Eco-climate drivers shape virome diversity in a globally invasive tick species.

Spillovers of viruses into human occur more frequently under warmer conditions, particularly arboviruses. The invasive tick species Haemaphysalis longicornis poses a significant public health threat due to its global expansion and its potential to carry a wide range of pathogens. We analyzed meta-transcriptomic data from 3595 adult H. longicornis ticks collected between 2016 and 2019 in 22 provinces across China, encompassing diverse ecological conditions. Generalized additive modelling revealed that climate factors exerted a stronger influence on the virome of H. longicornis compared to other ecological factors, such as ecotypes, distance to coastline, animal host, tick gender, and anti-viral immunity. We investigated the mechanistic understanding of how climate changes drive the tick virome using causality inference and emphasized its significance for public health. Our findings demonstrated that higher temperatures and lower relative humidity/precipitation contribute to variations in animal host diversity, leading to an increased diversity of tick virome, particularly the evenness of vertebrate associated viruses. This finding may explain the evolution of tick-borne viruses into generalists across multiple hosts, thereby increasing the probability of spillover events involving tick-borne pathogens. Deep learning projections indicate that the diversity of H. longicornis virome is expected to increase in 81.9% of regions under the SSP8.5 scenario from 2019-2030. Extension of surveillance should be implemented to avert the spread of tick-borne diseases.

ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences.

BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing. RESULTS: In this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30-50 aa; 100-150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG. CONCLUSIONS: ARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet , with an online service provided at https://ARGNet.hku.hk . Video Abstract.

Binding affinity between coronavirus spike protein and human ACE2 receptor.

Coronaviruses (CoVs) pose a major risk to global public health due to their ability to infect diverse animal species and potential for emergence in humans. The CoV spike protein mediates viral entry into the cell and plays a crucial role in determining the binding affinity to host cell receptors. With particular emphasis on α- and ß-coronaviruses that infect humans and domestic animals, current research on CoV receptor use suggests that the exploitation of the angiotensin-converting enzyme 2 (ACE2) receptor poses a significant threat for viral emergence with pandemic potential. This review summarizes the approaches used to study binding interactions between CoV spike proteins and the human ACE2 (hACE2) receptor. Solid-phase enzyme immunoassays and cell binding assays allow qualitative assessment of binding but lack quantitative evaluation of affinity. Surface plasmon resonance, Bio-layer interferometry, and Microscale Thermophoresis on the other hand, provide accurate affinity measurement through equilibrium dissociation constants (KD). In silico modeling predicts affinity through binding structure modeling, protein-protein docking simulations, and binding energy calculations but reveals inconsistent results due to the lack of a standardized approach. Machine learning and deep learning models utilize simulated and experimental protein-protein interaction data to elucidate the critical residues associated with CoV binding affinity to hACE2. Further optimization and standardization of existing approaches for studying binding affinity could aid pandemic preparedness. Specifically, prioritizing surveillance of CoVs that can bind to human receptors stands to mitigate the risk of zoonotic spillover.

Longitudinal monitoring reveals the emergence and spread of blaGES-5-harboring carbapenem-resistant Klebsiella quasipneumoniae in a Hong Kong hospital wastewater discharge line.

Testing hospital wastewater (HWW) is potentially an effective, long-term approach for monitoring trends in antimicrobial resistance (AMR) patterns in health care institutions. Over a year, we collected wastewater samples from the clinical and non-clinical sites of a tertiary hospital and from a downstream wastewater treatment plant (WWTP). We focused on the extent of carbapenem resistance among Enterobacteriaceae isolates given their clinical importance. Escherichia coli and Klebsiella spp. were the most frequently isolated Enterobacteriaceae species at all sampling sites. Additionally, a small number of isolates belonging to ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species), except K. pneumoniae, were detected. Of the 232 Klebsiella spp. isolates, 100 (43.1 %) were multi-drug resistant (MDR), with 46 being carbapenem-resistant. Most of these carbapenem-resistant isolates were K. quasipneumoniae (CRKQ) (n = 44). All CRKQ isolates were isolated from the wastewater of a clinical site that includes intensive care units, which also yielded significantly more multi-drug resistant isolates compared to all other sampling sites. Among the CRKQ isolates, blaGES-5 genes (n = 42) were the primary genetic determinant of carbapenem resistance. Notably, three different CRKQ isolates, collected within the same month in HWW and the influent and effluent flow of the WWTP, shared >99 % sequence similarity between their blaGES-5 genes and between their flanking regions and upstream integron-integrase region. The influent isolate was phylogenetically close to K. quasipnuemoniae isolates from wastewater collected in Japan. Its blaGES-5 gene and surrounding sequences were > 99 % identical to blaGES-24 genes found in the Japanese isolates. Our results suggest that testing samples from sites located closer to hospitals could support antibiotic stewardship programs compared to samples collected further downstream. Moreover, testing samples collected regularly from WWTPs may reflect the local and global spread of pathogens and their resistances.

Toward a Universal Unit for Quantification of Antibiotic Resistance Genes in Environmental Samples.

Surveillance of antibiotic resistance genes (ARGs) has been increasingly conducted in environmental sectors to complement the surveys in human and animal sectors under the "One-Health" framework. However, there are substantial challenges in comparing and synthesizing the results of multiple studies that employ different test methods and approaches in bioinformatic analysis. In this article, we consider the commonly used quantification units (ARG copy per cell, ARG copy per genome, ARG density, ARG copy per 16S rRNA gene, RPKM, coverage, PPM, etc.) for profiling ARGs and suggest a universal unit (ARG copy per cell) for reporting such biological measurements of samples and improving the comparability of different surveillance efforts.

Probable airborne transmission of Burkholderia pseudomallei causing an urban outbreak of melioidosis during typhoon season in Hong Kong, China.

Between January 2015 and October 2022, 38 patients with culture-confirmed melioidosis were identified in the Kowloon West (KW) Region, Hong Kong. Notably, 30 of them were clustered in the Sham Shui Po (SSP) district, which covers an estimated area of 2.5 km2. Between August and October 2022, 18 patients were identified in this district after heavy rainfall and typhoons. The sudden upsurge in cases prompted an environmental investigation, which involved collecting 20 air samples and 72 soil samples from residential areas near the patients. A viable isolate of Burkholderia pseudomallei was obtained from an air sample collected at a building site five days after a typhoon. B. pseudomallei DNA was also detected in 21 soil samples collected from the building site and adjacent gardening areas using full-length 16S rRNA gene sequencing, suggesting that B. psuedomallei is widely distributed in the soil environment surrounding the district. Core genome-multilocus sequence typing showed that the air sample isolate was phylogenetically clustered with the outbreak isolates in KW Region. Multispectral satellite imagery revealed a continuous reduction in vegetation region in SSP district by 162,255 m2 from 2016 to 2022, supporting the hypothesis of inhalation of aerosols from the contaminated soil as the transmission route of melioidosis during extreme weather events. This is because the bacteria in unvegetated soil are more easily spread by winds. In consistent with inhalational melioidosis, 24 (63.2%) patients had pneumonia. Clinicians should be aware of melioidosis during typhoon season and initiate appropriate investigation and treatment for patients with compatible symptoms.

Metavirome of 31 tick species provides a compendium of 1,801 RNA virus genomes.

The increasing prevalence and expanding distribution of tick-borne viruses globally have raised health concerns, but the full repertoire of the tick virome has not been assessed. We sequenced the meta-transcriptomes of 31 different tick species in the Ixodidae and Argasidae families from across mainland China, and identified 724 RNA viruses with distinctive virome compositions among genera. A total of 1,801 assembled and complete or nearly complete viral genomes revealed an extensive diversity of genome architectures of tick-associated viruses, highlighting ticks as a reservoir of RNA viruses. We examined the phylogenies of different virus families to investigate virome evolution and found that the most diverse tick-associated viruses are positive-strand RNA virus families that demonstrate more ancient divergence than other arboviruses. Tick-specific viruses are often associated with only a few tick species, whereas virus clades that can infect vertebrates are found in a wider range of tick species. We hypothesize that tick viruses can exhibit both 'specialist' and 'generalist' evolutionary trends. We hope that our virome dataset will enable much-needed research on vertebrate-pathogenic tick-associated viruses.

Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins.

The ongoing outbreak of viral pneumonia in China and across the world is associated with a new coronavirus, SARS-CoV-21. This outbreak has been tentatively associated with a seafood market in Wuhan, China, where the sale of wild animals may be the source of zoonotic infection2. Although bats are probable reservoir hosts for SARS-CoV-2, the identity of any intermediate host that may have facilitated transfer to humans is unknown. Here we report the identification of SARS-CoV-2-related coronaviruses in Malayan pangolins (Manis javanica) seized in anti-smuggling operations in southern China. Metagenomic sequencing identified pangolin-associated coronaviruses that belong to two sub-lineages of SARS-CoV-2-related coronaviruses, including one that exhibits strong similarity in the receptor-binding domain to SARS-CoV-2. The discovery of multiple lineages of pangolin coronavirus and their similarity to SARS-CoV-2 suggests that pangolins should be considered as possible hosts in the emergence of new coronaviruses and should be removed from wet markets to prevent zoonotic transmission.