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In vivo Monitoring of Transcriptional Dynamics After Lower-Limb Muscle Injury Enables Quantitative Classification of Healing.

Aguilar CA, Shcherbina A, Ricke DO, Pop R, Carrigan CT, Gifford CA, Urso ML, Kottke MA, Meissner A - Sci Rep (2015)

Bottom Line: Comprehensive dissection of the genome-wide datasets revealed the injured site to be a dynamic, heterogeneous environment composed of multiple cell types and thousands of genes undergoing significant expression changes in highly regulated networks.Four independent approaches were used to determine the set of genes, isoforms, and genetic pathways most characteristic of different time points post-injury and two novel approaches were developed to classify injured tissues at different time points.These results highlight the possibility to quantitatively track healing progression in situ via transcript profiling using high- throughput sequencing.

View Article: PubMed Central - PubMed

Affiliation: Massachusetts Institute of Technology - Lincoln Laboratory, Lexington, MA 02127, USA.

ABSTRACT
Traumatic lower-limb musculoskeletal injuries are pervasive amongst athletes and the military and typically an individual returns to activity prior to fully healing, increasing a predisposition for additional injuries and chronic pain. Monitoring healing progression after a musculoskeletal injury typically involves different types of imaging but these approaches suffer from several disadvantages. Isolating and profiling transcripts from the injured site would abrogate these shortcomings and provide enumerative insights into the regenerative potential of an individual's muscle after injury. In this study, a traumatic injury was administered to a mouse model and healing progression was examined from 3 hours to 1 month using high-throughput RNA-Sequencing (RNA-Seq). Comprehensive dissection of the genome-wide datasets revealed the injured site to be a dynamic, heterogeneous environment composed of multiple cell types and thousands of genes undergoing significant expression changes in highly regulated networks. Four independent approaches were used to determine the set of genes, isoforms, and genetic pathways most characteristic of different time points post-injury and two novel approaches were developed to classify injured tissues at different time points. These results highlight the possibility to quantitatively track healing progression in situ via transcript profiling using high- throughput sequencing.

No MeSH data available.


Related in: MedlinePlus

Pathways for each time point with the highest PCA loadings.The length of each bar denotes the fold change of the injured sample over the control at the time point. The color of each bar denotes the p-value for change in pathway activation level. Data derived from 12 test samples.
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f8: Pathways for each time point with the highest PCA loadings.The length of each bar denotes the fold change of the injured sample over the control at the time point. The color of each bar denotes the p-value for change in pathway activation level. Data derived from 12 test samples.

Mentions: The Time Point-Specific and Time Point-Weighted signatures method at the pathway level were able to classify 10 of the 12 test samples correctly (Supp. Figure 10b), with an uninjured control dataset misclassified as an injured 168 h dataset, and an injured 168 h dataset misclassified as an uninjured control dataset (Supp. Figure 10b). These were the same samples misclassified by performing the analysis at the gene level. The weighted loadings of the pathways in component space were used to identify a set of pathways that contribute the most to variation across the time points (Fig. 8 and Supplementary Figure 11). A number of these are associated with inflammation, the immune response and cell death for the early time period (IL-6 and Interferon- Gamma signaling, monocyte activation, triggering of coagulation and complement, apoptosis and hypoxia). In the middle phase, elements that regulate the immune system are still active (Nod-Like Receptors, Type 1 interferons, IL-12 signaling), while fibrinolysis and ECM remodeling (cytoskeletal protein cleavage, cell junction organization), cellular differentiation (Wnt signaling and syndecan-4 pathway) in addition to numerous metabolic pathways (HDL- mediated lipid transport pathways, threonine metabolism pathway) become significantly over- expressed. In the late phase, angiogenesis and endothelin pathways are activated in addition to neural regeneration pathways (NCAM signaling) as well as pathways associated with cellular adhesion and myoblast fusion, ECM remodeling and metabolism (integrin-cell surface interactions, GAG metabolism, chondroitin sulfate – dermatan sulfate metabolism). Many of the identified time point specific pathways matched the pathways discovered during the GSEA and DAVID analysis on the training RNA-seq data highlighted above.


In vivo Monitoring of Transcriptional Dynamics After Lower-Limb Muscle Injury Enables Quantitative Classification of Healing.

Aguilar CA, Shcherbina A, Ricke DO, Pop R, Carrigan CT, Gifford CA, Urso ML, Kottke MA, Meissner A - Sci Rep (2015)

Pathways for each time point with the highest PCA loadings.The length of each bar denotes the fold change of the injured sample over the control at the time point. The color of each bar denotes the p-value for change in pathway activation level. Data derived from 12 test samples.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585378&req=5

f8: Pathways for each time point with the highest PCA loadings.The length of each bar denotes the fold change of the injured sample over the control at the time point. The color of each bar denotes the p-value for change in pathway activation level. Data derived from 12 test samples.
Mentions: The Time Point-Specific and Time Point-Weighted signatures method at the pathway level were able to classify 10 of the 12 test samples correctly (Supp. Figure 10b), with an uninjured control dataset misclassified as an injured 168 h dataset, and an injured 168 h dataset misclassified as an uninjured control dataset (Supp. Figure 10b). These were the same samples misclassified by performing the analysis at the gene level. The weighted loadings of the pathways in component space were used to identify a set of pathways that contribute the most to variation across the time points (Fig. 8 and Supplementary Figure 11). A number of these are associated with inflammation, the immune response and cell death for the early time period (IL-6 and Interferon- Gamma signaling, monocyte activation, triggering of coagulation and complement, apoptosis and hypoxia). In the middle phase, elements that regulate the immune system are still active (Nod-Like Receptors, Type 1 interferons, IL-12 signaling), while fibrinolysis and ECM remodeling (cytoskeletal protein cleavage, cell junction organization), cellular differentiation (Wnt signaling and syndecan-4 pathway) in addition to numerous metabolic pathways (HDL- mediated lipid transport pathways, threonine metabolism pathway) become significantly over- expressed. In the late phase, angiogenesis and endothelin pathways are activated in addition to neural regeneration pathways (NCAM signaling) as well as pathways associated with cellular adhesion and myoblast fusion, ECM remodeling and metabolism (integrin-cell surface interactions, GAG metabolism, chondroitin sulfate – dermatan sulfate metabolism). Many of the identified time point specific pathways matched the pathways discovered during the GSEA and DAVID analysis on the training RNA-seq data highlighted above.

Bottom Line: Comprehensive dissection of the genome-wide datasets revealed the injured site to be a dynamic, heterogeneous environment composed of multiple cell types and thousands of genes undergoing significant expression changes in highly regulated networks.Four independent approaches were used to determine the set of genes, isoforms, and genetic pathways most characteristic of different time points post-injury and two novel approaches were developed to classify injured tissues at different time points.These results highlight the possibility to quantitatively track healing progression in situ via transcript profiling using high- throughput sequencing.

View Article: PubMed Central - PubMed

Affiliation: Massachusetts Institute of Technology - Lincoln Laboratory, Lexington, MA 02127, USA.

ABSTRACT
Traumatic lower-limb musculoskeletal injuries are pervasive amongst athletes and the military and typically an individual returns to activity prior to fully healing, increasing a predisposition for additional injuries and chronic pain. Monitoring healing progression after a musculoskeletal injury typically involves different types of imaging but these approaches suffer from several disadvantages. Isolating and profiling transcripts from the injured site would abrogate these shortcomings and provide enumerative insights into the regenerative potential of an individual's muscle after injury. In this study, a traumatic injury was administered to a mouse model and healing progression was examined from 3 hours to 1 month using high-throughput RNA-Sequencing (RNA-Seq). Comprehensive dissection of the genome-wide datasets revealed the injured site to be a dynamic, heterogeneous environment composed of multiple cell types and thousands of genes undergoing significant expression changes in highly regulated networks. Four independent approaches were used to determine the set of genes, isoforms, and genetic pathways most characteristic of different time points post-injury and two novel approaches were developed to classify injured tissues at different time points. These results highlight the possibility to quantitatively track healing progression in situ via transcript profiling using high- throughput sequencing.

No MeSH data available.


Related in: MedlinePlus