Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA271032 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR1732330_1.fastq.gz | Fastqsanger.gz | |||
| SRR1732330_2.fastq.gz | Fastqsanger.gz | |||
| SRR1732340_1.fastq.gz | Fastqsanger.gz | |||
| SRR1732340_2.fastq.gz | Fastqsanger.gz | |||
| SRR1732331_1.fastq.gz | Fastqsanger.gz |
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Project description:Arabidopsis thaliana contains two nuclear XRN2/3 5'-3' exonucleases that are homologs of yeast and human Rat1/Xrn2 proteins involved in the processing and degradation of several classes of nuclear RNAs and in transcription termination of RNA polymerase II. Using strand-specific short read sequencing we show that knockdown of XRN3 leads to an altered expression of hundreds of genes and the accumulation of uncapped and polyadenylated read-through transcripts generated by inefficiently terminated Pol II. Our data support the notion that XRN3-mediated changes in the expression of a subset of genes are caused by upstream read-through transcription and these effects are enhanced by RNA-mRNA chimeras generated in xrn3 plants. In turn, read-through transcripts that are antisense to downstream genes may trigger production of siRNA. Our results highlight the importance of XRN3 exoribonuclease in Pol II transcription termination in plants and show that disturbance in this process may significantly alter gene expression. Founded by National Science Centre Grant, Poland (UMO-2018/29/B/NZ3/01980, UMO‐2021/40/Q/NZ1/00014, UMO‐2014/13/B/NZ3/00405, MINIATURA 2017/01/X/NZ1/00332, UMO‐2021/41/B/NZ3/02605).
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Project description:Mathematical modeling of regulatory T cell effects on renal cell carcinoma treatment
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1.
Department of Mathematics, Harvey Mudd College, Claremont, CA 91711
2.
Harvey Mudd College, Claremont, CA 91711, United States, United States, United States
Abstract
We present a mathematical model to study the effects of the regulatory T cells (Treg) on Renal Cell Carcinoma (RCC) treatment with sunitinib. The drug sunitinib inhibits the natural self-regulation of the immune system, allowing the effector components of the immune system to function for longer periods of time. This mathematical model builds upon our non-linear ODE model by de Pillis et al. (2009) [13] to incorporate sunitinib treatment, regulatory T cell dynamics, and RCC-specific parameters. The model also elucidates the roles of certain RCC-specific parameters in determining key differences between in silico patients whose immune profiles allowed them to respond well to sunitinib treatment, and those whose profiles did not.
Simulations from our model are able to produce results that reflect clinical outcomes to sunitinib treatment such as: (1) sunitinib treatments following standard protocols led to improved tumor control (over no treatment) in about 40% of patients; (2) sunitinib treatments at double the standard dose led to a greater response rate in about 15% the patient population; (3) simulations of patient response indicated improved responses to sunitinib treatment when the patient's immune strength scaling and the immune system strength coefficients parameters were low, allowing for a slightly stronger natural immune response.
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