MT1X is an oncogene and indicates prognosis in ccRCC

Ding, Yanpeng; Fang, Jiayu; Chen, Mengge; Xu, Yan; Liu, Nuomin; Shang, Fang; Xiang, Wenbin; Chen, Rui; Wu, Caicong; Yu, Haijun

doi:10.1042/bsr20221128

Cited by 14 publications

(13 citation statements)

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“…MT1X supports proliferation while inhibiting apoptosis and p53 expression in several types of cancer (Liu et al, 2018). MT1X was overexpressed in many cancers, including breast cancer (Si and Lang, 2018), and was a prognostic marker for invasive ductal carcinoma (Zhang et al, 2000) and renal carcinoma (Ding et al, 2022).…”

Section: Holocarboxylase Synthetase Knockdown Perturbs Cellular Funct...mentioning

confidence: 99%

Proteomic analysis of holocarboxylase synthetase deficient-MDA-MB-231 breast cancer cells revealed the biochemical changes associated with cell death, impaired growth signaling, and metabolism

Sukjoi,

Young,

Acland

et al. 2024

Front. Mol. Biosci.

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We have previously shown that the holocarboxylase synthetase (HLCS) is overexpressed in breast cancer tissue of patients, and silencing of its expression in triple-negative cancer cell line inhibits growth and migration. Here we investigated the global biochemical changes associated with HLCS knockdown in MDA-MB-231 cells to discern the pathways that involve HLCS. Proteomic analysis of two independent HLCS knockdown cell lines identified 347 differentially expressed proteins (DEPs) whose expression change > 2-fold (p < 0.05) relative to the control cell line. GO enrichment analysis showed that these DEPs were mainly associated with the cellular process such as cellular metabolic process, cellular response to stimulus, and cellular component organization or biogenesis, metabolic process, biological regulation, response to stimuli, localization, and signaling. Among the 347 identified DEPs, 64 proteins were commonly found in both HLCS knockdown clones, confirming their authenticity. Validation of some of these DEPs by Western blot analysis showed that plasminogen activator inhibitor type 2 (SerpinB2) and interstitial collagenase (MMP1) were approximately 90% decreased in HLCS knockdown cells, consistent with a 50%–60% decrease in invasion ability of knockdown cells. Notably, argininosuccinate synthase 1 (ASS1), one of the enzymes in the urea cycle, showed approximately a 10-fold increase in the knockdown cells, suggesting the crucial role of HLCS in supporting the urea cycle in the triple-negative cancer cell line. Collectively, our proteomic data provide biochemical insights into how suppression of HLCS expression perturbs global changes in cellular processes and metabolic pathways, impairing cell growth and invasion.

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Section: Holocarboxylase Synthetase Knockdown Perturbs Cellular Funct...mentioning

confidence: 99%

Proteomic analysis of holocarboxylase synthetase deficient-MDA-MB-231 breast cancer cells revealed the biochemical changes associated with cell death, impaired growth signaling, and metabolism

Sukjoi,

Young,

Acland

et al. 2024

Front. Mol. Biosci.

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“…However, the efficiency of data-driven paradigms suits computer-science-oriented investigations. Examples can be found in the more recent proposals that integrate SNN handling functionalities into the PyTorch environment (Mozafari et al, 2019;Zimmer et al, 2019;Büller, 2020;Fang et al, 2020;Lenz and Sheik, 2020;Eshraghian et al, 2021;Pehle and Pedersen, 2021).…”

Section: Available Simulation Platformsmentioning

confidence: 99%

“…The numerical handling is instead carried out by a PyTorch backend, that enables SHIP to inherit PyTorch advantages and functionalities: (i) access to optimized libraries enabling fast matricial calculation, (ii) the network optimization algorithms and S1 for further information). The list of platform appears in order, from the earliest release to the most recent, and it encompasses the following: GENESIS (Bower and Beeman, 2007), XPPAUT (Bard, 1996), NEURON (Hines et al, 2020), NCS (Drewes, 2005;Hoang et al, 2013), EDLUT (Ros et al, 2006), NEST (Gewaltig and Diesmann, 2007), CARLSim (Niedermeier et al, 2022), NeMo (Fidjeland et al, 2009), CNS (Poggio et al, 2010), GeNN (Yavuz et al, 2016), N2D2 (Bichler et al, 2017), Nengo (Bekolay et al, 2014), Auryn (Zenke and Gerstner, 2014), Brian 2 (Stimberg et al, 2019), NEVESIM (Pecevski et al, 2014), ANNarchy (Vitay et al, 2015), MegaSim (Stromatias et al, 2017), BindsNET (Hazan et al, 2018), DynaSim (Sherfey et al, 2018), SPIKE (Ahmad et al, 2018), LSNN (Bellec et al, 2018), cuSNN (Paredes-Valles et al, 2020), Slayer (Shrestha and Orchard, 2018), RockPool (Muir et al, 2019), SpykeTorch (Mozafari et al, 2019), PySNN (Büller, 2020), s2net (Zimmer et al, 2019), sinabs (Lenz and Sheik, 2020), DECOLLE (Kaiser et al, 2020), Spice (Bautembach et al, 2020), Spiking Jelly (Fang et al, 2020), Sapicore (Moyal et al, 2021), Norse (Pehle and Pedersen, 2021), Lava (Richter et al, 2021), snnTorch…”

Section: Base Conceptsmentioning

confidence: 99%

SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks

Gemo,

Spiga,

Brivio

2024

Front. Neurosci.

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Investigations in the field of spiking neural networks (SNNs) encompass diverse, yet overlapping, scientific disciplines. Examples range from purely neuroscientific investigations, researches on computational aspects of neuroscience, or applicative-oriented studies aiming to improve SNNs performance or to develop artificial hardware counterparts. However, the simulation of SNNs is a complex task that can not be adequately addressed with a single platform applicable to all scenarios. The optimization of a simulation environment to meet specific metrics often entails compromises in other aspects. This computational challenge has led to an apparent dichotomy of approaches, with model-driven algorithms dedicated to the detailed simulation of biological networks, and data-driven algorithms designed for efficient processing of large input datasets. Nevertheless, material scientists, device physicists, and neuromorphic engineers who develop new technologies for spiking neuromorphic hardware solutions would find benefit in a simulation environment that borrows aspects from both approaches, thus facilitating modeling, analysis, and training of prospective SNN systems. This manuscript explores the numerical challenges deriving from the simulation of spiking neural networks, and introduces SHIP, Spiking (neural network) Hardware In PyTorch, a numerical tool that supports the investigation and/or validation of materials, devices, small circuit blocks within SNN architectures. SHIP facilitates the algorithmic definition of the models for the components of a network, the monitoring of states and output of the modeled systems, and the training of the synaptic weights of the network, by way of user-defined unsupervised learning rules or supervised training techniques derived from conventional machine learning. SHIP offers a valuable tool for researchers and developers in the field of hardware-based spiking neural networks, enabling efficient simulation and validation of novel technologies.

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“…Finally, each network is trained for 80 epochs. We used PyTorch and the SpikingJelly (Fang et al, 2020) framework for simulating SNNs. In the following sections, we report the accuracy as well as the latency and the sparsity of the SNN.…”

Section: Experimental Setupmentioning

confidence: 99%

Trainable quantization for Speedy Spiking Neural Networks

2023

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Spiking neural networks are considered as the third generation of Artificial Neural Networks. SNNs perform computation using neurons and synapses that communicate using binary and asynchronous signals known as spikes. They have attracted significant research interest over the last years since their computing paradigm allows theoretically sparse and low-power operations. This hypothetical gain, used from the beginning of the neuromorphic research, was however limited by three main factors: the absence of an efficient learning rule competing with the one of classical deep learning, the lack of mature learning framework, and an important data processing latency finally generating energy overhead. While the first two limitations have recently been addressed in the literature, the major problem of latency is not solved yet. Indeed, information is not exchanged instantaneously between spiking neurons but gradually builds up over time as spikes are generated and propagated through the network. This paper focuses on quantization error, one of the main consequence of the SNN discrete representation of information. We argue that the quantization error is the main source of accuracy drop between ANN and SNN. In this article we propose an in-depth characterization of SNN quantization noise. We then propose a end-to-end direct learning approach based on a new trainable spiking neural model. This model allows adapting the threshold of neurons during training and implements efficient quantization strategies. This novel approach better explains the global behavior of SNNs and minimizes the quantization noise during training. The resulting SNN can be trained over a limited amount of timesteps, reducing latency, while beating state of the art accuracy and preserving high sparsity on the main datasets considered in the neuromorphic community.

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MT1X is an oncogene and indicates prognosis in ccRCC

Cited by 14 publications

References 50 publications

Proteomic analysis of holocarboxylase synthetase deficient-MDA-MB-231 breast cancer cells revealed the biochemical changes associated with cell death, impaired growth signaling, and metabolism

Proteomic analysis of holocarboxylase synthetase deficient-MDA-MB-231 breast cancer cells revealed the biochemical changes associated with cell death, impaired growth signaling, and metabolism

SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks

Trainable quantization for Speedy Spiking Neural Networks

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