Biology subjects

Pawar, S.

Publications and source records attributed to Pawar, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Climatic limits to Atlantic salmon population fitness at continental scales

Anadromous fish populations are declining globally, partly due to acute pressure from rapid environmental change in their freshwater and marine habitats. A more mechanistic understanding of how climatic and land use changes impact their population-level fitness is needed to mitigate these declines. Here we develop a model that successfully captures the thermal niche of the declining Atlantic salmon. This allows us to predict the combined effects of changes in two dominant threats to this species: climate and resource availability. Specifically, the model predicts that a 50% reduction from metabolically optimal resource supply could constrict their thermal niche by [~]7{degrees}C. We also show that daily and seasonal temperature fluctuations have a relatively minor impact. A conservative increase of 1.5{degrees}C in global temperatures will cause fitness declines for populations in higher climatic regimes, across resource levels. Our results provide new and general insights into factors limiting the distribution of extant Atlantic salmon populations. They also highlight the relative importance of future threats from climatic warming, fluctuations, and changes in resource availability due to land use change.

ecology↗

bioRxiv2023

Deciphering fitness trade-offs in metabolite exchange at the origin of a bacterial cross-feeding community

Obligate cross-feeding is a common type of interaction among microbial communities, yet gaps persist in understanding its maintenance and limit practical applications. In particular, little is known about how contextualised metabolite exchange intensities affect community fitness, despite their influential role in shaping interdependencies1, diversities 2, and lifespan 3 of cross-feeding community members. Here I computationally test how amino acids isoleucine (ile) and lysine (lys) exchange intensities affect individual and community fitness of a two-strain auxotroph cross-feeding community. I innovatively integrated metabolite exchange intensities and multi-strain growth using Flux Balance Analysis (FBA) and evolutionary game theory, and showed that crossfeeding communities have the highest fitness when the metabolite exchange intensity is slightly above individual amino acid uptake demands, stimulated by cheaters presence. Using FBA with different metabolite uptake / secretion combinations, I discovered the individual amino acid demands are linearly correlated with sole carbon source (glucose) availability. Additionally, as cheating mutants emerge, costly exchange intensities can be better sustained when accumulated shared metabolites are accessible. This is the first study linking metabolite exchange intensities and cross-feeding community fitness considering all known metabolic reactions of a bacterial strain. The fittest metabolite exchange range and its relationships with glucose and shared metabolite availability shed lights on resilient microbial community engineering as well as metabolite exchange parameter constraints for multi-species population / metabolism models 4-6.

evolutionary biology↗

bioRxiv2023

No model to rule them all: a systematic comparison of 83 thermal performance curve models across traits and taxonomic groups

In ectotherms, the performance of physiological, ecological and life-history traits universally increases with temperature to a maximum before decreasing again. Identifying the most appropriate thermal performance model for a specific trait type has broad applications, from metabolic modelling at the cellular level to forecasting the effects of climate change on population, ecosystem and disease transmission dynamics. To date, numerous mathematical models have been designed, but a thorough comparison among them is lacking. In particular, we do not know if certain models consistently outperform others and how factors such as sampling resolution and trait or organismal identity influence model performance. To fill this knowledge gap, we compile 2,739 thermal performance datasets from diverse traits and taxa, to which we fit a comprehensive set of 83 existing mathematical models. We detect remarkable variation in model performance that is not primarily driven by sampling resolution, trait type, or taxonomic information. Our results reveal a surprising lack of well-defined scenarios in which certain models are more appropriate than others. To aid researchers in selecting the appropriate set of models for any given dataset or research objective, we derive a classification of the 83 models based on the average similarity of their fits.

ecology↗

bioRxiv2023

Heterogeneity of foam cell biogenesis across diseases

Foam cells are dysfunctional, lipid-laden macrophages associated with chronic inflammation of diverse origin. The long-standing paradigm that foam cells are cholesterol-laden derives from atherosclerosis research. We previously showed that, in tuberculosis, foam cells surprisingly accumulate triglycerides. Here, we utilized bacterial (Mycobacterium tuberculosis), fungal (Cryptococcus neoformans), and human papillary renal cell carcinoma (pRCC) models to address the need for a new explanation of foam cell biogenesis. We applied mass spectrometry-based imaging to assess the spatial distribution of storage lipids relative to foam-cell-rich areas in lesional tissues, and we characterized lipid-laden macrophages generated under corresponding in vitro conditions. The in vivo data and the in vitro findings showed that cryptococcus-infected macrophages accumulate triglycerides, while macrophages exposed to pRCC- conditioned-medium accumulated both triglycerides and cholesterol. Moreover, cryptococcus- and mycobacterium-infected macrophages accumulated triglycerides in different ways. Collectively, the data show that the molecular events underlying foam cell formation are specific to disease and microenvironment. Since foam cells are potential therapeutic targets, recognizing that their formation is disease-specific opens new biomedical research directions.

immunology↗

bioRxiv2023

Bacterial responses to complex mixtures of chemical pollutants

Our understanding of how microbes respond to pollutants is almost wholly based on single-species responses to individual chemicals. However, in natural environments, microbes experience the effects of multiple pollutants simultaneously, and their responses to these mixtures of chemicals may not be readily predictable based on their responses to each pollutant in isolation. Here we extended the scope and complexity of previous multi-stressor experiments by assaying the growth of model and non-model strains of bacteria in all 255 combinations of 8 chemical stressors. This approach allowed us to identify fitness effects arising from potential high-order interactions among stressor responses. We found that the bacterial strains responded in different ways to stressor mixtures, which could not be predicted simply from their phylogenetic relatedness. Responses to increasingly complex chemical mixtures were more likely to show a significant deviation from a null model based on the responses to each chemical alone. However, these net responses were mainly driven by lower-order interactions among a small number of chemicals, suggesting a limited role for complex high-order interactions. These results simplify the predictability of microbial populations and communities responding to multiple stressors, paving the way for the development of efficient next-generation eco-toxicological assays.

microbiology↗

bioRxiv2023

SPEAR: a Sparse Supervised Bayesian Factor Model for Multi-omic Integration

MotivationPredictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are identified from high-throughput profiling assays through a combination of dimensionality reduction and machine learning techniques. The genes, proteins, metabolites, and other biological analytes that compose signatures also generate hypotheses on the underlying mechanisms driving biological responses, thus improving biological understanding. Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive modeling. However, multi-omics integration and predictive modeling are generally performed independently in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful. ResultsWe developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the reconstruction of underlying factors in synthetic examples and prediction accuracy of COVID-19 severity and breast cancer tumor subtypes. AvailabilitySPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR. Contactjeremy.gygi@yale.edu; leying.guan@yale.edu

bioinformatics↗

bioRxiv2023

Variation in temperature of peak trait performance will constrain adaptation of arthropod populations to climatic warming.

The capacity of arthropod populations to adapt to long-term climatic warming is uncertain. Here, we combine theory and extensive data on diverse arthropod taxa to show that their rate of thermal adaptation to climatic warming will be constrained in two fundamental ways. First, the rate of thermal adaptation is predicted to be limited by the rate of shift in the temperature of peak performance of four life-history traits in a specific order: juvenile development, adult fecundity, juvenile mortality, and adult mortality. Second, thermal adaptation will be constrained due to differences in the temperature of peak performance among these four traits, which are expected to persist because of trade-offs. By compiling a new global dataset of 61 diverse arthropod species, we find strong evidence that contemporary populations have indeed evolved under these constraints. Our results provide a basis for using relatively feasible trait measurements to predict the adaptive capacity of diverse arthropod populations to climatic warming.

ecology↗

bioRxiv2022

Variation in thermal physiology can drive the temperature-dependence of microbial community richness

Predicting how species diversity changes along environmental gradients is an enduring problem in ecology. Current theories cannot explain the observation that microbial taxonomic richness can show positive, unimodal, as well as negative diversity-temperature gradients. Here we derive a general empirically-grounded theory that can explain this phenomenon by linking microbial species richness in local communities to variation in their temperature-driven competitive interaction and growth rates. It predicts that richness depends on variation in shape of the thermal performance curves of these metabolic traits across species in the community. Specifically, the shape of the microbial community temperature-richness relationship depends on how the strength of competition across the community and the degree of variation in growth rates changes across temperature. These in turn can be predicted from the variation in thermal performance across the community. We show that empirical variation in the thermal performance curves of metabolic traits across extant bacterial taxa is indeed sufficient to generate the variety of community-level temperature-richness responses observed in the real world. Our results provide a new mechanism that can help explain temperature-diversity gradients in microbial communities, and provide a quantitative framework for interlinking variation in the thermal physiology of microbial species to their community-level diversity.

ecology↗

bioRxiv2022

Integrative Multi-Omics Approaches for Identifying Cervical Cancer Therapeutic Targets

After breast cancer, cervical cancer (CC) is one of the most common malignancies in women globally. Over 90% of chronic infections are caused by human papillomavirus (HPV) and its subtypes. Extensive research efforts are required to identify the treatment targets and prognostic indicators for recurring and metastatic cancers. It may be possible because of omics methods, including genomes, epigenomics, transcriptomics, proteomics, and metabolomics. High throughput (HT) data on the differential mRNA and miRNA expression and their crucial interrelationships enable promising integration and interpretation of the results. Clinical data and multi-omics have risen to the top of the heap in delivering molecular and cellular activities. They aid in comparing data from different omics approaches and bridging the gap between genotype and phenotype. Therefore, multi-omic techniques may improve the knowledge of the molecular basis of the physiology and primary cause of disease, revealing a new route for the prognosis, diagnosis, prevention, and therapy of human diseases.

systems biology↗

bioRxiv2021

Less is worse than none: ineffective adaptive foraging can destabilise food webs

O_LIConsumers can potentially adjust their diet in response to changing resource abundances, thereby achieving better foraging payoffs. Although previous work has explored how such adaptive foraging scales up to determine the structure and dynamics of food webs, consumers may not be able to perform perfect diet adjustment due to sensory or cognitive limitations. Whether the effectiveness of consumers diet adjustment alters food-web consequences remains unclear. C_LIO_LIHere, we study how adaptive foraging, specifically the effectiveness (i.e. rate) with which consumers adjust their diet, influences the structure, dynamics, and overall species persistence in synthetic food webs. C_LIO_LIWe model metabolically-constrained optimal foraging as the mechanistic basis of adaptive diet adjustment and ensuing population dynamics within food webs. We compare food-web dynamical outcomes among simulations sharing initial states but differing in the effectiveness of diet adjustment. C_LIO_LIWe show that adaptive diet adjustment generally makes food-web structure resilient to species loss. Effective diet adjustment that maintains optimal foraging in the face of changing resource abundances facilitates species persistence in the community, particularly reducing the extinction of top consumers. However, a greater proportion of intermediate consumers goes extinct as optimal foraging becomes less-effective and, unexpectedly, slow diet adjustment leads to higher extinction rates than no diet adjustment at all. Therefore, food-web responses cannot be predicted from species responses in isolation, as even less-effective adaptive foraging benefits individual species (better than non-adaptive) but can harm species persistence in the food web as a whole (worse than non-adaptive). C_LIO_LIWhether adaptive foraging helps or harms species coexistence has been contradictory in literature Our finding that it can stabilise or destabilise the food web depending on how effectively it is performed help reconcile this conflict. Inspired by our simulations, we deduce that there may exist a positive association between consumers body size and adaptive-foraging effectiveness in the real world. We also infer that such effectiveness may be higher when consumers cognise complete information about their resources, or when trophic interactions are driven more by general traits than by specific trait-matching. We thereby suggest testable hypotheses on species persistence and food-web structure for future research, in both theoretical and empirical systems. C_LI

ecology↗

bioRxiv2021

Thermodynamic constraints on the diversity of microbial ecosystems

Non-equilibrium thermodynamics has long been an area of substantial interest to ecologists because most fundamental biological processes, such as protein synthesis and respiration, are inherently energy-consuming. However, most of this interest has focused on developing coarse ecosystem-level maximisation principles, providing little insight into underlying mechanisms that lead to such emergent constraints. Microbial communities are a natural system to decipher this mechanistic basis because their interactions in the form of substrate consumption, metabolite production, and cross-feeding can be described explicitly in thermodynamic terms. Previous work has considered how thermodynamic constraints impact competition between pairs of species, but restrained from analysing how this manifests in complex dynamical systems. To address this gap, we develop a thermodynamic microbial community model with fully reversible reaction kinetics, which allows direct consideration of free-energy dissipation. This also allows species to interact via products rather than just substrates, increasing the dynamical complexity, and allowing a more nuanced classification of interaction types to emerge. Using this model, we find that community diversity increases with substrate lability, because greater free-energy availability allows for faster generation of niches. Thus, more niches are generated in the time frame of community establishment, leading to higher final species diversity. We also find that allowing species to make use of near-to-equilibrium reactions increases diversity in a low free-energy regime. In such a regime, two new thermodynamic interaction types that we identify here reach comparable strengths to the conventional (competition and facilitation) types, emphasising the key role that thermodynamics plays in community dynamics. Our results suggest that accounting for realistic thermodynamic constraints is vital for understanding the dynamics of real-world microbial communities. Author summaryThere is a growing interest in microbial communities due to their important role in biogeochemical cycling as well as plant and animal health. Although our understanding of thermodynamic constraints on individual cells is rapidly improving, the impact of these constraints on complex microbial communities remains largely unexplored theoretically and empirically. Here, we develop a new microbial community model which allows thermodynamic efficiency and entropy production to be calculated directly. We find that availability of substrates with greater free-energy allows for a faster rate of niche generation, leading to higher final species diversity. We also show that when the free-energy availability is low, species with reactions close to thermodynamic equilibrium are favoured, leading to more diverse and efficient communities. In addition to the conventional interaction types (competition and facilitation), our model reveals the existence of two novel interaction types mediated by products rather than substrates. Though the conventional interactions are generally the strongest, the novel interaction types are significant when free-energy availability is low. Our results suggest that non-equilibrium thermodynamics need to be considered when studying microbial community dynamics.

ecology↗

bioRxiv2021

The role of competition versus cooperation in microbial community coalescence

New microbial communities often arise through the mixing of two or more separately assembled parent communities, a phenomenon that has been termed "community coalescence". Understanding how the interaction structures of complex parent communities determine the outcomes of coalescence events is an important challenge. While recent work has begun to elucidate the role of competition in coalescence, that of cooperation, a key interaction type commonly seen in microbial communities, is still largely unknown. Here, using a general consumer-resource model, we study the combined effects of competitive and cooperative interactions on the outcomes of coalescence events. In order to do so, we simulate coalescence events between pairs of communities with different degrees of competition for shared carbon resources and cooperation through cross-feeding on leaked metabolic by-products (facilitation). We also study how structural and functional properties of post-coalescence communities evolve when they are subjected to repeated coalescence events. We find that in coalescence events, the less competitive and more cooperative parent communities contribute a higher proportion of species to the new community, because this endows superior ability to deplete resources and resist invasions. Consequently, when a community is subjected to repeated coalescence events, it gradually evolves towards being less competitive and more cooperative, as well as more species rich, robust and efficient in resource use. Encounters between microbial communities are becoming increasingly frequent as a result of anthropogenic environmental change, and there is great interest in how the coalescence of microbial communities affects environmental and human health. Our study provides new insights into the mechanisms behind microbial community coalescence, and a framework to predict outcomes based on the interaction structures of parent communities. Author summaryIn nature, new microbial communities often arise from the fusion of whole, previously separate communities (community coalescence). Despite the crucial role that interactions among microbes play in the dynamics of complex communities, our ability to predict how these affect the outcomes of coalescence events remains limited. Here, using a general mathematical model, we study how the structure of species interactions confers an advantage upon a microbial community when it encounters another, and how communities evolve after undergoing repeated coalescence events. We find that competitive interactions between species preclude their survival upon a coalescence event, while cooperative interactions are advantageous for post-coalescence survival. Furthermore, after a community is exposed to many coalescence events, the remaining species become less competitive and more cooperative. Ultimately, this drives the community evolution, yielding post-coalescence communities that are more species-rich, productive, and resistant to invasions. There are many potential environmental and health implications of microbial community coalescence, which will benefit from the theoretical insights that we offer here about the fundamental mechanisms underlying this phenomenon.

ecology↗

bioRxiv2021

Species interactions amplify the temperature dependence of microbial community respiration

The respiratory release of CO2 by microbes is a dominant component of the global carbon cycle. However, large uncertainties exist about the effects of climatic warming on the respiration of microbial communities due to lack of mechanistic, empirically-tested theory that accounts for dynamic species interactions. We developed a general mathematical model which predicts that thermal sensitivity of microbial community respiration increases as species interactions become more positive, i.e., change from competition to facilitation. This is because facilitation disproportionately increases positive feedbacks between the thermal sensitivities of species-level metabolic and biomass accumulation rates at warmer temperatures. We experimentally validated this prediction in bacterial communities of 8 taxa, finding that a shift from competition to facilitation after a month of co-adaptation caused a 60% increase in the thermal sensitivity of their respiration relative to de novo communities that had not co-adapted. Thus, rapid changes in species interactions can profoundly change the temperature-dependence of microbial community respiration and should be considered in climate change models.

ecology↗

bioRxiv2021

Latent functional diversity may accelerate microbial community responses to environmental fluctuations

How complex microbial communities respond to climatic fluctuations remains an open question. Due to their relatively short generation times and high functional diversity, microbial populations harbor great potential to respond as a community through a combination of strain-level phenotypic plasticity, adaptation, and species sorting. However, the relative importance of these mechanisms remains unclear. We conducted a laboratory experiment to investigate the degree to which bacterial communities can respond to changes in environmental temperature through a combination of phenotypic plasticity and species sorting alone. We grew replicate soil communities from a single location at six temperatures between 4{degrees}C and 50{degrees}C. We found that phylogenetically- and functionally-distinct communities emerge at each of these temperatures, with K-strategist taxa favoured under cooler conditions, and r-strategist taxa under warmer conditions. We show that this dynamic emergence of distinct communities across a wide range of temperatures (in essence, community-level adaptation), is driven by the resuscitation of latent functional diversity: the parent community harbors multiple strains pre-adapted to different temperatures that are able to "switch on" at their preferred temperature without immigration or adaptation. Our findings suggest that microbial community function in nature is likely to respond rapidly to climatic temperature fluctuations through shifts in species composition by resuscitation of latent functional diversity.

ecology↗

bioRxiv2021

Competition in depleting resource environments shapes the thermal response of mosquito population fitness

The temperature-dependencies of life history traits are increasingly being used to predict how climatic warming will affect vector-borne disease dynamics, partially by affecting the abundance dynamics of the vector population. Such predictions generally arise from mathematical models that incorporate the temperature dependence of traits measured under laboratory conditions. These temperature-trait relationships are typically estimated from juvenile populations reared under optimal resource conditions, even though natural populations experience intermittent resource depletion. Using laboratory experiments on the mosquito Aedes aegypti, combined with a stage-structured population model, we show that resource depletion in the juvenile habitat can significantly depress the vectors maximal population growth rate (rm) across the entire temperature range, cause it to peak at a lower temperature, and narrow its thermal niche width. Our results provide compelling evidence for future studies to consider resource depletion when predicting the effects of global change on vector-borne disease transmission, disease vectors and other arthropods.

ecology↗

bioRxiv2020

rTPC and nls.multstart: a new pipeline to fit thermal performance curves in R.

O_LIThe quantification of thermal performance curves (TPCs) for biological rates has many applications to problems such as predicting species responses to climate change. There is currently no widely used open-source pipeline to fit mathematical TPC models to data, which limits the transparency and reproducibility of the curve fitting process underlying applications of TPCs. C_LIO_LIWe present a new pipeline in R that currently allows for reproducible fitting of 24 different TPC models using non-linear least squares (NLLS) regression. The pipeline consists of two packages - rTPC and nls. multstart - that allow multiple start values for NLLS fitting and provides helper functions for setting start parameters. This pipeline overcomes previous problems that have made NLLS fitting and estimation of key parameters difficult or unreliable. C_LIO_LIWe demonstrate how rTPC and nls.multstart can be combined with other packages in R to robustly and reproducibly fit multiple models to multiple TPC datasets at once. In addition, we show how model selection or averaging, weighted model fitting, and bootstrapping can easily be implemented within the pipeline. C_LIO_LIThis new pipeline provides a flexible and reproducible approach that makes the challenging task of fitting multiple TPC models to data accessible to a wide range of users. C_LI

ecology↗

bioRxiv2020

Higher temperatures worsen the effects of mutations on protein stability

Understanding whether and how temperature increases alter the effects of mutations on protein stability is crucial for understanding the limits to thermal adaptation by organisms. Currently, it is generally assumed that the stability effects of mutations are independent of temperature. Yet, mutations should become increasingly destabilizing as temperature rises due to the increase in the energy of atoms. Here, by performing an extensive computational analysis on the essential enzyme adenylate kinase in prokaryotes, we show, for the first time, that mutations become more destabilizing with temperature both across and within species. Consistent with these findings, we find that substitution rates of prokaryotes decrease nonlinearly with temperature. Our results suggest that life on Earth likely originated in a moderately thermophilic and thermally fluctuating environment, and indicate that global warming should decrease the per-generation rate of molecular evolution of prokaryotes.

evolutionary biology↗

bioRxiv2020

Systematic variation in the temperature dependence of bacterial carbon use efficiency

Understanding the temperature dependence of carbon use efficiency (CUE) is critical for understanding microbial physiology, population dynamics, and community-level responses to changing environmental temperatures 1,2. Currently, microbial CUE is widely assumed to decrease with temperature 3,4. However, this assumption is based largely on community-level data, which are influenced by many confounding factors 5, with little empirical evidence at the level of individual strains. Here, we experimentally characterise the CUE thermal response for a diverse set of environmental bacterial isolates. We find that contrary to current thinking, bacterial CUE typically responds either positively to temperature, or has no discernible temperature response, within biologically meaningful temperature ranges. Using a global data-synthesis, we show that our empirical results are generalisable across a much wider diversity of bacteria than have previously been tested. This systematic variation in the thermal responses of bacterial CUE stems from the fact that relative to respiration rates, bacterial population growth rates typically respond more strongly to temperature, and are also subject to weaker evolutionary constraints. Our results provide fundamental new insights into microbial physiology, and a basis for more accurately modelling the effects of shorter-term thermal fluctuations as well as longer-term climatic warming on microbial communities.

microbiology↗