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Arora, A.

Publications and source records attributed to Arora, A..

At least 37 records · Page 2Linked to original sources
bioRxiv2023

Impact of ectopic calcification on vascular oxygenation in placental dysfunction models: Simultaneous in vivo assessment with multi-parametric ultrasound and photoacoustic imaging

Preeclampsia (PE) is a prevalent gestational disorder occurring in 2-10% of all pregnancies. It is associated with placental dysfunction which can result in maternal mortality and preterm birth. Despite scientific advances, the underlying mechanisms of PE and the progression of placental dysfunction remain poorly understood without preventative or curative treatment. Other than blood tests and postdelivery placental diagnostics, there are limited detection methods for monitoring placental health. A major roadblock is the lack of imaging modalities available to monitor placental hemodynamics, as the commonly used clinical imaging systems are either harmful due to ionizing sources, too expensive and inaccessible, or provide insufficient data. We propose the combination of quantitative ultrasound and photoacoustic imaging (QUSPAI) to characterize the material and structural changes of the placenta during pregnancy in normal and disease states. The Slc20a2 murine model of placental dysfunction has previously been characterized by increased ectopic placental vascular calcification, reduced fetal growth, and decreased postnatal bone mineral density. In this study we utilized the Slc20a2 model and imaged knockout (KO) and wildtype (WT) placentae on embryonic day (E)18.5. The dual-wavelength images and radio frequency data were collected in vivo and ex vivo to provide quantitative ultrasound spectral (QUS) parameters and blood oxygen saturation. The photoacoustic images indicated that the WT tissue had statistically higher blood oxygen saturation (StO2) values than the KO tissues while QUS data revealed strain-specific calcification effects. Building upon the E18.5 gestation data collected here, future work will involve monitoring longitudinal changes in these parameters, including early calcification, to support translating this method for early diagnosis of placental vascular diseases.

bioengineering↗
bioRxiv2023

E-cadherin-dependent phosphorylation of EGFR governs a homeostatic feedback loop controlling intercellular junction viscosity and collective migration modes.

Actomyosin tension has been shown to be a ubiquitous driver of tissue morphogenesis1, 2. The Rho pathway, a prominent regulatory network influencing cortical tension, plays a central role in both tissue reorganisation and cell migration3-6. While viscous dissipation in the actin network is commonly regarded as a constant passive parameter in cell migration in both 2D and 3D contexts, there is limited knowledge concerning the regulation of dissipative forces arising from viscous drag between cells during collective rearrangement. Here, we found that the phosphorylation of Epithelial Growth Factor Receptor (EGFR) downstream of de novo E-cadherin adhesion7, 8 orchestrates a feedback loop, thereby governing intercellular viscosity via the Rac pathway regulating actin dynamics. Our findings highlight how the E-cadherin-dependent EGFR activity controls the migration mode of collective cell movements independently of intercellular tension. Combining molecular cell biology, micropatterning, and in silico simulation, our work suggests the existence of a regulatory loop by which cells can tune junctional actin viscosity, with implications for the phenomenology of morphogenetic movements.

cell biology↗
bioRxiv2023

Cortical ductility governs cell-cell adhesion mechanics

This paper challenges our understanding of cell-cell adhesion by emphasising the role of mechanical dissipation at the cellular level. We have developed new microdevices to measure the energy dissipated during the rupture of junctions between cell-cell doublets. Using a synthetic cadherin approach, we decoupled the role of cadherin binding energy, signalling and downstream regulation of cytoskeletal architecture. This yielded a phase diagram in which cell junctions transition from a ductile to a brittle fracture mode based on their ratio of cortical tension and shape relaxation time. We recapitulated our results using a descriptive mechanical simulation approach. Our results shift our understanding of cell-cell adhesion from the current focus on bond energy and tension to the key role played by energy dissipation in the cytoskeleton during junction deformation and its active mechanosensitive regulation.

cell biology↗
bioRxiv2023

Ultrasound-guided Photoacoustic image Annotation Toolkit in MATLAB (PHANTOM) for preclinical applications

Depth-dependent fluence-compensation in photoacoustic (PA) imaging is paramount for accurate quantification of chromophores from deep tissues. Here we present a user-friendly toolkit named PHANTOM (PHotoacoustic ANnotation TOolkit for MATLAB) that includes a graphical interface and assists in the segmentation of ultrasound-guided PA images. We modelled the light source configuration with Monte Carlo eXtreme and utilized 3D segmented tissues from ultrasound to generate fluence maps to depth compensate PA images. The methodology was used to analyze PA images of phantoms with varying blood oxygenation and results were validated with oxygen electrode measurements. Two preclinical models, a subcutaneous tumor and a calcified placenta, were imaged and fluence-compensated using the PHANTOM toolkit and the results were verified with immunohistochemistry. The PHANTOM toolkit provides scripts and auxiliary functions to enable biomedical researchers not specialized in optical imaging to apply fluence correction to PA images, enhancing accessibility of quantitative PAI for researchers in various fields.

bioengineering↗
bioRxiv2023

A Deep Learning method for classification of HNSCC and HPV patients using single-cell transcriptomics

Head and Neck Squamous Cell Carcinoma (HNSCC) is the seventh most highly prevalent cancer type worldwide. Early detection of HNSCC is one of the important challenges in managing the treatment of the cancer patients. Existing techniques for detecting HNSCC are costly, expensive, and invasive in nature. In this study, we aimed to address this issue by developing classification models using machine learning and deep learning techniques, focusing on single-cell transcriptomics to distinguish between HNSCC and normal samples. Additionally, we built models to classify HNSCC samples into HPV-positive (HPV+) and HPV-negative (HPV-) categories. The models developed in this study have been trained on 80% of the GSE181919 dataset and validated on the remaining 20%. To develop an efficient model, we performed feature selection using mRMR method to shortlist a small number of genes from a plethora of genes. Artificial Neural Network based model trained on 100 genes outperformed the other classifiers with an AUROC of 0.91 for HNSCC classification for the validation set. The same algorithm achieved an AUROC of 0.83 for the classification of HPV+ and HPV-patients on the validation set. We also performed Gene Ontology (GO) enrichment analysis on the 100 shortlisted genes and found that most genes were involved in binding and catalytic activities. To facilitate the scientific community, a software package has been developed in Python which allows users to identify HNSCC in patients along with their HPV status. It is available at https://webs.iiitd.edu.in/raghava/hnscpred/. Key PointsO_LIApplication of single cell transcriptomics in cancer diagnosis C_LIO_LIDevelopment of models for predicting HNSCC patients C_LIO_LIClassification of HPV+ and HPV-HNSCC patients C_LIO_LIIdentification of gene biomarkers from single cell sequencing C_LIO_LIA standalone software package HNSCpred for predicting HNSCC patients C_LI Authors BiographyO_LIAkanksha Jarwal is currently pursuing an M. Tech. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAnjali Dhall is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAkanksha Arora is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LISumeet Patiyal is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAman Srivastava is currently pursuing an M. Tech. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as a Professor and Head of the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗
bioRxiv2023

ToxinPred 3.0: An improved method for predicting the toxicity of peptides

Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity. Initially, we used BLAST for alignment-based toxicity prediction, yet coverage was limited. We adopted a motif-based approach with MERCI software to identify unique toxic patterns. Despite specificity gains, sensitivity was compromised. We developed alignment-free methods using machine/deep learning, achieving a balance sensitivity and specificity of prediction. A deep learning model (ANN - LSTM with fixed sequence length) developed using one-hot encoding attained a 0.93 AUROC and 0.71 MCC on independent data. The machine learning model (extra tree) developed using compositional features of peptides achieved 0.95 AUROC and 0.78 MCC. Lastly, we developed hybrid or ensemble methods combining two or more models to enhance performance. Hybrid approaches, including motif-based and machine learning, achieved a 0.98 AUROC and 0.81 MCC. Evaluation on independent data demonstrated our methods superiority. To cater to the needs of the scientific community, we have developed a standalone software, pip package and web-based server ToxinPred3 (https://github.com/raghavagps/toxinpred3 and https://webs.iiitd.edu.in/raghava/toxinpred3/). Authors BiographyO_LIAnand Singh Rathore is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAkanksha Arora is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIShubham Choudhury is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIPurava Tijare is a Project Fellow in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as a Professor and Head of the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI HighlightsO_LIImplementation of alignment or similarly based techniques for predicting toxic peptides. C_LIO_LIDiscovery of toxicity-associated patterns and identification of toxic regions in peptides. C_LIO_LIDevelopment of machine and deep learning-based models for toxicity prediction. C_LIO_LIEnsemble methods that combine alignment-based and alignment-free methods. C_LIO_LIWeb server and standalone software package for screening toxicity in peptides/proteins. C_LI

bioinformatics↗
bioRxiv2023

Metabolic Symbiosis between Oxygenated and Hypoxic Tumour Cells: An Agent-based Modelling Study

Deregulated metabolism is one of the hallmarks of cancer. It is well-known that tumour cells tend to metabolize glucose via glycolysis even when oxygen is available and mitochondrial respiration is functional. However, the lower energy efficiency of aerobic glycolysis with respect to mitochondrial respiration makes this behaviour, namely the Warburg effect, counter-intuitive, although it has now been recognized as a source of anabolic precursors. On the other hand, there is evidence that oxygenated tumour cells could be fuelled by exogenous lactate produced from glycolysis, and that glycolytic tumour cells export lactate to the environment. Using a multi-scale approach combining multi-agent modelling, diffusion-reaction and stoichiometric equations, we simulated these cell populations and their environment, and studied the metabolic co-operation between cells exposed to different oxygen concentration and nutrient abundance. The results show that the metabolic cooperation between cells reduces the depletion of environmental glucose, resulting in an overall advantage of using aerobic glycolysis. In addition, the environmental oxygen level was found to be decreased by the symbiosis, promoting a further shift towards anaerobic glycolysis. However, the oxygenated and hypoxic cell populations may gradually reach quasi-equilibrium. A sensitivity analysis based on Latin hypercube sampling and partial rank correlation shows that the symbiotic dynamics depends on properties of the specific cell such as the minimum glucose level needed for glycolysis. Our results suggest that strategies that block glucose transporters may be more effective than those blocking lactate intake transporters to reduce tumour growth by blocking lactate production. AUTHOR SUMMARYMetabolic alteration is one of the hallmarks of cancer and the well-known metabolic alteration of tumour cells is that cells prefer to do glycolysis over mitochondrial respiration even under well-oxygenated and functional mitochondrial conditions. On the other hand, there is evidence that oxygenated tumour cells could be fuelled by exogenous lactate produced from hypoxic glycolytic cells in which it can create a metabolic co-operation between oxygenated and hypoxic cell populations. This metabolic co-operation could allow tumour cells to economically share oxygen and glucose and promote tumour survival. Using a multi-scale approach combining multi-agent modelling, diffusion-reaction and stoichiometric equations, we studied this metabolic co-operation between different populations of cells, exposed to a changing microenvironment. We predict that the tumour environmental glucose depletion is decreased while the oxygen depletion is increased by this metabolic symbiosis, promoting a further shift towards glycolysis. Our results also show that blocking glucose transporters could be more effective than blocking lactate intake transporters, because the former would disrupt both glycolysis and lactate production, drastically reducing tumour growth.

systems biology↗
bioRxiv2023

Prediction of Alzheimer's Disease from Single Cell Transcriptomics Using Deep Learning

Alzheimers disease (AD) is a progressive neurological disorder characterized by brain cell death, brain atrophy, and cognitive decline. Early diagnosis of AD remains a significant challenge in effectively managing this debilitating disease. In this study, we aimed to harness the potential of single-cell transcriptomics data from 12 Alzheimers patients and 9 normal controls (NC) to develop a predictive model for identifying AD patients. The dataset comprised gene expression profiles of 33,538 genes across 169,469 cells, with 90,713 cells belonging to AD patients and 78,783 cells belonging to NC individuals. Employing machine learning and deep learning techniques, we developed prediction models. Initially, we performed data processing to identify genes expressed in most cells. These genes were then ranked based on their ability to classify AD and NC groups. Subsequently, two sets of genes, consisting of 35 and 100 genes, respectively, were used to develop machine learning-based models. Although these models demonstrated high performance on the training dataset, their performance on the validation/independent dataset was notably poor, indicating potential overoptimization. To address this challenge, we developed a deep learning method utilizing dropout regularization technique. Our deep learning approach achieved an AUC of 0.75 and 0.84 on the validation dataset using the sets of 35 and 100 genes, respectively. Furthermore, we conducted gene ontology enrichment analysis on the selected genes to elucidate their biological roles and gain insights into the underlying mechanisms of Alzheimers disease. While this study presents a prototype method for predicting AD using single-cell genomics data, it is important to note that the limited size of the dataset represents a major limitation. To facilitate the scientific community, we have created a website to provide with code and service. It is freely available at https://webs.iiitd.edu.in/raghava/alzscpred. Key PointsO_LIPredictive Model for Alzheimers Disease Using Single Cell Transcriptomics Data C_LIO_LIOveroptimization of models trained on single-cell genomics data. C_LIO_LIApplication of dropout regularization technique of ANN for reducing overoptimization C_LIO_LIRanking of genes based on their ability to predict patients Alzheimers Disease C_LIO_LIStandalone software package for predicting Alzheimers Disease C_LI Authors BiographyO_LIAman Srivastava is pursuing M. Tech. 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_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_LIAkanksha Arora is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAkanksha Jarwal is pursuing M. Tech. 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

Prediction of peptide hormones using an ensemble of machine learning and similarity-based methods

Peptide hormones are genome-encoded signal transduction molecules released in multicellular organisms. The dysregulation of hormone release can cause multiple health problems and it is crucial to study these hormones for therapeutic purposes. To help the research community working in this field, we developed a prediction server that classifies hormonal peptides and non-hormonal peptides. The dataset used in this study was collected for both plants and animals from Hmrbase2 and PeptideAtlas databases. It comprises non-redundant 1174 hormonal and 1174 non-hormonal peptide sequences which were combined and divided into 80% training and 20% validation sets. We extracted a wide variety of compositional features from these sequences to develop various Machine Learning (ML) and Deep Learning (DL) models. The best performing model was logistic regression model trained on top 50 features which achieved an AUROC of 0.93. To enhance the performance of ML model, we applied Basic Local Alignment Search Tool (BLAST) to identify hormonal sequences using similarity among them, and motif search using Motif-Emerging and Classes-Identification (MERCI) to detect motifs present in hormonal and non-hormonal sequences. We combined our best performing classification model, i.e., logistic regression model with BLAST and MERCI to form a hybrid model that can predict hormonal peptide sequences accurately. The hybrid model is able to achieve an AUROC of 0.96, an accuracy of 89.79%, and an MCC of 0.8 on the validation set. This hybrid model has been incorporated on the publicly available website of HOPPred at https://webs.iiitd.edu.in/raghava/hoppred/.

bioinformatics↗
bioRxiv2023

SARS-CoV-2 infection activates endogenous retroviruses of the LTR69 subfamily

Accumulating evidence suggests that endogenous retroviruses (ERVs) play an important role in the host response to infection and the development of disease. By combining RNA- and ChIP-sequencing analyses with RT-qPCR, we show that SARS-CoV-2 infection induces the LTR69 subfamily of ERVs, both in vitro and in vivo. Using functional assays, we identified one SARS-CoV-2-activated LTR69 locus, termed Dup69, which exhibits enhancer activity and is responsive to the transcription factors IRF3 and p65/RelA. LTR69-Dup69 is located about 500 bp upstream of a long non-coding RNA gene (ENSG00000289418) and within the PTPRN2 gene encoding a diabetes-associated autoantigen. Both ENSG00000289418 and PTPRN2 showed a significant increase in expression upon SARS-CoV-2 infection. Thus, our study sheds light on the interplay of exogenous with endogenous viruses and helps to understand how ERVs regulate gene expression during infection.

molecular biology↗
bioRxiv2023

Hmrbase2: A comprehensive database of hormones and their receptors

Background and objectiveHormones are essential for cell communication and hence regulate various physiological processes. The discrepancies in the hormones or their receptors can break this communication and cause major endocrinological disorders. It is, therefore, indispensable for the therapeutics and diagnostics of hormonal diseases. MethodsWe collected widespread information on peptide and non-peptide hormones and hormone receptors. The information was collected from HMDB, UniProt, HORDB, ENDONET, PubChem and literature. ResultsHmrbase2 is an updated version of Hmrbase. The current version contains a total of 12056 entries which is more than twice the entries in the previous version. These include 7406, 753, and 3897 entries for peptide hormones, non-peptide hormones and hormone receptors, respectively, from 803 organisms compared to the 562 organisms in the previous version. The database also hosts 5662 hormone receptor pairs. The source organism, function, and subcellular location are provided for peptide hormones and receptors and properties like melting point; water solubility is provided for non-peptide hormones. Besides browsing and keyword search, an advanced search option has also been provided. Additionally, a similarity search module has been incorporated, enabling users to run similarity searches against peptide hormone sequences using BLAST and Smith-Waterman. ConclusionsTo make the database accessible to various users, we designed a user-friendly, responsive website that can be easily used on smartphones, tablets, and desktop computers. The updated database version, Hmrbase2, offers improved data content compared to the previous version. Homebase 2.0 is freely available at https://webs.iiitd.edu.in/raghava/hmrbase2.

bioinformatics↗
bioRxiv2023

A random forest model for predicting exosomal proteins using evolutionary information and motifs

Identification of secretory proteins in body fluids is one of the key challenges in the development of non-invasive diagnostics. It has been shown in the part that a significant number of proteins are secreted by cells via exosomes called exosomal proteins. In this study, an attempt has been made to build a model that can predict exosomal proteins with high precision. All models are trained, tested, and evaluated on a non-redundant dataset comprising 2831 exosomal and 2831 non-exosomal proteins, where no two proteins have more than 40% similarity. Initially, the standard similarity-based method BLAST was used to predict exosomal proteins, which failed due to low-level similarity in the dataset. To overcome this challenge, machine learning based models have been developed using compositional features of proteins and achieved highest AUROC of 0.70. The performance of the ML-based models improved significantly to AUROC of 0.73 when evolutionary information in the form of PSSM profiles was used for building models. Our analysis indicates that exosomal proteins have wide range of motifs. In addition, it was observed that exosomal proteins contain different types of sequence-based motifs, which can be used for predicting exosomal proteins. Finally, a hybrid method has been developed that combines a motif-based approach and an ML-based model for predicting exosomal proteins, achieving a maximum AUROC 0.85 and MCC of 0.56 on an independent dataset. The hybrid model in this study performs better than the presently available methods when assessed on an independent dataset. A web server and a standalone software ExoProPred has been created for the scientific community to provide service, code, and data. (https://webs.iiitd.edu.in/raghava/exopropred/). KeypointsO_LIExosomal proteins or non-classical secretory proteins are secreted by via exosomes C_LIO_LIA method has been developed for predicting exosomal proteins C_LIO_LIModels have been trained, tested, and evaluated on non-redundant dataset C_LIO_LIWide range of sequence motifs have been discovered in exosomal proteins C_LIO_LIA web server and standalone software have been developed C_LI

bioinformatics↗
bioRxiv2022

Whole genome error-corrected sequencing for sensitive circulating tumor DNA cancer monitoring

Circulating cell-free DNA (ccfDNA) sequencing for low-burden cancer monitoring is limited by sparsity of circulating tumor DNA (ctDNA), the abundance of genomic material within a plasma sample, and pre-analytical error rates due to library preparation, and sequencing errors. Sequencing costs have historically favored the development of deep targeted sequencing approaches for overcoming sparsity in ctDNA detection, but these techniques are limited by the abundance of ccfDNA in samples, which imposes a ceiling on the maximal depth of coverage in targeted panels. Whole genome sequencing (WGS) is an orthogonal approach to ctDNA detection that can overcome the low abundance of ccfDNA by supplanting sequencing depth with breadth, integrating signal across the entire tumor mutation landscape. However, the higher cost of WGS limits the practical depth of coverage and hinders broad adoption. Lower sequencing costs may thus allow for enhanced ctDNA cancer monitoring via WGS. We therefore applied emerging lower-cost WGS (Ultima Genomics, 1USD/Gb) to plasma samples at [~]120x coverage. Copy number and single nucleotide variation profiles were comparable between matched Ultima and Illumina datasets, and the deeper WGS coverage enabled ctDNA detection at the parts per million range. We further harnessed these lower sequencing costs to implement duplex error-corrected sequencing at the scale of the entire genome, demonstrating a [~]1,500x decrease in errors in the plasma of patient-derived xenograft mouse models, and error rates of [~]10-7 in patient plasma samples. We leveraged this highly de-noised plasma WGS to undertake cancer monitoring in the more challenging context of resectable melanoma without matched tumor sequencing. In this context, duplex-corrected WGS allowed us to harness known mutational signature patterns for disease monitoring without matched tumors, paving the way for de novo cancer monitoring.

genomics↗
bioRxiv2022

Deficiency of Heterogeneous Nuclear Ribonucleoprotein U leads to delayed neurogenesis

Genetic variants affecting Heterogeneous Nuclear Ribonucleoprotein U (HNRNPU) have been identified in several neurodevelopmental disorders (NDDs). HNRNPU is widely expressed in the human brain and shows the highest postnatal expression in the cerebellum. Recent studies have investigated the role of HNRNPU in cerebral cortical development, but the effects of HNRNPU deficiency on cerebellar development remain unknown. Here, we describe the molecular and cellular outcomes of HNRNPU locus deficiency during in vitro neural differentiation of patient-derived and isogenic neuroepithelial stem cells with a hindbrain profile. We demonstrate that HNRNPU deficiency leads to chromatin remodeling of A/B compartments, and transcriptional rewiring, partly by impacting exon inclusion during mRNA processing. Genomic regions affected by the chromatin restructuring and host genes of exon usage differences show a strong enrichment for genes implicated in epilepsies, intellectual disability, and autism. Lastly, we show that at the cellular level. HNRNPU downregulation leads to altered neurogenesis and an increased fraction of neural progenitors in the maturing neuronal population. We conclude that, HNRNPU locus is involved in delayed commitment of neural progenitors to neuronal maturation in cell types with hindbrain profile.

neuroscience↗
bioRxiv2022

An intrinsic alkalization circuit turns on mntP-riboswitch under manganese stress in Escherichia coli

The trace metal manganese in excess affects iron-sulfur cluster and heme-protein biogenesis eliciting cellular toxicity. The manganese efflux protein MntP is crucial to evading manganese toxicity in bacteria. Recently, two Mn-sensing riboswitches upstream of mntP and alx in Escherichia coli have been reported to mediate the upregulation of their expression under manganese shock. As the alx-riboswitch is also responsive to alkaline shock administered externally, it is intriguing whether mntP-riboswitch is also responsive to alkaline stress. Furthermore, how both manganese and alkaline pH simultaneously regulate these two riboswitches under physiological conditions is a puzzle. Using multiple approaches, we show that manganese shock activated glutamine synthetase (GlnA) and glutaminases (GlsA and GlsB) to spike ammonia production in E. coli. The elevated ammonia intrinsically alkalizes the cytoplasm. We establish that this alkalization under manganese stress is crucial for attaining the highest degree of riboswitch activation. Additional studies showed that alkaline pH promotes a 17 to 22-fold tighter interaction between manganese and the mntP-riboswitch element. Our study uncovers a physiological linkage between manganese efflux and pH homeostasis that mediates enhanced manganese tolerance. Significance statementRiboswitch RNAs are cis-acting elements that can adopt alternative conformations in the presence or absence of a specific ligand(s) to modulate transcription termination or translation initiation processes. In the present work, we show that how manganese and alkaline pH both are necessary for maximal mntP-riboswitch activation to mitigate the manganese toxicity. This study bridges the gap between earlier studies that separately emphasize the importance of alkaline pH and manganese in activating the riboswitches belonging to the yybP-ykoY-family. This study also ascribes a physiological relevance as to how manganese can rewire cellular physiology to render cytoplasmic pH alkaline for its homeostasis.

biochemistry↗
bioRxiv2022

Transcriptomic and functional mapping of autism associated environmental factors in developing human neurons

Research continues to identify genetic variation, environmental exposures, and their mixtures underlying different diseases and conditions. There is a need for screening methods to understand the molecular outcomes of such factors. Here, we investigate a highly efficient and multiplexable, fractional factorial experimental design (FFED) to study six environmental factors and four human induced pluripotent stem cell line derived differentiating human neural progenitors. We showcase the FFED coupled with RNA-sequencing to identify the effects of low-grade exposures to these environmental factors and analyse the results in the context of autism spectrum disorder (ASD). We performed this after five-day exposures on differentiating human neural progenitors accompanied by a layered analytical approach and detected several convergent and divergent, gene and pathway level responses. We revealed significant upregulation of pathways related to synaptic function and lipid metabolism following lead and fluoxetine exposure, respectively. The lipid changes were validated using mass spectrometry- based metabolomics after fluoxetine exposure. Our study demonstrates that the FFED can be used for multiplexed transcriptomic analyses to detect relevant pathway-level changes in human neural development caused by low-grade environmental risk factors. Future studies will require multiple cell lines with different genetic backgrounds for characterising the effects of environmental exposures in ASD.

neuroscience↗
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↗