Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1029515 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR26421826_1.fastq.gz | Fastqsanger.gz | |||
| SRR26421826_2.fastq.gz | Fastqsanger.gz | |||
| SRR26421827_1.fastq.gz | Fastqsanger.gz | |||
| SRR26421827_2.fastq.gz | Fastqsanger.gz | |||
| SRR26421828_1.fastq.gz | Fastqsanger.gz |
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Project description:Pancreatic cancer is the 3rd most prevalent cause of cancer related deaths in United states alone, with over 55000 patients being diagnosed in 2019 alone and nearly as many succumbing to it. Late detection, lack of effective therapy and poor understanding of pancreatic cancer systemically contributes to its poor survival statistics. Obesity and high caloric intake linked co-morbidities like type 2 diabetes (T2D) have been attributed as being risk factors for a number of cancers including pancreatic cancer. Studies on gut microbiome has shown that lifestyle factors as well as diet has a huge effect on the microbial flora of the gut. Further, modulation of gut microbiome has been seen to contribute to effects of intensive insulin therapy in mice on high fat diet. In another study, abnormal gut microbiota was reported to contribute to development of diabetes in Db/Db mice. Recent studies indicate that microbiome and microbial dysbiosis plays a role in not only the onset of disease but also in its outcome. In colorectal cancer, Fusobacterium has been reported to promote therapy resistance. Certain intra-tumoral bacteria have also been shown to elicit chemo-resistance by metabolizing anti-cancerous agents. In pancreatic cancer, studies on altered gut microbiome have been relatively recent. Microbial dysbiosis has been observed to be associated with pancreatic tumor progression. Modulation of microbiome has been shown to affect response to anti-PD1 therapy in this disease as well. However, most of the studies in pancreatic cancer and microbiome have remained focused om immune modulation. In the current study, we observed that in a T2D mouse model, the microbiome changed significantly as the hyperglycemia developed in these animals. Our results further showed that, tumors implanted in the T2D mice responded poorly to Gemcitabine/Paclitaxel (Gem/Pac) standard of care compared to those in the control group. A metabolomic reconstruction of the WGS of the gut microbiota further revealed that an enrichment of bacterial population involved in drug metabolism in the T2D group.
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Project description:Zhu2015 - Combined gemcitabine and birinapant
in pancreatic cancer cells - basic PD model
Mathematical model to illustrate the
effectiveness of combination chemotherapy involving gemcitabine and
birinapant against pancreatic cancer.
This model is described in the article:
Mechanism-based mathematical
modeling of combined gemcitabine and birinapant in pancreatic
cancer cells.
Zhu X, Straubinger RM, Jusko
WJ.
J Pharmacokinet Pharmacodyn 2015 Oct;
42(5): 477-496
Abstract:
Combination chemotherapy is standard treatment for
pancreatic cancer. However, current drugs lack efficacy for
most patients, and selection and evaluation of new combination
regimens is empirical and time-consuming. The efficacy of
gemcitabine, a standard-of-care agent, combined with
birinapant, a pro-apoptotic antagonist of Inhibitor of
Apoptosis Proteins (IAPs), was investigated in pancreatic
cancer cells. PANC-1 cells were treated with vehicle,
gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and
combinations of the two drugs. Temporal changes in cell
numbers, cell cycle distribution, and apoptosis were measured.
A basic pharmacodynamic (PD) model based on cell numbers, and a
mechanism-based PD model integrating all measurements, were
developed. The basic PD model indicated that synergistic
effects occurred in both cell proliferation and death
processes. The mechanism-based model captured key features of
drug action: temporary cell cycle arrest in S phase induced by
gemcitabine alone, apoptosis induced by birinapant alone, and
prolonged cell cycle arrest and enhanced apoptosis induced by
the combination. A drug interaction term Ψ was employed in
the models to signify interactions of the combination when data
were limited. When more experimental information was utilized,
Ψ values approaching 1 indicated that specific mechanisms
of interactions were captured better. PD modeling identified
the potential benefit of combining gemcitabine and birinapant,
and characterized the key interaction pathways. An optimal
treatment schedule of pretreatment with gemcitabine for 24-48 h
was suggested based on model predictions and was verified
experimentally. This approach provides a generalizable modeling
platform for exploring combinations of cytostatic and cytotoxic
agents in cancer cell culture studies.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000668.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000668 | BioModels
Project description:Zhu2015 - combined gemcitabine and birinapant
in pancreatic cancer cells - mechanistic PD model
Mechanistic mathematical model to
illustrate the effectiveness of combination chemotherapy involving
gemcitabine and birinapant against pancreatic cancer.
This model is described in the article:
Mechanism-based mathematical
modeling of combined gemcitabine and birinapant in pancreatic
cancer cells.
Zhu X, Straubinger RM, Jusko
WJ.
J Pharmacokinet Pharmacodyn 2015 Oct;
42(5): 477-496
Abstract:
Combination chemotherapy is standard treatment for
pancreatic cancer. However, current drugs lack efficacy for
most patients, and selection and evaluation of new combination
regimens is empirical and time-consuming. The efficacy of
gemcitabine, a standard-of-care agent, combined with
birinapant, a pro-apoptotic antagonist of Inhibitor of
Apoptosis Proteins (IAPs), was investigated in pancreatic
cancer cells. PANC-1 cells were treated with vehicle,
gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and
combinations of the two drugs. Temporal changes in cell
numbers, cell cycle distribution, and apoptosis were measured.
A basic pharmacodynamic (PD) model based on cell numbers, and a
mechanism-based PD model integrating all measurements, were
developed. The basic PD model indicated that synergistic
effects occurred in both cell proliferation and death
processes. The mechanism-based model captured key features of
drug action: temporary cell cycle arrest in S phase induced by
gemcitabine alone, apoptosis induced by birinapant alone, and
prolonged cell cycle arrest and enhanced apoptosis induced by
the combination. A drug interaction term Ψ was employed in
the models to signify interactions of the combination when data
were limited. When more experimental information was utilized,
Ψ values approaching 1 indicated that specific mechanisms
of interactions were captured better. PD modeling identified
the potential benefit of combining gemcitabine and birinapant,
and characterized the key interaction pathways. An optimal
treatment schedule of pretreatment with gemcitabine for 24-48 h
was suggested based on model predictions and was verified
experimentally. This approach provides a generalizable modeling
platform for exploring combinations of cytostatic and cytotoxic
agents in cancer cell culture studies.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000669.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000669 | BioModels
Project description:Background & Aims: The complex interactions between diet and the microbiota that influence mucosal inflammation and inflammatory bowel disease are poorly understood. Experimental colitis models provide the opportunity to control and systematically perturb diet and the microbiota in parallel to quantify the contributions between multiple dietary ingredients and the microbiota on host physiology and colitis. Methods: To examine the interplay of diet and the gut microbiota on host health and colitis, we fed over 40 different diets with varied macronutrient sources and concentrations to specific pathogen free or germ free mice either in the context of healthy, unchallenged animals or dextran sodium sulfate colitis model. Results: Diet influenced physiology in both health and colitis across all models, with the concentration of protein and psyllium fiber having the most profound effects. Increasing dietary protein elevated gut microbial density and worsened DSS colitis severity. Depleting gut microbial density by using germ-free animals or antibiotics negated the effect of a high protein diet. Psyllium fiber influenced host physiology and attenuated colitis severity through microbiota-dependent and microbiota-independent mechanisms. Combinatorial perturbations to dietary protein and psyllium fiber in parallel explain most variation in gut microbial density, intestinal permeability, and DSS colitis severity, and changes in one ingredient can be offset by changes in the other. Conclusions: Our results demonstrate the importance of examining complex mixtures of nutrients to understand the role of diet in intestinal inflammation. Keywords: IBD; Diet; Microbiota; Mouse Models; Systems Biology
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