The research team, led by Max Kolton of Ben-Gurion University and Florida A&M University together with colleagues at the Savannah River Ecology Laboratory, compared floodplain soils with a long history of coal-ash contamination to nearby reference soils that remained relatively pristine. Coal ash, the residue left after coal is burned for power, carries a cocktail of metals including arsenic, chromium, and other elements that are toxic to living cells even at moderate concentrations. Because floodplains sit at the intersection of rivers and uplands, they are natural collection points for such industrial residues, and they are also among the most biologically active soils on the landscape, making them an ideal natural laboratory for asking what chronic metal stress does to the invisible life below ground.

To capture the full picture, the researchers sampled across five seasonal periods, an unusually thorough design for this kind of work. Microbial communities in soil are not static; they shift with temperature, moisture, and plant activity through the year. By sampling repeatedly across seasons, the team could distinguish a consistent contamination signal from the background noise of natural seasonal variation. The strongest divergence between contaminated and reference soils appeared during late-summer peak conditions, when heat and drought likely amplified the physiological stress that metals impose on microbial cells, pushing the two soil types furthest apart in composition and function.

The core of the study rested on three complementary molecular techniques. First, the team sequenced the 16S rRNA gene, a standard marker used to identify which bacteria and archaea are present in a sample and how diverse the community is. Second, they used a tool called PICRUSt2, which predicts the functional capabilities of a community from its taxonomic profile, offering a computational glimpse into what the microbes might be doing. Third, and most decisively, they turned to digital PCR, a highly sensitive technique that counts individual DNA molecules, to precisely quantify the abundance of 16S rRNA genes and a suite of nitrogen-cycling marker genes, including the ammonia monooxygenase genes carried by both bacteria and archaea, nitrite reductase genes, and the nrfA gene associated with dissimilatory nitrate reduction to ammonium.

The diversity results told a clear story. Metal contamination reduced both taxonomic and phylogenetic diversity, meaning that contaminated soils hosted fewer kinds of microbes drawn from a narrower slice of the evolutionary tree. Yet, crucially, the total abundance of prokaryotic DNA, measured by counting 16S rRNA gene copies, did not consistently decline. This is a subtle but important distinction. The contaminated soils were not sterile wastelands; rather, they had undergone a restructuring, with the microbial biomass largely maintained but redistributed among a smaller cast of survivors. Pollution, in other words, pruned the tree of soil life rather than cutting it down.

The pruning followed a predictable ecological logic. Contaminated soils were enriched in generalist taxa, microbes with broad environmental tolerances that can cope with a wide range of conditions, and depleted in specialists, organisms finely adapted to particular niches but vulnerable to disturbance. Ecologists call this pattern biotic homogenization: as environmental filters like metal toxicity eliminate the sensitive and the specialized, the remaining communities across contaminated sites come to resemble one another, dominated by the same resilient cosmopolitan players. The loss of specialists matters beyond simple headcounts, because specialist microbes often perform narrow but vital functions, such as breaking down specific organic compounds or mediating particular steps in nutrient transformations, that generalists may not fully replace.

The most surprising findings emerged from the nitrogen-cycling analysis. PICRUSt2’s functional predictions suggested that nitrification, the process by which microbes convert ammonia into nitrate, should be reduced in contaminated soils. But when the researchers actually counted the relevant genes with digital PCR, they found the opposite: the abundance of ammonia-oxidation genes was increased, driven primarily by archaeal ammonia oxidizers rather than their bacterial counterparts. This mismatch between prediction and measurement is itself a lesson in method. Gene-based functional prediction tools are calibrated largely on well-studied bacteria and can miss the idiosyncrasies of archaea, which are known to dominate ammonia oxidation in many soil environments. Direct gene quantification revealed a reality that the predictive model had inverted.

Why would archaeal ammonia oxidizers thrive under metal stress? Archaea are ancient, often extremophile lineages, and their ammonia-oxidizing members are famously tolerant of harsh conditions, including low pH and, apparently, elevated metal concentrations. As metal-sensitive competitors and grazers were filtered out, the archaeal oxidizers may have faced reduced competition for ammonia, allowing their populations to expand. Whatever the precise mechanism, the consequence is a shift in the architecture of the nitrogen cycle itself, with ammonia oxidation gaining ground in contaminated floodplains relative to uncontaminated ones.

The gene ratio analyses added a second layer of reorganization. Relative to the nrfA gene, which marks the dissimilatory nitrate and nitrite reduction to ammonium pathway, or DNRA, the contaminated soils showed an increased proportion of genes for ammonia oxidation and for denitrifying nitrite reduction. This matters because the two pathways have opposite consequences for the ecosystem. DNRA conserves nitrogen within the soil by converting nitrate back into ammonium, a form that plants and microbes can retain. Denitrification, by contrast, converts nitrate into gaseous forms, including nitrous oxide, a potent greenhouse gas, which escape to the atmosphere. A community shifted away from DNRA and toward denitrification is, in effect, a community that leaks nitrogen rather than holding it, with potential downstream consequences for fertility and emissions.

The authors caution that these are measurements of genetic potential rather than direct observations of nitrogen fluxes, and that linking gene abundances to actual process rates will require further work. Even so, the study carries a sober implication for the many floodplains worldwide that bear the legacy of industrial metal pollution, from former mining districts to power plant ash basins. Remediation efforts typically focus on the chemistry of the contamination itself, measuring metal concentrations and immobilizing them in place. This research shows that the biological legacy runs deeper and differently: even where microbial life persists in abundance, the identity of the organisms and the genetic toolkit they carry have been permanently reorganized. Restoring a contaminated floodplain, the findings suggest, may mean more than detoxifying the soil. It may mean waiting for, or actively assisting, the slow return of the specialist microbes that keep nitrogen locked in the landscape, a recovery measured not in years but potentially in decades, and one that begins with recognizing that a soil can look alive while functioning in an entirely altered way.

Subject of Research: Effects of legacy coal ash metal contamination on floodplain soil microbial communities and nitrogen-cycling gene abundance

Article Title: Legacy Metal Contamination Alters Floodplain Soil Microbiomes and Reorganizes Nitrogen-Cycling Genetic Potential

Article References: Kolton, M., Chukwujindu, C., Oo, W. Y. M., Pathak, A., Fincher, K., Xu, X., & Chauhan, A. (2026). Legacy Metal Contamination Alters Floodplain Soil Microbiomes and Reorganizes Nitrogen-Cycling Genetic Potential. Microbial Ecology. https://doi.org/10.1007/s00248-026-02865-5

Image Credits: AI Generated

DOI: 10.1007/s00248-026-02865-5

Keywords: coal ash, metal contamination, floodplain soils, soil microbiome, nitrogen cycle, archaeal ammonia oxidation, denitrification, DNRA, digital PCR, PICRUSt2, biotic homogenization, Savannah River Site

A new laboratory study, published in MicrobiologyOpen, suggests that a solution may lie in one of the most abundant molecules inside every living cell: ribosomal RNA. Previous work on prokaryotes demonstrated that when viruses lyse bacterial cells, substantial amounts of rRNA are released into the extracellular medium, where it can be detected and quantified. Building on that observation, researchers developed an approach called Mortality by Ribosomal Sequencing, or MoRS, which compares ribosomal RNA sequences in cellular and dissolved fractions of seawater to estimate taxon-specific cell lysis. What remained untested was whether the concept could be extended to eukaryotic microalgae, whose complex internal organization and diverse viral enemies make such an extrapolation far from trivial.

To test the idea, a research team led by investigators working with marine algal virus systems conducted controlled infection experiments with two contrasting host-virus pairs. The first was the diatom Chaetoceros tenuissimus, a small coastal phytoplankton species less than ten micrometers across that is susceptible to Chaetoceros tenuissimus RNA virus type II, a small single-stranded RNA virus with particles roughly 22 to 38 nanometers in diameter. The second was the raphidophyte Heterosigma akashiwo, a globally distributed, flagellated species notorious for forming harmful algal blooms, which was challenged with HaV strain 120, a large double-stranded DNA virus with strain-specific infectivity. Together, these systems span different host physiologies and viral genome types, providing a rigorous framework for testing whether cell-free rRNA behaves as a general lysis marker in eukaryotes.

The experimental design was carefully controlled. Host cultures were grown at 22 degrees Celsius under a 12-hour light and 12-hour dark cycle in sterile artificial seawater media and acclimated for multiple generations before infection. For each host-virus system, triplicate flasks received virus at a multiplicity of infection of ten, while triplicate control flasks received none. The cultures were incubated for six days and sampled daily. To track viral proliferation, the team used the most probable number method to determine infectious virus titers at the beginning and end of the incubation. Critically, they also added purified Escherichia coli ribosomes to every flask as spike-in controls, at a concentration of approximately 2.9 times ten to the seventh copies per milliliter, allowing them to measure how quickly free rRNA degrades in the medium.

Separating the cellular from the dissolved fraction was achieved by gentle filtration through 0.22-micrometer polycarbonate filters under low vacuum. RNA retained on the filter was operationally defined as the cellular fraction, while RNA passing through was defined as cell-free. Cellular RNA was extracted with a silica-column kit enhanced by bead-beating, whereas cell-free RNA was concentrated from twenty-milliliter filtrates using a column-based vacuum system optimized for large water volumes. After genomic DNA removal and complementary DNA synthesis, absolute quantification was performed with a digital PCR system, using newly designed primers targeting the V4 region of the algal 18S rRNA gene and primers specific to the major capsid protein genes of each virus. Extraction efficiencies of 21.9 percent for the cellular fraction and 83.1 percent for the cell-free fraction were used to correct the measured concentrations.

The growth dynamics of the two hosts diverged sharply. In the Chaetoceros experiment, cells in the virus-added treatment grew at rates comparable to controls until day three but then grew significantly more slowly, while cell density, chlorophyll a, and cellular rRNA all continued to rise until day three or four. In striking contrast, Heterosigma cultures declined immediately after viral addition, with most cells losing motility within 24 hours, echoing earlier reports on this host-virus system. Infectious virus particles increased dramatically in both systems, from 5.8 times ten to the fifth to 1.9 times ten to the eighth most probable number units per milliliter for the diatom RNA virus, and from 7.6 times ten to the fifth to 1.4 times ten to the seventh for the Heterosigma DNA virus, confirming active viral replication.

The central result concerned the redistribution of rRNA between particulate and dissolved pools. In the Chaetoceros experiment, cell-free 18S rRNA remained low in controls, below about 4 times ten to the eighth copies per milliliter, but rose sharply in infected flasks to a maximum of 3.7 times ten to the ninth copies per milliliter on day four. Per-cell rRNA content varied up to 2.8-fold within each condition but showed no significant treatment effect, and cellular rRNA correlated strongly with cell density in both species, with Pearson correlation coefficients of 0.72 and 0.94. These findings established that rRNA abundance tracks plankton biomass at the population scale, while its release into the dissolved phase signals cell rupture.

To convert rRNA dynamics into lysis rates, the team built a rate-based model describing daily changes in cell-free rRNA concentration. The model rests on first-order degradation kinetics, an assumption validated by the spike-in ribosomes, which decayed exponentially with rate constants of roughly 1.78 to 2.16 per day in the Chaetoceros experiment, corresponding to half-lives of 7.7 to 9.3 hours. By rearranging a simple mass-balance equation, the researchers solved for the cell-free rRNA production rate, a direct proxy for cell lysis. Normalizing this production rate by host cell abundance yielded a metric they call cell-free rRNA production per cell. In the Chaetoceros system, this metric was 46.1-fold higher in infected treatments than controls between days two and three, and in the Heterosigma system it reached 302.1-fold higher between days three and four, with maximum per-cell release rates up to 46.1- and 302.1-fold above controls across the experiments.

The two host-virus systems also revealed fundamentally different infection strategies. In Chaetoceros, viral marker genes accumulated progressively in the cellular fraction over days one to four, and lysis peaked between days two and three even as the population was still growing, demonstrating that active viral lysis can occur during the apparent growth phase of a bloom. In Heterosigma, elevated per-cell rRNA release appeared both early, during days zero to one, and late, during days three to five. The early mortality could not be explained by canonical viral lysis, since viral capsid gene expression peaked only on day two. The authors suggest several possible mechanisms, including abortive infection, membrane destabilization during viral entry, a phenomenon analogous to lysis from without described in bacteriophage systems, or damage caused by bacteria introduced with the nonaxenic viral lysate, which flow cytometry confirmed had proliferated to high densities in infected treatments.

The implications reach well beyond the laboratory. Unlike dilution assays, which are labor-intensive and lack taxonomic resolution, cell-free rRNA quantification combined with high-throughput sequencing could simultaneously assess lysis across diverse taxa in natural communities, offering a taxon-resolved measure of lytic mortality that plankton ecologists have long sought. Because viral lysis is estimated to contribute up to roughly 45 percent of the labile dissolved organic carbon pool in marine systems, better quantification of who is being lysed, and when, could sharpen models of carbon cycling and microbial loop dynamics. The authors caution that the approach has limits: ribosome content per cell varies several-fold, infected bacteria can actively reduce their ribosome pools, degradation constants differ among seawater samples and must be measured for each one, and nonviral processes such as sloppy feeding, parasitism, and algicidal bacteria can also release intracellular rRNA. They also note that the degradation kinetics of E. coli 16S rRNA may not perfectly match those of eukaryotic 18S rRNA, calling for spike-in standards derived from representative plankton. Even with these caveats, the study provides a mechanistic foundation for reading cell death directly from the RNA dissolved in seawater, turning a ubiquitous molecule into a quantitative witness of the viral shunt at work.

Subject of Research: Quantifying viral lysis of eukaryotic microalgae using cell-free ribosomal RNA as a molecular marker

Article Title: Quantifying Viral Lysis in Microalgae Using Cell‐Free rRNA

Article References: Kikuya, S., Tomaru, Y., Nagasaki, K., Morimoto, D., Yamagishi, Y., Ogata, H., & Endo, H. (2026). Quantifying Viral Lysis in Microalgae Using Cell‐Free rRNA. MicrobiologyOpen, 15(5), Article e70402. https://doi.org/10.1002/mbo3.70402

Image Credits: AI Generated

DOI: 10.1002/mbo3.70402

Keywords: viral lysis, microalgae, ribosomal RNA, phytoplankton, viral shunt, marine viruses, Chaetoceros tenuissimus, Heterosigma akashiwo, MoRS, digital PCR, plankton mortality, biogeochemistry

The research team, led by Vanessa DeClercq and Morgan G. I. Langille of Dalhousie University’s Faculty of Medicine, designed their study specifically to confront one of the most persistent controversies in microbiome science: whether bacteria genuinely reside within tumours and circulate in the bloodstream of cancer patients, or whether the DNA sequences detected in such samples are artefacts of laboratory contamination and the extraordinary sensitivity of modern sequencing technology. To do this, they applied an unusually comprehensive battery of molecular techniques to every sample, including full-length 16S rRNA gene amplicon sequencing, nested short-read 16S amplicon sequencing, metagenomic shotgun sequencing, and digital PCR for absolute quantification of bacterial DNA. Crucially, every sample was processed in duplicate, providing technical replicates against which the reliability of each measurement could be judged.

The results were striking in their asymmetry. When the researchers attempted full-length 16S gene sequencing on their lung tumour, adjacent lung tissue, and blood samples, the technique failed to generate usable data for nearly all of these specimens. This was not a subtle signal-to-noise problem but a fundamental inability of the method to amplify meaningful bacterial genetic material from samples that contained, in effect, almost nothing to amplify. In contrast, saliva samples and positive control specimens, which harbour rich and genuine microbial communities, yielded abundant, high-quality sequence data with strong reproducibility between replicates, confirming that the laboratory pipeline itself was functioning correctly.

A second strategy, nested short-read 16S sequencing, did manage to produce sequence data from the low biomass samples, but what it produced was far from reassuring. The tumour, adjacent tissue, and blood specimens contained very few bacterial taxa, and even those few varied dramatically between technical replicates of the same sample. When the researchers compared the taxonomic composition of these samples with their negative controls, which track DNA introduced during sample collection and laboratory processing, the low biomass specimens proved compositionally indistinguishable from the controls. In other words, there was no reliable way to tell a genuine tumour-associated bacterial community from background contamination picked up along the way.

Digital PCR provided the quantitative backbone for these observations. Unlike standard sequencing, which describes the relative proportions of different bacteria but says little about how much DNA is actually present, digital PCR counts target molecules directly, delivering an absolute measurement of bacterial biomass. The measurements confirmed what the sequencing results implied: lung tumour, adjacent tissue, and blood samples contained extremely low quantities of bacterial DNA, comparable to the levels found in negative controls. Saliva samples, by contrast, carried orders of magnitude more bacterial DNA. This quantitative gulf between genuinely microbe-rich samples and the near-sterile tumour and blood specimens goes to the heart of the technical challenge, because at such low biomass, even trace amounts of contaminating DNA from reagents, laboratory environments, or collection procedures can dominate the apparent microbial profile.

Metagenomic shotgun sequencing, which reads DNA across the whole genome rather than targeting a single gene, told a consistent story. In the lung tumour, adjacent tissue, and blood samples, this approach detected very few taxa, and, importantly, there was minimal overlap between the taxa identified by metagenomic sequencing and those identified by 16S sequencing from the same specimens. If the detected bacteria reflected real communities resident in the tumours, one would expect at least reasonable agreement between independent methods probing the same DNA. Saliva and positive control samples, in contrast, showed substantial overlap of detected genera across methods, exactly what one would expect when a genuine, abundant microbial community is being measured. The discordance in the low biomass samples is a hallmark of noise rather than signal.

These findings matter because the idea of a tumour microbiome has moved rapidly from curiosity to potential clinical application. Several high-profile studies have claimed that distinctive bacterial profiles can be found in tumours and circulating blood, and that these profiles might serve as biomarkers for early detection, prognosis, or treatment selection in cancers including lung cancer. If bacterial signatures could be read reliably from a blood sample, the logic goes, clinicians might one day supplement or even replace invasive biopsies with a simple liquid biopsy. But the new study demonstrates that, at least for lung cancer, the technical foundations of such ambitions remain shaky. The bacterial signals in tumour and blood samples were both weak, meaning barely above background, and inconsistent, meaning they failed to replicate even within the same sample processed twice.

The study’s methodological rigour is itself a lesson for the field. Technical replicates, in which the same sample is independently extracted, prepared, and sequenced, provide a direct test of measurement precision. The researchers found that while high biomass samples like saliva produced highly concordant replicates, the low biomass tumour, tissue, and blood specimens yielded wildly divergent replicate profiles, a clear indicator that the apparent diversity was driven by stochastic contamination rather than stable biological communities. The authors argue that this kind of replicate testing, combined with alternative sequencing strategies and absolute quantification of bacterial DNA, should become standard practice before any microbiome finding from low biomass samples is accepted as biologically meaningful.

The contrast between the different sample types in the study also offers a measure of reassurance about the underlying methods. Saliva proved to be highly diverse, strongly reproducible across replicates, and concordant across sequencing platforms, while positive control samples behaved as expected throughout. This means the researchers’ negative findings cannot be dismissed as a failure of their equipment or protocols. Instead, the problem appears to be intrinsic to the samples themselves: the lung tumours, adjacent lung tissue, and blood of these patients contained so little bacterial DNA that no current methodology could reliably distinguish any true signal from the noise of collection and processing environments. Whether lung tumours truly harbour sparse bacterial communities or none at all remains an open question that this study suggests may be extraordinarily difficult to answer.

For patients and clinicians hoping for microbiome-based diagnostics in lung cancer, the message is one of tempered expectations rather than closed doors. The authors emphasise that their work provides important insights into site-specific microbiomes from lung cancer patients and into the formidable challenges of assessing the tumour microbiome, and they call on the research community to adopt more rigorous validation standards before clinical claims are built on fragile data. As the field grapples with reproducibility concerns that have shadowed tumour microbiome research in recent years, this study stands as a model of the kind of scrutiny required: multiple sequencing approaches, technical replicates, careful controls, and absolute quantification, all applied to the same specimens. Only through such disciplined methods, the researchers conclude, can the field separate genuine biology from artefact and determine whether the dream of reading cancer’s microbial signature is grounded in reality or destined to dissolve at the boundaries of detection.

Subject of Research: Microbiome profiling of lung tumour and blood samples from lung cancer patients using replicates and multiple sequencing methods

Article Title: Technical replicates and multiple sequencing approaches reveal weak and inconsistent microbiome signals in lung tumour and blood samples from patients with lung cancer

Article References: DeClercq, V., Comeau, A. M., Kwawukume, A., Murphy, R., Parmar, N. R., Quinn, D. P., Wright, R., Wallace, A., & Langille, M. G. I. (2026). Technical replicates and multiple sequencing approaches reveal weak and inconsistent microbiome signals in lung tumour and blood samples from patients with lung cancer. Microbiome. https://doi.org/10.1186/s40168-026-02529-z

Image Credits: AI Generated

DOI: 10.1186/s40168-026-02529-z

Keywords: tumour microbiome, lung cancer, 16S rRNA sequencing, metagenomics, digital PCR, low biomass samples, contamination, technical replicates, bacterial biomarkers, microbiome reproducibility, non-small cell lung cancer, liquid biopsy

The question matters because plankton death is not simply an endpoint. When plankton cells die, their decomposing remains release dissolved organic carbon into the surrounding seawater, a form of carbon that microbes can transform and that can be stored in the ocean for thousands of years. The fate of this carbon shapes everything from microbial food webs to the ocean’s long-term capacity to sequester greenhouse gases. Understanding the mechanisms behind plankton mortality is therefore essential for reconstructing how marine ecosystems function and how material flows through them. The trouble is that a single plankton community can consist of hundreds of coexisting species, and pinpointing how many cells within such a crowded assemblage are dying, and at what rate, has long eluded researchers.

A team at Kyoto University has now tackled this challenge by focusing on one of the most pervasive causes of plankton death: viral infection. Viruses are extraordinarily abundant in seawater, and when they infect a plankton cell they often trigger cell lysis, the process by which the cell’s membrane breaks down and its contents, including genetic material, spill into the environment. This invisible death releases ribosomal RNA, or rRNA, into the water. Rather than trying to count dying cells directly, the researchers reasoned that they could measure the RNA these cells leave behind, turning the genetic debris of viral lysis into a quantitative signal of mortality.

To build such a measurement, the team first grew laboratory cultures of two phytoplankton groups, diatoms and raphidophytes, in a seawater-based medium. They then extracted the rRNA present in the medium and quantified it using digital PCR, a highly sensitive technique capable of counting individual nucleic acid molecules. The approach faced a fundamental obstacle, however: rRNA released into seawater does not persist. It degrades over time, meaning that any measured concentration reflects both the ongoing production of cell-free rRNA and its simultaneous disappearance. Without accounting for degradation, the method would systematically underestimate how much RNA dying cells actually release.

The researchers solved this problem with an elegant trick borrowed from analytical chemistry. They introduced a culture of spike-in ribosomes, a known quantity of ribosomal material that was not produced by the plankton, into the medium and tracked how quickly it degraded. This gave them a degradation rate constant specific to their experimental conditions. They then built a flux model that incorporated both the measured changes in host cell-free rRNA over time and this degradation constant. With both terms in hand, the model could correct for the RNA that had already broken down, making it possible to estimate the true rate of cell lysis at any given moment in the experiment.

The results were striking. Viral infection enhanced the rate of cell-free rRNA production approximately 46-fold compared with uninfected cultures, and subsequent cell lysis boosted it roughly 302-fold. In the non-infected solutions, only very small amounts of rRNA were actively released by living cells, underscoring how strongly lysis signals stand out from the background noise of a healthy population. The method effectively turns viral mortality into a measurable molecular beacon, one that can be detected while the deaths themselves are happening.

Perhaps the most surprising finding emerged from the timing. In the diatom experiment, dissolved rRNA production peaked before the population density began to decline as a result of viral infection. In other words, active cell lysis was already underway while the population as a whole was still growing steadily. From a biogeochemical perspective, this implies that the supply of dissolved organic matter to the environment through cell death may occur primarily during the growth phase of a bloom, rather than during its apparent decline, as conventional observations would suggest. Cells are dying and leaking their contents into the water long before anyone watching the population curve would notice.

We did not expect the temporal decoupling between population declines and cell lysis, says corresponding author Hisashi Endo of Kyoto University. Observing the dynamics of living cells is not enough to evaluate the dissolved organic carbon that phytoplankton contribute to marine environments. The statement carries significant weight for oceanographers who model carbon cycling, because it suggests that mortality-driven carbon fluxes may be systematically misattributed in time if they are inferred solely from changes in cell abundance. Carbon could be flowing into microbial food webs and the deep ocean at moments when blooms appear to be thriving.

The methods developed by the Kyoto team for quantifying this invisible death of plankton offer a valuable new lens for understanding material flows within ecosystems. The researchers are careful to note the study’s limits. The reasons for plankton mortality are diverse, spanning grazing, nutrient starvation, disease and viral attack, and this study did not distinguish between different causes of cell lysis. The flux model detects death, but not its perpetrator. The team now intends to develop approaches that can track both the impacts and the causes of cell lysis at the species level, a step that would allow ecologists to attribute carbon release to specific pathogens and specific hosts within complex natural communities.

For Endo, the work is the continuation of a long-standing fascination with the viral dark matter of the sea. Since we revealed that a wide variety of viruses are present in seawater, I have been interested in understanding their impact on the ecosystem, he says. Using this research as a starting point, I hope to shed light on the true nature of the plankton ecosystem. As the technique matures and moves from laboratory cultures toward field applications, it could transform how scientists monitor ocean health, refine global carbon models, and appreciate the ceaseless, mostly invisible cycle of life and death playing out in every drop of seawater.

Subject of Research: Quantifying viral cell lysis of marine plankton using extracellular ribosomal RNA

Article Title: Dead or alive, plankton support marine ecosystems

Article References: Dead or alive, plankton support marine ecosystems. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: plankton, viral lysis, phytoplankton, rRNA, digital PCR, dissolved organic carbon, marine ecosystems, biogeochemical cycles, diatoms, raphidophytes, carbon sequestration, ocean viruses