BMC genomic data最新文献

Enterobacter cloacae complex is a group of common opportunistic pathogens on intensive care units. On intensive care units sepsis is treated with high doses of antibiotics. This treatment does not only eliminate pathogenic bacteria but parts of the microbiome community as well. This leads to an imbalance of the gut microbiome. However, some bacteria can survive such treatment due to certain survival and resistance mechanisms. Not only antibiotic resistance mechanisms but also forming strong communities via biofilm formation promotes cell survival. Here, we investigated the properties of the isolate AT70PIP076 from a sepsis patient treated with piperacillin and tazobactam. After biochemical analysis and MALDI-TOF analysis, the strain was found to be Enterobacter cloacae. In addition to in vitro, antimicrobial susceptibility testing the genome was further investigated in situ regarding antibiotic resistance. Further live/dead staining was performed, and the biofilm formation was investigated using confocal laser microscopy (cLSM). The genome shows the presence of biofilm-associated genes EU554560, bcsABZC_AP010953, ehaB, KF662843, and crl. The understanding of the underlying mechanism of survival of potential pathogens might contribute to elucidate potential treatment options.ObjectivesGenomic analysis of a bacterium that can survive antibiotic treatment within the gut of an antibiotictreated patient to elucidate survival and resistance mechanisms.Data descriptionThe isolate AT70PIP076 was isolated in 2021 from feces collected from a patient treated with Piperacillin and tazobactam. Whole genome DNA was isolated using the Nextera DNA Flex microbial colony extraction protocol and the Nextera Flex DNA preparation kit according to the manufacturer's instructions. Following paired-end sequencing was performed on the MiSeq platform (Illumina, Inc., San Diego, CA, USA) using a 300-cycle MiSeq reagent kit and a read length of 151 bp. Contamination check and identification of 16 S RNA sequences was done by using ContESt16S. The genomic sequence contained 4,988,237 bp and the G + C content is represented at 54.80%. This genome and its associated data set will serve as a useful resource for further analyses.

Objectives: Plant endophytic fungusHormonema carpetanum is the only organism known to produce enfumafungin to date. Enfumafungin is a fernane-type triterpenoid glycoside compound with broad-spectrum antifungal activity, and this leading compound has been developed to antifungal agent ibrexafungerp approved by Food and Drug Administration (FDA). Here we report the first de novo assembled and annotated whole-genome sequence of H. carpetanum ATCC 74360, the original enfumafungin producing strain from the American Type Culture Collection (ATCC).

Data description: We sequenced the genome of H. carpetanum ATCC 74360 through a hybrid sequencing strategy combining Illumina NovaSeq X Plus short-read and PacBio Sequel IIe long-read technologies. Data from the two platforms were combined and the de novo assembly estimated a 32.93 Mbp genome divided into 16 contigs with a GC content of 49.85%. Genome annotation identified 10,433 genes, 253 tRNAs, and 62 rRNAs. antiSMASH predicted biosynthesis gene cluster (BGC) of secondary metabolite, including enfumafungin BGC. This genome and related data files presented here will provide a foundation for further gene editing of this important antibiotic-producing microorganism.

Objectives: Leek (Allium ampeloprasum) is a nutritious vegetable popularly cultivated in South Africa and most regions of the world. It is generally recognised as a source of vitamins and vegetables. Nevertheless, little is known about its rhizosphere microbiome and its microbial functional dataset under various fertilization systems. Therefore, this study intended to unveil the rhizosphere microbiome of Allium ampeloprasum and their functional datasets through shotgun metagenomics sequencing analysis.

Background: Respiratory Syncytial Virus (RSV) is a major cause of acute respiratory tract infections (ARTI) worldwide, with seasonal outbreaks often influenced by climatic factors. This study investigates the genetic diversity and phylogenetic relationships of RSV strains circulated in Sri Lanka between 2022 December and 2024 February.

Results: Subtyping of 12 RSV-positive samples identified both RSV A and B, with full-length F gene sequences obtained for four samples. Phylogenetic analysis revealed clustering with globally circulating strains. Mutation profiling highlighted key amino acid substitutions, including a rare mutation of N116D in the F protein, which may have functional implications. The limited sample size necessitates further studies with broader geographic coverage for more robust conclusions.

Conclusion: This study provides insights into the genetic diversity of RSV F gene in Sri Lanka.

Objective: This study presents the whole-genome sequence of Staphylococcus aureus strain MD02, isolated from a diabetic foot ulcer at a tertiary hospital in southwestern Uganda. The objective was to characterize the genome to understand the isolate's resistance and virulence potential.

Data description: Genomic DNA of S. aureus strain MD02 was extracted and sequenced using the Illumina platform, generating high-quality paired-end reads with an average genome coverage of 32.6×. Quality control was performed with FastQC and reads were trimmed using Trimmomatic. De novo assembly was carried out using SPAdes, resulting in a draft genome of 2,815,980 bp assembled into 68 contigs with a GC content of 32.5%. Quality analysis of the genome revealed 98% completeness, and 2.48% contamination and the closest type strain to our isolate was Staphylococcus aureus (GCA_000330825.2) with an average nucleotide identity of 97.14%% and genome coverage of 89.38% confirming species level identity and close relatedness. Annotation using NCBI Prokaryotic Genome Annotation Pipeline identified 2,701 protein coding genes.

The ASXL1 gene is one of the most frequently mutated genes in acute myeloid leukemia (AML). It is associated with signs of aggressiveness and adverse clinical outcomes. The aim of the current study was to analyze the genetic profile of ASXL1 gene mutations and its impact on the overall survival in AML patients from Pakistan.Thirty-eight well characterized AML patients were enrolled, and DNA sequencing of the ASXL1 was performed using the Illumina NextSeq500 next generation sequencing (NGS) system. Standard pipeline of bioinformatics tools was used to determine the mutational profile. The mutational profile of the enrolled AML patients was compared with that of 1000 Genomes project, and TCGA AML datasets.The analysis revealed 43 genetic variants in ASXL1 across the 38 AML patients (1.13 variant/patient). Eight rare variants were observed in exons 12, 13 of the ASXL1 gene. Notably, a recurrent rare nonsynonymous deleterious variant p.G1336S in exon 13 (NM_015338 transcript) was found in two patients (5.26%). The overall survival of the ASXL1+ (but TP53, FLT3, NPM1, EZH2, and WT1 negative) AML was shorter compared with the ASXL1- (p < 0.05). Further, the overall survival of current study ASXL1 + AML was found comparable with that of the TCGA AML.In conclusion, the non-silent mutations in ASXL1 were associated with lower survival in AML. Further studies with larger cohort are suggested for subsequent clinical implementation.

Background: Mycobacterium tuberculosis (MTB) is a human-specific pathogen that primarily infects humans, causing tuberculosis (TB). Antimicrobial resistance (AMR) in MTB presents a formidable challenge to global health. The employment of machine learning on whole-genome sequencing data (WGS) presents significant potential for uncovering the genomic mechanisms underlying drug resistance in MTB.

Methods: We used 18 binary matrices, each consisting of genotypes and antimicrobial susceptibility testing phenotypes from a specific MTB-antimicrobial dataset. By constructing training and test datasets on all SNPs, intersected SNPs, and randomly generated SNPs, we developed a Machine learning (ML) framework using twelve different algorithms. Then, we compared the performances of the various ML models and used the SHapley Additive exPlanations (SHAP) framework to decipher why and how decisions are made within the optimal algorithm. Lastly, we applied the models to predict the resistance phenotype to rifampicin (RIF) and isoniazid (INH) in the additional independent MTB isolate datasets from India and Israel.

Results: In our study, the Gradient Boosting Classifier (GBC) model was the best in terms of correctly identified percentages (97.28%, 96.06%, 94.19%, and 92.81% for the four first-line drugs, RIF, INH, pyrazinamide, and ethambutol respectively). By estimating the contributions of AMR-related SNPs by SHAP values, we found that position 761,155 (rpoB_p.Ser450), 2,155,168 (katG_p.Ser315) rank top in RIF and INH, their higher values (1 for alternative allele) tend to predict the resistance trait for these two drugs. In addition, the best model GBC generalizes well in predicting the resistance phenotypes for RIF and INH in the external independent MTB isolate datasets from India and Israel.

Conclusions: This study integrates ML methods into antimicrobial resistance research, develops a framework for predicting resistance phenotypes, and explores AMR-related SNPs in MTB. Quantifying the important SNPs' contribution to model decisions makes the ML algorithmic process more transparent, interpretable enabling and enables clinical practice.

Background: Local livestock breeds play a pivotal role in maintaining agricultural sustainability, conserving biodiversity, and preserving cultural heritage. These breeds often possess unique genetic characteristics tailored to their specific environments. The Cinisara is a dual-purpose local cattle breed of Podolian origin, primarily farmed in western Sicily, Italy. However, reports of spurious crossbreeding with cosmopolitan breeds aimed at improving the breed productivity exist. To assess the conservation status and ongoing selective pressures on this unique breed, we genotyped 71 unrelated Cinisara cattle (CIN_A) at 65k SNPs, and extended the dataset with publicly available genotype data of 30 Cinisara individuals sampled 20 years ago (CIN_B). We also included 194 individuals from seven cattle breeds, including the Podolica (POD) breed and the cosmopolitan Holstein (HOL) and Brown Swiss (BRW) breeds. We assessed the genetic diversity, population structure, and determined the extent of introgression from cosmopolitan breeds into Cinisara using local ancestry inference.

Results: Population structure analyses confirmed the Cinisara's Podolian lineage and revealed significant HOL and BRW introgression. While both Cinisara populations, CIN_A and CIN_B, displayed broadly comparable genetic diversity to larger breeds, CIN_B showed reduced heterozygosity and increased inbreeding. CIN_A exhibited higher introgression, suggesting ongoing crossbreeding. Local ancestry was inferred using POD, HOL, and BRW references. CIN_A showed about 258/257 HOL/BRW introgressed SNPs, intercepting 186/131 genes and 1,584/1,772 QTLs. CIN_B had approximately 256/254 HOL/BRW introgressed SNPs, intercepting 218/184 genes and 547/437 QTLs. Predominantly, these regions overlapped with milk production QTLs, but some intercepted genes linked to unique Cinisara traits, like milk quality and climate adaptation, potentially altering breed typicality. Notably, CIN_B shows a potentially higher relative BRW contribution, while CIN_A shows a higher HOL contribution.

Conclusion: Our findings align with the reports of crossbreeding with cosmopolitan breeds to enhance the production performance of Cinisara, and reflect breeding choices such as a reduction in BRW crossing or a preference for HOL. This raises significant concerns regarding the preservation of local breeds, livestock biodiversity, and their cultural and economic value, and highlights the importance of developing informed breeding strategies that balance production improvements with the conservation of genetic heritage.