BioTechniques

Quantitative PCR (qPCR) depends on reliable quantification cycle (Cq) estimation from amplification curves, which are not always well-behaved. We developed qPCR Guru to provide a complete analysis pipeline including data quality assessment, relative quantification, standard-curve diagnostics, and dual-method Cq evaluation. The latter compares the conventional instrument-derived threshold ("Reported") Cq versus the full-curve five-parameter logistic (5PL) second-derivative-maximum ("Fit") Cq and automatically flags curve-shape abnormalities and disagreement between the two estimates. On high-expressing targets (mRNA and microRNA), the two methods showed strong convergence, confirming general-purpose performance. On low-expressing targets, such as serum microRNA, baseline artifacts and biphasic amplification result in threshold miscalls that standard instrument analysis does not flag. Fit Cq restored replicate-concordant values where Reported Cq split the technical replicates by 17-20 cycles, recovered MIQE-compliant amplification efficiencies lost to biphasic miscalls (from 74% to 102% and 387% to 98%), and lowered within-group variability by 48% and 68% in feline and bovine samples, respectively. Together, these results demonstrate that full-curve estimation, with integrated curve-level diagnostics, strengthens qPCR analyses against threshold miscalls. ARTICLE HIGHLIGHTSO_LIqPCR Guru is a free, browser-based platform that provides a complete analysis pipeline and facilitates side-by-side comparisons of an instruments threshold (Reported) Cq and a full-curve (Fit) Cq, from the five-parameter logistic fitting with second-derivative-maximum (SDM/cpD2). C_LIO_LIFor every well the application automatically flags curve-shape abnormalities and disagreement between the two Cq estimates. C_LIO_LIOn clean, high-expressing mRNA and microRNA targets, the two estimators (Reported Cq and Fit Cq) were strongly concordant and produced equivalent relative quantification with comparable precision. C_LIO_LIIn low-expressing serum microRNA, baseline artifacts and biphasic amplification produced threshold Cq miscalls of up to [~]20 cycles and were detected by curve-shape flags and/or method disagreement. C_LIO_LIThe full-curve Cq estimate recovered replicate-concordant values, restored MIQE-compliant amplification efficiencies, and reduced within-group variability in serum microRNA. C_LI

A multi-omic approach utilizing a single biospecimen is important to avoid intra-sample heterogeneity associated with testing multiple omic single-samples, and for more efficient use of small volumes of precious biopsies (<30 mg). This is especially true for the microanatomy of post-mortem human brain samples. Using post-mortem human brain biospecimens from the NIH NeuroBioBank, a penta-omic sequential extraction method is described, Simultaneous Metabolomic, Proteomic, Lipidomic - DNA, RNA Extraction (SiMPL-DREx). Each sequential omic extract was compared to those obtained by the gold standard single omic method. Preserving RIN is critical for brain and tissue banks, as it is a primary measure of tissue quality. For all five omic extracts, the tissue integrity numbers and omic profiles did not significantly differ from those obtained by the respective omic gold standard method. Unlike past multi-omic studies, this study quantified the relative solvent percentages and upstream losses for both the organic and aqueous phases, confirming an omics loss of under 5%.

Luminescence is a powerful method for detecting trace analytes and monitoring biological processes. However, most bioluminescence reagents, including luciferase and its substrates, are sensitive to temperature, limiting their useable shelf lives, and resulting in inconsistent performance. Enhancing the stability of these reagents could improve data quality, simplify workflows, and address cold chain storage issues. In this study, we demonstrate the application of the platform stabilization technology, capillary-assisted vitrification (CAV), as a tool to stabilize different luciferases and their substrates, and the application of the stabilized reagents in both in vitro and in vivo bioluminescent assays. We demonstrate that CAV-stabilized reagents can be stored and shipped ambiently, maintain consistent performance over time, and are suitable for use in cell viability quantification, tumor monitoring, in vivo imaging, microbial detection, and immunoassays. Additionally, different reagents can be co-formulated to make ready-to-use assay kits that can also be shipped and stored ambiently. Our results demonstrate that CAV stabilization is a viable alternative to traditional storage methods, with broad potential to improve bioluminescence workflows.

Cystatin-C is an important renal function biomarker, and conventional quantification requires centralized laboratory analyzers, which limits timely testing in primary care and resource-limited settings. To address this need, we developed and validated a simple, rapid, and quantitative smartphone-based (SP) lateral flow immunoassay (LFIA) for measuring serum Cystatin-C. The SP-LFIA platform consists of a colorimetric LFIA strip and a custom SP reader with uniform LED illumination and macro lens for image capture. Quantitative image analysis of the colorimetric signal is performed by a dedicated application using a pre-defined third order polynomial calibration model. Following systematic optimization, the assay demonstrated a wide quantitative range of 0.32-8.00 mg/L, with a limit of detection of 0.15 mg/L. Analytical validation conducted according to CLSI guidelines showed excellent precision, with intra- and inter-assay coefficients of variation below 10%, and no significant interference from bilirubin, triglycerides, hemoglobin, or rheumatoid factor. Accelerated stability testing confirmed robust strip performance after storage at 50 {degrees}C for 28 days. Method comparison using 100 clinical serum samples showed high agreement with a commercial PETIA reference method (R{superscript 2} = 0.993) and minimal bias. These results indicate that the developed smartphone-based LFIA provides a reliable, cost-effective, and practical tool for point-of-care Cystatin-C monitoring.

Abstract Background: Acute Lymphoblastic Leukemia (ALL) is a highly heterogeneous pediatric malignancy. Despite high survival rates, relapse and the involvement of central nervous system (CNS) remains a significant clinical challenge. Traditional clinical parameters often lack the precision required for early detection and risk stratification. This study utilizes high-throughput proteomics and machine learning to identify molecular signatures in cerebrospinal fluid (CSF) that characterize disease effect and treatment response. Methods: 82 CSF samples from 41 pediatric ALL patients at diagnosis (VD) and remission (VR) were analyzed. Proteomic profiling of 276 proteins was performed using Olink Proximity Extension Assay. Differentially abundant proteins were identified (q-value< 0.05, |Log_2FC| > 0.5) using the Wilcoxon rank-sum test. Three machine-learning algorithms - Random Forest, LASSO, and SVM-RFE - were integrated to select the differentially abundant proteins in VR and VD and between CNS involvement levels. To validate the data Pan-Cancer Atlas analysis was done using two different platforms. Results: In the remission phase, we observed significant alterations in the expression of key proteins compared to diagnosis, with ADGRG1 and KYNU showing a marked increase, while CCL17, CD5, CD27, CXCL9, CXCL11, FASLG, GZMA, and TNFRSF9 were significantly downregulated. Furthermore, our analysis identified distinct protein signatures associated with CNS involvement: CCL4, CTSC, CXCL10, CXCL9, and MMP7 were differentially abundant at the VD stage, whereas CAIX, CASP-8, HAGH, CXCL9, MMP7, MCP-2, and VWC2 at the VR stage. Conclusion: Integrating Olink proteomics with machine learning identified molecular signatures in ALL that have the potential to be further developed to a biomarker panel for monitoring treatment response and guiding personalized therapeutic strategies shifting the focus toward the Precision One Health approaches.

Viroids are plant infectious agents that threaten agricultural production. Current viroid detection methods rely on RT-PCR-based assays, which require specialized laboratory equipment and can sometimes produce false-negative results or non-specific amplification due to the high sequence conservation among closely related viroid species. CRISPR-based diagnostics, particularly Cas12-based systems for DNA detection (DETECTR) and Cas13a-based systems (SHERLOCK) for RNA detection, have emerged as powerful tools for nucleic acid diagnostics. However, most existing workflows still rely on target amplification and, in the case of Cas13a systems, require additional in vitro transcription steps, limiting their simplicity and direct applicability for plant diagnostics. Here, we developed a direct amplification-free Cas13a-based detection platform for viroids using potato spindle tuber viroid (PSTVd) as a model. We optimized CRISPR RNA (crRNA) design, identified inhibitory effects of plant total RNA on readout signal, and employed simplified viroid RNA enrichment workflows enabling robust detection in plant samples. The system further supported both PSTVd-specific and broad-spectrum pospiviroid (genus Pospiviroid) detection and was successfully extended to avocado sunblotch viroid (family Avsunviroidae), demonstrating its adaptability across distinct viroid families. Together, these results establish a practical and modular Cas13a-based platform, not only for viroid diagnostics, but also for broader applications in RNA-derived plant pathogen detection. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=68 SRC="FIGDIR/small/736049v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1d04170org.highwire.dtl.DTLVardef@1783aa3org.highwire.dtl.DTLVardef@51baa7org.highwire.dtl.DTLVardef@1b542b9_HPS_FORMAT_FIGEXP M_FIG C_FIG Significance statementA simplified RNA enrichment workflow combined with CRISPR-Cas13a enables direct, amplification-free detection of plant viroids. The assay supports early and reliable diagnosis across different tomato varieties and provides a practical strategy for improving molecular detection of plant pathogens.

Rapidly detecting infectious diseases such as Covid-19 is essential to control outbreaks and treat patients early. However, no available screening method combines low cost, portability, speed (<20 min, ideally <5 min), and ultrasensitivity (e.g., <1 virus/L): lateral flow assays are fast, portable, and inexpensive but insensitive, whereas ultrasensitive assays require centralized labs with long turnaround times. We recently developed an ultrasensitive immunoassay that captures, separates, and counts saliva biomarker molecules using buoyant microbubbles and magnetic microspheres, but the original assay took 55 minutes and was not readily deployable. Here, we redesigned the assay protocol and reader for emergency medicine and mobile care by streamlining the workflow, collecting saliva with larger swabs, filtering it through a 10 m cap, and using larger microbubbles to accelerate flotation. A paramedic successfully ran the assay on the back of a parked medical van in 3.5 minutes (spit-to-results) while achieving a 1.3 fg/mL analytical detection limit for SARS-CoV-2 nucleocapsid protein (~0.04 virus1/L). The assay remained positive across 9 orders of magnitude. We describe the challenges and opportunities ahead for point-of-care deployment.

Premise: Rapid and accurate plant species identification is a critical challenge exacerbated by the taxonomic impediment. Although portable near-infrared (Micro NIR) spectroscopy represents a promising solution, the current absence of standardized protocols and a fundamental understanding of how critical acquisition and analysis parameters influence accuracy remain significant barriers. This study focused on the systematic optimization and validation of a comprehensive workflow designed to maximize the reliability of plant identification using this technology. To ensure methodological robustness across diverse foliar matrices, four vascular plant species were strategically selected as a representative test set to encompass morphological extremes, including significant variations in leaf thickness, pubescence, and surface texture. Methods: Using a portable spectrometer on herbarium specimens (exsiccate) of four vascular plant species, we systematically tested five spectral backgrounds, seven pre-processing methods, and four classification models. Subsequently, we optimized the number of spectral readings and evaluated the influence of the leaf scanning surface (adaxial vs. abaxial) on model accuracy. Results: The highest-performing combination was a Shiny Aluminum background, Second Derivative pre-processing, and a Random Forest model, which achieved a mean cross-validated accuracy of 99%. An average of just three spectral readings from the adaxial (upper) leaf face was sufficient to saturate model performance, proving statistically superior to other approaches (p < 0.001). Discussion: This study establishes a validated, high-accuracy protocol for plant species identification from herbarium specimens using portable NIR, offering a powerful tool for biodiversity studies. Direct applicability to fresh plants in the field requires future validation to account for the spectral influence of moisture variability.

Background: We propose the biological drift framework for the interpretation of biological test results: a z-score-like framework based on optimized and personalized reference populations and a distance-to-optimum drift metric for longitudinal interpretation relative to an estimated individual optimum. We benchmarked biological drifts against Reference Change Values (RCVs), which are used to interpret serial laboratory results by defining the minimum change expected to exceed normal within-subject biological variation CVi. Objectives: To benchmark biological drifts against the classical biological-variation framework and assess their consistency with RCV thresholds across routine biomarkers. Methods: For 62 routine biomarkers, biological drift levels were compared with RCVs after transformation to test the consistency between the two frameworks. Results: Severe biological drifts mostly exceeded the 95% RCV threshold, indicating changes unlikely to be explained by short-term biological variation alone. In contrast, moderate drifts reached the 95% RCV threshold for approximately one in two biomarkers, suggesting that many moderate distance-to-optimum deviations may remain within expected variability, particularly for biomarkers with large within-subject variation CVi. Results are particularly interesting for the follow-up of people with diabetes and for the management of thyroid and hepatic disorders. Conclusions: Biological drifts derived from optimized personalized reference populations are broadly consistent with the RCV framework for identifying biologically meaningful deviations from the optimum and may therefore be relevant for the monitoring of certain biomarkers across several medical conditions in clinical practice.

Reactive nitrogen species (RNS), particularly peroxynitrite generated from the reaction of superoxide and nitric oxide, are implicated in thermally-induced oxidative stress but remain difficult to resolve in live coral cells. We optimized fluorescent dye strategies to directly quantify superoxide, nitric oxide, and peroxynitrite production in thermally stressed Pocillopora acuta cell suspensions. Thermal stress was associated with an increase in intracellular peroxynitrite concentration, but not in its precursors, nitric oxide and superoxide, highlighting challenges with the application of fluorescent probes and their controls to live coral cells. Compounds developed for mammalian systems often translate poorly to non-model systems such as corals: strong endogenous fluorescence and multiple membrane barriers within the coral symbiocyte, for instance, limited the function of the nitric oxide probe, DAF-2DA. Despite these limitations, the detection of peroxynitrite in live, thermally stressed P. acuta cells represents a step forward in understanding the mechanism of coral bleaching. We also outline strategies for improving the performance of commercial dyes in non-model systems, including media optimization with EDTA treatment to preserve both cell viability and probe performance.

Cell-free DNA (cfDNA), released from apoptotic and necrotic cells into body fluids, represents a non-invasive source of genetic information for disease prediction, diagnosis, and monitoring. However, its low physiological abundance makes cfDNA highly susceptible to pre-analytical influences. In particular, genomic DNA (gDNA) released from lysed white blood cells (WBCs) can contaminate plasma and compromise downstream cfDNA analyses. This study evaluated the impact of different blood collection tubes and isolation methods on cfDNA stability and yield. Blood samples from 13 healthy donors were collected using cfDNA-stabilizing tubes (Cell-Free DNA BCT, Streck; S-Monovette cfDNA Exact, Sarstedt) and stored at room temperature for 1, 5, or 10 days before plasma isolation. CfDNA was extracted using either a magnetic bead-based method or a silica column-based approach. DNA quantity and quality were assessed by fluorometric quantification, automated fragment analysis, and gene-specific quantitative PCR. Streck-based workflows maintained stable cfDNA yields and characteristic mononucleosomal fragmentation profiles across all storage times. In contrast, Sarstedt tubes showed reduced cfDNA concentrations after 5 days and a pronounced increase at 10 Days, accompanied by high-molecular weight DNA patterns consistent with WBC lysis. These trends were largely independent of the extraction method. Overall, the results demonstrate that blood collection tube chemistry critically influences cfDNA integrity during delayed processing. Streck tubes, particularly when combined with QIAamp, provided the most robust and reproducible workflow for routine molecular diagnostics, whereas Sarstedt tubes produced physiologically implausible results after extended storage.

1. Detecting environmental DNA (eDNA) from rare or low-abundance aquatic species remains a major challenge, particularly when it is highly degraded, present at low concentrations, and dominated by DNA from non-target taxa. These challenges are further amplified in sedimentary ancient DNA (sedaDNA) studies, where thousands of years can degrade eDNA further, making the detection and quantitative interpretation of weak biological signals difficult. 2. Metabarcoding is commonly used to produce high-throughput community-level data from eDNA but is inherently compositional and influenced by amplification biases. Nonetheless, metabarcoding read abundance or PCR replicate detection frequency are increasingly used as proxies for relative DNA concentration, but their quantitative interpretation has rarely been evaluated against independent measures of absolute DNA abundance. 3. We used droplet digital PCR (ddPCR) to quantify mitochondrial DNA from Atlantic cod (Gadus morhua) and Atlantic herring (Clupea harengus) in 136 ancient eDNA extracts from Icelandic marine sediment cores spanning the last three millennia. We compared ddPCR copy number estimates with metabarcoding (18S) derived relative abundance and detection frequency, and evaluated whether temporal DNA trends corresponded with proxy reconstructed sea surface temperature (SST) variability. 4. We found that ddPCR-measured fish sedaDNA abundance was positively correlated with the proportion of metabarcoding PCR replicates for both Atlantic cod and Atlantic herring. Moreover, temporal trends in Atlantic herring DNA abundance were consistent with proxy reconstructed SST variability, supporting the ecological relevance of the molecular signal. 5. Overall, our results show that ddPCR-derived DNA concentrations and metabarcoding PCR replicate detection frequency capture consistent patterns in low-abundance fish sedaDNA from marine sediments. The observed agreement between approaches supports the use of PCR replicate detection frequency as a semi-quantitative proxy for low-abundance sedaDNA.

Targeted long read sequencing (LRS) of native genomic DNA (gDNA) using Oxford Nanopore Technologies (ONT) is an economically and computationally accessible method for sequencing selected genomic regions without the limitations associated with amplification-based approaches. At present, efficiency, multiplexing, and scalability remain key challenges for existing targeted LRS. We have developed Cas12a-Targeted Multiplexed Nanopore Sequencing (CTM-nSeq), which combines Cas12a-targeting, DNA fragment enrichment, and optimized adapter ligation using T7 DNA ligase. Unlike previously established protocols, CTM-nSeq is compatible with the latest ONT flow cell chemistry. Performing CTM-nSeq on a single sample with an R10.4 MinION flow cell routinely yields hundreds of on-target reads. Furthermore, CTM-nSeq enables targeting of multiple loci and is the first targeted ONT sequencing method, allowing reliable, barcode-assisted multiplexing. CTM-nSeq is an efficient and accessible method for sequencing native gDNA and analysing DNA methylation, repeat expansions, and sequence integrity. As such, CTM-nSeq has a wide range of analytical and diagnostic applications.

The purification of industrial enzymes typically relies on costly, multi-step chromatographic protocols. To address this, we developed a novel platform termed Coated Bacterial Enzymes (CBEs), which enables one-step purification and immobilization of recombinant proteins fused to the SlpA cell wall binding domain. As a proof of concept, we used a {beta}-galactosidase from Bifidobacterium bifidum of dairy relevance. The chimeric enzyme BbgII-SlpA was expressed in Escherichia coli and captured from crude lysate onto glutaraldehyde-inactivated Bacillus subtilis cells via SlpA domain. Binding was characterized by a dissociation constant (Kd) of 16.2 {micro}M and maximum binding capacity (Bmax) of 144 {micro}mol/g. The resulting CBE biocatalyst exhibited optimal activity at pH 6.0 for ONPG and lactose, with a broader pH profile than the free enzyme. Optimal temperatures were 60 {degrees}C for ONPG and 50 {degrees}C for lactose, and CBE retained >80% activity after 390 min at 45 {degrees}C, compared to 20% for the free enzyme. Catalytic efficiencies (kcat/Km) were 2.62 x106 M-1{middle dot}s-1 for ONPG and 4.40 x102 M-1{middle dot}s-1 for lactose. Moreover, CBE showed improved tolerance to cations such as Ca2+ and Fe2+. These results suggest that the CBE platform offers a cost-effective alternative for producing high-purity, immobilized enzymes for diverse industrial bioprocesses.

Structure prediction models have moved from single proteins to assemblies that include diverse biomolecules and their modifications. AlphaFold3 (AF3) and related models extended structural modelling via an all-atom framework, opening many new potential applications in structural biology. We evaluate how well the new capabilities of AF3 translate into application tasks in diverse areas: prediction of ubiquitinated protein structures, T-cell receptor (TCR)-epitope recognition, antibody-antigen complexes, protein-RNA and protein-lipid interactions. We find that, while AF3 can perform well in favourable settings, this performance is uneven across applications. In RNA-target predictions, the model confidence fails to separate genuine from decoy interaction partners and in several tasks accuracy depends on the presence of related complexes in the training set. Taken together, our assessment is more cautious than for AF2, whose gains in modelling monomers and complexes were clear and broadly generalisable. AF3s extension to new biomolecule types shows less consistent performance and generalisation. AF3 can be a powerful tool for hypothesis generation and prioritisation, but its predictions and use of confidence metrics will depend strongly on the specific application area and must be interpreted with respect to training-set overlap. We expect that the benchmarks provided here will serve for testing of future developments in the structure prediction field.

BackgroundReliable detection of Salmonella remains a major challenge for public health surveillance and food safety due to the growing diversity of circulating serovars and the limitations of existing molecular targets. This study aimed to identify an optimal molecular target and develop a TaqMan real-time PCR assay for the detection of Salmonella spp. MethodsBased on the results screening for Salmonella genes suitability as molecular markers, a TaqMan real-time PCR assay targeting the hilA gene was developed and validated. Analytical sensitivity, analytical specificity, and performance on bacterial isolates and artificially contaminated food samples were assessed. ResultsAmong all candidate targets, hilA demonstrated the broadest coverage and was detected in all tested Salmonella isolates, including representatives of rare serological groups, whereas invA conventionally used for this pathogen detection, was absent in a subset of strains. The assay exhibited a limit of detection of 100 bacterial cells/mL and 100 fg/L of genomic DNA. No cross-reactivity was observed with DNA from Shigella flexneri, Shigella sonnei, Yersinia pestis, Y. pseudotuberculosis, Y. enterocolitica, Y. kristensenii, Bacillus anthracis, Vibrio cholerae, or Francisella tularensis. The assay successfully detected Salmonella DNA in all artificially contaminated food samples tested. Evaluation using a collection of 25 bacterial isolates demonstrated positive amplification in all 24 confirmed Salmonella strains, while a strain initially identified by conventional bacteriology as Salmonella but subsequently confirmed by whole-genome sequencing as Proteus mirabilis yielded a negative result. ConclusionsThe hilA gene represents a highly conserved and reliable molecular target for the detection of Salmonella spp. The developed TaqMan real-time PCR assay demonstrated high analytical sensitivity, excellent specificity, and broad serovar coverage, supporting its application in laboratory detection of Salmonella, food safety monitoring, and epidemiological surveillance.

Background: Phenotypic antibiotic susceptibility testing (AST) for Helicobacter pylori (H. pylori) has relied on bacterial culture for three decades, requiring 5-7 days to yield results. Genotypic rapid tests can only detect known resistance mutations and fail to reliably identify amoxicillin resistance. To our knowledge, no culture-free rapid phenotypic AST method for H. pylori has been previously reported. Methods: We developed a phenotypic AST method based on fluorescence rapid on-site evaluation (ROSE) technology that completely bypasses bacterial culture. Gastric mucosal biopsy specimens from 40 H. pylori-positive patients were homogenized and co-incubated with an acridine orange/ethidium bromide (AO/EB)-based viability staining reagent and three first-line antibiotics (amoxicillin, clarithromycin, and levofloxacin) at concentrations corresponding to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints for H. pylori, at 37C for 1 hour. Fluorescence intensity was measured using a microplate reader. A reduction in fluorescence relative to an antibiotic-free control indicated susceptibility, whereas no significant reduction indicated resistance. Conventional culture-based AST (E-test) served as the reference method. The overall concordance rate, sensitivity, specificity, and Cohen's kappa coefficient were calculated. Results: Fourteen of the 40 samples had unsuccessful culture and were excluded, leaving 26 samples for statistical analysis of each antibiotic. The overall concordance rates between the ROSE method and culture-based AST were 84.6% (22/26) for amoxicillin, 76.9% (20/26) for levofloxacin, and 69.2% (18/26) for clarithromycin. Cohen's kappa coefficients indicated moderate agreement for all three antibiotics ({kappa} = 0.523, 0.539, and 0.412, respectively). Unlike genotypic methods, the ROSE method successfully assessed amoxicillin susceptibility in all 40 patients, a critical first-line antibiotic for which no reliable genetic resistance marker currently exists. The turnaround time was approximately 1 hour (55-65 minutes), compared with 5-7 days for culture-based methods; preliminary estimates indicated a cost reduction of approximately 3,000-5,000 Chinese yuan (CNY) per patient, mainly attributable to the elimination of culture media, prolonged incubation, and repeat clinic visits. Conclusions: This study reports, for the first time, a culture-free 1-hour phenotypic AST for H. pylori. The method enables same-day, susceptibility-guided treatment decisions, addressing an unmet clinical need spanning three decades. Algorithm optimization and a prospective randomized controlled trial are currently underway to further improve diagnostic accuracy and validate clinical utility.

Over the past decade, protein design has evolved from a specialized discipline into a broadly accessible approach for engineering and interrogating biological systems. Despite these advances, protein design continues to be a technically challenging task, often requiring knowledge of programming to be able to use and combine the different software packages. To address this challenge, we have developed Prosculpt, an easy-to-use protein design pipeline. Prosculpt integrates RFdiffusion for backbone generation, ProteinMPNN for sequence design and multiple structure-prediction platforms (AF2, AF3, Colabfold, Boltz2). Candidate designs are evaluated using customizable Rosetta-based scoring protocols. Each project is specified through a single configuration file, enabling users with minimal computational expertise to perform sophisticated protein design tasks without writing code, while also allowing advanced users to access the full capabilities of the underlying programs. Prosculpt supports a wide range of applications, including design of symmetric homo-oligomers, design of binders, motif scaffolding, partial diffusion and fixed-backbone sequence redesign. By combining these capabilities within a single, user-friendly platform, Prosculpt provides a practical entry point to modern protein design for both novice and expert users.

Lipid peroxidation (LPO) is widely used as a biomarker of oxidative stress in coral bleaching research, yet its measurement remains poorly standardized across the field. A systematic review of the coral LPO literature reveals substantial variation in methodological approaches, including tissue fraction analysis, lysis protocols, assay choice, and normalization metrics, confounding cross-study comparison and obscuring the biological interpretation of results. We experimentally investigate two key sources of variation: the use of bulk holobiont vs separated host and algal symbiont fractions, and the choice of normalization metric. To do so, we used Montastraea cavernosa (n = 6 colonies) exposed to ambient (28C), heat stress (30.5C), and heat stress + artificial upwelling (AU; heat stress intermitted by daily pulses of cooler water, 30.5/27.5C) conditions in a controlled mesocosm experiment. Using a TBARS-based MDA assay with a lysis buffer optimized for coral tissue, we measured LPO separately in coral host and algal symbiont fractions across four time points throughout the day. Host MDA remained stable across all treatments and time points, consistent with either sufficient antioxidant buffering capacity or thermal acclimation over the experimental period. Algal symbiont MDA, in contrast, exhibited pronounced diel and treatment-specific dynamics, and the two fractions responses were decoupled from one another. Normalizing MDA to coral surface area instead of total protein content produced largely consistent diel and treatment patterns, but the two metrics diverged at specific time points, indicating that normalization choice is not interchangeable and can itself affect interpretation. Together, our literature review and empirical results demonstrate that host and algal symbiont LPO dynamics are not comparable when aggregated and argue for host-symbiont fraction separation and consistent, explicitly reported normalization as minimum standards for interpretable and cross-comparable coral LPO measurement.

Probiotics produce antimicrobial peptides and small molecules that are secreted into the medium. Antimicrobial activity of cell-free supernatants can be tested using various qualitative and quantitative methods. Many of these techniques employ methods which introduce uncontrolled variables, impacting reproducibility and making comparison of reported results difficult. Here, we present a simple procedure for quantitative estimation of antimicrobial activity of cell-free supernatants which overcomes drawbacks of commonly used methods.