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PubMed2026

The Lipid Interactome: an interactive and open access platform for exploring cellular lipid-protein interactions.

SUMMARY: Lipid-protein interactions play essential roles in cellular signaling and membrane dynamics, yet their systematic characterization has long been hindered by the inherent biochemical properties of lipids. Recent advances in functionalized lipid probes-equipped with photoactivatable crosslinkers, affinity handles, and photocleavable protecting groups-have enabled proteomics-based identification of lipid interacting proteins with unprecedented specificity and resolution. Despite the growing number of published lipid interactomes, there remains no centralized effort to harmonize, compare, or integrate these datasets. The Lipid Interactome addresses this gap by providing a structured, interactive web portal that adheres to FAIR data principles-ensuring that lipid interactome studies are Findable, Accessible, Interoperable, and Reusable. Through standardized data formatting, interactive visualizations, and direct cross-study comparisons, this resource enables researchers to systematically explore the protein-binding partners of diverse bioactive lipids. By consolidating and curating lipid interactome proteomics data from multiple studies, the Lipid Interactome database serves as a critical tool for deciphering the biological functions of lipids in cellularsystems. AVAILABILITY AND IMPLEMENTATION: This site can be viewed at LipidInteractome.org. All data are available for download. No user information is collected or necessary for data navigation, interaction, or download.

Proteins↗

PubMed2026

Effectiveness of mass spectrometry and genomic analysis in the surveillance of nontuberculous Mycobacterium in Taiwan.

Nontuberculous mycobacteria (NTM) are diverse, and species-level identification remains challenging in routine diagnostics. We analyzed NTM isolates collected at three regional centers of the National Taiwan University Hospital (NTUH) from 2019 to 2024 to assess geographic variation and identification performance after implementation of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Among 3,188 cases meeting the microbiological criteria for probable pulmonary NTM disease, the species distribution differed by region: Mycobacterium avium complex predominated in central Taiwan (Yunlin, 47.3%), whereas M. abscessus complex (Taipei, 26.5%) and M. kansasii (Hsinchu, 12.4%) were more common in northern Taiwan. In 2019, 14.5% of isolates were reported to be unidentified by MALDI-TOF MS; with workflow optimization and database updates, this percentage decreased but plateaued at 4.5-4.8%. Whole-genome sequencing (WGS) of 61 randomly selected persistently unidentified isolates revealed eight average nucleotide identity (ANI)-defined clusters; 55 isolates (90.2%) could not be assigned to known species using current reference databases. Two clusters detected only in Hsinchu were phylogenetically closest to M. kyorinense, with ANI values below the species demarcation threshold. Overall, we observed marked regional heterogeneity of NTM in Taiwan and a persistent identification gap that remained after MALDI-TOF MS optimization and follow-up WGS.IMPORTANCEThis study characterized regional differences in the NTM species distribution across Taiwan, and the results highlight the limitations of current identification approaches. MALDI-TOF MS identifies most isolates, but locally circulating lineages represent a persistent gap in global reference libraries. Even with whole-genome sequencing (WGS), 90.2% (55/61) of persistently unresolved isolates could not be assigned to known species in the current reference databases despite the formation of clear ANI- and phylogeny-defined clusters. These findings show that both proteomic and genomic reference resources for clinical NTM remain incomplete. Expanding regionally representative databases and performing WGS for isolates that remain unresolved by MALDI-TOF MS will be necessary to improve species-level resolution for surveillance and clinical interpretation.

Taiwan↗

PubMed2025

ImmunoTar-integrative prioritization of cell surface targets for cancer immunotherapy.

MOTIVATION: Cancer remains a leading cause of mortality globally. Recent improvements in survival have been facilitated by the development of targeted and less toxic immunotherapies, such as chimeric antigen receptor (CAR)-T cells and antibody-drug conjugates (ADCs). These therapies, effective in treating both pediatric and adult patients with solid and hematological malignancies, rely on the identification of cancer-specific surface protein targets. While technologies like RNA sequencing and proteomics exist to survey these targets, identifying optimal targets for immunotherapies remains a challenge in the field. RESULTS: To address this challenge, we developed ImmunoTar, a novel computational tool designed to systematically prioritize candidate immunotherapeutic targets. ImmunoTar integrates user-provided RNA-sequencing or proteomics data with quantitative features from multiple public databases, selected based on predefined criteria, to generate a score representing the gene's suitability as an immunotherapeutic target. We validated ImmunoTar using three distinct cancer datasets, demonstrating its effectiveness in identifying both known and novel targets across various cancer phenotypes. By compiling diverse data into a unified platform, ImmunoTar enables comprehensive evaluation of surface proteins, streamlining target identification and empowering researchers to efficiently allocate resources, thereby accelerating the development of effective cancer immunotherapies. AVAILABILITY AND IMPLEMENTATION: Code and data to run and test ImmunoTar are available at https://github.com/sacanlab/immunotar.

Humans↗

PubMed2026

PDZ-binding kinase promotes ovarian cancer cell proliferation and invasion via CCNB1 regulation.

BACKGROUND: Ovarian cancer is one of the most lethal gynecological malignancies, characterized by late diagnosis, frequent recurrence, and high mortality. PDZ-binding kinase (PBK), a serine/threonine kinase of the mitogen-activated protein kinase kinase (MAPKK) family, has been implicated in the tumorigenesis of multiple cancers, yet its role in ovarian cancer remains incompletely characterized. This study aimed to investigate the effect of PBK on the proliferation and invasion of ovarian cancer cells. METHODS: The expression of PBK and cyclin B1 (CCNB1) in normal ovarian tissues and ovarian cancer tissues was analyzed using online databases including Gene Expression Profiling Interactive Analysis 2 (GEPIA2), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Kaplan-Meier Plotter. Clinical tissue specimens were collected to detect the expression of PBK and CCNB1 by immunohistochemistry. Quantitative real-time polymerase chain reaction (PCR) was performed to detect PBK messenger RNA (mRNA) expression levels in clinical specimens and cell lines. Western blot was used to detect PBK protein expression in ovarian cancer cell lines. ES2 and A2780 cells with higher PBK expression were selected to construct PBK knockdown cell lines using lentiviral interference vectors. Cell Counting Kit-8 (CCK-8) assay, colony formation assay, and 5-ethynyl-2'-deoxyuridine (EdU) assay were performed to explore the effect of PBK knockdown on cell proliferation. Transwell assay was used to investigate the effect on cell invasion. The Cancer Genome Atlas (TCGA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were utilized to analyze PBK-related pathways and predict CCNB1 as the gene most closely related to PBK. RESULTS: PBK was significantly overexpressed in ovarian cancer tissues and cell lines compared with normal controls, and high PBK expression was associated with poor overall survival (OS) and progression-free survival (PFS). Knockdown of PBK expression inhibited the proliferation, colony formation, and invasion of ovarian cancer cells. Bioinformatics analysis revealed that CCNB1 was significantly overexpressed in ovarian cancer and high CCNB1 expression was associated with poor OS. CCNB1 was also significantly highly expressed in ovarian cancer tissues as validated by immunohistochemistry and was associated with lymph node metastasis. PBK and CCNB1 expression showed a significant positive correlation in TCGA ovarian cancer datasets. Knockdown of PBK inhibited CCNB1 expression in ovarian cancer cells. CONCLUSIONS: PBK promotes ovarian cancer cell proliferation and invasion. PBK knockdown leads to CCNB1 downregulation. These findings suggest that CCNB1 contributes to PBK-mediated oncogenic effects and identify the PBK-CCNB1 axis as a potential therapeutic target for ovarian cancer treatment.

PDZ-binding kinase (PBK)↗

PubMed2026

Protein Language Model Decoys for Target Decoy Competition in Proteomics: Quality Assessment and Benchmarks.

Large-scale proteomics relies heavily on target-decoy competition for false discovery rate estimation in peptide identification, and the performance of this strategy depends strongly on the design of the decoy database. Classical generators such as reversal and shuffling remain widely used. Here, we introduce the first protein language model-based (PLM) decoy generation for peptide identification and benchmark it against classical strategies. We evaluate these approaches using three complementary quality-control layers: sequence-based separability, search-engine-agnostic spectral-space diagnostics, and end-to-end mass spectrometry benchmarks, including pipelines with rescoring. Across these analyses, PLM-based decoys are harder for sequence-only neural networks to distinguish than most classical generators, suggesting fewer obvious sequence-level artifacts. However, this signal is only weakly informative for search performance. Spectral diagnostics further show that short peptides occupy a particularly crowded target-decoy space and are therefore especially prone to local collisions across all generators. In full search pipelines, reverse decoys remain a strong baseline, and current PLM-based generators do not yet provide a clear overall advantage. We therefore view PLM-based decoys not as universal replacements for reverse decoys but as tunable tools for benchmarking, diagnostics, stress testing, and future adaptive decoy optimization, with increasing value as search models become more expressive.

Proteomics↗

PubMed2026

FANTASIA suite: a reproducible and configurable framework for embedding-based functional annotation of proteins.

Embedding-based annotation transfer is increasingly used for protein function inference due to protein language models capture sequence, structural, and functional signals that may extend beyond conventional pairwise similarity. However, systematic application of these approaches requires control over model choice, reference composition, lookup parameters, evidence traceability, and output formats. We developed the FANTASIA suite, a configurable framework for embedding-based functional annotation of proteins. The suite combines a database-backed implementation for reproducible and extensible analyses with a portable flat-file implementation for rapid local annotation and pipeline integration. Using non-model and model-organism proteomes, we show that larger neighbourhood sizes remain practical for proteome-scale analyses and that taxonomy and sequence-identity filtering support leakage-aware benchmarking. We also compare the supported models with baseline methods through external CAFA5 evaluation and provide practical guidance based on empirical evidence variables. FANTASIA provides a controlled, scalable, and reproducible framework for extending functional annotation across the rapidly expanding diversity of sequenced organisms.

Software↗

PubMed2026

A Proteogenomic Approach to Discover Novel lncRNA-Derived Microproteins and Their Potential Clinical Utility in Hepatocellular Carcinoma.

Microproteins (i.e., peptides) are increasingly recognized for their functions in versatile biological contexts, but their clinical relevance and utility remain largely unexplored. Proteogenomic approaches can accelerate microprotein discovery in clinical samples by integrating proteomic data with genomics and transcriptomics evidence. However, long noncoding RNA (lncRNA)-derived microproteins (lncPeps) remain largely unidentified, resulting in unmatchable MS/MS spectra. To solve this problem, we have used high-quality Ribo-seq translatomic datasets to generate an extensive database of human liver lncRNA-derived open reading frames (lncORFs), which we subsequently applied to proteomics data of tumor-adjacent normal tissue pairs from hepatocellular carcinoma (HCC) patients. Using the new database, we discovered 104 novel lncPeps, including 46 lncPeps differentially expressed between tumor and nontumor tissues, and 13 lncPeps with significant correlation with prognosis. Remarkably, combining the expression of lncPeps with canonical proteins in a LASSO regression model improved predictive performance for recurrence, increasing the AUC by 0.005 to 0.085 across three recurrence time points. These findings suggest that the discovery of lncPeps contributes to our understanding of the molecular heterogeneity and progression of HCC and broadens the range of potential biomarker candidates and treatment targets for the disease.

Humans↗

PubMed2026

Integrated analysis reveals the impact of obesity on triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a highly aggressive and heterogeneous breast cancer subtype with limited therapeutic options. While the prevalence of overweight/obese (OW/OB) women continues to rise, the impact of obesity on molecular features of TNBC remains incompletely understood. We investigated clinicopathological and molecular data (including genomic, transcriptomic, proteomic and metabolomic profiling) using our original multi-omics database of TNBC (N = 465) for associations with patient body mass index (BMI). Multi-omics profiling revealed that OW/OB patients exhibited worse survival as well as elevated inflammation of tumor microenvironment, higher expression of immune checkpoints, and dysregulated lipid metabolism. Our in vivo experiments demonstrated that tumors in obese mice displayed faster growth rates, a higher proportion of PD-1+CD8+ T cells and enhanced responsiveness to anti-PD-1 treatment. In addition, we analyzed data from four independent clinical trials and discovered that OW/OB patients demonstrated higher pathological complete response rates and longer progression-free survival following anti-PD-1-based immunotherapy. In conclusion, our study systematically revealed that obesity is associated with coordinated immune-metabolic remodeling in TNBC, characterized by checkpoint enrichment and lipid dysregulation, which may help explain the enhanced anti-PD-1 responsiveness and should be taken into account in the field of precision medicine.

Immunity↗

PubMed2024

The Hunt Lab Guide to De Novo Peptide Sequence Analysis by Tandem Mass Spectrometry.

Donald Hunt has made seminal contributions to the fields of proteomics, immunology, epigenetics, and glycobiology. The foundation of every important work to come out of the Hunt Laboratory is de novo peptide sequencing. For decades, he taught hundreds of students, postdocs, engineers, and scientists to directly interpret mass spectral data. To honor his legacy and ensure that the art of de novo sequencing is not lost, we have adapted his teaching materials into "The Hunt Lab Guide to De Novo Peptide Sequence Analysis by Tandem Mass Spectrometry". In addition to the de novo sequencing tutorials, we present two freely available software tools that facilitate manual interpretation of mass spectra and validation of search results. The first, "Hunt Lab Peptide Fragment Calculator", calculates precursor and fragment mass-to-charge ratios for any peptide. The second program, "Predator Protein Fragment Calculator", was inspired in part by the fragment calculator developed in the Hunt Lab. Its capabilities are enhanced to facilitate interpretation of mass spectral data derived from intact proteins. We hope that the combination of these educational tools will continue to benefit students and researchers by empowering them to interpret data on their own.

Tandem Mass Spectrometry↗

PubMed2026

ProtPen Combines Sequence- and Structure-based Approaches to Facilitate Protein Function Predictions on a Proteome-wide Scale.

Proteins of unknown function represent a significant gap in our understanding of biological processes, encompassing large portions of the proteomes of many organisms, especially prokaryotes. Addressing this gap is critical to understanding the biology and pathogenicity of such organisms. We introduce ProtPen, an open-source pipeline that facilitates protein function prediction by combining eggNOG-mapper for sequence-based annotation with Foldseek for rapid structural similarity searches using AlphaFold-predicted protein structures. Annotation results from both tools are merged and enriched with UniProt metadata to produce a comprehensive output suitable for downstream analysis. The pipeline requires only a FASTA input file with UniProt identifiers, and is designed to analyze data sets on the scale of whole proteomes. Benchmarking on a curated data set of well-characterized Pseudomonas aeruginosa proteins demonstrated an annotation accuracy of >90%, and highlighted the complementarity of sequence- and structure-based methods. Further evaluation of ProtPen included its application to biologically relevant data sets, comprising proteins of unknown function that exhibited significant differential abundances in a proteomics data set of P. aeruginosa, and uncharacterized glycoproteins from Haloferax volcanii. ProtPen is readily extensible to incorporate additional protein function prediction tools. In summary, this pipeline facilitates the systemwide annotation of proteins of unknown function from proteomic data sets and whole proteomes.

Pseudomonas aeruginosa↗

PubMed2026

Pathway-driven target prioritisation in drug discovery.

Genome-scale association studies and functional screens routinely implicate hundreds of candidate genes per disease, yet only a few will be clinically validated as drug targets. Choosing which to pursue is a central drug-discovery decision that depends on interpreting each candidate in its biological context. Curated pathway databases provide this context, while enrichment analysis applies it at scale, turning gene-level signals from genome-wide association, transcriptomic, proteomic and CRISPR studies into mechanistic hypotheses for prioritisation. This review examines how pathway-based methods inform target prioritisation, the databases and tools available for this purpose, and why pathway co-membership should be viewed as a starting point for validation rather than as evidence of causal involvement.

CRISPR↗

PubMed2026

Unveiling the Molecular Secrets of Seaweeds: A Comprehensive Review of Bioinformatics Applications in Algal Research.

Recent advances in high-throughput sequencing, bioinformatics, and multi-omics technologies have transformed seaweed research by overcoming long-standing challenges associated with complex genomes, diverse life cycles, and limited genomic resources. This review provides a comprehensive overview of bioinformatics approaches used to investigate seaweed genomics, transcriptomics, proteomics, metabolomics, microbiomes, and functional genomics, with emphasis on the computational tools and databases that support these analyses. Applications of bioinformatics in phylogenetics, drug discovery, microbiome characterization, and the development of biofuels, nutraceuticals, pharmaceuticals, and sustainable agriculture are also discussed. Particular attention is given to emerging strategies involving multi-omics integration, genome editing, artificial intelligence, machine learning, and synthetic biology that are reshaping seaweed research. The review further examines current challenges, including incomplete genomic resources, data standardization, and the need for experimental validation of computational predictions. Collectively, these advances highlight the growing role of bioinformatics in enabling systems-level understanding of seaweed biology and accelerating their translation into sustainable biotechnological and marine bioeconomy applications.

macroalgal genomics↗

PubMed2025

EucaMOD: a comprehensive multi-omics database for functional genomics research and molecular breeding of fast-growing eucalyptus trees.

Eucalyptus, one of the most widely planted plantation tree species globally, is primarily found in tropical and subtropical regions and contributes significantly to economic and social benefits. With advances in sequencing technologies, there is an increasing demand for the systematic analysis of multi-omics data among Eucalyptus species to enhance genetic breeding efforts. Although several early genomic databases have been established for eucalyptus, they have not been updated in a timely manner and lack recent multi-omics data, rendering them insufficient for current research needs. To address this gap, we developed the eucalyptus multi-omics database (EucaMOD, http://eucalyptusggd.net/eucamod), a comprehensive resource for cross-omics studies. In this study, we functionally annotated 45 eucalyptus genomes and structurally annotated 15, conducting comparative genomics and pan-proteomics analyses across all genomes. Additionally, we analyzed eucalyptus transcriptome, epigenome, and variome data through standardized workflows, enabling the in-depth mining and reanalysis of multi-omics datasets. EucaMOD is the most comprehensive multi-omics database for eucalyptus to date and includes data from 45 genomes (39 species), 870 mRNA-seq samples, 17 miRNA-seq samples, 52 epigenomic datasets (histone modifications and transcription factor binding), and genetic variation data from 1219 samples. To support functional genomics and molecular breeding research, the database is organized into the following 11 modules: Home, Species, Genomics, Comparative genomics, Pan-proteomics, Transcriptomics, Epigenetics, Variomics, Tools, Download, and Help. EucaMOD also offers online analysis tools for data mining, providing free public services to aid eucalyptus gene function and genetic engineering studies.

Eucalyptus↗

PubMed2026

The clinical promise of mass spectrometry-based single-cell proteomics: from bedside to bench.

INTRODUCTION: Single-cell proteomics (SCP) is entering into a transformative phase, moving beyond technically demanding benchmarking studies toward robust and reproducible workflows capable of quantifying thousands of proteins per cell. These advances highlight SCP's potential to address clinically relevant questions by resolving cellular and pathological heterogeneity that remains obscured in bulk proteomics. AREAS COVERED: This review discusses current advances, challenges, and clinical applications of SCP based on literature identified through searches in major scientific databases. Many clinically relevant samples remain underexplored in SCP studies, in part because their application requires careful evaluation of pre-analytical variables that can strongly influence proteomic readouts. Current SCP methodologies vary according to sample type, experimental conditions, and available resources. Compared with single-cell RNA sequencing, SCP remains limited in cellular throughput, making it challenging to define optimal sample sizes and to reliably detect both abundant and rare cell populations. These limitations also make dataset integration difficult, as reduced cellular coverage and sampling depth increase data sparsity. Moreover, implementing quality control strategies across sequential SCP experiments is essential to ensure data robustness, comparability, and accurate biological interpretation. EXPERT OPINION: Applying SCP to clinical samples advances our understanding of biological complexity and holds potential to drive progress in translational and precision medicine.

Humans↗

PubMed2025

The proteogenomic landscape of the human kidney and implications for cardio-kidney-metabolic health.

Nearly one-third of the global population is affected by cardio-kidney-metabolic (CKM) diseases; however, the molecular mechanisms underlying CKM diseases are poorly understood. Here we show that tissue proteomics provide critical insights not captured by tissue gene expression or blood proteomics information by performing whole-genome and RNA sequencing and proteomics analysis of human kidney samples (n = 337), and we generated a publicly available database. Via Bayesian co-localization and Mendelian randomization analyses of kidney protein quantitative trait loci and 36 CKM genome-wide association studies, we prioritized 89 proteins for CKM traits. We prioritized relationships that could underlie the interconnectedness of CKM traits and discovered multiple and targetable mechanisms for CKM diseases, including the potential role of kidney angiopoietin-like protein 3 (ANGPTL3) in serum lipid levels and kidney function as well as the role of charged multivesicular body protein 1A in kidney function and hypertension. Notably, we identify pathways with confluence of evidence from genetic loci, tissue gene expression and protein levels for CKM traits. In summary, our large-scale kidney proteomics study uncovers proteins and targetable mechanisms prioritized for CKM diseases.

Humans↗

PubMed2026

Mass Spectrometry-Based Profiling of Personalized Immunopeptidomes in Thai Renal Cell Carcinoma.

This study profiles the personalized immunopeptidomes of 13 Thai patients with renal cell carcinoma (RCC), addressing a critical knowledge gap in Southeast Asian populations characterized by distinct HLA allele distributions. We combined whole-exome sequencing (WES)-based personalized proteome construction with liquid chromatography-tandem mass spectrometry (LC-MS/MS), using both database-driven searches and de novo peptide sequencing. HLA typing identified several class I allotypes that are underrepresented in publicly available immunopeptidome resources, including seven alleles not previously represented in the databases examined; HLA-A*11:01 was the most frequent allele in this cohort. Database-based analysis identified a single tumor-specific neoantigen derived from a mutant JADE2 peptide in the patient with the highest tumor mutational burden, which was validated by a mutant-specific ELISPOT response. In contrast, de novo sequencing revealed numerous noncanonical peptides, a subset of which were supported by proteogenomic validation using PepQuery and detected exclusively in cancer proteomes but not in normal tissue data sets, indicating their potential as tumor-associated antigen candidates. Together, these results establish an integrated and scalable framework for identifying HLA-presented tumor-derived peptides and provide a foundational immunopeptidome resource to support personalized cancer immunotherapy development in Southeast Asia.

Humans↗

PubMed2025

DescribePROT Database of Residue-Level Protein Structure and Function Annotations.

DescribePROT is a freely available online database of structural and functional descriptors of proteins at the amino acid level. It provides access to 13 diverse descriptors that include sequence conservation, putative secondary structure, solvent accessibility, intrinsic disorder, and signal peptides, and putative annotations of residues that interact with proteins, peptides and nucleic acids. These data can be used to elucidate protein functions, to support efforts to develop therapeutics, and to develop and evaluate future predictors of protein structure and function. DescribePROT includes 7.8 billion predictions for 1.4 million proteins from 83 complete proteomes of popular model organisms. This information can be downloaded at multiple levels of scope (entire database, specific organisms, and individual proteins) and can be interacted with using a graphical interface that simultaneously displays data on multiple descriptors. We describe the contents of this resource, provide directions on how to use its interface, and offer instructions on how to obtain and interact with the underlying data. Moreover, we briefly discuss plans for a future expansion of this database. DescribePROT is available at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/ .

Databases, Protein↗

PubMed2026

Polyethylene transformation by a psychrotolerant Rhodococcus strain assessed by transcriptomics and 13C-isotope tracing.

Polyethylene is increasingly accumulating in nature, including remote places like the Arctic. While abiotic processes fragment polyethylene in situ, biotic transformation by microorganisms is assumed to occur. However, the enzymes and pathways involved remain poorly characterized. In this study, we used an in-house biobank from cold environments to screen for potential bacteria capable of degrading polyethylene by screening the strains in silico using the database PlasticDB and in vivo using a fluorescence-based assay. Using transcriptomic and proteomic analyses to identify genes in promising candidate strains that encode extracellular enzymes potentially capable of degrading PE, we selected a Rhodococcus erythropolis strain and two of its enzymes: a hypothetical protein (Hypr1) and a lipase family protein (Lip2). Expressing the candidate genes heterologously in Escherichia coli resulted in positive results in the fluorescence-based assay for polyethylene transformation. Applying 13C-labelled polyethylene for assessing and estimating polyethylene transformation and carbon assimilation, we found that R. erythropolis and both untransformed and recombinant E. coli extracellularly transformed the initially added polyethylene after 70 days. In addition, untransformed E. coli and R. erythropolis converted small, but significant amounts of polyethylene-derived carbon to carbon dioxide. The 13C-label was also traced into the bacterial biomass of R. erythropolis. Overall, our results provide evidence for biotic transformation of untreated polyethylene and suggests a hypothetical protein and a lipase family protein as two novel enzyme candidates associated with PE transformation.

Rhodococcus↗