Tommy Tsan-Yuk Lam’s research while affiliated with Hong Kong SAR Government and other places

In metabarcoding research, such as taxon identification, phylogenetic placement plays a critical role. However, many existing phylogenetic placement methods lack comprehensive features for downstream analysis and visualization. Visualization tools often ignore placement uncertainty, making it difficult to explore and interpret placement data effectively. To overcome these limitations, we introduce a scalable approach using treeio and ggtree for parsing and visualizing phylogenetic placement data. The treeio‐ggtree method supports placement filtration, uncertainty exploration, and customized visualization. It enhances scalability for large analyses by enabling users to extract subtrees from the full reference tree, focusing on specific samples within a clade. Additionally, this approach provides a clearer representation of phylogenetic placement uncertainty by visualizing associated placement information on the final placement tree.

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 β-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.

An extensive mosquito survey was carried out in Hong Kong from September to October 2022, employing a variety of collection methods. Specimens were identified using a combination of morphology and mitochondrial cytochrome C oxidase subunit 1 (COI) barcode sequences. Twenty-nine species, including three new records, i.e., Culex bicornutus (Theobald), Culex cinctellus Edwards, and Lutzia chiangmaiensis Somboon & Harbach, were collected. Phylogenetic analysis of COI sequences of Culex annulus Theobald and Culex vishnui Theobald collected in Hong Kong and elsewhere revealed that the sequences of the two nominal species are genetically very similar and are included in the same clade. Consequently, the synonymy of Cx. annulus with Cx. vishnui is reinstated. Lutzia halifaxii (Theobald) is removed from the list of species in Hong Kong and is replaced with Lutzia vorax Edwards, the identification of which is confirmed in the present study. The record of Culex spiculothorax Bram recorded in Hong Kong is replaced with the senior synonym Culex sasai Kano, Nitahara & Awaya. The occurrence of Anopheles fluviatilis James and Aedes aegypti (Linnaeus) is discussed. Finally, an updated checklist of the mosquitoes of Hong Kong, which now includes 76 species representing 14 genera, is provided, with notation of those species that vector pathogens of human diseases.

Geese, both wild and domestic, are generally considered part of the natural reservoir for influenza A viruses. The highly pathogenic H5 Goose/Guangdong avian influenza virus lineage that is still causing outbreaks worldwide was first detected in domestic geese in 1996. However, while wild geese might have a somewhat restricted role in the influenza ecosystem, the role of domestic geese is little studied. Here, 109 H6 viruses isolated from domestic geese during 2001-2018 in southern China had their phylogeny, evolutionary dynamics, and molecular signatures characterized to examine the role of domestic geese. Our findings demonstrated that all geese H6 viruses were derived from H6 viruses established in ducks and that they subsequently formed three distinct hemagglutinin lineages. Rapid evolution of the hemagglutinin genes was not detected after the duck-to-goose transmissions of H6 viruses that then circulated in geese. Despite long-term persistence in geese, H6 viruses were rarely observed to transmit back to ducks or terrestrial poultry and never exchanged genes with viruses from other subtypes. Most geese H6 viruses maintained the primary molecular signatures of their duck precursors. This study raises the possibility that, rather than being part of the natural reservoir, domestic geese might be more like an aberrant host species for influenza A viruses, and perhaps a “dead-end” host.

Background: This study explored neutralizing IgG antibody levels against COVID-19 decline over time post-vaccination. We conducted this prospective cohort study to investigate the function of gut microbiota in the host immune response following three doses of BNT162b2. Methods: Subjects who received three doses of BNT162b2 were recruited from three centers in Hong Kong. Blood samples were obtained before the first dose and at the one-year timepoint for IgG ELISA to determine the level of neutralizing antibody (NAb). The primary outcome was a high immune response (NAb > 600 AU/mL). We performed shotgun DNA metagenomic sequencing on baseline fecal samples to identify bacterial species and metabolic pathways associated with high immune response using linear discriminant analysis effect size analysis. Results: A total of 125 subjects were recruited (median age: 52 years [IQR: 46.2–59.0]; male: 43 [34.4%]), and 20 were regarded as low responders at the one-year timepoint. Streptococcus parasanguinis (log10LDA score = 2.38, p = 0.003; relative abundance of 2.97 × 10⁻⁵ vs. 0.03%, p = 0.001), Bacteroides stercoris (log10LDA score = 4.29, p = 0.024; relative abundance of 0.14% vs. 2.40%, p = 0.014) and Haemophilus parainfluenzae (log10LDA score = 2.15, p = 0.022; relative abundance of 0.01% vs. 0, p = 0.010) were enriched in low responders. Bifidobacterium pseudocatenulatum (log10LDA score = 2.99, p = 0.048; relative abundance of 0.09% vs. 0.36%, p = 0.049) and Clostridium leptum (log10LDA score = 2.38, p = 0.014; relative abundance of 1.2 × 10⁻⁵% vs. 0, p = 0.044) were enriched in high responders. S. parasanguinis was negatively correlated with the superpathway of pyrimidine ribonucleotides de novo biosynthesis (log10LDA score = 2.63), which contributes to inflammation and antibody production. H. parainfluenzae was positively correlated with pathways related to anti-inflammatory processes, including the superpathway of histidine, purine, and pyrimidine biosynthesis (log10LDA score = 2.14). Conclusion: Among three-dose BNT162b2 recipients, S. parasanguinis, B. stercoris and H. parainfluenzae were associated with poorer immunogenicity at one year, while B. pseudocatenulatum and C. leptum was associated with a better response.

Influenza A/H9 viruses circulate worldwide in wild and domestic avian species, continuing to evolve and posing a zoonotic risk. A substantial increase in human infections with A/H9N2 subtype avian influenza viruses (AIVs) and the emergence of novel reassortants carrying A/H9N2-origin internal genes has occurred in recent years. Different names have been used to describe the circulating and emerging A/H9 lineages. To address this issue, an international group of experts from animal and public health laboratories, endorsed by the WOAH/FAO Network of Expertise on Animal Influenza, has created a practical lineage classification and nomenclature system based on the analysis of 10,638 hemagglutinin sequences from A/H9 AIVs sampled worldwide. This system incorporates phylogenetic relationships and epidemiologic characteristics designed to trace emerging and circulating lineages and clades. To aid in lineage and clade assignment, an online tool has been created. This proposed classification enables rapid comprehension of the global spread and evolution of A/H9 AIVs.

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.

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 .