Biology subjects

Momeni, B.

Publications and source records attributed to Momeni, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Microbial interactions within a community are insensitive to recurrent disturbances

Environmental disturbances are ubiquitous in microbial ecosystems, yet their effects on community composition and interactions remain poorly understood. Here, we combine analytical derivations and large-scale generalized Lotka-Volterra simulations to examine how recurrent disturbances, varying in intensity (C), frequency (1/T), regularity, and evenness, affect microbial communities. We find that context-dependent interactions (ICD)--representing the net effect of the entire community on each species--are robust to changes in disturbance intensity and frequency. Community composition is influenced by disturbance, but this effect is primarily captured by a single composite parameter T/log(C), reducing the two-dimensional disturbance space onto a one-dimensional axis. Species abundances and ICD remain largely independent of specific values of T and C except near critical transition regions (survival thresholds and no-interaction zones). Sensitivity analyses show that disturbance-sensitive cases occur in the vicinity of these boundaries. This robustness extends to when the disturbance is irregular (even up to 50% stochastic variation in T or C), with uneven disturbances, when the disturbance is species-specific, and holds at any time point throughout the inter-disturbance growth phase. The patterns are independent of species richness, growth rates, carrying capacities, and interaction strengths. Our findings highlight that microbial community organization is largely conserved under recurrent environmental stress, with extinction acting as the dominant route by which disturbances reshape communities. These findings indicate that disturbance is not a strong driver of community structure and that minor variations in experimental or environmental conditions or sampling time are not expected to substantially impact microbiome composition.

ecology↗

bioRxiv2026

Early-Stage Cultures of Acidithiobacillus ferrooxidans Enable Efficient Bioleaching of Li-ion Battery Cathode Material

In response to the growing need to recycle E-waste, specifically that of lithium-ion batteries (LIBs), the development of sustainable recycling methodologies will be vital. As one of the most sustainable and low-cost options, biohydrometallurgy (BioHM) uses biological organisms to facilitate the recovery of critical metals from spent LIBs. Despite the advantages of BioHM, its slow kinetics, due to the reliance on the metabolic activity of microorganisms, limit its large-scale application for the closed-loop recycling of LIBs. In this work, we investigate the correlation between the incubation time of Acidithiobacillus ferrooxidans (Atf) and its leaching efficiency of four common elements in LIBs: Li, Ni, Mn, and Co. We assess how specific incubation times along the biological growth curve affect the leaching efficiency of Li[Ni0.6Mn0.2Co0.2]O2 (NMC622), a model electrode material. Our results show that pH alone is not an accurate descriptor of the leaching efficiency of Atf cultures used at different growth stages. The addition of NMC622 during the bacterial lag phase reaches similar or even improved extraction compared to cultures used after reaching the exponential or stationary phases. A simple model of leaching dynamics shows predictions consistent with our experimental observations under the condition that the inhibition of bacterial growth by NMC is not severe. Our findings indicate that early-stage cultures can alleviate the kinetic bottleneck and improve the throughput of recovering critical materials from spent batteries.

microbiology↗

bioRxiv2026

Partitioning the roots of interactions between microbes (PRISM): environment-supplied resources versus species-produced mediators

Microbial species in a community can interact through competition for resources present in the environment, as well as inhibition or facilitation by metabolites produced by other species. Understanding the relative contribution of these two types of interaction will help us modulate bacterial interactions more effectively. Our work focuses on partitioning the impact of metabolites that mediate the interactions between bacteria into the contribution of resources present in the environment versus that of mediators released as by-products of cellular activities. For this, we create a range of conditions in which the ratio of environmentally supplied resources (R) and the species-produced metabolites (M) is modulated to infer the contribution of each to the overall interaction. We performed this assay with six different nasal bacterial strains and saw an array of outcomes in terms of how secreted metabolites by one strain affected other nasal strains. Metabolites produced by Staphylococcus suppressed the growth of most strains tested. In contrast, metabolites produced by commensal strains such as Corynebacterium accolens and Corynebacterium tuberculostearicum could aid the growth of Staphylococcus aureus and Staphylococcus epidermidis strains. Interestingly, the growth of Staphylococcus epidermidis benefited from metabolites produced by four out of the five strains tested. Our proposed assay offers additional insights into the roots of bacterial interactions, enabling applications such as engrafting helpful probiotics and knocking out harmful bacteria.

microbiology↗

bioRxiv2025

Resource diversity and supply drive colonization resistance

AO_SCPLOWBSTRACTC_SCPLOWThe human microbiota play a key role in resisting the colonization of pathogenic microbes. However, the mechanisms by which our native microbial communities prevent invasion, a process known as colonization resistance, need to be better understood. Environmental resource supply and resource diversity are essential factors in forming these communities, but testing how the environment affects resistance in natural communities is challenging. Here we use a consumer-resource model and computational invasion simulations to investigate how environmental resource diversity and supply affect the richness-resistance relationship, overall colonization resistance, and cross-feeding dynamics. We find a non-monotonic trend between species richness and resistance, shaped by environmental characteristics. Our results show that colonization resistance is negatively correlated with both resource supply and resource diversity except when resource supply is limited. Lastly, we observe that cross-feeding weakens colonization resistance by increasing the diversity of available resources, but this trend disappears with limited resource supply. This work provides insights about colonization resistance in microbial communities of consumers, resources, and resource conversion and exchange.

ecology↗

bioRxiv2024

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

Host-associated resident microbiota can protect their host from pathogen--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 that 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. Our results state that colonization resistance often does not have a monotonic relationship with the species richness of the resident community. Instead, colonization outcomes are often the consequence of 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). We observe these two underlying 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 provide an explanation for the seemingly contradictory trend in the resistance-diversity relationship in previous reports.

ecology↗

bioRxiv2023

Weakly alkaline conditions degrade aflatoxins through lactone ring opening

Aflatoxins (AFs) are fungal metabolites that ubiquitously contaminate many common food crops and contribute to major foodborne diseases in humans and animals. The ability to remove AFs from common food and feed commodities will improve health standards and limit the economic impact inflicted by AF food contamination. Known chemical strategies have used strong acids and bases to remove contaminating AF, but these methods often lead to ecological waste issues downstream. In this study, we explore the application of weaker acidic and alkaline conditions to removes two types of AFs, AFB1 and AFG2. We find that a pH 9 buffered environment reduces AFB1 and AFG2 by more than 50% and 95%, respectively, within 24 hours. We show that AF degradation is through lactone ring opening, which is a known cause of AF toxicity, and provide a potential structure of the AFG2 degradation byproduct. Further, we confirm that incubation in the pH 9 environment reduces the genotoxicity of AFB1. Our findings indicate that a weakly alkaline environment may adequately detoxify AF-contaminated food or feed without the need to apply stronger or harsher basic conditions.

bioengineering↗

bioRxiv2023

Starch can expedite the screening for bacterial aflatoxin degraders

Aflatoxins (AFs) are secondary fungal metabolites that contaminate common food crops and are harmful to humans and animals. The ability to degrade or remove aflatoxins from common feed commodities will improve health standards and counter the economic drain inflicted by AF contamination. Bioremediation is a promising solution to AF contamination because of its low cost and few undesired environmental side-effects. Identifying new degrader species is highly beneficial in that it can offer alternatives to overcome the limitations of existing biodegraders, such as narrow working conditions and low degradation rates. Here, we screen several environmental isolates for their AF detoxification ability, using aflatoxin G2. We use different carbon sources (glucose and starch) as isolation and culturing media to examine the effect of the environment on degradation ability. Strains isolated in media with starch as the primary carbon source showed a higher percentage of good AF degraders, 16% compared to 2% when glucose was the primary carbon source. Additionally, the majority of species isolated in glucose medium exhibited improved degradation efficiency when moved into starch medium, with one isolate improving degradation levels from 30% to 70%. Our starch screen also revealed three previously unidentified AF degrader bacterial species. Good aflatoxin G2 degraders also appear to perform well against aflatoxin B1. Overall, for AF degradation, starch medium expedites the screening process and generally improves the performance of isolates. We thus propose that using starch as the carbon source is a promising means to identify new AF degraders in the environment.

microbiology↗

bioRxiv2022

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 filtrates obtained from other members--can be used as a test to decide whether a 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 medium 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.

microbiology↗

bioRxiv2022

Spatial structure may favor or disfavor microbial coexistence

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 used a model in which the spatial distributions of species and diffusible interaction mediators are explicitly included. We simulated 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 at low diffusion constants of diffusible mediators, because they lead 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.

microbiology↗

bioRxiv2022

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

Microbial enzymes have a broad potential to address many current needs, such as detoxification of harmful toxins and waste, but their native performance often does not match specific applications of interest. In attempting to evolve strains for a specific need, one challenge is that our functions of interest may not confer a fitness effect on the producer. As a result, a conventional selection scheme cannot be used to improve such secondary functions. We propose an alternative approach, partner-assisted artificial selection (PAAS), in which an assisting population acts as an intermediate to create a feedback from the function of interest to the fitness of the producer. We use a simplified model to examine how well and under what conditions such a scheme leads to improved enzymatic function, focusing on degradation of a toxin as a case example. We find that selection for improved growth in this scheme successfully leads to improved degradation performance, even in the presence of other sources of stochasticity. We find that standard selection considerations apply in PAAS: a more restrictive bottleneck leads to stronger selection but adds uncertainty. We also examine how much stochasticity in other traits can be tolerated in PAAS. Our findings offer a roadmap for successful implementation of PAAS to evolve improved functions of interest such as detoxification of harmful compounds.

microbiology↗

bioRxiv2021

Investigating the mutational landscape of the SARS-CoV-2 Omicron variant via ab initio quantum mechanical modeling

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 [~]13,000 atoms to predict and characterize binding of SARS-CoV-2 spike variants to the human receptor ACE2 (hACE2). 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 point mutations. We validate our computational predictions experimentally by comparing binding efficacy of spike variants to cells expressing hACE2. We argue that this computational model, QM-CR, can identify mutations critical for intermolecular interactions and inform the engineering of high-specificity interactors. One-Sentence SummaryAb initio modeling can predict the strength of SARS-CoV-2 variants binding to human cell receptor.

biophysics↗

bioRxiv2021

Anticipating future SARS-CoV-2 variants of concern through ab initio quantum mechanical modeling

Evolved SARS-CoV-2 variants are currently challenging the efficacy of first-generation vaccines, largely through the emergence of spike protein mutants. Among these variants, Delta is presently the most concerning. We employ an ab initio quantum mechanical model based on Density Functional Theory to characterize the spike protein Receptor Binding Domain (RBD) interaction with host cells and gain mechanistic insight into SARS-CoV-2 evolution. The approach is illustrated via a detailed investigation of the role of the E484K RBD mutation, a signature mutation of the Beta and Gamma variants. The simulation is employed to: predict the depleting effect of the E484K mutation on binding the RBD with select antibodies; identify residue E484 as a weak link in the original interaction with the human receptor hACE2; and describe SARS-CoV-2 Wuhan strand binding to the bat Rhinolophus macrotis ACE2 as more optimized than the human counterpart. Finally, we predict the hACE2 binding efficacy of a hypothetical E484K mutation added to the Delta variant RBD, identifying a potential future variant of concern. Results can be generalized to other mutations, and provide useful information to complement existing experimental datasets of the interaction between randomly generated libraries of hACE2 and viral spike mutants. We argue that ab initio modeling is at the point of being aptly employed to inform and predict events pertinent to viral and general evolution.

biophysics↗

bioRxiv2021

Enzyme fatigue limits the detoxification of aflatoxin by Rhodococcus species

Bacterial detoxification of mycotoxins has the potential to offer a low-cost solution to ensure that feed and food commodities contaminated by fungal growth become safe to consume. Among bacteria, Rhodococcus species are of particular interest because they can be metabolically versatile, non-pathogenic, and environment-friendly. However, the native response of Rhodococcus environmental isolates appears inadequate for current detoxification needs. By analyzing the detoxification of aflatoxin by two Rhodococcus species: R. pyridinivorans and R. erythropolis, we examine important features of the dynamics that could guide future optimization of bacterial detoxification. Our results for Rhodococcus species suggest that detoxification happens through a regulated process of secreting extracellular enzymes. We show that enzyme fatigue in the presence of the toxin determines the lifetime of the enzyme and limits the overall detoxification performance of these species. Additionally, we show that the regulation of enzyme production can be both species- and environment-dependent. Overall, our quantitative approach reveals that enzyme fatigue is a major determinant of overall detoxification and needs to be accounted for in assessing the performance of detoxification by live cells or cell-free filtrates.

microbiology↗

bioRxiv2021

Mechanistic Insight from Full Quantum Mechanical Modeling: Laccase as a Detoxifier of Aflatoxins

We demonstrate a path towards full Quantum Mechanics (QM) characterization of enzymatic activity. As a case-study, we investigate the detoxification of aflatoxin, a carcinogenic food contaminant, by laccase, a versatile oxidase capable of--but not efficient for--degrading aflatoxin. We use a combination of quantitative experimentation and QM modeling to show that low enzymatic steric affinity for aflatoxin is the main bottleneck, rather that the oxidative activity of laccase. To identify the structural elements responsible for low reaction rates, we perform a density functional theory (DFT) based modeling of both the substrate and the enzyme in a full QM simulation of more than 7,000 atoms. Thanks to our approach we point to amino acid residues that determine the affinity of laccase for aflatoxin. We show that these residues are substrate-dependent, making a full QM approach necessary for enzyme optimization. Altogether, we establish a roadmap for rational enzyme engineering applicable beyond our case study.

biophysics↗

bioRxiv2020

Impact of temporal pH fluctuations on the coexistence of nasal bacteria

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. We take advantage of the cultivability of nasal bacteria to experimentally assess their responses to pH. Based on experimentally observed responses, we formulate a mathematical model to numerically investigate the impact of temporal pH fluctuations on species coexistence. Through extensive numerical simulations, we find that the composition of nasal communities is robust against pH fluctuations. Our results suggest that nasal microbiota could be more robust than expected against environmental fluctuations.

ecology↗

bioRxiv2020

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 among resident microbes. Microbial communities differ from communities composed of other types of organisms in that many microbial interactions are mediated by chemicals that are released into or consumed from the environment. We ask what determines invasion outcomes in such microbial communities. Here, we use a model based on chemical-mediated interactions among microbial species to assess the impact of positive and negative interactions on invasion outcomes. We classified invasion outcomes as resistance, augmentation, displacement, or disruption depending on whether the richness of the resident community was maintained or dropped and whether the invader was maintained in the community or went extinct. We found that as the number of invaders increased relative to size of the resident community, resident communities were increasingly disrupted. As facilitation of the invader by the resident community increased, resistance outcomes were replaced by displacement and augmentation. By contrast, as facilitation increased among residents, displacement outcomes shifted to resistance. When facilitation of the resident community by the invader was eliminated, augmentation outcomes were replaced by displacement outcomes, while when inhibition of residents by invaders was eliminated, there was little change in the frequency of invasion outcomes. These results suggest that a better understanding of the chemical-mediated interactions within resident communities and between residents and invaders is crucial to predicting the success of invasions into microbial communities.

ecology↗

bioRxiv2020

Full quantum mechanical modeling of the enzyme-substrate system: how laccase detoxifies aflatoxin

This work focuses on: 1) the development of a methodology to perform a full Quantum Mechanics (QM) characterization of enzymatic activity; 2) the development of a rational approach to laccase engineering as a food bioremediator. Aflatoxins are among the most dangerous natural carcinogens, and regularly contaminate reserves of staple crops worldwide. Decontamination of aflatoxin-polluted food is of great interest for ensuring food safety, and bioremediation is regarded as the most promising solution. The fungal isoforms of laccase display the rare potential to detoxify aflatoxin by tackling its aromatic moieties.. Yet, because of a generally low efficiency, large-scale application of naturally occurring isoforms has so far been unfeasible. We perform a combination of quantitative experimentation and quantum mechanical modeling on aflatoxin and reveal that: (1) detoxification efficiency is limited by the low enzymatic affinity for the substrate; and (2) aflatoxin is not detoxified by oxidative activity of laccase alone, but requires additional stimulation from the environment. QM modeling also allowed identification of the residues in the laccase tertiary structure that determine affinity of the enzymatic pocket for aflatoxin. We conclude that, for our case-study, a full QM approach is mandatory as a first step towards rational optimization. We detail a feasible approach towards this endeavor and argue that our full QM characterization can serve as a roadmap for enzyme development in other applications pertaining laccase as well as other enzymes.

biophysics↗

bioRxiv2018

Microbial coexistence through chemical-mediated interactions

Many microbial functions happen within communities of interacting species. Explaining how species with intrinsically disparate fitness can coexist is important for applications such as manipulating host-associated microbiota or engineering industrial communities. Previous coexistence studies have often neglected interaction mechanisms. Here, we formulate and experimentally constrain a model in which chemical mediators of microbial interactions (e.g. metabolites or waste-products) are explicitly incorporated. We construct many instances of coexistence by simulating community assembly through enrichment and ask how species interactions can explain coexistence. We show that growth-facilitating influences between members are favored in assembled communities. Among negative influences, self-restraint, such as production of self-inhibiting waste, contributes to coexistence, whereas inhibition of other species disrupts coexistence. Coexistence is also favored when interactions are mediated by depletable chemicals that get consumed or degraded, rather than by reusable chemicals that are unaffected by recipients. Our model creates null predictions for coexistence driven by chemical-mediated interactions.

ecology↗