Effect of ubiquitin-activating enzyme inhibition on multiple myeloma cell line MM1.S
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE126254 | GEO | 2019/05/01
REPOSITORIES: GEO
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Project description:How cancer cells escape immune surveillance and resist immunotherapies remain to be elucidated. By screening candidate genes frequently amplified in cancer, we identified expression of ubiquitin-activating enzyme (UBA1) as being the most negatively correlated with signatures related to effector CD8+T cells. High expression of UBA1was strongly predictive of resistance to immune checkpoint blockade (ICB) and poor survival in ICB cohorts. In immunocompetent syngeneic models, but not in immunodeficient models, we found that overexpression ofUba1in cancer cells promoted tumor growth and reduced functional, intratumoral CD8+T cells. Conversely, depletion ofUba1impaired tumor progression, accompanied with increase of functional, intratumoral CD8+T cells. CD8+T cell depletion rescued tumor growth impairment mediated byUba1depletion. Additionally, a selective, small molecule inhibitor of UBA1, TAK-243, delayed tumor growth and augmented ICB in various syngeneic models, in a manner dependent on CD8+T cells. Intratumoral administration of TAK-243 and tumor rechallenge experiments revealed that TAK-243 treatment exhibited a remarkable abscopal effect and provided prolonged protection against cancer, accompanied with elevation of memory CD8+ T cells. Mechanistically, depletion or inactivation of UBA1 stabilized the short-lived interferon (IFN) pathway component, JAK1, upregulated both type-I and -II IFN signaling pathways, and resulted in elevation of key immune modulators, including CXCL9, CXCL10, and MHC-I. Taken together, our data supports that UBA1 mediates cancer immune escape via suppressing interferon signaling and suggests that targeting UBA1 may activate systemic cancer immunity.
2024-10-06 | GSE253880 | GEO
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Project description:Proctor2007 - Age related decline of proteolysis, ubiquitin-proteome system
This
is a stochastic model of the ubiquitin-proteasome system for a
generic pool of native proteins (NatP), which have a half-life of
about 10 hours under normal conditions. It is assumed that these
proteins are only degraded after they have lost their native
structure due to a damage event. This is represented in the model
by the misfolding reaction which depends on the level of reactive
oxygen species (ROS) in the cell. Misfolded proteins (MisP) are
first bound by an E3 ubiquitin ligase. Ubiquitin (Ub) is
activated by E1 (ubiquitin-activating enzyme) and then passed to
E2 (ubiquitin-conjugating enzyme). The E2 enzyme then passes the
ubiquitin molecule to the E3/MisP complex with the net effect
that the misfolded protein is monoubiquitinated and both E2 and
E3 are released. Further ubiquitin molecules are added in a
step-wise manner. When the chain of ubiquitin molecules is of
length 4 or more, the polyubiquitinated misfolded protein may
bind to the proteasome. The model also includes de-ubiquitinating
enzymes (DUB) which cleave ubiquitin molecules from the chain in
a step-wise manner. They work on chains attached to misfolded
proteins both unbound and bound to the proteasomes. Misfolded
proteins bound to the proteasome may be degraded releasing
ubiquitin. Misfolded proteins including ubiquitinated proteins
may also aggregate. Aggregates (AggP) may be sequestered
(Seq_AggP) which takes them out of harm's way or they may bind to
the proteasome (AggP_Proteasome). Proteasomes bound by aggregates
are no longer available for protein degradation.
Figure
2 and Figure 3 has been simulated using Gillespie2.
This model is described in the article:
An in silico model of the
ubiquitin-proteasome system that incorporates normal
homeostasis and age-related decline.
Proctor CJ, Tsirigotis M, Gray
DA.
BMC Syst Biol 2007; 1: 17
Abstract:
BACKGROUND: The ubiquitin-proteasome system is responsible
for homeostatic degradation of intact protein substrates as
well as the elimination of damaged or misfolded proteins that
might otherwise aggregate. During ageing there is a decline in
proteasome activity and an increase in aggregated proteins.
Many neurodegenerative diseases are characterised by the
presence of distinctive ubiquitin-positive inclusion bodies in
affected regions of the brain. These inclusions consist of
insoluble, unfolded, ubiquitinated polypeptides that fail to be
targeted and degraded by the proteasome. We are using a systems
biology approach to try and determine the primary event in the
decline in proteolytic capacity with age and whether there is
in fact a vicious cycle of inhibition, with accumulating
aggregates further inhibiting proteolysis, prompting
accumulation of aggregates and so on. A stochastic model of the
ubiquitin-proteasome system has been developed using the
Systems Biology Mark-up Language (SBML). Simulations are
carried out on the BASIS (Biology of Ageing e-Science
Integration and Simulation) system and the model output is
compared to experimental data wherein levels of ubiquitin and
ubiquitinated substrates are monitored in cultured cells under
various conditions. The model can be used to predict the
effects of different experimental procedures such as inhibition
of the proteasome or shutting down the enzyme cascade
responsible for ubiquitin conjugation. RESULTS: The model
output shows good agreement with experimental data under a
number of different conditions. However, our model predicts
that monomeric ubiquitin pools are always depleted under
conditions of proteasome inhibition, whereas experimental data
show that monomeric pools were depleted in IMR-90 cells but not
in ts20 cells, suggesting that cell lines vary in their ability
to replenish ubiquitin pools and there is the need to
incorporate ubiquitin turnover into the model. Sensitivity
analysis of the model revealed which parameters have an
important effect on protein turnover and aggregation kinetics.
CONCLUSION: We have developed a model of the
ubiquitin-proteasome system using an iterative approach of
model building and validation against experimental data. Using
SBML to encode the model ensures that it can be easily modified
and extended as more data become available. Important aspects
to be included in subsequent models are details of ubiquitin
turnover, models of autophagy, the inclusion of a pool of
short-lived proteins and further details of the aggregation
process.
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2024-09-02 | BIOMD0000000105 | BioModels
Project description:Effect of ubiquitin-activating enzyme inhibition on multiple myeloma cell line MM1.S
| PRJNA540712 | ENA
Project description:The ubiquitin proteasome system plays an important role in the pathophysiology of Multiple Myeloma, highlighted by the unique success of proteasome inhibitors in this malignancy. Using topic-defined microarrays we have looked at expression of ubiquitin proteasome system genes in 2 multiple myeloma cell lines and normal bone marrow samples.
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