Frontiers in Molecular Biosciences

Staphylococcus aureus (S. aureus) is an opportunistic pathogen that is a global health concern for its ability to cause a wide spectrum of clinical infections. Due to the emergence of resistance to commonly used antibiotics, there has been interest in exploring the use of antimicrobial peptides to treat S. aureus infections. However, changes in the lipid composition of the lipid bilayer membrane can alter the activity of peptides, and S. aureus is able to induce variations in lipid composition in response to environmental stress. Here, we explore how the main lipid components in S. aureus are altered when exposed to LL-37, a human cathelicidin involved in primary immune response, and ATRA-1, a short antimicrobial peptide derived from the snake Naja atra venom. A lipidomic study is conducted through HPLC-MS-MS (LC-ESI-MS/MS) to quantify phosphatidylglycerol, cardiolipin, lysyl-phosphatidylglycerol, monogalacto- and digalacto-diacylglycerol, and carotenoids. In addition, menaquinones, responsible for electron transport during oxidative phosphorylation, were also quantified. Biophysical properties such as membrane electric surface potential and lipid packing were assessed. We find that lipid adaptation is specific to the type of antimicrobial peptide, where ATRA-1 mainly induces changes in the electric surface potential through variations in Lysyl-PG, while exposure to LL-37 changes carotenoid levels, inducing an increase in membrane rigidity as measured by FTIR. In addition, both peptides induce a reduction in menaquinone and DGDG levels. These findings highlight the role of membrane lipid remodeling as a peptide-specific response mechanism in S. aureus, with implications for the development of AMP-based therapies. HighlightsO_LIStaphylococcus aureus responds through shifts in lipid composition and membrane biophysical properties to exposure to the antimicrobial peptides LL-37 and ATRA-1. C_LIO_LIBoth LL-37 and ATRA-1 lead to shifts in the glycolipids MGDG and DGDG; two lipids involved in regulating negative membrane curvature stress and responsible for shifting resistance to antimicrobial peptide activity in Staphylococcus aureus. C_LIO_LILL-37 treatment leads to an overall reduction in carotenoid content in Staphylococcus aureus, including the carotenoid end-product staphyloxanthin and the precursor 4,4-diaponeurosporenoic acid. Both lipids regulate membrane biophysical properties and protect Staphylococcus aureus from oxidative stress. C_LIO_LIBoth LL-37 and ATRA-1 lead to a reduction in menaquinone levels, which are involved in the electron transport chain during oxidative phosphorylation. Reduction in these menaquinones have been associated to the formation of small colony variants that are often observed in chronic Staphylococcus aureus infections. C_LI

Inflammasomes are cytosolic innate immune sensors that, once activated by a pathogenic threat, lead to activation of the inflammatory Caspase-1. Inflammasome activation and its consequences have been studied extensively in myeloid cells and in overexpression systems. Recent studies have identified cell type specific effects that are not fully explained by the known cleavage targets of Caspase-1. Here, we identified targets of caspase cleavage using mass spectrometry in primary intestinal epithelial cells by specifically activating the NAIP-NLRC4 inflammasome. We have taken an unbiased approach and developed a novel method for analyzing mass spectrometry data for evidence of caspase activity. Our approach can also be applied to existing proteomic datasets to establish the presence of caspase activity under various biological conditions. These results lay the groundwork for future studies on mechanisms of caspase-induced processes such as intestinal epithelial cell extrusion.

Kernel methods offer a robust framework for integrating multi-modal datasets into a unified representation, thereby facilitating more comprehensive data interpretation. In the presence of incomplete datasets, multiple kernel learning is employed to enhance the efficiency of data completion and integration. We investigate kernel-based approaches to address the incomplete-data problem with applications to yeast protein data. Biological data such as yeast proteins can be represented through multiple modalities, including gene expression profiles, amino acid sequences, three-dimensional structures, and protein interaction networks. We introduce a computational pipeline based on kernel matrix completion, in which topological data analysis (TDA) and persistent spectral analysis are incorporated into the classification setting. TDA captures geometric structure across scales while spectral descriptors reflect connectivity patterns through Laplacian eigenvalues. Kernel, topological, and spectral descriptors are used with support vector machines to discriminate between membrane and non-membrane yeast proteins. Empirical results show that the combined pipeline improves both kernel completion accuracy and ROC performance relative to baseline kernel-only approaches. The best-performing configuration achieves an ROC score of 0.8632 using the average of three kernels augmented with TDA features. These results demonstrate competitive performance relative to strong kernel-based baselines under incomplete data conditions. The proposed approach provides a unified approach for learning from incomplete heterogeneous data while enriching kernel representations with geometric and spectral information.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide. Current treatments offer limited efficacy and no definitive cure, underscoring the urgent need for more selective and effective therapeutic strategies. This study investigated the synthetic lethality caused by co-targeting two metabolic genes, ATP citrate lyase (ACLY) and oxoglutarate dehydrogenase (OGDH), in HCC cells. Using valproic acid (VPA) and bempedoic acid (BA) as pharmacological inhibitors of OGDH and ACLY, respectively, we observed a strong synergistic effect in inhibiting the proliferation of HCC cell lines (Hep3B and Huh7), compared to using these drugs individually. Importantly, this combination treatment exhibited little increased cytotoxicity in the non-cancerous liver cell line THLE-2, indicating a degree of selectivity. Our findings are consistent with previous reports implicating USP13 as a metabolic regulator of ACLY and OGDH in various cancers, suggesting that the inhibition of USP13 may prevent HCC cell proliferation primarily through its downstream effects on ACLY and OGDH. By directly co-targeting ACLY and OGDH, our approach may offer a more precise and safer alternative to USP13 inhibition. Additionally, while both VPA and BA have been individually associated with beneficial effects in liver disease, their combined application in the context of HCC has not been previously investigated. Limitations include the reliance on cell line models, highlighting the need for validation in more physiologically relevant systems such as human organoids and animal models. Overall, this study provides a compelling rationale for further investigation into ACLY and OGDH as a synthetic lethal pair and the therapeutic potential of the VPA-BA combination treatment in HCC.

Salla disease is caused by a genetic mutation in sialin, a lysosomal membrane transporter, which exports sialic acid from lysosomes. Substrate translocation occurs via a rocker-switch mechanism that alternately exposes the substrate-binding site to the lysosomal lumen and the cytosol. The pathogenic mutation R39C found in most Salla disease patients decreases the lysosomal localisation and the transport activity. In this study, we used computational and mutagenesis approaches to elucidate the molecular effects of the R39C mutation. Using three-dimensional models of human sialin in the lumen-open (LO) and cytosol-open (CO) states combined with the mutagenesis of selected residues, we identify a critical "triplet" motif comprising R39, E194, and E262, which is associated with an ionic lock formed between K197 and D350 in the LO conformation. Molecular dynamics simulations suggest that the electrostatic triplet negatively modulates the ionic lock, and are consistent with a strengthened ionic lock in R39C sialin, potentially favouring the LO state. To assess the global effects of the R39C mutation, we computed dynamic cross-correlation matrices and identified correlation patterns consistent with an allosteric coupling between the ionic lock K197/D350 and the region surrounding the sialic acid binding site in wild-type sialin, whereas in the LO state of R39C sialin, this communication preferentially bypasses this region. Therefore, the R39C mutation may impede the LO to CO conformational transition required for sialic acid transport, providing a plausible mechanistic framework for the decreased transport activity, and possibly the decreased lysosomal localisation, observed in Salla disease. HighlightsO_LIThe R39 residue participates in an interaction triplet, which negatively regulates an ionic lock stabilising the lumen-open conformation C_LIO_LIThe R39C mutation is associated with a stronger ionic lock in the simulations, and may favour the lumen-open state C_LIO_LICorrelation network analysis suggests an allosteric coupling between the ionic lock and the region surrounding the sialic acid binding site C_LIO_LIThe R39C mutation alters the inferred allosteric coupling between the ionic lock and the region surrounding the sialic acid binding site C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/719580v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@1ed0f72org.highwire.dtl.DTLVardef@913798org.highwire.dtl.DTLVardef@1d8e5adorg.highwire.dtl.DTLVardef@cf0060_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background: Elevated lipoprotein(a) [Lp(a)] is a known risk factor for several cardiovascular-related diseases established from multiple genetic and observational studies. However, the underlying mechanisms mediating the effects of Lp(a) levels on cardiovascular disease risk and major adverse cardiovascular events (MACE) are unclear. The aim of this study was to identify proteins downstream of Lp(a) using mendelian randomization (MR) - a genetic causal inference approach. Methods: A two-sample MR was performed by initially identifying Lp(a) genetic instruments based on data from genome wide association studies (GWAS) of Lp(a) blood concentrations. These instruments were then tested for association with proteins from proteomic pQTL data (Olink from UK Biobank, 2940 proteins and SomaScan from deCODE, 4907 proteins). Results: A total of 521 proteins associated with Lp(a) were identified. Using pathway enrichment analysis, the following MACE-relevant pathways were identified comprising a total of 91 Lp(a) downstream proteins: oxidized phospholipid-related, chemotaxis of immune cells and endothelial cell activation, pro-inflammatory monocyte activation, neutrophil activity, coagulation, and lipid metabolism. Conclusion: The results suggest that the influence of Lp(a) treatments is primarily through modifying inflammation rather than lipid-lowering, thus providing insight into the mechanistic framework which mediates the effects of elevated Lp(a) on atherosclerotic cardiovascular disease.

This protocol describes a computational approach for constructing correlation-based molecular networks from untargeted metabolomics data using MetVAE, a variational autoencoder-based framework. Complementing spectral similarity networks, it captures functional relationships re-flected in cross-sample correlations. The workflow imports metabolomics features and sample metadata, adjusts for compositionality, missingness, confounding, and high-dimensionality, esti-mates sparse metabolite correlations, and exports GraphML files for network visualization. In a hepatocellular carcinoma mouse model, it links lipid classes in high-fat-diet animals, suggesting an endogenous "auto-brewery" route to lipotoxic metabolites.

Fusobacterium nucleatum is an anaerobic bacterium strongly associated with the development and progression of colorectal cancer (CRC). Its pathogenic mechanisms involve the LuxS/AI-2 quorum sensing (QS) system, which regulates biofilm formation, virulence factor expression, and host immune evasion. Targeting LuxS represents a promising anti-virulence strategy that could disrupt bacterial communication without inducing selective pressure for antibiotic resistance. In this study, we employed a computational drug repurposing pipeline to identify FDA-approved drugs capable of inhibiting the LuxS enzyme in F. nucleatum. We performed structure-based virtual screening of 9,466 compounds from DrugBank using AutoDock Vina against the AlphaFold-predicted LuxS structure (UniProt: A0A133NIU3). From 1,082 initial hits (binding energy [&le;] - 7.0 kcal/mol), we applied ADMET filtering and composite scoring to select the top 5 candidates. Molecular dynamics simulations (10 ns each) using OpenMM with the AMBER14 force field confirmed the stability of all five protein-ligand complexes (RMSD < 2.0 [A]). The most promising candidates include Tubocurarine ({Delta}G = -16.97 kcal/mol, RMSD = 1.87 [A]), Docetaxel ({Delta}G = -13.22 kcal/mol, RMSD = 1.81 [A]), Metyrosine ({Delta}G = -13.78 kcal/mol, RMSD = 1.97 [A]), and Ergometrine ({Delta}G = -13.22 kcal/mol, RMSD = 1.92 [A]). These results constitute an exploratory computational basis that requires subsequent experimental validation through in vitro and in vivo assays, and provide candidates for testing as anti-quorum sensing agents against F. nucleatum, with potential implications for CRC prevention and treatment.

Background: Classification of central nervous system (CNS) tumors has become increasingly complex, raising concerns about the sustainability of comprehensive molecular diagnostics. We have evaluated nanopore whole genome sequencing (nWGS) as a single workflow to replace multiple diagnostic assays. Methods: We performed nWGS on DNA extracted from 90 adult CNS tumor samples (58 retrospective, 32 prospective) and compared the results to findings from standard of care (SoC) diagnostic work-up. Analysis was done through an automated workflow that consolidated diagnostically and therapeutically relevant genomic alterations, including copy-number variation, structural, and single-nucleotide variants, chromosomal aberrations, gene fusions, and methylation-based classification. Results: nWGS supported final diagnostic classification in all samples with >15% tumor cell content, requiring ~3 hours of hands-on library preparation, parallel sample processing, and sequencing times within 72 hours. Methylation-based classification was available within 1 hour and was concordant with the integrated final diagnosis in 89% of cases (80/90). All diagnostically relevant copy-number variations, single-nucleotide variants, and gene fusions were concordant with SoC testing. MGMT promoter methylation status matched in 94% of cases. In addition, nWGS identified prognostic and potentially actionable variants that were not reported or covered by SoC. Conclusions: nWGS delivers comprehensive genetic and epigenetic results with a fast turn-around compared to standard methods. This enables efficient, accurate, and scalable molecular diagnostics of CNS tumors using a single platform. This data supports its implementation in routine clinical practice and may be extended to other cancer types requiring complex genomic profiling.

Cancer cells exposed to nutrient deprivation activate adaptive programs to survive metabolic stress, often acquiring enhanced plasticity and motility. We have previously reported that colon cancer cell lines that survived nutrient depletion underwent partial epithelial-mesenchymal transition (pEMT), which was further exacerbated when these cells also underwent lysosomal alkalinization. Here, we have attempted to dissect the molecular mechanisms that drive the motility and shape change from cobblestone to elongated in subpopulations of cells. Using RNA-seq-based bioinformatic analyses integrated with pathway scoring, protein-protein interaction networks, probabilistic modeling and confirmatory experimental data, we have identified the coordinated activation of sublethal apoptotic signaling, fatty acid oxidation, mitochondrial ROS generation, and Ca{superscript 2}-dependent lysosomal exocytosis in the nutrient-depleted cells. Among these phenotypes, the cells undergoing starvation and lysosomal alkalinization exclusively mediated lysosomal exocytosis and cell motility. Probabilistic modeling further revealed non-linear relationships between metabolic stress signals and cell fate transitions, highlighting heterogeneous lysosomal functions as a key determinant of the altered phenotype of cells under nutrient depletion. Overall, our study has identified that aberrant lysosomal functioning in cells under nutrient depletion can specifically select for a subpopulation of cells that are highly viable, metabolically plastic and capable of motility.

This report identifies a bidirectional signaling axis connecting lipid metabolism to nuclear mechanotransduction, with the potential to control fatty acid/triglyceride metabolism. The sterol regulatory element-binding (SREBP) family of transcription factors control fatty acid, triglyceride and cholesterol synthesis and metabolism. The family consists of three members: SREBP1a, SREBP1c, and SREBP2, that are regulated by intracellular cholesterol levels and insulin signaling. The SREBP2-dependent control of the LDL receptor gene is a well-established target for cholesterol-lowering therapeutics and the activity of SREBP1c is an attractive target in metabolic disease. In the current report, we identify SYNE4 (nesprin-4), a component of the Linker of Nucleoskeleton and Cytoskeleton (LINC) complex, as a direct target of the SREBP family of transcription factors, and show that nesprin-4 in turn supports SREBP1c function. We identify functional SREBP binding sites in the human SYNE4 promoter and demonstrate that these are required for the sterol- and SREBP-dependent regulation of the promoter. Furthermore, we show that the endogenous SYNE4 gene is also regulated by SREBP1/2 and intracellular sterol levels. Interestingly, SREBP2 is responsible for the sterol regulation of the SYNE4 gene in HepG2 cells, while SREBP1 is the major regulator in MCF7 cells, demonstrating that diberent cell types use diberent SREBP paralogs to regulate the same promoter/gene. Importantly, we find that nesprin-4 is a positive regulator of SREBP1c expression and function in HepG2 cells and during the diberentiation of human adipose-derived stem cells. In summary, the current report identifies a novel regulatory interaction between lipid metabolism and the LINC complex. Importantly, we demonstrate that this signaling axis is bidirectional, forming a closed loop that has the potential to control SREBP1c activity and thereby fatty acid and triglyceride synthesis/metabolism. Based on our data, we propose that the nesprin-4-dependent regulation of SREBP1c could represent a novel therapeutic target in metabolic disease.

BackgroundInflammatory bowel disease (IBD) is associated with early structural changes in intestinal epithelial cells; however, the associated molecular alterations remain incompletely understood. The cytoskeletal protein keratin (K) 7 was recently found to be focally expressed in the colonic epithelium in IBD, while absent in the healthy colon. Here, we investigated the applicability of K7 as a noninvasive stool biomarker for pediatric IBD. MethodsIn this case-control study including adolescent patients with IBD (n=27) and healthy controls (n=15), stool lysates were analyzed by proteomics, immunoassay and qPCR to determine K7 protein and mRNA content, respectively. Additionally, stool mRNA levels of the simple epithelial keratins, K8, K18, K19 and K20, were measured. ResultsStool proteomic analysis identified focal K7 and K19 in IBD samples. Additionally, 23 differentially abundant proteins, of which 18 were higher in IBD, were identified and Gene Ontology enrichment analysis highlighted immune and inflammatory pathways. K7 specific immunoassay detected fecal K7 protein in all patients with active IBD, including both ulcerative colitis and Crohns disease, while K7 was near or below the detection limit in controls and IBD patients in remission (area under ROC curve=0.88, p<0.0001). While KRT7 mRNA levels were below the detection limit, KRT8 and KRT18 transcripts were elevated in IBD samples compared to controls (p<0.05). ConclusionsK7 protein is elevated in IBD patient stool, reflecting intestinal de novo expression and increased epithelial cell exfoliation. Fecal K7 may provide a novel, noninvasive marker for IBD diagnosis and monitoring.

High-Grade Serous Ovarian Cancer (HGSOC) is the most lethal gynecological malignancy due to aggressive growth, widespread metastases, and high intra-tumoral heterogeneity. Poor prognosis is largely due to late diagnosis, hence there is an urgent need to identify novel biomarkers for screening, diagnosis, and monitoring. Here, we propose the voltage-dependent calcium channel hCaV1.2 encoded by CACNA1C as a potential biomarker and therapeutic target in HGSOC. Using IHC analysis for ten ovarian cancer patients, cytotoxicity assay, TCGA gene expression and survival analyses, homology modeling, molecular docking, Calcium channel membrane assembly and molecular dynamics simulations, we tested CACNA1Cs role in HGSOC progression and the effect of blocking on cancer cell survival. We show that nifedipine (NIFE), a calcium channel blocker (CCB), had a tumor suppressive effect based on binding models predicted by three-dimensional computer assisted molecular modeling and in vitro validation using human HGSOC cell line. Using The Cancer Genome Atlas ovarian public cohort, we found CACNA1C mRNA expression strongly correlated with poor patient survival for late-stage and metastasis than primary. We also show strong correlation of CACNA1C protein expression using immunohistochemistry correlating with COH ovarian carcinomas patients disease progression. This research demonstrates that targeting HGSOC via CCBs may be therapeutically beneficial. By establishing further in vitro, in vivo, and clinical trials using FDA approved NIFE may be repurposed to target CACNA1C for HGSOC. Novelty and ImpactHigh-grade serous ovarian cancer (HGSOC) remains lethal due to late diagnosis and drug resistance. This study identifies CACNA1C (Cav1.2) as a novel prognostic biomarker and therapeutic target in HGSOC, showing that elevated expression correlates with metastatic/recurrent disease and poor survival. Using molecular dynamics and in vitro models, we demonstrate that the FDA-approved calcium channel blocker nifedipine binds stably to Cav1.2 and suppresses tumor cell growth more effectively than cisplatin. These findings support repurposing nifedipine for biomarker-driven HGSOC therapy. Translational RelevanceLate diagnosis and progressive relapses significantly contribute to the poor prognosis of ovarian cancer. Identification of a tumor biomarker that can be used for screening, diagnosis, and monitoring is critical for improving clinical outcome. Our findings demonstrate that CACNA1C is a viable diagnostic marker for HGSOC and that its blockade with CCBs reduces tumor progression, highlighting their therapeutic potential.

Intestinal epithelial cells are central players in mucosal barrier integrity and host-microbe interactions. Genetic studies have revealed that epithelial dysfunction is a key contributor to the pathogenesis of inflammatory bowel disease. Non-SMC condensin II complex subunit D3 (NCAPD3) is essential for chromatin organization and stability. NCAPD3 also promotes antimicrobial defense and autophagy responses in vitro. NCAPD3 expression is decreased in intestinal epithelial cells from patients with ulcerative colitis; however, it is not known whether loss of NCAPD3 expression drives intestinal barrier dysfunction or is a result of disease-associated inflammation. To investigate this relationship in vivo, a tissue-specific approach was required, as global constitutive knockout of NCAPD3 is embryonic lethal. Therefore, a transgenic mouse line with doxycycline-inducible expression of a short hairpin RNA targeting NCAPD3 restricted to villin-expressing cells was generated (NCAPD3KD mice) to enable the study of NCAPD3 function in the intestinal epithelium. Treatment of NCAPD3KD mice with 9-tert-butyl doxycycline resulted in [~]75% reduction of NCAPD3 protein in EpCAM intestinal cells. Short-term epithelial NCAPD3 knockdown did not induce spontaneous colitis but was associated with increased serum amyloid A and a trend towards increased intestinal permeability. Upon dextran sodium sulfate or Salmonella enterica serovar Typhimurium {Delta}AroA challenge, NCAPD3KD mice exhibited exacerbated weight loss, higher disease activity, increased histopathological damage, abnormal colonic cytokines and chemokines, and significantly increased intestinal permeability. These results indicate that NCAPD3 expression in the intestinal epithelium is required for optimal barrier maintenance and antimicrobial defense under chemical or microbial stress. These findings support prior in vitro observations and solidify NCAPD3 as a regulator of intestinal epithelial barrier function and mucosal host defense. Author SummaryNCAPD3 is a multifunctional protein with established roles in chromatin organization, genome stability, mitochondrial function, and antimicrobial defense. Dysregulated NCAPD3 is implicated in human diseases, such as inflammatory bowel disease (IBD) and microcephaly; however, due to its essential role in cellular division, determination of whether NCAPD3 loss drives these pathologies in vivo has been lacking. Using a new transgenic mouse model that selectively reduces NCAPD3 expression in intestinal epithelial cells, our study establishes NCAPD3 as an epithelial regulator of the mammalian intestine that enhances epithelial barrier resilience and antimicrobial defense during stress. Although dispensable for short-term basal homeostasis, NCAPD3 function becomes critical during epithelial injury and enteric infection. Reduced NCAPD3 expression may therefore lower the threshold for inflammatory disease by weakening barrier integrity, amplifying inflammatory cascades, and impairing antimicrobial defenses. These findings position NCAPD3 as a potential modulator of IBD susceptibility and highlight chromatin organization as an important, previously underappreciated layer of intestinal epithelial regulation.

We present a novel statistical method and a prototype computational implementation for estimating the diffusion coefficient from single particle tracking (SPT) data. Our method is based on anchored Brownian motion which is a novel representation that relaxes the restrictions of conventional Brownian motion. Our method is fully developed in Bayesian terms and allows for robust estimation of diffusion coefficient and quantification of the uncertainly propagated from limited data quantity and quality as appropriate for the analysis of live-cell SPT data. We compare our methods with conventional Brownian motion and demonstrate superior performance in estimating the correct value of the diffusion coefficient. Finally, we benchmark our methods with SPT data from in cellulo and in silico experiments.

Lipid metabolism is increasingly recognized as a hallmark of cancer, yet translating lipidomic discoveries into clinically actionable biomarkers remains constrained by analytical variability and limited standardized validation frameworks. This challenge is further compounded by a chicken-or-egg problem, where expensive standards and labelled internal standards are required to identify and quantitate target lipids, but the diagnostic importance of these targets is uncertain until they can be reliably measured. Previous work had indicated the potential of 48 lipid biomarker species for the prediction of breast cancer from plasma samples using high resolution liquid chromatography mass spectrometry. This study aimed to identify each of these 48 species and develop a quantitative method to determine the absolute concentrations of these lipids in plasma to provide the basis for the development of a clinical assay for use in breast cancer detection. In doing so, we present a pragmatic workflow that bridges lipid discovery with lipid identification and robust quantitative analysis. A curated library of 48 lipid species was established using authentic standards to verify plasma lipids through retention-time matching and high-resolution spectral comparison. In plasma, 41 lipids were confidently identified based on co-elution with standards and diagnostic fragment ions. Method qualification, including assessment of accuracy, precision, recovery, and linearity, was performed across all 48 lipids in parallel with identification, and 46 lipids ultimately met all predefined qualification criteria. Notably, practical constraints, including time, cost, and availability of authentic standards, necessitated performing identification and targeted method development in parallel, highlighting challenges inherent to translating lipidomics into commercial or clinical assays. This workflow provides a reproducible framework for harmonizing lipid identification and quantification, enabling the reliable integration of lipidomic data into biomarker discovery and clinical applications.

High level of protein expression is usually welcomed in industry and research, and codon optimization is widely used to achieve high expression. Methods of implementing codon optimization can be divided into two branches, one is classical methods which develop cost functions based on empirical law, another is AI methods which learn the codon choice principles from endogenous genes with neural networks. Here we develop two codon optimization tools based on two branches respectively, namely OptimWiz 2.1 and OptimWiz 3.0. Results of fusion protein fluorescence detection indicate that both OptimWiz 2.1 and OptimWiz 3.0 are superior to all the other commercially available codon optimization tools. Principles of codon optimization are revealed in the process of machine learning on both tools.

Perturbative X-ray crystallography can visualize functional dynamics and conformational changes in proteins at atomic resolution. During a typical perturbative crystallography experiment, only a fraction of protein molecules in a crystal will be perturbed, or "excited". As a result, the observed data represent a mixture of excited and ground states. The conventional approach to estimating the excited-state structure factor amplitudes is to linearly extrapolate the difference between the structure factor amplitudes of the perturbed and unperturbed data. This approach often fails to yield well-refined structural models because it amplifies experimental errors and neglects phase differences between the ground and excited states. Here, we introduce an approach to estimating excited-state structure factor amplitudes that starts from a statistical prior for the correlations between excited and ground states. Using benchmarks from time-resolved crystallography and a drug-fragment screen, we illustrate how this approach effectively addresses the limitations of traditional extrapolation.

Extracellular vesicles (EVs) are cell-secreted biological nanoparticles that play a crucial role in intercellular communication and are gaining increasing attention as diagnostic biomarkers, therapeutic agents, and drug delivery vehicles. Consequently, the development of robust and sensitive methods for their characterization is essential. Herein we present the use of a microscope-mounted nanofluidic device for direct size determination and multi-parametric (3-color) fluorescence-based phenotyping of single biological nanoparticles that are in the size range of 20-200 nm in a method we denote Nano-SMF (SMF; size and multiplexed fluorescence). We demonstrate that it is possible to accurately determine the size of nanoparticles by analyzing their one-dimensional Brownian motion during directional flow through nanochannels, achieving size distributions for monodisperse nanoparticle solutions that are on par with TEM analysis, and size discrimination of nanoparticle mixtures that is significantly improved compared to conventional nanoparticle tracking analysis (NTA). Furter, we demonstrate that the method can be applied to analyze EVs directly in minute volumes of cell supernatant, avoiding pre-isolation or concentration steps. The method was applied to phenotype CD63- and CD81-positive EVs from a human embryonic kidney cell model, demonstrating that vesicle sub-populations defined by these two tetraspanin biomarkers differ significantly in size.

Selecting representative subsets from large protein sequence datasets is a common challenge in enzyme discovery and related tasks under limited screening capacity. In practice, candidate panels are often constructed using clustering-based redundancy reduction or manual selection guided by phylogenetic or similarity-network analyses, which do not directly optimise subset diversity and require threshold tuning or expert interpretation. Here, we present a bi-level diversity-optimisation framework for representative protein panel selection implemented using a local search heuristic that iteratively updates panel composition to improve diversity. The method formulates panel design as a combinatorial optimisation problem over pairwise distance matrices, combining a MaxMin objective to enforce minimum separation between selected sequences with a MaxSum objective to increase global dispersion. This formulation enables the direct construction of fixed-cardinality panels while remaining independent of the similarity representation used to compute pairwise distances. Benchmarking across four Pfam families shows that the bi-level formulation consistently reduces redundancy among selected sequences, lowering maximum pairwise identity by 43-46% relative to the previous MaxSum-based formulation, while maintaining comparable or improved EC-label coverage. The framework can incorporate sequence- or structure-based similarity measures, providing a flexible strategy for constructing diverse representative panels across homologous protein families.