Biology subjects

Tian, K.

Publications and source records attributed to Tian, K..

17 recordsLinked to original sources

bioRxiv2026

Linoleic Acid-Lyso PG Axis promoting lipid droplet-mitochondria tethering by stabilizing Noncanonically Mitochondrial PPAR β/δ to Ameliorate Microglial Dysfunction in subarachnoid hemorrhage

Background Microglial lipid handling and mitochondrial failure contribute to brain injury after subarachnoid hemorrhage (SAH), but the lipid signals coupling these processes remain unclear. We investigated whether linoleic acid (LA) restores microglial homeostasis through lysophosphatidylglycerol 16:0 (LPG[16:0]) and peroxisome proliferator-activated receptor-{delta} (PPAR{delta}). Methods Cerebrospinal fluid metabolomics included 30 patients with aneurysmal SAH and 10 control participants. Mechanisms were examined in a blood-injection mouse model and hemoglobin-exposed primary mouse microglia using targeted lipidomics, RNA sequencing, mitochondrial and phagocytosis assays, pharmacological perturbation, fractionation, coimmunoprecipitation, thermal shift analysis, and structural modeling. Behavioral outcomes were evaluated by open-field, Y-maze, and Morris water-maze testing. Results; CSF LA was higher in SAH and discriminated the groups within this cohort (area under the curve, 0.9967 [95% CI, 0.9859-1.000]; P<0.001). LA attenuated inflammatory activation and restored phagocytosis, mitochondrial membrane potential, respiration, and ATP production in hemoglobin-exposed microglia. LA restored PLA2G15-associated LPG(16:0), which phenocopied these effects. Transcriptomic and inhibitor analyses identified PPAR{delta} as a downstream effector. LPG(16:0) increased PPAR{delta} stability, and fractionation and protease protection identified a PPAR{delta} pool on the cytosolic face of the outer mitochondrial membrane. PPAR{delta} associated with PLIN2 and CPT1A, promoted lipid droplet-mitochondria apposition, and supported fatty acid oxidation. In mice, LA reduced neuroinflammatory injury and partially improved anxiety-related behavior and spatial memory.

neuroscience↗

bioRxiv2026

Dynamic cortical routing mediates temporal attention

Selecting information from dynamic streams requires mechanisms that prioritize a visual stimulus at a specific moment over preceding and subsequent stimuli at the same location. Whereas selective temporal attention has been found to enhance neural responses to stimuli, its impact on communication between brain regions remains unexplored. Here, we investigated whether prioritizing a stimulus at a specific time is achieved through selective routing of stimulus information across cortical networks using MEG. We developed a dynamic informational connectivity approach to quantify shared stimulus information between each region and the rest of the network. When stimuli compete in time, we found that temporal attention modulated the network at both early and late post-target time windows, routing information along two possible pathways--occipito-fronto-cingulate and occipito-temporal--via both transient bursts of network communication and theta-rhythmic replay. These results provide evidence that under dynamic sensory input, the timing of neural communication determines stimulus selection.

neuroscience↗

bioRxiv2025

Pulmonary mRNA-LNP Vaccines for Rapid and Durable Protection Against Bacterial Infection

Pulmonary bacterial infections remain a major clinical challenge. Although vaccination reduces infection rates and mortality, the vulnerable post-vaccination immunity gap can still result in infection and vaccine failure. In addition, effective vaccines are unavailable for many clinically important bacterial pathogens. Here, we report a pulmonary mRNA-lipid nanoparticle (mRNA-LNP) vaccine incorporating a novel ionizable lipid engineered for localized high-level expression, which elicits both rapid and durable protections against bacterial lung infections, effectively bridging this critical window of vulnerability. Intratracheal delivery of mRNA-LNP rapidly primes lung neutrophils and macrophages into a transcriptionally pre-activated state, enhancing their phagocytic activity and enabling rapid, antigen-independent bacterial clearance during the early post-vaccination period (approximately 1-7 days). Subsequently, vaccination induces potent antigen-specific adaptive responses, conferring sustained protection against both laboratory and clinical drug-resistant Pseudomonas aeruginosa strains. Single-cell transcriptomics and immune profiling reveal coordinated activation of innate and adaptive immune programs. This dual-phase immune response exemplifies a paradigm-shifting vaccine design that integrates innate and adaptive immunity to confer both immediate and long-term protection. Our findings establish a mechanistic basis for rapid antibacterial defense and highlight pulmonary mRNA-LNP vaccination as a promising strategy for combating respiratory infections.

bioengineering↗

bioRxiv2025

Optic nerve regeneration requires the intracellular domain of LIFRa/CD118

Identifying cell-autonomous and non-autonomous factors that govern retinal ganglion cells (RGCs) ability to extend axons is an important step in developing therapies to achieve recovery after optic nerve injury. Here we report that the intracellular domain of the leukemia inhibitory factor receptor (LIFR/CD118) is essential for mature RGCs ability to regenerate injured axons independent of the cognate ligand (LIF) and other therapies. Overexpression of LIFR in adult RGCs induces neurite outgrowth in cultured RGCs and axon regeneration in vivo while strongly amplifying RGCs response to LIF itself and to unrelated growth factors. Conversely, in loss-of-function studies, down-regulation of LIFR eliminates the pro-regenerative effects of Pten deletion and other potent stimuli. LIFR bidirectionally regulates the constitutive activity of the MAP kinase pathway, in contrast to LIF itself, which primarily activates pSTAT3. Expression of a truncated LIFR lacking the extracellular domain is sufficient to promote axon regeneration and selectively increases the survival of particular RGC subtypes, placing the LIFR intracellular domain as an essential, cell-autonomous regulator of optic nerve regeneration.

neuroscience↗

bioRxiv2024

A multi-tissue developmental gene expression atlas towards understanding the biological basis of phenotypes in sheep

Sheep (Ovis aries) represents one of the most important livestock species for animal protein and wool production worldwide. However, little is known about the genetic and biological basis of ovine phenotypes, particularly for those of high economic value and environmental impact. Here, by generating and integrating 1,413 RNA-seq samples from 51 distinct tissues across 14 developmental time points, representing early prenatal, late prenatal, neonate, lamb, juvenile, adult, and elderly stages, we built a high-resolution developmental Gene Expression Atlas (dGEA) in sheep. We observed dynamic patterns of gene expression and regulatory networks across tissues and developmental stages. When harnessing this resource for interpreting genomic associations of 48 monogenetic and 12 complex traits in sheep, we found that genes upregulated at prenatal developmental stages played more important roles in shaping these phenotypes than those upregulated at postnatal stages. For instance, genetic associations of crimp number, mean staple length (MSL), and individual birth weight were significantly enriched in the prenatal rather than postnatal skin and immune tissues. By comprehensively integrating fine-mapping results and the sheep dGEA, we identified several key genes associated with complex traits in sheep, such as SOX9 (associated with MSL), GNRHR (associated with litter size at birth), and PRKDC (associated with live weight). These results provide novel insights into the gene regulatory and developmental architecture underlying ovine phenotypes. The dGEA (https://sheepdgea.njau.edu.cn/) will serve as an invaluable resource for sheep developmental biology, genetics, genomics, and selective breeding.

genetics↗

bioRxiv2024

Domain-specific Schema Reuse Supports Flexible Learning to Learn in Primate Brain

Prior knowledge accelerates subsequent learning of similarly structured problems - a phenomenon termed "learning to learn" - by forming and reusing generalizable neural representations, i.e., the schemas. However, the stability-plasticity dilemma, i.e., how to exploit stable schemas to facilitate learning while remaining flexible towards possible changes, is not well understood. We hypothesize that restricting schemas to specific functional, e.g., decision-making, subspace and making it orthogonal to other subspaces allows the brain to balance stability and plasticity. To test it, we trained three macaques on visuomotor mapping tasks and recorded neural activity in the dorsolateral premotor cortex. By delineating decision and stimulus subspaces, we identified a schema-like manifold within only the decision subspace. The reuse of decision schemas significantly facilitated subsequent learning. In addition, the decision subspace exhibited a trend to be orthogonal to the stimulus subspace, minimizing interference between these two domains. Our results revealed that restricting schemas to specific functional domains can preserve useful knowledge while maintaining orthogonality with other subspaces, allowing for flexible adaptation to new environments, thereby resolving the stability-plasticity dilemma. This finding provides new insights into the mechanisms underlying brains capability to learn both fast and flexibly, which can also inspire more efficient learning algorithms for artificial intelligence systems towards working in open, dynamic environments.

neuroscience↗

bioRxiv2024

Temporal attention recruits fronto-cingulate cortex to amplify stimulus representations

The human brain faces significant constraints in its ability to process every item in a sequence of stimuli. Voluntary temporal attention can selectively prioritize a task-relevant item over its temporal competitors to alleviate these constraints. However, it remains unclear when and where in the brain selective temporal attention modulates the visual representation of a prioritized item. Here, we manipulated temporal attention to successive stimuli in a two-target temporal cueing task, while controlling for temporal expectation with fully predictable stimulus timing. We used MEG and time-resolved decoding to track the spatiotemporal evolution of stimulus representations in human observers. We found that temporal attention enhanced the representation of the first target around 250 milliseconds after target onset, in a contiguous region spanning left frontal cortex and cingulate cortex. The results indicate that voluntary temporal attention recruits cortical regions beyond the ventral stream at an intermediate processing stage to amplify the representation of a target stimulus. This routing of stimulus information to anterior brain regions may provide protection from interference in visual cortex by a subsequent stimulus. Thus, voluntary temporal attention may have distinctive neural mechanisms to support specific demands of the sequential processing of stimuli. Significance statementWhen viewing a rapid sequence of visual input, the brain cannot fully process every item. Humans can attend to an item they know will be important to enhance its processing. However, how the brain selects one moment over others is little understood. We found that attending to visual information at a precise moment in time enhances visual representations around 250 ms after an item appears. Unexpectedly, this enhancement occurred not in the visual cortex, but in the left fronto-cingulate cortex. The involvement of frontal rather than posterior cortical regions in representing visual stimuli has not typically been observed for spatial or feature-based attention, suggesting that temporal attention may have specialized neural mechanisms to handle the distinctive demands of sequential processing.

neuroscience↗

bioRxiv2023

Cell context-specific Synthetic lethality Prediction and Mechanism Analysis

Synthetic lethality (SL) holds significant promise as a targeted cancer therapy by selectively eliminating tumor cells while sparing normal cells. However, the discovery of SL gene pairs has encountered tremendous challenges, including high costs and low efficiency of experimental methods. Current computational approaches only provide limited insights because of overlooking the crucial aspects of cellular context dependency and mechanistic understanding of SL pairs. To overcome these challenges, we have developed SLWise, a deep-learning model capable of predicting SL interactions in diverse cellular backgrounds. We evaluated SLWise using a real world ground truth standard. The evaluation demonstrated that SLWise outperformed benchmark models in SL prediction. Additionally, we proposed a novel analysis scheme called SLAD-CE (Synthetic Lethal Associated Gene Detection and Cell Damage Evaluation) for the identification of abnormal essential genes induced by SL gene pairs and tracking the extent of cell damage. Leveraging the cell-line-specific input feature L1000 and employing Gene Set Enrichment Analysis (GSEA), SLAD-CE provides valuable insights into the underlying mechanisms of SLWise-predicted gene pairs. The combined utilization of SLWise and SLAD-CE offers an approach for predicting and analyzing SL interactions in specific cellular contexts. Our findings highlight the potential of SLWise and SLAD-CE in advancing SL-based therapies by improving prediction accuracy and enhancing mechanistic understanding, ultimately contributing to the development of effective precision treatments for cancer.

cancer biology↗

bioRxiv2023

Sparse Modeling of Genomic Landscape Identifies Pathogenic Processes and Therapeutic Targets in Metastatic Breast Cancer

Breast cancer is a heterogeneous disease and ranks as one of the most lethal and frequently detected disease in the world. It poses significant challenges for precision therapy. To better decipher the patterns of heterogeneous nature in human genome and converge them into common functionals, mutational signatures are introduced to define the types of DNA damage, repair and replicative mechanisms that shape the genomic landscape of each cancer patient. In this study, we developed a deep learning (DL) model, MetaWise 2.0, based on pruning technology that improved model generalization with deep sparsity. We applied it to patient samples from multiple sequencing studies, and identified statistically significant mutational signatures associated with metastatic progression using Shapley additive explanations (SHAP). We also employed gene cumulative contribution abundance analysis to link the mutational signatures with relevant genes, which could unearth the shared molecular mechanisms behind tumorigenesis and metastasis of each patient and lead to novel therapeutic target identification. Our study illustrates that MetaWise 2.0 is an effective DL tool for discovering clinically meaningful mutational signatures in metastatic breast cancer (MBC) and relating them directly to relevant biological functions and gene targets. These findings could facilitate the development of novel therapeutic strategies and improve the clinical outcomes for individual patients.

bioinformatics↗

bioRxiv2022

Dynamics of temporal attention and expectation revealed by continuous tracking of visual cortical responses

We can often anticipate the precise moment when a stimulus will be relevant for our behavioral goals. Voluntary temporal attention, the prioritization of sensory information at task-relevant time points, enhances visual perception. However, the neural mechanisms of voluntary temporal attention have not been isolated from those of temporal expectation, which reflects timing predictability rather than relevance. Here we used time-resolved steady-state visual evoked responses (SSVER) to investigate how temporal attention dynamically modulates visual activity when temporal expectation is controlled. We recorded MEG while observers directed temporal attention to one of two sequential grating targets with predictable timing. Meanwhile, we used a co-localized SSVER probe to continuously track visual cortical modulations both before and after the target stimuli. In the pre-target period, the SSVER gradually ramped up as the targets approached, reflecting temporal expectation. Furthermore, we found a low-frequency modulation of the SSVER, which shifted approximately half a cycle in phase according to which target was attended. In the post-target period, temporal attention to the first target transiently modulated the SSVER shortly after target onset. Thus, temporal attention dynamically modulates visual cortical responses via both periodic pre-target and transient post-target mechanisms to prioritize sensory information at precise moments.

neuroscience↗

bioRxiv2022

St2cell: Reconstruction of in situ single-cell spatial transcriptomics by integrating high-resolution histological image.

Spatially resolved transcriptomics (SRT) has greatly expanded our understanding of the spatial patterns of gene expression in histological tissue sections. However, most currently available platforms could not provide in situ single-cell spatial transcriptomics, limiting their biological applications. Here, to in silico reconstruct SRT at the single-cell resolution, we propose St2cell which combines deep learning-based frameworks with a novel convex quadratic programming (CQP)-based model. St2cell can thoroughly leverage information in high-resolution (HR) histological images, enabling the accurate segmentation of in situ single cells and identification of their transcriptomics. Applying St2cell on various SRT datasets, we demonstrated the reliability of reconstructed transcriptomics. The single-cell resolution provided by our proposed method greatly promoted the detection of elaborate spatial architectures and further facilitated the integration with single-cell RNA-sequencing data. Moreover, in a breast cancer tissue, St2cell identified general spatial structures and co-occurrence patterns of cell types in the tumor microenvironment. St2cell is also computationally efficient and easily accessible, making it a promising tool for SRT studies.

bioinformatics↗

bioRxiv2021

Construction of continuously expandable single-cell atlases through integration of heterogeneous datasets in a generalized cell-embedding space

Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., scRNA-seq, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEXs advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build-upon previously hard-won scientific insights.

bioinformatics↗

bioRxiv2019

Automated Clear Cell Renal Carcinoma Grade Classification with Prognostic Significance

We developed an automated 2-tiered Fuhrmans grading system for clear cell renal cell carcinoma (ccRCC). Whole slide images (WSI) and clinical data were retrieved for 395 The Cancer Genome Atlas (TCGA) ccRCC cases. Pathologist 1 reviewed and selected regions of interests (ROIs). Nuclear segmentation was performed. Quantitative morphological, intensity, and texture features (n=72) were extracted. Features associated with grade were identified by constructing a Lasso model using data from cases with concordant 2-tiered Fuhrmans grades between TCGA and Pathologist 1 (training set n=235; held-out test set n=42). Discordant cases (n=118) were additionally reviewed by Pathologist 2. Cox proportional hazard model evaluated the prognostic efficacy of the predicted grades in an extended test set which was created by combining the test set and discordant cases (n=160). The Lasso model consisted of 26 features and predicted grade with 84.6% sensitivity and 81.3% specificity in the test set. In the extended test set, predicted grade was significantly associated with overall survival after adjusting for age and gender (Hazard Ratio 2.05; 95% CI 1.21-3.47); manual grades were not prognostic. Future work can adapt our computational system to predict WHO/ISUP grades, and validating this system on other ccRCC cohorts.

pathology↗

bioRxiv2018

Long Non-Coding RNA LncKdm2b Regulates Cortical Neuronal Differentiation by Cis-Activating Kdm2b

The mechanisms underlying spatial and temporal control of cortical neurogenesis of the brain are largely elusive. Long non-coding RNAs (lncRNAs) have emerged as essential cell fate regulators. Here we found LncKdm2b (also known as Kancr), a lncRNA divergently transcribed from a bidirectional promoter of Kdm2b, is transiently expressed during early differentiation of cortical projection neurons. Interestingly, Kdm2bs transcription is positively regulated in cis by LncKdm2b, which has intrinsic-activating function and facilitates a permissive chromatin environment at the Kdm2bs promoter by associating with hnRNPAB. Lineage tracing experiments and phenotypic analyses indicated LncKdm2b and Kdm2b are crucial in proper differentiation and migration of cortical projection neurons. Moreover, KDM2B exerts its role relying on its leucine-rich repeats (LRR) but independent of its PRC1-related function. These observations unveiled a lncRNA-dependent machinery in regulating cortical neuronal differentiation.

molecular biology↗

bioRxiv2018

PARylation modulates stress granule assembly, phase separation, and neurotoxicity of ALS-related RNA-binding proteins

Mutations in RNA-binding proteins localized in ribonucleoprotein (RNP) granules, such as hnRNP A1 and TDP-43, promote aberrant protein aggregations, which are pathological hallmarks in neurodegenerative diseases including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Protein posttranslational modifications (PTMs) are known to regulate RNP granules. In this study, we investigate the function of PARylation, an important PTM involved in DNA damage repair and cell death, in RNP-related neurodegeneration. We reveal that PARylation levels are a major regulator of the dynamic assembly-disassembly of RNP granules, and the disease-related RNPs such as hnRNP A1 and TDP-43 can both be PARylated and bind to PARylated proteins. We further identify the PARylation site of hnRNP A1 at K298, which controls the cytoplasmic translocation of hnRNP A1 in response to stress, as well as the PAR-binding motif (PBM) of hnRNP A1, which is required for the delivery and association of hnRNP A1 to stress granules. Moreover, we show that PAR not only dramatically enhances the liquid-liquid phase separation of hnRNP A1, but also promotes the co-phase separation of hnRNP A1 and TDP-43 in vitro and their interaction in vivo. Finally, we establish that both genetic and pharmacological inhibition of PARP mitigates hnRNP A1 and TDP-43-mediated neurotoxicity in cell and Drosophila models of ALS. Together, our findings indicate a novel and crucial role of PARylation in regulating the assembly and the dynamics of RNP granules, and dysregulation of PARylation may contribute to ALS disease pathogenesis.

neuroscience↗

bioRxiv2017

A novel k-mer set memory (KSM) motif representation improves regulatory variant prediction

The representation and discovery of transcription factor (TF) sequence binding specificities is critical for understanding gene regulatory networks and interpreting the impact of disease-associated non-coding genetic variants. We present a novel TF binding motif representation, the K-mer Set Memory (KSM), which consists of a set of aligned k-mers that are over-represented at TF binding sites, and a new method called KMAC for de novo discovery of KSMs. We find that KSMs more accurately predict in vivo binding sites than position weight matrix models (PWMs) and other more complex motif models across a large set of ChIP-seq experiments. KMAC also identifies correct motifs in more experiments than four state-of-the-art motif discovery methods. In addition, KSM derived features outperform both PWM and deep learning model derived sequence features in predicting differential regulatory activities of expression quantitative trait loci (eQTL) alleles. Finally, we have applied KMAC to 1488 ENCODE TF ChIP-seq datasets and created a public resource of KSM and PWM motifs. We expect that the KSM representation and KMAC method will be valuable in characterizing TF binding specificities and in interpreting the effects of non-coding genetic variations.

genomics↗

bioRxiv2017

Predicting Transcriptional Regulatory Activities with Deep Convolutional Networks

Massively parallel reporter assays (MPRAs) are a method to probe the effects of short sequences on transcriptional regulation activity. In a MPRA, short sequences are extracted from suspected regulatory regions, inserted into reporter plasmids, transfected into cell-types of interest, and the transcriptional activity of each reporter is assayed. Recently, Ernst et al. presented MPRA data covering 15750 putative regulatory regions. We trained a multitask convolutional neural network architecture using these sequence expression readouts which predicts as output the expression level outputs across four combinations of cell types and promoters. The model allows for the assigning of importance scores to each base through in silico mutagenesis, and the resulting importance scores correlated well with regions enriched for conservation and transcription factor binding.

genomics↗