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At least 19 recordsLinked to original sources
bioRxiv2017

A cell-free synthetic biochemistry platform for raspberry ketone production

Cell-free synthetic biochemistry provides a green solution to replace traditional petroleum or agricultural based methods for production of fine chemicals. 4-(4-hydroxyphenyl)-butan-2-one, also known as raspberry ketone, is the major fragrance component of raspberry fruit and is utilised as a natural additive in the food and sports industry. Current industrial processing standards involve chemical extraction with a yield of 1-4 mg per kilo of fruit. As such its market price can fluctuate up to $20,000 per kg. Metabolic engineering approaches to synthesise this molecule by microbial fermentation have only resulted in low yields of up to 5 mg L-1. In contrast, cell-free synthetic biochemistry offers an intriguing compromise to the engineering constraints provided by the living cell. Using purified enzymes or a two-step semisynthetic route, an optimised pathway was formed for raspberry ketone synthesis leading up to 100% yield conversion. The semi-synthetic route is potentially scalable and cost-efficient for industrial synthesis of raspberry ketone.

synthetic biology↗
bioRxiv2017

Knowledge of the Neighborhood of the Reactive Site up to Three Atoms Can Predict Biochemistry and Protein Sequences

Thousands of biochemical reactions with characterized activities are orphan, meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose a new method, BridgIT, to identify candidate genes and protein sequences for these reactions, and this method introduces, for the first time, information about the enzyme binding pocket into reaction similarity comparisons. BridgIT assesses the similarity of two reactions, one orphan and one well-characterized, nonorphan reaction, using their substrate reactive sites, their surrounding structures, and the structures of the generated products to suggest protein sequences and genes that catalyze the most similar non-orphan reactions as candidates for also catalyzing the orphan ones.\n\nWe performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from KEGG 2011 (a comprehensive enzymatic reaction database) that became non-orphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 out of 379 novel reactions in 2014 that were later catalogued in KEGG 2018, BridgIT predicted the exact or highly similar enzyme sequences.\n\nBridgIT requires knowledge about only three connecting bonds around the atoms of the reactive sites to correctly identify protein sequences for 93% of analyzed enzymatic reactions. Increasing to six connecting bonds allowed for the accurate identification of a sequence for nearly all known enzymatic reactions.\n\nSIGNIFICANCE STATEMENTRecent advances in synthetic biochemistry have resulted in a wealth of novel hypothetical enzymatic reactions that are not matched to protein-encoding genes, deeming them \"orphan\". Nearly half of known metabolic enzymes are also orphan, leaving important gaps in metabolic network maps. Proposing genes for the catalysis of orphan reactions is critical for applications ranging from biotechnology to medicine. In this work, a novel computational method, BridgIT, identified a potential enzyme sequence to orphan reactions and nearly all theoretically possible biochemical transformations, providing candidate genes to catalyze these reactions to the research community. BridgIT online tool will allow researchers to fill the knowledge gaps in metabolic networks and will act as a starting point for designing novel enzymes to catalyze non-natural transformations.

bioinformatics↗
bioRxiv2017

Hematology, biochemistry, and toxicology of wild hawksbill turtles (Eretmochelys imbricata) nesting in mangrove estuaries in the eastern Pacific Ocean

Sea turtles are a keystone species and are highly sensitive to changes in their environment, making them excellent environmental indicators. In light of environmental and climate changes, species are increasingly threatened by pollution, changes in ocean health, habitat alteration, and plastic ingestion. There may be additional health related threats and understanding these threats is key in directing future management and conservation efforts, particularly for severely reduced sea turtle populations. Hawksbill turtles (Eretmochelys imbricata) are critically endangered, with those in the eastern Pacific Ocean (Mexico-Peru) considered one of the most threatened sea turtle populations in the world. This study establishes baseline health parameters in hematology and blood biochemistry as well as tested for heavy metals and persitent organic pollutants in eastern Pacific hawksbills at a primary nesting colony located in a mangrove estuary. Whereas hematology and biochemistry results are consistent with healthy populations of other species of sea turtles, we identified differences in packed cell volume, heterophils and lympohcyte counts, and glucose when comparing our data to other adult hawksbill analysis (1), (2), (3). Our analysis of heavy metal contamination revealed a mean blood level of 0.245 ppm of arsenic, 0.045 ppm of lead, and 0.008 ppm of mercury. Blood levels of persistent organic pollutants were below the laboratory detection limit for all turtles. Our results suggest that differences in the feeding ecology of eastern Pacific hawksbills in mangrove estuaries may make them less likely to accumulate persistent organic pollutants and heavy metals in their blood. These baseline data on blood values in hawksbills nesting within a mangrove estuary in the eastern Pacific offer important guidance for health assessments of the species in the wild and in clinical rehabilitation facilities, and underscore the importance of preventing contamination from point and non-point sources in mangrove estuaries, which represent primary habitat to hawksbills and myriad other marine species in the eastern Pacific Ocean.

zoology↗
bioRxiv2017

Giantin Knockout Models Reveal The Capacity Of The Golgi To Regulate Its Biochemistry By Controlling Glycosyltransferase Expression

The Golgi is the cellular hub for complex glycosylation, controlling accurate processing of complex proteoglycans, receptors, ligands, and glycolipids. Its structure and organisation is dependent on golgins, which tether cisternal membranes and incoming transport vesicles. Here we show that knockout of the largest golgin, giantin, leads to substantial changes in gene expression despite only limited effects on Golgi structure. Notably, 22 Golgi-resident glycosyltransferases, but not glycan processing enzymes or the ER glycosylation machinery, are differentially expressed following giantin ablation. This includes near-complete loss-of-function of GALNT3 in both mammalian cell and zebrafish models. Giantin knockout zebrafish exhibit hyperostosis and ectopic calcium deposits, recapitulating phenotypes of hyperphosphatemic familial tumoral calcinosis, a disease caused by mutations in GALNT3. These data reveal a new feature of Golgi homeostasis, the ability to regulate glycosyltransferase expression to generate a functional proteoglycome.\n\nSummary statementKnockout of giantin in a genome-engineered cell line and zebrafish models reveals the capacity of the Golgi to control its own biochemistry through changes in gene expression.

cell biology↗
bioRxiv2017

Spatially organizing biochemistry: choosing a strategy to translate synthetic biology to the factory

Natural biochemical systems are ubiquitously organized both in space and time. Engineering the spatial organization of biochemistry has emerged as a key theme of synthetic biology, with numerous technologies promising improved biosynthetic pathway performance. One strategy, however, may produce disparate results for different biosynthetic pathways. We propose a spatially resolved kinetic model to explore this fundamental design choice in systems and synthetic biology. We predict that two example biosynthetic pathways have distinct optimal organization strategies that vary based on pathway-dependent and cell-extrinsic factors. Moreover, we outline this design space in general as a function of kinetic and biophysical properties, as well as culture conditions. Our results suggest that organizing biosynthesis has the potential to substantially improve performance, but that choosing the appropriate strategy is key. The flexible mathematical framework we propose can be adapted to diverse biosynthetic pathways, and lays a foundation to rationally choose organization strategies for biosynthesis.

synthetic biology↗
bioRxiv2018

The thermodynamic landscape of carbon redox biochemistry

Redox biochemistry plays a key role in the transduction of chemical energy in living systems. However, the compounds observed in metabolic redox reactions are a minuscule fraction of chemical space. It is not clear whether compounds that ended up being selected as metabolites display specific properties that distinguish them from non-biological compounds. Here we introduce a systematic approach for comparing the chemical space of all possible redox states of linear-chain carbon molecules to the corresponding metabolites that appear in biology. Using cheminformatics and quantum chemistry, we analyze the physicochemical and thermodynamic properties of the biological and non-biological compounds. We find that, among all compounds, aldose sugars have the highest possible number of redox connections to other molecules. Metabolites are enriched in carboxylic acid functional groups and depleted of carbonyls, and have higher solubility than non-biological compounds. Upon constructing the energy landscape for the full chemical space as a function of pH and electron donor potential, we find that over a large range of conditions metabolites tend to have lower Gibbs energies than non-biological molecules. Finally, we generate Pourbaix phase diagrams that serve as a thermodynamic atlas to indicate which compounds are local and global energy minima in redox chemical space across a set of pH values and electron donor potentials. Our work yields insight into the physicochemical principles governing redox metabolism, and suggests that thermodynamic stability in aqueous environments may have played an important role in early metabolic processes.

systems biology↗
bioRxiv2016

Local chromosome context is a major determinant of crossover pathway biochemistry during budding yeast meiosis

Meiotic chromosomes are divided into regions of enrichment and depletion for meiotic chromosome axis proteins, in budding yeast Hop1 and Red1. These proteins are important for formation of Spo11-catalyzed DSB, but their contribution to crossover recombination is undefined. By studying meiotic recombination initiated by the sequence-specific VMA1-derived endonuclease (VDE), we show that meiotic chromosome structure helps to determine the biochemical mechanism by which recombination intermediates are resolved to form crossovers. At a Hop1-enriched locus, most VDE-initiated crossovers required the MutL{gamma} resolvase, which forms most Spo11-initiated crossovers. In contrast, at a locus with lower Hop1 occupancy, most VDE-initiated crossovers were MutL{gamma}-independent. In pch2 mutants, the two loci displayed similar Hop1 occupancy levels, and also displayed similar MutL{gamma}-dependence of VDE-induced crossovers. We suggest that meiotic and mitotic recombination pathways coexist within meiotic cells, with features of meiotic chromosome structure partitioning the genome into regions where one pathway or the other predominates.

Genetics↗
bioRxiv2017

Effect of 40Gy irradiation on the ultrastructure, biochemistry, morphology and cytology during spermatogenesis in the southern green stink bug Nezara viridula (Hemiptera: Pentatomidae)

A study on the Nezara viridula male gonad cells was undertaken to compare normal development and development of insect cells after irradiation of 4th instar nymphs with a dose of 40 Gray. Visually, morphologically, biochemically and cytologically the insects were not all uniformly affected by the radiation. In all aspects of development there was a range from severely affected to nearly normal. Irradiated insects appeared to move slowly and were unable to mate with non-irradiated females. The testes of the males varied from bright orange to grey in colour and all were smaller in size than non-irradiated testes. The ultrastructure of the developing sperm showed abnormalities the axonemes, the mitochrondrial derivatives, nebenkern and centrioles. Cytochemically, the main difference observed was the presence of granules heavily stained with acid phosphatase in between mitochrondrial derivatives. The chromosomes of these irradiated insects were highly fragmented. Although a few sperm in irradiated insects appeared normal no progeny were produced as insect did not mate. The sterile insect technique (SIT) requires a balance between the effective radiation dose to achieve partial or full sterility, while maintaining physical fitness. The observation that abnormalities varied from almost none to severe at 40Gy could help the development of SIT for control of N. viridula and other Pentatomidae.

developmental biology↗
bioRxiv2017

Specialized plant biochemistry drives gene clustering in fungi

The fitness and evolution of both prokaryotes and eukaryotes are affected by the organization of their genomes. In particular, the physical clustering of functionally related genes can facilitate coordinated gene expression and can prevent the breakup of co-adapted alleles in recombining populations. While clustering may thus result from selection for phenotype optimization and persistence, the extent to which eukaryotic gene organization in particular is driven by specific environmental selection pressures has rarely been systematically explored. Here, we investigated the genetic architecture of fungal genes involved in the degradation of phenylpropanoids, a class of plant-produced secondary metabolites that mediate many ecological interactions between plants and fungi. Using a novel gene cluster detection method, we identified over one thousand gene clusters, as well as many conserved combinations of clusters, in a phylogenetically and ecologically diverse set of fungal genomes. We demonstrate that congruence in gene organization over small spatial scales in fungal genomes is often associated with similarities in ecological lifestyle. Additionally, we find that while clusters are often structured as independent modules with little overlap in content, certain gene families merge multiple modules in a common network, suggesting they are important components of phenylpropanoid degradation strategies. Together, our results suggest that phenylpropanoids have repeatedly selected for gene clustering in fungi, and highlight the interplay between gene organization and ecological evolution in this ancient eukaryotic lineage.

genomics↗
bioRxiv2018

Multi omics comparison reveals metabolome biochemistry, not microbiome composition or gene expression, corresponds to elevated biogeochemical function in the hyporheic zone

Biogeochemical hotspots are pervasive at terrestrial-aquatic interfaces, particularly within groundwater-surface water mixing zones (hyporheic zones), and they are critical to understanding spatiotemporal variation in biogeochemical cycling. Here, we use multi omic comparisons of hotspots to low-activity sediments to gain mechanistic insight into hyporheic zone organic matter processing. We hypothesized that microbiome structure and function, as described by metagenomics and metaproteomics, would distinguish hotspots from low-activity sediments through a shift towards carbohydrate-utilizing metabolic pathways and elucidate discrete mechanisms governing organic matter processing in each location. We also expected these differences to be reflected in the metabolome, whereby hotspot carbon (C) pools and metabolite transformations therein would be enriched in sugar-associated compounds. In contrast to expectations, we found pronounced phenotypic plasticity in the hyporheic zone microbiome that was denoted by similar microbiome structure, functional potential, and expression across sediments with dissimilar metabolic rates. Instead, diverse nitrogenous metabolites and biochemical transformations characterized hotspots. Metabolomes also corresponded more strongly to aerobic metabolism than bulk C content only (explaining 67% vs. 42% of variation), and bulk C did not improve statistical models based on metabolome composition alone. These results point to organic nitrogen as a significant regulatory factor influencing hyporheic zone organic matter processing. Based on our findings, we propose incorporating knowledge of metabolic pathways associated with different chemical fractions of C pools into ecosystem models will enhance prediction accuracy.

molecular biology↗
bioRxiv2018

Quantitative variations of ADF/cofilin’s multiple actions on actin filaments with pH

Actin Depolymerizing Factor (ADF)/cofilin is the main protein family promoting the disassembly of actin filaments, which is essential for numerous cellular functions. ADF/cofilin proteins disassemble actin filaments through different reactions, as they bind to their sides, sever them, and promote the depolymerization of the resulting ADF/cofilin-saturated filaments. Moreover, the efficiency of ADF/cofilin is known to be very sensitive to pH. ADF/cofilin thus illustrates two challenges in actin biochemistry: separating the different regulatory actions of a single protein, and characterizing them as a function of specific biochemical conditions. Here, we investigate the different reactions of ADF/cofilin on actin filaments, over four different values of pH ranging from pH 6.6 to pH 7.8, using single filament microfluidics techniques. We show that lowering pH reduces the effective filament severing rate by increasing the rate at which filaments become saturated by ADF/cofilin, thereby reducing the number of ADF/cofilin domain boundaries, where severing can occur. The severing rate per domain boundary, however, remains unchanged at different pH values. The ADF/cofilin-decorated filaments (refered to as \"cofilactin\" filaments) depolymerize from both ends. We show here that, at physiological pH (pH 7.0 to 7.4), the pointed end depolymerization of cofilactin filaments is barely faster than that of bare filaments. In contrast, cofilactin barbed ends undergo an \"unstoppable\" depolymerization (depolymerizing for minutes despite the presence of free actin monomers and capping protein in solution), throughout our range of pH. We thus show that, at physiological pH, the main contribution of ADF/cofilin to filament depolymerization is at the barbed end.\n\nA number of key cellular processes rely on the proper assembly and disassembly of actin filament networks 1. The central regulator of actin disassembly is the ADF/cofilin protein family 2, 3, which comprises three isoforms in mammals: cofilin-1 (cof1, found in nearly all cell types), cofilin-2 (cof2, found primarily in muscles) and Actin Depolymerization Factor (ADF, found mostly in neurons and epithelial cells). We refer to them collectively as \"ADF/cofilin\".\n\nOver the years, the combined efforts of several labs have led to the following understanding of actin filament disassembly by ADF/cofilin. Molecules of ADF/cofilin bind stoechiometrically 4, 5 to the sides of actin filaments, with a strong preference for ADP-actin subunits 6-10. Though ADF/cofilin molecules do not contact each other 11, they bind in a cooperative manner, leading to the formation of ADF/cofilin domains on the filaments 5, 7, 9, 12, 13. Compared to bare F-actin, the filament portions decorated by ADF/cofilin (refered to as \"cofilactin\") are more flexible 14, 15 and exhibit a shorter right-handed helical pitch, with a different subunit conformation 11, 16-19. Thermal fluctuations are then enough to sever actin filaments at (or near) domain boundaries8, 9, 13, 20, 21. Cofilactin filaments do not sever, but depolymerize from both ends 13 thereby renewing the actin monomer pool.\n\nADF/cofilin thus disassembles actin filaments through the combination of different actions. As such, it vividly illustrates a current challenge in actin biochemistry: identifying and quantifying the multiple reactions involving a single protein. This is a very difficult task for bulk solution assays, where a large number of reactions take place simultaneously, and single-filament techniques have played a key role in deciphering ADF/cofilins actions 9, 13, 20, 22-24. In particular, the microfluidics-based method that we have developed over the past years, is a powerful tool for such investigations 25. It has recently allowed us to quantify the kinetics of the aforementioned reactions, and to discover that ADF/cofilin-saturated filament (cofilactin) barbed ends can hardly stop depolymerizing, even when ATP-G-actin and capping protein are present in solution 13.\n\nIn addition, ADF/cofilin is very sensitive to pH 4, 5, 26-29. In cells, pH can be a key regulatory factor 30. It can vary between compartments, between cell types, and be specifically modulated. We can consider that a typical cytoplasmic pH would be comprised between 7.0 and 7.4. Recently, we have quantified the different reactions involving ADF/cofilin at pH 7.8 13, leaving open the question of how these reaction rates are indivdually affected by pH variations. For instance, it has been reported that ADF/cofilin is a more potent filament disassembler at higher pH values 4, 5, 26-29 but the actual impact of pH on the rate constants of individual reactions has yet to be characterized. Moreover, whether the unstoppable barbed end depolymerization that we have recently discovered for ADF/cofilin-saturated filaments at pH 7.8 13 remains significant at lower, more physiological pH values is an open question.\n\nHere, we investigate how the different contributions of ADF/cofilin (using unlabeled ADF, unlabeled cof1 and eGFP-cof1) to actin filament disassembly depend on pH, which we varied from 6.6 to 7.8. We first present the methods which we have used to do so, based on the observation of individual filaments, using microfluidics (Fig. 1). We measured cofilins abitility to decorate actin filament by binding to its sides (Fig. 2), and the rate at which individual cofilin domains severed actin filaments (Fig. 3). We next quantified the kinetic parameters of filament ends, for bare and ADF/cofilin-saturated (cofilactin) filaments (Fig. 4), and we specifically quantified the extent to which the barbed ends of cofilactin filaments are in a state which can hardly stop depolymerizing (Fig. 5). We finally summarize our results (Fig. 6).\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC=\"FIGDIR/small/422824_fig1.gif\" ALT=\"Figure 1\">\nView larger version (38K):\norg.highwire.dtl.DTLVardef@5a6fdorg.highwire.dtl.DTLVardef@1165d4borg.highwire.dtl.DTLVardef@146f0b7org.highwire.dtl.DTLVardef@658f72_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Using microfluidics to monitor individual actin filaments and the binding of cofilin. (A) Experiments are performed in microfluidic chambers, sketched from above. The main channel is connected through three inlets to different protein solutions. Controlling the pressure in each inlet allows one to rapidly change the solution in the field of view.\n\n(B) Sketch of a typical experiment (side view). Filaments are elongated from coverslip-anchored spectrin-actin seeds, by flowing in with ATP-G-actin. Filaments are then aged by flowing in a solution of ATP-G-actin at the critical concentration, for at least 15 min. This results in >99% of the monomers in the ADP-state. Finally, filaments are exposed to ADF/cofilin.\n\n(C) Example of a field of view, imaged with TIRFm. ADP-F-actin labelled with Alexa-488 is exposed to mCherry-cofilin-1, which forms observable domains on the filaments.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=180 SRC=\"FIGDIR/small/422824_fig2.gif\" ALT=\"Figure 2\">\nView larger version (47K):\norg.highwire.dtl.DTLVardef@1aca24borg.highwire.dtl.DTLVardef@d2f404org.highwire.dtl.DTLVardef@1924892org.highwire.dtl.DTLVardef@daaaac_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 2.C_FLOATNO Cofilin binds more slowly to filaments at higher pH values. (A). Experimental configuration. Actin filaments are grown from spectrin-actin seeds with a long middle segment of unlabelled ADP-actin.\n\n(B). Time-lapse showing an unlabeled ADP-actin filament become saturated by eGFP-cof1 over time.\n\n(C-D). Mean normalized eGFP-cofilin-1 fluorescence signal, binding onto unlabelled ADP-F-actin. 150 nM (C) and 400 nM (D) eGFP-cofilin-1 was injected in the chamber from time t=0 onwards. The fluorescence signal was averaged along 20 to 35 pixels (3.5 to 6 {micro}m) for each filment. Number of filaments (C) N = 10, 10, 18, 20, for pH 6.6 Hepes, 7.0 Hepes, 7.0 Tris and 7.4 Tris, respectively, and\n\n(D) N = 10 in all conditions.\n\n(E). Number of cofilin subunits in individual domains, increasing over time. For clarity, the time origin has been shifted for each curve. Lines: linear fit. Condition: 400 nM eGFP-cofilin-1, pH 7.0 Hepes.\n\n(F). Growth rate of individual cofilin domains at different eGFP-cofilin-1 concentrations and pH. Value: median, error bars: interquartile range. N = 10 domains, except N = 9 for pH 6.6 Hepes 150 nM cof1, and for pH 7.8 Tris 400 nM cof1.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=41 SRC=\"FIGDIR/small/422824_fig3.gif\" ALT=\"Figure 3\">\nView larger version (18K):\norg.highwire.dtl.DTLVardef@1fb31d4org.highwire.dtl.DTLVardef@846198org.highwire.dtl.DTLVardef@12379d0org.highwire.dtl.DTLVardef@127b96_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 3.C_FLOATNO The severing rate per cofilin domain is unaffected by pH. (A). Experimental setup. Alexa-568-labelled actin filaments are polymerized from actin-spectrin seeds, and aged before being exposed to eGFP-cof1.\n\n(B). Typical kymograph. 200 nM eGFP-cof1 (green) is constantly injected, binds F-actin (red) and induces severing (lightning symbols).\n\n(C). Fraction of cofilin domains with no severing event detected near their edges, over time. Time t=0 is defined for each domain as the last frame before they become visible. The survival fraction curves are calculated using the Kaplan-Meier method over 22 to 43 filaments, 78 to 90 cofilin domains and 30 to 33 severing events, for each data set.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC=\"FIGDIR/small/422824_fig4.gif\" ALT=\"Figure 4\">\nView larger version (32K):\norg.highwire.dtl.DTLVardef@16fdad8org.highwire.dtl.DTLVardef@889caborg.highwire.dtl.DTLVardef@e5b14aorg.highwire.dtl.DTLVardef@1da73c9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 4.C_FLOATNO Higher pH slows down polymerization and depolymerization of bare F-actin but accelerates depolymerization of ADF/cofilin-saturated filaments at both ends. (A-C) Polymerization from the barbed-end.\n\n(A) Sketch of the experimental configuration, where filaments were grown from actin-spectrin seeds with G-ATP-actin and profilin.\n\n(B) Kymograph of a typical elongating filament.\n\n(C) Polymerization rate at different pH. N = 20 filaments for each condition.\n\n(D-E) Depolymerization from the barbed-end.\n\n(D) Sketch of the experimental configuration. ADP-F-actin is exposed either to buffer only, or to 1-2{micro}M unlabelled ADF or cofilin-1 in order to fully saturate the filament in less than a minute.\n\n(E) Depolymerization rate for different pH values. Right: zoom into the 0-5 sub/s range. From left to right, N = 20, 32, 22, 32, 31 (buffer only); N=9, 14, 23, 33, 34 (ADF-saturated); N=17, 18, 16 (cofilin-1-saturated).\n\n(F-H) Depolymerization from the pointed-end.\n\n(F) Sketch of the experimental configuration. ADP-F-actin was bound to the surface by gelsolin. Filaments were exposed to buffer only (supplemented with 0.4 mM CaCl2 to ensure gelsolin-actin tight binding), containing 1 to 2 {micro}M unlabelled ADF or cofilin-1 to rapidly saturate filaments.\n\n(G) Typical kymograph of a depolymerizing filament saturated with ADF.\n\n(H) Pointed-end depolymerization rate at different pH. N = 14, 20, 15, 20, 20 (buffer); N=20, 20, 16, 20, 20 (ADF-saturated); N= 20, 20, 20 (cofilin-1-saturated).\n\n(C, E, H) Symbol: median, error bars: interquartile range.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC=\"FIGDIR/small/422824_fig5.gif\" ALT=\"Figure 5\">\nView larger version (22K):\norg.highwire.dtl.DTLVardef@e79318org.highwire.dtl.DTLVardef@16a4bb1org.highwire.dtl.DTLVardef@18f8fbdorg.highwire.dtl.DTLVardef@25dab6_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 5.C_FLOATNO The \"unstoppable\" depolymerization of cofilactin barbed ends is observed throughout our pH range. (A-C) Synergy of CP and ADF/cofilin to saturate filaments and initiate barbed end depolymerization.\n\n(A) Sketch of experimental configuration and events. Filaments grow until they are capped with CP. ADF/cofilin can then saturate the filaments, up to their BE which thus uncaps and depolymerizes.\n\n(B) Kymograph of a filament continuously exposed to the same solution containing 0.8 {micro}M G-ATP-actin, 1 {micro}M ADF and 2 nM CP. The filament polymerizes, pauses as it is capped by CP, and eventually depolymerizes.\n\n(C) Fraction of barbed ends that transitioned from a pause to depolymerization. Time t = 0 corresponds to the beginning of the pause (as shown on B). N = 24, 32, 32 filaments for pH 7.0 Hepes, pH 7.0 Tris, pH 7.4 Tris, respectively.\n\n(D-F) Cofilactin barbed ends sustain depolymerization in the presence of ATP-G-actin.\n\n(D) Sketch of the experimental configuration and events. Filaments are polymerized from spectrin-actin seeds and saturated with ADF. Depolymerizing cofilactin filaments are then constantly exposed to a solution of ATP-G-actin.\n\n(E) Fraction of barbed ends that transitioned from depolymerization to polymerization over time, when exposed to 1 {micro}M ATP-G-actin and 0.5 {micro}M ADF (to keep filaments saturated). N = 25, 16, 25, 24, 31 for pH 6.6 Hepes, 7.0 Hepes, 7.0 Tris, 7.4 Tris, 7.8 Tris, respectively.\n\n(F) Same as (E), with 1 {micro}M profilin added to the solution. N= 21, 27, 30 for pH 6.6 Hepes, 7.4 Tris, 7.8 Tris, respectively.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC=\"FIGDIR/small/422824_fig6.gif\" ALT=\"Figure 6\">\nView larger version (34K):\norg.highwire.dtl.DTLVardef@13a07bborg.highwire.dtl.DTLVardef@d19d2forg.highwire.dtl.DTLVardef@1a697daorg.highwire.dtl.DTLVardef@3b9d59_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 6.C_FLOATNO Summary of results: barbed end depolymerization is an important contribution of cofilin disassembly at physiological pH. Within the range of pH that we have explored (pH 6.6 to 7.8) we have made the following observations (from top to bottom, on this sketch). A lower pH favors the rapid decoration of filaments by ADF/cofilin, but the severing rate per cofilin domain does not vary with pH. As a consequence, at a higher pH, domain boundaries persist longer (before domains merge) and severing is more efficient. The acceleration of pointed end depolymerization for cofilactin filaments is mostly observed at high pH. The \"unstoppable\" depolymerization of cofilactin barbed ends is observed at all pH, and is more pronounced at lower pH values.\n\nC_FIG

biochemistry↗
bioRxiv2026

Scaffold Affinity Tunes Biomolecular Condensate Function

Biomolecular condensates (BMCs) organize cellular biochemistry by concentrating selected molecules into dynamic membrane-free compartments. Yet the molecular parameters that determine not only whether condensates form, but also how they behave and what they do, remain poorly defined. Here we show that scaffold binding affinity (Kd) is a quantitative determinant of condensate phase behavior, internal dynamics and biochemical output. Using a modular SUMO-SIM system in which scaffold valency was held constant while binding affinity was systematically varied, we found that affinity governs the phase boundary, resistance to chemical perturbation, and molecular mobility of condensates in vitro and in human cells. In multicomponent mixtures, the highest-affinity scaffold dominated dense-phase composition and dynamics, revealing a hierarchical rule for condensate organization. Finally, affinity-dependent changes in condensate dynamics translated into tunable enzyme activity, establishing binding energetics as an engineerable parameter for programming condensate biochemistry.

biochemistry↗
bioRxiv2014

Modelling reactions catalysed by carbohydrate-active enzymes

Carbohydrate polymers are ubiquitous in biological systems and their roles are highly diverse, ranging from energy storage over mechanical stabilisation to mediating cell-cell or cell-protein interactions. The functional diversity is mirrored by a chemical diversity that results from the high flexibility of how different sugar monomers can be arranged into linear, branched or cyclic polymeric structures. Mathematical models describing biochemical processes on polymers are faced with various difficulties. First, polymer-active enzymes are often specific to some local configuration within the polymer but are indifferent to other features. That is they are potentially active on a large variety of different chemical compounds, meaning that polymers of different size and structure simultaneously compete for enzymes. Second, especially large polymers interact with each other and form water-insoluble phases that restrict or exclude the formation of enzyme-substrate complexes. This heterogeneity of the reaction system has to be taken into account by explicitly considering processes at the, often complex, surface of the polymer matrix. We review recent approaches to theoretically describe polymer biochemical systems. All attempts address a particular challenge, which we discuss in more detail. We emphasise a recent attempt which draws novel analogies between polymer biochemistry and statistical thermodynamics and illustrate how this parallel leads to novel insights about non-uniform polymer reactant mixtures. Finally, we discuss the future challenges of the young and growing field of theoretical polymer biochemistry.

Systems Biology↗
bioRxiv2017

Universal Scaling in Biochemical Networks

The application of network science to biology has advanced our understanding of the metabolism of individual organisms and the organization of ecosystems but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, we constructed biochemical networks using a global database of 28,146 annotated genomes and metagenomes, and 8,658 cataloged biochemical reactions. We uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical networks to random chemical networks reveals the observed biological scaling is not solely a product of the biochemistry shared across life on Earth. Instead, it emerges due to how the global inventory of biochemical reactions is partitioned into individuals. We show the three domains of life are topologically distinguishable, with > 80% accuracy in predicting evolutionary domain based on biochemical network size and average topology. Taken together our results point to a deeper level of organization in biochemical networks than what has been understood so far.

systems biology↗
bioRxiv2018

A Bayesian Mixture Modelling Approach For Spatial Proteomics

AbstractAnalysis of the spatial sub-cellular distribution of proteins is of vital importance to fully understand context specific protein function. Some proteins can be found with a single location within a cell, but up to half of proteins may reside in multiple locations, can dynamically re-localise, or reside within an unknown functional compartment. These considerations lead to uncertainty in associating a protein to a single location. Currently, mass spectrometry (MS) based spatial proteomics relies on supervised machine learning algorithms to assign proteins to sub-cellular locations based on common gradient profiles. However, such methods fail to quantify uncertainty associated with sub-cellular class assignment. Here we reformulate the framework on which we perform statistical analysis. We propose a Bayesian generative classifier based on Gaussian mixture models to assign proteins probabilistically to sub-cellular niches, thus proteins have a probability distribution over sub-cellular locations, with Bayesian computation performed using the expectation-maximisation (EM) algorithm, as well as Markov-chain Monte-Carlo (MCMC). Our methodology allows proteome-wide uncertainty quantification, thus adding a further layer to the analysis of spatial proteomics. Our framework is flexible, allowing many different systems to be analysed and reveals new modelling opportunities for spatial proteomics. We find our methods perform competitively with current state-of-the art machine learning methods, whilst simultaneously providing more information. We highlight several examples where classification based on the support vector machine is unable to make any conclusions, while uncertainty quantification using our approach provides biologically intriguing results. To our knowledge this is the first Bayesian model of MS-based spatial proteomics data.\n\nAuthor summarySub-cellular localisation of proteins provides insights into sub-cellular biological processes. For a protein to carry out its intended function it must be localised to the correct sub-cellular environment, whether that be organelles, vesicles or any sub-cellular niche. Correct sub-cellular localisation ensures the biochemical conditions for the protein to carry out its molecular function are met, as well as being near its intended interaction partners. Therefore, mis-localisation of proteins alters cell biochemistry and can disrupt, for example, signalling pathways or inhibit the trafficking of material around the cell. The sub-cellular distribution of proteins is complicated by proteins that can reside in multiple micro-environments, or those that move dynamically within the cell. Methods that predict protein sub-cellular localisation often fail to quantify the uncertainty that arises from the complex and dynamic nature of the sub-cellular environment. Here we present a Bayesian methodology to analyse protein sub-cellular localisation. We explicitly model our data and use Bayesian inference to quantify uncertainty in our predictions. We find our method is competitive with state-of-the-art machine learning methods and additionally provides uncertainty quantification. We show that, with this additional information, we can make deeper insights into the fundamental biochemistry of the cell.

systems biology↗
bioRxiv2018

Current production as a rapid response expression reporter under micro-oxic and anoxic conditions

Inducible gene expression is crucial for regulating cellular processes and production of compounds within cellular pathways. Yet, inducing gene expression is only the first step to utilizing cellular processes for an applied purpose such as biosensors. Detecting when gene expression occurs is central to understanding the overall mechanism of the process as well as maximizing the process. Fluorescent proteins have been established as the primary tool for detecting gene expression in inducible systems. This study proposes electricity production as an alternate tool in reporting gene expression. Using a model organism for electricity production, Shewanella oneidensis MR-1, current was shown to be an efficient reporter for gene expression and comparable to superfolder green fluorescent protein (GFP). Through regulation of the lac operator and T7 promoter, current production was induced by isopropyl {beta}-D-1-thiogalactopyranoside (IPTG) addition. IPTG addition induced translation of GFP and the MtrB protein, which complemented a {triangleup}mtrB strain of S. oneidensis MR-1 and restored current production. This inducible system generated reproducible current in 18 minutes in both micro-oxic and anoxic conditions. These results show that current is a fast reporter for gene expression.\n\nFinancial DisclosureThe team was supported by the following departments and colleges at Michigan State University: College of Natural Science, College of Engineering, Biochemistry and Molecular Biology Department and Plant Research Laboratory. The team also received support from the DOE Great Lakes Bioenergy Research Center (DOE Office of Science BER DE-FC02-07ER64494) and startup funding from the Department of Molecular Biology and Biochemistry, Michigan State University and support from Michigan State University AgBioResearch (MICL02454) (to B.H.). This work was also supported by NSF CAREER (Award #1254238) to T.A.W. MSU Alpha Chi Sigma also supported the team.\n\nCompeting InterestsThe authors declare that no competing interests exist.\n\nEthics StatementN/A\n\nData AvailabilityAll data will be supplied upon request by the corresponding author.\n\nThis work was assessed during the iGEM/PLOS Realtime Peer Review Jamboree on 23rd February 2018 and has been revised in response to the reviewers comments.

synthetic biology↗
bioRxiv2018

Anisotropic growth is achieved through the additive mechanical effect of material anisotropy and elastic asymmetry

Fast directional growth is a necessity for the young seedling: after germination, the seedling needs to quickly reach through the soil to begin its autotrophic life. In most dicot plants, this rapid escape is due to the anisotropic elongation of the hypocotyl, the columnar organ between the root and the shoot meristems. Such anisotropic growth is common in many plant organs and is canonically attributed to cell wall anisotropy produced by oriented cellulose fibers in the cell wall. More recently, a mechanism based on asymmetric cell wall elasticity has been proposed, produced by differential pectin biochemistry. Here we present a harmonizing model for anisotropic growth control in the dark-grown Arabidopsis hypocotyl: basic anisotropic information is provided by cellulose orientation (proxied by microtubules) and additive anisotropic information is provided by pectin-based elastic asymmetry in the epidermis. We demonstrate that hypocotyl growth was always anisotropic with axial and transverse walls growing differently, from germination. We present experimental evidence for pectin biochemical differences and wall mechanics underlying this differential growth. We demonstrate that pectin biochemical changes control the transition to rapid growth characteristic of Arabidopsis hypocotyl elongation, and provide evidence for a substantial mechanical role for pectin in the cell wall when microtubules are compromised. Lastly, our in silico modelling experiments indicate an additive combination for pectin biochemistry and cellulose orientation in promoting anisotropic growth.

plant biology↗
bioRxiv2018

A role for long-range, through-lattice coupling in microtubule catastrophe

Microtubules are cylindrical polymers of {beta}-tubulin that play critical roles in fundamental processes like chromosome segregation and vesicular transport. Microtubules display dynamic instability, switching stochastically between growing and rapid shrinking as a consequence of GTPase activity in the lattice. The molecular mechanisms behind microtubule catastrophe, the switch from growing to rapid shrinking, remain poorly defined. Indeed, two-state stochastic models that seek to describe microtubule dynamics purely in terms of the biochemical properties of GTP- and GDP-bound {beta}-tubulin incorrectly predict the concentration-dependence of microtubule catastrophe. Recent studies provided evidence for three distinct conformations of {beta}-tubulin in the lattice that likely correspond to GTP, GDP.Pi, and GDP. The incommensurate lattices observed for these different conformations raises the possibility that in a mixed nucleotide state lattice, neighboring tubulin dimers might modulate each others conformations and hence their biochemistry. We explored whether incorporating a GDP.Pi state or the likely effects of conformational accommodation can improve predictions of catastrophe. Adding a GDP.Pi intermediate did not improve the model. In contrast, adding neighbor-dependent modulation of tubulin biochemistry improved predictions of catastrophe. Conformational accommodation should propagate beyond nearest-neighbor contacts, and consequently our modeling demonstrates that long-range, through-lattice effects are important determinants of microtubule catastrophe.

cell biology↗