Biology subjects

Goyal, A.

Publications and source records attributed to Goyal, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Improving rigor and reproducibility in chromatin immunoprecipitation assay data analysis workflows with Rocketchip

As genome sequencing technologies advance, the accumulation of sequencing data in public databases necessitates more robust and adaptable data analysis workflows. Here, we present Rocketchip, which aims to offer a solution to this problem by allowing researchers to easily compare and swap out different components of ChIP-seq, CUT&RUN, and CUT&Tag data analysis, thereby facilitating the identification of reliable analysis methodologies. Rocketchip enables researchers to efficiently process large datasets while ensuring reproducibility and allowing for the reanalysis of existing data. By supporting comparative analyses across different datasets and methodologies, Rocketchip contributes to the rigor and reproducibility of scientific findings. Furthermore, Rocketchip serves as a platform for benchmarking algorithms, allowing researchers to identify the most accurate and efficient analytical approaches to be applied to their data. In emphasizing reproducibility and adaptability, Rocketchip represents a significant step towards fostering robust scientific research practices.

bioinformatics↗

bioRxiv2024

Emergent ecological advantage of sequential metabolic strategies in complex microbial communities

Microbes growing in fluctuating environments employ two key metabolic strategies: sequential (diauxic) utilization and co-utilization of nutrients. Most work has focused on understanding and comparing these strategies physiologically for the growth of single species, rather than ecologically for the assembly of complex natural communities. This is in part because of the lack of good metrics quantifying the fitness of different metabolic strategies in ecological contexts. Here, we present a new consumer-resource framework that incorporates dynamic proteome reallocation, and use it to compare the fitness of metabolic strategies during community assembly. We introduce two notions of fitness of a strategy in fluctuating environments: the time-averaged growth rate and the biomass-weighted prevalence of microbes using a given strategy. We find that sequential utilizers, although disadvantaged in pairwise competitions, gain a significant edge during community assembly -- an advantage that becomes more pronounced with increasing community diversity and the size of the species pool from which they are assembled. Low diversity communities resemble pairwise competitions and are dominated by co-utilizers, whereas high diversity, mature communities are dominated by the sequential utilizers. This shift is driven by two factors: the difference in lag times and the increased structural stability conferred by sequential strategies. Overall, our work provides several testable predictions about the co-occurrence patterns of microbes using different metabolic strategies.

ecology↗

bioRxiv2024

Energy-ordered resource stratification as an agnostic signature of life

The search for extraterrestrial life hinges on identifying biosignatures, often focusing on gaseous metabolic byproducts as indicators. However, most such biosignatures require assuming specific metabolic processes. It is widely recognized that life on other planets may not resemble that of Earth, but identifying biosignatures "agnostic" to such assumptions has remained a challenge. Here, we propose a novel approach by considering the generic outcome of life: the formation of competing ecosystems. We use a minimal model to argue that the presence of ecosystem-level dynamics, characterized by ecological interactions and resource competition, may yield biosignatures independent of specific metabolic activities. Specifically, we propose the emergent stratification of chemical resources in order of decreasing energy content as a candidate new biosignature. While likely inaccessible to remote sensing, this signature could be relevant for sample return missions, or for detection of ancient signatures of life on Earth itself.

ecology↗

bioRxiv2024

Genome-Wide Transcription Response of Staphylococcus epidermidis to Heat Shock and Medically Relevant Glucose Levels

Skin serves as both barrier and interface between body and environment. Skin microbes are intermediaries evolved to respond, transduce, or act in response to changing environmental or physiological conditions. Here, we quantify genome-wide changes in gene expression levels for one abundant skin commensal, Staphylococcus epidermidis, in response to an internal physiological signal, glucose levels, and an external environmental signal, temperature. We find 85 of 2354 genes change up to[~] 34-fold in response to medically relevant changes in glucose concentration (0 mM to 17 mM; adj P value [≤] 0.05). We observed carbon catabolite repression in response to a range of glucose spikes, as well as upregulation of genes involved in glucose utilization in response to persistent glucose. We observed 366 differentially expressed genes in response to a physiologically relevant change in temperature (37{degrees}C to 45{degrees}C; adj P value [≤] 0.05) and an S. epidermidis heat-shock response that mostly resembles the heat-shock response of related staphylococcal species. DNA motif analysis also revealed CtsR and CIRCE operator sequences arranged in tandem upstream of dnaK and groESL operons. We further identified 38 glucose-responsive genes as candidate ON or OFF switches for use in controlling synthetic genetic systems. Such systems might be used to instrument the in-situ skin microbiome or help control microbes bioengineered to serve as embedded diagnostics, monitoring, or treatment platforms.

microbiology↗

bioRxiv2024

A universal niche geometry governs the response of ecosystems to environmental perturbations

How ecosystems respond to environmental perturbations is a fundamental question in ecology, made especially challenging due to the strong coupling between species and their environment. Here, we introduce a theoretical framework for calculating the linear response of ecosystems to environmental perturbations in generalized consumer-resource models. Our construction is applicable to a wide class of systems, including models with non-reciprocal interactions, cross-feeding, and non-linear growth/consumption rates. Within our framework, all ecological variables are embedded into four distinct vector spaces and ecological interactions are represented by geometric transformations between these spaces. We show that near a steady state, such geometric transformations directly map environmental perturbations - in resource availability and mortality rates - to shifts in niche structure. We illustrate these ideas in a variety of settings including a minimal model for pH-induced toxicity in bacterial denitrification.

ecology↗

bioRxiv2024

The proteome is a terminal electron acceptor

Microbial metabolism is impressively flexible, enabling growth even when available nutrients differ greatly from biomass in redox state. E. coli, for example, rearranges its physiology to grow on reduced and oxidized carbon sources through several forms of fermentation and respiration. To understand the limits on and evolutionary consequences of metabolic flexibility, we developed a mathematical model coupling redox chemistry with principles of cellular resource allocation. Our integrated model clarifies key phenomena, including demonstrating that autotrophs grow slower than heterotrophs because of constraints imposed by intracellular production of reduced carbon. Our model further indicates that growth is improved by adapting the redox state of biomass to nutrients, revealing an unexpected mode of evolution where proteins accumulate mutations benefiting organismal redox balance. One sentence summaryMicrobial proteins adapt their composition on physiological and evolutionary timescales to ensure organismal redox balance.

microbiology↗

bioRxiv2023

The ecological consequences of microbial metabolic strategies in fluctuating environments

Microbes adopt a variety of metabolic strategies to consume resources in fluctuating environments, but most work has focused on understanding these strategies in the context of isolated species, rather than diverse natural communities. We systematically measure the feasibility, dynamical and structural stability of multispecies microbial communities adopting different metabolic strategies. Our results reveal key distinctions between the ecological properties of different metabolic strategies, showing that communities containing sequential utilizers are more resilient to resource fluctuations, but are less feasible than co-utilizing communities.

ecology↗

bioRxiv2023

Closed ecosystems extract energy through self-organized nutrient cycles

Our planet is a self-sustaining ecosystem powered by light energy from the sun, but roughly closed to matter. Many ecosystems on Earth are also approximately closed to matter and recycle nutrients by self-organizing stable nutrient cycles, e.g., microbial mats, lakes, open ocean gyres. However, existing ecological models do not exhibit the self-organization and dynamical stability widely observed in such planetary-scale ecosystems. Here, we advance a new conceptual model that explains the self-organization, stability and emergent features of closed microbial ecosystems. Our model incorporates the bioenergetics of metabolism into an ecological framework. By studying this model, we uncover a crucial thermodynamic feedback loop that enables metabolically diverse communities to almost always stabilize nutrient cycles. Surprisingly, highly diverse communities self-organize to extract{approx} 10% of the maximum extractable energy, or{approx} 100 fold more than randomized communities. Further, with increasing diversity, distinct ecosystems show strongly correlated fluxes through nutrient cycles. However, as the driving force from light increases, the fluxes of nutrient cycles become more variable and species-dependent. Our results highlight that self-organization promotes the efficiency and stability of complex ecosystems at extracting energy from the environment, even in the absence of any centralized coordination.

ecology↗

bioRxiv2023

Micromagnetic Stimulation (μMS) Controls Dopamine Release: An in vivo Study Using WINCS Harmoni

ObjectiveResearch into the role of neurotransmitters in regulating normal and pathologic brain functions has made significant progress. Yet, clinical trials that aim to improve therapeutic interventions do not take advantage of the in vivo changes in the neurochemistry that occur in real time during disease progression, drug interactions or response to pharmacological, cognitive, behavioral, and neuromodulation therapies. In this work, we used the WINCS Harmoni tool to study the real time in vivo changes in dopamine release in rodent brains for the micromagnetic neuromodulation therapy. ApproachAlthough still in its infancy, micromagnetic stimulation (MS) using micro-meter sized coils or microcoils (coils) has shown incredible promise in spatially selective, galvanic contact free and highly focal neuromodulation. These coils are powered by a time-varying current which generates a magnetic field. As per Faradays Laws of Electromagnetic Induction, this magnetic field induces an electric field in a conducting medium (here, the brain tissues). We used a solenoidal-shaped coil to stimulate the medial forebrain bundle (MFB) of the rodent brain in vivo. The evoked in vivo dopamine releases in the striatum were tracked in real time by carbon fiber microelectrodes (CFM) using fast scan cyclic voltammetry (FSCV). ResultsOur experiments report that coils can successfully activate the MFB in rodent brains, triggering dopamine release in vivo. We further show that the successful release of dopamine upon micromagnetic stimulation is dependent on the orientation of the coil. Furthermore, varied intensities of MS can control the concentration of dopamine releases in the striatum. SignificanceThis work helps us better understand the brain and its conditions arising from a new therapeutic intervention, like MS, at the level of neurotransmitter release. Despite its early stage, this study potentially paves the path for MS to enter the clinical world as a precisely controlled and optimized neuromodulation therapy.

neuroscience↗

bioRxiv2023

DiscoGen: Learning to Discover Gene Regulatory Networks

Accurately inferring Gene Regulatory Networks (GRNs) is a critical and challenging task in biology. GRNs model the activatory and inhibitory interactions between genes and are inherently causal in nature. To accurately identify GRNs, perturbational data is required. However, most GRN discovery methods only operate on observational data. Recent advances in neural network-based causal discovery methods have significantly improved causal discovery, including handling interventional data, improvements in performance and scalability. However, applying state-of-the-art (SOTA) causal discovery methods in biology poses challenges, such as noisy data and a large number of samples. Thus, adapting the causal discovery methods is necessary to handle these challenges. In this paper, we introduce DiscoGen, a neural network-based GRN discovery method that can denoise gene expression measurements and handle interventional data. We demonstrate that our model outperforms SOTA neural network-based causal discovery methods.

genomics↗

bioRxiv2023

Air channels create a directional light signal to regulate hypocotyl phototropism

In light-limiting conditions, aerial organs of most plants reorient their growth towards the light to improve photosynthesis, through a process known as phototropism1-3. The blue light receptors phototropin control phototropic responses through light-induced protein kinase activity4. Current models posit that asymmetric activation of these sensory receptors across a unilaterally illuminated organ leads to asymmetric distribution of the growth hormone auxin ultimately leading to growth re-orientation4,5. However, the tissue properties required to generate a light gradient across the stem triggering phototropism remain unclear1. Here we show that inter-cellular air channels6,7 are required for an efficient phototropic response. These channels enhance light scattering (refraction and reflection) in Arabidopsis hypocotyls thereby enhancing the light gradient across the photo-stimulated organ. We identify an embryonically expressed ABC transporter that is required to keep air in inter-cellular spaces in seedlings and for efficient phototropism. Our work suggests that this transporter shapes cell wall properties to maintain air between cells. Moreover, we establish the functional importance of inter-cellular air channels in the hypocotyl for phototropism.

plant biology↗

bioRxiv2022

Annotation-free discovery of functional groups in microbial communities

Recent studies have shown that microbial communities are composed of groups of functionally cohesive taxa, whose abundance is more stable and better associated with metabolic fluxes than that of any individual taxon. However, identifying these functional groups in a manner that is independent from error-prone functional gene annotations remains a major open problem. Here, we develop a novel approach that identifies functional groups of taxa in an unsupervised manner, solely based on the patterns of statistical variation in species abundance and environmental parameters. We demonstrate the power of this approach on three distinct data sets. On data of replicate microcosm with heterotrophic soil bacteria, our unsupervised algorithm recovered experimentally validated functional groups that divide metabolic labor and remain stable despite large variation in species composition. When leveraged against the ocean microbiome data, our approach discovered a functional group that combines aerobic and anaerobic ammonia oxidizers, whose summed abundance tracks closely with nitrate concentrations in the water column. Finally, we show that our framework can enable the detection of species groups that are likely responsible for the production or consumption of metabolites abundant in animal gut microbiomes, serving as a hypothesis generating tool for mechanistic studies. Overall, this work advances our understanding of structure-function relationships in complex microbiomes and provides a powerful approach to discover functional groups in an objective and systematic manner.

ecology↗

bioRxiv2022

Community interactions drive the evolution of antibiotic tolerance in bacteria

The emergence of antibiotic tolerance (prolonged survival against exposure) in natural bacterial populations is a major concern. Since it has been studied primarily in isogenic populations, we do not yet understand how ecological interactions in a diverse community impact the evolution of tolerance. To address this, we studied the evolutionary dynamics of a synthetic bacterial community composed of two interacting strains. In this community, an antibiotic-resistant strain protected the other, susceptible strain by degrading the antibiotic ampicillin in the medium. Surprisingly, we found that in the presence of antibiotics, the susceptible strain evolved tolerance. Tolerance was typified by an increase in survival as well as an accompanying decrease in growth rate, highlighting a trade-off between the two. A simple mathematical model explained that the observed decrease in death rate, even when coupled with a decreased growth rate, is beneficial in a community with weak protective interactions. In the presence of strong interactions, the model predicted that the trade-off would instead be detrimental and tolerance would not emerge, which we experimentally verified. By whole genome sequencing the evolved tolerant isolates, we identified three genetic hotspots which accumulated mutations in parallel lines, suggesting their association with tolerance. Our work highlights that ecological interactions can promote antibiotic tolerance in bacterial communities, which has remained understudied. SignificanceBacteria evolve to evade antibiotic pressure, leading to adverse infection outcomes. Understanding the evolutionary dynamics which lead to different antibiotic responses has thus far focused on single-strain bacterial populations, with limited attention to multi-strain communities which are more common in nature. Here, we experimentally evolved a simple two-strain community, comprising an antibiotic-resistant strain protecting a susceptible one, and found that susceptible populations evolve tolerance, helping them better survive long antibiotic exposure. Using the interplay between community interactions, antibiotic dynamics, and resource availability, we explain this finding with a simple mathematical model, and predict and experimentally verify that an increased resistant strain carrying capacity would render tolerance detrimental. Our results highlight that community interactions can alter bacterial evolutionary responses to antibiotics.

evolutionary biology↗

bioRxiv2022

Fine-scale diversity of microbial communities due to satellite niches in boom-and-bust environments

Recent observations have revealed that closely related strains of the same microbial species can stably coexist in natural and laboratory settings subject to boom-and-bust dynamics and serial dilutions, respectively. However, the possible mechanisms enabling the coexistence of only a handful of strains, but not more, have thus far remained unknown. Here, using a consumer-resource model of microbial ecosystems, we propose that by differentiating on Monod parameters characterizing microbial growth rates in high and low nutrient conditions, strains can coexist in patterns similar to those observed. In our model, boom-and-bust environments create satellite niches due to resource concentrations varying in time. These satellite niches can be occupied by closely related strains, thereby enabling their coexistence. We demonstrate that this result is valid even in complex environments consisting of multiple resources and species. In these complex communities, each species partitions resources differently and creates separate sets of satellite niches for their own strains. While there is no theoretical limit to the number of coexisting strains, in our simulations, we always find between 1 and 3 strains coexisting, consistent with experiments and observations. Author summaryRecent genomic data has exposed to us the remarkable spectrum of microbial diversity in natural communities surrounding us, which harbor not only hundreds of species, but also a handful of closely related strains within each of those species (termed "oligo-colonization"). While the mechanisms behind species coexistence are much better studied, the mechanisms behind the coexistence of closely related strains have remained understudied. Here, using a simple consumer-resource model, we show that if strains differ on their Monod growth parameters, they can coexist even on a single limiting resource, provided that the environments vary with time in boom-and-bust cycles. The Monod growth parameters describe how a strains growth rate changes with resource concentration, namely the half-maximal concentration and maximal growth rate. Simulations of our model show that both in simple and complex environments, even though an arbitrary number of strains can coexist, typically it is between 1 and 3 strains of a species that coexist over several randomly assembled communities, consistent with some experimental observations. This is because the allowed parameter space for coexistence shrinks significantly with the number of strains that coexist.

ecology↗

bioRxiv2022

The chemical basis of metabolic interdependence in microbial communities

Microbial communities play a crucial role in determining the dynamics of soil and marine ecosystems. They strongly influence the physiological functioning of plants and animals, for instance, nutrient uptake, stress tolerance, immune responses in the gut, lung, skin, etc. The diverse species in such communities interact both competitively as well as cooperatively. Cross-feeding, the exchange of metabolites between a pair of microbial species for mutual benefit is a common interaction that probably explains why 99% of natural bacterial species are unculturable on their own in the laboratory. Here, we provide a theoretical, network-level understanding of the conditions under which cross-feeding between a pair of microbial species can be beneficial to both. Using the known microbial repertoire of metabolic reactions, as represented in the KEGG database, we construct a large ensemble of metabolic networks designed to synthesize a set of biomass precursors from specified nutrients. We construct both autonomous networks, that can perform this task on their own, as well as pairs of cross-feeding networks that can only perform this task together but not alone. Surprisingly, we find that there exist cross-feeding pairs that produce higher biomass or energy yields than even the best autonomous networks. We show that such "outperforming" cross-feeding pairs exist only because of certain nonlinearities in the way metabolic flux is distributed in these networks. By analyzing patterns in our ensemble of networks, we propose a set of necessary and (almost) sufficient conditions that the metabolic networks have to satisfy for cross-feeding to be beneficial. These conditions are based partly on the structure of the networks and partly on the chemical and thermodynamic properties of the underlying chemical reactions, phenomenologically quantified in terms of the effect of donating or accepting metabolites on the yield of our constructed networks. Our analysis not only provides a mechanistic understanding of why cross-feeding is prevalent in microbial communities, but also provides a theoretical basis for understanding the benefit of compartmentalization of chemical reactions in a variety of contexts, for instance with mitochondrial vs. cytoplasmic metabolism in eukaryotic cells, or multi-enzyme cascade reactions in industrial contexts.

ecology↗

bioRxiv2021

Conformational rearrangements upon start codon recognition in human 48S translation initiation complex

Selection of the translation start codon is a key step during protein synthesis in human cells. We obtained cryo-EM structures of human 48S initiation complexes and characterized the intermediates of codon recognition by kinetic methods using eIF1A as a reporter. Both approaches capture two distinct ribosome populations formed on an mRNA with a cognate AUG codon in the presence of eIF1, eIF1A, eIF2-GTP-Met-tRNAiMet, and eIF3. The open 40S subunit conformation differs from the human 48S scanning complex and represents an intermediate preceding the codon recognition step. The closed form is similar to reported structures of complexes from yeast and mammals formed upon codon recognition, except for the orientation of eIF1A, which is unique in our structure. Kinetic experiments show how various initiation factors mediate the population distribution of open and closed conformations until 60S subunit docking. Our results provide insights into the timing and structure of human translation initiation intermediates and suggest the differences in the mechanisms of start codon selection between mammals and yeast.

molecular biology↗

bioRxiv2021

Functional classification and validation of yeast prenylation motifs using machine learning and genetic reporters

Protein prenylation by farnesyltransferase (FTase) is often described as the targeting of a cysteine-containing motif (CaaX) that is enriched for aliphatic amino acids at the a1 and a2 positions, while quite flexible at the X position. Prenylation prediction methods often rely on these features despite emerging evidence that FTase has broader target specificity than previously considered. Using a machine learning approach and training sets based on canonical (prenylated, proteolyzed, and carboxymethylated) and recently identified shunted motifs (prenylation only), this study aims to improve prenylation predictions with the goal of determining the full scope of prenylation potential among the 8000 possible Cxxx sequence combinations. Further, this study aims to subdivide the prenylated sequences as either shunted (i.e., uncleaved) or cleaved (i.e., canonical). Predictions were determined for Saccharomyces cerevisiae FTase and compared to results derived using currently available prenylation prediction methods. In silico predictions were further evaluated using in vivo methods coupled to two yeast reporters, the yeast mating pheromone a-factor and Hsp40 Ydj1p, that represent proteins with canonical and shunted CaaX motifs, respectively. Our machine learning based approach expands the repertoire of predicted FTase targets and provides a framework for functional classification.

biochemistry↗

bioRxiv2021

Particle foraging strategies promote microbial diversity in marine environments

Microbial foraging in patchy environments, where resources are fragmented into particles or pockets embedded in a large matrix, plays a key role in natural environments. In the oceans and freshwater systems, particle-associated bacteria can interact with particle surfaces in different ways: some colonize only during short transients, while others form long-lived, stable colonies. We do not yet understand the ecological mechanisms by which both short-term and long-term colonizers can coexist. Here, we address this problem with a mathematical model that explains how marine populations with different detachment rates from particles can stably coexist. In our model, populations grow only while on particles, but also face the increased risk of mortality by predation and sinking. Key to coexistence is the idea that detachment from particles modulates both net growth and mortality, but in opposite directions, creating a trade-off between them. While slow-detaching populations show the highest growth return (i.e., produce more net offspring), they are more susceptible to suffer higher rates of mortality than fast-detaching populations. Surprisingly, fluctuating environments, manifesting as blooms of particles (favoring growth) and predators (favoring mortality) significantly expand the likelihood that populations with different detachment rates can coexist. Our study shows how the spatial ecology of microbes in the ocean can lead to a predictable diversification of foraging strategies and the coexistence of multiple taxa on a single growth-limiting resource.

ecology↗