Biology subjects

Swartz, A.

Publications and source records attributed to Swartz, A..

3 recordsLinked to original sources

bioRxiv2025

Informational Complexity as a Neural Marker of Cognitive Reserve

In Alzheimers disease (AD), a mismatch between neurological damage and cognitive functioning often is attributed to individual differences in cognitive reserve. Understanding the neural mechanism of cognitive reserve could help assessing the therapeutic effectiveness of interventions in AD. To address this, here, 38 elderly participants performed a sustained attention task during high-density EEG while awake and during drowsiness. Operationally, the degree to which performance was impaired under drowsiness signalled the extent of cognitive reserve, with less impairment indicating a higher level of cognitive reserve. Investigating performance variations during the active management of neural challenges offers a novel approach to studying cognitive reserve, capturing dynamics that mirror everyday cognitive demand. We related cognitive reserve to various measures, including informational complexity using the Lempel-Ziv (LZSUM) algorithm. We found a significant interaction effect between arousal and performance, where LZSUM values increased in high performers when drowsy but decreased in low performers. This effect was most pronounced in the frontal and central areas. Our findings suggest LZSUM to be indicative of a compensatory mechanism and thus show potential for LZSUM as a neural marker in assessing cognitive reserve. However, we found no consistent relationship between performance and structural brain measures, and proxies of cognitive reserve. Critically, our findings present a counterexample to the prevailing view that informational complexity purely reflects conscious level. Further research, such as a study with the same paradigm in patients with mild cognitive impairment (MCI) and AD, may lead to additional insights of whether we are truly measuring cognitive reserve.

neuroscience↗

bioRxiv2023

Differentiable Search of Evolutionary Trees from Leaves

Inferring the most probable evolutionary tree given leaf nodes is an important problem in computational biology that reveals the evolutionary relationships between species. Due to the exponential growth of possible tree topologies, finding the best tree in polynomial time becomes computationally infeasible. In this work, we propose a novel differentiable approach as an alternative to traditional heuristic-based combinatorial tree search methods in phylogeny. The optimization objective of interest in this work is to find the most parsimonious tree (i.e., to minimize the total number of evolutionary changes in the tree). We empirically evaluate our method using randomly generated trees of up to 128 leaves, with each node represented by a 256-length protein sequence. Our method exhibits promising convergence (< 1% error for trees up to 32 leaves, < 8% error up to 128 leaves, given only leaf node information), illustrating its potential in much broader phylogenetic inference problems and possible integration with end-to-end differentiable models. The code to reproduce the experiments in this paper can be found at https://github.ramith.io/diff-evol-tree-search.

bioinformatics↗

bioRxiv2023

Orthogonal CRISPR screens to identify transcriptional and epigenetic regulators of human CD8 T cell function

The clinical response to adoptive T cell therapies is strongly associated with transcriptional and epigenetic state. Thus, technologies to discover regulators of T cell gene networks and their corresponding phenotypes have great potential to improve the efficacy of T cell therapies. We developed pooled CRISPR screening approaches with compact epigenome editors to systematically profile the effects of activation and repression of 120 transcription factors and epigenetic modifiers on human CD8+ T cell state. These screens nominated known and novel regulators of T cell phenotypes with BATF3 emerging as a high confidence gene in both screens. We found that BATF3 overexpression promoted specific features of memory T cells such as increased IL7R expression and glycolytic capacity, while attenuating gene programs associated with cytotoxicity, regulatory T cell function, and T cell exhaustion. In the context of chronic antigen stimulation, BATF3 overexpression countered phenotypic and epigenetic signatures of T cell exhaustion. CAR T cells overexpressing BATF3 significantly outperformed control CAR T cells in both in vitro and in vivo tumor models. Moreover, we found that BATF3 programmed a transcriptional profile that correlated with positive clinical response to adoptive T cell therapy. Finally, we performed CRISPR knockout screens with and without BATF3 overexpression to define co-factors and downstream factors of BATF3, as well as other therapeutic targets. These screens pointed to a model where BATF3 interacts with JUNB and IRF4 to regulate gene expression and illuminated several other novel targets for further investigation.

genomics↗