Integrating Fréchet distance and AI reveals the evolutionary trajectory and origin of SARS-CoV-2.

A genome, composed of a precisely ordered sequence of four nucleotides (ATCG), encompasses a multitude of specific genome features like AAA motif. Mutations occurring within a genome disrupt the sequential order and composition of these features, thereby influencing the evolutionary trajectories and yielding variants. The evolutionary relatedness between a variant and its ancestor can be estimated by assessing evolutionary distances across a spectrum of genome features. This study develops a novel, alignment-free algorithm that considers both the sequential order and composition of genome features, enabling computation of the Fréchet distance (Fr) across multiple genome features to quantify the evolutionary status of a variant. Integrating this algorithm with an artificial recurrent neural network (RNN) reveals the quantitative evolutionary trajectory and origin of SARS-CoV-2, a puzzle unsolved by alignment-based phylogenetics. The RNN generates the evolutionary trajectory from Fr data at two levels: genome sequence mutations and organism variants. At the genome sequence level, SARS-CoV-2 evolutionarily shortens its genome to enhance its infectious capacity. Mutating signature features, such as TTA and GCT, increases its infectious potential and drives its evolution. At the organism level, variants mutating a single biomarker possess low infectious potential. However, mutating multiple markers dramatically increases their infectious capacity, propelling the COVID-19 pandemic. SARS-CoV-2 likely originates from mink coronavirus variants, with its origin trajectory traced as follows: mink, cat, tiger, mouse, hamster, dog, lion, gorilla, leopard, bat, and pangolin. Together, mutating multiple signature features and biomarkers delineates the evolutionary trajectory of mink-origin SARS-CoV-2, leading to the COVID-19 pandemic.

Conceptual breakthroughs of the long noncoding RNA functional system and its endogenous regulatory role in the cancerous regime.

Long noncoding RNAs (lncRNAs) derived from noncoding regions in the human genome were once regarded as junks with no biological significance, but recent studies have shown that these molecules are highly functional, prompting an explosion of studies on their biology. However, these recent efforts have only begun to recognize the biological significance of a small fraction (< 1%) of the lncRNAs. The basic concept of these lncRNA functions remains controversial. This controversy arises primarily from conventional biased observations based on limited datasets. Fortunately, emerging big data provides a promising path to circumvent conventional bias to understand an unbiased big picture of lncRNA biology and advance the fundamental principles of lncRNA biology. This review focuses on big data studies that break through the critical concepts of the lncRNA functional system and its endogenous regulatory roles in all cancers. lncRNAs have unique functional systems distinct from proteins, such as transcriptional initiation and regulation, and they abundantly interact with mitochondria and consume less energy. lncRNAs, rather than proteins as traditionally thought, function as the most critical endogenous regulators of all cancers. lncRNAs regulate the cancer regulatory regime by governing the endogenous regulatory network of all cancers. This is accomplished by dominating the regulatory network module and serving as a key hub and top inducer. These critical conceptual breakthroughs lay a blueprint for a comprehensive functional picture of the human genome. They also lay a blueprint for combating human diseases that are regulated by lncRNAs.

Safety and efficacy of a probiotic cocktail containing P. acidilactici and L. plantarum for gastrointestinal discomfort in endurance runners: randomized double-blinded crossover clinical trial.

Probiotics are increasingly used to treat conditions associated with gastrointestinal injury and permeability, including exercise-induced gastrointestinal discomfort. This study assessed safety and efficacy of a probiotic in altering the intestinal milieu and mitigating gastrointestinal symptoms (GIS) in endurance runners. In a double blind, crossover study, 16 runners were randomized to 4 weeks of daily supplementation with a probiotic cocktail containing Pediococcus acidilactici bacteria and Lactobacillus plantarum or placebo. Fasting blood and stool samples were collected for measurement of gut permeability markers, immune parameters, and microbiome analyses. Treadmill run tests were performed before and after treatment; participants ran at 65%-70% of VO2max at 27 °C for a maximum of 90 min or until fatigue/GIS developed. A blood sample was collected after the treadmill run test. In healthy individuals, 4 weeks of probiotic supplementation did not alter health parameters, although a marginal reduction in aspartate aminotransferase levels was observed with probiotic treatment only (p = 0.05). GIS, gut permeability-associated parameters (intestinal fatty acid binding protein, lipopolysaccharide binding protein, zonulin, and cytokines), and intestinal microbial content were not altered by the probiotic supplementation. Post-run measurements of GIS and gut-associated parameters did not differ between groups; however, the observed lack of differences is confounded by an absence of measurable functional outcome as GIS was not sufficiently induced during the run. Under the current study conditions, the probiotic was safe to use, and did not affect gut- or immune-associated parameters, or intestinal symptoms in a healthy population. The probiotic might reduce tissue damage, but more studies are warranted.

Game Theory-Based Signal Control Considering Both Pedestrians and Vehicles in Connected Environment.

Signal control, as an integral component of traffic management, plays a pivotal role in enhancing the efficiency of traffic and reducing environmental pollution. However, the majority of signal control research based on game theory primarily focuses on vehicular perspectives, often neglecting pedestrians, who are significant participants at intersections. This paper introduces a game theory-based signal control approach designed to minimize and equalize the queued vehicles and pedestrians across the different phases. The Nash bargaining solution is employed to determine the optimal green duration for each phase within a fixed cycle length. Several simulation tests were carried out by SUMO software to assess the effectiveness of this proposed approach. We select the actuated signal control approach as the baseline to demonstrate the superiority and stability of the proposed control strategy. The simulation results reveal that the proposed approach is able to reduce pedestrian and vehicle delay, vehicle queue length, fuel consumption, and CO2 emissions under different demand levels and demand patterns. Furthermore, the proposed approach consistently achieves more equalized queue length for each lane compared to the actuated control strategy, indicating a higher degree of fairness.

Ligand Activation of the Aryl Hydrocarbon Receptor Upregulates Epidermal Uridine Diphosphate Glucose Ceramide Glucosyltransferase and Glucosylceramides.

Ligand activation of the aryl hydrocarbon receptor (AHR) accelerates keratinocyte differentiation and the formation of the epidermal permeability barrier. Several classes of lipids, including ceramides, are critical to the epidermal permeability barrier. In normal human epidermal keratinocytes, the AHR ligand, 2,3,7,8-tetrachlorodibenzo-p-dioxin, increased RNA levels of ceramide metabolism and transport genes: uridine diphosphate glucose ceramide glucosyltransferase (UGCG), ABCA12, GBA1, and SMPD1. Levels of abundant skin ceramides were also increased by 2,3,7,8-tetrachlorodibenzo-p-dioxin. These included the metabolites synthesized by UGCG, glucosylceramides, and acyl glucosylceramides. Chromatin immunoprecipitation-sequence analysis and luciferase reporter assays identified UGCG as a direct AHR target. The AHR antagonist, GNF351, inhibited the 2,3,7,8-tetrachlorodibenzo-p-dioxin-mediated RNA and transcriptional increases. Tapinarof, an AHR ligand approved for the treatment of psoriasis, increased UGCG RNA, protein, and its lipid metabolites hexosylceramides as well as increased the RNA expression of ABCA12, GBA1, and SMPD1. In Ahr-null mice, Ugcg RNA and hexosylceramides were lower than those in the wild type. These results indicate that the AHR regulates the expression of UGCG, a ceramide-metabolizing enzyme required for ceramide trafficking, keratinocyte differentiation, and epidermal permeability barrier formation.

Palbociclib promotes the antitumor activity of Venetoclax plus Azacitidine against acute myeloid leukemia.

Around 70 % of patients diagnosed with acute myeloid leukemia (AML) survive less than 5 years due to drug resistance and disease relapse. Consequently, improved clinical treatments are urgently needed. Some but not all AML patients benefit from the combination of the BCL-2 inhibitor Venetoclax with the hypomethylation agent Azacitidine. Here we investigated the utility of employing the cyclin dependent kinase (CDK6) inhibitor Palbociclib to improve the efficacy of Venetoclax/Azacitidine combination therapy. Our analysis of publicly available RNA sequencing datasets showed CDK6 was highly expressed in the major acute forms of leukemia including AML. Consistently, using qPCR and flow cytometry we found that CDK6 was overexpressed in bone marrow mononuclear cells from AML patients compared to healthy controls. Subsequent in vitro testing of Palbociclib, Venetoclax and Azacitidine, alone and in combination against CDK6-overexpressing AML cells lines THP-1 and KG-1 and primary AML cells showed that the Palbociclib/Venetoclax/Azacitidine combination improved treatment efficacy compared to Venetoclax/Azacitidine treatment alone. Additional investigations in a subcutaneous KG-1 mouse model showed similarly the three-drug combination produced the most significant reductions in tumor load together with the least amount of spleen infiltration. We established Palbociclib functioned in combination with Venetoclax/Azacitidine by increasing the rates of apoptosis in AML cells. Further investigations revealed that Palbociclib does not affect BCL-2 activity but downregulated the anti-apoptotic proteins MCL-1 and BCL-XL, making AML cells more sensitive to Venetoclax/Azacitidine treatment. Our results propose that the Palbociclib/Venetoclax/Azacitidine regimen warrants further preclinical research for clinical application in AML patients.

Correlation Analysis and Prognostic Impacts of Biological Characteristics in Elderly Patients with Acute Myeloid Leukemia.

Background: The significant heterogeneity of elderly AML patients' biological features has caused stratification difficulties and adverse prognosis. This paper did a correlation study between their genetic mutations, clinical features, and prognosis to further stratify them. Methods: 90 newly diagnosed elderly acute myeloid leukemia (AML) patients (aged ≥60 years) who detected genetic mutations by next-generation sequencing (NGS) were enrolled between April 2015 and March 2021 in our medical center. Results: A total of 29 genetic mutations were identified in 82 patients among 90 cases with a frequency of 91.1%. DNMT3A, BCOR, U2AF1, and BCORL1 mutations were unevenly distributed among different FAB classifications (p < 0.05). DNMT3A, IDH2, NPM1, FLT3-ITD, ASXL1, IDH1, SRSF2, BCOR, NRAS, RUNX1, U2AF1, MPO, and WT1 mutations were distributed differently when an immunophenotype was expressed or not expressed (p<0.05). NPM1 and FLT3-ITD had higher mutation frequencies in patients with normal chromosome karyotypes than abnormal chromosome karyotypes (p<0.001, p=0.005). DNMT3A and NRAS mutations predicted lower CR rates. DNMT3A, TP53, and U2AF1 mutations were related to unfavorable OS. TET2 mutation with CD123+, CD11b+ or CD34- predicted lower CR rate. IDH2+/CD34- predicted lower CR rate. ASXL1+/CD38+ and SRSF2+/CD123- predicted shorter OS. Conclusion: The study showed specific correlations between elderly AML patients' genetic mutations and clinical features, some of which may impact prognosis.

Distinctive functional regime of endogenous lncRNAs in dark regions of human genome.

>98% of the human genome is composed of noncoding regions and >93% of these noncoding regions are actively transcribed, suggesting their criticality in the human genome. Yet <1% of these regions have been functionally characterized, leaving most of the human genomes in the dark. Here, this study processes petabyte level data and systematically decodes endogenous lncRNAs located in unannotated regions of the human genome and deciphers a distinctive functional regime of lncRNAs hidden in massive RNAseq data. LncRNAs divergently distribute across chromosomes, independent of protein-coding regions. Their transcriptions rarely initiate on promoters through polymerase II, but rather partially on enhancers. Yet conventional enhancer markers (e.g. H3K4me1) only account for a small proportion of lncRNA transcriptions, suggesting alternatively unknown mechanisms initiating the majority of lncRNAs. Furthermore, lncRNA-self regulation also notably contributes to lncRNA activation. LncRNAs regulate broad bioprocesses, including transcription and RNA processing, cell cycle, respiration, response to stress, chromatin organization, post-translational modification, and development. Therefore, lncRNAs functionally govern their own regime distinctive from protein coding genes. This finding establishes a clear framework to comprehend human genome-wide lncRNA-lncRNA and lncRNA-protein coding gene regulations.

[Clinical Study on Dynamic Detection of Minimal Residual Disease in Acute Myeloid Leukemia by Multiparameter Flow Cytometry].

OBJECTIVE: To investigate the prognostic significance of dynamic detection of minimal residual disease (MRD) in patients with acute myeloid leukemia (AML) by 8-color flow cytometry. METHODS: MRD of 282 AML patients who achieved remission after initial therapy was detected by 8-color flow cytometry. MRD threshold for predicting recurrence was determined by receiver operating characteristic (ROC) curve, and time from MRD-positive to clinical recurrence was analyzed. The differences in overall survival (OS) time and relapse-free survival (RFS) time of patients with different MRD-changes were compared, and the related factors of recurrence in patients with MRD-negative were analyzed by univariate and logistic regression analysis. RESULTS: ROC curve determined that the MFC-MRD threshold for predicting the recurrence of AML was 0.105%, and the recurrence rate of MRD-positive patients was significantly higher than that of MRD-negative patients [52.45% (75/143 cases) vs 35.97% (50/139 cases), P=0.005]. The patients in MRD persistent positive group and negative to positive group recurred earlier than those in positive to negative group and negative-positive fluctuation group (P<0.005). Survival analysis showed that OS and RFS time of patients with MRD persistent positive were significantly shorter than those of patients with MRD persistent negative, positive to negative, and negative-positive fluctuation (P<0.005). There was no significant difference in OS and RFS between MRD negative to positive group and MRD persistent positive group (P>0.005), either between MRD persistent negative group and MRD positive to negative group (P>0.005). Among 139 MRD-negative patients, 50 recurred. Univariate and logistic regression analysis showed that the risk of recurrence increased with the increase of white blood cells level (95%CI: 1.000-1.013, P=0.045). The risk of recurrence in patients without hematopoietic stem cell transplantation (HSCT) was 9.694 times higher than that in patients who received HSCT (95%CI: 1.720-54.651, P=0.010), and in the high-risk group was 5.848 times higher than that in the low-risk group (95%CI: 1.418-24.121, P=0.015). CONCLUSION: The prognosis of AML patients with different MRD changes is significantly different. No matter MRD-positive or MRD-negative at the initial remission, dynamic detection of MRD after treatment is more helpful to accurately guide treatment.

Noncoding RNAs endogenously rule the cancerous regulatory realm while proteins govern the normal.

Cancers evolve from normal tissues and share an endogenous regulatory realm distinctive from that of normal human tissues. Unearthing such an endogenous realm faces challenges due to heterogeneous biology data. This study computes petabyte level data and reveals the endogenous regulatory networks of normal and cancers and then unearths the most important endogenous regulators for normal and cancerous realm. In normal, proteins dominate the entire realm and trans-regulate their targets across chromosomes and ribosomal proteins serve as the most important drivers. However, in cancerous realm, noncoding RNAs dominate the whole realm and pseudogenes work as the most important regulators that cis-regulate their neighbors, in which they primarily regulate their targets within 1 million base pairs but they rarely regulate their cognates with complementary sequences as thought. Therefore, two distinctive mechanisms rule the normal and cancerous realm separately, in which noncoding RNAs endogenously regulate cancers, instead of proteins as currently conceptualized. This establishes a fundamental avenue to understand the basis of cancerous and normal physiology.

Noncoding RNAs and Deep Learning Neural Network Discriminate Multi-Cancer Types.

Detecting cancers at early stages can dramatically reduce mortality rates. Therefore, practical cancer screening at the population level is needed. To develop a comprehensive detection system to classify multiple cancer types, we integrated an artificial intelligence deep learning neural network and noncoding RNA biomarkers selected from massive data. Our system can accurately detect cancer vs. healthy objects with 96.3% of AUC of ROC (Area Under Curve of a Receiver Operating Characteristic curve), and it surprisingly reaches 78.77% of AUC when validated by real-world raw data from a completely independent data set. Even validating with raw exosome data from blood, our system can reach 72% of AUC. Moreover, our system significantly outperforms conventional machine learning models, such as random forest. Intriguingly, with no more than six biomarkers, our approach can easily discriminate any individual cancer type vs. normal with 99% to 100% AUC. Furthermore, a comprehensive marker panel can simultaneously multi-classify common cancers with a stable 82.15% accuracy rate for heterogeneous cancerous tissues and conditions. This detection system provides a promising practical framework for automatic cancer screening at population level. Key points: (1) We developed a practical cancer screening system, which is simple, accurate, affordable, and easy to operate. (2) Our system binarily classify cancers vs. normal with >96% AUC. (3) In total, 26 individual cancer types can be easily detected by our system with 99 to 100% AUC. (4) The system can detect multiple cancer types simultaneously with >82% accuracy.

The Impact of a Dried Fruit and Vegetable Supplement and Fiber Rich Shake on Gut and Health Parameters in Female Healthcare Workers: A Placebo-Controlled, Double-Blind, Randomized Clinical Trial.

Aim: Phytochemicals from fruits and vegetables are known to reduce inflammation and improve overall health. The objective of this study was to determine the effect of a fruit and vegetable concentrate (FVC) and high fiber component on the gut microbiome in an overweight/obese, female population. Methods: The study was a randomized, double blind, placebo-controlled trial with 57 asymptomatic, pre-menopausal, overweight/obese females between 25-50 years of age working in healthcare. Blood and fecal samples were collected before and after two, four and five months of daily supplementation. Metabolic parameters were measured, and the gut microbiome analyzed. Results: No effect was observed with FVC supplementation for blood lipids, glucose and immune parameters. There was an improvement in glucose clearance. The FVC supplement did not result in taxonomic alterations at phyla level, or changes in α or ß diversity, but reduced Bacteroides abundance and increased fecal butyrate. An additional high fiber component improved levels of health associated bacteria. Conclusion: The results suggest that a dried fruit and vegetable supplement, with a high fiber meal replacement can alter the intestinal microbiota and improve glucose clearance, suggesting that this combination of supplements can improve glucose metabolism and possibly reduce the risk of insulin resistance.

Noncoding RNAs Serve as the Deadliest Universal Regulators of all Cancers.

Numerous cancer drivers have been identified, but they are specific to a given cancer type and condition; universal cancer drivers and universal cancer mechanisms still remain largely unclear. Here, we identified the deadliest universal drivers for all cancers via developing algorithms to analyze massive RNAseqs and clinical data from The Cancer Genome Atlas (TCGA). In general, noncoding RNAs primarily serve as the most important inducers and suppressors for all types of cancers. In particular, pseudogenes are primary inducers, and specifically the antisense RNA RP11-335K5.2 serves as the most universal cancerous driver, independently of the cancer type and condition. Therefore, noncoding RNAs, instead of proteins as conventionally thought, primarily drive cancer, which establishes a novel field for future cancer research and therapy.

FINET: Fast Inferring NETwork.

OBJECTIVES: Numerous software has been developed to infer the gene regulatory network, a long-standing key topic in biology and computational biology. Yet the slowness and inaccuracy inherited in current software hamper their applications to the increasing massive data. Here, we develop a software, FINET (Fast Inferring NETwork), to infer a network with high accuracy and rapidity from big data. RESULTS: The high accuracy results from integrating algorithms with stability-selection, elastic-net, and parameter optimization. Tested by a known biological network, FINET infers interactions with over 94% precision. The high speed comes from partnering parallel computations implemented with Julia, a new compiled language that runs much faster than existing languages used in the current software, such as R, Python, and MATLAB. Regardless of FINET's implementations with Julia, users with no background in the language or computer science can easily operate it, with only a user-friendly single command line. In addition, FINET can infer other networks such as chemical networks and social networks. Overall, FINET provides a confident way to efficiently and accurately infer any type of network for any scale of data.

[Killing Effect of A CD7 Chimeric Antigen Receptor-Modified NK-92MI Cell Line on CD7-Positive Hematological Malignant Cells].

OBJECTIVE: To investigate the killing effect of NK-92MI cells modified by chimeric antigen receptor (CD7-CAR) and specifically targeting CD7 to CD7+ hematological malignant cells. METHODS: Three types of hematological malignant tumor cells, including 5 cases of CD7+ acute T-lymphoblastic leukemia (T-ALL), 10 cases of acute myeloid leukemia (AML) and 6 cases of T-cell lymphoma were collected, centrifuged, cultured and used to detect the expression levels of tumor cell surface targets; 7-AAD, CD56-APC, CD3-FITC, IgG Fc-PE flow cytometry were used to detected the transfection efficiency of NK-92MI and CD7-CAR-NK-92MI cells, killing efficiencies of CD7-CAR-NK-92MI cells to CD7+ hematological tumor cells in vitro were determined by flow cytometry using PE Annexin V Apoptosis Detection Kit. Secretion differences of NK-92MI and CD7-CAR-NK-92MI cytokines interleukin (IL)-2, interferon (IFN)-γ, and granzyme B detection were estimated by using CBA kit. RESULTS: The killing efficiencies of CD7-CAR-modified NK-92MI cells to CD7+ T-ALL, AML, T-cell lymphoma tumor cells were significantly higher than those of NK-92MI cells without genetical modification. The difference showed statistically significant (P＜0.05). The level of IFN-γ and granzyme B were significantly increased among cytokines secreted by CD7-CAR-modified NK-92MI cells as compared with those of NK-92MI cells without genetical modification (P＜0.05) . CONCLUSION: CD7-CAR-modified NK-92MI cells have significantly improved killing efficiency against CD7+ T-ALL, AML and T lymphoma cells, and shows specific targeting effects, which provides a clinical basis for the treatment of CD7+ hematological malignancies.

Zika virus genome biology and molecular pathogenesis.

Zika virus (ZIKV) is an emerging RNA virus in the widespread Flavivirus genus. Recently, ZIKV has rapidly spread around the world and has been implicated in human disease, including neurological disorders, triggering public and scientific attention. Understanding how ZIKV causes disease is the highest priority, yet little is known about this virus. Here we examine the currently published data from ZIKV studies to provide the latest understanding of ZIKV genome biology and molecular pathogenesis. The ZIKV genome evolved rapidly from the Flavivirus genus and diverged from the members of this genus, even within the dengue virus cluster to which ZIKV belongs. Genome variations and divergences also exist among ZIKV strains/isolates. These genome divergences might account for the uniqueness of Zika disease. ZIKV infection activates not only the antiviral immune response but also the pro-inflammatory responses associated with disease symptoms. Strikingly, ZIKV activates protein complexes that are functionally associated with disease process, such as glial cell activation and proliferation (for example, Toll-like receptors), apoptosis and cell death, and inflammation. The activation of these complexes may critically contribute to Zika disease. The novel insights into ZIKV genome divergence and disease mechanisms summarized in this review will help accelerate the development of anti-ZIKV strategies.

A combined biomarker and clinical panel for chronic graft versus host disease diagnosis.

Whilst many chronic graft versus host disease (cGVHD) biomarkers have been previously reported, few have been verified in an independent cGVHD cohort. We aimed to verify the diagnostic accuracy of previously reported markers of cGVHD in a multi-centre Chronic GVHD Consortium. A total of 42 RNA and 18 protein candidate biomarkers were assessed amongst 59 cGVHD cases and 33 matched non-GVHD controls. Total RNA was isolated from PBMC, and RNA markers were quantified using PCR. Serum protein markers were quantified using ELISA. A combined 3 RNA biomarker (IRS2, PLEKHF1 and IL1R2) and 2 clinical variables (recipient CMV serostatus and conditioning regimen intensity) panel accurately (AUC 0.81) segregated cGVHD cases from controls. Other studied RNA and protein markers were not confirmed as accurate cGVHD diagnostic biomarkers. The studied markers failed to segregate higher risk cGVHD (per overall NIH 0-3 score, and overlap versus classic cGVHD status). These data support the need for multiple independent verification studies for the ultimate clinical application of cGVHD diagnostic biomarkers.

Mining the human urine proteome for monitoring renal transplant injury.

The human urinary proteome provides an assessment of kidney injury with specific biomarkers for different kidney injury phenotypes. In an effort to fully map and decipher changes in the urine proteome and peptidome after kidney transplantation, renal allograft biopsy matched urine samples were collected from 396 kidney transplant recipients. Centralized and blinded histology data from paired graft biopsies was used to classify urine samples into diagnostic categories of acute rejection, chronic allograft nephropathy, BK virus nephritis, and stable graft. A total of 245 urine samples were analyzed by liquid chromatography-mass spectrometry using isobaric Tags for Relative and Absolute Quantitation (iTRAQ) reagents. From a group of over 900 proteins identified in transplant injury, a set of 131 peptides were assessed by selected reaction monitoring for their significance in accurately segregating organ injury causation and pathology in an independent cohort of 151 urine samples. Ultimately, a minimal set of 35 proteins were identified for their ability to segregate the 3 major transplant injury clinical groups, comprising the final panel of 11 urinary peptides for acute rejection (93% area under the curve [AUC]), 12 urinary peptides for chronic allograft nephropathy (99% AUC), and 12 urinary peptides for BK virus nephritis (83% AUC). Thus, urinary proteome discovery and targeted validation can identify urine protein panels for rapid and noninvasive differentiation of different causes of kidney transplant injury, without the requirement of an invasive biopsy.

The expression of ADAMTS13 in human microvascular endothelial cells.

ADAMTS13, as a specific von Willebrand factor (VWF)-cleaving protease, prevents microvascular thrombosis of VWF/platelet thrombi. It has been reported that human vascular endothelial cells could also synthesize and secrete ADAMTS13, and these reports were focused in human umbilical vascular endothelial cells. Considering the particularity of its huge quantity and structure of human microvascular endothelial cells (HMECs) in the body, whether ADAMTS13 is expressed in HMECs also needs to be confirmed. To investigate whether ADAMTS13 is expressed in HMECs. Real-time PCR (RT-PCR) amplification detected ADAMTS13 mRNA in HMEC-1 cell line. The expression and distribution of ADAMTS13 protein and VWF were detected by fluorescence immunoassay and western blot. We observed the expression and distribution of ADAMTS13 in HMECs. We confirmed the expression of ADAMTS13 mRNA in HMEC-1, and found that there were some partly common distributions of ADAMTS13 protein and VWF. This study provides the evidence that HMECs also express ADAMTS13. HMECs might also be a primary source for human plasma ADAMTS13. The overlap region for the distribution of ADAMTS13 and VWF suggests that ADAMTS13 might have a potential regulation role for VWF inside cells.