European and African-specific plasma protein-QTL and metabolite-QTL analyses identify ancestry-specific T2D effector proteins and metabolites.

Initially focused on the European population, multiple genome-wide association studies (GWAS) of complex diseases, such as type-2 diabetes (T2D), have now extended to other populations. However, to date, few ancestry-matched omics datasets have been generated or further integrated with the disease GWAS to nominate the key genes and/or molecular traits underlying the disease risk loci. In this study, we generated and integrated plasma proteomics and metabolomics with array-based genotype datasets of European (EUR) and African (AFR) ancestries to identify ancestry-specific muti-omics quantitative trait loci (QTLs). We further applied these QTLs to ancestry-stratified T2D risk to pinpoint key proteins and metabolites underlying the disease-associated genetic loci. We nominated five proteins and four metabolites in the European group and one protein and one metabolite in the African group to be part of the molecular pathways of T2D risk in an ancestry-stratified manner. Our study demonstrates the integration of genetic and omic studies of different ancestries can be used to identify distinct effector molecular traits underlying the same disease across diverse populations. Specifically, in the AFR proteomic findings on T2D, we prioritized the protein QSOX2; while in the AFR metabolomic findings, we pinpointed the metabolite GlcNAc sulfate conjugate of C21H34O2 steroid. Neither of these findings overlapped with the corresponding EUR results.

Evaluating tuberculosis knowledge and awareness of effective control practices among health care workers in primary- and secondary-level medical institutions in Beijing, China.

OBJECTIVE: Inadequate tuberculosis (TB) knowledge and awareness of proper TB control practices among health care workers (HCWs) may increase the risk of nosocomial TB transmission. This study aimed to assess HCWs' TB-related knowledge and control practices to guide the development of more effective targeted TB health education and training programs. METHODS: In January 2023 a cross-sectional survey was administered to 323 HCWs employed by five primary health care centers and three secondary comprehensive medical institutions in Beijing, China. Survey data were collected using a standard questionnaire. RESULTS: Analysis of survey responses revealed TB knowledge and practices awareness rates of 60.4% and 90.6%, respectively. The overall average awareness rate across all 19 TB knowledge- and practice-related questions was 70.0%. Intermediate- and senior-level HCW's average TB knowledge score was respectively 2.225 and 8.175 times higher than that of primary-level HCWs, while the average TB knowledge score of HCWs in secondary comprehensive medical institutions was 3.052 times higher than that of HCWs in primary health care centers. Higher average TB knowledge score correlated with higher-level professional titles and higher level work units, but higher average TB control practices score correlated with employment at primary health care center rather than secondary comprehensive medical institution. Notably, 13.6% of HCWs had not received TB training during the past three years, while 86.1% expressed willingness to undergo online TB training. CONCLUSION: These findings highlight inadequate TB knowledge and awareness of proper TB control practices among HCWs in primary health care centers and secondary comprehensive medical institutions in Beijing, underscoring the urgent need for targeted educational and training initiatives to improve TB awareness and control efforts.

Genetic and multi-omic resources for Alzheimer disease and related dementia from the Knight Alzheimer Disease Research Center.

The Knight-Alzheimer Disease Research Center (Knight-ADRC) at Washington University in St. Louis has pioneered and led worldwide seminal studies that have expanded our clinical, social, pathological, and molecular understanding of Alzheimer Disease. Over more than 40 years, research volunteers have been recruited to participate in cognitive, neuropsychologic, imaging, fluid biomarkers, genomic and multi-omic studies. Tissue and longitudinal data collected to foster, facilitate, and support research on dementia and aging. The Genetics and high throughput -omics core (GHTO) have collected of more than 26,000 biological samples from 6,625 Knight-ADRC participants. Samples available include longitudinal DNA, RNA, non-fasted plasma, cerebrospinal fluid pellets, and peripheral blood mononuclear cells. The GHTO has performed deep molecular profiling (genomic, transcriptomic, epigenomic, proteomic, and metabolomic) from large number of brain (n = 2,117), CSF (n = 2,012) and blood/plasma (n = 8,265) samples with the goal of identifying novel risk and protective variants, identify novel molecular biomarkers and causal and druggable targets. Overall, the resources available at GHTO support the increase of our understanding of Alzheimer Disease.

A genetic and proteomic comparison of key AD biomarkers across tissues.

INTRODUCTION: Plasma has been proposed as an alternative to cerebrospinal fluid (CSF) for measuring Alzheimer's disease (AD) biomarkers, but no studies have analyzed in detail which biofluid is more informative for genetics studies of AD. METHOD: Eleven proteins associated with AD (α-synuclein, apolipoprotein E [apoE], CLU, GFAP, GRN, NfL, NRGN, SNAP-25, TREM2, VILIP-1, YKL-40) were assessed in plasma (n = 2317) and CSF (n = 3107). Both plasma and CSF genome-wide association study (GWAS) analyses were performed for each protein, followed by functional annotation. Additional characterization for each biomarker included calculation of correlations and predictive power. RESULTS: Eighteen plasma protein quantitative train loci (pQTLs) associated with 10 proteins and 16 CSF pQTLs associated with 9 proteins were identified. Plasma and CSF shared some genetic loci, but protein levels between tissues correlated weakly. CSF protein levels better associated with AD compared to plasma. DISCUSSION: The present results indicate that CSF is more informative than plasma for genetic studies in AD. HIGHLIGHTS: The identification of novel protein quantitative trait loci (pQTLs) in both plasma and cerebrospinal fluid (CSF). Plasma and CSF levels of neurodegeneration-related proteins correlated weakly. CSF is more informative than plasma for genetic studies of Alzheimer's disease (AD). Neurofilament light (NfL), triggering receptor expressed on myeloid cells 2 (TREM2), and chitinase-3-like protein 1 (YKL-40) tend to show relatively strong inter-tissue associations. A novel signal in the apolipoprotein E (APOE) region was identified, which is an eQTL for APOC1.

Benchmarking of a multi-biomarker low-volume panel for Alzheimer's Disease and related dementia research.

Alzheimer's Disease (AD) biomarker measurement is key to aid in the diagnosis and prognosis of the disease. In the research setting, participant recruitment and retention and optimization of sample use, is one of the main challenges that observational studies face. Thus, obtaining accurate established biomarker measurements for stratification and maximizing use of the precious samples is key. Accurate technologies are currently available for established biomarkers, mainly immunoassays and immunoprecipitation liquid chromatography-mass spectrometry (IP-MS), and some of them are already being used in clinical settings. Although some immunoassays- and IP-MS based platforms provide multiplexing for several different coding proteins there is not a current platform that can measure all the stablished and emerging biomarkers in one run. The NUcleic acid Linked Immuno-Sandwich Assay (NULISA™) is a mid-throughput platform with antibody-based measurements with a sequencing output that requires 15µL of sample volume to measure more than 100 analytes, including those typically assayed for AD. Here we benchmarked and compared the AD-relevant biomarkers including in the NULISA against validated assays, in both CSF and plasma. Overall, we have found that CSF measures of Aß42/40, NfL, GFAP, and p-tau217 are highly correlated and have similar predictive performance when measured by immunoassay, mass-spectrometry or NULISA. In plasma, p-tau217 shows a performance similar to that reported with other technologies when predicting amyloidosis. Other established and exploratory biomarkers (total tau, p-tau181, NRGN, YKL40, sTREM2, VILIP1 among other) show a wide range of correlation values depending on the fluid and the platform. Our results indicate that the multiplexed immunoassay platform produces reliable results for established biomarkers in CSF that are useful in research settings, with the advantage of measuring additional novel biomarkers using minimal sample volume.

Waste rubber - Black pollution reframed as a global issue: Ecological challenges and sustainability initiatives.

In contrast to "white pollution" originating from waste plastics, waste rubber is often referred to as "black pollution." The quantity and variety of waste rubber are increasing at an alarming rate, with a considerable fraction entering the global ecosystem via various pathways. This study presents the first critical review of waste rubber research with a focus on the risks associated with toxicant discharge and existing problems in waste rubber disposal, management, and recycling practices. We aim to obtain a comprehensive understanding of current research, particularly regarding the ecological impacts of these wastes, highlight major gaps, and propose the most significant research directions. A total of 192 studies published in journals were critically analysed. The importance of conducting long-term and large-scale experiments and developing efficient waste rubber recycling systems is also emphasised. This study highlights the need to address the challenges posed by waste rubber pollution and offers insights and references for undertaking ecological risk assessments and understanding the mechanisms underlying toxicant behaviour. Suggestions and countermeasures are proposed with ecosystem sustainability as the ultimate goal. Further long-term, comprehensive, and systematic research in this area is required.

Raddeanin A Protects the BRB Through Inhibiting Inflammation and Apoptosis in the Retina of Alzheimer's Disease.

Neuroinflammation and endothelial cell apoptosis are prominent features of blood-brain barrier (BBB) disruption, which have been described in Alzheimer's disease (AD) and can predict cognitive decline. Recent reports revealed vascular ß-amyloid (Aß) deposits, Muller cell degeneration and microglial dysfunction in the retina of AD patients. However, there has been no in-depth research on the roles of inflammation, retinal endothelial cell apoptosis, and blood-retinal barrier (BRB) damage in AD retinopathy. We found that Raddeanin A (RDA) could improve pathological and cognitive deficits in a mouse model of Alzheimer's disease by targeting ß-amyloidosis, However, the effects of RDA on AD retinal function require further study. To clarify whether RDA inhibits inflammation and apoptosis and thus improves BRB function in AD-related retinopathy. In vitro we used Aß-treated HRECs and MIO-M1 cells, and in vivo we used 3×Tg-AD mice to investigate the effect of RDA on BRB in AD-related retinopathy. We found that RDA could improve BRB function in AD-related retinopathy by inhibiting NLRP3-mediated inflammation and suppressing Wnt/ß-catenin pathway-mediated apoptosis, which is expected to improve the pathological changes in AD-related retinopathy and the quality of life of AD patients.

Pitavastatin sensitizes the EGFR-TKI associated resistance in lung cancer by inhibiting YAP/AKT/BAD-BCL-2 pathway.

BACKGROUND: Despite effective strategies, resistance in EGFR mutated lung cancer remains a challenge. Metabolic reprogramming is one of the main mechanisms of tumor drug resistance. A class of drugs known as "statins" inhibit lipid cholesterol metabolism and are widely used in patients with cardiovascular diseases. Previous studies have also documented its ability to improve the therapeutic impact in lung cancer patients who receive EGFR-TKI therapy. Therefore, the effect of statins on targeted drug resistance to lung cancer remains to be investigated. METHODS: Prolonged exposure to gefitinib resulted in the emergence of a resistant lung cancer cell line (PC9GR) from the parental sensitive cell line (PC9), which exhibited a traditional EGFR mutation. The CCK-8 assay was employed to assess the impact of various concentrations of pitavastatin on cellular proliferation. RNA sequencing was conducted to detect differentially expressed genes and their correlated pathways. For the detection of protein expression, Western blot was performed. The antitumor activity of pitavastatin was evaluated in vivo via a xenograft mouse model. RESULTS: PC9 gefitinib resistant strains were induced by low-dose maintenance. Cell culture and animal-related studies validated that the application of pitavastatin inhibited the proliferation of lung cancer cells, promoted cell apoptosis, and restrained the acquired resistance to EGFR-TKIs. KEGG pathway analysis showed that the hippo/YAP signaling pathway was activated in PC9GR cells relative to PC9 cells, and the YAP expression was inhibited by pitavastatin administration. With YAP RNA interference, pAKT, pBAD and BCL-2 expression was decreased, while BAX expression as increased. Accordingly, YAP down-regulated significantly increased apoptosis and decreased the survival rate of gefitinib-resistant lung cancer cells. After pAKT was increased by SC79, apoptosis of YAP down-regulated cells induced by gefitinib was decreased, and the cell survival rate was increased. Mechanistically, these effects of pitavastatin are associated with the YAP pathway, thereby inhibiting the downstream AKT/BAD-BCL-2 signaling pathway. CONCLUSION: Our study provides a molecular basis for the clinical application of the lipid-lowering drug pitavastatin enhances the susceptibility of lung cancer to EGFR-TKI drugs and alleviates drug resistance.

Long-Term Outcomes in Patients Using Protocol-Directed Active Surveillance for Prostate Cancer.

Importance: Outcomes from protocol-directed active surveillance for favorable-risk prostate cancers are needed to support decision-making. Objective: To characterize the long-term oncological outcomes of patients receiving active surveillance in a multicenter, protocol-directed cohort. Design, Setting, and Participants: The Canary Prostate Active Surveillance Study (PASS) is a prospective cohort study initiated in 2008. A cohort of 2155 men with favorable-risk prostate cancer and no prior treatment were enrolled at 10 North American centers through August 2022. Exposure: Active surveillance for prostate cancer. Main Outcomes and Measures: Cumulative incidence of biopsy grade reclassification, treatment, metastasis, prostate cancer mortality, overall mortality, and recurrence after treatment in patients treated after the first or subsequent surveillance biopsies. Results: Among 2155 patients with localized prostate cancer, the median follow-up was 7.2 years, median age was 63 years, 83% were White, 7% were Black, 90% were diagnosed with grade group 1 cancer, and median prostate-specific antigen (PSA) was 5.2 ng/mL. Ten years after diagnosis, the incidence of biopsy grade reclassification and treatment were 43% (95% CI, 40%-45%) and 49% (95% CI, 47%-52%), respectively. There were 425 and 396 patients treated after confirmatory or subsequent surveillance biopsies (median of 1.5 and 4.6 years after diagnosis, respectively) and the 5-year rates of recurrence were 11% (95% CI, 7%-15%) and 8% (95% CI, 5%-11%), respectively. Progression to metastatic cancer occurred in 21 participants and there were 3 prostate cancer-related deaths. The estimated rates of metastasis or prostate cancer-specific mortality at 10 years after diagnosis were 1.4% (95% CI, 0.7%-2%) and 0.1% (95% CI, 0%-0.4%), respectively; overall mortality in the same time period was 5.1% (95% CI, 3.8%-6.4%). Conclusions and Relevance: In this study, 10 years after diagnosis, 49% of men remained free of progression or treatment, less than 2% developed metastatic disease, and less than 1% died of their disease. Later progression and treatment during surveillance were not associated with worse outcomes. These results demonstrate active surveillance as an effective management strategy for patients diagnosed with favorable-risk prostate cancer.

Detection of free DNA based on metal-enhanced fluorescence triggered by CRISPR-Cas12a and colorimetric analysis.

The CRISPR-Cas system has been found to be extremely sensitive and there is an urgent demand to extend its potential in bioassays. Herein, we developed a novel nanobiosensor to detect the human papillomavirus 16 genes (HPV-16 DNA), which is triggered by CRISPR-Cas12a to amplify the fluorescence signal by metal-enhanced fluorescence (CAMEF). Along with the changing of the fluorescence signal, the aggregation of the substrate of MEF also leads to a change in the color of the mixture solution, enabling dual signal detection with the fluorescence and the naked eye. Furthermore, the designed CAMEF probe was verified to detect the HPV-16 DNA accurately and reliably in biological samples. Triggered by the CRISPR system, the designed CAMEF probe allows quantitative detection of the HPV-16 DNA in the wide range of 10-500 pM. Owing to the MEF, the fluorescence signal of the CAMEF probe was significantly amplified with the detection limit as low as 1 pM. Besides, we can determine the concentration of HPV-16 DNA simply by the naked eye, which also drastically reduces the possibility of false-positive signals. Theoretically, the target ssDNA could be any strand of DNA obtained by designing the crRNA sequence in the CRISPR-Cas system. We believe that the designed CAMEF sensor can present a reliable approach for the accurate detection of low amounts of target ssDNA in complex biological samples.

Predicting fracture risk for elderly osteoporosis patients by hybrid machine learning model.

Background and Objective: Osteoporotic fractures significantly impact individuals's quality of life and exert substantial pressure on the social pension system. This study aims to develop prediction models for osteoporotic fracture and uncover potential risk factors based on Electronic Health Records (EHR). Methods: Data of patients with osteoporosis were extracted from the EHR of Xinhua Hospital (July 2012-October 2017). Demographic and clinical features were used to develop prediction models based on 12 independent machine learning (ML) algorithms and 3 hybrid ML models. To facilitate a nuanced interpretation of the results, a comprehensive importance score was conceived, incorporating various perspectives to effectively discern and mine critical features from the data. Results: A total of 8530 patients with osteoporosis were included for analysis, of which 1090 cases (12.8%) were fracture patients. The hybrid model that synergistically combines the Support Vector Machine (SVM) and XGBoost algorithms demonstrated the best predictive performance in terms of accuracy and precision (above 90%) among all benchmark models. Blood Calcium, Alkaline phosphatase (ALP), C-reactive Protein (CRP), Apolipoprotein A/B ratio and High-density lipoprotein cholesterol (HDL-C) were statistically found to be associated with osteoporotic fracture. Conclusions: The hybrid machine learning model can be a reliable tool for predicting the risk of fracture in patients with osteoporosis. It is expected to assist clinicians in identifying high-risk fracture patients and implementing early interventions.

Analysis of care-seeking and diagnosis delay among pulmonary tuberculosis patients in Beijing, China.

Background: Tuberculosis (TB) remains a significant public health challenge in China. Early detection and diagnosis of TB cases are crucial to interrupt disease transmission and prevent its progression. This study aims to describe the delay in seeking care and diagnosis among patients with pulmonary tuberculosis (PTB) and identify the influencing factors in two counties in Beijing. Methods: A retrospective analysis was carried out to investigate care-seeking and diagnosis delay in two counties in Beijing. Basic information of PTB patients from January 1 to December 31, 2021, was extracted from the Tuberculosis Information Management System of China (TBIMS), and all enrolled patients were interviewed via telephone using a standard questionnaire. Statistical description was performed using the median and interquartile range (IQR). Chi-square test and multivariate logistic regression model were used to analyze the influencing factors. Results: 537 patients were enrolled. The median duration of care-seeking and diagnosis delay was 11 (IQR: 5-26) days and 8 (IQR: 0-18) days, with 41.71 and 35.20% of patients experiencing delays (>14 days). The study found that being asymptomatic (OR = 2.791, 95%CI: 1.710-4.555) before seeking medical care and not attending work during treatment (OR = 2.990, 95%CI: 1.419-6.298) were identified as risk factors for care-seeking delay. Patients who were tracked (OR = 2.632, 95%CI: 1.062-6.521) and diagnosed at tuberculosis control and prevention institutions (OR = 1.843, 95%CI: 1.061-3.202) had higher odds of diagnostic delays. 44.69% of patients presented a total delay (>28 days), with a median duration of 25 (IQR: 13-39) days. A multivariate logistic regression analysis showed that healthy examination (OR = 0.136, 95%CI: 0.043-0.425) was a protective factor for total delay. Conclusion: Public interventions are necessary to improve the efficiency of PTB patients detection and treatment in Beijing. Medical services should focus on the target population and improve access to medical care to further reduce delays for PTB patients.

CSF proteomic profiling with amyloid/tau positivity identifies distinctive sex-different alteration of multiple proteins involved in Alzheimer's disease.

In Alzheimer's disease (AD), the most common cause of dementia, females have higher prevalence and faster progression, but sex-specific molecular findings in AD are limited. Here, we comprehensively examined and validated 7,006 aptamers targeting 6,162 proteins in cerebral spinal fluid (CSF) from 2,077 amyloid/tau positive cases and controls to identify sex-specific proteomic signatures of AD. In discovery (N=1,766), we identified 330 male-specific and 121 female-specific proteomic alternations in CSF (FDR <0.05). These sex-specific proteins strongly predicted amyloid/tau positivity (AUC=0.98 in males; 0.99 in females), significantly higher than those with age, sex, and APOE-ε4 (AUC=0.85). The identified sex-specific proteins were well validated (r≥0.5) in the Stanford study (N=108) and Emory study (N=148). Biological follow-up of these proteins led to sex differences in cell-type specificity, pathways, interaction networks, and drug targets. Male-specific proteins, enriched in astrocytes and oligodendrocytes, were involved in postsynaptic and axon-genesis. The male network exhibited direct connections among 152 proteins and highlighted PTEN, NOTCH1, FYN, and MAPK8 as hubs. Drug target suggested melatonin (used for sleep-wake cycle regulation), nabumetone (used for pain), daunorubicin, and verteporfin for treating AD males. In contrast, female-specific proteins, enriched in neurons, were involved in phosphoserine residue binding including cytokine activities. The female network exhibits strong connections among 51 proteins and highlighted JUN and 14-3-3 proteins (YWHAG and YWHAZ) as hubs. Drug target suggested biperiden (for muscle control of Parkinson's disease), nimodipine (for cerebral vasospasm), quinostatin and ethaverine for treating AD females. Together, our findings provide mechanistic understanding of sex differences for AD risk and insights into clinically translatable interventions.

Study on the differential diagnosis of benign and malignant breast lesions using a deep learning model based on multimodal images.

OBJECTIVE: To establish a multimodal model for distinguishing benign and malignant breast lesions. MATERIALS AND METHODS: Clinical data, mammography, and MRI images (including T2WI, diffusion-weighted images (DWI), apparent diffusion coefficient (ADC), and DCE-MRI images) of 132 benign and breast cancer patients were analyzed retrospectively. The region of interest (ROI) in each image was marked and segmented using MATLAB software. The mammography, T2WI, DWI, ADC, and DCE-MRI models based on the ResNet34 network were trained. Using an integrated learning method, the five models were used as a basic model, and voting methods were used to construct a multimodal model. The dataset was divided into a training set and a prediction set. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the model were calculated. The diagnostic efficacy of each model was analyzed using a receiver operating characteristic curve (ROC) and an area under the curve (AUC). The diagnostic value was determined by the DeLong test with statistically significant differences set at P < 0.05. RESULTS: We evaluated the ability of the model to classify benign and malignant tumors using the test set. The AUC values of the multimodal model, mammography model, T2WI model, DWI model, ADC model and DCE-MRI model were 0.943, 0.645, 0.595, 0.905, 0.900, and 0.865, respectively. The diagnostic ability of the multimodal model was significantly higher compared with that of the mammography and T2WI models. However, compared with the DWI, ADC, and DCE-MRI models, there was no significant difference in the diagnostic ability of these models. CONCLUSION: Our deep learning model based on multimodal image training has practical value for the diagnosis of benign and malignant breast lesions.

An anti-tumor protein PFAP specifically interacts with cholesterol-enriched membrane domains of A549 cells and induces paraptosis and endoplasmic reticulum stress.

Nowadays, non-small cell lung cancer (NSCLC) is still one of the most life-threatening diseases in the world. In previous studies, a fungal protein PFAP with anti-NSCLC properties was isolated and identified from Pleurotus ferulae lanzi. In this study, the amino acid sequence of PFAP was analyzed and found to be highly homologous to the aegerolysin family. PFAP, like other members of the aegerolysin family, specifically recognizes lipid raft domains rich in cholesterol and sphingomyelin, which is probably its specific anti-tumor mechanism. Previous studies have shown that PFAP can induce AMPK-mediated autophagy and G1-phase cell cycle arrest in A549 lung cancer cells. This study further revealed that PFAP can also induce paraptosis and endoplasmic reticulum stress (ERS) in A549 cells in vitro by targeting AMPK. PFAP induces multi-pathway death of A549 cells, and thus demonstrates its potential value for developing new drugs for NSCLC.

Multi-cohort cerebrospinal fluid proteomics identifies robust molecular signatures for asymptomatic and symptomatic Alzheimer's disease.

Changes in Amyloid-ß (A), hyperphosphorylated Tau (T) in brain and cerebrospinal fluid (CSF) precedes AD symptoms, making CSF proteome a potential avenue to understand the pathophysiology and facilitate reliable diagnostics and therapies. Using the AT framework and a three-stage study design (discovery, replication, and meta-analysis), we identified 2,173 proteins dysregulated in AD, that were further validated in a third totally independent cohort. Machine learning was implemented to create and validate highly accurate and replicable (AUC>0.90) models that predict AD biomarker positivity and clinical status. These models can also identify people that will convert to AD and those AD cases with faster progression. The associated proteins cluster in four different protein pseudo-trajectories groups spanning the AD continuum and were enrichment in specific pathways including neuronal death, apoptosis and tau phosphorylation (early stages), microglia dysregulation and endolysosomal dysfuncton(mid-stages), brain plasticity and longevity (mid-stages) and late microglia-neuron crosstalk (late stages).

Proteo-genomics of soluble TREM2 in cerebrospinal fluid provides novel insights and identifies novel modulators for Alzheimer's disease.

Triggering receptor expressed on myeloid cells 2 (TREM2) plays a critical role in microglial activation, survival, and apoptosis, as well as in Alzheimer's disease (AD) pathogenesis. We previously reported the MS4A locus as a key modulator for soluble TREM2 (sTREM2) in cerebrospinal fluid (CSF). To identify additional novel genetic modifiers of sTREM2, we performed the largest genome-wide association study (GWAS) and identified four loci for CSF sTREM2 in 3,350 individuals of European ancestry. Through multi-ethnic fine mapping, we identified two independent missense variants (p.M178V in MS4A4A and p.A112T in MS4A6A) that drive the association in MS4A locus and showed an epistatic effect for sTREM2 levels and AD risk. The novel TREM2 locus on chr 6 contains two rare missense variants (rs75932628 p.R47H, P=7.16×10-19; rs142232675 p.D87N, P=2.71×10-10) associated with sTREM2 and AD risk. The third novel locus in the TGFBR2 and RBMS3 gene region (rs73823326, P=3.86×10-9) included a regulatory variant with a microglia-specific chromatin loop for the promoter of TGFBR2. Using cell-based assays we demonstrate that overexpression and knock-down of TGFBR2, but not RBMS3, leads to significant changes of sTREM2. The last novel locus is located on the APOE region (rs11666329, P=2.52×10-8), but we demonstrated that this signal was independent of APOE genotype. This signal colocalized with cis-eQTL of NECTIN2 in the brain cortex and cis-pQTL of NECTIN2 in CSF. Overexpression of NECTIN2 led to an increase of sTREM2 supporting the genetic findings. To our knowledge, this is the largest study to date aimed at identifying genetic modifiers of CSF sTREM2. This study provided novel insights into the MS4A and TREM2 loci, two well-known AD risk genes, and identified TGFBR2 and NECTIN2 as additional modulators involved in TREM2 biology.

Long non-coding RNA small nucleolar RNA host gene 29 drives chronic myeloid leukemia progression via microRNA-483-3p/Casitas B-lineage Lymphoma axis-mediated activation of the phosphoinositide 3-kinase/Akt pathway.

The aberrant expression of the long non-coding RNA (lncRNA) Small Nucleolar RNA Host Gene 29 (SNHG29) has been associated with various human cancers. However, the role of SNHG29 in chronic myeloid leukemia (CML) remains elusive. Therefore, this study aimed to investigate the function of SNHG29 in CML and unveil its potential underlying mechanisms. Herein, peripheral blood samples from 44 CML patients and 17 healthy subjects were collected. The expressions of SNHG29, microRNA-483-3p (miR-483-3p), and Casitas B-lineage Lymphoma (CBL) were measured using quantitative polymerase chain reaction (qPCR) or Western Blot. Cell viability, apoptosis, and cell cycle progression were evaluated using the Cell Counting Kit-8 assay, 5-ethynyl-2'-deoxyuridine incorporation, and flow cytometry, respectively. Western Blot analysis was employed to assess protein expressions related to cellular proliferation, apoptosis, and oncogenesis. RNA immunoprecipitation and dual-luciferase reporter assays were utilized to verify the interactions among SNHG29, miR-483-3p, and CBL. SNHG29 was significantly overexpressed in both blood samples of CML patients and CML cell lines. In CML, increased expression of SNHG29 was positively correlated with clinical staging, and patients with high SNHG29 expression had poorer survival outcomes. Functionally, knocking down SNHG29 effectively inhibited CML cell proliferation and promoted apoptosis. Mechanistically, SNHG29 acted as a competing endogenous RNA for miR-483-3p to modulate CBL expression, thereby activating the Phosphoinositide 3-Kinase/Akt signaling pathway and mediating CML progression. In summary, these findings reveal that SNHG29 promotes tumorigenesis in CML, offering a potential therapeutic strategy for CML treatment.

Systematic proteomics in Autosomal dominant Alzheimer's disease reveals decades-early changes of CSF proteins in neuronal death, and immune pathways.

Background: To date, there is no high throughput proteomic study in the context of Autosomal Dominant Alzheimer's disease (ADAD). Here, we aimed to characterize early CSF proteome changes in ADAD and leverage them as potential biomarkers for disease monitoring and therapeutic strategies. Methods: We utilized Somascan® 7K assay to quantify protein levels in the CSF from 291 mutation carriers (MCs) and 185 non-carriers (NCs). We employed a multi-layer regression model to identify proteins with different pseudo-trajectories between MCs and NCs. We replicated the results using publicly available ADAD datasets as well as proteomic data from sporadic Alzheimer's disease (sAD). To biologically contextualize the results, we performed network and pathway enrichment analyses. Machine learning was applied to create and validate predictive models. Findings: We identified 125 proteins with significantly different pseudo-trajectories between MCs and NCs. Twelve proteins showed changes even before the traditional AD biomarkers (Aß42, tau, ptau). These 125 proteins belong to three different modules that are associated with age at onset: 1) early stage module associated with stress response, glutamate metabolism, and mitochondria damage; 2) the middle stage module, enriched in neuronal death and apoptosis; and 3) the presymptomatic stage module was characterized by changes in microglia, and cell-to-cell communication processes, indicating an attempt of rebuilding and establishing new connections to maintain functionality. Machine learning identified a subset of nine proteins that can differentiate MCs from NCs better than traditional AD biomarkers (AUC>0.89). Interpretation: Our findings comprehensively described early proteomic changes associated with ADAD and captured specific biological processes that happen in the early phases of the disease, fifteen to five years before clinical onset. We identified a small subset of proteins with the potentials to become therapy-monitoring biomarkers of ADAD MCs. Funding: Proteomic data generation was supported by NIH: RF1AG044546.