Biology subjects

WANG, A.

Publications and source records attributed to WANG, A..

5 recordsLinked to original sources

bioRxiv2026

Human lymphatic organoids reconstruct convergent developmental routes to functional lymphatic vessels

The lymphatic vasculature maintains fluid balance, regulates immune surveillance, and supports tissue repair, yet how human lymphatic vessels arise and acquire specialized functions remains poorly understood. Here, guided by single-cell and spatial analyses of human embryos, we found that both lateral plate and paraxial mesoderm converge on venous endothelial intermediates with cardinal vein-like characteristics prior to lymphatic specification. Informed by these developmental trajectories, we generated three-dimensional lymphatic organoids from human pluripotent stem cells through two distinct mesodermal routes. The organoids formed capillary-like vascular networks with emerging button-like junctions and distinct endothelial and stromal programs. The lymphatic organoids were transcriptionally benchmarked to human fetal lymphatic endothelium. The organoids supported immune-cell interactions and antigen accumulation and, following transplantation, exhibited interstitial tracer uptake and transport. These findings establish a platform for investigating how distinct developmental origins shape human lymphatic specification, vascular organization, and function, with applications to lymphatic disease modeling and tissue repair.

developmental biology↗

bioRxiv2020

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

Eukaryotic genomes gradually gain noncoding regions when advancing evolution and human genome actively transcribes >90% of its noncoding regions1, suggesting their criticality in evolutionary human genome. Yet <1% of them have been functionally characterized2, leaving most human genome in dark. Here we systematically decode endogenous lncRNAs located in unannotated regions of human genome and decipher a distinctive functional regime of lncRNAs hidden in massive RNAseq data. LncRNAs divergently distribute across chromosomes, independent of protein-coding regions. Their transcriptions barely initiate on promoters through polymerase II, but mostly on enhancers. Yet conventional enhancer activators(e.g. H3K4me1) only account for a small proportion of lncRNA activation, suggesting alternatively unknown mechanisms initiating the majority of lncRNAs. Meanwhile, lncRNA-self regulation also notably contributes to lncRNA activation. LncRNAs trans-regulate broad bioprocesses, including transcription and RNA processing, cell cycle, respiration, response to stress, chromatin organization, post-translational modification, and development. Overall lncRNAs govern their owned regime distinctive from proteins.

genomics↗

bioRxiv2019

Big-data analysis unearths the general regulatory regime in normal human genome and cancer

All cancers share a commonality in genome activation regulated by a systems endogenous network distinct from normal tissue, but such a network remains elusive. Here, we unearth a systems regulatory network endogenous for all types of cancers and normal human respectively from massive data, including all RNAseq data available from SRA and TCGA, and reveal distinctive systems realm for cancer and normal. In the cancerous realm, noncoding RNAs, especially pseudogenes, dominate endogenous network modules and centrality, and they work as the strongest systems inducers that cis-regulate their targets. However in the normal realm proteins dominate the entire endogenous network centrally controlled by ribosomal proteins and they trans-regulate their targets. Our finding establishes a systems picture of an endogenous mechanism overlooking the cancerous and normal realm, in which noncoding RNAs rule the overall cancer realm while proteins govern the normal one. This fundamentally refreshes the conventional concept of cancerous mechanism.

genomics↗

bioRxiv2019

FINET: Fast Inferring NETwork

Numerous software have 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. 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 more than 94% precision (true positives/total true callings). 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 FINETs implementations with Julia, users without any 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. Availability and implementation available in github https://github.com/anyouwang/finet.git

bioinformatics↗

bioRxiv2019

Self-reported sleep duration and daytime napping are associated with renal hyperfiltration and microalbuminuria in apparently healthy Chinese population

BackgroundSleep duration affects health in various way. The objective of this study was to investigate the relationship between sleep duration, daytime napping and kidney function in a middle-aged apparently healthy Chinese population.\n\nMethodsAccording to self-reported total sleep and daytime napping duration, 33,850 participants aged 38 to 90 years old from 8 regional centers were divided into subgroups. Height, weight, waistline, hipline, blood pressure, biochemical index, FBG, PBG, HbA1c, creatinine and urinary albumin-creatinine ratio (UACR) were measured and recorded in each subject. Microalbuminuria was defined as UACR>=30 mg/g, CKD was defined as eGFR<60 ml/min and hyperfiltration was defined as eGFR>=135 ml/min. Multiple logistic regressions were applied to investigate associations between sleep and kidney function.\n\nResultsCompared to participants with [7-8]h/day sleep, ORs of >9 h/day, (8, 9]h/day and <6h/day sleep for microalbuminuria were 1.317 (1.200-1.446, p<0.001), 1.215 (1.123-1.315, p<0.001) and 1.218 (0.967-1.534, p=0.094). eGFR levels were U-shaped associated with sleep duration among subjects with >=90ml/min eGFR, and N-shaped associated with sleep duration among subjects with <90ml/min eGFR. OR of >9h/day sleep for hyperfiltration was 1.400 (1.123-1.745, p=0.003) among eGFR>=90 ml/min participants. Daytime napping had a negative effect on renal health. Compared to participants did not have napping habit, the ORs of (0, 1]h/day, (1, 1.5]h/day and >1.5h/day daytime napping for microalbuminuria were 1.477 (1.370-1.591, p<0.001), 1.217 (1.056, 1.403, p=0.007) and 1.447 (1.242, 1.687, p<0.001).\n\nConclusionsTotal sleep duration are U-shaped associated with renal health outcomes. Daytime napping had a negative effect on renal health.

epidemiology↗