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Shen, T.

Publications and source records attributed to Shen, T..

At least 37 records · Page 2Linked to original sources

Risking your Tail: Modeling Individual Differences in Risk-sensitive Exploration using Conditional Value at Risk and Bayes Adaptive Markov Decision Processes

Novelty is a double-edged sword for agents and animals alike: they might benefit from untapped resources or face unexpected costs or dangers such as predation. The conventional exploration/exploitation tradeoff is thus coloured by risk-sensitivity. A wealth of experiments has shown how animals solve this dilemma, for example using intermittent approach. However, there are large individual differences in the nature of approach, and modeling has yet to elucidate how this might be based on animals differing prior expectations about reward and threat, and differing degrees of risk aversion. To capture these factors, we built a Bayes adaptive Markov decision process model with three key components: an adaptive hazard function capturing potential predation, an intrinsic reward function providing the urge to explore, and a conditional value at risk (CVaR) objective, which is a contemporary measure of trait risk-sensitivity. We fit this model to a coarse-grain abstraction of the behaviour of 26 animals who freely explored a novel object in an open-field arena (Akiti et al. Neuron 110, 2022). We show that the model captures both quantitative (frequency, duration of exploratory bouts) and qualitative (with distinguished, cautious, tail-behind approach) features of behavior, including the substantial idiosyncrasies that were observed. Some animals begin with cautious exploration, and quickly transition to confident approach to maximize exploration for reward; we classify them as potentially more risk neutral, and enjoying a flexible hazard prior. By contrast, other animals only ever approach in a cautious manner and display a form of self-censoring; they are characterized by potential risk aversion and high and inflexible hazard priors. Explaining risk-sensitive exploration using factorized parameters of reinforcement learning models could aid in the understanding, diagnosis, and treatment of psychiatric abnormalities such as anxiety disorders.

animal behavior and cognition↗

Node Features of Chromosome Structure Network and Their Connections to Genome Annotation

The 3D conformations of chromosomes can encode biological significance, and its implication is being increasingly appreciated recently. Certain chromosome structural features, such as A/B compartmentalization, are frequently extracted from pairwise contact information (physical association between different regions of the genome) and compared with linear annotations of the genome, such as histone modifications and lamina association. Here, we investigate how additional properties of chromosome structure can be deduced using the abstract graph representation of the contact heatmap, and how network properties can have a better connection with some of these biological annotations. We constructed chromosome structure networks (CSNs) from bulk Hi-C data and calculated a set of site-resolved (node-based) network properties of these CSNs. We found these network properties are useful for characterizing chromosome structure features. We examined the ability of network properties in differentiating several scenarios, such as haploid vs diploid cells, partially inverted nuclei vs conventional architecture, and structural changes during cell development. We also examined the connection between network properties and a series of other linear annotations, such as histone modifications and chromatin states including poised promoter and enhancer labels. We found that semi-local network properties are more capable of characterizing genome annotations than diffusive or ultra-local node features. For example, local square clustering coefficient can be a strong classifier of lamina-associated domains (LADs), whereas a path-based network property, closeness centrality, does not vary concordantly with LAD status. We demonstrated that network properties can be useful for discerning large-scale chromosome structures that emerge in different biological situations. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/573476v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@45df0forg.highwire.dtl.DTLVardef@13776daorg.highwire.dtl.DTLVardef@5c1519org.highwire.dtl.DTLVardef@16d447f_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

Simple and Highly Specific Targeting of Resident Microglia with Adeno-Associated Virus

Microglia, as the immune cells of the central nervous system (CNS), play dynamic roles in both health and diseased conditions. The ability to genetically target microglia using viruses is crucial for understanding their functions and advancing microglia-based treatments. We here show that resident microglia can be simply and specifically targeted using adeno-associated virus (AAV) vectors containing a 466-bp DNA fragment from the human IBA1 (hIBA1) promoter. This targeting approach is applicable to both resting and reactive microglia. When combining the short hIBA1 promoter with the target sequence of miR124, up to 95% of transduced cells are identified as microglia. Such a simple and highly specific microglia-targeting strategy may be further optimized for research and therapeutics. Significance StatementBrain microglia play critical roles in human health and diseases. Genetic manipulation of these cells will offer numerous therapeutic opportunities. However, there is a lack of relevant strategies to target these cells with high specificity since they are traditionally considered to be refractory to virus transduction. Through in vivo screening of many promoters, this study identified a short promoter from the human IBA1 gene. When incorporated into lentivirus or adeno-associated virus vectors, this promoter proves effective in driving gene expression with high specificity for brain microglia. Such a simple strategy will facilitate specific approaches for microglia-based research.

neuroscience↗

Evidence for peripheral and central actions of codeine to dysregulate deglutition in the anesthetized cat

Systemic administration of opioids has been associated with aspiration and swallow dysfunction in humans. We speculated that systemic administration of codeine would induce dysfunctional swallowing and that this effect would have a peripheral component. Experiments were conducted in spontaneously breathing, anesthetized cats. The animals were tracheotomized and electromyogram electrodes were placed in upper airway and chest wall respiratory muscles for recording swallow related motor activity. The animals were allocated into three groups: vagal intact (VI), cervical vagotomy (CVx), and supra-nodose ganglion vagotomy (SNGx). A dose response to intravenous codeine was performed in each animal. Swallowing was elicited by injection of 3 ml of water into the oropharynx. The number of swallows after vehicle was significantly higher in the VI group than in SNGx. Codeine had no significant effect on the number of swallows induced by water in any of the groups. However, the magnitudes of water swallow-related EMGs of the thyropharyngeus muscle were significantly increased in the VI and CVx groups by 2-4 fold in a dose-related manner. In the CVx group, the geniohyoid muscle EMG during water swallows was significantly increased. There was a significant codeine dose-related increase in spontaneous swallowing in each group. The spontaneous swallow number at the 10 mg/kg dose of codeine was significantly larger in the CVx than that in the SNGx groups. During water-evoked swallows, intravenous codeine increased upper airway motor drive in a dose-related manner, consistent with dysregulation. The appearance of spontaneous swallowing in response to codeine in all groups supports a central action of this drug on the swallow pattern generator and also is consistent with dysregulation. At the highest dose of codeine, the reduced spontaneous swallow number in the SNGx group relative to CVx is consistent with a peripheral excitatory action of codeine either on pharyngeal/laryngeal receptors or in the nodose ganglion itself.

physiology↗

Deep Reinforcement Learning Enables Better Bias Control in Benchmark for Virtual Screening

Virtual screening (VS) has been incorporated into the paradigm of modern drug discovery. This field is now undergoing a new wave of revolution driven by artificial intelligence and more specifically, machine learning (ML). In terms of those out-of-the- box datasets for model training or benchmarking, their data volume and applicability domain are limited. They are suffering from the biases constantly reported in the ML application. To address these issues, we present a novel benchmark named MUBDsyn. The utilization of synthetic decoys (i.e., presumed inactives) is the main feature of MUBDsyn, where deep reinforcement learning was leveraged for bias control during decoy generation. Then, we carried out extensive validations on this new benchmark. First, we confirmed that MUBDsyn was superior to the classical benchmarks in control of domain bias, artificial enrichment bias and analogue bias. Moreover, we found that the assessment of ML models based on MUBDsyn was less biased as revealed by the analysis of asymmetric validation embedding bias. In addition, MUBDsyn showed better setting of benchmarking challenge for deep learning models compared with NRLiSt- BDB. Overall, we have proven that MUBDsyn is the close-to-ideal benchmark for VS. The computational tool is publicly available for the easy extension of MUBDsyn.

bioinformatics↗

AutoMolDesigner for Antibiotic Discovery: An AI-based Open-source Software for Automated Design of Small-molecule Antibiotics

Discovery of small-molecule antibiotics with novel chemotypes serves as one of the essential strategies to address antibiotic resistance. Although a considerable number of computational tools committed to molecular design have been reported, there is a deficit in the holistic and efficient tool specifically developed for small-molecule antibiotic discovery. To address this issue, we report AutoMolDesigner, a computational modeling software dedicated to small-molecule antibiotic design. It is a generalized framework comprising two functional modules, i.e., generative deep learning-enabled molecular generation and automated machine learning based-antibacterial activity/property prediction, wherein individually trained models and curated datasets are out-of-the-box for whole cell-based antibiotic screening and design. It is open-source thus allows for the incorporation of new features for flexible use. Unlike most software programs based on Linux and command lines, this application equipped with Qt-based graphical user interface can be run on personal computers with multiple operating systems, making it much easier to use for experimental scientists. The software and related materials are freely available at GitHub (https://github.com/taoshen99/AutoMolDesigner) and Zenodo (https://zenodo.org/record/8366085).

bioinformatics↗

Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language Model

In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Anti-body Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% non-redundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold, and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials.

bioinformatics↗

A near-complete genome sequence of einkorn wheat provides insight into the evolution of wheat A subgenomes

Einkorn wheat (Triticum monococcum) is one of the oldest cereal crops to be domesticated by human beings, playing essential role in early agriculture development. Today, it is considered an important genomic resource for modern wheat improvement, especially for resistance against pests and diseases. However, the exploration and utilization of useful genes from T. monococcum is limited due to the lack of a reference genome and annotation for this species. Here, we report a near-complete genome assembly for T. monococcum with a total length of 5.11 Gb with a contig N50 of 131.2Mb and scaffold N50 of 728.66Mb, representing a genome assembly of highest quality for any wheat genome reported. Phylogenomic analysis confirmed T. monococcum is closely-related to T. urartu, the progenitor of wheat A subgenomes. A 4AL/5AL terminal translocation is present in the diploid species T. urartu and T. monococcum, taking place before wheat polyploidization. T. monococcum has significantly expanded and unique gene families involved in DNA damage repair and heat stress tolerance, reflecting its adaptive evolution to cope with historical harsh climate in its natural habitat, South East Turkey. The genome sequence confirmed the introgression of T. monococcum rust resistance genes at 5AmL into modern bread wheat varieties. This near-complete reference genome of T. monococcum will be an essential resource for wheat functional and evolutionary genomic studies and expedite the cloning of useful genes in T. monococcum for future wheat improvement.

genomics↗

The unknown lipids project: harmonized methods improve compound identification and data reproducibility in an inter-laboratory untargeted lipidomics study

Untargeted lipidomics allows analysis of a broader range of lipids than targeted methods and permits discovery of unknown compounds. Previous ring trials have evaluated the reproducibility of targeted lipidomics methods, but inter-laboratory comparison of compound identification and unknown feature detection in untargeted lipidomics has not been attempted. To address this gap, five laboratories analyzed a set of mammalian tissue and biofluid reference samples using both their own untargeted lipidomics procedures and a common chromatographic and data analysis method. While both methods yielded informative data, the common method improved chromatographic reproducibility and resulted in detection of more shared features between labs. Spectral search against the LipidBlast in silico library enabled identification of over 2,000 unique lipids. Further examination of LC-MS/MS and ion mobility data, aided by hybrid search and spectral networking analysis, revealed spectral and chromatographic patterns useful for classification of unknown features, a subset of which were highly reproducible between labs. Overall, our method offers enhanced compound identification performance compared to targeted lipidomics, demonstrates the potential of harmonized methods to improve inter-site reproducibility for quantitation and feature alignment, and can serve as a reference to aid future annotation of untargeted lipidomics data.

systems biology↗

Temporal and spatial staging of lung alveolar regeneration is determined by the grainyhead transcription factor Tfcp2l1

Alveolar epithelial type 2 (AT2) cells harbor the facultative progenitor capacity in the lung alveolus to drive regeneration after lung injury. Using single cell transcriptomics, software-guided segmentation of tissue damage, and in vivo lineage tracing, we have identified the grainyhead transcription factor Tfcp2l1 as a key regulator of this regenerative process. Tfcp2l1 expression is initiated late in lung development and restricted to the AT2 cell population in the postnatal lung. Loss of Tfcp2l1 in adult AT2 cells decreased self-renewal and enhanced AT2-AT1 differentiation during active tissue regeneration. Conversely, Tfcp2l1 blunts the proliferative response to inflammatory signaling during the early acute phase after injury. This ability of Tfcp2l1 to temporally regulate the balance of AT2 self-renewal and differentiation is spatially restricted to zones undergoing active alveolar regeneration. Single-cell transcriptomics and lineage tracing reveal that Tfcp2l1 regulates cell fate dynamics by balancing the traffic across the AT2-AT1 differentiation axis and restricting the inflammatory program in AT2 cells. Organoid modeling shows that these cell fate dynamics are controlled by Tfcp2l1 regulation of IL-1 receptor expression and activity in AT2 cells. Together, these studies reveal the critical importance of properly staging lung alveolar regeneration and the integral role of Tfcp2l1 plays in balancing epithelial cell self-renewal and differentiation in this process.

cell biology↗

Cardiac patch treatment alleviates ischemic cardiomyopathy correlated with reverting Piezo1/2 expression by unloading left ventricular myocardium

Pathologically elevated mechanical load promotes the adverse remodeling of left ventricle (LV) post myocardial infarction, which results in the progression from ischemic cardiomyopathy to heart failure. Cardiac patches could attenuate adverse LV remodeling by providing mechanical support to infarcted myocardium and border zone tissue. However, the mechanism of the translation from mechanical effects to favorable therapeutic outcome is still not clear. By transcriptome analysis, we found that the myocardial transcription levels of mechanosensitive ion channel proteins Piezo1 and Piezo2 significantly increased in patients with ischemic cardiomyopathy. In vitro tensile tests with local tissue information revealed a significant decrease in local strain and mechanical load in rat infarct. Cardiac function and geometry were preserved compared to non-treated control. Further, in LV myocardium of the patch-treated group, MI induced expression levels of Piezo1/2 were significantly reverted to the similar levels of the Sham group, indicating that cardiac patch beneficial effects were correlated with suppressing mechanosensitive genes, particularly Piezo1/2. These findings demonstrated the potential of cardiac patches in treating ICM patients with remodeling risks, and could provide guidance for improvement in next generation of patch devices.

pathology↗

Characterizing the variation in chromosome structure ensembles in the context of the nuclear microenvironment

Inside the nucleus, chromosomes are subjected to direct physical interaction between different components, active forces, and thermal noise, leading to the formation of an ensemble of three-dimensional structures. However, it is still not well understood to what extent and how the structural ensemble varies from one chromosome region or cell-type to another. We designed a statistical analysis technique and applied it to single-cell chromosome imaging data to reveal the heterogeneity of individual chromosome structures. By analyzing the resulting structural landscape, we find that the largest dynamic variation is the overall radius of gyration of the chromatin region, followed by domain reorganization within the region. By comparing different human cell-lines and experimental perturbation data using this statistical analysis technique and a network-based similarity quantification approach, we identify both cell-type and condition-specific features of the structural landscapes. We identify a relationship between epigenetic state and the properties of chromosome structure fluctuation and validate this relationship through polymer simulations. Overall, our study suggests that the types of variation in a chromosome structure ensemble are cell-type as well as region-specific and can be attributed to constraints placed on the structure by factors such as variation in epigenetic state.

genomics↗

SE(3)-Equivariant Energy-based Models for End-to-End Protein Folding

Accurate prediction of protein structures is critical for understanding the biological function of proteins. Nevertheless, most structure optimization methods are built upon pre-defined statistical energy functions, which may be sub-optimal in formulating the conformation space. In this paper, we propose an end-to-end approach for protein structure optimization, powered by SE(3)-equivariant energy-based models. The conformation space is characterized by a SE(3)-equivariant graph neural network, with substantial modifications to embed the protein-specific domain knowledge. Furthermore, we introduce continuously-annealed Langevin dynamics as a novel sampling algorithm, and demonstrate that such process converges to native protein structures with theoretical guarantees. Extensive experiments indicate that SE(3)-Fold achieves comparable structure optimization accuracy, compared against state-of-the-art baselines, with over 1-2 orders of magnitude speed-up.

bioinformatics↗

Oncogenic Activation of AKT by MAPK6

Mitogen-activated protein kinase 6 (MAPK6) is an atypical MAPK closely related to MAPK4. We recently reported that MAPK4 can promote cancer by activating the Protein Kinase B (PKB/AKT) pathway of cell growth and survival. Here we report that MAPK6 overexpression also activates AKT to induce oncogenic outcomes, including transforming "normal" human epithelial cells into anchorage-independent growth and enhancing cancer cell growth. Knockdown of MAPK6 inhibited cancer cell growth and xenograft growth, supporting the tumor-promoting activities of endogenous MAPK6. Unlike MAPK4, which binds AKT through its kinase domain and phosphorylates AKT at T308, MAPK6 interacts with AKT through its C34 region and the unique C-terminal tail and phosphorylates AKT at S473 independent of mTORC2, the major AKT S473 kinase. MAPK6 overexpression is associated with decreased overall survival and the survival of lung adenocarcinoma, mesothelioma, uveal melanoma, and breast cancer patients. We conclude that MAPK6 can promote cancer by activating AKT and that targeting MAPK6 may be effective in human cancers.

cancer biology↗

Robust Population Single Neuronal Calcium Signal Extraction Using SCOUT Allows for Longitudinal Analysis of Behavior-associated Neural Ensemble Dynamics

In vivo calcium imaging enables simultaneous recording of large neuronal ensembles while engaged in operations such as learning and memory. However, such in vivo optical recordings are typically subject to motion artifact and background contamination from neurons and blood vessels. Further, population cell tracking across multiple recordings is complicated by non-rigid transformation induced by cell movements and field shifts. We introduce the novel method SCOUT for Single-Cell SpatiOtemporal LongitUdinal Tracking, consisting of two crucial parts: (1) imposition of spatial constraints on neuronal footprints extracted from individual optical recordings to improve ROI selection and eliminate false discoveries, and (2) application of a predictor-corrector, using spatiotemporal correlation of extracted neurons across sessions, for population cell tracking across multiple sessions. SCOUT empirically outperforms current methods for cell extraction and tracking in long-term multi-session imaging experiments across multiple brain regions. Application of this method allows for robust longitudinal analysis of contextual discrimination associated neural ensemble dynamics in the hippocampus up to 60 days.

neuroscience↗

A Uropathogenic E. coli UTI89 model of prostatic inflammation and collagen accumulation for use in studying aberrant collagen production in the prostate

Bacterial infection is one known etiology of prostatic inflammation. Prostatic inflammation is associated with prostatic collagen accumulation and both are linked to progressive lower urinary tract symptoms in men. We characterized a model of prostatic inflammation utilizing transurethral instillations of E. coli UTI89 in C57BL/6J male mice with the goal of determining the optimal instillation conditions, understanding the impact of instillation conditions on urinary physiology, and identifying ideal prostatic lobes and collagen 1a1 prostatic cell types for further analysis. The smallest instillation volume tested (50 {micro}L) distributes exclusively to bladder, 100 and 200 {micro}L volumes distributes to bladder and prostate, and a 500 {micro}L volume distributes to bladder, prostate and ureter. A threshold optical density (OD) of 0.4 E. coli UTI89 in the instillation fluid is necessary for significant (p < 0.05) prostate colonization. E. coli UTI89 infection results in a low frequency, high volume spontaneous voiding pattern. This phenotype is due to exposure to E. coli UTI89, not catheterization alone, and is minimally altered by a 50 {micro}L increase in instillation volume and doubling of E. coli concentration. Prostate inflammation is isolated to the dorsal prostate and is accompanied by increased collagen density. This is partnered with increased density of PTPRC+, ProCOL1A1+ co-positive cells and decreased density of ACTA2+, ProCOL1A1+ co-positive cells. Overall, we determined that this model is effective in altering urinary phenotype and producing prostatic inflammation and collagen accumulation in mice.

physiology↗

Next generation of anti-PD-L1 Atezolizumab with better anti-tumor efficacy in vivo

Some cancer patients treated with Atezolizumab, PD-L1 antibody drug launched by Genentech, quickly developed anti-drug antibody (ADA), led to loss of efficacy. This was likely due to the heavy aggregation of Atezolizumab, caused by mutation of N297A for removing unwanted antibody-dependent cytotoxicity (ADCC) of IgG1 antibody drug. Here, we developed a new version of Atezolizumab (Maxatezo), which was demonstrated better anti-tumor efficacy in vivo. In Atezolizumab, we mutated 297A to 297N back to bring back the glycosylation, and inserted a short sequence GGGS between G237 and G238 in the hinge region of the IgG1 heavy chain. Our data shown that insertion of GGGS, without altering the anti-PD-L1 antibody affinity and inhibitory activity, completely abolished the ADCC activity, as same as Atezolizumab. Moreover, the insertion of GGGS, without altering the glycosylation profile of IgG1, increased the yields of anti-PD-L1 antibody considerately. Additionally, glycosylation improved the stability yet reduced the amounts of aggregations in the antibody solutions. In turn, the level of ADA in animals treated with Maxatezo was 70% lower than the ones treated with Atezolizumab. Most importantly, at the same 10mg/kg dose, the anti-tumor activity of Maxatezo had attained 98% compared to that of Atezolizumab at 68%.Competing Interest StatementThe authors declare that they are affiliated with four commercial entities with stakes in the data. Patent of De-ADCC pending.View Full Text

bioengineering↗

Inferring Chromosome Radial Organization from Hi-CData

BackgroundThe nonrandom radial organization of eukaryotic chromosome territories (CTs) inside the nucleus plays an important role in nuclear functional compartmentalization. Increasingly, chromosome conformation capture (Hi-C) based approaches are being used to characterize the genome structure of many cell types and conditions. Computational methods to extract 3D arrangements of CTs from this type of pairwise contact data will thus increase our ability to analyze CT organization in a wider variety of biological situations. ResultsA number of full-scale polymer models have successfully reconstructed the 3D structure of chromosome territories from Hi-C. To supplement such methods, we explore alternative, direct, and less computationally intensive approaches to capture radial CT organization from Hi-C data. We show that we can infer relative chromo-some ordering using PCA on a thresholded inter-chromosomal contact matrix. We simulate an ensemble of possible CT arrangements using a force-directed network layout algorithm and propose an approach to integrate additional chromosome properties into our predictions. Our CT radial organization predictions have a high correlation with microscopy imaging data for various cell nucleus geometries (lymphoblastoid, skin fibroblast, and breast epithelial cells), and we can capture previously documented changes in senescent and progeria cells. ConclusionsOur analysis approaches provide rapid and modular approaches to screen for alterations in CT organization across widely available Hi-C data. We demon-strate which stages of the approach can extract meaningful information, and also de-scribe limitations of pairwise contacts alone to predict absolute 3D positions.

bioinformatics↗