Yeast

Cancer, one of the worlds leading causes of death, is characterized by a complex metabolic reprogramming that features the Warburg effect as one of its hallmarks. The Warburg effect involves increased glucose and amino acid metabolism, which promotes tumor proliferation and progression. Although cancer has historically been attributed to genetic mutations, recent studies suggest a possible metabolic origin. However, a key characteristic of cancer cells is their greater adaptability than normal cells, as evidenced by their resistance to chemotherapy, which stems from their high mutability. This underscores the need to examine the relationship between metabolic reprogramming and cancer development from both metabolic and evolutionary perspectives. In this context, Saccharomyces cerevisiae snf1{Delta} strain has emerged as an ideal cellular model for studying the Warburg effect. This study aimed to determine whether deletion of the SNF1 gene in S. cerevisiae affects its chronological aging and competitiveness in a glucose and amino acid-dependent manner. Herein, we provide evidence that the snf1{Delta} strain changes the chronological aging depending on nutrimental condition, under low-nutrient levels shortens (0.1% glucose + 0.1x amino acids), and increases under high-nutrient levels (5% glucose + 3x amino acids). Competitiveness of the snf1{Delta} strain in co-cultivation with wild-type was also improved in 5% glucose + 3x amino acids, by approximately 2 Log10. These results indicate that snf1{Delta} strain aging and competitiveness are also sensitive to nutrimental status, as was observed in cancer cells.

Environmental surveys of wild yeasts typically rely on ribosomal barcodes, which cannot resolve cryptic species, interspecific gene flow, mixed cultures, or population structure. To determine what genome-scale characterization adds, we sequenced a representative panel of wild yeasts spanning the genera Saccharomyces, Schizosaccharomyces, and Lachancea using Oxford Nanopore long-read whole-genome sequencing and placed each isolate within published reference datasets. Whole-genome analyses revealed biologically important features that barcoding alone could not detect. A shagbark-hickory isolate resolved as a genuine two-species co-culture. An oak-bark isolate proved to be Schizosaccharomyces versatilis, a recently reinstated species represented by very few known strains, and its analysis demonstrated that standard assembly-quality benchmarks can be misleading for deep-branching taxa. Three Lachancea thermotolerans isolates formed a distinct, previously unsampled population within the wild tree-associated lineage, extending its known geographic range. In contrast, an apparent signal of Saccharomyces eubayanus introgression in two beer-associated S. cerevisiae isolates disappeared after analysis with matched negative controls and de novo assemblies, showing that it reflected mapping artifacts rather than genuine ancestry. Together, these results demonstrate that inexpensive long-read whole-genome sequencing transforms wild-yeast bioprospecting from species identification into a genome-scale framework for resolving cryptic diversity, population structure, and mixed cultures while providing stronger support - and stronger limits - for evolutionary inference. SIGNIFICANCEMost surveys of wild yeasts identify isolates using short DNA barcodes, which are well suited for naming species but often miss the evolutionary relationships and hidden diversity within them. By applying inexpensive whole-genome sequencing to a diverse collection of environmental yeasts, we uncovered previously undetected mixed cultures, a rare recently recognized species, and a distinct wild population, while also showing that an apparent case of interspecies gene exchange was instead a technical artifact. These results demonstrate that genome-scale analysis can both reveal biological diversity that simpler methods overlook and provide the evidence needed to avoid misleading evolutionary conclusions, making it a powerful new approach for studying natural microbial populations.

Lager is the most produced beer style world-wide and makes use of the bottom-fermenting hybrid yeast Saccharomyces pastorianus (S. cerevisiae x S. eubayanus). Previous research showed that flocculation in S. pastorianus, in contrast to the top-fermenting ale yeast S. cerevisiae, is triggered by nitrogen starvation. However, the cellular events leading to flocculation in S. pastorianus are not well characterized. Therefore, we conducted a proteomic screen of S. pastorianus TUM 34/70 and identified the protein kinase Ste20p and protein phosphatase regulatory subunit Ypi1p as higher abundant during flocculation. Overexpression of these genes caused a consistent and strong increase in flocculation rate over the complete duration of beer fermentation. Characterization of Ste20p and Ypi1p via a phospho-proteomics screen showed their targeting of proteins whose S. cerevisiae orthologues are involved in pseudohyphal growth. However, in contrast to this, the overexpression of STE20 and YPI1 led instead to the establishment of a flocculation morphology, giving first-time evidence that S. pastorianus repurposes the pseudohyphal signaling network for this phenotype.

Microbial metabolism can be represented as an energy-conserving process (catabolism) and a biomass-forming reaction (anabolism). Anabolism is traditionally measured through the turbidity of the culture, while catabolism is often assessed by the substrates consumed or the products formed. Standard measurements of biomass and products are intrusive and disrupt cultivation and headspace composition, potentially masking important analytical parameters and interactions. Online pressure and backscatter were combined in small-scale closed batch vials to obtain undisturbed real-time measurements of catabolic and anabolic rates, enabling mapping of metabolic phases throughout an entire batch cultivation cycle. The method identified discrete metabolic phases in yeast cultivation and thermophilic syngas fermentation. In nutrient-rich yeast cultivation, five metabolic phases were characterized, covering growth-associated and non-growth-associated gas formation. In a mixed community syngas fermentation, estimates of catabolic and anabolic rates distinguished early biomass increase from minimal net pressure change from two later gas-driven phases. An initial phase with a higher growth rate, linked to carboxydotrophy, followed by a phase with slightly lower growth and increased gas consumption, corresponding to hydrogenotrophic acetogenesis. The study demonstrates that a simple, affordable experimental setup with online pressure and backscatter measurements can be used to visualize phase-plane mapping of microbial metabolism. An additional advantage is the ability to detect sequential metabolic cascades in mixed microbial communities, which is not possible with gas-sparging bioreactor studies. Using a single simple batch culture, growth and maintenance data can be obtained, even when growth is low or absent, thereby yielding parameters applicable to phenotypic characterization and dynamic metabolic modelling.

The budding yeast Saccharomyces cerevisiae is a well-established model organism to study cellular stress response and underlying mechanistic regulation. Although glucose starvation fundamentally alters gene regulation and cell behaviour, inconsistent deprivation protocols often trigger gross morphological artefacts. These non-specific changes confound findings by activating pathways independently of true glucose-signalling mechanisms. Furthermore, a thorough transcriptomic profile of glucose starvation using non-confounding conditions remains lacking. Consequently, the precise transcriptional impact of losing key metabolic regulators that mediate adaptation to glucose starvation remains undefined. Here we have employed a refined glucose starvation protocol, utilising raffinose exchange, which shows induction of vast transcriptional stress response with minimal impact on cellular morphology confirmed by label-free imaging. Transcriptomic profiling revealed shifts in metabolic regulation, ATP turnover, and cell-to-cell communication as acute glucose deprivation driving cells towards oxidation-driven metabolism. Additionally, we characterise transcriptional alterations seen in deletion mutants of SNF12 and SPT20, known regulators of cellular metabolism, showing previously unappreciated transcriptional conservation, in part mimicking glucose starvation response. Finally, we identified cargo and stress-specific expression related to both eisosome components and surface transporters that are critical for metabolic adaptation. Overall, this dataset provides a comprehensive transcriptomic resource for dissecting stress signalling and driving novel hypothesis generation.

Cryptococcus neoformans and related species are major human pathogens that cause cryptococcosis, a disease with high mortality and morbidity despite antifungal therapy. Pathogenic Cryptococcus spp. cells express a polysaccharide capsule, which is the most important virulence factor. In this study we analyzed the distribution of capsule sizes for several strains from Cryptococcus spp. and found that they follow stochastic dynamics, with a heavy right-hand tail distribution, favoring larger capsules. The distribution for each strain is remarkably stable despite repeated perturbation of culture conditions including media refreshment, time, and macrophage ingestion. Growth in macrophages resulted in different capsule distributions, observed in vitro, with a suggestion of different polysaccharide-like materials formed or utilized in the resident phagosome. We propose that the stability in capsule size distributions represents a capsulestat mechanism for the population. An emergent property whereby individual cells manifest capsule size variation emanating from random effects on individual capsule assembly steps. This distribution balances between cells with large capsules that are less susceptible to a variety of environmental stresses at the price of slower replication, increased size, and increased energy requirements and cells with smaller, less protective capsules that reproduce faster. Thus, Cryptococcus spp. populations establish a bet hedging strategy that can enhance the viability of the population as conditions change at the cost of optimal short-term growth.

Coenzyme Q (ubiquinone, CoQ) is an essential component of the mitochondrial electron transport chain and a major lipid antioxidant in eukaryotic cells. Formation of its benzoquinone ring requires aromatic precursors whose metabolic origin remains incompletely defined. Here, we elucidate the biochemical link between tyrosine metabolism and the synthesis of the benzoquinone head group of coenzyme Q6 (Q6) in Saccharomyces cerevisiae through the 4-hydroxymandelate (4HMA) pathway. Using isotopic tracing with 13C6-tyrosine, 13C6-4-hydroxybenzoate, and 13C6-p-aminobenzoate (pABA), we demonstrate that tyrosine-derived 4-hydroxyphenylpyruvate is converted into 4-hydroxybenzaldehyde via benzoylformate decarboxylation, defining a functional 4HMA pathway in yeast. Chemical inhibition of benzoylformate decarboxylase with methylbenzoylphosphonate led to accumulation of pathway intermediates, which were identified by GCMS. Consistently, mutants lacking ARO10, DLD1, or DLD2 exhibited strongly decreased 4-hydroxybenzaldehyde formation. Despite disruption of the 4HMA pathway, the pABA route from chorismate compensated, demonstrating S. cerevisiae's metabolic flexibility to use pABA or 4 HB and maintain Q6 ring biosynthesis. Our results provide a mechanistic framework linking aromatic amino acid metabolism to respiratory quinone biosynthesis in eukaryotes and support the evolutionary conservation of the 4HMA-derived pathway as a source of 4-hydroxybenzoate for Q synthesis in higher organisms.

Microbial activity is often inferred from cell density measurements; however, biomass formation is merely an indirect, cumulative result of metabolism, known as anabolism. Microbial activity is more accurately indicated by energy conservation or catabolism. This is especially true under low or no-growth conditions, where anabolism remains constant, and shifts in catabolic fluxes go unnoticed with biomass measurements alone. In anaerobic and gas-based metabolic processes, net gas exchange is linked to energy conservation, and catabolism can then be quantified through headspace measurements. We introduce a sealed-vial, non-invasive workflow that uses high-resolution headspace pressure measurements to estimate gas exchange rates and catabolic reactions, enabling real-time visualisation of metabolic shifts throughout an entire batch cultivation cycle. The method was applied to carbon monoxide (CO) fermentations of three Clostridium autoethanogenum strains (JA1-1, LAbrini, and LAbrini_mut) cultivated in serum bottles. Two of them were indistinguishable by OD-derived max. Pressure-derived gas uptake rates resolved multiple exponential phases of gas consumption and identified specific shifts in the metabolism, consistent with transitions from mixotrophic to autotrophic growth. Small but significant differences in terminal headspace pressure were detected, providing an experimentally accessible end-state parameter for phenotypic characterisation that would be obscured by routine intrusive headspace sampling. Finally, pressure-derived catabolic rates further enabled estimates of relative product formation during the main autotrophic phase. The strains were successfully characterised and distinguished by identifying several exponential phases of gas consumption and their rates, as well as differences in the final absolute pressure threshold. This provided phenotypic characterisation and insights not obtainable from OD measurements alone. The work establishes a practical framework for catabolism-resolved microbial characterisation in sealed batch vials through high-resolution online pressure (gas exchange). The assumption that pressure measurements correlate with CO2 and catabolic rates is sensitive to solubility/buffering and temperature/vapour effects, but these limitations are addressable through controls and complementary analytics.

Three-dimensional live-cell fluorescence imaging of yeast cells is crucial for studying cell-cycle mechanics and regulation. However, extracting multi-channel phenotypes within dense cell clusters remains an image-processing bottleneck. Standard deep-learning models segment cells but fail to track mother-bud boundaries, mitotic spindle shapes and spindle-localizing proteins. Investigators rely on labour-intensive manual coordinate plotting, introducing observer bias and often exclude clustered cell data due to visual complexity. Here, we present an open-source Fiji pipeline for automated yeast cell image processing and deterministic classification of cell-cycle, spindle and protein dynamics. The workflow utilizes a dual-segmentation architecture via custom Cellpose models to capture the mother-bud cell boundaries. Extracted masks are integrated with multi-channel fluorescence data using a Difference-of-Gaussians framework to resolve SPB coordinates and localized protein kinetics, which a rule-based decision-tree maps to precise mitotic phenotypes. Validation demonstrates a 50-fold acceleration with ~6% deviation from manual analysis. Availability: Zenodo at https://doi.org/10.5281/zenodo.22083016.

Metabolomics enables the profiling of small-molecule metabolites and thereby captures the biochemical state of a living organism at a given moment and enables to monitor its cellular responses to stimuli. This technique has become a powerful tool in pharmaceutical research, the food industry, and microbial research. Metabolomics aims to obtain an unbiased metabolic profile; however, this is complicated by compound instability, complex and often extensive sample processing, and nonlinear responses in mass spectrometry. Therefore, correcting for metabolite loss and mass spectrometry-related artifacts is essential, typically achieved through relative quantification against an isotopically labelled internal standard for each metabolite of interest. This article describes how to produce 13C-labelled yeast extract and its use as internal standard for metabolomics. More specifically, it provides step-by-step protocols for the fed-batch fermentation, quenching, metabolite extraction, and LC-MS and GC-MS characterization of the internal standard. It also includes a protocol explaining how to use the internal standard for the quantification of metabolites in yeast samples.

Brettanomyces bruxellensis is a yeast species associated with diverse fermentation environments and characterized by extensive genetic diversity, including diploid, autotriploid, and allotriploid lineages resulting from independent hybridization events. These lineages are associated with distinct ecological niches and provide a framework for studying metabolic trait evolution in complex genomes. Nitrate assimilation is a relatively uncommon trait among yeasts and has been reported in B. bruxellensis, but its distribution and evolutionary history within the species remain poorly understood. Here, we combined phenotypic characterization of 151 strains with genomic analyses of 946 whole-genome sequences to investigate nitrate assimilation. Growth assays revealed that nitrate assimilation is widespread but unevenly distributed across genetic lineages, with some populations largely retaining the trait whereas others have frequently lost it. Genomic analyses identified extensive variation affecting the nitrate assimilation gene cluster composed of YNR1, YNI1, and YNT1. Nitrate assimilation was strongly associated with both gene copy number and predicted gene functionality, with nitrate-assimilating strains generally carrying more functional copies of the cluster. Leveraging the complex genomic architecture of the species, we independently analyzed primary and acquired genomes in allotriploid lineages and uncovered contrasting evolutionary trajectories following hybridization. While nitrate assimilation genes were generally maintained in primary genomes, acquired genomes showed a higher prevalence of gene loss and predicted loss-of-function variants, revealing asymmetric dynamics between subgenomes. Altogether, our results suggest that nitrate assimilation represents an ancestral trait that has been differentially maintained across B. bruxellensis lineages through a combination of copy number variation, gene degeneration, and genome-specific evolutionary dynamics. These findings provide new insights into how genome architecture and polyploid evolution shape the maintenance and loss of metabolic traits in an industrially relevant yeast species.

Raffinose family oligosaccharides (RFOs) are abundant in side streams from food and feed production from legumes, and the transition to plant-based diets increases the volume of such side streams. RFOs in the diet tend to have negative impacts on the consumers gut (e.g., nausea, bloating, diarrhoea), and in many ways, RFOs are comparable to lactose as a side stream from the dairy industry and symptoms associated with lactose intolerance. On the contrary, galactooligosaccharides (GOS) are recognized as prebiotics, and in this study we used a {beta}-galactosidase from Niallia circulans to produce potential prebiotics from RFOs (acceptors) and lactose (donor), which we hypothesized to have a lower fermentability than unmodified RFOs. The transglycosylation reactions resulted in RFO-based -{beta}-GOS, with NMR characterization showing ({beta}1-4) galactosylations on the non-reducing galactose end of RFOs as the major product. In reactions with RFOs, the characteristics were comparable to reactions with lactose alone and the new -{beta}-GOS products made up the largest fraction (by weight). A screening of 11 relevant gut and food microbe strains revealed that the gut commensal Bacteroides ovatus metabolised these modified oligosaccharides for growth whereas other strains grew only after adaption and others did not use them at all. This implies that mixed-linkage -{beta}-GOS are less fermentable by some microbes compared to raffinose, while other (beneficial) bacteria can still ferment them. The enzymatic synthesis established here is an interesting approach to upgrade abundant food side streams towards new prebiotics in a world where functional foods and food waste reduction receive increasing attention. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/731070v1_ufig1.gif" ALT="Figure 1000"> View larger version (22K): org.highwire.dtl.DTLVardef@18e0e62org.highwire.dtl.DTLVardef@1525b4borg.highwire.dtl.DTLVardef@1e7be88org.highwire.dtl.DTLVardef@18df278_HPS_FORMAT_FIGEXP M_FIG C_FIG

The coprophilous agaricomycete Coprinopsis cinerea is a model organism for antagonistic fungal-bacterial interactions. Previous studies showed that C. cinerea responds to antagonistic bacteria with strong induction of a set of genes encoding secreted antibacterial molecules. However, little is known about the elicitors of this response. Key open questions in this respect include whether individual antibacterial defence genes are induced by different bacteria and/or by specific bacterial soluble molecules. Here, we present a new C. cinerea reporter system to monitor antibacterial defence induction and address related outstanding issues with minimal hands-on time. In this system, the promoter of the endogenous bacterial-induced gene cclys1 drives the expression of cnluc, which encodes a secreted variant of the deep-sea shrimp luciferase Nluc. We show that cNluc allows to detect and quantify cclys1 induction by measuring luminescence directly in the culture medium of reporter strain colonies. Building on these features, we successfully leveraged the inducible cNluc reporter strain for the development of a novel 96-well plate assay that allows the high-throughput screening of antibacterial defence elicitors. As cNluc can be subject to degradation by secreted proteases of fungal or bacterial origin in the culture medium, we coupled this assay to confirmatory qRT-PCR. Testing this set-up by confronting the reporter strain with several different bacteria revealed that cclys1 induction occurs independently of the bacterial ecological niche. Based on these results, we also recommend qRT-PCR exclusively for validation of negative results. We conclude that cNluc offers significant advantages over cytoplasmic reporter proteins, especially for preliminary rapid screening of multiple conditions.

Recombinase polymerase amplification (RPA) enables rapid nucleic acid testing in low-resource environments, but poorly characterized byproducts can compromise assay specificity and cause false-positive results. Here, we amplified the thirteen original CODIS core loci and Amelogenin to characterize recurrent RPA artefacts and establish conditions that reduce their formation. First, RPA products were analyzed for two reference samples by Oxford Nanopore Technologies sequencing. This revealed two distinct classes of multimeric products: primer multimers and amplicon multimers, consisting of repeated primer or amplicon sequences, respectively. Individual artefacts contained up to 281 primer copies or 22 amplicon copies, demonstrating the extensive range of these products. Next, we performed an optimization study to evaluate the effects of reaction temperature and reagent concentrations at two representative loci, D3S1358 and D5S818. Among the conditions tested, temperature had the most pronounced effect. Reducing the temperature from 42{degrees}C to 34{degrees}C increased the relative target amplicon fraction from 15% to 83% for D3S1358 and from 84% to 98% for D5S818, while maintaining or increasing absolute target concentration. Lower primer concentrations and higher T4 UvsX concentrations also reduced multimer formation, although lower primer concentrations reduced target yield and caused allelic dropout. Finally, amplification at 34{degrees}C was evaluated across all fourteen loci by sequencing. Relative to 42{degrees}C, the target read fraction increased by more than 5 percentage points for 7/14 loci in one reference sample and 9/14 loci in the other, with the largest improvements at multimer-prone loci. These findings identify multimers as an important class of RPA artefacts and establish reaction temperature and T4 UvsX concentration as promising conditions to improve RPA specificity.

Sinking marine particles is a key process regulating carbon export through the biological carbon pump, yet direct measurements of sinking dynamics remain limited in many coastal environments. One barrier is that most existing approaches require expensive instrumentation and large research platforms. Here, we present a low-cost, modular method for concentrating fast-sinking particles and measuring their individual sinking velocities under controlled conditions. This combines large settling tanks (110 L) for field-based particle fractionation with a video-based tracking system that quantifies the sinking behavior of natural marine particles. The particle sinking speed chamber is surrounded on three sides by a temperature-controlled water chamber, minimizing the problem of advection during measurements. The post-processing Python script delivers sinking velocity, particle size, circularity, and RGB-based properties for large numbers of particles. The method accuracy was validated using reference beads with known theoretical sinking velocities derived from Stokes law. Field deployments in the Baltic Sea demonstrated successful enrichment of fast-sinking particles and stable operation from both a research vessel and a small boat. Compared to existing methods, the approach substantially reduces logistical and financial barriers while maintaining particle-resolved measurements and compatibility with complementary biogeochemical analyses. This enables a broader observational coverage of sinking particle processes across environments that are currently underrepresented in carbon export studies.

Food production is a significant contributor to global greenhouse gas emissions and deforestation, exacerbated by substantial food waste. Converting food waste into yeast protein offers a sustainable solution to enhance food security and contribute to a circular economy. However, due to the diverse and variable nature of food waste substrates, numerous experimental trials are required to optimise the preprocessing steps, yeast strain selection, nutrient addition, and fermentation conditions. This study presents a hybrid modelling approach where data-driven machine learning is used to predict microbial growth kinetics from process parameters. The hybrid model was trained on a comprehensive dataset consisting of 963 fermentation experiments from 55 publications, enabling transfer learning across 46 yeast strains and 79 food waste substrates. The hybrid modelling method was integrated with Bayesian optimisation, a sequential strategy to optimise expensive-to-evaluate functions, to efficiently maximise yeast biomass growth from different food waste substrates. The utility of the hybrid model was evaluated using five test datasets selected from previous literature and was shown to facilitate an average reduction of 66% in the number of experimental trials required to identify optimal fermentation conditions compared to without using the hybrid model. This proved that the transfer of knowledge between yeast strains and food wastes improved the optimisation efficiency of real, previously published datasets compared to traditional optimisation methods. The novelty and contributions of this study include the collation of the extensive dataset, provided as supplementary material; and the demonstration that transfer learning by training the hybrid model on this heterogeneous dataset can improve the optimisation efficiency for yeast biomass growth on new strains and substrates.

Course-based undergraduate research experiences (CUREs) are widely recognized as a high-impact practice in biology education, yet most existing CURE frameworks treat the research organism as an interchangeable teaching prop rather than a genuine scientific contribution. We argue that place-based, community-embedded CUREs - in which students isolate, characterize, and publicly deploy a locally meaningful wild organism - constitute a qualitatively distinct model warranting broader adoption. As proof of concept, we present the Declaration of Fermentation project at Indiana University Bloomington: graduate researchers isolated a wild Saccharomyces cerevisiae strain from the bark of a campus landmark tree, confirmed its wild provenance by whole-genome sequencing and phylogenomics, and partnered with local craft breweries to produce a colonial-era inspired ale released publicly for the 250th anniversary of the Declaration of Independence. Volunteer sensory panels at two independent public tasting events (combined n = 33-34 per attribute) confirmed a fruity-funky profile consistent with wild-strain fermentation, with no significant differences between events (Mann-Whitney U, Benjamini-Hochberg-corrected p > 0.05 for all 11 attributes). We describe three design principles - genomically confirmed strain identity, mandatory community partnership, and place-based historical narrative - that distinguish this model from prior wild yeast brewing CUREs, discuss how these principles generalize to other institutions and fermentation vehicles, and identify next steps for formal learning assessment. Complete implementation protocols are provided as supplemental Appendices 1-6, and the bioinformatics pipeline is freely available at https://doi.org/10.5281/zenodo.20679384.

Automated platforms such as Chi.Bio enable simultaneous monitoring of optical density and fluorescent reporter expression in 20 ml reactor cultures with controllable pump systems. As such, they provide an appealing option for contemporary gene expression quantification, quantitative physiology, and laboratory evolution and ecology experiments. While optical density calibration for this device is well established, no equivalent calibration framework exists for fluorescence, making quantitative comparison with reference instruments unreliable. Here, we characterize Chi.Bio fluorescence capabilities using fluorescent calibration microspheres and fixed GFP-expressing S. cerevisiae and E. coli cells, compared with orthogonal plate-reader measurements. We show that microsphere fluorescence is detectable and scales linearly with concentration, whereas the GFP signal from both species falls below the device detection limit. Comparison of background-correction strategies indicates that direct subtraction of a non-fluorescent control measured within the same device yields more reliable fluorescence estimates than the commonly used on-line normalization method. Knowledge of these sensitivity boundaries of the device provides practical guidelines for experimental design of future studies.

Ribosomal protein paralogs are increasingly implicated in the regulation of cellular metabolism and mitochondrial function. However, the mechanisms linking paralog composition of ribosomes to mitochondrial physiology remain largely unclear. Here, we investigate the two Rpl40 paralogs in the budding yeast Saccharomyces cerevisiae and find that deletion of either paralog induces compensatory upregulation of the remaining gene and causes mild mitochondrial stress. Despite this shared phenotype, the mutants display distinct mitochondrial adaptations. Loss of Rpl40a is accompanied by increased abundance of mitochondrial proteins, including MICOS components, whereas loss of Rpl40b leads to reduced levels of mitochondrial inner membrane proteins, including the translocase Tim22 and carrier proteins, together with increased sensitivity to membrane stress. Notably, the two mutants show opposing changes in triglyceride abundance, pointing to paralog-specific control of lipid metabolic remodeling during mitochondrial stress. These findings suggest that Rpl40 paralogs differentially modulate cellular adaptation to mitochondrial stress, linking ribosome composition to mitochondrial proteostasis and lipid homeostasis.

Diatom mating activity contributes to their enormous phenotypic and genetic diversity, yet little is known about patterns in diatom reproductive compatibility across genetically diverse strains, nor the effects on offspring phenotypes that may confer adaptive evolution, niche partitioning, or trait improvement. Here a panel of 38 Arctic Cylindrotheca sp. isolates were crossed pairwise to detect mating compatibility. Positive mating patterns were identified in multiple clades, including amongst crosses of different parental rbcL genotypes. F1 isolated from three different crosses presented phenotypic variation in growth rate, plastid traits, and associated photo-physiological responses to blue and green actinic light. Offspring gliding speed and behaviour also varied, providing insights into complex motility traits that link cell morphology, bioenergetics and sensory adaptation with emergent movement patterns. Exploratory analysis of the F1 trait landscape identified a varaible mixture of individual-level and cross-dependent effects, including substantial variation in growth rate between individuals and strong effects of different crosses on morphology and motility. Experimental diatom breeding may offer a unique strategy to study ocean protist evolution and phenotypic diversification and could complement other biotechnological innovations to enhance cultivation yields and crop resilience in mass cultivation.