Biology subjects

Choudhury, S.

Publications and source records attributed to Choudhury, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

p300 KAT regulates SOX10 stability and function in human melanoma

SOX10 is a lineage-specific transcription factor critical for melanoma tumor growth, while SOX10 loss-of-function drives the emergence of therapy-resistant, invasive melanoma phenotypes. A major challenge has been developing therapeutic strategies targeting SOX10s role in melanoma proliferation, while preventing a concomitant increase in tumor cell invasion. Here, we report that the lysine acetyltransferase (KAT) EP300 and SOX10 gene loci on Chromosome 22 are frequently co-amplified in melanomas, including UV-associated and acral tumors. We further show that p300 KAT activity mediates SOX10 protein stability and that the p300 inhibitor, A-485, downregulates SOX10 protein levels in melanoma cells via proteasome-mediated degradation. Additionally, A-485 potently inhibits proliferation of SOX10+ melanoma cells while decreasing invasion in AXLhigh/MITFlow melanoma cells through downregulation of metastasis-related genes. We conclude that the SOX10/p300 axis is critical to melanoma growth and invasion, and that inhibition of p300 KAT activity through A-485 may be a worthwhile therapeutic approach for SOX10-reliant tumors.

cancer biology↗

bioRxiv2023

AntiBP3: A hybrid method for predicting antibacterial peptides against gram-positive/negative/variable bacteria

This study focuses on the development of in silico models for predicting antibacterial peptides as a potential solution for combating antibiotic-resistant strains of bacteria. Existing methods for predicting antibacterial peptides are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we introduce a novel approach that enables the prediction of antibacterial peptides against several bacterial groups, including gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify antibacterial peptides and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict antibacterial peptides and obtained high precision with low sensitivity. To address the similarity issue, we developed machine learning-based models using a variety of compositional and binary features. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98 and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, when evaluated on a validation/independent dataset. Our attempts to develop hybrid or ensemble methods by merging machine learning models with similarity and motif-based techniques did not yield any improvements. To ensure robust evaluation, we employed standard techniques such as five-fold cross-validation, internal validation, and external validation. Our method performs better than existing methods when we compare our method with existing approaches on an independent dataset. In summary, this study makes significant contributions to the field of antibacterial peptide prediction by providing a comprehensive set of methods tailored to different bacterial groups. As part of our contribution, we have developed the AntiBP3 web server and standalone package, which will assist researchers in the discovery of novel antibacterial peptides for combating bacterial infections (https://webs.iiitd.edu.in/raghava/antibp3/). Key Points BLAST-based similarity for annotating antibacterial peptides. Machine learning-based models developed using composition and binary profiles. Identification and mapping of motifs exclusively found in antibacterial peptides Improved version of AntiBP and AntiBP2 for predicting antibacterial peptides. Web server for predicting/designing/scanning antibacterial peptides for all groups of bacteria Authors BiographyO_LINisha Bajiya is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIShubham Choudhury is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAnjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗

bioRxiv2023

Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

Generating large omics datasets has become routine practice to gain insights into cellular processes, yet deciphering such massive datasets and determining intracellular metabolic states remains challenging. Kinetic models of metabolism play a critical role in integrating omics data, as they provide explicit connections between metabolite concentrations, metabolic fluxes, and enzyme levels. Nevertheless, the challenges associated with determining the kinetic parameters that underlie cellular physiology pose significant obstacles to the broader acceptance and adoption of these models within the research community. Here, we present RENAISSANCE, a generative machine learning framework for efficiently parameterizing large-scale kinetic models with dynamic properties matching experimental observations. Through seamless integration and consolidation of diverse omics data and other relevant information, like extracellular medium composition, physicochemical data, and expertise of domain specialists, we show that the proposed framework accurately characterizes unknown intracellular metabolic states, including metabolic fluxes and metabolite concentrations in E. colis metabolic network. Moreover, we show that RENAISSANCE successfully estimates missing kinetic parameters and reconciles them with sparse and noisy experimental data, resulting in a substantial reduction in parameter uncertainty and a notable improvement in the accuracy and reliability of the parameter estimates. The proposed framework will be invaluable for researchers who seek to analyze metabolic variations involving changes in metabolite and enzyme levels and enzyme activity in health and biotechnological studies.

systems biology↗

bioRxiv2023

High-Quality Nuclei Isolation from Postmortem Human Heart Muscle Tissues for Single-Cell Studies

Single-cell approaches have become an increasingly popular way of understanding the genetic factors behind disease. Isolation of DNA and RNA from human tissues is necessary to analyze multi-omic data sets, providing information on the single-cell genome, transcriptome, and epigenome. Here, we isolated high-quality single-nuclei from postmortem human heart tissues for DNA and RNA analysis. Postmortem human tissues were obtained from 106 individuals, 33 with a history of myocardial disease, diabetes, or smoking, and 73 controls without heart disease. We demonstrated that the Qiagen EZ1 instrument and kit consistently isolated genomic DNA of high yield, which can be used for checking DNA quality before conducting single-cell experiments. Here, we provide a method for single-nuclei isolation from cardiac tissue, otherwise known as the SoNIC method, which allows for the isolation of single cardiomyocyte nuclei from postmortem tissue by nuclear ploidy status. We also provide a detailed quality control measure for single-nuclei whole genome amplification and a pre-amplification method for confirming genomic integrity.

genetics↗

bioRxiv2023

A hybrid approach for predicting multi-label subcellular localization of mRNA at genome scale

In the past, number of methods have been developed for predicting single label subcellular localization of mRNA in a cell. Only limited methods had been built to predict multi-label subcellular localization of mRNA. Most of the existing methods are slow and cannot be implemented at transcriptome scale. In this study, a fast and reliable method had been developed for predicting multi-label subcellular localization of mRNA that can be implemented at genome scale. Firstly, deep learning method based on convolutional neural network method have been developed using one-hot encoding and attained an average AUROC - 0.584 (0.543 - 0.605). Secondly, machine learning based methods have been developed using mRNA sequence composition, our XGBoost classifier achieved an average AUROC - 0.709 (0.668 - 0.732). In addition to alignment free methods, we also developed alignment-based methods using similarity and motif search techniques. Finally, a hybrid technique has been developed that combine XGBoost models and motif-based searching and achieved an average AUROC 0.742 (0.708 - 0.816). Our method - MRSLpred, developed in this study is complementary to the existing method. One of the major advantages of our method over existing methods is its speed, it can scan all mRNA of a transcriptome in few hours. A publicly accessible webserver and a standalone tool has been developed to facilitate researchers (Webserver: https://webs.iiitd.edu.in/raghava/mrslpred/). Key PointsO_LIPrediction of Subcellular localization of mRNA C_LIO_LIClassification of mRNA based on Motif and BLAST search C_LIO_LICombination of alignment based and alignment free techniques C_LIO_LIA fast method for subcellular localization of mRNA C_LIO_LIA web server and standalone software C_LI

bioinformatics↗

bioRxiv2022

A Multithreaded Model for Cancer Tissue Heterogeneity: An Application

Studying the heterogeneity in cancerous tissue is challenging in cancer research. It is vital to process the real-world data efficiently to understand the heterogeneous nature of cancer tissue. GPU compatible models, which can estimate the subpopulation of cancerous tissue, are fast if the size of input data, i.e., the number of qPCR (quantitative polymerase chain reaction) gene expression reading is extensive. In the real world, we rarely get that much data to reap the benefits of a GPUs parallelism. Real experimental data from fibroblasts are much less, and models using those data on a GPU are slower than the CPU multithreaded application. This paper will show a method to run GPU-compatible models for cancer tissue heterogeneity on a multithreaded CPU. Further, we also show that the model running on a multithreaded CPU is faster than the model running on a GPU with real experimental data.

bioinformatics↗

bioRxiv2022

Prediction, scanning and designing of TNF-α inducing epitopes for human and mouse

Tumor Necrosis Factor alpha (TNF-) is a pleiotropic pro-inflammatory cytokine that plays a crucial role in controlling signaling pathways within the immune cells. Recent studies reported that the higher expression levels of TNF- is associated with the progression of several diseases including cancers, cytokine release syndrome in COVID-19 and autoimmune disorders. Thus, it is the need of the hour to develop immunotherapies or subunit vaccines to manage TNF- progression in various disease conditions. In the pilot study, we have proposed a host-specific in-silico tool for the prediction, designing and scanning of TNF- inducing epitopes. The prediction models were trained and validated on the experimentally validated TNF- inducing/non-inducing for human and mouse hosts. Firstly, we developed alignment free (machine learning based models using composition of peptides) methods for predicting TNF- inducing peptides and achieved maximum AUROC of 0.79 and 0.74 for human and mouse hosts, respectively. Secondly, alignment based (using BLAST) method has been used for predicting TNF- inducing epitopes. Finally, a hybrid method (combination of alignment free and alignment-based method) has been developed for predicting epitopes. Our hybrid method achieved maximum AUROC of 0.83 and 0.77 on an independent dataset for human and mouse hosts, respectively. We have also identified the potential TNF- inducing peptides in different proteins of HIV-1, HIV-2, SARS-CoV-2 and human insulin. Best models developed in this study has been incorporated in a webserver TNFepitope (https://webs.iiitd.edu.in/raghava/tnfepitope/), standalone package and GitLab (https://gitlab.com/raghavalab/tnfepitope). Key PointsO_LITNF- is a multifunctional pleiotropic pro-inflammatory cytokine. C_LIO_LIAnti-TNF- therapy used as an effective treatment in several autoimmune disorders. C_LIO_LIComposition-based features generated using Pfeature for each peptide sequence. C_LIO_LIAlignment-based and alignment-free models developed. C_LIO_LIPrediction and scanning of TNF- inducing regions in antigens. C_LIO_LITNFepitope is available as a web-server, standalone package and GitLab. C_LI

bioinformatics↗

bioRxiv2022

Identification of Antigenic Regions Responsible for inducing Type 1 diabetes mellitus

There are a number of antigens that induce autoimmune response against {beta}-cells, leading to Type 1 diabetes mellitus (T1DM). Recently several antigen-specific immunotherapies have been developed to treat T1DM. Thus identification of T1DM associated peptides with antigenic regions or epitopes is important for peptide based-therapeutics (e.g., immunotherapeutic). In this study, for the first time an attempt has been made to develop a method for predicting, designing and scanning of T1DM associated peptides with high precision. We analyzed 815 T1DM associated peptides and observed that these peptides are not associated with a specific class of HLA alleles. Thus, HLA binder prediction methods are not suitable for predicting T1DM associated peptides. Firstly, we developed a similarity/alignment based method using BLAST and achieved a high probability of correct hits with poor coverage. Secondly, we developed an alignment free method using machine learning techniques and got maximum AUROC 0.89 using dipeptide composition. Finally, we developed a hybrid method that combines the strength of both alignment free and alignment based methods and achieve maximum AUROC 0.95 with MCC 0.81 on independent dataset. We developed a webserver "DMPPred" and standalone server, for predicting, designing and scanning of T1DM associated peptides (https://webs.iiitd.edu.in/raghava/dmppred/). Key PointsO_LIPrediction of peptides responsible for inducing immune system against {beta}-cells C_LIO_LICompilation and analysis of Type 1 diabetes associated HLA binders C_LIO_LIBLAST based similarity search against Type 1diabetes associated peptides C_LIO_LIAlignment free method using machine learning techniques and composition C_LIO_LIA hybrid method using alignment free and alignment based approach C_LI Authors BiographyO_LINishant Kumar is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LISumeet Patiyal is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LIShubham Choudhury is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LIRitu Tomer is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LIAnjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗

bioRxiv2022

An ensemble method for designing phage-based therapy against bacterial infections

Phage therapy is a viable alternative to antibiotics for treating microbial infections, particularly managing drug-resistant strains of bacteria. One of the major challenges in designing phage based therapy is to identify the most appropriate phage to treat a bacterial infection. In this study, an attempt has been made to predict phage-host interaction with high accuracy to identify the best virus for treating a bacterial infection. All models have been developed on a training dataset containing 826 phage host-interactions, whereas models have been evaluated on a validation dataset comprising 1201 phage-host interactions. Firstly, alignment based models have been developed using similarity between phage-phage (BLASTPhage), host-host (BLASTHost) and phage-CRISPR (CRISPRPred) where we achieved accuracy between 42.4%-66.2% for BLASTPhage, 55%-78.4% for BLASTHost, and 43.7%-80.2% for CRISPRPred at five taxonomic levels. Secondly, alignment free models have been developed using machine learning techniques. Thirdly, hybrid models have been developed by integrating alignment-free models and similarity-score where we achieved maximum performance of (60.6%-93.5%). Finally, an ensemble model has been developed that combines hybrid and alignment based model. Our ensemble model achieved highest accuracy of 67.9%, 80.6%, 85.5%, 90%, 93.5% at Genus, Family, Order, Class and Phylum levels, which is better than existing methods. In order to serve the scientific community we have developed a webserver named PhageTB and standalone software package (https://webs.iiitd.edu.in/raghava/phagetb/). Key PointsO_LIPhage therapy provides an alternative to mange drug resistant strains of bacteria C_LIO_LIPrediction bacterial strains that can be treated by a given phage C_LIO_LIAlignment-based, alignment-free and ensemble models have been developed. C_LIO_LIPrediction of appropriate phage/virus that can lyse a given strain of bacteria. C_LIO_LIWebserver and standalone package provided to predict phage-host interactions. C_LI

bioinformatics↗

bioRxiv2022

Deciphering the conformational dynamics of gephyrin-mediated collybistin activation

Efficient neuronal signaling depends on the proper assembly of the postsynaptic neurotransmitter machinery and at inhibitory GABAergic synapses is controlled by the scaffolding protein gephyrin and collybistin, a Dbl-family guanine nucleotide exchange factor and neuronal adaptor protein. Collybistin usually contains an N-terminal SH3 domain and exists in closed/inactive or open/active states. Here, we elucidate the molecular basis of the gephyrin-collybistin interaction with newly designed collybistin FRET sensors. Using fluorescence lifetime-based FRET measurements, we deduce the affinity of the gephyrin-collybistin complex, thereby confirming that the C-terminal dimer-forming E domain binds collybistin, an interaction, which does not require E domain dimerization. Simulations based on fluorescence lifetime and sensor distance distributions reveal a dynamic behavior of the SH3 domain already in the closed state of collybistin. Finally, our data provide strong evidence for a collybistin-gephyrin communication network, where, unexpectedly, switching of collybistin from closed/inactive to open/active states is efficiently triggered by gephyrin.

biophysics↗

bioRxiv2022

Doublecortin like kinase 1 is a target in squamous cell carcinoma

Doublecortin like kinase 1 (DCLK1) plays a crucial role in several cancers including colon and pancreatic adenocarcinomas. However, its role in squamous cell carcinoma (SCC) remains unknown. To this end, we examined DCLK1 expression in head and neck squamous cell carcinoma (HNSCC) and anal squamous cell carcinoma (ASCC). We found that DCLK1 is elevated in patient SCC tissue, which correlated with cancer progression and poorer overall survival. Furthermore, DCLK1 expression is significantly elevated in HPV negative cancer tissues, which are typically aggressive with poor responses to radiation therapy. To understand the role of DCLK1 in tumorigenesis, we used specific shRNA to suppress DCLK1 expression. This significantly reduced tumor growth, spheroid formation, and migration of HNSCC cancer cells. To further the translational relevance of our studies, we sought to identify a selective DCLK1 inhibitor. Current attempts to target DCLK1 using pharmacologic approaches have relied on non-specific suppression of DCLK1 kinase activity. Here, we demonstrate that DiFiD [3,5-bis (2,4-difluorobenzylidene)-4-piperidone] binds to DCLK1 with high selectivity. Moreover, DiFiD mediated suppression of DCLK1 led to G2/M arrest and apoptosis and significantly suppressed tumor growth of HNSCC xenografts and ASCC patient derived xenografts, supporting that DCLK1 is critical for SCC growth.

cancer biology↗

bioRxiv2022

Immune and Clotting Dysfunction Detected in Saliva and Blood Plasma after COVID-19.

A growing number of studies indicate that coronavirus disease 2019 (COVID-19) is associated with inflammatory sequelae, but molecular signatures governing the normal vs. pathologic convalescence process have not been well-delineated. We characterized global immune and proteome responses in matched plasma and saliva samples obtained from COVID-19 patients collected between 4-6 weeks after initial clinical symptoms resolved. Convalescent subjects showed robust IgA and IgG responses and positive antibody correlations between matched saliva and plasma samples. However, global shotgun proteomics revealed persistent inflammatory patterns in convalescent samples including dysfunction of salivary innate immune cells and clotting factors in plasma (e.g., fibrinogen and antithrombin), with positive correlations to acute COVID-19 disease severity. Saliva samples were characterized by higher concentrations of IgA, and proteomics showed altered pathways that correlated positively with IgA levels. Our study positions saliva as a viable fluid to monitor immunity beyond plasma to document COVID-19 immune, inflammatory, and coagulation-related sequelae.

immunology↗

bioRxiv2022

Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks

Kinetic models of metabolic networks relate metabolic fluxes, metabolite concentrations, and enzyme levels through well-defined mechanistic relations rendering them an essential tool for systems biology studies aiming to capture and understand the behavior of living organisms. However, due to the lack of information about the kinetic properties of enzymes and the uncertainties associated with available experimental data, traditional kinetic modeling approaches often yield only a few or no kinetic models with desirable dynamical properties making the computational analysis unreliable and computationally inefficient. We present REKINDLE (REconstruction of KINetic models using Deep LEarning), a deep-learning-based framework for efficiently generating large-scale kinetic models with dynamic properties matching the ones observed in living organisms. We showcase REKINDLEs efficiency and capabilities through three studies where we: (i) generate large populations of kinetic models that allow reliable in silico testing of hypotheses and systems biology designs, (ii) navigate the phenotypic space by leveraging the transfer learning capability of generative adversarial networks, demonstrating that the generators trained for one physiology can be fine-tuned for another physiology using a low amount of data, and (iii) expand upon existing datasets, making them amenable to thorough computational biology and data-science analyses. The results show that data-driven neural networks assimilate implicit kinetic knowledge and structure of metabolic networks and generate novel kinetic models with tailored properties and statistical diversity. We anticipate that our framework will advance our understanding of metabolism and accelerate future research in health, biotechnology, and systems and synthetic biology. REKINDLE is available as an open-access tool.

systems biology↗

bioRxiv2021

RNA binding protein RBM3 augments kissing loop formation with lncRNAs to enhance translational control

It is becoming apparent that translational regulation involves the coordinated actions of RNA binding proteins (RBPs) and non-coding RNAs. For efficient translation, mRNA needs to be circularized, which is catalyzed by RNA binding proteins and translation factors. However, the role of lncRNAs in the process is not yet defined. We first performed RNA-seq and RNA- immunoprecipitation coupled-Seq and identified LSAMP-3 and Flii-1. Moreover, modeling studies suggest enhanced kissing loop interactions including of transcripts that encode angiogenesis and epithelial mesenchymal transition. While intestinal epithelial cell specific RBM3 transgenic mice showed increased LSAMP-3 and Flii-1, this was reduced in knockout mice. Also, RBM3 overexpression increased tumor xenograft growth, this was suppressed by knockdown of the lncRNAs. Also, knockdown of endogenous RBM3 reduced lncRNA levels and tumor xenograft growth. In addition, it reduced colitis-associated cancers. We propose that RBPs such as RBM3 mediate their function through regulatory lncRNAs that enable circularization to control translation.

cancer biology↗

bioRxiv2021

Microtubule dynamics and the evolution of mitochondrial populations in fission yeast cells: A kinetic Monte Carlo study

Mitochondrial populations in cells are maintained by cycles of fission and fusion events. Perturbation of this balance has been observed in several diseases such as cancer and neurodegeneration. In fission yeast cells, the association of mitochondria with microtubules inhibits mitochondrial fission, [1] illustrating the intricate coupling between mitochondria and the dynamic population of microtubules within the cell. In order to understand this coupling, we carried out kinetic Monte Carlo (KMC) simulations to predict the evolution of mitochondrial size distributions for different cases; wild-type cells, cells with short and long microtubules, and cells without microtubules. Comparison are made with mitochondrial distributions reported in experiments with fission yeast cells. Using experimentally determined mitochondrial fission and fusion frequencies, simulations implemented without the coupling of microtubule dynamics predicted an increase in the mean number of mitochondria, equilibrating within 50 s. The mitochondrial length distribution in these models also showed a higher occurrence of shorter mitochondria, implying a greater tendency for fission, similar to the scenario observed in the absence of microtubules and cells with short microtubules. Interestingly, this resulted in overestimating the mean number of mitochondria and underestimating mitochondrial lengths in cells with wild-type and long microtubules. However, coupling mitochondrias fission and fusion events to the microtubule dynamics effectively captured the mitochondrial number and size distributions in wild-type and cells with long microtubules. Thus, the model provides greater physical insight into the temporal evolution of mitochondrial populations in different microtubule environments, allowing one to study both the short-time evolution as observed in the experiments (<5 minutes) as well as their transition towards a steady-state (>15 minutes). Our study illustrates the critical role of microtubules in mitochondrial dynamics and that coupling their growth and shrinkage dynamics is critical to predicting the evolution of mitochondrial populations within the cell. Author summaryMitochondria are semi-autonomous organelles that undergo fission and fusion to facilitate quality control and exchange of mitochondrial mass within the cell. Impaired mitochondrial fusion and fission dynamics are associated with disease states such as cancer and neurodegeneration. Recent experiments in fission yeast cells revealed a reduction in mitochondrial fission events when mitochondria were bound to the microtubules and longer microtubules shifted the mitochondrial population to longer lengths. In a distinct departure from earlier reports [2-16], we develop a generic framework to study the evolution of the mitochondrial population in fission yeast cells to predict the observed mitochondrial population by coupling the microtubule and mitochondrial dynamics. Using kinetic Monte Carlo (KMC) simulations we predict the temporal evolution of mitochondria in both the mutated and wild-type states of microtubules in fission yeast cells. The mitochondrial population evolves due to multiple fission and fusion reactions occurring between mitochondrial species of various lengths. Several models with varying complexity have been developed to study mitochondrial evolution, and predictions of the mitochondrial populations agree well with experimental data on fission yeast cells without microtubules and cells with short, wild-type and long microtubules. These set of microtubule states are consistent with not only the microtubule dynamics typically observed in cells under different physiological stimuli such as mitosis and disease states but also the stable microtubule states obtained through post-translational modification of and {beta} tubulin subunits of microtubules. Our study reveals that the temporal evolution of mitochondrial populations is an intrinsic function of the state of microtubules which modulates the fission and fusion frequencies to maintain mitochondrial homeostasis within cells.

systems biology↗

bioRxiv2021

Dual-color Fluorescence Cross-Correlation Spectroscopy to study Protein-Protein Interaction and Protein Dynamics in Live Cells

We present a protocol and workflow to perform live cell dual-color fluorescence crosscorrelation spectroscopy (FCCS) combined with Forster Resonance Energy transfer (FRET) to study membrane receptor dynamics in live cells using modern fluorescence labeling techniques. In dual-color FCCS, where the fluctuations in fluorescence intensity represents the dynamical "fingerprint" of the respective fluorescent biomolecule, we can probe co-diffusion or binding of the receptors. FRET, with its high sensitivity to molecular distances, serves as a well-known "nanoruler" to monitor intramolecular changes. Taken together, conformational changes and key parameters such as local receptor concentrations, and mobility constants become accessible in cellular settings. Quantitative fluorescence approaches are challenging in cells due to high noise levels and the vulnerable sample itself. We will show how to perform the experiments including the calibration steps. We use dual-color labeled {beta}2-adrenergic receptor ({beta}2AR) labeled (eGFP and SNAPtag-TAMRA). We will guide you step-by-step through the data analysis procedure using open-source software and provide templates that are easy to customize. Our guideline enables researchers to unravel molecular interactions of biomolecules in live cells in situ with high reliability despite the limited signal-to-noise levels in live cell experiments. The operational window of FRET and particularly FCCS at low concentrations allows quantitative analysis near-physiological conditions. Link to accompanying video: https://tr240.uni-wuerzburg.de/vippclass/index.php/s/TL8aWmwE9RjGfLE

biophysics↗

bioRxiv2020

Combinatorial phosphorylation modulates the structure and function of the G protein gamma subunit in yeast

Protein intrinsically disordered regions (IDRs) are often targets of combinatorial post-translational modifications (PTMs) that serve to regulate protein structure and/or function. Emerging evidence suggests that the N-terminal tails of G protein {gamma} subunits - essential components of heterotrimeric G protein complexes - are intrinsically disordered, highly phosphorylated governors of G protein signaling. Here, we demonstrate that the yeast G{gamma} Ste18 undergoes combinatorial, multi-site phosphorylation within its N-terminal IDR. Phosphorylation at S7 is responsive to GPCR activation and osmotic stress while phosphorylation at S3 is responsive to glucose stress and is a quantitative indicator of intracellular pH. Each site is phosphorylated by a distinct set of kinases and both are also interactive, such that phosphomimicry at one site affects phosphorylation on the other. Lastly, we show that phosphorylation produces subtle yet clear changes in IDR structure and that different combinations of phosphorylation modulate the activation rate and amplitude of the scaffolded MAPK Fus3. These data place G{gamma} subunits among the growing list of intrinsically disordered proteins that exploit combinatorial post-translational modification to govern signaling pathway output.

molecular biology↗

bioRxiv2020

Netome: The Molecular Characterization of Neutrophil Extracellular Traps (NETs)

Neutrophils are the most abundant type of white blood cells in humans with biological roles relevant to inflammation and fighting infections. The release of neutrophil extracellular DNA aims to control invasion by bacteria, viruses, fungi, and tissue damage. Neutrophil Extracellular Traps (NETs) act as antimicrobial agents triggering immune signaling through the release of the nuclear content into the extracellular space. Although intense investigations have elucidated the pathways preceding NET formation, the exact molecular composition of released NETs has not been mapped. We aimed to decode the sequences of DNA and proteins from NETs. With emerging needs to understand neutrophil functions precisely, we open the field of NETOMIC studies through isolation of NETs in combination with omics approaches including shotgun genomics and proteomics. Our in vitro NET isolation methodology allowed for unprecedented replicability with induction in a sterile inflammation model system. Enrichment of mitochondrial DNA and telomere sequences are significantly expressed in NET genomes. This study revealed that the genomic sequence released in the extracellular milieu is not stochastically serving as a scaffold for a repertoire of proteins involved in neutrophil protective functions. Collectively, we established the gene and protein signatures exclusive to the extracellular NETs in comparison to undifferentiated and differentiated neutrophil states, further guiding future detection of specific regions needed for diagnostics and targeted therapies of NET related conditions.

immunology↗