Ticks are vectors of a wide variety of pathogens that are implicated in mild to severe disease in humans and other animals. Nonetheless, the full range of tick-borne pathogens is unknown. Viruses, in particular, have been neglected in discovery efforts targeting tick-borne agents. High throughput sequencing was used to characterize the virome of 638 ticks, including Rhipicephalus microplus (n = 320), Rhipicephalus sanguineus (n = 300), and Amblyomma ovale (n = 18) collected throughout Trinidad and Tobago in 2017 and 2018. Sequences representing nine viruses were identified, including five novel species within Tymovirales, Bunyavirales, Chuviridae, Rhabdoviridae, and Flaviviridae. Thereafter the frequency of detection of viral sequences in individual tick species was investigated.

Tick-borne diseases are a serious threat to both public and veterinary health. In this study, we used high-throughput sequencing to characterize the virome of three tick species implicated in the spread of vector-borne disease throughout Croatia. Ten viruses were identified, including seven potential novel species within the viral families Flaviviridae, Nyamiviridae, Rhabdoviridae, Peribunyaviridae, Phenuiviridae, and Nairoviridae.

Hunters are at a higher risk for exposure to zoonotic pathogens due to their close interactions with wildlife and arthropod vectors. In this study, high throughput sequencing was used to explore the viromes of two tick species, Amblyomma dissimile and Haemaphysalis juxtakochi, removed from hunted wildlife in Trinidad and Tobago. We identified sequences from 3 new viral species, from the viral families Orthomyxoviridae, Chuviridae and Tetraviridae in A. dissimile.

The global burden of sepsis is concentrated in sub-Saharan Africa (SSA), where epidemic HIV and unique pathogen diversity challenge the effective management of severe infections. In this context, patient stratification based on biomarkers of a dysregulated host response may identify subgroups more likely to respond to targeted immunomodulatory therapeutics. In a prospective cohort of adults hospitalized with suspected sepsis in Uganda, we applied machine learning methods to develop a prediction model for 30-day mortality that integrates physiology-based risk scores with soluble biomarkers reflective of key domains of sepsis immunopathology. After model evaluation and internal validation, whole-blood RNA sequencing data were analyzed to compare biological pathway enrichment and inferred immune cell profiles between patients assigned differential model-based risks of mortality. Of 260 eligible adults (median age, 32 years; interquartile range, 26–43 years; 59.2% female, 53.9% living with HIV), 62 (23.8%) died by 30 days after hospital discharge. Among 14 biomarkers, soluble tumor necrosis factor receptor 1 (sTNFR1) and angiopoietin 2 (Ang-2) demonstrated the greatest importance for mortality prediction in machine learning models. A clinicomolecular model integrating sTNFR1 and Ang-2 with the Universal Vital Assessment (UVA) risk score optimized 30-day mortality prediction across multiple performance metrics. Patients assigned to the high-risk, UVA-based clinicomolecular subgroup exhibited a transcriptional profile defined by proinflammatory innate immune and necroptotic pathway activation, T-cell exhaustion, and expansion of key immune cell subsets including regulatory and gamma-delta T cells. Clinicomolecular stratification of adults with suspected sepsis in Uganda enhanced 30-day mortality prediction and identified a high-risk subgroup with a therapeutically targetable immunological profile. Further studies are needed to advance pathobiologically informed sepsis management in SSA.