Pfp-fm: an accelerated FM-index.

FM-indexes are crucial data structures in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [1] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. In 2022, Deng et al. [2] proposed parsing genomic data by induced suffix sorting, and showed that the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing-which takes parameters that let us tune the average length of the phrases-instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38, and is consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it seems our method accelerates the performance of count over all state-of-the-art methods with a moderate increase in the memory. The source code for PFP - FM is available at https://github.com/AaronHong1024/afm .

PFP-FM: An Accelerated FM-index.

FM-indexes are a crucial data structure in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [1] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. Last year, Deng et al. [2] proposed parsing genomic data by induced suffix sorting, and showed the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing-which takes parameters that let us tune the average length of the phrases-instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38, and is consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it seems our method accelerates the performance of count over all state-of-the-art methods with a minor increase in the memory. The source code for PFP-FM is available at https://github.com/marco-oliva/afm.

Recursive Prefix-Free Parsing for Building Big BWTs.

Prefix-free parsing is useful for a wide variety of purposes including building the BWT, constructing the suffix array, and supporting compressed suffix tree operations. This linear-time algorithm uses a rolling hash to break an input string into substrings, where the resulting set of unique substrings has the property that none of the substrings' suffixes (of more than a certain length) is a proper prefix of any of the other substrings' suffixes. Hence, the name prefix-free parsing. This set of unique substrings is referred to as the dictionary . The parse is the ordered list of dictionary strings that defines the input string. Prior empirical results demonstrated the size of the parse is more burdensome than the size of the dictionary for large, repetitive inputs. Hence, the question arises as to how the size of the parse can scale satisfactorily with the input. Here, we describe our algorithm, recursive prefix-free parsing , which accomplishes this by computing the prefix-free parse of the parse produced by prefix-free parsing an input string. Although conceptually simple, building the BWT from the parse-of-the-parse and the dictionaries is significantly more challenging. We solve and implement this problem. Our experimental results show that recursive prefix-free parsing is extremely effective in reducing the memory needed to build the run-length encoded BWT of the input. Our implementation is open source and available at https://github.com/marco-oliva/r-pfbwt .

Target-enriched long-read sequencing (TELSeq) contextualizes antimicrobial resistance genes in metagenomes.

BACKGROUND: Metagenomic data can be used to profile high-importance genes within microbiomes. However, current metagenomic workflows produce data that suffer from low sensitivity and an inability to accurately reconstruct partial or full genomes, particularly those in low abundance. These limitations preclude colocalization analysis, i.e., characterizing the genomic context of genes and functions within a metagenomic sample. Genomic context is especially crucial for functions associated with horizontal gene transfer (HGT) via mobile genetic elements (MGEs), for example antimicrobial resistance (AMR). To overcome this current limitation of metagenomics, we present a method for comprehensive and accurate reconstruction of antimicrobial resistance genes (ARGs) and MGEs from metagenomic DNA, termed target-enriched long-read sequencing (TELSeq). RESULTS: Using technical replicates of diverse sample types, we compared TELSeq performance to that of non-enriched PacBio and short-read Illumina sequencing. TELSeq achieved much higher ARG recovery (>1,000-fold) and sensitivity than the other methods across diverse metagenomes, revealing an extensive resistome profile comprising many low-abundance ARGs, including some with public health importance. Using the long reads generated by TELSeq, we identified numerous MGEs and cargo genes flanking the low-abundance ARGs, indicating that these ARGs could be transferred across bacterial taxa via HGT. CONCLUSIONS: TELSeq can provide a nuanced view of the genomic context of microbial resistomes and thus has wide-ranging applications in public, animal, and human health, as well as environmental surveillance and monitoring of AMR. Thus, this technique represents a fundamental advancement for microbiome research and application. Video abstract.

AMR-meta: a k-mer and metafeature approach to classify antimicrobial resistance from high-throughput short-read metagenomics data.

BACKGROUND: Antimicrobial resistance (AMR) is a global health concern. High-throughput metagenomic sequencing of microbial samples enables profiling of AMR genes through comparison with curated AMR databases. However, the performance of current methods is often hampered by database incompleteness and the presence of homology/homoplasy with other non-AMR genes in sequenced samples. RESULTS: We present AMR-meta, a database-free and alignment-free approach, based on k-mers, which combines algebraic matrix factorization into metafeatures with regularized regression. Metafeatures capture multi-level gene diversity across the main antibiotic classes. AMR-meta takes in reads from metagenomic shotgun sequencing and outputs predictions about whether those reads contribute to resistance against specific classes of antibiotics. In addition, AMR-meta uses an augmented training strategy that joins an AMR gene database with non-AMR genes (used as negative examples). We compare AMR-meta with AMRPlusPlus, DeepARG, and Meta-MARC, further testing their ensemble via a voting system. In cross-validation, AMR-meta has a median f-score of 0.7 (interquartile range, 0.2-0.9). On semi-synthetic metagenomic data-external test-on average AMR-meta yields a 1.3-fold hit rate increase over existing methods. In terms of run-time, AMR-meta is 3 times faster than DeepARG, 30 times faster than Meta-MARC, and as fast as AMRPlusPlus. Finally, we note that differences in AMR ontologies and observed variance of all tools in classification outputs call for further development on standardization of benchmarking data and protocols. CONCLUSIONS: AMR-meta is a fast, accurate classifier that exploits non-AMR negative sets to improve sensitivity and specificity. The differences in AMR ontologies and the high variance of all tools in classification outputs call for the deployment of standard benchmarking data and protocols, to fairly compare AMR prediction tools.

MONI: A Pangenomic Index for Finding Maximal Exact Matches.

Recently, Gagie et al. proposed a version of the FM-index, called the r-index, that can store thousands of human genomes on a commodity computer. Then Kuhnle et al. showed how to build the r-index efficiently via a technique called prefix-free parsing (PFP) and demonstrated its effectiveness for exact pattern matching. Exact pattern matching can be leveraged to support approximate pattern matching, but the r-index itself cannot support efficiently popular and important queries such as finding maximal exact matches (MEMs). To address this shortcoming, Bannai et al. introduced the concept of thresholds, and showed that storing them together with the r-index enables efficient MEM finding-but they did not say how to find those thresholds. We present a novel algorithm that applies PFP to build the r-index and find the thresholds simultaneously and in linear time and space with respect to the size of the prefix-free parse. Our implementation called MONI can rapidly find MEMs between reads and large-sequence collections of highly repetitive sequences. Compared with other read aligners-PuffAligner, Bowtie2, BWA-MEM, and CHIC- MONI used 2-11 times less memory and was 2-32 times faster for index construction. Moreover, MONI was less than one thousandth the size of competing indexes for large collections of human chromosomes. Thus, MONI represents a major advance in our ability to perform MEM finding against very large collections of related references.

Finding Maximal Exact Matches Using the r-Index.

Efficiently finding maximal exact matches (MEMs) between a sequence read and a database of genomes is a key first step in read alignment. But until recently, it was unknown how to build a data structure in [Formula: see text] space that supports efficient MEM finding, where r is the number of runs in the Burrows-Wheeler Transform. In 2021, Rossi et al. showed how to build a small auxiliary data structure called thresholds in addition to the r-index in [Formula: see text] space. This addition enables efficient MEM finding using the r-index. In this article, we present the tool that implements this solution, which we call MONI. Namely, we give a high-level view of the main components of the data structure and show how the source code can be downloaded, compiled, and used to find MEMs between a set of sequence reads and a set of genomes.

CSTs for Terabyte-Sized Data.

Generating pangenomic datasets is becoming increasingly common but there are still few tools able to handle them and even fewer accessible to non-specialists. Building compressed suffix trees (CSTs) for pangenomic datasets is still a major challenge but could be enormously beneficial to the community. In this paper, we present a method, which we refer to as RePFP-CST, for building CSTs in a manner that is scalable. To accomplish this, we show how to build a CST directly from VCF files without decompressing them, and to prune from the prefix-free parse (PFP) phrase boundaries whose removal reduces the total size of the dictionary and the parse. We show that these improvements reduce the time and space required for the construction of the CST, and the memory footprint of the finished CST, enabling us to build a CST for a terabyte of DNA for the first time in the literature.

Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation.

Antimicrobial resistance (AMR) is a significant and growing public health threat. Sequencing of bacterial isolates is becoming more common, and therefore automatic identification of resistant bacterial strains is of pivotal importance for efficient, wide-spread AMR detection. To support this approach, several AMR databases and gene identification algorithms have been recently developed. A key problem in AMR detection, however, is the need for computational approaches detecting potential novel AMR genes or variants, which are not included in the reference databases. Toward this direction, here we study the relation between AMR and relative solvent accessibility (RSA) of protein variants from an in silico perspective. We show how known AMR protein variants tend to correspond to exposed residues, while on the contrary their susceptible counterparts tend to be buried. Based on these findings, we develop RSA-AMR, a novel relative solvent accessibility-based AMR scoring system. This scoring system can be applied to any protein variant to estimate its propensity of altering the relative solvent accessibility, and potentially conferring (or hindering) AMR. We show how RSA-AMR score can be integrated with existing AMR detection algorithms to expand their range of applicability into detecting potential novel AMR variants, and provide a ten-fold increase in Specificity. The two main limitations of RSA-AMR score is that it is designed on single point changes, and a limited number of variants was available for model learning.

Physiological Basis of Salt Stress Tolerance in a Landrace and a Commercial Variety of Sweet Pepper (Capsicum annuum L.).

Salt stress is one of the most impactful abiotic stresses that plants must cope with. Plants' ability to tolerate salt stress relies on multiple mechanisms, which are associated with biomass and yield reductions. Sweet pepper is a salt-sensitive crop that in Mediterranean regions can be exposed to salt build-up in the root zone due to irrigation. Understanding the physiological mechanisms that plants activate to adapt to soil salinization is essential to develop breeding programs and agricultural practices that counteract this phenomenon and ultimately minimize yield reductions. With this aim, the physiological and productive performances of Quadrato D'Asti, a common commercial sweet pepper cultivar in Italy, and Cazzone Giallo, a landrace of the Campania region (Italy), were compared under different salt stress treatments. Quadrato D'Asti had higher tolerance to salt stress when compared to Cazzone Giallo in terms of yield, which was associated with higher leaf biomass vs. fruit ratio in the former. Ion accumulation and profiling between the two genoptypes revealed that Quadrato D'Asti was more efficient at excluding chloride from green tissues, allowing the maintenance of photosystem functionality under stress. In contrast, Cazzone Giallo seemed to compartmentalize most sodium in the stem. While sodium accumulation in the stems has been shown to protect shoots from sodium toxicity, in pepper and/or in the specific experimental conditions imposed, this strategy was less efficient than chloride exclusion for salt stress tolerance.

Portable nanopore analytics: are we there yet?

MOTIVATION: Oxford Nanopore technologies (ONT) add miniaturization and real time to high-throughput sequencing. All available software for ONT data analytics run on cloud/clusters or personal computers. Instead, a linchpin to true portability is software that works on mobile devices of internet connections. Smartphones' and tablets' chipset/memory/operating systems differ from desktop computers, but software can be recompiled. We sought to understand how portable current ONT analysis methods are. RESULTS: Several tools, from base-calling to genome assembly, were ported and benchmarked on an Android smartphone. Out of 23 programs, 11 succeeded. Recompilation failures included lack of standard headers and unsupported instruction sets. Only DSK, BCALM2 and Kraken were able to process files up to 16 GB, with linearly scaling CPU-times. However, peak CPU temperatures were high. In conclusion, the portability scenario is not favorable. Given the fast market growth, attention of developers to ARM chipsets and Android/iOS is warranted, as well as initiatives to implement mobile-specific libraries. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at: https://github.com/marco-oliva/portable-nanopore-analytics.

Unexpected Predictors of Antibiotic Resistance in Housekeeping Genes of Staphylococcus Aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) is currently the most commonly identified antibiotic-resistant pathogen in US hospitals. Resistance to methicillin is carried by SCCmec genetic elements. Multilocus sequence typing (MLST) covers internal fragments of seven housekeeping genes of S. aureus. In conjunction with mec typing, MLST has been used to create an international nomenclature for S. aureus. MLST sequence types with a single nucleotide polymorphism (SNP) considered distinct. In this work, relationships among MLST SNPs and methicillin/oxacillin resistance or susceptibility were studied, using a public data base, by means of cross-tabulation tests, multivariable (phylogenetic) logistic regression (LR), decision trees, rule bases, and random forests (RF). Model performances were assessed through multiple cross-validation. Hierarchical clustering of SNPs was also employed to analyze mutational covariation. The number of instances with a known methicillin (oxacillin) antibiogram result was 1526 (649), where 63% (54%) was resistant to methicillin (oxacillin). In univariable analysis, several MLST SNPs were found strongly associated with antibiotic resistance/susceptibility. A RF model predicted correctly the resistance/susceptibility to methicillin and oxacillin in 75% and 63% of cases (cross-validated). Results were similar for LR. Hierarchical clustering of the aforementioned SNPs yielded a high level of covariation both within the same and different genes; this suggests strong genetic linkage between SNPs of housekeeping genes and antibiotic resistant associated genes. This finding provides a basis for rapid identification of antibiotic resistant S. arues lineages using a small number of genomic markers. The number of sites could subsequently be increased moderately to increase the sensitivity and specificity of genotypic tests for resistance that do not rely on the direct detection of the resistance marker itself.

Clusia hilariana and Eugenia uniflora as bioindicators of atmospheric pollutants emitted by an iron pelletizing factory in Brazil.

The objectives of this work were to evaluate if the pollution emitted by the pelletizing factory causes visual symptoms and/or anatomical changes in exposed Eugenia uniflora and Clusia hilariana, in active biomonitoring, at different distances from a pelletizing factory. We characterize the symptomatology, anatomical, and histochemistry alterations induced in the two species. There was no difference in the symptomatology in relation to the different distances of the emitting source. The foliar symptoms found in C. hilariana were chlorosis, necrosis, and foliar abscission and, in E. uniflora, were observed necrosis punctuais, purple spots in the leaves, and increase in the emission of new leaves completely purplish. The two species presented formation of a cicatrization tissue. E. uniflora presented reduction in the thickness of leaf. In C. hilariana, it was visualized hyperplasia of the cells and the adaxial epidermis did not appear collapsed due to thick cuticle and cuticular flanges. Leaves of C. hilariana showed positive staining for iron, protein, starch, and phenolic compounds. E. uniflora showed positive staining for total phenolic compounds and starch. Micromorphologically, there was accumulation of particulate matter on the leaf surface, obstruction of the stomata, and scaling of the epicuticular wax in both species. It was concluded that the visual and anatomical symptoms were efficient in the diagnosis of the stress factor. C. hilariana and E. uniflora showed to be good bioindicators of the atmospheric pollutants emitted by the pelletizing factory.

Ecophysiological responses to excess iron in lowland and upland rice cultivars.

Iron (Fe) is an essential nutrient for plants but under high concentrations, such as that found naturally in clay and waterlogged soils, its toxic effect can limit production. This study aimed to investigate the stress tolerance responses exhibited by different rice cultivars. Both lowland and upland cultivars were grown under excess Fe and hypoxic conditions. Lowland cultivars showed higher Fe accumulation in roots compared with upland cultivars suggesting the use of different strategies to tolerate excess Fe. The upland Canastra cultivar displayed a mechanism to limit iron translocation from roots to the shoots, minimizing leaf oxidative stress induced by excess Fe. Conversely, the cultivar Curinga invested in the increase of R1/A, as an alternative drain of electrons. However, the higher iron accumulation in the leaves, was not necessarily related to high toxicity. Nutrient uptake and/or utilization mechanisms in rice plants are in accordance with their needs, which may be defined in relation to crop environments. Alterations in the biochemical parameters of photosynthesis suggest that photosynthesis in rice under excess Fe is primarily limited by biochemical processes rather than by diffusional limitations, particularly in the upland cultivars. The electron transport rate, carboxylation efficiency and electron excess dissipation by photorespiration demonstrate to be good indicators of iron tolerance. Altogether, these chemical and molecular patterns suggests that rice plants grown under excess Fe exhibit gene expression reprogramming in response to the Fe excess per se and in response to changes in photosynthesis and nutrient levels to maintain growth under stress.

Differential responses of C3 and CAM native Brazilian plant species to a SO2- and SPMFe-contaminated Restinga.

Aiming to evaluate responses in terms of growth rates, physiological parameters, and degree of sensitivity to SO2 and SPMFe in Eugenia uniflora L. (Myrtaceae, a C3 species) and Clusia hilariana Schlecht (Clusiaceae, a CAM species); saplings were exposed to emissions from a pelletizing factory for 7 months. The species were distributed along a transect (200, 500, 800, 1400, and 1700 m away from the emission source), and analyses were performed after 71, 118, and 211 days of exposure to the pollutants. E. uniflora received higher superficial deposition of particulate iron. The highest total iron foliar contents were observed 200 m away from the emission source in both plant species, while the highest total sulfur foliar contents were observed 200 m away in C. hilariana and 800 m away in E. uniflora. E. uniflora presented decreased values of height growth rate, number of necrotic leaves, chlorophyll analysis (SPAD index) and transpiration, in relation to the distances from the emission source. C. hilariana showed decreased values of height growth rate, number of leaves, number of necrotic leaves, total ionic permeability, stomatal conductance, transpiration, net CO2 assimilation, and total dry matter, in relation to distances from the emission source. In relation to the days of exposure, both species presented increased number of necrotic leaves and foliar phytotoxicity index, and decreased values in the chlorophyll analysis. The two native plant species, both of which occur in the Brazilian Restinga, showed damage when exposed to emissions from an iron ore pelletizing factory. C. hilariana was considered the most sensitive species due to the decreased values in a higher number of variables after exposition.

Iron excess affects rice photosynthesis through stomatal and non-stomatal limitations.

Iron toxicity is the most important stressor of rice in many lowland environments worldwide. Rice cultivars differ widely in their ability to tolerate excess iron. A physiological evaluation of iron toxicity in rice was performed using non-invasive photosynthesis, photorespiration and chlorophyll a fluorescence imaging measurements and chlorophyll content determination by SPAD. Four rice cultivars (BR IRGA 409; BR IRGA 412; BRA 041171 and BRA 041152) from the Brazilian breeding programs were used. Fe(2+) was supplied in the nutrient solution as Fe-EDTA (0.019, 4, 7 and 9 mM). Increases in shoot iron content due to Fe(2+) treatments led to changes in most of the non-invasive physiological variables assessed. The reduction in rice photosynthesis can be attributed to stomatal limitations at moderate Fe(2+) doses (4mM) and both stomatal and non-stomatal limitations at higher doses. Photorespiration was an important sink for electrons in rice cultivars under iron excess. A decreased chlorophyll content and limited photochemical ability to cope with light excess were characteristic of the more sensitive and iron accumulator cultivars (BRA 041171 and BRA 041152). Chlorophyll fluorescence imaging revealed a spatial heterogeneity of photosynthesis under excessive iron concentrations. The results showed the usefulness of non-invasive physiological measurements to assess differences among cultivars. The contributions toward understanding the rice photosynthetic response to toxic levels of iron in the nutrient solution are also discussed.

Diagnostic and prognostic characteristics of phytotoxicity caused by fluoride on Spondias dulcis Forst. F. (Anacardiaceae).

The goal of this study was to determine the symptoms and microscopic damage caused by fluoride on Spondias dulcis, a fluoride-sensitive species. The plants were exposed to simulated fog with fluoride (0, 5, 10, 15 and 20 mg L(-1)) for 20 min daily during four consecutive days. Samples from leaflets without any apparent fluoride injury were collected to microscopic analysis. The percentage of necrosed leaf area was measured, and the level of pollutant in the dry matter from the basal and apical portions of the plant was determined. The necroses began 24 h after the first simulation mainly from the base of the leaflets. A higher level of necrosis was observed at the apical portion of the plants, a region of higher fluoride accumulation. The damage on the surface of the leaflets was characterized as plasmolysis, erosion of the epicuticular waxes and epidermal rupture. Structurally, the noticeable accumulation of granules and droplets green stained by toluidine blue in the spongy parenchima and the boundaries of ending veinlets was observed. The limb thickness reduction occurred due to plasmolysis in the mesophyll, showing an apparent correlation with the damage observed on the surface. The parameters observed in the laboratory are promising for field biomonitoring studies.

Iron plaque formation and morphoanatomy of roots from species of restinga subjected to excess iron.

The restingas, a sandy coastal plain ecosystem of Brazil, have received an additional amount of iron due to the activity of mining industries. The present study aims to characterize morphoanatomically and histochemically the iron plaque formation on roots of Ipomoea pes-caprae L. and Canavalia rosea DC, cultivated in hydroponic solution with and without excess iron. The iron plaque formation as well as changes in the external morphology of the lateral roots of both species were observed after the subjection to excess iron. Changes in the nutrient uptake, and in the organization and form of the pericycle and cortex cells were observed for both species. Scanning electron microscopy showed evident iron plaques on the whole surface of the root. The iron was histolocalized in all root tissues of both species. The species of restinga studied here formed iron plaque in their roots when exposed to excess of this element, which may compromise their development in environments polluted by particulated iron.

Photosynthesis and oxidative stress in the restinga plant species Eugenia uniflora L. exposed to simulated acid rain and iron ore dust deposition: potential use in environmental risk assessment.

The Brazilian sandy coastal plain named restinga is frequently subjected to particulate and gaseous emissions from iron ore factories. These gases may come into contact with atmospheric moisture and produce acid rain. The effects of the acid rain on vegetation, combined with iron excess in the soil, can lead to the disappearance of sensitive species and decrease restinga biodiversity. The effects of iron ore dust deposition and simulated acid rain on photosynthesis and on antioxidant enzymes were investigated in Eugenia uniflora, a representative shrub species of the restinga. This study aimed to determine the possible utility of this species in environmental risk assessment. After the application of iron ore dust as iron solid particulate matter (SPM(Fe)) and simulated acid rain (pH 3.1), the 18-month old plants displayed brown spots and necrosis, typical symptoms of iron toxicity and injuries caused by acid rain, respectively. The acidity of the rain intensified leaf iron accumulation, which reached phytotoxic levels, mainly in plants exposed to iron ore dust. These plants showed the lowest values for net photosynthesis, stomatal conductance, transpiration, chlorophyll a content and electron transport rate through photosystem II (PSII). Catalase and superoxide dismutase activities were decreased by simulated acid rain. Peroxidase activity and membrane injury increased following exposure to acid rain and simultaneous SPM(Fe) application. Eugenia uniflora exhibited impaired photosynthetic and antioxidative metabolism in response to combined iron and acid rain stresses. This species could become a valuable tool in environmental risk assessment in restinga areas near iron ore pelletizing factories. Non-invasive evaluations of visual injuries, photosynthesis and chlorophyll a fluorescence, as well as invasive biochemical analysis could be used as markers.

Trioecy in Coccoloba cereifera Schwacke (Polygonaceae), a narrow endemic and threatened tropical species

Trioecy, the co-occurrence of the males, females, and hermaphrodite morphs in natural populations, is a rare and poorly studied breeding system. It is expressed in Coccoloba cereifera, an endemic, and endangered species from the rupestrian fields of Serra do Cipó, southeastern Brazil. Male individuals produce staminate flowers but no fruits. Female individuals produce pistillate flowers and set fruits. Both staminate and pistillate flowers present non-functional organs of the opposite sex that simulate perfect flowers. Hermaphrodite individuals produce two different perfect flowers, each one belonging to distinct individuals, and set fruits. Perfect flowers differ in the amount of pollen produced, in pollen viability, and in some morphological traits. Two Hymenoptera species visited the flowers sporadically. The low natural fructification indicated a limited pollination, while the fruit set recorded in bagged pistillate flowers indicated agamospermy. Female individuals represent more than 40 percent of the population studied and must be the result of agamospermic seeds.

A trioicia, a coocorrência, em população natural, de indivíduos masculinos, femininos e hermafroditas, é um sistema reprodutivo raro e pouco estudado. Coccoloba cereifera (Polygonaceae), espécie endêmica e em perigo de extinção dos campos rupestres da Serra do Cipó, sudeste brasileiro, apresenta esse sistema. Indivíduos masculinos produzem flores estaminadas e não frutificam. Indivíduos femininos produzem flores pistiladas e frutificam. Ambas flores estaminadas e pistiladas apresentam órgãos não-funcionais do sexo oposto, tornando-as semelhantes às flores perfeitas. Indivíduos hermafroditas produzem dois tipos de flores perfeitas, cada um pertencente a indivíduos distintos, e ambos frutificam. As flores perfeitas diferem entre si na quantidade de pólen produzido, na viabilidade do pólen e em algumas características morfológicas. Duas espécies de Hymenoptera visitaram as flores esporadicamente. A baixa frutificação natural indicou uma polinização limitada, ao passo que a frutificação registrada em flores pistiladas, isoladas por sacos, indicou agamospermia. Indivíduos femininos representaram mais que 40 por cento da população estudada e devem ser resultantes de sementes agamospérmicas.