Discover and read the best of Twitter Threads about #transcriptomic
Most recents (2)
Study from @salkinstitute found that partial reprogramming using the #Yamanaka transcription factors (#Oct4, #Sox2, #Klf4 & #cMyc) for short periods of time:
-restored a youthful #epigenetic signature to #aging cells & extends the life span of a #premature #aging mouse model.
-restored a youthful #epigenetic signature to #aging cells & extends the life span of a #premature #aging mouse model.
-long-term partial reprogramming led to rejuvenating effects in different tissues, such as the #kidney & #skin, and at the #organismal level; duration of the treatment determined the extent of the beneficial effects.
-The rejuvenating effects were associated with a reversion of the #epigenetic clock and metabolic and #transcriptomic changes, including reduced expression of genes involved in the #inflammation, #senescence & #stressresponse pathways.
nature.com/articles/s4358…
nature.com/articles/s4358…
Multi-Omic inTegrative Analysis (MOTA): an application of differential network analysis to #multiomics.
✓3 non-TCGA datasets (HCC vs CIRR)
✓explanations for parameters & rgCCA choice
✓good at known cancer drivers recovery
✓consistent across cohorts
#SundayMultiOmics 1/n
✓3 non-TCGA datasets (HCC vs CIRR)
✓explanations for parameters & rgCCA choice
✓good at known cancer drivers recovery
✓consistent across cohorts
#SundayMultiOmics 1/n
[[ Detailed description & comments follow ]]
Link: doi.org/10.3390/metabo…
Figures © by authors, reused under CC-BY 4.0
creativecommons.org/licenses/by/4.…
Developed @LombardiCancer @gumedcenter (sorry I could not to find authors on Twitter)
Link: doi.org/10.3390/metabo…
Figures © by authors, reused under CC-BY 4.0
creativecommons.org/licenses/by/4.…
Developed @LombardiCancer @gumedcenter (sorry I could not to find authors on Twitter)
["Layman" introduction]: many studies focus on the differences in the abundance of specific biomolecules; while very useful to highlight the obvious #biomarkers, it may not explain complex disease mechanisms & can be very sensitive to cohort differences (e.g. in #metabolomics)
