Biology subjects

Prosperi, M.

Publications and source records attributed to Prosperi, M..

16 recordsLinked to original sources

bioRxiv2026

Scalable machine learning improves resistance prediction and identifies novel determinants in Mycobacterium tuberculosis

Multidrug-resistant and extensively drug-resistant Mycobacterium tuberculosis (MTB) represents a growing global health crisis, characterized by limited treatment options and high mortality rates. Rapid and accurate prediction of resistance profiles is critical to guide effective therapy and curb transmission. Whole-genome sequencing (WGS) offers promise for individualized resistance profiling, yet existing computational tools remain constrained by predefined mutation catalogs and prohibitive resource requirements for large-scale analyses. Here, we present AURA, a GPU-accelerated, pangenome-scale machine learning framework for de novo resistance prediction. Trained on 12,185 globally diverse MTB isolates, AURA predicts resistance to 13 first-line, second-line, and repurposed antibiotics with high precision and identifies 59 novel resistance-associated loci, including variants in katG, pncA, rpoC, and members of the PE/PGRS gene family. By enabling model training on an unprecedented genomic scale, AURA provides new insights into the genetic architecture of resistance and establishes a scalable platform for precision-guided therapy and global surveillance of MTB.

bioinformatics↗

bioRxiv2025

Vir2vec: A Genome-Wide Viral Embedding

Genomic language models (gLMs) are powerful numerical surrogates for DNA, but existing architectures focus primarily on human DNA or limited viral sets, and no dedicated benchmark exists for viral genome understanding. Here we introduce Vir2vec (from 17M to 422M parameters), decoder-only gLMs continually pretrained on a curated pan-viral corpus of 565,747 complete genomes across 295 species. We also present the viral Genome Understanding Evaluation (vGUE), a benchmark probing viral representations across broad organism discrimination, evolutionary signatures, intra-genus separation, variant subtyping, and host tropism. Evaluated as frozen feature extractors under nested cross-validation, Vir2vec achieves top balanced accuracy across 5 of 7 vGUE tasks, outperforming human-trained (MistralDNA), viral-specific (ModernBERTvirus), and broad biological foundation (Evo1) model. Furthermore, Vir2vec displays robust zero-shot generalization to unseen viral families. Finally, we introduce an argmax-routed SHAP explainability framework that projects feature attributions directly onto single-codon coordinates, correctly isolating key receptor-binding domain mutations driving mouse host adaptation. Vir2vec and vGUE provide a foundation for viral surveillance and discovery pipelines.

bioinformatics↗

bioRxiv2025

Paired-Sample and Pathway-Anchored MLOps Framework for Robust Transcriptomic Machine Learning in Small Cohorts

BackgroundNinety percent of the 65,000 human diseases are infrequent, collectively affecting [~] 400 million people, substantially limiting cohort accrual. This low prevalence constrains the development of robust transcriptome-based machine learning (ML) classifiers. Standard data-driven classifiers typically require cohorts of over 100 subjects per group to achieve clinical accuracy while managing high-dimensional input ([~]25,000 transcripts). These requirements are infeasible for micro-cohorts of [~]20 individuals, where overfitting becomes pervasive. ObjectiveTo overcome these constraints, we developed a classification method that integrates three enabling strategies: (i) paired-sample transcriptome dynamics, (ii) N-of-1 pathway-based analytics, and (iii) reproducible machine learning operations (MLOps) for continuous model refinement. MethodsUnlike ML approaches relying on a single transcriptome per subject, within-subject paired-sample designs -- such as pre-versus post-treatment or diseased versus adjacent-normal tissue -- effectively control intra-individual variability under isogenic conditions and within-subject environmental exposures (e.g. smoking history, other medications, etc.), improve signal-to-noise ratios, and, when pre-processed as single-subject studies (N-of-1), can achieve statistical power comparable to that obtained in animal models. Pathway-level N-of-1 analytics further reduces each samples high-dimensional profile into [~]4,000 biologically interpretable features, annotated with effect sizes, dispersion, and significance. Complementary MLOps practices--automated versioning, continuous monitoring, and adaptive hyperparameter tuning--improve model reproducibility and generalization. ResultsIn two case studies--human rhinovirus infection versus matched healthy controls (n=16 training; 3 test) and breast cancer tissues harboring TP53 or PIK3CA mutations versus adjacent normal tissue (n=27 training; 9 test)--this approach achieved 90% precision and recall on an unseen breast cancer test set and 92% precision with 90% recall in rhinovirus fivefold cross-validation. Incorporating paired-sample dynamics boosted precision by 8.8% and recall by 6%, while the MLOps workflow yielded additional gains of 14.5% and 12.5%, respectively. Moreover, our method identified 42 critical gene sets (pathways) for rhinovirus response and 21 for cancer mutation status. ConclusionsThese proof-of-concept results support the utility of integrating intra-subject dynamics, "biological knowledge"-based feature reduction (pathway-level feature reduction grounded in prior biological knowledge; e.g., N-of-1-pathways analytics), and reproducible MLOps workflows can overcome cohort-size limitations in infrequent disease, offering a scalable, interpretable solution for high-dimensional transcriptomic classification. Future work will extend these advances across various therapeutic and small-cohort designs.

bioinformatics↗

bioRxiv2024

SARITA: A Large Language Model for Generating the S1 Subunit of the SARS-CoV-2 Spike Protein

The COVID-19 pandemic has profoundly impacted global health, economics, and daily life, with over 776 million cases and 7 million deaths from December 2019 to November 2024. Since the original SARS-CoV-2 Wuhan strain emerged, the virus has evolved into variants such as Alpha, Beta, Gamma, Delta, and Omicron, all characterized by mutations in the Spike glycoprotein, critical for viral entry into human cells via its S1 and S2 subunits. The S1 subunit, binding to the ACE2 receptor and mutating frequently, affects infectivity and immune evasion; the more conserved S2, on the other hand, facilitates membrane fusion. Predicting future mutations is crucial for developing vaccines and treatments adaptable to emerging strains, enhancing preparedness and intervention design. Generative Large Language Models (LLMs) are becoming increasingly common in the field of genomics, given their ability to generate realistic synthetic biological sequences, including applications in protein design and engineering. Here we present SARITA, an LLM with up to 1.2 billion parameters, based on GPT-3 architecture, designed to generate high-quality synthetic SARS-CoV-2 Spike S1 sequences. SARITA is trained via continuous learning on the pre-existing protein model RITA. When trained on Alpha, Beta, and Gamma variants (data up to February 2021 included), SARITA correctly predicts the evolution of future S1 mutations, including characterized mutations of Delta, Omicron and Iota variants. Furthermore, we show how SARITA outperforms alternative approaches, including other LLMs, in terms of sequence quality, realism, and similarity with real-world S1 sequences. These results indicate the potential of SARITA to predict future SARS-CoV-2 S1 evolution, potentially aiding in the development of adaptable vaccines and treatments.

bioinformatics↗

bioRxiv2024

OCTOPUS: Disk-based, Multiplatform, Mobile-friendly Metagenomics Classifier

Portable genomic sequencers such as Oxford Nanopores MinION enable real-time applications in clinical and environmental health. However, there is a bottleneck in the downstream analytics when bioinformatics pipelines are unavailable, e.g., when cloud processing is unreachable due to absence of Internet connection, or only low-end computing devices can be carried on site. Here we present a platform-friendly software for portable metagenomic analysis of Nanopore data, the Oligomer-based Classifier of Taxonomic Operational and Pan-genome Units via Singletons (OCTOPUS). OCTOPUS is written in Java, reimplements several features of the popular Kraken2 and KrakenUniq software, with original components for improving metagenomics classification on incomplete/sampled reference databases, making it ideal for running on smartphones or tablets. OCTOPUS obtains sensitivity and precision comparable to Kraken2, while dramatically decreasing (4-to 16-fold) the false positive rate, and yielding high correlation on real-word data. OCTOPUS is available along with customized databases at https://github.com/DataIntellSystLab/OCTOPUS and https://github.com/Ruiz-HCI-Lab/OctopusMobile.

molecular biology↗

bioRxiv2023

Scalable De Novo Classification of Antibiotic Resistance of Mycobacterium Tuberculosis

We develop a robust machine learning classifier using both linear and nonlinear models (i.e., LASSO logistic regression (LR) and random forests (RF)) to predict the phenotypic resistance of Mycobacterium tuberculosis (MTB) for a broad range of antibiotic drugs. We use data from the CRyPTIC consortium to train our classifier, which consists of whole genome sequencing and antibiotic susceptibility testing (AST) phenotypic data for 13 different antibiotics. To train our model, we assemble the sequence data into genomic contigs, identify all unique 31-mers in the set of contigs, and build a feature matrix M, where M [i, j] is equal to the number of times the i-th 31-mer occurs in the j-th genome. Due to the size of this feature matrix (over 350 million unique 31-mers), we build and use a sparse matrix representation. Our method, which we refer to as MTB++, leverages compact data structures and iterative methods to allow for the screening of all the 31-mers in the development of both LASSO LR and RF. MTB++ is able to achieve high discrimination (F-1 greater than 80%) for the first-line antibiotics. Moreover, MTB++ had the highest F-1 score in all but three classes and was the most comprehensive since it had a F-1 score greater than 75% in all but four (rare) antibiotic drugs. We use our feature selection to contextualize the 31-mers that are used for the prediction of phenotypic resistance, leading to some insights about sequence similarity to genes in MEGARes. Lastly, we give an estimate of the amount of data that is needed in order to provide accurate predictions.

bioinformatics↗

bioRxiv2023

Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders

The coronavirus disease of 2019 (COVID-19) pandemic is characterized by sequential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, lineages, and sublineages, outcompeting previously circulating ones because of, among other factors, increased transmissibility and immune escape. We propose DeepAutoCoV, an unsupervised deep learning anomaly detection system to predict future dominant lineages (FDLs). We define FDLs as viral (sub)lineages that will constitute more than 10% of all the viral sequences added to the GISAID database on a given week. DeepAutoCoV is trained and validated by assembling global and country-specific data sets from over 16 million Spike protein sequences sampled over a period of about 4 years. DeepAutoCoV successfully flags FDLs at very low frequencies (0.01% - 3%), with median lead times of 4-17 weeks, and predicts FDLs [~]5 and [~]25 times better than a baseline approach For example, the B.1.617.2 vaccine reference strain was flagged as FDL when its frequency was only 0.01%, more than a year before it was considered for an updated COVID-19 vaccine. Furthermore, DeepAutoCoV outputs interpretable results by pinpointing specific mutations potentially linked to increased fitness, and may provide significant insights for the optimization of public health pre-emptive intervention strategies. Key PointsO_LIIntroduction of DeepAutoCoV: The article introduces DeepAutoCoV, an unsupervised deep learning anomaly detection system designed to predict future dominant lineages (FDLs) of SARS-CoV-2. FDLs are defined as viral (sub)lineages that will constitute more than 10% of all viral sequences added to the GISAID database in a given week; C_LIO_LIPerformance and Predictive Capability: DeepAutoCoV successfully flags FDLs at very low frequencies (0.01% to 3%), with median lead times of 4 to 17 weeks before they become dominant. It predicts FDLs approximately 5 to 25 times better than baseline approaches. For instance, the B.1.617.2 vaccine reference strain was identified when its frequency was only 0.01%, over a year before it was considered for vaccine updates; C_LIO_LIInterpretable Results and Mutation Identification: The system provides interpretable results by pinpointing specific mutations that may be linked to increased fitness, offering insights that can optimize public health interventions. Key FDL mutations, such as those found in Delta and Omicron variants, are identified and analyzed for their potential impact on viral spread and immune escape; C_LIO_LIAdvantages and Applications: DeepAutoCoV is advantageous because it does not require prior assumptions about which protein sites are more likely to mutate. Its application in genomic surveillance systems could significantly reduce the time needed for public health responses to emerging variants, enabling early interventions such as vaccine updates; C_LIO_LIEvaluation and Comparisons: The performance of DeepAutoCoV was tested over four years of global and national surveillance data, demonstrating superior predictive power compared to other supervised and unsupervised methods. The system is periodically updated to adapt to the evolving viral landscape, making it a robust tool for ongoing surveillance efforts. C_LI

bioinformatics↗

bioRxiv2023

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful to reconstruct the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for infection forecasting in addition to backtracking, developing a phylogeny-based deep learning system, called DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, and it is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at: https://github.com/lab-smile/DeepDynaForcast. Author SummaryDuring an outbreak or sustained epidemic, accurate prediction of patterns in transmission risk can reliably inform public health strategies. Projections indicating growth or decline of transmission for specific risk groups can significantly enhance the optimization of interventions, especially when resources are limited. To address this, we present DeepDynaForecast, a cutting-edge deep learning algorithm designed for forecasting pathogen transmission dynamics. Uniquely, DeepDynaForecast was trained on in-depth simulation data and used more information from the phylogenetic tree of pathogen sequence data than any other algorithm in the field to date, allowing classification of samples according to their dynamics (growth, static, or decline) with incredible accuracy. We evaluated the models performance using both simulated outbreak data and empirical, large-scale data from the HIV epidemic in Florida between 2012 and 2020. We conclude DeepDynaForecast represents a significant advancement in genomics-mediated pathogen transmission characterization and has the potential to catalyze new research directions within virology, molecular biology, and public health.

bioinformatics↗

bioRxiv2022

The K-mer Antibiotic Resistance Gene Variant Analyzer (KARGVA)

Characterization of antibiotic resistance genes (ARGs) from high-throughput sequencing data of metagenomics and cultured bacterial samples is a challenging task, with the need to account for both computational (e.g., string algorithms) and biological (e.g., gene transfers, rearrangements) aspects. Curated ARG databases exist together with assorted ARG classification approaches (e.g., database alignment, machine learning). Besides ARGs that naturally occur in bacterial strains or are acquired through mobile elements, there are chromosomal genes that can render a bacterium resistant to antibiotics through point mutations, i.e., ARG variants (ARGVs). While ARG repositories also collect ARGVs, there are only a few tools that are able to identify ARGVs from metagenomics and high throughput sequencing data, with a number of limitations (e.g., pre-assembly, a posteriori verification of mutations, or specification of species). In this work we present the k-mer, i.e., strings of fixed length k, ARGV analyzer -KARGVA- an open-source, multi-platform tool that provides: (i) an ad hoc, large ARGV database derived from multiple sources; (ii) input capability for various types of high-throughput sequencing data; (iii) a three-way, hash-based, k-mer search setup to process data efficiently, linking k-mers to ARGVs, k-mers to point mutations, and ARGVs to k-mers, respectively; (iv) a statistical filter on sequence classification to reduce type I and II errors. On semi-synthetic data, KARGVA provides very high accuracy even in presence of high sequencing errors or mutations (99.2% and 86.6% accuracy within 1% and 5% base change rates, respectively), and genome rearrangements (98.2% accuracy), with robust performance on ad hoc false positive sets. On data from the worldwide MetaSUB consortium, comprising 3,700+ metagenomics experiments, KARGVA identifies more ARGVs than Resistance Gene Identifier (4.8x) and PointFinder (6.8x), yet all predictions are below the expected false positive estimates. The prevalence of ARGVs is correlated to ARGs but ecological characteristics do not explain well ARGV variance. KARGVA is publicly available at https://github.com/DataIntellSystLab/KARGVA under MIT license.

bioinformatics↗

bioRxiv2021

Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing

Antimicrobial resistance (AMR) is a growing threat to public health and farming at large. In clinical and veterinary practice, timely characterization of the antibiotic susceptibility profile of bacterial infections is a crucial step in optimizing treatment. High-throughput sequencing is a promising option for clinical point-of-care and ecological surveillance, opening the opportunity to develop genotyping-based AMR determination as a possibly faster alternative to phenotypic testing. In the present work, we compare the performance of state-of-the-art methods for detection of AMR using high-throughput sequencing data from clinical settings. We consider five computational approaches based on alignment (AMRPlusPlus), deep learning (DeepARG), k-mer genomic signatures (KARGA, ResFinder) or hidden Markov models (Meta-MARC). We use an extensive collection of 585 isolates with available AMR resistance profiles determined by phenotypic tests across nine antibiotic classes. We show how the prediction landscape of AMR classifiers is highly heterogeneous, with balanced accuracy varying from 0.40 to 0.92. Although some algorithms--ResFinder, KARGA, and AMRPlusPlus- exhibit overall better balanced accuracy than others, the high per-AMR-class variance and related findings suggest that: (1) all algorithms might be subject to sampling bias present both in data repositories used for training and experimental/clinical settings; and (2) a portion of clinical samples might contain uncharacterized AMR genes that the algorithms--mostly trained on known AMR genes--fail to generalize upon. These results lead us to formulate practical advice for software configuration and application, and give suggestions for future study designs to further develop AMR prediction tools from proof-of-concept to bedside.

genomics↗

bioRxiv2021

Finding Overlapping Rmaps via Gaussian Mixture Model Clustering

Optical mapping is a method for creating high resolution restriction maps of an entire genome. Optical mapping has been largely automated, and first produces single molecule restriction maps, called Rmaps, which are assembled to generate genome wide optical maps. Since the location and orientation of each Rmap is unknown, the first problem in the analysis of this data is finding related Rmaps, i.e., pairs of Rmaps that share the same orientation and have significant overlap in their genomic location. Although heuristics for identifying related Rmaps exist, they all require quantization of the data which leads to a loss in the precision. In this paper, we propose a Gaussian mixture modelling clustering based method, which we refer to as OMO_SCPLOWCLUSTC_SCPLOW, that finds overlapping Rmaps without quantization. Using both simulated and real datasets, we show that OMO_SCPLOWCLUSTC_SCPLOW substantially improves the precision (from 48.3% to 73.3%) over the state-of-the art methods while also reducing CPU time and memory consumption. Further, we integrated OMO_SCPLOWCLUSTC_SCPLOW into the error correction methods (Elmeri and O_SCPLOWCC_SCPLOWOMO_SCPLOWETC_SCPLOW) to demonstrate the increase in the performance of these methods. When OMO_SCPLOWCLUSTC_SCPLOW was combined with O_SCPLOWCC_SCPLOWOMO_SCPLOWETC_SCPLOW to error correct Rmap data generated from human DNA, it was able to error correct close to 3x more Rmaps, and reduced the CPU time by more than 35x. Our software is written in C++ and is publicly available under GNU General Public License at https://github.com/kingufl/OMclust

bioinformatics↗

bioRxiv2021

DYNAMITE: a phylogenetic tool for identification of dynamic transmission epicenters

Molecular data analysis is invaluable in understanding the overall behavior of a rapidly spreading virus population when epidemiological surveillance is problematic. It is also particularly beneficial in describing subgroups within the population, often identified as clades within a phylogenetic tree, that represent individuals connected via direct transmission or transmission via differing risk factors in viral spread. However, transmission patterns or viral dynamics within these smaller groups should not be expected to exhibit homogeneous behavior over time. As such, standard phylogenetic approaches that identify clusters based on summary statistics (e.g., median genetic distance over the clade) would not be expected to capture dynamic clusters of transmission. For this purpose, we have developed DYNAMITE (DYNAMic Identification of Transmission Epicenters), a cluster identification algorithm based on a branch-wise (rather than traditional clade-wise) search for cluster criteria, allowing partial clades to be recognized as clusters. Using simulated viral outbreaks with varying cluster types and dynamics, we show that DYNAMITE is consistently more sensitive than existing tools in detecting both static and dynamic transmission clusters. DYNAMITE has been implemented in R and released as open source at: github.com/ProsperiLab/DYNAMITE.

bioinformatics↗

bioRxiv2020

Differing impacts of global and regional responses on SARS-CoV-2 transmission cluster dynamics

Although the global response to COVID-19 has not been entirely unified, the opportunity arises to assess the impact of regional public health interventions and to classify strategies according to their outcome. Analysis of genetic sequence data gathered over the course of the pandemic allows us to link the dynamics associated with networks of connected individuals with specific interventions. In this study, clusters of transmission were inferred from a phylogenetic tree representing the relationships of patient sequences sampled from December 30, 2019 to April 17, 2020. Metadata comprising sampling time and location were used to define the global behavior of transmission over this earlier sampling period, but also the involvement of individual regions in transmission cluster dynamics. Results demonstrate a positive impact of international travel restrictions and nationwide lockdowns on global cluster dynamics. However, residual, localized clusters displayed a wide range of estimated initial secondary infection rates, for which uniform public health interventions are unlikely to have sustainable effects. Our findings highlight the presence of so-called "super-spreaders", with the propensity to infect a larger-than-average number of people, in countries, such as the USA, for which additional mitigation efforts targeting events surrounding this type of spread are urgently needed to curb further dissemination of SARS-CoV-2.

genomics↗

bioRxiv2020

Brain tissue transcriptomic analysis of SIV-infected macaques identifies Poly (ADP-ribose) polymerases (PARPs) as potential biomarkers for neuropathogenesis.

BackgroundDespite improvements in antiretroviral therapy, human immunodeficiency virus type 1 (HIV-1)-associated neurocognitive disorders (HAND) remain prevalent in subjects undergoing therapy. HAND significantly affects individuals quality of life, as well as adherence to therapy, and, despite the increasing understanding of neuropathogenesis, no definitive diagnostic or prognostic marker has been identified. ResultsWe investigated transcriptomic profiles in frontal cortex tissues of Simian immunodeficiency virus (SIV)-infected Rhesus macaques sacrificed at different stages of infection. Gene expression was compared among SIV-infected animals (n=11), with or without CD8+ lymphocyte depletion, based on detectable (n=6) or non-detectable (n=5) presence of the virus in frontal cortex tissues. Significant enrichment in activation of monocyte and macrophage cellular pathways was found in animals with detectable brain infection, independently from CD8+ lymphocyte depletion. In addition, transcripts of four poly (ADP-ribose) polymerases (PARPs) were up-regulated in the frontal cortex, which was confirmed by real-time polymerase chain reaction. ConclusionsOur results shed light on involvement of PARPs in SIV infection of the brain and their role in SIV-associated neurodegenerative processes. Inhibition of PARPs may provide an effective novel therapeutic target for HIV-related neuropathology.

pathology↗

bioRxiv2020

A snapshot of SARS-CoV-2 genome availability up to 30th March, 2020 and its implications

The SARS-CoV-2 pandemic has been growing exponentially, affecting nearly 900 thousand people and causing enormous distress to economies and societies worldwide. A plethora of analyses based on viral sequences has already been published, in scientific journals as well as through non-peer reviewed channels, to investigate SARS-CoV-2 genetic heterogeneity and spatiotemporal dissemination. We examined full genome sequences currently available to assess the presence of sufficient information for reliable phylogenetic and phylogeographic studies in countries with the highest toll of confirmed cases. Although number of-available full-genomes is growing daily, and the full dataset contains sufficient phylogenetic information that would allow reliable inference of phylogenetic relationships, country-specific SARS-CoV-2 datasets still present severe limitations. Studies assessing within country spread or transmission clusters should be considered preliminary at best, or hypothesis generating. Hence the need for continuing concerted efforts to increase number and quality of the sequences required for robust tracing of the epidemic. Significance StatementAlthough genome sequences of SARS-CoV-2 are growing daily and contain sufficient phylogenetic information, country-specific data still present severe limitations and should be interpreted with caution.

evolutionary biology↗

bioRxiv2020

Under-the-radar dengue virus infections in natural populations of Aedes aegypti mosquitoes

The incidence of locally acquired dengue infections increased during the last decade in the United States, compelling a sustained research effort on the dengue mosquito vector, Aedes aegypti, and its microbiome, which has been shown to influence virus transmission success. We examined the metavirome of four populations of Ae. aegypti mosquitoes collected in 2016-2017 from Manatee County, Florida. Unexpectedly, we discovered that dengue virus serotype 4 (DENV4) was circulating in these mosquito populations, representing the first documented case of such a phenomenon in the absence of a local DENV4 human case in this county over a two-year period. We confirmed that all of the mosquito populations carried the same DENV4 strain, assembled its full genome, validated infection orthogonally by reverse transcriptase PCR, traced the virus origin, estimated the time period of its introduction to the Caribbean region, as well as explored the viral genetic signatures and mosquito-specific virome associations that potentially mediated DENV4 persistence in mosquitoes. We discuss the significance of prolonged maintenance of these DENV4 infections in Ae. aegypti that occurred in the absence of a DENV4 human index case in Manatee County with respect to the inability of current surveillance paradigms to detect mosquito vector infections prior to a potential local outbreak. ImportanceSince 1999, dengue outbreaks in the continental United States (U.S.) involving local transmission have occurred episodically and only in Florida and Texas. In Florida, these episodes appear to be coincident with increased introductions of dengue virus into the region through human travel and migration from endemic countries. To date, the U.S. public health response to dengue outbreaks is largely reactive, and implementation of comprehensive arbovirus surveillance in advance of predictable transmission seasons, which would enable proactive preventative efforts, remains unsupported. The significance of our finding is that it is the first documented report of non-outbreak DENV4 transmission and maintenance within a local mosquito vector population in the continental U.S.in the absence of a human case during a two-year time period. Our data suggest that molecular surveillance of mosquito populations in high-risk, high tourism areas of the U.S., may allow for proactive, targeted vector control before potential arbovirus outbreaks.

ecology↗