Revisiting the invasion paradox: Resistance-richness relationship is driven by augmentation and displacement trends.

Host-associated resident microbiota can protect their host from pathogens-a community-level trait called colonization resistance. The effect of the diversity of the resident community in previous studies has shown contradictory results, with higher diversity either strengthening or weakening colonization resistance. To control the confounding factors that may lead to such contradictions, we use mathematical simulations with a focus on species interactions and their impact on colonization resistance. We use a mediator-explicit model that accounts for metabolite-mediated interactions to perform in silico invasion experiments. We show that the relationship between colonization resistance and species richness of the resident community is not monotonic because it depends on two underlying trends as the richness of the resident community increases: a decrease in instances of augmentation (invader species added, without driving out resident species) and an increase in instances of displacement (invader species added, driving out some of the resident species). These trends hold consistently under different parameters, regardless of the number of compounds that mediate interactions between species or the proportion of the facilitative versus inhibitory interactions among species. Our results show a positive correlation between resistance and diversity in low-richness communities and a negative correlation in high-richness communities, offering an explanation for the seemingly contradictory trend in the resistance-diversity relationship in previous reports.

Association of the gut microbiome and metabolome with wheeze frequency in childhood asthma.

BACKGROUND: While the microbiome has an established role in asthma development, less is known about its contribution to morbidity in children with asthma. OBJECTIVE: In this ancillary study of the Vitamin D Antenatal Asthma Reduction Trial (VDAART), we analyzed the gut microbiome and metabolome of wheeze frequency in children with asthma. METHODS: Bacterial 16S ribosomal RNA microbiome and untargeted metabolomic profiling were performed on fecal samples collected from 3-year-old children with parent-reported physician-diagnosed asthma. We analyzed wheeze frequency by calculating the proportion of quarterly questionnaires administered between ages 3 and 5 years in which parents reported the child had wheezed (wheeze proportion). Taxa and metabolites associated with wheeze were analyzed by identifying log fold changes with respect to wheeze frequency and correlation/linear regression analyses, respectively. Microbe-metabolite and microbe-microbe correlation networks were compared between subjects with high and low wheeze proportion. RESULTS: Specific taxa, including the genus Veillonella and histidine pathway metabolites, were enriched in subjects with high wheeze proportion. Among wheeze-associated taxa, Veillonella and Oscillospiraceae UCG-005, which was inversely associated with wheeze, were correlated with the greatest number of fecal metabolites. Microbial networks were similar between subjects with low versus high wheeze frequency. CONCLUSION: Gut microbiome features are associated with wheeze frequency in children with asthma, suggesting an impact of the gut microbiome on morbidity in childhood asthma.

Current and Emerging Tools of Computational Biology To Improve the Detoxification of Mycotoxins.

Biological organisms carry a rich potential for removing toxins from our environment, but identifying suitable candidates and improving them remain challenging. We explore the use of computational tools to discover strains and enzymes that detoxify harmful compounds. In particular, we focus on mycotoxins-fungus-produced toxins that contaminate food and feed-and biological enzymes that are capable of rendering them less harmful. We discuss the use of established and novel computational tools to complement existing empirical data in three directions: discovering the prospect of detoxification among underexplored organisms, finding important cellular processes that contribute to detoxification, and improving the performance of detoxifying enzymes. We hope to create a synergistic conversation between researchers in computational biology and those in the bioremediation field. We showcase open bioremediation questions where computational researchers can contribute and highlight relevant existing and emerging computational tools that could benefit bioremediation researchers.

Uncovering and resolving challenges of quantitative modeling in a simplified community of interacting cells.

Quantitative modeling is useful for predicting behaviors of a system and for rationally constructing or modifying the system. The predictive power of a model relies on accurate quantification of model parameters. Here, we illustrate challenges in parameter quantification and offer means to overcome these challenges, using a case example in which we quantitatively predict the growth rate of a cooperative community. Specifically, the community consists of two Saccharomyces cerevisiae strains, each engineered to release a metabolite required and consumed by its partner. The initial model, employing parameters measured in batch monocultures with zero or excess metabolite, failed to quantitatively predict experimental results. To resolve the model-experiment discrepancy, we chemically identified the correct exchanged metabolites, but this did not improve model performance. We then remeasured strain phenotypes in chemostats mimicking the metabolite-limited community environments, while mitigating or incorporating effects of rapid evolution. Almost all phenotypes we measured, including death rate, metabolite release rate, and the amount of metabolite consumed per cell birth, varied significantly with the metabolite environment. Once we used parameters measured in a range of community-like chemostat environments, prediction quantitatively agreed with experimental results. In summary, using a simplified community, we uncovered and devised means to resolve modeling challenges that are likely general to living systems.

The impact of interactions on invasion and colonization resistance in microbial communities.

In human microbiota, the prevention or promotion of invasions can be crucial to human health. Invasion outcomes, in turn, are impacted by the composition of resident communities and interactions of resident members with the invader. Here we study how interactions influence invasion outcomes in microbial communities, when interactions are primarily mediated by chemicals that are released into or consumed from the environment. We use a previously developed dynamic model which explicitly includes species abundances and the concentrations of chemicals that mediate species interaction. Using this model, we assessed how species interactions impact invasion by simulating a new species being introduced into an existing resident community. We classified invasion outcomes as resistance, augmentation, displacement, or disruption depending on whether the richness of the resident community was maintained or decreased and whether the invader was maintained in the community or went extinct. We found that as the number of invaders introduced into the resident community increased, disruption rather than augmentation became more prevalent. With more facilitation of the invader by the resident community, resistance outcomes were replaced by displacement and augmentation. By contrast, with more facilitation among residents, displacement outcomes shifted to resistance. When facilitation of the resident community by the invader was eliminated, the majority of augmentation outcomes turned into displacement, while when inhibition of residents by invaders was eliminated, invasion outcomes were largely unaffected. Our results suggest that a better understanding of interactions within resident communities and between residents and invaders is crucial to predicting the success of invasions into microbial communities.

Horizontal gene transfer can help maintain the equilibrium of microbial communities.

Horizontal gene transfer and species coexistence are two focal points in the study of microbial communities. Yet, the evolutionary advantage of horizontal gene transfer has not been well understood and is constantly being debated. Here we propose a simple population dynamics model based on frequency-dependent genotype interactions to evaluate the influence of horizontal gene transfer on microbial communities. In particular, we examine the structural stability of coexistence (i.e., the capability of the system to maintain species coexistence in response to small changes in parameters), as well as the robustness (defined as the maximal degree of perturbation the system can sustain around a stable coexistence steady state) of microbial communities. We find that both structural stability of coexistence and robustness of the microbial community are strongly affected by the gene transfer rate and direction. An optimal gene flux can stabilize the ecosystem, helping it recover from disturbance and maintain the species coexistence.

Predicting potential SARS-CoV-2 mutations of concern via full quantum mechanical modelling.

Ab initio quantum mechanical models can characterize and predict intermolecular binding, but only recently have models including more than a few hundred atoms gained traction. Here, we simulate the electronic structure for approximately 13 000 atoms to predict and characterize binding of SARS-CoV-2 spike variants to the human ACE2 (hACE2) receptor using the quantum mechanics complexity reduction (QM-CR) approach. We compare four spike variants in our analysis: Wuhan, Omicron, and two Omicron-based variants. To assess binding, we mechanistically characterize the energetic contribution of each amino acid involved, and predict the effect of select single amino acid mutations. We validate our computational predictions experimentally by comparing the efficacy of spike variants binding to cells expressing hACE2. At the time we performed our simulations (December 2021), the mutation A484K which our model predicted to be highly beneficial to ACE2 binding had not been identified in epidemiological surveys; only recently (August 2023) has it appeared in variant BA.2.86. We argue that our computational model, QM-CR, can identify mutations critical for intermolecular interactions and inform the engineering of high-specificity interactors.

When does a Lotka-Volterra model represent microbial interactions? Insights from in vitro nasal bacterial communities.

To alter microbial community composition for therapeutic purposes, an accurate and reliable modeling framework capable of predicting microbial community outcomes is required. Lotka-Volterra (LV) equations have been utilized to describe a breadth of microbial communities, yet, the conditions in which this modeling framework is successful remain unclear. Here, we propose that a set of simple in vitro experiments-growing each member in cell-free spent medium obtained from other members-can be used as a test to decide whether an LV model is appropriate for describing microbial interactions of interest. We show that for LV to be a good candidate, the ratio of growth rate to carrying capacity of each isolate when grown in the cell-free spent media of other isolates should remain constant. Using an in vitro community of human nasal bacteria as a tractable system, we find that LV can be a good approximation when the environment is low-nutrient (i.e., when growth is limited by the availability of nutrients) and complex (i.e., when multiple resources, rather than a few, determine growth). These findings can help clarify the range of applicability of LV models and reveal when a more complex model may be necessary for predictive modeling of microbial communities. IMPORTANCE Although mathematical modeling can be a powerful tool to draw useful insights in microbial ecology, it is crucial to know when a simplified model adequately represents the interactions of interest. Here, we take advantage of bacterial isolates from the human nasal passages as a tractable model system and conclude that the commonly used Lotka-Volterra model can represent interactions among microbes well when the environment is complex (with many interaction mediators) and low-nutrient. Our work highlights the importance of considering both realism and simplicity when choosing a model to represent microbial interactions.

Spatial structure favors microbial coexistence except when slower mediator diffusion weakens interactions.

Microbes often exist in spatially structured environments and many of their interactions are mediated through diffusible metabolites. How does such a context affect microbial coexistence? To address this question, we use a model in which the spatial distributions of species and diffusible interaction mediators are explicitly included. We simulate the enrichment process, examining how microbial species spatially reorganize and how eventually a subset of them coexist. In our model, we find that slower motility of cells promotes coexistence by allowing species to co-localize with their facilitators and avoid their inhibitors. We additionally find that a spatially structured environment is more influential when species mostly facilitate each other, rather than when they are mostly competing. More coexistence is observed when species produce many mediators and consume some (not many or few) mediators, and when overall consumption and production rates are balanced. Interestingly, coexistence appears to be disfavored when mediators are diffusing slowly because that leads to weaker interaction strengths. Overall, our results offer new insights into how production, consumption, motility, and diffusion intersect to determine microbial coexistence in a spatially structured environment.

Partner-assisted artificial selection of a secondary function for efficient bioremediation.

Microbial enzymes can address diverse challenges such as degradation of toxins. However, if the function of interest does not confer a sufficient fitness effect on the producer, the enzymatic function cannot be improved in the host cells by a conventional selection scheme. To overcome this limitation, we propose an alternative scheme, termed "partner-assisted artificial selection" (PAAS), wherein the population of enzyme producers is assisted by function-dependent feedback from an accessory population. Simulations investigating the efficiency of toxin degradation reveal that this strategy supports selection of improved degradation performance, which is robust to stochasticity in the model parameters. We observe that conventional considerations still apply in PAAS: more restrictive bottlenecks lead to stronger selection but add uncertainty. Overall, we offer a guideline for successful implementation of PAAS and highlight its potentials and limitations.

Experimental-theoretical study of laccase as a detoxifier of aflatoxins.

We investigate laccase-mediated detoxification of aflatoxins, fungal carcinogenic food contaminants. Our experimental comparison between two aflatoxins with similar structures (AFB1 and AFG2) shows significant differences in laccase-mediated detoxification. A multi-scale modeling approach (Docking, Molecular Dynamics, and Density Functional Theory) identifies the highly substrate-specific changes required to improve laccase detoxifying performance. We employ a large-scale density functional theory-based approach, involving more than 7000 atoms, to identify the amino acid residues that determine the affinity of laccase for aflatoxins. From this study we conclude: (1) AFB1 is more challenging to degrade, to the point of complete degradation stalling; (2) AFG2 is easier to degrade by laccase due to its lack of side products and favorable binding dynamics; and (3) ample opportunities to optimize laccase for aflatoxin degradation exist, especially via mutations leading to π-π stacking. This study identifies a way to optimize laccase for aflatoxin bioremediation and, more generally, contributes to the research efforts aimed at rational enzyme optimization.

Using artificial systems to explore the ecology and evolution of symbioses.

The web of life is weaved from diverse symbiotic interactions between species. Symbioses vary from antagonistic interactions such as competition and predation to beneficial interactions such as mutualism. What are the bases for the origin and persistence of symbiosis? What affects the ecology and evolution of symbioses? How do symbiotic interactions generate ecological patterns? How do symbiotic partners evolve and coevolve? Many of these questions are difficult to address in natural systems. Artificial systems, from abstract to living, have been constructed to capture essential features of natural symbioses and to address these key questions. With reduced complexity and increased controllability, artificial systems can serve as useful models for natural systems. We review how artificial systems have contributed to our understanding of symbioses.

Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures.

We employ a recently developed complexity-reduction quantum mechanical (QM-CR) approach, based on complexity reduction of density functional theory calculations, to characterize the interactions of the SARS-CoV-2 spike receptor binding domain (RBD) with ACE2 host receptors and antibodies. QM-CR operates via ab initio identification of individual amino acid residue's contributions to chemical binding and leads to the identification of the impact of point mutations. Here, we especially focus on the E484K mutation of the viral spike protein. We find that spike residue 484 hinders the spike's binding to the human ACE2 receptor (hACE2). In contrast, the same residue is beneficial in binding to the bat receptor Rhinolophus macrotis ACE2 (macACE2). In agreement with empirical evidence, QM-CR shows that the E484K mutation allows the spike to evade categories of neutralizing antibodies like C121 and C144. The simulation also shows how the Delta variant spike binds more strongly to hACE2 compared to the original Wuhan strain, and predicts that a E484K mutation can further improve its binding. Broad agreement between the QM-CR predictions and experimental evidence supports the notion that ab initio modeling has now reached the maturity required to handle large intermolecular interactions central to biological processes.

Fungal and bacterial oxylipins are signals for intra- and inter-cellular communication within plant disease.

Lipids are central at various stages of host-pathogen interactions in determining virulence and modulating plant defense. Free fatty acids may act as substrates for oxidizing enzymes [e.g., lipoxygenases (LOXs) and dioxygenases (DOXs)] that synthesize oxylipins. Fatty acids and oxylipins function as modulators of several pathways in cell-to-cell communication; their structural similarity among plant, fungal, and bacterial taxa suggests potential in cross-kingdom communication. We provide a prospect of the known role of fatty acids and oxylipins in fungi and bacteria during plant-pathogen interactions. In the pathogens, oxylipin-mediated signaling pathways are crucial both in development and host infection. Here, we report on case studies suggesting that oxylipins derived from oleic, linoleic, and linolenic acids are crucial in modulating the pathogenic lifestyle in the host plant. Intriguingly, overlapping (fungi-plant/bacteria-plant) results suggest that different inter-kingdom pathosystems use similar lipid signals to reshape the lifestyle of the contenders and occasionally determine the outcome of the challenge.

High resolution on-chip spectroscopy based on miniaturized microdonut resonators.

We experimentally demonstrate a high resolution integrated spectrometer on silicon on insulator (SOI) substrate using a large-scale array of microdonut resonators. Through top-view imaging and processing, the measured spectral response of the spectrometer shows a linewidth of ~0.6 nm with an operating bandwidth of ~50 nm. This high resolution and bandwidth is achieved in a compact size using miniaturized microdonut resonators (radius ~2 µm) with a high quality factor, single-mode operation, and a large free spectral range. The microspectrometer is realized using silicon process compatible fabrication and has a great potential as a high-resolution, large dynamic range, light-weight, compact, high-speed, and versatile microspectrometer.

Absorbing boundary conditions for low group velocity electromagnetic waves in photonic crystals.

We present an efficient method for the absorption of slow group velocity electromagnetic waves in photonic crystal waveguides (PCWs). We show that adiabatically matching the low group velocity waves to high group velocity waves of the PCW and extending the PCW structure into the perfectly matched layer (PML) region results in a 15 dB reduction of spurious reflections from the PML. We also discuss the applicability of this method to structures other than PCWs.

Impact of Temporal pH Fluctuations on the Coexistence of Nasal Bacteria in an in silico Community.

To manipulate nasal microbiota for respiratory health, we need to better understand how this microbial community is assembled and maintained. Previous work has demonstrated that the pH in the nasal passage experiences temporal fluctuations. Yet, the impact of such pH fluctuations on nasal microbiota is not fully understood. Here, we examine how temporal fluctuations in pH might affect the coexistence of nasal bacteria in in silico communities. We take advantage of the cultivability of nasal bacteria to experimentally assess their responses to pH and the presence of other species. Based on experimentally observed responses, we formulate a mathematical model to numerically investigate the impact of temporal pH fluctuations on species coexistence. We assemble in silico nasal communities using up to 20 strains that resemble the isolates that we have experimentally characterized. We then subject these in silico communities to pH fluctuations and assess how the community composition and coexistence is impacted. Using this model, we then simulate pH fluctuations-varying in amplitude or frequency-to identify conditions that best support species coexistence. We find that the composition of nasal communities is generally robust against pH fluctuations within the expected range of amplitudes and frequencies. Our results also show that cooperative communities and communities with lower niche overlap have significantly lower composition deviations when exposed to temporal pH fluctuations. Overall, our data suggest that nasal microbiota could be robust against environmental fluctuations.

Tuning of resonance-spacing in a traveling-wave resonator device.

In this work a traveling-wave resonator device is proposed and experimentally demonstrated in silicon-on-insulator platform in which the spacing between its adjacent resonance modes can be tuned. This is achieved through the tuning of mutual coupling of two strongly coupled resonators. By incorporating metallic microheaters, tuning of the resonance-spacing in a range of 20% of the free-spectral-range (0.4nm) is experimentally demonstrated with 27mW power dissipation in the microheater. To the best of our knowledge this is the first demonstration of the tuning of resonance-spacing in an integrated traveling-wave-resonator. It is also numerically shown that these modes exhibit high field-enhancements which makes this device extremely useful for nonlinear optics and sensing applications.

Athermal performance in high-Q polymer-clad silicon microdisk resonators.

We present a method for eliminating the temperature dependence of the resonance wavelength in high-Q silicon-based microdisk resonators by using a polymer cladding with a negative thermo-optic coefficient. Design requirements for athermal performance are derived based on theory and simulation, and their validity is experimentally verified.

Xylella fastidiosa subsp. pauca and olive produced lipids moderate the switch adhesive versus non-adhesive state and viceversa.

Global trade and climate change are re-shaping the distribution map of pandemic pathogens. One major emerging concern is Xylella fastidiosa, a tropical bacterium recently introduced into Europe from America. In last decades, X. fastidiosa was detected in several European countries. X. fastidiosa is an insect vector-transmitted bacterial plant pathogen associated with severe diseases in a wide range of hosts. X. fastidiosa through a tight coordination of the adherent biofilm and the planktonic states, invades the host systemically. The planktonic phase is correlated to low cell density and vessel colonization. Increase in cell density triggers a quorum sensing system based on mixture of cis 2-enoic fatty acids-diffusible signalling factors (DSF) that promote stickiness and biofilm. The lipidome profile of Olea europaea L. (cv. Ogliarola salentina) samples, collected in groves located in infected zones and uninfected zones was performed. The untargeted analysis of the lipid profiles of Olive Quick Decline Syndrome (OQDS) positive (+) and negative (-) plants showed a clustering of OQDS+ plants apart from OQDS-. The targeted lipids profile of plants OQDS+ and OQDS- identified a shortlist of 10 lipids that increase their amount in OQDS+ and X. fastidiosa positive olive trees. These lipid entities, provided to X. fastidiosa subsp. pauca pure culture, impact on the dual phase, e.g. planktonic ↔ biofilm. This study provides novel insights on OQDS lipid hallmarks and on molecules that might modulate biofilm phase in X. fastidiosa subsp. pauca.