Biology subjects

Shukla, A. K.

Publications and source records attributed to Shukla, A. K..

21 records · Page 2Linked to original sources

bioRxiv2019

Genetically encoded intrabody sensors illuminate structural and functional diversity in GPCR-β-arrestin complexes

Interaction of {beta}-arrestins ({beta}arrs) upon agonist-stimulation is a hallmark of G protein-coupled receptors (GPCRs) resulting in receptor desensitization, endocytosis and signaling. Although overall functional roles of {beta}arrs are typically believed to be conserved across different receptors, emerging data now clearly unveils receptor-specific functional contribution of {beta}arrs. The underlying mechanism however remains mostly speculative and represents a key missing link in our current understanding of GPCR signaling and regulatory paradigms. Here, we develop synthetic intrabody-based conformational sensors that help us visualize the assembly and trafficking of GPCR-{beta}arr1 complexes in cellular context for a broad set of receptors with spatio-temporal resolution. Surprisingly, these conformational sensors reveal a previously unappreciated level of diversity in GPCR-{beta}arr complexes that extends beyond the current framework of affinity-based classification and phosphorylation-code-based interaction patterns. More importantly, this conformational diversity arising from spatial signature of phosphorylation sites manifests directly in the form of distinct functional outcomes, including even opposite contribution of {beta}arrs in signal-transduction for different receptors. Taken together, these findings uncover that despite an overall similar interaction and trafficking patterns; critical structural and functional differences exist in {beta}arr complexes for different GPCRs that define and fine-tune receptor-specific downstream responses.

cell biology↗

bioRxiv2019

Partial ligand-receptor engagement yields functional bias at the human complement receptor, C5aR1

The human complement component, C5a, binds two different seven transmembrane receptors termed as C5aR1 and C5aR2. C5aR1 is a prototypical G protein-coupled receptor that couples to Gi sub-family of heterotrimeric G proteins and {beta}-arrestins ({beta}arr) following C5a stimulation. Peptide fragments derived from the carboxyl-terminus of C5a can still interact with the receptor, albeit with lower affinity, and can act as agonists or antagonists. However, whether such fragments might display ligand bias at C5aR1 remains unexplored. Here, we compare C5a and a modified C-terminal fragment of C5a, C5apep, in terms of G protein coupling, {beta}arr recruitment, endocytosis and ERK1/2 MAP kinase activation at the human C5aR1. We discover that C5apep acts as a full-agonist for G protein coupling, while only displaying partial agonism for {beta}arr recruitment. We also observe that whilst C5apep is significantly less efficient in inducing C5aR1 endocytosis compared to C5a, it exhibits robust activation of ERK1/2 phosphorylation at levels similar to C5a. Interestingly, C5apep displays full-agonist efficacy with respect to inhibiting LPS induced IL-6 secretion in human macrophages, but its ability to induce human neutrophil migration is substantially lower compared to C5a. Taken together, our findings reveal ligand-bias at C5aR1, not only with respect to transducer-coupling and receptor trafficking but also in terms of cellular responses. Our findings therefore establish a framework to explore additional levels of biased signaling and biased ligands at C5aR1 with therapeutic potential. More generally, our findings may be extended to discover biased ligands for the broad sub-family of chemokine GPCRs which also interact with chemokine ligands through a biphasic mechanism.

biochemistry↗

bioRxiv2018

Proteomics of natural bacterial isolates powered by deep learning-based de novo identification.

Metaproteomics has been increasingly utilized for high-throughput molecular characterization in complex environments and has been demonstrated to provide insights into microbial composition and functional roles in soil systems. Despite its potential for the study of microbiomes, significant challenges remain in data analysis, including the creation of a sample-specific protein sequence database as the taxonomic composition of soil is often unknown. Almost all metaproteome analysis tools require this database and their accuracy and sensitivity suffer when the database is incomplete or contains extraneous sequences from organisms which are not present. Here, we leverage a de novo peptide sequencing approach to identify sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data, and trained it on 5 million peptide-spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified unsequenced soil isolates directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S ribosomal RNA sequencing of the soil samples and also uncovered several additional species which were strongly represented only in proteomic data. Our pipeline offers an alternative and complementary method for metaproteomic data analysis by creating a protein database directly from proteomic data, thus removing the need for metagenomic sequencing. Significance StatementProteomic characterization of environmental samples, or metaproteomics, reveals microbial activity critical to our understanding of climate, nutrient cycling and human health. Metaproteomic samples originate from diverse environs, such as soil and oceans. One option for data analysis is a de novo interpretation of the mass spectra. Unfortunately, the current generation of de novo algorithms were primarily trained on data originating from human proteins. Therefore, these algorithms struggle with data from environmental samples, limiting our ability to analyze metaproteomics data. To address this challenge, we trained a new algorithm with data from dozens of diverse environmental bacteria and achieved significant improvements in accuracy across a broad range of organisms. This generality opens proteomics to the world of natural isolates and microbiomes.

bioinformatics↗