Biology subjects

Tran, O.

Publications and source records attributed to Tran, O..

5 recordsLinked to original sources

bioRxiv2026

Olfactory Deficits and Structural Anomalies of the Peripheral Olfactory System in 22q11.2 Deletion Syndrome

Background: Approximately 25% of individuals with 22q11.2 deletion syndrome (22q11DS) develop a psychotic disorder, and the syndrome shares key neurodevelopmental features with psychosis in the general population. The olfactory system develops early in utero, providing a window into gestational disruptions relevant to psychosis risk. We hypothesized that 22q11DS would show impairments in olfactory function and structural anomalies of the olfactory system relative to psychosis spectrum (PS) youth without a known genetic syndrome and typically developing controls (TD). Methods: Participants included individuals with 22q11DS (n=101), PS (n=56), and TD (n=52). Sinonasal symptoms, odor identification, and discrimination (Sniffin' Sticks) behaviorally. Structural 3T MRI measured olfactory bulb volume, sulcus depth, and sulcus length. Sample sizes vary per analysis. Results: Relative to TD, 22q11DS showed elevated sinonasal burden (p<.001), lower odor identification (p<.001), and lower odor discrimination (p<.001). PS showed elevated sinonasal burden (p<.001) and marginally lower odor identification (p=.06), but no discrimination deficit. Olfactory bulb volume was significantly smaller in 22q11DS versus TD (p=.007, r=.39); PS showed a non-significant trend (p=.07). Olfactory sulcus depth (p=.52) and length (p=.82) did not differ across groups. Conclusions: 22q11DS is associated with elevated sinonasal burden, robust olfactory identification and discrimination deficits exceeding those of PS and reduced olfactory bulb volume. The co-occurrence of functional deficits and reduced bulb volume without sulcal anomalies may distinguish the 22q11DS olfactory phenotype from that of PS. These hypothesis-generating findings are consistent with partially dissociable neurodevelopmental mechanisms and support the utility of 22q11DS as a genetic model for psychosis risk.

neuroscience↗

bioRxiv2024

Novel iGluSnFR3 variants with improved kinetics and dynamic range for tracking high frequency firing at glutamatergic synapses

The genetically-encoded fluorescent glutamate sensor, iGluSnFR3, characterised by a high dynamic range and rapid on-kinetics, is an attractive sensor for glutamate imaging in the central nervous system. However, kinetic variants with ultrafast or slow off-kinetics are needed to broaden the spectrum of applications from monitoring rapid synaptic glutamate transients to mesoscale imaging of brain activity. Here we report binding-site variant S72T (iGlu3Fast) optimised for reporting fast glutamate release at individual sites with a fluorescence dynamic range of 57 and a decay t1/2 of 2 ms in solution, [~]5-fold faster than iGluSnFR3. In contrast, variants D25A and D25R (termed iGlu3Slow1 and iGlu3Slow2, respectively) presented with slow off-kinetics (decay t1/2 43 ms and 30 ms, respectively, at 20{degrees}C), while retaining high dynamic ranges (48 and 65, respectively). These values were reduced when expressed in HEK293T cells, glutamate dissociation from iGlu3Fast slowed to a decay t1/2 of 6.9 ms, while decay t1/2-s of 115 and 234 ms were measured for iGlu3Slow1 and iGlu3Slow2, respectively. The slow decay rates in cells were in accordance with reduced Kd-s in the range of 200 M for iGlu3Fast and 6.4 and 8.4 M for iGlu3Slow1 and iGlu3Slow2, respectively. 2-photon imaging in organotypic hippocampal slices reported spontaneous activity with high sensitivity by both iGlu3Fast and iGlu3Slow2. Strikingly, different kinds of glutamate transient were detected by the fast- and slow-decay sensors indicating that the rapid kinetics and increased fluorescence dynamic range make iGlu3Fast an excellent candidate for imaging high-frequency glutamate release at synapses while iGlu3Slow2 has the potential for mesoscale imaging of brain activity to record global events with high sensitivity.

neuroscience↗

bioRxiv2024

Leveraging Machine Learning and AlphaFold2 Steering to Discover State-Specific Inhibitors Across the Kinome

Protein kinases are structurally dynamic proteins that control downstream signaling cascades by phosphorylating their substrates. Protein kinases regulate their function by adopting several conformational states in their active site determined by the movements of several motifs such as the C-Helix, DFG residues and the activation loop. Each conformational state represents a distinct physicochemical environment that accepts or precludes ligand binding. However, most of the kinome have not been crystalized across these possible conformational states. It has been shown that shallow Multiple Sequence Alignments (MSA) can enable AlphaFold2 (AF2) to model kinases in alternative conformations. However, it is unclear if these models can be leveraged for structure-based drug discovery. Additionally, there are several machine learning tools to predict protein-ligand interactions based on ligand chemotype and binding pocket properties, but these models cannot be used to identify ligands with clear state specificity. Here, we first present an approach called AlphaFold2 Steering (AF2-Steering), a systematic methodology to direct AF2 to sample kinases in the active and inactive conformations. We use our approach to model the protein kinome in precise conformational states. We demonstrate the utility of these AF2-steered kinase models by employing them in a prospective virtual screening study that integrates machine learning with docking to find state specific inhibitors for well-studied and dark kinases that lack structures in the active conformational state. We then experimentally validate the hits, an essential step often overlooked, and later experimentally confirm the conformation-specificity of the ligands identified for FLT3, a protein kinase that currently lacks an active state crystal structure. Against a strict binding criterion of at least 1M Kd, our modelled structures achieved an overall hit rate of 53%. We also confirm the conformation-specificity of 4/7 FLT3 ligands, thus demonstrating the value of MSA-steered AF2 modelled kinase structures combined with machine learning and docking to guide conformation-specific kinase drug discovery.

bioinformatics↗

bioRxiv2024

Optically Mapped Black Genomes: Distinct Structures and 22q11.2 Deletion Syndrome Mechanisms

The genomic architecture of 22q11.2 Deletion Syndrome (22q11.2DS) has focused on analysis of white genomes. However, Black individuals appear to have a lower prevalence of 22q11.2DS compared to whites. To improve the understanding of different populations in relation to 22q11.2DS, optical mapping data from 106 genomes across various Black and white genomes were used to determine the organization of 22q11.2 genomic structures. This revealed extensive variability between the groups regarding copy number and orientation changes of the elements comprising the 22q11.2 low copy repeats (LCR22s). Several novel CNVs and whole haplotype configurations, private and of different prevalence to each group were detected. The diversity of CNVs within Black genomes compared to white genomes was especially striking. To determine the impact of this variability, Black families with de novo 22q11.2DS probands were compared to white families. The highly variable configurations of Black and white haplotypes led to several unique non-allelic homologous recombination (NAHR) scenarios with recombinations at different loci. In particular, Black families had unique recombinations yet to be observed. Thus, the unique and highly variable haplotype configurations of LCR22s in Black individuals may play a role in their decreased incidence of 22q11.2DS.

genomics↗

bioRxiv2018

Clinical data specification and coding for cross-analyses with omics data in autoimmune disease trials

ObjectivesAutoimmune and inflammatory diseases (AIDs) form a continuum of autoimmune and inflammatory diseases, yet AIDs nosology is based on syndromic classification. The TRANSIMMUNOM trial (NCT02466217) was designed to re-evaluate AIDs nosology through clinic-biological and multi-omics investigations of patients with one of 19 selected AIDs. To allow cross-analyses of clinic-biological data together with omics data, we needed to integrate clinical data in a harmonized database.\n\nMaterials and MethodsWe assembled a clinical expert consortium (CEC) to select relevant clinic-biological features to be collected for all patients and a cohort management team comprising biologists, clinicians and computer scientists to design an electronic case report form (eCRF). The eCRF design and implementation has been done on OpenClinica, an open-source CFR-part 11 compliant electronic data capture system.\n\nResultsThe CEC selected 865 clinical and biological parameters. The CMT selected coded the items using CDISC standards into 5835 coded values organized in 28 structured eCRFs. Examples of such coding are check boxes for clinical investigation, numerical values with units, disease scores as a result of an automated calculations, and coding of possible treatment formulas, doses and dosage regimens per disease.\n\nDiscussion21 CRFs were designed using OpenClinica v3.14 capturing the 5835 coded values per patients. Technical adjustment have been implemented to allow data entry and extraction of this amount of data, rarely achieved in classical eCRFs designs.\n\nConclusionsA multidisciplinary endeavour offers complete and harmonized CRFs for AID clinical investigations that are used in TRANSIMMUNOM and will benefit translational research team.

clinical trials↗