Biology subjects

Emami, A.

Publications and source records attributed to Emami, A..

4 recordsLinked to original sources

bioRxiv2026

Small-molecule modulators of HIPK4 activity and proteostasis

Homeodomain-interacting protein kinase 4 (HIPK4) is a dual-specificity kinase that is predominantly expressed in differentiating spermatids, required for sperm development, and a promising target for nonhormonal male contraception. Genetic and functional studies have established an essential role for HIPK4 in spermiogenesis, where it acts at least in part through regulation of the F-actin-scaffolded acroplaxome during spermatid head shaping. The direct molecular targets of HIPK4 and their downstream effectors remain poorly defined, and small-molecule probes would be versatile tools for further investigating HIPK4 functions. Synthetic HIPK4 ligands could also be valuable leads for the development of nonhormonal male contraceptives. Here, we report the discovery of a cyanoquinoline-based series of HIPK4 inhibitors with nanomolar potency. Our lead compounds are selective for HIPK4, both within the HIPK family and across the broader kinome, establishing this scaffold as a useful starting point for probe and lead development. Unexpectedly, we found that a subset of these cyanoquinolines also perturbs HIPK4 proteostasis in a cell type-specific manner. In spermatids, these compounds induce the formation of detergent-insoluble HIPK4 aggregates and promote interactions between this kinase and the autophagy receptor Tax1-binding protein 1 (TAX1BP1). Together, our findings establish cyanoquinoline ligands as a new chemotype for probing HIPK4 biology and advancing male contraceptive discovery.

biochemistry↗

bioRxiv2025

Closed-loop Optogenetic Control in a Microplate Reader

Optogenetics integrates living cells and electronics into powerful cell-silicon systems, but prototyping their dynamics remains challenging. Current tools either require robotic liquid transfers into flow cytometers or rely on custom sensors with narrow dynamic range that limit controller performance. Additionally, current successful optogenetic feedback controllers only operate in chemostats or microfluidic devices that enforce constant growth, because models for growth-aware controller design in batch culture are lacking. Here we present LEMOS, a low-cost LED-embedded microplate that runs inside a commercial microplate reader. Coupled to a growth-aware multiscale model of gene expression for controller tuning, this platform enables rapid design-build-test-learn cycles for cell-silicon systems. We demonstrate closed loop setpoint tracking of gene expression in batch cultures within a standard microplate reader and show how growth dynamics complicates controller selection and tuning. Together, this platform reduces setup overhead and speed up iteration, enabling accurate real-time optogenetic feedback control.

synthetic biology↗

bioRxiv2024

A Field-Deployable Arsenic Sensor Integrating Bacillus Megaterium with CMOS Technology

Bacteria innately monitor their environment by dynamically regulating gene expression to respond to fluctuating conditions. Through synthetic biology, we can harness this natural capability to design cell-based sensors. Bacillus megaterium, a soil bacterium, stands out due to its remarkable heavy metal tolerance and sporulation ability, making it an ideal candidate for heavy metal detection with low transportation costs. However, challenges persist: the synthetic biology toolkit for this strain is underdeveloped and conventional whole-cell sensors necessitate specialized laboratory equipment to read the output. In our study, we genetically modified B. megaterium for arsenic detection, establishing a detection threshold below the EPA recommendation of 10 ppb for drinking water in both vegetative cell form and spore form. Additionally, we integrated both engineered B. megaterium living cells and spores with CMOS chip for field-deployable arsenic detection. We show that the limit of detection of our integrated sensor is applicable in soil and air arsenic contamination testing. As a proof of concept, this work paves the way for deploying our sensor in resource-limited settings, ensuring real-time arsenic detection in challenging environments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/604150v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1940e83org.highwire.dtl.DTLVardef@144da0aorg.highwire.dtl.DTLVardef@146e906org.highwire.dtl.DTLVardef@6cb8c1_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2019

Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

We present a new deep multi-state Dynamic Recurrent Neural Network (DRNN) architecture for Brain Machine Interface (BMI) applications. Our DRNN is used to predict Cartesian representation of a computer cursor movement kinematics from open-loop neural data recorded from the posterior parietal cortex (PPC) of a human subject in a BMI system. We design the algorithm to achieve a reasonable trade-off between performance and robustness, and we constrain memory usage in favor of future hardware implementation. We feed the predictions of the network back to the input to improve prediction performance and robustness. We apply a scheduled sampling approach to the model in order to solve a statistical distribution mismatch between the ground truth and predictions. Additionally, we configure a small DRNN to operate with a short history of input, reducing the required buffering of input data and number of memory accesses. This configuration lowers the expected power consumption in a neural network accelerator. Operating on wavelet-based neural features, we show that the average performance of DRNN surpasses other state-of-the-art methods in the literature on both single- and multi-day data recorded over 43 days. Results show that multi-state DRNN has the potential to model the nonlinear relationships between the neural data and kinematics for robust BMIs.

neuroscience↗