Limits...
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

Muscle tissue microenvironment signaling after traumatic muscle injury.(a) The left side illustrates the RNA-Seq read coverage for the fibronectin (Fn1) gene for the EDA exon during different time points after the injury. The MISO + (percent spliced in) values are on the right and show a shift in the EDA exon for the middle time points, indicating an increased detection of the ED-A splice variant. Detection of the splice variant decreases back to control values at the 672 h time point. (b) Top—Gene expression profile of Myogenin (MyoG), a transcription factor that regulates terminal differentiation of the myogenic program, and Id2, a helix-loop-helix protein that inhibits myogenic factor activity and modulates the terminal myogenic differentiation program. Bottom—Gene expression profile of Myomaker (Tmem8c), a transmembrane protein that fuses adjacent myoblasts, which remained upregulated until 672 h after the injury.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4585378&req=5

f4: Muscle tissue microenvironment signaling after traumatic muscle injury.(a) The left side illustrates the RNA-Seq read coverage for the fibronectin (Fn1) gene for the EDA exon during different time points after the injury. The MISO + (percent spliced in) values are on the right and show a shift in the EDA exon for the middle time points, indicating an increased detection of the ED-A splice variant. Detection of the splice variant decreases back to control values at the 672 h time point. (b) Top—Gene expression profile of Myogenin (MyoG), a transcription factor that regulates terminal differentiation of the myogenic program, and Id2, a helix-loop-helix protein that inhibits myogenic factor activity and modulates the terminal myogenic differentiation program. Bottom—Gene expression profile of Myomaker (Tmem8c), a transmembrane protein that fuses adjacent myoblasts, which remained upregulated until 672 h after the injury.

Mentions: Migrating fibroblasts play an essential role in tissue remodeling after muscle injury, through production of new extracellular matrix (ECM) components and development of a phenotype that contracts the surrounding matrix3139. The increased contractional forces are permitted by altered interactions between integrins and cell binding domains that modulate cell adhesion. These modified interactions are orchestrated through splicing changes to produced fibronectin transcripts such as the ED-A and ED-B exons40. Figure 4a demonstrates detection of the ED-A splice variant of fibronectin41 beginning at 24 h after the injury and shifting back at approximately 672 h. Detection of the ED-A splice variant indicates formation of new ECM and altered niche stiffness42, which in addition to the soluble factors emitted by invading immune cells, has previously been shown to activate satellite cells43.


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)

Muscle tissue microenvironment signaling after traumatic muscle injury.(a) The left side illustrates the RNA-Seq read coverage for the fibronectin (Fn1) gene for the EDA exon during different time points after the injury. The MISO + (percent spliced in) values are on the right and show a shift in the EDA exon for the middle time points, indicating an increased detection of the ED-A splice variant. Detection of the splice variant decreases back to control values at the 672 h time point. (b) Top—Gene expression profile of Myogenin (MyoG), a transcription factor that regulates terminal differentiation of the myogenic program, and Id2, a helix-loop-helix protein that inhibits myogenic factor activity and modulates the terminal myogenic differentiation program. Bottom—Gene expression profile of Myomaker (Tmem8c), a transmembrane protein that fuses adjacent myoblasts, which remained upregulated until 672 h after the injury.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Muscle tissue microenvironment signaling after traumatic muscle injury.(a) The left side illustrates the RNA-Seq read coverage for the fibronectin (Fn1) gene for the EDA exon during different time points after the injury. The MISO + (percent spliced in) values are on the right and show a shift in the EDA exon for the middle time points, indicating an increased detection of the ED-A splice variant. Detection of the splice variant decreases back to control values at the 672 h time point. (b) Top—Gene expression profile of Myogenin (MyoG), a transcription factor that regulates terminal differentiation of the myogenic program, and Id2, a helix-loop-helix protein that inhibits myogenic factor activity and modulates the terminal myogenic differentiation program. Bottom—Gene expression profile of Myomaker (Tmem8c), a transmembrane protein that fuses adjacent myoblasts, which remained upregulated until 672 h after the injury.
Mentions: Migrating fibroblasts play an essential role in tissue remodeling after muscle injury, through production of new extracellular matrix (ECM) components and development of a phenotype that contracts the surrounding matrix3139. The increased contractional forces are permitted by altered interactions between integrins and cell binding domains that modulate cell adhesion. These modified interactions are orchestrated through splicing changes to produced fibronectin transcripts such as the ED-A and ED-B exons40. Figure 4a demonstrates detection of the ED-A splice variant of fibronectin41 beginning at 24 h after the injury and shifting back at approximately 672 h. Detection of the ED-A splice variant indicates formation of new ECM and altered niche stiffness42, which in addition to the soluble factors emitted by invading immune cells, has previously been shown to activate satellite cells43.

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