Biology subjects

Deng, Z.-L.

Publications and source records attributed to Deng, Z.-L..

8 recordsLinked to original sources

bioRxiv2025

Detection of Paracoccus yeei in Spontaneous Bacterial Peritonitis using Rapid, Long Read Sequencing

Ascites is a common complication in patients with decompensated liver cirrhosis. Spontaneous bacterial peritonitis (SBP) is the most frequent infection, affecting up to 30% of hospitalized patients with ascites. Multidrug-resistant bacteria in patients with liver cirrhosis are becoming more common, particularly in nosocomial infections. With early detection and adequate antibiotic therapy, the mortality rate of SBP can be reduced from over 90% to 15-20%. However, the etiologic agent remains undetected by conventional pathogen diagnostics from ascites in more than half of the patients. Here, we describe a robust workflow combining long read metagenome sequencing with best practices for low microbial biomass sample processing and bioinformatic analytics, to detect and characterize pathogens from the cirrhotic ascites of SBP patients, as a complement to routine microbiological diagnostics. This approach identified Paracoccus yeei as a likely etiologic agent for a patient who developed a fever after successful treatment of an Escherichia coli infection. Analysis of the genome of P. yeei recovered directly from ascites delineated pathogenicity-related features and an antimicrobial resistance profile consistent with the patients treatment. Pangenome phylogenetics placed the P. yeei strain closest to isolates associated with abdominal infections. Our study underscores the utility of metagenomics for a rapid and comprehensive assessment of infectious agents, suggesting a novel and rarely reported pathogen contributing to SBP with potential implications for diagnostics and therapeutic strategies. ImportanceOur study demonstrates the clinical utility of real-time, long-read metagenomics in identifying elusive pathogens in Spontaneous Bacterial Peritonitis (SBP), a condition with high mortality and frequently inconclusive conventional diagnostics. By uncovering Paracoccus yeei as a likely secondary etiologic agent and characterizing its pathogenicity and AMR profile, our findings expand the known etiology of SBP and emphasize the potential of metagenomics to improve diagnostic accuracy, guide targeted treatment, and ultimately reduce mortality rates in infectious diseases. This approach could shape future clinical practice for managing complex infections.

microbiology↗

bioRxiv2024

In silico genomic surveillance by CoVerage predicts and characterizes SARS-CoV-2 Variants of Interest

Rapidly evolving viral pathogens such as SARS-CoV-2 continuously accumulate amino acid changes, some of which affect transmissibility, virulence or improve the virus ability to escape host immunity. Since the beginning of the pandemic and establishment of SARS-CoV-2 as a human pathogen, multiple lineages with concerning phenotypic alterations, so called Variants of Concern (VOCs), have emerged and risen to predominance. To optimize public health management and to ensure the continued efficacy of vaccines, the early detection of such variants of interest is essential. Therefore, large-scale viral genomic surveillance programs have been initiated worldwide, with data being deposited in public repositories in a timely manner. However, technologies for their continuous interpretation are currently lacking. Here, we describe the CoVerage system (www.sarscoverage.org) for viral genomic surveillance, which continuously predicts and characterizes novel and emerging potential Variants of Interest (pVOIs) from country-wise lineage frequency dynamics together with their antigenic and evolutionary alterations utilizing the GISAID viral genome resource. In a comprehensive assessment of VOIs, VOCs and VUMs identified, we demonstrate how CoVerage can be used to swiftly identify and characterize such variants, with a lead time of almost three months relative to them reaching their maximal abundances. CoVerage can facilitate the timely identification and assessment of future SARS-CoV-2 variants relevant for public health.

bioinformatics↗

bioRxiv2024

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes

The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno, and Aytan-Aktug), an ML baseline, and the rule-based ResFinder by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species-antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amox-clav, cefoxitin, ceftazidime, and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, C. jejuni and E. faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae, and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.

bioinformatics↗

bioRxiv2022

Evolution of resilience against heat stress in a red-tide dinoflagellate

"Red tides" are harmful algal blooms (HABs) caused by dinoflagellate microalgae that accumulate toxins lethal to other organisms, including humans via consumption of contaminated seafood. Increasingly frequent, HABs are driven by a combination of environmental factors including nutrient enrichment, particularly in warm waters. Here, we present the de novo assembled genome (~4.75 Gbp), transcriptome, proteome, and metabolome from Prorocentrum cordatum, a globally abundant, bloom-forming dinoflagellate. Using axenic algal cultures, we studied the molecular mechanisms that underpin response to temperature stress, which is relevant to current ocean warming trends. We discovered a complementary interplay between RNA editing and exon usage that regulates the expression and functional diversity of biomolecules, reflected by reduction in photosynthesis, central metabolism, and protein synthesis. Our multi-omics analyses uncover the molecular response to heat stress in an important HAB species, which is driven by complex gene structures in a large, high-G+C genome, combined with multi-level transcriptional regulation.

genomics↗

bioRxiv2021

Critical Assessment of Metagenome Interpretation - the second round of challenges

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the community-driven initiative for the Critical Assessment of Metagenome Interpretation (CAMI). In its second challenge, CAMI engaged the community to assess their methods on realistic and complex metagenomic datasets with long and short reads, created from [~]1,700 novel and known microbial genomes, as well as [~]600 novel plasmids and viruses. Altogether 5,002 results by 76 program versions were analyzed, representing a 22x increase in results. Substantial improvements were seen in metagenome assembly, some due to using long-read data. The presence of related strains still was challenging for assembly and genome binning, as was assembly quality for the latter. Taxon profilers demonstrated a marked maturation, with taxon profilers and binners excelling at higher bacterial taxonomic ranks, but underperforming for viruses and archaea. Assessment of clinical pathogen detection techniques revealed a need to improve reproducibility. Analysis of program runtimes and memory usage identified highly efficient programs, including some top performers with other metrics. The CAMI II results identify current challenges, but also guide researchers in selecting methods for specific analyses.

bioinformatics↗

bioRxiv2021

Haploflow: Strain-resolved de novo assembly of viral genomes

In viral infections often multiple related viral strains are present, due to coinfection or within-host evolution. We describe Haploflow, a de Bruijn graph-based assembler for de novo genome assembly of viral strains from mixed sequence samples using a novel flow algorithm. We assessed Haploflow across multiple benchmark data sets of increasing complexity, showing that Haploflow is faster and more accurate than viral haplotype assemblers and generic metagenome assemblers not aiming to reconstruct strains. Haplotype reconstructed high-quality strain-resolved assemblies from clinical HCMV samples and SARS-CoV-2 genomes from wastewater metagenomes identical to genomes from clinical isolates.

bioinformatics↗

bioRxiv2020

Evaluating assembly and variant calling software for strain-resolved analysis of large DNA-viruses

Infection with human cytomegalovirus (HCMV) can cause severe complications in immunocompromised individuals and congenitally infected children. Characterizing heterogeneous viral populations and their evolution by high-throughput sequencing of clinical specimens requires the accurate assembly of individual strains or sequence variants and suitable variant calling methods. However, the performance of most methods has not been assessed for populations composed of low divergent viral strains with large genomes, such as HCMV. In an extensive benchmarking study, we evaluated 15 assemblers and six variant callers on ten lab-generated benchmark data sets created with two different library preparation protocols, to identify best practices and challenges for analyzing such data. Most assemblers, especially metaSPAdes and IVA, performed well across a range of metrics in recovering abundant strains. However, only one, Savage, recovered low abundant strains and in a highly fragmented manner. Two variant callers, LoFreq and VarScan2, excelled across all strain abundances. Both shared a large fraction of false positive (FP) variant calls, which were strongly enriched in T to G changes in a "G.G" context. The magnitude of this context-dependent systematic error is linked to the experimental protocol. We provide all benchmarking data, results and the entire benchmarking workflow named QuasiModo, Quasispecies Metric determination on omics, under the GNU General Public License v3.0 (https://github.com/hzi-bifo/Quasimodo), to enable full reproducibility and further benchmarking on these and other data.

bioinformatics↗

bioRxiv2018

Metatranscriptome analysis of the vaginal microbiota reveals potential mechanisms for recurrence and protection against metronidazole in bacterial vaginosis

Bacterial vaginosis (BV) is a prevalent multifactorial disease of women in their reproductive years characterized by a shift from the Lactobacillus spp. dominated microbial community towards a taxonomically diverse anaerobic community. For unknown reasons, some women do not respond to therapy. In our recent clinical study, out of 37 women diagnosed with BV, 31 were successfully treated with metronidazole, while 6 still had BV after treatment. To discover possible reasons for the lack of response in those patients, we performed a metatranscriptome analysis of their vaginal microbiota, comparing them to patients who responded. Seven out of 8 Cas genes of Gardnerella vaginalis were highly up-regulated in non-responding patients. Cas genes, in addition to protecting against phages, might be involved in DNA repair thus mitigating the bactericidal effect of DNA damaging agents like metronidazole. In the second part of our study, we analyzed the vaginal metatranscriptomes of four patients over three months and showed high in vivo expression of genes for pore-forming toxins in L. iners and of genes encoding enzymes for the production of hydrogen peroxide and D-lactate in L. crispatus.

microbiology↗