Biology subjects

Basu, A.

Publications and source records attributed to Basu, A..

88 records · Page 5Linked to original sources

bioRxiv2020

Strategies for vaccine design for corona virus using Immunoinformatics techniques

The cutting-edge technology vaccinomics is the combination of two topics immunogenetics and immunogenomics with the knowledge of systems biology and immune profiling for designing vaccine against infectious disease. In our present study, an epitope-based peptide vaccine against nonstructural protein 4 of beta coronavirus, using a combination of B cell and T cell epitope predictions, followed by molecular docking methods are performed. Here, protein sequences of homologous nonstructural protein 4 of beta coronavirus are collected and conserved regions present in them are investigated via phylogenetic study to determine the most immunogenic part of protein. From the identified region of the target protein, the peptide sequence IRNTTNPSAR from the region ranging from 38-47 and the sequence PTDTYTSVYLGKFRG from the positions of 76-90 are considered as the most potential B cell and T cell epitopes respectively. Furthermore, this predicted T cell epitopes PTDTYTSVY and PTDTYTSVYLGKFRG interacted with MHC allelic proteins HLA-A*01:01 and HLA-DRB5*01:01 respectively with the low IC50 values. These epitopes are perfectly fitted into the epitope binding grooves of alpha helix of MHC I molecule and MHC II molecule with binding energy scores -725.0 Kcal/mole and -786.0 Kcal/mole respectively, showing stability in MHC molecules binding. This MHC restricted epitope PTDTYTSVY also showed a good conservancy of 50.16% in world population coverage. This MHC I HLA-A*01:01 allele is present among 58.87% of Chinese population also. Therefore, the epitopes IRNTTNPSAR and PTDTYTSVYLGKFRG may be considered as potential peptides for peptide-based vaccine for coronavirus after further experimental study.

immunology↗

bioRxiv2020

Characterizing the tumor microenvironment of metastatic ovarian cancer by single cell transcriptomics

Epithelial ovarian cancer is a highly heterogenous, metastatic and lethal disease. The presence of CD8+T cells within ovarian tumors is positively associated with overall patient survival. Determining if a patient has T cells that respond to immunotherapies, their characteristics and how they can be manipulated to target cancer cells is an area of intense investigation in cancer therapy. This study determines the cellular composition and the transcriptional state of immune cells in metastatic ovarian cancer samples from patients using single cell RNA sequencing (scRNA-seq). Hierarchical clustering stratified our patient cohort into 2 main groups: 1) a high T cell infiltration (high Tinf) group and 2) a low T cell infiltration (low Tinf) group. To assess the immune response in these patient samples, we performed an unsupervised clustering of the T cell population in each group. The T cell population clustered into 4 and 3 subpopulations in the high Tinf T cell and low Tinf respectively. A granulysin expressing T cell cluster was identified and unique to the High Tinf group. Interestingly, although both groups had resident memory CD8+T (CD8+Trm) cells, only the CD8+Trm cells in the high Tinf group expressed TOX, a recently described transcription factor. TOX confers longevity to T cells within immunosuppressive environment such as cancer. Interestingly, along with TOX+ T cells, we found a unique plasmablast cluster and an IRF8+ macrophage cluster unique to the high Tinf group. Our comprehensive scRNA-seq study provides important insights in elucidating the immune response in ovarian cancer patients.

cancer biology↗

bioRxiv2020

A Spiking Neural Network Model for Category Learning

The creation of useful categories from data is an important cognitive ability, and from the extensive research on categorization, it is now known that the brain has distinct systems for category learning. In this paper, we present the first spiking neural network (SNN) model of human category learning. Here categories are combinations of features - such categories are observed in the prefrontal cortex (PFC). The system follows an architecture commonly used to model the cortex - features are arranged in a topological 2D grid with short range excitatory connectivity and long range inhibitory connectivity - however, here, this architecture is used differently from other models to model higher level cognition. Earlier we presented an artificial neural network (ANN) model of category learning; however, here, a simpler model was adequate, as desired functionality emerges from the SNN dynamics. We identified the objectives that had to be fulfilled for the model to achieve the desired functionality, and performed a design space exploration (DSE) to identify the parameter range in which each of the objectives was fulfilled, and the parameter range for which the system exhibits good performance. Finally, we compared triphasic STDP (a variant of spike time dependant plasticity (STDP)) with standard STDP and observed that triphasic STDP exhibited quicker convergence.

bioengineering↗

bioRxiv2020

Towards Autonomous Intra-cortical Brain Machine Interfaces: Applying Bandit Algorithms for Online Reinforcement Learning

This paper presents application of Banditron - an online reinforcement learning algorithm (RL) in a discrete state intra-cortical Brain Machine Interface (iBMI) setting. We have analyzed two datasets from non-human primates (NHPs) - NHP A and NHP B each performing a 4-option discrete control task over a total of 8 days. Results show average improvements of {approx} 15%, 6% in NHP A and 15%, 21% in NHP B over state of the art algorithms - Hebbian Reinforcement Learning (HRL) and Attention Gated Reinforcement Learning (AGREL) respectively. Apart from yielding a superior decoding performance, Banditron is also the most computationally friendly as it requires two orders of magnitude less multiply-and-accumulate operations than HRL and AGREL. Furthermore, Banditron provides average improvements of at least 40%, 15% in NHPs A, B respectively compared to popularly employed supervised methods - LDA, SVM across test days. These results pave the way towards an alternate paradigm of temporally robust hardware friendly reinforcement learning based iBMIs.

bioengineering↗

bioRxiv2019

Sparse Ensemble Machine Learning to improve robustness of long-term decoding in iBMIs

This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracortical Brain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the ensemble is trained on a randomly sampled (with replacement) set of input channels. These sparse connections ensure that with a high chance, few of the base classifiers should be less affected by the variations in some of the recording channels. We have tested the generality of this technique on different base classifiers - linear discriminant analysis (LDA), support vector machine (SVM), extreme learning machine (ELM) and multilayer perceptron (MLP). Results show decoding accuracy improvements of up to {approx} 21%, 13%, 19%, 10% in non-human primate (NHP) A and 7%, 9%, 7%, 9% in NHP B across test days while using the sparse ensemble approach over a single classifier model for LDA, SVM, ELM and MLP algorithms respectively. The technique also holds ground when the most informative electrode on the test day is dropped. Accordingly, improvements of up to {approx} 24%, 11%, 22%, 9% in NHP A and 14%, 19%, 7%, 28% in NHP B are obtained for LDA, SVM, ELM and MLP respectively.

bioengineering↗

bioRxiv2019

Towards Intelligent Intra-cortical BMI (i2BMI):Low-power Neuromorphic Decoders that outperform Kalman Filters

Fully implantable wireless intra-cortical Brain Machine Interfaces (iBMI) is one of the most promising next frontiers in the nascent field of neurotechnology. However, scaling the number of channels in such systems by another 10X is difficult due to power and bandwidth requirements of the wireless transmitter. One promising solution for that is to include more processing, up to the decoder, in the implant so that transmission data rate is reduced drastically. Earlier work on neuromorphic decoders only showed classification of discrete states. We present results for continuous state decoding using a low power neuromorphic decoder chip termed Spike-input Extreme Learning Machine (SELMA). We compared SELMA against state of the art Steady State Kalman Filter (SSKF) across two different datasets involving a total of 4 non-human primates (NHPs). Results show at least a 10% or more increase in the fraction of variance accounted for by SELMA over SSKF across the datasets. Furthermore, estimated energy consumption comparison shows SELMA consuming {approx} 9 nJ/update against SSKFs {approx} 7.4 nJ/update for an iBMI with a 10 degree of freedom control. Thus, SELMA yields better performance against SSKF with a marginal increase in energy consumption paving the way for reducing transmission data rates in future scaled BMI systems.

bioengineering↗

bioRxiv2019

A Hedgehog-FGF signaling axis patterns anterior mesoderm during gastrulation

The application of single cell technologies to early development holds promise for resolving complex developmental phenotypes. Here we define a novel role for Hedgehog (Hh) signaling for the formation of anterior mesoderm lineages during gastrulation. Single-cell transcriptome analysis of Hh-deficient mesoderm revealed selective deficits in anterior mesoderm populations that later translate to physical defects to anterior embryonic structures including the first pharyngeal arch, heart, and anterior somites. We found that Hh-dependent anterior mesoderm defects were cell non-autonomous to Hh-signal reception. Transcriptional profiling of Hh-deficient mesoderm during gastrulation revealed disruptions to both transcriptional patterning of the mesoderm and a key FGF signaling pathway for mesoderm migration. FGF4 protein application was able to restore cellular migration during gastrulation that was decreased by Hh pathway antagonism. These findings implicate that primitive streak-mediated regulation of anterior mesoderm patterning is controlled by a multicomponent signaling hierarchy activated by Hh signaling and executed by FGF signal transduction.\n\nSIGNIFICANCE STATEMENTHow signaling events during gastrulation pattern the mesoderm is a fascinating developmental process. Although Hedgehog signaling has been implicated in early mesoderm development, its mechanistic role has not been described. We applied single cell sequencing to describe mesodermal defects in Hedgehog pathway mutants--revealing selective defects in anterior mesoderm populations. Transcriptional profiling of gastrulating Hedgehog mutants indicated that several pathways essential for primitive streak function, including FGF, required Hh signaling. Blocking Hedgehog signaling abrogated cell migration during gastrulation, which could be mitigated by addition of FGF4 ligand. This work uncovers a novel Hedgehog to FGF signaling event and describes a unique mechanism by which signals from the node impact to anterior mesoderm formation through the modulation of primitive streak function.

developmental biology↗

bioRxiv2019

Single Photon, Time-Gated, Phasor-based Fluorescence Lifetime Imaging Through Highly Scattering Medium

Fluorescence lifetime imaging (FLI) is a powerful tool for in vitro and non-invasive in vivo biomolecular and cellular investigations. Fluorescence lifetime is an intrinsic characteristic of any fluorescent dye which, to some extent, does not depend on excitation intensity and signal level. However, when used in vivo with visible wavelength emitting fluorophores, FLI is complicated by (i) light scattering as well as absorption by tissues, which significantly reduces fluorescence intensity, (ii) tissue autofluorescence (AF), which decreases the signal to noise ratio and (iii) broadening of the decay signal, which can result in incorrect lifetime estimation. Here, we report the use of a large-frame time-gated single-photon avalanche diode (SPAD) imager, SwissSPAD2, with a very short acquisition time (in the milliseconds range) and a wide-field microscopy format. We use the phasor approach to convert each pixels data into its local lifetime. The phasor transformation provides a simple and fast visual method for lifetime imaging and is particularly suitable for in vivo FLI which suffers from deformation of the fluorescence decay, and makes lifetime extraction by standard fitting challenging. We show, for single dyes, that the phasor cloud distribution (of pixels) increases with decay broadening due to scattering and decreasing fluorescence intensity. Yet, as long as the fluorescence signal is higher than the tissue-like phantom AF, a distinct lifetime can still be clearly identified with an appropriate background correction. Lastly, we demonstrate the detection of few hundred thousand A459 cells expressing the fluorescent protein mCyRFP1 through highly scattering phantom layers, despite significant scattering and the presence of the phantom AF.

bioengineering↗

bioRxiv2019

Multifaceted genome-wide study identifies novel regulatory loci for body mass index in Indians

Obesity, a risk factor for various human diseases originates through complex interactions between genes and prevailing environment that varies across populations. Indians exhibit a unique obesity phenotype likely attributed by specific gene pool and environmental factors. Here, we present genome-wide association study (GWAS) of 7,259 Indians to understand the genetic architecture of body mass index (BMI) in the population. Our study revealed novel association of variants in BAI3 (rs6913677) and SLC22A11 (rs2078267) at GWAS significance, and of ZNF45 (rs8100011) with near GWAS significance. As genetic loci may dictate the phenotype through modulation of epigenetic processes, we overlapped discovered genetic signatures with DNA methylation patterns of 236 Indian individuals, and analyzed expression of the candidate genes using publicly available data. The variants in BAI3 and SLC22A11 were found to dictate methylation patterns at unique CpGs harboring critical cis- regulatory elements. Further, BAI3, SLC22A11 and ZNF45 variants were found to overlie repressive chromatin, active enhancer, and active chromatin regions, in that order, in human subcutaneous adipose tissue in ENCODE database. Besides, the identified genomic regions represented potential binding sites for key transcription factors implicated in obesity and/or metabolic disorders. Interestingly, rs8100011 (ZNF45) acted as a robust cis-expression quantitative trait locus (cis-eQTL) in subcutaneous adipose tissue in GTEx portal, and ZNF45 gene expression showed an inverse correlation with BMI in skeletal muscle of Indian subjects. Further, gene-based GWAS analysis revealed CPS1 and UPP2 as additional leads regulating BMI in Indians. Our study decodes potential genomic mechanisms underlying obesity phenotype in Indians.

genomics↗

bioRxiv2019

Novel strategy for treating neurotropic viral infections using hypolipidemic drug Atorvastatin

Chandipura virus (CHPV) and Japanese Encephalitis Virus (JEV) are known to infect neurons followed by their successful propagation. Increased incidences of central nervous system invasion by the abovementioned viruses have been reported in case of children and elderly thus culminating into severe neurological damage. Literature suggests induction of endoplasmic reticulum (ER)-stress related proteins upon CHPV and JEV infection which help promote viral reproduction. Since earlier studies underscore the pleotropic role of atorvastatin (AT) in neuroprotection against flaviviruses like Hepatitis C and dengue, it was hypothesized that AT might also act as a neuroprotective agent against RNA viruses like CHPV and JEV. AT-mediated antiviral activity was evaluated by assessing survivability of virus-infected mouse pups treated with the drug. Balb C mice were used for in vivo experiments. Neuro2A cell line was used as the model for in vitro experiments. Cells subjected to AT treatment were infected by CHPV and JEV followed by evaluation of ER stress-related and apoptosis-related proteins by immunoblotting technique and immunofluorescence microscopy. Interaction of host protein with viral genome was assessed by RNA-Co-IP. AT treatment exhibited significant anti-viral activity against CHPV and JEV infections via hnRNPC-dependent manner. Viral genome-hnRNPC interaction was found to be abrogated upon AT action. AT was also observed to reduce secretion of proinflammatory cytokines by the neurons in response to viral infection. Moreover, AT treatment was also demonstrated to reduce neuronal death by abrogating virus-induced miR-21 upregulation in hnRNPC-dependent fashion. This study thus suggests probable candidature of AT as antiviral against CHPV and JEV infections.

microbiology↗

bioRxiv2019

Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation

A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. High-throughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3 RNA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of time-series transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. We compared the detected cell types from primary tissue with iPSC-derived cardiomyocytes profiled with DroNc-seq. Our data confirm that DroNc-seq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

genomics↗

bioRxiv2019

Identification and classification of hubs in miRNA target gene networks in human neural stem/progenitor cells following Japanese encephalitis virus infection

Micro RNA dysregulation is observed in many viral diseases. RNA viruses modulate host miRNA machinery for their own benefit. JEV, a neurotropic RNA virus has been reported to manipulate several miRNAs in neuron or microglia. However, no report indicates a complete sketch of the miRNA profile of NSPCs contributing to viral persistence; hence being focused in our current study. We performed a miRNA array of 84 miRNAs in human neuronal progenitor cell line and primary neural precursor cells isolated from aborted foetus. Several fold down-regulation of hsa-miR-9-5p, hsa-miR-22-3p, hsa-miR-124-3p and hsa-miR-132-3p were found in both of the cells. Subsequently, we screened for the target genes of these miRNAs and looked for the important biological pathways the genes significantly regulate. Then we sorted out the target genes which are involved in two or more than two pathways. We constructed a protein-protein interaction (PPI) network of the miRNA target genes based on their interaction patterns. A binary adjacency matrix for each gene network was prepared. Different modules or communities were identified in those networks using community detection algorithms. Mathematically, we identified the hub genes by analyzing their degree centrality and participation co-efficient in the network. The hub genes were classified either as provincial (P<0.4) or connector hubs (P>0.4). We validated the expression of hub genes in both cell line and primary cells through qRT-PCR post JEV infection and respective miR-mimic transfection. Taken together, our findings highlight the importance of specific target gene networks of miRNAs affected by JEV infection in NSPCs.\n\nImportanceJEV damages the neural stem/progenitor cell population of mammalian brain. However, JEV induced alteration in miRNA expression pattern of the cell population remains an open question, hence warrants our present study. In this study, we specifically address the down-regulation of four miRNAs and we prepared a protein-protein interaction network of miRNA target genes. We identified two types of hub genes in the PPI network namely connector hubs and provincial hubs. These two types of miRNA target hub genes critically influence the participation strength in the networks and thereby significantly influence up and down regulation in several key biological pathways. Computational analysis of the PPI networks identifies key protein interactions and hubs in those modules which opens up the possibility of precise identification and classification of host factors for viral infection in NSPCs and how RNA viruses modulate host miRNA machinery for their own benefit post JEV infection to the cells.

microbiology↗

bioRxiv2019

Harnessing patient-specific response dynamics to optimize evolutionary therapies for metastatic clear cell renal cell carcinoma - Learning to adapt

Renal cell carcinoma (RCC) is one of the ten most common and lethal cancers in the United States. Tumor heterogeneity and development of resistance to treatment suggest that patient-specific evolutionary therapies may hold the key to better patients prognosis. Mathematical models are a powerful tool to help develop such strategies; however, they depend on reliable biomarker information. In this paper, we present a dynamic model of tumor-immune interactions, as well as the treatment effect on tumor cells and the tumor-immune environment. We hypothesize that the neutrophil-to-lymphocyte ratio (NLR) is a powerful biomarker that can be used to predict an individual patients response to treatment. Using randomly sampled virtual patients, we show that the model recapitulates patient outcomes from clinical trials in RCC. Finally, we use in silico patient data to recreate realistic tumor behaviors and simulate various treatment strategies to find optimal treatments for each virtual patient.

cancer biology↗

bioRxiv2018

Evaluating Clinical Benefit in I-SPY 2

There are limited methods to analyze both efficacy of a drug and toxicity to a patient in response to treatment. We have developed a metric, the clinical benefit index (CBI) which is quantitative, and a scalable way to integrate patient-reported side effects and efficacy for adaptive randomization. Instead of using efficacy as a singular attribute in decision making for agent selection, we will consider the impact on QoL since many of the newer agents will be less toxic. This will be developed in effort to standardize PRO analysis methods. The clinical benefit index provides an alternative framework for evaluation and would help us to develop a regulatory model for approval of those drugs that have less toxicity by providing a replicable model for measuring toxicity and integrating it with efficacy. Reporting of clinical data can inform the management of toxicity and the integration into a clinical benefit index will help promote regulatory evaluation and preference for less toxic drug/combinations.

bioinformatics↗

bioRxiv2018

Japanese Encephalitis Virus Infected Microglial Cells Secrete Exosomes Containing let-7a/b that Facilitate Neuronal Damage via Caspase Activation

Extracellular microRNAs (miRNAs) are essential for the cell to cell communication in the healthy and diseased brain. MicroRNAs released from the activated microglia upon neurotropic virus infection may exacerbate CNS damage. Here, we identified let-7a and let-7b (let-7a/b) as the overexpressed miRNAs in Japanese Encephalitis virus (JEV) infected microglia and assessed their role in JEV pathogenesis. We measured the let-7a/b expressions in JEV infected post-mortem human brains, mice brains and in mouse microglial N9 cells by the qRT-PCR and in situ hybridization assay. The interaction between let-7a/b and NOTCH signaling pathway further examined in Toll-like receptor 7 knockdown (TLR7 KD) mice to assess the functions. Exosomes released from JEV infected or let-7a/b mimic transfected N9, and HEK-293 cells were isolated and evaluated their function. We observed an upregulation of let-7a/b in the infected brains as well as in microglia. Knockdown of TLR7 or Inhibition of let-7a/b suppressed the JEV induced NOTCH activation possibly via NF-{kappa}B dependent manner and subsequently, attenuated JEV induced TNF production in microglial cells. Further, exosomes secreted from JEV-infected microglial cells specifically contained let-7a/b. Exosomes overexpressed with let-7a/b were injected into BALB/c mice as well as co-incubated with mouse neuronal (Neuro2a) cells, or primary cortical neuron resulted in caspase activation leading to neuronal damage in the brain. Thus, our results provide evidence for the multifaceted role of let-7a/b miRNAs and unravel the exosomes mediated mechanism for JEV induced pathogenesis.

neuroscience↗

bioRxiv2017

DroNc-Seq: Deciphering cell types in human archived brain tissues by massively-parallel single nucleus RNA-seq

Single nucleus RNA-Seq (sNuc-Seq) profiles RNA from tissues that are preserved or cannot be dissociated, but does not provide the throughput required to analyse many cells from complex tissues. Here, we develop DroNc-Seq, massively parallel sNuc-Seq with droplet technology. We profile 29,543 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient and unbiased classification of cell types, paving the way for charting systematic cell atlases.

genomics↗