Sexually dimorphic renal expression of mouse Klotho is directed by a kidney-specific distal enhancer responsive to HNF1b.

Transcription enhancers are genomic sequences regulating common and tissue-specific genes and their disruption can contribute to human disease development and progression. Klotho, a sexually dimorphic gene specifically expressed in kidney, is well-linked to kidney dysfunction and its deletion from the mouse genome leads to premature aging and death. However, the sexually dimorphic regulation of Klotho is not understood. Here, we characterize two candidate Klotho enhancers using H3K27ac epigenetic marks and transcription factor binding and investigate their functions, individually and combined, through CRISPR-Cas9 genome engineering. We discovered that only the distal (E1), but not the proximal (E2) candidate region constitutes a functional enhancer, with the double deletion not causing Klotho expression to further decrease. E1 activity is dependent on HNF1b transcription factor binding site within the enhancer. Further, E1 controls the sexual dimorphism of Klotho as evidenced by qPCR and RNA-seq. Despite the sharp reduction of Klotho mRNA, unlike germline Klotho knockouts, mutant mice present normal phenotype, including weight, lifespan, and serum biochemistry. Lastly, only males lacking E1 display more prominent acute, but not chronic kidney injury responses, indicating a remarkable range of potential adaptation to isolated Klotho loss, especially in female E1 knockouts, retaining renoprotection despite over 80% Klotho reduction.

Causal relationship between circulating glutamine levels and idiopathic pulmonary fibrosis: a two-sample mendelian randomization study.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive and debilitating respiratory disease with a median survival of less than 5 years. In recent years, glutamine has been reported to be involved in the regulation of collagen deposition and cell proliferation in fibroblasts, thereby influencing the progression of IPF. However, the relationships between glutamine and the incidence, progression, and treatment response of IPF remain unclear. Our study aimed to investigate the relationship between circulating glutamine levels and IPF, as well as its potential as a therapeutic target. METHODS: We performed a comprehensive Mendelian Randomization (MR) analysis using the most recent genome-wide association study summary-level data. A total of 32 single nucleotide polymorphisms significantly correlated to glutamine levels were identified as instrumental variables. Eight MR analysis methods, including inverse variance weighted, MR-Egger, weighted median, weighted mode, constrained maximum likelihood, contamination mixture, robust adjusted profile score, and debiased inverse-variance weighted method, were used to assess the relationship between glutamine levels with IPF. RESULTS: The inverse variance weighted analysis revealed a significant inverse correlation between glutamine levels and IPF risk (Odds Ratio = 0.750; 95% Confidence Interval : 0.592-0.951; P = 0.017). Sensitivity analyses, including MR-Egger regression and MR-PRESSO global test, confirmed the robustness of our findings, with no evidence of horizontal pleiotropy or heterogeneity. CONCLUSION: Our study provides novel evidence for a causal relationship between lower circulating glutamine levels and increased risk of IPF. This finding may contribute to the early identification of high-risk individuals for IPF, disease monitoring, and development of targeted therapeutic strategies.

Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation.

BACKGROUND: Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials. Artificial intelligence (AI) has facilitated the identification of homogeneous subgroups, but how to estimate the uncertainty of the model outputs when applying AI to clinical decision-making remains unknown. OBJECTIVE: We aimed to design an AI-based model for purposeful patient enrollment, ensuring that a patient with sepsis recruited into a trial would still be persistently ill by the time the proposed therapy could impact patient outcome. We also expected that the model could provide interpretable factors and estimate the uncertainty of the model outputs at a customized confidence level. METHODS: In this retrospective study, 9135 patients with sepsis requiring vasopressor treatment within 24 hours after sepsis onset were enrolled from Beth Israel Deaconess Medical Center. This cohort was used for model development, and 10-fold cross-validation with 50 repeats was used for internal validation. In total, 3743 patients with sepsis from the eICU Collaborative Research Database were used as the external validation cohort. All included patients with sepsis were stratified based on disease progression trajectories: rapid death, recovery, and persistent ill. A total of 148 variables were selected for predicting the 3 trajectories. Four machine learning algorithms with 3 different setups were used. We estimated the uncertainty of the model outputs using conformal prediction (CP). The Shapley Additive Explanations method was used to explain the model. RESULTS: The multiclass gradient boosting machine was identified as the best-performing model with good discrimination and calibration performance in both validation cohorts. The mean area under the receiver operating characteristic curve with SD was 0.906 (0.018) for rapid death, 0.843 (0.008) for recovery, and 0.807 (0.010) for persistent ill in the internal validation cohort. In the external validation cohort, the mean area under the receiver operating characteristic curve (SD) was 0.878 (0.003) for rapid death, 0.764 (0.008) for recovery, and 0.696 (0.007) for persistent ill. The maximum norepinephrine equivalence, total urine output, Acute Physiology Score III, mean systolic blood pressure, and the coefficient of variation of oxygen saturation contributed the most. Compared to the model without CP, using the model with CP at a mixed confidence approach reduced overall prediction errors by 27.6% (n=62) and 30.7% (n=412) in the internal and external validation cohorts, respectively, as well as enabled the identification of more potentially persistent ill patients. CONCLUSIONS: The implementation of our model has the potential to reduce heterogeneity and enroll more homogeneous patients in sepsis clinical trials. The use of CP for estimating the uncertainty of the model outputs allows for a more comprehensive understanding of the model's reliability and assists in making informed decisions based on the predicted outcomes.

ISG15 accelerates acute kidney injury and the subsequent AKI-to-CKD transition by promoting TGFßR1 ISGylation.

Rationale: Acute kidney injury (AKI) has substantial rates of mortality and morbidity, coupled with an absence of efficacious treatment options. AKI commonly transits into chronic kidney disease (CKD) and ultimately culminates in end-stage renal failure. The interferon-stimulated gene 15 (ISG15) level was upregulated in the kidneys of mice injured by ischemia-reperfusion injury (IRI), cisplatin, or unilateral ureteral obstruction (UUO), however, its role in AKI development and subsequent AKI-to-CKD transition remains unknown. Methods: Isg15 knockout (Isg15 KO) mice challenged with bilateral or unilateral IRI, cisplatin, or UUO were used to investigate its role in AKI. We established cellular models with overexpression or knockout of ISG15 and subjected them to hypoxia-reoxygenation, cisplatin, or transforming growth factor- ß1 (TGF-ß1) stimulation. Renal RNA-seq data obtained from AKI models sourced from public databases and our studies, were utilized to examine the expression profiles of ISG15 and its associated genes. Additionally, published single cell RNA-seq data from human kidney allograft biopsies and mouse IRI model were analyzed to investigate the expression patterns of ISG15 and the type I TGF-ß receptor (TGFßR1). Western blotting, qPCR, co-immunoprecipitation, and immunohistochemical staining assays were performed to validate our findings. Results: Alleviated pathological injury and renal function were observed in Isg15 KO mice with IRI-, cisplatin-, or UUO-induced AKI and the following AKI-to-CKD transition. In hypoxia-reoxygenation, cisplatin or TGF-ß1 treated HK-2 cells, knockout ISG15 reduced stimulus-induced cell fibrosis, while overexpression of ISG15 with modification capacity exacerbated cell fibrosis. Immunoprecipitation assays demonstrated that ISG15 promoted ISGylation of TGFßR1, and inhibited its ubiquitination. Moreover, knockout of TGFßR1 blocked ISG15's fibrosis-exacerbating effect in HK-2 cells, while overexpression of TGFßR1 abolished the renal protective effect of ISG15 knockout during IRI-induced kidney injury. Conclusions: ISG15 plays an important role in the development of AKI and subsequent AKI-to-CKD transition by promoting TGFßR1 ISGylation.

SCG2 and CPE may be novel markers for the identification of pancreatic neuroendocrine tumors and solid pseudopapillary neoplasms.

Background: Distinguishing pancreatic neuroendocrine tumors (pNETs) from solid pseudopapillary neoplasms (SPNs) is challenging, primarily due to their overlapping pathological characteristics. To address this, our study aims to identify and validate novel biomarkers that effectively differentiate between these two conditions. We focus on the exploration of new immunohistochemical markers to enhance this distinction. Methods: In this study, we analyzed genetic variations in pNETs and SPNs using the GSE43795 dataset from the Gene Expression Omnibus (GEO) database. Our approach was to identify genes with higher expression in pNETs compared to SPNs and normal pancreatic tissues. We conducted enrichment analyses to understand the functions of these genes. Furthermore, protein-protein interaction (PPI) network analysis was utilized to identify key genes associated with pNETs. Our sample consisted of 163 pancreatic tumor specimens, comprising 78 pNETs and 85 SPNs. We also collected clinicopathological data and used immunohistochemistry to measure the expression levels of these key genes. Results: The enrichment analysis revealed that genes overexpressed in pNETs were mainly involved in signal release, vesicle transport, and ion pathway activation, playing significant roles in endocrine processes like insulin secretion, dopamine synapses, and circadian rhythm regulation. The PPI analysis identified secretogranin II (SCG2), carboxypeptidase E (CPE), and chromogranin A (CgA, CHGA) as key markers for differentiating pNETs from SPNs. Immunohistochemical validation of these markers demonstrated high sensitivity (SCG2: 98.7%, CPE: 97.4%) and specificity (100%), indicating their superior discriminative power compared to traditional markers like CgA, ß-catenin, lymphoid enhancer-binding factor 1 (LEF1), and vimentin. Conclusions: Our study indicates that SCG2 and CPE are effective, novel immunohistochemical biomarkers for differentiating pNETs from SPNs.

Tyrosine phosphorylation of both STAT5A and STAT5B is necessary for maximal IL-2 signaling and T cell proliferation.

Cytokine-mediated STAT5 protein activation is vital for lymphocyte development and function. In vitro tyrosine phosphorylation of a C-terminal tyrosine is critical for activation of STAT5A and STAT5B; however, the importance of STAT5 tyrosine phosphorylation in vivo has not been assessed. Here we generate Stat5a and Stat5b tyrosine-to-phenylalanine mutant knockin mice and find they have greatly reduced CD8+ T-cell numbers and profoundly diminished IL-2-induced proliferation of these cells, and this correlates with reduced induction of Myc, pRB, a range of cyclins and CDKs, and a partial G1→S phase-transition block. These mutant CD8+ T cells also exhibit decreased IL-2-mediated activation of pERK and pAKT, which we attribute in part to diminished expression of IL-2Rß and IL-2Rγ. Our findings thus demonstrate that tyrosine phosphorylation of both STAT5A and STAT5B is essential for maximal IL-2 signaling. Moreover, our transcriptomic and proteomic analyses elucidate the molecular basis of the IL-2-induced proliferation of CD8+ T cells.

Multikingdom and functional gut microbiota markers for autism spectrum disorder.

Associations between the gut microbiome and autism spectrum disorder (ASD) have been investigated although most studies have focused on the bacterial component of the microbiome. Whether gut archaea, fungi and viruses, or function of the gut microbiome, is altered in ASD is unclear. Here we performed metagenomic sequencing on faecal samples from 1,627 children (aged 1-13 years, 24.4% female) with or without ASD, with extensive phenotype data. Integrated analyses revealed that 14 archaea, 51 bacteria, 7 fungi, 18 viruses, 27 microbial genes and 12 metabolic pathways were altered in children with ASD. Machine learning using single-kingdom panels showed area under the curve (AUC) of 0.68 to 0.87 in differentiating children with ASD from those that are neurotypical. A panel of 31 multikingdom and functional markers showed a superior diagnostic accuracy with an AUC of 0.91, with comparable performance for males and females. Accuracy of the model was predominantly driven by the biosynthesis pathways of ubiquinol-7 or thiamine diphosphate, which were less abundant in children with ASD. Collectively, our findings highlight the potential application of multikingdom and functional gut microbiota markers as non-invasive diagnostic tools in ASD.

Continuous prediction and clinical alarm management of late-onset sepsis in preterm infants using vital signs from a patient monitor.

BACKGROUND AND OBJECTIVE: Continuous prediction of late-onset sepsis (LOS) could be helpful for improving clinical outcomes in neonatal intensive care units (NICU). This study aimed to develop an artificial intelligence (AI) model for assisting the bedside clinicians in successfully identifying infants at risk for LOS using non-invasive vital signs monitoring. METHODS: In a retrospective study from the NICU of the Máxima Medical Center in Veldhoven, the Netherlands, a total of 492 preterm infants less than 32 weeks gestation were included between July 2016 and December 2018. Data on heart rate (HR), respiratory rate (RR), and oxygen saturation (SpO2) at 1 Hz were extracted from the patient monitor. We developed multiple AI models using 102 extracted features or raw time series to provide hourly LOS risk prediction. Shapley values were used to explain the model. For the best performing model, the effect of different vital signs and also the input type of signals on model performance was tested. To further assess the performance of applying the best performing model in a real-world clinical setting, we performed a simulation using four different alarm policies on continuous real-time predictions starting from three days after birth. RESULTS: A total of 51 LOS patients and 68 controls were finally included according to the patient inclusion and exclusion criteria. When tested by seven-fold cross-validations, the mean (standard deviation) area under the receiver operating characteristic curve (AUC) six hours before CRASH was 0.875 (0.072) for the best performing model, compared to the other six models with AUC ranging from 0.782 (0.089) to 0.846 (0.083). The best performing model performed only slightly worse than the model learning from raw physiological waveforms (0.886 [0.068]), successfully detecting 96.1 % of LOS patients before CRASH. When setting the expected alarm window to 24 h and using a multi-threshold alarm policy, the sensitivity metric was 71.6 %, while the positive predictive value was 9.9 %, resulting in an average of 1.15 alarms per day per patient. CONCLUSIONS: The proposed AI model, which learns from routinely collected vital signs, has the potential to assist clinicians in the early detection of LOS. Combined with interpretability and clinical alarm management, this model could be better translated into medical practice for future clinical implementation.

Physiologic Network-Based Brain-Heart Interaction Quantification During Visual Emotional Elicitation.

In recent years, there has been a surge in interest regarding the intricate physiological interplay between the brain and the heart, particularly during emotional processing. This has led to the development of various signal processing techniques aimed at investigating Brain-Heart Interactions (BHI), reflecting a growing appreciation for their bidirectional communication and influence on each other. Our study contributes to this burgeoning field by adopting a network physiology approach, employing time-delay stability as a quantifiable metric to discern and measure the coupling strength between the brain and the heart, specifically during visual emotional elicitation. We extract and transform features from EEG and ECG signals into a 1 Hz format, facilitating the calculation of BHI coupling strength through stability analysis on their maximal cross-correlation. Notably, our investigation sheds light on the critical role played by low-frequency components in EEG, particularly in the δ , θ , and α bands, as essential mediators of information transmission during the complex processing of emotion-related stimuli by the brain. Furthermore, our analysis highlights the pivotal involvement of frontal pole regions, emphasizing the significance of δ - θ coupling in mediating emotional responses. Additionally, we observe significant arousal-dependent changes in the θ frequency band across different emotional states, particularly evident in the prefrontal cortex. By offering novel insights into the synchronized dynamics of cortical and heartbeat activities during emotional elicitation, our research enriches the expanding knowledge base in the field of neurophysiology and emotion research.

An ATP-responsive metal-organic framework against periodontitis via synergistic ion-interference-mediated pyroptosis.

Periodontitis involves hyperactivated stromal cells that recruit immune cells, exacerbating inflammation. This study presents an ATP-responsive metal-organic framework (Mg/Zn-MOF) designed for periodontitis treatment, utilizing ion interference to modulate immune responses and prevent tissue destruction. Addressing the challenges of synergistic ion effects and targeted delivery faced by traditional immunomodulatory nanomaterials, the Mg/Zn-MOF system is activated by extracellular ATP-a pivotal molecule in periodontitis pathology-ensuring targeted ion release. Magnesium and zinc ions released from the framework synergistically inhibit membrane pore formation by attenuating Gasdermin D (GSDMD) expression and activation. This action curtails pyroptosis, lactate dehydrogenase and IL-1ß release, thwarting the onset of inflammatory cascades. Mechanistically, Mg/Zn-MOF intervenes in both the NLRP3/Caspase-1/GSDMD and Caspase-11/GSDMD pathways to mitigate pyroptosis. In vivo assessments confirm its effectiveness in diminishing inflammatory cell infiltration and preserving collagen integrity, thereby safeguarding against periodontal tissue damage and bone loss. This investigation highlights the promise of ion-interference strategies in periodontitis immunotherapy, representing a significant stride in developing targeted therapeutic approaches.

The actin motor protein OsMYA1 associates with OsExo70H1 and contributes to rice secretory defense by modulating OsSyp121 distribution.

Magnaporthe oryzae (M. oryzae) is a devastating hemibiotrophic pathogen. Its biotrophic invasive hyphae (IH) are enclosed in the extrainvasive hyphal membrane produced by plant cells, thus generating a front line of the battlefield between the pathogen and the host plants. In plants, defense-related complexes such as proteins, callose-rich materials and vesicles, are directionally secreted to this interface to confer defense responses, but the underlying molecular mechanism is poorly understood. In this study, we found that a Myosin gene, Myosin A1 (OsMYA1), contributed to rice defense. The OsMYA1 knockout mutant exhibited decreased resistance to M. oryzae infection. OsMYA1 localizes to the actin cytoskeleton and surrounds the IH of M. oryzae. OsMYA1 interacts with an exocyst subunit, OsExo70H1, and regulates its accumulation at the plasma membrane (PM) and pathogen-plant interface. Furthermore, OsExo70H1 interacted with the rice syntaxin of the plants121 protein (OsSyp121), and the distribution of OsSyp121 to the PM or the pathogen-plant interface was disrupted in both the OsMYA1 and OsExo70H1 mutants. Overall, these results not only reveal a new function of OsMYA1 in rice blast resistance, but also uncover a molecular mechanism by which plants regulate defense against M. oryzae by OsMYA1-initiated vesicle secretory pathway, which originates from the actin cytoskeleton to the PM.

Evolution of blockchain accounting literature from the perspective of CiteSpace (2013-2023).

Against the backdrop of the Industrial Revolution 4.0, the advantages of blockchain technology in traceability, transparency, safety improvement, and efficiency improvement have made it possible to reduce the work of accounting personnel by 50 %, thus saving billions of dollars for global companies by combining this technology with accounting. However, the blockchain technology associated with accounting is in the experimental stage and has several problems to be solved including limited data processing capacity, information confidentiality, and regulatory difficulties. This innovation and progress in science and technology has provided more abundant, efficient, and professional technical support for the research of blockchain accounting documents. Among these advances, CiteSpace software has promoted the development of blockchain and accounting in the direction of visualization, comprehensiveness, security, and relevance. In this study, we used the knowledge map drawn by CiteSpace to search the core Blockchain Accounting database from 2013 to 2023 on the Web of Science (WoS). We obtained 1414 documents measured according to co-citation analysis, log-likelihood ratio (LLR) network clustering, co-occurrence keywords, and emergent time zone diagram method. We analyzed and summarized the important documents, research keywords, key research fields, and knowledge evolution related to "blockchain accounting" by network, literature integration, and popular research topics. We found that adopting blockchain technology in accounting information systems is expected to improve recordkeeping and reporting. Blockchain, as an innovative technology, provides a tamper-proof, traceable, and shareable platform for accounting information by using a distributed ledger system. By implementing blockchain, artificial intelligence can improve safety, transparency, and accuracy, and also may completely change the way we manage financial records. With its ability to improve overall efficiency and reduce errors, blockchain technology may change our familiar accounting methods. In addition, blockchain technology, intelligent contract, artificial intelligence, the Internet, information systems, and supply chain are the most important keywords, while blockchain technology, intelligent contract, and artificial intelligence are important components of blockchain accounting knowledge system. This research provided an important opportunity to advance the understanding of the crucial contribution of blockchain to the accounting field.

Poor sleep quality and overweight/obesity in healthcare professionals: a cross-sectional study.

Objective: This study aimed to analyze the relationship between the sleep quality of healthcare professionals and the incidence of overweight and obesity, exploring the potential impact of sleep quality on the onset of overweight and obesity in order to provide a scientific basis for formulating effective health intervention measures. Methods: A convenience sampling method was used to conduct a survey on the sleep characteristics and obesity status among healthcare professionals at Peking Union Medical College Hospital and Tianjin Dongli District Traditional Chinese Medicine Hospital. The survey was conducted via online questionnaires, which included demographic data, the Pittsburgh Sleep Quality Index (PSQI), height, weight, and related sleep, exercise, and dietary habits. Univariate and multivariate logistic regression analyses were applied to study the relationship between sleep quality and overweight/obesity among healthcare professionals. Results: A total of 402 questionnaires were distributed, with a 100% retrieval rate, yielding 402 valid questionnaires. The average body mass index of the 402 participants was 23.22 ± 3.87 kg/m^2. Among them, 144 cases were overweight or obese, accounting for 35.8% (144/402) of the total. The prevalence of poor sleep quality among healthcare professionals was 27.4% (110/402), with an average PSQI score of 8.37 ± 3.624. The rate of poor sleep quality was significantly higher in the overweight and obese group compared to the normal weight group (36.1% vs. 22.5%, p = 0.003). The multivariate analysis indicated that gender, marital status, lower education level, sleep duration (odds ratio [OR] =1.411, 95% confidence interval [CI] 1.043-1.910, p = 0.026), and sleep disturbances (OR = 1.574, 95%CI 1.123-2.206, p = 0.008) were significant risk factors for overweight and obesity among healthcare professionals. Conclusion: Overweight or obese healthcare professionals had poorer sleep quality compared to those with a normal weight. Sleep duration and sleep disorders were identified as independent risk factors for overweight or obesity in healthcare professionals. Increasing sleep duration and improving sleep disorders may play a positive role in controlling overweight and obesity among healthcare professionals.

Graph Convolutional Network With Connectivity Uncertainty for EEG-Based Emotion Recognition.

Automatic emotion recognition based on multichannel Electroencephalography (EEG) holds great potential in advancing human-computer interaction. However, several significant challenges persist in existing research on algorithmic emotion recognition. These challenges include the need for a robust model to effectively learn discriminative node attributes over long paths, the exploration of ambiguous topological information in EEG channels and effective frequency bands, and the mapping between intrinsic data qualities and provided labels. To address these challenges, this study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals based on Graph Convolutional Network (GCN) architecture that adaptively assigns weights to functional aggregate node features, enabling effective long-path capturing while mitigating over-smoothing phenomena. Moreover, the graph mixup technique is employed to enhance latent connected edges and mitigate noisy label issues. Furthermore, we integrate the uncertainty learning method with deep GCN weights in a one-way learning fashion, termed Connectivity Uncertainty GCN (CU-GCN). We evaluate our approach on two widely used datasets, namely SEED and SEEDIV, for emotion recognition tasks. The experimental results demonstrate the superiority of our methodology over previous methods, yielding positive and significant improvements. Ablation studies confirm the substantial contributions of each component to the overall performance.

STAT5B SH2 variants disrupt mammary enhancers and the stability of genetic programs during pregnancy.

During pregnancy, mammary tissue undergoes expansion and differentiation, leading to lactation, a process regulated by the hormone prolactin through the JAK2-STAT5 pathway. STAT5 activation is key to successful lactation making the mammary gland an ideal experimental system to investigate the impact of human missense mutations on mammary tissue homeostasis. Here, we investigated the effects of two human variants in the STAT5B SH2 domain, which convert tyrosine 665 to either phenylalanine (Y665F) or histidine (Y665H), both shown to activate STAT5B in cell culture. We ported these mutations into the mouse genome and found distinct and divergent functions. Homozygous Stat5bY665H mice failed to form functional mammary tissue, leading to lactation failure, with impaired alveolar development and greatly reduced expression of key differentiation genes. STAT5BY665H failed to recognize mammary enhancers and impeded STAT5A binding. In contrast, mice carrying the Stat5bY665F mutation exhibited abnormal precocious development, accompanied by an early activation of the mammary transcription program and the induction of otherwise silent genetic programs. Physiological adaptation was observed in Stat5bY665H mice as continued exposure to pregnancy hormones led to lactation. In summary, our findings highlight that human STAT5B variants can modulate their response to cytokines and thereby impact mammary homeostasis and lactation.

Optimal CXCR5 Expression during Tfh Maturation Involves the Bhlhe40-Pou2af1 Axis.

The pair of transcription factors Bcl6-Blimp1 is well-known for follicular T helper (Tfh) cell fate determination, however, the mechanism(s) for Bcl6-independent regulation of CXCR5 during Tfh migration into germinal center (GC) is still unclear. In this study, we uncovered another pair of transcription factors, Bhlhe40-Pou2af1, that regulates CXCR5 expression. Pou2af1 was specifically expressed in Tfh cells whereas Bhlhe40 expression was found high in non-Tfh cells. Pou2af1 promoted Tfh formation and migration into GC by upregulating CXCR5 but not Bcl6, while Bhlhe40 repressed this process by inhibiting Pou2af1 expression. RNA-Seq analysis of antigen-specific Tfh cells generated in vivo confirmed the role of Bhlhe40-Pou2af1 axis in regulating optimal CXCR5 expression. Thus, the regulation of CXCR5 expression and migration of Tfh cells into GC involves a transcriptional regulatory circuit consisting of Bhlhe40 and Pou2af1, which operates independent of the Bcl6-Blimp1 circuit that determines the Tfh cell fate.

Monitoring mitochondrial calcium in cardiomyocytes during coverslip hypoxia using a fluorescent lifetime indicator.

An increase in mitochondrial calcium via the mitochondrial calcium uniporter (MCU) has been implicated in initiating cell death in the heart during ischemia-reperfusion (I/R) injury. Measurement of calcium during I/R has been challenging due to the pH sensitivity of indicators coupled with the fall in pH during I/R. The development of a pH-insensitive indicator, mitochondrial localized Turquoise Calcium fluorescence Lifetime Sensor (mito-TqFLITS), allows for quantifying mitochondrial calcium during I/R via fluorescent lifetime imaging. Mitochondrial calcium was monitored using mito-TqFLITS, in neonatal mouse ventricular myocytes (NMVM) isolated from germline MCU-KO mice and MCUfl/fl treated with CRE-recombinase to acutely knockout MCU. To simulate ischemia, a coverslip was placed on a monolayer of NMVMs to prevent access to oxygen and nutrients. Reperfusion was induced by removing the coverslip. Mitochondrial calcium increases threefold during coverslip hypoxia in MCU-WT. There is a significant increase in mitochondrial calcium during coverslip hypoxia in germline MCU-KO, but it is significantly lower than in MCU-WT. We also found that compared to WT, acute MCU-KO resulted in no difference in mitochondrial calcium during coverslip hypoxia and reoxygenation. To determine the role of mitochondrial calcium uptake via MCU in initiating cell death, we used propidium iodide to measure cell death. We found a significant increase in cell death in both the germline MCU-KO and acute MCU-KO, but this was similar to their respective WTs. These data demonstrate the utility of mito-TqFLITS to monitor mitochondrial calcium during simulated I/R and further show that germline loss of MCU attenuates the rise in mitochondrial calcium during ischemia but does not reduce cell death.

Fecal Microbiota Transplantation for Sleep Disturbance in Post-acute COVID-19 Syndrome.

BACKGROUND & AIMS: Post-acute COVID-19 syndrome (PACS) is associated with sleep disturbance, but treatment options are limited. The etiology of PACS may be secondary to alterations in the gut microbiome. Here, we report the efficacy of fecal microbiota transplantation (FMT) in alleviating post-COVID insomnia symptoms in a nonrandomized, open-label prospective interventional study. METHODS: Between September 22, 2022, and May 22, 2023, we recruited 60 PACS patients with insomnia defined as Insomnia Severity Index (ISI) ≥8 and assigned them to the FMT group (FMT at weeks 0, 2, 4, and 8; n = 30) or the control group (n = 30). The primary outcome was clinical remission defined by an ISI of <8 at 12 weeks. Secondary outcomes included changes in the Pittsburgh Sleep Quality Index, Generalized Anxiety Disorder-7 scale, Epworth Sleepiness Scale, Multidimensional Fatigue Inventory, blood cortisol and melatonin, and gut microbiome analysis on metagenomic sequencing. RESULTS: At week 12, more patients in the FMT than the control group had insomnia remission (37.9% vs 10.0%; P = .018). The FMT group showed a decrease in ISI score (P < .0001), Pittsburgh Sleep Quality Index (P < .0001), Generalized Anxiety Disorder-7 scale (P = .0019), Epworth Sleepiness Scale (P = .0057), and blood cortisol concentration (P = .035) from baseline to week 12, but there was no significant change in the control group. There was enrichment of bacteria such as Gemmiger formicilis and depletion of microbial pathways producing menaquinol derivatives after FMT. The gut microbiome profile resembled that of the donor in FMT responders but not in nonresponders at week 12. There was no serious adverse event. CONCLUSIONS: This pilot study showed that FMT could be effective and safe in alleviating post-COVID insomnia, and further clinical trials are warranted. CLINICALTRIALS: gov, Number: NCT05556733.

Association between nutritional risk and fatigue in frailty conditions for older adult patients: a multicentre cross-sectional survey study.

BACKGROUND AND AIMS: Frailty is widespread in the elderly, while there is a bi-directional relationship between frailty and malnutrition. The objectives of this study were to investigate the prevalence and correlation of frailty and nutritional risk in older adult patients and to analyse the factors associated with fatigue which is one indicator of frailty. METHODS: This cross-sectional multicentre survey study was conducted in five hospitals in the same city from 01 January 2021 to 01 December 2021. We collected information on gender, age, diseases, medication and dietary status. Frailty status was diagnosed using the FRAIL scale, and Nutritional Risk Screening-2002 was used to screen the nutritional risk. Spearman rank correlation was used to analyse the correlation between frailty and nutritional risk. Univariate and multivariate logistic regression analyses were used to analyse the risk factors related to fatigue in all patients and inpatients. RESULTS: Among 2016 older adult patients, the prevalence of frailty was 15.1% (305/2016), the prevalence of nutritional risk was 16.2% (327/2016) and the overlap prevalence of frailty and nutritional risk was 7.3% (147/2016). Multivariate analysis showed that nutritional risk (OR 3.109, 95% CI 2.384 to 4.056, p<0.001) was an independent risk factor for fatigue in all patients; similar results were found for nutritional risk (OR 2.717, 95% CI 2.068 to 3.571, p<0.001) in hospitalised patients. CONCLUSIONS: Frailty and nutritional risk are prevalent among older adult patients, and nutritional risk is associated with the occurrence of fatigue in older adult patients and older adult inpatients. TRIAL REGISTRATION NUMBER: China Clinical Trial Registry (Registered No. ChiCTR-EPC-14005253).

Sexually dimorphic renal expression of Klotho is directed by a kidney-specific distal enhancer responsive to HNF1b.

Transcription enhancers are genomic sequences regulating common and tissue-specific genes and their disruption can contribute to human disease development and progression. Klotho, a sexually dimorphic gene specifically expressed in kidney, is well-linked to kidney dysfunction and its deletion from the mouse genome leads to premature aging and death. However, the sexually dimorphic regulation of Klotho is not understood. Here, we characterize two candidate Klotho enhancers using H3K27ac epigenetic marks and transcription factor binding and investigate their functions, individually and combined, through CRISPR-Cas9 genome engineering. We discovered that only the distal (E1), but not the proximal (E2) candidate region constitutes a functional enhancer, with the double deletion not causing Klotho expression to further decrease. E1 activity is dependent on HNF1b transcription factor binding site within the enhancer. Further, E1 controls the sexual dimorphism of Klotho as evidenced by qPCR and RNA-seq. Despite the sharp reduction of Klotho mRNA, unlike germline Klotho knockouts, mutant mice presented normal phenotype, including weight, lifespan, and serum biochemistry. Lastly, only males lacking E1 display more prominent acute, but not chronic kidney injury responses, indicating a remarkable range of potential adaptation to isolated Klotho loss, especially in female E1 knockouts, retaining renoprotection despite over 80% Klotho reduction.