Biology subjects

Goel, N.

Publications and source records attributed to Goel, N..

13 recordsLinked to original sources

bioRxiv2026

Adaptive respiratory bypass restores oxidative metabolism in NADH dehydrogenase-deficient Escherichia coli

Nicotinamide adenine dinucleotide is central to the cellular redox network and continuously cycles between its oxidized (NAD) and reduced (NADH) forms. Membrane-bound NADH dehydrogenases are the primary NADH-oxidizing enzymes and principal entry point for reducing equivalents into the bacterial respiratory chain. While maintaining optimal balance of this redox cofactor is among the most fundamental biochemical constraints on living systems, how cells adapt when the primary NADH oxidation route is lost remains poorly understood. Here we show that Escherichia coli lacking both NADH dehydrogenases initially compensates through costly fermentative lactate secretion, potentially to maintain redox and energy homeostasis, but at the cost of impaired growth and oxidative metabolism. Using adaptive laboratory evolution across four independent lineages, we found that all evolved populations derepressed malate:quinone oxidoreductase by disrupting an ArcA-binding site. This gain-of-function mutation establishes an alternative pathway that funnels electrons directly into the quinone pool, restoring respiratory oxygen consumption to wild-type level. Multi-omics and genome-scale metabolic modeling reveal that this bypass drives broad reorganization of central carbon metabolism, reinstates tricarboxylic acid cycle flux, and shifts cells toward a higher, more oxidative aero-type. These findings uncover a previously unrecognized metabolic buffering capacity that enables bacteria to circumvent loss of canonical respiratory entry points.

microbiology↗

bioRxiv2026

Loss of Adaptive Capacity Drives Climate Vulnerability Across Taxonomic Scales in an Alpine Specialist Species Complex

Accelerated warming at high elevations is having a disproportionate impact on alpine species. While assessments of climate vulnerability require quantifying the ecological and evolutionary components of adaptive capacity, such assessments are rare, especially in alpine systems. We leverage recent advances in population and landscape genomics to assess how variation in spatial heterogeneity and population connectivity across alpine systems influences adaptive capacity, using the North American Rosy-Finch species complex as a model system. In doing so, we clarify taxonomic relationships across the complex and identify one new ESU, the Sierra Nevada Rosy-Finch, based on its combined ecological and evolutionary distinctiveness. We then illustrate how combining genomic analyses with ecological data can improve estimates of adaptive capacity, sensitivity, and exposure and ultimately clarify climate vulnerability. Overall, our integrative analyses revealed that more isolated lineages, such as the Sierra Nevada Rosy-Finch, have lower adaptive capacity and face disproportionately high risks from climate change. This work highlights how conservation strategies that account for the multidimensional aspects of adaptive capacity can improve estimates of climate vulnerability.

evolutionary biology↗

bioRxiv2026

Identifying adaptive variation in spatially structured populations using low-coverage whole-genome sequencing data

Successful implementation of evolutionary programs to rescue climatically threatened species requires identification of adaptive variation. Although many genotype-environment association methods have been successful in identifying adaptive variation, current approaches can be improved in two important aspects. First, most existing methods do not account for genotype uncertainty in widely available low-coverage whole-genome sequencing data. Researchers often restrict analysis to loci for which genotypes can be inferred reliably or call the most probable genotype, allowing the use of genotype-based methods. However, discarding data and false genotype calls increase the uncertainty in estimates of genetic variation and can introduce systematic biases. Second, most methods use phenomenological approaches, such as logistic regression, to partition estimated variation into adaptive and non-adaptive components. Consequently, current approaches may fail to account for evolutionary processes, such as migration-selection balance. Structured migration between climatically disparate locations can produce deviations from a smooth S-shape response curve, which can be difficult to accommodate using generalized linear models. To overcome these challenges, we developed a method that accounts for genotype uncertainty in sequencing data and propagates this uncertainty to inform the parameters of an evolutionary model. A key feature of this model is that it describes mechanistically how genetic variation arises from joint interactions between local adaptation, structured migration, mutation, and drift. Our synthetic simulation tests reveal that accounting for genotype uncertainty and structured migration substantially reduces false negatives. We also applied our approach to analyze data on North American rosy-finches (3.7 million SNPs), a high-alpine, climatically threatened clade of bird species.

genomics↗

bioRxiv2025

Alternative NADH:ubiquinone oxidoreductase modulates disease susceptibility in rice by interfering ROS homeostasis and ferroptosis

Necrotrophic fungal pathogens, such as Rhizoctonia solani, the causal agent of sheath blight disease (SBD) in rice, enhance the production of reactive oxygen species (ROS) to induce necrosis and infect a broad range of plant species. Here, we present evidence that the upregulation of host alternative NADH:ubiquinone oxidoreductase (OsNUOR) is important for R. solani to induce ROS production and cause SBD in rice. The knock-out lines developed through genome editing are defective in ROS accumulation and demonstrate SBD resistance, whereas the overexpression lines exhibit enhanced disease susceptibility. We emphasize that the cross-talk between OsNUOR and ROS signaling is important for the suppression of antioxidant defense, followed by enhancement of lipid peroxidation and accumulation of ferric ions in rice under R. solani-infected conditions. Considering that treatment with deferoxamine, an iron chelator, and ferrostatin-1, a lipid peroxidation inhibitor, prevents SBD disease, whereas FeCl3 enhances disease severity, we propose that R. solani induces iron-dependent cell death, referred to as ferroptosis, in rice. As agronomic traits of OsNUOR-edited lines are comparable to wild-type lines, we emphasize that editing of OsNUOR and additional genes in ferroptotic pathway can be utilized as biotechnological interventions for control of SBD, for which effective control measures remain a challenge.

plant biology↗

bioRxiv2025

Measuring the Strength of Spatial Sorting

Empirical work suggests that spatial sorting--a mechanism of evolutionary change fueled by spatial assortment of phenotypes--may lead to phenotypic shifts on ecological timescales. However, we currently lack theoretical tools to measure the strength of spatial sorting, as we do to measure the strength of natural selection. To address this gap, we present a quantitative genetics model and identify an evolutionary parameter in the model to measure the strength of spatial sorting. This parameter, the standardized sorting gradient, is structurally akin to the standardized selection gradient, commonly used to measure the strength of natural selection. To show the utility of our approach, we analyzed wing-morphology data of soapberry bugs (Jadera haematoloma) recolonizing flooded habitats extirpated by a hurricane. We found that the estimated strength of spatial sorting ranked in the top ten percentile of standardized selection gradients documented in the scientific literature. Our results underscore that, like natural selection, spatial sorting, too, can yield rapid evolution after extreme events.

evolutionary biology↗

bioRxiv2024

Identifying genomic adaptation to local climate using a mechanistic evolutionary model

O_LIIdentifying genomic adaptation is key to understanding species evolutionary responses to environmental changes. However, current methods to identify adaptive variation have two major limitations. First, when estimating genetic variation, most methods do not account for observational uncertainty in genetic data because of finite sampling and missing genotypes. Second, many current methods use phenomenological models to partition genetic variation into adaptive and non-adaptive components. C_LIO_LIWe address these limitations by developing a hierarchical Bayesian model that explicitly accounts for observational uncertainty and underlying evolutionary processes. The first layer of the hierarchy is the data model that captures observational uncertainty by probabilistically linking RAD-sequence data to genetic variation. The second layer is a process model that represents how evolutionary forces, such as local adaptation, mutation, migration, and drift, maintain genetic variation. The third layer is the parameter model, which incorporates our knowledge about biological processes. For example, because most loci in the genome are expected to be neutral, the environmental sensitivity coefficients are assigned a regularized prior centered at zero. Together, the three models provide a rigorous probabilistic framework to identify local adaptation in wild organisms. C_LIO_LIAnalysis of simulated RAD-seq data shows that our statistical model can reliably infer adaptive genetic variation. To show the real-world applicability of our method, we re-analyzed RAD-seq data ([~]105k SNPs) from Willow Flycatchers (Empidonax traillii) in the USA. We found 30 genes close to loci that showed a statistically significant association with temperature seasonality. Gene ontology suggests that several of these genes play a crucial role in egg mineralization, feather development, and the ability to withstand extreme temperatures. C_LIO_LIMoreover, the data and process models can be modified to accommodate a wide range of genetic datasets (e.g., pool and low coverage genome sequencing) and demographic histories (e.g., range shifts) to study climatic adaptation in a wide range of natural systems. C_LI

genomics↗

bioRxiv2024

ecPath detects ecDNA in tumors from histopathology images

Circular extrachromosomal DNA (ecDNA) can drive tumor initiation, progression and resistance in some of the most aggressive cancers and is emerging as a promising anti-cancer target. However, detection currently requires costly whole-genome sequencing (WGS) or labor-intensive cytogenetic or FISH imaging, limiting its application in routine clinical diagnosis. To overcome this, we developed ecPath (ecDNA from histopathology), a computational method for predicting ecDNA status from routinely available hematoxylin and eosin (H&E) images. ecPath implements a deep-learning method we call transcriptomics-guided learning, which utilizes both transcriptomics and H&E images during the training phase to enable successful ecDNA prediction from H&E images alone, a task not achievable with models trained on H&E images only. It is trained on more than 6,000 tumor whole-slide images from the TCGA cohort with the best performance in predicting ecDNA status in brain and stomach tumors (average AUC=0.78). ecPath revealed that ecDNA-positive tumors are enriched with pleomorphic, larger and high-density nuclei. Testing in an independent cohort, ecPath predicted ecDNA status of 985 pediatric brain tumor patients with an AUC of 0.72. Finally, we applied ecPath to identify ecDNA-positive tumors in the TCGA cohort for which no WGS data were available. Like WGS-based ecDNA-positive labels, the predicted ecDNA-positive status also identify poor prognoses for low grade glioma patients. These results demonstrate that ecPath enables the detection of ecDNA from routinely available H&E imaging alone and help nominate aggressive tumors with ecDNA to study and target it.

pathology↗

bioRxiv2024

A mechanistic statistical approach to infer invasion characteristics of human-dispersed species with complex life cycle

The rising introduction of invasive species through trade networks threatens biodiversity and ecosystem services. Yet, we have a limited understanding of how transportation networks determine patterns of range expansion. This is partly because current analytical models fail to integrate the invaders life-history dynamics with heterogeneity in human-mediated dispersal patterns. And partly because classical statistical methods often fail to provide reliable estimates of model parameters due to spatial biases in the presence-only records and lack of informative demographic data. To address these gaps, we first formulate an age-structured metapopulation model that uses a probability matrix to emulate human-mediated dispersal patterns. The model reveals that an invader spreads along the shortest network path, such that the inter-patch network distances decrease with increasing traffic volume and reproductive value of hitchhikers. Next, we propose a Bayesian statistical method to estimate model parameters using presence-only data and prior demographic knowledge. To show the utility of the statistical approach, we analyze zebra mussel (Dreissena polymorpha) expansion in North America through the commercial shipping network. Our analysis underscores the importance of correcting spatial biases and leveraging priors to answer questions, such as where and when the zebra mussels were introduced and what life-history characteristics make these mollusks successful invaders.

ecology↗

bioRxiv2021

Algebraic theorem of selection by spatial sorting

The study of population expansions has produced a new class of directional and non-directional models of evolution that represent spatial sorting and gene surfing, respectively. These expansion models differ from their classical analogs, selection and genetic drift, in one key aspect. Unlike classical models that measure evolutionary change in a closed population, expansion models measure evolution in a moving frame of reference. This raises several fundamental questions, such as what drives evolution in a moving frame of reference, and whether spatial sorting and gene surfing are truly spatial analogs of selection and genetic drift. To answer these questions, we propose an identity, the sorting theorem, that is analogous to Prices theorem and is a general descriptor of evolution in a moving frame of reference. The sorting theorem reveals that evolutionary change in expansion models is driven by heritable variation in parental phenotypes and differential sorting fitness--the number of offspring a parent leaves in a newly colonized patch. Interestingly, this finding implies that spatial sorting, as currently defined, is not a spatial analog of selection. Our work shows that the sorting theorem can play a valuable role in organizing evolutionary thinking and building a unified theory of evolution during population expansion.

evolutionary biology↗

bioRxiv2021

Plasmodium falciparum Kelch13 and its artemisinin-resistant mutants assemble as hexamers in solution: a SAXS data driven shape restoration study.

Artemisinin-resistant mutations in PfKelch13 identified worldwide are mostly confined to its BTB/POZ and KRP domains. To date, only two crystal structures of the BTB/POZ-KRP domains as tight dimers are available, which limits structure-based interpretations of its functionality. Our solution Small-Angle X-ray Scattering (SAXS) data driven shape restoration of larger length of protein brought forth that: i) PfKelch13 forms a stable hexamer in P6 symmetry, ii) interactions of the N-termini drive the hexameric assembly, and iii) the six KRP domains project independently in space, forming a cauldron-like architecture. While artemisinin-sensitive mutant A578S packed like the wild-type, hexameric assemblies of dominant artemisinin-resistant mutant proteins R539T and C580Y displayed detectable differences in spatial positioning of their BTB/POZ-KRP domains. Lastly, mapping of mutations known to enable artemisinin resistance explained that most mutations exist mainly in these domains because they are non-detrimental to assembly of mutant PfKelch13 and yet can alter the flux of downstream events essential for susceptibility to artemisinin.

biochemistry↗

bioRxiv2020

Source-sink dynamics can maintain mismatched range and bioclimatic limits even at large spatial scales

Bioclimatic models assume that at broad spatial scales, climate is the primary determinant of species distribution. Meanwhile, processes such as source-sink dynamics can be ignored because they are thought to manifest at length scales comparable to species mean dispersal distance. We present a reaction-diffusion model to show species can use sink patches near the bioclimatic (or niche) limit as stepping-stones to occupy sinks much further than the mean dispersal distance, thereby extending the distribution far beyond the bioclimatic envelope. This mismatch between geographical and bioclimatic limits is mediated by the shape of the bioclimatic limit and may be significant for low growth sensitivity and fast dispersal life strategy. These findings challenge one of the core assumptions of the bioclimatic models. Therefore, we advocate that biogeographers consider the role of dispersal when using bioclimatic models to generate inferences about the ecological and evolutionary processes that determine the distribution of biota.

ecology↗

bioRxiv2020

Dispersal limitation and fire feedbacks maintain mesic savannas in Madagascar

Madagascar is regarded by some as one of the most degraded landscapes on Earth, with estimates suggesting that 90% of forests have been lost to indigenous Tavy farming. However, the extent of this degradation has been challenged: paleoecological data, phylogeographic analysis, and species diversity maps indicate that pyrogenic savannas in Central Madagascar pre-date human arrival, even though rainfall is sufficient to allow forest expansion into Central Madagascar. These observations raise a question--if savannas in Madagascar are not anthropogenic, how then are they maintained in regions where the climate can support forest? Observation reveals that the savanna-forest boundary coincides with a dispersal barrier--the escarpment of the Central Plateau. Using a stepping-stone model, we show that in a limited dispersal landscape, a stable savanna-forest boundary can form due to fire-vegetation feedbacks. This novel phenomenon, referred to as range pinning, could explain why eastern lowland forests have not expanded into the mesic savannas of the Central Highlands. This work challenges the view that highland savannas in Madagascar are derived by human-lit fires and, more importantly, suggests that partial dispersal barriers and strong non-linear feedbacks can pin biogeographical boundaries over a wide range of environmental conditions, providing a temporary buffer against climate change.

ecology↗

bioRxiv2018

Dispersal increases the resilience of tropical savanna and forest distributions

Global change may induce changes in savanna and forest distributions, but the dynamics of these changes remain unclear. Classical biome theory suggests that climate is predictive of biome distributions, such that shifts will be continuous and reversible. This view, however, cannot explain a widely observed mismatch between climate and tree cover, which some argue results from fire-vegetation feedbacks maintaining savanna and forest as bistable states, such that, instead, shifts will be discontinuous and irreversible. This bistable model, however, cannot reproduce the spatial aggregation of biomes. Here, we suggest that both models are limited in that they ignore spatial processes, such as dispersal. We examine the contributions of dispersal to determining savanna and forest distributions using a reaction-diffusion model, comparing results qualitatively to empirical savanna and forest distributions in Africa. The diffusion model induces spatially aggregated distributions, separated by a stable savanna-forest boundary. The equilibrium position of that boundary depends not only on precipitation but also on the curvature of precipitation contours with some history dependence (although less than in the bistable model). This model predicts different dynamics in response to global change: the boundary continuously tracks climate, recovering following disturbances, unless remnant biome patches are too small.

ecology↗