bioRxiv Science⌕ Search

bioRxiv · 10.64898/2026.06.04.730114

Recombinant GDF11 Improves Neurological Recovery in Models of Hemorrhagic Stroke and Traumatic Brain Injury

Abstract

BackgroundIntracerebral hemorrhage (ICH) and traumatic brain injury (TBI) are leading causes of long-term neurological disability and mortality worldwide, with no approved therapies that promote functional recovery. Growth differentiation factor 11 (GDF11), a circulating TGF{beta}-family protein, has shown regenerative and neurorestorative potential in models of ischemic stroke. MethodsWe evaluated recombinant GDF11 (rGDF11) in mouse models of ICH and TBI. ICH was induced by intrastriatal collagenase injection, and neurological recovery was assessed using Neuroseverity Score (NSS), Rotarod (RR), and CatWalk (CW) analyses up to 28 days post-injury. Histological assessments of vascularization, neuronal density, and microglial/macrophage density were performed 28 days after ICH. For TBI, a closed-head injury model using a pneumatic impactor was employed, and NSS and RR assessments were conducted through 28 days post-injury. ResultsrGDF11 treatment significantly improved neurobehavioral performance following ICH, including NSS, RR, and CW parameters (forelimb base of support and average speed). Histological analyses revealed enhanced vascular area and neuronal density, with reduced microglial/macrophage density in rGDF11-treated mice. Following TBI, rGDF11 accelerated functional recovery, improving RR latency by day 6 and NSS by day 28 post-injury. ConclusionrGDF11 promotes structural and functional recovery after both hemorrhagic and traumatic brain injury in mice. These findings, together with prior evidence in ischemic stroke, support rGDF11 as a promising neurorestorative biologic with broad therapeutic potential for diverse forms of brain injury. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC="FIGDIR/small/730114v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@8c624forg.highwire.dtl.DTLVardef@8a6107org.highwire.dtl.DTLVardef@e83ce6org.highwire.dtl.DTLVardef@f65f14_HPS_FORMAT_FIGEXP M_FIG C_FIG

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wang, H., Cohen, O. S., Ben Driss, L., Cantillana, V., Wang, Y., Sinha, M., Deshpande, A., Daman, T., Faw, T. D., Laskowitz, D. T., Sandrasagra, A., Lee, R. T.. 2026-06-08. Recombinant GDF11 Improves Neurological Recovery in Models of Hemorrhagic Stroke and Traumatic Brain Injury. https://doi.org/10.64898/2026.06.04.730114

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Same Movements, Different Skills: How Context Shapes Learning in Humans and Neural Networks

What determines whether experiences are integrated into shared representations or split? Motor skill learning offers a revealing test: when two tasks require an identical sequence of movements, both intuition and theory suggest that practice in one should generalize to the other. Yet we find that changing context can make identical movements different skills. In simple recurrent networks trained on the same sequence across contexts, splitting was greatest when strong contextual inputs coincided with a high initial weight scale; neither alone sufficed. Greater representational separation coincided with separate context-specific learning curves. Humans practicing the same finger sequence while alternating between two performance-irrelevant contexts likewise exhibited context-specific curves, rather than the single continuous trajectory observed when practicing in a single context. State-space modeling favored split learning states in humans and very weakly coupled states in the most context-dependent networks. These findings have broader implications for understanding how learning generalizes across contexts.

neuroscience↗

Postpartum resource scarcity reshapes maternal care, offspring prioritization, and the steroid-sensitive medial preoptic area

Parental care requires balancing offspring needs with environmental constraints, yet how resource scarcity alters the organization of maternal responses remains poorly understood. Here, we examined maternal behavior, offspring prioritization, and steroid receptor-associated extracellular matrix organization in postpartum rats exposed to limited bedding and nesting (LBN) or control conditions with ample nesting materials. LBN increased pup-directed and reduced self-directed behaviors but also increased behavioral entropy, a measure of behavioral fragmentation, indicating less predictable caregiving sequences. LBN dams preferentially retrieved male pups first, whereas control dams preferentially retrieved females first. LBN also increased the proportion of pup-investigation time directed toward males compared with controls. In offspring on postnatal day 10, corticosterone was elevated in female but not male LBN pups, a pattern that paralleled the maternal male bias. To identify associated neurobiological changes, we assessed steroid receptors and extracellular matrix organization in the medial preoptic area (mPOA), a key region for maternal behavior. LBN increased numbers of estrogen receptor (ER)- and androgen receptor (AR)-immunoreactive cells, reduced perineuronal net (PNN) abundance, and decreased receptor-positive cell enwrapment. Because PNNs constrain structural plasticity, these findings suggest that the postpartum environment alters the organization of steroid receptor-associated extracellular matrix, potentially modulating experience-dependent plasticity in neurosteroid-sensitive circuits. In sum, LBN increases the levels of maternal engagement while reshaping its temporal organization and allocation, accompanied by selective remodeling of steroid-sensitive preoptic architecture. Therefore, maternal adaptation to adversity may be better understood as changes in caregiving allocation strategies rather than a simple gain or loss of maternal care.

neuroscience↗

The Computational Grammar of Human Behaviour in a Biological Environment

Human behaviour unfolds as an extended stream of spontaneous actions shaped by environmental demands and the agent's goals. The organisation of these actions, situated between setting abstract goals and concrete movement execution, remains poorly characterised. Here, we introduce DynaClade, an unsupervised temporal action segmentation algorithm for human full-body motion capture data, and apply it to freely moving participants navigating ecologically realistic risky foraging environments in virtual reality. DynaClade identifies discrete actions whose kinematic characteristics and spatial distributions correspond to environmental affordances, supporting the validity of the approach. Among these actions, we discovered a hitherto undescribed movement pattern defined by increased body sway without locomotion. Action sequences were highly non-random and showed clear history dependence beyond first-order Markov structure, most of which was explained by a second-order prediction. This higher-order structure was well captured in a hidden Markov model, in which some of the observed actions were associated with more than one latent state. Each of these latent states corresponded to the same action in a distinct action context. These findings suggest that spontaneous human actions are predominantly selected based on the current action and context, rather than following pre-planned or habitually assembled sequences.

neuroscience↗