Khanna, P. | Search bioRxiv

bioRxiv2023

SOX4-SMARCA4 complex promotes glycolysis-dependent TNBC cell growth through transcriptional regulation of Hexokinase 2

Tumor cells rely on increased glycolytic capacity to promote cell growth and progression. While glycolysis is known to be upregulated in the majority of triple negative (TNBC) or basal-like subtype breast cancers, the mechanism remains unclear. Here, we used integrative genomic analyses to identify a subset of basal-like tumors characterized by increased expression of the oncogenic transcription factor SOX4 and its co-factor the SWI/SNF ATPase SMARCA4. These tumors are defined by unique gene expression programs that correspond with increased tumor proliferation and activation of key metabolic pathways, including glycolysis. Mechanistically, we demonstrate that the SOX4-SMARCA4 complex mediates glycolysis through direct transcriptional regulation of Hexokinase 2 (HK2) and that aberrant HK2 expression and altered glycolytic capacity are required to mediate SOX4-SMARCA4-dependent cell growth. Collectively, we have defined the SOX4-SMARCA4-HK2 signaling axis in basal-like breast tumors and established that this axis promotes metabolic reprogramming which is required to maintain tumor cell growth.

cancer biology↗

bioRxiv2021

The brain uses invariant dynamics to generalize outputs across movements

It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it re-uses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, where dynamics refer to how the instantaneous spatial pattern of population activity changes in time. Here we test whether invariant dynamics of neural populations are actually used to issue the commands that direct movement. Using a brain-machine interface that transformed rhesus macaques motor cortex activity into commands for a neuroprosthetic cursor, we discovered that the same command is issued with different neural activity patterns in different movements. However, these different patterns were predictable, as we found that the transitions between activity patterns are governed by the same dynamics across movements. These invariant dynamics are low-dimensional, and critically, they align with the brain-machine interface, so that they predict the specific component of neural activity that actually issues the next command. We introduce a model of optimal feedback control that shows that invariant dynamics can help transform movement feedback into commands, reducing the input that the neural population needs to control movement. Altogether our results demonstrate that invariant dynamics drive commands to control a variety of movements, and show how feedback can be integrated with invariant dynamics to issue generalizable commands.

neuroscience↗