulated growth dynamics of PAD (red) and the NPD (blue) strains following 6-APA treatment (a(0)=5.5). β1=0.04 and 0 were used for PAD and the NPD strains, respectively, and β2=5.5 was used. (C) Simulated growth of PAD (red) and the NPD (blue) strains following 6-APA treatment (a(0)=5.5) as a function of public-good production (solid lines). The PAD strain is fitter only when coupled with sufficiently fast production of public good (red zone; β2>3.6). Without 6-APA treatment, both strains reach the same high density (dotted lines). Cell densities at τ=20 are shown. (D) Growth of PAD (red) and the NPD (blue) strains following 400 μg/ml 6-APA treatment, at varying induction levels of BlaM modulated by IPTG (solid lines). Control cultures received no 6-APA (dotted lines). Cell densities (A600) at the 24th hour are shown. Source data is available for this figure in the Supplementary Information. Source data for Figure 3a [msb201257-sup-0001-SourceData-S1.xls] Source data for Figure 3d [msb201257-sup-0002-SourceData-S2.xls] Download figure Download PowerPoint Furthermore, our circuits can serve as a well-defined model system to examine the interplay between critical parameters associated with altruistic death in response to stress. To this end, we developed a kinetic model for the programmed circuit dynamics (Equations (1)–(6), Supplementary Text, and Figure 3B). In the model, we focused on the effects of several experimentally tunable parameters, including the synthesis rate of the public good (β2), the programmed death rate as modulated by the synthesis rate of the E protein (β1), and the initial stress level (or 6-APA concentration, a). Other key determinants for the growth advantage of altruistic death are the time frame within which the growth dynamics are compared and the cell density at which programmed death is triggered. At earlier time points, altruistic death is detrimental as the population has not yet fully enjoyed the benefit of the released public good, the degradation of 6-APA (Figure 3A and B). Also, if programmed death was triggered at a low cell density, the public-good release would be low (limited by the total number of cells that can be possibly killed) and thus altruistic death would not become advantageous after the same duration of culturing (Supplementary Figure S3). These properties underscore the importance of factors defining the bacterial life cycle (initial density and growth duration) in determining the potential adaptive advantage of altruistic death. In subsequent analysis, our experimental growth dynamics were initialized with density of A600=0.15–0.2 and compared at 24 h post 6-APA treatment. At a specific stress level, the rate of public-good production should affect the extent to which altruistic death can be advantageous for the population. This can be analyzed by varying the synthesis rate of BlaM. Indeed, for a=5.5 at time zero, our model predicts that although increasing BlaM expression improve growth of both PAD and NPD strains, PAD strain is more fit than the NPD strain only when BlaM expression is sufficiently high (β2>3.6) to compensate for the cost of death (Figure 3C). Consistent with the prediction, at 400 μg/ml 6-APA, increasing BlaM expression by IPTG enhanced overall growth, and the PAD strain outgrew the NPD strain only for IPTG greater than 0.25 mM (Figure 3D, Supplementary Figure S4). Thus, public-good release needs to be sufficiently high for altruistic death to be advantageous. Prediction and validation of optimal death rates Everything else being equal, the degree of programmed death should dictate the maximum net benefit of altruistic death owing to the trade-off between programmed death and public-good release. If too drastic, programmed death cannot be sufficiently compensated for by the released public good. If too little, the amount of released public good will also be low and the population is unable to deal with the stress within the time frame of interest. Indeed, our model predicts an optimal degree of programmed death (as modulated by β1, E synthesis rate; Figure 4A). We note that the emergence of the optimal death rate is critically dependent on temporal dynamics (Supplementary Figure S5a). The optimality emerges only after sufficient time; during the initial period, the bacterial density decreased monotonically with an increasing programmed death rate by the E protein. Figure 4.Predicted optimality in programmed death. (A) Simulated fitness landscape with respect to the E synthesis rate (β1; x axis) and the public-good production rate (β2; y axis) after 6-APA treatment (a(0)=5.5). An optimal β1 value shifts toward lower value as public-good production decreases (solid line). Inset: slices of the landscape along the x axis with different rates of public-good production (from blue to purple, β2=5.5, 4.5, 3.5, and 2.5). Cell densities at τ=20 are shown. (B) Simulated population dynamics with low public-good production rate (β2=3.3). 6-APA was added at time 0 and the following growth dynamics of population with either relatively high (blue; β1=0.03) or low E synthesis rate (red; β1=0.01) are shown. The fast-death population starts to recover after around time 14. (C) Simulated population dynamics with high public-good production rate (β2=5). The fast-death population starts to recover after around time 11, which is earlier than the case in (B). The same β1 values were used as in (B). Download figure Download PowerPoint Interestingly, the optimal degree of programmed death increases as the rate of public-good generation (β2) is increased. When public-good generation is too slow (β2<3.3), any programmed death is detrimental to the overall population because the amount of public good released is too small to cause substantial population recovery within the time frame of interest. For sufficiently fast public-good production (β2⩾3.3), the optimal degree of programmed death increases with the rate of public-good generation: that is, it is better to die faster if the public good is being released faster (per cell). This can be understood by considering the temporal dynamics of the system. In response to 6-APA, cells start to express the E gene and undergo a ‘death phase’. The cell death then releases BlaM, and 6-APA concentration starts to drop. When 6-APA is sufficiently reduced, cell density enters its ‘recovery phase’. Now consider two strategies, slow death and fast death. When public-good production rate is low (β2=3.3), the duration of the recovery phase is relatively short owing to slow removal of 6-APA (Figure 4B, Supplementary Figure S5b). This makes fast death less advantageous despite the fact that it enables the population to enter the recovery phase earlier with greater recovery rate (Supplementary Figure S5b). When the public-good production rate is high (β2=5), however, the duration of the recovery phase becomes relatively long. Now the fast growth rate in the recovery phase becomes more advantageous, making fast death a better strategy (Figure 4C). In other words, the increased public-good production renders more benefit for fast death than slow death owing to the nonlinearity of the system. We note that at sufficiently fast public-good production, the cost of drastic initial death can outweigh the benefit: a moderate degree of programmed death can release sufficient public good to neutralize 6-APA. As a result, the optimal death rate slightly decreases (Figures 4A, β2>5.5). At the same time, however, higher public-good production rates result in overall elevation of growth, leading to an insensitive dependence of cell density on the death rate around the optimum (Figure 4A, inset). These intricate dynamics highlight the complexity of the cost–benefit trade-off in programmed death in the temporal domain. They also underscore the need to use a kinetic model to capture the trade-off; this aspect is also evidenced by the requirement for sufficiently long growth duration for the emergence of optimality in death rate (Supplementary Figure S5a). We also note that the predicted optimality above is relevant for clonal populations. In mixed populations, the optimal degree of programmed death will likely be different and depend on the specific population structure (Ackermann et al, 2008; Chuang et al, 2009) (see Supplementary Text and Supplementary Figure S5c for further analysis and discussion). To test the predicted optimality in the E synthesis rate, we created variants of the PAD circuit, termed iPAD (intermediate-level PAD), by attenuating the strength of ribosome-binding site (RBS) of the E gene. Modulating translation as opposed to transcription likely maintains activation characteristics of PampC in response to 6-APA (e.g., dose–response curve). Upon 6-APA treatment, all the variants exhibited intermediate degrees of lysis, which were greater than that by the NPD strain but less than that by the PAD strain (Figure 5A; Supplementary Figure S6a). We repeated the experiment shown in Figure 3A using iPAD strains to obtain a fitness landscape. At 0.031 mM IPTG, neither PAD strain nor iPAD strains did better than NPD, suggesting that altruistic death was not sufficiently beneficial. However, at 0.063 mM IPTG, we found the optimum at iPAD1 strain, the lowest degree of programmed death (Figure 5B, light blue line). As the IPTG concentration was increased, the optimum shifted to higher degrees of programmed death, confirming the model prediction (Figure 5B). We note that the further increase in BlaM expression by arabinose resulted in a flat landscape (Supplementary Figure S6b), consistent with model prediction (Figure 4A, inset). Figure 5.Experimental test of optimal death rates. (A) Variants of PAD with varying programmed death rates. Cultures of PAD variants (iPAD1 through iPAD4) as well}, journal={Molecular Systems Biology}, author={Tanouchi, Y. and Pai, A. and Buchler, N.E. and You, L.}, year={2012} }
 @article{buchler_bai_2011, title={Chromatin: Bind at your own RSC}, volume={21}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79952802100&partnerID=MN8TOARS}, DOI={10.1016/j.cub.2011.01.060}, abstractNote={Recent work has identified a novel RSC–nucleosome complex that both strongly phases flanking nucleosomes and presents regulatory sites for ready access. These results challenge several widely held views. Recent work has identified a novel RSC–nucleosome complex that both strongly phases flanking nucleosomes and presents regulatory sites for ready access. These results challenge several widely held views. Genome-wide experiments in yeast, fly and mammalian cells have identified the existence of nucleosome-depleted regions in promoters and enhancers [1Yuan G.C. Liu Y.J. Dion M.F. Slack M.D. Wu L.F. Altschuler S.J. Rando O.J. Genome-scale identification of nucleosome positions in S. cerevisiae.Science. 2005; 309: 626-630Crossref PubMed Scopus (877) Google Scholar, 2Mavrich T.N. Jiang C. Ioshikhes I.P. Li X. Venters B.J. Zanton S.J. Tomsho L.P. Qi J. Glaser R.L. Schuster S.C. et al.Nucleosome organization in the Drosophila genome.Nature. 2008; 453: 358-362Crossref PubMed Scopus (539) Google Scholar, 3Schones D.E. Cui K. Cuddapah S. Roh T.Y. Barski A. Wang Z. Wei G. Zhao K. Dynamic regulation of nucleosome positioning in the human genome.Cell. 2008; 132: 887-898Abstract Full Text Full Text PDF PubMed Scopus (958) Google Scholar, 4Kaplan N. Moore I.K. Fondufe-Mittendorf Y. Gossett A.J. Tillo D. Field Y. LeProust E.M. Hughes T.R. Lieb J.D. Widom J. et al.The DNA-encoded nucleosome organization of a eukaryotic genome.Nature. 2009; 458: 362-366Crossref PubMed Scopus (871) Google Scholar]. Transcription factors are thought to bind to their cognate sites located in these nucleosome-depleted regions, subsequently recruit nucleosome-remodeling and modifying complexes, and evict or reposition flanking nucleosomes that block RNA polymerase assembly at the promoter. By using a novel, quantitative assay, recent work from the Ptashne lab has uncovered several striking insights into nucleosome occupancy at the GAL1/10 promoter of budding yeast [5Bryant G.O. Prabhu V. Floer M. Wang X. Spagna D. Schreiber D. Ptashne M. Activator control of nucleosome occupancy in activation and repression of transcription.PLoS Biol. 2008; 6: 2928-2939Crossref PubMed Scopus (81) Google Scholar, 6Floer M. Wang X. Prabhu V. Berrozpe G. Narayan S. Spagna D. Alvarez D. Kendall J. Krasnitz A. Stepansky A. et al.A RSC/nucleosome complex determines chromatin architecture and facilitates activator binding.Cell. 2010; 141: 407-418Abstract Full Text Full Text PDF PubMed Scopus (136) Google Scholar, 7Wang X. Bryant G.O. Floer M. Spagna D. Ptashne M. An effect of DNA sequence on nucleosome occupancy and removal upon induction at the yeast GAL1 promoter.Nat. Struct. Mol. Biol. 2011; (Advanced Online Publication)https://doi.org/10.1038/nsmb.2017Crossref Scopus (37) Google Scholar]. These results challenge current ideas of whether nucleosome-depleted regions are completely nucleosome-free, whether strongly positioned nucleosomes are always incompatible with the binding of regulatory proteins, and whether the occupancy of a DNA fragment by a nucleosome is mostly determined by its sequence. Nucleosome occupancy at a particular genomic location is measured by assessing nucleosome-mediated ‘protection’ (often assumed to be the canonical, mono-nucleosome size of 147 bp) of that sequence from digestion by micrococcal nuclease (MNase). Typical nucleosome occupancy assays fix chromatin in cells, lightly digest chromatin at a single concentration of MNase, and quantify protected DNA fragments by quantitative PCR (qPCR), tiling microarrays, or next-generation sequencing. Unfortunately, DNA sequence itself influences digestion efficiency of MNase, a bias that can create a false apparent protection of ‘naked’ genomic DNA. Strikingly, recent papers show that MNase digestion of naked genomic DNA infers similar nucleosome occupancies to that obtained by MNase digestion of chromatin DNA [8Locke G. Tolkunov D. Moqtaderi Z. Struhl K. Morozov A.V. High-throughput sequencing reveals a simple model of nucleosome energetics.Proc. Natl. Acad. Sci. USA. 2010; 107: 20998-21003Crossref PubMed Scopus (54) Google Scholar, 9Chung H.R. Dunkel I. Heise F. Linke C. Krobitsch S. Ehrenhofer-Murray A.E. Sperling S.R. Vingron M. The effect of micrococcal nuclease digestion on nucleosome positioning data.PLoS One. 2010; 5: e15754Crossref PubMed Scopus (82) Google Scholar]. Bryant et al. [5Bryant G.O. Prabhu V. Floer M. Wang X. Spagna D. Schreiber D. Ptashne M. Activator control of nucleosome occupancy in activation and repression of transcription.PLoS Biol. 2008; 6: 2928-2939Crossref PubMed Scopus (81) Google Scholar] developed a quantitative MNase protection assay that normalizes against such variability. The assay digests naked genomic DNA and fixed chromatin DNA over a wide range of MNase concentrations, followed by qPCR to quantify the relative amount of DNA. For any given amplicon (∼50 bp) of chromatin DNA, the measured digestion rate of nucleosomes is usually biphasic. One fraction of chromatin is digested at a rate comparable to naked DNA; the other (nucleosome-bound) fraction is digested ∼200-fold more slowly. Because of this separation of timescales, the occupancy of the nucleosome-protected DNA fragment is robustly determined by fitting a bi-exponential function to the MNase digestion series. Using this quantitative assay, Bryant et al. [5Bryant G.O. Prabhu V. Floer M. Wang X. Spagna D. Schreiber D. Ptashne M. Activator control of nucleosome occupancy in activation and repression of transcription.PLoS Biol. 2008; 6: 2928-2939Crossref PubMed Scopus (81) Google Scholar] illustrated that some unknown protective factor (not Gal4) is bound to the UASg in GAL1/10 in 100% of yeast cells both before and after galactose induction. In a follow-up study, Floer et al. [6Floer M. Wang X. Prabhu V. Berrozpe G. Narayan S. Spagna D. Alvarez D. Kendall J. Krasnitz A. Stepansky A. et al.A RSC/nucleosome complex determines chromatin architecture and facilitates activator binding.Cell. 2010; 141: 407-418Abstract Full Text Full Text PDF PubMed Scopus (136) Google Scholar] determined the identity of this factor. It is a ‘small’ RSC–nucleosome complex (containing all four histone components) that protects ∼130 bp and binds strongly to specific sequences within the UASg (Figure 1A ). RSC is a chromatin remodeling complex, and unlike its relative Swi/Snf, RSC is essential for yeast viability. The ∼130-bp footprint of the RSC nucleosome was further validated by genome-wide ‘paired-end’ DNA sequencing of digested chromatin. The genome-wide data of Floer et al. show the existence of hundreds of small nucleosome footprints that overlap regulatory sites in other yeast promoters. This is a striking result because many protocols and algorithms used to analyze nucleosome occupancy presume that mono-nucleosomes always protect an invariant ∼150-bp DNA fragment. Thus, the field may have been blind to a potentially important class of regulatory nucleosomes. Floer et al. subsequently showed that formation of this unusual and strongly positioned nucleosome depends on both the DNA-binding and catalytic subunit of the RSC complex. Mutants deficient in the RSC nucleosome were significantly delayed in Gal4 binding to UASg and GAL1 transcription, suggesting that a strongly positioned RSC nucleosome both prevents encroachment from flanking nucleosomes and facilitates the binding of Gal4 to the UASg. However, these encroaching nucleosomes do not prevent the eventual binding of Gal4 to UASg — they only make the process slower. To explain their results, Floer et al. proposed a structural model (based on [10Chaban Y. Ezeokonkwo C. Chung W.H. Zhang F. Kornberg R.D. Maier-Davis B. Lorch Y. Asturias F.J. Structure of a RSC-nucleosome complex and insights into chromatin remodeling.Nat. Struct. Mol. Biol. 2008; 15: 1272-1277Crossref PubMed Scopus (109) Google Scholar]) in which the DNA is partially unwrapped on the histone surface (presumably by RSC), so as to accommodate the binding of Gal4. Interestingly, a UASg ectopically inserted into the coding region of GAL1 sufficed to strongly position the RSC/nucleosome (100% occupancy) and strongly phase the flanking nucleosomes [6Floer M. Wang X. Prabhu V. Berrozpe G. Narayan S. Spagna D. Alvarez D. Kendall J. Krasnitz A. Stepansky A. et al.A RSC/nucleosome complex determines chromatin architecture and facilitates activator binding.Cell. 2010; 141: 407-418Abstract Full Text Full Text PDF PubMed Scopus (136) Google Scholar]. One explanation could be that these nucleosomes are not strongly positioned by their underlying DNA sequence and are relatively ‘fluid’, such that the strongly bound RSC nucleosome at UASg forms a barrier that statistically positions or phases these nucleosomes [11Kornberg R.D. Stryer L. Statistical distributions of nucleosomes: nonrandom locations by a stochastic mechanism.Nucleic Acids Res. 1988; 16: 6677-6690Crossref PubMed Scopus (217) Google Scholar, 12Möbius W. Gerland U. Quantitative test of the barrier nucleosome model for statistical positioning of nucleosomes up- and downstream of transcription start sites.PLoS Comput. Biol. 2010; 6: e1000891Crossref PubMed Scopus (55) Google Scholar]. In contrast to the ∼100% occupancy of the RSC–nucleosome complex, the nucleosomes at positions -1, -2, and -3 in GAL1/10 seem to be present in only ∼50% of the population before galactose induction [5Bryant G.O. Prabhu V. Floer M. Wang X. Spagna D. Schreiber D. Ptashne M. Activator control of nucleosome occupancy in activation and repression of transcription.PLoS Biol. 2008; 6: 2928-2939Crossref PubMed Scopus (81) Google Scholar] (Figure 1A). What determines the occupancy, and does that have any effect on the dynamics of GAL1 induction? To address these questions, Wang et al. [7Wang X. Bryant G.O. Floer M. Spagna D. Ptashne M. An effect of DNA sequence on nucleosome occupancy and removal upon induction at the yeast GAL1 promoter.Nat. Struct. Mol. Biol. 2011; (Advanced Online Publication)https://doi.org/10.1038/nsmb.2017Crossref Scopus (37) Google Scholar], as reported recently in Nature Sructural & Molecular Biology, replaced the DNA occupied by nucleosomes at postions -1 and -2 with a series of non-natural DNA sequences that are predicted to bind canonical nucleosomes with increasing affinity. As predicted, nucleosome affinity increased; the measured occupancy at positions -1 and -2 increased up to 100%. Upon induction with galactose, the occupancy of these strongly positioned ‘super-binder’ nucleosomes decreased (presumably by Swi/Snf), although eviction was less complete and induction occurred more slowly as nucleosome affinity increased. These data suggest that the wild-type GAL1/10 promoter has likely evolved a promoter sequence with low nucleosome occupancy to allow for rapid eviction upon galactose induction (Figure 1B,C). These studies raise important questions that will keep the chromatin field busy: how accurate are nucleosome occupancies derived from a single MNase digestion with no naked genomic DNA control? Do such artifacts change our current understanding of genome-wide nucleosome-depleted regions and whether nucleosome position is encoded in the DNA? How many other regulatory nucleosomes remain undiscovered because of our presumption that all nucleosomes protect ∼150 bp of DNA? How does partial nucleosome occupancy keep wild-type GAL1/10 transcription low? Is there a correlation in positioning and occupancy between adjacent nucleosomes at postions -1, -2, and -3? If this nucleosome depletion is a result of histone turnover, what is the on/off rate? Lastly, how does cell-to-cell variability in nucleosome configuration affect the noise in gene expression levels and dynamics? If we take our cue from Ptashne and co-workers, population-level and genome-wide assays may not be the best approach. Rather, biological insight will come from low-throughput approaches that measure nucleosome occupancy and gene expression of model genes in single cells [13Bai L. Charvin G. Siggia E. Cross F. Nucleosome-depleted regions in cell-cycle-regulated promoters ensure reliable gene expression in every cell cycle.Dev. Cell. 2010; 18: 544-555Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar, 14Zenklusen D. Larson D.R. Singer R.H. Single-RNA counting reveals alternative modes of gene expression in yeast.Nat. Struct. Mol. Biol. 2008; 15: 1263-1271Crossref PubMed Scopus (465) Google Scholar].}, number={6}, journal={Current Biology}, author={Buchler, N.E. and Bai, L.}, year={2011} }
 @article{cross_buchler_skotheim_2011, title={Evolution of networks and sequences in eukaryotic cell cycle control}, volume={366}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-81055155486&partnerID=MN8TOARS}, DOI={10.1098/rstb.2011.0078}, abstractNote={The molecular networks regulating the G1–S transition in budding yeast and mammals are strikingly similar in network structure. However, many of the individual proteins performing similar network roles appear to have unrelated amino acid sequences, suggesting either extremely rapid sequence evolution, or true polyphyly of proteins carrying out identical network roles. A yeast/mammal comparison suggests that network topology, and its associated dynamic properties, rather than regulatory proteins themselves may be the most important elements conserved through evolution. However, recent deep phylogenetic studies show that fungal and animal lineages are relatively closely related in the opisthokont branch of eukaryotes. The presence in plants of cell cycle regulators such as Rb, E2F and cyclins A and D, that appear lost in yeast, suggests cell cycle control in the last common ancestor of the eukaryotes was implemented with this set of regulatory proteins. Forward genetics in non-opisthokonts, such as plants or their green algal relatives, will provide direct information on cell cycle control in these organisms, and may elucidate the potentially more complex cell cycle control network of the last common eukaryotic ancestor.}, number={1584}, journal={Philosophical Transactions of the Royal Society B: Biological Sciences}, author={Cross, F.R. and Buchler, N.E. and Skotheim, J.M.}, year={2011}, pages={3532–3544} }
 @article{buchler_cross_2009, title={Protein sequestration generates a flexible ultrasensitive response in a genetic network}, volume={5}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-67149138163&partnerID=MN8TOARS}, DOI={10.1038/msb.2009.30}, abstractNote={Ultrasensitive responses are crucial for cellular regulation. Protein sequestration, where an active protein is bound in an inactive complex by an inhibitor, can potentially generate ultrasensitivity. Here, in a synthetic genetic circuit in budding yeast, we show that sequestration of a basic leucine zipper transcription factor by a dominant-negative inhibitor converts a graded transcriptional response into a sharply ultrasensitive response, with apparent Hill coefficients up to 12. A simple quantitative model for this genetic network shows that both the threshold and the degree of ultrasensitivity depend upon the abundance of the inhibitor, exactly as we observed experimentally. The abundance of the inhibitor can be altered by simple mutation; thus, ultrasensitive responses mediated by protein sequestration are easily tuneable. Gene duplication of regulatory homodimers and loss-of-function mutations can create dominant negatives that sequester and inactivate the original regulator. The generation of flexible ultrasensitive responses is an unappreciated adaptive advantage that could explain the frequent evolutionary emergence of dominant negatives.}, journal={Molecular Systems Biology}, author={Buchler, N.E. and Cross, F.R.}, year={2009} }
 @article{buchler_louis_2008, title={Molecular Titration and Ultrasensitivity in Regulatory Networks}, volume={384}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-56949098506&partnerID=MN8TOARS}, DOI={10.1016/j.jmb.2008.09.079}, abstractNote={Protein sequestration occurs when an active protein is sequestered by a repressor into an inactive complex. Using mathematical and computational modeling, we show how this regulatory mechanism (called "molecular titration") can generate ultrasensitive or "all-or-none" responses that are equivalent to highly cooperative processes. The ultrasensitive nature of the input-output response is mainly determined by two parameters: the dimer dissociation constant and the repressor concentration. Because in vivo concentrations are tunable through a variety of mechanisms, molecular titration represents a flexible mechanism for generating ultrasensitivity. Using physiological parameters, we report how details of in vivo protein degradation affect the strength of the ultrasensitivity at steady state. Given that developmental systems often transduce signals into cell-fate decisions on timescales incompatible with steady state, we further examine whether molecular titration can produce ultrasensitive responses within physiologically relevant time intervals. Using Drosophila somatic sex determination as a developmental paradigm, we demonstrate that molecular titration can generate ultrasensitivity on timescales compatible with most cell-fate decisions. Gene duplication followed by loss-of-function mutations can create dominant negatives that titrate and compete with the original protein. Dominant negatives are abundant in gene regulatory circuits, and our results suggest that molecular titration might be generating an ultrasensitive response in these networks.}, number={5}, journal={Journal of Molecular Biology}, author={Buchler, N.E. and Louis, M.}, year={2008}, pages={1106–1119} }
 @article{fritz_buchler_hwa_gerland_2007, title={Designing sequential transcription logic: A simple genetic circuit for conditional memory}, volume={1}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-34548533820&partnerID=MN8TOARS}, DOI={10.1007/s11693-007-9006-8}, abstractNote={The ability to learn and respond to recurrent events depends on the capacity to remember transient biological signals received in the past. Moreover, it may be desirable to remember or ignore these transient signals conditioned upon other signals that are active at specific points in time or in unique environments. Here, we propose a simple genetic circuit in bacteria that is capable of conditionally memorizing a signal in the form of a transcription factor concentration. The circuit behaves similarly to a “data latch” in an electronic circuit, i.e. it reads and stores an input signal only when conditioned to do so by a “read command.” Our circuit is of the same size as the well-known genetic toggle switch (an unconditional latch) which consists of two mutually repressing genes, but is complemented with a “regulatory front end” involving protein heterodimerization as a simple way to implement conditional control. Deterministic and stochastic analysis of the circuit dynamics indicate that an experimental implementation is feasible based on well-characterized genes and proteins. It is not known, to which extent molecular networks are able to conditionally store information in natural contexts for bacteria. However, our results suggest that such sequential logic elements may be readily implemented by cells through the combination of existing protein–protein interactions and simple transcriptional regulation.}, number={2}, journal={Systems and Synthetic Biology}, author={Fritz, G. and Buchler, N.E. and Hwa, T. and Gerland, U.}, year={2007}, pages={89–98} }
 @article{buchler_gerland_hwa_2005, title={Nonlinear protein degradation and the function of genetic circuits}, volume={102}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-22144443131&partnerID=MN8TOARS}, DOI={10.1073/pnas.0409553102}, abstractNote={The functions of most genetic circuits require a sufficient degree of cooperativity in the circuit components. Although mechanisms of cooperativity have been studied most extensively in the context of transcriptional initiation control, cooperativity from other processes involved in the operation of the circuits can also play important roles. In this work, we examine a simple kinetic source of cooperativity stemming from the nonlinear degradation of multimeric proteins. Ample experimental evidence suggests that protein subunits can degrade less rapidly when associated in multimeric complexes, an effect we refer to as "cooperative stability." For dimeric transcription factors, this effect leads to a concentration-dependence in the degradation rate because monomers, which are predominant at low concentrations, will be more rapidly degraded. Thus, cooperative stability can effectively widen the accessible range of protein levels in vivo. Through theoretical analysis of two exemplary genetic circuits in bacteria, we show that such an increased range is important for the robust operation of genetic circuits as well as their evolvability. Our calculations demonstrate that a few-fold difference between the degradation rate of monomers and dimers can already enhance the function of these circuits substantially. We discuss molecular mechanisms of cooperative stability and their occurrence in natural or engineered systems. Our results suggest that cooperative stability needs to be considered explicitly and characterized quantitatively in any systematic experimental or theoretical study of gene circuits.}, number={27}, journal={Proceedings of the National Academy of Sciences of the United States of America}, author={Buchler, N.E. and Gerland, U. and Hwa, T.}, year={2005}, pages={9559–9564} }
 @article{bintu_buchler_garcia_gerland_hwa_kondev_kuhlman_phillips_2005, title={Transcriptional regulation by the numbers: Applications}, volume={15}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-15744387380&partnerID=MN8TOARS}, DOI={10.1016/j.gde.2005.02.006}, abstractNote={With the increasing amount of experimental data on gene expression and regulation, there is a growing need for quantitative models to describe the data and relate them to their respective context. Thermodynamic models provide a useful framework for the quantitative analysis of bacterial transcription regulation. This framework can facilitate the quantification of vastly different forms of gene expression from several well-characterized bacterial promoters that are regulated by one or two species of transcription factors; it is useful because it requires only a few parameters. As such, it provides a compact description useful for higher-level studies (e.g. of genetic networks) without the need to invoke the biochemical details of every component. Moreover, it can be used to generate hypotheses on the likely mechanisms of transcriptional control.}, number={2}, journal={Current Opinion in Genetics and Development}, author={Bintu, L. and Buchler, N.E. and Garcia, H.G. and Gerland, U. and Hwa, T. and Kondev, J. and Kuhlman, T. and Phillips, R.}, year={2005}, pages={125–135} }
 @article{bintu_buchler_garcia_gerland_hwa_kondev_phillips_2005, title={Transcriptional regulation by the numbers: Models}, volume={15}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-15744394192&partnerID=MN8TOARS}, DOI={10.1016/j.gde.2005.02.007}, abstractNote={The expression of genes is regularly characterized with respect to how much, how fast, when and where. Such quantitative data demands quantitative models. Thermodynamic models are based on the assumption that the level of gene expression is proportional to the equilibrium probability that RNA polymerase (RNAP) is bound to the promoter of interest. Statistical mechanics provides a framework for computing these probabilities. Within this framework, interactions of activators, repressors, helper molecules and RNAP are described by a single function, the "regulation factor". This analysis culminates in an expression for the probability of RNA polymerase binding at the promoter of interest as a function of the number of regulatory proteins in the cell.}, number={2}, journal={Current Opinion in Genetics and Development}, author={Bintu, L. and Buchler, N.E. and Garcia, H.G. and Gerland, U. and Hwa, T. and Kondev, J. and Phillips, R.}, year={2005}, pages={116–124} }
 @article{archambault_buchler_wilmes_jacobson_cross_2005, title={Two-faced cyclins with eyes on the targets}, volume={4}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-14844287697&partnerID=MN8TOARS}, number={1}, journal={Cell Cycle}, author={Archambault, V. and Buchler, N.E. and Wilmes, G.M. and Jacobson, M.D. and Cross, F.R.}, year={2005}, pages={125–130} }
 @article{buchler_gerland_hwa_2003, title={On schemes of combinatorial transcription logic}, volume={100}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-0037965981&partnerID=MN8TOARS}, DOI={10.1073/pnas.0930314100}, abstractNote={Cells receive a wide variety of cellular and environmental signals, which are often processed combinatorially to generate specific genetic responses. Here we explore theoretically the potentials and limitations of combinatorial signal integration at the level of cis-regulatory transcription control. Our analysis suggests that many complex transcription-control functions of the type encountered in higher eukaryotes are already implementable within the much simpler bacterial transcription system. Using a quantitative model of bacterial transcription and invoking only specific protein-DNA interaction and weak glue-like interaction between regulatory proteins, we show explicit schemes to implement regulatory logic functions of increasing complexity by appropriately selecting the strengths and arranging the relative positions of the relevant protein-binding DNA sequences in the cis-regulatory region. The architectures that emerge are naturally modular and evolvable. Our results suggest that the transcription regulatory apparatus is a "programmable" computing machine, belonging formally to the class of Boltzmann machines. Crucial to our results is the ability to regulate gene expression at a distance. In bacteria, this can be achieved for isolated genes via DNA looping controlled by the dimerization of DNA-bound proteins. However, if adopted extensively in the genome, long-distance interaction can cause unintentional intergenic cross talk, a detrimental side effect difficult to overcome by the known bacterial transcription-regulation systems. This may be a key factor limiting the genome-wide adoption of complex transcription control in bacteria. Implications of our findings for combinatorial transcription control in eukaryotes are discussed.}, number={9}, journal={Proceedings of the National Academy of Sciences of the United States of America}, author={Buchler, N.E. and Gerland, U. and Hwa, T.}, year={2003}, pages={5136–5141} }
 @article{surveying determinants of protein structure designability across different energy models and amino-acid alphabets: a consensus_2000, url={https://publons.com/publon/2047266/}, DOI={10.1063/1.480893}, abstractNote={A variety of analytical and computational models have been proposed to answer the question of why some protein structures are more “designable” (i.e., have more sequences folding into them) than others. One class of analytical and statistical-mechanical models has approached the designability problem from a thermodynamic viewpoint. These models highlighted specific structural features important for increased designability. Furthermore, designability was shown to be inherently related to thermodynamically relevant energetic measures of protein folding, such as the foldability ℱ and energy gap Δ10. However, many of these models have been done within a very narrow focus: Namely, pair–contact interactions and two-letter amino-acid alphabets. Recently, two-letter amino-acid alphabets for pair–contact models have been shown to contain designability artifacts which disappear for larger-letter amino-acid alphabets. In addition, a solvation model was demonstrated to give identical designability results to previous two-letter amino-acid alphabet pair–contact models. In light of these discordant results, this report synthesizes a broad consensus regarding the relationship between specific structural features, foldability ℱ, energy gap Δ10, and structure designability for different energy models (pair–contact vs solvation) across a wide range of amino-acid alphabets. We also propose a novel measure Zdk which is shown to be well correlated to designability. Finally, we conclusively demonstrate that two-letter amino-acid alphabets for pair–contact models appear to be solvation models in disguise.}, journal={The Journal of Chemical Physics}, year={2000} }
 @article{buchler_goldstein_1999, title={Effect of alphabet size and foldability requirements on protein structure designability}, volume={34}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-0032939920&partnerID=MN8TOARS}, DOI={10.1002/(SICI)1097-0134(19990101)34:1<113::AID-PROT9>3.0.CO;2-J}, abstractNote={A number of investigators have addressed the issue of why certain protein structures are especially common by considering structure designability, defined as the number of sequences that would successfully fold into any particular native structure. One such approach, based on foldability, suggested that structures could be classified according to their maximum possible foldability and that this optimal foldability would be highly correlated with structure designability. Other approaches have focused on computing the designability of lattice proteins written with reduced two-letter amino acid alphabets. These different approaches suggested contrasting characteristics of the most designable structures. This report compares the designability of lattice proteins over a wide range of amino acid alphabets and foldability requirements. While all alphabets have a wide distribution of protein designabilities, the form of the distribution depends on how protein "viability" is defined. Furthermore, under increasing foldability requirements, the change in designabilities for all alphabets are in good agreement with the previous conclusions of the foldability approach. Most importantly, it was noticed that those structures that were highly designable for the two-letter amino acid alphabets are not especially designable with higher-letter alphabets.}, number={1}, journal={Proteins: Structure, Function and Genetics}, author={Buchler, N.E.G. and Goldstein, R.A.}, year={1999}, pages={113–124} }
 @article{universal correlation between energy gap and foldability for the random energy model and lattice proteins_1999, url={https://publons.com/publon/2047267/}, DOI={10.1063/1.479951}, abstractNote={The random energy model, originally used to analyze the physics of spin glasses, has been employed to explore what makes a protein a good folder versus a bad folder. In earlier work, the ratio of the folding temperature over the glass–transition temperature was related to a statistical measure of protein energy landscapes denoted as the foldability ℱ. It was posited and subsequently established by simulation that good folders had larger foldabilities, on average, than bad folders. An alternative hypothesis, equally verified by protein folding simulations, was that it is the energy gap Δ between the native state and the next highest energy that distinguishes good folders from bad folders. This duality of measures has led to some controversy and confusion with little done to reconcile the two. In this paper, we revisit the random energy model to derive the statistical distributions of the various energy gaps and foldability. The resulting joint distribution allows us to explicitly demonstrate the positive correlation between foldability and energy gap. In addition, we compare the results of this analytical theory with a variety of lattice models. Our simulations indicate that both the individual distributions and the joint distribution of foldability and energy gap agree qualitatively well with the random energy model. It is argued that the universal distribution of and the positive correlation between foldability and energy gap, both in lattice proteins and the random energy model, is simply a stochastic consequence of the “thermodynamic hypothesis.”}, journal={The Journal of Chemical Physics}, year={1999} }
 @article{buchler_zuiderweg_wang_goldstein_1997, title={Protein Heteronuclear NMR Assignments Using Mean-Field Simulated Annealing}, volume={125}, url={https://publons.com/publon/2047265/}, DOI={10.1006/JMRE.1997.1106}, abstractNote={A computational method for the assignment of the NMR spectra of larger (21 kDa) proteins using a set of six of the most sensitive heteronuclear multidimensional nuclear magnetic resonance experiments is described. Connectivity data obtained from HNC alpha, HN(CO)C alpha, HN(C alpha)H alpha, and H alpha (C alpha CO)NH and spin-system identification data obtained from CP-(H)CCH-TOCSY and CP-(H)C(C alpha CO)NH-TOCSY were used to perform sequence-specific assignments using a mean-field formalism and simulated annealing. This mean-field method reports the resonance assignments in a probabilistic fashion, displaying the certainty of assignments in an unambiguous and quantitative manner. This technique was applied to the NMR data of the 172-residue peptide-binding domain of the E. coli heat-shock protein, DnaK. The method is demonstrated to be robust to significant amounts of missing, spurious, noisy, extraneous, and erroneous data.}, number={1}, journal={Journal of Magnetic Resonance}, author={Buchler, N.E.G. and Zuiderweg, E.R.P. and Wang, H. and Goldstein, R.A.}, year={1997}, pages={34–42} }
 @article{buchler_zuiderweg_wang_goldstein_1997, title={Protein Heteronuclear NMR Assignments Using Mean-Field Simulated Annealing}, volume={125}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-0031083526&partnerID=MN8TOARS}, number={1}, journal={Journal of Magnetic Resonance}, author={Buchler, N.E.G. and Zuiderweg, E.R.P. and Wang, H. and Goldstein, R.A.}, year={1997}, pages={34–42} }
 @article{bl herculis model pulsations .3. livermore opacities_1994, url={https://publons.com/publon/51431452/}, journal={Astronomy & Astrophysics}, year={1994} }