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Biology subjects

Salem, S.

Publications and source records attributed to Salem, S..

2 recordsLinked to original sources

Statins and genetic inhibition of the mevalonate pathway activate an ATF3-STMN2 regenerative program

Loss of neuronal regenerative capacity is a common feature of neurodegenerative disease and axonal injury, yet the transcriptional programs governing this state remain poorly defined. Stathmin-2 (STMN2), a tubulin-binding protein essential for axon maintenance and repair, is profoundly depleted following loss of nuclear TDP-43 in neurodegenerative disease. Here, we identify statins as potent inducers of STMN2 expression. Pharmacological and genetic suppression of the mevalonate pathway, and subsequent prevention of protein geranylgeranylation, restored STMN2 levels in TDP-43 deficient cells and promoted neurite growth. STMN2 induction was abrogated when using a statin analogue unable to interact with HMG-CoA reductase, and through co-administration of mevalonate or geranylgeranyl diphosphate substrates. RNA-seq revealed that statins induce a coordinated pro-regenerative transcriptional response, including activation of the AP-1 transcription factor complex gene, ATF3. Loss of ATF3 attenuated STMN2 induction in vitro, and diminished injury-induced Stmn2 upregulation in spinal motor neurons in vivo. These results demonstrate statins as modulators of ATF3 and STMN2 expression and highlight their therapeutic potential in neurodegenerative disease.

neuroscience↗

FASTR: Reimagining FASTQ via Compact Image-inspired Representation

MotivationHigh-throughput sequencing (HTS) enables population-scale genomics but generates massive datasets, creating bottlenecks in storage, transfer, and analysis. FASTQ, the standard format for over two decades, stores one byte per base and one byte per quality score, leading to inefficient I/O, high storage costs, and redundancy. Existing compression tools can mitigate some issues, but often introduce costly decompression or complex dependency issues. ResultsWe introduce FASTR, a lossless, computation-native successor to FASTQ that encodes each nucleotide together with its base quality score into a single 8-bit value. FASTR reduces file size by at least 2x while remaining fully reversible and directly usable for downstream analyses. Applying general-purpose compression tools on FASTR consistently yields higher compression ratios, 2.47, 3.64, and 4.8x faster compression, and 2.34, 1.96, 1.75x faster decompression than on FASTQ across Illumina, HiFi, and ONT reads. FASTR is machine-learning-ready, allowing reads to be consumed directly as numerical vectors or image-like representations. We provide a highly parallel software ecosystem for FASTQ-FASTR conversion and show that FASTR integrates with existing tools, such as minimap2, with minimal interface changes and no performance overhead. By eliminating decompression costs and reducing data movement, FASTR lays the foundation for scalable genomics analyses and real-time sequencing workflows. Availability and Implementationhttps://github.com/ALSER-Lab/FASTR

bioinformatics↗