Splicing factor SRSF6 mediates pleural fibrosis

Lin, Limei; Xiong, Liang; Cheng, Peipei; Chen, Shuai-Jun; Feng, Xiaoyuan; Zhou, Yaya; Niu, Qian; Wang, Meng; Chen, Qianlan; Song, Linjie; Fan, Yu; He, Xiaowen; Fei, Xiang; Wang, Xiaorong; Yue, Hong; Ma, Wanli

doi:10.1172/jci.insight.146197

Cited by 11 publications

(7 citation statements)

References 41 publications

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“…. We also implement R-GCN [36] with TorchSparse++ and benchmark it on five representative heterogeneous graph datasets against state-of-the-art graph deep learning systems DGL [44], PyG [16] and the Graphiler [46]…”

Section: Resultsmentioning

confidence: 99%

TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs

Tang,

Yang,

Liu

et al. 2023

56th Annual IEEE/ACM International Symposium on Microarchitecture

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Sparse convolution plays a pivotal role in emerging workloads, including point cloud processing in AR/VR, autonomous driving, and graph understanding in recommendation systems. Since the computation pattern is sparse and irregular, specialized high-performance kernels are required. Existing GPU libraries offer two dataflow types for sparse convolution. The gather-GEMM-scatter dataflow is easy to implement but not optimal in performance, while the dataflows with overlapped computation and memory access (e.g. implicit GEMM) are highly performant but have very high engineering costs. In this paper, we introduce TorchSparse++, a new GPU library that achieves the best of both worlds. We create a highly efficient Sparse Kernel Generator that generates performant sparse convolution kernels at less than one-tenth of the engineering cost of the current state-of-the-art system. On top of this, we design the Sparse Autotuner, which extends the design space of existing sparse convolution libraries and searches for the best dataflow configurations for training and inference workloads. Consequently, TorchSparse++ achieves 2.9×, 3.3×, 2.2× and 1.7× measured end-to-end speedup on an NVIDIA A100 GPU over stateof-the-art MinkowskiEngine, SpConv 1.2, TorchSparse and SpConv v2 in inference; and is 1.2-1.3× faster than SpConv v2 in mixed precision training across seven representative autonomous driving benchmarks. It also seamlessly supports graph convolutions, achieving 2.6-7.6× faster inference speed compared with state-ofthe-art graph deep learning libraries. Our code is publicly released at https://github.com/mit-han-lab/torchsparse.

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Section: Resultsmentioning

confidence: 99%

TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs

Tang,

Yang,

Liu

et al. 2023

56th Annual IEEE/ACM International Symposium on Microarchitecture

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“…SRSF6 may also be involved in the cell proliferation of some normal cells. Liang et al showed that SRSF6 knockdown significantly inhibited cell proliferation of pleural mesothelial cells stimulated by inflammation ( Liang et al, 2021 ). In neuron cells, SRSF6 helps maintain microtubule stability by promoting the inclusion of Tau exon 10, which is important for the assembly and stability of microtubule in neuron cells ( Yin et al, 2012 ).…”

Section: The Normal Cellular Functions Of Serine/arginine-rich Splicing Factormentioning

confidence: 99%

“…SRSF6 is a multi-function protein that is involved in several biological processes besides RNA splicing, including translation ( Swanson et al, 2010 ) and transcription ( Juge et al, 2010 ). Besides cancer, SRSF6 has been associated with numerous human diseases, such as pleural fibrosis ( Liang et al, 2021 ), Huntington’s disease ( Cabrera and Lucas, 2017 ), Alzheimer’s disease ( Mai et al, 2019 ), diabetes ( Juan-Mateu et al, 2018 ), and systemic sclerosis ( Manetti et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Targeting Splicing Factor SRSF6 for Cancer Therapy

She

Shao

Jia

2021

Front. Cell Dev. Biol.

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Aberrant alternative splicing of pre-mRNA is an emerging cancer hallmark. Many cancer-associated genes undergo alternative splicing to produce multiple isoforms with diverse or even antagonistic functions. Oncogenic isoforms are often up-regulated, whereas tumor suppressive isoforms are down-regulated during tumorigenesis. Serine/arginine-rich splicing factor 6 (SRSF6) is an important splicing factor that regulates the alternative splicing of hundreds of target genes, including many cancer-associated genes. The potential roles of SRSF6 in cancers have attracted increasing attentions in the past decade. Accumulated pieces of evidence have shown that SRSF6 is a potential oncogenic gene that promotes oncogenic splicing when overexpressed. Targeting SRSF6 may suppress tumorigenesis. In this review, we describe the gene, mRNA, and protein structure of SRSF6; summarize the current understanding of the expression, functions, and regulatory mechanisms of SRSF6 during tumorigenesis; and discuss the potential application of targeting SRSF6 in cancer treatment.

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“…Furthermore, SRSF6 is a direct target of miR-506-3p, and its overexpression caused by the repression of miR-506-3p was observed in pleural fibrosis, a thickening of the membranes that cover the lungs [ 37 ]. Unfortunately, the downstream effects have yet to be directly identified.…”

Section: Rna Splicing Factors Regulated By Mirnasmentioning

confidence: 99%

miRNA-Based Regulation of Alternative RNA Splicing in Metazoans

Schorr¹,

Mangone

2021

IJMS

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Alternative RNA splicing is an important regulatory process used by genes to increase their diversity. This process is mainly executed by specific classes of RNA binding proteins that act in a dosage-dependent manner to include or exclude selected exons in the final transcripts. While these processes are tightly regulated in cells and tissues, little is known on how the dosage of these factors is achieved and maintained. Several recent studies have suggested that alternative RNA splicing may be in part modulated by microRNAs (miRNAs), which are short, non-coding RNAs (~22 nt in length) that inhibit translation of specific mRNA transcripts. As evidenced in tissues and in diseases, such as cancer and neurological disorders, the dysregulation of miRNA pathways disrupts downstream alternative RNA splicing events by altering the dosage of splicing factors involved in RNA splicing. This attractive model suggests that miRNAs can not only influence the dosage of gene expression at the post-transcriptional level but also indirectly interfere in pre-mRNA splicing at the co-transcriptional level. The purpose of this review is to compile and analyze recent studies on miRNAs modulating alternative RNA splicing factors, and how these events contribute to transcript rearrangements in tissue development and disease.

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Splicing factor SRSF6 mediates pleural fibrosis

Cited by 11 publications

References 41 publications

TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs

TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs

Targeting Splicing Factor SRSF6 for Cancer Therapy

miRNA-Based Regulation of Alternative RNA Splicing in Metazoans

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