Biology subjects

Gupta, G.

Publications and source records attributed to Gupta, G..

43 records · Page 3Linked to original sources

bioRxiv2019

DAVI:Deep Learning Based Tool for Alignment and Single Nucleotide Variant identification

The Next Generation Sequencing (NGS) technologies have provided affordable ways to generate errorful raw genetical data. To extract Variant Information from billions of NGS reads is still a daunting task which involves various hand-crafted and parameterized statistical tools. Here we propose a Deep Neural Networks (DNN) based alignment and SNV tool known as DAVI. DAVI consists of models for both global and local alignment and for Variant Calling. We have evaluated the performance of DAVI against existing state of the art tool-set and found that its accuracy and performance is comparable to existing tools used for benchmarking. We further demonstrate that while existing tools are based on data generated from a specific sequencing technology, the models proposed in DAVI are generic and can be used across different NGS technologies. Moreover, this approach is a migration from expert driven statistical models to generic, automated, self-learning models.

bioinformatics↗

bioRxiv2019

The Long Pentraxin 3 (PTX3) Suppresses Immunity to Cutaneous Leishmaniasis by Negatively Regulating Th17 Response

The long Pentraxin 3 (PTX3), a soluble pattern recognition molecule, plays a critical role in inflammation, tissue repair and wound healing. Here, we show that PTX3 regulates disease pathogenesis in cutaneous leishmaniasis (CL). PTX3 expression is increased in active skin lesions in patients and mice during CL, with higher levels being expressed in individuals with severe disease. PTX3 deficient (PTX3-/-) mice were highly resistant to L. major infection and the enhanced resistance was associated with increased IL-17 response. Neutralization of IL-17A abolished this enhanced resistance while treatment with recombinant PTX3 resulted in reduced IL-17A response and increased susceptibility to L. major infection. Naive CD4+ T cells from PTX3-/- mice displayed increased differentiation into Th17 cells, which was reversed in the presence of recombinant PTX3. The enhanced Th17 response observed in PTX3-/- cells was associated with increased Leishmania specific IL-6 production from dendritic cells along with enhanced expression of Th17-specific transcription factors including ROR{gamma}t, AhR and STAT3. Addition of recombinant PTX3 significantly inhibited the expression of Th17-specific transcription factors and dramatically reduced the frequency of Th17 cells in Th17-polarizing cultures of PTX3-/- CD4+ T cells. Collectively, our results show that PTX3 contributes to the pathogenesis of CL by suppressing Th17 differentiation and IL-17A production.\n\nAuthor SummaryCutaneous leishmaniasis (CL) is caused by several species of Leishmania. Currently, there is no approved vaccine against human CL because of the poor understanding of the mechanisms that regulate disease pathogenesis and correlates of protective immunity. Because the long pentraxin 3 (PTX3, a soluble pattern recognition molecule that forms an integral part of the host innate immunity), regulates inflammation and tissue repair, which are critical physiological events associated with resolution of skin lesions during CL, we investigated its role in disease pathogenesis.\n\nHere, we show that PTX3 levels were elevated in skin-lesions in patients and mice during CL. Using a loss of function approach, we showed that PTX3 contributes to pathogenesis, and this was associated with increased IL-17A responses. Neutralization and recombinant cytokine treatment studies showed that the increased resistance of PTX3 deficient mice to L. major is due to enhanced Th17 response in these mice. We further show that PTX3 negatively regulates IL-6 production by dendritic cells and the expression of IL-17A-specific transcription factors (including ROR{gamma}T, STAT3, IRF4, BATF and AhR) in CD4+ T cells. Collectively, these findings show that PTX3 is a negative regulator of Th17 response and protective immunity during L. major infection.

immunology↗

bioRxiv2018

A framework for evaluating correspondence between brain images using anatomical fiducials

Accurate spatial correspondence between template and subject images is a crucial step in neuroimaging studies and clinical applications like stereotactic neurosurgery. In the absence of a robust quantitative approach, we sought to propose and validate a set of point landmarks, anatomical fiducials (AFIDs), that could be quickly, accurately, and reliably placed on magnetic resonance images of the human brain. Using several publicly available brain templates and individual participant datasets, novice users could be trained to place a set of 32 AFIDs with millimetric accuracy. Furthermore, the utility of the AFIDs protocol is demonstrated for evaluating subject-to-template and template-to-template registration. Specifically, we found that commonly used voxel overlap metrics were relatively insensitive to focal misregistrations compared to AFID point-based measures. Our entire protocol and study framework leverages open resources and tools, and has been developed with full transparency in mind so that others may freely use, adopt, and modify. This protocol holds value for a broad number of applications including alignment of brain images and teaching neuroanatomy.

neuroscience↗

bioRxiv2018

A Pre-transplant Blood-based Lipid Signature for Prediction of Antibody-mediated Rejection in Kidney Transplant Patients

There is a lack of biomarkers for pre-kidney transplant immune risk stratification to avoid over- or under-immunosuppression, despite substantial advances in kidney transplant management. Since the circulating lipidome is integrally involved in various inflammatory process and pathophysiology of several immune response, we hypothesized that the lipidome may provide biomarkers that are helpful in the prediction of kidney rejection. Serial plasma samples collected over 1-year post-kidney transplant from a prospective, observational cohort of 45 adult Kidney Transplant [antibody-mediated rejection (AMR)=16; stable controls (SC) =29] patients, were assayed for 210 unique lipid metabolites by quantitative mass spectrometry. A stepwise regularized linear discriminant analysis (RLDA) was used to generate models of predictors of rejection and multivariate statistics was used to identify metabolic group differences. The RLDA models include lipids as well as of calculated panel reactive antibody (cPRA) and presence of significant donor-specific antibody (DSA) at the time of transplant. Analysis of lipids on day of transplant (T1) samples revealed a 7-lipid classifier (lysophosphatidylethanolamine and phosphatidylcholine species) which discriminated between AMR and SC with a misclassification rate of 8.9% [AUC = 0.95 (95% CI = 0.84-0.98), R2 = 0.63]. A clinical model using cPRA and DSA was inferior and produced a misclassification rate of 15.6% [AUC = 0.82 (95% CI = 0.69-0.93), R2 = 0.41]. A stepwise combined model using 4 lipid classifiers and DSA improved the AUC further to 0.98 (95% CI = 0.89-1.0, R2 = 0.83) with a misclassification of only 2.2%. Specific classes of lipids were lower in AMR compared with SC. Serial analysis of SC patients demonstrated metabolic changes between T1 and 6 months (T2) post-transplant, but not between 6 and 12 (T3) months post-transplant. There were no overtime changes in AMR patients. Analysis of SC T1 vs AMR T3 (that at time of AMR) showed sustained decreased levels of lipids in AMR at the time of rejection. These findings suggest that lack of anti-inflammatory polyunsaturated phospholipids differentiate SC from AMR pre-transplant and at the time of rejection, and a composite model using a 4-lipid classifier along with DSA could be used for prediction of antibody-mediated rejection before transplant. HighlightsO_LIDespite significant advancements in kidney transplant treatment and intensive clinical follow-up monitoring, all rejection events are unlikely to be recognized at the beginning. As a result, efforts have been made to identify new biomarkers for kidney rejection detection. C_LIO_LIWhile lipids are known to be potent mediators of inflammation, pro-resolving processes, and other cell signaling cascades, lipidomics can be applied to identify reliable biomarkers to monitor disease severity and may also allow prediction of kidney rejection. C_LIO_LIOur lipidomic study shows lipid profile changes between antibody-mediated rejection group and stable control group as a function of different time point, pre and post-kidney transplantation. Furthermore, our study demonstrates that combining lipid and clinical parameters allow prediction of rejection on the day of the transplant. C_LIO_LIThese findings have the potential to change the present paradigm of pre and post-transplant monitoring and management of these patients by implementing an evidence-based risk stratification technique, resulting in a substantial improvement in kidney transplant success. C_LI

biochemistry↗

bioRxiv2018

Nucl2Vec: Local alignment of DNA sequences using Distributed Vector Representation

The Next Generation Sequencing Technique (NGS) has provided affordable and fast method for generating genetic data. Generation of whole Genome Sequence and extract relevant information from this data is still a computationally expensive process. In this paper we demonstrate a novel approach for local alignment of DNA reads with respect to reference genome. For this process we have used Skip-gram model for creating encoding(Nucl2Vec) and k-nearest neighbor for the alignment. With our new approach we have reduced computation cost for local alignment, while achieving accuracy comparable to existing defacto standard BWA-MEM tool.\n\nIndex TermsGenome, Alignment, Local Alignment, k-nearest Neighbor, Distributed vector representation, Skip-gram

genomics↗

bioRxiv2017

Mapping DNA damage-dependent genetic interactions in yeast via orgy mating and barcode fusion genetics

Condition-dependent genetic interactions can reveal functional relationships between genes that are not evident under standard culture conditions. State-of-the-art yeast genetic interaction mapping, which relies on robotic manipulation of arrays of double mutant strains, does not scale readily to multi-condition studies. Here we describe Barcode Fusion Genetics to map Genetic Interactions (BFG-GI), by which double mutant strains generated via en masse party mating can also be monitored en masse for growth and genetic interactions. By using site-specific recombination to fuse two DNA barcodes, each representing a specific gene deletion, BFG-GI enables multiplexed quantitative tracking of double mutants via next-generation sequencing. We applied BFG-GI to a matrix of DNA repair genes under nine different conditions, including methyl methanesulfonate (MMS), 4-nitroquinoline 1-oxide (4NQO), bleomycin, zeocin, and three other DNA-damaging environments. BFG-GI recapitulated known genetic interactions and yielded new condition-dependent genetic interactions. We validated and further explored a subnetwork of condition-dependent genetic interactions involving MAG1, SLX4, and genes encoding the Shu complex, and inferred that loss of the Shu complex leads to a decrease in the activation or activity of the checkpoint protein kinase Rad53.

systems biology↗

bioRxiv2017

Scaling up DNA data storage and randomaccess retrieval

Current storage technologies can no longer keep pace with exponentially growing amounts of data. 1 Synthetic DNA offers an attractive alternative due to its potential information density of ~ 1018 B/mm3, 107 times denser than magnetic tape, and potential durability of thousands of years.2 Recent advances in DNA data storage have highlighted technical challenges, in particular, coding and random access, but have stored only modest amounts of data in synthetic DNA. 3,4,5 This paper demonstrates an end-to-end approach toward the viability of DNA data storage with large-scale random access. We encoded and stored 35 distinct files, totaling 200MB of data, in more than 13 million DNA oligonucleotides (about 2 billion nucleotides in total) and fully recovered the data with no bit errors, representing an advance of almost an order of magnitude compared to prior work. 6 Our data curation focused on technologically advanced data types and historical relevance, including the Universal Declaration of Human Rights in over 100 languages,7 a high-definition music video of the band OK Go,8 and a CropTrust database of the seeds stored in the Svalbard Global Seed Vault.9 We developed a random access methodology based on selective amplification, for which we designed and validated a large library of primers, and successfully retrieved arbitrarily chosen items from a subset of our pool containing 10.3 million DNA sequences. Moreover, we developed a novel coding scheme that dramatically reduces the physical redundancy (sequencing read coverage) required for error-free decoding to a median of 5x, while maintaining levels of logical redundancy comparable to the best prior codes. We further stress-tested our coding approach by successfully decoding a file using the more error-prone nanopore-based sequencing. We provide a detailed analysis of errors in the process of writing, storing, and reading data from synthetic DNA at a large scale, which helps characterize DNA as a storage medium and justify our coding approach. Thus, we have demonstrated a significant improvement in data volume, random access, and encoding/decoding schemes that contribute to a whole-system vision for DNA data storage.

bioengineering↗