A 3D structural SARS-CoV-2-human interactome to explore genetic and drug perturbations.

Emergence of new viral agents is driven by evolution of interactions between viral proteins and host targets. For instance, increased infectivity of SARS-CoV-2 compared to SARS-CoV-1 arose in part through rapid evolution along the interface between the spike protein and its human receptor ACE2, leading to increased binding affinity. To facilitate broader exploration of how pathogen-host interactions might impact transmission and virulence in the ongoing COVID-19 pandemic, we performed state-of-the-art interface prediction followed by molecular docking to construct a three-dimensional structural interactome between SARS-CoV-2 and human. We additionally carried out downstream meta-analyses to investigate enrichment of sequence divergence between SARS-CoV-1 and SARS-CoV-2 or human population variants along viral-human protein-interaction interfaces, predict changes in binding affinity by these mutations/variants and further prioritize drug repurposing candidates predicted to competitively bind human targets. We believe this resource ( http://3D-SARS2.yulab.org ) will aid in development and testing of informed hypotheses for SARS-CoV-2 etiology and treatments.

All-fiber miniature non-contact photoacoustic probe based on photoacoustic remote sensing microscopy for vascular imaging in vivo.

Photoacoustic (PA) remote sensing (PARS) microscopy represents a significant advancement by eliminating the need for traditional acoustic coupling media in PA microscopy (PAM), thereby broadening its potential applications. However, current PARS microscopy setups predominantly rely on free-space optical components, which can be cumbersome to implement and limit the scope of imaging applications. In this study, we develop an all-fiber miniature non-contact PA probe based on PARS microscopy, utilizing a 532-nm excitation wavelength, and showcase its effectiveness in in vivo vascular imaging. Our approach integrates various fiber-optic components, including a wavelength division multiplexer, a mode field adaptor, a fiber lens, and an optical circulator, to streamline the implementation of the PARS microscopy system. Additionally, we have successfully developed a miniature PA probe with a diameter of 4 mm. The efficacy of our imaging setup is demonstrated through in vivo imaging of mouse brain vessels. By introducing this all-fiber miniature PA probe, our work may open up new opportunities for non-contact PAM applications.

Adsorption Behavior of Diclofenac on Polystyrene and Poly(butylene adipate-co-terephthalate) Microplastics: Influencing Factors and Adsorption Mechanism.

To unveil the intricacies surrounding the interaction between microplastics (MPs) and pollutants, diligent investigation is warranted to mitigate the environmental perils they pose. This exposition delves into the sorption behavior and mechanism of diclofenac sodium (DCF), a contaminant, upon two distinct materials: polystyrene (PS) and poly(butylene adipate-co-terephthalate) (PBAT). Experimental adsorption endeavors solidify the observation that the adsorption capacity of DCF onto the designated MPs amounts to Q(PBAT) = 9.26 mg g-1 and Q(PS) = 9.03 mg g-1, respectively. An exploration of the factors governing these discrepant adsorption phenomena elucidates the influence of MPs and DCF properties, environmental factors, as well as surfactants. Fitting procedures underscore the suitability of the pseudo-second-order kinetic and Freundlich models in capturing the intricacies of the DCF adsorption process onto MPs, corroborating the notion that the mentioned process is characterized by non-homogeneous chemisorption. Moreover, this inquiry unveils that the primary adsorption mechanisms of DCF upon MPs encompass electrostatic interaction, hydrogen bonding, and halo hydrogen bonding. An additional investigation concerns the impact of commonly encountered surfactants in aqueous environments on the adsorption of DCF onto MPs. The presence of surfactants elicits modifications in the surface charge properties of MPs, consequently influencing their adsorption efficacy vis-à-vis DCF.

Desorption of sulfamethoxazole from polyamide 6 microplastics: Environmental factors, simulated gastrointestinal fluids, and desorption mechanisms.

Microplastics (MPs) can enrich pollutants after being released into the environment, and the contaminants-loaded MPs are usually ingested by organisms, resulting in a potential dual biotoxic effect. In this paper, the adsorption behavior of Sulfamethoxazole (SMX) on Polyamide 6 (PA6) MPs was systematically investigated and simulated by the kinetic and isotherm models. The effect of environmental conditions (pH, salinity) on the adsorption process was studied, and the desorption behavior of SMX-loaded PA6 MPs was focused on simulating the seawater, ultrapure water, gastric and intestinal fluids. We found that lower pH and solubilization of SMX by gastrointestinal components (bovine serum albumin (BSA), sodium taurocholate (NaT), and pepsin) can reduce the electrostatic interaction between the surface charge of PA6 MPs and SMX. The result will lead to an increase in the desorption capacity of SMX-loaded PA6 MPs in gastrointestinal fluids and therefore will provide a reasonable mechanism for the desorption of SMX-loaded PA6 MPs in the gastrointestinal fluids. This study will provide a theoretical reference for studying the desorption behavior of SMX-loaded PA6 MPs under gastrointestinal conditions.

SAAMBE-SEQ: a sequence-based method for predicting mutation effect on protein-protein binding affinity.

MOTIVATION: Vast majority of human genetic disorders are associated with mutations that affect protein-protein interactions by altering wild-type binding affinity. Therefore, it is extremely important to assess the effect of mutations on protein-protein binding free energy to assist the development of therapeutic solutions. Currently, the most popular approaches use structural information to deliver the predictions, which precludes them to be applicable on genome-scale investigations. Indeed, with the progress of genomic sequencing, researchers are frequently dealing with assessing effect of mutations for which there is no structure available. RESULTS: Here, we report a Gradient Boosting Decision Tree machine learning algorithm, the SAAMBE-SEQ, which is completely sequence-based and does not require structural information at all. SAAMBE-SEQ utilizes 80 features representing evolutionary information, sequence-based features and change of physical properties upon mutation at the mutation site. The approach is shown to achieve Pearson correlation coefficient (PCC) of 0.83 in 5-fold cross validation in a benchmarking test against experimentally determined binding free energy change (ΔΔG). Further, a blind test (no-STRUC) is compiled collecting experimental ΔΔG upon mutation for protein complexes for which structure is not available and used to benchmark SAAMBE-SEQ resulting in PCC in the range of 0.37-0.46. The accuracy of SAAMBE-SEQ method is found to be either better or comparable to most advanced structure-based methods. SAAMBE-SEQ is very fast, available as webserver and stand-alone code, and indeed utilizes only sequence information, and thus it is applicable for genome-scale investigations to study the effect of mutations on protein-protein interactions. AVAILABILITY AND IMPLEMENTATION: SAAMBE-SEQ is available at http://compbio.clemson.edu/saambe_webserver/indexSEQ.php#started. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ecological adaptation shaped the genetic structure of homoploid ferns against strong dispersal capacity.

The formation of spatial genetic structure with the presence of extensive gene flow, an evolutionary force which is generally expected to eliminate population-specific variation and maintain genetic homogeneity, remains poorly understood. Homosporous ferns, which spread by spores through wind and possess long distance dispersal capacity, provide an ideal system to investigate such a process. Here, using a homoploid fern lineage, the Athyrium sinense complex, we used reduced-representation genomic data to examine spatial genetic structure and explored potential driving forces including geographical distance, environment, climatic history and external dispersal constraints. Our findings showed a clear north-south divergence at the genetic, morphological and ecological levels between both sides of 35°N in East Asia. Fluctuant and heterogeneous climatic condition was demonstrated to play a crucial role during the formation of the divergence. Our results suggested that this lineage was able to migrate southward and colonize new habitat as a result of the Quaternary climatic fluctuation. Furthermore, the present genetic structure is attributed to adaptation to heterogeneous environments, especially temperature difference. In addition to ecological adaptation, we found clues showing that canopy density, wind direction as well as habitat continuity were all likely to constrain the effect of gene flow. These results demonstrated a diversification process without ploidy changes in ferns providing new insights for our present knowledge on ferns' spatio-temporal evolutionary pattern. In particular, our study highlights the influence of environmental heterogeneity in driving genetic divergence against strong dispersal capacity.

Cerebrovascular imaging in vivo by non-contact photoacoustic microscopy based on photoacoustic remote sensing with a laser diode for interrogation.

Photoacoustic microscopy (PAM) is a unique tool for biomedical applications because it can visualize optical absorption contrast in vivo. Recently, non-contact PAM based on non-interferometric photoacoustic remote sensing (PARS), termed PARS microscopy, has shown promise for selected imaging applications. A variety of superluminescent diodes (SLDs) have been employed in the PARS microscopy system as the interrogation light source. Here, we investigate the use of a low-cost laser diode (LD) as the interrogation light source in PARS microscopy, termed PARS-LD. A side-by-side comparison of PARS-LD and a PARS microscopy system using an SLD was conducted that showed comparable performance in terms of resolution and signal-to-noise ratio. More importantly, for the first time to our knowledge, in vivo PAM imaging of mouse brain vessels was conducted in a non-contact manner, and the results show that PARS-LD provides great performance.

Research Progress of Heavy Ion Radiotherapy for Non-Small-Cell Lung Cancer.

Non-small-cell lung cancer (NSCLC) has a high incidence and poses a serious threat to human health. However, the treatment outcomes of concurrent chemoradiotherapy for non-small-cell lung cancer are still unsatisfactory, especially for high grade lesions. As a new cancer treatment, heavy ion radiotherapy has shown promising efficacy and safety in the treatment of non-small-cell lung cancer. This article discusses the clinical progress of heavy ion radiotherapy in the treatment of non-small-cell lung cancer mainly from the different cancer stages, the different doses of heavy ion beams, and the patient's individual factors, and explores the deficiency of heavy ion radiotherapy in the treatment of non-small-cell lung cancer and the directions of future research, in order to provide reference for the wider and better application of heavy ion radiotherapy in the future.

Distinctive evolutionary pattern of organelle genomes linked to the nuclear genome in Selaginellaceae.

Plastids and mitochondria are endosymbiotic organelles that store genetic information. The genomes of these organelles generally exhibit contrasting patterns regarding genome architecture and genetic content. However, they have similar genetic features in Selaginellaceae, and little is known about what causes parallel evolution. Here, we document the multipartite plastid genomes (plastomes) and the highly divergent mitochondrial genomes (mitogenomes) from spikemoss obtained by combining short- and long-reads. The 188-kb multipartite plastome has three ribosomal operon copies in the master genomic conformation, creating the alternative subgenomic conformation composed of 110- and 78-kb subgenomes. The long-read data indicated that the two different genomic conformations were present in almost equal proportions in the plastomes of Selaginella nipponica. The mitogenome of S. nipponica was assembled into 27 contigs with a total size of 110 kb. All contigs contained directly arranged repeats at both ends, which introduced multiple conformations. Our results showed that plastomes and mitogenomes share high tRNA losses, GC-biased nucleotides, elevated substitution rates and complicated organization. The exploration of nuclear-encoded organelle DNA replication, recombination and repair proteins indicated that, several single-targeted proteins, particularly plastid-targeted recombinase A1, have been lost in Selaginellaceae; conversely, the dual-targeted proteins remain intact. According to the reported function of recombinase A1, we propose that the plastomes of spikemoss often fail to pair homologous sequences during recombination, and the dual-targeted proteins play a key role in the convergent genetic features of plastomes and mitogenomes. Our results provide a distinctive evolutionary pattern of the organelle genomes in Selaginellaceae and evidence of their convergent evolution.

Interactome INSIDER: a structural interactome browser for genomic studies.

We present Interactome INSIDER, a tool to link genomic variant information with structural protein-protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.

Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach.

MOTIVATION: In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases. RESULTS: Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug-gene phenotype similarity and gene expression profile similarity that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that potentially target unexplored genes. By providing newly predicted drug-target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Miniature non-contact photoacoustic probe based on fiber-optic photoacoustic remote sensing microscopy.

Photoacoustic (PA) remote sensing (PARS) microscopy, featured by non-contact operation, has shown great potential for PA microscopy (PAM) imaging applications. However, current PARS microscopy systems are mainly based on free-space light, making the imaging head bulky and inconvenient to use. These issues hinder selected applications such as PA endoscopy and handheld PAM. Here, we report a miniature probe capable of non-contact PAM based on PARS microscopy. By utilizing fiber-optic components including a wavelength division multiplexer and an optical circulator, the imaging head can be highly miniaturized with a diameter of ∼3.0mm. Also, since all light is transmitted via fibers, the fiber-optic PARS microscopy system is relatively easy to build and facilitates scanning of the probe. In vivo imaging of a zebrafish larva and imaging of lithium metal batteries are conducted using the probe, showing its good imaging capability.

Plastid phylogenomics provides novel insights into the infrafamilial relationship of Polypodiaceae.

The polygrammoids (Polypodiaceae) are the most species-rich and diversified epiphytic fern lineages, and hold an important role to understand the deep diverging events and rapid adaptation to changing environments in the plant tree of life. Despite progress in the phylogeny of this group of ferns in previous multilocus phylogenetic studies, uncertainty remains especially in backbone relationships among closely related clades, and the phylogenetic placement of recalcitrant species or lineages. Here, we investigated the deep phylogenetic relationships within Polypodiaceae by sampling all major lineages and using 81 plastid genomes (plastomes), of which 70 plastomes were newly sequenced with high-throughput sequencing technology. Based on parsimony, maximum-likelihood, Bayesian and multispecies coalescent analyses of genome skimming data, we achieved a better resolution of the backbone phylogeny of Polypodiaceae. Using simulated data matrices, we detected that potential phylogenetic artefacts, such as long-branch attraction and insufficient taxonomic sampling, may have a confounding impact on the incongruence of phylogenetic inferences. Furthermore, our phylogenetic analyses offer greater resolution than previous multilocus studies, providing a robust framework for future phylogenetic implications on the subfamilial taxonomy of Polypodiaceae. Our phylogenomic study not only demonstrates the advantage of a character-rich plastome dataset for resolving the recalcitrant lineages that have undergone rapid radiation, but also sheds new light on integrative explorations understanding the evolutionary history of large fern groups in the genomic era.

Leveraging genetic interactions for adverse drug-drug interaction prediction.

In light of increased co-prescription of multiple drugs, the ability to discern and predict drug-drug interactions (DDI) has become crucial to guarantee the safety of patients undergoing treatment with multiple drugs. However, information on DDI profiles is incomplete and the experimental determination of DDIs is labor-intensive and time-consuming. Although previous studies have explored various feature spaces for in silico screening of interacting drug pairs, their use of conventional cross-validation prevents them from achieving generalizable performance on drug pairs where neither drug is seen during training. Here we demonstrate for the first time targets of adversely interacting drug pairs are significantly more likely to have synergistic genetic interactions than non-interacting drug pairs. Leveraging genetic interaction features and a novel training scheme, we construct a gradient boosting-based classifier that achieves robust DDI prediction even for drugs whose interaction profiles are completely unseen during training. We demonstrate that in addition to classification power-including the prediction of 432 novel DDIs-our genetic interaction approach offers interpretability by providing plausible mechanistic insights into the mode of action of DDIs.

Golden retriever pigmentary uveitis: Vision loss, risk factors for glaucoma, and effect of treatment on disease progression.

OBJECTIVE: In previous reports, almost half of Golden Retrievers with Pigmentary Uveitis (GRPU) have lost vision in an eye within a year of diagnosis. The purpose of this study was to determine the proportion of GRPU affected dogs with vision loss, risk factors for the development of glaucoma, and effect of treatment on disease progression. ANIMALS STUDIED: Client owned Golden Retrievers. PROCEDURES: Two complete ophthalmic examinations were performed at least 6 months apart. Visual status, presence of glaucoma, GRPU score, and treatment were recorded. A proportional odds (ordinal logistic) model was fitted to determine whether the use of topical steroidal or non-steroidal (NSAID) ophthalmic preparations was associated with a change in GRPU scores. RESULTS: Twenty-nine Golden Retrievers, 58 eyes, were included. One eye was enucleated after the first examination. On first examination, 57/58 (98.3%) eyes and 29/29 (100%) dogs were visual. At the second examination, 48/57 (84.2%) eyes and 25/29 (86.2%) dogs were visual. Vision loss in 7/9 (77.8%) eyes was secondary to glaucoma. Posterior synechia and fibrinous material in the anterior chamber were significant risk factors for the development of glaucoma (P < .001). There was no significant difference in the change in GRPU score between eyes receiving topical steroids and topical NSAIDs (P = .14). Time between examinations was a significant factor in disease progression (increased GRPU score; P = .016). CONCLUSION: The number of eyes and dogs that retained vision was higher than previous reports. No topical treatment was superior in slowing disease progression. Golden Retriever Pigmentary Uveitis is a slowly progressive disease.

SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions.

Maintaining wild type protein-protein interactions is essential for the normal function of cell and any mutation that alter their characteristics can cause disease. Therefore, the ability to correctly and quickly predict the effect of amino acid mutations is crucial for understanding disease effects and to be able to carry out genome-wide studies. Here, we report a new development of the SAAMBE method, SAAMBE-3D, which is a machine learning-based approach, resulting in accurate predictions and is extremely fast. It achieves the Pearson correlation coefficient ranging from 0.78 to 0.82 depending on the training protocol in benchmarking five-fold validation test against the SKEMPI v2.0 database and outperforms currently existing algorithms on various blind-tests. Furthermore, optimized and tested via five-fold cross-validation on the Cornell University dataset, the SAAMBE-3D achieves AUC of 1.0 and 0.96 on a homo and hereto-dimer test datasets. Another important feature of SAAMBE-3D is that it is very fast, it takes less than a fraction of a second to complete a prediction. SAAMBE-3D is available as a web server and as well as a stand-alone code, the last one being another important feature allowing other researchers to directly download the code and run it on their local computer. Combined all together, SAAMBE-3D is an accurate and fast software applicable for genome-wide studies to assess the effect of amino acid mutations on protein-protein interactions. The webserver and the stand-alone codes (SAAMBE-3D for predicting the change of binding free energy and SAAMBE-3D-DN for predicting if the mutation is disruptive or non-disruptive) are available.

Closed-loop transcutaneous auricular vagus nerve stimulation for the improvement of upper extremity motor function in stroke patients: a study protocol.

Background: Transcutaneous auricular vagus nerve stimulation (taVNS) has garnered attention for stroke rehabilitation, with studies demonstrating its benefits when combined with motor rehabilitative training or delivered before motor training. The necessity of concurrently applying taVNS with motor training for post-stroke motor rehabilitation remains unclear. We aimed to investigate the necessity and advantages of applying the taVNS concurrently with motor training by an electromyography (EMG)-triggered closed-loop system for post-stroke rehabilitation. Methods: We propose a double-blinded, randomized clinical trial involving 150 stroke patients assigned to one of three groups: concurrent taVNS, sequential taVNS, or sham control condition. In the concurrent group, taVNS bursts will synchronize with upper extremity motor movements with EMG-triggered closed-loop system during the rehabilitative training, while in the sequential group, a taVNS session will precede the motor rehabilitative training. TaVNS intensity will be set below the pain threshold for both concurrent and sequential conditions and at zero for the control condition. The primary outcome measure is the Fugl-Meyer Assessment of Upper Extremity (FMA-UE). Secondary measures include standard upper limb function assessments, as well as EMG and electrocardiogram (ECG) features. Ethics and dissemination: Ethical approval has been granted by the Medical Ethics Committee, affiliated with Zhujiang Hospital of Southern Medical University for Clinical Studies (2023-QX-012-01). This study has been registered on ClinicalTrials (NCT05943431). Signed informed consent will be obtained from all included participants. The findings will be published in peer-reviewed journals and presented at relevant stakeholder conferences and meetings. Discussion: This study represents a pioneering effort in directly comparing the impact of concurrent taVNS with motor training to that of sequential taVNS with motor training on stroke rehabilitation. Secondly, the incorporation of an EMG-triggered closed-loop taVNS system has enabled the automation and individualization of both taVNS and diverse motor training tasks-a novel approach not explored in previous research. This technological advancement holds promise for delivering more precise and tailored training interventions for stroke patients. However, it is essential to acknowledge a limitation of this study, as it does not delve into examining the neural mechanisms underlying taVNS in the context of post-stroke rehabilitation.

Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations.

Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ~60,000 germline exomes and ~36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of ~11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

A structurally informed human protein-protein interactome reveals proteome-wide perturbations caused by disease mutations.

To assist the translation of genetic findings to disease pathobiology and therapeutics discovery, we present an ensemble deep learning framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that predicts protein-binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms to generate comprehensive structurally informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods and experimentally validate its predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces and explore their impact on disease prognosis and drug responses. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from analysis of approximately 11,000 whole exomes across 33 cancer types and show significant associations of oncoPPIs with patient survival and drug responses. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

Comparing the adsorption of methyl orange and malachite green on similar yet distinct polyamide microplastics: Uncovering hydrogen bond interactions.

Microplastics (MPs) and dye pollutants are widespread in aquatic environments. Here, the adsorption characteristics of anionic dye methyl orange (MO) and cationic dye malachite green (MG) on polyamide 6 (PA6) and polyamide 66 (PA66) MPs were investigated, including kinetics, isotherm equilibrium and thermodynamics. The co-adsorption of MO and MG under different pH was also evaluated. The results reveal that the adsorption process of MO and MG is suitably expounded by a pseudo-second-order kinetic model. The process can be characterized by two stages: internal diffusion and external diffusion. The isothermal adsorption equilibrium of MO and MG can be effectively described using the Langmuir model, signifying monolayer adsorption. Furthermore, the thermodynamic results indicated that the adsorption was spontaneous with exothermic and endothermic properties, respectively. The results of binary systems reveal that MO dominates the adsorption at low pH (2-5), while MG dominates at high pH (8-10). Strong competitive adsorption was observed between MO and MG in neutral conditions (pH 6-8). The desorption experiments confirm that PA6 and PA66 could serve as potential carriers of MO and MG. The interaction between dyes and polyamide MPs is primarily mediated through hydrogen bonds and electrostatic attraction. The results reveal that PA6 formed more hydrogen bonds with the dyes, resulting in higher adsorption capacity than that of PA66. This difference can be attributed to the disparities in the synthesis process and polymerization method. Our study uncovered the adsorption mechanism of dye pollutants on PA6 and PA66, and provided a more comprehensive theoretical basis for the risk assessment concerning different types of polyamide MPs in aquatic environments.