Reducing acetylated tau is neuroprotective in brain injury.

Traumatic brain injury (TBI) is the largest non-genetic, non-aging related risk factor for Alzheimer's disease (AD). We report here that TBI induces tau acetylation (ac-tau) at sites acetylated also in human AD brain. This is mediated by S-nitrosylated-GAPDH, which simultaneously inactivates Sirtuin1 deacetylase and activates p300/CBP acetyltransferase, increasing neuronal ac-tau. Subsequent tau mislocalization causes neurodegeneration and neurobehavioral impairment, and ac-tau accumulates in the blood. Blocking GAPDH S-nitrosylation, inhibiting p300/CBP, or stimulating Sirtuin1 all protect mice from neurodegeneration, neurobehavioral impairment, and blood and brain accumulation of ac-tau after TBI. Ac-tau is thus a therapeutic target and potential blood biomarker of TBI that may represent pathologic convergence between TBI and AD. Increased ac-tau in human AD brain is further augmented in AD patients with history of TBI, and patients receiving the p300/CBP inhibitors salsalate or diflunisal exhibit decreased incidence of AD and clinically diagnosed TBI.

Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction.

Synthetic lethality (SL) has shown great promise for the discovery of novel targets in cancer. CRISPR double-knockout (CDKO) technologies can only screen several hundred genes and their combinations, but not genome-wide. Therefore, good SL prediction models are highly needed for genes and gene pairs selection in CDKO experiments. However, lack of scalable SL properties prevents generalizability of SL interactions to out-of-sample data, thereby hindering modeling efforts. In this paper, we recognize that SL connectivity is a scalable and generalizable SL property. We develop a novel two-step multilayer encoder for individual sample-specific SL prediction model (MLEC-iSL), which predicts SL connectivity first and SL interactions subsequently. MLEC-iSL has three encoders, namely, gene, graph, and transformer encoders. MLEC-iSL achieves high SL prediction performance in K562 (AUPR, 0.73; AUC, 0.72) and Jurkat (AUPR, 0.73; AUC, 0.71) cells, while no existing methods exceed 0.62 AUPR and AUC. The prediction performance of MLEC-iSL is validated in a CDKO experiment in 22Rv1 cells, yielding a 46.8% SL rate among 987 selected gene pairs. The screen also reveals SL dependency between apoptosis and mitosis cell death pathways.

SLKB: synthetic lethality knowledge base.

Emerging CRISPR-Cas9 technology permits synthetic lethality (SL) screening of large number of gene pairs from gene combination double knockout (CDKO) experiments. However, the poor integration and annotation of CDKO SL data in current SL databases limit their utility, and diverse methods of calculating SL scores prohibit their comparison. To overcome these shortcomings, we have developed SL knowledge base (SLKB) that incorporates data of 11 CDKO experiments in 22 cell lines, 16,059 SL gene pairs and 264,424 non-SL gene pairs. Additionally, within SLKB, we have implemented five SL calculation methods: median score with and without background control normalization (Median-B/NB), sgRNA-derived score (sgRNA-B/NB), Horlbeck score, GEMINI score and MAGeCK score. The five scores have demonstrated a mere 1.21% overlap among their top 10% SL gene pairs, reflecting high diversity. Users can browse SL networks and assess the impact of scoring methods using Venn diagrams. The SL network generated from all data in SLKB shows a greater likelihood of SL gene pair connectivity with other SL gene pairs than non-SL pairs. Comparison of SL networks between two cell lines demonstrated greater likelihood to share SL hub genes than SL gene pairs. SLKB website and pipeline can be freely accessed at https://slkb.osubmi.org and https://slkb.docs.osubmi.org/, respectively.

Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease.

Because disease-associated microglia (DAM) and disease-associated astrocytes (DAA) are involved in the pathophysiology of Alzheimer's disease (AD), we systematically identified molecular networks between DAM and DAA to uncover novel therapeutic targets for AD. Specifically, we develop a network-based methodology that leverages single-cell/nucleus RNA sequencing data from both transgenic mouse models and AD patient brains, as well as drug-target network, metabolite-enzyme associations, the human protein-protein interactome, and large-scale longitudinal patient data. Through this approach, we find both common and unique gene network regulators between DAM (i.e., PAK1, MAPK14, and CSF1R) and DAA (i.e., NFKB1, FOS, and JUN) that are significantly enriched by neuro-inflammatory pathways and well-known genetic variants (i.e., BIN1). We identify shared immune pathways between DAM and DAA, including Th17 cell differentiation and chemokine signaling. Last, integrative metabolite-enzyme network analyses suggest that fatty acids and amino acids may trigger molecular alterations in DAM and DAA. Combining network-based prediction and retrospective case-control observations with 7.2 million individuals, we identify that usage of fluticasone (an approved glucocorticoid receptor agonist) is significantly associated with a reduced incidence of AD (hazard ratio [HR] = 0.86, 95% confidence interval [CI] 0.83-0.89, P < 1.0 × 10-8). Propensity score-stratified cohort studies reveal that usage of mometasone (a stronger glucocorticoid receptor agonist) is significantly associated with a decreased risk of AD (HR = 0.74, 95% CI 0.68-0.81, P < 1.0 × 10-8) compared to fluticasone after adjusting age, gender, and disease comorbidities. In summary, we present a network-based, multimodal methodology for single-cell/nucleus genomics-informed drug discovery and have identified fluticasone and mometasone as potential treatments in AD.

Continuous-variable quantum key distribution robust against environmental disturbances.

Continuous variable quantum key distribution (CV-QKD) can guarantee that two parties share secure keys even in the presence of an eavesdropper. However, the polarization direction of the coherent state transmitted in CV-QKD is susceptible to environmental disturbances during channel transmission, making it difficult to share keys consistently over long periods of time. Therefore, a CV-QKD system that can resist environmental disturbance is very urgent. In this paper, we propose a new optical architecture for CV-QKD based on the Faraday-Michelson interference (FMI) structure, and finally form an all-single-mode (SM) fiber-based stable CV-QKD system which employs transmitted local oscillator (TLO) scheme and discrete modulation coherent state (DMCS) protocol. Specifically, since the Faraday mirror rotates the polarization direction of light by 90o, the birefringence effect of light can be effectively dealt with, thus ensuring the same polarization state of light before and after reflection. The final simulation results show that the theoretical secret key rate of this scheme can reach 139 kbps at 70 km, which can further improve the stability and robustness of CV-QKD in the real environment, and provide technical support for the next-generation high-stability QKD system.

Deep-learning assisted fast orbital angular momentum complex spectrum analysis.

Analyzing the orbital angular momentum (OAM) distribution of a vortex beam is critical for OAM-based applications. Here, we propose a deep residual network (DRN) to model the relationship between characteristics of the multiplexed OAM beam and their complex spectrum. The favorable experimental results show that our proposal can obtain both the intensity and phase terms of multiplexed OAM beams, dubbed complex spectrum, with a wide range of OAM modes, varying in intensity, phase ratio, and mode intervals at high accuracy and real-time speed. Specifically, the root mean square error (RMSE) of intensity and phase spectrum is evaluated as 0.002 and 0.016, respectively, with a response time of only 0.020 s. To the best of our knowledge, this work opens a new sight for fast OAM complex spectrum analysis and paves the way for numerous advanced domains that need real-time OAM complex spectrum diagnostic like ultrahigh-dimensional OAM tailoring.

Single-frequency orbital angular momentum switchable modes from a microchip laser.

We demonstrate the direct generation of single-frequency switchable orbital angular momentum (OAM) modes in a 1 µm wavelength range using a Nd:YVO4 microchip laser. The 808 nm laser diode pump beam is shaped into annular through an axicon associated with a lens. By adjusting the diameter and power of the annular pump beam, various OAM modes with different mode volumes can oscillate inside the Nd:YVO4 microchip. Moreover, a single-frequency output is also available due to the short cavity of the microchip. In the proof-of-principle experiment, single-frequency twofold multiplexed OAM modes | ± 1> and | ± 2> are generated, with experimentally measured fidelity higher than 96%. This work presents a compact and versatile single-frequency OAM source and will inspire multiple advanced scenarios ranging from classical to quantum photonics.

Surpassing the Quantum Limit in Bosonic Loss Estimation without Quantum Probes.

Bosonic loss estimation has an important role in quantum metrology. It was once believed that the ultimate precision of this task is restricted to the standard quantum limit if no quantum probe is involved. Nevertheless, a recent proposal showed that this limit can be surpassed by utilizing ring resonators with coherent state probe. Here, we experimentally realize the resonator-based bosonic loss estimation and verify the resonant enhancement effect. This Letter explores the advantages of resonator-based metrology and sheds light on the development of high-precision miniature sensors.

A unique death pathway keeps RIPK1 D325A mutant mice in check at embryonic day 10.5.

Tumor necrosis factor receptor-1 (TNFR1) signaling, apart from its pleiotropic functions in inflammation, plays a role in embryogenesis as deficiency of varieties of its downstream molecules leads to embryonic lethality in mice. Caspase-8 noncleavable receptor interacting serine/threonine kinase 1 (RIPK1) mutations occur naturally in humans, and the corresponding D325A mutation in murine RIPK1 leads to death at early midgestation. It is known that both the demise of Ripk1D325A/D325A embryos and the death of Casp8-/- mice are initiated by TNFR1, but they are mediated by apoptosis and necroptosis, respectively. Here, we show that the defects in Ripk1D325A/D325A embryos occur at embryonic day 10.5 (E10.5), earlier than that caused by Casp8 knockout. By analyzing a series of genetically mutated mice, we elucidated a mechanism that leads to the lethality of Ripk1D325A/D325A embryos and compared it with that underlies Casp8 deletion-mediated lethality. We revealed that the apoptosis in Ripk1D325A/D325A embryos requires a scaffold function of RIPK3 and enzymatically active caspase-8. Unexpectedly, caspase-1 and caspase-11 are downstream of activated caspase-8, and concurrent depletion of Casp1 and Casp11 postpones the E10.5 lethality to embryonic day 13.5 (E13.5). Moreover, caspase-3 is an executioner of apoptosis at E10.5 in Ripk1D325A/D325A mice as its deletion extends life of Ripk1D325A/D325A mice to embryonic day 11.5 (E11.5). Hence, an unexpected death pathway of TNFR1 controls RIPK1 D325A mutation-induced lethality at E10.5.

Efficient testing of the biomarker positive and negative subgroups in a biomarker-stratified trial.

In many randomized placebo-controlled trials with a biomarker defined subgroup, it is believed that this subgroup has the same or higher treatment effect compared with its complement. These subgroups are often referred to as the biomarker positive and negative subgroups. Most biomarker-stratified pivotal trials are aimed at demonstrating a significant treatment effect either in the biomarker positive subgroup or in the overall population. A major shortcoming of this approach is that the treatment can be declared effective in the overall population even though it has no effect in the biomarker negative subgroup. We use the isotonic assumption about the treatment effects in the two subgroups to construct an efficient way to test for a treatment effect in both the biomarker positive and negative subgroups. A substantial reduction in the required sample size for such a trial compared with existing methods makes evaluating the treatment effect in both the biomarker positive and negative subgroups feasible in pivotal trials especially when the prevalence of the biomarker positive subgroup is less than 0.5.

Discovering clinical drug-drug interactions with known pharmacokinetics mechanisms using spontaneous reporting systems and electronic health records.

OBJECTIVE: Although the mechanisms behind pharmacokinetic (PK) drug-drug interactions (DDIs) are well-documented, bridging the gap between this knowledge and clinical evidence of DDIs, especially for serious adverse drug reactions (SADRs), remains challenging. While leveraging the FDA Adverse Event Reporting System (FAERS) database along with disproportionality analysis tends to detect a vast number of DDI signals, this abundance complicates further investigation, such as validation through clinical trials. Our study proposed a framework to efficiently prioritize these signals and assessed their reliability using multi-source Electronic Health Records (EHR) to identify top candidates for further investigation. METHODS: We analyzed FAERS data spanning from January 2004 to March 2023, employing four established disproportionality methods: Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagating Neural Network (BCPNN). Building upon these models, we developed four ranking models to prioritize DDI-SADR signals and cross-referenced signals with DrugBank. To validate the top-ranked signals, we employed longitudinal EHRs from Vanderbilt University Medical Center and the All of Us research program. The performance of each model was assessed by counting how many of the top-ranked signals were confirmed by EHRs and calculating the average ranking of these confirmed signals. RESULTS: Out of 189 DDI-SADR signals identified by all four disproportionality methods, only two were documented in the DrugBank database. By prioritizing the top 20 signals as determined by each of the four disproportionality methods and our four ranking models, 58 unique DDI-SADR signals were selected for EHR validations. Of these, five signals were confirmed. The ranking model, which integrated the MGPS and BCPNN, demonstrated superior performance by assigning the highest priority to those five EHR-confirmed signals. CONCLUSION: The fusion of disproportionality analysis with ranking models, validated through multi-source EHRs, presents a groundbreaking approach to pharmacovigilance. Our study's confirmation of five significant DDI-SADRs, previously unrecorded in the DrugBank database, highlights the essential role of advanced data analysis techniques in identifying ADRs.

Colchicine ameliorates myocardial injury induced by coronary microembolization through suppressing pyroptosis via the AMPK/SIRT1/NLRP3 signaling pathway.

BACKGROUND: Coronary microembolization(CME)is a common complication in acute coronary syndrome and percutaneous coronary intervention, which is closely related to poor prognosis. Pyroptosis, as an inflammatory programmed cell death, has been found to be associated with CME-induced myocardial injury. Colchicine (COL) has potential benefits in coronary artery disease due to its anti-inflammatory effect. However, the role of colchicine in pyroptosis-related CME-induced cardiomyocyte injury is unclear. This study was carried out to explore the effects and mechanisms of colchicine on myocardial pyroptosis induced by CME. METHODS: The CME animal model was constructed by injecting microspheres into the left ventricle with Sprague-Dawley rats, and colchicine (0.3 mg/kg) pretreatment seven days before and on the day of modeling or compound C(CC)co-treatment was given half an hour before modeling. The study was divided into 4 groups: Sham group, CME group, CME + COL group, and CME + COL + CC group (10 rats for each group). Cardiac function, serum myocardial injury markers, myocardial histopathology, and pyroptosis-related indicators were used to evaluate the effects of colchicine. RESULTS: Colchicine pretreatment improved cardiac dysfunction and reduced myocardial injury induced by CME. The main manifestations were the improvement of left ventricular systolic function, the decrease of microinfarction area, and the decrease of mRNA and protein indexes related to pyroptosis. Mechanistically, colchicine increased the phosphorylation level of adenosine monophosphate-activated protein kinase (AMPK), promoted the expression of silent information regulation T1 (SIRT1), and inhibited the expression of NOD-like receptor pyrin containing 3 (NLRP3) to reduce myocardial pyroptosis. However, after CC co-treatment with COL, the effect of colchicine was partially reversed. CONCLUSION: Colchicine improves CME-induced cardiac dysfunction and myocardial injury by inhibiting cardiomyocyte pyroptosis through the AMPK/SIRT1/NLRP3 signaling pathway.

Effects of Loquat Juice Addition on Sensory Characteristics and Volatile Organic Compounds of Loquat Beer.

Beer, as an ancient and widely consumed alcoholic beverage, holds a rich cultural heritage and history. In recent years, fruit beer has gained significant attention as a distinct beer type produced by incorporating fruit juice into traditional beer ingredients. This study employed headspace solid-phase microextraction-gas chromatography-mass spectrometry techniques, redundancy analysis, and orthogonal projections to latent structures discriminant analysis to analyze the sensory evaluation, physicochemical properties, organic acids, and volatile organic compounds (VOCs) of loquat beer with different proportions of loquat juice. The results shown that the addition of an appropriate amount of loquat juice (40%) enhanced the overall sensory quality of the beer; as the proportion of loquat juice increased, the contents of malic acid and tartaric acid significantly increased (p < 0.05). A total of 100 VOCs were identified, among which 23 key VOCs (VIP > 1, p < 0.05) represented the most important characteristic flavor components in loquat beer based on their odor activity value (OAV). This study holds significant importance for the value-added processing and economic development of loquat.

Ultrathin organic solvent nanofiltration membrane with polydopamine-HKUST-1 interlayer for organic solvent separation.

Polydopamine (PDA) and metal-organic skeleton HKUST-1 were co-deposited on the base membrane of hexamethylenediamine (HDA)-crosslinked polyetherimide (PEI) ultrafiltration membrane as the interlayer, and high-throughput organic solvent nanofiltration membrane (OSN) was prepared by interfacial polymerization and solvent activation reaction. The polyamide (PA) layer surface roughness from 28.4 nm in PA/PEI to 78.3 nm in PA/PDA-HKUST-10.6/PEI membrane, reduced the thickness of the separation layer from 79 to 14 nm, and significantly improved the hydrophilic, thermal and mechanical properties. The flux of the PA/PDA-HKUST-10.6/PEI membrane in a 0.1 g/L Congo Red (CR) ethanol solution at 0.6 MPa test pressure reached 21.8 L/(m2·hr) and the rejection of CR was 92.8%. Solvent adsorption test, N, N-dimethylformamide (DMF) immersion experiment, and long-term operation test in ethanol showed that the membranes had high solvent tolerance. The solvent flux test demonstrated that, under the test pressure of 0.6 MPa, the flux of different solvents ranked as follows: methanol (56.9 L/(m2·hr)) > DMF (39.6 L/(m2·hr)) > ethanol (31.2 L/(m2·hr)) > IPA (4.5 L/(m2·hr)) > N-hexane (1.9 L/(m2·hr)). The ability of the membranes to retain dyes in IPA/water dyes solution was also evaluated. The flux of the membrane was 30.4 L/(m2·hr) and the rejection of CR was 91.6% when the IPA concentration reached 50%. This OSN membrane-making strategy is economical, environment-friendly and efficient, and has a great application prospect in organic solvent separation systems.

Elucidation of Biological Networks across Complex Diseases Using Single-Cell Omics.

Single-cell multimodal omics (scMulti-omics) technologies have made it possible to trace cellular lineages during differentiation and to identify new cell types in heterogeneous cell populations. The derived information is especially promising for computing cell-type-specific biological networks encoded in complex diseases and improving our understanding of the underlying gene regulatory mechanisms. The integration of these networks could, therefore, give rise to a heterogeneous regulatory landscape (HRL) in support of disease diagnosis and drug therapeutics. In this review, we provide an overview of this field and pay particular attention to how diverse biological networks can be inferred in a specific cell type based on integrative methods. Then, we discuss how HRL can advance our understanding of regulatory mechanisms underlying complex diseases and aid in the prediction of prognosis and therapeutic responses. Finally, we outline challenges and future trends that will be central to bringing the field of HRL in complex diseases forward.

Upregulation of miR-29b-3p alleviates coronary microembolization-induced myocardial injury via regulating BMF and GSK-3ß.

Coronary microembolization (CME) is an intractable complication results from acute coronary syndrome. CME-induced myocardial apoptosis was associated with progressive cardiac contractile dysfunction. miR-29b-3p has been reported implicated in variety cardiovascular diseases, but its function in CME-induced myocardial injury is yet unknown. Herein, a rat model of CME was established by injecting microspheres into the left ventricle and found that the expression level of miR-29b-3p was markedly decreased in the CME rat heart tissues. By using echocardiography, CD31 immunohistochemistry staining, hematoxylin basic fuchsin picric acid (HBFP) staining, TUNEL staining, and western blotting analysis after CME, it was found that upregulating miR-29b-3p improved cardiac dysfunction, promoted angiogenesis, decreased myocardial microinfarct area, and inhibited myocardial apoptosis. Additionally, miR-29b-3p inhibition can reverse the protective benefits of miR-29b-3p overexpression. Mechanistically, the target genes of miR-29b-3p were identified as glycogen synthase kinase 3 (GSK-3ß) and Bcl-2 modifying factor (BMF) by bioinformatics analysis and luciferase reporter experiment. Overall, our findings imply that induction of miR-29b-3p, which negatively regulates GSK-3ß and BMF expression, attenuates CME-induced myocardial injury, suggesting a novel potential therapeutic target for cardioprotective after CME.

Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects.

Drug combinations have exhibited promising therapeutic effects in treating cancer patients with less toxicity and adverse side effects. However, it is infeasible to experimentally screen the enormous search space of all possible drug combinations. Therefore, developing computational models to efficiently and accurately identify potential anti-cancer synergistic drug combinations has attracted a lot of attention from the scientific community. Hypothesis-driven explicit mathematical methods or network pharmacology models have been popular in the last decade and have been comprehensively reviewed in previous surveys. With the surge of artificial intelligence and greater availability of large-scale datasets, machine learning especially deep learning methods are gaining popularity in the field of computational models for anti-cancer drug synergy prediction. Machine learning-based methods can be derived without strong assumptions about underlying mechanisms and have achieved state-of-the-art prediction performances, promoting much greater growth of the field. Here, we present a structured overview of available large-scale databases and machine learning especially deep learning methods in computational predictive models for anti-cancer drug synergy prediction. We provide a unified framework for machine learning models and detail existing model architectures as well as their contributions and limitations, shedding light into the future design of computational models. Besides, unbiased experiments are conducted to provide in-depth comparisons between reviewed papers in terms of their prediction performance.

CEDA: integrating gene expression data with CRISPR-pooled screen data identifies essential genes with higher expression.

MOTIVATION: Clustered regularly interspaced short palindromic repeats (CRISPR)-based genetic perturbation screen is a powerful tool to probe gene function. However, experimental noises, especially for the lowly expressed genes, need to be accounted for to maintain proper control of false positive rate. METHODS: We develop a statistical method, named CRISPR screen with Expression Data Analysis (CEDA), to integrate gene expression profiles and CRISPR screen data for identifying essential genes. CEDA stratifies genes based on expression level and adopts a three-component mixture model for the log-fold change of single-guide RNAs (sgRNAs). Empirical Bayesian prior and expectation-maximization algorithm are used for parameter estimation and false discovery rate inference. RESULTS: Taking advantage of gene expression data, CEDA identifies essential genes with higher expression. Compared to existing methods, CEDA shows comparable reliability but higher sensitivity in detecting essential genes with moderate sgRNA fold change. Therefore, using the same CRISPR data, CEDA generates an additional hit gene list. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Power amplification for 1.6†µm high-order vortex modes.

1.6 µm high-order vortex modes carrying orbital angular momentums (OAMs) play significant roles in long-range Doppler lidars and other remote sensing. Amplification of 1.6 µm high-order vortex modes is an important way to provide high-power laser sources for such lidars and also enable the weak echo signal to be amplified so that it can be analyzed. In this work, we propose a four-pass Er:YAG vortex master-oscillator-power-amplification (MOPA) system to amplify 1.6 µm high-order vortex modes. In the proof-of-concept experiments, 1.6 µm single OAM mode (l = 3) is amplified successfully and the gain ranging from 1.88 to 2.36 is achieved. Multiplexed OAM mode (l=±3) is also amplified with favorable results. This work addresses the issue as the low gain of Er:YAG vortex MOPA, which provides a feasible path for 1.6 µm high-order vortex modes amplification.

Multi-rate and multi-protocol continuous-variable quantum key distribution.

Quantum key distribution (QKD) is an effective solution to ensure the secure transmission of information. However, for the large-scale application of QKD, the interoperability and flexibility of the transmitter and receiver are urgent issues to be solved. Here, for the first time, to the best of our knowledge, we experimentally verify the feasibility of one continuous-variable (CV) QKD system to achieve multiple protocols and rates. The flexibility of the system comes from the modulator realizing multiple protocols and a broadband coherent detector realizing multiple symbol rates. The results show that this system can switch between different rates and protocols to generate the secure key, and reveal its similarity to classical optical communication. Therefore, It can be adjusted according to user needs and provides a system-level solution for building a flexible quantum network.