Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics.

Motivation: In the midst of an outbreak, identification of groups of individuals that represent risk for transmission of the pathogen under investigation is critical to public health efforts. Dynamic transmission patterns within these clusters, whether it be the result of changes at the level of the virus (e.g.

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

The COVID-19 pandemic is marked by the successive emergence of new SARS-CoV-2 variants, lineages, and sublineages that outcompete earlier strains, largely due to factors like 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 >10% of all the viral sequences added to the GISAID, a public database supporting viral genetic sequence sharing, in a given week.

COVID-19 lineages in a minimally vaccinated island population: Genomic epidemiology of SARS-CoV-2 in Haiti.

We monitored SARS-CoV-2 variants in Haiti from 2020-2023. Despite Haitian COVID-19 travel restrictions and in the setting of a vaccination rate of 2.7%, the timing and lineage evolution of the Haiti epidemic mirrored what was occurring in the rest of the world.

Novel insights on unraveling dynamics of transmission clusters in outbreaks using phylogeny-based methods.

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.

CSF1R inhibition depletes brain macrophages and reduces brain virus burden in SIV-infected macaques.

Perivascular macrophages (PVMs) and, to a lesser degree, microglia are targets and reservoirs of HIV and simian immunodeficiency virus (SIV) in the brain. Previously, we demonstrated that colony-stimulating factor 1 receptor (CSF1R) in PVMs was upregulated and activated in chronically SIV-infected rhesus macaques with encephalitis, correlating with SIV infection of PVMs. Herein, we investigated the role of CSF1R in the brain during acute SIV infection using BLZ945, a brain-penetrant CSF1R kinase inhibitor.

Phylodynamic Insights into Global Emergence and Diversification of the Tomato Pathogen pv. .

The emergence of plant pathogens is often associated with waves of unique evolutionary and epidemiological events. pv. is one of the major pathogens causing bacterial spot disease of tomatoes.

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 transmission 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.

Bacterial DnaK reduces the activity of anti-cancer drugs cisplatin and 5FU.

Background: Chemotherapy is a primary treatment for cancer, but its efficacy is often limited by cancer-associated bacteria (CAB) that impair tumor suppressor functions. Our previous research found that Mycoplasma fermentans DnaK, a chaperone protein, impairs p53 activities, which are essential for most anti-cancer chemotherapeutic responses.

Methods: To investigate the role of DnaK in chemotherapy, we treated cancer cell lines with M.

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.

Seafood-Associated Outbreak of ctx-Negative Vibrio mimicus Causing Cholera-Like Illness, Florida, USA.

Vibrio mimicus caused a seafood-associated outbreak in Florida, USA, in which 4 of 6 case-patients were hospitalized; 1 required intensive care for severe diarrhea. Strains were ctx-negative but carried genes for other virulence determinants (hemolysin, proteases, and types I-IV and VI secretion systems). Cholera toxin-negative bacterial strains can cause cholera-like disease.

Ancestral Origin and Dissemination Dynamics of Reemerging Toxigenic Vibrio cholerae, Haiti.

The 2010 cholera epidemic in Haiti was thought to have ended in 2019, and the Prime Minister of Haiti declared the country cholera-free in February 2022. On September 25, 2022, cholera cases were again identified in Port-au-Prince. We compared genomic data from 42 clinical Vibrio cholerae strains from 2022 with data from 327 other strains from Haiti and 1,824 strains collected worldwide.

ARCA: the interactive database for arbovirus reported cases in the Americas.

Background: Accurate case report data are essential to understand arbovirus dynamics, including spread and evolution of arboviruses such as Zika, dengue and chikungunya viruses. Giving the multi-country nature of arbovirus epidemics in the Americas, these data are not often accessible or are reported at different time scales (weekly, monthly) from different sources.

Results: We developed a publicly available and user-friendly database for arboviral case data in the Americas: ARCA.

Network typologies predict future molecular linkages in the network of HIV transmission.

Objective: HIV molecular transmission network typologies have previously demonstrated associations to transmission risk; however, few studies have evaluated their predictive potential in anticipating future transmission events. To assess this, we tested multiple models on statewide surveillance data from the Florida Department of Health.

Design: This was a retrospective, observational cohort study examining the incidence of new HIV molecular linkages within the existing molecular network of persons with HIV (PWH) in Florida.

Diversity and Genetic Reassortment of Keystone Virus in Mosquito Populations in Florida.

Keystone orthobunyavirus (KEYV), a member of the genus Orthobunyavirus, was first isolated in 1964 from mosquitoes in Keystone, Florida. Although data on human infections are limited, the virus has been linked to a fever/rash syndrome and, possibly, encephalitis, with early studies suggesting that 20% of persons in the Tampa, Florida, region had antibodies to KEYV. To assess the distribution and diversity of KEYV in other regions of Florida, we collected > 6,000 mosquitoes from 43 sampling sites in St.

Infectivity of three Mayaro Virus geographic isolates in human cell lines.

Mayaro virus (MAYV) is an emergent arthropod-borne virus that causes an acute febrile illness accompanied by arthralgia, similar to chikungunya virus. Increasing urbanization of MAYV outbreaks in the Americas has led to concerns for geographic expansion and spillover. Given the potential importance of this pathogen, we sought to fill critical gaps in knowledge regarding MAYV infectivity and geographic variation.

Application of Phylodynamic Tools to Inform the Public Health Response to COVID-19: Qualitative Analysis of Expert Opinions.

Background: In the wake of the SARS-CoV-2 pandemic, scientists have scrambled to collect and analyze SARS-CoV-2 genomic data to inform public health responses to COVID-19 in real time. Open source phylogenetic and data visualization platforms for monitoring SARS-CoV-2 genomic epidemiology have rapidly gained popularity for their ability to illuminate spatial-temporal transmission patterns worldwide. However, the utility of such tools to inform public health decision-making for COVID-19 in real time remains to be explored.

Machine Learning Prediction and Phyloanatomic Modeling of Viral Neuroadaptive Signatures in the Macaque Model of HIV-Mediated Neuropathology.

In human immunodeficiency virus (HIV) infection, virus replication in and adaptation to the central nervous system (CNS) can result in neurocognitive deficits in approximately 25% of patients with unsuppressed viremia. While no single viral mutation can be agreed upon as distinguishing the neuroadapted population, earlier studies have demonstrated that a machine learning (ML) approach could be applied to identify a collection of mutational signatures within the virus envelope glycoprotein (Gp120) predictive of disease. The S[imian]IV-infected macaque is a widely used animal model of HIV neuropathology, allowing in-depth tissue sampling infeasible for human patients.

Transmission cluster characteristics of global, regional, and lineage-specific SARS-CoV-2 phylogenies.

The SARS-CoV-2 pandemic has been presenting in periodic waves and multiple variants, of which some dominated over time with increased transmissibility. SARS-CoV-2 is still adapting in the human population, thus it is crucial to understand its evolutionary patterns and dynamics ahead of time. In this work, we analyzed transmission clusters and topology of SARS-CoV-2 phylogenies at the global, regional (North America) and clade-specific (Delta and Omicron) epidemic scales.

Predicting emerging SARS-CoV-2 variants of concern through a One Class dynamic anomaly detection algorithm.

Objectives: The objective of this study is the implementation of an automatic procedure to weekly detect new SARS-CoV-2 variants and non-neutral variants (variants of concern (VOC) and variants of interest (VOI)).

Methods: We downloaded spike protein primary sequences from the public resource GISAID and we represented each sequence as k-mer counts. For each week since 1 July 2020, we evaluate if each sequence represents an anomaly based on a One Class support vector machine (SVM) classification algorithm trained on neutral protein sequences collected from February to June 2020.

Molecular Basis of the Toxigenic Vibrio cholerae O1 Serotype Switch from Ogawa to Inaba in Haiti.

Toxigenic Vibrio cholerae O1 serotype Ogawa was introduced involuntarily into Haiti in October 2010, and virtually all of the clinical strains isolated during the first 5 years of the epidemic were Ogawa. Inaba strains were identified intermittently prior to 2015, with diverse mutations resulting in a common phenotype. In 2015, the percentage of clinical infections due to the Inaba serotype began to rapidly increase, with Inaba supplanting Ogawa as the dominant serotype during the subsequent 4 years.

Molecular Epidemiology of HIV-1 Subtype B Infection across Florida Reveals Few Large Superclusters with Metropolitan Origin.

Florida is considered an epicenter of HIV in the United States. The U.S.

Analysis of Zika Virus Sequence Data Associated with a School Cohort in Haiti.

Zika virus (ZIKV) infections occurred in epidemic form in the Americas in 2014-2016, with some of the earliest isolates in the region coming from Haiti. We isolated ZIKV from 20 children with acute undifferentiated febrile illness who were part of a cohort of children seen at a school clinic in the Gressier region of Haiti. The virus was also isolated from three pools of Aedes aegypti mosquitoes collected at the same location.