Ying Zhang

1 month ago | nature.com | Junchao Shi |Yunfang Zhang |Yun Li |Liwen Zhang |Xudong Zhang |Qi Chen | +2 more

Small noncoding RNAs (sncRNAs) are a diverse group of RNAs including small interfering RNAs, microRNAs, PIWI-interacting RNAs and RNAs derived from structured RNAs such as transfer RNAs, ribosomal RNAs and others. These sncRNAs have varied termini and RNA modifications, which can interfere with adaptor ligation and reverse transcription during cDNA library construction, hindering detection of many types of sncRNA by standard small RNA sequencing methods. To address this limitation, PANDORA sequencing introduces a refined methodology. The procedure includes sequential enzymatic treatments of size-selected RNAs with T4PNK and AlkB, which effectively circumvent the challenges presented by the ligation-blocking termini and reverse transcription-blocking RNA modifications, followed by tailored small RNA library construction protocols and deep sequencing. The obtained datasets are analyzed with the SPORTS pipeline, which can comprehensively analyze various types of sncRNA beyond the traditionally studied classes, to include those derived from various parental RNAs (for example, from transfer RNA and ribosomal RNA), as well as output the locations on the parental RNA from which these sncRNAs are derived. The entire protocol takes ~7 d, depending on the sample size and sequencing turnaround time. PANDORA sequencing provides a transformative tool to further our understanding of the expanding small RNA universe and to explore the uncharted functions of sncRNAs. This Protocol describes optimized procedures for purification and sequencing of small noncoding RNAs from a variety of samples to characterize these diverse RNA species and map them to the parental RNAs from which they originate.

Oct 13, 2024 | nature.com | Ying Zhang |Min Chen |Shen Zhong |Mingyu Liu

Against the backdrop of digitization and global warming, fintech plays a crucial role in accelerating the growth of green finance, driving innovation in the financial industry, and catalyzing the low-carbon transformation of economic activities. This paper utilizes city panel data from 2007 to 2019 to examine the direct impact of fintech on carbon emission efficiency (CEE), the transmission channels of green technological innovation and green finance, and the spatial spillover effects, using dynamic panel models, mediation effect models, and dynamic spatial Durbin models (SDM). The study finds that: (i) Fintech significantly improves CEE, and this conclusion remains robust after accounting for potential endogeneity issues and conducting robustness tests. (ii) Mechanism analysis reveals that green finance and green technological innovation are the primary channels through which fintech influences CEE. (iii) Results from the dynamic SDM model indicate that fintech has a significant positive spatial spillover effect on CEE, with the long-term spillover effect being smaller than the short-term spillover effect. (iv) Heterogeneity analysis reveals that fintech’s improvement effect on CEE is mainly evident in eastern regions, emerging first-tier and first-tier cities, and non-resource-based cities. Our research provides new insights for policymakers on achieving China’s dual-carbon goals through the promotion of fintech development, green finance, and green technological innovation. It also aids in the coordinated development of fintech among cities and the formulation of differentiated policies, providing a theoretical foundation and empirical support for future research.

Jan 25, 2024 | nature.com | Ran Xu |Shuo Liu |Lu-Yi Li |Guang-Cheng Luo |Xin-Jun Wang |Ying Zhang | +1 more

Erectile dysfunction ranks among the prevalent sexual disorders in men. Several studies have indicated a potential link between gut microbiota and erectile dysfunction. To validate this potential association, we were to screen statistical data from genome-wide association studies of gut microbiota and erectile dysfunction. p values of less than 1 × 10−5 were set as the threshold for screening instrumental variables that were strongly associated with gut microbiota. At the same time, in order to obtain more convincing findings, we further excluded instrumental variables with possible chain imbalance, instrumental variables with the presence of palindromes, instrumental variables with F-statistics less than 10, and instrumental variables associated with risk factors for erectile dysfunction. Five methods including inverse-variance weighted method, weighted median method, weighted mode, Mendelian randomization egger method and Mendelian randomization pleiotropy residual sum and outlier test were then used to analyse the 2591 instrumental variables obtained from the screening. We identified correlations between six gut microbiota and the risk of erectile dysfunction. The genus Ruminococcaceae UCG-013 exhibited an inverse association with the risk of developing erectile dysfunction (0.79 (0.65–0.97), P = 0.0214). Conversely, the genus Tyzzerella3 (1.13 (1.02–1.26), P = 0.0225), genus Erysipelotrichaceae UCG-003 (1.18 (1.01–1.38), P = 0.0412), genus LachnospiraceaeNC2004group (1.19 (1.03–1.37), P = 0.0191), genus Oscillibacter (1.23 (1.08–1.41), P = 0.0022), and family Lachnospiraceae (1.26 (1.05–1.52), P = 0.0123) demonstrated positive associations with an increased risk of erectile dysfunction. These sensitivity analyses of the gut microbiota were consistent. This study demonstrated a possible causal relationship between gut microbiota and erectile dysfunction risk through Mendelian randomization analysis, providing new potential possibilities for the prevention and treatment of erectile dysfunction.