Clement Tsui

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Prin. Scientific Officer, NCID, Singapore; Asst Prof UBC, Canada| #microbiology #genomics #evolution #ecology #fungi 🇭🇰 🇨🇦 tea lover
Discovery of the sixth Candida auris clade in Singapore

Background The emerging fungal pathogen Candida auris poses a serious threat to global public health due to its worldwide distribution, multidrug-resistance, high transmissibility, propensity to cause outbreaks and high mortality rates. We report three C. auris isolates detected in Singapore, which are genetically distinct from all known clades (I-V) and represent a new clade (Clade VI). Methods Three epidemiologically unlinked clinical isolates belonging to the potential new C. auris clade were whole-genome sequenced and phenotypically characterized. The complete genomes of these isolates were compared to representative genomes of all known clades. To provide a global context, 3,651 international whole-genome sequences (WGS) from the NCBI database were included in the high-resolution single nucleotide polymorphism (SNP) analysis. Antifungal resistance genes, mating type locus, and chromosomal rearrangements were characterized from the WGS data of the Clade VI isolates. We further implemented Bayesian logistic regression models to simulate the automatic detection of Clade V and VI as their WGS data became available. Findings The three Clade VI isolates were separated by >36,000 SNPs from all existing C. auris clades. These isolates had opposite mating type allele and different chromosomal rearrangements when compared to their closest Clade IV relatives. As a proof-of-concept, our classification model was able to flag these outlier genomes as a potential new clade. Furthermore, an independent WGS submission from Bangladesh was found to belong to this new clade. Interpretation The discovery of a new C. auris clade in Singapore and Bangladesh, showing close relationship to Clade IV members in South America, highlights the unknown genetic diversity and origin of C. auris, particularly in under-resourced regions. Active surveillance in clinical settings, along with effective sequencing strategies and downstream analysis, will be essential in the identification of novel strains, tracking of transmission, and containment of adverse clinical impacts caused by C. auris infections. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the Singapore National Medical Research Council (NMRC) research training fellowship (MOH-FLWSHP19may-0005), the NCRS Duke-NUS Academic Medical Center Academic Clinical Program grant (09/FY2022/P1/17-A32, GRDUKP003401), and the Genedant-GIS Innovation Program grant. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study used pre-existing retrospective collections of isolates and our analyses led to no clinical intervention. Epidemiological data collection was previously performed as part of routine surveillance and infection prevention measures and hence constituted a non-research infection control surveillance activity. Institutional review board exemption was granted by the SingHealth Centralised Institutional Review Board (Reference number 2017-2576). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Reads and genome assemblies from this study have been deposited in the National Centre for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra) under BioProject accession number PRJNA1000034. All data produced in the present study are available upon reasonable request to the authors.

medRxiv
A new #preprint #OpenScience #PeerReview by #PCIEvolBiol: Shang H, Rendón-Anaya M, Paun O, Field DL, Hess J, Vogl C, Liu J, Ingvarsson PK, Lexer C, Leroy T (2023) Drivers of genomic landscapes of differentiation across #Populus #divergencegradient. #bioRxiv https://doi.org/10.1101/2021.08.26.457771
Postdoctoral Research Fellow in Bioinformatics/Machine Learning

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Interactive genomic sequencing dataviz, code, acknowledgements and more info here:
https://github.com/Mike-Honey/covid-19-genomes#readme

World covid stats dataviz here:
https://github.com/Mike-Honey/covid-19-world-vaccinations#statistics-by-geography-page---pick-a-stat-and-countries-for-a-time-series

On this thread, https://twitter.com/yunlong_cao explained the source of the growth advantage enjoyed by XBB.1.5. "Kraken":
https://twitter.com/yunlong_cao/status/1607915567696203776

Here are some alternative versions of diagrams showing the new lineages among the swarm of the "great convergence":

https://twitter.com/dfocosi/status/1603385468254568448

https://twitter.com/rquiroga777/status/1607472191247118339

Here's a comparison the Immune Escape vs ACE2 Binding Scores for a range of current lineages. XBB.1.5 sits in the top-right corner, near XBF "Bythos". They look ideally balanced between the two attributes needed to succeed.
https://twitter.com/RajlabN/status/1605007724655566848

🧵 end

GitHub - Mike-Honey/covid-19-genomes: Projects on COVID-19 topic of genomic sequencing - mostly DataViz

Projects on COVID-19 topic of genomic sequencing - mostly DataViz - Mike-Honey/covid-19-genomes

GitHub
This is the best Christmas gift. what a joy to work with the lab of L. Cai.
Zhou, et al. Cross-kingdom synthetic microbiota supports tomato suppression of Fusarium wilt disease. Nat Commun 13, 7890 (2022). https://doi.org/10.1038/s41467-022-35452-6 https://rdcu.be/c19Zd #fungi #bacteria #SynCom

The community ecology perspective of omics data

Commentary published @ Microbiome on how sample prep/collection/processing impacts diversity estimates.

#microbiome #microbiota #science #ecology #research #metagenomics

https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01423-8

The community ecology perspective of omics data - Microbiome

The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data. Video Abstract

BioMed Central

OpenCitations Meta is out now! It stores and delivers bibliographic metadata for all publications involved in the OpenCitations citation #indexes, and presently contains #metadata describing > 87 MILLION bibliographic entities. Learn more about its data and technical features here:  https://opencitations.hypotheses.org/3140

#OpenCitationsMeta #OC2022releases

#Hybridization is frequent in the wild but are the outcomes predictable and if so, how quickly? @prrnhd @jonna_kulmuni &co reveal correlated sorting of genetic variation across 3 independent hybrid #WoodAnt populations in <50 generations #PLOSBiology https://plos.io/3GaO5uL
Rapid and predictable genome evolution across three hybrid ant populations

Hybridization between species is frequent in the wild but it is unclear when admixture events lead to predictable outcomes and if so, at what timescale. This study shows that selection contributes to correlated sorting of genetic variation across three independent hybrid wood ant populations in less than 50 generations.

Interesting idea of measuring TB lipids in exhaled breath: may have diagnostic potential

#Science #ScienceMastodon #tuberculosis #TB

https://www.nature.com/articles/s41467-022-35453-5#Sec8

A Mycobacterium tuberculosis fingerprint in human breath allows tuberculosis detection - Nature Communications

Most conventional tuberculosis diagnostic tests rely on difficult to obtain sputum samples. In this proof-of-concept study, authors analyse whether pulmonary tuberculosis can be detected using exhaled breath condensate samples.

Nature
Comparison of calling pipelines for whole genome sequencing: an empirical study demonstrating the importance of mapping and alignm https://www.nature.com/articles/s41598-022-26181-3
Comparison of calling pipelines for whole genome sequencing: an empirical study demonstrating the importance of mapping and alignment - Scientific Reports

Rapid advances in high-throughput DNA sequencing technologies have enabled the conduct of whole genome sequencing (WGS) studies, and several bioinformatics pipelines have become available. The aim of this study was the comparison of 6 WGS data pre-processing pipelines, involving two mapping and alignment approaches (GATK utilizing BWA-MEM2 2.2.1, and DRAGEN 3.8.4) and three variant calling pipelines (GATK 4.2.4.1, DRAGEN 3.8.4 and DeepVariant 1.1.0). We sequenced one genome in a bottle (GIAB) sample 70 times in different runs, and one GIAB trio in triplicate. The truth set of the GIABs was used for comparison, and performance was assessed by computation time, F1 score, precision, and recall. In the mapping and alignment step, the DRAGEN pipeline was faster than the GATK with BWA-MEM2 pipeline. DRAGEN showed systematically higher F1 score, precision, and recall values than GATK for single nucleotide variations (SNVs) and Indels in simple-to-map, complex-to-map, coding and non-coding regions. In the variant calling step, DRAGEN was fastest. In terms of accuracy, DRAGEN and DeepVariant performed similarly and both superior to GATK, with slight advantages for DRAGEN for Indels and for DeepVariant for SNVs. The DRAGEN pipeline showed the lowest Mendelian inheritance error fraction for the GIAB trios. Mapping and alignment played a key role in variant calling of WGS, with the DRAGEN outperforming GATK.

Nature