Biology subjects

Varshney, A.

Publications and source records attributed to Varshney, A..

5 recordsLinked to original sources

bioRxiv2018

Bright split red fluorescent proteins with enhanced complementation efficiency for the tagging of endogenous proteins and visualization of synapses

Self-associating split fluorescent proteins (FPs) have been widely used for labeling proteins, scaffolding protein assembly and detecting cell-cell contacts. Newly developed self-associating split FPs, however, have suffered from suboptimal fluorescence signal. Here, by investigating the complementation process, we have demonstrated two approaches to improve split FPs: assistance through SpyTag/SpyCatcher interaction and directed evolution. The latter has yielded two split sfCherry3 variants with substantially enhanced overall brightness, facilitating the tagging of endogenous proteins by gene editing. Based on sfCherry3, we have further developed a new red-colored trans-synaptic marker called Neuroligin-1 sfCherry3 Linker Across Synaptic Partners (NLG-1 CLASP) for multiplexed visualization of neuronal synapses in living animals, demonstrating its broad applications.

cell biology↗

bioRxiv2018

Robust and sensitive GFP-based cGMP sensor for real time imaging in intact Caenorhabditis elegans

cGMP is a ubiquitous second messenger that plays a role in sensory signaling and plasticity through its regulation of ion channels and kinases. Previous studies that primarily used genetic and biochemical tools suggest that cGMP is spatiotemporally regulated in multiple sensory modalities, including light, heat, gases, salt and odor. FRET- and GFP-based cGMP sensors were developed to visualize cGMP in primary cell culture and Caenorhabditis elegans to corroborate these findings. While a FRET-based sensor has been used in an intact animal to visualize cGMP, the requirement of a multiple emission system limits its ability to be used on its own as well as with other sensors and fluorescent markers. Here, we demonstrate that WincG2, a codon-optimized version of the cpEGFP-based cGMP sensor FlincG3, can be used in C. elegans to visualize rapidly changing cGMP levels in living, behaving animals using a single fluorophore. We coexpressed the sensor with the blue light-activated guanylyl cyclases BeCyclOp and bPGC in body wall muscles and found that the rate of WincG2 fluorescence correlated with the rate of cGMP production by each cyclase. Furthermore, we show that WincG2 responds linearly upon NaCl concentration changes and SDS presentation in the cell bodies of the gustatory neuron ASER and the nociceptive phasmid neuron PHB, respectively. Intriguingly, WincG2 fluorescence in the ASER cell body decreased in response to a NaCl concentration downstep and either stopped decreasing or increased in response to a NaCl concentration upstep, which is opposite in sign to previously published calcium recordings. These results illustrate that WincG2 can be used to report rapidly changing cGMP levels in an intact animal and that the reporter can potentially reveal unexpected spatiotemporal landscapes of cGMP in response to stimuli.\n\nAuthor SummarycGMP is a second messenger that plays an important role in sensory signaling and neural plasticity. Previous genetic and biochemical studies indirectly suggest that cGMP is spatiotemporally regulated in neurons to modulate neural activity. While a FRET-based sensor for cGMP has been used in intact Caenorhabditis elegans to examine its spatiotemporal regulation in neurobiological processes, its use has been limited due to the complicated setup required to image this type of sensor. Here, we describe a GFP-based cGMP sensor that has been codon optimized for use in C. elegans and demonstrate that it responds robustly and reliably to endogenously changing cGMP levels. We show that the sensor responds to cGMP production by coexpressing it with blue light-activated guanylyl cyclases, and we show that it responds to NaCl and sodium dodecyl sulfate when expressed in a gustatory and nociceptive neuron, respectively. We think that this sensor can be used to investigate the spatiotemporal regulation of cGMP in neurons and its relationship to neural activity.

neuroscience↗

bioRxiv2018

EndoC-βH1 multi-genomic profiling defines gene regulatory programs governing human pancreatic β cell identity and function

EndoC-{beta}H1 is emerging as a critical human beta cell model to study the genetic and environmental etiologies of beta cell function, especially in the context of diabetes. Comprehensive knowledge of its molecular landscape is lacking yet required to fully take advantage of this model. Here, we report extensive chromosomal (spectral karyotyping), genetic (genotyping), epigenetic (ChIP-seq, ATAC-seq), chromatin interaction (Hi-C, Pol2 ChIA-PET), and transcriptomic (RNA-seq, miRNA-seq) maps of this cell model. Integrated analyses of these maps define known (e.g., PDX1, ISL1) and putative (e.g., PCSK1, mir-375) beta cell-specific chromatin interactions and transcriptional cis-regulatory networks, and identify allelic effects on cis-regulatory element use and expression.\n\nImportantly, comparative analyses with maps generated in primary human islets/beta cells indicate substantial preservation of chromatin looping, but also highlight chromosomal heterogeneity and fetal genomic signatures in EndoC-{beta}H1. Together, these maps, and an interactive web application we have created for their exploration, provide important tools for the broad community in the design and success of experiments to probe and manipulate the genetic programs governing beta cell identity and (dys)function in diabetes.

genomics↗

bioRxiv2017

BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues

BackgroundBisulfite sequencing is widely employed to study the role of DNA methylation in disease; however, the data suffer from biases due to variability in depth of coverage. Imputation of methylation values at low-coverage sites may mitigate these biases while also identifying important genomic features and motifs associated with predictive power.\n\nResultsHere we describe BoostMe, a novel method for imputation of DNA methylation within whole-genome bisulfite sequencing (WGBS) data based on a gradient boosting algorithm. Importantly, we designed a new feature that leverages information from multiple samples in the same tissue and disease state, enabling BoostMe to outperform existing imputation methods in speed and accuracy. We show that imputation improves WGBS concordance with the Infinium MethylationEPIC array at low WGBS sequencing depth, suggesting improvement in WGBS accuracy after imputation. Furthermore, we compare the ability of BoostMe and DeepCpG - a deep neural network method - to identify interesting features and motifs associated with methylation in three human tissues implicated in type 2 diabetes (T2D) etiology. We find that while BoostMe only identifies features important to general methylation levels across tissues, DeepCpG is able to learn differences in methylation-associated sequence motifs among different tissues and identify tissue-specific regulators of differentiation such as EBF1 in adipose, ASCL2 in muscle, and FOXA1, TCF12, and NRF1 in islets. Neither algorithm readily identified T2D-associated features.\n\nConclusionsOur findings demonstrate the current power and limitations of machine and deep learning algorithms to both improve the quality of, and infer biological meaning from, genome-wide DNA methylation data.

genomics↗

bioRxiv2017

Interactions between genetic variation and cellular environment in skeletal muscle gene expression

From whole organisms to individual cells, responses to environmental conditions are influenced by genetic makeup, where the effect of genetic variation on a trait depends on the environmental context. RNA-sequencing quantifies gene expression as a molecular trait, and is capable of capturing both genetic and environmental effects. In this study, we explore opportunities of using allele-specific expression (ASE) to discover cis acting genotype-environment interactions (GxE) - genetic effects on gene expression that depend on an environmental condition. Treating 17 common, clinical traits as approximations of the cellular environment of 267 skeletal muscle biopsies, we identify 10 candidate interaction quantitative trait loci (iQTLs) across 6 traits (12 unique gene-environment trait pairs; 10% FDR per trait) including sex, systolic blood pressure, and low-density lipoprotein cholesterol. Although using ASE is in principle a promising approach to detect GxE effects, replication of such signals can be challenging as validation requires harmonization of environmental traits across cohorts and a sufficient sampling of heterozygotes for a transcribed SNP. Comprehensive discovery and replication will require large human transcriptome datasets, or the integration of multiple transcribed SNPs, coupled with standardized clinical phenotyping.

genetics↗