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During meiotic prophase I, telomeres attach to and move on the nuclear envelope (NE), regulating chromosome movement to promote homologous pairing. Meiosis-specific proteins TERB1, TERB2 and MAJIN play a key role in this process. Here, we report the crystal structures of human TERB1-TERB2 and TERB2-MAJIN subcomplexes. Specific disruption of the TERB1-TERB2 or the TERB2-MAJIN interaction in the mouse Terb2 gene abolishes the telomere attachment to the NE and causes aberrant homologous pairing and disordered synapsis. In addition, depletion of SUN1 also partially disrupts the telomere-NE connection. We propose that the telomere-TRF1-TERB1-TERB2-MAJIN-NE interaction network and the telomere-LINC complex connection are likely two separate but cooperative pathways to stably recruit telomeres to the NE in meiosis prophase I. Our work provides a molecular model of the connection between telomeres and the NE and reveals the correlation between aberrant synapsis and the defective telomere attachment to the NE. The TERB1-TERB2-MAJIN complex mediates the attachment of telomeres to the nuclear envelope. Here the authors present the crystal structures of the human TERB1-TERB2 and TERB2-MAJIN subcomplexes and show that Terb2 mutations, which abolish complex formation cause aberrant homologous pairing and disordered synapsis in mouse.

During meiosis, chromosomes exhibit dramatic changes in morphology and intranuclear positioning. How these changes influence homolog pairing, alignment, and recombination remain elusive. Using Hi-C, we systematically mapped 3D genome architecture throughout all meiotic prophase substages during mouse spermatogenesis. Our data uncover two major chromosome organizational features varying along the chromosome axis during early meiotic prophase, when homolog alignment occurs. First, transcriptionally active and inactive genomic regions form alternating domains consisting of shorter and longer chromatin loops, respectively. Second, the force-transmitting LINC complex promotes the alignment of ends of different chromosomes over a range of up to 20% of chromosome length. Both features correlate with the pattern of homolog interactions and the distribution of recombination events. Collectively, our data reveal the influences of transcription and force on meiotic chromosome structure and suggest chromosome organization may provide an infrastructure for the modulation of meiotic recombination in higher eukaryotes. During meiosis, chromosomes undergo dramatic changes in morphology and intranuclear positioning. Here the authors mapped the 3D genome architecture throughout mouse spermatogenesis by Hi-C of sorted cells to reveal the contributions of transcriptional activity and mechanical force in modulating homolog alignment and recombination.

Chromosomes pair and synapse with their homologous partners to segregate correctly at the first meiotic division. Association of telomeres with the LINC (Linker of Nucleoskeleton and Cytoskeleton) complex composed of SUN1 and KASH5 enables telomere-led chromosome movements and telomere bouquet formation, facilitating precise pairwise alignment of homologs. Here, we identify a direct interaction between SUN1 and Speedy A (SPDYA) and determine the crystal structure of human SUN1-SPDYA-CDK2 ternary complex. Analysis of meiosis prophase I process in SPDYA-binding-deficient SUN1 mutant mice reveals that the SUN1-SPDYA interaction is required for the telomere-LINC complex connection and the assembly of a ring-shaped telomere supramolecular architecture at the nuclear envelope, which is critical for efficient homologous pairing and synapsis. Overall, our results provide structural insights into meiotic telomere structure that is essential for meiotic prophase I progression. Telomeres attach to the nuclear envelope to facilitate homolog pairing during meiosis prophase I. Here, the authors show that SUN1 and SPDYA interact, and demonstrate that this interaction is important for telomere structure and function, and essential to mice gametogenesis.

Deep learning-based impact load identification technology for the next generation of large aircraft structures has garnered significant attention and has become one of the focal points in aircraft structural health monitoring. However, this technology relies on a large number of training samples and exhibits poor scalability. One of the current challenges in system-level multi-structure monitoring is how to construct deep learning models with a small number or even zero impact training samples, and improve the models’ ability to migrate between different structures. To address this challenge, a novel method for impact load identification using only a small number of samples, based on a supervised scene adaptive model, is proposed. The performance of the model is validated on real aircraft structures. For large and complex structures, the model can be applied to other similar structural areas or different structural areas by using samples from the baseline area for training. Then, a very small number of calibration samples from the migrated area can be used for calibration. The results demonstrate that the proposed model, calibrated with just a single sample, achieves 97.22% accuracy in impact location identification and 99.44% accuracy in energy identification under similar regional structural conditions. Under different structural region conditions, the location identification accuracy of the proposed model is 87.65%, while the energy identification accuracy remains at 98.85%. The position identification accuracy of the model is 91.98% under different impact energy level conditions, and the identification accuracy remains at 87.04% even under varying impact energy levels and structural region conditions.

Intraflagellar transport (IFT) trains, the polymers composed of two multi-subunit complexes, IFT-A and IFT-B, carry out bidirectional intracellular transport in cilia, vital for cilia biogenesis and signaling. IFT-A plays crucial roles in the ciliary import of membrane proteins and the retrograde cargo trafficking. However, the molecular architecture of IFT-A and the assembly mechanism of the IFT-A into the IFT trains in vivo remains elusive. Here, we report the cryo-electron microscopic structures of the IFT-A complex from protozoa Tetrahymena thermophila . We find that IFT-A complexes present two distinct, elongated and folded states. Remarkably, comparison with the in situ cryo-electron tomography structure of the anterograde IFT train unveils a series of adjustments of the flexible arms in apo IFT-A when incorporated into the anterograde train. Our results provide an atomic-resolution model for the IFT-A complex and valuable insights into the assembly mechanism of anterograde IFT trains. IFT-A plays crucial roles in bidirectional ciliary transport, vital for cilia biogenesis and signaling. Here, Ma et al. report the IFT-A structure in two distinct states and unveil the assembly mechanism of IFT-A into the anterograde train.

Knee joint function deterioration significantly impacts quality of life. This study developed estimation models for ten knee indicators using data from in-shoe motion sensors to assess knee movement during everyday activities. Sixty-six healthy young participants were involved, and multivariate linear regression was employed to construct the models. The results showed that eight out of ten models achieved a “fair” to “good” agreement based on intra-class correlation coefficients (ICCs), with three knee joint angle indicators reaching the “fair” agreement. One temporal indicator model displayed a “good” agreement, while another had a “fair” agreement. For the angular jerk cost indicators, three out of four attained a “fair” or “good” agreement. The model accuracy was generally acceptable, with the mean absolute error ranging from 0.54 to 0.75 times the standard deviation of the true values and errors less than 1% from the true mean values. The significant predictors included the sole-to-ground angles, particularly the foot posture angles in the sagittal and frontal planes. These findings support the feasibility of estimating knee function solely from foot motion data, offering potential for daily life monitoring and rehabilitation applications. However, discrepancies in the two models were influenced by the variance in the baseline knee flexion and sensor placement. Future work will test these models on older and osteoarthritis-affected individuals to evaluate their broader applicability, with prospects for user-tailored rehabilitation applications. This study is a step towards simplified, accessible knee health monitoring through wearable technology.

EDA is a tumor necrosis factor (TNF) family member, which functions together with its cognate receptor EDAR during ectodermal organ development. Mutations of EDA have long been known to cause X‐linked hypohidrotic dysplasia in humans characterized by primary defects in teeth, hair and sweat glands. However, the structural information of EDA interaction with EDAR is lacking and the pathogenic mechanism of EDA variants is poorly understood. Here, we report the crystal structure of EDA C-terminal TNF homology domain bound to the N-terminal cysteine-rich domains of EDAR. Together with biochemical, cellular and mouse genetic studies, we show that different EDA mutations lead to varying degrees of ectodermal developmental defects in mice, which is consistent with the clinical observations on human patients. Our work extends the understanding of the EDA signaling mechanism, and provides important insights into the molecular pathogenesis of disease-causing EDA variants. EDA variants are associated with X-linked hypohidrotic dysplasia. Here, the authors report the crystal structure of the human EDA-EDAR complex, reveal the important role of this complex in ectodermal development and uncover the structural mechanism of disease-related mutations in EDA.

Frailty poses a threat to the daily lives of healthy older adults, highlighting the urgent need for technologies that can monitor and prevent its progression. Our objective is to demonstrate a method for providing long-term daily frailty monitoring using an in-shoe motion sensor (IMS). We undertook two steps to achieve this goal. Firstly, we used our previously established SPM-LOSO-LASSO (SPM: statistical parametric mapping; LOSO: leave-one-subject-out; LASSO: least absolute shrinkage and selection operator) algorithm to construct a lightweight and interpretable hand grip strength (HGS) estimation model for an IMS. This algorithm automatically identified novel and significant gait predictors from foot motion data and selected optimal features to construct the model. We also tested the robustness and effectiveness of the model by recruiting other groups of subjects. Secondly, we designed an analog frailty risk score that combined the performance of the HGS and gait speed with the aid of the distribution of HGS and gait speed of the older Asian population. We then compared the effectiveness of our designed score with the clinical expert-rated score. We discovered new gait predictors for HGS estimation via IMSs and successfully constructed a model with an “excellent” intraclass correlation coefficient and high precision. Moreover, we tested the model on separately recruited subjects, which confirmed the robustness of our model for other older individuals. The designed frailty risk score also had a large effect size correlation with clinical expert-rated scores. In conclusion, IMS technology shows promise for long-term daily frailty monitoring, which can help prevent or manage frailty for older adults.

To expand the potential use of in-shoe motion sensors (IMSs) in daily healthcare or activity monitoring applications for healthy subjects, we propose a real-time temporal estimation method for gait parameters concerning bilateral lower limbs (GPBLLs) that uses a single IMS and is based on a gait event detection approach. To validate the established methods, data from 26 participants recorded by an IMS and a reference 3D motion analysis system were compared. The agreement between the proposed method and the reference system was evaluated by the intraclass correlation coefficient (ICC). The results showed that, by averaging over five continuous effective strides, all time parameters achieved precisions of no more than 30 ms and agreement at the “excellent” level, and the symmetry indexes of the stride time and stance phase time achieved precisions of 1.0% and 3.0%, respectively, and agreement at the “good” level. These results suggest our method is effective and shows promise for wide use in many daily healthcare or activity monitoring applications for healthy subjects.

Telomeres are nucleoprotein complexes that play essential roles in protecting chromosome ends. Mammalian telomeres consist of repetitive DNA sequences bound by the shelterin complex. In this complex, the POT1-TPP1 heterodimer binds to single-stranded telomeric DNAs, while TRF1 and TRF2-RAP1 interact with double-stranded telomeric DNAs. TIN2, the linchpin of this complex, simultaneously interacts with TRF1, TRF2, and TPP1 to mediate the stable assembly of the shelterin complex. However, the molecular mechanism by which TIN2 interacts with these proteins to orchestrate telomere protection remains poorly understood. Here, we report the crystal structure of the N-terminal domain of TIN2 in complex with TIN2-binding motifs from TPP1 and TRF2, revealing how TIN2 interacts cooperatively with TPP1 and TRF2. Unexpectedly, TIN2 contains a telomeric repeat factor homology （TRFH）-Iike domain that functions as a protein-protein interaction platform. plays an important role in maintaining the stable shelterin Structure-based mutagenesis analyses suggest that TIN2 complex required for proper telomere end protection.