Biology subjects

Ramanathan, P.

Publications and source records attributed to Ramanathan, P..

4 recordsLinked to original sources

bioRxiv2021

mRNA-1273 efficacy in a severe COVID-19 model: attenuated activation of pulmonary immune cells after challenge

The mRNA-1273 vaccine was recently determined to be effective against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from interim Phase 3 results. Human studies, however, cannot provide the controlled response to infection and complex immunological insight that are only possible with preclinical studies. Hamsters are the only model that reliably exhibit more severe SARS-CoV-2 disease similar to hospitalized patients, making them pertinent for vaccine evaluation. We demonstrate that prime or prime-boost administration of mRNA-1273 in hamsters elicited robust neutralizing antibodies, ameliorated weight loss, suppressed SARS-CoV-2 replication in the airways, and better protected against disease at the highest prime-boost dose. Unlike in mice and non-human primates, mRNA-1273- mediated immunity was non-sterilizing and coincided with an anamnestic response. Single-cell RNA sequencing of lung tissue permitted high resolution analysis which is not possible in vaccinated humans. mRNA-1273 prevented inflammatory cell infiltration and the reduction of lymphocyte proportions, but enabled antiviral responses conducive to lung homeostasis. Surprisingly, infection triggered transcriptome programs in some types of immune cells from vaccinated hamsters that were shared, albeit attenuated, with mock-vaccinated hamsters. Our results support the use of mRNA-1273 in a two-dose schedule and provides insight into the potential responses within the lungs of vaccinated humans who are exposed to SARS-CoV-2.

immunology↗

bioRxiv2020

Active and machine learning-based approaches to rapidly enhance microbial chemical production

In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic models (e.g., kinetic/stoichiometric models of metabolism) -- requiring many experimental datasets for their parameterization--while experimental methods may require screening large mutant libraries to explore the design space for the few mutants with desired behaviors. To address these limitations, we developed an active and machine learning approach (ActiveOpt) to intelligently guide experiments to arrive at an optimal phenotype with minimal measured datasets. ActiveOpt was applied to two separate case studies to evaluate its potential to increase valine yields and neurosporene productivity in Escherichia coli. In both the cases, ActiveOpt identified the best performing strain in fewer experiments than the case studies used. This work demonstrates that machine and active learning approaches have the potential to greatly facilitate metabolic engineering efforts to rapidly achieve its objectives.

systems biology↗

bioRxiv2018

RNA sequencing identifies dysregulated circular RNAs in early-stage breast cancer

BackgroundBreast cancer is a major cause of cancer related death in women worldwide. Molecular diagnostic markers that are detectable in early-stage breast cancer can aid in effective clinical intervention. Circular RNAs are a recently identified group of non-coding RNA with potential role in cancer development and progression. In this study, we aimed to identify circular RNAs specific for early stage breast cancer.\n\nMethodCircular RNA expression profile was analyzed in early-stage breast cancer tissues (N=5), matched normal counterparts (N=5) and absolute normal samples (N=5) by RNA-sequencing that enables a comprehensive analysis of RNA expression across the transcriptome. Two different algorithms, find_circ and DCC were used to identify the differentially expressed circular RNAs.\n\nResultsA total of 58 and 87 circular RNAs were found to be differentially expressed by find_circ and DCC algorithms, respectively, among which 26 circular RNAs were common. Hsa_circ_0001946 (CDR1-as) was found to be upregulated in early stage breast cancer along with other novel circular RNAs (hsa_circ_0008225, hsa_circ_0007766, hsa_circ_0016601). We also found that a few of the identified circular RNAs harbor microRNA binding sites which can lead to microRNA sponging activity and pre-microRNA sequences which can generate mature microRNAs. The identified circular RNAs that are differentially regulated in early stage breast cancer can be of potential diagnostic/prognostic importance.\n\nConclusionCircular RNA are differentially expressed in the early-stage breast cancer with potential application in early diagnosis and prognosis. The differentially expressed circular RNA can sequester microRNA and can act as microRNA precursor as well.

cancer biology↗

bioRxiv2018

The RNA helicase UPF1 associates with mRNAs co-transcriptionally and is required in nuclear processes of gene expression

UPF1 is an RNA helicase that is required for efficient nonsense-mediated mRNA decay (NMD) in eukaryotes, and the predominant view is that UPF1 mainly operates on the 3UTRs of mRNAs that are directed for NMD in the cytoplasm. Here we offer evidence, obtained from Drosophila, that UPF1 constantly moves between the nucleus and cytoplasm and that it has multiple functions in the nucleus. It is associated, genome-wide, with nascent RNAs at most of the active Pol II transcription sites and at some Pol III-transcribed genes, as demonstrated microscopically on the polytene chromosomes of salivary gland and by ChIP-seq analysis in S2 cells. Intron recognition seems to interfere with association and translocation of UPF1 on nascent pre-mRNA transcripts, and cells depleted of UPF1 show defects in several nuclear processes essential to correct gene expression - most strikingly, the release of mRNAs from transcription sites and mRNA export from the nucleus.

cell biology↗