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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

Temporal evolution of transcriptional coregulated networks organized by function after traumatic injury.Each network diagram is composed of statistically significant functional enrichments, where Gene Ontology (GO) terms are clustered by functional category such that all terms with a common ancestor term are the same color. The size of each circle corresponds to the corrected P-value of the associated GO term, and edges in the graph represent interactions between associated GO terms.
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f5: Temporal evolution of transcriptional coregulated networks organized by function after traumatic injury.Each network diagram is composed of statistically significant functional enrichments, where Gene Ontology (GO) terms are clustered by functional category such that all terms with a common ancestor term are the same color. The size of each circle corresponds to the corrected P-value of the associated GO term, and edges in the graph represent interactions between associated GO terms.

Mentions: Summation of the different transcriptional networks for all of the time points shows the injury site is a complex environment with multiple cell types executing a wide variety of functions. Figure 5 illustrates the temporal transcriptome dynamics organized into three time periods, whereby co-regulated networks are categorized by Gene Ontology (GO) terms. The resulting network diagram captures the evolution of different transcriptional groups such as the immune network and cell-death program in the early time period, cytokines and growth and development in the middle and late periods, both of which were described above. The diagram also highlights combinatorial regulation of the injured site and healing progression. For example, in the middle time period, cytokines, immune cell genes and elements of the ECM are observed to interact with genes involved with growth and development. As illustrated above, these collective interactions drove satellite cell proliferation through the Complement and Notch signaling pathways followed by differentiation and active Wnt signaling, all of which have previously been shown to influence satellite cell activation and differentiation5822303132364344. Consequently, the collective interaction of many transcriptional programs such as inflammation, cytokine signaling, immunity, ECM remodeling, metabolism, and myogenic differentiation converge to influence the dynamics of satellite cells and muscle repair and regeneration (Supp. Fig. 7). The observed transcriptional patterns suggest the possibility that their detection can be utilized as bioinformatics classifiers1617 to track healing progression after injury.


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)

Temporal evolution of transcriptional coregulated networks organized by function after traumatic injury.Each network diagram is composed of statistically significant functional enrichments, where Gene Ontology (GO) terms are clustered by functional category such that all terms with a common ancestor term are the same color. The size of each circle corresponds to the corrected P-value of the associated GO term, and edges in the graph represent interactions between associated GO terms.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f5: Temporal evolution of transcriptional coregulated networks organized by function after traumatic injury.Each network diagram is composed of statistically significant functional enrichments, where Gene Ontology (GO) terms are clustered by functional category such that all terms with a common ancestor term are the same color. The size of each circle corresponds to the corrected P-value of the associated GO term, and edges in the graph represent interactions between associated GO terms.
Mentions: Summation of the different transcriptional networks for all of the time points shows the injury site is a complex environment with multiple cell types executing a wide variety of functions. Figure 5 illustrates the temporal transcriptome dynamics organized into three time periods, whereby co-regulated networks are categorized by Gene Ontology (GO) terms. The resulting network diagram captures the evolution of different transcriptional groups such as the immune network and cell-death program in the early time period, cytokines and growth and development in the middle and late periods, both of which were described above. The diagram also highlights combinatorial regulation of the injured site and healing progression. For example, in the middle time period, cytokines, immune cell genes and elements of the ECM are observed to interact with genes involved with growth and development. As illustrated above, these collective interactions drove satellite cell proliferation through the Complement and Notch signaling pathways followed by differentiation and active Wnt signaling, all of which have previously been shown to influence satellite cell activation and differentiation5822303132364344. Consequently, the collective interaction of many transcriptional programs such as inflammation, cytokine signaling, immunity, ECM remodeling, metabolism, and myogenic differentiation converge to influence the dynamics of satellite cells and muscle repair and regeneration (Supp. Fig. 7). The observed transcriptional patterns suggest the possibility that their detection can be utilized as bioinformatics classifiers1617 to track healing progression after injury.

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