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A significant burden is placed on the lives of females due to cervical cancer, which is currently the leading cause of cancer death among women. Preferentially expressed antigen in melanoma (PRAME) belongs to the CTA gene family and was found to be abnormally expressed among different types of cancers. Our previous research also indicated that PRAME was highly expressed in cervical cancer compared with normal tissues. However, the roles and detailed mechanisms of PRAME have not been explored in cervical cancer. In the present study, the expression of PRAME in cervical tissues and cells was detected by immunohistochemistry (IHC), qRT-PCR, and Western blotting. Additionally, CCK-8, BrdU, scratch, transwell, and flow cytometry assays were conducted to explore the function of PRAME in regulating the malignant biological behaviors of cervical cancer cells. Nude mice were used to confirm the role of PRAME in tumor growth in vivo. Furthermore, the Wnt inhibitor MSAB was used to verify the role of PRAME in regulating the Wnt/β-catenin pathway both in vitro and in vivo. The results of IHC, qRT-PCR, and Western blotting showed that PRAME was highly expressed in cervical cancer tissues and cells. PRAME knockdown attenuated cell growth, migration, and invasion; induced G0/G1 arrest; and increased cell apoptosis in C33A and SiHa cells through Wnt/β-catenin signaling regulation. However, the upregulation of PRAME exhibited the opposite effects accordingly, which could be partly reversed via MSAB treatment. The growth rate of xenograft tumors was enhanced when PRAME was overexpressed via Wnt/β-catenin signaling activation. Taken together, PRAME is associated with cervical cancer occurrence and progression mediated by Wnt/β-catenin signaling, suggesting that PRAME might be a factor in manipulating cervical carcinogenesis and a potential therapeutic target.

Spatial-spectral classification (SSC) has become a trend for hyperspectral image (HSI) classification. However, most SSC methods mainly consider local information, so that some correlations may not be effectively discovered when they appear in regions that are not contiguous. Although many SSC methods can acquire spatial-contextual characteristics via spatial filtering, they lack the ability to consider correlations in non-Euclidean spaces. To address the aforementioned issues, we develop a new semisupervised HSI classification approach based on normalized spectral clustering with kernel-based learning (NSCKL), which can aggregate local-to-global correlations to achieve a distinguishable embedding to improve HSI classification performance. In this work, we propose a normalized spectral clustering (NSC) scheme that can learn new features under a manifold assumption. Specifically, we first design a kernel-based iterative filter (KIF) to establish vertices of the undirected graph, aiming to assign initial connections to the nodes associated with pixels. The NSC first gathers local correlations in the Euclidean space and then captures global correlations in the manifold. Even though homogeneous pixels are distributed in noncontiguous regions, our NSC can still aggregate correlations to generate new (clustered) features. Finally, the clustered features and a kernel-based extreme learning machine (KELM) are employed to achieve the semisupervised classification. The effectiveness of our NSCKL is evaluated by using several HSIs. When compared with other state-of-the-art (SOTA) classification approaches, our newly proposed NSCKL demonstrates very competitive performance. The codes will be available at https://github.com/yuanchaosu/TCYB-nsckl.

Background: The x-ray repair cross-complementing (XRCC) family is essential in DNA repair processes. The predictive roles of XRCCs remain unclear in ovarian carcinomas. Therefore, detecting the relationship between XRCCs expression and ovarian carcinomas prognosis is increasingly pivotal. Methods: Using the "Kaplan-Meier (KM) plotter" database, progression-free survival (PFS) and overall survival (OS) were utilized to evaluate the prognosis of XRCCs mRNA expression in ovarian carcinoma patients with clinical outcomes. Then, mRNA level and protein levels of XRCCs were assessed in normal ovarian cells and ovarian carcinoma cell lines by real-time qPCR, Western blotting and immunofluorescence analysis. Additionally, expression of the XRCCs protein in tissues from ovarian carcinomas and normal ovary was identified by immunohistochemical staining. Results: Higher mRNA levels of XRCC2 and XRCC9 predicted longer PFS and OS in all women with ovarian malignance, while elevated XRCC4 mRNA levels were linked to poor PFS and OS in all ovarian cancer patients. Elevated mRNA of XRCC2 was also correlated with better PFS in patients with serous ovarian carcinomas, and better PFS and OS in grade III and stage III+IV ovarian carcinomas patients. What's more, highly expressed levels of XRCC9 mRNA were also linked to favorable PFS and OS in patients with serous, grade III and stage III+IV ovarian carcinomas. Nevertheless, elevated mRNA expression of XRCC4 was linked to worse PFS and OS for patients with serous, grade III as well as all stages of ovarian malignance. Additionally, when compared to ovarian carcinoma cell lines, elevated mRNA and protein levels of XRCC2 and XRCC9 were detected in normal ovarian cells. Consistently, higher staining of XRCC2 and XRCC9 was also detected in normal ovarian cells than that in ovarian cancer cells. Then, higher staining levels of XRCC2 and XRCC9 were discovered in healthy control tissues than that in ovarian carcinoma tissues. Meanwhile, XRCC4 was identified to be overexpressed in tissues of ovarian malignance as compared to normal control tissues. However, XRCC4 mRNA and protein levels were lower in ovarian cancer cells than that in normal cell line. Conclusion: Elevated XRCC2 and XRCC9 expression levels were observed in normal ovarian cells and tissues than that in ovarian malignance cells and tissues, and exhibited better prognostic value especially in patients with serous, poor differentiated and late stage, suggesting that XRCC2 and XRCC9 may be potent prognostic markers in ovarian cancer patients and can guide personalized surveillance for ovarian malignance.

Study Design: Retrospective study. Objective: Lumbar magnetic resonance imaging (MRI) findings are believed to be associated with low back pain (LBP). This study sought to develop a new predictive classification system for low back pain. Method: Normal subjects with repeated lumbar MRI scans were retrospectively enrolled. A new classification system, based on the radiological features on MRI, was developed using an unsupervised clustering method. Results: One hundred and fifty-nine subjects were included. Three distinguishable clusters were identified with unsupervised clustering that were significantly correlated with LBP ( P = .017). The incidence of LBP was highest in cluster 3 (57.14%), nearly twice the incidence in cluster 1 (30.11%). There were obvious differences in the sagittal parameters among the 3 clusters. Cluster 3 had the smallest intervertebral height. Based on follow-up findings, 27% of subjects changed clusters. More subjects changed from cluster 1 to clusters 2 or 3 (14.5%) than changed from cluster 2 or cluster 3 to cluster 1 (5%). Participation in sport was more frequent in subjects who changed from cluster 3 to cluster 1. Conclusion: Using an unsupervised clustering method, we developed a new classification system comprising 3 clusters, which were significantly correlated with LBP. The prediction of LBP is independent of age and better than that based on individual sagittal parameters derived from MRI. A change in cluster during follow-up may partially predict lumbar degeneration. This study provides a new system for the prediction of LBP that should be useful for its diagnosis and treatment.

Skin photoaging is a complicated pathological process and is mainly due to UV irradiation, especially UVB irradiation. Damage induction by UVB is a complex process, involving intricate molecular mechanisms. The formation of bulky photoproducts in the DNA globally affects transcription and splicing and results in the dysfunction of keratinocytes. In this study, we show that δ-catenin is predominantly distributed in keratinocytes of the skin epidermis and functionally accelerates cell proliferation and DNA repair. Ex vivo protein profiling reveals that δ-catenin upregulates the phosphorylation of RSK2

Ferroptosis exhibits a potent antitumor effect and dihydroorotate dehydrogenase (DHODH) has recently been identified as a novel ferroptosis defender. However, the role of DHODH inhibition in cervical cancer cells is unclear, particularly in synergy with cisplatin via ferroptosis. Herein, shRNA and brequinar were used to knock down DHODH and directly inhibit DHODH, respectively. Immunohistochemistry and Western blotting assays were performed to measure the expression of proteins. CCK-8 and colony formation assays were employed to assess the cell viability and proliferation. Ferroptosis was monitored through flow cytometry, the malondialdehyde assay kit and JC-1 staining analyses. The nude mouse xenograft model was generated to examine the effect of combination of DHODH inhibition and cisplatin on tumor growth in vivo. The expression of DHODH was increased in cervical cancer tissues. DHODH inhibition inhibited the proliferation and promoted the ferroptosis in cervical cancer cells. A combination of DHODH inhibition and cisplatin synergistically induced both in vitro and in vivo ferroptosis and downregulated the ferroptosis defender mTOR pathway. Therefore, the combination of DHODH inhibition and cisplatin exhibits synergistic effects on ferroptosis induction via inhibiting the mTOR pathway could provide a promising way for cervical cancer therapy.

The surgical masks have been essential consumables for public in the COVID-19 pandemic. However, long-time wearing masks will make wearers feel uncomfortable and massive discarded non-biodegradable masks lead to a heavy burden on our environment. In this paper, we adopt degradable chitosan@silver (CS@Ag) core-shell fibers and plant fibers to prepare an eco-friendly mask with excellent thermal comfort, self-sterilization, and antiviral effects. The thermal network of CS@Ag core-shell fibers highly improves the in-plane thermal conductivity of masks, which is 4.45 times higher than that of commercial masks. Because of the electrical conductivity of Ag, the fabricated mask can be electrically heated to warm the wearer in a cold environment and disinfect COVID-19 facilely at room temperature. Meanwhile, the in-situ reduced silver nanoparticles (AgNPs) endow the mask with superior antibacterial properties. Therefore, this mask shows a great potential to address the urgent need for a thermally comfortable, antibacterial, antiviral, and eco-friendly mask.

Systemic sclerosis is a complex process of pathogenesis, and the contributions of inherited genes, infections, and chemicals remain largely unknown. In this study, we showed that p90 ribosomal S6 protein kinase 2 (RSK2) was selectively upregulated in fibrotic skin and fibroblasts treated with the profibrotic cytokine TGF-β. Moreover, knockout of Rsk2 specifically in skin fibroblasts or pharmacological inhibition of RSK2 attenuated skin fibrosis in a mouse model. Mechanistically, RSK2 directly interacted with glycogen synthase kinase 3β in vivo and in vitro and thereby induced phosphorylation of glycogen synthase kinase 3β at Ser9 to inhibit ubiquitination and degradation of GLI1, which promoted fibroblast differentiation and skin fibrosis. Consequently, RSK2 plays an important role in the dermal skin of systemic sclerosis. These findings provided a potential therapeutic target for systemic sclerosis.

Skin wound healing is a complex process involving intricate molecular mechanisms that remain unknown. Restoration of homeostasis after wounding requires the remodeling function of fibroblasts. Here, we demonstrate that phosphorylation of α-cateninS641 was upregulated in fibroblasts upon wounding, which accelerated their proliferation and migration to restore the skin barrier. At the wound edge, phospho-α-cateninS641 stabilized IκBα and thereby impaired expression of NF-κB target genes to promote proliferation and migration of fibroblasts. Mechanically, phospho-α-cateninS641 blocked K48-linked polyubiquitination and proteasomal degradation of IκBα. Moreover, we also demonstrated that EGF/EGFR/CK2α was function as a key upstream signaling of α-catenin by phosphorylating α-catenin at S641. Wound repair was significantly disrupted in skin of mice in which α-catenin phosphorylation and CK2α kinase activity were perturbed in fibroblasts. These findings provide insights into the molecular control of fibroblast proliferation and migration in response to wounding and identify potential targets for the treatment of defective wound repair.

This article introduces the EU Horizon 2020 research project MIX-UP, “Mixed plastics biodegradation and upcycling using microbial communities”. The project focuses on the ambitious vision to change the traditional linear value chain of plastics to a sustainable, biodegradable based one. In MIX-UP, plastic mixtures containing five of the top six fossil-based recalcitrant plastics (PE, PUR, PP, PET, and PS), along with upcoming biobased and biodegradable plastics (bioplastics) such as PHA and PLA, will be used as feedstock for microbial transformations. The generated new workflow increases recycling quotas and adds value to present poorly recycled plastic waste streams. Consecutive controlled enzymatic and microbial degradation of mechanically pre-treated plastics waste combined with subsequent microbial conversion to polymers and value-added chemicals by mixed cultures. Through optimization of known plastic-degrading enzymes by integrated protein engineering, high specific binding capacities, stability, and catalytic efficacy towards a broad spectrum of plastic polymers under high salt content and temperature conditions will be achieved. Another focus lies in the search and isolation of novel enzymes active on recalcitrant polymers. MIX-UP will also enhance the production of enzymes and formulate enzyme cocktails tailored to specific waste streams. In vivo and in vitro application of these cocktails enables stable, self-sustaining microbiomes to convert the released plastic monomers selectively into value-added products, key building blocks, and biomass. Any of the remaining material recalcitrant to the enzymatic activity will be recirculated into the process by physicochemical treatment. The Chinese-European MIX-UP is a multidisciplinary and industry-participating consortium to address the market need for novel sustainable routes to valorize plastic waste streams. MIX-UP realizes a circular (bio) plastic economy and will contribute where mechanical and chemical plastic recycling show limits.

Skin wound healing is a complex process involving intricate molecular mechanisms that remain unknown. Restoration of homeostasis after wounding requires the remodeling function of fibroblasts. In this study, we show that phosphorylation of α-catenin

Metabolic engineering 66, 167-178 (2021). doi:10.1016/j.ymben.2021.03.011, Published by Academic Press, Orlando, Fla.

Background Bioleaching has been attracting attention recent years as an emerging sediment heavy metal pollution remediation technology. However, the use of sulfur powder as sulfur substrate causes the problem of “post-acidification”, and the free bioleaching functional bacteria which are susceptible to environmental impact during reactor operation cannot be used efficiently for multiple rounds. These problems can be solved if the sulfur substrate and the bioleaching functional bacteria can be recycled simultaneously after bioleaching. A new kind of sulfur substrate, the laboratory-made sulfur-covered biochar particles, were used in the bioleaching experiment, compared with sulfur powder and sulfur powder mixed with the surfactant rhamnolipid. Results The sulfur-covered biochar particles exhibited superior bioleaching performance, including faster acidification rate, SO 4 2- production rate and heavy metal bioleaching rate, and higher heavy metal solubilization percentage (Ni 33.76%; Cu 66.16%; Zn 65.494%), which was resulted from the acceleration of bioleaching reaction by the bioleaching functional bacteria immobilized on the biochar surface. Otherwise, the sulfur-covered biochar particles could be reused in the second round, and the heavy metal solubilization percentage (Ni32.84%, Cu69.93%, Zn67.17%) was comparable with that of the first round. Nevertheless, the sulfur content became the main limiting factor causing poor bioleaching performance during the third round. The sulfur mixed with the surfactant rhamnolipid did not show significant effect in promoting acidification and heavy metal solubilization due to high levels of organic matter and the impact of the low pH value. Conclusion The research indicated the laboratory-made sulfur-covered biochar particles could realize the dual immobilization of the bioleaching functional bacteria and the sulfur substrate to support their recycling and reuse in the second bioleaching round. In the future research, the way to maintain the reuse of the sulfur-covered biochar particle for more rounds will be explored.

Additional file 1: Fig. S1. Amount of heavy metal leached from the sediment during the bioleaching process. Fig. S2. Infrared spectrum analysis of bamboo biochar. Fig. S3. Amount of heavy metal leached from the sediment during the second and the third round of bioleaching.

The applications of extreme learning machine (ELM) to the hyperspectral-image (HSI) classification have attracted a great deal of research attention because of its excellent performance and fast learning speed. However, conventional ELM is unable to achieve satisfactory accuracy since it only exploits the spectral information to conduct the HSI classification. To address the above issues, we propose a novel classification algorithm based on both spectral and multiscale spatial information, referred to as ELM with enhanced composite feature (ELM-ECF). To be specific, we adopt the original ELM, exploit a multiscale spatial weighted-mean-filtering-based approach to extract multiple spatial information, and use the majority vote method to select the final classification result. The proposed ELM-ECF significantly improves the classification accuracy of the original ELM. Experimental results on three public HSIs (i.e., Indian Pines data set, Pavia University data set, and Salinas data set) illustrate that the proposed ELM-ECF outperforms a variety of the state-of-the-art HSI classification counterparts in terms of classification accuracy.

Physalis L., an important genus of the family Solanaceae, includes many commercially important edible and medicinal species. Traditionally, species identification is based on morphological traits; however, the highly similar morphological traits among species of Physalis make this approach difficult. In this study, we evaluated the feasibility of using a popular DNA barcode, the chloroplast psbA–trnH intergenic region, in the identification of species of Physalis. Thirty-six psbA–trnH regions of species of Physalis and of the closely related plant Nicandra physalodes were analyzed. The success rates of PCR amplification and sequencing of the psbA–trnH region were 100%. MEGA V6.0 was utilized to align the psbA–trnH sequences and to compute genetic distances. The results show an apparent barcoding gap between intra- and interspecific variations. Results of both BLAST1 and nearest-distance methods prove that the psbA–trnH regions can be used to identify all species examined in the present study. In addition, phylogenetic analysis using psbA–trnH data revealed a distinct boundary between species. It also confirmed the relationship between species of Physalis and closely related species, as established by previous studies. In conclusion, the psbA–trnH intergenic region can be used as an efficient DNA barcode for the identification of species of Physalis.

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program "e-Bitter" is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

The over-collection and habitat destruction of natural Dendrobium populations for their commercial medicinal value has led to these plants being under severe threat of extinction. In addition, many Dendrobium plants are similarly shaped and easily confused during the absence of flowering stages. In the present study, we examined the application of the ITS2 region in barcoding and phylogenetic analyses of Dendrobium species (Orchidaceae). For barcoding, ITS2 regions of 43 samples in Dendrobium were amplified. In combination with sequences from GenBank, the sequences were aligned using Clustal W and genetic distances were computed using MEGA V5.1. The success rate of PCR amplification and sequencing was 100%. There was a significant divergence between the inter- and intra-specific genetic distances of ITS2 regions, while the presence of a barcoding gap was obvious. Based on the BLAST1, nearest distance and TaxonGAP methods, our results showed that the ITS2 regions could successfully identify the species of most Dendrobium samples examined, Second, we used ITS2 as a DNA marker to infer phylogenetic relationships of 64 Dendrobium species. The results showed that cluster analysis using the ITS2 region mainly supported the relationship between the species of Dendrobium established by traditional morphological methods and many previous molecular analyses. To sum up, the ITS2 region can not only be used as an efficient barcode to identify Dendrobium species, but also has the potential to contribute to the phylogenetic analysis of the genus Dendrobium.

Two molecular marker systems, start codon targeted (SCoT) and target region amplification polymorphism (TRAP), were used for genetic relationship analysis of 36 Dendrobium species collected from China. Twenty-two selected SCoT primers produced 337 loci, of which 324 (96%) were polymorphic, whereas 13 TRAP primer combinations produced a total of 510 loci, with 500 (97.8%) of them being polymorphic. An average polymorphism information content of 0.953 and 0.983 was detected using the SCoT and TRAP primers, respectively, showing that a high degree of genetic diversity exists among Chinese Dendrobium species. The partition of clusters in the unweighted pair group method with arithmetic mean dendrogram and principal coordinate analysis plot based on the SCoT and TRAP markers was similar and clustered the 36 Dendrobium species into four main groups. Our results will provide useful information for resource protection and will also be useful to improve the current Dendrobium breeding programs. Our results also demonstrate that SCoT and TRAP markers are informative and can be used to evaluate genetic relationships between Dendrobium species.

As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances. However, when directly applied to the hyperspectral image (HSI) classification, the recognition rate is too low. This is because ELM does not use the spatial information which is very important for HSI classification. In view of this, this paper proposes a new framework for spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are the improvement of linear ELM (LELM). However, based on lots of experiments and analysis, we found out that the LELM is a better choice than nonlinear ELM for spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learn such distribution using the LBP. The proposed method not only maintain the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines and Pavia University, demonstrate the good performances of the proposed method., 13 pages,8 figures,3 tables,article

Recently, commercial interest in Physalis species has grown worldwide due to their high nutritional value, edible fruit and potential medicinal properties. However, many Physalis species have similar shapes and are easily confused, and consequently the phylogenetic relationships between Physalis species are poorly understood. This hinders their safe utilization and genetic resource conservation. In this study, the nuclear ribosomal ITS2 region was used to identify species and phylogenetically examine Physalis. Eighty-six ITS2 regions from 45 Physalis species were analyzed. The ITS2 sequences were aligned using Clustal W and genetic distances were calculated using MEGA V6.0. The results showed that ITS2 regions have significant intra- and inter-specific divergences, obvious barcoding gaps, and higher species discrimination rates (82.2% for both the BLASTA1 and nearest distance methods). In addition, the secondary structure of ITS2 provided another way to differentiate species. Cluster analysis based on ITS2 regions largely concurred with the relationships among Physalis species established by many previous molecular analyses, and showed that most sections of Physalis appear to be polyphyletic. Our results demonstrated that ITS2 can be used as an efficient and powerful marker in the identification and phylogenetic study of Physalis species. The technique provides a scientific basis for the conservation of Physalis plants and for utilization of resources.

Chrysanthemum morifolium, is a well-known flowering plant worldwide, and has a high commercial, floricultural, and medicinal value. In this study, simple-sequence repeat (SSR) markers were generated from EST datasets and were applied to assess the genetic diversity among 32 cultivars. A total of 218 in silico SSR loci were identified from 7300 C. morifolium ESTs retrieved from GenBank. Of all SSR loci, 61.47% of them (134) were hexa-nucleotide repeats, followed by tri-nucleotide repeats (17.89%), di-nucleotide repeats (12.39%), tetra-nucleotide repeats (4.13%), and penta-nucleotide repeats (4.13%). In this study, 17 novel EST-SSR markers were verified. Along with 38 SSR markers reported previously, 55 C. morifolium SSR markers were selected for further genetic diversity analysis. PCR amplification of these EST-SSRs produced 1319 fragments, 1306 of which showed polymorphism. The average polymorphism information content of the SSR primer pairs was 0.972 (0.938–0.993), which showed high genetic diversity among C. morifolium cultivars. Based on SSR markers, 32 C. morifolium cultivars were separated into two main groups by partitioning of the clusters using the unweighted pair group method with arithmetic mean dendrogram, which was further supported by a principal coordinate analysis plot. Phylogenetic relationship among C. morifolium cultivars as revealed by SSR markers was highly consistent with the classification of medicinal C. morifolium populations according to their origin and ecological distribution. Our results demonstrated that SSR markers were highly reproducible and informative, and could be used to evaluate genetic diversity and relationships among medicinal C. morifolium cultivars.

The system uses the ultra-low power MSP430F449 MCU as the control core, and uses the infrared wireless communication, achieving a simple underwater wireless communication. The entire system consists of 3 parts, the land-based unit, the electrical unit and the submarine model. The land-based unit uses the ASK modulation to achieve wireless communication with the submarine model. The submarine model receives and decodes signals, then communicates with model unit via infrared light waves .The motor unit receives the infrared signals and keeps the floating and diving of the submarine model by controlling the suspension. The system realizes the controlling of the submarine model to achieve various forms of movement when the antenna is 3 meters above the water. The distance error, time error and speed error all meet the requirements .The whole system operates simply and functions well.

Over 359 million tons of plastics were produced worldwide in 2018, with significant growth expected in the near future, resulting in the global challenge of end-of-life management. The recent identification of enzymes that degrade plastics previously considered non-biodegradable opens up opportunities to steer the plastic recycling industry into the realm of biotechnology. Here, we present the sequential conversion of polyethylene terephthalate (PET) into two types of bioplastics: a medium chain-length polyhydroxyalkanoate (PHA) and a novel bio-based poly(amide urethane) (bio-PU). PET films were hydrolyzed by a thermostable polyester hydrolase yielding 100% terephthalate and ethylene glycol. A terephthalate-degrading Pseudomonas was evolved to also metabolize ethylene glycol and subsequently produced PHA. The strain was further modified to secrete hydroxyalkanoyloxy-alkanoates (HAAs), which were used as monomers for the chemo-catalytic synthesis of bio-PU. In short, we present a novel value-chain for PET upcycling, adding technological flexibility to the global challenge of end-of-life management of plastics.Graphical abstract