Biology subjects

Kadakia, N.

Publications and source records attributed to Kadakia, N..

12 recordsLinked to original sources

bioRxiv2024

Bifurcation enhances temporal information encoding in the olfactory periphery

Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.

neuroscience↗

bioRxiv2023

Integration of single-cell multiomic measurements across disease states with genetics identifies mechanisms of beta cell dysfunction in type 2 diabetes

Altered function and gene regulation of pancreatic islet beta cells is a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of mechanisms driving T2D is still missing. Here we integrate information from measurements of chromatin activity, gene expression and function in single beta cells with genetic association data to identify disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 non-diabetic, pre-T2D and T2D donors, we robustly identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift in T2D. Subtype-defining active chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is likely induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for identifying mechanisms of complex diseases.

systems biology↗

bioRxiv2022

Novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments

To survive, insects must effectively navigate odors plumes to their source. In natural plumes, turbulent winds break up smooth odor regions into disconnected patches, so navigators encounter brief bursts of odor interrupted by bouts of clean air. The timing of these encounters plays a critical role in navigation, determining the direction, rate, and magnitude of insects orientation and speed dynamics. Still, disambiguating the specific role of odor timing from other cues, such as spatial structure, is challenging due to natural correlations between plumes temporal and spatial features. Here, we use optogenetics to isolate temporal features of odor signals, examining how the frequency and duration of odor encounters shape the navigational decisions of freely-walking Drosophila. We find that fly angular velocity depends on signal frequency and intermittency - fraction of time signal can be detected - but not directly on durations. Rather than switching strategies when signal statistics change, flies smoothly transition between signal regimes, by combining an odor offset response with a frequency-dependent novelty-like response. In the latter, flies are more likely to turn in response to each odor hit only when the hits are sparse. Finally, the upwind bias of individual turns relies on a filtering scheme with two distinct timescales, allowing rapid and sustained responses in a variety of signal statistics. A quantitative model incorporating these ingredients recapitulates fly orientation dynamics across a wide range of environments.

animal behavior and cognition↗

bioRxiv2022

Optimal inference of molecular interactions in live FRET imaging

Intensity-based live-cell fluorescence resonance energy transfer (FRET) imaging converts otherwise unobservable molecular interactions inside cells into fluorescence time-series signals. However, inferring the degree of molecular interactions from these observables is challenging, due to experimental complications such as spectral crosstalk, photobleaching, and measurement noise. Conventional methods solve this inverse problem through algebraic manipulations of the observables, but such manipulations inevitably accumulate measurement noise, limiting the scope of FRET analysis. Here, we introduce a Bayesian inference framework, B-FRET, which estimates molecular interactions from FRET data in a statistically optimal manner. B-FRET requires no additional measurements beyond those routinely conducted in standard 3-cube FRET imaging methods, and yet, by using the information contained in the data more efficiently, dramatically improves the signal-to-noise ratio (SNR). We validate B-FRET using simulated data, and then apply it to FRET data measured from single bacterial cells, a system with notoriously low SNR, to reveal signaling dynamics that are otherwise hidden in noise.

biophysics↗

bioRxiv2022

Optimal control methods for nonlinear parameter estimation in biophysical neuron models

Functional forms of biophysically-realistic neuron models are constrained by neurobiological and anatomical considerations, such as cell morphologies and the presence of known ion channels. Despite these constraints, neurons models still contain unknown static parameters which must be inferred from experiment. This inference task is most readily cast into the framework of state-space models, which systematically takes into account partial observability and measurement noise. Inferring only dynamical state variables such as membrane voltages is a well-studied problem, and has been approached with a wide range of techniques beginning with the well-known Kalman filter. Inferring both states and fixed parameters, on the other hand, is less straightforward. Here, we develop a method for joint parameter and state inference that combines traditional state space modeling with chaotic synchronization and optimal control. Our methods are tailored particularly to situations with considerable measurement noise, sparse observability, very nonlinear or chaotic dynamics, and highly uninformed priors. We illustrate our approach both in a canonical chaotic model and in a phenomenological neuron model, showing that many unknown parameters can be uncovered reliably and accurately from short and noisy observed time traces. Our method holds promise for estimation in larger-scale systems, given ongoing improvements in calcium reporters and genetically-encoded voltage indicators.

neuroscience↗

bioRxiv2021

Odor motion sensing enables complex plume navigation

Studies dating back a century (Flugge, 1934) have stressed the critical role of the wind as the primary directional cue in odor plume navigation. Here, we show that Drosophila shape their navigational decisions using a second directional cue - the direction of motion of odors - which they detect from the temporal correlations of the odor signal between their two antennae. Using a high-resolution virtual reality paradigm to deliver spatiotemporally complex fictive odors to freely-walking flies, we demonstrate that such odor direction sensing is computationally equivalent to motion detection algorithms underlying motion detection in vision. Simulations and theoretical analysis of turbulent plumes reveal that odor motion contains valuable directional information absent from the airflow; indeed, this information is used by both Drosophila and virtual agents to navigate naturalistic odor environments. The generality of our findings suggests that odor direction sensing is likely used throughout the animal kingdom, and could significantly improve olfactory robot navigation in harsh chemical environments.

neuroscience↗

bioRxiv2021

Navigating a diversity of turbulent plumes is enhanced by sensing complementary temporal features of odor signals

We and others have shown that during odor plume navigation, walking Drosophila melanogaster bias their motion upwind in response to both the frequency of their encounters with the odor (Demir et al., 2020), and the intermittency of the odor signal, i.e. the fraction of time the signal is above a detection threshold (Alvarez-Salvado et al., 2018). Here we combine and simplify previous mathematical models that recapitulated these data to investigate the benefits of sensing both of these temporal features, and how these benefits depend on the spatiotemporal statistics of the odor plume. Through agent-based simulations, we find that navigators that only use frequency or intermittency perform well in some environments - achieving maximal performance when gains are near those inferred from experiment - but fail in others. Robust performance across diverse environments requires both temporal modalities. However, we also find a steep tradeoff when using both sensors simultaneously, suggesting a strong benefit to modulating how much each sensor is weighted, rather than using both in a fixed combination across plumes.

neuroscience↗

bioRxiv2020

Walking Drosophila navigate complex plumes using stochastic decisions biased by the timing of odor encounters

Insects find food, mates, and egg-laying sites by tracking odor plumes swept by complex wind patterns. Previous studies have shown that moths and flies localize plumes by surging upwind at odor onset and turning cross- or downwind at odor offset. Less clear is how, once within the expanding cone of the odor plume, insects use their brief encounters with individual odor packets, whose location and timing are random, to progress towards the source. Experiments and theory have suggested that the timing of odor encounters might assist navigation, but connecting behaviors to individual encounters has been challenging. Here, we imaged complex odor plumes simultaneous with freely-walking flies, allowing us to quantify how behavior is shaped by individual odor encounters. Combining measurements, dynamical models, and statistical inference, we found that within the plume cone, individual encounters did not trigger reflexive surging, casting, or counterturning. Instead, flies turned stochastically with stereotyped saccades, whose direction was biased upwind by the timing of prior odor encounters, while the magnitude and rate of saccades remained constant. Odor encounters did not strongly affect walking speed. Instead, flies used encounter timing to modulate the rate of transitions between walks and stops. When stopped, flies initiated walks using information from multiple odor encounters, suggesting that integrating evidence without losing position was part of the strategy. These results indicate that once within the complex odor plume, where odor location and timing are unpredictable, animals navigate with biased random walks shaped by the entire sequence of encounters.

neuroscience↗

bioRxiv2019

Members of the Arabidopsis auxin receptor gene family are essential early in embryogenesis and have broadly overlapping functions

The TIR1/AFB auxin co-receptors mediate diverse responses to the plant hormone auxin. The Arabidopsis genome encodes six TIR1/AFB proteins representing three of the four clades that were established prior to angiosperm radiation. To determine the role of these proteins in plant development we performed an extensive genetic analysis involving the generation and characterization of all possible multiply mutant lines. We find that loss of all six TIR1/AFB proteins results in defects in embryogenesis as early as the 8-cell stage, and possibly earlier. Mutant embryos progress but exhibit frequent cell division errors followed by proliferation of the suspensor, and eventually seed abortion. Despite this dramatic phenotype, a single wild-type allele of TIR1 or AFB2 is sufficient to support growth throughout plant development. Further, gametophytic expression of the TIR1/AFB genes is not essential for development of the male or female gametophyte. Our analysis reveals extensive functional overlap between even the most distantly related TIR1/AFB genes except for AFB1. Surprisingly, the AFB1 protein has a specialized function in rapid auxin-dependent inhibition of root growth and early phase of root gravitropism. This activity may be related to a difference in subcellular localization compared to the other members of the family.

plant biology↗

bioRxiv2018

Front-end Weber-Fechner gain control enhances the fidelity of combinatorial odor coding

Odor identity is encoded by spatiotemporal patterns of activity in olfactory receptor neurons (ORNs). In natural environments, the intensity and timescales of odor signals can span several orders of magnitude, and odors can mix with one another, potentially scrambling the combinatorial code mapping neural activity to odor identity. Recent studies have shown that in Drosophila melanogaster the ORNs that express the olfactory co-receptor Orco scale their gain inversely with mean odor concentration according to the Weber-Fechner Law of psychophysics. Here we use a minimal biophysical model of signal transduction, ORN firing, and signal decoding to investigate the implications of this front-end scaling law for the neural representations of odor identity. We find that Weber-Fechner scaling enhances coding capacity and promotes the reconstruction of odor identity from dynamic odor signals, even in the presence of confounding background odors and rapid intensity fluctuations. We show that these enhancements are further aided by downstream transformations in the antennal lobe and mushroom body. Thus, despite the broad overlap between individual ORN tuning curves, a mechanism of front-end adaptation, when endowed with Weber-Fechner scaling, may play a vital role in preserving representations of odor identity in naturalistic odor landscapes.

neuroscience↗

bioRxiv2018

Shared genetic contribution to type 1 and type 2 diabetes risk

The role of shared genetic risk in the etiology of type 1 diabetes (T1D) and type 2 diabetes (T2D) and the mechanisms of these effects is unknown. In this study, we generated T1D association data of 15k samples imputed into the HRC reference panel which we compared to T2D association data of 159k samples imputed into 1000 Genomes. The effects of genetic variants on T1D and T2D risk at known loci and genome-wide were positively correlated, which we replicated using data from the UK Biobank and clinically-defined diabetes in the WTCCC. Increased risk of T1D and T2D was correlated with higher fasting insulin and fasting glucose level and decreased birth weight, among T1D- and T2D-specifc correlations, and T1D and T2D associated variants were enriched in regulatory elements for pancreatic, insulin resistance (adipose, CD19+ B cell), and developmental (CD184+ endoderm) cell types. We fine-mapped causal variants at known T1D and T2D loci and found evidence for co-localization at five signals, four of which had same direction of effect, including CENPW and GLIS3. Shared risk variants at GLIS3 and other signals were associated with measures of islet function, while CENPW was associated with early growth, and we identified shared risk variants at GLIS3 in islet accessible chromatin with allelic effects on islet regulatory activity. Our findings support shared genetic risk involving variants affecting islet function as well as insulin resistance, growth and development in the etiology of T1D and T2D.

genetics↗

bioRxiv2018

Pancreatic islet chromatin accessibility and conformation defines distal enhancer networks of type 2 diabetes risk

The gene targets of enhancer activity in pancreatic islets are largely unknown, impeding discovery of islet regulatory networks involved in type 2 diabetes (T2D) risk. We mapped chromatin state, accessibility and conformation using ChIP-seq, ATAC-seq and Hi-C in human pancreatic islets, which we integrated with T2D genetic fine-mapping and islet expression QTL data. Active islet regulatory elements preferentially interacted with other active elements, often at distances over 1MB, and we identified target genes for thousands of distal islet enhancers. A third of T2D risk signals mapped in islet enhancers, and target genes regulated by these signals were specifically involved in processes related to protein transport and secretion. Among implicated target genes of T2D islet enhancer signals with no prior known role in islet function, we demonstrated that reduced IGF2BP2 activity in mouse islets leads to impaired glucose-stimulated insulin secretion. These results link distal islet enhancer regulation of protein secretion and transport to genetic risk of T2D, and highlight the utility of high-throughput chromatin conformation maps to uncover the gene regulatory networks of complex disease.

genomics↗