The evolution of cutinase Est1 based on the clustering strategy and its application for commercial PET bottles degradation.

The rapid increase in global plastic consumption, especially the worldwide use of polyethylene terephthalate (PET), has caused serious pollution problems. Due to the low recycling rate of PET, a substantial amount of waste accumulates in the environment, which prompts a growing focus on enzymatic degradation for its efficiency and environmentally friendliness. This study systematically designed and modified a cutinase, Est1 from Thermobifida alba AHK119, known for its potential of plastic-degradation at high temperatures. Additionally, the introduction of clustering algorithms provided the ability to understand and modify biomolecules, to accelerate the process of finding the optimal mutations. K-means was further proceeded based on the positive mutations. After comprehensive screening for thermostability and activity mutation sites, the dominant mutation Est1_5M (Est1 with the mutations of N213M, T215P, S115P, Q93A, and L91W) exhibited satisfying degradation ability for commercial PET bottles. The results showed that Est1_5M achieved a degradation rate of 90.84% in 72 h, 65-fold higher than the wild type. This study offers reliable theoretical and practical support for the development of efficient PET-degrading enzymes, providing a reference for plastic pollution management.

Extracellular RNA in a single droplet of human serum reflects physiologic and disease states.

Extracellular RNAs (exRNAs) are present in human serum. It remains unclear to what extent these circulating exRNAs may reflect human physiologic and disease states. Here, we developed SILVER-seq (Small Input Liquid Volume Extracellular RNA Sequencing) to efficiently sequence both integral and fragmented exRNAs from a small droplet (5 µL to 7 µL) of liquid biopsy. We calibrated SILVER-seq in reference to other RNA sequencing methods based on milliliters of input serum and quantified droplet-to-droplet and donor-to-donor variations. We carried out SILVER-seq on more than 150 serum droplets from male and female donors ranging from 18 y to 48 y of age. SILVER-seq detected exRNAs from more than a quarter of the human genes, including small RNAs and fragments of mRNAs and long noncoding RNAs (lncRNAs). The detected exRNAs included those derived from genes with tissue (e.g., brain)-specific expression. The exRNA expression levels separated the male and female samples and were correlated with chronological age. Noncancer and breast cancer donors exhibited pronounced differences, whereas donors with or without cancer recurrence exhibited moderate differences in exRNA expression patterns. Even without using differentially expressed exRNAs as features, nearly all cancer and noncancer samples and a large portion of the recurrence and nonrecurrence samples could be correctly classified by exRNA expression values. These data suggest the potential of using exRNAs in a single droplet of serum for liquid biopsy-based diagnostics.

A temporal extracellular transcriptome atlas of human pre-implantation development.

Non-invasively evaluating gene expression products in human pre-implantation embryos remains a significant challenge. Here, we develop a non-invasive method for comprehensive characterization of the extracellular RNAs (exRNAs) in a single droplet of spent media that was used to culture human in vitro fertilization embryos. We generate the temporal extracellular transcriptome atlas (TETA) of human pre-implantation development. TETA consists of 245 exRNA sequencing datasets for five developmental stages. These data reveal approximately 4,000 exRNAs at each stage. The exRNAs of the developmentally arrested embryos are enriched with the genes involved in negative regulation of the cell cycle, revealing an exRNA signature of developmental arrest. Furthermore, a machine-learning model can approximate the morphology-based rating of embryo quality based on the exRNA levels. These data reveal the widespread presence of coding gene-derived exRNAs at every stage of human pre-implantation development, and these exRNAs provide rich information on the physiology of the embryo.

Presymptomatic Increase of an Extracellular RNA in Blood Plasma Associates with the Development of Alzheimer's Disease.

The extracellular RNAs (exRNAs) from human biofluid have recently been systematically characterized. However, the correlations of biofluid exRNA levels and human diseases remain largely untested. Here, considering the unmet need for presymptomatic biomarkers of sporadic Alzheimer's disease (AD), we leveraged the recently developed SILVER-seq (small-input liquid volume extracellular RNA sequencing) technology to generate exRNA profiles from a longitudinal collection of human plasma samples. These 164 plasma samples were collected from research subjects 70 years or older with up to 15 years of clinical follow-up prior to death and whose clinical diagnoses were confirmed by pathological analysis of their post mortem brains. The exRNAs of AD-activated genes and transposons in the brain exhibited a concordant trend of increase in AD plasma in comparison with age-matched control plasma. However, when we required statistical significance with multiple testing adjustments, phosphoglycerate dehydrogenase (PHGDH) was the only gene that exhibited consistent upregulation in AD brain transcriptomes from 3 independent cohorts and an increase in AD plasma as compared to controls. We validated PHGDH's serum exRNA and brain protein expression increases in AD by using 5 additional published cohorts. Finally, we compared the time-course exRNA trajectories between "converters" and controls. Plasma PHGDH exRNA exhibited presymptomatic increases in each of the 11 converters during their transitions from normal to cognitive impairment but remained stable over the entire follow-up period in 8 out of the 9 control elderly subjects. These data suggest the potential utilities of plasma exRNA levels for screening and longitudinal exRNA changes as a presymptomatic indication of sporadic AD.

GITAR: An Open Source Tool for Analysis and Visualization of Hi-C Data.

Interactions between chromatin segments play a large role in functional genomic assays and developments in genomic interaction detection methods have shown interacting topological domains within the genome. Among these methods, Hi-C plays a key role. Here, we present the Genome Interaction Tools and Resources (GITAR), a software to perform a comprehensive Hi-C data analysis, including data preprocessing, normalization, and visualization, as well as analysis of topologically-associated domains (TADs). GITAR is composed of two main modules: (1) HiCtool, a Python library to process and visualize Hi-C data, including TAD analysis; and (2) processed data library, a large collection of human and mouse datasets processed using HiCtool. HiCtool leads the user step-by-step through a pipeline, which goes from the raw Hi-C data to the computation, visualization, and optimized storage of intra-chromosomal contact matrices and TAD coordinates. A large collection of standardized processed data allows the users to compare different datasets in a consistent way, while saving time to obtain data for visualization or additional analyses. More importantly, GITAR enables users without any programming or bioinformatic expertise to work with Hi-C data. GITAR is publicly available at http://genomegitar.org as an open-source software.

GIVE: portable genome browsers for personal websites.

Growing popularity and diversity of genomic data demand portable and versatile genome browsers. Here, we present an open source programming library called GIVE that facilitates the creation of personalized genome browsers without requiring a system administrator. By inserting HTML tags, one can add to a personal webpage interactive visualization of multiple types of genomics data, including genome annotation, "linear" quantitative data, and genome interaction data. GIVE includes a graphical interface called HUG (HTML Universal Generator) that automatically generates HTML code for displaying user chosen data, which can be copy-pasted into user's personal website or saved and shared with collaborators. GIVE is available at: https://www.givengine.org/ .

Rainbow-Seq: Combining Cell Lineage Tracing with Single-Cell RNA Sequencing in Preimplantation Embryos.

We developed the Rainbow-seq technology to trace cell division history and reveal single-cell transcriptomes. With distinct fluorescent protein genes as lineage markers, Rainbow-seq enables each single-cell RNA sequencing (RNA-seq) experiment to simultaneously decode the lineage marker genes and read single-cell transcriptomes. We triggered lineage tracking in each blastomere at the 2-cell stage, observed microscopically inequivalent contributions of the progeny to the two embryonic poles at the blastocyst stage, and analyzed every single cell at either 4- or 8-cell stage with deep paired-end sequencing of full-length transcripts. Although lineage difference was not marked unequivocally at a single-gene level, it became clear when the transcriptome was analyzed as a whole. Moreover, several groups of novel transcript isoforms with embedded repeat sequences exhibited lineage difference, suggesting a possible link between DNA demethylation and cell fate decision. Rainbow-seq bridged a critical gap between division history and single-cell RNA-seq assays.