GigaByte (Hong Kong, China)最新文献

Antibiotic resistance is increasing at an alarming rate, and three related mycobacteria are sources of widespread infections in humans. According to the World Health Organization, Mycobacterium leprae, which causes leprosy, is still endemic in tropical countries; Mycobacterium tuberculosis is the second leading infectious killer worldwide after COVID-19; and Mycobacteroides abscessus, a group of non-tuberculous mycobacteria, causes lung infections and other healthcare-associated infections in humans. Due to the rise in resistance to common antibacterial drugs, it is critical that we develop alternatives to traditional treatment procedures. Furthermore, an understanding of the biochemical mechanisms underlying pathogenic evolution is important for the treatment and management of these diseases. In this study, metabolic models have been developed for two bacterial pathogens, M. leprae and My. abscessus, and a new computational tool has been used to identify potential drug targets, which are referred to as bottleneck reactions. The genes, reactions, and pathways in each of these organisms have been highlighted; the potential drug targets can be further explored as broad-spectrum antibacterials and the unique drug targets for each pathogen are significant for precision medicine initiatives. The models and associated datasets described in this paper are available in GigaDB, Biomodels, and PatMeDB repositories.

Cosmocercoid nematodes are common parasites of the digestive tract of amphibians. Genomic resources are important for understanding the evolution of a species and the molecular mechanisms of parasite adaptation. So far, no genome resource of Cosmocercoid has been reported. In 2020, a massive Cosmocercoid infection was found in the small intestine of a toad, causing severe intestinal blockage. We morphologically identified this parasite as A. chamaeleonis. Here, we report the first A. chamaeleonis genome with a genome size of 1.04 Gb. The repeat content of this A. chamaeleonis genome is 72.45%, and the total length is 751 Mb. This resource is fundamental for understanding the evolution of Cosmocercoid and provides the molecular basis for Cosmocercoid infection and control.

Like their shallow-water counterparts, cold-water corals create reefs that support highly diverse communities, and these structures are subject to numerous anthropogenic threats. Here, we present the genome assembly of Lophelia pertusa from the southeastern coast of the USA, the first one for a deep-sea scleractinian coral species. We generated PacBio continuous long reads data for an initial assembly and proximity ligation data for scaffolding. The assembly was annotated using evidence from transcripts, proteins, and ab initio gene model predictions. This assembly is comparable to high-quality reference genomes from shallow-water scleractinian corals. The assembly comprises 2,858 scaffolds (N50 1.6 Mbp) and has a size of 556.9 Mbp. Approximately 57% of the genome comprises repetitive elements and 34% of coding DNA. We predicted 41,089 genes, including 91.1% of complete metazoan orthologs. This assembly will facilitate investigations into the ecology of this species and the evolution of deep-sea corals.

In silico models of biological systems are usually very complex and rely on a large number of parameters describing physical and biological properties that require validation. As such, parameter space exploration is an essential component of computational model development to fully characterize and validate simulation results. Experimental data may also be used to constrain parameter space (or enable model calibration) to enhance the biological relevance of model parameters. One widely used computational platform in the mathematical biology community is PhysiCell, which provides a standardized approach to agent-based models of biological phenomena at different time and spatial scales. Nonetheless, one limitation of PhysiCell is the lack of a generalized approach for parameter space exploration and calibration that can be run without high-performance computing access. Here, we present PhysiCOOL, an open-source Python library tailored to create standardized calibration and optimization routines for PhysiCell models.

The Gulf pipefish Syngnathus scovelli has emerged as an important species for studying sexual selection, development, and physiology. Comparative evolutionary genomics research involving fishes from Syngnathidae depends on having a high-quality genome assembly and annotation. However, the first S. scovelli genome assembled using short-read sequences and a smaller RNA-sequence dataset has limited contiguity and a relatively poor annotation. Here, using PacBio long-read high-fidelity sequences and a proximity ligation library, we generate an improved assembly to obtain 22 chromosome-level scaffolds. Compared to the first assembly, the gaps in the improved assembly are smaller, the N75 is larger, and our genome is ~95% BUSCO complete. Using a large body of RNA-Seq reads from different tissue types and NCBI's Eukaryotic Annotation Pipeline, we discovered 28,162 genes, of which 8,061 are non-coding genes. Our new genome assembly and annotation are tagged as a RefSeq genome by NCBI and provide enhanced resources for research work involving S. scovelli..

Rapid screening of hospital admissions to detect asymptomatic carriers of resistant bacteria can prevent pathogen outbreaks. However, the resulting isolates rarely have their genome sequenced due to cost constraints and long turn-around times to get and process the data, limiting their usefulness to the practitioner. Here we used real-time, on-device target enrichment ("adaptive") sequencing as a highly multiplexed assay covering 1,147 antimicrobial resistance genes. We compared its utility against standard and metagenomic sequencing, focusing on an isolate of Raoultella ornithinolytica harbouring three carbapenemases (NDM, KPC, VIM). Based on this experimental data, we then modelled the influence of several variables on the enrichment results and predicted the large effect of nucleotide identity (higher is better) and read length (shorter is better). Lastly, we showed how all relevant resistance genes are detected using adaptive sequencing on a miniature ("Flongle") flow cell, motivating its use in a clinical setting to monitor similar cases and their surroundings.

Mosquitoes play a crucial role as primary vectors for various infectious diseases in Thailand. Therefore, accurate distribution information is vital for effectively combating and better controlling mosquito-borne diseases. Here, we present a curated dataset of the mosquito distribution in Thailand comprising 12,278 records of at least 117 mosquito species (Diptera: Culicidae). The main genera included in the dataset are Aedes, Anopheles, Armigeres, Culex, and Mansonia. From 2007 to 2023, data were collected through routine mosquito surveillance and research projects from 1,725 locations across 66 (out of 77) Thai provinces. The majority of the data were extracted from a Thai database of the Thailand Malaria Elimination Program. To facilitate broader access to mosquito-related data and support further exploration of the Thai mosquito fauna, the data were translated into English. Our dataset has been published in the Global Biodiversity Information Facility, making it available for researchers worldwide.

Extraction-free HTG EdgeSeq protocols are used to profile sets of genes and measure their expression. Thus, these protocols are frequently used to characterise tumours and their microenvironments. However, although positive and control genes are provided, little indication is given concerning the assessment of the technical success of each sample within the sequencing run. We developed HTGQC, an R package for the quality control of HTG EdgeSeq protocols. Additionally, shinyHTGQC is a shiny application for users without computing knowledge, providing an easy-to-use interface for data quality control and visualisation. Quality checks can be performed on the raw sequencing outputs, and samples are flagged as FAIL or ALERT based on the expression levels of the positive and negative control genes.

Availability & implementation: The code is freely available at https://github.com/LodovicoTerzi/HTGQC (R package) and https://lodovico.shinyapps.io/shinyHTGQC/ (shiny application), including test datasets.