Biology subjects

Luan, T.

Publications and source records attributed to Luan, T..

5 recordsLinked to original sources

bioRxiv2025

Axon Trafficking Counteracts Aberrant Protein Aggregation in Neurons

Directed axon trafficking of mRNA via ribonucleoprotein complexes (RNPs) is essential for the proper function and survival of neurons. However, the mechanisms governing RNP transport in axons remain poorly understood. Here, we identify Annexin A7 (ANXA7) as a critical adaptor facilitating the retrograde transport of T-cell intracellular antigen 1 (TIA1)-containing RNPs by linking them to the cytoplasmic dynein. Persistent axonal Ca{superscript 2} elevation disrupts ANXA7s linker role, causing the detachment of TIA1 granules from dynein, consequently impairing transport and triggering pathological TIA1 aggregation within axons. Similarly, ANXA7 knockdown decouples TIA1 granules from dynein, severely obstructing trafficking and causing pathological aggregation of TIA1 in axons, which culminates in axonopathy and neurodegeneration both in vitro and in vivo. Conversely, ANXA7 overexpression enhances trafficking and counteracts aberrant aggregation of TIA1-containing RNPs in axons. Our findings elucidate a novel mechanism underlying RNP axonal transport, highlighting its significance in the biology and pathology of central neurons.

neuroscience↗

bioRxiv2023

Database size positively correlates with the loss of species-level taxonomic resolution for the 16S rRNA and other prokaryotic marker genes

For decades, the 16S rRNA gene has been used to taxonomically classify prokaryotic species and to taxonomically profile microbial communities. The 16S rRNA gene has been criticized for being too conserved to differentiate between distinct species. We argue that the inability to differentiate between species is not a unique feature of the 16S rRNA gene. Rather, we observe the gradual loss of species-level resolution for other marker genes as the number of gene sequences increases in reference databases. We demonstrate this effect through the analysis of three commonly used databases of nearly-universal prokaryotic marker genes: the SILVA 16S rRNA gene database, the Genome Taxonomy Database (GTDB), and a set of 40 taxonomically-informative single-copy genes. Our results reflect a more fundamental property of the taxonomies themselves and have broad implications for bioinformatic analyses beyond taxonomic classification. Effective solutions for fine-level taxonomic classification require a more precise, and operationally-relevant, definition of the taxonomic labels being sought, and the use of combinations of genomic markers in the classification process. ImportanceThe use of reference databases for assigning taxonomic labels to genomic and metagenomic sequences is a fundamental bioinformatic task in the characterization of microbial communities. The increasing accessibility of high throughput sequencing has led to a rapid increase in the size and number of sequences in databases. This has been beneficial for improving our understanding of the global microbial genetic diversity. However, there is evidence that as the microbial diversity is more densely sampled, increasingly longer genomic segments are needed to differentiate between distinct species. The scientific community needs to be aware of this issue and needs to develop methods that better account for it when assigning taxonomic labels to metagenomic sequences from microbial communities.

bioinformatics↗

bioRxiv2023

Cardiac disease diagnosis based on GAN in case of missing data

In daily life, two common algorithms are used for collecting medical disease data: data integration of medical institutions and questionnaires. However, these statistical methods require collecting data from the entire research area, which consumes a significant amount of manpower and material resources. Additionally, data integration is difficult and poses privacy protection challenges, resulting in a large number of missing data in the dataset. The presence of incomplete data significantly reduces the quality of the published data, hindering the timely analysis of data and the generation of reliable knowledge by epidemiologists, public health authorities, and researchers. Consequently, this affects the downstream tasks that rely on this data. To address the issue of discrete missing data in cardiac disease, this paper proposes the AGAN (Attribute Generative Adversarial Nets) architecture for missing data filling, based on generative adversarial networks. This algorithm takes advantage of the strong learning ability of generative adversarial networks. Given the ambiguous meaning of filling data in other network structures, the attribute matrix is designed to directly convert it into the corresponding data type, making the actual meaning of the filling data more evident. Furthermore, the distribution deviation between the generated data and the real data is integrated into the loss function of the generative adversarial networks, improving their training stability and ensuring consistency between the generated data and the real data distribution. This approach establishes the missing data filling mechanism based on the generative adversarial networks, which ensures the rationality of the data distribution while filling the missing data samples. The experimental results demonstrate that compared to other filling algorithms, the data matrix filled by the proposed algorithm in this paper has more evident practical significance, fewer errors, and higher accuracy in downstream classification prediction.

physiology↗

bioRxiv2023

Actomyosin-II Proactively Shields Axons of the Central Nervous System from Mild Mechanical Stress

SummaryPan et al found that actomyosin-II-driven radial contractility underpins the resilience of central axons to mild mechanical stress by suppressing the propagation and firing of injurious Ca2+ waves. Boosting actomyosin-II activity alleviates axon degeneration in mice with traumatic brain injury. Traumatic brain injury (TBI) remains a significant and unmet health challenge. However, our understanding of how neurons, particularly their fragile axons, withstand the abrupt mechanical impacts within the central nervous system remains largely unknown. Using a microfluidic device applying discrete levels of transverse forces to axons, we identified the stress levels that most axons could resist and explored their instant responses at nanoscale resolution. Mild stress induces rapid and reversible axon beading, driven by actomyosin-II-dependent radial contraction, which restricts the spreading and bursting of stress-induced Ca2+ waves. More severe stress causes irreversible focal swelling and Ca2+ overload, ultimately leading to focal axonal swelling and degeneration. Up-regulating actomyosin-II activity prevented the progression of initial injury in vivo, protecting commissural axons from degeneration in a mice TBI model. Our study established a scalable axon injury model and uncovered the critical roles of actomyosin-II in shielding neurons against detrimental mechanical stress.

neuroscience↗

bioRxiv2022

Spatiotemporal orchestration of multicellular transcriptional programs and communications in spinal cord injury

While spinal cord injury (SCI) involves a complex cascade of cellular and pathological changes that last for months to years, the most dramatic and comprehensive molecular rewiring and multicellular re-organization occur in the first few days, which determine the overall progression and prognosis of SCI, yet remain poorly understood. Here, we resolved the spatiotemporal architecture of multicellular gene expression in a mouse model of acute SCI, and revealed the coordinated gene co-expression networks, the upstream regulatory programs, and in situ cell-cell interactions that underlay the anatomic disorganization as well as the immune and inflammatory responses conferring the secondary injury. The spatial transcriptomic analysis highlights that the genes and cell types in the white matter (WM) play a more active and predominant role in the early stage of SCI. In particular, we identified a distinct population of WM-originated, Igfbp2-expressing reactive astrocytes, which migrated to the grey matter and expressed multiple axon/synapse-supporting molecules that may foster neuron survival and spinal cord recovery in the acute phase. Together, our dataset and analyses not only showcase the spatially-defined molecular features endowing the cell (sub)types with new biological significance but also provide a molecular atlas for disentangling the spatiotemporal organization of the mammalian SCI and advancing the injury management.

neuroscience↗