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Staphylococcus epidermidis is one of the most commonly isolated species from human skin and the second leading cause of bloodstream infections. Here, we performed a large-scale comparative study without any pre-assigned reference to identify genomic determinants associated with the diversity and adaptation of S. epidermidis strains to various environments. Pan-genome of S. epidermidis was open with 435 core proteins and had a pan-genome size of 8,034 proteins. Genome-wide phylogenetic tree showed high heterogeneity and suggested that routine whole genome sequencing was a powerful tool for analyzing the complex evolution of S. epidermidis and for investigating the infection sources. Comparative genome analyses demonstrated a range of antimicrobial resistance (AMR) genes, especially those within mobile genetic elements. The complicated host-bacterium and bacterium-bacterium relationships help S. epidermidis to play a vital role in balancing the epithelial microflora. The highly variable and dynamic nature of the S. epidermidis genome may contribute to its success in adapting to broad habitats. Genes related to biofilm formation and cell toxicity were significantly enriched in the blood and skin, demonstrating their potentials in identifying risk genotypes. This study gave a general landscape of S. epidermidis pan-genome and provided valuable insights into mechanisms for genome evolution and lifestyle adaptation of this ecologically flexible species.Birt-Hogg-Dubé syndrome (BHDS), which is also called Hornstein-Knickenberg syndrome (HKS), is a hereditary autosomal dominant disorder caused by germline mutations in the folliculin gene (FLCN, NM_144997). More pulmonary manifestations (pulmonary cysts and recurrent pneumothoraxes) but fewer skin fibrofolliculomas and renal malignancy are found in Asian BHDS patients compared with other BHDS patients. The atypical manifestation can easily lead to a missed or delayed diagnosis. Here, we report a Chinese family with BHDS that presented with primary spontaneous pneumothorax (PSP) and extensive pulmonary cysts in the absence of skin lesions or renal neoplasms. Next-generation sequencing (NGS) was used to sequence the FLCN gene, and Sanger sequencing was carried out on the samples to confirm the presence of these variants. Among the 13 family members, a novel frameshift variant of FLCN (c.912delT/p.E305KfsX18) was identified in seven individuals. This variant has not been reported before. Bioinformatics analysis showed that the novel variant might lead to a premature stop codon after 18 amino acid residues in exon 9, and this may affect the expression level of FLCN. The identification of this novel frameshift variant of FLCN not only further confirms the familial inheritance of BHDS in the proband but also expands the mutational spectrum of the FLCN gene in patients with BHDS.To identify next-generation-sequencing (NGS) clinical usability and to propose a standard diagnostic routine for critically ill infants, aged less than 100 days and suspected of having a genetically heterogeneous condition, a retrospective study was conducted between January 2016 and December 2018 at neonatal intensive care units (NICUs) of three tertiary hospitals in Shanghai, China. Whole-exome sequencing (WES) or panel sequencing was performed on 307 patients. Trio-WES, trio-panel, proband-WES, and proband-panel diagnostic yields were 39.71% (83/209), 68.75% (22/32), 59.09% (26/44), and 33.33% (4/12), respectively. Definitive molecular diagnoses of 142 infants (46.25%) uncovered 99 disorders; 21 disorders displayed on 44.37% of the diagnosed patients. Genetic etiologies were identified for 61.73% (50/81) of the deceased infants. One in three (29.58%) diagnosed infants exhibited one of the following four clinical traits which had a higher odds of diagnostic rate integument abnormality (adjusted odds ratio [aOR], 19.7; 95% confidence interval [CI], 2.5-156.3), complex immune-related phenotypes (aOR, 9.2; 95% CI, 1.4-83.5), mixed nervous system phenotypes and congenital anomalies (aOR, 5.0; 95% CI, 1.3-19.1), or mixed metabolism and nervous system phenotypes (aOR, 4.5; 95% CI, 1.0-21.5). Our results demonstrated that NGS was an effective diagnostic tool. Infants exhibiting integument, complex immune-related conditions, metabolism, and nervous signs have higher chances of carrying variants in known disease-causing genes. The number of specific phenotypes could be used as an independent predictor of a positive molecular diagnosis, rather than an isolated abnormality. We developed a molecular diagnostic procedure for the use of NGS for diagnosis in Chinese NICU population based on individual characteristics.A multitude of factors, such as drug misuse, lack of strong regulatory measures, improper sewage disposal, and low-quality medicine and medications, have been attributed to the emergence of drug resistant microbes. The emergence and outbreaks of multidrug resistance to last-line antibiotics has become quite common. This is further fueled by the slow rate of drug development and the lack of effective resistome surveillance systems. In this review, we provide insights into the recent advances made in computational approaches for the surveillance of antibiotic resistomes, as well as experimental formulation of combinatorial drugs. We explore the multiple roles of antibiotics in nature and the current status of combinatorial and adjuvant-based antibiotic treatments with nanoparticles, phytochemical, and other non-antibiotics based on synergetic effects. [https://www.selleckchem.com/products/azd9291.html AZD9291 cost] Furthermore, advancements in machine learning algorithms could also be applied to combat the spread of antibiotic resistance. Development of resistance to new antibiotics is quite rapid. Hence, we review the recent literature on discoveries of novel antibiotic resistant genes though shotgun and expression-based metagenomics. To decelerate the spread of antibiotic resistant genes, surveillance of the resistome is of utmost importance. Therefore, we discuss integrative applications of whole-genome sequencing and metagenomics together with machine learning models as a means for state-of-the-art surveillance of the antibiotic resistome. We further explore the interactions and negative effects between antibiotics and microbiomes upon drug administration.