Biology subjects

Allam, A.

Publications and source records attributed to Allam, A..

7 recordsLinked to original sources

bioRxiv2026

Individualized AI-driven Neuromodulation Selectively Enhances and Reorganizes Human Tongue Sensorimotor Networks.

Precise modulation of brain networks for tongue motor and sensory control (TMSC) is critical for neruorehabilitation. Existing neuromodulation approaches use fixed targets, ignoring individual network architecture. We present an AI-driven, individualized fMRI neuromodulation (iNM) platform adaptively targeting subject-specific TMSC networks in real-time. Using a neurofunctional twin built from each participant's anatomy and function, iNM reinforces targeted activity and suppresses competing states. Unsupervised machine learning and hidden Markov modeling showed iNM increased TMSC-state occupancy from 82% to 97% and suppressed swallow-state interference by 83%. The inferior parietal lobule emerged as a sensorimotor integration hub, with increased magnitude (d' 90.4 right, 84 left) and spatial expansion (134.8% right, 81.8% left). Cortico-subcortical connectivity strengthened 53% to 74% across motor, sensory, and attention networks and persisted into non-iNM blocks. Thus, iNM may offer precision neurorehabilitation for CNS or PNS disorders, such as amyotrophic lateral sclerosis, and treatment-induced cranial neuropathy in head-and-neck-cancer survivors, respectively.

neuroscience↗

bioRxiv2025

Uncovering Cas9 PAM diversity through metagenomic mining and machine learning

Recognition of protospacer adjacent motifs (PAMs) is crucial for target site recognition by CRISPR-Cas systems. In genome editing applications, the requirement for specific PAM sequences at the target locus imposes substantial constraints, driving efforts to search for novel Cas9 orthologs with extended or alternative PAM compatibilities. Here, we present CRISPR-PAMdb, a comprehensive and publicly accessible database compiling Cas9 protein sequences from 3.8 million bacterial and archaeal genomes and PAM profiles from 7.4 million phage and plasmid sequences. Through spacer-protospacer alignment, we inferred consensus PAM preferences for 8,003 unique Cas9 clusters. To extend PAM discovery beyond traditional alignment-based approaches, we developed CICERO, a machine learning model predicting PAM preferences directly from Cas9 protein sequences. Built on the ESM2 protein language model and trained on the CRISPR-PAMdb database, CICERO achieved an average accuracy of 0.68 on test data and 0.75 on experimentally validated Cas9 orthologs. For Cas9 clusters where alignment-based predictions were infeasible, CICERO generated PAM profiles for an additional 50,308 Cas9 proteins, including 17,453 high-confidence predictions with accuracies above 0.86. CRISPR-PAMdb, alongside CICERO models, enables large-scale exploration of PAM diversity across Cas9 proteins, accelerating design of next-generation CRISPR-Cas9 tools for precise genome engineering.

synthetic biology↗

bioRxiv2025

Individualized AI-driven neuromodulation enhances tongue motor and sensory control: preliminary efficacy targeted towards the alleviation of chronic cranial neuropathies

Precise modulation of brain networks responsible for tongue motor and sensory control (TMSC) is critical for restoring functions, such as speech and swallowing in neurodegenerative disease or in treatment-induced chronic cranial neuropathy. We present an individualized, AI-driven fMRI neuromodulation (iNM) platform that adaptively targets subject-specific TMSC networks in real time. To enhance iNM precision and encodability --critical for neurorehabilitation--we mapped each healthy participants individualized TMSC selectivity network, creating a subject-specific TMSC digital twin. iNM increased signal strength, spatial expansion, and consistency across motor, sensory, and attention regions, while it reduced signal variability. The bilateral inferior parietal lobule emerged as key sensorimotor integration hub, as it exhibited exclusive activation under iNM along with highest discriminability, and largest spatial expansion. iNM also significantly strengthened and expanded motor, sensory, and attention-related networks -- medial-middle frontal areas, insula-claustrum, S1, M1, basal ganglia, motor cerebellum, and inferior temporal-- supporting interoceptive and proprioceptive-motor integration. Machine learning and unsupervised hidden Markov modeling revealed that iNM enhanced the decodability and stability of TMSC-neural states, while it suppressed competing swallow-neural state interference. Notably, the iNM effects extended beyond the neuromodulation window, indicating functional persistence--a key requirement for rehabilitation. iNM reconfigured TMSC networks by strengthening cortico-subcortical connectivity and adaptive circuit dynamics. Our findings show iNM as a non-invasive, personalized intervention capable of selectively enhancing sensorimotor control with high spatiotemporal specificity. By demonstrating mechanistic network-precision and functional carryover, iNM offers a promising intervention for individuals with limited treatment options, including head and neck cancer survivors and early-stage neurodegenerative disease patients.

neuroscience↗

bioRxiv2025

GPR161 mechanosensitivity at the primary cilium drives neuronal saltatory migration

The saltatory migration of neurons is essential for brain formation. Whether mechanical stimuli regulate this process is unknown. Here we show that the primary cilium acts as a mechanical sensor through GPR161. Using an ex vivo neuronal migration model and microfluidic assays, we demonstrate that fluid shear stress induces migration via the mechanoreceptor GPR161 at the primary cilium, with its mechanosensitive Helix 8 being essential. We demonstrate that GPR161 activates a recently discovered cAMP/PKA signaling pathway leading to the phosphorylation of NDE1, a dynein complex regulator, and microtubule organization to regulate migration. These findings unveil a dynamic primary cilium-based pathway sensing mechanical stimulus to drive cyclic saltatory neuronal migration during brain development.

neuroscience↗

bioRxiv2023

Predicting prime editing efficiency across diverse edit types and chromatin contexts with machine learning

Prime editing is a powerful genome editing technology, but its efficiency varies depending on the pegRNA design and target locus. Existing computational models for predicting prime editing rates are limited by their focus on specific edit types and by omitting the local chromatin environment. In our study, we developed machine learning models that predict prime editing efficiencies across a wide range of edit types up to 15 bp (PRIDICT2.0) and in different chromatin contexts (ePRIDICT). Both models can be accessed at www.pridict.it.

molecular biology↗

bioRxiv2023

FMRP regulates postnatal neuronal migration via MAP1B

The Fragile X Syndrome (FXS) represents the most prevalent form of inherited intellectual disability and is the first monogenic cause of Autism Spectrum Disorder. FXS results from the absence of the RNA-binding protein FMRP (Fragile X Messenger Ribonucleoprotein). Neuronal migration is an essential step of brain development allowing displacement of neurons from their germinal niches to their final integration site. The precise role of FMRP in neuronal migration remains largely unexplored. Using live imaging of postnatal Rostral Migratory Stream (RMS) neurons in Fmr1-null mice, we observed that the absence of FMRP leads to delayed neuronal migration and altered trajectory, associated with defects of centrosomal movement. RNA-interference-induced knockdown of Fmr1 shows that these migratory defects are cell-autonomous. Notably, the primary FMRP mRNA target implicated in these migratory defects is MAP1B (Microtubule-Associated Protein 1B). Knocking-down MAP1B expression effectively rescued most of the observed migratory defects. Finally, we elucidate the molecular mechanisms at play by demonstrating that the absence of FMRP induces defects in the cage of microtubules surrounding the nucleus of migrating neurons, which is rescued by MAP1B knockdown. Our findings reveal a novel neurodevelopmental role for FMRP in collaboration with MAP1B, jointly orchestrating neuronal migration by influencing the microtubular cytoskeleton.

neuroscience↗

bioRxiv2020

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens

Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable conversion of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have vast potential as genome editing tools for basic research and gene therapy, their application has been hampered by a broad variation in editing efficiencies on different genomic loci. Here we perform an extensive analysis of adenine- and cytosine base editors on thousands of lentivirally integrated genetic sequences and establish BE-DICT, an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy. BE-DICT is a versatile tool that in principle can be trained on any novel base editor variant, facilitating the application of base editing for research and therapy.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genomics↗