Biology subjects

Khan, E. A.

Publications and source records attributed to Khan, E. A..

3 recordsLinked to original sources

bioRxiv2026

Astrocytes instructively regulate neuronal translation

Neuronal protein synthesis is essential for synaptic plasticity and long-term memory, yet whether its regulation is shaped by other cell types remains poorly understood. Here, we show that astrocyte-secreted proteins regulate global neuronal translation depending on astrocytic state. Astrocyte-conditioned medium (ACM) increased neuronal translation under basal conditions, an effect enhanced by astrocyte stimulation with the activity-dependent factor BDNF, whereas ACM from neurotoxic reactive astrocytes, a state linked to neuroinflammation and Alzheimers disease, suppressed neuronal translation. Across these conditions, neuronal mTORC1 activity consistently tracked with translational output, whereas the integrated stress response (ISR) acted through distinct, state-specific mechanisms that did not always track with neuronal translation. Furthermore, we identified astrocyte-secreted apolipoprotein E (APOE) and its associated cargo as a negative regulator of neuronal translation that contributed to the decreased translation induced by neurotoxic reactive astrocytes. We also found that astrocyte-secreted signals required neuronal endocytosis to influence translation and drove synaptic remodeling dependent on glutamatergic signaling and neuronal mTORC1 activity. Together, these findings identify astrocytes as active, instructive regulators of neuronal translation and synaptic structure, with implications for understanding how astrocyte dysfunction may disrupt the translational mechanisms underlying impairments in synaptic plasticity and long-term memory in neurodegenerative disease. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/741020v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@74ecborg.highwire.dtl.DTLVardef@1c620a0org.highwire.dtl.DTLVardef@881b8corg.highwire.dtl.DTLVardef@1c8b7e4_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Integrated microdroplet workflow for high-throughput cell-free transcription in double emulsion picoreactors

Precise characterization of regulatory sequence performance is fundamental to synthetic biology and next-generation gene therapies, driving the need for scalable and quantitative screening of genetic libraries. While droplet-based microfluidics offers the ultra-high throughput required to scale these assays, it often depends on complex, custom fluorescence-activated droplet sorting platforms. To address this limitation, we introduce an integrated microfluidic workflow that enables cell-free transcription in water-in-oil-in-water double emulsion picoreactors compatible with commercial flow cytometers. The core innovation is an integrated device that combines emulsion reinjection, electric-field-mediated step-injection of in vitro transcription (IVT) reagents, and downstream double emulsification, thereby reducing manual handling and preserving droplet integrity across multistep workflows. We validate the system by coupling isothermal rolling circle amplification (RCA) of DNA templates with on-chip IVT and Mango III aptamer-based fluorescence readout, demonstrating robust detection, binning, and sorting of transcription-active droplets. This workflow provides an accessible and modular platform for quantitative, high-throughput functional screening of regulatory sequences without the need for specialized optical sorting instrumentation.

synthetic biology↗

bioRxiv2024

High-resolution mapping of Sigma Factor DNA Binding Sequences using Artificial Promoters, RNA Aptamers and Deep Sequencing

The variable sigma ({sigma}) subunit of the bacterial RNA polymerase holoenzyme determines promoter specificity and facilitate open complex formation during transcription initiation. Understanding {sigma}-factor binding sequences is therefore crucial for deciphering bacterial gene regulation. Here, we present a data-driven high-throughput approach that utilizes an extensive library of 1.54 million DNA templates providing artificial promoters and 5' UTR sequences for {sigma}-factor DNA binding motif discovery. This method combines the generation of extensive DNA libraries, in vitro transcription, RNA aptamer selection, and deep DNA and RNA sequencing. It allows direct assessment of promoter activity, identification of transcription start sites, and quantification of promoter strength based on mRNA production levels. We applied this approach to map {sigma}54 DNA binding sequences in Pseudomonas putida. Deep sequencing of the enriched RNA pool revealed 64,966 distinct {sigma}54 binding motifs, significantly expanding the known repertoire. This data-driven approach surpasses traditional methods by directly evaluating promoter function and avoiding selection bias based solely on binding affinity. This comprehensive dataset enhances our understanding of {sigma}-factor binding sequences and their regulatory roles, opening avenues for new research in biology and biotechnology.

biochemistry↗