Biology subjects

Chan, M.

Publications and source records attributed to Chan, M..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Cell Sex and Sex Hormones Modulate Kidney Glucose and Glutamine Metabolism in Health and Diabetes

Male sex is a risk factor for progression of diabetic kidney disease, but the reasons for this predilection are unclear. Here, we demonstrate that cell sex and sex hormones alter the metabolic phenotype of human proximal tubular epithelial cells (PTECs). Male PTECs displayed increased glycolysis, mitochondrial respiration, oxidative stress, apoptosis, and high glucose-induced injury, compared to female PTECs. This phenotype was enhanced by dihydrotestosterone (DHT) and linked to increased mitochondrial utilization of glucose and glutamine. Studies in vivo pointed towards increased glutamine anaplerosis in diabetic male kidneys. Male sex was linked to increased levels of glutamate, TCA cycle, and glutathione cycle metabolites, in PTECs and in the blood metabolome of healthy youth and youth with type 2 diabetes. Conversely, female PTECs displayed increased levels of pyruvate, glutamyl-cysteine, cysteinylglycine, and a higher GSH/GSSG ratio than male PTECs, indicative of enhanced redox homeostasis. Finally, we identified transcriptional mechanisms that control kidney metabolism in a sex-specific fashion. While X-linked demethylase KDM6A facilitated metabolic homeostasis in female PTECs, transcription factor HNF4A mediated the deleterious effects of DHT in male PTECs. This work uncovers the role of sex in glucose/glutamine metabolism, that may explain the roots of sex dimorphism in the healthy and diabetic kidney.

cell biology↗

bioRxiv2021

Exposure route, sex, and age influence disease outcome in a golden Syrian hamster model of SARS-CoV-2 infection

The emergence of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the resultant pandemic of coronavirus disease 2019 (COVID-19) has led to over one hundred million confirmed infections, greater than three million deaths, and severe economic and social disruption. Animal models of SARS-CoV-2 are critical tools for the pre-clinical evaluation of antivirals, vaccines, and candidate therapeutics currently under urgent development to curb COVID-19-associated morbidity and mortality. The golden (Syrian) hamster model of SARS-CoV-2 infection recapitulates key characteristics of severe COVID-19, including high-titer viral replication in the upper and lower respiratory tract and the development of pathogenic lesions in the lungs. In this work we examined the influence of the route of exposure, sex, and age on SARS-CoV-2 pathogenesis in golden hamsters. We report that delivery of SARS-CoV-2 primarily to the nasal passages (low-volume intranasal), the upper and lower respiratory tract (high-volume intranasal), or the digestive tract (intragastric) results in comparable viral titers in the lung tissue and similar levels of viral shedding during acute infection. However, low-volume intranasal exposure results in milder weight loss during acute infection while intragastric exposure leads to a diminished capacity to regain body weight following the period of acute illness. Further, we examined both sex and age differences in response to SARS-CoV-2 infection. Male hamsters, and to a greater extent older male hamsters, display an impaired capacity to recover from illness and a delay in viral clearance compared to females. Lastly, route of exposure, sex, and age were found to influence the nature of the host inflammatory cytokine response, but they had a minimal effect on both the quality and durability of the humoral immune response as well as the susceptibility of hamsters to SARS-CoV-2 re-infection. Together, these data indicate that the route of exposure, sex, and age have a meaningful impact SARS-CoV-2 pathogenesis in hamsters and that these variables should be considered when designing pre-clinical challenge studies.

microbiology↗

bioRxiv2021

Dissecting Functional, Structural, and Molecular Requirements for Serotonin Release from Mouse Enterochromaffin Cells

Serotonergic enterochromaffin (EC) cells of the gut epithelium are secretory sensory cells that communicate with vagal neurons. EC cells exhibit many features of neurons in the brain, raising the hypothesis that synapse-like contacts may mediate fast and directed signalling. To dissect functional, structural, and molecular properties underlying serotonin release from genetically identified EC cells, we employed a multidisciplinary in vitro approach combining intestinal epithelial cell and organoid cultures, electrochemistry, correlated light- and electron microscopy, and gene expression and biochemical analyses. Despite the presence of key molecules of the synaptic neurotransmitter release machinery, we found that the majority of serotonin is released with slow kinetics from large dense-core rather than small synaptic-like vesicles. While we cannot exclude synapse-like transmission between EC cells and neurons in vivo, our data support the notion that the predominant mode of serotonin secretion is similar to that of other endocrine cell types.

neuroscience↗

bioRxiv2021

Revisiting the Hayflick Limit: Insights from an Integrated Analysis of Changing Transcripts, Proteins, Metabolites and Chromatin

The process wherein dividing cells exhaust proliferative capacity and enter into replicative senescence has become a prominent model for cellular aging in vitro. Despite decades of study, this cellular state is not fully understood in culture and even much less so during aging. Here, we revisit Leonard Hayflicks original observation of replicative senescence in WI-38 human lung fibroblasts equipped with a battery of modern techniques including RNA-seq, single cell RNA-seq, proteomics, metabolomics, and ATAC-seq. We find evidence that the transition to a senescent state manifests early, increases gradually, and corresponds to a concomitant global increase in DNA accessibility in nucleolar and lamin associated domains. Furthermore, we demonstrate that senescent WI-38 cells acquire a striking resemblance to myofibroblasts in a process similar to the epithelial to mesenchymal transition (EMT) that is regulated by the transcription factors YAP1/TEAD1 and TGF-{beta}2. Lastly, we show that verteporfin inhibition of YAP1/TEAD1 activity in aged WI-38 cells robustly attenuates this gene expression program.

genomics↗

bioRxiv2021

Deep Mutagenesis of a Transporter for Uptake of a Non-Native Substrate Identifies Conformationally Dynamic Regions

The serotonin transporter, SERT, catalyzes serotonin reuptake at the synapse to terminate neurotransmission via an alternating access mechanism, and SERT inhibitors are the most widely prescribed antidepressants. Here, deep mutagenesis is used to determine the effects of nearly all amino acid substitutions on human SERT surface expression and transport of the fluorescent substrate APP+, identifying many mutations that enhance APP+ import. Comprehensive simulations of the entire ion-coupled import process reveal that while binding of the native substrate, serotonin, reduces free energy barriers between conformational states to promote SERT dynamics, the conformational free energy landscape in the presence of APP+ instead resembles Na+ bound-SERT, with a higher free energy barrier for transitioning to an inward-facing state. The deep mutational scan for SERT-catalyzed import of APP+ finds mutations that promote the necessary conformational changes that would otherwise be facilitated by the native substrate. Indeed, hundreds of gain-of-function mutations for APP+ import are found along the permeation pathway, most notably mutations that favor the formation of a solvent-exposed intracellular vestibule. The mutagenesis data support the simulated mechanism in which the neurotransmitter and a symported sodium share a common cytosolic exit pathway to achieve coupling. Furthermore, the mutational landscape for SERT surface expression, which likely filters out misfolded sequences, reveals that residues along the permeation pathway are mutationally tolerant, providing plausible evolutionary pathways for changes in transporter properties while maintaining folded structure.

biochemistry↗

bioRxiv2021

Machine Learning Identifies Ponatinib as a Potent Inhibitor of SARS-CoV2-induced Cytokine Storm

Although 15-20% of COVID-19 patients experience hyper-inflammation induced by massive cytokine production, cellular triggers of this process and strategies to target them remain poorly understood. Here, we show that the N-terminal domain (NTD) of the spike protein from the SARS-CoV-2 and emerging variants B1.1.7 and B.1.351 substantially induces multiple inflammatory molecules in human monocytes and PBMCs. Further, we identified several protein kinases, including JAK1, EPHA7, IRAK1, MAPK12, and MAP3K8, as essential downstream mediators of NTD-induced cytokine release. Additionally, we found that the FDA-approved, multi-kinase inhibitor Ponatinib is a potent inhibitor of the NTD-mediated cytokine storm. Taken together, we propose that agents targeting multiple kinases required for the SARS-CoV-2-mediated cytokine storm, such as Ponatinib, may represent an attractive therapeutic option for treating moderate to severe COVID-19.

systems biology↗

bioRxiv2020

Prunella vulgaris extract and suramin block SARS-coronavirus 2 virus Spike protein D614 and G614 variants mediated receptor association and virus entry in cell culture system

Until now, no approved effective vaccine and antiviral therapeutic are available for treatment or prevention of SARS-coronavirus 2 (SCoV-2) virus infection. In this study, we established a SCoV-2 Spike glycoprotein (SP), including a SP mutant D614G, pseudotyped HIV-1-based vector system and tested their ability to infect ACE2-expressing cells. This study revealed that a C-terminal 17 amino acid deletion in SCoV-2 SP significantly increases the incorporation of SP into the pseudotyped viruses and enhanced its infectivity, which may be helpful in the design of SCoV2-SP-based vaccine strategies. Moreover, based on this system, we have demonstrated that an aqueous extract from the Chinese herb Prunella vulgaris (CHPV) and a compound, suramin, displayed potent inhibitory effects on both wild type and mutant (G614) SCoV-2 SP pseudotyped virus (SCoV-2-SP-PVs)-mediated infection. The 50% inhibitory concentration (IC50) for CHPV and suramin on SCoV-2-SP-PVs are 30, and 40 g/ml, respectively. To define the mechanisms of their actions, we demonstrated that both CHPV and suramin are able to directly interrupt SCoV-2-SP binding to its receptor ACE2 and block the viral entry step. Importantly, our results also showed that CHPV or suramin can efficiently reduce levels of cytopathic effect caused by SARS-CoV-2 virus (hCoV-19/Canada/ON-VIDO-01/2020) infection in Vero cells. Furthermore, our results demonstrated that the combination of CHPV/suramin with an anti-SARS-CoV-2 neutralizing antibody mediated more potent blocking effect against SCoV2-SP-PVs. Overall, this study provides evidence that CHPV and suramin has anti-SARS-CoV-2 activity and may be developed as a novel antiviral approach against SARS-CoV-2 infection.

microbiology↗

bioRxiv2020

North American deer mice are susceptible to SARS-CoV-2

The zoonotic spillover of the pandemic SARS-coronavirus 2 (SARS-CoV-2) from an animal reservoir, currently presumed to be the Chinese horseshoe bat, into a naive human population has rapidly resulted in a significant global public health emergency. Worldwide circulation of SARS-CoV-2 in humans raises the theoretical risk of reverse zoonosis events with wildlife, reintroductions of SARS-CoV-2 into permissive non-domesticated animals, potentially seeding new host reservoir species and geographic regions in which bat SARS-like coronaviruses have not historically been endemic. Here we report that North American deer mice (Peromyscus maniculatus) and some closely related members of the Cricetidae family of rodents possess key amino acid residues within the angiotensin-converting enzyme 2 (ACE2) receptor known to confer SARS-CoV-2 spike protein binding. Peromyscus rodent species are widely distributed across North America and are the primary host reservoirs of several emerging pathogens that repeatedly spill over into humans including Borrelia burgdorferi, the causative agent of Lyme disease, deer tick virus, and Sin Nombre orthohantavirus, the causative agent of hantavirus pulmonary syndrome (HPS). We demonstrate that adult deer mice are susceptible to SARS-CoV-2 infection following intranasal exposure to a human isolate, resulting in viral replication in the upper and lower respiratory tract with little or no signs of disease. Further, shed infectious virus is detectable in nasal washes, oropharyngeal and rectal swabs, and viral RNA is detectable in feces and occasionally urine. We further show that deer mice are capable of transmitting SARS-CoV-2 to naive deer mice through direct contact. The extent to which these observations may translate to wild deer mouse populations remains unclear, and the risk of reverse zoonosis and/or the potential for the establishment of Peromyscus rodents as a North American reservoir for SARS-CoV-2 is unknown. Nevertheless, efforts to monitor wild, peri-domestic Peromyscus rodent populations are likely warranted as the SARS-CoV-2 pandemic progresses.

microbiology↗

bioRxiv2020

An aptamer-based magnetic flow cytometer using matched filtering

Facing unprecedented population-ageing, the management of noncommunicable diseases (NCDs) urgently needs a point-of-care (PoC) testing infrastructure. Magnetic flow cytometers are one such solution for rapid cancer cellular detection in a PoC setting. In this work, we report a giant magnetoresistive spin-valve (GMR SV) biosensor array with a multi-stripe sensor geometry and matched filtering to improve detection accuracy without compromising throughput. The carefully designed sensor geometry generates a characteristic signature when cells labeled with magnetic nanoparticles (MNPs) pass by thus enabling multi-parametric measurement like optical flow cytometers (FCMs). Enumeration and multi-parametric information were successfully measured across two decades of throughput. 10-{micro}m polymer microspheres were used as a biomimetic model where MNPs and MNP-decorated polymer conjugates were flown over the GMR SV sensor array and detected with a signal-to-noise ratio (SNR) as low as 2.5 dB due to the processing gain afforded by the matched filtering. The performance was compared against optical observation, exhibiting a 92% detection efficiency. The system achieved a 95% counting accuracy for biomimetic models and 98% for aptamer-based pancreatic cancer cell detection. This system demonstrates the ability to perform reliable PoC diagnostics towards the benefit for NCD control plans.

bioengineering↗

bioRxiv2020

A Fob1 independent role for the Smc5/6 complex in the rDNA that modulates lifespan.

The ribosomal DNA (rDNA) in Saccharomyces cerevisiae is in one tandem repeat array on Chromosome XII. Two spacer regions within each repetitive element, called non-transcribed spacer 1 (NTS1) and NTS2, are important in nucleolar organization. Smc5/6 localizes to both NTS1 and NTS2 and is involved in the regulation of Sir2 and Cohibin binding at NTS1, whereas Fob1 and Sir2 are required for optimal binding of the complex to NTS1 and NTS2, respectively. We demonstrate that Smc5/6 functions in chromatin silencing at NTS1 independently of its role in homologous recombination (HR) when forks pause at the replication fork barrier (RFB). In contrast, when the complex does not localize to the rDNA in nse3-1 mutants, the shortened lifespan correlates with NTS2 homeostasis independently of FOB1 status. Our data identify the importance of Smc5/6 integrity in NTS2 transcriptional silencing and repeat tethering, which in turn underscores rDNA stability and replicative lifespan. HighlightsO_LISmc5/6 is important for transcriptional silencing in the rDNA. C_LIO_LISmc5/6 tethers the rDNA array to the periphery. C_LIO_LITranscriptional silencing of ncRNA at NTS1 and NTS2 is differentially regulated. C_LIO_LISmc5/6 has a role in rDNA maintenance independent of HR processing at the RFB. C_LIO_LIFob1-independent disruption of Smc5/6 at NTS2 leads to lifespan reduction. C_LI

molecular biology↗

bioRxiv2020

TLmutation: predicting the effects of mutations using transfer learning

A reoccurring challenge in bioinformatics is predicting the phenotypic consequence of amino acid variation in proteins. With the recent advancements in sequencing techniques, sufficient genomic data has become available to train models that predict the evolutionary statistical energies, but there is still inadequate experimental data to directly predict functional effects. One approach to overcome this data scarcity is to apply transfer learning and train more models with available datasets. In this study, we propose a set of transfer learning algorithms we call TLmutation, which implements a supervised transfer learning algorithm that transfers knowledge from survival data of a protein to a particular function of that protein. This is followed by an unsupervised transfer learning algorithm that extends the knowledge to a homologous protein. We explore the application of our algorithms in three cases. First, we test the supervised transfer on 17 previously published deep mutagenesis datasets to complete and refine missing datapoints. We further investigate these datasets to identify which mutations build better predictors of variant functions. In the second case, we apply the algorithm to predict higher-order mutations solely from single point mutagenesis data. Finally, we perform the unsupervised transfer learning algorithm to predict mutational effects of homologous proteins from experimental datasets. These algorithms are generalized to transfer knowledge between Markov random field models. We show the benefit of our transfer learning algorithms to utilize informative deep mutational data and provide new insights into protein variant functions. As these algorithms are generalized to transfer knowledge between Markov random field models, we expect these algorithms to be applicable to other disciplines.

bioinformatics↗

bioRxiv2019

Kinome-centric pharmacoproteomics identifies signaling pathways underlying cellular responses to targeted cancer drugs

ABSTRACTKinase-dependent signaling networks are frequently dysregulated in cancer, driving disease progression. While kinase inhibition has become an important therapeutic approach many cancers resist drug treatment. Therefore, we need both reliable biomarkers that predict drug responses and new targets to overcome drug resistance. Determining the kinase(s) that control cancer progression in individual cancers can pose a significant challenge. Genomics has identified important, yet limited numbers of kinase driver mutations. Transcriptomics can quantify aberrant gene expression, but it cannot measure the protein phosphorylation that regulates kinase-dependent signaling network activity. Proteomics measures protein expression and phosphorylation and, therefore, quantifies aberrant signaling network activity directly. We developed a kinome-centric pharmacoproteomics platform to study signaling pathways that determine cancer drug response. Using hepatocellular carcinoma (HCC) as our model, we determined kinome activity with kinobead/LC-MS profiling, and screened 299 kinase inhibitors for growth inhibition. Integrating kinome activity with drug responses, we obtained a comprehensive database of predictive biomarkers, and kinase targets that promote drug sensitivity and resistance. Our dataset specified pathway-based biomarkers for the clinical HCC drugs sorafenib, regorafenib and lenvatinib, and we found these biomarkers enriched in human HCC specimens. Strikingly, our database also revealed signaling pathways that promote HCC cell epithelial-mesenchymal transition (EMT) and drug resistance, and that NUAK1 and NUAK2 regulate these pathways. Inhibition of these kinases reversed the EMT and sensitized HCC cells to kinase inhibition. These results demonstrate that our kinome pharmacoproteomics platform discovers both predictive biomarkers for personalized oncology and novel cancer drug targets.

cancer biology↗

bioRxiv2019

Defining variation in pre-human ecosystems can guide conservation: An example from a Caribbean coral reef

There is a consensus that Caribbean coral reefs are a pale shadow of what they once were, yet a reef’s pre-human state is typically assumed or estimated using space-for-time substitution approaches. These approaches may fail to account for past variation before human impact which could mislead conservation priorities and actions. In this study we use a suite of fossilised mid-Holocene (7.2-5.6 ka) fringing reefs in Caribbean Panama to define the Historical Range of Variation (HRV) in coral community structure before human-impact to provide context for the states of modern reefs in the same area. Using the abundances of coral taxa to quantify communities, we found that most of the modern coral communities exist in novel ecosystem states with no fossil precedence. We do however identify one modern reef that is indistinguishable in coral community structure from the mid-Holocene reefs. Reef-matrix cores show that the community on this reef has remained in a stable state for over 760 years, suggesting long-term resistance to the region-wide shift to novel states. Without historical context this robust and stable reef would be overlooked since it does not fulfil expectations of what a “pristine” coral reef should look like. This example illustrates how defining past variation using the fossil record can place modern degradation in historical context and improve conservation recommendations.View Full Text

paleontology↗

bioRxiv2019

Genetically encoded cell-death indicators (GEDI) to detect an early irreversible commitment to neurodegeneration

Cell death is a critical process that occurs normally in health and disease. However, its study is limited due to available technologies that only detect very late stages in the process or specific death mechanisms. Here, we report the development of a new fluorescent biosensor called genetically encoded death indicator (GEDI). GEDI specifically detects an intracellular Ca2+ level that cells achieve early in the cell death process and marks a stage at which cells are irreversibly committed to die. The time-resolved nature of GEDI delineates a binary demarcation of cell life and death in real time, reformulating the definition of cell death. We demonstrate that GEDI acutely and accurately reports death of rodent and human neurons in vitro, and show GEDI enables a novel automated imaging platform for single cell detection of neuronal death in vivo in zebrafish larvae. With a quantitative pseudo-ratiometric signal, GEDI facilitates high-throughput analysis of cell death in time lapse imaging analysis, providing the necessary resolution and scale to identify early factors leading to cell death in studies of neurodegeneration.

neuroscience↗

bioRxiv2018

Disrupting Transcriptional Feedback Yields an Escape-Resistant Antiviral

From microbes to cancers, drug-resistant escape variants cause significant morbidity and mortality1-7. Here we present proof-of-concept that disruption of viral auto-regulatory (feedback) circuits strongly inhibits viral replication and confers an extremely high barrier to the evolution of resistance. Using DNA duplexes, we develop single-molecule feedback-circuit disruptors that interfere with transcriptional negative feedback in human herpesviruses (both Herpes Simplex Virus 1 and Cytomegalovirus) thereby increasing viral transcription factors to cytotoxic levels. Feedback disruptors exhibit low-nanomolar to picomolar IC-50s, reduce viral replication >100-fold in culture and in mice, and synergize with the standard-of-care antivirals. Strikingly, no feedback-disruptor escape mutants evolved over >60 days of culture, in contrast to approved antivirals to which resistance rapidly evolved. Overall, the results demonstrate that molecular targeting of feedback circuitry could yield escape-resistant antivirals, potentially enabling development of a new class of antimicrobials.

microbiology↗

bioRxiv2018

Molecular recording of mammalian embryogenesis

Understanding the emergence of complex multicellular organisms from single totipotent cells, or ontogenesis, represents a foundational question in biology. The study of mammalian development is particularly challenging due to the difficulty of monitoring embryos in utero, the variability of progenitor field sizes, and the indeterminate relationship between the generation of uncommitted progenitors and their progression to subsequent stages. Here, we present a flexible, high information, multi-channel molecular recorder with a single cell (sc) readout and apply it as an evolving lineage tracer to define a mouse cell fate map from fertilization through gastrulation. By combining lineage information with scRNA-seq profiles, we recapitulate canonical developmental relationships between different tissue types and reveal an unexpected transcriptional convergence of endodermal cells from extra-embryonic and embryonic origins, illustrating how lineage information complements scRNA-seq to define cell types. Finally, we apply our cell fate map to estimate the number of embryonic progenitor cells and the degree of asymmetric partitioning within the pluripotent epiblast during specification. Our approach enables massively parallel, high-resolution recording of lineage and other information in mammalian systems to facilitate a quantitative framework for describing developmental processes.

developmental biology↗

bioRxiv2018

Radiogenomics-based Risk Prediction of Glioblastoma Multiforme with Clinical Relevance

GBM is the most common and aggressive primary brain tumor. Although the TMZ-based radiochemotherapy improves overall GBM patients survival, it also increases the frequency of false positive post-treatment magnetic resonance imaging (MRI) assessments for tumor progression. Pseudoprogression is a treatment-related reaction with an increase in contrast-enhancing lesion size at the tumor site or resection margins which mimics tumor recurrence on MRI. Accurate and reliable prognostication of GBM progression is urgently needed in the clinical management of GBM patients. Clinical data analysis indicates that the patients with PsP had superior overall and progression-free survival rates. In this study, we aimed to develop a prognostic model to evaluate tumor progression potential of GBM patients following standard therapies. We applied a dictionary learning scheme to obtain imaging features of GBM patients with PsP or TTP from the Wake dataset. Based on these radiographic features, we then conducted radiogenomics analysis to identify the significantly associated genes. These significantly associated genes were then used as features to construct a 2YS logistic regression model. GBM patients were classified into low-and high-survival risk groups based on the individual 2YS scores derived from this model. We tested our model using an independent TCGA dataset and found that 2YS scores were significantly associated with the patients overall survival. We further used two cohorts of the TCGA data to train and test our model. Our results show that 2YS scores-based classification results from the training and testing TGCA datasets were significantly associated with the overall survival of patients. We also analyzed the survival prediction ability of other clinical factors (gender, age, KPS, normal cell ratio) and found that these factors were not related or weakly correlated with patients survival. Overall, our studies have demonstrated the effectiveness and robustness of the 2YS model in predicting clinical outcomes of GBM patients after standard therapies.

bioinformatics↗

bioRxiv2018

Spatiotemporal dynamics of river viruses, bacteria and microeukaryotes

Freshwater is an essential resource of increasing value, as clean water sources diminish. Microorganisms in rivers, a major source of renewable freshwater, are significant due to their role in drinking water safety, signalling environmental contamination1, and driving global nutrient cycles2,3. However, a foundational understanding of microbial communities in rivers is lacking4, especially temporally and for viruses5-7. No studies to date have examined the composition of the free-floating river virome over time, and explanations of the underlying causes of spatial and temporal changes in riverine microbial composition, especially for viruses, remain unexplored. Here, we report relationships among riverine microbial communities and their environment across time, space, and superkingdoms (viruses, bacteria, and microeukaryotes), using metagenomics and marker-based microbiome analysis methods. We found that many superkingdom pairs were synchronous and had consistent shifts with sudden environmental change. However, synchrony strength, and relationships with environmental conditions, varied across space and superkingdoms. Variable relationships were observed with seasonal indicators and chemical conditions previously found to be predictive of bacterial community composition4,8-10, emphasizing the complexity of riverine ecosystems and raising questions around the generalisability of single-site and bacteria-only studies. In this first study of riverine viromes over time, DNA viral communities were stably distinct between sites, suggesting the similarity in riverine bacteria across significant geographic distances10-12 does not extend to viruses, and synchrony was surprisingly observed between DNA and RNA viromes. This work provides foundational data for riverine microbial dynamics in the context of environmental and chemical conditions and illustrates how a bacteria-only or single-site approach would lead to an incorrect description of microbial dynamics. We show how more holistic microbial community analysis, including viruses, is necessary to gain a more accurate and deeper understanding of microbial community dynamics.

microbiology↗