Spliceosomal complex components are critical for adjusting the C:N balance during high-light acclimation.

Plant acclimation to an ever-changing environment is decisive for growth, reproduction, and survival. Light availability limits biomass production on both ends of the intensity spectrum. Therefore, the adjustment of plant metabolism is central to high-light (HL) acclimation, and the accumulation of photoprotective anthocyanins is commonly observed. However, mechanisms and factors regulating the HL acclimation response are less clear. Two Arabidopsis mutants of spliceosome components exhibiting a pronounced anthocyanin overaccumulation in HL were isolated from a forward genetic screen for new factors crucial for plant acclimation. Time-resolved physiological, transcriptome, and metabolome analysis revealed a vital function of the spliceosome components for rapidly adjusting gene expression and metabolism. Deficiency of INCREASED LEVEL OF POLYPLOIDY1 (ILP1), NTC-RELATED PROTEIN1 (NTR1), and PLEIOTROPIC REGULATORY LOCUS1 (PRL1) resulted in a marked overaccumulation of carbohydrates and strongly diminished amino acid biosynthesis in HL. While not generally limited in N-assimilation, ilp1, ntr1, and prl1 showed higher glutamate levels and reduced amino acid biosynthesis in HL. The comprehensive analysis reveals a function of the spliceosome components in the conditional regulation of the carbon:nitrogen balance and the accumulation of anthocyanins during HL acclimation. The importance of gene expression, metabolic regulation, and re-direction of carbon towards anthocyanin biosynthesis for HL acclimation are discussed.

Degradation of FATTY ACID EXPORT PROTEIN1 by RHOMBOID-LIKE PROTEASE11 contributes to cold tolerance in Arabidopsis.

Plants need to acclimate to different stresses to optimize growth under unfavorable conditions. In Arabidopsis (Arabidopsis thaliana), the abundance of the chloroplast envelope protein FATTY ACID EXPORT PROTEIN1 (FAX1) decreases after the onset of low temperatures. However, how FAX1 degradation occurs and whether altered FAX1 abundance contributes to cold tolerance in plants remains unclear. The rapid cold-induced increase in RHOMBOID-LIKE PROTEASE11 (RBL11) transcript levels, the physical interaction of RBL11 with FAX1, the specific FAX1 degradation after RBL11 expression, and the absence of cold-induced FAX1 degradation in rbl11 loss-of-function mutants suggest that this enzyme is responsible for FAX1 degradation. Proteomic analyses showed that rbl11 mutants have higher levels of FAX1 and other proteins involved in membrane lipid homeostasis, suggesting that RBL11 is a key element in the remodeling of membrane properties during cold conditions. Consequently, in the cold, rbl11 mutants show a shift in lipid biosynthesis toward the eukaryotic pathway, which coincides with impaired cold tolerance. To test whether cold sensitivity is due to increased FAX1 levels, we analyzed FAX1 overexpressors. The rbl11 mutants and FAX1 overexpressor lines show superimposable phenotypic defects upon exposure to cold temperatures. Our re-sults show that the cold-induced degradation of FAX1 by RBL11 is critical for Arabidop-sis to survive cold and freezing periods.

Temporal classification of short time series data.

MOTIVATION: Within the frame of their genetic capacity, organisms are able to modify their molecular state to cope with changing environmental conditions or induced genetic disposition. As high throughput methods are becoming increasingly affordable, time series analysis techniques are applied frequently to study the complex dynamic interplay between genes, proteins, and metabolites at the physiological and molecular level. Common analysis approaches fail to simultaneously include (i) information about the replicate variance and (ii) the limited number of responses/shapes that a biological system is typically able to take. RESULTS: We present a novel approach to model and classify short time series signals, conceptually based on a classical time series analysis, where the dependency of the consecutive time points is exploited. Constrained spline regression with automated model selection separates between noise and signal under the assumption that highly frequent changes are less likely to occur, simultaneously preserving information about the detected variance. This enables a more precise representation of the measured information and improves temporal classification in order to identify biologically interpretable correlations among the data. AVAILABILITY AND IMPLEMENTATION: An open source F# implementation of the presented method and documentation of its usage is freely available in the TempClass repository, https://github.com/CSBiology/TempClass  [58].

Moderate high temperature is beneficial or detrimental depending on carbon availability in the green alga Chlamydomonas reinhardtii.

High temperatures impair plant growth and reduce agricultural yields, but the underlying mechanisms remain elusive. The unicellular green alga Chlamydomonas reinhardtii is an excellent model to study heat responses in photosynthetic cells due to its fast growth rate, many similarities in cellular processes to land plants, simple and sequenced genome, and ample genetic and genomics resources. Chlamydomonas grows in light by photosynthesis and with externally supplied acetate as an organic carbon source. Understanding how organic carbon sources affect heat responses is important for the algal industry but remains understudied. We cultivated wild-type Chlamydomonas under highly controlled conditions in photobioreactors at 25 °C (control), 35 °C (moderate high temperature), or 40 °C (acute high temperature) with or without constant acetate supply for 1 or 4 day. Treatment at 35 °C increased algal growth with constant acetate supply but reduced algal growth without sufficient acetate. The overlooked and dynamic effects of 35 °C could be explained by induced acetate uptake and metabolism. Heat treatment at 40 °C for more than 2 day was lethal to algal cultures with or without constant acetate supply. Our findings provide insights to understand algal heat responses and help improve thermotolerance in photosynthetic cells.

PLANTdataHUB: a collaborative platform for continuous FAIR data sharing in plant research.

In modern reproducible, hypothesis-driven plant research, scientists are increasingly relying on research data management (RDM) services and infrastructures to streamline the processes of collecting, processing, sharing, and archiving research data. FAIR (i.e., findable, accessible, interoperable, and reusable) research data play a pivotal role in enabling the integration of interdisciplinary knowledge and facilitating the comparison and synthesis of a wide range of analytical findings. The PLANTdataHUB offers a solution that realizes RDM of scientific (meta)data as evolving collections of files in a directory - yielding FAIR digital objects called ARCs - with tools that enable scientists to plan, communicate, collaborate, publish, and reuse data on the same platform while gaining continuous quality control insights. The centralized platform is scalable from personal use to global communities and provides advanced federation capabilities for institutions that prefer to host their own satellite instances. This approach borrows many concepts from software development and adapts them to fit the challenges of the field of modern plant science undergoing digital transformation. The PLANTdataHUB supports researchers in each stage of a scientific project with adaptable continuous quality control insights, from the early planning phase to data publication. The central live instance of PLANTdataHUB is accessible at (https://git.nfdi4plants.org), and it will continue to evolve as a community-driven and dynamic resource that serves the needs of contemporary plant science.

Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale.

Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas.

Competition co-immunoprecipitation reveals the interactors of the chloroplast CPN60 chaperonin machinery.

The functionality of all metabolic processes in chloroplasts depends on a balanced integration of nuclear- and chloroplast-encoded polypeptides into the plastid's proteome. The chloroplast chaperonin machinery is an essential player in chloroplast protein folding under ambient and stressful conditions, with a more intricate structure and subunit composition compared to the orthologous GroEL/ES chaperonin of Escherichia coli. However, its exact role in chloroplasts remains obscure, mainly because of very limited knowledge about the interactors. We employed the competition immunoprecipitation method for the identification of the chaperonin's interactors in Chlamydomonas reinhardtii. Co-immunoprecipitation of the target complex in the presence of increasing amounts of isotope-labelled competitor epitope and subsequent mass spectrometry analysis specifically allowed to distinguish true interactors from unspecifically co-precipitated proteins. Besides known substrates such as RbcL and the expected complex partners, we revealed numerous new interactors with high confidence. Proteins that qualify as putative substrate proteins differ from bulk chloroplast proteins by a higher content of beta-sheets, lower alpha-helical conformation and increased aggregation propensity. Immunoprecipitations targeted against a subunit of the co-chaperonin lid revealed the ClpP protease as a specific partner complex, pointing to a close collaboration of these machineries to maintain protein homeostasis in the chloroplast.

Characterization of a soluble library of the Pseudomonas aeruginosa PAO1 membrane proteome with emphasis on c-di-GMP turnover enzymes.

Studies of protein-protein interactions in membranes are very important to fully understand the biological function of a cell. The extraction of proteins from the native membrane environment is a critical step in the preparation of membrane proteins that might affect the stability of protein complexes. In this work, we used the amphiphilic diisobutylene/maleic acid copolymer to extract the membrane proteome of the opportunistic pathogen Pseudomonas aeruginosa, thereby creating a soluble membrane-protein library within a native-like lipid-bilayer environment. Size fractionation of nanodisc-embedded proteins and subsequent mass spectrometry enabled the identification of 3358 proteins. The native membrane-protein library showed a very good overall coverage compared to previous proteome data. The pattern of size fractionation indicated that protein complexes were preserved in the library. More than 20 previously described complexes, e.g. the SecYEG and Pili complexes, were identified and analyzed for coelution. Although the mass-spectrometric dataset alone did not reveal new protein complexes, combining pulldown assays with mass spectrometry was successful in identifying new protein interactions in the native membrane-protein library. Thus, we identified several candidate proteins for interactions with the membrane phosphodiesterase NbdA, a member of the c-di-GMP network. We confirmed the candidate proteins CzcR, PA4200, SadC, and PilB as novel interaction partners of NbdA using the bacterial adenylate cyclase two-hybrid assay. Taken together, this work demonstrates the usefulness of the native membrane-protein library of P. aeruginosa for the investigation of protein interactions and membrane-protein complexes. Data are available via ProteomeXchange with identifiers PXD039702 and PXD039700.

DeepSTABp: A Deep Learning Approach for the Prediction of Thermal Protein Stability.

Proteins are essential macromolecules that carry out a plethora of biological functions. The thermal stability of proteins is an important property that affects their function and determines their suitability for various applications. However, current experimental approaches, primarily thermal proteome profiling, are expensive, labor-intensive, and have limited proteome and species coverage. To close the gap between available experimental data and sequence information, a novel protein thermal stability predictor called DeepSTABp has been developed. DeepSTABp uses a transformer-based protein language model for sequence embedding and state-of-the-art feature extraction in combination with other deep learning techniques for end-to-end protein melting temperature prediction. DeepSTABp can predict the thermal stability of a wide range of proteins, making it a powerful and efficient tool for large-scale prediction. The model captures the structural and biological properties that impact protein stability, and it allows for the identification of the structural features that contribute to protein stability. DeepSTABp is available to the public via a user-friendly web interface, making it accessible to researchers in various fields.

Systems-wide analysis revealed shared and unique responses to moderate and acute high temperatures in the green alga Chlamydomonas reinhardtii.

Different intensities of high temperatures affect the growth of photosynthetic cells in nature. To elucidate the underlying mechanisms, we cultivated the unicellular green alga Chlamydomonas reinhardtii under highly controlled photobioreactor conditions and revealed systems-wide shared and unique responses to 24-hour moderate (35°C) and acute (40°C) high temperatures and subsequent recovery at 25°C. We identified previously overlooked unique elements in response to moderate high temperature. Heat at 35°C transiently arrested the cell cycle followed by partial synchronization, up-regulated transcripts/proteins involved in gluconeogenesis/glyoxylate-cycle for carbon uptake and promoted growth. But 40°C disrupted cell division and growth. Both high temperatures induced photoprotection, while 40°C distorted thylakoid/pyrenoid ultrastructure, affected the carbon concentrating mechanism, and decreased photosynthetic efficiency. We demonstrated increased transcript/protein correlation during both heat treatments and hypothesize reduced post-transcriptional regulation during heat may help efficiently coordinate thermotolerance mechanisms. During recovery after both heat treatments, especially 40°C, transcripts/proteins related to DNA synthesis increased while those involved in photosynthetic light reactions decreased. We propose down-regulating photosynthetic light reactions during DNA replication benefits cell cycle resumption by reducing ROS production. Our results provide potential targets to increase thermotolerance in algae and crops.

Complexome profiling on the Chlamydomonas lpa2 mutant reveals insights into PSII biogenesis and new PSII associated proteins.

While the composition and function of the major thylakoid membrane complexes are well understood, comparatively little is known about their biogenesis. The goal of this work was to shed more light on the role of auxiliary factors in the biogenesis of photosystem II (PSII). Here we have identified the homolog of LOW PSII ACCUMULATION 2 (LPA2) in Chlamydomonas. A Chlamydomonas reinhardtii lpa2 mutant grew slower in low light, was hypersensitive to high light, and exhibited aberrant structures in thylakoid membrane stacks. Chlorophyll fluorescence (Fv/Fm) was reduced by 38%. Synthesis and stability of newly made PSII core subunits D1, D2, CP43, and CP47 were not impaired. However, complexome profiling revealed that in the mutant CP43 was reduced to ~23% and D1, D2, and CP47 to ~30% of wild type levels. Levels of PSI and the cytochrome b6f complex were unchanged, while levels of the ATP synthase were increased by ~29%. PSII supercomplexes, dimers, and monomers were reduced to ~7%, ~26%, and ~60% of wild type levels, while RC47 was increased ~6-fold and LHCII by ~27%. We propose that LPA2 catalyses a step during PSII assembly without which PSII monomers and further assemblies become unstable and prone to degradation. The LHCI antenna was more disconnected from PSI in the lpa2 mutant, presumably as an adaptive response to reduce excitation of PSI. From the co-migration profiles of 1734 membrane-associated proteins, we identified three novel putative PSII associated proteins with potential roles in regulating PSII complex dynamics, assembly, and chlorophyll breakdown.

A longer isoform of Stim1 is a negative SOCE regulator but increases cAMP-modulated NFAT signaling.

Alternative splicing is a potent modifier of protein function. Stromal interaction molecule 1 (Stim1) is the essential activator of store-operated Ca2+ entry (SOCE) triggering activation of transcription factors. Here, we characterize Stim1A, a splice variant with an additional 31 amino acid domain inserted in frame within its cytosolic domain. Prominent expression of exon A is found in astrocytes, heart, kidney, and testes. Full-length Stim1A functions as a dominant-negative regulator of SOCE and ICRAC, facilitating sequence-specific fast calcium-dependent inactivation and destabilizing gating of Orai channels. Downregulation or absence of native Stim1A results in increased SOCE. Despite reducing SOCE, Stim1A leads to increased NFAT translocation. Differential proteomics revealed an interference of Stim1A with the cAMP-SOCE crosstalk by altered modulation of phosphodiesterase 8 (PDE8), resulting in reduced cAMP degradation and increased PIP5K activity, facilitating NFAT activation. Our study uncovers a hitherto unknown mechanism regulating NFAT activation and indicates that cell-type-specific splicing of Stim1 is a potent means to regulate the NFAT signalosome and cAMP-SOCE crosstalk.

Topology of the redox network during induction of photosynthesis as revealed by time-resolved proteomics in tobacco.

Photosynthetically produced electrons provide energy for various metabolic pathways, including carbon reduction. Four Calvin-Benson cycle enzymes and several other plastid proteins are activated in the light by reduction of specific cysteines via thioredoxins, a family of electron transporters operating in redox regulation networks. How does this network link the photosynthetic chain with cellular metabolism? Using a time-resolved redox proteomic method, we have investigated the redox network in vivo during the dark­to­low light transition. We show that redox states of some thioredoxins follow the photosynthetic linear electron transport rate. While some redox targets have kinetics compatible with an equilibrium with one thioredoxin (TRXf), reduction of other proteins shows specific kinetic limitations, allowing fine-tuning of each redox-regulated step of chloroplast metabolism. We identified five new redox-regulated proteins, including proteins involved in Mg2+ transport and 1O2 signaling. Our results provide a system-level functional view of the photosynthetic redox regulation network.

Data Management and Modeling in Plant Biology.

The study of plant-environment interactions is a multidisciplinary research field. With the emergence of quantitative large-scale and high-throughput techniques, amount and dimensionality of experimental data have strongly increased. Appropriate strategies for data storage, management, and evaluation are needed to make efficient use of experimental findings. Computational approaches of data mining are essential for deriving statistical trends and signatures contained in data matrices. Although, current biology is challenged by high data dimensionality in general, this is particularly true for plant biology. Plants as sessile organisms have to cope with environmental fluctuations. This typically results in strong dynamics of metabolite and protein concentrations which are often challenging to quantify. Summarizing experimental output results in complex data arrays, which need computational statistics and numerical methods for building quantitative models. Experimental findings need to be combined by computational models to gain a mechanistic understanding of plant metabolism. For this, bioinformatics and mathematics need to be combined with experimental setups in physiology, biochemistry, and molecular biology. This review presents and discusses concepts at the interface of experiment and computation, which are likely to shape current and future plant biology. Finally, this interface is discussed with regard to its capabilities and limitations to develop a quantitative model of plant-environment interactions.

In Vivo Structure-Function Analysis and Redox Interactomes of Leishmania tarentolae Erv.

Import and oxidative folding of proteins in the mitochondrial intermembrane space differ among eukaryotic lineages. While opisthokonts such as yeast rely on the receptor and oxidoreductase Mia40 in combination with the Mia40:cytochrome c oxidoreductase Erv, kinetoplastid parasites and other Excavata/Discoba lack Mia40 but have a functional Erv homologue. Whether excavate Erv homologues rely on a Mia40 replacement or directly interact with imported protein substrates remains controversial. Here, we used the CRISPR-Cas9 system to generate a set of tagged and untagged homozygous mutants of LTERV from the kinetoplastid model parasite Leishmania tarentolae. Modifications of the shuttle cysteine motif of LtErv were lethal, whereas replacement of clamp residue Cys17 or removal of the kinetoplastida-specific second (KISS) domain had no impact on parasite viability under standard growth conditions. However, removal of the KISS domain rendered parasites sensitive to heat stress and led to the accumulation of homodimeric and mixed LtErv disulfides. We therefore determined and compared the redox interactomes of tagged wild-type LtErv and LtErvΔKISS using stable isotope labeling by amino acids in cell culture (SILAC) and quantitative mass spectrometry. While the Mia40-replacement candidate Mic20 and all but one typical substrate with twin Cx3/9C-motifs were absent in both redox interactomes, we identified a small set of alternative potential interaction partners with putative redox-active cysteine residues. In summary, our study reveals parasite-specific intracellular structure-function relationships and redox interactomes of LtErv with implications for current hypotheses on mitochondrial protein import in nonopisthokonts. IMPORTANCE The discovery of the redox proteins Mia40/CHCHD4 and Erv1/ALR, as well as the elucidation of their relevance for oxidative protein folding in the mitochondrial intermembrane space of yeast and mammals, founded a new research topic in redox biology and mitochondrial protein import. The lack of Mia40/CHCHD4 in protist lineages raises fundamental and controversial questions regarding the conservation and evolution of this essential pathway. Do protist Erv homologues act alone, or do they use the candidate Mic20 or another protein as a Mia40 replacement? Furthermore, we previously showed that Erv homologues in L. tarentolae and the human pathogen L. infantum are not only essential but also differ structurally and mechanistically from yeast and human Erv1/ALR. Here, we analyzed the relevance of such structural differences in vivo and determined the first redox interactomes of a nonopisthokont Erv homologue. Our data challenge recent hypotheses on mitochondrial protein import in nonopisthokonts.

Differential degradation of RNA species by autophagy-related pathways in Arabidopsis.

The plant vacuole recycles proteins and RNA delivered to it by autophagy. In this study, by isolating intact vacuoles from Arabidopsis plants, followed by subsequent RNA purification, and deep sequencing, we provide a comprehensive characterization of Arabidopsis vacuolar RNAome. In the vacuolar RNAome, we detected ribosomal RNAs, transfer RNAs, including those of chloroplast origin, and in addition small RNA types. As autophagy is a main mechanism for the transport of RNA to the vacuole, atg5-1 mutants deficient in autophagy were included in our analysis. We observed severely reduced amounts of most chloroplast-derived RNA species in these mutants. Comparisons with cellular RNA composition provided an indication of possible up-regulation of alternative RNA breakdown pathways. By contrast, vacuolar RNA processing and composition in plants lacking vacuolar ribonuclease 2, involved in cellular RNA homeostasis, only showed minor alterations, possibly because of the presence of further so far unknown vacuolar RNase species. Among the small RNA types, we detected mature miRNAs in all vacuolar preparations but at much lower frequency in atg5-1, raising the possibility of a biological role for vacuolar miRNAs.

iMLP, a predictor for internal matrix targeting-like sequences in mitochondrial proteins.

Matrix targeting sequences (MTSs) direct proteins from the cytosol into mitochondria. Efficient targeting often relies on internal matrix targeting-like sequences (iMTS-Ls) which share structural features with MTSs. Predicting iMTS-Ls was tedious and required multiple tools and webservices. We present iMLP, a deep learning approach for the prediction of iMTS-Ls in protein sequences. A recurrent neural network has been trained to predict iMTS-L propensity profiles for protein sequences of interest. The iMLP predictor considerably exceeds the speed of existing approaches. Expanding on our previous work on iMTS-L prediction, we now serve an intuitive iMLP webservice available at http://iMLP.bio.uni-kl.de and a stand-alone command line tool for power user in addition.

Parkinson mice show functional and molecular changes in the gut long before motoric disease onset.

BACKGROUND: There is increasing evidence that Parkinson's disease (PD) might start in the gut, thus involving and compromising also the enteric nervous system (ENS). At the clinical onset of the disease the majority of dopaminergic neurons in the midbrain is already destroyed, so that the lack of early biomarkers for the disease represents a major challenge for developing timely treatment interventions. Here, we use a transgenic A30P-α-synuclein-overexpressing PD mouse model to identify appropriate candidate markers in the gut before hallmark symptoms begin to manifest. METHODS: Based on a gait analysis and striatal dopamine levels, we defined 2-month-old A30P mice as pre-symptomatic (psA30P), since they are not showing any motoric impairments of the skeletal neuromuscular system and no reduced dopamine levels, but an intestinal α-synuclein pathology. Mice at this particular age were further used to analyze functional and molecular alterations in both, the gastrointestinal tract and the ENS, to identify early pathological changes. We examined the gastrointestinal motility, the molecular composition of the ENS, as well as the expression of regulating miRNAs. Moreover, we applied A30P-α-synuclein challenges in vitro to simulate PD in the ENS. RESULTS: A retarded gut motility and early molecular dysregulations were found in the myenteric plexus of psA30P mice. We found that i.e. neurofilament light chain, vesicle-associated membrane protein 2 and calbindin 2, together with the miRNAs that regulate them, are significantly altered in the psA30P, thus representing potential biomarkers for early PD. Many of the dysregulated miRNAs found in the psA30P mice are reported to be changed in PD patients as well, either in blood, cerebrospinal fluid or brain tissue. Interestingly, the in vitro approaches delivered similar changes in the ENS cultures as seen in the transgenic animals, thus confirming the data from the mouse model. CONCLUSIONS: These findings provide an interesting and novel approach for the identification of appropriate biomarkers in men.

Identifying the minimum amplicon sequence depth to adequately predict classes in eDNA-based marine biomonitoring using supervised machine learning.

Environmental DNA metabarcoding is a powerful approach for use in biomonitoring and impact assessments. Amplicon-based eDNA sequence data are characteristically highly divergent in sequencing depth (total reads per sample) as influenced inter alia by the number of samples simultaneously analyzed per sequencing run. The random forest (RF) machine learning algorithm has been successfully employed to accurately classify unknown samples into monitoring categories. To employ RF to eDNA data, and avoid sequencing-depth artifacts, sequence data across samples are normalized using rarefaction, a process that inherently loses information. The aim of this study was to inform future sampling designs in terms of the relationship between sampling depth and RF accuracy. We analyzed three published and one new bacterial amplicon datasets, using a RF, based initially on the maximal rarefied data available (minimum mean of > 30,000 reads across all datasets) to give our baseline performance. We then evaluated the RF classification success based on increasingly rarefied datasets. We found that extreme to moderate rarefaction (50-5000 sequences per sample) was sufficient to achieve prediction performance commensurate to the full data, depending on the classification task. We did not find that the number of classification classes, data balance across classes, or the total number of sequences or samples, were associated with predictive accuracy. We identified the ability of the training data to adequately characterize the classes being mapped as the most important criterion and discuss how this finding can inform future sampling design for eDNA based biomonitoring to reduce costs and computation time.