New quasi-Co/Cu-MOF nanozyme coupled with entropy-driven DNA amplification cascades for highly sensitive electrochemical aptamer growth differentiation factor 15 assay.

BACKGROUND: The monitoring of concentration variation of the newly developed growth differentiation factor 15 (GDF15) biomarker in human serum is of great significance for diagnosing cardiovascular diseases. Current methods for the detection of the GDF15 protein mainly are based on antibody-assisted immunoassays, which encounter the limitations in terms of sensitivity, complexity and costs. The development of simple and sensitive biosensors for GDF15 can therefore facilitate the diagnosis of cardiovascular diseases. RESULTS: A new bimetallic quasi-Cu/Co-MOF nanozyme with high catalytic performance for electrochemical reduction of H2O2 is synthesized via simple one-step precipitation and low-temperature calcination method. Such nanozymes are further employed as amplification tags and coupled with cyclic entropy-driven DNA signal enhancement strategies to construct ultrasensitive aptamer-based biosensor for detecting GDF15 in human serums. GDF15 molecules associate with two aptamers and release the ssDNA trigger sequences via target-binding induced displacement reaction. These ssDNAs subsequently initiate cyclic DNA-fueled strand displacement and catalytic hairpin assembly (CHA) reaction cascades for confining many quasi-Cu/Co-MOF nanozymes on sensor electrode, which yield drastically amplified H2O2 reduction current for detecting GDF15 down to 0.12 pg mL-1 with a dynamic range of 0.5 pg mL-1 to 20 ng mL-1. The electrochemical aptasensor also presents good reproducibility and selectivity and exhibits the capability to detect GDF15 in diluent serums. SIGNIFICANCE: Our aptamer-based GDF15 protein electrochemical assay clearly outperforms current existing antibody-based methods and the quasi-Cu/Co-MOF nanozyme/entropy-driven cascaded signal amplification means can be used as a universal strategy for sensitive monitoring of different biomolecular markers for diverse applications.

WGBS of embryonic gonads revealed that long non-coding RNAs in the MHM region might be involved in cell autonomous sex identity and female gonadal development in chickens.

DNA methylation plays a key role in sex determination and differentiation in vertebrates. However, there are few studies on DNA methylation involved in chicken gonad development, and most focused on male hypermethylated regions (MHM). It is unclear whether there are specific differentially methylated regions (DMRs) in chicken embryonic gonads regulating sex determination and differentiation. Here, the DNA methylation maps showed that the difference of DNA methylation level between sexes was much higher at embryonic day 10 (E10) than that at embryonic day 6 (E6), and the significant differentially methylated regions at both stages were mainly distributed on the Z chromosome, including MHM1 and MHM2. The results of bisulphite sequencing PCR (BSP) and qRT-PCR showed hypomethylation of female MHM and upregulation of long non-coding RNAs (lncRNAs) whose promoter in the MHM region was consistent with the sequencing results, and similar results were in brain and muscle. In female sex-reversed gonads, the methylation pattern of MHM remained unchanged, and the expression levels of the three candidate lncRNAs were significantly decreased compared with those in females, but were significantly increased compared to males. The fluorescence in situ hybridization (FISH) results also showed that these lncRNAs were highly expressed in female embryonic gonads. The results of methyltransferase inhibitor and dual-luciferase reporter assay suggest that lncRNA expression may be regulated by DNA methylation within their promoters. Therefore, we speculated that MHM may be involved in cell-autonomous sex identity in chickens, and that lncRNAs regulated by MHM may be involved in female sexual differentiation.

[Identification and treatment of missing data].

Missing data plagues almost all surveys and researches. The occurrence of missing data will cause losses of original sample information and undermine the validity of the research results to some extents, so researchers should attach great importance to this problem. In this article, we introduced 3 kinds of missingness mechanism, namely missing completely at random, missing at random, and not missing at random. We summarized some common approaches to deal with missing data, including deletion, weighting approach, imputation and parameter likelihood method. Since these methods had its pros and cons, we should carefully select the proper way to handle missing data according to the missingness mechanism.