Analytica Chimica Acta

Cultivated meat production requires robust and validated analytical methods for comprehensive characterization. While transcriptomics-based approaches establish the foundational profile of molecular analysis, proteomics provides additional resolution that further enhances scientific certainty in both product development and safety characterization. However, the industry adoption of proteomics is currently hindered by technical complexity and a critical lack of analytical standardization, which leads to significant workflow-dependent variations in proteome coverage. To address this gap, we investigated the influence of key workflow steps (digestion, cleanup, LC-MS conditions) on the proteome profile of cultivated duck biomass. We compared five bottom-up sample preparation protocols - two traditional in-solution options (urea and SDC-based protocols), two device-based approaches (PreOmics iST and EasyPep kits), and an innovative protocol (SPEED), and demonstrated that device-based protocols offered the highest peptide yield and proteome coverage. However, optimization allowed cost-effective in-solution methods to achieve comparable performance. Specifically, an optimal digestion time of 3 hours at 37{degrees}C and the use of polymer-based desalting columns significantly enhanced protein identification ([~]4500 - 5000 IDs). Moreover, data independent acquisition (DIA) provided deeper proteome coverage than data dependent acquisition (DDA) with higher precision ([~]6500 vs 5000 IDs). The validated Standard Operating Procedures presented here establish a standardized framework for bulk bottom-up proteomics in cultivated meat, facilitating the generation of reliable and comparable data required for robust multi-omics characterization. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/713501v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@5b61b8org.highwire.dtl.DTLVardef@16c7e65org.highwire.dtl.DTLVardef@1de21d2org.highwire.dtl.DTLVardef@7e984a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIComplexity and non-standardization limit MS-proteomics use in cultivated meat (CM). C_LIO_LICM protein profile varies with sample prep, LC-MS, and data processing pipeline. C_LIO_LIDevice-based and optimized cost-effective protocols offer a high proteome coverage. C_LIO_LIProteomics can complement transcriptomics for a comprehensive CM characterization. C_LIO_LIProposed standardized methods ensure reliable data for future regulatory submissions. C_LI

Polymeric 3D printing of microfluidic devices for biosensing is an appealing fabrication alternative for rapid manufacturing of biosensing devices with complex geometry in a streamlined, repeatable and cost-effective manner without the need for expensive instrumentation such as those employed in photochemical etching and soft lithography. Hybrid 3D printed paper-based microfluidics is an emerging area which harnesses the unique properties of both, merging the construction of microfluidic structures and the inherent capillary-driven flow within paper substrates. In this work, we have fabricated hydrophobic barriers by 3D printing a single layer of machinable wax, thermoplastic polyurethane, polylactic acid and polypropylene directly on chromatography paper to create open microchannels and determine the most suitable material. Characterization of each open microchannel using the four materials revealed polypropylene as the most reliable material with high hydrophobic barrier integrity and resolution. Polypropylene achieved functional microchannels with a resolution of 621 {+/-} 33{micro}m, hydrophobic barrier integrity of (93.75 {+/-} 9.16%), wicking speed of 0.38mm/s and optimal hydrophilicity of channels (51.4 {+/-} 8.36 {degrees}) with minimal embedding during thermal curing. To demonstrate proof of principle, a fluorescence assay demonstrating the formation of a dimeric g-quadruplex structure from a g-rich sequence which significantly enhances fluorescence of thioflavin T was implemented.

Phytohormones are key players in the regulation of plant development and metabolism. The different phytohormone classes comprise numerous chemically very diverse compounds, which are often present at very low concentrations. The chemical properties of phytohormones range from acidic to basic and from polar to non-polar. Furthermore, concentration varies strongly among different phytohormones, between plant species, tissues and developmental stages. Challenges often arise when only small amounts of plant material are available and when plant species are investigated in which the phytohormone profile has not yet been characterized. To establish a method for comprehensive phytohormone analysis we addressed these challenges by choosing and optimizing a suitable extraction method followed by optimized HPLC separation. We compared the most widely-used mass spectrometric detection methods, multiple reaction monitoring (MRM) on a triple quad instrument with high-resolution mass spectrometry (HRMS) on a Q-TOF instrument, and discuss the advantages of both methods and their limitations. O_LIWe compared various methods described in literature for the extraction of six phytohormone classes by liquid-liquid extraction and solid phase extraction purification and describe our optimizations to the selected method. C_LIO_LIWe optimized HPLC separation for 50 different phytohormones. C_LIO_LIWe evaluated the application of MRM and HRMS detection strategies. C_LI

IntroductionRetinoids are bioactive vitamin A derivatives that regulate cellular differentiation and gene expression, yet their reliable quantification remains challenging due to low abundance, structural isomerism, and sensitivity to ionization conditions while handling. ObjectivesIn this study, we performed a systematic optimization of liquid chromatography-mass spectrometry (LC-MS)-based detection of retinoids across tissues and biofluids. MethodsChromatographic separation, adduct formation, ionization parameters, fragmentation behavior, and extraction procedures were evaluated in an integrated workflow. ResultsChromatographic conditions influenced not only retention time but also the ionic species detected, affecting precursor selection for MS{superscript 2} analysis. Retinoids exhibited compound-dependent responses to electrospray ionization and collision energy, requiring tailored acquisition parameters. Extraction experiments demonstrated differential recovery among retinoid classes and revealed matrix-dependent behavior, indicating that protocols used for tissues cannot be directly transferred to low-abundance biofluids. Using optimized conditions, retinoids were detected in mouse cerebrospinal fluid (CSF) at concentrations approaching the analytical detection limit, where MS{superscript 2} confirmation was necessary for reliable identification. ConclusionTogether, our results provide a framework for reproducible retinoid profiling across biological matrices and enables comparative studies of retinoid biology in low-volume and low-abundance biofluids.

Peptide-level analyses are becoming increasingly popular in mass spectrometry-based proteomics and are being applied, for example, in immunopeptidomics, structural proteomics, and analyses of post-translational modifications. In such analyses, peptides that are not biologically meaningful but instead arise as artifacts prior to mass spectrometry analysis pose the risk of data misinterpretation. Here, we describe an approach based on retention time analysis and precise chromatographic peak matching to identify peptides generated by in-source fragmentation (ISF), which occurs between chromatographic separation of peptide mixtures and the first mass filter of a tandem mass spectrometer (MS). To understand the prevalence and properties of ISF, we generated 13 proteomics datasets and analyzed them along with additional 25 previously published datasets spanning a broad range of sample types, MS, and proteomics approaches including classical bottom-up proteomics, immunopeptidomics, structural proteomics, and phosphoproteomics. We found that, in typical trypsin-digested samples on average 1 % of fully-tryptic peptides and 22 % of semi-tryptic peptides originated from ISF. However, we observed large variations between datasets, and in-source fragments exceeded, in some cases, a third of the total peptide identifications. The extent of ISF was dependent on the peptide sequence, the instrument, method parameters, and sample complexity. Although ISF did not impair relative quantification across samples, it generated peptides that could be misinterpreted qualitatively, inflated peptide identifications, and comprised up to 37 percent of peptides shorter than 9 amino acids in immunopeptidomics datasets. We propose that, for peptide-centric applications, our open-source ISF detection approach be used to re-annotate peptides generated by ISF and remove them to avoid misinterpretation of data. ISF is an increasing concern with improving mass spectrometers, as they enable detection of an ever-increasing number of m/z features, including low abundance features like ISF products. Our work thus addresses a growing issue in proteomics and presents solutions to mitigate the impact of in-source fragment peptides. In the future, improved feature detection algorithms may enable elucidation of new ISF patterns affecting side chains that have been missed so far, which could contribute to explaining the vast space of as-yet unannotated proteomics data.

Tumour biomarkers such as CA125, CA15-3, CA19-9, AFP and CEA are routinely used in the oncology clinic to diagnose cancer, monitor response to therapy, and detect relapse. However, their quantification depends on immunoassay-based methods that are time-consuming, reagent-dependent, and poorly suited to resource-limited settings. Here, we present a machine learning-assisted ATR-FTIR spectroscopy approach for label-free tumour biomarker analysis to enable simple and rapid quantification at the bedside. Using principal component analysis (PCA), we first demonstrate that these five clinically relevant biomarkers are spectrally separable, with the protein-associated region (1200-1700 cm-1) providing the greatest discriminative information. We then develop partial least squares regression (PLSR) models to quantify CA125 in phosphate-buffered saline (R2 = 0.95) and in human serum across a clinically relevant concentration range, achieving reliable predictions at and above the clinical decision threshold of 35 U/mL. A semi-quantitative classification model further demonstrated robust identification of elevated CA125, with a macro-average sensitivity of 0.86 and specificity of 0.92. These results support ATR-FTIR spectroscopy as a rapid, reagent-free platform for cancer biomarker monitoring, with potential utility in resource-limited settings. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/714840v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1be9c03org.highwire.dtl.DTLVardef@f49e5eorg.highwire.dtl.DTLVardef@1c93e39org.highwire.dtl.DTLVardef@1141e6f_HPS_FORMAT_FIGEXP M_FIG C_FIG

MicroRNAs (miRNAs) are ultra-short RNA molecules characterized by high sequence homology, frequent post-transcriptional modifications, and typically low abundance, particularly in circulating biofluids. These inherent biological features present substantial technical challenges for RT-qPCR- based quantification. Consequently, the development of miRNA RT-qPCR assays has required architectural adaptations at the reverse transcription (RT) stage to generate extended cDNA templates, thereby enabling effective downstream quantitative PCR amplification. One widely adopted approach involves the enzymatic addition of a poly(A) tail to the 3' end of miRNAs, followed by poly(T)-primed universal reverse transcription, which has gained broad acceptance due to its perceived sensitivity and simplified workflow. However, independent experimental evidence indicates that this architecture does not consistently provide the level of specificity required for reliable single-nucleotide (SN) discrimination, particularly when quantifying low-abundance circulating miRNA targets, as demonstrated in our previous study. An alternative strategy relies on miRNA-specific reverse transcription using stem-loop priming has been equally well accepted. When generically generated, this approach offers certain improved specificity, but its performance in resolving single-nucleotide differences remains limited. In this article, we employed precision engineering to maximize specificity for both reverse transcription and qPCR steps. By tailoring both primer design and reaction architecture to the specific sequence features of each miRNA, we enable robust single nucleotide discrimination among these ultra-short targets. Prototype of ten different miRNova assays quantifying miRNAs whose sequences are differed in various configurations were tested on synthetic miRNA targets. For miRNova assay validation, saliva samples were elite rugby players submitted to small RNA extraction, then RT-qPCR. Spike-in of synthetic targets was applied for each quantification point to characterized the sensitivity, specificity and accuracy of the assays. Comparative analysis was performed between miRNova and two commercially available kits on the same sample set. The obtained results show a superior performance of miRNova assays allowing for sensitive and accurate quantification of miRNAs in saliva samples. Altogether, this results in modular, reproducible assays optimized for low-abundance miRNA detection in challenging biofluids, including saliva, positioning the platform beyond existing sensitivity-focused solutions toward true diagnostic-grade specificity.

A programmable 4-input cascade DNA logic gate utilizing toehold mediated strand displacement (TMSD) was implemented on a 3D printed hybrid paper-polymer vertical flow device (3D HPVF) for on/off sensitive and specific fluorescence detection of platelet derived growth factor BB (PDGF BB). Polypropylene was 3D printed directly on paper and thermally cured to create micro paper analytical devices ({micro}PADs). The 3D HPVF comprised of three layers of {micro}PADs enclosed in a casing that clamped each {micro}PAD securely to ensure seamless and efficient wicking between layers. In the presence of PDGF BB, a partially complementary strand to a PDGF B aptamer (PDGF B Apt), cApt, was liberated from a PDGF B Apt/cApt duplex in solution. The solution was then deposited on the 3D HPVF with a dimeric g-quadruplex hairpin. The 4-nucleotide toehold region on the cApt started the hybridization reaction with the dimeric g-quadruplex hairpin (dGH) opening it up allowing formation of a dimeric g-quadruplex structure that binds with thioflavin T (ThT) with enhanced fluorescence intensity at room temperature. The 3D HPVF exhibits a pico molar range of detection from 10pM to 100pM with a 10pM limit of detection (LOD) for PDGF BB concentrations relevant for pregnant women predisposed to early-onset preeclampsia with clear differentiation when compared to similarly competing analytes PDGF AA and AB.

Accelerating the development of enzymatic degradation of polyesters such as poly(ethylene terephthalate) (PET) and poly(butylene terephthalate) (PBT) requires a rapid and parallelizable detection method. We developed a protein-based biosensor for the fast and accurate quantification of the PET and PBT degradation product, terephthalate (TPA), which we named TPAsense. Engineering TPAsense required overcoming low thermal stability and aggregation of the initial construct by introducing stabilizing mutations without disrupting the binding affinity to TPA. The sensor performance was validated by screening for the PBT degrading activity of a Leaf-branch Compost Cutinase (LCC) mutant library and comparing with liquid chromatography data. TPAsense detects nanomolar concentrations of TPA enabling shorter incubation times for screening workflows. In addition, a comparative analysis of PETase and PBTase kinetics was performed with TPAsense. Finally, we demonstrated the detection of PET microplastic in samples from a wastewater treatment plant by combining the biosensor and a PETase. TPAsense offers a platform to accelerate PETase and PBTase development for plastic waste recycling and detection of microplastic in the environment.

Persistent infection with high-risk human papillomavirus (HPV) is the primary cause of cervical cancer and other HPV-related malignancies. Effective screening and early detection of HPV, particularly in point-of-care (POC) settings, can reduce disease progression and associated mortality. Although PCR-based assays provide high sensitivity, their dependence on centralized laboratory infrastructure limits accessibility in POC settings. CRISPR-Cas diagnostics enable programmable, isothermal detection of HPV with lateral flow assay (LFA) readouts; however, visual interpretation of faint bands can be subjective and inconsistent. Our objective was to develop a machine learning (ML)-enhanced, smartphone-native CRISPR-LFA platform for highly sensitive and reliable detection of HPV DNA in plasma. A smartphone-based diagnostic system integrating CRISPR-LFA with a ML framework was developed using standardized image acquisition within a light-controlled enclosure. Radiomics-inspired strip features were extracted and analyzed using a multivariable logistic regression model. A total of 150 plasma samples were used for model development and 60 independent samples for validation. An optimized model was developed that had 96.7% sensitivity and 100% specificity for detection of HPV DNA. The smartphone-enabled CRISPR platform demonstrated higher sensitivity than visual interpretation, particularly for faint-band results, and reduced false positives. Validation in the independent cohort confirmed the robustness of the assay. Performance remained stable across smartphone models, lighting conditions, and operators, and on-device inference enabled reliable operation. In sum, the smartphone-integrated CRISPR-LFA platform can facilitate accurate and reliable detection of plasma HPV DNA in POC settings and has the potential to enhance early detection, prevention, and treatment of cervical cancer.

Rapid quantification of sulfur-containing amino acids, particularly cysteine, in legumes is critical for assessing nutritional quality, supporting breeding program screening, and ensuring consistency in quality control processes. However, conventional methods, such as high-performance liquid chromatography (HPLC), are time-consuming and resource-intensive for high-throughput applications. This study evaluated artificial intelligence models for predicting cysteine concentration from surface-enhanced Raman spectroscopy (SERS) spectra of pea extracts. SERS spectra were acquired from 20 cultivars grown at three geographically distinct locations, with HPLC-measured cysteine concentrations as a ground truth reference. Linear regression, partial least squares regression, support vector regression, random forest regression, and a one-dimensional convolutional neural network (1D-CNN) were compared using within-cultivar splits and leave-one-cultivar-out (LOCO) evaluation. The 1D-CNN achieved RMSE 0.008 g/100 g within cultivars and maintained performance under LOCO, while other models showed limited generalization. Shapley Additive Explanations highlighted informative bands in the 630-760 cm-1 range, and noise modeling optimized scan-count selection.

Multiplexing samples in long-read sequencing with Oxford Nanopore Next Generation Sequencing Technology (ONT) by ligating specific native barcodes to individual DNA samples enables significant increases of high throughput sequencing combined with a significant reduction of sequencing costs. However, this advantage carries the risk of barcode misassignment / crosstalk. Employing ONT multiplex sequencing with samples, we observed misassigned barcodes so called barcode crosstalk, after ONT library preparation according to the standard protocol, particularly in samples with low input DNA concentrations. We assumed that these barcode misassignments are largely due to misligation of remaining native barcodes during subsequent the subsequent sequencing adapter ligation. To systematically investigate and quantify barcode crosstalk, genomic DNA (gDNA) from four bacterial type strains with different DNA input concentrations was prepared using three protocols for library preparation: the Nanopore standard protocol (protocol A: version valid until July 2, 2025) the new Nanopore protocol (protocol B: version from July 2, 2025), and an in house protocol with pooling of the barcoded samples only after the sequencing adapter ligation step (protocol C: in house). All samples were sequenced on a Nanopore PromethIon device. The results clearly showed that the use of protocol A resulted in a pronounced barcode crosstalk especially detectable in samples with low DNA input concentrations (up to 2.4% misassigned reads). The ONT adjustment in protocol B (altered washing buffer vs. protocol A) significantly alleviated the barcode crosstalk to below 0.01%, whereas protocol C eliminated barcode crosstalk virtually completely. These observations emphasize that sequencing results obtained with older ONT native barcoding protocol variants should be critically reviewed. The newer ONT barcoding protocol is preferable for sequencing, but it does not completely eliminate the barcode crosstalk effect. In conclusion, for low DNA input and high accuracy sequencing, protocol C is recommended.

Extracellular vesicles (EVs) are lipid spheres released from cells. Research utilizing EVs has met several hurdles owing to the small size of the majority of EVs and other nanoparticles (<150 nm) and the lack of detection technologies capable of providing high-throughput single particle measurements at this scale. The use of high-throughput single particle measurements is critical for the assessment of EV heterogeneity and abundance which are features often used to assess the development of isolation protocols or particle characterization. The Coulter principle, known in the field as resistive pulse sensing (RPS), has been used for several decades to size and count cells. More recently, this technology has evolved to accommodate nanoparticle analysis. In the last decade a platform utilizing microfluidic resistive pulse sensing (MRPS) has been demonstrated for nanoparticles, offering ergonomic characterization of nanoparticles along with utilizing open format data. To date, assessment of MRPS accuracy and reporting standards have not been assessed. With the aim of increasing data accuracy, ergonomics, and reporting transparency, we developed a microfluidic resistive pulse sensing post-acquisition analysis software (RPSPASS) application for automated cohort calibration, population gating, statistical output, QC plot generation, alternative data file outputs, and standardized reporting templates.

Over the past decades, the frequency of viral outbreaks has increased substantially worldwide, driven in part by global travel and resulting in millions of deaths each year. This trend underscores the urgent need for rapid, simple, and accessible diagnostic tools for infectious disease detection. Here, we present a nanofluidic digital chip (Nano-dChip) for point-of-care viral RNA detection that delivers results within 30 minutes at a cost of less than $0.50 per chip. The Nano-dChip employs reverse transcription loop-mediated isothermal amplification (RT-LAMP) for highly sensitive and specific target amplification. Reaction reagents are compartmentalized into numerous nanofluidic reservoirs, enabling highly quantitative detection while minimizing contamination risks. Using a single chip, we successfully detect both SARS-CoV-2 and Influenza H3 RNA with a detection limit of 10 fM, demonstrating the Nano-dChips potential as a rapid, low-cost, and scalable diagnostic platform for timely outbreak control.

Accurate detection of RNA splice variants is often hindered when transcripts lack large distinguishable exonic regions, making conventional PCR strategies challenging. We developed a simple melting temperature (Tm)-guided exon-exon junction (EEJ) RT-PCR method to enable variant-specific detection under these conditions. Uni-directional primers spanning exon-exon junctions were designed so that approximately each half anneals to adjacent exons. The Tm of each half-site was set >7{degrees}C below the annealing temperature, preventing stable binding to individual exons and enforcing junction-dependent amplification. The method was evaluated using HTRA1-AS1 long noncoding RNA variants that share overlapping exon sequences but differ in splice connectivity. HTRA1-AS1 comprises five variants, only one with a large distinguishable exon. Tm-guided EEJ primers robustly discriminated the remaining four variants. After optimization, amplification yielded sharp, single bands with minimal cross-reactivity. Compared with conventional designs, this approach reduced heteroduplex and heteroquadruplex formation, improving band clarity. Sanger sequencing confirmed junction specificity, and the method performed well in multiplex settings. Overall, Tm-guided EEJ RT-PCR is a cost-effective, high-resolution approach for detecting RNA variants lacking easily distinguishable exonic regions, readily compatible with standard RT-PCR and qPCR workflows.

Latent tuberculosis infection (LTBI) remains a significant barrier to global TB control and elimination efforts. The QuantiFERON-TB Gold (QFT) assay is commonly used for the diagnosis of LTBI. However, blood collected in QFT tubes is seldom utilized for molecular and genetic analysis due to the presence of heparin and a dense gel barrier that hinders efficient DNA extraction. To address this limitation, we aimed to develop a method for directly isolating high-quality DNA from blood in QFT tubes, eliminating the need for additional blood sampling and enabling their use in both diagnostic and molecular workflows. In this study, DNA was extracted from blood in EDTA and QFT tubes using a hybrid approach that combined manual lysis with three commercial kits: Thermo Scientific GeneJET, QIAamp DNA Blood Kit, and FavorPrep Blood Genomic DNA Extraction Kit. DNA concentration and purity were measured with a Multiskan SkyHigh Microplate Spectrophotometer, while integrity was assessed through agarose gel electrophoresis. Two nucleic acid amplification techniques (NAATs), ARMS-PCR and whole exome sequencing (WES) were performed to validate applicability of extracted DNA for molecular biology applications. We did not find any differences in the quantity, quality, or application of PCR or sequencing for DNA extracted from EDTA or QFT tubes. The extracted DNA from both EDTA and QFT tubes exhibited A260/280 ratios of 1.7-1.9 and concentrations ranging from 4.9 to 118.5 {micro}g/mL, indicating an adequate yield and purity. Intact genomic DNA and PCR product bands on agarose gel indicated suitability for downstream applications. Additionally, WES produced 6.47-8.71 GB of data per sample, with 42.8-57.7 M reads and GC content between 49.29% and 52.54%. Sequencing metrics were consistently strong, with Q20 values exceeding 98.6% and Q30 values above 95%. Our study presents an optimized and reproducible protocol for extracting high-quality DNA from QFT tubes, producing DNA suitable for both PCR and sequencing technologies. This protocol provides a cost-effective and practical strategy to integrate LTBI diagnosis with genomic research, particularly beneficial in resource-limited settings. This study introduces a novel analytical workflow applicable to diagnostic laboratory settings, enabling the integration of routine LTBI immunodiagnostic testing with downstream genomic analysis. The approach supports improved utilization of clinical specimens in laboratory medicine and may facilitate future biomarker and precision diagnostics research.

Microplastic (MP) pollution, which is present in the ecosystem in vast quantities, adversely affects human health and the environment, making it imperative to develop methods for its mitigation. The challenge of detecting or capturing MPs could potentially be addressed using plastic-binding peptides (PBPs). The ideal PBP for MP remediation would not only bind strongly to plastic, but also have other properties such as high solubility in water or great binding specificity to a certain plastic. However, the scarcity or absence of known PBPs for common plastics along with the lack of methods that can discover PBPs with all of the desired properties precludes the development of peptide-based MP remediation strategies. In this study, we discovered short linear PBPs with high predicted water solubility and binding specificity by employing an in-silico discovery pipeline that combines deep learning and biophysical modeling. First, a long short-term memory (LSTM) network was trained on biophysical modeling data to predict peptide affinity to plastic. High affinity peptides were generated by pairing the trained LSTM with a Monte Carlo tree search (MCTS) algorithm. Molecular dynamics (MD) simulations showed that the PBPs discovered for polyethylene, the most common plastic, had 15% lower binding free energy than PBPs obtained using biophysical modeling alone. PBPs with both high affinity and high predicted solubility in water were found by including the CamSol solubility score in the MCTS peptide scoring function, increasing the average solubility score from 0.2 to 0.9, while only minimally decreasing affinity for polyethylene. The framework also discovered peptides with high binding specificity between polystyrene and polyethylene, two major constituents of MP pollution, using a competitive MCTS approach that optimized the difference in affinity between the two plastics. MD simulations showed that competitive MCTS increased the binding specificity of PBPs for polystyrene and identified peptides with relatively great preference for either of the two plastics. The framework can readily be applied to design PBPs for other types of plastic. Overall, the high-affinity PBPs with desirable properties discovered by marrying artificial intelligence and biophysics can be valuable for remediating MP pollution and protecting the health of humans and the environment.

This article presents the development of an advanced modeling and simulation platform for carbon capture systems, with a focus on integrated process analysis from upstream CO2 capture through to bioethanol production. The platform supports the evaluation of CO2 mitigation technology by coupling mathematical bioprocess models with an interactive desktop application. The biological system employs Chlorella vulgaris microalgae to fix CO2 through photosynthesis and generate carbohydrate substrates, which are subsequently converted to bioethanol by Saccharomyces cerevisiae yeast via fermentation. The simulation integrates three established kinetic models--the Monod, Logistic, and Luedeking-Piret models--to predict biomass growth, substrate consumption, and ethanol yield under varying operational conditions. A closed-loop CO2 recycling subsystem captures fermentation off-gases and reintroduces them into the bioreactor, enhancing overall carbon utilization efficiency. Three representative simulation scenarios demonstrated process efficiencies ranging from 1.09% to 93.78% of the theoretical maximum CO2-to-ethanol conversion efficiency, confirming the platforms capacity to evaluate a wide operational envelope. The Electron/React-based desktop application provides real-time visualization, interactive 3D bioreactor models, and a simulation history module, making it accessible to researchers, engineers, and students. The platform serves as a digital twin that bridges rigorous bioprocess mathematics with intuitive user interaction, providing a cost-effective tool for designing and optimizing sustainable carbon capture and biofuel production systems.

Open modification search (OMS) strategies have gained popularity in mass spectrometry-based proteomics for identification of peptides carrying unknown or unexpected post-translational modifications. However, most OMS search engines report only the overall mass difference between the precursor and the matched peptide and do not explicitly identify or score combinations of multiple modifications at the peptide-spectrum match (PSM) level, leaving the interpretation of mass shifts up to the end user and to using downstream analysis tools. Here, we introduce MS Andrea, a novel OMS search engine developed to directly identify and score combinations of up to four variable modifications per peptide without having to predefine them. MS Andrea uses a sequence tag-based strategy to efficiently filter candidate peptides prior to scoring. Remaining candidates are evaluated using the MS Amanda scoring function, first considering fixed modifications only, followed by a second scoring stage in which combinations of modifications from the Unimod database are considered based on the observed mass difference and matched to the spectrum. We evaluated MS Andrea using phosphopeptide datasets from HeLa cells and Arabidopsis thaliana and compared its performance with the widely used OMS engines MSFragger and Sage. Across datasets, MS Andrea identified the highest number of PSMs at 1% false discovery rate while achieving comparable peptide-level identifications. Importantly, MS Andrea directly reports modification identities and sites at the PSM level and enables the identification of peptides having up to four variable modifications. Together, these results demonstrate that MS Andrea facilitates more detailed and interpretable characterization of peptide modifications while maintaining competitive identification performance in OMS-based proteomic analyses. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/714851v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@52f65forg.highwire.dtl.DTLVardef@acf4e3org.highwire.dtl.DTLVardef@10171caorg.highwire.dtl.DTLVardef@1d594ad_HPS_FORMAT_FIGEXP M_FIG C_FIG

Boar semen contains spermatozoa and seminal plasma (SP) that carries extracellular vesicles (EVs) among other components. However, artificial insemination (AI) doses produced by AI companies are highly diluted based solely on sperm concentration. The aim of this study was to evaluate the integrity of EVs isolated from AI doses, characterize the protein and miRNA content from high-fertility (HF) and reduced-fertility (RF) boars, and evaluate their functional impact on spermatozoa after dilution by a coincubation up to 24 hours at 38 {degrees}C. Proteomics identified 108 differentially expressed proteins between HF and RF EVs (97 upregulated in HF, 11 in RF), and transcriptomics revealed 80 differentially expressed miRNAs (DEMs) in EVs, 52 in SP, and 3 in spermatozoa, showing inverse expression in various shared DEMs between fertility rates, suggesting compartment-specific regulation. Functional coincubation demonstrated that EVs remain biologically active after dilution. HF EVs improved sperm quality parameters and reduced oxidative stress, while RF EVs increased total and progressive motility. Overall, our findings show that EVs from AI doses retain structural integrity, carry fertility-associated protein and miRNA signatures, and functionally modulate sperm quality in vitro. These features highlight porcine EVs as promising biomarkers and potential tools to optimize reproductive performance in swine production.