Synthetic mammalian trigger-controlled bipartite transcription factors

Synthetic biology has significantly advanced the design of synthetic control devices, gene circuits and networks that can reprogram mammalian cells in a trigger-inducible manner. Prokaryotic helix-turn-helix motifs have become the standard resource to design synthetic mammalian transcription factors that tune chimeric promoters in a small molecule-responsive manner. We have identified a family of Actinomycetes transcriptional repressor proteins showing a tandem TetR-family signature and have used a synthetic biology-inspired approach to reveal the potential control dynamics of these bi-partite regulators. Daisy-chain assembly of well-characterized prokaryotic repressor proteins such as TetR, ScbR, TtgR or VanR and fusion to either the Herpes simplex transactivation domain VP16 or the Krueppel-associated box domain (KRAB) of the human kox-1 gene resulted in synthetic bi- and even tri-partite mammalian transcription factors that could reversibly program their individual chimeric or hybrid promoters for trigger-adjustable transgene expression using tetracycline (TET), γ-butyrolactones, phloretin and vanillic acid. Detailed characterization of the bi-partite ScbR-TetR-VP16 (ST-TA) transcription factor revealed independent control of TET- and γ-butyrolactone-responsive promoters at high and double-pole double-throw (DPDT) relay switch qualities at low intracellular concentrations. Similar to electromagnetically operated mechanical DPDT relay switches that control two electric circuits by a fully isolated low-power signal, TET programs ST-TA to progressively switch from TetR-specific promoter-driven expression of transgene one to ScbR-specific promoter-driven transcription of transgene two while ST-TA flips back to exclusive transgene 1 expression in the absence of the trigger antibiotic. We suggest that natural repressors and activators with tandem TetR-family signatures may also provide independent as well as DPDT-mediated control of two sets of transgenes in bacteria, and that their synthetic transcription-factor analogs may enable the design of compact therapeutic gene circuits for gene and cell-based therapie

Antagonistic control of a dual-input mammalian gene switch by food additives

Synthetic biology has significantly advanced the design of mammalian trigger-inducible transgene-control devices that are able to programme complex cellular behaviour. Fruit-based benzoate derivatives licensed as food additives, such as flavours (e.g. vanillate) and preservatives (e.g. benzoate), are a particularly attractive class of trigger compounds for orthogonal mammalian transgene control devices because of their innocuousness, physiological compatibility and simple oral administration. Capitalizing on the genetic componentry of the soil bacterium Comamonas testosteroni, which has evolved to catabolize a variety of aromatic compounds, we have designed different mammalian gene expression systems that could be induced and repressed by the food additives benzoate and vanillate. When implanting designer cells engineered for gene switch-driven expression of the human placental secreted alkaline phosphatase (SEAP) into mice, blood SEAP levels of treated animals directly correlated with a benzoate-enriched drinking programme. Additionally, the benzoate-/vanillate-responsive device was compatible with other transgene control systems and could be assembled into higher-order control networks providing expression dynamics reminiscent of a lap-timing stopwatch. Designer gene switches using licensed food additives as trigger compounds to achieve antagonistic dual-input expression profiles and provide novel control topologies and regulation dynamics may advance future gene- and cell-based therapie

ConvFormer: Revisiting Transformer for Sequential User Modeling

Sequential user modeling, a critical task in personalized recommender systems, focuses on predicting the next item a user would prefer, requiring a deep understanding of user behavior sequences. Despite the remarkable success of Transformer-based models across various domains, their full potential in comprehending user behavior remains untapped. In this paper, we re-examine Transformer-like architectures aiming to advance state-of-the-art performance. We start by revisiting the core building blocks of Transformer-based methods, analyzing the effectiveness of the item-to-item mechanism within the context of sequential user modeling. After conducting a thorough experimental analysis, we identify three essential criteria for devising efficient sequential user models, which we hope will serve as practical guidelines to inspire and shape future designs. Following this, we introduce ConvFormer, a simple but powerful modification to the Transformer architecture that meets these criteria, yielding state-of-the-art results. Additionally, we present an acceleration technique to minimize the complexity associated with processing extremely long sequences. Experiments on four public datasets showcase ConvFormer's superiority and confirm the validity of our proposed criteria

Segregated Nanocompartments Containing Therapeutic Enzymes and Imaging Compounds within DNA-Zipped Polymersome Clusters for Advanced Nanotheranostic Platform

Abstract Nanotheranostics is an emerging field that brings together nanoscale-engineered materials with biological systems providing a combination of therapeutic and diagnostic strategies. However, current theranostic nanoplatforms have serious limitations, mainly due to a mismatch between the physical properties of the selected nanomaterials and their functionalization ease, loading ability, or overall compatibility with bioactive molecules. Herein, a nanotheranostic system is proposed based on nanocompartment clusters composed of two different polymersomes linked together by DNA. Careful design and procedure optimization result in clusters segregating the therapeutic enzyme human Dopa decarboxylase (DDC) and fluorescent probes for the detection unit in distinct but colocalized nanocompartments. The diagnostic compartment provides a twofold function: trackability via dye loading as the imaging component and the ability to attach the cluster construct to the surface of cells. The therapeutic compartment, loaded with active DDC, triggers the cellular expression of a secreted reporter enzyme via production of dopamine and activation of dopaminergic receptors implicated in atherosclerosis. This two-compartment nanotheranostic platform is expected to provide the basis of a new treatment strategy for atherosclerosis, to expand versatility and diversify the types of utilizable active molecules, and thus by extension expand the breadth of attainable applications

A synthetic biology-based device prevents liver injury in mice

BACKGROUND & AIMS The liver performs a panoply of complex activities coordinating metabolic, immunologic and detoxification processes. Despite the liver's robustness and unique self-regeneration capacity, viral infection, autoimmune disorders, fatty liver disease, alcohol abuse and drug-induced hepatotoxicity contribute to the increasing prevalence of liver failure. Liver injuries impair the clearance of bile acids from the hepatic portal vein which leads to their spill over into the peripheral circulation where they activate the G-protein-coupled bile acid receptor TGR5 to initiate a variety of hepatoprotective processes. METHODS By functionally linking activation of ectopically expressed TGR5 to an artificial promoter controlling transcription of the hepatocyte growth factor (HGF), we created a closed-loop synthetic signalling network that coordinated liver injury-associated serum bile acid levels to expression of HGF in a self-sufficient, reversible and dose-dependent manner. RESULTS After implantation of genetically engineered human cells inside auto-vascularizing, immunoprotective and clinically validated alginate-poly-(L-lysine)-alginate beads into mice, the liver-protection device detected pathologic serum bile acid levels and produced therapeutic HGF levels that protected the animals from acute drug-induced liver failure. CONCLUSIONS Genetically engineered cells containing theranostic gene circuits that dynamically interface with host metabolism may provide novel opportunities for preventive, acute and chronic healthcare. LAY SUMMARY Liver diseases leading to organ failure may go unnoticed as they do not trigger any symptoms or significant discomfort. We have designed a synthetic gene circuit that senses excessive bile acid levels associated with liver injuries and automatically produces a therapeutic protein in response. When integrated into mammalian cells and implanted into mice, the circuit detects the onset of liver injuries and coordinates the production of a protein pharmaceutical which prevents liver damage

Is personal growth initiative associated with later life satisfaction in Chinese college students? A 15‐week prospective analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151959/1/ajsp12386.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151959/2/ajsp12386_am.pd

STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation

The impressive development of facial manipulation techniques has raised severe public concerns. Identity-aware methods, especially suitable for protecting celebrities, are seen as one of promising face forgery detection approaches with additional reference video. However, without in-depth observation of fake video’s characteristics, most existing identity-aware algorithms are just naive imitation of face verification model and fail to exploit discriminative information. In this article, we argue that it is necessary to take both spatial and temporal perspectives into consideration for adequate inconsistency clues and propose a novel forgery detector named SpatioTemporal IDentity network (STIDNet). To effectively capture heterogeneous spatiotemporal information in a unified formulation, our STIDNet is following a knowledge distillation architecture that the student identity extractor receives supervision from a spatial information encoder (SIE) and a temporal information encoder (TIE) through multiteacher training. Specifically, a regional sensitive identity modelling paradigm is proposed in SIE by introducing facial blending augmentation but with uniform identity label, thus encourage model to focus on spatial discriminative region like outer face. Meanwhile, considering the strong temporal correlation between audio and talking face video, our TIE is devised in a cross-modal pattern that the audio information is introduced to supervise model exploiting temporal personalized movements. Benefit from knowledge transfer from SIE and TIE, STIDNet is able to capture individual’s essential spatiotemporal identity attributes and sensitive to even subtle identity deviation caused by manipulation. Extensive experiments indicate the superiority of our STIDNet compared with previous works. Moreover, we also demonstrate STIDNet is more suitable for real-world implementation in terms of model complexity and reference set size

An Empirical Comparison of Information-Theoretic Criteria in Estimating the Number of Independent Components of fMRI Data

BACKGROUND: Independent Component Analysis (ICA) has been widely applied to the analysis of fMRI data. Accurate estimation of the number of independent components of fMRI data is critical to reduce over/under fitting. Although various methods based on Information Theoretic Criteria (ITC) have been used to estimate the intrinsic dimension of fMRI data, the relative performance of different ITC in the context of the ICA model hasn't been fully investigated, especially considering the properties of fMRI data. The present study explores and evaluates the performance of various ITC for the fMRI data with varied white noise levels, colored noise levels, temporal data sizes and spatial smoothness degrees. METHODOLOGY: Both simulated data and real fMRI data with varied Gaussian white noise levels, first-order auto-regressive (AR(1)) noise levels, temporal data sizes and spatial smoothness degrees were carried out to deeply explore and evaluate the performance of different traditional ITC. PRINCIPAL FINDINGS: Results indicate that the performance of ITCs depends on the noise level, temporal data size and spatial smoothness of fMRI data. 1) High white noise levels may lead to underestimation of all criteria and MDL/BIC has the severest underestimation at the higher Gaussian white noise level. 2) Colored noise may result in overestimation that can be intensified by the increase of AR(1) coefficient rather than the SD of AR(1) noise and MDL/BIC shows the least overestimation. 3) Larger temporal data size will be better for estimation for the model of white noise but tends to cause severer overestimation for the model of AR(1) noise. 4) Spatial smoothing will result in overestimation in both noise models. CONCLUSIONS: 1) None of ITC is perfect for all fMRI data due to its complicated noise structure. 2) If there is only white noise in data, AIC is preferred when the noise level is high and otherwise, Laplace approximation is a better choice. 3) When colored noise exists in data, MDL/BIC outperforms the other criteria