Functional Genomics Research Articles

CRISPR-Mediated Library Screening of Gene-Knockout Cell Lines for Investigating Antiviral Innate Immunity.

The application of CRISPR-mediated library screening has fundamentally transformed functional genomics by revealing the complexity of virus-host interactions. This protocol describes the use of CRISPR-mediated library screening to identify key functional genes regulating the innate immune response to PEDV infection. We detail a step-by-step process, starting from the design and construction of a customized CRISPR knockout library targeting genes involved in innate immunity to the effective delivery of these constructs into cells using lentiviral vectors. Subsequently, we outline the process of identifying functional genes postviral attack, including the use of next-generation sequencing (NGS), to analyze and identify knockout cells that exhibit altered responses to infection. This integrated approach provides researchers in immunology and virology with a resource and a robust framework for uncovering the genetic basis of host-pathogen interactions and the arsenal of the innate immune system against viral invasions.

Heterochromatin protein 1 alpha (HP1α) undergoes a monomer to dimer transition that opens and compacts live cell genome architecture.

Our understanding of heterochromatin nanostructure and its capacity to mediate gene silencing in a living cell has been prevented by the diffraction limit of optical microscopy. Thus, here to overcome this technical hurdle, and directly measure the nucleosome arrangement that underpins this dense chromatin state, we coupled fluorescence lifetime imaging microscopy (FLIM) of Förster resonance energy transfer (FRET) between histones core to the nucleosome, with molecular editing of heterochromatin protein 1 alpha (HP1α). Intriguingly, this super-resolved readout of nanoscale chromatin structure, alongside fluorescence fluctuation spectroscopy (FFS) and FLIM-FRET analysis of HP1α protein-protein interaction, revealed nucleosome arrangement to be differentially regulated by HP1α oligomeric state. Specifically, we found HP1α monomers to impart a previously undescribed global nucleosome spacing throughout genome architecture that is mediated by trimethylation on lysine 9 of histone H3 (H3K9me3) and locally reduced upon HP1α dimerisation. Collectively, these results demonstrate HP1α to impart a dual action on chromatin that increases the dynamic range of nucleosome proximity. We anticipate that this finding will have important implications for our understanding of how live cell heterochromatin structure regulates genome function.

The Genetics and Functional Genomics of Osteoarthritis.

Osteoarthritis is the most prevalent whole-joint degenerative disorder, and is characterized by the degradation of articular cartilage and the underlying bone structures. Almost 600 million people are affected by osteoarthritis worldwide. No curative treatments are available, and management strategies focus mostly on pain relief. Here, we provide a comprehensive overview of the available human genetic and functional genomics studies for osteoarthritis to date and delineate how these studies have helped shed light on disease etiopathology. We highlight genetic discoveries from genome-wide association studies and provide a detailed overview of molecular-level investigations in osteoarthritis tissues, including methylation-, transcriptomics-, and proteomics-level analyses. We review how functional genomics data from different molecular levels have helped to prioritize effector genes that can be used as drug targets or drug-repurposing opportunities. Finally, we discuss future directions with the potential to drive a step change in osteoarthritis research.

Rare germline genetic variation in PAX8 transcription factor binding sites and susceptibility to epithelial ovarian cancer.

Common genetic variation throughout the genome together with rare coding variants identified to date explain about a half of the inherited genetic component of epithelial ovarian cancer risk. It is likely that rare variation in the non-coding genome will explain some of the unexplained heritability, but identifying such variants is challenging. The primary problem is lack of statistical power to identifying individual risk variants by association as power is a function of sample size, effect size and allele frequency. Power can be increased by using burden tests which test for association of carriers of any variant in a specified genomic region. This has the effect of increasing the putative effect allele frequency. PAX8 is a transcription factor that plays a critical role in tumour progression, migration and invasion. Furthermore, regulatory elements proximal to target genes of PAX8 are enriched for common ovarian cancer risk variants. We hypothesised that rare variation in PAX8 binding sites are also associated with ovarian cancer risk, but unlikely to be associated with risk of breast, colorectal or endometrial cancer. We have used publicly available, whole-genome sequencing data from the UK 100,000 Genomes Project to evaluate the burden of rare variation in PAX8 binding sites across the genome. Data were available for 522 ovarian cancers, 2,984 breast cancers, 2,696 colorectal cancers, 836 endometrial cancers and 2253 non-cancer controls. Active binding sites were defined using data from multiple PAX8 and H3K27 ChIPseq experiments. We found no association between the burden of rare variation in PAX8 binding sites (defined in several ways) and risk of ovarian, breast or endometrial cancer. An apparent association with colorectal cancer was likely to be a technical artefact as a similar association was also detected for rare variation in random regions of the genome. Despite the null result this study provides a proof-of -principle for using burden testing to identify rare, non-coding germline genetic variation associated with disease. Larger sample sizes available from large-scale sequencing projects together with improved understanding of the function of the non-coding genome will increase the potential of similar studies in the future.

A simple and efficient non-tissue culture method for genetic transformation of grape immature zygotic embryos via VvBBM overexpression

The process of genetic transformation of grapevine has long been hampered by the inefficiency and complexity of tissue culture-based methods, severely limiting cultivar improvement and functional genome research. In this study, we aimed to develop a novel, non-tissue culture-based method for the genetic transformation of grape immature zygotic embryos without relying on tissue culture. This approach involves the overexpression of the Vitis vinifera BABY BOOM (VvBBM) gene, a key member of the APETALA2/ETHYLENE RESPONSE FACTOR (AP2/ERF) transcription factor family, which is known to enhance plant regeneration and transformation efficiency. Using ‘Cabernet Sauvignon’ immature zygotic embryos, we achieved a transformation efficiency of 42.85 % under non-tissue culture conditions, significantly reducing the cycle duration to approximately two months compared to traditional genetic transformation methods. Phenotypic and molecular analyses of the transformed grapevine plants confirmed the integration and overexpression of the VvBBM gene, resulting in improved growth metrics, including increased plant height, root length, and total root number, in a genotype-independent manner. Our results highlight the potential of VvBBM overexpression to circumvent the limitations imposed by tissue culture dependence, providing a rapid, efficient, and scalable alternative to conventional methods. Our study not only provides a practical approach to overcoming the barriers to genetic transformation in grapevine, but also opens new avenues for the application of this technology in the breeding of other economically important crops.

Enhancing personalized gene expression prediction from DNA sequences using genomic foundation models

Artificial intelligence/deep learning (AI/DL) models that predict molecular phenotypes like gene expression directly from DNA sequences have recently emerged. While these models have proven effective at capturing the variation across genes, their ability to explain inter-individual differences has been limited. We hypothesize that the performance gap can be narrowed through the use of pre-trained embeddings from the Nucleotide Transformer, a large foundation model trained on 3,000+ genomes. We train a transformer model using the pre-trained embeddings and compare its predictive performance to Enformer, the current state-of-the-art model, using genotype and expression data from 290 individuals. Our model significantly outperforms Enformer in terms of correlation across individuals and narrows the performance gap with an elastic net regression approach using just the genetic variants as predictors. Although simple regression models have their advantages in personalized prediction tasks, DL approaches based on foundation models pre-trained on diverse genomes have unique strengths in flexibility and interpretability. With further methodological and computational improvements with more training data, these models may someday predict molecular phenotypes from DNA sequences with accuracy surpassing that of regression-based approaches. Our work demonstrates the potential for large pre-trained AI/DL models to advance functional genomics.

Genome-wide analysis of Brucella melitensis growth in spleen of infected mice allows rational selection of new vaccine candidates.

Live attenuated vaccines (LAVs) whose virulence would be controlled at the tissue level could be a crucial tool to effectively fight intracellular bacterial pathogens, because they would optimize the induction of protective immune memory while avoiding the long-term persistence of vaccine strains in the host. Rational development of these new LAVs implies developing an exhaustive map of the bacterial virulence genes according to the host organs implicated. We report here the use of transposon sequencing to compare the bacterial genes involved in the multiplication of Brucella melitensis, a major causative agent of brucellosis, in the lungs and spleens of C57BL/6 infected mice. We found 257 and 135 genes predicted to be essential for B. melitensis multiplication in the spleen and lung, respectively, with 87 genes common to both organs. We selected genes whose deletion is predicted to produce moderate or severe attenuation in the spleen, the main known reservoir of Brucella, and compared deletion mutants for these genes for their ability to protect mice against challenge with a virulent strain of B. melitensis. The protective efficacy of a deletion mutant for the plsC gene, implicated in phospholipid biosynthesis, is similar to that of the reference Rev.1 vaccine but with a shorter persistence in the spleen. Our results demonstrate that B. melitensis faces different selective pressures depending on the organ and underscore the effectiveness of functional genome mapping for the design of new safer LAV candidates.

Identification and functional analysis of genes selected using different chemometric techniques on multiarray expression data of liver from high-fat diet treated mice

The prevalence of obesity is increasing, and this lifestyle disease is related to a high-fat diet, a surplus in caloric intake, and increased inflammation. This study aimed to use a publicly available dataset of microarray gene expression data from the liver of high-fat diet fed mice (GSE39549) to determine the functional importance of small subsets of the overall genes. The regulatory aspects of the chosen mice genes were extrapolated to human genes for the determination of potential diagnostic and therapeutic targets. The chemometric approaches of principal component analysis (PCA), random forest (RF), and genetic algorithm (GA) were used as data reduction techniques to select 50 genes from a total of 15,000 genes to differentiate liver samples from high-fat diet and normal diet-fed mice. A subset of 30 genes from each of the techniques were processed with classification techniques of k-nearest neighbor and support vector machines. The results showed that random forest was best at differentiating the samples and GA was the least accurate. The results of functional annotation and protein-protein interactions showed that genes selected by PCA and RF were more associated with obesity as they identified functions related to inflammatory processes, as well as lipid and cholesterol metabolic processes. The genes selected by GA identified processes related to cilium and cell projection. The proteins identified by RF, such as Msmo and Sqle, had roles in cholesterol metabolic and biosynthetic processes. The results showed that combining the genes selected by PCA and RF allowed a better understanding of the overall functional protein modules. The crosstalk genes such as Abcg5 as well as Abcg8 that relate cholesterol metabolic and biosynthetic process to glutathione metabolic process were identified. Various miRNAs-gene interactions are present in humans for most of the genes identified by PCA, RF, or GA. Some genes that showed fewer interactions with human miRNAs are CIDEA, PLIN4, and NME8. The results suggest the use of different chemometric analyses in combination with functional genomics to identify different sets of targets for diagnostic, therapeutic, and future research.

Repeated migration, interbreeding and bottlenecking shaped the phylogeography of the selfing grass Brachypodium stacei.

Brachypodium stacei is the most ancestral lineage in the genus Brachypodium, a model system for grass functional genomics. B. stacei shows striking and sometimes contradictory biological and evolutionary features, including a high selfing rate yet extensive admixture, an ancient Miocene origin yet with recent evolutionary radiation, and adaptation to different dry climate conditions in its narrow distribution range. Therefore, it constitutes an ideal system to study these life history traits. We studied the phylogeography of 17 native circum-Mediterranean B. stacei populations (39 individuals) using genome-wide RADseq SNP data and complete plastome sequences. Nuclear SNP data revealed the existence of six distinct genetic clusters, low levels of intra-population genetic diversity and high selfing rates, albeit with signatures of admixture. Coalescence-based dating analysis detected a recent split between crown lineages in the Late Quaternary. Plastome sequences showed incongruent evolutionary relationships with those recovered by the nuclear data, suggesting interbreeding and chloroplast capture events between genetically distant populations. Demographic and population dispersal coalescent models identified an ancestral origin of B. stacei in the western-central Mediterranean islands, followed by an early colonization of the Canary Islands and two independent colonization events of the eastern Mediterranean region through long-distance dispersal and bottleneck events as the most likely evolutionary history. Climate niche data identified three arid niches of B. stacei in the southern Mediterranean region. Our findings indicate that the phylogeography of B. stacei populations was shaped by recent radiations, frequent extinctions, long-distance dispersal events, occasional interbreeding, and adaptation to local climates.

Uncovering methylation-dependent genetic effects on regulatory element function in diverse genomes.

A major goal in evolutionary biology and biomedicine is to understand the complex interactions between genetic variants, the epigenome, and gene expression. However, the causal relationships between these factors remain poorly understood. mSTARR-seq, a methylation-sensitive massively parallel reporter assay, is capable of identifying methylation-dependent regulatory activity at many thousands of genomic regions simultaneously, and allows for the testing of causal relationships between DNA methylation and gene expression on a region-by-region basis. Here, we developed a multiplexed mSTARR-seq protocol to assay naturally occurring human genetic variation from 25 individuals sampled from 10 localities in Europe and Africa. We identified 6,957 regulatory elements in either the unmethylated or methylated state, and this set was enriched for enhancer and promoter annotations, as expected. The expression of 58% of these regulatory elements was modulated by methylation, which was generally associated with decreased RNA expression. Within our set of regulatory elements, we used allele-specific expression analyses to identify 8,020 sites with genetic effects on gene regulation; further, we found that 42.3% of these genetic effects varied between methylated and unmethylated states. Sites exhibiting methylation-dependent genetic effects were enriched for GWAS and EWAS annotations, implicating them in human disease. Compared to datasets that assay DNA from a single European individual, our multiplexed assay uncovers dramatically more genetic effects and methylation-dependent genetic effects, highlighting the importance of including diverse individuals in assays which aim to understand gene regulatory processes.

Molecular Marker Techniques and Genotypic Characterization Approaches in Plant Breeding

Plant breeding, often known as the science of plant development, is the study and practice of modifying a plant’s genetic makeup through a variety of breeding techniques to produce higher-quality, more prolific, and more resistant to harsh environmental circumstances. Classical breeding programs are indispensable techniques for increasing yields and improving plant characteristics, but they are progressing too slowly to meet the increasing food demand of the rapidly growing world population alone. Considering that the development periods of plants are generally long in traditional plant breeding, the opportunity to develop higher quality and more productive species that are more resistant to abiotic and biotic stress factors is very limited. Because there are multiple steps required in producing new plant varieties, including hybridization, selection, and testing, the process of creating a new variation takes several years. However, it is important to rapidly develop plant varieties with desirable characteristics to meet the increasing food demand of the rapidly growing world population, so the application of biotechnological methods integrated into plant breeding and combined with traditional methods can help reduce food shortages. Today, with the quick acceleration of biotechnology, molecular DNA marker technology has been developed in plant breeding and very important developments have been experienced. Thanks to the development of molecular tools for genetic research aimed at improving agricultural traits in plants related to crop yield, crop quality, or tolerance to adverse environmental conditions, we now have a much better understanding of plant genetics and the architecture and function of plant genomes. Therefore, it is of critical importance to revise current breeding procedures by incorporating molecular markers into breeding programs in the future.

Identification of KIFC1 as a putative vulnerability in lung cancers with centrosome amplification

Centrosome amplification (CA), an abnormal increase in the number of centrosomes in the cell, is a recurrent phenomenon in lung and other malignancies. Although CA promotes tumor development and progression by inducing genomic instability (GIN), it also induces mitotic stress that jeopardizes cellular integrity. CA leads to the formation of multipolar mitotic spindles that can cause lethal chromosome segregation errors. To sustain the benefits of CA by mitigating its consequences, malignant cells are dependent on adaptive mechanisms that represent therapeutic vulnerabilities. We aimed to discover genetic dependencies associated with CA in lung cancer. Combining a CRISPR/Cas9 functional genomics screen with tumor genomic analyses, we identified the motor protein KIFC1, also known as HSET, as a putative vulnerability specifically in lung adenocarcinoma (LUAD) with CA. KIFC1 expression was positively correlated with CA in LUAD and associated with worse patient outcomes, smoking history, and indicators of GIN. KIFC1 loss-of-function sensitized LUAD cells with high basal KIFC1 expression to potentiation of CA, which was associated with a diminished ability to cluster extra centrosomes into pseudo-bipolar mitotic spindles. Our work suggests that KIFC1 inhibition represents a novel approach for potentiating GIN to lethal levels in LUAD with CA by forcing cells to divide with multipolar spindles, rationalizing further studies to investigate its therapeutic potential.

Propagating pluripotency - The conundrum of self-renewal.

The discovery of mouse embryonic stem cells in 1981 transformed research in mammalian developmental biology and functional genomics. The subsequent generation of human pluripotent stem cells (PSCs) and the development of molecular reprogramming have opened unheralded avenues for drug discovery and cell replacement therapy. Here, I review the history of PSCs from the perspective that long-term self-renewal is a product of the in vitro signaling environment, rather than an intrinsic feature of embryos. I discuss the relationship between pluripotent states captured in vitro to stages of epiblast in the embryo and suggest key considerations for evaluation of PSCs. A remaining fundamental challenge is to determine whether naïve pluripotency can be propagated from the broad range of mammals by exploiting common principles in gene regulatory architecture.

Cross-species modeling of plant genomes at single nucleotide resolution using a pre-trained DNA language model.

Interpreting function and fitness effects in diverse plant genomes requires transferable models. Language models (LMs) pre-trained on large-scale biological sequences can learn evolutionary conservation and offer cross-species prediction better than supervised models through fine-tuning limited labeled data. We introduce PlantCaduceus, a plant DNA LM based on the Caduceus and Mamba architectures, pre-trained on a curated dataset of 16 Angiosperm genomes. Fine-tuning PlantCaduceus on limited labeled Arabidopsis data for four tasks, including predicting translation initiation/termination sites and splice donor and acceptor sites, demonstrated high transferability to 160 million year diverged maize, outperforming the best existing DNA LM by 1.45 to 7.23-fold. PlantCaduceus is competitive to state-of-the-art protein LMs in terms of deleterious mutation identification, and is threefold better than PhyloP. Additionally, PlantCaduceus successfully identifies well-known causal variants in both Arabidopsis and maize. Overall, PlantCaduceus is a versatile DNA LM that can accelerate plant genomics and crop breeding applications.

Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality.

Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) and genome-wide association studies (GWAS) approaches. Transcriptomics, proteomics, and metabolomics provide insights into underlying genetic mechanisms by identifying differentially expressed genes, proteins, and metabolic pathways linked to quality traits, complementing GWAS data. Leveraging these functional genomics techniques can optimize beef cattle breeding for enhanced quality traits to meet high-quality beef demand. This paper provides a comprehensive overview of the current state of applications of omics technologies in uncovering functional variants underlying beef quality complexities. By highlighting the latest findings from GWAS, GS, transcriptomics, proteomics, and metabolomics studies, this work seeks to serve as a valuable resource for fostering a deeper understanding of the complex relationships between genetics, gene expression, protein dynamics, and metabolic pathways in shaping beef quality.

Gradient Boosting Prediction of Overlapping Genes From Weighted Co-expression and Differential Gene Expression Analysis of Wnt Pathway: An Artificial Intelligence-Based Bioinformatics Study.

Introduction The Wnt (wingless-related integration site) signalling pathway is crucial for bone formation and remodelling, regulating the commitment of mesenchymal stem cells (MSCs) to the osteoblastic lineage. It triggers the transcriptional activation of Wnt target genes and promotes osteoblast proliferation and survival. Weighted co-expression network analysis (WGCNA) and differential gene expression analysis help researchers understand gene roles. Gradient boosting, a machine learning technique, enhances understanding of genetic and molecular mechanisms contributing to overlap genes, improving gene regulation and functional genomics. The aim is to predict overlapping genes in the Wnt signalling pathway. Methods Differential gene expression analysis was performed using the National Center for Biotechnology Information (NCBI) geo dataset-GSE251951, focusing on the effect of Wnt signaling on treatment. The WGCNA module was analyzed using the iDEP tool to identify interconnected gene clusters. Hub genes were identified by calculating module eigengenes, correlated with external traits, and ranked based on module membership values. The study utilized gradient boosting, an ensemble learning method, to predict models, evaluate their performance using metrics like accuracy, precision, recall, and F1 score, and adjust predictions based on gradient and learning rate. Results The dendrogram uses the "Dynamic TreeCut" algorithm to analyze gene clusters, aiding researchers in understanding gene modules and biological processes, identifying co-expressed genes, and discovering new pathways. The confusion matrix displays 88 actual and predicted cases. The gradient boosting model achieves 78.9% accuracy in predicting Wnt pathway overlapping genes, with a respectable area under the curve (AUC) and classification accuracy values. It accurately predicts 73.9% of samples, with a high precision ratio and low recall. Conclusion Future research should enhance differential expression analysis and WGCNA to identify key Wnt pathway genes, improve sensitivity, specificity, hyperparameter tuning, and validation experiments, and use larger datasets.

Insights into a functional synthetic plant genome.

Synthetic genomics involves the design, assembly, and transfer of artificially synthesized DNA fragments into target hosts to replace the native genome and construct viable forms of life. With advances in DNA synthesis and assembly techniques, the application of synthetic genomics in viruses, bacteria, and yeast has improved our knowledge of genome organization and function. Multicellular eukaryotic organisms are characterized by larger genomes, more complex epigenetic regulation, and widespread transposable elements, making genome synthesis challenging. Recently, the first synthetic multicellular eukaryotic organism was generated in the model plant Physcomitrium patens with a partially synthetic chromosome arm. Here, we introduce the design and assembly principles of moss genome synthesis. We also discuss the remaining technical barriers in the application of synthetic genomics in seed plants.

Creating and leveraging bespoke large-scale knowledge graphs for comparative genomics and multi-omics drug discovery with SocialGene.

The rapid expansion of multi-omics data has transformed biological research, offering unprecedented opportunities to explore complex genomic relationships across diverse organisms. However, the vast volume and heterogeneity of these datasets presents significant challenges for analyses. Here we introduce SocialGene, a comprehensive software suite designed to collect, analyze, and organize multi-omics data into structured knowledge graphs, with the ability to handle small projects to repository-scale analyses. Originally developed to enhance genome mining for natural product drug discovery, SocialGene has been effective across various applications, including functional genomics, evolutionary studies, and systems biology. SocialGene's concerted Python and Nextflow libraries streamline data ingestion, manipulation, aggregation, and analysis, culminating in a custom Neo4j database. The software not only facilitates the exploration of genomic synteny but also provides a foundational knowledge graph supporting the integration of additional diverse datasets and the development of advanced search engines and analyses. This manuscript introduces some of SocialGene's capabilities through brief case studies including targeted genome mining for drug discovery, accelerated searches for similar and distantly related biosynthetic gene clusters in biobank-available organisms, integration of chemical and analytical data, and more. SocialGene is free, open-source, MIT-licensed, designed for adaptability and extension, and available from github.com/socialgene.

New insights on β-glycan synthases using in vitro GT-array (i-GT-ray) platform

Cellulose and hemicellulose are the major structural β-glycan polysaccharides in cell walls of land plants. They are characterized by a backbone of β-(1,3)- and/or β-(1,4)-linked sugars such as glucose, mannose, or xylose. The backbones of these polymers are produced by processive glycosyltransferases (GTs) called synthases having multiple transmembrane domains anchoring them to the membrane. Thus, they are among the most difficult membrane proteins to test in vitro and to purify. Recently, we developed an in vitro GT-array (i-GTray) platform and showed that non-processive type II membrane GTs could be produced via cell-free system in a soluble and active form and tested in this platform. To determine whether i-GT-ray platform is adequate for the production and testing of β-glycan synthases, we tested five synthases involved in cellulose, xyloglucan, (gluco)mannan, and β-(1,3)(1,4)-mixed-linkage glucan synthesis. Our results revealed unsuspected features of these enzymes. For example, all these synthases could be produced in a soluble and active form and are active in the absence of detergent or membrane lipids, and none of them required a primer for initiation of synthesis. All synthases produced ethanol-insoluble products that were susceptible to the appropriate hydrolases (i.e., cellulase, lichenase, mannanase). Using this platform, we showed that AtCslC4 and AtXXT1 interact directly to form an active xyloglucan synthase that produced xylosylated cello-oligosaccharides (up to three xylosyl residues) when supplied with UDP-Glc and UDP-Xyl. i-GTray platform represents a simple and powerful functional genomics tool for discovery of new insights of synthase activities and can be adapted to other enzymes.

The genomic potential of photosynthesis in piconanoplankton is functionally redundant but taxonomically structured at a global scale.

Carbon fixation is a key metabolic function shaping marine life, but the underlying taxonomic and functional diversity involved is only partially understood. Using metagenomic resources targeted at marine piconanoplankton, we provide a reproducible machine learning framework to derive the potential biogeography of genomic functions through the multi-output regression of gene read counts on environmental climatologies. Leveraging the Marine Atlas of Tara Oceans Unigenes, we investigate the genomic potential of primary production in the global ocean. The latter is performed by ribulose-1,5-bisphosphate carboxylase/oxygenase (RUBISCO) and is often associated with carbon concentration mechanisms in piconanoplankton, major marine unicellular photosynthetic organisms. We show that the genomic potential supporting C4 enzymes and RUBISCO exhibits strong functional redundancy and important affinity toward tropical oligotrophic waters. This redundancy is taxonomically structured by the dominance of Mamiellophyceae and Prymnesiophyceae in mid and high latitudes. These findings enhance our understanding of the relationship between functional and taxonomic diversity of microorganisms and environmental drivers of key biogeochemical cycles.