Biology subjects

Patil, P.

Publications and source records attributed to Patil, P..

31 records · Page 2Linked to original sources

bioRxiv2021

Realistic retinal modeling unravels the differential role of excitation and inhibition to starburst amacrine cells in direction selectivity

Retinal direction-selectivity originates in starburst amacrine cells (SACs), which display a centrifugal preference, responding with greater depolarization to a stimulus expanding from soma to dendrites than to a collapsing stimulus. Various mechanisms were hypothesized to underlie SAC centrifugal preference, but dissociating them is experimentally challenging and the mechanisms remain debatable. To address this issue, we developed the Retinal Stimulation Modeling Environment (RSME), a multifaceted data-driven retinal model that encompasses detailed neuronal morphology and biophysical properties, retina-tailored connectivity scheme and visual input. Using a genetic algorithm, we demonstrated that spatiotemporally diverse excitatory inputs - sustained in the proximal and transient in the distal processes - are sufficient to generate experimentally validated centrifugal preference in a single SAC. Reversing these input kinetics did not produce any centrifugal-preferring SAC. We then explored the contribution of SAC-SAC inhibitory connections in establishing the centrifugal preference. SAC inhibitory network enhanced the centrifugal preference, but failed to generate it in its absence. Embedding a direction selective ganglion cell (DSGC) in a SAC network showed that the known SAC-DSGC asymmetric connectivity by itself produces direction selectivity. Still, this selectivity is sharpened in a centrifugal-preferring SAC network. Finally, we use RSME to demonstrate the contribution of SAC-SAC inhibitory connections in mediating direction selectivity and recapitulate recent experimental findings. Thus, using RSME, we obtained a comprehensive mechanistic understanding of SACs centrifugal preference and its contribution to direction selectivity.

neuroscience↗

bioRxiv2021

Improved HIV-1 drug resistance mutation prediction using quasispecies reconstruction supported analysis

Accurate and sensitive approaches to detect HIV-1 drug resistance mutations (DRMs) are indispensable for the paradigm of treatment as prevention. While HIV-1 proviral DNA allows sensitive high throughput sequencing (HTS)-based DRM detection, its applicability is limited by presence of defective genomes. This study demonstrates application of quasispecies reconstruction algorithms (QRAs) to improve DRM detection sensitivity from proviral DNA. A robust benchmarking of 5 QRAs was performed with 2 distinct experimental control-datasets including a stringent, novel control: DCPM, simulating in-vivo variant distribution (0.08%-86.5%). Selected QRA was further evaluated for its ability to differentiate DRMs from hypermutated sequences using an in-silico control. PredictHaplo outperformed all others in terms of precision and was selected for further analysis. Near full-genome HTS was performed on proviral DNA from 20 HIV-1C infected individuals, at different stages of ART, from Mumbai, India. DRM detection was performed through residue-wise variation analysis and implementation of QRAs. Both analyses were highly concordant for DRM frequencies >10% (spearman r=0.91, p<0.0001). Phylogenetic association in HTS datasets with shared transmission history could also be demonstrated by PredictHaplo. This study highlights utility of QRAs as an adjunct to traditional residue-wise variation-based DRM detection leading to optimal personalized ART as well as better disease management.

bioinformatics↗

bioRxiv2021

ROS-Degradable Polythioketal Urethane Foam Dressings to Promote Porcine Skin Wound Repair

Impaired skin healing and progression into chronic wounds is a prevalent and growing medical problem. Porous, resorbable biomaterials can be used as temporary substrates placed into skin defects to support cell infiltration, neo-tissue formation, and remodeling of nonhealing wounds. Naturally-derived biomaterials have promising healing benefits, but their low mechanical properties and exuberant costs limit their performance and use. Synthetic materials can be affordably manufactured and tuned across a broader range of physiochemical properties, but opportunities remain for tailoring them for ideal host immune and regenerative responses. Polyesters are the most clinically-tested class of synthetic biomaterials, but their hydrolysis releases acidic degradation products that can cause autocatalytic degradation processes that are poorly controlled and are not tied to cellular or other biologic activities. Here, we systemically explored a series of ROS-degradable polythioketal (PTK) urethane (UR) foams with varied hydrophilicity as an alternative class of synthetic biomaterials for wound healing. It was found that the most hydrophilic PTK- UR variant, which had 7 ethylene glycol (EG7) repeats flanking each side of each thioketal bond, had the highest ROS reactivity of the PTK-URs tested. In an in vivo porcine excisional skin wound healing model, hydrophilic EG7 PTK-UR foams more effectively promoted tissue integration, ECM deposition, and re- epithelialization of full-thickness skin wound compared to more hydrophobic PTK-UR variants. Resolution of type 1 inflammation and lower foreign body response to scaffold remnants was also observed for EG7 versus more hydrophobic PTK-UR scaffolds. Finally, porcine wound healing studies showed that EG7 PTK-UR foams had similar wound healing response to a collagen-based clinical gold standard product, Integra Bilayer Wound Matrix (BWM), while outperforming polyester UR foam-based NovoSorb Biodegradable Temporizing Matrix (BTM) with respect to increased ECM production, vascularization, and biomaterial-associated immune phenotype. In sum, PTK-UR foams warrant further development toward a new class of synthetic biomaterial foams for skin wound healing applications.

bioengineering↗

bioRxiv2021

Optogenetic silencing of neurotransmitter release with a naturally occurring invertebrate rhodopsin

Information is carried between brain regions through neurotransmitter release from axonal presynaptic terminals. Understanding the functional roles of defined neuronal projection pathways in cognitive and behavioral processes requires temporally precise manipulation of their activity in vivo. However, existing optogenetic tools have low efficacy and off-target effects when applied to presynaptic terminals, while chemogenetic tools are difficult to control in space and time. Here, we show that a targeting-enhanced mosquito homologue of the vertebrate encephalopsin (eOPN3) can effectively suppress synaptic transmission through the Gi/o signaling pathway. Brief illumination of presynaptic terminals expressing eOPN3 triggers a lasting suppression of synaptic output that recovers spontaneously within minutes in vitro as well as in vivo. In freely moving mice, eOPN3-mediated suppression of dopaminergic nigrostriatal afferents leads to an ipsiversive rotational bias. We conclude that eOPN3 can be used to selectively suppress neurotransmitter release at synaptic terminals with high spatiotemporal precision, opening new avenues for functional interrogation of long-range neuronal circuits in vivo.

neuroscience↗

bioRxiv2021

Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference

Non-ignorable technical variation is commonly observed across data from multiple experimental runs, platforms, or studies. These so-called batch effects can lead to difficulty in merging data from multiple sources, as they can severely bias the outcome of the analysis. Many groups have developed approaches for removing batch effects from data, usually by accommodating batch variables into the analysis (one-step correction) or by preprocessing the data prior to the formal or final analysis (two-step correction). One-step correction is often desirable due it its simplicity, but its flexibility is limited and it can be difficult to include batch variables uniformly when an analysis has multiple stages. Two-step correction allows for richer models of batch mean and variance. However, prior investigation has indicated that two-step correction can lead to incorrect statistical inference in downstream analysis. Generally speaking, two-step approaches introduce a correlation structure in the corrected data, which, if ignored, may lead to either exaggerated or diminished significance in downstream applications such as differential expression analysis. Here, we provide more intuitive and more formal evaluations of the impacts of two-step batch correction compared to existing literature. We demonstrate that the undesired impacts of two-step correction (exaggerated or diminished significance) depend on both the nature of the study design and the batch effects. We also provide strategies for overcoming these negative impacts in downstream analyses using the estimated correlation matrix of the corrected data. We compare the results of our proposed workflow with the results from other published one-step and two-step methods and show that our methods lead to more consistent false discovery controls and power of detection across a variety of batch effect scenarios. Software for our method is available through GitHub (https://github.com/jtleek/sva-devel) and will be available in future versions of the sva R package in the Bioconductor project (https://bioconductor.org/packages/release/bioc/html/sva.html). Batch effect; Two-step batch adjustment; ComBat; Sample correlation adjustment; Generalized least squares

bioinformatics↗

bioRxiv2020

Kupffer Cell Release of Platelet Activating Factor Drives Dose Limiting Toxicities of Nucleic Acid Nanocarriers

In vivo nanocarrier-associated toxicity is a significant and poorly understood hurdle to clinical translation of siRNA nanomedicines. In this work, we demonstrate that platelet activating factor (PAF), an inflammatory lipid mediator, plays a key role in nanocarrier-associated toxicities, and that prophylactic inhibition of the PAF receptor (PAFR) completely prevents these toxicities. High-dose intravenous injection of siRNA-polymer nano-complexes (si-NPs) elicited acute, shock-like symptoms (vasodilation and vascular leak) in mice and caused a three-fold increase in blood PAF levels. PAFR inhibition completely prevented these toxicities, indicating PAF activity is a primary driver of systemic si-NP toxicity. Pre-treatment with clodronate liposomes fully abrogated si-NP-associated increases in blood PAF and consequent toxicities, suggesting that nanoparticle uptake by Kupffer macrophages is the source of PAF. Assessment of varied si-NP chemistries further confirmed that toxicity level correlated to relative uptake of the carrier by liver Kupffer cells and that this toxicity mechanism is dependent on the endosome disruptive function of the carrier. Finally, the PAF toxicity mechanism was shown to be generalizable to commercial delivery reagent in vivo-jetPEI(R) and an MC3 lipid nanoparticle formulated to match an FDA-approved siRNA nanomedicine. Greater sensitivity to the PAF mechanism occurs in 4T1 tumor-bearing mice, a mammary tumor model known to exhibit increased circulating leukocytes and potential to respond to inflammatory insult. These results establish Kupffer cell release of PAF as a key mediator of in vivo nucleic acid nanocarrier toxicity and identify PAFR inhibition as an effective prophylactic strategy to increase maximum tolerated dose and reduce nanocarrier-associated adverse events. SignificanceNon-viral nucleic acid nanocarriers can enable in vivo gene therapy, but their potential interaction with innate immune cells can cause dose-limiting toxicities. Nanoparticle toxicities are currently poorly understood, making it difficult to identify relevant design criteria for maximizing nanoparticle safety. This work connects nanoparticle-associated toxicities to the release of platelet activating factor (PAF) by liver Kupffer cells. Small molecule inhibition of the PAF receptor (PAFR) completely prevents severe adverse events associated with high doses of multiple polymer-based formulations and a lipid nanoparticle matching the composition of the first clinically-approved siRNA nanomedicine. This study identifies PAF as a toxicity biomarker for future nanomedicine discovery programs. Further, PAFR inhibition should be explored as a strategy to expand the therapeutic index of nanomedicines.

bioengineering↗

bioRxiv2020

Deep phylo-taxono-genomics (DEEPT genomics) reveals misclassification of Xanthomonas species complexes into Xylella, Stenotrophomonas and Pseudoxanthomonas

Genus Xanthomonas encompasses specialized group of phytopathogenic bacteria with genera Xylella, Stenotrophomonas and Pseudoxanthomonas being its closest relatives. While species of genera Xanthomonas and Xylella are known as serious phytopathogens, members of other two genera are found in diverse habitats with metabolic versatility of biotechnological importance. Few species of Stenotrophomonas are multidrug resistant opportunistic nosocomial pathogens. In the present study, we report genomic resource of genus Pseudoxanthomonas and further in-depth comparative studies with publically available genome resources of other three genera. Surprisingly, based on deep phylo-taxono-genomic or DEEPT criteria, all the four genera were found to be one genus and hence synonyms of Xanthomonas. The members of Pseudoxanthomonas are more diverse and ancestral and rest forming two mega species groups (MSGs) i.e. Xanthomonas Xylella (XX-MSG) and Stenotrophomonas (S-MSG). Within XX-MSG, there are 3 species complexes i.e., X. citri complex (Xcc) member species are primarily pathogenic to dicots, X. hyacinthi complex (Xhc) member species are primarily pathogenic to monocots and Xanthomonas (Xylella) fastidosa complex (Xfc) with diverse phytopathogenic potential. Interestingly, Xylella seems to be a variant Xanthomonas lineage or species complex that is sandwiched between Xcc and Xhc. Like Xylella, within S-MSG, we find a species complex of clinical origin Xanthomonas (Stenotrophomonas) maltophilia complex (Xmc). Comparative studies revealed selection and role of xanthomonadin pigment and xanthan gum in emergence of plant pathogenic XX-MSG. Pan genome analysis also revealed large set of unique genes with particular functions suited for plant/animal lifestyle responsible for emergence of variant Xfc and Xmc species complexes. Overall, our systematic and large scale genera based study has allowed us to understand the origin and to clarify the taxonomic breadth of genus of high importance in agriculture, medicine and industry. Such DEEPT genomics studies are also way forward to identify right markers or functions for diagnosis and drug development of any pathogenic bacteria. RepositoriesGenome Submission Accession Number: MWIP00000000, PDWO00000000, PDWN00000000, PDWT00000000, PDWS00000000, PDWW00000000, PDWU00000000, PDWR00000000, PDWL00000000, PDWQ00000000, PDWM00000000, PDWP00000000, PDWV00000000, PDWK00000000 and QOVG00000000

genomics↗

bioRxiv2019

Covariate-Profile Similarity Weighting and Bagging Studies with the Study Strap: Multi-Study Learning for Human Neurochemical Sensing

We propose the "study strap ensemble," which combines advantages of two common approaches to fitting prediction models when multiple training datasets ("studies") are available: pooling studies and fitting one model versus averaging predictions from multiple models each fit to individual studies. The study strap ensemble fits models to bootstrapped datasets, or "pseudo-studies." These are generated by resampling from multiple studies with a hierarchical resampling scheme that generalizes the randomized cluster bootstrap. The study strap is controlled by a tuning parameter that determines the proportion of observations to draw from each study. When the parameter is set to its lowest value, each pseudo-study is resampled from only a single study. When it is high, the study strap ignores the multi-study structure and generates pseudo-studies by merging the datasets and drawing observations like a standard bootstrap. We empirically show the optimal tuning value often lies in between, and prove that special cases of the study strap draw the merged dataset and the set of original studies as pseudo-studies. We extend the study strap approach with an ensemble weighting scheme that utilizes information in the distribution of the covariates of the test dataset. Our work is motivated by neuroscience experiments using real-time neurochemical sensing during awake behavior in humans. Current techniques to perform this kind of research require measurements from an electrode placed in the brain during awake neurosurgery and rely on prediction models to estimate neurotransmitter concentrations from the electrical measurements recorded by the electrode. These models are trained by combining multiple datasets that are collected in vitro under heterogeneous conditions in order to promote accuracy of the models when applied to data collected in the brain. A prevailing challenge is deciding how to combine studies or ensemble models trained on different studies to enhance model generalizability. Our methods produce marked improvements in simulations and in this application. All methods are available in the studyStrap CRAN package.

neuroscience↗

bioRxiv2019

Tree-weighting for multi-study ensemble learners

Multi-study learning uses multiple training studies, separately trains classifiers on individual studies, and then forms ensembles with weights rewarding members with better cross-study prediction ability. This article considers novel weighting approaches for constructing tree-based ensemble learners in this setting. Using Random Forests as a single-study learner, we perform a comparison of either weighting each forest to form the ensemble, or extracting the individual trees trained by each Random Forest and weighting them directly. We consider weighting approaches that reward cross-study replicability within the training set. We find that incorporating multiple layers of ensembling in the training process increases the robustness of the resulting predictor. Furthermore, we explore the mechanisms by which the ensembling weights correspond to the internal structure of trees to shed light on the important features in determining the relationship between the Random Forests algorithm and the true outcome model. Finally, we apply our approach to genomic datasets and show that our method improves upon the basic multi-study learning paradigm.

bioinformatics↗

bioRxiv2019

A multidimensional functional fitness score is a stronger predictor of type 2 diabetes than obesity parameters in cross sectional data.

ObjectiveWe examine here whether multidimensional functional fitness is a better predictor of type 2 diabetes as compared to morphometric indices of obesity such as body mass index (BMI) and waist to hip ratio (WHR).\n\nResearch design and methodWe analysed retrospective data of 663 volunteer participants (285 males and 378 females between age 28 and 84), from an exercise clinic in which every participant routinely undergoes a health related physical fitness (HRPF) assessment consisting of 15 different tasks examining 8 different aspects of functional fitness.\n\nResultsThe odds of being diabetic in the highest quartile of BMI were not significantly higher than that in the lowest quartile in either of the sexes. The odds of being a diabetic in the highest WHR quartile were significantly greater than the lowest quartile in females (OR = 4.54 (1.95, 10.61) as well as in males (OR = 3.81 (1.75, 8.3). In both sexes the odds of being a diabetic were significantly greater in the lowest quartile of HRPF score than the highest (males OR = 10.52 (4.21, 26.13); females OR = 10.50 (3.53, 31.35)). HRPF was not correlated with BMI in both sexes but was negatively correlated with WHR. After removing confounding, the predictive power of HRPF was significantly greater than that of WHR.\n\nConclusionMultidimensional functional fitness score was a better predictor of type 2 diabetes than obesity parameters in the Indian population.

epidemiology↗

bioRxiv2018

A strain of an emerging Indian pathotype of Xanthomonas oryzae pv. oryzae defeats the rice bacterial blight resistance gene xa13 without inducing a clade III SWEET gene and is nearly identical to a recent Thai isolate

The rice bacterial blight pathogen Xanthomonas oryzae pv. oryzae (Xoo) injects transcription activator-like effectors (TALEs) that bind and activate host susceptibility (S) genes important for disease. Clade III SWEET genes are major S genes for bacterial blight. The resistance genes xa5, which reduces TALE activity generally, and xa13, a SWEET11 allele not recognized by the cognate TALE, have been effectively deployed. However, strains that defeat both resistance genes individually were recently reported in India and Thailand. To gain insight into the mechanism(s), we completely sequenced the genome of one such strain from each country and examined the encoded TALEs. Strikingly, the two strains are clones, sharing nearly identical TALE repertoires, including a TALE known to activate SWEET11 strongly enough to be effective even when diminished by xa5. We next investigated SWEET gene induction by the Indian strain. The Indian strain induced no clade III SWEET in plants harbouring xa13, indicating a pathogen adaptation that relieves dependence on these genes for susceptibility. The findings open a door to mechanistic understanding of the role SWEET genes play in susceptibility and illustrate the importance of complete genome sequence-based monitoring of Xoo populations in developing varieties with effective disease resistance.

pathology↗

bioRxiv2017

High-efficiency optogenetic silencing with soma-targeted anion-conducting channelrhodopsins

Optogenetic silencing allows time-resolved functional interrogation of defined neuronal populations. However, the limitations of inhibitory optogenetic tools impose stringent constraints on experimental paradigms. The high light power requirement of light-driven ion pumps and their effects on intracellular ion homeostasis pose unique challenges, particularly in experiments that demand inhibition of a widespread neuronal population in vivo. Guillardia theta anion-conducting channelrhodopsins (GtACRs) are promising in this regard, due to their high single-channel conductance and favorable photon-ion stoichiometry. However, GtACRs show poor membrane targeting in mammalian cells, and the activity of such channels can cause transient excitation in the axon due to an excitatory chloride reversal potential in this compartment. Here we address both problems by enhancing membrane targeting and subcellular compartmentalization of GtACRs. The resulting GtACR-based optogenetic tools show improved photocurrents, greatly reduced axonal excitation, high light sensitivity and rapid kinetics, allowing highly efficient inhibition of neuronal activity in the mammalian brain.

neuroscience↗

bioRxiv2016

A statistical definition for reproducibility and replicability

Everyone agrees that reproducibility and replicability are fundamental characteristics of scientific studies. These topics are attracting increasing attention, scrutiny, and debate both in the popular press and the scientific literature. But there are no formal statistical definitions for these concepts, which leads to confusion since the same words are used for different concepts by different people in different fields. We provide formal and informal definitions of scientific studies, reproducibility, and replicability that can be used to clarify discussions around these concepts in the scientific and popular press.

Scientific Communication and Education↗