1
|
Su C, Kent CL, Pierpoint M, Floyd W, Luo L, Wiliams NT, Ma Y, Peng B, Lazarides AL, Subramanian A, Himes JE, Perez VM, Hernansaiz-Ballesteros RD, Roche KE, Modliszewski JL, Selitsky SR, Mari Shinohara, Wisdom AJ, Moding EJ, Mowery YM, Kirsch DG. Enhancing radiotherapy response via intratumoral injection of the TLR9 agonist CpG to stimulate CD8 T cells in an autochthonous mouse model of sarcoma. bioRxiv 2024:2024.01.03.573968. [PMID: 38260522 PMCID: PMC10802286 DOI: 10.1101/2024.01.03.573968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Radiation therapy is frequently used to treat cancers including soft tissue sarcomas. Prior studies established that the toll-like receptor 9 (TLR9) agonist cytosine-phosphate-guanine oligodeoxynucleotide (CpG) enhances the response to radiation therapy (RT) in transplanted tumors, but the mechanism(s) remain unclear. Here, we used CRISPR/Cas9 and the chemical carcinogen 3-methylcholanthrene (MCA) to generate autochthonous soft tissue sarcomas with high tumor mutation burden. Treatment with a single fraction of 20 Gy RT and two doses of CpG significantly enhanced tumor response, which was abrogated by genetic or immunodepletion of CD8+ T cells. To characterize the immune response to RT + CpG, we performed bulk RNA-seq, single-cell RNA-seq, and mass cytometry. Sarcomas treated with 20 Gy and CpG demonstrated increased CD8 T cells expressing markers associated with activation and proliferation, such as Granzyme B, Ki-67, and interferon-γ. CpG + RT also upregulated antigen presentation pathways on myeloid cells. Furthermore, in sarcomas treated with CpG + RT, TCR clonality analysis suggests an increase in clonal T-cell dominance. Collectively, these findings demonstrate that RT + CpG significantly delays tumor growth in a CD8 T cell-dependent manner. These results provide a strong rationale for clinical trials evaluating CpG or other TLR9 agonists with RT in patients with soft tissue sarcoma.
Collapse
Affiliation(s)
- Chang Su
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Collin L. Kent
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Matthew Pierpoint
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | | | - Lixia Luo
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Nerissa T. Wiliams
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Yan Ma
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Brian Peng
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | | | - Ajay Subramanian
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Jonathan E. Himes
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | | | | | | | | | | | - Mari Shinohara
- Department of Immunology, Duke University, Durham, NC, USA
| | - Amy J. Wisdom
- Department of Radiation Oncology, Harvard University, Cambridge, MA, USA
| | - Everett J. Moding
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University, Stanford, CA, USA
| | - Yvonne M. Mowery
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
- MD Anderson Cancer Center, Houston, TX, USA
- Department of Radiation Oncology, University of Pittsburgh, Pittsburgh, PA, USA
| | - David G. Kirsch
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC, USA
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| |
Collapse
|
2
|
Roche KE, Bjork JR, Dasari MR, Grieneisen L, Jansen D, Gould TJ, Gesquiere LR, Barreiro LB, Alberts SC, Blekhman R, Gilbert JA, Tung J, Mukherjee S, Archie EA. Universal gut microbial relationships in the gut microbiome of wild baboons. eLife 2023; 12:e83152. [PMID: 37158607 PMCID: PMC10292843 DOI: 10.7554/elife.83152] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/08/2023] [Indexed: 05/10/2023] Open
Abstract
Ecological relationships between bacteria mediate the services that gut microbiomes provide to their hosts. Knowing the overall direction and strength of these relationships is essential to learn how ecology scales up to affect microbiome assembly, dynamics, and host health. However, whether bacterial relationships are generalizable across hosts or personalized to individual hosts is debated. Here, we apply a robust, multinomial logistic-normal modeling framework to extensive time series data (5534 samples from 56 baboon hosts over 13 years) to infer thousands of correlations in bacterial abundance in individual baboons and test the degree to which bacterial abundance correlations are 'universal'. We also compare these patterns to two human data sets. We find that, most bacterial correlations are weak, negative, and universal across hosts, such that shared correlation patterns dominate over host-specific correlations by almost twofold. Further, taxon pairs that had inconsistent correlation signs (either positive or negative) in different hosts always had weak correlations within hosts. From the host perspective, host pairs with the most similar bacterial correlation patterns also had similar microbiome taxonomic compositions and tended to be genetic relatives. Compared to humans, universality in baboons was similar to that in human infants, and stronger than one data set from human adults. Bacterial families that showed universal correlations in human infants were often universal in baboons. Together, our work contributes new tools for analyzing the universality of bacterial associations across hosts, with implications for microbiome personalization, community assembly, and stability, and for designing microbiome interventions to improve host health.
Collapse
Affiliation(s)
- Kimberly E Roche
- Program in Computational Biology and Bioinformatics, Duke UniversityDurhamUnited States
| | - Johannes R Bjork
- University of Groningen and University Medical Center Groningen, Department of Gastroenterology and HepatologyGroningenNetherlands
- University of Groningen and University Medical Center Groningen, Department of GeneticsGroningenNetherlands
- Department of Biological Sciences, University of Notre DameNotre DameUnited States
| | - Mauna R Dasari
- Department of Biological Sciences, University of Notre DameNotre DameUnited States
| | - Laura Grieneisen
- Department of Biology, University of British Columbia-Okanagan CampusKelownaCanada
| | - David Jansen
- Department of Biological Sciences, University of Notre DameNotre DameUnited States
| | - Trevor J Gould
- Department of Ecology, Evolution, and Behavior, University of MinnesotaMinneapolisUnited States
| | | | - Luis B Barreiro
- Committee on Genetics, Genomics, and Systems Biology, University of ChicagoChicagoUnited States
- Section of Genetic Medicine, Department of Medicine, University of ChicagoChicagoUnited States
- Committee on Immunology, University of ChicagoChicagoUnited States
| | - Susan C Alberts
- Department of Biology, Duke UniversityDurhamUnited States
- Department of Evolutionary Anthropology, Duke UniversityDurhamUnited States
- Duke University Population Research Institute, Duke UniversityDurhamUnited States
| | - Ran Blekhman
- Section of Genetic Medicine, Department of Medicine, University of ChicagoChicagoUnited States
| | - Jack A Gilbert
- Department of Pediatrics and the Scripps Institution of Oceanography, University of California, San DiegoSan DiegoUnited States
| | - Jenny Tung
- Department of Biology, Duke UniversityDurhamUnited States
- Department of Evolutionary Anthropology, Duke UniversityDurhamUnited States
- Duke University Population Research Institute, Duke UniversityDurhamUnited States
- Department of Primate Behavior and Evolution, Max Planck Institute for Evolutionary AnthropologyLeipzigGermany
| | - Sayan Mukherjee
- Program in Computational Biology and Bioinformatics, Duke UniversityDurhamUnited States
- Departments of Statistical Science, Mathematics, Computer Science, and Bioinformatics & Biostatistics, Duke UniversityDurhamUnited States
- Center for Scalable Data Analytics and Artificial Intelligence, University of LeipzigLeipzigGermany
- Max Plank Institute for Mathematics in the Natural SciencesLeipzigGermany
| | - Elizabeth A Archie
- Department of Biological Sciences, University of Notre DameNotre DameUnited States
| |
Collapse
|
3
|
Roche KE, Mukherjee S. The accuracy of absolute differential abundance analysis from relative count data. PLoS Comput Biol 2022; 18:e1010284. [PMID: 35816553 PMCID: PMC9302745 DOI: 10.1371/journal.pcbi.1010284] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/21/2022] [Accepted: 06/07/2022] [Indexed: 11/29/2022] Open
Abstract
Concerns have been raised about the use of relative abundance data derived from next generation sequencing as a proxy for absolute abundances. For example, in the differential abundance setting, compositional effects in relative abundance data may give rise to spurious differences (false positives) when considered from the absolute perspective. In practice however, relative abundances are often transformed by renormalization strategies intended to compensate for these effects and the scope of the practical problem remains unclear. We used simulated data to explore the consistency of differential abundance calling on renormalized relative abundances versus absolute abundances and find that, while overall consistency is high, with a median sensitivity (true positive rates) of 0.91 and specificity (1—false positive rates) of 0.89, consistency can be much lower where there is widespread change in the abundance of features across conditions. We confirm these findings on a large number of real data sets drawn from 16S metabarcoding, expression array, bulk RNA-seq, and single-cell RNA-seq experiments, where data sets with the greatest change between experimental conditions are also those with the highest false positive rates. Finally, we evaluate the predictive utility of summary features of relative abundance data themselves. Estimates of sparsity and the prevalence of feature-level change in relative abundance data give reasonable predictions of discrepancy in differential abundance calling in simulated data and can provide useful bounds for worst-case outcomes in real data. Molecular sequence counting is a near-ubituiqous method for taking “snapshots” of the state of biological systems at the molecular level and is applied to problems as diverse as profiling gene expression and characterizing bacterial community composition. However, concerns exist about the interpretation of these data, given they are relative counts. In particular some feature-level differences between samples may be technical, not biological, stemming from compositional effects. Here, we quantify the accuracy of estimates of sample-sample differences made from relative versus “absolute” molecular count data, using a comprehensive simulation strategy and published experimental data. We find the accuracy of difference estimation is high in at least 50% of simulated and real data sets but that low accuracy outcomes are far from rare. Further, we observe similar numbers of these low accuracy cases when using any of several popular methods for estimating differences in biological count data. Our results support the use of complementary reference measures of absolute abundance (like RNA spike-ins) for normalizing next-generation sequencing data. We briefly validate the use of these reference quantities and of stringent effect size thresholds as strategies for mitigating interpretational problems with relative count data.
Collapse
Affiliation(s)
- Kimberly E. Roche
- Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, United States of America
- * E-mail:
| | - Sayan Mukherjee
- Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, United States of America
- Departments of Statistical Science, Mathematics, Computer Science, Biostatistics & Bioinformatics, Duke University, Durham, North Carolina, United States of America
- Center for Scalable Data Analytics and Artificial Intelligence, Universität Leipzig and the Max Planck Institute for Mathematics in the Natural Sciences, Leipzig, Germany
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
| |
Collapse
|
4
|
Smith LM, Motta FC, Chopra G, Moch JK, Nerem RR, Cummins B, Roche KE, Kelliher CM, Leman AR, Harer J, Gedeon T, Waters NC, Haase SB. An intrinsic oscillator drives the blood stage cycle of the malaria parasite Plasmodium falciparum. Science 2020; 368:754-759. [PMID: 32409472 DOI: 10.1126/science.aba4357] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 02/11/2020] [Accepted: 04/06/2020] [Indexed: 12/14/2022]
Abstract
The blood stage of the infection of the malaria parasite Plasmodium falciparum exhibits a 48-hour developmental cycle that culminates in the synchronous release of parasites from red blood cells, which triggers 48-hour fever cycles in the host. This cycle could be driven extrinsically by host circadian processes or by a parasite-intrinsic oscillator. To distinguish between these hypotheses, we examine the P. falciparum cycle in an in vitro culture system and show that the parasite has molecular signatures associated with circadian and cell cycle oscillators. Each of the four strains examined has a different period, which indicates strain-intrinsic period control. Finally, we demonstrate that parasites have low cell-to-cell variance in cycle period, on par with a circadian oscillator. We conclude that an intrinsic oscillator maintains Plasmodium's rhythmic life cycle.
Collapse
Affiliation(s)
| | - Francis C Motta
- Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, FL, USA
| | - Garima Chopra
- Malaria Biologics Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - J Kathleen Moch
- Malaria Biologics Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Robert R Nerem
- Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA
| | - Bree Cummins
- Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA
| | - Kimberly E Roche
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA
| | | | - Adam R Leman
- Department of Biology, Duke University, Durham, NC, USA
| | - John Harer
- Department of Mathematics, Duke University, Durham, NC, USA
| | - Tomas Gedeon
- Department of Mathematical Sciences, Montana State University, Bozeman, MT, USA
| | - Norman C Waters
- Malaria Biologics Branch, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Steven B Haase
- Department of Biology, Duke University, Durham, NC, USA. .,Department of Medicine, Duke University, Durham, NC, USA
| |
Collapse
|
5
|
Ficklin SP, Dunwoodie LJ, Poehlman WL, Watson C, Roche KE, Feltus FA. Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study. Sci Rep 2017; 7:8617. [PMID: 28819158 PMCID: PMC5561081 DOI: 10.1038/s41598-017-09094-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Accepted: 07/21/2017] [Indexed: 01/10/2023] Open
Abstract
A gene co-expression network (GCN) describes associations between genes and points to genetic coordination of biochemical pathways. However, genetic correlations in a GCN are only detectable if they are present in the sampled conditions. With the increasing quantity of gene expression samples available in public repositories, there is greater potential for discovery of genetic correlations from a variety of biologically interesting conditions. However, even if gene correlations are present, their discovery can be masked by noise. Noise is introduced from natural variation (intrinsic and extrinsic), systematic variation (caused by sample measurement protocols and instruments), and algorithmic and statistical variation created by selection of data processing tools. A variety of published studies, approaches and methods attempt to address each of these contributions of variation to reduce noise. Here we describe an approach using Gaussian Mixture Models (GMMs) to address natural extrinsic (condition-specific) variation during network construction from mixed input conditions. To demonstrate utility, we build and analyze a condition-annotated GCN from a compendium of 2,016 mixed gene expression data sets from five tumor subtypes obtained from The Cancer Genome Atlas. Our results show that GMMs help discover tumor subtype specific gene co-expression patterns (modules) that are significantly enriched for clinical attributes.
Collapse
Affiliation(s)
- Stephen P Ficklin
- Department of Horticulture, Washington State University, Pullman, WA, 99164, USA.
| | - Leland J Dunwoodie
- Department of Genetics & Biochemistry, Clemson University, Clemson, SC, 29631, USA
| | - William L Poehlman
- Department of Genetics & Biochemistry, Clemson University, Clemson, SC, 29631, USA
| | - Christopher Watson
- Molecular Plant Sciences Program, Washington State University, Pullman, WA, 99164, USA
| | - Kimberly E Roche
- Department of Genetics & Biochemistry, Clemson University, Clemson, SC, 29631, USA
| | - F Alex Feltus
- Department of Genetics & Biochemistry, Clemson University, Clemson, SC, 29631, USA.
| |
Collapse
|
6
|
Osborne CG, McTyre RB, Dudek J, Roche KE, Scheuplein R, Silverstein B, Weinberg MS, Salkeld AA. Evidence for the relationship of calcium to blood pressure. Nutr Rev 1996; 54:365-81. [PMID: 9155209 DOI: 10.1111/j.1753-4887.1996.tb03850.x] [Citation(s) in RCA: 35] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Affiliation(s)
- C G Osborne
- Weinberg Group Inc., Washington, DC 20036, USA
| | | | | | | | | | | | | | | |
Collapse
|
7
|
Abstract
Male mice were given a single injection of either adrenocorticotropic hormone (ACTH) or lysine vasopressin immediately after a defeat in an encounter with an aggressive male mouse. The defeated mice were tested for submissiveness at either 24 hours, 48 hours, or 7 days after the initial encounter. Both hormone treatments increased future submissiveness, although the time courses of the effects were different: The effects of ACTH disappeared after 48 hours, whereas those of vasopressin persisted for 7 days. These results suggest that changes in peptide hormone levels following naturally stressful experiences can affect the memory of those experiences, as expressed in future adaptive responses.
Collapse
|
8
|
|