Thomas Gurry

Strong underwater adhesives made by self-assembling multi-protein nanofibres

Many natural underwater adhesives harness hierarchically assembled amyloid nanostructures to achieve strong and robust interfacial adhesion under dynamic and turbulent environments. Despite recent advances, our understanding of the molecular design, self-assembly and structure-function relationships of these natural amyloid fibres remains limited. Thus, designing biomimetic amyloid-based adhesives remains challenging. Here, we report strong and multi-functional underwater adhesives obtained from fusing mussel foot proteins (Mfps) of Mytilus galloprovincialis with CsgA proteins, the major subunit of Escherichia coli amyloid curli fibres. These hybrid molecular materials hierarchically self-assemble into higher-order structures, in which, according to molecular dynamics simulations, disordered adhesive Mfp domains are exposed on the exterior of amyloid cores formed by CsgA. Our fibres have an underwater adhesion energy approaching 20.9 mJ m(-2), which is 1.5 times greater than the maximum of bio-inspired and bio-derived protein-based underwater adhesives reported thus far. Moreover, they outperform Mfps or curli fibres taken on their own and exhibit better tolerance to auto-oxidation than Mfps at pH ≥ 7.0.Many natural underwater adhesives harness hierarchically assembled amyloid nanostructures to achieve strong and robust interfacial adhesion under dynamic and turbulent environments. Despite recent advances, our understanding of the molecular design, self-assembly, and structure-function relationship of those natural amyloid fibers remains limited. Thus, designing biomimetic amyloid-based adhesives remains challenging. Here, we report strong and multi-functional underwater adhesives obtained from fusing mussel foot proteins (Mfps) of Mytilus galloprovincialis with CsgA proteins, the major subunit of Escherichia coli amyloid curli fibers. These hybrid molecular materials hierarchically self-assemble into higher-order structures, in which, according to molecular dynamics simulations, disordered adhesive Mfp domains are exposed on the exterior of amyloid cores formed by CsgA. Our fibers have an underwater adhesion energy approaching 20.9 mJ/m2, which is 1.5 times greater than the maximum of bio-inspired and bio-derived protein-based underwater adhesives reported thus far. Moreover, they outperform Mfps or curli fibers taken on their own at all pHs and exhibit better tolerance to auto-oxidation than Mfps at pH ≥7.0. This work establishes a platform for engineering multi-component self-assembling materials inspired by nature.

The Dynamic Structure of α‑Synuclein Multimers

α-Synuclein, a protein that forms ordered aggregates in the brains of patients with Parkinsons disease, is intrinsically disordered in the monomeric state. Several studies, however, suggest that it can form soluble multimers in vivo that have significant secondary structure content. A number of studies demonstrate that α-synuclein can form β-strand-rich oligomers that are neurotoxic, and recent observations argue for the existence of soluble helical tetrameric species in cellulo that do not form toxic aggregates. To gain further insight into the different types of multimeric states that this protein can adopt, we generated an ensemble for an α-synuclein construct that contains a 10-residue N-terminal extension, which forms multimers when isolated from Escherichia coli. Data from NMR chemical shifts and residual dipolar couplings were used to guide the construction of the ensemble. Our data suggest that the dominant state of this ensemble is a disordered monomer, complemented by a small fraction of helical trimers and tetramers. Interestingly, the ensemble also contains trimeric and tetrameric oligomers that are rich in β-strand content. These data help to reconcile seemingly contradictory observations that indicate the presence of a helical tetramer in cellulo on the one hand, and a disordered monomer on the other. Furthermore, our findings are consistent with the notion that the helical tetrameric state provides a mechanism for storing α-synuclein when the protein concentration is high, thereby preventing non-membrane-bound monomers from aggregating.

Fecal microbiota transplant from a rational stool donor improves hepatic encephalopathy: A randomized clinical trial

Recurrent hepatic encephalopathy (HE) is a leading cause of readmission despite standard of care (SOC) associated with microbial dysbiosis. Fecal microbiota transplantation (FMT) may improve dysbiosis; however, it has not been studied in HE. We aimed to define whether FMT using a rationally derived stool donor is safe in recurrent HE compared to SOC alone. An open‐label, randomized clinical trial with a 5‐month follow‐up in outpatient men with cirrhosis with recurrent HE on SOC was conducted with 1:1 randomization. FMT‐randomized patients received 5 days of broad‐spectrum antibiotic pretreatment, then a single FMT enema from the same donor with the optimal microbiota deficient in HE. Follow‐up occurred on days 5, 6, 12, 35, and 150 postrandomization. The primary outcome was safety of FMT compared to SOC using FMT‐related serious adverse events (SAEs). Secondary outcomes were adverse events, cognition, microbiota, and metabolomic changes. Participants in both arms were similar on all baseline criteria and were followed until study end. FMT with antibiotic pretreatment was well tolerated. Eight (80%) SOC participants had a total of 11 SAEs compared to 2 (20%) FMT participants with SAEs (both FMT unrelated; P = 0.02). Five SOC and no FMT participants developed further HE (P = 0.03). Cognition improved in the FMT, but not the SOC, group. Model for End‐Stage Liver Disease (MELD) score transiently worsened postantibiotics, but reverted to baseline post‐FMT. Postantibiotics, beneficial taxa, and microbial diversity reduction occurred with Proteobacteria expansion. However, FMT increased diversity and beneficial taxa. SOC microbiota and MELD score remained similar throughout. Conclusion: FMT from a rationally selected donor reduced hospitalizations, improved cognition, and dysbiosis in cirrhosis with recurrent HE. (Hepatology 2017;66:1727–1738)

Biophysical Mechanism for Ras-Nanocluster Formation and Signaling in Plasma Membrane

Ras GTPases are lipid-anchored G proteins, which play a fundamental role in cell signaling processes. Electron micrographs of immunogold-labeled Ras have shown that membrane-bound Ras molecules segregate into nanocluster domains. Several models have been developed in attempts to obtain quantitative descriptions of nanocluster formation, but all have relied on assumptions such as a constant, expression-level independent ratio of Ras in clusters to Ras monomers (cluster/monomer ratio). However, this assumption is inconsistent with the law of mass action. Here, we present a biophysical model of Ras clustering based on short-range attraction and long-range repulsion between Ras molecules in the membrane. To test this model, we performed Monte Carlo simulations and compared statistical clustering properties with experimental data. We find that we can recover the experimentally-observed clustering across a range of Ras expression levels, without assuming a constant cluster/monomer ratio or the existence of lipid rafts. In addition, our model makes predictions about the signaling properties of Ras nanoclusters in support of the idea that Ras nanoclusters act as an analog-digital-analog converter for high fidelity signaling.

Mechanism of Amyloid-β Fibril Elongation

Amyloid-β is an intrinsically disordered protein that forms fibrils in the brains of patients with Alzheimers disease. To explore factors that affect the process of fibril growth, we computed the free energy associated with disordered amyloid-β monomers being added to growing amyloid fibrils using extensive molecular dynamics simulations coupled with umbrella sampling. We find that the mechanisms of Aβ40 and Aβ42 fibril elongation have many features in common, including the formation of an obligate on-pathway β-hairpin intermediate that hydrogen bonds to the fibril core. In addition, our data lead to new hypotheses for how fibrils may serve as secondary nucleation sites that can catalyze the formation of soluble oligomers, a finding in agreement with recent experimental observations. These data provide a detailed mechanistic description of amyloid-β fibril elongation and a structural link between the disordered free monomer and the growth of amyloid fibrils and soluble oligomers.

Designing fecal microbiota transplant trials that account for differences in donor stool efficacy

Fecal microbiota transplantation is a highly effective intervention for patients suffering from recurrent Clostridium difficile, a common hospital-acquired infection. Fecal microbiota transplantation’s success as a therapy for C. difficile has inspired interest in performing clinical trials that experiment with fecal microbiota transplantation as a therapy for other conditions like inflammatory bowel disease, obesity, diabetes, and Parkinson’s disease. Results from clinical trials that use fecal microbiota transplantation to treat inflammatory bowel disease suggest that, for at least one condition beyond C. difficile, most fecal microbiota transplantation donors produce stool that is not efficacious. The optimal strategies for identifying and using efficacious donors have not been investigated. We therefore examined the optimal Bayesian response-adaptive strategy for allocating patients to donors and formulated a computationally tractable myopic heuristic. This heuristic computes the probability that a donor is efficacious by updating prior expectations about the efficacy of fecal microbiota transplantation, the placebo rate, and the fraction of donors that produce efficacious stool. In simulations designed to mimic a recent fecal microbiota transplantation clinical trial, for which traditional power calculations predict ∼ 100 % statistical power, we found that accounting for differences in donor stool efficacy reduced the predicted statistical power to ∼ 9 % . For these simulations, using the heuristic Bayesian allocation strategy more than quadrupled the statistical power to ∼ 39 % . We use the results of similar simulations to make recommendations about the number of patients, the number of donors, and the choice of clinical endpoint that clinical trials should use to optimize their ability to detect if fecal microbiota transplantation is effective for treating a condition.

Synbiotic approaches to human health and well-being

Synbiotics refer to combinations of probiotics and prebiotics that act synergistically to confer health benefits to the host. As a therapeutic strategy, they provide a gentle yet powerful method for modulating the composition and metabolic output of the human gut microbiota. In the context of achieving the UN Sustainable Development Goals, synbiotics have the potential to act as cost‐effective prophylactic measures against a variety of human ailments, ranging from infant diarrhoea to metabolic and inflammatory diseases in adults, by maintaining commensal microbial communities and metabolic networks that are conducive to human health.

Analyzing Ensembles of Amyloid Proteins Using Bayesian Statistics.

Intrinsically disordered proteins (IDPs) are notoriously difficult to study experimentally because they rapidly interconvert between many dissimilar conformations during their biological lifetime, and therefore cannot be described by a single structure. The importance of studying these systems, however, is underscored by the fact that they form toxic aggregates that play a role in the pathogenesis of many disorders. The first step towards a comprehensive understanding of the aggregation mechanism of these proteins involves a description of their thermally accessible states under physiologic conditions. The resulting conformational ensembles correspond to coarse-grained descriptions of their energy landscapes, where the number of structures in the ensemble is related to the resolution in which one views the free energy surface. Here, we provide step-by-step instructions on how to use experimental data to construct a conformational ensemble for an IDP using a Variational Bayesian Weighting (VBW) algorithm. We further discuss how to leverage this Bayesian approach to identify statistically significant ensemble-wide observations that can form the basis of further experimental studies.

Meta Analysis Of Microbiome Studies Identifies Shared And Disease-Specific Patterns

Hundreds of clinical studies have been published that demonstrate associations between the human microbiome and a variety of diseases. Yet, fundamental questions remain on how we can generalize this knowledge. For example, if diseases are mainly characterized by a small number of pathogenic species, then new targeted antimicrobial therapies may be called for. Alternatively, if diseases are characterized by a lack of healthy commensal bacteria, then new probiotic therapies might be a better option. Results from individual studies, however, can be inconsistent or in conflict, and comparing published data is further complicated by the lack of standard processing and analysis methods. Here, we introduce the MicrobiomeHD database, which includes 29 published case-control gut microbiome studies spanning ten different diseases. Using standardized data processing and analyses, we perform a comprehensive crossdisease meta-analysis of these studies. We find consistent and specific patterns of disease-associated microbiome changes. A few diseases are associated with many individual bacterial associations, while most show only around 20 genus-level changes. Some diseases are marked by the presence of pathogenic microbes whereas others are characterized by a depletion of health-associated bacteria. Furthermore, over 60% of microbes associated with individual diseases fall into a set of “core” health and disease-associated microbes, which are associated with multiple disease states. This suggests a universal microbial response to disease.

Identifying predictive features of Clostridium difficile infection recurrence before, during, and after primary antibiotic treatment

BackgroundColonization by the pathogen Clostridium difficile often occurs in the background of a disrupted microbial community. Identifying specific organisms conferring resistance to invasion by C. difficile is desirable because diagnostic and therapeutic strategies based on the human microbiota have the potential to provide more precision to the management and treatment of Clostridium difficile infection (CDI) and its recurrence.MethodsWe conducted a longitudinal study of adult patients diagnosed with their first CDI. We investigated the dynamics of the gut microbiota during antibiotic treatment, and we used microbial or demographic features at the time of diagnosis, or after treatment, to predict CDI recurrence. To check the validity of the predictions, a meta-analysis using a previously published dataset was performed.ResultsWe observed that patients’ microbiota “before” antibiotic treatment was predictive of disease relapse, but surprisingly, post-antibiotic microbial community is indistinguishable between patients that recur or not. At the individual OTU level, we identified Veillonella dispar as a candidate organism for preventing CDI recurrence; however, we did not detect a corresponding signal in the conducted meta-analysis.ConclusionAlthough in our patient population, a candidate organism was identified for negatively predicting CDI recurrence, results suggest the need for larger cohort studies that include patients with diverse demographic characteristics to generalize species that robustly confer colonization resistance against C. difficile and accurately predict disease relapse.