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      Tumor-derived IL-6 and trans-signaling among tumor, fat, and muscle mediate pancreatic cancer cachexia

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          Abstract

          Cachexia increases morbidity and mortality. Rupert et al. show PDAC cachexia results from tissue crosstalk via an IL-6 trans-signaling loop. Malignant cells signal via IL-6 to muscle and fat, muscle to fat via sIL6R, and fat to muscle via lipids and IL-6.

          Abstract

          Most patients with pancreatic adenocarcinoma (PDAC) suffer cachexia; some do not. To model heterogeneity, we used patient-derived orthotopic xenografts. These phenocopied donor weight loss. Furthermore, muscle wasting correlated with mortality and murine IL-6, and human IL-6 associated with the greatest murine cachexia. In cell culture and mice, PDAC cells elicited adipocyte IL-6 expression and IL-6 plus IL-6 receptor (IL6R) in myocytes and blood. PDAC induced adipocyte lipolysis and muscle steatosis, dysmetabolism, and wasting. Depletion of IL-6 from malignant cells halved adipose wasting and abolished myosteatosis, dysmetabolism, and atrophy. In culture, adipocyte lipolysis required soluble (s)IL6R, while IL-6, sIL6R, or palmitate induced myotube atrophy. PDAC cells activated adipocytes to induce myotube wasting and activated myotubes to induce adipocyte lipolysis. Thus, PDAC cachexia results from tissue crosstalk via a feed-forward, IL-6 trans-signaling loop. Malignant cells signal via IL-6 to muscle and fat, muscle to fat via sIL6R, and fat to muscle via lipids and IL-6, all targetable mechanisms for treatment of cachexia.

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          STAR: ultrafast universal RNA-seq aligner.

          Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)
          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Mark Robinson, Davis J. McCarthy, Gordon K. Smyth (2009)
            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              Cancer statistics, 2019

              Rebecca Siegel, Kimberly D Miller, Ahmedin Jemal (2019)
              Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data, available through 2015, were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data, available through 2016, were collected by the National Center for Health Statistics. In 2019, 1,762,450 new cancer cases and 606,880 cancer deaths are projected to occur in the United States. Over the past decade of data, the cancer incidence rate (2006-2015) was stable in women and declined by approximately 2% per year in men, whereas the cancer death rate (2007-2016) declined annually by 1.4% and 1.8%, respectively. The overall cancer death rate dropped continuously from 1991 to 2016 by a total of 27%, translating into approximately 2,629,200 fewer cancer deaths than would have been expected if death rates had remained at their peak. Although the racial gap in cancer mortality is slowly narrowing, socioeconomic inequalities are widening, with the most notable gaps for the most preventable cancers. For example, compared with the most affluent counties, mortality rates in the poorest counties were 2-fold higher for cervical cancer and 40% higher for male lung and liver cancers during 2012-2016. Some states are home to both the wealthiest and the poorest counties, suggesting the opportunity for more equitable dissemination of effective cancer prevention, early detection, and treatment strategies. A broader application of existing cancer control knowledge with an emphasis on disadvantaged groups would undoubtedly accelerate progress against cancer.
              Article 0 comments Cited 6666 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Joseph E. Rupert:
                ORCID: https://orcid.org/0000-0001-7698-5588
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Ashok Narasimhan:
                ORCID: https://orcid.org/0000-0003-2065-0580
                Role: Formal analysisRole: SoftwareRole: VisualizationRole: Writing - review & editing
                Daenique H.A. Jengelley:
                ORCID: https://orcid.org/0000-0003-3882-0277
                Role: InvestigationRole: Writing - review & editing
                Yanlin Jiang:
                ORCID: https://orcid.org/0000-0003-1235-2671
                Role: MethodologyRole: Resources
                Ernie Au:
                ORCID: https://orcid.org/0000-0002-8782-4488
                Role: Investigation
                Libbie M. Silverman:
                ORCID: https://orcid.org/0000-0002-1287-6494
                Role: Investigation
                George Sandusky:
                ORCID: https://orcid.org/0000-0003-0769-057X
                Role: Investigation
                Andrea Bonetto:
                ORCID: https://orcid.org/0000-0002-3235-1871
                Role: InvestigationRole: Writing - review & editing
                Sha Cao:
                ORCID: https://orcid.org/0000-0002-8645-848X
                Role: Formal analysisRole: Software
                Xiaoyu Lu:
                ORCID: https://orcid.org/0000-0002-6211-6215
                Role: SoftwareRole: Visualization
                Thomas M. O’Connell:
                ORCID: https://orcid.org/0000-0003-4342-9539
                Role: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: Visualization
                Yunlong Liu:
                ORCID: https://orcid.org/0000-0002-2699-626X
                Role: Formal analysis
                Leonidas G. Koniaris:
                ORCID: https://orcid.org/0000-0003-4864-6380
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: VisualizationRole: Writing - review & editing
                Teresa A. Zimmers:
                ORCID: https://orcid.org/0000-0001-7872-0540
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Journal
                Journal ID (nlm-ta): J Exp Med
                Journal ID (iso-abbrev): J Exp Med
                Journal ID (publisher-id): jem
                Title: The Journal of Experimental Medicine
                Publisher: Rockefeller University Press
                ISSN (Print): 0022-1007
                ISSN (Electronic): 1540-9538
                Publication date Collection: 07 June 2021
                Publication date (Electronic): 14 April 2021
                Publication date PMC-release: 14 April 2021
                Volume: 218
                Issue: 6
                Electronic Location Identifier: e20190450
                Affiliations
                [1 ]Department of Biochemistry, Indiana University School of Medicine, Indianapolis, IN
                [2 ]Department of Surgery, Indiana University School of Medicine, Indianapolis, IN
                [3 ]Indiana University Simon Comprehensive Cancer Center, Indianapolis, IN
                [4 ]Department of Pathology, Indiana University School of Medicine, Indianapolis, IN
                [5 ]Department of Otolaryngology–Head and Neck Surgery, Indiana University School of Medicine, Indianapolis, IN
                [6 ]Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN
                [7 ]Indiana Center for Musculoskeletal Health, Indiana University School of Medicine, Indianapolis, IN
                [8 ]Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN
                [9 ]Department of Molecular and Medical Genetics, Indiana University School of Medicine, Indianapolis, IN
                [10 ]Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN
                [11 ]Richard L. Roudebush Veterans Administration Medical Center, Indianapolis, IN
                Author notes
                Correspondence to Teresa A. Zimmers: zimmerst@ 123456iu.edu

                Disclosures: T.A. Zimmers has been compensated for consulting work on cancer cachexia and is a member of the Scientific Advisory Board of Emmyon, Inc.; however, none of these financial relationships concern the pathways or research presented here. The authors declare no further conflicts of interest.

                Author information
                https://orcid.org/0000-0001-7698-5588
                https://orcid.org/0000-0003-2065-0580
                https://orcid.org/0000-0003-3882-0277
                https://orcid.org/0000-0003-1235-2671
                https://orcid.org/0000-0002-8782-4488
                https://orcid.org/0000-0002-1287-6494
                https://orcid.org/0000-0003-0769-057X
                https://orcid.org/0000-0002-3235-1871
                https://orcid.org/0000-0002-8645-848X
                https://orcid.org/0000-0002-6211-6215
                https://orcid.org/0000-0003-4342-9539
                https://orcid.org/0000-0002-2699-626X
                https://orcid.org/0000-0003-4864-6380
                https://orcid.org/0000-0001-7872-0540
                Article
                Publisher ID: jem.20190450
                DOI: 10.1084/jem.20190450
                PMC ID: 8185651
                PubMed ID: 33851955
                SO-VID: a9a22e78-e4ff-454b-952b-56dc18119f93
                Copyright © © 2021 Rupert et al.
                License:

                This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 12 March 2019
                Date revision received : 20 December 2020
                Date accepted : 26 February 2021
                Page count
                Pages: 23
                Funding
                Funded by: National Institutes of Health, DOI http://dx.doi.org/10.13039/100000002;
                Award ID: R01CA122596
                Award ID: R01CA194593
                Award ID: R01GM137656
                Funded by: U.S. Department of Veterans Affairs, DOI http://dx.doi.org/10.13039/100000738;
                Award ID: I01BX004177
                Award ID: I01CX002046
                Funded by: Indiana University Simon Comprehensive Cancer Center, DOI http://dx.doi.org/10.13039/100011749;
                Funded by: Lustgarten Foundation, DOI http://dx.doi.org/10.13039/100005979;
                Funded by: National Institutes of Health, DOI http://dx.doi.org/10.13039/100000002;
                Award ID: R01GM137656
                Funded by: Lilly Endowment, DOI http://dx.doi.org/10.13039/100006976;
                Funded by: National Institutes of Health, DOI http://dx.doi.org/10.13039/100000002;
                Award ID: R21CA190028
                Funded by: Purdue University Center for Cancer Research, DOI http://dx.doi.org/10.13039/100013415;
                Award ID: P30CA023168
                Funded by: Walther Cancer Foundation, DOI http://dx.doi.org/10.13039/100001377;
                Funded by: Indiana University Simon Comprehensive Cancer Center, DOI http://dx.doi.org/10.13039/100011749;
                Award ID: P30CA082709
                Funded by: National Institutes of Health, DOI http://dx.doi.org/10.13039/100000002;
                Award ID: T32HL007910
                Funded by: Indiana University Simon Comprehensive Cancer Center, DOI http://dx.doi.org/10.13039/100011749;
                Funded by: Developmental Studies Hybridoma Bank;
                Funded by: Eunice Kennedy Shriver National Institute of Child Health and Human Development, DOI http://dx.doi.org/10.13039/100009633;
                Funded by: National Institutes of Health, DOI http://dx.doi.org/10.13039/100000002;
                Funded by: The University of Iowa, DOI http://dx.doi.org/10.13039/100008893;
                Categories
                Subject: Article
                Subject: Metabolism
                Subject: Solid Tumors
                Subject: Innate immunity and inflammation

                ScienceOpen disciplines: Medicine
                Data availability:
                ScienceOpen disciplines: Medicine

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